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.

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.

The interactions between aerosols and clouds are still one of the largest sources of uncertainty in quantifying anthropogenic radiative forcing. To reduce this uncertainty, we must first determine the baseline natural aerosol loading for different environments. In the pristine and hardly accessible polar regions, the exact nature of local aerosol sources remains poorly understood. It is unclear how oceans, including sea ice, control the aerosol budget, influence cloud formation, and determine the cloud phase. One critical question relates to the abundance and characteristics of biological aerosol particles that are important for the formation and microphysical properties of Arctic mixed-phase clouds. Within this work, we conducted a comprehensive analysis of various potential local sources of natural aerosols in the high Arctic over the pack ice during the ARTofMELT expedition in May–June 2023. Samples of snow, sea ice, seawater, and the sea surface microlayer (SML) were analysed for their microphysical, chemical, and fluorescent properties immediately after collection. Accompanied analyses of ice nucleating properties and biological cell quantification were performed at a later stage. We found that increased biological activity in seawater and the SML during the late Arctic spring led to higher emissions of fluorescent primary biological aerosol particles (fPBAPs) and other highly fluorescent particles (OHFPs, here organic-coated sea salt particles). Surprisingly, the concentrations of ice nucleating particles (INPs) in the corresponding liquid samples did not follow this trend. Gradients in OHFPs, fPBAPs, and black carbon indicated an anthropogenic pollution signal in surface samples especially in snow but also in the top layer of the sea ice core and SML samples. Salinity did not affect the aerosolisation of fPBAPs or sample ice nucleating activity. Compared to seawater, INP and fPBAP concentrations were enriched in sea ice samples. All samples showed distinct differences in their biological, chemical, and physical properties, which can be used in future work for an improved source apportionment of natural Arctic aerosol to reduce uncertainties associated with their representation in models and impacts on Arctic mixed-phase clouds.

While climate change is a global issue, the change in itself is not homogeneously distributed over the globe. It is well established that near-surface Arctic warming is on average 3-4 times larger than the global-average warming. This so-called Arctic Amplification is due to a number of positive feedbacks in the Arctic, some of which are poorly understood. On a basic level, Arctic climate is determined by a balance between inflows of energy from the south and the net loss of energy by radiation at the top of the Arctic atmosphere. Both are large while their difference is small. The inflow of heat from the south occurs in both the atmosphere and ocean. From a numerical modeling perspective it occurs on sufficiently large spatial and temporal scales that it is considered resolved, however, a disproportionately large fraction of the heat transport into the Arctic happens in discrete localized events, sometimes referred to as atmospheric rivers. The net energy flux at the top of the atmosphere also has a very large annual cycle: positive but small in summer, when the solar radiation is at its maximum, but large and negative in winter when the sun is absent. The net radiative flux at TOA depends on a number of processes, including sea-ice cover, surface temperature and albedo, atmospheric chemical composition, clouds and aerosols etc. All of these have in common that they are not resolved in numerical models and hence have to be parameterized, described parametrically as functions of larger-scale resolved variables. Different in different models, models typically have substantial systematic but sometimes compensating errors in these descriptions and to a large extent this explains the spread in climate model projections of future climate and systematic errors in weather forecasts. Arctic Ocean near-surface air temperature, as a proxy for climate, goes through a substantial annual cycle with two main states; these can be characterized as either freezing or melting. Physically, in some sense, the Arctic Ocean surface only has two seasons – the melt season and the freeze season. In winter with surface temperature below the melting point, the surface temperature reacts to changes in the surface energy budget, hence, it features large and fast changes in response to changes mainly in incoming radiation. In summer, or the melt season, the surface temperature is prevented from increasing above the melting point by the phase change of melting, as long as there is substantial ice and snow remaining, and all excess energy goes into melting rather than into warming. Consequently, the summer near-surface air temperature varies only a little. How much ice melts over the melt season is directly related to the length of the melt period but also indirectly to what happens in winter. If the melt season becomes longer it follows that sea ice extent at its minimum in September will decrease. The length of the melt season is therefore one important component of the Arctic climate system, and studying and understanding the so-called shoulder seasons – the transition between melt and freeze both in spring and autumn – is of great interest in order to understand the Arctic climate system. Historically, icebreaker-based expeditions, capable of performing scientific-grade process-level observations have occurred in summer or early autumn because the ice is easier to navigate in the Arctic Ocean, when melting. Hence, a number of Oden expeditions have been able to observe the transition from surface melt to surface freeze in late August or early September. However, only a few have collected such observations at the melt onset. Hence, on a process level, there are no relevant observations of the melt onset. The ARTofMELT expedition was conceived to rectify this, studying the relationship between this onset and atmospheric rivers.

Dimethyl sulfide (DMS) is the major sulfur species emitted from the ocean. The gas-phase oxidation of DMS by hydroxyl radicals proceeds through the stable, soluble intermediate hydroperoxymethyl thioformate (HPMTF), eventually forming carbonyl sulfide (OCS) and sulfur dioxide (SO₂). Recent work has shown that HPMTF is efficiently lost to marine boundary layer (MBL) clouds, thus arresting OCS and SO₂ production and their contributions to new-particle formation and growth events. To date, no long-term field studies exist to assess the extent to which frequent cloud processing impacts the fate of HPMTF. Here, we present 6 weeks of measurements of the cloud fraction and the marine sulfur species methanethiol, DMS, and HPMTF made at the Atmospheric Radiation Measurement (ARM) research facility on Graciosa Island, Azores, Portugal. Using an observationally constrained chemical box model, we determine that cloud loss is the dominant sink of HPMTF in this region of the MBL during the study, accounting for 79 %–91 % of HPMTF loss on average. When accounting for HPMTF uptake to clouds, we calculate campaign average reductions in DMS-derived MBL SO₂ and OCS of 52 %–60 % and 80 %–92 % for the study period. Using yearly measurements of the site- and satellite-measured 3D cloud fraction and DMS climatology, we infer that HPMTF cloud loss is the dominant sink of HPMTF in the eastern North Atlantic during all seasons and occurs on timescales faster than what is prescribed in global chemical transport models. Accurately resolving this rapid loss of HPMTF to clouds has important implications for constraining drivers of MBL new-particle formation.

While aerosol–cloud interactions have been extensively investigated, large knowledge gaps still exist. Atmospheric organic nitrogen (ON) species and their formation in the aqueous phase are potentially important due to (1) their influence on aerosol optical and hygroscopic properties and (2) their adverse effects on human health. This study aimed to characterize the wintertime aerosol and fog chemical composition, with a focus on the formation of ON, at a rural site in the Italian Po Valley. Online chemical characterization of interstitial aerosol (nonactivated particles) and fog residuals (dried fog droplets) were performed in parallel. Fog residuals were sampled using a ground-based counterflow virtual impactor (GCVI) inlet and analyzed by a soot particle aerosol mass spectrometer (SP-AMS), while the interstitial aerosol was characterized by a high-resolution time-of-flight AMS (HR-ToF-AMS). Our results revealed an enhancement of nitrate (NO₃^-; 43.3% vs. 34.6%), ammonium (NH₄⁺; 15.2% vs. 11.7%), and sulfate (SO₄^2-; 10.5% vs. 6.6%) in the fog residuals compared to the ambient non-fog aerosol, while organic aerosol (OA; 27.6% vs. 39.4%) and refractory black carbon (rBC; 2.3% vs. 6.3%) were less abundant. An enrichment of ON was observed in the fog, mainly consisting of C_xH_yN₁⁺ ions, partly originating from amines in the fog. C_xH_yN₂⁺ ions, fragments linked to imidazoles, were overproportionally present in the fog, which was verified by proton nuclear magnetic resonance (¹H-NMR) spectroscopy, suggesting aqueous-phase formation. This study demonstrates that fogs and clouds are potentially important sinks for gaseous nitrogen species and media for the aqueous production of nitrogen-containing organic aerosol in the atmosphere.

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.

Ice nucleating particles (INPs) initiate ice formation, affecting the liquid versus ice distribution and radiative properties of clouds. INPs have been measured around the Arctic, but few INP concentration measurements have been reported for air during movement south out of central Arctic pack ice regions during cold air outbreaks (CAOs). We analyzed cases of transports connecting the Central Arctic location of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition to the near sea ice edge in Svalbard and across ice-free ocean to the Cold-air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) site at Andenes, Norway, during the 2019–2020 Arctic winter. Aerosol surface area concentration measurements during CAOs indicate a switch from primarily accumulation mode at MOSAiC toward marine coarse mode (from sea spray emissions) at COMBLE. INP concentrations were independent of aerosol surface area or volume over the pack ice in MOSAiC in winter. At Svalbard, INPs related best to supermicron aerosol surface area and supermicron volume. At the COMBLE site, INPs related best with total aerosol surface area and total aerosol volume. In 5 of 6 case studies analyzed, INP concentrations increased in association with the transition to a dominance of sea spray aerosols. The INPs at COMBLE had a unique INP concentration mode near −18°C and higher ice nucleation active site densities (e.g., INPs per surface area) compared to those previously reported for other open ocean regions dominated by marine aerosols. While the INP sources in this case appear to be from oceanic emissions from shallower oceans under turbid water conditions, attribution solely to sea spray aerosols versus mixing down of free tropospheric aerosols by CAO clouds remains as a future topic. These studies provide a basis for parameterization of INPs for numerical modeling studies of CAO cloud systems.

Chemical ionization mass spectrometers are widely used for the detection of trace gases, particularly in the field of atmospheric science. Depending on the analytes of interest, chemical ionization instruments are operated under varying reactor conditions, which can make it difficult to compare instrument performance, even for the same reagent ion chemistry. This variability leads to inconsistent sensitivity distributions, particularly for weakly bound or labile analytes. As a result, determining sensitivity – instrument response per unit analyte concentration – is challenging, even when comparing the same compound detected with the same reagent ion across different studies. To address this issue, we employed multiple Vocus AIM reactors (Tofwerk AG) to systematically identify the critical parameters affecting sensitivity in flow tube chemical ionization mass spectrometers. Controlling these parameters for a given reactor geometry can significantly reduce sensitivity variations across instruments and operators. We demonstrate that sensitivity normalized to reagent ion concentration serves as a fundamental metric for interpreting results from different datasets operating under uniform chemical ionization conditions, such as those within regional networks or other monitoring applications. Calibrating the sensitivity of benzene cations to a group of hydrocarbons, and comparing it to the sensitivity of iodide anions to levoglucosan, a molecule known to react near the collision limit, reveals that it is possible to map kinetic constraints on sensitivity from one ion mode polarity to another, as long as the critical parameters are held constant. Additionally, we show that collision-limited sensitivity relative to the reagent ion is nearly constant across different ionization mechanisms for a given reactor geometry and set of conditions. This consistency enables the determination of the upper limit of sensitivity, even for reagent ions where the specific molecules reacting at the collision limit are unknown. As a result, the use of the voltage-scanning approach can be extended to a broader range of reagent ion chemistries. This study highlights how collision-limited sensitivity can enhance our understanding of the relationships between different instruments and simplify calibration requirements across various reagent ion chemistries.