Regime Classification and AI-Enhanced Causal Analysis of Aerosol-Cloud Interactions Based on Long-Term Observations

Despite decades of research and progress, climate models still suffer from large uncertainty in estimated aerosol indirect effects and large discrepancy with observations. Understanding of aerosol-cloud interactions (ACI) and their representation in climate models still pose vital challenges even for the simplest of all clouds – warm liquid clouds. In particular, different, even opposite, results have been reported in different studies of both the first and second aerosol indirect effects, awaiting physical explanation. This study conducts systematic classification of ACI regimes by analyzing decade-long surface-based measurements collected by the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) facility under the frame of Twomey vs anti-Twomey and Albrecht vs. anti-Albrecht effects. Inspection of confounding factors (e.g., vertical motion, entrainment, decoupling, and stability) and potential micro-macro interactions will also made to provide physical understanding of the occurrence of the different ACI regimes, together with  AI-based causal analysis.