Optimization of Model Parameters, Uncertainty Quantification and Experimental Designs for a Global Marine Biogeochemical Model

TL;DR

This paper presents methods for optimizing model parameters, quantifying uncertainties, and designing experiments for a global marine biogeochemical model, improving model accuracy and guiding future measurements.

Contribution

It introduces a comprehensive approach combining parameter estimation, uncertainty quantification, and experimental design tailored for marine biogeochemical modeling.

Findings

Enhanced model parameter accuracy with measurement data

Quantified uncertainties in model outputs due to measurement errors

Predicted the impact of new measurements on uncertainty reduction

Abstract

Methods for model parameter estimation, uncertainty quantification and experimental design are summarized in this paper. They are based on the generalized least squares estimator and different approximations of its covariance matrix using the first and second derivative of the model regarding its parameters. The methods have been applied to a model for phosphate and dissolved organic phosphorus concentrations in the global ocean. As a result, model parameters have been determined which considerably improved the consistency of the model with measurement results. The uncertainties regarding the estimated model parameters caused by uncertainties in the measurement results have been quantified as well as the uncertainties associated with the corresponding model output implied by the uncertainty in the model parameters. This allows to better assess the model parameters as well as the model output. Furthermore, it has been determined to what extent new measurements can reduce these uncertainties. For this, the information content of new measurements has been predicted depending on the measured process as well as the time and the location of the measurement. This is very useful for planning new measurements.