Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Volume 10, issue 2
Biogeosciences, 10, 1169–1182, 2013
https://doi.org/10.5194/bg-10-1169-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Biogeosciences, 10, 1169–1182, 2013
https://doi.org/10.5194/bg-10-1169-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 21 Feb 2013

Research article | 21 Feb 2013

Reducing the model-data misfit in a marine ecosystem model using periodic parameters and linear quadratic optimal control

M. El Jarbi1, J. Rückelt1, T. Slawig1, and A. Oschlies2 M. El Jarbi et al.
  • 1Institute for Computer Science, Cluster The Future Ocean, Christian-Albrechts Universität zu Kiel, 24098 Kiel, Germany
  • 2Helmholtz Centre for Ocean Research Kiel (GEOMAR), 24148 Kiel, Germany

Abstract. This paper presents the application of the Linear Quadratic Optimal Control (LQOC) method to a parameter optimization problem for a one-dimensional marine ecosystem model of NPZD (N for dissolved inorganic nitrogen, P for phytoplankton, Z for zooplankton and D for detritus) type. This ecosystem model, developed by Oschlies and Garcon, simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column and is driven by ocean circulation data. The LQOC method is used to introduce annually periodic model parameters in a linearized version of the model. We show that the obtained version of the model gives a significant reduction of the model-data misfit, compared to the one obtained for the original model with optimized constant parameters. The found inner-annual variability of the optimized parameters provides hints for improvement of the original model. We use the obtained optimal periodic parameters also in validation and prediction experiments with the original non-linear version of the model. In both cases, the results are significantly better than those obtained with optimized constant parameters.

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