Automatic reduction of ocean biogeochemical models: a case study with BFM (v5.3)

Modeling biogeochemical processes in ocean fluid dynamics simulations is computationally expensive, necessitating efficient model reduction techniques. Large-scale biophysical simulations, such as high-resolution large-eddy simulations (LES) of the upper ocean, require significant computing resources to capture small-scale turbulent processes while also resolving the evolution of reactive biogeochemical tracers. However, the complexity of existing biogeochemical models, such as the Biogeochemical Flux Model (BFM) which resolves 56 state variables, leads to unfeasibly high computational costs when represented in detailed LES. To address this, we applied model reduction techniques from the field of combustion to systematically reduce the complexity of the BFM while maintaining high fidelity. Specifically, we developed a modified version of the Directed Relation Graph with Error Propagation method and applied it to a 50-state-variable BFM. By analyzing 24 reduction scenarios, we produced five reduced models containing between 1 and 36 state variables capable of accurately capturing trends in concentration of the target fields. The results demonstrate the effectiveness of this reduction approach in preserving key biogeochemical dynamics while significantly reducing model size and complexity, paving the way for more efficient high-resolution ocean biogeochemical simulations.