How cloud droplet number concentration impacts liquid water path and precipitation in marine stratocumulus clouds - a satellite-based analysis using explainable machine learning

In this work, a data set comprised of satellite observations and reanalysis data is used in explainable machine learning models to analyse the relationship between the cloud droplet number concentration (N_d), cloud liquid water path (LWP) and the fraction of precipitating clouds (PF) in 5 distinct marine stratocumulus (MSC) regions.

Aerosol--cloud--precipitation interactions (ACI) are a known major cause of uncertainties in simulations of the future climate. An improved understanding of the in-cloud feedback processes accompanying ACI could help in advancing their implementation in global climate models. This is especially the case for marine stratocumulus clouds which constitute the most common cloud type globally.

The machine learning framework applied here makes use of Shapley additive explanation (SHAP) values, allowing to isolate the impact of N_d from other confounding factors which proved to be very difficult in previous satellite based studies.

All examined MSC regions display a decrease of PF and an increase in LWP with increasing N_d, despite marked inter-regional differences in the distribution of N_d. The negative N_d-PF relationship is stronger in high LWP conditions, while the positive N_d-LWP relationship is amplified in precipitating clouds. While these results for the N_d-LWP relationship differ from the findings in recent satellite-based global analyses, they are consistent with previous studies using model simulations. The results presented here indicate that precipitation suppression plays an important role in MSC adjusting to aerosol-driven perturbations in N_d.