OceanBench: A Benchmark for Data-Driven Global Ocean Forecasting systems

Data-driven approaches, particularly deep learning, are rapidly transforming earth system modeling. OceanBench has established a standardized benchmark for global short-range data-driven ocean forecasting, providing operationally consistent datasets and evaluation protocols that support reproducible development and assessment of ML-based ocean forecasting systems.

Building on this foundation, we introduce new extensions to OceanBench that broaden its accessibility and applicability under realistic computational constraints. These include the integration of coarser-resolution (~1°) global models, enabling computationally efficient experimentation, regional evaluation capabilities, and the inclusion of new candidate models spanning both physics-based and machine-learning approaches. By supporting multiple resolutions and modeling paradigms, the extended OceanBench framework enables more flexible and application-relevant assessment of ocean forecasts, accelerating research and operational adoption of data-driven and hybrid ocean modeling systems.