Ocean Science (Apr 2010)

Sensitivity of oxygen dynamics in the water column of the Baltic Sea to external forcing

  • S. Miladinova,
  • A. Stips

DOI
https://doi.org/10.5194/os-6-461-2010
Journal volume & issue
Vol. 6, no. 2
pp. 461 – 474

Abstract

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A 1-D biogeochemical/physical model of marine systems has been applied to study the oxygen cycle in four stations of different sub-basins of the Baltic Sea, namely, in the Gotland Deep, Bornholm, Arkona and Fladen. The model consists of the biogeochemical model of Neumann et al. (2002) coupled with the 1-D General Ocean Turbulence Model (GOTM). The model has been forced with meteorological data from the ECMWF reanalysis project for the period 1998–2003, producing a six year hindcast which is validated with datasets from the Baltic Environmental Database (BED) for the same period. The vertical profiles of temperature and salinity are relaxed towards both profiles provided by 3-D simulations of General Estuarine Transport Model (GETM) and observed profiles from BED. Modifications in the parameterisation of the air-sea oxygen fluxes have led to a significant improvement of the model results in the surface and intermediate water layers. The largest mismatch with observations is found in simulating the oxygen dynamics in the Baltic Sea bottom waters. The model results demonstrate the good capability of the model to predict the time-evolution of the physical and biogeochemical variables at all different stations. Comparative analysis of the modelled oxygen concentrations with respect to observation data is performed to distinguish the relative importance of several factors on the seasonal, interannual and long-term variations of oxygen. It is found that natural physical factors, like the magnitude of the vertical turbulent mixing, wind speed and the variation of temperature and salinity fields are the major factors controlling the oxygen dynamics in the Baltic Sea. The influence of limiting nutrients is less pronounced, at least under the nutrient flux parameterisation assumed in the model.