{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07f255d7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c9258cc-3800-4485-bdd8-889b299b9133",
   "metadata": {},
   "source": [
    "# Import objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6af1a96e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from climateqa.engine.llm import get_llm\n",
    "from climateqa.engine.llm.ollama import get_llm as get_llm_ollama\n",
    "\n",
    "llm = get_llm(provider=\"openai\")\n",
    "# llm = get_llm_ollama(model = \"llama3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a9128bfc-4b24-4b25-b7a7-68423b1124b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading FlashRankRanker model ms-marco-TinyBERT-L-2-v2\n",
      "Loading model FlashRank model ms-marco-TinyBERT-L-2-v2...\n"
     ]
    }
   ],
   "source": [
    "from climateqa.engine.reranker import get_reranker\n",
    "\n",
    "# reranker = get_reranker(\"large\")\n",
    "reranker = get_reranker(\"nano\")\n",
    "# from rerankers import Reranker\n",
    "# # Specific flashrank model.\n",
    "# # reranker = Reranker('ms-marco-TinyBERT-L-2-v2', model_type='flashrank')\n",
    "# # reranker = Reranker('ms-marco-MiniLM-L-12-v2', model_type='flashrank')\n",
    "# # reranker = Reranker('cross-encoder/ms-marco-MiniLM-L-4-v2', model_type='cross-encoder')\n",
    "# # reranker = Reranker(\"mixedbread-ai/mxbai-rerank-xsmall-v1\", model_type='cross-encoder')\n",
    "# # reranker = Reranker(\"mixedbread-ai/mxbai-rerank-xsmall-v1\", model_type='cross-encoder')\n",
    "# reranker = Reranker(\"cohere\", lang='en', api_key = \"XXX\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "942d2705-22dd-46cf-8c31-6daa127e4743",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading embeddings model:  BAAI/bge-base-en-v1.5\n"
     ]
    },
    {
     "name": "stderr",
     "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: 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"
     ]
    }
   ],
   "source": [
    "from climateqa.engine.vectorstore import get_pinecone_vectorstore\n",
    "from climateqa.engine.embeddings import get_embeddings_function\n",
    "from climateqa.knowledge.retriever import ClimateQARetriever\n",
    "\n",
    "embeddings_function = get_embeddings_function()\n",
    "vectorstore = get_pinecone_vectorstore(embeddings_function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "882811c8-5890-4048-8630-d052c5179d7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "51aed81d-860b-409a-bae0-f0e1eeb0f120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16eb91cb-3bfb-4c0c-b29e-753c954c2399",
   "metadata": {},
   "source": [
    "# Semantic Routing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e769371-1f8c-4f34-89c5-c0f9914d47a0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from climateqa.engine.chains.intent_categorization import make_intent_categorization_chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "480ad611-33c7-49ea-b02c-94d6ce1f1d1a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cat_chain = make_intent_categorization_chain(llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82cf49d9-d48e-4d5c-8666-bcc95f637371",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# for question in SAMPLE_QUESTIONS:\n",
    "#     output = router_chain.invoke({\"input\":question})\n",
    "#     print(question)\n",
    "#     print(output)\n",
    "#     print(\"-\"*100)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8ef7e0f-ac5f-4323-b02e-753ce2b4afda",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cat_chain.invoke({\"input\":\"Which industries have the highest GHG emissions?\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a9c89f2d-c597-47b8-ae50-75eda964e609",
   "metadata": {},
   "outputs": [],
   "source": [
    "from climateqa.knowledge.openalex import OpenAlexRetriever\n",
    "from climateqa.engine.chains.keywords_extraction import make_keywords_extraction_chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c609d34-5767-47a6-90a4-d5987e9499ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "kec = make_keywords_extraction_chain(llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d781cd4-228b-462f-bd0e-55c02084a616",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = kec.invoke(\"What is the environmental footprint of artificial intelligence\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "553fd4d4-fe5a-4050-ae31-dffa2c7af7b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "50a57f08-40e5-4419-8568-0739a13af904",
   "metadata": {},
   "outputs": [],
   "source": [
    "ROUTING_INDEX = {\n",
    "    \"Vector\":[\"IPCC\",\"IPBES\",\"IPOS\"],\n",
    "    \"OpenAlex\":[\"OpenAlex\"],\n",
    "}\n",
    "\n",
    "POSSIBLE_SOURCES = [y for values in ROUTING_INDEX.values() for y in values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6b45dd9c-58c3-4204-bf44-7b9eb35dcb9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "questions = [\n",
    "    {\"question\":\"What is climate change ?\",\"sources\":[\"IPCC\",\"IPBES\"]},\n",
    "    {\"question\":\"What is the link between El Nino and climate change ?\",\"sources\":[\"IPCC\",\"OpenAlex\"]},\n",
    "]\n",
    "\n",
    "state = {\"remaining_questions\":questions,\"auto_mode\":False,\"sources_input\":[\"OpenAlex\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "90b02a8e-6e67-4592-8bc7-959b544e914b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from climateqa.engine.chains.search import make_search_node\n",
    "from climateqa.engine.graph import search\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "347737d8-c4cb-45a7-9e5d-74d1a2e3f5a2",
   "metadata": {},
   "outputs": [
    {
     "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(search(state))\n",
    "state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a27425de-1297-417a-af02-fba270df8e38",
   "metadata": {},
   "outputs": [],
   "source": [
    "from climateqa.engine.chains.retrieve_documents import make_retriever_node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fde71c99-8bdd-4fd5-b36e-6804ec21bf2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "retrieve = make_retriever_node(vectorstore,reranker,llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdad5b2f-2103-43d6-9d7f-e2addc2c2c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_state = await retrieve.ainvoke(state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "751a9a6a-8271-4e89-8754-e2065cd7d737",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(new_state[\"documents\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06db606d-0b04-4e7d-9f55-4f7cefbce654",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_state[\"documents\"][1][\"reranking_score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5ed23f9-bbbe-4a0b-b919-429e08837765",
   "metadata": {},
   "outputs": [],
   "source": [
    "set([\"IPCC\",\"OpenAlex\"]).intersection(set([\"IPCC\",\"IPBES\",\"IPOS\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca801dc5-958b-4392-88ac-5cb69e5f6fab",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_questions = []\n",
    "\n",
    "for q in questions:\n",
    "    question,sources = q[\"question\"],q[\"sources\"]\n",
    "\n",
    "    for index,index_sources in ROUTING_INDEX.items():\n",
    "        selected_sources = list(set(sources).intersection(index_sources))\n",
    "        new_questions.append({\"question\":question,\"sources\":selected_sources,\"index\":index})\n",
    "\n",
    "new_questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "6091ada7-851c-4ffc-859e-ea33a6fbb310",
   "metadata": {},
   "outputs": [],
   "source": [
    "oa = OpenAlex()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "8c5771bb-9074-45e0-afdb-f9eda2cb5ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = oa.search(\"warming AND impoverished communities\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ef2094a-2e98-4ed9-b0a6-7536ae8057ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0996d61d-905e-4e88-bce3-3f905846dd73",
   "metadata": {},
   "outputs": [],
   "source": [
    "test[[\"title\",\"abstract\"]].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ee7ab03-2cb5-4c43-b38e-0996951d1649",
   "metadata": {},
   "source": [
    "##### test[\"content\"].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "677b95e3-ad04-41eb-85e6-76d45db81933",
   "metadata": {},
   "outputs": [],
   "source": [
    "oa = OpenAlexRetriever(min_year = 1960)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "c4d15feb-9faa-44bd-af2d-425351747725",
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = oa.invoke(\"cloud formations AND Earth's radiative balance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8eac98e-1efc-4f85-b8f5-41a8c0c03a8e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "docs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b27c6f48-f4d3-4525-aa1d-8c3c52eb2179",
   "metadata": {},
   "outputs": [],
   "source": [
    "docs[0].metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f50f767-64e9-4f5d-82b4-496530532ad9",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Query Rewriter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3345a24e-e794-4e84-93ae-98be5ca61af3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from climateqa.engine.chains.query_transformation import make_query_rewriter_chain,make_query_decomposition_chain\n",
    "from climateqa.engine.chains.translation import make_translation_chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16bd17cd-f9ea-489c-923f-acd851b09cd8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rewriter_chain = make_query_rewriter_chain(llm)\n",
    "translation_chain = make_translation_chain(llm)\n",
    "decomposition_chain = make_query_decomposition_chain(llm)\n",
    "router_chain = make_intent_router_chain(llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a2147fc-8437-44a3-87ae-52fe5b8f8f87",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def transform_query(user_input):\n",
    "    \n",
    "    state = {\"user_input\":user_input}\n",
    "    \n",
    "    # Route\n",
    "    output_router = router_chain.invoke({\"input\":user_input})\n",
    "    if \"language\" not in output_router: output_router[\"language\"] = \"English\"\n",
    "    state.update(output_router)\n",
    "    \n",
    "    # Translation\n",
    "    if output_router[\"language\"].lower() != \"english\":\n",
    "        translation = translation_chain.invoke({\"input\":user_input})\n",
    "        state[\"query\"] = translation[\"translation\"]\n",
    "    else:\n",
    "        state[\"query\"] = user_input\n",
    "        \n",
    "    # Decomposition\n",
    "    decomposition_output = decomposition_chain.invoke({\"input\":state[\"query\"]})\n",
    "    state.update(decomposition_output)\n",
    "    \n",
    "    # Query Analysis\n",
    "    questions = []\n",
    "    for question in state[\"questions\"]:\n",
    "        question_state = {\"question\":question}\n",
    "        analysis_output = rewriter_chain.invoke({\"input\":question})\n",
    "        question_state.update(analysis_output)\n",
    "        questions.append(question_state)\n",
    "    state[\"questions\"] = questions\n",
    "    \n",
    "    return state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4151d2e4-0bfe-4642-a185-2dcb639e6f78",
   "metadata": {
    "tags": []
   },
   "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",
    "question = \"I need to search the president of the United States, find their age, then find how old they will be in June 2023.\"\n",
    "question = \"What does Morrison argue about IK and LK on internal migration ?\"\n",
    "state = transform_query(question)\n",
    "state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a322f737-191d-407f-b499-2b79476fac8b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "questions = [\n",
    "    \"Super thanks, Which industries have the highest GHG emissions?\",\n",
    "    \"How do you compare the view on biodiversity between the IPCC and IPBES ?\",\n",
    "    \"Est-ce que l'IA a un impact sur l'environnement ?\",\n",
    "    \"Que dit le GIEC sur l'impact de l'IA\",\n",
    "    \"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",
    "    \"Ok that's nice, but I don't really understand. What is the impact of El Nino ?\",\n",
    "    \"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",
    "    \"Which industries have the highest GHG emissions?\",\n",
    "    \"What are invasive alien species and how do they threaten biodiversity and ecosystems?\",\n",
    "    \"Are human activities causing global warming?\",\n",
    "    \"What is the motivation behind mining the deep seabed?\",\n",
    "    \"Tu peux m'écrire un poème sur le changement climatique ?\",\n",
    "    \"Tu peux m'écrire un poème sur les bonbons ?\",\n",
    "    \"What will be the temperature in 2100 in Strasbourg?\",\n",
    "    \"C'est quoi le lien entre biodiversity and changement climatique ?\",\n",
    "    \"Can you tell me more about ESRS2 ?\"\n",
    "]\n",
    "\n",
    "question = questions[0]\n",
    "question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcee82a3-342d-4da1-8c27-a6f6079da4a7",
   "metadata": {},
   "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",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d13d6a1a-9fa5-4e47-861e-30a79ea97d05",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for question in questions:\n",
    "    print(question)\n",
    "    output = transform_query(question)\n",
    "    print(output)\n",
    "    print(\"-\"*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a0d82ad-44e7-40a7-a594-48823429d70e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rewriter_chain.invoke({\"input\":question})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05aeeb92-9408-42b8-9f99-1e907c6a0798",
   "metadata": {},
   "source": [
    "# Langgraph\n",
    "Inspired from https://colab.research.google.com/drive/1WemHvycYcoNTDr33w7p2HL3FF72Nj88i?usp=sharing#scrollTo=YJ77ZCzkiGTL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8664f5f1-0db8-4c3d-8229-e1719224cde5",
   "metadata": {},
   "source": [
    "## Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2376e1d7-5893-4022-a0af-155bb8c1950f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from climateqa.engine.graph import make_graph_agent,display_graph\n",
    "agent = make_graph_agent(llm,vectorstore,reranker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1f78d00-e4a0-40f3-a1e9-61dea83fa6cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "display_graph(agent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a07607b-eb02-467f-a255-0c6bcdc6f62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "verbose = False\n",
    "# question = \"What does the IPCC and IPBES think about deforestation ?\"\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",
    "# question = \"Are human activities causing global warming?\"\n",
    "# question = \"What are the evidence of climate change?\"\n",
    "# question = \"What specific mechanisms link solar activity, particularly sunspots, to changes in global climate?\"\n",
    "# question = \"In what ways does warming specifically help impoverished communities?\"\n",
    "question = \"What's the impact of PFAS on babies ?\"\n",
    "\n",
    "steps_display = {\n",
    "    \"categorize_intent\":(\"... Analyzing user message\",True),\n",
    "    \"transform_query\":(\"... Thinking step by step to answer the question\",True),\n",
    "    # \"retrieve_documents\":(\"... Searching in the knowledge base\",False),\n",
    "}\n",
    "\n",
    "def display_steps(event):\n",
    "\n",
    "    for event_name,(event_description,display_output) in steps_display.items():\n",
    "        if event[\"name\"] == event_name:\n",
    "            if event[\"event\"] == \"on_chain_start\":\n",
    "                print(event_description)\n",
    "            elif event[\"event\"] == \"on_chain_end\":\n",
    "                if display_output:\n",
    "                    print(event[\"data\"][\"output\"])\n",
    "    if event[\"name\"] == \"log_retriever\" and event[\"event\"] == \"on_chain_start\":\n",
    "        question = event[\"data\"][\"input\"][\"question\"]\n",
    "        sources = event[\"data\"][\"input\"][\"sources\"]\n",
    "        print(f\"\"\"... Searching for \"{question}\" in {\", \".join(sources)}\"\"\")\n",
    "\n",
    "async for event in agent.astream_events({\"user_input\":question,\"sources_input\":[\"IPCC\"],\"sources_auto\":False,\"audience\":\"experts\"},version = \"v1\"):\n",
    "# async for event in agent.astream_log({\"user_input\":question,\"sources_input\":[\"auto\"],\"audience\":\"experts\"}):\n",
    "\n",
    "    if verbose:\n",
    "        print(str(event)[:1000])\n",
    "        print(\"-\"*50)\n",
    "    else:\n",
    "    \n",
    "        if event[\"event\"] == \"on_chat_model_stream\":\n",
    "            print(event[\"data\"][\"chunk\"].content,end = \"\")\n",
    "\n",
    "        if event[\"name\"] == \"retrieve_documents\" and event[\"event\"] == \"on_chain_end\":\n",
    "            docs = event[\"data\"][\"output\"][\"documents\"]\n",
    "            \n",
    "        display_steps(event)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a09f58e5-368b-486b-a458-7cfe56445c08",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b41f377-b3a9-41eb-acef-fb3ea131c374",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = await agent.ainvoke({\"user_input\":\"C'est quoi l'empreinte carbone de l'IA\"})\n",
    "output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d89f36b-fc04-4d0c-b9c4-990b489b7fc3",
   "metadata": {},
   "source": [
    "## Test simple route chains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1d884bb7-7230-47cb-a778-36cffed1fcde",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from climateqa.engine.chains.answer_chitchat import make_chitchat_chain\n",
    "from climateqa.engine.chains.answer_ai_impact import make_ai_impact_chain\n",
    "\n",
    "chitchat_chain = make_chitchat_chain(llm)\n",
    "ai_impact_chain = make_ai_impact_chain(llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "586a4ead-6064-42e8-9f3c-8973b8d754c6",
   "metadata": {
    "tags": []
   },
   "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",
    "        print(event[\"data\"][\"chunk\"],end = \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f3a1c52-f92a-442b-9442-22415087da8b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "async for event in chitchat_chain.astream_events({\"question\":\"Vas y blbablablala\"},version = \"v1\"):\n",
    "    if event[\"event\"] == \"on_chain_stream\":\n",
    "        print(event[\"data\"][\"chunk\"],end = \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0e380744-e03d-408d-9282-3fcb6925413b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.schema import Document\n",
    "from langgraph.graph import END, StateGraph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1788fbb7-90df-41e5-88c4-83c5e59fe23c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Retriever & Reranker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbe2f9f0-0c0b-46f8-929c-51a94eab5f22",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "query = \"Is global warming caused by humans?\"\n",
    "\n",
    "retriever = ClimateQARetriever(\n",
    "    vectorstore=vectorstore,\n",
    "    sources = [\"IPCC\"],\n",
    "    # reports = ias_reports,\n",
    "   min_size = 200,\n",
    "   k_summary = 5,k_total = 100,\n",
    "   threshold = 0.5,\n",
    ")\n",
    "\n",
    "docs_question = retriever.get_relevant_documents(query)\n",
    "len(docs_question)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e6892d2-a5bd-456a-8284-6e844d02af0e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "from scipy.special import expit, logit\n",
    "\n",
    "def rerank_docs(reranker,docs,query):\n",
    "    \n",
    "    # Get a list of texts from langchain docs\n",
    "    input_docs = [x.page_content for x in docs]\n",
    "    \n",
    "    # Rerank using rerankers library\n",
    "    results = reranker.rank(query=query, docs=input_docs)\n",
    "\n",
    "    # Prepare langchain list of docs\n",
    "    docs_reranked = []\n",
    "    for result in results.results:\n",
    "        doc_id = result.document.doc_id\n",
    "        doc = docs[doc_id]\n",
    "        doc.metadata[\"rerank_score\"] = result.score\n",
    "        doc.metadata[\"query_used_for_retrieval\"] = query\n",
    "        docs_reranked.append(doc)\n",
    "    return docs_reranked\n",
    "\n",
    "docs_reranked = rerank_docs(reranker,docs_question,query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "430209db-5ffb-4017-8ebd-12fee30df327",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "docs_reranked[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ed0b677-bcc4-48b0-bc99-2f515f2e7293",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def divide_into_parts(target, parts):\n",
    "    # Base value for each part\n",
    "    base = target // parts\n",
    "    # Remainder to distribute\n",
    "    remainder = target % parts\n",
    "    # List to hold the result\n",
    "    result = []\n",
    "    \n",
    "    for i in range(parts):\n",
    "        if i < remainder:\n",
    "            # These parts get base value + 1\n",
    "            result.append(base + 1)\n",
    "        else:\n",
    "            # The rest get the base value\n",
    "            result.append(base)\n",
    "    \n",
    "    return result\n",
    "\n",
    "divide_into_parts(15,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1886fe98-4a29-4419-b436-adcbdbda35ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "questions = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "4fca0c26-cc42-4f40-996a-c915b7c03a85",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "state = {'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?\",\n",
    " 'questions': [{'question': \"What role do cloud formations play in modulating the Earth's radiative balance?\",\n",
    "   'sources': ['IPCC']},\n",
    "  {'question': 'How are cloud formations represented in current climate models?',\n",
    "   'sources': ['IPCC']}]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "28307adf-42bb-4067-a95d-c5c4867d2657",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "state = {'query': \"What does Morrison argue about the role of IK and LK ?\",\n",
    " 'questions': [{'question': \"How is the manga One Piece cited in the IPCC\",\n",
    "   'sources': ['IPCC']}]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "9daa8285-919b-4eab-b071-70ea866d473e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "from contextlib import contextmanager\n",
    "\n",
    "@contextmanager\n",
    "def suppress_output():\n",
    "    # Open a null device\n",
    "    with open(os.devnull, 'w') as devnull:\n",
    "        # Store the original stdout and stderr\n",
    "        old_stdout = sys.stdout\n",
    "        old_stderr = sys.stderr\n",
    "        # Redirect stdout and stderr to the null device\n",
    "        sys.stdout = devnull\n",
    "        sys.stderr = devnull\n",
    "        try:\n",
    "            yield\n",
    "        finally:\n",
    "            # Restore stdout and stderr\n",
    "            sys.stdout = old_stdout\n",
    "            sys.stderr = old_stderr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "4fc2efe2-783d-44ba-a246-2472ce6fd19b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def retrieve_documents(state):\n",
    "    \n",
    "    POSSIBLE_SOURCES = [\"IPCC\",\"IPBES\",\"IPOS\",\"OpenAlex\"]\n",
    "    questions = state[\"questions\"]\n",
    "    \n",
    "    # Use sources from the user input or from the LLM detection\n",
    "    sources_input = state[\"sources_input\"] if \"sources_input\" in state else [\"auto\"]\n",
    "    auto_mode = \"auto\" in sources_input\n",
    "    \n",
    "    # Constants\n",
    "    k_final = 15\n",
    "    k_before_reranking = 100\n",
    "    k_summary = 5\n",
    "    rerank_by_question = True\n",
    "    \n",
    "    # There are several options to get the final top k\n",
    "    # Option 1 - Get 100 documents by question and rerank by question\n",
    "    # Option 2 - Get 100/n documents by question and rerank the total\n",
    "    if rerank_by_question:\n",
    "        k_by_question = divide_into_parts(k_final,len(questions))\n",
    "    \n",
    "    docs = []\n",
    "    \n",
    "    for i,q in enumerate(questions):\n",
    "        \n",
    "        sources = q[\"sources\"]\n",
    "        question = q[\"question\"]\n",
    "        \n",
    "        # If auto mode, we use the sources detected by the LLM\n",
    "        if auto_mode:\n",
    "            sources = [x for x in sources if x in POSSIBLE_SOURCES]\n",
    "            \n",
    "        # Otherwise, we use the config\n",
    "        else:\n",
    "            sources = sources_input\n",
    "            \n",
    "        # Search the document store using the retriever\n",
    "        # Configure high top k for further reranking step\n",
    "        retriever = ClimateQARetriever(\n",
    "            vectorstore=vectorstore,\n",
    "            sources = sources,\n",
    "            # reports = ias_reports,\n",
    "           min_size = 200,\n",
    "           k_summary = k_summary,k_total = k_before_reranking,\n",
    "           threshold = 0.5,\n",
    "        )\n",
    "        docs_question = retriever.get_relevant_documents(question)\n",
    "        \n",
    "        # Rerank\n",
    "        with suppress_output():\n",
    "            docs_question = rerank_docs(reranker,docs_question,question)\n",
    "        \n",
    "        # If rerank by question we select the top documents for each question\n",
    "        if rerank_by_question:\n",
    "            docs_question = docs_question[:k_by_question[i]]\n",
    "            \n",
    "        # Add sources used in the metadata\n",
    "        for doc in docs_question:\n",
    "            doc.metadata[\"sources_used\"] = sources\n",
    "        \n",
    "        # Add to the list of docs\n",
    "        docs.extend(docs_question)\n",
    "        \n",
    "    # Sorting the list in descending order by rerank_score\n",
    "    # Then select the top k\n",
    "    docs = sorted(docs, key=lambda x: x.metadata[\"rerank_score\"], reverse=True)\n",
    "    docs = docs[:k_final]\n",
    "    \n",
    "    new_state = {\"documents\":docs}\n",
    "    return new_state\n",
    "\n",
    "def search(state):\n",
    "    return {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "299cdae3-2e97-4e47-bad3-98643dfaf4ea",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "output = retrieve_documents(state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "638be0ea-bd41-45d7-ac7c-57ed789da3c5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rerank_scores = np.array([doc.metadata[\"rerank_score\"] for doc in output[\"documents\"]])\n",
    "print(rerank_scores)\n",
    "print(np.mean(rerank_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69ba19d3-97fb-4a23-99ee-c5827e0664e6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for doc in output[\"documents\"]:\n",
    "    print(doc)\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef9b1be-d913-4dbd-ad67-b87c4e948d11",
   "metadata": {},
   "source": [
    "## Create the RAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4638a7a-08c4-4259-a2de-77d1eb45a47c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# async def answer_ai_impact(state,config):\n",
    "#     answer = await ai_impact_chain.ainvoke({\"question\":state[\"user_input\"]},config)\n",
    "#     return {\"answer\":answer}\n",
    "            \n",
    "async def answer_rag(state):\n",
    "    \n",
    "    # Get the docs\n",
    "    docs = state[\"documents\"]\n",
    "    \n",
    "    # Compute the trust average score\n",
    "    rerank_scores = np.array([doc.metadata[\"rerank_score\"] for doc in docs])\n",
    "    trust_score = np.mean(rerank_scores)\n",
    "    \n",
    "    # \n",
    "    answer = \"\\n\".join([x[\"question\"] for x in state[\"questions\"]])\n",
    "    return {\"answer\":answer}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827873ee-f74d-4ed6-a4f8-fc8a3ef6aea8",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Test the graph"
   ]
  },
  {
   "cell_type": "code",
   "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": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
      "\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",
      "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",
      "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."
     ]
    }
   ],
   "source": [
    "# question = \"Tu penses quoi de Shakespeare ?\"\n",
    "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",
    "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",
    "    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",
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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. 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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.  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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. 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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\"), 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 <Section-header> 7.4.2.4 Cloud Feedbacks </Section-header> \\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 <Section-header> 7.4.2.4 Cloud Feedbacks </Section-header> \\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 <Section-header> Tropical high-cloud amount feedback </Section-header>', '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 <Section-header> Tropical high-cloud amount feedback </Section-header>'), 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 <Section-header> 4.6.3.3 Climate Response to Solar Radiation Modification </Section-header> \\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 <Section-header> 4.6.3.3 Climate Response to Solar Radiation Modification </Section-header> \\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 <Section-header> Radiative forcing </Section-header> \\n\\n <Section-header> Reasons for Concern (RFCs) </Section-header>', '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 <Section-header> Radiative forcing </Section-header> \\n\\n <Section-header> Reasons for Concern (RFCs) </Section-header>'), 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 <Section-header> Radiative forcing  </Section-header> \\n\\n <Section-header> Reasons for concern (RFC)  </Section-header>', '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 <Section-header> Radiative forcing  </Section-header> \\n\\n <Section-header> Reasons for concern (RFC)  </Section-header>'), 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 <Section-header> 4.6.3.3.7 Synthesis of the climate response to solar  radiation modification </Section-header>', '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 <Section-header> 4.6.3.3.7 Synthesis of the climate response to solar  radiation modification </Section-header>'), 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 <Section-header> 1.3.3 Lines of Evidence: Identifying Natural  and Human Drivers </Section-header>\", '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 <Section-header> 1.3.3 Lines of Evidence: Identifying Natural  and Human Drivers </Section-header>\"), 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 <Section-header> 6.4 SLCF Radiative Forcing  and Climate Effects </Section-header>', '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 <Section-header> 6.4 SLCF Radiative Forcing  and Climate Effects </Section-header>'), 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 <Section-header> 7.4.2.4.2 Assessment for individual cloud regimes </Section-header> \\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 <Section-header> High-cloud altitude feedback </Section-header>\", '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 <Section-header> 7.4.2.4.2 Assessment for individual cloud regimes </Section-header> \\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 <Section-header> High-cloud altitude feedback </Section-header>\"), 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 <Section-header> 7.3.3 Aerosols </Section-header> \\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 <Section-header> 7.3.3 Aerosols </Section-header> \\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 <Section-header> Overshoot pathways  </Section-header>\", '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 <Section-header> Overshoot pathways  </Section-header>\"), 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. 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_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. 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': '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. 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', '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. 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). 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. 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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). 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. 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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",
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      "{'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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> 8.1.3 Chapter Motivations, Framing and Preview </Section-header>', '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 <Section-header> 8.1.3 Chapter Motivations, Framing and Preview </Section-header>'), 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 <Section-header> Atlas.7.2.4 Assessment and Synthesis of Projections </Section-header> \\n\\nAtlas.7.2.4 Assessment and Synthesis of Projections\\n <Section-header> Atlas.7.2.4 Assessment and Synthesis of Projections </Section-header>', '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 <Section-header> Atlas.7.2.4 Assessment and Synthesis of Projections </Section-header> \\n\\nAtlas.7.2.4 Assessment and Synthesis of Projections\\n <Section-header> Atlas.7.2.4 Assessment and Synthesis of Projections </Section-header>'), 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 <Section-header> 11.7.1.4 Detection and Attribution, Event Attribution </Section-header>', '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 <Section-header> 11.7.1.4 Detection and Attribution, Event Attribution </Section-header>'), 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 <Section-header> 11.7.2.3 Detection and Attribution, Event Attribution </Section-header>', '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 <Section-header> 11.7.2.3 Detection and Attribution, Event Attribution </Section-header>'), 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 <Section-header> 10.3.3.9 Fitness of Climate Models for  Projecting Regional Climate </Section-header>', '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 <Section-header> 10.3.3.9 Fitness of Climate Models for  Projecting Regional Climate </Section-header>'), 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 <Section-header> 1.5.3.1 Earth System Models </Section-header>', '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 <Section-header> 1.5.3.1 Earth System Models </Section-header>'), 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 <Section-header> 8.5.1.1.2 Aerosol microphysical effects on clouds and precipitation </Section-header>', '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 <Section-header> 8.5.1.1.2 Aerosol microphysical effects on clouds and precipitation </Section-header>'), 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 <Section-header> 1.5.3.3 From Global to Regional Models </Section-header> \\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 <Section-header> 1.5.3.3 From Global to Regional Models </Section-header> \\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 <Section-header> 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 <Section-header> 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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. (See Figure SPM.2 for the assessed contributions to warming). \\n <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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<context,query,language,audience>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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. (See Figure SPM.2 for the assessed contributions to warming). \\n <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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).   <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>\\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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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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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': 'expert'}, 'parent_ids': []}\n",
      "{'event': 'on_chain_end', 'name': 'RunnableParallel<context,query,language,audience>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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).   <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>\\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).   <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>\\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).   <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>\\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).   <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>\\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).   <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>\\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).   <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>\\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. 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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. 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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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': \"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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>', '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 <Section-header> Figure SPM.1 | History of global temperature change and causes of recent warming </Section-header>'), 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"
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    "for event in events_list:\n",
    "    print(event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1ba9fad4",
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       "[{'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. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\",\n",
       "    'sources': ['auto'],\n",
       "    'audience': 'expert'}},\n",
       "  'parent_ids': []},\n",
       " {'event': 'on_chain_start',\n",
       "  'name': '__start__',\n",
       "  'run_id': '3a1114ca-865e-48e5-bb41-74f2eeceacdf',\n",
       "  'tags': ['graph:step:0', 'langsmith:hidden', 'langsmith:hidden'],\n",
       "  'metadata': {'langgraph_step': 0,\n",
       "   'langgraph_node': '__start__',\n",
       "   'langgraph_triggers': ['__start__'],\n",
       "   'langgraph_path': ('__pregel_pull', '__start__'),\n",
       "   'langgraph_checkpoint_ns': '__start__:d7171ff5-c9e3-ff42-6716-523efb4f8ac0'},\n",
       "  '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?\",\n",
       "    'sources': ['auto'],\n",
       "    'audience': 'expert'}},\n",
       "  'parent_ids': []},\n",
       " {'event': 'on_chain_end',\n",
       "  'name': '__start__',\n",
       "  'run_id': '3a1114ca-865e-48e5-bb41-74f2eeceacdf',\n",
       "  'tags': ['graph:step:0', 'langsmith:hidden', 'langsmith:hidden'],\n",
       "  'metadata': {'langgraph_step': 0,\n",
       "   'langgraph_node': '__start__',\n",
       "   'langgraph_triggers': ['__start__'],\n",
       "   'langgraph_path': ('__pregel_pull', '__start__'),\n",
       "   'langgraph_checkpoint_ns': '__start__:d7171ff5-c9e3-ff42-6716-523efb4f8ac0'},\n",
       "  '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?\",\n",
       "    'sources': ['auto'],\n",
       "    'audience': 'expert'},\n",
       "   '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?\",\n",
       "    'sources': ['auto'],\n",
       "    'audience': 'expert'}},\n",
       "  'parent_ids': []},\n",
       " {'event': 'on_chain_start',\n",
       "  'name': 'categorize_intent',\n",
       "  'run_id': 'c1dbc947-7723-44b6-819b-6df4aeb7fd1a',\n",
       "  'tags': ['graph:step:1'],\n",
       "  'metadata': {'langgraph_step': 1,\n",
       "   'langgraph_node': 'categorize_intent',\n",
       "   'langgraph_triggers': ['start:categorize_intent'],\n",
       "   'langgraph_path': ('__pregel_pull', 'categorize_intent'),\n",
       "   'langgraph_checkpoint_ns': 'categorize_intent:e59da5fd-192c-44d7-ed6e-40297a9a1864'},\n",
       "  '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?\",\n",
       "    'audience': 'expert'}},\n",
       "  'parent_ids': []},\n",
       " {'event': 'on_chain_start',\n",
       "  'name': 'RunnableSequence',\n",
       "  'run_id': '930d6ee8-107a-49e2-a19f-2f848ecda0a9',\n",
       "  'tags': ['seq:step:1'],\n",
       "  'metadata': {'langgraph_step': 1,\n",
       "   'langgraph_node': 'categorize_intent',\n",
       "   'langgraph_triggers': ['start:categorize_intent'],\n",
       "   'langgraph_path': ('__pregel_pull', 'categorize_intent'),\n",
       "   'langgraph_checkpoint_ns': 'categorize_intent:e59da5fd-192c-44d7-ed6e-40297a9a1864',\n",
       "   'checkpoint_ns': 'categorize_intent:e59da5fd-192c-44d7-ed6e-40297a9a1864'},\n",
       "  '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?\"}},\n",
       "  'parent_ids': []},\n",
       " {'event': 'on_prompt_start',\n",
       "  'name': 'ChatPromptTemplate',\n",
       "  'run_id': '5df8b6c2-9b25-4dec-9d56-17cd221ec6aa',\n",
       "  'tags': ['seq:step:1'],\n",
       "  'metadata': {'langgraph_step': 1,\n",
       "   'langgraph_node': 'categorize_intent',\n",
       "   'langgraph_triggers': ['start:categorize_intent'],\n",
       "   'langgraph_path': ('__pregel_pull', 'categorize_intent'),\n",
       "   'langgraph_checkpoint_ns': 'categorize_intent:e59da5fd-192c-44d7-ed6e-40297a9a1864',\n",
       "   'checkpoint_ns': 'categorize_intent:e59da5fd-192c-44d7-ed6e-40297a9a1864'},\n",
       "  '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?\"}},\n",
       "  'parent_ids': []},\n",
       " {'event': 'on_prompt_end',\n",
       "  'name': 'ChatPromptTemplate',\n",
       "  'run_id': '5df8b6c2-9b25-4dec-9d56-17cd221ec6aa',\n",
       "  'tags': ['seq:step:1'],\n",
       "  'metadata': {'langgraph_step': 1,\n",
       "   'langgraph_node': 'categorize_intent',\n",
       "   'langgraph_triggers': ['start:categorize_intent'],\n",
       "   'langgraph_path': ('__pregel_pull', 'categorize_intent'),\n",
       "   'langgraph_checkpoint_ns': 'categorize_intent:e59da5fd-192c-44d7-ed6e-40297a9a1864',\n",
       "   'checkpoint_ns': 'categorize_intent:e59da5fd-192c-44d7-ed6e-40297a9a1864'},\n",
       "  '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?\"},\n",
       "   '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?\")])},\n",
       "  'parent_ids': []},\n",
       " {'event': 'on_chat_model_start',\n",
       "  'name': 'ChatOpenAI',\n",
       "  'run_id': '90f65514-af42-4c64-b793-43bff670810a',\n",
       "  'tags': ['seq:step:2'],\n",
       "  'metadata': {'langgraph_step': 1,\n",
       "   'langgraph_node': 'categorize_intent',\n",
       "   'langgraph_triggers': ['start:categorize_intent'],\n",
       "   'langgraph_path': ('__pregel_pull', 'categorize_intent'),\n",
       "   'langgraph_checkpoint_ns': 'categorize_intent:e59da5fd-192c-44d7-ed6e-40297a9a1864',\n",
       "   'checkpoint_ns': 'categorize_intent:e59da5fd-192c-44d7-ed6e-40297a9a1864',\n",
       "   'ls_provider': 'openai',\n",
       "   'ls_model_type': 'chat',\n",
       "   'ls_model_name': 'gpt-3.5-turbo-0125',\n",
       "   'ls_temperature': 0.0,\n",
       "   'ls_max_tokens': 1024},\n",
       "  'data': {'input': {'messages': [[SystemMessage(content='You are a helpful assistant, you will analyze, translate and reformulate the user input message using the function provided'),\n",
       "      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?\")]]}},\n",
       "  'parent_ids': []},\n",
       " {'event': 'on_chat_model_end',\n",
       "  'name': 'ChatOpenAI',\n",
       "  'run_id': '90f65514-af42-4c64-b793-43bff670810a',\n",
       "  'tags': ['seq:step:2'],\n",
       "  'metadata': {'langgraph_step': 1,\n",
       "   'langgraph_node': 'categorize_intent',\n",
       "   'langgraph_triggers': ['start:categorize_intent'],\n",
       "   'langgraph_path': ('__pregel_pull', 'categorize_intent'),\n",
       "   'langgraph_checkpoint_ns': 'categorize_intent:e59da5fd-192c-44d7-ed6e-40297a9a1864',\n",
       "   'checkpoint_ns': 'categorize_intent:e59da5fd-192c-44d7-ed6e-40297a9a1864',\n",
       "   'ls_provider': 'openai',\n",
       "   'ls_model_type': 'chat',\n",
       "   'ls_model_name': 'gpt-3.5-turbo-0125',\n",
       "   'ls_temperature': 0.0,\n",
       "   'ls_max_tokens': 1024},\n",
       "  'data': {'input': {'messages': [[SystemMessage(content='You are a helpful assistant, you will analyze, translate and reformulate the user input message using the function provided'),\n",
       "      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?\")]]},\n",
       "   'output': {'generations': [[{'text': '',\n",
       "       'generation_info': {'finish_reason': 'stop'},\n",
       "       'type': 'ChatGeneration',\n",
       "       'message': AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"intent\":\"search\"}', 'name': 'IntentCategorizer'}}, response_metadata={'finish_reason': 'stop'}, id='run-90f65514-af42-4c64-b793-43bff670810a')}]],\n",
       "    'llm_output': None,\n",
       "    'run': None}},\n",
       "  'parent_ids': []},\n",
       " {'event': 'on_parser_start',\n",
       "  'name': 'JsonOutputFunctionsParser',\n",
       "  'run_id': 'b411b4d6-df67-48a5-8645-f18bb1295fc0',\n",
       "  'tags': ['seq:step:3'],\n",
       "  'metadata': {'langgraph_step': 1,\n",
       "   'langgraph_node': 'categorize_intent',\n",
       "   'langgraph_triggers': ['start:categorize_intent'],\n",
       "   'langgraph_path': ('__pregel_pull', 'categorize_intent'),\n",
       "   'langgraph_checkpoint_ns': 'categorize_intent:e59da5fd-192c-44d7-ed6e-40297a9a1864',\n",
       "   'checkpoint_ns': 'categorize_intent:e59da5fd-192c-44d7-ed6e-40297a9a1864'},\n",
       "  'data': {'input': AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"intent\":\"search\"}', 'name': 'IntentCategorizer'}}, response_metadata={'finish_reason': 'stop'}, id='run-90f65514-af42-4c64-b793-43bff670810a')},\n",
       "  'parent_ids': []}]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_list[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "33f1d95b",
   "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. What role do cloud formations play in modulating the Earth's radiative balance, and how are they represented in current climate models?\",\n",
       "   'sources': ['auto'],\n",
       "   'audience': 'expert'}},\n",
       " 'parent_ids': []}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "339ee1cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'on_chain_end',\n",
       " 'on_chain_start',\n",
       " 'on_chain_stream',\n",
       " 'on_chat_model_end',\n",
       " 'on_chat_model_start',\n",
       " 'on_parser_end',\n",
       " 'on_parser_start',\n",
       " 'on_prompt_end',\n",
       " 'on_prompt_start',\n",
       " 'on_retriever_end',\n",
       " 'on_retriever_start'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([event[\"event\"] for event in events_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "743649a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "events_list"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "climateqa",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}