2025/10/14 by Pouria Behnoudfar, Behnoudfar, Pouria, Marc Bocquet +5
Decision Sciences · #Bridging (networking) #Data assimilation #Earth system science #Exploit #FOS: Computer and information sciences #Hierarchy #Machine Learning (cs.LG) #Probabilistic logic #Scientific Computing and Data Management #Statistical model
paper · pdf · doi:10.48550/arxiv.2510.13030
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/10/14 · openalex created_date 2025/10/17 · openalex updated_date 2026/08/05
Computer models are indispensable tools for understanding the Earth system. While high-resolution operational models have achieved many successes, they exhibit persistent biases, particularly in simulating extreme events and statistical distributions. In contrast, 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. By leveraging the complementary strengths of models of varying complexity, we develop an explainable AI framework for Earth system emulators. It 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. This work also highlights the importance of pushing idealized model development and advancing communication between modeling communities.