AxiomOcean: Forecasting the Three-Dimensional Structure of the Upper Ocean

2026-05-11

AI Summary

This study addresses the challenge that existing AI-based ocean forecasting models struggle to preserve the three-dimensional structure of the upper ocean, often yielding overly smoothed subsurface features and insufficient physical consistency. To overcome this limitation, the authors propose AxiomOcean, a global AI ocean forecasting model that explicitly represents vertical water-column layers and their interdependencies within a deep learning framework for the first time. Built upon a fully three-dimensional encoder–backbone–decoder architecture and incorporating atmospheric forcing inputs, AxiomOcean jointly predicts global temperature, salinity, and three-dimensional currents at 1/12° resolution down to 643 meters depth. The model achieves a 20%–35% reduction in RMSE over 10-day forecasts, substantially improves anomaly correlation, and effectively retains eddy kinetic energy and temperature-salinity variance, with particularly notable performance in the equatorial Pacific, Kuroshio Extension region, and Southern Ocean.

Abstract

Short-term ocean forecast skill depends strongly on the three-dimensional ocean structure of the upper ocean, which governs stratification, subsurface heat storage, and the response of the ocean to atmospheric forcing. However, AI ocean forecasting models often fail to preserve this vertical structure, resulting in over-smoothed subsurface features and weak physical consistency under strong forcing. Here, we present AxiomOcean, a global AI ocean forecasting model that explicitly represents vertical hierarchy and cross-layer dependence within the water column. By combining a fully three-dimensional encoder-backbone-decoder architecture with surface atmospheric forcing, AxiomOcean jointly predicts upper-ocean temperature, salinity, and three-dimensional currents at global 1/12° resolution down to 643 m depth. In 10-day forecasts, AxiomOcean outperforms an advanced AI comparison model across variables and lead times, reducing day-1 RMSE by approximately 20 to 35% while maintaining higher anomaly correlation. The gain is not achieved through excessive smoothing: AxiomOcean better preserves eddy kinetic energy, temperature and salinity variance. Its advantage also extends through the water column and remains evident across the equatorial Pacific, Kuroshio Extension, and Southern Ocean, yielding a more realistic reconstruction of upper-ocean heat content. These results show that explicitly preserving upper-ocean three-dimensional structure can improve both forecast accuracy and physical fidelity in AI ocean prediction.

Problem

• ocean forecasting
• three-dimensional structure
• vertical hierarchy
• physical consistency
• subsurface features

Innovation

• three-dimensional ocean structure
• vertical hierarchy
• AI ocean forecasting
• physical consistency
• eddy kinetic energy