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Ocean Science An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 5
Ocean Sci., 7, 609-627, 2011
https://doi.org/10.5194/os-7-609-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Ocean Sci., 7, 609-627, 2011
https://doi.org/10.5194/os-7-609-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 06 Oct 2011

Research article | 06 Oct 2011

An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI

J. Xie1, F. Counillon2, J. Zhu3, and L. Bertino2 J. Xie et al.
  • 1ICCES, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2Mohn-Sverdrup Center, Nansen Environmental and Remote Sensing Center, Bergen, Norway
  • 3LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Abstract. The upper ocean circulation in the South China Sea (SCS) is driven by the Asian monsoon, the Kuroshio intrusion through the Luzon Strait, strong tidal currents, and a complex topography. Here, we demonstrate the benefit of assimilating along-track altimeter data into a nested configuration of the HYbrid Coordinate Ocean Model that includes tides. Including tides in models is important because they interact with the main circulation. However, assimilation of altimetry data into a model including tides is challenging because tides and mesoscale features contribute to the elevation of ocean surface at different time scales and require different corrections. To address this issue, tides are filtered out of the model output and only the mesoscale variability is corrected with a computationally cheap data assimilation method: the Ensemble Optimal Interpolation (EnOI). This method uses a running selection of members to handle the seasonal variability and assimilates the track data asynchronously. The data assimilative system is tested for the period 1994–1995, during which time a large number of validation data are available. Data assimilation reduces the Root Mean Square Error of Sea Level Anomalies from 9.3 to 6.9 cm and improves the representation of the mesoscale features. With respect to the vertical temperature profiles, the data assimilation scheme reduces the errors quantitatively with an improvement at intermediate depth and deterioration at deeper depth. The comparison to surface drifters shows an improvement of surface current by approximately −9% in the Northern SCS and east of Vietnam. Results are improved compared to an assimilative system that does not include tides and a system that does not consider asynchronous assimilation.

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