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

==== 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]] ).

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===== 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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===== 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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===== 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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