Simultaneous Emulation and Downscaling With Physically Consistent Deep Learning‐Based Regional Ocean Emulators

Abstract Building upon recent advancements in AI‐driven atmospheric emulation, we present a novel framework for AI‐based ocean emulation, downscaling, and bias correction, with a specific focus on high‐resolution modeling of the regional ocean in the Gulf of Mexico. Emulating regional ocean dynamics poses distinct challenges due to intricate bathymetry, complex lateral boundary conditions, and inherent limitations of deep learning models, including instability and the potential for hallucinations. In this study, we introduce a deep learning framework that autoregressively integrates ocean surface variables at 8 km spatial resolution over the Gulf of Mexico, maintaining physical consistency over decadal time scales. Simultaneously, the framework downscales and bias‐corrects the outputs to 4 km resolution using a physics‐informed generative model. Our approach demonstrates short‐term predictive skill comparable to high‐resolution physics‐based simulations, while also accurately capturing long‐term statistical properties, including temporal mean and variability.