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Ocean Science An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 4
Ocean Sci., 8, 683–701, 2012
https://doi.org/10.5194/os-8-683-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: The MyOcean project: scientific advances for operational ocean...

Ocean Sci., 8, 683–701, 2012
https://doi.org/10.5194/os-8-683-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 30 Aug 2012

Research article | 30 Aug 2012

Assessment of a physical-biogeochemical coupled model system for operational service in the Baltic Sea

Z. Wan1, J. She1, M. Maar2, L. Jonasson1, and J. Baasch-Larsen3 Z. Wan et al.
  • 1Centre for Ocean and Ice, Danish Meteorological Institute, Lyngbyvej 100, Copenhagen, Denmark
  • 2National Environmental Research Institute, Aarhus University, Department of Marine Ecology,
    Frederiksborgvej 399, P.O. Box 358, Roskilde, Denmark
  • 3Danish Defence Center for Operational Oceanography, Overgaden Oven Vandet 62B, Copenhagen K, Denmark

Abstract. Thanks to the abundant observation data, we are able to deploy the traditional point-to-point comparison and statistical measures in combination with a comprehensive model validation scheme to assess the skills of the biogeochemical model ERGOM in providing an operational service for the Baltic Sea. The model assessment concludes that the operational products can resolve the main observed seasonal features for phytoplankton biomass, dissolved inorganic nitrogen, dissolved inorganic phosphorus and dissolved oxygen in euphotic layers as well as their vertical profiles. This assessment reflects that the model errors of the operational system at the current stage are mainly caused by insufficient light penetration, excessive organic particle export downward, insufficient regional adaptation and some from improper initialization. This study highlights the importance of applying multiple schemes in order to assess model skills rigidly and identify main causes for major model errors.

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