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

TL;DR

AxiomOcean is a novel AI model that explicitly captures the three-dimensional structure of the upper ocean, significantly improving short-term forecast accuracy and physical realism over existing models.

Contribution

It introduces a 3D encoder-decoder architecture that jointly predicts temperature, salinity, and currents, enhancing forecast skill and physical consistency in global ocean modeling.

Findings

AxiomOcean reduces 1-day RMSE by 20-35% compared to previous models.

It better preserves eddy kinetic energy and variance in temperature and salinity.

The model performs well across different ocean regions, including the equatorial Pacific 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{\deg} 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.