Affiliations: [a] Institute for Computer Science, Cluster The Future Ocean, Christian-Albrechts Universität zu Kiel, Kiel, Germany | [b] Engineering Optimization and Modeling Center, School of Science and Engineering, Reykjavik University, Reykjavik, Iceland | [x] Engineering Optimization and Modeling Center, School of Science and Engineering, Reykjavik University, Reykjavik, Iceland | [y] Mathematics and Scientific Computing, National Physical Laboratory, Teddington, UK
Correspondence:
[*]
Corresponding author: M. Prieß, Institute for Computer Science, Cluster The Future Ocean, Christian-Albrechts Universität zu Kiel, 24098 Kiel, Germany. Tel.: +49 431 880 7452; Fax: +49 431 880 7618; E-mail: mpr@informatik.uni-kiel.de.
Abstract: We present initial steps and first results of a surrogate-based optimization (SBO) approach for parameter optimization in climate models. In SBO, a computationally cheap, but yet reasonably accurate representation of the original high-fidelity (or fine) model, the so-called surrogate, replaces the fine model in the optimization process. We choose two representatives, namely two marine ecosystem models, to verify our approach. We present two ways to obtain a physics-based low-fidelity (or coarse) model, one based on a coarser time discretization, the other on an inaccurate fixed point iteration. Since in both cases, the low-fidelity model is less accurate, we use a multiplicative response correction technique, aligning the low- and the high-fidelity model output to obtain a reliable surrogate at the current iterate in the optimization process. We verify the approach by using model generated target data. We show that the proposed SBO method leads to a very satisfactory solution at the cost of a few evaluations of the high-fidelity model only.
