Final Government Distribution                                        Chapter 1                                                   IPCC AR6 WGI

 1   Table of contents
 3   Executive Summary ................................................................................................................................. 5
 5   1.1     Report and chapter overview .......................................................................................................... 8
 6      1.1.1 The AR6 WGI Report ................................................................................................................... 8
 7      1.1.2 Rationale for the new AR6 WGI structure and its relation to the previous AR5 WGI Report.......... 9
 8      1.1.3 Integration of AR6 WGI assessments with other Working Groups ............................................... 12
 9      1.1.4 Chapter preview .......................................................................................................................... 13
11   1.2     Where we are now .......................................................................................................................... 13
12      1.2.1 The changing state of the physical climate system ....................................................................... 13
13 Recent changes in multiple climate indicators............................................................................. 14
14 Long-term perspectives on anthropogenic climate change........................................................... 15
15      1.2.2 The policy and governance context ............................................................................................. 18
17   Cross-Chapter Box 1.1: The WGI contribution to the AR6 and its potential relevance for the global
18                      stocktake ................................................................................................................ 19
20      1.2.3 Linking science and society: communication, values, and the IPCC assessment process .............. 29
21 Climate change understanding, communication, and uncertainties .............................................. 29
23      Box 1.1: Treatment of uncertainty and calibrated uncertainty language in AR6 ............................ 30
25 Values, science, and climate change communication .................................................................. 32
26 Climate information, co-production, and climate services ........................................................... 34
27 Media coverage of climate change .............................................................................................. 35
29   1.3     How we got here: the scientific context ......................................................................................... 36
30      1.3.1 Lines of evidence: instrumental observations ............................................................................... 36
31      1.3.2 Lines of evidence: paleoclimate .................................................................................................. 39
32      1.3.3 Lines of evidence: identifying natural and human drivers ............................................................ 40
33      1.3.4 Lines of evidence: understanding and attributing climate change ................................................. 43
34      1.3.5 Projections of future climate change ............................................................................................ 45
35      1.3.6 How do previous climate projections compare with subsequent observations? ............................. 48
37      Box 1.2: Special Reports in the sixth IPCC assessment cycle: key findings...................................... 50
39   1.4     AR6 foundations and concepts ...................................................................................................... 53
40      1.4.1 Baselines, reference periods and anomalies ................................................................................. 53
42   Cross-Chapter Box 1.2: Changes in global temperature between 1750 and 1850 ................................. 55
44      1.4.2 Variability and emergence of the climate change signal ............................................................... 56
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 1 Climate variability can influence trends over short periods .......................................................... 57
 2 The emergence of the climate change signal ............................................................................... 57
 3      1.4.3 Sources of uncertainty in climate simulations .............................................................................. 59
 4 Sources of uncertainty ................................................................................................................ 59
 5 Uncertainty quantification .......................................................................................................... 60
 6      1.4.4 Considering an uncertain future................................................................................................... 61
 7 Low-likelihood outcomes ........................................................................................................... 62
 8 Storylines          ............................................................................................................................... 62
10   Cross-Chapter Box 1.3: Risk framing in IPCC AR6 .............................................................................. 63
12 Abrupt change, tipping points and surprises ................................................................................ 65
14   Cross-Working Group Box: Attribution................................................................................................. 67
16      1.4.5 Climate regions used in AR6 ....................................................................................................... 70
17 Defining climate regions ............................................................................................................ 70
18 Types of regions used in AR6..................................................................................................... 71
20   1.5     Major developments and their implications .................................................................................. 72
21      1.5.1 Observational data and observing systems ................................................................................... 72
22 Major expansions of observational capacity................................................................................ 73
23 Threats to observational capacity or continuity ........................................................................... 77
24      1.5.2 New developments in reanalyses ................................................................................................. 78
25      1.5.3 Climate Models ........................................................................................................................... 82
26 Earth System Models .................................................................................................................. 82
27 Model tuning and adjustment ..................................................................................................... 84
28 From global to regional models .................................................................................................. 85
29 Models of lower complexity ....................................................................................................... 86
31      Box 1.3: Emission metrics in AR6 WGI............................................................................................. 88
33      1.5.4 Modelling techniques, comparisons and performance assessments ............................................... 89
34 Model ‘fitness for purpose’ ......................................................................................................... 89
35 Ensemble modelling techniques ................................................................................................. 89
36 The sixth phase of the Coupled Model Intercomparison Project (CMIP6) ................................... 91
37 Coordinated Regional Downscaling Experiment (CORDEX)...................................................... 93
38 Model Evaluation Tools ............................................................................................................. 94
39 Evaluation of process-based models against observations ........................................................... 94
40 Emergent constraints on climate feedbacks, sensitivities and projections .................................... 95
41 Weighting techniques for model comparisons ............................................................................. 96
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 1   1.6     Dimensions of Integration: Scenarios, global warming levels and cumulative carbon emissions 97
 2      1.6.1 Scenarios             ............................................................................................................................... 98
 3 Shared Socio-economic Pathways ............................................................................................ 100
 5   Cross-Chapter Box 1.4: The SSP scenarios as used in Working Group I ............................................ 102
 7 Scenario generation process for CMIP6 .................................................................................... 107
 8 History of scenarios within the IPCC ........................................................................................ 108
 9 The likelihood of reference scenarios, scenario uncertainty and storylines ................................ 109
10      1.6.2 Global warming levels .............................................................................................................. 111
11      1.6.3 Cumulative CO2 emissions ........................................................................................................ 112
13      Box 1.4: The relationships between ‘net zero’ emissions, temperature outcomes and carbon dioxide
14               removal ............................................................................................................................. 113
16   1.7     Final remarks ............................................................................................................................. 114
18   Frequently Asked Questions .................................................................................................................. 116
19   FAQ 1.1:                      Do we understand climate change better now compared to when the IPCC started? 116
20   FAQ 1.2:                      Where is climate change most apparent?................................................................ 117
21   FAQ 1.3:                      What can past climate teach us about the future? ................................................... 118
23   Acknowledgements               ............................................................................................................................. 120
25   References                     ............................................................................................................................. 121
27   Appendix 1.A                   ............................................................................................................................. 165
29   Figures                        ............................................................................................................................. 175

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 1   Executive Summary
 3   Working Group I (WGI) of the Intergovernmental Panel on Climate Change (IPCC) assesses the current
 4   evidence on the physical science of climate change, evaluating knowledge gained from observations,
 5   reanalyses, paleoclimate archives and climate model simulations, as well as physical, chemical and
 6   biological climate processes. This chapter sets the scene for the WGI assessment, placing it in the context of
 7   ongoing global and regional changes, international policy responses, the history of climate science and the
 8   evolution from previous IPCC assessments, including the Special Reports prepared as part of this
 9   Assessment Cycle. Key concepts and methods, relevant recent developments, and the modelling and scenario
10   framework used in this assessment are presented.
12   Framing and Context of the WGI Report
14   The WGI contribution to the IPCC Sixth Assessment Report (AR6) assesses new scientific evidence
15   relevant for a world whose climate system is rapidly changing, overwhelmingly due to human
16   influence. The five IPCC assessment cycles since 1990 have comprehensively and consistently laid out the
17   rapidly accumulating evidence of a changing climate system, with the Fourth Assessment Report (AR4,
18   2007) being the first to conclude that warming of the climate system is unequivocal. Sustained changes have
19   been documented in all major elements of the climate system, including the atmosphere, land, cryosphere,
20   biosphere and ocean. Multiple lines of evidence indicate the unprecedented nature of recent large-scale
21   climatic changes in context of all human history, and that they represent a millennial-scale commitment for
22   the slow-responding elements of the climate system, resulting in continued worldwide loss of ice, increase in
23   ocean heat content, sea level rise and deep ocean acidification. {1.2.1, 1.3, Box 1.2, Appendix 1.A}
25   Since the IPCC Fifth Assessment Report (AR5), the international policy context of IPCC reports has
26   changed. The UN Framework Convention on Climate Change (UNFCCC, 1992) has the overarching
27   objective of preventing ‘dangerous anthropogenic interference with the climate system’. Responding to that
28   objective, the Paris Agreement (2015) established the long-term goals of ‘holding the increase in global
29   average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the
30   temperature increase to 1.5°C above pre-industrial levels’ and of achieving ‘a balance between
31   anthropogenic emissions by sources and removals by sinks of greenhouse gases in the second half of this
32   century’. Parties to the Agreement have submitted Nationally Determined Contributions (NDCs) indicating
33   their planned mitigation and adaptation strategies. However, the NDCs submitted as of 2020 are insufficient
34   to reduce greenhouse gas emission enough to be consistent with trajectories limiting global warming to well
35   below 2°C above pre-industrial levels (high confidence). {1.1, 1.2}
37   This report provides information of potential relevance to the 2023 global stocktake. The 5-yearly
38   stocktakes called for in the Paris Agreement will evaluate alignment among the Agreement’s long-term
39   goals, its means of implementation and support, and evolving global efforts in climate change mitigation
40   (efforts to limit climate change) and adaptation (efforts to adapt to changes that cannot be avoided). In this
41   context, WGI assesses, among other topics, remaining cumulative carbon emission budgets for a range of
42   global warming levels, effects of long-lived and short-lived climate forcers, projected changes in sea level
43   and extreme events, and attribution to anthropogenic climate change. {Cross-Chapter Box 1.1}
45   Understanding of the fundamental features of the climate system is robust and well established.
46   Scientists in the 19th-century identified the major natural factors influencing the climate system. They also
47   hypothesized the potential for anthropogenic climate change due to carbon dioxide (CO2) emitted by fossil
48   fuel combustion. The principal natural drivers of climate change, including changes in incoming solar
49   radiation, volcanic activity, orbital cycles, and changes in global biogeochemical cycles, have been studied
50   systematically since the early 20th century. Other major anthropogenic drivers, such as atmospheric aerosols
51   (fine solid particles or liquid droplets), land-use change and non-CO2 greenhouse gases, were identified by
52   the 1970s. Since systematic scientific assessments began in the 1970s, the influence of human activity on the
53   warming of the climate system has evolved from theory to established fact. Past projections of global surface
54   temperature and the pattern of warming are broadly consistent with subsequent observations (limited
55   evidence, high agreement), especially when accounting for the difference in radiative forcing scenarios used
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 1   for making projections and the radiative forcings that actually occurred. {1.3.1 - 1.3.6}
 3   Global surface temperatures increased by about 0.1°C (likely range –0.1°C to +0.3°C, medium
 4   confidence) between the period around 1750 and the 1850–1900 period, with anthropogenic factors
 5   responsible for a warming of 0.0°C–0.2°C (likely range, medium confidence). This assessed change in
 6   temperature before 1850–1900 is not included in the AR6 assessment of global warming to date, to ensure
 7   consistency with previous IPCC assessment reports, and because of the lower confidence in the estimate.
 8   There was likely a net anthropogenic forcing of 0.0–0.3 Wm-2 in 1850–1900 relative to 1750 (medium
 9   confidence), with radiative forcing from increases in atmospheric greenhouse gas concentrations being
10   partially offset by anthropogenic aerosol emissions and land-use change. Net radiative forcing from solar and
11   volcanic activity is estimated to be smaller than ±0.1 Wm-2 for the same period. {Cross Chapter Box 1.2,
12   1.4.1, Cross Chapter Box 2.3}
14   Natural climate variability can temporarily obscure or intensify anthropogenic climate change on
15   decadal time scales, especially in regions with large internal interannual-to-decadal variability. At the
16   current level of global warming, an observed signal of temperature change relative to the 1850–1900
17   baseline has emerged above the levels of background variability over virtually all land regions (high
18   confidence). Both the rate of long-term change and the amplitude of interannual (year-to-year) variability
19   differ from global to regional to local scales, between regions and across climate variables, thus influencing
20   when changes become apparent. Tropical regions have experienced less warming than most others, but also
21   exhibit smaller interannual variations in temperature. Accordingly, the signal of change is more apparent in
22   tropical regions than in regions with greater warming but larger interannual variations (high confidence).
23   {1.4.2, FAQ1.2}
25   The AR6 has adopted a unified framework of climate risk, supported by an increased focus in WGI on
26   low-likelihood, high-impact events. Systematic risk framing is intended to aid the formulation of effective
27   responses to the challenges posed by current and future climatic changes and to better inform risk assessment
28   and decision-making. AR6 also makes use of the ‘storylines’ approach, which contributes to building a
29   robust and comprehensive picture of climate information, allows a more flexible consideration and
30   communication of risk, and can explicitly address low-likelihood, high-impact events. {1.1.2, 1.4.4, Cross-
31   Chapter Box 1.3}
33   The construction of climate change information and communication of scientific understanding are
34   influenced by the values of the producers, the users and their broader audiences. Scientific knowledge
35   interacts with pre-existing conceptions of weather and climate, including values and beliefs stemming from
36   ethnic or national identity, traditions, religion or lived relationships to land and sea (high confidence).
37   Science has values of its own, including objectivity, openness and evidence-based thinking. Social values
38   may guide certain choices made during the construction, assessment and communication of information
39   (high confidence). {1.2.3, Box 1.1}
41   Data, Tools and Methods Used across the WGI Report
43   Capabilities for observing the physical climate system have continued to improve and expand overall,
44   but some reductions in observational capacity are also evident (high confidence). Improvements are
45   particularly evident in ocean observing networks and remote-sensing systems, and in paleoclimate
46   reconstructions from proxy archives. However, some climate-relevant observations have been interrupted by
47   the discontinuation of surface stations and radiosonde launches, and delays in the digitisation of records.
48   Further reductions are expected to result from the COVID-19 pandemic. In addition, paleoclimate archives
49   such as mid-latitude and tropical glaciers as well as modern natural archives used for calibration (e.g., corals
50   and trees) are rapidly disappearing owing to a host of pressures, including increasing temperatures (high
51   confidence). {1.5.1}
53   Reanalyses have improved since AR5 and are increasingly used as a line of evidence in assessments of
54   the state and evolution of the climate system (high confidence). Reanalyses, where atmosphere or ocean
55   forecast models are constrained by historical observational data to create a climate record of the past, provide
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 1   consistency across multiple physical quantities and information about variables and locations that are not
 2   directly observed. Since AR5, new reanalyses have been developed with various combinations of increased
 3   resolution, extended records, more consistent data assimilation, estimation of uncertainty arising from the
 4   range of initial conditions, and an improved representation of the ocean. While noting their remaining
 5   limitations, the WGI report uses the most recent generation of reanalysis products alongside more standard
 6   observation-based datasets. {1.5.2, Annex 1}
 8   Since AR5, new techniques have provided greater confidence in attributing changes in climate
 9   extremes to climate change. Attribution is the process of evaluating the relative contributions of multiple
10   causal factors to an observed change or event. This includes the attribution of the causal factors of changes in
11   physical or biogeochemical weather or climate variables (e.g., temperature or atmospheric CO2) as done in
12   WGI, or of the impacts of these changes on natural and human systems (e.g., infrastructure damage or
13   agricultural productivity), as done in WGII. Attributed causes include human activities (such as emissions of
14   greenhouse gases and aerosols, or land-use change), and changes in other aspects of the climate, or natural or
15   human systems. {Cross-WG Box 1.1}
17   The latest generation of complex climate models has an improved representation of physical processes,
18   and a wider range of Earth system models now represent biogeochemical cycles. Since the AR5,
19   higher-resolution models that better capture smaller-scale processes and extreme events have become
20   available. Key model intercomparisons supporting this assessment include the Coupled Model
21   Intercomparison Project Phase 6 (CMIP6) and the Coordinated Regional Climate Downscaling Experiment
22   (CORDEX), for global and regional models respectively. Results using CMIP Phase 5 (CMIP5) simulations
23   are also assessed. Since the AR5, large ensemble simulations, where individual models perform multiple
24   simulations with the same climate forcings, are increasingly used to inform understanding of the relative
25   roles of internal variability and forced change in the climate system, especially on regional scales. The
26   broader availability of ensemble model simulations has contributed to better estimations of uncertainty in
27   projections of future change (high confidence). A broad set of simplified climate models is assessed and used
28   as emulators to transfer climate information across research communities, such as for evaluating impacts or
29   mitigation pathways consistent with certain levels of future warming. {1.4.2, 1.5.3, 1.5.4, Cross-chapter Box
30   7.1}
32   Assessments of future climate change are integrated within and across the three IPCC Working
33   Groups through the use of three core components: scenarios, global warming levels, and the
34   relationship between cumulative carbon emissions and global warming. Scenarios have a long history in
35   the IPCC as a method for systematically examining possible futures. A new set of scenarios, derived from
36   the Shared Socio-economic Pathways (SSPs), is used to synthesize knowledge across the physical sciences,
37   impact, and adaptation and mitigation research. The core set of SSP scenarios used in the WGI report, SSP1-
38   1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, cover a broad range of emission pathways, including new
39   low-emissions pathways. The feasibility or likelihood of individual scenarios is not part of this assessment,
40   which focuses on the climate response to possible, prescribed emission futures. Levels of global surface
41   temperature change (global warming levels), which are closely related to a range of hazards and regional
42   climate impacts, also serve as reference points within and across IPCC Working Groups. Cumulative carbon
43   emissions, which have a nearly linear relationship to increases in global surface temperature, are also used.
44   {1.6.1-1.6.4, Cross-Chapter Box 1.5, Cross-Chapter Box 11.1}

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 1   1.1     Report and chapter overview
 3   The role of the Intergovernmental Panel on Climate Change (IPCC) is to critically assess the scientific,
 4   technical, and socio-economic information relevant to understanding the physical science and impacts of
 5   human-induced climate change and natural variations, including the risks, opportunities and options for
 6   adaptation and mitigation. This task is performed through a comprehensive assessment of the scientific
 7   literature. The robustness of IPCC assessments stems from the systematic consideration and combination of
 8   multiple lines of independent evidence. In addition, IPCC reports undergo one of the most comprehensive,
 9   open, and transparent review and revision processes ever employed for science assessments.
11   Starting with the First Assessment Report (FAR; IPCC, 1990) the IPCC assessments have been structured
12   into three working groups. Working Group I (WGI) assesses the physical science basis of climate change,
13   Working Group II (WGII) assesses associated impacts, vulnerability and adaptation options, and Working
14   Group III (WGIII) assesses mitigation response options. Each report builds on the earlier comprehensive
15   assessments by incorporating new research and updating previous findings. The volume of knowledge
16   assessed and the cross-linkages between the three working groups have substantially increased over time.
18   As part of its sixth assessment cycle, from 2015 to 2022, the IPCC is producing three Working Group
19   Reports, three targeted Special Reports, a Refinement to the 2006 IPCC Guidelines for National Greenhouse
20   Gas Inventories, and a Synthesis Report. The AR6 Special Reports covered the topics of ‘Global Warming of
21   1.5°C’ (SR1.5; IPCC, 2018), ‘Climate Change and Land’ (SRCCL; IPCC, 2019a) and ‘The Ocean and
22   Cryosphere in a Changing Climate’ (SROCC; IPCC, 2019b). The SR1.5 and SRCCL are the first IPCC
23   reports jointly produced by all three Working Groups. This evolution towards a more integrated assessment
24   reflects a broader understanding of the interconnectedness of the multiple dimensions of climate change.
27   1.1.1    The AR6 WGI Report
29   The Sixth Assessment Report (AR6) of the IPCC marks more than 30 years of global collaboration to
30   describe and understand, through expert assessments, one of the defining challenges of the 21st century:
31   human-induced climate change. Since the inception of the IPCC in 1988, our understanding of the physical
32   science basis of climate change has advanced markedly. The amount and quality of instrumental
33   observations and information from paleoclimate archives have substantially increased. Understanding of
34   individual physical, chemical and biological processes has improved. Climate model capabilities have been
35   enhanced, through the more realistic treatment of interactions among the components of the climate system,
36   and improved representation of the physical processes, in line with the increased computational capacities of
37   the world's supercomputers.
39   This report assesses both observed changes, and the components of these changes that are attributable to
40   anthropogenic influence (or human-induced), distinguishing between anthropogenic and naturally forced
41   changes (see Section, Section 1.4.1, Cross Working Group Box: Attribution, and Chapter 3). The
42   core assessment conclusions from previous IPCC reports are confirmed or strengthened in this report,
43   indicating the robustness of our understanding of the primary causes and consequences of anthropogenic
44   climate change.
46   The WGI contribution to AR6 is focused on physical and biogeochemical climate science information, with
47   particular emphasis on regional climate changes. These are relevant for mitigation, adaptation and risk
48   assessment in the context of complex and evolving policy settings, including the Paris Agreement, the
49   Global Stocktake, the Sendai Framework and the Sustainable Development Goals (SDGs) Framework.
51   The core of this report consists of twelve chapters plus the Atlas (Figure 1.1), which can together be grouped
52   into three categories (excluding this framing chapter):
54   Large-Scale Information (Chapters 2, 3 and 4). These chapters assess climate information from global to
55   continental or ocean-basin scales. Chapter 2 presents an assessment of the changing state of the climate
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 1   system, including the atmosphere, biosphere, ocean and cryosphere. Chapter 3 continues with an assessment
 2   of the human influence on this changing climate, covering the attribution of observed changes, and
 3   introducing the fitness-for-purpose approach for the evaluation of climate models used to conduct the
 4   attribution studies. Finally, Chapter 4 assesses climate change projections, from the near to the long term,
 5   including climate change beyond 2100, as well as the potential for abrupt and ‘low-likelihood, high-impact’
 6   changes.
 8   Process Understanding (Chapters 5, 6, 7, 8 and 9). These five chapters provide end-to-end assessments of
 9   fundamental Earth system processes and components: the carbon budget and biogeochemical cycles (Chapter
10   5), short-lived climate forcers and their links to air quality (Chapter 6), the Earth’s energy budget and climate
11   sensitivity (Chapter 7), the water cycle (Chapter 8), and the ocean, cryosphere and sea-level changes
12   (Chapter 9). All these chapters provide assessments of observed changes, including relevant paleoclimatic
13   information and understanding of processes and mechanisms as well as projections and model evaluation.
15   Regional Information (Chapters 10, 11, 12 and Atlas). New knowledge on climate change at regional
16   scales is reflected in this report with four chapters covering regional information. Chapter 10 provides a
17   framework for assessment of regional climate information, including methods, physical processes, an
18   assessment of observed changes at regional scales, and the performance of regional models. Chapter 11
19   addresses extreme weather and climate events, including temperature, precipitation, flooding, droughts and
20   compound events. Chapter 12 provides a comprehensive, region-specific assessment of changing climatic
21   conditions that may be hazardous or favourable (hence influencing climate risk) for various sectors to be
22   assessed in WGII. Lastly, the Atlas assesses and synthesizes regional climate information from the whole
23   report, focussing on the assessments of mean changes in different regions and on model assessments for the
24   regions. It also introduces the online Interactive Atlas, a novel compendium of global and regional climate
25   change observations and projections. It includes a visualization tool combining various warming levels and
26   scenarios on multiple scales of space and time.
28   Embedded in the chapters are Cross-Chapter Boxes that highlight cross-cutting issues. Each chapter also
29   includes an Executive Summary (ES), and several Frequently Asked Questions (FAQs). To enhance
30   traceability and reproducibility of report figures and tables, detailed information on the input data used to
31   create them, as well as links to archived code, are provided in the Input Data Tables in chapter
32   Supplementary Material. Additional metadata on the model input datasets is provided via the report website.
34   The AR6 WGI report includes a Summary for Policy Makers (SPM) and a Technical Summary (TS). The
35   integration among the three IPCC Working Groups is strengthened by the implementation of the Cross-
36   Working-Group Glossary.
41   Figure 1.1: The structure of the AR6 WGI Report. Shown are the three pillars of the AR6 WGI, its relation to the
42               WGII and WGIII contributions, and the cross-working-group AR6 Synthesis Report (SYR).
44   [END FIGURE 1.1 HERE]
47   1.1.2   Rationale for the new AR6 WGI structure and its relation to the previous AR5 WGI Report
49   The AR6 WGI report, as a result of its scoping process, is structured around topics such as large-scale
50   information, process understanding and regional information (Figure 1.1). This represents a rearrangement
51   relative to the structure of the WGI contribution to the IPCC Fifth Assessment Report (AR5; IPCC, 2013a),
52   as summarized in Figure 1.2. The AR6 approach aims at a greater visibility of key knowledge developments
53   potentially relevant for policymakers, including climate change mitigation, regional adaptation planning
54   based on a risk management framework, and the Global Stocktake.
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 4   Figure 1.2: Main relations between AR5 WGI and AR6 WGI chapters. The left column shows the AR5 WGI
 5               chapter categories. The central column lists the AR5 WGI chapters, with the colour code indicating their
 6               relation to the AR6 WGI structure shown in Figure 1.1: Large-Scale Information (red), Process
 7               Understanding (gold), Regional Information (light blue), and Whole-Report Information (dark blue). AR5
 8               WGI chapters depicted in white have their topics distributed over multiple AR6 WGI chapters and
 9               categories. The right column explains where to find related information in the AR6 WGI report.
11   [END FIGURE 1.2 HERE]
14   Two key subjects presented separately in AR5, paleoclimate and model evaluation, are now distributed
15   among multiple AR6 WGI chapters. Various other cross-cutting themes are also distributed throughout this
16   report. A summary of these themes and their integration across chapters is described in Table 1.1.
21   Table 1.1: Cross-cutting themes in AR6 WGI, and the main chapters that deal with them. Bold numbers in the table
22   indicate the chapters that have extensive coverage.
      Thematic focus                              Main chapters; additional chapters

      Aerosols                                    2, 6, 7, 8, 9, 10, 11; 3, 4, Atlas

      Atmospheric circulation                     3, 4, 8; 2, 5, 10, 11

      Biosphere                                   2, 3, 5, 11, Cross-Chapter Box 5.1; 1, 4, 6, 8

      Carbon dioxide removal (CDR)                4, 5; 8

      Cities and urban aspects                    10, 11, 12; 2, 8, 9, Atlas

      Climate services                            12, Atlas, Cross-Chapter Box 12.2; 1, 10

      Climatic impact-drivers                     12, Annex VI; 1, 9, 10, 11, Atlas

      CO2 concentration levels                    1, 2, 5, Cross-Chapter Box 1.1; 12, Atlas

      Coronavirus pandemic (COVID-19)             Cross-Chapter Box 6.1; 1

      Cryosphere                                  2, 3, 9; 1, 4, 8, 12, Atlas

      Deep uncertainty                            9; 4, 7, 8, Cross-Chapter Box 11.2, Cross-Chapter Box 12.1

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 Detection and attribution             3, 10, 11, Cross-Working Group Box: Attribution; 5, 6, 8,
                                       9, 12, Atlas

 Emergence                             1, 10, 12; 8, 11

 Extremes and abrupt change            11, 12; 1, 5, 7, 8, 9, 10, Atlas, Cross-Chapter Box 12.1

 Global warming hiatus                 Cross-Chapter Box 3.1; 10, 11

 Land use                              5; 2, 7, 8, 10, 11

 Limits of habitability                9, 12; 11

 Low-likelihood, high-impact/warming   1, 4, 11; 7, 8, 9, 10, Cross-Chapter Box 1.1, Cross-Chapter
                                       Box 1.3, Cross-Chapter Box 4

 Model evaluation                      1, 3, 9, 10, 11, Atlas; 5, 6, 8

 Modes of variability                  1, 2, 3, 4, 8, 9, Annex IV; 7, 10, 11, 12, Atlas

 Monsoons                              8; 3, 4, 9, 10, 11, 12, Atlas

 Natural variability                   1, 2, 3, 4, 9, 11; 5, 8, 10

 Ocean                                 3, 5, 9; 1, 2, 4, 7, 12, Atlas

 Paleoclimate                          1, 2; 3, 5, 7, 8, 9, Atlas, Box 11.3

 Polar regions                         9, 12, Atlas; 2, 3, 7, 8

 Radiative Forcing                     7; 1, 2, 6, 11

 Regional case studies                 10, 11, Atlas; 12, Box 8.1, Box 11.4, Cross-Chapter
                                       Box 12.2

 Risk                                  1, 11, 12, Cross-Chapter Box 1.3; 4, 5, 9, Cross-Chapter
                                       Box 12.1

 Sea level                             9, 12; 1, 2, 3, 4, 7, 8, 10, 11, Atlas

 Short-lived climate forcers (SLCF)    6, 7; 1, 2, 4, Atlas

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      Solar radiation modification (SRM)        4, 5; 6, 8

      Tipping points                            5, 8, 9; 4, 11, 12, Cross-Chapter Box 12.1

      Values and beliefs                        1, 10; 12

      Volcanic forcing                          2, 4, 7, 8; 1, 3, 5, 9, 10, Annex III

      Water cycle                               8, 11; 2, 3, 10, Box 11.1

 2   [END TABLE 1.1 HERE]
 5   1.1.3   Integration of AR6 WGI assessments with other Working Groups
 7   Integration of assessments across the chapters of the WGI Report, and with WGII and WGIII, occurs in a
 8   number of ways, including work on a common Glossary, risk framework (see Cross-Chapter Box 1.3),
 9   scenarios and projections of future large-scale changes, and the presentation of results at various global
10   warming levels (see Section 1.6).
12   Chapters 8 through 12, and the Atlas, cover topics also assessed by WGII in several areas, including regional
13   climate information and climate-related risks. This approach produces a more integrated assessment of
14   impacts of climate change across Working Groups. In particular, Chapter 10 discusses the generation of
15   regional climate information for users, the co-design of research with users, and the translation of
16   information into the user context (in particular directed towards WGII). Chapter 12 provides a direct bridge
17   between physical climate information (climatic impact-drivers) and sectoral impacts and risk, following the
18   chapter organization of the WGII assessment. Notably, Cross-Chapter Box 12.1 draws a connection to
19   representative key risks and Reasons for Concern (RFC).
21   The science assessed in Chapters 2 to 7, such as the carbon budget, short-lived climate forcers and emission
22   metrics, are topics in common with WGIII, and relevant for the mitigation of climate change. This includes a
23   consistent presentation of the concepts of carbon budget and net zero emission targets within chapters, in
24   order to support integration in the Synthesis Report. Emission-driven emulators (simple climate models),
25   summarised in Cross-Chapter Box 7.1 in Chapter 7 are used to approximate large-scale climate responses of
26   complex Earth System Models (ESMs) and have been used as tools to explore the expected GSAT response
27   to multiple scenarios consistent with those assessed in WGI for the classification of scenarios in WGIII.
28   Chapter 6 provides information about the impact of climate change on global air pollution, relevant for
29   WGII, including Cross-Chapter Box 6.1 on the implications of the recent coronavirus pandemic (COVID-19)
30   for climate and air quality. Cross-Chapter Box 2.3 in Chapter 2 presents an integrated cross-WG discussion
31   of global temperature definitions, with implications for many aspects of climate change science.
33   In addition, Chapter 1 sets out a shared terminology on cross-cutting topics, including climate risk,
34   attribution and storylines, as well as an introduction to emission scenarios, global warming levels and
35   cumulative carbon emissions as an overarching topic for integration across all three Working Groups.
37   All these integration efforts are aimed at enhancing the bridges and ‘handshakes’ among Working Groups,
38   enabling the final cross-working group exercise of producing the integrated Synthesis Report.

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 1   1.1.4    Chapter preview
 3   The main purposes of this chapter are: (1) to set the scene for the WGI assessment and to place it in the
 4   context of ongoing global changes, international policy processes, the history of climate science and the
 5   evolution from previous IPCC assessments, including the Special Reports prepared as part of the sixth
 6   assessment cycle; (2) to describe key concepts and methods, relevant developments since AR5, and the
 7   modelling framework used in this assessment; and (3) together with the other chapters of this report, to
 8   provide context and support for the WGII and WGIII contributions to AR6, particularly on climate
 9   information to support mitigation, adaptation and risk management.
11   The chapter comprises seven sections (Figure 1.3). Section 1.2 describes the present state of Earth’s climate,
12   in the context of reconstructed and observed long-term changes and variations caused by natural and
13   anthropogenic factors. It also provides context for the present assessment by describing recent changes in
14   international climate change governance and fundamental scientific values. The evolution of knowledge
15   about climate change and the development of earlier IPCC assessments are presented in Section 1.3.
16   Approaches, methods, and key concepts of this assessment are introduced in Section 1.4. New developments
17   in observing networks, reanalyses, modelling capabilities and techniques since the AR5 are discussed in
18   Section 1.5. The three main ‘dimensions of integration’ across Working Groups in the AR6, i.e. emission
19   scenarios, global warming levels and cumulative carbon emissions, are described in Section 1.6. The Chapter
20   closes with a discussion of opportunities and gaps in knowledge integration in Section 1.7.
25   Figure 1.3: A roadmap to the contents of Chapter 1.
27   [END FIGURE 1.3 HERE]
30   1.2     Where we are now
32   The IPCC sixth assessment cycle occurs in the context of increasingly apparent climatic changes observed
33   across the physical climate system. Many of these changes can be attributed to anthropogenic influences,
34   with impacts on natural and human systems. AR6 also occurs in the context of efforts in international climate
35   governance such as the Paris Agreement, which sets a long-term goal to hold the increase in global average
36   temperature to ‘well below 2°C above pre-industrial levels, and to pursue efforts to limit the temperature
37   increase to 1.5°C above pre-industrial levels, recognizing that this would significantly reduce the risks and
38   impacts of climate change’. This section summarises key elements of the broader context surrounding the
39   assessments made in the present report.
42   1.2.1    The changing state of the physical climate system
44   The WGI contribution to the AR5 (AR5 WGI; IPCC, 2013a) assessed that ‘warming of the climate system is
45   unequivocal’, and that since the 1950s, many of the observed changes are unprecedented over decades to
46   millennia. Changes are evident in all components of the climate system: the atmosphere and the ocean have
47   warmed, amounts of snow and ice have diminished, sea level has risen, the ocean has acidified and its
48   oxygen content has declined, and atmospheric concentrations of greenhouse gases have increased (IPCC,
49   2013b). This Report documents that, since the AR5, changes to the state of the physical and biogeochemical
50   climate system have continued, and these are assessed in full in later chapters. Here, we summarize changes
51   to a set of key large-scale climate indicators over the modern era (1850 to present). We also discuss the
52   changes in relation to the longer-term evolution of the climate. These ongoing changes throughout the
53   climate system form a key part of the context of the present report.
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 1   Recent changes in multiple climate indicators
 3   The physical climate system comprises all processes that combine to form weather and climate. The early
 4   chapters of this report broadly organize their assessments according to overarching realms: the atmosphere,
 5   the biosphere, the cryosphere (surface areas covered by frozen water, such as glaciers and ice sheets), and the
 6   ocean. Elsewhere in the report, and in previous IPCC assessments, the land is also used as an integrating
 7   realm that includes parts of the biosphere and the cryosphere. These overarching realms have been studied
 8   and measured in increasing detail by scientists, institutions, and the general public since the 18th century,
 9   over the era of instrumental observation (see Section 1.3). Today, observations include those taken by
10   numerous land surface stations, ocean surface measurements from ships and buoys, underwater
11   instrumentation, satellite and surface-based remote sensing, and in situ atmospheric measurements from
12   airplanes and balloons. These instrumental observations are combined with paleoclimate reconstructions and
13   historical documentations to produce a highly detailed picture of the past and present state of the whole
14   climate system, and to allow assessments about rates of change across the different realms (see Chapter 2
15   and Section 1.5).
17   Figure 1.4 documents that the climate system is undergoing a comprehensive set of changes. It shows a
18   selection of key indicators of change through the instrumental era that are assessed and presented in the
19   subsequent chapters of this report. Annual mean values are shown as stripes, with colours indicating their
20   value. The transitions from one colour to another over time illustrate how conditions are shifting in all
21   components of the climate system. For these particular indicators, the observed changes go beyond the
22   yearly and decadal variability of the climate system. In this Report, this is termed an ‘emergence’ of the
23   climate signal (see Section 1.4.2 and FAQ 1.2).
25   Warming of the climate system is most commonly presented through the observed increase in global mean
26   surface temperature (GMST). Taking a baseline of 1850–1900, GMST change until present (2011–2020) is
27   1.09 °C (0.95–1.20 °C) (see Chapter 2, Section 2.3, Cross-Chapter Box 2.3). This evolving change has been
28   documented in previous Assessment Reports, with each reporting a higher total global temperature change
29   (see Section 1.3, Cross-Chapter Box 1.2). The total change in Global Surface Air Temperature (GSAT; see
30   Section 1.4.1 and Cross-Chapter Box 2.3 in Chapter 2) attributable to anthropogenic activities is assessed to
31   be consistent with the observed change in GSAT (see Chapter 3, Section 3.3) 1.
33   Similarly, atmospheric concentrations of a range of greenhouse gases are increasing. Carbon dioxide (CO2,
34   shown in Figure 1.4 and Figure 1.5a), found in AR5 and earlier reports to be the current strongest driver of
35   anthropogenic climate change, has increased from 285.5 ± 2.1 ppm in 1850 to 409.9 ± 0.4 ppm in 2019;
36   concentrations of methane (CH4), and nitrous oxide (N2O) have increased as well (see Chapter 2, Sections
37   2.2, Chapter 5, section 5.2, and Annex V). These observed changes are assessed to be in line with known
38   anthropogenic and natural emissions, when accounting for observed and inferred uptake by land, ocean, and
39   biosphere respectively (see Chapter 5, Section 5.2), and are a key source of anthropogenic changes to the
40   global energy balance (or radiative forcing; see Chapter 2, Section 2.2 and Chapter 7, Section 7.3).
42   The hydrological (or water) cycle is also changing and is assessed to be intensifying, through a higher
43   exchange of water between the surface and the atmosphere (see Chapter 3, Section 2.3 and Chapter 8,
44   Section 8.3). The resulting regional patterns of changes to precipitation are, however, different from surface
45   temperature change, and interannual variability is larger, as illustrated in Figure 1.4. Annual land area mean
46   precipitation in the Northern Hemisphere temperate regions has increased, while the sub-tropical dry regions
47   have experienced a decrease in precipitation in recent decades (see Chapter 2, Section 2.3).
49   The cryosphere is undergoing rapid changes, with increased melting and loss of frozen water mass in most

      Note that GMST and GSAT are physically distinct but closely related quantities, see Section 1.4.1 and Cross-Chapter
     Box 2.3 in Chapter 2.

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 1   regions. This includes all frozen parts of the globe, such as terrestrial snow, permafrost, sea ice, glaciers,
 2   freshwater ice, solid precipitation, and the ice sheets covering Greenland and Antarctica (see Chapter 9;
 3   SROCC, IPCC, 2019b). Figure 1.4 illustrates how, globally, glaciers have been increasingly losing mass for
 4   the last fifty years. The total glacier mass in the most recent decade (2010-2019) was the lowest since the
 5   beginning of the 20th century. (See Chapter 2, Section 2.3 and Chapter 9, Section 9.5).
 7   The global ocean has warmed unabatedly since at least 1970 (Sections 1.3, 2.3, 9.2; SROCC, IPCC, 2019b) .
 8   Figure 1.4 shows how the averaged ocean heat content is steadily increasing, with a total increase of [0.28–
 9   0.55] yottajoule (1024 joule) between 1971 and 2018. (see Chapter 9, Section 9.2). In response to this ocean
10   warming, as well as to the loss of mass from glaciers and ice sheets, the global mean sea level (GMSL) has
11   risen by 0.20 [0.15 to 0.25] metres between 1900 and 2018. GMSL rise has accelerated since the late 1960s.
12   (See Chapter 9, Section 9.6).
14   Overall, the changes in these selected climatic indicators have progressed beyond the range of natural year-
15   to-year variability (see Chapters 2, 3, 8, and 9, and further discussion in Sections and 1.4.2). The
16   indicators presented in Figure 1.4 document a broad set of concurrent and emerging changes across the
17   physical climate system. All indicators shown here, along with many others, are further presented in the
18   coming chapters, together with a rigorous assessment of the supporting scientific literature. Later chapters
19   (Chapter 10, 11, 12, and the Atlas) present similar assessments at the regional level, where observed changes
20   do not always align with the global mean picture shown here.
25   Figure 1.4: Changes are occurring throughout the climate system. Left: Main realms of the climate system:
26               atmosphere, biosphere, cryosphere, and ocean. Right: Six key indicators of ongoing changes since 1850,
27               or the start of the observational or assessed record, through 2018. Each stripe indicates the global (except
28               for precipitation which shows two latitude band means), annual mean anomaly for a single year, relative
29               to a multi-year baseline (except for CO2 concentration and glacier mass loss, which are absolute values).
30               Grey indicates that data are not available. Datasets and baselines used are: (1) CO2: Antarctic ice cores
31               (Lüthi et al., 2008; Bereiter et al., 2015) and direct air measurements (Tans and Keeling, 2020) (see
32               Figure 1.5 for details); (2) precipitation: Global Precipitation Climatology Centre (GPCC) V8 (updated
33               from Becker et al. 2013), baseline 1961-1990 using land areas only with latitude bands 33°N–66°N and
34               15°S–30°S; (3) glacier mass loss: Zemp et al., 2019; (4) global surface air temperature (GMST):
35               HadCRUT5 (Morice et al., 2021), baseline 1961–1990; (5) sea level change: (Dangendorf et al., 2019),
36               baseline 1900–1929; (6) ocean heat content (model-observation hybrid): Zanna et al., (2019), baseline
37               1961–1990. Further details on data sources and processing are available in the chapter data table (Table
38               1.SM.1).
40   [END FIGURE 1.4 HERE]
43   Long-term perspectives on anthropogenic climate change
45   Paleoclimate archives (e.g, ice cores, corals, marine and lake sediments, speleothems, tree rings, borehole
46   temperatures, soils) permit the reconstruction of climatic conditions before the instrumental era. This
47   establishes an essential long-term context for the climate change of the past 150 years and the projected
48   changes in the 21st century and beyond (IPCC, 2013a; Masson-Delmotte et al., 2013) Chapter 3). Figure 1.5
49   shows reconstructions of three key indicators of climate change over the past 800,000 years 2 – atmospheric
50   CO2 concentrations, global mean surface temperature (GMST) and global mean sea level (GMSL) –

      as old as the longest continuous climate records based on the ice core from EPICA Dome Concordia (Antarctica).
     Polar ice cores are the only paleoclimatic archive providing direct information on past greenhouse gas concentrations.

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 1   comprising at least eight complete glacial-interglacial cycles (EPICA Community Members, 2004; Jouzel et
 2   al., 2007) that are largely driven by oscillations in the Earth’s orbit and consequent feedbacks on multi-
 3   millennial time scales (Berger, 1978; Laskar et al., 1993). The dominant cycles – recurring approximately
 4   every 100,000 years – can be found imprinted in the natural variations of these three key indicators. Before
 5   industrialisation, atmospheric CO2 concentrations varied between 174 ppm and 300 ppm, as measured
 6   directly in air trapped in ice at Dome Concordia, Antarctica (Bereiter et al., 2015; Nehrbass-Ahles et al.,
 7   2020). Relative to 1850–1900, the reconstructed GMST changed in the range of -6 to +1°C across these
 8   glacial-interglacial cycles (see Chapter 2, Section 2.3.1 for an assessment of different paleo reference
 9   periods). GMSL varied between about -130 m during the coldest glacial maxima and +5 to +25 m during the
10   warmest interglacial periods (Spratt and Lisiecki, 2016; Chapter 2). They represent the amplitudes of natural,
11   global-scale climate variations over the last 800,000 years prior to the influence of human activity. Further
12   climate information from a variety of paleoclimatic archives are assessed in Chapters 2, 5, 7, 9.
14   Paleoclimatic information also provides a long-term perspective on rates of change of these three key
15   indicators. The rate of increase in atmospheric CO2 observed over 1919-2019 CE is one order of magnitude
16   higher than the fastest CO2 fluctuations documented during the last glacial maximum and the last deglacial
17   transition in high-resolution reconstructions from polar ice cores (Marcott et al., 2014, see Chapter 2, Section
18 Current multi-decadal GMST exhibit a higher rate of increase than over the past two thousand
19   years (PAGES 2k Consortium, 2019; Chapter 2, Section, and in the 20th century GMSL rise was
20   faster than during any other century over the past three thousand years (Chapter 2, Section
25   Figure 1.5: Long-term context of anthropogenic climate change based on selected paleoclimatic reconstructions
26               over the past 800,000 years for three key indicators: atmospheric CO2 concentrations, Global Mean
27               Surface Temperature (GMST), and Global Mean Sea Level (GMSL). a) Measurements of CO2 in air
28               enclosed in Antarctic ice cores (Lüthi et al., 2008; Bereiter et al., 2015 [a compilation]; uncertainty
29               ±1.3ppm; see Chapter 2, Section 2.2.3 and Chapter 5, Section 5.1.2 for an assessment) and direct air
30               measurements (Tans and Keeling, 2020; uncertainty ±0.12 ppm). Projected CO2 concentrations for five
31               Shared Socioeconomic Pathways (SSP) scenarios are indicated by dots on the right-hand side panels of
32               the figure (grey background) (Meinshausen et al., 2020; SSPs are described in Section 1.6). b)
33               Reconstruction of GMST from marine paleoclimate proxies (light grey: Snyder (2016); dark grey:
34               Hansen et al. (2013); see Chapter 2, Section 2.3.1 for an assessment). Observed and reconstructed
35               temperature changes since 1850 are the AR6 assessed mean (referenced to 1850–1900; Box TS.3;
36     ; dots/whiskers on the right-hand side panels of the figure (grey background) indicate the
37               projected mean and ranges of warming derived from Coupled Model Intercomparison Project Phase 6
38               (CMIP6) SSP-based (2081–2100) and Model for the Assessment of Greenhouse Gas Induced Climate
39               Change (MAGICC7) (2300) simulations (Chapter 4, Tables 4.5 and 4.9). c) Sea level changes
40               reconstructed from a stack of oxygen isotope measurements on seven ocean sediment cores (Spratt and
41               Lisiecki, 2016; see Chapter 2, Section and Chapter 9, Section 9.6.2 for an assessment). The sea
42               level record from 1850 to 1900 is from Kopp et al. (2016), while the 20th century record is an updated
43               ensemble estimate of GMSL change (Palmer et al., 2021; see also Chapter 2, Section and Chapter
44               9, Section Dots/whiskers on the right-hand side panels of the figure (grey background) indicate
45               the projected median and ranges derived from SSP-based simulations (2081–2100: Chapter 9, Table 9.9;
46               2300: Chapter 9, Section Best estimates (dots) and uncertainties (whiskers) as assessed by
47               Chapter 2 are included in the left and middle panels for each of the three indicators and selected paleo-
48               reference periods used in this report (CO2: Chapter 2, Table 2.1; GMST: Chapter 2, Section and
49               Cross-Chapter Box 2.3, Table 1 in Chapter 2; GMSL: Chapter 2, Section and Chapter 9, Section
50               9.6.2. See also Cross-Chapter Box 2.1 in Chapter 2). Selected paleo-reference periods: LIG – Last
51               Interglacial; LGM – Last Glacial Maximum; MH – mid-Holocene (Cross-Chapter Box 2.1, Table 1 in
52               Chapter 2). The non-labelled best estimate in panel c) corresponds to the sea level high-stand during
53               Marine Isotope Stage 11, about 410,000 years ago (see Chapter 9, Section 9.6.2). Further details on data
54               sources and processing are available in the chapter data table (Table 1.SM.1).
56   [END FIGURE 1.5 HERE]
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 1   Paleoclimate reconstructions also shed light on the causes of these variations, revealing processes that need
 2   to be considered when projecting climate change. The paleorecords show that sustained changes in global
 3   mean temperature of a few degrees Celsius are associated with increases in sea level of several tens of metres
 4   (Figure 1.5). During two extended warm periods (interglacials) of the last 800,000 years, sea level is
 5   estimated to have been at least six metres higher than today (Chapter 2; Dutton et al., 2015). During the last
 6   interglacial, sustained warmer temperatures in Greenland preceded the peak of sea level rise (Figure 5.15 in
 7   Masson-Delmotte et al., 2013). The paleoclimate record therefore provides substantial evidence directly
 8   linking warmer GMST to substantially higher GMSL.
10   GMST will remain above present-day levels for many centuries even if net CO2 emissions are reduced to
11   zero, as shown in simulations with coupled climate models (Plattner et al., 2008; Section 12.5.3 in Collins et
12   al., 2013; Zickfeld et al., 2013; MacDougall et al., 2020; Chapter 4, Section 4.7.1). Such persistent warm
13   conditions in the atmosphere represent a multi-century commitment to long-term sea level rise, summer sea
14   ice reduction in the Arctic, substantial ice sheet melting, potential ice sheet collapse, and many other
15   consequences in all components of the climate system (Clark et al., 2016; Pfister and Stocker, 2016; Fischer
16   et al., 2018; see also Chapter 9, Section 9.4) (Figure 1.5).
18   Paleoclimate records also show centennial- to millennial-scale variations, particularly during the ice ages,
19   which indicate rapid or abrupt changes of the Atlantic Meridional Overturning Circulation (AMOC; see
20   Chapter 9, Section and the occurrence of a ‘bipolar seesaw’ (opposite-phase surface temperature
21   changes in both hemispheres; Stocker and Johnsen, 2003; EPICA Community Members, 2006; Members
22   WAIS Divide Project et al., 2015; Lynch-Stieglitz, 2017; Pedro et al., 2018; Weijer et al., 2019, see Chapter
23   2, Section This process suggests that instabilities and irreversible changes could be triggered if
24   critical thresholds are passed (Section Several other processes involving instabilities are identified
25   in climate models (Drijfhout et al., 2015), some of which may now be close to critical thresholds (Joughin et
26   al., 2014; Section; see also Chapters 5, 8 and 9 regarding tipping points).
28   Based on Figure 1.5, the reconstructed, observed and projected ranges of changes in the three key indicators
29   can be compared. By the first decade of the 20th century, atmospheric CO2 concentrations had already
30   moved outside the reconstructed range of natural variation over the past 800,000 years. On the other hand,
31   global mean surface temperature and sea level were higher than today during several interglacials of that
32   period (Chapter 2, Section 2.3.1, Figure 2.34 and Section 2.3.3). Projections for the end of the 21st century,
33   however, show that GMST will have moved outside of its natural range within the next few decades, except
34   for the strong mitigation scenarios (Section 1.6). There is a risk that GMSL may potentially leave the
35   reconstructed range of natural variations over the next few millennia (Clark et al., 2016; Chapter 9, Section
36; SROCC (IPCC, 2019b). In addition, abrupt changes can not be excluded (Section
38   An important time period in the assessment of anthropogenic climate change is the last 2000 years. Since
39   AR5, new global datasets have emerged, aggregating local and regional paleorecords (PAGES 2k
40   Consortium, 2013, 2017, 2019; McGregor et al., 2015; Tierney et al., 2015; Abram et al., 2016; Hakim et al.,
41   2016; Steiger et al., 2018; Brönnimann et al., 2019b). Before the global warming that began around the mid-
42   19th century (Abram et al., 2016), a slow cooling in the Northern Hemisphere from roughly 1450 to 1850 is
43   consistently recorded in paleoclimate archives (PAGES 2k Consortium, 2013; McGregor et al., 2015). While
44   this cooling, primarily driven by an increased number of volcanic eruptions (PAGES 2k Consortium, 2013;
45   Owens et al., 2017; Brönnimann et al., 2019b; Chapter 3, Section 3.3.1), shows regional differences, the
46   subsequent warming over the past 150 years exhibits a global coherence that is unprecedented in the last
47   2000 years (Neukom et al., 2019).
49   The rate, scale, and magnitude of anthropogenic changes in the climate system since the mid-20th century
50   suggested the definition of a new geological epoch, the Anthropocene (Crutzen and Stoermer, 2000; Steffen
51   et al., 2007), referring to an era in which human activity is altering major components of the Earth system
52   and leaving measurable imprints that will remain in the permanent geological record (IPCC, 2018) (Figure
53   1.5). These alterations include not only climate change itself, but also chemical and biological changes in the
54   Earth system such as rapid ocean acidification due to uptake of anthropogenic carbon dioxide, massive
55   destruction of tropical forests, a worldwide loss of biodiversity and the sixth mass extinction of species
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 1   (Hoegh-Guldberg and Bruno, 2010; Ceballos et al., 2017; IPBES, 2019). According to the key messages of
 2   the last global assessment of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem
 3   Services (IPBES, 2019), climate change is a ‘direct driver that is increasingly exacerbating the impact of
 4   other drivers on nature and human well-being’, and ‘the adverse impacts of climate change on biodiversity
 5   are projected to increase with increasing warming’.
 8   1.2.2   The policy and governance context
10   The contexts of both policymaking and societal understanding about climate change have evolved since the
11   AR5 was published (2013–2014). Increasing recognition of the urgency of the climate change threat, along
12   with still-rising emissions and unresolved issues of mitigation and adaptation, including aspects of
13   sustainable development, poverty eradication and equity, have led to new policy efforts. This section
14   summarizes these contextual developments and how they have shaped, and been used during the preparation
15   of this Report.
17   IPCC reports and the UN Framework Convention on Climate Change (UNFCCC). The IPCC First
18   Assessment Report (FAR, IPCC, 1990a) provided the scientific background for the establishment of the
19   United Nations Framework Convention on Climate Change (UNFCCC, 1992), which committed parties to
20   negotiate ways to ‘prevent dangerous anthropogenic interference with the climate system’ (the ultimate
21   objective of the UNFCCC). The Second Assessment Report (SAR, IPCC, 1995a) informed governments in
22   negotiating the Kyoto Protocol (1997), the first major agreement focusing on mitigation under the UNFCCC.
23   The Third Assessment report (TAR, IPCC, 2001a) highlighted the impacts of climate change and need for
24   adaptation and introduced the treatment of new topics such as policy and governance in IPCC reports. The
25   Fourth and Fifth Assessment Reports (AR4, IPCC, 2007a; AR5, IPCC, 2013a) provided the scientific
26   background for the second major agreement under the UNFCCC: the Paris Agreement (2015), which entered
27   into force in 2016.
29   The Paris Agreement (PA). Parties to the PA commit to the goal of limiting global average temperature
30   increase to ‘well below 2°C above pre-industrial levels, and to pursue efforts to limit the temperature
31   increase to 1.5°C in order to ‘significantly reduce the risks and impacts of climate change’. In AR6, as in
32   many previous IPCC reports, observations and projections of changes in global temperature are expressed
33   relative to 1850-1900 as an approximation for pre-industrial levels (Cross-Chapter Box 1.2).
35   The PA further addresses mitigation (Article 4) and adaptation to climate change (Article 7), as well as loss
36   and damage (Article 8), through the mechanisms of finance (Article 9), technology development and transfer
37   (Article 10), capacity-building (Article 11) and education (Article 12). To reach its long-term temperature
38   goal, the PA recommends ‘achieving a balance between anthropogenic emissions by sources and removals
39   by sinks of greenhouse gases in the second half of this century’, a state commonly described as ‘net zero’
40   emissions (Article 4) (Section 6, Box 1.4). Each Party to the PA is required to submit a Nationally
41   Determined Contribution (NDC) and pursue, on a voluntary basis, domestic mitigation measures with the
42   aim of achieving the objectives of its NDC (Article 4).
44   Numerous studies of the NDCs submitted since adoption of the PA in 2015 (Fawcett et al., 2015; UNFCCC,
45   2015, 2016; Lomborg, 2016; Rogelj et al., 2016, 2017; Benveniste et al., 2018; Gütschow et al., 2018;
46   United Nations Environment Programme (UNEP), 2019) conclude that they are insufficient to meet the Paris
47   temperature goal. In the present IPCC Sixth Assessment cycle, a Special Report on Global Warming of
48   1.5°C (SR1.5, IPCC, 2018) assessed high agreement that current NDCs ‘are not in line with pathways that
49   limit warming to 1.5°C by the end of the century’. The PA includes a ratcheting mechanism designed to
50   increase the ambition of voluntary national pledges over time. Under this mechanism, NDCs will be
51   communicated or updated every five years. Each successive NDC will represent a ‘progression beyond’ the
52   ‘then current’ NDC and reflect the ‘highest possible ambition’ (Article 4). These updates will be informed by
53   a five-yearly periodic review including the ‘Structured Expert Dialogue’ (SED), as well as a ‘global
54   stocktake’, to assess collective progress toward achieving the PA long-term goals. These processes will rely
55   upon the assessments prepared during the IPCC sixth assessment cycle (e.g., Schleussner et al., 2016b;
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 1   Cross-Chapter Box 1.1).
 3   The Structured Expert Dialogue (SED). Since AR5, the formal dialogue between the scientific and policy
 4   communities has been strengthened through a new science-policy interface, the Structured Expert Dialogue
 5   (SED). The SED was established by UNFCCC to support the work of its two subsidiary bodies, the
 6   Subsidiary Body for Scientific and Technological Advice (SBSTA) and the Subsidiary Body for
 7   Implementation (SBI). The first SED aimed to ‘ensure the scientific integrity of the first periodic review’ of
 8   the UNFCCC, the 2013-2015 review. The Mandate of the periodic review is to ‘assess the adequacy of the
 9   long-term (temperature) goal in light of the ultimate objective of the convention’ and the ‘overall progress
10   made towards achieving the long-term global goal, including a consideration of the implementation of the
11   commitments under the Convention'.
13   The SED of the first periodic review (2013-2015) provided an important opportunity for face-to-face
14   dialogue between decision makers and experts on review themes, based on ‘the best available scientific
15   knowledge, including the assessment reports of the IPCC’. That SED was instrumental in informing the
16   long-term global goal of the PA and in providing the scientific argument of the consideration of limiting
17   warming to 1.5°C warming (Fischlin et al., 2015; Fischlin, 2017). The SED of the second periodic review,
18   initiated in the second half of 2020, focuses on, inter alia, ‘enhancing Parties’ understanding of the long-term
19   global goal and the scenarios towards achieving it in the light of the ultimate objective of the Convention’.
20   The second SED provides a formal venue for the scientific and the policy communities to discuss the
21   requirements and benchmarks to achieve the ‘long-term temperature goal’ (LTTG) of 1.5°C and well below
22   2°C global warming. The discussions also concern the associated timing of net zero emissions targets and the
23   different interpretations of the PA LTTG, including the possibility of overshooting the 1.5° C warming level
24   before returning to it by means of negative emissions (e.g., Schleussner and Fyson, 2020; Section 1.6). The
25   second periodic review is planned to continue until November 2022 and its focus includes the review of the
26   progress made since the first review, with minimising ‘possible overlaps’ and profiting from ‘synergies with
27   the Global Stocktake’.
32   Cross-Chapter Box 1.1:          The WGI contribution to the AR6 and its potential relevance for the global
33                                   stocktake
35   Contributing Authors: Malte Meinshausen (Australia/Germany), Gian-Kasper Plattner (Switzerland), Aïda
36   Diongue-Niang (Senegal), Francisco Doblas-Reyes (Spain), David Frame (New Zealand), Nathan Gillett
37   (Canada/UK), Helene Hewitt (UK), Richard Jones (UK), Hong Liao (China), Jochem Marotzke (Germany),
38   James Renwick (New Zealand), Joeri Rogelj (Belgium), Maisa Rojas (Chile), Sonia I. Seneviratne
39   (Switzerland), Claudia Tebaldi (USA), Blair Trewin (Australia)
41   The global stocktake under the Paris Agreement (PA) evaluates the collective progress of countries’
42   actions towards attaining the Agreement’s purpose and long-term goals every five years. The first
43   global stocktake is due in 2023, and then every five years thereafter, unless otherwise decided by the
44   Conference of the Parties. The purpose and long-term goals of the PA are captured in Article 2: to
45   ‘strengthen the global response to the threat of climate change, in the context of sustainable development and
46   efforts to eradicate poverty, including by’: mitigation3, specifically, ‘holding the increase in the global
47   average temperature to well below 2°C above pre-industrial levels and to pursue efforts to limit the
48   temperature increase to 1.5°C above pre-industrial levels, recognizing that this would significantly reduce

      the labels of mitigation, adaptation and means of implementation and support are here provided for reader's guidance
     only, with no presumption about the actual legal content of the paragraphs and to which extent they encompass
     mitigation, adaptation and means of implementation in its entirety.

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 1   the risks and impacts of climate change’; adaptation, that is, ‘Increasing the ability to adapt to the adverse
 2   impacts of climate change and foster climate resilience and low greenhouse gas emissions development, in a
 3   manner that does not threaten food production’; and means of implementation and support, that is, ‘Making
 4   finance flows consistent with a pathway towards low greenhouse gas emissions and climate-resilient
 5   development’.
 7   The PA further specifies that the stocktake shall be undertaken in a ‘comprehensive and facilitative manner,
 8   considering mitigation, adaptation and the means of implementation and support, and in the light of equity
 9   and the best available science’ (Article 14).
11   The sources of input envisaged for the global stocktake include the ‘latest reports of the Intergovernmental
12   Panel on Climate Change’ as a central source of information 4. The global stocktake is one of the key formal
13   avenues for scientific inputs into the UNFCCC and PA negotiation process alongside, for example, the
14   Structured Expert Dialogues under the UNFCCC5 (Section 1.2.2).
16   The WGI assessment provides a wide range of information potentially relevant for the global
17   stocktake, complementing the IPCC AR6 Special Reports, the contributions from WGII and WGIII
18   and the Synthesis Report. This includes the state of greenhouse gas emissions and concentrations, the
19   current state of the climate, projected long-term warming levels under different scenarios, near-term
20   projections, the attribution of extreme events, and remaining carbon budgets. Cross-Chapter Box 1.1, Table 1
21   provide pointers to the in-depth material that WGI has assessed and may be relevant for the global stocktake.
23   The following tabularised overview of potentially relevant information from the WGI contribution for
24   the global stocktake is structured into three sections: the current state of the climate, the long-term
25   future, and the near-term. These sections and their order align with the three questions of the Talanoa
26   dialogue, launched during COP23 based on the Pacific concept of talanoa 6: ‘Where are we’, ‘Where do we
27   want to go’ and ‘How do we get there?’.
32   Cross-Chapter Box 1.1, Table 1: WGI assessment findings and their potential relevance for the global stocktake. The
33                                   table combines information assessed in this report that could potentially be relevant
34                                   for the global stocktake process. Section 1 focuses on the current state of the climate
35                                   and its recent past. Section 2 focuses on long-term projections in the context of the
36                                   PA’s 1.5°C and 2.0°C goals and on progress towards net zero greenhouse gas
37                                   emissions. Section 3 considers challenges and key insights for mitigation and
38                                   adaptation in the near term from a WGI perspective. Further Information on
39                                   potential relevance of the aspects listed here in terms of, for example, impacts and
40                                   socio-economic aspects can be found in the WGII and WGIII reports
         Section 1: State of the Climate – ‘Where are we?’
         WGI assessment to inform about past changes in the climate system, current climate and committed

         Question                         Chapter          Potential Relevance and Explanatory Remarks

      paragraph 37b in 19/CMA.1 in FCCC/PA/CMA/2018/3/Add.2, pursuant decision 1/CP.21, paragraph 99 of the
     adoption of the PA in FCCC/CP/2015/10/Add.1, available at:
         Decision 5/CP.25, available at:
         Decision 1/CP.23, in FCCC/CP/2017/L.13, available at
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 How much warming have          Cross-          Knowledge about the current warming relative to pre-
 we observed in global          Chapter         industrial levels allows us to quantify the remaining
 mean surface air               Box 1.2;        distance to the PA goal of keeping global mean
 temperatures?                  Cross-          temperatures well below 2°C above pre-industrial level or
                                Chapter         pursue best efforts to limit warming to 1.5°C above pre-
                                Box 2.3;        industrial level. Many of the report’s findings are provided
                      ,        against a proxy for pre-industrial temperature levels with
                                especially      Cross-Chapter Box 1.2 examining the difference between
                             pre-industrial levels and the 1850–1900 period.

 How much has the ocean,        A warming ocean can affect marine life (e.g., coral
 warmed?              ; Box    bleaching) and also are among the main contributors to
                                9.1; 7.2;       long-term sea level rise (thermal expansion). Marine
                                Box 7.2         heatwaves can accentuate the impacts of ocean warming on
                                                marine ecosystems. Also, knowing the heat uptake of the
                                                ocean helps to better understand the response of the
                                                climate system and hence helps to project future warming.

 How much have the land         2.3.4; 5.4.3;   A stronger than global-average warming over land,
 areas warmed and how has       5.4.8; 8.2.1;   combined with changing precipitation patterns, and/or
 precipitation changed?         8.2.3; 8.5.1;   increased aridity in some regions (like the Mediterranean)
                                                can severely affect land ecosystems and species
                                                distributions, the terrestrial carbon cycle and food
                                                production systems. Amplified warming in the Arctic can
                                                enhance permafrost thawing, which in turn can result in
                                                overall stronger anthropogenic warming (a positive
                                                feedback loop). Intensification of heavy precipitation
                                                events can cause more severe impacts related to flooding.

 How did the sea ice area;      Sea ice area influences mass and energy (ice-albedo, heat
 change in recent decades;      and momentum) exchange between the atmosphere and the
 in both the Arctic and         9.3; Cross-     ocean, and its changes in turn impact polar life, adjacent
 Antarctic?                     Chapter         land and ice masses and complex dynamical flows in the
                                Box 10.1;       atmosphere. The loss of a year-round sea-ice cover in the
                                12.4.9          Arctic can severely impact Arctic ecosystems, affect the
                                                livelihood of First Nations in the Arctic, and amplify
                                                Arctic warming with potential consequences for the
                                                warming of the surrounding permafrost regions and ice

 How much have                  2.2.3; 2.2.4;   The main human influence on the climate is via
 atmospheric CO2 and other      5.1.1; 5.2.2;   combustion of fossil fuels and land use-change-related CO2
 GHG concentrations             5.2.3; 5.2.4    emissions, the principal causes of increased CO2
 increased?                                     concentrations since the pre-industrial period. Historical
                                                observations indicate that current atmospheric
                                                concentrations are unprecedented within at least the last
                                                800,000 years. An understanding of historical fossil fuel
                                                emissions and the carbon cycle interactions, as well as CH4
                                                and N2O sinks and sources are crucial for better estimates
                                                of future GHG emissions compatible with the PA’s long-
                                                term goals.

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 How much did sea level;        Sea level rise is a comparatively slow consequence of a
 rise in past centuries and     9.6.1; 9.6.2;   warming world. Historical warming committed the world
 how large is the long-term     FAQ 9.1;        already to long-term sea level rise that is not reversed in
 commitment?                    Box 9.1;        even the lowest emissions scenarios (such as 1.5°C), which
                                9.6.3; 9.6.4    come with a multimeter sea level commitment. Regional
                                                sea level change near the coastlines differs from global-
                                                mean sea level change due to vertical land movement, ice
                                                mass changes, and ocean dynamical changes.

 How much has the ocean;        Ocean acidification is affecting marine life, especially
 acidified and how much; 5.3    organisms that build calciferous shells and structures (e.g.,
 oxygen have they lost?                         coral reefs). Together with less oxygen in upper ocean
                                                waters and increasingly widespread oxygen minimum
                                                zones and in addition to ocean warming, this poses
                                                adaptation challenges for coastal and marine ecosystems
                                                and their services, including seafood supply.

 How much of the observed       3.3.1           To monitor progress toward the PA’s long-term goals it is
 warming was due to                             important to know how much of the observed warming is
 anthropogenic influences?                      due to human activities. Chapter 3 assesses human-induced
                                                warming in global mean near-surface air temperature for
                                                the decade 2010–2019, relative to 1850–1900 with
                                                associated uncertainties, based on detection and attribution
                                                studies. This estimate can be compared with observed
                                                estimates of warming for the same decade reported in
                                                Chapter 2, and is typically used to calculate carbon budgets
                                                consistent with remaining below a particular temperature

 How much has                   3.3.2; 3.3.3;   Climate change impacts are driven by changes in many
 anthropogenic influence        3.4; 3.5;       aspects of the climate system, including changes in the
 changed other aspects of       3.6; 3.7; 8;    water cycle, atmospheric circulation, ocean, cryosphere,
 the climate system?            12; 10.4        biosphere and modes of variability, and to better plan
                                                climate change adaptation it is relevant to know which
                                                observed changes have been driven by human influence.

 How much are                   9.6.4; 11.3-    Adaptation challenges are often accentuated in the face of
 anthropogenic emissions        11.8; 12.3;     extreme events, including floods, droughts, bushfires, and
 contributing to changes in     Cross-          tropical cyclones. For agricultural management,
 the severity and frequency     Chapter         infrastructure planning, and designing for climate
 of extreme events?             Box 3.2;        resilience it is relevant to know whether extreme events
                                1.5; Cross-     will become more frequent in the near future. In that
                                Chapter         respect it is important to understand whether observed
                                Box 1.3         extreme events are part of a natural background variability
                                                or caused by past anthropogenic emissions. This attribution
                                                of extreme events is therefore key to understanding current
                                                events, as well as to better project the future evolution of
                                                these events, such as temperature extremes; heavy
                                                precipitation; floods; droughts; extreme storms and
                                                compound events; extreme sea level. Also, loss and
                                                damage events are often related to extreme events, which
                                                means that future disasters can be fractionally attributed to

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                                                 past human emissions

 Section 2: Long-term climate futures. – ‘Where do we want to go?’
 WGI assessment to inform how long-term climate change could unfold depending on chosen emission

 Question                       Chapter        Potential Relevance and Explanatory Remarks

 How are climate model          Box 4.1;       The scientific literature provides new insights in a
 projections used to            3.8.2;         developing field of scientific research regarding evaluating
 project the range of future    Cross-         model performance and weighting. This can lead to more
 global and regional            Chapter        constrained projection ranges for a given scenario and some
 climate changes?               Box 3.1;       variables, which take into account the performance of
                                10.3; 10.4;    climate models and interdependencies among them. These
                                12.4           techniques have a strong relevance to quantifying future
                                               uncertainties, for example regarding the likelihood of the
                                               various scenarios exceeding the PA’s long-term temperature
                                               goals of 1.5°C or 2°C.

 If emission scenarios are      1.2.2; 4.6,    Understanding of the response to a change of anthropogenic
 pursued that achieve           FAQ 4.2,       emissions is important to estimate the scale and timing of
 mitigation goals by 2050,      12.4, 9, 11;   mitigation compatible with the PA’s long-term goals. The
 what are the differences       Atlas;         new generation of scenarios spans the response space from
 in climate over the 21st       Interactive    very low emission scenarios (SSP1-1.9) under the
 century compared to            Atlas;         assumption of accelerated and effective climate policy
 emission scenarios where                      implementation, to very high emission scenarios in the
 no additional climate                         absence of additional climate policies (SSP3-7.0 or SSP5-
 policies are implemented?                     8.5). It can be informative to place current NDCs and their
                                               emission mitigation pledges within this low and high-end
                                               scenario range, that is, in the context of medium-high
                                               emission scenarios (RCP4.5, RCP6.0 or SSP4-6.0). Climate
                                               response differences between those future medium or high
                                               emission scenarios and those compatible with the PA’s
                                               long-term temperature goals can help inform policymakers
                                               about the corresponding adaptation challenges.

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 What is the climatic           Box 1.4;       Understanding the long-term climate effect of global
 effect of net zero GHG         4.7.2;         emission levels, including the effect of net zero emission
 emissions and a balance        5.2.2; 5.2.3   targets adopted by countries as part of their long-term
 between anthropogenic          and 5.2.4;     climate strategies, can be important when assessing whether
 sources and                    7.6            the collective level of mitigation action is consistent with
 anthropogenic sinks?                          long-term goals of the PA. Understanding the dynamics of
                                               natural sources of CO2, CH4 and N2O is a fundamental
                                               prerequisite to derive climate projections. Net zero GHG
                                               emissions, that is, the balance between anthropogenic
                                               sources and anthropogenic sinks of CO2 and other
                                               greenhouse gases, will halt human-induced global warming
                                               and/or lead to slight reversal below peak warming levels.
                                               Net zero CO2 emissions will approximately lead to a
                                               stabilisation of CO2-induced global warming.

 What is the remaining          5.5            The remaining carbon budget provides an estimate of how
 carbon budget that is                         much CO2 can still be emitted into the atmosphere by
 consistent with the PA’s                      human activities while keeping global mean surface
 long-term temperature                         temperature to a specific warming level. It thus provides key
 goals?                                        geophysical information about emissions limits consistent
                                               with limiting global warming to well below 2°C above pre-
                                               industrial levels and to pursue efforts to limit the
                                               temperature increase to 1.5°C. Remaining carbon budgets
                                               can be seen in the context of historical CO2 emissions to
                                               date. The concept of the transient climate response to
                                               cumulative emissions of CO2 (TCRE) indicates that one
                                               tonne of CO2 has the same effect on global warming
                                               irrespective of whether it is emitted in the past, today, or in
                                               the future. In contrast, the global warming from short-lived
                                               climate forcers is dependent on their rate of emission rather
                                               than their cumulative emission.

 What is our current            Cross-         Synthesis information on projected changes in indices of
 knowledge on the               Chapter        climatic impact-drivers feeds into different ‘Reasons for
 ‘Reasons for Concern’          Box 12.1;      Concern’. Where possible, an explicit transfer function
 related to the PA’s long-      with           between different warming levels and indices quantifying
 term temperature goals         individual     characteristics of these hazards is provided, or the
 and higher warming             domains        difficulties in doing so documented. Those indices include
 levels?                        discussed      Arctic sea ice area in September; global average change in
                                in 2.3.3,      ocean acidification; volume of glaciers or snow cover; ice
                                3.5.4,         volume change for the West Antarctic Ice Sheet (WAIS)
                                4.3.2; 5.3;    and Greenland Ice Sheet (GIS); Atlantic Meridional
                                8.4.1;         Overturning Circulation (AMOC) strength ; amplitude and
                                9.4.2, 9.5;    variance of El Niño Southern Oscillation (ENSO) mode
                                11; 12         (Nino3.4 index); and weather and climate extremes.

 What are the climate           6.6.3 ;        Understanding to what degree rapid decarbonisation
 effects and air pollution      6.7.3 ; Box    strategies bring about reduced air pollution due to
 co-benefits of rapid           6.2            reductions in co-emitted short-lived climate forcers can be
 decarbonisation due to                        useful to consider integrated and/or complementary policies,
 the reduction of co-                          with synergies for pursuing the PA goals, the World Health
 emitted short-lived                           Organization (WHO) air quality guidelines and the
 climate forcers (SLCF)?                       Sustainable Development Goals (SDGs).

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 What are the Equilibrium       Box 4.1;       Equilibrium Climate Sensitivity (ECS) measures the long-
 Climate Sensitivity, the       5.4, 5.5.1,    term global-mean warming in response to doubling CO2
 Transient Climate              7.5            concentrations from pre-industrial levels, while Transient
 Response, and Transient                       Climate Response (TCR) also takes into account the inertia
 Climate Response to                           of the climate system and is an indicator for the near- and
 Emissions and what do                         medium-term warming. TCRE is similar to TCR, but asks
 these indicators tell us                      the question of what is the implied warming is in response
 about expected warming                        to cumulative CO2 emissions (rather than CO2 concentration
 over the 21st century                         changes). The higher the ECS, TCR or TCRE, the lower are
 under various scenarios?                      the greenhouse gas emissions that are consistent with the
                                               PA’s long-term temperature goals.

 What is the Earth's            7.2.2          The current global energy imbalance implies that one can
 energy imbalance and                          expect additional warming before the Earth’s climate
 why does it matter?                           system attains equilibrium with the current level of
                                               concentrations and radiative forcing. Note though, that
                                               future warming commitments can be different depending on
                                               how future concentrations and radiative forcing change.

 What are the regional and      8.4.1, 8.5;    Changes in regional precipitation – in terms of both
 long-term changes in           8.6; 10.4;     extremes and long-term averages – are important for
 precipitation, evaporation     10.6; 12.4;    estimating adaptation challenges. Projected changes of
 and runoff?                    11.4; 11.9;    precipitation minus evaporation (P-E) are closely related to
                                11.6; 11.7;    surface water availability and drought probability.
                                Atlas;         Understanding water cycle changes over land, including
                                Interactive    seasonality, variability and extremes, and their uncertainties,
                                Atlas          is important to estimate a broad range of climate impacts
                                               and adaptation, including food production, water supply and
                                               ecosystem functioning.

 Are we committed to            4.7.2;         Unlike many regional climate responses, global-mean sea
 irreversible sea level rise    9.6.3;         level keeps rising even in the lowest scenarios and is not
 and what is the expected       9.6.4; 12.4;   halted when warming is halted. This is due to the long
 sea level rise by the end      Interactive    timescales on which ocean heat uptake, glacier melt, ice
 of the century if we           Atlas          sheets react to temperature changes. Tipping points and
 pursue strong mitigation                      thresholds in polar ice sheets need to be considered. Thus,
 or high emission                              sea level rise commitments and centennial-scale
 scenarios?                                    irreversibility of ocean warming and sea level rise are
                                               important for future impacts under even the lowest of the
                                               emission scenarios.

 Can we project future          11, 12.4;      Projections of future weather and climate extreme events
 climate extremes under         Interactive    and their regional occurrence, including at different global
 various global warming         Atlas          warming levels, are important for adaptation and disaster
 levels in the long term?                      risk reduction. The attribution of these extreme events to
                                               natural variability and human-induced changes can be of
                                               relevance for both assessing adaptation challenges and
                                               issues of loss and damage.

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 What is the current            1.4.4;          From a risk perspective, it is useful to have information of
 knowledge of potential         4.7.2; 4.8;     lower probability events and system changes, if they could
 surprises, abrupt changes,     5.4.8; Box      potentially result in high impacts, given the dynamic
 tipping points and low-        5.1;            interactions between climate-related hazards and socio-
 likelihood, high impact;        economic drivers (exposure, vulnerability of the affected
 events related to different    8.6.2; Box      human or ecological systems). Examples include permafrost
 levels of future emissions     9.4; 11.2.4;    thaw, CH4 clathrate feedbacks, ice sheet mass loss, ocean
 or warming?                    Cross-          turnover circulation changes, either accelerating warming
                                Chapter         globally or yielding particular regional responses and
                                Box 4.1;        impacts.
                                Box 12.1

 Section 3: The near term. – ‘How do we get there?’
 WGI assessment to inform near-term adaptation and mitigation options

 Questions                      Chapter        Potential Relevance and Explanatory Remarks

 What are projected key         4.3; 4.4;      Much of the near-term information and comparison to
 climate indices under          FAQ 4.1,       historical observations allows us to quantify the climate
 low, medium and high           10.6; 12.3;    adaptation challenges for the next decades as well as the
 emission scenarios in the      Atlas;         opportunities to reduce climate change by pursuing lower
 near term, that is, the        Interactive    emissions. For this timescale both the forced changes and the
 next 20 years?                 Atlas          internal variability are important.

 How can the climate            7.6            For mitigation challenges, it is important to compare efforts to
 benefit of mitigating                         reduce emissions of CO2 versus emissions of other climate
 emissions of different                        forcers, such as, short-lived CH4 or long-lived N2O. Global
 greenhouse gases be                           Warming Potentials (GWPs), which are used in the UNFCCC
 compared?                                     and in emission inventories, are updated and various other
                                               metrics are also investigated. While the NDCs of Parties to
                                               the PA, emission inventories under the UNFCCC, and various
                                               emission trading schemes work on the basis of GWP-
                                               weighted emissions, some recent discussion in the scientific
                                               literature also considers projecting temperatures induced by
                                               short-lived climate forcers on the basis of emission changes,
                                               not emissions per se.

 Do mountain glaciers ;       Mountain glaciers and seasonal snow cover often feed
 shrink currently and in        9.5;           downstream river systems during the melting period, and can
 the near-future in regions     Cross-         be an important source of freshwater. Changing river
 that are currently             chapter        discharge can pose adaptation challenges. Melting mountain
 dependent on this              Box 10.4;      glaciers are among the main contributors to observed global
 seasonal freshwater            12.4:          mean sea level rise.
 supply?                        8.4.1;

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 What are the capacities        10.5;10.6;    Challenges for adaptation and risk management are
 and limitations in the         Box 10.2;     predominantly local, even if globally interlinked. There are a
 provision of regional          Cross-        number of approaches used in the production of regional
 climate information for        Chapter       climate information for adaptation purposes focusing on
 adaptation and risk            Box 10.4;     regional scales. All of them consider a range of sources of
 management?                    11.9;         data and knowledge that are distilled into, at times contextual,
                                Cross-        climate information. A wealth of examples can be found in
                                Chapter       this Report, including assessments of extremes and climatic
                                Box 1.3;      impact-drivers, and attribution at regional scales. Specific
                                12.6;         regions and case studies for regional projections are
                                Cross-        considered, like the Sahel and West African monsoon drought
                                Chapter       and recovery, the Southern Australian rainfall decline, the
                                Box 12.1      Caribbean small island summer drought, and regional
                                              projections are discussed for Cape Town, the Mediterranean
                                              region and Hindu Kush Himalaya.

 How important are              6.1; 6.6;     While most of the radiative forcing which causes climate
 reductions in short-lived      6.7; 7.6      change comes from CO2 emissions, short-lived climate
 climate forcers compared                     forcers also play an important role in the anthropogenic effect
 to the reduction of CO2                      on climate change. Many aerosol species, especially SO4, tend
 and other long-lived                         to cool the climate and their reduction leads to a masking of
 greenhouse gases?                            greenhouse gas induced warming. On the other hand, many
                                              short-lived species themselves exert a warming effect,
                                              including black carbon and CH4, the second most important
                                              anthropogenic greenhouse gas (in terms of current radiative
                                              forcing). Notably, the climate response to aerosol emissions
                                              has a strong regional pattern and is different from that of
                                              greenhouse gas driven warming.

 What are potential co-         5.6.2; 6.1;   The reduction of fossil-fuel-related emissions often goes
 benefits and side-effects      6.7.5         hand-in-hand with a reduction of air pollutants, such as
 of climate change                            aerosols and ozone. Reductions will improve air quality and
 mitigation?                                  result in broader environmental benefits (reduced
                                              acidification, eutrophication, and often tropospheric ozone
                                              recovery). More broadly, various co-benefits are discussed in
                                              WGII and WGIII, as well as co-benefits and side-effects
                                              related to certain mitigation actions, like increased biomass
                                              use and associated challenges to food security and
                                              biodiversity conservation.

 What large near-term           1.4; 4.4.4;   Surprises can come from a range of sources: from incomplete
 surprises could result in      Cross-        understanding of the climate system, from surprises in
 particular adaptation          Chapter       emissions of natural (e.g., volcanic) sources, or from
 challenges?                    Box 4.1;      disruptions to the carbon cycle associated with a warming
                                8.5.2;        climate (e.g., methane release from permafrost thawing,
                                11.2.4;       tropical forest dieback). There could be large natural
                                Cross-        variability in the near-term; or also accelerated climate

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                                     Chapter    change due to a markedly more sensitive climate than
                                     Box 12.1   previously thought. When the next large explosive volcanic
                                                eruption will happen is unknown. The largest volcanic
                                                eruptions over the last few hundred years led to substantial
                                                but temporary cooling, including precipitation changes.
 8   Sustainable Development Goals (SDGs). Many interactions among environmental problems and
 9   development are addressed in the United Nations 2030 Agenda for Sustainable Development and its
10   Sustainable Development Goals. The 2030 Agenda, supported by the finance-oriented Addis Ababa Action
11   Agenda (UN DESA, 2015), calls on nations to ‘take the bold and transformative steps which are urgently
12   needed to shift the world onto a sustainable and resilient path.’ The 2030 Agenda recognizes that ‘climate
13   change is one of the greatest challenges of our time and its adverse impacts undermine the ability of all
14   countries to achieve sustainable development.’ SDG 13 deals explicitly with climate change, establishing
15   several targets for adaptation, awareness-raising and finance. Climate and climate change are also highly
16   relevant to most other SDGs, while acknowledging UNFCCC as the main forum to negotiate the global
17   response to climate change. For example, both long-lived greenhouse gases (LLGHGs), through mitigation
18   decisions, and SLCFs, through air quality, are relevant to SDG 11 (sustainable cities and communities).
19   Chapter 6 assesses SLCF effects on climate and the implications of changing climate for air quality,
20   including opportunities for mitigation relevant to the SDGs (Chapter 6, Box 6.2). Also, the UN Conference
21   on Housing and Sustainable Development established a New Urban Agenda (United Nations, 2017)
22   envisaging cities as part of the solutions for sustainable development, climate change adaptation and
23   mitigation.
25   The Sendai Framework for Disaster Risk Reduction (SFDRR). The Sendai Framework for Disaster Risk
26   Reduction is a non-binding agreement to reduce risks associated with disasters of all scales, frequencies and
27   onset rates caused by natural or human-made hazards, including climate change. The SFDRR outlines targets
28   and priorities for action including ‘Understanding disaster risk’, along the dimensions of vulnerability,
29   exposure of persons and assets and hazard characteristics. Chapter 12 assesses climate information relevant
30   to regional impact and risk assessment with a focus on climate hazards and other aspects of climate that
31   influence society and ecosystem and makes the link with Working Group II. AR6 adopts a consistent risk
32   and solution-oriented framing (Cross-Chapter Box 1.3) that calls for a multidisciplinary approach and cross-
33   working group coordination in order to ensure integrative discussions of major scientific issues associated
34   with integrative risk management and sustainable solutions (IPCC, 2017).
36   The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES).
37   Efforts to address climate change take place alongside and in the context of other major environmental
38   problems, such as biodiversity loss. The Intergovernmental Science-Policy Platform on Biodiversity and
39   Ecosystem Services (IPBES), established in 2012, builds on the IPCC model of a science-policy interface
40   and assessment. The Platform's objective is to ‘strengthen the science-policy interface for biodiversity and
41   ecosystem services for the conservation and sustainable use of biodiversity, long-term human well-being and
42   sustainable development’ (UNEP, 2012). SROCC (IPCC, 2019b) and SRCCL (IPCC, 2019a) assessed the
43   relations between changes in biodiversity and in the climate system. The rolling work programme of IPBES
44   up to 2030 will address interlinkages among biodiversity, water, food and health. This assessment will use a
45   nexus approach to examine interlinkages between biodiversity and the above-mentioned issues, including
46   climate change mitigation and adaptation. Furthermore, IPBES and IPCC will directly collaborate on
47   biodiversity and climate change under the rolling work programme.
49   Addressing climate change alongside other environmental problems, while simultaneously supporting
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 1   sustainable socioeconomic development, requires a holistic approach. Since AR5, there is increasing
 2   attention on the need for coordination among previously independent international agendas, recognizing that
 3   climate change, disaster risk, economic development, biodiversity conservation and human well-being are
 4   tightly interconnected. The current COVID-19 pandemic provides an example of the need for such
 5   interconnection, with its widespread impacts on economy, society and environment (e.g., Shan et al., 2020).
 6   Cross Chapter Box 6.1 in Chapter 6 assesses the consequences of the COVID-19 lockdowns on emissions of
 7   GHGs and SLCFs and related implications for the climate. Another example is the close link between SLCF
 8   emissions, climate evolution and air quality concerns (see Chapter 6). Emissions of halocarbons have
 9   previously been successfully regulated under the Montreal Protocol and its Kigali Amendment. This has
10   been achieved in an effort to reduce ozone depletion that has also modulated anthropogenic climate influence
11   (Estrada et al., 2013; Wu et al., 2013). In the process, emissions of some SLCFs are jointly regulated to
12   reduce environmental and health impacts from air pollution (e.g., Gothenburg Protocol; Reis et al., 2012).
13   Considering the recognized importance of SLCFs for climate, the IPCC decided in May 2019 to approve that
14   the IPCC Task Force on National Greenhouse Gas Inventories produces an IPCC Methodology Report on
15   SLCFs to develop guidance for national SLCFs inventories.
17   The evolving governance context since AR5 challenges the IPCC to provide policymakers and other actors
18   with information relevant for both adaptation to and mitigation of climate change and for the loss and
19   damage induced.
22   1.2.3     Linking science and society: communication, values, and the IPCC assessment process
24   This section assesses how the process of communicating climate information has evolved since AR5. It
25   summarizes key issues regarding scientific uncertainty addressed in previous IPCC assessments and
26   introduces the IPCC calibrated uncertainty language. Next it discusses the role of values in problem-driven,
27   multidisciplinary science assessments such as this one. The section introduces climate services and how
28   climate information can be tailored for greatest utility in specific contexts, such as the global stocktake.
29   Finally, we briefly evaluate changes in media coverage of climate information since AR5, including the
30   increasing role of internet sources and social media.
33    Climate change understanding, communication, and uncertainties
35   The response to climate change is facilitated when leaders, policymakers, resource managers, and their
36   constituencies share basic understanding of the causes, effects, and possible future course of climate change
37   (SR1.5, IPCC, 2018; SRCCL, IPCC, 2019a). Achieving shared understanding is complicated, since scientific
38   knowledge interacts with pre-existing conceptions of weather and climate built up in diverse world cultures
39   over centuries and often embedded in strongly held values and beliefs stemming from ethnic or national
40   identities, traditions, religion, and lived relationships to weather, land and sea (Van Asselt and Rotmans,
41   1996; Rayner and Malone, 1998; Hulme, 2009, 2018; Green et al., 2010; Jasanoff, 2010; Orlove et al., 2010;
42   Nakashima et al., 2012; Shepherd and Sobel, 2020). These diverse, more local understandings can both
43   contrast with and enrich the planetary-scale analyses of global climate science (high confidence).
45   Political cultures also give rise to variation in how climate science knowledge is interpreted, used, and
46   challenged (Leiserowitz, 2006; Oreskes and Conway, 2010; Brulle et al., 2012; Dunlap and Jacques, 2013;
47   Mahony, 2014, 2015; Brulle, 2019). A meta-analysis of 87 studies carried out between 1998 and 2016 (62
48   USA national, 16 non-USA national, 9 cross-national) found that political orientation and political party
49   identification were the second-most important predictors of views on climate change after environmental
50   values (the strongest predictor) (McCright et al. 2016). Ruiz et al. (2020) systematically reviewed 34 studies
51   of non-US nations or clusters of nations and 30 studies of the USA alone. They found that in the non-US
52   studies, ‘changed weather’ and ‘socio-altruistic values’ were the most important drivers of public attitudes.
53   For the USA case, by contrast, political affiliation and the influence of corporations were most important.
54   Widely varying media treatment of climate issues also affects public responses (see Section In
55   summary, environmental and socio-altruistic values are the most significant influences on public opinion
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 1   about climate change globally, while political views, political party affiliation, and corporate influence also
 2   had strong effects, especially in the USA (high confidence).
 4   Furthermore, climate change itself is not uniform. Some regions face steady, readily observable change,
 5   while others experience high variability that masks underlying trends (Section 1.4.1); most regions are
 6   subject to hazards, but some may also experience benefits, at least temporarily (see Chapters 11, 12, and
 7   Atlas). This non-uniformity may lead to wide variation in public climate change awareness and risk
 8   perceptions at multiple scales (Howe et al., 2015; Lee et al., 2015). For example, short-term temperature
 9   trends, such as cold spells or warm days, have been shown to influence public concern (Hamilton and
10   Stampone, 2013; Zaval et al., 2014; Bohr, 2017).
12   Given these manifold influences and the highly varied contexts of climate change communication, special
13   care is required when expressing findings and uncertainties, including IPCC assessments that inform
14   decision making. Throughout the IPCC’s history, all three Working Groups (WGs) have sought to explicitly
15   assess and communicate scientific uncertainty (Le Treut et al., 2007; Cubasch et al., 2013). Over time, the
16   IPCC has developed and revised a framework to treat uncertainties consistently across assessment cycles,
17   reports, and Working Groups through the use of calibrated language (Moss and Schneider, 2000; IPCC,
18   2005). Since its First Assessment Report (IPCC, 1990a), the IPCC specified terms and methods for
19   communicating authors’ expert judgments (Mastrandrea and Mach, 2011). During the AR5 cycle, this
20   calibrated uncertainty language was updated and unified across all WGs (Mastrandrea et al., 2010, 2011).
21   Box 1.1 summarizes this framework as used in AR6.
24   [START BOX 1.1 HERE]
26   Box 1.1: Treatment of uncertainty and calibrated uncertainty language in AR6
28   The AR6 follows the approach developed for AR5 (Box 1.1, Figure 1), as described in the ‘Guidance Notes
29   for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties’
30   (Mastrandrea et al., 2010). The uncertainty Guidance Note used in AR6 clarifies the relationship between the
31   qualitative description of confidence and the quantitative representation of uncertainty expressed by the
32   likelihood scale. The calibrated uncertainty language emphasizes traceability of the assessment throughout
33   the process. Key chapter findings presented in the chapter Executive Summary are supported in the chapter
34   text by a summary of the underlying literature that is assessed in terms of evidence and agreement,
35   confidence, and also likelihood if applicable.
37   In all three WGs, author teams evaluate underlying scientific understanding and use two metrics to
38   communicate the degree of certainty in key findings. These metrics are:
40       1. Confidence: a qualitative measure of the validity of a finding, based on the type, amount, quality and
41          consistency of evidence (e.g., data, mechanistic understanding, theory, models, expert judgment) and
42          the degree of agreement.
43       2. Likelihood: a quantitative measure of uncertainty in a finding, expressed probabilistically (e.g.,
44          based on statistical analysis of observations or model results, or both, and expert judgement by the
45          author team or from a formal quantitative survey of expert views, or both).
47   Throughout IPCC reports, the calibrated language indicating a formal confidence assessment is clearly
48   identified by italics (e.g., medium confidence). Where appropriate, findings can also be formulated as
49   statements of fact without uncertainty qualifiers.
54   Box 1.1, Figure 1: The IPCC AR6 approach for characterizing understanding and uncertainty in assessment findings.
55                      This diagram illustrates the step-by-step process authors use to evaluate and communicate the state
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 1                     of knowledge in their assessment (Mastrandrea et al., 2010). Authors present evidence/agreement,
 2                     confidence, or likelihood terms with assessment conclusions, communicating their expert judgments
 3                     accordingly. Example conclusions drawn from this report are presented in the box at the bottom of
 4                     the figure. [adapted from Mach et al. (2017)].
 6   [END BOX 1.1, FIGURE 1 HERE]
 9   Box.1.1, Figure 1 (adapted from Mach et al., 2017) shows the idealized step-by-step process by which IPCC
10   authors assess scientific understanding and uncertainties. It starts with the evaluation of the available
11   evidence and agreement (Steps 1–2). The following summary terms are used to describe the available
12   evidence: limited, medium, or robust; and the degree of agreement: low, medium, or high. Generally,
13   evidence is most robust when there are multiple, consistent, independent lines of high-quality evidence.
15   If the author team concludes that there is sufficient evidence and agreement, the level of confidence can be
16   evaluated. In this step, assessments of evidence and agreement are combined into a single metric (Steps 3–5).
17   The assessed level of confidence is expressed using five qualifiers: very low, low, medium, high, and very
18   high. Step 4 depicts how summary statements for evidence and agreement relate to confidence levels. For a
19   given evidence and agreement statement, different confidence levels can be assigned depending on the
20   context, but increasing levels of evidence and degrees of agreement correlate with increasing confidence.
21   When confidence in a finding is assessed to be low, this does not necessarily mean that confidence in its
22   opposite is high, and vice versa. Similarly, low confidence does not imply distrust in the finding; instead, it
23   means that the statement is the best conclusion based on currently available knowledge. Further research and
24   methodological progress may change the level of confidence in any finding in future assessments.
26   If the expert judgement of the author team concludes that there is sufficient confidence and
27   quantitative/probabilistic evidence, assessment conclusions can be expressed with likelihood statements
28   (Box.1.1, Figure 1, Steps 5–6). Unless otherwise indicated, likelihood statements are related to findings for
29   which the authors’ assessment of confidence is ‘high’ or ‘very high’. Terms used to indicate the assessed
30   likelihood of an outcome include: virtually certain: 99–100% probability, very likely: 90–100%, likely: 66–
31   100%, about as likely as not: 33–66%, unlikely: 0–33%, very unlikely: 0–10%, exceptionally unlikely: 0–1%.
32   Additional terms (extremely likely: 95–100%, more likely than not >50–100%, and extremely unlikely 0–5%)
33   may also be used when appropriate.
35   Likelihood can indicate probabilities for single events or broader outcomes. The probabilistic information
36   may build from statistical or modelling analyses, other quantitative analyses, or expert elicitation. The
37   framework encourages authors, where appropriate, to present probability more precisely than can be done
38   with the likelihood scale, for example with complete probability distributions or percentile ranges, including
39   quantification of tails of distributions important for risk management (Mach et al., 2017; see also Sections
40   1.2.2 and 1.4.4). In some instances, multiple combinations of confidence and likelihood are possible to
41   characterize key findings. For example, a very likely statement might be made with high confidence, whereas
42   a likely statement might be made with very high confidence. In these instances, the author teams consider
43   which statement will convey the most balanced information to the reader.
45   Throughout this WGI report and unless stated otherwise, uncertainty is quantified using 90% uncertainty
46   intervals. The 90% uncertainty interval, reported in square brackets [x to y], is estimated to have a 90%
47   likelihood of covering the value that is being estimated. The range encompasses the median value and there
48   is an estimated 10% combined likelihood of the value being below the lower end of the range (x) and above
49   its upper end (y). Often the distribution will be considered symmetric about the corresponding best estimate
50   (as in the illustrative example in the figure), but this is not always the case. In this report, an assessed 90%
51   uncertainty interval is referred to as a ‘very likely range’. Similarly, an assessed 66% uncertainty interval is
52   referred to as a ‘likely range’.
54   [END BOX 1.1 HERE]

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 2   Considerable critical attention has focused on whether applying the IPCC framework effectively achieves
 3   consistent treatment of uncertainties and clear communication of findings to users (Shapiro et al., 2010;
 4   Adler and Hirsch Hadorn, 2014). Specific concerns include, for example, the transparency and traceability of
 5   expert judgements underlying the assessment conclusions (Oppenheimer et al., 2016) and the context-
 6   dependent representations and interpretations of probability terms (Budescu et al., 2009, 2012; Janzwood,
 7   2020). Budescu et al. (2014) surveyed 25 samples in 24 countries (a total of 10,792 individual responses),
 8   finding that even when shown IPCC uncertainty guidance, lay readers systematically misunderstood IPCC
 9   likelihood statements. When presented with a ‘high likelihood’ statement, they understood it as indicating a
10   lower likelihood than intended by the IPCC authors. Conversely, they interpreted ‘low likelihood’ statements
11   as indicating a higher likelihood than intended. In another study, British lay readers interpreted uncertainty
12   language somewhat differently from IPCC guidance, but Chinese lay people reading the same uncertainty
13   language translated into Chinese differed much more in their interpretations (Harris et al., 2013). Further,
14   even though it is objectively more probable that wide uncertainty intervals will encompass true values, wide
15   intervals were interpreted by lay people as implying subjective uncertainty or lack of knowledge on the part
16   of scientists (Løhre et al., 2019). Mach et al. (2017) investigated the advances and challenges in approaches
17   to expert judgment in the AR5. Their analysis showed that the shared framework increased the overall
18   comparability of assessment conclusions across all WGs and topics related to climate change, from the
19   physical science basis to resulting impacts, risks, and options for response. Nevertheless, many challenges in
20   developing and communicating assessment conclusions persist, especially for findings drawn from multiple
21   disciplines and Working Groups, for subjective aspects of judgments, and for findings with substantial
22   uncertainties (Adler and Hirsch Hadorn, 2014). In summary, the calibrated language cannot entirely prevent
23   misunderstandings, including a tendency to systematically underestimate the probability of the IPCC’s
24   higher-likelihood conclusions and overestimate the probability of the lower-likelihood ones (high
25   confidence), however a consistent and systematic approach across Working Groups to communicate the
26   assessment outcomes is an important characteristic of the IPCC.
28   Some suggested alternatives are impractical, such as always including numerical values along with calibrated
29   language (Budescu et al., 2014). Others, such as using positive instead of negative expressions of low to
30   medium probabilities, show promise but were not proposed in time for adoption in AR6 (Juanchich et al.,
31   2020). This report therefore retains the same calibrated language used in AR5 (Box 1.1). Like previous
32   reports, AR6 also includes FAQs that express its chief conclusions in plain language designed for lay
33   readers.
35   The framework for communicating uncertainties does not address when "deep uncertainty" is identified in
36   the assessment (Adler and Hirsch Hadorn, 2014). The definition of deep uncertainty in IPCC assessments
37   has been described in the context of the SROCC (IPCC, 2019b; Box 5 in Abram et al. (2019)). A situation of
38   deep uncertainty exists when experts or stakeholders do not know or cannot agree on: (1) appropriate
39   conceptual models that describe relationships among key driving forces in a system; (2) the probability
40   distributions used to represent uncertainty about key variables and parameters; and/or (3) how to weigh and
41   value desirable alternative outcomes (Abram et al., 2019). (See also Cross-Chapter Box 1.2, Annex VII
42   Glossary) Since AR5, the ‘storylines’ or ‘narratives’ approach has been used to address issues related to deep
43   uncertainty, for example low-likelihood events that would have high impact if they occurred, to better inform
44   risk assessment and decision making (see Section 1.4.4). Chapter 9 (Section 9.2.3) notes deep uncertainty in
45   long term projections for sea level rise, and in processes related to Marine Ice Sheet Instability and Marine
46   Ice Cliff Instability.
49   Values, science, and climate change communication
51   As noted above, values — fundamental attitudes about what is important, good, and right — play critical
52   roles in all human endeavours, including climate science. In AR5, Chapters 3 and 4 of the WGIII assessment
53   addressed the role of cultural, social, and ethical values in climate change mitigation and sustainable
54   development (Fleurbaey et al., 2014; Kolstad et al., 2014). These values include widely accepted concepts of
55   human rights, enshrined in international law, that are relevant to climate impacts and policy objectives (Hall
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 1   and Weiss, 2012; Peel and Osofsky, 2018; Setzer and Vanhala, 2019). Specific values – human life,
 2   subsistence, stability, and equitable distribution of the costs and benefits of climate impacts and policies –
 3   are explicit in the texts of the UNFCCC and the PA (Breakey et al., 2016; Dooley and Parihar, 2016). Here
 4   we address the role of values in how scientific knowledge is created, verified, and communicated. Chapters
 5   10, 12, and Cross-Chapter Box 12.2 address how the specific values and contexts of users can be addressed
 6   in the co-production of climate information.
 8   The epistemic (knowledge-related) values of science include explanatory power, predictive accuracy,
 9   falsifiability, replicability, and justification of claims by explicit reasoning (Popper, 1959; Kuhn, 1977).
10   These are supported by key institutional values, including openness, ‘organized scepticism,’ and objectivity
11   or ‘disinterestedness’ (Merton, 1973), operationalized as well-defined methods, documented evidence,
12   publication, peer review, and systems for institutional review of research ethics (COSEPUP, 2009). In recent
13   decades, open data, open code, and scientific cyberinfrastructure (notably the Earth System Grid Federation,
14   a partnership of climate modeling centers dedicated to supporting climate research by providing secure, web-
15   based, distributed access to climate model data) have facilitated scrutiny from a larger range of participants,
16   and FAIR data stewardship principles – making data Findable, Accessible, Interoperable and Reusable
17   (FAIR) – are being mainstreamed in many fields (Wilkinson et al., 2016). Climate science norms and
18   practices embodying these scientific values and principles include the publication of data and model code,
19   multiple groups independently analysing the same problems and data, model intercomparison projects
20   (MIPs), explicit evaluations of uncertainty, and comprehensive assessments by national academies of science
21   and the IPCC.
23   The formal Principles Governing IPCC Work (1998, amended 2003, 2006, 2012, 2013) specify that
24   assessments should be ‘comprehensive, objective, open and transparent.’ The IPCC assessment process
25   seeks to achieve these goals in several ways: by evaluating evidence and agreement across all relevant peer-
26   reviewed literature, especially that published or accepted since the previous assessment; by maintaining a
27   traceable, transparent process that documents the reasoning, data, and tools used in the assessment; and by
28   maximizing the diversity of participants, authors, experts, reviewers, institutions, and communities
29   represented, across scientific discipline, geographical location, gender, ethnicity, nationality, and other
30   characteristics. The multi-stage review process is critical to ensure an objective, comprehensive and robust
31   assessment, with hundreds of scientists, other experts, and governments providing comments to a series of
32   drafts before the report is finalised.
34   Social values are implicit in many choices made during the construction, assessment, and communication of
35   climate science information (Heymann et al., 2017; Skelton et al., 2017). Some climate science questions are
36   prioritised for investigation, or given a specific framing or context, because of their relevance to climate
37   policy and governance. One example is the question of how the effects of a 1.5°C global warming would
38   differ from those of a 2°C warming, an assessment specifically requested by Parties to the PA. SR1.5 (2018)
39   explicitly addressed this issue ‘within the context of sustainable development; considerations of ethics,
40   equity and human rights; and the problem of poverty’ (Chapters 1 and 5; see also Hoegh-Guldberg et al.,
41   2019) following the outcome of the approval of the outline of the Special Report by the IPCC during its 44th
42   Session (Bangkok, Thailand, 17-20 October 2016). Likewise, particular metrics are sometimes prioritized in
43   climate model improvement efforts because of their practical relevance for specific economic sectors or
44   stakeholders. Examples include reliable simulation of precipitation in a specific region, or attribution of
45   particular extreme weather events to inform rebuilding and future policy (see Chapters 8 and 11; Intemann,
46   2015; Otto et al., 2018; James et al., 2019). Sectors or groups whose interests do not influence research and
47   modelling priorities may thus receive less information in support of their climate-related decisions (Parker
48   and Winsberg, 2018).
50   Recent work also recognizes that choices made throughout the research process can affect the relative
51   likelihood of false alarms (overestimating the probability and/or magnitude of hazards) or missed warnings
52   (underestimating the probability and/or magnitude of hazards), known respectively as Type I and Type II
53   errors. Researchers may choose different methods depending on which type of error they view as most
54   important to avoid, a choice that may reflect social values (Douglas, 2009; Knutti, 2018; Lloyd and Oreskes,
55   2018). This reflects a fundamental trade-off between the values of reliability and informativeness. When
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 1   uncertainty is large, researchers may choose to report a wide range as ‘very likely’, even though it is less
 2   informative about potential consequences. By contrast, high-likelihood statements about a narrower range
 3   may be more informative, yet also prove less reliable if new evidence later emerges that widens the range.
 4   Furthermore, the difference between narrower and wider uncertainty intervals has been shown to be
 5   confusing to lay readers, who often interpret wider intervals as less certain (Løhre et al., 2019).
 8   Climate information, co-production, and climate services
10   In AR6, ‘climate information’ refers to specific information about the past, current, or future state of the
11   climate system that is relevant for mitigation, adaptation and risk management. Cross-Chapter Box 1.1 is an
12   example of climate information at the global scale. It provides climate change information potentially
13   relevant to the global stocktake, and indicates where in AR6 this information may be found.
15   Responding to national and regional policymakers' needs for tailored information relevant to risk assessment
16   and adaptation, AR6 emphasizes assessment of regional information more than earlier reports. Here the
17   phrase ‘regional climate information’ refers to predefined reference sets of land and ocean regions; various
18   typological domains (such as mountains or monsoons); temporal frames including baseline periods as well as
19   near-term (2021–2040), medium-term (2041–2060), and long-term (2081–2100); and global warming levels
20   (Sections 1.4.1 and 1.4.5; Chapters 10, 12, and Atlas). Regional climate change information is constructed
21   from multiple lines of evidence including observations, paleoclimate proxies, reanalyses, attribution of
22   changes and climate model projections from both global and regional climate models (Section 1.5.3, Chapter
23   10, Section 10.2 to 10.4). The constructed regional information needs to take account of user context and
24   values for risk assessment, adaptation and policy decisions (Section 1.2.3, Chapter 10, Section 10.5).
26   As detailed in Chapter 10, scientific climate information often requires ‘tailoring’ to meet the requirements
27   of specific decision-making contexts. In a study of the UK Climate Projections 2009 project, researchers
28   concluded that climate scientists struggled to grasp and respond to users’ information needs because they
29   lacked experience interacting with users, institutions, and scientific idioms outside the climate science
30   domain (Porter and Dessai, 2017). Economic theory predicts the value of ‘polycentric’ approaches to climate
31   change informed by specific global, regional, and local knowledge and experience (Ostrom, 1996, 2012).
32   This is confirmed by numerous case studies of extended, iterative dialogue among scientists, policymakers,
33   resource managers, and other stakeholders to produce mutually understandable, usable, task-related
34   information and knowledge, policymaking and resource management around the world (Lemos and
35   Morehouse, 2005; Lemos et al., 2012, 2014, 2018; see Vaughan and Dessai, 2014 for a critical view). SR1.5
36   (2018) assessed that ‘education, information, and community approaches, including those that are informed
37   by indigenous knowledge and local knowledge, can accelerate the wide-scale behaviour changes consistent
38   with adapting to and limiting global warming to 1.5°C. These approaches are more effective when combined
39   with other policies and tailored to the motivations, capabilities and resources of specific actors and contexts
40   (high confidence).’ These extended dialogic ‘co-production’ and education processes have thus been
41   demonstrated to improve the quality of both scientific information and governance (high confidence)
42   (Chapter 10, Section 10.5; Cross Chapter Box 12.2 in Chapter 12).
44   Since AR5, ‘climate services’ have increased at multiple levels (local, national, regional, and global) to aid
45   decision-making of individuals and organizations and to enable preparedness and early climate change
46   action. These services include appropriate engagement from users and providers, are based on scientifically
47   credible information and producer and user expertise, have an effective access mechanism, and respond to
48   the users’ needs (Hewitt et al., 2012; Annex VII Glossary). A Global Framework for Climate Services
49   (GFCS) was established in 2009 by the World Meteorological Organization (WMO) in support of these
50   efforts (Hewitt et al., 2012; Lúcio and Grasso, 2016). Climate services are provided across sectors and
51   timescales, from sub-seasonal to multi-decadal and support co-design and co-production processes that
52   involve climate information providers, resource managers, planners, practitioners and decision makers
53   (Brasseur and Gallardo, 2016; Trenberth et al., 2016; Hewitt et al., 2017). For example, they may provide
54   high-quality data on temperature, rainfall, wind, soil moisture and ocean conditions, as well as maps, risk
55   and vulnerability analyses, assessments, and future projections and scenarios. These data and information
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 1   products may be combined with non-meteorological data, such as agricultural production, health trends,
 2   population distributions in high-risk areas, road and infrastructure maps for the delivery of goods, and other
 3   socio-economic variables, depending on users’ needs (WMO, 2020a). Cross-chapter Box 12.2 in Chapter 12
 4   illustrates the diversity of climate services with three examples from very different contexts.
 6   The current landscape of climate services is assessed in detail in Chapter 12 (Section 12.6), with a focus on
 7   multi-decadal timescales relevant for climate change risk assessment. Other information relevant to
 8   improving climate services for decision making includes the assessment of methods to construct regional
 9   information (Chapter 10), as well as projections at the regional level (Atlas) relevant for impact and risk
10   assessment in different sectors (Chapter 12).
13   Media coverage of climate change
15   Climate services focus on users with specific needs for climate information, but most people learn about
16   climate science findings from media coverage. Since AR5, research has expanded on how mass media report
17   climate change and how their audiences respond (Dewulf, 2013; Jaspal and Nerlich, 2014; Jaspal et al.,
18   2014). For example, in five European Union (EU) countries, television coverage of the AR5 used ‘disaster’
19   and ‘opportunity’ as its principal themes, but virtually ignored the ‘risk’ framing introduced by AR5 WGII
20   (Painter, 2015) and now extended by the AR6 (see Cross-Chapter Box 1.3). Other studies show that people
21   react differently to climate change news when it is framed as a catastrophe (Hine et al., 2015), as associated
22   with local identities (Sapiains et al., 2016), or as a social justice issue (Howell, 2013). Similarly, audience
23   segmentation studies show that responses to climate change vary between groups of people with different,
24   although not necessarily opposed, views on this phenomenon (e.g., Maibach et al., 2011; Sherley et al., 2014;
25   Detenber et al., 2016). In Brazil, two studies have shown the influence of mass media on the high level of
26   public climate change concern in that country (Rodas and DiGiulio, 2017; Dayrell, 2019). In the USA,
27   analyses of television network news show that climate change receives minimal attention, is most often
28   framed in a political context, and largely fails to link extreme weather events to climate change using
29   appropriate probability framing (Hassol et al., 2016). However, recent evidence suggests that Climate
30   Matters (an Internet resource for US TV weathercasters to link weather to climate change trends) may have
31   had a positive effect on public understanding of climate change (Myers et al., 2020). Also, some media
32   outlets have recently adopted and promoted terms and phrases stronger than the more neutral ‘climate
33   change’ and ‘global warming’, including ‘climate crisis’, ‘global heating’, and ‘climate emergency’ (Zeldin-
34   O’Neill, 2019). Google searches on those terms, and on ‘climate action,’ increased 20-fold in 2019, when
35   large social movements such as the School Strikes for Climate gained worldwide attention (Thackeray et al.,
36   2020). We thus assess that specific characteristics of media coverage play a major role in climate
37   understanding and perception (high confidence), including how IPCC assessments are received by the
38   general public.
40   Since AR5, social media platforms have dramatically altered the mass-media landscape, bringing about a
41   shift from uni-directional transfer of information and ideas to more fluid, multi-directional flows (Pearce et
42   al., 2019). A survey covering 18 Latin American countries (StatKnows-CR2, 2019) found that the main
43   sources of information about climate change mentioned were the Internet (52% of mentions), followed by
44   social media (18%). There are well-known challenges with social media, such as misleading or false
45   presentations of scientific findings, incivility that diminishes the quality of discussion around climate change
46   topics, and ‘filter bubbles’ that restrict interactions to those with broadly similar views (Anderson and
47   Huntington, 2017). However, at certain moments (such as at the release of the AR5 WGI report), Twitter
48   studies have found that more mixed, highly-connected groups existed, within which members were less
49   polarized (Pearce et al., 2014; Williams et al., 2015). Thus, social media platforms may in some
50   circumstances support dialogic or co-production approaches to climate communication. Because the contents
51   of IPCC reports speak not only to policymakers, but also to the broader public, the character and effects of
52   media coverage are important considerations across Working Groups.

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 1   1.3     How we got here: the scientific context
 3   Scientific understanding of the climate system’s fundamental features is robust and well established. This
 4   section briefly presents the major lines of evidence in climate science (Figure 1.6). It illustrates their long
 5   history and summarises key findings from the WGI contribution to AR5, where relevant referencing
 6   previous IPCC assessments for comparison. Box 1.2 summarises major findings from three Special Reports
 7   already released during the sixth IPCC assessment cycle. This chapter’s Appendix 1A summarises the
 8   principal findings of all six IPCC WGI Assessment Reports, including the present one, in a single table for
 9   ready reference.
14   Figure 1.6: Climate science milestones, between 1817-2021. Milestones in observations (top); Curves of global
15               surface air temperature (GMST) using HadCRUT5 (Morice et al., 2021) and atmospheric CO2
16               concentrations from Antarctic ice cores (Lüthi et al., 2008; Bereiter et al., 2015) and direct air
17               measurements from 1957 onwards (Tans and Keeling, 2020) (see Figure 1.4 for details) (middle).
18               Milestone in scientific understanding of the CO2 enhanced greenhouse effect (bottom). Further details on
19               each milestone are available in Chapter 1, Section 1.3, and Chapter 1 of AR4.
21   [END FIGURE 1.6 HERE]
24   1.3.1    Lines of evidence: instrumental observations
26   Instrumental observations of the atmosphere, ocean, land, biosphere, and cryosphere underpin all
27   understanding of the climate system. This section describes the evolution of instrumental data for major
28   climate variables at Earth’s land and ocean surfaces, at altitude in the atmosphere, and at depth in the ocean.
29   Many data records exist, of varying length, continuity, and spatial distribution; Figure 1.7 gives a schematic
30   overview of temporal coverage.
32   Instrumental weather observation at the Earth’s surface dates to the invention of thermometers and
33   barometers in the 1600s. National and colonial weather services built networks of surface stations in the
34   1800s. By the mid-19th century, semi-standardized naval weather logs recorded winds, currents,
35   precipitation, air pressure, and temperature at sea, initiating the longest continuous quasi-global instrumental
36   record (Maury, 1849, 1855, 1860). Because the ocean covers over 70% of global surface area and constantly
37   exchange energy with the atmosphere, both air and sea surface temperatures (SST) recorded in these naval
38   logs are crucial variables in climate studies. Dove (1853) mapped seasonal isotherms over most of the globe.
39   By 1900, a patchy weather data-sharing system reached all continents except Antarctica. Regular
40   compilation of climatological data for the world began in 1905 with the Réseau Mondial (Meteorological
41   Office and Shaw, 1920), and the similar compilations World Weather Records (Clayton, 1927) and Monthly
42   Climatic Data for the World (est. 1948) have been published continuously since their founding.
44   Land and ocean surface temperature data have been repeatedly evaluated, refined, and extended (Section
45   1.5.1). As computer power increased and older data were recovered from handwritten records, the number of
46   surface station records used in published global land temperature time series grew. A pioneering study for
47   1880–1935 used fewer than 150 stations (Callendar, 1938). A benchmark study of 1880–2005 incorporated
48   4300 stations (Brohan et al., 2006). A study of the 1753–2011 period included previously unused station
49   data, for a total of 36,000 stations (Rohde et al., 2013); recent versions of this dataset comprise over 40,000
50   land stations (Rohde and Hausfather, 2020). Several centres, including NOAA, Hadley, and Japan
51   Meteorological Agency (JMA), each produce SST datasets independently calculated from instrumental
52   records. In the 2000s, adjustments for bias due to different measurement methods (buckets, engine intake
53   thermometers, moored and drifting buoys) resulted in major improvements of SST data (Thompson et al.,
54   2008), and these improvements continue (Huang et al., 2017; Kennedy et al., 2019). SST and land-based data
55   are incorporated into global surface temperature datasets calculated independently by multiple research

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 1   groups, including NOAA, NASA, Berkeley Earth, Hadley-CRU, JMA, and China Meteorological
 2   Administration (CMA). Each group aggregates the raw measurement data, applies various adjustments for
 3   non-climatic biases such as urban heat-island effects, and addresses unevenness in geospatial and temporal
 4   sampling with various techniques (see Chapter 2, Section and Table 2.4 for references). Other
 5   research groups provide alternative interpolations of these datasets using different methods (e.g., Cowtan and
 6   Way, 2014; Kadow et al., 2020). Using the then available global surface temperature datasets, WGI AR5
 7   assessed that the global mean surface temperature (GMST) increased by 0.85°C from 1880 to 2012 and
 8   found that each of the three decades following 1980 was successively warmer at the Earth’s surface than any
 9   preceding decade since 1850 (IPCC, 2013b). Marine air temperatures, especially those measured during
10   night-time, are increasingly also used to examine variability and long-term trends (e.g., Rayner et al., 2006;
11   Kent et al., 2013; Cornes et al., 2020; Junod and Christy, 2020). Cross-Chapter Box 2.3 in Chapter 2
12   discusses updates to the global temperature datasets, provides revised estimates for the observed changes and
13   considers whether marine air temperatures are changing at the same rate as SSTs.
15   Data at altitude came initially from scattered mountain summits, balloons, and kites, but the upper
16   troposphere and stratosphere were not systematically observed until radiosonde (weather balloon) networks
17   emerged in the 1940s and 1950s. These provide the longest continuous quasi-global record of the
18   atmosphere’s vertical dimension (Stickler et al., 2010). New methods for spatial and temporal
19   homogenisation (intercalibration and quality control) of radiosonde records were introduced in the 2000s
20   (Sherwood et al., 2008, 2015; Haimberger et al., 2012). Since 1978, Microwave Sounding Units (MSU)
21   mounted on Earth-orbiting satellites have provided a second high-altitude data source, measuring
22   temperature, humidity, ozone, and liquid water throughout the atmosphere. Over time, these satellite data
23   have required numerous adjustments to account for such factors as orbital precession and decay (Edwards,
24   2010). Despite repeated adjustments, however, marked differences remain in the temperature trends from
25   surface, radiosonde, and satellite observations; between the results from three research groups that analyse
26   satellite data (UAH, RSS, and NOAA); and between modelled and satellite-derived tropospheric warming
27   trends (Thorne et al., 2011; Santer et al., 2017). These differences are the subject of ongoing research
28   (Maycock et al., 2018). In the 2000s, Atmospheric Infrared Sounder (AIRS) and radio occultation (GNSS-
29   RO) measurements provided new ways to measure temperature at altitude, complementing data from the
30   MSU. GNSS-RO is a new independent, absolutely calibrated source, using the refraction of radio-frequency
31   signals from the Global Navigation Satellite System (GNSS) to measure temperature, pressure, and water
32   vapour (Chapter 2, Section; Foelsche et al., 2008; Anthes, 2011).
34   Heat-retaining properties of the atmosphere’s constituent gases were closely investigated in the 19th century.
35   Foote, (1856) measured solar heating of CO2 experimentally and argued that higher concentrations in the
36   atmosphere would increase Earth’s temperature. Water vapour, ozone, carbon dioxide, and certain
37   hydrocarbons were found to absorb longwave (infrared) radiation, the principal mechanism of the
38   greenhouse effect (Tyndall, 1861). 19th-century investigators also established the existence of a natural
39   biogeochemical carbon cycle. CO2 emitted by volcanoes is removed from the atmosphere through a
40   combination of silicate rock weathering, deep-sea sedimentation, oceanic absorption, and biological storage
41   in plants, shellfish, and other organisms. On multi-million-year timescales, the compression of fossil organic
42   matter stores carbon as coal, oil, and natural gas (Chamberlin, 1897, 1898; Ekholm, 1901).
44   Arrhenius (1896) calculated that a doubling of atmospheric carbon dioxide would produce a 5–6°C warming,
45   but in 1900 new measurements seemed to rule out CO2 as a greenhouse gas due to overlap with the
46   absorption bands of water vapour (Ångström, 1900; Very and Abbe, 1901). Further investigation and more
47   sensitive instruments later overturned Ångström’s conclusion (Fowle, 1917; Callendar, 1938). Nonetheless,
48   the major role of CO2 in the energy balance of the atmosphere was not widely accepted until the 1950s
49   (Callendar, 1949; Plass, 1956, 1961; Manabe and Möller, 1961; Weart, 2008; Edwards, 2010). Revelle and
50   Keeling established carbon dioxide monitoring stations in Antarctica and Hawaii during the 1957–1958
51   International Geophysical Year (Revelle and Suess, 1957; Keeling, 1960). These stations have tracked rising
52   atmospheric CO2 concentrations from 315 ppm in 1958 to 414 ppm in 2020. Ground-based monitoring of
53   other greenhouse gases followed. The Greenhouse Gases Observing Satellite (GOSat) was launched in 2009,
54   and two Orbiting Carbon Observatory satellite instruments have been in orbit since 2014.
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 1   WGI AR5 highlighted ‘the other CO2 problem’ (Doney et al., 2009), that is, ocean acidification caused by
 2   the absorption of some 20–30% of anthropogenic carbon dioxide from the atmosphere and its conversion to
 3   carbonic acid in seawater. WGI AR5 assessed that the pH of ocean surface water has decreased by 0.1 since
 4   the beginning of the industrial era (high confidence), indicating approximately a 30% increase in acidity
 5   (IPCC, 2013b).
 7   With a heat capacity about 1000 times greater than that of the atmosphere, Earth’s ocean stores the vast
 8   majority of energy retained by the planet. Ocean currents transport the stored heat around the globe and, over
 9   decades to centuries, from the surface to its greatest depths. The ocean’s thermal inertia moderates faster
10   changes in radiative forcing on land and in the atmosphere, reaching full equilibrium with the atmosphere
11   only after hundreds to thousands of years (Yang and Zhu, 2011). The earliest subsurface measurements in
12   the open ocean date to the 1770s (Abraham et al., 2013). From 1872–76, the research ship HMS Challenger
13   measured global ocean temperature profiles at depths up to 1700 m along its cruise track. By 1900, research
14   ships were deploying instruments such as Nansen bottles and Mechanical BathyThermographs (MBTs) to
15   develop profiles of the upper 150 m in areas of interest to navies and commercial shipping (Abraham et al.,
16   2013). Starting in 1967, eXpendable BathyThermographs (XBTs) were deployed by scientific and
17   commercial ships along repeated transects to measure temperature to 700 m (Goni et al., 2019). Ocean data
18   collection expanded in the 1980s with the Tropical Ocean Global Experiment (TOGA; Gould, 2003). Marine
19   surface observations for the globe, assembled in the mid-1980s in the International Comprehensive Ocean-
20   Atmosphere Data Set (ICOADS; Woodruff et al., 1987, 2005), were extended to 1662–2014 using newly
21   recovered marine records and metadata (Woodruff et al., 1998; Freeman et al., 2017). The Argo submersible
22   float network developed in the early 2000s provided the first systematic global measurements of the 700–
23   2000 m layer. Comparing the HMS Challenger data to data from Argo submersible floats revealed global
24   subsurface ocean warming on the centennial scale (Roemmich et al., 2012). WGI AR5 assessed with high
25   confidence that ocean warming accounted for more than 90% of the additional energy accumulated by the
26   climate system between 1971 and 2010 (IPCC, 2013b). In comparison, warming of the atmosphere
27   corresponds to only about 1% of the additional energy accumulated over that period (IPCC, 2013a). Chapter
28   2 summarises the ocean heat content datasets used in AR6 (Chapter 2, Section; Table 2.7).
30   Water expands as it warms. This thermal expansion, along with glacier mass loss, were the dominant
31   contributors to global mean sea level rise during the 20th century (high confidence) according to AR5 (IPCC,
32   2013b). Sea level can be measured by averaging across tide gauges, some of which date to the 18th century.
33   However, translating tide gauge readings into global mean sea levevl (GMSL) is challenging, since their
34   spatial distribution is limited to continental coasts and islands, and their readings are relative to local coastal
35   conditions that may shift vertically over time. Satellite radar altimetry, introduced operationally in the 1990s,
36   complements the tide gauge record with geocentric measurements of GMSL at much greater spatial coverage
37   (Katsaros and Brown, 1991; Fu et al., 1994). WGI AR5 assessed that global mean sea level rose by 0.19
38   [0.17 to 0.21] m over the period 1901–2010, and that the rate of sea level rise increased from 2.0 [1.7 to 2.3]
39   mm yr–1 in 1971–2010 to 3.2 [2.8 to 3.6] mm yr–1 from 1993–2010. Warming of the ocean very likely
40   contributed 0.8 [0.5 to 1.1] mm yr–1 of sea level change during 1971–2010, with the majority of that
41   contribution coming from the upper 700 m (IPCC, 2013b). Chapter 2, Section assesses current
42   understanding of the extent and rate of sea level rise, past and present.
44   Satellite remote sensing also revolutionised studies of the cryosphere (Chapter 2, Section 2.3.2 and Chapter
45   9, Sections 9.3 to 9.5), particularly near the poles where conditions make surface observations very difficult.
46   Satellite mapping and measurement of snow cover began in 1966, with land and sea ice observations
47   following in the mid-1970s. Yet prior to the Third Assessment Repor, researchers lacked sufficient data to
48   tell whether the Greenland and Antarctic Ice Sheets were shrinking or growing. Through a combination of
49   satellite and airborne altimetry and gravity measurements, and improved knowledge of surface mass balance
50   and perimeter fluxes, a consistent signal of ice loss for both ice sheets was established by the time of AR5
51   (Shepherd et al., 2012). After 2000, satellite radar interferometry revealed rapid changes in surface velocity
52   at ice-sheet margins, often linked to reduction or loss of ice shelves (Scambos et al., 2004; Rignot and
53   Kanagaratnam, 2006). Whereas sea ice area and concentration were continuously monitored since 1979 from
54   microwave imagery, datasets for ice thickness emerged later from upward sonar profiling by submarines
55   (Rothrock et al., 1999) and radar altimetry of sea-ice freeboards (Laxon et al., 2003). A recent reconstruction
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 1   of Arctic sea ice extent back to 1850 found no historical precedent for the Arctic sea ice minima of the 21st
 2   century (Walsh et al., 2017). Glacier length has been monitored for decades to centuries; internationally
 3   coordinated activities now compile worldwide glacier length and mass balance observations (World Glacier
 4   Monitoring Service, Zemp et al., 2015), global glacier outlines (Randolph Glacier Inventory, Pfeffer et al.,
 5   2014), and ice thickness of about 1100 glaciers (GlaThiDa, Gärtner-Roer et al., 2014). In summary, these
 6   data allowed WGI AR5 to assess that over the last two decades, the Greenland and Antarctic Ice Sheets have
 7   been losing mass, glaciers have continued to shrink almost worldwide, and Arctic sea ice and Northern
 8   Hemisphere spring snow cover have continued to decrease in extent (high confidence) (IPCC, 2013b).
13   Figure 1.7: Schematic of temporal coverage of selected instrumental climate observations (top) and selected
14               paleoclimate archives (bottom). The satellite era began in 1979 CE (Common Era). The width of the taper
15               gives an indication of the amount of available records.
18   [END FIGURE 1.7 HERE]
21   1.3.2   Lines of evidence: paleoclimate
23   With the gradual acceptance of geological ‘deep time’ in the 19th century came investigation of fossils,
24   geological strata, and other evidence pointing to large shifts in the Earth’s climate, from ice ages to much
25   warmer periods, across thousands to billions of years. This awareness set off a search for the causes of
26   climatic changes. The long-term perspective provided by paleoclimate studies is essential to understanding
27   the causes and consequences of natural variations in climate, as well as crucial context for recent
28   anthropogenic climatic change. The reconstruction of climate variability and change over recent millennia
29   began in the 1800s (Brückner et al., 2000; Brückner, 2018 [1890]; Coen, 2018, 2020). In brief,
30   paleoclimatology reveals the key role of carbon dioxide and other greenhouse gases in past climatic
31   variability and change, the magnitude of recent climate change in comparison to past glacial-interglacial
32   cycles, and the unusualness recent climate change (Section; Cross Chapter Box 2.1 in Chapter 2;
33   Tierney et al., 2020). FAQ 1.3 provides a plain-language summary of its importance.
35   Paleoclimate studies reconstruct the evolution of Earth’s climate over hundreds to billions of years using pre-
36   instrumental historical archives, indigenous knowledge and natural archives left behind by geological,
37   chemical, and biological processes (Figure 1.7). Paleoclimatology covers a wide range of temporal scales,
38   ranging from the human historical past (decades to millennia) to geological deep time (millions to billions of
39   years). Paleoclimate reference periods are presented in Cross Chapter Box 2.1 in Chapter 2.
41   Historical climatology aids near-term paleoclimate reconstructions using media such as diaries, almanacs,
42   and merchant accounts that describe climate-related events such as frosts, thaws, flowering dates, harvests,
43   crop prices, and droughts (Lamb, 1965, 1995; Le Roy Ladurie, 1967; Brázdil et al., 2005). Meticulous
44   records by Chinese scholars and government workers, for example, have permitted detailed reconstructions
45   of China’s climate back to 1000 CE, and even beyond (Louie and Liu, 2003; Ge et al., 2008). Climatic
46   phenomena such as large-scale, regionally and temporally distributed warmer and cooler periods of the past
47   2000 years were originally reconstructed from European historical records (Lamb, 1965, 1995; Le Roy
48   Ladurie, 1967; Neukom et al., 2019).
50   Indigenous and local knowledge have played an increasing role in historical climatology, especially in areas
51   where instrumental observations are sparse. Peruvian fishermen named the periodic El Niño warm current in
52   the Pacific, linked by later researchers to the Southern Oscillation (Cushman, 2004). Inuit communities have
53   contributed to climatic history and community based monitoring across the Arctic (Riedlinger and Berkes,
54   2001; Gearheard et al., 2010). Indigenous Australian knowledge of climatic patterns has been offered as a
55   complement to sparse observational records (Green et al., 2010; Head et al., 2014), such as those of sea-level

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 1   rise (Nunn and Reid, 2016). Ongoing research seeks to conduct further dialogue, utilise Indigenous and local
 2   knowledge as an independent line of evidence complementing scientific understanding, and analyse their
 3   utility for multiple purposes, especially adaptation (Laidler, 2006; Alexander et al., 2011; IPCC, 2019c).
 4   Indigenous and local knowledge are used most extensively by IPCC Working Group II.
 6   Certain geological and biological materials preserve evidence of past climate changes. These ‘natural
 7   archives’ include corals, trees, glacier ice, speleothems (stalactites and stalagmites), loess deposits (dust
 8   sediments), fossil pollen, peat, lake sediment, and marine sediment (Stuiver, 1965; Eddy, 1976; Haug et al.,
 9   2001; Wang et al., 2001; Jones et al., 2009; Bradley, 2015). By the early 20th century, laboratory research
10   had begun using tree rings to reconstruct precipitation and the possible influence of sunspots on climatic
11   change (Douglass, 1914, 1919, 1922). Radiocarbon dating, developed in the 1940s (Arnold and Libby,
12   1949), allows accurate determination of the age of carbon-containing materials from the past 50,000 years;
13   this dating technique ushered in an era of rapid progress in paleoclimate studies.
15   On longer timescales, tiny air bubbles trapped in polar ice sheets provide direct evidence of past atmospheric
16   composition, including CO2 levels (Petit et al., 1999), and the 18O isotope in frozen precipitation serves as a
17   proxy marker for temperature (Dansgaard, 1954). Sulphate deposits in glacier ice and as ash layers within
18   sediment record major volcanic eruptions, providing another mechanism for dating. The first paleoclimate
19   reconstructions used an almost 100,000-year ice core taken at Camp Century, Greenland (Dansgaard et al.,
20   1969; Langway Jr, 2008). Subsequent cores from Antarctica extended this climatic record to 800,000 years
21   (EPICA Community Members, 2004; Jouzel, 2013). Comparisons of air contained in these ice samples
22   against measurements from the recent past enabled WGI AR5 to assess that atmospheric concentrations of
23   CO2, methane (CH4), and nitrous oxide (N2O) had all increased to levels unprecedented in at least the last
24   800,000 years (IPCC, 2013b) (see Section, Figure 1.5).
26   Global reconstructions of sea surface temperature were developed from material contained in deep-sea
27   sediment cores (CLIMAP Project Members et al., 1976), providing the first quantitative constraints for
28   model simulations of ice age climates (e.g., Rind and Peteet, 1985). Paleoclimate data and modelling showed
29   that the Atlantic Ocean circulation has not been stable over glacial-interglacial time periods, and that many
30   changes in ocean circulation are associated with abrupt transitions in climate in the North Atlantic region
31   (Ruddiman and McIntyre, 1981; Broecker et al., 1985; Boyle and Keigwin, 1987; Manabe and Stouffer,
32   1988).
34   By the early 20th century, cyclical changes in insolation due to the interacting periodicities of orbital
35   eccentricity, axial tilt, and axial precession had been hypothesised as a chief pacemaker of ice age-
36   interglacial cycles on multi-millennial timescales (Milankovich, 1920). Paleoclimate information derived
37   from marine sediment provides quantitative estimates of past temperature, ice volume, and sea level over
38   millions of years (Section, Figure 1.5) (Emiliani, 1955; Shackleton and Opdyke, 1973; Siddall et al.,
39   2003; Lisiecki and Raymo, 2005; Past Interglacials Working Group of PAGES, 2016). These estimates have
40   bolstered the orbital cycles hypothesis (Hays et al., 1976; Berger, 1977, 1978). However, paleoclimatology
41   of multi-million to billion-year periods reveals that methane, carbon dioxide, continental drift, silicate rock
42   weathering, and other factors played a greater role than orbital cycles in climate changes during ice-free
43   ‘hothouse’ periods of Earth’s distant past (Frakes et al., 1992; Bowen et al., 2015; Zeebe et al., 2016).
45   The WGI AR5 (IPCC, 2013b) used paleoclimatic evidence to put recent warming and sea level rise in a
46   multi-century perspective and assessed that 1983–2012 was likely the warmest 30-year period of the last
47   1400 years in the Northern Hemisphere (medium confidence). AR5 also assessed that the rate of sea level
48   rise since the mid-19th century has been larger than the mean rate during the previous two millennia (high
49   confidence).
52   1.3.3   Lines of evidence: identifying natural and human drivers
54   The climate is a globally interconnected system driven by solar energy. Scientists in the 19th-century
55   established the main physical principles governing Earth’s temperature. By 1822, the principle of radiative
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 1   equilibrium (the balance between absorbed solar radiation and the energy Earth re-radiates into space) had
 2   been articulated, and the atmosphere’s role in retaining heat had been likened to a greenhouse (Fourier,
 3   1822). The primary explanations for natural climate change — greenhouse gases, orbital factors, solar
 4   irradiance, continental position, volcanic outgassing, silicate rock weathering, and the formation of coal and
 5   carbonate rock — were all identified by the late 1800s (Fleming, 1998; Weart, 2008).
 7   The natural and anthropogenic factors responsible for climate change are known today as radiative ‘drivers’
 8   or ‘forcers’. The net change in the energy budget at the top of the atmosphere, resulting from a change in one
 9   or more such drivers, is termed radiative forcing (RF; see Annex VII: Glossary) and measured in Watts per
10   square metre (W m-2). The total radiative forcing over a given time interval (often since 1750) represents the
11   sum of positive drivers (inducing warming) and negative ones (inducing cooling). Past IPCC reports have
12   assessed scientific knowledge of these drivers, quantified their range for the period since 1750, and presented
13   the current understanding of how they interact in the climate system. Like all previous IPCC reports, AR5
14   assessed that total radiative forcing has been positive at least since 1850–1900, leading to an uptake of
15   energy by the climate system, and that the largest single contribution to total radiative forcing is the rising
16   atmospheric concentration of CO2 since 1750 (IPCC, 2013a; see Cross-Chapter Box 1.2 and Chapter 7).
18   Natural drivers include changes in solar irradiance, ocean currents, naturally occurring aerosols, and natural
19   sources and sinks of radiatively active gases such as water vapour, carbon dioxide, methane, and sulphur
20   dioxide. Detailed global measurements of surface-level solar irradiance were first conducted during the
21   1957–1958 International Geophysical Year (Landsberg, 1961), while top-of-atmosphere irradiance has been
22   measured by satellites since 1959 (House et al., 1986). Measured changes in solar irradiance have been small
23   and slightly negative since about 1980 (Matthes et al., 2017). Water vapour is the most abundant radiatively
24   active gas, accounting for about 75% of the terrestrial greenhouse effect, but because its residence time in the
25   atmosphere averages just 8–10 days, its atmospheric concentration is largely governed by temperature (van
26   der Ent and Tuinenburg, 2017; Nieto and Gimeno, 2019). As a result, non-condensing greenhouse gases with
27   much longer residence times serve as ‘control knobs’, regulating planetary temperature, with water vapour
28   concentrations as a feedback effect (Lacis et al., 2010, 2013). The most important of these non-condensing
29   gases is carbon dioxide (a positive driver), released naturally by volcanism at about 637 MtCO2 yr-1 in recent
30   decades, or roughly 1.6% of the 37 GtCO2 emitted by human activities in 2018 (Burton et al., 2013; Le
31   Quéré et al., 2018). Absorption by the ocean and uptake by plants and soils are the primary natural CO2 sinks
32   on decadal to centennial time scales (see Chapter 5, Section 5.1.2 and Figure 5.3).
34   Aerosols (tiny airborne particles) interact with climate in numerous ways, some direct (e.g. reflecting solar
35   radiation back into space) and others indirect (e.g., cloud droplet nucleation); specific effects may cause
36   either positive or negative radiative forcing. Major volcanic eruptions inject sulphur dioxide (SO2, a negative
37   driver) into the stratosphere, creating aerosols that can cool the planet for years at a time by reflecting some
38   incoming solar radiation. The history and climatic effects of volcanic activity have been traced through
39   historical records, geological traces, and observations of major eruptions by aircraft, satellites, and other
40   instruments (Dörries, 2006). The negative RF of major volcanic eruptions was considered in the First
41   Assessment Report (FAR; IPCC, 1990a). In subsequent assessments, the negative RF of smaller eruptions
42   has also been considered (e.g., Chapter 2, section 2.4.3 in IPCC, 1995; Cross-Chapter Box 4.1 in Chapter 4
43   of this report). Dust and other natural aerosols have been studied since the 1880s (e.g., Aitken, 1889;
44   Ångström, 1929, 1964; Twomey, 1959), particularly in relation to their role in cloud nucleation, an aerosol
45   indirect effect whose RF may be either positive or negative depending on such factors as cloud altitude,
46   depth, and albedo (Stevens and Feingold, 2009; Boucher et al., 2013).
48   Anthropogenic (human) drivers of climatic change were hypothesised as early as the 17th century, with a
49   primary focus on forest clearing and agriculture (Grove, 1995; Fleming, 1998). In the 1890s, Arrhenius was
50   first to calculate the effects of increased or decreased CO2 concentrations on planetary temperature, and
51   Högbom estimated that worldwide coal combustion of about 500 Mt yr-1 had already completely offset the
52   natural absorption of CO2 by silicate rock weathering (Högbom, 1894; Arrhenius, 1896; Berner, 1995;
53   Crawford, 1997). As coal consumption reached 900 Mt yr-1 only a decade later, Arrhenius wrote that
54   anthropogenic carbon dioxide from fossil fuel combustion might eventually warm the planet (Arrhenius,
55   1908). In 1938, analysing records from 147 stations around the globe, Callendar calculated atmospheric
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 1   warming over land at 0.3-0.4°C from 1880-1935 and attributed about half of this warming to anthropogenic
 2   CO2 (Callendar, 1938; Fleming, 2007; Hawkins and Jones, 2013; Figure 1.8).
 7   Figure 1.8: G.S. Callendar’s estimates of global land temperature variations and their possible causes. (a) The
 8               original figure from Callendar (1938), using measurements from 147 surface stations for 1880–1935,
 9               showing: (top) ten-year moving departures from the mean of 1901-1930 (°C), with the dashed line
10               representing his estimate of the ‘CO2 effect’ on temperature rise, and (bottom) annual departures from the
11               1901–1930 mean (°C). (b) Comparing the estimates of global land (60°S–60°N) temperatures tabulated
12               in Callendar (1938, 1961) with a modern reconstruction (Osborn et al., 2021) for the same period, after
13               (Hawkins and Jones (2013). Further details on data sources and processing are available in the chapter
14               data table (Table 1.SM.1).
16   [END FIGURE 1.8 HERE]
19   Studies of radiocarbon (14C) in the 1950s established that increasing atmospheric CO2 concentrations were
20   due to fossil fuel combustion. Since all the 14C once contained in fossil fuels long ago decayed into non-
21   radioactive 12C, the CO2 produced by their combustion reduces the overall concentration of atmospheric 14C
22   (Suess, 1955). Related work demonstrated that while the ocean was absorbing around 30% of anthropogenic
23   CO2, these emissions were also accumulating in the atmosphere and biosphere (see Section 1.3.1 and
24   Chapter 5, Section Further work later established that atmospheric oxygen levels were decreasing in
25   inverse relation to the anthropogenic CO2 increase, because combustion of carbon consumes oxygen to
26   produce CO2 (Keeling and Shertz, 1992; IPCC, 2013a, Chapters 2 and 6). Revelle and Suess (1957)
27   famously described fossil fuel emissions as a ‘large scale geophysical experiment’, in which ‘within a few
28   centuries we are returning to the atmosphere and ocean the concentrated organic carbon stored in
29   sedimentary rocks over hundreds of millions of years’. The 1960s saw increasing attention to other
30   radiatively active gases, especially ozone (Manabe and Möller, 1961; Plass, 1961). Methane and nitrous
31   oxide were not considered systematically until the 1970s, when anthropogenic increases in those gases were
32   first noted (Wang et al., 1976). In the 1970s and 1980s, scientists established that synthetic halocarbons (see
33   Annex VII: Glossary), including widely used refrigerants and propellants, were extremely potent greenhouse
34   gases (Ramanathan, 1975; Chapter 2, Section; Chapter 6, section When these chemicals
35   were also found to be depleting the stratospheric ozone layer, they were stringently and successfully
36   regulated on a global basis by the 1987 Montreal Protocol on the Ozone Layer and successor agreements
37   (Parson, 2003).
39   Radioactive fallout from atmospheric nuclear weapons testing (1940s–1950s) and urban smog (1950s–
40   1960s) first provoked widespread attention to anthropogenic aerosols and ozone in the troposphere
41   (Edwards, 2012). Theory, measurement, and modelling of these substances developed steadily from the
42   1950s (Hidy, 2019). However, the radiative effects of anthropogenic aerosols did not receive sustained study
43   until around 1970 (Bryson and Wendland, 1970; Rasool and Schneider, 1971), when their potential as
44   cooling agents was recognised (Peterson et al., 2008). The US Climatic Impact Assessment Program (CIAP)
45   found that proposed fleets of supersonic aircraft, flying in the stratosphere, might cause substantial aerosol
46   cooling and depletion of the ozone layer, stimulating efforts to understand and model stratospheric
47   circulation, atmospheric chemistry, and aerosol radiative effects (Mormino et al., 1975; Toon and Pollack,
48   1976). Since the 1980s, aerosols have increasingly been integrated into comprehensive modelling studies of
49   transient climate evolution and anthropogenic influences, through treatment of volcanic forcing, links to
50   global dimming and cloud brightening, and their influence on cloud nucleation and other properties (e.g.,
51   thickness, lifetime, and extent) and precipitation (e.g., Hansen et al., 1981; Charlson et al., 1987, 1992;
52   Albrecht, 1989; Twomey, 1991).
54   The FAR (1990) focused attention on human emissions of carbon dioxide, methane, tropospheric ozone,
55   chlorofluorocarbons (CFCs), and nitrous oxide. Of these, at that time only the emissions of CO2 and CFCs
56   were well measured, with methane sources known only ‘semi-quantitatively’ (IPCC, 1990a). The FAR
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 1   assessed that some other trace gases, especially CFCs, have global warming potentials hundreds to thousands
 2   of times greater than CO2 and methane, but are emitted in much smaller amounts. As a result, CO2 remains
 3   by far the most important positive anthropogenic driver, with methane next most significant (Section 1.6.3);
 4   anthropogenic methane stems from such sources as fossil fuel extraction, natural gas pipeline leakage,
 5   agriculture, and landfills. In 2001, increased greenhouse forcing attributable to CO2, methane, ozone, CFC-
 6   11, and CFC-12 was detected by comparing satellite measurements of outgoing longwave radiation
 7   measurements taken in 1970 and in 1997 (Harries et al., 2001). AR5 assessed that the 40% increase in
 8   atmospheric CO2 contributed most to positive RF since 1750. Together, changes in atmospheric
 9   concentrations of CO2, methane, nitrous oxide, and halocarbons from 1750–2011 were assessed to contribute
10   a positive RF of 2.83 [2.26 to 3.40] W m–2 (IPCC, 2013b).
12   All IPCC reports have assessed the total RF as positive when considering all sources. However, due to the
13   considerable variability of both natural and anthropogenic aerosol loads, the FAR characterised total aerosol
14   RF as ‘highly uncertain’ and was unable even to determine its sign (positive or negative). Major advances in
15   quantification of aerosol loads and their effects have taken place since then, and IPCC reports since 1992
16   have consistently assessed total forcing by anthropogenic aerosols as negative (IPCC, 1992, 1995a, 1996).
17   However, due to their complexity and the difficulty of obtaining precise measurements, aerosol effects have
18   been consistently assessed as the largest single source of uncertainty in estimating total RF (Stevens and
19   Feingold, 2009; IPCC, 2013a). Overall, AR5 assessed that total aerosol effects, including cloud adjustments,
20   resulted in a negative RF of –0.9 [–1.9 to −0.1] W m−2 (medium confidence), offsetting a substantial portion
21   of the positive RF resulting from the increase in greenhouse gases (high confidence) (IPCC, 2013b). Chapter
22   7 provides an updated assessment of the total and per-component RF for the WGI contribution to AR6.
25   1.3.4   Lines of evidence: understanding and attributing climate change
27   Understanding the global climate system requires both theoretical understanding and empirical measurement
28   of the major forces and factors that govern the transport of energy and mass (air, water and water vapour)
29   around the globe; the chemical and physical properties of the atmosphere, ocean, cryosphere, and land
30   surfaces; and the biological and physical dynamics of natural ecosystems, as well as the numerous feedbacks
31   (both positive and negative) among these processes. Attributing climatic changes or extreme weather events
32   to human activity (see Cross Working Group Box: Attribution) requires, additionally, understanding of the
33   many ways that human activities may affect the climate, along with statistical and other techniques for
34   separating the ‘signal’ of anthropogenic climate change from the ‘noise’ of natural climate variability (see
35   Section 1.4.2). This inter- and trans-disciplinary effort requires contributions from many sciences.
37   Due to the complexity of many interacting processes ranging in scale from the molecular to the global, and
38   occurring on timescales from seconds to millennia, attribution makes extensive use of conceptual,
39   mathematical, and computer simulation models. Modelling allows scientists to combine a vast range of
40   theoretical and empirical understanding from physics, chemistry, and other natural sciences, producing
41   estimates of their joint consequences as simulations of past, present, or future states and trends (Nebeker,
42   1995; Edwards, 2010, 2011).
44   In addition to radiative transfer (discussed above in Section 1.3.3), forces and factors such as
45   thermodynamics (energy conversions), gravity, surface friction, and the Earth's rotation govern the
46   planetary-scale movements or ‘circulation’ of air and water in the climate system. The scientific theory of
47   climate began with Halley (1686), who hypothesized vertical atmospheric circulatory cells driven by solar
48   heating, and Hadley (1735), who showed how the Earth’s rotation affects that circulation. Ferrel (1856)
49   added the Coriolis force to existing theory, explaining the major structures of the global atmospheric
50   circulation. In aggregate, prevailing winds and ocean currents move energy poleward from the equatorial
51   regions where the majority of incoming solar radiation is received.
53   Climate models provide the ability to simulate these complex circulatory processes, and to improve the
54   physical theory of climate by testing different mathematical formulations of those processes. Since
55   controlled experiments at planetary scale are impossible, climate simulations provide one important way to
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 1   explore the differential effects and interactions of variables such as solar irradiance, aerosols, and greenhouse
 2   gases. To assess their quality, models or compontents of models may be compared with observations. For
 3   this reason, they can be used to attribute observed climatic effects to different natural and human drivers
 4   (Hegerl et al., 2011). As early as Arrhenius (1896), simple mathematical models were used to calculate the
 5   effects of doubling atmospheric carbon dioxide over pre-industrial concentrations (~550 ppm vs ~275 ppm).
 6   In the early 1900s Bjerknes formulated the Navier-Stokes equations of fluid dynamics for motion of the
 7   atmosphere (Bjerknes, 1906; Bjerknes et al., 1910), and Richardson (1922) developed a system for numerical
 8   weather prediction based on these equations. When electronic computers became available in the late 1940s,
 9   the methods of Bjerknes and Richardson were successfully applied to weather forecasting (Charney et al.,
10   1950; Nebeker, 1995; Harper, 2008).
12   In the 1960s similar approaches to modelling the weather were used to model the climate, but with much
13   longer runs than daily forecasting (Smagorinsky et al., 1965; Manabe and Wetherald, 1967). Simpler
14   statistical and one- and two-dimensional modelling approaches continued in tandem with the more complex
15   General Circulation Models (GCMs) (Manabe and Wetherald, 1967; Budyko, 1969; Sellers, 1969). The first
16   coupled atmosphere-ocean model (AOGCM) with realistic topography appeared in 1975 (Bryan et al., 1975;
17   Manabe et al., 1975). Rapid increases in computer power enabled higher resolutions, longer model
18   simulations, and the inclusion of additional physical processes in GCMs, such as aerosols, atmospheric
19   chemistry, sea ice, and snow.
21   In the 1990s, AOGCMs were state of the art. By the 2010s, Earth system models (ESMs, also known as
22   coupled carbon-cycle climate models) incorporated land surface, vegetation, the carbon cycle, and other
23   elements of the climate system. Since the 1990s, some major modelling centres have deployed ‘unified’
24   models for both weather prediction and climate modelling, with the goal of a seamless modelling approach
25   that uses the same dynamics, physics, and parameterisations at multiple scales of time and space (Cullen,
26   1993; Brown et al., 2012; NRC Committee on a National Strategy for Advancing Climate Modeling, 2012;
27   Brunet et al., 2015; Chapter 10, Section 10.1.2). Because weather forecast models make short-term
28   predictions that can be frequently verified, and improved models are introduced and tested iteratively on
29   cycles as short as 18 months, this approach allows major portions of the climate model to be evaluated as a
30   weather model and more frequently improved. However, all climate models exhibit biases of different
31   degrees and types, and the practice of ‘tuning’ parameter values in models to make their outputs match
32   variables such as historical warming trajectories has generated concern throughout their history (Randall and
33   Wielicki, 1997; Edwards, 2010; Hourdin et al., 2017; see also Overall, the WGI AR5 assessed that
34   climate models had improved since previous reports (IPCC, 2013b) .
36   Since climate models vary along many dimensions, such as grid type, resolution, and parameterizations,
37   comparing their results requires special techniques. To address this problem, the climate modelling
38   community developed increasingly sophisticated Model Intercomparison Projects (MIPs) (Gates et al., 1999;
39   Covey et al., 2003). MIPs prescribe standardised experiment designs, time periods, output variables, or
40   observational reference data, to facilitate direct comparison of model results. This aids in diagnosing the
41   reasons for biases and other differences among models, and furthers process understanding (Section 1.5).
42   Both the CMIP3 and CMIP5 model intercomparison projects included experiments testing the ability of
43   models to reproduce 20th century global surface temperature trends both with and without anthropogenic
44   forcings. Although some individual model runs failed to achieve this (Hourdin et al., 2017), the mean trends
45   of multi-model ensembles did so successfully (Meehl et al., 2007a; Taylor et al., 2012). When only natural
46   forcings were included (creating the equivalent of a ‘control Earth’ without human influences), similar multi-
47   model ensembles could not reproduce the observed post-1970 warming at either global or regional scales
48   (Edwards, 2010; Jones et al., 2013). The GCMs and ESMs compared in CMIP6 (used in this report) offer
49   more explicit documentation and evaluation of tuning procedures (Schmidt et al., 2017; Burrows et al., 2018;
50   Mauritsen and Roeckner, 2020); see Section 1.5).
52   The FAR (IPCC, 1990a) concluded that while both theory and models suggested that anthropogenic
53   warming was already well underway, its signal could not yet be detected in observational data against the
54   ‘noise’ of natural variability (also see Barnett and Schlesinger (1987) and Section 1.4.2). Since then,
55   increased warming and progressively more conclusive attribution studies have identified human activities as
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 1   the ‘dominant cause of the observed warming since the mid-20th century’ (IPCC, 2013b). ‘Fingerprint’
 2   studies seek to detect specific observed changes – expected from theoretical understanding and model results
 3   – that could not be explained by natural drivers alone, and to attribute statistically the proportion of such
 4   changes that is due to human influence. These include global-scale surface warming, nights warming faster
 5   than days, tropospheric warming and stratospheric cooling, a rising tropopause, increasing ocean heat
 6   content, changed global patterns of precipitation and sea-level air pressure, increasing downward longwave
 7   radiation, and decreasing upward longwave radiation (Hasselmann, 1979; Schneider, 1994; Karoly et al.,
 8   1994; Santer et al., 1995, 2013, Hegerl et al., 1996, 1997; Gillett et al., 2003; Santer, 2003; Zhang et al.,
 9   2007; Stott et al., 2010; Davy et al., 2017; Mann et al., 2017). Cross Working Group Box 1.1 outlines
10   attribution methods and uses from across the AR6, now including event attribution (specifying the influence
11   of climate change on individual extreme events such as floods, or on the frequency of classes of events such
12   as tropical cyclones). Overall, the evidence for human influence has grown substantially over time and from
13   each IPCC report to the subsequent one.
15   A key indicator of climate understanding is whether theoretical climate system budgets or ‘inventories’, such
16   as the balance of incoming and outgoing energy at the surface and at the top of the atmosphere, can be
17   quantified and closed observationally. The global energy budget, for example, includes energy retained in
18   the atmosphere, upper ocean, deep ocean, ice, and land surface. Church et al. (2013) assessed in AR5 with
19   high confidence that independent estimates of effective radiative forcing (ERF), observed heat storage, and
20   surface warming combined to give an energy budget for the Earth that is consistent with the WGI AR5
21   assessed likely range of equilibrium climate sensitivity (ECS) [1.5°C to 4.5°C] to within estimated
22   uncertainties (IPCC, 2013a; on ECS, see Section 1.3.5 below). Similarly, over the period 1993 to 2010, when
23   observations of all sea level components were available, WGI AR5 assessed the observed global mean sea
24   level rise to be consistent with the sum of the observed contributions from ocean thermal expansion (due to
25   warming) combined with changes in glaciers, the Antarctic and Greenland Ice Sheets, and land water storage
26   (high confidence). Verification that the terms of these budgets balance over recent decades provides strong
27   evidence for our understanding of anthropogenic climate change (Cross-Chapter Box 9.1 in Chapter 9).
29   The Appendix to Chapter 1 (Appendix 1A) lists the key detection and attribution statements in the
30   Summaries for Policymakers of WGI reports since 1990. The evolution of these statements over time reflects
31   the improvement of scientific understanding and the corresponding decrease in uncertainties regarding
32   human influences. The SAR stated that ‘the balance of evidence suggests a discernible human influence on
33   global climate’ (IPCC, 1995b). Five years later, the TAR concluded that ‘there is new and stronger evidence
34   that most of the warming observed over the last 50 years is attributable to human activities’ (IPCC, 2001b).
35   AR4 further strengthened previous statements, concluding that ‘most of the observed increase in global
36   average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic
37   greenhouse gas concentrations’ (IPCC, 2007b). AR5 assessed that a human contribution had been detected to
38   changes in warming of the atmosphere and ocean; changes in the global water cycle; reductions in snow and
39   ice; global mean sea level rise; and changes in some climate extremes. AR5 concluded that ‘it is extremely
40   likely that human influence has been the dominant cause of the observed warming since the mid-20th
41   century’ (IPCC, 2013b).
44   1.3.5   Projections of future climate change
46   It was recognised in IPCC AR5 that information about the near term was increasingly relevant for adaptation
47   decisions. In response, WGI AR5 made a specific assessment for how global surface temperature was
48   projected to evolve over the next two decades, concluding that the change for the period 2016–2035 relative
49   to 1986–2005 will likely be in the range of 0.3°C to 0.7°C (medium confidence), assuming no major volcanic
50   eruptions or secular changes in total solar irradiance (IPCC, 2013b). AR5 was also the first IPCC assessment
51   report to assess ‘decadal predictions’ of the climate, where the observed state of the climate system was used
52   to start forecasts for a few years ahead. AR6 examines updates to these decadal predictions (Chapter 4,
53   Section 4.4.1).
55   The assessments and predictions for the near-term evolution of global climate features are largely
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 1   independent of future carbon emissions pathways. However, WGI AR5 assessed that limiting climate change
 2   in the long-term future will require substantial and sustained reductions of greenhouse gas emissions (IPCC,
 3   2013b). This assessment results from decades of research on understanding the climate system and its
 4   perturbations, and projecting climate change into the future. Each IPCC report has considered a range of
 5   emission scenarios, typically including a scenario in which societies choose to continue on their present
 6   course as well as several others reflecting socioeconomic and policy responses that may limit emissions
 7   and/or increase the rate of carbon dioxide removal from the atmosphere. Climate models are used to project
 8   the outcomes of each scenario. However, future human climate influence cannot be precisely predicted
 9   because greenhouse gas and aerosol emissions, land use, energy use, and other human activities may change
10   in numerous ways. Common emission scenarios used in the WGI contribution to AR6 are detailed in Section
11   1.6.
13   Based on model results and steadily increasing CO2 concentrations (Bolin and Bischof, 1970; SMIC, 1971;
14   Meadows et al., 1972), concerns about future ‘risk of effects on climate’ were addressed in Recommendation
15   70 of the Stockholm Action Plan, resulting from the 1972 United Nations Conference on the Human
16   Environment. Numerous other scientific studies soon amplified these concerns (summarised in Schneider
17   (1975), and Williams (1978); see also Nordhaus (1975, 1977). In 1979, a US National Research Council
18   (NRC) group led by Jule Charney reported on the ‘best present understanding of the carbon dioxide/climate
19   issue for the benefit of policymakers’, initiating an era of regular and repeated large-scale assessments of
20   climate science findings.
22   The 1979 Charney NRC report estimated equilibrium climate sensitivity (ECS) at 3°C, stating the range as
23   2°C–4.5°C, based on ‘consistent and mutually supporting’ model results and expert judgment (NRC, 1979).
24   ECS is defined in IPCC assessments as the global surface air temperature (GSAT) response to CO2 doubling
25   (from pre-industrial levels) after the climate has reached equilibrium (stable energy balance between the
26   atmosphere and ocean). Another quantity, transient climate response (TCR), was later introduced as the
27   global surface air temperature change, averaged over a 20-year period, at the time of CO2 doubling in a
28   scenario of concentration increasing at 1% per year). Calculating ECS from historical or paleoclimate
29   temperature records in combination with energy budget models has produced estimates both lower and
30   higher than those calculated using GCMs and ESMs; in AR6, these are assessed in Chapter 7, Section 7.5.2.
32   ECS is typically characterised as most relevant on centennial timescales, while TCR was long seen as a more
33   appropriate measure of the 50-100 year response to gradually increasing CO2; however, recent studies have
34   raised new questions about how accurately both quantities are estimated by GCMs and ESMs (Grose et al.,
35   2018; Meehl et al., 2020; Sherwood et al., 2020). Further, as climate models evolved to include a full-depth
36   ocean, the time scale for reaching full equilibrium became longer and new methods to estimate ECS had to
37   be developed (Gregory et al., 2004; Meehl et al., 2020; Meinshausen et al., 2020). Because of these
38   considerations as well as new estimates from observation-based, paleoclimate, and emergent-constraints
39   studies (Sherwood et al., 2020), the AR6 definition of ECS has changed from previous reports; it now
40   includes all feedbacks except those associated with ice sheets. Accordingly, unlike previous reports, the AR6
41   assessments of ECS and TCR are not based primarily on GCM and ESM model results (see Chapter 7, Box.
42   7.1 and Section 7.5.5 for a full discussion).
44   Today, other sensitivity terms are sometimes used, such as transient climate response to emissions (TCRE,
45   defined as the ratio of warming to cumulative CO2 emissions in a CO2-only simulation) and Earth system
46   sensitivity (ESS), which includes multi-century Earth system feedbacks such as changes in ice sheets. Table
47   1.2 shows estimates of ECS and TCR for major climate science assessments since 1979. The table shows
48   that despite some variation in the range of GCM and (for the later assessments) ESM results, expert
49   assessment of ECS changed little between 1979 and the present report. Based on multiple lines of evidence,
50   AR6 has narrowed the likely range of ECS to 2.5-4.0 °C (Chapter 7, Section 7.5.5).

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 1   Table 1.2: Estimates of equilibrium climate sensitivity (ECS) and transient climate response (TCR) from successive
 2   major scientific assessments since 1979. No likelihood statements are available for reports prior to 2001 because those
 3   reports did not use the IPCC calibrated uncertainty language. The assessed range of ECS differs from the range derived
 4   from General Circulation Model (GCM) and Earth System Model (ESM) results because assessments take into account
 5   other evidence, other types of models, and expert judgment. The AR6 definition of ECS differs from previous reports,
 6   now including all long-term feedbacks except those associated with ice sheets. AR6 estimates of ECS are derived
 7   primarily from process understanding, historical observations, and emergent constraints, informed by (but not based on)
 8   GCM and ESM model results. CMIP6 is the 6th phase of the Coupled Model Intercomparison Project. See Chapter 7,
 9   Box 7.1 and Section 7.5.5.
                     Assessment                      ECS range           Assessed       Assessed       Assessed
                                                    derived from         range of        central       range of
                                                     GCM and             ECS (°C)      estimate of     TCR (°C)
                                                    ESM results                         ECS (°C)
      NAS 1979 (NRC, 1979)                         2.0–3.5            1.5–4.5         3.0
      NAS 1983 (National Research Council          2.0–3.5            1.5–4.5         3.0
      and Carbon Dioxide Assessment
      Committee, 1983)
      Villach 1985 (WMO/UNEP/ICSU, 1986)           1.5–5.5            1.5–4.5         3.0
      IPCC FAR 1990 (IPCC, 1990a)                  1.9–5.2            1.5–4.5         2.5
      IPCC 1992 Supplementary Report (IPCC,        1.7–5.4            1.5–4.5         2.5            discussed
      1992)                                                                                          but not
      IPCC 1994 Radiative Forcing report           not given          1.5–4.5         2.5
      (IPCC, 1995a)
      IPCC SAR (IPCC, 1996)                        1.9–5.2            1.5–4.5         2.5            discussed
                                                                                                     but not
      IPCC TAR (IPCC, 2001a)                       2.0–5.1            1.5–4.5         2.5            1.1–3.1
      IPCC AR4 (IPCC, 2007a)                       2.1–4.4            2.0–4.5         3.0            1.0–3.0
      IPCC AR5 (IPCC, 2013a)                       2.1–4.7            1.5–4.5         not given      1.0–2.5
      World Climate Research Programme             Models not         2.6–3.9         not given      not given
      (Sherwood et al., 2020)                      used in            (66%
                                                   estimate           uncertainty

                                                                      very likely)
      IPCC AR6 2021                                1.8–5.6            2.5–4.0         3.0            1.4–2.2
                                                   (CMIP6). Not       (likely)                       (likely)
                                                   used directly
                                                   in assessing       2.0-5.0
                                                   ECS range (Ch      (very likely)

12   [END TABLE 1.2 HERE]
15   WGI AR5 assessed that there is a close relationship of cumulative total emissions of CO2 and global mean
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 1   surface temperature response that is approximately linear (IPCC, 2013b). This finding implies that continued
 2   emissions of carbon dioxide will cause further warming and changes in all components of the climate
 3   system, independent of any specific scenario or pathway. Scenario-based climate projections using the
 4   Representative Concentration Pathways (RCPs) assessed in WGI AR5 result in continued warming over the
 5   21st century in all scenarios except a strong climate change mitigation scenario (RCP2.6). Similarly, under
 6   all RCP scenarios, AR5 assessed that the rate of sea level rise over the 21st century will very likely exceed
 7   that observed during 1971–2010 due to increased ocean warming and increased loss of mass from glaciers
 8   and ice sheets. Further increases in atmospheric CO2 will also lead to further uptake of carbon by the ocean,
 9   which will increase ocean acidification. By the mid-21st century the magnitudes of the projected changes are
10   substantially affected by the choice of scenario. The set of scenarios used in climate change projections
11   assessed as part of the AR6 are discussed in Section 1.6.
13   From the close link between cumulative emissions and warming it follows that any given level of global
14   warming is associated with a total budget of GHG emissions, especially CO2 as it is the largest long-lasting
15   contributor to radiative forcing (Allen et al., 2009; Collins et al., 2013; Rogelj et al., 2019). Higher emissions
16   in earlier decades imply lower emissions later on to stay within the Earth's carbon budget. Stabilising the
17   anthropogenic influence on global surface temperature thus requires that CO2 emissions and removals reach
18   net zero once the remaining carbon budget is exhausted (see Cross-Chapter Box 1.4).
20   Past, present and future emissions of CO2 therefore commit the world to substantial multi-century climate
21   change, and many aspects of climate change would persist for centuries even if emissions of CO2 were
22   stopped immediately (IPCC, 2013b). According to AR5, a large fraction of this change is essentially
23   irreversible on a multi-century to millennial time scale, barring large net removal (‘negative emissions’) of
24   CO2 from the atmosphere over a sustained period through as yet unavailable technological means (IPCC,
25   2013a, 2018; see Chapters 4 and 5). However, significant reductions of warming due to SLCFs could reduce
26   the level at which temperature stabilises once CO2 emissions reach net zero, and also reduce the long-term
27   global warming commitment by reducing radiative forcing from SLCFs (Chapter 5).
29   In summary, major lines of evidence – observations, paleoclimate, theoretical understanding, and natural and
30   human drivers — have been studied and developed for over 150 years. Methods for projecting climate
31   futures have matured since the 1950s and attribution studies since the 1980s. We conclude that
32   understanding of the principal features of the climate system is robust and well established.
35   1.3.6   How do previous climate projections compare with subsequent observations?
37   Many different sets of climate projections have been produced over the past several decades, so it is valuable
38   to assess how well those projections have compared against subsequent observations. Consisent findings
39   build confidence in the process of making projections for the future. For example, Stouffer and Manabe
40   (2017) compared projections made in the early 1990s with subsequent observations. They found that the
41   projected surface pattern of warming, and the vertical structure of temperature change in both the atmosphere
42   and ocean, were realistic. Rahmstorf et al. (2007, 2012) examined projections of global surface
43   temperatureand global mean sea level assessed by the TAR and AR4 and found that the global surface
44   temperature projections were in good agreement with the subsequent observations, but that sea level
45   projections were underestimates compared to subsequent observations. WGI AR5 also examined earlier
46   IPCC Assessment Reports to evaluate their projections of how global surface temperature and global mean
47   sea level would change (Cubasch et al., 2013) with similar conclusions.
49   Although these studies generally showed good agreement between the past projections and subsequent
50   observations, this type of analysis is complicated because the scenarios of future radiative forcing used in
51   earlier projections do not precisely match the actual radiative forcings that subsequently occurred.
52   Mismatches between the projections and subsequent observations could be due to incorrectly projected
53   radiative forcings (e.g., aerosol emissions, greenhouse gas concentrations or volcanic eruptions that were not
54   included), an incorrect modelled response to those forcings, or both. Alternatively, agreement between
55   projections and observations may be fortuitous due to a compensating balance of errors, for example, too low
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 1   climate sensitivity but too strong radiative forcings.
 3   One approach to partially correct for mismatches between the forcings used in the projections and the
 4   forcings that actually occurred is described by Hausfather et al. (2020). Model projections of global surface
 5   temperatureand estimated radiative forcings were taken from several historical studies, along with the
 6   baseline no-policy scenarios from the first four IPCC assessment reports. These model projections of
 7   temperature and radiative forcing are then compared to (a) the observed change in temperature through time
 8   over the projection period, and (b) the observed change in temperature relative to the observationally-
 9   estimated radiative forcing over the projection period (Figure 1.9; data from Hausfather et al. (2020)).
11   Although this approach has limitations when the modelled forcings differ greatly from the forcings
12   subsequently experienced, they were generally able to project actual future global warming when the
13   mismatches between forecast and observed radiative forcings are accounted for. For example, the Scenario B
14   presented in Hansen et al. (1988) projected around 50% more warming than has been observed during the
15   1988–2017 period, but this is largely because it overestimated subsequent radiative forcings. Similarly, while
16   the FAR (IPCC, 1990a) projected a higher rate of global surface temperature warming than has been
17   observed, this is largely because it overestimated future greenhouse gas concentrations: the FAR’s projected
18   increase in total anthropogenic forcing between 1990 and 2017 was 1.6 W m-2, while the observational
19   estimate of actual forcing during that period is 1.1 W m-2 (Dessler and Forster, 2018). Under these actual
20   forcings, the change in temperature in the FAR aligns with observations (Hausfather et al., 2020).
25   Figure 1.9: Assessing past projections of global temperature change. Projected temperature change post-publication
26               on a temperature vs time (1970–2020, top panel) and temperature vs radiative forcing (1970–2017,
27               bottom panel) basis for a selection of prominent climate model projections (taken from Hausfather et al.,
28               2020). Model projections (using global surface air temperature, GSAT) are compared to temperature
29               observations (using global mean surface temperature, GMST) from HadCRUT5 (black) and
30               anthropogenic forcings (through 2017) from Dessler and Forster (2018), and have a baseline generated
31               from the first five years of the projection period. Projections shown are: Manabe (1970), Rasool and
32               Schneider (1971), Broecker (1975), Nordhaus (1977), Hansen et al. (1981, H81), Hansen et al. (1988,
33               H88), Manabe and Stouffer (1993), along with the Energy Balance Model (EBM) projections from the
34               FAR, SAR and TAR, and the multi-model mean projection using CMIP3 simulations of the Special
35               Reports on Emission Scenarios (SRES) A1B scenario from AR4. H81 and H88 show most excpected
36               scenarios 1 and B, respectively. See Hausfather et al. (2020) for more details of the projections. Further
37               details on data sources and processing are available in the chapter data table (Table 1.SM.1).
39   [END FIGURE 1.9 HERE]
42   In addition to global surface temperature, past regional projections can be evaluated. For example, the FAR
43   presented a series of temperature projections for 1990 to 2030 for several regions around the world. Regional
44   projections were given for a best global warming estimate of 1.8°C since 1850-1900 by 2030, and were
45   assigned low confidence. The FAR also suggested that regional temperature changes should be scaled by -
46   30% to +50% to account for the uncertainty in projected global warming.
48   The regional projections presented in the FAR are compared to the observed temperature change in the
49   period since 1990 (Figure 1.10), following Grose et al. (2017). Subsequent observed temperature change has
50   tracked within the FAR projected range for the best estimate of regional warming in the Sahel, South Asia
51   and Southern Europe. Temperature change has tracked at or below this range for the Central North America
52   and Australia, yet remains within the range reduced by 30% to generate the FAR’s lower global warming
53   estimate, consistent with the smaller observed estimate of radiative forcing compared to the FAR central
54   estimate. Note that the projections assessed in Chapter 4 of AR6 WGI suggest that global temperatures will
55   be around 1.2°C–1.8°C above 1850–1900 by 2030, also lower than the FAR central estimate.
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 1   Overall, there is medium confidence that past projections of global temperature are consistent with
 2   subsequent observations, especially when accounting for the difference in radiative forcings used and those
 3   which actually occurred (limited evidence, high agreement). FAR regional projections are broadly consistent
 4   with subsequent observations, allowing for regional-scale climate variability and differences in projected and
 5   actual forcings. There is medium confidence that the spatial warming pattern has been reliably projected in
 6   past IPCC reports (limited evidence, high agreement).
11   Figure 1.10: Range of projected temperature change for 1990–2030 for various regions defined in IPCC First
12                Assessment Report (FAR).The left panel shows the FAR projections (IPCC, 1990a) for Southern
13                Europe, with darker red bands representing the range of projected change given for the best estimate of
14                1.8°C global warming since pre-industrial to 2030, and the fainter red bands show the range scaled by –
15                30% to +50% for lower and higher estimates of global warming. Blue lines show the regionally averaged
16                observations from several global temperature gridded datasets, and blue dashed lines show the linear
17                trends in those datasets for 1990–2020 extrapolated to 2030. Observed datasets are: HadCRUT5, Cowtan
18                and Way, GISTEMP, Berkeley Earth and NOAA GlobalTemp. The inset map shows the definition of the
19                FAR regions used. The right panel shows projected temperature changes by 2030 for the various FAR
20                regions, compared to the extrapolated observational trends, following Grose et al. (2017). Further details
21                on data sources and processing are available in the chapter data table (Table 1.SM.1).
23   [END FIGURE 1.10 HERE]
26   [START BOX 1.2 HERE]
28   Box 1.2:     Special Reports in the sixth IPCC assessment cycle: key findings
30   The Sixth Assessment Cycle started with three Special Reports. The Special Report on Global Warming of
31   1.5°C (SR1.5, (IPCC, 2018), invited by the Parties to the UNFCCC in the context of the Paris Agreement,
32   assessed current knowledge on the impacts of global warming of 1.5°C above pre-industrial levels and
33   related global greenhouse gas (GHG) emission pathways. The Special Report on Climate Change and Land
34   (SRCCL, IPCC, 2019a) addressed GHG fluxes in land-based ecosystems, land use and sustainable land
35   management in relation to climate change adaptation and mitigation, desertification, land degradation and
36   food security. The Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC, IPCC,
37   2019b) assessed new literature on observed and projected changes of the ocean and the cryosphere, and their
38   associated impacts, risks, and responses.
40   The SR1.5 and SRCCL were produced through a collaboration between the three IPCC Working Groups, the
41   SROCC by only WGs I and II. Here we focus on key findings relevant to the physical science basis covered
42   by WGI.
44   1)    Observations of climate change
46   The SR1.5 estimated with high confidence that human activities caused a global warming of approximately
47   1°C between the 1850-1900 and 2017. For the period 2006–2015, observed global mean surface temperature
48   (GMST 7) was 0.87±0.12°C higher than the average over the 1850–1900 period (very high confidence).
49   Anthropogenic global warming was estimated to be increasing at 0.2±0.1°C per decade (high confidence)
50   and likely matches the level of observed warming to within ±20%. The SRCCL found with high confidence
51   that over land, mean surface air temperature increased by 1.53±0.15°C from 1850–1900 to 2006–2015, or
52   nearly twice as much as the global average. This observed warming has already led to increases in the

      Box 1.2 reproduces the temperature metrics as they appeared in the respective SPMs of the SRs. In AR6 long-term
     changes of GMST (Global Mean Surface Temperature) and GSAT (Global Surface Air Temperature) are considered to
     be equivalent, differing in uncertainty estimates only (see Cross-Chapter Box 2.3 in Chapter 2).
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 1   frequency and intensity of climate and weather extremes in many regions and seasons, including heat waves
 2   in most land regions (high confidence), increased droughts in some regions (medium confidence), and
 3   increases in the intensity of heavy precipitation events at the global scale (medium confidence). These
 4   climate changes have contributed to desertification and land degradation in many regions (high confidence).
 5   Increased urbanisation can enhance warming in cities and their surroundings (heat island effect), especially
 6   during heat waves (high confidence), and intensify extreme rainfall (medium confidence).
 8   With respect to the ocean, the SROCC assessed that it is virtually certain that the ocean has warmed
 9   unabated since 1970 and has taken up more than 90% of the excess heat contributed by global warming. The
10   rate of ocean warming has likely more than doubled since 1993. Over the period 1982–2016, marine
11   heatwaves have very likely doubled in frequency and are increasing in intensity (very high confidence). In
12   addition, the surface ocean acidified further (virtually certain) and loss of oxygen occurred from the surface
13   to a depth of 1000 m (medium confidence). The report expressed medium confidence that the Atlantic
14   Meridional Overturning Circulation (AMOC) weakened in 2004–2017 relative to 1850–1900.
16   Concerning the cryosphere, the SROCC reported widespread continued shrinking of nearly all components.
17   Mass loss from the Antarctic Ice Sheet tripled over the period 2007–2016 relative to 1997–2006, while mass
18   loss doubled for the Greenland Ice Sheet (likely, medium confidence). The report concludes with very high
19   confidence that due to the combined increased loss from the ice sheets, global mean sea level (GMSL) rise
20   has accelerated (extremely likely). The rate of recent GMSL rise (3.6±0.5 mm yr-1 for 2006–2015) is about
21   2.5 times larger than for 1901–1990. The report also found that Arctic sea ice extent has very likely
22   decreased for all months of the year since 1979 and that September sea ice reductions of 12.8±2.3% per
23   decade are likely unprecedented for at least 1000 years. Feedbacks from the loss of summer sea ice and
24   spring snow cover on land have contributed to amplified warming in the Arctic (high confidence), where
25   surface air temperature likely increased by more than double the global average over the last two decades. By
26   contrast, Antarctic sea ice extent overall saw no statistically significant trend for the period 1979 to 2018
27   (high confidence).
29   The SROCC assessed that anthropogenic climate change has increased observed precipitation (medium
30   confidence), winds (low confidence), and extreme sea level events (high confidence) associated with some
31   tropical cyclones. It also found evidence for an increase in annual global proportion of Category 4 or 5
32   tropical cyclones in recent decades (low confidence).
34   2)   Drivers of climate change
36   The SRCCL stated that the land is simultaneously a source and sink of CO2 due to both anthropogenic and
37   natural drivers. It estimates with medium confidence that Agriculture, Forestry and Other Land Use
38   (AFOLU) activities accounted for around 13% of CO2, 44% of methane, and 82% of nitrous oxide emissions
39   from human activities during 2007–2016, representing 23% (12.0±3.0 GtCO2 equivalent yr-1) of the total net
40   anthropogenic emissions of GHGs. The natural response of land to human-induced environmental change
41   such as increasing atmospheric CO2 concentration, nitrogen deposition, and climate change, caused a net
42   CO2 sink equivalent of around 29% of total CO2 emissions (medium confidence); however, the persistence of
43   the sink is uncertain due to climate change (high confidence).
45   The SRCCL also assessed how changes in land conditions affect global and regional climate. It found that
46   changes in land cover have led to both a net release of CO2, contributing to global warming, and an increase
47   in global land albedo, causing surface cooling. However, the report estimated that the resulting net effect on
48   globally averaged surface temperature was small over the historical period (medium confidence).
50   The SROCC found that the carbon content of Arctic and boreal permafrost is almost twice that of the
51   atmosphere (medium confidence), and assessed medium evidence with low agreement that thawing northern
52   permafrost regions are currently releasing additional net methane and CO2.
54   3)   Projections of climate change
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 1   The SR1.5 concluded that global warming is likely to reach 1.5°C between 2030 and 2052 if it continues to
 2   increase at the current rate (high confidence). However, even though warming from anthropogenic emissions
 3   will persist for centuries to millennia and will cause ongoing long-term changes, past emissions alone are
 4   unlikely to raise global surface temperatur to 1.5°C above 1850-1900 levels.
 6   The SR1.5 also found that reaching and sustaining net zero anthropogenic CO2 emissions and reducing net
 7   non-CO2 radiative forcing would halt anthropogenic global warming on multi-decadal time scales (high
 8   confidence). The maximum temperature reached is then determined by cumulative net global anthropogenic
 9   CO2 emissions up to the time of net zero CO2 emissions (high confidence) and the level of non-CO2 radiative
10   forcing in the decades prior to the time that maximum temperatures are reached (medium confidence).
12   Furthermore, climate models project robust differences in regional climate characteristics between the
13   present day and a global warming of 1.5°C, and between 1.5°C and 2°C, including mean temperature in most
14   land and ocean regions and hot extremes in most inhabited regions (high confidence). There is medium
15   confidence in robust differences in heavy precipitation events in several regions and the probability of
16   droughts in some regions.
18   The SROCC projected that global-scale glacier mass loss, permafrost thaw, and decline in snow cover and
19   Arctic sea ice extent will continue in the near term (2031–2050) due to surface air temperature increases
20   (high confidence). The Greenland and Antarctic Ice Sheets are projected to lose mass at an increasing rate
21   throughout the 21st century and beyond (high confidence). Sea level rise will also continue at an increasing
22   rate. For the period 2081–2100 with respect to 1986–2005, the likely ranges of global mean sea level
23   (GMSL) rise are projected at 0.26–0.53 m for RCP2.6 and 0.51–0.92 m for RCP8.5. For the RCP8.5
24   scenario, projections of GMSL rise by 2100 are higher by 0.1 m than in AR5 due to a larger contribution
25   from the Antarctic Ice Sheet (medium confidence). Extreme sea level events that occurred once per hundred
26   years in the recent past are projected to occur at least once per year at many locations by 2050, especially in
27   tropical regions, under all RCP scenarios (high confidence). According to SR1.5, by 2100, GMSL rise would
28   be around 0.1 m lower with 1.5°C global warming compared to 2°C (medium confidence). If warming is held
29   to 1.5°, GMSLwill still continue to rise well beyond 2100, but at a slower rate and a lower magnitude.
30   However, instability and/or irreversible loss of the Greenland and Antarctic Ice Sheets, resulting in multi-
31   metre rise in sea level over hundreds to thousands of years, could be triggered at 1.5°C to 2°C of global
32   warming (medium confidence). According to the SROCC, sea level rise in an extended RCP2.6 scenario
33   would be limited to around 1 m in 2300 (low confidence) while multi-metre sea-level rise is projected under
34   RCP8.5 by then (medium confidence).
36   The SROCC projected that over the 21st century, the ocean will transition to unprecedented conditions with
37   increased temperatures (virtually certain), further acidification (virtually certain), and oxygen decline
38   (medium confidence). Marine heatwaves are projected to become more frequent (very high confidence) as are
39   extreme El Niño and La Niña events (medium confidence). The AMOC is projected to weaken during the
40   21st century (very likely), but a collapse is deemed very unlikely (albeit with medium confidence due to
41   known biases in the climate models used for the assessment).
43   4)   Emission pathways to limit global warming
45   The SR1.5 focused on emission pathways and system transitions consistent with 1.5°C global warming over
46   the 21st century. Building upon the understanding from WGI AR5 of the quasi-linear relationship between
47   cumulative net anthropogenic CO2 emissions since 1850–1900 and maximum global mean temperature, the
48   report assessed the remaining carbon budgets compatible with the 1.5°C or 2°C warming goals of the Paris
49   Agreement. Starting from year 2018, the remaining carbon budget for a one-in-two chance of limiting global
50   warming to 1.5°C is about 580 GtCO2, and about 420 GtCO2 for a two-in-three chance (medium confidence).
51   At constant 2017 emissions, these budgets would be depleted by about the years 2032 and 2028,
52   respectively. Using GMST instead of GSAT gives estimates of 770 and 570 GtCO2, respectively (medium
53   confidence). Each budget is further reduced by approximately 100 GtCO2 over the course of this century
54   when permafrost and other less well represented Earth-system feedbacks are taken into account.
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 1   It is concluded that all emission pathways with no or limited overshoot of 1.5°C imply that global net
 2   anthropogenic CO2 emissions would need to decline by about 45% from 2010 levels by 2030, reaching net
 3   zero around 2050, together with deep reductions in other anthropogenic emissions, such as methane and
 4   black carbon. To limit global warming to below 2°C, CO2 emissions would have to decline by about 25% by
 5   2030 and reach net zero around 2070.
 7   [END BOX 1.2 HERE]
10   1.4     AR6 foundations and concepts
12   AR6 WGI builds on previous assessments using well established foundations and concepts. This section
13   highlights some of the cross-cutting methods applied in the climate change literature and topics discussed
14   repeatedly throughout this report. The choices related to baseline, or reference periods, are first highlighted
15   (Section 1.4.1), including a specific discussion on the pre-industrial baseline used in AR6 WGI (Cross-
16   Chapter Box 1.2). The relationships between long-term trends, climate variability and the concept of
17   emergence of changes (Section 1.4.2) and the sources of uncertainty in climate simulations (Section 1.4.3)
18   are discussed next. The topic of low-likelihood outcomes, storylines, abrupt changes and surprises follows
19   (Section 1.4.4), including a description of the AR6 WGI risk framing (Cross-Chapter Box 1.3). The Cross-
20   Working Group Box: Attribution describes attribution methods, including those for extreme events. Various
21   sets of geographical regions used in later Chapters are also defined and introduced (Section 1.4.5).
24   1.4.1    Baselines, reference periods and anomalies
26   Several ‘baselines’ or ‘reference periods’ are used consistently throughout AR6 WGI. Baseline refers to a
27   period against which differences are calculated whereas reference period is used more generally to indicate a
28   time period of interest, or a period over which some relevant statistics are calculated (see Annex VII:
29   Glossary). Variations in observed and simulated climate variables over time are often presented as
30   ‘anomalies’, i.e., the differences relative to a baseline, rather than using the absolute values. This is done for
31   several reasons.
33   First, anomalies are often used when combining data from multiple locations, because the absolute values
34   can vary over small spatial scales which are not densely observed or simulated, whereas anomalies are
35   representative for much larger scales (e.g., for temperature, Hansen and Lebedeff 1987). Since their baseline
36   value is zero by definition, anomalies are also less susceptible to biases arising from changes in the
37   observational network. Second, the seasonality in different climate indicators can be removed using
38   anomalies to more clearly distinguish variability from long-term trends.
40   Third, different datasets can have different absolute values for the same climate variable that should be
41   removed for effective comparisons of variations with time. This is often required when comparing climate
42   simulations with each other, or when comparing simulations with observations, as simulated climate
43   variables are also affected by model bias that can be removed when they are presented as anomalies. It can
44   also be required when comparing observational datasets or reanalyses (see Section 1.5.2) with each other,
45   due to systematic differences in the underlying measurement system (see Figure 1.11). Understanding the
46   reasons for any absolute difference is important, but whether the simulated absolute value matters when
47   projecting future change will depend on the variable of interest. For example, there is not a strong
48   relationship between climate sensitivity of a model (which is an indicator of the degree of future warming)
49   and the simulated absolute global surface temperature (Mauritsen et al. 2012; Hawkins and Sutton 2016).
51   For some variables, such as precipitation, anomalies are often expressed as percentages in order to more
52   easily compare changes in regions with very different climatological means. However, for situations where
53   there are important thresholds (e.g., phase transitions around 0°C) or for variables which can only take a
54   particular sign or be in a fixed range (e.g., sea ice extent or relative humidity), absolute values are normally
55   used.
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 2   The choice of a baseline period has important consequences for evaluating both observations and simulations
 3   of the climate, for comparing observations with simulations, and for presenting climate projections. There is
 4   usually no perfect choice of baseline as many factors have to be considered and compromises may be
 5   required (Hawkins and Sutton 2016). It is important to evaluate the sensitivity of an analysis or assessment to
 6   the choice of the baseline.
 8   For example, the collocation of observations and reanalyses within the model ensemble spread depends on
 9   the choice of the baseline, and uncertainty in future projections of climate is reduced if using a more recent
10   baseline, especially for the near-term (Figure 1.11). The length of an appropriate baseline or reference period
11   depends on the variable being considered, the rates of change of the variable and the purpose of the period,
12   but is usually 20 to 50 years long. The World Meteorological Organization (WMO) uses 30-year periods to
13   define ‘climate normals’, which indicate conditions expected to be experienced in a given location.
18   Figure 1.11: Choice of baseline matters when comparing observations and model simulations. Global surface air
19                temperature (GSAT, grey) from a range of CMIP6 historical simulations (1850–2014, 25 models) and
20                SSP1-2.6 (2015–2100) using absolute values (top) and anomalies relative to two different baselines:
21                1850–1900 (middle) and 1995–2014 (bottom). An estimate of GSAT from a reanalysis (ERA-5, orange,
22                1979–2020) and an observation-based estimate of global mean surface air temperature (GMST) (Berkeley
23                Earth, black, 1850–2020) are shown, along with the mean GSAT for 1961–1990 estimated by Jones et al.
24                (1999), light blue shading, 14.0±0.5°C). Using the more recent baseline (bottom) allows the inclusion of
25                datasets which do not include the periods of older baselines. The middle and bottom panels have scales
26                which are the same size but offset. Further details on data sources and processing are available in the
27                chapter data table (Table 1.SM.1).
29   [END FIGURE 1.11 HERE]
32   For AR6 WGI, the period 1995–2014 is used as a baseline to calculate the changes in future climate using
33   model projections and also as a ‘modern’ or ‘recent past’ reference period when estimating past observed
34   warming. The equivalent period in AR5 was 1986–2005, and in SR1.5, SROCC and SRCCL it was 2006–
35   2015. The primary reason for the different choice in AR6 is that 2014 is the final year of the historical
36   CMIP6 simulations. These simulations subsequently assume different emission scenarios and so choosing
37   any later baseline end date would require selecting a particular emissions scenario. For certain assessments,
38   the most recent decade possible (e.g. 2010–2019 or 2011–2020, depending on the availability of
39   observations) is also used as a reference period (see Cross Chapter Box 2.3 in Chapter 2).
41   Figure 1.12 shows changes in observed global mean surface temperature (GMST) relative to 1850–1900 and
42   illustrates observed global warming levels for a range of reference periods that are either used in AR6 or
43   were used in previous IPCC Reports. This allows changes to be calculated between different periods and
44   compared to previous assessments. For example, AR5 assessed the change in GMST from the 1850–1900
45   baseline to 1986–2005 reference period as 0.61 (0.55–0.67) °C, whereas it is now assessed to be 0.69 (0.52–
46   0.82) °C using improved GMST datasets (also see Cross-Chapter Box 2.3 in Chapter 2).
48   The commonly used metric for global surface warming tends to be global mean surface temperature (GMST)
49   but, as shown in Figure 1.11, climate model simulations tend to use global surface air temperature (GSAT).
50   Although GMST and GSAT are closely related, the two measures are physically distinct. GMST is a
51   combination of land surface air temperatures (LSAT) and sea surface temperatures (SSTs), whereas GSAT is
52   surface air temperatures over land, ocean and ice. A key development in AR6 is the assessment that long-
53   term changes in GMST and GSAT differ by at most 10% in either direction, with low confidence in the sign
54   of any differences (see Cross Chapter Box 2.3 for details).
56   Three future reference periods are used in AR6 WGI for presenting projections: near-term (2021–2040),
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 1   mid-term (2041–2060) and long-term (2081–2100) (see Figure 1.11). In AR6, 20-year reference periods are
 2   considered long enough to show future changes in many variables when averaging over ensemble members
 3   of multiple models, and short enough to enable the time dependence of changes to be shown throughout the
 4   21st century. Projections with alternative recent baselines (such as 1986–2005 or the current WMO climate
 5   normal period of 1981–2010) and a wider range of future reference periods are presented in the Interactive
 6   Atlas. Note that ‘long-term’ is also sometimes used to refer to durations of centuries to millennia when
 7   examining past climate, as well as future climate change beyond the year 2100. Cross-Chapter Box 2.1 in
 8   Chapter 2 discusses the paleo reference periods used in AR6.
13   Figure 1.12: Global warming over the instrumental period. Observed global mean surface temperature (GMST) from
14                four datasets, relative to the average temperature of 1850–1900 in each dataset (see Cross-Chapter Box
15                2.3 and Section for more details). The shaded grey band indicates the assessed likely range for the
16                period around 1750 (see Cross-Chapter Box 1.2). Different reference periods are indicated by the
17                coloured horizontal lines, and an estimate of total GMST change up to that period is given, enabling a
18                translation of the level of warming between different reference periods. The reference periods are all
19                chosen because they have been used in the AR6 or previous IPCC assessment reports. The value for the
20                1981–2010 reference period, used as a ‘climate normal’ period by the World Meteorological
21                Organization, is the same as the 1986–2005 reference period shown. Further details on data sources and
22                processing are available in the chapter data table (Table 1.SM.1).
24   [END FIGURE 1.12 HERE]
29   Cross-Chapter Box 1.2:          Changes in global temperature between 1750 and 1850
31   Contributing Authors: Ed Hawkins (UK), Paul Edwards (USA), Piers Forster (UK), Darrell Kaufman
32   (USA), Jochem Marotzke (Germany), Malte Meinshausen (Australia/Germany), Maisa Rojas (Chile), Bjørn
33   H. Samset (Norway), Peter Thorne (Ireland/UK).
36   The Paris Agreement aims to limit global temperatures to specific thresholds ‘above pre-industrial levels’. In
37   AR6 WGI, as in previous IPCC reports, observations and projections of changes in global temperature are
38   generally expressed relative to 1850–1900 as an approximate pre-industrial state (SR1.5, IPCC, 2018). This
39   is a pragmatic choice based upon data availability considerations, though both anthropogenic and natural
40   changes to the climate occurred before 1850. The remaining carbon budgets, the chance of crossing global
41   temperature thresholds, and projections of extremes and sea level rise at a particular level of global warming
42   can all be sensitive to the chosen definition of the approximate pre-industrial baseline (Millar et al., 2017a;
43   Schurer et al., 2017; Pfleiderer et al., 2018; Rogelj et al., 2019; Tokarska et al., 2019). This Cross-Chapter
44   Box assesses the evidence on change in radiative forcing and global temperature from the period around
45   1750 to 1850–1900; variations in the climate before 1750 are discussed in Chapter 2.
47   Although there is some evidence for human influence on climate before 1750 (e.g., Ruddiman and Thomson,
48   2001; Koch et al., 2019), the magnitude of the effect is still disputed (e.g., Joos et al., 2004; Beck et al.,
49   2018b; see Chapter 5, Section, and most studies analyse the human influence on climate over the
50   industrial period. Historically, the widespread use of coal-powered machinery started the Industrial
51   Revolution in Britain in the late 18th century (Ashton, 1997), but the global effects were small for several
52   decades. In line with this, previous IPCC assessment reports considered changes in radiative forcing relative
53   to 1750, and temperature changes were often reported relative to the ‘late 19th century’. AR5 and SR1.5
54   made the specific pragmatic choice to approximate pre-industrial global temperatures by the average of the
55   1850–1900 period, when permanent surface observing networks emerged that provide sufficiently accurate

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 1   and continuous measurements on a near-global scale (see Sections 1.3.1 and Chapter 2, Section, and
 2   because the model simulations of the historical period used 1850 as their start date. For the same reasons, to
 3   ensure continuity with previous assessments, and because of larger uncertainties and lower confidence in
 4   climatic changes before 1850 than after, AR6 makes the same choice to approximate pre-industrial global
 5   temperatures by the average of the 1850-1900 period.
 7   Here we assess improvements in our understanding of climatic changes in the period 1750-1850.
 8   Anthropogenic influences on climate between 1750 and 1900 were primarily increased anthropogenic GHG
 9   and aerosol emissions, and changes in land use. Between 1750 and 1850 atmospheric CO2 levels increased
10   by from about 278 ppm to about 285 ppm (Chapter 2, Section 2.2.3, equivalent to around 3 years of current
11   rates of increase), corresponding to about 55 GtCO2 in the atmosphere. Estimates of emissions from fossil
12   fuel burning (about 4 GtCO2, Boden et al., 2017) cannot explain the pre-1850 increase, so CO2 emissions
13   from land use changes are implicated as the dominant source. The atmospheric concentration of other GHGs
14   also increased over the same period, and there was a cooling influence from other anthropogenic radiative
15   forcings (such as aerosols and land use changes), but with a larger uncertainty than for GHGs (e.g., Carslaw
16   et al., 2017; Owens et al., 2017; Hamilton et al., 2018; Chapter 2, Section 2.2.6; Chapter 7, Section;
17   Cross-Chapter Box 1.2, Figure 1). It is likely that there was a net anthropogenic forcing of 0.0–0.3 Wm-2 in
18   1850–1900 relative to 1750 (medium confidence). The net radiative forcing from changes in solar activity
19   and volcanic activity in 1850–1900, compared to the period around 1750, is estimated to be smaller than
20   ± 0.1 W m-2, but note there were several large volcanic eruptions between 1750 and 1850 (Cross-Chapter
21   Box 1.2, Figure 1).
23   Several studies since AR5 have estimated changes in global temperatures following industrialisation and
24   before 1850. Hawkins et al. (2017) used observations, radiative forcing estimates and model simulations to
25   estimate the warming from 1720–1800 until 1986–2005 and assessed a likely range of 0.55°C–0.80°C,
26   slightly broader than the equivalent range starting from 1850–1900 (0.6°C–0.7°C). From proxy evidence,
27   PAGES 2k Consortium (2019) found that GMST for 1850–1900 was 0.02°C [-0.22 to 0.16°C] warmer than
28   the 30-year period centred on 1750. Schurer et al. (2017) used climate model simulations of the last
29   millennium to estimate that the increase in GHG concentrations before 1850 caused an additional likely
30   range of 0.0–0.2°C global warming when considering multiple reference periods. Haustein et al. (2017)
31   implies an additional warming of around 0.05°C attributable to human activity from 1750 to 1850–1900, and
32   the AR6 emulator (Chapter 7, Section estimates the likely range of this warming to be 0.04°C–
33   0.14°C.
35   Combining these different sources of evidence, we assess that from the period around 1750 to 1850–1900
36   there was a change in global temperature of around 0.1°C [-0.1 to +0.3°C](medium confidence), with an
37   anthropogenic component of a likely range of 0.0°C–0.2°C (medium confidence).
42   Cross-Chapter Box 1.2, Figure 1: Changes in radiative forcing from 1750 to 2019. The radiative forcing estimates
43   from the AR6 emulator (see Cross-Chapter Box 7.1 in Chapter 7) are split into GHG, other anthropogenic (mainly
44   aerosols and land use) and natural forcings, with the average over the 1850–1900 baseline shown for each. Further
45   details on data sources and processing are available in the chapter data table (Table 1.SM.1).
53   1.4.2   Variability and emergence of the climate change signal
55   Climatic changes since the pre-industrial era are a combination of long-term anthropogenic changes and

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 1   natural variations on time scales from days to decades. The relative importance of these two factors depends
 2   on the climate variable or region of interest. Natural variations consist of both natural radiatively forced
 3   trends (e.g. due to volcanic eruptions or solar variations) and ‘internal’ fluctuations of the climate system
 4   which occur even in the absence of any radiative forcings. The internal ‘modes of variability’, such as ENSO
 5   and the NAO, are discussed further in Annex IV.
 8   Climate variability can influence trends over short periods
10   Natural variations in both weather and longer timescale phenomena can temporarily obscure or intensify any
11   anthropogenic trends (e.g., Deser et al., 2012; Kay et al., 2015). These effects are more important on small
12   spatial and temporal scales but can also occur on the global scale as well (see Cross-Chapter Box 3.1in
13   Chapter 3).
15   Since AR5, many studies have examined the role of internal variability through the use of ‘large ensembles’.
16   Each such ensemble consists of many different simulations by a single climate model for the same time
17   period and using the same radiative forcings. These simulations differ only in their phasing of the internal
18   climate variations (also see Section A set of illustrative examples using one such large ensemble
19   (Maher et al., 2019) demonstrates how variability can influence trends on decadal timescales (Figure 1.13).
20   The long-term anthropogenic trends in this set of climate indicators are clearly apparent when considering
21   the ensemble as a whole (grey shading), and all the individual ensemble members have very similar trends
22   for ocean heat content (OHC), which is a robust estimate of the total energy stored in the climate system
23   (e.g., Palmer and McNeall, 2014). However, the individual ensemble members can exhibit very different
24   decadal trends in global surface air temperature (GSAT), UK summer temperatures, and Arctic sea-ice
25   variations. More specifically, for a representative 11-year period, both positive and negative trends can be
26   found in all these surface indicators, even though the long-term trend is for increasing temperatures and
27   decreasing sea ice. Periods in which the long-term trend is substantially obscured or intensified for more than
28   20 years are also visible in these regional examples, highlighting that observations are expected to exhibit
29   short-term trends which are larger or smaller than the long-term trend or differ from the average projected
30   trend from climate models, especially on continental spatial scales or smaller (see Cross Chapter Box 3.1 in
31   Chapter 3). The actual observed trajectory can be considered as one realisation of many possible alternative
32   worlds which experienced different weather, as also demonstrated by the construction of ‘observation-based
33   large ensembles’ that are alternate possible realisations of historical observations, which retain the statistical
34   properties of observed regional weather (e.g., McKinnon and Deser, 2018).
39   Figure 1.13: Simulated changes in various climate indicators under historical and RCP4.5 scenarios using the
40                MPI ESM Grand Ensemble. The grey shading shows the 5–95% range from the 100-member ensemble.
41                The coloured lines represent individual example ensemble members, with linear trends for the 2011–2021
42                period indicated by the thin dashed lines. Changes in Ocean Heat Content (OHC) over the top 2000m
43                represents the integrated signal of global warming (left). The top row shows surface air temperature-
44                related indicators (annual GSAT change and UK summer temperatures) and the bottom row shows Arctic
45                sea-ice related indicators (annual ice volume and September sea ice extent). For smaller regions and for
46                shorter time period averages the variability increases and simulated short-term trends can temporarily
47                obscure or intensify anthropogenic changes in climate. Data from Maher et al., (2019). Further details on
48                data sources and processing are available in the chapter data table (Table 1.SM.1).
50   [END FIGURE 1.13 HERE]
53   The emergence of the climate change signal
55   In the 1930s it was noted that temperatures were increasing at both local and global scales (Kincer, 1933;
56   Callendar, 1938; Figure 1.8). At the time it was unclear whether the observed changes were part of a longer-
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 1   term trend or a natural fluctuation; the ‘signal’ had not yet clearly emerged from the ‘noise’ of natural
 2   variability. Numerous studies have since focused on the emergence of changes in temperature using
 3   instrumental observations (e.g., Madden and Ramanathan, 1980; Wigley and Jones, 1981; Mahlstein et al.,
 4   2011, 2012; Lehner and Stocker, 2015; Lehner et al., 2017) and paleo-temperature data (e.g., Abram et al.,
 5   2016).
 7   Since the IPCC Third’s Assessment report in 2001, the observed signal of climate change has been
 8   unequivocally detected at the global scale (see Section 1.3), and this signal is increasingly emerging from the
 9   noise of natural variability on smaller spatial scales and in a range of climate variables (see also FAQ1.2). In
10   this Report emergence of a climate change signal or trend refers to when a change in climate (the ‘signal’)
11   becomes larger than the amplitude of natural or internal variations (defining the ‘noise’). This concept is
12   often expressed as a ‘signal to-noise’ ratio (S/N) and emergence occurs at a defined threshold of this ratio
13   (e.g. S/N > 1 or 2). Emergence can be estimated using observations and/or model simulations and can refer
14   to changes relative to a historical or modern baseline (see Chapter 12, Section 12.5.2, Annex VII: Glossary).
15   The concept can also be expressed in terms of time (the ‘time of emergence’; Annex VII: Glossary) or in
16   terms of a global warming level (Kirchmeier‐ Young et al., 2019; see Chapter 11, Section 11.2.5) and is also
17   used to refer to a time when we can expect to see a response of mitigation activities that reduce emissions of
18   greenhouse gases or enhance their sinks (emergence with respect to mitigation, see Chapter 4, Section
19 Whenever possible, emergence should be discussed in the context of a clearly defined level of S/N
20   or other quantification, such as ‘the signal has emerged at the level of S/N > 2’, rather than as a simple
21   binary statement. For an extended discussion, see Chapter 10 (Section 10.4.3).
23   Related to the concept of emergence is the detection of change (see Chapter 3). Detection of change is
24   defined as the process of demonstrating that some aspect of the climate or a system affected by climate has
25   changed in some defined statistical sense, often using spatially aggregating methods that try to maximise
26   S/N, such as ‘fingerprints’ (e.g., Hegerl et al., 1996), without providing a reason for that change. An
27   identified change is detected in observations if its likelihood of occurrence by chance due to internal
28   variability alone is determined to be small, for example, <10% (Annex VII: Glossary).
30   An example of observed emergence in surface air temperatures is shown in Figure 1.14. Both the largest
31   changes in temperature and the largest amplitude of year-to-year variations are observed in the Arctic, with
32   lower latitudes showing less warming and smaller year-to-year variations. For the six example regions
33   shown (Figure 1.14), the emergence of changes in temperature is more apparent in northern South America,
34   East Asia and central Africa, than for northern North America or northern Europe. This pattern was predicted
35   by Hansen et al. (1988) and noted in subsequent observations by Mahlstein et al. (2011) (see Chapter 10,
36   Section, Chapter 12, Section 12.5.2). Overall, tropical regions show earlier emergence of
37   temperature changes than at higher latitudes (high confidence).
39   Since AR5, the emergence of projected future changes has also been extensively examined, in variables
40   including surface air temperature (Hawkins and Sutton, 2012; Kirtman et al., 2013; Tebaldi and
41   Friedlingstein, 2013), ocean temperatures and salinity (Banks and Wood, 2002), mean precipitation (Giorgi
42   and Bi, 2009; Maraun, 2013), drought (Orlowsky and Seneviratne, 2013), extremes (Diffenbaugh and
43   Scherer, 2011; Fischer et al., 2014; King et al., 2015; Schleussner and Fyson, 2020), and regional sea level
44   change (Lyu et al., 2014). The concept has also been applied to climate change impacts such as effects on
45   crop growing regions (Rojas et al., 2019). In AR6, the emergence of oceanic signals such as regional sea
46   level change and changes in water mass properties is assessed in Chapter 9 (Section, emergence of
47   future regional changes is assed in Chapter 10 (Section 10.4.3), the emergence of extremes as a function of
48   global warming levels is assessed in Chapter 11 (Section 11.2.5) and the emergence of climatic impact-
49   drivers for AR6 regions and many climate variables is assessed in Chapter 12 (Section 12.5.2).
51   Although the magnitude of any change is important, regions which have a larger signal of change relative to
52   the background variations will potentially face greater risks than other regions, as they will see unusual or
53   novel climate conditions more quickly (Frame et al., 2017). As in Figure 1.14, the signal of temperature
54   change is often smaller in tropical countries, but their lower amplitude of variability means they may
55   experience the effects of climate change earlier than the mid-latitudes. In addition, these tropical countries
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 1   are often amongst the most exposed, due to large populations (Lehner and Stocker, 2015), and often more
 2   vulnerable (Harrington et al., 2016; Harrington and Otto, 2018; Russo et al., 2019); both of these factors
 3   increase the risk from climate-related impacts (Cross Chapter Box 1.3). The rate of change is also important
 4   for many hazards (e.g., Loarie et al., 2009). Providing more information about changes and variations on
 5   regional scales, and the associated attribution to particular causes (see Cross-Working Group Box:
 6   Attribution), is therefore important for adaptation planning.
11   Figure 1.14: The observed emergence of changes in temperature. Top left: the total change in temperature
12                estimated for 2020 relative to 1850–1900 (following Hawkins et al. 2020), showing the largest warming
13                in the Arctic. Top right: the amplitude of estimated year-to-year variations in temperature. Middle left:
14                the ratio of the observed total change in temperature and the amplitude of temperature variability (the
15                ‘signal-to-noise (S/N) ratio’), showing that the warming is most apparent in the tropical regions (also see
16                FAQ1.2). Middle right: the global warming level at which the change in local temperature becomes larger
17                than the local year-to-year variability. The bottom panels show time series of observed annual mean
18                surface air temperatures over land in various example regions, as indicated as boxes in the top left panel.
19                The 1 and 2 standard deviations of estimated year-to-year variations for that region are shown by the pink
20                shaded bands. Observed temperature data from Berkeley Earth (Rohde and Hausfather, 2020). Further
21                details on data sources and processing are available in the chapter data table (Table 1.SM.1).
23   [END FIGURE 1.14 HERE]
26   1.4.3     Sources of uncertainty in climate simulations
28   When evaluating and analysing simulations of the physical climate system, several different sources of
29   uncertainty need to be considered (e.g., Hawkins and Sutton, 2009; Lehner et al., 2020). Broadly, these
30   sources are: uncertainties in radiative forcings (both those observed in the past and those projected for the
31   future); uncertainty in the climate response to particular radiative forcings; internal and natural variations of
32   the climate system (which may be somewhat predictable) and interactions among these sources of
33   uncertainty.
35   Ensembles of climate simulations (see Section, such as those produced as part of the sixth phase of
36   the Coupled Model Intercomparison Project (CMIP6), can be used to explore these different sources of
37   uncertainty and estimate their magnitude. Relevant experiments with climate models include both historical
38   simulations constrained by past radiative forcings and projections of future climate which are constrained by
39   specified drivers, such as GHG concentrations, emissions, or radiative forcings. (The term ‘prediction’ is
40   usually reserved for estimates of the future climate state which are also constrained by the observed initial
41   conditions of the climate system, analogous to a weather forecast.)
44    Sources of uncertainty
46   Radiative forcing uncertainty
47   Future radiative forcing is uncertain due to as-yet-unknown societal choices that will determine future
48   anthropogenic emissions; this is considered ‘scenario uncertainty’. The RCP and SSP scenarios, which form
49   the basis for climate projections assessed in this report, are designed to span a plausible range of future
50   pathways (see Section 1.6) and can be used to estimate the magnitude of scenario uncertainty, but the real
51   world may also differ from any one of these example pathways.
53   Uncertainties also exist regarding past emissions and radiative forcings. These are especially important for
54   simulations of paleoclimate time periods, such as the Pliocene, Last Glacial Maximum or the last
55   millennium, but are also relevant for the CMIP historical simulations of the instrumental period since 1850.
56   In particular, historical radiative forcings due to anthropogenic and natural aerosols are less well constrained
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 1   by observations than the greenhouse gas radiative forcings. There is also uncertainty in the size of large
 2   volcanic eruptions (and in the location for some that occurred before around 1850), and the amplitude of
 3   changes in solar activity, before satellite observations. The role of historical radiative forcing uncertainty was
 4   considered previously (Knutti et al., 2002; Forster et al., 2013) but, since AR5, specific simulations have
 5   been performed to examine this issue, particularly for the effects of uncertainty in anthropogenic aerosol
 6   radiative forcing (e.g., Jiménez-de-la-Cuesta and Mauritsen, 2019; Dittus et al., 2020).
 8   Climate response uncertainty
 9   Under any particular scenario (see Section 1.6.1), there is uncertainty in how the climate will respond to the
10   specified emissions or radiative forcing combinations. A range of climate models is often used to estimate
11   the range of uncertainty in our understanding of the key physical processes and to define the ‘model response
12   uncertainty’ (see Section 1.5.4 and Chapter 4, Section 4.2.5). However, this range does not necessarily
13   represent the full ‘climate response uncertainty’ in how the climate may respond to a particular radiative
14   forcing or emissions scenario. This is because, for example, the climate models used in CMIP experiments
15   have structural uncertainties not explored in a typical multi-model exercise (e.g., Murphy et al., 2004) and
16   are not entirely independent of each other (Masson and Knutti, 2011; Abramowitz et al., 2019; see Section
17; there are small spatial-scale features which cannot be resolved; and long time-scale processes or
18   tipping points are not fully represented. Section 1.4.4 discusses how some of these issues can still be
19   considered in a risk assessment context. For some metrics, such as Equilibrium Climate Sensitivity (ECS),
20   the CMIP6 model range is found to be broader than the very likely range assessed by combining multiple
21   lines of evidence (see Chapter 4, Section 4.3.4 and Chapter 7, Section 7.5.6).
23   Natural and internal climate variations
24   Even without any anthropogenic radiative forcing, there would still be uncertainty in projecting future
25   climate because of unpredictable natural factors such as variations in solar activity and volcanic eruptions.
26   For projections of future climate, such as those presented in Chapter 4, the uncertainty in these factors is not
27   normally considered. However, the potential effects on the climate of large volcanic eruptions (Cross-
28   Chapter Box 4.1in Chapter 4, Zanchettin et al., 2016; Bethke et al., 2017) and large solar variations (Feulner
29   and Rahmstorf, 2010; Maycock et al., 2015) are studied. On longer timescales, orbital effects and plate
30   tectonics also play a role.
32   Further, even in the absence of any anthropogenic or natural changes in radiative forcing, Earth’s climate
33   fluctuates on timescales from days to decades or longer. These ‘internal’ variations, such as those associated
34   with modes of variability (e.g., ENSO, Pacific Decadal Variability (PDV), or Atlantic Multi-decadal
35   Variability (AMV) – see Annex IV) are unpredictable on timescales longer than a few years ahead and are a
36   source of uncertainty for understanding how the climate might become in a particular decade, especially
37   regionally. The increased use of ‘large ensembles’ of complex climate model simulations to sample this
38   component of uncertainty is discussed above in Section and further in Chapter 4.
40   Interactions between variability and radiative forcings
41   It is plausible that there are interactions between radiative forcings and climate variations, such as influences
42   on the phasing or amplitude of internal or natural climate variability (Zanchettin, 2017). For example, the
43   timing of volcanic eruptions may influence Atlantic multi-decadal variability (e.g., Otterå et al., 2010; Birkel
44   et al., 2018) or ENSO (e.g., Maher et al., 2015; Khodri et al., 2017; Zuo et al., 2018), and anthropogenic
45   aerosols may influence decadal modes of variability in the Pacific (e.g., Smith et al., 2016). In addition,
46   melting of glaciers and ice caps due to anthropogenic influences has been speculated to increase volcanic
47   activity (e.g., a specific example for Iceland is discussed in Swindles et al., 2018).
50   Uncertainty quantification
52   Not all of these listed sources of uncertainty are of the same type. For example, internal climate variations
53   are an intrinsic uncertainty that can be estimated probabilistically, and could be more precisely quantified,
54   but cannot usually be reduced. However, advances in decadal prediction offer the prospect of narrowing
55   uncertainties in the trajectory of the climate for a few years ahead (e.g., Meehl et al., 2014; Yeager and
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 1   Robson, 2017; Chapter 4, Section 4.2.3).
 3   Other sources of uncertainty, such as model response uncertainty, can in principle be reduced, but are not
 4   amenable to a frequency-based interpretation of probability, and Bayesian methods to quantify the
 5   uncertainty have been considered instead (e.g., Tebaldi, 2004; Rougier, 2007; Sexton et al., 2012). The
 6   scenario uncertainty component is distinct from other uncertainties, given that future anthropogenic
 7   emissions can be considered as the outcome of a set of societal choices (see Section 1.6.1).
 9   For climate model projections it is possible to approximately quantify the relative amplitude of various
10   sources of uncertainty (e.g., Hawkins and Sutton, 2009; Lehner et al., 2020). A range of different climate
11   models are used to estimate the model response uncertainty to a particular emissions pathway, and multiple
12   pathways are used to estimate the scenario uncertainty. The unforced component of internal variability can
13   be estimated from individual ensemble members of the same climate model (e.g., Deser et al., 2012; Maher
14   et al., 2019; Section
16   Figure 1.15 illustrates the relative size of these different uncertainty components using a ‘cascade of
17   uncertainty’ (Wilby and Dessai, 2010), with examples shown for global mean temperature, northern South
18   American annual temperatures and East Asian summer precipitation changes. For global mean temperature,
19   the role of internal variability is small, and the total uncertainty is dominated by emissions scenario and
20   model response uncertainties. Note that there is considerable overlap between individual simulations for
21   different emissions scenarios even for the mid-term (2041–2060). For example, the slowest-warming
22   simulation for SSP5-8.5 produces less mid-term warming than the fastest-warming simulation for SSP1-1.9.
23   For the long-term, emissions scenario uncertainty becomes dominant.
25   The relative uncertainty due to internal variability and model uncertainty increases for smaller spatial scales.
26   In the regional example shown for changes in temperature, the same scenario and model combination has
27   produced two simulations which differ by 1°C in their projected 2081–2100 averages due solely to internal
28   climate variability. For regional precipitation changes, emissions scenario uncertainty is often small relative
29   to model response uncertainty. In the example shown, the SSPs overlap considerably, but SSP1-1.9 shows
30   the largest precipitation change in the near-term even though global mean temperature warms the least; this
31   is due to differences between regional aerosol emissions projected in this and other scenarios (Wilcox et al.,
32   2020). These cascades of uncertainty would branch out further if applying the projections to derive estimates
33   of changes in hazard (e.g., Wilby and Dessai, 2010; Halsnæs and Kaspersen, 2018; Hattermann et al., 2018).
38   Figure 1.15: The ‘cascade of uncertainties’ in CMIP6 projections. Changes in GSAT (left), northern South
39                America (region NSA) temperature change (middle), and East Asia (region EAS) summer (JJA)
40                precipitation change (right) are shown for two time periods (2041–2060, top, and 2081–2100, bottom).
41                The SSP-radiative forcing combination is indicated at the top of each cascade at the value of the multi-
42                model mean for each scenario. This branches downwards to show the ensemble mean for each model, and
43                further branches into the individual ensemble members, although often only a single member is available.
44                These diagrams highlight the relative importance of different sources of uncertainty in climate
45                projections, which varies for different time periods, regions and climate variables. See Section 1.4.5 for
46                the definition of the regions used. Further details on data sources and processing are available in the
47                chapter data table (Table 1.SM.1).
49   [END FIGURE 1.15 HERE]
52   1.4.4   Considering an uncertain future
54   Since AR5 there have been developments in how to consider and describe future climate outcomes which are
55   considered possible but very unlikely, highly uncertain, or potentially surprising. To examine such futures
56   there is a need to move beyond the usual ‘likely’ or ‘very likely’ assessed ranges and consider low-likelihood
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 1   outcomes, especially those that would result in significant impacts if they occurred (e.g., Sutton, 2018;
 2   Sillmann et al., 2021). This section briefly outlines some of the different approaches used in the AR6 WGI.
 5   Low-likelihood outcomes
 7   In the AR6, certain low-likelihood outcomes are described and assessed because they may be associated with
 8   high levels of risk and the greatest risks may not be associated with the most expected outcome. The aim of
 9   assessing these possible futures is to better inform risk assessment and decision making. Two types are
10   considered: (1) low-likelihood high warming (LLHW) scenarios, which describe the climate in a world with
11   very high climate sensitivity, and (2) low-likelihood high impact (LLHI) events that have a low likelihood of
12   occurring, but would cause large potential impacts on society or ecosystems.
14   An illustrative example of how low-likelihood outcomes can produce significant additional risks is shown in
15   Figure 1.16. The Reasons for Concern (RFCs) produced by the IPCC AR5 WGII define the additional risks
16   due to climate change at different global warming levels. These have been combined with Chapter 4
17   assessments of projected global temperature for different emissions scenarios (SSPs; see Section 1.6), and
18   Chapter 7 assessments about ECS. For example, even following a medium emissions scenario could result in
19   high levels of additional risk if ECS is at the upper end of the very likely range. However, not all possible
20   low-likelihood outcomes relate to ECS, and AR6 considers these issues in more detail than previous IPCC
21   assessment reports (see Table 1.1 and below for some examples).
26   Figure 1.16: Illustrating concepts of low-likelihood scenarios. Left: schematic likelihood distribution consistent with
27                the IPCC AR6 assessments that equilibrium climate sensitivity (ECS) is likely in the range 2.5 to 4.0°C,
28                and very likely between 2.0 and 5.0°C (Chapter 7). ECS values outside the assessed very likely range are
29                designated low-likelihood scenarios in this example (light grey). Middle and right columns: additional
30                risks due to climate change for 2020–2090 using the Reasons For Concern (RFCs, see IPCC, 2014),
31                specifically RFC1 describing the risks to unique and threatened systems and RFC3 describing risks from
32                the distribution of impacts (O’Neill et al., 2017b; Zommers et al., 2020). The projected changes of GSAT
33                used are the 95%, median and 5% assessed ranges from Chapter 4 for each SSP (top, middle and bottom);
34                these are designated High ECS, Mid-range ECS and Low ECS respectively. The burning-ember risk
35                spectrum is usually associated with levels of committed GSAT change; instead, this illustration associates
36                the risk spectrum with the GSAT reached in each year from 2020 to 2090. Note that this illustration does
37                not include the vulnerability aspect of each SSP scenario. Further details on data sources and processing
38                are available in the chapter data table (Table 1.SM.1).
40   [END FIGURE 1.16 HERE]
43   Storylines
45   As societies are increasingly experiencing the impacts of climate change related events, the climate science
46   community is developing climate information tailored for particular regions and sectors. There is a growing
47   focus on explaining and exploring complex physical chains of events or on predicting climate under various
48   future socio-economic developments. Since AR5, ‘storylines’ or ‘narratives’ approaches have been used to
49   better inform risk assessment and decision making, to assist understanding of regional processes, and
50   represent and communicate climate projection uncertainties more clearly. The aim is to help build a cohesive
51   overall picture of potential climate change pathways that moves beyond the presentation of data and figures
52   (Annex VII: Glossary; Fløttum and Gjerstad, 2017; Moezzi et al., 2017; Dessai et al., 2018; Shepherd et al.,
53   2018b).
55   In the broader IPCC context, the term ‘scenario storyline’ refers to a narrative description of one or more
56   scenarios, highlighting their main characteristics, relationships between key driving forces and the dynamics
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 1   of their evolution (for example, short-lived climate forcers emissions assessed in Chapter 6 are driven by
 2   ‘scenario storylines’, see Section 1.6). WGI is mainly concerned with ‘physical climate storylines’. These are
 3   self-consistent and possible unfolding of a physical trajectory of the climate system, or a weather or climate
 4   event, on timescales from hours to multiple decades (Shepherd et al., 2018b). This approach can be used to
 5   constrain projected changes or specific events on specified explanatory elements such as projected changes
 6   of large-scale indicators (Chapter 10, Box 10.2). For example, Hazeleger et al. (2015) suggested using ‘tales
 7   of future weather’, blending numerical weather prediction with a climate projection to illustrate the potential
 8   behaviour of future high-impact events (also see Hegdahl et al. 2020). Several studies describe how possible
 9   large changes in atmospheric circulation would affect regional precipitation and other climate variables, and
10   discuss the various climate drivers which could cause such a circulation response (James et al., 2015; Zappa
11   and Shepherd, 2017; Mindlin et al., 2020). Physical climate storylines can also help frame the causal factors
12   of extreme weather events (Shepherd, 2016) and then be linked to event attribution (Chapter 11, Section
13   11.2.2; Cross Working Group Box: Attribution).
15   Storyline approaches can be used to communicate and contextualise climate change information in the
16   context of risk for policymakers and practitioners (e.g., de Bruijn et al., 2016; Dessai et al., 2018; Scott et al.,
17   2018; Jack et al., 2020; Chapter 10, Box 10.2). They can also help in assessing risks associated with LLHI
18   events (Weitzman, 2011; Sutton, 2018), because they consider the ‘physically self-consistent unfolding of
19   past events, or of plausible future events or pathways’ (Shepherd et al., 2018b), which would be masked in a
20   probabilistic approach. These aspects are important as the greatest risk need not be associated with the
21   highest-likelihood outcome, and in fact will often be associated with low-likelihood outcomes. The storyline
22   approach can also acknowledge that climate-relevant decisions in a risk-oriented framing will rarely be taken
23   on the basis of physical climate change alone; instead, such decisions will normally take into account socio-
24   economic factors as well (Shepherd, 2019).
26   In the AR6 WGI Assessment Report, these different storyline approaches are used in several places (see
27   Table 1.1). Chapter 4 uses a storyline approach to assess the upper tail of the distribution of global warming
28   levels (the storylines of high global warming levels) and their manifestation in global patterns of temperature
29   and precipitation changes. Chapter 9 uses a storyline approach to examine the potential for, and early
30   warning signals of, a high-end sea-level scenario, in the context of deep uncertainty related to our current
31   understanding the physical processes that contribute to long-term sea-level rise. Chapter 10 assesses the use
32   of physical climate storylines and narratives as a way to explore uncertainties in regional climate projections,
33   and to link to the specific risk and decision context relevant to a user, for developing integrated and context-
34   relevant regional climate change information. Chapter 11 uses the term storyline in the framework of
35   extreme event attribution. Chapter 12 assesses the use of a storylines approach with narrative elements for
36   communicating climate (change) information in the context of climate services (Cross-Chapter Box 12.2 in
37   Chapter 12).
42   Cross-Chapter Box 1.3:          Risk framing in IPCC AR6
44   Contributing Authors: Andy Reisinger (New Zealand), Maisa Rojas (Chile), Maarten van Aalst
45   (Netherlands), Aïda Diongue-Niang (Senegal), Mathias Garschagen (Germany), Mark Howden (Australia),
46   Margot Hurlbert (Canada), Katie Mach (USA), Sawsan Mustafa (Sudan), Brian O’Neill (USA), Roque
47   Pedace (Argentina), Jana Sillmann (Norway), Carolina Vera (Argentina), David Viner (UK).
49   The IPCC SREX presented a framework for assessing risks from climate change, linking hazards (due to
50   changes in climate) with exposure and vulnerability (Cardona et al., 2012). This framework was further
51   developed by AR5 WGII (IPCC, 2014b), while AR5 WGI focussed only on the hazard component of risk.
52   As part of AR6, a cross-Working Group process expanded and refined the concept of risk to allow for a
53   consistent risk framing to be used across the three IPCC working groups (IPCC, 2019b; Box 2 in Abram et
54   al., 2019; Reisinger et al., 2020).
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 1   In this revised definition, risk is the ‘potential for adverse consequences for human or ecological systems,
 2   recognising the diversity of values and objectives associated with such systems. In the context of climate
 3   change, risks can arise not only from impacts of climate change, but also from potential human responses
 4   to climate change. Relevant adverse consequences include those on lives, livelihoods, health and wellbeing,
 5   economic, social and cultural assets and investments, infrastructure, services (including ecosystem services),
 6   ecosystems and species.
 8   In the context of climate change impacts, risks result from dynamic interactions between climate-related
 9   hazards with the exposure and vulnerability of the affected human or ecological system to hazards. Hazards,
10   exposure and vulnerability may each be subject to uncertainty in terms of magnitude and likelihood of
11   occurrence, and each may change over time and space due to socio-economic changes and human decision-
12   making.
14   In the context of climate change responses, risks result from the potential for such responses not achieving
15   the intended objective(s), or from potential trade-offs with, or negative side-effects on, other societal
16   objectives, such as the Sustainable Development Goals. Risks can arise for example from uncertainty in
17   implementation, effectiveness or outcomes of climate policy, climate-related investments, technology
18   development or adoption, and system transitions’.
20   The following concepts are also relevant for the definition of risk (see Annex VII: Glossary):
22   Exposure: The presence of people, livelihoods, species or ecosystems, environmental functions, services,
23   and resources, infrastructure, or economic, social, or cultural assets in places and settings that could be
24   adversely affected.
26   Vulnerability: The propensity or predisposition to be adversely affected. Vulnerability encompasses a
27   variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope
28   and adapt.
30   Hazard: The potential occurrence of a natural or human-induced physical event or trend that may cause loss
31   of life, injury, or other health impacts, as well as damage and loss to property, infrastructure, livelihoods,
32   service provision, ecosystems and environmental resources.
34   Impacts: The consequences of realised risks on natural and human systems, where risks result from the
35   interactions of climate-related hazards (including extreme weather and climate events), exposure, and
36   vulnerability. Impacts generally refer to effects on lives, livelihoods, health and wellbeing, ecosystems and
37   species, economic, social and cultural assets, services (including ecosystem services), and infrastructure.
38   Impacts may be referred to as consequences or outcomes, and can be adverse or beneficial.
40   Risk in AR6 WGI
42   The revised risk framing clarifies the role and contribution of WGI to risk assessment. Risk in IPCC
43   terminology applies only to human or ecological systems, not to physical systems on their own.
45   Climatic impact-drivers: CIDs are physical climate system conditions (e.g., means, extremes, events) that
46   affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be
47   detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions.
49   In AR6, WGI uses the term ‘climatic impact-drivers’ (CIDs) to describe changes in physical systems rather
50   than ‘hazards’, because the term hazard already assumes an adverse consequence. The terminology of
51   ‘climatic impact-driver’ therefore allows WGI to provide a more value-neutral characterisation of climatic
52   changes that may be relevant for understanding potential impacts, without pre-judging whether specific
53   climatic changes necessarily lead to adverse consequences, as some could also result in beneficial outcomes
54   depending on the specific system and associated values. Chapter 12 and the Atlas assess and provide
55   information on climatic impact-drivers for different regions and sectors to support and link to WGII
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 1   assessment of the impacts and risks (or opportunities) related to the changes in the climatic impact-drivers.
 2   Although CIDs can lead to adverse or beneficial outcomes, focus is given to CIDs connected to hazards, and
 3   hence inform risk.
 5   ‘Extremes’ are a category of CID, corresponding to unusual events with respect to the range of observed
 6   values of the variable. Chapter 11 assesses changes in weather and climate extremes, their attribution and
 7   future projections.
 9   As examples of the use of this terminology, the term ‘flood risk’ should not be used if it only describes
10   changes in the frequency and intensity of flood events (a hazard); the risk from flooding to human and
11   ecological systems is caused by the flood hazard, the exposure of the system affected (e.g., topography,
12   human settlements or infrastructure in the area potentially affected by flooding) and the vulnerability of the
13   system (e.g., design and maintenance of infrastructure, existence of early warning systems). As another
14   example, climate-related risk to food security can arise from both potential climate change impacts and
15   responses to climate change and can be exacerbated by other stressors. Drivers for risks related to climate
16   change impacts include climate hazards (e.g., drought, temperature extremes, humidity), mediated by other
17   climatic impact-drivers (e.g., increased CO2 fertilisation of certain types of crops may help increase yields),
18   the potential for indirect climate-related impacts (e.g., pest outbreaks triggered by ecosystem responses to
19   weather patterns), exposure of people (e.g., how many people depend on a particular crop) and vulnerability
20   or adaptability (how able are affected people to substitute other sources of food, which may be related to
21   financial access and markets).
23   Information provided by WGI may or may not be relevant to understand risks related to climate change
24   responses. For example, the risk to a company arising from emissions pricing, or the societal risk from
25   reliance on an unproven mitigation technology, are not directly dependent on actual or projected changes in
26   climate but arise largely from human choices. However, WGI climate information may be relevant to
27   understand the potential for maladaptation, such as the potential for specific adaptation responses not
28   achieving the desired outcome or having negative side-effects. For example, WGI information about the
29   range of sea level rise can help inform understanding of whether coastal protection, accommodation, or
30   retreat would be the most effective risk management strategy in a particular context.
32   From a WGI perspective also relevant for risk assessment are low-likelihood high impact events and the
33   concept of deep uncertainty.
35   Low-likelihood, high-impact events: (LLHI) ‘These are events whose probability of occurrence is low but
36   whose potential impacts on society and ecosystems are high. To better inform risk assessment and decision
37   making, such low likelihood outcomes are described as they may be associated with very high levels of risk
38   and because the greatest risks might not be associated with the most expected outcome.
40   The AR6 WGI report provides more detailed information about these types of events compared to the AR5
41   (see Table 1.1, Section 1.4.4).
43   Recognising the need for assessing and managing risk in situations of high uncertainty, the SROCC
44   advanced the treatment of situations with deep uncertainty (IPCC, 2019b; Box 5 in Abram et al., 2019);
45   Section 1.2.3). A situation of deep uncertainty exists when experts or stakeholders do not know or cannot
46   agree on: (1) appropriate conceptual models that describe relationships among key driving forces in a
47   system; (2) the probability distributions used to represent uncertainty about key variables and parameters;
48   and/or (3) how to weigh and value desirable alternative outcomes (Abram et al., 2019). The concept of deep
49   uncertainty can complement the IPCC calibrated language and thereby broaden the communication of risk.
54   Abrupt change, tipping points and surprises
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 1   An abrupt change is defined in this report as a change that takes place substantially faster than the rate of
 2   change in the recent history of the affected component of a system (see Annex VII: Glossary). In some cases,
 3   abrupt change occurs because the system state actually becomes unstable, such that the subsequent rate of
 4   change is independent of the forcing. We refer to this class of abrupt change as a tipping point, defined as a
 5   critical threshold beyond which a system reorganizes, often abruptly and/or irreversibly (Lenton et al., 2008);
 6   Annex VII: Glossary). Some of the abrupt climate changes and climate tipping points discussed in this report
 7   could have severe local climate responses, such as extreme temperature, droughts, forest fires, ice sheet loss
 8   and collapse of the thermohaline circulation (see Chapter 4, Section 4.7.2, Chapter 5, Section 5.4.9, Chapter
 9   8, Section 8.6 and Chapter 9, Section 9.2.3).
11   There is evidence of abrupt change in Earth’s history, and some of these events have been interpreted as
12   tipping points (Dakos et al., 2008) . Some of these are associated with significant changes in the global
13   climate, such as deglaciations in the Quaternary (past 2.5 million years) and rapid warming at the
14   Palaeocene-Eocene Thermal Maximum (around 55.5 million years ago) (Bowen et al., 2015; Hollis et al.,
15   2019). Such events changed the planetary climate for tens to hundreds of thousands of years, but at a rate
16   that is actually much slower than projected anthropogenic climate change over this century, even in the
17   absence of tipping points.
19   Such paleoclimate evidence has even fuelled concerns that anthropogenic GHGs could tip the global climate
20   into a permanent hot state (Steffen et al., 2018). However, there is no evidence of such non-linear responses
21   at the global scale in climate projections for the next century, which indicate a near-linear dependence of
22   global temperature on cumulative GHG emissions (Section 1.3.5, Chapter 5, Section 5.5 and Chapter 7,
23   Section At the regional scale, abrupt changes and tipping points, such as Amazon forest dieback and
24   permafrost collapse, have occurred in projections with Earth System Models (Drijfhout et al., 2015; Bathiany
25   et al., 2020; Chapter 4, Section 4.7.3). In such simulations, tipping points occur in narrow regions of
26   parameter space (e.g., CO2 concentration or temperature increase), and for specific climate background
27   states. This makes them difficult to predict using ESMs relying on parmeterizations of known processes. In
28   some cases, it is possible to detect forthcoming tipping points through time-series analysis that identifies
29   increased sensitivity to perturbations as the tipping point is approached (e.g., ‘critical slowing-down’,
30   Scheffer et al., 2012).
32   Some suggested climate tipping points prompt transitions from one steady state to another (see Figure 1.17).
33   Transitions can be prompted by perturbations such as climate extremes which force the system outside of its
34   current well of attraction in the stability landscape; this is called noise-induced tipping (Ashwin et al., 2012;
35   Figure 1.17, panels a/b). For example, the tropical forest dieback seen in some ESM projections is
36   accelerated by longer and more frequent droughts over tropical land (Good et al., 2013).
38   Alternatively, transitions from one state to another can occur if a critical threshold is exceeded; this is called
39   bifurcation tipping (Ashwin et al., 2012; Figure 1.17, panels c/d). The new state is defined as irreversible on
40   a given timescale if the recovery from this state takes substantially longer than the timescale of interest,
41   which is decades to centuries for the projections presented in this report. A well-known example is the
42   modelled irreversibility of the ocean’s thermohaline circulation in response to North Atlantic changes such
43   as freshwater input from rainfall and ice-sheet melt (Rahmstorf et al., 2005; Alkhayuon et al., 2019), which
44   is assessed in detail in Chapter 9, Section 9.2.3.
46   The tipping point concept is most commonly framed for systems in which the forcing changes relatively
47   slowly. However, this is not the case for most scenarios of anthropogenic forcing projected for the 21st
48   century. Systems with inertia lag behind rapidly-increasing forcing, which can lead to the failure of early
49   warning signals or even the possibility of temporarily overshooting a bifurcation point without provoking
50   tipping (Ritchie et al., 2019).
55   Figure 1.17: Illustration of two types of tipping points: noise-induced (panels a, b) and bifurcation (panels c, d).
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 1                (a), (c) example time-series (coloured lines) through the tipping point with black solid lines indicating
 2                stable climate states (e.g., low or high rainfall) and dashed lines represent the boundary between stable
 3                states. (b), (d) stability landscapes provide an intuitive understanding for the different types of tipping
 4                point. The valleys represent different climate states the system can occupy, with hill tops separating the
 5                stable states. The resilience of a climate state is implied by the depth of the valley. The current state of the
 6                system is represented by a ball. Both scenarios assume that the ball starts in the left-hand valley (black
 7                dashed lines) and then through different mechanisms dependent on the type of tipping transitions to the
 8                right valley (coloured lines). Noise-induced tipping events, for instance drought events causing sudden
 9                dieback of the Amazonian rainforest, develop from fluctuations within the system. The stability landscape
10                in this scenario remains fixed and stationary. A series of perturbations in the same direction or one large
11                perturbation are required to force the system over the hill top and into the alternative stable state.
12                Bifurcation tipping events, such as a collapse of the thermohaline circulation in the Atlantic Ocean under
13                climate change, occur when a critical level in the forcing is reached. Here the stability landscape is
14                subjected to a change in shape. Under gradual anthropogenic forcing the left valley begins to shallow and
15                eventually vanishes at the tipping point, forcing the system to transition to the right-hand valley.
17   [END FIGURE 1.17 HERE]
20   Surprises are a class of risk that can be defined as low-likelihood but well-understood events, and events that
21   cannot be predicted with current understanding. The risk from such surprises can be accounted for in risk
22   assessments (Parker and Risbey, 2015). Examples relevant to climate science include: a series of major
23   volcanic eruptions or a nuclear war, either of which would cause substantial planetary cooling (Robock et al.,
24   2007; Mills et al., 2014); significant 21st century sea level rise due to marine ice sheet instability (MISI,
25   Chapter 9, Box 9.4); the potential for collapse of the stratocumulus cloud decks (Schneider et al., 2019) or
26   other substantial changes in climate feedbacks (see Chapter 7, Section 7.4); and unexpected biological
27   epidemics among humans or other species, such as the COVID-19 pandemic (Forster et al., 2020; Le Quéré
28   et al., 2020; see Cross-Chapter Box 6.1 in Chapter 6). The discovery of the ozone hole was also a surprise
29   even though some of the relevant atmospheric chemistry was known at the time. The term ‘unknown
30   unknowns’ (Parker and Risbey, 2015) is also sometimes used in this context to refer to events that cannot be
31   anticipated with present knowledge or were of an unanticipated nature before they occurred.
36   Cross-Working Group Box: Attribution
38   Contributing Authors: Wolfgang Cramer (France/Germany), Pandora Hope (Australia), Maarten van Aalst
39   (Netherlands), Greg Flato (Canada), Katja Frieler (Germany), Nathan Gillett (Canada/UK), Christian Huggel
40   (Switzerland), Jan Minx (Germany), Friederike Otto (UK/Germany), Camille Parmesan (France/UK/USA),
41   Joeri Rogelj (UK/Belgium), Maisa Rojas (Chile), Sonia I. Seneviratne (Switzerland), Aimee Slangen
42   (Netherlands), Daithi Stone (New Zealand), Laurent Terray (France), Robert Vautard (France), Xuebin
43   Zhang (Canada)
45   Introduction
47   Changes in the climate system are becoming increasingly apparent, as are the climate-related impacts on
48   natural and human systems. Attribution is the process of evaluating the contribution of one or more causal
49   factors to such observed changes or events. Typical questions addressed by the IPCC are for example: ‘To
50   what degree is an observed change in global temperature induced by anthropogenic greenhouse gas and
51   aerosol concentration changes or influenced by natural variability?’ or ‘What is the contribution of climate
52   change to observed changes in crop yields that are also influenced by changes in agricultural management?’
53   Changes in the occurrence and intensity of extreme events can also be attributed, addressing questions such
54   as: ‘Have human greenhouse gas emissions increased the likelihood or intensity of an observed heat wave?’
56   This Cross-Working Group Box briefly describes why attribution studies are important. It also describes

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 1   some new developments in the methods used and provides recommendations for interpretation.
 3   Attribution studies serve to evaluate and communicate linkages associated with climate change, for example:
 4   between the human-induced increase in greenhouse gas concentrations and the observed increase in air
 5   temperature or extreme weather events (WGI Chapter 3, 10, 11); or between observed changes in climate
 6   and changing species distributions and food production (WGII Chapters 2 and others, summarised in Chapter
 7   16) (e.g., Verschuur et al., 2021); or between climate change mitigation policies and atmospheric greenhouse
 8   gas concentrations (WGI Chapter 5; WGIII Chapter 14). As such, they support numerous statements made
 9   by the IPCC (IPCC, 2013b, 2014b; WGI Chapter 1, Section 1.3, Appendix 1A).
11   Attribution assessments can also serve to monitor mitigation and assess the efficacy of applied climate
12   protection policies (e.g., Nauels et al., 2019; Banerjee et al., 2020; WGI Chapter 4, Section 4.6.3), inform
13   and constrain projections (Gillett et al., 2021; Ribes et al., 2021; WGI Chapter 4, Section 4.2.3) or inform the
14   loss and damages estimates and potential climate litigation cases by estimating the costs of climate change
15   (Huggel et al., 2015; Marjanac et al., 2017; Frame et al., 2020). These findings can thus inform mitigation
16   decisions as well as risk management and adaptation planning (e.g., CDKN, 2017).
18   Steps towards an attribution assessment
20   The unambiguous framing of what is being attributed to what is a crucial first step for an assessment
21   (Easterling et al., 2016; Hansen et al., 2016; Stone et al., 2021), followed by the identification of the possible
22   and plausible drivers of change and the development of a hypothesis or theory for the linkage (see Cross-
23   Working Group Box: Attribution, Figure 1). The next step is to clearly define the indicators of the observed
24   change or event and note the quality of the observations. There has been significant progress in the
25   compilation of fragmented and distributed observational data, broadening and deepening the data basis for
26   attribution research (e.g., Poloczanska et al., 2013; Ray et al., 2015; Cohen et al., 2018; WGI Chapter 1,
27   Section 1.5). The quality of the observational record of drivers should also be considered (e.g., volcanic
28   eruptions: WGI Chapter 2, section 2.2.2). Impacted systems also change in the absence of climate change;
29   this baseline and its associated modifiers such as agricultural developments or population growth need to be
30   considered, alongside the exposure and vulnerability of people depending on these systems.
32   There are many attribution approaches, and several methods are detailed below. In physical and biological
33   systems, attribution often builds on the understanding of the mechanisms behind the observed changes and
34   numerical models are used, while in human systems other methods of evidence-building are employed.
35   Confidence in the attribution can be increased if more than one approach is used and the model is evaluated
36   as fit-for-purpose (Hegerl et al., 2010; Vautard et al., 2019; Otto et al., 2020; Philip et al., 2020) (WGI
37   Chapter 1, Section 1.5). Finally, appropriate communication of the attribution assessment and the
38   accompanying confidence in the result (e.g., Lewis et al., 2019).
40   Attribution methods
42   Attribution of changes in atmospheric greenhouse gas concentrations to anthropogenic activity
44   AR6 WGI Chapter 5 presents multiple lines of evidence that unequivocally establish the dominant role of
45   human activities in the growth of atmospheric CO2, including through analysing changes in atmospheric
46   carbon isotope ratios and the atmospheric O2-N2 ratio (WGI Chapter 5, Section Decomposition
47   approaches can be used to attribute emissions underlying those changes to various drivers such as
48   population, energy efficiency, consumption or carbon intensity (Hoekstra and van den Bergh, 2003; Raupach
49   et al., 2007; Rosa and Dietz, 2012). Combined with attribution of their climate outcomes, the attribution of
50   the sources of greenhouse gas emissions can inform the attribution of anthropogenic climate change to
51   specific countries or actors (Matthews, 2016; Otto et al., 2017; Skeie et al., 2017; Nauels et al., 2019), and in
52   turn inform discussions on fairness and burden sharing (WGIII Chapter 14).
55   Attribution of observed climate change to anthropogenic forcing
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 2   Changes in large-scale climate variables (e.g., global mean temperature) have been reliably attributed to
 3   anthropogenic and natural forcings (e.g., Hegerl et al., 2010; Bindoff et al., 2013; WGI Chapter 1, Section
 4   1.3.4). The most established method is to identify the ‘fingerprint’ of the expected space-time response to a
 5   particular climate forcing agent such as the concentration of anthropogenically induced greenhouse gases or
 6   aerosols, or natural variation of solar radiation. This technique disentangles the contribution of individual
 7   forcing agents to an observed change (e.g., Gillett et al., 2021). New statistical approaches have been applied
 8   to better account for internal climate variability and the uncertainties in models and observations (e.g.,
 9   Naveau et al., 2018; Santer et al., 2019) (WGI, Chapter 3 Section 3.2). There are many other approaches, for
10   example, global mean sea-level change has been attributed to anthropogenic climate forcing by attributing
11   the individual contributions from, for example, glacier melt or thermal expansion, while also examining
12   which aspects of the observed change are inconsistent with internal variability (WGI Chapter 3, Section 3.5.2
13   and WGI Chapter 9, Section
15   Specific regional conditions and responses may simplify or complicate attribution on those scales. For
16   example, some human forcings, such as regional land use change or aerosols, may enhance or reduce
17   regional signals of change (Lejeune et al., 2018; Undorf et al., 2018; Boé et al., 2020; Thiery et al., 2020; see
18   also WGI Chapter 10, Section 10.4.2; WGI Chapter 11, Sections 11.1.6 and 11.2.2. In general, regional
19   climate variations are larger than the global mean climate, adding additional uncertainty to attribution (e.g.,
20   in regional sea-level change, WGI Chapter 9, Section 9.6.1). These statistical limitations may be reduced by
21   ‘process-based attribution’, focusing on the physical processes known to influence the response to external
22   forcing and internal variability (WGI Chapter 10, Section 10.4.2).
24   Attribution of weather and climate events to anthropogenic forcing
26   New methods have emerged since AR5 to attribute the change in likelihood or characteristics of weather or
27   climate events or classes of events to underlying drivers (National Academies of Sciences Engineering and
28   Medicine, 2016; Stott et al., 2016; Jézéquel et al., 2018; Wehner et al., 2018; Wang et al., 2020; WGI
29   Chapter 10, Section 10.4.1; WG1 Chapter 11, Section 11.2.2). Typically, historical changes, simulated under
30   observed forcings, are compared to a counterfactual climate simulated in the absence of anthropogenic
31   forcing. Another approach examines facets of the weather and thermodynamic status of an event through
32   process-based attribution (Hauser et al., 2016; Shepherd et al., 2018b; Grose et al., 2019; WGI Chapter 10
33   Section 10.4.1 and Chapter 11). Events where attributable human influences have been found include hot and
34   cold temperature extremes (including some with wide-spread impacts), heavy precipitation, and certain types
35   of droughts and tropical cyclones (e.g., Vogel et al., 2019; Herring et al., 2021; AR6 WGI Chapter 11,
36   Section 11.9). Event attribution techniques have sometimes been extended to ‘end-to-end’ assessments from
37   climate forcing to the impacts of events on natural or human systems (Otto, 2017, examples in WGII Table
38   16.1, SI of WGII Chapter 16, Section 16.2).
40   Attribution of observed changes in natural or human systems to climate-related drivers
42   The attribution of observed changes to climate-related drivers across a diverse set of sectors, regions and
43   systems is part of each chapter in the WGII contribution to the AR6 and is synthesised in WGII Chapter 16
44   (Section 16.2). The number of attribution studies on climate change impacts has grown substantially since
45   AR5, generally leading to higher confidence levels in attributing the causes of specific impacts. New studies
46   include the attribution of changes in socio-economic indicators such as economic damages due to river
47   floods (e.g., Schaller et al., 2016; Sauer et al., 2021), the occurrence of heat related human mortality (e.g.,
48   Sera et al., 2020, Vicedo-Cabrera et al., 2018;) or economic inequality (e.g., Diffenbaugh and Burke, 2019).
50   Impact attribution covers a diverse set of qualitative and quantitative approaches, building on experimental
51   approaches, observations from remote sensing, long-term in situ observations, and monitoring efforts,
52   teamed with local knowledge, process understanding and empirical or dynamical modelling (WGII Chapter
53   16, Section 16.2; Stone et al., 2013; Cramer et al., 2014). The attribution of a change in a natural or human
54   system (e.g., wild species, natural ecosystems, crop yields, economic development, infrastructure or human
55   health) to changes in climate-related systems (i.e., climate, and ocean acidification, permafrost thawing or
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 1   sea-level rise) requires accounting for other potential drivers of change, such as technological and economic
 2   changes in agriculture affecting crop production (Hochman et al., 2017; Butler et al., 2018), changes in
 3   human population patterns and vulnerability affecting flood or wildfire induced damages (Huggel et al.,
 4   2015; Sauer et al., 2021), or habitat loss driving declines in wild species (IPBES, 2019). These drivers are
 5   accounted for by estimating a baseline condition that would exist in the absence of climate change. The
 6   baseline might be stationary and be approximated by observations from the past, or it may change over time
 7   and be simulated by statistical or process-based impact models (Cramer et al. 2014, WGII Chapter 16,
 8   Section 16.2). Assessment of multiple independent lines of evidence, taken together, can provide rigorous
 9   attribution when more quantitative approaches are not available (Parmesan et al., 2013). These include
10   paleodata, physiological and ecological experiments, natural ‘experiments’ from very long-term datasets
11   indicating consistent responses to the same climate trend/event, and ‘fingerprints’ in species' responses that
12   are uniquely expected from climate change (e.g. poleward range boundaries expanding and equatorial range
13   boundaries contracting in a coherent pattern world-wide, Parmesan and Yohe, 2003). Meta-analyses of
14   species/ecosystem responses, when conducted with wide geographic coverage, also provide a globally
15   coherent signal of climate change at an appropriate scale for attribution to anthropogenic climate change
16   (Parmesan and Yohe, 2003; Parmesan et al., 2013).
18   Impact attribution does not always involve attribution to anthropogenic climate forcing. However, a growing
19   number of studies include this aspect (e.g., Frame et al., 2020 for the attribution of damages induced by
20   hurricane Harvey; or Diffenbaugh and Burke, 2019 for the attribution of economic inequality between
21   countries; or Schaller et al., 2016 for flood damages).
26   Cross-Working Group Box: Attribution, Figure 1: Schematic of the steps to develop an attribution assessment,
27                                                   and the purposes of such assessments. Methods and systems
28                                                   used to test the attribution hypothesis or theory include model-
29                                                   based fingerprinting, other model-based methods, evidence-
30                                                   based fingerprinting, process-based approaches, empirical or
31                                                   decomposition methods and the use of multiple lines of
32                                                   evidence. Many of the methods are based on the comparison of
33                                                   the observed state of a system to a hypothetical counterfactual
34                                                   world that does not include the driver of interest to help estimate
35                                                   the causes of the observed response.
43   1.4.5     Climate regions used in AR6
45    Defining climate regions
47   AR5 assessed regional scale detection and attribution and assessed key regional climate phenomena and their
48   relevance for future regional climate projections. This report shows that past and future climate changes and
49   extreme weather events can be substantial on local and regional scales (Chapters 8–12, Atlas), where they
50   may differ considerably from global trends, not only in intensity but even in sign (e.g., Fischer et al., 2013).
52   Although the evolution of global climate trends emerges as the net result of regional phenomena, average or
53   aggregate estimates often do not reflect the intensity, variability, and complexity of regional climate changes
54   (Stammer et al., 2018; Shepherd, 2019). A fundamental aspect of the study of regional climate changes is the
55   definition of characteristic climate zones, clusters or regions, across which the emergent climate change
56   signal can be properly analysed and projected (see Atlas). Suitable sizes and shapes of such zones strongly
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 1   depend not only on the climate variable and process of interest, but also on relevant multiscale feedbacks.
 3   There are several approaches to the classification of climate regions. When climate observation data was
 4   sparse and limited, the aggregation of climate variables was implicitly achieved through the consideration of
 5   biomes, giving rise to the traditional vegetation-based classification of Köppen (1936). In the last decades,
 6   the substantial increases in climate observations, climate modelling, and data processing capabilities have
 7   allowed new approaches to climate classification, e.g., through interpolation of aggregated global data from
 8   thousands of stations (Peel et al., 2007; Belda et al., 2014; Beck et al., 2018a) or through data-driven
 9   approaches applied to delineate ecoregions that behave in a coherent manner in response to climate
10   variability (Papagiannopoulou et al., 2018). Experience shows that each method has strengths and
11   weaknesses through trade-offs between detail and convenience. For instance, a very detailed classification,
12   with numerous complexly shaped regions derived from a large set of variables, may be most useful for the
13   evaluation of climate models (Rubel and Kottek, 2010; Belda et al., 2015; Beck et al., 2018a) and climate
14   projections (Feng et al., 2014; Belda et al., 2016). In contrast, geometrically simple regions are often best
15   suited for regional climate modelling and downscaling (e.g., the Coordinated Regional Climate Downscaling
16   Experiment (CORDEX) domains; see Giorgi and Gutowski, 2015, and Section 1.5.3).
19   Types of regions used in AR6
21   IPCC’s recognition of the importance of regional climates can be traced back to its First Assessment Report
22   (IPCC, 1990a), where climate projections for 2030 were presented for five subcontinental regions (see
23   Section 1.3.6 for an assessment of those projections). In subsequent reports, there has been a growing
24   emphasis on the analysis of regional climate, including two special reports: one on regional impacts (IPCC,
25   1998) and another on extreme events (SREX, IPCC, 2012). A general feature of previous IPCC reports is
26   that the number and coverage of climate regions vary according to the subject and across WGs. Such varied
27   definitions have the advantage of optimizing the results for a particular application (e.g., national boundaries
28   are crucial for decision making, but they rarely delimit distinctive climate regions), whereas variable region
29   definitions may have the disadvantage of hindering multidisciplinary assessments and comparisons between
30   studies or WGs.
32   In this Report, regional climate change is primarily addressed through the introduction of four classes of
33   regions (unless otherwise explicitly mentioned and justified). The first two are the unified WGI Reference
34   Sets of (1) Land and (2) Ocean Regions, which are used in the entire Report. These are supplemented by
35   additional sets of (3) Typological Regions — used in Chapters 5, 8–12 and Atlas — and (4) Continental
36   Regions, which are mainly used for linking Chapters 11, 12 and Atlas with WGII (Figure 1.18). All four
37   classes of regions are defined and described in detail in the Atlas. Here we summarize their basic features.
42   Figure 1.18: Main types of regions used in this report. (a) AR6 WGI Reference Set of Land and Ocean Regions
43                (Iturbide et al., 2020), consisting of 46 land regions and 15 ocean regions, including 3 hybrid regions
44                (CAR, MED, SEA) that are both land and ocean regions. Acronyms are explained on the right of the map.
45                Notice that RAR, SPO, NPO and EPO extend beyond the 180º meridian, therefore appearing at both sides
46                of the map (indicated by dashed lines). A comparison with the previous reference regions of AR5 WGI
47                (IPCC, 2013a) is presented in the Atlas. (b) Example of typological regions: monsoon domains adopted in
48                Chapter 8. Acronyms are explained on the right of the map. The black contour lines represent the global
49                monsoon zones, while the coloured regions denote the regional monsoon domains. The two stippled
50                regions (EqAmer and SAfri) do receive seasonal rainfall, but their classification as monsoon regions is
51                still under discussion. (c) Continental Regions used mainly in Chapter 12 and the Atlas. Stippled zones
52                define areas that are assessed in both regions (e.g., the Caribbean is assessed as Small Islands and also as
53                part of Central America). Small Islands are ocean regions containing small islands with consistent climate
54                signals and/or climatological coherence.
56   [END FIGURE 1.18 HERE]
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 3   Reference Land and Ocean Regions are polygonal, sub-continental domains defined through a combination
 4   of environmental, climatic and non-climatic (e.g., pragmatic, technical, historical) factors, in accordance
 5   with the literature and climatological reasoning based on observed and projected future climate. Merging the
 6   diverse functions and purposes of the regions assessed in the literature into a common reference set implies a
 7   certain degree of compromise between simplicity, practicality, and climate consistency. For instance, Spain
 8   is fully included in the Mediterranean (MED) Reference Region, but is one of the most climatically diverse
 9   countries in the world. Likewise, a careful comparison of panels (a) and (b) of Figure 1.18) reveals that the
10   simplified southern boundary of the Sahara (SAH) Reference Region slightly overlaps the northern boundary
11   of the West African Monsoon Typological Region. As such, the resulting Reference Regions are not
12   intended to precisely represent climates, but rather to provide simple domains suitable for regional synthesis
13   of observed and modelled climate and climate change information (Iturbide et al., 2020). In particular,
14   CMIP6 model results averaged over Reference Regions are presented in the Atlas.
16   The starting point for defining the AR6 Reference Sets of Land Regions was the collection of 26 regions
17   introduced in SREX (IPCC, 2012). The SREX collection was then revised, reshaped, complemented and
18   optimized to reflect the recent scientific literature and observed climate-change trends, giving rise to the
19   novel AR6 reference set of 46 land regions. Additionally, AR6 introduces a new reference set of 15 ocean
20   regions (including 3 hybrid regions that are treated as both, land and ocean), which complete the coverage of
21   the whole Earth (Iturbide et al., 2020).
23   Particular aspects of regional climate change are described by specialized domains called Typological
24   Regions (Figure 1.18b). These regions cover a wide range of spatial scales and are defined by specific
25   features, called typologies. Examples of typologies include: tropical forests, deserts, mountains, monsoon
26   regions, and megacities, among others. Typological Regions are powerful tools to summarize complex
27   aspects of climate defined by a combination of multiple variables. For this reason, they are used in many
28   chapters of AR6 WGI and WGII (e.g., Chapters 8–12 and the Atlas).
30   Finally, consistency with WGII is also pursued in Chapter 11, 12 and Atlas through the use of a set of
31   Continental Regions (Figure 1.18c), based on the nine continental domains defined in AR5 WGII Part B
32   (Hewitson et al., 2014). These are classical geopolitical divisions of Africa, Asia, Australasia, Europe, North
33   America, Central and South America, plus Small Islands, Polar Regions, and the Ocean. In AR6 WGI, five
34   hybrid zones (Caribbean–Small islands, East Europe–Asia, European Arctic, North American Arctic, and
35   North Central America) are also identified, which are assessed in more than one continental region.
36   Additional consistency with WGIII is pursued by Chapter 6 through the use of sub-continental domains
37   which essentially form a subset of the Continental Set of Regions (Figure 1.18c and Chapter 6, Section 6.1).
40   1.5     Major developments and their implications
42   This section presents a selection of key developments since the AR5 of the capabilities underlying the lines
43   of evidence used in the present report: observational data and observing systems (Section 1.5.1), new
44   developments in reanalyses (Section 1.5.2), climate models (Section 1.5.3), and modelling techniques,
45   comparisons and performance assessments (Section 1.5.4). For brevity, we focus on the developments that
46   are of particular importance to the conclusions drawn in later chapters, though we also provide an assessment
47   of potential losses of climate observational capacity.
50   1.5.1    Observational data and observing systems
52   Progress in climate science relies on the quality and quantity of observations from a range of platforms:
53   surface-based instrumental measurements, aircraft, radiosondes and other upper-atmospheric observations,
54   satellite-based retrievals, ocean observations, and paleoclimatic records. An historical perspective to these
55   types of observations is presented in Section 1.3.1.
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 2   Observed large-scale climatic changes assessed in Chapter 2, attribution of these changes in Chapter 3, and
 3   regional observations of specific physical or biogeochemical processes presented in other Chapters, are
 4   supported by improvements in observational capacity since the AR5. Attribution assessments can be made at
 5   a higher likelihood level than AR5, due in part to the availability of longer observational datasets (Chapter
 6   3). Updated assessments are made based on new and improved datasets, for example of global temperature
 7   change (Cross-Chapter Box 2.3 in Chapter 2) or regional climate information (Chapter 10, Section 10.2). Of
 8   particular relevance to the AR6 assessment are the ‘Essential Climate Variables’ (ECVs, Hollmann et al.,
 9   2013; Bojinski et al., 2014), and ‘Essential Ocean Variables’ (EOVs; Lindstrom et al., 2012), compiled by
10   the Global Climate Observing System (GCOS; WMO, 2016), and the Global Ocean Observing System
11   (GOOS), respectively. These variables include physical, chemical, and biological variables or groups of
12   linked variables and underpin ‘headline indicators’ for climate monitoring (Trewin et al., 2021).
14   We highlight below the key advances in observational capacity since the AR5, including major expansions
15   of existing observational platforms as well as new and/or emerging observational platforms that play a key
16   role in AR6. We then discuss potential near-term losses in key observational networks due to climate change
17   or other adverse human-caused influence.
20   Major expansions of observational capacity
22   Atmosphere, land and hydrological cycle
24   Satellites provide observations of a large number of key atmospheric and land surface variables, ensuring
25   sustained observations over wide areas. Since AR5, such observations have expanded to include satellite
26   retrievals of atmospheric CO2 via the NASA Orbiting Carbon Observatory satellites (OCO-2 and OCO-3,
27   Eldering et al., 2017), following on from similar efforts employing the Greenhouse Gases Observing
28   Satellite (GOSAT, Yokota et al., 2009; Inoue et al., 2016). Improved knowledge of fluxes between the
29   atmosphere and land surface results from combining remote sensing and in situ measurements (Rebmann et
30   al., 2018). FLUXNET ( has been providing eddy covariance measurements of carbon,
31   water, and energy fluxes between the land and the atmosphere, with some of the stations operating for over
32   20 years (Pastorello et al., 2017), while the Baseline Surface Radiation Network (BSRN) has been
33   maintaining high-quality radiation observations since the 1990s (Ohmura et al., 1998; Driemel et al., 2018).
35   Observations of the composition of the atmosphere have been further improved through expansions of
36   existing surface observation networks (Bodeker et al., 2016; De Mazière et al., 2018) and through in situ
37   measurements such as aircraft campaigns (Chapter 2, Section 2.2; Chapter 5, Section 5.2; Chapter 6, Section
38   6.2). Examples of expanded networks include Aerosols, Clouds, and Trace Gases Research InfraStructure
39   (ACTRIS) (Pandolfi et al., 2018), which focuses on short-lived climate forcers, and the Integrated Carbon
40   Observation System (ICOS), which allows scientists to study and monitor the global carbon cycle and
41   greenhouse gas emissions (Colomb et al., 2018). Examples of recent aircraft observations include the
42   Atmospheric Tomography Mission (ATom), which has flown repeatedly along the north-south axis of both
43   the Pacific and Atlantic oceans, and the continuation of the In-service Aircraft for a Global Observing
44   System (IAGOS) effort, which measures atmospheric composition from commercial aircraft (Petzold et al.,
45   2015).
47   Two distinctly different but important remote sensing systems can provide information about temperature
48   and humidity since the early 2000s. Global Navigation Satellite Systems (e.g., GPS) radio occultation and
49   limb soundings provide information, although only data for the upper troposphere and lower stratosphere are
50   suitable to support climate change assessments (Angerer et al., 2017; Scherllin-Pirscher et al., 2017; Steiner
51   et al., 2019; Gleisner et al., 2020). These measurements complement those from the Atmospheric Infrared
52   Sounder (AIRS; Chahine et al., 2006). AIRS has limitations in cloudy conditions, although these limitations
53   have been partly solved using new methods of analysis (Blackwell and Milstein, 2014; Susskind et al.,
54   2014). These new data sources now have a sufficient length of the record to strengthen the analysis of
55   atmospheric warming in Chapter 2, Section
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 2   Assessments of the hydrological cycle in Chapters 2 and 8 are supported by longer time series and new
 3   developments. Examples are new satellites (McCabe et al., 2017) and measurements of water vapor using
 4   commercial laser absorption spectrometers and water vapor isotopic composition (Steen-Larsen et al., 2015;
 5   Zannoni et al., 2019). Data products of higher quality have been developed since AR5, such as the multi-
 6   source weighted ensemble precipitation (Beck et al., 2017), and multi-satellite terrestrial evaporation (Fisher
 7   et al., 2017). Longer series are available for satellite-derived global inundation (Prigent et al., 2020).
 8   Observations of soil moisture are now available via the Soil Moisture and Ocean Salinity (SMOS) and the
 9   Soil Moisture Active Passive (SMAP) satellite retrievals, filling critical gaps in the observation of
10   hydrological trends and variability over land (Dorigo et al., 2017). Similarly, the Gravity Recovery and
11   Climate Experiment GRACE and GRACE-FO satellites (Tapley et al., 2019) have provided key constraints
12   on groundwater variability and trends around the world (Frappart and Ramillien, 2018). The combination of
13   new observations with other sources of information has led to updated estimates of heat storage in inland
14   waters (Vanderkelen et al., 2020), contributing to revised estimates of heat storage on the continents (von
15   Schuckmann et al., 2020; Chapter 7, Section
17   The ongoing collection of information about the atmosphere as it evolves is supplemented by the
18   reconstruction and digitization of data about past conditions. Programs aimed at recovering information from
19   sources such as handwritten weather journals and ship logs continue to make progress, and are steadily
20   improving spatial coverage and extending our knowledge backward in time. For example, Brönnimann et al.
21   (2019) has recently identified several thousand sources of climate data for land areas in the pre-1890 period,
22   with many from the 18th century. The vast majority of these data are not yet contained in international
23   digital data archives, and substantial quantities of undigitized ship’s weather log data exist for the same
24   period (Kaspar et al., 2015). Since the AR5 there has been a growth of ‘citizen science’ activities to rapidly
25   transcribe substantial quantities of weather observations involving volunteers. Examples of projects include:
26, and that both used ship-based logbook sources, and the
27   DRAW (Data Rescue: Archival and Weather) project,, and the
28   Climate History Australia project, which recovered land-based station data from Canada, Europe, the Congo
29   and Australia respectively (e.g., Park et al., 2018; Hawkins et al., 2019). Undergraduate students have also
30   been recruited to successfully digitise rainfall data in Ireland (Ryan et al., 2018). Such observations are an
31   invaluable source of weather and climate information for the early historical period that continues to expand
32   the digital archives (e.g., Freeman et al., 2017) which underpin observational datasets used across several
33   Chapters.
35   Ocean
37   Observations of the ocean have expanded significantly since the AR5, with expanded global coverage of in
38   situ ocean temperature and salinity observations, in situ ocean biogeochemistry observations, and satellite
39   retrievals of a variety of EOVs. Many recent advances are extensively documented in a compilation by Lee
40   et al. (2019). Below we discuss those most relevant for the current assessment.
42   Argo is a global network of nearly 4000 autonomous profiling floats (Roemmich et al., 2019), delivering
43   detailed constraints on the horizontal and vertical structure of temperature and salinity across the global
44   ocean. Argo has greatly expanded since AR5, including biogeochemistry and measurements deeper than
45   2000 m (Jayne et al., 2017), and the longer timeseries enable more rigorous climate assessments of direct
46   relevance to estimates of ocean heat content (Chapter 2, Section ; Chapter 7, section Argo
47   profiles are complemented by animal-borne sensors in several key areas, such as the seasonally ice-covered
48   sectors of the Southern Ocean (Harcourt et al., 2019).
50   Most basin-scale arrays of moored ocean instruments have expanded since AR5, providing decades-long
51   records of the ocean and atmosphere properties relevant for climate, such as the El Niño-Southern
52   Oscillation (Chen et al., 2018), deep convection (de Jong et al., 2018) or transports through straits
53   (Woodgate, 2018). Key basin-scale arrays include transport-measuring arrays in the Atlantic Ocean,
54   continuing (McCarthy et al., 2020) or newly added since AR5 (Lozier et al., 2019), supporting the
55   assessment of regional ocean circulation (Chapter 9, section 9.2.3). Tropical ocean moorings in the Pacific,
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 1   Indian and Atlantic oceans include new sites, improved capability for real time transmission, and new
 2   oxygen and CO2 sensors (Bourlès et al., 2019; Hermes et al., 2019; Smith et al., 2019b).
 4   A decade of observations of sea-surface salinity is now available via the SMOS and SMAP satellite
 5   retrievals, providing continuous and global monitoring of surface salinity in the open ocean and coastal areas
 6   for the first time (Vinogradova et al., 2019; Reul et al., 2020) (Chapter 9, Section
 8   The global network of tide gauges, complemented by a growing number of satellite-based altimetry datasets,
 9   allows for more robust estimates of global and regional sea level rise (Chapter 2, Section; Chapter 9,
10   Section Incorporating vertical land motion derived from the Global Positioning System (GPS), the
11   comparison with tide gauges has allowed the correction of a drift in satellite altimetry series over the period
12   1993–1999 (Watson et al., 2015; Chen et al., 2017), thus improving our knowledge of the recent acceleration
13   of sea level rise (Chapter 2, Section These datasets, combined with Argo and observations of the
14   cryosphere, allow a consistent closure of the global mean sea level budget (Cross-Chapter Box 9.1 in
15   Chapter 9; WCRP Global Sea Level Budget, 2018).
17   Cryosphere
19   For the cryosphere, there has been much recent progress in synthesizing global datasets covering larger areas
20   and longer time periods from multi-platform observations. For glaciers, the Global Terrestrial Network for
21   Glaciers, which combines data on glacier fluctuations, mass balance and elevation change with glacier
22   outlines and ice thickness, has expanded and provided input for assessing global glacier evolution and its
23   role in sea level rise (Chapter 2, Section; Chapter 9, Section 9.5.1; Zemp et al., 2019). New data
24   sources include archived and declassified aerial photographs and satellite missions, and high-resolution (10
25   m or less) digital elevation models (Porter et al., 2018; Braun et al., 2019).
27   Improvements have also been made in the monitoring of permafrost. The Global Terrestrial Network for
28   Permafrost (Biskaborn et al., 2015) provides long-term records of permafrost temperature and active layer
29   thickness at key sites to assess their changes over time. Substantial improvements to our assessments of
30   large-scale snow changes come from intercomparison and blending of several datasets, for snow water
31   equivalent (Mortimer et al., 2020) and snow cover extent (Mudryk et al., 2020), and from bias corrections of
32   combined datasets using in situ data (Pulliainen et al., 2020; Chapter 2, Section; Chapter 9, Section
33   9.5.2).
35   The value of gravity-based estimates of changes in ice sheet mass has increased as the time series from the
36   GRACE and GRACE-FO satellites, homogenised and absolutely calibrated, is close to 20 years in length.
37   The ESA’s Cryosat-2 radar altimetry satellite mission has continued to provide measurements of the changes
38   in the thickness of sea ice and the elevation of the Greenland and Antarctic Ice Sheets (Tilling et al., 2018).
39   Other missions include NASA’s Operation IceBridge, collecting airborne remote sensing measurements to
40   bridge the gap between ICESat (Ice, Cloud and land Elevation Satellite) and the upcoming ICESat-2 laser
41   altimetry missions. Longer time series from multiple missions have led to considerable advances in
42   understanding the origin of inconsistencies between the mass balances of different glaciers and reducing
43   uncertainties in estimates of changes in the Greenland and Antarctic Ice Sheets (Bamber et al., 2018;
44   Shepherd et al., 2018a, 2020). Last, the first observed climatology of snowfall over Antarctica was obtained
45   using the cloud/precipitation radar onboard NASA’s CloudSat (Palerme et al., 2014).
47   Biosphere
49   Satellite observations have recently expanded to include data on the fluorescence of land plants as a measure
50   of photosynthetic activity via the Global Ozone Monitoring Experiment (Guanter et al., 2014; Yang et al.,
51   2015) and OCO-2 satellites (Sun et al., 2017). Climate data records of Leaf Area Index (LAI), characterizing
52   the area of green leaves per unit of ground area, and the fraction of absorbed photosynthetically active
53   radiation (FAPAR) – an important indicator of photosynthetic activity and plant health (Gobron et al., 2009)
54   – are now available for over 30-years (Claverie et al., 2016). In addition, key indicators such as fire
55   disturbances/burned areas are now retrieved via satellite (Chuvieco et al., 2019). In the US, the National
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 1   Ecological Observational Network (NEON) provides continental-scale observations relevant to the
 2   assessment of changes in aquatic and terrestrial ecosystems via a wide variety of ground-based, airborne, and
 3   satellite platforms (Keller et al., 2008). All these long-term records reveal range shifts in ecosystems
 4   (Chapter 2, Section 2.3.4).
 6   The ability to estimate changes in global land biomass has improved due to the use of different microwave
 7   satellite data (Liu et al., 2015) and in situ forest census data and co-located lidar, combined with the
 8   MODerate resolution Imaging Spectroradiometer (MODIS; Baccini et al., 2017). This has allowed for
 9   improved quantification of land temperature (Duan et al., 2019), carbon stocks and human-induced changes
10   due to deforestation (Chapter 2, Section 2.2.7). Time series of Normalized Difference Vegetation Index
11   (NDVI) from MODIS and other remote sensing platforms is widely applied to assess the effects of climate
12   change on vegetation in drought-sensitive regions (Atampugre et al., 2019). New satellite imaging
13   capabilities for meteorological observations, such as the advanced multi-spectral imager aboard Himawari-8
14   (Bessho et al., 2016), also allow for improved monitoring of challenging quantities such as seasonal changes
15   of vegetation in cloudy regions (Miura et al., 2019; Chapter 2, section
17   In the ocean, efforts are underway to coordinate observations of biologically-relevant EOVs around the
18   globe (Muller-Karger et al., 2018; Canonico et al., 2019) and to integrate observations across disciplines
19   (e.g., the Global Ocean Acidification Observing Network; Tilbrook et al., 2019). A large number of
20   coordinated field campaigns during the 2015/2016 El Niño event enabled the collection of short-lived
21   biological phenomena such as coral bleaching and mortality caused by a months-long ocean heatwave
22   (Hughes et al., 2018); beyond this event, coordinated observations of coral reef systems are increasing in
23   number and quality (Obura et al., 2019). Overall, globally coordinated efforts focused on individual
24   components of the biosphere (e.g., the Global Alliance of Continuous Plankton Recorder Surveys; Batten et
25   al., 2019) contribute to improved knowledge of the changing marine ecosystems (Chapter 2, Section
27   Given widespread evidence for decreases in global biodiversity in recent decades related to climate change
28   and other forms of human disturbance (IPBES, 2019), a new international effort to identify a set of Essential
29   Biodiversity Variables is underway (Pereira et al., 2013; Navarro et al., 2017).
31   In summary, the observational coverage of ongoing changes to the climate system is improved at the time of
32   AR6, relative to what was available for AR5 (high confidence).
34   Paleoclimate
36   Major paleo reconstruction efforts completed since AR5 include a variety of large-scale, multi-proxy
37   temperature datasets and associated reconstructions spanning the last 2000 years (PAGES 2k Consortium,
38   2017, 2019; Neukom et al., 2019), the Holocene (Kaufman et al., 2020), the Last Glacial Maximum (Cleator
39   et al., 2020; Tierney et al., 2020b), the Mid-Pliocene Warm Period (McClymont et al., 2020), and the Early
40   Eocene Climate Optimum (Hollis et al., 2019). Newly compiled borehole data (Cuesta-Valero et al., 2019),
41   as well as advances in statistical applications to tree ring data, result in more robust reconstructions of key
42   indices such as Northern Hemisphere temperature over the last millennium (e.g., Wilson et al., 2016;
43   Anchukaitis et al., 2017). Such reconstructions provide a new context for recent warming trends (Chapter 2)
44   and serve to constrain the response of the climate system to natural and anthropogenic forcing (Chapters 3
45   and 7).
47   Ongoing efforts have expanded the number of large-scale, tree-ring-based drought reconstructions that span
48   the last centuries to millennium at annual resolution (Chapter 8; Cook et al., 2015; Stahle et al., 2016;
49   Aguilera-Betti et al., 2017; Morales et al., 2020). Likewise, stalagmite records of oxygen isotopes have
50   increased in number, resolution, and geographic distribution since AR5, providing insights into regional to
51   global-scale hydrological change over the last centuries to millions of years (Chapter 8; Cheng et al., 2016;
52   Denniston et al., 2016; Comas-Bru and Harrison, 2019). A new global compilation of water isotope-based
53   paleoclimate records spanning the last 2,000 years (PAGES Iso2K) lays the groundwork for quantitative
54   multi-proxy reconstructions of regional to global scale hydrological and temperature trends and extremes
55   (Konecky et al., 2020).
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 2   Recent advances in the reconstruction of climate extremes beyond temperature and drought include
 3   expanded datasets of past El Niño-Southern Oscillation extremes (Chapter 2, Section 2.4.2; e.g., Barrett et
 4   al., 2018; Freund et al., 2019; Grothe et al., 2019) and other modes of variability (Hernández et al., 2020),
 5   hurricane activity (e.g., Burn and Palmer, 2015; Donnelly et al., 2015), jet stream variability (Trouet et al.,
 6   2018), and wildfires (e.g., Taylor et al., 2016).
 8   New datasets as well as recent data compilations and syntheses of sea level over the last millennia (Kopp et
 9   al., 2016; Kemp et al., 2018), the last 20,000 years (Khan et al., 2019), the last interglacial period (Dutton et
10   al., 2015; Chapter 2, Section, and the Pliocene (Dumitru et al., 2019; Grant et al., 2019; Cross-
11   Chapter Box 2.4 in Chapter 2) help constrain sea level variability and its relationship to global and regional
12   temperature variability, and to contributions from different sources on centennial to millennial timescales
13   (Chapter 9, Section 9.6.2).
15   Reconstructions of paleoocean pH (Chapter 2, Section have increased in number and accuracy,
16   providing new constraints on ocean pH across the last centuries (e.g., Wu et al., 2018), the last glacial cycles
17   (e.g., Moy et al., 2019), and the last several million years (e.g., Anagnostou et al., 2020). Such
18   reconstructions inform processes and act as benchmarks for Earth system models of the global carbon cycle
19   over the recent geologic past (Chapter 5, Section 5.3.1), including previous high-CO2 warm intervals such as
20   the Pliocene (Cross-Chapter Box 2.4 in Chapter 2). Particularly relevant to such investigations are
21   reconstructions of atmospheric CO2 (Hönisch et al., 2012; Foster et al., 2017) that span the past millions to
22   tens of millions of years.
24   Constraints on the timing and rates of past climate changes have improved since AR5. Analytical methods
25   have increased the precision and reduced sample-size requirements for key radiometric dating techniques
26   including radiocarbon (Gottschalk et al., 2018; Lougheed et al., 2018) and Uranium-Thorium dating (Cheng
27   et al., 2013). More accurate ages of many paleoclimate records are also facilitated by recent improvements in
28   the radiocarbon calibration datasets (IntCal20, Reimer et al., 2020). A recent compilation of global
29   cosmogenic nuclide-based exposure dates (Balco, 2020b) allows for a more rigorous assessment of the
30   evolution of glacial landforms since the Last Glacial Maximum (Balco, 2020a).
32   Advances in paleoclimate data assimilation (Chapter 10, Section leverage the expanded set of
33   paleoclimate observations to create physically consistent gridded fields of climate variables for data-rich
34   intervals of interest (e.g., over the last millennium, Hakim et al. 2016) or last glacial period (Cleator et al.,
35   2020; Tierney et al., 2020b). Such efforts mirror advances in our understanding of the relationship between
36   proxy records and climate variables of interest, as formalized in so-called proxy system models (e.g.,
37   Tolwinski-Ward et al., 2011; Dee et al., 2015; Dolman and Laepple, 2018).
39   Overall, the number, temporal resolution, and chronological accuracy of paleoclimate reconstructions have
40   increased since AR5, leading to improved understanding of climate system processes (or Earth system
41   processes) (high confidence).
44   Threats to observational capacity or continuity
46   The lock-downs and societal outcomes arising due to the COVID-19 pandemic pose a new threat to
47   observing systems. For example, WMO and UNESCO-IOC published a summary of the changes to Earth
48   system observations during COVID-19 (WMO, 2020b). Fewer aircraft flights (down 75–90% in May 2020,
49   depending on region) and ship transits (down 20% in May 2020) mean that onboard observations from those
50   networks have reduced in number and frequency (James et al., 2020; Ingleby et al., 2021). Europe has
51   deployed more radiosonde soundings to account for the reduction in data from air traffic. Fewer ocean
52   observing buoys were deployed during 2020, and reductions have been particularly prevalent in the tropics
53   and Southern Hemisphere. The full consequences of the pandemic and responses will come to light over
54   time. Estimates of the effect of the reduction in aircraft data assimilation on weather forecasting skill are
55   small (James et al., 2020; Ingleby et al., 2021), potentially alleviating concerns about veracity of future
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 1   atmospheric reanalyses of the COVID-19 pandemic period.
 3   Surface-based networks have reduced in their coverage or range of variables measured due to COVID-19
 4   and other factors. Over land, several factors, including the ongoing transition from manual to automatic
 5   observations of weather, have reduced the spatial coverage of certain measurement types including rainfall
 6   intensity, radiosonde launches and pan evaporation, posing unique risks to datasets used for climate
 7   assessment (WMO, 2017; Lin and Huybers, 2019). Ship-based measurements, which are important for ocean
 8   climate and reanalyses through time (Smith et al., 2019c), have been in decline due to the number of ships
 9   contributing observations. There has also been a decline in the number of variables recorded by ships, but an
10   increase in the quality and time-resolution of others (e.g., sea level pressure, Kent et al. 2019).
12   Certain satellite frequencies are used to detect meteorological features that are vital to climate change
13   monitoring. These can be disturbed by certain radio communications (Anterrieu et al., 2016), although
14   scientists work to remove noise from the signal (Oliva et al., 2016). For example, water vapour in the
15   atmosphere naturally produces a weak signal at 23.8 gigahertz, which is within the range of frequencies of
16   the 5G communications network (Liu et al., 2021). Concern has been raised about potential leakage from 5G
17   network transmissions into the operating frequencies of passive sensors on existing weather satellites, which
18   could adversely influence their ability to remotely observe water vapour in the atmosphere (Yousefvand et
19   al., 2020).
21   Threats to observational capacity also include the loss of natural climate archives that are disappearing as a
22   direct consequence of warming temperatures. Ice core records from vulnerable alpine glaciers in the tropics
23   (Permana et al., 2019) and the mid-latitudes (Gabrielli et al., 2016; Winski et al., 2018; Moreno et al., 2021)
24   document more frequent melt layers in recent decades, with glacial retreat occurring at a rate and geographic
25   scale that is unusual in the Holocene (Solomina et al., 2015). The scope and severity of coral bleaching and
26   mortality events have increased in recent decades (Hughes et al., 2018), with profound implications for the
27   recovery of coral climate archives from new and existing sites. An observed increase in the mortality of
28   larger, long-lived trees over the last century is attributed to a combination of warming, land use change, and
29   disturbance (e.g., McDowell et al., 2020). The ongoing loss of these natural, high-resolution climate archives
30   endanger an end in their coverage over recent decades, given that many of the longest monthly- to annually-
31   resolved paleoclimate records were collected in the 1960s to 1990s (e.g., the PAGES2K database as
32   represented in PAGES 2k Consortium, 2017). This gap presents a barrier to the calibration of existing
33   decades-to-centuries-long records needed to constrain past temperature and hydrology trends and extremes.
35   Historical archives of weather and climate observations contained in ship’s logs, weather diaries, observatory
36   logbooks and other sources of documentary data are also at jeopardy of loss from natural disasters or
37   accidental destruction. These include measurements of temperature (air and sea surface), rainfall, surface
38   pressure, wind strength and direction, sunshine amount, and many other variables back into the 19th century.
39   While internationally coordinated data rescue efforts are focused on recovering documentary sources of past
40   weather and climate data (e.g., Allan et al., 2011), no such coordinated efforts exist for vulnerable
41   paleoclimate archives. Furthermore, oral traditions about local and regional weather and climate from
42   indigenous peoples represent valuable sources of information, especially when used in combination with
43   instrumental climate data (Makondo and Thomas, 2018), but are in danger of being lost as indigenous
44   knowledge-holders pass away.
46   In summary, while the quantity, quality, and diversity of climate system observations have grown since AR5,
47   the loss or potential loss of several critical components of the observational network is also evident (high
48   confidence).
51   1.5.2   New developments in reanalyses
53   Reanalyses are usually the output of a model (e.g., a numerical weather prediction model) constrained by
54   observations using data assimilation techniques, but the term has also been used to describe observation-
55   based datasets produced using simpler statistical methods and models (see Annex I). This section focuses on
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 1   the model-based methods and their recent developments.
 3   Reanalyses complement datasets of observations in describing changes through the historical record and are
 4   sometimes considered as ‘maps without gaps’ because they provide gridded output in space and time, often
 5   global, with physical consistency across variables on sub-daily timescales, and information about sparsely-
 6   observed variables (such as evaporation) (Hersbach et al., 2020). They can be globally complete, or
 7   regionally focussed and constrained by boundary conditions from a global reanalysis (Chapter 10, Section
 8 They can also provide feedback about the quality of the observations assimilated, including
 9   estimates of biases and critical gaps for some observing systems.
11   Many early reanalyses are described in Box 2.3 of Hartmann et al. (2013). These were often limited by the
12   underlying model, the data assimilation schemes and observational issues (Thorne and Vose, 2010; Zhou et
13   al., 2018). Observational issues include the lack of underlying observations in some regions, changes in the
14   observational systems over time (e.g., spatial coverage, introduction of satellite data), and time-dependent
15   errors in the underlying observations or in the boundary conditions, which may lead to stepwise biases in
16   time. The assimilation of sparse or inconsistent observations can introduce mass or energy imbalances
17   (Valdivieso et al., 2017; Trenberth et al., 2019). Further limitations and some efforts to reduce the
18   implications of these observational issues will be detailed below.
20   The methods used in the development of reanalyses have progressed since AR5 and, in some cases, this has
21   important implications for the information they provide on how the climate is changing. Annex I includes a
22   list of reanalysis datasets used in the AR6. Recent major developments in reanalyses include the assimilation
23   of a wider range of observations, higher spatial and temporal resolution, extensions further back in time, and
24   greater efforts to minimise the influence of a temporally varying observational network.
26   Atmospheric reanalyses
27   Extensive improvements have been made in global atmospheric reanalyses since AR5. The growing demand
28   for high-resolution data has led to the development of higher-resolution atmospheric reanalyses, such as the
29   Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2; Gelaro et al.,
30   2017) and ERA5 (Hersbach et al., 2020). There is a focus on ERA5 here because it has been assessed as of
31   high enough quality to present temperature trends alongside more traditional observational datasets (Chapter
32   2, Section and is also used in the Interactive Atlas.
34   Atmospheric reanalyses that were assessed in AR5 are still being used in the literature, and results from
35   ERA-Interim (Dee et al., 2011, ~80 km resolution, production stopped in August 2019), the Japanese 55-year
36   Reanalysis (JRA-55) (Ebita et al., 2011; Kobayashi et al., 2015; Harada et al., 2016) and Climate Forecast
37   System Reanalysis (CFSR) (Saha et al., 2010) are assessed in AR6. Some studies still also use the
38   NCEP/NCAR reanalysis, particularly because it extends back to 1948 and is updated in near real-time
39   (Kistler et al., 2001). Older reanalyses have a number of limitations, which have to be accounted for when
40   assessing the results of any study that uses them.
42   ERA5 provides hourly atmospheric fields at about 31 km resolution on 137 levels in the vertical, as well as
43   land surface variables and ocean waves, and is available from 1979 onwards and is updated in near real-time,
44   with plans to extend back to 1950. A 10-member ensemble is also available at coarser resolution, allowing
45   uncertainty estimates to be provided (e.g., Chapter 2, Section 2.3). MERRA-2 includes many updates from
46   the earlier version, including the assimilation of aerosol observations, several improvements to the
47   representation of the stratosphere, including ozone, and improved representations of cryospheric processes.
48   All of these improvements increase the usefulness of these reanalyses (Hoffmann et al., 2019; Chapter 7,
49   Section 7.3).
51   Models of atmospheric composition and emission sources and sinks allow the forecast and reanalysis of
52   constituents such as O3, CO, NOx and aerosols. The Copernicus Atmosphere Monitoring Service (CAMS)
53   reanalysis shows improvement against earlier atmospheric composition reanalyses, giving greater confidence
54   for its use to study trends and evaluate models (e.g., Inness et al., 2019; Chapter 7, Section 7.3).
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 1   The inter-comparison of reanalyses with each other, or with earlier versions, is often done for particular
 2   variables or aspects of the simulation. ERA5 is assessed as the most reliable reanalysis for climate trend
 3   assessment (Chapter 2, Section 2.3). Compared to ERA-Interim, the ERA5 forecast model and assimilation
 4   system, as well as the availability of improved reprocessing of observations, resulted in relatively smaller
 5   errors when compared to observations, including a better representation of global energy budgets, radiative
 6   forcing from volcanic eruptions (e.g., Mt. Pinatubo: Allan et al., 2020), the partitioning of surface energy
 7   (Martens et al., 2020) and wind (Kaiser-Weiss et al., 2015, 2019; Borsche et al., 2016; Scherrer, 2020). In
 8   ERA5, higher resolution means a better representation of Lagrangian motion convective updrafts, gravity
 9   waves, tropical cyclones, and other meso- to synoptic-scale features of the atmosphere (Hoffmann et al.,
10   2019; Martens et al., 2020). Low-frequency variability is found to be generally well represented and, from 10
11   hPa downwards, patterns of anomalies in temperature match those from the ERA-Interim, MERRA-2 and
12   JRA-55 reanalyses. Inhomogeneities in the water cycle have also been reduced (Hersbach et al., 2020).
14   Precipitation is not usually assimilated in reanalyses and, depending on the region, reanalysis precipitation
15   can differ from observations by more than the observational error (Zhou and Wang, 2017; Sun et al., 2018;
16   Alexander et al., 2020; Bador et al., 2020), although these studies did not include ERA5. Assimilation of
17   radiance observations from microwave imagers which, over ice-free ocean surfaces, improve the analysis of
18   lower-tropospheric humidity, cloud liquid water and ocean surface wind speed have resulted in improved
19   precipitation outputs in ERA5 (Hersbach et al., 2020). Global averages of other fields, particularly
20   temperature, from ERA-Interim and JRA-55 reanalyses continue to be consistent over the last 20 years with
21   surface observational data sets that include the polar regions (Simmons and Poli, 2015), although biases in
22   precipitation and radiation can influence temperatures regionally (Zhou et al., 2018). The global average
23   surface temperature from MERRA-2 is far cooler in recent years than temperatures derived from ERA-
24   Interim and JRA-55, which may be due to the assimilation of aerosols and their interactions (see Chapter 2,
25   Section 2.3).
27   A number of regional atmospheric reanalyses (see Chapter 10, Section have been developed, such
28   as COSMO-REA (Wahl et al., 2017), and the Australian Bureau of Meteorology Atmospheric high-
29   resolution Regional Reanalysis for Australia (BARRA) (Su et al., 2019). Regional reanalyses can add value
30   to global reanalyses due to the lower computational requirements, and can allow multiple numerical weather
31   prediction models to be tested (e.g., Kaiser-Weiss et al., 2019). There is some evidence that these higher
32   resolution reanalyses better capture precipitation variability than global lower resolution reanalyses (Jermey
33   and Renshaw, 2016; Cui et al., 2017) and are further assessed in Chapter 10, Section and used in the
34   Interactive Atlas.
36   In summary, the improvements in atmospheric reanalyses, and the greater number of years since the routine
37   ingestion of satellite data began relative to AR5, mean that there is increased confidence in using
38   atmospheric reanalyses products alongside more standard observation-based datasets in AR6 (high
39   confidence).
41   Sparse input reanalyses of the instrumental era
43   Although reanalyses such as ERA5 take advantage of new observational datasets and present a great
44   improvement in atmospheric reanalyses, the issues introduced by the evolving observational network remain.
45   Sparse input reanalyses, where only a limited set of reliable and long observed records are assimilated,
46   address these issues, with the limitation of fewer observational constraints. These efforts are sometimes
47   called centennial-scale reanalyses. One example is the atmospheric 20th Century Reanalysis (Compo et al.,
48   2011; Slivinski et al., 2021) which assimilates only surface and sea-level pressure observations, and is
49   constrained by time-varying observed changes in atmospheric constituents, prescribed sea surface
50   temperatures and sea ice concentration, creating a reconstruction of the weather over the whole globe every 3
51   hours for the period 1806–2015. The ERA-20C atmospheric reanalysis (covering 1900–2010; Poli et al.,
52   2016) also assimilates marine wind observations, and CERA-20C is a centennial-scale reanalysis that
53   assimilates both atmospheric and oceanic observations for the 1901–2010 period (Laloyaux et al., 2018).
54   These centennial-scale reanalyses are often run as ensembles that provide an estimate of the uncertainty in
55   the simulated variables over space and time. Slivinski et al. (2021) conclude that the uncertainties in surface
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 1   circulation fields in version 3 of the 20th Century Reanalysis are reliable and that there is also skill in its
 2   tropospheric reconstruction over the 20th century. Long-term changes in other variables, such as
 3   precipitation, also agree well with direct observation-based datasets (Chapter 2, Section; Chapter 8,
 4   Section
 6   Ocean reanalyses
 8   Since AR5, ocean reanalyses have improved due to: increased model resolution (Zuo et al., 2017; Lellouche
 9   et al., 2018; Heimbach et al., 2019); improved physics (Storto et al., 2019); improvements in the atmospheric
10   forcing from atmospheric reanalyses (see preceding subsection); and improvements in the data quantity and
11   quality available for assimilation (e.g., Lellouche et al., 2018; Heimbach et al., 2019), particularly due to
12   Argo observations (Zuo et al., 2019) (see Annex I).
14   The first Ocean Reanalyses Intercomparison project (Balmaseda et al., 2015) focussed on the uncertainty in
15   key climate indicators, such as ocean heat content (Palmer et al., 2017), thermosteric sea level (Storto et al.,
16   2017, 2019), salinity (Shi et al., 2017), sea ice extent (Chevallier et al., 2017), and the AMOC (Karspeck et
17   al., 2017). Reanalysis uncertainties occur in areas of inhomogeneous or sparse observational data sampling,
18   such as for the deep ocean, the Southern Ocean and western boundary currents (Lellouche et al., 2018; Storto
19   et al., 2019). Intercomparisons have also been dedicated to specific variables such as mixed-layer depths
20   (Toyoda et al., 2017), eddy kinetic energy (Masina et al., 2017) of the polar regions (Uotila et al., 2019).
21   Karspeck et al. (2017) found disagreement in the Atlantic meridional overturning circulation (AMOC)`
22   variability and strength in reanalyses over observation-sparse periods, whereas Jackson et al. (2019) reported
23   a lower spread in AMOC strength across an ensemble of ocean reanalyses of the recent period (1993-2010)
24   linked to improved observation availability for assimilation. Reanalyses also have a larger spread of ocean
25   heat uptake than data-only products and can produce spurious overestimates of heat uptake (Palmer et al.,
26   2017), which is important in the context of estimating climate sensitivity (Storto et al., 2019). The ensemble
27   approach for ocean reanalyses provides another avenue for estimating uncertainties across ocean reanalyses
28   (Storto et al., 2019).
30   While there are still limitations in their representation of oceanic features, ocean reanalyses add value to
31   observation-only based products and are used to inform assessments in AR6 (Chapters 2, 3, 7 and 9).
32   Reanalyses of the atmosphere or ocean alone may not account for important atmosphere-ocean coupling,
33   motivating the development of coupled reanalyses (Laloyaux et al., 2018; Schepers et al., 2018; Penny et al.,
34   2019), but these are not assessed in AR6.
36   Reanalyses of the pre-instrumental era
38   Longer reanalyses that extend further back in time than the beginning of the instrumental record are being
39   developed. They include the complete integration of paleoclimate archives and newly available early
40   instrumental data into extended reanalysis datasets. Such integration leverages ongoing development of
41   climate models that can simulate paleoclimate records in their units of analysis (i.e., oxygen isotope
42   composition, tree ring width, etc.), in many cases using physical climate variables as input for so-called
43   ‘proxy system models’ (Evans et al., 2013; Dee et al., 2015). Ensemble Kalman filter data assimilation
44   approaches allow to combine paleoclimate data and climate model data to generate annually resolved fields
45   (Last Millenium Reanalysis, Hakim et al., 2016; Tardif et al., 2018) or even monthly fields (Franke et al.,
46   2017). This allows for a greater understanding of decadal variability (Parsons and Hakim, 2019) and greater
47   certainty around the full range of the frequency and severity of climate extremes, allowing for better-defined
48   detection of change. It also helps to identify the links between biogeochemical cycles, ecosystem structure
49   and ecosystem functioning, and to provide initial conditions for further model experiments or downscaling
50   (see Chapter 2).
52   Applications of reanalyses
54   The developments in reanalyses described above mean that they are now used across a range of applications.
55   In AR6, reanalyses provide information for fields and in regions where observations are limited. There is
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 1   growing confidence that modern reanalyses can provide another line of evidence in describing recent
 2   temperature trends (see Chapter 2, Tables 2.4 and 2.5) As their spatial resolution increases, the exploration of
 3   fine-scale extremes in both space and time becomes possible (e.g., wind; Kaiser-Weiss et al., 2015). Longer
 4   reanalyses can be used to describe the change in the climate over the last 100 to 1000 years. Reanalyses have
 5   been used to help post-process climate model output, and drive impact models, however, they are often bias
 6   adjusted first (e.g., Weedon et al., 2014). See Cross-Chapter Box 10.2 in Chapter 10. Copernicus Climate
 7   Change Service (C3S) provides a bias adjusted dataset for global land areas based on ERA5 called WFDE5
 8   (Cucchi et al., 2020) which, combined with ERA5 information over the ocean (W5E5; Lange, 2019), is used
 9   as the AR6 Interactive Atlas reference for the bias adjustment of model output.
11   The growing interest in longer-term climate forecasts (from seasonal to multi-year and decadal) means that
12   reanalyses are now more routinely being used to develop the initial state for these forecasts, such as for the
13   Decadal Climate Prediction Project (DCPP; Boer et al., 2016). Ocean reanalyses are now being used
14   routinely in the context of climate monitoring, (e.g., the Copernicus Marine Environment Monitoring Service
15   Ocean State Report; von Schuckmann et al., 2019).
17   In summary, reanalyses have improved since AR5 and can increasingly be used as a line of evidence in
18   assessments of the state and evolution of the climate system (high confidence). Reanalyses provide
19   consistency across multiple physical quantities, and information about variables and locations that are not
20   directly observed. Since AR5, new reanalyses have been developed with various combinations of increased
21   resolution, extended records, more consistent data assimilation, estimation of uncertainty arising from the
22   range of initial conditions, and an improved representation of the atmosphere or ocean system. While noting
23   their remaining limitations, the WGI Report uses the most recent generation of reanalysis products alongside
24   more standard observation-based datasets.
27   1.5.3     Climate Models
29   A wide range of numerical models are widely used in climate science to study the climate system and its
30   behaviour across multiple temporal and spatial scales. These models are the main tools available to look
31   ahead into possible climate futures under a range of scenarios (see Section 1.6). Global Earth System Models
32   (ESMs) are the most complex models which contribute to AR6. At the core of each ESM is a GCM (General
33   Circulation Model) representing the dynamics of the atmosphere and ocean. ESMs are complemented by
34   regional models (see Chapter 10, Section 10.3.1) and by a hierarchy of models of lower complexity. This
35   section summarizes major developments in these different types of models since AR5. Past IPCC reports
36   have made use of multi-model ensembles generated through various phases of the World Climate Research
37   Programme (WCRP) Coupled Model Intercomparison Project (CMIP). Analysis of the latest CMIP Phase 6
38   (CMIP6, Eyring et al., 2016) simulations constitute a key line of evidence supporting this assessment report
39   (see Section 1.5.4). The key characteristics of models participating in CMIP6 are listed in Annex II.
42    Earth System Models
44   Earth system models are mathematical formulations of the natural laws that govern the evolution of climate-
45   relevant systems: atmosphere, ocean, cryosphere, land, and biosphere, and the carbon cycle (Flato, 2011).
46   They build on the fundamental laws of physics (e.g., Navier-Stokes or Clausius-Clapeyron equations) or
47   empirical relationships established from observations and, when possible, constrained by fundamental
48   conservation laws (e.g., mass and energy). The evolution of climate-relevant variables is computed
49   numerically using high performance computers (André et al., 2014; Balaji et al., 2017), on three-dimensional
50   discrete grids (Staniforth and Thuburn, 2012). The spatial (and temporal) resolution of these grids in both the
51   horizontal and vertical directions determines which processes need to be parameterised or whether they can
52   be explicitly resolved. Developments since AR5 in model resolution, parameterizations and modelling of the
53   land and ocean biosphere and of biogeochemical cycles are discussed below.
55   Model grids and resolution
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 2   The horizontal resolution and the number of vertical levels in ESMs is generally higher in CMIP6 than in
 3   CMIP5 (Figure 1.19). Global models with finer horizontal grids represent better many aspects of the
 4   circulation of the atmosphere (Gao et al., 2020; Schiemann et al., 2020) and ocean (Bishop et al., 2016;
 5   Storkey et al., 2018), bringing improvements in the simulation of the global hydrological cycle (Roberts et
 6   al., 2018). CMIP6 includes a dedicated effort (HighResMIP, Haarsma et al., 2016) to explore the effect of
 7   higher horizontal resolution, such as ~50 km, ~25 km and even ~10 km (see and Annex II, Table
 8   AII.6). Improvements are documented in the highest resolution coupled models used for HighResMip
 9   (Hewitt et al., 2017b; Roberts et al., 2019). Flexible grids allowing spatially variable resolution are more
10   widely used than at the time of the AR5 in the atmosphere (McGregor, 2015; Giorgetta et al., 2018) and in
11   the ocean (Wang et al., 2014; Petersen et al., 2019).
13   The number of vertical levels in the atmosphere of global models has increased (Figure 1.19) partly to enable
14   simulations to include higher levels in the atmosphere and better represent stratospheric processes (Charlton-
15   Perez et al., 2013; Kawatani et al., 2019). Half the modelling groups now use ‘high top’ models with a top
16   level above the stratopause (a pressure of about 1 hPa). The number of vertical levels in the ocean models
17   has also increased in order to achieve finer resolution over the water column and especially in the upper
18   mixed layer, and better resolve the diurnal cycle (Bernie et al., 2008) (see Chapter 3, Section 3.5 and Annex
19   II).
21   Despite the documented progress of higher resolution, the model evaluation carried out in subsequent
22   chapters shows that improvements between CMIP5 and CMIP6 remain modest at the global scale (Bock et
23   al., 2020; Chapter 3, Section 3.8.2). Lower resolution alone does not explain all model biases, for example, a
24   low blocking frequency (Davini and D’Andrea, 2020) or a wrong shape of the Intertropical Convergence
25   Zone (Tian and Dong, 2020). Model performance depends on model formulation and parameterizations as
26   much as on resolution (Chapter 3, Chapter 8, Chapter 10).
31   Figure 1.19: Resolution of the atmospheric and oceanic components of global climate models participating in
32                CMIP5, CMIP6, and HighResMIP: (a) (b) horizontal resolution (km), and (c) (d) number of vertical
33                levels. Darker colour circles indicate high-top models (whose top of the atmosphere is above 50 km). The
34                crosses are the median values. These models are documented in Annex II. Note that duplicated models in
35                a modelling group are counted as one entry when their horizontal and vertical resolutions are same. For
36                HighResMIP, one atmosphere-ocean coupled model with the highest resolution from each modelling
37                group is used. The horizontal resolution (rounded to 10km) is the square root of the number of grid points
38                divided by the surface area of the Earth, or the number of surface ocean grid points divided by the area of
39                the ocean surface, for the atmosphere and ocean respectively.
41   [END FIGURE 1.19 HERE]
44   Representation of physical and chemical processes in ESMs
45   Atmospheric models include representations of physical processes such as clouds, turbulence, convection
46   and gravity waves that are not fully represented by grid-scale dynamics. The CMIP6 models have undergone
47   updates in some of their parameterization schemes compared to their CMIP5 counterparts, with the aim of
48   better representing the physics and bringing the climatology of the models closer to newly available
49   observational datasets. Most notable developments are to schemes involving radiative transfer, cloud
50   microphysics, and aerosols, in particular a more explicit representation of the aerosol indirect effects through
51   aerosol-induced modification of cloud properties. Broadly, aerosol-cloud microphysics has been a key topic
52   for the aerosol and chemistry modelling communities since AR5, leading to improved understanding of the
53   climate influence of short-lived climate forcers, but they remain the single largest source of spread in ESM
54   calculations of climate sensitivity (Meehl et al., 2020), with numerous parameterization schemes in use
55   (Gettelman and Sherwood, 2016; Zhao et al., 2018; Gettelman et al., 2019). See also Chapter 6, section 6.4.
56   The treatment of droplet size and mixed-phase clouds (liquid and ice) was found to lead to changes in
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 1   climate sensitivity (Annex VII: Glossary) of some models between AR5 and AR6 (Bodas-Salcedo et al.,
 2   2019; Gettelman et al., 2019; Zelinka et al., 2020, Chapter 7, Section 7.4).
 4   The representation of ocean and cryosphere processes has also evolved significantly since CMIP5. The
 5   explicit representation of ocean eddies, due to increased grid resolution (typically, from 1° to ¼°), is a major
 6   advance in a number of CMIP6 ocean model components (Hewitt et al., 2017b). Advances in sea ice models
 7   have been made, for example, through correcting known shortcomings in CMIP5 simulations, in particular
 8   the persistent underestimation of the rapid decline in summer Arctic sea ice extent (Rosenblum and
 9   Eisenman, 2016, 2017; Turner and Comiso, 2017; Notz and Stroeve, 2018). The development of glacier and
10   ice-sheet models has been motivated and guided by an improved understanding of key physical processes,
11   including grounding line dynamics, stratigraphy and microstructure evolution, sub-shelf melting, and glacier
12   and ice-shelf calving, among others (Faria et al., 2014, 2018; Hanna et al., 2020). The resolution of ice sheet
13   models has continuously increased, including the use of nested grids, sub-grid interpolation schemes, and
14   adaptive mesh approaches (Cornford et al., 2016), mainly for a more accurate representation of grounding-
15   line migration and data assimilation (Pattyn, 2018). Ice-sheet models are increasingly interactively coupled
16   with global and regional climate models, accounting for the height mass-balance feedback (Vizcaino et al.,
17   2015; Le clec’h et al., 2019), and enabling a better representation of ice-ocean processes, in particular for the
18   Antarctic Ice Sheet (Asay-Davis et al., 2017).
20   Sea level rise is caused by multiple processes acting on multiple time scales: ocean warming, glaciers and ice
21   sheet melting, change in water storage on land, glacial isostatic adjustment (Chapter 9, Box 9.1) but no
22   single model can represent all these processes (Chapter 9, Section 9.6). In this report, the contributions are
23   computed separately (Chapter 9, Figure 9.28) and merged into a common probabilistic framework and
24   updated from AR5 (Church et al., 2013; Kopp et al., 2014; Chapter 9, Section 9.6).
26   Another notable development since AR5 is the inclusion of stochastic parameterizations of sub-grid
27   processes in some comprehensive climate models (Sanchez et al., 2016). Here, the deterministic differential
28   equations that govern the dynamical evolution of the model are complemented by knowledge of the
29   stochastic variability in unresolved processes. While not yet widely implemented, the approach has been
30   shown to improve the forecasting skill of weather models, to reduce systematic biases in global models
31   (Berner et al., 2017; Palmer, 2019) and to influence simulated climate sensitivity (Strommen et al., 2019).
33   Representation of biogeochemistry, including the carbon cycle
34   Since AR5, more sophisticated land use and land cover change representations in ESMs have been
35   developed to simulate the effects of land management on surface fluxes of carbon, water and energy
36   (Lawrence et al., 2016), although the integration of many processes (e.g., wetland drainage, fire as a
37   management tool) remains a challenge (Pongratz et al., 2018). The importance of nitrogen availability to
38   limit the terrestrial carbon sequestration has been recognised (Zaehle et al., 2014; Chapter 5, Section 5.4) and
39   so an increasing number of models now include a prognostic representation of the terrestrial nitrogen cycle
40   and its coupling to the land carbon cycle (Jones et al., 2016a; Arora et al., 2020), leading to a reduction in
41   uncertainty for carbon budgets (Jones and Friedlingstein, 2020; Chapter 5, Section 5.1). As was the case in
42   CMIP5 (Ciais et al., 2013), the land surface processes represented vary across CMIP6 models, with at least
43   some key processes (fire, permafrost carbon, microbes, nutrients, vegetation dynamics, plant demography)
44   absent from any particular ESM land model (Chapter 5, Table 5.4). Ocean biogeochemical models have
45   evolved to enhance the consistency of the exchanges between ocean, atmosphere and land, through riverine
46   input and dust deposition (Stock et al., 2014; Aumont et al., 2015). Other developments include flexible
47   plankton stoichiometric ratios (Galbraith and Martiny, 2015), improvements in the representation of nitrogen
48   fixation (Paulsen et al., 2017), and the limitation of plankton growth by iron (Aumont et al., 2015). Due to
49   the long time scale of biogeochemical processes, spin-up strategies have been shown to affect the
50   performance of models used in AR5 (Séférian et al., 2016).
53   Model tuning and adjustment
55   When developing climate models, choices have to be made in a number of areas. Besides model formulation
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 1   and resolution, parameterizations of unresolved processes also involve many choices as, for each of these,
 2   several parameters can be set. The acceptable range for these parameters is set by mathematical consistency
 3   (e.g., convergence of a numerical scheme), physical considerations (e.g., energy conservation), observations,
 4   or a combination of factors. Model developers choose a set of parameters that both falls within this range and
 5   mimics observations of individual processes or their statistics.
 7   An initial set of such choices is usually made by (often extensive) groups of modellers working on individual
 8   components of the Earth system (e.g., ocean, atmosphere, land or sea ice). As components are assembled to
 9   build an ESM, the choices are refined so that the simulated climate best represents a number of pre-defined
10   climate variables, or ‘tuning targets’. When these are met the model is released for use in intercomparisons
11   such as CMIP. Tuning targets can be one of three types: mean climate, regional phenomena and features, and
12   historical trends (Hourdin et al., 2017). One example of such a goal is that the climate system should reach a
13   mean equilibrium temperature close to observations when energy received from the sun is close to its
14   observed value. Whether tuning should be performed to accurate simulating long-term trends such as
15   changes in global mean temperature over the historical era, or rather be performed for each process
16   independently such that all collective behaviour is emergent, is an open question (Schmidt et al., 2017;
17   Burrows et al., 2018).
19   Each modelling group has its own strategy and, after AR5, a survey was conducted to understand the tuning
20   approach used in 23 CMIP5 modelling centres. The results are discussed in Hourdin et al. (2017), which
21   stresses that the behaviour of ESMs depends on the tuning strategy. An important recommendation is that the
22   calibration steps that lead to particular model tuning should be carefully documented. In CMIP6 each
23   modelling group now describes the three levels of tuning, both for the complete ESM and for the individual
24   components (available at and in the published model descriptions, Annex II). The
25   most important global tuning target for CMIP6 models is the net top-of-the-atmosphere (TOA) heat flux and
26   its radiative components. Other global targets include: the decomposition of each of these TOA fluxes into a
27   clear sky component and a component due to the radiative effect of clouds, global mean air and ocean
28   temperature, sea ice extent, sea ice volume, glacial mass balance, global root mean square error of
29   precipitation. The TOA heat flux balance is achieved using a diversity of approaches, usually unique to each
30   modelling group. Adjustments are made for parameters associated with uncertain or poorly constrained
31   processes (Schmidt et al., 2017), for example the aerosol indirect effects, adjustments to ocean albedo,
32   marine dimethyl sulfide (DMS) parameterization, or cloud properties (Mauritsen and Roeckner, 2020).
34   Regional tuning targets include the meridional overturning circulation in the Atlantic Ocean, the Southern
35   Ocean circulation and temperature profiles in ocean basins (Golaz et al., 2019; Sellar et al., 2019); regional
36   land properties and precipitations (Mauritsen et al., 2019; Yukimoto et al., 2019), latitudinal distribution of
37   radiation (Boucher et al., 2020), spatial contrasts in TOA radiative fluxes or surface fluxes, and stationary
38   waves in the Northern Hemisphere (Schmidt et al., 2017; Yukimoto et al., 2019).
40   Even with some core commonalities of approaches to model tuning, practices can differ, such as the use of
41   initial drift from initialized forecasts, the explicit use of the transient observed record for the historical
42   period, or the use of the present-day radiative imbalance at the TOA as a tuning target rather than an
43   equilibrated pre-industrial balance. The majority of CMIP6 modelling groups report that they do not tune
44   their model for the observed trends during the historical period (23 out of 29), nor for equilibrium climate
45   sensitivity (25 out of 29). ECS and TCR are thus emergent properties for a large majority of models. The
46   effect of tuning on model skill and ensemble spread in CMIP6 is further discussed in Chapter 3, Section 3.3.
49   From global to regional models
51   The need for accurate climate information at the regional scale is increasing (Chapter 10, Section 10.1).
52   High-resolution global climate models, such as those taking part in HighResMIP, provide more detailed
53   information at the regional scale (Roberts et al., 2018). However, due to the large computational resources
54   required by these models, only a limited number of simulations per model are available. In addition to CMIP
55   global models, regional information can be derived using Regional Climate Models (RCMs) and
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 1   downscaling techniques, presented in Chapter 10 and the Atlas. RCMs are dynamical models similar to
 2   GCMs that simulate a limited region and are forced with boudary conditions from a global simulation, often
 3   correcting for biases (Chapter 10, Section 10.3 and Cross-Chapter Box 10.2, Annex II). This approach allows
 4   the use of a higher resolution within the chosen domain, and thus better represent important drivers of
 5   regional climate such as mountain ranges, land management and urban effects. RCMs resolving atmospheric
 6   convection explicitly are now included in intercomparisons (Coppola et al., 2020) and used in Chapters 10,
 7   11 and 12. Other approaches are also used to generate regional climate projections, such as statistical
 8   downscaling (Maraun and Widmann, 2018; Chapter 10, Section 10.3).
10   The number of climate centres or consortia that carry out global climate simulations and projections has
11   grown from 11 in the first CMIP to 19 in CMIP5 and 28 for CMIP6 (see Section and Annex II).
12   Regional climate models participating in the Coordinated Regional Downscaling Experiment (CORDEX) are
13   more diverse than the global ESMs (see Section and Annex II) and engage an even wider
14   international community (Figure 1.20).
19   Figure 1.20: A world map showing the increased diversity of modelling centres contributing to CMIP and
20                CORDEX. Climate models are often developed by international consortia. EC-Earth is shown as an
21                example (involving SMHI, Sweden; KNMI, The Netherlands; DMI, Denmark; AEMET, Spain; Met
22                Éireann, Ireland; CNR‐ISAC, Italy; Instituto de Meteorologia, Portugal; FMI, Finland), but there are too
23                many such collaborations to display all of them on this map. More complete information about
24                institutions contributing to CORDEX and CMIP6 is found in Annex II.
26   [END FIGURE 1.20 HERE]
29   Models of lower complexity
31   Earth System Models of Intermediate Complexity (EMICs) complement the model hierarchy and fill the
32   gap between conceptual, simple climate models and complex GCMs or ESMs (Claussen et al., 2002).
33   EMICs are simplified; they include processes in a more parameterized, rather than explicitly calculated, form
34   and generally have lower spatial resolution compared to the complex ESMs. As a result, EMICs require
35   much less computational resource and can be integrated for many thousands of years without
36   supercomputers (Hajima et al., 2014). The range of EMICs used in climate change research is highly
37   heterogeneous, ranging from zonally averaged or mixed-layer ocean models coupled to statistical-dynamical
38   models of the atmosphere to low-resolution 3-dimensional ocean models coupled to simplified dynamical
39   models of the atmosphere. An increasing number of EMICs include interactive representations of the global
40   carbon cycle, with varying levels of complexity and numbers of processes considered (Plattner et al., 2008;
41   Zickfeld et al., 2013; MacDougall et al., 2020). Given the heterogeneity of the EMIC community, modelers
42   tend to focus on specific research questions and develop individual models accordingly. As for any type of
43   models assessed in this report, the set of EMICs undergoes thorough evaluation and fit-for-purpose testing
44   before being applied to address specific climate aspects.
46   EMICs have been used extensively in past IPCC reports, providing long-term integrations on paleoclimate
47   and future timescales, including stabilization pathways and a range of commitment scenarios, with perturbed
48   physics ensembles and sensitivity studies, or with simulations targeting the uncertainty in global climate-
49   carbon cycle systems (e.g., Meehl et al., 2007; Collins et al., 2013). More recently, a number of studies have
50   pointed to the possibility of systematically different climate responses to external forcings in EMICs and
51   complex ESMs (Frölicher and Paynter, 2015; Pfister and Stocker, 2017, 2018) that need to be considered in
52   the context of this report. For example, Frölicher and Paynter (2015) showed that EMICs have a higher
53   simulated realized warming fraction (i.e., the TCR/ECS ratio) than CMIP5 ESMs and speculated that this
54   may bias the temperature response to zero carbon emissions. But, in a recent comprehensive multi-model
55   analysis of the zero CO2 emissions commitment, MacDougall et al. (2020) did not find any significant

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 1   differences in committed temperatures 90 years after halting emissions between EMICs and ESMs. While
 2   some EMICs contribute to parts of the CMIP6-endorsed MIPs, a coordinated EMICs modeling effort similar
 3   to the ones for the AR4 (Plattner et al., 2008) and AR5 (Eby et al., 2013; Zickfeld et al., 2013) is not in place
 4   for IPCC AR6; however, EMICs are assessed in a number of chapters. For example, Chapters 4 and 5 use
 5   EMICs in the assessment of long-term climate change beyond 2100 (Chapter 5, Section 5.5), zero-emission
 6   commitments, overshoot and recovery (Chapter 4, Section 4.7), consequences of carbon dioxide removal
 7   (CDR) on the climate system and the carbon cycle (Chapter 4, Sections 4.6 and Chapter 5, Section 5.6) and
 8   long-term carbon cycle–climate feedbacks (Chapter 5, Section 5.4).
10   Physical emulators and simple climate models make up a broad class of heavily parametrized models
11   designed to reproduce the responses of the more complex, process-based models, and provide rapid
12   translations of emissions, via concentrations and radiative forcing, into probabilistic estimates of changes to
13   the physical climate system. The main application of emulators is to extrapolate insights from ESMs and
14   observational constraints to a larger set of emission scenarios (see Cross-Chapter Box 7.1 in Chapter 7). The
15   computational efficiency of various emulating approaches opens new analytical possibilities given that
16   ESMs take a lot of computational resources for each simulation. The applicability and usefulness of
17   emulating approaches are however constrained by their skill in capturing the global mean climate responses
18   simulated by the ESMs (mainly limited to global-mean or hemispheric land/ocean temperatures) and by their
19   ability to extrapolate skilfully outside the calibrated range.
21   The terms emulator and simple climate model (SCM) are different, although they are sometimes used
22   interchangeably. SCM refers to a broad class of lower-dimensional models of the energy balance, radiative
23   transfer, carbon cycle, or a combination of such physical components. SCMs can also be tuned to reproduce
24   the calculations of climate-mean variables of a given ESM, assuming that their structural flexibility can
25   capture both the parametric and structural uncertainties across process-oriented ESM responses. When run in
26   this setup, they are termed emulators. Simple climate models do not have to be run in ‘emulation’ mode,
27   though, as they can also be used to test consistency across multiple lines of evidence with regard to ranges in
28   ECS, TCR, TCRE and carbon cycle feedbacks (see Chapters 5 and 7). Physical emulation can also be
29   performed with very simple parameterisations (‘one-or-few-line climate models’), statistical methods like
30   neural networks, genetic algorithms, or other artificial intelligence approaches, where the emulator behaviour
31   is explicitly tuned to reproduce the response of a given ESM or model ensemble (Chapters 4, 5, and 7).
33   Current emulators and SCMs include the generic impulse response model outlined in Chapter 8 of the AR5
34   (AR5-IR (Supplementary Material 8.SM.11 of Myhre et al. (2013)), two-layer models (Held et al., 2010;
35   Rohrschneider et al., 2019; Nicholls et al., 2020), and higher complexity approaches that include upwelling,
36   diffusion and entrainment in the ocean component (e.g., MAGICC Version 5.3 (Raper et al., 2001; Wigley et
37   al., 2009), Version 6/7 (Meinshausen et al., 2011a); OSCAR (Gasser et al., 2017); CICERO SCM (Skeie et
38   al., 2017); FaIR (Millar et al., 2017b; Smith et al., 2018); and a range of statistical approaches (Schwarber et
39   al., 2019; Beusch et al., 2020b)). An example of recent use of an emulator approach is an early estimate of
40   the climate implications of the COVID-19 lockdowns (Forster et al. 2020; see Cross-Chapter Box 6.1 in
41   Chapter 6).
43   Since AR5, simplified climate models have been developed further, and their use is increasing. Different
44   purposes motivating development include: being as simple as possible for teaching purposes (e.g., a two-
45   layer energy balance model), as comprehensive as possible to allow for propagation of uncertainties across
46   multiple Earth System domains (MAGICC and others), or focus on higher complexity representation of
47   specific domains (e.g., OSCAR). The common theme in many models is to improve parameterisations that
48   reflect the latest findings in complex ESM interactions, such as the nitrogen cycle addition to the carbon
49   cycle, or tropospheric and stratospheric ozone exchange, with the aim of emulating their global mean
50   temperature response. Also, within the simple models that have a rudimentary representation of spatial
51   heterogeneity (e.g., four-box simple climate models), the ambition is to represent heterogeneous forcers such
52   as black carbon more adequately (Stjern et al., 2017), provide an appropriate representation of the forcing-
53   feedback framework (see e.g., Sherwood et al., 2015), investigate new parameterisations of ocean heat
54   uptake, and implement better representations of volcanic aerosol induced cooling (Gregory et al., 2016a).
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 1   MAGICC (Wigley et al., 2009; Meinshausen et al., 2011a) and FaIR (Smith et al., 2018) were used in the
 2   SR1.5 (IPCC, 2018) to categorize mitigation pathways into classes of scenarios that peak near 1.5°C,
 3   overshoot 1.5°C, or stay below 2°C. The SR1.5 (Rogelj et al., 2018) concluded that there was a high
 4   agreement in the relative temperature response of pathways, but medium agreement on the precise absolute
 5   magnitude of warming, introducing a level of imprecision in the attribution of a single pathway to a given
 6   category.
 8   In this Report, there are two notable uses of simple climate models. One is the connection between the
 9   assessed range of ECS in Chapter 7, and the projections of future global surface air temperature (GSAT)
10   change in Chapter 4, which is done via a two-layer model based on Held et al. (2010). It is also used as input
11   to sea level projections in Chapter 9. The other usage is the transfer of Earth system assessment knowledge
12   to Working Group III, via a set of models (MAGICC, FaIR, CICERO-SCM) specifically tuned to represent
13   the Working Group I assessment. For an overview of the uses, and an assessment of the related Reduced
14   Complexity Model Intercomparison Project (RCMIP), see Nicholls et al. (2020) and Cross-Chapter Box 7.1
15   in Chapter 7.
18   [START BOX 1.3 HERE]
20   Box 1.3: Emission metrics in AR6 WGI

21   Emission metrics compare the radiative forcing, temperature change, or other climate effects arising from
22   emissions of CO2 versus those from emissions of non-CO2 radiative forcing agents (such as CH4 or N2O).
23   They have been discussed in the IPCC since the First Assessment Report and are used as a means of
24   aggregating emissions and removals of different gases and placing them on a common (‘CO2 equivalent’, or
25   ‘CO2-eq’) scale.

26   AR5 included a thorough assessment of common pulse emission metrics, and how these address various
27   indicators of future climate change (Myhre et al., 2013). Most prominently used are the Global Warming
28   Potentials (GWPs), which integrate the calculated radiative forcing contribution following an idealized pulse
29   (or one-time) emission, over a chosen time horizon (IPCC, 1990a), or the Global Temperature-change
30   Potential (GTP), which considers the contribution of emission to the global-mean temperature at a specific
31   time after emission. Yet another metric is the Global Precipitation change Potential (GPP), used to quantify
32   the precipitation change per unit mass of emission of a given forcing agent (Shine et al., 2015).

33   As an example of usage, the Paris Rulebook [Decision 18/CMA.1, annex, paragraph 37] states that ‘Each
34   Party shall use the 100-year time-horizon global warming potential (GWP) values from the IPCC Fifth
35   Assessment Report, or 100-year time-horizon GWP values from a subsequent IPCC assessment report as
36   agreed upon by the ‘Conference of the Parties serving as the meeting of the Parties to the Paris Agreement’
37   (CMA), to report aggregate emissions and removals of GHGs, expressed in CO2-eq. Each Party may in
38   addition also use other metrics (e.g., global temperature potential) to report supplemental information on
39   aggregate emissions and removals of GHGs, expressed in CO2-eq’.

40   Since AR5, improved knowledge of the radiative properties, lifetimes, and other characteristics of emitted
41   species, and the response of the climate system, have led to updates to the numerical values of a range of
42   metrics; see Chapter 7, Table 7.15. Another key development is a set of metrics that compare a pulse
43   emission of CO2 (as considered by GWP and GTP) to step-changes of emission rates for short-lived
44   components (i.e., also considering emission trends). Termed GWP* (which also includes a pulse component)
45   and Combined Global Temperature change Potential (CGTP), these metrics allow the construction of a near-
46   linear relationship between global surface temperature change and cumulative CO2 and CO2-equivalent
47   emissions of both short and long lived forcing agents (Allen et al., 2016; Cain et al., 2019; Collins et al.,
48   2019). For example, the temperature response to a sustained methane reduction has a similar behaviour to the
49   temperature response to a pulse CO2 removal (or avoided emission).

50   In this Report, recent scientific developments underlying emission metrics, as relevant for Working Group I,
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 1   are assessed in full in Chapter 7, Section 7.6. In particular, see Box 7.3, which discusses the choice of metric
 2   for different usages, and Section 7.6.1, which treats the challenge of comparing the climate implication of
 3   emissions of short-lived and long-lived compounds. Also, the choice of metric is of key importance when
 4   defining and quantifying net zeronet-zero greenhouse gas emissions; see Box 1.4 and Chapter 7, Section
 5   7.6.2. Chapter 6 applies metrics to attribute GSAT change to short-lived climate forcer (SLCF) and long-
 6   lived greenhouse gas (LLGHG) emissions from different sectors and regions (Section 6.6.2).

 7   The metrics assessed in this Report are also used, and separately assessed, by Working Group III. See Cross-
 8   Chapter Box 2: GHG emissions metrics and Annex B in Chapter 2 of the WGIII contribution to the AR6.

 9   [END BOX 1.3 HERE]
12   1.5.4     Modelling techniques, comparisons and performance assessments
14   Numerical models, however complex, cannot be a perfect representation of the real world. Results from
15   climate modelling simulations constitute a key line of evidence for the present report, which requires
16   considering the limitations of each model simulation. This section presents recent developments in
17   techniques and approaches to robustly extract, quantify and compare results from multiple, independent
18   climate models, and how their performance can be assessed and validated.
21    Model ‘fitness for purpose’
23   A key issue addressed in this report is whether climate models are adequate or ‘fit’ for purposes of interest,
24   that is, whether they can be used to successfully answer particular research questions, especially about the
25   causes of recent climate change and the future evolution of climate (e.g., Parker, 2009; Notz, 2015; Knutti,
26   2018; Winsberg, 2018). Assessment of a model’s fitness-for-purpose can be informed both by how the
27   model represents relevant physical processes and by relevant performance metrics (Baumberger et al., 2017;
28   Parker, 2020). The processes and metrics that are most relevant can vary with the question of interest, for
29   example, a question about changes in deep ocean circulation versus a question about changes in regional
30   precipitation (Notz, 2015; Gramelsberger et al., 2020). New model evaluation tools (Section and
31   emergent constraint methodologies (Section can also aid the assessment of fitness-for-purpose,
32   especially in conjunction with process understanding (Klein and Hall, 2015; Knutti, 2018). The broader
33   availability of large model ensemble may allow for novel tests of fitness that better account for natural
34   climate variability (see Section Fitness-for-purpose of models used in this report is discussed in
35   Chapter 3 (Section 3.8.4) for the global scale, in Chapter 10 (Section 10.3) for regional climate, and in the
36   other chapters at the process level.
38   Typical strategies for enhancing the fitness-for-purpose of a model include increasing resolution in order to
39   explicitly simulate key processes, improving relevant parameterizations, and careful tuning. Changes to a
40   model that enhance its fitness for one purpose can sometimes decrease its fitness for others, by upsetting a
41   pre-existing balance of approximations. When it is unclear whether a model is fit for a purpose of interest,
42   there is often a closely-related purpose for which the evidence of fitness is clearer; for example, it might be
43   unclear whether a model is fit for providing highly accurate projections of precipitation changes in a region,
44   but reasonable to think that the model is fit for providing projections of precipitation changes that cannot yet
45   be ruled out (Parker, 2009). Such information about plausible or credible changes can be useful to inform
46   adaptation. Note that challenges associated with assessing model fitness-for-purpose need not prevent
47   reaching conclusions with high confidence if there are multiple other lines of evidence supporting those
48   same conclusions.
51    Ensemble modelling techniques
53   A key approach in climate science is the comparison of results from multiple model simulations with each
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 1   other and against observations. These simulations have typically been performed by separate models with
 2   consistent boundary conditions and prescribed emissions or radiative forcings, as in the Coupled Model
 3   Intercomparison Project phases (CMIP, Meehl et al., 2000, 2007a; Taylor et al., 2012; Eyring et al., 2016).
 4   Such multi-model ensembles (MMEs) have proven highly useful in sampling and quantifying model
 5   uncertainty, within and between generations of climate models. They also reduce the influence on
 6   projections of the particular sets of parametrizations and physical components simulated by individual
 7   models. The primary usage of MMEs is to provide a well quantified model range, but when used carefully
 8   they can also increase confidence in projections (Knutti et al., 2010). Presently, however, many models also
 9   share provenance (Masson and Knutti, 2011) and may have common biases that should be acknowledged
10   when presenting and building on MME-derived conclusions (Boé, 2018; Abramowitz et al., 2019) (see
11   Section
13   Since AR5, an increase in computing power has made it possible to investigate simulated internal variability
14   and to provide robust estimates of forced model responses, using Large Initial Condition Ensembles (ICEs),
15   also referred to as Single Model Initial condition Large Ensembles (SMILEs). Examples using GCMs or
16   ESMs that support assessments in AR6 include the CESM Large Ensemble (Kay et al., 2015), the MPI
17   Grand Ensemble (Maher et al., 2019), and the CanESM2 large ensembles (Kirchmeier-Young et al., 2017).
18   Such ensembles employ a single GCM or ESM in a fixed configuration, but starting from a variety of
19   different initial states. In some experiments, these initial states only differ slightly. As the climate system is
20   chaotic, such tiny changes in initial conditions lead to different evolutions for the individual realizations of
21   the system as a whole. Other experiments start from a set of well-separated ocean initial conditions to sample
22   the uncertainty in the circulation state of the ocean and its role in longer-timescale variations. These two
23   types of ICEs have been referred to as ‘micro’ and ‘macro’ perturbation ensembles respectively (Hawkins et
24   al., 2016). In support of this report, most models contributing to CMIP6 have produced ensembles of
25   multiple realizations of their historical and scenario simulations (see Chapters 3 and 4).
27   Recently, the ICE technique has been extended to atmosphere-only simulations (Mizuta et al., 2017), single-
28   forcer influences such as volcanic eruptions (Bethke et al., 2017) to regional modelling (Mote et al., 2015;
29   Fyfe et al., 2017; Schaller et al., 2018; Leduc et al., 2019) and to attribution of extreme weather events using
30   crowd-sourced computing (; Massey et al., 2015).
32   ICEs can also be used to evaluate climate model parameterizations, if models are initialized appropriately
33   (Phillips et al., 2004; Williams et al., 2013), mostly within the framework of seamless weather and climate
34   predictions (e.g., Palmer et al., 2008; Hurrell et al., 2009; Brown et al., 2012). Initializing an atmospheric
35   model in hindcast mode and observing the biases as they develop permits testing of the parameterized
36   processes, by starting from a known state rather than one dominated by quasi-random short term variability
37   (Williams et al., 2013; Ma et al., 2014; Vannière et al., 2014). However, single-model initial-conditions
38   ensembles cannot cover the same degrees of freedom as a multi-model ensemble, because model
39   characteristics substantially affect model behaviour (Flato et al., 2013).
41   A third common modelling technique is the perturbed parameter ensemble (PPE; note that the abbreviation
42   also sometimes refers to the sub-category ‘perturbed physics ensemble’). These methods are used to assess
43   uncertainty based on a single model, with individual parameters perturbed to reflect the full range of their
44   uncertainty (Murphy et al., 2004; Knutti et al., 2010; Lee et al., 2011; Shiogama et al., 2014). Statistical
45   methods can then be used to detect which parameters are the main causes of uncertainty across the ensemble.
46   PPEs have been used frequently in simpler models, such as EMICs, and are being applied to more complex
47   models. A caveat of PPEs is that the estimated uncertainty will depend on the specific parameterizations of
48   the underlying model and may well be an underestimation of the ‘true’ uncertainty. It is also challenging to
49   disentangle forced responses from internal variability using a PPE alone.
51   Together, the three ensemble methods (MMEs, ICEs, PPEs) allow investigation of climate model uncertainty
52   arising from internal variability, initial and internal boundary conditions, model formulations and
53   parameterizations (Parker, 2013). Figure 1.21 illustrates the different ensemble types. Recent studies have
54   also started combining multiple ensemble types or using ensembles in combination with statistical analytical
55   techniques. For example, Murphy et al. (2018) combine MMEs and PPEs to give a fuller assessment of
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 1   modelling uncertainty. Wagman and Jackson (2018) use PPEs to evaluate the robustness of MME-based
 2   emergent constraints. Sexton et al. (2019) study the robustness of ICE approaches by identifying parameters
 3   and processes responsible for model errors at the two different timescales.
 5   Overall, we assess that increases in computing power and the broader availability of larger and more varied
 6   ensembles of model simulations have contributed to better estimations of uncertainty in projections of future
 7   change (high confidence). Note, however, that despite their widespread use in climate science today, the cost
 8   of the ensemble approach in human and computational resources, and the challenges associated with the
 9   interpretation of multi-model ensembles, has been questioned (Palmer and Stevens, 2019; Touzé-Peiffer et
10   al., 2020).
15   Figure 1.21: Illustration of common types of model ensemble, simulating the time evolution of a quantity Q
16                (such as global mean surface temperature). (a) Multi-model ensemble, where each model has its own
17                realization of the processes affecting Q, and its own internal variability around the baseline value (dashed
18                line). The multi-model mean (black) is commonly taken as the ensemble average. (b) Initial condition
19                ensemble, where several realizations from a single model are compared. These differ only by minute
20                (‘micro’) perturbations to the initial conditions of the simulation, such that over time, internal variability
21                will progress differently in each ensemble member. (c) Perturbed physics ensemble, which also compares
22                realizations from a single model, but where one or more internal parameters that may affect the
23                simulations of Q are systematically changed to allow for a quantification of the effects of those quantities
24                on the model results. Additionally, each parameter set may be taken as the starting point for an initial
25                condition ensemble. In this figure, each set has three ensemble members.
27   [END FIGURE 1.21 HERE]
30   The sixth phase of the Coupled Model Intercomparison Project (CMIP6)
32   The Coupled Model Intercomparison Project (CMIP) provides a framework to compare the results of
33   different GCMs or ESMs performing similar experiments. Since its creation in the mid-1990s, it has evolved
34   in different phases, involving all major climate modelling centres in the world (Figure 1.20). The results of
35   these phases have played a key role in previous IPCC reports, and the present Report assesses a range of
36   results from CMIP5 that were not published until after the AR5, as well as the first results of the 6th phase of
37   CMIP (CMIP6) (Eyring et al., 2016). The CMIP6 experiment design is somewhat different from previous
38   phases. It now consists of a limited set of DECK (Diagnostic, Evaluation and Characterization of Klima)
39   simulations and an historical simulation that must be performed by all participating models, as well as a wide
40   range of CMIP6-endorsed Model Intercomparison Projects (MIPs) covering specialized topics (Eyring et al.,
41   2016) (see Figure 1.22). Each MIP activity consists of a series of model experiments, documented in the
42   literature (see Table 1.3) and in an online database (, see Pascoe et al. (2019) and Annex II).
44   The CMIP DECK simulations form the basis for a range of assessments and projections in the following
45   chapters. As in CMIP5, they consist of a ‘pre-industrial’ control simulation (piControl, where ‘pre-industrial’
46   is taken as fixed 1850 conditions in these experiments), an idealized, abrupt quadrupling of CO2
47   concentrations relative to piControl (to estimate equilibrium climate sensitivity), a 1% per year increase in
48   CO2 concentrations relative to piControl (to estimate the transient climate response), and a transient
49   simulation with prescribed sea-surface temperatures for the period 1979–2014 (termed ‘AMIP’ for historical
50   reasons). In addition, all participating models perform a historical simulation for the period 1850–2014. For
51   the latter, common CMIP6 forcings are prescribed (Cross-Chapter Box 1.4, Table 2). Depending on the
52   model setup, these include emissions and concentrations of short-lived species (Hoesly et al., 2018; Gidden
53   et al., 2019), long-lived greenhouse gases (Meinshausen et al., 2017), biomass burning emissions (van Marle
54   et al., 2017), global gridded land use forcing data (Ma et al., 2020a), solar forcing (Matthes et al., 2017), and
55   stratospheric aerosol data from volcanoes (Zanchettin et al., 2016). The methods for generating gridded
56   datasets are described in (Feng et al., 2019). For AMIP simulations, common sea surface temperatures
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 1   (SSTs) and sea ice concentrations (SICs) are prescribed. For simulations with prescribed aerosol abundances
 2   (i.e., not calculated from emissions), optical properties and fractional changes in cloud droplet effective
 3   radius are generally prescribed in order to provide a more consistent representation of aerosol forcing
 4   relative to earlier CMIP phases (Fiedler et al., 2017; Stevens et al., 2017). For models without ozone
 5   chemistry, time-varying gridded ozone concentrations and nitrogen deposition are also provided (Checa-
 6   Garcia et al., 2018).
 8   Beyond the DECK and the historical simulations, the CMIP6-endorsed MIPs aim to investigate how models
 9   respond to specific forcings, their potential systematic biases, their variability, and their responses to detailed
10   future scenarios such as the Shared Socioeconomic Pathways (SSPs, Section 1.6). Table 1.3 lists the 23
11   CMIP6-endorsed MIPs and key references. Results from a range of these MIPs, and many others outside of
12   the most recent CMIP6 cycle, will be assessed in the following chapters (also shown in Table 1.3).
13   References to all the CMIP6 datasets used in the report are found in Annex II, Table AII.10.
18   Figure 1.22: Structure of CMIP6, the 6th phase of the Coupled Model Intercomparison Project. The centre shows
19                the common DECK (Diagnostic, Evaluation and Characterization of Klima) and historical experiments
20                that all participating models must perform. The outer circles show the topics covered by the endorsed
21                (blue) and other MIPs (red). See Table 1.3 for explanation of the MIP acronyms. (Expanded from Eyring
22                et al., 2016).
24   [END FIGURE 1.22 HERE]
29   Table 1.3: CMIP6-Endorsed MIPs, their key references, and where they are used or referenced throughout this report.
        CMIP6-Endorsed MIP
                                        Long name                     Key references            Used in chapters
                                        Aerosols and Chemistry
        AerChemMIP                      Model Intercomparison         (Collins et al., 2017)    4, 6, Atlas
                                        Coupled Climate Carbon
        C4MIP                           Cycle Model                   (Jones et al., 2016a)     4, 5, Atlas
                                        Intercomparison Project
                                        The Carbon Dioxide
        CDRMIP                          Removal Model                 (Keller et al., 2018)     4, 5, Atlas
                                        Intercomparison Project
                                        Cloud Feedback Model
        CFMIP                                                         (Webb et al., 2017)       4, 7, Atlas
                                        Intercomparison Project
                                        Coordinated Regional
                                                                      (Gutowski Jr. et al.,     4, 8, 9, 10, 11, 12,
        CORDEX                          Climate Downscaling
                                                                      2016)                     Atlas
                                        Detection and
        DAMIP                           Attribution Model             (Gillett et al., 2016)    3, 10, Atlas
                                        Intercomparison Project
                                        Decadal Climate
        DCPP                                                          (Boer et al., 2016)       4, 8, Atlas
                                        Prediction Project
                                        Dynamics and
                                                                      (Gerber and Manzini,
        DynVarMIP                       Variability Model                                       Atlas
                                        Intercomparison Project

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        FAFMIP                          Model Intercomparison        (Gregory et al., 2016b)   9, Atlas
                                        Geoengineering Model
        GeoMIP                                                       (Kravitz et al., 2015)    4, 5, 8, 12, Atlas
                                        Intercomparison Project
                                        Global Monsoons Model
        GMMIP                                                        (Zhou et al., 2016)       2,3,4, 10, Atlas
                                        Intercomparison Project
                                        High Resolution Model                                  3, 8, 9, 10, 11,
        HighResMIP                                                   (Haarsma et al., 2016)
                                        Intercomparison Project                                Atlas
                                        Ice Sheet Model
        ISMIP6                          Intercomparison Project      (Nowicki et al., 2016)    3, 7, 9, Atlas
                                        for CMIP6
                                        Land Surface, Snow and       (van den Hurk et al.,
        LS3MIP                                                                                 3, 9, 11, Atlas
                                        Soil Moisture                2016)
                                        Land Use Model
        LUMIP                                                        (Lawrence et al., 2016)   4, 6, Atlas
                                        Intercomparison Project

                                        Ocean Model                  (Griffies et al., 2016;
        OMIP                                                                                   3, 9, Atlas
                                        Intercomparison Project      Orr et al., 2017)

                                        Polar Amplification
        PAMIP                           Model Intercomparison        (Smith et al., 2019a)     10, Atlas
                                                                     (Haywood et al., 2016;
                                                                     Jungclaus et al., 2017;
                                        Paleoclimate Modelling                                 2, 3, 7, 8, 9, 10,
        PMIP                                                         Otto-Bliesner et al.,
                                        Intercomparison Project                                Atlas
                                                                     2017; Kageyama et al.,
                                        Radiative Forcing Model
        RFMIP                                                        (Pincus et al., 2016)     6, 7, Atlas
                                        Intercomparison Project
                                        Scenario Model                                         4, 5, 6, 9, 10, 12,
        ScenarioMIP                                                  (O’Neill et al., 2016)
                                        Intercomparison Project                                Atlas
                                        Sea Ice Model
        SIMIP                                                        (Notz et al., 2016)       4, 9, 12, Atlas
                                        Intercomparison Project
                                        Vulnerability, Impacts,
        VIACS AB                        Adaptation and Climate       (Ruane et al., 2016)      12, Atlas
                                        Services Advisory Board
                                        Volcanic Forcings
        VolMIP                          Model Intercomparison        (Zanchettin et al., 2016) 4, 8, Atlas
 2   [END TABLE 1.3 HERE]
 5   Coordinated Regional Downscaling Experiment (CORDEX)
 7   The Coordinated Regional Downscaling Experiment (CORDEX, Gutowski Jr. et al., 2016) is an
 8   intercomparison project for regional models and statistical downscaling techniques, coordinating simulations
 9   on common domains and under common experimental conditions in a similar way to the CMIP effort.
10   Dynamical and statistical downscaling techniques can provide higher-resolution climate information than is
11   available directly from global climate models (Chapter 10, Section 10.3). These techniques require
12   evaluation and quantification of their performance before they can be considered appropriate as usable
13   regional climate information or be used in support of climate services. CORDEX simulations have been
14   provided by a range of regional downscaling models, for 14 regions together covering much of the globe
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 1   (Atlas, Figure Atlas.7), and they are used extensively in the AR6 WGI Atlas (Atlas.1.4; see also Annex II).
 3   In support of AR6, CORDEX has undertaken a new experiment (CORDEX-CORE) where regional climate
 4   models downscale a common set of global model simulations, performed at a coarser resolution, to a spatial
 5   resolution spanning from 12 to 25 km over most of the CORDEX domains (Atlas, Box Atlas.1). CORDEX-
 6   CORE represents an improved level of coordinated intercomparison of downscaling models (Remedio et al.,
 7   2019).
10   Model Evaluation Tools
12   For the first time in CMIP, a range of comprehensive evaluation tools are now available that can run
13   alongside the commonly used distributed data platform Earth System Grid Federation (ESGF, see Annex II),
14   to produce comprehensive results as soon as the model output is published to the CMIP archive.
16   For instance, the Earth System Model Evaluation Tool (ESMValTool; Eyring et al., 2020; Lauer et al., 2020;
17   Righi et al., 2020) is used by a number of chapters. It is an open-source community software tool that
18   includes a large variety of diagnostics and performance metrics relevant for coupled Earth System processes,
19   such as for the mean, variability and trends, and it can also examine emergent constraints (see Section
20 ESMValTool also includes routines provided by the WMO Expert Team on Climate Change
21   Detection and Indices for the evaluation of extreme events (Min et al., 2011; Sillmann et al., 2013) and
22   diagnostics for key processes and variability. Another example of evaluation tool is the CLIVAR 2020
23   ENSO metrics package (Planton et al., 2021).
25   These tools are used in several chapters of this report for the creation of the figures that show CMIP results.
26   Together with the Interactive Atlas, they allow for traceability of key results, and an additional level of
27   quality control on whether published figures can be reproduced. It also provides the capability to update
28   published figures with, as much as possible, the same set of models in all figures, and to assess model
29   improvements across different phases of CMIP (Chapter 3, Section 3.8.2).
31   These new developments are facilitated by the definition of common formats for CMIP model output (Balaji
32   et al., 2018) and the availability of reanalyses and observations in the same format as CMIP output
33   (obs4MIPs, Ferraro et al., 2015). The tools are also used to support routine evaluation at individual model
34   centres and simplify the assessment of improvements of individual models or generations of model
35   ensembles (Eyring et al., 2019). Note, however, that while tools such as ESMValTool can produce an
36   estimate of overall model performance, dedicated model evaluation still needs to be performed when
37   analysing projections for a particular purpose, such as assessing changing hazards in a given particular
38   region. Such evaluation is discussed in the next section, and in greater detail in later chapters of this Report.
41   Evaluation of process-based models against observations
43   Techniques used for evaluating process-based climate models against observations were assessed in AR5
44   (Flato et al., 2013), and have progressed rapidly since (Eyring et al., 2019). The most widely used technique
45   is to compare climatologies (long-term averages of specific climate variables) or time series of simulated
46   (process-based) model output with observations, considering the observational uncertainty. A further
47   approach is to compare the results of process-based models with those from statistical models. In addition to
48   a comparison of climatological means, trends and variability, AR5 already made use of a large set of
49   performance metrics for a quantitative evaluation of the models.
51   Since AR5, a range of studies has investigated model agreement with observations well beyond large scale
52   mean climate properties (e.g., Bellenger et al., 2014; Covey et al., 2016; Pendergrass and Deser, 2017;
53   Goelzer et al., 2018; Beusch et al., 2020a), providing information on the performance of recent model
54   simulations across multiple variables and components of the Earth system (e.g., Anav et al., 2013; Guan and
55   Waliser, 2017). Based on such studies, this Report assesses model improvements across different CMIP
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 1   DECK, CMIP6 historical and CMIP6-Endorsed MIP simulations, and of differences in model performance
 2   between different classes of models, such as high- versus low-resolution models (see e.g., Chapter 3, Section
 3   3.8.2).
 5   In addition, process- or regime-oriented evaluation of models has been expanded since AR5. By focusing on
 6   processes, causes of systematic errors in the models can be identified and insights can be gained as to
 7   whether a mean state or trend is correctly simulated for the right reasons. This approach is commonly used
 8   for the evaluation of clouds (e.g., Williams and Webb, 2009; Konsta et al., 2012; Bony et al., 2015; Dal
 9   Gesso et al., 2015; Jin et al., 2017), dust emissions (e.g., Parajuli et al., 2016; Wu et al., 2016) as well as
10   aerosol-cloud (e.g., Gryspeerdt and Stier, 2012) and chemistry-climate (SPARC, 2010) interactions. Process-
11   oriented diagnostics have also been used to evaluate specific phenomena such as the El Niño Southern
12   Oscillation (ENSO, Guilyardi et al., 2016), the Madden–Julian Oscillation (MJO; Ahn et al., 2017; Jiang et
13   al., 2018), Southern Ocean clouds (Hyder et al., 2018), monsoons (Boo et al., 2011; James et al., 2015), and
14   tropical cyclones (Kim et al., 2018).
16   Instrument simulators provide estimates of what a satellite would see if looking down on the model
17   simulated planet, and improve the direct comparison of modelled variables such as clouds, precipitation and
18   upper tropospheric humidity with observations from satellites (e.g., Kay et al., 2011; Klein et al., 2013;
19   Cesana and Waliser, 2016; Konsta et al., 2016; Jin et al., 2017; Chepfer et al., 2018; Swales et al., 2018;
20   Zhang et al., 2018). Within the framework of the Cloud Feedback Model Intercomparison Project (CFMIP)
21   contribution to CMIP6 (Webb et al., 2017), a new version of the Cloud Feedback Model Intercomparison
22   Project Observational Simulator (COSP, Swales et al., 2018) has been released which makes use of a
23   collection of observation proxies or satellite simulators. Related approaches in this rapidly evolving field
24   include simulators for Arctic Ocean observations (Burgard et al., 2020) and measurements of aerosol
25   observations along aircraft trajectories (Watson-Parris et al., 2019).
27   In this Report, model evaluation is performed in the individual chapters, rather than in a separate chapter as
28   was the case for AR5. This applies to the model types discussed above, and also to dedicated models of
29   subsystems that are not (or not yet) part of usual climate models, for example, glacier or ice sheet models
30   (Annex II). Further discussions are found in Chapter 3 (attribution), Chapter 5 (carbon cycle), Chapter 6
31   (short-lived climate forcers), Chapter 8 (water cycle), Chapter 9 (ocean, cryosphere and sea level), Chapter
32   10 (regional scale information) and the Atlas (regional models).
35   Emergent constraints on climate feedbacks, sensitivities and projections
37   An emergent constraint is the relationship between an uncertain aspect of future climate change and an
38   observable feature of the Earth System, evident across an ensemble of models (Allen and Ingram, 2002;
39   Mystakidis et al., 2016; Wenzel et al., 2016; Hall et al., 2019; Winkler et al., 2019). Complex Earth System
40   Models (ESMs) simulate variations on timescales from hours to centuries, telling us how aspects of the
41   current climate relate to its sensitivity to anthropogenic forcing. Where an ensemble of different ESMs
42   displays a relationship between a short-term observable variation and a longer-term sensitivity, an
43   observation of the short-term variation in the real world can be converted, via the model-based relationship,
44   into an ‘emergent constraint’ on the sensitivity. This is shown schematically in Figure 1.23 (Eyring et al.,
45   2019), see also Annex VII: Glossary.
47   Emergent constraints use the spread in model projections to estimate the sensitivities of the climate system to
48   anthropogenic forcing, providing another type of ensemble-wide information that is not readily available
49   from simulations with one ESM alone. As emergent constraints depend on identifying those observable
50   aspects of the climate system that are most related to climate projections, they also help to focus model
51   evaluation on the most relevant observations (Hall et al., 2019). However, there is a chance that
52   indiscriminate data-mining of the multi-dimensional outputs from ESMs could lead to spurious correlations
53   (Caldwell et al., 2014; Wagman and Jackson, 2018) and less than robust emergent constraints on future
54   changes (Bracegirdle and Stephenson, 2013). To avoid this, emergent constraints need to be tested ‘out of
55   sample’ on parts of the dataset that were not included in its construction (Caldwell et al., 2018) and should
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 1   also always be based on sound physical understanding and mathematical theory (Hall et al., 2019). Their
 2   conclusions should also be reassessed when a new generation of MMEs becomes available, such as CMIP6.
 3   As an example, Chapter 7 (Section 7.5.4) discusses and assesses recent studies where equilibrium climate
 4   sensitivities (ECS) diagnosed in a multi-model ensemble are compared with the same models’ estimates of
 5   an observable quantity, such as post-1970s global warming or tropical sea-surface temperatures of past
 6   climates like the last glacial maximum or the Pliocene. Assessments of other emergent constraints appear
 7   throughout later chapters, such as Chapter 4 (Section 4.2.5), Chapter 5 (Section 5.4.6) and Chapter 7 (Section
 8   7.5.4).
13   Figure 1.23: The principle of emergent constraints. An ensemble of models (blue dots) defines a relationship
14                between an observable, mean, trend or variation in the climate (x-axis) and an uncertain projection,
15                climate sensitivity or feedback (y-axis). An observation of the x-axis variable can then be combined with
16                the model-derived relationship to provide a tighter estimate of the climate projection, sensitivity or
17                feedback on the y-axis (adapted from Eyring et al. 2019).
19   [END FIGURE 1.23 HERE]
22   Weighting techniques for model comparisons
24   Assessments of climate model ensembles have commonly assumed that each individual model is of equal
25   value (‘model democracy’) and when combining simulations to estimate the mean and variance of quantities
26   of interest, they are typically unweighted (Haughton et al., 2015). This practice has been noted to diminish
27   the influence of models exhibiting a good match with observations (Tapiador et al., 2020). However,
28   exceptions to this approach exist, notably AR5 projections of sea ice, which only selected a few models
29   which passed a model performance assessment (Collins et al., 2013), and more studies on this topic have
30   appeared since the AR5 (e.g., Eyring et al., 2019). Ensembles are typically sub-selected by removing either
31   poorly performing model simulations (McSweeney et al., 2015) or model simulations that are perceived to
32   add little additional information, typically where multiple simulations have come from the same model. They
33   may also be weighted based on model performance.
35   Several recent studies have attempted to quantify the effect of various strategies for selection or weighting of
36   ensemble members based on some set of criteria (Haughton et al., 2015; Olonscheck and Notz, 2017;
37   Sanderson et al., 2017). Model weighting strategies have been further employed since AR5 to reduce the
38   spread in climate projections for a given scenario by using weights based on one or more model performance
39   metrics (Wenzel et al., 2016; Knutti et al., 2017; Sanderson et al., 2017; Lorenz et al., 2018; Liang et al.,
40   2020). However, models may share representations of processes, parameterization schemes, or even parts of
41   code, leading to common biases. The models may therefore not be fully independent, calling into question
42   inferences derived from multi-model ensembles (Abramowitz et al., 2019). Emergent constraints (see
43   Section also represent an implicit weighting technique that explicitly links present performance to
44   future projections (Bracegirdle and Stephenson, 2013).
46   Concern has been raised about the large extent to which code is shared within the CMIP5 multi-model
47   ensemble (Sanderson et al., 2015a). Boé (2018) showed that a clear relationship exists between the number
48   of components shared by climate models and how similar the simulations are. The resulting similarities in
49   behaviour need to be accounted for in the generation of best-estimate multi-model climate projections. This
50   has led to calls to move beyond equally-weighted multi-model means towards weighted means that take into
51   account both model performance and model independence (Sanderson et al., 2015b, 2017; Knutti et al.,
52   2017). Model independence has been defined in terms of performance differences within an ensemble
53   (Masson and Knutti, 2011; Knutti et al., 2013, 2017, Sanderson et al., 2015b, 2015a, 2017; Lorenz et al.,
54   2018). However, this definition is sensitive to the choice of variable, observational data set, metric, time
55   period, and region, and a performance ranked ensemble has been shown to sometimes perform worse than a
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 1   random selection (Herger et al., 2018a). The adequacy of the constraint provided by the data and
 2   experimental methods can be tested using a calibration-validation style partitioning of observations into two
 3   sets (Bishop and Abramowitz, 2013), or a ‘perfect model approach’ where one of the ensemble members is
 4   treated as the reference dataset and all model weights are calibrated against it (Bishop and Abramowitz,
 5   2013; Wenzel et al., 2016; Knutti et al., 2017; Sanderson et al., 2017; Herger et al., 2018a, 2018b). Sunyer et
 6   al. (2014) use a Bayesian framework to account for model dependencies and changes in model biases. Annan
 7   and Hargreaves (2017) provides a statistical, quantifiable definition of independence that is independent of
 8   performance-based measures.
10   The AR5 quantified uncertainty in CMIP5 climate projections by selecting one realization per model per
11   scenario, and calculating the 5–95% range of the resulting ensemble (see Chapter 4, Box 4.1) and the same
12   strategy is generally still used in AR6. Broadly, the following chapters take the CMIP6 5–95% ensemble
13   range as the likely uncertainty range for projections 8, with no further weighting or consideration of model
14   ancestry and as long as no universal, robust method for weighting a multi-model projection ensemble is
15   available (Box 4.1, Chapter 4). A notable exception to this approach is the assessment of future changes in
16   global surface air temperature (GSAT), which also draws on the updated best estimate and range of
17   equilibrium climate sensitivity assessed in Chapter 7. For a thorough description of the model weighting
18   choices made in this Report, and the assessment of GSAT, see Chapter 4 (Box 4.1). Model selection and
19   weighting in downscaling approaches for regional assessment is discussed in Chapter 10 (Section 10.3.4).
22   1.6    Dimensions of Integration: Scenarios, global warming levels and cumulative carbon emissions
24   This section introduces three ways to synthesize climate change knowledge across topics and chapters. These
25   ‘dimensions of integration’ include (1) emission and concentration scenarios underlying the climate change
26   projections assessed in this report, (2) levels of global mean surface warming relative to the 1850-1900
27   baseline (‘global warming levels’), and (3) cumulative carbon emissions (Figure 1.24). All three dimensions
28   can, in principle, be used to synthesize physical science knowledge across WGI, and also across climate
29   change impacts, adaptation, and mitigation research. Scenarios, in particular, have a long history of serving
30   as a common reference point within and across IPCC Working Groups and research communities. Similarly,
31   cumulative carbon emissions and global warming levels provide key links between WGI assessments and
32   those of the other WGs; these two dimensions frame the cause-effect chain investigated by WGI. The closest
33   links to WGIII are the emissions scenarios, as WGIII considers drivers of emissions and climate change
34   mitigation options. The links to WGII are the geophysical climate projections from the Earth System Models
35   which the climate impacts and adaptation literature often uses as their starting point.
40   Figure 1.24: The Dimensions of Integration across Chapters and Working Groups in the IPCC AR6 assessment.
41                This report adopts three explicit dimensions of integration to integrate knowledge across chapters and
42                Working Groups. The first dimension is scenarios, the second dimension is global-mean warming levels
43                relative to 1850-1900, and the third dimension is cumulative CO2 emissions. For the scenarios, illustrative
44                2100 end-points are also indicated (white circles). Further details on data sources and processing are
45                available in the chapter data table (Table 1.SM.1).
47   [END FIGURE 1.24 HERE]
50   The section is structured as follows: first, the scenarios used in AR6 are introduced and discussed in relation
51   to scenarios used in earlier IPCC assessments (Section 1.6.1). Cross-Chapter Box 1.4 provides an overview
52   of the new scenarios and how they are used in this report. Next, the two additional dimensions of integration
53   are introduced: global warming levels (Section 1.6.2) and cumulative emissions (Section 1.6.3). Net zero

       Note that the 5–95% is a very likely range (See Box 1.1 on the use of calibrated uncertainty language in AR6), though
     if this is purely a multi-model likelihood range, it is generally treated as likely, in absence of other lines of evidence
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 1   emissions are discussed in Box 1.4. The relation between global warming levels and scenarios is further
 2   assessed in Cross-Chapter Box 11.1 in Chapter 11.
 5   1.6.1   Scenarios
 7   A scenario is a description of how the future may develop based on a coherent and internally consistent set
 8   of assumptions about key drivers including demography, economic processes, technological innovation,
 9   governance, lifestyles and relationships among these driving forces (IPCC, 2000; Rounsevell and Metzger,
10   2010; O’Neill et al., 2014; see Section Scenarios can also be defined by geophysical driving forces
11   only, such as emissions or abundances of greenhouse gases, aerosols, and aerosol precursors or land use
12   patterns. Scenarios are not predictions; instead, they provide a ‘what-if’ investigation of the implications of
13   various developments and actions (Moss et al., 2010). WGI investigates potential future climate change
14   principally by assessing climate model simulations using emission scenarios originating from the WGIII
15   community (Section The scenarios used in this WGI report cover various hypothetical ‘baseline
16   scenarios’ or ‘reference futures’ that could unfold in the absence of any or any additional climate policies
17   (see Annex VII: Glossary). These ‘reference scenarios’ originate from a comprehensive analysis of a wide
18   array of socio-economic drivers, such as population growth, technological development, and economic
19   development, and their broad spectrum of associated energy, land use and emission implications (Riahi et al.,
20   2017). With direct policy relevance to the Paris Agreement’s 1.5°C and ‘well below’ 2°C goals, this report
21   also assesses climate futures where the effects of additional climate change mitigation action are explored,
22   i.e., so-called mitigation scenarios (for a broader discussion on scenarios and futures analysis, see Cross-
23   Chapter Box 1, Table 1 in SRCCL, IPCC, 2019b).
25   For this Report, the main emissions, concentration and land use scenarios considered are a subset of
26   scenarios recently developed using the Shared Socioeconomic Pathways framework (SSPs) (Riahi et al.,
27   2017; see Section and Cross-Chapter Box 1.4). Initially, the term ‘SSP’ described five broad
28   narratives of future socio-economic development only (O’Neill et al., 2014). However, at least in the WGI
29   community, the term ‘SSP scenario’ is now more widely used to refer directly to future emission and
30   concentration scenarios that result from combining these socio-economic development pathways with
31   climate change mitigation assumptions. These are assessed in detail in WGIII (WGIII, Chapter 3; Cross-
32   Chapter Box 1.4, Table 1).
34   The WGI report uses a core set of five SSP scenarios to assist cross-Chapter integration and cross-WG
35   applications: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 (Cross-Chapter Box 1.4, Table 1).
36   These scenarios span a wide range of plausible societal and climatic futures from potentially below 1.5°C
37   best-estimate warming to over 4°C warming by 2100 (Figure 1.25). The set of five SSP scenarios includes
38   those in ‘Tier 1’ simulations of the CMIP6 ScenarioMIP intercomparison project (O’Neill et al., 2016; see
39   Section 1.5.4) that participating climate modelling groups were asked to prioritize (SSP1-2.6, SSP2-4.5,
40   SSP3-7.0 and SSP5-8.5), plus the low emission scenario SSP1-1.9. SSP1-1.9 is used in combination with
41   SSP1-2.6 to explore differential outcomes of approximately 1.5 and 2.0 °C warming relative to pre-industrial
42   levels, relevant to the Paris Agreement goals. Further SSP scenarios are used in this report to assess specific
43   aspects, e.g., air pollution policies in Chapter 6 (Cross-Chapter Box 1.4). In addition, the previous generation
44   of Representative Concentration Pathways (RCPs) is also used in this report when assessing future climate
45   change (Section; Cross-Chapter Box 1.4, Table 1).
47   Climatic changes over the 21st century (and beyond) are projected and assessed in subsequent chapters,
48   using a broad range of climate models, conditional on the various SSP scenarios. The projected future
49   changes can then be put into the context of longer-term paleoclimate data and historical observations,
50   showing how the higher emission and higher concentration scenarios diverge further from the range of
51   climate conditions that ecosystems and human societies experienced in the past 2000 years in terms of global
52   mean temperature and other key climate variables (Figure 1.26; see also Figure 1.5).
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 2   Figure 1.25: Global mean surface air temperature (GSAT) illustrated as warming stripes from blue (cold) to red
 3                (warm) over three different time periods. From 1750 to 1850 based on PAGES 2K reconstructions
 4                (2017, 2019); from 1850 to 2018 showing the composite GSAT time series assessed in Chapter 2; and
 5                from 2020 onwards using the assessed GSAT projections for each Shared Socio-economic Pathway (SSP)
 6                (from Chapter 4). For the projections, the upper end of each arrow aligns with colour corresponding to the
 7                95th percentile of the projected temperatures and the lower end aligns with the colour corresponding to the
 8                5th percentile of the projected temperature range. Projected temperatures are shown for five scenarios
 9                from ‘very low’ SSP1-1.9 to ‘very high’ SSP5-8.5 (see Cross-Chapter Box 1.4 for more details on the
10                scenarios). For illustrative purposes, natural variability has been added from a single CMIP6 Earth system
11                model (MRI ESM2). The points in time when total CO2 emissions peak, reach halved levels of the peak
12                and reach net-zero emissions are indicated with arrows, ‘½’ and ‘0’ marks, respectively. Further details
13                on data sources and processing are available in the chapter data table (Table 1.SM.1).
16   [END FIGURE 1.25 HERE]
19   While scenarios are a key tool for integration across IPCC Working Groups, they also allow the integration
20   of knowledge among scientific communities and across timescales. For example, agricultural yield,
21   infrastructure and human health impacts of increased drought frequency, extreme rainfall events and
22   hurricanes are often examined in isolation. New insights on climate impacts in WGII can be gained if
23   compound effects of multiple cross-sectoral impacts are considered across multiple research communities
24   under consistent scenario frameworks (Leonard et al., 2014; Warszawski et al., 2014; see also Chapter 11,
25   Section 11.8). Similarly, a synthesis of WGI knowledge on sea level rise contributions is enabled by a
26   consistent application of future scenarios across all specialised research communities, such as ice-sheet mass
27   balance analyses, glacier loss projections and thermosteric change from ocean heat uptake (e.g. Kopp et al.,
28   2014; see Chapter 9).
33   Figure 1.26: Historical and projected future concentrations of CO2, CH4 and N2O and global mean surface
34                temperatures (GMST). GMST temperature reconstructions over the last 2000 years were compiled by
35                the PAGES 2k Consortium (2017, 2019) (grey line, with 95% uncertainty range), joined by historical
36                GMST timeseries assessed in Chapter 2 (black line) – both referenced against the 1850-1900 period.
37                Future GSAT temperature projections are from CMIP6 ESM models across all concentration-driven SSP
38                scenario projections (Chapter 4). The discontinuity around year 2100 for CMIP6 temperature projections
39                results from the fact that not all ESM models ran each scenario past 2100. The grey vertical band
40                indicates the future 2015-2300 period. The concentrations used to drive CMIP6 Earth System Models are
41                derived from ice core, firn and instrumental datasets (Meinshausen et al., 2017) and projected using an
42                emulator (Cross-Chapter Box 7.1 in Chapter 7; Meinshausen et al., 2020). The colours of the lines
43                indicate the SSP scenarios used in this report (see Cross-Chapter Box 1.4, Figure 1). Further details on
44                data sources and processing are available in the chapter data table (Table 1.SM.1).
46   [END FIGURE 1.26 HERE]
49   In addition to the comprehensive SSP scenario set and the RCPs, multiple idealized scenarios and time-slice
50   experiments using climate models are assessed in this report. Idealized scenarios refer to experiments where,
51   for example, CO2 concentrations are increased by 1% per year, or instantly quadrupled. Such idealized
52   experiments have been extensively used in previous model intercomparison projects and constitute the core
53   ‘DECK’ set of model experiments of CMIP6 (see Section 1.5.4). They are, for example, used to diagnose the
54   patterns of climate feedbacks across the suite of models assessed in this report (Chapter 7).
56   In the following, we further introduce the SSP scenarios and how they relate to the Shared Socioeconomic
57   Pathways framework (Section, describe the scenario generation process (Section, and

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 1   provide a historical review of scenarios used in IPCC assessment reports (Section, before briefly
 2   discussing questions of scenario likelihood, scenario uncertainty and the use of scenario storylines (Section
 6   Shared Socio-economic Pathways
 8   The Shared Socioeconomic Pathways SSP1 to SSP5 describe a range of plausible trends in the evolution of
 9   society over the 21st century. They were developed in order to connect a wide range of research
10   communities (Nakicenovic et al., 2014) and consist of two main elements: a set of qualitative, narrative
11   storylines describing societal futures (O’Neill et al., 2017a) and a set of quantified measures of development
12   at aggregated and/or spatially resolved scales. Each pathway is an internally consistent, plausible and
13   integrated description of a socio-economic future, but these socio-economic futures do not account for the
14   effects of climate change, and no new climate policies are assumed. The SSPs’ quantitative projections of
15   socio-economic drivers include population, gross domestic product (GDP) and urbanization (Dellink et al.,
16   2017; Jiang and O’Neill, 2017; Samir and Lutz, 2017). By design, the SSPs differ in terms of the socio-
17   economic challenges they present for climate change mitigation and adaptation (Rothman et al., 2014;
18   Schweizer and O’Neill, 2014) and the evolution of these drivers within each SSP reflects this design.
19   Broadly, the five SSPs represent ‘sustainability’ (SSP1), a ‘middle of the road’ path (SSP2), ‘regional
20   rivalry’ (SSP3), ‘inequality’ (SSP4), and ‘fossil fuel intensive’ development (SSP5) (Cross-Chapter Box 1.4,
21   Figure 1) (O’Neill et al., 2017a). More specific information on the SSP framework and the assumptions
22   underlying the SSPs will be provided in the IPCC WGIII report (WGIII, Chapter 3; see also Box SPM.1 in
23   SRCCL (IPCC, 2019d)).
25   The SSP narratives and drivers were used to develop scenarios of energy use, air pollution control, land use
26   and greenhouse gas (GHG) emissions developments using integrated assessment models (IAMs) (Riahi et
27   al., 2017; Rogelj et al., 2018a). An IAM can derive multiple emission futures for each socio-economic
28   development pathway, assuming no new mitigation policies or various levels of additional mitigation action
29   (in the case of reference scenarios and mitigation scenarios, respectively (Riahi et al., 2017). By design, the
30   evolution of drivers and emissions within the SSP scenarios do not take into account the effects of climate
31   change.
33   The SSPX-Y scenarios and the RCP scenarios are categorized similarly, by reference to the approximate
34   radiative forcing levels each one entails at the end of the 21st century. For example, the ‘1.9’ in the SSP1-1.9
35   scenario stands for an approximate radiative forcing level of 1.9 W m-2 in 2100. The first number (X) in the
36   ‘SSPX-Y’ acronym refers to one of the five shared socio-economic development pathways (Cross-Chapter
37   Box 1.4, Figure 1; Table 1.4).
42   Table 1.4: Overview of different RCP and SSP acronyms as used in this report.
      Scenario Acronym Description

      ‘SSPX’ with X          The shared socioeconomic pathway family, i.e., the socioeconomic developments with
      standing for the       storylines regarding (among other things) GDP, population, urbanisation, economic
      shared                 collaboration, human and technological development projections that describe different
      socioeconomic          future worlds in the absence of climate change and additional climate policy (O’Neill et
      pathway family (1,     al., 2014). The quantification of energy, land use and emission implications in those
      2, …, 5)               storylines is not part of the SSPX narratives, but follows in a second step in which their
                             climate outcomes are defined. This second step is dependent upon the IAM that is used
                             for this quantification (Riahi et al., 2017) (see SSPX-Y)

      ‘RCPY’ with Y          Representative Concentration Pathways (Moss et al., 2010; van Vuuren et al., 2011).

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      standing for           These are GHG concentrations (Meinshausen et al., 2011b) , aerosol emissions
      approximate            (Lamarque et al., 2011) and land use pattern time series (Hurtt et al., 2011) derived from
      radiative forcing      several IAMs. The pathways were originally generated from specific sets of socio-
      level in 2100, at      economic drivers, but these are no longer considered. Instead, these RCP emission and
      levels 2.6, 4.5, 6.0   concentration time series are used in combination with a range of socio-economic futures
      or 8.5.                (see SSPX-RCPY). For example, the CMIP5 intercomparison (assessed in IPCC AR5)
                             developed climate futures based on these emission and concentration pathways from the

      The SSP and RCP        Combination of the SSP socioeconomic pathway X with climate futures stemming from
      combination            GCMs, AOGCMs or Earth system model runs that used the RCPY. This combination is
      ‘SSPX-RCPY’            widely used in the impact literature assessed by WGII (see for example the Special Issue
      with X and Y as        on SSPs by van Vuuren et al. (2014) and the large literature collection in the
      above.                 International Committee On New Integrated Climate change assessment Scenarios
                             database (ICONICS, 2021). These SSPX-RCPY scenarios differ from the SSPX-Y group
                             (below) in that the respective socio-economic futures (SSPXs) and emission and
                             concentration futures (RCPYs) were developed separately before being used in

      ‘SSPX-Y’ with X        SSPX-Y is the abbreviation for a scenario, where X is the numbering of the SSP
      and Y as above.        socioeconomic family (1 to 5) that was used to develop the emission pathway, and the Y
                             indicates the approximate radiative forcing value reached by the end of the century. The
                             SSPX-Y scenarios span the nominal range from 1.9 to 8.5 W m2. A range of different
                             IAMs were used to quantify the SSPX-Y scenarios, but each IAM quantified both the
                             scenario-economic futures (energy use, land use, population etc) and various emission
                             futures within the same IAM modelling framework, thus enhancing the consistency
                             between the socio-economic backgrounds and their resulting emission futures. In
                             contrast, the SSPX-RCPY framework combines the SSP socio-economic futures and
                             RCP emission and concentration futures at random (see above). For more details, see
 2   [END TABLE 1.4 HERE]
 5   This SSP scenario categorisation, focused on end-of-century radiative forcing levels, reflects how scenarios
 6   were conceptualized until recently, namely, to reach a particular climate target in 2100 at the lowest cost and
 7   irrespective of whether the target was exceeded over the century. More recently, and in particular since the
 8   IPCC SR1.5 report focused attention on peak warming scenarios (Rogelj et al., 2018b), scenario
 9   development started to explicitly consider peak warming, cumulative emissions and the amount of net
10   negative emissions (Rogelj et al., 2018b; Fujimori et al., 2019).
12   The SSP scenarios can be used for either emission- or concentration-driven model experiments (Cross-
13   Chapter Box 1.4). ESMs can be run with emissions and concentrations data for GHGs and aerosols and land
14   use or landcover maps and calculate levels of radiative forcing internally. The radiative forcing labels of the
15   RCP and SSP scenarios, such as ‘2.6’ in RCP2.6 or SSP1-2.6, are thus approximate labels for the year 2100
16   only. The actual global mean effective radiative forcing varies across ESMs due to different radiative
17   transfer schemes, uncertainties in aerosol-cloud interactions and different feedback mechanisms, among
18   other reasons. Nonetheless, using approximate radiative forcing labels is advantageous because it establishes
19   a clear categorization of scenarios, with multiple climate forcings and different combinations in those
20   scenarios summarized in a single number. The classifications according to cumulative carbon emissions (see
21   Section 1.6.3) and global warming level (see Section 1.6.2 and Cross-Chapter Box 7.1 on emulators in
22   Chapter 7) complement those forcing labels.
24   A key advance of the SSP scenarios relative to the RCPs is a wider span of assumptions on future air quality

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 1   mitigation measures, and hence emissions of short-lived climate forcers (SLCFs) (Rao et al., 2017; Lund et
 2   al., 2020). This allows for a more detailed investigation into the relative roles of GHG and SLCF emissions
 3   in future global and regional climate change, and hence the implications of policy choices. For instance,
 4   SSP1-2.6 builds on an assumption of stringent air quality mitigation policy, leading to rapid reductions in
 5   particle emissions, while SSP3-7.0 assumes slow improvements, with pollutant emissions over the 21st
 6   century comparable to current levels (Cross-Chapter Box 1.4, Figure 2, Chapter 6, Figure 6.19).
 8   One limitation of the SSP scenarios used for CMIP6 and in this Report is that they reduce emissions from all
 9   the major ozone-depleting substances controlled under the Montreal Protocol (CFCs, halons, and
10   hydrochlorofluorocarbons (HCFCs)) uniformly, rather than representing a fuller range of possible high and
11   low emission futures (UNEP, 2016). Hydrofluorocarbon (HFC) emissions, on the other hand, span a wider
12   range within the SSPs than in the RCPs (Cross-Chapter Box 1.4, Figure 2).
14   The SSP scenarios and previous RCP scenarios are not directly comparable. First, the gas-to-gas
15   compositions differ; for example, the SSP5-8.5 scenario has higher CO2 concentrations but lower methane
16   concentrations compared to RCP8.5. Second, the projected 21st-century trajectories may differ, even if they
17   result in the same radiative forcing by 2100. Third, the overall effective radiative forcing (see Chapter 7)
18   may differ, and tends to be higher for the SSPs compared to RCPs that share the same nominal stratospheric-
19   temperature adjusted radiative forcing label. The stratospheric-temperature adjusted radiative forcings of the
20   SSPs and RCPs, however, remain relatively close, at least by 2100 (Tebaldi et al., 2021). In summary,
21   differences in, for example, CMIP5 RCP8.5 and CMIP6 SSP5-8.5 ESM outputs, are partially due to different
22   scenario characteristics rather than different ESM characteristics only (Chapter 4, Section 4.6.2).
24   When investigating various mitigation futures, WGIII goes beyond the core set of SSP scenarios assessed in
25   WGI (SSP1-1.9, SSP1-2.6, etc.) to consider the characteristics of more than 1000 scenarios (see Cross-
26   Chapter Box 7.1 in Chapter 7). In addition, while staying within the framework of socio-economic
27   development pathways (SSP1 to SSP5), WGIII also considers various mitigation possibilities through so-
28   called illustrative pathways (IPs). These illustrative pathways help to highlight key narratives in the literature
29   concerning various technological, social and behavioral options for mitigation, various timings for
30   implementation, or varying emphasis on different GHG and land use options. Just as with the SSPX-Y
31   scenarios considered in this report, these illustrative pathways can be placed in relation to the matrix of SSP
32   families and approximate radiative forcing levels in 2100 (see Cross-Chapter Box 1.4, Figure 1 and Working
33   Group III, Chapter 3).
35   No likelihood is attached to the scenarios assessed in this report, and the feasibility of specific scenarios in
36   relation to current trends is best informed by the WGIII contribution to AR6. In the scenario literature, the
37   plausibility of the high emissions levels underlying scenarios such as RCP8.5 or SSP5-8.5 has been debated
38   in light of recent developments in the energy sector. (see Section
43   Cross-Chapter Box 1.4:          The SSP scenarios as used in Working Group I
45   Contributing Authors: Jan Fuglestvedt (Norway), Celine Guivarch (France), Chris Jones (UK), Malte
46   Meinshausen (Australia/Germany), Zebedee Nicholls (Australia), Gian-Kasper Plattner (Switzerland),
47   Keywan Riahi (Austria), Joeri Rogelj (UK/Belgium), Sophie Szopa (France), Claudia Tebaldi (USA/Italy),
48   Anne-Marie Treguier (France), and Detlef van Vuuren (Netherlands)
50   The new nine SSP emission and concentration scenarios (SSP1-1.9 to SSP5-8.5; Cross-Chapter Box 1.4,
51   Table 1) offer unprecedented detail of input data for climate model simulations. They allow for a more
52   comprehensive assessment of climate drivers and responses than has previously been available, in particular
53   because some of the scenarios’ time series, e.g., pollutants, emissions or changes in land use and land cover,
54   are more diverse in the SSP scenarios than in the RCPs used in AR5 (e.g., Chuwah et al., 2013) (Cross-
55   Chapter Box 1.4, Figure 2).
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 2   The core set of five SSP scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 was selected in
 3   this Report to align with the objective that the new generation of SSP scenarios should fill certain gaps
 4   identified in the RCPs. For example, a scenario assuming reduced air pollution control and thus higher
 5   aerosol emissions was missing from the RCPs. Likewise, nominally the only ‘no-additional-climate-policy’
 6   scenario in the set of RCPs was RCP8.5. The new SSP3-7.0 ‘no-additional-climate-policy’ scenario fills both
 7   these gaps. A very strong mitigation scenario in line with the 1.5°C goal of the Paris Agreement was also
 8   missing from the RCPs, and the SSP1-1.9 scenario now fills this gap, complementing the other strong
 9   mitigation scenario SSP1-2.6. The five core SSPs were also chosen to ensure some overlap with the RCP
10   levels for radiative forcing at the year 2100 (specifically 2.6, 4.5, and 8.5) (O’Neill et al., 2016; Tebaldi et
11   al., 2021), although effective radiative forcings are generally higher in the SSP scenarios compared to the
12   equivalently-named RCP pathways (Cross-Chapter Box 1.4, Figure 1; Chapter 4, Section 4.6.2). In theory,
13   running scenarios with similar radiative forcings would permit analysis of the CMIP5 and CMIP6 outcomes
14   for pairs of scenarios (e.g., RCP8.5 and SSP5-8.5) in terms of varying model characteristics rather than
15   differences in the underlying scenarios. In practice, however, there are limitations to this approach (Section
16 and Chapter 4, Section 4.6.2).
19   [START Cross-Chapter Box 1.4, FIGURE 1 HERE]
21   Cross-Chapter Box 1.4, Figure 1: The SSP scenarios used in this report, their indicative temperature evolution
22   and radiative forcing categorization, and the five socio-economic storylines upon which they are built. The core
23   set of scenarios used in this report, i.e., SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, is shown together with
24   an additional four SSPs that are part of ScenarioMIP, as well as previous RCP scenarios. In the left panel, the indicative
25   temperature evolution is shown (adapted from Meinshausen et al., 2020). The black stripes on the respective scenario
26   family panels on the left side indicate a larger set of IAM-based SSP scenarios that span the scenario range more fully,
27   but are not used in this report. The SSP-radiative forcing matrix is shown on the right, with the SSP socioeconomic
28   narratives shown as columns and the indicative radiative forcing categorisation by 2100 shown as rows. Note that the
29   descriptive labels for the five SSP narratives refer mainly to the reference scenario futures without additional climate
30   policies. For example, SSP5 can accommodate strong mitigation scenarios leading to net zero emissions; these do not
31   match a ‘fossil-fueled development’ label. Further details on data sources and processing are available in the chapter
32   data table (Table 1.SM.1).
34   [END Cross-Chapter Box 1.4, FIGURE 1 HERE]
37   [START Cross-Chapter Box 1.4, TABLE 1 HERE]
39   Cross-Chapter Box 1.4, Table 1: Overview of SSP scenarios used in this report. The middle column briefly
40   describes the SSP scenarios and the right column indicates the previous RCP scenarios that most closely
41   match that SSP’s assessed global-mean temperatures (GSAT) trajectory. RCP scenarios are generally found
42   to result in larger modelled warming for the same nominal radiative forcing label (Chapter 4, Section
43 The five core SSP scenarios used most commonly in this report are highlighted in bold. Further SSP
44   scenarios are used where they allow assessment of specific aspects, e.g., air pollution policies in Chapter 6
45   (SSP3-7.0-lowNTCF). RCPs are used in this report wherever the relevant scientific literature makes
46   substantial use of regional or domain-specific model output that is based on these previous RCP pathways,
47   such as sea level rise projections in Chapter 9 (Section or regional climate aspects in Chapters 10
48   and 12. See Chapter 4 (Section 4.3.4) for the GSAT assessment for the SSP scenarios and Section for
49   a comparison between SSPs and RCPs in terms of both radiative forcing and global surface temperature.
         SSPX-Y          Description from an emission /                    Closest RCP scenarios
         scenario        concentration and temperature
                         perspective (Chapter 4, Table 4.2)
         SSP1-1.9        Holds warming to approximately 1.5°C              Not available. No equivalently low RCP
                         above 1850-1900 in 2100 after slight              scenario exists.
                         overshoot (median) and implied net zero
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                  CO2 emissions around the middle of the
   SSP1-2.6       Stays below 2.0°C warming relative to        RCP2.6, although RCP2.6 might be cooler
                  1850-1900 (median) with implied net          for the same model settings.
                  zero emissions in the second half of the
   SSP4-3.4       A scenario in between SSP1-2.6 and           No 3.4 level of end-of-century radiative
                  SSP2-4.5 in terms of end-of-century          forcing was available in the RCPs.
                  radiative forcing. It does not stay below    Nominally SSP4-3.4 sits between RCP 2.6
                  2.0°C in most CMIP6 runs (Chapter 4)         and RCP 4.5, although SSP4-3.4 might be
                  relative to 1850-1900.                       more similar to RCP4.5. Also, in the early
                                                               decades of the 21st century, SSP4-3.4 is
                                                               close to RCP6.0, which featured lower
                                                               radiative forcing than RCP4.5 in the first
                                                               decades of the 21st century.
   SSP2-4.5       Scenario approximately in line with the      RCP4.5 and, until 2050, also RCP6.0.
                  upper end of aggregate NDC emission          Forcing in the latter was even lower than
                  levels by 2030 (see Section 1.2.2 and        RCP4.5 in the early decades of the 21st
                  Chapter 4, Section 4.3; SR1.5, (IPCC,        century.
                  2018) , Box 1). SR1.5 assessed
                  temperature projections for NDCs to be
                  between 2.7 and 3.4°C by 2100 (Section
                  1.2.2; SR1.5 (IPCC, 2018); Cross-
                  Chapter Box 11 in Chapter 11),
                  corresponding to the upper half of
                  projected warming under SSP2-4.5
                  (Chapter 4). New or updated NDCs by
                  the end of 2020 did not significantly
                  change the emissions projections up to
                  2030, although more countries adopted
                  2050 net zero targets in line with SSP1-
                  1.9 or SSP1-2.6. The SSP2-4.5 scenario
                  deviates mildly from a ‘no-additional-
                  climate-policy’ reference scenario,
                  resulting in a best-estimate warming
                  around 2.7°C by the end of the 21st
                  century relative to 1850-1900 (Chapter
   SSP4-6.0       The end-of-century nominal radiative         RCP6.0 is nominally closest in the second
                  forcing level of 6.0 W/m2 can be             half of the century, although global mean
                  considered a ‘no-additional-climate-         temperatures are estimated to be generally
                  policy’ reference scenario, under SSP1       lower in RCPs compared to SSPs.
                  and SSP4 socioeconomic development           Furthermore, RCP6.0 features lower
                  narratives.                                  warming than SSP4-6.0, as it has very
                                                               similar temperature projections compared
                                                               to the nominally lower RCP4.5 scenario in
                                                               the first half of the century.
   SSP3-7.0       A medium to high reference scenario          In between RCP6.0 and RCP8.5, although
                  resulting from no additional climate         SSP3-7.0 non-CO2 emissions and aerosols
                  policy under the SSP3 socioeconomic          are higher than in any of the RCPs.
                  development narrative. SSP3-7.0 has
                  particularly high non-CO2 emissions,
                  including high aerosols emissions.
   SSP3-7.0-      A variation of the medium to high            SSP3-7.0-lowNTCF is between RCP6.0
   lowNTCF        reference scenario SSP3-7.0 but with         and RCP8.5, as RCP scenarios generally
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                       mitigation of CH4 and/or short-lived          incorporated a narrow and comparatively
                       species such as black carbon and other        low level of SLCF emissions across the
                       short-lived climate forcers (SLCF). Note      range of RCPs.
                       that variants of SSP3-7.0-lowNTCF
                       differ in terms of whether methane
                       emissions are reduced 9 (Chapter 4,
                       Section 4.4 and Chapter 6, Section 6.6).
         SSP5-3.4      A mitigation-focused variant of SSP5-8.5      Not available. Initially, until 2040, similar
         OS            that initially follows unconstrained          to RCP8.5.
         (Overshoot    emission growth in a fossil-intensive
         )             setting until 2040 and then implements
                       the largest net negative CO2 emissions of
                       all SSP scenarios in the second half of
                       21st century to reach SSP1-2.6 forcing
                       levels in the 22nd century. Used to
                       consider reversibility and strong
                       overshoot scenarios in, e.g., Chapters 4
                       and 5.
         SSP5-8.5      A high reference scenario with no             RCP8.5, although CO2 emissions under
                       additional climate policy. Emission           SSP5-8.5 are higher towards the end of the
                       levels as high as SSP5-8.5 are not            century (Cross-Chapter Box 1.4, Figure 2).
                       obtained by Integrated Assessment             Methane emissions under SSP5-8.5 are
                       Models (IAMs) under any of the SSPs           lower than under RCP 8.5. When used
                       other than the fossil fueled SSP5             with the same model settings, SSP5-8.5
                       socioeconomic development pathway.            may result in slightly higher temperatures
                                                                     than RCP8.5 (Chapter 4, Section 4.6.2).
 2   [END Cross-Chapter Box 1.4, TABLE 1 HERE]
 5   In contrast to stylized assumptions about the future evolution of emissions (e.g., a linear phase-out from year
 6   A to year B), these SSP scenarios are the result of a detailed scenario generation process (see Sections
 7 and While IAMs produce internally-consistent future emission time series for CO2, CH4,
 8   N2O, and aerosols for the SSP scenarios (Riahi et al., 2017; Rogelj et al., 2018a), these emission scenarios
 9   are subject to several processing steps for harmonisation (Gidden et al., 2018) and in-filling (Lamboll et al.,
10   2020), before also being complemented by several datasets so that ESMs can run these SSPs (Durack et al.,
11   2018; Tebaldi et al., 2021). Although five scenarios are the primary focus of WGI, a total of nine SSP
12   scenarios have been prepared with all the necessary detail to drive the ESMs as part of the CMIP6 (Cross-
13   Chapter Box 1.4, Figure 1 and Table 2).
16   [START Cross-Chapter Box 1.4, TABLE 2 HERE]
18   Cross-Chapter Box 1.4, Table 2: Overview of key climate forcer datasets used as input by ESMs for
19   historical and future SSP scenario experiments. The data is available from the Earth System Grid Federation
20   (ESGF, 2021) described in Eyring et al. (2016).
       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. The SSP3-7.0-lowNTCF variant by the Integrated Assessment Models also
     reduced methane emissions (Gidden et al., 2019), which creates differences between SSP3-7.0-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).

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      Climate Forcer             Description

      CO2 emissions              Harmonized historical and future gridded emissions of anthropogenic CO2
      (emission-driven runs      emissions (Hoesly et al., 2018; Gidden et al., 2019) are used instead of the
      only)                      prescribed CO2 concentrations. See Chapter 4 (Section 4.3.1).

      Historical and future      Greenhouse gas surface air mole fractions of 43 species, including CO2, CH4,
      greenhouse gas             N2O, HFCs, PFCs, halons, HCFCs, CFCs, SF6, NF3, including latitudinal
      concentrations             gradients and seasonality from year 1 to 2500 (Meinshausen et al., 2017,

      Land use change and        Globally gridded land use and land cover change datasets (Hurtt et al., 2020;
      management patterns        Ma et al., 2020b)

      Biomass burning            Historical fire-related gridded emissions, including SO2, NOx, CO, BC, OC,
      emissions                  NH3, NMVOCs, relevant to concentration-driven historical and future SSP
                                 scenario runs (van Marle et al., 2017).

      Stratospheric and          Historical and future ozone dataset, also with total column ozone (CCMI,
      tropospheric ozone         2021).

      Reactive gas emissions     Gridded global anthropogenic emissions of reactive gases and aerosol
                                 precursors, including CO, SOx, CH4, NOx, NMVOCs, or NH3 (Hoesly et al.,
                                 2018; Feng et al., 2020)

      Solar forcing              Radiative and particle input of solar variability from 1850 through to 2300
                                 (Matthes et al., 2017). Future variations in solar forcing also reflect long-term
                                 multi-decadal trends.

      Volcanic forcing           Historical stratospheric aerosol climatology (Thomason et al., 2018), with the
                                 mean stratospheric volcanic aerosol prescribed in future projections.
 2   [END Cross-Chapter Box 1.4, TABLE 2 HERE]
 5   ESMs are driven by either emission or concentration scenarios. Inferring concentration changes from
 6   emission time series requires using carbon cycle and other gas cycle models. To aid comparability across
 7   ESMs, and in order to allow participation of ESMs that do not have coupled carbon and other gas cycles
 8   models in CMIP6, most of the CMIP6 ESM experiments are so-called ‘concentration-driven’ runs, with
 9   concentrations of CO2, CH4, N2O and other well-mixed GHGs prescribed in conjunction with aerosol
10   emissions, ozone changes and effects from human-induced land cover changes that may be radiatively active
11   via albedo changes (Cross-Chapter Box 1.4, Figure 2). In these concentration-driven climate projections, the
12   uncertainty in projected future climate change resulting from our limited understanding of how the carbon
13   cycle and other gas cycles will evolve in the future is not captured. For example, when deriving the default
14   concentrations for these scenarios, permafrost and other carbon cycle feedbacks are considered using default
15   settings, with a single time series prescribed for all ESMs (Meinshausen et al., 2020). Thus, associated
16   uncertainties (Joos et al., 2013; Schuur et al., 2015) are not considered.
18   The so-called ‘emission-driven’ experiments (Jones et al., 2016b) use the same input datasets as
19   concentration-driven ESM experiments, except that they use CO2 emissions rather than concentrations
20   (Chapter 4, Section 4.3.1; Chapter 5). In these experiments, atmospheric CO2 concentrations are calculated
21   internally using the ESM interactive carbon cycle module and thus differ from the prescribed default CO2
22   concentrations used in the concentration-driven runs. In the particular case of SSP5-8.5, the emission-driven
23   runs are assessed to add no significant additional uncertainty to future global surface air temperature (GSAT)
24   projections (Chapter 4, Section 4.3.1). However, generally, when assessing uncertainties in future climate
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 1   projections, it is important to consider which elements of the cause-effect chain from emissions to the
 2   resulting climate change are interactively included as part of the model projections, and which are externally
 3   prescribed using default settings.
 6   [START Cross-Chapter Box 1.4, FIGURE 2 HERE]
 8   Cross-Chapter Box 1.4, Figure 2: Comparison between the Shared Socio-economic Pathways (SSP) scenarios
 9   and the Representative Concentration Pathway (RCP) scenarios in terms of their CO2, CH4 and N2O
10   atmospheric concentrations (panels a-c), and their global emissions (panels d-o). Also shown are gridded emission
11   differences for sulfur (panel p) and black carbon (panel q) for the year 2000 between the input emission datasets that
12   underpinned the CMIP5 and CMIP6 model intercomparisons. Historical emission estimates are provided in black in
13   panels d to o. The range of concentrations and emissions investigated under the RCP pathways is grey shaded. Panels p
14   and q adapted from Figure 7 in Hoesly et al. (2018). Further details on data sources and processing are available in the
15   chapter data table (Table 1.SM.1).
17   [END Cross-Chapter Box 1.4, FIGURE 2 HERE]
23   Scenario generation process for CMIP6
25   The scenario generation process involves research communities linked to all three IPCC Working Groups
26   (Figure 1.27). It generally starts in the scientific communities associated with WGII and WGIII with the
27   definition of new socio-economic scenario storylines (IPCC, 2000; O’Neill et al., 2014) that are quantified in
28   terms of their drivers, i.e., GDP, population, technology, energy and land use and their resulting emissions
29   (Riahi et al., 2017). Then, numerous complementation and harmonisation steps are necessary for datasets
30   within the WGI and WGIII science communities, including gridding emissions of anthropogenic short-lived
31   forcers, providing open biomass burning emission estimates, preparing land use patterns, aerosol fields,
32   stratospheric and tropospheric ozone, nitrogen deposition datasets, solar irradiance and aerosol optical
33   property estimates, and observed and projected greenhouse gas concentration time series (documented for
34   CMIP6 through input4mips; Durack et al., 2018; Cross-Chapter Box 1.4, Table 2).
36   Once these datasets are completed, ESMs are run in coordinated model intercomparison projects in the WGI
37   science community, using standardized simulation protocols and scenario data. The most recent example of
38   such a coordinated effort is the CMIP6 exercise (Eyring et al., 2016; see also Section 1.5.4) with, in
39   particular, ScenarioMIP (O’Neill et al., 2016). The WGI science community feeds back climate information
40   to WGIII via climate emulators (Cross-Chapter Box 7.1 in Chapter 7) that are updated and calibrated with
41   the ESMs’ temperature responses and other lines of evidence. Next, this climate information is used to
42   compute several high-level global climate indicators (e.g., atmospheric concentrations, global temperatures)
43   for a much wider set of hundreds of scenarios that are assessed as part of IPCC WGIII assessment (WGIII
44   Annex C). The outcomes from climate models run under the different scenarios are then used to calculate the
45   evolution of climatic impact-drivers (Chapter 12), and utilized by impact researchers together with exposure
46   and vulnerability information, in order to characterize risk from future climate change to human and natural
47   systems. The climate impacts associated with these scenarios or different warming levels are then assessed as
48   part of WGII reports (Figure 1.27).
53   Figure 1.27: A simplified illustration of the scenario generation process that involves the scientific communities
54                represented in the three IPCC Working Groups. The circular set of arrows at the top indicate the main
55                set of models and workflows used in that scenario generation process, with the lower level indicating the
56                datasets.
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 2   [END FIGURE 1.27 HERE]
 5   History of scenarios within the IPCC
 7   Scenario modelling experiments have been a core element of physical climate science since the first transient
 8   simulations with a General Circulation Model in 1988 (Hansen et al., 1988; see Section 1.3). Scenarios and
 9   modelling experiments assessed in IPCC reports have evolved over time, which provides a ‘history of how
10   the future was seen’. The starting time for the scenarios moves as actual emissions supersede earlier
11   emission assumptions, while new scientific insights into the range of plausible population trends,
12   behavioural changes and technology options and other key socioeconomic drivers of emissions also emerge
13   (see WGIII; Legget, 1992; IPCC, 2000; Moss et al., 2010; Riahi et al., 2017). Many different sets of climate
14   projections have been produced over the past several decades, using different sets of scenarios. Here, we
15   compare those earlier scenarios against the most recent ones.
17   Climate science research involving scenarios necessarily follows a series of consecutive steps (see Figure
18   1.27). As each step waits for input from the preceding one, delays often occur that result in the impact
19   literature basing its analyses on earlier scenarios than those most current in the climate change mitigation and
20   climate system literature. It is hence important to provide an approximate comparison across the various
21   scenario generations (Figure 1.28; Cross-Chapter Box 1.4, Table 1; Chapter 4).
26   Figure 1.28: Comparison of the range of fossil and industrial CO2 emissions from scenarios used in previous
27                assessments up to AR6. Previous assessments are the IS92 scenarios from 1992 (top panel), the Special
28                Report on Emissions Scenarios (SRES) scenarios from the year 2000 (second panel), the Representative
29                Concentration Pathway (RCP) scenarios designed around 2010 (third panel) and the Shared Socio-
30                economic Pathways (SSP) scenarios (second bottom panel). In addition, historical emissions are shown
31                (black line) (Chapter 5, Figure 5.5); a more complete set of scenarios is assessed in SR1.5 (bottom panel)
32                (Huppmann et al., 2018). Further details on data sources and processing are available in the chapter data
33                table (Table 1.SM.1).
35   [END FIGURE 1.28 HERE]
38   The first widely used set of IPCC emission scenarios was the IS92 scenarios in 1992 (Leggett et al., 1992).
39   Apart from reference scenarios, IS92 also included a set of stabilisation scenarios, the so-called ‘S’
40   scenarios. Those ‘S’ pathways were designed to lead to CO2 stabilisation levels such as 350 ppm or 450
41   ppm. By 1996, those latter stabilisation levels were complemented in the scientific literature by alternative
42   trajectories that assumed a delayed onset of climate change mitigation action (Figure 1.28; Wigley et al.,
43   1996).
45   By 2000, the IPCC Special Report on Emission Scenarios (SRES) produced the SRES scenarios (IPCC,
46   2000), albeit without assuming any climate-policy-induced mitigation. The four broad groups of SRES
47   scenarios (scenario ‘families’) A1, A2, B1 and B2 were the first scenarios to emphasize socio-economic
48   scenario storylines, and also first to emphasize other greenhouse gases, land use change and aerosols.
49   Represented by three scenarios for the high-growth A1 scenario family, those 6 SRES scenarios (A1FI, A1B,
50   A1T, A2, B1, and B2) can still sometimes be found in today’s climate impact literature. The void of missing
51   climate change mitigation scenarios was filled by a range of community exercises, including the so-called
52   post-SRES scenarios (Swart et al., 2002).
54   The RCP scenarios (van Vuuren et al., 2011) then broke new ground by providing low emission pathways
55   that implied strong climate change mitigation including an example with negative CO2 emissions on a large
56   scale, namely RCP2.6. As shown in Figure 1.28, the upper end of the scenario range has not substantially
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 1   shifted. Building on the SRES multi-gas scenarios, the RCPs include time series of emissions and
 2   concentrations of the full suite of greenhouse gases and aerosols and chemically active gases, as well as land
 3   use and land cover (Moss et al., 2010). The word ‘representative’ signifies that each RCP is only one of
 4   many possible scenarios that would lead to the specific radiative forcing characteristics. The term pathway
 5   emphasizes that not only the long-term concentration levels are of interest, but also the trajectory taken over
 6   time to reach that outcome (Moss et al., 2010). RCPs usually refer to the concentration pathway extending to
 7   2100, for which IAMs produced corresponding emission scenarios. Four RCPs produced from IAMs were
 8   selected from the published literature and are used in AR5 as well as in this report, spanning approximately
 9   the range from below 2°C warming to high (>4°C) warming best-estimates by the end of the 21st century:
10   RCP2.6, RCP4.5 and RCP6.0 and RCP8.5 (Cross-Chapter Box 1.4, Table 1). Extended Concentration
11   Pathways (ECPs) describe extensions of the RCPs from 2100 to 2300 that were calculated using simple rules
12   generated by stakeholder consultations; these do not represent fully consistent scenarios (Meinshausen et al.,
13   2011b).
15   By design, the RCP emission and concentration pathways were originally developed using particular socio-
16   economic development pathways, but those are no longer considered (Moss et al., 2010). The different levels
17   of emissions and climate change represented in the RCPs can hence be explored against the backdrop of
18   different socio-economic development pathways (SSP1 to SSP5) (Section; Cross-Chapter Box 1.4).
19   This integrative SSP-RCP framework (‘SSPX-RCPY’ in Table 1.4) is now widely used in the climate impact
20   and policy analysis literature (e.g., ICONICS, 2021; Green et al., 2020; O’Neill et al., 2020), where climate
21   projections obtained under the RCP scenarios are analysed against the backdrop of various SSPs.
22   Considering various levels of future emissions and climate change for each socio-economic development
23   pathway was an evolution from the previous SRES framework (IPCC, 2000), in which socio-economic and
24   emission futures were closely aligned.
26   The new set of scenarios (SSP1-1.9 to SSP5-8.5) now features a higher top level of CO2 emissions (SSP5-8.5
27   compared to RCP8.5), although the most significant change is again the addition of a very low climate
28   change mitigation scenario (SSP1-1.9, compared to the previous low scenario, RCP2.6). Also, historically,
29   none of the previous scenario sets featured a scenario that involves a very pronounced peak-and-decline
30   emissions trajectory, but SSP1-1.9 does so now. The full set of nine SSP scenarios now includes a high
31   aerosol emission scenario (SSP3-7.0). The RCPs featured more uniformly low aerosol trajectories across all
32   scenarios (Cross-Chapter Box 1.4, Figure 2). More generally, the SSP scenarios feature a later peak of global
33   emission for the lower scenarios, simply as a consequence of historical emissions not having followed the
34   trajectory projected by previous low scenarios (Figure 1.28).
36   Over the last decades, discussions around scenarios have often focussed on whether recent trends make
37   certain future scenarios more or less probable or whether all scenarios are too high or too low. When the
38   SRES scenarios first appeared, the debate was often whether the scenarios were overestimating actual world
39   emissions developments (e.g., Castles and Henderson, 2003). With the strong emissions increase throughout
40   the 2000s, that debate then shifted towards the question of whether the lower future climate change
41   mitigation scenarios were rendered unfeasible (Pielke et al., 2008; van Vuuren and Riahi, 2008). Historical
42   emissions over 2000 to 2010 approximately track the upper half of SRES and RCP projections (Figure 1.28).
43   More generally, the global fossil and industrial CO2 emissions of recent decades tracked approximately the
44   middle of the projected scenario ranges (see Fig 1.28), although with regional differences (Pedersen et al.,
45   2020).
48   The likelihood of reference scenarios, scenario uncertainty and storylines
50   In general, no likelihood is attached to the scenarios assessed in this Report. The use of different scenarios
51   for climate change projections allows to explore ‘scenario uncertainty’ (Collins et al., 2013; SR1.5; see also
52   Section 1.4.4). Scenario uncertainty is fundamentally different from geophysical uncertainties, which result
53   from limitations in the understanding and predictability of the climate system (Smith and Stern, 2011). In
54   scenarios, by contrast, future emissions depend to a large extent on the collective outcome of choices and
55   processes related to population dynamics and economic activity, or on choices that affect a given activity’s
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 1   energy and emissions intensity (Jones, 2000; Knutti et al., 2008; Kriegler et al., 2012; van Vuuren et al.,
 2   2014). Even if identical socio-economic futures are assumed, the associated future emissions still face
 3   uncertainties, since different experts and model frameworks diverge in their estimates of future emission
 4   ranges (Ho et al., 2019).
 6   When exploring various climate futures, scenarios with no, or no additional, climate policies are often
 7   referred to as ‘baseline’ or ‘reference scenarios’ (Section; Annex VII: Glossary). Among the five core
 8   scenarios used most in this report, SSP3-7.0 and SSP5-8.5 are explicit ‘no-climate-policy’ scenarios (Gidden
 9   et al., 2019; Cross-Chapter Box 1.4, Table 1), assuming a carbon price of zero. These future ‘baseline’
10   scenarios are hence counterfactuals that include less climate policies compared to ‘business-as-usual’
11   scenarios – given that ‘business-as-usual’ scenarios could be understood to imply a continuation of existing
12   climate policies. Generally, future scenarios are meant to cover a broad range of plausible futures, due for
13   example to unforeseen discontinuities in development pathways (Raskin and Swart, 2020), or to large
14   uncertainties in underlying long-term projections of economic drivers (Christensen et al., 2018). However,
15   the likelihood of high emission scenarios such as RCP8.5 or SSP5-8.5 is considered low in light of recent
16   developments in the energy sector (Hausfather and Peters, 2020a, 2020b). Studies that consider possible
17   future emission trends in the absence of additional climate policies, such as the recent IEA 2020 World
18   Energy Outlook ‘stated policy’ scenario (International Energy Agency, 2020), project approximately
19   constant fossil and industrial CO2 emissions out to 2070, approximately in line with the medium RCP4.5,
20   RCP6.0 and SSP2-4.5 scenarios (Hausfather and Peters, 2020b) and the 2030 global emission levels that are
21   pledged as part of the Nationally Determined Contributions (NDCs) under the Paris Agreement (Section
22   1.2.2; (Fawcett et al., 2015; Rogelj et al., 2016; UNFCCC, 2016; IPCC, 2018). On the other hand, the default
23   concentrations aligned with RCP8.5 or SSP5-8.5 and resulting climate futures derived by ESMs could be
24   reached by lower emission trajectories than RCP8.5 or SSP5-8.5. That is because the uncertainty range on
25   carbon-cycle feedbacks includes stronger feedbacks than assumed in the default derivation of RCP8.5 and
26   SSP5-8.5 concentrations (Ciais et al., 2013; Friedlingstein et al., 2014; Booth et al., 2017; see also Chapter 5,
27   Section 5.4).
29   To address long-term scenario uncertainties, scenario storylines (or ‘narratives’) are often used (Rounsevell
30   and Metzger, 2010; O’Neill et al., 2014) (see Section 1.4.4 for a more general discussion on ‘storylines’ also
31   covering ‘physical climate storylines’). Scenario storylines are descriptions of a future world, and the related
32   large-scale socio-economic development path towards that world that are deemed plausible within the
33   current state of knowledge and historical experience (WGIII; Section 1.2.3). Scenario storylines attempt to
34   ‘stimulate, provoke, and communicate visions of what the future could hold for us’ (Rounsevell and
35   Metzger, 2010) in settings where either limited knowledge or inherent unpredictability in social systems
36   prevent a forecast or numerical prediction. Scenario storylines have been used in previous climate research,
37   and they are the explicit or implicit starting point of any scenario exercise, including for the SRES scenarios
38   (IPCC, 2000) and the SSPs (e.g., O’Neill et al., 2017a).
40   Recent technological or socio-economic trends might be informative for bounding near-term future trends,
41   for example, if technological progress renders a mitigation technology cheaper than previously assumed.
42   However, short-term emission trends alone do not generally rule out an opposite trend in the future (van
43   Vuuren et al., 2010). The ranking of individual RCP emission scenarios from the IAMs with regard to
44   emission levels is different for different time horizons, e.g., 2020 versus longer-term emission levels; For
45   example, the strongest climate change mitigation scenario RCP2.6 was in fact the second highest CO2
46   emission scenario (jointly with RCP4.5) before 2020 in the set of RCPs and the strong global emission
47   decline in RCP2.6 only followed after 2020. Implicitly, this scenario feature was cautioning against the
48   assumption that short-term trends predicate particular long-term trajectories. This is also the case in relation
49   to the COVID-19 related drop in 2020 emissions. Potential changes in underlying drivers of emissions, such
50   as those potentially incentivised by COVID-19 recovery stimulus packages, are more significant for longer-
51   term emissions than the short-term deviation from recent emission trends (Cross-Chapter Box 6.1 on
52   COVID-19 in Chapter 6).

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 1   1.6.2   Global warming levels
 3   The global mean surface temperature change, or ‘global warming level’ (GWL), is a ‘dimension of
 4   integration’ that is highly relevant across scientific disciplines and socioeconomic actors. First, global
 5   warming levels relative to pre-industrial conditions are the quantity in which the 1.5°C and ‘well below 2°C’
 6   Paris Agreement goals were formulated. Second, global mean temperature change has been found to be
 7   nearly-linearly related to a number of regional climate effects (Mitchell et al., 2000; Mitchell, 2003; Tebaldi
 8   and Arblaster, 2014; Seneviratne et al., 2016; Li et al., 2020; Seneviratne and Hauser, 2020). Even where
 9   non-linearities are found, some regional climate effects can be considered to be almost scenario-independent
10   for a given level of warming (Cross-Chapter Box 11.1 in Chapter 11; Chapter 4, Sections 4.2.4 and 4.6.1;
11   Chapter 8, Section 8.5.3; Chapter 10, Section Finally, the evolution of aggregated impacts with
12   warming levels has been widely used and embedded in the assessment of the ‘Reasons for Concern’ (RFC)
13   in IPCC WGII (Smith et al., 2009; IPCC, 2014a). The RFC framework was further expanded in SR1.5
14   (2018), SROCC (2019) and SRCCL (2019) by explicitly describing the differential impacts of half-degree
15   warming steps (cf. King et al., 2017) (Section 1.4.4; Cross-Chapter Box 12.1 in Chapter 12).
17   In this Report, the term ‘global warming level’ refers to the categorisation of global and regional climate
18   change, associated impacts, emission and concentration scenarios by global mean surface temperature
19   relative to 1850-1900, which is the period used as a proxy for pre-industrial levels (see Cross-Chapter Box
20   11.1 in Chapter 11). By default, GWLs are expressed as global surface air temperature (GSAT; see Section
21   1.4.1; Cross-Chapter Box 2.3 in Chapter 2).
23   As the SR1.5 concluded, even half-degree global mean temperature steps carry robust differences in climate
24   impacts (see SR1.5,IPCC, 2018; Schleussner et al., 2016a; Wartenburger et al., 2017; see also Chapter 11).
25   This Report adopts half-degree warming levels which allows integration within and across the three WGs for
26   climate projections, impacts, adaptation challenges and mitigation challenges. The core set of - GWLs 1.5,
27   2.0, 3.0 and 4.0°C - are highlighted (Chapters 4, 8, 11, 12 and the Atlas). Given that much impact analysis is
28   based on previous scenarios, i.e., RCPs or SRES, and climate change mitigation analysis is based on new
29   emission scenarios in addition to the main SSP scenarios, these global warming levels assist in the
30   comparison of climate states across scenarios and in the synthesis across the broader literature.
32   The transient and equilibrium states of certain global warming levels can differ in their climate impacts
33   (IPCC, 2018; King et al., 2020). Climate impacts in a ‘transient’ world relate to a scenario in which the
34   world is continuing to warm. On the other hand, climate impacts at the same warming levels can also be
35   estimated from equilibrium states after a (relatively) short-term stabilisation by the end of the 21st century or
36   at a (near-) equilibrium state after a long-term (multi-decadal to multi-millennial) stabilisation. Different
37   methods to estimate these climate states come with challenges and limitations (Chapter 4, Section 4.6.1;
38   Cross-Chapter Box 11.1 in Chapter 11). First, information can be drawn from GCM or ESM simulations that
39   ‘pass through’ the respective warming levels (as used and demonstrated in the Interactive Atlas to this
40   report), also called ‘epoch’ or ‘time-shift’ approaches (Chapter 4, Sections 4.2.4 and 4.6.1) (Herger et al.,
41   2015; James et al., 2017; Tebaldi and Knutti, 2018). Information from transient simulations can also be used
42   through an empirical scaling relationship (Seneviratne et al., 2016, 2018; Wartenburger et al., 2017) or using
43   ‘time sampling’ approaches, as described in James et al. (2017). Second, information can be drawn from
44   large ESM ensembles with prescribed SST at particular global warming levels (Mitchell et al., 2017),
45   although an underrepresentation of variability can arise when using prescribed SST temperatures (Fischer et
46   al., 2018a).
48   In order to fully derive climate impacts, warming levels will need to be complemented by additional
49   information, such as their associated CO2 concentrations (e.g., fertilization or ocean acidification),
50   composition of the total radiative forcing (aerosols vs greenhouse gases, with varying regional distributions)
51   or socioeconomic conditions (e.g., to estimate societal impacts). More fundamentally, while a global
52   warming level is a good proxy for the state of the climate (Cross-Chapter Box 11.1 in Chapter 11), it does
53   not uniquely define a change in global or regional climate state. For example, regional precipitation
54   responses depend on the details of the individual forcing mechanisms that caused the change (Samset et al.,
55   2016), on whether the temperature level is stabilized or transient (King et al., 2020; Zappa et al., 2020), on
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 1   the vertical structure of the troposphere (Andrews et al., 2010), and, in particular, on the global distribution
 2   of atmospheric aerosols (Frieler et al., 2012). Another aspect is how Earth system components with century
 3   to millennial response timescales, such as long-term sea level rise or permafrost thaw, are affected by global
 4   mean warming. For example, sea level rise 50 years after a 1°C warming will be lower than sea level rise
 5   150 years after that same 1°C warming (Chapter 9).
 7   Also, forcing or response patterns that vary in time can create differences in regional climates for the same
 8   global mean warming level, or can create non-linearities when scaling patterns from one warming level to
 9   another (King et al., 2018), depending on whether near-term transient climate, end of the century,
10   equilibrium climate or climate states after an initial overshoot are considered.
12   In spite of these challenges, and thanks to recent methodological advances in quantifying or overcoming
13   them, global warming levels provide a robust and useful integration mechanism. They allow knowledge from
14   various domains within WGI and across the three WGs to be integrated and communicated (Cross Chapter
15   Box 11.1). In this report, Chapters 4, 8, 11, 12 and the Atlas provide information specific to certain warming
16   levels, highlighting the regional differences, but also the approximate scalability of regional climate change,
17   that can arise from even a 0.5°C shift in global-mean temperatures. Furthermore, building on WGI insights
18   into physical climate system responses (Cross-Chapter Box 7.1 in Chapter 7), WGIII will use peak and end-
19   of-century global warming levels to classify a broad set of scenarios.
22   1.6.3   Cumulative CO2 emissions
24   The WGI AR5 (IPCC, 2013a) and the SR1.5 (IPCC, 2018) highlighted the near-linear relationship between
25   cumulative carbon emissions and global mean warming (Section 1.3; Section 5.5). This implies that
26   continued CO2 emissions will cause further warming and changes in all components of the climate system,
27   independent of any specific scenario or pathway. This is captured in the TCRE concept, which relates CO2-
28   induced global mean warming to cumulative carbon emissions (Chapter 5). This Report thus uses cumulative
29   CO2 emissions to compare the climate response across scenarios, and to categorise emission scenarios
30   (Figure 1.29). The advantage of using cumulative CO2 emissions is that it is an inherent emission scenario
31   characteristic rather than an outcome of the scenario-based projections, where uncertainties in the cause-
32   effect chain from emissions to atmospheric concentrations to temperature change are important.
34   There is also a close relationship between cumulative total greenhouse gas emissions and cumulative CO2
35   emissions for scenarios in the SR1.5 scenario database (IPCC, 2018; Figure 1.29). The dominance of CO2
36   compared to other well-mixed greenhouse gases (Figure 1.29; Chapter 5, Section 5.2.4) allows policymakers
37   to make use of the carbon budget concept (Chapter 5, Section 5.5) in a policy context, in which GWP-
38   weighted combinations of multiple greenhouse gases are used to define emission targets. A caveat is that
39   cumulative GWP-weighted CO2 equivalent emissions over the next decades do not yield exactly the same
40   temperature outcomes as the same amount of cumulative CO2 emissions, because atmospheric perturbation
41   lifetimes of the various greenhouse gases differ. While carbon budgets are not derived using GWP-weighted
42   emission baskets but rather by explicit modelling of non-CO2 induced warming (Chapter 5, Section 5.5;
43   Cross-Chapter Box 7.1 in Chapter 7), the policy frameworks based on GWP-weighted emission baskets can
44   still make use of the insights from remaining cumulative carbon emissions for different warming levels.
46   The same cumulative CO2 emissions could lead to a slightly different level of warming over time (Box 1.4).
47   Rapid emissions followed by steep cuts and potentially net-negative emissions would be characterised by a
48   higher maximum warming and faster warming rate, compared with the same cumulative CO2 emissions
49   spread over a longer period. As further explored in the WGIII assessment, one potential limitation when
50   presenting emission pathway characteristics in cumulative emission budget categories is that path
51   dependencies and lock-in effects (e.g. today’s decisions regarding fossil fuel related infrastructure) play an
52   important role in long-term mitigation strategies (Davis et al., 2010; Luderer et al., 2018). Similarly, high
53   emissions early on might imply strongly net negative emissions (Minx et al., 2018) later on to reach the same
54   cumulative emission and temperature target envelope by the end of the century (Box 1.4). This report
55   explores options to address some of those potential issues from a WGI perspective (see Chapter 5, Sections
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 1   5.5.2 and 5.6.2).
 6   Figure 1.29: The role of CO2 in driving future climate change in comparison to other greenhouse gases (GHGs).
 7                The GHGs included here are CH4, N2O, and 40 other long-lived, well-mixed GHGs. The blue shaded area
 8                indicates the approximate forcing exerted by CO2 in Shared Socio-economic Pathways (SSP) scenarios,
 9                ranging from very low SSP1-1.9 to very high SSP5-8.5 (Chapter 7). The CO2 concentrations under the
10                SSP1-1.9 scenarios reach approximately 350 ppm after 2150, while those of SSP5-8.5 exceed 2000 ppm
11                CO2 in the longer term (through year 2300). Similarly to the dominant radiative forcing share at each
12                point in time (lower area plots), cumulative GWP-100-weighted GHG emissions happen to be closely
13                correlated with cumulative CO2 emissions, allowing policymakers to make use of the carbon budget
14                concept in a policy context with multi-gas GHG baskets as it exhibits relatively low variation across
15                scenarios with similar cumulative emissions until 2050 (inset panel). Further details on data sources and
16                processing are available in the chapter data table (Table 1.SM.1).
18   [END FIGURE 1.29 HERE]
21   [START BOX 1.4 HERE]
23   Box 1.4: The relationships between ‘net zero’ emissions, temperature outcomes and carbon dioxide
24               removal
25   Article 4 of the Paris Agreement sets an objective to ‘achieve a balance between anthropogenic emissions by
26   sources and removals by sinks of greenhouse gases’ (Section 1.2). This box addresses the relationship
27   between such a balance and the corresponding evolution of global surface temperature, with or without the
28   deployment of large-scale Carbon Dioxide Removal (CDR), using the definitions of ‘net zero CO2
29   emissions’ and ‘net zero greenhouse gas (GHG) emissions’ of the AR6 Glossary (Annex VII: Glossary).
31   ‘Net zero CO2 emissions’ is defined in AR6 as the condition in which anthropogenic CO2 emissions are
32   balanced by anthropogenic CO2 removals over a specified period. Similarly, ‘net zero GHG emissions’ is the
33   condition in which metric-weighted anthropogenic GHG emissions are balanced by metric-weighted
34   anthropogenic GHG removals over a specified period. The quantification of net zero GHG emissions thus
35   depends on the GHG emission metric chosen to compare emissions of different gases, as well as the time
36   horizon chosen for that metric. (For a broader discussion of metrics, see Box 1.3 and Chapter 7, Section 7.6,
37   and WGIII Cross-Chapter Box 2.)
39   Technical notes expanding on these definitions can be found as part of their respective entries in the Annex
40   VII: Glossary. The notes clarify the relation between ‘net zero’ CO2 and GHG emissions and the concept of
41   carbon and GHG neutrality, and the metric usage set out in the Paris Rulebook (Decision 18/CMA.1, annex,
42   paragraph 37).
44   A global net zero level of CO2, or GHG, emissions will be achieved when the sum of anthropogenic
45   emissions and removals across all countries, sectors, sources and sinks reaches zero. Achieving net zero CO2
46   or GHG emissions globally, at a given time, does not imply that individual entities (i.e., countries, sectors)
47   have to reach net zero emissions at that same point in time, or even at all (see WGIII, TS Box 4 and Chapter
48   3).
50   Net zero CO2 and net zero GHG emissions differ in their implications for the subsequent evolution of global
51   surface temperature. Net zero CO2 emissions result in approximately stable CO2-induced warming, but
52   overall warming will depend on any further warming contribution of non-CO2 GHGs. The effect of net zero
53   GHG emissions on global surface temperature depends on the GHG emission metric chosen to aggregate
54   emissions and removals of different gases. For GWP100 (the metric in which Parties to the Paris Agreement
55   have decided to report their aggregated emissions and removals), net zero GHG emissions would generally
56   imply a peak in global surface temperature, followed by a gradual decline (Chapter 7, Section 7.6.2; see also
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 1   Chapter 4, Section 4.7.1 regarding the Zero Emission Commitment). However, other anthropogenic factors,
 2   such as aerosol emissions or land use-induced changes in albedo, may still affect the climate.
 4   The definitions of net zero CO2 and GHG should also be seen in relation to the various CDR methods
 5   discussed in the context of climate change mitigation (see Chapter 5, Section 5.6, which also includes an
 6   assessment of the response of natural sinks to CDR), and how it is employed in scenarios used throughout
 7   the WG I and III reports, as described in Section 1.6.1. (See also WGIII, Chapters 3, 7 and 12.)
 9   For virtually all scenarios assessed by the IPCC, CDR is necessary to reach both global net zero CO2 and net
10   zero GHG emissions, to compensate for residual anthropogenic emissions. This is in part because for some
11   sources of CO2 and non-CO2 emissions, abatement options to eliminate them have not yet been identified.
12   For a given scenario, the choice of GHG metric determines how much net CDR is necessary to compensate
13   for residual non-CO2 emissions, in order to reach net zero GHG emissions (Chapter 7, Section 7.6.2).
15   If CDR is further used to go beyond net zero, to a situation with net-negative CO2 emissions (i.e., where
16   anthropogenic removals exceed anthropogenic emissions), anthropogenic CO2-induced warming will
17   decline. A further increase of CDR, until a situation with net zero or even net-negative GHG emissions is
18   reached, would increase the pace at which historical human-induced warming is reversed after its peak
19   (SR1.5, IPCC, 2018). Net-negative anthropogenic GHG emissions may become necessary to stabilize the
20   global surface temperature in the long term, should climate feedbacks further affect natural GHG sinks and
21   sources (see Chapter 5).
23   CDR can be achieved through a number of measures (Chapter 5, Section 5.6, and SRCCL). These include
24   additional afforestation, reforestation, soil carbon management, biochar, direct air capture and carbon capture
25   and storage (DACCS), and bioenergy with carbon capture and storage (BECCS) (de Coninck et al., 2018,
26   SR1.5 Ch4; Minx et al., 2018; see also WGIII Chapters 7 and 12). Differences between Land Use, Land Use
27   Change and Forestry (LULUCF) accounting rules, and scientific book-keeping approaches for CO2
28   emissions and removals from the terrestrial biosphere, can result in significant differences between the
29   amount of CDR that is reported in different studies (Grassi et al., 2017). Different measures to achieve CDR
30   come with different risks, negative side effects and potential co-benefits – also in conjunction with
31   sustainable development goals – that can inform choices around their implementation (Fuss et al., 2018; Roe
32   et al., 2019, Chapter 5, Section 5.6). Technologies to achieve direct large-scale anthropogenic removals of
33   non-CO2 GHGs are speculative at present (Yoon et al., 2009; Ming et al., 2016; Kroeger et al., 2017; Jackson
34   et al., 2019).
36   [END BOX 1.4 HERE]
39   1.7   Final remarks
41   The assessment in this Report is based on a rapidly growing body of new evidence from the peer-reviewed
42   literature. Recently, scientific climate change research has doubled in output every 5–6 years; the majority of
43   publications deal with issues related to the physical climate system (Burkett et al., 2014; Haunschild et al.,
44   2016). The sheer volume of published, peer-reviewed literature on climate change presents a challenge to
45   comprehensive, robust and transparent assessment.
47   The enhanced focus on regional climate in WGI AR6 further expands the volume of literature relative to
48   AR5, including non-English language publications sometimes presented as reports (‘grey’ literature),
49   particularly on topics such as regional observing networks and climate services. These factors enhance the
50   challenge of discovering, accessing and assessing the relevant literature. The international, multi-lingual
51   author teams of the IPCC AR6, combined with the open expert review process, help to minimise these
52   concerns, but they remain a challenge.
54   Despite the key role of CMIP6 in this Report (Section 1.5), the number of studies evaluating its results and
55   modelling systems remains relatively limited. At the time of publication, additional model results are still
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 1   becoming available. This reflects the need for close temporal alignment of the CMIP cycle with the IPCC
 2   assessment process, and the growing complexity of coordinated international modelling efforts.
 4   Indigenous and local knowledge includes information about past and present climate states. However,
 5   assessing this knowledge, and integrating it with the scientific literature, remains a challenge to be met. This
 6   lack of assessment capability and integration leads to most WGI chapters still not including Indigenous and
 7   local knowledge in their assessment findings.
 9   Spatial and temporal gaps in both historical and current observing networks, and the limited extent of
10   paleoclimatic archives, have always posed a challenge for IPCC assessments. A relative paucity of long-term
11   observations is particularly evident in Antarctica and in the depths of the ocean. Knowledge of previous
12   cryospheric and oceanic processes is therefore incomplete. Sparse instrumental temperature observations
13   prior to the industrial revolution makes it difficult to uniquely characterize a ‘pre-industrial’ baseline,
14   although this report extends the assessment of anthropogenic temperature change further back in time than
15   previous assessment cycles (Cross-Chapter Box 1.2, Chapter 7).
17   Common, integrating scenarios can never encompass all possible events that might induce radiative forcing
18   in the future (Section 1.4). These may include large volcanic eruptions (see Cross-Chapter Box 4.1 in
19   Chapter 4), the consequences of a major meteorite, smoke plumes following a conflict involving nuclear
20   weapons, extensive geo-engineering, or a major pandemic (Cross-Chapter Box 1.6). Scenario-related
21   research also often focuses on the 21st century. Post-2100 climate changes are not covered as
22   comprehensively, and their assessment is limited. Those long-term climate changes, potentially induced by
23   forcing over the 21st century (as in the case of sea level rise), are nevertheless relevant for decision making.
25   At the time of publication, the consequences of the COVID-19 pandemic on emissions, atmospheric
26   abundances, radiative forcing and the climate (see Cross-Chapter Box 6.1 in Chapter 6), and on observations
27   (Section 1.5.1), are not yet fully evident. Their assessment in this report is thus limited.

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 1   Frequently Asked Questions
 4   FAQ 1.1: Do we understand climate change better now compared to when the IPCC started?
 5   Yes, much better. The first IPCC report, released in 1990, concluded that human-caused climate change
 6   would soon become evident, but could not yet confirm that it was already happening. Today, evidence is
 7   overwhelming that the climate has indeed changed since the pre-industrial era and that human activities are
 8   the principal cause of that change. With much more data and better models, we also understand more about
 9   how the atmosphere interacts with the ocean, ice, snow, ecosystems and land surfaces of the Earth.
10   Computer climate simulations have also improved dramatically, incorporating many more natural processes
11   and providing projections at much higher resolutions.
13   Since the first IPCC report in 1990, large numbers of new instruments have been deployed to collect data in
14   the air, on land, at sea and from outer space. These instruments measure temperature, clouds, winds, ice,
15   snow, ocean currents, sea level, soot and dust in the air, and many other aspects of the climate system. New
16   satellite instruments have also provided a wealth of increasingly fine-grained data. Additional data from
17   older observing systems and even hand-written historical records are still being incorporated into
18   observational datasets, and these datasets are now better integrated and adjusted for historical changes in
19   instruments and measurement techniques. Ice cores, sediments, fossils, and other new evidence from the
20   distant past have taught us much about how Earth’s climate has changed throughout its history.
22   Understanding of climate system processes has also improved. For example, in 1990 very little was known
23   about how the deep ocean responds to climate change. Today, reconstructions of deep ocean temperatures
24   extend as far back as 1871. We now know that the oceans absorb most of the excess energy trapped by
25   greenhouse gases and that even the deep ocean is warming up. As another example, in 1990, relatively little
26   was known about exactly how or when the gigantic ice sheets of Greenland and Antarctica would respond to
27   warming. Today, much more data and better models of ice sheet behaviour reveal unexpectedly high melt
28   rates that will lead to major changes within this century, including substantial sea level rise (see FAQ 9.2).
30   The major natural factors contributing to climate change on time scales of decades to centuries are volcanic
31   eruptions and variations in the sun’s energy output. Today, data show that changes in incoming solar energy
32   since 1900 have contributed only slightly to global warming, and they exhibit a slight downward trend since
33   the 1970s. Data also show that major volcanic eruptions have sometimes cooled the entire planet for
34   relatively short periods of time (typically several years) by erupting aerosols (tiny airborne particles) high
35   into the atmosphere.
36   The main human causes of climate change are the heat-absorbing greenhouse gases released by fossil fuel
37   combustion, deforestation, and agriculture, which warm the planet, and aerosols such as sulphate from
38   burning coal, which have a short-term cooling effect that partially counteracts human-caused warming. Since
39   1990, we have more and better observations of these human factors as well as improved historical records,
40   resulting in more precise estimates of human influences on the climate system (see FAQ 3.1).
42   While most climate models in 1990 focused on the atmosphere, using highly simplified representations of
43   oceans and land surfaces, today’s Earth system simulations include detailed models of oceans, ice, snow,
44   vegetation and many other variables. An important test of models is their ability to simulate Earth’s climate
45   over the period of instrumental records (since about 1850). Several rounds of such testing have taken place
46   since 1990, and the testing itself has become much more rigorous and extensive. As a group and at large
47   scales, models have predicted the observed changes well in these tests (see FAQ 3.3). Since there is no way
48   to do a controlled laboratory experiment on the actual Earth, climate model simulations can also provide a
49   kind of ‘alternate Earth’ to test what would have happened without human influences. Such experiments
50   show that the observed warming would not have occurred without human influence.             .
52   Finally, physical theory predicts that human influences on the climate system should produce specific
53   patterns of change, and we see those patterns in both observations and climate simulations. For example,
54   nights are warming faster than days, less heat is escaping to space, and the lower atmosphere (troposphere) is
55   warming but the upper atmosphere (stratosphere) has cooled. These confirmed predictions are all evidence of
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 1   changes driven primarily by increases in greenhouse gas concentrations rather than natural causes.
 6   FAQ 1.1, Figure 1: Sample elements of climate understanding, observations and models as assessed in the IPCC First
 7   Assessment Report (1990) and Sixth Assessment Report (2021). Many other advances since 1990, such as key aspects
 8   of theoretical understanding, geological records and attribution of change to human influence, are not included in this
 9   figure because they are not readily represented in this simple format. Fuller explications of the history of climate
10   knowledge are available in the introductory chapters of the IPCC Fourth and Sixth Assessment Reports.
12   [END FAQ 1.1, FIGURE 1 HERE]
15   FAQ 1.2: Where is climate change most apparent?
17   The signs of climate change are unequivocal at the global scale and are increasingly apparent on smaller
18   spatial scales. The high northern latitudes show the largest temperature increase with clear effects on sea
19   ice and glaciers. The warming in the tropical regions is also apparent because the natural year-to-year
20   variations in temperature there are small. Long-term changes in other variables such as rainfall and some
21   weather and climate extremes have also now become apparent in many regions.
23   It was first noticed that the planet’s land areas were warming in the 1930s. Although increasing atmospheric
24   carbon dioxide concentrations were suggested as part of the explanation, it was not certain at the time
25   whether the observed warming was part of a long-term trend or a natural fluctuation – global warming had
26   not yet become apparent. But the planet continued to warm, and by the 1980s the changes in temperature had
27   become obvious or, in other words, the signal had emerged.
29   Imagine you had been monitoring temperatures at the same location for the past 150 years. What would you
30   have experienced? When would the warming have become noticeable in your data? The answers to these
31   questions depend on where on the planet you are.
33   Observations and climate model simulations both demonstrate that the largest long-term warming trends are
34   in the high northern latitudes and the smallest warming trends over land are in tropical regions. However, the
35   year-to-year variations in temperature are smallest in the tropics, meaning that the changes there are also
36   apparent, relative to the range of past experiences (see FAQ 1.2, Figure 1).
38   Changes in temperature also tend to be more apparent over land areas than over the open ocean and are often
39   most apparent in regions which are more vulnerable to climate change. It is expected that future changes will
40   continue to show the largest signals at high northern latitudes, but with the most apparent warming in the
41   tropics. The tropics also stand to benefit the most from climate change mitigation in this context, as limiting
42   global warming will also limit how far the climate shifts relative to past experience.
44   Changes in other climate variables have also become apparent at smaller spatial scales. For example,
45   changes in average rainfall are becoming clear in some regions, but not in others, mainly because natural
46   year-to-year variations in precipitation tend to be large relative to the magnitude of the long-term trends.
47   However, extreme rainfall is becoming more intense in many regions, potentially increasing the impacts
48   from inland flooding (see FAQ 8.2). Sea levels are also clearly rising on many coastlines, increasing the
49   impacts of inundation from coastal storm surges, even without any increase in the number of storms reaching
50   land. A decline in the amount of Arctic sea ice is apparent, both in the area covered and in its thickness, with
51   implications for polar ecosystems.
53   When considering climate-related impacts, it is not necessarily the size of the change which is most
54   important. Instead, it can be the rate of change or it can also be the size of the change relative to the natural
55   variations of the climate to which ecosystems and society are adapted. As the climate is pushed further away

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 1   from past experiences and enters an unprecedented state, the impacts can become larger, along with the
 2   challenge of adapting to them.
 4   How and when a long-term trend becomes distinguishable from shorter-term natural variations depends on
 5   the aspect of climate being considered (e.g., temperature, rainfall, sea ice or sea level), the region being
 6   considered, the rate of change, and the magnitude and timing of natural variations. When assessing the local
 7   impacts from climate change, both the size of the change and the amplitude of natural variations matter.
12   FAQ 1.2, Figure 1: Observed variations in regional temperatures since 1850 (data from Berkeley Earth). Regions in
13   high latitudes, such as mid-North America (40ºN–64ºN, 140ºW–60ºW, left), have warmed by a larger amount than
14   regions at lower latitudes, such as Tropical South America (10ºS–10ºN, 84ºW–16ºW, right), but the natural variations
15   are also much larger at high latitudes (darker and lighter shading represents 1 and 2 standard deviations, respectively, of
16   natural year-to-year variations). The signal of observed temperature change emerged earlier in Tropical South America
17   than mid-North America even though the changes were of a smaller magnitude. (Note that those regions were chosen
18   because of the longer length of their observational record, see Figure 1.14 for more regions).
20   [END FAQ 1.2, FIGURE 1 HERE]
23   FAQ 1.3: What can past climate teach us about the future?
25   In the past, the Earth has experienced prolonged periods of elevated greenhouse gas concentrations that
26   caused global temperatures and sea levels to rise. Studying these past warm periods informs us about the
27   potential long-term consequences of increasing greenhouse gases in the atmosphere.
29   Rising greenhouse gas concentrations are driving profound changes to the Earth system, including global
30   warming, sea level rise, increases in climate and weather extremes, ocean acidification, and ecological shifts
31   (see FAQ 2.2, FAQ 7.1). The vast majority of instrumental observations of climate began during the 20th
32   century, when greenhouse gas emissions from human activities became the dominant driver of changes in
33   Earth’s climate (see FAQ 3.1).
35   As scientists seek to refine our understanding of Earth’s climate system and how it may evolve in coming
36   decades to centuries, past climate states provide a wealth of insights. Data about these past states help to
37   establish the relationship between natural climate drivers and the history of changes in global temperature,
38   global sea levels, the carbon cycle, ocean circulation, and regional climate patterns, including climate
39   extremes. Guided by such data, scientists use Earth system models to identify the chain of events underlying
40   the transitions between past climatic states (see FAQ 3.3). This is important because during present-day
41   climate change, just as in past climate changes, some aspects of the Earth system (e.g., surface temperature)
42   respond to changes in greenhouse gases on a time scale of decades to centuries, while others (e.g., sea level
43   and the carbon cycle) respond over centuries to millennia (see FAQ 5.3). In this way, past climate states
44   serve as critical benchmarks for climate model simulations, improving our understanding of the sequences,
45   rates, and magnitude of future climate change over the next decades to millennia.
47    Analyzing previous warm periods caused by natural factors can help us understand how key aspects of the
48   climate system evolve in response to warming. For example, one previous warm-climate state occurred
49   roughly 125,000 years ago, during the Last Interglacial period, when slight variations in the Earth’s orbit
50   triggered a sequence of changes that caused about 1°C–2°C of global warming and about 2–8 m of sea level
51   rise relative to the 1850-1900, even though atmospheric carbon dioxide concentrations were similar to 1850-
52   1900 values (FAQ 1.3, Figure 1). Modelling studies highlight that increased summer heating in the higher
53   latitudes of the Northern Hemisphere during this time caused widespread melting of snow and ice, reducing
54   the reflectivity of the planet and increasing the absorption of solar energy by the Earth’s surface. This gave
55   rise to global-scale warming, which led in turn to further ice loss and sea level rise. These self-reinforcing
56   positive feedback cycles are a pervasive feature of Earth’s climate system, with clear implications for future
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     Final Government Distribution                        Chapter 1                                      IPCC AR6 WGI

 1   climate change under continued greenhouse gas emissions. In the case of sea level rise, these cycles evolved
 2   over several centuries to millennia, reminding us that the rates and magnitude of sea level rise in the 21st
 3   century are just a fraction of the sea level rise that will ultimately occur after the Earth system fully adjusts to
 4   current levels of global warming.
 6   Roughly 3 million years ago, during the Pliocene Epoch, the Earth witnessed a prolonged period of elevated
 7   temperatures (2.5°C–4°C higher than 1850-1900) and higher sea levels (5–25 m higher than 1850-1900), in
 8   combination with atmospheric carbon dioxide concentrations similar to present-day. The fact that Pliocene
 9   atmospheric carbon dioxide concentrations were similar to present, while global temperatures and sea levels
10   were significantly higher, reflects the difference between an Earth system that has fully-adjusted to changes
11   in natural drivers (the Pliocene) and one where greenhouse gases concentrations, temperature, and sea level
12   rise are still increasing (present-day). Much about the transition into the Pliocene climate state – in terms of
13   key causes, the role of cycles that hastened or slowed the transition, and the rate of change in climate
14   indicators such as sea level – remain topics of intense study by climate researchers using a combination of
15   paleoclimate observations and Earth system models. Insights from such studies may help to reduce the large
16   uncertainties around estimates of global sea level rise by 2300, which range from 0.3 m to 3 m above 1850-
17   1900 (in a low-emissions scenario) to as much as 16 m higher than 1850-1900 (in a very high-emissions
18   scenario that includes accelerating structural disintegration of the polar ice sheets).
20   While present-day warming is unusual in the context of the recent geologic past in several different ways
21   (see FAQ 2.1), past warm climate states present a stark reminder that the long-term adjustment to present-
22   day atmospheric carbon dioxide concentrations has only just begun. That adjustment will continue over the
23   coming centuries to millennia.
28   FAQ 1.3, Figure 1: Comparison of past, present and future. Schematic of atmospheric carbon dioxide
29   concentrations, global temperature, and global sea level during previous warm periods as compared to 1850-1900,
30   present-day (2011-2020), and future (2100) climate change scenarios corresponding to low-emissions scenarios (SSP1-
31   2.6; lighter colour bars) and very high emissions scenarios (SSP5-8.5; darker colour bars).
34   [END FAQ 1.3, FIGURE 1 HERE]

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