Can GraphCast learn skillful subseasonal-to-seasonal global ocean forecasting using the ARCO-OCEAN testbed?

Data-driven models promise higher-fidelity Earth system forecasts at a fraction of the computational cost of numerical models, enabling the use of large ensembles for more robust statistics. Consequently, the number of purely data-driven atmospheric models has grown explosively in recent years. However, the sheer diversity of architectures and the absence of a clear "winner" pose a significant design challenge for those seeking to replicate these successes in oceanography.

GraphCast was one of the first models in this arena and remains state-of-the-art. Based on graph neural networks, it lacks specific atmospheric inductive biases, such as fixed physical dimensions, conservation laws, or explicit evolution equations. Its only relational inductive bias is the physical proximity between interacting elements. When provided with an appropriate graph, this principle should hold equally well for the ocean, making GraphCast an ideal candidate for cross-domain application.

To test this hypothesis, we introduce ARCO-OCEAN: a new Analysis-Ready, Cloud-Optimized curated dataset designed for training such models. ARCO-OCEAN contains global reanalyses and hindcasts of multiple Earth system components, including ocean physical state, waves, sea ice, and atmospheric/hydrological forcing. Widely available through the AWS Open Data program, this dataset decouples AI/ML-related methodological development from domain-specific scientific knowledge (e.g., variable selection, spatial and temporal resolution) and data engineering (e.g., choice of format, chunking), relieving data scientists of the heavy burden of data preparation.

We detail the specific design choices of ARCO-OCEAN intended for coupled atmosphere-ocean modeling at subseasonal-to-seasonal timescales. Finally, by equipping GraphCast with land-masking capabilities and a global ocean mesh graph, we present preliminary results on its training performance within the ocean domain.