A General Circulation Model for Upper Ocean Simulation

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Abstract
A general circulation model (GCM) of the ocean that emphasizes the simulation of the upper ocean has been developed. This emphasis is in keeping with its future intent, that of an air-sea coupled model. The basic model is the primitive equation model of Bryan and Cox with the additions, of optional usage, of the Mellor-Yamada level 2.5 turbulence closure scheme and horizontal nonlinear viscosity. These modifications are intended to improve the upper ocean simulations, particularly sea surface temperature and heat content. The horizontal grid spacing is 1° latitude × 1° longitude and is global in domain. The equatorial region between 10°N and 10°S is further refined in the north–south direction to ⅓° resolution. There are 12 vertical levels, with six levels in the top 70 m. The model incorporates varying bottom topography. Prior to coupling the ocean model to an atmospheric GCM, experiments have been carried out to determine the ocean GCM's performance using atmospheric forcing from observed data.... Abstract A general circulation model (GCM) of the ocean that emphasizes the simulation of the upper ocean has been developed. This emphasis is in keeping with its future intent, that of an air-sea coupled model. The basic model is the primitive equation model of Bryan and Cox with the additions, of optional usage, of the Mellor-Yamada level 2.5 turbulence closure scheme and horizontal nonlinear viscosity. These modifications are intended to improve the upper ocean simulations, particularly sea surface temperature and heat content. The horizontal grid spacing is 1° latitude × 1° longitude and is global in domain. The equatorial region between 10°N and 10°S is further refined in the north–south direction to ⅓° resolution. There are 12 vertical levels, with six levels in the top 70 m. The model incorporates varying bottom topography. Prior to coupling the ocean model to an atmospheric GCM, experiments have been carried out to determine the ocean GCM's performance using atmospheric forcing from observed data....

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