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Aerosol-cloud interactions are of central importance for understanding climate processes but remains the largest uncertainty associated with climate change. Hence, the effective radiative forcing (ERF) due to ACI and rapid adjustments (ERFaci) is still assessed only with medium confidence. An important part of this uncertainty originates from the difficulty of quantifying ACI using observations, especially for ice-containing clouds. In this study, we present a novel Cloud-by-Cloud (CxC) approach for studying ACI in satellite observations that merges properties of individual clouds that have been tracked from geostationary satellite observations with height-resolved concentrations of cloud condensation nuclei (nCCN) and ice nucleating particles (nINP) from polar-orbiting lidar data. This approach lays the foundations for better understanding of ACI through a thorough investigation of matched aerosol-cloud cases at cloud level. The methodology is applied to satellite observations over Central Europe and Northern Africa for several years, resulting in a bottom-up dataset of combining parameters that can be stratified accordingly for assessing the impact of changes in cloud-relevant aerosol concentrations on the surrounding quality assured liquid and ice-containing clouds. The first preliminary results of this novel CxC approach are promising and constitute a step forward in the quantification of ERFaci from space.
Alexandri et al. (Fri,) studied this question.