Developing a data-driven global ocean model at ECMWF

Machine learning (ML) techniques have emerged as a powerful tool for predicting weather and climate systems, particularly in predicting the short-term evolution of the atmosphere. Here, we look at the potential for ML to predict the evolution of the 3d-ocean. 

We present a data-driven global ocean model, developed within the Destination Earth project, to form the ocean component of a fully data-driven earth system model. Following the skill shown by the AIFS (Lang et al, 2024), we use a graph-based encoder-decoder design, with a transformer backbone. Our model is trained on the ECMWF ORAS6 reanalysis dataset (Zuo et al, 2024). Work focuses on short-term predictions, up to a 2-week forecast period. The model predicts temperature, salinity, zonal and meridional current components throughout the full ocean depth, along with sea-surface height and sea-ice. 

In this presentation we will discuss the design choices of our network architecture, including comparisons between networks trained to predict future fields, and those trained to predict increments to fields. We will show results from our data-driven model and put these into the context of other similar models. 

Simon Lang, Mihai Alexe, Matthew Chantry, Jesper Dramsch, Florian Pinault, Baudouin Raoult, Mariana C. A. Clare, Christian Lessig, Michael Maier-Gerber, Linus Magnusson, Zied Ben Bouallègue, Ana Prieto Nemesio, Peter D. Dueben, Andrew Brown, Florian Pappenberger, and Florence Rabier (2024). AIFS – ECMWF’s data-driven forecasting system. arXiv preprint https://arxiv.org/abs/2406.01465.  

Hao Zuo, Magdalena Alonso-Balmaseda, Eric de Boisseson, Philip Browne, Marcin Chrust, Sarah Keeley, Kristian Mogensen, Charles Pelletier, Patricia de Rosnay, Toshinari Takakura (2024). ECMWF’s next ensemble reanalysis system for ocean and sea ice: ORAS6. ECMWF newsletter. https://doi.org/10.21957/hzd5y821lk