Search for a command to run...
Aerosol–cloud interactions (ACI) remain a major source of uncertainty in understanding the cloud microphysical and macrophysical responses to aerosol perturbations, with important implications for cloud evolution and climate processes. Within the Satellite observations to improve our understanding of aerosol-cloud interactions (SATACI) framework, we exploit synergistic satellite observations to derive robust observational constraints on aerosol effects on liquid clouds.This study focuses on using high-frequency geostationary observations, relying on the heritage of the ESA aerosol and cloud CCI projects. Aerosol and cloud properties are collocated at the pixel level and harmonized in space and time to characterize aerosol loading, cloud droplet number concentration (CDNC), cloud fraction (CF), and cloud phase under controlled meteorological stratifications. The geostationary perspective enables systematic investigation of temporal offsets between aerosol and cloud observations, allowing assessment of time-lagged aerosol–cloud responses that are not accessible from polar-orbiting sensors alone.We quantify CDNC and CF sensitivities to aerosol perturbations using large-sample, stratified analyses that explicitly account for spatial aggregation, aerosol loading regimes, surface type, and temporal co-variability. Aerosol loading is analysed using stepwise binning approaches to separate distinct loading regimes and to identify changes in aerosol–cloud sensitivities that are not well represented by a single linear relationship. Separate analyses over land and ocean reveal distinct sensitivity patterns in both magnitude and variability, highlighting the non-uniform nature of ACI across environments. The robustness of derived sensitivities is assessed across multiple aerosol proxies and independent cloud datasets, and uncertainty information is propagated throughout the analysis to support quantitative interpretation.Additional stratifications are used to assess the influence of environmental factors such as relative humidity and precipitation occurrence on inferred ACI metrics. Comparisons with climate model simulations from NorESM, performed under matched spatial and temporal stratifications, provide a consistency check on observed aerosol–cloud sensitivities and support interpretation of the observational diagnostics.The analysis underscores the importance of high-frequency observations, regime-aware stratification, and uncertainty-aware methodologies for constraining aerosol effects on liquid clouds. By providing statistically robust, observation-based diagnostics of aerosol–cloud interactions, SATACI contributes to the efforts of the ACI cluster (involving CERTAINTY, CleanCloud and AirSense) to improve process understanding and reduce observational uncertainty in aerosol–cloud studies.