Optimisation of the World Ocean Model of Biogeochemistry and Trophic-dynamics (WOMBAT) using surrogate machine learning methods
Abstract. The introduction of new processes in biogeochemical models brings new model parameters that must be set. Optimisation of the model parameters is crucial to ensure model performance based on process representation, rather than poor parameter values. However, for most biogeochemical models, standard optimisation techniques are not viable due to computational cost. Typically, (tens of) thousands of simulations are required to accurately estimate optimal parameter values of complex non-linear models. To overcome this persistent challenge, we apply surrogate machine learning methods to optimise the model parameters of a new version of the World Ocean Model of Biogeochemistry and Trophic dynamics (WOMBAT), which we call WOMBAT-lite. WOMBAT-lite has undergone numerous updates described herein with many new model parameters to prescribe. A computationally inexpensive surrogate machine learning model based on Gaussian Process Regression was trained on a set of 512 simulations with WOMBAT-lite. These simulations explored model fidelity to 8 observation-based target datasets by varying 26 uncertain parameters across their a priori ranges. The surrogate model, trained on these 512 simulations, facilitated a global sensitivity analysis to identify the most important parameters and facilitated Bayesian parameter optimisation. Our approach returned optimal posterior distributions of 13 important parameters that, when input to WOMBAT-lite, ensured excellent fidelity to the target datasets. This process improved the representation of chlorophyll-a concentrations, air-sea carbon dioxide fluxes and patterns of phytoplankton nutrient limitation. We present an optimal parameter set for use by the modelling community. Overall, we show that surrogate-based calibration can deliver optimal parameter values for the biogeochemical components of earth system models and can improve the simulation of key processes in the global carbon cycle.