Graduate Student / Postdoc Seminar

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Bridging idealized and operational models: an explainable AI framework for Earth system emulators

Speaker: Pouria Behnoudfar, UW Madison

Location: Warren Weaver Hall 1302

Date: Friday, April 10, 2026, 1 p.m.

Synopsis:

Computational models are indispensable for understanding complex dynamical systems. High-fidelity operational models offer rich resolution and comprehensive state descriptions but often suffer from persistent biases, particularly in extreme events and long-term statistics. At the other end of the spectrum, coarse-grained idealized models isolate fundamental processes and can be precisely calibrated to excel in characterizing specific dynamical and statistical features. However, different models remain siloed by disciplinary boundaries. In this talk, we present an explainable AI framework that bridges the model hierarchy through a reconfigured latent data assimilation technique, uniquely suited to exploit the sparse output from the idealized models. The resulting bridging model inherits the high resolution and comprehensive variables of operational models while achieving global accuracy enhancements through targeted improvements from idealized models. Crucially, the mechanism of AI provides a clear rationale for these advancements, moving beyond black-box correction to physically insightful understanding in a computationally efficient framework that enables effective physics-assisted digital twins and uncertainty quantification. We demonstrate its power by significantly correcting biases in CMIP6 simulations of El Niño spatiotemporal patterns, leveraging statistically accurate idealized models.