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Parameter identification for marine ecosystem models is important for theassessment and validation of marine ecosystem models against observationaldata. The surrogate-based optimization (SBO) is a computationally efficientmethod to optimize complex models. SBO replaces the computationally expensive(high-fidelity) model by a surrogate constructed from a less accurate butcomputationally cheaper (low-fidelity) model in combination with an appropriatecorrection approach, which improves the accuracy of the low-fidelity model. Toconstruct a computationally cheap low-fidelity model, we tested three differentapproaches to compute an approximation of the annual periodic solution (i.e., asteady annual cycle) of a marine ecosystem model: firstly, a reduced number ofspin-up iterations (several decades instead of millennia), secondly, anartificial neural network (ANN) approximating the steady annual cycle and,finally, a combination of both approaches. Except for the low-fidelity modelusing only the ANN, the SBO yielded a solution close to the target and reducedthe computational effort significantly. If an ANN approximating appropriately amarine ecosystem model is available, the SBO using this ANN as low-fidelitymodel presents a promising and computational efficient method for thevalidation.

Surrogate-based optimization using an artificial neural network for a parameter identification in a 3D marine ecosystem model