Data-driven global ocean model resolving ocean-atmosphere coupling dynamics

TL;DR

This paper introduces KIST-Ocean, a deep learning-based global ocean model that effectively simulates ocean-atmosphere interactions and improves climate prediction accuracy, especially for phenomena like El Nino.

Contribution

The study develops a novel DL-based 3D ocean model with attention and adversarial training, advancing ocean-atmosphere coupling simulation beyond traditional methods.

Findings

Accurately captures Kelvin and Rossby wave propagation.

Demonstrates realistic vertical motions from wind stress.

Shows robust predictive skill and efficiency.

Abstract

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.