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Learn how artificial intelligence can revolutionize Earth system modeling by addressing fundamental limitations in aerosol and cloud microphysics representations. Explore the replacement of simplified parameterizations with fast, accurate, data-driven surrogates for aerosol optics, droplet nucleation, and warm-rain processes while maintaining physical constraints. Discover methods for using observational data to investigate rapid cloud adjustments to aerosol perturbations and disentangle aerosol-cloud causal relationships from meteorological co-variability. Examine the critical role of large, AI-ready libraries derived from observations and high-resolution simulations as training and validation datasets, with emphasis on sampling across different regimes. Understand how changes in aerosol and cloud microphysical representations propagate to top-of-atmosphere radiative fluxes through a hybrid Earth system modeling framework, focusing on observational consistency and uncertainty reduction in Earth system predictions. Gain insights into emerging best practices for incorporating AI into Earth system modeling and prediction, and learn approaches for bringing Earth system science to AI to accelerate scientific discovery and improve predictability in climate modeling.
