Estimating Carbon Pools in the European Shelf Sea Environment: Replacing Reanalysis by Model‐Informed Machine Learning?

Abstract Shelf seas are important for the economy and the carbon cycle, but shelf sea observations for carbon pools are often sparse or highly uncertain. An alternative can be provided by carbon reanalyses (whether assimilating proxy variables, such as chlorophyll‐, or directly carbon), but these are often expensive to run. We propose to use a computationally cheap ensemble of neural networks (i.e., deep ensemble) to learn the relationship between the directly observable (atmospheric, riverine, and ocean) variables and marine carbon pools from a coupled physics‐biogeochemistry model. The deep ensemble was trained on a North‐West European Shelf (NWES) physical‐biogeochemistry model free run simulation. After training, the deep ensemble was run using inputs from the NWES reanalysis instead of the free run, demonstrating that it can efficiently predict several NWES carbon pools (e.g., detritus, zooplankton, and heterotrophic bacteria) in much better agreement with the reanalysis than the free run, while also providing uncertainty information. We further show that the deep ensemble performs similarly well when it is driven directly by the observations assimilated into