A cloud-by-cloud approach for studying aerosol-cloud interaction in satellite observations

Abstract

Abstract. The effective radiative forcing (ERF) due to aerosol-cloud interactions (ACI) and rapid adjustments (ERFaci) still causes the largest uncertainty in the assessment of climate change. It is understood only with medium confidence and studied primarily for warm clouds. Here, we present a novel cloud-by-cloud (C×C) approach for studying ACI in satellite observations that combines the concentration of cloud condensation nuclei (n_CCN) and ice nucleating particles (n_INP) from polar-orbiting lidar measurements with the development of the properties of individual clouds from tracking them in geostationary observations. We present a step-by-step description for obtaining matched aerosol-cloud cases. The application to satellite observations over Central Europe and Northern Africa during 2014 together with rigorous quality assurance leads to 399 liquid-only clouds and 95 ice-containing clouds that can be matched to surrounding n_CCN and n_INP, respectively, at cloud level. We use this initial data set for assessing the impact of changes in cloud-relevant aerosol concentrations on the cloud droplet number concentration (N_d) and effective radius (r_eff) of liquid clouds and the phase of clouds in the regime of heterogeneous ice formation. We find a Δ ln N_d/Δ ln n_CCN of 0.13 to 0.30 which is at the lower end of commonly inferred values of 0.3 to 0.8. The Δ ln r_eff/Δ ln n_CCN between -0.09 and -0.21 suggests that r_eff decreases by -0.81 to -3.78 nm per increase in n_CCN of 1 cm^-3. We also find a tendency towards more cloud ice and more fully glaciated clouds with increasing n_INP that cannot be explained by the increasingly lower cloud-top temperature of super-cooled liquid, mixed-phase, and fully glaciated clouds alone. Applied to a larger amount of observations, the C×C approach has the potential to enable the systematic investigation of warm and cold clouds. This marks a step change in the quantification of ERFaci from space.