# document

id: f06586449ff2242e5ca2adc421d346fb
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"The WGI contribution to AR5 (AR5 WGI; IPCC, 2013a) assessed \r\nthat 'warming of the climate system is unequivocal', and that since \r\nthe 1950s, many of the observed changes are unprecedented over \r\ndecades to millennia. Changes are evident in all components  of \r\nthe climate system: the atmosphere and the ocean have warmed, \r\namounts of  snow and ice have diminished, sea level has risen, \r\nthe ocean has acidified and its oxygen content has declined, and \r\natmospheric  concentrations of greenhouse gases (GHGs) have \r\nincreased (IPCC, 2013b). This Report documents that, since the AR5, \r\nchanges to the state of the physical and biogeochemical climate "


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"recent decade (2010-2019) was the lowest since the beginning of \r\nthe 20th century (Sections 2.3 and 9.5)."


id: 88bbd83d7cfb02608c11bb911bd47a4
created_at: 2023-03-16 14:00:37
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"Similarly, atmospheric concentrations of a  range of GHGs are \r\nincreasing. Carbon dioxide (CO2, shown in Figure 1.4 and Figure 1.5a, \r\nfound in AR5 and earlier reports to be the current strongest driver of \r\nanthropogenic climate change), has increased from 285.5 +- 2.1 ppm \r\nin 1850 to 409.9  +-  0.4  ppm in 2019; concentrations of methane \r\n(CH4), and nitrous oxide (N2O) have increased as well (Sections 2.2 \r\nand 5.2, and Annex V). These observed changes are assessed \r\nto be in line with known anthropogenic and natural emissions, \r\nwhen accounting for observed and inferred uptake by land, ocean \r\nand biosphere respectively (Section  5.2), and are a  key source of \r\nanthropogenic changes to the global energy balance (or radiative \r\nforcing; Sections 2.2 and 7.3)."


id: 4fda399663ef46798ad843d3d2c94caa
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"The global ocean has warmed unabatedly since at least 1970 \r\n(Sections 1.3, 2.3 and 9.2; SROCC, IPCC, 2019b). Figure 1.4 shows \r\nhow the averaged ocean heat content is steadily increasing, with \r\na total increase of [0.28 to 0.55] yottajoule (YJ; 1024 joule) between \r\n1971 and 2018 (Section 9.2). In response to this ocean warming, as \r\nwell as to the loss of mass from glaciers and ice sheets, the global \r\nmean sea level (GMSL) has risen by 0.20 [0.15 to 0.25] metres \r\nbetween 1900 and 2018. GMSL rise has accelerated since the late \r\n1960s (see Section 9.6)."


id: cbff8c53575dc8400fdd68b058bf6406
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"Overall, the changes in these selected climatic indicators have progressed \r\nbeyond the range of natural year-to-year variability (Chapters  2, 3, \r\n8 and 9, and Sections 1.2.1.2 and 1.4.2). The indicators presented in \r\nFigure 1.4 document a broad set of concurrent and emerging changes \r\nacross the physical climate system. All indicators shown here, along with \r\nmany others, are further presented in the coming chapters, together \r\nwith a rigorous assessment of the supporting scientific literature. Later \r\nchapters (Chapters 10, 11, 12 and Atlas) present similar assessments \r\nat the regional level, where observed changes do not always align with \r\nthe global mean picture shown here."


id: e3c0f5cf7278dd25851460cfe19489ac
created_at: 2023-03-16 14:00:37
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"The hydrological (or water) cycle is also changing and is assessed \r\nto be intensifying, through a higher exchange of water between the \r\nsurface and the atmosphere (Sections  2.3 and 8.3). The resulting \r\nregional patterns of changes to precipitation are, however, different \r\nfrom surface temperature change, and interannual variability is larger, \r\nas illustrated in Figure 1.4. Annual land area mean precipitation in \r\nthe Northern Hemisphere temperate regions has increased, while the \r\nsubtropical dry regions have experienced a decrease in precipitation \r\nin recent decades (Section 2.3)."


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"The cryosphere is undergoing rapid changes, with increased melting \r\nand loss of frozen water mass in most regions. This includes all \r\nfrozen parts of the globe, such as terrestrial snow, permafrost, sea \r\nice, glaciers, freshwater ice, solid precipitation, and the ice sheets \r\ncovering Greenland and Antarctica (Chapter 9; SROCC, IPCC, 2019b). \r\nFigure 1.4 illustrates how, globally, glaciers have been increasingly \r\nlosing mass for the last fifty years. The total glacier mass in the most "


id: 95892bbe26ba1834690acae0b83cb95e
created_at: 2023-03-16 14:00:37
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"Paleoclimate archives (e.g., ice cores, corals, marine and lake sediments, \r\nspeleothems, tree rings, borehole temperatures, soils) permit the \r\nreconstruction of climatic conditions before the instrumental era. "


id: 6cc860a707868e72c9bacfc2aae00e80
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"changes in global mean temperature of a few degrees Celsius are \r\nassociated with increases in sea level of several tens of metres \r\n(Figure  1.5). During two extended warm periods (interglacials) of \r\nthe last 800 kyr, sea level is estimated to have been at least six \r\nmetres higher than today (Chapter  2; Dutton et al., 2015). During \r\nthe last interglacial, sustained warmer temperatures in Greenland \r\npreceded the peak of sea level rise (Figure 5.15 in Masson-Delmotte \r\net al., 2013). The paleoclimate record therefore provides substantial \r\nevidence directly linking warmer GMST to substantially higher GMSL."


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"GMST will remain above present-day levels for many centuries even \r\nif net CO2 emissions are reduced to zero, as shown in simulations \r\nwith coupled climate models (Section  4.7.1; Plattner et al., 2008; \r\nSection 12.5.3 in Collins et al., 2013; Zickfeld et al., 2013; MacDougall \r\net al., 2020). Such persistent warm conditions in the atmosphere \r\nrepresent a  multi-century commitment to long-term sea level rise, \r\nsummer sea ice reduction in the Arctic, substantial ice-sheet melting, \r\npotential ice-sheet collapse, and many other consequences in all \r\ncomponents of the climate system (Section 9.4 and Figure 1.5; Clark \r\net al., 2016; Pfister and Stocker, 2016; H. Fischer et al., 2018)."


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"Paleoclimate records also show centennial- to millennial-scale \r\nvariations, particularly during the ice ages, which indicate rapid or \r\nabrupt changes of the Atlantic Meridional Overturning Circulation \r\n(AMOC; Section  9.2.3.1) and the occurrence of a 'bipolar seesaw' \r\n(opposite-phase surface temperature changes in both hemispheres; \r\nSection  2.3.3.4.1; Stocker and Johnsen, 2003; EPICA Community \r\nMembers, 2006; WAIS Divide Project Members et al., 2015; Lynch\u0002Stieglitz, 2017; Pedro et al., 2018; Weijer et al., 2019). This process \r\nsuggests that instabilities and irreversible changes could be triggered \r\nif critical thresholds are passed (Section  1.4.4.3). Several other \r\nprocesses involving instabilities are identified in climate models \r\n(Drijfhout et al., 2015), some of which may now be close to critical \r\nthresholds (Section 1.4.4.3; see also Chapters 5, 8 and 9 regarding \r\ntipping points; Joughin et al., 2014)."


id: 36eecce85fe6c286987d97828d2a4bdd
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"Paleoclimatic information also provides a  long-term perspective \r\non rates of change of these three key indicators. In high-resolution \r\nreconstructions from polor ice cores, the rate of increase in \r\natmospheric CO2 observed over 1919-2019 CE is one order of \r\nmagnitude higher than the fastest CO2 fluctuations documented \r\nduring the Last Glacial Maximum and the last deglacial transition \r\n(Marcott et al., 2014, see Chapter 2, Section 2.2.3.2.1). Current multi\u0002decadal GMST exhibit a higher rate of increase than over the past \r\n2  kyr (Section  2.3.1.1.2; PAGES 2k Consortium, 2019), and in the \r\n20th  century GMSL rise was faster than during any other century \r\nover the past 3 kyr (Section 2.3.3.3)."


id: 352ea27e77f27d9b9009c52ab1ebc6fe
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"Based on Figure 1.5, the reconstructed, observed and projected ranges \r\nof changes in the three key indicators can be compared. By the first \r\ndecade of the 20th  century, atmospheric CO2 concentrations had \r\nalready moved outside the reconstructed range of natural variation "


id: f90f9f4ee7203cbf01bf655bddcf253b
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"Paleoclimate reconstructions also shed light on the causes of these \r\nvariations, revealing processes that need to be considered when \r\nprojecting climate change. The paleorecords show that sustained "


id: 78d07940bde8f77bd9e8db3b3ca922cc
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"over the past 800 kyr. On the other hand, GMST and GMSL were higher \r\nthan today during several interglacials of that period (Sections 2.3.1 \r\nand 2.3.3, and Figure 2.34). Projections for the end of the 21st century, \r\nhowever, show that GMST will have moved outside of its natural range \r\nwithin the next few decades, except for the strong mitigation scenarios \r\n(Section  1.6). There is a  risk that GMSL may potentially leave the \r\nreconstructed range of natural variations over the next few millennia \r\n(Section 9.6.3.5; Clark et al., 2016; SROCC, IPCC, 2019b). In addition, \r\nabrupt changes can not be excluded (Section 1.4.4.3)."


id: 5ebeac41b57a3b2e451a3e52d299b186
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"The IPCC First Assessment Report (FAR, IPCC, 1990a) provided the \r\nscientific background for the establishment of the UNFCCC (UNFCCC, \r\n1992), which committed parties to negotiate ways to 'prevent \r\ndangerous anthropogenic interference with the climate system' (the \r\nultimate objective of the UNFCCC). The Second Assessment Report \r\n(SAR, IPCC, 1996) informed governments in negotiating the Kyoto \r\nProtocol (1997), the first major agreement focusing on mitigation \r\nunder the UNFCCC. The Third Assessment report (TAR, IPCC, \r\n2001a) highlighted the impacts of climate change and the need for \r\nadaptation, and introduced the treatment of new topics such as policy \r\nand governance in IPCC reports. The Fourth and Fifth Assessment \r\nReports (AR4, IPCC, 2007a; AR5, IPCC, 2013a) provided the scientific \r\nbackground for the second major agreement under the UNFCCC: the \r\nParis Agreement (2015), which entered into force in 2016."


id: 51da0056a3cc89f313037aef4b1448fb
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"An important time period in the assessment of anthropogenic climate \r\nchange is the last 2 kyr. Since AR5, new global datasets have been \r\nproduced that aggregate aggregating local and regional paleorecords \r\n(PAGES 2k Consortium, 2013, 2017, 2019; McGregor et al., 2015; \r\nTierney et al., 2015; Abram et al., 2016; Hakim et al., 2016; Steiger \r\net al., 2018; Bronnimann et al., 2019b). Before the global warming \r\nthat began around the mid-19th century (Abram et al., 2016), a slow \r\ncooling in the Northern Hemisphere from roughly 1450-1850 CE is \r\nconsistently recorded in paleoclimate archives (PAGES 2k Consortium, \r\n2013; McGregor et al., 2015). While this cooling, primarily driven by \r\nan increased number of volcanic eruptions (Section 3.3.1; PAGES 2k \r\nConsortium, 2013; Owens et al., 2017; Bronnimann et al., 2019b), \r\nshows regional differences, the subsequent warming over the past \r\n150 years exhibits a global coherence that is unprecedented in the \r\nlast 2 kyr (Neukom et al., 2019)."


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"Parties to the PA commit to the goal of limiting global average \r\ntemperature increase to 'well below 2degC above pre-industrial levels, \r\nand to pursue efforts to limit the temperature increase to 1.5degC \r\nin order to significantly reduce the risks and impacts of climate \r\nchange'. In AR6, as in many previous IPCC reports, observations and \r\nprojections of changes in global temperature are expressed relative \r\nto 1850-1900 as an approximation for pre-industrial levels (Cross\u0002Chapter Box 1.2)."


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"The PA further addresses mitigation (Article  4) and adaptation to \r\nclimate change (Article 7), as well as loss and damage (Article 8), \r\nthrough the mechanisms of finance (Article  9), technology \r\ndevelopment and transfer (Article 10), capacity-building (Article 11) \r\nand education (Article 12). To reach its long-term temperature goal, \r\nthe PA recommends 'achieving a  balance between anthropogenic \r\nemissions by sources and removals by sinks of greenhouse gases in \r\nthe second half of this century', a state commonly described as 'net \r\nzero' emissions (Article 4) (Section 1.6 and Box 1.4). Each Party to the \r\nPA is required to submit a Nationally Determined Contribution (NDC) \r\nand pursue, on a voluntary basis, domestic mitigation measures with \r\nthe aim of achieving the objectives of its NDC (Article 4)."


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"The contexts of both policymaking and societal understanding about \r\nclimate change have evolved since AR5 was published (2013-2014). \r\nIncreasing recognition of the urgency of the climate change threat, \r\nalong with still-rising emissions and unresolved issues of mitigation \r\nand adaptation, including aspects of sustainable development, \r\npoverty eradication and equity, have led to new policy efforts. This \r\nsection summarizes these contextual developments and how they \r\nhave shaped, and been used during the preparation of this Report."


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"collective progress toward achieving the PA long-term goals. These \r\nprocesses will rely upon the assessments prepared during the IPCC \r\nSixth Assessment Cycle (e.g.,  Cross-Chapter Box  1.1; Schleussner \r\net al., 2016b)."


id: 99738f0f123b28f5f73dcb47f4be768a
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"Since AR5, the formal dialogue between the scientific and policy \r\ncommunities has been strengthened through a  new science-\r\npolicy interface, the Structured Expert Dialogue (SED). The SED was \r\nestablished by UNFCCC to support the work of its two subsidiary \r\nbodies, the Subsidiary Body for Scientific and Technological Advice \r\n(SBSTA) and the Subsidiary Body for Implementation (SBI). The first \r\nSED aimed to 'ensure the scientific integrity of the first periodic review' \r\nof the UNFCCC, the 2013-2015 review. The Mandate of the periodic \r\nreview is to 'assess the adequacy of the long-term (temperature) \r\ngoal in light of the ultimate objective of the convention' and the \r\n'overall progress made towards achieving the long-term global goal, \r\nincluding a consideration of the implementation of the commitments \r\nunder the Convention.'"


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"Cross-Chapter Box 1.1 | The WGI Contribution to AR6 and Its Potential Relevance for the \r\nGlobal Stocktake"


id: 6385eecd10521dc62ec2c9e405d63e48
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"Contributing Authors: Malte Meinshausen (Australia/Germany), Gian-Kasper Plattner (Switzerland), Aida Diongue-Niang (Senegal), \r\nFrancisco J. Doblas-Reyes (Spain), David Frame (New Zealand), Nathan P. Gillett (Canada), Helene T. Hewitt (United Kingdom), \r\nRichard G. Jones (United Kingdom), Hong Liao (China), Jochem Marotzke (Germany), James Renwick (New Zealand), Joeri Rogelj \r\n(United Kingdom, Belgium), Maisa Rojas (Chile), Sonia I. Seneviratne (Switzerland), Claudia Tebaldi (United States of America), \r\nBlair Trewin (Australia)"


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"The PA further specifies that the stocktake shall be undertaken in a 'comprehensive and facilitative manner, considering mitigation, \r\nadaptation and the means of implementation and support, and in the light of equity and the best available science' (Article 14)."


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"The sources of input envisaged for the global stocktake include the 'latest reports of the Intergovernmental Panel on Climate Change' \r\nas a central source of information.4 The global stocktake is one of the key formal avenues for scientific inputs into the UNFCCC and PA \r\nnegotiation process alongside, for example, the Structured Expert Dialogues (SEDs) under the UNFCCC (Section 1.2.2).5"


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"The WGI Assessment provides a  wide range of information with potential relevance for the global stocktake, \r\ncomplementing the IPCC AR6 Special Reports, the contributions from WGII and WGIII and the Synthesis Report. This \r\nincludes the state of GHG emissions and concentrations, the current state of the climate, projected long-term warming levels under \r\ndifferent scenarios, near-term projections, the attribution of extreme events, and remaining carbon budgets. Cross-Chapter Box 1.1, \r\nTable 1 provides pointers to the in-depth material that WGI has assessed and that may be relevant for the global stocktake."


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"The following tabular overview of potentially relevant information from the WGI contribution for the global stocktake is \r\nstructured into three sections: the current state of the climate, the long-term future, and the near-term. These sections and \r\ntheir order align with the three questions of the Talanoa dialogue, launched during COP23, based on the Pacific concept of talanoa6: \r\n'Where are we', 'Where do we want to go' and 'How do we get there?'"


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"Cross-Chapter Box 1.1, Table 1 | WGI assessment findings and their potential relevance for the global stocktake. The table combines information \r\nassessed in this report that could potentially be relevant for the global stocktake process. Section 1 focuses on the current state of the climate and its recent \r\npast. Section 2 focuses on long-term projections in the context of the PA's 1.5degC and 2.0degC goals and on progress towards net zero greenhouse gas emissions. \r\nSection 3 considers challenges and key insights for mitigation and adaptation in the near term from a WGI perspective. Further information on potential relevance of \r\nthe aspects listed here in terms of, for example, impacts and socio-economic aspects can be found in the WGII and WGIII reports"


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"The Sendai Framework for Disaster Risk Reduction is a non-binding \r\nagreement to reduce risks associated with disasters of all scales, \r\nfrequencies and onset rates caused by natural or human-made \r\nhazards, including climate change. The SFDRR outlines targets and \r\npriorities for action including 'understanding disaster risk', along \r\nthe dimensions of vulnerability, exposure of persons and assets, \r\nand hazard characteristics. Chapter 12 assesses climate information \r\nrelevant to regional impact and risk assessment, with a  focus on \r\nclimate hazards and other aspects of climate that influence society \r\nand ecosystems and makes the link with Working Group II. AR6 \r\nadopts a  consistent risk- and solution-oriented framing (Cross\u0002Chapter Box  1.3) that calls for a  multidisciplinary approach and \r\ncross-Working Group coordination in order to ensure integrative \r\ndiscussions of major scientific issues associated with integrative risk \r\nmanagement and sustainable solutions (IPCC, 2017)."


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"1.2.2.6 The Intergovernmental Science-Policy Platform \r\non Biodiversity and Ecosystem Services (IPBES)"


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"Efforts to address climate change take place alongside and in the \r\ncontext of other major environmental problems, such as biodiversity \r\nloss. IPBES, established in 2012, builds on the IPCC model of \r\na science-policy interface and assessment. The Platform's objective \r\nis to 'strengthen the science-policy interface for biodiversity "


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"and ecosystem services for the conservation and sustainable \r\nuse of biodiversity, long-term human well-being and sustainable \r\ndevelopment' (UNEP, 2012). The SROCC (IPCC, 2019b) and SRCCL \r\n(IPCC, 2019a) assessed the relations between changes in biodiversity \r\nand in the climate system. The rolling work programme of IPBES up \r\nto 2030 will address interlinkages among biodiversity, water, food \r\nand health. This assessment will use a nexus approach to examine \r\ninterlinkages between biodiversity and the above-mentioned issues, \r\nincluding climate change mitigation and adaptation. Furthermore, \r\nIPBES and IPCC will directly collaborate on biodiversity and climate \r\nchange under the rolling work programme."


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"Responses to climate change are facilitated when leaders, \r\npolicymakers, resource managers and their constituencies share \r\na  basic understanding of the causes, effects, and possible future \r\ncourse of climate change (SR1.5, IPCC, 2018; SRCCL, IPCC, 2019a). \r\nAchieving shared understanding is complicated, since scientific \r\nknowledge interacts with pre-existing conceptions of weather and \r\nclimate that have built up in diverse world cultures over centuries, \r\nand which are often embedded in strongly held values and beliefs \r\nstemming from ethnic or national identities, traditions, religions, and \r\nlived relationships to weather, land and sea (Van Asselt and Rotmans, \r\n1996; Rayner and Malone, 1998; Hulme, 2009, 2018; Green et al., \r\n2010; Jasanoff, 2010; Orlove et al., 2010; Nakashima et al., 2012; \r\nShepherd and Sobel, 2020). These diverse, more local understandings \r\ncan both contrast with and enrich the planetary-scale analyses of \r\nglobal climate science (high confidence)."


id: 2895f6cc04d3a7e19d4553d02f051c27
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 3808
content:
"The evolving governance context since AR5 challenges the IPCC to \r\nprovide policymakers and other actors with information relevant for \r\nboth adaptation to and mitigation of climate change, and for the loss \r\nand damage induced."


id: 98985c031e1b3cf8512c11724d34a292
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 21523
content:
"Furthermore, climate change itself is not uniform. Some regions face \r\nsteady, readily observable change, while others experience high \r\nvariability that masks underlying trends (Section 1.4.1); most regions \r\nare subject to hazards, but some may also experience benefits, \r\nat least temporarily (Chapters 11, 12 and Atlas). This non-uniformity \r\nmay lead to wide variation in public climate change awareness and \r\nrisk perceptions at multiple scales (Howe et al., 2015; Lee et al., 2015). \r\nFor example, short-term temperature trends, such as cold spells or \r\nwarm days, have been shown to influence public concern (Hamilton \r\nand Stampone, 2013; Zaval et al., 2014; Bohr, 2017)."


id: 802c057f1f28439d23b03dcdf6c8cb72
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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content:
"This section assesses how the process of communicating climate \r\ninformation has evolved since AR5. It summarizes key issues regarding \r\nscientific uncertainty addressed in previous IPCC assessments and \r\nintroduces the IPCC calibrated uncertainty language. Next it discusses \r\nthe role of values in problem-driven, multidisciplinary science \r\nassessments such as this one. The section introduces climate services \r\nand how climate information can be tailored for greatest utility in \r\nspecific contexts, such as the global stocktake. Finally, we briefly \r\nevaluate changes in media coverage of climate information since AR5, \r\nincluding the increasing role of Internet sources and social media."


id: f114856f4c8ec8ee5b0e434f663e580a
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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content:
"Given these manifold influences and the highly varied contexts \r\nof climate change communication, special care is required when \r\nexpressing findings and uncertainties, including IPCC assessments \r\nthat inform decision making. Throughout the IPCC's history, all three \r\nWorking Groups have sought to explicitly assess and communicate \r\nscientific uncertainty (Le Treut et al., 2007; Cubasch et al., 2013). "


id: 3ab55524189133e8e8c38d038a2b41aa
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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content:
"communicating authors' expert judgments (Mastrandrea and Mach, \r\n2011). During the AR5 cycle, this calibrated uncertainty language was \r\nupdated and unified across all Working Groups (Mastrandrea et al., \r\n2010, 2011). Box 1.1 summarizes this framework as it is used in AR6."


id: 5bf6fc0f8f754495dc4bd4fd7974c2aa
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 11983
content:
"Over time, the IPCC has developed and revised a  framework to \r\ntreat uncertainties consistently across assessment cycles, reports, \r\nand Working Groups through the use of calibrated language (Moss \r\nand Schneider, 2000; IPCC, 2005). Since its First Assessment Report \r\n(FAR; IPCC, 1990a), the IPCC has specified terms and methods for "


id: 6805aa94e34787d7677e2a21ca0da681
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 13953
content:
"The AR6 follows the approach developed for AR5 (Box 1.1, Figure 1), as described in the 'Guidance Notes for Lead Authors of the IPCC \r\nFifth Assessment Report on Consistent Treatment of Uncertainties' (Mastrandrea et al., 2010). The uncertainty Guidance Note used \r\nin AR6 clarifies the relationship between the qualitative description of confidence and the quantitative representation of uncertainty \r\nexpressed by the likelihood scale. The calibrated uncertainty language emphasizes traceability of the assessment throughout the \r\nprocess. Key chapter findings presented in each chapter's Executive Summary are supported in the chapter text by a summary of the \r\nunderlying literature that is assessed in terms of evidence and agreement, confidence, and also likelihood, if applicable."


id: 491895334a5bb6cb8e70ceb14b6a6b75
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 8888
content:
"In all three Working Groups, author teams evaluate underlying scientific understanding and use two metrics to communicate the \r\ndegree of certainty in key findings. These metrics are:"


id: f66589d1f9663de4b7188c3617388dce
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 36275
content:
"Throughout IPCC reports, the calibrated language indicating a formal confidence assessment is clearly identified by italics (e.g., medium \r\nconfidence). Where appropriate, findings can also be formulated as statements of fact without uncertainty qualifiers."


id: 5dd0c7e3447ed294053238dd44c63e73
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 12295
content:
"Box.1.1, Figure 1 (adapted from Mach et al., 2017) shows the idealized step-by-step process by which IPCC authors assess scientific \r\nunderstanding and uncertainties. It starts with the evaluation of the available evidence and agreement (steps 1-2). The following \r\nsummary terms are used to describe the available evidence: limited, medium, or robust; and the degree of agreement: low, medium, or \r\nhigh. Generally, evidence is most robust when there are multiple, consistent, independent lines of high-quality evidence."


id: 25f1ce56eb0b1eec6333497c12b6ead
created_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 3393
content:
"If the expert judgement of the author team concludes that there is sufficient confidence and quantitative/probabilistic evidence, \r\nassessment conclusions can be expressed with likelihood statements (steps 5-6). Unless otherwise indicated, likelihood statements \r\nare related to findings for which the authors' assessment of confidence is high or very high. Terms used to indicate the assessed \r\nlikelihood of an outcome include: virtually certain: 99-100% probability, very likely: 90-100%, likely: 66-100%, about as likely as not: \r\n33-66%, unlikely: 0-33%, very unlikely: 0-10%, exceptionally unlikely: 0-1%. Additional terms (extremely likely: 95-100%, more \r\nlikely than not >50-100%, and extremely unlikely 0-5%) may also be used when appropriate."


id: 11f080746ccc9b75b1ae75ccf1ab419f
created_at: 2023-03-16 14:00:37
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vector_id: 322
content:
"Likelihood can indicate probabilities for single events or broader outcomes. The probabilistic information may build from statistical \r\nor modelling analyses, other quantitative analyses, or expert elicitation. The framework encourages authors, where appropriate, to \r\npresent probability more precisely than can be done with the likelihood scale, for example with complete probability distributions or \r\npercentile ranges, including quantification of tails of distributions, which are important for risk management (Sections 1.2.2 and 1.4.4; \r\nMach et al., 2017). In some instances, multiple combinations of confidence and likelihood are possible to characterize key findings"


id: 712f0cca9011353e58596cea6409ef73
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 15443
content:
"Box 1.1, Figure 1 | The IPCC AR6 approach for characterizing understanding and uncertainty in assessment findings. This diagram illustrates the \r\nstep-by-step process authors use to evaluate and communicate the state of knowledge in their assessment (Mastrandrea et al., 2010). Authors present evidence/\r\nagreement, confidence, or likelihood terms with assessment conclusions, communicating their expert judgments accordingly. Example conclusions drawn from Report \r\nare presented in the box at the bottom of the figure. Figure adapted from Mach et al. (2017) ."


id: 9b8742388db96c7f7d07a73a0290a18b
created_at: 2023-03-16 14:00:37
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vector_id: 22023
content:
"For example, a very likely statement might be made with high confidence, whereas a likely statement might be made with very high \r\nconfidence. In these instances, the author teams consider which statement will convey the most balanced information to the reader."


id: d4310006457f913763bbdb92f847a16a
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 30906
content:
"Throughout this WGI Report, unless stated otherwise, uncertainty is quantified using 90% uncertainty intervals. The 90% uncertainty \r\ninterval, reported in square brackets [x to y], is estimated to have a 90% likelihood of covering the value that is being estimated. \r\nThe range encompasses the median value and there is an estimated 10% combined likelihood of the value being below the lower end \r\nof the range (x) and above its upper end (y). Often the distribution will be considered symmetric about the corresponding best estimate \r\n(as in the illustrative example in the figure), but this is not always the case. In this report, an assessed 90% uncertainty interval is \r\nreferred to as a 'very likely range'. Similarly, an assessed 66% uncertainty interval is referred to as a 'likely range'."


id: 74b2d9a5f0bbb9c6ff99c9f2940ba7c1
created_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 15956
content:
"for example, the transparency and traceability of expert judgements \r\nunderlying the assessment conclusions (Oppenheimer et al., 2016) \r\nand the context-dependent representations and interpretations \r\nof probability terms (Budescu et al., 2009, 2012; Janzwood, 2020). "


id: 189b5d136f1808584e352d100b001d19
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 1365
content:
"Considerable critical attention has focused on whether applying \r\nthe IPCC framework effectively achieves consistent treatment of \r\nuncertainties and clear communication of findings to users (Shapiro \r\net al., 2010; Adler and Hirsch Hadorn, 2014). Specific concerns include, "


id: 58cd753f81f1c37bf584f7828c53024b
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 11468
content:
"Chapter  9  (Section  9.2.3) notes deep uncertainty in long-term \r\nprojections for sea level rise, and in processes related to marine ice\u0002sheet instability and marine ice cliff instability."


id: 60a2ad556f490a5c9418dfa3014b4118
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 12774
content:
"Some suggested alternatives are impractical, such as always \r\nincluding numerical values along with calibrated language (Budescu \r\net al., 2014). Others, such as using positive instead of negative \r\nexpressions of low-to-medium probabilities, show promise but were \r\nnot proposed in time for adoption in AR6 (Juanchich et al., 2020). This \r\nreport therefore retains the same calibrated language used in AR5 \r\n(Box 1.1). Like previous reports, AR6 also includes FAQs that express \r\nits chief conclusions in plain language designed for lay readers."


id: f317047b4470361ad55878418cf28717
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 35760
content:
"The formal Principles Governing IPCC Work (1998, amended 2003, \r\n2006, 2012, 2013) specify that assessments should be 'comprehensive, \r\nobjective, open and transparent.' The IPCC assessment process seeks \r\nto achieve these goals in several ways: by evaluating evidence and \r\nagreement across all relevant peer-reviewed literature, especially that \r\npublished or accepted since the previous assessment; by maintaining \r\na  traceable, transparent process that documents the reasoning, \r\ndata and tools used in the assessment; and by maximizing the \r\ndiversity of participants, authors, experts, reviewers, institutions and \r\ncommunities represented, across scientific discipline, geographical "


id: b8cf298a93cbf201ef5505e0e4e9b7e6
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 26598
content:
"location, gender, ethnicity, nationality and other characteristics. \r\nThe multi-stage review process is critical to ensure an objective, \r\ncomprehensive and robust assessment, with hundreds of scientists, \r\nother experts and governments providing comments to a series of \r\ndrafts before the report is finalized."


id: d7839919b6fa6ec4e6b564e5663444c8
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 31427
content:
"Since AR5, climate services have increased at multiple levels (local, \r\nnational, regional and global) to aid decision-making of individuals \r\nand organizations and to enable preparedness and early climate \r\nchange action. These services include appropriate engagement from \r\nusers and providers, are based on scientifically credible information \r\nand producer and user expertise, have an effective access mechanism, \r\nand respond to the users' needs (Glossary; Hewitt et al., 2012). \r\nA Global Framework for Climate Services (GFCS) was established in \r\n2009 by the World Meteorological Organization (WMO) in support \r\nof these efforts (Hewitt et al., 2012; Lucio and Grasso, 2016). \r\nClimate services are provided across sectors and time scales, from \r\nsub-seasonal to multi-decadal, and support co-design and co-"


id: 60c0566e0bb89696b72181ab8a645c7a
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 12792
content:
"In AR6, 'climate information' refers to specific information about the \r\npast, current or future state of the climate system that is relevant for \r\nmitigation, adaptation and risk management. Cross-Chapter Box 1.1 \r\nis an example of climate information at the global scale. It provides \r\nclimate change information with potential relevance for the global \r\nstocktake, and indicates where in AR6 this information may be found."


id: ef0f88da1d86bbe4a033f66dad0bdcb2
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 35082
content:
"large social movements such as School Strikes for Climate gained \r\nworldwide attention (Thackeray et al., 2020). We thus assess that \r\nspecific characteristics of media coverage play a major role in climate \r\nunderstanding and perception (high confidence), including how IPCC \r\nassessments are received by the general public."


id: b2add9d1dd1ce8a9e9013229b69e32bc
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 25631
content:
"production processes that involve climate information providers, \r\nresource managers, planners, practitioners and decision makers \r\n(Brasseur and Gallardo, 2016; Trenberth et al., 2016; C.D.  Hewitt \r\net al., 2017). For example, they may provide high-quality data on \r\ntemperature, rainfall, wind, soil moisture and ocean conditions, as \r\nwell as maps, risk and vulnerability analyses, assessments, and future \r\nprojections and scenarios. These data and information products may \r\nbe combined with non-meteorological data, such as agricultural \r\nproduction, health trends, population distributions in high-risk areas, \r\nroad and infrastructure maps for the delivery of goods, and other \r\nsocio-economic variables, depending on users' needs (WMO, 2020a). \r\nCross-Chapter Box 12.2 illustrates the diversity of climate services \r\nwith three examples from very different contexts."


id: e7c1500c63815f276fe231fb7b83f222
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content:
"The current landscape of climate services is assessed in detail \r\nin Chapter  12 (Section  12.6), with a  focus on multi-decadal \r\ntime scales relevant for climate change risk assessment. Other \r\ninformation relevant to improving climate services for decision\u0002making includes the assessment of methods to construct regional \r\ninformation (Chapter  10), as well as projections at the regional \r\nlevel (Atlas) relevant for impact and risk assessment in different \r\nsectors (Chapter 12)."


id: 25f7591319c01612996d9939fbd2e7de
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 3398
content:
"Scientific understanding of the climate system's fundamental features \r\nis robust and well established. This section briefly presents the major \r\nlines of evidence in climate science (Figure  1.6). It illustrates their \r\nlong history and summarizes key findings from the WGI contribution \r\nto AR5, referencing previous IPCC assessments for comparison, \r\nwhere relevant. Box  1.2 summarizes major findings from three \r\nSpecial Reports already released during the IPCC Sixth Assessment \r\nCycle. This chapter's Appendix 1A summarizes the principal findings \r\nof all six IPCC WGI Assessment Reports, including the present Report, \r\nin a single table for ease of reference."


id: 63bf67228bc9054a9b5447ebbed17b70
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 13263
content:
"Instrumental observations of the atmosphere, ocean, land, biosphere \r\nand cryosphere underpin all understanding of the climate system. \r\nThis section describes the evolution of instrumental data for major \r\nclimate variables at Earth's land and ocean surfaces, at altitude in \r\nthe atmosphere, and at depth in the ocean. Many data records exist, \r\nof varying length, continuity and spatial distribution; Figure 1.7 gives \r\na schematic overview of temporal coverage."


id: d905721fb51a123f96d341faa1e39e95
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 31653
content:
"Instrumental weather observation at the Earth's surface dates to \r\nthe invention of thermometers and barometers in the 17th century. "


id: 325032084ca39206c07da6e00df03ac6
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 5328
content:
"Figure 1.6 | Climate science milestones, between 1817 and 2021. Top: Milestones in observations. Middle: Curves of global surface air temperature (GMST) anomaly \r\nrelative to 1850-1900, using HadCRUT5 (Morice et al., 2021); atmospheric CO2 concentrations from Antarctic ice cores (Luthi et al., 2008; Bereiter et al., 2015); direct air \r\nmeasurements from 1957 onwards (see Figure 1.4 for details; Tans and Keeling, 2020). Bottom: Milestones in scientific understanding of the CO2-enhanced greenhouse effect. \r\nFurther details on each milestone are available in Section 1.3, and in Chapter 1 of AR4 (Le Treut et al., 2007)."


id: 3256e3723cb07c82eabb8f1772a6bb61
created_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
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content:
"Heat-retaining properties of the atmosphere's constituent gases were \r\nclosely investigated in the 19th century. Foote (1856) measured solar \r\nheating of CO2 experimentally and argued that higher concentrations \r\nin the atmosphere would increase Earth's temperature. Water vapour, \r\nozone, CO2 and certain hydrocarbons were found to absorb longwave \r\n(infrared) radiation, the principal mechanism of the greenhouse effect \r\n(Tyndall, 1861). Nineteenth-century investigators also established the \r\nexistence of a natural biogeochemical carbon cycle. Carbon dioxide \r\nemitted by volcanoes is removed from the atmosphere through \r\na combination of silicate rock weathering, deep-sea sedimentation, \r\noceanic absorption, and biological storage in plants, shellfish, and \r\nother organisms. On multi-million-year time scales, the compression \r\nof fossil organic matter is stored as carbon as coal, oil and natural gas \r\n(Chamberlin, 1897, 1898; Ekholm, 1901)."


id: cdc6468f63a685e07f194090ab75d87d
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content:
"Arrhenius (1896) calculated that a  doubling of atmospheric CO2\r\nwould produce warming of 5degC-6degC, but in 1900 new measurements \r\nseemed to rule out CO2 as a  greenhouse gas due to overlap with \r\nthe absorption bands of water vapour (Angstrom, 1900; Very and \r\nAbbe, 1901). Further investigation and more sensitive instruments \r\nlater overturned Angstrom's conclusion (Fowle, 1917; Callendar, \r\n1938). Nonetheless, the major role of CO2 in the energy balance of \r\nthe atmosphere was not widely accepted until the 1950s (Callendar, \r\n1949; Plass, 1956, 1961; Manabe and Moller, 1961; Weart, 2008; \r\nEdwards, 2010). Revelle and Keeling established CO2 monitoring \r\nstations in Antarctica and Hawaii during the 1957-1958 International \r\nGeophysical Year (Revelle and Suess, 1957; Keeling, 1960). These \r\nstations have tracked rising atmospheric CO2 concentrations from "


id: a80f9edaffb57b9ad2f17fd841a01d9a
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 23954
content:
"315  ppm in 1958 to 414  ppm in 2020. Ground-based monitoring \r\nof other GHGs followed. The Greenhouse Gases Observing Satellite \r\n(GOSat) was launched in 2009, and two Orbiting Carbon Observatory \r\nsatellite instruments have been in orbit since 2014."


id: cba8a295adf5b1302479050ed9fbd03e
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 29584
content:
"The AR5 WGI highlighted 'the other CO2 problem' (Doney et al., 2009), \r\nthat is, ocean acidification caused by the absorption of some 20-30% \r\nof anthropogenic CO2 from the atmosphere and its conversion to \r\ncarbonic acid in seawater. The AR5 WGI assessed that the pH of \r\nocean surface water has decreased by 0.1 since the beginning of \r\nthe industrial era (high confidence), indicating approximately a 30% \r\nincrease in acidity (IPCC, 2013b)."


id: 603ed221afbcff3f487cc356b4fc0230
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 12718
content:
"With a  heat capacity about 1000 times greater than that of the \r\natmosphere, Earth's ocean stores the vast majority of energy \r\nretained by the planet. Ocean currents transport the stored heat \r\naround the globe and, over decades to centuries, from the surface \r\nto its greatest depths. The ocean's thermal inertia moderates \r\nfaster changes in radiative forcing on land and in the atmosphere, "


id: 8f08f4fe1a26e5c69735093bacd72dea
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index: climate_gpt_only_giec
vector_id: 20042
content:
"(Glacier Thickness Database (GlaThiDa), Gartner-Roer et al., 2014). \r\nIn summary, these data allowed AR5 WGI to assess that over the \r\nlast two decades, the Greenland and Antarctic ice sheets have been \r\nlosing mass, glaciers have continued to shrink almost worldwide, \r\nand Arctic sea ice and Northern Hemisphere spring snow cover have \r\ncontinued to decrease in extent (high confidence) (IPCC, 2013b)."


id: 4f080246aa01e809c695174a45429c1d
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 9875
content:
"the first systematic global measurements of the 700-2000 m layer. \r\nComparing the HMS Challenger data to data from Argo submersible \r\nfloats revealed global subsurface ocean warming on the centennial \r\nscale (Roemmich et al., 2012). The AR5 WGI assessed with high \r\nconfidence that ocean warming accounted for more than 90% of the \r\nadditional energy accumulated by the climate system between 1971 \r\nand 2010 (IPCC, 2013b). In comparison, warming of the atmosphere \r\ncorresponds to only about 1% of the additional energy accumulated \r\nover that period (IPCC, 2013a). Chapter 2 summarizes the ocean heat \r\ncontent datasets used in AR6 (Section 2.3.3.1 and Table 2.7)."


id: daccd09eb7918f395964db3681f28ba4
created_at: 2023-03-16 14:00:37
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"Paleoclimate studies reconstruct the evolution of Earth's climate \r\nover hundreds to billions of years using pre-instrumental historical \r\narchives, indigenous knowledge, and natural archives left behind \r\nby geological, chemical and biological processes (Figure  1.7). \r\nPaleoclimatology covers a  wide range of temporal scales, ranging \r\nfrom the human historical past (decades to millennia) to geological \r\ndeep time (millions to billions of years). Paleoclimate reference \r\nperiods are presented in Cross-Chapter Box 2.1."


id: 4ddcbf88f233206b8eddad8cafcf17ae
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"Historical climatology aids near-term paleoclimate reconstructions \r\nusing media such as diaries, almanacs and merchant accounts that \r\ndescribe climate-related events such as frosts, thaws, flowering \r\ndates, harvests, crop prices and droughts (Lamb, 1965, 1995; Le Roy \r\nLadurie, 1967; Brazdil et al., 2005). Meticulous records by Chinese \r\nscholars and government workers, for example, have permitted \r\ndetailed reconstructions of China's climate back to 1000 CE, and even \r\nbeyond (Louie and Liu, 2003; Ge et al., 2008). Climatic phenomena \r\nsuch as large-scale, regionally and temporally distributed warmer \r\nand cooler periods of the past 2000 years were reconstructed from \r\nEuropean historical records (Lamb, 1965, 1995; Le Roy Ladurie, 1967; \r\nNeukom et al., 2019)."


id: ab4df75d88009d98795cd4d2486280a8
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"Indigenous and local knowledge has played an increasing role \r\nin historical climatology, especially in areas where instrumental \r\nobservations are sparse. Peruvian fishermen named the periodic \r\nEl  Nino warm current in the Pacific, which was linked by later \r\nresearchers to the Southern Oscillation (Cushman, 2004). Inuit \r\ncommunities have contributed to climatic history and community\u0002based monitoring across the Arctic (Riedlinger and Berkes, 2001; "


id: fa3838130cf1436315b3cb94ba2f4610
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"Gearheard et al., 2010). Indigenous Australian knowledge of climatic \r\npatterns has been offered as a complement to sparse observational \r\nrecords (Green et al., 2010; Head et al., 2014), such as those of sea\u0002level rise (Nunn and Reid, 2016). Ongoing research seeks to conduct \r\nfurther dialogue, utilize indigenous and local knowledge as an \r\nindependent line of evidence complementing scientific understanding, \r\nand analyse their utility for multiple purposes, especially adaptation \r\n(Laidler, 2006; Alexander et al., 2011; IPCC, 2019c). Indigenous and \r\nlocal knowledge is used most extensively by IPCC WGII."


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"and Opdyke, 1973; Siddall et al., 2003; Lisiecki and Raymo, 2005; \r\nPast Interglacials Working Group of PAGES, 2016). These estimates \r\nhave bolstered the orbital cycles hypothesis (Hays et al., 1976; \r\nBerger, 1977, 1978). However, paleoclimatology of multi-million to \r\nbillion-year periods reveals that CH4, CO2, continental drift, silicate \r\nrock weathering and other factors played a greater role than orbital \r\ncycles in climate changes during ice-free 'hothouse' periods of Earth's \r\ndistant past (Frakes et al., 1992; Bowen et al., 2015; Zeebe et al., 2016)."


id: 710514d9184b94b457f3e8e02a327b08
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"The AR5 WGI (IPCC, 2013b) used paleoclimatic evidence to put \r\nrecent warming and sea level rise in a  multi-century perspective \r\nand assessed that 1983-2012 was likely to have been the warmest \r\n30-year period of the last 1400 years in the Northern Hemisphere \r\n(medium confidence). The AR5 also assessed that the rate of sea level \r\nrise since the mid-19th century has been larger than the mean rate \r\nduring the previous two millennia (high confidence)."


id: 49d225e4fea4d260e797d8db842af9c4
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"Certain geological and biological materials preserve evidence of past \r\nclimate changes. These 'natural archives' include corals, trees, glacier \r\nice, speleothems (stalactites and stalagmites), loess deposits (dust \r\nsediments), fossil pollen, peat, lake sediment and marine sediment \r\n(Stuiver, 1965; Eddy, 1976; Haug et al., 2001; Wang et al., 2001; Jones \r\net al., 2009; Bradley, 2015). By the early 20th  century, laboratory \r\nresearch had begun to use tree rings to reconstruct precipitation \r\nand the possible influence of sunspots on climatic change (Douglass, \r\n1914, 1919, 1922). Radiocarbon dating, developed in the 1940s \r\n(Arnold and Libby, 1949), allows accurate determination of the age of \r\ncarbon-containing materials from the past 50,000 years; this dating \r\ntechnique ushered in an era of rapid progress in paleoclimate studies."


id: 7afd33a4f1ce3c614048c86f25b77568
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content:
"The climate is a  globally interconnected system driven by solar \r\nenergy. Scientists in the 19th century established the main physical \r\nprinciples governing Earth's temperature. By 1822, the principle of \r\nradiative equilibrium (the balance between absorbed solar radiation \r\nand the energy Earth re-radiates into space) had been articulated, \r\nand the atmosphere's role in retaining heat had been likened to \r\na greenhouse (Fourier, 1822). The primary explanations for natural \r\nclimate change - greenhouse gases, orbital factors, solar irradiance, \r\ncontinental position, volcanic outgassing, silicate rock weathering, \r\nand the formation of coal and carbonate rock - were all identified by \r\nthe late 19th century (Fleming, 1998; Weart, 2008)."


id: 3026a247fcd09e58850325e453629f5b
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"Global reconstructions of sea surface temperature were developed \r\nfrom material contained in deep-sea sediment cores (CLIMAP Project \r\nMembers et al., 1976), providing the first quantitative constraints for \r\nmodel simulations of ice-age climates (e.g., Rind and Peteet, 1985). \r\nPaleoclimate data and modelling showed that the Atlantic Ocean \r\ncirculation has not been stable over glacial-interglacial time periods, \r\nand that many changes in ocean circulation are associated with \r\nabrupt transitions in climate in the North Atlantic region (Ruddiman \r\nand McIntyre, 1981; Broecker et al., 1985; Boyle and Keigwin, 1987; \r\nManabe and Stouffer, 1988)."


id: 1b5bd081603773feb6549457d38c9d73
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"Natural drivers include changes in solar irradiance, ocean currents, \r\nnaturally occurring aerosols, and natural sources and sinks of \r\nradiatively active gases such as water vapour, CO2, CH4, and \r\nsulphur dioxide (SO2). Detailed global measurements of surface\u0002level solar irradiance were first conducted during the 1957-1958 \r\nInternational Geophysical Year (Landsberg, 1961), while top-of\u0002atmosphere irradiance has been measured by satellites since 1959 "


id: e7aa9de73eabbaf0bcd4e11f169a568b
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"By the early 20th century, cyclical changes in insolation due to the \r\ninteracting periodicities of orbital eccentricity, axial tilt and axial \r\nprecession had been hypothesized as a chief pacemaker of ice age-\r\ninterglacial cycles on multi-millennial time scales (Milankovitch, \r\n1920). Paleoclimate information derived from marine sediment \r\nprovides quantitative estimates of past temperature, ice volume and \r\nsea level over millions of years (Figure 1.5; Emiliani, 1955; Shackleton "


id: 9185641771d98e7cc2c5c4307ed33dc9
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"2010, 2013). The most important of these non-condensing gases \r\nis CO2 (a positive driver), released naturally by volcanism at about \r\n637 MtCO2 yr-1 in recent decades, or roughly 1.6% of the 37 GtCO2\r\nemitted by human activities in 2018 (Burton et al., 2013; Le Quere \r\net al., 2018). Absorption by the ocean and uptake by plants and soils \r\nare the primary natural CO2 sinks on decadal to centennial time \r\nscales (Section 5.1.2 and Figure 5.3)."


id: 76f8746cd5543ab4df79c8bdb6b261d0
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"(House  et al.,  1986). Measured changes in solar irradiance have \r\nbeen small and slightly negative since about 1980 (Matthes et al., \r\n2017). Water vapour is the most abundant radiatively active gas, \r\naccounting for about 75% of the terrestrial greenhouse effect, \r\nbut because its residence time in the atmosphere averages just \r\n8-10 days, its atmospheric concentration is largely governed by \r\ntemperature (van der Ent and Tuinenburg, 2017; Nieto and Gimeno, \r\n2019). As a result, non-condensing GHGs with much longer residence \r\ntimes serve as 'control knobs', regulating planetary temperature, \r\nwith water vapour concentrations as a feedback effect (Lacis et al., "


id: 3823c7613988e54bbadaf96477c72f2a
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content:
"Aerosols (tiny airborne particles) interact with climate in numerous \r\nways, some direct (e.g., reflecting solar radiation back into space) and "


id: e7dcef4c9de109e89ecc04624def4137
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content:
"(see Glossary), including widely used refrigerants and propellants, \r\nwere extremely potent greenhouse gases (Sections  2.2.4.3 and \r\n6.2.2.9; Ramanathan, 1975). When these chemicals were also \r\nfound to be depleting the stratospheric ozone layer, they were \r\nstringently and successfully regulated on a global basis by the 1987 \r\nMontreal Protocol on the Ozone Layer and successor agreements \r\n(Parson, 2003)."


id: a96d42db16283ea1c47b02b4a1dcc8a
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content:
"All IPCC reports have assessed the total RF as positive when \r\nconsidering all sources. However, due to the considerable variability \r\nof both natural and anthropogenic aerosol loads, FAR characterized \r\ntotal aerosol RF as 'highly uncertain' and was unable even to \r\ndetermine its sign (positive or negative). Major advances in \r\nquantification of aerosol loads and their effects have taken place \r\nsince then, and IPCC reports since 1992 have consistently assessed "


id: 92310319049332a27531cc393004834e
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"total forcing by anthropogenic aerosols as negative (IPCC, 1992, \r\n1995a, 1996). However, due to their complexity and the difficulty \r\nof obtaining precise measurements, aerosol effects have been \r\nconsistently assessed as the largest single source of uncertainty \r\nin estimating total RF (Stevens and Feingold, 2009; IPCC, 2013a). \r\nOverall, AR5 assessed that total aerosol effects, including cloud \r\nadjustments, resulted in a negative RF of -0.9 [-1.9 to -0.1] W m-2\r\n(medium confidence), offsetting a substantial portion of the positive \r\nRF resulting from the increase in GHGs (high confidence) (IPCC, \r\n2013b). Chapter 7 provides an updated assessment of the total and \r\nper-component RF for the WGI contribution to AR6."


id: 41994cb5f4711bf76b42d6eda1334288
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content:
"Understanding the global climate system requires both theoretical \r\nunderstanding and empirical measurement of the major forces \r\nand factors that govern the transport of energy and mass (air, water and \r\nwater vapour) around the globe; the chemical and physical properties \r\nof the atmosphere, ocean, cryosphere and land surfaces; and the \r\nbiological and physical dynamics of natural ecosystems, as well as \r\nthe numerous feedbacks (both positive and negative) among these \r\nprocesses. Attributing climatic changes or extreme weather events \r\nto human activity (Cross-Working Group Box: Attribution) also \r\nrequires an understanding of the many ways that human activities \r\nmay affect the climate, along with statistical and other techniques \r\nfor separating the 'signal' of anthropogenic climate change from the \r\n'noise' of natural climate variability (Section 1.4.2). This inter- and \r\ntrans-disciplinary effort requires contributions from many sciences."


id: 8f72df9ed91afbd1d74f3322c009b101
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content:
"In the 1960s similar approaches to modelling the weather were used \r\nto model the climate, but with much longer runs than daily forecasting \r\n(Smagorinsky et al., 1965; Manabe and Wetherald, 1967). Simpler \r\nstatistical and one- and two-dimensional modelling approaches \r\ncontinued in tandem with the more complex general circulation \r\nmodels (GCMs; Manabe and Wetherald, 1967; Budyko, 1969; Sellers, \r\n1969). The first coupled atmosphere-ocean model (AOGCM) with \r\nrealistic topography appeared in 1975 (Bryan et al., 1975; Manabe \r\net al., 1975). Rapid increases in computer power enabled higher \r\nresolutions, longer model simulations, and the inclusion of additional \r\nphysical processes in GCMs, such as aerosols, atmospheric chemistry, \r\nsea ice, and snow."


id: f188b0e25b9b488d07250dd5a768192
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"Due to the complexity of many interacting processes, ranging \r\nin scale from the molecular to the global, and occurring on time \r\nscales from seconds to millennia, attribution makes extensive use \r\nof conceptual, mathematical, and computer simulation models. \r\nModelling allows scientists to combine a vast range of theoretical \r\nand empirical understanding from physics, chemistry and other \r\nnatural sciences, producing estimates of their joint consequences \r\nas simulations of past, present or future states and trends (Nebeker, \r\n1995; Edwards, 2010, 2011)."


id: a4499d107920017d799bfd1d296ae390
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"In addition to radiative transfer (discussed above in Section 1.3.3), \r\nforces and factors such as thermodynamics (energy conversions), \r\ngravity, surface friction, and the Earth's rotation govern the planetary\u0002scale movements or 'circulation' of air and water in the climate \r\nsystem. The scientific theory of climate began with Halley (1686), \r\nwho hypothesized vertical atmospheric circulatory cells driven by \r\nsolar heating, and Hadley (1735), who showed how the Earth's \r\nrotation affects that circulation. Ferrel (1856) added the Coriolis \r\nforce to existing theory, explaining the major structures of the global \r\natmospheric circulation. In aggregate, prevailing winds and ocean \r\ncurrents move energy poleward from the equatorial regions where \r\nthe majority of incoming solar radiation is received."


id: 83b380954667d99e19293bc07bc5aee9
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vector_id: 18254
content:
"Since climate models vary along many dimensions, such as grid \r\ntype, resolution, and parameterizations, comparing their results \r\nrequires special techniques. To address this problem, the climate "


id: 6b1f3f959d1b27d0426077e899a745c1
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content:
"Climate models provide the ability to simulate these complex \r\ncirculatory processes, and to improve the physical theory of climate \r\nby testing different mathematical formulations of those processes. "


id: c171368de748b4207c4943c331627ad5
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"consistent with the AR5 WGI assessed likely range of equilibrium \r\nclimate sensitivity (ECS) [1.5degC to 4.5degC] to within estimated \r\nuncertainties (on ECS, see Section  1.3.5; IPCC, 2013a). Similarly, \r\nover the period 1993-2010, when observations of all sea level \r\ncomponents were available, AR5 WGI assessed the observed global \r\nmean sea level rise to be consistent with the sum of the observed \r\ncontributions from ocean thermal expansion (due to warming) \r\ncombined with changes in glaciers, the Antarctic and Greenland ice \r\nsheets, and land-water storage (high confidence). Verification that \r\nthe terms of these budgets balance over recent decades provides \r\nstrong evidence for our understanding of anthropogenic climate \r\nchange (Cross-Chapter Box 9.1)."


id: 23f38864fa3a5483beda34fd5cfb964c
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"It was recognized in IPCC AR5 that information about the near term \r\nwas increasingly relevant for adaptation decisions. In response, AR5 \r\nWGI made a specific assessment for how global surface temperature \r\nwas projected to evolve over the next two decades, concluding that \r\nthe change for the period 2016-2035 relative to 1986-2005 will likely \r\nbe in the range of 0.3degC-0.7degC (medium confidence), assuming no \r\nmajor volcanic eruptions or secular changes in total solar irradiance \r\n(IPCC, 2013b). The AR5 was also the first IPCC assessment report \r\nto assess 'decadal predictions' of the climate, where the observed \r\nstate of the climate system was used as a starting point for forecasts \r\nseveral years ahead. The AR6 examines updates to these decadal \r\npredictions (Section 4.4.1)."


id: aa499fea0714d4e66529d4ae69125e51
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"A key indicator of climate understanding is whether theoretical climate \r\nsystem budgets or 'inventories', such as the balance of incoming and \r\noutgoing energy at the surface and at the top of the atmosphere, \r\ncan be quantified and balanced observationally. The global energy \r\nbudget, for example, includes energy retained in the atmosphere, \r\nupper ocean, deep ocean, ice, and land surface. Church et al. (2013) \r\nassessed in AR5 with high confidence that independent estimates of \r\neffective radiative forcing (ERF), observed heat storage, and surface \r\nwarming combined to give an energy budget for the Earth that is "


id: 750c9ffe53048086f687cfb4a618d72e
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"The assessments and predictions for the near-term evolution of global \r\nclimate features are largely independent of future CO2 emissions \r\npathways. However, AR5 WGI assessed that limiting climate change \r\nin the long-term future will require substantial and sustained \r\nreductions of GHG emissions (IPCC, 2013b). This assessment results \r\nfrom decades of research on understanding the climate system and "


id: 3c7554c92d35346635836259b784a9cf
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"The 1979 Charney NRC report estimated ECS at 3degC, stating the range \r\nas 2degC-4.5degC, based on 'consistent and mutually supporting' model \r\nresults and expert judgment (NRC, 1979). ECS is defined in IPCC \r\nassessments as the global surface air temperature (GSAT) response \r\nto CO2 doubling (from pre-industrial levels) after the climate has \r\nreached equilibrium (stable energy balance between the atmosphere \r\nand ocean). Another quantity, transient climate response (TCR), was \r\nlater introduced as the change in GSAT, averaged over a  20-year \r\nperiod, at the time of CO2 doubling in a scenario of concentration \r\nincreasing at 1% per year. Calculating ECS from historical or \r\npaleoclimate temperature records, in combination with energy \r\nbudget models, has produced estimates both lower and higher than \r\nthose calculated using GCMs and ESMs; in this Report, these are \r\nassessed in Chapter 7, Section 7.5.2."


id: 368ecf0fd49e5cc7a2a49ec18d870f58
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"its perturbations, and projecting climate change into the future. Each \r\nIPCC report has considered a range of emissions scenarios, typically \r\nincluding a scenario in which societies choose to continue on their \r\npresent course, as well as several others reflecting socio-economic \r\nand policy responses that may limit emissions and/or increase the \r\nrate of CO2 removal from the atmosphere. Climate models are used \r\nto project the outcomes of each scenario. However, future human \r\nclimate influence cannot be precisely predicted because GHG and \r\naerosol emissions, land use, energy use and other human activities \r\nmay change in numerous ways. Common emissions scenarios used in \r\nthe WGI contribution to AR6 are detailed in Section 1.6."


id: e4bb792454e948f13b113ef11fdee020
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content:
"Based on model results and steadily increasing CO2 concentrations \r\n(Bolin and Bischof, 1970; SMIC, 1971; Meadows et al., 1972), \r\nconcerns about future 'risk of effects on climate' were addressed in \r\nRecommendation 70 of the Stockholm Action Plan, resulting from \r\nthe 1972 United Nations Conference on the Human Environment \r\n(UN, 1973). Numerous other scientific studies soon amplified these \r\nconcerns (summarized in Schneider (1975) and Williams (1978); see \r\nalso Nordhaus (1975, 1977). In 1979, a US National Research Council \r\n(NRC) group led by Jule Charney reported on the 'best present \r\nunderstanding of the carbon dioxide/climate issue for the benefit of \r\npolicymakers', initiating an era of regular and repeated large-scale \r\nassessments of climate science findings."


id: 9450d96ffc6b96ca73bc7907c36cfe62
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content:
"ECS is typically characterized as most relevant on centennial time \r\nscales, while TCR was long seen as a more appropriate measure of \r\nthe 50-100-year response to gradually increasing CO2. However, \r\nrecent studies have raised new questions about how accurately both \r\nquantities are estimated by GCMs and ESMs (Grose et al., 2018; \r\nMeehl et al., 2020; Sherwood et al., 2020). Further, as climate models \r\nevolved to include a full-depth ocean, the time scale for reaching full \r\nequilibrium became longer and new methods to estimate ECS had to \r\nbe developed (Gregory et al., 2004; Meehl et al., 2020; Meinshausen \r\net al., 2020). Because of these considerations, as well as new \r\nestimates from observation-based, paleoclimate, and emergent\u0002"


id: 7d017650f4cad319213336f868589ac4
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content:
"Table 1.2 | Estimates of equilibrium climate sensitivity (ECS) and transient climate response (TCR) from successive major scientific assessments since \r\n1979. No likelihood statements are available for reports prior to 2001 because those reports did not use the IPCC calibrated uncertainty language. The assessed range of \r\nECS differs from the range derived from general circulation model (GCM) and Earth system model (ESM) results because assessments take into account other evidence, other \r\ntypes of models, and expert judgment. The AR6 definition of ECS differs from previous reports, now including all long-term feedbacks except those associated with ice sheets. \r\nAR6 estimates of ECS are derived primarily from process understanding, historical observations and emergent constraints, informed by (but not based on) GCM and ESM model \r\nresults. CMIP6 is the 6th phase of the Coupled Model Intercomparison Project (Section 7.5.5 and Box 7.1)."


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"constraints studies (Sherwood et al., 2020), the AR6 definition of ECS \r\nhas changed from previous reports; it now includes all feedbacks \r\nexcept those associated with ice sheets. Accordingly, unlike previous \r\nreports, the AR6 assessments of ECS and TCR are not based primarily \r\non GCM and ESM model results (see Section 7.5.5 and Box. 7.1 for \r\na full discussion)."


id: e5e705df065dc62fdbafe1817613460
created_at: 2023-03-16 14:00:37
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content:
"significant reductions of warming due to short-lived climate forcers \r\n(SLCFs) could reduce the level at which temperature stabilizes once CO2\r\nemissions reach net zero, and also reduce the long-term global warming \r\ncommitment by reducing radiative forcing from SLCFs (Chapter 5)."


id: 10a20b22f1f7fc1e2b593d4200ffb659
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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content:
"In summary, major lines of evidence  - observations, paleoclimate, \r\ntheoretical understanding and natural and human drivers - have been \r\nstudied and developed for over 150  years. Methods for projecting \r\nclimate futures have matured since the 1950s and attribution \r\nstudies since the 1980s. We conclude that understanding of the \r\nprincipal features of the climate system is robust and well established."


id: 357adb453dab10a072418394d3be16ce
created_at: 2023-03-16 14:00:37
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content:
"Today, other sensitivity terms are sometimes used, such as 'transient \r\nclimate response to emissions' (TCRE, defined as the ratio of warming \r\nto cumulative CO2 emissions in a  CO2-only simulation) and 'Earth \r\nsystem sensitivity' (ESS), which includes multi-century Earth system \r\nfeedbacks such as changes in ice sheets."


id: 243771ab7b33bd11b3f894933b29ce31
created_at: 2023-03-16 14:00:37
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content:
"Table 1.2 shows estimates of ECS and TCR for major climate science \r\nassessments since 1979. The table shows that despite some variation \r\nin the range of GCM and (for the later assessments) ESM results, \r\nexpert assessment of ECS changed little between 1979 and the \r\npresent Report. Based on multiple lines of evidence, AR6 has narrowed \r\nthe likely range of ECS to 2.5degC-4.0degC (Chapter 7, Section 7.5.5)."


id: 2cb8d99239c8f7876e7bcee990aacceb
created_at: 2023-03-16 14:00:37
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"Although these studies generally showed good agreement between \r\npast projections and subsequent observations, this type of analysis is \r\ncomplicated because the scenarios of future radiative forcing used in \r\nearlier projections do not precisely match the actual radiative forcings \r\nthat subsequently occurred. Mismatches between the projections \r\nand subsequent observations could be due to incorrectly projected \r\nradiative forcings (e.g.,  aerosol emissions, GHG concentrations or \r\nvolcanic eruptions that were not included), an incorrectly modelled \r\nresponse to those forcings, or both. Alternatively, agreement \r\nbetween projections and observations could be fortuitous due to \r\na  compensating balance of errors, for example, too low climate \r\nsensitivity but too strong radiative forcings."


id: b851f3f2ce5836757b75f5486e48ab44
created_at: 2023-03-16 14:00:37
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content:
"From the close link between cumulative emissions and warming it \r\nfollows that any given level of global warming is associated with \r\na total budget of GHG emissions, especially CO2 as it is the largest long\u0002lived contributor to radiative forcing (Allen et al., 2009; Collins et al., \r\n2013; Rogelj et al., 2019). Higher emissions in earlier decades imply \r\nlower emissions later on to stay within the Earth's carbon budget. \r\nStabilizing the anthropogenic influence on global surface temperature \r\nthus requires that CO2 emissions and removals reach net zero once the \r\nremaining carbon budget is exhausted (Cross-Chapter Box 1.4)."


id: b6320bb28347d359993cc5a0fa2b4a4
created_at: 2023-03-16 14:00:37
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vector_id: 26195
content:
"One approach to partially correct for mismatches between the \r\nforcings used in the projections and the forcings that actually occurred \r\nis described by Hausfather et al. (2020). Model projections of global \r\nsurface temperature and estimated radiative forcings were taken \r\nfrom several historical studies, along with the baseline 'no-policy' \r\nscenarios from the first four IPCC assessment reports. These model \r\nprojections of temperature and radiative forcing are then compared \r\nto (i) the observed change in temperature through time over the \r\nprojection period, and (ii) the observed change in temperature \r\nrelative to the observationally estimated radiative forcing over the \r\nprojection period (Figure 1.9; data from Hausfather et al., 2020). "


id: b91d30562dfe2be7accb9ba825f59d0
created_at: 2023-03-16 14:00:37
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content:
"Past, present and future emissions of CO2 therefore commit the world to \r\nsubstantial multi-century climate change, and many aspects of climate \r\nchange would persist for centuries even if emissions of CO2 were \r\nstopped immediately (IPCC, 2013b). According to AR5, a large fraction \r\nof this change is essentially irreversible on a multi-century to millennial \r\ntime scale, barring large net removal ('negative emissions') of CO2 from \r\nthe atmosphere over a  sustained period through as yet unavailable \r\ntechnological means (Chapters 4 and 5l; IPCC, 2013a, 2018). However, "


id: 4066bf51d9b85b44ea835dffb29c1345
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"the world. Regional projections were given for the best estimate of 1.8degC \r\nof global warming by 2030, compared to a baseline of 1850-1900, and \r\nwere assigned low confidence. The FAR also suggested that regional \r\ntemperature changes should be scaled by -30% to +50% to account \r\nfor the uncertainty in projected global warming."


id: 4c90d6bd9b2519e51f51ae391d2c04ea
created_at: 2023-03-16 14:00:37
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content:
"The regional projections presented in FAR are compared to \r\nthe observed temperature change in the period since 1990 \r\n(Figure  1.10), following Grose et al. (2017). Subsequent observed \r\ntemperature change has tracked within the FAR projected range \r\nfor the best estimate of regional warming in the Sahel, South Asia \r\nand southern Europe. Temperature change has tracked at or below \r\nthis range for the central North America and Australia regions, \r\nyet remains within the range reduced by 30% to generate FAR's \r\nlower global warming estimate. This is consistent with the smaller \r\nobserved estimate of radiative forcing compared to the FAR central \r\nestimate. Note that the projections assessed in Chapter  4  of this \r\nReport suggest that global temperatures will be around 1.2degC-1.8degC \r\nabove 1850-1900 levels by 2030, a range which is also lower than \r\nthe FAR central estimate."


id: ac4282701bb37fb6d5204db8fce2db09
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vector_id: 24639
content:
"In addition to global surface temperature, past regional projections \r\ncan be evaluated. For example, FAR (IPCC, 1990a) presented a series \r\nof temperature projections for 1990-2030 for several regions around "


id: 857a2df3e216cc312994fed25eded905
created_at: 2023-03-16 14:00:37
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content:
"observations, allowing for regional-scale climate variability and \r\ndifferences in projected and actual forcings. There is medium \r\nconfidence that the spatial warming pattern has been reliably \r\nprojected in past IPCC reports (limited evidence, high agreement)."


id: 769966bffac6ec55c2a40b208b01a069
created_at: 2023-03-16 14:00:37
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vector_id: 16243
content:
"Overall, there is medium confidence that past projections of global \r\ntemperature are consistent with subsequent observations, especially \r\nwhen accounting for the difference in radiative forcings used and \r\nthose which actually occurred (limited evidence, high agreement). \r\nThe FAR regional projections are broadly consistent with subsequent "


id: f66d3610df72bb1fd88419b189a4eb0b
created_at: 2023-03-16 14:00:37
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content:
"The Sixth Assessment Cycle started with three Special Reports. The Special Report on Global Warming of 1.5degC (SR1.5, IPCC, 2018), \r\ninvited by the Parties to the UNFCCC in the context of the Paris Agreement, assessed current knowledge on the impacts of global \r\nwarming of 1.5degC above pre-industrial levels and related global greenhouse gas (GHG) emissions pathways. The Special Report on \r\nClimate Change and Land (SRCCL, IPCC, 2019a) addressed GHG fluxes in land-based ecosystems, land use and sustainable land \r\nmanagement in relation to climate change adaptation and mitigation, desertification, land degradation and food security. The Special \r\nReport on the Ocean and Cryosphere in a Changing Climate (SROCC, IPCC, 2019b) assessed new literature on observed and projected \r\nchanges of the ocean and the cryosphere, and their associated impacts, risks and responses."


id: 48008d59fa74c1a42b0732096e648a8b
created_at: 2023-03-16 14:00:37
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content:
"The SR1.5 and SRCCL were produced through a collaboration between the three IPCC Working Groups, SROCC by only Working \r\nGroups I and II. Here we focus on key findings relevant to the physical science basis covered by WGI."


id: 4037a1502fad1a33e23795dc73d49d30
created_at: 2023-03-16 14:00:37
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content:
"With respect to the ocean, SROCC assessed that it is virtually certain that the ocean has warmed unabated since 1970 and has taken \r\nup more than 90% of the excess heat contributed by global warming. The rate of ocean warming has likely more than doubled since \r\n1993. Over the period 1982-2016, marine heatwaves have very likely doubled in frequency and are increasing in intensity (very high \r\nconfidence). In addition, the surface ocean acidified further (virtually certain) and loss of oxygen occurred from the surface to a depth \r\nof 1000 m  (medium confidence). The Report expressed medium confidence that the Atlantic Meridional Overturning Circulation \r\n(AMOC) weakened in 2004-2017 relative to 1850-1900."


id: 75965e90555818e54bc332d1d53f42cd
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"The SROCC assessed that anthropogenic climate change has increased observed precipitation (medium confidence), winds (low \r\nconfidence), and extreme sea level events (high confidence) associated with some tropical cyclones. It also found evidence for an \r\nincrease in the annual global proportion of Category 4 or 5 tropical cyclones in recent decades (low confidence)."


id: 9f47f152059446e66e81daeb5a9647cc
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"The SRCCL stated that the land is simultaneously a source and sink of CO2, due to both anthropogenic and natural drivers. It estimates \r\nwith medium confidence that agriculture, forestry and other land use (AFOLU) activities accounted for around 13% of CO2, 44% of \r\nCH4, and 82% of N2O emissions from human activities during 2007-2016, representing 23% (12.0 +- 3.0 GtCO2 equivalent yr-1) of \r\nthe total net anthropogenic emissions of GHGs. The natural response of land to human-induced environmental change - such as \r\nincreasing atmospheric CO2 concentration, nitrogen deposition and climate change - caused a net CO2 sink equivalent of around 29% \r\nof total CO2 emissions (medium confidence); however, the persistence of the sink is uncertain due to climate change (high confidence)."


id: b9f94f4e61efa626a6e72b29bf51c387
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content:
"The SRCCL also assessed how changes in land conditions affect global and regional climate. It found that changes in land cover have \r\nled to both a net release of CO2, contributing to global warming, and an increase in global land albedo, causing surface cooling. \r\nHowever, the report estimated that the resulting net effect on globally averaged surface temperature was small over the historical \r\nperiod (medium confidence)."


id: 40b74e341bcfe8643437601e209652f9
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"The SROCC found that the carbon content of Arctic and boreal permafrost is almost twice that of the atmosphere (medium confidence), \r\nand assessed medium evidence with low agreement that thawing northern permafrost regions are currently releasing additional \r\nnet CH4 and CO2."


id: f841536b310e838cb710f28d86e4ca0e
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"jg\r\nThe SR1.5 concluded that global warming is likely to reach 1.5degC between 2030 and 2052 if it continues to increase at the current rate \r\n(high confidence). However, even though warming from anthropogenic emissions will persist for centuries to millennia and will cause \r\nongoing long-term changes, past emissions alone are unlikely to raise global surface temperature to 1.5degC above 1850-1900 levels."


id: b5e2086bae85861fc58a10cfca161e6d
created_at: 2023-03-16 14:00:37
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content:
"The SR1.5 also found that reaching and sustaining net zero anthropogenic CO2 emissions and reducing net non-CO2 radiative forcing \r\nwould halt anthropogenic global warming on multi-decadal time scales (high confidence). The maximum temperature reached is then \r\ndetermined by (i) cumulative net global anthropogenic CO2 emissions up to the time of net zero CO2 emissions (high confidence) and (ii) \r\nthe level of non-CO2 radiative forcing in the decades prior to the time that maximum temperatures are reached (medium confidence)."


id: dd7c88e8bedf53ca5def5739653ae0cf
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content:
"Furthermore, climate models project robust differences in regional climate characteristics between the present day and a  global \r\nwarming of 1.5degC, and between 1.5degC and 2degC, including mean temperature in most land and ocean regions and hot extremes in most \r\ninhabited regions (high confidence). There is medium confidence in robust differences in heavy precipitation events in several regions \r\nand the probability of droughts in some regions."


id: be48efe9e87b069c749ffb302b1e4d7d
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content:
"The SROCC projected that over the 21st century, the ocean will transition to unprecedented conditions, with increased temperatures \r\n(virtually certain), further acidification (virtually certain), and oxygen decline (medium confidence). Marine heatwaves are projected to \r\nbecome more frequent (very high confidence) as are extreme El Nino and La Nina events (medium confidence). The AMOC is projected"


id: c2d0aea65a3d8335e738a6ccd4f178d6
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content:
"to weaken during the 21st century (very likely), but a collapse is deemed very unlikely (albeit with medium confidence due to known \r\nbiases in the climate models used for the assessment)."


id: 4d2c94bb8b5de2b967bd62a646cce0e4
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updated_at: 2023-03-16 14:00:37
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content:
"pygg\r\nThe SR1.5 focused on emissions pathways and system transitions consistent with 1.5degC global warming over the 21st century. Building \r\nupon the understanding from AR5 WGI of the quasi-linear relationship between cumulative net anthropogenic CO2 emissions since \r\n1850-1900 and maximum global mean temperature, the Report assessed the remaining carbon budgets compatible with the 1.5degC \r\nor 2degC warming goals of the Paris Agreement. Starting from year 2018, the remaining carbon budget for a one-in-two (50%) chance \r\nof limiting global warming to 1.5degC is about 580 GtCO2, and about 420 GtCO2 for a two-in-three (66%) chance (medium confidence)."


id: fd69dfa12d1223b854b8bc053decb22a
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content:
"At  constant 2017 emissions, these budgets would be depleted by about the years 2032 and 2028, respectively. Using GMST \r\ninstead of GSAT gives estimates of 770 GtCO2 and 570 GtCO2, respectively (medium confidence). Each budget is further reduced by \r\napproximately 100 GtCO2 over the course of this century when permafrost and other less well represented Earth system feedbacks \r\nare taken into account."


id: 219801d6202e169e838f81d39c22de7d
created_at: 2023-03-16 14:00:37
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content:
"It is concluded that all emissions pathways with no or limited overshoot of 1.5degC imply that global net anthropogenic CO2 emissions \r\nwould need to decline by about 45% from 2010 levels by 2030, reaching net zero around 2050, together with deep reductions in \r\nother anthropogenic emissions, such as methane and black carbon. To limit global warming to below 2degC, CO2 emissions would have \r\nto decline by about 25% by 2030 and reach net zero around 2070."


id: 6b7b181b01e3b54a8a90529f7e33c011
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vector_id: 14526
content:
"First, anomalies are often used when combining data from multiple \r\nlocations, because the absolute values can vary over small spatial scales \r\nwhich are not densely observed or simulated, whereas anomalies are \r\nrepresentative for much larger scales (e.g., for temperature; Hansen \r\nand Lebedeff, 1987). Since their baseline value is zero by definition, \r\nanomalies are also less susceptible to biases arising from changes \r\nin the observational network. Second, the seasonality in different \r\nclimate indicators can be removed using anomalies to more clearly \r\ndistinguish variability from long-term trends."


id: 809fb2314f409f5bb15b7a69201d0f83
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vector_id: 17764
content:
"Several baselines or reference periods are used consistently \r\nthroughout AR6 WGI. Baseline refers to a  period against which \r\ndifferences are calculated, whereas reference period is used more \r\ngenerally to indicate a  time period of interest, or a  period over \r\nwhich some relevant statistics are calculated (Glossary). Variations \r\nin observed and simulated climate variables over time are often \r\npresented as 'anomalies', that is, the differences relative to a baseline, \r\nrather than using the absolute values. This is done for several reasons."


id: 64782062c9fd5d77ff76147237f923ef
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content:
"variable being considered, the rates of change of the variable and the \r\npurpose of the chosen period, but is usually 20 to 50  years long. \r\nThe World Meteorological Organization (WMO) uses 30-year periods \r\nto define 'climate normals', which indicate conditions expected to be \r\nexperienced in a given location."


id: 2ec28245b8fef548913f9e267dbfe676
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vector_id: 4795
content:
"For some variables, such as precipitation, anomalies are often \r\nexpressed as percentages in order to more easily compare changes \r\nin regions with very different climatological means. However, \r\nfor situations where there are important thresholds (e.g.,  phase \r\ntransitions around 0degC) or for variables which can only take \r\na particular sign or be in a fixed range (e.g., sea ice extent or relative \r\nhumidity), absolute values are normally used."


id: bf9a8bc8ec643ed43115dc53da739b07
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index: climate_gpt_only_giec
vector_id: 27704
content:
"For AR6 WGI, the period 1995-2014 is used as a baseline to calculate \r\nthe changes in future climate using model projections and also as \r\na 'modern' or 'recent past' reference period when estimating past \r\nobserved warming. The equivalent period in AR5 was 1986-2005, and \r\nin SR1.5, SROCC and SRCCL it was 2006-2015. The primary reason for \r\nthe different choice in AR6 is that 2014 is the final year of the historical \r\nCMIP6 simulations. These simulations subsequently assume different \r\nemissions scenarios and so choosing any later baseline end date \r\nwould require selecting a particular emissions scenario. For certain \r\nassessments, the most recent decade possible (e.g., 2010-2019 or \r\n2011-2020, depending on the availability of observations) is also \r\nused as a reference period (Cross-Chapter Box 2.3)."


id: 6b79dcad771ab6988377cf2eb472c77
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content:
"The choice of a  baseline period has important consequences for \r\nevaluating both observations and simulations of the climate, \r\nfor  comparing observations with simulations, and for presenting \r\nclimate projections. There is usually no perfect choice of baseline \r\nas many factors have to be considered and compromises may be \r\nrequired (Hawkins and Sutton, 2016). It is important to evaluate the \r\nsensitivity of an analysis or assessment to the choice of the baseline."


id: 4d4056402d0a8db4922249ba595691dd
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content:
"For example, the collocation of observations and reanalyses within the \r\nmodel ensemble spread depends on the choice of the baseline, and \r\nuncertainty in future projections of climate is reduced if using a more \r\nrecent baseline, especially for the near term (Figure 1.11). The length \r\nof an appropriate baseline or reference period depends on the "


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content:
"Figure  1.12 shows changes in observed global mean surface \r\ntemperature (GMST) relative to 1850-1900 and illustrates "


id: ad90b2eb608b089cb1d6796e39f103a5
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content:
"observed global warming levels for a  range of reference periods \r\nthat are either used in AR6 or were used in previous IPCC reports. \r\nThis allows changes to be calculated between different periods \r\nand compared to previous assessments. For example, AR5 assessed \r\nthe change in GMST from the 1850-1900 baseline to 1986-2005 \r\nreference period as 0.61 [0.55  to  0.67]  degC, whereas it is now \r\nassessed to be 0.69 [0.52 to 0.82] degC using improved GMST datasets \r\n(Cross-Chapter Box 2.3)."


id: 9a84b6e3793c88d3eee698453ba78e42
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content:
"either direction, with low confidence in the sign of any differences \r\n(see Cross Chapter Box 2.3 for details). "


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content:
"Three future reference periods are used in AR6 WGI for presenting \r\nprojections: near term (2021-2040), mid-term (2041-2060) and \r\nlong-term (2081-2100; Figure  1.11). In AR6, 20-year reference \r\nperiods are considered long enough to show future changes in \r\nmany variables when averaging over ensemble members of multiple \r\nmodels, and short enough to enable the time dependence of changes \r\nto be shown throughout the 21st century. Projections with alternative \r\nrecent baselines (such as 1986-2005 or the current WMO climate\u0002normal period of 1981-2010) and a wider range of future reference \r\nperiods are presented in the Interactive Atlas. Note that 'long term' \r\nis also sometimes used in a more general sense to refer to durations \r\nof centuries to millennia when examining past climate, as well as \r\nfuture climate change beyond the year 2100. Cross-Chapter Box 2.1 \r\ndiscusses the paleo-reference periods used in AR6."


id: 2f3cb4132a9f819c745f803b3267bf82
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 4849
content:
"The commonly used metric for global surface warming tends to be \r\nGMST but, as shown in Figure 1.11, climate model simulations tend to \r\nuse global surface air temperature (GSAT). Although GMST and GSAT \r\nare closely related, the two measures are physically distinct. GMST is \r\na combination of land surface air temperature (LSAT) and sea surface \r\ntemperature (SST), whereas GSAT is surface air temperatures over \r\nland, ocean and ice. A  key development in AR6 is the assessment \r\nthat long-term changes in GMST and GSAT differ by at most 10% in "


id: c9b8fca8ad707ee010ffcebd765b4aae
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
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vector_id: 29295
content:
"Contributing Authors: Ed Hawkins (United Kingdom), Paul Edwards (United States of America), Piers Forster (United Kingdom), \r\nDarrell S. Kaufman (United States of America), Jochem Marotzke (Germany), Malte Meinshausen (Australia/Germany), Maisa Rojas \r\n(Chile), Bjorn H. Samset (Norway), Peter Thorne (Ireland/United Kingdom)"


id: e7ebca33b9176d90bfc1a466b746e42f
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 33983
content:
"Owens et al., 2017; Hamilton et al., 2018). It is likely that there was a net anthropogenic forcing of 0.0-0.3 Wm-2 in 1850-1900 relative \r\nto 1750 (medium confidence). The net radiative forcing from changes in solar activity and volcanic activity in 1850-1900, compared \r\nto the period around 1750, is estimated to be smaller than +-0.1 W m-2, but note there were several large volcanic eruptions between \r\n1750 and 1850 (Cross-Chapter Box 1.2, Figure 1)."


id: 65ae4d7bd3eda388699d7fb01b525b09
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 13575
content:
"Combining these different sources of evidence, we assess that from the period around 1750 to 1850-1900 there was a change in \r\nglobal temperature of around 0.1 [-0.1 to +0.3] degC (medium confidence), with an anthropogenic component in a likely range of \r\n0.0degC-0.2degC (medium confidence)."


id: 2ceb98c08cdee8ea1bbe7eb09df9709f
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 4505
content:
"Climatic changes since the pre-industrial era are a combination of \r\nlong-term anthropogenic changes and natural variations on time \r\nscales from days to decades. The relative importance of these two \r\nfactors depends on the climate variable or region of interest. Natural \r\nvariations consist of both natural radiatively forced trends (e.g., due \r\nto volcanic eruptions or solar variations) and 'internal' fluctuations of \r\nthe climate system which occur even in the absence of any radiative \r\nforcings. The internal 'modes of variability', such as the El Nino-\r\nSouthern Oscillation (ENSO) and the North Atlantic Oscillation (NAO), \r\nare discussed further in Annex IV."


id: 1fef0ec1d21f3efa22ae6d38bc7ee195
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 2462
content:
"Natural variations in both weather and longer time scale phenomena \r\ncan temporarily mask or enhance any anthropogenic trends \r\n(e.g.,  Deser et al., 2012; Kay et al., 2015). These effects are more \r\nimportant on small spatial and temporal scales but can also occur on \r\nthe global scale (Cross-Chapter Box 3.1)."


id: 21db6b14568ccbd7b3be8d52c82f50dd
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 2765
content:
"Since AR5, many studies have examined the role of internal variability \r\nthrough the use of 'large ensembles'. Each such ensemble consists of \r\nmany different simulations by a single climate model for the same \r\ntime period and using the same radiative forcings. These simulations \r\ndiffer only in their phasing of the internal climate variations (also "


id: 6177811df14f18e49140a5821dc30640
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 12906
content:
"Natural climate variations can temporarily mask or \r\nenhance anthropogenic climatic changes over a \r\ndecade or more, especially for smaller regions and \r\nshorter averaging periods."


id: b074f6567603dd1bdad0d802581df95d
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 25283
content:
"the construction of 'observation-based large ensembles', which \r\nare alternate possible realizations of historical observations that \r\nretain the statistical properties of observed regional weather \r\n(e.g., McKinnon and Deser, 2018)."


id: 977507dfe76d11766f827c6c88b77405
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 21356
content:
"In the 1930s it was noted that temperatures were increasing at both \r\nlocal and global scales (Figure  1.8; Kincer, 1933; Callendar, 1938). \r\nAt the time it was unclear whether the observed changes were \r\npart of a  longer-term trend or a  natural fluctuation; the 'signal' \r\nhad not yet clearly emerged from the 'noise' of natural variability. \r\nNumerous studies have since focused on the emergence of changes \r\nin temperature using instrumental observations (e.g., Madden and \r\nRamanathan, 1980; Wigley and Jones, 1981; Mahlstein et al., 2011, \r\n2012; Lehner and Stocker, 2015; Lehner et al., 2017) and paleo\u0002temperature data (e.g., Abram et al., 2016)."


id: 9b6e0ba5f322f9a24b9d6291994bdee5
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 22004
content:
"Since the IPCC Third's Assessment Report in 2001, the observed \r\nsignal of climate change has been unequivocally detected at the \r\nglobal scale (Section 1.3), and this signal is increasingly emerging \r\nfrom the noise of natural variability on smaller spatial scales and \r\nin a range of climate variables (FAQ 1.2). In this Report emergence "


id: fd5d08685f797dc66d8d1d732e6fc26d
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 37377
content:
"(Section  11.2.5); and the emergence of climatic impact-drivers for \r\nAR6 regions and many climate variables is assessed in Chapter 12 \r\n(Section 12.5.2)."


id: 515a43497bade9b23a4fc28a393b5fd2
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 10254
content:
"Related to the concept of emergence is the detection of change \r\n(Chapter  3). Detection of change is defined as the process of \r\ndemonstrating that some aspect of the climate, or a system affected \r\nby climate, has changed in some defined statistical sense, often using \r\nspatially aggregating methods that try to maximize S/N, such as \r\n'fingerprints' (e.g., Hegerl et al., 1996), without providing a reason \r\nfor that change. An identified change is detected in observations if its \r\nlikelihood of occurrence by chance due to internal variability alone is \r\ndetermined to be small, for example, <10% (Glossary)."


id: fac3d49e509c9c40f1f5a36255b28b2a
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 36974
content:
"When evaluating and analysing simulations of the physical climate \r\nsystem, several different sources of uncertainty need to be considered \r\n(e.g., Hawkins and Sutton, 2009; Lehner et al., 2020). Broadly, these \r\nsources are: uncertainties in radiative forcings (both those observed \r\nin the past and those projected for the future); uncertainty in the \r\nclimate response to particular radiative forcings; internal and \r\nnatural variations of the climate system (which may be somewhat \r\npredictable); and interactions among these sources of uncertainty."


id: 24ffbb77145f16b6b2a6a07b2bbd4c76
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 3266
content:
"An example of observed emergence in surface air temperatures is \r\nshown in Figure 1.14. Both the largest changes in temperature and \r\nthe largest amplitude of year-to-year variations are observed in the \r\nArctic, with lower latitudes showing less warming and smaller year-to\u0002year variations. For the six example regions shown in Figure 1.14, the \r\nemergence of changes in temperature is more apparent in Northern \r\nSouth America, East Asia and Central Africa, than for northern North \r\nAmerica or Northern Europe. This pattern was predicted by Hansen \r\net al. (1988) and noted in subsequent observations by Mahlstein et al. \r\n(2011) (Sections 10.3.4.3 and 12.5.2). Overall, tropical regions show \r\nearlier emergence of temperature changes than at higher latitudes \r\n(high confidence)."


id: ad0cd79d11a0a55305d08931c77ebef3
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 24772
content:
"Ensembles of climate simulations (Section  1.5.4.2), such as \r\nthose produced as part of the sixth phase of the Coupled Model \r\nIntercomparison Project (CMIP6), can be used to explore these \r\ndifferent sources of uncertainty and estimate their magnitude. \r\nRelevant experiments with climate models include both historical \r\nsimulations constrained by past radiative forcings, and projections \r\nof future climate which are constrained by specified drivers, such \r\nas GHG concentrations, emissions, or radiative forcings. (The term \r\n'prediction' is usually reserved for estimates of the future climate \r\nstate which are also constrained by the observed initial conditions of \r\nthe climate system, analogous to a weather forecast.)"


id: 59558fbdc52b051858d17cb4dedcd8c2
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 11542
content:
"Future radiative forcing is uncertain due to as-yet-unknown societal \r\nchoices that will determine future anthropogenic emissions; this is \r\nconsidered 'scenario uncertainty'. The RCP and SSP scenarios, which \r\nform the basis for climate projections assessed in this Report, are \r\ndesigned to span a plausible range of future pathways (Section 1.6) \r\nand can be used to estimate the magnitude of scenario uncertainty, but \r\nthe real world may also differ from any one of these example pathways."


id: f77b5bde7de38f956dc38bb3416341ed
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 36446
content:
"Annex IV) are unpredictable on time scales longer than a few years \r\nahead and are a source of uncertainty for understanding how the \r\nclimate might become in a particular decade, especially regionally. \r\nThe increased use of 'large ensembles' of complex climate model \r\nsimulations to sample this component of uncertainty is discussed \r\nabove in Section 1.4.2.1 and further in Chapter 4."


id: 2c7514124c63088ff7de155255188630
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 4426
content:
"It is plausible that there are interactions between radiative forcings \r\nand climate variations, such as influences on the phasing or \r\namplitude of internal or natural climate variability (Zanchettin, 2017). \r\nFor example, the timing of volcanic eruptions may influence Atlantic \r\nMulti-decadal Variability (e.g., Ottera et al., 2010; Birkel et al., 2018) \r\nor ENSO (e.g.,  Maher et al., 2015; Khodri et al., 2017; Zuo et al., \r\n2018), and anthropogenic aerosols may influence decadal modes of \r\nvariability in the Pacific (e.g., Smith et al., 2016). In addition, melting \r\nof glaciers and ice caps due to anthropogenic influences has been \r\nspeculated to increase volcanic activity (e.g., a specific example for \r\nIceland is discussed in Swindles et al., 2018)."


id: 71bb21fd2d9a9906858260d3255e412
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 15521
content:
"Not all of these listed sources of uncertainty are of the same type. \r\nFor example, internal climate variations are an intrinsic uncertainty \r\nthat can be estimated probabilistically, and could be more precisely \r\nquantified, but cannot usually be reduced. However, advances in \r\ndecadal prediction offer the prospect of narrowing uncertainties in \r\nthe trajectory of the climate for a few years ahead (Section 4.2.3; \r\ne.g., Meehl et al., 2014; Yeager and Robson, 2017)."


id: e424ff0a00b97bbd48bd655fe548ab2d
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 33374
content:
"Other sources of uncertainty, such as model response uncertainty, \r\ncan in principle be reduced, but are not amenable to a frequency\u0002based interpretation of probability, and Bayesian methods to \r\nquantify the uncertainty have been considered instead (e.g., Tebaldi, \r\n2004; Rougier, 2007; Sexton et al., 2012). The scenario uncertainty \r\ncomponent is distinct from other uncertainties, given that future \r\nanthropogenic emissions can be considered as the outcome of a set \r\nof societal choices (Section 1.6.1)."


id: 746df508c80d4c912ab94404b127cc07
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 15909
content:
"For climate model projections it is possible to approximately quantify \r\nthe relative amplitude of various sources of uncertainty (e.g., Hawkins \r\nand Sutton, 2009; Lehner et al., 2020). A range of different climate \r\nmodels are used to estimate the model response uncertainty to \r\na  particular emissions pathway, and multiple pathways are used \r\nto estimate the scenario uncertainty. The unforced component \r\nof internal variability can be estimated from individual ensemble \r\nmembers of the same climate model (Section  1.5.4.8; e.g.,  Deser \r\net al., 2012; Maher et al., 2019)."


id: da3ca0418de271145331166124773cc2
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 31859
content:
"Even without any anthropogenic radiative forcing, there would still \r\nbe uncertainty in projecting future climate because of unpredictable \r\nnatural factors such as variations in solar activity and volcanic \r\neruptions. For projections of future climate, such as those presented \r\nin Chapter  4, the uncertainty in these factors is not normally \r\nconsidered. However, the potential effects on the climate of large \r\nvolcanic eruptions (Cross-Chapter Box 4.1; Zanchettin et al., 2016; \r\nBethke et al., 2017) and large solar variations (Feulner and Rahmstorf, \r\n2010; Maycock et al., 2015) are studied. On longer time scales, orbital \r\neffects and plate tectonics also play a role."


id: ef90bc113867b2d7c8aaf0485f3d6e86
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 35187
content:
"Figure 1.15 illustrates the relative size of these different uncertainty \r\ncomponents using a 'cascade of uncertainty' (Wilby and Dessai, 2010), \r\nwith examples shown for global mean temperature, Northern South \r\nAmerican annual temperatures and East Asian summer precipitation \r\nchanges. For global mean temperature, the role of internal variability \r\nis small, and the total uncertainty is dominated by emissions scenario \r\nand model response uncertainties. Note that there is considerable "


id: d5c16687d1ead0976c615dc69c6186e9
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 31148
content:
"Further, even in the absence of any anthropogenic or natural changes \r\nin radiative forcing, Earth's climate fluctuates on time scales from \r\ndays to decades or longer. These 'internal' variations, such as those \r\nassociated with modes of variability (e.g.,  ENSO, Pacific Decadal \r\nVariability (PDV), or Atlantic Multi-decadal Variability (AMV); "


id: b5024004d2c403b6dbbba532be3b1792
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 26012
content:
"overlap between individual simulations for different emissions \r\nscenarios, even for the mid-term (2041-2060). For example, the \r\nslowest-warming simulation for SSP5-8.5 produces less mid-term \r\nwarming than the fastest-warming simulation for SSP1-1.9. For the \r\nlong term, emissions scenario uncertainty becomes dominant."


id: b477ce6217054ba65c91e565d040fe5e
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 25924
content:
"Since AR5 there have been developments in how to consider and \r\ndescribe future climate outcomes which are considered possible but \r\nvery unlikely, highly uncertain, or potentially surprising. To examine \r\nsuch futures there is a  need to move beyond the usual likely or \r\nvery likely assessed ranges and consider low-likelihood outcomes, \r\nespecially those that would result in significant impacts if they \r\noccurred (e.g., Sutton, 2018; Sillmann et al., 2021). This section briefly \r\noutlines some of the different approaches used in the AR6 WGI."


id: 87f420af3edcd9269ee48e749ceac6fe
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 18909
content:
"In the AR6, certain low-likelihood outcomes are described and \r\nassessed because they may be associated with high levels of \r\nrisk, and  the greatest risks may not be associated with the most \r\nlikely outcome. The aim of assessing these possible futures is to \r\nbetter inform risk assessment and decision-making. Two types are \r\nconsidered: (i) low-likelihood high-warming (LLHW) scenarios, which \r\ndescribe the climate in a  world with very high climate sensitivity; \r\nand (ii) low-likelihood, high-impact outcomes that have a  low "


id: 5092fa1915fb09f6a0bf0a0c94a03d72
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 10123
content:
"approaches have been used to better inform risk assessment and \r\ndecision-making, to assist understanding of regional processes, and \r\nrepresent and communicate climate projection uncertainties more \r\nclearly. The aim is to help build a cohesive overall picture of potential \r\nclimate change pathways that moves beyond the presentation of \r\ndata and figures (Glossary; Flottum and Gjerstad, 2017; Moezzi et al., \r\n2017; Dessai et al., 2018; T.G. Shepherd et al., 2018)."


id: 9378e727595aaf15e14d6b0145b650b8
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 20719
content:
"An illustrative example of how low-likelihood outcomes can produce \r\nsignificant additional risks is shown in Figure  1.16. The Reasons \r\nfor Concern (RFCs) produced by the IPCC AR5 WGII define the \r\nadditional risks due to climate change at different global warming \r\nlevels. These have been combined with Chapter  4  assessments of \r\nprojected global temperature for different emissions scenarios (SSPs; \r\nSection  1.6), and Chapter  7  assessments about ECS. For example, \r\neven following an intermediate emissions scenario could result in \r\nhigh levels of additional risk if ECS is at the upper end of the very \r\nlikely range. However, not all possible low-likelihood outcomes \r\nrelate to ECS, and AR6 considers these issues in more detail than \r\nprevious IPCC assessment reports (see Table 1.1 and Section 1.4.4.2 \r\nfor some examples)."


id: 12cd583272a57f084421e72325182ee9
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 458
content:
"As societies are increasingly experiencing the impacts of climate \r\nchange-related events, the climate science community is developing \r\nclimate information tailored for particular regions and sectors. There \r\nis a  growing focus on explaining and exploring complex physical \r\nchains of events or on predicting climate under various future \r\nsocio-economic developments. Since AR5, 'storylines' or 'narratives' "


id: dcb058aa78f55044bbdbf2fb2df725a5
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 32221
content:
"climate change alone; instead, such decisions will normally take into \r\naccount socio-economic factors as well (Shepherd, 2019)."


id: 57a5f2300f2f9340facfca12751ccfea
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 11288
content:
"would affect regional precipitation and other climate variables, and \r\ndiscuss the various climate drivers that could cause such a circulation \r\nresponse (James et al., 2015; Zappa and Shepherd, 2017; Mindlin \r\net al., 2020). Physical climate storylines can also help frame the \r\ncausal factors of extreme weather events (Shepherd, 2016) and then \r\nbe linked to event attribution (Section  11.2.2 and Cross-Working \r\nGroup Box: Attribution)."


id: 1e5fac98e8b8055b3d70754b14e73863
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 2219
content:
"Storyline approaches can be used to communicate and contextualize \r\nclimate change information in the context of risk for policymakers \r\nand practitioners (Box  10.2; e.g.,  de Bruijn et al., 2016; Dessai \r\net al., 2018; Scott et al., 2018; Jack et al., 2020). They can also help \r\nin assessing risks associated with LLHI events (Weitzman, 2011; \r\nSutton, 2018), because they consider the 'physically self-consistent \r\nunfolding of past events, or of plausible future events or pathways' \r\n(Shepherd et al., 2018), which would be masked in a  probabilistic \r\napproach. These aspects are important as the greatest risk need \r\nnot be associated with the highest-likelihood outcome, and in fact \r\nwill often be associated with low-likelihood outcomes. The storyline \r\napproach can also acknowledge that climate-relevant decisions in \r\na risk-oriented framing will rarely be taken on the basis of physical "


id: 48de573d1ce1515be813f7843a287f2b
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 8845
content:
"Contributing Authors: Andy Reisinger (New Zealand), Maisa Rojas (Chile), Aida Diongue-Niang (Senegal), Maarten K. van Aalst \r\n(The  Netherlands), Mathias Garschagen (Germany), Mark Howden (Australia), Margot Hurlbert (Canada), Katharine Mach \r\n(United States of America), Sawsan Khair Elsied Abdel Rahim Mustafa (Sudan), Brian O'Neill (United States of America), Roque Pedace \r\n(Argentina), Jana Sillmann (Norway/Germany), Carolina Vera (Argentina), David Viner (United Kingdom)"


id: 6ed9000ea0afb9a3d4be043f1aced9fd
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updated_at: 2023-03-16 14:00:37
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"The IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX; \r\nIPCC, 2012) presented a framework for assessing risks from climate change, which linked hazards (due to changes in climate) with \r\nexposure and vulnerability (Cardona et al., 2012). This framework was further developed by AR5 WGII (IPCC, 2014b), while AR5 WGI \r\nfocussed only on the hazard component of risk. As part of AR6, a cross-Working Group process expanded and refined the concept of \r\nrisk to allow for a consistent risk framing to be used across the three IPCC Working Groups (IPCC, 2019b; Box 2 in Abram et al., 2019; \r\nReisinger et al., 2020)."


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"The potential for adverse consequences for human or ecological systems, recognizing the diversity of values and objectives \r\nassociated with such systems. In the context of climate change, risks can arise from potential impacts of climate change \r\nas well as human responses to climate change. Relevant adverse consequences include those on lives, livelihoods, health \r\nand well-being, economic, social and cultural assets and investments, infrastructure, services (including ecosystem services), \r\necosystems and species."


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"In the context of climate change impacts, risks result from dynamic interactions between climate-related hazards with the \r\nexposure and vulnerability of the affected human or ecological system to the hazards. Hazards, exposure and vulnerability \r\nmay each be subject to uncertainty in terms of magnitude and likelihood of occurrence, and each may change over time and \r\nspace due to socio-economic changes and human decision-making (see also risk management, adaptation and mitigation)."


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"In the context of climate change responses, risks result from the potential for such responses not achieving the intended \r\nobjective(s), or from potential trade-offs with, or negative side-effects on, other societal objectives, such as the Sustainable \r\nDevelopment Goals (SDGs) (see also risk trade-off). Risks can arise, for example, from uncertainty in implementation, \r\neffectiveness or outcomes of climate policy, climate-related investments, technology development or adoption, and \r\nsystem transitions."


id: f679d6a7a1011d2acacbe561d7b47e5
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"Exposure: The presence of people; livelihoods; species or ecosystems; environmental functions, services, and resources; infrastructure; \r\nor economic, social, or cultural assets in places and settings that could be adversely affected."


id: cfa304f6c441be0a1dd1711665234a40
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"Vulnerability: The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements \r\nincluding sensitivity or susceptibility to harm and lack of capacity to cope and adapt."


id: 8a89975ca8b5132ba01f7ad11f79ae4
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"Hazard: The potential occurrence of a natural or human-induced physical event or trend that may cause loss of life, injury, or other health \r\nimpacts, as well as damage and loss to property, infrastructure, livelihoods, service provision, ecosystems and environmental resources."


id: 19d96ea616e9eb0f116d9e61b534c565
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"Impacts: The consequences of realized risks on natural and human systems, where risks result from the interactions of climate-related \r\nhazards (including extreme weather/climate events), exposure, and vulnerability. Impacts generally refer to effects on lives, livelihoods, \r\nhealth and well-being, ecosystems and species, economic, social and cultural assets, services (including ecosystem services), and \r\ninfrastructure. Impacts may be referred to as consequences or outcomes and can be adverse or beneficial."


id: 4e179b35039b1426d805ea6185e4dfaf
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content:
"Risk in AR6 WGI\r\nThe revised risk framing clarifies the role and contribution of WGI to risk assessment. 'Risk' in IPCC terminology applies only to human \r\nor ecological systems, not to physical systems on their own."


id: 26c6e750b3a2231c576d9ad085e0ef9f
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content:
"Climatic impact-drivers (CIDs): CIDs are physical climate system conditions (e.g., means, events, extremes) that affect an element \r\nof society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral or a mixture \r\nof each across interacting system elements and regions."


id: 4f85dddfd6292d1f71a4b587233f1cf2
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"In AR6, WGI uses the term 'climatic impact-drivers' to describe changes in physical systems rather than 'hazards', because the term \r\nhazard already assumes an adverse consequence. The terminology of 'climatic impact-driver' therefore allows WGI to provide a more \r\nvalue-neutral characterization of climatic changes that may be relevant for understanding potential impacts, without pre-judging \r\nwhether specific climatic changes necessarily lead to adverse consequences, as some could also result in beneficial outcomes \r\ndepending on the specific system and associated values. Chapter 12 and the Atlas assess and provide information on climatic impact\u0002drivers for different regions and sectors to support and link to the WGII assessment of the impacts and risks (or opportunities) related \r\nto the changes in the climatic impact-drivers. Although CIDs can lead to adverse or beneficial outcomes, focus is given to CIDs \r\nconnected to hazards, and hence inform risk."


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"'Extremes' are a category of CID, corresponding to unusual events with respect to the range of observed values of the variable. \r\nChapter 11 assesses changes in weather and climate extremes, their attribution and future projections."


id: e924e582eb1194053e105447a9a9a953
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"Information provided by WGI may or may not be relevant to understand risks related to climate change responses. For example, the \r\nrisk to a company arising from emissions pricing, or the societal risk from reliance on an unproven mitigation technology, is not directly \r\ndependent on actual or projected changes in climate but arise largely from human choices. However, WGI climate information may be \r\nrelevant to understand the potential for maladaptation, such as the potential for specific adaptation responses not achieving the desired \r\noutcome or having negative side effects. For example, WGI information about the range of sea level rise can help inform understanding \r\nof whether coastal protection, accommodation, or retreat would be the most effective risk management strategy in a particular context. "


id: 5782ea67612e2fa43a72abbf370347ea
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"From a WGI perspective, low-likelihood, high-impact outcomes and the concept of deep uncertainty are also relevant for risk assessment."


id: d473f02df93cf692f4fe495acce14c55
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"Low-likelihood, high-impact (LLHI) outcomes: Outcomes/events whose probability of occurrence is low or not well known \r\n(as in the context of deep uncertainty) but whose potential impacts on society and ecosystems could be high. To better inform risk \r\nassessment and decision-making, such low-likelihood outcomes are considered if they are associated with very large consequences \r\nand may therefore constitute material risks, even though those consequences do not necessarily represent the most likely outcome."


id: e187bdc61598b2bad11d66decb62fc07
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"The AR6 WGI Report provides more detailed information about these types of events compared to AR5 (Table 1.1, Section 1.4.4)."


id: ff8d03df9578c5fc60e403a55db03c38
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"Recognizing the need for assessing and managing risk in situations of high uncertainty, SROCC advanced the treatment of situations \r\nwith deep uncertainty (Section 1.2.3; IPCC, 2019b; Box 5 in Abram et al., 2019). A situation of deep uncertainty exists when experts or \r\nstakeholders do not know or cannot agree on: (i) appropriate conceptual models that describe relationships among key driving forces \r\nin a system; (ii) the probability distributions used to represent uncertainty about key variables and parameters; and/or (iii) how to \r\nweigh and value desirable alternative outcomes (Abram et al., 2019). The concept of deep uncertainty can complement the IPCC \r\ncalibrated uncertainty language and thereby broaden the communication of risk."


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"et al., 2015; Bathiany et al., 2020). In such simulations, tipping points \r\noccur in narrow regions of parameter space (e.g., CO2 concentration \r\nor temperature increase), and for specific climate background states. \r\nThis makes them difficult to predict using Earth system models (ESMs) \r\nrelying on parmeterizations of known processes. In some cases, \r\nit is possible to detect forthcoming tipping points through time\u0002series analysis that identifies increased sensitivity to perturbations \r\nas the tipping point is approached (e.g.,  'critical slowing-down', \r\nScheffer et al., 2012)."


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"An 'abrupt change' is defined in this report as a change that takes \r\nplace substantially faster than the rate of change in the recent history \r\nof the affected component of a  system (Glossary). In some cases, \r\nabrupt change occurs because the system state actually becomes \r\nunstable, such that the subsequent rate of change is independent of \r\nthe forcing. We refer to this class of abrupt change as a 'tipping point',\r\ndefined as a critical threshold beyond which a system reorganizes, \r\noften abruptly and/or irreversibly (Glossary; Lenton et al., 2008). \r\nSome of the abrupt climate changes and climate tipping points \r\ndiscussed in this Report could have severe local climate responses, \r\nsuch as extreme temperature, droughts, forest fires, ice-sheet loss \r\nand collapse of the thermohaline circulation (Sections 4.7.2, 5.4.9, \r\n8.6 and 9.2.3)."


id: 27257ba3e6bf4a29f0477e71f7d5bafa
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"Some suggested climate tipping points prompt transitions from one \r\nsteady state to another (Figure 1.17). Transitions can be prompted \r\nby perturbations such as climate extremes which force the system \r\noutside of its current well of attraction in the stability landscape; \r\nthis is called noise-induced tipping (Figure  1.17a,b; Ashwin et al., \r\n2012). For example, the tropical forest dieback seen in some ESM \r\nprojections is accelerated by longer and more frequent droughts over \r\ntropical land (Good et al., 2013)."


id: da29f074a301ad8060be4a0f2cfb61a7
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"There is evidence of abrupt changes in Earth's history, and some of \r\nthese events have been interpreted as tipping points (Dakos et al., \r\n2008). Some of these are associated with significant changes in the \r\nglobal climate, such as deglaciations in the Quaternary (past 2.5 \r\nmillion years) and rapid warming at the Palaeocene-Eocene Thermal \r\nMaximum (around 55.5 million years ago; Bowen et al., 2015; Hollis \r\net al., 2019). Such events changed the planetary climate for tens to \r\nhundreds of thousands of years, but at a rate that is actually much \r\nslower than projected anthropogenic climate change over this \r\ncentury, even in the absence of tipping points."


id: f36983944288a77e1b16d3fafaa7ea8a
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"Alternatively, transitions from one state to another can occur if \r\na  critical threshold is exceeded; this is called 'bifurcation tipping' \r\n(Figure  1.17c,d; Ashwin et al., 2012). The new state is defined as \r\n'irreversible' on a  given time scale if the recovery from this state \r\ntakes substantially longer than the time scale of interest, which is \r\ndecades to centuries for the projections presented in this report. \r\nA well-known example is the modelled irreversibility of the ocean's \r\nthermohaline circulation in response to North Atlantic changes such \r\nas freshwater input from rainfall and ice-sheet melt (Rahmstorf \r\net al., 2005; Alkhayuon et al., 2019), which is assessed in detail in \r\nChapter 9 (Section 9.2.3)."


id: 4347655274559ad6bc5f50fd021c8808
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"Such paleoclimate evidence has even fuelled concerns that \r\nanthropogenic GHGs could tip the global climate into a permanent \r\nhot state (Steffen et al., 2018). However, there is no evidence of such \r\nnon-linear responses at the global scale in climate projections for \r\nthe next century, which indicates a near-linear dependence of global \r\ntemperature on cumulative GHG emissions (Sections 1.3.5, 5.5 and \r\n7.4.3.1). At the regional scale, abrupt changes and tipping points, such \r\nas Amazon rainforest dieback and permafrost collapse, have occurred \r\nin projections with Earth System Models (Section  4.7.3; Drijfhout "


id: b2b96f54b736e15c3c8d375179d00395
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"The tipping point concept is most commonly framed for systems \r\nin which the forcing changes relatively slowly. However, this is not \r\nthe case for most scenarios of anthropogenic forcing projected for \r\nthe 21st century. Systems with inertia lag behind rapidly increasing \r\nforcing, which can lead to the failure of early warning signals or "


id: 556b5e1f263caef3f034f1c8a14f0aa4
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"even the possibility of temporarily overshooting a bifurcation point \r\nwithout provoking tipping (Ritchie et al., 2019)."


id: 7725b37b6ccda12af8ec4d6736e79721
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"among humans or other species, such as the COVID-19 pandemic \r\n(Cross-Chapter Box 6.1; Forster et al., 2020; Le Quere et al., 2020). \r\nThe discovery of the hole in the ozone layerwas also a surprise even \r\nthough some of the relevant atmospheric chemistry was known at \r\nthe time. The term 'unknown unknowns' (Parker and Risbey, 2015) \r\nis also sometimes used in this context to refer to events that cannot \r\nbe anticipated with present knowledge or were of an unanticipated \r\nnature before they occurred."


id: e526887da25d78254482222c0b98e514
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content:
"'Surprises' are a class of risk that can be defined as low-likelihood \r\nbut well-understood events: they are events that cannot be predicted \r\nwith current understanding. The risk from such surprises can be \r\naccounted for in risk assessments (Parker and Risbey, 2015). Examples \r\nrelevant to climate science include: a  series of major volcanic \r\neruptions or a nuclear war, either of which would cause substantial \r\nplanetary cooling (Robock et al., 2007; Mills et al., 2014); significant \r\n21st century sea level rise due to marine ice sheet instability (MISI; \r\nBox 9.4); the potential for collapse of the stratocumulus cloud decks \r\n(Schneider et al., 2019) or other substantial changes in climate \r\nfeedbacks (Section  7.4); and unexpected biological epidemics "


id: 884eff17f0d29f1b10e1c06e9c22d443
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"Contributing Authors: Pandora Hope (Australia), Wolfgang Cramer (France/Germany), Gregory M. Flato (Canada), Katja Frieler \r\n(Germany), Nathan P. Gillett (Canada), Christian Huggel (Switzerland), Jan Minx (Germany), Friederike Otto (United Kingdom/\r\nGermany), Camille Parmesan (France, United Kingdom/United States of America), Joeri Rogelj (United Kingdom/Belgium), Maisa Rojas \r\n(Chile), Sonia I. Seneviratne (Switzerland), Aimee B.A. Slangen (The Netherlands), Daithi Stone (New Zealand), Laurent Terray (France), \r\nMaarten K. van Aalst (The Netherlands), Robert Vautard (France), Xuebin Zhang (Canada)"


id: e0fd44a389714430ff4b92f152c0a69b
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"Changes in the climate system are becoming increasingly apparent, as are the climate-related impacts on natural and human systems. \r\nAttribution is the process of evaluating the contribution of one or more causal factors to such observed changes or events. Typical \r\nquestions addressed by the IPCC include: 'To what extent is an observed change in global temperature induced by anthropogenic GHG \r\nand aerosol concentration changes, or influenced by natural variability?' and 'What is the contribution of climate change to observed \r\nchanges in crop yields, which are also influenced by changes in agricultural management?' Changes in the occurrence and intensity of \r\nextreme events can also be attributed, addressing questions such as: 'Have human GHG emissions increased the likelihood or intensity \r\nof an observed heatwave?'"


id: 30c47fceed19067b74810f6f8d382997
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"This Cross-Working Group Box briefly describes why attribution studies are important. It also describes some new developments in the \r\nmethods used in those studies and provides recommendations for interpretation."


id: 13053d3570b9566f2beb5f2124b07d77
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"Attribution studies serve to evaluate and communicate linkages associated with climate change, for example: between the human\u0002induced increase in GHG concentrations and the observed increase in air temperature or extreme weather events (AR6 WGI \r\nChapters 3, 10 and 11); or between observed changes in climate and changing species distributions and food production (AR6 WGII \r\nChapters 2 and others, summarized in WGII Chapter 16; e.g., Verschuur et al., 2021); or between climate change mitigation policies \r\nand atmospheric GHG concentrations (AR6 WGI Chapter 5; AR6 WGIII Chapter 14). As such, they support numerous statements made \r\nby the IPCC (AR6 WGI Section 1.3 and Appendix 1A; IPCC, 2013b, 2014b)."


id: bb984dd8cf9310af4d0c81e2d2164559
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"Attribution assessments can also serve to monitor mitigation and assess the efficacy of applied climate protection policies (AR6 WGI \r\nSection 4.6.3; e.g., Nauels et al., 2019; Banerjee et al., 2020), inform and constrain projections (WGI Section 4.2.3; Gillett et al., 2021; \r\nRibes et al., 2021) or inform the loss and damages estimates and potential climate litigation cases by estimating the costs of climate \r\nchange (Huggel et al., 2015; Marjanac et al., 2017; Frame et al., 2020). These findings can thus inform mitigation decisions as well as \r\nrisk management and adaptation planning (e.g., CDKN, 2017)."


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"There are many attribution approaches, and several methods are detailed below. In physical and biological systems, attribution often \r\nbuilds on the understanding of the mechanisms behind the observed changes and numerical models are used, while in human systems \r\nother methods of evidence-building are employed. Confidence in the attribution can be increased if more than one approach is used \r\nand the model is evaluated as fit-for-purpose (WGI Section 1.5, WGI Section 3.8, WGI Section 10.3.3.4; Hegerl et al., 2010; Vautard \r\net al., 2019; Otto et al., 2020; Philip et al., 2020). The final step includes appropriate communication of the attribution assessment and \r\nthe accompanying confidence in the result (e.g., Lewis et al., 2019)."


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"ratio (WGI Section 5.2.1.1). Decomposition approaches can be used to attribute emissions underlying those changes to various drivers \r\nsuch as population, energy efficiency, consumption or carbon intensity (Hoekstra and van den Bergh, 2003; Raupach et al., 2007; Rosa \r\nand Dietz, 2012). Combined with attribution of their climate outcomes, the attribution of the sources of GHG emissions can inform \r\nthe attribution of anthropogenic climate change to specific countries or actors (Matthews, 2016; Otto et al., 2017; Skeie et al., 2017; \r\nNauels et al., 2019), and in turn inform discussions on fairness and burden sharing (WGIII Chapter 14)."


id: 67e0f555ed545be35612297f0d863cc9
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"Specific regional conditions and responses may simplify or complicate attribution on those scales. For example, some human forcings, \r\nsuch as regional land-use change or aerosols, may enhance or reduce regional signals of change (WGI Sections 10.4.2, 11.1.6 and \r\n11.2.2; Lejeune et al., 2018; Undorf et al., 2018; Boe et al., 2020; Thiery et al., 2020). In general, regional climate variations are larger \r\nthan the global mean climate, adding additional uncertainty to \r\nattribution (e.g., in regional sea level change, WGI Section 9.6.1). \r\nThese statistical limitations may be reduced by 'process-based \r\nattribution', focusing on the physical processes known to \r\ninfluence the response to external forcing and internal variability \r\n(WGI Section 10.4.2).\r\nAttribution in the IPCC\r\nThe process of evaluating the contribution of one or more causal \r\nfactors to observed changes or events"


id: 248452c04db5bcc915c0e4dc2f103d8b
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"Cross-Working Group Box: Attribution, Figure 1 | Schematic of the \r\nsteps to develop an attribution assessment, and the purposes of \r\nsuch assessments. Methods and systems used to test the attribution \r\nhypothesis or theory include: model-based fingerprinting; other \r\nmodel-based methods; evidence-based fingerprinting; process\u0002based approaches; empirical or decomposition methods; and the \r\nuse of multiple lines of evidence. Many of the methods are based on the \r\ncomparison of the observed state of a system to a hypothetical counterfactual \r\nworld that does not include the driver of interest to help estimate the causes of \r\nthe observed response."


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"The attribution of observed changes to climate-related drivers \r\nacross a  diverse set of sectors, regions and systems is part of \r\neach chapter in the WGII contribution to AR6 and is synthesized \r\nin WGII Chapter  16 (Section  16.2). The number of attribution "


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"studies on climate change impacts has grown substantially since AR5, generally leading to higher confidence levels in attributing the \r\ncauses of specific impacts. New studies include the attribution of changes in socio-economic indicators such as economic damages \r\ndue to river floods (e.g., Schaller et al., 2016; Sauer et al., 2021), the occurrence of heat-related human mortality (e.g., Vicedo-Cabrera \r\net al., 2018; Sera et al., 2020) or economic inequality (e.g., Diffenbaugh and Burke, 2019)."


id: 5b55587c5b3f015d95fe16b61934fd55
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content:
"Impact attribution does not always involve attribution to anthropogenic climate forcing. However, a  growing number of studies \r\ninclude this aspect (e.g., Frame et al. (2020) for the attribution of damages induced by Hurricane Harvey; or Diffenbaugh and Burke \r\n(2019) for the attribution of economic inequality between countries; or Schaller et al. (2016) for flood damages)."


id: 7a89a31af8486efb155405693d5f7ff3
created_at: 2023-03-16 14:00:37
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"The AR5 assessed regional-scale detection and attribution and \r\nassessed key regional climate phenomena and their relevance for \r\nfuture regional climate projections. This report shows that past \r\nand future climate changes and extreme weather events can be \r\nsubstantial on local and regional scales (Chapters 8-12 and Atlas), \r\nwhere they may differ considerably from global trends, not only in \r\nintensity but even in the direction of change (e.g., Fischer et al., 2013)."


id: cfa5381628d344e19dcdf41f511a340b
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content:
"Although the evolution of global climate trends emerges as the \r\nnet result of regional phenomena, average or aggregate estimates \r\noften do not reflect the intensity, variability and complexity of \r\nregional climate changes (Stammer et al., 2018; Shepherd, 2019). \r\nA fundamental aspect of the study of regional climate changes is the \r\ndefinition of characteristic climate zones, clusters or regions, across \r\nwhich the emergent climate change signal can be properly analysed \r\nand projected (see Atlas). Suitable sizes and shapes of such zones \r\nstrongly depend not only on the climate variable and process of \r\ninterest, but also on relevant multi-scale feedbacks."


id: dd09b48d82fa2d99df613f4412f76074
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content:
"There are several approaches to the classification of climate \r\nregions. When climate observation data was sparse and limited, the "


id: e7affa61ab77f6fb02b1cc49060c2048
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"climate projections for 2030 were presented for five sub-continental \r\nregions (see Section 1.3.6 for an assessment of those projections). \r\nIn subsequent reports, there has been a growing emphasis on the \r\nanalysis of regional climate, including two special reports: one on \r\nregional impacts (IPCC, 1998) and another on extreme events "


id: 69856cd7424d4744cf0824d787d55339
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"(SREX, IPCC, 2012). A  general feature of previous IPCC reports is \r\nthat the number and coverage of climate regions vary according to \r\nthe subject and across Working Groups. Such varied definitions have \r\nthe advantage of optimizing the results for a particular application \r\n(e.g., national boundaries are crucial for decision-making, but they "


id: caff15fea76c8a4646ba1a887ade200b
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content:
"rarely delimit distinctive climate regions), whereas variable definitions \r\nof regions may have the disadvantage of hindering multidisciplinary \r\nassessments and comparisons between studies or Working Groups."


id: 67ac7e0e26bdab99348b2d425a077460
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"(Figure 1.18c), based on the nine continental domains defined in AR5 \r\nWGII Part B (Hewitson et al., 2014). These are classical geopolitical \r\ndivisions of Africa, Asia, Australasia, Europe, North America, Central \r\nand South America, plus Small Islands, Polar Regions, and the \r\nOcean. In AR6 WGI, five hybrid zones (Caribbean-Small Islands, East \r\nEurope-Asia, European Arctic, North American Arctic, and Northern \r\nCentral America) are also identified, which are assessed in more \r\nthan one Continental Region. Additional consistency with WGIII is \r\npursued in Chapter  6  through the use of sub-continental domains \r\nwhich essentially form a  subset of the Continental Set of Regions \r\n(Figure 1.18c and Section 6.1)."


id: ec1e6b4878477b840ab32bc3e00e94a0
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content:
"In this Report, regional climate change is primarily addressed through \r\nthe introduction of four classes of regions (unless otherwise explicitly \r\nmentioned and justified). The first two are the unified WGI Reference \r\nSets of (i) Land Regions and (ii) Ocean Regions, which are used \r\nthroughout the Report. These are supplemented by additional sets \r\nof (iii) Typological Regions - used in Chapters 5, 8-12 and Atlas - \r\nand (iv) Continental Regions, which are mainly used for linking \r\nChapters 11, 12 and Atlas with Working Group II (Figure 1.18). All \r\nfour classes of regions are defined and described in detail in the \r\nAtlas. Here we summarize their basic features."


id: d6a7d51d46a0d6e1def18422aef626dd
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"This section presents a selection of key developments since AR5 of \r\nthe capabilities underlying the lines of evidence used in the present \r\nreport: observational data and observing systems (Section  1.5.1); \r\nnew developments in reanalyses (Section  1.5.2); climate models \r\n(Section  1.5.3); and modelling techniques, comparisons and \r\nperformance assessments (Section 1.5.4). For brevity, we focus on the \r\ndevelopments that are of particular importance to the conclusions \r\ndrawn in later chapters, though we also provide an assessment of \r\npotential losses of climate observational capacity."


id: 8522c5403a80f4bef27f0a93e44bb1d3
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"Progress in climate science relies on the quality and quantity of \r\nobservations from a range of platforms: surface-based instrumental \r\nmeasurements, aircraft, radiosondes and other upper-atmospheric \r\nobservations, satellite-based retrievals, ocean observations, and \r\npaleoclimatic records. An historical perspective to these types of \r\nobservations is presented in Section 1.3.1."


id: 3689178e33053feeddc5a5ad503e8d9d
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"The starting point for defining the AR6 Reference Sets of Land Regions \r\nwas the collection of 26 regions introduced in SREX (IPCC, 2012). \r\nThe SREX collection was then revised, reshaped, complemented and \r\noptimized to reflect the recent scientific literature and observed \r\nclimate-change trends, giving rise to the novel AR6 Reference Set of \r\n46 Land Regions. Additionally, AR6 introduces a new Reference Set \r\nof 15 Ocean Regions (including 3 Hybrid Regions that are treated as \r\nboth: land and ocean), which complete the coverage of the whole \r\nEarth (Iturbide et al., 2020)."


id: 41e9fae1f3f0e9831a114c38071308c8
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"Particular aspects of regional climate change are described by \r\nspecialized domains called Typological Regions (Figure 1.18b). These \r\nregions cover a  wide range of spatial scales and are defined by \r\nspecific features, called typologies. Examples of typologies include: \r\ntropical forests, deserts, mountains, monsoon regions and megacities, \r\namong others. Typological Regions are powerful tools to summarize \r\ncomplex aspects of climate defined by a  combination of multiple \r\nvariables. For this reason, they are used in many chapters of AR6 WGI \r\nand WGII (e.g., Chapters 8-12 and Atlas)."


id: e66637b5decf34ccde61c45968a2d9a4
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content:
"as well as new and/or emerging observational platforms that play \r\na key role in AR6. We then discuss potential near-term losses in key \r\nobservational networks due to climate change or other adverse \r\nhuman-caused influence."


id: c045b6cba6e0a7ee06b416987283844c
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content:
"Two distinctly different but important remote-sensing systems can \r\nprovide information about temperature and humidity since the \r\nearly 2000s. Global navigation satellite systems (e.g.,  GPS), radio \r\noccultation and limb soundings provide information, although only \r\ndata for the upper troposphere and lower stratosphere are suitable \r\nto support climate change assessments (Angerer et al., 2017; \r\nScherllin-Pirscher et al., 2017; Gleisner et al., 2020; Steiner et al., \r\n2020). These measurements complement those from the Atmospheric \r\nInfrared Sounder (AIRS; Chahine et al., 2006). AIRS has limitations \r\nin cloudy conditions, although these limitations have been partly \r\nsolved using new methods of analysis (Blackwell and Milstein, 2014; \r\nSusskind et al., 2014). These new data sources now have sufficiently \r\nlong records to strengthen the analysis of atmospheric warming in \r\nChapter 2 (Section 2.3.1.2)."


id: e93f46d43923135cdb903dcf7a31b5fe
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content:
"Observations of the ocean have expanded significantly since AR5, \r\nwith expanded global coverage of in situ ocean temperature and \r\nsalinity observations, in situ ocean biogeochemistry observations, \r\nand satellite retrievals of a variety of EOVs. Many recent advances "


id: 13c906a6cdb960af66673e8ca8c34fa2
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"are extensively documented in a  compilation by Lee et al. (2019). \r\nBelow we discuss those most relevant for the current assessment."


id: cccfe45254d18db73fb22f1b327a83bf
created_at: 2023-03-16 14:00:37
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content:
"Argo is a global network of nearly 4000 autonomous profiling floats \r\n(Roemmich et al., 2019), delivering detailed constraints on the \r\nhorizontal and vertical structure of temperature and salinity across \r\nthe global ocean. Argo has greatly expanded since AR5, including \r\nbiogeochemistry and measurements deeper than 2000 m  (Jayne \r\net al., 2017), and the longer time series enable more rigorous climate \r\nassessments of direct relevance to estimates of ocean heat content \r\n(Sections  2.3.3.1 and 7.2.2.2). Argo profiles are complemented by \r\nanimal-borne sensors in several key areas, such as the seasonally ice\u0002covered sectors of the Southern Ocean (Harcourt et al., 2019)."


id: 8f639e2da1d7008a185201b9f08f145b
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"Improvements have also been made in the monitoring of permafrost. \r\nThe Global Terrestrial Network for Permafrost (GTN-P; Biskaborn \r\net al., 2015) provides long-term records of permafrost temperature \r\nand active layer thickness at key sites to assess their changes over \r\ntime. Substantial improvements to our assessments of large-scale \r\nsnow changes come from intercomparison and blending of several \r\ndatasets, for snow water equivalent (Mortimer et al., 2020) and \r\nsnow cover extent (Mudryk et al., 2020), and from bias corrections \r\nof combined datasets using in situ data (Sections 2.3.2.5 and 9.5.2; \r\nPulliainen et al., 2020)."


id: d625b399050300337f6d8475e4424d1f
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"Most basin-scale arrays of moored ocean instruments have \r\nexpanded since AR5, providing decades-long records of the ocean \r\nand atmosphere properties relevant for climate, such as the El Nino-\r\nSouthern Oscillation (Chen et al., 2018), deep convection (de Jong \r\net al., 2018) or transports through straits (Woodgate, 2018). Key \r\nbasin-scale arrays include transport-measuring arrays in the Atlantic \r\nOcean, continuing (McCarthy et al., 2020) or newly added since AR5 \r\n(Lozier et al., 2019), supporting the assessment of regional ocean \r\ncirculation (Section  9.2.3). Tropical ocean moorings in the Pacific, \r\nIndian and Atlantic oceans include new sites, improved capability for \r\nreal-time transmission, and new oxygen and CO2 sensors (Bourles \r\net al., 2019; Hermes et al., 2019; Smith et al., 2019)."


id: c51d37e368328717b82684432e5010b6
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"A decade of observations of sea-surface salinity is now available via \r\nthe SMOS and SMAP satellite retrievals, providing continuous and \r\nglobal monitoring of surface salinity in the open ocean and coastal \r\nareas for the first time (Section  9.2.2.2; Vinogradova et al., 2019; \r\nReul et al., 2020)."


id: 123eb788fdfeaa31a6da63aa20148e91
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"The global network of tide gauges, complemented by a  growing \r\nnumber of satellite-based altimetry datasets, allows for more robust \r\nestimates of global and regional sea level rise (Sections  2.3.3.3 \r\nand 9.6.1.3). Incorporating vertical land motion derived from the \r\nGlobal Positioning System (GPS), the comparison with tide gauges \r\nhas allowed the correction of a drift in satellite altimetry series over \r\nthe period 1993-1999 (Watson et al., 2015; Chen et al., 2017), thus \r\nimproving our knowledge of the recent acceleration of sea level rise \r\n(Chapter  2, Section  2.3.3.3). These datasets, combined with Argo \r\nand observations of the cryosphere, allow a consistent closure of the \r\nglobal mean sea level budget (Cross-Chapter Box 9.1; WCRP Global \r\nSea Level Budget Group, 2018)."


id: 1a267630ca66c810915d41e3a7f4baa4
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"For the cryosphere, there has been much recent progress in \r\nsynthesizing global datasets covering larger areas and longer time \r\nperiods from multi-platform observations. For glaciers, the Global \r\nTerrestrial Network for Glaciers, which combines data on glacier \r\nfluctuations, mass balance and elevation change with glacier outlines \r\nand ice thickness, has expanded and provided input for assessing \r\nglobal glacier evolution and its role in sea level rise (Sections 2.3.2.3 \r\nand 9.5.1; Zemp et al., 2019). New data sources include archived \r\nand declassified aerial photographs and satellite missions, and high\u0002"


id: 456d5712a2beded902c2d6dfd6bf81bd
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content:
"The ability to estimate changes in global land biomass has improved \r\ndue to the use of different microwave satellite data (Liu et al., 2015) \r\nand in situ forest census data and co-located lidar, combined with \r\nthe Moderate Resolution Imaging Spectroradiometer (MODIS; \r\nBaccini et al., 2017). This has allowed for improved quantification "


id: 3edf855fc88be654c63a2dae8c22b4bc
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content:
"Ongoing efforts have expanded the number of large-scale, tree \r\nring-based drought reconstructions that span the last centuries \r\nto millennium at annual resolution (Chapter  8; Cook et al., 2015; \r\nStahle et al., 2016; Aguilera-Betti et al., 2017; Morales et al., 2020). \r\nLikewise, stalagmite records of oxygen isotopes have increased in \r\nnumber, resolution and geographic distribution since AR5, providing \r\ninsights into regional-to-global-scale hydrological change over the \r\nlast centuries to millions of years (Chapter  8; Cheng et al., 2016; \r\nDenniston et al., 2016; Comas-Bru and Harrison, 2019). A new global \r\ncompilation of water isotope-based paleoclimate records spanning \r\nthe last 2000 years (PAGES Iso2K) lays the groundwork for quantitative \r\nmulti-proxy reconstructions of regional- to global-scale hydrological \r\nand temperature trends and extremes (Konecky et al., 2020)."


id: 3025be478bd33ca7a803a239c936b0bd
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vector_id: 4999
content:
"of land temperature (Duan et al., 2019), carbon stocks and human\u0002induced changes due to deforestation (Chapter  2, Section  2.2.7). \r\nTime series of Normalized Difference Vegetation Index (NDVI) \r\nfrom MODIS and other remote-sensing platforms is widely applied \r\nto assess the effects of climate change on vegetation in drought\u0002sensitive regions (Atampugre et al., 2019). New satellite imaging \r\ncapabilities for meteorological observations, such as the advanced \r\nmultispectral imager aboard Himawari-8 (Bessho et al., 2016), also \r\nallow for improved monitoring of challenging quantities such as \r\nseasonal changes of vegetation in cloudy regions (Section 2.3.4.3; \r\nMiura et al., 2019)."


id: 6e36fe66e2f39c72dac8b983ab647db2
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"In the ocean, efforts are underway to coordinate observations of \r\nbiologically relevant EOVs around the globe (Muller-Karger et al., \r\n2018; Canonico et al., 2019) and to integrate observations across \r\ndisciplines (e.g., the Global Ocean Acidification Observing Network, \r\nGOA-ON; Tilbrook et al., 2019). A  large number of coordinated \r\nfield campaigns during the 2015/2016 El Nino event enabled \r\nthe collection of short-lived biological phenomena such as coral \r\nbleaching and mortality caused by a months-long ocean heatwave \r\n(Hughes et al., 2018); beyond this event, coordinated observations of \r\ncoral reef systems are increasing in number and quality (Obura et al., \r\n2019). Overall, globally coordinated efforts focused on individual \r\ncomponents of the biosphere (e.g., the Global Alliance of Continuous \r\nPlankton Recorder Surveys, GACS; Batten et al., 2019) contribute to \r\nimproved knowledge of the ways in which marine ecosystems are \r\nchanging (Section 2.3.4.2)."


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"Recent advances in the reconstruction of climate extremes - aside \r\nfrom temperature and drought - include expanded datasets of past \r\nEl Nino-Southern Oscillation extremes (Section  2.4.2; e.g.,  Barrett \r\net al., 2018; Freund et al., 2019; Grothe et al., 2020) and other modes \r\nof variability (Hernandez et al., 2020), hurricane activity (e.g., Burn \r\nand Palmer, 2015; Donnelly et al., 2015), jet stream variability (Trouet \r\net al., 2018) and wildfires (e.g., Taylor et al., 2016)."


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"New datasets as well as recent data compilations and syntheses \r\nof sea level over the last millennia (Kopp et al., 2016; Kemp et al., \r\n2018), the last 20 kyr (Khan et al., 2019), the last interglacial period \r\n(Section 2.3.3.3: Dutton et al., 2015), and the Pliocene (Cross-Chapter \r\nBox  2.4; Dumitru et al., 2019; Grant et al., 2019) help constrain \r\nsea level variability and its relationship to global and regional \r\ntemperature variability, and to estimates of contributions to sea level \r\nchange from different sources on centennial to millennial time scales \r\n(Section 9.6.2)."


id: 5c2c767ad96a517e0e98bb5ac154849d
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"Given widespread evidence for decreases in global biodiversity in \r\nrecent decades  - and that these decreases are related to climate \r\nchange and other forms of human disturbance (IPBES, 2019) - a new \r\ninternational effort to identify a set of Essential Biodiversity Variables \r\n(EBVs) is underway (Pereira et al., 2013; Navarro et al., 2017)."


id: e977689aead4e560aee70a18c4cc4a70
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content:
"Reconstructions of paleo ocean pH (Section 2.3.3.5) have increased \r\nin number and accuracy, providing new constraints on ocean pH \r\nacross the last centuries (e.g.,  Wu et al., 2018), the last glacial \r\ncycles (e.g.,  Moy et al., 2019), and the last several million years \r\n(e.g., Anagnostou et al., 2020). Such reconstructions inform processes \r\nand act as benchmarks for Earth system models of the global carbon \r\ncycle over the recent geologic past (Section 5.3.1), including previous \r\nhigh-CO2 warm intervals such as the Pliocene (Cross-Chapter Box 2.4). \r\nParticularly relevant to such investigations are reconstructions of \r\natmospheric CO2 (Honisch et al., 2012; Foster et al., 2017) that span \r\nthe past millions to tens of millions of years."


id: 3b32c0ffe753d19d6e4d388c516d8b10
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updated_at: 2023-03-16 14:00:37
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vector_id: 6669
content:
"In summary, the observational coverage of ongoing changes to the \r\nclimate system is improved at the time of AR6, relative to what was \r\navailable for AR5 (high confidence)."


id: 496110cdf439a5758665623367b8a37f
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content:
"Major paleoreconstruction efforts completed since AR5 include \r\na  variety of large-scale, multi-proxy temperature datasets and \r\nassociated reconstructions spanning the last 2000  years (PAGES \r\n2k Consortium, 2017, 2019; Neukom et al., 2019), the Holocene \r\n(Kaufman et al., 2020), the Last Glacial Maximum (Cleator et al., 2020; \r\nTierney et al., 2020b), the mid-Pliocene Warm Period (McClymont \r\net al., 2020), and the Early Eocene Climatic Optimum (Hollis et al., \r\n2019). Newly compiled borehole data (Cuesta-Valero et al., 2019), \r\nas well as advances in statistical applications to tree ring data, \r\nresult in more robust reconstructions of key indices such as Northern \r\nHemisphere temperature over the last millennium (e.g., Wilson et al., \r\n2016; Anchukaitis et al., 2017). Such reconstructions provide a new \r\ncontext for recent warming trends (Chapter 2) and serve to constrain \r\nthe response of the climate system to natural and anthropogenic \r\nforcing (Chapters 3 and 7)."


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content:
"Constraints on the timing and rates of past climate changes have \r\nimproved since AR5. Analytical methods have increased the precision \r\nand reduced sample-size requirements for key radiometric dating \r\ntechniques, including radiocarbon (Gottschalk et al., 2018; Lougheed \r\net al., 2018) and uranium-thorium dating (Cheng et al., 2013). More \r\naccurate ages of many paleoclimate records are also facilitated \r\nby recent improvements in the radiocarbon calibration datasets \r\n(IntCal20, Reimer et al., 2020). A  recent compilation of global \r\ncosmogenic nuclide-based exposure dates (Balco, 2020b) allows for \r\na  more rigorous assessment of the evolution of glacial landforms \r\nsince the Last Glacial Maximum (Balco, 2020a)."


id: 3728fdff6abfa8f6212c49c6b4e66c2b
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 6065
content:
"Advances in paleoclimate data assimilation (Section  10.2.3.2) \r\nleverage the expanded set of paleoclimate observations to create \r\nphysically consistent gridded fields of climate variables for data\u0002rich intervals of interest (e.g.,  over the last millennium, (Hakim \r\net al., 2016) or last glacial period (Cleator et al., 2020; Tierney et al., \r\n2020b)). Such efforts mirror advances in our understanding of the \r\nrelationship between proxy records and climate variables of interest, \r\nas formalized in so-called proxy system models (e.g., Tolwinski-Ward \r\net al., 2011; Dee et al., 2015; Dolman and Laepple, 2018)."


id: 1f74a728135ad3631931b0b0855b71d0
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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index: climate_gpt_only_giec
vector_id: 2378
content:
"(Liu et al., 2021). Concern has been raised about potential leakage \r\nfrom 5G network transmissions into the operating frequencies of \r\npassive sensors on existing weather satellites, which could adversely \r\ninfluence their ability to remotely observe water vapour in the \r\natmosphere (Yousefvand et al., 2020)."


id: ac1a0fa546e5f3b71bb62385dd1ed972
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 24599
content:
"Overall, the number, temporal resolution and chronological accuracy \r\nof paleoclimate reconstructions have increased since AR5, leading \r\nto improved understanding of climate system processes (or Earth \r\nsystem processes) (high confidence)."


id: 9865f7e3ee604ed3cd6fbfe00e0aae98
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 21487
content:
"Surface-based networks have reduced in their coverage or range of \r\nvariables measured due to COVID-19 and other factors. Over land, \r\nseveral factors, including the ongoing transition from manual to \r\nautomatic observations of weather, have reduced the spatial coverage \r\nof certain measurement types, including rainfall intensity, radiosonde \r\nlaunches and pan evaporation, posing unique risks to datasets used \r\nfor climate assessment (WMO, 2017; Lin and Huybers, 2019). Ship\u0002based measurements, which are important for ocean climate and \r\nreanalyses through time (Smith et al., 2019), have been in decline \r\ndue to the number of ships contributing observations. There has also \r\nbeen a decline in the number of variables recorded by ships, but an \r\nincrease in the quality and time-resolution of others (e.g., sea level \r\npressure, Kent et al., 2019)."


id: 4e6c2a8cb6ce1162c8661c67b631f9a4
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
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vector_id: 9767
content:
"In summary, while the quantity, quality and diversity of climate \r\nsystem observations have grown since AR5, the loss or potential loss \r\nof several critical components of the observational network is also \r\nevident (high confidence)."


id: 172e55c25a08468b394caa2e7353ed66
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 1160
content:
"Certain satellite frequencies are used to detect meteorological \r\nfeatures that are vital to climate change monitoring. These can be \r\ndisturbed by certain radio communications (Anterrieu et al., 2016), \r\nalthough scientists work to remove noise from the signal (Oliva \r\net al., 2016). For example, water vapour in the atmosphere naturally \r\nproduces a weak signal at 23.8 gigahertz (GHz), which is within the \r\nrange of frequencies of the 5G cellular communications network "


id: 33044ba4d4e7ee3d03ede81f56d322b
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 5450
content:
"Reanalyses are usually the output of a  model (e.g.,  a  numerical \r\nweather prediction model) constrained by observations using data \r\nassimilation techniques, but the term has also been used to describe \r\nobservation-based datasets produced using simpler statistical "


id: ca7f730f7a4d4e459b5912530c63b007
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
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vector_id: 29421
content:
"et al., 2001). Older reanalyses have a number of limitations, which \r\nhave to be accounted for when assessing the results of any study \r\nthat uses them."


id: 7be2834c898f0e1c6b22373121f3ce41
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
index: climate_gpt_only_giec
vector_id: 17095
content:
"methods and models (Annex I: Observational Products). This section \r\nfocuses on the model-based methods and their recent developments."


id: 8758996b2b50d34536e90fe3026bb11e
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
content_type: text
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vector_id: 18837
content:
"Reanalyses complement datasets of observations in describing \r\nchanges through the historical record and are sometimes considered \r\nas 'maps without gaps' because they provide gridded output in \r\nspace and time, often global, with physical consistency across \r\nvariables on sub-daily time scales, and information about sparsely \r\nobserved variables (such as evaporation; Hersbach et al., 2020). They \r\ncan be globally complete, or regionally focussed and constrained \r\nby boundary conditions from a global reanalysis (Section 10.2.1.2). \r\nThey can also provide feedback about the quality of the observations \r\nassimilated, including estimates of biases and critical gaps for some \r\nobserving systems."


id: a4b566452a1539fb659e8c711275f10b
created_at: 2023-03-16 14:00:37
updated_at: 2023-03-16 14:00:37
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vector_id: 23482
content:
"ERA5 provides hourly atmospheric fields at about 31 km resolution \r\non 137 levels in the vertical, as well as land-surface variables and \r\nocean waves. It is available from 1979 onwards and is updated in \r\nnear-real time, with plans to extend back to 1950. A  10-member \r\nensemble is also available at coarser resolution, allowing uncertainty \r\nestimates to be provided (e.g.,  Section  2.3). MERRA-2 includes \r\nmany updates from the earlier version, including the assimilation of \r\naerosol observations, several improvements to the representation \r\nof the stratosphere, including ozone, and improved representations \r\nof cryospheric processes. All of these improvements increase the \r\nusefulness of these reanalyses (Section 7.3; Hoffmann et al., 2019)."


id: 4c9321bc497ea999f4c5e0ff3acc1a34
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 9471
content:
"Many early reanalyses are described in Box 2.3 of Hartmann et al. \r\n(2013). These were often limited by the underlying model, the data \r\nassimilation schemes and observational issues (Thorne and Vose, \r\n2010; Zhou et al., 2018). Observational issues include the lack of \r\nunderlying observations in some regions, changes in the observational \r\nsystems over time (e.g.,  spatial coverage, introduction of satellite \r\ndata), and time-dependent errors in the underlying observations or in \r\nthe boundary conditions, which may lead to stepwise biases in time. \r\nThe assimilation of sparse or inconsistent observations can introduce \r\nmass or energy imbalances (Valdivieso et al., 2017; Trenberth et al., \r\n2019). Further limitations and some efforts to reduce the implications \r\nof these observational issues are detailed below."


id: 2a1b2e57096d6d0caa01b5350c034b61
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
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vector_id: 4049
content:
"Models of atmospheric composition and emissions sources and \r\nsinks allow the forecast and reanalysis of constituents such as O3, \r\ncarbon monoxide (CO), nitrogen oxides (NOx) and aerosols. The \r\nCopernicus Atmosphere Monitoring Service (CAMS) reanalysis shows \r\nimprovement against earlier atmospheric composition reanalyses, \r\ngiving greater confidence for its use to study trends and evaluate \r\nmodels (Section 7.3; e.g., Inness et al., 2019)."


id: 9853f44dfb22d4386f27c2eb95001138
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 21477
content:
"The methods used in the development of reanalyses have progressed \r\nsince AR5 and, in some cases, this has important implications for \r\nthe information they provide on how the climate is changing. Annex \r\nI  includes a  list of reanalysis datasets used in AR6. Recent major \r\ndevelopments in reanalyses include the assimilation of a wider range \r\nof observations, higher spatial and temporal resolution, extensions \r\nfurther back in time, and greater efforts to minimize the influence of \r\na temporally varying observational network."


id: d20b6719f26e19e3d5682f9de29a13b4
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 30576
content:
"Extensive improvements have been made in global atmospheric \r\nreanalyses since AR5. The growing demand for high-resolution \r\ndata has led to the development of higher-resolution atmospheric \r\nreanalyses, such as the Modern-Era Retrospective Analysis for \r\nResearch and Applications, version 2 (MERRA-2; Gelaro et al., 2017) \r\nand ERA5 (Hersbach et al., 2020). There is a  focus on ERA5 here \r\nbecause it has been assessed as of high enough quality to present \r\ntemperature trends alongside more traditional observational datasets \r\n(Section 2.3.1.1) and is also used in the Interactive Atlas."


id: fdd419296aaa3ee2d515ff9cb7c7a65f
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 37452
content:
"Precipitation is not usually assimilated in reanalyses and, depending \r\non the region, reanalysis precipitation can differ from observations \r\nby more than the observational error (Zhou and Wang, 2017; Sun \r\net al., 2018; Alexander et al., 2020; Bador et al., 2020), although \r\nthese studies did not include ERA5. Assimilation of radiance \r\nobservations from microwave imagers which, over ice-free ocean \r\nsurfaces, improve the analysis of lower-tropospheric humidity, \r\ncloud liquid water and ocean-surface wind speed have resulted \r\nin improved precipitation outputs in ERA5 (Hersbach et al., 2020). \r\nGlobal averages of other fields, particularly temperature, from ERA\u0002Interim and JRA-55 reanalyses continue to be consistent over the last \r\n20 years with surface observational data sets that include the polar \r\nregions (Simmons and Poli, 2015), although biases in precipitation "


id: 5e74c8e1645207c3393a7281b913b41f
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 12395
content:
"Atmospheric reanalyses that were assessed in AR5 are still being \r\nused in the literature, and results from ERA-Interim (about 80 km \r\nresolution, production stopped in August 2019; Dee et al., 2011), the \r\nJapanese 55-year Reanalysis (JRA-55; Ebita et al., 2011; Kobayashi \r\net al., 2015; Harada et al., 2016) and Climate Forecast System \r\nReanalysis (CFSR; Saha et al., 2010) are assessed in AR6. Some \r\nstudies still also use the NCEP/NCAR reanalysis, particularly because \r\nit extends back to 1948 and is updated in near-real time (Kistler "


id: f16ccacf89c4c5cee6ac5aede9d1bd35
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 35490
content:
"and radiation can influence temperatures regionally (Zhou et al., \r\n2018). The global average surface temperature from MERRA-2 is far \r\ncooler in recent years than temperatures derived from ERA-Interim \r\nand JRA-55, which may be due to the assimilation of aerosols and \r\ntheir interactions (Section 2.3)."


id: 7ad38d88905eaaff0f451f6a33dad202
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 16915
content:
"in the atmospheric forcing from atmospheric reanalyses \r\n(see  Section  1.5.2.1.3); and improvements in the data quantity \r\nand quality available for assimilation (e.g.,  Lellouche et al., 2018; \r\nHeimbach et al., 2019), particularly due to Argo observations (Annex \r\nI; Zuo et al., 2019)."


id: cf32ca94185444caf1ac4c3a33acd9f
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 30135
content:
"A number of regional atmospheric reanalyses (Section  10.2.1.2) \r\nhave been developed, such as COSMO-REA (Wahl et al., 2017), and \r\nthe Australian Bureau of Meteorology Atmospheric high-resolution \r\nRegional Reanalysis for Australia (BARRA; Su et al., 2019). Regional \r\nreanalyses can add value to global reanalyses due to the lower \r\ncomputational requirements, and can allow multiple numerical \r\nweather prediction models to be tested (e.g.,  Kaiser-Weiss et al., \r\n2019). There is some evidence that these higher-resolution reanalyses \r\nbetter capture precipitation variability than global lower-resolution \r\nreanalyses (Jermey and Renshaw, 2016; Cui et al., 2017). They are \r\nfurther assessed in Section 10.2.1.2 and used in the Interactive Atlas."


id: b4ecbae8680cc28e06fc2e62eda33988
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 26001
content:
"In summary, the improvements in atmospheric reanalyses, and the \r\ngreater number of years since the routine ingestion of satellite data \r\nbegan, relative to AR5, mean that there is increased confidence in \r\nusing atmospheric reanalysis products alongside more standard \r\nobservation-based datasets in AR6 (high confidence)."


id: 649c04ef4cbde4feffd98682ff634a5f
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 13402
content:
"While there are still limitations in their representation of oceanic \r\nfeatures, ocean reanalyses add value to products based only on \r\nobservation, and are used to inform assessments in AR6 (Chapters 2, \r\n3, 7 and 9). Reanalyses of the atmosphere or ocean alone may not \r\naccount for important atmosphere-ocean coupling, motivating the \r\ndevelopment of coupled reanalyses (Laloyaux et al., 2018; Schepers \r\net al., 2018; Penny et al., 2019), but these are not assessed in AR6."


id: b9a49db32aa8ce4b1f943502e0e382d7
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 26748
content:
"Since AR5, ocean reanalyses have improved due to: increased \r\nmodel resolution (Zuo et al., 2017; Lellouche et al., 2018; Heimbach \r\net al., 2019); improved physics (Storto et al., 2019); improvements "


id: 2a01a4fcf9f84758d85a8b54a74cb106
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 4040
content:
"This section summarizes major developments in these different types \r\nof models since AR5. Past IPCC reports have made use of multi-model \r\nensembles generated through various phases of the World Climate \r\nResearch Programme (WCRP) Coupled Model Intercomparison \r\nProject (CMIP). Analysis of the latest CMIP Phase 6 (CMIP6; Eyring \r\net al., 2016) simulations constitute a key line of evidence supporting \r\nthis Assessment Report (Section  1.5.4). The key characteristics of \r\nmodels participating in CMIP6 are listed in Annex II: Models."


id: 983d236e191f95b9bb4cf15a0d71a94
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 21464
content:
"cycles, ecosystem structure and ecosystem functioning, and \r\nto provide initial conditions for further model experiments or \r\ndownscaling (Chapter 2)."


id: d007549ea669eba26e51557cc647e263
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 30263
content:
"The growing interest in longer-term climate forecasts (from seasonal \r\nto multi-year and decadal) means that reanalyses are now more \r\nroutinely being used to develop the initial state for these forecasts, \r\nsuch as for the Decadal Climate Prediction Project (DCPP; Boer et al., \r\n2016). Ocean reanalyses are now being used routinely in the context \r\nof climate monitoring, (e.g.,  the Copernicus Marine Environment \r\nMonitoring Service Ocean State Report; von Schuckmann et al., 2019)."


id: 7f469a7dc529ed6d27b97ab59303db40
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 17576
content:
"In summary, reanalyses have improved since AR5 and can \r\nincreasingly be used as a  line of evidence in assessments of the \r\nstate and evolution of the climate system (high confidence). \r\nReanalyses provide consistency across multiple physical quantities, \r\nand information about variables and locations that are not directly \r\nobserved. Since AR5, new reanalyses have been developed with \r\nvarious combinations of increased resolution, extended records, \r\nmore consistent data assimilation, estimation of uncertainty arising \r\nfrom the range of initial conditions, and an improved representation \r\nof the atmosphere or ocean system. While noting their remaining \r\nlimitations, this Report uses the most recent generation of reanalysis \r\nproducts alongside more standard observation-based datasets."


id: be7d5bac9b0ae5f55b3d7fe50b78824f
created_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 27524
content:
"The number of vertical levels in the atmosphere of global models \r\nhas increased (Figure 1.19), partly to enable simulations to include \r\nhigher levels in the atmosphere and better represent stratospheric \r\nprocesses (Charlton-Perez et al., 2013; Kawatani et al., 2019). Half \r\nthe modelling groups now use 'high-top' models with a  top level \r\nabove the stratopause (a pressure of about 1 hPa). The number of \r\nvertical levels in the ocean models has also increased in order to \r\nachieve finer resolution over the water column and especially in the \r\nupper mixed layer and to better resolve the diurnal cycle (Section 3.5 \r\nand Annex II; Bernie et al., 2008)."


id: eb7f35a34a0321c2a226d4894cbfae8e
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 34550
content:
"A wide range of numerical models is widely used in climate science to \r\nstudy the climate system and its behaviour across multiple temporal \r\nand spatial scales. These models are the main tools available to \r\nlook ahead into possible climate futures under a range of scenarios \r\n(Section  1.6). Global Earth system models (ESMs) are the most \r\ncomplex models that contribute to AR6. At the core of each ESM is \r\na GCM (general circulation model) representing the dynamics of the \r\natmosphere and ocean. ESMs are complemented by regional models \r\n(Section 10.3.1) and by a hierarchy of models of lower complexity. "


id: 26aa5b32435f48bc6e8ff861b98d1416
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 3483
content:
"Despite the documented progress of higher resolution, the \r\nmodel evaluation carried out in subsequent chapters shows that \r\nimprovements between CMIP5 and CMIP6 remain modest at the \r\nglobal scale (Section  3.8.2; Bock et al., 2020). Lower resolution \r\nalone does not explain all model biases, for example, a low blocking \r\nfrequency (Davini and D'Andrea, 2020) or a  wrong shape of the \r\nIntertropical Convergence Zone (Tian and Dong, 2020). Model \r\nperformance depends on model formulation and parameterizations \r\nas much as on resolution (Chapters 3, 8 and 10)."


id: 7ab165153bb888f70ccd4f6ebc11c098
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 16881
content:
"Atmospheric models include representations of physical processes \r\nsuch as clouds, turbulence, convection and gravity waves that are \r\nnot fully represented by grid-scale dynamics. The CMIP6 models \r\nhave undergone updates in some of their parameterization schemes \r\ncompared to their CMIP5 counterparts, with the aim of better \r\nrepresenting the physics and bringing the climatology of the models \r\ncloser to newly available observational datasets. Most notable \r\ndevelopments are to schemes involving radiative transfer, cloud "


id: c4fb79c64d3c3ec903513fa4b9f417f
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 28566
content:
"microphysics, and aerosols, in particular a more explicit representation \r\nof the aerosol indirect effects through aerosol-induced modification \r\nof cloud properties. Broadly, aerosol-cloud microphysics has been \r\na  key topic for the aerosol and chemistry modelling communities \r\nsince AR5, leading to improved understanding of the climate \r\ninfluence of short-lived climate forcers, but they remain the single \r\nlargest source of spread in ESM calculations of climate sensitivity \r\n(Meehl et al., 2020), with numerous parameterization schemes in \r\nuse (Section 6.4; Gettelman and Sherwood, 2016; Zhao et al., 2018; \r\nGettelman et al., 2019). The treatment of droplet size and mixed\u0002phase clouds (liquid and ice) was found to lead to changes in the \r\nclimate sensitivity (Glossary) of some models between AR5 and AR6 \r\n(Section  7.4; Bodas-Salcedo et al., 2019; Gettelman et al., 2019; \r\nZelinka et al., 2020)."


id: 8273f0055273e7fe38deec82fb2c1137
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 18043
content:
"When developing climate models, choices have to be made in a number \r\nof areas. Besides model formulation and resolution, parameterizations \r\nof unresolved processes also involve many choices as, for each of \r\nthese, several parameters can be set. The acceptable range for these \r\nparameters is set by mathematical consistency (e.g.,  convergence \r\nof a  numerical scheme), physical considerations (e.g.,  energy \r\nconservation), observations, or a  combination of factors. Model \r\ndevelopers choose a set of parameters that both falls within this range \r\nand mimics observations of individual processes or their statistics."


id: 6730e49b6219129eb4ad259ba4049eff
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 13832
content:
"Sea level rise is caused by multiple processes acting on multiple time \r\nscales: ocean warming, glaciers and ice-sheet melting, change in \r\nwater storage on land, and glacial isostatic adjustment (Box 9.1) but \r\nno single model can represent all these processes (Section 9.6). In \r\nthis Report, the contributions are computed separately (Figure 9.28) \r\nand merged into a  common probabilistic framework and updated \r\nfrom AR5 (Section 9.6; Church et al., 2013; Kopp et al., 2014)."


id: 433b611f99285efe50cc527b72fe90b7
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 7980
content:
"Another notable development since AR5 is the inclusion of stochastic \r\nparameterizations of sub-grid processes in some comprehensive \r\nclimate models (Sanchez et al., 2016). Here, the deterministic \r\ndifferential equations that govern the dynamical evolution of the \r\nmodel are complemented by knowledge of the stochastic variability \r\nin unresolved processes. While not yet widely implemented, the \r\napproach has been shown to improve the forecasting skill of weather \r\nmodels, to reduce systematic biases in global models (Berner et al., \r\n2017; Palmer, 2019) and to influence simulated climate sensitivity \r\n(Strommen et al., 2019)."


id: 351b3d5b171b194b89e15a19ccfeca65
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 5764
content:
"independently such that all collective behaviour is emergent, is an \r\nopen question (Schmidt et al., 2017; Burrows et al., 2018)."


id: be82287a986102ad6fa304122ad9c28b
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 27530
content:
"Regional tuning targets include: the AMOC, the Southern Ocean \r\ncirculation, and temperature profiles in ocean basins (Golaz et al., \r\n2019; Sellar et al., 2019); regional land properties and precipitations \r\n(Mauritsen et al., 2019; Yukimoto et al., 2019) ; latitudinal distribution \r\nof radiation (Boucher et al., 2020); spatial contrasts in TOA radiative \r\nfluxes or surface fluxes; and stationary waves in the Northern \r\nHemisphere (Schmidt et al., 2017; Yukimoto et al., 2019)."


id: 7fb16629d65af06bacf316b1a4c16783
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 17637
content:
"Even with some core commonalities of approaches to model tuning, \r\npractices can differ, such as the use of initial drift from initialized \r\nforecasts, the explicit use of the transient observed record for the \r\nhistorical period, or the use of the present-day radiative imbalance at \r\nthe TOA as a tuning target rather than an equilibrated pre-industrial \r\nbalance. The majority of CMIP6 modelling groups report that they \r\ndo not tune their model for the observed trends during the historical \r\nperiod (23 out of 29 groups), nor for ECS (25 out of 29). ECS and \r\nTCR are thus emergent properties for a  large majority of models. \r\nThe effect of tuning on model skill and ensemble spread in CMIP6 is \r\nfurther discussed in Section 3.3."


id: 6b4187db61edbebc64306a76a9703a1e
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 14498
content:
"The need for accurate climate information at the regional scale is \r\nincreasing (Section  10.1). High-resolution global climate models, \r\nsuch as those taking part in HighResMIP, provide more detailed "


id: 47566579276e1f51f046c827615fe0c7
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 8610
content:
"Figure 1.20 | World map showing the increased diversity of modelling centres contributing to CMIP and CORDEX. Climate models are often developed by \r\ninternational consortia. One such consortium, EC-Earth, is shown as an example under the label 8 EU Cities (involving SMHI, Sweden; KNMI, The Netherlands; DMI, Denmark; \r\nAEMET, Spain; Met Eireann, Ireland; CNR-ISAC, Italy; Instituto de Meteorologia, Portugal; and FMI, Finland). There are too many such collaborations to display all of them on \r\nthis map. More complete information about institutions contributing to CORDEX and CMIP6 is found in Annex II."


id: 38d195152c506cfc6e58da545e108900
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 6310
content:
"The number of climate centres or consortia that carry out global \r\nclimate simulations and projections has grown from 11 in the \r\nfirst CMIP to 19 in CMIP5 and 28 for CMIP6 (Section  1.5.4.2 and \r\nAnnex II). Regional climate models participating in the Coordinated \r\nRegional Downscaling Experiment (CORDEX) are more diverse than \r\nthe global ESMs (Section 1.5.4.3 and Annex II) and engage an even \r\nwider international community (Figure 1.20)."


id: eff52e92139b7385ddf54e24f50a710f
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 35259
content:
"EMICs have been used extensively in past IPCC reports, providing \r\nlong-term integrations on paleoclimate and future time scales, \r\nincluding stabilization pathways and a  range of commitment \r\nscenarios, with perturbed physics ensembles and sensitivity studies, \r\nor with simulations targeting the uncertainty in global climate-\r\ncarbon cycle systems (e.g., Meehl et al., 2007b; Collins et al., 2013). "


id: 5d61f5f63d9981e50477f8cf889b06c6
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 12219
content:
"Current emulators and SCMs include the generic impulse response \r\nmodel outlined in Chapter  8  of AR5 (AR5-IR; Supplementary \r\nMaterial 8.SM.11 of Myhre et al., 2013), two-layer models (Held \r\net al., 2010; Rohrschneider et al., 2019; Nicholls et al., 2020), and \r\nhigher-complexity approaches that include upwelling, diffusion and \r\nentrainment in the ocean component (e.g.,  MAGICC Version 5.3 \r\n(Raper et al., 2001; Wigley et al., 2009); Version 6/7 (Meinshausen \r\net al., 2011a); OSCAR (Gasser et al., 2017); CICERO SCM (Skeie et al., \r\n2017); FaIR (Millar et al., 2017a; Smith et al., 2018); and a range of \r\nstatistical approaches (Schwarber et al., 2019; Beusch et al., 2020b). \r\nAn example of recent use of an emulator approach is an early \r\nestimate of the climate implications of the COVID-19 lockdowns \r\n(Cross-Chapter Box 6.1; Forster et al., 2020)."


id: cdf13114b6a5fdc01f9fb1123f287e35
created_at: 2023-03-16 14:00:38
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vector_id: 29925
content:
"heterogeneous forcers such as black carbon more adequately (Stjern \r\net al., 2017), provide an appropriate representation of the forcing-\r\nfeedback framework (e.g., Sherwood et al., 2015), investigate new \r\nparameterizations of ocean heat uptake, and implement better \r\nrepresentations of volcanic aerosol-induced cooling (Gregory \r\net al., 2016a)."


id: 5d4f6f8b851a83e1e33ce455a18c1f3d
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 12196
content:
"MAGICC (Wigley et al., 2009; Meinshausen et al., 2011a) and \r\nFaIR (Smith et al., 2018) were used in IPCC SR1.5 (IPCC, 2018) to \r\ncategorize mitigation pathways into classes of scenarios that peak \r\nnear 1.5degC, overshoot 1.5degC, or stay below 2degC. The SR1.5 (Rogelj \r\net al., 2018b) concluded that there was high agreement on the \r\nrelative temperature response of pathways, but medium agreement\r\non the precise absolute magnitude of warming, introducing a level of \r\nimprecision in the attribution of a single pathway to a given category."


id: 996a64b9d6ef657f0d4cd2039604ec02
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
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vector_id: 21658
content:
"Since AR5, simplified climate models have been developed further, and \r\ntheir use is increasing. Different purposes motivating development \r\ninclude: being as simple as possible for teaching purposes (e.g., a two\u0002layer energy balance model); being as comprehensive as possible to \r\nallow for propagation of uncertainties across multiple Earth system \r\ndomains (MAGICC and others); or focusing on higher-complexity \r\nrepresentation of specific domains (e.g.,  OSCAR). The common \r\ntheme motivating many models is to improve parameterizations \r\nthat reflect the latest findings in complex ESM interactions - such \r\nas the nitrogen cycle addition to the carbon cycle, or tropospheric \r\nand stratospheric ozone exchange - with the aim of emulating their \r\nglobal mean temperature response. Also, within the simple models \r\nthat have a  rudimentary representation of spatial heterogeneity \r\n(e.g., four-box simple climate models), the ambition is to represent "


id: b4a027ae06d7997af11619713d5ed05e
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 25951
content:
"In this Report, there are two notable uses of simple climate \r\nmodels. One is the connection between the assessed range of \r\nECS in Chapter  7, and the projections of future global surface air \r\ntemperature (GSAT) change in Chapter 4, which is done via a two\u0002layer model based on Held et al. (2010). It is also used as input to \r\nsea level projections in Chapter 9. The other usage is the transfer of \r\nEarth system assessment knowledge to WGIII, via a set of models \r\n(MAGICC, FaIR, CICERO-SCM) specifically tuned to represent the WGI \r\nassessment. For an overview of the uses, and an assessment of the \r\nrelated Reduced Complexity Model Intercomparison Project (RCMIP), \r\nsee Nicholls et al. (2020) and Cross-Chapter Box 7.1."


id: a09d32577ba5be37000962ce9aadfa58
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 22810
content:
"Emissions metrics compare the radiative forcing, temperature change, or other climate effects arising from emissions of CO2 against \r\nthose from emissions of non-CO2 radiative forcing agents (such as CH4 or N2O). They have been discussed in the IPCC since the First \r\nAssessment Report and are used as a means of aggregating emissions and removals of different gases and placing them on a common \r\n('CO2 equivalent', or 'CO2-eq') scale."


id: 6b62c4e011068acb1982fe6fa5eea6df
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
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vector_id: 14512
content:
"AR5 included a thorough assessment of common pulse emissions metrics, and how these address various indicators of future climate \r\nchange (Myhre et al., 2013). Most prominently used are the global warming potentials (GWPs), which integrate the calculated radiative \r\nforcing contribution following an idealized pulse (or one-time) emission, over a chosen time horizon (IPCC, 1990a), or the global \r\ntemperature change potential (GTP), which considers the contribution of emissions to the global-mean temperature at a specific time \r\nafter emission. Yet another metric is the global precipitation change potential (GPP), used to quantify the precipitation change per unit \r\nmass of emission of a given forcing agent (Shine et al., 2015)."


id: f16b55451bf953461309b004cc304ebe
created_at: 2023-03-16 14:00:38
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content:
"Each Party shall use the 100-year time-horizon global warming potential (GWP) values from the IPCC Fifth Assessment Report, \r\nor 100-year time-horizon GWP values from a subsequent IPCC assessment report as agreed upon by the 'Conference of the \r\nParties serving as the meeting of the Parties to the Paris Agreement' (CMA), to report aggregate emissions and removals of \r\nGHGs, expressed in CO2-eq. Each Party may in addition also use other metrics (e.g., global temperature potential) to report \r\nsupplemental information on aggregate emissions and removals of GHGs, expressed in CO2-eq."


id: 353e43391754303c8617e59f149d3af6
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 5787
content:
"Since AR5, improved knowledge of the radiative properties, lifetimes and other characteristics of emitted species, and the response of \r\nthe climate system, have led to updates to the numerical values of a range of metrics (Table 7.15). Another key development is a set \r\nof metrics that compare a pulse emission of CO2 (as considered by GWP and GTP) to step-changes of emission rates for short-lived \r\ncomponents (i.e., also considering emissions trends). Termed GWP* (which also includes a pulse component) and combined global"


id: 6cd460101789a15db6429f5622564e9a
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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content:
"temperature change potential (CGTP), these metrics allow the construction of a  near-linear relationship between global surface \r\ntemperature change and cumulative CO2 and CO2-eq emissions of both short- and long-lived forcing agents (Allen et al., 2016; Cain \r\net al., 2019; Collins et al., 2020). For example, the temperature response to a sustained methane reduction has a similar behaviour to \r\nthe temperature response to a pulse CO2 removal (or avoided emission)."


id: f596fb0fc9b3f0d4225084d3210c4301
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 36158
content:
"In this Report, recent scientific developments underlying emissions metrics, as relevant for WGI, are assessed in full in Section 7.6. \r\nIn particular, see Box 7.3, which discusses the choice of metric for different usages, and Section 7.6.1, which treats the challenge \r\nof comparing the climate implication of emissions of short-lived and long-lived compounds. Also, the choice of metric is of key \r\nimportance when defining and quantifying net zero GHG emissions (Box 1.4 and Section 7.6.2). Chapter 6 applies metrics to attribute \r\nGSAT change to short-lived climate forcer (SLCF) and long-lived GHG emissions from different sectors and regions (Section 6.6.2)."


id: c7d45477571cc5d8c93859670db47116
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 29015
content:
"The metrics assessed in this Report are also used, and separately assessed, by WGIII. See Cross-Chapter Box  2  and Annex B  in \r\nChapter 2 of the WGIII contribution to AR6."


id: 2ba77926e1b768e0535d67ee3e29adf4
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 4301
content:
"Numerical models, however complex, cannot be a  perfect \r\nrepresentation of the real world. Results from climate modelling \r\nsimulations constitute a key line of evidence for the present Report, \r\nwhich requires considering the limitations of each model simulation. \r\nThis section presents recent developments in techniques and \r\napproaches to robustly extract, quantify and compare results from \r\nmultiple, independent climate models, and how their performance \r\ncan be assessed and validated."


id: b210e4954f6c70d3a8a5ab80062458d6
created_at: 2023-03-16 14:00:38
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vector_id: 25515
content:
"Together, the three ensemble methods (MMEs, ICEs, PPEs) allow \r\ninvestigation of climate model uncertainty arising from internal \r\nvariability, initial and internal boundary conditions, model \r\nformulations and parameterizations (Parker, 2013). Figure  1.21 \r\nillustrates the different ensemble types. Recent studies have also \r\nstarted combining multiple ensemble types or using ensembles in \r\ncombination with statistical analytical techniques. For example, \r\nMurphy et al. (2018) combine MMEs and PPEs to give a  fuller \r\nassessment of modelling uncertainty. Wagman and Jackson (2018) \r\nuse PPEs to evaluate the robustness of MME-based emergent \r\nconstraints. Sexton et al. (2019) study the robustness of ICE "


id: 96780608b2950a003c0458cac0bc4c4f
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content:
"Recently, the ICE technique has been extended to atmosphere-only \r\nsimulations (Mizuta et al., 2017), single-forcer influences such as \r\nvolcanic eruptions (Bethke et al., 2017), regional modelling (Mote \r\net al., 2015; Fyfe et al., 2017; Schaller et al., 2018; Leduc et al., 2019), \r\nand to attribution of extreme weather events using crowdsourced \r\ncomputing (climateprediction.net; Massey et al., 2015)."


id: 601141a6c80eafd2fdd4096e1847c92c
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 12692
content:
"ICEs can also be used to evaluate climate model parameterizations, \r\nif models are initialized appropriately (Phillips et al., 2004; Williams \r\net al., 2013), mostly within the framework of seamless weather and \r\nclimate predictions (e.g.,  Palmer et al., 2008; Hurrell et al., 2009; \r\nBrown et al., 2012). Initializing an atmospheric model in hindcast \r\nmode and observing the biases as they develop permits testing of the \r\nparameterized processes, by starting from a known state rather than \r\none dominated by quasi-random short-term variability (Williams \r\net al., 2013; Ma et al., 2014; Vanniere et al., 2014). However, single\u0002model initial-conditions ensembles cannot cover the same degrees of \r\nfreedom as a multi-model ensemble, because model characteristics \r\nsubstantially affect model behaviour (Flato et al., 2013)."


id: e97868bbe093a26ebd3991d1b576ecad
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content:
"A third common modelling technique is the perturbed parameter \r\nensemble (PPE; note that the abbreviation also sometimes refers to \r\nthe sub-category 'perturbed physics ensemble'). These methods are \r\nused to assess uncertainty based on a single model, with individual \r\nparameters perturbed to reflect the full range of their uncertainty \r\n(Murphy et al., 2004; Knutti et al., 2010; Lee et al., 2011; Shiogama \r\net al., 2014). Statistical methods can then be used to detect which \r\nparameters are the main causes of uncertainty across the ensemble. \r\nPPEs have been used frequently in simpler models, such as EMICs, and \r\nare being applied to more complex models. A caveat of PPEs is that the \r\nestimated uncertainty will depend on the specific parameterizations \r\nof the underlying model and may well be an underestimation of \r\nthe 'true' uncertainty. It is also challenging to disentangle forced \r\nresponses from internal variability using a PPE alone."


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content:
"approaches by identifying parameters and processes responsible for \r\nmodel errors at the two different time scales."


id: c1ef894f4a770dd541c3b2f16f3a6aba
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 28091
content:
"Overall, we assess that increases in computing power and the \r\nbroader availability of larger and more varied ensembles of model \r\nsimulations have contributed to better estimations of uncertainty in \r\nprojections of future change (high confidence). Note, however, that \r\ndespite their widespread use in climate science today, the cost of \r\nthe ensemble approach in human and computational resources, and \r\nthe challenges associated with the interpretation of multi-model \r\nensembles, has been questioned (Palmer and Stevens, 2019; Touze\u0002Peiffer et al., 2020)."


id: 20698d23a1a171a2aa9d100e8d838a21
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updated_at: 2023-03-16 14:00:38
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vector_id: 2547
content:
"The Coupled Model Intercomparison Project (CMIP) provides \r\na  framework to compare the results of different GCMs or ESMs \r\nperforming similar experiments. Since its creation in the mid-1990s, it \r\nhas evolved in different phases, involving all major climate modelling \r\ncentres in the world (Figure 1.20). The results of these phases have \r\nplayed a key role in previous IPCC reports, and the present Report \r\nassesses a  range of results from CMIP5 that were not published \r\nuntil after the AR5, as well as the first results of the 6th phase of "


id: 710c31cfed4250691c4e910291ea147d
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vector_id: 15419
content:
"Figure  1.22 |  Structure of CMIP6, the 6th phase of the Coupled Model \r\nIntercomparison Project. The centre shows the common DECK (Diagnostic, \r\nEvaluation and Characterization of Klima) and historical experiments that all \r\nparticipating models must perform. The outer circles show the topics covered by the \r\nendorsed (red) and other MIPs (orange). See Table 1.3 for explanation of the MIP \r\nacronyms. Figure is adapted from Eyring et al. (2016)."


id: ee6690d8e63bd299db7a1b87301465a6
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content:
"CMIP (CMIP6; Eyring et al., 2016). The CMIP6 experiment design is \r\nsomewhat different from previous phases. It now consists of a limited \r\nset of DECK (Diagnostic, Evaluation and Characterization of Klima) \r\nsimulations and an historical simulation that must be performed by \r\nall participating models, as well as a wide range of CMIP6-Endorsed \r\nmodel intercomparison projects (MIPs) covering specialized topics \r\n(Figure 1.22; Eyring et al., 2016). Each MIP activity consists of a series \r\nof model experiments, documented in the literature (Table 1.3) and in \r\nan online database (es-doc.org; Annex II; Pascoe et al., 2020)."


id: 78b42cc95876c57da4f9335690e46f26
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content:
"simulations on common domains and under common experimental \r\nconditions in a  similar way to the CMIP effort. Dynamical and \r\nstatistical downscaling techniques can provide higher-resolution \r\nclimate information than is available directly from global climate \r\nmodels (Section  10.3). These techniques require evaluation and \r\nquantification of their performance before they can be considered \r\nappropriate as usable regional climate information or be used in \r\nsupport of climate services. CORDEX simulations have been provided \r\nby a range of regional downscaling models for 14 regions, together \r\ncovering much of the globe (Figure Atlas.7), and they are used \r\nextensively in the AR6 WGI Atlas (Atlas.1.4 and Annex II)."


id: 31dc307f0b3af519c83a2902d3a0924a
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content:
"In support of AR6, CORDEX has undertaken a  new experiment \r\n(CORDEX-CORE) in which regional climate models downscale \r\na common set of global model simulations, performed at a coarser \r\nresolution, to a spatial resolution spanning from 12-25 km over most \r\nof the CORDEX domains (Box Atlas.1). CORDEX-CORE represents \r\nan improved level of coordinated intercomparison of downscaling \r\nmodels (Remedio et al., 2019)."


id: 7668a6376e410cbcd52cb1693dd6d486
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content:
"For the first time in CMIP, a  range of comprehensive evaluation \r\ntools are now available that can run alongside the commonly used \r\ndistributed data platform - Earth System Grid Federation (ESGF; see \r\nAnnex II) - to produce comprehensive results as soon as the model \r\noutput is published to the CMIP archive."


id: 972b388f1df628994e010bfda35746e0
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content:
"For instance, the Earth System Model Evaluation Tool (ESMValTool; \r\nEyring et al., 2020; Lauer et al., 2020; Righi et al., 2020) is used by \r\na number of chapters. It is an open-source community software tool \r\nthat includes a large variety of diagnostics and performance metrics \r\nrelevant for coupled Earth system processes, such as for the mean, \r\nvariability and trends, and it can also examine emergent constraints \r\n(Section 1.5.4.7). ESMValTool also includes routines provided by the \r\nWMO Expert Team on Climate Change Detection and Indices for the \r\nevaluation of extreme events (Min et al., 2011; Sillmann et al., 2013) \r\nand diagnostics for key processes and variability. Another example \r\nof an evaluation tool is the CLIVAR 2020 ENSO metrics package \r\n(Planton et al., 2021)."


id: c07202807dfbd66a906e5d705b14141f
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content:
"Beyond the DECK and the historical simulations, the CMIP6-Endorsed \r\nMIPs aim to investigate how models respond to specific forcings, \r\ntheir potential systematic biases, their variability, and their responses \r\nto detailed future scenarios such as the Shared Socio-economic \r\nPathways (SSPs; Section 1.6). Table 1.3 lists the 23 CMIP6-Endorsed \r\nMIPs and key references. Results from a range of these MIPs, and \r\nmany others outside of the most recent CMIP6 cycle, will be assessed \r\nin the following chapters (also shown in Table  1.3). References to \r\nall the CMIP6 datasets used in the report are found in Annex II, \r\nTable AII.10."


id: a68942905c8de564928f537cc2e09135
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content:
"These tools are used in several chapters of this report for the creation \r\nof the figures that show CMIP results. Together with the Interactive \r\nAtlas, they allow for traceability of key results, and an additional level \r\nof quality control on whether published figures can be reproduced. \r\nIt also provides the capability to update published figures with, as \r\nmuch as possible, the same set of models in all figures, and to assess \r\nmodel improvements across different phases of CMIP (Section 3.8.2)."


id: d5f67244f8eaf7c8b518d7144fb8c29
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"These new developments are facilitated by the definition of common \r\nformats for CMIP model output (Balaji et al., 2018) and the availability \r\nof reanalyses and observations in the same format as CMIP output \r\n(obs4MIPs; Ferraro et al., 2015). The tools are also used to support \r\nroutine evaluation at individual model centres and simplify the \r\nassessment of improvements in individual models or generations \r\nof model ensembles (Eyring et al., 2019). Note, however, that while \r\ntools such as ESMValTool can produce an estimate of overall model "


id: fbf36c404be2c3b63a2cc0c3a328910
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content:
"The Coordinated Regional Downscaling Experiment (CORDEX; \r\nGutowski Jr. et al., 2016) is an intercomparison project for regional \r\nmodels and statistical downscaling techniques, coordinating "


id: 68dd9531961266eb50bf0aa23a2e10b9
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"Model Intercomparison Project Observational Simulator (COSP; \r\nSwales et al., 2018) has been released which makes use of a collection \r\nof observation proxies or satellite simulators. Related approaches \r\nin this rapidly evolving field include simulators for Arctic Ocean \r\nobservations (Burgard et al., 2020) and measurements of aerosol \r\nobservations along aircraft trajectories (Watson-Parris et al., 2019)."


id: 8180785925a4dfdececaf673071db4df
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"performance, dedicated model evaluation still needs to be performed \r\nwhen analysing projections for a particular purpose, such as assessing \r\nchanging hazards in a given region. Such evaluation is discussed in \r\nthe next section, and in greater detail in later chapters of this Report."


id: 8e91ab1f19864213e53aa2cbfa2bd26b
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"In this Report, model evaluation is performed in the individual \r\nchapters, rather than in a separate chapter as was the case for AR5. \r\nThis applies to the model types discussed above, and also to dedicated \r\nmodels of subsystems that are not (or not yet) part of usual climate \r\nmodels, for example, glacier or ice-sheet models (Annex II). Further \r\ndiscussions are found in Chapter 3 (attribution), Chapter 5 (carbon \r\ncycle), Chapter 6 (short-lived climate forcers), Chapter 8 (water cycle), \r\nChapter  9  (ocean, cryosphere and sea level), Chapter  10 (regional \r\nscale information) and the Atlas (regional models)."


id: baabdfe80ed51e4df4cf9a052123098a
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"Techniques used for evaluating process-based climate models against \r\nobservations were assessed in AR5 (Flato et al., 2013), and have \r\nprogressed rapidly since (Eyring et al., 2019). The most widely used \r\ntechnique is to compare climatologies (long-term averages of specific \r\nclimate variables) or time series of simulated (process-based) model \r\noutput with observations, considering the observational uncertainty. \r\nA further approach is to compare the results of process-based models \r\nwith those from statistical models. In addition to a comparison of \r\nclimatological means, trends and variability, AR5 already made \r\nuse of a  large set of performance metrics for a  quantitative \r\nevaluation of the models."


id: b3e48de4f10b06bdf3ceb8455813b62b
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"An emergent constraint is the relationship between an uncertain \r\naspect of future climate change and an observable feature of the \r\nEarth System, evident across an ensemble of models (Allen and \r\nIngram, 2002; Mystakidis et al., 2016; Wenzel et al., 2016; Hall et al., \r\n2019; Winkler et al., 2019). Complex Earth system models (ESMs) \r\nsimulate variations on time scales from hours to centuries, telling \r\nus how aspects of the current climate relate to its sensitivity to \r\nanthropogenic forcing. Where an ensemble of different ESMs \r\ndisplays a  relationship between a  short-term observable variation \r\nand a  longer-term sensitivity, an observation of the short-term "


id: 72bd4e6da90a7b452913b6a02565e547
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"Since AR5, a  range of studies has investigated model agreement \r\nwith observations well beyond large-scale mean climate properties \r\n(e.g., Bellenger et al., 2014; Covey et al., 2016; Pendergrass and Deser, \r\n2017; Goelzer et al., 2018; Beusch et al., 2020a), providing information \r\non the performance of recent model simulations across multiple \r\nvariables and components of the Earth system (e.g.,  Anav et al., \r\n2013; Guan and Waliser, 2017). Based on such studies, this Report \r\nassesses model improvements across different CMIP DECK, CMIP6 \r\nhistorical and CMIP6-Endorsed MIP simulations, and of differences \r\nin model performance between different classes of models, such as \r\nhigh- versus low-resolution models (see e.g., Section 3.8.2)."


id: 50eaf2c052c4104e689a9374b471f607
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"Instrument simulators provide estimates of what a  satellite would \r\nsee if looking down on the model-simulated planet, and improve the \r\ndirect comparison of modelled variables such as clouds, precipitation \r\nand upper tropospheric humidity with observations from satellites \r\n(e.g., Kay et al., 2011; Klein et al., 2013; Cesana and Waliser, 2016; \r\nKonsta et al., 2016; Jin et al., 2017; Chepfer et al., 2018; Swales \r\net al., 2018; Zhang et al., 2018). Within the framework of the Cloud \r\nFeedback Model Intercomparison Project (CFMIP) contribution to \r\nCMIP6 (Webb et al., 2017), a  new version of the Cloud Feedback "


id: 6e96443acff5bf5e4cbac0f8d2923b1
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"variation in the real world can be converted, via the model-based \r\nrelationship, into an 'emergent constraint' on the sensitivity. This is \r\nshown schematically in Figure 1.23 (see Glossary; Eyring et al., 2019)."


id: e9e7f69bf961aaf32d5cca4bbcc938ea
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"et al., 2017; Lorenz et al., 2018; Liang et al., 2020). However, models \r\nmay share representations of processes, parameterization schemes, \r\nor even parts of code, leading to common biases. The models may \r\ntherefore not be fully independent, calling into question inferences \r\nderived from multi-model ensembles (Abramowitz et al., 2019). \r\nEmergent constraints (Section  1.5.4.5) also represent an implicit \r\nweighting technique that explicitly links present performance to future \r\nprojections (Bracegirdle and Stephenson, 2013)."


id: d6c3f72244ea5630fbfb3fc3cbb88f92
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"Several recent studies have attempted to quantify the effect of various \r\nstrategies for selection or weighting of ensemble members based \r\non some set of criteria (Haughton et al., 2015; Olonscheck and Notz, \r\n2017; Sanderson et al., 2017). Model weighting strategies have been \r\nfurther employed since AR5 to reduce the spread in climate projections \r\nfor a given scenario by using weights based on one or more model \r\nperformance metrics (Wenzel et al., 2016; Knutti et al., 2017; Sanderson "


id: e141f79657b062ca495d55473017ab8c
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"This section is structured as follows: first, the scenarios used in AR6 \r\nare introduced and discussed in relation to scenarios used in earlier \r\nIPCC assessments (Section 1.6.1). Cross-Chapter Box 1.4 provides an \r\noverview of the new set of illustrative scenarios and how they are \r\nused in this report. Next, the two additional dimensions of integration \r\nare introduced: global warming levels (Section 1.6.2) and cumulative \r\nCO2 emissions (Section 1.6.3). Net zero emissions are discussed in \r\nBox 1.4. The relation between global warming levels and scenarios is \r\nfurther assessed in Cross-Chapter Box 11.1 in Chapter 11."


id: e4598e588c98e7357bd6fb43eea4fc00
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"A scenario is a description of how the future may develop, based on \r\na coherent and internally consistent set of assumptions about key \r\ndrivers including demography, economic processes, technological \r\ninnovation, governance, lifestyles, and relationships among these \r\ndriving forces (Section 1.6.1.1; IPCC, 2000; Rounsevell and Metzger, \r\n2010; O'Neill et al., 2014). Scenarios can also be defined by \r\ngeophysical driving forces only, such as emissions or abundances of \r\nGHGs, aerosols, and aerosol precursors or land-use patterns. Scenarios \r\nare not predictions; instead, they provide a 'what-if' investigation of \r\nthe implications of various developments and actions (Moss et al., \r\n2010). WGI investigates potential future climate change principally \r\nby assessing climate model simulations using emissions scenarios "


id: 9def0da8feb516628b9f949a292c65a3
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"Figure 1.24 | The dimensions of integration across chapters and Working Groups in the IPCC AR6 Assessment. This Report adopts three explicit dimensions of \r\nintegration to integrate knowledge across chapters and Working Groups. The first dimension is scenarios; the second dimension is global mean warming levels relative to pre\u0002industrial levels; and the third dimension is cumulative CO2 emissions. For the scenarios, illustrative 2100 end-points are also indicated (white circles). Further details on data \r\nsources and processing are available in the chapter data table (Table 1.SM.1)."


id: 88a45a043d46ea9f3c47053e7ff84a4f
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"originating from the WGIII community (Section 1.6.1.2). The scenarios \r\nused in this WGI Report cover various hypothetical 'baseline \r\nscenarios' or 'reference futures' that could unfold in the absence of \r\nany - or any additional - climate policies (Glossary). These 'reference \r\nscenarios' originate from a comprehensive analysis of a wide array \r\nof socio-economic drivers, such as population growth, technological \r\ndevelopment, and economic development, and their broad spectrum \r\nof associated energy, land use and emissions implications (Riahi et al., \r\n2017). With direct policy relevance to the Paris Agreement's 1.5degC \r\nand 'well below' 2degC goals, this Report also assesses climate futures \r\nwhere the effects of additional climate change mitigation action are \r\nexplored, i.e., so-called mitigation scenarios (for a broader discussion \r\nof scenarios and futures analysis, see Cross-Chapter Box 1, Table 1 in \r\nSRCCL, IPCC, 2019a)."


id: e86cca4d2f5785556f10ae9db6c05f10
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"economic development only (O'Neill et al., 2014). However, at least in \r\nthe WGI community, the term 'SSP scenario' is now more widely used \r\nto refer directly to future emissions and concentration scenarios that \r\nresult from combining these socio-economic development pathways \r\nwith climate change mitigation assumptions. These are assessed in \r\ndetail in WGIII (AR6 WGIII Chapter 3) and in Cross-Chapter Box 1.4, \r\nTable 1 in this chapter."


id: ecb312189874930c94d776dc800d377c
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"This Report uses a  core set of five illustrative SSP scenarios \r\nto assist  cross-Chapter integration and cross-Working Group \r\napplications: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 \r\n(Cross-Chapter Box  1.4, Table  1). These scenarios span a  wide \r\nrange of plausible societal and climatic futures from potentially \r\nbelow 1.5degC best-estimate warming to over 4degC warming by 2100 \r\n(Figure  1.25). The  set of five SSP scenarios includes those in 'Tier \r\n1' simulations of the CMIP6 ScenarioMIP intercomparison project \r\n(Section  1.5.4; O'Neill et al., 2016) that participating climate \r\nmodelling groups were asked to prioritize (SSP1-2.6, SSP2-4.5, \r\nSSP3-7.0 and SSP5-8.5), plus the low emissions scenario SSP1-1.9. \r\nSSP1-1.9 is used in combination with SSP1-2.6 to explore differential "


id: 32b9f08de7f02381b0da350992c95533
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"For this Report, the main emissions, concentration and land-use \r\nscenarios considered are a  subset of scenarios recently developed \r\nusing the Shared Socio-economic Pathways framework (SSPs; \r\nSection  1.6.1.1 and Cross-Chapter Box  1.4; Riahi et al., 2017). \r\nInitially, the term 'SSP' described five broad narratives of future socio\u0002"


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"Pathways (RCPs) is also used in this Report when assessing future \r\nclimate change (Section 1.6.1.3 and Cross-Chapter Box 1.4, Table 1)."


id: bb351eaa60bad2f93746a6f3a29cbf8c
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"outcomes of approximately 1.5degC and 2.0degC warming relative to \r\npre-industrial levels, relevant to the Paris Agreement goals. Further \r\nSSP scenarios are used in this report to assess specific aspects of, for \r\nexample, air pollution policies in Chapter 6 (Cross-Chapter Box 1.4). \r\nIn addition, the previous generation of Representative Concentration "


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"models, conditional on the various SSP scenarios. The projected future \r\nchanges can then be put into the context of longer-term paleoclimate \r\ndata and historical observations, showing how the higher emissions \r\nand higher concentration scenarios diverge further from the range of \r\nclimate conditions that ecosystems and human societies experienced \r\nin the past 2000  years in terms of global mean temperature and \r\nother key climate variables (Figures 1.26 and 1.5)."


id: 7b65bc1309e57a7d764c90fb30e056f1
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"(GDP) and urbanization (Dellink et al., 2017; Jiang and O'Neill, 2017; \r\nSamir and Lutz, 2017). By design, the SSPs differ in terms of the socio\u0002economic challenges they present for climate change mitigation and \r\nadaptation (Rothman et al., 2014; Schweizer and O'Neill, 2014) and \r\nthe evolution of these drivers within each SSP reflects this design. \r\nBroadly, the five SSPs represent 'sustainability' (SSP1), a 'middle-of\u0002the-road' path (SSP2), 'regional rivalry' (SSP3), 'inequality' (SSP4), \r\nand 'fossil fuel-intensive' development (SSP5; Cross-Chapter Box 1.4, \r\nFigure 1; O'Neill et al., 2017a). More specific information on the SSP \r\nframework and the assumptions underlying the SSPs will be provided \r\nin the IPCC WGIII report (WGIII Chapter  3; see also Box SPM.1 in \r\nSRCCL (IPCC, 2019d))."


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"While scenarios are a key tool for integration across IPCC Working \r\nGroups, they also allow the integration of knowledge among \r\nscientific communities and across time scales. For example, \r\nagricultural yield, infrastructure and human health impacts of \r\nincreased drought frequency, extreme rainfall events and hurricanes \r\nare often examined in isolation. New insights on climate impacts in \r\nWGII can be gained if compound effects of multiple cross-sectoral \r\nimpacts are considered across multiple research communities under \r\nconsistent scenario frameworks (Section 11.8; Leonard et al., 2014; \r\nWarszawski et al., 2014). Similarly, a synthesis of WGI knowledge \r\non sea level rise contributions is enabled by a consistent application \r\nof future scenarios across all specialized research communities, \r\nsuch as ice-sheet mass balance analyses, glacier loss projections \r\nand thermosteric change from ocean heat uptake (Chapter  9; \r\ne.g. Kopp et al., 2014)."


id: 994b6f59dab2d8959ddc91f69f05db7d
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"The SSP narratives and drivers were used to develop scenarios of \r\nenergy use, air pollution control, land use, and GHG emissions \r\ndevelopments using integrated assessment models (IAMs; Riahi et al., \r\n2017; Rogelj et al., 2018a). An IAM can derive multiple emissions \r\nfutures for each socio-economic development pathway, assuming \r\nno new mitigation policies or various levels of additional mitigation \r\naction (in the case of reference scenarios and mitigation scenarios, \r\nrespectively; Riahi et al., 2017). By design, the evolution of drivers \r\nand emissions within the SSP scenarios do not take into account the \r\neffects of climate change."


id: 74838c09021e15ac8535c56bc98af39a
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"In addition to the comprehensive SSP scenario set and the RCPs, \r\nmultiple idealized scenarios and time-slice experiments using \r\nclimate models are assessed in this Report. Idealized scenarios refer \r\nto experiments where, for example, CO2 concentrations are increased \r\nby 1% per year, or instantly quadrupled. Such idealized experiments \r\nhave been extensively used in previous model intercomparison \r\nprojects and constitute the core 'DECK' set of model experiments of \r\nCMIP6 (Section 1.5.4). They are, for example, used to diagnose the \r\npatterns of climate feedbacks across the suite of models assessed in \r\nthis Report (Chapter 7)."


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"The SSPX-Y scenarios and the RCP scenarios are categorized similarly, \r\nby reference to the approximate radiative forcing levels each one \r\nentails at the end of the 21st century. For example, the '1.9' in the \r\nSSP1-1.9 scenario stands for an approximate radiative forcing level of \r\n1.9 W m-2 in 2100. The first number (X) in the 'SSPX-Y' acronym refers \r\nto one of the five shared socio-economic development pathways \r\n(Cross-Chapter Box 1.4, Figure 1 and Table 1.4)."


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"This SSP scenario categorization, focused on end-of-century radiative \r\nforcing levels, reflects how scenarios were conceptualized until \r\nrecently, namely, to reach a particular climate target in 2100 at the \r\nlowest cost and irrespective of whether the target was exceeded over \r\nthe century. More recently, and in particular since IPCC SR1.5 report \r\nfocused attention on peak warming scenarios (Rogelj et al., 2018b), \r\nscenario development started to explicitly consider peak warming, \r\ncumulative emissions and the amount of net negative emissions \r\n(Rogelj et al., 2018b; Fujimori et al., 2019)."


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"In the following section, we further introduce the SSP scenarios and \r\nhow they relate to the Shared Socio-economic Pathways framework \r\n(Section  1.6.1.1); describe the scenario generation process \r\n(Section 1.6.1.2); and provide a historical review of scenarios used in \r\nIPCC assessment reports (Section 1.6.1.3); before briefly discussing \r\nquestions of scenario likelihood, scenario uncertainty and the use of \r\nscenario storylines (Section 1.6.1.4)."


id: df6c9d8f326ce0097faf360c4c72c21b
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"The Shared Socio-economic Pathways SSP1 to SSP5 describe a range \r\nof plausible trends in the evolution of society over the 21st century. \r\nThey were developed in order to connect a wide range of research \r\ncommunities (Nakicenovic et al., 2014) and consist of two main \r\nelements: a set of qualitative, narrative storylines describing societal \r\nfutures (O'Neill et al., 2017a) and a  set of quantified measures \r\nof development at aggregated and/or spatially resolved scales. \r\nEach pathway is an internally consistent, plausible and integrated \r\ndescription of a  socio-economic future, but these socio-economic \r\nfutures do not account for the effects of climate change, and no new \r\nclimate policies are assumed. The SSPs' quantitative projections of \r\nsocio-economic drivers include population, gross domestic product "


id: 36dcf0a93187b5ec46a786ab3eca44d0
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"may differ, and tends to be higher for the SSPs compared to RCPs \r\nthat share the same nominal stratospheric-temperature-adjusted \r\nradiative forcing label. The stratospheric-temperature-adjusted \r\nradiative forcings of the SSPs and RCPs, however, remain relatively \r\nclose, at least by 2100 (Tebaldi et al., 2021). In summary, differences \r\nin, for example, CMIP5 RCP8.5 and CMIP6 SSP5-8.5 ESM outputs, are \r\npartially due to different scenario characteristics rather than different \r\nESM characteristics only (Section 4.6.2)."


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"A key advance of the SSP scenarios relative to the RCPs is a wider \r\nspan of assumptions on future air-quality mitigation measures, and \r\nhence emissions of short-lived climate forcers (SLCFs; Rao et al., \r\n2017; Lund et al., 2020). This allows for a more detailed investigation \r\ninto the relative roles of GHG and SLCF emissions in future global \r\nand regional climate change, and hence the implications of policy \r\nchoices. For instance, SSP1-2.6 builds on an assumption of stringent \r\nair-quality mitigation policy, leading to rapid reductions in particle \r\nemissions, while SSP3-7.0 assumes slow improvements, with \r\npollutant emissions over the 21st  century comparable to current \r\nlevels (Figure 6.19 and Cross-Chapter Box 1.4, Figure 2)."


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"When investigating various mitigation futures, WGIII goes beyond \r\nthe core set of SSP scenarios assessed in WGI (SSP1-1.9, SSP1-2.6, \r\netc.) to consider the characteristics of more than 1000 scenarios \r\n(Cross-Chapter Box  7.1). In addition, while staying within the \r\nframework of socio-economic development pathways (SSP1 to \r\nSSP5), WGIII also considers various mitigation possibilities through \r\nso-called illustrative pathways (IPs). These illustrative pathways \r\nhelp to highlight key narratives in the literature concerning various \r\ntechnological, social and behavioural options for mitigation, various \r\ntimings for implementation, or varying emphasis on different GHG \r\nand land-use options. Just as with the SSPX-Y scenarios considered \r\nin this Report, these illustrative pathways can be placed in relation to \r\nthe matrix of SSP families and approximate radiative forcing levels in \r\n2100 (Cross-Chapter Box 1.4, Figure 1; IPCC WGIII, Chapter 3)."


id: 5240cf4d630840dde10887b4f45db701
created_at: 2023-03-16 14:00:38
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content:
"One limitation of the SSP scenarios used for CMIP6 and in this Report \r\nis that they reduce emissions from all the major ozone-depleting \r\nsubstances controlled under the Montreal Protocol (CFCs, halons, \r\nand hydrochlorofluorocarbons (HCFCs)) uniformly, rather than \r\nrepresenting a  fuller range of possible high- and low-emissions \r\nfutures (UNEP, 2016). Hydrofluorocarbon (HFC) emissions, on the \r\nother hand, span a wider range within the SSPs than in the RCPs \r\n(Cross-Chapter Box 1.4, Figure 2)."


id: 2bfedc928b05bf86e62bd8f6a254fdd1
created_at: 2023-03-16 14:00:38
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content:
"No likelihood is attached to the scenarios assessed in this report, \r\nand the feasibility of specific scenarios in relation to current trends \r\nis best informed by the WGIII contribution to AR6. In the scenario \r\nliterature, the plausibility of the high emissions levels underlying \r\nscenarios such as RCP8.5 or SSP5-8.5 has been debated in light of \r\nrecent developments in the energy sector (Section 1.6.1.4)."


id: a477b15be90e1e36cc2f663fed78f822
created_at: 2023-03-16 14:00:38
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vector_id: 23443
content:
"The SSP scenarios and previous RCP scenarios are not directly \r\ncomparable. First, the gas-to-gas compositions differ; for example, \r\nthe SSP5-8.5 scenario has higher CO2 concentrations but lower CH4\r\nconcentrations compared to RCP8.5. Second, the projected 21st\u0002century trajectories may differ, even if they result in the same radiative \r\nforcing by 2100. Third, the overall effective radiative forcing (Chapter 7) "


id: 4ceafb5a8d0ef771afe9e05f443b1932
created_at: 2023-03-16 14:00:38
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content:
"Contributing Authors: Jan S. Fuglestvedt (Norway), Celine Guivarch (France), Christopher Jones (United Kingdom), Malte \r\nMeinshausen (Australia/Germany), Zebedee R. J. Nicholls (Australia), Gian-Kasper Plattner (Switzerland), Keywan Riahi (Austria), Joeri \r\nRogelj (United Kingdom/Belgium), Sophie Szopa (France), Claudia Tebaldi (United States of America), Anne-Marie Treguier (France), \r\nand Detlef van Vuuren (The Netherlands)"


id: c43a3c90e636e7f670826d3abe638f04
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 28439
content:
"The nine new SSP emissions and concentrations scenarios (SSP1-1.9 to SSP5-8.5; Cross-Chapter Box 1.4, Table 1) offer unprecedented \r\ndetail of input data for climate model simulations. They allow for a more comprehensive assessment of climate drivers and responses \r\nthan has previously been available, in particular because some of the scenarios' time series, (e.g., pollutants, emissions or changes \r\nin land use and land cover), are more diverse in the SSP scenarios than in the RCPs used in AR5 (Cross-Chapter Box 1.4, Figure 2; \r\ne.g., Chuwah et al., 2013)."


id: 32e100b607a7d4e819251c06f7abc933
created_at: 2023-03-16 14:00:38
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vector_id: 5424
content:
"a\r\n The AerChemMIP variant of SSP3-7.0-lowNTCF (Collins et al., 2017) only reduced aerosol and ozone precursors compared to SSP3-7.0, not methane. \r\nThe SSP3-7.0-lowNTCF variant by the integrated assessment models also reduced methane emissions (Gidden et al., 2019), which creates differences between \r\nSSP3-7.0-lowNTCF and SSP3-7.0 also in terms of methane concentrations and some fluorinated gas concentrations that have OH related sinks (Meinshausen et al., 2020)."


id: 9b172ce8b829137b953e337cc64ca376
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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content:
"Cross-Chapter Box 1.4, Table 2 | Overview of key climate forcer datasets used as input by ESMs for historical and future SSP scenario experiments.\r\nThe data is available from the Earth System Grid Federation (ESGF, 2021) described in Eyring et al. (2016)."


id: 69fbb9695fe7d6da99ace13db1affa59
created_at: 2023-03-16 14:00:38
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content:
"In contrast to stylized assumptions about the future evolution of emissions (e.g., a linear phase-out from year A to year B), these \r\nSSP scenarios are the result of a detailed scenario generation process (Sections 1.6.1.1 and 1.6.1.2). While IAMs produce internally \r\nconsistent future-emissions time series for CO2, CH4, N2O, and aerosols for the SSP scenarios (Riahi et al., 2017; Rogelj et al., 2018a), \r\nthese emissions scenarios are subject to several processing steps for harmonization (Gidden et al., 2018) and in-filling (Lamboll et al., \r\n2020), before also being complemented by several datasets so that ESMs can run these SSPs (Durack et al., 2018; Tebaldi et al., 2021). \r\nAlthough five scenarios are the primary focus of WGI, a total of nine SSP scenarios have been prepared with all the necessary detail \r\nto drive the ESMs as part of the CMIP6 (Cross-Chapter Box 1.4, Figure 1 and Table 2)."


id: 835aaa109d92c905b72e5c18bfac9eeb
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content:
"The so-called 'emissions-driven' experiments (Jones et al., 2016) use the same input datasets as concentration-driven ESM experiments, \r\nexcept that they use CO2 emissions rather than concentrations (Chapter 5 and Section 4.3.1). In these experiments, atmospheric CO2\r\nconcentrations are calculated internally using the ESM interactive carbon cycle module and thus differ from the prescribed default CO2\r\nconcentrations used in the concentration-driven runs. In the particular case of SSP5-8.5, the emissions-driven runs are assessed to add \r\nno significant additional uncertainty to future global surface air temperature (GSAT) projections (Section 4.3.1). However, generally, \r\nwhen assessing uncertainties in future climate projections, it is important to consider which elements of the cause-effect chain, \r\nfrom emissions to the resulting climate change, are interactively included as part of the model projections, and which are externally \r\nprescribed using default settings."


id: 9abc2f977326b22e81878b7da58745b8
created_at: 2023-03-16 14:00:38
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content:
"ss-Chapter Box  1.4, Figure  2 | Comparison between the Shared Socio-economic Pathways (SSP) scenarios and the Representative \r\ncentration Pathway (RCP) scenarios in terms of their CO2, CH4 and N2O atmospheric concentrations (a-c), and their global emissions of CO2, \r\n N2O, black carbon (BC), organic carbon (OC), sulphur dioxide (SO2), ammonia (NH3), nitrogen oxides (NOx), volatile organic compounds \r\nC), sulphur hexafluoride (SF6), perfluorocarbons (PFCs), and hydrofluorocarbons (HFCs) (d-o). "


id: ca36f13bf559d8fa963466e380673a11
created_at: 2023-03-16 14:00:38
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content:
"Cross-Chapter Box 1.4, Figure 2 (continued): Also shown are gridded emissions differences for SO2 (p) and black carbon (q) for the year 2000 between the \r\ninput emissions datasets that underpinned the CMIP5 and CMIP6 model intercomparisons. Historical emissions estimates are provided in black in panels (d-o). The \r\nrange of concentrations and emissions investigated under the RCP pathways is shaded grey. Panels (p) and (q) adapted from Figure 7 in Hoesly et al. (2018). Further \r\ndetails on data sources and processing are available in the chapter data table (Table 1.SM.1)."


id: 61826b33d56e55735e04a081ab016da9
created_at: 2023-03-16 14:00:38
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vector_id: 12912
content:
"series (documented for CMIP6 through input4mips; Cross-Chapter \r\nBox 1.4, Table 2; Durack et al., 2018)."


id: a526a30d2869f564c53592d9b2fd8d8d
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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content:
"The scenario generation process involves research communities \r\nlinked to all three IPCC Working Groups (Figure 1.27). It generally \r\nstarts in the scientific communities associated with WGII and WGIII \r\nwith the definition of new socio-economic scenario storylines (IPCC, \r\n2000; O'Neill et al., 2014) that are quantified in terms of their \r\ndrivers  - i.e.,  GDP, population, technology, energy and land use  - \r\nand their resulting emissions (Riahi et al., 2017). Then, numerous \r\ncomplementation and harmonization steps are necessary for datasets \r\nwithin the WGI and WGIII science communities, including gridding \r\nemissions of anthropogenic short-lived forcers, providing open \r\nbiomass-burning emissions estimates, preparing land-use patterns, \r\naerosol fields, stratospheric and tropospheric ozone, nitrogen \r\ndeposition datasets, solar irradiance and aerosol optical property \r\nestimates, and observed and projected GHG concentration time "


id: d1ab6b76be096af8dc4101ba66ddd6ce
created_at: 2023-03-16 14:00:38
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content:
"Once these datasets are completed, ESMs are run in coordinated \r\nmodel intercomparison projects in the WGI science community, \r\nusing standardized simulation protocols and scenario data. The most \r\nrecent example of such a coordinated effort is the CMIP6 exercise \r\n(Section  1.5.4; Eyring et al., 2016) with, in particular, ScenarioMIP \r\n(O'Neill et al., 2016). The WGI science community feeds back climate \r\ninformation to WGIII via climate emulators (Cross-Chapter Box 7.1) \r\nthat are updated and calibrated with the ESMs' temperature \r\nresponses and other lines of evidence. Next, this climate information \r\nis used to compute several high-level global climate indicators \r\n(e.g., atmospheric concentrations, global temperatures) for a much \r\nwider set of hundreds of scenarios that are assessed as part of the \r\nIPCC WGIII Assessment (WGIII Annex C). The outcomes from climate \r\nmodels run under the different scenarios are then used to calculate "


id: 1a527f4af6c896522b196f17086718d2
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content:
"Figure  1.28 |  Comparison of the range of fossil fuel and industrial CO\r\n2\r\nemissions from scenarios used in previous assessments up to AR6. Previous \r\nassessments are the IS92 scenarios from 1992 (top), the Special Report on Emissions \r\nScenarios (SRES) scenarios from the year 2000 (second panel), the Representative \r\nConcentration Pathway (RCP) scenarios designed around 2010 (third panel) and \r\nthe Shared Socio-economic Pathways (SSP) scenarios (fourth panel). In addition, \r\nhistorical emissions are shown (black line; Figure  5.5); a  more complete set of \r\nscenarios is assessed in SR1.5 (bottom); (Huppmann et al., 2018). Further details on \r\ndata sources and processing are available in the chapter data table (Table 1.SM.1)."


id: ee41eb9efa22e92d8ff77aed4d736eef
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vector_id: 34955
content:
"the evolution of climatic impact-drivers (Chapter  12), and utilized \r\nby impact researchers together with exposure and vulnerability \r\ninformation, in order to characterize risk to human and natural \r\nsystems from future climate change. The climate impacts associated \r\nwith these scenarios or different warming levels are then assessed as \r\npart of WGII reports (Figure 1.27)."


id: 1d3869e0b2e8794420ba0f26f993d93c
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content:
"Scenario modelling experiments have been a  core element of \r\nphysical climate science since the first transient simulations with \r\na general circulation model in 1988 (Section 1.3; Hansen et al., 1988). \r\nScenarios and modelling experiments assessed in IPCC reports have \r\nevolved over time, which provides a 'history of how the future was \r\nseen'. The starting time for the scenarios moves as actual emissions \r\nsupersede earlier emissions assumptions, while new scientific insights \r\ninto the range of plausible population trends, behavioural changes \r\nand technology options and other key socio-economic drivers of \r\nemissions also emerge (see WGIII; Leggett et al., 1992; IPCC, 2000; \r\nMoss et al., 2010; Riahi et al., 2017). Many different sets of climate \r\nprojections have been produced over the past several decades, using \r\ndifferent sets of scenarios. Here, we compare those earlier scenarios \r\nagainst the most recent ones."


id: 23b94b3c806aa6309915735408b864b8
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content:
"Climate science research involving scenarios necessarily follows \r\na  series of consecutive steps (Figure  1.27). As each step waits for \r\ninput from the preceding one, delays often occur that result in the \r\nimpact literature basing its analyses on earlier scenarios than those \r\nmost current in the climate change mitigation and climate system \r\nliterature. It is therefore important to provide an approximate \r\ncomparison across the various scenario generations (Chapter  4, \r\nFigure 1.28, and Cross-Chapter Box 1.4, Table 1)."


id: f51e240822bbbfd0fc213a35e7178424
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content:
"The first widely used set of IPCC emissions scenarios was the \r\nIS92 scenarios in 1992 (Leggett et al., 1992). Apart from reference \r\nscenarios, IS92 also included a  set of stabilization scenarios, the \r\nso-called 'S' scenarios. Those 'S' pathways were designed to lead \r\nto CO\r\n2 stabilization levels such as 350 ppm or 450 ppm. By 1996, \r\nthose latter stabilization levels were complemented in the scientific \r\nliterature by alternative trajectories that assumed a delayed onset of \r\nclimate change mitigation action (Figure 1.28; Wigley et al., 1996)."


id: 1306cbee069d0b5c1c52fe35434135ab
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content:
"By 2000, the IPCC Special Report on Emissions Scenarios (SRES) \r\nproduced the SRES scenarios (IPCC, 2000), albeit without assuming \r\nany climate policy-induced mitigation. The four broad groups of \r\nSRES scenarios (scenario 'families') - A1, A2, B1 and B2 - were the \r\nfirst scenarios to emphasize socio-economic scenario storylines, and \r\nalso first to emphasize other GHGs, land-use change and aerosols. "


id: b3170c26781430df7af185f536a1ea09
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content:
"Represented by three scenarios for the high-growth A1 scenario \r\nfamily, those 6 SRES scenarios (A1FI, A1B, A1T, A2, B1, and B2) can \r\nstill sometimes be found in today's climate impact literature. The void \r\nof missing climate change mitigation scenarios was filled by a range \r\nof community exercises, including the so-called 'post-SRES scenarios' \r\n(Swart et al., 2002)."


id: 5e148dafaf1f576c0c4f7d3e31763b14
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content:
"low aerosol trajectories across all scenarios (Cross-Chapter Box 1.4, \r\nFigure 2). More generally, the SSP scenarios feature a later peak of \r\nglobal emissions for the lower scenarios, simply as a consequence of \r\nhistorical emissions not having followed the trajectory projected by \r\nprevious low scenarios (Figure 1.28)."


id: 157e90822a64ae3a057c46413826f853
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updated_at: 2023-03-16 14:00:38
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content:
"Over the last decades, discussions around scenarios have often \r\nfocussed on whether recent trends make certain future scenarios \r\nmore or less probable or whether all scenarios are too high or too \r\nlow. When the SRES scenarios first appeared, the debate was often \r\nwhether the scenarios were overestimating actual world emissions \r\ndevelopments (e.g., Castles and Henderson, 2003). With the strong \r\nemissions increase throughout the 2000s, that debate then shifted \r\ntowards the question of whether the lower future climate change \r\nmitigation scenarios were rendered unfeasible (Pielke et al., 2008; \r\nvan Vuuren and Riahi, 2008). Historical emissions between 2000 and \r\n2010 approximately track the upper half of SRES and RCP projections \r\n(Figure 1.28). More generally, the global fossil fuel and industrial CO2\r\nemissions of recent decades tracked approximately the middle of \r\nthe projected scenario ranges (Figure 1.28), although with regional \r\ndifferences (Pedersen et al., 2020)."


id: 39bf46785d60adb4b6285fb125b71fe4
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content:
"By design, the RCP emissions and concentrations pathways were \r\noriginally developed using particular socio-economic development \r\npathways, but those are no longer considered (Moss et al., 2010). \r\nThe different levels of emissions and climate change represented in \r\nthe RCPs can hence be explored against the backdrop of different \r\nsocio-economic development pathways (SSP1 to SSP5; Section 1.6.1.1 \r\nand Cross-Chapter Box  1.4). This integrative SSP-RCP framework \r\n('SSPX-RCPY' in Table 1.4) is now widely used in the climate impact \r\nand policy analysis literature (e.g., ICONICS, 2021; Green et al., 2020; \r\nO'Neill et al., 2020), where climate projections obtained under the \r\nRCP scenarios are analysed against the backdrop of various SSPs. \r\nConsidering various levels of future emissions and climate change for \r\neach socio-economic development pathway was an evolution from \r\nthe previous SRES framework (IPCC, 2000), in which socio-economic \r\nand emissions futures were closely aligned."


id: 185075bfb0aeddd9dfc623f654ce5773
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content:
"The new set of scenarios (SSP1-1.9 to SSP5-8.5) now features \r\na higher top level of CO2 emissions (SSP5-8.5 compared to RCP8.5), \r\nalthough the most significant change is again the addition of a very \r\nlow climate change mitigation scenario (SSP1-1.9, compared to the \r\nprevious low scenario, RCP2.6). Also, historically, none of the previous \r\nscenario sets featured a  scenario that involves a  very pronounced \r\npeak-and-decline emissions trajectory, but SSP1-1.9 does so now. \r\nThe full set of nine SSP scenarios now includes a  high-aerosol\u0002emissions scenario (SSP3-7.0). The RCPs featured more uniformly "


id: 7d27fe7da09b0bc574bdf7b88023f530
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content:
"In this Report, the term 'global warming level' refers to the \r\ncategorization of global and regional climate change, associated \r\nimpacts, emissions and concentrations scenarios by GMST relative \r\nto 1850-1900, which is the period used as a proxy for pre-industrial \r\nlevels (Cross-Chapter Box 11.1). By default, GWLs are expressed in \r\nterms of global surface air temperature (GSAT; Section  1.4.1 and \r\nCross-Chapter Box 2.3)."


id: 32894926d9ff9dc3d4383b7779328d68
created_at: 2023-03-16 14:00:38
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content:
"As SR1.5 concluded, even half-degree global mean temperature \r\nsteps carry robust differences in climate impacts (Chapter 11; SR1.5, \r\nIPCC, 2018; Schleussner et al., 2016a; Wartenburger et al., 2017). This \r\nReport adopts half-degree warming levels, which allows integration \r\nfor climate projections, impacts, adaptation challenges and mitigation \r\nchallenges within and across the three WGs. The core set of GWLs - \r\n1.5degC, 2.0degC, 3.0degC and 4.0degC - are highlighted (Chapters 4, 8, 11, \r\n12 and Atlas). Given that much impact analysis is based on previous \r\nscenarios, (i.e., RCPs or SRES), and climate change mitigation analysis \r\nis based on new emissions scenarios in addition to the main SSP \r\nscenarios, these GWLs assist in the comparison of climate states \r\nacross scenarios and in the synthesis across the broader literature."


id: b3e27bb95301acd721c617c04668850b
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content:
"The transient and equilibrium states of certain global warming levels \r\ncan differ in their climate impacts (IPCC, 2018; King et al., 2020). \r\nClimate impacts in a 'transient' world relate to a scenario in which \r\nthe world is continuing to warm. On the other hand, climate impacts \r\nat the same warming levels can also be estimated from equilibrium \r\nstates after a  (relatively) short-term stabilization by the end of \r\nthe 21st  century or at a  (near-)equilibrium state after a  long-term \r\n(multi-decadal to multi-millennial) stabilization. Different methods to \r\nestimate these climate states come with challenges and limitations \r\n(Section  4.6.1 and Cross-Chapter Box  11.1). First,  information \r\ncan be drawn from GCM or ESM simulations that 'pass through' \r\nthe respective warming levels (as used and demonstrated in the \r\nInteractive Atlas), also called 'epoch' or 'time-shift' approaches \r\n(Sections  4.2.4 and 4.6.1; Herger et al., 2015; James et al., 2017; "


id: 454107f4cf0c0d266a3b3cd9b6f06a34
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content:
"Tebaldi and Knutti, 2018). Information from transient simulations can \r\nalso be used through an empirical scaling relationship (Seneviratne \r\net al., 2016, 2018; Wartenburger et al., 2017) or using 'time sampling' \r\napproaches, as described in James et al. (2017). Second, information \r\ncan be drawn from large ESM ensembles with prescribed SST at \r\nparticular global warming levels (Mitchell et al., 2017), although an \r\nunderrepresentation of variability can arise when using prescribed \r\nSST temperatures (E.M. Fischer et al., 2018)."


id: f8a5bca32040c564452767ec1857425c
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
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content:
"The AR5 WGI (IPCC, 2013a) and SR1.5 (IPCC, 2018) highlighted \r\nthe near-linear relationship between cumulative carbon emissions \r\nand global mean warming (Sections 1.3 and 5.5). This implies that \r\ncontinued CO2 emissions will cause further warming and changes \r\nin all components of the climate system, independent of any \r\nspecific scenario or pathway. This is captured in the TCRE concept, \r\nwhich relates CO2-induced global mean warming to cumulative \r\ncarbon emissions (Chapter 5). This Report thus uses cumulative CO2\r\nemissions to compare the climate response across scenarios, and \r\nto categorize emissions scenarios (Figure  1.29). The advantage of \r\nusing cumulative CO2 emissions is that it is an inherent emissions \r\nscenario characteristic rather than an outcome of the scenario\u0002based projections, where uncertainties in the cause-effect chain - \r\nfrom emissions to atmospheric concentrations to temperature \r\nchange - are important."


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"Also, forcing or response patterns that vary in time can create \r\ndifferences in regional climates for the same global mean warming \r\nlevel, or can create non-linearities when scaling patterns from one \r\nwarming level to another (King et al., 2018), depending on whether \r\nnear-term transient climate, end of the century, equilibrium climate \r\nor climate states after an initial overshoot are considered."


id: 6da699c6b5b142cd131539268cdac730
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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content:
"In spite of these challenges, and thanks to recent methodological \r\nadvances in quantifying or overcoming them, global warming \r\nlevels provide a  robust and useful integration mechanism. They \r\nallow knowledge from various domains within WGI and across \r\nthe three WGs to be integrated and communicated (Cross-Chapter \r\nBox 11.1). In this report, Chapters 4, 8, 11, 12 and the Atlas provide \r\ninformation specific to certain warming levels, highlighting the \r\nregional differences, but also the approximate scalability of regional \r\nclimate change, that can arise from even a 0.5degC shift in global mean \r\ntemperatures. Furthermore, building on WGI insights into physical \r\nclimate system responses (Cross-Chapter Box  7.1), WGIII will use \r\npeak and end-of-century global warming levels to classify a broad \r\nset of scenarios."


id: ec6ed4ee6159f147c7394dbc5a281a88
created_at: 2023-03-16 14:00:38
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content:
"Box 1.4 | The Relationships Between 'Net Zero' Emissions, Temperature Outcomes \r\nand Carbon Dioxide Removal"


id: 768a911b3437c666e070f43ec3aaac7a
created_at: 2023-03-16 14:00:38
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"Article 4 of the Paris Agreement sets an objective to 'achieve a balance between anthropogenic emissions by sources and removals by \r\nsinks of greenhouse gases' (Section 1.2). This box addresses the relationship between such a balance and the corresponding evolution \r\nof global surface temperature, with or without the deployment of large-scale carbon dioxide removal (CDR), using the definitions of \r\n'net zero CO2 emissions' and 'net zero greenhouse gas (GHG) emissions' of the AR6 Glossary (Annex VII)."


id: 599220293f5ae5e81bf948074c5b82b0
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"'Net zero CO2 emissions' is defined in AR6 as the condition in which anthropogenic CO2 emissions are balanced by anthropogenic CO2\r\nremovals over a specified period. Similarly, 'net zero GHG emissions' is the condition in which metric-weighted anthropogenic GHG\r\nemissions are balanced by metric-weighted anthropogenic GHG removals over a  specified period. The quantification of net zero \r\nGHG emissions thus depends on the GHG emissions metric chosen to compare emissions of different gases, as well as the time horizon \r\nchosen for that metric. (For a broader discussion of metrics, see Box 1.3 and Section 7.6, and WGIII Cross-Chapter Box 2.)"


id: c00f0028eccbc7d86c7bf529ff11bc3e
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"Technical notes expanding on these definitions can be found as part of their respective entries in the Glossary. The notes clarify the \r\nrelation between 'net zero' CO2 and GHG emissions and the concept of carbon and GHG neutrality, and the metric usage set out in \r\nthe Paris Rulebook [Decision 18/CMA.1, annex, paragraph 37]."


id: 17a86f9a593a8ca46b740ed23bf63ad4
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content:
"A global net zero level of CO2, or GHG, emissions will be achieved when the sum of anthropogenic emissions and removals across all \r\ncountries, sectors, sources and sinks reaches zero. Achieving net zero CO2 or GHG emissions globally, at a given time, does not imply \r\nthat individual entities (i.e., countries, sectors) have to reach net zero emissions at that same point in time, or even at all (see WGIII, \r\nTS Box 4 and Chapter 3)."


id: e8836d33ae97a3d21cbbc29d1822ffc0
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content:
"Net zero CO2 and net zero GHG emissions differ in their implications for the subsequent evolution of global surface temperature. Net \r\nzero CO2 emissions result in approximately stable CO2-induced warming, but overall warming will depend on any further warming \r\ncontribution of non-CO2 GHGs. The effect of net zero GHG emissions on global surface temperature depends on the GHG emissions \r\nmetric chosen to aggregate emissions and removals of different gases. For GWP100 (the metric in which Parties to the Paris Agreement \r\nhave decided to report their aggregated emissions and removals), net zero GHG emissions would generally imply a peak in global \r\nsurface temperature, followed by a gradual decline (Section 7.6.2; see also Section 4.7.1 regarding the zero emissions commitment). \r\nHowever, other anthropogenic factors, such as aerosol emissions or land use-induced changes in albedo, may still affect the climate."


id: 13e2478b0633acddebe25d01e4474c57
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"The definitions of net zero CO2 and GHG should also be seen in relation to the various CDR methods discussed in the context of \r\nclimate change mitigation (see Section 5.6, which also includes an assessment of the response of natural sinks to CDR), and how it is \r\nemployed in scenarios used throughout the WGI and WGIII reports (Section 1.6.1; see also WGIII Chapters 3, 7 and 12.)"


id: 814ee93c9492208135dcfe1a65849742
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content:
"For virtually all scenarios assessed by the IPCC, CDR is necessary to reach both global net zero CO2 and net zero GHG emissions, to \r\ncompensate for residual anthropogenic emissions. This is in part because for some sources of CO2 and non-CO2 emissions, abatement \r\noptions to eliminate them have not yet been identified. For a given scenario, the choice of GHG metric determines how much net CDR \r\nis necessary to compensate for residual non-CO2 emissions, in order to reach net zero GHG emissions (Section 7.6.2)."


id: 29f9925d2d0acd1ef6fd4b57465af5f5
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content:
"If CDR is further used to go beyond net zero, to a situation with net-negative CO2 emissions (i.e., where anthropogenic removals \r\nexceed anthropogenic emissions), anthropogenic CO2-induced warming will decline. A further increase of CDR, until a situation with \r\nnet zero or even net-negative GHG emissions is reached, would increase the pace at which historical human-induced warming is \r\nreversed after its peak (SR1.5, IPCC, 2018). Net negative anthropogenic GHG emissions may become necessary to stabilize the global \r\nsurface temperature in the long term, should climate feedbacks further affect natural GHG sinks and sources (Chapter 5)."


id: f8e4d4e3fa960b4365f368ac6b691394
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content:
"the 21st century (as in the case of sea level rise), are nevertheless \r\nrelevant for decision-making."


id: a3e923bc26421bb229d9c53fda94b657
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 23349
content:
"The assessment in this Report is based on a rapidly growing body \r\nof new evidence from the peer-reviewed literature. Recently, \r\nscientific climate change research has doubled in output every \r\n5-6 years; the majority of publications deal with issues related to \r\nthe physical climate system (Burkett et al., 2014; Haunschild et al., \r\n2016). The sheer volume of published, peer-reviewed literature on \r\nclimate change presents a challenge to comprehensive, robust and \r\ntransparent assessment."


id: 96cc29d04bbf88b8de8e5b055f85721e
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vector_id: 21241
content:
"At the time of publication, the consequences of the COVID-19 \r\npandemic on emissions, atmospheric abundances, radiative forcing \r\nand the climate (Cross-Chapter Box  6.1), and on observations \r\n(Section  1.5.1), are not yet fully evident. Their assessment in this \r\nReport is thus limited."


id: ac1f6d2294ee3f12aca412a813094466
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content:
"The enhanced focus on regional climate in AR6 WGI further \r\nexpands the volume of literature relative to AR5, including non\u0002English language publications sometimes presented as reports \r\n('grey' literature), particularly on topics such as regional observing \r\nnetworks and climate services. These factors enhance the challenge \r\nof discovering, accessing and assessing the relevant literature. \r\nThe international, multilingual author teams of IPCC AR6, combined \r\nwith the open expert-review process, help to minimize these concerns, \r\nbut they remain a challenge."


id: 20c64f2a61fb0d63352b5dd400f91aa1
created_at: 2023-03-16 14:00:38
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vector_id: 2594
content:
"Despite the key role of CMIP6 in this Report (Section 1.5), the number \r\nof studies evaluating its results and modelling systems remains \r\nrelatively limited. At the time of publication, additional model \r\nresults are still becoming available. This reflects the need for close \r\ntemporal alignment of the CMIP cycle with the IPCC assessment \r\nprocess, and the growing complexity of coordinated international \r\nmodelling efforts."


id: d9562cd3bc505f28b69f301c764e2160
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updated_at: 2023-03-16 14:00:38
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vector_id: 31705
content:
"Indigenous and local knowledge includes information about past \r\nand present climate states. However, assessing this knowledge, and \r\nintegrating it with the scientific literature, remains a challenge to be \r\nmet. This lack of assessment capability and integration leads to most \r\nWGI chapters still not including indigenous and local knowledge in \r\ntheir assessment findings."


id: d24736e92c656086562e25e20e9290c0
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content:
"Spatial and temporal gaps in both historical and current observing \r\nnetworks, and the limited extent of paleoclimatic archives, have \r\nalways posed a challenge for IPCC assessments. A relative paucity of \r\nlong-term observations is particularly evident in Antarctica and in the \r\ndepths of the ocean. Knowledge of previous cryospheric and oceanic \r\nprocesses is therefore incomplete. Sparse instrumental temperature \r\nobservations prior to the industrial revolution make it difficult to \r\nuniquely characterize a 'pre-industrial' baseline, although this Report \r\nextends the assessment of anthropogenic temperature change \r\nfurther back in time than previous assessment cycles (Chapter 7 and \r\nCross-Chapter Box 1.2)."


id: 5c5308bdf7090081038032407500a6d1
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content:
"Common, integrating scenarios can never encompass all possible \r\nevents that might induce radiative forcing in the future (Section 1.4). \r\nThese may include large volcanic eruptions (Cross-Chapter Box 4.1), \r\nthe consequences of a  major meteorite, smoke plumes following \r\na conflict involving nuclear weapons, extensive geoengineering, or \r\na major pandemic (Cross-Chapter Box 1.6). Scenario-related research \r\nalso often focuses on the 21st century. Post-2100 climate changes \r\nare not covered as comprehensively, and their assessment is limited. \r\nThose long-term climate changes, potentially induced by forcing over "


id: c24b058e03c57135e7f337ff202437bc
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content:
"Yes, much better. The first IPCC report, released in 1990, concluded that human-caused climate change would \r\nsoon become evident, but could not yet confirm that it was already happening. Today, evidence is overwhelming \r\nthat the climate has indeed changed since the pre-industrial era and that human activities are the principal cause \r\nof that change. With much more data and better models, we also understand more about how the atmosphere \r\ninteracts with the ocean, ice, snow, ecosystems and land surfaces of the Earth. Computer climate simulations \r\nhave also improved dramatically, incorporating many more natural processes and providing projections at much \r\nhigher resolutions."


id: 4d1eeb06c75a374464f0cd80d519022a
created_at: 2023-03-16 14:00:38
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vector_id: 9561
content:
"Since the first IPCC report in 1990, large numbers of new instruments have been deployed to collect data in the \r\nair, on land, at sea and from outer space. These instruments measure temperature, clouds, winds, ice, snow, \r\nocean currents, sea level, soot and dust in the air, and many other aspects of the climate system. New satellite \r\ninstruments have also provided a wealth of increasingly fine-grained data. Additional data from older observing \r\nsystems and even hand-written historical records are still being incorporated into observational datasets, and \r\nthese datasets are now better integrated and adjusted for historical changes in instruments and measurement \r\ntechniques. Ice cores, sediments, fossils, and other new evidence from the distant past have taught us much \r\nabout how Earth's climate has changed throughout its history."


id: 3292be0a009e73effe25de7dcb561c11
created_at: 2023-03-16 14:00:38
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content:
"Understanding of climate system processes has also improved. For example, in 1990 very little was known about \r\nhow the deep ocean responds to climate change. Today, reconstructions of deep-ocean temperatures extend as \r\nfar back as 1871. We now know that the oceans absorb most of the excess energy trapped by greenhouse gases \r\nand that even the deep ocean is warming up. As another example, in 1990, relatively little was known about \r\nexactly how or when the gigantic ice sheets of Greenland and Antarctica would respond to warming. Today, \r\nmuch more data and better models of ice-sheet behaviour reveal unexpectedly high melt rates that will lead to \r\nmajor changes within this century, including substantial sea level rise (FAQ 9.2)."


id: 83d4dbdbb2e97b96b3276949611a961c
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vector_id: 18271
content:
"The major natural factors contributing to climate change on time scales of decades to centuries are volcanic \r\neruptions and variations in the sun's energy output. Today, data show that changes in incoming solar energy \r\nsince 1900 have contributed only slightly to global warming, and they exhibit a slight downward trend since the \r\n1970s. Data also show that major volcanic eruptions have sometimes cooled the entire planet for relatively short \r\nperiods of time (typically several years) by erupting aerosols (tiny airborne particles) high into the atmosphere."


id: 191e48e346a1552c1a0a24bf998f5136
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content:
"The main human causes of climate change are the heat-absorbing greenhouse gases released by fossil fuel \r\ncombustion, deforestation, and agriculture, which warm the planet; and aerosols such as sulphate from burning \r\ncoal, which have a short-term cooling effect that partially counteracts human-caused warming. Since 1990, we \r\nhave more and better observations of these human factors as well as improved historical records, resulting in \r\nmore precise estimates of human influence on the climate system (FAQ 3.1)."


id: 56c1a165a3b16a856965401e7157c720
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content:
"While most climate models in 1990 focused on the atmosphere, using highly simplified representations of oceans \r\nand land surfaces, today's Earth system simulations include detailed models of oceans, ice, snow, vegetation and \r\nmany other variables. An important test of models is their ability to simulate Earth's climate over the period \r\nof instrumental records (since about 1850). Several rounds of such testing have taken place since 1990, and \r\nthe testing itself has become much more rigorous and extensive. As a group and at large scales, models have \r\npredicted the observed changes well in these tests (FAQ 3.3). Since there is no way to do a controlled laboratory \r\nexperiment on the actual Earth, climate model simulations can also provide a kind of 'alternate Earth' to test \r\nwhat would have happened without human influence. Such experiments show that the observed warming \r\nwould not have occurred without human influence."


id: 6ba2cd1940d3014db4c716672ca63017
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content:
"Finally, physical theory predicts that human influence on the climate system should produce specific patterns \r\nof change, and we see those patterns in both observations and climate simulations. For example, nights are \r\nwarming faster than days, less heat is escaping to space, and the lower atmosphere (troposphere) is warming but \r\nthe upper atmosphere (stratosphere) has cooled. These confirmed predictions are all evidence of changes driven \r\nprimarily by increases in GHG concentrations rather than natural causes."


id: 4e3cd058191b6b62df6ccc093467690a
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content:
"Yes. Between 1990 and 2021, observations, models and climate understanding improved, while the dominant \r\nrole of human influence in global warming was confirmed.\r\nFAQ 1.1: Do we understand climate change better than when the IPCC started?"


id: c1c07e3600729c3654e3f633044d5c36
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content:
"FAQ 1.1, Figure 1 | Sample elements of climate understanding, observations and models as assessed in the IPCC First Assessment Report \r\n(1990) and Sixth Assessment Report (2021). Many other advances since 1990, such as key aspects of theoretical understanding, geological records and \r\nattribution of change to human influence, are not included in this figure because they are not readily represented in this simple format. Fuller explanations of \r\nthe history of climate knowledge are available in the introductory chapters of the IPCC Fourth and Sixth assessment reports."


id: 57158d3c5da1fd5df17a8e12e0919f55
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content:
"The signs of climate change are unequivocal at the global scale and are increasingly apparent on smaller spatial \r\nscales. The high northern latitudes show the largest temperature increase, with clear effects on sea ice and \r\nglaciers. The warming in the tropical regions is also apparent because the natural year-to-year variations in \r\ntemperature there are small. Long-term changes in other variables such as rainfall and some weather and climate \r\nextremes have also now become apparent in many regions."


id: 9dfa0332596b45025a7d70b86a1cfe0e
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"It was first noticed that the planet's land areas were warming in the 1930s. Although increasing atmospheric \r\ncarbon dioxide (CO2) concentrations were suggested as part of the explanation, it was not certain at the time \r\nwhether the observed warming was part of a long-term trend or a natural fluctuation: global warming had not \r\nyet become apparent. But the planet continued to warm, and by the 1980s the changes in temperature had \r\nbecome obvious or, in other words, the signal had emerged."


id: 97047aaf5a29560400e2057f3c1d1dd8
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content:
"Imagine you had been monitoring temperatures at the same location for the past 150 years. What would you \r\nhave experienced? When would the warming have become noticeable in your data? The answers to these \r\nquestions depend on where on the planet you are."


id: 6dfc43cc1fece97c6f2b2b584a9fb60c
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"Observations and climate model simulations both demonstrate that the largest long-term warming trends are in \r\nthe high northern latitudes and the smallest warming trends over land are in tropical regions. However, the year\u0002to-year variations in temperature are smallest in the tropics, meaning that the changes there are also apparent, \r\nrelative to the range of past experiences (FAQ 1.2, Figure 1)."


id: 16b632d48d38f06b390b08bcf3ff356
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content:
"Changes in temperature also tend to be more apparent over land areas than over the open ocean and are often \r\nmost apparent in regions which are more vulnerable to climate change. It is expected that future changes will \r\ncontinue to show the largest signals at high northern latitudes, but with the most apparent warming in the \r\ntropics. The tropics also stand to benefit the most from climate change mitigation in this context, as limiting \r\nglobal warming will also limit how far the climate shifts relative to past experience."


id: e8fdb58d7f3f24220128302ec65885ab
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content:
"Changes in other climate variables have also become apparent at smaller spatial scales. For example, changes \r\nin average rainfall are becoming clear in some regions, but not in others, mainly because natural year-to-year \r\nvariations in precipitation tend to be large relative to the magnitude of the long-term trends. However, extreme \r\nrainfall is becoming more intense in many regions, potentially increasing the impacts from inland flooding (FAQ \r\n8.2). Sea levels are also clearly rising on many coastlines, increasing the impacts of inundation from coastal storm \r\nsurges, even without any increase in the number of storms reaching land. A decline in the amount of Arctic sea \r\nice is apparent, both in the area covered and in its thickness, with implications for polar ecosystems."


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updated_at: 2023-03-16 14:00:38
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content:
"When considering climate-related impacts, it is not necessarily the size of the change that is most important. \r\nInstead, it can be the rate of change or it can also be the size of the change relative to the natural variations \r\nof the climate to which ecosystems and society are adapted. As the climate is pushed further away from past \r\nexperiences and enters an unprecedented state, the impacts can become larger, along with the challenge of \r\nadapting to them."


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content:
"How and when a long-term trend becomes distinguishable from shorter-term natural variations depends on the \r\naspect of climate being considered (e.g., temperature, rainfall, sea ice or sea level), the region being considered, \r\nthe rate of change, and the magnitude and timing of natural variations. When assessing the local impacts from \r\nclimate change, both the size of the change and the amplitude of natural variations matter."


id: 88420ded3d09f54a910a24fe94210870
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content:
"FAQ 1.2, Figure 1 | Observed variations in regional temperatures since 1850 (data from Berkeley Earth). Regions in high latitudes, such as \r\nmid-North America (40degN-64degN, 140degW-60degW, left), have warmed by a larger amount than regions at lower latitudes, such as tropical South America \r\n(10degS-10degN, 84degW-16degW, right), but the natural variations are also much larger at high latitudes (darker and lighter shading represents 1 and 2 standard \r\ndeviations, respectively, of natural year-to-year variations). The signal of observed temperature change emerged earlier in tropical South America than mid\u0002North America even though the changes were of a smaller magnitude. (Note that those regions were chosen because of the longer length of their observational \r\nrecord; see Figure 1.14 for more regions)."


id: a76f8664836b45a5f7555c1f67ca9022
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content:
"In the past, the Earth has experienced prolonged periods of elevated greenhouse gas concentrations that caused \r\nglobal temperatures and sea levels to rise. Studying these past warm periods informs us about the potential long\u0002term consequences of increasing greenhouse gases in the atmosphere."


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vector_id: 8576
content:
"Rising greenhouse gas concentrations are driving profound changes to the Earth system, including global \r\nwarming, sea level rise, increases in climate and weather extremes, ocean acidification, and ecological shifts \r\n(FAQ 2.2 and FAQ 7.1). The vast majority of instrumental observations of climate began during the 20th century, \r\nwhen greenhouse gas emissions from human activities became the dominant driver of changes in Earth's \r\nclimate (FAQ 3.1)."


id: ce2e3aff7a9ec922c1e0be1bb658e818
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 29958
content:
"While present-day warming is unusual in the context of the recent geologic past in several different ways \r\n(FAQ 2.1), past warm climate states present a  stark reminder that the long-term adjustment to present-day \r\natmospheric carbon dioxide concentrations has only just begun. That adjustment will continue over the coming \r\ncenturies to millennia."


id: adb4deea8ae082d6c05e59350a9c4d35
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 24868
content:
"FAQ 1.3, Figure 1 | Comparison of past, present and future. Schematic of atmospheric carbon dioxide concentrations, global temperature, and global \r\nsea level during previous warm periods as compared to 1850-1900, present-day (2011-2020), and future (2100) climate change scenarios corresponding to \r\nlow-emissions scenarios (SSP1-2.6; lighter colour bars) and very high-emissions scenarios (SSP5-8.5; darker colour bars)."


id: 4a84940462a9e1162ac679c72aeb32c9
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 9126
content:
"nt Report (AR5\r\nessment Repor\r\n3b), and the I\r\nstat\r\nde, \r\nve \r\n of \r\nnd \r\nres \r\nare \r\n \r\nose \r\nare \r\nTotal rais positito an upby the cThe largto total is causein the aconcentsince 17\r\n \r\nThe totaradiativ2011 re2.29 [1.and it hrapidly during ptotal anestimathigher tin AR4 fThe totasolar irrand straaerosols contriburadiativthe last brief pevolcanic"


id: dacb4ae985ef46fa2017e263505096b8
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 31952
content:
"Jinho Ahn (Republic of Korea), Frank J. Dentener (EU/The Netherlands), Catia M. Domingues \r\n(Australia, United Kingdom/Brazil), Sebastian Gerland (Norway/Germany), Daoyi Gong (China), \r\nDarrell S. Kaufman (United States of America), Hyacinth C. Nnamchi (Nigeria, Germany/Nigeria), \r\nJohannes Quaas (Germany), Juan A. Rivera (Argentina), Shubha Sathyendranath (United Kingdom/\r\nCanada, Overseas citizen of India, United Kingdom), Sharon L. Smith (Canada), Blair Trewin \r\n(Australia), Karina von Schuckmann (France/Germany), Russell S. Vose (United States of America)"


id: 4ff1b1b7b91bcac8137920cc2ce7a2c2
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 10016
content:
"Katherine J. Dooley (Ireland), Therese A. Myslinski (Ireland), David N. Smyth (Ireland/United \r\nKingdom, Ireland)"


id: e9bc72c6acccf57522a9bbc65cde4af9
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 34273
content:
"Gulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, \r\nJ.  Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing \r\nState of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group \r\nI to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, \r\nA. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, \r\nJ.B.R.  Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, \r\nCambridge, United Kingdom and New York, NY, USA, pp. 287-422, doi:10.1017/9781009157896.004. "


id: 87b0a891ca047a84666e646c5834ac64
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 18877
content:
"Tropospheric aerosol concentrations across the Northern \r\nHemisphere mid-latitudes increased from 1700 to the last \r\nquarter of the 20th century, but have subsequently declined \r\n(high confidence). Aerosol optical depth (AOD) has decreased \r\nsince 2000 over Northern Hemisphere mid-latitudes and Southern \r\nHemisphere mid-latitude continents, but increased over South \r\nAsia and East Africa (high confidence). These trends are even more \r\npronounced in AOD from sub-micrometre aerosols for which the \r\nanthropogenic contribution is particularly large. The best-estimate of \r\naerosol ERF in 2019 relative to 1750 is -1.1 W m-2. {2.2.6, 7.3.3}"


id: 175b00efae2608971600a3fe1a6d5448
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 1182
content:
"Chapter 2 assesses observed large-scale changes in climate system \r\ndrivers, key climate indicators and principal modes of variability. \r\nChapter 3 considers model performance and detection/attribution, \r\nand Chapter 4 covers projections for a subset of these same indicators \r\nand modes of variability. Collectively, these chapters provide the basis \r\nfor later chapters, which focus upon processes and regional changes. \r\nWithin Chapter 2, changes are assessed from in situ and remotely \r\nsensed data and products and from indirect evidence of longer-term \r\nchanges based upon a diverse range of climate proxies. The time\u0002evolving availability of observations and proxy information dictate \r\nthe periods that can be assessed. Wherever possible, recent changes \r\nare assessed for their significance in a longer-term context, including \r\ntarget proxy periods, both in terms of mean state and rates of change."


id: 61cff051c17def10ed51bd013b0f323e
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 12955
content:
"Changes in other short-lived gases are associated with an \r\noverall positive ERF (medium confidence). Stratospheric ozone \r\nhas declined between 60degS and 60degN by 2.2% from the 1980s to \r\n2014-2017 (high confidence). Since the mid-20th century, tropospheric \r\nozone has increased by 30-70% across the Northern Hemisphere \r\n(medium confidence). Since the mid-1990s, free tropospheric ozone \r\nincreases were 2-7% per decade in the northern mid-latitudes (high \r\nconfidence), 2-12% per decade in the tropics (high confidence) and \r\n<5% per decade in southern mid-latitudes (medium confidence). The \r\nbest estimate of ozone column ERF (0.5 W m-2 relative to 1750) is \r\ndominated by changes in tropospheric ozone. Due to discrepancies \r\nin satellite and in situ records, there is low confidence in estimates of \r\nstratospheric water vapour change. {2.2.5, 7.3.2}"


id: bc4b739322fc004d8d028a3f497b6145
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 27201
content:
"Climate system drivers lead to climate change by altering the Earth's \r\nenergy balance. The influence of a climate driver is described in terms \r\nof its effective radiative forcing (ERF), measured in W m-2. Positive \r\nERF values exert a warming influence and negative ERF values exert \r\na cooling influence (Chapter 7)."


id: 1d15ac565ca4432a611fc656fd96104b
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 2019
content:
"Present-day global concentrations of atmospheric carbon \r\ndioxide (CO2) are at higher levels than at any time in at least \r\nthe past two million years (high confidence). Changes in ERF \r\nsince the late 19th century are dominated by increases in \r\nconcentrations of greenhouse gases and trends in aerosols; \r\nthe net ERF is positive and changing at an increasing rate since \r\nthe 1970s (medium confidence). {2.2, 7.2, 7.3}"


id: b58547387858feb53f2e223b363a38cd
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 26087
content:
"Biophysical effects from historical changes in land use have \r\nan overall negative ERF (medium confidence). The best-estimate \r\nERF from the increase in global albedo is -0.15 W m-2 since 1700 and \r\n-0.12 W m-2 since 1850 (medium confidence). {2.2.7, 7.3.4}"


id: 4e568ae71a64074f2460904e1e549722
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 9757
content:
"Observed changes in the atmosphere, oceans, cryosphere and \r\nbiosphere provide unequivocal evidence of a world that has \r\nwarmed. Over the past several decades, key indicators of the \r\nclimate system are increasingly at levels unseen in centuries \r\nto millennia, and are changing at rates unprecedented in at \r\nleast the last 2000 years (high confidence). Temperatures as high \r\nas during the most recent decade (2011-2020) exceed the warmest \r\ncentennial-scale range reconstructed for the present interglacial, \r\naround 6,500 years ago [0.2degC-1degC relative to 1850-1900] (medium \r\nconfidence). The next older warm period is the last interglacial \r\nwhen the multi-centennial temperature range about 125,000 years \r\nago [0.5degC-1.5degC relative to 1850-1900] encompassed the recent \r\ndecade values (medium confidence). {2.3}"


id: 9ab1d285833d88857e2f69eca562da15
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 21868
content:
"Change in ERF from natural factors since 1750 is negligible in \r\ncomparison to anthropogenic drivers (very high confidence). \r\nSolar activity since 1900 was high but not exceptional compared \r\nto the past 9000 years (high confidence). The average magnitude \r\nand variability of volcanic aerosol forcing since 1900 have not been \r\nunusual compared to the past 2500 years (medium confidence). \r\n{2.2.1, 2.2.2}"


id: 11574d76eafdbaa2d1a24d7e4a71dca
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 211
content:
"In 2019, concentrations of CO2, methane (CH4) and nitrous \r\noxide (N2O) reached levels of 409.9 (+-0.4) parts per million \r\n(ppm), 1866.3 (+-3.3) parts per billion (ppb) and 332.1 (+-0.4) \r\nppb, respectively. Since 1850, these well-mixed greenhouse \r\ngases (GHGs) have increased at rates that have no precedent \r\non centennial time scales in at least the past 800,000 years.\r\nConcentrations of CO2, CH4, and N2O increased from 1750 to 2019 by \r\n131.6 +- 2.9 ppm (47.3%), 1137 +- 10 ppb (156%), and 62 +- 6 ppb \r\n(23.0%) respectively. These changes are larger than those between \r\nglacial and interglacial periods over the last 800,000 years for CO2\r\nand CH4 and of comparable magnitude for N2O (very high confidence). \r\nThe best estimate of the total ERF from CO2, CH4 and N2O in 2019 \r\nrelative to 1750 is 2.9 W m-2, an increase of 12.5% from 2011. ERF \r\nfrom halogenated components in 2019 was 0.4 W m-2, an increase of \r\n3.5% since 2011. {2.2.3, 2.2.4, 7.3.2}"


id: f297d077d9c2971f4fd6d3a54d51f2cf
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 35699
content:
"GMST increased by 0.85 [0.69 to 0.95] degC between 1850-1900 \r\nand 1995-2014 and by 1.09 [0.95 to 1.20] degC between \r\n1850-1900 and 2011-2020. From 1850-1900 to 2011-2020, \r\nthe temperature increase over land (1.59 [1.34 to 1.83] degC) \r\nhas been faster than over the oceans (0.88 [0.68 to 1.01] degC).\r\nGMST in the first two decades of the 21st century (2001-2020) was \r\n0.99 [0.84-1.10] degC higher than 1850-1900. Each of the last four \r\ndecades has successively been warmer than all preceding decades \r\nsince 1850. Over the last 50 years, observed GMST has increased at \r\na rate unprecedented in at least the last 2000 years (high confidence). "


id: ee8ee45360c4bdeeb503f23dc4cd2dfd
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 35002
content:
"minor differences between sea ice area decadal means for 1979-1988 \r\n(2.04 million km2 for February, 15.39 million km2 for September) and \r\n2010-2019 (2.17 million km2 for February, 15.75  million km2 for \r\nSeptember). {2.3.2.1}"


id: 54a975d7b6dedaad7d25b9fb834d1220
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 10777
content:
"The increase in GMST since the mid-19th century was preceded by \r\na slow decrease that began in the mid-Holocene (around 6500 years \r\nago) (medium confidence). {2.3.1.1, Cross-Chapter Box 2.1}"


id: 9ec6f0c331a9a5346530a00f32937299
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 22518
content:
"Changes in GMST and global surface air temperature (GSAT) \r\nover time differ by at most 10% in either direction  (high \r\nconfidence), and the long-term changes in GMST and GSAT are \r\npresently assessed to be identical. There is expanded uncertainty \r\nin GSAT estimates, with the assessed change from 1850-1900 to \r\n1995-2014 being 0.85 [0.67 to 0.98] degC. {Cross-Chapter Box 2.3}"


id: 85d673e0a91d6ce4b3c9bfc32468bb6
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 18600
content:
"The troposphere has warmed since at least the 1950s, and it is \r\nvirtually certain that the stratosphere has cooled. In the Tropics, \r\nthe upper troposphere has warmed faster than the near-surface since \r\nat least 2001, the period over which new observational techniques \r\npermit more robust quantification (medium confidence). It is virtually \r\ncertain that the tropopause height has risen globally over 1980-2018, \r\nbut there is low confidence in the magnitude. {2.3.1.2}"


id: f2d673ad9a148146b12138a8e698027b
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 35733
content:
"Changes in several components of the global hydrological \r\ncycle provide evidence for overall strengthening since at least \r\n1980 (high confidence). However, there is low confidence\r\nin comparing recent changes with past variations due to \r\nlimitations in paleoclimate records at continental and global \r\nscales. Global land precipitation has likely increased since 1950, \r\nwith a faster increase since the 1980s (medium confidence). Near\u0002surface specific humidity has increased over both land (very likely) \r\nand the oceans (likely) since at least the 1970s. Relative humidity has \r\nvery likely decreased over land areas since 2000. Global total column \r\nwater vapour content has very likely increased during the satellite \r\nera. Observational uncertainty leads to low confidence in global \r\ntrends in precipitation minus evaporation and river runoff. {2.3.1.3}"


id: 1f96ac053eda6c9060c2761facc0fc47
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 2402
content:
"Global mean sea level (GMSL) is rising, and the rate of GMSL \r\nrise since the 20th century is faster than over any preceding \r\ncentury in at least the last three millennia (high confidence). \r\nSince 1901, GMSL has risen by 0.20 [0.15 to 0.25] m, and the \r\nrate of rise is accelerating. The average rate of sea level rise \r\nwas 1.3 [0.6 to 2.1] mm yr -1 between 1901 and 1971, increasing \r\nto 1.9 [0.8 to 2.9] mm yr -1 between 1971 and 2006, and further \r\nincreasing to 3.7 [3.2 to 4.2] mm yr -1 between 2006 and 2018 \r\n(high confidence). Further back in time, there is medium confidence\r\nthat GMSL was within -3.5 to +0.5 m (very likely) of present during \r\nthe mid-Holocene (6000 years ago), 5 to 10 m (likely) higher during \r\nthe Last Interglacial (125,000 years ago), and 5 to 25 m (very likely) \r\nhigher during the mid-Pliocene Warm Period (MPWP) (3.3 million \r\nyears ago). {2.3.3.3}"


id: fdb5a3dc9d862504e25983ff231bb234
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 37434
content:
"Several aspects of the large-scale atmospheric circulation have \r\nlikely changed since the mid-20th century, but limited proxy \r\nevidence yields low confidence in how these changes compare \r\nto longer-term climate. The Hadley circulation has likely widened \r\nsince at least the 1980s, and extratropical storm tracks have likely\r\nshifted poleward in both hemispheres. Global monsoon precipitation \r\nhas likely increased since the 1980s, mainly in the Northern Hemisphere \r\n(medium confidence). Since the 1970s, near-surface winds have \r\nlikely weakened over land. Over the oceans, near-surface winds likely\r\nstrengthened over 1980-2000, but divergent estimates lead to low \r\nconfidence in the sign (direction) of change thereafter. It is likely that \r\nthe northern stratospheric polar vortex has weakened since the 1980s \r\nand experienced more frequent excursions toward Eurasia. {2.3.1.4}"


id: df1ed7ff68a384a7f4af75b429ebdbc6
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 32601
content:
"Current Arctic sea ice coverage levels are the lowest since at \r\nleast 1850 for both annual mean and late-summer values (high \r\nconfidence) and for the past 1000 years for late-summer values \r\n(medium confidence). Between 1979 and 2019, Arctic sea ice area \r\nhas decreased in both summer and winter, with sea ice becoming \r\nyounger, thinner and more dynamic (very high confidence). Decadal \r\nmeans for Arctic sea ice area decreased from 6.23 million  km2 in \r\n1979-1988 to 3.76 million km2 in 2010-2019 for September and \r\nfrom 14.52 to 13.42 million km2 for March. Antarctic sea ice area has \r\nexperienced little net change since 1979 (high confidence), with only "


id: 5918f2c801d85b15df8c4b316bab1902
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 11514
content:
"most open ocean regions during the mid 20th to early 21st centuries \r\n(high confidence), with decadal variability (medium confidence). \r\nOxygen minimum zones are expanding at many locations (high \r\nconfidence). {2.3.3}"


id: e7da438591de50625daabdeb6f2f6859
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 33972
content:
"the 23rd century [1.0degC to 2.2degC]. During the mid-Pliocene Warm \r\nPeriod, the GMST estimate [2.5degC to 4.0degC] is similar to the range \r\nprojected under SSP2-4.5 for the end of the 23rd century [2.3degC to \r\n4.6degC]. GMST estimates for the Miocene Climatic Optimum [5degC to \r\n10degC] and Early Eocene Climatic Optimum [10degC to 18degC], about \r\n15 and 50 million years ago, respectively, overlap with the range \r\nprojected for the end of the 23rd century under SSP5-8.5 [6.6degC to \r\n14.1degC]. {Cross-Chapter Box 2.1, 2.3.1, 4.3.1.1, 4.7.1.1}"


id: e096237655db0c6c1884942841e37efc
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 32835
content:
"Changes in key global aspects of the terrestrial biosphere \r\nare consistent with large-scale warming (high confidence).\r\nOver the last century, there have been poleward and upslope shifts \r\nin the distributions of many land species (very high confidence) as \r\nwell as increases in species turnover within many ecosystems (high \r\nconfidence). Over the past half century, climate zones have shifted \r\npoleward, accompanied by an increase in the length of the growing \r\nseason in the Northern Hemisphere extratropics and an increase in \r\nthe amplitude of the seasonal cycle of atmospheric CO2 above 45degN \r\n(high confidence). Since the early 1980s, there has been a global-scale \r\nincrease in the greenness of the terrestrial surface (high confidence). \r\n{2.3.4.1, 2.3.4.3}"


id: 6b680ce7d31b1d91e407596dca72fa13
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 14516
content:
"During the mid-Pliocene warm period (MPWP, 3.3 to 3.0 million \r\nyears ago) slowly changing large-scale indicators reflect \r\na  world that was warmer than present, with CO2 similar to \r\ncurrent levels. CO2 levels during the MPWP were similar to present \r\nfor a sustained period, within a range of 360-420 ppm (medium \r\nconfidence). Relative to the present, GMST, GMSL and precipitation \r\nrate were all higher, the Northern Hemisphere latitudinal temperature \r\ngradient was lower, and major terrestrial biomes were shifted \r\nnorthward (very high confidence). There is high confidence that \r\ncryospheric indicators were diminished and medium confidence\r\nthat the Pacific longitudinal temperature gradient weakened and \r\nmonsoon systems strengthened. {2.3, Cross-Chapter Box 2.4, 9.6.2}"


id: 88f4b13f0daeb902088e8ed69d9ecb38
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 19087
content:
"Inferences from past climate states based on proxy records can \r\nbe compared with climate projections over coming centuries \r\nto place the range of possible futures into a longer-term \r\ncontext. There is medium confidence in the following mappings \r\nbetween selected paleo periods and future projections: during the \r\nLast Interglacial, GMST is estimated to have been 0.5degC-1.5degC \r\nwarmer than the 1850-1900 reference for a sustained period, which \r\noverlaps the low end of the range of warming projected under \r\nSSP1-2.6, including its negative-emissions extension to the end of "


id: 593592cd6c8c44f647b7a9c815cee12
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 11528
content:
"the recent past is applied to the extent permitted by available data \r\n(Section 1.4.1 and Cross-Chapter Box 2.1). In all cases, the narrative \r\nproceeds from as far in the past as the data permit through to the \r\npresent. Each sub-section starts by highlighting the key findings \r\nfrom AR5 and any relevant AR6-cycle Special Reports (SROCC, \r\nSR1.5, SRCCL), and then outlines the new evidence-basis arising \r\nfrom a combination of: (i) new findings reported in the literature, \r\nincluding new datasets and new versions of existing datasets; \r\nand (ii) recently observed changes, before closing with a  new \r\nsummary assessment. "


id: 15fb40f2dc2acf9c67610231504d8bfb
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 982
content:
"Trends, when calculated as part of this assessment, have wherever \r\npossible been calculated using a common approach following \r\nthat adopted in Box 2.2 of Chapter 2 of AR5 (Hartmann et  al., \r\n2013). In addition to trends, consideration is also made of \r\nchanges between various time slices/periods in performing the \r\nassessment (Section 1.4.1 and Cross-Chapter Box 2.1). Statistical \r\nsignificance of trends and changes are assessed at the two-tailed \r\n90% confidence (very likely) level unless otherwise stated. Limited \r\nuse is also made of published analyses that have employed a range \r\nof methodological choices. In each such case the method/metric \r\nis stated."


id: 5f1f30b6189060c73636cf5635b23668
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 12518
content:
"Use is made of paleoclimate, in situ, ground- and satellite-based \r\nremote sensing, and reanalysis data products where applicable \r\n(Section 1.5). All observational products used in the chapter are \r\ndetailed in Annex I, and information on data sources and processing \r\nfor each figure and table can be found in the associated chapter Table \r\n2.SM.1 available as an electronic supplement to the chapter.  Use \r\nof common periods ranging from 56 million years ago through to "


id: b6b20b5ef20bab34d109ab9fc23a2fed
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 26262
content:
"There exist a variety of inevitable and, in some cases, irreducible \r\nuncertainties in performing an assessment of the observational \r\nevidence for climate change. In some instances, a combination of \r\nsources of uncertainty is important. For example, the assessment \r\nof global surface temperature over the instrumental record in "


id: 2e9e20027f13d82c23d823585cf30756
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 4778
content:
"Section  2.3.1.1.3 considers a combination of observational-dataset \r\nand trend-estimate uncertainties. Furthermore, estimates of parametric \r\nuncertainty are often not comprehensive in their consideration of \r\nall possible factors and, when such estimates are constructed in \r\ndistinct manners, there are often significant limitations to their direct \r\ncomparability (Hartmann et al., 2013, their Box 2.1)."


id: b0f236df526181f574af221808071d1a
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 25350
content:
"Contributing Authors: Darrell S. Kaufman (United States of America), Kevin D. Burke (United States of America), Samuel Jaccard \r\n(Switzerland), Christopher Jones (United Kingdom), Wolfgang Kiessling (Germany), Daniel J. Lunt (United Kingdom), Olaf Morgenstern \r\n(New Zealand/Germany), John W. Williams (United States of America)"


id: 4ef5af88781a8f1441560b529a9f4b9d
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
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vector_id: 9865
content:
"Over the long evolution of the Earth's climate system, several periods have been extensively studied as examples of distinct \r\nclimate states. This Cross-Chapter Box places multiple paleoclimate reference periods into the unifying context of Earth's long-term \r\nclimate history, and points to sections in the report with additional information about each period. Other reference periods, including \r\nthose of the industrialized era, are described in Section 1.4.1."


id: fce8ae8749c7b9ff589b322481b577df
created_at: 2023-03-16 14:00:38
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vector_id: 37303
content:
"The reference periods represent times that were both colder and warmer than present, and periods of rapid climate change, many \r\nwith informative parallels to projected climate (Cross-Chapter Box 2.1, Table 1). They are used to address a wide variety of questions \r\nrelated to natural climate variations in the past (FAQ 1.3). Most of them are used as targets to evaluate the performance of climate \r\nmodels under different climate forcings (Section 3.8.2), while also providing insight into the ocean-atmospheric circulation changes \r\nassociated with various radiative forcings and geographical changes. "


id: db967df6647f2208603cdfc93a4a630
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 32067
content:
"radiative forcing (ERF) performed in Section 7.3. For some SLCFs \r\nwith insufficient spatial or temporal observational coverage, ERFs are \r\nbased on model estimates, but also reported here for completeness \r\nand context. Tabulated global mixing ratios of all well-mixed GHGs \r\nand ERFs from 1750-2019 are provided in Annex III."


id: ab538a1cd0ceb489b93084ea85cd4740
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 24468
content:
"The AR5 assessed solar variability over multiple time scales, concluding \r\nthat total solar irradiance (TSI) multi-millennial fluctuations over the \r\npast 9 kyr were <1 W m-2, but with no assessment of confidence \r\nprovided. For multi-decadal to centennial variability over the last \r\nmillennium, AR5 emphasized reconstructions of TSI that show \r\nlittle change (<0.1%) since the Maunder Minimum (1645-1715) \r\nwhen solar activity was particularly low, again without providing \r\na confidence level. The AR5 further concluded that the best estimate \r\nof radiative forcing due to TSI changes for the period 1750-2011 "


id: 21ee2db8090993eaac68cb7847df950b
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 2783
content:
"Recent estimates of TSI and spectral solar irradiance (SSI) for \r\nthe past millennium are based upon updated irradiance models \r\n(e.g., Egorova et al., 2018; C.-J. Wu et al., 2018) and employ updated \r\nand revised direct sunspot observations over the last three centuries \r\n(Clette et al., 2014; Chatzistergos et al., 2017) as well as records of \r\nsunspot numbers reconstructed from cosmogenic isotope data prior \r\nto this (Usoskin et  al., 2016). These reconstructed TSI time series \r\n(Figure  2.2a) feature little variation in TSI averaged over the past \r\nmillennium. The TSI between the Maunder Minimum (1645-1715) \r\nand second half of the 20th century increased by 0.7-2.7 W m-2\r\n(Jungclaus et al., 2017; Egorova et al., 2018; Lean, 2018; C.-J. Wu et al., \r\n2018; Lockwood and Ball, 2020; Yeo et al., 2020). This TSI increase \r\nimplies a change in ERF of 0.09-0.35 W m-2 (Section 7.3.4.4)."


id: a51a20f4fc78935c9f99509452602f3e
created_at: 2023-03-16 14:00:38
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vector_id: 23543
content:
"was 0.05-0.10 W m-2 (medium confidence), and that TSI very likely \r\nchanged by -0.04 [-0.08 to 0.00] W m-2 between 1986 and 2008. \r\nPotential solar influences on climate due to feedbacks arising from \r\ninteractions with galactic cosmic rays are assessed in Section 7.3.4.5. "


id: dd74569dcd6dadc378ef8d93c6761f18
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 32343
content:
"Slow periodic changes in the Earth's orbit around the Sun mainly \r\ncause variations in seasonal and latitudinal receipt of incoming solar \r\nradiation. Precise calculations of orbital variations are available for \r\ntens of millions of years (Berger and Loutre, 1991; Laskar et al., 2011). \r\nThe range of insolation averaged over boreal summer at 65degN was \r\nabout 83 W m-2 during the past million years, and 3.2 W m-2 during \r\nthe past millennium, but there was no substantial effect upon global \r\naverage radiative forcing (0.02 W m-2 during the past millennium)."


id: 9983179f6e9a3298a440be0080baa63a
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 21680
content:
"Estimation of TSI changes since 1900 (Figure 2.2b) has further \r\nstrengthened, and confirms a small (less than about 0.1  W  m-2) \r\ncontribution to global climate forcing (Section 7.3.4.4). New \r\nreconstructions of TSI over the 20th century (Lean, 2018; C.-J. Wu \r\net  al., 2018) support previous results that the TSI averaged over \r\nthe solar cycle very likely increased during the first seven decades \r\nof the 20th century and decreased thereafter (Figure 2.2b). TSI did \r\nnot change significantly between 1986 and 2019. Improved insights \r\n(Krivova et al., 2006; Yeo et al., 2015, 2017; Coddington et al., 2016) \r\nshow that variability in the 200-400 nm UV range was greater than \r\npreviously assumed. Building on these results, the forcing proposed \r\nby Matthes et  al. (2017) has a 16% stronger contribution to TSI "


id: 8b74ce16495c1b191e579b2213faf02a
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 19459
content:
"A new reconstruction of solar irradiance extends back 9 kyr based \r\nupon updated cosmogenic isotope datasets and improved models \r\nfor production and deposition of cosmogenic nuclides (Poluianov \r\net al., 2016), and shows that solar activity during the second half of \r\nthe 20th century was in the upper decile of the range. TSI features \r\nmillennial-scale changes with typical magnitudes of 1.5 [1.4 to 2.1] \r\nW m-2 (C.-J. Wu et  al., 2018). Although stronger variations in the \r\ndeeper past cannot be ruled out completely (Egorova et al., 2018; \r\nReinhold et al., 2019), there is no indication of such changes having \r\nhappened over the last 9 kyr."


id: 933f4c99786fbc9ce44fd27e61dd7236
created_at: 2023-03-16 14:00:38
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vector_id: 20681
content:
"variability in this wavelength range compared to the forcing used in \r\nthe 5th Phase of the Coupled Model Intercomparison Project (CMIP5)."


id: 4352d2a57989b017456a124a9925a419
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 7999
content:
"the previous 24 centuries (back to 2.5 ka), and well within the range \r\nof centennial-scale variability (Toohey and Sigl, 2017)."


id: c8bc670db73faa15d121e4a8d58e7747
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 29141
content:
"To conclude, solar activity since the late 19th century was relatively \r\nhigh but not exceptional in the context of the past 9 kyr (high \r\nconfidence). The associated global mean ERF is in the range of -0.06 \r\nto +0.08 W m-2 (Section 7.3.4.4)."


id: 5ec1c06eb071e3d6c9f5847875294397
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 12449
content:
"To conclude, strong individual volcanic eruptions cause multi-annual \r\nvariations in radiative forcing. However, the average magnitude \r\nand variability of SAOD and its associated volcanic aerosol forcing \r\nsince 1900 are not unusual in the context of at least the past 2.5 kyr \r\n(medium confidence)."


id: a55ae783e4c9c1e155cdb6880af84177
created_at: 2023-03-16 14:00:38
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vector_id: 23581
content:
"SAOD averaged over the period 950-1250 CE (0.012) was lower \r\nthan for the period 1450-1850 CE (0.017) and similar to the \r\nperiod  1850-1900 (0.011). Uncertainties associated with these \r\ninter-period differences are not well quantified but have little effect \r\nbecause the uncertainties are mainly systematic throughout the record. \r\nOver the past 100 years, SAOD averaged 14% lower than the mean of "


id: 84ec256d93bdc70cc614937f5d80297d
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 18435
content:
"Isotopes from continental and marine sediments using improved \r\nanalytical techniques and sampling resolution have reinforced the \r\nunderstanding of long-term changes in atmospheric CO2 during the \r\npast 450 Myr (Table 2.1 and Figure 2.3). In particular, for the last \r\n60 Myr, sampling resolution and accuracy of the boron isotope proxy "


id: 3b976d5d5165491b319f9f9cb505200a
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
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vector_id: 6728
content:
"of the terrestrial proxies, preference here is given to the marine based \r\nproxies (and boron in particular) where possible. "


id: adcebc046e272c776d5b2e94052a0ba
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 24883
content:
"a\r\n Centennial rate of change estimated by extrapolation of data from a shorter time period. The values (x to y) represent very likely ranges (90% CIs).\r\nb\r\n Data uncertainty is not estimated. "


id: 9694260deb92aee00502706a733dda47
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 21208
content:
"Following the MPWP, the atmospheric CO2 mixing ratio generally \r\ndecreased at a rate of about 30 ppm Myr -1. It is very likely that CO2\r\nlevels as high as the present were not experienced in the last 2 Myr \r\n(Honisch et al., 2009; Bartoli et al., 2011; Martinez-Boti et al., 2015a; \r\nChalk et al., 2017; Dyez et al., 2018; Da et al., 2019; Stoll et al., 2019). \r\nRelated to the shift of glacial-interglacial cycle frequency from 40 to \r\n100 kyr at 0.8-1.2 Ma, there was a decrease of glacial-period CO2\r\n(Chalk et  al., 2017; Dyez et  al., 2018). These boron isotope-based \r\nCO2  results agree with available records based on ancient ice \r\nexposed near the surface of the AIS (Yan et  al., 2019), however, \r\ndirect comparison is limited due to a lack of ancient ice cores with \r\nsufficiently continuous stratigraphy (Higgins et al., 2015; Brook and \r\nBuizert, 2018)."


id: c161e50d686b110bb3a3f900c7c46add
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 28013
content:
"To conclude, there is high confidence that average EECO and MPWP \r\n(KM5c) CO2 concentrations were higher than those preceding \r\nindustrialization at 1150-2500 ppm and 360-420 ppm, respectively. \r\nAlthough there is some uncertainty due to the non-continuous nature \r\nof marine sediment records, the last time atmospheric CO2 mixing \r\nratio was as high as present was very likely more than 2 Ma."


id: e4a6de52864c577a2656ac1048c99a04
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 33451
content:
"Since AR5, the number of ice cores for the last 800 kyr has increased \r\nand their temporal resolution has improved (Figure 2.4), especially for \r\nthe last 60 kyr and when combined with analyses of firn air, leading \r\nto improved quantification of greenhouse gas concentrations prior to \r\nthe mid-20th century."


id: 2f79b7116e3e3342efd0d61e3a24a019
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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content:
"In this section and for calculation of ERF, surface global averages are \r\ndetermined from measurements representative of the well-mixed lower \r\ntroposphere. Global averages that include sites subject to significant \r\nanthropogenic activities or influenced by strong regional biospheric \r\nemissions are typically larger than those from remote sites, and require \r\nweighting accordingly (Table 2.2). This section focusses on global mean \r\nmixing ratios estimated from networks with global spatial coverage, \r\nand updated from the CMIP6 historical dataset (Meinshausen et al., \r\n2017) for periods prior to the existence of global networks."


id: cffcbf6eff2e112e03dd1c6965462a1
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 30249
content:
"The globally averaged surface mixing ratio of CH4 in 2019 was \r\n1866.3 +- 3.3 ppb, which is 3.5% higher than 2011, while observed \r\nincreases from various networks range from 3.3-3.9% (Table 2.2 \r\nand Figure 2.5b). There are marked growth rate changes over the \r\nperiod of direct observations, with a decreasing rate from the late\u00021970s through the late-1990s, very little change in concentrations \r\nfrom 1999-2006, and resumed increases since 2006. Atmospheric \r\nCH4 fluctuations result from complex variations of sources and sinks. \r\nA detailed discussion of recent methane trends and our understanding \r\nof their causes is presented in Cross-Chapter Box 5.2."


id: c779829db361eca0f28283f90f20d2c5
created_at: 2023-03-16 14:00:38
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content:
"The AR5 reported 324.2 +- 0.1 ppb for global surface annual mean \r\nN2O in 2011; since then, it has increased by 2.4% to 332.1 +- 0.4 ppb \r\nin 2019. Independent measurement networks agree well for both \r\nthe global mean mixing ratio and relative change since 2011 \r\n(Table 2.2). Over 1995-2011, N2O increased at an average rate of \r\n0.79 +- 0.05 ppb yr -1. The growth rate has been higher in recent years, \r\namounting to 0.96 +- 0.05 ppb yr -1 from 2012 to 2019 (Figure 2.5c \r\nand Section 5.2.3.5)."


id: 761d191c5776174e626236c4ee40c332
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 16187
content:
"In summary, CO2 has fluctuated by at least 2000 ppm over the last \r\n450 Myr (medium confidence). The last time CO2 concentrations were \r\nsimilar to the present-day was over 2 Ma (high confidence). Further, \r\nit is certain that WMGHG mixing ratios prior to industrialization were \r\nlower than present-day levels and the growth rates of the WMGHGs \r\nfrom 1850 are unprecedented on centennial timescales in at least \r\nthe last 800 kyr. During the glacial-interglacial climate cycles over \r\nthe last 800 kyr, the concentration variations of the WMGHG were \r\n50-100 ppm for CO2, 210-430 ppb for CH4 and 60-90 ppb for N2O. \r\nBetween 1750-2019 mixing ratios increased by 131.6 +- 2.9 ppm \r\n(47%), 1137 +- 10 ppb (156%), and 62 +- 6 ppb (23%), for CO2, \r\nCH4, and N2O, respectively (very high confidence). Since 2011 (AR5) \r\nmixing ratios of CO2, CH4, and N2O have further increased by 19 ppm, "


id: 7e075e8a21c61b0a96a5c91599003cec
created_at: 2023-03-16 14:00:38
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content:
"63 ppb, and 7.7 ppb, reaching in 2019 levels of 409.9 (+- 0.4) ppm, \r\n1866.3 (+-  3.3)  ppb, and 332.1 (+- 0.4) ppb, respectively. By 2019, \r\nthe combined ERF (relative to 1750) of CO2, CH4 and N2O was \r\n2.9 +- 0.5 W m-2 (Table 2.2; Section 7.3.2)."


id: 76b7f4e0aaf777c2cd6a72e5afa32ea1
created_at: 2023-03-16 14:00:38
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vector_id: 16258
content:
"This category includes ozone depleting substances (ODS), their \r\nreplacements, and gases used industrially or produced as by-products. \r\nSome have natural sources (Section 6.2.2.4). The AR5 reported \r\nthat atmospheric abundances of chlorofluorocarbons (CFCs) were \r\ndecreasing in response to controls on production and consumption \r\nmandated by the Montreal Protocol on Substances that Deplete \r\nthe Ozone Layer and its amendments. In contrast, abundances of \r\nboth hydrochlorofluorocarbons (HCFCs, replacements for CFCs) and \r\nhydrofluorocarbons (HFCs, replacements for HCFCs) were increasing. \r\nAtmospheric abundances of perfluorocarbons (PFCs), SF6, and NF3\r\nwere also increasing."


id: 758262c47459b7879778fe97e9feb5ae
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index: climate_gpt_only_giec
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content:
"Further details on ODS and other minor greenhouse gases can be found \r\nin the Scientific Assessment of Ozone Depletion: 2018 (Engel et al., 2018; \r\nMontzka et al., 2018b). Updated mixing ratios of the most radiatively \r\nimportant gases (ERF >0.001 W m-2) are reported in Table  2.2, and \r\nadditional gases (ERF <0.001 W m-2) are shown in Annex III."


id: 5af9db4eab78cfb6385771cd45ff5575
created_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
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content:
"Atmospheric abundances of most CFCs have continued to decline since \r\n2011 (AR5). The globally-averaged abundance of CFC-12 decreased \r\nby 25 ppt (4.8%) from 2011 to 2019, while CFC-11 decreased by \r\nabout 11 ppt (4.7%) over the same period (Table 2.2 and Figure 2.6). \r\nAtmospheric abundances of some minor CFCs (CFC-13, CFC-115, \r\nCFC-113a) have increased since 2011 (Annex III), possibly related to \r\nuse of HFCs (Laube et al., 2014). Overall, as of 2019 the ERF from CFCs \r\nhas declined by 9 +- 0.5% from its maximum in 2000, and 4.7 +- 0.6% \r\nsince 2011 (Table 7.5)."


id: 3443751dc600b10e39f9526f212d5eba
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vector_id: 5634
content:
"and AGAGE networks. Reasons for the discrepancy are presently \r\nunverified, but could be related to differences in sampling locations \r\nin the networks (Simpson et al., 2012). Emissions of HCFC-22, derived \r\nfrom atmospheric data, have remained relatively stable since 2012, \r\nwhile those of HCFC-141b and HCFC-142b have declined (Engel \r\net  al., 2018). Minor HCFCs, HCFC-133a and HCFC-31, have been \r\ndetected in the atmosphere (currently less than 1 ppt) and may be \r\nunintentional by-products of HFC production (Engel et al., 2018)."


id: 499c163156f94ca208a5938680bbdc0c
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 8968
content:
"While global reporting indicated that CFC-11 production had \r\nessentially ceased by 2010, and the atmospheric abundance of CFC-11 \r\nis still decreasing, emissions inferred from atmospheric observations \r\nbegan increasing in 2013-2014 and remained elevated for 5-6 years, \r\nsuggesting renewed and unreported production (Montzka et  al., \r\n2018a, 2021; Rigby et al., 2019; Park et al., 2021). The global lifetimes \r\nof several ozone-depleting substances have been updated (SPARC, \r\n2013), in particular for CFC-11 from 45 to 52 years. "


id: e64cfa893b122c50c307ee03cb28950f
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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index: climate_gpt_only_giec
vector_id: 33724
content:
"The atmospheric abundances of the major HCFCs (HCFC-22, \r\nHCFC-141b, HCFC-142b), primarily used in refrigeration and foam \r\nblowing, are increasing, but rates of increase have slowed in recent \r\nyears (Figure 2.6). Global mean mixing ratios (Table 2.2) showed \r\ngood concordance at the time of AR5 for the period 2005-2011. For \r\nthe period 2011-2019, the UCI network detected larger increases \r\nin HCFC-22, HCFC-141b, and HCFC-142b compared to the NOAA "


id: 47000a0e78d11d4429fd14245bf83665
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vector_id: 8554
content:
"Hydrofluorocarbons (HFCs) are replacements for CFCs and HCFCs. \r\nThe atmospheric abundances of many HFCs increased between 2011 \r\nand 2019. HFC-134a (mobile air conditioning, foam blowing, and \r\ndomestic refrigerators) increased by 71 % from 63 ppt in 2011 to \r\n107.6 ppt in 2019 (Table 2.2). The UCI network detected a slightly \r\nsmaller relative increase (53%). HFC-23, which is emitted as a by\u0002product of HCFC-22 production, increased by 8.4 ppt (35%) over "


id: 65b400b88b39ab6b9ffa3130c9389784
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 13581
content:
"2011-2019. HFC-32 used as a substitute for HCFC-22, increased at \r\nleast by 300%, and HFC-143a and HFC-125 showed increases \r\nof 100% and 187%, respectively. While the ERF of HFC-245fa is \r\ncurrently <0.001 W m-2, its atmospheric abundance doubled since \r\n2011 to 3.1 ppt in 2019 (Annex III). In contrast, HFC-152a is showing \r\nsigns of stable (steady-state) abundance. "


id: c089f7077de71050634a563edebf39c4
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 27850
content:
"The AR5 assessed low confidence in stratospheric water vapour (SWV) \r\ntrends based on substantial seasonal and interannual variability in \r\nsatellite data from 1992 to 2011. The 1980-2010 record of balloon\u0002borne frost point hygrometer measurements over Boulder, Colorado \r\n(40degN), showed an average net increase of 1.0 +- 0.2 ppm (27 +- 6%) \r\nin the 16-26 km layer."


id: 11f0975e78a9c3fd2b45bfdc5fe28f64
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 323
content:
"Since AR5, bias-adjusted spatially comprehensive SWV measurements \r\nby different satellite sensors were merged to form continuous records \r\n(Hegglin et al., 2014; Froidevaux et al., 2015; Davis et al., 2016). These \r\nindicate no net global increase of SWV in the lower stratosphere since \r\nthe late 1980s. Hegglin et al. (2014) reported a latitudinal dependence \r\nof SWV trends and suggested that the upward trend over Boulder \r\nshould not be considered representative of the global stratosphere, \r\nwhile Lossow et al. (2018) showed insignificant differences between \r\nSWV trends at Boulder and those for the 35-45degN zonal mean from \r\n1980 to 2010 using model simulations and satellite observations. "


id: 24a9f92ffe37d971fb3171ef031fccc1
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 3214
content:
"The global abundance of CCl4 continues to decline, down about \r\n9.6% since 2011. Following a revision of the global lifetime from \r\n26 to 32  years, and discovery of previously unknown sources \r\n(e.g.,  biproducts of industrial emissions), knowledge of the CCl4\r\nbudget has improved. There is now better agreement between top\u0002down emissions estimates (based on atmospheric measurements) \r\nand industry-based estimates (Engel et al., 2018). Halon-1211, mainly \r\nused for fire suppression, is also declining, and its ERF dropped below \r\n0.001 W m-2 in 2019. While CH2Cl2 has a short atmospheric lifetime \r\n(6 months), and is not well-mixed, its abundance is increasing and its \r\nERF is approaching 0.001 W m-2."


id: 79c566fb7fc6de0cda24ae7b736582f
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 16727
content:
"Perfluorocarbons CF4 and C2F6, which have exceedingly long global \r\nlifetimes, showed modest increases from 2011 to 2019. CF4, which \r\nhas both natural and anthropogenic sources, increased 8.2% to \r\n85.5 ppt, and C2F6 increased 16.3% to 4.85 ppt. c-C4F8, which is used \r\nin the electronics industry and may also be generated during the \r\nproduction of polytetrafluoroethylene (PTFE, also known as 'Teflon') \r\nand other fluoropolymers (Muhle et al., 2019), has increased 34% \r\nsince 2011 to 1.75 ppt, although its ERF remains below 0.001 W m-2. \r\nOther PFCs, present at mixing ratios <1 ppt, have also been quantified \r\n(Droste et al., 2020; see Annex III)."


id: 6d27864e750066799fe49480e12fad86
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 14795
content:
"In summary, in situ measurements at a single mid-latitude location \r\nindicate about a 25% net increase in stratospheric water vapour \r\nsince 1980, while merged satellite data records since the late 1980s \r\nsuggest little net change. Recent studies of dynamical influences \r\non SWV have highlighted their substantial roles in driving large \r\ninterannual variability that complicates trend detection. There \r\nthus continues to be low confidence in trends of SWV over the \r\ninstrumental period. Disregarding dynamic influences on SWV, an \r\nERF of 0.05 +-  0.05 W m-2 is estimated for SWV produced by CH4\r\noxidation (Section 7.3.2.6), unchanged from AR5."


id: ffecfa53575554133ae3eb073cc03db1
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 37802
content:
"In summary, by 2019 the ERF of halogenated GHGs had increased \r\nby 3.5% since 2011, reflecting predominantly a decrease in \r\nthe atmospheric mixing ratios of CFCs and an increase in their \r\nreplacements. However, average annual ERF growth rates associated \r\nwith halogenated gases since 2011 are a factor of seven lower than \r\nin the 1970s and 1980s. Direct radiative forcings from CFCs, HCFCs, \r\nHFCs, and other halogenated greenhouse gases were 0.28, 0.06, \r\n0.04, and 0.03 W  m-2 respectively, totalling 0.41 +- 0.07 W m-2 in \r\n2019 (see Table 7.5). "


id: d2666c0d028bdab9c4f623e46b4ef08
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 30631
content:
"that as much as 40% of the long-term ozone loss occurred between \r\n1960 and 1980 (Shepherd et  al., 2014), lowering the 1964-1980 \r\nbaseline values by about 1% (outside the polar regions), a value \r\nclose to observational uncertainties. The world's longest record of \r\ntotal ozone measurements from Arosa, Switzerland, initiated in 1926, \r\ndoes not show any substantial long-term changes before about 1980 \r\n(Staehelin et al., 2018). "


id: 9c65a714075dc9723d1ea79168444f5f
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 22162
content:
"The AR5 assessed that it was certain that global stratospheric ozone \r\nfrom the mid-1990s to 2011 was nearly constant and about 3.5% \r\nlower than in the reference period 1964-1980. Most of the declines \r\noccurred prior to the mid-1990s. "


id: 5a53c3a7478bdcdf97bbd31360d0cc19
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 11718
content:
"Global annual mean total ozone (Figure 2.7) significantly declined \r\nby about 3.5% during the 1980s and the early 1990s and by 2.5% \r\nover 60degS-60degN (near-global). Then, during 2000-2017, both \r\nglobal and near-global concentrations increased slightly, but not \r\nsignificantly, all in line with production and consumption limits of \r\nODS regulated under the Montreal Protocol and its amendments. \r\nNear-global 2014-2017 mean total ozone is about 2.2% below the \r\npre-ozone depletion 1964-1980 average (Braesicke et  al., 2018). \r\nAt  southern and northern mid-latitudes, declines are 5.5% and \r\n3.0% compared to the 1964-1980 average respectively. Total ozone \r\nremained practically unchanged in the tropics (Braesicke et al., 2018). \r\nEmission of ODS started before 1980 and some estimates suggest "


id: d0c224b0d06cf22fbe190c9cef24e685
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 30378
content:
"ERF depends strongly on the altitude of ozone changes. Two \r\nstratospheric regions are mainly responsible for long-term changes \r\noutside the polar regions. In the upper stratosphere (35-45 km), \r\nthere was a strong decline (about 10%) from the start of observations \r\nin 1979 up to the mid-1990s and a subsequent increase by about \r\n4% to present (SPARC/IO3C/GAW, 2019). In the lower stratosphere \r\n(20-25 km), there also was a statistically significant decline (7-8%) \r\nup to the mid-1990s, followed by stabilization or a small further \r\ndecline (Ball et al., 2018, 2019), although the natural variability is \r\ntoo strong to make a conclusive statement (Chipperfield et al., 2018)."


id: 9d85a3b260560f72c48cdcfcb059718a
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 22318
content:
"the SH tropics and mid-latitudes since the mid-20th century, with \r\ntropospheric ozone exhibiting little change across Antarctica (Tarasick \r\net  al., 2019; Cooper et  al., 2020). Based on reliable UV absorption \r\nmeasurements at remote locations (surface and lower troposphere), \r\nozone trends since the mid-1990s varied spatially at northern mid\u0002latitudes, but increased in the northern tropics (2-17%; 1-6 ppbv \r\nper decade; (Cooper et al., 2020; Gaudel et al., 2020). Across the SH \r\nthese more recent observations are too limited to determine zonal \r\ntrends (e.g., tropics, mid-latitudes, high latitudes)."


id: 61071394d5cf7f6d100aad33d133221a
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 12835
content:
"The strongest ozone loss in the stratosphere continues to occur in \r\naustral spring over Antarctica (ozone hole) with emergent signs of \r\nrecovery after 2000 (Langematz et al., 2018). Interannual variability \r\nin polar stratospheric ozone is driven by large scale winds and \r\ntemperatures, and, to a lesser extent, by the stratospheric aerosol \r\nloading and the solar cycle. This variability is particularly large in the \r\nArctic, where the largest depletion events, comparable to a typical \r\nevent in the Antarctic, occurred in 2011 (Manney et  al., 2011; \r\nLangematz et  al., 2018) and again in 2020 (Manney et  al., 2020; \r\nGrooss and Muller, 2021). Further details on trends and ERF can be \r\nfound in Sections 6.3.2 and 7.3.2.5. "


id: 894f94142fe26f34e1103e7197b1a013
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 19143
content:
"In summary, compared to the 1964-1980 average, stratospheric ozone \r\ncolumns outside polar regions (60degS-60degN) declined by about 2.5% \r\nover 1980-1995, and stabilized after 2000, with 2.2% lower values in \r\n2014-2017. Large ozone depletions continue to appear in spring in \r\nthe Antarctic and, in particularly cold years, also in the Arctic. Model\u0002based estimates disagree on the sign of the ERF due to stratospheric \r\nozone changes, but agree that it is much smaller in magnitude than \r\nthat due to tropospheric ozone changes (Section 7.3.2.5)."


id: 1b50d4f04519dbe967aecb6a7ed0c782
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 1759
content:
"The AR5 assessed medium confidence in large-scale increases of \r\ntropospheric ozone at rural surface sites across the NH (1970-2010), \r\nand in a doubling of European surface ozone during the 20th \r\ncentury, with the increases of surface ozone in the SH being of \r\nlow confidence. Surface ozone likely increased in East Asia, but \r\nlevelled off or decreased in the eastern USA and western Europe. \r\nFree tropospheric trends (1971-2010) from ozonesondes and aircraft \r\nshowed positive trends in most, but not all, assessed regions, and \r\nfor most seasons and altitudes. This section focuses on large scale \r\nozone changes; chemical and physical processes and regional \r\nchanges in tropospheric ozone are assessed in Section 6.3.2.1 and \r\nSection 7.3.2.5 assesses radiative forcing."


id: c4f4520bff78950466a49674e238a65
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 28561
content:
"In summary, limited available isotopic evidence constrains the \r\nglobal tropospheric ozone increase to less than 40% between 1850 \r\nand 2005 (low confidence). Based on sparse historical surface/low \r\naltitude data tropospheric ozone has increased since the mid-20th \r\ncentury by 30-70% across the NH (medium confidence). Surface/low \r\naltitude ozone trends since the mid-1990s are variable at northern \r\nmid-latitudes, but positive in the tropics [2 to 17% per decade] \r\n(high confidence). Since the mid-1990s, free tropospheric ozone \r\nhas increased by 2-7% per decade in most regions of the northern \r\nmid-latitudes, and 2-12% per decade in the sampled regions of the \r\nnorthern and southern tropics (high confidence). Limited coverage \r\nby surface observations precludes identification of zonal trends in \r\nthe SH, while observations of tropospheric column ozone indicate \r\nincreases of less than 5% per decade at southern mid-latitudes \r\n(medium confidence)."


id: e4cb5af2531036f966f2acbb0d7fb52
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 33475
content:
"loadings for mid- and high-latitude ocean of both hemispheres \r\n(Lamy et al., 2014; Martinez-Garcia et al., 2014; Serno et al., 2015). \r\nImproved quantification of changes in dust deposition from North \r\nAfrica and North Atlantic sediment records confirms dust deposition \r\nrates lower by a factor 2-5 during the African Humid Period (10-5 ka) \r\ncompared to the late Holocene (McGee et  al., 2013; Albani et  al., \r\n2015; Middleton et al., 2018; Palchan and Torfstein, 2019). During the \r\nHolocene, biogenic emissions and volcanic activity drove significant \r\nvariability (up to one order of magnitude) in sulphate concentrations \r\n(Schupbach et al., 2018). "


id: 12e8d52b17dd458c7823f8ae880262fb
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 479
content:
"The AR5 assessed large-scale aerosol optical depth (AOD) trends \r\nover 2000-2009, concluding that there was low confidence in \r\na  global trend, but that AOD very likely decreased from 1990 \r\nonwards over Europe and the eastern USA, and increased since 2000 \r\nover eastern and southern Asia. The ERF associated with aerosol-\r\nradiation interactions for 2011 (relative to 1750) was estimated to \r\nbe -0.45  +-  0.5 W m-2 and of aerosol-cloud interaction estimated \r\nas -0.45 [-1.2 to 0.0] W m-2. Aerosol ERF uncertainty was assessed \r\nas the largest contributor to the overall ERF uncertainty since 1750. "


id: 8b4d03808f4ceeb9579efed7fac226ab
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 19440
content:
"Ice cores allow for estimation of multi-centennial trends in mid- and \r\nhigh-latitude aerosol deposition, including those for sulphate \r\nand black carbon (Figure 2.9a,b). Sulphate in ice cores increased \r\nby a factor of 8 from the end of the 19th century to the 1970s in \r\ncontinental Europe, by a factor of 4 from the 1940s to the 1970s \r\nin Russia, and by a factor of 3 from the end of the 19th century to \r\n1950 in the Arctic (Svalbard). In all regions studied, concentrations \r\nhave declined by about a factor of 2 following their peak (around \r\n1970 in Europe and Russia, and 1950 in the Arctic). Strong increases \r\nof black carbon (BC) were observed in the 20th century over Europe, \r\nRussia, Greenland (primarily originating from emissions from North \r\nAmerica), and in the Arctic (Svalbard). South America exhibits a small \r\npositive trend (Figure 2.9). BC concentrations in various Antarctic ice \r\ncores were below 1 ng g-1 without a clear trend. "


id: e4f3700463520031bfe201dd49e6d28d
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 33506
content:
"This section assesses the observed large-scale temporal evolution \r\nof tropospheric aerosols. Aerosol-related processes, chemical and \r\nphysical properties, and links to air quality, are assessed in Chapter 6. \r\nAn in-depth assessment of aerosol interactions with radiation and \r\nclouds is provided in Section 7.3.3."


id: 9edc2a66520543cd3288fa7777743b3d
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 22536
content:
"Aerosol proxy records of improved temporal resolution and quality \r\nare now available (Kylander et al., 2016; Stevens et al., 2016, 2018; \r\nJacobel et al., 2017; Dornelas et al., 2018; Middleton et al., 2018), \r\nwhich further advance synthesis of new global compilations of \r\naerosol loadings (Lambert et al., 2015; Albani et al., 2016). Estimates \r\nof the glacial/interglacial ratio in global dust deposition are within \r\nthe range of 2-4 (Albani et  al., 2015; Lambert et  al., 2015). New \r\nreconstructions indicate a ratio of 3-5 for the glacial/interglacial "


id: 4051ce8b3240f64b7f4879ad4f3fb40e
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 7500
content:
"Satellite-derived trends are further supported by in situ regional surface \r\nconcentration measurements, operational since the 1980s (sulphate) \r\nand 1990s (PM2.5) from a global compilation (Collaud Coen et al., \r\n2020) of networks over Europe (Stjern et al., 2011), North America \r\n(Jongeward et  al., 2016), and China (Zheng et  al., 2018). Collaud \r\nCoen et al. (2020) report from surface observations across the NH \r\nmid-latitudes that aerosol absorption coefficients decreased since \r\nthe first decade of the 21st century."


id: 98da0aab6fd058d91adfad056fadebba
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 21565
content:
"Anthropogenic aerosol is predominantly found in the fraction of \r\nparticles with radii <1 um that comprise the fine-mode AOD (AODf; \r\nFigure 2.9d; Kinne, 2019). A significant decline in AODf of more than \r\n1.5% per year from 2000 to 2019 has occurred over Europe and \r\nNorth America, while there have been positive trends of up to 1.5% \r\nper year over Southern Asia and East Africa. The global-scale trend in \r\nAODf of -0.03% per year (Figure 2.9) is significant. The results are \r\nconsistent with trend estimates from an aerosol reanalysis (Bellouin \r\net al., 2020), and the trends in satellite-derived cloud droplet number \r\nconcentrations are consistent with the aerosol trends (Cherian and \r\nQuaas, 2020). Cloudiness and cloud radiative properties trends \r\nare, however, less conclusive possibly due to their large variability \r\n(Norris  et  al., 2016; Cherian and Quaas, 2020). Further details on \r\naerosol-cloud interactions are assessed in Section 7.3.3.2."


id: 738078cef4bc8d97b30b160a56a36a76
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 15793
content:
"To conclude, atmospheric aerosols sampled by ice cores, influenced \r\nby northern mid-latitude emissions, show positive trends from 1700 \r\nuntil the last quarter of the 20th century and decreases thereafter \r\n(high confidence), but there is low confidence in observations of \r\nsystematic changes in other parts of the world in these periods. \r\nSatellite data and ground-based records indicate that AOD exhibits \r\npredominantly negative trends since 2000 over NH mid-latitudes and \r\nSH continents, but increased over South Asia and East Africa (high \r\nconfidence). A globally deceasing aerosol abundance is thus assessed \r\nwith medium confidence. This implies increasing net positive ERF, \r\nsince the overall negative aerosol ERF has become smaller."


id: dda91c1b8fcc59a24db2641c8081e64c
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 32378
content:
"The AR5 assessed that land use change very likely increased the \r\nEarth's albedo with a radiative forcing of -0.15 (+- 0.10) W m-2. \r\nAR5 also assessed that a net cooling of the surface, accounting for \r\nprocesses that are not limited to the albedo, was about as likely as not. \r\nThe SRCCL concluded with medium confidence that the biophysical \r\neffects of land cover change (mainly increased albedo) had a cooling \r\neffect on surface temperatures. The SRCCL also concluded with very \r\nhigh confidence that the biogeochemical effects of land cover change \r\n(i.e., GHG emissions) resulted in a mean annual surface warming."


id: 219125d0ef75585f0fadccaca1fd4164
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 2707
content:
"In summary, biophysical effects from historical changes in land use \r\nhave an overall negative ERF (medium confidence). The best-estimate \r\nERF from the increase in global albedo is -0.15 W m-2 since 1700 \r\nand -0.12 W m-2 since 1850 (medium confidence) (Section 7.3.4.1). \r\nBiophysical effects of land-use change likely resulted in a net \r\nglobal cooling of about 0.1degC since 1750 (medium confidence) \r\n(Section 7.3.5.3)."


id: 42059bda32158dff8ee67753fd20ebb1
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 7793
content:
"The AR5 concluded that changes in climate drivers over the industrial \r\nperiod corresponded to a positive ERF which increased more rapidly \r\nafter 1970 than before. There was very high confidence in the positive \r\nERF due to WMGHG, with CO2 the single largest contributor. The AR5 \r\nconcluded that there was high confidence that aerosols have offset \r\na substantial portion of the WMGHG forcing. "


id: fc69a75cfd9c7978051868c091e86cf4
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 37219
content:
"This section reports the evolution in ERF with respect to 1750 \r\nas assessed in Section 7.3 and relies on the observed changes in \r\nclimate drivers as assessed in Section 2.2 wherever possible, and \r\nmodels otherwise. The ERF is assessed using the methods and details \r\ndescribed in Section 7.3.1 and includes, in addition to the radiative \r\nforcing, the rapid adjustments, especially implied by clouds. The time \r\nseries are shown in Figure 2.10."


id: 491658c5d75a40717b5f66299411d80
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 8884
content:
"The impact of historical land-cover change on global climate is \r\nassessed with model simulations that consider multiple climate and \r\nbiophysical processes (e.g.,  changes in albedo, evapotranspiration, \r\nand roughness) and/or biogeochemical processes (e.g.,  changes in \r\natmospheric composition such as carbon release from deforestation). \r\nThe dominant biophysical response to land cover changes is albedo, \r\nwhich is estimated (using a MODIS albedo product and a historical \r\nland-use harmonization product) to have increased gradually prior to \r\nthe mid-19th century and then strongly through the mid-20th century, "


id: 5ab46da664eca6f8666ab0e07653cbb
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 11788
content:
"Increasing TSI (Section 2.2.1) implies a small ERF of less than \r\n0.1 W m-2 between 1900 and 1980. TSI varies over the 11-year solar \r\ncycle with ERF of order +- 0.1 W m-2 in the assessed period. Strong "


id: 96f5be8aa0fa19a23ff981e08ac8f096
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
index: climate_gpt_only_giec
vector_id: 21267
content:
"volcanic eruptions (Section 2.2.2) with periods of strong negative \r\nERF lasting 2-5 years in duration occurred in the late 19th and early \r\n20th centuries. There followed a relatively quiescent period between \r\nabout 1920 and 1960, and then three strong eruptions in 1963, \r\n1982 and 1991, and only small-to-moderate eruptions thereafter \r\n(Schmidt et al., 2018). "


id: 7e97ed31eb2aa6d62019956e5ea68a06
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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content:
"Among the gaseous short-lived climate forcers (Chapter 6 and \r\nSections 2.2.5 and 7.3; excluding CH4 here), ozone (O3) is the \r\ncomponent with the largest (positive) ERF. Concentrations from \r\ndirect observations have increased since the mid-20th century and, \r\nmostly based on models, this extends to since 1750. Other gaseous \r\nshort-lived climate forcers have small contributions to total ERF."


id: 1e95141dbd37e56f70df0fefee9939f4
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 2256
content:
"The atmospheric concentrations of WMGHGs (Section 2.2.3) have \r\ncontinuously increased since the early 19th century, with CO2\r\ncontributing the largest share of the positive ERF. Compared to \r\nthe last two decades of the 20th century, the growth rate of CO2 in the \r\natmosphere increased in the 21st century, showed strong fluctuations \r\nfor CH4, and was about constant for N2O. Mixing ratios of the most \r\nabundant CFCs declined (Section 2.2.4). Mixing  ratios of HCFCs \r\nincreased, but growth rates are starting to decelerate. Mixing ratios \r\nof HFCs and some other human-made components are increasing \r\n(Section 2.2.4). The ERF for CO2 alone is stronger than for all the other \r\nanthropogenic WMGHGs taken together throughout the industrial "


id: 3beb0ac27e6b2eb41ded1a1d721fc295
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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content:
"The net effect of aerosols (Sections 2.2.6 and 6.4) on the radiation \r\nbudget, including their effect on clouds, and cloud adjustments, as \r\nwell as the deposition of black carbon on snow (Section 7.3.4.3), \r\nwas negative throughout the industrial period (high confidence). \r\nThe net effect strengthened (becoming more negative) over most \r\nof the 20th  century, but more likely than not weakened (becoming \r\nless negative) since the late 20th century. These trends are reflected \r\nin measurements of surface solar radiation (Section 7.2.2.3) and the "


id: 44379f2d39d06864f4ea27e8f4a229cc
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 8156
content:
"Earth's energy imbalance (Section 7.2.2.1). The relative importance \r\nof aerosol forcing compared to other forcing agents has decreased \r\nglobally in the most recent 30 years (medium confidence) and the \r\nreduction of the negative forcing in the 21st century enhances \r\nthe overall positive ERF."


id: 9b6a45312fbd62dcb5e9d076bb456114
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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content:
"rapid adjustments and thus there is low confidence in its magnitude. \r\nBiogeochemical feedbacks can be substantial (Section 5.4) and are \r\nnot included in ERF."


id: d850836fa1c365539c2cd9ff471ad58c
created_at: 2023-03-16 14:00:38
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vector_id: 31561
content:
"In conclusion, the net ERF due to all observed changes in climate drivers \r\nis positive, except for short periods (up to a few years in duration) \r\nfollowing moderate to large volcanic eruptions, and has grown in \r\nmagnitude since the late 19th century. The rate of change likely has \r\nincreased in the last 30 years, since CO2 concentrations increased at an \r\nincreasing rate due to growing CO2 emissions (very likely), and since \r\nthe aerosol forcing became less negative (more likely than not). "


id: e189b2a8c327a5c5eeaf7a33a47d459b
created_at: 2023-03-16 14:00:38
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content:
"Land use and land cover changes (Section 2.2.7) over the industrial \r\nperiod introduce a negative radiative forcing by increasing the surface \r\nalbedo. This effect increased since 1750, reaching current values of \r\nabout -0.20 W m-2 (medium confidence). This ERF value is taken from \r\nSection 7.3.4.1 and is different from the assessment in Section 2.2.7 \r\nin that it also includes the effect of irrigation. It also includes uncertain "


id: 724596725fe49cd7fb14df2e33c64d3f
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 15603
content:
"Contributing Authors: Veronika Eyring (Germany), Nathan P. Gillett (Canada), Sergey K. Gulev (Russian Federation), Jochem Marotzke \r\n(Germany), June-Yi Lee (Republic of Korea), Peter W. Thorne (Ireland/United Kingdom)"


id: 8ba825383a522f1b21510aef9621ea0e
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
content_type: text
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content:
"Chapters 2, 3 and 4 assess the current evidence basis for climatic changes, their causes, and their potential future under different \r\npossible emissions pathways using a combination of observations and state-of-the-art Earth system models (ESMs). The assessment in \r\nthese chapters focuses on selected large-scale indicators and modes as defined in this Box. These indicators and modes of variability \r\ntaken together characterize overall changes to the climate system as a whole. "


id: 2e251282f5672471caad26cff801beaa
created_at: 2023-03-16 14:00:38
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content:
"Understanding of large-scale climate variability and change requires knowledge of both the response to forcings and the role of \r\ninternal variability. Many forcings have substantial hemispheric or continental scale variations. Modes of climate variability are \r\ngenerally driven by ocean basin scale processes. The climate system involves process interactions from the micro- to the global-scale \r\nand as such, any threshold for defining 'large-scale' is arbitrary, but, within these chapters on the basis of these considerations \r\nlarge-scale is defined to include ocean basin and continental scales as well as hemispheric and global scales. "


id: 9710c3439373fcf88d0341ff8159867b
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 21282
content:
"Key climate indicators should constitute a finite set of distinct variables and/or metrics that may collectively point to important overall \r\nchanges in the climate system that provide a synthesis of climate system evolution and are of broad societal relevance. Key indicators \r\nhave been selected across the atmospheric, oceanic, cryospheric and biospheric domains, with land as a cross-cutting component. These \r\nindicators, and their use across Chapters 2, 3 and 4, as well as the broader report, are summarized in Cross-Chapter Box 2.2, Table 1. \r\nAll selected indicators are Essential Climate Variables as defined by the Global Climate Observing System (Bojinski et al., 2014)."


id: bf9e399808f3987309d93866544a7589
created_at: 2023-03-16 14:00:38
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content:
"Cross-Chapter Box 2.2, Table 1 | Summary of the large-scale indicators used across Chapters 2 through 4 and their principal applications in \r\nremaining chapters. Indicators are sub-divided by Earth-system domain and their inclusion in individual chapters is indicated by a blue dot. The list of additional \r\nchapters is limited to those where the variable is a principal consideration. "


id: 14548cbeed0839cf5c36de75a1083d9f
created_at: 2023-03-16 14:00:38
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content:
"Changes in ice sheets are indicators of the longest-term impacts of climate change and associated with changes in global and \r\nregional sea level. Seasonal snow cover has many implications for mid- to high-latitude regions (albedo, hydrological cycle, etc.) with \r\nimpacts on biospheric components of the system. Changes in sea ice extent, seasonality and thickness have potential impacts for \r\nhemispheric-scale circulation (Cross-Chapter Box 10.1). Changes in glacier mass balance contribute to changes in sea level but also \r\nhave substantial implications for water supply for a substantial proportion of the global population. Finally, changes in permafrost and \r\nthe seasonally thawed active layer have substantial implications in mid- to high-latitudes and have been hypothesized to be important \r\nin potential feedbacks through degassing of WMGHGs as the permafrost thaws."


id: 899d91f3a1630b97ffc070cc84858092
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 19187
content:
"Most of the energy imbalance (Box 7.2) in the climate system is taken up by the ocean, resulting in changes in ocean temperature and \r\nheat content. Salinity changes indicate broad-scale hydrological cycle and circulation changes. Global-mean sea-level change is a key \r\nindicator of the impacts of both global warming and changes in global ice volume. Furthermore, it is integral to assessing the global \r\nenergy budget (Cross-Chapter Box 9.1). The oceanic overturning circulation redistributes heat, carbon, oxygen and salinity within the \r\nocean. Declines in ocean pH result from air-sea exchange of carbon dioxide and loss of ocean oxygen results from ocean warming; \r\nboth lead to changes in marine ecosystems."


id: 183a46ad5fd274d010de22150c5d9552
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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content:
"The seasonal cycle of CO2 is an integrated measure of the biogeochemical activity across the global biosphere. Changes in marine \r\nand terrestrial ecosystems can also be observed directly at large scales. For small, free-floating organisms such as phytoplankton, the \r\ndynamics can be rapid in nature, whereas on land slower changes in plant assemblages may occur, with commensurate changes in \r\naltitude and latitude of the tree-line. Lengthening of the growing season and the associated changes in phenology, distribution and \r\nabundance of species would be expected in most of the extratropics. Biospheric indicators and their impacts are assessed in much \r\ngreater detail in WGII Chapters 2 and 3."


id: acfb5d5126b8c67fe10ffd9cc0424b26
created_at: 2023-03-16 14:00:38
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content:
"Defining a selection of modes of variability\r\nMany modes of climate variability affect global, hemispheric or regional climate across a range of timescales. Conversely, their \r\nbehaviour may be influenced by global climate change. Modes were selected for inclusion that: (i) have effects at large spatial scales; \r\nand (ii) have substantial potential to modify interannual to multi-decadal climate. The selected modes are considered in multiple \r\nchapters (Cross-Chapter Box 2.2, Table 2) and are defined in Annex IV."


id: 1478c08693bdc114eb9d9bb17f366cfe
created_at: 2023-03-16 14:00:38
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vector_id: 728
content:
"Cross-Chapter Box 2.2, Table 2 | Summary of the modes of variability used across Chapters 2 through 4 and their principal applications in \r\nremaining chapters. Inclusion in each of Chapters 2 through 4 is indicated by a blue dot of the relevant table cell. The list of remaining chapters is limited to those \r\nwhere the mode of variability is a principal consideration of that chapter and is not intended to be exhaustive."


id: 9ae4b0d0eecebb393e75a934a31c1b8e
created_at: 2023-03-16 14:00:38
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vector_id: 21900
content:
"For the EECO, new GMST reconstructions fall at the high end of the range \r\nassessed by AR5. These include estimates of 7degC-18degC (90% range; \r\nInglis et al., 2020) and 12degC-18degC (95% range; Zhu et al., 2019) warmer \r\nthan 1850-1900, and 10degC-16degC warmer than 1995-2014 'recent \r\npast' conditions (2 standard error range; Caballero and Huber, 2013). "


id: cc327d227d206924eff4dc67bd11f726
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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content:
"Together, they indicate that GMST was 10degC-18degC warmer during the \r\nEECO compared with 1850-1900 (medium confidence). "


id: de5fcd6b3a1a4b0efde80915eb2ae054
created_at: 2023-03-16 14:00:38
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content:
"The AR5 did not assess the GMST for the MCO. Reconstructions \r\nbased on data from multiple study sites include estimates of about \r\n4degC (uncertainty range not specified; You et al., 2009) and 5degC-10degC \r\n(2 standard error range; Goldner et al., 2014) warmer than 1850-1900. \r\nTogether, these studies indicate that GMST was 4degC-10degC warmer \r\nduring the MCO (medium confidence)."


id: 9fc9677c34b493600666b2f7deaaceb2
created_at: 2023-03-16 14:00:38
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content:
"For the MPWP, new proxy-based estimates of global sea surface \r\ntemperatures (SST) are about 2.0degC-3.5degC warmer than 1850-1900, \r\ndepending on which proxy types are included in the analysis (Foley \r\nand Dowsett, 2019; McClymont et al., 2020). On the basis of model\u0002derived relationships between land versus sea surface temperatures \r\nunder different climate states (Figure 3.2b), the increase in GMST is \r\nestimated to have been roughly 15% greater than the increase in \r\nglobal SST. Therefore, GMST during the MPWP is estimated to have \r\nbeen 2.5degC-4.0degC warmer than 1850-1900 (medium confidence)."


id: e4e7916ef1165701fd0027190238dc59
created_at: 2023-03-16 14:00:38
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content:
"For the LDT (Cross-Chapter Box 2.1, Figure 1), no new large-scale \r\nstudies have been published since AR5 (Shakun et  al., 2012) to \r\nfurther assess the rate of GMST change during this period of rapid \r\nglobal warming (estimated at 1degC-1.5degC per kyr). The reconstruction \r\nof Shakun et  al. (2012) was based primarily on SST records and \r\ntherefore underrepresents the change in GMST during the LDT. \r\nTemperature over Greenland increased by about ten times that rate \r\nduring the centuries of most rapid warming (Jansen et al., 2020)."


id: a789bbc0b025eb62cdeefad65621bf2e
created_at: 2023-03-16 14:00:38
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vector_id: 23866
content:
"The AR5 did not include an assessment of large-scale temperature \r\nestimates for the MH, although it assigned high confidence to the \r\nlong-term cooling trend over mid- to high-latitudes of the Northern \r\nHemisphere (NH) during the 5 kyr that preceded recent warming. For \r\naverage annual NH temperatures, the period 1983-2012 was assessed \r\nas very likely the warmest 30-year period of the past 800 years (high \r\nconfidence) and likely the warmest 30-year period of the past 1.4 kyr \r\n(medium confidence); the warm multi-decadal periods prior to the \r\n20th century were unsynchronized across regions, in contrast to \r\nthe warming since the mid-20th century (high confidence), although \r\nonly sparse information was available from the SH. "


id: 89e83cfb86f5a6ade08b19ba2d77b30a
created_at: 2023-03-16 14:00:38
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content:
"New GMST reconstructions for the LGM fall near the middle \r\nof AR5's very likely range, which was based on a combination of \r\nproxy reconstructions and model simulations. Two of these new \r\nreconstructions use marine proxies to reconstruct global SST that \r\nwere scaled to GMST based on different assumptions. One indicates \r\nthat GMST was 6.2 [4.5 to 8.1] degC cooler than the late Holocene \r\naverage (Snyder, 2016), and the other, 5.7degC +- 0.8degC (2 SD) cooler \r\nthan the average of the first part of the Holocene (10-5 ka) (Friedrich \r\nand Timmermann, 2020). A third new estimate (Tierney et al., 2020) \r\nuses a much larger compilation of marine proxies along with a data\u0002assimilation procedure, rather than scaling, to reconstruct a GMST of "


id: d770c19b18d859b0c22bbcffa4c4ad69
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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content:
"Taking all lines of evidence into account, the GMST averaged over \r\nthe warmest centuries of the current interglacial period (sometime \r\nbetween around 6 and 7 ka) is estimated to have been 0.2degC-1.0degC \r\nhigher than 1850-1900 (medium confidence). It is therefore more \r\nlikely than not that no multi-centennial interval during the post\u0002glacial period was warmer globally than the most recent decade \r\n(which was 1.1degC warmer than 1850-1900; Section 2.3.1.1.3); the LIG \r\n(129-116 ka) is the next most recent candidate for a period of higher \r\nglobal temperature. Zonally averaged mean annual temperature \r\nreconstructions (Routson et al., 2019) indicate that MH warmth was \r\nmost pronounced north of 30degN latitude, and that GMST subsequently \r\ndecreased in general, albeit with multi-century variability, with greater \r\ncooling in the NH than in the SH (Kaufman et al., 2020a)."


id: 6edb0c0b6c66a0f770b11775b9c5c850
created_at: 2023-03-16 14:00:38
updated_at: 2023-03-16 14:00:38
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vector_id: 15064
content:
"The temperature history of the last millennium and the methods \r\nused to reconstruct it have been studied extensively, both prior \r\nto and following AR5, as summarized recently by Smerdon and \r\nPollack (2016) and Christiansen and Ljungqvist (2017). New regional \r\n(e.g., Shi et al., 2015; Stenni et al., 2017; Werner et al., 2018), global \r\nocean (McGregor et  al., 2015), quasi-hemispheric (Neukom et  al., \r\n2014; Schneider et  al., 2015; Anchukaitis et  al., 2017), and global \r\n(Tardif et al., 2019) temperature reconstructions, and new regional "


id: ecb11c99ef73b313417cffd0ea77b232
created_at: 2023-03-16 14:00:38
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vector_id: 34712
content:
"To conclude, following approximately 6 ka, GMST generally decreased, \r\nculminating in the coldest multi-century interval of the post-glacial \r\nperiod (since 8 ka), which occurred between around 1450 and 1850 \r\n(high confidence). This multi-millennial cooling trend was reversed in \r\nthe mid-19th century. Since around 1950, GMST has increased at an \r\nobserved rate unprecedented for any 50-year period in at least the \r\nlast 2000 years (high confidence)."


id: 4d722196e0f8553a298c192617a6a98
created_at: 2023-03-16 14:00:38
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content:
"Contributing Authors:  Peter W. Thorne (Ireland/United Kingdom), Blair Trewin (Australia), Richard P. Allan (United Kingdom), \r\nRichard Betts (United Kingdom), Lea Beusch (Switzerland), Chris Fairall (United States of America), Piers Forster (United Kingdom), \r\nBaylor Fox-Kemper (United States of America), Jan S. Fuglestvedt (Norway), John C. Fyfe (Canada), Nathan P. Gillett (Canada), \r\nEd Hawkins (United Kingdom), Christopher Jones (United Kingdom), Elizabeth Kent (United Kingdom), Svitlana Krakovska (Ukraine), \r\nElmar Kriegler (Germany), Jochem Marotzke (Germany), H. Damon Matthews (Canada), Thorsten Mauritsen (Germany/Denmark), Anna \r\nPirani (Italy), Joeri Rogelj (United Kingdom, Austria/Belgium), Steven K. Rose (United States of America), Bjorn H. Samset (Norway), \r\nSonia I. Seneviratne (Switzerland), Claudia Tebaldi (United States of America), Andrew Turner (United Kingdom), Russell S. Vose (United \r\nStates of America), Rachel Warren (United Kingdom) "


