Data-driven global ocean model resolving atmospherically forced ocean dynamics

Artificial intelligence has advanced global weather forecasting, outperforming traditional numerical models in both accuracy and computational efficiency. Nevertheless, extending predictions beyond subseasonal timescales requires the development of deep learning (DL)–based ocean–atmosphere coupled models that can realistically simulate complex oceanic responses to atmospheric forcing. This study presents KIST-Ocean, a DL-based global three-dimensional ocean general circulation model. Comprehensive evaluations confirmed the model’s robust ocean predictive skill and efficiency. Moreover, it accurately reproduces realistic ocean responses, such as Kelvin and Rossby wave propagation, and vertical motions induced by rotational wind stress, demonstrating its ability to represent key ocean–atmosphere interactions underlying climate phenomena, including the El Niño–Southern Oscillation. These findings reinforce confidence in DL-based global weather and climate models by demonstrating their capacity to capture essential ocean-atmosphere relationships. Building upon this foundation, the present study paves the way for extending DL-based modeling frameworks toward integrated Earth system simulations, thereby offering substantial potential for advancing long-range climate prediction capabilities.