Data-driven ensemble prediction of the global ocean

• URL: http://arxiv.org/abs/2603.19591v1
• Date: Fri, 20 Mar 2026 03:05:41 GMT
• Title: Data-driven ensemble prediction of the global ocean
• Abstract summary: FuXi-ONS is the first machine-learning ensemble forecasting system for the global ocean.<n>It provides 5-day forecasts on a global 1 grid up to 365 days for sea-surface temperature, sea-surface height, subsurface temperature, salinity and ocean currents.<n>FuXi-ONS learns physically structured perturbations and incorporates an atmospheric encoding module to stabilize long-range forecasts.
• Score: 9.068376705993009
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-driven models have advanced deterministic ocean forecasting, but extending machine learning to probabilistic global ocean prediction remains an open challenge. Here we introduce FuXi-ONS, the first machine-learning ensemble forecasting system for the global ocean, providing 5-day forecasts on a global 1° grid up to 365 days for sea-surface temperature, sea-surface height, subsurface temperature, salinity and ocean currents. Rather than relying on repeated integration of computationally expensive numerical models, FuXi-ONS learns physically structured perturbations and incorporates an atmospheric encoding module to stabilize long-range forecasts. Evaluated against GLORYS12 reanalysis, FuXi-ONS improves both ensemble-mean skill and probabilistic forecast quality relative to deterministic and noise-perturbed baselines, and shows competitive performance against established seasonal forecast references for SST and Niño3.4 variability, while running orders of magnitude faster than conventional ensemble systems. These results provide a strong example of machine learning advancing a core problem in ocean science, and establish a practical path toward efficient probabilistic ocean forecasting and climate risk assessment.

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