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

Anass El Aouni (Mercator Ocean International, Toulouse, France.) · Quentin Gaudel (Mercator Ocean international) · J. Emmanuel Johnson (University of Valencia) · REGNIER Charly (Mercator Ocean International) · Julien Le Sommer (CNRS) · Simon van Gennip (Mercator Ocean International) · ronan fablet (IMT Atlantique) · Marie Drevillon (Mercator Ocean International) · Yann DRILLET (mercator ocean international) · Pierre Le Traon (Mercator Ocean)
atmospheric forcingsdata-driven approachesdynamical consistencyfirst-guess trajectorieshigher-resolution physical analysisnowcastsobservational dataocean forecastingoperational physical ocean modelsphysical plausibilityprocess-oriented diagnosticsreanalysis datasetstandardized metrics

Data-driven approaches, particularly those based on deep learning, are rapidly advancing Earth system modeling. However, their application to ocean forecasting remains limited despite the ocean's pivotal role in climate regulation and marine ecosystems. To address this gap, we present OceanBench, a benchmark designed to evaluate and accelerate global short-range (1–10 days) data-driven ocean forecasting.OceanBench is constructed from a curated dataset comprising first-guess trajectories, nowcasts, and atmospheric forcings from operational physical ocean models, typically unavailable in public datasets due to assimilation cycles. Matched observational data are also included, enabling realistic evaluation in an operational-like forecasting framework.The benchmark defines three complementary evaluation tracks: (i) Model-to-Reanalysis, where models are compared against the reanalysis dataset commonly used for training; (ii) Model-to-Analysis, assessing generalization to a higher-resolution physical analysis; and (iii) Model-to-Observations, Intercomparison and Validation (IV-TT) CLASS-4 evaluation against independent observational data. The first two tracks are further supported by process-oriented diagnostics to assess the dynamical consistency and physical plausibility of forecasts.OceanBench includes key ocean variables: sea surface height, temperature, salinity, and currents, along with standardized metrics grounded in physical oceanography. Baseline comparisons with operational systems and state-of-the-art deep learning models are provided. All data, code, and evaluation protocols are openly available at https://github.com/mercator-ocean/oceanbench, establishing OceanBench as a foundation for reproducible and rigorous research in data-driven ocean forecasting.