Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea

Abstract. Mathematical models are an invaluable tool for understanding marine ecosystems. The performance of these models is often highly dependent on their parameters. Traditionally, refining these models has involved a time-consuming trial-and-error approach to identify model parameter values that are able to reproduce observations well. However, as ecosystem models become more complex, this approach becomes impractical. With advances in computing power, optimization techniques have emerged as a viable alternative. Nevertheless, these techniques often exhibit model-specific customization, which limits their broader application. In this study, we present a parameterization toolbox based on the particle swarm optimization (PSO) algorithm, implemented within the Framework for Aquatic Biogeochemical Models (FABM). This implementation encapsulates the optimizer – that is, the toolbox itself – as a reusable component, enabling its integration across multiple models within the framework and thus making it more accessible to the community. The effectiveness of the PSO toolbox is showcased through its application in a 1D physical–biogeochemical model (GOTM–ECOSMO E2E), which successfully parameterized the Sylt–Rømø Bight ecosystem. The toolbox was able to define optimal values for most of the tuned parameters and to suggest potential ranges for poorly constrained parameters. In addition, the toolbox uncovers a number of parameter sets with notable differences in some parameter values but leading to only minor variations in biomass and fluxes. Furthermore, by experimenting with optimization models of varying complexity, the toolbox was able to define an optimal model for the Sylt–Rømø Bight.