A Global Multilevel Atmospheric Model Using a Vector Semi-Lagrangian Finite-Difference Scheme. Part II: Version with Physics

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  • 1 Development Division, NMC/NWS/NOAA, Washington, D.C.
  • | 2 Climate Analysis Center, NMC/NWS/NOAA, Washington, D.C.
  • | 3 NASA/Goddard Laboratory for Atmospheres, Greenbelt, Maryland
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Abstract

Full physical parameterzations have been incorporated into the global model using a two-time-level, semi-Lagrangian, semi-implicit finite-difference integration scheme that was described in Part I of this work. Virtual temperature effects have also been incorporated into the adiabatic part of the model. The diurnal and seasonal cycles have been included, with prescribed seasonally varying climatological surface boundary conditions.

The model has been integrated in both forecast and climate mode, at a resolution of 2° × 2.5° in latitude/longitude, 20 levels in the vertical, and a time step of 45 min for the dynamics. Medium-range forecasts using January and July initial conditions give highly encouraging anomaly correlation skill scores. Two parallel 17-month climate simulations, one without and one with mass restoration, show that the model successfully simulates many features of the observed climate. Mass restoration is shown to have no significant impact on the seasonally averaged climate statistics.

Abstract

Full physical parameterzations have been incorporated into the global model using a two-time-level, semi-Lagrangian, semi-implicit finite-difference integration scheme that was described in Part I of this work. Virtual temperature effects have also been incorporated into the adiabatic part of the model. The diurnal and seasonal cycles have been included, with prescribed seasonally varying climatological surface boundary conditions.

The model has been integrated in both forecast and climate mode, at a resolution of 2° × 2.5° in latitude/longitude, 20 levels in the vertical, and a time step of 45 min for the dynamics. Medium-range forecasts using January and July initial conditions give highly encouraging anomaly correlation skill scores. Two parallel 17-month climate simulations, one without and one with mass restoration, show that the model successfully simulates many features of the observed climate. Mass restoration is shown to have no significant impact on the seasonally averaged climate statistics.

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