Editing IPCC:AR6/WGI/Chapter-6 (section)

== 6.7 Future Projections of Atmospheric Composition and Climate Response in SSP Scenarios ==

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This section assesses the 21st-century projections of SLCF emissions, abundances and responses in terms of climate and air quality following the SSPs (Chapter 1, [[IPCC:Wg1:Chapter:Chapter-1#1.6.1.3|Section 1.6.1.3]] and Cross-Chapter Box 1.5; [[#Riahi--2017|Riahi et al., 2017]] ; [[#Gidden--2019|Gidden et al., 2019]] ). The future evolution of atmospheric abundances and the resulting climate and AQ responses is driven mainly by anthropogenic emissions and by natural emissions modulated by chemical, physical and biological processes as discussed in Sections 6.2 and 6.3. Like the RCP scenarios used in AR5, the SSP emissions scenarios consider only direct anthropogenic (including biomass burning) emissions and do not project natural emissions changes due to climate or land-use changes; ESMs intrinsically consider these biogeochemical feedbacks to varying degrees (Section 6.4.5). We rely on future projections based on CMIP6 ESMs with comprehensive representation of chemistry, aerosol microphysics and biospheric processes that participated in the ScenarioMIP ( [[#O’Neill--2016|O’Neill et al., 2016]] ) and AerChemMIP ( [[#Collins--2017|Collins et al., 2017]] ). However, due to the high computational costs of running coupled ESMs, they cannot be used for quantifying the contributions from individual species, regions and sectors, and across the scenarios. Therefore, reduced complexity models (Box 1.3 and Cross-Chapter Box 7.1), which represent chemistry and complex ESM interactions in parametrized forms updated since the AR5, are also applied here.

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=== 6.7.1 Projections of Emissions and Atmospheric Abundances ===

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==== 6.7.1.1 SLCF Emissions and atmospheric abundances ====

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The trajectory of future SLCF emissions is driven by the evolution of socio-economic drivers described in [[IPCC:Wg1:Chapter:Chapter-1#1.6.1.1|Section 1.6.1.1]] but dedicated, SSP-specific, air pollution policy storylines can change the regional and global trends ( [[#Rao--2017|Rao et al., 2017]] ). Additionally, assumptions about urbanization ( [[#Jiang--2017|Jiang and O’Neill, 2017]] ) will affect the spatial distribution of emissions and consequently air quality.

Growing urbanization worldwide has strongly modified the spatial distribution and intensity of SLCF emissions. The effect and extent of urbanization on air pollution and other emissions species are captured within Integrated Assessment Models (IAMs) at varying levels of complexity. In most cases, models use a combination of proxies and assumptions of end-use efficiency and technological improvement assumptions to estimate emissions arising from rural-to-urban migration and population growth within cities, utilizing quantifications of urbanization for the SSPs ( [[#Jiang--2017|Jiang and O’Neill, 2017]] ). In addition, spatial patterns of future rural and urban population growth, migration, and decline have been quantified for the SSPs using a gravity model ( [[#Jiang--2017|Jiang and O’Neill, 2017]] ). However, linking these spatial patterns with IAM regional emissions pathways is still an ongoing area of study and has not yet been represented in spatial emissions estimates provided by IAMs ( [[#Riahi--2017|Riahi et al., 2017]] ; [[#Gidden--2019|Gidden et al., 2019]] ; [[#Feng--2020|Feng et al., 2020]] ). As described in [[#Feng--2020|Feng et al. (2020)]] , spatial emissions estimates derived for CMIP6 are largely a product of existing spatial patterns of population, but do not vary dynamically in future patterns. To the extent urbanization is accounted for in gridded emissions, IAM native region resolution (varying, for example, from 11 world regions to more than 30, depending on the model) provides urbanization-based dynamics. Despite the interest of studying the effect of well-planned, densely populated urban centres, which can help to maximize the benefits of agglomeration, by providing proximity to infrastructure and services, the opportunity for energy saving, and providing a frame for air-quality control, IAM realizations of SSPs are not sufficient to assess this effect. The opportunities and risks associated with this rapid urbanization for SLCF emissions and air quality are analysed in the Chapter 6 of the WGII report and [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] of the WGIII report.

All the RCP trajectories started in 2005 and relied on the assumption that economic growth will bring rapid strengthening of air pollution legislation, effectively reducing emissions of non-methane SLCFs (e.g., [[#Chuwah--2013|Chuwah et al., 2013]] ). While in the long-term such trends are expected if more ambitious air pollution control goes on par with economic growth. The near-term developments, however, might be much more diverse across regions and species, as has been observed in the last three decades ( [[#Amann--2013|Amann et al., 2013]] ; [[#Rafaj--2014|Rafaj et al., 2014]] ; [[#Rafaj--2018|Rafaj and Amann, 2018]] ; [[#Ru--2018|Ru et al., 2018]] ), especially in several fast-growing economies, leading to the difference between CMIP6 historical estimates for the post-2000 period ( [[#Hoesly--2018|Hoesly et al., 2018]] ) and those used in RCPs (Figure 6.18). Since several SLCFs are also air pollutants, the narrow range of the RCP emissions trajectories in the future allowed for only limited analysis of near-future air quality (Amann et al. , 2013; Chuwah et al. , 2013; von Schneidemesser et al. , 2015) . However, the range of storylines in the SSPs lead to a wider range of assumed pollution-control policies in the SSPs ( [[#Rao--2017|Rao et al., 2017]] ; [[#Riahi--2017|Riahi et al., 2017]] ). In SSP1 and SSP5, strong air-quality policies are assumed to minimize the adverse effects of pollution on population and ecosystems. In SSP2, a medium pollution control, with lower than current policy targets, is considered. Only weak, regionally varied, air pollution policies are applied in the SSP3 and SSP4. Additional climate policies introduced to reach defined radiative forcing targets will also affect SLCF emissions. The SSP SLCF-emissions trajectories ( [[#Rao--2017|Rao et al., 2017]] ; [[#Gidden--2019|Gidden et al., 2019]] ) assume a long-term coupling of economic growth and specific emissions indicators, such as sectoral emission densities. The pace of change varies across regions and SSPs resulting in a wider range of future air pollutants evolution '''(''' Figure 6.18), reflecting the differences in assumed levels of air pollution controls across regions (Figure 6.19). At the end of the century, the range across the SSPs is about four times that of RCPs for SO <sub>2</sub> and NO <sub>x</sub> , two to four times for BC and NMVOCs, and up to three times for CO and OC, while indicating a slightly smaller range than RCPs for methane (Figure 6.18). The originally developed SSP scenarios ( [[#Rao--2017|Rao et al., 2017]] ) have been harmonized with the CMIP6 historical emissions ( [[#Hoesly--2018|Hoesly et al., 2018]] ) and include updated SO <sub>2</sub> emissions to account for the recent decline in China ( [[#Gidden--2019|Gidden et al., 2019]] ).

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[[File:9425c6446c5b729ba5e722685f63d660 IPCC_AR6_WGI_Figure_6_18.png]]

'''Figure 6.18 |''' '''Global anthropogenic and biomass burning short-lived climate forcer (SLCF) and CO''' <sub>2</sub> '''emissions from 1850 to 2100 and HFC emissions from 1990 to 2100.''' Emissions for the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the period 1850–2014 are based on [[#Hoesly--2018|Hoesly et al. (2018)]] and [[#van%20Marle--2017|van Marle et al. (2017)]] ; emissions for CMIP5 for the period 1850–2005 are from [[#Lamarque--2010|Lamarque et al. (2010)]] ; CO <sub>2</sub> emissions are from EDGAR database ( [[#Crippa--2020|Crippa et al., 2020]] ); methane (CH <sub>4</sub> ) and HFCs are from ( [[#Crippa--2019|Crippa et al., 2019]] ); and air pollutants are from EC-JRC / PBL (2020), [[#Höglund-Isaksson--2012|Höglund-Isaksson (2012)]] and [[#Klimont--2017a|Klimont et al. (2017a)]] for ECLIPSE. Projections originate from the Shared Socio-Economic Pathway (SSP) database ( [[#Riahi--2017|Riahi et al., 2017]] ; [[#Rogelj--2018a|Rogelj et al., 2018a]] ; [[#Gidden--2019|Gidden et al., 2019]] ); Representative Concentration Pathway (RCP) database ( [[#van%20Vuuren--2011|van Vuuren et al., 2011]] ); GAINS (CLE – current legislation baseline, KA – Kigali Amendment, MTFR – maximum technical mitigation potential) for HFCs ( [[#Purohit--2020|Purohit et al., 2020]] ; Velders et al. 2015); and, ECLIPSE ( [[#Stohl--2015|Stohl et al., 2015]] ). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

All SSP scenarios (Figure 6.18), except SSP3-7.0, project decline in global total emissions for all SLCFs by the end of the 21st century, except for ammonia and for HFCs (more on HFCs below). Similar to RCPs, ammonia emissions continue to increase in most SSPs, except SSP1 and SSP2, accounting for the expected growth in food demand and a general lack of effective policies targeting agricultural emissions. Additionally, mitigation potential for NH <sub>3</sub> is generally smaller than for other species owing to fugitive and widely distributed sources ( [[#Pinder--2007|Pinder et al., 2007]] ; [[#Klimont--2015|Klimont and Winiwarter, 2015]] ; [[#Mohankumar%20Sajeev--2018|Mohankumar]] [[#Sajeev--2018|Sajeev et al., 2018]] ; [[#Sajeev--2018|Sajeev et al., 2018]] ). Most significant changes of SLCF emissions in the near and long-term compared to the present day are expected for SO <sub>2.</sub> This is due to ever more stringent (and enforced) legislation in China’s power sector, extended recently to industrial sources ( [[#Zheng--2018b|Zheng et al., 2018b]] ; [[#Tong--2020|Tong et al., 2020]] ), declining coal use in most SSPs, recently announced stricter emissions limits for the power sector in India, and reduction of the sulphur content of oil fuel used in international shipping from 2020 ( [[#IMO--2016|IMO, 2016]] ). For the lower forcing targets (e.g., SSP1-2.6), the SO <sub>2</sub> trajectories are similar to the RCPs resulting in over 50–90% decline by 2050 and 2100, respectively, while for the scenarios with no climate policies, the SSPs show a large spread even at the end of the century.

Until the mid-21st century, SSP3-7.0 and SSP5-8.5 scenarios project no reduction in NO <sub>x</sub> emissions at the global level with decline in most OECD countries and Eastern Asia, driven by existing legislation in power, industry and transportation (e.g., [[#Tong--2020|Tong et al., 2020]] ), and continued increase in the rest of the world (Figures 6.18 and 6.19). Towards the end of the century, similar trends continue in SSP3-7.0 while emissions in SSP5 decline strongly owing to faster technological progress and stronger air-quality action ( [[#Rao--2017|Rao et al., 2017]] ; [[#Riahi--2017|Riahi et al., 2017]] ). By 2100, the ‘Regional Rivalry’ (SSP3) scenario emissions of NO <sub>x</sub> (and most other SLCFs, except ammonia) are typically twice as high as the next highest SSP projection, both at the global (Figure 6.18) and regional levels (Figure 6.19). In emissions pathways consistent with Paris Agreement goals (SSP1-1.9 or SSP1-2.6; [[IPCC:Wg1:Chapter:Chapter-1#1.6.1|Section 1.6.1]] ), NO <sub>x</sub> drops, compared to 2015, by 50% in SSP1-2.6 and by 65% in SSP1-1.9 by 2050, is reduced by about 70% by 2100, resulting in global emissions levels comparable to the 1950s and below the RCP range. In these pathways considering strong climate change mitigation, similar reductions are projected at the regional level, except in Africa (less than 50% decline) due to its high share of biomass emissions as well as strong growth in population and fossil fuel use. The trends in anthropogenic and biomass-burning emissions for other ozone precursors (NMVOC, CO) are similar to that of NO <sub>x</sub> .

An additional scenario, based on the SSP3-7.0, has been designed specifically to assess the effect of a strong SLCF emissions abatement and is called SSP3-7.0-lowNTCF in the literature ( [[#Collins--2017|Collins et al., 2017]] ; [[#Gidden--2019|Gidden et al., 2019]] ). It has been applied in the modelling studies (e.g., AerChemMIP) with or without consideration of additional methane reduction and we refer here to these scenarios, respectively, as SSP3-7.0-lowSLCF-lowCH <sub>4</sub> or SSP3-7.0-lowSLCF-highCH <sub>4</sub> . In these scenarios, aerosols, their precursors, and non-methane tropospheric ozone precursors are mitigated by applying the same emissions factors as in SSP1-1.9.

For global methane emissions, the range is similar for SSPs and RCPs over the entire century (Figure 6.18), with highest projections in SSP3-7.0 (slightly below RCP8.5) estimating doubling of the current emissions and a reduction of about 75% by 2100 in scenarios consistent with 1.5°C to 2°C targets; similar to RCP2.6. At the regional level, the evolution of methane emissions in climate change mitigation scenarios is comparable to RCPs but there are significant differences for some regions with respect to high CO <sub>2</sub> emissions scenarios. In particular, projections for Eastern Asia differ significantly, the highest SSP3-7.0 is about half of the highest RCP by 2100 (Figure 6.19), which is due to much lower projections of coal use in China driven largely by efforts during the last decade to combat poor air quality. At the same time, the SSP scenarios without climate change mitigation project faster growth in methane emissions in Africa, the Middle East and Latin America (Figure 6.19) driven by developments in agriculture, the oil and gas sectors, and, especially in Africa, waste management. There are significant differences in the assessment and feasibility of rapid methane mitigation. [[#Höglund-Isaksson--2020|Höglund-Isaksson et al. (2020)]] review most recent studies and assess feasibility of rapid widespread mitigation, concluding that significant (over 50%) reductions are attainable but the feasibility of such reductions could be constrained in the short term due to locked capital. This might have implications for near-term evolution assumed in, for example, SSP1-1.9 or SSP3-lowSLCF-lowCH <sub>4</sub> , where emissions drop very quickly due to fast decarbonization and reductions in agriculture. Such high reduction potential in agriculture has been also assumed in other studies ( [[#Lucas--2007|Lucas et al., 2007]] ; [[#Harmsen--2020a|Harmsen et al., 2020a]] ) but is questioned by [[#Höglund-Isaksson--2020|Höglund-Isaksson et al. (2020)]] who indicate that widespread implementation (within decades) of policies bringing about institutional and behavioural changes would be important for transition towards very low methane emissions from livestock production.

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[[File:5ed41c853630d6e5c492f45cdbac3fa7 IPCC_AR6_WGI_Figure_6_19.png]]

'''Figure 6.19 |''' '''Regional anthropogenic and biomass burning short-lived climate forcer (SLCF) emissions from 1850 to 2100.''' Emissions for the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the period 1850–2014 are based on [[#Hoesly--2018|Hoesly et al. (2018)]] and [[#van%20Marle--2017|van Marle et al. (2017)]] and emissions for CMIP5 for the period 1850–2005 are from [[#Lamarque--2010|Lamarque et al. (2010)]] . Projections originate from the Shared Socio- economic Pathway (SSP) database ( [[#Riahi--2017|Riahi et al., 2017]] ; [[#Rogelj--2018a|Rogelj et al., 2018a]] ; [[#Gidden--2019|Gidden et al., 2019]] ) and Representative Concentration Pathway (RCP) database ( [[#van%20Vuuren--2011|van Vuuren et al., 2011]] ). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

Global emissions of carbonaceous aerosols are projected to decline in all SSP scenarios (Figure 6.18) except SSP3-7.0. In that scenario, which also has much higher emissions than any of the RCPs, about half of the anthropogenic BC originates from cooking and heating on solid fuels, mostly in Asia and Africa (Figure 6.19), where only limited progress in access to clean energy is achieved. Slow progress in improving waste management, high coal use in energy and industry, and no further progress in controlling diesel engines in Asia, Africa and Latin America contributes most of the remaining emissions, resulting in about 90% of anthropogenic BC emitted in the non-OECD world by 2100 in SSP3-7.0. A similar picture emerges for OC but with greater contribution of the waste management sector and biomass burning, and lower impact of transportation and industry developments. Since scenarios compliant with Paris Agreement goals (SSP1-1.9 or SSP1-2.6; [[IPCC:Wg1:Chapter:Chapter-1#1.6.1|Section 1.6.1]] ) include widespread access to clean energy already by 2050, the global and regional emissions of BC decline by 70–75% by 2050 and 80% by 2100 relayive to 2015. The decline in the residential sector (about 90% by 2050 and over 95% by 2100) is accompanied by a strong reduction in transport (over 98%) and the decarbonization of the industry and energy sector. About 50% of remaining BC emissions in SSP1-1.9 or SSP1-2.6 are projected to originate from waste and open biomass burning of which open burning of waste represent a significant part. Some studies suggest this might be pessimistic as, for example, efficient waste management (consistent with SDG goals) could potentially eliminate the open burning of solid residues ( [[#Gómez-Sanabria--2018|Gómez-Sanabria et al., 2018]] ), which accounts for over 30% of BC emissions in SSP1-1.9 in 2050 or 2100.

The SSP scenarios draw on the HFC projections developed by [[#Velders--2015|Velders et al. (2015)]] considering, in climate change mitigation scenarios, the provisions of the Kigali Amendment (2016) to the Montreal Protocol leading to phase-down of HFCs (Section 6.6.3.2; [[#Papanastasiou--2018|Papanastasiou et al., 2018]] ). The SSP scenarios without climate change mitigation (e.g, SSP3-7.0, SSP5-8.5) show a range in HFC emissions of 3.2–5.3 GtCO <sub>2</sub> -eq yr <sup>–1</sup> in 2050 and about 4–7.2 GtCO <sub>2</sub> -eq yr <sup>–1</sup> by 2100 while in deep climate change mitigation scenarios (SSP1-1.9 and SSP1-2.6), consistent with the 1.5–2°C targets, they are expected to drop to 0.1–0.35 GtCO <sub>2</sub> -eq yr <sup>–1</sup> (Figure 6.18). In SSP1-1.9, the extent of reduction and its pace is more ambitious than current estimates of the effect of the fully implemented and enforced Kigali Amendment (Figure 6.18; [[#Höglund-Isaksson--2017|Höglund-Isaksson et al., 2017]] ; [[#Purohit--2017|Purohit and Höglund-Isaksson, 2017]] ). The best representation of the HFC emissions trajectories in the SSP framework compliant with the Kigali Amendment is the SSP1-2.6 and the baseline (including only pre-Kigali national legislation; Section 6.6.3) is best represented by SSP5-8.5 (Figure 6.18). However, since HFC emissions in SSPs were developed shortly after the Kigali Amendment had been agreed, none of these projections represents accurately the HFC emissions trajectory corresponding to the phase-out emissions levels agreed to in the Kigali Amendment ( [[#Meinshausen--2020|Meinshausen et al., 2020]] ), leading to ''medium confidence'' in the assessment of the benefits of the Kigali Amendment when using SSP projections for HFCs.

The SSP SLCF trajectories reflect the effect of recent legislation and the assumed evolution thereof in the longer term, however, they do not necessarily reflect the full mitigation potential for several SLCFs, within particular SSPs (Figure 6.18), that could be achieved with air-quality- or SDG-targeted policies (Amann et al. , 2013; Rogelj et al. , 2014a; Haines et al. , 2017; Klimont et al. , 2017b; [[#Rafaj--2018|Rafaj and Amann, 2018]] ; Shindell et al. , 2018; Tong et al. , 2020) . Such policies could bring more rapid mitigation of SLCFs, independent of the climate strategy (Section 6.6.3).

The projections of future SLCF abundances typically follow their emissions trajectories except for SLCFs that are formed from precursor reactions (e.g., tropospheric ozone) or are influenced by biogeochemical feedbacks (Sections 6.2.2 and 6.4.5). According to multi-model CMIP6 simulations, total column ozone (reflecting mostly stratospheric ozone) is projected to return to 1960s values by the middle of the 21st century under the SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5 scenarios ( [[#Keeble--2021|Keeble et al., 2021]] ). ESMs project increasing tropospheric ozone burden over the 2015–2100 period for the SSP3-7.0 scenario (Figure 6.4; [[#Griffiths--2021|Griffiths et al., 2021]] ), there is, however, a large spread in the magnitude of this increase reflecting structural uncertainties associated with the model representation of processes that influence tropospheric ozone. Sources of uncertainties in SLCF-abundance projections include scenario uncertainties, or parametric and structural uncertainties in the model representation of the processes affecting simulated abundances with implications for radiative forcing and air quality. The evolution of methane abundances in SSP scenarios, for example, is derived from integrated assessment models (IAMs) which do not include the effects from biogeochemical feedbacks (e.g., climate-driven changes in wetland emissions; [[#Meinshausen--2020|Meinshausen et al., 2020]] ) introducing uncertainty.

In summary, in SSPs, in addition to the socio-economic development and climate change mitigation policies shaping the GHG emissions trajectories, the SLCF emissions trajectories are also steered by varying levels of air pollution control originating from SSP narratives and independent from climate change mitigation. Consequently, SSPs span a wider range of SLCF emissions than considered in the RCPs, better covering the diversity of future options in air pollution management and SLCF-induced climate effects ( ''high confidence'' ). In addition to SSP-driven emissions, the future evolution of SLCFs abundance is also sensitive to chemical and biogeochemical feedbacks involving SLCFs, particularly natural emissions, whose magnitude and sign are poorly constrained.

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==== 6.7.1.2 Future Evolution of Surface Ozone and PM Concentrations ====

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The projection of air-quality relevant abundances (surface ozone and PM <sub>2.5</sub> ) under the SSP scenarios are assessed here. Future changes in global and regional annual mean surface ozone and PM <sub>2.5</sub> driven by the evolution of emissions as well as by climate change have been quantified by CMIP6 models analysed in AerChemMIP ( [[#Allen--2020|Allen et al., 2020]] , 2021; [[#Turnock--2020|Turnock et al., 2020]] ).

Surface ozone increases continuously until 2050 across most regions in SSP3-7.0 and SSP5-8.5, ( [[#Turnock--2020|Turnock et al., 2020]] ), particularly over Eastern Asia, Southern Asia, the Middle East, Africa, South East Asia and Developing Pacific, where this increase can reach and even exceed 5 ppb for annual mean averaged over land areas (Figure 6.20). After 2050, surface ozone concentrations decrease in SSP5-8.5, reaching levels below their 2005–2014 mean levels in most regions, but level off or continue to increase under SSP3-7.0. The increase in surface ozone in the SSP5-8.5 scenario occurs despite an emissions decrease of several ozone precursors because the methane emissions increase until about 2080 in the absence of climate change mitigation. Ozone decreases over all regions in response to strong emissions mitigation in SSP1-1.9 and SSP1-2.6 ( [[#Turnock--2020|Turnock et al., 2020]] ), with decreases of 5 to 10 ppb as soon as 2030 in North America, Europe, Eurasia, Eastern Asia, the Middle East and Southern Asia in their annual means over land areas. In most regions surface ozone is reduced slightly or remains near present day values in the middle of the road scenario, SSP2-4.5. In 2100, the largest differences in surface ozone changes across the scenarios occur for the Middle East, Southern Asia and Eastern Asia with differences ranging up to 40 ppb between SSP3-7.0 and SSP1-1.9 at the end of the century. In each scenario, despite discrepancies in the magnitude of changes, especially over North America, Europe, Eurasia, Eastern Asia and Southern Asia, the models are in high agreement regarding the signs of the changes and are thus assessed as of ''high confidence'' .

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'''Figure 6.20 |''' '''Projected changes in regional annual mean surface ozone (O''' <sub>3</sub> '''; ppb) from 2015 to 2100 in different shared socio-economic pathways (SSPs)''' . Each panel represents values averaged over the corresponding land area (except for ‘Global’) shown on the map in Figure 6.7. Solid coloured lines and shading indicate the multi-model mean and ±1 ''standard deviation'' across the available CMIP6 models (Turnock et al. 2020; Allen et al. 2021) for each scenario. Changes are relative to annual mean values calculated over the period 2005–2014 from the historical experiment as indicated in the top left of each regional panel along with ±1 standard deviation. For each model all available ensemble members are averaged before being used to calculate the multi-model mean. Ozone changes are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

The strong abatement of ozone precursor emissions (except those of methane; SSP3-7.0-lowSLCF-highCH <sub>4</sub> ) lead to a decrease of global average surface ozone by 15% (6 ppb) between 2015 and 2055 ( [[#Allen--2020|Allen et al., 2020]] ), and ozone decreases in all regions except Southern Asia. However, this decrease is twice as large when methane emissions are abated simultaneously (SSP3-7.0-lowSLCF-lowCH <sub>4</sub> ), underlying the importance of methane emissions reduction as an important lever to reduce ozone pollution ( ''high confidence'' ) (Section 6.6.4).

A decrease in surface PM <sub>2.5</sub> concentrations is estimated for SSP1-1.9, SSP1-2.6 and SSP2-4.5 ( [[#Turnock--2020|Turnock et al., 2020]] ) (Figure 6.21). A decrease in PM <sub>2.5</sub> , is also projected in SSP5-8.5, which does not consider any climate change mitigation but has a strong air pollution control. The decrease is largest in the regions with the highest 2005–2014 mean concentrations (the Middle East, Southern Asia and Eastern Asia). Under the SSP3-7.0 scenario, PM <sub>2.5</sub> is predicted to increase or remain at near present-day values across Asia; regions where present-day concentrations are currently the highest. There is large model spread over regions with large natural aerosol sources, for example, in North Africa, where dust sources are important. The mitigation of non-methane SLCFs in the SSP3-7.0-lowSLCF-highCH <sub>4</sub> scenario is predicted to reduce PM <sub>2.54</sub> by 25% (in 2055, relative to the SSP3-7.0 scenario) over global land surface areas ( [[#Allen--2020|Allen et al., 2020]] ).

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[[File:280f7a98d564363be0f5efb811dea334 IPCC_AR6_WGI_Figure_6_21.png]]

'''Figure 6.21 |''' '''Future changes in regional five-year mean surface PM''' <sub>2.5</sub> '''from 2015 to 2100 in different shared socio-economic pathways (SSPs).''' PM <sub>2.5</sub> stands for micrograms per cubic meter of aerosols with diameter less than 2.5 μm and is calculated by summing up individual aerosol mass components from each model as: black carbon + particulate organic matter + sulphate + 0.25 × sea salt + 0.1 × dust. Since not all CMIP6 models reported nitrate aerosol, it is not included here. See Figure 6.20 for further details. PM <sub>2.5</sub> changes are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

The magnitude of the annual mean change in surface ozone and PM <sub>2.5</sub> for all the SSPs (accounting for both emissions and climate change) is greater than that expected from climate change in isolation ( [[#Turnock--2020|Turnock et al., 2020]] ). The uncertainty in the projections comes from how natural emissions will respond to climate change. However, multiple lines of evidence (along with Sections 6.2.2, 6.5, and 6.7.1) provide ''high confidence'' (compared to ''medium'' in AR5) that changes in emissions, and in particular in human-induced emissions, will drive future air pollution levels rather than physical climate change.

In summary, future air pollution levels are strongly driven by precursor emissions trajectories in the SSPs with substantial reductions in global surface ozone and PM (when air pollution and climate change are both strongly mitigated, e.g., SSP1-2.6) to no improvement and even degradation ( ''high confidence'' ) (when no climate change mitigation and only weak air pollution control are considered, SSP3-7.0). In the latter case, PM levels are estimated to increase until 2050 over large parts of Asia and surface ozone pollution worsens over all continental areas throughout the whole century ( ''high confidence'' ). In scenarios without climate change mitigation but with strong air pollution control (SSP5-8.5), high methane levels hamper the decline in global surface ozone in the near term and only PM levels decrease ( ''high confidence'' ).

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=== 6.7.2 Evolution of Future Climate in Response to Changes in SLCF Emissions ===

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==== 6.7.2.1 Effects of changes in SLCFs on ERF and Climate Response ====

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This section assesses how the different spatial and temporal evolution of SLCF emissions in the SSPs affects the future global and regional ERFs, and GSAT and precipitation responses. In CMIP6, only a very limited set of simulations (all based on the SSP3-7.0 scenario) have been carried out with coupled ESMs to specifically address the future role of SLCFs (Sections 4.3 and 4.4; [[#Collins--2017|Collins et al., 2017]] ). Note that the ScenarioMIP simulations ( [[IPCC:Wg1:Chapter:Chapter-4#4.3|Section 4.3]] ) include the SLCF emissions (as shown in Figures 6.18 and 6.19), however, they cannot be used to quantify the effect of individual forcers. Coupled ESMs can in principle be used for this through a series of sensitivity simulations (e.g., [[#Allen--2020|Allen et al., 2020]] , 2021), but the amount of computer time required has made this approach prohibitive across the full SSP range. Therefore, to quantify the contribution from emissions of individual forcers spanning the range of the SSP scenarios to GSAT response, the analysis is mainly based on estimates using a two-layer emulator configuration derived from the medians of MAGICC7 and FaIRv1.6.2 ( [[IPCC:Wg1:Chapter:Chapter-1#1.5.3.4|Section 1.5.3.4]] , Cross-Chapter Box 7.1 and Supplementary Material 7.SM5.2). The contribution from SLCFs to changes in GSAT have been calculated based on the global mean ERF for the various components as assessed in [[IPCC:Wg1:Chapter:Chapter-7#7.3.5|Section 7.3.5]] , using the two-layer emulator for the climate response.

The projections of GSAT for a broad group of forcing agents (aerosols, methane, tropospheric ozone and HFCs with lifetimes lower than 50 years) for the SSP scenarios show how much of the future warming or cooling (relative to 2019) can be attributed to the SLCFs (Figure 6.22). Note that during the first two decades, some of these changes in GSAT are due to emissions before 2019, in particular for the longer-lived SLCFs such as methane and HFCs (Figure 6.15). The scenarios SSP3-7.0-lowSLCF-highCH <sub>4</sub> and SSP3-7.0-lowSLCF-lowCH <sub>4</sub> are special cases of the SSP3-7.0 scenario with strong, but realistic, reductions in non-methane SLCFs and all SLCFs, respectively ( [[#Gidden--2019|Gidden et al., 2019]] ).

As discussed in Sections 6.2, 6.3 and 6.4, there are uncertainties relating emissions of SLCFs to changes in abundance (Box 6.2) and further to ERF, in particular for aerosols and tropospheric ozone. Furthermore, there are uncertainties related to climate sensitivity, that is, the relation between ERF and change in GSAT. Uncertainties in the ERF are assessed in [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] and calibrated impulse response function also includes the assessed range (Box 7.1). There are also uncertainties related to the radiative efficacies of the different SLCFs and time scales for the response, in particular for regional emissions ( [[#Schwarber--2019|Schwarber et al., 2019]] ; [[#Yang--2019b|Yang et al., 2019b]] ) that cannot be accounted for with the simple models used here.

Historical emissions have been updated until 2019 (see Supplementary Material 7.SM.1.3.1) and used for ERF for calculating GSAT in Figure 6.22. The year 2019 has been chosen as the base year to be consistent with the attributed temperature changes since 1750 (Figure 7.8). The warming attributed to SLCFs (methane, ozone and aerosols) over the last decade (Figure 7.8) constitutes about 30% of the peak SLCF-driven warming in the most stringent scenarios (SSP1), in good agreement with [[#Shindell--2019|Shindell and Smith (2019)]] , and supported by the recent observed decline in AOD ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.6|Section 2.2.6]] ).

From 2019 and until about 2040, SLCFs and HFCs will contribute to increase GSAT in the WGI core set of SSP scenarios, with a ''very likely'' range of 0.04°C–0.41°C relative to 2019. The warming is most pronounced in the strong mitigation scenarios (i.e., SSP1-1.9 and SSP1-2.6) due to rapid cuts in aerosols. In scenario SSP3-7.0, there is no reduction of aerosols until mid-century and it is the increases in methane and ozone that give a net warming in 2040. The warming is similar in magnitude to that in the SSP1-scenarios, in which the reduction in aerosols is the main driver. Contributions to warming from methane, ozone, aerosols and HFCs make SSP5-8.5 the scenario with the highest warming in 2040 and throughout the century.

After about 2040, it is ''likely'' that, across the scenarios, the net effect of the removal of aerosols is a further increase in GSAT. However, their contribution to the rate of change decreases towards the end of the century (from up to 0.2°C per decade before 2040 to about 0.03°C per decade after 2040). After 2040, the changes in methane, HFCs and tropospheric ozone become equally important as the changes in the aerosols for the GSAT trends. In the low-emissions scenarios (SSP1-1.9 and SSP1-2.6), the contribution to warming from the SLCFs peaks around 2040 with a ''very likely'' range of 0.04°C to 0.34°C. After the peak, the reduced warming from reductions in methane and ozone dominates, giving a best total estimate warming induced by SLCF and HFC changes of 0.12°C and 0.14°C respectively, in 2100, with a ''very likely'' range of –0.07°C to +0.45°C (Figure 6.22). However, in the longer term towards the end of the century there are very significant differences between the scenarios. In SSP3-7.0 there is a near-linear warming due to SLCFs of 0.08°C per decade, while for SSP5-8.5 there is a more rapid early warming. In SSP3-7.0, the limited reductions in aerosols, but a steady increase in methane, HFCs and ozone lead to a nearly linear contribution to GSAT reaching a best estimate of 0.5°C in 2100. Contributions from methane and ozone decrease towards 2100 in SSP5-8.5, however the warming from HFCs still increase and the SSP5-8.5 has the largest SLCF and HFC warming in 2100 with a best estimate of 0.6°C. In the SSP2-4.5 scenario, a reduction in aerosols contributes to about 0.3°C warming in 2100, while contributions from ozone and methane in this scenario are small.

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[[File:fa6856a0df781584e847af369e681192 IPCC_AR6_WGI_Figure_6_22.png]]

'''Figure 6.22 |''' '''Time evolution of the effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-Economic Pathways (SSPs)''' . Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes &lt;50years), and the sum of these, relative to the year 2019 and to the year 1750. The GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; [[IPCC:Wg1:Chapter:Chapter-7#7.3.5|Section 7.3.5]] ). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO <sub>2</sub> (feedback parameter of –1.31 W m <sup>–2</sup> °C <sup>–1</sup> , see Cross-Chapter Box 7.1). The vertical bars to the right in each panel show the uncertainties (5–95% ranges) for the GSAT change between 2019 and 2100. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

The simplified approach used to estimate the contributions to GSAT in Figure 6.22 has been supplemented with ESM simulations driven by the two versions of the SSP3-7.0-lowSLCF scenario (Section 6.7.1.1). Results from five CMIP6 ESMs with fully interactive atmospheric chemistry and aerosols for the high-methane scenario show ( [[#Allen--2020|Allen et al., 2020]] , 2021) that reductions in emissions of air pollutants would lead to an additional increase in GSAT by 2055 relative to 2015 compared to the standard SSP3-7.0 scenario, with a best estimate of 0.23°C ± 0.05°C, and a corresponding increase in global mean precipitation of 1.3 ± 0.17% (note that uncertainties from the work of Allen et al. here and elsewhere are reported as twice standard deviation). Including methane mitigation (SSP3-7.0-lowSLCF-lowCH <sub>4</sub> ) would lead to a small increase in global precipitation (0.7 ± 0.1%) by mid-century despite a decrease in GSAT (Section 6.7.3), which is related to the higher sensitivity of precipitation to sulphate aerosols than greenhouse gases ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.1|Section 8.2.1]] ; [[#Allen--2021|Allen et al., 2021]] ).

Regionally inhomogeneous ERFs can lead to regionally dependent responses (Section 6.4.3). Mitigation of non-methane SLCFs over the period 2015–2055 (SSP3-7.0-lowSLCF-highCH <sub>4</sub> versus SSP3-7.0) will lead to positive ERF over land regions (Allen et al. , 2020) . There are large regional differences in the ERF from no significant trend over northern Africa to about 0.5 W m <sup>–2</sup> decade <sup>–1</sup> for Southern Asia. The differences are mainly driven by differences in the reductions of sulphate aerosols. There is no strong correspondence between regional warming and the ERF trends. As expected, the sensitivity (temperature change per unit ERF) increases towards higher latitudes due to climate feedbacks and teleconnections. Regionally, the warming rates are higher over continental regions, with the highest increase in temperatures for Central and northern Asia and the Arctic in 2055 relative to 2015. The models agree on an increasing global mean trend in precipitation due to SLCFs, however precipitation trends over land are more uncertain ( [[#Allen--2020|Allen et al., 2020]] ), in agreement with the relationship between aerosol and precipitation trends assessed in Chapter 8.

ESM estimates of future concentrations of various SLCFs vary considerably even when using the same future emissions scenarios, which is related to sources of model structural uncertainty in the several physical, chemical and natural emissions model parametrizations. The general uncertainties in understanding and representing chemical and physical processes governing the life cycle of SLCFs (Box 6.1) necessarily also applies to simulations of future concentrations and ERF. In addition, how the models are able to simulate climate changes (i.e., circulation and precipitation) that affect the dispersion and removal of SLCFs constitute a structural uncertainty in the models. Also SLCF-related climate feedbacks (e.g., NO <sub>x</sub> from lightning or BVOCs from vegetation; Section 6.4.5) add to the uncertainty.

In the near term (2035–2040), it is ''unlikely'' that differences in the socio-economic developments and emissions controls induced by policies (as embedded in the SSPs) can lead to a discernible difference in the net effect of changes of SLCFs on GSAT. This is because the inter-model spread in the estimated net effect of SLCFs on GSAT is as large as the difference between the scenarios due to the compensating effects of change in emissions leading to cooling and warming. However, in the longer term, there is ''high confidence'' that the net warming induced by changes in SLCFs will be lower in the scenario considering strong climate change mitigation (SSP1-1.9 and SSP1-2.6 that include reductions in methane emissions) than in the high CO <sub>2</sub> emissions scenarios (SSP3-7.0 and SSP5-8.5).

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==== 6.7.2.2 Effect of Regional Emissions of SLCFs on GSAT ====

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For SLCFs with lifetimes shorter than typical mixing times in the atmosphere (days to weeks), the effects on secondary forcing agents (e.g., tropospheric ozone, sulphate and nitrate aerosols) depend on where and when the emissions occur due to non-linear chemical and physical processes. Also, the ERF following a change in concentrations depends on the local conditions (Sections 6.2, 6.3 and 6.4).

While the emulators used for GSAT projections shown in Figure 6.22 do not take the regional perspective into account, the set of simulations performed within the Hemispheric Transport of Air Pollutants Phase 2 (HTAP2) project ( [[#Galmarini--2017|Galmarini et al., 2017]] ) allows for this perspective. The results from the chemistry–transport model OsloCTM3 taking part in the HTAP2 have been used by [[#Lund--2020|Lund et al. (2020)]] to derive region-specific absolute global warming potentials (AGTPs; cf. [[#Aamaas--2016|Aamaas et al., 2016]] ) for each emitted SLCF and each HTAP2 region. With this set of AGTPs, [[#Lund--2020|Lund et al. (2020)]] estimate the transient response in GSAT to the regional anthropogenic emissions. There are important differences in the contributions to GSAT in 2040 and 2100 (relative to 2020) between the regions and scenarios, mainly due to the differences in the mixture of emitted SLCFs (Figure 6.23). There is overall good agreement between the total net contribution from all regions to GSAT and the estimate based on global ERF and the two-layer emulator (Figure 6.22).

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[[File:11a875528002601bbe0a1271982b2cde IPCC_AR6_WGI_Figure_6_23.png]]

'''Figure 6.23 |''' '''Contribution from regional emissions of short-lived climate forcers (SLCFs) to changes in global surface air temperature (GSAT) in 2040 (upper row) and 2100 (lower row), relative to 2020 for four Shared Socio-economic Pathways (SSPs).''' Adapted from [[#Lund--2020|Lund et al. (2020)]] . NO x , CO, and NMVOC account for the impact through changes in ozone and methane, NO x additionally includes the impact through formation of nitrate aerosols. BC, SO <sub>2</sub> and OC accounts for the direct aerosol effect (aerosol–radiation interactions), as well as an estimate of the semi-direct effect for BC due to rapid adjustments and indirect effect (aerosol–cloud interactions) of sulphate aerosols. Regions are the same as shown in the map in Figure 6.7. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

In the low- and medium-emissions scenarios (SSP1-2.6 and SSP2-4.5), the warming effects induced by changes in SLCFs on GSAT are dominated by emissions in North America, Europe and Eastern Asia (Figure 6.23). In SSP1-2.6 the emissions of all SLCFs in all regions decrease and the net effect of the changes in SLCFs from all of these three regions is an increase in GSAT of about 0.02°C (per region) in 2040 and about 0.04°C in 2100. For SSP2-4.5, emissions of most SLCFs continue to increase in Southern Asia (Figure 6.19), leading to a net cooling in the near term (–0.03°C in 2040), while in 2100, North America, Europe, Eastern and Southern Asia all contribute to a warming, most pronounced from Eastern Asia (0.05°C). In the SSP3-7.0 scenario, the net effect induced by changes in SLCFs in all regions is an enhanced warming towards the end of the century, driven predominantly by change in methane. Africa is the region contributing the most to predicted global warming due to SLCF changes in 2100 (0.24°C). In SSP5-8.5, methane emissions increase in North America, Europe and Africa, while there is a decrease in the Asian regions. For North America and Europe, the methane increase combined with a reduction in aerosol leads to highest net contribution to GSAT in this scenario (0.06°C and 0.04°C in 2100, respectively). The high growth in methane makes Africa the region with the largest contribution to future warming by SLCFs (0.18°C in 2100 versus 2020) in this scenario.

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=== 6.7.3 Effect of SLCF Mitigation in SSP Scenarios ===

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Air-quality policies lead to a decrease in emissions of both warming and cooling SLCFs. Here we assess the contribution of SLCFs to the total warming (also including the LLGHGs) in the case of stringent SLCF mitigation to improve air quality in scenarios with continued high use of fossil fuels (e.g., SSP3-7.0-lowSLCF and SSP5-8.5). Conversely, we also assess the effect on air quality of strategies aiming to mitigate air pollution or climate change under the SSP3-7.0 framework (using the SSP3-7.0-lowSLCF-lowCH <sub>4</sub> , SSP3-7.0-lowSLCF-highCH <sub>4</sub> and SSP3-3.4 scenarios).

As illustrated in Figure 2.2 of SR1.5 ( [[#Rogelj--2018a|Rogelj et al., 2018a]] ), the total aerosol ERF change in stringent mitigation pathways is expected to be positive and to contribute to a warming since it is dominated by the effects from the phase-out of SO 2 (Figure 6.24, Section 6.7.2.2). Recent emissions inventories and observations of trends in AOD (Sections 2.2.6 and 6.2.1) show that it is ''very likely'' that there have been reductions in global SO <sub>2</sub> emissions and in aerosol burdens over the last decade. Here, we use 2019 as the reference year rather than the ‘Recent Past’ defined as the average over 1995–2014 ( [[IPCC:Wg1:Chapter:Chapter-4#4.1|Section 4.1]] ) in order to exclude the recent emissions reductions when discussing the different possible futures.

The role of the different SLCFs, and also the net of all the SLCFs relative to the total warming in the scenarios, is quite different across the SSP scenarios varying with the summed levels of climate change mitigation and air pollution control (Figure 6.24). In the scenario without climate change mitigation but with strong air pollution control (SSP5-8.5) all the SLCFs (methane, aerosols and tropospheric ozone) and the HFCs (with lifetimes less than 50 years) add to the warming, while in the strong climate change and air pollution mitigation scenarios (SSP1-1.9 and SSP1-2.6), the emissions controls act to reduce methane, ozone and BC, and these reductions thus contribute to cooling. In all scenarios, except SSP3-7.0, emissions controls lead to a reduction of the aerosols relative to 2019, causing a warming. However, the warming from aerosol reductions is stronger in the SSP1 scenarios (with best estimates of 0.21°C in 2040 and 0.4°C in 2100 in SSP1-2.6) because of higher emissions reductions from stronger decrease of fossil fuel use in these scenarios than in SSP5-8.5 (0.13°C in 2040 and 0.22°C in 2100). The changes in methane abundance contribute a warming of 0.14°C in SSP5-8.5, but a cooling of 0.14°C in SSP1-2.6 by the end of the 21st century relative to 2019. Furthermore, under SSP5-8.5, HFCs induce a warming of 0.06°C with a ''very'' ''likely'' range of [0.04 to 0.08] °C in 2050 and 0.2 [0.1 to 0.3] °C by the end of the 21st century, relative to 2019, while under SSP1-2.6, warming due to HFCs is negligible (below 0.02°C) ( ''high confidence'' ). This assessment relies on these estimates, which are based on updated ERFs and HFC lifetimes. It is in accordance with previous estimates (Section 6.6.3.2) of the efficiency of the implementation of the Kigali Amendment and national regulations. It is ''very likely'' that under a stringent climate and air pollution mitigation scenario (SSP1-2.6), the warming induced by changes in methane, ozone, aerosols and HFCs towards the end of the 21st century, will be very low compared with the warming they would cause under the SSP5-8.5 scenario (0.14°C in SSP1-2.6 versus 0.62°C in SSP5-8.5).

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[[File:ab55f93a96af4801e067c83b49900846 IPCC_AR6_WGI_Figure_6_24.png]]

'''Figure 6.24 |''' '''Effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs)''' . Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes &lt;50years), are compared with those of total anthropogenic forcing for 2040 and 2100 relative to the year 2019. The GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; [[IPCC:Wg1:Chapter:Chapter-7#7.3.5|Section 7.3.5]] ). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO <sub>2</sub> (feedback parameter of –1.31 W m <sup>–2</sup> °C <sup>–1</sup> ; Cross-Chapter Box 7.1). Uncertainties are 5–95% ranges. The scenario total (grey bar) includes all anthropogenic forcings (long- and short-lived climate forcers, and land-use changes) whereas the white diamonds and bars show the net effects of SLCFs and HFCs and their uncertainties. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

For the SSP3-7.0-lowSLCF-highCH <sub>4</sub> and SSP3-7.0-lowSLCF-lowCH <sub>4</sub> scenarios, a five-ESM ensemble has been analysed relative to the standard SSP3-7.0 scenario ( [[#Allen--2020|Allen et al., 2020]] , 2021). For SSP3-7.0-lowSLCF-highCH <sub>4</sub> , in which the methane emissions are as in the standard SSP3-7.0 scenario, [[#Allen--2021|Allen et al. (2021)]] found an enhanced global mean surface warming of 0.23°C ± 0.05°C by mid-century and 0.21°C ± 0.03°C by 2100 relative to the warming in the standard SSP3-7.0 scenario. Also including strong mitigation of methane emissions, the same models ( [[#Allen--2021|Allen et al., 2021]] ) find that the warming is offset resulting in a net cooling of 0.15°C ± 0.05°C at mid-century (2050–2059) and 0.50°C ± 0.02°C at the end of the century (2090–2099) relative to SSP3-7.0.

There is ''robust evidence'' and ''high agreement'' that non-methane SLCF mitigation measures, through reductions in aerosols and non-methane ozone precursors to improve air quality (SSP3-7.0-lowSLCF-highCH <sub>4</sub> vs SSP3-7.0), would lead to additional near-term warming with a range of 0.1°C–0.3°C. Methane mitigation that also reduces tropospheric ozone, stands out as an option that combines near- and long-term gains on surface temperature ( ''high confidence)'' . With stringent but realistic methane mitigation (SSP3-7.0-lowSLCF-lowCH <sub>4</sub> ), it is ''very likely'' that warming (relative to SSP3-7.0) from non-methane SLCFs can be offset (Figure 6.24; [[#Allen--2021|Allen et al., 2021]] ). Due to the slower response to the methane mitigation, this offset becomes more robust over time and it is ''very likely'' to be an offset after 2050. However, when comparing to the present day, it is ''unlikely'' that methane mitigation can fully cancel out the warming over the 21st century from reduction of non-methane cooling SLCFs.

The SSP3 storyline assumes ‘regional rivalry’ ( [[IPCC:Wg1:Chapter:Chapter-1#1.6.1.1|Section 1.6.1.1]] ) with weak air pollution legislation and no climate change mitigation, and is compared here against SSP3-7.0-lowSLCF-lowCH <sub>4</sub> (strong air pollution control) and SSP3-3.4 (the most ambitious climate policy feasible under the SSP3 narrative). In the SSP3-3.4 scenario, all emissions follow the SSP3-7.0 scenario until about 2030 and then deep and rapid cuts in fossil fuel use are imposed ( [[#Fujimori--2017|Fujimori et al., 2017]] ). In the case of climate change mitigation, such as in the SSP3-3.4 scenario, the decrease of SLCF emissions is a co-benefit from the targeted decrease of CO <sub>2</sub> (when SLCFs are co-emitted), but also directly targeted as in the case of methane. For SLCFs, this means that emissions of aerosols and methane increase until 2030 and are reduced quickly thereafter ( [[#Fujimori--2017|Fujimori et al., 2017]] ). The effect on GSAT (relative to 2019) is shown in Figures 6.22 and 6.24. The net GSAT response to the SLCFs is dominated by the aerosols, with an initial cooling until 2030, then a fast rebound for 15 years followed by a very moderate warming reaching 0.21°C in 2100. The ozone reduction causes a slight cooling (up to 0.06°C), in contrast to the warming in the SSP3-7.0-lowSLCF-highCH <sub>4</sub> scenario in which the methane emissions increase until 2100.

To assess the effect of dedicated air-quality versus climate policy on air quality, PM <sub>2.5</sub> and ozone indicators were estimated for three SSP3 scenarios by applying a widely used approach for the analysis of air-quality implications for given emissions scenarios ( [[#Rao--2017|Rao et al., 2017]] ; [[#Van%20Dingenen--2018|Van Dingenen et al., 2018]] ; [[#Vandyck--2018|Vandyck et al., 2018]] ) and whose sensitivity of surface concentrations to emissions changes is comparable to that in the ESM ensemble (Supplementary Material 6.SM.5). The assessment shows that both strong air pollution control and strong climate change mitigation, implemented independently, lead to a large reduction of exposure to PM <sub>2.5</sub> and ozone by the end of the century ( ''high confidence'' ) (Figures 6.25 and 6.26). However, implementation of air pollution control, relying on the deployment of existing technologies, leads to benefits more rapidly than climate change mitigation ( ''high confidence'' ), which requires systemic changes and is thus implemented later in this scenario. Notably, under the underlying SSP3 context, significant parts of the population remain exposed to air quality exceeding the WHO guidelines for PM <sub>2.5</sub> over the whole century ( ''high confidence'' ), in particular in Africa, Eastern and Southern Asia and the Middle East, and for ozone only a small improvement in population exposure is expected in Africa and Asia. Confidence levels here result from expert judgement on the whole chain of evidence.

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[[File:f2a3bb2c54c35a1bd8579a4b0c6cbba1 IPCC_AR6_WGI_Figure_6_25.png]]

'''Figure 6.25 |''' '''Effect of dedicated air pollution or climate policy on population-weighted PM''' <sub>2.5</sub> '''concentrations (µg m''' <sup>–3</sup> ''') and share of population (%) exposed to different PM''' <sub>2.5</sub> '''levels across 10 world regions.''' Thresholds of 10 µg m <sup>–3</sup> and 35 µg m <sup>–3</sup> represent the WHO air quality guideline and the WHO interim target 1, respectively; [[#WHO--2017|WHO (2017)]] . Results are compared for SSP3-7.0 (no major improvement of current legislation is assumed), SSP3-lowSLCF (strong air pollution controls are assumed), and a climate change mitigation scenario SSP3-3.4; details of scenario assumptions are discussed in [[#Riahi--2017|Riahi et al. (2017)]] and [[#Rao--2017|Rao et al. (2017)]] . Analysis performed with the TM5-FASST model ( [[#Van%20Dingenen--2018|Van Dingenen et al., 2018]] ) using emissions projections from the Shared economic Pathway (SSP) database ( [[#Riahi--2017|Riahi et al., 2017]] ; [[#Rogelj--2018a|Rogelj et al., 2018a]] ; [[#Gidden--2019|Gidden et al., 2019]] ). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

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[[File:383534a75c6196a557e8a37ed592543d IPCC_AR6_WGI_Figure_6_26.png]]

'''Figure 6.26 |''' '''Effect of dedicated air pollution or climate policy on population-weighted ozone concentrations (ppb) and share of population (%) exposed to different ozone levels across 10 world regions.''' Results are compared for SSP3-7.0 (no major improvement of current legislation is assumed), SSP3-low SLCF (strong air pollution controls are assumed), and a climate change mitigation scenario (SSP3-3.4); details of scenario assumptions are discussed in [[#Riahi--2017|Riahi et al. (2017)]] and [[#Rao--2017|Rao et al. (2017)]] . Analysis performed with the TM5-FASST model ( [[#Van%20Dingenen--2018|Van Dingenen et al., 2018]] ) using emissions projections from the Socio-economic Pathway (SSP) database ( [[#Riahi--2017|Riahi et al., 2017]] ; [[#Rogelj--2018a|Rogelj et al., 2018a]] ; [[#Gidden--2019|Gidden et al., 2019]] ). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

In summary, the warming induced by SLCF changes is stable after 2040 in the WGI core set of SSP scenarios associated with lower global air pollution as long as methane emissions are also mitigated, but the overall warming induced by SLCF changes is higher in scenarios in which air quality continues to deteriorate (caused by growing fossil fuel use and limited air pollution control) ( ''high confidence'' ). In the SSP3-7.0 context, applying an additional strong air pollution control resulting in reductions in anthropogenic aerosols and non-methane ozone precursors would lead to an additional near-term global warming of 0.08 °C with a ''very likely'' range of [–0.05 to 0.25] °C (compared with SSP3-7.0 for the same period). A simultaneous methane mitigation consistent with SSP1’s stringent climate change mitigation policy implemented in the SSP3 world, could entirely alleviate this warming and even lead to a cooling of 0.07°C with a ''very likely'' range of [–0.08 to +0.18] °C (compared with SSP3-7.0 for the same period) . Across the SSPs, the reduction of methane , ozone precursors and HFCs can make a 0.2 [0.1 to 0.4] °C difference on GSAT in 2040 and a 0.8 [0.5 to 1.3] °C difference at the end of the 21st century (Figure 6.24), which is substantial in the context of the Paris Agreement. Sustained methane mitigation, wherever it occurs, stands out as an option that combines near- and long-term gains on surface temperature ( ''high confidence)'' and leads to air pollution benefits by reducing surface ozone level globally ( ''high confidence'' ).

Strong air pollution control as well as strong climate change mitigation, implemented independently, lead to large reduction of the exposure to air pollution by the end of the century ( ''high confidence'' ). Implementation of air pollution control, relying on the deployment of existing technologies, leads more rapidly to air-quality benefits than climate change mitigation which requires systemic changes but, in both cases, significant parts of the population remain exposed to air pollution exceeding the WHO guidelines ( ''high confidence'' ).

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