This project will advance our capabilities to understand and predict two critical aspects of the Earth system by pioneering a new approach driven by artificial intelligence (AI). For decades, the complexity of the underlying physics has limited progress in quantifying the cooling effect due to atmospheric aerosols and their effect on clouds and the rate of Arctic sea ice melt. This project tackles these challenges by using advanced AI to learn directly from satellite observations and laboratory data, developing more accurate and reliable computer simulations. These will in turn result in improved predictive capabilities that are vital for U.S. strategic interests, for example, as a warming Arctic opens new maritime shipping routes essential for commerce and security. More reliable environmental intelligence will support better-informed decisions for infrastructure planning and risk assessment. The project will also make all its AI tools and software openly available and will train a new generation of researchers in these cutting-edge methods. To address current limitations in Earth System Models (ESMs), this project will develop and implement novel parameterizations for aerosol-cloud interactions (ACI) and Arctic sea ice thermodynamics. The research will leverage AI, specifically a novel framework called Ensemble Kalman Diffusion Guidance (EnKG), to learn from a diverse range of observational and laboratory data. For ACI, the project will develop new data-driven models for