Learning‐Based Calibration of Ocean Carbon Models to Tackle Physical Forcing Uncertainties and Observation Sparsity
2025
Littaye, J. | Fablet, Ronan | Memery, Laurent
Biogeochemical (BGC) ocean models are simplified representations of complex coupled processes, usually resulting in a large number of parameters, that need to be calibrated. In general, these parameters are constrained relying on incomplete and very heterogeneous sets of data. In addition, as biogeochemical tracers strongly depend on ocean circulation, the spatio‐temporal uncertainties in the physical forcing can bias the circulation, which makes the calibration of ocean carbon models challenging. This study addresses the calibration of ocean biogeochemical models when dealing with imperfect physical forcings and sparse observations. We design a numerical testbed based on a simple BGC box model. It comprises different uncertainty scenarios for the physical forcing as well as different observation configurations of the considered nutrient, phytoplankton, zooplankton, detritus dynamics. We propose and benchmark a learning‐based scheme against a variational data assimilation (DA) approach. The former frames the calibration as learning a neural operator between observations and model parameters. The experiments revealed that the DA‐based calibration is highly sensitive to imperfect physical forcing and limited observations, often leading to significant estimation errors in BGC parameters. Conversely, the learning‐based approach demonstrated a greater robustness in parameter estimation and simulated BGC patterns. We discuss further how these results could transfer to more realistic BGC models and real observing systems.
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