Skillful Global Ocean Emulation and the Role of Correlation-Aware Loss
This work provides a more accurate ocean emulator for medium-range forecasting, benefiting climate and weather prediction communities.
The authors adapted GraphCast into an ocean-only emulator for medium-range predictions, achieving skillful forecasts for 10-15 day lead times. Using Mahalanobis distance as a loss function improved forecast skill over Mean Squared Error by accounting for correlations between target variables.
Machine learning emulators have shown extraordinary skill in forecasting atmospheric states, and their application to global ocean dynamics offers similar promise. Here, we adapt the GraphCast architecture into a dedicated ocean-only emulator, driven by prescribed atmospheric conditions, for medium-range predictions. The emulator is trained on NOAA's UFS-Replay dataset. Using a 24 hour time step, single initial condition, and without using autoregressive training, we produce an emulator that provides skillful forecasts for 10-15 day lead times. We further demonstrate the use of Mahalanobis distance as loss that improves the forecast skill compared to the Mean Squared Error loss by explicitly accounting for the correlations between tendencies of the target variables. Using spatial correlation analysis of the forecasted fields, we also show that the proposed correlation-aware loss acts as a statistical-dynamical regularizer for the slow, correlated dynamics of the global oceans, offering a better background forecast for downstream tasks like data assimilation.