Editing IPCC:AR6/WGI/TS (section)

== TS.1 A Changing Climate ==

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This section introduces the assessment of the physical science basis of climate change in the AR6 and presents the climate context in which this assessment takes place, recent progress in climate science and the relevance of global and regional climate information for impact and risk assessments. The future emissions scenarios and global warming levels, used to integrate assessments across this Report, are introduced and their applications for future climate projections are briefly addressed. Paleoclimate science provides a long-term context for observed climate change of the past 150 years and the projected changes in the 21st century and beyond (Box TS.2). The assessment of past, current and future global surface temperature changes relative to the standard baselines and reference periods <sup>[[#footnote-008|13]]</sup> used throughout this Report is summarized in Cross-Section Box TS.1.

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=== TS1.1 Context of a Changing Climate ===

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'''This Report assesses new scientific evidence relevant for a world whose climate system is rapidly changing, overwhelmingly due to human influence. The five IPCC assessment cycles since 1990 have comprehensively and consistently laid out the rapidly accumulating evidence of a changing climate system, with the Fourth Assessment Report in 2007 being the first to conclude that warming of the climate system is unequivocal. Sustained changes have been documented in all major elements of the climate system: the atmosphere, land, cryosphere, biosphere and ocean (Section TS.2). Multiple lines of evidence indicate the recent large-scale climatic changes are unprecedented in a multi-millennial context and that they represent a millennial-scale commitment for the slow-responding elements of the climate system, resulting in continued worldwide loss of ice, increase in ocean heat content, sea level rise and deep ocean acidification (Box TS.2; Section TS.2). Links to chapters 1.2.1, 1.3, Box 1.2, 2.2, 2.3, Figure 2.34, 5.1, 5.3, 9.2, 9.4–9.6, Appendix 1.A'''
Earth’s climate system has evolved over many millions of years, and evidence from natural archives provides a long-term perspective on observed changes and projected changes over the coming centuries. These reconstructions of past climate also show that atmospheric CO <sub>2</sub> concentrations and global surface temperature are strongly coupled (Figure TS.1), based on evidence from a variety of proxy records over multiple time scales (Box TS.2, Section TS.2). Levels of global warming (see Core Concepts Box) that have not been seen in millions of years could be reached by 2300, depending on the emissions pathway that is followed (Section TS.1.3). For example, there is ''medium confidence'' that, by 2300, an intermediate scenario <sup>[[#footnote-007|14]]</sup> used in this Report leads to global surface temperatures of [2.3°C to 4.6°C] higher than 1850–1900, similar to the mid-Pliocene Warm Period [2.5°C to 4°C], about 3.2 million years ago, whereas the high CO <sub>2</sub> emissions scenario SSP5-8.5 leads to temperatures of [6.6°C to 14.1°C] by 2300, which overlaps with the Early Eocene Climate Optimum [10°C to 18°C], about 50 million years ago. Links to chapters Cross-Chapter Boxes 2.1 and 2.4, 2.3.1, 4.3.1.1, 4.7.1.2, 7.4.4.1

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'''Figure TS.1 |''' '''Changes in atmospheric CO''' 2 '''and global surface temperature (relative to 1850–1900) from the deep past to the next 300 years.''' ''The intent of this figure is to show that CO'' 2 ''and temperature covary, both in the past and into the future, and that projected CO'' 2 ''and temperatures are similar to those only from many millions of years ago.'' CO <sub>2</sub> concentrations from millions of years ago are reconstructed from multiple proxy records (grey dots are data from [[IPCC:Wg1:Chapter:Chapter-2#2.2.3.1|Section 2.2.3.1]] , Figure 2.3 shown with cubic-spline fit). CO <sub>2</sub> levels for the last 800,000 years through the mid-20th century are from air trapped in polar ice; recent values are from direct air measurements. Global surface temperature prior to 1850 is estimated from marine oxygen isotopes, one of multiple sources of evidence used to assess paleo temperatures in this Report. Temperature of the past 170 years is the AR6 assessed mean. CO <sub>2</sub> levels and global surface temperature change for the future are shown for three Shared Socio-economic Pathway (SSP) scenarios through 2300 CE, using Earth system model emulators calibrated to the assessed global surface temperatures. Their smooth trajectories do not account for inter-annual to inter-decadal variability, including transient response to potential volcanic eruptions. Global maps for two paleo reference periods are based on Coupled Model Intercomparison Project Phase 6 (CMIP6) and pre-CMIP6 multi-model means, with site-level proxy data for comparison (squares and circles are marine and terrestrial, respectively). The map for 2020 is an estimate of the total observed warming since 1850–1900. Global maps at right show two SSP scenarios at 2100 (2081–2100) and at 2300 (2281–2300; map from CMIP6 models; temperature assessed in 4.7.1). A brief account of the major climate forcings associated with past global temperature changes is in Cross-Chapter Box 2.1. (Section TS.1.3, Figure TS.9, Cross-Section Box TS.1, Box TS.2) Links to chapters 1.2.1.2; Figures 1.14 and 1.5; 2.2.3; 2.3.1.1; 2.3.1.1.1; Figures 2.4 and 2.5; Cross-Chapter Box 2.1, Figure 1; 4.5.1; 4.7.1; Cross-Chapter Box 4.1; Cross-Chapter Box 7.1; Figure 7.13

Understanding of the climate system’s fundamental elements is robust and well established. Scientists in the 19th century identified the major natural factors influencing the climate system. They also hypothesized the potential for anthropogenic climate change due to CO <sub>2</sub> emitted by combustion of fossil fuels (petroleum, coal, natural gas). The principal natural drivers of climate change, including changes in incoming solar radiation, volcanic activity, orbital cycles and changes in global biogeochemical cycles, have been studied systematically since the early 20th century. Other major anthropogenic drivers, such as atmospheric aerosols (fine solid particles or liquid droplets), land-use change and non-CO <sub>2</sub> greenhouse gases, were identified by the 1970s. Since systematic scientific assessments began in the 1970s, the influence of human activities on the warming of the climate system has evolved from theory to established fact (see also Section TS.2). The evidence for human influence on recent climate change strengthened from the IPCC First Assessment Report in 1990 to the IPCC Fifth Assessment Report in 2013/14, and is now even stronger in this assessment (Sections TS.1.2.4 and TS.2). Changes across a greater number of climate system components, including changes in regional climate and extremes can now be attributed to human influence (see Sections TS.2 and TS.4). Links to chapters 1.3.1–1.3.5, 3.1, 11.2, 11.9

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'''Box TS.2 | Paleoclimate'''

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'''Paleoclimate evidence is integrated within multiple lines of evidence across the WGI Report to more fully understand the climate system. Paleo evidence extends instrument-based observations of climate variables and climate drivers back in time, providing the long-term context needed to gauge the extent to which recent and potential future changes are unusual (Section TS.2, Figure TS.1). Pre-industrial climate states complement evidence from climate model projections by providing real-world examples of climate characteristics for past global warming levels, with empirical evidence for how the slow-responding components of the climate system operate over centuries to millennia – the time scale for committed climate change (Core Concepts Box, Box TS.4, Box TS.9). Information about the state of the climate system during well-described paleoclimate reference periods helps narrow the uncertainty range in the overall assessment of Earth’s sensitivity to climate forcing (Section TS.3.2.1). Links to chapters Cross-Chapter Box 2.1, FAQ 1.3, FAQ 2.1'''
'''Paleoclimate reference periods.''' Over the long evolution of Earth’s climate, several periods have received extensive research attention as examples of distinct climate states and rapid climate transitions (Box TS.2, Figure 1). These paleoclimate reference periods represent the present geological era (Cenozoic; past 65 million years) and are used across chapters to help structure the assessment of climate changes prior to industrialization. Cross-Chapter Box 2.1 describes the reference periods, along with a brief account of their climate forcings, and lists where each is discussed in other chapters. Cross-Chapter Box 2.4 summarizes information on one of the reference periods, the mid-Pliocene Warm Period. The Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] includes model output from the World Climate Research Programme Coupled Model Intercomparison Project Phase 6 (CMIP6) for four of the paleoclimate reference periods.

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'''Box TS.2, Figure 1 |'''

'''Paleoclimate and recent reference periods, with selected key indicators.''' ''The intent of this figure is to list the paleoclimate reference periods used in this Report, to summarize three key global climate indicators, and compare CO'' 2 ''with global temperature over multiple periods.'' '''(a)''' Three large-scale climate indicators (atmospheric CO 2 , global surface temperature relative to 1850–1900, and global mean sea level relative to 1900), based on assessments in Chapter 2, with confidence levels ranging from ''low'' to ''very high'' . '''(b)''' Comparison between global surface temperature (relative to 1850–1900) and atmospheric CO 2 concentration (shown on a log scale) for multiple reference periods (mid-points with 5–95% ranges). Links to chapters 2.2.3, 2.3.1.1, 2.3.3.3, Figure 2.34
'''Paleoclimate models and reconstructions.''' Climate models that target paleoclimate reference periods have been featured by the IPCC since the First Assessment Report. Under the framework of CMIP6-PMIP4 (Paleoclimate Modelling Intercomparison Project), new protocols for model intercomparisons have been developed for multiple paleoclimate reference periods. These modelling efforts have led to improved understanding of the climate response to different external forcings, including changes in Earth’s orbital and plate movements, solar irradiance, volcanism, ice-sheet size and atmospheric greenhouse gases. Likewise, quantitative reconstructions of climate variables from proxy records that are compared with paleoclimate simulations have improved as the number of study sites and variety of proxy types have expanded, and as records have been compiled into new regional and global datasets. Links to chapters 1.3.2, 1.5.1, Cross-Chapter Boxes 2.1 and 2.4

'''Global surface temperature.''' Since AR5, updated climate forcings, improved models, new understanding of the strengths and weaknesses of a growing array of proxy records, better chronologies and more robust proxy data products have led to better agreement between models and reconstructions. For global surface temperature, the mid-point of the AR6-assessed range and the median of the model-simulated temperatures differ by an average of 0.5°C across five reference periods; they overlap within their 90% ranges in four of five cases, which together span from about 6 [5 to 7]°C colder during the Last Glacial Maximum to about 14 [10 to 18] °C warmer during the Early Eocene, relative to 1850–1900 (Box TS.2, Figure 2a). Changes in temperature by latitude in response to multiple forcings show that polar amplification (stronger warming at high latitudes than the global average) is a prominent feature of the climate system across multiple climate states, and the ability of models to simulate this polar amplification in past warm climates has improved since AR5 ( ''high confidence'' ). Over the past millennium, and especially since about 1300 CE, simulated global surface temperature anomalies are well within the uncertainty of reconstructions ( ''medium confidence'' ), except for some short periods immediately following large volcanic eruptions, for which different forcing datasets disagree (Box TS.2, Figure 2b). Links to chapters 2.3.1.1, 3.3.3.1, 3.8.2.1, 7.4.4.1.2

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'''Box TS.2, Figure 2 |'''

'''Global surface temperature as estimated from proxy records (reconstructed) and climate models (simulated).''' ''The intent of this figure is to show the agreement between observations and models of global temperatures during paleo reference periods.'' '''(a)''' For individual paleoclimate reference periods. '''(b)''' For the last millennium, with instrumental temperature (AR6 assessed mean, 10-year smoothed). Model uncertainties in (a) and (b) are 5–95% ranges of multi-model ensemble means; reconstructed uncertainties are 5–95% ranges ( ''medium confidence'' ) of (a) midpoints and (b) multi-method ensemble median. Links to chapters 2.3.1.1, Figure 2.34, Figure 3.2c, Figure 3.44
'''Equilibrium climate sensitivity.''' Paleoclimate data provide evidence to estimate equilibrium climate sensitivity (ECS <sup>[[#footnote-006|15]]</sup> ) (Section TS.3.2.1). In AR6, refinements in paleo data for paleoclimate reference periods indicate that ECS is ''very likely'' greater than 1.5°C and ''likely'' less than 4.5°C, which is largely consistent with other lines of evidence and helps narrow the uncertainty range of the overall assessment of ECS. Some of the CMIP6 climate models that have either high (&gt;5°C) or low (&lt;2°C) ECS also simulate past global surface temperature changes outside the range of proxy-based reconstructions for the coldest and warmest reference periods. Since AR5, independent lines of evidence, including proxy records from past warm periods and glacial–interglacial cycles, indicate that sensitivity to forcing increases as temperature increases (Section TS.3.2.2). Links to chapters 7.4.3.2, 7.5.3, 7.5.6, Table 7.11

'''Water cycle.''' New hydroclimate reconstructions and model-data comparisons have improved the understanding of the causes and effects of long-term changes in atmospheric and ocean circulation, including monsoon variability and modes of variability (Box TS.13, Section TS.4.2). Climate models are able to reproduce decadal drought variability on large regional scales, including the severity, persistence and spatial extent of past megadroughts known from proxy records ( ''medium confidence'' ). Some long-standing discrepancies remain, however, such as the magnitude of African monsoon precipitation during the early Holocene (the past 11,700 years), suggesting continuing knowledge gaps. Paleoclimate evidence shows that, in relatively high CO <sub>2</sub> climates such as the Pliocene, Walker circulation over the equatorial Pacific Ocean weakens, supporting the ''high confidence'' model projections of weakened Walker cells by the end of the 21st century. Links to chapters 3.3.2, 8.3.1.6, 8.4.1.6, 8.5.2.1, 9.2

'''Sea level and ice sheets.''' Although past and future global warming differ in their forcings, evidence from paleoclimate records and modelling show that ice-sheet mass and global mean sea level (GMSL) responded dynamically over multiple millennia ( ''high confidence'' ). This evidence helps to constrain estimates of the committed GMSL response to global warming (Box TS.4). For example, under a past global warming levels of around [2.5°C to 4°C] relative to 1850–1900, like during the mid-Pliocene Warm Period, sea level was [5 to 25 m] higher than 1900 ( ''medium confidence'' ); under past global warming levels of [10°C to 18°C], like during the Early Eocene, the planet was essentially ice free ( ''high confidence'' ). Constraints from these past warm periods, combined with physical understanding, glaciology and modelling, indicate a committed long-term GMSL rise over 10,000 years, reaching about 8 to 13 m for sustained peak global warming of 2°C and up to 28 to 37 m for 5°C, which exceeds the AR5 estimate. Links to chapters 2.3.3.3, 9.4.1.4, 9.4.2.6, 9.6.2, 9.6.3.5

'''Ocean.''' Since AR5, better integration of paleo-oceanographic data with modelling along with higher-resolution analyses of transient changes have improved understanding of long-term ocean processes. Low-latitude sea surface temperatures at the Last Glacial Maximum cooled more than previously inferred, resolving some inconsistencies noted in AR5. This paleo context supports the assessment that ongoing increase in ocean heat content (OHC) represents a long-term commitment (see Core Concepts Box), essentially irreversible on human time scales ( ''high confidence'' ). Estimates of past global OHC variations generally track those of sea surface temperatures around Antarctica, underscoring the importance of Southern Ocean processes in regulating deep-ocean temperatures. Paleoclimate data, along with other evidence of glacial–interglacial changes, show that Antarctic Circumpolar flow strengthened and that ventilation of Antarctic Bottom Water accelerated during warming intervals, facilitating release of CO <sub>2</sub> stored in the deep ocean to the atmosphere. Paleo evidence suggests significant reduction of deep-ocean ventilation associated with meltwater input during times of peak warmth. Links to chapters 2.3.1.1, 2.3.3.1, 9.2.2, 9.2.3.2

'''Carbon cycle.''' Past climate states were associated with substantial differences in the inventories of the various carbon reservoirs, including the atmosphere (Section TS.2.2). Since AR5, the quantification of carbon stocks has improved due to the development of novel sedimentary proxies and stable-isotope analyses of air trapped in polar ice. Terrestrial carbon storage decreased markedly during the Last Glacial Maximum by 300–600 PgC, possibly by 850 PgC when accounting for interactions with the lithosphere and ocean sediments, a larger reduction than previously estimated, owing to a colder and drier climate. At the same time, the storage of remineralized carbon in the ocean interior increased by as much as 750–950 PgC, sufficient to balance the removal of carbon from the atmosphere (200 PgC) and terrestrial biosphere reservoirs combined ( ''high confidence'' ). Links to chapters 5.1.2.2

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=== TS.1.2 Progress in Climate Science ===

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==== TS.1.2.1 Observation-based Products and their Assessments ====

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'''Observational capabilities have continued to improve and expand overall since AR5, enabling improved consistency between independent estimates of climate drivers, the combined climate feedbacks, and the observed energy and sea level increase. Satellite climate records and improved reanalyses are used as an additional line of evidence for assessing changes at the global and regional scales. However, there have also been reductions in some observational data coverage or continuity and limited access to data resulting from data policy issues. Natural archives of past climate, such as tropical glaciers, have also been subject to losses (in part due to anthropogenic climate change). Links to chapters 1.5.1, 1.5.2, 10.2.2'''
Earth system observations are an essential driver of progress in our understanding of climate change. Overall, capabilities to observe the physical climate system have continued to improve and expand. Improvements are particularly evident in ocean observing networks and remote-sensing systems. Records from several recently instigated satellite measurement techniques are now long enough to be relevant for climate assessments. For example, globally distributed, high-vertical-resolution profiles of temperature and humidity in the upper troposphere and stratosphere can be obtained from the early 2000s using global navigation satellite systems, leading to updated estimates of recent atmospheric warming. Improved measurements of ocean heat content, warming of the land surface, ice-sheet mass loss and sea level changes allow a better closure of the global energy and sea level budgets relative to AR5. For surface and balloon-based networks, apparent regional data reductions result from a combination of data policy issues, data curation/provision challenges, and real cessation of observations, and are to an extent counter-balanced by improvements elsewhere. Limited observational records of extreme events and spatial data gaps currently limit the assessment of some observed regional climate change. Links to chapters 1.5.1, 2.3.2, 7.2.2, Box 7.2, Cross-Chapter Box 9.1, 9.6.1, 10.2.2, 10.6, 11.2, 12.4

New paleoclimate reconstructions from natural archives have enabled more robust reconstructions of the spatial and temporal patterns of past climate changes over multiple time scales (Box TS.2). However, paleoclimate archives, such as tropical glaciers and modern natural archives used for calibration (e.g., corals and trees), are rapidly disappearing owing to a host of pressures, including increasing temperatures ( ''high confidence'' ). Substantial quantities of past instrumental observations of weather and other climate variables, over both land and ocean, which could fill gaps in existing datasets, remain un-digitized or inaccessible. These include measurements of temperature (air and sea surface), rainfall, surface pressure, wind strength and direction, sunshine amount and many other variables dating back into the 19th century. Links to chapters 1.5.1

Reanalyses combine observations and models (e.g., a numerical weather prediction model) using data assimilation techniques to provide a spatially complete, dynamically consistent estimate of multiple variables describing the evolving climate state. Since AR5, new reanalyses have been developed for the atmosphere and the ocean with various combinations of increased resolution, extended records, more consistent data assimilation and larger availability of uncertainty estimates. Limitations remain, for example, in how reanalyses represent global-scale changes to the water cycle. Regional reanalyses use high-resolution, limited-area models constrained by regional observations and with boundary conditions from global reanalyses. There is ''high confidence'' that regional reanalyses better represent the frequencies of extremes and variability in precipitation, surface air temperature and surface wind than global reanalyses and provide estimates that are more consistent with independent observations than dynamical downscaling approaches. Links to chapters 1.5.2, 10.2.1.2, Annex I

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==== TS.1.2.2 Climate Model Performance ====

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'''This report assesses results from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representation of physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous IPCC Assessment Reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate change and many other aspects across the climate system. Some differences from observations remain, for example in regional precipitation patterns. Projections of the increase in global surface temperature, the pattern of warming, and global mean sea level rise from previous IPCC Assessment Reports and other studies are broadly consistent with subsequent observations, especially when accounting for the difference in radiative forcing scenarios used for making projections and the radiative forcings that actually occurred.''' '''The CMIP6 historical simulations assessed in this report have an ensemble mean global surface temperature change within 0.2°C of the observations over most of the historical period, and observed warming is within the ''very likely'' range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above or below the assessed ''very likely'' range of observed warming. The information about how well models simulate past warming, as well as other insights from observations and theory, are used to assess projections of global warming (see Cross-Section Box TS.1). Increasing horizontal resolution in global climate models improves the representation of small-scale features and the statistics of daily precipitation ( ''high confidence'' ). Earth system models, which include additional biogeochemical feedbacks, often perform as well as their lower-complexity global climate model counterparts, which do not account for these additional feedbacks ( ''medium confidence'' ). Links to chapters 1.3.6, 1.5.3, 3.1, 3.5.1, 3.8.2, 4.3.1, 4.3.4, 7.5, 8.5.1, 9.6.3.1'''
Climate model simulations coordinated and collected as part of the World Climate Research Programme’s Coupled Model Intercomparison Project Phase 6 (CMIP6), complemented by a range of results from the previous phase (CMIP5), constitute a key line of evidence supporting this Report. The latest generation of CMIP6 models have an improved representation of physical processes relative to previous generations, and a wider range of Earth system models now represent biogeochemical cycles. Higher-resolution models that better capture smaller-scale processes are also increasingly becoming available for climate change research (Figure TS.2, Panels a and b). Results from coordinated regional climate modelling initiatives, such as the Coordinated Regional Climate Downscaling Experiment (CORDEX) complement and add value to the CMIP global models, particularly in complex topography zones, coastal areas and small islands, as well as for extremes. Links to chapters 1.5.3, 1.5.4, 2.8.2, FAQ 3.3, 6.2.2, 6.4, 6.4.5, 8.5.1, 10.3.3, Atlas.1.4

Projections of the increase in global surface temperature and the pattern of warming from previous IPCC Assessment Reports and other studies are broadly consistent with subsequent observations ( ''limited evidence, high agreement'' ), especially when accounting for the difference in radiative forcing scenarios used for making projections and the radiative forcings that actually occurred (Figure TS.3). The AR5 and SROCC projections of GMSL for the 2007–2018 period have been shown to be consistent with observed trends in GMSL and regional weighted mean tide gauges. Links to chapters 1.3.6, 9.6.3.1

For most large-scale indicators of climate change, the simulated recent mean climate from CMIP6 models underpinning this assessment have improved compared to the CMIP5 models used in AR5 ( ''high confidence'' ). This is evident from the performance of 18 simulated atmospheric and land large-scale indicators of climate change between the three generations of models (CMIP3, CMIP5, and CMIP6) when benchmarked against reanalysis and observational data (Figure TS.2, Panel c). Earth system models, characterized by additional biogeochemical feedbacks, often perform at least as well as related, more constrained, lower-complexity models lacking these feedbacks ( ''medium confidence'' ). Links to chapters 3.8.2, 10.3.3.3

The CMIP6 multi-model mean global surface temperature change from 1850–1900 to 2010–2019 is close to the best estimate of the observed warming. However, some CMIP6 models simulate a warming that is below or above the assessed ''very likely'' range. The CMIP6 models also reproduce surface temperature variations over the past millennium, including the cooling that follows periods of intense volcanism ( ''medium confidence'' ). For upper air temperature, an overestimation of the upper tropical troposphere warming by about 0.1°C per decade between 1979 and 2014 persists in most CMIP5 and CMIP6 models ( ''medium confidence'' ), whereas the differences between simulated and improved satellite-derived estimates of change in global mean temperature through the depth of the stratosphere have decreased. Links to chapters 3.3.1

Some CMIP6 models demonstrate an improvement in how clouds are represented. CMIP5 models commonly displayed a negative shortwave cloud radiative effect that was too weak in the present climate. These errors have been reduced, especially over the Southern Ocean, due to a more realistic simulation of supercooled liquid droplets with sufficient numbers and an associated increase in the cloud optical depth. Because a negative cloud optical depth feedback in response to surface warming results from ‘brightening’ of clouds via active phase change from ice to liquid cloud particles (increasing their shortwave cloud radiative effect), the extratropical cloud shortwave feedback in CMIP6 models tends to be less negative, leading to a better agreement with observational estimates ( ''medium confidence'' ). CMIP6 models generally represent more processes that drive aerosol–cloud interactions than the previous generation of climate models, but there is only ''medium confidence'' that those enhancements improve their fitness-for-purpose of simulating radiative forcing of aerosol–cloud interactions. Links to chapters 6.4, 7.4.2, FAQ 7.2

CMIP6 models still have deficiencies in simulating precipitation patterns, particularly in the tropical ocean. Increasing horizontal resolution in global climate models improves the representation of small-scale features and the statistics of daily precipitation ( ''high confidence'' ). There is ''high confidence'' that high-resolution global, regional and hydrological models provide a better representation of land surfaces, including topography, vegetation and land-use change, which can improve the accuracy of simulations of regional changes in the terrestrial water cycle. Links to chapters 3.3.2, 8.5.1, 10.3.3, 11.2.3

There is ''high confidence'' that climate models can reproduce the recent observed mean state and overall warming of temperature extremes globally and in most regions, although the magnitude of the trends may differ. There is ''high confidence'' in the ability of models to capture the large-scale spatial distribution of precipitation extremes over land. The overall performance of CMIP6 models in simulating the intensity and frequency of extreme precipitation is similar to that of CMIP5 models ( ''high confidence'' ). Links to chapters Cross-Chapter Box 3.2, 11.3.3, 11.4.3

The structure and magnitude of multi-model mean ocean temperature biases have not changed substantially between CMIP5 and CMIP6 ( ''medium confidence'' ). Since AR5, there is improved consistency between recent observed estimates and model simulations of changes in upper (&lt;700 m) ocean heat content. The mean zonal and overturning circulations of the Southern Ocean and the mean overturning circulation of the North Atlantic (AMOC) are broadly reproduced by CMIP5 and CMIP6 models. Links to chapters 3.5.1, 3.5.4, 9.2.3, 9.3.2, 9.4.2

CMIP6 models better simulate the sensitivity of Arctic sea ice area to anthropogenic CO <sub>2</sub> emissions, and thus better capture the time evolution of the satellite-observed Arctic sea ice loss ( ''high confidence'' ). The ability to model ice-sheet processes has improved substantially since AR5. As a consequence, there is ''medium confidence'' in the representation of key processes related to surface-mass balance and retreat of the grounding-line (the junction between a grounded ice sheet and an ice shelf, where the ice starts to float) in the absence of instabilities. However, there remains ''low confidence'' in simulations of ice-sheet instabilities, ice-shelf disintegration and basal melting owing to their high sensitivity to both uncertain oceanic forcing and uncertain boundary conditions and parameters. Links to chapters 1.5.3, 2.3.2, 3.4.1, 3.4.2, 3.8.2, 9.3.1, 9.3.2, 9.4.1, 9.4.2

CMIP6 models are able to reproduce most aspects of the spatial structure and variance of the El Niño–Southern Oscillation (ENSO) and Indian Ocean Basin and Dipole modes of variability ( ''medium confidence'' ). However, despite a slight improvement in CMIP6, some underlying processes are still poorly represented. Models reproduce observed spatial features and variance of the Southern Annular Mode (SAM) and Northern Annular Mode (NAM) very well ( ''high confidence'' ). The summertime SAM trend is well captured, with CMIP6 models outperforming CMIP5 models ( ''medium confidence'' ). By contrast, the cause of the NAM trend towards its positive phase is not well understood. In the Tropical Atlantic basin, which contains the Atlantic Zonal and Meridional modes, major biases in modelled mean state and variability remain. Model performance is limited in reproducing sea surface temperature anomalies for decadal modes of variability, despite improvements from CMIP5 to CMIP6 ( ''medium confidence'' ) (see also Section TS.1.4.2.2, Table TS.4). Links to chapters 3.7.3–3.7.7

Earth system models (ESMs) simulate globally averaged land carbon sinks within the range of observation-based estimates ( ''high confidence'' ), but global-scale agreement masks large regional disagreements. There is also ''high confidence'' that the ESMs simulate the weakening of the global net flux of CO <sub>2</sub> into the ocean during the 1990s, as well as the strengthening of the flux from 2000. Links to chapters 3.6

Two important quantities used to estimate how the climate system responds to changes in greenhouse gas (GHG) concentrations are the equilibrium climate sensitivity (ECS) and transient climate response (TCR <sup>[[#footnote-005|16]]</sup> ). The CMIP6 ensemble has broader ranges of ECS and TCR values than CMIP5 (see Section TS.3.2 for the assessed range). These higher sensitivity values can, in some models, be traced to changes in extratropical cloud feedbacks ( ''medium confidence'' ). To combine evidence from CMIP6 models and independent assessments of ECS and TCR, various emulators are used throughout the report. Emulators are a broad class of simple climate models or statistical methods that reproduce the behaviour of complex ESMs to represent key characteristics of the climate system, such as global surface temperature and sea level projections. The main application of emulators in AR6 is to extrapolate insights from ESMs and observational constraints to produce projections from a larger set of emissions scenarios, which is achieved due to their computational efficiency. These emulated projections are also used for scenario classification in WGIII. Links to chapters Box 4.1, 4.3.4, 7.4.2, 7.5.6, Cross-Chapter Box 7.1, FAQ 7.2

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'''Figure TS.2 |''' '''Progress in climate models.''' ''The intent of this figure is to show present improvements in climate models in resolution, complexity and representation of key variables.'' '''(a)''' Evolution of model horizontal resolution and vertical levels (based on Figure 1.19). '''(b)''' Evolution of inclusion of processes and resolution from Coupled Model Intercomparison Project Phase 3 (CMIP3), Phase 5 (CMIP5) and Phase 6 (CMIP6; Annex II). '''(c)''' Centred pattern correlations between models and observations for the annual mean climatology over the period 1980–1999. Results are shown for individual CMIP3 (cyan), CMIP5 (blue) and CMIP6 (red) models (one ensemble member is used) as short lines, along with the corresponding ensemble averages (long lines). The correlations are shown between the models and the primary reference observational data set (from left to right: ERA5, GPCP-SG, CERES-EBAF, CERES-EBAF, CERES-EBAF, CERES-EBAF, JR-55, ERA5, ERA5, ERA5, ERA5, ERA5, ERA5, AIRS, ERA5, ESACCI-Soilmoisture, LAI3g, MTE). In addition, the correlation between the primary reference and additional observational data sets (from left to right: NCEP, GHCN, -, -, -, -, ERA5, HadISST, NCEP, NCEP, NCEP, NCEP, NCEP, NCEP, ERA5, NCEP, -, -, FLUXCOM) are shown (solid grey circles) if available. To ensure a fair comparison across a range of model resolutions, the pattern correlations are computed after regridding all datasets to a resolution of 4º in longitude and 5º in latitude. (Expanded from Figure 3.43; produced with ESMValTool version 2). Links to chapters Figure 3.43

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==== TS.1.2.3 Understanding Climate Variability and Emerging Changes ====

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'''Observed changes in climate are unequivocal at the global scale and are increasingly apparent on regional and local spatial scales. Both the rate of long-term change and the amplitude of year-to-year variations differ between regions and across climate variables, thus influencing when changes emerge or become apparent compared to natural variations (see Emergence in Core Concepts Box). The signal of temperature change has emerged more clearly in tropical regions, where year-to-year variations tend to be small over land, than in regions with greater warming but larger year-to-year variations ( ''high confidence'' ) (Figure TS.3). Long-term changes in other variables have emerged in many regions, such as for some weather and climate extremes and Arctic sea ice area. Links to chapters 1.4.2, Cross-Chapter Box 3.1, 9.3.1, 11.3.2, 12.5.2'''
Observational datasets have been extended and improved since AR5, providing stronger evidence that the climate is changing and allowing better estimates of natural climate variability on decadal time scales. There is ''very high confidence'' that the slower rate of global surface temperature change observed over 1998–2012 compared to 1951–2012 was temporary, and was, with ''high confidence'' , induced by internal variability (particularly Pacific Decadal Variability) and variations in solar irradiance and volcanic forcing that partly offset the anthropogenic warming over this period. Global ocean heat content continued to increase throughout this period, indicating continuous warming of the entire climate system ( ''very high confidence'' ). Hot extremes also continued to increase during this period over land ( ''high confidence'' ). Even in a continually warming climate, periods of reduced and increased trends in global surface temperature at decadal time scales will continue to occur in the 21st century ( ''very high confidence'' ). Links to chapters Cross-Chapter Box 3.1, 3.3.1, 3.5.1, 4.6.2, 11.3.2

Since AR5, the increased use of ‘large ensembles’, or multiple simulations with the same climate model but using different initial conditions, supports improved understanding of the relative roles of internal variability and forced change in the climate system. Simulations and understanding of modes of climate variability, including teleconnections, have improved since AR5 ( ''medium confidence'' ), and larger ensembles allow a better quantification of uncertainty in projections due to internal climate variability. Links to chapters 1.4.2, 1.5.3, 1.5.4, 4.2, 4.4.1, Box 4.1, 8.5.2, 10.3.4, 10.4

Changes in regional climate can be detected even though natural climate variations can temporarily increase or obscure anthropogenic climate change on decadal time scales. While anthropogenic forcing has contributed to multi-decadal mean precipitation changes in several regions, internal variability can delay emergence of the anthropogenic signal in long-term precipitation changes in many land regions ( ''high confidence'' ). Links to chapters 10.4

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'''Figure TS.3 |''' '''Emergence of changes in temperature over the historical period.''' ''The intent of this figure is to show how observed changes in temperature have emerged and that the emergence pattern agrees with model simulations.'' The observed change in temperature at a global warming level of 1°C (a), and the signal-to-noise ratio (the change in temperature at a global warming level of 1°C, divided by the size of year-to-year variations, ( b) ) using data from Berkeley Earth. The right panels show the zonal means of the maps and include data from different observational datasets (red) and the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations (black, including the 5–95% range) processed in the same way as the observations. Links to chapters 1.4.2, 10.4.3

Mean temperatures and heat extremes have emerged above natural variability in almost all land regions with ''high confidence'' . Changes in temperature-related variables, such as regional temperatures, growing season length, extreme heat and frost, have already occurred, and there is ''medium confidence'' that many of these changes are attributable to human activities. Several impact-relevant changes have not yet emerged from natural variability but will emerge sooner or later in this century depending on the emissions scenario ( ''high confidence'' ). Ocean acidification and deoxygenation have already emerged over most of the global open ocean, as has a reduction in Arctic sea ice ( ''high confidence'' ). Links to chapters 9.3.1, 9.6.4, 11.2, 11.3, 12.4, 12.5, Atlas.3–Atlas.11

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<span id="ts.1.2.4-understanding-of-human-influence"></span>
==== TS.1.2.4 Understanding of Human Influence ====

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'''The evidence for human influence on recent climate change has strengthened progressively from the IPCC Second Assessment Report to AR5 and is even stronger in this assessment, including for regional scales and for extremes. Human influence in the IPCC context refers to the human activities that lead to or contribute to a climate response, such as the human-induced emissions of greenhouse gases that subsequently alter the atmosphere’s radiative properties, resulting in warming of the atmosphere, ocean and land components of the climate system. Other human activities influencing climate include the emission of aerosols and other short-lived climate forcers, and land-use change such as urbanization. Progress in our understanding of human influence is gained from longer observational datasets, improved paleoclimate information, a stronger warming signal since AR5, and improvements in climate models, physical understanding and attribution techniques (see Core Concepts Box). Since AR5, the attribution to human influence has become possible across a wider range of climate variables and climatic impact-drivers (CIDs, see Core Concepts Box). New techniques and analyses drawing on several lines of evidence have provided greater confidence in attributing changes in regional weather and climate extremes to human influence ( ''high confidence'' ). Links to chapters 1.3, 1.5.1, Appendix 1.A, 3.1–3.8, 5.2, 6.4.2, 7.3.5, 7.4.4, 8.3.1, 10.4, Cross-Chapter Box 10.3, 11.2–11.9, 12.4'''
Combining the evidence from across the climate system increases the level of confidence in the attribution of observed climate change to human influence and reduces the uncertainties associated with assessments based on single variables. Links to chapters Cross-Chapter Box 10.3

Since AR5, the accumulation of energy in the Earth system has become established as a robust measure of the rate of global climate change on interannual-to-decadal time scales. The rate of accumulation of energy is equivalent to Earth’s energy imbalance and can be quantified by changes in the global energy inventory for all components of the climate system, including global ocean heat uptake, warming of the atmosphere, warming of the land and melting of ice. Compared to changes in global surface temperature, Earth’s energy imbalance (see Core Concepts Box) exhibits less variability, enabling more accurate identification and estimation of trends. Links to chapters Box 7.2 and [[IPCC:Wg1:Chapter:Chapter-7#7.2|Section 7.2]]

Identifying the human-induced components contributing to the energy budget provides an implicit estimate of the human influence on global climate change (Sections TS.2 and TS.3.1). Links to chapters Cross-Working Group Box: Attribution in Chapter 1, 3.8, 7.2.2, Box 7.2, Cross-Chapter Box 9.1

Regional climate changes can be moderated or amplified by regional forcing from land-use and land-cover changes or from aerosol concentrations and other short-lived climate forcers (SLCFs). For example, the difference in observed warming trends between cities and their surroundings can partly be attributed to urbanization ( ''very high confidence'' ). While established attribution techniques provide confidence in our assessment of human influence on large-scale climate changes (as described in Section TS.2), new techniques developed since AR5, including attribution of individual events, have provided greater confidence in attributing changes in climate extremes to climate change (Box TS.10). Multiple attribution approaches support the contribution of human influence to several regional multi-decadal mean precipitation changes ( ''high confidence'' ). Understanding about past and future changes in weather and climate extremes has increased due to better observation-based datasets, physical understanding of processes, an increasing proportion of scientific literature combining different lines of evidence, and improved accessibility to different types of climate models ( ''high confidence'' ) (see Sections TS.2 and TS.4). Links to chapters Cross-Working Group Box: Attribution in Chapter 1, 1.5, 3.2, 3.5, 5.2, 6.4.3, 8.3, 9.6, 10.1, 10.2, 10.3.3, 10.4.1, 10.4.2, 10.4.3, 10.5, 10.6, Cross-Chapter Box 10.3, Box 10.3, 11.1.6, 11.2–11.9, 12.4

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<span id="ts.1.3-assessing-future-climate-change"></span>
=== TS.1.3 Assessing Future Climate Change ===

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Various frameworks can be used to assess future climatic changes and to synthesize knowledge across climate change assessment in WGI, WGII and WGIII. These frameworks include: (i) scenarios, (ii) global warming levels and (iii) cumulative CO <sub>2</sub> emissions (see Core Concepts Box). The latter two offer scenario- and path-independent approaches to assess future projections. Additional choices, for instance with regard to common reference periods and time windows for which changes are assessed, can further help to facilitate integration across the WGI report and across the whole AR6 (see Section TS.1.1). Links to chapters 1.4.1, 1.6, Cross-Chapter Box 1.4, 4.2.2, 4.2.4, Cross-Chapter Box 11.1

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<span id="ts.1.3.1-climate-change-scenarios"></span>
==== TS.1.3.1 Climate Change Scenarios ====

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'''A core set of five illustrative scenarios based on the Shared Socio-economic Pathways (SSPs) are used consistently across this Report: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. These scenarios cover a broader range of greenhouse gas and air pollutant futures than assessed in earlier WGI reports, and they include high-CO <sub>2</sub> emissions pathways without climate change mitigation as well as new low-CO <sub>2</sub> emissions pathways (Figure TS.4). In these scenarios, differences in air pollution control and variations in climate change mitigation stringency strongly affect anthropogenic emissions trajectories of SLCFs. Modelling studies relying on the Representative Concentration Pathways (RCPs) used in AR5 complement the assessment based on SSP scenarios, for example at the regional scale.'''
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'''Figure TS.4 |''' '''The climate change cause–effect chain:''' ''The intent of this figure is to illustrate the process chain starting from anthropogenic emissions, to changes in atmospheric concentration, to changes in Earth’s energy balance (‘forcing’), to changes in global climate and ultimately regional climate and climatic impact-drivers'' . Shown is the core set of five Shared Socio-economic Pathway (SSP) scenarios as well as emissions and concentration ranges for the previous Representative Concentration Pathway (RCP) scenarios in year 2100; carbon dioxide (CO 2 ) emissions (GtCO 2 yr <sup>–1</sup> ), panel top left; methane (CH 4 ) emissions (middle) and sulphur dioxide (SO 2 ), nitrogen oxide (NOx) emissions (all in Mt yr <sup>–1</sup> ), top right; concentrations of atmospheric CO 2 (ppm) and CH <sub>4</sub> (ppb), second row left and right; effective radiative forcing for both anthropogenic and natural forcings (W m <sup>–2</sup> ), third row; changes in global surface air temperature (°C) relative to 1850–1900, fourth row; maps of projected temperature change (°C) (left) and changes in annual-mean precipitation (%) (right) at a global warming level (GWL) of 2°C relative to 1850–1900 (see also Figure TS.5), bottom row. Carbon cycle and non-CO 2 biogeochemical feedbacks will also influence the ultimate response to anthropogenic emissions (arrows on the left). Links to chapters 1.6.1, Cross-Chapter Box 1.4, 4.2.2, 4.3.1, 4.6.1, 4.6.2

'''A comparison of simulations from CMIP5 using the RCPs with SSP-based simulations from CMIP6 shows that about half of the increase in simulated warming in CMIP6 versus CMIP5 arises because higher climate sensitivity is more prevalent in CMIP6 model versions; the other half arises from higher radiative forcing in nominally corresponding scenarios (e.g., RCP8.5 and SSP5-8.5; ''medium confidence'' ). The feasibility or likelihood of individual scenarios is not part of this assessment, which focuses on the climate response to a large range of emissions scenarios. Links to chapters 1.5.4, 1.6, Cross-Chapter Box 1.4, 4.2, 4.3, 4.6, 6.6, 6.7, Cross-Chapter Box 7.1, Atlas.2.1'''
Climate change projections with climate models require information about future emissions or concentrations of greenhouse gases, aerosols, ozone-depleting substances, and land use over time (Figure TS.4). This information can be provided by scenarios, which are internally consistent projections of these quantities based on assumptions of how socio-economic systems could evolve over the 21st century. Emissions from natural sources, such as the ocean and the land biosphere, are usually assumed to be constant, or to evolve in response to changes in anthropogenic forcings or to projected climate change. Natural forcings, such as past changes in solar irradiance and historical volcanic eruptions, are represented in model simulations covering the historical era. Future simulations assessed in this Report account for projected changes in solar irradiance and for the long-term mean background forcing from volcanoes, but not for individual volcanic eruptions. Scenarios have a long history in IPCC as a method for systematically examining possible futures and following the cause–effect chain: from anthropogenic emissions, to changes in atmospheric concentrations, to changes in Earth’s energy balance (‘forcing’), to changes in global climate and ultimately regional climate and climatic impact-drivers (Figure TS.4, Section TS.2, Infographic TS.1). Links to chapters 1.5.4, 1.6.1, 4.2.2, 4.4.4, Cross-Chapter Box 4.1, 11.1

The uncertainty in climate change projections that results from assessing alternative socio-economic futures, the so-called scenario uncertainty, is explored through the use of scenario sets. Designed to span a wide range of possible future conditions, these scenarios do not intend to match how events actually unfold in the future, and they do not account for impacts of climate change on the socio-economic pathways. Besides scenario uncertainty, climate change projections are also subject to climate response uncertainty (i.e., the uncertainty related to our understanding of the key physical processes and structural uncertainties in climate models) and irreducible and intrinsic uncertainties related to internal variability. Depending on the spatial and temporal scales of the projection, and on the variable of interest, the relative importance of these different uncertainties may vary substantially. Links to chapters 1.4.3, 1.6, 4.2.5, Box 4.1, 8.5.1

Scenarios in AR6 cover a broader range of emissions futures than considered in AR5, including high CO <sub>2</sub> emissions scenarios without climate change mitigation as well as a low CO <sub>2</sub> emissions scenario reaching net zero CO <sub>2</sub> emissions (see Core Concepts Box) around mid-century. In this Report, a core set of five illustrative scenarios is used to explore climate change over the 21st century and beyond (Section TS.2). They are labelled SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 <sup>[[#footnote-004|17]]</sup> and span a wide range of radiative forcing levels in 2100. They start in 2015 and include scenarios with high and very high GHG emissions and CO <sub>2</sub> emissions that roughly double from current levels by 2100 and 2050, respectively (SSP3-7.0 and SSP5-8.5); scenarios with intermediate GHG emissions and CO <sub>2</sub> emissions remaining around current levels until the middle of the century (SSP2-4.5); and scenarios with very low and low GHG emissions and CO <sub>2</sub> emissions declining to net zero around or after 2050, followed by varying levels of net negative CO <sub>2</sub> emissions (SSP1-1.9 and SSP1-2.6). These SSP scenarios offer unprecedented detail of input data for ESM simulations and allow for a more comprehensive assessment of climate drivers and responses, in particular because some aspects, such as the temporal evolution of pollutants, emissions or changes in land use and land cover, span a broader range in the SSP scenarios than in the RCPs used in AR5. Modelling studies utilizing the RCPs complement the assessment based on SSP scenarios, for example, at the regional scale (Section TS.4). Scenario extensions are based on assumptions about the post-2100 evolution of emissions or of radiative forcing that are independent from the modelling of socio-economic dynamics, which does not extend beyond 2100. To explore specific dimensions, such as air pollution or temporary overshoot of a given warming level, scenario variants are used in addition to the core set. Links to chapters 1.6.1, Cross-Chapter Box 1.4, 4.2.2, 4.2.6, 4.7.1, Cross-Chapter Box 7.1

SSP1-1.9 represents the low end of future emissions pathways, leading to warming below 1.5°C in 2100 and limited temperature overshoot of 1.5°C over the course of the 21st century (see Figure TS.6). At the opposite end of the range, SSP5-8.5 represents the very high warming end of future emissions pathways from the literature. SSP3-7.0 has overall lower GHG emissions than SSP5-8.5 but, for example, CO <sub>2</sub> emissions still almost double by 2100 compared to today’s levels. SSP2-4.5 and SSP1-2.6 represent scenarios with stronger climate change mitigation and thus lower GHG emissions. SSP1-2.6 was designed to limit warming to below 2°C. Infographic TS.1 presents a narrative depiction of SSP-related climate futures. No likelihood is attached to the scenarios assessed in this Report, and the feasibility of specific scenarios in relation to current trends is best informed by the WGIII contribution to AR6. In the scenario literature, the plausibility of some scenarios with high CO <sub>2</sub> emissions, such as RCP8.5 or SSP5-8.5, has been debated in light of recent developments in the energy sector. However, climate projections from these scenarios can still be valuable because the concentration levels reached in RCP8.5 or SSP5-8.5 and corresponding simulated climate futures cannot be ruled out. That is because of uncertainty in carbon-cycle feedbacks which, in nominally lower emissions trajectories, can result in projected concentrations that are higher than the central concentration levels typically used to drive model projections. Links to chapters 1.6.1; Cross-Chapter Box 1.4; 4.2.2, 5.4; SROCC; Chapter 3 in WGIII

The socio-economic narratives underlying SSP-based scenarios differ in their assumed level of air pollution control. Together with variations in climate change mitigation stringency, this difference strongly affects anthropogenic emissions trajectories of SLCFs, some of which are also air pollutants. SSP1 and SSP5 assume strong pollution control, projecting a decline of global emissions of ozone precursors (except methane; CH <sub>4</sub> ) and of aerosols and most of their precursors in the mid- to long term. The reductions due to air pollution controls are further strengthened in scenarios that assume a marked decarbonization, such as SSP1-1.9 or SSP1-2.6. SSP2-4.5 is a medium pollution-control scenario with air pollutant emissions following current trends, and SSP3-7.0 is a weak pollution-control scenario with strong increases in emissions of air pollutants over the 21st century. Methane emissions in SSP-based scenarios vary with the overall climate change mitigation stringency, declining rapidly in SSP1-1.9 and SSP1-2.6 but declining only after 2070 in SSP5-8.5. SSP trajectories span a wider range of air pollutant emissions than considered in the RCP scenarios (see Figure TS.4), reflecting the potential for large regional differences in their assumed pollution policies. Their effects on climate and air pollution are assessed in Box TS.7. Links to chapters 4.4.4, 6.6.1, Figure 6.4, 6.7.1, Figure 6.19

Since the RCPs are also labelled by the level of radiative forcing they reach in 2100, they can in principle be related to the core set of AR6 scenarios (Figure TS.4). However, the RCPs and SSP-based scenarios are not directly comparable. First, the gas-to-gas compositions differ; for example, the SSP5-8.5 scenario has higher CO <sub>2</sub> but lower CH <sub>4</sub> concentrations compared to RCP8.5. Second, the projected 21st-century trajectories may differ, even if they result in the same radiative forcing by 2100. Third, the overall effective radiative forcing (see Core Concepts Box) may differ, and tends to be higher for the SSPs compared to RCPs that share the same nominal stratospheric-temperature-adjusted radiative forcing label. Comparing the differences between CMIP5 and CMIP6 projections (Cross-Section Box TS.1) that were driven by RCPs and SSP-based scenarios, respectively, indicates that about half of the difference in simulated warming arises because of higher climate sensitivity being more prevalent in CMIP6 model versions; the remainder arises from higher ERF in nominally corresponding scenarios (e.g., RCP8.5 and SSP5-8.5; ''medium confidence'' ) (see Section TS.1.2.2). In SSP1-2.6 and SSP2-4.5, changes in ERF also explain about half of the changes in the range of warming ( ''medium confidence'' ). For SSP5-8.5, higher climate sensitivity is the primary reason behind the upper end of the CMIP6-projected warming being higher than for RCP8.5 in CMIP5 ( ''medium confidence'' ). Note that AR6 uses multiple lines of evidence beyond CMIP6 results to assess global surface temperature under various scenarios (see Cross-Section Box TS.1 for the detailed assessment). Links to chapters 1.6, 4.2.2, 4.6.2.2, Cross-Chapter Box 7.1

Earth system models can be driven by anthropogenic CO <sub>2</sub> emissions (‘emissions-driven’ runs), in which case atmospheric CO <sub>2</sub> concentration is a projected variable; or by prescribed time-varying atmospheric concentrations (‘concentration-driven’ runs). In emissions-driven runs, changes in climate feed back on the carbon cycle and interactively modify the projected CO <sub>2</sub> concentration in each ESM, thus adding the uncertainty in the carbon cycle response to climate change to the projections. Concentration-driven simulations are based on a central estimate of carbon cycle feedbacks, while emissions-driven simulations help quantify the role of feedback uncertainty. The differences in the few ESMs for which both emissions and concentration-driven runs were available for the same scenario are small and do not affect the assessment of global surface temperature projections discussed in Cross-Section Box TS.1 and Section TS.2 ( ''high confidence'' ). By the end of the 21st century, emissions-driven simulations are on average around 0.1°C cooler than concentration-driven runs, reflecting the generally lower CO <sub>2</sub> concentrations simulated by the emissions-driven ESMs, and have a spread about 0.1°C greater, reflecting the range of simulated CO <sub>2</sub> concentrations. However, these carbon cycle–climate feedbacks do affect the transient climate response to cumulative CO <sub>2</sub> emissions (TCRE <sup>[[#footnote-003|18]]</sup> ), and their quantification is crucial for the assessment of remaining carbon budgets consistent with global warming levels simulated by ESMs (see Section TS.3). Links to chapters 1.6.1, Cross-Chapter Box 1.4, 4.2, 4.3.1, 5.4.5, Cross-Chapter Box 7.1

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==== TS.1.3.2 Global Warming Levels and Cumulative CO <sub>2</sub> Emissions ====

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'''Quantifying geographical response patterns of climate change at various global warming levels (GWLs), such as 1.5°C or 2°C above the 1850–1900 period, is useful for characterizing changes in mean climate, extremes and climatic impact-drivers. Global warming levels are used in this Report as a dimension of integration independent of the timing when the warming level is reached and of the emissions scenario that led to the warming. For many climate variables the response pattern for a given GWL is consistent across different scenarios. However, this is not the case for slowly responding processes, such as ice-sheet and glacier mass loss, deep ocean warming, and the related sea level rise. The response of these variables depends on the time it takes to reach the GWL, differs if the warming is reached in a transient warming state or after a temporary overshoot of the warming level, and will continue to evolve, over centuries to millennia, even after global warming has stabilized. Different GWLs correspond closely to specific cumulative CO <sub>2</sub> emissions due to their near-linear relationship with global surface temperature. This Report uses 1.0°C, 1.5°C, 2.0°C, 3.0°C and 4.0°C above 1850–1900 conditions as a primary set of GWLs. Links to chapters 1.6.2, 4.2.4, 4.6.1, 5.5, Cross-Chapter Box 11.1, Cross-chapter Box 12.1'''
For many indicators of climate change, such as seasonal and annual mean and extreme surface air temperatures and precipitation, the geographical patterns of changes are well estimated by the level of global surface warming, independently of the details of the emissions pathways that caused the warming, or the time at which the level of warming is attained. GWLs, defined as a global surface temperature increase of, for example, 1.5°C or 2°C relative to the mean of 1850–1900, are therefore a useful way to integrate climate information independently of specific scenarios or time periods. Links to chapters 1.6.2, 4.2.4, 4.6.1, 11.2.4, Cross-Chapter Box 11.1

The use of GWLs allows disentangling the contribution of changes in global warming from regional aspects of the climate response, as scenario differences in response patterns at a given GWL are often smaller than model uncertainty and internal variability. The relationship between the GWL and response patterns is often linear, but integration of information can also be done for non-linear changes, like the frequency of heat extremes. The requirement is that the relationship to the GWL is broadly independent of the scenario and relative contribution of radiative forcing agents. Links to chapters 1.6, 11.2.4, Cross-Chapter Box 11.1

The GWL approach to integration of climate information also has some limitations. Variables that are quick to respond to warming, like temperature and precipitation, including extremes, sea ice area, permafrost and snow cover, show little scenario dependence for a given GWL, whereas slow-responding variables such as glacier and ice-sheet mass, warming of the deep ocean and their contributions to sea level rise, have substantial dependency on the trajectory of warming taken to reach the GWL. A given GWL can also be reached for different balances between anthropogenic forcing agents, such as long-lived greenhouse gas and SLCF emissions, and the response patterns may depend on this balance. Finally, there is a difference in the response even for temperature-related variables if a GWL is reached in a rapidly warming transient state or in an equilibrium state when the land–sea warming contrast is less pronounced. In this Report, the climate responses at different GWLs are calculated based on climate model projections for the 21st century (see Figure TS.5), which are mostly not in equilibrium. The SSP1-1.9 scenario allows assessing the response to a GWL of about 1.5°C after a (relatively) short-term stabilization by the end of the 21st century. Links to chapters 4.6.2, 9.3.1.1, 9.5.2.3, 9.5.3.3, 11.2.4, Cross-Chapter Box 11.1, Cross-Chapter Box 12.1

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'''Figure TS.5 |''' '''Scenarios, global warming levels, and patterns of change.''' ''The intent of this figure is to show how scenarios are linked to global warming levels (GWLs) and to provide examples of the evolution of patterns of change with global warming levels.'' (a) Illustrative example of GWLs defined as global surface temperature response to anthropogenic emissions in unconstrained Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations, for two illustrative scenarios (SSP1-2.6 and SSP3-7.0). The time when a given simulation reaches a GWL, for example, +2°C, relative to 1850–1900 is taken as the time when the central year of a 20-year running mean first reaches that level of warming. See the dots for +2°C, and how not all simulations reach all levels of warming. The assessment of the timing when a GWL is reached takes into account additional lines of evidence and is discussed in Cross-Section Box TS.1. (b) Multi-model, multi-simulation average response patterns of change in near-surface air temperature, precipitation (expressed as percentage change) and soil moisture (expressed in standard deviations of interannual variability) for three GWLs. The number to the top right of the panels shows the number of model simulations averaged across including all models that reach the corresponding GWL in any of the five Shared Socio-economic Pathways (SSPs). See Section TS.2 for discussion. Links to chapters Cross-Chapter Box 11.1

Global warming levels are highly relevant as a dimension of integration across scientific disciplines and socio-economic actors and are motivated by the long-term goal in the Paris Agreement of ‘holding the increase in the global average temperature to well below 2°C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5°C above pre-industrial levels’. The evolution of aggregated impacts with temperature levels has also been widely used and embedded in the WGII assessment. This includes the ‘Reasons for Concern’ (RFC) and other ‘burning ember’ diagrams in IPCC WGII. The RFC framework has been further expanded in SR1.5, SROCC and SRCCL by explicitly looking at the differential impacts between half-degree GWLs and the evolution of risk for different socio-economic assumptions. Links to chapters 1.4.4, 1.6.2, 11.2.4, 12.5.2, Cross-Chapter Box 11.1, Cross-Chapter Box 12.1

SR1.5 concluded that ‘climate models project robust differences in regional climate characteristics between present-day and global warming of 1.5°C, and between 1.5°C and 2°C’. This Report adopts a set of common GWLs across which climate projections, impacts, adaptation challenges and climate change mitigation challenges can be integrated, within and across the three Working Groups, relative to 1850–1900. The core set of GWLs in this Report are 1.0°C (close to present day conditions), 1.5°C, 2.0°C, 3.0°C and 4.0°C. Links to chapters 1.4, 1.6.2, Cross-Chapter Box 1.2, Table 1.5, Cross-Chapter Box 11.1

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===== Connecting Scenarios and Global Warming Levels =====

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In this Report, scenario-based climate projections are translated into GWLs by aggregating the ESM model response at specific GWLs across scenarios (see Figure TS.5 and Figure TS.6). The climate response pattern for the 20-year period around when individual simulations reach a given GWL are averaged across all models and scenarios that reach that GWL. The best estimate and ''likely'' range of the timing of when a certain GWL is reached under a particular scenario (or ‘GWL-crossing time’), however, is based not only on CMIP6 output, but on a combined assessment taking into account the observed warming to date, CMIP6 output and additional lines of evidence (see Cross-Section Box TS.1). Links to chapters 4.3.4, Cross-Chapter Box 11.1, Atlas.2, Interactive Atlas

Global warming levels are closely related to cumulative CO <sub>2</sub> (and in some cases CO <sub>2</sub> -equivalent) emissions. This Report confirms the assessment of the WGI contribution to AR5 and SR1.5 that a near-linear relationship exists between cumulative CO <sub>2</sub> emissions and the resulting increase in global surface temperature (Section TS.3.2). This implies that continued CO <sub>2</sub> emissions will cause further warming and associated changes in all components of the climate system. For declining cumulative CO <sub>2</sub> emissions (i.e., if negative net emissions are achieved), the relationship is less strong for some components, such as the hydrological cycle. The WGI report uses cumulative CO <sub>2</sub> emissions to compare climate response across scenarios and provides a link to the emissions pathways assessment in WGIII. The advantage of using cumulative CO <sub>2</sub> emissions is that it is an inherent emissions scenario characteristic rather than an outcome of the scenario-based projections, where uncertainties in the cause–effect chain from emissions to temperature change are important (Figure TS.4), for example, the uncertainty in ERF and TCR. Cumulative CO <sub>2</sub> emissions can also provide a link to the assessments of mitigation options. Cumulative CO <sub>2</sub> emissions do not carry information about non-CO <sub>2</sub> emissions, although these can be included with specific emissions metrics to estimate CO <sub>2</sub> -equivalent emissions. (Section TS.3.3) Links to chapters 1.3.2, 1.6, 4.6.2, 5.5, 7.6

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=== TS.1.4 From Global to Regional Climate Information for Impact and Risk Assessment ===

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'''The AR6 WGI Report has an expanded focus on regional information supported by the increased availability of coordinated regional climate model ensemble projections and improvements in the sophistication and resolution of global and regional climate models ( ''high confidence'' ). Multiple lines of evidence can be used to construct climate information on a global to regional scale and can be further distilled in a co-production process to meet user needs ( ''high confidence'' ). To better support risk assessment, a common risk framework across all three Working Groups has been implemented in AR6, and low-likelihood but high-impact outcomes are explicitly addressed in WGI by using physical climate storylines (see Core Concepts Box).''' '''Climatic impact-drivers are physical climate system conditions (e.g., means, events, extremes) that affect an element of society or ecosystems. They are the WGI contribution to the risk framing without anticipating whether their impact provides potential opportunities or is detrimental (i.e., as for hazards). Many global and regional climatic impact-drivers have a direct relation to global warming levels ( ''high confidence'' ). Links to chapters 1.4.4, 1.5.2–1.5.4, Cross-Chapter Box 1.3, 4.8, 10.1, 10.5.1, Box 10.2, Cross-Chapter Box 10.3, 11.2.4, 11.9, Box 11.2, Cross-Chapter Box 11.1, 12.1–12.3, 12.6, Cross-Chapter Boxes 12.1 and 12.2, Atlas.1.3.3–1.3.4, Atlas.1.4, Atlas.1.4.4'''
Climate change is a global phenomenon, but manifests differently in different regions. The impacts of climate change are generally experienced at local, national and regional scales, and these are also the scales at which decisions are typically made. Robust climate change information is increasingly available at regional scales for impact and risk assessments. Depending on the climate information context, geographical regions in AR6 may refer to larger areas, such as sub-continents and oceanic regions, or to typological regions, such as monsoon regions, coastlines, mountain ranges or cities, as used in Section TS.4. A new set of standard AR6 WGI reference regions has also been included in this Report (Figure TS.6, bottom panels). Links to chapters 1.4.5, 10.1, 11.9, 12.1–12.4, Atlas.1.3.3–1.3.4

[[File:7883996611279b2ba154dd86af2f0d1b IPCC_AR6_WGI_TS_Figure_6.png]]

'''Figure TS.6 |''' '''A graphical abstract for key aspects of the Technical Summary.''' ''The intent of this figure is to summarize many different aspects of the Technical Summary related to observed and projected changes in global temperature and associated regional changes in climatic impact-drivers relevant for impact and risk assessment.'' Top left: a schematic representation of the likelihood for equilibrium climate sensitivity (ECS), consistent with the AR6 assessment (see [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] and Section TS.3). ECS values above 5°C and below 2°C are termed low-likelihood, high warming (LLHW) and low-likelihood, low warming, respectively (Box TS.3). Top right: Observed (see Cross-Section Box TS.1) and projected global surface temperature changes, shown as global warming levels (GWLs) relative to 1850–1900, using the assessed 95% (top), 50% (middle) and 5% (bottom) likelihood time series (see [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] and Section TS.2). Bottom panels show maps of Coupled Model Intercomparison Project Phase 6 (CMIP6) median projections of two climatic impact-drivers (CIDs, see Section TS.1.4) at three different GWLs (columns for 1.5, 2 and 4°C) for the AR6 land regions (see Chapters 1, 10, and [[IPCC:Wg1:Chapter:Atlas|Atlas]] and Section TS.4). The heat warning index is the number of days per year averaged across each region at which a heat warning for human health at level ‘danger’ would be issued according to the U.S. National Oceanic and Atmospheric Administration (NOAA) (NOAA HI41, see [[IPCC:Wg1:Chapter:Chapter-12|Chapter 12]] and Annex VI). The maps of extreme rainfall changes show the percentage change in the amount of rain falling on the wettest day of a year (Rx1day, relative to 1995–2014, see Chapter 11) averaged across each region when the respective GWL is reached. Additional CIDs are discussed in Section TS.4. Links to chapters 1.4.4, Box 4.1, 7.5, 11.4.3, 12.4

Global and regional climate models are important sources of climate information at the regional scale. Since AR5, a more comprehensive assessment of past and future evolution of a range of climate variables on a regional scale has been enabled by the increased availability of coordinated ensemble regional climate model projections and improvements in the level of sophistication and resolution of global and regional climate models. This has been complemented by observational, attribution and sectoral-vulnerability studies informing, for instance, about impact-relevant tolerance thresholds. Links to chapters 10.3.3, 11.9, 12.1, 12.3, 12.6, Atlas.3–Atlas.11

Multiple lines of evidence derived from observations, model simulations and other approaches can be used to construct climate information on a regional scale as described in detail in Sections TS.4.1.1 and TS.4.1.2. Depending on the phenomena and specific context, these sources and methodologies include theoretical understanding of the relevant processes, drivers and feedbacks of climate at regional scale; trends in observed data from multiple datasets; and the attribution of these trends to specific drivers. Furthermore, simulations from different model types (including global and regional climate models, emulators, statistical downscaling methods, etc.) and experiments (e.g., CMIP, CORDEX, and large ensembles of single-model simulations with different initial conditions), attribution methodologies and other relevant local knowledge (e.g., indigenous knowledge) are utilized (see Box TS.11). Links to chapters 1.5.3, 1.5.4, Cross-Chapter Box 7.1, 10.2–10.6, 11.2, Atlas.1.4, Cross-Chapter Box 10.3

From the multiple lines of evidence, climate information can be distilled in a co-production process that involves users, related stakeholders and producers of climate information, considering the specific context of the question at stake, the underlying values and the challenge of communicating across different communities. The co-production process is an essential part of climate services, which are discussed in Section TS.4.1.2. Links to chapters 10.5, 12.6, Cross-Chapter Box 12.2

With the aim of informing decision-making at local or regional scales, a common risk framework has been implemented in AR6. Methodologies have been developed to construct more impact- and risk-relevant climate change information tailored to regions and stakeholders. Physical storyline approaches are used in order to build climate information based on multiple lines of evidence, and which can explicitly address physically plausible, but low-likelihood, high-impact outcomes and uncertainties related to climate variability for consideration in risk assessments (Figure TS.6). Links to chapters Cross-Chapter Box 1.3, 4.8, Box 9.4, 10.5, Box 10.2, Box 11.2, 12.1–12.3, 12.6, Glossary

The climatic impact-driver framework developed in AR6 supports an assessment of changing climate conditions that are relevant for sectoral impacts and risk assessment. Climatic impact-drivers (CIDs) are physical climate system conditions (e.g., means, extremes, events) that affect an element of society or ecosystems and are thus a potential priority for providing climate information. For instance, the heat index used by the U.S. National Oceanic and Atmospheric Administration (NOAA HI) for issuing heat warnings is a CID index that can be associated with adverse human health impacts due to heat stress (see Figure TS.6). Depending on system tolerance, CIDs and their changes can be detrimental (i.e., hazards in the risk framing), beneficial, neutral, or a mixture of each across interacting system elements, regions and sectors (aligning with WGII Sectoral Chapters 2–8). Each sector is affected by multiple CIDs, and each CID affects multiple sectors. Climate change has already altered CID profiles and resulted in shifting magnitude, frequency, duration, seasonality and spatial extent of associated indices ( ''high confidence'' ) (see regional details in Section TS.4.3). Links to chapters 12.1–12.4, Table 12.1, Table 12.2, Annex VI

Many global- and regional-scale CIDs, including extremes, have a direct relation to global warming levels (GWLs) and can thus inform the hazard component of ‘Representative Key Risks’ and ‘Reasons for Concern’ assessed by AR6 WGII. These include heat, cold, wet and dry hazards, both mean and extremes; cryospheric hazards (snow cover, ice extent, permafrost) and oceanic hazards (marine heatwaves) ( ''high confidence'' ) (Figure TS.6). Establishing links between specific GWLs with tipping points and irreversible behaviour is challenging due to model uncertainties and lack of observations, but their occurrence cannot be excluded, and their likelihood of occurrence generally increases at greater warming levels (Box TS.1, Section TS.9). Links to chapters 11.2.4, Box 11.2, Cross-Chapter Boxes 11.1 and 12.1

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'''Cross-Section Box TS.1: Global Surface Temperature Change'''

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This box synthesizes the outcomes of the assessment of past, current and future global surface temperature. Global mean surface temperature (GMST) and global surface air temperature (GSAT) are the two primary metrics of global surface temperature used to estimate global warming in IPCC reports. GMST merges sea surface temperature (SST) over the ocean and 2 m air temperature over land and sea ice areas and is used in most paleo, historical and present-day observational estimates. The GSAT metric is 2 m air temperature over all surfaces and is the diagnostic generally used from climate models. Changes in GMST and GSAT over time differ by at most 10% in either direction ( ''high confidence'' ), but conflicting lines of evidence from models and direct observations, combined with limitations in theoretical understanding, lead to ''low confidence'' in the sign of any difference in long-term trend. Therefore, long-term changes in GMST/GSAT are presently assessed to be identical, with expanded uncertainty in GSAT estimates. Hence the term global surface temperature is used in reference to both quantities in the text of the TS and SPM. Links to chapters Cross-Chapter Box 2.3

'''Global surface temperature has increased by 0.99 [0.84 to 1.10] °C from 1850–1900 to the first two decades of the 21st century (2001–2020) and by 1.09 [0.95 to 1.20] °C from 1850–1900 to 2011–2020. Temperatures as high as during the most recent decade (2011–2020) exceed the warmest centennial-scale range reconstructed for the present interglacial, around 6500 years ago [0.2°C to 1°C] ( medium confidence ). The next most recent warm period was about 125,000 years ago during the last interglacial when the multi-centennial temperature range [0.5°C to 1.5°C] encompasses the 2011–2020 values ( medium confidence ). The likely range of human-induced change in global surface temperature in 2010–2019 relative to 1850–1900 is 0.8°C to 1.3°C, with a central estimate of 1.07°C, encompassing the best estimate of observed warming for that period, which is 1.06°C with a very likely range of [0.88°C to 1.21°C], while The likely range of the change attributable to natural forcing is only –0.1°C to +0.1°C.''' '''Compared to 1850–1900, average global surface temperature over the period 2081–2100 is very likely to be higher by [1.0°C to 1.8°C] in the low CO 2 emissions scenario SSP1-1.9 and by [3.3°C to 5.7°C] in the high CO 2 emissions scenario SSP5-8.5. In all scenarios assessed here except SSP5-8.5, the central estimate of 20-year averaged global surface warming crossing the 1.5°C level lies in the early 2030s, which is in the early part of The likely range (2030–2052) assessed in SR1.5. It is more likely than not that under SSP1-1.9, global surface temperature relative to 1850–1900 will remain below 1.6°C throughout the 21st century, implying a potential temporary overshoot of 1.5°C global warming of no more than 0.1°C. Global surface temperature in any individual year could exceed 1.5°C relative to 1850–1900 by 2030 with a likelihood between 40% and 60% across the scenarios considered here ( medium confidence ). A 2°C increase in global surface temperature relative to 1850–1900 will be crossed under SSP5-8.5 but is extremely unlikely to be crossed under SSP1-1.9. Periods of reduced and increased global surface temperature trends at decadal time scales will continue to occur in the 21st century ( very high confidence ). The effect of strong mitigation on 20-year global surface temperature trends would be likely to emerge during the near term (2021–2040), assuming no major volcanic eruptions occur. (Figure TS.8, Cross-Section Box TS.1, Figure 1) Links to chapters 2.3, 3.3, 4.3, 4.4, 4.5, 4.6, 7.3''' '''Surface Temperature History'''
Dataset innovations, particularly more comprehensive representation of polar regions, and the availability of new datasets have led to an assessment of increased global surface temperature change relative to the directly equivalent estimates reported in AR5. The contribution of changes in observational understanding alone between AR5 and AR6 in assessing temperature changes from 1850–1900 to 1986–2005 is estimated at 0.08 [–0.01 to 0.12] °C. Global surface temperature increased from 1850–1900 to 1995–2014 by 0.85 [0.69 to 0.95] °C, between 1850–1900 and the first two decades of the 21st century (2001–2020) by 0.99 [0.84 to 1.20] °C, and to the most recent decade (2011–2020) by 1.09 [0.95 to 1.20] °C. Each of the last four decades has in turn been warmer than any decade that preceded it since 1850. Temperatures have increased faster over land than over the ocean since 1850–1900, with warming to 2011–2020 of 1.59 [1.34 to 1.83] °C over land and 0.88 [0.68 to 1.01] °C over the ocean. Links to chapters 2.3.1, Cross-Chapter Box 2.3

Global surface temperature during the period 1850–1900 is used as an approximation for pre-industrial conditions for consistency with AR5 and AR6 Special Reports, whilst recognizing that radiative forcings have a baseline of 1750 for the start of anthropogenic influences. It is ''likely'' that there was a net anthropogenic forcing of 0.0–0.3 Wm <sup>–2</sup> in 1850–1900 relative to 1750 ( ''medium confidence'' ), and from the period around 1750 to 1850–1900, there was a change in global surface temperature of around 0.1°C ( ''likely'' range –0.1 to +0.3°C, ''medium confidence'' ), with an anthropogenic component of 0.0°C to 0.2°C ( ''likely'' range '', medium confidence'' ). Links to chapters Cross-Chapter Box 1.2, 7.3.5

Global surface temperature has evolved over geological time (Figure TS.1, Box TS.2). Beginning approximately 6500 years ago, global surface temperature generally decreased, culminating in the coldest multi-century interval of the post-glacial period (since roughly 7000 years ago), which occurred between around 1450 and 1850 ( ''high confidence'' ). Over the last 50 years, global surface temperature has increased at an observed rate unprecedented in at least the last two thousand years ( ''high confidence'' ). Temperatures as high as during the most recent decade (2011–2020) exceed the warmest centennial-scale range reconstructed for the present interglacial, around 6500 years ago [0.2°C to 1°C] ( ''medium confidence'' ). The next most recent warm period was about 125,000 years ago during the Last Interglacial when the multi-centennial temperature range [0.5°C to 1.5°C] encompasses the 2011–2020 values ( ''medium confidence'' ) (Cross-Section Box TS.1, Figure 1). During the mid-Pliocene Warm Period, around 3.3–3.0 million years ago, global surface temperature was 2.5°C to 4°C warmer ( ''medium confidence'' ). Links to chapters 2.3.1, Cross-Chapter Box 2.1 and 2.4

'''Current Warming'''
There is ''very high confidence'' that the CMIP6 model ensemble reproduces observed global surface temperature trends and variability since 1850 with errors small enough to allow for detection and attribution of human-induced warming. The CMIP6 multi-model mean global surface warming between 1850–1900 and 2010–2019 is close to the best estimate of observed warming, though some CMIP6 models simulate a warming that is outside the assessed ''very likely'' observed range. Links to chapters 3.3.1

The ''likely'' range of human-induced change in global surface temperature in 2010–2019 relative to 1850–1900 is 0.8°C to 1.3°C, with a central estimate of 1.07°C (Figure Cross-Section Box TS.1, Figure 1), encompassing the best estimate of observed warming for that period, which is 1.06°C with a ''very likely'' range of [0.88°C to 1.21°C], while the ''likely'' range of the change attributable to natural forcing is only –0.1°C to +0.1°C. This assessment is consistent with an estimate of the human-induced global surface temperature rise based on assessed ranges of perturbations to the top of the atmosphere (effective radiative forcing) and with metrics of feedbacks of the climate response (equilibrium climate sensitivity and the transient climate response). Over the same period, well-mixed greenhouse gas forcing ''likely'' warmed global surface temperature by 1.0°C to 2.0°C, while aerosols and other anthropogenic forcings ''likely'' cooled global surface temperature by 0.0°C to 0.8°C. Links to chapters 2.3.1, 3.3.1, 7.3.5, Cross-Chapter Box 7.1

The observed slower increase in global surface temperature (relative to preceding and following periods) in the 1998–2012 period, sometimes referred to as ‘the hiatus’, was temporary ( ''very high confidence'' ). The increase in global surface temperature during the 1998–2012 period is also greater in the data sets used in the AR6 assessment than in those available at the time of AR5. Using these updated observational data sets and a like-for-like consistent comparison of simulated and observed global surface temperature, all observed estimates of the 1998–2012 trend lie within the ''very likely'' range of CMIP6 trends. Furthermore, the heating of the climate system continued during this period, as reflected in the continued warming of the global ocean ( ''very high confidence'' ) and in the continued rise of hot extremes over land ( ''medium confidence'' ). Since 2012, global surface temperature has risen strongly, with the past five years (2016–2020) being the hottest five-year period between 1850 and 2020 ( ''high confidence'' ). Links to chapters 2.3.1, 3.3.1, 3.5.1, Cross-Chapter Box 3.1

'''Future Changes in Global Surface Temperature'''
The AR6 assessment of future change in global surface temperature is, for the first time in an IPCC report, explicitly constructed by combining new projections for the SSP scenarios with observational constraints based on past simulated warming as well as the AR6-updated assessment of equilibrium climate sensitivity and transient climate response. In addition, climate forecasts initialized from the observed climate state have been used for the period 2019–2028. The inclusion of additional lines of evidence has reduced the assessed uncertainty ranges for each scenario (Cross-Section Box TS.1, Figure 1). Links to chapters 4.3.1, 4.3.4, Box 4.1, 7.5

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'''Cross-Section Box TS.1, Figure 1 |'''

'''Earth’s surface temperature history and future with key findings annotated within each panel.''' ''The intent of this figure is to show global surface temperature observed changes from the Holocene to now, and projected changes.'' '''(a)''' Global surface temperature over the Holocene divided into three time scales: (i) 12,000 to 1000 years ago (10,000 BCE to 1000 CE) in 100-year time steps, (ii) 1000 to 1900 CE, 10-year smooth, and (iii) 1900 to 2020 CE (mean of four datasets in panel c). Bold lines show the median of the multi-method reconstruction, with 5% and 95% percentiles of the ensemble members (thin lines). Vertical bars are 5–95th percentile ranges of estimated global surface temperature for the Last Interglacial and mid-Holocene ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1|Section 2.3.1.1]] ). All temperatures are relative to 1850–1900. '''(b)''' Spatially resolved trends (°C per decade) for (upper map) HadCRUTv5 over 1981–2020, and (lower map, total change) multi-model mean projected changes from 1995–2014 to 2081–2100 in the SST3-7.0 scenario. Observed trends have been calculated where data are present in both the first and last decade and for at least 70% of all years within the period using ordinary least squares. Significance is assessed with autoregressive AR(1) model correction and denoted by stippling. Hatched areas in the lower map show areas of conflicting model evidence on significance of changes. '''(c)''' Temperature from instrumental data for 1850–2020, including annually resolved averages for the four global surface temperature datasets assessed in [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1.3|Section 2.3.1.1.3]] (see text for references). The grey shading shows the uncertainty associated with the HadCRUTv5 estimate. All temperatures are relative to the 1850–1900 reference period. '''(d)''' Recent past and 2015–2050 evolution of annual mean global surface temperature change relative to 1850–1900, from HadCRUTv5 (black), Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations (up to 2014, in grey, ensemble mean solid, 5% and 95% percentiles dashed, individual models thin), and CMIP6 projections under scenario SSP2-4.5, from four models that have an equilibrium climate sensitivity near the assessed central value (thick yellow). Solid thin coloured lines show the assessed central estimate of 20-year change in global surface temperature for 2015–2050 under three scenarios, and dashed thin coloured lines the corresponding 5% and 95% quantiles. '''(e)''' Assessed projected change in 20-year running mean global surface temperature for five scenarios (central estimate solid, ''very likely'' range shaded for SSP1-2.6 and SSP3-7.0), relative to 1995–2014 (left y-axis) and 1850–1900 (right y-axis). The y-axis on the right-hand side is shifted upward by 0.85°C, the central estimate of the observed warming for 1995–2014, relative to 1850–1900. The right y-axis in (e) is the same as the y-axis in (d). Links to chapters 2.3, 4.3, 4.4
During the near term (2021–2040), a 1.5°C increase in global surface temperature, relative to 1850–1900, is ''very likely'' to occur in scenario SSP5-8.5, ''likely'' to occur in scenarios SSP2-4.5 and SSP3-7.0, and ''more likely than not'' to occur in scenarios SSP1-1.9 and SSP1-2.6. The time of crossing a warming level is defined here as the midpoint of the first 20-year period during which the average global surface temperature exceeds the level. In all scenarios assessed here except SSP5-8.5, the central estimate of crossing the 1.5°C level lies in the early 2030s. This is in the early part of the ''likely'' range (2030 '''–''' 2052) assessed in SR1.5, which assumed continuation of the then-current warming rate; this rate has been confirmed in the AR6. Roughly half of this difference arises from a larger historical warming diagnosed in AR6. The other half arises because for central estimates of climate sensitivity, most scenarios show stronger warming over the near term than was estimated as ‘current’ in SR1.5 ( ''medium confidence'' ). When considering scenarios similar to SSP1-1.9 instead of linear extrapolation, the SR1.5 estimate of when 1.5°C global warming is crossed is close to the central estimate reported here. (Cross-Section Box TS.1, Table 1) Links to chapters 2.3.1, Cross-Chapter Box 2.3, 3.3.1, 4.3.4, Box 4.1

It is ''more likely than not'' that under SSP1-1.9, global surface temperature relative to 1850–1900 will remain below 1.6°C throughout the 21st century, implying a potential temporary overshoot of 1.5°C global warming of no more than 0.1°C. If climate sensitivity lies near the lower end of the assessed ''very likely'' range, crossing the 1.5°C warming level is avoided in scenarios SSP1-1.9 and SSP1-2.6 ( ''medium confidence'' ). Global surface temperature in any individual year, in contrast to the 20-year average, could by 2030 exceed 1.5°C relative to 1850–1900 with a likelihood between 40% and 60%, across the scenarios considered here ( ''medium confidence'' ). (Cross-Section Box TS.1, Table 1) Links to chapters 4.3.4, 4.4.1, Box 4.1, 7.5

During the 21st century, a 2°C increase in global surface temperature relative to 1850–1900 will be crossed under SSP5-8.5 and SSP3-7.0, is ''extremely likely'' to be crossed under SSP2-4.5, but is ''unlikely'' to be crossed under SSP1-2.6 and ''extremely unlikely'' to be crossed under SSP1-1.9. For the mid-term period 2041–2060, this 2°C global warming level is ''very likely'' to be crossed under SSP5-8.5, ''likely'' to be crossed under SSP3-7.0, and ''more likely than not'' to be crossed under SSP2-4.5. (Cross-Section Box TS.1, Table 1) Links to chapters 4.3.4

Events of reduced and increased global surface temperature trends at decadal time scales will continue to occur in the 21st century but will not affect the centennial-scale warming ( ''very high confidence'' ). If strong mitigation is applied from 2020 onward as reflected in SSP1-1.9, its effect on 20-year trends in global surface temperature would ''likely'' emerge during the near term (2021–2040), measured against an assumed non-mitigation scenario such as SSP3-7.0 or SSP5-8.5. All statements about crossing the 1.5°C level assume that no major volcanic eruption occurs during the near term (Cross-Section Box TS.1, Table 1). Links to chapters 2.3.1, Cross-Chapter Box 2.3, 4.3.4, 4.4.1, 4.6.3, Box 4.1

Compared to 1850–1900, average global surface temperature over the period 2081–2100 is ''very'' ''likely'' to be higher by [1.0°C to 1.8°C] in the low CO <sub>2</sub> emissions scenario SSP1-1.9 and by [3.3°C to 5.7°C] in the high CO <sub>2</sub> emissions scenario SSP5-8.5. For the scenarios SSP1-2.6, SSP2-4.5, and SSP3-7.0, the corresponding ''very'' ''likely'' ranges are [1.3°C to 2.4°C], [2.1°C to 3.5°C], and [2.8°C to 4.6°C], respectively. The uncertainty ranges for the period 2081–2100 continue to be dominated by the uncertainty in equilibrium climate sensitivity and transient climate response ( ''very high confidence'' ) (Cross-Section Box TS.1, Table 1). Links to chapters 4.3.1, 4.3.4, 4.4.1, 7.5

The CMIP6 models project a wider range of global surface temperature change than the assessed range ( ''high confidence'' ); furthermore, the CMIP6 global surface temperature increase tends to be larger than that in CMIP5 ( ''very high confidence'' ). Links to chapters 4.3.1, 4.3.4, 4.6.2, 7.5.6

'''Cross-Section Box TS.1, Table 1 |'''

'''Assessment results for 20-year averaged change in global surface temperature based on multiple lines of evidence.''' The change is displayed in °C relative to the 1850–1900 reference period for selected time periods (first three rows), and as the first 20-year period during which the average global surface temperature change exceeds the specified level relative to the period 1850–1900 (last four rows). The entries give both the central estimate and, in parentheses, the ''very likely'' (5–95%) range. An entry n.c. means that the global warming level is not crossed during the period 2021–2100.
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