Calibrating ocean biogeochemistry (BGC) parameters in Earth System Models is challenging because there are multiple sources of error, and the parameters' sensitivities are interlinked. Reducing the bias in the ocean physical component of the Norwegian Earth System Model (NorESM) diminishes the BGC state bias at intermediate depth but leads to a greater bias increase near the surface. This suggests that BGC parameters are currently tuned to compensate for the ocean physics biases. We successfully apply the iterative ensemble smoother data assimilation technique to re-calibrate BGC parameters in NorESM with reduced bias in its ocean physics component.  We calibrated BGC parameters from the monthly climatological error of nitrate, phosphate, and oxygen in a coupled reanalysis of NorESM that assimilates monthly climatology of temperature and salinity.  First, we compare the performance of globally and spatially varying parameter estimations. Both approaches reduce BGC bias obtained with default parameters, even for variables not assimilated in the parameter estimation (such as CO2 fluxes and primary production). While spatial parameter estimation performs locally best, it also increases biases in areas with few observations, and overall performs poorer than global parameter estimation. A second iteration further reduces the bias in the near-surface BGC with global parameter estimation. Finally, we verify the global estimated parameters in a 30-year coupled reanalysis, which assimilates time-varying temperature and salinity observations. This reanalysis reduces error by  10-20% for phosphate, nitrate, oxygen, and dissolved inorganic carbon compared to a reanalysis done with default parameters.