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Aerosol-cloud interactions include a myriad of effects that all begin whenaerosol enters a cloud and acts as cloud condensation nuclei (CCN). An increasein CCN results in a decrease in the mean cloud droplet size (r$_{e}$). Thesmaller droplet size leads to brighter, more expansive, and longer lastingclouds that reflect more incoming sunlight, thus cooling the earth. Globally,aerosol-cloud interactions cool the Earth, however the strength of the effectis heterogeneous over different meteorological regimes. Understanding howaerosol-cloud interactions evolve as a function of the local environment canhelp us better understand sources of error in our Earth system models, whichcurrently fail to reproduce the observed relationships. In this work we userecent non-linear, causal machine learning methods to study the heterogeneouseffects of aerosols on cloud droplet radius.

Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific