The aim of the project FORCeS is to understand the radiative forcing effect of anthropogenic aerosol in the climate system. The effects of aerosol are among the least understood components of the climate system; the reduction of this uncertainty requires a coordinated effort. In the European FORCeS project, coordinated by Stockholm University, models are evaluated and improved at various scales using insights gained from observations, also at various scales. Our role in the consortium is to contribute remote sensing observations, and to evaluate the sensitivities of climate system components to changes in the aerosol situation within remote sensing observations.

In two publications (Atmosphere 2022, Atmosphere 2024), we analyze the relationship between the cloud droplet number concentration (Nd), cloud liquid water path (LWP), and drizzle (D) in satellite observations of stratocumulus clouds using explainable machine learning. We consistently find a decrease of D and an increase in LWP with increasing Nd in all stratocumulus decks. The machine learning framework suggests that drizzle is suppression is particularly pronounced in high LWP conditions, whereas LWP increases with Nd are amplified in precipitating clouds. Our findings indicate that precipitation suppression plays an important role in marine stratocumulus clouds adjusting to aerosol-driven perturbations in Nd.