{"paper_id":"4e27321a-575e-4d47-b199-e62b43daeb69","body_text":"China’s aerosol cleanup has contributed strongly to the recent acceleration in global warming | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Physical Sciences - Article China’s aerosol cleanup has contributed strongly to the recent acceleration in global warming Bjørn Samset, Laura Wilcox, Robert Allen, Camilla Stjern, Marianne Lund, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6005409/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jul, 2025 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Abstract Global surface warming has accelerated since around 2010, relative to the preceding half century 1-3 . This has coincided with China’s efforts to reduce air pollution through restricted atmospheric aerosol and precursor emissions 4,5 . A direct link between the two has, however, not yet been established. Here we show, using a large set of simulations from eight Earth System Models, how a time evolving 75% reduction in Chinese sulfate emissions partially unmasks greenhouse driven warming and influences the pattern of surface temperature change. We find a rapidly evolving global, annual-mean warming of 0.07 ± 0.05 ºC, sufficient to explain a majority of the uptick in global warming rate since 2010. We also find North-Pacific warming and a top-of-atmosphere radiative imbalance that are consistent with recent observations. China’s aerosol cleanup is thus likely a key contributor to recent global warming acceleration, and to Pacific warming trends. Earth and environmental sciences/Climate sciences/Climate change Earth and environmental sciences/Climate sciences/Climate change/Climate and Earth system modelling Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Over the industrial era, anthropogenic emissions of atmospheric aerosols, and their gaseous precursors, have strongly influenced the Earth’s climate and energy balance 6 . Aerosols have recently been assessed to have cooled the global surface by 0.4 ºC (year 2019, relative to pre-industrial conditions), partially masking greenhouse gas driven warming, predominantly through aerosol-cloud interactions affecting the albedo of clouds, alongside direct aerosol shortwave effects 7 . The geographical distribution of this forcing has, however, shifted after 1980, with China and India having replaced the US and Europe as the major emitters 8 . It has shifted again since the early 2010s, following China’s strong effort to reduce air pollution, which has led to strong (~20 Tg/year, or around 75%) sustained reductions in the emission rate of SO 2 4,9 , the precursor gas of sulfate aerosols, which in turn is the dominating aerosol specie currently cooling the Earth 7 . Concurrently, the rate of global mean surface warming, which has overall been constant at around 0.18 ºC/decade since around 1970, has increased 1,2 . Recent studies find an acceleration in the rate of surface warming and ocean heat uptake after 1990, and the most recent decade (2013-2022) had a warming rate of 0.25 ºC/decade, even after reducing the influence of internal variability 1,3 . 2023 and 2024 were both record-setting in terms of surface temperature anomaly, dominated by strong positive sea-surface temperature anomalies in most ocean basins 10 . Recent improvements in satellite-based constraints on the Earth’s Radiative Imbalance at top-of-atmosphere have also revealed increased energy absorption into the global Earth system 11 . A range of studies have suggested that aerosol emissions changes may have been contributing factors. These include recent overall trends in global aerosol loading 12 , and the regulations by the International Maritime Organization that caused a strong drop in SO 2 emissions from the global shipping fleet from 2020 and onwards 13,14 . Low planetary albedo due to reduced cloud amounts, or cloud albedo, in the North Pacific was also recently implicated as a key process leading to increased surface warming 15 . However, no study has to date quantified the influence of the recent emissions reductions in China on global and regional climate evolution, despite their being larger in magnitude than the shipping emission changes, and sustained for longer. Such analysis requires dedicated simulations with Earth System Models that have not been readily available, partly due to a lack of updated emissions inventories that capture the decrease in Chinese emissions. The regional nature of the emissions change also means that internal variability is a major limiting factor in quantifying a climate response, necessitating multiple ensemble realizations of these simulations. Recent global modelling exercises such as CMIP6 used global emissions changes 16 , where the influence of Chinese SO 2 emissions cannot be separated from other, concurrent changes. Further, the emissions dataset used in CMIP6 did not accurately represent the recent reduction in Chinese aerosol precursors 17 . Here, we present results from the Regional Aerosol Model Intercomparison Project (RAMIP), where aerosol emissions were systematically perturbed in individual source regions, in eight CMIP6 era Earth System Models 18 . We show results from a transient emissions reduction experiment in an East Asia region, consisting primarily of mainland China, that are closely analogous to recent emissions changes. RAMIP simulations span 2015-2051, but they contain the same East Asian SO 2 emission reductions of 20 Tg/year that have occurred in the real world since around 2010. Thus, we can use the RAMIP simulations to quantify the real-world climate impacts of recent efforts by China to improve air quality, including surface temperatures, precipitation, and the global energy imbalance. By comparing to observations of regional temperature changes and the top-of-atmosphere radiation imbalance, we argue that the recent aerosol emissions changes in China are a key contributing factor, among others, to the recent uptick in the rate of global mean surface warming, through an unmasking of greenhouse gas driven climate change. Results RAMIP simulations and recent emissions changes in China We first document the emissions perturbation applied in the RAMIP baseline and East Asia simulations 18 (see Methods), and compare them to the actual emissions reductions from the same region since around 2010. Briefly, RAMIP isolates the climate effects of aerosol emissions in one region by comparing two sets of transient emission simulations; one following a global, high emissions pathway (SSP3-7.0, which assumes weak air quality policies), and one where aerosol emissions in one region ( East Asia , consisting mainly of mainland China emissions) have been replaced by those from a strong air quality policy trajectory (SSP1-2.6). See Methods, or 18 , for a full description. In the present analysis, we use simulations from 8 global models, each with 10 ensemble members, for a total of 80 ensemble members. This simulation set effectively samples both model uncertainty and internal climate variability. Figure 1a shows changes in aerosol optical depth (AOD) retrieved by MODIS Terra and Aqua between the two previous decades. Consistent with previous literature, we find a dipole pattern consisting of an increase over India, and a strong decrease over China following their air quality improvement initiatives. For comparison, Figure 1b shows the pattern of AOD change between the RAMIP East Asia and baseline simulations, for the simulated period 2035-2049. Figures 1c and 1d show the corresponding SO 2 emissions and AOD change, for observations and simulations, within the box labeled East Asia in Figure 1a (a geographical box that covers the main emission regions of mainland China). For observations, relative to the 2005-2010 period, we find an AOD change of -0.13 units for the period 2014-2023, resulting primarily from emissions reductions of around 20 Tg SO 2 / year (Supplementary Figure 1b). Emissions data are from the December 2024 release of the Community Emissions Data System (CEDS) 19 . Concurrent changes in black carbon (BC) aerosol emissions are shown in Supplementary Figure 1; they are smaller, in absolute terms and in particle number, and are not expected to contribute strongly to the AOD change, though they may influence climate features through their strong atmospheric shortwave absorption 20 . RAMIP transient simulations start in 2015, but use CMIP6 emissions based on a CEDS version that projected a delayed reduction in East Asia emissions compared to the actual, realized changes. The RAMIP East Asia and baseline simulations however still have an emissions difference trajectory that broadly corresponds to recent observations (20 Tg SO 2 /year), for a later range of years. We also find a multi-model mean AOD change trajectory and magnitude that broadly tracks MODIS observations (DAOD of -0.11 ± 0.05 units for the RAMIP period 2035-2049). We do note, however, that even though all models used the same emissions, the RAMIP 2035-2049 mean East Asia AOD change ranges from -0.08 to -0.28 (see Fig.S1). This is due to a combination of factors including the optical properties of the simulated aerosols, the cloud fields, wind and precipitation climatologies, and aerosol removal rates. Physically, AOD decreases are associated with less scattering of incoming solar radiation, and hence increases in downwelling surface solar radiation. Supplementary Figure 1 shows the corresponding changes in downwelling shortwave radiation at the surface, in response to aerosol emissions reductions. Here, we find a multi-model mean change of 7.7 ± 2.5 Wm -2 ,over the East Asia domain, with inter-model variation and spatial pattern that broadly follow that of AOD. Based on Figure 1 and Supplementary Figure 1, we conclude that the RAMIP East Asia results for the 2035-2049 period can be used as a proxy for the response to the emission rate change that has occurred in the real world over the 2010-2023 period (i.e. a 20 Tg / year sustained reduction in SO 2 emissions). Modeled temperature and precipitation changes In Figure 2, we show the resulting global, annual mean temperature responses to a 20 Tg/year reduction in SO 2 emissions from East Asia. For 2035-2049, we find a multi-model mean global warming of 0.07 ± 0.05 ºC, where the uncertainty is the standard deviation of the eight individual model results. The signal evolves smoothly, along with the emission reduction, with a rate-of-change of 0.02 ºC/decade for the full 2015-2050 period. Note, however, the strong inter-model variability (Fig. 2b), with one model (NorESM2-LM) showing an ensemble mean warming of 0.15 ºC, while another outlier (GISS-E2-1-G) even shows a slight cooling (-0.02 ºC). We link these model differences primarily to Arctic amplification and aerosol-cloud interactions in the North Pacific; see Supplementary Figure 2 and further discussion below. There is also a strong contribution from internal variability, with marked diversity between ensemble members (Figure 2b). This illustrates the difficulties of quantifying the climate impacts of the recent East Asian aerosol emission reductions, and other notable emissions changes like those resulting from the recent IMO shipping regulations 21 and the importance of conducting large ensemble simulations when investigating climate forcings that are strong regionally but weaker on a global scale 18 . Geographically, the seasonal temperature change is strongest near the source (East Asia, notably Eastern and Northen China) both in boreal summer (JJA, Fig. 2c) and winter (DJF, Fig. 2d). However, we also find significant warming (>0.2 ºC; paired Student’s t-test, p<0.05) over much of the North Pacific, in both seasons. For DJF, we also find a significant warming of North America, and throughout the Arctic. A wintertime cooling patch in Central Europe that has been reported by previous studies 22,23 is however not visible in our dataset. For annual mean responses, including for individual models and ensemble members, see Supplementary Figure 2. Supplementary Figure 3 shows the corresponding precipitation response, which broadly tracks that of surface temperature. We find an overall global wettening of 0.009 ± 0.004 mm/day (0.3 ± 0.1 %), yielding an overall hydrological sensitivity of 4 ± 2 %/ºC, which is broadly consistent with previous estimates of the climate impacts of aerosol emissions changes ( 20 ). Geographically, we find a strong summertime (JJA) precipitation increase in East China, and along the East Asian coastline, as well as a wettening along the North Pacific storm tracks extending well into North America. We also find a northward shift of the ITCZ, consistent with expectations from preferential warming of the Northern Hemisphere relative to the Southern Hemisphere 24 The regions with statistical significance are smaller than for temperature, as expected due to the higher internal variability and greater model diversity in precipitation simulations. Influence on recent global warming and radiative imbalance We now put the RAMIP results in the context of recent trends in global warming, in Figure 3. In panel 3a, we show the global mean surface temperature anomaly (GSTA) relative to 1850-1900 since 1980, as the average of four observational reconstructions (HadCRUT5, NOAA, GISTEMP and Berkeley Earth; see Methods). For the 30-year period of 1980-2009, the average observed warming rate is 0.18 ºC / decade. For illustration, we also show the time series from ( 7 ), where internal variability from Pacific ENSO and other ocean modes of variability has been filtered out (our conclusions do not rely on the usage of this dataset). For the subsequent period of 2010 - 2023, we find an elevated observed warming rate of 0.33 ºC / decade, and 0.25 ºC / decade when interannual variability is filtered, consistent with previous studies 2,3 . In the main panel (Fig. 3b), we zoom in on the latter period. Most post-2010 GSTA values fall above the continuation of the 1980-2009 trend (dotted black line), in the reconstructions (dots) and in the reduced variability time series (solid black line), indicating a recent increase in the global warming rate. To estimate the contribution of East Asian aerosol emissions changes to this increase, we take the RAMIP quantified global warming of a 0.07 ± 0.05 ℃, and convert it to a warming rate over the 2010-2023 period (0.05 ± 0.04 ºC / decade). See the box-and-whisker on the right of Fig. 3b, which shows how we’ve added this aerosol cleanup induced warming rate (red line and range) to the continuation of the 1980-2009 trend. Assuming, for now, no change in the underlying greenhouse gas induced global warming rate, this suggests a combined post-2010 warming rate, from greenhouse gas increases and East Asian aerosol cleanup, of 0.18 ℃/decade + 0.05 ℃/decade = 0.23 ℃/decade, approaching the 0.25 ºC / decade found after filtering the effects of internal variability. For context, see further discussion below of other sources of recent warming. Note that we assume here that the full warming due to the observed recent East Asian emissions reduction has already been realized, so that it is justifiable to compare warming in observations during the 2010-2023 period to warming in the RAMIP simulations during the 2035-2049 period. This is supported by recent modelling exercises using sustained step changes in SO 2 emissions ( 21 ), finding that the majority of subsequent global mean surface temperature change has been realized within the first 24 months, while the rest develops slowly at a multi-decadal timescale. The transient RAMIP simulations also do not show any appreciable delay in the climate response to SO 2 reductions. However, our estimate should still be taken as an upper limit, to take this limitation into account. Since aerosol changes have regionally heterogeneous climate influences, as shown above, we next investigate the correspondence of simulated changes to observed regional warming rate. In Figure 3c, we show the observed difference between the 2010-2023 and 1980-2009 warming rates, in the four reconstructions. See Supplementary Figure 4 for individual time series and the two trend periods in isolation. While a 13-year trend will be strongly influenced by decadal scale variability, we do find a very clear pattern of observed warming in the North Pacific, with two distinct maxima: one along the East Asian coastline, and the other following the west coast of North America, extending west to the center of the Pacific. In Figure 3d, we show the annual mean surface warming pattern from East Asian aerosol emission reductions in RAMIP. As documented above, we also here find a two-maxima pattern along the East Asian and western North American coastlines. This shows that the simulated increased global mean warming rate comes from a geographical region where observations also find an elevated warming rate since 2010, relative to previous decades. See Supplementary Figure 7 for the transient evolution of regional means (Western and Eastern North Pacific) for individual models. Top-of-atmosphere radiative imbalance The next question is what physical processes lead to this warming rate. In Figure 4, we investigate the top-of-atmosphere (TOA) all-sky change in radiative imbalance (shortwave plus longwave) in response to East Asian aerosol emissions reductions in RAMIP, and compare them to observations (CERES) and reanalysis (ERA5). Fig. 4a-b show the time series and 2035-2049 means of the TOA all-sky radiative imbalance, which has a mean of 0.06 ± 0.04 Wm -2 . While there is substantial inter-model and ensemble member variability, the overall evolution is very similar between the eight RAMIP models. Using the same logic as above, this corresponds to an evolving increase in the TOA imbalance since 2010 of 0.05 ± 0.03 Wm -2 / decade. Fig. 4c-d shows the geographical distributions of clear sky and all-sky radiative imbalances. Again, for all-sky conditions, we find a geographical pattern displaying two clear maxima, near the source region, and in the western North Pacific, with peak values exceeding 2 Wm -2 . There is little influence on other regions. For clear sky conditions, only the East Asian and eastern North Pacific influence remains, indicating it has a major contribution from the direct interaction of aerosols with incoming sunlight. The maximum west of North America, however, is likely primarily a result of aerosol-cloud interactions, seen in a region with high prevalence of low clouds (stratocumulus decks). Supplementary Figure 5 shows individual models, while Supplementary Figure 6 shows a further breakdown into shortwave and long wave components. This result also explains part of the inter-model diversity in RAMIP responses; the models that have low overall temperature response to East Asian aerosol changes (notably CNRM and GISS), also have weak responses in this region, related to their cloud climatologies and their simplified treatment of aerosol-cloud interactions. See also Supplementary Figure 7, which shows the transient evolution of Western and Eastern North Pacific means of temperature and surface shortwave fluxes. We further note that that for clear sky conditions, there is a strong positive anomaly over Eastern China in most models. This indicates the presence of compensating aerosol-cloud effects, and perhaps other aerosol processes such as wet removal by precipitation, over this region in the RAMIP models. Finally, in Fig. 4e-f, we show recent all-sky TOA imbalance trends from observations (CERES), and its difference from a reanalysis product (ERA5). While the CERES observations (Fig. 4e) have a strong influence of internal variability, and will also be influenced by other recent changes in the climate system, we do find a positive anomaly in the North Pacific. The ERA5 reanalysis itself shows a very similar pattern (Supplementary Figure 3). However, when taking the difference between CERES and ERA5 (Fig. 4f), we find two striking features. One is a positive anomaly in the low cloud region of the eastern North Pacific, the other is a negative anomaly over Eastern China. ERA5 does include a treatment of aerosols, but it uses a CMIP5 era combination of historical emissions up to 2009 and subsequently the RCP emissions 25 . These pathways do not include the recent reductions in East Asian emissions. Hence, the difference between CERES and ERA5 may be indicative of regions where the recent aerosol changes are important for the reconstruction, and have an influence on observed rates of change of TOA radiative fluxes. This is supported by RAMIP finding that these two regions are strongly influenced by aerosol-cloud interactions. We do note, however, that trends in sea surface temperatures will also influence ERA5 reconstructions, meaning that we cannot make a clear attribution of the observed changes to aerosol emissions changes with this method. Other sources of recent warming We have shown how recent aerosol emissions reductions in East Asia may have had a strong influence on post-2010 elevated rates of surface warming, both globally and in the North Pacific. To put these results in context, we here discuss some other, concurrent changes that we cannot consider in the same framework. One anthropogenic factor is the accelerated increase in atmospheric CH 4 concentrations over the same period. As an estimate, using recent global near-surface concentrations from NOAA 26 and the IPCC AR6 27 , combined with the forcing estimation methods from ( 22 ), we find a global mean CH 4 radiative forcing (RF) of 0.06 Wm -2 for the 2010-2023 period, corresponding to a rate of 0.047 Wm -2 / decade. This is a marked increase over the previous decade (2000-2010), where we find 0.01 Wm -2 / decade. However, for the full 30-year period of 1980-2010, we estimate a forcing of 0.043 Wm -2 / decade. This means that while changes in CH 4 atmospheric concentration growth rates may have contributed to decadal variability, and clearly can enhance the overall rate of global warming, the recent decade has not seen markedly strong influence from CH 4 increases compared to recent history. We also note that the above numbers are for RF, while the Effective Radiative Forcing (ERF), and the temperature influences of methane, may be muted due to rapid adjustments ( 23 ). Another, more recent, anthropogenic factor is the post-2020 reduction in SO2 emissions from the shipping sector, following the recent regulations of the International Maritime Organization. Here, a range of studies have concluded that the global mean ERF from the 80% reduction in emissions, corresponding to around 9 Tg SO 2 /year, is in the range of 0.05-0.10 Wm -2 ( 10, 11 ). While studies using ensembles of simulations from fully coupled models do find a surface temperature response to this emissions change over time, the magnitude and detectability relative to internal variability for the years 2020-2023 is still disputed 21,28 . Coming in at the end of the time period studied here, the IMO regulations are unlikely to have had a major influence on the above conclusions regarding the influence of Chinese aerosol emission changes. ERF estimates are unfortunately not available from all RAMIP models. For those that have delivered the required simulations (see Methods), we find an ERF from the emissions changes in China discussed above ranging from 0.06 – 0.21 Wm -2 ( 15 ). Recently, in the context of the 2023 record global mean surface temperatures, Goessling et al. 15 identified a record-low planetary albedo as a contributing cause. Using the same datasets as here (CERES and ERA5), they highlight low cloud cover over the North Pacific as a key component of this, and note that the role of aerosols in this change is still unclear. Their results are well in line with the present study, both geographically and in terms of physical processes, however their main focus is on reduced cloud fraction in selected years while our results concern a reduced aerosol-cloud induced albedo on a decadal scale. The regional similarity, however, opens the possibility that the results of Goessling et al. are, at least in part, interpretable as a sustained reduction in aerosol-cloud interactions. Discussion Using a set of 10-ensemble-member simulations from eight CMIP6-era Earth System Models, we have quantified the transient climate response to gradually reducing aerosol emissions from East Asia. We find a global, annual-mean warming of 0.07 ± 0.05 ºC, and a corresponding wettening of around 4 ± 2 %/ºC. The emission reductions in our simulations correspond closely to the emissions reductions realized in East Asia over the period 2010-2023, in magnitude and geographical location. This allows us to put our results in the context of the recent uptick in the observed rate of global mean surface warming. Here, we find that emissions reductions in China have contributed up to 0.05 ºC / decade since 2010, explaining a large fraction of the observed increase of 0.06 ºC / decade over the same period, after filtering out the effects of interannual variability. We also find that the geographical location of the temperature influence of a reduction in Chinese SO 2 emissions correspond to where observations show a recent surge in warming, and also where satellite observations find an increase in TOA radiative imbalance. This lends support to the conclusion that the recent intensive effort to tackle air pollution in China has caused, as an unintended side effect, an unmasking of greenhouse gas driven global warming and a marked contribution to the recently observed warming trend. Looking ahead, emissions from East Asia are projected to keep going down. However, the rate of change has slowed markedly, and the CEDS inventory estimates that there is less than 10 Tg SO 2 /year (~25% of the 2010 value) left to reduce. This means that for the coming years and decades, the influence on global warming rates from East Asian emissions reductions is likely to be less prominent, although this depends crucially on the still unresolved question of whether the influence of aerosols on the climate – in particular through aerosol-cloud interactions - is linear. Methods Simulations This study uses Earth System Model simulations performed for the Regional Aerosol Model Intercomparison Project (RAMIP) 18 . RAMIP is part of the extended phase of the 6 th Coordinated Model Intercomparison Project (CMIP6Plus), and builds on and extends historical (1850-2014) simulations delivered to CMIP6. The RAMIP baseline simulation is a Shared Socioeconomic Pathway with weak air quality policy, and consequently continued strong aerosol emissions (SSP3-7.0). In RAMIP perturbations, these emissions are exchanged for a strong air quality policy case (SSP1-2.6), for individual emission regions. In this study, we use the RAMIP East Asia simulation (SSP370-eas126aer), where all anthropogenic aerosol emissions from a geographical box consisting mainly of China are perturbed. All participating models ran the baseline and signal simulations for the period 2015-2050, and delivered 10 ensemble members for each simulation. See Supplementary Table 1 for an overview of the simulations. A subset of the models also delivered simulations with fixed sea-surface temperatures, which can be used to quantify the Effective Radiative Forcing (ERF). See 18 for details. As part of Figure 1, we also show the aerosol emissions trajectories of the signal and baseline simulations. All non-aerosol emissions are identical in signal and baseline, and they each branch off from the same ensemble members from the historical simulation in 2014. Land use change is also identical in signal and baseline (SSP3-7.0). Models Supplementary Table 2 lists the eight models used for the present study, together with the spatial resolutions, aerosol representation, and main references. Analysis Climate responses are defined as the difference between SSP370 and SSP370-eas126aer, for the period 2035-2049. For statistical significance, we require that the multi-model, multi-ensemble member sets (consisting of 80 global or regional means, or grid point values) are significantly different according to a paired t-test with p<0.05. Map figures are hatched for all grid points that show statistically significant changes. ERF is quantified as the difference in net top-of-atmosphere (shortwave + longwave) radiation between 30-year fSST simulations, following Forster et al. 2016 29 . Uncertainties All uncertainties quoted in this paper are ±1 standard deviation, generally of the set of ensemble mean results from each model. Regions East Asia is here defined as a geographical box covering 20N-35N, 95E-133E. We also refer to this as China, as the box predominantly covers the mainland part of that country. Observational data Additionally, we use gridded aerosol emissions data from CEDS (2024 Gridded Data Release: December 3, 2024; v_2024_11_25), observations of aerosol optical depths from the MODIS instruments on the Terra and Aqua satellites (Combined Dark Target and Deep Blue AOD at 0.55 micron; MYD08_M3 v6.1 and MOD08_M3 v6.1), top-of-atmosphere (TOA) energy imbalance observations from the CERES instrument (EBAF-TOA_v4.2.1), and TOA energy imbalance estimates from the ERA5 reanalysis 25 . Declarations Data availability Analysis code and processed RAMIP datasets used in this paper are available from figshare archive 10.6084/m9.figshare.28296344 (to be made public on publication). Other datasets are available as described in Methods, with full links to the simulation datasets shown in Supplementary Table 3. Contributions BHS and LJW conceived the study. BHS, LJW, CWS, RJA and MTL performed the analysis and wrote the paper. PK, LFL, RA, DMW, KT, JM, DO, RM, AL, LW, SR, JK, PG, NO, TK, PN, LN delivered simulations. LJW, SA and MTE performed dataset preparations. All authors contributed to editing and finalizing the paper. Competing interests The authors declare no competing interests. Acknowledgements We acknowledge support by the Center for Advanced Study in Oslo, Norway which funded and hosted the HETCLIF centre during the academic year of 2023/24. The NorESM simulations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS). We also acknowledge the following funding sources: Research Council of Norway grant 324182 (BHS, LJW, CWS, MTL, RJA). National Science Foundation grant #AGS-2153486 (RJA). Natural Environment Research Council (NERC) grant TerraFIRMA NE/W004895/1 (LJW, PTG, STR). National Center for Atmospheric Science, UK (LJW, STR). Environment Research and Technology Development Fund (JPMEERF20232001) of the Environmental Restoration and Conservation Agency provided by Ministry of the Envi-ronment of Japan (TK, NO). Arctic Challenge for Sustainability II (ArCS II), Program Grant Number JPMXD1420318865 (TK, NO). Global Environmental Research Coordination System from Ministry of the Environment of Japan grant MLIT2253 (TK, NO). H2020 Societal Challenges grant no. 101003826 (JM, RM, DOD). Research Council Finland grant no. 337552. (JM, RM, DOD). Columbia Center for Climate and Life (DMW). Natural Sciences and Engineering Research Council of Canada (NSERC) (PK, LJF). Swedish Research Council through grant agreement no. 2022-06725 (AL) References Merchant, C. J., Allan, R. P. & Embury, O. Quantifying the acceleration of multidecadal global sea surface warming driven by Earth's energy imbalance. Environmental Research Letters 20 , doi:10.1088/1748-9326/adaa8a (2025). Forster, P. M. et al. 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Samset, B. H. Aerosol absorption has an underappreciated role in historical precipitation change. Communications Earth & Environment 3 , doi:10.1038/s43247-022-00576-6 (2022). Watson-Parris, D. et al. Weak surface temperature effects of recent reductions in shipping SO2 emissions, with quantification confounded by internal variability. doi:10.5194/egusphere-2024-1946 (2024). Xiang, B., Xie, S.-P., Kang, S. M. & Kramer, R. J. An emerging Asian aerosol dipole pattern reshapes the Asian summer monsoon and exacerbates northern hemisphere warming. npj Climate and Atmospheric Science 6 , doi:10.1038/s41612-023-00400-8 (2023). Wilcox, L. J. et al. Mechanisms for a remote response to Asian anthropogenic aerosol in boreal winter. Atmospheric Chemistry and Physics 19 , 9081-9095, doi:10.5194/acp-19-9081-2019 (2019). Allen, R. J., Evan, A. T. & Booth, B. B. B. Interhemispheric Aerosol Radiative Forcing and Tropical Precipitation Shifts during the Late Twentieth Century. Journal of Climate 28 , 8219-8246, doi:10.1175/jcli-d-15-0148.1 (2015). Hersbach, H. et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146 , 1999-2049, doi:10.1002/qj.3803 (2020). Lan, X., Thoning, K. W. & Dlugokencky, E. J. (ed NOAA) (2025). Gulev, S. K. et al. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds V. Masson-Delmotte et al. ) 287–422 (Cambridge University Press, 2021). Quaglia, I. & Visioni, D. Modeling 2020 regulatory changes in international shipping emissions helps explain anomalous 2023 warming. Earth System Dynamics 15 , 1527-1541, doi:10.5194/esd-15-1527-2024 (2024). Forster, P. M. et al. Recommendations for diagnosing effective radiative forcing from climate models for CMIP6. Journal of Geophysical Research: Atmospheres , doi:10.1002/2016JD025320 (2016). Additional Declarations There is NO Competing Interest. Supplementary Files 24RAMIPClimateResponseToEastAsianAerosolsSupplementaryInformationV6.docx Supplementaty Information Cite Share Download PDF Status: Published Journal Publication published 14 Jul, 2025 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6005409\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Physical Sciences - Article\",\"associatedPublications\":[],\"authors\":[{\"id\":416046294,\"identity\":\"3dd54821-f5d1-49cc-bffe-64e1ed24059f\",\"order_by\":0,\"name\":\"Bjørn 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08:41:28\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6005409/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6005409/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1038/s43247-025-02527-3\",\"type\":\"published\",\"date\":\"2025-07-14T04:00:00+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":76631362,\"identity\":\"0e4b251c-da57-404b-8e2b-99e44f47fb99\",\"added_by\":\"auto\",\"created_at\":\"2025-02-19 06:40:01\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":281366,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eObserved changes in aerosol optical depth since 2010, and the corresponding changes in emissions and in the RAMIP model simulations.\\u003c/strong\\u003e (a) AOD observations, difference between 2014-2023 and 2005-2014. Mean of MODIS Aqua and Terra. Inset box shows the East Asia domain used throughout this paper. (b) Spatial distribution of AOD change in RAMIP. Multi-model (80 ensemble member) mean, difference between the East Asia and Base-line simulations. Hatching indicates statistical significance (see Methods). (c) Annual SO2 emissions difference, relative to 2010, in CEDSv2024 (red), and between the two scenarios used by RAMIP (black). Mean over the East Asia domain. (d) AOD change, mean over the East Asia domain, from MODIS (red) and in RAMIP (black).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"RAMIPChinaFig11.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6005409/v1/ed14239f298b74f64828e95e.png\"},{\"id\":76631359,\"identity\":\"75bb9f11-a9a6-4617-aaa1-022b296809bc\",\"added_by\":\"auto\",\"created_at\":\"2025-02-19 06:40:00\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":317601,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSurface temperature responses to reductions in East Asian aerosol emissions.\\u003c/strong\\u003e(a) Global, annual mean surface temperature response to the RAMIP East Asian aerosol emissions perturbations, multi-model mean and ±1 standard deviation range. (b) Ensemble member, model mean and multi-model mean temperature response for 2035-2049. (c) Multi-model spatial response, for June-July-August. (d) As c, for December-January-February.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"RAMIPChinaFig12.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6005409/v1/577dd8dad731e3f8ced48319.png\"},{\"id\":76631622,\"identity\":\"3a305bb1-7e58-4ebc-a9ad-5d1699f1a7ff\",\"added_by\":\"auto\",\"created_at\":\"2025-02-19 06:48:00\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":311835,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eWarming from reductions in East Asian aerosol emissions in the context of recent surface temperature changes.\\u003c/strong\\u003e (a) Global Surface Temperature Anomaly (GSTA) relative to 1850-1900, mean of four data series (HadCRUT5, NOAA, BEST, GISTEMP). Black line shows a derived dataset where interannual variability has been filtered out based on oceanic modes of variability (Samset et al. 2024). Dashed line: 1980-2010 linear trend, extended through 2023. (b) As (a), with added trend lines for 2010-2023. Red line and range, and right hand box, show the RAMIP estimate of warming due to East Asian aerosol emissions reductions, added to the extension of the observed 1980-2010 linear trend. (c) Regional differences in 1980-2010 and 2010-2023 trends. Mean of the four data series. (d) RAMIP spatial annual mean surface temperature response to East Asian aerosol emission reductions. (e) AOD change from MODIS, as Figure 1a.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"RAMIPChinaFig13.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6005409/v1/422da1ff8fc7627243630f4c.png\"},{\"id\":76631361,\"identity\":\"a3b4e2db-bc99-4618-806d-cc0e80e30068\",\"added_by\":\"auto\",\"created_at\":\"2025-02-19 06:40:00\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":779460,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eInfluence of East Asian aerosol emissions reductions on top-of-atmosphere radiative imbalance.\\u003c/strong\\u003e (a) Global, annual mean response of the top-of-atmosphere (TOA) radiative flux imbalance to the RAMIP East Asian aerosol emissions perturbations. Multi-model mean and ±1 standard deviation range. (b) Ensemble member, model mean and multi-model mean TOA imbalance response for 2035-2049. (c) Spatial distribution of TOA imbalance response, all-sky. (d) As (c), for clear sky conditions. (e) TOA imbalance from CERES observations for recent decades (2001-2010 vs 2014-2023). (f) Difference between (e) and the ERA5 reanalysis TOA imbalance for the same time periods.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"RAMIPChinaFig14.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6005409/v1/c0cf8fc1df561396037d48e9.png\"},{\"id\":86742456,\"identity\":\"f624453c-ca36-4c90-9256-f90a1354ee77\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 07:09:59\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2519318,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6005409/v1/9c4e57b0-6025-4d46-a69f-c4f784029fcf.pdf\"},{\"id\":76631364,\"identity\":\"c0a8b9eb-b047-4304-b2c9-0c5de24dd79c\",\"added_by\":\"auto\",\"created_at\":\"2025-02-19 06:40:01\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":4036238,\"visible\":true,\"origin\":\"\",\"legend\":\"Supplementaty Information\",\"description\":\"\",\"filename\":\"24RAMIPClimateResponseToEastAsianAerosolsSupplementaryInformationV6.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6005409/v1/fa9c07c5c939d7fd0aae9d53.docx\"}],\"financialInterests\":\"There is \\u003cb\\u003eNO\\u003c/b\\u003e Competing Interest.\",\"formattedTitle\":\"China’s aerosol cleanup has contributed strongly to the recent acceleration in global warming\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eOver the industrial era, anthropogenic emissions of atmospheric aerosols, and their gaseous precursors, have strongly influenced the Earth\\u0026rsquo;s climate and energy balance \\u003csup\\u003e6\\u003c/sup\\u003e. Aerosols have recently been assessed to have cooled the global surface by 0.4 \\u0026ordm;C (year 2019, relative to pre-industrial conditions), partially masking greenhouse gas driven warming, predominantly through aerosol-cloud interactions affecting the albedo of clouds, alongside direct aerosol shortwave effects \\u003csup\\u003e7\\u003c/sup\\u003e. The geographical distribution of this forcing has, however, shifted after 1980, with China and India having replaced the US and Europe as the major emitters \\u003csup\\u003e8\\u003c/sup\\u003e. It has shifted again since the early 2010s, following China\\u0026rsquo;s strong effort to reduce air pollution, which has led to strong (~20 Tg/year, or around 75%) sustained reductions in the emission rate of SO\\u003csub\\u003e2\\u003c/sub\\u003e \\u003csup\\u003e4,9\\u003c/sup\\u003e, the precursor gas of sulfate aerosols, which in turn is the dominating aerosol specie currently cooling the Earth \\u003csup\\u003e7\\u003c/sup\\u003e.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eConcurrently, the rate of global mean surface warming, which has overall been constant at around 0.18 \\u0026ordm;C/decade since around 1970, has increased \\u003csup\\u003e1,2\\u003c/sup\\u003e. Recent studies find an acceleration in the rate of surface warming and ocean heat uptake after 1990, and the most recent decade (2013-2022) had a warming rate of 0.25 \\u0026ordm;C/decade, even after reducing the influence of internal variability \\u003csup\\u003e1,3\\u003c/sup\\u003e. 2023 and 2024 were both record-setting in terms of surface temperature anomaly, dominated by strong positive sea-surface temperature anomalies in most ocean basins \\u003csup\\u003e10\\u003c/sup\\u003e.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eRecent improvements in satellite-based constraints on the Earth\\u0026rsquo;s Radiative Imbalance at top-of-atmosphere have also revealed increased energy absorption into the global Earth system \\u003csup\\u003e11\\u003c/sup\\u003e. A range of studies have suggested that aerosol emissions changes may have been contributing factors. These include recent overall trends in global aerosol loading \\u003csup\\u003e12\\u003c/sup\\u003e, and the regulations by the International Maritime Organization that caused a strong drop in SO\\u003csub\\u003e2\\u003c/sub\\u003e emissions from the global shipping fleet from 2020 and onwards\\u0026nbsp;\\u003csup\\u003e13,14\\u003c/sup\\u003e. Low planetary albedo due to reduced cloud amounts, or cloud albedo, in the North Pacific was also recently implicated as a key process leading to increased surface warming\\u0026nbsp;\\u003csup\\u003e15\\u003c/sup\\u003e.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eHowever, no study has to date quantified the influence of the recent emissions reductions in China on global and regional climate evolution, despite their being larger in magnitude than the shipping emission changes, and sustained for longer. Such analysis requires dedicated simulations with Earth System Models that have not been readily available, partly due to a lack of updated emissions inventories that capture the decrease in Chinese emissions. The regional nature of the emissions change also means that internal variability is a major limiting factor in quantifying a climate response, necessitating multiple ensemble realizations of these simulations. Recent global modelling exercises such as CMIP6 used global emissions changes \\u003csup\\u003e16\\u003c/sup\\u003e, where the influence of Chinese SO\\u003csub\\u003e2\\u003c/sub\\u003e emissions cannot be separated from other, concurrent changes.\\u0026nbsp;Further, the emissions dataset used in CMIP6 did not accurately represent the recent reduction in Chinese aerosol precursors\\u0026nbsp;\\u003csup\\u003e17\\u003c/sup\\u003e.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eHere, we present results from the Regional Aerosol Model Intercomparison Project (RAMIP), where aerosol emissions were systematically perturbed in individual source regions, in eight CMIP6 era Earth System Models \\u003csup\\u003e18\\u003c/sup\\u003e. We show results from a transient emissions reduction experiment in an East Asia region, consisting primarily of mainland China, that are closely analogous to recent emissions changes. RAMIP simulations span 2015-2051, but they contain the same East Asian SO\\u003csub\\u003e2\\u003c/sub\\u003e emission reductions of 20 Tg/year that have occurred in the real world since around 2010. Thus, we can use the RAMIP simulations to quantify the real-world climate impacts of recent efforts by China to improve air quality, including surface temperatures, precipitation, and the global energy imbalance. By comparing to observations of regional temperature changes and the top-of-atmosphere radiation imbalance, we argue that the recent aerosol emissions changes in China are a key contributing factor, among others, to the recent uptick in the rate of global mean surface warming, through an unmasking of greenhouse gas driven climate change.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eRAMIP simulations and recent emissions changes in China\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe first document the emissions perturbation applied in the RAMIP\\u0026nbsp;\\u003cem\\u003ebaseline\\u003c/em\\u003e and\\u0026nbsp;\\u003cem\\u003eEast Asia\\u003c/em\\u003e simulations \\u003csup\\u003e18\\u003c/sup\\u003e (see Methods), and compare them to the actual emissions reductions from the same region since around 2010. Briefly, RAMIP isolates the climate effects of aerosol emissions in one region by comparing two sets of transient emission simulations; one following a global, high emissions pathway (SSP3-7.0, which assumes weak air quality policies), and one where aerosol emissions in one region (\\u003cem\\u003eEast Asia\\u003c/em\\u003e, consisting mainly of mainland China emissions) have been replaced by those from a strong air quality policy trajectory (SSP1-2.6). See Methods, or\\u0026nbsp;\\u003csup\\u003e18\\u003c/sup\\u003e, for a full description. In the present analysis, we use simulations from 8 global models, each with 10 ensemble members, for a total of 80 ensemble members. This simulation set effectively samples both model uncertainty and internal climate variability.\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 1a shows changes in aerosol optical depth (AOD) retrieved by MODIS Terra and Aqua between the two previous decades. Consistent with previous literature, we find a dipole pattern consisting of an increase over India, and a strong decrease over China following their air quality improvement initiatives. For comparison, Figure 1b shows the pattern of AOD change between the RAMIP \\u003cem\\u003eEast Asia\\u003c/em\\u003e and \\u003cem\\u003ebaseline\\u003c/em\\u003e simulations, for the simulated period 2035-2049. Figures 1c and 1d show the corresponding SO\\u003csub\\u003e2\\u003c/sub\\u003e emissions and AOD change, for observations and simulations, within the box labeled East Asia in Figure 1a (a geographical box that covers the main emission regions of mainland China).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eFor observations, relative to the 2005-2010 period, we find an AOD change of -0.13 units for the period 2014-2023, resulting primarily from emissions reductions of around 20 Tg SO\\u003csub\\u003e2\\u003c/sub\\u003e / year (Supplementary Figure 1b). Emissions data are from the December 2024 release of the Community Emissions Data System (CEDS) \\u003csup\\u003e19\\u003c/sup\\u003e. Concurrent changes in black carbon (BC) aerosol emissions are shown in Supplementary Figure 1; they are smaller, in absolute terms and in particle number, and are not expected to contribute strongly to the AOD change, though they may influence climate features through their strong atmospheric shortwave absorption \\u003csup\\u003e20\\u003c/sup\\u003e.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eRAMIP transient simulations start in 2015, but use CMIP6 emissions based on a CEDS version that projected a delayed reduction in East Asia emissions compared to the actual, realized changes. The RAMIP \\u003cem\\u003eEast Asia\\u003c/em\\u003e and \\u003cem\\u003ebaseline\\u003c/em\\u003e simulations however still have an emissions difference trajectory that broadly corresponds to recent observations (20 Tg SO\\u003csub\\u003e2\\u003c/sub\\u003e/year), for a later range of years. We also find a multi-model mean AOD change trajectory and magnitude that broadly tracks MODIS observations (DAOD of -0.11 \\u0026plusmn; 0.05 units for the RAMIP period 2035-2049). We do note, however, that even though all models used the same emissions, the RAMIP 2035-2049 mean East Asia AOD change ranges from -0.08 to -0.28 (see Fig.S1). This is due to a combination of factors including the optical properties of the simulated aerosols, the cloud fields, wind and precipitation climatologies, and aerosol removal rates. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003ePhysically, AOD decreases are associated with less scattering of incoming solar radiation, and hence increases in downwelling surface solar radiation. Supplementary Figure 1 shows the corresponding changes in downwelling shortwave radiation at the surface, in response to aerosol emissions reductions. Here, we find a multi-model mean change of 7.7 \\u0026plusmn; 2.5 Wm\\u003csup\\u003e-2\\u003c/sup\\u003e,over the East Asia domain, with inter-model variation and spatial pattern that broadly follow that of AOD.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eBased on Figure 1 and Supplementary Figure 1, we conclude that the RAMIP East Asia results for the 2035-2049 period can be used as a proxy for the response to the emission rate change that has occurred in the real world over the 2010-2023 period (i.e. a 20 Tg / year sustained reduction in SO\\u003csub\\u003e2\\u003c/sub\\u003e emissions).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eModeled temperature and precipitation changes\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn Figure 2, we show the resulting global, annual mean temperature responses to a 20 Tg/year reduction in SO\\u003csub\\u003e2\\u003c/sub\\u003e emissions from East Asia. For 2035-2049, we find a multi-model mean global warming of 0.07 \\u0026plusmn; 0.05 \\u0026ordm;C, where the uncertainty is the standard deviation of the eight individual model results. The signal evolves smoothly, along with the emission reduction, with a rate-of-change of 0.02 \\u0026ordm;C/decade for the full 2015-2050 period. Note, however, the strong inter-model variability (Fig. 2b), with one model (NorESM2-LM) showing an ensemble mean warming of 0.15 \\u0026ordm;C, while another outlier (GISS-E2-1-G) even shows a slight cooling (-0.02 \\u0026ordm;C). We link these model differences primarily to Arctic amplification and aerosol-cloud interactions in the North Pacific; see Supplementary Figure 2 and further discussion below. There is also a strong contribution from internal variability, with marked diversity between ensemble members (Figure 2b). This illustrates the difficulties of quantifying the climate impacts of the recent East Asian aerosol emission reductions, and other notable emissions changes like those resulting from the recent IMO shipping regulations \\u003csup\\u003e21\\u003c/sup\\u003e and the importance of conducting large ensemble simulations when investigating climate forcings that are strong regionally but weaker on a global scale \\u003csup\\u003e18\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eGeographically, the seasonal temperature change is strongest near the source (East Asia, notably Eastern and Northen China) both in boreal summer (JJA, Fig. 2c) and winter (DJF, Fig. 2d). However, we also find significant warming (\\u0026gt;0.2 \\u0026ordm;C; paired Student\\u0026rsquo;s t-test, p\\u0026lt;0.05) over much of the North Pacific, in both seasons. For DJF, we also find a significant warming of North America, and throughout the Arctic. A wintertime cooling patch in Central Europe that has been reported by previous studies \\u003csup\\u003e22,23\\u003c/sup\\u003e is however not visible in our dataset. For annual mean responses, including for individual models and ensemble members, see Supplementary Figure 2.\\u003c/p\\u003e\\n\\u003cp\\u003eSupplementary Figure 3 shows the corresponding precipitation response, which broadly tracks that of surface temperature. We find an overall global wettening of 0.009 \\u0026plusmn; 0.004 mm/day (0.3 \\u0026plusmn; 0.1 %), yielding an overall hydrological sensitivity of 4 \\u0026plusmn; 2 %/\\u0026ordm;C, which is broadly consistent with previous estimates of the climate impacts of aerosol emissions changes (\\u003cem\\u003e20\\u003c/em\\u003e). Geographically, we find a strong summertime (JJA) precipitation increase in East China, and along the East Asian coastline, as well as a wettening along the North Pacific storm tracks extending well into North America. We also find a northward shift of the ITCZ, consistent with expectations from preferential warming of the Northern Hemisphere relative to the Southern Hemisphere \\u003csup\\u003e24\\u003c/sup\\u003e The regions with statistical significance are smaller than for temperature, as expected due to the higher internal variability and greater model diversity in precipitation simulations.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInfluence on recent global warming and radiative imbalance\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe now put the RAMIP results in the context of recent trends in global warming, in Figure 3. In panel 3a, we show the global mean surface temperature anomaly (GSTA) relative to 1850-1900 since 1980, as the average of four observational reconstructions (HadCRUT5, NOAA, GISTEMP and Berkeley Earth; see Methods). For the 30-year period of 1980-2009, the average observed warming rate is 0.18 \\u0026ordm;C / decade. For illustration, we also show the time series from (\\u003cem\\u003e7\\u003c/em\\u003e), where internal variability from Pacific ENSO and other ocean modes of variability has been filtered out (our conclusions do not rely on the usage of this dataset). For the subsequent period of 2010 - 2023, we find an elevated observed warming rate of 0.33 \\u0026ordm;C / decade, and 0.25 \\u0026ordm;C / decade when interannual variability is filtered, consistent with previous studies \\u003csup\\u003e2,3\\u003c/sup\\u003e.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn the main panel (Fig. 3b), we zoom in on the latter period. Most post-2010 GSTA values fall above the continuation of the 1980-2009 trend (dotted black line), in the reconstructions (dots) and in the reduced variability time series (solid black line), indicating a recent increase in the global warming rate. To estimate the contribution of East Asian aerosol emissions changes to this increase, we take the RAMIP quantified global warming of a 0.07 \\u0026plusmn; 0.05\\u0026nbsp;℃, and convert it to a warming rate over the 2010-2023 period (0.05 \\u0026plusmn; 0.04 \\u0026ordm;C / decade). See the box-and-whisker on the right of Fig. 3b, which shows how we\\u0026rsquo;ve added this aerosol cleanup induced warming rate (red line and range) to the continuation of the 1980-2009 trend.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAssuming, for now, no change in the underlying greenhouse gas induced global warming rate, this suggests a combined post-2010 warming rate, from greenhouse gas increases and East Asian aerosol cleanup, of 0.18\\u0026nbsp;℃/decade + 0.05\\u0026nbsp;℃/decade = 0.23\\u0026nbsp;℃/decade, approaching the 0.25 \\u0026ordm;C / decade found after filtering the effects of internal variability. For context, see further discussion below of other sources of recent warming.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eNote that we assume here that the full warming due to the observed recent East Asian emissions reduction has already been realized, so that it is justifiable to compare warming in observations during the 2010-2023 period to warming in the RAMIP simulations during the 2035-2049 period. This is supported by recent modelling exercises using sustained step changes in SO\\u003csub\\u003e2\\u003c/sub\\u003e emissions (\\u003cem\\u003e21\\u003c/em\\u003e), finding that the majority of subsequent global mean surface temperature change has been realized within the first 24 months, while the rest develops slowly at a multi-decadal timescale. The transient RAMIP simulations also do not show any appreciable delay in the climate response to SO\\u003csub\\u003e2\\u003c/sub\\u003e reductions. However, our estimate should still be taken as an upper limit, to take this limitation into account.\\u003c/p\\u003e\\n\\u003cp\\u003eSince aerosol changes have regionally heterogeneous climate influences, as shown above, we next investigate the correspondence of simulated changes to observed regional warming rate. In Figure 3c, we show the observed difference between the 2010-2023 and 1980-2009 warming rates, in the four reconstructions. See Supplementary Figure 4 for individual time series and the two trend periods in isolation. While a 13-year trend will be strongly influenced by decadal scale variability, we do find a very clear pattern of observed warming in the North Pacific, with two distinct maxima: one along the East Asian coastline, and the other following the west coast of North America, extending west to the center of the Pacific.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn Figure 3d, we show the annual mean surface warming pattern from East Asian aerosol emission reductions in RAMIP. As documented above, we also here find a two-maxima pattern along the East Asian and western North American coastlines. This shows that the simulated increased global mean warming rate comes from a geographical region where observations also find an elevated warming rate since 2010, relative to previous decades. See Supplementary Figure 7 for the transient evolution of regional means (Western and Eastern North Pacific) for individual models.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTop-of-atmosphere radiative imbalance\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe next question is what physical processes lead to this warming rate. In Figure 4, we investigate the top-of-atmosphere (TOA) all-sky change in radiative imbalance (shortwave plus longwave) in response to East Asian aerosol emissions reductions in RAMIP, and compare them to observations (CERES) and reanalysis (ERA5). Fig. 4a-b show the time series and 2035-2049 means of the TOA all-sky radiative imbalance, which has a mean of 0.06 \\u0026plusmn; 0.04 Wm\\u003csup\\u003e-2\\u003c/sup\\u003e. While there is substantial inter-model and ensemble member variability, the overall evolution is very similar between the eight RAMIP models. Using the same logic as above, this corresponds to an evolving increase in the TOA imbalance since 2010 of 0.05 \\u0026plusmn; 0.03 Wm\\u003csup\\u003e-2\\u003c/sup\\u003e / decade.\\u003c/p\\u003e\\n\\u003cp\\u003eFig. 4c-d shows the geographical distributions of clear sky and all-sky radiative imbalances. Again, for all-sky conditions, we find a geographical pattern displaying two clear maxima, near the source region, and in the western North Pacific, with peak values exceeding 2 Wm\\u003csup\\u003e-2\\u003c/sup\\u003e. There is little influence on other regions. For clear sky conditions, only the East Asian and eastern North Pacific influence remains, indicating it has a major contribution from the direct interaction of aerosols with incoming sunlight. The maximum west of North America, however, is likely primarily a result of aerosol-cloud interactions, seen in a region with high prevalence of low clouds (stratocumulus decks). Supplementary Figure 5 shows individual models, while Supplementary Figure 6 shows a further breakdown into shortwave and long wave components.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThis result also explains part of the inter-model diversity in RAMIP responses; the models that have low overall temperature response to East Asian aerosol changes (notably CNRM and GISS), also have weak responses in this region, related to their cloud climatologies and their simplified treatment of aerosol-cloud interactions. See also Supplementary Figure 7, which shows the transient evolution of Western and Eastern North Pacific means of temperature and surface shortwave fluxes. We further note that that for clear sky conditions, there is a strong positive anomaly over Eastern China in most models. This indicates the presence of compensating aerosol-cloud effects, and perhaps other aerosol processes such as wet removal by precipitation, over this region in the RAMIP models.\\u003c/p\\u003e\\n\\u003cp\\u003eFinally, in Fig. 4e-f, we show recent all-sky TOA imbalance trends from observations (CERES), and its difference from a reanalysis product (ERA5). While the CERES observations (Fig. 4e) have a strong influence of internal variability, and will also be influenced by other recent changes in the climate system, we do find a positive anomaly in the North Pacific. The ERA5 reanalysis itself shows a very similar pattern (Supplementary Figure 3). However, when taking the difference between CERES and ERA5 (Fig. 4f), we find two striking features. One is a positive anomaly in the low cloud region of the eastern North Pacific, the other is a negative anomaly over Eastern China. ERA5 does include a treatment of aerosols, but it uses a CMIP5 era combination of historical emissions up to 2009 and subsequently the RCP emissions \\u003csup\\u003e25\\u003c/sup\\u003e. These pathways do not include the recent reductions in East Asian emissions. Hence, the difference between CERES and ERA5 may be indicative of regions where the recent aerosol changes are important for the reconstruction, and have an influence on observed rates of change of TOA radiative fluxes. This is supported by RAMIP finding that these two regions are strongly influenced by aerosol-cloud interactions. We do note, however, that trends in sea surface temperatures will also influence ERA5 reconstructions, meaning that we cannot make a clear attribution of the observed changes to aerosol emissions changes with this method.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eOther sources of recent warming\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe have shown how recent aerosol emissions reductions in East Asia may have had a strong influence on post-2010 elevated rates of surface warming, both globally and in the North Pacific. To put these results in context, we here discuss some other, concurrent changes that we cannot consider in the same framework.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eOne anthropogenic factor is the accelerated increase in atmospheric CH\\u003csub\\u003e4\\u003c/sub\\u003e concentrations over the same period. As an estimate, using recent global near-surface concentrations from NOAA \\u003csup\\u003e26\\u003c/sup\\u003e and the IPCC AR6 \\u003csup\\u003e27\\u003c/sup\\u003e, combined with the forcing estimation methods from (\\u003cem\\u003e22\\u003c/em\\u003e), we find a global mean CH\\u003csub\\u003e4\\u003c/sub\\u003e radiative forcing (RF) of 0.06 Wm\\u003csup\\u003e-2\\u003c/sup\\u003e for the 2010-2023 period, corresponding to a rate of 0.047 Wm\\u003csup\\u003e-2\\u0026nbsp;\\u003c/sup\\u003e/ decade. This is a marked increase over the previous decade (2000-2010), where we find 0.01 Wm\\u003csup\\u003e-2\\u0026nbsp;\\u003c/sup\\u003e/ decade. However, for the full 30-year period of 1980-2010, we estimate a forcing of 0.043 Wm\\u003csup\\u003e-2\\u0026nbsp;\\u003c/sup\\u003e/ decade. This means that while changes in CH\\u003csub\\u003e4\\u003c/sub\\u003e atmospheric concentration growth rates may have contributed to decadal variability, and clearly can enhance the overall rate of global warming, the recent decade has not seen markedly strong influence from CH\\u003csub\\u003e4\\u003c/sub\\u003e increases compared to recent history. We also note that the above numbers are for RF, while the Effective Radiative Forcing (ERF), and the temperature influences of methane, may be muted due to rapid adjustments (\\u003cem\\u003e23\\u003c/em\\u003e).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAnother, more recent, anthropogenic factor is the post-2020 reduction in SO2 emissions from the shipping sector, following the recent regulations of the International Maritime Organization. Here, a range of studies have concluded that the global mean ERF from the 80% reduction in emissions, corresponding to around 9 Tg SO\\u003csub\\u003e2\\u003c/sub\\u003e/year, is in the range of 0.05-0.10 Wm\\u003csup\\u003e-2\\u003c/sup\\u003e (\\u003cem\\u003e10, 11\\u003c/em\\u003e). While studies using ensembles of simulations from fully coupled models do find a surface temperature response to this emissions change over time, the magnitude and detectability relative to internal variability for the years 2020-2023 is still disputed \\u003csup\\u003e21,28\\u003c/sup\\u003e. Coming in at the end of the time period studied here, the IMO regulations are unlikely to have had a major influence on the above conclusions regarding the influence of Chinese aerosol emission changes.\\u003c/p\\u003e\\n\\u003cp\\u003eERF estimates are unfortunately not available from all RAMIP models. For those that have delivered the required simulations (see Methods), we find an ERF from the emissions changes in China discussed above ranging from 0.06 \\u0026ndash; 0.21 Wm\\u003csup\\u003e-2\\u003c/sup\\u003e (\\u003cem\\u003e15\\u003c/em\\u003e). \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eRecently, in the context of the 2023 record global mean surface temperatures, Goessling et al. \\u003csup\\u003e15\\u003c/sup\\u003e identified a record-low planetary albedo as a contributing cause. Using the same datasets as here (CERES and ERA5), they highlight low cloud cover over the North Pacific as a key component of this, and note that the role of aerosols in this change is still unclear. Their results are well in line with the present study, both geographically and in terms of physical processes, however their main focus is on reduced cloud fraction in selected years while our results concern a reduced aerosol-cloud induced albedo on a decadal scale. The regional similarity, however, opens the possibility that the results of Goessling et al. are, at least in part, interpretable as a sustained reduction in aerosol-cloud interactions.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eUsing a set of 10-ensemble-member simulations from eight CMIP6-era Earth System Models, we have quantified the transient climate response to gradually reducing aerosol emissions from East Asia. We find a global, annual-mean warming of 0.07 \\u0026plusmn; 0.05 \\u0026ordm;C, and a corresponding wettening of around 4 \\u0026plusmn; 2 %/\\u0026ordm;C.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe emission reductions in our simulations correspond closely to the emissions reductions realized in East Asia over the period 2010-2023, in magnitude and geographical location. This allows us to put our results in the context of the recent uptick in the observed rate of global mean surface warming. Here, we find that emissions reductions in China have contributed up to 0.05 \\u0026ordm;C / decade since 2010, explaining a large fraction of the observed increase of 0.06 \\u0026ordm;C / decade over the same period, after filtering out the effects of interannual variability.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eWe also find that the geographical location of the temperature influence of a reduction in Chinese SO\\u003csub\\u003e2\\u003c/sub\\u003e emissions correspond to where observations show a recent surge in warming, and also where satellite observations find an increase in TOA radiative imbalance. This lends support to the conclusion that the recent intensive effort to tackle air pollution in China has caused, as an unintended side effect, an unmasking of greenhouse gas driven global warming and a marked contribution to the recently observed warming trend.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eLooking ahead, emissions from East Asia are projected to keep going down. However, the rate of change has slowed markedly, and the CEDS inventory estimates that there is less than 10 Tg SO\\u003csub\\u003e2\\u003c/sub\\u003e/year (~25% of the 2010 value) left to reduce. This means that for the coming years and decades, the influence on global warming rates from East Asian emissions reductions is likely to be less prominent, although this depends crucially on the still unresolved question of whether the influence of aerosols on the climate \\u0026ndash; in particular through aerosol-cloud interactions - is linear.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eSimulations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study uses Earth System Model simulations performed for the Regional Aerosol Model Intercomparison Project (RAMIP)\\u003csup\\u003e18\\u003c/sup\\u003e. RAMIP is part of the extended phase of the 6\\u003csup\\u003eth\\u003c/sup\\u003e Coordinated Model Intercomparison Project (CMIP6Plus), and builds on and extends historical (1850-2014) simulations delivered to CMIP6. The RAMIP baseline simulation is a Shared Socioeconomic Pathway with weak air quality policy, and consequently continued strong aerosol emissions (SSP3-7.0). In RAMIP perturbations, these emissions are exchanged for a strong air quality policy case (SSP1-2.6), for individual emission regions.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, we use the RAMIP \\u003cem\\u003eEast Asia\\u003c/em\\u003e simulation (SSP370-eas126aer), where all anthropogenic aerosol emissions from a geographical box consisting mainly of China are perturbed. All participating models ran the baseline and signal simulations for the period 2015-2050, and delivered 10 ensemble members for each simulation. See Supplementary Table 1 for an overview of the simulations.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eA subset of the models also delivered simulations with fixed sea-surface temperatures, which can be used to quantify the Effective Radiative Forcing (ERF). See \\u003csup\\u003e18\\u003c/sup\\u003e for details.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAs part of Figure 1, we also show the aerosol emissions trajectories of the signal and baseline simulations. All non-aerosol emissions are identical in signal and baseline, and they each branch off from the same ensemble members from the historical simulation in 2014. Land use change is also identical in signal and baseline (SSP3-7.0). \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eModels\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSupplementary Table 2 lists the eight models used for the present study, together with the spatial resolutions, aerosol representation, and main references.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAnalysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eClimate responses are defined as the difference between SSP370 and SSP370-eas126aer, for the period 2035-2049. For statistical significance, we require that the multi-model, multi-ensemble member sets (consisting of 80 global or regional means, or grid point values) are significantly different according to a paired t-test with p\\u0026lt;0.05. Map figures are hatched for all grid points that show statistically significant changes.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eERF is quantified as the difference in net top-of-atmosphere (shortwave + longwave) radiation between 30-year fSST simulations, following Forster et al. 2016\\u003csup\\u003e29\\u003c/sup\\u003e. \\u0026nbsp; \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eUncertainties\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll uncertainties quoted in this paper are \\u0026plusmn;1 standard deviation, generally of the set of ensemble mean results from each model.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRegions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eEast Asia is here defined as a geographical box covering 20N-35N, 95E-133E. We also refer to this as China, as the box predominantly covers the mainland part of that country.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eObservational data\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAdditionally, we use gridded aerosol emissions data from CEDS (2024 Gridded Data Release: December 3, 2024; v_2024_11_25), observations of aerosol optical depths from the MODIS instruments on the Terra and Aqua satellites (Combined Dark Target and Deep Blue AOD at 0.55 micron; MYD08_M3 v6.1 and MOD08_M3 v6.1), top-of-atmosphere (TOA) energy imbalance observations from the CERES instrument (EBAF-TOA_v4.2.1), and TOA energy imbalance estimates from the ERA5 reanalysis \\u003csup\\u003e25\\u003c/sup\\u003e.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAnalysis code and processed RAMIP datasets used in this paper are available from figshare archive 10.6084/m9.figshare.28296344 (to be made public on publication). Other datasets are available as described in Methods, with full links to the simulation datasets shown in Supplementary Table 3.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eContributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBHS and LJW conceived the study. BHS, LJW, CWS, RJA and MTL performed the analysis and wrote the paper. PK, LFL, RA, DMW, KT, JM, DO, RM, AL, LW, SR, JK, PG, NO, TK, PN, LN delivered simulations. LJW, SA and MTE performed dataset preparations. All authors contributed to editing and finalizing the paper.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe acknowledge support by the Center for Advanced Study in Oslo, Norway which funded and hosted the HETCLIF centre during the academic year of 2023/24. The NorESM simulations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS). We also acknowledge the following funding sources: Research Council of Norway grant 324182 (BHS, LJW, CWS, MTL, RJA). National Science Foundation grant #AGS-2153486 (RJA). Natural Environment Research Council (NERC) grant TerraFIRMA NE/W004895/1 (LJW, PTG, STR). National Center for Atmospheric Science, UK (LJW, STR). Environment Research and Technology Development Fund (JPMEERF20232001) of the Environmental Restoration and Conservation Agency provided by Ministry of the Envi-ronment of Japan (TK, NO). Arctic Challenge for Sustainability II (ArCS II), Program Grant Number JPMXD1420318865 (TK, NO). Global Environmental Research Coordination System from Ministry of the Environment of Japan grant MLIT2253 (TK, NO). H2020 Societal Challenges grant no. 101003826 (JM, RM, DOD). Research Council Finland grant no. 337552. (JM, RM, DOD). Columbia Center for Climate and Life (DMW). Natural Sciences and Engineering Research Council of Canada (NSERC) (PK, LJF). Swedish Research Council through grant agreement no. 2022-06725 (AL)\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eMerchant, C. J., Allan, R. P. \\u0026amp; Embury, O. 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M.\\u003cem\\u003e\\u0026nbsp;et al.\\u003c/em\\u003e Recommendations for diagnosing effective radiative forcing from climate models for CMIP6. \\u003cem\\u003eJournal of Geophysical Research: Atmospheres\\u003c/em\\u003e, doi:10.1002/2016JD025320 (2016).\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"nature-portfolio\",\"isNatureJournal\":true,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Nature Portfolio\",\"twitterHandle\":\"\",\"acdcEnabled\":false,\"dfaEnabled\":false,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6005409/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6005409/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eGlobal surface warming has accelerated since around 2010, relative to the preceding half century\\u003csup\\u003e1-3\\u003c/sup\\u003e. This has coincided with China’s efforts to reduce air pollution through restricted atmospheric aerosol and precursor emissions\\u003csup\\u003e4,5\\u003c/sup\\u003e. A direct link between the two has, however, not yet been established. Here we show, using a large set of simulations from eight Earth System Models, how a time evolving 75% reduction in Chinese sulfate emissions partially unmasks greenhouse driven warming and influences the pattern of surface temperature change. We find a rapidly evolving global, annual-mean warming of 0.07 ± 0.05 ºC, sufficient to explain a majority of the uptick in global warming rate since 2010. We also find North-Pacific warming and a top-of-atmosphere radiative imbalance that are consistent with recent observations. China’s aerosol cleanup is thus likely a key contributor to recent global warming acceleration, and to Pacific warming trends.\\u003c/p\\u003e\",\"manuscriptTitle\":\"China’s aerosol cleanup has contributed strongly to the recent acceleration in global warming\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-02-19 06:39:56\",\"doi\":\"10.21203/rs.3.rs-6005409/v1\",\"editorialEvents\":[],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"communications-earth-and-environment\",\"isNatureJournal\":true,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"commsenv\",\"sideBox\":\"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Communications Earth \\u0026 Environment\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Communications Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"532ade82-e9f8-40de-9a8e-8c5f3bb05e63\",\"owner\":[],\"postedDate\":\"February 19th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":44369338,\"name\":\"Earth and environmental sciences/Climate sciences/Climate change\"},{\"id\":44369339,\"name\":\"Earth and environmental sciences/Climate sciences/Climate change/Climate and Earth system modelling\"},{\"id\":44369340,\"name\":\"Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction\"}],\"tags\":[],\"updatedAt\":\"2025-07-15T07:09:51+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-6005409\",\"link\":\"https://doi.org/10.1038/s43247-025-02527-3\",\"journal\":{\"identity\":\"communications-earth-and-environment\",\"isVorOnly\":false,\"title\":\"Communications Earth \\u0026 Environment\"},\"publishedOn\":\"2025-07-14 04:00:00\",\"publishedOnDateReadable\":\"July 14th, 2025\"},\"versionCreatedAt\":\"2025-02-19 06:39:56\",\"video\":\"\",\"vorDoi\":\"10.1038/s43247-025-02527-3\",\"vorDoiUrl\":\"https://doi.org/10.1038/s43247-025-02527-3\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6005409\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6005409\",\"identity\":\"rs-6005409\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}