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Explore how Earth System Models (ESMs) can be utilized to assess regional climate responses to global climate change in this conference talk. Learn about the fundamental principles of ESMs as simplified models that simulate various aspects of the Earth System, including climate, hydrology, ecology, biogeochemistry, and human activities at global scales with applications to local and regional dynamics. Discover the essential role these models play in simulating carbon cycles and environmental conditions crucial for understanding climate-ecosystem variability and change. Understand the computational challenges and parameterization limitations that ESMs face, particularly for localized processes, and how these issues can lead to inaccurate simulations. Examine the potential of Machine Learning approaches, including Physics-Inspired Neural Networks (PINN) and Generative Modeling, to develop low-cost surrogate models and improve parameterization accuracy. Gain insights into how Explainable AI approaches can extract new understanding from ESM simulations and explore the growing adoption of data-driven models in weather forecasting powered by deep learning technologies such as Transformer-based models and Graph Neural Networks.
