Aerosol-cloud interactions (ACI) are a major source of uncertainty in climate science, critically affecting our ability to project near-term climate evolution and assess societal risks. These interactions influence effective radiative forcing, cloud dynamics, and precipitation patterns, yet remain insufficiently constrained due to limitations in observations, modeling, and process understanding. This uncertainty hampers robust policy advice across multiple domains—from estimating remaining carbon budgets and climate sensitivity, to anticipating regional extreme events and evaluating climate interventions such as solar radiation modification. In many cases, the influence of ACI is either underappreciated or excluded from decision-making frameworks due to its complexity and lack of quantification. This perspective outlines a path forward to overcome these barriers by leveraging emerging opportunities in satellite remote sensing, ground-based and airborne observations, high-resolution climate modeling, and machine learning. We identify key areas where rapid progress is feasible, including improved retrievals of cloud microphysical properties, better representation of natural aerosols in a warming world, and enhanced integration of observational and modeling communities. Even as anthropogenic aerosol and its impacts on clouds is reducing owing to emissions controls, addressing ACI uncertainties remains essential for refining climate projections, supporting effective mitigation and adaptation strategies, and delivering actionable science to policymakers in a rapidly changing climate system.

In response to continued greenhouse gas (GHG) increases, the Atlantic Meridional Overturning Circulation (AMOC) is expected to weaken through the 21st century. However, AMOC impacts associated with efforts to improve air quality are less well understood. Here, eight models from the Regional Aerosol Model Intercomparison Project are examined to quantify mid-21st century AMOC changes resulting from global and regional anthropogenic aerosol and precursor gas (AA) emissions reductions (industrial and biomass burning), by comparing strong air pollution control shared socioeconomic pathway (SSP1-2.6) to a baseline with weak air pollution control (SSP3-7.0). Global AA reductions and subsequent warming yield multi-model mean AMOC weakening of 6% ( (Formula presented) (Formula presented) Sv; 1 Sv = 106 m3 s−1) by the last 12 years of the simulation (2039–2050). This is ⅓ of the magnitude of the corresponding weakening associated with the high GHG emissions scenario SSP3-7.0. Of the regional perturbations, combined North American and European AA reductions drive the largest AMOC weakening, followed by combined African and Middle Eastern reductions and then East Asian reductions, with South Asian reductions yielding non-significant weakening. Across these experiments, AMOC weakening is significantly correlated with the North Atlantic Ocean aerosol effective radiative forcing ( (Formula presented) (Formula presented) ) and aerosol optical depth response ( (Formula presented) (Formula presented) ). AMOC weakening under AA reductions is associated with a thermally driven reduction in buoyancy in the subpolar North Atlantic, which is largely driven by surface shortwave radiation increases, consistent with the forcing from AA reductions. Africa + Middle East AA reductions also involve excitation of a negative North Atlantic Oscillation pattern, which contributes to AMOC weakening. Our results show that efforts to improve air quality, particularly around the Atlantic basin but also far away in East Asia, will contribute to future AMOC weakening.

The Regional Aerosol Model Intercomparison Project (RAMIP) is designed to quantify the forcing and climate impacts of mid-21st century anthropogenic aerosol and precursor gas (AA) emissions reductions (both industrial and biomass burning), by comparing a weak (SSP3-7.0) versus strong (SSP1-2.6) level of air quality control aerosol emissions pathway. AA emissions reductions experiments include global (GLO), East Asia (EAS), South Asia, Africa and the Middle East (AFR), and North America and Europe (NAE). Here, we use RAMIP time-slice simulations with fixed sea surface temperatures and sea-ice distributions from nine models to quantify the aerosol effective radiative forcing (ERF), including aerosol radiation (ERF_ari) and aerosol cloud interactions (ERF_aci). The multi-model global mean net ERF_ari+aci is 0.77 ± 0.25 W m⁻² for GLO, and three of the four regional perturbations yield a significant positive net ERF_ari+aci (up to 0.15 ± 0.07 W m⁻² for EAS). In all cases, net ERF_ari+aci is dominated by aerosol-cloud interactions, which are largely due to reduced cloud scattering. Of the four regions, NAE yields the largest forcing efficiency whereas AFR yields the weakest. Although the areas outside our four target regions contribute 25% to the GLO aerosol optical depth reduction, they disproportionately contribute 44% to the GLO net ERF_ari+aci. The multimodel regional mean net ERF_ari+aci for three regional perturbations is much larger (up to  1.64 ± 1.36 W m⁻² for EAS) than the corresponding global mean value. However, these regional values are even larger (up to 2.69 ± 1.72 W m⁻² for EAS) under global aerosol reductions, implying remote emission reductions represent a sizable contribution (up to 1.05 ± 0.56 W m⁻² for EAS). These large regional ERFs will in turn drive relatively large regional climate impacts, which continue to be underappreciated in most policy discussions.

Decreases in anthropogenic aerosols will reduce fine particulate matter (PM2.5); however, meteorological feedbacks alter dust emissions, modifying air quality gains. We use eight Earth System Models from the Regional Aerosol Model Intercomparison Project (RAMIP) simulations to assess African climate and air quality responses to anthropogenic aerosol emission perturbations, including meteorological feedbacks on dust emissions. By 2050, African and global emissions reductions drive the largest continent-average PM2.5 decrease (0.92 ± 0.17 μg m−3; 5% and 1.35 ± 0.50 μg m−3; 7%, respectively) relative to SSP3-7.0, though regional dust increases partially offset these reductions. Anthropogenic emissions reductions in the U.S. and Europe also lower African PM2.5 by 0.29 ± 0.32 μg m−3 (2%) due to teleconnections of Northern Hemisphere warming influencing the Intertropical Convergence Zone. Inter-model variability in dust and total PM2.5 reflects differences in meteorological responses and dust emission parameterizations. Meteorological responses explain 90% of dust emissions variability across regions. Aerosol-driven climate feedbacks on dust account for up to 70% of total PM2.5 changes in the Sahara and Namib, offsetting up to 20% of anthropogenic PM2.5 reductions across Africa. Under 2050 global and Africa-wide anthropogenic aerosol reductions, 96,000 (95% CI: 54,000–137,000) and 84,000 (95% CI: 43,000–125,000) PM2.5-related deaths are avoided in Africa, respectively. Dust PM2.5 contributes an uncertain 3.4% of the avoided deaths under global reductions and has no net effect under Africa-wide reductions. Aerosol-driven climate feedbacks may partially offset direct air quality gains, though their continental-scale contribution remains small and uncertain.

Uncertainty in estimations of the net contribution of anthropogenic aerosol particles, particularly of aerosol-cloud interactions (ACIs) to the Earth’s radiation budget, limits our ability to understand past and project future climate change. Earth System Models (ESMs) are among the key tools for assessing the magnitude and impacts of changes in various forcing agents on the global climate system. Hence, improving aerosol and cloud descriptions in ESMs is an important way forward to increase the confidence in estimates of climate impacts of aerosol perturbations in the past, present and future. In the framework of the FORCeS project, experimental and theoretical approaches were combined to bridge the current key gaps in the fundamental understanding of essential aerosol and cloud processes and their descriptions in selected European ESMs. Regarding aerosol types and processes, we focused on organic aerosol, particulate nitrate, absorbing aerosols, and ultrafine aerosol sources including new particle formation and growth. In terms of cloud processes, we targeted cloud droplet activation, hydrometeor growth and evaporation, ice formation and multiplication as well as aerosol processing and scavenging by clouds. The selection was made based on the identified knowledge gaps in the scientific understanding of these processes and/or their current representation in ESMs, as well as a novel perturbed parameter ensemble approach to detecting potential structural deficiencies in an ESM. Here, we review the state-of-the-art, outline our approach for arriving at recommendations for improving the representation of key aerosol and cloud processes within ESMs, and then provide such recommendations applicable in models operating at the Earth system scale. The limitations of the recommendations, applicability, as well as alternative approaches and future research directions are discussed. Overall, the findings highlight the need for continuous efforts towards smart ways for representing the aerosol number size distribution as well as consistent representations of key parameters (e.g., liquid water content and cloud droplet number concentration). Furthermore, we provide guidance for future ESM evaluation emphasising, in particular, the need for exploring the consistency of key parameters, process-based (as opposed to parameter-based), and the complementarity of in-situ and remote-sensed measurements for model evaluation.

Ongoing deforestation and global warming are driving critical changes in the Amazon rainforest, a region essential to the global climate system. Model and observational studies have found drier conditions and changes in rainfall patterns in the Amazon; however, fewer studies have examined the role of clouds. Using the state-of-the-art CLARA-A3 satellite climate data record and ERA5 reanalysis data between 1982 and 2020, this study analyzes spatiotemporal trends in cloud cover and surface properties. Our results show a slight but statistically insignificant increase in total cloud cover across the Amazon, but regional and seasonal variations are pronounced. The northern Amazon exhibits a significant increase in high-level cloud fractional cover during the dry–wet transition, while the southern Amazon shows significant declining trends during the dry season across all cloud types. A significant correlation is observed between El Niño–Southern Oscillation and high-level cloud cover variability, especially in the northeastern Amazon, while mid- and low-level clouds show a weak relationship with sea surface temperature variability. Consistent trends in surface heat fluxes and humidity, likely tied to land use and surface changes, may influence these lower cloud layers. However, the application of multiple linear regression reveals the analyzed variables poorly predict medium- and low-level cloud cover, leaving the exact drivers of the observed cloud cover trends uncertain. These findings are consistent with prior research on hydroclimatic trends, implying a drier dry season and a wetter wet season. Further research is recommended to clarify causality and implications for the Amazon climate system.

Aerosol particles from both natural and anthropogenic sources play a critical role in the Earth's climate by interacting with solar radiation and clouds. Anthropogenic aerosol and precursor emissions have historically exerted a global cooling effect, which has partially offset the warming from concurrent greenhouse gas emissions. Recent reductions and shifts in aerosol and precursor emission patterns may reduce this offset and introduce spatially and temporarily varying climate impacts. Investigating aerosol-climate effects is typically done with computationally expensive Earth System Models, which include complex representations of physical, chemical, biological, and geological processes and their coupled interactions for the entire global climate system. In this study, we develop a machine-learning climate emulator using Gaussian processes, called AeroGP, that can be used to quickly assess, for example, the impact of different policy decisions on future climate mitigation strategies. The emulator is trained on a unique data set from the Norwegian Earth System Model (NorESM), analyzed as an ensemble here for the first time. AeroGP accounts for the joint spatial covariance of the output variables and captures the complex, heterogeneous impacts of aerosols on surface temperature using coregionalization. We believe this is the first time this method has been used to account for the spatial correlation of such climate data. We show that AeroGP retains the spatial complexity of NorESM at a fraction of the computational cost and demonstrate its usefulness to assess the sensitivity of temperature to idealized future aerosol emission scenarios.

Global surface warming has accelerated since around 2010, relative to the preceding half century. This has coincided with East Asian efforts to reduce air pollution through restricted atmospheric aerosol and precursor emissions. 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 East Asian sulfate emissions partially unmasks greenhouse gas-driven warming and influences the spatial pattern of surface temperature change. We find a rapidly evolving global, annual mean warming of 0.07 ± 0.05 °C, sufficient to be a main driver of the uptick in global warming rate since 2010. We also find North-Pacific warming and a top-of-atmosphere radiative imbalance that are qualitatively consistent with recent observations. East Asian aerosol cleanup is thus likely a key contributor to recent global warming acceleration and to Pacific warming trends.

The Italian Po Valley is one of the most polluted regions in Europe. During winter, meteorological conditions favor long and dense fogs, which strongly affect visibility and human health. In spring, the frequency of nighttime fogs reduces while daytime new particle formation events become more common. This transition is likely caused by a reduction in particulate matter (PM_2.5), leading to a decrease in the relevant condensation sink. The physics and chemistry of fog and aerosol have been studied at the San Pietro Capofiume site since the 1980s, but the detailed processes driving the observed trends are not fully understood. Hence, during winter and spring 2021/22, the Fog and Aerosol Interaction Research Italy (FAIRARI) campaign was carried out, using a wide spectrum of approaches, including in situ measurements, outdoor chamber experiments, and remote sensing. Atmospheric constituents and their properties were measured ranging from gas molecules and molecular clusters to fog droplets. One unique aspect of this study is the direct measurement of the aerosol composition inside and outside of fog, showing a slightly greater dominance of organic compounds in the interstitial compared to the droplet phase. Satellite observations of fog provided a spatial context and agreed well with in situ measurements of droplet size. They were complemented with in situ chamber experiments, providing insights into oxidative processes and revealing a large secondary organic aerosol-forming potential of ambient air upon chemical aging. The oxidative potential of aerosol and fog water inferred the impact of aerosol–fog interactions on particle toxicity.

The susceptibility of cloud droplet number to cloud condensation nuclei number is one of the major factors controlling the highly uncertain change in the amount of solar radiation reflected by clouds when aerosol emissions are perturbed (the radiative forcing due to aerosol–cloud interactions). We investigate this susceptibility in low-level stratiform clouds using long-term (3–10-yr) in situ observations of aerosols and clouds at three high-latitude locations. The in situ observations show higher susceptibility for low-level stratiform clouds than values reported for satellite data. We estimate −1.16 W m−2 for the aerosol indirect radiative forcing on the basis of our observations, which is at the higher end of satellite-derived forcing estimates and the uncertainty range of the most recent Intergovernmental Panel on Climate Change report. We evaluate four Earth system models against the observations and find large inter-model variability in the susceptibility. Our results demonstrate that, even if the susceptibility in some of the models is relatively close to observations, the underlying physics in the models is unrealistic when compared with observations. We show that the inter-model variability is driven by differences in sub-grid-scale updraught velocities and aerosol size distributions, raising a need to improve these aspects in models.