ABSTRACT A tighter integration of modeling frameworks for climate and air quality is urgently needed to assess the impacts of clean air policies on future Arctic and global climate. We

combined a new model emulator and comprehensive emissions scenarios for air pollutants and greenhouse gases to assess climate and human health co-benefits of emissions reductions. Fossil

fuel use is projected to rapidly decline in an increasingly sustainable world, resulting in far-reaching air quality benefits. Despite human health benefits, reductions in sulfur emissions

in a more sustainable world could enhance Arctic warming by 0.8 °C in 2050 relative to the 1995–2014, thereby offsetting climate benefits of greenhouse gas reductions. Targeted and

CLIMATE CO-BENEFITS OF AIR QUALITY AND CLEAN ENERGY POLICY IN INDIA Article 14 December 2020 AMPLIFIED POSITIVE EFFECTS ON AIR QUALITY, HEALTH, AND RENEWABLE ENERGY UNDER CHINA’S CARBON

NEUTRAL TARGET Article Open access 29 April 2024 WEAKENING AEROSOL DIRECT RADIATIVE EFFECTS MITIGATE CLIMATE PENALTY ON CHINESE AIR QUALITY Article 03 August 2020 INTRODUCTION The Arctic

annual mean surface temperature has warmed three times faster than the global average between 1971 and 2019, with consequences that reach far beyond the Arctic environment and people1,2. To

limit Arctic warming and sea ice melt, and to mitigate risks associated with potential climate tipping points in the Arctic3, reductions in emissions of carbon dioxide (CO2) and other

greenhouse gases are urgently needed. Many countries have pledged to bring emissions of greenhouse gases to “net zero” by 2050, primarily targeting CO2. Despite these commitments, the world

is not currently on track to meet the goals of the Paris Agreement4. At the same time, strong national regulations and regional agreements are in place to reduce transboundary air

pollution5,6,7, which is a major health threat in many parts of the world8. Air pollutants such as particulate matter and tropospheric ozone (O3) act as Short-Lived Climate Forcers (SLCFs).

Although SLCFs, including methane (CH4), are known to contribute to both air quality degradation and climate change, these are often dealt with as separate environmental concerns, despite

scientific evidence indicating strong linkages between the two9,10,11,12,13,14,15. Since the atmospheric lifetimes of SLCFs range from a few hours to several years, their control has

substantial potential for rapidly mitigating warming, in the Arctic and globally. However, the climate change mitigation potential of SLCFs is still poorly understood. SLCF mitigation

actions to limit Arctic warming over the next few decades have not yet been robustly assessed or compared with greenhouse gas (GHG) mitigation actions. Climate modeling studies have

consistently failed to meaningfully constrain the competing influences of scattering and absorbing particulate matter components on future Arctic climate16. Unsurprisingly, detailed

modelling studies disagree on the magnitude of SLCF temperature impacts in the Arctic15,16,17,18. Although important information about process uncertainties has become available from

multiple multi-model assessments, this information is not yet widely used in studies of SLCFs. Furthermore, highly idealized SLCF scenarios have been used, which are disconnected from

greenhouse gas scenarios, or do not reflect technological changes in the real world. Here we assess the climate impacts of a swift adoption of best available technologies to reduce key air

pollutants (Table 1) and CH4. In our analysis, we distinguish between different air pollutants, depending on whether their control leads to cooling or warming impacts. While regulation of

sulfur dioxide (SO2) and other chemically reactive sulfur compounds reduces particulate matter pollution and thereby yields far-reaching health benefits19,20, influences of sulfur emissions

on global climate are well documented15,17,21,22,23. In particular, sulfur emissions lead to the formation of sulfate (SO4) particulate matter, which efficiently scatters short-wave

radiation and enhances the cloud albedo. This has masked some of the past climate warming from increasing GHGs and light-absorbing black carbon (BC) particulate matter24,25. With globally

declining levels of sulfate, this masking of global warming is diminishing, which enhances warming in the Arctic and globally. Currently, the unmasking of warming from declining sulfur

emissions cannot be observed with sufficient accuracy or continuity. Air quality and climate models remain as essential tools for assessing the efficacy of sulfur mitigation actions. Similar

limitations exist for assessments of BC climate impacts. In addition to the observational constraints, previous climate modelling studies have often been limited to combined impacts of all

emitted particulate matter components, without clearly distinguishing sulfur from BC emissions impacts. Here we compare the impacts of sulfur and BC emissions on radiatively forced

temperature changes and use our results to identify scenarios for improving both climate and air quality in the near to mid-term using multiple emissions scenarios. FUTURE EMISSIONS

SCENARIOS Given the uncertain nature of global socio-economic development trajectories, we consider eight alternative future GHG and air pollutant emissions scenarios. This includes a set of

future emissions scenarios underpinning the 6th assessment report of the IPCC4,26, the Shared Socioeconomic Pathways (SSPs), and a dedicated set of new scenarios specifically focusing on

mitigation of tropospheric O3 precursors, CH4, and particulate matter. The new scenarios are subsequently referred to as “AMAP scenarios” because we recently used these to assess the impacts

of SLCFs on Arctic climate and human health for the Arctic Monitoring and Assessment Programme (AMAP)27, which provides the scientific basis of our analysis here (Supplementary Note 1). We

selected four SSP scenarios that capture a wide possible range of GHGs and other anthropogenic drivers of climate change. This includes emissions which depend, beyond consumption of fossil

fuels, on SSP-specific assumptions about air quality and development policies28,29. Global emissions of SO2 and carbon aerosol particles are projected to decline after 2015 in all SSP

scenarios, except in the scenario depicting regional rivalry (SSP3-7.0), which is based on the narrative of material-intensive consumption, with low international priority for addressing

environmental concerns26 (Fig. 1). However, emissions from North America, Europe, and North-East Asia are still projected to decline in this scenario, which is of relevance for the Arctic

(Supplementary Fig. 1). The emission reductions are even more rapid in the sustainability scenario (SSP1-2.6), with low challenges to climate mitigation and increasing shares of renewables,

resulting in considerable reductions already before 2030. The remaining SSP scenarios describe narratives of continued socio-economic development, similar to historical patterns (SSP2-4.5),

and fossil-fueled economic growth (SSP5-8.5). In addition to the SSP scenarios, the four new AMAP scenarios (Table 2) were developed to explore the impacts of dedicated air quality and SLCF

policies for two distinct socio-economic futures, broadly consistent with the SSP2-4.5 and SSP1-2.6 scenario CO2 emissions trajectories, using the GAINS model30. We used these scenarios to

assess SLCF mitigation options and to address health challenges from air pollution exceeding national standards and WHO air quality guidelines. The pace and degree of SLCF emission

reductions in the AMAP Current LEgislation (CLE) scenario are comparable to the SSP2-4.5 scenario. The Maximum technically Feasible Reduction (MFR and MFR_SDS) scenarios assume strong

mitigation of BC, CH4, and other air pollutants beyond those in the CLE scenario. Neither the CLE nor MFR scenario were developed from the perspective of addressing global or Arctic warming;

thereby they result in strong reductions of both warming and cooling SLCFs. Therefore, the focus of policy actions in the Climate Forcing Mitigation (CFM) scenario is on only the warming

SLCFs, assuming compliance with current air quality legislation. RESULTS AND DISCUSSION UNMASKING OF WARMING We used state-of-the-art Earth System Models (ESMs, see Methods) to simulate the

changes in global and Arctic climate in the future scenarios. For emissions following the SSP scenarios, the global Surface Air Temperature (SAT) is projected to increase between 1 °C

(0.5–1.6 °C) and 1.7 °C (1–2.4 °C) from the 1995–2014 average to the 2046–2055 average, for SSP1-2.6 and SSP5-8.5, respectively (Fig. 2c). The corresponding increase in Arctic SAT ranges

from 1.9 °C (0.2–4.3 °C) to 3.4 °C (1.6–6 °C), for SSP1-2.6 and SSP5-8.5, respectively (Fig. 2d). Note that we use 60°N as the boundary of the Arctic. We further used a new Earth System

model emulator (see Methods) to compare the Arctic warming contributions from global anthropogenic CO2 emissions and the unmasking of warming from sulfate reductions, which are not available

from the ESMs. The simulations show that these contributions could be nearly equal in magnitude until at least 2030 (Fig. 2 and Supplementary Fig. 9). The unmasking of warming would be

particularly strong if the world shifted toward a more sustainable path of improved air quality following SSP1-2.6. This would result in reductions in global sulfate concentrations and could

lead to enhanced warming in the Arctic by 0.8 °C (0.4–1.4 °C) and by 0.4 °C (0.3–0.8 °C) globally, from the 1995 – 2014 average to 2050. In comparison, this represents roughly 70% of the

Arctic and 60% of the global warming from anthropogenic CO2. This is larger than the contributions of the changes in other SLCFs emissions in this scenario. In broad agreement with our

results, earlier ESM studies31,32 showed that particulate matter reductions could increase temperatures in 2050 by up to 0.7 °C in the Arctic, (relative to 2010), and by about 0.4 °C

globally (relative to 2000). The unmasking of Arctic warming could also be considerable with less rapid reductions in sulfur emissions (0.4 °C, from 0 to 0.7 °C, for SSP3-7.0). Overall, a

substantial Arctic warming commitment from sulfur emission reductions does not appear to be avoidable within the context of the available climate scenarios and considering the likelihood of

near-term renewable energy technology and air quality advancements, which are essential for advancing global progress on the United Nations Sustainable Development Goals. While the unmasking

of warming by reductions in sulfate exacerbates the CO2-induced Arctic warming in all of the available SSP scenarios, the warming is enhanced further by increasing CH4 emissions in two of

the scenarios (SSP3-7.0 and SSP5-8.5). Ultimately, the consequence of the changing emissions of these and other chemically reactive compounds is an enhancement of CO2-induced Arctic warming,

which is directly and indirectly associated with changes in air pollution, primarily from declining sulfate and increasing CH4. The magnitude of the air pollution warming enhancement is

uncertain, owing to uncertainties in aerosol radiative forcings and climate sensitivity (see Methods). For the SSP2-4.5, SSP3-7.0, SSP5-8.5, and CLE scenarios we find that forced temperature

increases could reach or exceed 2 °C globally and 4 °C in the Arctic during the time period 2046−2055, relative to 1880–1920 (Fig. 2). In comparison, the contribution of the CO2-induced

warming to the forced temperatures is less than 2 °C (global) and 4 °C (Arctic). Consequently, the rate of Arctic warming to 2050, and successful implementation of the Paris Agreement, may

considerably depend upon changes in air quality, within the uncertainties of the emissions scenarios and emulator simulations. Note that physical process uncertainties affect the scenario

simulations in a systematic manner; they either enhance or reduce the changes in all scenarios, permitting robust comparisons of different scenarios. ARCTIC CLIMATE BENEFITS OF BLACK CARBON

AND METHANE MITIGATION Historically, emissions of BC contributed to global warming by enhancing the atmospheric absorption of solar radiation and through impacts on clouds4,18. When

deposited on Arctic snow and ice, BC also decreases the ability of the snow and ice to reflect solar radiation. The absorption of solar radiation by BC in the snow and ice results in a

positive radiative forcing from interactions of BC with surface albedo16,33. Thereby interactions of BC with radiation, clouds, and surface albedo have warmed the Arctic in recent

decades10,15,16. As an encouraging step, the Arctic Council aims to reduce black carbon particulate matter emissions by 2025, which aligns with research showing that Arctic climate is

sensitive to black carbon from sources at high latitudes in the Northern Hemisphere15,16. Our emulator simulations, using newly developed AMAP scenarios, show that deep reductions of BC and

CH4 emissions would help to mitigate the unmasking of warming from declining sulfur emissions (Fig. 2). Specifically, BC emissions reductions would reduce Arctic warming by 0.3 °C (0.1–0.4 

°C) in the MFR scenario, compared to the CLE scenario, as a consequence of diminishing interactions of BC with radiation, clouds, and surface albedo (Fig. 3a). In addition, diminishing

emissions and interactions of CH4 with radiation would further reduce the Arctic warming by 0.2 °C (0.1–0.2 °C). In the hypothetical case, where future sulfur emissions are driven by current

legislation but BC and CH4 emission reductions are prioritized, beyond those mandated by current legislation (the CFM scenario), it would be possible to notably reduce the air

pollution-driven warming enhancement. This could lower the Arctic temperature by 0.4 °C in 2050 (Fig. 3b). In comparison, the avoided Arctic warming from global CO2 emission reductions alone

in the SSP1-2.6 versus the SSP5-8.5 scenario is 0.5 °C (0.4–0.7 °C), according to results shown in Fig. 2d. This indicates that ambitious global reductions of BC and CH4 could lead to

Arctic climate benefits by 2050, similar to those from global CO2 reductions in a climate-focused mitigation strategy. Climate benefits of global BC and CH4 emission reductions are similar

for the CFM, MFR, and MFR_SDS scenarios (Fig. 3). The combined global SLCF emission changes in the MFR or MFR_SDS scenarios could reduce the forced Arctic warming by 0.2 °C. This is still

notable, considering the rapid sulfur emission reductions and associated warming impacts in these two scenarios. Overall, Arctic temperature is projected to increase less rapidly in the

MFR_SDS than in the SSP1-2.6 scenario (1.5 vs. 1.9 °C), given the deeper SLCF emission reductions in that scenario. With global BC emission reductions, Arctic warming would be reduced

largely as a consequence of diminishing interactions of BC with surface albedo (0.2 °C, Fig. 3), relative to the CLE scenario. Consistent with earlier research15,16, we find that policies

targeting emissions of BC from sources in the Arctic Council countries would be particularly efficient at slowing Arctic warming, mainly due to reduced absorption of solar radiation by BC in

snow and ice. Although roughly 40% of the Arctic warming reduction from BC is attributable to emissions in the Arctic Council countries, these account for only 6% of the global BC emission

reductions in the AMAP scenarios. On the other hand, BC emission reductions in the Asian Arctic Council observer countries yield much smaller Arctic climate benefits (Fig. 3). Consequently,

BC emissions at high latitudes have a disproportionately large impact on the Arctic climate, which may provide particularly interesting opportunities for climate policy development. For

instance, ambitious BC and CO2 emission reductions in Arctic Council countries could yield comparable Arctic climate benefits by 2050 (0.1 °C warming reductions for BC and CO2), according to

the available AMAP and SSP scenarios. AIR QUALITY AND HUMAN HEALTH CO-BENEFITS Over the next few decades, maximum feasible reductions in air pollutant emissions (MFR and MFR_SDS scenarios)

would lead to systematic reductions in annual mean PM2.5 concentrations globally, in the Arctic Council, and in Asian Arctic Council observer countries, relative to the CLE scenario (Fig. 

4). Changes in long-range transport of air pollutants between the regions contribute to the PM2.5 reductions, but are less important than local emission reductions. Most of the PM2.5

reductions are projected to occur before 2030 and are particularly large for the Asian countries, given the rapid reductions of the emissions in these scenarios (Fig. 4c). Here, reduced

emissions of sulfur and carbon particles would contribute about equally to PM2.5 reductions in the MFR and MFR_SDS scenarios, relative to the CLE scenario. Despite the rapidly declining

emissions in these scenarios, the PM2.5 concentration is projected to continue to exceed the World Health Organization’s 2021 Air Quality Guideline34 for annual average PM2.5 of 5 µg/m3. For

the Arctic Council countries (Fig. 4b), reductions in sulfur emissions would contribute more strongly to reductions in PM2.5 than reductions in emissions of carbon particulate matter (OC

and BC). PM2.5- and ozone-attributable mortality are calculated using the TM5-FASST model (Supplementary Note 6). Air pollution-related mortality follows a different pattern from

concentrations, due to simultaneously changing population, age structure, and disease rates. Despite relatively constant PM2.5 concentrations in the CLE scenario, global PM2.5 mortality is

estimated to increase by approximately 300,000 (9%) annual deaths from 2015 to 2030, and further in 2050 (1.2 M deaths, 38%; Fig. 4). The increase from 2015 to 2050 is particularly large in

the Asian countries (600,000 deaths, 36%), while PM2.5 mortality decreases slightly under CLE in Arctic Council countries (−3,000 deaths, −2%). PM2.5 mortality in 2050 is reduced under MFR

compared with CLE globally (−1.2 M deaths, −28%), in Arctic Council countries (−100,000 deaths, −58%), and in the Asian countries (−700,000 deaths, −31%). In the Climate Forcing Mitigation

(CFM) scenario, global PM2.5 concentrations would be reduced less strongly than in the MFR scenario, primarily because of the weak sulfur mitigation in the CFM scenario. PM2.5 mortality is

increased under CFM compared with MFR and is slightly lower for MFR_SDS compared with MFR. For O3, associated mortality increases steadily under CLE from 2015 to 2050 globally (by 72% from

400,000) and in both Arctic Council (by 11% from 30,000) and the Asian countries (by 77% from 300,000; Fig. 5). As for PM2.5, ozone concentrations and mortality are lower under MFR and

MFR_SDS compared with CLE and CFM. Since ozone mortality is an order of magnitude smaller than PM2.5 mortality, total air pollution mortality impacts largely follow the patterns of PM2.5

mortality. BENEFITS AND LIMITS OF MAXIMUM FEASIBLE EMISSION REDUCTIONS Globally, air pollution is a major driver of climate change and the top environmental human health threat. Our results

indicate that the understanding of future climate and health impacts of air pollutants can be advanced by using a combination of new emissions scenarios and an Earth System model emulator

for simulating air pollutants and climate. Cutting particulate carbon compounds and methane globally using best available technologies according to our AMAP scenarios would reduce

particulate matter and tropospheric ozone pollution. More ambitious efforts than currently legislated emission reductions could prevent hundreds of thousands of premature deaths in Arctic

Council Member and the Asian countries. This could be rapidly accomplished by increasing the use of best available technologies for reducing emissions of carbon particulate matter,

particularly black carbon, according to the three AMAP mitigation scenarios that we considered here. In addition to reduced mortality, the use of best available technologies would yield

rapid benefits for Arctic and global climate. Deep reductions in emissions of particulate carbon compounds and methane will be required to compensate for the additional Arctic warming that

is caused by globally reducing sulfur emissions and sulfate, which will be a major contributor to Arctic warming in the next few decades under any of the available future scenario.

Addressing black carbon particulate matter and methane would be highly beneficial for Arctic climate over the next few decades, with climate benefits comparable to those from CO2 reductions

in a climate-focused mitigation strategy, according to the SSP scenarios. Reducing the black carbon component of particulate matter in Arctic nations would be particularly impactful. Arctic

Council’s goal of reducing black carbon emissions of 25-33 percent below 2013 levels by 2025 is a welcome step but still not ambitious enough in that regard. In turn, without deep black

carbon and methane future reductions, global society may need to prepare for enhanced near-term Arctic warming from declining sulfate particulate matter, well beyond the warming driven by

carbon dioxide. Although the magnitude of the additional warming is subject to uncertainties in interactions of sulfate with radiative processes in the atmosphere, the climate consequences

would be considerable, for all available scenarios and within plausible uncertainty ranges. To achieve the 2021 World Health Organization Air Quality Guidelines for more of the global

population, and to stabilize Arctic climate in the longer term, sharp and immediate reductions of fossil fuel emissions of carbon dioxide and all other climate and air pollutants are needed.

This requires an integrated approach addressing air quality, development, and climate policies, including a major shift away from fossil fuels as well as fundamental behavioral changes that

go beyond the scenarios that we considered. METHODS EARTH SYSTEM AND AIR QUALITY MODELS To assess changes in climate due to the AMAP emissions scenarios we used five Earth System Models35

(ESMs; NorESM-happi, CESM2, MRI-ESM2, GISS-E2.1, and UKESM1; Supplementary Note 2, Supplementary Data 1) and simulated changes in global and Arctic temperatures from 2015 to 2050. For the

SSP scenarios, we used ESM multi-model ensemble results from the Coupled Model Intercomparison Project Phase 6 (CMIP6; Supplementary Data 2)36,37, which includes the same five ESMs. We

included results from all available models in the analysis, regardless of the fact that some of the models are known to project global climate warming in response to carbon dioxide emissions

that is larger than expected, based on various lines of evidence38. In addition to temperature, we assessed changes in air quality using results from 10 global models, including four of the

ESMs (CESM2, MRI-ESM2, GISS-E2.1, and UKESM1) and additional models (CanAM5-PAM, CIESM-MAM7, ECHAM6-SALSA, EMEP MSC-W, GEOS-Chem, Oslo CTM). EMULATOR We employed an Earth System model

emulator to assess the impacts of regional emissions of different air pollutants on radiative forcings, global and Arctic temperature, and PM2.5 trends. These were not provided by the ESMs,

given that the computational demands to compute these would have been prohibitive. Simple climate models, which are comparable to our emulator, have previously been used to analyze

radiatively forced changes in temperatures and have been shown to match ESM simulations well, globally39 and regionally10. Emulator simulations of PM2.5 concentrations are based on

pre-calculated equilibrium concentration pattern responses to specified regional emission perturbations. These were derived from simulations with CanAM5-PAM, CESM, MRI-ESM2, and UKESM1, for

emissions from the Western Arctic Council (Canada and United States), Eastern Arctic Council (Kingdom of Denmark, Finland, Iceland, Norway, the Russian Federation, and Sweden), Rest of

Europe, Arctic Council Asian observer countries (Japan, People’s Republic of China, Republic of India, Republic of Korea, Republic of Singapore), and the Rest of the World. To reproduce

concentration gradients for the analysis of health impacts, we first downscale the PM2.5 concentrations that are simulated in the 3D models using satellite-based data. This allows us to

conduct the emulator simulations at a resolution of 0.5° latitude and longitude. The downscaled concentrations from the models are averaged and scaled linearly by the specified regional

emission changes to obtain gridded and annual mean concentrations. The rigorous linearization of complex physical and chemical atmospheric processes in the emulator can limit the accuracy of

simulated concentration responses involving non-linear processes. However, the emulator reproduces the simulated PM2.5 trends in the ESMs during the simulation time period of interest. The

climate component of the emulator is based on simplified models of the heat, carbon and air pollutant cycles in the global Earth System. The simplicity of the emulator allows us to compare

the climate influences of anthropogenic emissions of CO2, CH4, CO, NO_x_, VOC, sulfur, BC, and OC. No other chemical species are represented in the emulator. On the other hand, ESM

simulations usually account for more species and emission sources than we consider here, which limits comparisons between the simulated total warming trends in the emulator and ESMs.

However, the focus of our study is on comparisons for SLCFs and CO2 climate impacts, which can be approximated to be independent of other species. It is possible to add other chemical

species to the emulator, as needed. The model parameterizations are constrained by effective radiative forcing sensitivities from a series of equilibrium simulations with regionally

perturbed emissions in CanAM5-PAM, CESM2, MRI-ESM2, and UKESM1. The emulator simulates global and Arctic temperature changes, assuming a linear relationship between the emissions and

effective radiative forcings for the different regions, similar to the simulation of PM2.5 concentrations. Separate effective radiative forcing sensitivities were obtained for each emitted

species, region, and forcing process, for interactions of aerosols with radiation, clouds, and the surface albedo (snow and ice). For global effective radiative forcings of CO2 and CH4,

global atmospheric greenhouse gas mass budgets are simulated which account for key physical and chemical loss processes. A more detailed description of the emulator is available40. Simulated

global aerosol effective radiative forcings in the emulator are comparable to results from previous multi-model based assessments (Fig. 6). Compared to an earlier assessment of Arctic

climate by AMAP41, differences in simulated forcings are due to a combination of differences in regional emissions, time periods, and changes in the climate models that were used to generate

the radiative forcing sensitivities. The ESMs that we used to calculate effective radiative forcing sensitivities tend to produce concentrations of BC that are too low, especially in the

Arctic (Supplementary Note 3), which indicates that BC effective radiative forcings in the emulator may also be low. Given the concentration biases and the tendency of the ESMs to

underestimate absorption aerosol optical depth, we cannot rule out larger impacts of BC on effective radiative forcings and temperatures than our best estimates provided here. The Arctic

temperature responses to radiative forcing changes are simulated accounting for transport of heat to the Arctic but omitting natural climate variability, changes in natural air pollutants,

and land-use. Specifically, the emulator simulates the forced response of the global and Arctic mean surface air temperatures to a series of annual emission pulses. The temporal evolution of

the regional mean temperature in response to the pulse emissions is approximated using a specified Equilibrium Climate Sensitivity (ECS), time scales of heat dissipation, and other

parameters derived from simulations with climate and other models. Global surface air temperature responses following an emission pulse are simulated by employing the Absolute Global

Temperature-Change Potential42 (AGTP). For simulation of Arctic temperatures, the AGTP is linearly decomposed into an Absolute Regional Temperature-Change Potential (ARTP), using Regional

Temperature-Change Potentials (RTPs). This builds on the approach used by AMAP15,41 to simulate the responses of Arctic and global temperatures to mean SLCF radiative forcings in 4 latitude

bands. Some improvements were made, including updated linear relationships between regional emission perturbations, vertically distributed Arctic black carbon, and aerosol effective

radiative forcing responses. The RTP originally used by AMAP produces an ECS of 2.7 °C, which is low compared than that derived43 from results of ESM simulations in Phase 6 of the Coupled

Model Intercomparison Project, CMIP6. In order to match ESM simulations, the ARTP is scaled so that the emulator simulations are conducted for an ECS of 3.7 °C instead. However, the scaling

does not affect the warming patterns. It seems possible that the simulated impacts of BC on Arctic temperature are underestimated in the emulator simulations. We note that Arctic warming

rates are lower in the emulator than in ESMs simulations (Fig. 2). This seems plausible given that the original RTPs were derived from simulations with an ESM with a low ECS and weak Arctic

sea ice response to temperature changes15,44. ASSESSMENT OF UNCERTAINTIES Physical climate process uncertainties, particularly including climate feedbacks and aerosol radiative forcings, are

a major source of uncertainty in climate model simulations and climate assessments4. An ESM assessment of uncertainties in Arctic SLCFs and climate would require very large ensembles of

multiple ESMs, which are not available. Here we used the emulator to analyze the impacts of key climate process uncertainties on global and Arctic mean temperatures confidence ranges (Fig. 

2). First, we estimated model confidence ranges for global radiative forcings, based on recent results from the CMIP6 multi-model ensemble. Second, the radiative forcings in the emulator

were scaled to match the end points of these ranges (Table 3). Third, the ECS in the emulator was varied by 30%, corresponding to an ECS range43 from 2.6 to 4.8 °C. Subsequently, emulator

simulations were conducted with the scaled forcings and ECS to infer corresponding temperature confidence ranges for the different processes. Finally, confidence ranges for global and Arctic

mean temperatures, from the combination of all forcing and ECS uncertainties, were determined assuming statistical independence of all forcing process and ECS uncertainties. We tested the

robustness of this approach by applying different combinations of forcing and ECS choices in Monte Carlo simulations with the emulator. These tests provide evidence for a highly systematic

warming effect from reduced sulfur emissions for any scenario, global radiative forcing, or equilibrium climate sensitivity, within the specified physical process uncertainty ranges.

Similarly, the sign of the cooling impact of reducing black carbon and methane emissions in all of the AMAP mitigation scenarios (MFR, MFR_SDS, and CFM) is highly robust. Given that several

ESMs are known to project global climate warming in response to carbon dioxide emissions that is larger than expected38, we also specifically assessed the impacts of using a lower ECS of 3 

°C in the emulator and removing ESMs that have an equilibrium climate sensitivity outside of a range from 2.5 to 4 °C from the analysis. This yields results that are similar to the original

results (Supplementary Fig. 11 and Fig. 2). With the reduced ECS in the emulator, all the SLCF impacts on Arctic temperatures in 2050 are reduced by about 20%, whereas the CO2 impacts are

reduced by about 10–20%, which is well within the uncertainty of the analysis. DATA AVAILABILITY Model data sets used here are publicly available: AMAP emissions and data from AMAP models

(https://doi.org/10.18164/e0a0ac5c-d851-45b9-b6d9-4abc29d7d419, https://iiasa.ac.at/web/home/research/researchPrograms/air/ECLIPSEv6b.html), CEDS and SSP emissions

(https://esgf-node.llnl.gov/projects/input4mips/), and the CMIP6 ESM data (https://esgf-node.llnl.gov/projects/cmip6/). See the Supplementary Data 1, 2 for specific model and data

references. CODE AVAILABILITY The AMAP emulator code and input files for all scenarios are available at https://zenodo.org/record/5555173 (https://doi.org/10.5281/zenodo.5555173). REFERENCES

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and organizers of the AMAP assessment report “Impacts of Short-lived Climate Forcers on Arctic Climate, Air Quality, and Human Health” for providing the scientific foundation of our

analysis. We are also very grateful to the many colleagues that contributed to the CMIP6 model data sets, IPCC Working Group I, and 6th IPCC assessment report, three anonymous reviewers, and

John Fyfe for their helpful comments. The authors further acknowledge funding provided through the following agencies and programs: European Union Action on BC in the Arctic (Z.K., K.K.,

V.V.P.); The European Union’s Horizon 2020 Research and Innovation Assessment Programme (A.M.L.E., T.K., S.K.); Swedish Environmental Protection Agency, Swedish Clean Air and Climate

research program (A.M.L.E., S.K., M.A.T.); Arctic climate Across Scales (S.T.T.); The Knut and Alice Wallenberg Foundation (M.A.T.); Swiss National Science Foundation (J.S.); The Ingvar

Kamprad Chair for Extreme Environments Research sponsored by Ferring Pharmaceuticals (J.S.); The Academy of Finland (T.K.); Japan Society for the Promotion of Science KAKENHI (N.O.);

Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency of Japan (N.O.); Arctic Challenge for Sustainability II (N.O.); Grant for the

Global Environmental Research Coordination System from the Ministry of the Environment, Japan (N.O.); Aarhus University Interdisciplinary Centre for Climate Change (U.I.); FREYA project

funded by the Nordic Council of Ministers (U.I., M.Sa.); EVAM-SLCF project funded by the Danish Environmental Agency (U.I.); Danish Environmental Protection Agency, DANCEA funds for

Environmental Support to the Arctic Region project (J.H.C.); ACCEPT project funded by Research Council of Norway (M.Sa.); French IDRIS HPC computing resources and Institut Pierre Simon

Laplace computing center (K.S.L., L.M., T.O., J.C.R.), and the Arctic Monitoring and Assessment Programme (S.R.A., M.G., S.T., S.T.T.). AUTHOR INFORMATION Author notes * Rashed Mahmood

Present address: University of Montreal, Montreal, QC, Canada * Luca Pozzoli Present address: FINCONS SPA, Vimercate, Italy AUTHORS AND AFFILIATIONS * Canadian Centre for Climate Modelling

and Analysis, Environment and Climate Change Canada, Victoria, BC, Canada Knut von Salzen, Cynthia H. Whaley, David Plummer, Michael Sigmond & Barbara Winter * Milken Institute School of

Public Health, George Washington University, Washington, DC, USA Susan C. Anenberg * European Commission, Joint Research Centre (JRC), Ispra, Italy Rita Van Dingenen & Luca Pozzoli *

International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria Zbigniew Klimont * Department of Climate and Space Sciences and Engineering, University of Michigan, Ann

Arbor, MI, USA Mark G. Flanner * Barcelona Supercomputing Center, Barcelona, Spain Rashed Mahmood * Institute for Climate and Atmospheric Science, School of Earth and Environment, University

of Leeds, Leeds, United Kingdom Stephen R. Arnold & Steven T. Turnock * Air Quality Research Division, Environment and Climate Change Canada, Toronto, ON, Canada Stephen Beagley & 

Wanmin Gong * Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA Rong-You Chien & Joshua S. Fu * Department of Environmental Science, Aarhus

University, Roskilde, Denmark Jesper H. Christensen, Jens L. Hjorth & Ulas Im * Aarhus University, Interdisciplinary Centre for Climate Change (iClimate), Roskilde, Denmark Jesper H.

Christensen & Ulas Im * NILU – Norwegian Institute for Air Research, Kjeller, Norway Sabine Eckhardt & Nikolaos Evangeliou * Department of Meteorology, Stockholm University,

Stockholm, Sweden Annica M. L. Ekman * The Bolin Centre of Climate Research, Stockholm University, Stockholm, Sweden Annica M. L. Ekman & Srinath Krishnan * Center for Climate Systems

Research, Columbia University, New York, USA Greg Faluvegi & Kostas Tsigaridis * NASA Goddard Institute for Space Studies, New York, USA Greg Faluvegi & Kostas Tsigaridis * Norwegian

Meteorological Institute, Oslo, Norway Michael Gauss, Dirk Olivié & Svetlana Tsyro * CICERO Center for International Climate Research, Oslo, Norway Srinath Krishnan & Maria Sand *

Ministry of the Environment (YM); Government, Helsinki, Finland Kaarle Kupiainen * Department of Applied Physics, University of Eastern Finland (UEF), Kuopio, Finland Thomas Kühn *

Atmospheric Research Centre of Eastern Finland, Finnish Meteorological Institute (FMI), Kuopio, Finland Thomas Kühn * Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

Joakim Langner & Manu A. Thomas * Laboratoire, Atmosphères, Observations Spatiales (LATMOS)/IPSL, Sorbonne Université, UVSQ, CNRS, Paris, France Kathy S. Law, Louis Marelle, Tatsuo

Onishi & Jean-Christophe Raut * Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Ibaraki, Japan Naga Oshima * Finnish Environment Institute (SYKE), Helsinki,

Finland Ville-Veikko Paunu * Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University,

Beijing, China Yiran Peng & Minqi Wang * Division for Climate, Environment and Health at Norwegian Institute of Public Health, Oslo, Norway Shilpa Rao * Extreme Environments Research

Laboratory, École Polytechnique Fédérale de Lausanne, Sion, Switzerland Julia Schmale * Met Office Hadley Centre, Exeter, UK Steven T. Turnock Authors * Knut von Salzen View author

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for this author inPubMed Google Scholar * Barbara Winter View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS The study was initiated and

conceptualized by K.v.S., C.H.W., S.C.A., R.V.D., Z.K., M.G.F., S.R.A., S.E., A.M.L.E., K.K., K.S.L., D.O., N.O., S.R., M.Sa., J.S., and S.T.T.; K.v.S. wrote the initial draft of the main

manuscript and conducted much of the analysis, with major contributions from C.H.W., S.C.A., R.V.D., Z.K., R.M., A.M.L.E., G.F., N.O., M.Si., and S.T.T.; Key model data sets were generated

and analyzed by K.v.S., R.V.D., Z.K., M.G.F., R.M., S.B., R.Y.C., J.H.C., N.E., J.S.F., M.G., W.G., J.L.H., U.I., S.K., T.K., J.L., L.M., D.O., T.O., N.O., V.V.P., Y.P., D.P., L.P., J.C.R.,

M.Sa., M.Si., M.A.T., K.T., S.T., S.T.T., M.W., B.W.; All authors discussed the analysis at all stages of writing. CORRESPONDING AUTHOR Correspondence to Knut von Salzen. ETHICS DECLARATIONS

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