diff --git "a/sandbox/20240310 - CQA - Semantic Routing 1.ipynb" "b/sandbox/20240310 - CQA - Semantic Routing 1.ipynb" --- "a/sandbox/20240310 - CQA - Semantic Routing 1.ipynb" +++ "b/sandbox/20240310 - CQA - Semantic Routing 1.ipynb" @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "07f255d7", "metadata": { "tags": [] @@ -14,7 +14,7 @@ "True" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -30,6 +30,7 @@ "import sys\n", "sys.path.append(\"C:/git/climate-question-answering\")\n", "sys.path.append(\"/mnt/c/git/climate-question-answering\")\n", + "sys.path.append(\"/home/tim/ai4s/climate_qa/climate-question-answering\")\n", "\n", "from dotenv import load_dotenv\n", "load_dotenv()" @@ -45,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "6af1a96e", "metadata": { "tags": [] @@ -61,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "a9128bfc-4b24-4b25-b7a7-68423b1124b1", "metadata": {}, "outputs": [ @@ -69,7 +70,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/theolvs/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/home/tim/anaconda3/envs/climateqa/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, @@ -99,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "942d2705-22dd-46cf-8c31-6daa127e4743", "metadata": {}, "outputs": [ @@ -115,9 +116,9 @@ "output_type": "stream", "text": [ "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: BAAI/bge-base-en-v1.5\n", - "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: cuda\n", - "/home/theolvs/miniconda3/envs/climateqa/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `Pinecone` was deprecated in LangChain 0.0.18 and will be removed in 0.2.0. An updated version of the class exists in the langchain-pinecone package and should be used instead. To use it run `pip install -U langchain-pinecone` and import as `from langchain_pinecone import Pinecone`.\n", - " warn_deprecated(\n" + "INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: cpu\n", + "/home/tim/ai4s/climate_qa/climate-question-answering/climateqa/engine/vectorstore.py:32: LangChainDeprecationWarning: The class `Pinecone` was deprecated in LangChain 0.0.18 and will be removed in 0.3.0. An updated version of the class exists in the langchain-pinecone package and should be used instead. To use it run `pip install -U langchain-pinecone` and import as `from langchain_pinecone import Pinecone`.\n", + " vectorstore = PineconeVectorstore(\n" ] } ], @@ -132,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "882811c8-5890-4048-8630-d052c5179d7d", "metadata": {}, "outputs": [], @@ -142,17 +143,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "51aed81d-860b-409a-bae0-f0e1eeb0f120", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "True" + "False" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -183,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "id": "480ad611-33c7-49ea-b02c-94d6ce1f1d1a", "metadata": { "tags": [] @@ -195,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "id": "82cf49d9-d48e-4d5c-8666-bcc95f637371", "metadata": { "tags": [] @@ -212,37 +213,19 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "d8ef7e0f-ac5f-4323-b02e-753ce2b4afda", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "data": { - "text/plain": [ - "{'intent': 'search'}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "cat_chain.invoke({\"input\":\"Which industries have the highest GHG emissions?\"})" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "id": "a9c89f2d-c597-47b8-ae50-75eda964e609", "metadata": {}, "outputs": [], @@ -253,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 20, "id": "0c609d34-5767-47a6-90a4-d5987e9499ee", "metadata": {}, "outputs": [], @@ -263,46 +246,27 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "5d781cd4-228b-462f-bd0e-55c02084a616", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - } - ], + "outputs": [], "source": [ "output = kec.invoke(\"What is the environmental footprint of artificial intelligence\")" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "553fd4d4-fe5a-4050-ae31-dffa2c7af7b2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'keywords': ['environmental footprint', 'artificial intelligence']}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "output" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 23, "id": "50a57f08-40e5-4419-8568-0739a13af904", "metadata": {}, "outputs": [], @@ -317,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "id": "6b45dd9c-58c3-4204-bf44-7b9eb35dcb9d", "metadata": {}, "outputs": [], @@ -332,54 +296,45 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "id": "90b02a8e-6e67-4592-8bc7-959b544e914b", "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'climateqa.engine.chains.search'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mclimateqa\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mengine\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchains\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msearch\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m make_search_node\n\u001b[1;32m 3\u001b[0m start_search \u001b[38;5;241m=\u001b[39m make_search_node()\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'climateqa.engine.chains.search'" - ] - } - ], + "outputs": [], "source": [ - "from climateqa.engine.chains.search import make_search_node\n", - "\n", - "start_search = make_search_node()" + "# from climateqa.engine.chains.search import make_search_node\n", + "from climateqa.engine.graph import search\n" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "id": "347737d8-c4cb-45a7-9e5d-74d1a2e3f5a2", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'start_search' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m state\u001b[38;5;241m.\u001b[39mupdate(\u001b[43mstart_search\u001b[49m(state))\n\u001b[1;32m 2\u001b[0m state\n", - "\u001b[0;31mNameError\u001b[0m: name 'start_search' is not defined" - ] + "data": { + "text/plain": [ + "{'remaining_questions': [{'question': 'What is climate change ?',\n", + " 'sources': ['IPCC', 'IPBES']},\n", + " {'question': 'What is the link between El Nino and climate change ?',\n", + " 'sources': ['IPCC', 'OpenAlex']}],\n", + " 'auto_mode': False,\n", + " 'sources_input': ['OpenAlex']}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "state.update(start_search(state))\n", + "state.update(search(state))\n", "state" ] }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 26, "id": "a27425de-1297-417a-af02-fba270df8e38", "metadata": {}, "outputs": [], @@ -389,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 27, "id": "fde71c99-8bdd-4fd5-b36e-6804ec21bf2c", "metadata": {}, "outputs": [], @@ -399,119 +354,50 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": null, "id": "cdad5b2f-2103-43d6-9d7f-e2addc2c2c3f", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "... OpenAlex query: climate change\n" - ] - } - ], + "outputs": [], "source": [ "new_state = await retrieve.ainvoke(state)" ] }, { "cell_type": "code", - "execution_count": 183, + "execution_count": null, "id": "751a9a6a-8271-4e89-8754-e2065cd7d737", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7" - ] - }, - "execution_count": 183, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "len(new_state[\"documents\"])" ] }, { "cell_type": "code", - "execution_count": 182, + "execution_count": null, "id": "06db606d-0b04-4e7d-9f55-4f7cefbce654", "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'Document' object is not subscriptable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[182], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnew_state\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdocuments\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mreranking_score\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n", - "\u001b[0;31mTypeError\u001b[0m: 'Document' object is not subscriptable" - ] - } - ], + "outputs": [], "source": [ "new_state[\"documents\"][1][\"reranking_score\"]" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "a5ed23f9-bbbe-4a0b-b919-429e08837765", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'IPCC'}" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "set([\"IPCC\",\"OpenAlex\"]).intersection(set([\"IPCC\",\"IPBES\",\"IPOS\"]))" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "ca801dc5-958b-4392-88ac-5cb69e5f6fab", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'question': \"C'est quoi le réchauffement?\",\n", - " 'sources': ['IPCC', 'IPBES'],\n", - " 'index': 'Vector'},\n", - " {'question': \"C'est quoi le réchauffement?\",\n", - " 'sources': ['OpenAlex'],\n", - " 'index': 'OpenAlex'},\n", - " {'question': \"C'est quoi le YOO?\", 'sources': ['IPBES'], 'index': 'Vector'},\n", - " {'question': \"C'est quoi le YOO?\",\n", - " 'sources': ['OpenAlex'],\n", - " 'index': 'OpenAlex'}]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "new_questions = []\n", "\n", @@ -547,538 +433,20 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": null, "id": "5ef2094a-2e98-4ed9-b0a6-7536ae8057ca", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iddoititledisplay_namerelevance_scorepublication_yearpublication_dateidslanguageprimary_location...cited_by_api_urlcounts_by_yearupdated_datecreated_dateabstractis_oapdf_urlurlcontentnum_tokens
0https://openalex.org/W2117777590https://doi.org/10.1111/j.1523-1739.1990.tb002...The Onslaught of Alien Species, and Other Chal...The Onslaught of Alien Species, and Other Chal...155.60701019901990-09-01{'openalex': 'https://openalex.org/W2117777590...en{'is_oa': False, 'landing_page_url': 'https://......https://api.openalex.org/works?filter=cites:W2...[{'year': 2024, 'cited_by_count': 1}, {'year':...2024-07-01T21:42:17.5926812016-06-24Abstract: Among the many environmental challen...FalseNonehttps://openalex.org/W2117777590The Onslaught of Alien Species, and Other Chal...231
1https://openalex.org/W2057596265https://doi.org/10.1016/j.bjid.2014.11.004Impact of human schistosomiasis in sub-Saharan...Impact of human schistosomiasis in sub-Saharan...136.45924020152015-03-01{'openalex': 'https://openalex.org/W2057596265...en{'is_oa': True, 'landing_page_url': 'https://d......https://api.openalex.org/works?filter=cites:W2...[{'year': 2024, 'cited_by_count': 18}, {'year'...2024-06-26T01:44:20.8317262016-06-24Schistosomiasis, a neglected tropical disease ...TrueNonehttps://openalex.org/W2057596265Impact of human schistosomiasis in sub-Saharan...385
2https://openalex.org/W2094131597https://doi.org/10.1111/j.1472-4642.2010.00637.xHalf a century of succession in a temperate oa...Half a century of succession in a temperate oa...129.60660020102010-02-21{'openalex': 'https://openalex.org/W2094131597...en{'is_oa': True, 'landing_page_url': 'https://d......https://api.openalex.org/works?filter=cites:W2...[{'year': 2024, 'cited_by_count': 8}, {'year':...2024-07-01T13:42:04.9868772016-06-24Abstract Aim Lowland woodlands in Europe went ...Truehttps://onlinelibrary.wiley.com/doi/pdfdirect/...https://openalex.org/W2094131597Half a century of succession in a temperate oa...394
3https://openalex.org/W2042539814https://doi.org/10.1080/0028825x.1964.10428748Facets of the distribution of forest vegetatio...Facets of the distribution of forest vegetatio...100.42501019641964-12-01{'openalex': 'https://openalex.org/W2042539814...en{'is_oa': True, 'landing_page_url': 'https://d......https://api.openalex.org/works?filter=cites:W2...[{'year': 2024, 'cited_by_count': 2}, {'year':...2024-06-26T19:33:50.8444632016-06-24Summary The altitudinal sequences of vegetatio...Truehttps://www.tandfonline.com/doi/pdf/10.1080/00...https://openalex.org/W2042539814Facets of the distribution of forest vegetatio...463
4https://openalex.org/W1982291004https://doi.org/10.1016/j.jhevol.2010.04.002The Early–Middle Pleistocene environmental and...The Early–Middle Pleistocene environmental and...97.53789520112011-04-01{'openalex': 'https://openalex.org/W1982291004...en{'is_oa': False, 'landing_page_url': 'https://......https://api.openalex.org/works?filter=cites:W1...[{'year': 2024, 'cited_by_count': 3}, {'year':...2024-07-01T20:12:53.4095762016-06-24The dispersal of hominins may have been favore...TrueNonehttps://openalex.org/W1982291004The Early–Middle Pleistocene environmental and...312
..................................................................
95https://openalex.org/W2079111689https://doi.org/10.1111/j.0030-1299.2004.13255.xImpacts of multiple stressors on biodiversity ...Impacts of multiple stressors on biodiversity ...19.72595020042004-02-16{'openalex': 'https://openalex.org/W2079111689...en{'is_oa': False, 'landing_page_url': 'https://......https://api.openalex.org/works?filter=cites:W2...[{'year': 2024, 'cited_by_count': 18}, {'year'...2024-06-26T10:03:56.8711062016-06-24Ecosystem resistance to a single stressor reli...FalseNonehttps://openalex.org/W2079111689Impacts of multiple stressors on biodiversity ...193
96https://openalex.org/W2911810635https://doi.org/10.1007/978-981-13-2101-6_1Introduction: The Dynamics of Sustainability a...Introduction: The Dynamics of Sustainability a...19.62872920192019-01-01{'openalex': 'https://openalex.org/W2911810635...en{'is_oa': False, 'landing_page_url': 'https://......https://api.openalex.org/works?filter=cites:W2...[{'year': 2019, 'cited_by_count': 1}]2024-06-27T09:59:31.3405592019-02-21Challenges to achieving sustainable communitie...FalseNonehttps://openalex.org/W2911810635Introduction: The Dynamics of Sustainability a...204
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Controlled laboratory experiments have resolved, in detail, the different nucleation pathways likely responsible for atmospheric new particle formation, yet very little is known from field studies about the molecular steps and compounds involved in different regions of the atmosphere. The scarcity of primary particle sources makes secondary aerosol formation particularly important in the Antarctic atmosphere. Here, we report on the observation of ion-induced nucleation of sulfuric acid and ammonia-a process experimentally investigated by the CERN CLOUD experiment-as a major source of secondary aerosol particles over coastal Antarctica. We further show that measured high sulfuric acid concentrations, exceeding 107 molecules cm-3, are sufficient to explain the observed new particle growth rates. Our findings show that ion-induced nucleation is the dominant particle formation mechanism, implying that galactic cosmic radiation plays a key role in new particle formation in the pristine Antarctic atmosphere.\", metadata={'id': 'https://openalex.org/W2902694464', 'doi': 'https://doi.org/10.1126/sciadv.aat9744', 'title': 'Ion-induced sulfuric acid–ammonia nucleation drives particle formation in coastal Antarctica', 'display_name': 'Ion-induced sulfuric acid–ammonia nucleation drives particle formation in coastal Antarctica', 'relevance_score': 174.29054, 'publication_year': 2018, 'publication_date': '2018-11-02', 'ids': {'openalex': 'https://openalex.org/W2902694464', 'doi': 'https://doi.org/10.1126/sciadv.aat9744', 'mag': '2902694464', 'pmid': 'https://pubmed.ncbi.nlm.nih.gov/30498779', 'pmcid': 'https://www.ncbi.nlm.nih.gov/pmc/articles/6261657'}, 'language': 'en', 'primary_location': {'is_oa': True, 'landing_page_url': 'https://doi.org/10.1126/sciadv.aat9744', 'pdf_url': 'https://advances.sciencemag.org/content/advances/4/11/eaat9744.full.pdf', 'source': {'id': 'https://openalex.org/S2737427234', 'display_name': 'Science advances', 'issn_l': '2375-2548', 'issn': ['2375-2548'], 'is_oa': True, 'is_in_doaj': True, 'is_core': True, 'host_organization': 'https://openalex.org/P4310315823', 'host_organization_name': 'American Association for the Advancement of Science', 'host_organization_lineage': ['https://openalex.org/P4310315823'], 'host_organization_lineage_names': ['American Association for the Advancement of Science'], 'type': 'journal'}, 'license': 'cc-by-nc', 'license_id': 'https://openalex.org/licenses/cc-by-nc', 'version': 'publishedVersion', 'is_accepted': True, 'is_published': True}, 'type': 'article', 'type_crossref': 'journal-article', 'indexed_in': ['crossref', 'pubmed'], 'open_access': {'is_oa': True, 'oa_status': 'gold', 'oa_url': 'https://advances.sciencemag.org/content/advances/4/11/eaat9744.full.pdf', 'any_repository_has_fulltext': True}, 'authorships': [{'author_position': 'first', 'author': {'id': 'https://openalex.org/A5043850385', 'display_name': 'Tuija Jokinen', 'orcid': 'https://orcid.org/0000-0002-1280-1396'}, 'institutions': [{'id': 'https://openalex.org/I133731052', 'display_name': 'University of Helsinki', 'ror': 'https://ror.org/040af2s02', 'country_code': 'FI', 'type': 'education', 'lineage': ['https://openalex.org/I133731052']}], 'countries': ['FI'], 'is_corresponding': True, 'raw_author_name': 'T. Jokinen', 'raw_affiliation_strings': ['INAR–Institute for Atmospheric and Earth System Research, P.O. Box 64, 00014 University of Helsinki, Finland.'], 'affiliations': [{'raw_affiliation_string': 'INAR–Institute for Atmospheric and Earth System Research, P.O. Box 64, 00014 University of Helsinki, Finland.', 'institution_ids': ['https://openalex.org/I133731052']}]}, {'author_position': 'middle', 'author': {'id': 'https://openalex.org/A5049530714', 'display_name': 'Mikko Sipilä', 'orcid': 'https://orcid.org/0000-0002-8594-7003'}, 'institutions': [{'id': 'https://openalex.org/I133731052', 'display_name': 'University of Helsinki', 'ror': 'https://ror.org/040af2s02', 'country_code': 'FI', 'type': 'education', 'lineage': ['https://openalex.org/I133731052']}], 'countries': ['FI'], 'is_corresponding': False, 'raw_author_name': 'M. Sipilä', 'raw_affiliation_strings': ['INAR–Institute for Atmospheric and Earth System Research, P.O. Box 64, 00014 University of Helsinki, Finland.'], 'affiliations': [{'raw_affiliation_string': 'INAR–Institute for Atmospheric and Earth System Research, P.O. Box 64, 00014 University of Helsinki, Finland.', 'institution_ids': ['https://openalex.org/I133731052']}]}, {'author_position': 'middle', 'author': {'id': 'https://openalex.org/A5041077752', 'display_name': 'Jenni Kontkanen', 'orcid': 'https://orcid.org/0000-0002-5373-3537'}, 'institutions': [{'id': 'https://openalex.org/I133731052', 'display_name': 'University of Helsinki', 'ror': 'https://ror.org/040af2s02', 'country_code': 'FI', 'type': 'education', 'lineage': ['https://openalex.org/I133731052']}], 'countries': ['FI'], 'is_corresponding': False, 'raw_author_name': 'J. Kontkanen', 'raw_affiliation_strings': ['INAR–Institute for Atmospheric and Earth System Research, P.O. Box 64, 00014 University of Helsinki, Finland.'], 'affiliations': [{'raw_affiliation_string': 'INAR–Institute for Atmospheric and Earth System Research, P.O. Box 64, 00014 University of Helsinki, Finland.', 'institution_ids': ['https://openalex.org/I133731052']}]}, {'author_position': 'middle', 'author': {'id': 'https://openalex.org/A5067947954', 'display_name': 'Ville Vakkari', 'orcid': None}, 'institutions': [{'id': 'https://openalex.org/I1285790362', 'display_name': 'Finnish Meteorological Institute', 'ror': 'https://ror.org/05hppb561', 'country_code': 'FI', 'type': 'government', 'lineage': ['https://openalex.org/I1284112523', 'https://openalex.org/I1285790362']}], 'countries': ['FI'], 'is_corresponding': False, 'raw_author_name': 'V. Vakkari', 'raw_affiliation_strings': ['Finnish Meteorological Institute, Erik Palménin aukio 1, 00560 Helsinki, Finland.'], 'affiliations': [{'raw_affiliation_string': 'Finnish Meteorological Institute, Erik Palménin aukio 1, 00560 Helsinki, Finland.', 'institution_ids': ['https://openalex.org/I1285790362']}]}, {'author_position': 'middle', 'author': {'id': 'https://openalex.org/A5004260866', 'display_name': 'Priit Tisler', 'orcid': None}, 'institutions': [{'id': 'https://openalex.org/I1285790362', 'display_name': 'Finnish Meteorological Institute', 'ror': 'https://ror.org/05hppb561', 'country_code': 'FI', 'type': 'government', 'lineage': ['https://openalex.org/I1284112523', 'https://openalex.org/I1285790362']}], 'countries': ['FI'], 'is_corresponding': False, 'raw_author_name': 'P. 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Junninen', 'raw_affiliation_strings': ['INAR–Institute for Atmospheric and Earth System Research, P.O. Box 64, 00014 University of Helsinki, Finland.', 'Laboratory of Environmental Physics, Institute of Physics, University of Tartu, Tartu 50090, Estonia.'], 'affiliations': [{'raw_affiliation_string': 'INAR–Institute for Atmospheric and Earth System Research, P.O. 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'created_date': '2018-12-11', 'is_authors_truncated': nan, 'abstract': \"Formation of new aerosol particles from trace gases is a major source of cloud condensation nuclei (CCN) in the global atmosphere, with potentially large effects on cloud optical properties and Earth's radiative balance. Controlled laboratory experiments have resolved, in detail, the different nucleation pathways likely responsible for atmospheric new particle formation, yet very little is known from field studies about the molecular steps and compounds involved in different regions of the atmosphere. The scarcity of primary particle sources makes secondary aerosol formation particularly important in the Antarctic atmosphere. Here, we report on the observation of ion-induced nucleation of sulfuric acid and ammonia-a process experimentally investigated by the CERN CLOUD experiment-as a major source of secondary aerosol particles over coastal Antarctica. We further show that measured high sulfuric acid concentrations, exceeding 107 molecules cm-3, are sufficient to explain the observed new particle growth rates. Our findings show that ion-induced nucleation is the dominant particle formation mechanism, implying that galactic cosmic radiation plays a key role in new particle formation in the pristine Antarctic atmosphere.\", 'is_oa': True, 'pdf_url': 'https://advances.sciencemag.org/content/advances/4/11/eaat9744.full.pdf', 'url': 'https://openalex.org/W2902694464', 'content': \"Ion-induced sulfuric acid–ammonia nucleation drives particle formation in coastal Antarctica\\nFormation of new aerosol particles from trace gases is a major source of cloud condensation nuclei (CCN) in the global atmosphere, with potentially large effects on cloud optical properties and Earth's radiative balance. Controlled laboratory experiments have resolved, in detail, the different nucleation pathways likely responsible for atmospheric new particle formation, yet very little is known from field studies about the molecular steps and compounds involved in different regions of the atmosphere. The scarcity of primary particle sources makes secondary aerosol formation particularly important in the Antarctic atmosphere. Here, we report on the observation of ion-induced nucleation of sulfuric acid and ammonia-a process experimentally investigated by the CERN CLOUD experiment-as a major source of secondary aerosol particles over coastal Antarctica. We further show that measured high sulfuric acid concentrations, exceeding 107 molecules cm-3, are sufficient to explain the observed new particle growth rates. Our findings show that ion-induced nucleation is the dominant particle formation mechanism, implying that galactic cosmic radiation plays a key role in new particle formation in the pristine Antarctic atmosphere.\", 'num_tokens': 230})" - ] - }, - "execution_count": 234, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "docs[0]" ] }, { "cell_type": "code", - "execution_count": 220, + "execution_count": null, "id": "b27c6f48-f4d3-4525-aa1d-8c3c52eb2179", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'https://openalex.org/W2117777590',\n", - " 'doi': 'https://doi.org/10.1111/j.1523-1739.1990.tb00283.x',\n", - " 'title': 'The Onslaught of Alien Species, and Other Challenges in the Coming Decades *',\n", - " 'display_name': 'The Onslaught of Alien Species, and Other Challenges in the Coming Decades *',\n", - " 'relevance_score': 155.60701,\n", - " 'publication_year': 1990,\n", - " 'publication_date': '1990-09-01',\n", - " 'ids': {'openalex': 'https://openalex.org/W2117777590',\n", - " 'doi': 'https://doi.org/10.1111/j.1523-1739.1990.tb00283.x',\n", - " 'mag': '2117777590'},\n", - " 'language': 'en',\n", - " 'primary_location': {'is_oa': False,\n", - " 'landing_page_url': 'https://doi.org/10.1111/j.1523-1739.1990.tb00283.x',\n", - " 'pdf_url': None,\n", - " 'source': {'id': 'https://openalex.org/S98137347',\n", - " 'display_name': 'Conservation biology',\n", - " 'issn_l': '0888-8892',\n", - " 'issn': ['0888-8892', '1523-1739'],\n", - " 'is_oa': False,\n", - " 'is_in_doaj': False,\n", - " 'is_core': True,\n", - " 'host_organization': 'https://openalex.org/P4310320503',\n", - " 'host_organization_name': 'Wiley-Blackwell',\n", - " 'host_organization_lineage': ['https://openalex.org/P4310320503',\n", - " 'https://openalex.org/P4310320595'],\n", - " 'host_organization_lineage_names': ['Wiley-Blackwell', 'Wiley'],\n", - " 'type': 'journal'},\n", - " 'license': None,\n", - " 'license_id': None,\n", - " 'version': None,\n", - " 'is_accepted': False,\n", - " 'is_published': False},\n", - " 'type': 'article',\n", - " 'type_crossref': 'journal-article',\n", - " 'indexed_in': ['crossref'],\n", - " 'open_access': {'is_oa': False,\n", - " 'oa_status': 'closed',\n", - " 'oa_url': None,\n", - " 'any_repository_has_fulltext': False},\n", - " 'authorships': [{'author_position': 'first',\n", - " 'author': {'id': 'https://openalex.org/A5002662609',\n", - " 'display_name': 'Michael E. 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Although these invasions will homogenize and impoverish the world's biota, they will lead to a deeper understanding of ecological communities. One consequence of the current biotic interchange is that the public's use of the outdoors will continue to decline as new and alien pathogens and parasites, their distributions and survival enhanced by climate warming and other anthropogenic factors, reduce the safety and enjoyment of hunting fishing and hiking. The forthcoming and massive ecological disruptions are bound to produce misunderstanding and conflict among environmentalists. Attempts by conservation biologists to manage wild and feral animals, including vector species, will be blocked by animal's rights groups. Even within the conservation biology movement there are many real and potential conflicts, especially over turf and resources. Such conflicts are a serious threat to biological diversity. Tolerance and compromise are essential if conservation biology is to accomplish its mission.\",\n", - " 'is_oa': False,\n", - " 'pdf_url': None,\n", - " 'url': 'https://openalex.org/W2117777590',\n", - " 'content': \"The Onslaught of Alien Species, and Other Challenges in the Coming Decades *\\nAbstract: Among the many environmental challenges faced by conservation scientists and managers in the coming decades, the inexorable invasion of alien species from distant land masses and between heretofore isolated regions within continents may be the most revolutionary. Although these invasions will homogenize and impoverish the world's biota, they will lead to a deeper understanding of ecological communities. One consequence of the current biotic interchange is that the public's use of the outdoors will continue to decline as new and alien pathogens and parasites, their distributions and survival enhanced by climate warming and other anthropogenic factors, reduce the safety and enjoyment of hunting fishing and hiking. The forthcoming and massive ecological disruptions are bound to produce misunderstanding and conflict among environmentalists. Attempts by conservation biologists to manage wild and feral animals, including vector species, will be blocked by animal's rights groups. Even within the conservation biology movement there are many real and potential conflicts, especially over turf and resources. Such conflicts are a serious threat to biological diversity. Tolerance and compromise are essential if conservation biology is to accomplish its mission.\",\n", - " 'num_tokens': 231}" - ] - }, - "execution_count": 220, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "docs[0].metadata" ] @@ -1498,32 +582,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "4151d2e4-0bfe-4642-a185-2dcb639e6f78", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'user_input': 'What does Morrison argue about IK and LK on internal migration ?',\n", - " 'intent': 'search',\n", - " 'language': 'English',\n", - " 'query': 'What does Morrison argue about IK and LK on internal migration ?',\n", - " 'questions': [{'question': 'What does Morrison argue about internal migration?',\n", - " 'sources': ['OpenAlex']},\n", - " {'question': 'What does Morrison argue about interregional migration?',\n", - " 'sources': ['OpenAlex']},\n", - " {'question': 'What does Morrison argue about intraregional migration?',\n", - " 'sources': ['OpenAlex']}]}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# question = \"Franchement je sais pas trop de quoi on parle là, qui sont les membres du GIEC en fait ? Et quelles sont leurs dernières publications ?\"\n", "question = \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\"\n", @@ -1535,23 +599,12 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "a322f737-191d-407f-b499-2b79476fac8b", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "'Super thanks, Which industries have the highest GHG emissions?'" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "questions = [\n", " \"Super thanks, Which industries have the highest GHG emissions?\",\n", @@ -1578,21 +631,10 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "fcee82a3-342d-4da1-8c27-a6f6079da4a7", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'sources': ['IPCC']}" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "question = \"Very nice thank you, What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\"\n", "output = rewriter_chain.invoke({\"input\":question})\n", @@ -1601,67 +643,12 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "d13d6a1a-9fa5-4e47-861e-30a79ea97d05", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Super thanks, Which industries have the highest GHG emissions?\n", - "{'user_input': 'Super thanks, Which industries have the highest GHG emissions?', 'intent': 'search', 'language': 'English', 'query': 'Super thanks, Which industries have the highest GHG emissions?', 'questions': [{'question': 'Which industries are the largest contributors to greenhouse gas emissions?', 'sources': ['IPCC']}]}\n", - "----------------------------------------------------------------------------------------------------\n", - "How do you compare the view on biodiversity between the IPCC and IPBES ?\n", - "{'user_input': 'How do you compare the view on biodiversity between the IPCC and IPBES ?', 'intent': 'search', 'language': 'English', 'query': 'How do you compare the view on biodiversity between the IPCC and IPBES ?', 'questions': [{'question': 'What is the view on biodiversity according to the IPCC?', 'sources': ['IPCC']}, {'question': 'What is the view on biodiversity according to the IPBES?', 'sources': ['IPBES']}]}\n", - "----------------------------------------------------------------------------------------------------\n", - "Est-ce que l'IA a un impact sur l'environnement ?\n", - "{'user_input': \"Est-ce que l'IA a un impact sur l'environnement ?\", 'intent': 'ai_impact', 'language': 'French', 'query': 'Does AI have an impact on the environment?', 'questions': [{'question': 'What is the impact of AI on the environment?', 'sources': ['IPCC', 'OpenAlex']}]}\n", - "----------------------------------------------------------------------------------------------------\n", - "Que dit le GIEC sur l'impact de l'IA\n", - "{'user_input': \"Que dit le GIEC sur l'impact de l'IA\", 'intent': 'search', 'language': 'French', 'query': 'What does the IPCC say about the impact of AI', 'questions': [{'question': \"What is the IPCC's stance on artificial intelligence and its impact on climate change?\", 'sources': ['IPCC']}]}\n", - "----------------------------------------------------------------------------------------------------\n", - "Franchement je sais pas trop de quoi on parle là, qui sont les membres du GIEC en fait ? 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threaten biodiversity and ecosystems?\n", - "{'user_input': 'What are invasive alien species and how do they threaten biodiversity and ecosystems?', 'intent': 'search', 'language': 'English', 'query': 'What are invasive alien species and how do they threaten biodiversity and ecosystems?', 'questions': [{'question': 'Definition of invasive alien species', 'sources': ['IPBES']}, {'question': 'How do invasive alien species threaten biodiversity and ecosystems?', 'sources': ['IPBES']}]}\n", - "----------------------------------------------------------------------------------------------------\n", - "Are human activities causing global warming?\n", - "{'user_input': 'Are human activities causing global warming?', 'intent': 'search', 'language': 'English', 'query': 'Are human activities causing global warming?', 'questions': [{'question': 'What are the main causes of global warming?', 'sources': ['IPCC']}, {'question': 'How do human activities contribute to global warming?', 'sources': ['IPCC']}]}\n", - "----------------------------------------------------------------------------------------------------\n", - "What is the motivation behind mining the deep seabed?\n", - "{'user_input': 'What is the motivation behind mining the deep seabed?', 'intent': 'search', 'language': 'English', 'query': 'What is the motivation behind mining the deep seabed?', 'questions': [{'question': 'What are the reasons for mining the deep seabed?', 'sources': ['IPOS']}, {'question': 'What are the benefits of mining the deep seabed?', 'sources': ['IPOS']}, {'question': 'What are the environmental impacts of mining the deep seabed?', 'sources': ['IPOS']}]}\n", - "----------------------------------------------------------------------------------------------------\n", - "Tu peux m'écrire un poème sur le changement climatique ?\n", - "{'user_input': \"Tu peux m'écrire un poème sur le changement climatique ?\", 'language': 'French', 'intent': 'search', 'query': 'Can you write me a poem about climate change?', 'questions': [{'question': 'What are the key themes and impacts of climate change?', 'sources': ['IPCC']}, {'question': 'How does climate change affect the environment and ecosystems?', 'sources': ['IPCC', 'IPBES']}, {'question': 'What are some common metaphors or symbols used to describe climate change in literature?', 'sources': ['IPCC']}]}\n", - "----------------------------------------------------------------------------------------------------\n", - "Tu peux m'écrire un poème sur les bonbons ?\n", - "{'user_input': \"Tu peux m'écrire un poème sur les bonbons ?\", 'language': 'French', 'intent': 'search', 'query': 'Can you write me a poem about candies?', 'questions': [{'question': 'What are the key elements of a poem about candies?', 'sources': ['OpenAlex']}, {'question': 'What are some common themes in poems about candies?', 'sources': ['OpenAlex']}, {'question': 'How can I structure a poem about candies?', 'sources': ['OpenAlex']}]}\n", - "----------------------------------------------------------------------------------------------------\n", - "What will be the temperature in 2100 in Strasbourg?\n", - "{'user_input': 'What will be the temperature in 2100 in Strasbourg?', 'intent': 'geo_info', 'language': 'English', 'query': 'What will be the temperature in 2100 in Strasbourg?', 'questions': [{'question': 'What are the projected temperature changes for the year 2100 according to IPCC reports?', 'sources': ['IPCC']}, {'question': 'How does climate change affect temperature projections for the future?', 'sources': ['IPCC']}]}\n", - "----------------------------------------------------------------------------------------------------\n", - "C'est quoi le lien entre biodiversity and changement climatique ?\n", - "{'user_input': \"C'est quoi le lien entre biodiversity and changement climatique ?\", 'intent': 'search', 'language': 'French', 'query': 'What is the link between biodiversity and climate change?', 'questions': [{'question': 'What is biodiversity?', 'sources': ['IPBES']}, {'question': 'How does climate change affect biodiversity?', 'sources': ['IPCC', 'IPBES']}, {'question': 'How does biodiversity affect climate change?', 'sources': ['IPCC', 'IPBES']}]}\n", - "----------------------------------------------------------------------------------------------------\n", - "Can you tell me more about ESRS2 ?\n", - "{'user_input': 'Can you tell me more about ESRS2 ?', 'intent': 'esg', 'language': 'English', 'query': 'Can you tell me more about ESRS2 ?', 'questions': [{'question': 'What is ESRS2?', 'sources': ['OpenAlex']}, {'question': 'What are the key features of ESRS2?', 'sources': ['IPCC']}, {'question': 'How is ESRS2 related to climate change, energy, biodiversity, and nature?', 'sources': ['IPCC', 'IPBES']}]}\n", - "----------------------------------------------------------------------------------------------------\n" - ] - } - ], + "outputs": [], "source": [ "for question in questions:\n", " print(question)\n", @@ -1672,27 +659,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "7a0d82ad-44e7-40a7-a594-48823429d70e", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'query': 'Industries with highest GHG emissions',\n", - " 'sources': ['IPCC'],\n", - " 'topics': ['Climate change', 'Decarbonization'],\n", - " 'date': '',\n", - " 'location': {'country': '', 'location': ''}}" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "rewriter_chain.invoke({\"input\":question})" ] @@ -1727,90 +699,20 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "a1f78d00-e4a0-40f3-a1e9-61dea83fa6cb", "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display_graph(agent)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "3a07607b-eb02-467f-a255-0c6bcdc6f62c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "... Analyzing user message\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'intent': 'ai_impact', 'language': 'English', 'query': \"What's the impact of PFAS on babies ?\"}\n", - "... Thinking step by step to answer the question\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'remaining_questions': [{'question': 'What are PFAS?', 'sources': ['IPCC'], 'index': 'Vector'}, {'question': 'What are the potential impacts of PFAS exposure on babies?', 'sources': ['IPCC'], 'index': 'Vector'}], 'n_questions': 2}\n", - "... Searching for \"What are PFAS?\" in IPCC\n", - "... Searching for \"What are the potential impacts of PFAS exposure on babies?\" in IPCC\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The impact of PFAS on babies can have significant consequences on their health and development. PFAS exposure has been linked to adverse effects on cognitive function in children [Doc 6]. Prenatal exposure to PFAS can affect children's cognitive function, highlighting the potential risks associated with these chemicals [Doc 6].\n", - "\n", - "Furthermore, PFAS exposure can also have implications for food safety. Climate change may affect animal health management practices, potentially leading to increased use of pesticides or veterinary drugs that could result in increased levels of residues in foods, including PFAS [Doc 7]. This indicates a potential pathway for PFAS contamination in food sources, which can pose risks to human health, especially vulnerable populations like babies.\n", - "\n", - "In summary, PFAS exposure can impact babies by affecting their cognitive function and potentially leading to contamination in food sources, highlighting the importance of monitoring and regulating these chemicals to protect infant health and development." - ] - } - ], + "outputs": [], "source": [ "verbose = False\n", "# question = \"What does the IPCC and IPBES think about deforestation ?\"\n", @@ -1861,79 +763,22 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "id": "a09f58e5-368b-486b-a458-7cfe56445c08", "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(page_content='Climate change adaptation entails the process of adjustment to actual or expected climate change and its effects \\r\\nin order to moderate harm or exploit beneficial opportunities. At a high level, international frameworks, including \\r\\nthe Paris Agreement and the SDGs, have come to provide a direction for coordinating, financing and assessing \\r\\nglobal progress in these terms. The Paris Agreement calls for climate change adaptation actions, referring to these \\r\\nactions as those that reduce risk and vulnerability, strengthen resilience, enhance the capacity to anticipate and \\r\\nrespond successfully, and ensure the availability of necessary financial resources, as these processes and outcomes \\r\\nrelate to climate change. In addition, the Sustainable Development Goals include 17 targets (with a specific goal \\r\\nSDG 13 on climate action) to fulfil its mission to end extreme poverty by 2030, protect the planet and build more \\r\\npeaceful, just and inclusive societies. These goals are difficult to reach without successful adaptation to climate \\r\\nchange. Other notable frameworks that identify climate change adaptation as important global priorities include \\r\\nthe SFDRR, the finance-oriented Addis Ababa Action Agenda and the New Urban Agenda.', metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 1244.0, 'num_tokens': 223.0, 'num_tokens_approx': 269.0, 'num_words': 202.0, 'page_number': 189, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'FAQ 1.1 | What are the goals of climate change adaptation?', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Chapters and Cross-Chapter Papers ', 'toc_level1': 'Chapter 1 Point of Departure and Key Concepts', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.728290737, 'content': 'Climate change adaptation entails the process of adjustment to actual or expected climate change and its effects \\r\\nin order to moderate harm or exploit beneficial opportunities. At a high level, international frameworks, including \\r\\nthe Paris Agreement and the SDGs, have come to provide a direction for coordinating, financing and assessing \\r\\nglobal progress in these terms. The Paris Agreement calls for climate change adaptation actions, referring to these \\r\\nactions as those that reduce risk and vulnerability, strengthen resilience, enhance the capacity to anticipate and \\r\\nrespond successfully, and ensure the availability of necessary financial resources, as these processes and outcomes \\r\\nrelate to climate change. In addition, the Sustainable Development Goals include 17 targets (with a specific goal \\r\\nSDG 13 on climate action) to fulfil its mission to end extreme poverty by 2030, protect the planet and build more \\r\\npeaceful, just and inclusive societies. These goals are difficult to reach without successful adaptation to climate \\r\\nchange. Other notable frameworks that identify climate change adaptation as important global priorities include \\r\\nthe SFDRR, the finance-oriented Addis Ababa Action Agenda and the New Urban Agenda.', 'reranking_score': 0.9998205, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'}),\n", - " Document(page_content='Frequently Asked Questions\\nFAQ 1.1 | What are the goals of climate change adaptation?\\nThe goals of climate change adaptation, as a broad concept, are to reduce risk and vulnerability to climate change, strengthen resilience, \\r\\nenhance well-being and the capacity to anticipate, and respond successfully to change. Existing international frameworks provide a high-level \\r\\ndirection for coordinating, financing and assessing progress toward these goals. However, specifying the goals for specific adaptation actions \\r\\nis not straightforward because the impacts of climate change affect people and nature in many different ways requiring different adaptation \\r\\nactions. Thereby, goals that accompany these actions are diverse. Goals can relate to health, water or food security, jobs and employment, \\r\\npoverty eradication and social equity, biodiversity and ecosystem services at international, national and local levels.\\n FAQ 1.1 | What are the goals of climate change adaptation? ', metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 1013.0, 'num_tokens': 185.0, 'num_tokens_approx': 234.0, 'num_words': 176.0, 'page_number': 189, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': '1.6 Structure of the Report', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Chapters and Cross-Chapter Papers ', 'toc_level1': 'Chapter 1 Point of Departure and Key Concepts', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.701966941, 'content': 'Frequently Asked Questions\\nFAQ 1.1 | What are the goals of climate change adaptation?\\nThe goals of climate change adaptation, as a broad concept, are to reduce risk and vulnerability to climate change, strengthen resilience, \\r\\nenhance well-being and the capacity to anticipate, and respond successfully to change. Existing international frameworks provide a high-level \\r\\ndirection for coordinating, financing and assessing progress toward these goals. However, specifying the goals for specific adaptation actions \\r\\nis not straightforward because the impacts of climate change affect people and nature in many different ways requiring different adaptation \\r\\nactions. Thereby, goals that accompany these actions are diverse. Goals can relate to health, water or food security, jobs and employment, \\r\\npoverty eradication and social equity, biodiversity and ecosystem services at international, national and local levels.\\n FAQ 1.1 | What are the goals of climate change adaptation? ', 'reranking_score': 0.9979572, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'}),\n", - " Document(page_content='The success of climate change adaptation is dependent on the extent to which relevant actions reduce risk and vulnerability, as well as achieve \\r\\ntheir respective goals. At a global scale, these goals are set and tracked according to international frameworks and conventions. At smaller \\r\\nscales, such as local and national, goals are dependent on the specific impacts being managed, the actions being taken and the relevant scale. \\r\\nWhile success can take shape as uniquely as goals can, the degree to which an adaptation is feasible, effective and conforms to principles of \\r\\njustice represents important attributes for measuring success across actions. Adaptation responses that lead to increased risk and impacts are \\r\\nconsidered maladaptation.\\n FAQ 1.3 | What constitutes successful adaptation to climate change? ', metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 852.0, 'num_tokens': 156.0, 'num_tokens_approx': 196.0, 'num_words': 147.0, 'page_number': 190, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'FAQ 1.3 | What constitutes successful adaptation to climate change?', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Chapters and Cross-Chapter Papers ', 'toc_level1': 'Chapter 1 Point of Departure and Key Concepts', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.691562712, 'content': 'The success of climate change adaptation is dependent on the extent to which relevant actions reduce risk and vulnerability, as well as achieve \\r\\ntheir respective goals. At a global scale, these goals are set and tracked according to international frameworks and conventions. At smaller \\r\\nscales, such as local and national, goals are dependent on the specific impacts being managed, the actions being taken and the relevant scale. \\r\\nWhile success can take shape as uniquely as goals can, the degree to which an adaptation is feasible, effective and conforms to principles of \\r\\njustice represents important attributes for measuring success across actions. Adaptation responses that lead to increased risk and impacts are \\r\\nconsidered maladaptation.\\n FAQ 1.3 | What constitutes successful adaptation to climate change? ', 'reranking_score': 0.9957848, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'}),\n", - " Document(page_content='Adaptation involves actions to lessen the harm associated with \\r\\nclimate change, or take advantage of potential gains (Smit and \\r\\nWandel 2006). It can seek to reduce present and future exposure \\r\\nto specific climate risks (Adger et al. 2003), mainstream climate \\r\\ninformation into existing planning efforts (Gupta et al. 2010; van der \\r\\nVoorn et al. 2012; van der Voorn et al. 2017), and reduce vulnerability \\r\\n(or increase resilience) of people or communities to the effects of \\r\\nclimate change (Kasperson and Kasperson 2001). There is a body \\r\\nof literature highlighting potential synergies and conflicts between \\r\\nadaptation actions - in any of the three areas above - and mitigation \\r\\nactions - and potential strategies for resolving them (Locatelli \\r\\net al. 2011; Casado-Asensio and Steurer 2014; Duguma et al. 2014;', metadata={'chunk_type': 'text', 'document_id': 'document9', 'document_number': 9.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2258.0, 'name': 'Full Report. In: Climate Change 2022: Mitigation of Climate Change. 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It can seek to reduce present and future exposure \\r\\nto specific climate risks (Adger et al. 2003), mainstream climate \\r\\ninformation into existing planning efforts (Gupta et al. 2010; van der \\r\\nVoorn et al. 2012; van der Voorn et al. 2017), and reduce vulnerability \\r\\n(or increase resilience) of people or communities to the effects of \\r\\nclimate change (Kasperson and Kasperson 2001). There is a body \\r\\nof literature highlighting potential synergies and conflicts between \\r\\nadaptation actions - in any of the three areas above - and mitigation \\r\\nactions - and potential strategies for resolving them (Locatelli \\r\\net al. 2011; Casado-Asensio and Steurer 2014; Duguma et al. 2014;', 'reranking_score': 0.99499834, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'}),\n", - " Document(page_content='For decades, communities worldwide have already been adapting to climate change-induced hydrological changes \\r\\nto maintain their livelihood and safety. Adaptation is a multi-faceted process that is implemented differently \\r\\ndepending on the sector affected by changes in the hydrological cycle and the region where these changes happen. \\r\\nFor instance, farmers in the semiarid areas might adapt to changing rain patterns through irrigation (see also \\r\\nFAQ4.4). At the same time, urban dwellers can adopt measures such as rainwater harvesting and other nature-based \\r\\nsolutions. Several principles have been documented as crucial for achieving sustainable adaptation as they support \\r\\ncommunities in becoming more resilient to climate change. However, these principles can be implemented singularly \\r\\nor in tandem, and it is essential to acknowledge that long-term adaptation success is context-specific. Therefore, it \\r\\nis critical to involve local communities in co-designing effective adaptation responses.', metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. 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Adaptation is a multi-faceted process that is implemented differently \\r\\ndepending on the sector affected by changes in the hydrological cycle and the region where these changes happen. \\r\\nFor instance, farmers in the semiarid areas might adapt to changing rain patterns through irrigation (see also \\r\\nFAQ4.4). At the same time, urban dwellers can adopt measures such as rainwater harvesting and other nature-based \\r\\nsolutions. Several principles have been documented as crucial for achieving sustainable adaptation as they support \\r\\ncommunities in becoming more resilient to climate change. However, these principles can be implemented singularly \\r\\nor in tandem, and it is essential to acknowledge that long-term adaptation success is context-specific. Therefore, it \\r\\nis critical to involve local communities in co-designing effective adaptation responses.', 'reranking_score': 0.99424744, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'}),\n", - " Document(page_content='Adaptation, in response to current climate change, is reducing climate risks and vulnerability mostly via adjustment of existing systems. Many \\r\\nadaptation options exist and are used to help manage projected climate change impacts, but their implementation depends upon the capacity and \\r\\neffectiveness of governance and decision-making processes. These and other enabling conditions can also support climate resilient development \\r\\n(Section D).\\nCurrent Adaptation and its Benefits\\nC.1 Progress in adaptation planning and implementation has been observed across all sectors and regions, generating multiple \\r\\nbenefits (very high confidence). However, adaptation progress is unevenly distributed with observed adaptation gaps40 (high \\r\\nconfidence). Many initiatives prioritize immediate and near-term climate risk reduction which reduces the opportunity for \\r\\ntransformational adaptation (high confidence). {2.6, 5.14, 7.4, 10.4, 12.5, 13.11, 14.7, 16.3, 17.3, CCP5.2, CCP5.4}', metadata={'chunk_type': 'text', 'document_id': 'document4', 'document_number': 4.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. 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Many \\r\\nadaptation options exist and are used to help manage projected climate change impacts, but their implementation depends upon the capacity and \\r\\neffectiveness of governance and decision-making processes. These and other enabling conditions can also support climate resilient development \\r\\n(Section D).\\nCurrent Adaptation and its Benefits\\nC.1 Progress in adaptation planning and implementation has been observed across all sectors and regions, generating multiple \\r\\nbenefits (very high confidence). However, adaptation progress is unevenly distributed with observed adaptation gaps40 (high \\r\\nconfidence). Many initiatives prioritize immediate and near-term climate risk reduction which reduces the opportunity for \\r\\ntransformational adaptation (high confidence). {2.6, 5.14, 7.4, 10.4, 12.5, 13.11, 14.7, 16.3, 17.3, CCP5.2, CCP5.4}', metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. 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These and other enabling conditions can also support climate resilient development \\r\\n(Section D).\\nCurrent Adaptation and its Benefits\\nC.1 Progress in adaptation planning and implementation has been observed across all sectors and regions, generating multiple \\r\\nbenefits (very high confidence). However, adaptation progress is unevenly distributed with observed adaptation gaps40 (high \\r\\nconfidence). Many initiatives prioritize immediate and near-term climate risk reduction which reduces the opportunity for \\r\\ntransformational adaptation (high confidence). {2.6, 5.14, 7.4, 10.4, 12.5, 13.11, 14.7, 16.3, 17.3, CCP5.2, CCP5.4}', 'reranking_score': 0.9935619, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'}),\n", - " Document(page_content='Frequently Asked Questions\\nFAQ 16.4 | What adaptation-related responses to climate change have already been observed, and do they help \\r\\nreduce climate risk?\\nAdaptation-related responses are the actions taken with the intention of managing risks by reducing vulnerability or exposure to climate \\r\\nhazards. Responses are increasing and expanding across global regions and sectors, although there is still a lot of opportunity for improvement. \\r\\nExamining the adequacy and effectiveness of the responses is important to guide planning, implementation and expansion.\\nThe most frequently reported adaptation-related responses are behavioural changes made by individuals \\r\\nand households in response to drought, flooding and rainfall variability in Africa and Asia. Governments are \\r\\nincreasingly undertaking planning, and implementing policy and legislation, including, for example, new zoning \\r\\nregulations and building codes, coordination mechanisms, disaster and emergency planning, or extension services \\r\\nto support farmer uptake of drought tolerant crops. Local governments are particularly active in adaptation-related \\r\\nresponses, particularly in protecting infrastructure and services, such as water and sanitation. Across all regions, \\r\\nadaptation-related responses are strongly linked to food security, with poverty alleviation a key strategy in the', metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 1356.0, 'num_tokens': 231.0, 'num_tokens_approx': 284.0, 'num_words': 213.0, 'page_number': 2518, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': '(a) Low-lying coastal systems', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Chapters and Cross-Chapter Papers ', 'toc_level1': 'Chapter 16 Key Risks across Sectors and Regions', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.723861217, 'content': 'Frequently Asked Questions\\nFAQ 16.4 | What adaptation-related responses to climate change have already been observed, and do they help \\r\\nreduce climate risk?\\nAdaptation-related responses are the actions taken with the intention of managing risks by reducing vulnerability or exposure to climate \\r\\nhazards. Responses are increasing and expanding across global regions and sectors, although there is still a lot of opportunity for improvement. \\r\\nExamining the adequacy and effectiveness of the responses is important to guide planning, implementation and expansion.\\nThe most frequently reported adaptation-related responses are behavioural changes made by individuals \\r\\nand households in response to drought, flooding and rainfall variability in Africa and Asia. Governments are \\r\\nincreasingly undertaking planning, and implementing policy and legislation, including, for example, new zoning \\r\\nregulations and building codes, coordination mechanisms, disaster and emergency planning, or extension services \\r\\nto support farmer uptake of drought tolerant crops. Local governments are particularly active in adaptation-related \\r\\nresponses, particularly in protecting infrastructure and services, such as water and sanitation. Across all regions, \\r\\nadaptation-related responses are strongly linked to food security, with poverty alleviation a key strategy in the', 'reranking_score': 0.991938, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'}),\n", - " Document(page_content='Current observed climate impacts and expected future risks include stronger and longer heat waves, unprecedented \\r\\ndroughts and floods, accelerating sea level rise and storm surges affecting many geographies and communities. \\r\\nPeople around the world are increasingly perceiving changing climates, regarding these changes as significant and \\r\\nconsidering climate action as a matter of high urgency. In particular, marginalised and poor people, as well as island \\r\\nand coastal communities, experience relatively higher risks and vulnerability. The available evidence suggests that \\r\\ncurrent adaptation efforts may be insufficient to help ensure sustainable development and other societal goals in \\r\\nmany communities worldwide even under the most optimistic GHG emissions scenarios.\\nClimate change adaptation is, therefore, urgent to the extent that meeting important societal goals requires \\r\\nimmediate and long-term action by governments, business, civil society and individuals at a scale and speed \\r\\nsignificantly faster than that represented by current trends.\\nFrequently Asked Questions', metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 1090.0, 'num_tokens': 182.0, 'num_tokens_approx': 218.0, 'num_words': 164.0, 'page_number': 190, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'FAQ 1.2 | Is climate change adaptation urgent?', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Chapters and Cross-Chapter Papers ', 'toc_level1': 'Chapter 1 Point of Departure and Key Concepts', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.679264843, 'content': 'Current observed climate impacts and expected future risks include stronger and longer heat waves, unprecedented \\r\\ndroughts and floods, accelerating sea level rise and storm surges affecting many geographies and communities. \\r\\nPeople around the world are increasingly perceiving changing climates, regarding these changes as significant and \\r\\nconsidering climate action as a matter of high urgency. In particular, marginalised and poor people, as well as island \\r\\nand coastal communities, experience relatively higher risks and vulnerability. The available evidence suggests that \\r\\ncurrent adaptation efforts may be insufficient to help ensure sustainable development and other societal goals in \\r\\nmany communities worldwide even under the most optimistic GHG emissions scenarios.\\nClimate change adaptation is, therefore, urgent to the extent that meeting important societal goals requires \\r\\nimmediate and long-term action by governments, business, civil society and individuals at a scale and speed \\r\\nsignificantly faster than that represented by current trends.\\nFrequently Asked Questions', 'reranking_score': 0.9919067, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'}),\n", - " Document(page_content='As discussed in Chapter 16, adaptation is a key mechanism for \\r\\nmanaging climate risks, and therefore for pursuing CRD. The lower \\r\\nestimates in Table 18.2 are associated with higher levels of adaptation \\r\\nand more conducive development conditions. Furthermore, additional \\r\\nadaptation demand is associated with greater levels of climate change. \\r\\nAdaptation is a broad term referring to many different levels of response \\r\\nand options for natural and human systems, from individuals, specific \\r\\nlocations and specific technologies, to nations, markets, global dynamics \\r\\nand strategies at the system level. Adaptation also includes endogenous \\r\\nreflexive and exogenous policy responses. Perspectives on limits to \\r\\nadaptation, synergies, trade-offs and feasibility therefore depend on \\r\\nwhere the boundaries are drawn and the objective. Overall, there are \\r\\na broad range of adaptation options relevant to reducing risks posed \\r\\nby climate change to development. However, current understanding \\r\\nof how such options are implemented in practice, their effectiveness \\r\\nacross a range of possible climate futures and their potential limits, is \\r\\nmodest.', metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 1151.0, 'num_tokens': 218.0, 'num_tokens_approx': 248.0, 'num_words': 186.0, 'page_number': 2692, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': '(b) Sustainable development related climate impacts indicators', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Chapters and Cross-Chapter Papers ', 'toc_level1': 'Chapter 18 Climate Resilient Development Pathways', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.734549761, 'content': 'As discussed in Chapter 16, adaptation is a key mechanism for \\r\\nmanaging climate risks, and therefore for pursuing CRD. The lower \\r\\nestimates in Table 18.2 are associated with higher levels of adaptation \\r\\nand more conducive development conditions. Furthermore, additional \\r\\nadaptation demand is associated with greater levels of climate change. \\r\\nAdaptation is a broad term referring to many different levels of response \\r\\nand options for natural and human systems, from individuals, specific \\r\\nlocations and specific technologies, to nations, markets, global dynamics \\r\\nand strategies at the system level. Adaptation also includes endogenous \\r\\nreflexive and exogenous policy responses. Perspectives on limits to \\r\\nadaptation, synergies, trade-offs and feasibility therefore depend on \\r\\nwhere the boundaries are drawn and the objective. Overall, there are \\r\\na broad range of adaptation options relevant to reducing risks posed \\r\\nby climate change to development. However, current understanding \\r\\nof how such options are implemented in practice, their effectiveness \\r\\nacross a range of possible climate futures and their potential limits, is \\r\\nmodest.', 'reranking_score': 0.99029154, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'}),\n", - " Document(page_content='Adaptation plays a key role in reducing risks and vulnerability \\r\\nfrom climate change. Implementing adaptation and mitigation \\r\\nactions together with the SDGs helps to exploit synergies, \\r\\nreduce trade-offs and makes all three more effective. From \\r\\na risk perspective, limiting atmospheric greenhouse gas (GHG) \\r\\nconcentrations reduces climate-related hazards while adaptation and \\r\\nsustainable development reduce exposure and vulnerability to those \\r\\nhazards. Adaptation facilitates development, which is increasingly \\r\\nhindered by impacts and risks from climate change. Development \\r\\nfacilitates adaptation by expanding the resources and capacity to \\r\\nreduce climate risks and vulnerability. {1.1.3; 1.5.1; 1.5.3}', metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 716.0, 'num_tokens': 147.0, 'num_tokens_approx': 161.0, 'num_words': 121.0, 'page_number': 135, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'Executive Summary', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Chapters and Cross-Chapter Papers ', 'toc_level1': 'Chapter 1 Point of Departure and Key Concepts', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.676665783, 'content': 'Adaptation plays a key role in reducing risks and vulnerability \\r\\nfrom climate change. Implementing adaptation and mitigation \\r\\nactions together with the SDGs helps to exploit synergies, \\r\\nreduce trade-offs and makes all three more effective. From \\r\\na risk perspective, limiting atmospheric greenhouse gas (GHG) \\r\\nconcentrations reduces climate-related hazards while adaptation and \\r\\nsustainable development reduce exposure and vulnerability to those \\r\\nhazards. Adaptation facilitates development, which is increasingly \\r\\nhindered by impacts and risks from climate change. Development \\r\\nfacilitates adaptation by expanding the resources and capacity to \\r\\nreduce climate risks and vulnerability. {1.1.3; 1.5.1; 1.5.3}', 'reranking_score': 0.9901782, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'}),\n", - " Document(page_content=\"The IPCC defines adaptation as: 'in human systems, the process of \\r\\nadjustment to actual or expected climate and its effects, in order to \\r\\nmoderate harm or exploit beneficial opportunities. In natural systems, \\r\\nthe process of adjustment to actual climate and its effect; human \\nintervention may facilitate adjustment to expected climate and its \\r\\neffects' (Annex I: Glossary).\", metadata={'chunk_type': 'text', 'document_id': 'document9', 'document_number': 9.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2258.0, 'name': 'Full Report. In: Climate Change 2022: Mitigation of Climate Change. Contribution of the WGIII to the AR6 of the IPCC', 'num_characters': 378.0, 'num_tokens': 77.0, 'num_tokens_approx': 89.0, 'num_words': 67.0, 'page_number': 1510, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': '14.5.1.2 Linkages with Sustainable Development, Adaptation, \\r\\nLoss and Damage, and Human Rights', 'short_name': 'IPCC AR6 WGIII FR', 'source': 'IPCC', 'toc_level0': '14.5 Multi-level, Multi-actor Governance', 'toc_level1': '14.5.1 International Cooperation at Multiple Governance Levels', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_FullReport.pdf', 'similarity_score': 0.760399759, 'content': \"The IPCC defines adaptation as: 'in human systems, the process of \\r\\nadjustment to actual or expected climate and its effects, in order to \\r\\nmoderate harm or exploit beneficial opportunities. In natural systems, \\r\\nthe process of adjustment to actual climate and its effect; human \\nintervention may facilitate adjustment to expected climate and its \\r\\neffects' (Annex I: Glossary).\", 'reranking_score': 0.9898303, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'}),\n", - " Document(page_content='Adaptation In human systems, the process of adjustment to actual \\r\\nor expected climate and its effects, in order to moderate harm or exploit \\r\\nbeneficial opportunities. In natural systems, the process of adjustment \\r\\nto actual climate and its effects; human intervention may facilitate \\r\\nadjustment to expected climate and its effects. See also Adaptation \\r\\noptions, Adaptive capacity, and Maladaptive actions (Maladaptation).\\n Adaptation limits \\n\\n[Note: For a discussion of the term forest and related terms such as \\r\\nafforestation, reforestation and deforestation, see the 2006 IPCC Guidelines \\r\\nfor National Greenhouse Gas Inventories and their 2019 Refinement, and \\r\\ninformation provided by the United Nations Framework Convention on \\r\\nClimate Change (IPCC 2006, 2019; UNFCCC 2021a,b).] \\nSee also Deforestation, Reducing Emissions from Deforestation \\r\\nand Forest Degradation (REDD+), Reforestation, Anthropogenic \\r\\nRemovals, and Carbon dioxide removal (CDR).', metadata={'chunk_type': 'text', 'document_id': 'document9', 'document_number': 9.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2258.0, 'name': 'Full Report. In: Climate Change 2022: Mitigation of Climate Change. Contribution of the WGIII to the AR6 of the IPCC', 'num_characters': 999.0, 'num_tokens': 223.0, 'num_tokens_approx': 221.0, 'num_words': 166.0, 'page_number': 1807, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': '1.5degC pathway See Pathways.', 'short_name': 'IPCC AR6 WGIII FR', 'source': 'IPCC', 'toc_level0': '_Hlk111724995', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_FullReport.pdf', 'similarity_score': 0.742421, 'content': 'Adaptation In human systems, the process of adjustment to actual \\r\\nor expected climate and its effects, in order to moderate harm or exploit \\r\\nbeneficial opportunities. In natural systems, the process of adjustment \\r\\nto actual climate and its effects; human intervention may facilitate \\r\\nadjustment to expected climate and its effects. See also Adaptation \\r\\noptions, Adaptive capacity, and Maladaptive actions (Maladaptation).\\n Adaptation limits \\n\\n[Note: For a discussion of the term forest and related terms such as \\r\\nafforestation, reforestation and deforestation, see the 2006 IPCC Guidelines \\r\\nfor National Greenhouse Gas Inventories and their 2019 Refinement, and \\r\\ninformation provided by the United Nations Framework Convention on \\r\\nClimate Change (IPCC 2006, 2019; UNFCCC 2021a,b).] \\nSee also Deforestation, Reducing Emissions from Deforestation \\r\\nand Forest Degradation (REDD+), Reforestation, Anthropogenic \\r\\nRemovals, and Carbon dioxide removal (CDR).', 'reranking_score': 0.9870296, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'}),\n", - " Document(page_content='Because the impacts of climate change affect people and nature in so many different ways, the specific goals of \\r\\nadaptation depend on the impact being managed and the action being taken. For human systems, adaptation \\r\\nincludes actions aimed at reducing a specific risk, such as by fortifying a building against flooding, or actions aimed \\r\\nat multiple risks, such as requiring climate risk assessments in financial reporting in anticipation of different kinds \\r\\nof risk. At the local level, communities can take actions that include updating building codes and land use plans, \\r\\nimproving soil management, enhancing water use efficiency, supporting migrants and taking measures to reduce \\r\\npoverty. For natural systems, adaptation includes organisms changing behaviours, migrating to new locations and \\r\\ngenetic modifications in response to changing climate conditions. The goals for these adaptation actions can relate \\r\\nto health, water or food security, jobs and employment, poverty eradication and social equity, biodiversity and \\r\\necosystem services, among others. Articulating the goals of adaptation thus requires engaging with the concepts of \\r\\nequity, justice and effectiveness at the international, national and local levels.\\n177177', metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 1244.0, 'num_tokens': 225.0, 'num_tokens_approx': 269.0, 'num_words': 202.0, 'page_number': 189, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'FAQ 1.1 | What are the goals of climate change adaptation?', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Chapters and Cross-Chapter Papers ', 'toc_level1': 'Chapter 1 Point of Departure and Key Concepts', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.682544589, 'content': 'Because the impacts of climate change affect people and nature in so many different ways, the specific goals of \\r\\nadaptation depend on the impact being managed and the action being taken. For human systems, adaptation \\r\\nincludes actions aimed at reducing a specific risk, such as by fortifying a building against flooding, or actions aimed \\r\\nat multiple risks, such as requiring climate risk assessments in financial reporting in anticipation of different kinds \\r\\nof risk. At the local level, communities can take actions that include updating building codes and land use plans, \\r\\nimproving soil management, enhancing water use efficiency, supporting migrants and taking measures to reduce \\r\\npoverty. For natural systems, adaptation includes organisms changing behaviours, migrating to new locations and \\r\\ngenetic modifications in response to changing climate conditions. The goals for these adaptation actions can relate \\r\\nto health, water or food security, jobs and employment, poverty eradication and social equity, biodiversity and \\r\\necosystem services, among others. Articulating the goals of adaptation thus requires engaging with the concepts of \\r\\nequity, justice and effectiveness at the international, national and local levels.\\n177177', 'reranking_score': 0.9862577, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'}),\n", - " Document(page_content='Adaptation in this report is defined, in human systems, as the process \\r\\nof adjustment to actual or expected climate and its effects, in order \\r\\nto moderate harm or exploit beneficial opportunities. In natural \\r\\nsystems, adaptation is the process of adjustment to actual climate \\r\\nand its effects; human intervention may facilitate adjustment to \\r\\nexpected climate and its effects (see Annex II: Glossary). Adaptation \\r\\nplanning in human systems generally entails a process of iterative risk \\r\\nmanagement. Different types of adaptation have been distinguished, \\r\\nincluding anticipatory versus reactive, autonomous versus planned \\r\\nand incremental versus transformational adaptation (Chapters 16-\\r\\n18; IPCC WGII glossaries for the TAR, AR4, AR5, and AR6 (Annex II)). \\r\\nAdaptation is often seen as having five general stages: (a) awareness, \\r\\n(b) assessment, (c) planning, (d) implementation and (e) M&E (Moser \\r\\nand Boykoff, 2013; Jones et al., 2014; Mimura et al., 2014; Noble et al.,', metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 984.0, 'num_tokens': 224.0, 'num_tokens_approx': 249.0, 'num_words': 187.0, 'page_number': 146, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': '1.2.1.3 Adaptation', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Chapters and Cross-Chapter Papers ', 'toc_level1': 'Chapter 1 Point of Departure and Key Concepts', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.762178361, 'content': 'Adaptation in this report is defined, in human systems, as the process \\r\\nof adjustment to actual or expected climate and its effects, in order \\r\\nto moderate harm or exploit beneficial opportunities. In natural \\r\\nsystems, adaptation is the process of adjustment to actual climate \\r\\nand its effects; human intervention may facilitate adjustment to \\r\\nexpected climate and its effects (see Annex II: Glossary). Adaptation \\r\\nplanning in human systems generally entails a process of iterative risk \\r\\nmanagement. Different types of adaptation have been distinguished, \\r\\nincluding anticipatory versus reactive, autonomous versus planned \\r\\nand incremental versus transformational adaptation (Chapters 16-\\r\\n18; IPCC WGII glossaries for the TAR, AR4, AR5, and AR6 (Annex II)). \\r\\nAdaptation is often seen as having five general stages: (a) awareness, \\r\\n(b) assessment, (c) planning, (d) implementation and (e) M&E (Moser \\r\\nand Boykoff, 2013; Jones et al., 2014; Mimura et al., 2014; Noble et al.,', 'reranking_score': 0.98532796, 'query_used_for_retrieval': 'What is climate change adaptation?', 'sources_used': ['IPCC'], 'question_used': 'What is climate change adaptation?'})]" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "docs" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "9b41f377-b3a9-41eb-acef-fb3ea131c374", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "L'impact environnemental de l'intelligence artificielle (IA) n'est pas couvert spécifiquement par les rapports du GIEC ou de l'IPBES. Cependant, il existe des recherches récentes sur ce sujet. Par exemple, vous pouvez consulter le travail de l'experte en IA et climat, Sasha Luccioni, qui a publié plusieurs articles sur l'empreinte carbone de l'IA, notamment sur le coût énergétique de l'inférence des modèles d'IA et sur l'estimation de l'empreinte carbone de l'entraînement de grands modèles de langage.\n", - "\n", - "De plus, il existe des outils tels que CodeCarbon et Ecologits qui peuvent vous aider à calculer l'empreinte carbone de vos modèles d'IA. Si vous souhaitez en savoir plus sur l'empreinte carbone de l'IA, je vous recommande de consulter ces ressources et outils. Vous pouvez également visiter cette page pour en apprendre davantage sur l'empreinte carbone de ClimateQ&A : [Lien vers la page sur l'empreinte carbone de ClimateQ&A](https://climateqa.com/docs/carbon-footprint/)\n" - ] - }, - { - "data": { - "text/plain": [ - "{'user_input': \"C'est quoi l'empreinte carbone de l'IA\",\n", - " 'language': 'French',\n", - " 'intent': 'ai_impact',\n", - " 'query': \"C'est quoi l'empreinte carbone de l'IA\",\n", - " 'answer': \"L'impact environnemental de l'intelligence artificielle (IA) n'est pas couvert spécifiquement par les rapports du GIEC ou de l'IPBES. Cependant, il existe des recherches récentes sur ce sujet. Par exemple, vous pouvez consulter le travail de l'experte en IA et climat, Sasha Luccioni, qui a publié plusieurs articles sur l'empreinte carbone de l'IA, notamment sur le coût énergétique de l'inférence des modèles d'IA et sur l'estimation de l'empreinte carbone de l'entraînement de grands modèles de langage.\\n\\nDe plus, il existe des outils tels que CodeCarbon et Ecologits qui peuvent vous aider à calculer l'empreinte carbone de vos modèles d'IA. Si vous souhaitez en savoir plus sur l'empreinte carbone de l'IA, je vous recommande de consulter ces ressources et outils. Vous pouvez également visiter cette page pour en apprendre davantage sur l'empreinte carbone de ClimateQ&A : [Lien vers la page sur l'empreinte carbone de ClimateQ&A](https://climateqa.com/docs/carbon-footprint/)\"}" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "output = await agent.ainvoke({\"user_input\":\"C'est quoi l'empreinte carbone de l'IA\"})\n", "output" @@ -1965,34 +810,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "586a4ead-6064-42e8-9f3c-8973b8d754c6", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "L'impact environnemental de l'intelligence artificielle n'est pas couvert par les rapports du GIEC ou de l'IPBES. Cependant, il existe des recherches récentes sur ce sujet. Par exemple, vous pouvez consulter le travail de l'experte en IA et climat, Sasha Luccioni, qui a publié des articles sur l'empreinte carbone de l'IA, notamment sur le coût énergétique de l'inférence des modèles d'IA et sur l'estimation de l'empreinte carbone de l'entraînement de grands modèles de langage.\n", - "\n", - "Si vous souhaitez en savoir plus sur l'empreinte carbone de l'IA, je vous recommande de consulter les liens suivants :\n", - "- \"Power Hungry Processing: Watts Driving the Cost of AI Deployment?\" - https://arxiv.org/abs/2311.16863\n", - "- \"Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning\" - https://arxiv.org/abs/2302.08476\n", - "- \"Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model\" - https://arxiv.org/abs/2211.02001\n", - "\n", - "De plus, il existe des outils tels que CodeCarbon (https://github.com/mlco2/codecarbon) pour mesurer l'empreinte carbone de votre code, et Ecologits (https://ecologits.ai/) pour mesurer l'empreinte carbone de l'utilisation des API de grands modèles de langage. Ces ressources pourraient vous aider à mieux comprendre l'impact environnemental de l'IA. Si vous souhaitez en savoir plus sur l'empreinte carbone de ClimateQ&A, vous pouvez consulter cette page : https://climateqa.com/docs/carbon-footprint/." - ] - } - ], + "outputs": [], "source": [ "async for event in ai_impact_chain.astream_events({\"question\":\"Mais c'est quoi l'empreinte carbone de cet outil, ça doit consommer pas mal ...\"},version = \"v1\"):\n", " if event[\"event\"] == \"on_chain_stream\":\n", @@ -2001,27 +824,12 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "6f3a1c52-f92a-442b-9442-22415087da8b", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Je suis désolé, je ne peux répondre qu'aux questions liées à l'environnement et au climat. N'hésitez pas à poser une question sur ces sujets!" - ] - } - ], + "outputs": [], "source": [ "async for event in chitchat_chain.astream_events({\"question\":\"Vas y blbablablala\"},version = \"v1\"):\n", " if event[\"event\"] == \"on_chain_stream\":\n", @@ -2053,23 +861,12 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": null, "id": "cbe2f9f0-0c0b-46f8-929c-51a94eab5f22", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "96" - ] - }, - "execution_count": 140, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "query = \"Is global warming caused by humans?\"\n", "\n", @@ -2088,21 +885,12 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "id": "6e6892d2-a5bd-456a-8284-6e844d02af0e", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: total: 1.97 s\n", - "Wall time: 681 ms\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "from scipy.special import expit, logit\n", @@ -2130,46 +918,24 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": null, "id": "430209db-5ffb-4017-8ebd-12fee30df327", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "Document(page_content='Observed Warming and its Causes\\nA.1 Human activities, principally through emissions of greenhouse gases, have unequivocally \\r\\ncaused global warming, with global surface temperature reaching 1.1degC above 1850-1900 \\r\\nin 2011-2020. Global greenhouse gas emissions have continued to increase, with unequal \\r\\nhistorical and ongoing contributions arising from unsustainable energy use, land use and \\r\\nland-use change, lifestyles and patterns of consumption and production across regions, \\r\\nbetween and within countries, and among individuals (high confidence). {2.1, Figure 2.1, \\r\\nFigure 2.2}', metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 587.0, 'num_tokens': 130.0, 'num_tokens_approx': 142.0, 'num_words': 107.0, 'page_number': 10, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Observed Warming and its Causes', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.759439, 'content': 'Observed Warming and its Causes\\nA.1 Human activities, principally through emissions of greenhouse gases, have unequivocally \\r\\ncaused global warming, with global surface temperature reaching 1.1degC above 1850-1900 \\r\\nin 2011-2020. Global greenhouse gas emissions have continued to increase, with unequal \\r\\nhistorical and ongoing contributions arising from unsustainable energy use, land use and \\r\\nland-use change, lifestyles and patterns of consumption and production across regions, \\r\\nbetween and within countries, and among individuals (high confidence). {2.1, Figure 2.1, \\r\\nFigure 2.2}', 'rerank_score': 0.9993778467178345, 'query_used_for_retrieval': 'Is global warming caused by humans?'})" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "docs_reranked[0]" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": null, "id": "5ed0b677-bcc4-48b0-bc99-2f515f2e7293", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "[5, 5, 5]" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def divide_into_parts(target, parts):\n", " # Base value for each part\n", @@ -2349,21 +1115,12 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": null, "id": "299cdae3-2e97-4e47-bad3-98643dfaf4ea", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: total: 1.64 s\n", - "Wall time: 3.23 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "output = retrieve_documents(state)" @@ -2371,23 +1128,12 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": null, "id": "638be0ea-bd41-45d7-ac7c-57ed789da3c5", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.20497774 0.20197058 0.20149879 0.16000335 0.1522842 0.14817041\n", - " 0.14743239 0.1468197 0.14330755 0.13432105 0.12885363 0.12517229\n", - " 0.12262824 0.12241826 0.12210386]\n", - "0.15079746933333335\n" - ] - } - ], + "outputs": [], "source": [ "rerank_scores = np.array([doc.metadata[\"rerank_score\"] for doc in output[\"documents\"]])\n", "print(rerank_scores)\n", @@ -2396,49 +1142,12 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": null, "id": "69ba19d3-97fb-4a23-99ee-c5827e0664e6", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "page_content='This Technical Summary should be cited as:\\nIPCC, 2019: Technical Summary [H.-O. Portner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, E. Poloczanska, K. Mintenbeck, \\r\\nM. Tignor, A. Alegria, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In: IPCC Special Report on the \\r\\nOcean and Cryosphere in a Changing Climate [H.- O. Portner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, \\r\\nE. Poloczanska, K. Mintenbeck, A. Alegria, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge \\r\\nUniversity Press, Cambridge, UK and New York, NY, USA, pp. 39-69. https://doi.org/10.1017/9781009157964.002' metadata={'chunk_type': 'text', 'document_id': 'document14', 'document_number': 14.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Technical Summary. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 623.0, 'num_tokens': 237.0, 'num_tokens_approx': 250.0, 'num_words': 188.0, 'page_number': 4, 'release_date': 2019.0, 'report_type': 'TS', 'section_header': 'This Technical Summary should be cited as:', 'short_name': 'IPCC SR OC TS', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/02_SROCC_TS_FINAL.pdf', 'similarity_score': 0.611846924, 'content': 'This Technical Summary should be cited as:\\nIPCC, 2019: Technical Summary [H.-O. Portner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, E. Poloczanska, K. Mintenbeck, \\r\\nM. Tignor, A. Alegria, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In: IPCC Special Report on the \\r\\nOcean and Cryosphere in a Changing Climate [H.- O. Portner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, \\r\\nE. Poloczanska, K. Mintenbeck, A. Alegria, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge \\r\\nUniversity Press, Cambridge, UK and New York, NY, USA, pp. 39-69. https://doi.org/10.1017/9781009157964.002', 'rerank_score': 0.20497774, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='This chapter should be cited as:\\nCabeza, L. F., Q. Bai, P. Bertoldi, J.M. Kihila, A.F.P. Lucena, E. Mata, S. Mirasgedis, A. Novikova, Y. Saheb, 2022: Buildings. \\r\\nIn IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth \\r\\nAssessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, \\r\\nR. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, \\r\\n(eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. doi: 10.1017/9781009157926.011\\n This chapter should be cited as: \\n\\n953953' metadata={'chunk_type': 'text', 'document_id': 'document9', 'document_number': 9.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2258.0, 'name': 'Full Report. In: Climate Change 2022: Mitigation of Climate Change. Contribution of the WGIII to the AR6 of the IPCC', 'num_characters': 706.0, 'num_tokens': 248.0, 'num_tokens_approx': 261.0, 'num_words': 196.0, 'page_number': 966, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'This chapter should be cited as:', 'short_name': 'IPCC AR6 WGIII FR', 'source': 'IPCC', 'toc_level0': 'References', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_FullReport.pdf', 'similarity_score': 0.603373706, 'content': 'This chapter should be cited as:\\nCabeza, L. F., Q. Bai, P. Bertoldi, J.M. Kihila, A.F.P. Lucena, E. Mata, S. Mirasgedis, A. Novikova, Y. Saheb, 2022: Buildings. \\r\\nIn IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth \\r\\nAssessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, \\r\\nR. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, \\r\\n(eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. doi: 10.1017/9781009157926.011\\n This chapter should be cited as: \\n\\n953953', 'rerank_score': 0.20197058, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='This chapter should be cited as:\\nBashmakov, I.A., L.J. Nilsson, A. Acquaye, C. Bataille, J.M. Cullen, S. de la Rue du Can, M. Fischedick, Y. Geng, K. Tanaka, \\r\\n2022: Industry. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III \\r\\nto the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, \\r\\nA. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, \\r\\nG. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. \\r\\ndoi: 10.1017/9781009157926.013\\n This chapter should be cited as: \\n\\n11611161' metadata={'chunk_type': 'text', 'document_id': 'document9', 'document_number': 9.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2258.0, 'name': 'Full Report. In: Climate Change 2022: Mitigation of Climate Change. Contribution of the WGIII to the AR6 of the IPCC', 'num_characters': 724.0, 'num_tokens': 251.0, 'num_tokens_approx': 264.0, 'num_words': 198.0, 'page_number': 1174, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'This chapter should be cited as:', 'short_name': 'IPCC AR6 WGIII FR', 'source': 'IPCC', 'toc_level0': 'Appendix 10.3: Line of Sight for Feasibility\\xa0Assessment', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_FullReport.pdf', 'similarity_score': 0.619030833, 'content': 'This chapter should be cited as:\\nBashmakov, I.A., L.J. Nilsson, A. Acquaye, C. Bataille, J.M. Cullen, S. de la Rue du Can, M. Fischedick, Y. Geng, K. Tanaka, \\r\\n2022: Industry. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III \\r\\nto the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, \\r\\nA. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, \\r\\nG. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. \\r\\ndoi: 10.1017/9781009157926.013\\n This chapter should be cited as: \\n\\n11611161', 'rerank_score': 0.20149879, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='This Annex should be cited as:\\nIPCC, 2022: Annex II: Glossary [Moller, V., R. van Diemen, J.B.R. Matthews, C. Mendez, S. Semenov, J.S. Fuglestvedt, \\r\\nA. Reisinger (eds.)]. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to \\r\\nthe Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Portner, D.C. Roberts, M. Tignor, \\r\\nE.S. Poloczanska, K. Mintenbeck, A. Alegria, M. Craig, S. Langsdorf, S. Loschke, V. Moller, A. Okem, B. Rama (eds.)]. \\r\\nCambridge University Press, Cambridge, UK and New York, NY, USA, pp. 2897-2930, doi:10.1017/9781009325844.029.\\n This Annex should be cited as: \\n\\n28972897' metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 702.0, 'num_tokens': 239.0, 'num_tokens_approx': 250.0, 'num_words': 188.0, 'page_number': 2909, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'This Annex should be cited as:', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Annexes', 'toc_level1': 'Annex II Glossary', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.612448752, 'content': 'This Annex should be cited as:\\nIPCC, 2022: Annex II: Glossary [Moller, V., R. van Diemen, J.B.R. Matthews, C. Mendez, S. Semenov, J.S. Fuglestvedt, \\r\\nA. Reisinger (eds.)]. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to \\r\\nthe Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Portner, D.C. Roberts, M. Tignor, \\r\\nE.S. Poloczanska, K. Mintenbeck, A. Alegria, M. Craig, S. Langsdorf, S. Loschke, V. Moller, A. Okem, B. Rama (eds.)]. \\r\\nCambridge University Press, Cambridge, UK and New York, NY, USA, pp. 2897-2930, doi:10.1017/9781009325844.029.\\n This Annex should be cited as: \\n\\n28972897', 'rerank_score': 0.16000335, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='Annex IX: Contributors \\r\\nto the IPCC WGI Sixth \\r\\nAssessment Report\\nThis annex should be cited as:\\nIPCC, 2021: Annex IX: Contributors to the IPCC Working Group I Sixth Assessment Report. In Climate Change 2021: The \\r\\nPhysical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel \\r\\non Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, \\r\\nM.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou \\r\\n(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2267-2286.\\n This annex should be cited as: \\n\\n22672267' metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 766.0, 'num_tokens': 233.0, 'num_tokens_approx': 260.0, 'num_words': 195.0, 'page_number': 2284, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'This annex should be cited as:', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IX: Contributors', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.652021468, 'content': 'Annex IX: Contributors \\r\\nto the IPCC WGI Sixth \\r\\nAssessment Report\\nThis annex should be cited as:\\nIPCC, 2021: Annex IX: Contributors to the IPCC Working Group I Sixth Assessment Report. In Climate Change 2021: The \\r\\nPhysical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel \\r\\non Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, \\r\\nM.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou \\r\\n(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2267-2286.\\n This annex should be cited as: \\n\\n22672267', 'rerank_score': 0.1522842, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='This Summary for Policymakers should be cited as:\\nIPCC, 2019: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate \\r\\n[H.-O. Portner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegria, \\r\\nM. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and \\r\\nNew York, NY, USA, pp. 3-35. https://doi.org/10.1017/9781009157964.001. \\n This Summary for Policymakers should be cited as: ' metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 548.0, 'num_tokens': 183.0, 'num_tokens_approx': 197.0, 'num_words': 148.0, 'page_number': 3, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'This Summary for Policymakers should be cited as:', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.624829769, 'content': 'This Summary for Policymakers should be cited as:\\nIPCC, 2019: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate \\r\\n[H.-O. Portner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegria, \\r\\nM. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and \\r\\nNew York, NY, USA, pp. 3-35. https://doi.org/10.1017/9781009157964.001. \\n This Summary for Policymakers should be cited as: ', 'rerank_score': 0.14817041, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='Annex VIII: Acronyms\\nThis annex should be cited as:\\nIPCC, 2021: Annex VIII: Acronyms. In Climate Change 2021: The Physical Science Basis. Contribution of Working \\r\\nGroup I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, \\r\\nV., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. \\r\\nLeitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge \\r\\nUniversity Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2257-2266.\\n This annex should be cited as: \\n\\n22572257' metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 667.0, 'num_tokens': 218.0, 'num_tokens_approx': 238.0, 'num_words': 179.0, 'page_number': 2274, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'land-use-land-use-change-and-forestry-lulucf/reporting-and-accounting\\x02of-lulucf-activities-under-the-kyoto-protocol.', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex VIII: Acronyms', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.595291555, 'content': 'Annex VIII: Acronyms\\nThis annex should be cited as:\\nIPCC, 2021: Annex VIII: Acronyms. In Climate Change 2021: The Physical Science Basis. Contribution of Working \\r\\nGroup I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, \\r\\nV., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. \\r\\nLeitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge \\r\\nUniversity Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2257-2266.\\n This annex should be cited as: \\n\\n22572257', 'rerank_score': 0.14743239, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='This annex should be cited as:\\nIPCC, 2022: Annex I: Global to Regional Atlas [Portner, H.-O., A. Alegria, V. Moller, E.S. Poloczanska, K. Mintenbeck, \\r\\nS. Gotze (eds.)]. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to \\r\\nthe Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Portner, D.C. Roberts, M. Tignor, \\r\\nE.S. Poloczanska, K. Mintenbeck, A. Alegria, M. Craig, S. Langsdorf, S. Loschke, V. Moller, A. Okem, B. Rama (eds.)]. \\r\\nCambridge University Press, Cambridge, UK and New York, NY, USA, pp. 2811-2896, doi:10.1017/9781009325844.028.\\n This annex should be cited as: \\n\\n28112811' metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 700.0, 'num_tokens': 235.0, 'num_tokens_approx': 245.0, 'num_words': 184.0, 'page_number': 2823, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'This annex should be cited as:', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Annexes', 'toc_level1': 'Annex I Global to Regional Atlas', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.626139402, 'content': 'This annex should be cited as:\\nIPCC, 2022: Annex I: Global to Regional Atlas [Portner, H.-O., A. Alegria, V. Moller, E.S. Poloczanska, K. Mintenbeck, \\r\\nS. Gotze (eds.)]. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to \\r\\nthe Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Portner, D.C. Roberts, M. Tignor, \\r\\nE.S. Poloczanska, K. Mintenbeck, A. Alegria, M. Craig, S. Langsdorf, S. Loschke, V. Moller, A. Okem, B. Rama (eds.)]. \\r\\nCambridge University Press, Cambridge, UK and New York, NY, USA, pp. 2811-2896, doi:10.1017/9781009325844.028.\\n This annex should be cited as: \\n\\n28112811', 'rerank_score': 0.1468197, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='This chapter should be cited as:\\nIPCC, 2022: Annex I: Glossary [van Diemen, R., J.B.R. Matthews, V. Moller, J.S. Fuglestvedt, V. Masson-Delmotte, \\r\\nC. Mendez, A. Reisinger, S. Semenov (eds)]. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. \\r\\nContribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\\r\\n[P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, \\r\\nM. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, \\r\\nNY, USA. doi: 10.1017/9781009157926.020' metadata={'chunk_type': 'text', 'document_id': 'document9', 'document_number': 9.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2258.0, 'name': 'Full Report. In: Climate Change 2022: Mitigation of Climate Change. Contribution of the WGIII to the AR6 of the IPCC', 'num_characters': 659.0, 'num_tokens': 235.0, 'num_tokens_approx': 242.0, 'num_words': 182.0, 'page_number': 1806, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'This chapter should be cited as:', 'short_name': 'IPCC AR6 WGIII FR', 'source': 'IPCC', 'toc_level0': '_Hlk111724995', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_FullReport.pdf', 'similarity_score': 0.608979702, 'content': 'This chapter should be cited as:\\nIPCC, 2022: Annex I: Glossary [van Diemen, R., J.B.R. Matthews, V. Moller, J.S. Fuglestvedt, V. Masson-Delmotte, \\r\\nC. Mendez, A. Reisinger, S. Semenov (eds)]. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. \\r\\nContribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\\r\\n[P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, \\r\\nM. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, \\r\\nNY, USA. doi: 10.1017/9781009157926.020', 'rerank_score': 0.14330755, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='This annex should be cited as:\\nIPCC, 2022: Annex III: Acronyms. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of \\r\\nWorking Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Portner, \\r\\nD.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegria, M. Craig, S. Langsdorf, S. Loschke, V. Moller, \\r\\nA. Okem, B. Rama (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 2931-2938, \\r\\ndoi:10.1017/9781009325844.030. \\n This annex should be cited as: \\n\\n29312931' metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 597.0, 'num_tokens': 194.0, 'num_tokens_approx': 201.0, 'num_words': 151.0, 'page_number': 2943, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'This annex should be cited as:', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Annexes', 'toc_level1': 'Annex III Acronyms', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.598234832, 'content': 'This annex should be cited as:\\nIPCC, 2022: Annex III: Acronyms. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of \\r\\nWorking Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Portner, \\r\\nD.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegria, M. Craig, S. Langsdorf, S. Loschke, V. Moller, \\r\\nA. Okem, B. Rama (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 2931-2938, \\r\\ndoi:10.1017/9781009325844.030. \\n This annex should be cited as: \\n\\n29312931', 'rerank_score': 0.13432105, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='This annex should be cited as:\\nIPCC, 2021: Annex V: Monsoons [Cherchi, A., A. Turner (eds.)]. In Climate Change 2021: The Physical Science Basis. \\r\\nContribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\\r\\n[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, \\r\\nK. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University \\r\\nPress, Cambridge, United Kingdom and New York, NY, USA, pp. 2193-2204, doi:10.1017/9781009157896.019.\\n This annex should be cited as: \\n\\n21932193' metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 704.0, 'num_tokens': 238.0, 'num_tokens_approx': 260.0, 'num_words': 195.0, 'page_number': 2210, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'This annex should be cited as:', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex V: Monsoons', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.621438146, 'content': 'This annex should be cited as:\\nIPCC, 2021: Annex V: Monsoons [Cherchi, A., A. Turner (eds.)]. In Climate Change 2021: The Physical Science Basis. \\r\\nContribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\\r\\n[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, \\r\\nK. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University \\r\\nPress, Cambridge, United Kingdom and New York, NY, USA, pp. 2193-2204, doi:10.1017/9781009157896.019.\\n This annex should be cited as: \\n\\n21932193', 'rerank_score': 0.12885363, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='This annex should be cited as:\\nIPCC, 2019: Annex I: Glossary [Weyer, N.M. (ed.)]. In: IPCC Special Report on the Ocean and Cryosphere in a Changing \\r\\nClimate [H.-O. Portner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegria, M. \\r\\nNicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, \\r\\nUSA, pp. 677-702. https://doi.org/10.1017/9781009157964.010.\\n This annex should be cited as: \\n\\n677677' metadata={'chunk_type': 'text', 'document_id': 'document22', 'document_number': 22.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 28.0, 'name': 'Annex I: Glossary In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 533.0, 'num_tokens': 188.0, 'num_tokens_approx': 206.0, 'num_words': 155.0, 'page_number': 3, 'release_date': 2019.0, 'report_type': 'Special Report', 'section_header': 'This annex should be cited as:', 'short_name': 'IPCC SR OC A1 G', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/10_SROCC_AnnexI-Glossary_FINAL.pdf', 'similarity_score': 0.644453526, 'content': 'This annex should be cited as:\\nIPCC, 2019: Annex I: Glossary [Weyer, N.M. (ed.)]. In: IPCC Special Report on the Ocean and Cryosphere in a Changing \\r\\nClimate [H.-O. Portner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegria, M. \\r\\nNicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, \\r\\nUSA, pp. 677-702. https://doi.org/10.1017/9781009157964.010.\\n This annex should be cited as: \\n\\n677677', 'rerank_score': 0.12517229, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='This index should be cited as:\\nIPCC, 2022: Index. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to \\r\\nthe Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Portner, D.C. Roberts, M. Tignor, \\r\\nE.S. Poloczanska, K. Mintenbeck, A. Alegria, M. Craig, S. Langsdorf, S. Loschke, V. Moller, A. Okem, B. Rama (eds.)]. \\r\\nCambridge University Press, Cambridge, UK and New York, NY, USA, pp. 3005-3056, doi:10.1017/9781009325844.033. \\n This index should be cited as: \\n\\n30053005' metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 581.0, 'num_tokens': 189.0, 'num_tokens_approx': 197.0, 'num_words': 148.0, 'page_number': 3017, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'This index should be cited as:', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Index', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.616092443, 'content': 'This index should be cited as:\\nIPCC, 2022: Index. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to \\r\\nthe Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Portner, D.C. Roberts, M. Tignor, \\r\\nE.S. Poloczanska, K. Mintenbeck, A. Alegria, M. Craig, S. Langsdorf, S. Loschke, V. Moller, A. Okem, B. Rama (eds.)]. \\r\\nCambridge University Press, Cambridge, UK and New York, NY, USA, pp. 3005-3056, doi:10.1017/9781009325844.033. \\n This index should be cited as: \\n\\n30053005', 'rerank_score': 0.12262824, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='This chapter should be cited as:\\nIPCC, 2022: Index. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working \\r\\nGroup III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, \\r\\nR. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, \\r\\nA. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. \\r\\ndoi: 10.1017/9781009157926.026\\n This chapter should be cited as: \\n\\n19791979' metadata={'chunk_type': 'text', 'document_id': 'document9', 'document_number': 9.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2258.0, 'name': 'Full Report. In: Climate Change 2022: Mitigation of Climate Change. Contribution of the WGIII to the AR6 of the IPCC', 'num_characters': 602.0, 'num_tokens': 199.0, 'num_tokens_approx': 205.0, 'num_words': 154.0, 'page_number': 1992, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'This chapter should be cited as:', 'short_name': 'IPCC AR6 WGIII FR', 'source': 'IPCC', 'toc_level0': 'References', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_FullReport.pdf', 'similarity_score': 0.614085197, 'content': 'This chapter should be cited as:\\nIPCC, 2022: Index. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working \\r\\nGroup III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, \\r\\nR. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, \\r\\nA. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. \\r\\ndoi: 10.1017/9781009157926.026\\n This chapter should be cited as: \\n\\n19791979', 'rerank_score': 0.12241826, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n", - "page_content='This chapter should be cited as:\\nGrubb, M., C. Okereke, J. Arima, V. Bosetti, Y. Chen, J. Edmonds, S. Gupta, A. Koberle, S. Kverndokk, A. Malik, L. Sulistiawati, \\r\\n2022: Introduction and Framing. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of \\r\\nWorking Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, \\r\\nJ. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, \\r\\nA. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. \\r\\ndoi: 10.1017/9781009157926.003\\n This chapter should be cited as: \\n\\n151151' metadata={'chunk_type': 'text', 'document_id': 'document9', 'document_number': 9.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2258.0, 'name': 'Full Report. In: Climate Change 2022: Mitigation of Climate Change. Contribution of the WGIII to the AR6 of the IPCC', 'num_characters': 741.0, 'num_tokens': 254.0, 'num_tokens_approx': 264.0, 'num_words': 198.0, 'page_number': 164, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'This chapter should be cited as:', 'short_name': 'IPCC AR6 WGIII FR', 'source': 'IPCC', 'toc_level0': 'TS.7 Mitigation in the Context of\\xa0Sustainable\\xa0Development', 'toc_level1': 'Box TS.15 | A Harmonised Approach to Assessing Feasibility', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_FullReport.pdf', 'similarity_score': 0.603973, 'content': 'This chapter should be cited as:\\nGrubb, M., C. Okereke, J. Arima, V. Bosetti, Y. Chen, J. Edmonds, S. Gupta, A. Koberle, S. Kverndokk, A. Malik, L. Sulistiawati, \\r\\n2022: Introduction and Framing. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of \\r\\nWorking Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, \\r\\nJ. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, \\r\\nA. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. \\r\\ndoi: 10.1017/9781009157926.003\\n This chapter should be cited as: \\n\\n151151', 'rerank_score': 0.12210386, 'query_used_for_retrieval': 'How is the manga One Piece cited in the IPCC', 'sources_used': ['IPCC']}\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "for doc in output[\"documents\"]:\n", " print(doc)\n", @@ -2491,67 +1200,71 @@ }, { "cell_type": "code", - "execution_count": 105, - "id": "a24b8dd3-54b7-4209-a532-c3d6dfd1530d", - "metadata": { - "tags": [] - }, + "execution_count": 8, + "id": "b91f4f58", + "metadata": {}, + "outputs": [], + "source": [ + "from climateqa.engine.graph import make_graph_agent,display_graph\n", + "\n", + "app = make_graph_agent(llm,vectorstore,reranker)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c3c70cdb", + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'event': 'on_chain_start', 'run_id': '54411858-94b4-423d-933e-45e86a7d6c4b', 'name': 'LangGraph', 'tags': [], 'metadata': {}, 'data': {'input': {'user_input': \"I am not really sure what you mean. 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What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'sources': ['auto']}, 'output': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'sources': ['auto']}}}\n", - "\n", - "{'event': 'on_chain_start', 'name': 'route_input_message', 'run_id': 'c5328959-b052-4920-a538-fb9a9f482ccb', 'tags': ['graph:step:1'], 'metadata': {}, 'data': {}}\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CATEGORIZE {'intent': 'search', 'language': 'English'}\n", - "{'event': 'on_chain_start', 'name': 'ChannelWrite', 'run_id': '09cda419-7333-4bf6-aeb7-385ee456f1d9', 'tags': ['seq:step:2', 'langsmith:hidden'], 'metadata': {}, 'data': {'input': {'intent': 'search', 'language': 'English'}}}\n", - "\n", - "{'event': 'on_chain_end', 'name': 'ChannelWrite', 'run_id': '09cda419-7333-4bf6-aeb7-385ee456f1d9', 'tags': ['seq:step:2', 'langsmith:hidden'], 'metadata': {}, 'data': {'input': {'intent': 'search', 'language': 'English'}, 'output': {'intent': 'search', 'language': 'English'}}}\n", - "\n", - "{'event': 'on_chain_start', 'name': 'route_entry_point', 'run_id': '478ecc36-08b2-4732-8e70-6efc0fcfc22f', 'tags': ['seq:step:3'], 'metadata': {}, 'data': {'input': {'user_input': \"I am not really sure what you mean. 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What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search'}, 'output': 'transform_query'}}\n", - "\n", - "{'event': 'on_chain_start', 'name': 'ChannelWrite', 'run_id': '32752ceb-d15d-4ae3-aaf6-639f5564f559', 'tags': ['seq:step:3', 'langsmith:hidden'], 'metadata': {}, 'data': {'input': {'intent': 'search', 'language': 'English'}}}\n", - "\n", - "{'event': 'on_chain_end', 'name': 'ChannelWrite', 'run_id': '32752ceb-d15d-4ae3-aaf6-639f5564f559', 'tags': ['seq:step:3', 'langsmith:hidden'], 'metadata': {}, 'data': {'input': {'intent': 'search', 'language': 'English'}, 'output': {'intent': 'search', 'language': 'English'}}}\n", - "\n", - "{'event': 'on_chain_stream', 'name': 'route_input_message', 'run_id': 'c5328959-b052-4920-a538-fb9a9f482ccb', 'tags': ['graph:step:1'], 'metadata': {}, 'data': {'chunk': {'intent': 'search', 'language': 'English'}}}\n", - "\n", - "{'event': 'on_chain_end', 'name': 'route_input_message', 'run_id': 'c5328959-b052-4920-a538-fb9a9f482ccb', 'tags': ['graph:step:1'], 'metadata': {}, 'data': {'input': {'user_input': \"I am not really sure what you mean. 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display_graph(app)" + ] + }, + { + "cell_type": "markdown", + "id": "3c387c53", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "05ead97d", + "metadata": {}, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ + "/tmp/ipykernel_3686/3877529797.py:6: LangChainBetaWarning: This API is in beta and may change in the future.\n", + " async for event in app.astream_events({\"user_input\": question,\"sources\":[\"auto\"], \"audience\" : 'expert'}, version=\"v1\"):\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "WARNING:langchain_core.callbacks.manager:Error in LogStreamCallbackHandler.on_chain_start callback: ValidationError(model='Run', errors=[{'loc': ('__root__',), 'msg': \"argument of type 'NoneType' is not iterable\", 'type': 'type_error'}])\n", + "WARNING:langchain_core.callbacks.manager:Error in LangChainTracer.on_chain_start callback: ValidationError(model='Run', errors=[{'loc': ('__root__',), 'msg': \"argument of type 'NoneType' is not iterable\", 'type': 'type_error'}])\n", + "WARNING:langchain_core.callbacks.manager:Error in LogStreamCallbackHandler.on_chain_end callback: TracerException('No indexed run ID c2319973-e00d-4c8e-a0c2-6bd9c310f00b.')\n", + "WARNING:langchain_core.callbacks.manager:Error in LangChainTracer.on_chain_end callback: TracerException('No indexed run ID c2319973-e00d-4c8e-a0c2-6bd9c310f00b.')\n", + "WARNING:langchain_core.callbacks.manager:Error in LogStreamCallbackHandler.on_chain_start callback: ValidationError(model='Run', errors=[{'loc': ('__root__',), 'msg': \"argument of type 'NoneType' is not iterable\", 'type': 'type_error'}])\n", + "WARNING:langchain_core.callbacks.manager:Error in LangChainTracer.on_chain_start callback: ValidationError(model='Run', errors=[{'loc': ('__root__',), 'msg': \"argument of type 'NoneType' is not iterable\", 'type': 'type_error'}])\n", + "WARNING:langchain_core.callbacks.manager:Error in LogStreamCallbackHandler.on_chain_end callback: TracerException('No indexed run ID 6613117a-1e4c-42c1-8088-eff11f07cd7d.')\n", + "WARNING:langchain_core.callbacks.manager:Error in LangChainTracer.on_chain_end callback: TracerException('No indexed run ID 6613117a-1e4c-42c1-8088-eff11f07cd7d.')\n", + "WARNING:langchain_core.callbacks.manager:Error in LogStreamCallbackHandler.on_chain_start callback: ValidationError(model='Run', errors=[{'loc': ('__root__',), 'msg': \"argument of type 'NoneType' is not iterable\", 'type': 'type_error'}])\n", + "WARNING:langchain_core.callbacks.manager:Error in LangChainTracer.on_chain_start callback: ValidationError(model='Run', errors=[{'loc': ('__root__',), 'msg': \"argument of type 'NoneType' is not iterable\", 'type': 'type_error'}])\n", + "WARNING:langchain_core.callbacks.manager:Error in LogStreamCallbackHandler.on_chain_end callback: TracerException('No indexed run ID 866c5686-8072-4291-b809-16dd2c248b6d.')\n", + "WARNING:langchain_core.callbacks.manager:Error in LangChainTracer.on_chain_end callback: TracerException('No indexed run ID 866c5686-8072-4291-b809-16dd2c248b6d.')\n", "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" ] }, @@ -2559,51 +1272,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_chain_start', 'name': 'ChannelWrite', 'run_id': 'f0d11fc9-cdc0-4c51-8576-4fa59b668d17', 'tags': ['seq:step:2', 'langsmith:hidden'], 'metadata': {}, 'data': {'input': {'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'questions': [{'question': 'What are the main factors contributing to climate change?', 'sources': ['IPCC']}, {'question': 'How does human activity impact biodiversity?', 'sources': ['IPBES']}, {'question': 'What are the key findings of the latest IPCC report?', 'sources': ['IPCC']}]}}}\n", - "\n", - "{'event': 'on_chain_end', 'name': 'ChannelWrite', 'run_id': 'f0d11fc9-cdc0-4c51-8576-4fa59b668d17', 'tags': ['seq:step:2', 'langsmith:hidden'], 'metadata': {}, 'data': {'input': {'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'questions': [{'question': 'What are the main factors contributing to climate change?', 'sources': ['IPCC']}, {'question': 'How does human activity impact biodiversity?', 'sources': ['IPBES']}, {'question': 'What are the key findings of the latest IPCC report?', 'sources': ['IPCC']}]}, 'output': {'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'questions': [{'question': 'What are the main factors contributing to climate change?', 'sources': ['IPCC']}, {'question': 'How does human activity impact biodiversity?', 'sources': ['IPBES']}, {'question': 'What are the key findings of the latest IPCC report?', 'sources': ['IPCC']}]}}}\n", + "Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\n", "\n", - "{'event': 'on_chain_stream', 'name': 'transform_query', 'run_id': 'debea710-be86-44af-a6ef-094ea95d5a3d', 'tags': ['graph:step:2'], 'metadata': {}, 'data': {'chunk': {'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'questions': [{'question': 'What are the main factors contributing to climate change?', 'sources': ['IPCC']}, {'question': 'How does human activity impact biodiversity?', 'sources': ['IPBES']}, {'question': 'What are the key findings of the latest IPCC report?', 'sources': ['IPCC']}]}}}\n", + "In current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\n", "\n", - "{'event': 'on_chain_end', 'name': 'transform_query', 'run_id': 'debea710-be86-44af-a6ef-094ea95d5a3d', 'tags': ['graph:step:2'], 'metadata': {}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': None, 'questions': None, 'answer': None, 'audience': None, 'sources_input': None}, 'output': {'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'questions': [{'question': 'What are the main factors contributing to climate change?', 'sources': ['IPCC']}, {'question': 'How does human activity impact biodiversity?', 'sources': ['IPBES']}, {'question': 'What are the key findings of the latest IPCC report?', 'sources': ['IPCC']}]}}}\n", + "Despite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \n", "\n", - "{'event': 'on_chain_stream', 'run_id': '54411858-94b4-423d-933e-45e86a7d6c4b', 'tags': [], 'metadata': {}, 'name': 'LangGraph', 'data': {'chunk': {'transform_query': {'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'questions': [{'question': 'What are the main factors contributing to climate change?', 'sources': ['IPCC']}, {'question': 'How does human activity impact biodiversity?', 'sources': ['IPBES']}, {'question': 'What are the key findings of the latest IPCC report?', 'sources': ['IPCC']}]}}}}\n", - "\n", - "{'event': 'on_chain_start', 'name': 'retrieve_documents', 'run_id': '6aee01df-8b06-47d5-b338-b774c7b76641', 'tags': ['graph:step:3'], 'metadata': {}, 'data': {}}\n", - "\n", - "{'event': 'on_chain_end', 'name': 'retrieve_documents', 'run_id': '6aee01df-8b06-47d5-b338-b774c7b76641', 'tags': ['graph:step:3'], 'metadata': {}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'questions': [{'question': 'What are the main factors contributing to climate change?', 'sources': ['IPCC']}, {'question': 'How does human activity impact biodiversity?', 'sources': ['IPBES']}, {'question': 'What are the key findings of the latest IPCC report?', 'sources': ['IPCC']}], 'answer': None, 'audience': None, 'sources_input': None}, 'output': None}}\n", - "\n" - ] - }, - { - "ename": "TypeError", - "evalue": "argument of type 'NoneType' is not iterable", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[105], line 6\u001b[0m\n\u001b[0;32m 3\u001b[0m question \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mC\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mest quoi l\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimpact de ChatGPT ?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 4\u001b[0m question \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mI am not really sure what you mean. What role do cloud formations play in modulating the Earth\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms radiative balance, and how are they represented in current climate models?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 6\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m event \u001b[38;5;129;01min\u001b[39;00m app\u001b[38;5;241m.\u001b[39mastream_events({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser_input\u001b[39m\u001b[38;5;124m\"\u001b[39m: question,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msources\u001b[39m\u001b[38;5;124m\"\u001b[39m:[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m\"\u001b[39m]}, version\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mv1\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m event[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mevent\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mon_chat_model_stream\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 8\u001b[0m token \u001b[38;5;241m=\u001b[39m event[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchunk\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mcontent\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langchain_core\\runnables\\base.py:1131\u001b[0m, in \u001b[0;36mRunnable.astream_events\u001b[1;34m(self, input, config, version, include_names, include_types, include_tags, exclude_names, exclude_types, exclude_tags, **kwargs)\u001b[0m\n\u001b[0;32m 1126\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1127\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[0;32m 1128\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOnly versions \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mv1\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mv2\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m of the schema is currently supported.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 1129\u001b[0m )\n\u001b[1;32m-> 1131\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m event \u001b[38;5;129;01min\u001b[39;00m event_stream:\n\u001b[0;32m 1132\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m event\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langchain_core\\tracers\\event_stream.py:570\u001b[0m, in \u001b[0;36m_astream_events_implementation_v1\u001b[1;34m(runnable, input, config, include_names, include_types, include_tags, exclude_names, exclude_types, exclude_tags, **kwargs)\u001b[0m\n\u001b[0;32m 566\u001b[0m root_name \u001b[38;5;241m=\u001b[39m config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m, runnable\u001b[38;5;241m.\u001b[39mget_name())\n\u001b[0;32m 568\u001b[0m \u001b[38;5;66;03m# Ignoring mypy complaint about too many different union combinations\u001b[39;00m\n\u001b[0;32m 569\u001b[0m \u001b[38;5;66;03m# This arises because many of the argument types are unions\u001b[39;00m\n\u001b[1;32m--> 570\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m log \u001b[38;5;129;01min\u001b[39;00m _astream_log_implementation( \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 571\u001b[0m runnable,\n\u001b[0;32m 572\u001b[0m \u001b[38;5;28minput\u001b[39m,\n\u001b[0;32m 573\u001b[0m config\u001b[38;5;241m=\u001b[39mconfig,\n\u001b[0;32m 574\u001b[0m stream\u001b[38;5;241m=\u001b[39mstream,\n\u001b[0;32m 575\u001b[0m diff\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 576\u001b[0m with_streamed_output_list\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 577\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m 578\u001b[0m ):\n\u001b[0;32m 579\u001b[0m run_log \u001b[38;5;241m=\u001b[39m run_log \u001b[38;5;241m+\u001b[39m log\n\u001b[0;32m 581\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m encountered_start_event:\n\u001b[0;32m 582\u001b[0m \u001b[38;5;66;03m# Yield the start event for the root runnable.\u001b[39;00m\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langchain_core\\tracers\\log_stream.py:617\u001b[0m, in \u001b[0;36m_astream_log_implementation\u001b[1;34m(runnable, input, config, stream, diff, with_streamed_output_list, **kwargs)\u001b[0m\n\u001b[0;32m 614\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 615\u001b[0m \u001b[38;5;66;03m# Wait for the runnable to finish, if not cancelled (eg. by break)\u001b[39;00m\n\u001b[0;32m 616\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 617\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m task\n\u001b[0;32m 618\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mCancelledError:\n\u001b[0;32m 619\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langchain_core\\tracers\\log_stream.py:571\u001b[0m, in \u001b[0;36m_astream_log_implementation..consume_astream\u001b[1;34m()\u001b[0m\n\u001b[0;32m 568\u001b[0m prev_final_output: Optional[Output] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 569\u001b[0m final_output: Optional[Output] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m--> 571\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m runnable\u001b[38;5;241m.\u001b[39mastream(\u001b[38;5;28minput\u001b[39m, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 572\u001b[0m prev_final_output \u001b[38;5;241m=\u001b[39m final_output\n\u001b[0;32m 573\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m final_output \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langgraph\\pregel\\__init__.py:1208\u001b[0m, in \u001b[0;36mPregel.astream\u001b[1;34m(self, input, config, stream_mode, output_keys, input_keys, interrupt_before, interrupt_after, debug)\u001b[0m\n\u001b[0;32m 1206\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m 1207\u001b[0m \u001b[38;5;66;03m# wait for all background tasks to finish\u001b[39;00m\n\u001b[1;32m-> 1208\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mgather(\u001b[38;5;241m*\u001b[39mbg)\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langgraph\\pregel\\__init__.py:1107\u001b[0m, in \u001b[0;36mPregel.astream\u001b[1;34m(self, input, config, stream_mode, output_keys, input_keys, interrupt_before, interrupt_after, debug)\u001b[0m\n\u001b[0;32m 1100\u001b[0m done, inflight \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mwait(\n\u001b[0;32m 1101\u001b[0m futures,\n\u001b[0;32m 1102\u001b[0m return_when\u001b[38;5;241m=\u001b[39masyncio\u001b[38;5;241m.\u001b[39mFIRST_EXCEPTION,\n\u001b[0;32m 1103\u001b[0m timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstep_timeout,\n\u001b[0;32m 1104\u001b[0m )\n\u001b[0;32m 1106\u001b[0m \u001b[38;5;66;03m# panic on failure or timeout\u001b[39;00m\n\u001b[1;32m-> 1107\u001b[0m \u001b[43m_panic_or_proceed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdone\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minflight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1109\u001b[0m \u001b[38;5;66;03m# combine pending writes from all tasks\u001b[39;00m\n\u001b[0;32m 1110\u001b[0m pending_writes \u001b[38;5;241m=\u001b[39m deque[\u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any]]()\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langgraph\\pregel\\__init__.py:1334\u001b[0m, in \u001b[0;36m_panic_or_proceed\u001b[1;34m(done, inflight, step)\u001b[0m\n\u001b[0;32m 1332\u001b[0m inflight\u001b[38;5;241m.\u001b[39mpop()\u001b[38;5;241m.\u001b[39mcancel()\n\u001b[0;32m 1333\u001b[0m \u001b[38;5;66;03m# raise the exception\u001b[39;00m\n\u001b[1;32m-> 1334\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[0;32m 1336\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inflight:\n\u001b[0;32m 1337\u001b[0m \u001b[38;5;66;03m# if we got here means we timed out\u001b[39;00m\n\u001b[0;32m 1338\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m inflight:\n\u001b[0;32m 1339\u001b[0m \u001b[38;5;66;03m# cancel all pending tasks\u001b[39;00m\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langgraph\\pregel\\retry.py:111\u001b[0m, in \u001b[0;36marun_with_retry\u001b[1;34m(task, retry_policy, stream)\u001b[0m\n\u001b[0;32m 109\u001b[0m \u001b[38;5;66;03m# run the task\u001b[39;00m\n\u001b[0;32m 110\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stream:\n\u001b[1;32m--> 111\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m task\u001b[38;5;241m.\u001b[39mproc\u001b[38;5;241m.\u001b[39mastream(task\u001b[38;5;241m.\u001b[39minput, task\u001b[38;5;241m.\u001b[39mconfig):\n\u001b[0;32m 112\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m 113\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langchain_core\\runnables\\base.py:2769\u001b[0m, in \u001b[0;36mRunnableSequence.astream\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m 2766\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minput_aiter\u001b[39m() \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m AsyncIterator[Input]:\n\u001b[0;32m 2767\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n\u001b[1;32m-> 2769\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39matransform(input_aiter(), config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 2770\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langchain_core\\runnables\\base.py:2752\u001b[0m, in \u001b[0;36mRunnableSequence.atransform\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m 2746\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21matransform\u001b[39m(\n\u001b[0;32m 2747\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 2748\u001b[0m \u001b[38;5;28minput\u001b[39m: AsyncIterator[Input],\n\u001b[0;32m 2749\u001b[0m config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 2750\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[0;32m 2751\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m AsyncIterator[Output]:\n\u001b[1;32m-> 2752\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_atransform_stream_with_config(\n\u001b[0;32m 2753\u001b[0m \u001b[38;5;28minput\u001b[39m,\n\u001b[0;32m 2754\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_atransform,\n\u001b[0;32m 2755\u001b[0m patch_config(config, run_name\u001b[38;5;241m=\u001b[39m(config \u001b[38;5;129;01mor\u001b[39;00m {})\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname),\n\u001b[0;32m 2756\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m 2757\u001b[0m ):\n\u001b[0;32m 2758\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langchain_core\\runnables\\base.py:1854\u001b[0m, in \u001b[0;36mRunnable._atransform_stream_with_config\u001b[1;34m(self, input, transformer, config, run_type, **kwargs)\u001b[0m\n\u001b[0;32m 1849\u001b[0m chunk: Output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mcreate_task( \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m 1850\u001b[0m py_anext(iterator), \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[0;32m 1851\u001b[0m context\u001b[38;5;241m=\u001b[39mcontext,\n\u001b[0;32m 1852\u001b[0m )\n\u001b[0;32m 1853\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1854\u001b[0m chunk \u001b[38;5;241m=\u001b[39m cast(Output, \u001b[38;5;28;01mawait\u001b[39;00m py_anext(iterator))\n\u001b[0;32m 1855\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n\u001b[0;32m 1856\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m final_output_supported:\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langchain_core\\tracers\\log_stream.py:238\u001b[0m, in \u001b[0;36mLogStreamCallbackHandler.tap_output_aiter\u001b[1;34m(self, run_id, output)\u001b[0m\n\u001b[0;32m 234\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtap_output_aiter\u001b[39m(\n\u001b[0;32m 235\u001b[0m \u001b[38;5;28mself\u001b[39m, run_id: UUID, output: AsyncIterator[T]\n\u001b[0;32m 236\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m AsyncIterator[T]:\n\u001b[0;32m 237\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Tap an output async iterator to stream its values to the log.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 238\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m output:\n\u001b[0;32m 239\u001b[0m \u001b[38;5;66;03m# root run is handled in .astream_log()\u001b[39;00m\n\u001b[0;32m 240\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_id \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mroot_id:\n\u001b[0;32m 241\u001b[0m \u001b[38;5;66;03m# if we can't find the run silently ignore\u001b[39;00m\n\u001b[0;32m 242\u001b[0m \u001b[38;5;66;03m# eg. because this run wasn't included in the log\u001b[39;00m\n\u001b[0;32m 243\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m:=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_key_map_by_run_id\u001b[38;5;241m.\u001b[39mget(run_id):\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langchain_core\\runnables\\base.py:2722\u001b[0m, in \u001b[0;36mRunnableSequence._atransform\u001b[1;34m(self, input, run_manager, config)\u001b[0m\n\u001b[0;32m 2714\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step \u001b[38;5;129;01min\u001b[39;00m steps:\n\u001b[0;32m 2715\u001b[0m final_pipeline \u001b[38;5;241m=\u001b[39m step\u001b[38;5;241m.\u001b[39matransform(\n\u001b[0;32m 2716\u001b[0m final_pipeline,\n\u001b[0;32m 2717\u001b[0m patch_config(\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2720\u001b[0m ),\n\u001b[0;32m 2721\u001b[0m )\n\u001b[1;32m-> 2722\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m final_pipeline:\n\u001b[0;32m 2723\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m output\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langchain_core\\runnables\\base.py:1182\u001b[0m, in \u001b[0;36mRunnable.atransform\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m 1179\u001b[0m final: Input\n\u001b[0;32m 1180\u001b[0m got_first_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m-> 1182\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m ichunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28minput\u001b[39m:\n\u001b[0;32m 1183\u001b[0m \u001b[38;5;66;03m# The default implementation of transform is to buffer input and\u001b[39;00m\n\u001b[0;32m 1184\u001b[0m \u001b[38;5;66;03m# then call stream.\u001b[39;00m\n\u001b[0;32m 1185\u001b[0m \u001b[38;5;66;03m# It'll attempt to gather all input into a single chunk using\u001b[39;00m\n\u001b[0;32m 1186\u001b[0m \u001b[38;5;66;03m# the `+` operator.\u001b[39;00m\n\u001b[0;32m 1187\u001b[0m \u001b[38;5;66;03m# If the input is not addable, then we'll assume that we can\u001b[39;00m\n\u001b[0;32m 1188\u001b[0m \u001b[38;5;66;03m# only operate on the last chunk,\u001b[39;00m\n\u001b[0;32m 1189\u001b[0m \u001b[38;5;66;03m# and we'll iterate until we get to the last chunk.\u001b[39;00m\n\u001b[0;32m 1190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m got_first_val:\n\u001b[0;32m 1191\u001b[0m final \u001b[38;5;241m=\u001b[39m ichunk\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langchain_core\\runnables\\base.py:1200\u001b[0m, in \u001b[0;36mRunnable.atransform\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m 1197\u001b[0m final \u001b[38;5;241m=\u001b[39m ichunk\n\u001b[0;32m 1199\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m got_first_val:\n\u001b[1;32m-> 1200\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mastream(final, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 1201\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m output\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langchain_core\\runnables\\base.py:820\u001b[0m, in \u001b[0;36mRunnable.astream\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m 810\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mastream\u001b[39m(\n\u001b[0;32m 811\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 812\u001b[0m \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[0;32m 813\u001b[0m config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 814\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[0;32m 815\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m AsyncIterator[Output]:\n\u001b[0;32m 816\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 817\u001b[0m \u001b[38;5;124;03m Default implementation of astream, which calls ainvoke.\u001b[39;00m\n\u001b[0;32m 818\u001b[0m \u001b[38;5;124;03m Subclasses should override this method if they support streaming output.\u001b[39;00m\n\u001b[0;32m 819\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 820\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mainvoke(\u001b[38;5;28minput\u001b[39m, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langgraph\\utils.py:115\u001b[0m, in \u001b[0;36mRunnableCallable.ainvoke\u001b[1;34m(self, input, config)\u001b[0m\n\u001b[0;32m 111\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mcreate_task(\n\u001b[0;32m 112\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mafunc(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs), context\u001b[38;5;241m=\u001b[39mcontext\n\u001b[0;32m 113\u001b[0m )\n\u001b[0;32m 114\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 115\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mafunc(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 116\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(ret, Runnable) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrecurse:\n\u001b[0;32m 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m ret\u001b[38;5;241m.\u001b[39mainvoke(\u001b[38;5;28minput\u001b[39m, config)\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\langchain_core\\runnables\\config.py:514\u001b[0m, in \u001b[0;36mrun_in_executor\u001b[1;34m(executor_or_config, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m 501\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Run a function in an executor.\u001b[39;00m\n\u001b[0;32m 502\u001b[0m \n\u001b[0;32m 503\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 510\u001b[0m \u001b[38;5;124;03m Output: The output of the function.\u001b[39;00m\n\u001b[0;32m 511\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 512\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m executor_or_config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(executor_or_config, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m 513\u001b[0m \u001b[38;5;66;03m# Use default executor with context copied from current context\u001b[39;00m\n\u001b[1;32m--> 514\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mget_running_loop()\u001b[38;5;241m.\u001b[39mrun_in_executor(\n\u001b[0;32m 515\u001b[0m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 516\u001b[0m cast(Callable[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, T], partial(copy_context()\u001b[38;5;241m.\u001b[39mrun, func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)),\n\u001b[0;32m 517\u001b[0m )\n\u001b[0;32m 519\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mget_running_loop()\u001b[38;5;241m.\u001b[39mrun_in_executor(\n\u001b[0;32m 520\u001b[0m executor_or_config, partial(func, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs), \u001b[38;5;241m*\u001b[39margs\n\u001b[0;32m 521\u001b[0m )\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\concurrent\\futures\\thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m 57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 58\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfn(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[0;32m 59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m 60\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n", - "Cell \u001b[1;32mIn[89], line 7\u001b[0m, in \u001b[0;36mretrieve_documents\u001b[1;34m(state)\u001b[0m\n\u001b[0;32m 5\u001b[0m questions \u001b[38;5;241m=\u001b[39m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestions\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 6\u001b[0m sources_input \u001b[38;5;241m=\u001b[39m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msources_input\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m----> 7\u001b[0m auto_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mauto\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msources_input\u001b[49m\n\u001b[0;32m 9\u001b[0m k_final \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m15\u001b[39m\n\u001b[0;32m 10\u001b[0m k_before_reranking \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m\n", - "\u001b[1;31mTypeError\u001b[0m: argument of type 'NoneType' is not iterable" + "Overall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance." ] } ], @@ -2612,14 +1287,378 @@ "question = \"C'est quoi la recette de la tarte aux pommes ?\"\n", "question = \"C'est quoi l'impact de ChatGPT ?\"\n", "question = \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\"\n", - "\n", - "async for event in app.astream_events({\"user_input\": question,\"sources\":[\"auto\"]}, version=\"v1\"):\n", + "events_list = []\n", + "async for event in app.astream_events({\"user_input\": question,\"sources\":[\"auto\"], \"audience\" : 'expert'}, version=\"v1\"):\n", " if event[\"event\"] == \"on_chat_model_stream\":\n", " token = event[\"data\"][\"chunk\"].content\n", " print(token,end = \"\")\n", - " \n", - " print(event)\n", - " print(\"\")" + " else :\n", + " events_list.append(event)\n", + " # print(event)\n", + " # print(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ad6b4819", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'event': 'on_chain_start', 'run_id': 'debff86f-5fee-42e7-9f9e-6f9f4147948a', 'name': 'LangGraph', 'tags': [], 'metadata': {}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'sources': ['auto'], 'audience': 'expert'}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': '__start__', 'run_id': '97a94fac-af1d-40b6-9f37-0275e976ed9f', 'tags': ['graph:step:0', 'langsmith:hidden', 'langsmith:hidden'], 'metadata': {'langgraph_step': 0, 'langgraph_node': '__start__', 'langgraph_triggers': ['__start__'], 'langgraph_path': ('__pregel_pull', '__start__'), 'langgraph_checkpoint_ns': '__start__:125a97f0-1abc-0d8e-723b-8d12595e172b'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'sources': ['auto'], 'audience': 'expert'}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': '__start__', 'run_id': '97a94fac-af1d-40b6-9f37-0275e976ed9f', 'tags': ['graph:step:0', 'langsmith:hidden', 'langsmith:hidden'], 'metadata': {'langgraph_step': 0, 'langgraph_node': '__start__', 'langgraph_triggers': ['__start__'], 'langgraph_path': ('__pregel_pull', '__start__'), 'langgraph_checkpoint_ns': '__start__:125a97f0-1abc-0d8e-723b-8d12595e172b'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'sources': ['auto'], 'audience': 'expert'}, 'output': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'sources': ['auto'], 'audience': 'expert'}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': 'categorize_intent', 'run_id': 'b0bc86b0-069a-49ea-b05c-af4d57e92e8f', 'tags': ['graph:step:1'], 'metadata': {'langgraph_step': 1, 'langgraph_node': 'categorize_intent', 'langgraph_triggers': ['start:categorize_intent'], 'langgraph_path': ('__pregel_pull', 'categorize_intent'), 'langgraph_checkpoint_ns': 'categorize_intent:946c26f6-6fe9-aae4-7a23-d6b24d691e75'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'audience': 'expert'}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': 'RunnableSequence', 'run_id': 'c21aa0ed-2517-43b6-8c98-f3cac9624f19', 'tags': ['seq:step:1'], 'metadata': {'langgraph_step': 1, 'langgraph_node': 'categorize_intent', 'langgraph_triggers': ['start:categorize_intent'], 'langgraph_path': ('__pregel_pull', 'categorize_intent'), 'langgraph_checkpoint_ns': 'categorize_intent:946c26f6-6fe9-aae4-7a23-d6b24d691e75', 'checkpoint_ns': 'categorize_intent:946c26f6-6fe9-aae4-7a23-d6b24d691e75'}, 'data': {'input': {'input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\"}}, 'parent_ids': []}\n", + "{'event': 'on_prompt_start', 'name': 'ChatPromptTemplate', 'run_id': '3ece9fb1-6944-4335-a9c5-c5cba9af2504', 'tags': ['seq:step:1'], 'metadata': {'langgraph_step': 1, 'langgraph_node': 'categorize_intent', 'langgraph_triggers': ['start:categorize_intent'], 'langgraph_path': ('__pregel_pull', 'categorize_intent'), 'langgraph_checkpoint_ns': 'categorize_intent:946c26f6-6fe9-aae4-7a23-d6b24d691e75', 'checkpoint_ns': 'categorize_intent:946c26f6-6fe9-aae4-7a23-d6b24d691e75'}, 'data': {'input': {'input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\"}}, 'parent_ids': []}\n", + "{'event': 'on_prompt_end', 'name': 'ChatPromptTemplate', 'run_id': '3ece9fb1-6944-4335-a9c5-c5cba9af2504', 'tags': ['seq:step:1'], 'metadata': {'langgraph_step': 1, 'langgraph_node': 'categorize_intent', 'langgraph_triggers': ['start:categorize_intent'], 'langgraph_path': ('__pregel_pull', 'categorize_intent'), 'langgraph_checkpoint_ns': 'categorize_intent:946c26f6-6fe9-aae4-7a23-d6b24d691e75', 'checkpoint_ns': 'categorize_intent:946c26f6-6fe9-aae4-7a23-d6b24d691e75'}, 'data': {'input': {'input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\"}, 'output': ChatPromptValue(messages=[SystemMessage(content='You are a helpful assistant, you will analyze, translate and reformulate the user input message using the function provided'), HumanMessage(content=\"input: I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\")])}, 'parent_ids': []}\n", + "{'event': 'on_chat_model_start', 'name': 'ChatOpenAI', 'run_id': '6e006a57-ab2f-4810-a6a6-a2df22dd0db5', 'tags': ['seq:step:2'], 'metadata': {'langgraph_step': 1, 'langgraph_node': 'categorize_intent', 'langgraph_triggers': ['start:categorize_intent'], 'langgraph_path': ('__pregel_pull', 'categorize_intent'), 'langgraph_checkpoint_ns': 'categorize_intent:946c26f6-6fe9-aae4-7a23-d6b24d691e75', 'checkpoint_ns': 'categorize_intent:946c26f6-6fe9-aae4-7a23-d6b24d691e75', 'ls_provider': 'openai', 'ls_model_type': 'chat', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'data': {'input': {'messages': [[SystemMessage(content='You are a helpful assistant, you will analyze, translate and reformulate the user input message using the function provided'), HumanMessage(content=\"input: I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\")]]}}, 'parent_ids': []}\n", + "{'event': 'on_chat_model_end', 'name': 'ChatOpenAI', 'run_id': '6e006a57-ab2f-4810-a6a6-a2df22dd0db5', 'tags': ['seq:step:2'], 'metadata': {'langgraph_step': 1, 'langgraph_node': 'categorize_intent', 'langgraph_triggers': ['start:categorize_intent'], 'langgraph_path': ('__pregel_pull', 'categorize_intent'), 'langgraph_checkpoint_ns': 'categorize_intent:946c26f6-6fe9-aae4-7a23-d6b24d691e75', 'checkpoint_ns': 'categorize_intent:946c26f6-6fe9-aae4-7a23-d6b24d691e75', 'ls_provider': 'openai', 'ls_model_type': 'chat', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'data': {'input': {'messages': [[SystemMessage(content='You are a helpful assistant, you will analyze, translate and reformulate the user input message using the function provided'), HumanMessage(content=\"input: I am not really sure what you mean. 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In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1056.0, 'num_tokens': 219.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 7, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.2 | Assessed contributions to observed warming in 2010-2019 relative to 1850-1900', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.595214307, 'content': 'Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}', 'reranking_score': 0.9769342541694641, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 638.0, 'num_tokens': 177.0, 'num_tokens_approx': 200.0, 'num_words': 150.0, 'page_number': 11, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.3 | Synthesis of assessed observed and attributable regional changes ', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.592556596, 'content': 'A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}', 'reranking_score': 0.907663881778717, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 827.0, 'num_tokens': 238.0, 'num_tokens_approx': 278.0, 'num_words': 209.0, 'page_number': 19, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.6 | Projected changes in the intensity and frequency of hot temperature extremes over land, extreme precipitation over land, \\r\\nand agricultural and ecological droughts in drying regions', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.582914889, 'content': 'B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919', 'reranking_score': 0.8376452922821045, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919'), Document(metadata={'chunk_type': 'text', 'document_id': 'document4', 'document_number': 4.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 806.0, 'num_tokens': 147.0, 'num_tokens_approx': 162.0, 'num_words': 122.0, 'page_number': 19, 'release_date': 2022.0, 'report_type': 'SPM', 'section_header': 'Complex, Compound and Cascading Risks', 'short_name': 'IPCC AR6 WGII SPM', 'source': 'IPCC', 'toc_level0': 'B: Observed and Projected Impacts and Risks', 'toc_level1': 'Impacts of Temporary Overshoot', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_SummaryForPolicymakers.pdf', 'similarity_score': 0.581911206, 'content': 'B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 686.0, 'num_tokens': 163.0, 'num_tokens_approx': 182.0, 'num_words': 137.0, 'page_number': 19, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.581764042, 'content': 'Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 644.0, 'num_tokens': 151.0, 'num_tokens_approx': 165.0, 'num_words': 124.0, 'page_number': 950, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.741419494, 'content': \"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\", 'reranking_score': 0.7089773416519165, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1121.0, 'num_tokens': 231.0, 'num_tokens_approx': 288.0, 'num_words': 216.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727996647, 'content': \"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\", 'reranking_score': 0.47518521547317505, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 534.0, 'num_tokens': 102.0, 'num_tokens_approx': 124.0, 'num_words': 93.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': \"FAQ 7.1: The Earth's energy budget and climate change\", 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.69055146, 'content': 'Frequently Asked Questions\\nFAQ 7.2 | What Is the Role of Clouds in a Warming Climate?\\nOne of the biggest challenges in climate science has been to predict how clouds will change in a warming world and whether those changes will amplify or partially offset the warming caused by increasing concentrations of greenhouse gases and other human activities. Scientists have made significant progress over the past decade and are now more confident that changes in clouds will amplify, rather than offset, global warming in the future.', 'reranking_score': 0.46917641162872314, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Frequently Asked Questions\\nFAQ 7.2 | What Is the Role of Clouds in a Warming Climate?\\nOne of the biggest challenges in climate science has been to predict how clouds will change in a warming world and whether those changes will amplify or partially offset the warming caused by increasing concentrations of greenhouse gases and other human activities. Scientists have made significant progress over the past decade and are now more confident that changes in clouds will amplify, rather than offset, global warming in the future.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 843.0, 'num_tokens': 164.0, 'num_tokens_approx': 204.0, 'num_words': 153.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.68601656, 'content': \"Since the pre-industrial period, the Earth's surface and atmosphere have warmed, altering the properties of clouds, such as their altitude, amount and composition (water or ice), thereby affecting the Earth's energy budget and, in turn, changing temperature. This cascading effect of clouds, known as the cloud feedback, could either amplify or offset some of the future warming and has long been the biggest source of uncertainty in climate projections. The problem stems from the fact that clouds can change in many ways and that their processes occur on much smaller scales than global climate models can explicitly represent. As a result, global climate models have disagreed on how clouds, particularly over the subtropical ocean, will change in the future and whether the change will amplify or suppress the global warming.\", 'reranking_score': 0.4625747501850128, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"Since the pre-industrial period, the Earth's surface and atmosphere have warmed, altering the properties of clouds, such as their altitude, amount and composition (water or ice), thereby affecting the Earth's energy budget and, in turn, changing temperature. This cascading effect of clouds, known as the cloud feedback, could either amplify or offset some of the future warming and has long been the biggest source of uncertainty in climate projections. The problem stems from the fact that clouds can change in many ways and that their processes occur on much smaller scales than global climate models can explicitly represent. As a result, global climate models have disagreed on how clouds, particularly over the subtropical ocean, will change in the future and whether the change will amplify or suppress the global warming.\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 871.0, 'num_tokens': 226.0, 'num_tokens_approx': 214.0, 'num_words': 161.0, 'page_number': 596, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '4.3.3.1.1 Northern Annular Mode', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '4: Future Global Climate: Scenario-based Projections and Near-term Information', 'toc_level1': '4.3 Projected Changes in Global Climate Indices in the 21st\\xa0Century', 'toc_level2': '4.3.3 Modes of Variability', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.682466626, 'content': 'Another area of progress is new understanding the role of cloud radiative effects in shaping the mid-latitude circulation response to anthropogenic forcing. Through their non-uniform distribution of radiative heating, cloud changes can modify meridional temperature gradients and alter mid-latitude circulation and the annular modes in both hemispheres (Ceppi et al., 2014; Voigt and Shaw, 2015, 2016; Ceppi and Hartmann, 2016; Ceppi and Shepherd, 2017; Lipat et al., 2018; Albern et al., 2019; Voigt et al., 2019). In addition to the effects of changing upper and lower tropospheric temperature gradients on the NAM, progress has been made since AR5 in understanding the effect of simulated changes in the strength of the stratospheric polar vortex on winter NAM projections (Manzini et al., 2014; Zappa and Shepherd, 2017; Simpson et al., 2018).', 'reranking_score': 0.4539980888366699, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Another area of progress is new understanding the role of cloud radiative effects in shaping the mid-latitude circulation response to anthropogenic forcing. Through their non-uniform distribution of radiative heating, cloud changes can modify meridional temperature gradients and alter mid-latitude circulation and the annular modes in both hemispheres (Ceppi et al., 2014; Voigt and Shaw, 2015, 2016; Ceppi and Hartmann, 2016; Ceppi and Shepherd, 2017; Lipat et al., 2018; Albern et al., 2019; Voigt et al., 2019). In addition to the effects of changing upper and lower tropospheric temperature gradients on the NAM, progress has been made since AR5 in understanding the effect of simulated changes in the strength of the stratospheric polar vortex on winter NAM projections (Manzini et al., 2014; Zappa and Shepherd, 2017; Simpson et al., 2018).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 897.0, 'num_tokens': 222.0, 'num_tokens_approx': 225.0, 'num_words': 169.0, 'page_number': 1274, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '9.4.1.1 Recent Observed Changes', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '9: Ocean, Cryosphere and Sea Level Change', 'toc_level1': '9.4 Ice Sheets', 'toc_level2': '9.4.1 Greenland Ice Sheet', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.679714382, 'content': 'The SROCC did not assess the role of cloud changes in detail. Studies since AR5 have shown that higher incident shortwave radiation in conjunction with reduced cloud cover leads to increased melt rates, particularly over the low-albedo ablation zone in the southern part of the Greenland Ice Sheet (Hofer et al., 2017; Niwano et al., 2019; Ruan et al., 2019). Conversely, an increase in cloud cover over the high-albedo central parts of the ice sheet, leading to higher downwelling longwave radiation, was shown to lead either to increased melt (Bennartz et al., 2013) or reduced refreezing of meltwater (van Tricht et al., 2016). The elevation dependence of the cloud radiative effect and its control on surface meltwater generation and refreezing (W. Wang et al., 2019; Hahn et al., 2020) can induce a spatially consistent response of the integrated Greenland Ice Sheet', 'reranking_score': 0.45154306292533875, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='The SROCC did not assess the role of cloud changes in detail. Studies since AR5 have shown that higher incident shortwave radiation in conjunction with reduced cloud cover leads to increased melt rates, particularly over the low-albedo ablation zone in the southern part of the Greenland Ice Sheet (Hofer et al., 2017; Niwano et al., 2019; Ruan et al., 2019). Conversely, an increase in cloud cover over the high-albedo central parts of the ice sheet, leading to higher downwelling longwave radiation, was shown to lead either to increased melt (Bennartz et al., 2013) or reduced refreezing of meltwater (van Tricht et al., 2016). The elevation dependence of the cloud radiative effect and its control on surface meltwater generation and refreezing (W. Wang et al., 2019; Hahn et al., 2020) can induce a spatially consistent response of the integrated Greenland Ice Sheet'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 830.0, 'num_tokens': 199.0, 'num_tokens_approx': 229.0, 'num_words': 172.0, 'page_number': 988, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.2.4.1 Decomposition of clouds into regimes', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.678828418, 'content': \"In the global energy budget at TOA, clouds affect shortwave (SW) radiation by reflecting sunlight due to their high albedo (cooling the climate system) and also longwave (LW) radiation by absorbing the energy from the surface and emitting at a lower temperature to space, that is, contributing to the greenhouse effect, warming the climate system. In general, the greenhouse effect of clouds strengthens with height whereas the SW reflection depends on the cloud optical properties. The effects of clouds on Earth's energy budget are measured by the cloud radiative effect (CRE), which is the difference in the TOA radiation between clear and all skies \\n7.4.2.4 Cloud Feedbacks\\n 7.4.2.4 Cloud Feedbacks \\n\\n7.4.2.4 Cloud Feedbacks\\n7.4.2.4.1 Decomposition of clouds into regimes\", 'reranking_score': 0.392482191324234, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"In the global energy budget at TOA, clouds affect shortwave (SW) radiation by reflecting sunlight due to their high albedo (cooling the climate system) and also longwave (LW) radiation by absorbing the energy from the surface and emitting at a lower temperature to space, that is, contributing to the greenhouse effect, warming the climate system. In general, the greenhouse effect of clouds strengthens with height whereas the SW reflection depends on the cloud optical properties. The effects of clouds on Earth's energy budget are measured by the cloud radiative effect (CRE), which is the difference in the TOA radiation between clear and all skies \\n7.4.2.4 Cloud Feedbacks\\n 7.4.2.4 Cloud Feedbacks \\n\\n7.4.2.4 Cloud Feedbacks\\n7.4.2.4.1 Decomposition of clouds into regimes\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 819.0, 'num_tokens': 182.0, 'num_tokens_approx': 192.0, 'num_words': 144.0, 'page_number': 989, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Tropical high-cloud amount feedback', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.67528522, 'content': 'Tropical high-cloud amount feedback\\nUpdrafts in convective plumes lead to detrainment of moisture at a level where the buoyancy diminishes, and thus deep convective clouds over high SSTs in the tropics are accompanied by anvil and cirrus clouds in the upper troposphere. These clouds, rather than the convective plumes themselves, play a substantial role in the global TOA radiation budget. In the present climate, the net CRE of these clouds is small due to a cancellation between the SW and LW components (Hartmann et al., 2001). However, high-clouds with different optical properties could respond to surface warming differently, potentially perturbing this radiative balance and therefore leading to a non-zero feedback.\\n Tropical high-cloud amount feedback ', 'reranking_score': 0.30762800574302673, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Tropical high-cloud amount feedback\\nUpdrafts in convective plumes lead to detrainment of moisture at a level where the buoyancy diminishes, and thus deep convective clouds over high SSTs in the tropics are accompanied by anvil and cirrus clouds in the upper troposphere. These clouds, rather than the convective plumes themselves, play a substantial role in the global TOA radiation budget. In the present climate, the net CRE of these clouds is small due to a cancellation between the SW and LW components (Hartmann et al., 2001). However, high-clouds with different optical properties could respond to surface warming differently, potentially perturbing this radiative balance and therefore leading to a non-zero feedback.\\n Tropical high-cloud amount feedback '), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 622.0, 'num_tokens': 132.0, 'num_tokens_approx': 145.0, 'num_words': 109.0, 'page_number': 995, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.2.7 Synthesis', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.673939586, 'content': 'cloud feedbacks, which may originate from the Arctic regions: a reduction in sea ice enhances the shortwave cloud radiative effect because the ocean surface is darker than sea ice (Gilgen et al., 2018). This covariance is reinforced as the decrease of sea ice leads to an increase in low-level clouds (Mauritsen et al., 2013). However, the mechanism causing these co-dependences between feedbacks is not well understood yet and a quantitative assessment based on multiple lines of evidence is difficult. Therefore, this synthesis assessment does not consider any co-dependency across individual feedbacks.', 'reranking_score': 0.30197522044181824, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='cloud feedbacks, which may originate from the Arctic regions: a reduction in sea ice enhances the shortwave cloud radiative effect because the ocean surface is darker than sea ice (Gilgen et al., 2018). This covariance is reinforced as the decrease of sea ice leads to an increase in low-level clouds (Mauritsen et al., 2013). However, the mechanism causing these co-dependences between feedbacks is not well understood yet and a quantitative assessment based on multiple lines of evidence is difficult. Therefore, this synthesis assessment does not consider any co-dependency across individual feedbacks.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 205.0, 'num_tokens': 58.0, 'num_tokens_approx': 54.0, 'num_words': 41.0, 'page_number': 1079, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.1.1.2 Overview of the Global Water Cycle \\r\\nin the Climate System', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.1 Introduction', 'toc_level2': '8.1.2 Summary of Water Cycle Changes From AR5 and\\xa0Special Reports', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.672232032, 'content': 'The cloud response to anthropogenic radiative forcings, both in the tropics and in the extratropics (Zelinka et al., 2020), is therefore also crucial for understanding climate change (Section 7.4.2.4).', 'reranking_score': 0.26861870288848877, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='The cloud response to anthropogenic radiative forcings, both in the tropics and in the extratropics (Zelinka et al., 2020), is therefore also crucial for understanding climate change (Section 7.4.2.4).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 913.0, 'num_tokens': 218.0, 'num_tokens_approx': 225.0, 'num_words': 169.0, 'page_number': 989, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Tropical high-cloud amount feedback', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.670264423, 'content': \"A thermodynamic mechanism referred to as the 'stability iris effect' has been proposed to explain that the anvil cloud amount decreases with surface warming (Bony et al., 2016). In this mechanism, a temperature-mediated increase of static stability in the upper troposphere, where convective detrainment occurs, acts to balance a weakened mass outflow from convective clouds, and thereby reduce anvil cloud areal coverage (Figure 7.9). The reduction of anvil cloud amount is accompanied by enhanced convective aggregation that causes a drying of the surrounding air and thereby increases the LW emission to space that acts as a negative feedback (Bony et al., 2020). This phenomenon is found in many CRM simulations (Emanuel et al., 2014; Wing and Emanuel, 2014; Wing et al., 2020) and also identified in observed interannual variability (Stein et al., 2017; Saint-Lu et al., 2020).\", 'reranking_score': 0.2583097815513611, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"A thermodynamic mechanism referred to as the 'stability iris effect' has been proposed to explain that the anvil cloud amount decreases with surface warming (Bony et al., 2016). In this mechanism, a temperature-mediated increase of static stability in the upper troposphere, where convective detrainment occurs, acts to balance a weakened mass outflow from convective clouds, and thereby reduce anvil cloud areal coverage (Figure 7.9). The reduction of anvil cloud amount is accompanied by enhanced convective aggregation that causes a drying of the surrounding air and thereby increases the LW emission to space that acts as a negative feedback (Bony et al., 2020). This phenomenon is found in many CRM simulations (Emanuel et al., 2014; Wing and Emanuel, 2014; Wing et al., 2020) and also identified in observed interannual variability (Stein et al., 2017; Saint-Lu et al., 2020).\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1028.0, 'num_tokens': 217.0, 'num_tokens_approx': 216.0, 'num_words': 162.0, 'page_number': 988, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.2.4.1 Decomposition of clouds into regimes', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.66944629, 'content': 'Clouds have various types, from optically thick convective clouds to thin stratus and cirrus clouds, depending upon thermodynamic conditions and large-scale circulation (Figure 7.9). Over the equatorial warm pool and inter-tropical convergence zone (ITCZ) regions, high SSTs stimulate the development of deep convective cloud systems, which are accompanied by anvil and cirrus clouds near the tropopause where the convective air outflows. The large-scale circulation associated with these convective clouds leads to subsidence over the subtropical cool ocean, where deep convection is suppressed by a lower tropospheric inversion layer maintained by the subsidence and promoting the formation of shallow cumulus and stratocumulus clouds. In the extratropics, mid-latitude storm tracks control cloud formation, which occurs primarily in the frontal bands of extratropical cyclones. Since liquid droplets do not freeze spontaneously at temperatures warmer than approximately -40degC and ice nucleating', 'reranking_score': 0.2330094873905182, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Clouds have various types, from optically thick convective clouds to thin stratus and cirrus clouds, depending upon thermodynamic conditions and large-scale circulation (Figure 7.9). Over the equatorial warm pool and inter-tropical convergence zone (ITCZ) regions, high SSTs stimulate the development of deep convective cloud systems, which are accompanied by anvil and cirrus clouds near the tropopause where the convective air outflows. The large-scale circulation associated with these convective clouds leads to subsidence over the subtropical cool ocean, where deep convection is suppressed by a lower tropospheric inversion layer maintained by the subsidence and promoting the formation of shallow cumulus and stratocumulus clouds. In the extratropics, mid-latitude storm tracks control cloud formation, which occurs primarily in the frontal bands of extratropical cyclones. Since liquid droplets do not freeze spontaneously at temperatures warmer than approximately -40degC and ice nucleating'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 541.0, 'num_tokens': 108.0, 'num_tokens_approx': 121.0, 'num_words': 91.0, 'page_number': 988, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.2.4.1 Decomposition of clouds into regimes', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.667222738, 'content': 'Clouds can be formed almost anywhere in the atmosphere when moist air parcels rise and cool, enabling the water vapour to condense. Clouds consist of liquid water droplets and/or ice crystals, and these droplets and crystals can grow into larger particles of rain, snow or drizzle. These microphysical processes interact with aerosols, \\nradiation and atmospheric circulation, resulting in a highly complex set of processes governing cloud formation and life cycles that operate across a wide range of spatial and temporal scales.', 'reranking_score': 0.19094012677669525, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Clouds can be formed almost anywhere in the atmosphere when moist air parcels rise and cool, enabling the water vapour to condense. Clouds consist of liquid water droplets and/or ice crystals, and these droplets and crystals can grow into larger particles of rain, snow or drizzle. These microphysical processes interact with aerosols, \\nradiation and atmospheric circulation, resulting in a highly complex set of processes governing cloud formation and life cycles that operate across a wide range of spatial and temporal scales.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1095.0, 'num_tokens': 220.0, 'num_tokens_approx': 248.0, 'num_words': 186.0, 'page_number': 2241, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Natural variability', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex VII: Glossary', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.667198241, 'content': \"tolerance, CIDs and their changes can be detrimental, beneficial, neutral or a mixture of each across interacting system elements and regions. See also Risk, Hazard and Impacts (consequences, outcomes).\\nCloud condensation nuclei (CCN) The subset of aerosol particles that serve as an initial site for the condensation of liquid water, which can lead to the formation of cloud droplets, under typical cloud formation conditions. The main factor that determines which aerosol particles are CCN at a given supersaturation is their size.\\nCloud feedback A climate feedback involving changes in any of the properties of clouds as a response to a change in the local or global surface temperature. Understanding cloud feedbacks and determining their magnitude and sign requires an understanding of how a change in climate may affect the spectrum of cloud types, the cloud fraction and height, the radiative properties of clouds, and finally the Earth's radiation budget. \\nCloud radiative effect The radiative effect of clouds relative to the identical situation without clouds.\", 'reranking_score': 0.1841827929019928, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"tolerance, CIDs and their changes can be detrimental, beneficial, neutral or a mixture of each across interacting system elements and regions. See also Risk, Hazard and Impacts (consequences, outcomes).\\nCloud condensation nuclei (CCN) The subset of aerosol particles that serve as an initial site for the condensation of liquid water, which can lead to the formation of cloud droplets, under typical cloud formation conditions. The main factor that determines which aerosol particles are CCN at a given supersaturation is their size.\\nCloud feedback A climate feedback involving changes in any of the properties of clouds as a response to a change in the local or global surface temperature. Understanding cloud feedbacks and determining their magnitude and sign requires an understanding of how a change in climate may affect the spectrum of cloud types, the cloud fraction and height, the radiative properties of clouds, and finally the Earth's radiation budget. \\nCloud radiative effect The radiative effect of clouds relative to the identical situation without clouds.\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 871.0, 'num_tokens': 218.0, 'num_tokens_approx': 190.0, 'num_words': 143.0, 'page_number': 2404, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Sulphur dioxide (SO2)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Index', 'toc_level1': 'T', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.66647613, 'content': 'changes following a perturbation, 91 changes in, 935 cloud effects on, 971 effective radiative forcing driving of, 941 estimates of equilibrium climate sensitivity based on variability, 998 imbalance with anthropogenic forcing, 933 instantaneous radiative forcing from aerosol-cloud interactions, 948 mechanism of anthropogenic effects on climate, 925 monitoring methods for, 929 net energy flux of Earth system, 931 radiative adjustments for climate drivers, 943 radiative flux evaluation, 968 reconstruction of variations, 937 response to spatial pattern of warming, 989 Tornadoes association with extratropical cyclones, 1594 convective systems with, 1594-1600 spatial and temporal scales of, 1522 Total aerosol effective radiative forcing, assessment of, 954 Total alkalinity* (of ocean), 742 Total column ozone (TCO), in stratosphere, 838', 'reranking_score': 0.1790597140789032, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='changes following a perturbation, 91 changes in, 935 cloud effects on, 971 effective radiative forcing driving of, 941 estimates of equilibrium climate sensitivity based on variability, 998 imbalance with anthropogenic forcing, 933 instantaneous radiative forcing from aerosol-cloud interactions, 948 mechanism of anthropogenic effects on climate, 925 monitoring methods for, 929 net energy flux of Earth system, 931 radiative adjustments for climate drivers, 943 radiative flux evaluation, 968 reconstruction of variations, 937 response to spatial pattern of warming, 989 Tornadoes association with extratropical cyclones, 1594 convective systems with, 1594-1600 spatial and temporal scales of, 1522 Total aerosol effective radiative forcing, assessment of, 954 Total alkalinity* (of ocean), 742 Total column ozone (TCO), in stratosphere, 838'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 559.0, 'num_tokens': 133.0, 'num_tokens_approx': 152.0, 'num_words': 114.0, 'page_number': 641, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '4.6.3.3 Climate Response to Solar Radiation Modification', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '4: Future Global Climate: Scenario-based Projections and Near-term Information', 'toc_level1': '4.6 Implications of Climate Policy', 'toc_level2': '4.6.3 Climate Response to Mitigation, Carbon Dioxide Removal and Solar Radiation Modification', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.665777624, 'content': '4.6.3.3 Climate Response to Solar Radiation Modification\\n4.6.3.3 Climate Response to Solar Radiation Modification\\n 4.6.3.3 Climate Response to Solar Radiation Modification \\n\\nMost SRM approaches, including stratospheric aerosol injection (SAI), marine cloud brightening (MCB), and surface albedo enhancements (Table 4.7), aim to cool the Earth by deflecting more solar radiation to space. Although cirrus cloud thinning (CCT) aims to cool the planet by increasing the longwave emission to space, it is included in the', 'reranking_score': 0.1790597140789032, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='4.6.3.3 Climate Response to Solar Radiation Modification\\n4.6.3.3 Climate Response to Solar Radiation Modification\\n 4.6.3.3 Climate Response to Solar Radiation Modification \\n\\nMost SRM approaches, including stratospheric aerosol injection (SAI), marine cloud brightening (MCB), and surface albedo enhancements (Table 4.7), aim to cool the Earth by deflecting more solar radiation to space. Although cirrus cloud thinning (CCT) aims to cool the planet by increasing the longwave emission to space, it is included in the'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 941.0, 'num_tokens': 227.0, 'num_tokens_approx': 222.0, 'num_words': 167.0, 'page_number': 877, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '6.4.6 ERF by Aerosols in Proposed Solar \\r\\nRadiation Modification ', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '6: Short-lived Climate Forcers', 'toc_level1': '6.4 SLCF Radiative Forcing and\\xa0Climate\\xa0Effects', 'toc_level2': '6.4.6 ERF by Aerosols in Proposed Solar Radiation\\xa0Modification ', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.664611638, 'content': 'the range of -3 to -10 W m-2/(Pg yr-1), when emitted over tropical oceans in ESMs in the Geoengineering Intercomparison Project (GeoMIP; Ahlm et al., 2017). Cloud-resolving models reveal the complex behaviour and response of stratocumulus clouds to seeding, in that the ERF efficiency depends on meteorological conditions, and the ambient aerosol composition, where lower background particle concentrations may increase the ERFaci efficiency (Wang et al., 2011). Seeding could suppress precipitation formation and drizzle, and hence increase the lifetime of clouds, preserving their cooling effect (Ferek et al., 2000). In contrast, cloud lifetime could be decreased by making the smaller droplets more susceptible to evaporation. Modelling studies have shown that a positive ERFaci (warming) could also result from seeding clouds with too large aerosols (Pringle et al., 2012; Alterskjaer and Kristjansson, 2013).', 'reranking_score': 0.1428375095129013, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='the range of -3 to -10 W m-2/(Pg yr-1), when emitted over tropical oceans in ESMs in the Geoengineering Intercomparison Project (GeoMIP; Ahlm et al., 2017). Cloud-resolving models reveal the complex behaviour and response of stratocumulus clouds to seeding, in that the ERF efficiency depends on meteorological conditions, and the ambient aerosol composition, where lower background particle concentrations may increase the ERFaci efficiency (Wang et al., 2011). Seeding could suppress precipitation formation and drizzle, and hence increase the lifetime of clouds, preserving their cooling effect (Ferek et al., 2000). In contrast, cloud lifetime could be decreased by making the smaller droplets more susceptible to evaporation. Modelling studies have shown that a positive ERFaci (warming) could also result from seeding clouds with too large aerosols (Pringle et al., 2012; Alterskjaer and Kristjansson, 2013).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1117.0, 'num_tokens': 224.0, 'num_tokens_approx': 273.0, 'num_words': 205.0, 'page_number': 1037, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': \"FAQ 7.1 | What Is the Earth's Energy Budget, and What Does It Tell Us About Climate Change?\", 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.664263725, 'content': \"We measure the influence of various human and natural factors on the energy flows at the top of our atmosphere in terms of radiative forcings, where a positive radiative forcing has a warming effect and a negative radiative forcing has a cooling effect. In response to these forcings, the Earth system will either warm or cool, so as to restore balance through changes in the amount of outgoing thermal radiation (the warmer the Earth, the more radiation it emits). Changes in Earth's temperature in turn lead to additional changes in the climate system (known as climate feedbacks) that either amplify or dampen the original effect. For example, Arctic sea ice has been melting as the Earth warms, reducing the amount of reflected sunlight and adding to the initial warming (an amplifying feedback). The most uncertain of those climate feedbacks are clouds, as they respond to warming in complex ways that affect both the emission of thermal radiation and the reflection of sunlight. However, we are now more confident that cloud changes, taken together, will amplify climate warming (see FAQ 7.2).\", 'reranking_score': 0.11089885979890823, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"We measure the influence of various human and natural factors on the energy flows at the top of our atmosphere in terms of radiative forcings, where a positive radiative forcing has a warming effect and a negative radiative forcing has a cooling effect. In response to these forcings, the Earth system will either warm or cool, so as to restore balance through changes in the amount of outgoing thermal radiation (the warmer the Earth, the more radiation it emits). Changes in Earth's temperature in turn lead to additional changes in the climate system (known as climate feedbacks) that either amplify or dampen the original effect. For example, Arctic sea ice has been melting as the Earth warms, reducing the amount of reflected sunlight and adding to the initial warming (an amplifying feedback). The most uncertain of those climate feedbacks are clouds, as they respond to warming in complex ways that affect both the emission of thermal radiation and the reflection of sunlight. However, we are now more confident that cloud changes, taken together, will amplify climate warming (see FAQ 7.2).\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1160.0, 'num_tokens': 221.0, 'num_tokens_approx': 246.0, 'num_words': 185.0, 'page_number': 121, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Box TS.8 | Earth System Response to Solar Radiation Modification', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Technical Summary', 'toc_level1': 'TS.3 Understanding the Climate System Response and Implications for Limiting Global Warming', 'toc_level2': 'Box TS.8 | Earth System Response to Solar Radiation Modification', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.661996603, 'content': 'Since AR5, further modelling work has been conducted on aerosol-based solar radiation modification (SRM) options such as stratospheric aerosol injection, marine cloud brightening, and cirrus cloud thinning21 and their climate and biogeochemical effects. These investigations have consistently shown that SRM could offset some of the effects of increasing greenhouse gases on global and regional climate, including the carbon and water cycles (high confidence). However, there would be substantial residual or overcompensating climate change at the regional scales and seasonal time scales (high confidence), and large uncertainties associated with aerosol-cloud-radiation interactions persist. The cooling caused by SRM would increase the global land and ocean CO2 sinks (medium confidence), but this would not stop CO2 from increasing in the atmosphere or affect the resulting ocean acidification under continued anthropogenic emissions (high confidence). It is likely that abrupt water cycle changes will occur if SRM techniques are implemented rapidly. A sudden and sustained termination of SRM in a high CO2 emissions scenario would cause', 'reranking_score': 0.07576695084571838, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Since AR5, further modelling work has been conducted on aerosol-based solar radiation modification (SRM) options such as stratospheric aerosol injection, marine cloud brightening, and cirrus cloud thinning21 and their climate and biogeochemical effects. These investigations have consistently shown that SRM could offset some of the effects of increasing greenhouse gases on global and regional climate, including the carbon and water cycles (high confidence). However, there would be substantial residual or overcompensating climate change at the regional scales and seasonal time scales (high confidence), and large uncertainties associated with aerosol-cloud-radiation interactions persist. The cooling caused by SRM would increase the global land and ocean CO2 sinks (medium confidence), but this would not stop CO2 from increasing in the atmosphere or affect the resulting ocean acidification under continued anthropogenic emissions (high confidence). It is likely that abrupt water cycle changes will occur if SRM techniques are implemented rapidly. A sudden and sustained termination of SRM in a high CO2 emissions scenario would cause'), Document(metadata={'chunk_type': 'text', 'document_id': 'document3', 'document_number': 3.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 112.0, 'name': 'Technical Summary. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1160.0, 'num_tokens': 221.0, 'num_tokens_approx': 246.0, 'num_words': 185.0, 'page_number': 72, 'release_date': 2021.0, 'report_type': 'TS', 'section_header': 'Box TS.8 | Earth System Response to Solar Radiation Modification', 'short_name': 'IPCC AR6 WGI TS', 'source': 'IPCC', 'toc_level0': 'TS.3 Understanding the Climate System Response and Implications for Limiting Global Warming', 'toc_level1': 'Box TS.8 | Earth System Response to Solar Radiation Modification', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf', 'similarity_score': 0.661996603, 'content': 'Since AR5, further modelling work has been conducted on aerosol-based solar radiation modification (SRM) options such as stratospheric aerosol injection, marine cloud brightening, and cirrus cloud thinning21 and their climate and biogeochemical effects. These investigations have consistently shown that SRM could offset some of the effects of increasing greenhouse gases on global and regional climate, including the carbon and water cycles (high confidence). However, there would be substantial residual or overcompensating climate change at the regional scales and seasonal time scales (high confidence), and large uncertainties associated with aerosol-cloud-radiation interactions persist. The cooling caused by SRM would increase the global land and ocean CO2 sinks (medium confidence), but this would not stop CO2 from increasing in the atmosphere or affect the resulting ocean acidification under continued anthropogenic emissions (high confidence). It is likely that abrupt water cycle changes will occur if SRM techniques are implemented rapidly. A sudden and sustained termination of SRM in a high CO2 emissions scenario would cause', 'reranking_score': 0.06748388707637787, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Since AR5, further modelling work has been conducted on aerosol-based solar radiation modification (SRM) options such as stratospheric aerosol injection, marine cloud brightening, and cirrus cloud thinning21 and their climate and biogeochemical effects. These investigations have consistently shown that SRM could offset some of the effects of increasing greenhouse gases on global and regional climate, including the carbon and water cycles (high confidence). However, there would be substantial residual or overcompensating climate change at the regional scales and seasonal time scales (high confidence), and large uncertainties associated with aerosol-cloud-radiation interactions persist. The cooling caused by SRM would increase the global land and ocean CO2 sinks (medium confidence), but this would not stop CO2 from increasing in the atmosphere or affect the resulting ocean acidification under continued anthropogenic emissions (high confidence). It is likely that abrupt water cycle changes will occur if SRM techniques are implemented rapidly. A sudden and sustained termination of SRM in a high CO2 emissions scenario would cause'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1014.0, 'num_tokens': 223.0, 'num_tokens_approx': 222.0, 'num_words': 167.0, 'page_number': 1028, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Contributions to global mean warming in CMIP6 ESMs in response to CO2 quadrupling', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.660910845, 'content': \"More computationally intensive approaches evaluate how the climate response depends on perturbations to key parameter or structural choices within ESMs. Large 'perturbed parameter ensembles', wherein a range of parameter settings associated with cloud physics are explored within atmospheric ESMs, produce a wide range of ECS due to changes in cloud feedbacks, but often produce unrealistic climate states (Joshi et al., 2010). Rowlands et al. (2012) generated an ESM perturbed-physics ensemble of several thousand members by perturbing model parameters associated with radiative forcing, cloud feedbacks and ocean vertical diffusivity (an important parameter for ocean heat uptake). After constraining the ensemble to have a reasonable climatology and to match the observed historical surface warming, they found a wide range of projected warming by the year 2050 under the SRES A1B scenario (1.4degC-3degC relative to the 1961-1990 average) that is dominated by differences in cloud\", 'reranking_score': 0.066981740295887, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"More computationally intensive approaches evaluate how the climate response depends on perturbations to key parameter or structural choices within ESMs. Large 'perturbed parameter ensembles', wherein a range of parameter settings associated with cloud physics are explored within atmospheric ESMs, produce a wide range of ECS due to changes in cloud feedbacks, but often produce unrealistic climate states (Joshi et al., 2010). Rowlands et al. (2012) generated an ESM perturbed-physics ensemble of several thousand members by perturbing model parameters associated with radiative forcing, cloud feedbacks and ocean vertical diffusivity (an important parameter for ocean heat uptake). After constraining the ensemble to have a reasonable climatology and to match the observed historical surface warming, they found a wide range of projected warming by the year 2050 under the SRES A1B scenario (1.4degC-3degC relative to the 1961-1990 average) that is dominated by differences in cloud\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 739.0, 'num_tokens': 143.0, 'num_tokens_approx': 158.0, 'num_words': 119.0, 'page_number': 1028, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Contributions to global mean warming in CMIP6 ESMs in response to CO2 quadrupling', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.660891235, 'content': 'There is robust evidence and high agreement across a diverse range of modelling approaches and thus high confidence that radiative feedbacks are the largest source of uncertainty in projected global warming out to 2100 under increasing or stable emissions scenarios, and that cloud feedbacks in particular are the dominant source of that uncertainty. Uncertainty in radiative forcing plays an important but generally secondary role. Uncertainty in global ocean heat uptake plays a lesser role in global warming uncertainty, but ocean circulation could play an important role through its effect on sea surface warming patterns which in turn project onto radiative feedbacks through the pattern effect (Section 7.4.4.3).', 'reranking_score': 0.060062456876039505, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='There is robust evidence and high agreement across a diverse range of modelling approaches and thus high confidence that radiative feedbacks are the largest source of uncertainty in projected global warming out to 2100 under increasing or stable emissions scenarios, and that cloud feedbacks in particular are the dominant source of that uncertainty. Uncertainty in radiative forcing plays an important but generally secondary role. Uncertainty in global ocean heat uptake plays a lesser role in global warming uncertainty, but ocean circulation could play an important role through its effect on sea surface warming patterns which in turn project onto radiative feedbacks through the pattern effect (Section 7.4.4.3).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 1092.0, 'num_tokens': 237.0, 'num_tokens_approx': 260.0, 'num_words': 195.0, 'page_number': 2932, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'Radiative forcing', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Annexes', 'toc_level1': 'Annex II Glossary', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.65996474, 'content': 'Radiative forcing\\nThe change in the net, downward minus upward, radiative flux (expressed in W m-2) at the tropopause or top of atmosphere due to a change in an (external) driver of climate change, such as a change in the concentration of carbon dioxide (CO2), the concentration of volcanic aerosols or the output of the Sun. The traditional radiative forcing is computed with all tropospheric properties held fixed at their unperturbed values, and after allowing for stratospheric temperatures, if perturbed, to readjust to radiative-dynamical equilibrium. Radiative forcing is called instantaneous if no change in stratospheric temperature is accounted for. The radiative forcing once rapid adjustments are accounted for is termed the effective radiative forcing. Radiative forcing is not to be confused with cloud radiative forcing, which describes an unrelated measure of the impact of clouds on the radiative flux at the top of the atmosphere.\\n Radiative forcing \\n\\n Reasons for Concern (RFCs) ', 'reranking_score': 0.05967465043067932, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Radiative forcing\\nThe change in the net, downward minus upward, radiative flux (expressed in W m-2) at the tropopause or top of atmosphere due to a change in an (external) driver of climate change, such as a change in the concentration of carbon dioxide (CO2), the concentration of volcanic aerosols or the output of the Sun. The traditional radiative forcing is computed with all tropospheric properties held fixed at their unperturbed values, and after allowing for stratospheric temperatures, if perturbed, to readjust to radiative-dynamical equilibrium. Radiative forcing is called instantaneous if no change in stratospheric temperature is accounted for. The radiative forcing once rapid adjustments are accounted for is termed the effective radiative forcing. Radiative forcing is not to be confused with cloud radiative forcing, which describes an unrelated measure of the impact of clouds on the radiative flux at the top of the atmosphere.\\n Radiative forcing \\n\\n Reasons for Concern (RFCs) '), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 770.0, 'num_tokens': 198.0, 'num_tokens_approx': 226.0, 'num_words': 170.0, 'page_number': 952, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.2 Changes in Earth’s Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.659944475, 'content': 'of clouds, with otherwise identical atmospheric and surface radiative properties. It has been derived by taking into account information contained in both in situ and satellite radiation measurements taken under cloud-free conditions (Wild et al., 2019). A comparison of the upper and lower panels in Figure 7.2 shows that without clouds, 47 W m-2 less solar radiation is reflected back to space globally (53 +- 2 W m-2 instead of 100 +- 2 W m-2), while 28 W m-2 more thermal radiation is emitted to space (267 +- 3 W m-2 instead of 239 +- 3 W m-2). As a result, there is a 20 W m-2 radiative imbalance at the TOA in the clear-sky energy budget (Figure 7.2, lower panel), suggesting that the Earth would warm substantially if there were no clouds.', 'reranking_score': 0.04170629009604454, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='of clouds, with otherwise identical atmospheric and surface radiative properties. It has been derived by taking into account information contained in both in situ and satellite radiation measurements taken under cloud-free conditions (Wild et al., 2019). A comparison of the upper and lower panels in Figure 7.2 shows that without clouds, 47 W m-2 less solar radiation is reflected back to space globally (53 +- 2 W m-2 instead of 100 +- 2 W m-2), while 28 W m-2 more thermal radiation is emitted to space (267 +- 3 W m-2 instead of 239 +- 3 W m-2). As a result, there is a 20 W m-2 radiative imbalance at the TOA in the clear-sky energy budget (Figure 7.2, lower panel), suggesting that the Earth would warm substantially if there were no clouds.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 888.0, 'num_tokens': 204.0, 'num_tokens_approx': 210.0, 'num_words': 158.0, 'page_number': 1155, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.5.1.1.2 Aerosol microphysical effects on clouds and precipitation', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.5 What Are the Limits for Projecting Water Cycle Changes?', 'toc_level2': '8.5.1 Model Uncertainties of Relevance for the Water Cycle', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.658766448, 'content': 'In AR5 Chapter 7, there was low confidence in the representation of cloud-aerosol interactions in climate models. Despite progresses in this field since AR5, cloud-aerosol interactions remain a major obstacle to understanding climate and severe weather (Varble, 2018). High aerosol concentrations have been observed to suppress rain in water clouds (Campos Braga et al., 2017; Fan et al., 2020). However, such aerosol effects are muted in GCMs, which tend to produce precipitation from shallow clouds too frequently at the expense of rain intensity (Suzuki et al., 2015; Jing et al., 2017). This arises from incomplete knowledge of how clouds adjust to aerosol primary effects such as cloud condensation nuclei (CCN). The adjustment occurs mainly as a dynamic response to the impacts of CCN on cloud droplet size and number concentrations on precipitation-forming', 'reranking_score': 0.03291070833802223, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='In AR5 Chapter 7, there was low confidence in the representation of cloud-aerosol interactions in climate models. Despite progresses in this field since AR5, cloud-aerosol interactions remain a major obstacle to understanding climate and severe weather (Varble, 2018). High aerosol concentrations have been observed to suppress rain in water clouds (Campos Braga et al., 2017; Fan et al., 2020). However, such aerosol effects are muted in GCMs, which tend to produce precipitation from shallow clouds too frequently at the expense of rain intensity (Suzuki et al., 2015; Jing et al., 2017). This arises from incomplete knowledge of how clouds adjust to aerosol primary effects such as cloud condensation nuclei (CCN). The adjustment occurs mainly as a dynamic response to the impacts of CCN on cloud droplet size and number concentrations on precipitation-forming'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 696.0, 'num_tokens': 226.0, 'num_tokens_approx': 232.0, 'num_words': 174.0, 'page_number': 1045, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'References', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'References', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.658277452, 'content': 'Bony, S. et al., 2020: Observed Modulation of the Tropical Radiation Budget by Deep Convective Organization and Lower-Tropospheric Stability. AGU Advances, 1(3), e2019AV000155, doi:10.1029/2019av000155. Booth, B.B.B. et al., 2018: Comments on \"Rethinking the Lower Bound on Aerosol Radiative Forcing\". Journal of Climate, 31(22), 9407-9412, doi:10.1175/jcli-d-17-0369.1. Boucher, O., 2012: Comparison of physically- and economically-based CO2-equivalences for methane. Earth System Dynamics, 3(1), 49-61, doi:10.5194/esd-3-49-2012. Boucher, O., P. Friedlingstein, B. Collins, and K.P. Shine, 2009: The indirect global warming potential and global temperature change potential due', 'reranking_score': 0.03214704990386963, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Bony, S. et al., 2020: Observed Modulation of the Tropical Radiation Budget by Deep Convective Organization and Lower-Tropospheric Stability. AGU Advances, 1(3), e2019AV000155, doi:10.1029/2019av000155. Booth, B.B.B. et al., 2018: Comments on \"Rethinking the Lower Bound on Aerosol Radiative Forcing\". Journal of Climate, 31(22), 9407-9412, doi:10.1175/jcli-d-17-0369.1. Boucher, O., 2012: Comparison of physically- and economically-based CO2-equivalences for methane. Earth System Dynamics, 3(1), 49-61, doi:10.5194/esd-3-49-2012. Boucher, O., P. Friedlingstein, B. Collins, and K.P. Shine, 2009: The indirect global warming potential and global temperature change potential due'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 836.0, 'num_tokens': 200.0, 'num_tokens_approx': 213.0, 'num_words': 160.0, 'page_number': 989, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Tropical high-cloud amount feedback', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.657746375, 'content': 'Despite the reduction of anvil cloud amount supported by several lines of evidence, estimates of radiative feedback due to high-cloud amount changes is highly uncertain in models. The assessment presented here is guided by combined analyses of TOA radiation and cloud fluctuations at interannual time scale using multiple satellite datasets. The observationally based local cloud amount feedback associated with optically thick high-clouds is negative, leading to its global contribution (by multiplying the mean tropical anvil cloud fraction of about 8%) of -0.24 +- 0.05 W m-2 degC-1 (one standard deviation) for LW (Vaillant de Guelis et al., 2018). Also, there is a positive feedback due to increase of optically thin cirrus clouds in the tropopause layer, estimated to be 0.09 +- 0.09 W m-2 degC-1 \\n972972', 'reranking_score': 0.03214704990386963, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Despite the reduction of anvil cloud amount supported by several lines of evidence, estimates of radiative feedback due to high-cloud amount changes is highly uncertain in models. The assessment presented here is guided by combined analyses of TOA radiation and cloud fluctuations at interannual time scale using multiple satellite datasets. The observationally based local cloud amount feedback associated with optically thick high-clouds is negative, leading to its global contribution (by multiplying the mean tropical anvil cloud fraction of about 8%) of -0.24 +- 0.05 W m-2 degC-1 (one standard deviation) for LW (Vaillant de Guelis et al., 2018). Also, there is a positive feedback due to increase of optically thin cirrus clouds in the tropopause layer, estimated to be 0.09 +- 0.09 W m-2 degC-1 \\n972972'), Document(metadata={'chunk_type': 'text', 'document_id': 'document22', 'document_number': 22.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 28.0, 'name': 'Annex I: Glossary In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1095.0, 'num_tokens': 241.0, 'num_tokens_approx': 269.0, 'num_words': 202.0, 'page_number': 21, 'release_date': 2019.0, 'report_type': 'Special Report', 'section_header': 'Radiative forcing ', 'short_name': 'IPCC SR OC A1 G', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/10_SROCC_AnnexI-Glossary_FINAL.pdf', 'similarity_score': 0.657620072, 'content': 'Radiative forcing \\nThe change in the net, downward minus upward, radiative flux (expressed in W m-2) at the tropopause or top of atmosphere due to a change in an external driver of climate change, such as a change in the concentration of carbon dioxide (CO2 ), the concentration of volcanic aerosols or in the output of the Sun. The traditional radia\\x02tive forcing is computed with all tropospheric properties held fixed at their unperturbed values, and after allowing for stratospheric temper\\x02atures, if perturbed, to readjust to radiative-dynamical equilibrium. Radiative forcing is called instantaneous if no change in stratospheric temperature is accounted for. The radiative forcing once rapid adjust\\x02ments are accounted for is termed the effective radiative forcing. Radiative forcing is not to be confused with cloud radiative forc\\x02ing, which describes an unrelated measure of the impact of clouds on the radiative flux at the top of the atmosphere. \\n Radiative forcing \\n\\n Reasons for concern (RFC) ', 'reranking_score': 0.02326352521777153, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Radiative forcing \\nThe change in the net, downward minus upward, radiative flux (expressed in W m-2) at the tropopause or top of atmosphere due to a change in an external driver of climate change, such as a change in the concentration of carbon dioxide (CO2 ), the concentration of volcanic aerosols or in the output of the Sun. The traditional radia\\x02tive forcing is computed with all tropospheric properties held fixed at their unperturbed values, and after allowing for stratospheric temper\\x02atures, if perturbed, to readjust to radiative-dynamical equilibrium. Radiative forcing is called instantaneous if no change in stratospheric temperature is accounted for. The radiative forcing once rapid adjust\\x02ments are accounted for is termed the effective radiative forcing. Radiative forcing is not to be confused with cloud radiative forc\\x02ing, which describes an unrelated measure of the impact of clouds on the radiative flux at the top of the atmosphere. \\n Radiative forcing \\n\\n Reasons for concern (RFC) '), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1048.0, 'num_tokens': 225.0, 'num_tokens_approx': 248.0, 'num_words': 186.0, 'page_number': 1168, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.6.3 Abrupt Water Cycle Responses to Initiation or \\r\\nTermination of Solar Radiation Modification', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.7 Final Remarks', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.655445814, 'content': \"Solar radiation modification (SRM) techniques seek to reduce the impacts of climate change by modifying the Earth's radiation budget, either by reflecting incoming solar radiation or increasing the amount of heat lost to space. Note that, following SR1.5, the definition of SRM in this Report refers to changes in both solar and longwave radiation (Section 4.6.3.3 and Glossary). A variety of methods have been proposed, including injection of aerosols or their precursors into the stratosphere, cloud brightening, and cirrus cloud thinning (Table 4.8). Since SRM alters the planetary energy balance, changes in the hydrological cycle are theoretically expected (Section 8.2). These changes can be abrupt if the initial magnitude of SRM is large, rather than increased gradually. Since AR5, a diversity of SRM techniques have been tested using climate model simulations, with an increasing focus on consequences for regional water availability. Techniques targeting shortwave radiation (sulfate injection, surface albedo\", 'reranking_score': 0.022857487201690674, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"Solar radiation modification (SRM) techniques seek to reduce the impacts of climate change by modifying the Earth's radiation budget, either by reflecting incoming solar radiation or increasing the amount of heat lost to space. Note that, following SR1.5, the definition of SRM in this Report refers to changes in both solar and longwave radiation (Section 4.6.3.3 and Glossary). A variety of methods have been proposed, including injection of aerosols or their precursors into the stratosphere, cloud brightening, and cirrus cloud thinning (Table 4.8). Since SRM alters the planetary energy balance, changes in the hydrological cycle are theoretically expected (Section 8.2). These changes can be abrupt if the initial magnitude of SRM is large, rather than increased gradually. Since AR5, a diversity of SRM techniques have been tested using climate model simulations, with an increasing focus on consequences for regional water availability. Techniques targeting shortwave radiation (sulfate injection, surface albedo\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 976.0, 'num_tokens': 210.0, 'num_tokens_approx': 238.0, 'num_words': 179.0, 'page_number': 991, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.2.4.3 Synthesis for the net cloud feedback', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.654895902, 'content': '7.4.2.4.3 Synthesis for the net cloud feedback\\nThe understanding of the response of clouds to warming and associated radiative feedback has deepened since AR5 (Figure 7.9 and FAQ 7.2). Particular progress has been made in the assessment of the marine low-cloud feedback, which has historically been a major contributor to the cloud feedback uncertainty but is no longer the largest source of uncertainty. Multiple lines of evidence (theory, observations, emergent constraints and process modelling) are now available in addition to ESM simulations, and the positive low-cloud feedback is consequently assessed with high confidence.\\nThe best estimate of net cloud feedback is obtained by summing feedbacks associated with individual cloud regimes and assessed to be aC = 0.42 W m-2 degC-1. By assuming that the uncertainties of individual cloud feedbacks are independent of each other, their standard deviations are added in quadrature, leading to the', 'reranking_score': 0.020251456648111343, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='7.4.2.4.3 Synthesis for the net cloud feedback\\nThe understanding of the response of clouds to warming and associated radiative feedback has deepened since AR5 (Figure 7.9 and FAQ 7.2). Particular progress has been made in the assessment of the marine low-cloud feedback, which has historically been a major contributor to the cloud feedback uncertainty but is no longer the largest source of uncertainty. Multiple lines of evidence (theory, observations, emergent constraints and process modelling) are now available in addition to ESM simulations, and the positive low-cloud feedback is consequently assessed with high confidence.\\nThe best estimate of net cloud feedback is obtained by summing feedbacks associated with individual cloud regimes and assessed to be aC = 0.42 W m-2 degC-1. By assuming that the uncertainties of individual cloud feedbacks are independent of each other, their standard deviations are added in quadrature, leading to the'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 961.0, 'num_tokens': 228.0, 'num_tokens_approx': 232.0, 'num_words': 174.0, 'page_number': 645, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '4.6.3.3.3 Cirrus cloud thinning', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '4: Future Global Climate: Scenario-based Projections and Near-term Information', 'toc_level1': '4.6 Implications of Climate Policy', 'toc_level2': '4.6.3 Climate Response to Mitigation, Carbon Dioxide Removal and Solar Radiation Modification', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.65455687, 'content': 'Under present-day climate, cirrus clouds exerts a net positive radiative forcing of about 5 W m-2 (Gasparini and Lohmann, 2016; Hong et al., 2016), indicating a maximum cooling potential of the same magnitude if all cirrus cloud were removed from the climate system. However, modelling results show a much smaller cooling effect of CCT. For the optimal ice nuclei seeding concentration and globally non-uniform seeding strategy, a net negative cloud radiative forcing of about 1 to 2 W m-2 is achieved (Storelvmo and Herger, 2014; Gasparini et al., 2020). A few studies find that no seeding strategy could achieve a significant cooling effect, owing to complex microphysical mechanisms limiting robust climate responses to cirrus seeding (Penner et al., 2015; Gasparini and Lohmann, 2016). A higher than optimal concentration of ice nucleating particles could also result in over-seeding that increases rather than decreases cirrus', 'reranking_score': 0.018827350810170174, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Under present-day climate, cirrus clouds exerts a net positive radiative forcing of about 5 W m-2 (Gasparini and Lohmann, 2016; Hong et al., 2016), indicating a maximum cooling potential of the same magnitude if all cirrus cloud were removed from the climate system. However, modelling results show a much smaller cooling effect of CCT. For the optimal ice nuclei seeding concentration and globally non-uniform seeding strategy, a net negative cloud radiative forcing of about 1 to 2 W m-2 is achieved (Storelvmo and Herger, 2014; Gasparini et al., 2020). A few studies find that no seeding strategy could achieve a significant cooling effect, owing to complex microphysical mechanisms limiting robust climate responses to cirrus seeding (Penner et al., 2015; Gasparini and Lohmann, 2016). A higher than optimal concentration of ice nucleating particles could also result in over-seeding that increases rather than decreases cirrus'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 483.0, 'num_tokens': 124.0, 'num_tokens_approx': 118.0, 'num_words': 89.0, 'page_number': 197, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '1.3.3 Lines of Evidence: Identifying Natural \\r\\nand Human Drivers', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '1: Framing, Context, and Methods', 'toc_level1': '1.3 How We Got Here: The Scientific Context', 'toc_level2': '1.3.3 Lines of Evidence: Identifying Natural and Human Drivers', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.65395689, 'content': 'and Pollack, 1976). Since the 1980s, aerosols have increasingly been integrated into comprehensive modelling studies of transient climate evolution and anthropogenic influences, through treatment of volcanic forcing, links to global dimming and cloud brightening, and their influence on cloud nucleation and other properties (e.g., thickness, lifetime and extent), and precipitation (e.g., Hansen et al., 1981; Charlson et al., 1987, 1992; Albrecht, 1989; Twomey, 1991).', 'reranking_score': 0.018462074920535088, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='and Pollack, 1976). Since the 1980s, aerosols have increasingly been integrated into comprehensive modelling studies of transient climate evolution and anthropogenic influences, through treatment of volcanic forcing, links to global dimming and cloud brightening, and their influence on cloud nucleation and other properties (e.g., thickness, lifetime and extent), and precipitation (e.g., Hansen et al., 1981; Charlson et al., 1987, 1992; Albrecht, 1989; Twomey, 1991).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1114.0, 'num_tokens': 220.0, 'num_tokens_approx': 256.0, 'num_words': 192.0, 'page_number': 992, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Table 7.9 | Assessed sign and confidence level of cloud feedbacks in different regimes in AR5 and AR6. For some cloud regimes, the feedback was not assessed \\r\\nin AR5, indicated by N/A.', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.653172314, 'content': 'In reality, different types of cloud feedback may occur simultaneously in one cloud regime. For example, an upward shift of high-clouds associated with the altitude feedback could be coupled to an increase/decrease of cirrus/anvil cloud fractions associated with the cloud amount feedback. Alternatively, slowdown of the tropical circulation with surface warming (Section 4.5.3 and Figure 7.9) could affect both high and low-clouds so that their feedbacks are co-dependent. Quantitative assessments of such covariances require further knowledge about cloud feedback mechanisms, which will further narrow the uncertainty range.\\nIn summary, deepened understanding of feedback processes in individual cloud regimes since AR5 leads to an assessment of the positive net cloud feedback with high confidence. A small probability (less than 10%) of a net negative cloud feedback cannot be ruled out, but this would require an extremely large negative feedback due to decreases in the amount of tropical anvil clouds or increases in optical depth of extratropical clouds over the Southern Ocean;', 'reranking_score': 0.015802541747689247, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='In reality, different types of cloud feedback may occur simultaneously in one cloud regime. For example, an upward shift of high-clouds associated with the altitude feedback could be coupled to an increase/decrease of cirrus/anvil cloud fractions associated with the cloud amount feedback. Alternatively, slowdown of the tropical circulation with surface warming (Section 4.5.3 and Figure 7.9) could affect both high and low-clouds so that their feedbacks are co-dependent. Quantitative assessments of such covariances require further knowledge about cloud feedback mechanisms, which will further narrow the uncertainty range.\\nIn summary, deepened understanding of feedback processes in individual cloud regimes since AR5 leads to an assessment of the positive net cloud feedback with high confidence. A small probability (less than 10%) of a net negative cloud feedback cannot be ruled out, but this would require an extremely large negative feedback due to decreases in the amount of tropical anvil clouds or increases in optical depth of extratropical clouds over the Southern Ocean;'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 680.0, 'num_tokens': 210.0, 'num_tokens_approx': 202.0, 'num_words': 152.0, 'page_number': 665, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'References', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '4: Future Global Climate: Scenario-based Projections and Near-term Information', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.653167367, 'content': 'Boucher, O., C. Kleinschmitt, and G. Myhre, 2017: Quasi-Additivity of the Radiative Effects of Marine Cloud Brightening and Stratospheric Sulfate Aerosol Injection. Geophysical Research Letters, 44(21), 11158-11165, doi:10.1002/2017gl074647. Boucher, O. et al., 2012: Reversibility in an Earth System model in response to CO2 concentration changes. Environmental Research Letters, 7(2), 024013, doi:10.1088/1748-9326/7/2/024013. Boucher, O. et al., 2013: Clouds and Aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin,', 'reranking_score': 0.01135291624814272, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Boucher, O., C. Kleinschmitt, and G. Myhre, 2017: Quasi-Additivity of the Radiative Effects of Marine Cloud Brightening and Stratospheric Sulfate Aerosol Injection. Geophysical Research Letters, 44(21), 11158-11165, doi:10.1002/2017gl074647. Boucher, O. et al., 2012: Reversibility in an Earth System model in response to CO2 concentration changes. Environmental Research Letters, 7(2), 024013, doi:10.1088/1748-9326/7/2/024013. Boucher, O. et al., 2013: Clouds and Aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin,'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1104.0, 'num_tokens': 222.0, 'num_tokens_approx': 240.0, 'num_words': 180.0, 'page_number': 942, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Executive Summary', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': 'Executive Summary', 'toc_level2': 'Effective Radiative Forcing', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.651689827, 'content': \"Executive Summary This chapter assesses the present state of knowledge of Earth's energy budget: that is, the main flows of energy into and out of the Earth system, and how these energy flows govern the climate response to a radiative forcing. Changes in atmospheric composition and land use, like those caused by anthropogenic greenhouse gas emissions and emissions of aerosols and their precursors, affect climate through perturbations to Earth's top-of-atmosphere energy budget. The effective radiative forcings (ERFs) quantify these perturbations, including any consequent adjustment to the climate system (but excluding surface temperature response). How the climate system responds to a given forcing is determined by climate feedbacks associated with physical, biogeophysical and biogeochemical processes. These feedback processes are assessed, as are useful measures of global climate response, namely equilibrium climate sensitivity (ECS) and the transient climate response (TCR). This chapter also assesses emissions metrics, which are used to quantify how the\", 'reranking_score': 0.011049534194171429, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"Executive Summary This chapter assesses the present state of knowledge of Earth's energy budget: that is, the main flows of energy into and out of the Earth system, and how these energy flows govern the climate response to a radiative forcing. Changes in atmospheric composition and land use, like those caused by anthropogenic greenhouse gas emissions and emissions of aerosols and their precursors, affect climate through perturbations to Earth's top-of-atmosphere energy budget. The effective radiative forcings (ERFs) quantify these perturbations, including any consequent adjustment to the climate system (but excluding surface temperature response). How the climate system responds to a given forcing is determined by climate feedbacks associated with physical, biogeophysical and biogeochemical processes. These feedback processes are assessed, as are useful measures of global climate response, namely equilibrium climate sensitivity (ECS) and the transient climate response (TCR). This chapter also assesses emissions metrics, which are used to quantify how the\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 964.0, 'num_tokens': 212.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 990, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Subtropical marine low-cloud feedback', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.651528239, 'content': \"In order to disentangle the large-scale processes that cause the cloud amount either to increase or decrease in response to the surface warming, the cloud feedback has been expressed in terms of several 'cloud controlling factors' (Qu et al., 2014, 2015; Zhai et al., 2015; Brient and Schneider, 2016; Myers and Norris, 2016; McCoy et al., 2017a). The advantage of this approach over conventional calculation of cloud feedbacks is that the temperature-mediated cloud response can be estimated without using information of the simulated cloud responses that are less well-constrained than the changes in the environmental conditions. Two dominant factors are identified for the subtropical low-clouds: a thermodynamic effect due to rising SST that acts to reduce low-cloud by enhancing cloud-top entrainment of dry air, and a stability effect accompanied by an enhanced inversion strength that acts to increase low-cloud (Qu et al., 2014,\", 'reranking_score': 0.010157172568142414, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"In order to disentangle the large-scale processes that cause the cloud amount either to increase or decrease in response to the surface warming, the cloud feedback has been expressed in terms of several 'cloud controlling factors' (Qu et al., 2014, 2015; Zhai et al., 2015; Brient and Schneider, 2016; Myers and Norris, 2016; McCoy et al., 2017a). The advantage of this approach over conventional calculation of cloud feedbacks is that the temperature-mediated cloud response can be estimated without using information of the simulated cloud responses that are less well-constrained than the changes in the environmental conditions. Two dominant factors are identified for the subtropical low-clouds: a thermodynamic effect due to rising SST that acts to reduce low-cloud by enhancing cloud-top entrainment of dry air, and a stability effect accompanied by an enhanced inversion strength that acts to increase low-cloud (Qu et al., 2014,\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 800.0, 'num_tokens': 209.0, 'num_tokens_approx': 248.0, 'num_words': 186.0, 'page_number': 56, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Characteristics of Climate Change Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Technical Summary', 'toc_level1': 'Introduction', 'toc_level2': 'Box TS.1 | Core Concepts Central to This Report', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.651351452, 'content': \"Earth's energy imbalance: In a stable climate, the amount of energy that Earth receives from the Sun is approximately in balance with the amount of energy that is lost to space in the form of reflected sunlight and thermal radiation. 'Climate drivers', such as an increase in greenhouse gases or aerosols, interfere with this balance, causing the system to either gain or lose energy. The strength of a climate driver is quantified by its effective radiative forcing (ERF), measured in W m-2. Positive ERF leads to warming, and negative ERF leads to cooling. That warming or cooling in turn can change the energy imbalance through many positive (amplifying) or negative (dampening) climate feedbacks. (Sections TS.2.2, TS.3.1 and TS.3.2) {2.2.8, 7.2, 7.3, 7.4, Box 7.1, Box 7.2, Glossary}\", 'reranking_score': 0.008570753037929535, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"Earth's energy imbalance: In a stable climate, the amount of energy that Earth receives from the Sun is approximately in balance with the amount of energy that is lost to space in the form of reflected sunlight and thermal radiation. 'Climate drivers', such as an increase in greenhouse gases or aerosols, interfere with this balance, causing the system to either gain or lose energy. The strength of a climate driver is quantified by its effective radiative forcing (ERF), measured in W m-2. Positive ERF leads to warming, and negative ERF leads to cooling. That warming or cooling in turn can change the energy imbalance through many positive (amplifying) or negative (dampening) climate feedbacks. (Sections TS.2.2, TS.3.1 and TS.3.2) {2.2.8, 7.2, 7.3, 7.4, Box 7.1, Box 7.2, Glossary}\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document3', 'document_number': 3.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 112.0, 'name': 'Technical Summary. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 800.0, 'num_tokens': 209.0, 'num_tokens_approx': 248.0, 'num_words': 186.0, 'page_number': 7, 'release_date': 2021.0, 'report_type': 'TS', 'section_header': 'Characteristics of Climate Change Assessment', 'short_name': 'IPCC AR6 WGI TS', 'source': 'IPCC', 'toc_level0': 'Introduction', 'toc_level1': 'Box TS.1 | Core Concepts Central to This Report', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf', 'similarity_score': 0.651351452, 'content': \"Earth's energy imbalance: In a stable climate, the amount of energy that Earth receives from the Sun is approximately in balance with the amount of energy that is lost to space in the form of reflected sunlight and thermal radiation. 'Climate drivers', such as an increase in greenhouse gases or aerosols, interfere with this balance, causing the system to either gain or lose energy. The strength of a climate driver is quantified by its effective radiative forcing (ERF), measured in W m-2. Positive ERF leads to warming, and negative ERF leads to cooling. That warming or cooling in turn can change the energy imbalance through many positive (amplifying) or negative (dampening) climate feedbacks. (Sections TS.2.2, TS.3.1 and TS.3.2) {2.2.8, 7.2, 7.3, 7.4, Box 7.1, Box 7.2, Glossary}\", 'reranking_score': 0.008275200612843037, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"Earth's energy imbalance: In a stable climate, the amount of energy that Earth receives from the Sun is approximately in balance with the amount of energy that is lost to space in the form of reflected sunlight and thermal radiation. 'Climate drivers', such as an increase in greenhouse gases or aerosols, interfere with this balance, causing the system to either gain or lose energy. The strength of a climate driver is quantified by its effective radiative forcing (ERF), measured in W m-2. Positive ERF leads to warming, and negative ERF leads to cooling. That warming or cooling in turn can change the energy imbalance through many positive (amplifying) or negative (dampening) climate feedbacks. (Sections TS.2.2, TS.3.1 and TS.3.2) {2.2.8, 7.2, 7.3, 7.4, Box 7.1, Box 7.2, Glossary}\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 801.0, 'num_tokens': 220.0, 'num_tokens_approx': 208.0, 'num_words': 156.0, 'page_number': 1083, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.2.1 Global Water Cycle Constraints', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.2 Why Should We Expect Water Cycle Changes?', 'toc_level2': '8.2.1 Global Water Cycle Constraints', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.651062489, 'content': \"controlled by thermal deepening of the troposphere (Jeevanjee and Romps, 2018) and limited by surface evaporation and consequent atmospheric latent heat release and warming (Webb et al., 2018). Climate feedbacks (e.g., temperature lapse rate and clouds) that vary across models (Sections 7.4 and 3.8.2) also modulate the magnitude of e (O'Gorman et al., 2012; Flaschner et al., 2016; T.B. Richardson et al., 2018a). Uncertainty in e across CMIP5 models relating to deficiencies in representing low-altitude cloud feedbacks (Watanabe et al., 2018) and absorption of shortwave radiation by atmospheric water vapour (DeAngelis et al., 2015) do not apply well to CMIP6 simulations, the latter improvement explained by more accurate radiative transfer modelling (Pendergrass, 2020b).\", 'reranking_score': 0.0076806917786598206, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"controlled by thermal deepening of the troposphere (Jeevanjee and Romps, 2018) and limited by surface evaporation and consequent atmospheric latent heat release and warming (Webb et al., 2018). Climate feedbacks (e.g., temperature lapse rate and clouds) that vary across models (Sections 7.4 and 3.8.2) also modulate the magnitude of e (O'Gorman et al., 2012; Flaschner et al., 2016; T.B. Richardson et al., 2018a). Uncertainty in e across CMIP5 models relating to deficiencies in representing low-altitude cloud feedbacks (Watanabe et al., 2018) and absorption of shortwave radiation by atmospheric water vapour (DeAngelis et al., 2015) do not apply well to CMIP6 simulations, the latter improvement explained by more accurate radiative transfer modelling (Pendergrass, 2020b).\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 950.0, 'num_tokens': 208.0, 'num_tokens_approx': 214.0, 'num_words': 161.0, 'page_number': 991, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Mid-latitude cloud amount feedback', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.650601506, 'content': 'Thermodynamics play an important role in controlling extratropical cloud amount equatorward of about 50deg latitude. Recent studies showed, using observed cloud controlling factors, that the mid-latitude low-cloud fractions decrease with rising SST, which also acts to weaken stability of the atmosphere unlike in the subtropics (McCoy et al., 2017a). ESMs consistently show a decrease of cloud amounts and a resultant positive SW feedback in the 30deg-40deg latitude bands, which can be constrained using observations of seasonal migration of cloud amount (Zhai et al., 2015). Based on the qualitative agreement between observations and ESMs, the mid-latitude cloud amount feedback is assessed as positive with medium confidence. Following these emergent constraint studies using observations and CMIP5/6 models, the global contribution of net cloud amount feedback over 30deg-60deg ocean areas, covering 27% of the globe,', 'reranking_score': 0.0076036169193685055, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Thermodynamics play an important role in controlling extratropical cloud amount equatorward of about 50deg latitude. Recent studies showed, using observed cloud controlling factors, that the mid-latitude low-cloud fractions decrease with rising SST, which also acts to weaken stability of the atmosphere unlike in the subtropics (McCoy et al., 2017a). ESMs consistently show a decrease of cloud amounts and a resultant positive SW feedback in the 30deg-40deg latitude bands, which can be constrained using observations of seasonal migration of cloud amount (Zhai et al., 2015). Based on the qualitative agreement between observations and ESMs, the mid-latitude cloud amount feedback is assessed as positive with medium confidence. Following these emergent constraint studies using observations and CMIP5/6 models, the global contribution of net cloud amount feedback over 30deg-60deg ocean areas, covering 27% of the globe,'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 937.0, 'num_tokens': 192.0, 'num_tokens_approx': 214.0, 'num_words': 161.0, 'page_number': 646, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '4.6.3.3.7 Synthesis of the climate response to solar \\r\\nradiation modification', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '4: Future Global Climate: Scenario-based Projections and Near-term Information', 'toc_level1': '4.7 Climate Change Beyond 2100', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.650119483, 'content': '4.6.3.3.7 Synthesis of the climate response to solar radiation modification\\nModelling studies have consistently shown that SRM has the potential to offset some effect of increasing GHGs on global and regional climate (high confidence), but there would be substantial residual or overcompensating climate change at the regional scale and seasonal time scale (high confidence). Large uncertainties associated with aerosol-cloud-radiation interactions persist in our understanding of climate response to aerosol-based SRM options. For the same amount of global mean cooling, different SRM options would cause different patterns of climate change (medium confidence). Modelling studies suggest that it is conceptually possible to achieve multiple climate policy goals by optimally designed SRM strategies.\\n 4.6.3.3.7 Synthesis of the climate response to solar radiation modification ', 'reranking_score': 0.0062230792827904224, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='4.6.3.3.7 Synthesis of the climate response to solar radiation modification\\nModelling studies have consistently shown that SRM has the potential to offset some effect of increasing GHGs on global and regional climate (high confidence), but there would be substantial residual or overcompensating climate change at the regional scale and seasonal time scale (high confidence). Large uncertainties associated with aerosol-cloud-radiation interactions persist in our understanding of climate response to aerosol-based SRM options. For the same amount of global mean cooling, different SRM options would cause different patterns of climate change (medium confidence). Modelling studies suggest that it is conceptually possible to achieve multiple climate policy goals by optimally designed SRM strategies.\\n 4.6.3.3.7 Synthesis of the climate response to solar radiation modification '), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 849.0, 'num_tokens': 190.0, 'num_tokens_approx': 205.0, 'num_words': 154.0, 'page_number': 195, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '1.3.3 Lines of Evidence: Identifying Natural \\r\\nand Human Drivers', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '1: Framing, Context, and Methods', 'toc_level1': '1.3 How We Got Here: The Scientific Context', 'toc_level2': '1.3.3 Lines of Evidence: Identifying Natural and Human Drivers', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.650068879, 'content': \"The climate is a globally interconnected system driven by solar energy. Scientists in the 19th century established the main physical principles governing Earth's temperature. By 1822, the principle of radiative equilibrium (the balance between absorbed solar radiation and the energy Earth re-radiates into space) had been articulated, and the atmosphere's role in retaining heat had been likened to a greenhouse (Fourier, 1822). The primary explanations for natural climate change - greenhouse gases, orbital factors, solar irradiance, continental position, volcanic outgassing, silicate rock weathering, and the formation of coal and carbonate rock - were all identified by the late 19th century (Fleming, 1998; Weart, 2008).\\n 1.3.3 Lines of Evidence: Identifying Natural and Human Drivers \", 'reranking_score': 0.005609897896647453, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"The climate is a globally interconnected system driven by solar energy. Scientists in the 19th century established the main physical principles governing Earth's temperature. By 1822, the principle of radiative equilibrium (the balance between absorbed solar radiation and the energy Earth re-radiates into space) had been articulated, and the atmosphere's role in retaining heat had been likened to a greenhouse (Fourier, 1822). The primary explanations for natural climate change - greenhouse gases, orbital factors, solar irradiance, continental position, volcanic outgassing, silicate rock weathering, and the formation of coal and carbonate rock - were all identified by the late 19th century (Fleming, 1998; Weart, 2008).\\n 1.3.3 Lines of Evidence: Identifying Natural and Human Drivers \"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 874.0, 'num_tokens': 203.0, 'num_tokens_approx': 209.0, 'num_words': 157.0, 'page_number': 868, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '6.4 SLCF Radiative Forcing \\r\\nand Climate Effects', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '6: Short-lived Climate Forcers', 'toc_level1': '6.4 SLCF Radiative Forcing and\\xa0Climate\\xa0Effects', 'toc_level2': '6.4.1 Historical Estimates of Regional Short‑lived Climate Forcing ', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.649517417, 'content': 'The radiative forcing on the climate system introduced by SLCFs is distinguished from that of long-lived greenhouse gases (LLGHGs) by the diversity of forcing mechanisms for SLCFs, and the challenges of constraining these mechanisms via observations and of inferring their global forcings from available data. Chapter 7 assesses the global estimates of effective radiative forcing (ERF) due to SLCF abundance changes. This section assesses the characteristics (e.g., spatial patterns, temporal evolution) of forcings, emissions\\x02based SLCF forcings, climate response and feedbacks due to SLCFs relying primarily on results from CMIP6 models. Additionally, the ERFs for several aerosol-based forms of solar -radiation modification (SRM) are discussed in Section 6.4.6. \\n 6.4 SLCF Radiative Forcing and Climate Effects ', 'reranking_score': 0.004304219968616962, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='The radiative forcing on the climate system introduced by SLCFs is distinguished from that of long-lived greenhouse gases (LLGHGs) by the diversity of forcing mechanisms for SLCFs, and the challenges of constraining these mechanisms via observations and of inferring their global forcings from available data. Chapter 7 assesses the global estimates of effective radiative forcing (ERF) due to SLCF abundance changes. This section assesses the characteristics (e.g., spatial patterns, temporal evolution) of forcings, emissions\\x02based SLCF forcings, climate response and feedbacks due to SLCFs relying primarily on results from CMIP6 models. Additionally, the ERFs for several aerosol-based forms of solar -radiation modification (SRM) are discussed in Section 6.4.6. \\n 6.4 SLCF Radiative Forcing and Climate Effects '), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1092.0, 'num_tokens': 222.0, 'num_tokens_approx': 250.0, 'num_words': 188.0, 'page_number': 1095, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Aerosol radiative effects on precipitation', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.2 Why Should We Expect Water Cycle Changes?', 'toc_level2': 'Box\\xa08.1 |\\xa0Role of Anthropogenic Aerosols in Water Cycle Changes', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.64934051, 'content': 'Box 8.1, Figure 2 | Schematic depiction of the atmospheric effects of light-absorbing aerosols on convection and cloud formation: (a) without and (b) with the presence of absorbing aerosols in the planetary boundary layer. The dashed and solid blue lines correspond to the vertical temperature profiles in the absence and presence of the absorbing aerosol layer, respectively, and the solid and dashed red lines denote the dry and moist adiabats, respectively. Absorbing aerosols result in an increasing temperature in the atmosphere but a reduced temperature at the surface. The reduced surface temperature and the increased temperature aloft led to a larger negative energy associated with convective inhibition (-) and a higher convection condensation level (CCL) under the polluted conditions. On the other hand, the absorbing aerosol layer induces a larger convective available potential energy (+) above CCL, facilitating more intensive vertical development of clouds, if lifting is sufficient to overcome the larger convective inhibition. Figure from Y. Wang et al. (2013).', 'reranking_score': 0.00408297311514616, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Box 8.1, Figure 2 | Schematic depiction of the atmospheric effects of light-absorbing aerosols on convection and cloud formation: (a) without and (b) with the presence of absorbing aerosols in the planetary boundary layer. The dashed and solid blue lines correspond to the vertical temperature profiles in the absence and presence of the absorbing aerosol layer, respectively, and the solid and dashed red lines denote the dry and moist adiabats, respectively. Absorbing aerosols result in an increasing temperature in the atmosphere but a reduced temperature at the surface. The reduced surface temperature and the increased temperature aloft led to a larger negative energy associated with convective inhibition (-) and a higher convection condensation level (CCL) under the polluted conditions. On the other hand, the absorbing aerosol layer induces a larger convective available potential energy (+) above CCL, facilitating more intensive vertical development of clouds, if lifting is sufficient to overcome the larger convective inhibition. Figure from Y. Wang et al. (2013).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 788.0, 'num_tokens': 212.0, 'num_tokens_approx': 209.0, 'num_words': 157.0, 'page_number': 645, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '4.6.3.3.2 Marine cloud brightening', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '4: Future Global Climate: Scenario-based Projections and Near-term Information', 'toc_level1': '4.6 Implications of Climate Policy', 'toc_level2': '4.6.3 Climate Response to Mitigation, Carbon Dioxide Removal and Solar Radiation Modification', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.6477651, 'content': 'Several modelling studies suggest that the direct scattering effect by injected particles might also play an important role in the cooling effect of MCB, but the relative contribution of aerosol-cloud and aerosol- cloud-radiation effect is uncertain (Partanen et al., 2012; Kravitz et al., 2013b; Ahlm et al., 2017). Relative to the high-GHG climate, it is likely that MCB would increase precipitation over tropical land due to the inhomogeneous forcing pattern of MCB over ocean and land (medium confidence) (Bala et al., 2011; Alterskjaer et al., 2013; Niemeier et al., 2013; Ahlm et al., 2017; Muri et al., 2018; Stjern et al., 2018). Because of the high level of uncertainty associated with cloud microphysics and aerosol-cloud-radiation interaction (Section 7.3),', 'reranking_score': 0.003935177344828844, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Several modelling studies suggest that the direct scattering effect by injected particles might also play an important role in the cooling effect of MCB, but the relative contribution of aerosol-cloud and aerosol- cloud-radiation effect is uncertain (Partanen et al., 2012; Kravitz et al., 2013b; Ahlm et al., 2017). Relative to the high-GHG climate, it is likely that MCB would increase precipitation over tropical land due to the inhomogeneous forcing pattern of MCB over ocean and land (medium confidence) (Bala et al., 2011; Alterskjaer et al., 2013; Niemeier et al., 2013; Ahlm et al., 2017; Muri et al., 2018; Stjern et al., 2018). Because of the high level of uncertainty associated with cloud microphysics and aerosol-cloud-radiation interaction (Section 7.3),'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 987.0, 'num_tokens': 202.0, 'num_tokens_approx': 226.0, 'num_words': 170.0, 'page_number': 991, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Extratropical cloud optical depth feedback', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.647670686, 'content': '(one standard deviation) by combining estimates based on observed interannual variability and the cloud controlling factors. Arctic cloud feedback Clouds in polar regions, especially over the Arctic, form at low altitude above or within a stable to neutral boundary layer and are known to co-vary with sea ice variability beneath. Because the clouds reflect sunlight during summer but trap LW radiation throughout the year, seasonality plays an important role in cloud effects on Arctic climate (Kay et al., 2016b). AR5 assessed that Arctic low-cloud amount will increase in boreal autumn and winter in response to declining sea ice in a warming climate, due primarily to an enhanced upward moisture flux over open water. The cloudier conditions during these seasons result in more downwelling LW radiation, acting as a positive feedback on surface warming (Kay and Gettelman, 2009). Over recent years, further evidence of the cloud contribution to the Arctic', 'reranking_score': 0.003851410001516342, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='(one standard deviation) by combining estimates based on observed interannual variability and the cloud controlling factors. Arctic cloud feedback Clouds in polar regions, especially over the Arctic, form at low altitude above or within a stable to neutral boundary layer and are known to co-vary with sea ice variability beneath. Because the clouds reflect sunlight during summer but trap LW radiation throughout the year, seasonality plays an important role in cloud effects on Arctic climate (Kay et al., 2016b). AR5 assessed that Arctic low-cloud amount will increase in boreal autumn and winter in response to declining sea ice in a warming climate, due primarily to an enhanced upward moisture flux over open water. The cloudier conditions during these seasons result in more downwelling LW radiation, acting as a positive feedback on surface warming (Kay and Gettelman, 2009). Over recent years, further evidence of the cloud contribution to the Arctic'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1090.0, 'num_tokens': 215.0, 'num_tokens_approx': 262.0, 'num_words': 197.0, 'page_number': 951, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.646721303, 'content': 'Figure 7.2 | Schematic representation of the global mean energy budget of the Earth (upper panel), and its equivalent without considerations of cloud effects (lower panel). Numbers indicate best estimates for the magnitudes of the globally averaged energy balance components in W m-2 together with their uncertainty ranges in parentheses (5-95% confidence range), representing climate conditions at the beginning of the 21st century. Note that the cloud-free energy budget shown in the lower panel is not the one that Earth would achieve in equilibrium when no clouds could form. It rather represents the global mean fluxes as determined solely by removing the clouds but otherwise retaining the entire atmospheric structure. This enables the quantification of the effects of clouds on the Earth energy budget and corresponds to the way clear-sky fluxes are calculated in climate models. Thus, the cloud-free energy budget is not closed and therefore the sensible and latent heat fluxes are not quantified in the lower panel. Figure adapted from Wild et al. (2015, 2019).\\n934934', 'reranking_score': 0.003658813424408436, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Figure 7.2 | Schematic representation of the global mean energy budget of the Earth (upper panel), and its equivalent without considerations of cloud effects (lower panel). Numbers indicate best estimates for the magnitudes of the globally averaged energy balance components in W m-2 together with their uncertainty ranges in parentheses (5-95% confidence range), representing climate conditions at the beginning of the 21st century. Note that the cloud-free energy budget shown in the lower panel is not the one that Earth would achieve in equilibrium when no clouds could form. It rather represents the global mean fluxes as determined solely by removing the clouds but otherwise retaining the entire atmospheric structure. This enables the quantification of the effects of clouds on the Earth energy budget and corresponds to the way clear-sky fluxes are calculated in climate models. Thus, the cloud-free energy budget is not closed and therefore the sensible and latent heat fluxes are not quantified in the lower panel. Figure adapted from Wild et al. (2015, 2019).\\n934934'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1017.0, 'num_tokens': 223.0, 'num_tokens_approx': 258.0, 'num_words': 194.0, 'page_number': 112, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'TS.3.2.2 Earth System Feedbacks', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Technical Summary', 'toc_level1': 'TS.3 Understanding the Climate System Response and Implications for Limiting Global Warming', 'toc_level2': 'TS.3.2 Climate Sensitivity and Earth System Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.645854771, 'content': 'The net effect of changes in clouds in response to global warming is to amplify human-induced warming, that is, the net cloud feedback is positive (high confidence). Compared to AR5, major advances in the understanding of cloud processes have increased the level of confidence and decreased the uncertainty range in the cloud feedback by about 50% (Figure TS.17a). An assessment of the low\\x02altitude cloud feedback over the subtropical ocean, which was previously the major source of uncertainty in the net cloud feedback, is improved owing to a combined use of climate model simulations, satellite observations, and explicit simulations of clouds, altogether leading to strong evidence that this type of cloud amplifies global warming. The net cloud feedback is assessed to be +0.42 [-0.10 to 0.94] W m-2 degC-1. A net negative cloud feedback is very unlikely. The CMIP5 and CMIP6 ranges of cloud feedback are similar to this assessed range, with CMIP6 having a slightly more positive median', 'reranking_score': 0.0031600510701537132, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='The net effect of changes in clouds in response to global warming is to amplify human-induced warming, that is, the net cloud feedback is positive (high confidence). Compared to AR5, major advances in the understanding of cloud processes have increased the level of confidence and decreased the uncertainty range in the cloud feedback by about 50% (Figure TS.17a). An assessment of the low\\x02altitude cloud feedback over the subtropical ocean, which was previously the major source of uncertainty in the net cloud feedback, is improved owing to a combined use of climate model simulations, satellite observations, and explicit simulations of clouds, altogether leading to strong evidence that this type of cloud amplifies global warming. The net cloud feedback is assessed to be +0.42 [-0.10 to 0.94] W m-2 degC-1. A net negative cloud feedback is very unlikely. The CMIP5 and CMIP6 ranges of cloud feedback are similar to this assessed range, with CMIP6 having a slightly more positive median'), Document(metadata={'chunk_type': 'text', 'document_id': 'document3', 'document_number': 3.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 112.0, 'name': 'Technical Summary. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1017.0, 'num_tokens': 223.0, 'num_tokens_approx': 258.0, 'num_words': 194.0, 'page_number': 63, 'release_date': 2021.0, 'report_type': 'TS', 'section_header': 'TS.3.2.2 Earth System Feedbacks', 'short_name': 'IPCC AR6 WGI TS', 'source': 'IPCC', 'toc_level0': 'TS.3 Understanding the Climate System Response and Implications for Limiting Global Warming', 'toc_level1': 'TS.3.2 Climate Sensitivity and Earth System Feedbacks', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf', 'similarity_score': 0.645854771, 'content': 'The net effect of changes in clouds in response to global warming is to amplify human-induced warming, that is, the net cloud feedback is positive (high confidence). Compared to AR5, major advances in the understanding of cloud processes have increased the level of confidence and decreased the uncertainty range in the cloud feedback by about 50% (Figure TS.17a). An assessment of the low\\x02altitude cloud feedback over the subtropical ocean, which was previously the major source of uncertainty in the net cloud feedback, is improved owing to a combined use of climate model simulations, satellite observations, and explicit simulations of clouds, altogether leading to strong evidence that this type of cloud amplifies global warming. The net cloud feedback is assessed to be +0.42 [-0.10 to 0.94] W m-2 degC-1. A net negative cloud feedback is very unlikely. The CMIP5 and CMIP6 ranges of cloud feedback are similar to this assessed range, with CMIP6 having a slightly more positive median', 'reranking_score': 0.002952353097498417, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='The net effect of changes in clouds in response to global warming is to amplify human-induced warming, that is, the net cloud feedback is positive (high confidence). Compared to AR5, major advances in the understanding of cloud processes have increased the level of confidence and decreased the uncertainty range in the cloud feedback by about 50% (Figure TS.17a). An assessment of the low\\x02altitude cloud feedback over the subtropical ocean, which was previously the major source of uncertainty in the net cloud feedback, is improved owing to a combined use of climate model simulations, satellite observations, and explicit simulations of clouds, altogether leading to strong evidence that this type of cloud amplifies global warming. The net cloud feedback is assessed to be +0.42 [-0.10 to 0.94] W m-2 degC-1. A net negative cloud feedback is very unlikely. The CMIP5 and CMIP6 ranges of cloud feedback are similar to this assessed range, with CMIP6 having a slightly more positive median'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 733.0, 'num_tokens': 234.0, 'num_tokens_approx': 232.0, 'num_words': 174.0, 'page_number': 1060, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': \"The Earth's Energy Budget, Climate Feedbacks and Climate Sensitivity\", 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'References', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.645847857, 'content': 'Ohmura, A., A. Bauder, H. Mueller, and G. Kappenberger, 2007: Long-term change of mass balance and the role of radiation. Annals of Glaciology, 46, 367-374, doi:10.3189/172756407782871297. Ohno, T., M. Satoh, and A. Noda, 2019: Fine Vertical Resolution Radiative\\x02Convective Equilibrium Experiments: Roles of Turbulent Mixing on the High-Cloud Response to Sea Surface Temperatures. Journal of Advances in Modeling Earth Systems, 11(6), 1637-1654, doi:10.1029/2019ms001704. Oldenburg, D., K.C. Armour, L.A. Thompson, and C.M. Bitz, 2018: Distinct Mechanisms of Ocean Heat Transport Into the Arctic Under Internal Variability and Climate Change. Geophysical Research Letters, 45(15), 7692-7700, doi:10.1029/2018gl078719.', 'reranking_score': 0.0020338217727839947, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Ohmura, A., A. Bauder, H. Mueller, and G. Kappenberger, 2007: Long-term change of mass balance and the role of radiation. Annals of Glaciology, 46, 367-374, doi:10.3189/172756407782871297. Ohno, T., M. Satoh, and A. Noda, 2019: Fine Vertical Resolution Radiative\\x02Convective Equilibrium Experiments: Roles of Turbulent Mixing on the High-Cloud Response to Sea Surface Temperatures. Journal of Advances in Modeling Earth Systems, 11(6), 1637-1654, doi:10.1029/2019ms001704. Oldenburg, D., K.C. Armour, L.A. Thompson, and C.M. Bitz, 2018: Distinct Mechanisms of Ocean Heat Transport Into the Arctic Under Internal Variability and Climate Change. Geophysical Research Letters, 45(15), 7692-7700, doi:10.1029/2018gl078719.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1072.0, 'num_tokens': 201.0, 'num_tokens_approx': 222.0, 'num_words': 167.0, 'page_number': 66, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'TS.1.2.2 Climate Model Performance', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Technical Summary', 'toc_level1': 'TS.1 A Changing Climate', 'toc_level2': 'TS.1.2 Progress in Climate Science', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.645673513, 'content': \"Some CMIP6 models demonstrate an improvement in how clouds are represented. CMIP5 models commonly displayed a negative shortwave cloud radiative effect that was too weak in the present climate. These errors have been reduced, especially over the Southern Ocean, due to a more realistic simulation of supercooled liquid droplets with sufficient numbers and an associated increase in the cloud optical depth. Because a negative cloud optical depth feedback in response to surface warming results from 'brightening' of clouds via active phase change from ice to liquid cloud particles (increasing their shortwave cloud radiative effect), the extratropical cloud shortwave feedback in CMIP6 models tends to be less negative, leading to a better agreement with observational estimates (medium confidence). CMIP6 models generally represent more processes that drive aerosol-cloud interactions than the previous generation of climate models, but there is only medium confidence that those enhancements improve their fitness-for-purpose of simulating\", 'reranking_score': 0.0016388249350711703, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"Some CMIP6 models demonstrate an improvement in how clouds are represented. CMIP5 models commonly displayed a negative shortwave cloud radiative effect that was too weak in the present climate. These errors have been reduced, especially over the Southern Ocean, due to a more realistic simulation of supercooled liquid droplets with sufficient numbers and an associated increase in the cloud optical depth. Because a negative cloud optical depth feedback in response to surface warming results from 'brightening' of clouds via active phase change from ice to liquid cloud particles (increasing their shortwave cloud radiative effect), the extratropical cloud shortwave feedback in CMIP6 models tends to be less negative, leading to a better agreement with observational estimates (medium confidence). CMIP6 models generally represent more processes that drive aerosol-cloud interactions than the previous generation of climate models, but there is only medium confidence that those enhancements improve their fitness-for-purpose of simulating\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document3', 'document_number': 3.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 112.0, 'name': 'Technical Summary. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1072.0, 'num_tokens': 201.0, 'num_tokens_approx': 222.0, 'num_words': 167.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'TS', 'section_header': 'TS.1.2.2 Climate Model Performance', 'short_name': 'IPCC AR6 WGI TS', 'source': 'IPCC', 'toc_level0': 'TS.1 A Changing Climate', 'toc_level1': 'TS.1.2 Progress in Climate Science', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf', 'similarity_score': 0.645673513, 'content': \"Some CMIP6 models demonstrate an improvement in how clouds are represented. CMIP5 models commonly displayed a negative shortwave cloud radiative effect that was too weak in the present climate. These errors have been reduced, especially over the Southern Ocean, due to a more realistic simulation of supercooled liquid droplets with sufficient numbers and an associated increase in the cloud optical depth. Because a negative cloud optical depth feedback in response to surface warming results from 'brightening' of clouds via active phase change from ice to liquid cloud particles (increasing their shortwave cloud radiative effect), the extratropical cloud shortwave feedback in CMIP6 models tends to be less negative, leading to a better agreement with observational estimates (medium confidence). CMIP6 models generally represent more processes that drive aerosol-cloud interactions than the previous generation of climate models, but there is only medium confidence that those enhancements improve their fitness-for-purpose of simulating\", 'reranking_score': 0.0013943830272182822, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"Some CMIP6 models demonstrate an improvement in how clouds are represented. CMIP5 models commonly displayed a negative shortwave cloud radiative effect that was too weak in the present climate. These errors have been reduced, especially over the Southern Ocean, due to a more realistic simulation of supercooled liquid droplets with sufficient numbers and an associated increase in the cloud optical depth. Because a negative cloud optical depth feedback in response to surface warming results from 'brightening' of clouds via active phase change from ice to liquid cloud particles (increasing their shortwave cloud radiative effect), the extratropical cloud shortwave feedback in CMIP6 models tends to be less negative, leading to a better agreement with observational estimates (medium confidence). CMIP6 models generally represent more processes that drive aerosol-cloud interactions than the previous generation of climate models, but there is only medium confidence that those enhancements improve their fitness-for-purpose of simulating\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 890.0, 'num_tokens': 220.0, 'num_tokens_approx': 229.0, 'num_words': 172.0, 'page_number': 1007, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.4.3 Dependence of Feedbacks on Temperature Patterns', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.4 Relationship Between Feedbacks and\\xa0Temperature Patterns', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.644913077, 'content': 'The radiation changes most sensitive to warming patterns are those associated with low-cloud cover (affecting global albedo) and the tropospheric temperature profile (affecting thermal emission to space) (Ceppi and Gregory, 2017; Zhou et al., 2017b; Andrews et al., 2018; Dong et al., 2019). The mechanisms and radiative effects of these changes are illustrated in Figure 7.14a,b. SSTs in regions of deep convective ascent (e.g., in the western Pacific warm pool) govern the temperature of the tropical free troposphere and, in turn, affect low-clouds through the strength of the inversion that caps the boundary layer (i.e., the lower-tropospheric stability) in subsidence regions (Wood and Bretherton, 2006; Klein et al., 2017). Surface warming within ascent regions thus warms the free troposphere and increases low-cloud cover, causing an increase in emission', 'reranking_score': 0.001353559666313231, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='The radiation changes most sensitive to warming patterns are those associated with low-cloud cover (affecting global albedo) and the tropospheric temperature profile (affecting thermal emission to space) (Ceppi and Gregory, 2017; Zhou et al., 2017b; Andrews et al., 2018; Dong et al., 2019). The mechanisms and radiative effects of these changes are illustrated in Figure 7.14a,b. SSTs in regions of deep convective ascent (e.g., in the western Pacific warm pool) govern the temperature of the tropical free troposphere and, in turn, affect low-clouds through the strength of the inversion that caps the boundary layer (i.e., the lower-tropospheric stability) in subsidence regions (Wood and Bretherton, 2006; Klein et al., 2017). Surface warming within ascent regions thus warms the free troposphere and increases low-cloud cover, causing an increase in emission'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 708.0, 'num_tokens': 142.0, 'num_tokens_approx': 168.0, 'num_words': 126.0, 'page_number': 988, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.2.4.1 Decomposition of clouds into regimes', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.644340932, 'content': 'Figure 7.9 | Schematic cross section of diverse cloud responses to surface warming from the tropics to polar regions. Thick solid and dashed curves indicate the tropopause and the subtropical inversion layer in the current climate, respectively. Thin grey text and arrows represent robust responses in the thermodynamic structure to greenhouse warming, of relevance to cloud changes. Text and arrows in red, orange and green show the major cloud responses assessed with high, medium and low confidence, respectively, and the sign of their feedbacks to the surface warming is indicated in the parenthesis. Major advances since AR5 are listed in the box. Figure adapted from Boucher et al. (2013).\\n971971', 'reranking_score': 0.0012581485789269209, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Figure 7.9 | Schematic cross section of diverse cloud responses to surface warming from the tropics to polar regions. Thick solid and dashed curves indicate the tropopause and the subtropical inversion layer in the current climate, respectively. Thin grey text and arrows represent robust responses in the thermodynamic structure to greenhouse warming, of relevance to cloud changes. Text and arrows in red, orange and green show the major cloud responses assessed with high, medium and low confidence, respectively, and the sign of their feedbacks to the surface warming is indicated in the parenthesis. Major advances since AR5 are listed in the box. Figure adapted from Boucher et al. (2013).\\n971971'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 906.0, 'num_tokens': 217.0, 'num_tokens_approx': 236.0, 'num_words': 177.0, 'page_number': 976, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.3.5 Synthesis of Global Mean Radiative \\r\\nForcing, Past and Future', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.3 Effective Radiative Forcing', 'toc_level2': '7.3.5 Synthesis of Global Mean Radiative Forcing,\\xa0Past\\xa0and Future', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.644245625, 'content': 'The AR5 introduced the concept of effective radiative forcing (ERF) and radiative adjustments, and made a preliminary assessment that the tropospheric adjustments were zero for all species other than the effects of aerosol-cloud interaction and black carbon. Since AR5, new studies have allowed for a tentative assessment of values for tropospheric adjustments to CO2, CH4, N2O, some CFCs, solar forcing, and stratospheric aerosols, and to place a tighter constraint on adjustments from aerosol-cloud interaction (Sections 7.3.2, 7.3.3 and 7.3.4). In AR6, the definition of ERF explicitly removes the land\\x02surface temperature change as part of the forcing, in contrast to AR5 where only sea surface temperatures were fixed. The ERF is assessed to be a better predictor of modelled equilibrium temperature change (i.e., less variation in feedback parameter) than SARF (Section 7.3.1).', 'reranking_score': 0.0012581485789269209, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='The AR5 introduced the concept of effective radiative forcing (ERF) and radiative adjustments, and made a preliminary assessment that the tropospheric adjustments were zero for all species other than the effects of aerosol-cloud interaction and black carbon. Since AR5, new studies have allowed for a tentative assessment of values for tropospheric adjustments to CO2, CH4, N2O, some CFCs, solar forcing, and stratospheric aerosols, and to place a tighter constraint on adjustments from aerosol-cloud interaction (Sections 7.3.2, 7.3.3 and 7.3.4). In AR6, the definition of ERF explicitly removes the land\\x02surface temperature change as part of the forcing, in contrast to AR5 where only sea surface temperatures were fixed. The ERF is assessed to be a better predictor of modelled equilibrium temperature change (i.e., less variation in feedback parameter) than SARF (Section 7.3.1).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 295.0, 'num_tokens': 84.0, 'num_tokens_approx': 77.0, 'num_words': 58.0, 'page_number': 197, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '1.3.3 Lines of Evidence: Identifying Natural \\r\\nand Human Drivers', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '1: Framing, Context, and Methods', 'toc_level1': '1.3 How We Got Here: The Scientific Context', 'toc_level2': '1.3.3 Lines of Evidence: Identifying Natural and Human Drivers', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.643884, 'content': '1889; Angstrom, 1929, 1964; Twomey, 1959), particularly in relation to their role in cloud nucleation, an aerosol indirect effect whose RF may be either positive or negative depending on such factors as cloud altitude, depth and albedo (Stevens and Feingold, 2009; Boucher et al., 2013).', 'reranking_score': 0.001232236041687429, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='1889; Angstrom, 1929, 1964; Twomey, 1959), particularly in relation to their role in cloud nucleation, an aerosol indirect effect whose RF may be either positive or negative depending on such factors as cloud altitude, depth and albedo (Stevens and Feingold, 2009; Boucher et al., 2013).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 962.0, 'num_tokens': 207.0, 'num_tokens_approx': 206.0, 'num_words': 155.0, 'page_number': 2262, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Proxy records See Proxy.', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex VII: Glossary', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.643754601, 'content': 'Radiative forcing The change in the net, downward minus upward, radiative flux (expressed in W m-2) due to a change in an external driver of climate change, such as a change in the concentration of carbon dioxide (CO2), the concentration of volcanic aerosols or the output of the Sun. The stratospherically adjusted radiative forcing is computed with all tropospheric properties held fixed at their unperturbed values, and after allowing for stratospheric temperatures, if perturbed, to readjust to radiative-dynamical equilibrium. Radiative forcing is called instantaneous if no change in stratospheric temperature is accounted for. The radiative forcing once both stratospheric and tropospheric adjustments are accounted for is termed the effective radiative forcing.\\nsystem that occur in operational analyses. Although continuity is improved, global reanalyses still suffer from changing coverage and biases in the observing systems.', 'reranking_score': 0.0010635214857757092, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Radiative forcing The change in the net, downward minus upward, radiative flux (expressed in W m-2) due to a change in an external driver of climate change, such as a change in the concentration of carbon dioxide (CO2), the concentration of volcanic aerosols or the output of the Sun. The stratospherically adjusted radiative forcing is computed with all tropospheric properties held fixed at their unperturbed values, and after allowing for stratospheric temperatures, if perturbed, to readjust to radiative-dynamical equilibrium. Radiative forcing is called instantaneous if no change in stratospheric temperature is accounted for. The radiative forcing once both stratospheric and tropospheric adjustments are accounted for is termed the effective radiative forcing.\\nsystem that occur in operational analyses. Although continuity is improved, global reanalyses still suffer from changing coverage and biases in the observing systems.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 750.0, 'num_tokens': 181.0, 'num_tokens_approx': 184.0, 'num_words': 138.0, 'page_number': 645, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '4.6.3.3.3 Cirrus cloud thinning', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '4: Future Global Climate: Scenario-based Projections and Near-term Information', 'toc_level1': '4.6 Implications of Climate Policy', 'toc_level2': '4.6.3 Climate Response to Mitigation, Carbon Dioxide Removal and Solar Radiation Modification', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.643602192, 'content': 'Relative to the high-GHG climate and for the same amount of global cooling, CCT is simulated to cause an increase in global precipitation compared to shortwave-based SRM options such as SAI and MCB (Duan et al., 2018; Muri et al., 2018) because of the opposing effects of CCT and increased CO2 on outgoing longwave radiation (Kristjansson et al., 2015; Jackson et al., 2016). Combining SAI and CCT has suggested that GHG-induced changes in global mean temperature and precipitation can be simultaneously offset (Cao et al., 2017), but there is low confidence in the applicability of this result to the real world owing to the large uncertainty in simulating aerosol forcing and the complex cirrus microphysical processes.\\n628628', 'reranking_score': 0.0007931159343570471, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Relative to the high-GHG climate and for the same amount of global cooling, CCT is simulated to cause an increase in global precipitation compared to shortwave-based SRM options such as SAI and MCB (Duan et al., 2018; Muri et al., 2018) because of the opposing effects of CCT and increased CO2 on outgoing longwave radiation (Kristjansson et al., 2015; Jackson et al., 2016). Combining SAI and CCT has suggested that GHG-induced changes in global mean temperature and precipitation can be simultaneously offset (Cao et al., 2017), but there is low confidence in the applicability of this result to the real world owing to the large uncertainty in simulating aerosol forcing and the complex cirrus microphysical processes.\\n628628'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 674.0, 'num_tokens': 226.0, 'num_tokens_approx': 220.0, 'num_words': 165.0, 'page_number': 1210, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'monsoon. Climate Dynamics, 56(5-6), 1643-1662, doi:10.1007/s00382-\\r\\n020-05551-5.', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.643265069, 'content': 'A New Cloud Scheme and the Working Mechanisms. Journal of Advances in Modeling Earth Systems, 10(9), 2318-2332, doi:10.1029/2018ms001343. Qiu, B., W. Guo, Y. Xue, and Q. Dai, 2016: Implementation and evaluation of a generalized radiative transfer scheme within canopy in the soil-vegetation-atmosphere transfer (SVAT) model. Journal of Geophysical Research: Atmospheres, 121(20), 12145-12163, doi:10.1002/2016jd025328. Rach, O., A. Kahmen, A. Brauer, and D. Sachse, 2017: A dual-biomarker approach for quantification of changes in relative humidity from sedimentary lipid D/H ratios. Climate of the Past, 13(7), 741-757, doi:10.5194/cp-13-741-2017.', 'reranking_score': 0.000688760366756469, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='A New Cloud Scheme and the Working Mechanisms. Journal of Advances in Modeling Earth Systems, 10(9), 2318-2332, doi:10.1029/2018ms001343. Qiu, B., W. Guo, Y. Xue, and Q. Dai, 2016: Implementation and evaluation of a generalized radiative transfer scheme within canopy in the soil-vegetation-atmosphere transfer (SVAT) model. Journal of Geophysical Research: Atmospheres, 121(20), 12145-12163, doi:10.1002/2016jd025328. Rach, O., A. Kahmen, A. Brauer, and D. Sachse, 2017: A dual-biomarker approach for quantification of changes in relative humidity from sedimentary lipid D/H ratios. Climate of the Past, 13(7), 741-757, doi:10.5194/cp-13-741-2017.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 909.0, 'num_tokens': 213.0, 'num_tokens_approx': 220.0, 'num_words': 165.0, 'page_number': 1095, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Aerosol cloud microphysical effects', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.2 Why Should We Expect Water Cycle Changes?', 'toc_level2': 'Box\\xa08.1 |\\xa0Role of Anthropogenic Aerosols in Water Cycle Changes', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.643162847, 'content': 'Cloud droplets nucleate on pre-existing aerosol particles which act as cloud condensation nuclei (CCN). Anthropogenic aerosols add CCN, compared to a pristine background, and produce clouds with more numerous and smaller droplets, slower to coalesce into raindrops and to freeze into ice hydrometeors at temperatures below 0degC. Adding CCN suppresses light rainfall from shallow and short-lived clouds, but it is compensated by heavier rainfall from deep clouds. Adding aerosols to clouds in extremely clean air invigorates them by more efficient vapour condensation on the added drop surfaces (Koren et al., 2014; Fan et al., 2018). Clouds forming in more polluted air masses (hence with more numerous and smaller drops) need to grow deeper to initiate rain (Freud and Rosenfeld, 2012; Konwar et al., 2012; Campos Braga et al., 2017). This leads to larger amount of cloud water evaporating aloft', 'reranking_score': 0.000688760366756469, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Cloud droplets nucleate on pre-existing aerosol particles which act as cloud condensation nuclei (CCN). Anthropogenic aerosols add CCN, compared to a pristine background, and produce clouds with more numerous and smaller droplets, slower to coalesce into raindrops and to freeze into ice hydrometeors at temperatures below 0degC. Adding CCN suppresses light rainfall from shallow and short-lived clouds, but it is compensated by heavier rainfall from deep clouds. Adding aerosols to clouds in extremely clean air invigorates them by more efficient vapour condensation on the added drop surfaces (Koren et al., 2014; Fan et al., 2018). Clouds forming in more polluted air masses (hence with more numerous and smaller drops) need to grow deeper to initiate rain (Freud and Rosenfeld, 2012; Konwar et al., 2012; Campos Braga et al., 2017). This leads to larger amount of cloud water evaporating aloft'), Document(metadata={'chunk_type': 'text', 'document_id': 'document3', 'document_number': 3.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 112.0, 'name': 'Technical Summary. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 714.0, 'num_tokens': 227.0, 'num_tokens_approx': 246.0, 'num_words': 185.0, 'page_number': 81, 'release_date': 2021.0, 'report_type': 'TS', 'section_header': 'TS.4.2.1 Regional Fingerprints of Anthropogenic \\r\\nand Natural Forcing', 'short_name': 'IPCC AR6 WGI TS', 'source': 'IPCC', 'toc_level0': 'TS.4 Regional Climate Change', 'toc_level1': 'TS.4.2 Drivers of Regional Climate Variability and\\xa0Change', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf', 'similarity_score': 0.643108249, 'content': \"Multi-decadal dimming and brightening trends in incoming solar radiation at Earth's surface occurred at widespread locations (high confidence). Multi-decadal variation in anthropogenic aerosol emissions are thought to be a major contributor (medium confidence), but multi-decadal variability in cloudiness may also have played a role. Volcanic eruptions affect regional climate through their spatially heterogeneous effect on the radiative budget as well as through triggering dynamical responses by favouring a given phase from some MoVs, for instance. {1.4.1, Cross-Chapter Box 1.2, 2.2.1, 2.2.2, 3.7.1, 3.7.3, 4.3.1, 4.4.1, 4.4.4, Cross-Chapter Box 4.1, 7.2.2, 8.5.2, 10.1.4, 11.1.6, 11.3.1}\", 'reranking_score': 0.0006530744722113013, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"Multi-decadal dimming and brightening trends in incoming solar radiation at Earth's surface occurred at widespread locations (high confidence). Multi-decadal variation in anthropogenic aerosol emissions are thought to be a major contributor (medium confidence), but multi-decadal variability in cloudiness may also have played a role. Volcanic eruptions affect regional climate through their spatially heterogeneous effect on the radiative budget as well as through triggering dynamical responses by favouring a given phase from some MoVs, for instance. {1.4.1, Cross-Chapter Box 1.2, 2.2.1, 2.2.2, 3.7.1, 3.7.3, 4.3.1, 4.4.1, 4.4.4, Cross-Chapter Box 4.1, 7.2.2, 8.5.2, 10.1.4, 11.1.6, 11.3.1}\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 714.0, 'num_tokens': 227.0, 'num_tokens_approx': 246.0, 'num_words': 185.0, 'page_number': 130, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'TS.4.2.1 Regional Fingerprints of Anthropogenic \\r\\nand Natural Forcing', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Technical Summary', 'toc_level1': 'TS.4 Regional Climate Change', 'toc_level2': 'TS.4.2 Drivers of Regional Climate Variability and\\xa0Change', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.643108249, 'content': \"Multi-decadal dimming and brightening trends in incoming solar radiation at Earth's surface occurred at widespread locations (high confidence). Multi-decadal variation in anthropogenic aerosol emissions are thought to be a major contributor (medium confidence), but multi-decadal variability in cloudiness may also have played a role. Volcanic eruptions affect regional climate through their spatially heterogeneous effect on the radiative budget as well as through triggering dynamical responses by favouring a given phase from some MoVs, for instance. {1.4.1, Cross-Chapter Box 1.2, 2.2.1, 2.2.2, 3.7.1, 3.7.3, 4.3.1, 4.4.1, 4.4.4, Cross-Chapter Box 4.1, 7.2.2, 8.5.2, 10.1.4, 11.1.6, 11.3.1}\", 'reranking_score': 0.0006386771565303206, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"Multi-decadal dimming and brightening trends in incoming solar radiation at Earth's surface occurred at widespread locations (high confidence). Multi-decadal variation in anthropogenic aerosol emissions are thought to be a major contributor (medium confidence), but multi-decadal variability in cloudiness may also have played a role. Volcanic eruptions affect regional climate through their spatially heterogeneous effect on the radiative budget as well as through triggering dynamical responses by favouring a given phase from some MoVs, for instance. {1.4.1, Cross-Chapter Box 1.2, 2.2.1, 2.2.2, 3.7.1, 3.7.3, 4.3.1, 4.4.1, 4.4.4, Cross-Chapter Box 4.1, 7.2.2, 8.5.2, 10.1.4, 11.1.6, 11.3.1}\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1112.0, 'num_tokens': 233.0, 'num_tokens_approx': 270.0, 'num_words': 203.0, 'page_number': 1170, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 8.1 | How Does Land Use Change Alter the Water Cycle? ', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': 'Frequently Asked Questions', 'toc_level2': 'FAQ 8.1 |\\xa0How Does Land Use Change Alter the Water Cycle? ', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.643015385, 'content': 'Changing land use can also alter how wet the soil is, influencing how quickly the ground heats up and cools down and the local water cycle. Drier soils evaporate less water into the air but heat up more in the day. This can lead to warmer, more buoyant plumes of air that can promote cloud development and precipitation if there is enough moisture in the air. \\nChanges in land use can also modify the amount of tiny aerosol particles in the air. For instance, industrial and domestic activities can contribute to aerosol emissions, as do natural environments such as forests or salt lakes. Aerosols cool down global temperature by blocking out sunlight but can also affect the formation of clouds and therefore the occurrence of precipitation (see FAQ 7.2). \\nVegetation plays an important role in soaking up soil moisture and evaporating water into the air (transpiration) through tiny holes (stomata) that allow the plants to take in carbon dioxide. Some plants are better at retaining water than others, so changes in vegetation can affect how much water infiltrates into the ground, flows into', 'reranking_score': 0.0005133794620633125, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Changing land use can also alter how wet the soil is, influencing how quickly the ground heats up and cools down and the local water cycle. Drier soils evaporate less water into the air but heat up more in the day. This can lead to warmer, more buoyant plumes of air that can promote cloud development and precipitation if there is enough moisture in the air. \\nChanges in land use can also modify the amount of tiny aerosol particles in the air. For instance, industrial and domestic activities can contribute to aerosol emissions, as do natural environments such as forests or salt lakes. Aerosols cool down global temperature by blocking out sunlight but can also affect the formation of clouds and therefore the occurrence of precipitation (see FAQ 7.2). \\nVegetation plays an important role in soaking up soil moisture and evaporating water into the air (transpiration) through tiny holes (stomata) that allow the plants to take in carbon dioxide. Some plants are better at retaining water than others, so changes in vegetation can affect how much water infiltrates into the ground, flows into'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 588.0, 'num_tokens': 201.0, 'num_tokens_approx': 192.0, 'num_words': 144.0, 'page_number': 1058, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': \"The Earth's Energy Budget, Climate Feedbacks and Climate Sensitivity\", 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'References', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.642782927, 'content': 'radiation budget. Journal of Geophysical Research: Atmospheres, 122(5), 2559-2578, doi:10.1002/2016jd025951. Mauritsen, T., 2016: Global warming: Clouds cooled the Earth. Nature Geoscience, 9(12), 865-867, doi:10.1038/ngeo2838. Mauritsen, T. and B. Stevens, 2015: Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models. Nature Geoscience, 8(5), 346-351, doi:10.1038/ngeo2414. Mauritsen, T. and R. Pincus, 2017: Committed warming inferred from observations. Nature Climate Change, 7(9), 652-655, doi:10.1038/ nclimate3357.', 'reranking_score': 0.00048389495350420475, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='radiation budget. Journal of Geophysical Research: Atmospheres, 122(5), 2559-2578, doi:10.1002/2016jd025951. Mauritsen, T., 2016: Global warming: Clouds cooled the Earth. Nature Geoscience, 9(12), 865-867, doi:10.1038/ngeo2838. Mauritsen, T. and B. Stevens, 2015: Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models. Nature Geoscience, 8(5), 346-351, doi:10.1038/ngeo2414. Mauritsen, T. and R. Pincus, 2017: Committed warming inferred from observations. Nature Climate Change, 7(9), 652-655, doi:10.1038/ nclimate3357.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 963.0, 'num_tokens': 222.0, 'num_tokens_approx': 228.0, 'num_words': 171.0, 'page_number': 645, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '4.6.3.3.3 Cirrus cloud thinning', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '4: Future Global Climate: Scenario-based Projections and Near-term Information', 'toc_level1': '4.6 Implications of Climate Policy', 'toc_level2': '4.6.3 Climate Response to Mitigation, Carbon Dioxide Removal and Solar Radiation Modification', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.642620206, 'content': 'Cirrus clouds trap more outgoing thermal radiation than they reflect incoming solar radiation and thus have an overall warming effect on the climate system (Mitchell and Finnegan, 2009). The aim of cirrus cloud thinning (CCT) is to reduce cirrus cloud optical depth by increasing the heterogeneous nucleation via seeding cirrus clouds with an optimal concentration of ice nucleating particles, which might cause larger ice crystals and rapid fallout, resulting in reduced lifetime and coverage of cirrus clouds (Muri et al., 2014; Gasparini et al., 2017; Lohmann and Gasparini, 2017; Gruber et al., 2019). CCT aims to achieve the opposite effect of contrails that increase cirrus cover and cause a small positive ERF (Section 7.3). A high-resolution modelling study of CCT over a limited area of the Arctic suggested that cirrus seeding causes a decrease in ice crystal number concentration and a reduction in mixed-phase cloud cover,', 'reranking_score': 0.00044499398791231215, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Cirrus clouds trap more outgoing thermal radiation than they reflect incoming solar radiation and thus have an overall warming effect on the climate system (Mitchell and Finnegan, 2009). The aim of cirrus cloud thinning (CCT) is to reduce cirrus cloud optical depth by increasing the heterogeneous nucleation via seeding cirrus clouds with an optimal concentration of ice nucleating particles, which might cause larger ice crystals and rapid fallout, resulting in reduced lifetime and coverage of cirrus clouds (Muri et al., 2014; Gasparini et al., 2017; Lohmann and Gasparini, 2017; Gruber et al., 2019). CCT aims to achieve the opposite effect of contrails that increase cirrus cover and cause a small positive ERF (Section 7.3). A high-resolution modelling study of CCT over a limited area of the Arctic suggested that cirrus seeding causes a decrease in ice crystal number concentration and a reduction in mixed-phase cloud cover,'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 971.0, 'num_tokens': 226.0, 'num_tokens_approx': 229.0, 'num_words': 172.0, 'page_number': 1089, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.2.3.2 Processes Determining Heavy Precipitation \\r\\nand Flooding', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.2 Why Should We Expect Water Cycle Changes?', 'toc_level2': '8.2.3 Local-scale Physical Processes Affecting the\\xa0Water Cycle', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.642336786, 'content': 'Intensification of sub-daily rainfall is inhibited in regions and seasons where available moisture is limited (Prein et al., 2017). However, a fixed threshold temperature above which precipitation is limited by moisture availability is not supported by modelling evidence (Neelin et al., 2017; Prein et al., 2017). Enhanced latent heating within storms can also suppress convection at larger scales due to atmospheric stabilization as demonstrated with high resolution, idealized and large ensemble modelling studies (Loriaux et al., 2017; Chan et al., 2018; Nie et al., 2018; Tandon et al., 2018; Kendon et al., 2019). Stability is also increased by the direct radiative heating effect of higher CO2 concentrations (Baker et al., 2018) and influenced by aerosol effects on the atmospheric energy budget and cloud development (Box 8.1). Since AR5, modelling evidence shows increases in convective precipitation extremes are limited by droplet/', 'reranking_score': 0.00041049186256714165, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Intensification of sub-daily rainfall is inhibited in regions and seasons where available moisture is limited (Prein et al., 2017). However, a fixed threshold temperature above which precipitation is limited by moisture availability is not supported by modelling evidence (Neelin et al., 2017; Prein et al., 2017). Enhanced latent heating within storms can also suppress convection at larger scales due to atmospheric stabilization as demonstrated with high resolution, idealized and large ensemble modelling studies (Loriaux et al., 2017; Chan et al., 2018; Nie et al., 2018; Tandon et al., 2018; Kendon et al., 2019). Stability is also increased by the direct radiative heating effect of higher CO2 concentrations (Baker et al., 2018) and influenced by aerosol effects on the atmospheric energy budget and cloud development (Box 8.1). Since AR5, modelling evidence shows increases in convective precipitation extremes are limited by droplet/'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1103.0, 'num_tokens': 218.0, 'num_tokens_approx': 241.0, 'num_words': 181.0, 'page_number': 868, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '6.4 SLCF Radiative Forcing \\r\\nand Climate Effects', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '6: Short-lived Climate Forcers', 'toc_level1': '6.4 SLCF Radiative Forcing and\\xa0Climate\\xa0Effects', 'toc_level2': '6.4.1 Historical Estimates of Regional Short‑lived Climate Forcing ', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.642164111, 'content': 'In summary, CMIP6 models generally represent more processes that drive aerosol-cloud interactions than the previous generation of climate models, but there is only medium confidence that those enhancements improve their fitness for the purpose of simulating radiative forcing due to aerosol-cloud interactions because only a few studies have identified the level of sophistication required to do so. In addition, the challenge of representing the small-scale processes involved in aerosol-cloud interactions, and a lack of relevant model\\x02data comparisons, does not allow a quantitative assessment of the progress of the models from CMIP5 to CMIP6 in simulating the underlying conditions relevant for aerosol-cloud interactions at this time. \\n6.4.1 Historical Estimates of Regional Short-lived Climate Forcing \\nThe highly heterogeneous distribution of SLCF abundances (Section 6.3) translates to strong heterogeneity in the spatial pattern and temporal evolution of forcing and climate responses due to SLCFs. This section assesses the spatial patterns of the current forcing', 'reranking_score': 0.000388551241485402, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='In summary, CMIP6 models generally represent more processes that drive aerosol-cloud interactions than the previous generation of climate models, but there is only medium confidence that those enhancements improve their fitness for the purpose of simulating radiative forcing due to aerosol-cloud interactions because only a few studies have identified the level of sophistication required to do so. In addition, the challenge of representing the small-scale processes involved in aerosol-cloud interactions, and a lack of relevant model\\x02data comparisons, does not allow a quantitative assessment of the progress of the models from CMIP5 to CMIP6 in simulating the underlying conditions relevant for aerosol-cloud interactions at this time. \\n6.4.1 Historical Estimates of Regional Short-lived Climate Forcing \\nThe highly heterogeneous distribution of SLCF abundances (Section 6.3) translates to strong heterogeneity in the spatial pattern and temporal evolution of forcing and climate responses due to SLCFs. This section assesses the spatial patterns of the current forcing'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1120.0, 'num_tokens': 220.0, 'num_tokens_approx': 265.0, 'num_words': 199.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.642138898, 'content': 'Since the last IPCC Report in 2013 (the Fifth Assessment Report, or AR5), understanding of cloud processes has advanced with better observations, new analysis approaches and explicit high-resolution numerical simulation of clouds. Also, current global climate models simulate cloud behaviour better than previous models, due both to advances in computational capabilities and process understanding. Altogether, this has helped to build a more complete picture of how clouds will change as the climate warms (FAQ 7.2, Figure 1). For example, the amount of low-clouds will reduce over the subtropical ocean, leading to less reflection of incoming solar energy, and the altitude of high-clouds will rise, making them more prone to trapping outgoing energy; both processes have a warming effect. In contrast, clouds in high latitudes will be increasingly made of water droplets rather than ice crystals. This shift from fewer, larger ice crystals to smaller but more numerous water droplets will result in more of the incoming solar energy being reflected back to space and produce a cooling effect. Better', 'reranking_score': 0.0003855253744404763, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Since the last IPCC Report in 2013 (the Fifth Assessment Report, or AR5), understanding of cloud processes has advanced with better observations, new analysis approaches and explicit high-resolution numerical simulation of clouds. Also, current global climate models simulate cloud behaviour better than previous models, due both to advances in computational capabilities and process understanding. Altogether, this has helped to build a more complete picture of how clouds will change as the climate warms (FAQ 7.2, Figure 1). For example, the amount of low-clouds will reduce over the subtropical ocean, leading to less reflection of incoming solar energy, and the altitude of high-clouds will rise, making them more prone to trapping outgoing energy; both processes have a warming effect. In contrast, clouds in high latitudes will be increasingly made of water droplets rather than ice crystals. This shift from fewer, larger ice crystals to smaller but more numerous water droplets will result in more of the incoming solar energy being reflected back to space and produce a cooling effect. Better'), Document(metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 684.0, 'num_tokens': 162.0, 'num_tokens_approx': 169.0, 'num_words': 127.0, 'page_number': 2485, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'Cross-Working Group Box SRM | Solar Radiation Modification', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Chapters and Cross-Chapter Papers ', 'toc_level1': 'Chapter 16 Key Risks across Sectors and Regions', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.64183861, 'content': 'SRM refers to proposals to increase the reflection of shortwave radiation (sunlight) back to space to counteract anthropogenic warming and some of its harmful impacts (de Coninck et al., 2018) (Cross-Chapter Box 10; WGI Chapters 4, 5). A number of SRM options have been proposed, including: stratospheric aerosol interventions (SAI), marine cloud brightening (MCB), ground-based albedo modifications (GBAM) and ocean albedo change (OAC). Although not strictly a form of SRM, cirrus cloud thinning (CCT) has been proposed to cool the planet by increasing the escape of longwave thermal radiation to space and is included here for consistency with previous assessments \\n24732473', 'reranking_score': 0.00036505103344097733, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='SRM refers to proposals to increase the reflection of shortwave radiation (sunlight) back to space to counteract anthropogenic warming and some of its harmful impacts (de Coninck et al., 2018) (Cross-Chapter Box 10; WGI Chapters 4, 5). A number of SRM options have been proposed, including: stratospheric aerosol interventions (SAI), marine cloud brightening (MCB), ground-based albedo modifications (GBAM) and ocean albedo change (OAC). Although not strictly a form of SRM, cirrus cloud thinning (CCT) has been proposed to cool the planet by increasing the escape of longwave thermal radiation to space and is included here for consistency with previous assessments \\n24732473'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 692.0, 'num_tokens': 173.0, 'num_tokens_approx': 169.0, 'num_words': 127.0, 'page_number': 1028, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Contributions to global mean warming in CMIP6 ESMs in response to CO2 quadrupling', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.641371608, 'content': 'feedbacks. The finding that cloud feedbacks are the largest source of spread in the net radiative feedback has since been confirmed in perturbed parameter ensemble studies using several different ESMs (Gettelman et al., 2012; Tomassini et al., 2015; Kamae et al., 2016b; Rostron et al., 2020; Tsushima et al., 2020). By swapping out different versions of the atmospheric or oceanic components in a coupled ESM, Winton et al. (2013) found that TCR and ECS depend on which atmospheric component was used (using two versions with different atmospheric physics), but that only TCR is sensitive to which oceanic component of the model was used (using two versions with different', 'reranking_score': 0.000247651623794809, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='feedbacks. The finding that cloud feedbacks are the largest source of spread in the net radiative feedback has since been confirmed in perturbed parameter ensemble studies using several different ESMs (Gettelman et al., 2012; Tomassini et al., 2015; Kamae et al., 2016b; Rostron et al., 2020; Tsushima et al., 2020). By swapping out different versions of the atmospheric or oceanic components in a coupled ESM, Winton et al. (2013) found that TCR and ECS depend on which atmospheric component was used (using two versions with different atmospheric physics), but that only TCR is sensitive to which oceanic component of the model was used (using two versions with different'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 372.0, 'num_tokens': 84.0, 'num_tokens_approx': 101.0, 'num_words': 76.0, 'page_number': 1040, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.641049743, 'content': 'FAQ 7.2, Figure 1 | Interactions between clouds and the climate, today and in a warmer future. Global warming is expected to alter the altitude (left) and the amount (centre) of clouds, which will amplify warming. On the other hand, cloud composition will change (right), offsetting some of the warming. Overall, clouds are expected to amplify future warming.\\n10231025', 'reranking_score': 0.00020096411753911525, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='FAQ 7.2, Figure 1 | Interactions between clouds and the climate, today and in a warmer future. Global warming is expected to alter the altitude (left) and the amount (centre) of clouds, which will amplify warming. On the other hand, cloud composition will change (right), offsetting some of the warming. Overall, clouds are expected to amplify future warming.\\n10231025'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 797.0, 'num_tokens': 229.0, 'num_tokens_approx': 222.0, 'num_words': 167.0, 'page_number': 956, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': \"7.2.2.3 Changes in Earth's Surface Energy Budget\", 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': 'Box 7.2 | The Global Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.640619457, 'content': 'Soni et al., 2016; Tanaka et al., 2016; Kazadzis et al., 2018; J. Li et al., 2018; Yang et al., 2019; Wild et al., 2021). This suggests that changes in the composition of the cloud-free atmosphere, primarily in aerosols, contributed to these variations, particularly since the second half of the 20th century (Wild, 2016). Water vapour and other radiatively active gases seem to have played a minor role (Wild, 2009; Mateos et al., 2013; Posselt et al., 2014; Yang et al., 2019). For Europe and East Asia, modelling studies also point to aerosols as an important factor for dimming and brightening by comparing simulations that include or exclude variations in anthropogenic aerosol and aerosol-precursor emissions (Golaz et al., 2013; Nabat et al., 2014; Persad et al., 2014;', 'reranking_score': 0.00016132999735418707, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Soni et al., 2016; Tanaka et al., 2016; Kazadzis et al., 2018; J. Li et al., 2018; Yang et al., 2019; Wild et al., 2021). This suggests that changes in the composition of the cloud-free atmosphere, primarily in aerosols, contributed to these variations, particularly since the second half of the 20th century (Wild, 2016). Water vapour and other radiatively active gases seem to have played a minor role (Wild, 2009; Mateos et al., 2013; Posselt et al., 2014; Yang et al., 2019). For Europe and East Asia, modelling studies also point to aerosols as an important factor for dimming and brightening by comparing simulations that include or exclude variations in anthropogenic aerosol and aerosol-precursor emissions (Golaz et al., 2013; Nabat et al., 2014; Persad et al., 2014;'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 985.0, 'num_tokens': 223.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1079, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.1.1.2 Overview of the Global Water Cycle \\r\\nin the Climate System', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.1 Introduction', 'toc_level2': '8.1.2 Summary of Water Cycle Changes From AR5 and\\xa0Special Reports', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.640571833, 'content': \"Understanding the interactions between the water and energy cycles is one of the four core projects of the World Climate Research Programme (WCRP). Latent heat fluxes, released by condensation of atmospheric water vapour and absorbed by evaporative processes, are critical to driving the circulation of the atmosphere on scales ranging from individual thunderstorm cells to the global circulation of the atmosphere (Stocker et al., 2013; Miralles et al., 2019). Water vapour is the most important gaseous absorber in the Earth's atmosphere, playing a key role in the Earth's radiative budget (Schneider et al., 2010). As atmospheric water vapour content increases with temperature, it has a considerable influence on climate change (Section 7.4.2.2). Additionally, a small fraction of the atmospheric water content is liquid or solid and has a major effect on both solar and longwave radiative fluxes, from the Earth's surface to the top of the atmosphere.\", 'reranking_score': 0.00015977399016264826, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"Understanding the interactions between the water and energy cycles is one of the four core projects of the World Climate Research Programme (WCRP). Latent heat fluxes, released by condensation of atmospheric water vapour and absorbed by evaporative processes, are critical to driving the circulation of the atmosphere on scales ranging from individual thunderstorm cells to the global circulation of the atmosphere (Stocker et al., 2013; Miralles et al., 2019). Water vapour is the most important gaseous absorber in the Earth's atmosphere, playing a key role in the Earth's radiative budget (Schneider et al., 2010). As atmospheric water vapour content increases with temperature, it has a considerable influence on climate change (Section 7.4.2.2). Additionally, a small fraction of the atmospheric water content is liquid or solid and has a major effect on both solar and longwave radiative fluxes, from the Earth's surface to the top of the atmosphere.\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1100.0, 'num_tokens': 251.0, 'num_tokens_approx': 288.0, 'num_words': 216.0, 'page_number': 989, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.2.4.1 Decomposition of clouds into regimes', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.640084505, 'content': \"7.4.2.4.2 Assessment for individual cloud regimes\\n 7.4.2.4.2 Assessment for individual cloud regimes \\n\\n7.4.2.4.2 Assessment for individual cloud regimes\\nHigh-cloud altitude feedback\\nIt has long been argued that cloud-top altitude rises under global warming, concurrent with the rising of the tropopause at all latitudes (Marvel et al., 2015; Thompson et al., 2017). This increasing altitude of high-clouds was identified in early generation GCMs and the tropical high-cloud altitude feedback was assessed to be positive with high confidence in AR5 (Boucher et al., 2013). This assessment is supported by a theoretical argument called the 'fixed anvil temperature mechanism', which ensures that the temperature of the convective detrainment layer does not change when the altitude of high-cloud tops increases with the rising tropopause (Hartmann and Larson, 2002). Because the cloud-top temperature does not change significantly with global warming, cloud LW emission does not \\n High-cloud altitude feedback \", 'reranking_score': 0.00015034705575089902, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"7.4.2.4.2 Assessment for individual cloud regimes\\n 7.4.2.4.2 Assessment for individual cloud regimes \\n\\n7.4.2.4.2 Assessment for individual cloud regimes\\nHigh-cloud altitude feedback\\nIt has long been argued that cloud-top altitude rises under global warming, concurrent with the rising of the tropopause at all latitudes (Marvel et al., 2015; Thompson et al., 2017). This increasing altitude of high-clouds was identified in early generation GCMs and the tropical high-cloud altitude feedback was assessed to be positive with high confidence in AR5 (Boucher et al., 2013). This assessment is supported by a theoretical argument called the 'fixed anvil temperature mechanism', which ensures that the temperature of the convective detrainment layer does not change when the altitude of high-cloud tops increases with the rising tropopause (Hartmann and Larson, 2002). Because the cloud-top temperature does not change significantly with global warming, cloud LW emission does not \\n High-cloud altitude feedback \"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 612.0, 'num_tokens': 166.0, 'num_tokens_approx': 156.0, 'num_words': 117.0, 'page_number': 2357, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'This index should be cited as:', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Index', 'toc_level1': 'A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.640011609, 'content': 'Aerosol-cloud interactions* (ERFaci) alteration of cloud radiative properties, 951 AR5 assessment of, 825, 953 cirrus cloud thinning effects, 861 cooking and heating emissions effect, 866 direct interactions of, 852 effective radiative forcing (1750-2014), 926 effects on water clouds, 950 historical estimates of, 321-322 model-based evidence for, 953 observation-based evidence for, 951 overall assessment of, 953 quantification of forcing from, 951 satellite-based estimates of, 953 sea-spray feedback effects, 858 seeding and cloud-thinning effects, 860 sulphur dioxide emissions in, 820, 855', 'reranking_score': 0.0001251594367204234, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Aerosol-cloud interactions* (ERFaci) alteration of cloud radiative properties, 951 AR5 assessment of, 825, 953 cirrus cloud thinning effects, 861 cooking and heating emissions effect, 866 direct interactions of, 852 effective radiative forcing (1750-2014), 926 effects on water clouds, 950 historical estimates of, 321-322 model-based evidence for, 953 observation-based evidence for, 951 overall assessment of, 953 quantification of forcing from, 951 satellite-based estimates of, 953 sea-spray feedback effects, 858 seeding and cloud-thinning effects, 860 sulphur dioxide emissions in, 820, 855'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 541.0, 'num_tokens': 130.0, 'num_tokens_approx': 136.0, 'num_words': 102.0, 'page_number': 990, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Subtropical marine low-cloud feedback', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.639511466, 'content': '2015; Kawai et al., 2017). These controlling factors compensate with a varying degree in different ESMs, but can be constrained by referring to the observed seasonal or interannual relationship between the low-cloud amount and the controlling factors in the environment as a surrogate. The analysis leads to a positive local feedback that has the global contribution of 0.14 to 0.36 W m-2 degC-1 (Klein et al., 2017), to which the feedback in the stratocumulus regime dominates over the feedback in the trade cumulus regime', 'reranking_score': 0.00012453064846340567, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='2015; Kawai et al., 2017). These controlling factors compensate with a varying degree in different ESMs, but can be constrained by referring to the observed seasonal or interannual relationship between the low-cloud amount and the controlling factors in the environment as a surrogate. The analysis leads to a positive local feedback that has the global contribution of 0.14 to 0.36 W m-2 degC-1 (Klein et al., 2017), to which the feedback in the stratocumulus regime dominates over the feedback in the trade cumulus regime'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 506.0, 'num_tokens': 129.0, 'num_tokens_approx': 136.0, 'num_words': 102.0, 'page_number': 1007, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '(a) Atmospheric response to observed Pacific ocean warming', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.4 Relationship Between Feedbacks and\\xa0Temperature Patterns', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.639287531, 'content': 'resulting in an observed increase in low-cloud cover over the tropical eastern Pacific (Figure 7.14a; Zhou et al., 2016; Ceppi and Gregory, 2017; Fueglistaler and Silvers, 2021). Thus, tropical low-cloud cover increased over recent decades even as global surface temperature increased, resulting in a negative low-cloud feedback which is at odds with the positive low-cloud feedback expected for the pattern of equilibrium warming under CO2 forcing (Section 7.4.2.4 and Figure 7.14b).\\n990990', 'reranking_score': 0.00011221654858672991, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='resulting in an observed increase in low-cloud cover over the tropical eastern Pacific (Figure 7.14a; Zhou et al., 2016; Ceppi and Gregory, 2017; Fueglistaler and Silvers, 2021). Thus, tropical low-cloud cover increased over recent decades even as global surface temperature increased, resulting in a negative low-cloud feedback which is at odds with the positive low-cloud feedback expected for the pattern of equilibrium warming under CO2 forcing (Section 7.4.2.4 and Figure 7.14b).\\n990990'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 414.0, 'num_tokens': 94.0, 'num_tokens_approx': 100.0, 'num_words': 75.0, 'page_number': 1094, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Aerosol radiative effects on precipitation', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.2 Why Should We Expect Water Cycle Changes?', 'toc_level2': 'Box\\xa08.1 |\\xa0Role of Anthropogenic Aerosols in Water Cycle Changes', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.639222145, 'content': 'Absorption of solar radiation by anthropogenic aerosols such as black carbon warms the lower troposphere and increases moist static energy, but also results in larger convection inhibition that suppresses light rainfall (Box 8.1, Figure 2; Y. Wang et al., 2013). Release of aerosol-induced instability, often triggered by topographical barriers, produces intense rainfall, flooding (Fan et al., 2015; \\n10771077', 'reranking_score': 0.00010988663416355848, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Absorption of solar radiation by anthropogenic aerosols such as black carbon warms the lower troposphere and increases moist static energy, but also results in larger convection inhibition that suppresses light rainfall (Box 8.1, Figure 2; Y. Wang et al., 2013). Release of aerosol-induced instability, often triggered by topographical barriers, produces intense rainfall, flooding (Fan et al., 2015; \\n10771077'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 954.0, 'num_tokens': 217.0, 'num_tokens_approx': 217.0, 'num_words': 163.0, 'page_number': 645, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '4.6.3.3.2 Marine cloud brightening', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '4: Future Global Climate: Scenario-based Projections and Near-term Information', 'toc_level1': '4.6 Implications of Climate Policy', 'toc_level2': '4.6.3 Climate Response to Mitigation, Carbon Dioxide Removal and Solar Radiation Modification', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.638640523, 'content': \"Marine cloud brightening (MCB) involves injecting small aerosols such as sea salt into the base of marine stratocumulus clouds where the aerosols act as cloud condensation nuclei (CCN). In the absence of other changes, an increase in CCN would produce higher cloud droplet number concentration with reduced droplet sizes, increasing cloud albedo. Increased droplet concentration may also increase cloud water content and optical thickness, but recent studies suggest that liquid water path response to anthropogenic aerosols is weak due to the competing effects of suppressed precipitation and enhanced cloud water evaporation (Toll et al., 2019). An analogue for MCB are reflective, persistent 'ship tracks' observed after the passage of a sea-going vessel emitting combustion aerosols into susceptible clouds (Christensen and Stephens, 2011; Chen et al., 2012; Gryspeerdt et al., 2019). A recent study (Diamond et al., 2020)\", 'reranking_score': 0.0001073575476766564, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"Marine cloud brightening (MCB) involves injecting small aerosols such as sea salt into the base of marine stratocumulus clouds where the aerosols act as cloud condensation nuclei (CCN). In the absence of other changes, an increase in CCN would produce higher cloud droplet number concentration with reduced droplet sizes, increasing cloud albedo. Increased droplet concentration may also increase cloud water content and optical thickness, but recent studies suggest that liquid water path response to anthropogenic aerosols is weak due to the competing effects of suppressed precipitation and enhanced cloud water evaporation (Toll et al., 2019). An analogue for MCB are reflective, persistent 'ship tracks' observed after the passage of a sea-going vessel emitting combustion aerosols into susceptible clouds (Christensen and Stephens, 2011; Chen et al., 2012; Gryspeerdt et al., 2019). A recent study (Diamond et al., 2020)\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 651.0, 'num_tokens': 220.0, 'num_tokens_approx': 212.0, 'num_words': 159.0, 'page_number': 1051, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': \"The Earth's Energy Budget, Climate Feedbacks and Climate Sensitivity\", 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'References', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.637683451, 'content': 'Warm Clouds: A Review of the Current State of Knowledge and Perspectives. Reviews of Geophysics, 56(2), 409-453, doi:10.1029/2017rg000593. Gryspeerdt, E., P. Stier, and B.S. Grandey, 2014a: Cloud fraction mediates the aerosol optical depth-cloud top height relationship. Geophysical Research Letters, 41(10), 3622-3627, doi:10.1002/2014gl059524. Gryspeerdt, E., P. Stier, and D.G. Partridge, 2014b: Satellite observations of cloud regime development: the role of aerosol processes. Atmospheric Chemistry and Physics, 14(3), 1141-1158, doi:10.5194/acp-14-1141-2014. Gryspeerdt, E., J. Quaas, and N. Bellouin, 2016: Constraining the aerosol', 'reranking_score': 0.0001041992218233645, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Warm Clouds: A Review of the Current State of Knowledge and Perspectives. Reviews of Geophysics, 56(2), 409-453, doi:10.1029/2017rg000593. Gryspeerdt, E., P. Stier, and B.S. Grandey, 2014a: Cloud fraction mediates the aerosol optical depth-cloud top height relationship. Geophysical Research Letters, 41(10), 3622-3627, doi:10.1002/2014gl059524. Gryspeerdt, E., P. Stier, and D.G. Partridge, 2014b: Satellite observations of cloud regime development: the role of aerosol processes. Atmospheric Chemistry and Physics, 14(3), 1141-1158, doi:10.5194/acp-14-1141-2014. Gryspeerdt, E., J. Quaas, and N. Bellouin, 2016: Constraining the aerosol'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1040.0, 'num_tokens': 252.0, 'num_tokens_approx': 284.0, 'num_words': 213.0, 'page_number': 965, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.3.3 Aerosols', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.3 Effective Radiative Forcing', 'toc_level2': '7.3.3 Aerosols', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.637638211, 'content': \"Anthropogenic activity, and particularly burning of biomass and fossil fuels, has led to a substantial increase in emissions of aerosols and their precursors, and thus to increased atmospheric aerosol concentrations since the pre-industrial era (Sections 2.2.6 and 6.3.5, and Figure 2.9). This is particularly true for sulphate and carbonaceous aerosols (Section 6.3.5). This has in turn led to changes in the scattering and absorption of incoming solar radiation, and also affected cloud micro- and macro-physics and thus cloud radiative properties. Aerosol changes are heterogeneous in both space and time and have impacted not just Earth's radiative energy budget but also air quality (Sections 6.1.1 and 6.6.2). Here, the assessment is focused exclusively on the global mean effects of aerosols on Earth's energy budget, while regional changes and changes associated \\n 7.3.3 Aerosols \\n\\nwith individual aerosol compounds are assessed in Chapter 6 (Sections 6.4.1 and 6.4.2).\", 'reranking_score': 9.947567741619423e-05, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"Anthropogenic activity, and particularly burning of biomass and fossil fuels, has led to a substantial increase in emissions of aerosols and their precursors, and thus to increased atmospheric aerosol concentrations since the pre-industrial era (Sections 2.2.6 and 6.3.5, and Figure 2.9). This is particularly true for sulphate and carbonaceous aerosols (Section 6.3.5). This has in turn led to changes in the scattering and absorption of incoming solar radiation, and also affected cloud micro- and macro-physics and thus cloud radiative properties. Aerosol changes are heterogeneous in both space and time and have impacted not just Earth's radiative energy budget but also air quality (Sections 6.1.1 and 6.6.2). Here, the assessment is focused exclusively on the global mean effects of aerosols on Earth's energy budget, while regional changes and changes associated \\n 7.3.3 Aerosols \\n\\nwith individual aerosol compounds are assessed in Chapter 6 (Sections 6.4.1 and 6.4.2).\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 673.0, 'num_tokens': 221.0, 'num_tokens_approx': 225.0, 'num_words': 169.0, 'page_number': 1057, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': \"The Earth's Energy Budget, Climate Feedbacks and Climate Sensitivity\", 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'References', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.637552083, 'content': \"Loeb, N.G. et al., 2018b: Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product. Journal of Climate, 31(2), 895-918, doi:10.1175/ jcli-d-17-0208.1. Loeb, N.G. et al., 2020: New Generation of Climate Models Track Recent Unprecedented Changes in Earth's Radiation Budget Observed by CERES. Geophysical Research Letters, 47(5), e2019GL086705, doi:10.1029/ 2019gl086705. Lohmann, U. and D. Neubauer, 2018: The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol-climate model ECHAM6-HAM2. Atmospheric Chemistry and Physics, 18(12), 8807-8828,\", 'reranking_score': 9.827027679421008e-05, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"Loeb, N.G. et al., 2018b: Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product. Journal of Climate, 31(2), 895-918, doi:10.1175/ jcli-d-17-0208.1. Loeb, N.G. et al., 2020: New Generation of Climate Models Track Recent Unprecedented Changes in Earth's Radiation Budget Observed by CERES. Geophysical Research Letters, 47(5), e2019GL086705, doi:10.1029/ 2019gl086705. Lohmann, U. and D. Neubauer, 2018: The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol-climate model ECHAM6-HAM2. Atmospheric Chemistry and Physics, 18(12), 8807-8828,\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 264.0, 'num_tokens': 51.0, 'num_tokens_approx': 65.0, 'num_words': 49.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.637470722, 'content': 'understanding of how clouds respond to warming has led to more confidence than before that future changes in clouds will, overall, cause additional warming (i.e., by weakening the current cooling effect of clouds). This is called a positive net cloud feedback.', 'reranking_score': 8.48046547616832e-05, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='understanding of how clouds respond to warming has led to more confidence than before that future changes in clouds will, overall, cause additional warming (i.e., by weakening the current cooling effect of clouds). This is called a positive net cloud feedback.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document9', 'document_number': 9.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2258.0, 'name': 'Full Report. In: Climate Change 2022: Mitigation of Climate Change. Contribution of the WGIII to the AR6 of the IPCC', 'num_characters': 863.0, 'num_tokens': 193.0, 'num_tokens_approx': 213.0, 'num_words': 160.0, 'page_number': 1823, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'Overshoot pathways See Pathways.', 'short_name': 'IPCC AR6 WGIII FR', 'source': 'IPCC', 'toc_level0': '_Hlk111724995', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_FullReport.pdf', 'similarity_score': 0.63711977, 'content': \"(O2). Stratospheric O3 plays a dominant role in the stratospheric radiative balance. Its concentration is highest in the ozone layer. Pareto optimum A state in which no one's welfare can be increased without reducing someone else's welfare.\\nOvershoot pathways \\nPathways that first exceed a specified concentration, forcing, or global warming level, and then return to or below that level again before the end of a specified period of time (e.g., before 2100). Sometimes the magnitude and likelihood of the overshoot is also characterised. The overshoot duration can vary from one pathway to the next, but in most overshoot pathways in the literature and referred to as overshoot pathways in the AR6, the overshoot occurs over a period of at least one decade and up to several decades.\\n Overshoot pathways \", 'reranking_score': 8.385351247852668e-05, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"(O2). Stratospheric O3 plays a dominant role in the stratospheric radiative balance. Its concentration is highest in the ozone layer. Pareto optimum A state in which no one's welfare can be increased without reducing someone else's welfare.\\nOvershoot pathways \\nPathways that first exceed a specified concentration, forcing, or global warming level, and then return to or below that level again before the end of a specified period of time (e.g., before 2100). Sometimes the magnitude and likelihood of the overshoot is also characterised. The overshoot duration can vary from one pathway to the next, but in most overshoot pathways in the literature and referred to as overshoot pathways in the AR6, the overshoot occurs over a period of at least one decade and up to several decades.\\n Overshoot pathways \"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 354.0, 'num_tokens': 128.0, 'num_tokens_approx': 125.0, 'num_words': 94.0, 'page_number': 1060, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': \"The Earth's Energy Budget, Climate Feedbacks and Climate Sensitivity\", 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'References', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.636806428, 'content': 'Norris, J.R. et al., 2016: Evidence for climate change in the satellite cloud record. Nature, 536(7614), 72, doi:10.1038/nature18273.\\nrsta.2014.0164.\\nOhmura, A., A. Bauder, H. Mueller, and G. Kappenberger, 2007: Long-term change of mass balance and the role of radiation. Annals of Glaciology, 46, 367-374, doi:10.3189/172756407782871297.\\n10431043', 'reranking_score': 7.811773684807122e-05, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='Norris, J.R. et al., 2016: Evidence for climate change in the satellite cloud record. Nature, 536(7614), 72, doi:10.1038/nature18273.\\nrsta.2014.0164.\\nOhmura, A., A. Bauder, H. Mueller, and G. Kappenberger, 2007: Long-term change of mass balance and the role of radiation. Annals of Glaciology, 46, 367-374, doi:10.3189/172756407782871297.\\n10431043'), Document(metadata={'chunk_type': 'text', 'document_id': 'document15', 'document_number': 15.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 60.0, 'name': 'Chapter 1 - Framing and Context of the Report. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 964.0, 'num_tokens': 225.0, 'num_tokens_approx': 253.0, 'num_words': 190.0, 'page_number': 10, 'release_date': 2019.0, 'report_type': 'Special Report', 'section_header': \"1.2.1 Ocean and Cryosphere in Earth's \\r\\nEnergy, Water and Biogeochemical Cycles\", 'short_name': 'IPCC SR OC C1', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/03_SROCC_Ch01_FINAL.pdf', 'similarity_score': 0.636738896, 'content': \"During an equilibrium (stable) climate state, the amount of incoming solar energy is balanced by an equal amount of outgoing radiation at the top of Earth's atmosphere (Hansen et al., 2011). At the Earth's surface energy from the Sun is transformed into various forms (heat, potential, latent, kinetic, and chemical), that drive weather systems in the atmosphere and currents in the ocean, fuel photosynthesis on land and in the ocean, and fundamentally determine the climate (Trenberth et al., 2014). The ocean has a large capacity to store and release heat, and the Earth's energy budget can be effectively monitored through the heat content of the ocean on time scales longer than one year (Palmer and McNeall, 2014; von Schuckmann et al., 2016; Cheng et al., 2018). The large heat capacity of the ocean leads to different characteristics of the ocean response to external forcings compared with the atmosphere (Sections 1.3, 1.4). The\", 'reranking_score': 6.046539783710614e-05, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"During an equilibrium (stable) climate state, the amount of incoming solar energy is balanced by an equal amount of outgoing radiation at the top of Earth's atmosphere (Hansen et al., 2011). At the Earth's surface energy from the Sun is transformed into various forms (heat, potential, latent, kinetic, and chemical), that drive weather systems in the atmosphere and currents in the ocean, fuel photosynthesis on land and in the ocean, and fundamentally determine the climate (Trenberth et al., 2014). The ocean has a large capacity to store and release heat, and the Earth's energy budget can be effectively monitored through the heat content of the ocean on time scales longer than one year (Palmer and McNeall, 2014; von Schuckmann et al., 2016; Cheng et al., 2018). The large heat capacity of the ocean leads to different characteristics of the ocean response to external forcings compared with the atmosphere (Sections 1.3, 1.4). The\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 767.0, 'num_tokens': 200.0, 'num_tokens_approx': 209.0, 'num_words': 157.0, 'page_number': 877, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '6.4.6 ERF by Aerosols in Proposed Solar \\r\\nRadiation Modification ', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '6: Short-lived Climate Forcers', 'toc_level1': '6.4 SLCF Radiative Forcing and\\xa0Climate\\xa0Effects', 'toc_level2': '6.4.6 ERF by Aerosols in Proposed Solar Radiation\\xa0Modification ', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.63658154, 'content': '6.4.6 ERF by Aerosols in Proposed Solar Radiation Modification \\nSolar radiation modification (SRM; Sections 4.6.3.3 and 8.6.3) has the potential to exert a significant ERF on the climate, mainly by affecting the SW component of the radiation budget (e.g., Caldeira et al., 2013; NRC, 2015; Lawrence et al., 2018). The possible ways and the extent to which the most commonly discussed options may affect radiative forcing is addressed in this section. Side effects of SRM on stratospheric ozone and changes in atmospheric transport due to radiative heating of the lower stratosphere are discussed in Section 4.6.3.3. \\naerosol layer, and hence the ERF efficiency, which also depends on the dispersion, transport, and residence time of the aerosols.', 'reranking_score': 5.79287952859886e-05, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='6.4.6 ERF by Aerosols in Proposed Solar Radiation Modification \\nSolar radiation modification (SRM; Sections 4.6.3.3 and 8.6.3) has the potential to exert a significant ERF on the climate, mainly by affecting the SW component of the radiation budget (e.g., Caldeira et al., 2013; NRC, 2015; Lawrence et al., 2018). The possible ways and the extent to which the most commonly discussed options may affect radiative forcing is addressed in this section. Side effects of SRM on stratospheric ozone and changes in atmospheric transport due to radiative heating of the lower stratosphere are discussed in Section 4.6.3.3. \\naerosol layer, and hence the ERF efficiency, which also depends on the dispersion, transport, and residence time of the aerosols.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1230.0, 'num_tokens': 214.0, 'num_tokens_approx': 248.0, 'num_words': 186.0, 'page_number': 1169, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.7 Final Remarks', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': 'Acknowledgements', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.635869622, 'content': \"* An improvement of the general circulation model (GCM)- simulated precipitation, latent heating and radiative effects of deep convective clouds would benefit from an improved representation of their interactions with aerosols. * Further research on land surface processes, including groundwater recharge, the role of plant physiological changes, land use change, dams and irrigation, will improve future projections of key aspects of the terrestrial water cycle such as aridity and drought. * Ongoing efforts to develop higher-resolution 'convection permitting' regional or global climate models will lead to an improved simulation of clouds and precipitation, their coupling with boundary layer and surface processes, their diurnal cycle and high-frequency variability, and their response to climate change, including extreme precipitation events. * Further analysis of past and current climate variability alongside future climate change projections will provide physically understood constraints for improving the accuracy of regional water cycle simulations, adding value to the results obtained from global climate models. * Increased understanding of internal variability and interactions\", 'reranking_score': 2.9899905712227337e-05, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"* An improvement of the general circulation model (GCM)- simulated precipitation, latent heating and radiative effects of deep convective clouds would benefit from an improved representation of their interactions with aerosols. * Further research on land surface processes, including groundwater recharge, the role of plant physiological changes, land use change, dams and irrigation, will improve future projections of key aspects of the terrestrial water cycle such as aridity and drought. * Ongoing efforts to develop higher-resolution 'convection permitting' regional or global climate models will lead to an improved simulation of clouds and precipitation, their coupling with boundary layer and surface processes, their diurnal cycle and high-frequency variability, and their response to climate change, including extreme precipitation events. * Further analysis of past and current climate variability alongside future climate change projections will provide physically understood constraints for improving the accuracy of regional water cycle simulations, adding value to the results obtained from global climate models. * Increased understanding of internal variability and interactions\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 783.0, 'num_tokens': 148.0, 'num_tokens_approx': 178.0, 'num_words': 134.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.635536432, 'content': 'The concentration of aerosols in the atmosphere has markedly increased since the pre-industrial era, and this has had two important effects on clouds. First, clouds now reflect more incoming energy because cloud droplets have become more numerous and smaller. Second, smaller droplets may delay rain formation, thereby making the clouds last longer, although this effect remains uncertain. Hence, aerosols released by human activities have had a cooling effect, counteracting a considerable portion of the warming caused by increases in greenhouse gases over the last century (see FAQ 3.1). Nevertheless, this cooling effect is expected to diminish in the future, as air pollution policies progress worldwide, reducing the amount of aerosols released into the atmosphere.', 'reranking_score': 2.7568323275772855e-05, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='The concentration of aerosols in the atmosphere has markedly increased since the pre-industrial era, and this has had two important effects on clouds. First, clouds now reflect more incoming energy because cloud droplets have become more numerous and smaller. Second, smaller droplets may delay rain formation, thereby making the clouds last longer, although this effect remains uncertain. Hence, aerosols released by human activities have had a cooling effect, counteracting a considerable portion of the warming caused by increases in greenhouse gases over the last century (see FAQ 3.1). Nevertheless, this cooling effect is expected to diminish in the future, as air pollution policies progress worldwide, reducing the amount of aerosols released into the atmosphere.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 525.0, 'num_tokens': 109.0, 'num_tokens_approx': 118.0, 'num_words': 89.0, 'page_number': 1274, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '9.4.1.1 Recent Observed Changes', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '9: Ocean, Cryosphere and Sea Level Change', 'toc_level1': '9.4 Ice Sheets', 'toc_level2': '9.4.1 Greenland Ice Sheet', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.635476232, 'content': 'melt to dominant patterns of cloud and atmospheric variability. The shortwave and longwave radiation effects on surface melt by clouds have been shown to compensate for each other during strong atmospheric river events, and the increase in melt is caused by increased sensible heat fluxes during such events (Mattingly et al., 2020). In summary, there is medium confidence that cloud cover changes are an important driver of the increasing melt rates in the southern and western part of the Greenland Ice Sheet.', 'reranking_score': 2.4480457796016708e-05, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content='melt to dominant patterns of cloud and atmospheric variability. The shortwave and longwave radiation effects on surface melt by clouds have been shown to compensate for each other during strong atmospheric river events, and the increase in melt is caused by increased sensible heat fluxes during such events (Mattingly et al., 2020). In summary, there is medium confidence that cloud cover changes are an important driver of the increasing melt rates in the southern and western part of the Greenland Ice Sheet.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document27', 'document_number': 27.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 494.0, 'name': 'Full Report. Regional Assessment Report on Biodiversity and Ecosystem Services for Africa', 'num_characters': 1018.0, 'num_tokens': 220.0, 'num_tokens_approx': 246.0, 'num_words': 185.0, 'page_number': 268, 'release_date': 2018.0, 'report_type': 'Full Report', 'section_header': '4.2.1 Natural direct drivers', 'short_name': 'IPBES RAR AF FR', 'source': 'IPBES', 'toc_level0': '4.2 Direct drivers of biodiversity change and flow of ecosystem services', 'toc_level1': '4.2.1.1 Natural climate variability and\\xa0weather patterns', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://zenodo.org/record/3236178/files/ipbes_assessment_report_africa_EN.pdf', 'similarity_score': 0.635242343, 'content': \"The long-term natural drivers of change are now known to be paced by orbital forcing, and display dominant periodicities at 100,000, 41,000 and 23,000 years, which are related to the earth's eccentricity, tilt and precession, respectively. They subtly modulate the incoming radiation from the sun at the surface of the earth, but their effects are amplified by earth-intrinsic factors such as the volume and extent of sea and land ice, vegetation and soil cover, ocean and atmospheric circulation, and variations in cloud cover and type, to an extent where the resultant climatic and environmental changes are large enough to be etched visibly on the geological record (O'Hare et al., 2005). Studies of long\\x02term changes in vegetation indicate that there is a close and dynamical relationship between such changes and variations in temperature, precipitation and atmospheric CO2 concentrations (Olago, 2001), and the present day distribution of vegetation in Africa largely reflects the\", 'reranking_score': 1.7225082046934403e-05, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\"}, page_content=\"The long-term natural drivers of change are now known to be paced by orbital forcing, and display dominant periodicities at 100,000, 41,000 and 23,000 years, which are related to the earth's eccentricity, tilt and precession, respectively. They subtly modulate the incoming radiation from the sun at the surface of the earth, but their effects are amplified by earth-intrinsic factors such as the volume and extent of sea and land ice, vegetation and soil cover, ocean and atmospheric circulation, and variations in cloud cover and type, to an extent where the resultant climatic and environmental changes are large enough to be etched visibly on the geological record (O'Hare et al., 2005). Studies of long\\x02term changes in vegetation indicate that there is a close and dynamical relationship between such changes and variations in temperature, precipitation and atmospheric CO2 concentrations (Olago, 2001), and the present day distribution of vegetation in Africa largely reflects the\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_stream', 'name': 'retrieve_documents', 'run_id': '06d7a138-098c-41f9-8be4-1735f5c1e0a4', 'tags': ['seq:step:1'], 'metadata': {'langgraph_step': 4, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['transform_query'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:abb15d66-a55a-7547-aeb5-e24822632d58', 'checkpoint_ns': 'retrieve_documents:abb15d66-a55a-7547-aeb5-e24822632d58'}, 'data': {'chunk': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1056.0, 'num_tokens': 219.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 7, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.2 | Assessed contributions to observed warming in 2010-2019 relative to 1850-1900', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.595214307, 'content': 'Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}', 'reranking_score': 0.9769342541694641, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 638.0, 'num_tokens': 177.0, 'num_tokens_approx': 200.0, 'num_words': 150.0, 'page_number': 11, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.3 | Synthesis of assessed observed and attributable regional changes ', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.592556596, 'content': 'A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}', 'reranking_score': 0.907663881778717, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 827.0, 'num_tokens': 238.0, 'num_tokens_approx': 278.0, 'num_words': 209.0, 'page_number': 19, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.6 | Projected changes in the intensity and frequency of hot temperature extremes over land, extreme precipitation over land, \\r\\nand agricultural and ecological droughts in drying regions', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.582914889, 'content': 'B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919', 'reranking_score': 0.8376452922821045, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919'), Document(metadata={'chunk_type': 'text', 'document_id': 'document4', 'document_number': 4.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 806.0, 'num_tokens': 147.0, 'num_tokens_approx': 162.0, 'num_words': 122.0, 'page_number': 19, 'release_date': 2022.0, 'report_type': 'SPM', 'section_header': 'Complex, Compound and Cascading Risks', 'short_name': 'IPCC AR6 WGII SPM', 'source': 'IPCC', 'toc_level0': 'B: Observed and Projected Impacts and Risks', 'toc_level1': 'Impacts of Temporary Overshoot', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_SummaryForPolicymakers.pdf', 'similarity_score': 0.581911206, 'content': 'B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 686.0, 'num_tokens': 163.0, 'num_tokens_approx': 182.0, 'num_words': 137.0, 'page_number': 19, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.581764042, 'content': 'Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 644.0, 'num_tokens': 151.0, 'num_tokens_approx': 165.0, 'num_words': 124.0, 'page_number': 950, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.741419494, 'content': \"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\", 'reranking_score': 0.7089773416519165, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1121.0, 'num_tokens': 231.0, 'num_tokens_approx': 288.0, 'num_words': 216.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727996647, 'content': \"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\", 'reranking_score': 0.47518521547317505, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\")], 'remaining_questions': [{'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'retrieve_documents', 'run_id': '06d7a138-098c-41f9-8be4-1735f5c1e0a4', 'tags': ['seq:step:1'], 'metadata': {'langgraph_step': 4, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['transform_query'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:abb15d66-a55a-7547-aeb5-e24822632d58', 'checkpoint_ns': 'retrieve_documents:abb15d66-a55a-7547-aeb5-e24822632d58'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [{'question': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}, {'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}], 'n_questions': 2, 'audience': 'expert'}, 'output': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1056.0, 'num_tokens': 219.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 7, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.2 | Assessed contributions to observed warming in 2010-2019 relative to 1850-1900', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.595214307, 'content': 'Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}', 'reranking_score': 0.9769342541694641, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. 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The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}', 'reranking_score': 0.907663881778717, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. 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Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.582914889, 'content': 'B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). 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There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919'), Document(metadata={'chunk_type': 'text', 'document_id': 'document4', 'document_number': 4.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. 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Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 686.0, 'num_tokens': 163.0, 'num_tokens_approx': 182.0, 'num_words': 137.0, 'page_number': 19, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.581764042, 'content': 'Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 644.0, 'num_tokens': 151.0, 'num_tokens_approx': 165.0, 'num_words': 124.0, 'page_number': 950, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.741419494, 'content': \"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\", 'reranking_score': 0.7089773416519165, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1121.0, 'num_tokens': 231.0, 'num_tokens_approx': 288.0, 'num_words': 216.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727996647, 'content': \"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\", 'reranking_score': 0.47518521547317505, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\")], 'remaining_questions': [{'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': '_write', 'run_id': '628f38f6-97b5-4658-89d8-bc54a8b50211', 'tags': ['seq:step:2', 'langsmith:hidden'], 'metadata': {'langgraph_step': 4, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['transform_query'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:abb15d66-a55a-7547-aeb5-e24822632d58'}, 'data': {'input': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1056.0, 'num_tokens': 219.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 7, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.2 | Assessed contributions to observed warming in 2010-2019 relative to 1850-1900', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.595214307, 'content': 'Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}', 'reranking_score': 0.9769342541694641, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 638.0, 'num_tokens': 177.0, 'num_tokens_approx': 200.0, 'num_words': 150.0, 'page_number': 11, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.3 | Synthesis of assessed observed and attributable regional changes ', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.592556596, 'content': 'A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}', 'reranking_score': 0.907663881778717, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 827.0, 'num_tokens': 238.0, 'num_tokens_approx': 278.0, 'num_words': 209.0, 'page_number': 19, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.6 | Projected changes in the intensity and frequency of hot temperature extremes over land, extreme precipitation over land, \\r\\nand agricultural and ecological droughts in drying regions', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.582914889, 'content': 'B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919', 'reranking_score': 0.8376452922821045, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919'), Document(metadata={'chunk_type': 'text', 'document_id': 'document4', 'document_number': 4.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 806.0, 'num_tokens': 147.0, 'num_tokens_approx': 162.0, 'num_words': 122.0, 'page_number': 19, 'release_date': 2022.0, 'report_type': 'SPM', 'section_header': 'Complex, Compound and Cascading Risks', 'short_name': 'IPCC AR6 WGII SPM', 'source': 'IPCC', 'toc_level0': 'B: Observed and Projected Impacts and Risks', 'toc_level1': 'Impacts of Temporary Overshoot', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_SummaryForPolicymakers.pdf', 'similarity_score': 0.581911206, 'content': 'B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 686.0, 'num_tokens': 163.0, 'num_tokens_approx': 182.0, 'num_words': 137.0, 'page_number': 19, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.581764042, 'content': 'Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 644.0, 'num_tokens': 151.0, 'num_tokens_approx': 165.0, 'num_words': 124.0, 'page_number': 950, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.741419494, 'content': \"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\", 'reranking_score': 0.7089773416519165, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1121.0, 'num_tokens': 231.0, 'num_tokens_approx': 288.0, 'num_words': 216.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727996647, 'content': \"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\", 'reranking_score': 0.47518521547317505, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\")], 'remaining_questions': [{'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': '_write', 'run_id': '628f38f6-97b5-4658-89d8-bc54a8b50211', 'tags': ['seq:step:2', 'langsmith:hidden'], 'metadata': {'langgraph_step': 4, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['transform_query'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:abb15d66-a55a-7547-aeb5-e24822632d58'}, 'data': {'input': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1056.0, 'num_tokens': 219.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 7, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.2 | Assessed contributions to observed warming in 2010-2019 relative to 1850-1900', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.595214307, 'content': 'Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}', 'reranking_score': 0.9769342541694641, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 638.0, 'num_tokens': 177.0, 'num_tokens_approx': 200.0, 'num_words': 150.0, 'page_number': 11, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.3 | Synthesis of assessed observed and attributable regional changes ', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.592556596, 'content': 'A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}', 'reranking_score': 0.907663881778717, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 827.0, 'num_tokens': 238.0, 'num_tokens_approx': 278.0, 'num_words': 209.0, 'page_number': 19, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.6 | Projected changes in the intensity and frequency of hot temperature extremes over land, extreme precipitation over land, \\r\\nand agricultural and ecological droughts in drying regions', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.582914889, 'content': 'B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919', 'reranking_score': 0.8376452922821045, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919'), Document(metadata={'chunk_type': 'text', 'document_id': 'document4', 'document_number': 4.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 806.0, 'num_tokens': 147.0, 'num_tokens_approx': 162.0, 'num_words': 122.0, 'page_number': 19, 'release_date': 2022.0, 'report_type': 'SPM', 'section_header': 'Complex, Compound and Cascading Risks', 'short_name': 'IPCC AR6 WGII SPM', 'source': 'IPCC', 'toc_level0': 'B: Observed and Projected Impacts and Risks', 'toc_level1': 'Impacts of Temporary Overshoot', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_SummaryForPolicymakers.pdf', 'similarity_score': 0.581911206, 'content': 'B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 686.0, 'num_tokens': 163.0, 'num_tokens_approx': 182.0, 'num_words': 137.0, 'page_number': 19, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.581764042, 'content': 'Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 644.0, 'num_tokens': 151.0, 'num_tokens_approx': 165.0, 'num_words': 124.0, 'page_number': 950, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.741419494, 'content': \"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\", 'reranking_score': 0.7089773416519165, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1121.0, 'num_tokens': 231.0, 'num_tokens_approx': 288.0, 'num_words': 216.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727996647, 'content': \"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\", 'reranking_score': 0.47518521547317505, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\")], 'remaining_questions': [{'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}]}, 'output': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1056.0, 'num_tokens': 219.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 7, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.2 | Assessed contributions to observed warming in 2010-2019 relative to 1850-1900', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.595214307, 'content': 'Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}', 'reranking_score': 0.9769342541694641, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 638.0, 'num_tokens': 177.0, 'num_tokens_approx': 200.0, 'num_words': 150.0, 'page_number': 11, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.3 | Synthesis of assessed observed and attributable regional changes ', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.592556596, 'content': 'A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}', 'reranking_score': 0.907663881778717, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 827.0, 'num_tokens': 238.0, 'num_tokens_approx': 278.0, 'num_words': 209.0, 'page_number': 19, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.6 | Projected changes in the intensity and frequency of hot temperature extremes over land, extreme precipitation over land, \\r\\nand agricultural and ecological droughts in drying regions', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.582914889, 'content': 'B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919', 'reranking_score': 0.8376452922821045, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919'), Document(metadata={'chunk_type': 'text', 'document_id': 'document4', 'document_number': 4.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 806.0, 'num_tokens': 147.0, 'num_tokens_approx': 162.0, 'num_words': 122.0, 'page_number': 19, 'release_date': 2022.0, 'report_type': 'SPM', 'section_header': 'Complex, Compound and Cascading Risks', 'short_name': 'IPCC AR6 WGII SPM', 'source': 'IPCC', 'toc_level0': 'B: Observed and Projected Impacts and Risks', 'toc_level1': 'Impacts of Temporary Overshoot', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_SummaryForPolicymakers.pdf', 'similarity_score': 0.581911206, 'content': 'B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 686.0, 'num_tokens': 163.0, 'num_tokens_approx': 182.0, 'num_words': 137.0, 'page_number': 19, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.581764042, 'content': 'Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 644.0, 'num_tokens': 151.0, 'num_tokens_approx': 165.0, 'num_words': 124.0, 'page_number': 950, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.741419494, 'content': \"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\", 'reranking_score': 0.7089773416519165, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1121.0, 'num_tokens': 231.0, 'num_tokens_approx': 288.0, 'num_words': 216.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727996647, 'content': \"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\", 'reranking_score': 0.47518521547317505, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\")], 'remaining_questions': [{'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_stream', 'name': 'retrieve_documents', 'run_id': '02a7aaaf-1037-46d0-a7a7-092ae0f5e619', 'tags': ['graph:step:4'], 'metadata': {'langgraph_step': 4, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['transform_query'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:abb15d66-a55a-7547-aeb5-e24822632d58'}, 'data': {'chunk': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1056.0, 'num_tokens': 219.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 7, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.2 | Assessed contributions to observed warming in 2010-2019 relative to 1850-1900', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.595214307, 'content': 'Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}', 'reranking_score': 0.9769342541694641, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 638.0, 'num_tokens': 177.0, 'num_tokens_approx': 200.0, 'num_words': 150.0, 'page_number': 11, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.3 | Synthesis of assessed observed and attributable regional changes ', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.592556596, 'content': 'A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}', 'reranking_score': 0.907663881778717, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. 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Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.582914889, 'content': 'B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919', 'reranking_score': 0.8376452922821045, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919'), Document(metadata={'chunk_type': 'text', 'document_id': 'document4', 'document_number': 4.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 806.0, 'num_tokens': 147.0, 'num_tokens_approx': 162.0, 'num_words': 122.0, 'page_number': 19, 'release_date': 2022.0, 'report_type': 'SPM', 'section_header': 'Complex, Compound and Cascading Risks', 'short_name': 'IPCC AR6 WGII SPM', 'source': 'IPCC', 'toc_level0': 'B: Observed and Projected Impacts and Risks', 'toc_level1': 'Impacts of Temporary Overshoot', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_SummaryForPolicymakers.pdf', 'similarity_score': 0.581911206, 'content': 'B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 686.0, 'num_tokens': 163.0, 'num_tokens_approx': 182.0, 'num_words': 137.0, 'page_number': 19, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.581764042, 'content': 'Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. 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Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\", 'reranking_score': 0.7089773416519165, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1121.0, 'num_tokens': 231.0, 'num_tokens_approx': 288.0, 'num_words': 216.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727996647, 'content': \"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\", 'reranking_score': 0.47518521547317505, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\")], 'remaining_questions': [{'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'retrieve_documents', 'run_id': '02a7aaaf-1037-46d0-a7a7-092ae0f5e619', 'tags': ['graph:step:4'], 'metadata': {'langgraph_step': 4, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['transform_query'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:abb15d66-a55a-7547-aeb5-e24822632d58'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [{'question': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}, {'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}], 'n_questions': 2, 'audience': 'expert'}, 'output': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1056.0, 'num_tokens': 219.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 7, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.2 | Assessed contributions to observed warming in 2010-2019 relative to 1850-1900', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.595214307, 'content': 'Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}', 'reranking_score': 0.9769342541694641, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 638.0, 'num_tokens': 177.0, 'num_tokens_approx': 200.0, 'num_words': 150.0, 'page_number': 11, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.3 | Synthesis of assessed observed and attributable regional changes ', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.592556596, 'content': 'A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}', 'reranking_score': 0.907663881778717, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 827.0, 'num_tokens': 238.0, 'num_tokens_approx': 278.0, 'num_words': 209.0, 'page_number': 19, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.6 | Projected changes in the intensity and frequency of hot temperature extremes over land, extreme precipitation over land, \\r\\nand agricultural and ecological droughts in drying regions', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.582914889, 'content': 'B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919', 'reranking_score': 0.8376452922821045, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919'), Document(metadata={'chunk_type': 'text', 'document_id': 'document4', 'document_number': 4.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. 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Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 686.0, 'num_tokens': 163.0, 'num_tokens_approx': 182.0, 'num_words': 137.0, 'page_number': 19, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.581764042, 'content': 'Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 644.0, 'num_tokens': 151.0, 'num_tokens_approx': 165.0, 'num_words': 124.0, 'page_number': 950, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.741419494, 'content': \"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\", 'reranking_score': 0.7089773416519165, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1121.0, 'num_tokens': 231.0, 'num_tokens_approx': 288.0, 'num_words': 216.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727996647, 'content': \"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\", 'reranking_score': 0.47518521547317505, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\")], 'remaining_questions': [{'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_stream', 'run_id': 'debff86f-5fee-42e7-9f9e-6f9f4147948a', 'tags': [], 'metadata': {}, 'name': 'LangGraph', 'data': {'chunk': {'retrieve_documents': {'remaining_questions': [{'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}], 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. 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The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}', 'reranking_score': 0.9769342541694641, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 638.0, 'num_tokens': 177.0, 'num_tokens_approx': 200.0, 'num_words': 150.0, 'page_number': 11, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.3 | Synthesis of assessed observed and attributable regional changes ', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.592556596, 'content': 'A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}', 'reranking_score': 0.907663881778717, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 827.0, 'num_tokens': 238.0, 'num_tokens_approx': 278.0, 'num_words': 209.0, 'page_number': 19, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.6 | Projected changes in the intensity and frequency of hot temperature extremes over land, extreme precipitation over land, \\r\\nand agricultural and ecological droughts in drying regions', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.582914889, 'content': 'B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919', 'reranking_score': 0.8376452922821045, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919'), Document(metadata={'chunk_type': 'text', 'document_id': 'document4', 'document_number': 4.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 806.0, 'num_tokens': 147.0, 'num_tokens_approx': 162.0, 'num_words': 122.0, 'page_number': 19, 'release_date': 2022.0, 'report_type': 'SPM', 'section_header': 'Complex, Compound and Cascading Risks', 'short_name': 'IPCC AR6 WGII SPM', 'source': 'IPCC', 'toc_level0': 'B: Observed and Projected Impacts and Risks', 'toc_level1': 'Impacts of Temporary Overshoot', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_SummaryForPolicymakers.pdf', 'similarity_score': 0.581911206, 'content': 'B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 686.0, 'num_tokens': 163.0, 'num_tokens_approx': 182.0, 'num_words': 137.0, 'page_number': 19, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.581764042, 'content': 'Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 644.0, 'num_tokens': 151.0, 'num_tokens_approx': 165.0, 'num_words': 124.0, 'page_number': 950, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.741419494, 'content': \"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\", 'reranking_score': 0.7089773416519165, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1121.0, 'num_tokens': 231.0, 'num_tokens_approx': 288.0, 'num_words': 216.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727996647, 'content': \"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\", 'reranking_score': 0.47518521547317505, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\")]}}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': 'retrieve_documents', 'run_id': 'a5ec2c2e-c7eb-4fe2-b7c8-e009264d3df9', 'tags': ['graph:step:5'], 'metadata': {'langgraph_step': 5, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['branch:retrieve_documents:condition:retrieve_documents'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [{'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1056.0, 'num_tokens': 219.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 7, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.2 | Assessed contributions to observed warming in 2010-2019 relative to 1850-1900', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.595214307, 'content': 'Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}', 'reranking_score': 0.9769342541694641, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 638.0, 'num_tokens': 177.0, 'num_tokens_approx': 200.0, 'num_words': 150.0, 'page_number': 11, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.3 | Synthesis of assessed observed and attributable regional changes ', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.592556596, 'content': 'A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}', 'reranking_score': 0.907663881778717, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 827.0, 'num_tokens': 238.0, 'num_tokens_approx': 278.0, 'num_words': 209.0, 'page_number': 19, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.6 | Projected changes in the intensity and frequency of hot temperature extremes over land, extreme precipitation over land, \\r\\nand agricultural and ecological droughts in drying regions', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.582914889, 'content': 'B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919', 'reranking_score': 0.8376452922821045, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919'), Document(metadata={'chunk_type': 'text', 'document_id': 'document4', 'document_number': 4.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 806.0, 'num_tokens': 147.0, 'num_tokens_approx': 162.0, 'num_words': 122.0, 'page_number': 19, 'release_date': 2022.0, 'report_type': 'SPM', 'section_header': 'Complex, Compound and Cascading Risks', 'short_name': 'IPCC AR6 WGII SPM', 'source': 'IPCC', 'toc_level0': 'B: Observed and Projected Impacts and Risks', 'toc_level1': 'Impacts of Temporary Overshoot', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_SummaryForPolicymakers.pdf', 'similarity_score': 0.581911206, 'content': 'B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 686.0, 'num_tokens': 163.0, 'num_tokens_approx': 182.0, 'num_words': 137.0, 'page_number': 19, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.581764042, 'content': 'Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 644.0, 'num_tokens': 151.0, 'num_tokens_approx': 165.0, 'num_words': 124.0, 'page_number': 950, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.741419494, 'content': \"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\", 'reranking_score': 0.7089773416519165, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1121.0, 'num_tokens': 231.0, 'num_tokens_approx': 288.0, 'num_words': 216.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727996647, 'content': \"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\", 'reranking_score': 0.47518521547317505, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': 'retrieve_documents', 'run_id': 'bceac6db-d865-4123-9ceb-98d88083c1b8', 'tags': ['seq:step:1'], 'metadata': {'langgraph_step': 5, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['branch:retrieve_documents:condition:retrieve_documents'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b', 'checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b'}, 'data': {}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': 'log_retriever', 'run_id': '29da9b63-8043-45e9-b5c6-47370e45a5a9', 'tags': [], 'metadata': {'langgraph_step': 5, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['branch:retrieve_documents:condition:retrieve_documents'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b', 'checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b'}, 'data': {'input': {'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'log_retriever', 'run_id': '29da9b63-8043-45e9-b5c6-47370e45a5a9', 'tags': [], 'metadata': {'langgraph_step': 5, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['branch:retrieve_documents:condition:retrieve_documents'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b', 'checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b'}, 'data': {'input': {'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}, 'output': {'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}}, 'parent_ids': []}\n", + "{'event': 'on_retriever_start', 'name': 'ClimateQARetriever', 'run_id': 'b9d89398-9308-46e8-aa7e-cb4e5cbdd7ba', 'tags': [], 'metadata': {'langgraph_step': 5, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['branch:retrieve_documents:condition:retrieve_documents'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b', 'checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b', 'ls_retriever_name': 'climateqa'}, 'data': {'input': {'query': 'How are cloud formations represented in current climate models?'}}, 'parent_ids': []}\n", + "{'event': 'on_retriever_end', 'name': 'ClimateQARetriever', 'run_id': 'b9d89398-9308-46e8-aa7e-cb4e5cbdd7ba', 'tags': [], 'metadata': {'langgraph_step': 5, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['branch:retrieve_documents:condition:retrieve_documents'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b', 'checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b', 'ls_retriever_name': 'climateqa'}, 'data': {'input': {'query': 'How are cloud formations represented in current climate models?'}, 'output': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1014.0, 'num_tokens': 223.0, 'num_tokens_approx': 222.0, 'num_words': 167.0, 'page_number': 1028, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Contributions to global mean warming in CMIP6 ESMs in response to CO2 quadrupling', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.736498773, 'content': \"More computationally intensive approaches evaluate how the climate response depends on perturbations to key parameter or structural choices within ESMs. Large 'perturbed parameter ensembles', wherein a range of parameter settings associated with cloud physics are explored within atmospheric ESMs, produce a wide range of ECS due to changes in cloud feedbacks, but often produce unrealistic climate states (Joshi et al., 2010). Rowlands et al. (2012) generated an ESM perturbed-physics ensemble of several thousand members by perturbing model parameters associated with radiative forcing, cloud feedbacks and ocean vertical diffusivity (an important parameter for ocean heat uptake). After constraining the ensemble to have a reasonable climatology and to match the observed historical surface warming, they found a wide range of projected warming by the year 2050 under the SRES A1B scenario (1.4degC-3degC relative to the 1961-1990 average) that is dominated by differences in cloud\", 'reranking_score': 0.9910033941268921, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"More computationally intensive approaches evaluate how the climate response depends on perturbations to key parameter or structural choices within ESMs. Large 'perturbed parameter ensembles', wherein a range of parameter settings associated with cloud physics are explored within atmospheric ESMs, produce a wide range of ECS due to changes in cloud feedbacks, but often produce unrealistic climate states (Joshi et al., 2010). Rowlands et al. (2012) generated an ESM perturbed-physics ensemble of several thousand members by perturbing model parameters associated with radiative forcing, cloud feedbacks and ocean vertical diffusivity (an important parameter for ocean heat uptake). After constraining the ensemble to have a reasonable climatology and to match the observed historical surface warming, they found a wide range of projected warming by the year 2050 under the SRES A1B scenario (1.4degC-3degC relative to the 1961-1990 average) that is dominated by differences in cloud\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document3', 'document_number': 3.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 112.0, 'name': 'Technical Summary. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1072.0, 'num_tokens': 201.0, 'num_tokens_approx': 222.0, 'num_words': 167.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'TS', 'section_header': 'TS.1.2.2 Climate Model Performance', 'short_name': 'IPCC AR6 WGI TS', 'source': 'IPCC', 'toc_level0': 'TS.1 A Changing Climate', 'toc_level1': 'TS.1.2 Progress in Climate Science', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf', 'similarity_score': 0.734275, 'content': \"Some CMIP6 models demonstrate an improvement in how clouds are represented. CMIP5 models commonly displayed a negative shortwave cloud radiative effect that was too weak in the present climate. These errors have been reduced, especially over the Southern Ocean, due to a more realistic simulation of supercooled liquid droplets with sufficient numbers and an associated increase in the cloud optical depth. Because a negative cloud optical depth feedback in response to surface warming results from 'brightening' of clouds via active phase change from ice to liquid cloud particles (increasing their shortwave cloud radiative effect), the extratropical cloud shortwave feedback in CMIP6 models tends to be less negative, leading to a better agreement with observational estimates (medium confidence). CMIP6 models generally represent more processes that drive aerosol-cloud interactions than the previous generation of climate models, but there is only medium confidence that those enhancements improve their fitness-for-purpose of simulating\", 'reranking_score': 0.986099123954773, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"Some CMIP6 models demonstrate an improvement in how clouds are represented. CMIP5 models commonly displayed a negative shortwave cloud radiative effect that was too weak in the present climate. These errors have been reduced, especially over the Southern Ocean, due to a more realistic simulation of supercooled liquid droplets with sufficient numbers and an associated increase in the cloud optical depth. Because a negative cloud optical depth feedback in response to surface warming results from 'brightening' of clouds via active phase change from ice to liquid cloud particles (increasing their shortwave cloud radiative effect), the extratropical cloud shortwave feedback in CMIP6 models tends to be less negative, leading to a better agreement with observational estimates (medium confidence). CMIP6 models generally represent more processes that drive aerosol-cloud interactions than the previous generation of climate models, but there is only medium confidence that those enhancements improve their fitness-for-purpose of simulating\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1072.0, 'num_tokens': 201.0, 'num_tokens_approx': 222.0, 'num_words': 167.0, 'page_number': 66, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'TS.1.2.2 Climate Model Performance', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Technical Summary', 'toc_level1': 'TS.1 A Changing Climate', 'toc_level2': 'TS.1.2 Progress in Climate Science', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.734275, 'content': \"Some CMIP6 models demonstrate an improvement in how clouds are represented. CMIP5 models commonly displayed a negative shortwave cloud radiative effect that was too weak in the present climate. These errors have been reduced, especially over the Southern Ocean, due to a more realistic simulation of supercooled liquid droplets with sufficient numbers and an associated increase in the cloud optical depth. Because a negative cloud optical depth feedback in response to surface warming results from 'brightening' of clouds via active phase change from ice to liquid cloud particles (increasing their shortwave cloud radiative effect), the extratropical cloud shortwave feedback in CMIP6 models tends to be less negative, leading to a better agreement with observational estimates (medium confidence). CMIP6 models generally represent more processes that drive aerosol-cloud interactions than the previous generation of climate models, but there is only medium confidence that those enhancements improve their fitness-for-purpose of simulating\", 'reranking_score': 0.9836174249649048, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"Some CMIP6 models demonstrate an improvement in how clouds are represented. CMIP5 models commonly displayed a negative shortwave cloud radiative effect that was too weak in the present climate. These errors have been reduced, especially over the Southern Ocean, due to a more realistic simulation of supercooled liquid droplets with sufficient numbers and an associated increase in the cloud optical depth. Because a negative cloud optical depth feedback in response to surface warming results from 'brightening' of clouds via active phase change from ice to liquid cloud particles (increasing their shortwave cloud radiative effect), the extratropical cloud shortwave feedback in CMIP6 models tends to be less negative, leading to a better agreement with observational estimates (medium confidence). CMIP6 models generally represent more processes that drive aerosol-cloud interactions than the previous generation of climate models, but there is only medium confidence that those enhancements improve their fitness-for-purpose of simulating\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 536.0, 'num_tokens': 102.0, 'num_tokens_approx': 106.0, 'num_words': 80.0, 'page_number': 233, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '1.5.3.1.2 Representation of physical and chemical \\r\\nprocesses in ESMs', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '1: Framing, Context, and Methods', 'toc_level1': '1.5 Major Developments and\\xa0Their Implications', 'toc_level2': '1.5.3 Climate Models', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.732051373, 'content': 'Atmospheric models include representations of physical processes such as clouds, turbulence, convection and gravity waves that are not fully represented by grid-scale dynamics. The CMIP6 models have undergone updates in some of their parameterization schemes compared to their CMIP5 counterparts, with the aim of better representing the physics and bringing the climatology of the models closer to newly available observational datasets. Most notable developments are to schemes involving radiative transfer, cloud \\n216216', 'reranking_score': 0.9751958250999451, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Atmospheric models include representations of physical processes such as clouds, turbulence, convection and gravity waves that are not fully represented by grid-scale dynamics. The CMIP6 models have undergone updates in some of their parameterization schemes compared to their CMIP5 counterparts, with the aim of better representing the physics and bringing the climatology of the models closer to newly available observational datasets. Most notable developments are to schemes involving radiative transfer, cloud \\n216216'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 787.0, 'num_tokens': 153.0, 'num_tokens_approx': 182.0, 'num_words': 137.0, 'page_number': 536, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 3.3 | Are Climate Models Improving?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '3: Human Influence on the Climate System', 'toc_level1': 'Frequently Asked Questions', 'toc_level2': 'FAQ 3.3 | Are Climate Models Improving?', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.730883479, 'content': \"Climate models are important tools for understanding past, present and future climate change. They are sophisticated computer programs that are based on fundamental laws of physics of the atmosphere, ocean, ice, and land. Climate models perform their calculations on a three-dimensional grid made of small bricks or 'gridcells' of about 100 km across. Processes that occur on scales smaller than the model grid cells (such as the transformation of cloud moisture into rain) are treated in a simplified way. This simplification is done differently in different models. Some models include more processes and complexity than others; some represent processes in finer detail (smaller grid cells) than others. Hence the simulated climate and climate change vary between models.\", 'reranking_score': 0.9704611301422119, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"Climate models are important tools for understanding past, present and future climate change. They are sophisticated computer programs that are based on fundamental laws of physics of the atmosphere, ocean, ice, and land. Climate models perform their calculations on a three-dimensional grid made of small bricks or 'gridcells' of about 100 km across. Processes that occur on scales smaller than the model grid cells (such as the transformation of cloud moisture into rain) are treated in a simplified way. This simplification is done differently in different models. Some models include more processes and complexity than others; some represent processes in finer detail (smaller grid cells) than others. Hence the simulated climate and climate change vary between models.\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1130.0, 'num_tokens': 210.0, 'num_tokens_approx': 249.0, 'num_words': 187.0, 'page_number': 536, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 3.3 | Are Climate Models Improving?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '3: Human Influence on the Climate System', 'toc_level1': 'Frequently Asked Questions', 'toc_level2': 'FAQ 3.3 | Are Climate Models Improving?', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.729721963, 'content': 'Climate modelling started in the 1950s and, over the years, models have become increasingly sophisticated as computing power, observations and our understanding of the climate system have advanced. The models used in the IPCC First Assessment Report published in 1990 correctly reproduced many aspects of climate (FAQ 1.1). The actual evolution of the climate since then has confirmed these early projections, when accounting for the differences between the simulated scenarios and actual emissions. Models continue to improve and get better and better at simulating the large variety of important processes that affect climate. For example, many models now simulate complex interactions between different aspects of the Earth system, such as the uptake of carbon dioxide by vegetation on land and by the ocean, and the interaction between clouds and air pollutants. While some models are becoming more comprehensive, others are striving to represent processes at higher resolution, for example to better represent the vortices and swirls in currents responsible for much of the transport of heat in the ocean.', 'reranking_score': 0.9653986096382141, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Climate modelling started in the 1950s and, over the years, models have become increasingly sophisticated as computing power, observations and our understanding of the climate system have advanced. The models used in the IPCC First Assessment Report published in 1990 correctly reproduced many aspects of climate (FAQ 1.1). The actual evolution of the climate since then has confirmed these early projections, when accounting for the differences between the simulated scenarios and actual emissions. Models continue to improve and get better and better at simulating the large variety of important processes that affect climate. For example, many models now simulate complex interactions between different aspects of the Earth system, such as the uptake of carbon dioxide by vegetation on land and by the ocean, and the interaction between clouds and air pollutants. While some models are becoming more comprehensive, others are striving to represent processes at higher resolution, for example to better represent the vortices and swirls in currents responsible for much of the transport of heat in the ocean.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1120.0, 'num_tokens': 220.0, 'num_tokens_approx': 265.0, 'num_words': 199.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.72886616, 'content': 'Since the last IPCC Report in 2013 (the Fifth Assessment Report, or AR5), understanding of cloud processes has advanced with better observations, new analysis approaches and explicit high-resolution numerical simulation of clouds. Also, current global climate models simulate cloud behaviour better than previous models, due both to advances in computational capabilities and process understanding. Altogether, this has helped to build a more complete picture of how clouds will change as the climate warms (FAQ 7.2, Figure 1). For example, the amount of low-clouds will reduce over the subtropical ocean, leading to less reflection of incoming solar energy, and the altitude of high-clouds will rise, making them more prone to trapping outgoing energy; both processes have a warming effect. In contrast, clouds in high latitudes will be increasingly made of water droplets rather than ice crystals. This shift from fewer, larger ice crystals to smaller but more numerous water droplets will result in more of the incoming solar energy being reflected back to space and produce a cooling effect. Better', 'reranking_score': 0.9562049508094788, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Since the last IPCC Report in 2013 (the Fifth Assessment Report, or AR5), understanding of cloud processes has advanced with better observations, new analysis approaches and explicit high-resolution numerical simulation of clouds. Also, current global climate models simulate cloud behaviour better than previous models, due both to advances in computational capabilities and process understanding. Altogether, this has helped to build a more complete picture of how clouds will change as the climate warms (FAQ 7.2, Figure 1). For example, the amount of low-clouds will reduce over the subtropical ocean, leading to less reflection of incoming solar energy, and the altitude of high-clouds will rise, making them more prone to trapping outgoing energy; both processes have a warming effect. In contrast, clouds in high latitudes will be increasingly made of water droplets rather than ice crystals. This shift from fewer, larger ice crystals to smaller but more numerous water droplets will result in more of the incoming solar energy being reflected back to space and produce a cooling effect. Better'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1051.0, 'num_tokens': 223.0, 'num_tokens_approx': 248.0, 'num_words': 186.0, 'page_number': 989, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.2.4.1 Decomposition of clouds into regimes', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.72876966, 'content': \"Since AR5, community efforts have been undertaken to understand and quantify the cloud feedbacks in various cloud regimes coupled with large-scale atmospheric circulation (Bony et al., 2015). For some cloud regimes, alternative tools to ESMs, such as observations, theory, high-resolution cloud resolving models (CRMs), and large eddy simulations (LES), help quantify the feedbacks. Consequently, the net cloud feedback derived from ESMs has been revised by assessing the regional cloud feedbacks separately and summing them with weighting by the ratio of fractional coverage of those clouds over the globe to give the global feedback, following an approach adopted in Sherwood et al. (2020). This 'bottom-up' assessment is explained below with a summary of updated confidence of individual cloud feedback components (Table 7.9). Dependence of cloud feedbacks on evolving patterns of surface warming will be discussed in Section 7.4.4 and is not explicitly taken into account in the assessment presented in this section.\", 'reranking_score': 0.9536903500556946, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"Since AR5, community efforts have been undertaken to understand and quantify the cloud feedbacks in various cloud regimes coupled with large-scale atmospheric circulation (Bony et al., 2015). For some cloud regimes, alternative tools to ESMs, such as observations, theory, high-resolution cloud resolving models (CRMs), and large eddy simulations (LES), help quantify the feedbacks. Consequently, the net cloud feedback derived from ESMs has been revised by assessing the regional cloud feedbacks separately and summing them with weighting by the ratio of fractional coverage of those clouds over the globe to give the global feedback, following an approach adopted in Sherwood et al. (2020). This 'bottom-up' assessment is explained below with a summary of updated confidence of individual cloud feedback components (Table 7.9). Dependence of cloud feedbacks on evolving patterns of surface warming will be discussed in Section 7.4.4 and is not explicitly taken into account in the assessment presented in this section.\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 825.0, 'num_tokens': 178.0, 'num_tokens_approx': 202.0, 'num_words': 152.0, 'page_number': 1080, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.1.3 Chapter Motivations, Framing and Preview', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.1 Introduction', 'toc_level2': '8.1.3 Chapter Motivations, Framing and Preview', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727772653, 'content': 'modelling clouds, precipitation, surface fluxes, vegetation, snow, floodplains, groundwater and other processes relevant to the water cycle. Convection permitting and cloud-resolving models have been implemented over increasingly large domains and can be used as benchmarks for the evaluation of the current-generation climate models. The added value of increased resolution in global or regional climate models can be also assessed more thoroughly based on dedicated model intercomparison projects (Sections 10.3.3 and 8.5.1). Ongoing research activities on decadal predictions and observational constraints are aimed at narrowing the plausible range of near-term (2021-2040) to long-term (2081-2100) water cycle changes.\\n 8.1.3 Chapter Motivations, Framing and Preview ', 'reranking_score': 0.9480205178260803, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='modelling clouds, precipitation, surface fluxes, vegetation, snow, floodplains, groundwater and other processes relevant to the water cycle. Convection permitting and cloud-resolving models have been implemented over increasingly large domains and can be used as benchmarks for the evaluation of the current-generation climate models. The added value of increased resolution in global or regional climate models can be also assessed more thoroughly based on dedicated model intercomparison projects (Sections 10.3.3 and 8.5.1). Ongoing research activities on decadal predictions and observational constraints are aimed at narrowing the plausible range of near-term (2021-2040) to long-term (2081-2100) water cycle changes.\\n 8.1.3 Chapter Motivations, Framing and Preview '), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 957.0, 'num_tokens': 208.0, 'num_tokens_approx': 242.0, 'num_words': 182.0, 'page_number': 529, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '3.8.2.2 Process Representation in Different Classes of Models', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '3: Human Influence on the Climate System', 'toc_level1': '3.8 Synthesis Across Earth System\\xa0Components', 'toc_level2': '3.8.2 Multivariate Model Evaluation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727271795, 'content': 'Based on new scientific insights and newly available observations, many improvements have been made to models from CMIP5 to CMIP6, including changes in the representation of physics of the atmosphere, ocean, sea ice, and land surface. In many cases, changes in the detailed representation of cloud and aerosol processes have been implemented. The new generation of CMIP6 climate models also features increases in spatial resolution, as well as inclusion of additional Earth system processes and new components (see further details in Section 1.5.3.1 and in Tables AII.5 and AII.6). Such changes to model physics and resolution are often designed to improve the fitness-for-purpose of a model such as for projecting regional aspects of climate (Section 10.3) or to more fully represent feedbacks to make the models more fit for long-term climate projections affected for example by carbon cycle feedbacks (see also Section 1.5.3.1).', 'reranking_score': 0.9093816876411438, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Based on new scientific insights and newly available observations, many improvements have been made to models from CMIP5 to CMIP6, including changes in the representation of physics of the atmosphere, ocean, sea ice, and land surface. In many cases, changes in the detailed representation of cloud and aerosol processes have been implemented. The new generation of CMIP6 climate models also features increases in spatial resolution, as well as inclusion of additional Earth system processes and new components (see further details in Section 1.5.3.1 and in Tables AII.5 and AII.6). Such changes to model physics and resolution are often designed to improve the fitness-for-purpose of a model such as for projecting regional aspects of climate (Section 10.3) or to more fully represent feedbacks to make the models more fit for long-term climate projections affected for example by carbon cycle feedbacks (see also Section 1.5.3.1).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1001.0, 'num_tokens': 212.0, 'num_tokens_approx': 224.0, 'num_words': 168.0, 'page_number': 1154, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.5.1.1 Fitness-for-purpose and Poorly Constrained \\r\\nKey Processes', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.5 What Are the Limits for Projecting Water Cycle Changes?', 'toc_level2': '8.5.1 Model Uncertainties of Relevance for the Water Cycle', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.723808825, 'content': 'The AR5 Chapter 7 recognized that the simulation of clouds and precipitation remains challenging for state-of-the-art GCMs. Model development and evaluation have continued since AR5, with a particular emphasis on the representation of new model components, like interactive vegetation, aerosols and biogeochemical cycles. For example, the comparison of simulated tropical precipitation across three successive generations of CMIP models (including CMIP6) indicates overall little improvement for the summer monsoons, the double-ITCZ bias, the diurnal cycle and the frequency of precipitation (Fiedler et al., 2020). Some of these issues are related to inherent model limitations in three specific areas: atmospheric convection, cloud-aerosol interactions and land surface processes (ocean and cryosphere-related processes are addressed in Chapter 9). These limitations do not weaken the overall progress made in the large-scale simulation of present-day climate (FAQ 3.3', 'reranking_score': 0.8714073300361633, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='The AR5 Chapter 7 recognized that the simulation of clouds and precipitation remains challenging for state-of-the-art GCMs. Model development and evaluation have continued since AR5, with a particular emphasis on the representation of new model components, like interactive vegetation, aerosols and biogeochemical cycles. For example, the comparison of simulated tropical precipitation across three successive generations of CMIP models (including CMIP6) indicates overall little improvement for the summer monsoons, the double-ITCZ bias, the diurnal cycle and the frequency of precipitation (Fiedler et al., 2020). Some of these issues are related to inherent model limitations in three specific areas: atmospheric convection, cloud-aerosol interactions and land surface processes (ocean and cryosphere-related processes are addressed in Chapter 9). These limitations do not weaken the overall progress made in the large-scale simulation of present-day climate (FAQ 3.3'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 634.0, 'num_tokens': 212.0, 'num_tokens_approx': 213.0, 'num_words': 160.0, 'page_number': 2145, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Huang, W., 2019b: THU CIESM model output prepared for CMIP6 ScenarioMIP. \\r\\nEarth System Grid Federation, doi:10.22033/esgf/cmip6.1357.', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex II: Models', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.723277509, 'content': 'Liu, X. et al., 2012: Toward a minimal representation of aerosols in climate models: description and evaluation in the Community Atmosphere Model CAM5. Geoscientific Model Development, 5(3), 709-739, doi:10.5194/ gmd-5-709-2012. Liu, X. et al., 2016: Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model. Geoscientific Model Development, 9(2), 505-522, doi:10.5194/gmd-9-505-2016. Lovato, T. and D. Peano, 2020a: CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Earth System Grid Federation, doi:10.22033/ esgf/cmip6.1362.', 'reranking_score': 0.8641304969787598, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Liu, X. et al., 2012: Toward a minimal representation of aerosols in climate models: description and evaluation in the Community Atmosphere Model CAM5. Geoscientific Model Development, 5(3), 709-739, doi:10.5194/ gmd-5-709-2012. Liu, X. et al., 2016: Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model. Geoscientific Model Development, 9(2), 505-522, doi:10.5194/gmd-9-505-2016. Lovato, T. and D. Peano, 2020a: CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP. Earth System Grid Federation, doi:10.22033/ esgf/cmip6.1362.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 708.0, 'num_tokens': 142.0, 'num_tokens_approx': 168.0, 'num_words': 126.0, 'page_number': 988, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.2.4.1 Decomposition of clouds into regimes', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.722961068, 'content': 'Figure 7.9 | Schematic cross section of diverse cloud responses to surface warming from the tropics to polar regions. Thick solid and dashed curves indicate the tropopause and the subtropical inversion layer in the current climate, respectively. Thin grey text and arrows represent robust responses in the thermodynamic structure to greenhouse warming, of relevance to cloud changes. Text and arrows in red, orange and green show the major cloud responses assessed with high, medium and low confidence, respectively, and the sign of their feedbacks to the surface warming is indicated in the parenthesis. Major advances since AR5 are listed in the box. Figure adapted from Boucher et al. (2013).\\n971971', 'reranking_score': 0.847446084022522, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Figure 7.9 | Schematic cross section of diverse cloud responses to surface warming from the tropics to polar regions. Thick solid and dashed curves indicate the tropopause and the subtropical inversion layer in the current climate, respectively. Thin grey text and arrows represent robust responses in the thermodynamic structure to greenhouse warming, of relevance to cloud changes. Text and arrows in red, orange and green show the major cloud responses assessed with high, medium and low confidence, respectively, and the sign of their feedbacks to the surface warming is indicated in the parenthesis. Major advances since AR5 are listed in the box. Figure adapted from Boucher et al. (2013).\\n971971'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 910.0, 'num_tokens': 228.0, 'num_tokens_approx': 224.0, 'num_words': 168.0, 'page_number': 996, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.2.8 Climate Feedbacks in ESMs', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.3 Dependence of Feedbacks on Climate Mean State', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.722643375, 'content': \"A remarkable improvement of cloud representation in some CMIP6 models is the reduced error of the too-weak negative shortwave CRE over the Southern Ocean (Bodas-Salcedo et al., 2019; Gettelman et al., 2019) due to a more realistic simulation of supercooled liquid droplets and associated cloud optical depths that were biased low commonly in CMIP5 models (McCoy et al., 2014a, b). Because the negative cloud optical depth feedback occurs due to 'brightening' of clouds via phase change from ice to liquid cloud particles in response to surface warming (Cesana and Storelvmo, 2017), the extratropical cloud shortwave feedback tends to be less negative or even slightly positive in models with reduced errors (Bjordal et al., 2020; Zelinka et al., 2020). The assessment of cloud feedbacks in Section 7.4.2.4 incorporates estimates from these improved ESMs. Yet, there still remain other\", 'reranking_score': 0.7553779482841492, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"A remarkable improvement of cloud representation in some CMIP6 models is the reduced error of the too-weak negative shortwave CRE over the Southern Ocean (Bodas-Salcedo et al., 2019; Gettelman et al., 2019) due to a more realistic simulation of supercooled liquid droplets and associated cloud optical depths that were biased low commonly in CMIP5 models (McCoy et al., 2014a, b). Because the negative cloud optical depth feedback occurs due to 'brightening' of clouds via phase change from ice to liquid cloud particles in response to surface warming (Cesana and Storelvmo, 2017), the extratropical cloud shortwave feedback tends to be less negative or even slightly positive in models with reduced errors (Bjordal et al., 2020; Zelinka et al., 2020). The assessment of cloud feedbacks in Section 7.4.2.4 incorporates estimates from these improved ESMs. Yet, there still remain other\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1110.0, 'num_tokens': 240.0, 'num_tokens_approx': 274.0, 'num_words': 206.0, 'page_number': 2014, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Atlas.7.2.3 Assessment of Model Performance', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Atlas', 'toc_level1': 'Atlas.7 Central and South America', 'toc_level2': 'Atlas.7.2 South America', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.722359061, 'content': 'Overall, climate modelling has made some progress in the past decade but there is no model that performs well in simulating all aspects of the present climate over South America (high confidence). The performance of the models varies according to the region, time scale and variables analysed (Abadi et al., 2018). There is also a fairly narrow spread in the representation of temperature and precipitation over South America by the CMIP5 GCMs and also the RCMs, with biases that can be associated with the parametrizations and schemes of surface, boundary layer, microphysics and radiation used by the models. Finally, observational reference datasets, such as reanalysis products, used in the calibration and validation of climate models can also be quite uncertain and may explain part of the apparent biases present in climate models (high confidence).\\n Atlas.7.2.4 Assessment and Synthesis of Projections \\n\\nAtlas.7.2.4 Assessment and Synthesis of Projections\\n Atlas.7.2.4 Assessment and Synthesis of Projections ', 'reranking_score': 0.7157607078552246, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Overall, climate modelling has made some progress in the past decade but there is no model that performs well in simulating all aspects of the present climate over South America (high confidence). The performance of the models varies according to the region, time scale and variables analysed (Abadi et al., 2018). There is also a fairly narrow spread in the representation of temperature and precipitation over South America by the CMIP5 GCMs and also the RCMs, with biases that can be associated with the parametrizations and schemes of surface, boundary layer, microphysics and radiation used by the models. Finally, observational reference datasets, such as reanalysis products, used in the calibration and validation of climate models can also be quite uncertain and may explain part of the apparent biases present in climate models (high confidence).\\n Atlas.7.2.4 Assessment and Synthesis of Projections \\n\\nAtlas.7.2.4 Assessment and Synthesis of Projections\\n Atlas.7.2.4 Assessment and Synthesis of Projections '), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 209.0, 'num_tokens': 70.0, 'num_tokens_approx': 69.0, 'num_words': 52.0, 'page_number': 439, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Changing State of the Climate System ', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '2: Changing State of the Climate System', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.721580148, 'content': 'Zhu, J., C.J. Poulsen, and J.E. Tierney, 2019: Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks. Science Advances, 5(9), eaax1874, doi:10.1126/sciadv.aax1874.\\n422422', 'reranking_score': 0.6988133788108826, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Zhu, J., C.J. Poulsen, and J.E. Tierney, 2019: Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks. Science Advances, 5(9), eaax1874, doi:10.1126/sciadv.aax1874.\\n422422'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 890.0, 'num_tokens': 220.0, 'num_tokens_approx': 225.0, 'num_words': 169.0, 'page_number': 1154, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.5.1.1.1 Atmospheric convection', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.5 What Are the Limits for Projecting Water Cycle Changes?', 'toc_level2': '8.5.1 Model Uncertainties of Relevance for the Water Cycle', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.717401, 'content': 'et al., 2014; Han et al., 2017; Walters et al., 2019) or evaluated against convection-permitting models (CPMs; J. Chen et al., 2020a). To increase the sensitivity of convection to tropospheric humidity, several models now include a representation of deep convective entrainment dependent on relative humidity (Bechtold et al., 2008; Han et al., 2017; M. Zhao et al., 2018; Walters et al., 2019). Other efforts have focused on the improvement of shallow convection and low-level cloudiness due to their major contribution to uncertainty in climate sensitivity (Section 7.4.2.4). A cloud-regime-based study however highlights an apparent disconnection between cloud and precipitation processes in GCMs (Tan et al., 2018), suggesting that a good representation of clouds does not lead to systematic improvement in simulated precipitation. A global simulation in which', 'reranking_score': 0.6867156624794006, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='et al., 2014; Han et al., 2017; Walters et al., 2019) or evaluated against convection-permitting models (CPMs; J. Chen et al., 2020a). To increase the sensitivity of convection to tropospheric humidity, several models now include a representation of deep convective entrainment dependent on relative humidity (Bechtold et al., 2008; Han et al., 2017; M. Zhao et al., 2018; Walters et al., 2019). Other efforts have focused on the improvement of shallow convection and low-level cloudiness due to their major contribution to uncertainty in climate sensitivity (Section 7.4.2.4). A cloud-regime-based study however highlights an apparent disconnection between cloud and precipitation processes in GCMs (Tan et al., 2018), suggesting that a good representation of clouds does not lead to systematic improvement in simulated precipitation. A global simulation in which'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 976.0, 'num_tokens': 210.0, 'num_tokens_approx': 238.0, 'num_words': 179.0, 'page_number': 991, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.2.4.3 Synthesis for the net cloud feedback', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.717377067, 'content': '7.4.2.4.3 Synthesis for the net cloud feedback\\nThe understanding of the response of clouds to warming and associated radiative feedback has deepened since AR5 (Figure 7.9 and FAQ 7.2). Particular progress has been made in the assessment of the marine low-cloud feedback, which has historically been a major contributor to the cloud feedback uncertainty but is no longer the largest source of uncertainty. Multiple lines of evidence (theory, observations, emergent constraints and process modelling) are now available in addition to ESM simulations, and the positive low-cloud feedback is consequently assessed with high confidence.\\nThe best estimate of net cloud feedback is obtained by summing feedbacks associated with individual cloud regimes and assessed to be aC = 0.42 W m-2 degC-1. By assuming that the uncertainties of individual cloud feedbacks are independent of each other, their standard deviations are added in quadrature, leading to the', 'reranking_score': 0.659690797328949, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='7.4.2.4.3 Synthesis for the net cloud feedback\\nThe understanding of the response of clouds to warming and associated radiative feedback has deepened since AR5 (Figure 7.9 and FAQ 7.2). Particular progress has been made in the assessment of the marine low-cloud feedback, which has historically been a major contributor to the cloud feedback uncertainty but is no longer the largest source of uncertainty. Multiple lines of evidence (theory, observations, emergent constraints and process modelling) are now available in addition to ESM simulations, and the positive low-cloud feedback is consequently assessed with high confidence.\\nThe best estimate of net cloud feedback is obtained by summing feedbacks associated with individual cloud regimes and assessed to be aC = 0.42 W m-2 degC-1. By assuming that the uncertainties of individual cloud feedbacks are independent of each other, their standard deviations are added in quadrature, leading to the'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 954.0, 'num_tokens': 210.0, 'num_tokens_approx': 205.0, 'num_words': 154.0, 'page_number': 1155, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.5.1.1.2 Aerosol microphysical effects on clouds and precipitation', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.5 What Are the Limits for Projecting Water Cycle Changes?', 'toc_level2': '8.5.1 Model Uncertainties of Relevance for the Water Cycle', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.717354238, 'content': 'A major challenge in representing convective clouds and related precipitation events in GCMs is a lack of sophisticated cloud microphysics in convective parametrization schemes (e.g., Fan et al., 2016). Most of these schemes only include simple microphysical treatments, such as direct partition between cloud condensation and precipitation, and do not include advanced treatment of conversion among different types of hydrometeors. As such these schemes are unable to simulate microphysical cloud and precipitation responses to aerosol-related perturbations in cloud droplet concentration and ice crystals (see Box 8.1), or perturbations in thermodynamical states from global warming. Efforts have been made to include more advanced cloud microphysical treatment in cumulus parametrizations (Song and Zhang, 2011; Grell and Freitas, 2014; Berg et al., 2015) or to use explicit cloud microphysics schemes in climate models with', 'reranking_score': 0.6570985317230225, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='A major challenge in representing convective clouds and related precipitation events in GCMs is a lack of sophisticated cloud microphysics in convective parametrization schemes (e.g., Fan et al., 2016). Most of these schemes only include simple microphysical treatments, such as direct partition between cloud condensation and precipitation, and do not include advanced treatment of conversion among different types of hydrometeors. As such these schemes are unable to simulate microphysical cloud and precipitation responses to aerosol-related perturbations in cloud droplet concentration and ice crystals (see Box 8.1), or perturbations in thermodynamical states from global warming. Efforts have been made to include more advanced cloud microphysical treatment in cumulus parametrizations (Song and Zhang, 2011; Grell and Freitas, 2014; Berg et al., 2015) or to use explicit cloud microphysics schemes in climate models with'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 882.0, 'num_tokens': 219.0, 'num_tokens_approx': 220.0, 'num_words': 165.0, 'page_number': 623, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Seasonal mean sea level pressure change', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '4: Future Global Climate: Scenario-based Projections and Near-term Information', 'toc_level1': '4.5 Mid- to Long-term Global Climate\\xa0Change', 'toc_level2': '4.5.1 Atmosphere', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.717198431, 'content': 'response of clouds, their non-spatially uniform radiative feedbacks shaping the meridional profile of warming (Ceppi et al., 2014; Voigt and Shaw, 2015, 2016; Ceppi and Hartmann, 2016; Ceppi and Shepherd, 2017; Lipat et al., 2018; Albern et al., 2019; Voigt et al., 2019). Climate models seem to underestimate the forced component of the year-to-year variability in the atmospheric circulation, particularly in the North Atlantic sector (Scaife and Smith, 2018), which suggests some relevant dynamical processes may not be well represented. Whether and how this may affect long-term projections is unknown. In conclusion, due to the influence from competing dynamical drivers and the absence of observational evidence, there is medium confidence in a projected poleward shift of the NH zonal\\x02mean low-level westerlies in autumn and summer and low confidence', 'reranking_score': 0.5121383666992188, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='response of clouds, their non-spatially uniform radiative feedbacks shaping the meridional profile of warming (Ceppi et al., 2014; Voigt and Shaw, 2015, 2016; Ceppi and Hartmann, 2016; Ceppi and Shepherd, 2017; Lipat et al., 2018; Albern et al., 2019; Voigt et al., 2019). Climate models seem to underestimate the forced component of the year-to-year variability in the atmospheric circulation, particularly in the North Atlantic sector (Scaife and Smith, 2018), which suggests some relevant dynamical processes may not be well represented. Whether and how this may affect long-term projections is unknown. In conclusion, due to the influence from competing dynamical drivers and the absence of observational evidence, there is medium confidence in a projected poleward shift of the NH zonal\\x02mean low-level westerlies in autumn and summer and low confidence'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 897.0, 'num_tokens': 226.0, 'num_tokens_approx': 213.0, 'num_words': 160.0, 'page_number': 1154, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.5.1.1.1 Atmospheric convection', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.5 What Are the Limits for Projecting Water Cycle Changes?', 'toc_level2': '8.5.1 Model Uncertainties of Relevance for the Water Cycle', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.716955245, 'content': 'Since AR5, spatial aggregation of tropical convection has also received growing attention in both observational (Holloway et al., 2017) and modelling studies (Muller and Bony, 2015; Wing et al., 2017; Tan et al., 2018). The changing degree of convective organization was highlighted as a key mechanism for dynamic changes in extreme precipitation (Pendergrass, 2020a). Yet, convective parametrizations do not represent all aspects of mesoscale convective systems (Hourdin et al., 2013; Park et al., 2019). This is related to the complexity of mechanisms involved from synoptic to mesoscale dynamics, which are only partially resolved by models. Cloud-resolving models (CRMs, Section 8.5.1.2.2) represent a useful benchmark for improving the parametrization of mesoscale convective systems. Machine learning can also be used to parametrize moist convection after training', 'reranking_score': 0.4698672592639923, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Since AR5, spatial aggregation of tropical convection has also received growing attention in both observational (Holloway et al., 2017) and modelling studies (Muller and Bony, 2015; Wing et al., 2017; Tan et al., 2018). The changing degree of convective organization was highlighted as a key mechanism for dynamic changes in extreme precipitation (Pendergrass, 2020a). Yet, convective parametrizations do not represent all aspects of mesoscale convective systems (Hourdin et al., 2013; Park et al., 2019). This is related to the complexity of mechanisms involved from synoptic to mesoscale dynamics, which are only partially resolved by models. Cloud-resolving models (CRMs, Section 8.5.1.2.2) represent a useful benchmark for improving the parametrization of mesoscale convective systems. Machine learning can also be used to parametrize moist convection after training'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 940.0, 'num_tokens': 214.0, 'num_tokens_approx': 216.0, 'num_words': 162.0, 'page_number': 1002, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.4.1.2 Polar amplification from proxies and models during \\r\\npast climates associated with CO2 change', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.4 Relationship Between Feedbacks and\\xa0Temperature Patterns', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.716811657, 'content': 'Since AR5, there has been progress in the simulation of polar amplification by paleoclimate models of the Early Eocene. Initial work indicated that changes to model parameters associated with aerosols and/or clouds could increase simulated polar amplification and improve agreement between models and paleoclimate data (Kiehl and Shields, 2013; Sagoo et al., 2013), but such parameter changes were not physically based. In support of these initial findings, a more recent (CMIP5) climate model, that includes a process-based representation of cloud microphysics, exhibits polar amplification in better agreement with proxies when compared to the models assessed in AR5 (Zhu et al., 2019a). Since then, some other CMIP3 and CMIP5 models in the DeepMIP multi-model ensemble (Lunt et al., 2021) have obtained polar amplification for the EECO that is consistent with proxy indications of both polar amplification and', 'reranking_score': 0.4180181622505188, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Since AR5, there has been progress in the simulation of polar amplification by paleoclimate models of the Early Eocene. Initial work indicated that changes to model parameters associated with aerosols and/or clouds could increase simulated polar amplification and improve agreement between models and paleoclimate data (Kiehl and Shields, 2013; Sagoo et al., 2013), but such parameter changes were not physically based. In support of these initial findings, a more recent (CMIP5) climate model, that includes a process-based representation of cloud microphysics, exhibits polar amplification in better agreement with proxies when compared to the models assessed in AR5 (Zhu et al., 2019a). Since then, some other CMIP3 and CMIP5 models in the DeepMIP multi-model ensemble (Lunt et al., 2021) have obtained polar amplification for the EECO that is consistent with proxy indications of both polar amplification and'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 967.0, 'num_tokens': 226.0, 'num_tokens_approx': 233.0, 'num_words': 175.0, 'page_number': 989, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.2.4.1 Decomposition of clouds into regimes', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.714518368, 'content': 'In a first attempt to systematically evaluate equilibrium climate sensitivity (ECS) based on fully coupled general circulation models (GCMs) in AR4, diverging cloud feedbacks were recognized as a dominant source of uncertainty. An advance in understanding the cloud feedback was to assess feedbacks separately for different cloud regimes (Gettelman and Sherwood, 2016). A thorough assessment of cloud feedbacks in different cloud regimes was carried out in AR5 (Boucher et al., 2013), which assigned high or medium confidence for some cloud feedbacks but low or no confidence for others (Table 7.9). Many studies that estimate the net cloud feedback using CMIP5 simulations (Vial et al., 2013; Caldwell et al., 2016; Zelinka et al., 2016; Colman and Hanson, 2017) show different values depending on the methodology and the set of models used, but often report a large inter-model spread of the feedback, with the 90% confidence interval', 'reranking_score': 0.4161548316478729, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='In a first attempt to systematically evaluate equilibrium climate sensitivity (ECS) based on fully coupled general circulation models (GCMs) in AR4, diverging cloud feedbacks were recognized as a dominant source of uncertainty. An advance in understanding the cloud feedback was to assess feedbacks separately for different cloud regimes (Gettelman and Sherwood, 2016). A thorough assessment of cloud feedbacks in different cloud regimes was carried out in AR5 (Boucher et al., 2013), which assigned high or medium confidence for some cloud feedbacks but low or no confidence for others (Table 7.9). Many studies that estimate the net cloud feedback using CMIP5 simulations (Vial et al., 2013; Caldwell et al., 2016; Zelinka et al., 2016; Colman and Hanson, 2017) show different values depending on the methodology and the set of models used, but often report a large inter-model spread of the feedback, with the 90% confidence interval'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 683.0, 'num_tokens': 218.0, 'num_tokens_approx': 216.0, 'num_words': 162.0, 'page_number': 1054, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': \"The Earth's Energy Budget, Climate Feedbacks and Climate Sensitivity\", 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'References', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.714431703, 'content': '34(1), 16-34, doi:10.1029/2018pa003380. Khairoutdinov, M. and K. Emanuel, 2013: Rotating radiative-convective equilibrium simulated by a cloud-resolving model. Journal of Advances in Modeling Earth Systems, 5(4), 816-825, doi:10.1002/2013ms000253. Kiehl, J.T., 2007: Twentieth century climate model response and climate sensitivity. Geophysical Research Letters, 34(22), 1-4, doi:10.1029/2007gl031383. Kiehl, J.T. and C.A. Shields, 2013: Sensitivity of the Palaeocene-Eocene Thermal Maximum climate to cloud properties. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(2001), 20130093, doi:10.1098/rsta.2013.0093.', 'reranking_score': 0.40870213508605957, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='34(1), 16-34, doi:10.1029/2018pa003380. Khairoutdinov, M. and K. Emanuel, 2013: Rotating radiative-convective equilibrium simulated by a cloud-resolving model. Journal of Advances in Modeling Earth Systems, 5(4), 816-825, doi:10.1002/2013ms000253. Kiehl, J.T., 2007: Twentieth century climate model response and climate sensitivity. Geophysical Research Letters, 34(22), 1-4, doi:10.1029/2007gl031383. Kiehl, J.T. and C.A. Shields, 2013: Sensitivity of the Palaeocene-Eocene Thermal Maximum climate to cloud properties. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(2001), 20130093, doi:10.1098/rsta.2013.0093.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 888.0, 'num_tokens': 204.0, 'num_tokens_approx': 210.0, 'num_words': 158.0, 'page_number': 1155, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.5.1.1.2 Aerosol microphysical effects on clouds and precipitation', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.5 What Are the Limits for Projecting Water Cycle Changes?', 'toc_level2': '8.5.1 Model Uncertainties of Relevance for the Water Cycle', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.714334726, 'content': 'In AR5 Chapter 7, there was low confidence in the representation of cloud-aerosol interactions in climate models. Despite progresses in this field since AR5, cloud-aerosol interactions remain a major obstacle to understanding climate and severe weather (Varble, 2018). High aerosol concentrations have been observed to suppress rain in water clouds (Campos Braga et al., 2017; Fan et al., 2020). However, such aerosol effects are muted in GCMs, which tend to produce precipitation from shallow clouds too frequently at the expense of rain intensity (Suzuki et al., 2015; Jing et al., 2017). This arises from incomplete knowledge of how clouds adjust to aerosol primary effects such as cloud condensation nuclei (CCN). The adjustment occurs mainly as a dynamic response to the impacts of CCN on cloud droplet size and number concentrations on precipitation-forming', 'reranking_score': 0.39471152424812317, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='In AR5 Chapter 7, there was low confidence in the representation of cloud-aerosol interactions in climate models. Despite progresses in this field since AR5, cloud-aerosol interactions remain a major obstacle to understanding climate and severe weather (Varble, 2018). High aerosol concentrations have been observed to suppress rain in water clouds (Campos Braga et al., 2017; Fan et al., 2020). However, such aerosol effects are muted in GCMs, which tend to produce precipitation from shallow clouds too frequently at the expense of rain intensity (Suzuki et al., 2015; Jing et al., 2017). This arises from incomplete knowledge of how clouds adjust to aerosol primary effects such as cloud condensation nuclei (CCN). The adjustment occurs mainly as a dynamic response to the impacts of CCN on cloud droplet size and number concentrations on precipitation-forming'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 681.0, 'num_tokens': 233.0, 'num_tokens_approx': 245.0, 'num_words': 184.0, 'page_number': 568, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'References', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '3: Human Influence on the Climate System', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.712190449, 'content': 'on cloud formation. Nature Communications, 8(1), 14067, doi:10.1038/ ncomms14067. Zheng, F., J. Li, R.T. Clark, and H.C. Nnamchi, 2013: Simulation and Projection of the Southern Hemisphere Annular Mode in CMIP5 Models. Journal of Climate, 26(24), 9860-9879, doi:10.1175/jcli-d-13-00204.1. Zheng, X.-T., L. Gao, G. Li, and Y. Du, 2016: The Southwest Indian Ocean thermocline dome in CMIP5 models: Historical simulation and future projection. Advances in Atmospheric Sciences, 33(4), 489-503, doi:10.1007/ s00376-015-5076-9. Zhou, S., G. Huang, and P. Huang, 2020: Excessive ITCZ but Negative SST Biases in the Tropical Pacific Simulated by CMIP5/6 Models: The Role of', 'reranking_score': 0.325069397687912, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='on cloud formation. Nature Communications, 8(1), 14067, doi:10.1038/ ncomms14067. Zheng, F., J. Li, R.T. Clark, and H.C. Nnamchi, 2013: Simulation and Projection of the Southern Hemisphere Annular Mode in CMIP5 Models. Journal of Climate, 26(24), 9860-9879, doi:10.1175/jcli-d-13-00204.1. Zheng, X.-T., L. Gao, G. Li, and Y. Du, 2016: The Southwest Indian Ocean thermocline dome in CMIP5 models: Historical simulation and future projection. Advances in Atmospheric Sciences, 33(4), 489-503, doi:10.1007/ s00376-015-5076-9. Zhou, S., G. Huang, and P. Huang, 2020: Excessive ITCZ but Negative SST Biases in the Tropical Pacific Simulated by CMIP5/6 Models: The Role of'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 838.0, 'num_tokens': 174.0, 'num_tokens_approx': 204.0, 'num_words': 153.0, 'page_number': 1605, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '11.7.1.3 Model Evaluation', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '11: Weather and Climate Extreme Events in a Changing Climate', 'toc_level1': '11.7 Extreme Storms', 'toc_level2': '11.7.1 Tropical Cyclones', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.711990833, 'content': 'In summary, various types of models are useful to study how TCs change in response to climate changes, and there is no unique solution for choosing a model type. However, higher-resolution models generally capture TC properties more realistically (high confidence). In particular, models with horizontal resolutions of 10-60 km are capable of reproducing strong TCs with Category 4-5 and those of 1-10 km are capable of the eye wall structure of TCs. Uncertainties in TC simulations come from details of the model configuration of both dynamical and physical processes. Models with realistic atmosphere- ocean interactions are generally better than atmosphere-only models at reproducing realistic TC evolutions (high confidence).\\n 11.7.1.4 Detection and Attribution, Event Attribution ', 'reranking_score': 0.30527421832084656, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='In summary, various types of models are useful to study how TCs change in response to climate changes, and there is no unique solution for choosing a model type. However, higher-resolution models generally capture TC properties more realistically (high confidence). In particular, models with horizontal resolutions of 10-60 km are capable of reproducing strong TCs with Category 4-5 and those of 1-10 km are capable of the eye wall structure of TCs. Uncertainties in TC simulations come from details of the model configuration of both dynamical and physical processes. Models with realistic atmosphere- ocean interactions are generally better than atmosphere-only models at reproducing realistic TC evolutions (high confidence).\\n 11.7.1.4 Detection and Attribution, Event Attribution '), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1230.0, 'num_tokens': 214.0, 'num_tokens_approx': 248.0, 'num_words': 186.0, 'page_number': 1169, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.7 Final Remarks', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': 'Acknowledgements', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.711966515, 'content': \"* An improvement of the general circulation model (GCM)- simulated precipitation, latent heating and radiative effects of deep convective clouds would benefit from an improved representation of their interactions with aerosols. * Further research on land surface processes, including groundwater recharge, the role of plant physiological changes, land use change, dams and irrigation, will improve future projections of key aspects of the terrestrial water cycle such as aridity and drought. * Ongoing efforts to develop higher-resolution 'convection permitting' regional or global climate models will lead to an improved simulation of clouds and precipitation, their coupling with boundary layer and surface processes, their diurnal cycle and high-frequency variability, and their response to climate change, including extreme precipitation events. * Further analysis of past and current climate variability alongside future climate change projections will provide physically understood constraints for improving the accuracy of regional water cycle simulations, adding value to the results obtained from global climate models. * Increased understanding of internal variability and interactions\", 'reranking_score': 0.2958179712295532, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"* An improvement of the general circulation model (GCM)- simulated precipitation, latent heating and radiative effects of deep convective clouds would benefit from an improved representation of their interactions with aerosols. * Further research on land surface processes, including groundwater recharge, the role of plant physiological changes, land use change, dams and irrigation, will improve future projections of key aspects of the terrestrial water cycle such as aridity and drought. * Ongoing efforts to develop higher-resolution 'convection permitting' regional or global climate models will lead to an improved simulation of clouds and precipitation, their coupling with boundary layer and surface processes, their diurnal cycle and high-frequency variability, and their response to climate change, including extreme precipitation events. * Further analysis of past and current climate variability alongside future climate change projections will provide physically understood constraints for improving the accuracy of regional water cycle simulations, adding value to the results obtained from global climate models. * Increased understanding of internal variability and interactions\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 983.0, 'num_tokens': 226.0, 'num_tokens_approx': 222.0, 'num_words': 167.0, 'page_number': 868, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '6.4 SLCF Radiative Forcing \\r\\nand Climate Effects', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '6: Short-lived Climate Forcers', 'toc_level1': '6.4 SLCF Radiative Forcing and\\xa0Climate\\xa0Effects', 'toc_level2': '6.4.1 Historical Estimates of Regional Short‑lived Climate Forcing ', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.70871, 'content': 'unclear what level of sophistication is required to properly quantify aerosol effects on climate (Boucher et al., 2013). Since the AR5, Ekman (2014) found that the CMIP5 models with the most complex representations of aerosol impacts on cloud microphysics had the largest reduction in biases in surface temperature trends. CMIP6-generation CCMs that simulate aerosol and cloud-size distributions better represent the effect of a volcanic eruption on lower atmosphere clouds than a model with aerosol-mass only (Malavelle et al., 2017). This highlights the need for skilful simulation of conditions underlying aerosol-cloud interactions, such as the distribution, transport and properties of aerosol species, in addition to the interactions themselves (Chapter 7). In advance of CMIP6, representations of aerosol processes and aerosol-cloud interactions in ESMs have generally become more comprehensive (Meehl et al., 2020; Gliss et al., 2021; Thornhill', 'reranking_score': 0.28701096773147583, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='unclear what level of sophistication is required to properly quantify aerosol effects on climate (Boucher et al., 2013). Since the AR5, Ekman (2014) found that the CMIP5 models with the most complex representations of aerosol impacts on cloud microphysics had the largest reduction in biases in surface temperature trends. CMIP6-generation CCMs that simulate aerosol and cloud-size distributions better represent the effect of a volcanic eruption on lower atmosphere clouds than a model with aerosol-mass only (Malavelle et al., 2017). This highlights the need for skilful simulation of conditions underlying aerosol-cloud interactions, such as the distribution, transport and properties of aerosol species, in addition to the interactions themselves (Chapter 7). In advance of CMIP6, representations of aerosol processes and aerosol-cloud interactions in ESMs have generally become more comprehensive (Meehl et al., 2020; Gliss et al., 2021; Thornhill'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 813.0, 'num_tokens': 154.0, 'num_tokens_approx': 189.0, 'num_words': 142.0, 'page_number': 532, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 3.1 | How Do We Know Humans Are Responsible for Climate Change?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '3: Human Influence on the Climate System', 'toc_level1': 'Frequently Asked Questions', 'toc_level2': 'FAQ 3.1 | How Do We Know Humans Are Responsible for Climate Change?', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.708666325, 'content': 'The current rates of increase of the concentration of the major greenhouse gases (carbon dioxide, methane and nitrous oxide) are unprecedented over at least the last 800,000 years. Several lines of evidence clearly show that these increases are the results of human activities. The basic physics underlying the warming effect of greenhouse gases on the climate has been understood for more than a century, and our current understanding has been used to develop the latest generation climate models (see FAQ 3.3). Like weather forecasting models, climate models represent the state of the atmosphere on a grid and simulate its evolution over time based on physical principles. They include a representation of the ocean, sea ice and the main processes important in driving climate and climate change.', 'reranking_score': 0.2726522982120514, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='The current rates of increase of the concentration of the major greenhouse gases (carbon dioxide, methane and nitrous oxide) are unprecedented over at least the last 800,000 years. Several lines of evidence clearly show that these increases are the results of human activities. The basic physics underlying the warming effect of greenhouse gases on the climate has been understood for more than a century, and our current understanding has been used to develop the latest generation climate models (see FAQ 3.3). Like weather forecasting models, climate models represent the state of the atmosphere on a grid and simulate its evolution over time based on physical principles. They include a representation of the ocean, sea ice and the main processes important in driving climate and climate change.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 927.0, 'num_tokens': 221.0, 'num_tokens_approx': 234.0, 'num_words': 176.0, 'page_number': 198, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '1.3.4 Lines of Evidence: Understanding \\r\\nand Attributing Climate Change', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '1: Framing, Context, and Methods', 'toc_level1': '1.3 How We Got Here: The Scientific Context', 'toc_level2': '1.3.4 Lines of Evidence: Understanding and\\xa0Attributing Climate Change', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.708651662, 'content': \"In the 1990s, AOGCMs were state of the art. By the 2010s, Earth system models (ESMs, also known as coupled carbon-cycle climate models) incorporated land surface, vegetation, the carbon cycle, and other elements of the climate system. Since the 1990s, some major modelling centres have deployed 'unified' models for both weather prediction and climate modelling, with the goal of a seamless modelling approach that uses the same dynamics, physics and parameterisations at multiple scales of time and space (Section 10.1.2; Cullen, 1993; Brown et al., 2012; NRC, 2012; WMO, 2015). Because weather forecast models make short-term predictions that can be frequently verified, and improved models are introduced and tested iteratively on cycles as short as 18 months, this approach allows major portions of the climate model to be evaluated as a weather model and more frequently improved. However, all\", 'reranking_score': 0.2214583456516266, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"In the 1990s, AOGCMs were state of the art. By the 2010s, Earth system models (ESMs, also known as coupled carbon-cycle climate models) incorporated land surface, vegetation, the carbon cycle, and other elements of the climate system. Since the 1990s, some major modelling centres have deployed 'unified' models for both weather prediction and climate modelling, with the goal of a seamless modelling approach that uses the same dynamics, physics and parameterisations at multiple scales of time and space (Section 10.1.2; Cullen, 1993; Brown et al., 2012; NRC, 2012; WMO, 2015). Because weather forecast models make short-term predictions that can be frequently verified, and improved models are introduced and tested iteratively on cycles as short as 18 months, this approach allows major portions of the climate model to be evaluated as a weather model and more frequently improved. However, all\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 946.0, 'num_tokens': 219.0, 'num_tokens_approx': 234.0, 'num_words': 176.0, 'page_number': 1610, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '11.7.2.2 Model Evaluation', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '11: Weather and Climate Extreme Events in a Changing Climate', 'toc_level1': '11.7 Extreme Storms', 'toc_level2': '11.7.2 Extratropical Storms', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.70859468, 'content': 'particularly those related to the release of latent heat (Willison et al., 2013; Trzeciak et al., 2016) and the formation of clouds (Govekar et al., 2014). There is medium confidence that climate models simulate well the spatial distribution of precipitation associated with ETCs over the Northern Hemisphere, together with some of the main features of the ETC life cycle, including the maximum in precipitation occurring just before the peak in dynamical intensity (e.g., vorticity) as observed in a reanalysis and observations (Hawcroft et al., 2018). There is, however, large observational uncertainty in ETC-associated precipitation (Hawcroft et al., 2018) and limitations in the simulation of frontal precipitation, including overly low rainfall intensity over mid-latitude oceanic areas in both hemispheres (Catto et al., 2015).\\n 11.7.2.3 Detection and Attribution, Event Attribution ', 'reranking_score': 0.14809398353099823, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='particularly those related to the release of latent heat (Willison et al., 2013; Trzeciak et al., 2016) and the formation of clouds (Govekar et al., 2014). There is medium confidence that climate models simulate well the spatial distribution of precipitation associated with ETCs over the Northern Hemisphere, together with some of the main features of the ETC life cycle, including the maximum in precipitation occurring just before the peak in dynamical intensity (e.g., vorticity) as observed in a reanalysis and observations (Hawcroft et al., 2018). There is, however, large observational uncertainty in ETC-associated precipitation (Hawcroft et al., 2018) and limitations in the simulation of frontal precipitation, including overly low rainfall intensity over mid-latitude oceanic areas in both hemispheres (Catto et al., 2015).\\n 11.7.2.3 Detection and Attribution, Event Attribution '), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 785.0, 'num_tokens': 183.0, 'num_tokens_approx': 196.0, 'num_words': 147.0, 'page_number': 852, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '6.3 Evolution of Atmospheric \\r\\nSLCF Abundances', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '6: Short-lived Climate Forcers', 'toc_level1': '6.3 Evolution of Atmospheric SLCF Abundances', 'toc_level2': '6.3.1 Methane (CH4)', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.708483577, 'content': 'However, caution is exercised as nudging can alter the model climate resulting in unintentional impacts on the simulated atmospheric physics and/or chemistry (Orbe et al., 2018; Chrysanthou et al., 2019). Chemical mechanisms implemented in CCMs are evaluated and intercompared to assess their skill in capturing relevant chemistry features (e.g., Brown-Steiner et al., 2018). The multi-model ensemble approach, employed for evaluating climate models, has been particularly useful for characterizing errors in CCM simulations of SLCFs related to structural uncertainty and internal variability (Naik et al., 2013; Shindell et al., 2013; Young et al., 2013; Turnock et al., 2020). However, as discussed in Box 4.1, this approach is unable to capture the full uncertainty range.', 'reranking_score': 0.1368158757686615, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='However, caution is exercised as nudging can alter the model climate resulting in unintentional impacts on the simulated atmospheric physics and/or chemistry (Orbe et al., 2018; Chrysanthou et al., 2019). Chemical mechanisms implemented in CCMs are evaluated and intercompared to assess their skill in capturing relevant chemistry features (e.g., Brown-Steiner et al., 2018). The multi-model ensemble approach, employed for evaluating climate models, has been particularly useful for characterizing errors in CCM simulations of SLCFs related to structural uncertainty and internal variability (Naik et al., 2013; Shindell et al., 2013; Young et al., 2013; Turnock et al., 2020). However, as discussed in Box 4.1, this approach is unable to capture the full uncertainty range.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 968.0, 'num_tokens': 211.0, 'num_tokens_approx': 217.0, 'num_words': 163.0, 'page_number': 238, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '1.5.4.2 Ensemble Modelling Techniques', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '1: Framing, Context, and Methods', 'toc_level1': '1.5 Major Developments and\\xa0Their Implications', 'toc_level2': '1.5.4 Modelling Techniques, Comparisons and\\xa0Performance Assessments', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.708310544, 'content': 'A key approach in climate science is the comparison of results from multiple model simulations with each other and against observations. These simulations have typically been performed by separate models with consistent boundary conditions and prescribed emissions or radiative forcings, as in the Coupled Model Intercomparison Project phases (CMIP, Meehl et al., 2000, 2007a; Taylor et al., 2012; Eyring et al., 2016). Such multi-model ensembles (MMEs) have proven highly useful in sampling and quantifying model uncertainty, within and between generations of climate models. They also reduce the influence on projections of the particular sets of parametrizations and physical components simulated by individual models. The primary usage of MMEs is to provide a well-quantified model range, but when used carefully they can also increase confidence in projections (Knutti et al., 2010). Presently, however, many models also share provenance', 'reranking_score': 0.13537892699241638, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='A key approach in climate science is the comparison of results from multiple model simulations with each other and against observations. These simulations have typically been performed by separate models with consistent boundary conditions and prescribed emissions or radiative forcings, as in the Coupled Model Intercomparison Project phases (CMIP, Meehl et al., 2000, 2007a; Taylor et al., 2012; Eyring et al., 2016). Such multi-model ensembles (MMEs) have proven highly useful in sampling and quantifying model uncertainty, within and between generations of climate models. They also reduce the influence on projections of the particular sets of parametrizations and physical components simulated by individual models. The primary usage of MMEs is to provide a well-quantified model range, but when used carefully they can also increase confidence in projections (Knutti et al., 2010). Presently, however, many models also share provenance'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 794.0, 'num_tokens': 225.0, 'num_tokens_approx': 212.0, 'num_words': 159.0, 'page_number': 1415, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '10.3.3.4.1 Convection including tropical cyclones', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '10: Linking Global to Regional Climate Change', 'toc_level1': '10.3 Using Models for Constructing Regional\\xa0Climate Information', 'toc_level2': '10.3.3 Model Performance and Added Value in Simulating and Projecting Regional Climate', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.708301127, 'content': '(Prein et al., 2015; Fumiere et al., 2020; Ban et al., 2021; Pichelli et al., 2021), the scaling of precipitation with temperature (Ban et al., 2014), cloud cover (Bohme et al., 2011; Langhans et al., 2013) and its resultant radiative effects (Stratton et al., 2018), as well as the annual cycle of tropical convection (Hart et al., 2018) are improved. Phenomena such as supercells, mesoscale convective systems, or the local weather associated with squall lines are not captured by global models and standard RCMs. Convection-permitting RCM simulations, however, have been shown to realistically simulate supercells (Trapp et al., 2011), mesoscale convective systems, their life cycle and motion (Prein et al., 2017; Crook et al., 2019), and heavy precipitation associated', 'reranking_score': 0.1303734928369522, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='(Prein et al., 2015; Fumiere et al., 2020; Ban et al., 2021; Pichelli et al., 2021), the scaling of precipitation with temperature (Ban et al., 2014), cloud cover (Bohme et al., 2011; Langhans et al., 2013) and its resultant radiative effects (Stratton et al., 2018), as well as the annual cycle of tropical convection (Hart et al., 2018) are improved. Phenomena such as supercells, mesoscale convective systems, or the local weather associated with squall lines are not captured by global models and standard RCMs. Convection-permitting RCM simulations, however, have been shown to realistically simulate supercells (Trapp et al., 2011), mesoscale convective systems, their life cycle and motion (Prein et al., 2017; Crook et al., 2019), and heavy precipitation associated'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 657.0, 'num_tokens': 213.0, 'num_tokens_approx': 225.0, 'num_words': 169.0, 'page_number': 281, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'References', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '1: Framing, Context, and Methods', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.707129598, 'content': 'CD-ROM and documentation. Bulletin of the American Meteorological Society, 74, 247-268, doi:10.1175/1520-0477(2001)082<0247:tnnyrm>2. 3.co;2. Klein, S.A. and A. Hall, 2015: Emergent Constraints for Cloud Feedbacks. Current Climate Change Reports, 1(4), 276-287, doi:10.1007/s40641-015-0027-1. Klein, S.A. et al., 2013: Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator. Journal of Geophysical Research: Atmospheres, 118(3), 1329-1342, doi:10.1002/jgrd.50141. Knutti, R., 2018: Climate Model Confirmation: From Philosophy to Predicting Climate in the Real World. In: Climate Modelling: Philosophical and', 'reranking_score': 0.1303734928369522, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='CD-ROM and documentation. Bulletin of the American Meteorological Society, 74, 247-268, doi:10.1175/1520-0477(2001)082<0247:tnnyrm>2. 3.co;2. Klein, S.A. and A. Hall, 2015: Emergent Constraints for Cloud Feedbacks. Current Climate Change Reports, 1(4), 276-287, doi:10.1007/s40641-015-0027-1. Klein, S.A. et al., 2013: Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator. Journal of Geophysical Research: Atmospheres, 118(3), 1329-1342, doi:10.1002/jgrd.50141. Knutti, R., 2018: Climate Model Confirmation: From Philosophy to Predicting Climate in the Real World. In: Climate Modelling: Philosophical and'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 644.0, 'num_tokens': 151.0, 'num_tokens_approx': 165.0, 'num_words': 124.0, 'page_number': 950, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.707094491, 'content': \"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\", 'reranking_score': 0.11457688361406326, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 556.0, 'num_tokens': 188.0, 'num_tokens_approx': 194.0, 'num_words': 146.0, 'page_number': 1483, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'References', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '10: Linking Global to Regional Climate Change', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.705982506, 'content': 'Europe. Climate Dynamics, 49(7-8), 2665-2683, doi:10.1007/s00382- 016-3471-2. Barton, N.P., S.A. Klein, J.S. Boyle, and Y.Y. Zhang, 2012: Arctic synoptic regimes: Comparing domain-wide Arctic cloud observations with CAM4 and CAM5 during similar dynamics. Journal of Geophysical Research: Atmospheres, 117(D15), D15205, doi:10.1029/2012jd017589. Bathiany, S., V. Dakos, M. Scheffer, and T.M. Lenton, 2018: Climate models predict increasing temperature variability in poor countries. Science Advances, 4(5), eaar5809, doi:10.1126/sciadv.aar5809.', 'reranking_score': 0.10800255835056305, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Europe. Climate Dynamics, 49(7-8), 2665-2683, doi:10.1007/s00382- 016-3471-2. Barton, N.P., S.A. Klein, J.S. Boyle, and Y.Y. Zhang, 2012: Arctic synoptic regimes: Comparing domain-wide Arctic cloud observations with CAM4 and CAM5 during similar dynamics. Journal of Geophysical Research: Atmospheres, 117(D15), D15205, doi:10.1029/2012jd017589. Bathiany, S., V. Dakos, M. Scheffer, and T.M. Lenton, 2018: Climate models predict increasing temperature variability in poor countries. Science Advances, 4(5), eaar5809, doi:10.1126/sciadv.aar5809.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 632.0, 'num_tokens': 228.0, 'num_tokens_approx': 220.0, 'num_words': 165.0, 'page_number': 2138, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'References', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex II: Models', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.705896199, 'content': 'Bao, Q. and B. He, 2019a: CAS FGOALS-f3-H model output prepared for CMIP6 HighResMIP. Earth System Grid Federation, doi:10.22033/esgf/cmip6.2041. Bao, Q. and B. He, 2019b: CAS FGOALS-f3-L model output prepared for CMIP6 HighResMIP. Earth System Grid Federation, doi:10.22033/esgf/ cmip6.12001. Bao, Y., Z. Song, and F. Qiao, 2020: FIO-ESM Version 2.0: Model Description and Evaluation. Journal of Geophysical Research: Oceans, 125(6), e2019JC016036, doi:10.1029/2019jc016036. Bauer, S.E. et al., 2020: Historical (1850-2014) Aerosol Evolution and Role on Climate Forcing Using the GISS ModelE2.1 Contribution to CMIP6.', 'reranking_score': 0.1048036590218544, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Bao, Q. and B. He, 2019a: CAS FGOALS-f3-H model output prepared for CMIP6 HighResMIP. Earth System Grid Federation, doi:10.22033/esgf/cmip6.2041. Bao, Q. and B. He, 2019b: CAS FGOALS-f3-L model output prepared for CMIP6 HighResMIP. Earth System Grid Federation, doi:10.22033/esgf/ cmip6.12001. Bao, Y., Z. Song, and F. Qiao, 2020: FIO-ESM Version 2.0: Model Description and Evaluation. Journal of Geophysical Research: Oceans, 125(6), e2019JC016036, doi:10.1029/2019jc016036. Bauer, S.E. et al., 2020: Historical (1850-2014) Aerosol Evolution and Role on Climate Forcing Using the GISS ModelE2.1 Contribution to CMIP6.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 212.0, 'num_tokens': 74.0, 'num_tokens_approx': 77.0, 'num_words': 58.0, 'page_number': 669, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'References', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '4: Future Global Climate: Scenario-based Projections and Near-term Information', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.70586729, 'content': 'Fasullo, J.T., B.M. Sanderson, and K.E. Trenberth, 2015: Recent Progress in Constraining Climate Sensitivity With Model Ensembles. Current Climate Change Reports, 1(4), 268-275, doi:10.1007/s40641-015-0021-7.', 'reranking_score': 0.099406398832798, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Fasullo, J.T., B.M. Sanderson, and K.E. Trenberth, 2015: Recent Progress in Constraining Climate Sensitivity With Model Ensembles. Current Climate Change Reports, 1(4), 268-275, doi:10.1007/s40641-015-0021-7.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 202.0, 'num_tokens': 73.0, 'num_tokens_approx': 66.0, 'num_words': 50.0, 'page_number': 952, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.2 Changes in Earth’s Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.705522418, 'content': 'often related to their representation of clouds (Trenberth and Fasullo, 2010; Donohoe and Battisti, 2012; Hwang and Frierson, 2013; J.-L.F. Li et al., 2013; Dolinar et al., 2015; Wild et al., 2015).', 'reranking_score': 0.08365446329116821, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='often related to their representation of clouds (Trenberth and Fasullo, 2010; Donohoe and Battisti, 2012; Hwang and Frierson, 2013; J.-L.F. Li et al., 2013; Dolinar et al., 2015; Wild et al., 2015).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document9', 'document_number': 9.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2258.0, 'name': 'Full Report. In: Climate Change 2022: Mitigation of Climate Change. Contribution of the WGIII to the AR6 of the IPCC', 'num_characters': 961.0, 'num_tokens': 217.0, 'num_tokens_approx': 236.0, 'num_words': 177.0, 'page_number': 1872, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'A.III.I.9.2.3 Climate System Component', 'short_name': 'IPCC AR6 WGIII FR', 'source': 'IPCC', 'toc_level0': 'Part I: Modelling Methods', 'toc_level1': 'A.III.I.9 Integrated Assessment Modelling', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_FullReport.pdf', 'similarity_score': 0.705187261, 'content': 'Reduced complexity climate models (often called simple climate models or emulators) are used for communicating WGI physical climate science knowledge to the research communities associated with other IPCC working groups (Annex III.I.8). They are used by IAMs to model the climate outcome of the multi-gas emissions trajectories that IAMs produce (van Vuuren et al. 2011a). A main application of such models is related to scenario classifications in WGIII (Clarke et al. 2014; Rogelj et al. 2018a). Since WGIII assesses a large number of scenarios, it must rely on the use of these simple climate models; more computationally demanding models (as used by WGI) will not be feasible to apply. For consistency across the AR6 reports, it is important that these reduced-complexity models are up to date with the latest assessments from WGI. This relies on calibrating these models so that they match, as closely as possible, the assessments', 'reranking_score': 0.06980902701616287, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Reduced complexity climate models (often called simple climate models or emulators) are used for communicating WGI physical climate science knowledge to the research communities associated with other IPCC working groups (Annex III.I.8). They are used by IAMs to model the climate outcome of the multi-gas emissions trajectories that IAMs produce (van Vuuren et al. 2011a). A main application of such models is related to scenario classifications in WGIII (Clarke et al. 2014; Rogelj et al. 2018a). Since WGIII assesses a large number of scenarios, it must rely on the use of these simple climate models; more computationally demanding models (as used by WGI) will not be feasible to apply. For consistency across the AR6 reports, it is important that these reduced-complexity models are up to date with the latest assessments from WGI. This relies on calibrating these models so that they match, as closely as possible, the assessments'), Document(metadata={'chunk_type': 'text', 'document_id': 'document19', 'document_number': 19.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 142.0, 'name': 'Chapter 5 - Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 888.0, 'num_tokens': 212.0, 'num_tokens_approx': 224.0, 'num_words': 168.0, 'page_number': 11, 'release_date': 2019.0, 'report_type': 'Special Report', 'section_header': '5.2.2.2 Changing Temperature, Salinity, Circulation', 'short_name': 'IPCC SR OC C5', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/07_SROCC_Ch05_FINAL.pdf', 'similarity_score': 0.704832613, 'content': 'To understand the recent and future climate, we use ensembles of coupled ocean-atmosphere-cryosphere-ecosystem models (ESMs) with the full-time history of atmospheric forcing (greenhouse gases, aerosols, solar radiation and volcanic eruptions) for the historical period and projections of the concentrations or emissions of these forcings to 2100. For these projections the RCPs of atmospheric emissions scenarios are used as specified by the Coupled Model Intercomparison Project, Phase 5 (CMIP5) (see Section 1.8.2.3, Cross-Chapter Box 1, and also IPCC AR5)3. This chapter focuses on the low and high emissions scenarios RCP2.6 and RCP8.5, respectively. When these scenarios are used to drive ESMs, it is possible to simulate the recent and future patterns of changes in the ocean temperature, salinity and circulation (and other oceanic properties such as ocean', 'reranking_score': 0.05601799488067627, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='To understand the recent and future climate, we use ensembles of coupled ocean-atmosphere-cryosphere-ecosystem models (ESMs) with the full-time history of atmospheric forcing (greenhouse gases, aerosols, solar radiation and volcanic eruptions) for the historical period and projections of the concentrations or emissions of these forcings to 2100. For these projections the RCPs of atmospheric emissions scenarios are used as specified by the Coupled Model Intercomparison Project, Phase 5 (CMIP5) (see Section 1.8.2.3, Cross-Chapter Box 1, and also IPCC AR5)3. This chapter focuses on the low and high emissions scenarios RCP2.6 and RCP8.5, respectively. When these scenarios are used to drive ESMs, it is possible to simulate the recent and future patterns of changes in the ocean temperature, salinity and circulation (and other oceanic properties such as ocean'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 773.0, 'num_tokens': 215.0, 'num_tokens_approx': 213.0, 'num_words': 160.0, 'page_number': 1604, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '11.7.1.3 Model Evaluation', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '11: Weather and Climate Extreme Events in a Changing Climate', 'toc_level1': '11.7 Extreme Storms', 'toc_level2': '11.7.1 Tropical Cyclones', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.704818606, 'content': \"Moon et al., 2020). Confidence in the projection of intense TCs, such as those of Category 4-5, generally becomes higher as the resolution of the models becomes higher. The Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5/6) class climate models (around 100-200 km grid spacing) cannot simulate TCs of Category 4-5 intensity. They do simulate storms of relatively high vorticity that are at best described as 'TC-like', but metrics such as storm counts are highly dependent on tracking algorithms (Camargo, 2013; Wehner et al., 2015; Zarzycki and Ullrich, 2017; Roberts et al., 2020a). High-resolution GCMs (around 10-60 km grid spacing), as used in HighResMIP (Haarsma et al., 2016; Roberts et al., 2020a), begin to capture some structures\", 'reranking_score': 0.054121967405080795, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"Moon et al., 2020). Confidence in the projection of intense TCs, such as those of Category 4-5, generally becomes higher as the resolution of the models becomes higher. The Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5/6) class climate models (around 100-200 km grid spacing) cannot simulate TCs of Category 4-5 intensity. They do simulate storms of relatively high vorticity that are at best described as 'TC-like', but metrics such as storm counts are highly dependent on tracking algorithms (Camargo, 2013; Wehner et al., 2015; Zarzycki and Ullrich, 2017; Roberts et al., 2020a). High-resolution GCMs (around 10-60 km grid spacing), as used in HighResMIP (Haarsma et al., 2016; Roberts et al., 2020a), begin to capture some structures\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document15', 'document_number': 15.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 60.0, 'name': 'Chapter 1 - Framing and Context of the Report. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1164.0, 'num_tokens': 229.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 38, 'release_date': 2019.0, 'report_type': 'Special Report', 'section_header': 'Cross-Chapter Box 5 (continued)', 'short_name': 'IPCC SR OC C1', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/03_SROCC_Ch01_FINAL.pdf', 'similarity_score': 0.704707205, 'content': \"The IPCC, and earlier assessments, encountered deep uncertainty when evaluating numerous aspects of the climate change problem. Examining these cases sheds light on approaches to quantifying and reducing deep uncertainty. An assessment by the US National Academy of Sciences (Charney et al., 1979; commonly referred to as the Charney Report) provides a classic example. Evaluating climate sensitivity to a doubling of carbon dioxide concentration, and developing a probability distribution for it, was challenging because only two 3-D climate models and a handful of model variants and realisations were available. The panel invoked three strategies to eliminate some of these simulations: (1) Using multiple lines of evidence to complement the limited model results; (2) estimating the consequences of poor or absent model representations of certain physical processes (particularly cumulus convection, high-altitude cloud formation, and non-cloud entrainment); and, (3) evaluating mismatches between model results and observations. This triage yielded 'probable bounds' of 2o C-3.5oC on climate sensitivity. The panel then invoked expert judgment\", 'reranking_score': 0.03134722635149956, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"The IPCC, and earlier assessments, encountered deep uncertainty when evaluating numerous aspects of the climate change problem. Examining these cases sheds light on approaches to quantifying and reducing deep uncertainty. An assessment by the US National Academy of Sciences (Charney et al., 1979; commonly referred to as the Charney Report) provides a classic example. Evaluating climate sensitivity to a doubling of carbon dioxide concentration, and developing a probability distribution for it, was challenging because only two 3-D climate models and a handful of model variants and realisations were available. The panel invoked three strategies to eliminate some of these simulations: (1) Using multiple lines of evidence to complement the limited model results; (2) estimating the consequences of poor or absent model representations of certain physical processes (particularly cumulus convection, high-altitude cloud formation, and non-cloud entrainment); and, (3) evaluating mismatches between model results and observations. This triage yielded 'probable bounds' of 2o C-3.5oC on climate sensitivity. The panel then invoked expert judgment\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 687.0, 'num_tokens': 206.0, 'num_tokens_approx': 213.0, 'num_words': 160.0, 'page_number': 2148, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Earth system model. Journal of Advances in Modeling Earth Systems, 5(2), \\r\\n173-194, doi:10.1002/jame.20016.', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex II: Models', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.704632223, 'content': 'HighResMIP. Earth System Grid Federation, doi:10.22033/esgf/cmip6.11003. Rong, X. et al., 2018: The CAMS Climate System Model and a Basic Evaluation of Its Climatology and Climate Variability Simulation. Journal of Meteorological Research, 32(6), 839-861, doi:10.1007/s13351-018-8058-x. Rotstayn, L.D. and U. Lohmann, 2002: Simulation of the tropospheric sulfur cycle in a global model with a physically based cloud scheme. Journal of Geophysical Research: Atmospheres, 107(D21), AAC 20-1-AAC 20-21, doi:10.1029/2002jd002128. Rotstayn, L.D. et al., 2011: Simulated enhancement of ENSO-related rainfall variability due to Australian dust. Atmospheric Chemistry and Physics,', 'reranking_score': 0.023412764072418213, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='HighResMIP. Earth System Grid Federation, doi:10.22033/esgf/cmip6.11003. Rong, X. et al., 2018: The CAMS Climate System Model and a Basic Evaluation of Its Climatology and Climate Variability Simulation. Journal of Meteorological Research, 32(6), 839-861, doi:10.1007/s13351-018-8058-x. Rotstayn, L.D. and U. Lohmann, 2002: Simulation of the tropospheric sulfur cycle in a global model with a physically based cloud scheme. Journal of Geophysical Research: Atmospheres, 107(D21), AAC 20-1-AAC 20-21, doi:10.1029/2002jd002128. Rotstayn, L.D. et al., 2011: Simulated enhancement of ENSO-related rainfall variability due to Australian dust. Atmospheric Chemistry and Physics,'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1103.0, 'num_tokens': 218.0, 'num_tokens_approx': 241.0, 'num_words': 181.0, 'page_number': 868, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '6.4 SLCF Radiative Forcing \\r\\nand Climate Effects', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '6: Short-lived Climate Forcers', 'toc_level1': '6.4 SLCF Radiative Forcing and\\xa0Climate\\xa0Effects', 'toc_level2': '6.4.1 Historical Estimates of Regional Short‑lived Climate Forcing ', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.704219759, 'content': 'In summary, CMIP6 models generally represent more processes that drive aerosol-cloud interactions than the previous generation of climate models, but there is only medium confidence that those enhancements improve their fitness for the purpose of simulating radiative forcing due to aerosol-cloud interactions because only a few studies have identified the level of sophistication required to do so. In addition, the challenge of representing the small-scale processes involved in aerosol-cloud interactions, and a lack of relevant model\\x02data comparisons, does not allow a quantitative assessment of the progress of the models from CMIP5 to CMIP6 in simulating the underlying conditions relevant for aerosol-cloud interactions at this time. \\n6.4.1 Historical Estimates of Regional Short-lived Climate Forcing \\nThe highly heterogeneous distribution of SLCF abundances (Section 6.3) translates to strong heterogeneity in the spatial pattern and temporal evolution of forcing and climate responses due to SLCFs. This section assesses the spatial patterns of the current forcing', 'reranking_score': 0.022220991551876068, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='In summary, CMIP6 models generally represent more processes that drive aerosol-cloud interactions than the previous generation of climate models, but there is only medium confidence that those enhancements improve their fitness for the purpose of simulating radiative forcing due to aerosol-cloud interactions because only a few studies have identified the level of sophistication required to do so. In addition, the challenge of representing the small-scale processes involved in aerosol-cloud interactions, and a lack of relevant model\\x02data comparisons, does not allow a quantitative assessment of the progress of the models from CMIP5 to CMIP6 in simulating the underlying conditions relevant for aerosol-cloud interactions at this time. \\n6.4.1 Historical Estimates of Regional Short-lived Climate Forcing \\nThe highly heterogeneous distribution of SLCF abundances (Section 6.3) translates to strong heterogeneity in the spatial pattern and temporal evolution of forcing and climate responses due to SLCFs. This section assesses the spatial patterns of the current forcing'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 906.0, 'num_tokens': 222.0, 'num_tokens_approx': 228.0, 'num_words': 171.0, 'page_number': 1004, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.4.1.2 Polar amplification from proxies and models during \\r\\npast climates associated with CO2 change', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.4 Relationship Between Feedbacks and\\xa0Temperature Patterns', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.703883529, 'content': 'For the MPWP, model simulations are now in better agreement with proxies than at the time of AR5 (Haywood et al., 2020; McClymont et al., 2020). In particular, in the tropics new proxy reconstructions of SSTs are warmer and in better agreement with the models, due in part to the narrower time window in the proxy reconstructions. There is also better agreement at higher latitudes (primarily in the North Atlantic), due in part to the absence of some very warm proxy SSTs due to the narrower time window (McClymont et al., 2020), and in part to a modified representation of Arctic gateways in the most recent Pliocene model simulations (Otto-Bliesner et al., 2017), which have resulted in warmer modelled SSTs in the North Atlantic (Haywood et al., 2020). Furthermore, as for the Eocene, improvements in the representation of aerosol-cloud interactions have also led to improved', 'reranking_score': 0.01930353231728077, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='For the MPWP, model simulations are now in better agreement with proxies than at the time of AR5 (Haywood et al., 2020; McClymont et al., 2020). In particular, in the tropics new proxy reconstructions of SSTs are warmer and in better agreement with the models, due in part to the narrower time window in the proxy reconstructions. There is also better agreement at higher latitudes (primarily in the North Atlantic), due in part to the absence of some very warm proxy SSTs due to the narrower time window (McClymont et al., 2020), and in part to a modified representation of Arctic gateways in the most recent Pliocene model simulations (Otto-Bliesner et al., 2017), which have resulted in warmer modelled SSTs in the North Atlantic (Haywood et al., 2020). Furthermore, as for the Eocene, improvements in the representation of aerosol-cloud interactions have also led to improved'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 296.0, 'num_tokens': 86.0, 'num_tokens_approx': 101.0, 'num_words': 76.0, 'page_number': 1422, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '10.3.3.9 Fitness of Climate Models for \\r\\nProjecting Regional Climate', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '10: Linking Global to Regional Climate Change', 'toc_level1': '10.3 Using Models for Constructing Regional\\xa0Climate Information', 'toc_level2': '10.3.3 Model Performance and Added Value in Simulating and Projecting Regional Climate', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.70223707, 'content': 'representing present and past climate (Sections 10.3.3.3-10.3.3.8) to the confidence in future projections (Section 1.3.5; Baumberger et al., 2017) and it is addressed in this subsection.\\n 10.3.3.9 Fitness of Climate Models for Projecting Regional Climate ', 'reranking_score': 0.01814422383904457, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='representing present and past climate (Sections 10.3.3.3-10.3.3.8) to the confidence in future projections (Section 1.3.5; Baumberger et al., 2017) and it is addressed in this subsection.\\n 10.3.3.9 Fitness of Climate Models for Projecting Regional Climate '), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 659.0, 'num_tokens': 195.0, 'num_tokens_approx': 206.0, 'num_words': 155.0, 'page_number': 2138, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'References', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex II: Models', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.702214837, 'content': 'Bi, D. et al., 2020: Configuration and spin-up of ACCESS-CM2, the new generation Australian Community Climate and Earth System Simulator Coupled Model. Journal of Southern Hemisphere Earth Systems Science, 70(1), 225, doi:10.1071/es19040. Bossing Christensen, O. et al., 2007: The HIRHAM Regional Climate Model. Version 5 (beta). Technical Report 06-17, Danish Climate Centre, Danish Meteorological Institute, Denmark, 22 pp., https://backend.orbit.dtu.dk/ ws/portalfiles/portal/51950450/HIRHAM.pdf. Boucher, O. et al., 2018a: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CFMIP. Earth System Grid Federation, doi:10.22033/esgf/cmip6.1522.', 'reranking_score': 0.01728619821369648, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Bi, D. et al., 2020: Configuration and spin-up of ACCESS-CM2, the new generation Australian Community Climate and Earth System Simulator Coupled Model. Journal of Southern Hemisphere Earth Systems Science, 70(1), 225, doi:10.1071/es19040. Bossing Christensen, O. et al., 2007: The HIRHAM Regional Climate Model. Version 5 (beta). Technical Report 06-17, Danish Climate Centre, Danish Meteorological Institute, Denmark, 22 pp., https://backend.orbit.dtu.dk/ ws/portalfiles/portal/51950450/HIRHAM.pdf. Boucher, O. et al., 2018a: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CFMIP. Earth System Grid Federation, doi:10.22033/esgf/cmip6.1522.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 929.0, 'num_tokens': 220.0, 'num_tokens_approx': 224.0, 'num_words': 168.0, 'page_number': 1026, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.7 Processes Underlying Uncertainty in the Global\\xa0Temperature Response to Forcing', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.701975107, 'content': 'In summary, the distribution of CMIP6 models have higher average ECS and TCR values than the CMIP5 generation of models and the assessed values of ECS and TCR in Section 7.5.5. The high ECS and TCR values can in some CMIP6 models be traced to improved representation of extratropical cloud feedbacks (medium confidence). The ranges of ECS and TCR from the CMIP6 models are not considered robust samples of possible values and the models are not considered a separate line of evidence for ECS and TCR. Solely based on its ECS or TCR values an individual ESM cannot be ruled out as implausible, though some models with high (greater than 5degC) and low (less than 2degC) ECS are less consistent with past climate change (high confidence). High climate sensitivity in models leads to generally higher projected warming in CMIP6 compared to \\nboth CMIP5 and that assessed based on multiple lines of evidence', 'reranking_score': 0.016032056882977486, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='In summary, the distribution of CMIP6 models have higher average ECS and TCR values than the CMIP5 generation of models and the assessed values of ECS and TCR in Section 7.5.5. The high ECS and TCR values can in some CMIP6 models be traced to improved representation of extratropical cloud feedbacks (medium confidence). The ranges of ECS and TCR from the CMIP6 models are not considered robust samples of possible values and the models are not considered a separate line of evidence for ECS and TCR. Solely based on its ECS or TCR values an individual ESM cannot be ruled out as implausible, though some models with high (greater than 5degC) and low (less than 2degC) ECS are less consistent with past climate change (high confidence). High climate sensitivity in models leads to generally higher projected warming in CMIP6 compared to \\nboth CMIP5 and that assessed based on multiple lines of evidence'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1136.0, 'num_tokens': 252.0, 'num_tokens_approx': 277.0, 'num_words': 208.0, 'page_number': 232, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '1.5.3.1 Earth System Models', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '1: Framing, Context, and Methods', 'toc_level1': '1.5 Major Developments and\\xa0Their Implications', 'toc_level2': '1.5.3 Climate Models', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.701476038, 'content': 'Earth system models are mathematical formulations of the natural laws that govern the evolution of climate-relevant systems: atmosphere, ocean, cryosphere, land, and biosphere, as well as the carbon cycle (Flato, 2011). They build on the fundamental laws of physics (e.g., Navier-Stokes or Clausius-Clapeyron equations) or empirical relationships established from observations and, when possible, they are constrained by fundamental conservation laws (e.g., mass and energy). The evolution of climate-relevant variables is computed numerically using high-performance computers (Andre et al., 2014; Balaji et al., 2017), on three-dimensional discrete grids (Staniforth and Thuburn, 2012). The spatial (and temporal) resolution of these grids in both the horizontal and vertical directions determines which processes need to be parameterized or whether they can be explicitly resolved. Developments since AR5 in model resolution, parameterizations and modelling of the land and ocean biosphere and of biogeochemical cycles are discussed below.\\n 1.5.3.1 Earth System Models ', 'reranking_score': 0.015564106404781342, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Earth system models are mathematical formulations of the natural laws that govern the evolution of climate-relevant systems: atmosphere, ocean, cryosphere, land, and biosphere, as well as the carbon cycle (Flato, 2011). They build on the fundamental laws of physics (e.g., Navier-Stokes or Clausius-Clapeyron equations) or empirical relationships established from observations and, when possible, they are constrained by fundamental conservation laws (e.g., mass and energy). The evolution of climate-relevant variables is computed numerically using high-performance computers (Andre et al., 2014; Balaji et al., 2017), on three-dimensional discrete grids (Staniforth and Thuburn, 2012). The spatial (and temporal) resolution of these grids in both the horizontal and vertical directions determines which processes need to be parameterized or whether they can be explicitly resolved. Developments since AR5 in model resolution, parameterizations and modelling of the land and ocean biosphere and of biogeochemical cycles are discussed below.\\n 1.5.3.1 Earth System Models '), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 755.0, 'num_tokens': 230.0, 'num_tokens_approx': 236.0, 'num_words': 177.0, 'page_number': 1045, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Bretherton, C.S., 2015: Insights into low-latitude cloud feedbacks from \\r\\nhigh-resolution models. Philosophical Transactions of the Royal Society A: \\r\\nMathematical, Physical and Engineering Sciences, 373(2054), doi:10.1098/\\r\\nrsta.2014.0415.', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'References', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.70117867, 'content': 'Bretherton, C.S., 2015: Insights into low-latitude cloud feedbacks from high-resolution models. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2054), doi:10.1098/ rsta.2014.0415. Bretherton, C.S., P.N. Blossey, and C.R. Jones, 2013: Mechanisms of marine low cloud sensitivity to idealized climate perturbations: A single-LES exploration extending the CGILS cases. Journal of Advances in Modeling Earth Systems, 5(2), 316-337, doi:10.1002/jame.20019. Bretherton, C.S., P.N. Blossey, and C. Stan, 2014: Cloud feedbacks on greenhouse warming in the superparameterized climate model SP-CCSM4. Journal of Advances in Modeling Earth Systems, 6(4), 1185-1204, doi:10.1002/ 2014ms000355.', 'reranking_score': 0.012862779200077057, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Bretherton, C.S., 2015: Insights into low-latitude cloud feedbacks from high-resolution models. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2054), doi:10.1098/ rsta.2014.0415. Bretherton, C.S., P.N. Blossey, and C.R. Jones, 2013: Mechanisms of marine low cloud sensitivity to idealized climate perturbations: A single-LES exploration extending the CGILS cases. Journal of Advances in Modeling Earth Systems, 5(2), 316-337, doi:10.1002/jame.20019. Bretherton, C.S., P.N. Blossey, and C. Stan, 2014: Cloud feedbacks on greenhouse warming in the superparameterized climate model SP-CCSM4. Journal of Advances in Modeling Earth Systems, 6(4), 1185-1204, doi:10.1002/ 2014ms000355.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 888.0, 'num_tokens': 214.0, 'num_tokens_approx': 221.0, 'num_words': 166.0, 'page_number': 1416, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '10.3.3.4.1 Convection including tropical cyclones', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '10: Linking Global to Regional Climate Change', 'toc_level1': '10.3 Using Models for Constructing Regional\\xa0Climate Information', 'toc_level2': '10.3.3 Model Performance and Added Value in Simulating and Projecting Regional Climate', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.700980544, 'content': 'Initial studies with convection-permitting global models suggests that improvements in representing convection, as described for RCMs above, have a positive impact on the tropical and extratropical atmospheric circulation and, thus, regional climate (Satoh et al., 2019; Stevens et al., 2019; see also Section 8.5.1.2 and Chapter 7). Computational constraints currently limit these simulations to a length of few months only, such that they cannot yet be used for routine climate change studies.\\n4 km resolution showed good skill in simulating the diurnal cycle of temperature and wind on days of weak synoptic forcing in the Rocky Mountains (Letcher and Minder, 2017) as well as in simulating the mountain-plain wind circulation over the Tianshan mountains in central Asia (Cai et al., 2019), while in the Alps, a 1 km resolution has been required (Zangl, 2004).', 'reranking_score': 0.012862779200077057, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Initial studies with convection-permitting global models suggests that improvements in representing convection, as described for RCMs above, have a positive impact on the tropical and extratropical atmospheric circulation and, thus, regional climate (Satoh et al., 2019; Stevens et al., 2019; see also Section 8.5.1.2 and Chapter 7). Computational constraints currently limit these simulations to a length of few months only, such that they cannot yet be used for routine climate change studies.\\n4 km resolution showed good skill in simulating the diurnal cycle of temperature and wind on days of weak synoptic forcing in the Rocky Mountains (Letcher and Minder, 2017) as well as in simulating the mountain-plain wind circulation over the Tianshan mountains in central Asia (Cai et al., 2019), while in the Alps, a 1 km resolution has been required (Zangl, 2004).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 724.0, 'num_tokens': 225.0, 'num_tokens_approx': 216.0, 'num_words': 162.0, 'page_number': 1069, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': \"The Earth's Energy Budget, Climate Feedbacks and Climate Sensitivity\", 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'References', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.700858414, 'content': 'and carbon cycling. Environmental Research Letters, 15(9), 0940c1, doi:10.1088/1748-9326/ab97c9. Wing, A.A. and K.A. Emanuel, 2014: Physical mechanisms controlling self-aggregation of convection in idealized numerical modeling simulations. Journal of Advances in Modeling Earth Systems, 6(1), 59-74, doi:10.1002/2013ms000269. Wing, A.A. et al., 2020: Clouds and Convective Self-Aggregation in a Multimodel Ensemble of Radiative-Convective Equilibrium Simulations. Journal of Advances in Modeling Earth Systems, 12(9), e2020MS002138, doi:10.1029/2020ms002138. Winguth, A., C. Shellito, C. Shields, and C. Winguth, 2010: Climate Response at the Paleocene-Eocene Thermal Maximum to Greenhouse Gas Forcing -', 'reranking_score': 0.010783408768475056, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='and carbon cycling. Environmental Research Letters, 15(9), 0940c1, doi:10.1088/1748-9326/ab97c9. Wing, A.A. and K.A. Emanuel, 2014: Physical mechanisms controlling self-aggregation of convection in idealized numerical modeling simulations. Journal of Advances in Modeling Earth Systems, 6(1), 59-74, doi:10.1002/2013ms000269. Wing, A.A. et al., 2020: Clouds and Convective Self-Aggregation in a Multimodel Ensemble of Radiative-Convective Equilibrium Simulations. Journal of Advances in Modeling Earth Systems, 12(9), e2020MS002138, doi:10.1029/2020ms002138. Winguth, A., C. Shellito, C. Shields, and C. Winguth, 2010: Climate Response at the Paleocene-Eocene Thermal Maximum to Greenhouse Gas Forcing -'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1017.0, 'num_tokens': 223.0, 'num_tokens_approx': 258.0, 'num_words': 194.0, 'page_number': 112, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'TS.3.2.2 Earth System Feedbacks', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Technical Summary', 'toc_level1': 'TS.3 Understanding the Climate System Response and Implications for Limiting Global Warming', 'toc_level2': 'TS.3.2 Climate Sensitivity and Earth System Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.700683236, 'content': 'The net effect of changes in clouds in response to global warming is to amplify human-induced warming, that is, the net cloud feedback is positive (high confidence). Compared to AR5, major advances in the understanding of cloud processes have increased the level of confidence and decreased the uncertainty range in the cloud feedback by about 50% (Figure TS.17a). An assessment of the low\\x02altitude cloud feedback over the subtropical ocean, which was previously the major source of uncertainty in the net cloud feedback, is improved owing to a combined use of climate model simulations, satellite observations, and explicit simulations of clouds, altogether leading to strong evidence that this type of cloud amplifies global warming. The net cloud feedback is assessed to be +0.42 [-0.10 to 0.94] W m-2 degC-1. A net negative cloud feedback is very unlikely. The CMIP5 and CMIP6 ranges of cloud feedback are similar to this assessed range, with CMIP6 having a slightly more positive median', 'reranking_score': 0.01039742212742567, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='The net effect of changes in clouds in response to global warming is to amplify human-induced warming, that is, the net cloud feedback is positive (high confidence). Compared to AR5, major advances in the understanding of cloud processes have increased the level of confidence and decreased the uncertainty range in the cloud feedback by about 50% (Figure TS.17a). An assessment of the low\\x02altitude cloud feedback over the subtropical ocean, which was previously the major source of uncertainty in the net cloud feedback, is improved owing to a combined use of climate model simulations, satellite observations, and explicit simulations of clouds, altogether leading to strong evidence that this type of cloud amplifies global warming. The net cloud feedback is assessed to be +0.42 [-0.10 to 0.94] W m-2 degC-1. A net negative cloud feedback is very unlikely. The CMIP5 and CMIP6 ranges of cloud feedback are similar to this assessed range, with CMIP6 having a slightly more positive median'), Document(metadata={'chunk_type': 'text', 'document_id': 'document3', 'document_number': 3.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 112.0, 'name': 'Technical Summary. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1017.0, 'num_tokens': 223.0, 'num_tokens_approx': 258.0, 'num_words': 194.0, 'page_number': 63, 'release_date': 2021.0, 'report_type': 'TS', 'section_header': 'TS.3.2.2 Earth System Feedbacks', 'short_name': 'IPCC AR6 WGI TS', 'source': 'IPCC', 'toc_level0': 'TS.3 Understanding the Climate System Response and Implications for Limiting Global Warming', 'toc_level1': 'TS.3.2 Climate Sensitivity and Earth System Feedbacks', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf', 'similarity_score': 0.700683236, 'content': 'The net effect of changes in clouds in response to global warming is to amplify human-induced warming, that is, the net cloud feedback is positive (high confidence). Compared to AR5, major advances in the understanding of cloud processes have increased the level of confidence and decreased the uncertainty range in the cloud feedback by about 50% (Figure TS.17a). An assessment of the low\\x02altitude cloud feedback over the subtropical ocean, which was previously the major source of uncertainty in the net cloud feedback, is improved owing to a combined use of climate model simulations, satellite observations, and explicit simulations of clouds, altogether leading to strong evidence that this type of cloud amplifies global warming. The net cloud feedback is assessed to be +0.42 [-0.10 to 0.94] W m-2 degC-1. A net negative cloud feedback is very unlikely. The CMIP5 and CMIP6 ranges of cloud feedback are similar to this assessed range, with CMIP6 having a slightly more positive median', 'reranking_score': 0.008927966468036175, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='The net effect of changes in clouds in response to global warming is to amplify human-induced warming, that is, the net cloud feedback is positive (high confidence). Compared to AR5, major advances in the understanding of cloud processes have increased the level of confidence and decreased the uncertainty range in the cloud feedback by about 50% (Figure TS.17a). An assessment of the low\\x02altitude cloud feedback over the subtropical ocean, which was previously the major source of uncertainty in the net cloud feedback, is improved owing to a combined use of climate model simulations, satellite observations, and explicit simulations of clouds, altogether leading to strong evidence that this type of cloud amplifies global warming. The net cloud feedback is assessed to be +0.42 [-0.10 to 0.94] W m-2 degC-1. A net negative cloud feedback is very unlikely. The CMIP5 and CMIP6 ranges of cloud feedback are similar to this assessed range, with CMIP6 having a slightly more positive median'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 568.0, 'num_tokens': 179.0, 'num_tokens_approx': 170.0, 'num_words': 128.0, 'page_number': 1067, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': \"The Earth's Energy Budget, Climate Feedbacks and Climate Sensitivity\", 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'References', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.700672448, 'content': 'a perturbed parameter ensemble. Climate Dynamics, 55(5-6), 1159-1185, doi:10.1007/s00382-020-05318-y. Tsushima, Y. et al., 2014: High cloud increase in a perturbed SST experiment with a global nonhydrostatic model including explicit convective processes. Journal of Advances in Modeling Earth Systems, 6(3), 571-585, doi:10.1002/2013ms000301. Tsutsui, J., 2020: Diagnosing Transient Response to CO2 Forcing in Coupled Atmosphere-Ocean Model Experiments Using a Climate Model Emulator. Geophysical Research Letters, 47(7), 1-12, doi:10.1029/2019gl085844.', 'reranking_score': 0.008603464812040329, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='a perturbed parameter ensemble. Climate Dynamics, 55(5-6), 1159-1185, doi:10.1007/s00382-020-05318-y. Tsushima, Y. et al., 2014: High cloud increase in a perturbed SST experiment with a global nonhydrostatic model including explicit convective processes. Journal of Advances in Modeling Earth Systems, 6(3), 571-585, doi:10.1002/2013ms000301. Tsutsui, J., 2020: Diagnosing Transient Response to CO2 Forcing in Coupled Atmosphere-Ocean Model Experiments Using a Climate Model Emulator. Geophysical Research Letters, 47(7), 1-12, doi:10.1029/2019gl085844.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 605.0, 'num_tokens': 218.0, 'num_tokens_approx': 208.0, 'num_words': 156.0, 'page_number': 2138, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'References', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex II: Models', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.69993037, 'content': 'doi:10.1029/2011jf002064. Anderson, J.L. et al., 2004: The New GFDL Global Atmosphere and Land Model AM2-LM2: Evaluation with Prescribed SST Simulations. Journal of Climate, 17(24), 4641-4673, doi:10.1175/jcli-3223.1. Andrews, T., 2019: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 RFMIP. Earth System Grid Federation, doi:10.22033/esgf/cmip6.475. Archibald, A.T. et al., 2020: Description and evaluation of the UKCA stratosphere-troposphere chemistry scheme (StratTrop vn 1.0) implemented in UKESM1. Geoscientific Model Development, 13(3), 1223- 1266, doi:10.5194/gmd-13-1223-2020.', 'reranking_score': 0.008394444361329079, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='doi:10.1029/2011jf002064. Anderson, J.L. et al., 2004: The New GFDL Global Atmosphere and Land Model AM2-LM2: Evaluation with Prescribed SST Simulations. Journal of Climate, 17(24), 4641-4673, doi:10.1175/jcli-3223.1. Andrews, T., 2019: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 RFMIP. Earth System Grid Federation, doi:10.22033/esgf/cmip6.475. Archibald, A.T. et al., 2020: Description and evaluation of the UKCA stratosphere-troposphere chemistry scheme (StratTrop vn 1.0) implemented in UKESM1. Geoscientific Model Development, 13(3), 1223- 1266, doi:10.5194/gmd-13-1223-2020.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1090.0, 'num_tokens': 212.0, 'num_tokens_approx': 248.0, 'num_words': 186.0, 'page_number': 1405, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '10.3.1 Model Types', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '10: Linking Global to Regional Climate Change', 'toc_level1': '10.3 Using Models for Constructing Regional\\xa0Climate Information', 'toc_level2': '10.3.1 Model Types', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.699837744, 'content': 'Regional climate change information may be derived from a hierarchy of different model types covering a wide range of spatial scales and processes (Figure 10.5). The application of any model relies on assumptions, depending on the specific model as well as the application. Table 10.1 gives an overview of the generic assumptions of the different model types discussed here for generating regional climate information. The violation of these assumptions will affect the model performance, which is discussed in Section 10.3.3.\\nOne of the main scientific challenges related to high-resolution regional climate modelling is dealing with the representation of fine-scale processes (e.g., Yano et al., 2018) in observational datasets. Additionally, reliable observation networks following WMO standards have a very sparse geographical representation. Hence, regional climate models have started to use high-resolution data combined with crowdsourced observations (Zheng et al., 2018). Recent efforts have led to the production of homogeneously processed long-term', 'reranking_score': 0.008136891759932041, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Regional climate change information may be derived from a hierarchy of different model types covering a wide range of spatial scales and processes (Figure 10.5). The application of any model relies on assumptions, depending on the specific model as well as the application. Table 10.1 gives an overview of the generic assumptions of the different model types discussed here for generating regional climate information. The violation of these assumptions will affect the model performance, which is discussed in Section 10.3.3.\\nOne of the main scientific challenges related to high-resolution regional climate modelling is dealing with the representation of fine-scale processes (e.g., Yano et al., 2018) in observational datasets. Additionally, reliable observation networks following WMO standards have a very sparse geographical representation. Hence, regional climate models have started to use high-resolution data combined with crowdsourced observations (Zheng et al., 2018). Recent efforts have led to the production of homogeneously processed long-term'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 565.0, 'num_tokens': 132.0, 'num_tokens_approx': 141.0, 'num_words': 106.0, 'page_number': 232, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '1.5.3 Climate Models', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '1: Framing, Context, and Methods', 'toc_level1': '1.5 Major Developments and\\xa0Their Implications', 'toc_level2': '1.5.3 Climate Models', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.699118853, 'content': 'This section summarizes major developments in these different types of models since AR5. Past IPCC reports have made use of multi-model ensembles generated through various phases of the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project (CMIP). Analysis of the latest CMIP Phase 6 (CMIP6; Eyring et al., 2016) simulations constitute a key line of evidence supporting this Assessment Report (Section 1.5.4). The key characteristics of models participating in CMIP6 are listed in Annex II: Models.\\n1.5.3.1 Earth System Models', 'reranking_score': 0.007757794111967087, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='This section summarizes major developments in these different types of models since AR5. Past IPCC reports have made use of multi-model ensembles generated through various phases of the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project (CMIP). Analysis of the latest CMIP Phase 6 (CMIP6; Eyring et al., 2016) simulations constitute a key line of evidence supporting this Assessment Report (Section 1.5.4). The key characteristics of models participating in CMIP6 are listed in Annex II: Models.\\n1.5.3.1 Earth System Models'), Document(metadata={'chunk_type': 'text', 'document_id': 'document16', 'document_number': 16.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 72.0, 'name': 'Chapter 2 - High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1007.0, 'num_tokens': 215.0, 'num_tokens_approx': 241.0, 'num_words': 181.0, 'page_number': 10, 'release_date': 2019.0, 'report_type': 'Special Report', 'section_header': 'High Mountain Areas', 'short_name': 'IPCC SR OC C2', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/04_SROCC_Ch02_FINAL.pdf', 'similarity_score': 0.699032426, 'content': 'Projected changes of mountain snow cover are studied based on climate model experiments, either directly from GCM or RCM output, or following downscaling and the use of snowpack models. These projections generally do not specifically account for future changes in the deposition rate of light absorbing particles on snow (or, if so, simple approaches have been used hitherto; e.g., Deems et al., 2013), so that future changes in snow conditions are mostly driven by changes in meteorological drivers assessed in Section 2.2.1. Evidence from regional studies is provided in Table SM2.7. Although existing studies in mountain regions do not use homogenous reference periods and model configurations, common future trends can be summarised as follows. At lower elevation in many regions such as the European Alps, Western North America, Himalaya and subtropical Andes, the snow depth or mass is projected to decline by 25% (likely range between 10 and 40%), between the recent past', 'reranking_score': 0.0070152487605810165, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Projected changes of mountain snow cover are studied based on climate model experiments, either directly from GCM or RCM output, or following downscaling and the use of snowpack models. These projections generally do not specifically account for future changes in the deposition rate of light absorbing particles on snow (or, if so, simple approaches have been used hitherto; e.g., Deems et al., 2013), so that future changes in snow conditions are mostly driven by changes in meteorological drivers assessed in Section 2.2.1. Evidence from regional studies is provided in Table SM2.7. Although existing studies in mountain regions do not use homogenous reference periods and model configurations, common future trends can be summarised as follows. At lower elevation in many regions such as the European Alps, Western North America, Himalaya and subtropical Andes, the snow depth or mass is projected to decline by 25% (likely range between 10 and 40%), between the recent past'), Document(metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 742.0, 'num_tokens': 221.0, 'num_tokens_approx': 233.0, 'num_words': 175.0, 'page_number': 2546, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'Seneviratne, S.I., et al., 2018b: Land radiative management as contributor to \\r\\nregional-scale climate adaptation and mitigation, 88-96.', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Chapters and Cross-Chapter Papers ', 'toc_level1': 'Chapter 16 Key Risks across Sectors and Regions', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.69889611, 'content': 'Stjern, C.W., et al., 2018: Response to marine cloud brightening in a multi\\x02model ensemble. Atmos. Chem. Phys., 18(2), 621-634.\\nStoerk, T., G. Wagner and R.E. Ward, 2018: Policy brief--Recommendations for improving the treatment of risk and uncertainty in economic estimates of climate impacts in the sixth Intergovernmental Panel on Climate Change assessment report. Rev. Environ. Econ. Policy, 12(2), 371-376.\\nStorelvmo, T. and N. Herger, 2014: Cirrus cloud susceptibility to the injection of ice nuclei in the upper troposphere. J. Geophys. Res. Atmos., 119(5), 2375-2389. Storlazzi, C.D., et al., 2018: Most atolls will be uninhabitable by the mid-21st century because of sea-level rise exacerbating wave-driven flooding. Sci.', 'reranking_score': 0.00682080490514636, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Stjern, C.W., et al., 2018: Response to marine cloud brightening in a multi\\x02model ensemble. Atmos. Chem. Phys., 18(2), 621-634.\\nStoerk, T., G. Wagner and R.E. Ward, 2018: Policy brief--Recommendations for improving the treatment of risk and uncertainty in economic estimates of climate impacts in the sixth Intergovernmental Panel on Climate Change assessment report. Rev. Environ. Econ. Policy, 12(2), 371-376.\\nStorelvmo, T. and N. Herger, 2014: Cirrus cloud susceptibility to the injection of ice nuclei in the upper troposphere. J. Geophys. Res. Atmos., 119(5), 2375-2389. Storlazzi, C.D., et al., 2018: Most atolls will be uninhabitable by the mid-21st century because of sea-level rise exacerbating wave-driven flooding. Sci.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document3', 'document_number': 3.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 112.0, 'name': 'Technical Summary. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1099.0, 'num_tokens': 224.0, 'num_tokens_approx': 241.0, 'num_words': 181.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'TS', 'section_header': 'TS.1.2.2 Climate Model Performance', 'short_name': 'IPCC AR6 WGI TS', 'source': 'IPCC', 'toc_level0': 'TS.1 A Changing Climate', 'toc_level1': 'TS.1.2 Progress in Climate Science', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf', 'similarity_score': 0.698856175, 'content': 'Two important quantities used to estimate how the climate system responds to changes in greenhouse gas (GHG) concentrations are the equilibrium climate sensitivity (ECS) and transient climate response (TCR16). The CMIP6 ensemble has broader ranges of ECS and TCR values than CMIP5 (see Section TS.3.2 for the assessed range). These higher sensitivity values can, in some models, be traced to changes in extratropical cloud feedbacks (medium confidence). To combine evidence from CMIP6 models and independent assessments of ECS and TCR, various emulators are used throughout the report. Emulators are a broad class of simple climate models or statistical methods that reproduce the behaviour of complex ESMs to represent key characteristics of the climate system, such as global surface temperature and sea level projections. The main application of emulators in AR6 is to extrapolate insights from ESMs and observational constraints to produce projections from a larger set of emissions scenarios, which is achieved due to their computational efficiency. These emulated', 'reranking_score': 0.006720034405589104, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Two important quantities used to estimate how the climate system responds to changes in greenhouse gas (GHG) concentrations are the equilibrium climate sensitivity (ECS) and transient climate response (TCR16). The CMIP6 ensemble has broader ranges of ECS and TCR values than CMIP5 (see Section TS.3.2 for the assessed range). These higher sensitivity values can, in some models, be traced to changes in extratropical cloud feedbacks (medium confidence). To combine evidence from CMIP6 models and independent assessments of ECS and TCR, various emulators are used throughout the report. Emulators are a broad class of simple climate models or statistical methods that reproduce the behaviour of complex ESMs to represent key characteristics of the climate system, such as global surface temperature and sea level projections. The main application of emulators in AR6 is to extrapolate insights from ESMs and observational constraints to produce projections from a larger set of emissions scenarios, which is achieved due to their computational efficiency. These emulated'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1099.0, 'num_tokens': 224.0, 'num_tokens_approx': 241.0, 'num_words': 181.0, 'page_number': 66, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'TS.1.2.2 Climate Model Performance', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Technical Summary', 'toc_level1': 'TS.1 A Changing Climate', 'toc_level2': 'TS.1.2 Progress in Climate Science', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.698856175, 'content': 'Two important quantities used to estimate how the climate system responds to changes in greenhouse gas (GHG) concentrations are the equilibrium climate sensitivity (ECS) and transient climate response (TCR16). The CMIP6 ensemble has broader ranges of ECS and TCR values than CMIP5 (see Section TS.3.2 for the assessed range). These higher sensitivity values can, in some models, be traced to changes in extratropical cloud feedbacks (medium confidence). To combine evidence from CMIP6 models and independent assessments of ECS and TCR, various emulators are used throughout the report. Emulators are a broad class of simple climate models or statistical methods that reproduce the behaviour of complex ESMs to represent key characteristics of the climate system, such as global surface temperature and sea level projections. The main application of emulators in AR6 is to extrapolate insights from ESMs and observational constraints to produce projections from a larger set of emissions scenarios, which is achieved due to their computational efficiency. These emulated', 'reranking_score': 0.0065247551538050175, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Two important quantities used to estimate how the climate system responds to changes in greenhouse gas (GHG) concentrations are the equilibrium climate sensitivity (ECS) and transient climate response (TCR16). The CMIP6 ensemble has broader ranges of ECS and TCR values than CMIP5 (see Section TS.3.2 for the assessed range). These higher sensitivity values can, in some models, be traced to changes in extratropical cloud feedbacks (medium confidence). To combine evidence from CMIP6 models and independent assessments of ECS and TCR, various emulators are used throughout the report. Emulators are a broad class of simple climate models or statistical methods that reproduce the behaviour of complex ESMs to represent key characteristics of the climate system, such as global surface temperature and sea level projections. The main application of emulators in AR6 is to extrapolate insights from ESMs and observational constraints to produce projections from a larger set of emissions scenarios, which is achieved due to their computational efficiency. These emulated'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 848.0, 'num_tokens': 223.0, 'num_tokens_approx': 241.0, 'num_words': 181.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.698842824, 'content': 'given for CMIP6 models in Supplementary Material 7.SM.4 based on Schlund et al. (2020) for ECS and Meehl et al. (2020) for TCR (see also Figure 7.18 and FAQ 7.3). The upward shift does not apply to all models traceable to specific modelling centres, but a substantial subset of models have seen an increase in ECS between the two model generations. The increased ECS values, as discussed in Section 7.4.2.8, are partly due to shortwave cloud feedbacks (Flynn and Mauritsen, 2020) and it appears that in some models extra-tropical clouds with mixed ice and liquid phases are central to the behaviour (Zelinka et al., 2020), probably borne out of a recent focus on biases in these types of clouds (McCoy et al., 2016; Tan et al., 2016). These biases have recently been reduced in many ESMs, guided by process understanding from', 'reranking_score': 0.00562676414847374, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='given for CMIP6 models in Supplementary Material 7.SM.4 based on Schlund et al. (2020) for ECS and Meehl et al. (2020) for TCR (see also Figure 7.18 and FAQ 7.3). The upward shift does not apply to all models traceable to specific modelling centres, but a substantial subset of models have seen an increase in ECS between the two model generations. The increased ECS values, as discussed in Section 7.4.2.8, are partly due to shortwave cloud feedbacks (Flynn and Mauritsen, 2020) and it appears that in some models extra-tropical clouds with mixed ice and liquid phases are central to the behaviour (Zelinka et al., 2020), probably borne out of a recent focus on biases in these types of clouds (McCoy et al., 2016; Tan et al., 2016). These biases have recently been reduced in many ESMs, guided by process understanding from'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 831.0, 'num_tokens': 223.0, 'num_tokens_approx': 202.0, 'num_words': 152.0, 'page_number': 2018, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'tlas.8.3 Assessment of Model Performance', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Atlas', 'toc_level1': 'Atlas.8 Europe', 'toc_level2': 'Atlas.8.4 Assessment and Synthesis of Projections', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.698799193, 'content': 'Specific assessments of convection-permitting RCMs (CPRCMs, running at a resolution of typically 1 to 3 km and designed for extreme precipitation characteristics) is undertaken in Section 10.3.3.4.1. A unique CPRCM ensemble has been applied over the great Alpine domain and improves representation of mean and extreme precipitation compared to coarser resolution models (Ban et al., 2021; Pichelli et al., 2021). The role of aerosol forcing is increasingly analysed as new and more realistic aerosol datasets become available (Nabat et al., 2013; Pavlidis et al., 2020), and as RCMs begin to include interactive aerosols (Nabat et al., 2012, 2015, 2020; Druge et al., 2019). Explicitly accounting for aerosol effects in RCMs leads to improved representation of the surface shortwave radiation at various', 'reranking_score': 0.005449835676699877, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Specific assessments of convection-permitting RCMs (CPRCMs, running at a resolution of typically 1 to 3 km and designed for extreme precipitation characteristics) is undertaken in Section 10.3.3.4.1. A unique CPRCM ensemble has been applied over the great Alpine domain and improves representation of mean and extreme precipitation compared to coarser resolution models (Ban et al., 2021; Pichelli et al., 2021). The role of aerosol forcing is increasingly analysed as new and more realistic aerosol datasets become available (Nabat et al., 2013; Pavlidis et al., 2020), and as RCMs begin to include interactive aerosols (Nabat et al., 2012, 2015, 2020; Druge et al., 2019). Explicitly accounting for aerosol effects in RCMs leads to improved representation of the surface shortwave radiation at various'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1055.0, 'num_tokens': 228.0, 'num_tokens_approx': 258.0, 'num_words': 194.0, 'page_number': 1155, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.5.1.1.1 Atmospheric convection', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.5 What Are the Limits for Projecting Water Cycle Changes?', 'toc_level2': '8.5.1 Model Uncertainties of Relevance for the Water Cycle', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.698670685, 'content': 'In summary, since AR5 empirical convective parametrization schemes and associated precipitation biases have improved in some but not all global climate models. There is still low confidence in their ability to accurately simulate the spatio-temporal features of present-day precipitation, especially in the tropics where a double-ITCZ bias is still apparent in many models. While such biases limit the reliability of precipitation projections in some cases, there is currently only medium confidence that model selection or weighting is a better alternative to the one-model-one-vote approach (Box 4.1). Improved water cycle projections can be achieved by focusing on phenomena or weather events, such as a thermodynamic intensification of convective events (high confidence, Section 8.2.2.1), however accurate quantitative estimates are currently hampered by complex, model-dependent dynamical responses (Section 8.2.2.2).\\n 8.5.1.1.2 Aerosol microphysical effects on clouds and precipitation ', 'reranking_score': 0.004717639181762934, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='In summary, since AR5 empirical convective parametrization schemes and associated precipitation biases have improved in some but not all global climate models. There is still low confidence in their ability to accurately simulate the spatio-temporal features of present-day precipitation, especially in the tropics where a double-ITCZ bias is still apparent in many models. While such biases limit the reliability of precipitation projections in some cases, there is currently only medium confidence that model selection or weighting is a better alternative to the one-model-one-vote approach (Box 4.1). Improved water cycle projections can be achieved by focusing on phenomena or weather events, such as a thermodynamic intensification of convective events (high confidence, Section 8.2.2.1), however accurate quantitative estimates are currently hampered by complex, model-dependent dynamical responses (Section 8.2.2.2).\\n 8.5.1.1.2 Aerosol microphysical effects on clouds and precipitation '), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 251.0, 'num_tokens': 52.0, 'num_tokens_approx': 56.0, 'num_words': 42.0, 'page_number': 2241, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Natural variability', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex VII: Glossary', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.698509455, 'content': 'Cloud-resolving models (CRMs) Numerical models that are that are of high enough resolution and have the necessary physics to represent the dynamical and physical processes of cloud formation.\\nCMIP6 See Coupled Model Intercomparison Project (CMIP).', 'reranking_score': 0.004378741141408682, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Cloud-resolving models (CRMs) Numerical models that are that are of high enough resolution and have the necessary physics to represent the dynamical and physical processes of cloud formation.\\nCMIP6 See Coupled Model Intercomparison Project (CMIP).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 940.0, 'num_tokens': 229.0, 'num_tokens_approx': 229.0, 'num_words': 172.0, 'page_number': 265, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': '2.5 Projected Impacts and Risk for Species, \\r\\nCommunities, Biomes, Key Ecosystems \\r\\nand Their Services', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Chapters and Cross-Chapter Papers ', 'toc_level1': 'Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services ', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.69841522, 'content': 'SDMs or niche-based models assess potential geographic areas of suitable climate for the species in current conditions and then project them into future conditions (Trisurat, 2018; Vieira et al., 2018). There are limitations in all models and it is critical that modellers understand the assumptions, proper parameterization and limitations of each model technique, including differences between climate models, emission scenarios or RCPs and baselines (Araujo et al., 2019). Several systems automate the development of SDMs, including R-packages (Beaumont et al., 2016; Hallgren et al., 2016), and other model types (Foden et al., 2019) and aid in the use of climate model data (Suggitt et al., 2017), including allowing for connectivity constraints (Peterson et al., 2013). Buisson et al. (2010) found most variation in model outputs stems from differences in design, followed by general circulation models (GCMs).', 'reranking_score': 0.0037147952243685722, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='SDMs or niche-based models assess potential geographic areas of suitable climate for the species in current conditions and then project them into future conditions (Trisurat, 2018; Vieira et al., 2018). There are limitations in all models and it is critical that modellers understand the assumptions, proper parameterization and limitations of each model technique, including differences between climate models, emission scenarios or RCPs and baselines (Araujo et al., 2019). Several systems automate the development of SDMs, including R-packages (Beaumont et al., 2016; Hallgren et al., 2016), and other model types (Foden et al., 2019) and aid in the use of climate model data (Suggitt et al., 2017), including allowing for connectivity constraints (Peterson et al., 2013). Buisson et al. (2010) found most variation in model outputs stems from differences in design, followed by general circulation models (GCMs).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 251.0, 'num_tokens': 97.0, 'num_tokens_approx': 93.0, 'num_words': 70.0, 'page_number': 1342, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'References', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '9: Ocean, Cryosphere and Sea Level Change', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.698401, 'content': 'Dynamics, 40(11-12), 2993-3007, doi:10.1007/s00382-012-1525-7. Boucher, O. et al., 2020: Presentation and Evaluation of the IPSL-CM6A-LR Climate Model. Journal of Advances in Modeling Earth Systems, 12(7), e2019MS002010, doi:10.1029/2019ms002010.', 'reranking_score': 0.001994052901864052, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Dynamics, 40(11-12), 2993-3007, doi:10.1007/s00382-012-1525-7. Boucher, O. et al., 2020: Presentation and Evaluation of the IPSL-CM6A-LR Climate Model. Journal of Advances in Modeling Earth Systems, 12(7), e2019MS002010, doi:10.1029/2019ms002010.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1053.0, 'num_tokens': 245.0, 'num_tokens_approx': 277.0, 'num_words': 208.0, 'page_number': 235, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '1.5.3.2 Model Tuning and Adjustment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '1: Framing, Context, and Methods', 'toc_level1': '1.5 Major Developments and\\xa0Their Implications', 'toc_level2': '1.5.3 Climate Models', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.698268, 'content': 'Even with some core commonalities of approaches to model tuning, practices can differ, such as the use of initial drift from initialized forecasts, the explicit use of the transient observed record for the historical period, or the use of the present-day radiative imbalance at the TOA as a tuning target rather than an equilibrated pre-industrial balance. The majority of CMIP6 modelling groups report that they do not tune their model for the observed trends during the historical period (23 out of 29 groups), nor for ECS (25 out of 29). ECS and TCR are thus emergent properties for a large majority of models. The effect of tuning on model skill and ensemble spread in CMIP6 is further discussed in Section 3.3.\\n 1.5.3.3 From Global to Regional Models \\n\\n1.5.3.3 From Global to Regional Models\\nThe need for accurate climate information at the regional scale is increasing (Section 10.1). High-resolution global climate models, such as those taking part in HighResMIP, provide more detailed', 'reranking_score': 0.0019578728824853897, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Even with some core commonalities of approaches to model tuning, practices can differ, such as the use of initial drift from initialized forecasts, the explicit use of the transient observed record for the historical period, or the use of the present-day radiative imbalance at the TOA as a tuning target rather than an equilibrated pre-industrial balance. The majority of CMIP6 modelling groups report that they do not tune their model for the observed trends during the historical period (23 out of 29 groups), nor for ECS (25 out of 29). ECS and TCR are thus emergent properties for a large majority of models. The effect of tuning on model skill and ensemble spread in CMIP6 is further discussed in Section 3.3.\\n 1.5.3.3 From Global to Regional Models \\n\\n1.5.3.3 From Global to Regional Models\\nThe need for accurate climate information at the regional scale is increasing (Section 10.1). High-resolution global climate models, such as those taking part in HighResMIP, provide more detailed'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 729.0, 'num_tokens': 186.0, 'num_tokens_approx': 188.0, 'num_words': 141.0, 'page_number': 1991, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Atlas.5.1.3 Assessment of Model Performance', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Atlas', 'toc_level1': 'Atlas.5 Asia', 'toc_level2': 'Atlas.5.1 East Asia', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.697911859, 'content': 'T. Wu et al., 2019). The performance of models is sensitive to cumulus convection schemes and horizontal resolution (Haarsma et al., 2016; Wu et al., 2017; Kusunoki, 2018b). High-resolution atmospheric global climate models (AGCM) successfully reproduce the intensity and the spatial pattern of the EASM rainfall (Li et al., 2015; Yao et al., 2017; Ito et al., 2020a) and improve the simulation of the diurnal cycle of precipitation rates and the probability density distributions of daily precipitation over Korea, Japan and northern China (Lin et al., 2019), but increasing horizontal resolution (at the typical scales used in GCMs) is not always a panacea for solving model biases (Roberts et al., 2018).', 'reranking_score': 0.0017488134326413274, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='T. Wu et al., 2019). The performance of models is sensitive to cumulus convection schemes and horizontal resolution (Haarsma et al., 2016; Wu et al., 2017; Kusunoki, 2018b). High-resolution atmospheric global climate models (AGCM) successfully reproduce the intensity and the spatial pattern of the EASM rainfall (Li et al., 2015; Yao et al., 2017; Ito et al., 2020a) and improve the simulation of the diurnal cycle of precipitation rates and the probability density distributions of daily precipitation over Korea, Japan and northern China (Lin et al., 2019), but increasing horizontal resolution (at the typical scales used in GCMs) is not always a panacea for solving model biases (Roberts et al., 2018).'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 615.0, 'num_tokens': 172.0, 'num_tokens_approx': 169.0, 'num_words': 127.0, 'page_number': 2216, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'AV.4.5 The South American Monsoon', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex V: Monsoons', 'toc_level1': 'AV.4 Definition of Regional Monsoons', 'toc_level2': 'AV.4.6 The Australian-Maritime Continent Monsoon', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.697755754, 'content': 'The general large-scale features of the SAmerM are reasonably well simulated by coupled climate models although they do not adequately reproduce maximum precipitation over the core of the monsoon, even when considering simulations under past natural forcings, such as those during the last millennium (Rojas et al., 2016; Diaz and Vera, 2018). However, CMIP5 models featured an improved representation of the SAmerM with respect to CMIP3 (Joetzjer et al., 2013; Jones and Carvalho, 2013; Gulizia and Camilloni, 2015; Diaz and Vera, 2017). \\nThe SAmerM is assessed in Sections 8.3.2.4.5 and 8.4.2.4.5.', 'reranking_score': 0.0017172765219584107, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='The general large-scale features of the SAmerM are reasonably well simulated by coupled climate models although they do not adequately reproduce maximum precipitation over the core of the monsoon, even when considering simulations under past natural forcings, such as those during the last millennium (Rojas et al., 2016; Diaz and Vera, 2018). However, CMIP5 models featured an improved representation of the SAmerM with respect to CMIP3 (Joetzjer et al., 2013; Jones and Carvalho, 2013; Gulizia and Camilloni, 2015; Diaz and Vera, 2017). \\nThe SAmerM is assessed in Sections 8.3.2.4.5 and 8.4.2.4.5.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 554.0, 'num_tokens': 200.0, 'num_tokens_approx': 200.0, 'num_words': 150.0, 'page_number': 281, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'References', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '1: Framing, Context, and Methods', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.697572231, 'content': 'Conceptual Issues [A. Lloyd, E. and E. Winsberg (eds.)]. Palgrave Macmillan, Cham, Switzerland, pp. 325-359, doi:10.1007/978-3-319-65058-6_11. Knutti, R., D. Masson, and A. Gettelman, 2013: Climate model genealogy: Generation CMIP5 and how we got there. Geophysical Research Letters, 40(6), 1194-1199, doi:10.1002/grl.50256. Knutti, R., T.F. Stocker, F. Joos, and G.-K. Plattner, 2002: Constraints on radiative forcing and future climate change from observations and climate model ensembles. Nature, 416(6882), 719-723, doi:10.1038/416719a.', 'reranking_score': 0.0017110711196437478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Conceptual Issues [A. Lloyd, E. and E. Winsberg (eds.)]. Palgrave Macmillan, Cham, Switzerland, pp. 325-359, doi:10.1007/978-3-319-65058-6_11. Knutti, R., D. Masson, and A. Gettelman, 2013: Climate model genealogy: Generation CMIP5 and how we got there. Geophysical Research Letters, 40(6), 1194-1199, doi:10.1002/grl.50256. Knutti, R., T.F. Stocker, F. Joos, and G.-K. Plattner, 2002: Constraints on radiative forcing and future climate change from observations and climate model ensembles. Nature, 416(6882), 719-723, doi:10.1038/416719a.'), Document(metadata={'chunk_type': 'image', 'document_id': 'document3', 'document_number': 3.0, 'element_id': 'Picture_0_17', 'figure_code': 'Figure TS.2', 'file_size': 195.7666015625, 'image_path': '/dbfs/mnt/ai4sclqa/raw/climateqa/documents/document3/images/Picture_0_17.png', 'n_pages': 112.0, 'name': 'Technical Summary. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 'N/A', 'num_tokens': 'N/A', 'num_tokens_approx': 'N/A', 'num_words': 'N/A', 'page_number': 18, 'release_date': 2021.0, 'report_type': 'TS', 'section_header': 'N/A', 'short_name': 'IPCC AR6 WGI TS', 'source': 'IPCC', 'toc_level0': 'TS.1 A Changing Climate', 'toc_level1': 'TS.1.2 Progress in Climate Science', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf', 'similarity_score': 0.697530031, 'content': 'Summary: This image illustrates advancements in climate modeling as presented in an IPCC report, highlighting three aspects: the evolution of model resolution, the increase in model complexity over time, and the pattern correlation of different model outputs with observational reference data. The comparison shows data from successive Coupled Model Intercomparison Projects (CMIP3, CMIP5, and CMIP6) demonstrating the improvements made in simulating climate variables with more refined spatial resolution and depth levels, the inclusion of a greater number of processes such as atmospheric chemistry and the nitrogen cycle, and an overall increase in the accuracy of models in matching observed climate patterns over the period 1980-1999. This information is crucial for understanding the precision and reliability of climate projections used to guide policy and scientific research related to climate change.', 'reranking_score': 0.0011455357307568192, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Summary: This image illustrates advancements in climate modeling as presented in an IPCC report, highlighting three aspects: the evolution of model resolution, the increase in model complexity over time, and the pattern correlation of different model outputs with observational reference data. The comparison shows data from successive Coupled Model Intercomparison Projects (CMIP3, CMIP5, and CMIP6) demonstrating the improvements made in simulating climate variables with more refined spatial resolution and depth levels, the inclusion of a greater number of processes such as atmospheric chemistry and the nitrogen cycle, and an overall increase in the accuracy of models in matching observed climate patterns over the period 1980-1999. This information is crucial for understanding the precision and reliability of climate projections used to guide policy and scientific research related to climate change.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 666.0, 'num_tokens': 216.0, 'num_tokens_approx': 233.0, 'num_words': 175.0, 'page_number': 1060, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': \"The Earth's Energy Budget, Climate Feedbacks and Climate Sensitivity\", 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'References', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.697122276, 'content': 'response. Geoscientific Model Development, 13(11), 5175-5190, doi:10.5194/gmd-13-5175-2020. Nijsse, F.J.M.M., P.M. Cox, and M.S. Williamson, 2020: Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models. Earth System Dynamics, 11(3), 737-750, doi:10.5194/esd-11-737-2020. Norris, J.R. et al., 2016: Evidence for climate change in the satellite cloud record. Nature, 536(7614), 72, doi:10.1038/nature18273. Notaro, M., S. Vavrus, and Z. Liu, 2007: Global vegetation and climate change due to future increases in CO2 as projected by a fully coupled model with', 'reranking_score': 0.0010937325423583388, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='response. Geoscientific Model Development, 13(11), 5175-5190, doi:10.5194/gmd-13-5175-2020. Nijsse, F.J.M.M., P.M. Cox, and M.S. Williamson, 2020: Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models. Earth System Dynamics, 11(3), 737-750, doi:10.5194/esd-11-737-2020. Norris, J.R. et al., 2016: Evidence for climate change in the satellite cloud record. Nature, 536(7614), 72, doi:10.1038/nature18273. Notaro, M., S. Vavrus, and Z. Liu, 2007: Global vegetation and climate change due to future increases in CO2 as projected by a fully coupled model with'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 479.0, 'num_tokens': 135.0, 'num_tokens_approx': 129.0, 'num_words': 97.0, 'page_number': 500, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '3.5.4.1 Atlantic Meridional Overturning Circulation (AMOC)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '3: Human Influence on the Climate System', 'toc_level1': '3.5 Human Influence on the Ocean', 'toc_level2': '3.5.4 Ocean Circulation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.696608126, 'content': 'biases (C. Wang et al., 2014). Both coupled and ocean-only models also underestimate the depth of the AMOC cell (Danabasoglu et al., 2014; Weijer et al., 2020; Figure 3.30a). Paleo-climatic evidence has also raised questions regarding the accuracy of the representation of the strength and depth of the modelled AMOC during past periods (Otto-Bliesner et al., 2007; Muglia and Schmittner, 2015). Overall, however, both the CMIP5 and CMIP6 model ensembles simulate the', 'reranking_score': 0.0010113953612744808, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='biases (C. Wang et al., 2014). Both coupled and ocean-only models also underestimate the depth of the AMOC cell (Danabasoglu et al., 2014; Weijer et al., 2020; Figure 3.30a). Paleo-climatic evidence has also raised questions regarding the accuracy of the representation of the strength and depth of the modelled AMOC during past periods (Otto-Bliesner et al., 2007; Muglia and Schmittner, 2015). Overall, however, both the CMIP5 and CMIP6 model ensembles simulate the'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 900.0, 'num_tokens': 192.0, 'num_tokens_approx': 220.0, 'num_words': 165.0, 'page_number': 1022, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.4.3 Assessed ECS and TCR Based on Emergent Constraints', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.5 Combined Assessment of ECS and TCR', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.696337461, 'content': 'present-day climate biases. The former class is arguably superior in representing ECS, since it is a global surface temperature or energy budget change, whereas the latter class is perhaps best thought of as providing constraints on individual climate feedbacks, for example, the determination that low-level cloud feedbacks are positive. The latter result is consistent with and confirms process-based estimates of low-cloud feedbacks (Section 7.4.2.4), but are potentially biased as a group by missing or biased feedbacks in ESMs and is accordingly not taken into account here. A limiting case here is Dessler and Forster (2018) which is focused on monthly co-variability in the global TOA energy budget with mid-tropospheric temperature, at which time scale the surface-albedo feedback is unlikely to operate, thus implicitly assuming it is unbiased in the model ensemble.', 'reranking_score': 0.0009855100652202964, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='present-day climate biases. The former class is arguably superior in representing ECS, since it is a global surface temperature or energy budget change, whereas the latter class is perhaps best thought of as providing constraints on individual climate feedbacks, for example, the determination that low-level cloud feedbacks are positive. The latter result is consistent with and confirms process-based estimates of low-cloud feedbacks (Section 7.4.2.4), but are potentially biased as a group by missing or biased feedbacks in ESMs and is accordingly not taken into account here. A limiting case here is Dessler and Forster (2018) which is focused on monthly co-variability in the global TOA energy budget with mid-tropospheric temperature, at which time scale the surface-albedo feedback is unlikely to operate, thus implicitly assuming it is unbiased in the model ensemble.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1090.0, 'num_tokens': 215.0, 'num_tokens_approx': 262.0, 'num_words': 197.0, 'page_number': 951, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.696293294, 'content': 'Figure 7.2 | Schematic representation of the global mean energy budget of the Earth (upper panel), and its equivalent without considerations of cloud effects (lower panel). Numbers indicate best estimates for the magnitudes of the globally averaged energy balance components in W m-2 together with their uncertainty ranges in parentheses (5-95% confidence range), representing climate conditions at the beginning of the 21st century. Note that the cloud-free energy budget shown in the lower panel is not the one that Earth would achieve in equilibrium when no clouds could form. It rather represents the global mean fluxes as determined solely by removing the clouds but otherwise retaining the entire atmospheric structure. This enables the quantification of the effects of clouds on the Earth energy budget and corresponds to the way clear-sky fluxes are calculated in climate models. Thus, the cloud-free energy budget is not closed and therefore the sensible and latent heat fluxes are not quantified in the lower panel. Figure adapted from Wild et al. (2015, 2019).\\n934934', 'reranking_score': 0.00033549402724020183, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Figure 7.2 | Schematic representation of the global mean energy budget of the Earth (upper panel), and its equivalent without considerations of cloud effects (lower panel). Numbers indicate best estimates for the magnitudes of the globally averaged energy balance components in W m-2 together with their uncertainty ranges in parentheses (5-95% confidence range), representing climate conditions at the beginning of the 21st century. Note that the cloud-free energy budget shown in the lower panel is not the one that Earth would achieve in equilibrium when no clouds could form. It rather represents the global mean fluxes as determined solely by removing the clouds but otherwise retaining the entire atmospheric structure. This enables the quantification of the effects of clouds on the Earth energy budget and corresponds to the way clear-sky fluxes are calculated in climate models. Thus, the cloud-free energy budget is not closed and therefore the sensible and latent heat fluxes are not quantified in the lower panel. Figure adapted from Wild et al. (2015, 2019).\\n934934'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 706.0, 'num_tokens': 232.0, 'num_tokens_approx': 240.0, 'num_words': 180.0, 'page_number': 2054, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'References', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Atlas', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.696224511, 'content': 'E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1327-1370, doi:10.1017/cbo9781107415386.004. Hines, K.M. et al., 2019: Microphysics of summer clouds in central West Antarctica simulated by the Polar Weather Research and Forecasting Model (WRF) and the Antarctic Mesoscale Prediction System (AMPS). Atmospheric Chemistry and Physics, 19(19), 12431-12454, doi:10.5194/acp-19-12431- 2019. Hock, R. et al., 2019a: GlacierMIP - A model intercomparison of global\\x02scale glacier mass-balance models and projections. Journal of Glaciology, 65(251), 453-467, doi:10.1017/jog.2019.22.', 'reranking_score': 0.0002404050756013021, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1327-1370, doi:10.1017/cbo9781107415386.004. Hines, K.M. et al., 2019: Microphysics of summer clouds in central West Antarctica simulated by the Polar Weather Research and Forecasting Model (WRF) and the Antarctic Mesoscale Prediction System (AMPS). Atmospheric Chemistry and Physics, 19(19), 12431-12454, doi:10.5194/acp-19-12431- 2019. Hock, R. et al., 2019a: GlacierMIP - A model intercomparison of global\\x02scale glacier mass-balance models and projections. Journal of Glaciology, 65(251), 453-467, doi:10.1017/jog.2019.22.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 893.0, 'num_tokens': 221.0, 'num_tokens_approx': 221.0, 'num_words': 166.0, 'page_number': 242, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '1.5.4.6 Evaluation of Process-Based Models \\r\\nAgainst Observations', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '1: Framing, Context, and Methods', 'toc_level1': '1.5 Major Developments and\\xa0Their Implications', 'toc_level2': '1.5.4 Modelling Techniques, Comparisons and\\xa0Performance Assessments', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.696094573, 'content': 'Instrument simulators provide estimates of what a satellite would see if looking down on the model-simulated planet, and improve the direct comparison of modelled variables such as clouds, precipitation and upper tropospheric humidity with observations from satellites (e.g., Kay et al., 2011; Klein et al., 2013; Cesana and Waliser, 2016; Konsta et al., 2016; Jin et al., 2017; Chepfer et al., 2018; Swales et al., 2018; Zhang et al., 2018). Within the framework of the Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6 (Webb et al., 2017), a new version of the Cloud Feedback \\nModel Intercomparison Project Observational Simulator (COSP; Swales et al., 2018) has been released which makes use of a collection of observation proxies or satellite simulators. Related approaches in this rapidly evolving field include simulators for Arctic Ocean', 'reranking_score': 0.00023418113414663821, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Instrument simulators provide estimates of what a satellite would see if looking down on the model-simulated planet, and improve the direct comparison of modelled variables such as clouds, precipitation and upper tropospheric humidity with observations from satellites (e.g., Kay et al., 2011; Klein et al., 2013; Cesana and Waliser, 2016; Konsta et al., 2016; Jin et al., 2017; Chepfer et al., 2018; Swales et al., 2018; Zhang et al., 2018). Within the framework of the Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6 (Webb et al., 2017), a new version of the Cloud Feedback \\nModel Intercomparison Project Observational Simulator (COSP; Swales et al., 2018) has been released which makes use of a collection of observation proxies or satellite simulators. Related approaches in this rapidly evolving field include simulators for Arctic Ocean'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 425.0, 'num_tokens': 95.0, 'num_tokens_approx': 89.0, 'num_words': 67.0, 'page_number': 1155, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '8.5.1.1.2 Aerosol microphysical effects on clouds and precipitation', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '8: Water Cycle Changes', 'toc_level1': '8.5 What Are the Limits for Projecting Water Cycle Changes?', 'toc_level2': '8.5.1 Model Uncertainties of Relevance for the Water Cycle', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.696051061, 'content': \"a 'super parametrization' (Wang et al., 2015), which have been shown to improve the performance in simulating cloud properties and precipitation. However, few of these improvements have been incorporated into CMIP6 climate models so the projected precipitation response to anthropogenic perturbation may still be hindered by the inadequate microphysical treatment in cumulus parametrization (Smith et al., 2020).\", 'reranking_score': 0.0002219185553258285, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"a 'super parametrization' (Wang et al., 2015), which have been shown to improve the performance in simulating cloud properties and precipitation. However, few of these improvements have been incorporated into CMIP6 climate models so the projected precipitation response to anthropogenic perturbation may still be hindered by the inadequate microphysical treatment in cumulus parametrization (Smith et al., 2020).\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 225.0, 'num_tokens': 71.0, 'num_tokens_approx': 76.0, 'num_words': 57.0, 'page_number': 543, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'References', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '3: Human Influence on the Climate System', 'toc_level1': 'References', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.695928633, 'content': \"Davini, P. and F. D'Andrea, 2016: Northern Hemisphere Atmospheric Blocking Representation in Global Climate Models: Twenty Years of Improvements? Journal of Climate, 29(24), 8823-8840, doi:10.1175/jcli-d-16-0242.1.\\n526526\", 'reranking_score': 0.00021343691332731396, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"Davini, P. and F. D'Andrea, 2016: Northern Hemisphere Atmospheric Blocking Representation in Global Climate Models: Twenty Years of Improvements? Journal of Climate, 29(24), 8823-8840, doi:10.1175/jcli-d-16-0242.1.\\n526526\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document6', 'document_number': 6.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 3068.0, 'name': 'Full Report. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 769.0, 'num_tokens': 236.0, 'num_tokens_approx': 246.0, 'num_words': 185.0, 'page_number': 2546, 'release_date': 2022.0, 'report_type': 'Full Report', 'section_header': 'Seneviratne, S.I., et al., 2018b: Land radiative management as contributor to \\r\\nregional-scale climate adaptation and mitigation, 88-96.', 'short_name': 'IPCC AR6 WGII FR', 'source': 'IPCC', 'toc_level0': 'Chapters and Cross-Chapter Papers ', 'toc_level1': 'Chapter 16 Key Risks across Sectors and Regions', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg2/IPCC_AR6_WGII_FullReport.pdf', 'similarity_score': 0.695747674, 'content': 'Stjern, C.W., et al., 2018: Response to marine cloud brightening in a multi\\x02model ensemble. Atmos. Chem. Phys., 18(2), 621-634. Stoerk, T., G. Wagner and R.E. Ward, 2018: Policy brief--Recommendations for improving the treatment of risk and uncertainty in economic estimates of climate impacts in the sixth Intergovernmental Panel on Climate Change assessment report. Rev. Environ. Econ. Policy, 12(2), 371-376. Stone, Jr, B., et al., 2014: Avoided heat-related mortality through climate adaptation strategies in three US cities. PloS One, 9(6), e100852, doi:10.1371/journal.pone.0100852. Storelvmo, T. and N. Herger, 2014: Cirrus cloud susceptibility to the injection of ice nuclei in the upper troposphere. J. Geophys. Res. Atmos., 119(5), 2375-2389.', 'reranking_score': 0.0001739229919621721, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Stjern, C.W., et al., 2018: Response to marine cloud brightening in a multi\\x02model ensemble. Atmos. Chem. Phys., 18(2), 621-634. Stoerk, T., G. Wagner and R.E. Ward, 2018: Policy brief--Recommendations for improving the treatment of risk and uncertainty in economic estimates of climate impacts in the sixth Intergovernmental Panel on Climate Change assessment report. Rev. Environ. Econ. Policy, 12(2), 371-376. Stone, Jr, B., et al., 2014: Avoided heat-related mortality through climate adaptation strategies in three US cities. PloS One, 9(6), e100852, doi:10.1371/journal.pone.0100852. Storelvmo, T. and N. Herger, 2014: Cirrus cloud susceptibility to the injection of ice nuclei in the upper troposphere. J. Geophys. Res. Atmos., 119(5), 2375-2389.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 889.0, 'num_tokens': 229.0, 'num_tokens_approx': 241.0, 'num_words': 181.0, 'page_number': 2018, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'tlas.8.3 Assessment of Model Performance', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Atlas', 'toc_level1': 'Atlas.8 Europe', 'toc_level2': 'Atlas.8.4 Assessment and Synthesis of Projections', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.695686936, 'content': \"New, or updated, higher-resolution, coupled atmosphere-ocean-ice model systems have been found to improve simulations of observed climate features over the Baltic area compared to atmosphere-only model versions, including correlation between precipitation and SST, between surface heat-flux components and SST, and weather events like convective snow bands over the Baltic Sea (e.g., Tian et al., 2013; Van Pham et al., 2014; Groger et al., 2015; S. Wang et al., 2015; Pham et al., 2017). Coupled atmosphere-land-river-ocean regional climate system models (RCSMs) from Med-CORDEX have similar skill as the ENSEMBLES and the Euro-CORDEX ensembles to represent decadal variability of Mediterranean climate and its extremes (Cavicchia et al., 2018; Dell'Aquila et al., 2018; Gaertner et al., 2018). Panthou et al. (2018a) showed that, over land, differences between\", 'reranking_score': 0.00016190341557376087, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"New, or updated, higher-resolution, coupled atmosphere-ocean-ice model systems have been found to improve simulations of observed climate features over the Baltic area compared to atmosphere-only model versions, including correlation between precipitation and SST, between surface heat-flux components and SST, and weather events like convective snow bands over the Baltic Sea (e.g., Tian et al., 2013; Van Pham et al., 2014; Groger et al., 2015; S. Wang et al., 2015; Pham et al., 2017). Coupled atmosphere-land-river-ocean regional climate system models (RCSMs) from Med-CORDEX have similar skill as the ENSEMBLES and the Euro-CORDEX ensembles to represent decadal variability of Mediterranean climate and its extremes (Cavicchia et al., 2018; Dell'Aquila et al., 2018; Gaertner et al., 2018). Panthou et al. (2018a) showed that, over land, differences between\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 541.0, 'num_tokens': 108.0, 'num_tokens_approx': 121.0, 'num_words': 91.0, 'page_number': 988, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.4.2.4.1 Decomposition of clouds into regimes', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.4 Climate Feedbacks', 'toc_level2': '7.4.2 Assessing Climate Feedbacks', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.695204914, 'content': 'Clouds can be formed almost anywhere in the atmosphere when moist air parcels rise and cool, enabling the water vapour to condense. Clouds consist of liquid water droplets and/or ice crystals, and these droplets and crystals can grow into larger particles of rain, snow or drizzle. These microphysical processes interact with aerosols, \\nradiation and atmospheric circulation, resulting in a highly complex set of processes governing cloud formation and life cycles that operate across a wide range of spatial and temporal scales.', 'reranking_score': 0.00012115899880882353, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='Clouds can be formed almost anywhere in the atmosphere when moist air parcels rise and cool, enabling the water vapour to condense. Clouds consist of liquid water droplets and/or ice crystals, and these droplets and crystals can grow into larger particles of rain, snow or drizzle. These microphysical processes interact with aerosols, \\nradiation and atmospheric circulation, resulting in a highly complex set of processes governing cloud formation and life cycles that operate across a wide range of spatial and temporal scales.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 700.0, 'num_tokens': 227.0, 'num_tokens_approx': 244.0, 'num_words': 183.0, 'page_number': 1045, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'Bretherton, C.S., 2015: Insights into low-latitude cloud feedbacks from \\r\\nhigh-resolution models. Philosophical Transactions of the Royal Society A: \\r\\nMathematical, Physical and Engineering Sciences, 373(2054), doi:10.1098/\\r\\nrsta.2014.0415.', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'References', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.6951949, 'content': \"Brient, F. and T. Schneider, 2016: Constraints on climate sensitivity from space-based measurements of low-cloud reflection. Journal of Climate, 29(16), 5821-5835, doi:10.1175/jcli-d-15-0897.1. Brient, F. et al., 2016: Shallowness of tropical low clouds as a predictor of climate models' response to warming. Climate Dynamics, 47(1-2), 433-449, doi:10.1007/s00382-015-2846-0. Brierley, C., N. Burls, C. Ravelo, and A. Fedorov, 2015: Pliocene warmth and gradients. Nature Geoscience, 8(6), 419-420, doi:10.1038/ngeo2444.\\n Bretherton, C.S., 2015: Insights into low-latitude cloud feedbacks from high-resolution models. Philosophical Transactions of the Royal Society A:\", 'reranking_score': 9.6050018328242e-05, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content=\"Brient, F. and T. Schneider, 2016: Constraints on climate sensitivity from space-based measurements of low-cloud reflection. Journal of Climate, 29(16), 5821-5835, doi:10.1175/jcli-d-15-0897.1. Brient, F. et al., 2016: Shallowness of tropical low clouds as a predictor of climate models' response to warming. Climate Dynamics, 47(1-2), 433-449, doi:10.1007/s00382-015-2846-0. Brierley, C., N. Burls, C. Ravelo, and A. Fedorov, 2015: Pliocene warmth and gradients. Nature Geoscience, 8(6), 419-420, doi:10.1038/ngeo2444.\\n Bretherton, C.S., 2015: Insights into low-latitude cloud feedbacks from high-resolution models. Philosophical Transactions of the Royal Society A:\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1015.0, 'num_tokens': 220.0, 'num_tokens_approx': 253.0, 'num_words': 190.0, 'page_number': 488, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '3.4.3 Glaciers and Ice Sheets', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '3: Human Influence on the Climate System', 'toc_level1': '3.4 Human Influence on the Cryosphere', 'toc_level2': '3.4.3 Glaciers and Ice Sheets', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.695061684, 'content': 'While Chapter 9 (Sections 9.4 and 9.5) discusses process understanding for glaciers and ice sheets, as well as evaluation of global and regional-scale glacier and ice-sheet models, our focus here is on the attribution of large-scale changes in glaciers and ice sheets. Land ice in the form of glaciers has been included in CMIP climate and Earth system models as components of the land surface models for many years. However, their representation is simplified and is omitted altogether in the less complex modelling systems. In CMIP3 (Meehl et al., 2007) and CMIP5 (Taylor et al., 2012) land ice area fraction, a component of land surface models, was defined as a time-independent quantity, and in most model configurations was preset at the simulation initialization as a permanent land feature. In CMIP6 considerable progress has been made in improving and evaluating the representation of modelled land ice. For glaciers, an example is the expansion of the Joint UK Land Environment', 'reranking_score': 9.541400504531339e-05, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='While Chapter 9 (Sections 9.4 and 9.5) discusses process understanding for glaciers and ice sheets, as well as evaluation of global and regional-scale glacier and ice-sheet models, our focus here is on the attribution of large-scale changes in glaciers and ice sheets. Land ice in the form of glaciers has been included in CMIP climate and Earth system models as components of the land surface models for many years. However, their representation is simplified and is omitted altogether in the less complex modelling systems. In CMIP3 (Meehl et al., 2007) and CMIP5 (Taylor et al., 2012) land ice area fraction, a component of land surface models, was defined as a time-independent quantity, and in most model configurations was preset at the simulation initialization as a permanent land feature. In CMIP6 considerable progress has been made in improving and evaluating the representation of modelled land ice. For glaciers, an example is the expansion of the Joint UK Land Environment'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 964.0, 'num_tokens': 220.0, 'num_tokens_approx': 224.0, 'num_words': 168.0, 'page_number': 1413, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '10.3.3.3.1 Mid- to high-latitude atmospheric variability \\r\\nphenomena: Blocking and extratropical cyclones', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '10: Linking Global to Regional Climate Change', 'toc_level1': '10.3 Using Models for Constructing Regional\\xa0Climate Information', 'toc_level2': '10.3.3 Model Performance and Added Value in Simulating and Projecting Regional Climate', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.69494468, 'content': 'accurate representation of blocking and extratropical cyclones in global and regional climate models is needed to better understand regional climate variability and extremes as well as to project future changes (Section 11.7.2; Grotjahn et al., 2016; Mitchell et al., 2017; Rohrer et al., 2018; Huguenin et al., 2020). An overview of CMIP5 and CMIP6 model performance in simulating blocking and extratropical cyclones is given in Section 3.3.3.3. CMIP6 models still suffer from long-standing blocking biases identified in previous generations of models. However, blocking location has improved compared to CMIP5, while comparable performance is seen for blocking frequency and persistence (Figure 10.7). Increasing horizontal model resolution to about 20 km in the HighResMIP experiments improves the representation of blocking frequency and its spatial pattern in most models, but no clear effect could be shown for blocking persistence.', 'reranking_score': 6.302902329480276e-05, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='accurate representation of blocking and extratropical cyclones in global and regional climate models is needed to better understand regional climate variability and extremes as well as to project future changes (Section 11.7.2; Grotjahn et al., 2016; Mitchell et al., 2017; Rohrer et al., 2018; Huguenin et al., 2020). An overview of CMIP5 and CMIP6 model performance in simulating blocking and extratropical cyclones is given in Section 3.3.3.3. CMIP6 models still suffer from long-standing blocking biases identified in previous generations of models. However, blocking location has improved compared to CMIP5, while comparable performance is seen for blocking frequency and persistence (Figure 10.7). Increasing horizontal model resolution to about 20 km in the HighResMIP experiments improves the representation of blocking frequency and its spatial pattern in most models, but no clear effect could be shown for blocking persistence.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 697.0, 'num_tokens': 176.0, 'num_tokens_approx': 172.0, 'num_words': 129.0, 'page_number': 642, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '4.6.3.3 Climate Response to Solar Radiation Modification', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '4: Future Global Climate: Scenario-based Projections and Near-term Information', 'toc_level1': '4.6 Implications of Climate Policy', 'toc_level2': '4.6.3 Climate Response to Mitigation, Carbon Dioxide Removal and Solar Radiation Modification', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.6948632, 'content': 'As assessed in SR1.5 (de Coninck et al., 2018), most of the knowledge about SRM is based on idealized model simulations and some natural analogues. In addition to single-model studies, more results from the coordinated modelling work of Geoengineering Model Intercomparison Project (GeoMIP) have become available. GeoMIP was initiated at the time of AR5 (Kravitz et al., 2011, 2013a) and is now in its second phase under the framework of CMIP6 (GEOMIP6, Kravitz et al., 2015). However, studies based on GeoMIP6 data are currently limited and hence the assessment on climate response to SRM here is derived mostly from GeoMIP literature together with studies with single models.', 'reranking_score': 5.558468183153309e-05, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?'}, page_content='As assessed in SR1.5 (de Coninck et al., 2018), most of the knowledge about SRM is based on idealized model simulations and some natural analogues. In addition to single-model studies, more results from the coordinated modelling work of Geoengineering Model Intercomparison Project (GeoMIP) have become available. GeoMIP was initiated at the time of AR5 (Kravitz et al., 2011, 2013a) and is now in its second phase under the framework of CMIP6 (GEOMIP6, Kravitz et al., 2015). However, studies based on GeoMIP6 data are currently limited and hence the assessment on climate response to SRM here is derived mostly from GeoMIP literature together with studies with single models.')]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_stream', 'name': 'retrieve_documents', 'run_id': 'bceac6db-d865-4123-9ceb-98d88083c1b8', 'tags': ['seq:step:1'], 'metadata': {'langgraph_step': 5, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['branch:retrieve_documents:condition:retrieve_documents'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b', 'checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b'}, 'data': {'chunk': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")], 'remaining_questions': []}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'retrieve_documents', 'run_id': 'bceac6db-d865-4123-9ceb-98d88083c1b8', 'tags': ['seq:step:1'], 'metadata': {'langgraph_step': 5, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['branch:retrieve_documents:condition:retrieve_documents'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b', 'checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [{'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1056.0, 'num_tokens': 219.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 7, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.2 | Assessed contributions to observed warming in 2010-2019 relative to 1850-1900', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.595214307, 'content': 'Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}', 'reranking_score': 0.9769342541694641, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 638.0, 'num_tokens': 177.0, 'num_tokens_approx': 200.0, 'num_words': 150.0, 'page_number': 11, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.3 | Synthesis of assessed observed and attributable regional changes ', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.592556596, 'content': 'A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}', 'reranking_score': 0.907663881778717, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 827.0, 'num_tokens': 238.0, 'num_tokens_approx': 278.0, 'num_words': 209.0, 'page_number': 19, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.6 | Projected changes in the intensity and frequency of hot temperature extremes over land, extreme precipitation over land, \\r\\nand agricultural and ecological droughts in drying regions', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.582914889, 'content': 'B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919', 'reranking_score': 0.8376452922821045, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919'), Document(metadata={'chunk_type': 'text', 'document_id': 'document4', 'document_number': 4.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 806.0, 'num_tokens': 147.0, 'num_tokens_approx': 162.0, 'num_words': 122.0, 'page_number': 19, 'release_date': 2022.0, 'report_type': 'SPM', 'section_header': 'Complex, Compound and Cascading Risks', 'short_name': 'IPCC AR6 WGII SPM', 'source': 'IPCC', 'toc_level0': 'B: Observed and Projected Impacts and Risks', 'toc_level1': 'Impacts of Temporary Overshoot', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_SummaryForPolicymakers.pdf', 'similarity_score': 0.581911206, 'content': 'B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 686.0, 'num_tokens': 163.0, 'num_tokens_approx': 182.0, 'num_words': 137.0, 'page_number': 19, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.581764042, 'content': 'Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 644.0, 'num_tokens': 151.0, 'num_tokens_approx': 165.0, 'num_words': 124.0, 'page_number': 950, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.741419494, 'content': \"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\", 'reranking_score': 0.7089773416519165, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1121.0, 'num_tokens': 231.0, 'num_tokens_approx': 288.0, 'num_words': 216.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727996647, 'content': \"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\", 'reranking_score': 0.47518521547317505, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\")]}, 'output': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")], 'remaining_questions': []}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': '_write', 'run_id': '0c762529-601f-40ca-848d-f017fe0118bd', 'tags': ['seq:step:2', 'langsmith:hidden'], 'metadata': {'langgraph_step': 5, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['branch:retrieve_documents:condition:retrieve_documents'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b'}, 'data': {'input': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")], 'remaining_questions': []}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': '_write', 'run_id': '0c762529-601f-40ca-848d-f017fe0118bd', 'tags': ['seq:step:2', 'langsmith:hidden'], 'metadata': {'langgraph_step': 5, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['branch:retrieve_documents:condition:retrieve_documents'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b'}, 'data': {'input': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")], 'remaining_questions': []}, 'output': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")], 'remaining_questions': []}}, 'parent_ids': []}\n", + "{'event': 'on_chain_stream', 'name': 'retrieve_documents', 'run_id': 'a5ec2c2e-c7eb-4fe2-b7c8-e009264d3df9', 'tags': ['graph:step:5'], 'metadata': {'langgraph_step': 5, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['branch:retrieve_documents:condition:retrieve_documents'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b'}, 'data': {'chunk': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")], 'remaining_questions': []}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'retrieve_documents', 'run_id': 'a5ec2c2e-c7eb-4fe2-b7c8-e009264d3df9', 'tags': ['graph:step:5'], 'metadata': {'langgraph_step': 5, 'langgraph_node': 'retrieve_documents', 'langgraph_triggers': ['branch:retrieve_documents:condition:retrieve_documents'], 'langgraph_path': ('__pregel_pull', 'retrieve_documents'), 'langgraph_checkpoint_ns': 'retrieve_documents:bed6e373-8ab3-2fae-c18d-12b411ab436b'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [{'question': 'How are cloud formations represented in current climate models?', 'sources': ['IPOS', 'IPCC', 'IPBES'], 'index': 'Vector'}], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1056.0, 'num_tokens': 219.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 7, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.2 | Assessed contributions to observed warming in 2010-2019 relative to 1850-1900', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.595214307, 'content': 'Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}', 'reranking_score': 0.9769342541694641, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. {Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 638.0, 'num_tokens': 177.0, 'num_tokens_approx': 200.0, 'num_words': 150.0, 'page_number': 11, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.3 | Synthesis of assessed observed and attributable regional changes ', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.592556596, 'content': 'A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}', 'reranking_score': 0.907663881778717, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='A.4.1 Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m-2 in 2019 relative to 1750 has warmed the climate system. This warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. The radiative forcing has increased by 0.43 W m-2 (19%) relative to AR5, of which 0.34 W m-2 is due to the increase in GHG concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of aerosol forcing, which include decreases in concentration and improvement in its calculation (high confidence). {2.2, 7.3, TS.2.2, TS.3.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 827.0, 'num_tokens': 238.0, 'num_tokens_approx': 278.0, 'num_words': 209.0, 'page_number': 19, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.6 | Projected changes in the intensity and frequency of hot temperature extremes over land, extreme precipitation over land, \\r\\nand agricultural and ecological droughts in drying regions', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.582914889, 'content': 'B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919', 'reranking_score': 0.8376452922821045, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.3.4 A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated precipitation is likely in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term the effect of stratospheric ozone recovery counteracts these changes (high confidence). There is medium confidence in a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence in projected changes in the North Atlantic storm tracks. {4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\n{4.4, 4.5, 8.4, TS.2.3, TS.4.2}\\nB.4 Under scenarios with increasing CO2 emissions, the ocean and land carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere. {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)\\n1919'), Document(metadata={'chunk_type': 'text', 'document_id': 'document4', 'document_number': 4.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 34.0, 'name': 'Summary for Policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of the WGII to the AR6 of the IPCC', 'num_characters': 806.0, 'num_tokens': 147.0, 'num_tokens_approx': 162.0, 'num_words': 122.0, 'page_number': 19, 'release_date': 2022.0, 'report_type': 'SPM', 'section_header': 'Complex, Compound and Cascading Risks', 'short_name': 'IPCC AR6 WGII SPM', 'source': 'IPCC', 'toc_level0': 'B: Observed and Projected Impacts and Risks', 'toc_level1': 'Impacts of Temporary Overshoot', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_SummaryForPolicymakers.pdf', 'similarity_score': 0.581911206, 'content': 'B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO2 concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). {CWGB SRM}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 686.0, 'num_tokens': 163.0, 'num_tokens_approx': 182.0, 'num_words': 137.0, 'page_number': 19, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.581764042, 'content': 'Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313', 'reranking_score': 0.7240780591964722, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content='Permafrost, seasonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, an\\nsonal snow cover, glaciers, the Greenland and Antarctic Ice Sheets, and Arctic se\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n35 Based on 2500-year reconstructions, eruptions with a radiative forcing more negative than -1 W m-2, related to the radiative effect of volcanic stratospheric aerosols in the literature assessed in this report, occur on average twice per century. {4.3}\\n1313'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 644.0, 'num_tokens': 151.0, 'num_tokens_approx': 165.0, 'num_words': 124.0, 'page_number': 950, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.2.1 Present-day Energy Budget', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.2 Earth’s Energy Budget and its Changes\\xa0Through Time', 'toc_level2': '7.2.1 Present-day Energy Budget', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.741419494, 'content': \"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\", 'reranking_score': 0.7089773416519165, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Figure 7.2 (upper panel) shows a schematic representation of Earth's energy budget for the early 21st century, including globally averaged estimates of the individual components (Wild et al., 2015). Clouds are important modulators of global energy fluxes. Thus, any perturbations in the cloud fields, such as forcing by aerosol-cloud interactions (Section 7.3) or through cloud feedbacks (Section 7.4) can have a strong influence on the energy distribution in the climate system. To illustrate the overall effects that clouds exert on energy fluxes, Figure 7.2 (lower panel) also shows the energy budget in the absence \\n933933\"), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1121.0, 'num_tokens': 231.0, 'num_tokens_approx': 288.0, 'num_words': 216.0, 'page_number': 1039, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.6 Metrics to Evaluate Emissions', 'toc_level2': 'Frequently Asked Questions', 'toc_level3': 'FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.727996647, 'content': \"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\", 'reranking_score': 0.47518521547317505, 'query_used_for_retrieval': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': \"What role do cloud formations play in modulating the Earth's radiative balance?\", 'index_used': 'Vector'}, page_content=\"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol\")]}, 'output': {'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")], 'remaining_questions': []}}, 'parent_ids': []}\n", + "{'event': 'on_chain_stream', 'run_id': 'debff86f-5fee-42e7-9f9e-6f9f4147948a', 'tags': [], 'metadata': {}, 'name': 'LangGraph', 'data': {'chunk': {'retrieve_documents': {'remaining_questions': [], 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': 'answer_search', 'run_id': 'e20087f9-f7a8-4dbd-b3b7-0077ab704edd', 'tags': ['graph:step:6'], 'metadata': {'langgraph_step': 6, 'langgraph_node': 'answer_search', 'langgraph_triggers': ['branch:retrieve_documents:condition:answer_search'], 'langgraph_path': ('__pregel_pull', 'answer_search'), 'langgraph_checkpoint_ns': 'answer_search:1d9d622b-3e7a-d296-cc30-156c83253af7'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': '_write', 'run_id': '8967028d-9d7d-4c5b-9a85-b92583543cf4', 'tags': ['seq:step:2', 'langsmith:hidden'], 'metadata': {'langgraph_step': 6, 'langgraph_node': 'answer_search', 'langgraph_triggers': ['branch:retrieve_documents:condition:answer_search'], 'langgraph_path': ('__pregel_pull', 'answer_search'), 'langgraph_checkpoint_ns': 'answer_search:1d9d622b-3e7a-d296-cc30-156c83253af7'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': '_write', 'run_id': '8967028d-9d7d-4c5b-9a85-b92583543cf4', 'tags': ['seq:step:2', 'langsmith:hidden'], 'metadata': {'langgraph_step': 6, 'langgraph_node': 'answer_search', 'langgraph_triggers': ['branch:retrieve_documents:condition:answer_search'], 'langgraph_path': ('__pregel_pull', 'answer_search'), 'langgraph_checkpoint_ns': 'answer_search:1d9d622b-3e7a-d296-cc30-156c83253af7'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}, 'output': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_stream', 'name': 'answer_search', 'run_id': 'e20087f9-f7a8-4dbd-b3b7-0077ab704edd', 'tags': ['graph:step:6'], 'metadata': {'langgraph_step': 6, 'langgraph_node': 'answer_search', 'langgraph_triggers': ['branch:retrieve_documents:condition:answer_search'], 'langgraph_path': ('__pregel_pull', 'answer_search'), 'langgraph_checkpoint_ns': 'answer_search:1d9d622b-3e7a-d296-cc30-156c83253af7'}, 'data': {'chunk': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'answer_search', 'run_id': 'e20087f9-f7a8-4dbd-b3b7-0077ab704edd', 'tags': ['graph:step:6'], 'metadata': {'langgraph_step': 6, 'langgraph_node': 'answer_search', 'langgraph_triggers': ['branch:retrieve_documents:condition:answer_search'], 'langgraph_path': ('__pregel_pull', 'answer_search'), 'langgraph_checkpoint_ns': 'answer_search:1d9d622b-3e7a-d296-cc30-156c83253af7'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. 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(See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}, 'output': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_stream', 'run_id': 'debff86f-5fee-42e7-9f9e-6f9f4147948a', 'tags': [], 'metadata': {}, 'name': 'LangGraph', 'data': {'chunk': {'answer_search': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': 'answer_rag', 'run_id': '0100669a-1dbb-47b2-8ecf-dec81fc9365a', 'tags': ['graph:step:7'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': 'RunnableSequence', 'run_id': '4aa53aa8-484e-4763-a4de-a3f60f884935', 'tags': ['seq:step:1'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': 'RunnableParallel', 'run_id': 'df65b5ee-c7f2-42d5-8acb-5e20c117263f', 'tags': ['seq:step:1'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': 'RunnableLambda', 'run_id': '352ca151-c5d1-4923-8d98-70e7d72d2155', 'tags': ['map:key:context'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': 'RunnableLambda', 'run_id': '2660c7ab-e52c-40bc-a2a3-b45554d0fa17', 'tags': ['map:key:query'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': 'RunnableLambda', 'run_id': '967b638b-114b-491c-8908-0607cad3cf41', 'tags': ['map:key:language'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': 'RunnableLambda', 'run_id': 'cba5c4fe-6d2a-46a2-9062-21d7c5a618d7', 'tags': ['map:key:audience'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'RunnableLambda', 'run_id': '2660c7ab-e52c-40bc-a2a3-b45554d0fa17', 'tags': ['map:key:query'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}, 'output': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\"}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'RunnableLambda', 'run_id': '967b638b-114b-491c-8908-0607cad3cf41', 'tags': ['map:key:language'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}, 'output': 'English'}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'RunnableLambda', 'run_id': '352ca151-c5d1-4923-8d98-70e7d72d2155', 'tags': ['map:key:context'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. 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(See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}, 'output': \"Doc 1: Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.\\n\\nDoc 2: Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}\\n\\nDoc 3: 30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1) 31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}\\n\\nDoc 4: Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). Figure SPM.1 | History of global temperature change and causes of recent warming \\n\\nDoc 5: indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in\\n\\nDoc 6: global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.\\n\\nDoc 7: The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\"}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'RunnableLambda', 'run_id': 'cba5c4fe-6d2a-46a2-9062-21d7c5a618d7', 'tags': ['map:key:audience'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. 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These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. 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Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. 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The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. 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Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. 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Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}, 'output': 'expert'}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'RunnableParallel', 'run_id': 'df65b5ee-c7f2-42d5-8acb-5e20c117263f', 'tags': ['seq:step:1'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}, 'output': {'context': \"Doc 1: Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.\\n\\nDoc 2: Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}\\n\\nDoc 3: 30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1) 31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}\\n\\nDoc 4: Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). Figure SPM.1 | History of global temperature change and causes of recent warming \\n\\nDoc 5: indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in\\n\\nDoc 6: global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.\\n\\nDoc 7: The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'audience': 'expert'}}, 'parent_ids': []}\n", + "{'event': 'on_prompt_start', 'name': 'ChatPromptTemplate', 'run_id': '5c45def8-1f34-422e-8453-7036c477b192', 'tags': ['seq:step:2'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'context': \"Doc 1: Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.\\n\\nDoc 2: Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}\\n\\nDoc 3: 30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1) 31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}\\n\\nDoc 4: Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). Figure SPM.1 | History of global temperature change and causes of recent warming \\n\\nDoc 5: indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in\\n\\nDoc 6: global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.\\n\\nDoc 7: The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'audience': 'expert'}}, 'parent_ids': []}\n", + "{'event': 'on_prompt_end', 'name': 'ChatPromptTemplate', 'run_id': '5c45def8-1f34-422e-8453-7036c477b192', 'tags': ['seq:step:2'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'context': \"Doc 1: Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.\\n\\nDoc 2: Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}\\n\\nDoc 3: 30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1) 31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}\\n\\nDoc 4: Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). Figure SPM.1 | History of global temperature change and causes of recent warming \\n\\nDoc 5: indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in\\n\\nDoc 6: global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.\\n\\nDoc 7: The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'audience': 'expert'}, 'output': ChatPromptValue(messages=[HumanMessage(content=\"\\nYou are ClimateQ&A, an AI Assistant created by Ekimetrics. You are given a question and extracted passages of the IPCC and/or IPBES reports. Provide a clear and structured answer based on the passages provided, the context and the guidelines.\\n\\nGuidelines:\\n- If the passages have useful facts or numbers, use them in your answer.\\n- When you use information from a passage, mention where it came from by using [Doc i] at the end of the sentence. i stands for the number of the document.\\n- Do not use the sentence 'Doc i says ...' to say where information came from.\\n- If the same thing is said in more than one document, you can mention all of them like this: [Doc i, Doc j, Doc k]\\n- Do not just summarize each passage one by one. Group your summaries to highlight the key parts in the explanation.\\n- If it makes sense, use bullet points and lists to make your answers easier to understand.\\n- You do not need to use every passage. Only use the ones that help answer the question.\\n- If the documents do not have the information needed to answer the question, just say you do not have enough information.\\n- Consider by default that the question is about the past century unless it is specified otherwise. \\n- If the passage is the caption of a picture, you can still use it as part of your answer as any other document.\\n\\n-----------------------\\nPassages:\\nDoc 1: Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.\\n\\nDoc 2: Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}\\n\\nDoc 3: 30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1) 31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}\\n\\nDoc 4: Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). Figure SPM.1 | History of global temperature change and causes of recent warming \\n\\nDoc 5: indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in\\n\\nDoc 6: global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.\\n\\nDoc 7: The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\\n\\n-----------------------\\nQuestion: I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models? - Explained to expert\\nAnswer in English with the passages citations:\\n\")])}, 'parent_ids': []}\n", + "{'event': 'on_chat_model_start', 'name': 'ChatOpenAI', 'run_id': 'a155bb6b-716d-4ad1-9bb7-46e72656b090', 'tags': ['seq:step:3'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'ls_provider': 'openai', 'ls_model_type': 'chat', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'data': {'input': {'messages': [[HumanMessage(content=\"\\nYou are ClimateQ&A, an AI Assistant created by Ekimetrics. You are given a question and extracted passages of the IPCC and/or IPBES reports. Provide a clear and structured answer based on the passages provided, the context and the guidelines.\\n\\nGuidelines:\\n- If the passages have useful facts or numbers, use them in your answer.\\n- When you use information from a passage, mention where it came from by using [Doc i] at the end of the sentence. i stands for the number of the document.\\n- Do not use the sentence 'Doc i says ...' to say where information came from.\\n- If the same thing is said in more than one document, you can mention all of them like this: [Doc i, Doc j, Doc k]\\n- Do not just summarize each passage one by one. Group your summaries to highlight the key parts in the explanation.\\n- If it makes sense, use bullet points and lists to make your answers easier to understand.\\n- You do not need to use every passage. Only use the ones that help answer the question.\\n- If the documents do not have the information needed to answer the question, just say you do not have enough information.\\n- Consider by default that the question is about the past century unless it is specified otherwise. \\n- If the passage is the caption of a picture, you can still use it as part of your answer as any other document.\\n\\n-----------------------\\nPassages:\\nDoc 1: Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.\\n\\nDoc 2: Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}\\n\\nDoc 3: 30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1) 31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}\\n\\nDoc 4: Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). Figure SPM.1 | History of global temperature change and causes of recent warming \\n\\nDoc 5: indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in\\n\\nDoc 6: global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.\\n\\nDoc 7: The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\\n\\n-----------------------\\nQuestion: I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models? - Explained to expert\\nAnswer in English with the passages citations:\\n\")]]}}, 'parent_ids': []}\n", + "{'event': 'on_chat_model_end', 'name': 'ChatOpenAI', 'run_id': 'a155bb6b-716d-4ad1-9bb7-46e72656b090', 'tags': ['seq:step:3'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'ls_provider': 'openai', 'ls_model_type': 'chat', 'ls_model_name': 'gpt-3.5-turbo-0125', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'data': {'input': {'messages': [[HumanMessage(content=\"\\nYou are ClimateQ&A, an AI Assistant created by Ekimetrics. You are given a question and extracted passages of the IPCC and/or IPBES reports. Provide a clear and structured answer based on the passages provided, the context and the guidelines.\\n\\nGuidelines:\\n- If the passages have useful facts or numbers, use them in your answer.\\n- When you use information from a passage, mention where it came from by using [Doc i] at the end of the sentence. i stands for the number of the document.\\n- Do not use the sentence 'Doc i says ...' to say where information came from.\\n- If the same thing is said in more than one document, you can mention all of them like this: [Doc i, Doc j, Doc k]\\n- Do not just summarize each passage one by one. Group your summaries to highlight the key parts in the explanation.\\n- If it makes sense, use bullet points and lists to make your answers easier to understand.\\n- You do not need to use every passage. Only use the ones that help answer the question.\\n- If the documents do not have the information needed to answer the question, just say you do not have enough information.\\n- Consider by default that the question is about the past century unless it is specified otherwise. \\n- If the passage is the caption of a picture, you can still use it as part of your answer as any other document.\\n\\n-----------------------\\nPassages:\\nDoc 1: Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.\\n\\nDoc 2: Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}\\n\\nDoc 3: 30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1) 31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}\\n\\nDoc 4: Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). Figure SPM.1 | History of global temperature change and causes of recent warming \\n\\nDoc 5: indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in\\n\\nDoc 6: global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.\\n\\nDoc 7: The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\\n\\n-----------------------\\nQuestion: I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models? - Explained to expert\\nAnswer in English with the passages citations:\\n\")]]}, 'output': {'generations': [[{'text': \"Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\\n\\nIn current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\\n\\nDespite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \\n\\nOverall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance.\", 'generation_info': {'finish_reason': 'stop'}, 'type': 'ChatGeneration', 'message': AIMessage(content=\"Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\\n\\nIn current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\\n\\nDespite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \\n\\nOverall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance.\", response_metadata={'finish_reason': 'stop'}, id='run-a155bb6b-716d-4ad1-9bb7-46e72656b090')}]], 'llm_output': None, 'run': None}}, 'parent_ids': []}\n", + "{'event': 'on_parser_start', 'name': 'StrOutputParser', 'run_id': '91adb470-1e63-476a-b378-e68ff7b60a12', 'tags': ['seq:step:4'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': AIMessage(content=\"Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\\n\\nIn current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\\n\\nDespite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \\n\\nOverall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance.\", response_metadata={'finish_reason': 'stop'}, id='run-a155bb6b-716d-4ad1-9bb7-46e72656b090')}, 'parent_ids': []}\n", + "{'event': 'on_parser_end', 'name': 'StrOutputParser', 'run_id': '91adb470-1e63-476a-b378-e68ff7b60a12', 'tags': ['seq:step:4'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': AIMessage(content=\"Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\\n\\nIn current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\\n\\nDespite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \\n\\nOverall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance.\", response_metadata={'finish_reason': 'stop'}, id='run-a155bb6b-716d-4ad1-9bb7-46e72656b090'), 'output': \"Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\\n\\nIn current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\\n\\nDespite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \\n\\nOverall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance.\"}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'RunnableSequence', 'run_id': '4aa53aa8-484e-4763-a4de-a3f60f884935', 'tags': ['seq:step:1'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71', 'checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. 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The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. 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In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}, 'output': \"Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\\n\\nIn current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\\n\\nDespite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \\n\\nOverall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance.\"}, 'parent_ids': []}\n", + "{'event': 'on_chain_start', 'name': '_write', 'run_id': '0b52324a-ab2d-4522-a46e-bb8479927b72', 'tags': ['seq:step:2', 'langsmith:hidden'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'answer': \"Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\\n\\nIn current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\\n\\nDespite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \\n\\nOverall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance.\"}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': '_write', 'run_id': '0b52324a-ab2d-4522-a46e-bb8479927b72', 'tags': ['seq:step:2', 'langsmith:hidden'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'answer': \"Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\\n\\nIn current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\\n\\nDespite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \\n\\nOverall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance.\"}, 'output': {'answer': \"Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\\n\\nIn current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\\n\\nDespite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \\n\\nOverall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance.\"}}, 'parent_ids': []}\n", + "{'event': 'on_chain_stream', 'name': 'answer_rag', 'run_id': '0100669a-1dbb-47b2-8ecf-dec81fc9365a', 'tags': ['graph:step:7'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'chunk': {'answer': \"Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\\n\\nIn current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\\n\\nDespite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \\n\\nOverall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance.\"}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'answer_rag', 'run_id': '0100669a-1dbb-47b2-8ecf-dec81fc9365a', 'tags': ['graph:step:7'], 'metadata': {'langgraph_step': 7, 'langgraph_node': 'answer_rag', 'langgraph_triggers': ['branch:answer_search:condition:answer_rag'], 'langgraph_path': ('__pregel_pull', 'answer_rag'), 'langgraph_checkpoint_ns': 'answer_rag:e1a63201-8d0b-c3f8-fb80-45b97976fb71'}, 'data': {'input': {'user_input': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'language': 'English', 'intent': 'search', 'query': \"I am not really sure what you mean. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\", 'remaining_questions': [], 'n_questions': 2, 'audience': 'expert', 'documents': [Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 1034.0, 'num_tokens': 198.0, 'num_tokens_approx': 230.0, 'num_words': 173.0, 'page_number': 12, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Box SPM.1 | Scenarios, Climate Models and Projections', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.6897524, 'content': 'Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.', 'reranking_score': 0.9990547299385071, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global surface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is within the very likely range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed very likely range of observed warming.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 711.0, 'num_tokens': 193.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 17, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.5 | Changes in annual mean surface temperature, precipitation, and soil moisture', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'B. Possible Climate Futures', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.67004168, 'content': 'Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}', 'reranking_score': 0.998754620552063, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Maps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6. Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness. {Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document10', 'document_number': 10.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Synthesis report of the IPCC Sixth Assesment Report AR6', 'num_characters': 694.0, 'num_tokens': 232.0, 'num_tokens_approx': 266.0, 'num_words': 200.0, 'page_number': 18, 'release_date': 2023.0, 'report_type': 'SPM', 'section_header': 'Future Climate Change ', 'short_name': 'IPCC AR6 SYR', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf', 'similarity_score': 0.664377, 'content': '30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='30 The best estimates [and very likely ranges] for the different scenarios are: 1.4 [1.0 to 1.8 ]degC (SSP1-1.9); 1.8 [1.3 to 2.4]degC (SSP1-2.6); 2.7 [2.1 to 3.5]degC (SSP2-4.5); 3.6 [2.8 to 4.6]degC (SSP3-7.0); and 4.4 [3.3 to 5.7 ]degC (SSP5-8.5). {3.1.1} (Box SPM.1)\\n31 Assessed future changes in global surface temperature have been constructed, for the first time, by combining multi-model projections with observational constraints and the assessed equilibrium climate sensitivity and transient climate response. The uncertainty range is narrower than in the AR5 thanks to improved knowledge of climate processes, paleoclimate evidence and model-based emergent constraints. {3.1.1}'), Document(metadata={'chunk_type': 'text', 'document_id': 'document1', 'document_number': 1.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 32.0, 'name': 'Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 529.0, 'num_tokens': 113.0, 'num_tokens_approx': 138.0, 'num_words': 104.0, 'page_number': 6, 'release_date': 2021.0, 'report_type': 'SPM', 'section_header': 'Figure SPM.1 | History of global temperature change and causes of recent warming', 'short_name': 'IPCC AR6 WGI SPM', 'source': 'IPCC', 'toc_level0': 'A. The Current State of the Climate', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf', 'similarity_score': 0.658697307, 'content': 'Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming ', 'reranking_score': 0.9964662790298462, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming). \\n Figure SPM.1 | History of global temperature change and causes of recent warming '), Document(metadata={'chunk_type': 'text', 'document_id': 'document13', 'document_number': 13.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 36.0, 'name': 'Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate', 'num_characters': 1032.0, 'num_tokens': 214.0, 'num_tokens_approx': 252.0, 'num_words': 189.0, 'page_number': 24, 'release_date': 2019.0, 'report_type': 'SPM', 'section_header': 'Summary for Policymakers', 'short_name': 'IPCC SR OC SPM', 'source': 'IPCC', 'toc_level0': 'N/A', 'toc_level1': 'N/A', 'toc_level2': 'N/A', 'toc_level3': 'N/A', 'url': 'https://www.ipcc.ch/site/assets/uploads/sites/3/2022/03/01_SROCC_SPM_FINAL.pdf', 'similarity_score': 0.65217036, 'content': 'indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in', 'reranking_score': 0.9940266609191895, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='indicate areas of model inconsistency, shaded areas represent regions where models disagree in the direction of change for more than: (a) and (b) 3 out of 10 model projections, and (c) one out of two models. Although unshaded, the projected change in the Arctic and Antarctic regions in (b) total animal biomass and (c) fisheries catch potential have low confidence due to uncertainties associated with modelling multiple interacting drivers and ecosystem responses. Projections presented in (b) and (c) are driven by changes in ocean physical and biogeochemical conditions e.g., temperature, oxygen level, and net primary production projected from CMIP5 Earth system models. **The epipelagic refers to the uppermost part of the ocean with depth <200 m from the surface where there is enough sunlight to allow photosynthesis. (d) Assessment of risks for coastal and open ocean ecosystems based on observed and projected climate impacts on ecosystem structure, functioning and biodiversity. Impacts and risks are shown in'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 352.0, 'num_tokens': 98.0, 'num_tokens_approx': 102.0, 'num_words': 77.0, 'page_number': 2196, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': 'The Madden-Julian Oscillation (MJO)', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': 'Annex IV: Modes of Variability', 'toc_level1': 'AIV.2 The Main Modes of Climate Variability\\xa0Assessed in AR6', 'toc_level2': 'AIV.2.8 Madden–Julian Oscillation', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738178253, 'content': 'global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.', 'reranking_score': 0.9927944540977478, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content='global cloud system-resolving models (Miyakawa et al., 2014) and high-resolution global climate models with an improved seasonal cycle (Mizuta et al., 2012) have been shown to produce a more realistic simulation of the MJO. Progresses in the representation of the MJO across model generations are assessed in Sections 1.5.4.6 and 8.3.2.9.1.'), Document(metadata={'chunk_type': 'text', 'document_id': 'document2', 'document_number': 2.0, 'element_id': 'N/A', 'figure_code': 'N/A', 'file_size': 'N/A', 'image_path': 'N/A', 'n_pages': 2409.0, 'name': 'Full Report. In: Climate Change 2021: The Physical Science Basis. Contribution of the WGI to the AR6 of the IPCC', 'num_characters': 975.0, 'num_tokens': 211.0, 'num_tokens_approx': 237.0, 'num_words': 178.0, 'page_number': 1025, 'release_date': 2021.0, 'report_type': 'Full Report', 'section_header': '7.5.6 Considerations on the ECS and TCR in Global \\r\\nClimate Models and Their Role in the Assessment', 'short_name': 'IPCC AR6 WGI FR', 'source': 'IPCC', 'toc_level0': '7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity', 'toc_level1': '7.5 Estimates of ECS and TCR', 'toc_level2': '7.5.6 Considerations on the ECS and TCR in Global Climate Models and Their Role in the Assessment', 'toc_level3': 'N/A', 'url': 'https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf', 'similarity_score': 0.738108695, 'content': \"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\", 'reranking_score': 0.9924079179763794, 'query_used_for_retrieval': 'How are cloud formations represented in current climate models?', 'sources_used': ['IPOS', 'IPCC', 'IPBES'], 'question_used': 'How are cloud formations represented in current climate models?', 'index_used': 'Vector'}, page_content=\"The ECS of a model is the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are\")]}, 'output': {'answer': \"Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\\n\\nIn current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\\n\\nDespite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \\n\\nOverall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance.\"}}, 'parent_ids': []}\n", + "{'event': 'on_chain_stream', 'run_id': 'debff86f-5fee-42e7-9f9e-6f9f4147948a', 'tags': [], 'metadata': {}, 'name': 'LangGraph', 'data': {'chunk': {'answer_rag': {'answer': \"Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\\n\\nIn current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\\n\\nDespite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \\n\\nOverall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance.\"}}}, 'parent_ids': []}\n", + "{'event': 'on_chain_end', 'name': 'LangGraph', 'run_id': 'debff86f-5fee-42e7-9f9e-6f9f4147948a', 'tags': [], 'metadata': {}, 'data': {'output': {'answer_rag': {'answer': \"Cloud formations play a crucial role in modulating the Earth's radiative balance. They can either reflect incoming solar radiation back to space, cooling the Earth, or trap outgoing infrared radiation, contributing to warming. The representation of clouds in climate models is essential for accurately simulating the Earth's energy balance and predicting future climate changes.\\n\\nIn current climate models, the Effective Climate Sensitivity (ECS) is a key metric that includes the net result of the model's effective radiative forcing from a doubling of CO2 and the sum of individual feedbacks, including cloud feedbacks. The ECS is influenced by the response of low-level clouds, which are particularly important due to their impact on the Earth's radiative balance. However, the representation of low-level clouds in climate models is challenging because they are small-scale and shallow, requiring sub-grid-scale parametrizations.\\n\\nDespite efforts to improve parametrizations and model resolution, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5, indicating ongoing challenges in accurately representing cloud formations and their impact on the Earth's radiative balance [Doc 7]. \\n\\nOverall, while current climate models have made advancements in representing physical, chemical, and biological processes, including cloud formations, there are still uncertainties and challenges in accurately capturing the complexities of cloud feedbacks and their role in modulating the Earth's radiative balance.\"}}}, 'parent_ids': []}\n" + ] + } + ], + "source": [ + "for event in events_list:\n", + " print(event)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1ba9fad4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'event': 'on_chain_start',\n", + " 'run_id': 'f405c636-8152-4d9f-ad15-f292600e7ae8',\n", + " 'name': 'LangGraph',\n", + " 'tags': [],\n", + " 'metadata': {},\n", + " 'data': {'input': {'user_input': \"I am not really sure what you mean. 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