Editing IPCC:AR6/WGI/Chapter-4 (section)

== 4.6 Implications of Climate Policy ==

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=== 4.6.1 Patterns of Climate Change for Specific Levels of Global Warming ===

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This subsection provides an assessment of changes in climate at 1.5°C, 2°C, 3°C, and 4°C of globalwarming relative to the period 1850–1900 ( [[IPCC:Wg1:Chapter:Chapter-1#1.6.2|Section 1.6.2]] ), in particular a discussion of the regional patterns of change in temperature ( [[#4.6.1.1|Section 4.6.1.1]] ), precipitation ( [[#4.6.1.2|Section 4.6.1.2]] ), and aspects of atmospheric circulation ( [[#4.6.1.3|Section 4.6.1.3]] ). An assessment of changes in extreme weather events as a function of different levels of global warming is provided in Chapter 11, while corresponding analyses of regional climate change are provided in [[IPCC:Wg1:Chapter:Chapter-12|Chapter 12]] and in the Atlas. This section builds upon assessments from AR5 ( [[#Bindoff--2013|Bindoff et al., 2013]] ; [[#Christensen--2013|Christensen et al., 2013]] ; [[#Collins--2013|Collins et al., 2013]] ; [[#Hartmann--2013|Hartmann et al., 2013]] ) and SR1.5 (SR1.5; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ), as well as new literature related to projections of climate at 1.5°C, 2°C, and higher levels of global warming above pre-industrial levels.

Several methodologies have been applied to estimate the spatial patterns of climate change associated with a given level of global warming. These include performing model simulations under stabilisation scenarios designed to achieve a specific level of global warming, the analysis of epochs identified within transient simulations that systematically exceed different thresholds of global warming (e.g., [[#Mitchell--2017|Mitchell et al., 2017]] ; [[#Dosio--2018|Dosio et al., 2018]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Kjellström--2018|Kjellström et al., 2018]] ), and analysis based on statistical methodologies that include empirical scaling relationships (ESR; [[#Schleussner--2017|Schleussner et al., 2017]] ; [[#Dosio--2018|Dosio and Fischer, 2018]] ; [[#Seneviratne--2018|Seneviratne et al., 2018]] ) and statistical pattern scaling (e.g., [[#Kharin--2018|Kharin et al., 2018]] ). These different methodologies are assessed in some detail in [[#4.2.4|Section 4.2.4]] ( [[#James--2017|James et al., 2017]] ) and generally provide qualitatively consistent results regarding changes in the spatial patterns of temperature and rainfall means and extremes (see Chapter 11) at different levels of global warming.

In this subsection, we present the projected patterns of climate change obtained following the epoch approach (also called the time-shift method, see [[#4.2.4|Section 4.2.4]] ) under the Tier 1 SSPs (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). For each model simulation considered under each of these SSPs, 20-year moving averages of the global average atmospheric surface temperature are first constructed, then this time series is used to detect the first year during when GSAT exceeds the 1.5°C, 2°C, 3°C and 4°C thresholds with respect to the 1850–1900 (Cross-Chapter Box 11.1). The temperature thresholds are not exceeded in all the model simulations across the Tier 1 SSPs. That is, decreasing numbers of simulations are available for the analysis of patterns of change as the temperature threshold increases. For each simulation within which a given temperature threshold is exceeded, a 20-year global climatology is subsequently constructed to represent that level of global warming, centred on the year for which the threshold was first exceeded. The composite of all such climatologies across the Tier 1 SSPs and model simulations constitute the spatial patterns of change for a given temperature threshold. Some of the complexities of scaling patterns of climate change with different levels of global warming are also discussed in the following sections. These include overshoot versus stabilization scenarios and limitations of pattern scaling for strong mitigation and stabilization scenarios ( [[#Tebaldi--2014|Tebaldi and Arblaster, 2014]] ). At least for the case of annual mean temperature and precipitation, strong evidence exists that even for strong mitigation and stabilization scenarios, patterns of change at lower levels of warming scale similarly to those reconstructed from transient simulations using either standard pattern-correlation or time-shift methodologies ( [[#Tebaldi--2018|Tebaldi and Knutti, 2018]] ).

Pattern scaling performance based on scenario experiments is generally better for near-surface temperature than for precipitation ( [[#Ishizaki--2013|Ishizaki et al., 2013]] ). For precipitation, rapid adjustments due to different forcing agents must be accounted for ( [[#Richardson--2016|Richardson et al., 2016]] ). Possible non-linear responses to different forcing levels are also important ( [[#Good--2015|Good et al., 2015]] , 2016). Pattern scaling does not work as well at high forcing levels ( [[#Osborn--2018|Osborn et al., 2018]] ). It is also important to distinguish the forced response from internal variability when comparing similar warming levels ( [[#Suárez-Gutiérrez--2018|Suárez-Gutiérrez et al., 2018]] ). The purpose of this section is not to repeat the analysis for all the variables considered in Sections 4.4 and 4.5, but rather to show a selected number of key variables that are important from the perspective of understanding the response of the physical climate system to different levels of warming.

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==== 4.6.1.1 Temperature ====

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Global warming of 1.5°C implies higher mean temperatures compared to 1850–1900, with generally higher warming over land compared to ocean areas ( ''virtuallycertain'' ) and larger warming in high latitudes compared to low latitudes (Figure 4.31). In addition, global warming of 2°C versus 1.5°C results in robust increases in the mean temperatures in almost all locations, both on land and in the ocean ( ''virtually certain'' ), with subsequent further warming at almost all locations at higher levels of global warming ( ''virtually certain'' ) ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). For each particular level of global warming, relatively larger mean warming is projected for land regions ( ''virtually certain'' ) (see Figure 4.31; [[#Christensen--2013|Christensen et al., 2013]] ; [[#Collins--2013|Collins et al., 2013]] ; [[#Seneviratne--2016|Seneviratne et al., 2016]] ). The projected changes at 1.5°C and 2°C global warming are consistent with observed historical global trends in temperature and their attribution to anthropogenic forcing (Chapter 3), as well as with observed changes under the recent global warming of 0.5°C ( [[#Schleussner--2017|Schleussner et al., 2017]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). That is, spatial patterns of temperature changes associated with the 0.5°C difference in GMST warming between 1991–2010 and 1960–1979 ( [[#Schleussner--2017|Schleussner et al., 2017]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) are consistent with projected changes under 1.5°C and 2°C of global warming.

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[[File:68da52712f4289321d6b5d7412f0fcaf IPCC_AR6_WGI_Figure_4_31.png]]

'''Figure 4.31''' '''|''' '''Projected spatial patterns of change in annual average near-surface temperature (°C) at different levels of global warming.''' Displayed are '''(a–d)''' spatial patterns of change in annual average near-surface temperature at 1.5°C, 2°C, 3°C, and 4°C of global warming relative to the period 1850–1900 and '''(e–g)''' spatial patterns of differences in temperature change at 2°C, 3°C, and 4°C of global warming compared to 1.5°C of global warming. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and ''likely'' emerges from internal variability. That is, where at least 66% of the models show a change greater than the internal-variability threshold ( [[#4.2.6|Section 4.2.6]] ) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Values were assessed from a 20-year period at a given warming level, based on model simulations under the Tier-1 SSPs of CMIP6. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

The largest increase in annual mean temperature is found in the high latitudes of the Northern Hemisphere across all levels of global warming ( ''virtually certain'' ) (Figure 4.31). This phenomenon peaks in the Arctic and is known as Arctic amplification, with the underlying physical mechanisms assessed in detail in [[#4.5.1|Section 4.5.1]] and [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] (Section 7.4.4.1). For the CMIP6 ensemble average considered here, Arctic annual mean temperatures warm by a factor of 2.3, 2.5, 2.4 and 2.4 for 1.5°C, 2°C, 3°C and 4°C of global warming, respectively. That is, Arctic warming scales approximately linearly with GSAT. Generally, when Arctic amplification is considered across individual models, warming occurs at a factor of two to four times the global level of warming. It is ''unlikely'' that warming in the Southern Hemisphere high latitudes in the 21st century will exceed the change in GSAT, or that it will substantially exceed warming in the tropics, for GSAT change ranging between 1.5°C and 4°C (Figure 4.31 and Table 4.2). Correspondingly, there is ''low confidence'' of Antarctic amplification occurring under transient, 21st century low mitigation scenarios (Table 4.2 and Section 7.4.4.1). The Antarctic continent is projected to warm at a higher rate than the mid-latitude Southern Ocean, however, at all levels of global warming (Figure 4.31). The relevant physical mechanisms that reduce the amplitude of polar amplification over Antarctica compared to the Arctic are assessed in detail in [[#4.5.1|Section 4.5.1]] and [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] (Section 7.4.4.1). In the Southern Hemisphere the strongest warming over land is to occur, at any given level of global warming, over the subtropical areas of South America, southern Africa and Australia ( ''high confidence'' ). The relatively strong warming in subtropical southern Africa may be attributed to strong soil-moisture–temperature coupling and projected increased dryness under enhanced subsidence ( [[#Engelbrecht--2015|Engelbrecht et al., 2015]] ; [[#Vogel--2017|Vogel et al., 2017]] ). Across the globe, in the tropics, subtropics, and mid- to high latitudes, temperatures tend to scale linearly with the level of increase in GSAT and patterns of change are largely scenario independent ( ''high confidence'' ).

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==== 4.6.1.2 Precipitation ====

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While global mean precipitation increases as GSAT rises with the ''very likely'' range of 1–3% per 1°C ( ''high confidence'' ) (Sections 8.2.1 and 8.4.1), patterns of precipitation change do not scale as linearly with GSAT increase. Nevertheless, common features of precipitation change in the multi-model mean across scenarios still exist for different levels of global warming (Figure 4.32). Precipitation will ''very likely'' increase in the high latitudes and over tropical regions, and will ''likely'' increase in large parts of the monsoon region, but are ''likely'' to decrease over the subtropical regions, including the Mediterranean, southern Africa, parts of Australia and South America at all four levels of global warming. The increases and decreases in precipitation will amplify at higher levels of global warming ( ''high confidence'' ) (Figure 4.32). Changes in extreme precipitation events under different levels of global warming are assessed in Chapter 11.

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[[File:03b9f70ce7cce3b59a103943cd279d67 IPCC_AR6_WGI_Figure_4_32.png]]

'''Figure 4.32 |''' '''Projected spatial patterns of change in annual average precipitation (expressed as a percentage change) at different levels of global warming.''' Displayed are '''(a–d)''' spatialpatterns of change in annual precipitation at 1.5°C, 2°C, 3°C, and 4°C of global warming relative to the period 1850–1900. No map overlay indicates regions where the change is robust and ''likely'' emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold ( [[#4.2.6|Section 4.2.6]] ) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Values were assessed from a 20-year period at a given warming level, based on model simulations under the Tier-1 SSPs of CMIP6. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

The SR1.5 stated ''low confidence'' regarding changes in global monsoons at 1.5°C versus 2°C of global warming, as well as differences in monsoon responses at 1.5°C versus 2°C. Generally, statistically significant changes in regional annual average precipitation are expected at a global mean warming of 2.5°C–3°C or more ( [[#Tebaldi--2015|Tebaldi et al., 2015]] ). Over the austral-winter rainfall regions of south-western South America, South Africa and Australia, projected decreases in mean annual rainfall show '''high agreement''' across models and a strong climate change signal even under 1.5°C of global warming, with further amplification of the signal at higher levels of global warming ( ''high confidence'' ) ( [[#Mindlin--2020|Mindlin et al., 2020]] ). This is a signal evident in observed rainfall trends over these regions (Sections 2.3.1.3 and 8.3.1.6 ). Also, over the Asian monsoon regions, increases in rainfall will occur at 1.5°C and 2°C of global warming ( [[#Chevuturi--2018|Chevuturi et al., 2018]] ). At warming levels of 1.5°C and 2°C, the changes in global monsoons are strongly dependent on the modelling strategies used, such as fully coupled transient, fully coupled quasi-equilibrium, and atmosphere-only quasi-equilibrium simulations. In particular, the differences of regional monsoon changes among model setups are dominated by strategy choices such as transient versus quasi-equilibrium set-up, prescription of SST, and treatment of aerosols ( [[#Zhang--2021|Zhang and Zhou, 2021]] ).

The global and land area fractions with significant precipitation changes with global warming are shown in Figure 4.33. It is ''virtually certain'' that average warming will be higher over land. As warming increases, a larger global and land area will experience statistically significant increases or decreases in precipitation ( ''medium confidence'' ). The increase of the area fraction with significant precipitation increase is larger over land than over the ocean, but the increase of the area fraction with significant precipitation decrease is larger over the ocean than over land (Figure 4.33). Precipitation variability in most climate models increases over the global land area in response to warming ( [[#Pendergrass--2017|Pendergrass et al., 2017]] ).

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[[File:234ba024605e8d9502eda050a0989ea3 IPCC_AR6_WGI_Figure_4_33.png]]

'''Figure 4.33''' '''|''' '''Area fraction of significant precipitation change at 1.5°C, 2°C, 3°C, and 4°C of global warming.''' Range of land fraction '''(top)''' and global area fraction '''(bottom)''' with significant precipitation increase '''(left-hand side)''' and decrease '''(right-hand side)''' in the projected annual precipitation change (%) at levels of global warming compared to the period 1850–1900. Values were assessed from a 20-year period at a given warming level from SSP1-2.6, SSP3-7.0 and SSP5-8.5 in CMIP6. The solid line illustrates the CMIP6-multi model mean and the shaded band is the 5–95% range across models that reach a given level of warming. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

In summary, based on the assessment of CMIP6 models, there is ''high confidence'' that global mean precipitation will increase with increase in global mean surface temperature. Precipitation will ''very likely'' increase in the high latitudes and over tropical regions, ''likely'' increase in large parts of the monsoon region, but will ''likely'' decrease over the subtropical regions. There is ''high confidence'' that increases and decreases in precipitation will amplify over higher levels of global warming. As warming increases, there is ''medium confidence'' that a larger land area will experience statistically significant increases or decreases in precipitation.

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==== 4.6.1.3 Atmospheric Circulation ====

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The AR5 reported that the application of pattern scaling to extract information on variables other than surface temperature and precipitation was relativelyunexplored. Since AR5, new studies have examined the relationship between projections of mid-latitude atmospheric circulation and GSAT both in terms of interpreting spread in responses across the CMIP5 multi-model ensemble ( [[#Grise--2014a|Grise and Polvani, 2014a]] , 2016) and to investigate variations in the circulation response as a function of GSAT change over time within a given forcing experiment ( [[#Grise--2017|Grise and Polvani, 2017]] ; [[#Ceppi--2018|Ceppi et al., 2018]] ).

At a fixed time horizon, the CMIP5 multi-model spread in GSAT explains only a small fraction of the spread in the shift of the Northern Hemisphere mid-latitude circulation due to an abrupt quadrupling in CO <sub>2</sub> ( [[#Grise--2016|Grise and Polvani, 2016]] ). The fraction of model spread explained by GSAT in the shift of the Southern Hemisphere circulation is larger, but still fairly small ( [[#Grise--2014a|Grise and Polvani, 2014a]] , 2016). At a fixed time horizon and for a given emissions scenario, CMIP5 multi-model spread in storm track shifts, and the closely related mid-latitude jets, can be better explained by multi-model spread in lower and upper level meridional temperature gradients than by GSAT ( [[#Harvey--2014|Harvey et al., 2014]] ; [[#Grise--2016|Grise and Polvani, 2016]] ).

In the North Atlantic, North Pacific, and Southern Hemisphere, the transient response of the mid-latitude jets to forcing behaves non-linearly with GSAT ( [[#Grise--2017|Grise and Polvani, 2017]] ; [[#Ceppi--2018|Ceppi et al., 2018]] ). This is a consequence of the time-dependence of the relationship between radiative forcing and GSAT and the temporal evolution of SST patterns ( [[#Ceppi--2018|Ceppi et al., 2018]] ), with a potential seasonal component in the SH associated with polar stratospheric temperature changes ( [[#Grise--2017|Grise and Polvani, 2017]] ). Consequently, the epoch approach applied to a transient simulation of the 21st century will overestimate the mid-latitude circulation response in a stabilized climate. Dedicated time slice experiments simulating stabilized climates are therefore required to assess differences in mid-latitude circulation at given levels of global warming ( [[#Li--2018|Li et al., 2018]] ). A further complication in the SH is the competing influences of ozone recovery and increasing GHG concentrations on the austral-summer mid-latitude circulation during the first half the 21st century ( [[#Barnes--2013|Barnes and Polvani, 2013]] ; [[#Barnes--2014|Barnes et al., 2014]] ). Using transient 21st century experiments to diagnose changes in SH mid-latitude circulation at different levels of warming therefore confounds the effects of ozone recovery and GHG increases ( [[#Ceppi--2018|Ceppi et al., 2018]] ). Given these various limitations, we do not apply epoch analysis to assess mid-latitude atmospheric circulation changes and related annular modes of variability.

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=== 4.6.2 Climate Goals, Overshoot, and Path-Dependence ===

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Many scenarios aiming at limiting warming by 2100 to 1.5°C involve overshoot – ERF temporarily exceeds a certain level before peaking and declining again (Annex VII: Glossary). To quantify the implications of any such overshoot, this subsection assesses reversibility of climate due to temporary overshoot of GSAT levels during the 21st century, and implications for the use of carbon budgets. It also assesses differences in climate outcomes under different pathways, with a focus on comparing the SSPs used in CMIP6 with the RCPs used in CMIP5.

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==== 4.6.2.1 Climate Change Under Overshoot ====

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The SR1.5 ( [[#IPCC--2018b|IPCC, 2018b]] ) concluded with ''high confidence'' that overshoot trajectories ‘result in higher impacts and associated challenges compared to pathways that limit global warming to 1.5°C with no or limited overshoot’. The degree and duration of overshoot affects the risks and impacts likely to be experienced ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) and the emissions pathway required to achieve it ( [[#Akimoto--2018|Akimoto et al., 2018]] ). Consequences relating to ice sheets and climatic extremes have been found to be greater at 2°C of global warming than at 1.5°C ( [[#Schleussner--2016|Schleussner et al., 2016]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) but even on recovery to lower temperatures, these effects may not reverse. Overshoot has been found to lead to irreversible changes in thermosteric sea level ( [[#Tokarska--2015|Tokarska and Zickfeld, 2015]] ; [[#Palter--2018|Palter et al., 2018]] ; [[#Tachiiri--2019|Tachiiri et al., 2019]] ), AMOC ( [[#Palter--2018|Palter et al., 2018]] ), ice sheets, and permafrost carbon (Sections 4.7.2 and 5.4.9) and to long-lasting effects on ocean heat ( [[#Tsutsui--2006|Tsutsui et al., 2006]] ). Abrupt changes and tipping points are not well understood, but the higher the warming level and the longer the duration of overshoot, the greater the risk of unexpected changes ( [[#4.7.2|Section 4.7.2]] ). Non-reversal of the hydrological cycle has also been found in some studies with an increase in global precipitation following CO <sub>2</sub> decrease being attributed to a build-up of ocean heat ( [[#Wu--2010|Wu et al., 2010]] ), and to a fast atmospheric adjustment to CO <sub>2</sub> radiative forcing ( [[#Cao--2011|Cao et al., 2011]] ).

Global temperature is expected to remain approximately constant if emissions of CO <sub>2</sub> were to cease ( [[#4.7.1.1|Section 4.7.1.1]] ), and so reductions in GSAT are only possible in the event of net negative global CO <sub>2</sub> emissions. We assess here results from an overshoot scenario (SSP5-3.4-OS; [[#O’Neill--2016|O’Neill et al., 2016]] ), which explores the implications of a peak and decline in forcing during the 21st century. Reversibility under more extreme and idealized carbon dioxide removal (CDR) scenarios is assessed in [[#4.6.3|Section 4.6.3]] . In SSP5-3.4-OS, CO <sub>2</sub> peaks at 571 ppm in the year 2062 and reverts to 497 ppm by 2100 – approximately the same level as in 2040. SSP5-3.4-OS has strong net negative emissions of CO <sub>2</sub> , exceeding those in SSP1-2.6 and SSP1-1.9 from 2070 onwards and reaching –5.5 PgC yr <sup>–1</sup> (–20 GtCO <sub>2</sub> yr <sup>–1</sup> ) by 2100. While this causes global mean temperature to decline, changes in climate have not fully reversed by 2100 under this reversal of CO <sub>2</sub> concentration (Figure 4.34). Quantities are compared for 2081–2100 relative to a 20-year period (2034–2053) of the same average CO <sub>2</sub> . Differences between these two periods of the same CO <sub>2</sub> are: GSAT: 0.28 ± 0.30°C (mean ± standard deviation); global land precipitation: 0.026 ± 0.011 mm day <sup>–1</sup> ; September Arctic sea ice area: –0.32 ± 0.53 million km <sup>2</sup> ; thermosteric sea level: 12 ± 0.8 cm. As assessed in Section 9.3.1.1, Arctic sea ice area is linearly reversible with GSAT. Although these climate quantities are not fully reversible, the overshoot scenario results in reduced climate change compared with stabilisation or continued increase in greenhouse gases ( [[#Tsutsui--2006|Tsutsui et al., 2006]] ; [[#Palter--2018|Palter et al., 2018]] ; [[#Tachiiri--2019|Tachiiri et al., 2019]] ) ( ''high confidence'' ).

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[[File:61bb4e57a44ee1dcc8c841ad587e826e IPCC_AR6_WGI_Figure_4_34.png]]

'''Figure 4.34''' '''|''' '''Simulated changes in climate indices for SSP5-3.4-OS plotted against atmospheric CO''' <sub>2</sub> '''concentration (ppm) from 480 up to 571 and back to 496 by 2100. (a)''' Global surface air temperature change; '''(b)''' Global land precipitation change; '''(c)''' September Arctic sea ice area change; '''(d)''' Global thermosteric sea level change. Plotted changes are relative to the 2034–2053 mean which has same CO <sub>2</sub> as 2081–2100 mean (shaded grey bar). Red lines denote changes during the period up to 2062 when CO <sub>2</sub> is rising, blue lines denote changes after 2062 when CO <sub>2</sub> is decreasing again. Thick line is multi-model mean; thin lines and shading show individual models and complete model range. Numbers in square brackets indicate number of models used in each panel. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

The transient climate response to cumulative CO <sub>2</sub> emissions, TCRE, allows climate policy goals to be associated with remaining carbon budgets as global temperature increase is near-linear with cumulative emissions (Section 5.5). Research since AR5 has shown that the concept of near-linearity of climate change to cumulative carbon emissions holds for measures other than just GSAT, such as regional climate ( [[#Leduc--2016|Leduc et al., 2016]] ) or extremes ( [[#Harrington--2016|Harrington et al., 2016]] ; [[#Seneviratne--2016|Seneviratne et al., 2016]] ). However, ocean heat and carbon uptake do exhibit path dependence, leading to deviation from the TCRE relationship for levels of overshoot above 300 PgC ( [[#Zickfeld--2016|Zickfeld et al., 2016]] ; [[#Tokarska--2019|Tokarska et al., 2019]] ). Sea level rise, loss of ice sheets, and permafrost carbon release may not reverse under overshoot and recovery of GSAT and cumulative emissions ( [[#4.7|Section 4.7]] ). TCRE remains a valuable concept to assess climate policy goals and how to achieve them but given the non-reversibility of different climate metrics with CO <sub>2</sub> and GSAT reductions, it has limitations associated with evaluating the climate response under overshoot scenarios and CO <sub>2</sub> removal ( ''medium confidence'' ).

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==== 4.6.2.2 Consistency Between Shared Socio-economic Pathways and Representative Concentration Pathways ====

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As CMIP5 and CMIP6 employed different scenario sets (RCPs and SSPs, respectively; see [[IPCC:Wg1:Chapter:Chapter-1#1.6.1.1|Section 1.6.1.1]] and Cross-Chapter Box 1.4), we assess how much of the differences in projections are due to the scenario change and how much due to model changes. The CMIP6-simulated GSAT increases tend to be larger than in CMIP5, for nominally comparable scenarios ( [[#4.3.1|Section 4.3.1]] ; [[#Tebaldi--2021|Tebaldi et al., 2021]] ).

The radiative forcing labels on SSP and RCP scenarios is approximate and enables the multiple climate forcings within the scenario to be characterized by a single number. While the scenarios are similar in terms of the stratospheric adjusted radiative forcing ( [[#Tebaldi--2021|Tebaldi et al., 2021]] ), they differ more in their effective radiative forcing (ERF). The combination of component forcings (CO <sub>2</sub> , non-CO <sub>2</sub> greenhouse gases, aerosols) within the scenario also differ ( [[#Meinshausen--2020|Meinshausen et al., 2020]] ). The ERF levels in the RCP and SSP scenarios have been calculated by sampling uncertainty in forcing from a range of different GHG species and aerosols (see 7.SM.1.4 for details). Figure 4.35 shows the time evolution and 2081–2100 mean across the families of scenarios and how this affects projections of GSAT. That the ERFs differ between corresponding SSP and RCP scenarios makes a comparison between CMIP6 and CMIP5 projections challenging ( [[#Tebaldi--2021|Tebaldi et al., 2021]] ). [[#Wyser--2020|Wyser et al. (2020)]] find the EC-Earth3-Veg model exhibits stronger radiative forcing and substantially greater warming under SSP5-8.5 than RCP8.5, and similar, but smaller additional warmings for SSP2-4.5and SSP1-2.6 compared with RCP4.5 and RCP2.6, respectively. In addition to the global response, climate can vary regionally due to non-CO <sub>2</sub> components of forcing ( [[#Samset--2016|Samset et al., 2016]] ; [[#Richardson--2018a|Richardson et al., 2018a]] , b).

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[[File:68e2a399ae3deaecc5953de3fef12860 IPCC_AR6_WGI_Figure_4_35.png]]

'''Figure''' '''4.35 |''' '''Comparison of RCPs and SSPs run by a single emulator to estimate scenario differences.''' Time series with 5–95% ranges and medians of '''(a)''' effective radiative forcings, calculated as described in Annex 7.A.1; and '''(b)''' global surface air temperature projections relative to 1850–1900 for the RCP and SSP scenarios from MAGICC 7.5. Note that the nameplate radiative forcing level refers to stratospheric adjusted radiative forcings in AR5-consistent settings ( [[#Tebaldi--2021|Tebaldi et al., 2021]] ) while ERFs may differ. MAGICC7.5 is here run in the recommended setup for WGIII, prescribing observed GHG concentrations for the historical period and switching to emissions-driven runs in 2015. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

Emulators ( [[#cross-chapter-box-7.1|Cross-Chapter Box 7.1]] ) can be used to aid understanding of differences between generations of scenarios. The AR5 ( [[#Collins--2013|Collins et al., 2013]] ) explored the differences between CMIP3 and CMIP5 (their Figure 12.40). Here we use an emulator calibrated to AR6 assessed GSAT ranges, thus eliminating the effect of differences in the model ensembles, to analyse the differences between SSP and RCP scenarios. MAGICC7.5 in its WGIII-calibrated setup (see [[#cross-chapter-box-7.1|Cross-Chapter Box 7.1]] ) projects differences in 2081–2100 mean warming between the RCP2.6 and SSP1-2.6 scenarios of around 0.2°C, between RCP4.5 and SSP2-4.5 ofaround 0.3°C and between RCP8.5 and SSP5-8.5 of around 0.3°C (Figure 4.35b). The SSP scenarios also have a wider 5–95% range simulated by MAGICC7.5 explaining about half of the increased range seen when comparing CMIP5 and CMIP6 models. Higher climate sensitivity is, though, the primary reason behind the upper end of the warming for SSP5-8.5 reaching 1.5°C higher than the CMIP5 results. Compared with the differences between the CMIP5 and CMIP6 multi-model ensembles for the same scenario pairs (Table A6 in [[#Tebaldi--2021|Tebaldi et al., 2021]] ), the higher ERFs of the SSP scenarios contribute approximately half of the warmer CMIP6 SSP outcomes ( ''medium confidence'' ).

In summary, there is ''medium confidence'' that about half of the warming increase in CMIP6 compared to CMIP5 is due to higher climate sensitivity in CMIP6 models; the other half arises from higher ERF in nominally comparable scenarios (e.g., RCP8.5 and SSP5-8.5).

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<span id="climate-response-to-mitigation-carbon-dioxide-removal-and-solar-radiation-modification"></span>
=== 4.6.3 Climate Response to Mitigation, Carbon Dioxide Removal and Solar Radiation Modification ===

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Most strong-mitigation scenarios assume – in addition to emissions reductions – some form of carbon dioxide removal (CDR). Anthropogenic activities that remove CO <sub>2</sub> from the atmosphere and durably store it in geological, terrestrial, or ocean reservoirs, or in products (see Glossary). The SR1.5 ( [[#Rogelj--2018b|Rogelj et al., 2018b]] ) assessed that all pathways that limit warming to 1.5°C by 2100 with no or limited overshoot use CDR. In the SSP class of scenarios, SSP1-1.9 is characterized by a rapid decline of net CO <sub>2</sub> emissions to zero by 2050 and net negative CO <sub>2</sub> emissions in the second half of this century ( [[#O’Neill--2016|O’Neill et al., 2016]] ; [[#Rogelj--2018a|Rogelj et al., 2018a]] ), implying the use of CDR. The term ‘net CO <sub>2</sub> emissions’ refers to the difference between anthropogenic CO <sub>2</sub> emissions and removal by CDR options, and ‘net negative CO <sub>2</sub> emissions’ imply a scenario where CO <sub>2</sub> removal exceeds emissions ( [[#van%20Vuuren--2011|van Vuuren et al., 2011]] , 2016). The terms ‘negative emissions’ and ‘net negative emissions’ refer to and include all GHGs (see Glossary).

Climate change can be also offset by solar radiation modification (SRM) measures that modify the Earth’s radiation budget to reduce global warming (see Glossary). CDR and SRM approaches have been together referred to as ‘geoengineering’ or ‘climate engineering’ in the literature ( [[#The%20Royal%20Society--2009|The Royal Society, 2009]] ; [[#NRC--2015a|NRC, 2015a]] , b; [[#Schäfer--2015|Schäfer et al., 2015]] ). However, following SR1.5 ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ), these terms are inconsistently used in the literature, so that CDR and SRM are explicitly differentiated here. SRM contrasts with climate change mitigation because it introduces a ‘mask’ to the climate change problem by altering the Earth’s radiation budget, rather than attempting to address the root cause of the problem, which is the increase in GHGs in the atmosphere.

( [[#4.6.3.1|Section 4.6.3.1]] assesses the emergence of the climate response to mitigation, which is reflected by the difference between high- and low-emissions scenarios. [[#4.6.3.2|Section 4.6.3.2]] then assesses the climate response to mitigation through CDR options, usually assumed against the background of some emissions scenario; note that the CDR options themselves are assessed in [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] (Section 5.6.2). [[#4.6.3.3|Section 4.6.3.3]] assesses the climate system response to SRM options. The biogeochemical implications of CDR and SRM are assessed in [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] (Sections 5.6.2 and 5.6.3, respectively). The importance of CDR for reaching net zero or negative CO <sub>2</sub> emissions in mitigation pathways is assessed in the AR6 WGIII report (Chapters 3, 4, 6, 7 and 12). The risks for and impacts on human and natural systems due to SRM are assessed in the AR6 WGII report (Chapter 16), and the international governance issues related to SRM and CDR are assessed in the AR6 WGIII report (Chapter 14).

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<span id="emergence-of-the-climate-response-to-mitigation"></span>
==== 4.6.3.1 Emergence of the Climate Response to Mitigation ====

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Reducing GHG emissions will eventually slow and limit the degree of climate change relative to high-emissions scenarios such as SSP5-8.5 ( ''very'' ''high confidence'' ). Even when CO <sub>2</sub> emissions are reduced, however, atmospheric CO <sub>2</sub> concentrations continue to increase as long as emissions exceed removal by sinks ( [[#Millar--2017|Millar et al., 2017]] ). Surface warming would likewise initially continue under scenarios of decreasing emissions, resulting in a substantial lag between a peak in CO <sub>2</sub> emissions and peak warming ( ''high confidence'' ) ( [[#Ricke--2014|Ricke and Caldeira, 2014]] ; [[#Zickfeld--2015|Zickfeld and Herrington, 2015]] ). The lag between peak emissions and warming depends on the emissions history prior to the peak and also on the rate of the subsequent emissions reductions ( [[#Matthews--2010|Matthews, 2010]] ; [[#Ricke--2014|Ricke and Caldeira, 2014]] ; [[#Zickfeld--2015|Zickfeld and Herrington, 2015]] ).

In addition to the lag between peak emissions and peak warming,the climate response to reduced emissions would be overlain by internal variability, which can amplify or attenuate the forced response. The resulting masking of differences between scenarios is illustrated in Figure 4.36 for GSAT trends over 2021–2040 ( [[#Maher--2020|Maher et al., 2020]] ). The overall trends conform to expectations in that most simulations show warming almost everywhere, especially under scenario RCP8.5 (Figure 4.36 bottom row). But any individual grid point can in principle show no warming or even cooling, even under RCP 8.5, over the near term (Figure 4.36, middle row). The magnitude of pointwise maximum and minimum temperature trends can be as large as 0.5 <sup>°</sup> C per year (Figure 4.36, top and middle rows), exceeding possible trends in the global mean by one order of magnitude. While it is only a small fraction of the surface that simultaneously can show cooling, cooling at any given location is fully consistent with globally averaged surface warming over the near term ( ''high confidence'' , since the findings of [[#Maher--2020|Maher et al. (2020)]] are consistent across six different large initial-condition ensembles).

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[[File:f83d9eafc0470a6d1c009854fbbaeaee IPCC_AR6_WGI_Figure_4_36.png]]

'''Figure''' '''4.36 |''' '''Masking of climate response to mitigation by internal variability in the near term.''' Near-term (2021–2040) pointwise maximum '''(top row)''' and pointwise minimum '''(middle row)''' surface air temperature trends in the large initial-condition ensemble from MPI '''(left and centre columns)''' , and CESM '''(right-hand column)''' models in the RCP2.6 '''(left-hand column)''' and RCP8.5 scenarios '''(centre and right columns)''' . The percentage of ensemble members with a warming trend in the near term is shown in the bottom panels. Figure modified from [[#Maher--2020|Maher et al. (2020)]] . Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

An important development since AR5 has been the quantification of when the climate response to mitigation can be expected to emerge from the background noise of internal variability (illustrated in Figure 4.36; see [[IPCC:Wg1:Chapter:Chapter-1#1.4.2.2|Section 1.4.2.2]] and Glossary). A basic ambiguity arises because once mitigation measures are in place, it is no longer possible to observe what the climate would have been without these measures, and any statement about emergence of the response to mitigation is contingent upon the assumed strength of mitigation in relation to an assumed (‘counterfactual’) no-mitigation scenario. Still, there is ''high agreement'' on the emergence of the climate response to mitigation across a number of independent studies using different models and different statistical approaches.

Among global quantities, emergence of the response to differing CO <sub>2</sub> emissions – representing differences between low- and high-emissions scenarios – is first expected to arise in global mean CO <sub>2</sub> concentrations, about 10 years after emissions pathways have started diverging ( ''high confidence'' ) ( [[#Tebaldi--2013|Tebaldi and Friedlingstein, 2013]] ; [[#Peters--2017|Peters et al., 2017]] ; [[#Schwartzman--2020|Schwartzman and Keeling, 2020]] ; [[#Spring--2020|Spring et al., 2020]] ). In these studies, emergence is generally defined as the time at which the global mean concentration first differs between mitigation and non-mitigation scenarios by more than two standard deviations of internal variability, although there are some methodological differences.

Emergence in GSAT would be delayed further, owing to the inertia in the climate system. Although not investigating emergence as defined here in AR6, [[#Tebaldi--2021|Tebaldi et al. (2021)]] used a 20-year running-mean GSAT and compared pairwise either model-by-model or between CM IP6 ensemble means from the core set of five scenarios assessed in this chapter. Differences by more than 0.1°C showed up in most cases in the near term, with only some of the individual models and the comparisons of the closest scenarios showing a delay until the mid-term. Taking internal variability explicitly into account, [[#Tebaldi--2013|Tebaldi and Friedlingstein (2013)]] and [[#Samset--2020|Samset et al. (2020)]] found emergence of mitigation benefits in GSAT changes about 25–30 years after RCP2.6 emissions diverge from the higher-emissions trajectories in RCP4.5 and RCP8.5. Consistently, [[#Marotzke--2019|Marotzke (2019)]] found about one-third likelihood that a trend reduction in GSAT, over the period 2021–2035 relative to 2005–2020, would be attributable to the emissions reductions implied by the difference between RCP2.6 and RCP4.5. Emergence of the GSAT response to mitigation of individual short-lived climate forcers (SLCFs) would likewise not occur until several decades after emissions trajectories diverge, owing to the relatively small influence of individual SLCFs on the total ERF ( [[#Samset--2020|Samset et al., 2020]] ), see also [[#4.4.4|Section 4.4.4]] and Figure 4.18.

In contrast to the earlier studies, emergence in GSAT within the near-term has recently been found by [[#McKenna--2021|McKenna et al. (2021)]] who investigated the likelihood that under the SSP scenarios GSAT trends will exceed the largest historical observed 20-year trends. They found that under scenario SSP1-1.9, the 20-year GSAT trends would ''likely'' be lower than in SSP3-7.0 and SSP5-8.5 within the near term. This earlier diagnosed time of emergence compared to [[#Marotzke--2019|Marotzke (2019)]] , while using a similar statistical approach, presumably arose because of the longer-period trends (20 rather than 15 years) and the larger difference between emissions trajectories considered ( ''medium confidence'' ). Using 20-year temperature anomalies relative to 1995–2014 instead of 20-year trends yielded a low probability of emergence ( [[#McKenna--2021|McKenna et al., 2021]] ), consistent with the AR5 (Collins et al., 2013; [[#Kirtman--2013|Kirtman et al., 2013]] ; [[#Tebaldi--2013|Tebaldi and Friedlingstein, 2013]] ; [[#Samset--2020|Samset et al., 2020]] ). It is not yet understood why GSAT trends appear to show faster emergence of mitigation benefits, compared to GSAT anomalies.

Emergence of mitigation benefits has been studied much less for quantities other than globally and annually averaged CO <sub>2</sub> concentration and surface temperature. Boreal-winter temperatures are more challenging for emergence, due to larger variability in boreal winter and adding a decade to the time of emergence, whereas emergence times for boreal-summer averages are similar to the annual temperature averages ( [[#Tebaldi--2013|Tebaldi and Friedlingstein, 2013]] ). Emergence happens later at the regional scale, with a median time of emergence of 30–45 years after emissions paths separate in RCP2.6 relative to RCP4.5 and RCP8.5; a stricter requirement of 95% confidence level instead of median induces a delay of several decades, bringing time of emergence toward the end of the 21st century at regional scales ( [[#Tebaldi--2013|Tebaldi and Friedlingstein, 2013]] ).

Attribution to emissions reductions, for the case of RCP2.6 relative to RCP4.5, is not substantially more likely for 2021–2035 trends in upper-2000 m OHC than for GSAT ( [[#Marotzke--2019|Marotzke, 2019]] ), although OHC change is thought to be less susceptible to internal variability. Furthermore, [[#Marotzke--2019|Marotzke (2019)]] found only around 10% likelihood of mitigation-benefit emergence during 2021–2035 for change in AMOC and September Arctic sea ice area. [[#Tebaldi--2018|Tebaldi and Wehner (2018)]] showed that the differences in temperature extremes between RCP4.5 and RCP8.5 over all land areas become statistically significant by 2050. The seemingly contrasting result of [[#Ciavarella--2017|Ciavarella et al. (2017)]] that mitigation benefits arise earlier for climate extremes poses no contradiction, because [[#Ciavarella--2017|Ciavarella et al. (2017)]] did not look at emergence as defined here but at the extremes of a distribution, which differ between scenarios already at a time when the distributions are still largely overlapping.

In summary, 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, 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 globally averaged surface warming ( ''high confidence'' ).

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<span id="climate-response-to-mitigation-by-carbon-dioxide-removal"></span>
==== 4.6.3.2 Climate Response to Mitigation by Carbon Dioxide Removal ====

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CDR options include afforestation, soil carbon sequestration, bioenergy with carbon capture and storage (BECCS), wet land restoration, ocean fertilization, ocean alkalinisation, enhanced terrestrial weathering and direct air capture and storage (see [[IPCC:Wg1:Chapter:Chapter-5#5.6.2|Section 5.6.2]] and Table 5.9 for a more complete discussion). [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] (Section 8.4.3) assesses the implications of CDR for water cycle changes. The potential of different CDR options in terms of the amount of CO <sub>2</sub> removed per year from the atmosphere, costs, co-benefits and side effects of the CDR approaches are assessed in SR1.5 ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ), the AR6 WGIII Report (see [[IPCC:Wg1:Chapter:Chapter-7|Chapters 7]] and [[IPCC:Wg1:Chapter:Chapter-12|12]] ), and in several review papers ( [[#Fuss--2018|Fuss et al., 2018]] ; [[#Lawrence--2018|Lawrence et al., 2018]] ; [[#Nemet--2018|Nemet et al., 2018]] ). In the literature, CDR options are also referred to as ‘negative CO <sub>2</sub> emissions technologies’.

Deployment of CDR will lead to a reduction in atmospheric CO <sub>2</sub> levels only if uptake by sinks exceeds net CO <sub>2</sub> emissions. Hence, there could be a substantial delay between the initiation of CDR and net CO <sub>2</sub> emissions turning negative ( [[#van%20Vuuren--2016|van Vuuren et al., 2016]] ), and the time to reach net negative CO <sub>2</sub> emissions and the evolution of atmospheric CO <sub>2</sub> and climate thereafter would depend on the combined pathways of anthropogenic CO <sub>2</sub> emissions, CDR, and natural sinks. The cooling (or avoided warming) due to CDR would be proportional to the cumulative amount of CO <sub>2</sub> removed from the atmosphere by CDR ( [[#Tokarska--2015|Tokarska and Zickfeld, 2015]] ; [[#Zickfeld--2016|Zickfeld et al., 2016]] ), as implied by the near-linear relationship between cumulative carbon emissions and GSAT change (Section 5.5).

Emissions pathways that limit globally averaged warming to 1.5°C or 2°C by the year 2100 assume the use of CDR approaches in combination with emissions reductions to follow net negative CO <sub>2</sub> emissions trajectory in the second half of this century. For instance ''',''' in SR1.5, all analysed pathways limiting warming to 1.5°C by 2100 with no or limited overshoot include the use of CDR to some extent to offset anthropogenic CO <sub>2</sub> emissions and the median of CO <sub>2</sub> removal across all scenarios was 730 GtCO <sub>2</sub> in the 21st century ( [[#Rickels--2018|Rickels et al., 2018]] ; [[#Rogelj--2018b|Rogelj et al., 2018b]] ). Affordable and environmentally and socially acceptable CDR options at scale well before 2050 are an important element of 1.5°C-consistent pathways especially in overshoot scenarios ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ). The required scale of removal by CDR can vary from 1–2 GtCO <sub>2</sub> yr <sup>–1</sup> year from 2050 onwards to as much as 20 GtCO <sub>2</sub> yr <sup>–1</sup> ( [[#Waisman--2019|Waisman et al., 2019]] ). In the SSP class of scenarios, net CO <sub>2</sub> emissions turn negative from around 2050 in SSP1-1.9 and around 2070 in SSP1-2.6 and in the overshoot scenario SSP5-3.4-OS ( [[#O’Neill--2016|O’Neill et al., 2016]] ). Thus, CDR would play a pivotal role in limiting climate warming to 1.5°C or 2°C ( [[#Minx--2018|Minx et al., 2018]] ). In stark contrast, however, two extensive reviews ( [[#Lawrence--2018|Lawrence et al., 2018]] ; [[#Nemet--2018|Nemet et al., 2018]] ) conclude that it is implausible that any CDR technique can be implemented at the scale needed by 2050.

When CDR is applied continuously and at scales as large as currently deemed possible, under RCP8.5 as the background scenario, the widely discussed CDR options such as afforestation, ocean iron fertilization and surface ocean alkalinisation are individually expected to be relatively ineffective, with limited (8%) warming reductions relative to the scenario with no CDR option ( [[#Keller--2014|Keller et al., 2014]] ). Hence, the potential role that CDR will play in lowering the temperature in high-emissions scenarios is limited ( ''medium confidence'' ). The challenges involved in comparing the climatic effects of various CDR options has also been recognized in recent studies ( [[#Sonntag--2018|Sonntag et al., 2018]] ; [[#Mengis--2019|Mengis et al., 2019]] ). For instance, due to compensating processes such as biogeophysical effects of afforestation (warming from albedo decrease when croplands are converted to forests) more carbon is expected to be removed from the atmosphere by afforestation than by ocean alkalinisation to reach the same global mean cooling.

The climate response to CDR-caused net negative CO <sub>2</sub> emissions has been studied in Earth system models by prescribing idealized ramp-down of CO <sub>2</sub> concentrations (MacDougall,2013; [[#Zickfeld--2016|Zickfeld et al., 2016]] ; [[#Schwinger--2018|Schwinger and Tjiputra, 2018]] ), CO <sub>2</sub> concentrations of RCP scenarios that have net negative CO <sub>2</sub> emissions (C.D. [[#Jones--2016|Jones et al., 2016]] b), and idealized net negative CO <sub>2</sub> emissions scenarios ( [[#Tokarska--2015|Tokarska and Zickfeld, 2015]] ). The Carbon Dioxide Removal Model Intercomparison Project (CDRMIP) uses multiple ESMs to explore the climate response, effectiveness of CO <sub>2</sub> removal, and challenges of CDR options ( [[#Keller--2018|Keller et al., 2018]] ). Idealized CDRMIP simulations increase CO <sub>2</sub> concentrations at 1% per year from the level in the pre-industrial control run (piControl) to 4×CO <sub>2</sub> <sub></sub> and subsequently decrease at the same rate to the piControl level. This section assesses the lag in climate response to CDR-caused negative emissions; climate ‘reversibility’ is assessed in [[#4.7.2|Section 4.7.2]] . The ramp-down phase, though unrealistic, represents the ‘net negative CO <sub>2</sub> emissions’ phase.

Figure 4.37 illustrates the first results from CDRMIP ( [[#Keller--2018|Keller et al., 2018]] ). Other studies that use similar (Zickfeld et al.,2016; [[#Schwinger--2018|Schwinger and Tjiputra, 2018]] ; [[#Jeltsch-Thömmes--2020|Jeltsch-Thömmes et al., 2020]] ) or other idealized scenarios ( [[#MacDougall--2013|MacDougall, 2013]] ) or more realistic net negative CO <sub>2</sub> emissions scenarios such as RCP2.6 (C.D. [[#Jones--2016|Jones et al., 2016]] b) and scenarios that limit warming to 2°C or less after different levels of overshoot ( [[#Tokarska--2015|Tokarska and Zickfeld, 2015]] ) arrive at similar conclusions. Changes in key climate variables substantially lag behind the decline in CO <sub>2</sub> (Figure 4.37). The precipitation increase at the beginning of the ramp-down phase agrees with the increase in precipitation for an abrupt decline in CO <sub>2</sub> ( [[#Cao--2011|Cao et al., 2011]] ). Notwithstanding a decline in atmospheric CO <sub>2</sub> , global mean thermosteric sea level would continue to rise. When atmospheric CO <sub>2</sub> returns to the piControl level, global mean thermosteric sea level is higher than its value at peak CO <sub>2</sub> (Figure 4.37), and it is ''likely'' that thermosteric global sea level would not return to piControl levels for over 1000 years after atmospheric CO <sub>2</sub> is restored to piControl concentrations ( [[#Tokarska--2015|Tokarska and Zickfeld, 2015]] ; [[#Ehlert--2018|Ehlert and Zickfeld, 2018]] ). Therefore, there is ''high confidence'' that sea level rise will not be reversed by CDR at least for several centuries ( [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] (Section 9.6.3.5). A comparison of different models shows recovery of AMOC intensity during net negative CO <sub>2</sub> emissions, but the results are model dependent – strengthening with an overshoot in most models ( [[#Jackson--2014|Jackson et al., 2014]] ) and strengthening but not reaching the initial state in some models ( [[#Sgubin--2015|Sgubin et al., 2015]] ). The overall lag in response is qualitatively similar to the lagged climate system response in the overshoot scenario SSP5-34-OS where CO <sub>2</sub> rises until 2062 and decreases thereafter (Figure 4.34). The lag in climate response to CDR causes hysteresis between key climate variables such as temperature, precipitation, AMOC and sea level, and atmosphere CO <sub>2</sub> with the hysteresis characteristics dependent on the rate of CDR and climate sensitivity ( [[#MacDougall--2013|MacDougall, 2013]] ; [[#Jeltsch-Thömmes--2020|Jeltsch-Thömmes et al., 2020]] ).

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[[File:3711ae45733f8a3f5cb71f1a85e01b3d IPCC_AR6_WGI_Figure_4_37.png]]

'''Figure 4.37''' '''|''' '''Delayed climate response to carbon dioxide removal (CDR)-caused net negative CO''' <sub>2</sub> '''emissions.''' Multi-model simulated response in global and annual mean climate variables for a ramp-up followed by ramp-down of CO <sub>2</sub> . Atmospheric CO <sub>2</sub> increases from the pre-industrial level at a rate of 1% yr <sup>–1</sup> to 4×CO <sub>2</sub> , then decreases at the same rate to the pre-industrial level and then remains constant. The ramp-down phase represents the period of net negative CO <sub>2</sub> emissions. '''(a)''' Normalized ensemble mean anomaly of key variables as a function of year, including atmospheric CO <sub>2</sub> , surface air temperature, precipitation, thermosteric sea level change (see Glossary), global sea ice area, Northern Hemisphere sea ice area in September, and Atlantic meridional overturning circulation (AMOC); '''(b)''' surface air temperature; '''(c)''' precipitation; '''(d)''' September Arctic sea ice area; '''(e)''' AMOC; '''(f)''' thermosteric sea level; five-year running means are shown for all variables except the sea level change. In (b, f), red lines represent the phase of CO <sub>2</sub> ramp-up, blue lines represent the phase of CO <sub>2</sub> ramp-down, brown lines represent the period after CO <sub>2</sub> has returned to pre-industrial level, and black lines represent the multi-model mean. For all of the segments in (b, f), the solid coloured lines are CMIP6 models, and the dashed lines are other models (i.e., EMICs, CMIP5-era models). Vertical dashed lines indicate peak CO <sub>2</sub> and when CO <sub>2</sub> again reaches pre-industrial value. The number of CMIP6 and non-CMIP6 models used is indicated in each panel. The time series for the multi-model means (b, f) and the normalized anomalies (a) are terminated when data from all models are not available, in order to avoid the discontinuity in the time series. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

Termination of CDR refers to a sudden and sustained discontinuation of CDR deployment (see [[#4.6.3.3|Section 4.6.3.3]] for termination effects of SRM). The literature on the termination effects of CDR is limited, mostly considering scenarios where CDR implementation is explicit and does not result in net negative CO <sub>2</sub> emissions ( [[#Keller--2014|Keller et al., 2014]] ; [[#González--2018|González et al., 2018]] ). In simulations where CDR is applied on the RCP8.5 scenario at scales as large as currently deemed possible, the increase in global mean warming rates following CDR termination are relatively small in comparison to SRM termination ( [[#Keller--2014|Keller et al., 2014]] ). The exception is artificial ocean upwelling where surface cooling is mainly caused by bringing cold water from the deep ocean; upon termination this causes larger rates of surface warming ( [[#Oschlies--2010|Oschlies et al., 2010]] ). When background emissions are as high as in RCP8.5, termination of a large global-scale application of CDR such as ocean alkalinisation for multiple decades could also result in large regional warming rates (up to 0.15°C per year) that are comparable to those caused by termination of SRM ( [[#González--2018|González et al., 2018]] ). In such cases, large amounts of CO <sub>2</sub> would be removed from the atmosphere before termination, and termination would cause a temporal trajectory of atmospheric CO <sub>2</sub> that is parallel to the high-emissions scenario but from an atmosphere with much lower CO <sub>2</sub> levels. Because CO <sub>2</sub> radiative forcing is a logarithmic function of CO <sub>2</sub> concentration, large regional warming rates are simulated in such terminations. Thus, there is ''high confidence'' that the climate effect of CDR termination would depend on the amount CO <sub>2</sub> removed by CDR prior to termination and the rate of background CO <sub>2</sub> emissions at the time of termination. See also Chapter 5, Table 5.9, which summarizes the termination effects of individual CDR options.

In summary, there is ''high confidence'' that, due to the near-linear relationship between cumulative carbon emissions and GSAT change, cooling or avoided warming due to a CDR option would depend on the cumulative amount of CO <sub>2</sub> removed by that CDR option. The climate system response to the deployment of CDR is expected to be delayed by years (e.g., in temperature, precipitation, sea ice extent) to centuries (e.g., sea level and AMOC) ( ''high confidence'' ). The climate response to 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'' ).

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<span id="climate-response-to-solar-radiation-modification"></span>
==== 4.6.3.3 Climate Response to Solar Radiation Modification ====

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Most SRM approaches, including stratospheric aerosol injection (SAI), marine cloud brightening (MCB), and surface albedo enhancements (Table 4.7), aim to cool the Earth by deflecting more solar radiation to space. Although cirrus cloud thinning (CCT) aims to cool the planet by increasing the longwave emission to space, it is included in the portfolio of SRM options (Table 4.7) for consistency with AR5 ( [[#Boucher--2013|Boucher et al., 2013]] ) and SR1.5 ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ). Other approaches such as injection of sulphate aerosols into the Arctic troposphere and sea ice albedo enhancements for moderating ''regional'' warming have also been suggested ( [[#MacCracken--2016|MacCracken, 2016]] ; [[#Field--2018|Field et al., 2018]] ). As noted in SR1.5 ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ), SRM is only considered as a potential supplement to deep mitigation, for example in overshoot scenarios ( [[#MacMartin--2018|MacMartin et al., 2018]] ).

The AR5 assessed the climate response to, as well as risks and side effects of, several SRM options ( [[#Boucher--2013|Boucher et al., 2013]] ) and concluded with ''high confidence'' that SRM, if practicable, could substantially offset a global temperature rise and partially offset some other impacts of global warming, but the compensation for the climate change caused by GHGs would be imprecise. The AR5 furthermore concluded that models consistently suggest that SRM would generally reduce climate differences compared to a world with elevated GHG concentrations and no SRM; however, there would also be residual regional differences in climate (e.g., temperature and rainfall) when compared to a climate without elevated GHGs. The AR5 concluded with ''high confidence'' that scaling SRM to substantial levels would carry the risk that if the SRM were terminated for any reason, surface temperatures would increase rapidly (within a decade or two) to values consistent with the GHG forcing ( [[#Boucher--2013|Boucher et al., 2013]] ).

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'''Table''' '''4.7 |''' '''A summary of the various SRM approaches.'''

{| class="wikitable"
|-
| '''SRM Approach'''

| '''Proposed Mechanism and Associated Uncertainties of the SRM Approac''' h

| '''Global Mean Negative Radiative Forcing Potential and Characteristics'''

| '''Key Climate and Environmental Effects'''

| '''References'''

|-
| '''Stratospheric Aerosol Injection (SAI)'''

| Injection of aerosols or their precursor gases into the stratosphere to scatter sunlight back to space; Aerosol types such as sulphates, calcium carbonate, and titanium dioxide have been proposed; large uncertainties associated with type of aerosol, aerosol radiative properties, microphysics, chemistry, stratospheric processes, and temporal and spatial strategy of aerosol injection.

| 1–8 W m <sup>–2</sup> , depending on the amount and pattern of injection, and transport and growth of injected particles; compared to other SRM approaches, radiative forcing could be more homogenously distributed.

| Change in temperature and precipitation pattern; precipitation reduction in some monsoon regions; decrease in direct and increase in diffuse sunlight at surface; stratospheric heating and changes to stratospheric dynamics and chemistry; potential delay in ozone hole recovery; changes in surface UV radiation; changes in crop yields.

| [[#Visioni--2017|Visioni et al. (2017)]] ; [[#Tilmes--2018b|Tilmes et al. (2018b)]] ; [[#Simpson--2019b|Simpson et al. (2019b)]]

|-
| '''Marine Cloud Brightening (MCB)'''

| Injection of sea salt or other types of aerosols to increase the albedo of marine stratocumulus clouds; regional option to reduce SST in hurricane formation regions and in coral reef areas; large uncertainties associated with cloud microphysics and aerosol–cloud-radiation interactions.

| 1–5 W m <sup>–2</sup> , depending on the scale and amount of sea salt injection; heterogeneous radiative forcing.

| Change in land–sea contrast and precipitation patterns.

| Latham et al., (2012, 2014); [[#Ahlm--2017|Ahlm et al. (2017)]] ; [[#Stjern--2018|Stjern et al. (2018)]]

|-
| '''Cirrus Cloud Thinning (CCT)'''

| Inject ice nuclei in the upper troposphere to reduce the lifetime and optical thickness of cirrus clouds to allow more longwave radiation to escape to space; large uncertainties associated with cirrus cloud formation processes, cirrus microphysics, and interaction with aerosol.

| 1–2 W m <sup>–2</sup> , depending on cirrus microphysical response and seeding strategy; heterogeneous radiative forcing; loss in cirrus clouds could also cause significant shortwave forcing regionally; risk of overseeding and consequent warming.

| Changes in temperature and precipitation pattern; increase in solar radiation reaching surface.

| [[#Storelvmo--2014|Storelvmo and Herger (2014)]] ; [[#Jackson--2016|Jackson et al. (2016)]] ; [[#Gasparini--2020|Gasparini et al. (2020)]]

|-
| '''Surface-Based Albedo Modification'''

| Increase ocean albedo by creating microbubbles; add reflective material to increase desert albedo; paint the roof of buildings white to increase roof reflectivity; increase albedo of agriculture land via no-till farming or modifying crop albedo, add reflective material to increase sea ice albedo.

| Radiative forcing of a few W m <sup>–2</sup> might be achieved via increase in ocean and desert albedo, but the large-scale implementation is not feasible; less than 0.5 W m <sup>–2</sup> for white roof and crop albedo enhancement; heterogeneous radiative forcing.

| Change in land–sea contrast and precipitation pattern for ocean and desert albedo increase; more localized effect for white roofs, no-till farming, and crop albedo modification.

| [[#Evans--2010|Evans et al. (2010)]] ; [[#Davin--2014|Davin et al. (2014)]] ; [[#Zhang--2016|Zhang et al. (2016)]] ; [[#Field--2018|Field et al. (2018)]] ; [[#Kravitz--2018|Kravitz et al. (2018)]]

|}

The SR1.5 ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ) assessed SRM in terms of its potential to limit warming to below 1.5°C in temporary overshoot scenarios and the associated impacts. It concluded that SAI could limit warming to below 1.5°C but that the climate response to SAI is uncertain and varies across climate models. Overall, the assessment concluded that the combined uncertainties related to SRM approaches, including technological maturity, limited physical understanding of the response to SRM, potential impacts, and challenges of governance, constrain potential deployment of SRM in the near future.

This subsection assesses the global and large-scale physical climate system response to SRM based on theoretical and modelling studies. There is no mature technology today to implement any of the SRM options assessed here. A short summary of the SRM options, including the proposed mechanism of each SRM approach, radiative forcing potential, and key climate and environmental effects, is listed in Table 4.7. [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] (Section 5.6.3) assesses the biogeochemical implications of SRM, [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] (Section 6.4.6) assesses the potential ERF of the aerosol-based SRM options and [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] (Section 8.6.3) assesses the abrupt water cycle changes in response to initiation or termination of SRM. The risks to human and natural systems, impacts of SRM, ethics, and perceptions are assessed in the WGII Report (Chapter 16). Governance issues associated with SRM research and deployment are assessed in the WGII and WGIII Reports. The assessment of technical feasibility and engineering aspects of SRM is beyond the scope of this Report.

The AR5 assessed SRM modelling mainly based on idealized simulations that used solar constant reductions. Since then, more in-depth investigations into specific SRM approaches have been conducted with more sophisticated treatment of aerosol–cloud–radiative interactions and stratospheric dynamics and chemistry underlying SAI, MCB, and CCT. Another major development since AR5 is the investigation into whether multiple climate policy goals may be met by optimally designed SRM strategies, including large-ensemble SAI simulations using multiple injection locations. There are large uncertainties in important SRM-related processes such as aerosol microphysics and aerosol–cloud–radiation interaction and hence the level of understanding is low.

As assessed in SR1.5 ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ), most of the knowledge about SRM is based on idealized model simulations and some natural analogues. In addition to single-model studies, more results from the coordinated modelling work of Geoengineering Model Intercomparison Project (GeoMIP) have become available. GeoMIP was initiated at the time of AR5 (Kravitz et al., 2011, 2013a) and is now in its second phase under the framework of CMIP6 (GEOMIP6, [[#Kravitz--2015|Kravitz et al., 2015]] ). However, studies based on GeoMIP6 data are currently limited and hence the assessment on climate response to SRM here is derived mostly from GeoMIP literature together with studies with single models.

Simple calculations and climate modelling studies show that about 2% extra solar irradiance reflected away from Earth or a one percentage point increase in planetary albedo (0.31 to 0.32) would suffice to offset global mean warming from a doubling of the CO <sub>2</sub> concentration (TheRoyal Society, 2009; [[#Kravitz--2013a|Kravitz et al., 2013a]] , 2021). To offset the same amount of CO <sub>2</sub> -induced GSAT increase, different levels of ERF are required for different methods of SRM (Schmidt et al., 2012; [[#Chiodo--2016|Chiodo and Polvani, 2016]] ; [[#Modak--2016|Modak et al., 2016]] ; [[#Duan--2018|Duan et al., 2018]] ; [[#Russotto--2018|Russotto and Ackerman, 2018]] ; [[#Krishnamohan--2019|Krishnamohan et al., 2019]] ; [[#Zhao--2021|Zhao et al., 2021]] ).

As assessed in AR5 ( [[#Boucher--2013|Boucher et al., 2013]] ), abruptly introducing SRM to fully offset global warming reduces temperature toward 1850–1900values with an e-folding time of only about five years ( [[#Matthews--2007|Matthews and Caldeira, 2007]] ). A more realistic approach would be a slow ramp-up of SRM to offset further warming (MacCracken, 2016; [[#Tilmes--2016|Tilmes et al., 2016]] ). Modelling studies have consistently shown that SRM has the potential to offset some effects of increasing GHGs on global and regional climate, including the melting of Arctic sea ice (Berdahl et al., 2014; [[#Moore--2014|Moore et al., 2014]] ) and mountain glaciers ( [[#Zhao--2017|Zhao et al., 2017]] ), weakening of Atlantic meridional overturning circulation (AMOC; [[#Cao--2016|Cao et al., 2016]] ; [[#Hong--2017|Hong et al., 2017]] ; [[#Tilmes--2020|Tilmes et al., 2020]] ), changes in extremes of temperature and precipitation (Curry et al., 2014; [[#Ji--2018|Ji et al., 2018]] ; [[#Muthyala--2018|Muthyala et al., 2018]] ), and changes in frequency and intensity of tropical cyclone ( [[#Moore--2015|Moore et al., 2015]] ; [[#Jones--2017|Jones et al., 2017]] ).

The climate response to SRM depends greatly on the characteristics of SRM implementation approaches. There could be substantial residual or overcompensating climate change at both the global and regional scales and seasonal time scales (Kravitz et al., 2014; [[#McCusker--2015|McCusker et al., 2015]] ; [[#Irvine--2016|Irvine et al., 2016]] ; [[#Fasullo--2018|Fasullo et al., 2018]] ; [[#Jiang--2019|Jiang et al., 2019]] ; [[#Gertler--2020|Gertler et al., 2020]] ). This is because the climate response to SRM options is different from the response to GHG increase (Figure 4.38). For instance, when global mean warming is offset by a uniform reduction in incoming sunlight, there is residual warming in the high latitudes and overcooling in the tropics ( [[#Kravitz--2013a|Kravitz et al., 2013a]] ; [[#Kalidindi--2015|Kalidindi et al., 2015]] ), and a reduction in tropical mean rainfall ( [[#Tilmes--2013|Tilmes et al., 2013]] ). In simulations of stratospheric SO <sub>2</sub> injection, SRM diminishes the amplitude of the seasonal cycle of temperature at many high‐latitude locations, with warmer winters and cooler summers ( [[#Jiang--2019|Jiang et al., 2019]] ). Further, the rates of response could differ between surface temperature and slow components in the climate system such as sea level rise ( [[#Irvine--2012|Irvine et al., 2012]] ; [[#Jones--2018|Jones et al., 2018]] ). SRM implemented at a moderate intensity, for example by offsetting half of the global warming, has the potential to reduce negative effects such as reduced precipitation that are associated with fully offsetting global mean warming (Irvine et al., 2019; [[#Irvine--2020|Irvine and Keith, 2020]] ).

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[[File:9fbadade999a137b4b57bf954820ced0 IPCC_AR6_WGI_Figure_4_38.png]]

'''Figure''' '''4.38 |''' '''Multi-model response per degree global mean cooling in temperature and precipitation in response to CO''' <sub>2</sub> '''forcing and SRM forcing. Top row''' shows the response to a CO <sub>2</sub> decrease, calculated as the difference between pre-industrial control simulation and ''abrupt4xCO'' ''2'' simulations where the CO <sub>2</sub> concentration is quadrupled abruptly from the pre-industrial level (11-model average); '''second row''' shows the response to a globally uniform solar reduction, calculated as the difference between GeoMIP experiment G1 and ''abrupt4xCO'' 2 (11-model average); '''third row''' shows the response to stratospheric sulphate aerosol injection, calculated as the difference between GeoMIP experiment G4 (a continuous injection of 5 Tg SO <sub>2</sub> year <sup>–1</sup> at one point on the equator into the lower stratosphere against the RCP4.5 background scenario) and RCP4.5 (six-model average); and The '''bottom row''' shows the response to marine cloud brightening, calculated as the difference between GeoMIP experiment G4cdnc (increase cloud droplet concentration number in marine low cloud by 50% over the global ocean against RCP4.5 background scenario) and RCP4.5 (eight-model average). All differences (average of years 11–50 of simulation) are normalized by the global mean cooling in each scenario, averaged over years 11–50. Diagonal lines represent regions where fewer than 80% of the models agree on the sign of change. The values of correlation represent the spatial correlation of each SRM-induced temperature and precipitation change pattern with the pattern of change caused by a reduction of atmospheric CO <sub>2</sub> . RMS (root mean square) is calculated based on the fields shown in the maps (normalized by global mean cooling). Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

For the same amount of global mean cooling achieved, the pattern of climate response would depend on SRM characteristics (Niemeier et al., 2013; [[#Duan--2018|Duan et al., 2018]] ; [[#Muri--2018|Muri et al., 2018]] ). This is illustrated in Figure 4.38 for temperature and precipitation change relative to a high-CO <sub>2</sub> world for scenarios of CO <sub>2</sub> reduction, solar irradiance reduction, SAI, and MCB. The pattern differences for different methods are much larger for precipitation than for temperature. The pattern of climate change resulting from SRM is also different from that resulting from CO <sub>2</sub> reduction (Figure 4.38). It is ''virtually certain'' that SRM approaches would not be able to precisely offset the GHG-induced anthropogenic climate change at global and regional scales.

Because of different sensitivity of precipitation change to CO <sub>2</sub> and solar forcings ( [[#Myhre--2017|Myhre et al., 2017]] ), if shortwave-based SRM is used to fully offset GHG-induced global mean warming, there would be a overcompensation of GHG-induced increase in global mean precipitation ( [[#Kravitz--2013a|Kravitz et al., 2013a]] ; [[#Tilmes--2013|Tilmes et al., 2013]] ; [[#Irvine--2016|Irvine et al., 2016]] ). Further, regional SRM approaches such as aerosol injections into the Arctic stratosphere are ''likely'' to remotely influence on tropical monsoon precipitation by shifting the mean position of ITCZ ( [[#Nalam--2018|Nalam et al., 2018]] ). However, the shift could be avoided by simultaneously cooling the southern hemisphere ( [[#MacCracken--2013|MacCracken et al., 2013]] ; [[#Kravitz--2016|Kravitz et al., 2016]] ; [[#Nalam--2018|Nalam et al., 2018]] ). The SRM response of precipitation minus evapotranspiration (P–E) is found to be smaller than that of precipitation because of reduction in both precipitation and evapotranspiration ( [[#Tilmes--2013|Tilmes et al., 2013]] ; [[#Nalam--2018|Nalam et al., 2018]] ; [[#Irvine--2019|Irvine et al., 2019]] ). Thus, global mean soil moisture could be effectively maintained, though with significant regional variability ( [[#Cheng--2019|Cheng et al., 2019]] ).

The Geoengineering Large Ensemble Project (GLENS) has investigated achieving multiple climate policy goals by adjusting the rate of stratospheric SO <sub>2</sub> injection at four different latitudes. GSAT, the inter-hemispheric temperature difference, and the equator-to-pole temperature gradient could be maintained simultaneously at the year-2020 level under RCP 8.5 ( [[#Tilmes--2018a|Tilmes et al., 2018a]] ). The possibility of using SAI to simultaneously stabilize non-temperature metrics such as tropical precipitation and Arctic sea ice extent is also explored ( [[#Lee--2020|Lee et al., 2020]] ). Furthermore, the potential of achieving multiple climate policy goals by combining two SRM approaches is also examined in a few modelling studies, with ''low confidence'' in the outcome of combining various approaches and the related climate response ( [[#Boucher--2017|Boucher et al., 2017]] ; [[#Cao--2017|Cao et al., 2017]] ).

<div id="4.6.3.3.1" class="h4-container"></div>

<span id="stratospheric-aerosol-injection"></span>
===== 4.6.3.3.1 Stratospheric aerosol injection =====

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Most SRM research has focused on stratospheric aerosol injection (SAI) and most SAI studies have assessed the effects of injection. Most research has focused on stratospheric aerosol injection (SAI): the injection of sulphate particles or its precursor gases such as SO <sub>2</sub> , which would then be oxidized to H <sub>2</sub> SO <sub>4</sub> . Injection of other types of aerosol particles, such as calcite (CaCO <sub>3</sub> ), titanium dioxide (TiO <sub>2</sub> ), aluminium oxide (Al <sub>2</sub> O <sub>3</sub> ), and engineered nanoparticles has also been proposed (Keith, 2010; [[#Ferraro--2011|Ferraro et al., 2011]] ; [[#Pope--2012|Pope et al., 2012]] ; [[#Weisenstein--2015|Weisenstein et al., 2015]] ; [[#Jones--2016|A.C. Jones et al., 2016]] ; [[#Keith--2016|Keith et al., 2016]] ), but are much less studied compared to sulphate injection. The natural analogue for sulphate aerosol injection is major volcanic eruptions ( [[#cross-chapter-box-4.1|Cross-Chapter Box 4.1]] ). While volcanic eruptions are not perfect analogues for SAI ( [[#Robock--2013|Robock et al., 2013]] ; [[#Plazzotta--2018|Plazzotta et al., 2018]] ; [[#Duan--2019|Duan et al., 2019]] ), studies on climate impacts of past volcanic eruptions can inform on the potential impact of stratospheric sulphate injection. For example, emergent constraints (Chapters 1 and 5) that relate the climate system response to volcanic eruptions can be used to reduce uncertainty of the land surface temperature response to SAI ( [[#Plazzotta--2018|Plazzotta et al., 2018]] ).

The cooling potential of SAI using sulphate aerosols depends on many factors ( [[#Visioni--2017|Visioni et al., 2017]] ) including the amount of injection ( [[#Niemeier--2015|Niemeier and Timmreck, 2015]] ), aerosol microphysics ( [[#Krishnamohan--2020|Krishnamohan et al., 2020]] ), the spatial and temporal pattern of injection ( [[#Tilmes--2017|Tilmes et al., 2017]] ), response of stratospheric dynamics and chemistry ( [[#Richter%20Jadwiga--2018|Richter Jadwiga et al., 2018]] ), and aerosol effect on cirrus clouds ( [[#Visioni--2018|Visioni et al., 2018]] ). A negative radiative forcing of a few W m <sup>–2</sup> (ranging from one to eight W m <sup>–2</sup> ) could be achieved depending on the amount and location of SO <sub>2</sub> injected into the stratosphere ( [[#Aquila--2014|Aquila et al., 2014]] ; [[#Pitari--2014|Pitari et al., 2014]] ; [[#Niemeier--2015|Niemeier and Timmreck, 2015]] ; [[#Kravitz--2017|Kravitz et al., 2017]] ; [[#Kleinschmitt--2018|Kleinschmitt et al., 2018]] ; [[#Tilmes--2018a|Tilmes et al., 2018a]] ). The simulated efficacy of SAI by emission of SO <sub>2</sub> (radiative forcing per mass of injection rate) generally decreases with the increase in injection rate because of the growth of larger particles (about 0.5 microns) through condensation and coagulation reducing the mass scattering efficiency ( [[#Niemeier--2015|Niemeier and Timmreck, 2015]] ; [[#Kleinschmitt--2018|Kleinschmitt et al., 2018]] ). However, efficacy changes little for total injection rate up to about 25 Tg sulphur per year when SO <sub>2</sub> is injected at multiple locations simultaneously ( [[#Kravitz--2017|Kravitz et al., 2017]] ; [[#Tilmes--2018a|Tilmes et al., 2018a]] ). Differences in model representation of aerosol microphysics, evolution of particle size, stratospheric dynamics and chemistry, and aerosol microphysics–radiation–circulation interactions all contribute to the uncertainty in simulated cooling efficiency of SAI. Compared to sulphate aerosols, injection of non-sulphate particles would result in different cooling efficacy, but understanding is limited (Pope et al.,2012; [[#Weisenstein--2015|Weisenstein et al., 2015]] ; [[#Jones--2016|A.C. Jones et al., 2016]] ).

Earlier modelling studies focused on the effect of equatorial sulphate injection that tends to overcool the tropics and undercool the poles. Compared to equatorial injection, off-equatorial injection at multiple locations shows a closer resemblance to the baseline climate in many aspects, including temperature, precipitation, and sea ice coverage ( [[#Kravitz--2019|Kravitz et al., 2019]] ). However, significant regional and seasonal residual and overcompensating climate change is reported, including regional shifts in precipitation, continued warming of polar oceans, and shifts in the seasonal cycle of snow depth and sea ice cover ( [[#Fasullo--2018|Fasullo et al., 2018]] ; [[#Jiang--2019|Jiang et al., 2019]] ; [[#Simpson--2019b|Simpson et al., 2019b]] ). By appropriately adjusting the amount, latitude, altitude, and timing of the aerosol injection, modelling studies suggest that SAI is conceptually able to achieve some desired combination of radiative forcing and climate response ( ''medium confidence'' ) ( [[#MacMartin--2017|MacMartin et al., 2017]] ; [[#Dai--2018|Dai et al., 2018]] ; [[#Lee--2020|Lee et al., 2020]] ; [[#Visioni--2020b|Visioni et al., 2020b]] ).

There is large uncertainty in the stratospheric response to SAI, and the change in stratospheric dynamics and chemistry would depend on the amount, size, type, location, and timing of injection. There is ''high confidence'' that aerosol-induced stratospheric heating will play an important role in surface climate change ( [[#Simpson--2019b|Simpson et al., 2019b]] ) by altering the effective radiative forcing ( [[#Krishnamohan--2019|Krishnamohan et al., 2019]] ), lower stratosphere stability ( [[#Ferraro--2016|Ferraro and Griffiths, 2016]] ), quasi-biennial oscillation (QBO) ( [[#Aquila--2014|Aquila et al., 2014]] ; [[#Niemeier--2017|Niemeier and Schmidt, 2017]] ; [[#Kleinschmitt--2018|Kleinschmitt et al., 2018]] ), polar vortexes ( [[#Visioni--2020a|Visioni et al., 2020a]] ), and North Atlantic Oscillation ( [[#Jones--2021|Jones et al., 2021]] ). Model simulations indicate stronger polar jets and weaker storm tracks and a poleward shift of the tropospheric mid-latitude jets in response to stratospheric sulphate injections in the tropics ( [[#Ferraro--2015|Ferraro et al., 2015]] ; [[#Richter%20Jadwiga--2018|Richter Jadwiga et al., 2018]] ), as the meridional temperature gradient is increased in the lower stratosphere by the aerosol-induced heating. The aerosol-induced warming would also offset some of the GHG-induced stratospheric cooling. Compared to equatorial injection, off-equatorial injection is ''likely'' to result in reduced change in stratospheric heating, circulation, and QBO ( [[#Richter%20Jadwiga--2018|Richter Jadwiga et al., 2018]] ; [[#Kravitz--2019|Kravitz et al., 2019]] ). Stratospheric ozone response to sulphate injection is uncertain depending on the amount, altitude, and location of injection ( [[#WMO--2018|WMO, 2018]] ). It is ''likely'' that sulphate injection would cause a reduction in polar column ozone concentration and delay the recovery of Antarctic ozone hole ( [[#Pitari--2014|Pitari et al., 2014]] ; [[#Richter%20Jadwiga--2018|Richter Jadwiga et al., 2018]] ; [[#Tilmes--2018b|Tilmes et al., 2018b]] ), which would have implications for UV radiation and surface ozone ( [[#Pitari--2014|Pitari et al., 2014]] ; [[#Xia--2017|Xia et al., 2017]] ; [[#Richter%20Jadwiga--2018|Richter Jadwiga et al., 2018]] ; [[#Tilmes--2018b|Tilmes et al., 2018b]] ). Injection of non-sulphate aerosols is ''likely'' to result in less stratospheric heating and ozone loss ( [[#Pope--2012|Pope et al., 2012]] ; [[#Weisenstein--2015|Weisenstein et al., 2015]] ; [[#Keith--2016|Keith et al., 2016]] ). One side effect of SAI is increased sulphate deposition at surface. A recent modelling study indicates that to maintain global temperature at 2020 levels under RCP 8.5, increased sulphate deposition from stratospheric sulphate injection could be globally balanced by the projected decrease in tropospheric anthropogenic SO <sub>2</sub> emissions, but the spatial distribution of sulphate deposition would move from low to high latitudes ( [[#Visioni--2020c|Visioni et al., 2020c]] ).

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<span id="marine-cloud-brightening"></span>
===== 4.6.3.3.2 Marine cloud brightening =====

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Marine cloud brightening (MCB) involves injecting small aerosols such as sea salt into the base of marine stratocumulus clouds where the aerosols act as cloud condensation nuclei (CCN). In the absence of other changes, an increase in CCN would produce higher cloud droplet number concentration with reduced droplet sizes, increasing cloud albedo. Increased droplet concentration may also increase cloud water content and optical thickness, but recent studies suggest that liquid water path response to anthropogenic aerosols is weak due to the competing effects of suppressed precipitation and enhanced cloud water evaporation ( [[#Toll--2019|Toll et al., 2019]] ). An analogue for MCB are reflective, persistent ‘ship tracks’ observed after the passage of a sea-going vessel emitting combustion aerosols into susceptible clouds (Christensen and Stephens, 2011; [[#Chen--2012|Chen et al., 2012]] ; [[#Gryspeerdt--2019|Gryspeerdt et al., 2019]] ). A recent study ( [[#Diamond--2020|Diamond et al., 2020]] ) found a substantial increase in cloud reflectivity from shipping in south-east Atlantic basin, suggesting that a regional-scale test of MCB in stratocumulus‐dominated regions could be successful.

Modelling studiessuggest that MCB has the potential to achieve a negative forcing of about 1 to 5 W m <sup>–2</sup> , depending on the deployment area and strategies of cloud seeding (Hill and Ming, 2012; [[#Partanen--2012|Partanen et al., 2012]] ; [[#Alterskjær--2013|Alterskjær et al., 2013]] ; [[#Ahlm--2017|Ahlm et al., 2017]] ; [[#Stjern--2018|Stjern et al., 2018]] ). Regional applications of MCB has also been suggested for offsetting severe impacts from tropical cyclones whose genesis is associated with higher SST ( [[#MacCracken--2016|MacCracken, 2016]] ; [[#Latham--2014|Latham et al., 2014]] ) and for protecting coral reefs from higher SST ( [[#Latham--2013|Latham et al., 2013]] ). However, such regional approaches also involve large uncertainties in the magnitude of the responses and consequences.

Several modelling studies suggest that the direct scattering effect by injected particles might also play an important role in the cooling effect of MCB, but the relative contribution of aerosol–cloud and aerosol–cloud–radiation effect is uncertain (Partanen et al., 2012; [[#Kravitz--2013b|Kravitz et al., 2013b]] ; [[#Ahlm--2017|Ahlm et al., 2017]] ). Relative to the high-GHG climate, it is ''likely'' that MCB would increase precipitation over tropical land due to the inhomogeneous forcing pattern of MCB over ocean and land ( ''medium confidence'' ) ( [[#Bala--2011|Bala et al., 2011]] ; [[#Alterskjær--2013|Alterskjær et al., 2013]] ; [[#Niemeier--2013|Niemeier et al., 2013]] ; [[#Ahlm--2017|Ahlm et al., 2017]] ; [[#Muri--2018|Muri et al., 2018]] ; [[#Stjern--2018|Stjern et al., 2018]] ). Because of the high level of uncertainty associated with cloud microphysics and aerosol–cloud–radiation interaction (Section 7.3), the climate response to MCB is as uncertain. Results from global climate models are subject to large uncertainty because of different treatment of cloud microphysics and inadequate representation of sub-grid aerosol and cloud processes (Alterskjær and Kristjánsson, 2013; [[#Stuart--2013|Stuart et al., 2013]] ; [[#Connolly--2014|Connolly et al., 2014]] ; [[#Stjern--2018|Stjern et al., 2018]] ). Sea salt deposition over land ( [[#Muri--2015|Muri et al., 2015]] ) and the effect of sea salt emission on atmospheric chemistry ( [[#Horowitz--2020|Horowitz et al., 2020]] ) are some of the potential side effects of MCB.

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<span id="cirrus-cloud-thinning"></span>
===== 4.6.3.3.3 Cirrus cloud thinning =====

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Cirrus clouds trap more outgoing thermal radiation than they reflect incoming solar radiation and thus have an overall warming effect on the climate system ( [[#Mitchell--2009|Mitchell and Finnegan, 2009]] ). The aim of cirrus cloud thinning (CCT) is to reduce cirrus cloud optical depth by increasing the heterogeneous nucleation via seeding cirrus clouds with an optimal concentration of ice nucleating particles, which might cause larger ice crystals and rapid fallout, resulting in reduced lifetime and coverage of cirrus clouds ( [[#Muri--2014|Muri et al., 2014]] ; Gasparini et al., 2017; [[#Lohmann--2017|Lohmann and Gasparini, 2017]] ; [[#Gruber--2019|Gruber et al., 2019]] ). CCT aims to achieve the opposite effect of contrails that increase cirrus cover and cause a small positive ERF (Section 7.3). A high-resolution modelling study of CCT over a limited area of the Arctic suggested that cirrus seeding causes a decrease in ice crystal number concentration and a reduction in mixed-phase cloud cover, both of which cause a cooling effect ( [[#Gruber--2019|Gruber et al., 2019]] ).

Under present-day climate, cirrus clouds exerts a net positive radiative forcing of about 5 W m <sup>–2</sup> ( [[#Gasparini--2016|Gasparini and Lohmann, 2016]] ; [[#Hong--2016|Hong et al., 2016]] ), indicating a maximum cooling potential of the same magnitude if all cirrus cloud were removed from the climate system. However, modelling results show a much smaller cooling effect of CCT. For the optimal ice nuclei seeding concentration and globally non-uniform seeding strategy, a net negative cloud radiative forcing of about 1 to 2 W m <sup>–2</sup> is achieved (Storelvmo and Herger, 2014; [[#Gasparini--2020|Gasparini et al., 2020]] ). A few studies find that no seeding strategy could achieve a significant cooling effect, owing to complex microphysical mechanisms limiting robust climate responses to cirrus seeding ( [[#Penner--2015|Penner et al., 2015]] ; [[#Gasparini--2016|Gasparini and Lohmann, 2016]] ). A higher than optimal concentration of ice nucleating particles could also result in over-seeding that increases rather than decreases cirrus optical thickness ( [[#Storelvmo--2013|Storelvmo et al., 2013]] ; [[#Gasparini--2016|Gasparini and Lohmann, 2016]] ). Thus, there is ''low confidence'' in the cooling effect of CCT, due to limited understanding of cirrus microphysics, its interaction with aerosols, and the complexity of seeding strategy.

Relative to the high-GHG climate and for the same amount of global cooling, CCT is simulated to cause an increase in global precipitation compared to shortwave-based SRM options such as SAI and MCB ( [[#Duan--2018|Duan et al., 2018]] ; Muriet al., 2018) because of the opposing effects of CCT and increased CO <sub>2</sub> on outgoing longwave radiation ( [[#Kristjánsson--2015|Kristjánsson et al., 2015]] ; [[#Jackson--2016|Jackson et al., 2016]] ). Combining SAI and CCT has suggested that GHG-induced changes in global mean temperature and precipitation can be simultaneously offset ( [[#Cao--2017|Cao et al., 2017]] ), but there is ''low confidence'' in the applicability of this result to the real world owing to the large uncertainty in simulating aerosol forcing and the complex cirrus microphysical processes.

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<span id="surface-based-albedo-modification"></span>
===== 4.6.3.3.4 Surface-based albedo modification =====

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Surface-based albedo modification could, in principle, achieve a negative radiative forcing of a few W m <sup>–2</sup> by enhancing the albedo of the ocean surface ( [[#Gabriel--2017|Gabriel et al., 2017]] ; [[#Kravitz--2018|Kravitz et al., 2018]] ). However, the technology does not exist today to increase ocean albedo at large scale. An increase in crop albedo or roof albedo in urban areas could help to reduce warming in densely populated and important agricultural regions, but the effect would be limited to local scales and ineffective at counteracting global warming ( [[#Crook--2015|Crook et al., 2015]] ; Zhang et al., 2016). Large changes in desert albedo could in principle result in substantial global cooling, but would severely alter the hydrological cycle ( [[#Crook--2015|Crook et al., 2015]] ).

In addition to above-mentioned SRM methods, a number of local intervention methods have been proposed to limit the loss of cryosphere, such as applying reflective materials over sea ice ( [[#Field--2018|Field et al., 2018]] ), pumping seawater on top of the ice surface ( [[#Desch--2017|Desch et al., 2017]] ; Zampieri and Goessling, 2019), depositing a massive amount of snow over ice sheets ( [[#Feldmann--2019|Feldmann et al., 2019]] ), and blocking warm seawater from reaching glaciers ( [[#Moore--2018|]] [[#Moore--2018|J.C. Moore et al., 2018]] ). The stabilization of ice sheets through local intervention methods would reduce sea level commitment (Section 9.6.3.5). However, these methods are subject to large uncertainty concerning their feasibility and effectiveness, and their effects would be largely localized.

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<span id="detectability-of-climate-response-to-solar-radiation-modification"></span>
===== 4.6.3.3.5 Detectability of climate response to solar radiation modification =====

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Internal variability could mask the response to solar radiation modification (SRM)-related forcing in the near term ( [[#4.6.3.1|Section 4.6.3.1]] ). A detection of the global scale climate system response to stratospheric sulphate aerosol injection will ''likely'' require a forcing of the size produced by the 1991 Mount Pinatubo eruption ( [[#Robock--2010|Robock et al., 2010]] ). In model simulations of where 5 Tg SO <sub>2</sub> is injected into the stratosphere continuously (roughly one fourth of the 1991 Pinatubo eruption per year) under RCP 4.5, it is shown that, relative to the high-GHG world without SRM, the effect of SRM on global temperature and precipitation is detectable after one to two decades (Bürger and Cubasch, 2015; [[#Lo--2016|Lo et al., 2016]] ) which is similar to the time scale for the emergence of GSAT trends due to strong mitigation ( [[#4.6.3.1|Section 4.6.3.1]] ). The detection time is sensitive to detection methods and filtering techniques ( [[#Lo--2016|Lo et al., 2016]] ). An analysis using GLENS simulation ( [[#MacMartin--2019|MacMartin et al., 2019]] ) compares response in temperature, precipitation, and precipitation minus evapotranspiration (P-E) between a climate state with GHG-induced 1.5°C global mean temperature change and that with the same global mean temperature but under RCP4.5 emissions and a limited deployment of SO <sub>2</sub> injection. It is found that at grid-scale, difference in climate response between these two climate states are not detectable by the end of this century. However, for higher emissions scenarios of the RCP8.5 and correspondingly larger SRM deployment for maintaining the same global mean temperature change of 1.5°C, the regional differences are detectable before the end of the century. In addition to surface temperature and precipitation, observations of aerosol burden and temperature in the stratosphere via the deployment of stratospheric aerosol observing system might facilitate the detection of climate response to SAI.

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<span id="climate-response-to-termination-of-solar-radiation-modification"></span>
===== 4.6.3.3.6 Climate response to termination of solar radiation modification =====

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A hypothetical, sudden and sustained termination of SRM in a world with high GHG concentrations has been simulated to cause climate rebound effects such as rapid increase in global temperature, precipitation, and sea level, and rapid reduction in sea ice area ( [[#Jones--2013|Jones et al., 2013]] ; [[#McCusker--2014|McCusker et al., 2014]] ; [[#Crook--2015|Crook et al., 2015]] ; [[#Muri--2018|Muri et al., 2018]] ). Model simulations also show reduced precipitation over land areas in the first few years following termination, indicating general drying that would exacerbate the effects of rapid warming ( [[#McCusker--2014|McCusker et al., 2014]] ). A sudden and sustained termination of SRM is also expected to weaken carbon sinks, accelerating atmospheric CO <sub>2</sub> accumulation andwarming ( [[#Tjiputra--2016|Tjiputra et al., 2016]] ; [[#Muri--2018|Muri et al., 2018]] ; [[#Plazzotta--2019|Plazzotta et al., 2019]] ). A gradual phase-out of SRM combined with mitigation and CDR could reduce the large warming rates from sudden SRM termination ( [[#MacMartin--2014|MacMartin et al., 2014]] ; [[#Keith--2015|Keith and MacMartin, 2015]] ; [[#Tilmes--2016|Tilmes et al., 2016]] ), though this would be limited by how rapidly emission reductions can be scaled up ( [[#Ekholm--2016|Ekholm and Korhonen, 2016]] ).

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<span id="synthesis-of-theclimate-response-to-solar-radiation-modification"></span>
===== 4.6.3.3.7 Synthesis of theclimate response to solar radiation modification =====

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Modelling studies have consistently shown that SRM has the potential to offset some effect of increasing GHGs on global and regional climate ( ''high confidence'' ), but there would be substantial residual or overcompensating climate change at the regional scale and seasonal time scale ( ''high confidence'' ). Large uncertainties associated with aerosol–cloud–radiation interactions persist in our understanding of climate response to aerosol-based SRM options. For the same amount of global mean cooling, different SRM options would cause different patterns of climate change ( ''medium confidence'' ). Modelling studies suggest that it is conceptually possible to achieve multiple climate policy goals by optimally designed SRM strategies.

The effect of SRM options on global temperature and precipitation response would be detectable after one or two decades, which is similar to the time scale for the detection of strong mitigation. There is ''high confidence'' that a sudden and sustained termination of a high level of SRM against a high-GHG background would cause a rapid increase in temperature at a rate that far exceeds that projected for climate change without SRM. However, a gradual phase-out of SRM combined with mitigation and CDR would ''more likely than not'' avoid large rates of warming '''.'''

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