Marine ecosystem model calibration with real data using enhanced surrogate-based optimization

We have already shown in a previous methodological work that the surrogate-based optimization (SBO) approach can be successful and computationally very efficient when reconstructing parameters in a typical nonlinear, time-dependent marine ecosystem model, where a one-dimensional application has been considered to test the method's functionality in a first step. The application on real (measurement) data is covered in this paper. Essential here are a special model data treatment and further methodological enhancements which allow us to improve the robustness of the algorithm and the accuracy of the solution. By numerical experiments, we demonstrate that SBO is able to yield a solution close to the original model's optimum while time savings are again up to 85% when compared to a conventional direct optimization of the original model.