IPCC:AR6/WGI/Chapter-3 - Revision history

Laura: /* References */

2026-06-24T09:55:36Z

References

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	Zuo, M., T. Zhou, and W. Man, 2019: Hydroclimate Responses over Global Monsoon Regions Following Volcanic Eruptions at Different Latitudes. ''Journal of Climate'' , '''32(14)''' , 4367–4385, doi: [https://dx.doi.org/10.1175/jcli-d-18-0707.1 10.1175/jcli-d-18-0707.1] .		Zuo, M., T. Zhou, and W. Man, 2019: Hydroclimate Responses over Global Monsoon Regions Following Volcanic Eruptions at Different Latitudes. ''Journal of Climate'' , '''32(14)''' , 4367–4385, doi: [https://dx.doi.org/10.1175/jcli-d-18-0707.1 10.1175/jcli-d-18-0707.1] .

	~~-----~~		== Footnotes ==

	<div id="footnote-002" class="_idFootnote"></div>		<div id="footnote-002" class="_idFootnote"></div>

Laura: /* 3.8.2.1 Integrative Measures of Model Performance */

2026-06-22T12:17:20Z

3.8.2.1 Integrative Measures of Model Performance

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	[[File:010a3587f1a6573bfacb264de3bea8fd IPCC_AR6_WGI_Figure_3_43.png]] Figure 3.43 \| '''Centred pattern correlations between models and observations for the annual mean climatology over the period 1980–1999.''' Results are shown for individual CMIP3 (green), CMIP5 (blue) and CMIP6 (red) models (one ensemble member from each model is used) as short lines, along with the corresponding multi-model ensemble averages (long lines). 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, JRA-55, ESACCI-SST, ERA5, ERA5, ERA5, ERA5, ERA5, ERA5, AIRS, ERA5). In addition, the correlation between the primary reference and additional observational datasets (from left to right: NCEP, GHCN, -, -, -, -, ERA5, HadISST, NCEP, NCEP, NCEP, NCEP, NCEP, NCEP, ERA5, NCEP) 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 4° in longitude and 5° in latitude. Figure is updated and expanded from [[#Bock--2020\|Bock et al. (2020)]] , their Figure 7 CC BY 4.0 [https://dx.doi.org/10.1038/nature19082 https://creativecommons.org/licenses/by/4.0/] . Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).		[[File:010a3587f1a6573bfacb264de3bea8fd IPCC_AR6_WGI_Figure_3_43.png]]

			Figure 3.43 \| '''Centred pattern correlations between models and observations for the annual mean climatology over the period 1980–1999.''' Results are shown for individual CMIP3 (green), CMIP5 (blue) and CMIP6 (red) models (one ensemble member from each model is used) as short lines, along with the corresponding multi-model ensemble averages (long lines). 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, JRA-55, ESACCI-SST, ERA5, ERA5, ERA5, ERA5, ERA5, ERA5, AIRS, ERA5). In addition, the correlation between the primary reference and additional observational datasets (from left to right: NCEP, GHCN, -, -, -, -, ERA5, HadISST, NCEP, NCEP, NCEP, NCEP, NCEP, NCEP, ERA5, NCEP) 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 4° in longitude and 5° in latitude. Figure is updated and expanded from [[#Bock--2020\|Bock et al. (2020)]] , their Figure 7 CC BY 4.0 [https://dx.doi.org/10.1038/nature19082 https://creativecommons.org/licenses/by/4.0/] . Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

	In addition to the multivariate assessments of simulations of the recent historical period, simulations of selected periods of the Earth’s more distant history can be used to benchmark climate models by exposing them to climate forcings that are radically different from the present and recent past ([[#Harrison--2015\|Harrison et al., 2015]] , 2016; [[#Kageyama--2018\|Kageyama et al., 2018]] ; [[#Tierney--2020a\|Tierney et al., 2020a]]). These time periods provide an out-of-sample test of models because they are not in general used in the process of model development. They encompass a range of climate drivers, such as volcanic and solar forcing for the Last Millennium, orbital forcing for the mid-Holocene and Last Interglacial, and changes in greenhouse gases and ice sheets for the LGM, mid-Pliocene Warm Period, and early Eocene (Sections 2.2 and 2.3). These drivers led to climate changes, including in surface temperature ([[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1\|Section 2.3.1.1]]) and the hydrological cycle ([[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.1\|Section 2.3.1.3.1]]), which are described by paleoclimate proxies that have been synthesized to support evaluations of models on a global and regional scale. However, the more sparse, indirect, and regionally incomplete climate information available from paleo-archives motivates a different form of the multivariate analysis of simulations covering these periods versus the equivalent for the historical period, as described below.		In addition to the multivariate assessments of simulations of the recent historical period, simulations of selected periods of the Earth’s more distant history can be used to benchmark climate models by exposing them to climate forcings that are radically different from the present and recent past ([[#Harrison--2015\|Harrison et al., 2015]] , 2016; [[#Kageyama--2018\|Kageyama et al., 2018]] ; [[#Tierney--2020a\|Tierney et al., 2020a]]). These time periods provide an out-of-sample test of models because they are not in general used in the process of model development. They encompass a range of climate drivers, such as volcanic and solar forcing for the Last Millennium, orbital forcing for the mid-Holocene and Last Interglacial, and changes in greenhouse gases and ice sheets for the LGM, mid-Pliocene Warm Period, and early Eocene (Sections 2.2 and 2.3). These drivers led to climate changes, including in surface temperature ([[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1\|Section 2.3.1.1]]) and the hydrological cycle ([[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.1\|Section 2.3.1.3.1]]), which are described by paleoclimate proxies that have been synthesized to support evaluations of models on a global and regional scale. However, the more sparse, indirect, and regionally incomplete climate information available from paleo-archives motivates a different form of the multivariate analysis of simulations covering these periods versus the equivalent for the historical period, as described below.
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	<span id="process-representation-in-different-classes-of-models"></span>		<span id="process-representation-in-different-classes-of-models"></span>

	==== 3.8.2.2 Process Representation in Different Classes of Models ====		==== 3.8.2.2 Process Representation in Different Classes of Models ====

Laura: /* 3.3.2.4 Streamflow */

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3.3.2.4 Streamflow

Laura: /* 3.3.1.2.2 Stratospheric temperature */

2026-06-22T12:13:41Z

3.3.1.2.2 Stratospheric temperature

Laura: /* Human Influence on Modes of Climate Variability */

2026-06-22T12:11:33Z

Human Influence on Modes of Climate Variability

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	'''It is''' very likely '''that human influence has contributed to the observed trend towards the positive phase of the Southern Annular Mode (SAM) since the 1970s and to the associated~~''' '''~~strengthening and southward shift of the Southern~~''' '''~~Hemispheric extratropical jet in austral summer.''' The influence of ozone forcing on the SAM trend has been small since the early 2000s compared to earlier decades, contributing to a weaker SAM trend observed over 2000–2019 (''medium confidence''). Climate models reproduce the summertime SAM trend well, with CMIP6 models outperforming CMIP5 models (''medium confidence''). By contrast, the cause of the Northern Annular Mode (NAM) trend towards its positive phase since the 1960s and associated northward shifts of the Northern Hemispheric extratropical jet and storm track in boreal winter is not well understood. Models reproduce the observed spatial features and variance of the SAM and NAM very well (''high confidence''). {3.3.3, 3.7.1, 3.7.2}		'''It is ''very likely'' that human influence has contributed to the observed trend towards the positive phase of the Southern Annular Mode (SAM) since the 1970s and to the associated strengthening and southward shift of the Southern Hemispheric extratropical jet in austral summer.''' The influence of ozone forcing on the SAM trend has been small since the early 2000s compared to earlier decades, contributing to a weaker SAM trend observed over 2000–2019 (''medium confidence''). Climate models reproduce the summertime SAM trend well, with CMIP6 models outperforming CMIP5 models (''medium confidence''). By contrast, the cause of the Northern Annular Mode (NAM) trend towards its positive phase since the 1960s and associated northward shifts of the Northern Hemispheric extratropical jet and storm track in boreal winter is not well understood. Models reproduce the observed spatial features and variance of the SAM and NAM very well (''high confidence''). {3.3.3, 3.7.1, 3.7.2}

	'''Human influence has not affected the principal tropical modes~~''' '''~~of interannual climate variability or their associated regional~~''' '''~~teleconnections beyond the range of internal variability''~~' (~~high confidence ''').''' Further assessment since AR5 confirms that climate and Earth system models are able to reproduce most aspects of the spatial structure and variance of the El Niño–Southern Oscillation and Indian Ocean Basin and Dipole modes (''medium confidence''). However, despite a slight improvement in CMIP6, some underlying processes are still poorly represented. In the Tropical Atlantic basin, which contains the Atlantic Zonal and Meridional modes, major biases in modelled mean state and variability remain. {3.7.3 to 3.7.5}		'''Human influence has not affected the principal tropical modes of interannual climate variability or their associated regional teleconnections beyond the range of internal variability (''high confidence'').''' Further assessment since AR5 confirms that climate and Earth system models are able to reproduce most aspects of the spatial structure and variance of the El Niño–Southern Oscillation and Indian Ocean Basin and Dipole modes (''medium confidence''). However, despite a slight improvement in CMIP6, some underlying processes are still poorly represented. In the Tropical Atlantic basin, which contains the Atlantic Zonal and Meridional modes, major biases in modelled mean state and variability remain. {3.7.3 to 3.7.5}

	'''There is''' medium confidence '''that anthropogenic and volcanic aerosols contributed to observed changes in the Atlantic Multi-decadal Variability (AMV) index and associated regional teleconnections since the 1960s, but there is''' low confidence '''in the magnitude of this influence.''' There is ''high confidence'' that internal variability is the main driver of Pacific Decadal Variability (PDV) observed since pre-industrial times, despite some modelling evidence for potential human influence. Uncertainties remain in quantification of the human influence on AMV and PDV due to brevity of the observational records, limited model performance in reproducing related sea surface temperature (SST) anomalies despite improvements from CMIP5 to CMIP6 (''medium confidence''), and limited process understanding of their key drivers. {3.7.6, 3.7.7}		'''There is ''medium confidence'' that anthropogenic and volcanic aerosols contributed to observed changes in the Atlantic Multi-decadal Variability (AMV) index and associated regional teleconnections since the 1960s, but there is ''low confidence'' in the magnitude of this influence.''' There is ''high confidence'' that internal variability is the main driver of Pacific Decadal Variability (PDV) observed since pre-industrial times, despite some modelling evidence for potential human influence. Uncertainties remain in quantification of the human influence on AMV and PDV due to brevity of the observational records, limited model performance in reproducing related sea surface temperature (SST) anomalies despite improvements from CMIP5 to CMIP6 (''medium confidence''), and limited process understanding of their key drivers. {3.7.6, 3.7.7}

	<div id="3.1" class="h1-container"></div>		<div id="3.1" class="h1-container"></div>

	<span id="scope-and-overview"></span>		<span id="scope-and-overview"></span>

	== 3.1 Scope and Overview ==		== 3.1 Scope and Overview ==

Laura: /* Human Influence on the Biosphere */

2026-06-22T12:10:27Z

Human Influence on the Biosphere

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	'''The main driver of the observed increase in the amplitude of the seasonal cycle of atmospheric CO~~'''~~ <sub>2</sub> ~~'''~~is enhanced fertilization of plant growth by the increasing concentration of atmospheric CO~~'''~~ <sub>2</sub> (''medium confidence'').''' However, there is only ''low confidence'' that this CO <sub>2</sub> fertilization has also been the main driver of observed greening because land management is the dominating factor in some regions. Earth system models simulate globally averaged land carbon sinks within the range of observation-based estimates (''high confidence''), but global-scale agreement masks large regional disagreements. {3.6.1}		'''The main driver of the observed increase in the amplitude of the seasonal cycle of atmospheric CO <sub>2</sub> is enhanced fertilization of plant growth by the increasing concentration of atmospheric CO <sub>2</sub> (''medium confidence'').''' However, there is only ''low confidence'' that this CO <sub>2</sub> fertilization has also been the main driver of observed greening because land management is the dominating factor in some regions. Earth system models simulate globally averaged land carbon sinks within the range of observation-based estimates (''high confidence''), but global-scale agreement masks large regional disagreements. {3.6.1}

	'''It is ''virtually certain'' that the uptake of anthropogenic CO''' <sub>2</sub> '''was the main driver of the observed acidification of the global surface open ocean.''' The observed increase in CO <sub>2</sub> concentration in the subtropical and equatorial North Atlantic since 2000 is ''likely'' associated in part with an increase in ocean temperature, a response that is consistent with the expected weakening of the ocean carbon sink with warming. Consistent with AR5 there is ''medium confidence'' that deoxygenation in the upper ocean is due in part to human influence. There is ''high confidence'' that Earth system models simulate a realistic time evolution of the global mean ocean carbon sink. {3.6.2}		'''It is ''virtually certain'' that the uptake of anthropogenic CO''' <sub>2</sub> '''was the main driver of the observed acidification of the global surface open ocean.''' The observed increase in CO <sub>2</sub> concentration in the subtropical and equatorial North Atlantic since 2000 is ''likely'' associated in part with an increase in ocean temperature, a response that is consistent with the expected weakening of the ocean carbon sink with warming. Consistent with AR5 there is ''medium confidence'' that deoxygenation in the upper ocean is due in part to human influence. There is ''high confidence'' that Earth system models simulate a realistic time evolution of the global mean ocean carbon sink. {3.6.2}

Laura: /* Human Influence on the Biosphere */

2026-06-22T12:09:50Z

Human Influence on the Biosphere

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	'''The main driver of the observed increase in the amplitude of the seasonal cycle of atmospheric CO''' <sub>2</sub> '''is enhanced fertilization of plant growth by the increasing concentration of atmospheric CO''' <sub>2</sub> ~~'''~~(''' medium confidence~~) '~~''.''' However, there is only ''low confidence'' that this CO <sub>2</sub> fertilization has also been the main driver of observed greening because land management is the dominating factor in some regions. Earth system models simulate globally averaged land carbon sinks within the range of observation-based estimates (''high confidence''), but global-scale agreement masks large regional disagreements. {3.6.1}		'''The main driver of the observed increase in the amplitude of the seasonal cycle of atmospheric CO''' <sub>2</sub> '''is enhanced fertilization of plant growth by the increasing concentration of atmospheric CO''' <sub>2</sub> (''medium confidence'').''' However, there is only ''low confidence'' that this CO <sub>2</sub> fertilization has also been the main driver of observed greening because land management is the dominating factor in some regions. Earth system models simulate globally averaged land carbon sinks within the range of observation-based estimates (''high confidence''), but global-scale agreement masks large regional disagreements. {3.6.1}

	'''It is''' virtually certain '''that the uptake of anthropogenic CO''' <sub>2</sub> '''was the main driver of the observed acidification of the global~~''' '''~~surface open ocean.''' The observed increase in CO <sub>2</sub> concentration in the subtropical and equatorial North Atlantic since 2000 is ''likely'' associated in part with an increase in ocean temperature, a response that is consistent with the expected weakening of the ocean carbon sink with warming. Consistent with AR5 there is ''medium confidence'' that deoxygenation in the upper ocean is due in part to human influence. There is ''high confidence'' that Earth system models simulate a realistic time evolution of the global mean ocean carbon sink. {3.6.2}		'''It is ''virtually certain'' that the uptake of anthropogenic CO''' <sub>2</sub> '''was the main driver of the observed acidification of the global surface open ocean.''' The observed increase in CO <sub>2</sub> concentration in the subtropical and equatorial North Atlantic since 2000 is ''likely'' associated in part with an increase in ocean temperature, a response that is consistent with the expected weakening of the ocean carbon sink with warming. Consistent with AR5 there is ''medium confidence'' that deoxygenation in the upper ocean is due in part to human influence. There is ''high confidence'' that Earth system models simulate a realistic time evolution of the global mean ocean carbon sink. {3.6.2}

	<span id="human-influence-on-modes-of-climate-variability"></span>		<span id="human-influence-on-modes-of-climate-variability"></span>

	=== Human Influence on Modes of Climate Variability ===		=== Human Influence on Modes of Climate Variability ===

Laura: /* Human Influence on the Ocean */

2026-06-22T12:09:12Z

Human Influence on the Ocean

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	'''It is''' extremely likely '''that human influence was the main driver~~''' '''~~of the ocean heat content increase observed since the 1970s, which extends into the deeper ocean''~~' (~~very high confidence ''').''' Since AR5, there is improved consistency between recent observed estimates and model simulations of changes in upper (<700 m) ocean heat content, when accounting for both natural and anthropogenic forcings. Updated observations and model simulations show that warming extends throughout the entire water column (''high confidence''), with CMIP6 models simulating 58% of industrial-era heat uptake (1850–2014) in the upper layer (0–700 m), 21% in the intermediate layer (700–2000 m) and 22% in the deep layer (>2000 m). The structure and magnitude of multi-model mean ocean temperature biases have not changed substantially between CMIP5 and CMIP6 (''medium confidence''). {3.5.1}		'''It is ''extremely likely'' that human influence was the main driver of the ocean heat content increase observed since the 1970s, which extends into the deeper ocean (''very high confidence'').''' Since AR5, there is improved consistency between recent observed estimates and model simulations of changes in upper (<700 m) ocean heat content, when accounting for both natural and anthropogenic forcings. Updated observations and model simulations show that warming extends throughout the entire water column (''high confidence''), with CMIP6 models simulating 58% of industrial-era heat uptake (1850–2014) in the upper layer (0–700 m), 21% in the intermediate layer (700–2000 m) and 22% in the deep layer (>2000 m). The structure and magnitude of multi-model mean ocean temperature biases have not changed substantially between CMIP5 and CMIP6 (''medium confidence''). {3.5.1}

	'''It is''' extremely likely '''that human influence has contributed~~''' '''~~to observed near-surface and subsurface ocean salinity~~''' '''~~changes since the mid-20th century.''' The associated pattern of change corresponds to fresh regions becoming fresher and salty regions becoming saltier (''high confidence''). Changes to the coincident atmospheric water cycle and ocean-atmosphere fluxes (evaporation and precipitation) are the primary drivers of the observed basin-scale salinity changes (''high confidence''). The observed depth-integrated basin-scale salinity changes have been attributed to human influence, with CMIP5 and CMIP6 models able to reproduce these patterns only in simulations that include greenhouse gas increases (''medium confidence''). The basin-scale changes are consistent across models and intensify through the historical period (''high confidence''). The structure of the biases in the multi-model mean has not changed substantially between CMIP5 and CMIP6 (''medium confidence''). {3.5.2}		'''It is ''extremely likely'' that human influence has contributed to observed near-surface and subsurface ocean salinity changes since the mid-20th century.''' The associated pattern of change corresponds to fresh regions becoming fresher and salty regions becoming saltier (''high confidence''). Changes to the coincident atmospheric water cycle and ocean-atmosphere fluxes (evaporation and precipitation) are the primary drivers of the observed basin-scale salinity changes (''high confidence''). The observed depth-integrated basin-scale salinity changes have been attributed to human influence, with CMIP5 and CMIP6 models able to reproduce these patterns only in simulations that include greenhouse gas increases (''medium confidence''). The basin-scale changes are consistent across models and intensify through the historical period (''high confidence''). The structure of the biases in the multi-model mean has not changed substantially between CMIP5 and CMIP6 (''medium confidence''). {3.5.2}

	'''Combining the attributable contributions from glaciers,~~''' '''i''' '''ce~~-~~she''' '''et~~ surface mass balance and thermal expansion, it is''' very likely '''that human influence was the main driver of the observed global mean sea level rise since at least 1971.''' Since AR5, studies have shown that simulations that exclude anthropogenic greenhouse gases are unable to capture the sea level rise due to thermal expansion (thermosteric) during the historical period and that model simulations that include all forcings (anthropogenic and natural) most closely match observed estimates. It is ''very likely'' that human influence was the main driver of the observed global mean thermosteric sea level increase since 1970. {3.5.3, 3.5.1, 3.4.3}		'''Combining the attributable contributions from glaciers, ice-sheet surface mass balance and thermal expansion, it is ''very likely'' that human influence was the main driver of the observed global mean sea level rise since at least 1971.''' Since AR5, studies have shown that simulations that exclude anthropogenic greenhouse gases are unable to capture the sea level rise due to thermal expansion (thermosteric) during the historical period and that model simulations that include all forcings (anthropogenic and natural) most closely match observed estimates. It is ''very likely'' that human influence was the main driver of the observed global mean thermosteric sea level increase since 1970. {3.5.3, 3.5.1, 3.4.3}

	'''While observations show that the Atlantic Meridional Overturning Circulation (AMOC) has weakened from the mid-2000s to the mid-2010s''~~' (~~high confidence~~) '~~''and the Southern Ocean upper overturning cell has strengthened since the 1990s''~~' (~~low confidence '''), observational records are too short to determine the relative contributions of internal variability, natural forcing, and anthropogenic forcing to these changes''~~' (~~high confidence ''').''' No changes in Antarctic Circumpolar Current transport or meridional position have been observed. The mean zonal and overturning circulations of the Southern Ocean and the mean overturning circulation of the North Atlantic (the Atlantic Meridional Overturning Circulation, AMOC) are broadly reproduced by CMIP5 and CMIP6 models. However, biases are apparent in the modelled circulation strengths (''high confidence'') and their variability (''medium confidence''). {3.5.4}		'''While observations show that the Atlantic Meridional Overturning Circulation (AMOC) has weakened from the mid-2000s to the mid-2010s (''high confidence'') and the Southern Ocean upper overturning cell has strengthened since the 1990s (''low confidence''), observational records are too short to determine the relative contributions of internal variability, natural forcing, and anthropogenic forcing to these changes (''high confidence'').''' No changes in Antarctic Circumpolar Current transport or meridional position have been observed. The mean zonal and overturning circulations of the Southern Ocean and the mean overturning circulation of the North Atlantic (the Atlantic Meridional Overturning Circulation, AMOC) are broadly reproduced by CMIP5 and CMIP6 models. However, biases are apparent in the modelled circulation strengths (''high confidence'') and their variability (''medium confidence''). {3.5.4}

	<span id="human-influence-on-the-biosphere"></span>		<span id="human-influence-on-the-biosphere"></span>

	=== Human Influence on the Biosphere ===		=== Human Influence on the Biosphere ===

Laura: /* Human Influence on the Cryosphere */

2026-06-22T12:07:28Z

Human Influence on the Cryosphere

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	'''It is''' very likely '''that anthropogenic forcing, mainly due to~~''' '''~~greenhouse gas increases, was the main driver of Arctic sea ice loss since the late 1970s.''' There is new evidence that increases in anthropogenic aerosols have offset part of the greenhouse gas-induced Arctic sea ice loss since the 1950s (''medium confidence''). In the Arctic, despite large differences in the mean sea ice state, loss of sea ice extent and thickness during recent decades is reproduced in all CMIP5 and CMIP6 models (''high confidence''). By contrast, global climate models do not generally capture the small observed increase in Antarctic sea ice extent during the satellite era, and there is ''low confidence'' in attributing the causes of this change. {3.4.1}		'''It is ''very likely'' that anthropogenic forcing, mainly due to greenhouse gas increases, was the main driver of Arctic sea ice loss since the late 1970s.''' There is new evidence that increases in anthropogenic aerosols have offset part of the greenhouse gas-induced Arctic sea ice loss since the 1950s (''medium confidence''). In the Arctic, despite large differences in the mean sea ice state, loss of sea ice extent and thickness during recent decades is reproduced in all CMIP5 and CMIP6 models (''high confidence''). By contrast, global climate models do not generally capture the small observed increase in Antarctic sea ice extent during the satellite era, and there is ''low confidence'' in attributing the causes of this change. {3.4.1}

	'''It is''' very likely '''that human influence contributed to the~~''' '''~~observed reductions in Northern Hemisphere spring snow cover~~''' '''~~since 1950.''' The seasonal cycle in Northern Hemisphere snow cover is better reproduced by CMIP6 than by CMIP5 models (''high confidence''). Human influence was ''very likely'' the main driver of the recent global, near-universal retreat of glaciers. It is ''very likely'' that human influence has contributed to the observed surface melting of the Greenland Ice Sheet over the past two decades, and there is ''medium confidence'' in an anthropogenic contribution to recent overall mass loss from the Greenland Ice Sheet. However, there is only ''limited evidence'' , with ''medium agreement'' , of human influence on Antarctic Ice Sheet mass balance through changes in ice discharge. {3.4.2, 3.4.3}		'''It is ''very likely'' that human influence contributed to the observed reductions in Northern Hemisphere spring snow cover since 1950.''' The seasonal cycle in Northern Hemisphere snow cover is better reproduced by CMIP6 than by CMIP5 models (''high confidence''). Human influence was ''very likely'' the main driver of the recent global, near-universal retreat of glaciers. It is ''very likely'' that human influence has contributed to the observed surface melting of the Greenland Ice Sheet over the past two decades, and there is ''medium confidence'' in an anthropogenic contribution to recent overall mass loss from the Greenland Ice Sheet. However, there is only ''limited evidence'' , with ''medium agreement'' , of human influence on Antarctic Ice Sheet mass balance through changes in ice discharge. {3.4.2, 3.4.3}

	<span id="human-influence-on-the-ocean"></span>		<span id="human-influence-on-the-ocean"></span>

	=== Human Influence on the Ocean ===		=== Human Influence on the Ocean ===

Laura: /* Human Influence on the Atmosphere and Surface */

2026-06-22T12:06:47Z

Human Influence on the Atmosphere and Surface

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	'''It is''' likely '''that human influence has contributed to''' <sup>[[#footnote-000\|3]]</sup> '''moistening''' '''in the upper troposphere since 1979.''' Also, there ''is medium confidence'' that human influence contributed to a global increase in annual surface specific humidity, and ''medium confidence'' that it contributed to a decrease in surface relative humidity over mid-latitude Northern Hemisphere continents during summertime. {3.3.2}		'''It is''' likely '''that human influence has contributed to''' <sup>[[#footnote-000\|3]]</sup> '''moistening''' '''in the upper troposphere since 1979.''' Also, there ''is medium confidence'' that human influence contributed to a global increase in annual surface specific humidity, and ''medium confidence'' that it contributed to a decrease in surface relative humidity over mid-latitude Northern Hemisphere continents during summertime. {3.3.2}

	'''It is''' likely '''that human influence has contributed to observed large-scale precipitation changes since the mid-20th century.''' New attribution studies strengthen previous findings of a detectable increase in Northern Hemisphere mid- to high-latitude land precipitation (''high confidence''). Human influence has contributed to strengthening the zonal mean precipitation contrast between the wet tropics and dry subtropics (''medium confidence''). Yet, anthropogenic aerosols contributed to decreasing global land summer monsoon precipitation from the 1950s to 1980s (''medium confidence''). There is also ''medium confidence'' that human influence has contributed to high-latitude increases and mid-latitude decreases in Southern Hemisphere summertime precipitation since 1979 associated with the trend of the Southern Annular Mode toward its positive phase. Despite improvements, models still have deficiencies in simulating precipitation patterns, particularly over the tropical ocean (''high confidence''). {3.3.2, 3.3.3, 3.5.2}		'''It is ''likely'' that human influence has contributed to observed large-scale precipitation changes since the mid-20th century.''' New attribution studies strengthen previous findings of a detectable increase in Northern Hemisphere mid- to high-latitude land precipitation (''high confidence''). Human influence has contributed to strengthening the zonal mean precipitation contrast between the wet tropics and dry subtropics (''medium confidence''). Yet, anthropogenic aerosols contributed to decreasing global land summer monsoon precipitation from the 1950s to 1980s (''medium confidence''). There is also ''medium confidence'' that human influence has contributed to high-latitude increases and mid-latitude decreases in Southern Hemisphere summertime precipitation since 1979 associated with the trend of the Southern Annular Mode toward its positive phase. Despite improvements, models still have deficiencies in simulating precipitation patterns, particularly over the tropical ocean (''high confidence''). {3.3.2, 3.3.3, 3.5.2}

	'''Human-induced greenhouse gas forcing is the main driver of the observed changes in hot and cold extremes on the global scale''~~' (~~virtually certain ''') and on most continents''~~' (~~very likely ''').''' It is ''likely'' that human influence, in particular due to greenhouse gas forcing, is the main driver of the observed intensification of heavy precipitation in global land regions during recent decades. There is ''high confidence'' in the ability of models to capture the large-scale spatial distribution of precipitation extremes over land. The magnitude and frequency of extreme precipitation simulated by CMIP6 models are similar to those simulated by CMIP5 models (''high confidence''). {Cross-Chapter Box 3.2}		'''Human-induced greenhouse gas forcing is the main driver of the observed changes in hot and cold extremes on the global scale (''virtually certain'') and on most continents (''very likely'').''' It is ''likely'' that human influence, in particular due to greenhouse gas forcing, is the main driver of the observed intensification of heavy precipitation in global land regions during recent decades. There is ''high confidence'' in the ability of models to capture the large-scale spatial distribution of precipitation extremes over land. The magnitude and frequency of extreme precipitation simulated by CMIP6 models are similar to those simulated by CMIP5 models (''high confidence''). {Cross-Chapter Box 3.2}

	'''It is''' likely '''that human influence has contributed to the poleward expansion of the zonal mean Hadley cell in the Southern Hemisphere since the 1980s.''' There is ''medium confidence'' that the observed poleward expansion of the zonal mean Hadley cell in the Northern Hemisphere is within the range of internal variability. The causes of the observed strengthening of the Pacific Walker circulation since the 1980s are not well understood, and the observed strengthening trend is outside the range of trends simulated in the coupled models (''medium confidence''). While CMIP6 models capture the general characteristics of the tropospheric large-scale circulation (''high confidence''), systematic biases exist in the mean frequency of atmospheric blocking events, especially in the Euro-Atlantic sector, some of which reduce with increasing model resolution (''medium confidence''). {3.3.3}		'''It is ''likely'' that human influence has contributed to the poleward expansion of the zonal mean Hadley cell in the Southern Hemisphere since the 1980s.''' There is ''medium confidence'' that the observed poleward expansion of the zonal mean Hadley cell in the Northern Hemisphere is within the range of internal variability. The causes of the observed strengthening of the Pacific Walker circulation since the 1980s are not well understood, and the observed strengthening trend is outside the range of trends simulated in the coupled models (''medium confidence''). While CMIP6 models capture the general characteristics of the tropospheric large-scale circulation (''high confidence''), systematic biases exist in the mean frequency of atmospheric blocking events, especially in the Euro-Atlantic sector, some of which reduce with increasing model resolution (''medium confidence''). {3.3.3}

	<span id="human-influence-on-the-cryosphere"></span>		<span id="human-influence-on-the-cryosphere"></span>

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	~~'''~~Cross-Chapter Box 3.2 \| Human Influence on Large-scale Changes in Temperature and Precipitation Extremes~~'''~~		== Cross-Chapter Box 3.2 \| Human Influence on Large-scale Changes in Temperature and Precipitation Extremes ==

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	'''Attribution of temperature extremes'''		'''Attribution of temperature extremes'''

	One important aspect of various indicators of temperature extremes is their connection to mean temperature at local, regional and global scales. For example, the highest daily temperature in a summer is often highly correlated with the summer mean temperature. Model projections show that changes in temperature extremes are often closely related to shifts in mean temperature ([[#Seneviratne--2016\|Seneviratne et al., 2016]] ; [[#Kharin--2018\|Kharin et al., 2018]]). It is thus no surprise that changes in temperature extremes are consistent with warming mean temperature, with warming leading to more hot extremes and fewer cold extremes. Given the attribution of mean warming to human influence ([[#3.3.1\|Section 3.3.1]]), and the connection between changes in mean and extreme temperatures, it is to be expected that anthropogenic forcing has also influenced temperature extremes.		One important aspect of various indicators of temperature extremes is their connection to mean temperature at local, regional and global scales. For example, the highest daily temperature in a summer is often highly correlated with the summer mean temperature. Model projections show that changes in temperature extremes are often closely related to shifts in mean temperature ([[#Seneviratne--2016\|Seneviratne et al., 2016]] ; [[#Kharin--2018\|Kharin et al., 2018]]). It is thus no surprise that changes in temperature extremes are consistent with warming mean temperature, with warming leading to more hot extremes and fewer cold extremes. Given the attribution of mean warming to human influence ([[#3.3.1\|Section 3.3.1]]), and the connection between changes in mean and extreme temperatures, it is to be expected that anthropogenic forcing has also influenced temperature extremes.

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	'''Attribution of precipitation extremes'''		'''Attribution of precipitation extremes'''

	An important piece of evidence supporting the SREX and AR5 assessment that there is ''medium confidence'' that anthropogenic forcing has contributed to a global scale intensification of heavy precipitation during the second half of the 20th century is the evidence for anthropogenic influence on other aspects of the global hydrological cycle. The most significant aspect of that is the increase in atmospheric moisture content associated with warming which should, in general, lead to enhanced extreme precipitation, particularly associated with enhanced convergence in tropical and extratropical cyclones (Sections 8.2.3.2 and 11.4.1). Such a connection is supported by the fact that annual maximum one-day precipitation increases with global mean temperature at a rate similar to the increase in the moisture holding capacity in response to warming, both in observations and in model simulations. Additionally, models project an increase in extreme precipitation across global land regions even in areas in which total annual or seasonal precipitation is projected to decrease.		An important piece of evidence supporting the SREX and AR5 assessment that there is ''medium confidence'' that anthropogenic forcing has contributed to a global scale intensification of heavy precipitation during the second half of the 20th century is the evidence for anthropogenic influence on other aspects of the global hydrological cycle. The most significant aspect of that is the increase in atmospheric moisture content associated with warming which should, in general, lead to enhanced extreme precipitation, particularly associated with enhanced convergence in tropical and extratropical cyclones (Sections 8.2.3.2 and 11.4.1). Such a connection is supported by the fact that annual maximum one-day precipitation increases with global mean temperature at a rate similar to the increase in the moisture holding capacity in response to warming, both in observations and in model simulations. Additionally, models project an increase in extreme precipitation across global land regions even in areas in which total annual or seasonal precipitation is projected to decrease.

	The overall performance of CMIP6 models in simulating extreme precipitation intensity and frequency is similar to that of CMIP5 models (''high confidence''), and there is ''high confidence'' in the ability of models to capture the large-scale spatial distribution of precipitation extremes over land (Section 11.4.3). Evidence of human influence on extreme precipitation has become stronger since AR5. Considering changes in precipitation intensity averaged over all wet days, there is ''high confidence'' that daily mean precipitation intensities have increased since the mid-20th century in a majority of land regions, including Europe, North America and Asia, and it is ''likely'' that such an increase is mainly due to anthropogenic emissions of greenhouse gases (Sections 8.3.1.3 and 11.4.4). Section 11.4.4 also finds a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation than expected by chance, which can only be explained when anthropogenic greenhouse gas forcing is considered. The right-hand		The overall performance of CMIP6 models in simulating extreme precipitation intensity and frequency is similar to that of CMIP5 models (''high confidence''), and there is ''high confidence'' in the ability of models to capture the large-scale spatial distribution of precipitation extremes over land (Section 11.4.3). Evidence of human influence on extreme precipitation has become stronger since AR5. Considering changes in precipitation intensity averaged over all wet days, there is ''high confidence'' that daily mean precipitation intensities have increased since the mid-20th century in a majority of land regions, including Europe, North America and Asia, and it is ''likely'' that such an increase is mainly due to anthropogenic emissions of greenhouse gases (Sections 8.3.1.3 and 11.4.4). Section 11.4.4 also finds a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation than expected by chance, which can only be explained when anthropogenic greenhouse gas forcing is considered. The right-hand panel of Cross-Chapter Box 3.2, Figure 1 demonstrates the consistency between changes in global average annual maximum daily precipitation in the observations and model simulations under combined anthropogenic and natural forcing, and inconsistency with simulations under natural forcing alone. While there is more evidence in the literature to quantify the net anthropogenic influence on extreme precipitation than the influence of individual forcing components, a dominant contribution of greenhouse gas forcing to the long-term intensification of extreme precipitation on global and continental scales has recently been quantified separately from the influence of anthropogenic aerosol and natural forcings ([[#Dong--2020\|Dong et al., 2020]] ; [[#Paik--2020b\|Paik et al., 2020b]]).

	~~Cross-Chapter Box 3.2~~

	panel of Cross-Chapter Box 3.2, Figure 1 demonstrates the consistency between changes in global average annual maximum daily precipitation in the observations and model simulations under combined anthropogenic and natural forcing, and inconsistency with simulations under natural forcing alone. While there is more evidence in the literature to quantify the net anthropogenic influence on extreme precipitation than the influence of individual forcing components, a dominant contribution of greenhouse gas forcing to the long-term intensification of extreme precipitation on global and continental scales has recently been quantified separately from the influence of anthropogenic aerosol and natural forcings ([[#Dong--2020\|Dong et al., 2020]] ; [[#Paik--2020b\|Paik et al., 2020b]]).

	[[IPCC:Wg1:Chapter:Chapter-11\|Chapter 11]] assesses that it is ''likely'' that human influence, in particular due to greenhouse gas forcing, is the main driver of the observed intensification of heavy precipitation in global land regions during recent decades (Section 11.4.4).		[[IPCC:Wg1:Chapter:Chapter-11\|Chapter 11]] assesses that it is ''likely'' that human influence, in particular due to greenhouse gas forcing, is the main driver of the observed intensification of heavy precipitation in global land regions during recent decades (Section 11.4.4).

	[[File:0e05877b182295b6f9fbd194fdc03b74 IPCC_AR6_WGI_CCBox_3_2_Figure_1.png]]		[[File:0e05877b182295b6f9fbd194fdc03b74 IPCC_AR6_WGI_CCBox_3_2_Figure_1.png]]

	'''Cross-Chapter Box 3.2, Figure 1 \| Comparison of observed and simulated changes in global mean temperature and precipitation extremes.''' Time series of globally averaged five-year mean anomalies of the annual maximum daily maximum temperature (TXx in °C) and annual maximum 1-day precipitation (Rx1day as standardized probability index in %) between 1953 and 2017 from the HadEX3 observations and the CMIP5 and CMIP6 multi-model ensembles with natural and human forcing '''(top)''' and natural forcing only '''(bottom)''' . For CMIP5, historical simulations for 1953–2005 are combined with corresponding RCP4.5 scenario runs for 2006–2017. For CMIP6, historical simulations for 1953–2014 are combined with SSP2-4.5 scenario simulations for 2015–2017. Numbers in brackets represent the number of models used. The time-fixed observational mask has been applied to model data throughout the whole period. Grid cells with more than 70% of data available between 1953 and 2017 plus data for at least three years between 2013 and 2017 are used. Coloured lines indicate multi-model means, while shading represents 5th–95th percentile ranges, based on all available ensemble members with equal weight given to each model ([[#3.2\|Section 3.2]]). Anomalies are relative to 1961–1990 means. Figure is updated from [[#Seong--2021\|Seong et al. (2021)]] , their Figure 3 and [[#Paik--2020b\|Paik et al. (2020b)]] , their Figure 3. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).		'''Cross-Chapter Box 3.2, Figure 1 \| Comparison of observed and simulated changes in global mean temperature and precipitation extremes.''' Time series of globally averaged five-year mean anomalies of the annual maximum daily maximum temperature (TXx in °C) and annual maximum 1-day precipitation (Rx1day as standardized probability index in %) between 1953 and 2017 from the HadEX3 observations and the CMIP5 and CMIP6 multi-model ensembles with natural and human forcing '''(top)''' and natural forcing only '''(bottom)''' . For CMIP5, historical simulations for 1953–2005 are combined with corresponding RCP4.5 scenario runs for 2006–2017. For CMIP6, historical simulations for 1953–2014 are combined with SSP2-4.5 scenario simulations for 2015–2017. Numbers in brackets represent the number of models used. The time-fixed observational mask has been applied to model data throughout the whole period. Grid cells with more than 70% of data available between 1953 and 2017 plus data for at least three years between 2013 and 2017 are used. Coloured lines indicate multi-model means, while shading represents 5th–95th percentile ranges, based on all available ensemble members with equal weight given to each model ([[#3.2\|Section 3.2]]). Anomalies are relative to 1961–1990 means. Figure is updated from [[#Seong--2021\|Seong et al. (2021)]] , their Figure 3 and [[#Paik--2020b\|Paik et al. (2020b)]] , their Figure 3. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).
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	<div id="3.3.3" class="h2-container"></div>		<div id="3.3.3" class="h2-container"></div>

	<span id="atmospheric-circulation"></span>		<span id="atmospheric-circulation"></span>

	=== 3.3.3 Atmospheric Circulation ===		=== 3.3.3 Atmospheric Circulation ===