Guiding Machine-Learned Biogeochemical Forecasts with Observations

Climate change is reshaping ocean ecosystems faster than we can monitor them. Predicting shifts in productivity, carbon uptake, and oxygen levels requires forecasting interacting biogeochemical variables, a task where traditional process-based models struggle with computational cost and parameter uncertainty.  

BG4Sea is a machine-learned seasonal forecast that was trained on Mercator Océan's operational biogeochemical analysis. The model can generate skillful seasonal predictions of the carbon cycle, nutrients, oxygen, pH, chlorophyll, and plankton dynamics at a fraction of the computational cost, all while remaining competitive even at longer forecasting horizons. However, while the model demonstrates skill when evaluated against reanalysis data, this is likely to share the parametrization assumptions and constraints that are characteristic of process-based models.

This contribution explores strategies for evaluating against real-world measurements and for using observations to guide and constrain the model. We investigate “global-first” approaches, which prioritize remote-sensing data, as well as “regional-first” approaches, which use the model’s grid-independent structure to produce region-specific updates from in-situ stations.