Abstract. Ice nucleating particles (INPs) are rare aerosols essential for cloud ice formation in the mixed-phase temperature range between -38 °C and 0 °C. Due to measurement challenges and limitations in instrument capabilities, the availability of atmospheric observations of INPs remains scarce in time and space. Consequently, no observation-based global distribution of INPs exists so far. This study applies a machine learning (gradient boosting) algorithm to predict the INP concentration over the mixed-phase temperature range across the globe using aerosol mass concentration reanalysis and observed immersion-mode INPs. This proof-of-concept exercise demonstrates that even with limited measurements, the occurrence of INPs can be estimated, following the spatial pattern of key aerosol species. Point-based evaluation metrics, R2 and log-based RMSE, reach 0.83 and 0.71 for the gradient boosting model, compared to 0.75 and 0.86 for the linear regression benchmark. Regional INP spectra with temperature extracted from the machine learning model agree within one order of magnitude with observations, except over Antarctica (mean bias factor of 150). INPs in continental (oceanic) regions are well predicted within a mean bias of 2.4 (overestimated within a mean bias of 4) by the machine learning model. The prediction is driven by the strong temperature dependence followed by mid-sized dust particles. This approach can resolve a long-standing source of INP prediction uncertainty in regional weather and climate models.

Recent studies show that warm and moist air intrusions are major sources of aerosol particles in the Arctic, affecting local radiative impacts by supplying Cloud Condensation Nuclei (CCN). However, their influence on aerosol size modes, CCN, and cloud droplet number concentrations remains poorly constrained. Here, we use long-term aerosol observations from five Arctic observatories to quantify intrusion impacts. We find that intrusions strongly perturb Arctic CCN, especially in summer, when accumulation-mode and CCN concentrations increase markedly at all sites. In winter and spring, two regimes emerge: intrusions reduce number concentrations at sites near 0° longitude (Zeppelin, Villum, Alert) but enhance them near 180° (Tiksi, Utqiaġvik/Barrow), consistent with competing effects of pollution sources and wet scavenging along trajectories. Intrusions also systematically modify cloud droplet number concentration (Nd): Nd increases at all sites in summer, while in winter it increases at Tiksi and Utqiaġvik/Barrow but decreases at Zeppelin, Villum, and Alert. Overall, intrusions are a key regulator of Arctic aerosol and cloud properties and an important component of the evolving Arctic climate system.Beyond aerosol–cloud number effects, it is unclear how intrusion events modulate cloud optical depth, liquid water content, and precipitation across Arctic sites and seasons. To address this, we will combine cloud and precipitation observations from the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition with reanalysis data to quantify systematic intrusion-driven changes in liquid water content and precipitation occurrence. Finally, we will use the non-hydrostatic mesoscale Weather Research and Forecasting (WRF) model to examine the sensitivity of mixed-phase cloud lifetime and associated precipitation to intrusion occurrence, providing process-level constraints on how intrusions shape Arctic mixed-phase cloud persistence and hydrometeor production.

Abstract. Closed-to-open cell mixed-phase cloud transitions within marine cold air outbreaks subjected to strong turbulent surface fluxes remain poorly understood despite their importance to high-latitude climate. The Cold-Air outbreak Experiment in the Sub-Arctic Region (CAESAR) research aircraft sampled closed-cells with cloud condensation nuclei concentrations surpassing 680 cm-3, decreasing to 90 cm-3 across a transition to open-cells. The aerosol likely originated from Siberian industrial emissions. With fetch, liquid water paths (LWPs) increase from 120 g m-2 to 270 g m-2 and cloud-top effective diameters increase from 10 μm to 16 μm, coincident with more riming. Ice particle number concentrations (Ni) are generally 2 L-1 or less, but exceed ice nucleating particle number concentrations by 100x. As the cloud-top inversion weakens and the boundary layer deepens further, ice precipitation co-exists with lidar-observed surface cold pools, modulated by entrainment events, juxtaposed with surface-based plumes of warm moist air. Open-cells contain isolated LWP peaks surpassing 500 g m-2 collocated with strong updrafts, adjacent to glaciated cloud. Ni surpasses 10 L-1 at cloud temperatures < -15 °C. Precipitation shafts contain abundant large graupel (> 5 mm diameter) with liquid-equivalent precipitation intensities reaching 3 mm hr-1 developing cold pools with virtual potential temperature depressions reaching 1.3 K. Nonetheless, buoyancy fluxes of 200-250 W m-2 prevent sub-cloud decoupling. The updrafts supporting liquid water production occur at the upwind edge of the cold pools. This case expands the observations needed to better understand mixed-phase Arctic cloud processes.

Abstract. Fine aerosol liquid water content (ALWC) and acidity (pH) are co-determined, pH primarily reflects the ratio of hydrogen-ion concentrations in air (H+air) to ALWC. Inorganic ions dominate H+air and often ALWC, whereas organic aerosol (OA) mainly adds water. Added OA water, however, shifts gas–particle partitioning of semi-volatile species, altering H+air itself, and thus pH and the aqueous-phase processes it governs. We characterize PM1 ALWC and pH over four North American cities using airborne AEROMMA measurements (June–August 2023), including periods influenced by aged wildfire smoke with high OA but little effect on inorganic species. ALWC and pH were predicted with ISORROPIA-Lite, which includes OA water, and evaluated against measured partitioning of NH3–NH4+ and HNO3–NO3-. Predicted ammonia partitioning agreed with observations (R2 > 0.75, within ~±10 %), whereas nitrate was systematically over-predicted by ~27 %. Outside smoke, inorganic ions dominated ALWC despite being a minor mass fraction; within smoke, organic water dominated (45–65 %). Across all cities, pH remained low and varied little (1.5–2.5, 10th–90th percentile). Particle-phase fractions were 0.17–0.56 for NH4+ but only 0.1–0.22 for NO3-, giving nitrate less redistribution and pH-buffering capacity than NH4+. Including OA water raised pH during smoke by at most 0.62 units and improved HNO3–NO3- agreement. Summertime PM1 acidity thus remains persistently low and thermodynamically stabilized across diverse composition regimes, with wide-ranging implications for regulatory, environmental, and human-health impacts.

Interactions between aerosols, clouds, and radiation remain a major source of uncertainty in effective radiative forcing (ERF), limiting the accuracy of climate projections. This study aims to quantify parametric uncertainties in aerosol-cloud and aerosol-radiation interactions using a perturbed parameter ensemble (PPE) of 221 simulations with the ECHAM6.3-HAM2.3 climate model, varying 23 aerosol-related parameters that control emissions, removal, chemistry, and microphysics.The resulting global mean aerosol ERF is -1.24 W m-2 (5-95 percentile: -1.56 to -0.89 W m-2). Uncertainty in ERF is dominated by sulfate-related processes, biomass burning, aerosol size, and natural emissions. For aerosol-cloud interactions, dimethyl sulfide (DMS) and biomass burning emissions are key drivers, whereas sulfate chemistry and dry deposition exert the strongest influence on aerosol-radiation interactions. Structural uncertainty is difficult to characterize, and this study focuses primarily on evaluating parametric uncertainty. The leading sources of ERF parametric uncertainty identified here are consistent with those found in other PPE studies, highlighting common sensitivities across climate models.Comparison with POLDER-3/PARASOL satellite retrievals reveals persistent model biases in aerosol optical depth (AOD), & Aring;ngstr & ouml;m exponent (AE), and single-scattering albedo (SSA), many of which fall within the parametric uncertainty range. Sulfate-related processes account for over 40 % of AOD uncertainty, while AE and SSA are most sensitive to DMS, sea salt, and black carbon parameters. Correlation analysis between key parameters and observables indicates that several biases may be reduced by tuning through physically consistent parameter adjustments for bias reduction. Our results highlight the need for combined efforts in parameter optimization and structural model development to improve confidence in aerosol-forcing estimates and future climate projections.

Health effects associated with particulate matter (PM) exposure are closely linked to the ability of particles to induce the formation of reactive oxygen species (ROS) and trigger oxidative stress. Accordingly, the oxidative potential (OP) of PM is considered a more health-relevant toxicity metric than mass concentration alone. However, in densely populated and ecologically sensitive areas in the northwestern Mediterranean, the main sources contributing to OP remain poorly constrained, particularly regarding differences between urban and rural environments.This sutdy systematically evaluated the OP of total suspended particulate matter (TSP) at an urban coastal site (Endoume) and a rural coastal site (Banyuls) in the region. OP was quantified using the dithiothreitol (DTT) assay and evaluated the contributions of primary emission sources and secondary formation to OP. Chemical tracers, dual carbon isotopes (¹³C & ¹⁴C), and positive matrix factorization (PMF) were used to apportion the main local sources.The results reveal pronounced differences in both magnitude and source contribution to OP between urban and rural coastal aerosols. The annual mean organic-carbon-normalized DTT activity (DTTm) at Endoume was 20.0 ± 9.1 pmol min⁻¹ μg⁻¹ and 17.0 ± 5.3 pmol min⁻¹ μg⁻¹ at Banyuls (Mann–Whitney U test, p = 0.06). The annual mean volume-normalized OP (DTTv) was comparable at both sites (≈ 0.04 ± 0.02 pmol min⁻¹ m⁻³, Mann–Whitney U test, p 0.75); in summer, ship emission emerged as the dominant driver (V–DTTv: ρ > 0.9, p < 0.01); while in autumn and winter, the contribution from biomass burning (BB) increased substantially (DTTv–nssK⁺: ρ = 0.74, p < 0.01). In contrast, the OP at Banyuls was dominated by traffic emission in spring (Zn–DTTv: ρ > 0.7, p < 0.01), whereas BB and ship emission jointly influenced OP in summer (V–Ni: ρ ≈ 0.7, p < 0.01). Additionally, dust and sea salt contributed significantly to OP at both sites, with a more pronounced influence at Banyuls (nss-Ca²⁺–DTTv: ρ ≈ 0.85, p < 0.01). Carbon isotope analysis showed that autumn samples at both sites exhibited lower OCNF and DTTv values, indicating that the influence of FF on OP may be more pronounced.PMF results further show that at Banyuls, traffic emission and BB together accounted for approximately 25% of OPv, with natural dust contributing about 14%, whereas at Endoume, industrial emissions (25%), BB (20%), and traffic emission (19%) were the major contributors to OPv. For OPm, industrial emission dominated at Endoume, while natural sources such as sea salt and dust were the primary contributors at Banyuls; secondary formation processes contributed substantially to OPm at both sites. Overall, this study demonstrates strong spatial and seasonal source dependence of PM oxidative toxicity in the northwestern Mediterranean coastal region, providing important constraints for health-oriented air pollution assessments.

Abstract. Oxidative potential (OP) of atmospheric particulate matter (PM) is a metric of increasing scientific interest because it potentially links chemical particle properties to particle health effects. OP has been recently introduced as a recommended monitoring metric in the European Air Quality Directive. However, inconsistent protocols in the existing literature make it difficult to compare results across studies. Following a 2023 inter-laboratory comparison that focused on PM OP measured using the dithiothreitol (DTT) assay, this paper presents the findings and lessons learnt from a second inter-laboratory study focused on the ascorbic acid assay (OP-AA). In this study, twenty-six laboratories worldwide quantified OP of four PM filter samples and of one chemical compound to evaluate the entire analytical chain, including the extraction step, using a simplified OP-AA protocol. While most laboratories produced repeatable internal results when applying the simplified protocol, significant discrepancies between participants highlight the need for each laboratory to carefully evaluate deviations from the simplified OP-AA protocol. Over half of the 26 participants achieved satisfactory results, suggesting that the protocol is suitable for large-scale implementation. Beyond assessing performance, this work investigates technical, analytical, and mathematical refinements to measurement protocols. Building on the first DTT assay study, this second inter-laboratory comparison represents a significant step toward harmonizing OP assays, and provides specific recommendations to ensure consistent future measurements, ready to be applied in the new air quality directive EU 2024/2881.

Aerosol acidity is a key regulator of atmospheric processes, influencing particulate matter (PM) composition, toxicity, and the deposition of reactive nitrogen (Nr) to ecosystems. Emission controls in Europe have strongly reduced SO x and NO x but left NH3 largely unchanged, creating an imbalance between acidic and basic species that could shift aerosol pH and its impacts. Thermodynamic analysis of long-term observations (2008-2024) from Swiss monitoring sites combined with SHapley Additive exPlanations (SHAP) quantified aerosol pH and its key drivers. Annual mean pH shows a slight increasing trend, with consistently lower values in summer than in winter. SHAP analysis indicates that temperature drives seasonal pH variability at agricultural sites, whereas total ammonia (NH3 T) dominates at the semialpine site. The Nr deposition analysis shows that the fast deposition of NH3 T predominates across both the lowland and Alpine regions, which increases local soil nitrogen burden. In contrast, PM has become increasingly insensitive to NH3 and more sensitive to HNO3, particularly in the agricultural sites. These results highlight that, although HNO3 precursor controls have effectively reduced PM pollution without the need for NH3 reductions, evermore significant ecological concerns remain from a lack of NH3 control. This underscores the need for coordinated reductions in both NO x and NH3 emissions.

Clouds and aerosol-cloud interactions remain major sources of uncertainty in climate projections. Here, we improve the representation of mixed-phase clouds (MPCs) in the EC-Earth3-AerChem Earth System Model by replacing the default temperature-dependent nucleation scheme with a physically based aerosol-sensitive heterogeneous ice nucleation parameterization. This scheme accounts for immersion freezing by K-feldspar, quartz, and marine organic aerosols, and is combined with a machine-learning-based parameterization of secondary ice production (SIP) to represent ice crystal multiplication processes.The new configuration improves agreement with global in situ ice nucleating particle (INP) observations and reveals realistic spatial patterns of ice crystal number concentrations (ICNC) across diverse environments. While these improvements do not eliminate the persistent structural cloud biases in EC-Earth3-AerChem, the aerosol-sensitive primary ice production scheme increases supercooled liquid water and cloud cover, particularly in the extratropics. Critically, the addition of SIP rebalances the cloud phase by enhancing ICNC in regions with low primary ice formation.Compared to the default scheme, the aerosol-sensitive primary ice production configuration with SIP reduces cloud radiative effect biases at mid- and high latitudes, while increasing them in the lower latitudes, leading to comparable global biases across configurations. Our results highlight the importance of explicitly representing both aerosol-sensitive nucleation and SIP for realistic simulations of MPCs and their radiative impacts. Unlike previous schemes, in which ice concentrations depend directly on INPs, the presence of effective SIP enhances ice formation in all MPCs and reduces the sensitivity of ICNC to aerosols, especially at low INP levels.

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. ACI influence effective radiative forcing, cloud dynamics, and precipitation patterns, yet remain insufficiently constrained due to limitations in observations, modeling, and process understanding. Uncertainty from ACI 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. Despite these important issues, ACI is often underappreciated or excluded from decision-making frameworks due to its complexity and lack of quantification.This talk 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.

Volatile methyl siloxanes (VMS) are anthropogenic compounds widely used in personal care products and industrial applications and are frequently detected at elevated concentrations in urban air. However, their sources in urban areas remain poorly constrained. Here, we use chassis dynamometer experiments to quantify gas-phase VMS emissions from a range of on-road vehicles, including light-duty gasoline and diesel vehicles. Hexamethylcyclotrisiloxane (D-3) dominated the emitted VMS, over octamethylcyclotetrasiloxane (D-4), and decamethylcyclopentasiloxane (D-5). VMS emissions increased with driving speed and were substantially higher from gasoline than from diesel vehicles. Comparison with tunnel measurements showing elevated ambient VMS concentrations supports a significant contribution from traffic-related sources, including tailpipe and potentially nontailpipe emissions. These results identify vehicular emissions as a previously underrecognized source of VMS near highways, and provide new constraints for their atmospheric budget and source apportionment.

Changes in aerosols since the preindustrial era have altered the top-of-the-atmosphere radiation balance by directly scattering solar radiation and indirectly interacting with clouds, known as aerosol effective radiative forcing (ERFaer). ERFaer persistently remains one of the most uncertain components in global climate model simulations, due to the imperfect representations of aerosol and cloud properties and processes. Perturbed parameter ensembles (PPEs) are increasingly used to quantify these sources of uncertainty and to constrain models with observations.Here, we first present a single-model PPE using the ICON-A-HAM2.3 model, designed to identify key sources of ERFaer uncertainty. This PPE comprises 383 simulations for both preindustrial and present-day conditions, in which 42 parameters related to aerosol emissions, aerosol properties and processes, cloud microphysics, convection, and turbulence are perturbed simultaneously. Gaussian process emulators are trained on model outputs to enable efficient sampling of this high-dimensional parameter space. Our analysis focuses on uncertainty quantification and attribution for aerosol and cloud properties as well as ERFaer, along with comparisons against satellite observations from SPEXone/PACE and MODIS. Our results show a global mean ERFaer of −1.10 W m⁻² (5–95 percentile: −1.54 to −0.68 W m⁻²), with the overall uncertainty dominated by aerosol-related processes, particularly aerosol emissions.Building on this single-model framework, we further propose a Multi-Model PPE (MMPPE) initiative within the AeroCom Phase IV experiments. This multi-model approach allows us to simultaneously address structural and parametric uncertainties across models, providing a coordinated pathway toward reducing ERFaer uncertainty in current climate models. An overview of the MMPPE design and objectives will be presented.

Atmospheric aerosols represent one of the largest sources of uncertainty in estimates of future climate predictions. A key challenge arises from the large variety of aerosol types differing in physical properties, e.g. size and shape, and chemical composition as well as concentration. Coastal regions are particularly complex environments, where natural and anthropogenic aerosols co-exist, mix and interact, often fundamentally altering their original properties. At the same time, coastal areas are densely populated, hosting approximately 40 % of the global population. Consequently, improved knowledge of aerosol properties in coastal regions is essential not only for climate studies but also because of their relevance to human health.The aerosols’ optical properties, defined by their interactions with sunlight through scattering and absorption, provide valuable insight into both their physical and chemical properties. The wavelength-dependent light scattering signal can be predominantly related to the particles size, while the wavelength-dependent absorption signal rather more reflects the aerosol particles’ chemical composition. By combining these types of information within a so-called Ångström matrix, the aerosol sources and types can be assessed.In this work, aerosol optical properties were measured at three different coastal sites representing contrasting environments to identify dominant aerosol sources and types. Measurement campaigns were conducted in an urban environment at Aarhus Bay, Denmark, a rural environment at Askö, Sweden and a pristine Arctic environment at Villum Research Station, Northwest Greenland. Wavelength-dependent scattering coefficients were measured using a nephelometer (AURORA 3000, Ecotech) and wavelength-dependent absorption coefficients were obtained by an aethalometer (AE33 or AE36s, MAGEE). In addition, aerosol number size distributions were measured and air-mass back-trajectory analysis was performed. One intense measurement campaign of approximately five weeks was carried out at each site between spring 2023 and spring 2025. The resulting datasets were analysed regarding dominant aerosol sources, determining the importance of natural vs. anthropogenic emissions and locally emitted vs. long-range transported aerosols.

Aerosol–cloud and aerosol–radiation interactions remain among the dominant sources of uncertainty in estimates of effective radiative forcing (ERF). Perturbed parameter ensembles (PPEs) are now increasingly used to evaluate climate model forcings and to diagnose sources of uncertainty. PPEs systematically sample uncertainty by performing large sets of simulations in which key model parameters are perturbed, allowing the sensitivity of model outcomes to individual processes to be quantified. When combined with Gaussian process emulators, PPE outputs can be efficiently extended to millions of model surrogates, enabling robust statistical assessments of model uncertainty. Here, we focus on aerosol-related sources of uncertainty in ERF.This work applies a PPE–emulator framework in a two-model, one-to-one configuration to study both parametric and structural uncertainties in two Earth system models: OpenIFS/AC cycle48r1 (EC-Earth4) and ECHAM6.3-HAM2.3. Parameters are selected based on aerosol ERF uncertainty analyses in ECHAM6-HAM (Bhatti et al., 2026), with corresponding perturbations applied in OpenIFS/AC using identical parameter ranges.Both model ensembles are evaluated against satellite observations from MODIS/Terra and POLDER-3/PARASOL for the year 2010, focusing on annual mean aerosol optical depth, single-scattering albedo, and Ångström exponent as key observables linking aerosol microphysics to ERF. In addition to the two-model comparison, we perform a detailed evaluation of the OpenIFS/AC PPE in its own right. This includes an assessment of regional patterns in aerosol properties and ERF, as well as a quantification of the relative contributions of individual parameters to model uncertainty. From the parametric uncertainty within OpenIFS/AC, we can identify model-specific sensitivities and regional responses for parameter constraining and model development.Despite identical parameter perturbations, the two models exhibit systematic differences in their climate responses, associated with differences in aerosol life-cycle representation, cloud microphysics, and radiative coupling. Initial results indicate that sea-salt emissions contribute significantly to the largest global uncertainties in AOD at 550 nm in both models. The ERF uncertainties are driven by a more diverse set of parameters between the models, with fossil fuel, SO₂, dimethylsulfide (DMS), and biomass-burning emissions among the dominant contributors. The resulting inter-model spread can provide a quantitative measure of structural uncertainty that is not captured by single-model PPE studies. This two-model framework adds a structural dimension to previous PPE approaches by isolating structural effects under controlled parametric sampling. Bhatti, Y. A., Watson-Parris, D., Regayre, L. A., Jia, H., Neubauer, D., Im, U., Svenhag, C., Schutgens, N., Tsikerdekis, A., Nenes, A., Irfan, M., van Diedenhoven, B., Arifi, A., Fu, G., and Hasekamp, O. P.: Uncertainty in aerosol effective radiative forcing from anthropogenic and natural aerosol parameters in ECHAM6.3-HAM2.3, Atmos. Chem. Phys., 26, 269–293, https://doi.org/10.5194/acp-26-269-2026, 2026.

Aerosol acidity is a key regulator of atmospheric processes, influencing particulate matter (PM) composition, toxicity, and the deposition of reactive nitrogen (Nr) to ecosystems. Emission controls in Europe have strongly reduced SOx and NOx but left NH3 largely unchanged, creating an imbalance between acidic and basic species that could shift aerosol pH and its impacts. Thermodynamic analysis of long-term observations (2008-2024) from Swiss monitoring sites combined with SHapley Additive exPlanations (SHAP) quantified aerosol pH and its key drivers. Annual mean pH shows a slight increasing trend, with consistently lower values in summer than in winter. SHAP analysis indicates that temperature drives seasonal pH variability at agricultural sites, whereas total ammonia (NH3T) dominates at the semialpine site. The Nr deposition analysis shows that the fast deposition of NH3T predominates across both the lowland and Alpine regions, which increases local soil nitrogen burden. In contrast, PM has become increasingly insensitive to NH3 and more sensitive to HNO3, particularly in the agricultural sites. These results highlight that, although HNO3 precursor controls have effectively reduced PM pollution without the need for NH3 reductions, evermore significant ecological concerns remain from a lack of NH3 control. This underscores the need for coordinated reductions in both NOx and NH3 emissions.

Volatile methyl siloxanes (VMS) are anthropogenic compounds widely used in personal care products and industrial applications and are frequently detected at elevated concentrations in urban air. However, their sources in urban areas remain poorly constrained. Here, we use chassis dynamometer experiments to quantify gas-phase VMS emissions from a range of on-road vehicles, including light-duty gasoline and diesel vehicles. Hexamethylcyclotrisiloxane (D3) dominated the emitted VMS, over octamethylcyclotetrasiloxane (D4), and decamethylcyclopentasiloxane (D5). VMS emissions increased with driving speed and were substantially higher from gasoline than from diesel vehicles. Comparison with tunnel measurements showing elevated ambient VMS concentrations supports a significant contribution from traffic-related sources, including tailpipe and potentially nontailpipe emissions. These results identify vehicular emissions as a previously underrecognized source of VMS near highways, and provide new constraints for their atmospheric budget and source apportionment.

Abstract. Changes in aerosols since the preindustrial era have altered the top-of-the-atmosphere radiation balance by scattering and absorbing solar radiation (ARI) and indirectly interacting with clouds (ACI), known as aerosol effective radiative forcing (ERFaer). ERFaer persistently remains one of the most uncertain components in climate projections, due to imperfect representations of aerosol and cloud processes in climate models. Here, we construct a perturbed parameter ensemble (PPE) with the aerosol–climate model ICON2.6.4–A–HAM2.3 (hereafter ICON–HAM) to quantify key sources of ERFaer uncertainty. We perturb 42 aerosol and cloud parameters over 383 PPE members. Parametric uncertainties in aerosol and cloud processes yield an ERFaer of −1.04Wm−2, with a 90 % credible range of −1.42 to −0.65 Wm−2 for the period 2024–2025. The parameters related to emissions (anthropogenic sulfur dioxide, natural dimethyl sulfide, and emitted particle size) dominate ACI uncertainty and hence ERFaer uncertainty (80 %), while absorption-related parameters (anthropogenic black carbon emissions and aerosol refractive indices) drive ARI uncertainty (60 %). Cloud parameters account for 13 % of ERFaer uncertainty, mainly via convection and entrainment processes. The sensitivity analysis of model diagnostics to parameters reveals that many present-day aerosol and cloud observables share dominant causes of uncertainty with ACI and ARI forcing, highlighting the potential for constraining ERFaer using existing space- and ground-based measurements. Notably, model biases against SPEXone and MODIS observations coincide spatially with parametric uncertainties, suggesting that much of these biases may be mitigated through appropriate constraint with observations, while the remainder requires structural model developments in combination with improved observations.