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this radiation burden, radiation transfer is only computed every few model hours. For example, with full radiation computations performed every 2 h at all grid points, radiation transfer accounts for 27% of the run time of the “GME” forecast model ( Majewski et al. 2002 ). The recent introduction of the McRad package for radiation computations ( Morcrette et al. 2008 ) in the Integrated Forecasting System (IFS) has increased the cost of the radiation computations and required revisiting the use of
this radiation burden, radiation transfer is only computed every few model hours. For example, with full radiation computations performed every 2 h at all grid points, radiation transfer accounts for 27% of the run time of the “GME” forecast model ( Majewski et al. 2002 ). The recent introduction of the McRad package for radiation computations ( Morcrette et al. 2008 ) in the Integrated Forecasting System (IFS) has increased the cost of the radiation computations and required revisiting the use of
1. Introduction The Community Climate System Model (CCSM) is a global coupled ocean–atmosphere modeling framework designed to simulate the climate of the Earth. It is a comprehensive General Circulation Model that consists of complex submodels for the atmosphere, ocean, ice and land. The fidelity of the simulations using the most recent version CCSM3 was described in detail in the June 2006 special issue of the Journal of Climate . The global-scale overview of the present-day climate
1. Introduction The Community Climate System Model (CCSM) is a global coupled ocean–atmosphere modeling framework designed to simulate the climate of the Earth. It is a comprehensive General Circulation Model that consists of complex submodels for the atmosphere, ocean, ice and land. The fidelity of the simulations using the most recent version CCSM3 was described in detail in the June 2006 special issue of the Journal of Climate . The global-scale overview of the present-day climate
speed peaks at , or about 13° from the rotational poles. For a given initial distribution ψ 0 ( λ ′, φ ′) as a function of the rotated coordinates, the exact solution at time t generated by this deformation field is given by ψ 0 ( λ ′ − ωt , φ ′). The following analytical solution has been recommended by Nair and Machenhauer (2002) : The rotational pole is usually set apart from the grid pole by using a rotated coordinate system. The deformation velocity field [Eq. (7) ] and the exact
speed peaks at , or about 13° from the rotational poles. For a given initial distribution ψ 0 ( λ ′, φ ′) as a function of the rotated coordinates, the exact solution at time t generated by this deformation field is given by ψ 0 ( λ ′ − ωt , φ ′). The following analytical solution has been recommended by Nair and Machenhauer (2002) : The rotational pole is usually set apart from the grid pole by using a rotated coordinate system. The deformation velocity field [Eq. (7) ] and the exact
requests for guidance at other specific locations; for instance, an observation location recently established that had no observational record sufficient with which to develop so-called single-station (SS) MOS equations. With the advent of the National Digital Forecast Database (NDFD; Glahn and Ruth 2003 ) and the method of producing fields for it—the Interactive Forecast Preparation System (IFPS; Ruth 2002 )—guidance is needed on a grid (specific values at grid points) rather than, or in addition to
requests for guidance at other specific locations; for instance, an observation location recently established that had no observational record sufficient with which to develop so-called single-station (SS) MOS equations. With the advent of the National Digital Forecast Database (NDFD; Glahn and Ruth 2003 ) and the method of producing fields for it—the Interactive Forecast Preparation System (IFPS; Ruth 2002 )—guidance is needed on a grid (specific values at grid points) rather than, or in addition to
predominantly west and southwest (sea breeze) in the afternoon, accompanied by an increase in wind velocity [mean maximum wind velocity of 5 m s −1 at 1700 South African standard time (SAST)]. However, modeling soon appeared necessary and was undertaken using the Regional Atmospheric Modeling System (RAMS). The first numerical simulations performed over the wine-producing Stellenbosch area used two nested grids (25- and 5-km resolution) for 2 days under onshore synoptic wind conditions (3–4 February 2000
predominantly west and southwest (sea breeze) in the afternoon, accompanied by an increase in wind velocity [mean maximum wind velocity of 5 m s −1 at 1700 South African standard time (SAST)]. However, modeling soon appeared necessary and was undertaken using the Regional Atmospheric Modeling System (RAMS). The first numerical simulations performed over the wine-producing Stellenbosch area used two nested grids (25- and 5-km resolution) for 2 days under onshore synoptic wind conditions (3–4 February 2000
1. Introduction The choice of a spherical grid system is crucial for designing efficient scalable global atmospheric models that can exploit the enormous computing potentials of present-day distributed memory parallel machines. Because of the problems associated with the polar grid singularities, the regular latitude–longitude (RLL; see Table 1 for a complete list of acronyms used in this paper) spherical grid is not well suited for being a candidate for developing such scalable models. In
1. Introduction The choice of a spherical grid system is crucial for designing efficient scalable global atmospheric models that can exploit the enormous computing potentials of present-day distributed memory parallel machines. Because of the problems associated with the polar grid singularities, the regular latitude–longitude (RLL; see Table 1 for a complete list of acronyms used in this paper) spherical grid is not well suited for being a candidate for developing such scalable models. In
used to downscale the results from the ECHAM4.5 model to a smaller and larger domain and to grid sizes ranging from 100 to 20 km. The ECHAM4.5 is an atmospheric global spectral model based on the Navier–Stokes equations of the atmosphere with a hydrostatic approximation in the vertical. The prognostic variables are vorticity, divergence, surface pressure, temperature, specific humidity, and mixing ratio of total cloud water. To account for the inherent uncertainties in the nonlinear climate system
used to downscale the results from the ECHAM4.5 model to a smaller and larger domain and to grid sizes ranging from 100 to 20 km. The ECHAM4.5 is an atmospheric global spectral model based on the Navier–Stokes equations of the atmosphere with a hydrostatic approximation in the vertical. The prognostic variables are vorticity, divergence, surface pressure, temperature, specific humidity, and mixing ratio of total cloud water. To account for the inherent uncertainties in the nonlinear climate system
1. Introduction Land, ocean, and atmosphere components of coupled climate system models are often implemented on different spherical grids, individually designed to enhance the accuracy or capture features unique to their respective settings. Historically, the regular latitude–longitude (RLL; see Table 1 for a complete list of acronyms used in this paper) grid has been the predominant choice for global atmospheric models, but problems associated with the polar singularity persist, and hence
1. Introduction Land, ocean, and atmosphere components of coupled climate system models are often implemented on different spherical grids, individually designed to enhance the accuracy or capture features unique to their respective settings. Historically, the regular latitude–longitude (RLL; see Table 1 for a complete list of acronyms used in this paper) grid has been the predominant choice for global atmospheric models, but problems associated with the polar singularity persist, and hence
1. Introduction Solvers for the Navier–Stokes equations on the sphere are used as the basis for both climate and global numerical weather prediction models. For these applications there is a growing appreciation of the need for conservative positive-definite and/or monotonic transport of chemical species, aerosols, and moisture. In the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM), a second dynamical core incorporating a finite-volume-based solver
1. Introduction Solvers for the Navier–Stokes equations on the sphere are used as the basis for both climate and global numerical weather prediction models. For these applications there is a growing appreciation of the need for conservative positive-definite and/or monotonic transport of chemical species, aerosols, and moisture. In the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM), a second dynamical core incorporating a finite-volume-based solver
boundaries and grid refinement: positions of the vertices in the coarse, the refined, and the fine grids used for the planar Munk gyre test. While energy is introduced to the system via the wind forcing, the energy is reduced by diffusion. The value used for the eddy viscosity ν and the wind forcing τ 0 need to be balanced at a realistic level for the resulting velocity field. In principle, we want to use as little viscosity as possible, since viscosity dampens the flow patterns and reduces details
boundaries and grid refinement: positions of the vertices in the coarse, the refined, and the fine grids used for the planar Munk gyre test. While energy is introduced to the system via the wind forcing, the energy is reduced by diffusion. The value used for the eddy viscosity ν and the wind forcing τ 0 need to be balanced at a realistic level for the resulting velocity field. In principle, we want to use as little viscosity as possible, since viscosity dampens the flow patterns and reduces details
