Using a global aerosol model to interpret satellite-derived aerosol-cloud interactions

Determining the susceptibilities of cloud properties to perturbations in aerosols has been a persisting challenge in climate research. For example, satellite-retrieved susceptibility of cloud droplet number concentration (CDNC) to changes in cloud condensation nuclei (CCN) varies regionally, also having opposite correlations over land and ocean. Over the oceans, the correlation between CCN and CDNC is positive and in many cases, with a proper regression method, the dlogCDNC/dlogCCN exceeds 1. On the other hand, over land, many studies have found a negative correlation. As our preliminary global climate model simulations give qualitatively similar results to satellite retrievals, we have used the model together with reanalysis data of aerosol, meteorological properties, and cloud properties to interpret how other parameters such as cloud activation updrafts and vertical mixing of aerosol affect the satellite derived CCN vs CDNC correlations. In this study, we focus on ocean regions determining how these different atmospheric properties affect the derived slope between CCN and CDNC. Our results indicate that as satellite derived CCN is a columnar value, does not properly represent the true variability of cloud base CCN. Thus, the mixing of aerosol as well as cloud activation updrafts cause biases in the satellite determined CCN vs CDNC correlations.