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.

Transpiration drives up to 40 % of terrestrial precipitation, with forests playing a critical role. This study combines long-term sap flow measurements and surface-based cloud observations to examine how different clouds affect tree transpiration across boreal and temperate European forests. Under specific cloudy conditions, sap flow can exceed clear-sky levels, reflecting distinct radiation effects of various cloud types. However, overall cloudiness reduces maximum sap flow by up to 40 %. A key finding is the contrasting influence of low- and high-altitude clouds: low-altitude clouds suppress transpiration by limiting incoming radiation, while high-altitude clouds have negligible impact even when overcast. Structural equation modelling further indicates a pathway linking transpiration to cloudiness when other meteorological and site factors are accounted for. Satellite data show a decline in low‑altitude cloud fraction over boreal forests, and a highly simplified model-based order‑of‑magnitude estimate suggests a potential associated increase in transpiration equivalent to ∼0.6–1.2 mm of precipitation annually. These results emphasize how climate change driven changes in cloud cover and type may alter the moisture flux to the atmosphere, impacting regional and global water cycles.

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.

We present direct observations of 2-methyltetrol (C5H12O4) in the gas- and particle phase from the deployment of a Filter Inlet for Gases and Aerosols coupled to a Time-of-Flight Chemical Ionization Mass Spectrometer (FIGAERO-CIMS) during the Southern Hemisphere High Altitude Experiment on Particle Nucleation and Growth (SALTENA), which took place between December 2017 and June 2018 at the high-altitude Global Atmosphere Watch station Chacaltaya (CHC) located at 5240 m a s l in the Bolivian Andes. 2-Methyltetrol signals were dominant in a factor resulting from Positive Matrix Factorization (PMF) identified as influenced by Amazon emissions. We combine these observations with investigations of isoprene oxidation chemistry and uptake in an isolated deep convective cloud in the Amazon using a photochemical box model with coupled cloud microphysics and show that, likely, 2-methyltetrol is taken up by hydrometeors or formed in situ in the convective cloud, and then transported in the particle phase in the cold environment of the Amazon outflow and to the station, where it partially evaporates.

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.

The contribution of natural aerosol particles from boreal forests to total aerosol loadings may increase with reduction in anthropogenic emissions. Aitken and accumulation mode particles in boreal regions differ significantly in hygroscopicity, and ignoring this size dependence can cause large uncertainty in Cloud Condensation Nuclei (CCN) prediction. We applied κ-Köhler theory to a multi-year dataset (2016–2020) from Hyytiälä, Finland, to evaluate different representations of aerosol chemical composition for CCN prediction. Overpredictions by forward closures using either bulk chemical composition from an Aerosol Chemical Speciation Monitor (ACSM) or a constant κ= 0.18 were mitigated to a great extent by optimizing size-resolved composition using two inverse modeling approaches: (1) Nelder–Mead method with the size distribution fixed to its median during each 2 h CCN measurement cycle, and (2) MCMC (Markov Chain Monte Carlo) accounting also for the variability in the size distribution during each cycle. Both methods improved closure at SS =  0.2 %–1.0 % (with Geometric Mean Bias GMB values 1.12–1.20 and 0.95–1.05, respectively), with moderate improvement at 0.1 % (GMBs of 1.53 and 1.32, respectively). The Aitken mode was enriched in organics in 77 % of cases using method (1) and 46 % using method (2) – with typical κ values of ∼ 0.1 for Aitken and ∼ 0.3 for accumulation modes. The results generally align with known size-dependent chemical composition in Hyytiälä and indicate that variability in CCN hygroscopicity is largely driven by Aitken mode composition. Our results demonstrate the potential of inverse CCN closure methods for obtaining valuable information of the size-dependent chemical composition.

The years 2023 and 2024 were characterized by unprecedented warming across the globe, underscoring the urgency of climate action. Robust science advice for decision makers on subjects as complex as climate change requires deep cross- and interdisciplinary understanding. However, navigating the ever-expanding and diverse peer-reviewed literature on climate change is enormously challenging for individual researchers. We elicited expert input through an online questionnaire (188 respondents from 45 countries) and prioritized 10 key advances in climate-change research with high policy relevance. The insights span a wide range of areas, from changes in methane and aerosol emissions to the factors shaping citizens’ acceptance of climate policies. This synthesis and communications effort forms the basis for a science-policy report distributed to party delegations ahead of the 29th session of the Conference of the Parties (COP29) to inform their positions and arguments on critical issues, including heat-adaptation planning, comprehensive mitigation strategies, and strengthened governance in energy-transition minerals value chains.

Accounting for the condensation of organic vapors along with water vapor (co-condensation) has been shown in adiabatic cloud parcel model (CPM) simulations to enhance the number of aerosol particles that activate to form cloud droplets. The boreal forest is an important source of biogenic organic vapors, but the role of these vapors in co-condensation has not been systematically investigated. In this work, the environmental conditions under which strong co-condensation-driven cloud droplet number enhancements would be expected over the boreal biome are identified. Recent measurement technology, specifically the Filter Inlet for Gases and AEROsols (FIGAERO) coupled to an iodide-adduct chemical ionization mass spectrometer (I-CIMS), is utilized to construct volatility distributions of the boreal atmospheric organics. Then, a suite of CPM simulations initialized with a comprehensive set of concurrent aerosol observations collected in the boreal forest of Finland during spring 2014 is performed. The degree to which co-condensation impacts droplet formation in the model is shown to be dependent on the initialization of temperature, relative humidity, updraft velocity, aerosol size distribution, organic vapor concentration, and the volatility distribution. The predicted median enhancements in cloud droplet number concentration (CDNC) due to accounting for the co-condensation of water and organics fall on average between 16 % and 22 %. This corresponds to activating particles 10–16 nm smaller in dry diameter that would otherwise remain as interstitial aerosol. The highest CDNC enhancements (ΔCDNC) are predicted in the presence of a nascent ultrafine aerosol mode with a geometric mean diameter of ∼ 40 nm and no clear Hoppel minimum, indicative of pristine environments with a source of ultrafine particles (e.g., via new particle formation processes). Such aerosol size distributions are observed 30 %–40 % of the time in the studied boreal forest environment in spring and fall when new particle formation frequency is the highest. To evaluate the frequencies with which such distributions are experienced by an Earth system model over the whole boreal biome, 5 years of UK Earth System Model (UKESM1) simulations are further used. The frequencies are substantially lower than those observed at the boreal forest measurement site (< 6 % of the time), and the positive values, peaking in spring, are modeled only over Fennoscandia and the western parts of Siberia. Overall, the similarities in the size distributions between observed and modeled (UKESM1) are limited, which would limit the ability of this model, or any model with a similar aerosol representation, to project the climate relevance of co-condensation over the boreal forest. For the critical aerosol size distribution regime, ΔCDNC is shown to be sensitive to the concentrations of semi-volatile and some intermediate-volatility organic compounds (SVOCs and IVOCs), especially when the overall particle surface area is low. The magnitudes of ΔCDNC remain less affected by the more volatile vapors such as formic acid and extremely low- and low-volatility organic compounds (ELVOCs and LVOCs). The reasons for this are that most volatile organic vapors condense inefficiently due to their high volatility below the cloud base, and the concentrations of LVOCs and ELVOCs are too low to gain significant concentrations of soluble mass to reduce the critical supersaturations enough for droplet activation to occur. A reduction in the critical supersaturation caused by organic condensation emerges as the main driver of the modeled ΔCDNC. The results highlight the potential significance of co-condensation in pristine boreal environments close to sources of fresh ultrafine particles. For accurate predictions of co-condensation effects on CDNC, also in larger-scale models, an accurate representation of the aerosol size distribution is critical. Further studies targeted at finding observational evidence and constraints for co-condensation in the field are encouraged.