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

==== 6.6.3.3 Assessment of SLCF Mitigation Strategies and Opportunities ====

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There is a consensus in the literature that mitigation of SLCF emissions plays a central role in simultaneous mitigation of climate change, air quality and other development goals including SDG targets (UNEP and WMO, 2011; Shindell et al. , 2012, 2017b; Rogelj et al. , 2014b, 2018b; [[#AMAP--2015a|AMAP, 2015a]] ; Haines et al. , 2017; Klimont et al. , 2017b; McCollum et al. , 2018; Rafaj et al. , 2018; UNEP and CCAC, 2018; [[#UNEP--2019|UNEP, 2019]] ) . There is less agreement in the literature with respect to the actual mitigation potential (or its potential rate of implementation), necessary policies to trigger successful implementation, and resulting climate impacts. Most studies agree that climate policies, especially those aiming to keep warming below 1.5°C or 2°C, trigger large SLCF mitigation co-benefits, (e.g., [[#Rogelj--2014b|Rogelj et al., 2014b]] , 2018b), however, discussion of practical implementation of respective policies and SDGs has only started ( [[#Haines--2017|Haines et al., 2017]] ). Note that mitigation scenarios outside of the SSP framework are assessed here while those within the SSPs are assessed in Section 6.7.3.

Focusing on air quality, specifically addressing aerosols, by introducing the best available technology reducing PM <sub>2.5</sub> , SO <sub>2</sub> and NO <sub>x</sub> in most Asian countries within the 2030–2050 time frame (a strategy that has indeed shown reduction in PM <sub>2.5</sub> exposure in China) comes, in many regions, short of national regulatory PM <sub>2.5</sub> concentration standards (often set at 35 µg m <sup>–3</sup> for annual mean; [[#UNEP--2019|UNEP, 2019]] ). Similarly, global studies ( [[#Rafaj--2018|Rafaj et al., 2018]] ; [[#Amann--2020|Amann et al., 2020]] ) show that strengthening current air-quality policies, that address primarily aerosols and their precursors, will not enable the achievement of WHO air quality guidelines (annual average concentration of PM <sub>2.54</sub> below 10 µg m <sup>–3</sup> ) in many regions.

A multi-model study (four ESMs and six CTMs) found a consistent response to the removal of SO <sub>2</sub> emissions that resulted in a global mean surface temperature increase of 0.69°C (0.4°C–0.84°C). However, results are mixed for a global BC-focused deep SLCF reduction without SO <sub>2</sub> and methane mitigation which remain as in the baseline (see ECLIPSE in Figure 6.18). BC contributed about –0.022°C temperature reduction for the decade 2041–2050 based on the assumption that mitigation of the non-methane species contributed only about 10% of the global temperature reduction for the strategy where methane mitigation was also included (–0.22°C ± 0.07°C; [[#Stohl--2015|Stohl et al., 2015]] ). These results are consistent with studies analysing similar strategies using emulators (e.g., [[#Smith--2013|Smith and Mizrahi, 2013]] ; [[#Rogelj--2014b|Rogelj et al., 2014b]] ). [[#Stohl--2015|Stohl et al. (2015)]] also analysed the impact of BC-focused mitigation on air quality, estimating large-scale regional reduction in PM <sub>2.5</sub> mean concentration from about 2% in Europe to 20% over India for the decade 2041–2050.

Local response to global reduction can be higher than the global temperature response, particularly for regions subjected to rapid changes. Hence, mitigation of rapid warming in the Arctic has been subject to an increasing number of studies (Sand et al. , 2013b, 2016; Jiao et al. , 2014; [[#AMAP--2015a|AMAP, 2015a]] , b; Mahmood et al. , 2016; Christensen et al. , 2019) . Considering maximum technically feasible reductions (MTFR) for methane globally and an idealized strategy reducing key global anthropogenic sources of BC (about 80% reduction by 2030 and sustained thereafter) and precursors of ozone was estimated to jointly bring a reduction of Arctic warming, averaged over the 2041–2050 period, between 0.2°C and 0.6°C ( [[#AMAP--2015a|AMAP, 2015a]] ; [[#Sand--2016|Sand et al., 2016]] ). [[#Stohl--2015|Stohl et al. (2015)]] have estimated that a global SLCF mitigation strategy (excluding further reduction of SO <sub>2</sub> ) would lead to about twice as high a temperature reduction (–0.44 (–0.39 to –0.49) °C) in the Arctic than the global response to such mitigation.

While there is robust evidence that air-quality policies resulting in reductions of aerosols and ozone can be beneficial for human health but can lead to ‘disbenefits’ for near-term climate change, the existence of such trade-offs in response to climate change mitigation policies is less certain ( [[#Shindell--2019|Shindell and Smith, 2019]] ). Recent studies show that very ambitious but plausible gradual phasing out of fossil fuels in 1.5°C-compatible pathways with little or no overshoot, lead to a near-term future warming of less than 0.1°C, when considering associated emissions reduction of both warming and cooling species. This suggests that there may not be a strong conflict, at least at the global scale, between climate and air-quality benefits in the case of a worldwide transition to clean energy ( [[#Shindell--2019|Shindell and Smith, 2019]] ; [[#Smith--2019|Smith et al., 2019]] ). However, at the regional scale, the changes in spatially variable emissions and abundance changes might result in different responses, including implications for precipitation and monsoons (Chapter 8), especially over Southern Asia (e.g., [[#Wilcox--2020|Wilcox et al., 2020]] ).

Decarbonization of energy supply and end-use sectors is among key pillars of any ambitious climate change mitigation strategy and it would result in improved air quality owing to associated reduction of co-emitted SLCF emissions (e.g., [[#McCollum--2013|McCollum et al., 2013]] ; [[#Rogelj--2014b|Rogelj et al., 2014b]] ; [[#Braspenning%20Radu--2016|Braspenning Radu et al., 2016]] ; [[#Rao--2016|Rao et al., 2016]] ; [[#Stechow--2016|Stechow et al., 2016]] ; [[#Lelieveld--2019|Lelieveld et al., 2019]] ; [[#Shindell--2019|Shindell and Smith, 2019]] ). Regional studies ( [[#Lee--2016|Lee et al., 2016]] ; [[#Shindell--2016|Shindell et al., 2016]] ; [[#Chen--2018|Chen et al., 2018]] ; [[#Li--2018|Li et al., 2018]] ), where significant CO <sub>2</sub> reductions were assumed for 2030 and 2050, show consistently reduced of PM <sub>2.5</sub> and ozone concentrations resulting in important health benefits. However, these improvements are not sufficient to bring PM <sub>2.5</sub> levels in agreement with the WHO air-quality guidelines in several regions. [[#Amann--2020|Amann et al. (2020)]] and [[#UNEP--2019|UNEP (2019)]] highlight that only the combination of strong air-quality, development and climate policies, including societal transformations, could pave the way towards the achievement of such a target at a regional and global level.

At a global level, [[#Rao--2016|Rao et al. (2016)]] showed that climate policies, compatible with Copenhagen pledges and a long-term CO <sub>2</sub> target of 450 ppm, result in important air-quality benefits, reducing the share of the global population exposed to PM <sub>2.5</sub> levels above the WHO Tier 1 standard (35 µg m <sup>–3</sup> ) in 2030 from 21% to 5%. The impacts are similar to a strong air-quality policy but still leave large parts of population, especially in Asia and Africa, exposed to levels well above the WHO air quality guideline level of 10 µg m <sup>–3</sup> . The latter can be partly alleviated by combining such climate policy with strong air-quality policy. [[#Shindell--2018|Shindell et al. (2018)]] analysed more ambitious climate change mitigation scenarios than [[#Rao--2016|Rao et al. (2016)]] and highlighted the opportunities to improve air quality and avert societal effects associated with warmer climate by accelerated decarbonization strategies. Most climate change mitigation strategies compatible with limiting global warming to well below 2°C rely on future negative CO <sub>2</sub> emissions postponing immediate reduction. Alternatively, a faster decarbonization could allow the achievement of a 2°C goal without negative CO <sub>2</sub> emissions and, with currently known and effectively applied emissions-control technologies, this would also have immediate and significant air-quality benefits, reducing premature deaths worldwide ( [[#Shindell--2018|Shindell et al., 2018]] ). For a 2°C-compatible pathway, [[#Vandyck--2018|Vandyck et al. (2018)]] estimated 5% and 15% reduction in premature mortality due to PM <sub>2.5</sub> in 2030 and 2050, respectively, compared to reference scenarios.

There is robust evidence that reducing atmospheric methane will benefit climate and improve air quality through near-surface ozone reduction ( [[#Fiore--2015|Fiore et al., 2015]] ; [[#Shindell--2017a|Shindell et al., 2017a]] ) and wide agreement that strategies reducing methane offer larger (and less uncertain) climate benefits than policies addressing BC (e.g., Smith andMizrahi, 2013; [[#Rogelj--2014b|Rogelj et al., 2014b]] , 2018b ; [[#Stohl--2015|Stohl et al., 2015]] ; [[#Christensen--2019|Christensen et al., 2019]] ; [[#Shindell--2019|Shindell and Smith, 2019]] ). SR1.5 ( [[#Rogelj--2018b|Rogelj et al., 2018b]] ) highlighted the importance of methane mitigation in limiting warming to 1.5ºC in addition to net zero CO <sub>2</sub> emissions by 2050. Implementation of the identified maximum technically feasible reductions (MTFR) potential for methane globally, estimated at nearly 50% reduction (or 205 Tg CH <sub>4</sub> in 2050) of anthropogenic emissions from the baseline, would lead to a reduction in warming, calculated as the differences between the baseline and MTFR scenario, for the 2036–2050 period of about 0.20°C ± 0.02°C globally ( [[#AMAP--2015b|AMAP, 2015b]] ). Plausible levels of methane mitigation, achieved with proven technologies, can increase the feasibility of achieving the Paris Agreement goal through slightly slowing down the pace of CO <sub>2</sub> reductions (but not changing the final CO <sub>2</sub> reduction goal) while this benefit is enhanced by the indirect effects of methane mitigation on ozone levels ( [[#Collins--2018|Collins et al., 2018]] ). Adressing methane mitigation appears even more important in view of recently observed growth in atmospheric concentrations that is linked to increasing anthropogenic emissions ( [[IPCC:Wg1:Chapter:Chapter-5#5.2.2|Section 5.2.2]] ).

Neither ambitious climate change policy nor air-quality abatement policy can automatically yield co-benefits without integrated policies aimed at co-beneficial solutions ( [[#Zusman--2013|Zusman et al., 2013]] ; [[#Schmale--2014a|Schmale et al., 2014a]] ; [[#Melamed--2016|Melamed et al., 2016]] ), particularly in the energy generation and transport sectors (Rao et al. , 2013; Thompson et al. , 2016; Shindell et al. , 2018; Vandyck et al. , 2018) . Integrated policies are necessary to yield multiple benefits of mitigating climate change, improving air quality, protecting human health and achieving several SDGs.

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'''Box 6.2 | SLCF Mitigation and Sustainable Development Goals (SDG) Opportunities'''

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Striving to achieve air-quality and climate targets will bring significant SLCF reductions. These reductions contribute first and foremost to the attainment of SDGs targeting improved human health and sustainable cities (SDGs 3 and 11), specifically related to PM exposure (goals 3.9 and 11.6; [[#Lelieveld--2017|Lelieveld, 2017]] ; [[#Amann--2020|Amann et al., 2020]] ), but also access to affordable and clean energy, responsible consumption and production, and climate, as well as reducing nutrient losses and consequently protecting biodiversity (SDG 7, 12, 13, 14 and 15; [[#UNEP--2019|UNEP, 2019]] ; [[#Amann--2020|Amann et al., 2020]] ). Furthermore, declining SLCF emissions will result in reduced crop losses (SDG 2; zero hunger) due to decrease of ozone exposure ( [[#Feng--2009|Feng and Kobayashi, 2009]] ; [[#Ainsworth--2012|Ainsworth et al., 2012]] ; [[#Emberson--2018|Emberson et al., 2018]] ).

However, the design of suitable policies addressing these SDGs can be difficult because of the complexity of linking emissions to impacts on human health, ecosystems, equity, infrastructure and costs. Beyond the fact that several species are co-emitted, interlinkage between species, such as through atmospheric chemistry, can weaken the benefit of emissions reduction efforts. An illustration lies in the recent (2013–2017) reduction of aerosols over China ( [[#Silver--2018|Silver et al., 2018]] ; [[#Zheng--2018b|Zheng et al., 2018b]] ) resulting from the strategy to improve air quality (‘Clean Air Action’); this has successfully reduced the level of PM <sub>2.54</sub> but has led to a concurrent increase in surface ozone, partly due to declining heterogeneous interactions of ozone precursors with aerosols (K. [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ; [[#Yu--2019|Yu et al., 2019]] ). This side effect on ozone has been addressed since then by amending the legislation to target NMVOC sources, especially solvent use. Complex interactions between anthropogenic and biogenic volatile compounds are also at play and reduction of certain SLCFs could possibly promote new particle formation from organic vapours (e.g., [[#Lehtipalo--2018|Lehtipalo et al., 2018]] ). Finally, a recent example of this complexity is the mixed effects on ozone pollution induced by NO <sub>x</sub> decrease during the COVID-19 pandemic (Cross-Chapter Box 6.1). Thus, the climate and air pollution effects of policies depend strongly on the choice of regulated compounds and the degree of reduction. Such policies have to be informed by strong science support, including for example multi-model analyses such as HTAP (UNECE, 2010) and AMAP ( [[#AMAP--2015a|AMAP, 2015a]] , b), based on global and regional CCMs. This is essential to capture the complexity and inform the policy development process.

In addition, pursuing SDG objectives, apparently decoupled from air pollution, such as improved waste management, access to clean energy, or improved agricultural practices, would also stimulate and lead to mitigation of SLCFs. Amann et al. (2020) show that a global strategy to achieve the WHO air quality guidelines, cannot only rely on air pollution control but also on a combination of SDG-aligned policies. Such actions would include energy efficiency improvements, increased use of renewables, reduction of methane from waste management and agriculture, and CO <sub>2</sub> and methane due to lower fossil fuel consumption, resulting in climate co-benefits. Consideration of SDGs including local air-quality co-benefits, creates an opportunity to support and gain acceptance for ambitious climate change mitigation ( [[#Jakob--2016|Jakob and Steckel, 2016]] ; [[#Stechow--2016|Stechow et al., 2016]] ; [[#Vandyck--2018|Vandyck et al., 2018]] ). Such near-term policies targeting SDGs and air quality would enable longer-term transformations necessary to achieve climate goals (Chapter 17, WGIII).

In summary, there is ''high confidence'' that effective decarbonization strategies could lead to air-quality improvements but are not sufficient to achieve, in the near term, air-quality WHO guideline values set for fine particulate matter, especially in parts of Asia and in some highly polluted regions. Additional policies (e.g., access to clean energy, waste management) envisaged to attain SDGs bring complementary SLCF reduction ( ''high confidence)'' . 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 an air pollution benefit by reducing ozone levels globally ( ''high confidence'' ) ''.''

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'''Cross-Chapter Box 6.1 | Implications of COVID-19 Restrictions for Emissions, Air Quality and Climate'''

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'''Coordinators:''' Astrid Kiendler-Scharr (Germany/Austria), John C. Fyfe (Canada)

'''Contributors:''' Josep G. Canadell (Australia), Sergio Henrique Faria (Spain/Brazil), Piers Forster (UK), Sandro Fuzzi (Italy), Nathan P. Gillett (Canada), Christopher Jones (UK), Zbigniew Klimont (Austria/Poland), Svitlana Krakovska (Ukraine), Prabir Patra (Japan/India), Joeri Rogelj (Austria/Belgium), Bjørn Samset (Norway), Sophie Szopa (France), Izuru Takayabu (Japan), Hua Zhang (China)

In response to the outbreak of COVID-19 (officially the severe acute respiratory syndrome–coronavirus 2 or SARS-CoV-2), which was declared a pandemic on March 11 2020 by the World Health Organization (WHO), regulations were imposed by many countries to contain the spread of COVID-19. Restrictions were implemented on the movement of people, such as closing borders or requiring the majority of population to stay at home, for periods of several months. This Cross-Chapter Box assesses the influence of the COVID-19 containment on short-lived climate forcers (SLCFs) and long-lived greenhouse gases (LLGHGs), and related implications for the climate. Note that this assessment was developed late in the AR6 WGI process and is based on the available emerging literature.

'''Emissions'''

Global fossil CO <sub>2</sub> emissions are estimated to have declined by 7% ( ''medium confidence'' ) in 2020 compared to 2019 emissions, with estimates ranging from 5.8% to 13.0% based on various combinations of data on energy production and consumption, economic activity and proxy activity data for emissions and their drivers (Forster et al. , 2020; Friedlingstein et al. , 2020; Le Quéré et al. , 2020; Liu et al. , 2020) . However, the concentration of atmospheric CO <sub>2</sub> continued to grow in 2020 compared to previous years ( [[#Dlugokencky--2021|Dlugokencky and Tans, 2021]] ). Given the large natural interannual variability of CO <sub>2</sub> ( [[IPCC:Wg1:Chapter:Chapter-5#5.2.1|Section 5.2.1]] ), and the small expected impact of emissions in the CO <sub>2</sub> growth rate, there were no observed changes in CO <sub>2</sub> concentration that could be attributed to COVID-19 containment ( ''medium confidence'' ) ( [[#Chevallier--2020|Chevallier et al., 2020]] ; [[#Tohjima--2020|Tohjima et al., 2020]] ).

Global daily CO <sub>2</sub> emissions from fossil fuel sources had a maximum decline of 17% in early April, compared with the mean 2019 levels, and coinciding with the global peak pandemic lockdown ( [[#Le%20Quéré--2020|Le Quéré et al., 2020]] ). The reductions in CO <sub>2</sub> emissions in 2020 were dominated by the drop in emissions from surface transport followed, in order of absolute emissions reductions, by industry, power and aviation ( [[#Le%20Quéré--2020|Le Quéré et al., 2020]] ; [[#Liu--2020|Liu et al., 2020]] ). Residential emissions showed little change ( [[#Liu--2020|Liu et al., 2020]] ) or rose slightly ( [[#Forster--2020|Forster et al., 2020]] ; [[#Le%20Quéré--2020|Le Quéré et al., 2020]] ). Aviation had the biggest relative drop in activity. CO <sub>2</sub> emissions due to land use (based on early and uncertain evidence on deforestation and forest fires) were higher than average in 2020 ( [[#Amador-Jiménez--2020|Amador-Jiménez et al., 2020]] ).

Using similar methodologies, [[#Forster--2020|Forster et al. (2020)]] assembled activity data and emissions estimates for other greenhouse gases and aerosols and their precursors. Anthropogenic NO <sub>x</sub> emissions, which are largely from the transport sector, are estimated to have decreased by a maximum of 35% in April ( ''medium confidence'' ). Species whose emissions are dominated by other sectors, such as methane and NH <sub>3</sub> from agriculture, saw smaller reductions.

'''Abundances and air quality'''

Owing to the short atmospheric lifetimes of SLCFs relevant to air quality, changes in their concentrations were detected within a few days after lockdowns had been implemented (e.g., Bauwens et al. , 2020; Venter et al. , 2020; Gkatzelis et al. , 2021; Shi et al. , 2021) . The COVID-19-driven economic slowdown has illustrated how complex the relationship is between emissions and air pollutant concentrations due to non-linearity in the atmospheric chemistry leading to secondary compound formation (Section 6.1, Box 6.1; [[#Kroll--2020|Kroll et al., 2020]] ).

Several studies have examined the effect of COVID-19 containment on air quality, showing that multi-year datasets with proper statistical/modelling analysis are required to discriminate the effects of meteorology from that of emissions reduction (Dhaka et al. , 2020; L. Li et al. , 2020; Wang et al. , 2020; Zhao et al. , 2020b) . Accounting for meteorological influences and with an increasing stringency index, the median observed change in NO <sub>2</sub> decreased from –13% to –48%, and in PM <sub>2.5</sub> decreased from –10% to –33%, whereas the median change in ozone increased from 0% to 4% ( [[#Gkatzelis--2021|Gkatzelis et al., 2021]] ). The latter can be explained by the decrease of NO emissions that titrate ozone in specific highly polluted areas, leading to the observed increase in surface ozone concentration in cities (Le et al. , 2020; Sicard et al. , 2020; Huang et al. , 2021).

The temporary decrease of PM <sub>2.5</sub> concentrations should be put in perspective of the sustained reduction (estimated at 30–70%), which could be achieved by implementing policies addressing air quality and climate change (Section 6.6.3). Such sustained reductions can lead to multiple benefits and simultaneously achieve several SDGs (Section 6.6.3). These policies would also result in reduction of ground-level ozone by up to 20% (Section 6.7.1.3).

Except for ozone, temporary improvement of air quality during lockdown periods was observed in most regions of the world ( ''high confidence'' ), resulting from a combination of interannual meteorological variability and the impact of COVID-19 containment measures ( ''high confidence'' ). Estimated air pollution reductions associated with lockdown periods are lower than what can be expected from integrated mitigation policy leading to lasting reductions ( ''medium confidence'' ).

'''Radiative forcings'''

COVID-19-related emissions changes primarily exerted effective radiative forcing (ERF) through reduced emissions rates of CO <sub>2</sub> and methane, altered abundance of SLCFs, notably ozone, NO <sub>2</sub> and aerosols, and through other changes in anthropogenic activities, notably a reduction in the formation of aviation-induced cirrus clouds.

[[#Forster--2020|Forster et al. (2020)]] combined the FaIR emulator (Cross-Chapter Box 7.1) with emissions changes for a range of species, relative to a continuation of Nationally Determined Contributions ( [[#Rogelj--2017|Rogelj et al., 2017]] ). They found a negative ERF from avoided CO <sub>2</sub> emissions that strengthens through 2020 to –0.01 W m <sup>–2</sup> . During the spring lockdown, they found a peak positive ERF of 0.1 W m <sup>–2</sup> from loss of aerosol-induced cooling, and a peak negative ERF of –0.04 W m <sup>–2</sup> from reductions in tropospheric ozone (from reduced photochemical production via NO <sub>x</sub> ). Overall, they estimated a net ERF of +0.05 W m <sup>–2</sup> for spring 2020, declining to +0.025 W m <sup>–2</sup> by the end of the year.

Cross-Chapter Box 6.1

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'''Cross-Chapter Box 6.1, Figure 1'''

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'''Emissions reductions and their effect on aerosols and climate in response to COVID-19.''' Estimated reductions in emissions of CO <sub>2</sub> , SO <sub>2</sub> and NO <sub>x</sub> are shown in panel '''(a)''' based on reconstructions using activity data (updated from [[#Forster--2020|Forster et al., 2020]] ). Eight Earth system models (ESMs) performed multiple ensemble simulations of the response to COVID-19 emissions reductions forced with these assumed emissions reductions up until August 2020 followed by a constant continuation near the August value to the end of 2020. Emissions reductions were applied relative to the SSP2-4.5 scenario. Panel '''(b)''' shows ESM-simulated AOD at 550nm (only seven models reported this variable). Panel '''(c)''' shows ESM-simulated GSAT anomalies during 2020; curves denote the ensemble mean result for each model with shading used for ±1 standard deviation for each model. ESM data from these simulations (‘ssp245-covid’) is archived on the Earth System Grid CMIP6 database. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where &lt;80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1.
[[#Gettelman--2021|Gettelman et al. (2021)]] extended Forster et al.’s (2020) results using two ESMs, and found a spring peak aerosol-induced ERF ranging from 0.12 to 0.3 W m <sup>–2</sup> , depending on the aerosol parametrization. They also estimated an ERF of –0.04 W m <sup>–2</sup> from loss of contrail warming. Overall, they report a peak ERF of 0.04 to 0.2 W m <sup>–2</sup> , and a subsequent decline to around half the peak value. Two independent ESM studies [[#Weber--2020|Weber et al. (2020)]] and [[#Yang--2020|Yang et al. (2020)]] found consistent results in time evolution and component contributions but included fewer forcing components.

The available studies are in broad agreement on the sign and magnitude of contributions to ERF from COVID-19-related emissions changes during 2020. The range in peak global mean ERF in spring 2020 was [0.025 to 0.2] W m <sup>–2</sup> ( ''medium confidence'' ), composed of a positive forcing from aerosol–climate interactions of [0.1 to 0.3] W m <sup>–2</sup> , and negative forcings from CO <sub>2</sub> (–0.01 W m <sup>–2</sup> ), NO <sub>x</sub> (–0.04 W m <sup>–2</sup> ) and contrail cirrus (–0.04 W m <sup>–2</sup> ) ( ''limited evidence'' , ''medium agreement'' ). By the end of 2020, the ERF was at half the peak value ( ''medium confidence'' ).

'''Climate responses'''

Changes in atmospheric composition due to COVID-19 emissions reductions are not thought to have caused a detectable change in global temperature or rainfall in 2020 ( ''high confidence'' ). A large ensemble of Earth system model (ESM) simulations show an ensemble average reduction in Aerosol Optical Depth (AOD) in some regions, notably Eastern and Southern Asia ( [[#Fyfe--2021|Fyfe et al., 2021]] ). This result is supported by observational studies finding decreases in optical depth in 2020 ( [[#Gkatzelis--2021|Gkatzelis et al., 2021]] ; [[#Ming--2021|Ming et al., 2021]] ; [[#van%20Heerwaarden--2021|van Heerwaarden et al., 2021]] ), which may have contributed to observed increases in solar irradiance ( [[#van%20Heerwaarden--2021|van Heerwaarden et al., 2021]] ) or solar clear-sky reflection ( [[#Ming--2021|Ming et al., 2021]] ).

Cross-Chapter Box 6.1

Model simulations of the response to COVID-19 emissions reductions indicate a small warming of global surface air temperature (GSAT) due to a decrease in sulphate aerosols ( [[#Forster--2020|Forster et al., 2020]] ; [[#Fyfe--2021|Fyfe et al., 2021]] ), balanced by cooling due to an ozone decrease ( [[#Forster--2020|Forster et al., 2020]] ; [[#Weber--2020|Weber et al., 2020]] ), black carbon decrease ( [[#Weber--2020|Weber et al., 2020]] ) and CO <sub>2</sub> decrease. It is noted that observational studies report little SO <sub>2</sub> change, at least locally near the surface ( [[#Shi--2021|Shi et al., 2021]] ), and do not correlate with emissions inventory-based changes ( [[#Gkatzelis--2021|Gkatzelis et al., 2021]] ). One study suggests a small net warming while another using idealized simulations suggests a small cooling ( [[#Weber--2020|Weber et al., 2020]] ). Simulated GSAT and rainfall changes are unlikely to be detectable in observations ( ''high confidence'' ) ( [[#Samset--2020|Samset et al., 2020]] ; [[#Fyfe--2021|Fyfe et al., 2021]] ). Multi-model ESM simulations based on a realistic COVID-19 containment forcing scenario ( [[#Forster--2020|Forster et al., 2020]] ) indicate a model mean reduction in regional AOD but no discernible response in GSAT (Figure 1, Cross-Chapter Box 6.1).

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