Building on top of the success in AI-based atmospheric emulation, we propose an AI-based ocean emulation and downscaling framework focusing on the high-resolution regional ocean over Gulf of Mexico. Regional ocean emulation presents unique challenges owing to the complex bathymetry and lateral boundary conditions as well as from fundamental biases in deep learning-based frameworks, such as instability and hallucinations. In this paper, we develop a deep learning-based framework to autoregressively integrate ocean-surface variables over the Gulf of Mexico at $8$ Km spatial resolution without unphysical drifts over decadal time scales and simulataneously downscale and bias-correct it to $4$ Km resolution using a physics-constrained generative model. The framework shows both short-term skills as well as accurate long-term statistics in terms of mean and variability.

本研究针对墨西哥湾高分辨率区域海洋仿真中的挑战，提出了一种基于深度学习的海洋仿真和降尺度框架。通过自回归整合海洋表面变量，该框架能够在不出现不自然漂移的情况下，实现8公里分辨率的仿真，并使用物理约束生成模型将其降尺度至4公里分辨率，展示出短期技能和准确的长期统计特性。

基于物理一致性的深度学习区域海洋仿真与降尺度的同时仿真