Abstract
The South China Sea is a large marginal sea surrounded by land masses and island chains, and characterized by complex bathymetry and irregular coastlines. An unstructured-grid SUNTANS model is employed to perform depth-averaged simulations of the circulation in the South China Sea. The model is tidally forced at the open ocean boundaries using the eight main tidal constituents as derived from the OSU Tidal Prediction Software. The model simulations are performed for the year 2005 using a time step of 60 s. The model reproduces the spring-neap and diurnal and semidiurnal variability in the observed data. Skill assessment of the model is performed by comparing model-predicted surface elevations with observations. For stations located in the central region of the South China Sea, the root mean squared errors (RMSE) are less than 10 % and the Pearson’s correlation coefficient (r) is as high as 0.9. The simulations show that the quality of the model prediction is dependent on the horizontal grid resolution, coastline accuracy, and boundary locations. The maximum RMSE errors and minimum correlation coefficients occur at Kaohsiung (located in northern South China Sea off Taiwan coast) and Tioman (located in southern South China Sea off Malaysia coast). This may be explained with spectral analysis of sea level residuals and winds, which reveal dynamics at Kaohsiung and Tioman are strongly influenced by the seasonal monsoon winds. Our model demonstrates the importance of tidally driven circulation in the central region of the South China Sea.
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Acknowledgments
The authors acknowledge the support of the National University of Singapore research grant (WBS R-302-000-021-133). We also thank the useful comments from participants of IWMO 2013.
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Responsible Editor: Yasumasa Miyazawa
This article is part of the Topical Collection on the 5th International Workshop on Modelling the Ocean (IWMO) in Bergen, Norway 17-20 June 2013
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Nelko, V., Saha, A. & Chua, V.P. On the tidally driven circulation in the South China Sea: modeling and analysis. Ocean Dynamics 64, 413–428 (2014). https://doi.org/10.1007/s10236-013-0687-0
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DOI: https://doi.org/10.1007/s10236-013-0687-0