PE-TA-ViT: Dual-Resolution Residual Forecasting of Global Surface and Near-Surface Ocean States

Accurate and computationally efficient forecasting of global surface and near-surface ocean states remains challenging in contemporary oceanography. High-resolution numerical models can better resolve mesoscale dynamics but incur prohibitive computational costs, whereas coarse-resolution models often suffer from biases associated with unresolved subgrid-scale processes. Here, we propose a dual-resolution deep learning framework for forecasting global surface and near-surface ocean states, with near-surface variables represented at 0.49 m depth. The framework adopts a residual reconstruction paradigm in which a Physics-Enhanced Time-Aware Vision Transformer (PE-TA-ViT), incorporating physics-guided inductive biases and adaptive temporal conditioning, predicts coarse-scale spatiotemporal increments at $1^{\circ }$ resolution, which are subsequently interpolated to $1/4^{\circ }$ and added to the evolving high-resolution background state. Over a 9-day forecast horizon, PE-TA-ViT reduces the average Root Mean Square Error (RMSE) relative to persistence by 6.0% to 24.8% across the evaluated ocean variables. It also extends the useful forecast window (ACC $>$ 0.6) for highly dynamic velocity fields by approximately one day. The resulting forecasts retain transient mesoscale structures and large-scale flow patterns, with the deviation in the global energy cascading fraction remaining below 0.009. This framework enables efficient high-resolution forecasting of global surface and near-surface ocean states.