Deep learning-Based Global Ocean prediction model on the HEALPix Mesh

While deep learning-based atmospheric have been actively developed, in contrast, the development of ocean prediction models which allows multi-decade simulations through the autoregressive operation has been largely limited. This study developed a deep learning-based global ocean prediction model using the HEALPix grid system that capable of multi-decades integration in daily time step by successfully reproducing the observed global ocean statistics. Model training uses Fourier amplitude and phase losses to preserve low-frequency spatial structure and phase consistency, batch anomaly loss to learn anomalous variability, and sequentially ingests past-to-present atmospheric forcing to enable physically consistent coupled atmosphere–ocean dynamics in long-term integration. Long-term ocean model integration experiments with the observed atmospheric forcing demonstrate drift-free stable climatology for 20-yr simulations, with realistic Niño3.4 variations and ENSO-related global oceanic anomaly patterns consistent with observations. Furthermore, oceanic subsurface temperature responses to the westerly wind bursts (WWBs) over the equatorial western Pacific successfully capture the eastward propagation properties associated with the oceanic Kelvin waves.