Data-driven global ocean model resolving ocean-atmosphere coupling dynamics
This work addresses the need for efficient and accurate climate prediction models for researchers and policymakers, representing an incremental improvement by integrating existing deep learning techniques into ocean modeling.
The study tackled the challenge of extending deep learning-based predictions to subseasonal timescales by developing KIST-Ocean, a global ocean model that accurately simulates ocean-atmosphere coupling dynamics, such as capturing Kelvin and Rossby wave propagation and vertical motions induced by wind stress.
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 using a U-shaped visual attention adversarial network architecture. KIST-Ocean integrates partial convolution, adversarial training, and transfer learning to address coastal complexity and predictive distribution drift in auto-regressive models. Comprehensive evaluations confirmed the model's robust ocean predictive skill and efficiency. Moreover, it accurately captures realistic ocean response, such as Kelvin and Rossby wave propagation in the tropical Pacific, and vertical motions induced by cyclonic and anticyclonic wind stress, demonstrating its ability to represent key ocean-atmosphere coupling mechanisms underlying climate phenomena, including the El Nino-Southern Oscillation. These findings reinforce confidence in DL-based global weather and climate models and their extending DL-based approaches to broader Earth system modeling, offering potential for enhancing climate prediction capabilities.