A realistic representation of ocean carbon exchanges through a biogeochemical (BGC) model depends heavily on its parameterisation. However, this calibration is often hindered by an inaccurate representation of small-scale ocean physical dynamics, which are common in physical reanalysis. Here, a novel learning-based method enables a robust estimation of BGC states and parameters, and correction of physical forcing, despite physical forcing uncertainties and sparse observations.