This paper is mainly the brilliant work of Pouria Behnoudfar, Charlotte Moser, and Nan Chen at the University of Madison, Wisconsin, USA. The idea is to drive a high-resolution climate model with a principled idealised model of some phenomenon such as ENSO. Information is exchanged between both models within a latent space where data assimilation is implemented. The latent space is implemented using machine learning (an auto encoder). Since the idealised model sheds light onto the climate phenomenology of the operational model, the method can be seen as an explainable AI framework.

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.

The paper is entitled Bridging idealized and operational models: an explainable AI framework for Earth system emulators, and is published in npj Climate and Atmsopheric Science.