Optimizing a large number of parameters in a Biogeochemical Model: A Multi-Variable BGC-Argo Data Assimilation Approach

The predictive accuracy of marine biogeochemical models is fundamentally limited by uncertainty in their parameter values. We present a parameter optimization framework using iterative Importance Sampling (iIS) to constrain the PISCES model by leveraging the rich, multi-variable dataset provided by Biogeochemical-Argo (BGC-Argo) floats. Using data from a BGC-Argo float in the North Atlantic, we assimilate a comprehensive suite of 20 biogeochemical metrics to constrain all 95 parameters of the PISCES model within a 1D vertical configuration. Our global sensitivity analysis (GSA) identifies parameters controlling zooplankton dynamics as the dominant source of model sensitivity for this specific site. We compare three strategies: (1) optimizing a subset of parameters for their strong direct influence (Main effects); (2) optimizing a larger subset that also includes parameters influential through non-linear interactions (Total effects); and (3) simultaneously optimizing all 95 parameters. All three approaches achieve a statistically indistinguishable and significant improvement in model skill, reducing Normalized Root Mean Square Error (NRMSE) by 54–56 %. The rich, multi-variable dataset provides sufficient orthogonal constraints to yield posterior parameter distributions with negligible inter-correlation, shifting the long-standing challenge of correlated equifinality to uncorrelated equifinality, where a range of optimal parameter sets can be found independently. Parameter uncertainty is reduced by 16–41 %, and the optimized ensembles demonstrate strong portability. While all strategies produce a similar, tightly constrained predictive spread for the assimilated variables, they differ significantly in computational cost and in their estimation of uncertainty for unobserved parts of the model. The prerequisite GSA was ~40 times more computationally expensive than the optimization, while the All-parameters strategy, by exploring the full parameter space, provides a more comprehensive and robust quantification of the model's uncertainty in unassimilated variables. We therefore conclude that directly optimizing all model parameters is the recommended strategy. This work delivers a validated, parameter set for the North Atlantic and demonstrates a scalable framework to advance biogeochemical modeling from using static, globally-uniform parametrization to developing a map of regionally-tuned parameters.