Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
20 Jun 2017
Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Geoscientific Model Development (GMD) and is expected to appear here in due course.
Error assessment of biogeochemical models by lower bound methods
Volkmar Sauerland1, Ulrike Löptien2, Claudine Leonhard1, Andreas Oschlies2, and Anand Srivastav1 1Department of Computer Science, Kiel University, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
2GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
Abstract. Biogeochemical models, capturing the major feedbacks of the pelagic ecosystem of the world ocean, are today often embedded into Earth System models which are increasingly used for decision making regarding climate policies. These models contain poorly constrained parameters (e.g., maximum phytoplankton growth rate) which are typically adjusted until the model shows a reasonable behavior. Systematic approaches determine these parameters by minimizing the misfit between the model and observational data. In most common model approaches, however, the underlying functions mimicking the biogeochemical processes are non-linear and non-convex. Thus, systematic optimization algorithms are likely to get trapped in a local minimum and might lead to non-optimal results. To judge the quality of an obtained parameter estimate, we propose to determine a preferably large lower bound for the global optimum, that is relatively easy to obtain and that will help to assess the quality of an optimum, generated by an optimization algorithm. Due to the unavoidable noise component in all observations, such a lower bound is typically larger than zero. We suggest to derive such lower bounds based on typical properties of biogeochemical models (e.g., a limited number of extremes and a bounded time-derivative). We evaluate this approach with synthetic observations and demonstrate a real-world example, consisting of phytoplankton observations in the Baltic Sea.
Citation: Sauerland, V., Löptien, U., Leonhard, C., Oschlies, A., and Srivastav, A.: Error assessment of biogeochemical models by lower bound methods, Geosci. Model Dev. Discuss.,, in review, 2017.
Volkmar Sauerland et al.
Volkmar Sauerland et al.


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Short summary
We presented a concept to prove that a parametric model is well calibrated, i.e., that changes of its free parameters cannot lead to a much better model-data misfit anymore. The intention is motivated by the fact that calibrating global biogeochemical ocean models is important for assessment and inter-model comparison but computationally expensive.
We presented a concept to prove that a parametric model is well calibrated, i.e., that changes...