IPCC:AR6/WGI/Chapter-4 - Revision history

Laura: /* 4.4.4 Response to Short-lived Climate Forcers and Volcanic Eruptions */

2026-06-22T13:02:44Z

4.4.4 Response to Short-lived Climate Forcers and Volcanic Eruptions

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	~~'''~~Cross-Chapter Box 4.1 \| The Climate Effects of Volcanic Eruption~~'''~~		== Cross-Chapter Box 4.1 \| The Climate Effects of Volcanic Eruption==

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	[[File:d5013b2ba1c5abd9898efdb7a90aece2 IPCC_AR6_WGI_CCBox_4_1_Figure_1.png]]		[[File:d5013b2ba1c5abd9898efdb7a90aece2 IPCC_AR6_WGI_CCBox_4_1_Figure_1.png]]

	'''Cross-Chapter Box 4.1, ~~Fig'''~~		'''Cross-Chapter Box 4.1, Figure 1 \|''' '''Potential impact of volcanic eruption on future global temperature change.''' CMIP5 projections of possible 21st-century futures under RCP4.5 after a 1257 Samalas magnitude volcanic eruption in 2044, from [[#Bethke--2017\|Bethke et al. (2017)]] . '''(a)''' Volcanic ERF of the most volcanically active ensemble member, estimated from SAOD. '''(b)''' Annual mean global surface air temperature. Ensemble mean (solid) of future projections including volcanoes (blue) and excluding volcanoes (red) with 5–95% range (shading) and ensemble minima/maxima (dots); evolution of the most volcanically active member (black). Data created using a SMILE approach with NorESM1 in its CMIP5 configuration. See Sections 2.2.2 and 4.4.4 for more details. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

	~~'''ure~~ 1 \|'''

	'''Potential impact of volcanic eruption on future global temperature change.''' CMIP5 projections of possible 21st-century futures under RCP4.5 after a 1257 Samalas magnitude volcanic eruption in 2044, from [[#Bethke--2017\|Bethke et al. (2017)]] . '''(a)''' Volcanic ERF of the most volcanically active ensemble member, estimated from SAOD. '''(b)''' Annual mean global surface air temperature. Ensemble mean (solid) of future projections including volcanoes (blue) and excluding volcanoes (red) with 5–95% range (shading) and ensemble minima/maxima (dots); evolution of the most volcanically active member (black). Data created using a SMILE approach with NorESM1 in its CMIP5 configuration. See Sections 2.2.2 and 4.4.4 for more details. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

	'''Summary'''		'''Summary'''

	It is ''likely'' that at least one large eruption will occur during the 21st century. Such an eruption would reduce GSAT for several years, decrease global mean land precipitation, alter monsoon circulation, modify extreme precipitation, and change the profile of many regional climatic impact-drivers. A low-likelihood, high-impact outcome would be several large eruptions that would greatly alter the 21st century climate trajectory compared to SSP-based ESM projections.		It is ''likely'' that at least one large eruption will occur during the 21st century. Such an eruption would reduce GSAT for several years, decrease global mean land precipitation, alter monsoon circulation, modify extreme precipitation, and change the profile of many regional climatic impact-drivers. A low-likelihood, high-impact outcome would be several large eruptions that would greatly alter the 21st century climate trajectory compared to SSP-based ESM projections.
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Laura: /* 4.2.6 Display of Model Agreement and Spread */

2026-06-22T12:59:26Z

4.2.6 Display of Model Agreement and Spread

← Older revision		Revision as of 12:59, 22 June 2026
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	~~'''~~Box 4.1 \| Ensemble Evaluation and Weighting~~'''~~		== Box 4.1 \| Ensemble Evaluation and Weighting ==

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	The constrained range of GSAT change is useful for quantifying uncertainties in changes of other climate quanties that scale well with GSAT change, such as September Arctic sea ice area, global mean precipitation, and many climate extremes (Cross-Chapter Box 11.1). However, there are also quantities that do not scale linearly with GSAT change, such as global mean land precipitation, atmospheric circulation, AMOC, and modes of variability, especially ENSO SST variability. Because we do not have robust scientific evidence to constrain changes in other quantities, uncertainty quantification for their changes is based on CMIP6 projections and expert judgement. For the assessment for changes in GMSL, the contribution from land-ice melt has been added offline to the CMIP6 simulated contributions from thermal expansion, consistent with [[IPCC:Wg1:Chapter:Chapter-9\|Chapter 9]] (Section 9.6).		The constrained range of GSAT change is useful for quantifying uncertainties in changes of other climate quanties that scale well with GSAT change, such as September Arctic sea ice area, global mean precipitation, and many climate extremes (Cross-Chapter Box 11.1). However, there are also quantities that do not scale linearly with GSAT change, such as global mean land precipitation, atmospheric circulation, AMOC, and modes of variability, especially ENSO SST variability. Because we do not have robust scientific evidence to constrain changes in other quantities, uncertainty quantification for their changes is based on CMIP6 projections and expert judgement. For the assessment for changes in GMSL, the contribution from land-ice melt has been added offline to the CMIP6 simulated contributions from thermal expansion, consistent with [[IPCC:Wg1:Chapter:Chapter-9\|Chapter 9]] (Section 9.6).

	[[File:b5a07ed31e0fc1b9384e6fbb11840f02 IPCC_AR6_WGI_Box_4_1_Figure_1.png]]		[[File:b5a07ed31e0fc1b9384e6fbb11840f02 IPCC_AR6_WGI_Box_4_1_Figure_1.png]]
	~~'''Box 4.1 Figure 1'''~~

	~~'''\|'''~~

	'''CMIP6 annual mean global surface air temperature (GSAT) simulations and various contributions to uncertainty in the projections ensemble.''' The figure shows anomalies relative to the period 1995–2014 (left y-axis), converted to anomalies relative to 1850–1900 (right y-axis); the difference between the y-axes is 0.85°C (Cross-Chapter Box 2.3). Shown are historical simulations with 39 CMIP6 models (grey) and projections following scenario SSP2-4.5 (dark yellow; thin lines: individual simulations; heavy line; ensemble mean; dashed lines: 5% and 95% ranges). The black curve shows the observations-based estimate (HadCRUT5; [[#Morice--2021\|Morice et al., 2021]]). Light blue shading shows the 50-member ensemble CanESM5, such that the deviations from the CanESM5 ensemble mean have been added to the CMIP6 multi-model mean. The green curves are from the emulator and show the central estimate (solid) and ''very likely'' range (dashed) for GSAT. The inset shows a cut-out from the main plot and additionally in light purple for the period 2019–2028 the initialized forecasts from eight models contributing to DCPP ([[#Boer--2016\|Boer et al., 2016]]); the deep-purple curve shows the average of the forecasts. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

			'''Box 4.1 Figure 1 \|''' '''CMIP6 annual mean global surface air temperature (GSAT) simulations and various contributions to uncertainty in the projections ensemble.''' The figure shows anomalies relative to the period 1995–2014 (left y-axis), converted to anomalies relative to 1850–1900 (right y-axis); the difference between the y-axes is 0.85°C (Cross-Chapter Box 2.3). Shown are historical simulations with 39 CMIP6 models (grey) and projections following scenario SSP2-4.5 (dark yellow; thin lines: individual simulations; heavy line; ensemble mean; dashed lines: 5% and 95% ranges). The black curve shows the observations-based estimate (HadCRUT5; [[#Morice--2021\|Morice et al., 2021]]). Light blue shading shows the 50-member ensemble CanESM5, such that the deviations from the CanESM5 ensemble mean have been added to the CMIP6 multi-model mean. The green curves are from the emulator and show the central estimate (solid) and ''very likely'' range (dashed) for GSAT. The inset shows a cut-out from the main plot and additionally in light purple for the period 2019–2028 the initialized forecasts from eight models contributing to DCPP ([[#Boer--2016\|Boer et al., 2016]]); the deep-purple curve shows the average of the forecasts. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).
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Laura: /* 4.2.6 Display of Model Agreement and Spread */

2026-06-22T12:58:26Z

4.2.6 Display of Model Agreement and Spread

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	Chapter 4 uses the advanced approach, taking into account the sign and significance of the change (Cross-Chapter Box Atlas.1, approach C). Where not applicable, such as due to a lack of the necessary model output, the simple method is used taking into account only agreement on the sign of the change across the multi-model ensemble (Cross-Chapter Box Atlas.1, approach B). The advanced approach is similar to the method used in AR5 but isolates conflicting signals as proposed in [[#Zappa--2021\|Zappa et al. (2021)]] . It uses three mutually exclusive categories and distinguishes (i) areas with significant change and high model agreement (no overlay), (ii) areas with no change or no robust change (diagonal lines), and (iii) areas with significant change but '''low agreement''' (crossed lines). Category (i) marks areas where the climate change signals ''likely'' emerge from internal variability, where two-thirds or more of the models project changes greater than internal variability and 80% or more of the models agree on the sign of the change. Category (ii) marks areas where fewer than two-thirds of the models project changes greater than internal variability, and category (iii) marks areas with significant but conflicting signals, where two-thirds or more of the models project changes greater than internal variability but less than 80% agree on the sign of the change.		Chapter 4 uses the advanced approach, taking into account the sign and significance of the change (Cross-Chapter Box Atlas.1, approach C). Where not applicable, such as due to a lack of the necessary model output, the simple method is used taking into account only agreement on the sign of the change across the multi-model ensemble (Cross-Chapter Box Atlas.1, approach B). The advanced approach is similar to the method used in AR5 but isolates conflicting signals as proposed in [[#Zappa--2021\|Zappa et al. (2021)]] . It uses three mutually exclusive categories and distinguishes (i) areas with significant change and high model agreement (no overlay), (ii) areas with no change or no robust change (diagonal lines), and (iii) areas with significant change but '''low agreement''' (crossed lines). Category (i) marks areas where the climate change signals ''likely'' emerge from internal variability, where two-thirds or more of the models project changes greater than internal variability and 80% or more of the models agree on the sign of the change. Category (ii) marks areas where fewer than two-thirds of the models project changes greater than internal variability, and category (iii) marks areas with significant but conflicting signals, where two-thirds or more of the models project changes greater than internal variability but less than 80% agree on the sign of the change.

	In this chapter variability is defined as <code>1.645 * √ 2 σ <sub>yr</sub></code>, where σ <sub>yr</sub> is the standard deviation of 20-year means in the pre-industrial control simulations (see Cross-Chapter Box, Atlas.1). Category (a) uses a definition very similar to the AR5 method for stippling, except that the model signal is compared to its corresponding internal rather than the multi-model mean variability, to account for the substantial model differences in pre-industrial internal variability ([[#Parsons--2020\|Parsons et al., 2020]]). Changes smaller than internal variability can have potential impacts particularly if they persist over sustained periods such as several decades. Finally, even when changes do not exceed variability at the grid point level they may exceed variability if aggregated over catchment basins, regions, or continents (Cross-Chapter Box Atlas.1). Maps of mean changes also ignore potential changes in variability addressed by a more comprehensive assessment of changes in temperature variability ([[#4.5.1\|Section 4.5.1]]) and modes of internal variability ([[#4.4.3\|Section 4.4.3]]).		In this chapter variability is defined as <code>1.645 * √ 2 σ <sub>yr</sub></code>, where <code>σ <sub>yr</sub></code> is the standard deviation of 20-year means in the pre-industrial control simulations (see Cross-Chapter Box, Atlas.1). Category (a) uses a definition very similar to the AR5 method for stippling, except that the model signal is compared to its corresponding internal rather than the multi-model mean variability, to account for the substantial model differences in pre-industrial internal variability ([[#Parsons--2020\|Parsons et al., 2020]]). Changes smaller than internal variability can have potential impacts particularly if they persist over sustained periods such as several decades. Finally, even when changes do not exceed variability at the grid point level they may exceed variability if aggregated over catchment basins, regions, or continents (Cross-Chapter Box Atlas.1). Maps of mean changes also ignore potential changes in variability addressed by a more comprehensive assessment of changes in temperature variability ([[#4.5.1\|Section 4.5.1]]) and modes of internal variability ([[#4.4.3\|Section 4.4.3]]).

	<div id="box-4.1" class="h2-container box-container"></div>		<div id="box-4.1" class="h2-container box-container"></div>

Laura: /* 4.2.6 Display of Model Agreement and Spread */

2026-06-22T12:58:07Z

4.2.6 Display of Model Agreement and Spread

← Older revision		Revision as of 12:58, 22 June 2026
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	Chapter 4 uses the advanced approach, taking into account the sign and significance of the change (Cross-Chapter Box Atlas.1, approach C). Where not applicable, such as due to a lack of the necessary model output, the simple method is used taking into account only agreement on the sign of the change across the multi-model ensemble (Cross-Chapter Box Atlas.1, approach B). The advanced approach is similar to the method used in AR5 but isolates conflicting signals as proposed in [[#Zappa--2021\|Zappa et al. (2021)]] . It uses three mutually exclusive categories and distinguishes (i) areas with significant change and high model agreement (no overlay), (ii) areas with no change or no robust change (diagonal lines), and (iii) areas with significant change but '''low agreement''' (crossed lines). Category (i) marks areas where the climate change signals ''likely'' emerge from internal variability, where two-thirds or more of the models project changes greater than internal variability and 80% or more of the models agree on the sign of the change. Category (ii) marks areas where fewer than two-thirds of the models project changes greater than internal variability, and category (iii) marks areas with significant but conflicting signals, where two-thirds or more of the models project changes greater than internal variability but less than 80% agree on the sign of the change.		Chapter 4 uses the advanced approach, taking into account the sign and significance of the change (Cross-Chapter Box Atlas.1, approach C). Where not applicable, such as due to a lack of the necessary model output, the simple method is used taking into account only agreement on the sign of the change across the multi-model ensemble (Cross-Chapter Box Atlas.1, approach B). The advanced approach is similar to the method used in AR5 but isolates conflicting signals as proposed in [[#Zappa--2021\|Zappa et al. (2021)]] . It uses three mutually exclusive categories and distinguishes (i) areas with significant change and high model agreement (no overlay), (ii) areas with no change or no robust change (diagonal lines), and (iii) areas with significant change but '''low agreement''' (crossed lines). Category (i) marks areas where the climate change signals ''likely'' emerge from internal variability, where two-thirds or more of the models project changes greater than internal variability and 80% or more of the models agree on the sign of the change. Category (ii) marks areas where fewer than two-thirds of the models project changes greater than internal variability, and category (iii) marks areas with significant but conflicting signals, where two-thirds or more of the models project changes greater than internal variability but less than 80% agree on the sign of the change.

	In this chapter variability is defined as		In this chapter variability is defined as <code>1.645 * √ 2 σ <sub>yr</sub></code>, where σ <sub>yr</sub> is the standard deviation of 20-year means in the pre-industrial control simulations (see Cross-Chapter Box, Atlas.1). Category (a) uses a definition very similar to the AR5 method for stippling, except that the model signal is compared to its corresponding internal rather than the multi-model mean variability, to account for the substantial model differences in pre-industrial internal variability ([[#Parsons--2020\|Parsons et al., 2020]]). Changes smaller than internal variability can have potential impacts particularly if they persist over sustained periods such as several decades. Finally, even when changes do not exceed variability at the grid point level they may exceed variability if aggregated over catchment basins, regions, or continents (Cross-Chapter Box Atlas.1). Maps of mean changes also ignore potential changes in variability addressed by a more comprehensive assessment of changes in temperature variability ([[#4.5.1\|Section 4.5.1]]) and modes of internal variability ([[#4.4.3\|Section 4.4.3]]).

	<~~code> </~~code> 1.645 * √ ~~<code>~~ 2 ~~</code>~~ σ <sub>yr</sub>

	, where

	σ <sub>yr</sub>

	is the standard deviation of 20-year means in the pre-industrial control simulations (see Cross-Chapter Box, Atlas.1). Category (a) uses a definition very similar to the AR5 method for stippling, except that the model signal is compared to its corresponding internal rather than the multi-model mean variability, to account for the substantial model differences in pre-industrial internal variability ([[#Parsons--2020\|Parsons et al., 2020]]). Changes smaller than internal variability can have potential impacts particularly if they persist over sustained periods such as several decades. Finally, even when changes do not exceed variability at the grid point level they may exceed variability if aggregated over catchment basins, regions, or continents (Cross-Chapter Box Atlas.1). Maps of mean changes also ignore potential changes in variability addressed by a more comprehensive assessment of changes in temperature variability ([[#4.5.1\|Section 4.5.1]]) and modes of internal variability ([[#4.4.3\|Section 4.4.3]]).

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	== 4.3 Projected Changes in Global Climate Indices in the 21st Century ==		== 4.3 Projected Changes in Global Climate Indices in the 21st Century ==

Laura at 12:55, 22 June 2026

2026-06-22T12:55:05Z

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Laura at 12:54, 22 June 2026

2026-06-22T12:54:24Z

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Laura: /* Climate Change Commitment and Change Beyond 2100 */

2026-06-22T12:53:06Z

Climate Change Commitment and Change Beyond 2100

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	'''Earth system modelling experiments since AR5 confirm that the zero CO~~'''~~ <sub>2</sub> ~~'''~~emissions commitment (the additional rise in GSAT after all CO~~'''~~ <sub>2</sub> ~~'''~~emissions cease) is small~~'''~~ ( ''likely~~'' '~~''less than 0.3°C in magnitude) on decadal time scales, but that it may be positive or negative.''' There is ''low~~'' ''~~confidence'' in the sign of the zero CO <sub>2</sub> emissions commitment. Consistent with SR1.5, the central estimate is taken as zero for assessments of remaining carbon budgets for global warming levels of 1.5°C or 2°C. {4.7.2, 5.5.2} .		'''Earth system modelling experiments since AR5 confirm that the zero CO <sub>2</sub> emissions commitment (the additional rise in GSAT after all CO <sub>2</sub> emissions cease) is small (''likely'' less than 0.3°C in magnitude) on decadal time scales, but that it may be positive or negative.''' There is ''low confidence'' in the sign of the zero CO <sub>2</sub> emissions commitment. Consistent with SR1.5, the central estimate is taken as zero for assessments of remaining carbon budgets for global warming levels of 1.5°C or 2°C. {4.7.2, 5.5.2} .

	'''Overshooting specific global warming levels such as 2°C has effects on the climate system that persist beyond 2100~~'''~~ ( ''medium confidence~~'' '~~'').''' Under one scenario including a peak and decline in atmospheric CO <sub>2</sub> concentration (SSP5-3.4-OS), some climate metrics such as GSAT begin to decline but do not fully reverse by 2100 to levels prior to the CO <sub>2</sub> peak ( ''medium confidence'' ). GMSL continues to rise in all models up to 2100 despite a reduction in CO <sub>2</sub> to 2040 levels. {4.6.3, 4.7.1, 4.7.2}		'''Overshooting specific global warming levels such as 2°C has effects on the climate system that persist beyond 2100 (''medium confidence'').''' Under one scenario including a peak and decline in atmospheric CO <sub>2</sub> concentration (SSP5-3.4-OS), some climate metrics such as GSAT begin to decline but do not fully reverse by 2100 to levels prior to the CO <sub>2</sub> peak ( ''medium confidence'' ). GMSL continues to rise in all models up to 2100 despite a reduction in CO <sub>2</sub> to 2040 levels. {4.6.3, 4.7.1, 4.7.2}

	'''Using extended scenarios beyond 2100, projections show~~'''~~ ''likely~~'' '~~''warming by 2300, relative to 1850-1900, of 1.0°C-2.2°C for SSP1-2.6 and 6.6°C-14.1°C for SSP5-8.5.''' By 2300, warming under the SSP5-3.4-OS overshoot scenario decreases from a peak around year 2060 to a level very similar to SSP1-2.6. Precipitation over land continues to increase strongly under SSP5-8.5. GSAT projected for the end of the 23rd century under SSP2-4.5 ( ''likely'' 2.3°C–4.6°C higher than over the period 1850–1900) has not been experienced since the mid-Pliocene, about 3 million years ago. GSAT projected for the end of the 23rd century under SSP5-8.5 ( ''likely'' 6.6°C–14.1°C higher than over the period 1850–1900) overlaps with the range estimated for the Miocene Climatic Optimum (5°C–10°C higher) and Early Eocene Climatic Optimum (10°C–18°C higher), about 15 and 50 million years ago, respectively ( ''medium confidence'' ). {2.3.1.1, 4.7.1}		'''Using extended scenarios beyond 2100, projections show ''likely'' warming by 2300, relative to 1850-1900, of 1.0°C-2.2°C for SSP1-2.6 and 6.6°C-14.1°C for SSP5-8.5.''' By 2300, warming under the SSP5-3.4-OS overshoot scenario decreases from a peak around year 2060 to a level very similar to SSP1-2.6. Precipitation over land continues to increase strongly under SSP5-8.5. GSAT projected for the end of the 23rd century under SSP2-4.5 ( ''likely'' 2.3°C–4.6°C higher than over the period 1850–1900) has not been experienced since the mid-Pliocene, about 3 million years ago. GSAT projected for the end of the 23rd century under SSP5-8.5 ( ''likely'' 6.6°C–14.1°C higher than over the period 1850–1900) overlaps with the range estimated for the Miocene Climatic Optimum (5°C–10°C higher) and Early Eocene Climatic Optimum (10°C–18°C higher), about 15 and 50 million years ago, respectively ( ''medium confidence'' ). {2.3.1.1, 4.7.1}

	<div id="4.1" class="h1-container"></div>		<div id="4.1" class="h1-container"></div>

	<span id="scope-and-overview-of-this-chapter"></span>		<span id="scope-and-overview-of-this-chapter"></span>

	== 4.1 Scope and Overview of This Chapter ==		== 4.1 Scope and Overview of This Chapter ==

Laura: /* Climate Response to Emissions Reduction, Carbon Dioxide Removal and Solar Radiation Modification */

2026-06-22T12:52:21Z

Climate Response to Emissions Reduction, Carbon Dioxide Removal and Solar Radiation Modification

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	'''If strong mitigation is applied from 2020 onward as reflected in SSP1-1.9, its effect on 20-year trends in GSAT would~~'''~~ ''likely~~'' '~~''emerge during the near term (2021–2040), measured against an assumed non-mitigation scenario such as ~~S''' '''SP3~~-7.~~''' '''~~0 and SSP5-8.5. However, the response of many other climate quantities to mitigation would be largely masked by internal variability during the near term, especially on the regional scale~~'''~~ ( ''high confidence~~'' '~~'').''' The mitigation benefits for these quantities would emerge only later during the 21st century ( ''high confidence'' ). During the near term, a small fraction of the surface can show cooling under all scenarios assessed here, so near-term cooling at any given location is fully consistent with GSAT increase ( ''high confidence'' ). Events of reduced and increased GSAT trends at decadal time scales will continue to occur in the 21st century but will not affect the centennial warming ( ''very high confidence'' ). {4.6.3, Cross-Chapter Box 3.1}		'''If strong mitigation is applied from 2020 onward as reflected in SSP1-1.9, its effect on 20-year trends in GSAT would ''likely'' emerge during the near term (2021–2040), measured against an assumed non-mitigation scenario such as SSP3-7.0 and SSP5-8.5. However, the response of many other climate quantities to mitigation would be largely masked by internal variability during the near term, especially on the regional scale (''high confidence'').''' The mitigation benefits for these quantities would emerge only later during the 21st century ( ''high confidence'' ). During the near term, a small fraction of the surface can show cooling under all scenarios assessed here, so near-term cooling at any given location is fully consistent with GSAT increase ( ''high confidence'' ). Events of reduced and increased GSAT trends at decadal time scales will continue to occur in the 21st century but will not affect the centennial warming ( ''very high confidence'' ). {4.6.3, Cross-Chapter Box 3.1}

	'''Because of the near-linear relationship between cumulative carbon emissions and GSAT change, the cooling or avoided warming from carbon dioxide removal (CDR) is proportional to the cumulative amount of CO~~'''~~ <sub>2</sub> ~~'''~~removed by CDR~~'''~~ ( ''high confidence~~'' '~~'').''' The climate system response to net negative CO <sub>2</sub> emissions is expected to be delayed by years to centuries. Net negative CO <sub>2</sub> emissions due to CDR will not reverse some climate change, such as sea level rise, at least for several centuries ( ''high confidenc'' e). The climate effect of a sudden and sustained CDR termination would depend on the amount of CDR-induced cooling prior to termination and the rate of background CO <sub>2</sub> emissions at the time of termination ( ''high confidence'' ). {4.6.3, 5.5, 5.6}		'''Because of the near-linear relationship between cumulative carbon emissions and GSAT change, the cooling or avoided warming from carbon dioxide removal (CDR) is proportional to the cumulative amount of CO <sub>2</sub> removed by CDR (''high confidence'').''' The climate system response to net negative CO <sub>2</sub> emissions is expected to be delayed by years to centuries. Net negative CO <sub>2</sub> emissions due to CDR will not reverse some climate change, such as sea level rise, at least for several centuries ( ''high confidenc'' e). The climate effect of a sudden and sustained CDR termination would depend on the amount of CDR-induced cooling prior to termination and the rate of background CO <sub>2</sub> emissions at the time of termination ( ''high confidence'' ). {4.6.3, 5.5, 5.6}

	'''Solar radiation modification (SRM) could offset some of the effects of anthropogenic warming on global and regional climate, but there would be substantial residual and overcompensating climate change at the regional scale and seasonal time scale~~'''~~ ( ''high confidence~~'' '~~''), and~~''' '''~~there is~~'''~~ ''low confidence~~'' '~~''in our understanding of the climate response to SRM, specifically at the regional scale.''' Since AR5, understanding of the global and regional climate response to SRM has improved, due to modelling work with more sophisticated treatment of aerosol-based SRM options and stratospheric processes. Improved modelling suggests that multiple climate goals could be met simultaneously. A sudden and sustained termination of SRM in a high-emissions scenario such as SSP5-8.5 would cause a rapid climate change ( ''high confidence'' ). However, a gradual phase-out of SRM combined with emissions reductions and CDR would ''more'' ''likely than not'' avoid larger rates of warming '''.''' {4.6.3}		'''Solar radiation modification (SRM) could offset some of the effects of anthropogenic warming on global and regional climate, but there would be substantial residual and overcompensating climate change at the regional scale and seasonal time scale (''high confidence''), and there is ''low confidence'' in our understanding of the climate response to SRM, specifically at the regional scale.''' Since AR5, understanding of the global and regional climate response to SRM has improved, due to modelling work with more sophisticated treatment of aerosol-based SRM options and stratospheric processes. Improved modelling suggests that multiple climate goals could be met simultaneously. A sudden and sustained termination of SRM in a high-emissions scenario such as SSP5-8.5 would cause a rapid climate change ( ''high confidence'' ). However, a gradual phase-out of SRM combined with emissions reductions and CDR would ''more'' ''likely than not'' avoid larger rates of warming '''.''' {4.6.3}

	<span id="climate-change-commitment-and-change-beyond-2100"></span>		<span id="climate-change-commitment-and-change-beyond-2100"></span>

	=== Climate Change Commitment and Change Beyond 2100 ===		=== Climate Change Commitment and Change Beyond 2100 ===

Laura: /* Cryosphere and Ocean */

2026-06-22T12:51:01Z

Cryosphere and Ocean

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	'''Under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, it is~~'''~~ ''likely~~'' '~~''that the Arctic Ocean in September, the month of annual minimum sea ice area, will become practically ice-free (sea ice area less than 1 million km~~'''~~ <sup>2</sup> ~~'''~~) averaged over 2081–2100 and all available simulations.''' Arctic sea ice area in March, the month of annual maximum sea ice area, also decreases in the future under each of the considered scenarios, but to a much lesser degree (in percentage terms) than in September ( ''high confidence'' ). {4.3.2}		'''Under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, it is ''likely'' that the Arctic Ocean in September, the month of annual minimum sea ice area, will become practically ice-free (sea ice area less than 1 million km <sup>2</sup>) averaged over 2081–2100 and all available simulations.''' Arctic sea ice area in March, the month of annual maximum sea ice area, also decreases in the future under each of the considered scenarios, but to a much lesser degree (in percentage terms) than in September ( ''high confidence'' ). {4.3.2}

	'''Under the five scenarios assessed, it is~~'''~~ ''virtually certain~~'' '~~''that global mean sea level (GMSL) will continue to rise through the 21st century.''' For the period 2081–2100 relative to 1995–2014, GMSL is ''likely'' to rise by 0.46–0.74 m under SSP3-7.0 and by 0.30–0.54 m under SSP1-2.6 ( ''medium confidence'' ). For the assessment of change in GMSL, the contribution from land-ice melt has been added offline to the CMIP6-simulated contributions from thermal expansion. {4.3.2. 9.6}		'''Under the five scenarios assessed, it is ''virtually certain'' that global mean sea level (GMSL) will continue to rise through the 21st century.''' For the period 2081–2100 relative to 1995–2014, GMSL is ''likely'' to rise by 0.46–0.74 m under SSP3-7.0 and by 0.30–0.54 m under SSP1-2.6 ( ''medium confidence'' ). For the assessment of change in GMSL, the contribution from land-ice melt has been added offline to the CMIP6-simulated contributions from thermal expansion. {4.3.2. 9.6}

	'''It is~~'''~~ ''very likely~~'' '~~''that the cumulative uptake of carbon by the ocean and by land will increase through to the end of the 21st century.''' Carbon uptake by land shows greater increases but with greater uncertainties than for ocean carbon uptake. The fraction of emissions absorbed by land and ocean sinks will be smaller under high emissions scenarios than under low emissions scenarios ( ''high confidence'' ). Ocean surface pH will decrease steadily through the 21st century, except for SSP1-1.9 and SSP1-2.6 where values decrease until around 2070 and then increase slightly to 2100 ( ''high confidence'' ). {4.3.2, 5.4}		'''It is ''very likely'' that the cumulative uptake of carbon by the ocean and by land will increase through to the end of the 21st century.''' Carbon uptake by land shows greater increases but with greater uncertainties than for ocean carbon uptake. The fraction of emissions absorbed by land and ocean sinks will be smaller under high emissions scenarios than under low emissions scenarios ( ''high confidence'' ). Ocean surface pH will decrease steadily through the 21st century, except for SSP1-1.9 and SSP1-2.6 where values decrease until around 2070 and then increase slightly to 2100 ( ''high confidence'' ). {4.3.2, 5.4}

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	<span id="climate-response-to-emissions-reduction-carbon-dioxide-removal-and-solar-radiation-modification"></span>		<span id="climate-response-to-emissions-reduction-carbon-dioxide-removal-and-solar-radiation-modification"></span>

	=== Climate Response to Emissions Reduction, Carbon Dioxide Removal and Solar Radiation Modification ===		=== Climate Response to Emissions Reduction, Carbon Dioxide Removal and Solar Radiation Modification ===

Laura: /* Large-scale Circulation and Modes of Variability */

2026-06-22T12:50:20Z

Large-scale Circulation and Modes of Variability

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	'''In the near term, the forced change in Southern Annular Mode in austral summer is~~'''~~ ''likely~~'' '~~''to be weaker than observed during the late 20th century under all five SSPs assessed.''' This is because of the opposing influence in the near- to mid-term from stratospheric ozone recovery and increases in other greenhouse gases on the Southern Hemisphere summertime mid-latitude circulation ( ''high confidence'' ). In the near term, forced changes in the Southern Annular Mode in austral summer are therefore ''likely'' to be smaller than changes due to natural internal variability. {4.3.3, 4.4.3}		'''In the near term, the forced change in Southern Annular Mode in austral summer is ''likely'' to be weaker than observed during the late 20th century under all five SSPs assessed.''' This is because of the opposing influence in the near- to mid-term from stratospheric ozone recovery and increases in other greenhouse gases on the Southern Hemisphere summertime mid-latitude circulation ( ''high confidence'' ). In the near term, forced changes in the Southern Annular Mode in austral summer are therefore ''likely'' to be smaller than changes due to natural internal variability. {4.3.3, 4.4.3}

	'''In the long term, the Southern Hemisphere mid-latitude jet is~~'''~~ ''likely~~'' '~~''to shift poleward and strengthen under SSP5-8.5 relative to 1995–2014.''' This is ''likely'' to be accompanied by an increase in the Southern Annular Mode index in all seasons relative to 1995–2014. For SSP1-2.6, CMIP6 models project no robust change in the Southern Annular Mode index in the long term. It is ''likely'' that wind speeds associated with extratropical cyclones will strengthen in the Southern Hemisphere storm track for SSP5-8.5. {4.5.1, 4.5.3}		'''In the long term, the Southern Hemisphere mid-latitude jet is ''likely'' to shift poleward and strengthen under SSP5-8.5 relative to 1995–2014.''' This is ''likely'' to be accompanied by an increase in the Southern Annular Mode index in all seasons relative to 1995–2014. For SSP1-2.6, CMIP6 models project no robust change in the Southern Annular Mode index in the long term. It is ''likely'' that wind speeds associated with extratropical cyclones will strengthen in the Southern Hemisphere storm track for SSP5-8.5. {4.5.1, 4.5.3}

	'''The CMIP6 multi-model ensemble projects a long-term increase in the boreal wintertime Northern Annular Mode index under the high-emissions scenarios of SSP3-7.0 and~~''' '''~~SSP5-8.5~~''' '''~~, but regional changes may deviate from a simple shift in the ~~m''' '''id~~-~~latit''' '''ude~~ circulation.''' Substantial uncertainty and thus ''low confidence'' remain in projecting regional changes in Northern Hemisphere jet streams and storm tracks, especially for the North Atlantic basin in winter; this is due to large natural internal variability, the competing effects of projected upper- and lower-tropospheric temperature gradient changes, and new evidence of weaknesses in simulating past variations in North Atlantic atmospheric circulation on seasonal-to-decadal time scales. One exception is the expected decrease in frequency of atmospheric blocking events over Greenland and the North Pacific in boreal winter in SSP3-7.0 and SSP5-8.5 scenarios ( ''medium confidence'' ). {4.5.1}		'''The CMIP6 multi-model ensemble projects a long-term increase in the boreal wintertime Northern Annular Mode index under the high-emissions scenarios of SSP3-7.0 and SSP5-8.5, but regional changes may deviate from a simple shift in the mid-latitude circulation.''' Substantial uncertainty and thus ''low confidence'' remain in projecting regional changes in Northern Hemisphere jet streams and storm tracks, especially for the North Atlantic basin in winter; this is due to large natural internal variability, the competing effects of projected upper- and lower-tropospheric temperature gradient changes, and new evidence of weaknesses in simulating past variations in North Atlantic atmospheric circulation on seasonal-to-decadal time scales. One exception is the expected decrease in frequency of atmospheric blocking events over Greenland and the North Pacific in boreal winter in SSP3-7.0 and SSP5-8.5 scenarios ( ''medium confidence'' ). {4.5.1}

	'''Near-term predictions and projections of the sub-polar branch of the Atlantic Multi-decadal Variability (AMV) on the decadal time scale have improved in CMP6 models compared to CMIP5~~'''~~ ( ''high confidence~~'' '~~'').''' This is ''likely'' to be related to a more accurate response to natural forcing in CMIP6 models. Initialization contributes to the reduction of uncertainty and to predicting subpolar sea surface temperature. AMV influences on the nearby regions can be predicted over lead times of 5–8 years ( ''medium'' ''confidence'' ). {4.4.3}		'''Near-term predictions and projections of the sub-polar branch of the Atlantic Multi-decadal Variability (AMV) on the decadal time scale have improved in CMP6 models compared to CMIP5 (''high confidence'').''' This is ''likely'' to be related to a more accurate response to natural forcing in CMIP6 models. Initialization contributes to the reduction of uncertainty and to predicting subpolar sea surface temperature. AMV influences on the nearby regions can be predicted over lead times of 5–8 years ( ''medium'' ''confidence'' ). {4.4.3}

	'''It is~~'''~~ ''virtually certain~~'' '~~''that the El Niño–Southern Oscillation (ENSO) will remain the dominant mode of interannual variability in a warmer world.''' There is no model consensus for a systematic change in intensity of ENSO sea surface temperature variability over the 21st century in any of the SSP scenarios assessed ( ''medium confidence'' ). However, it is ''very'' ''likely'' that ENSO rainfall variability, used for defining extreme El Niños and La Niñas, will increase significantly, regardless of amplitude changes in ENSO SST variability, by the second half of the 21st century in scenarios SSP2-4.5, SSP3-7.0, and SSP5-8.5. {4.3.3, 4.5.3, 8.4.2}		'''It is ''virtually certain'' that the El Niño–Southern Oscillation (ENSO) will remain the dominant mode of interannual variability in a warmer world.''' There is no model consensus for a systematic change in intensity of ENSO sea surface temperature variability over the 21st century in any of the SSP scenarios assessed ( ''medium confidence'' ). However, it is ''very'' ''likely'' that ENSO rainfall variability, used for defining extreme El Niños and La Niñas, will increase significantly, regardless of amplitude changes in ENSO SST variability, by the second half of the 21st century in scenarios SSP2-4.5, SSP3-7.0, and SSP5-8.5. {4.3.3, 4.5.3, 8.4.2}

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	=== Cryosphere and Ocean ===		=== Cryosphere and Ocean ===