Attributing Long‐Term Trends in Marine Low Cloud Morphologies to Aerosols and Large‐Scale Meteorology With Deep Learning

Abstract The response of marine low‐cloud mesoscale morphologies to climate change and emission reductions remains poorly understood. Here, we link long‐term trends in six cloud morphologies to variations in large‐scale meteorology and aerosols. The trends show strong spatial heterogeneity, with closed and disorganized mesoscale cellular convection decreasing in the Northeast Pacific and Southeast Atlantic. We develop a deep learning model (UMorNet) to predict instantaneous cloud morphologies from meteorology and cloud droplet number concentration ( N d ), a proxy for aerosols. UMorNet achieves an average test accuracy of 0.55 and captures spatial patterns of climatology and long‐term trends. Out‐of‐sample test with a marine heatwave event further demonstrates the model's performance. Sensitivity experiments identify N d , marine cold‐air outbreak index, sea surface temperature, and inversion strength as key drivers. Different responses of clustered Cu and suppressed Cu to N d was identified. These findings highlight the potential role of aerosols in shaping cloud morphological changes.