A semi-automated sensitivity-based approach for simplifying marine biogeochemical models for targeted applications: A case study with the Eco3M-MED model

Marine biogeochemical models are being increasingly used to support scenario-based analyses of climate change and ecosystem dynamics. However, their high structural complexity and large parameter space often limit computational efficiency, interpretability, and adaptability in applications requiring the exploration of many scenarios. To address these issues, we propose a Semi-Automated Iterative Simplification (SAIS) approach that integrates local sensitivity analysis with model mechanistic guidance and Kling–Gupta Efficiency (KGE) metrics to evaluate each simplification step. Using the marine biogeochemical model Eco3M-MED as an example, we specified three objectives for model simplification: (1) fidelity of state variables, (2) fidelity of marine ecosystem indicators, and (3) applicability for coupling with higher trophic level models. For each objective, we assessed model sensitivity to parameters and applied the SAIS approach to simplify the model, and obtained three simplified models. KGE-based fidelity evaluations are used to validate each final simplified model against the reference model. The results show that computational time can be reduced by up to approximately 30% without compromising the model’s mechanistic foundation. Overall, this method offers a flexible and scalable approach for generating simplified versions of complex biogeochemical models, suitable for applications in regional marine ecosystem assessments, climate scenario explorations, and model coupling frameworks. • A semi-automated approach to reduce model complexity is described. • Sensitivity analysis with parameter-dependency constraints guides simplification. • Three simplified models obtained for three application objectives. • Up to 30% reduction in computational cost without loss of key mechanisms. • Up to 26% reduction in parameters that are used in model computation.