Surrogate-based optimization of climate model parameters using response correction

We present a computationally efficient methodology for the optimization of climate model parameters applied to a (one-dimensional) representative of a class of marine ecosystem models. We use a response correction technique to create a surrogate from a temporarily coarser discretized physics-based low-fidelity model. We demonstrate that replacing the direct parameter optimization of the high-fidelity ecosystem model by iteratively updating and re-optimizing the surrogate leads to a very satisfactory solution while yielding significant cost saving – about 84% when compared to the direct high-fidelity model optimization.

► Computationally efficient methodology for optimization of climate model parameters. ► We use a response correction technique to create a surrogate. ► The optimization based on this surrogate leads to a very satisfactory solution. ► Our approach leads to significant cost saving.