A variable‐resolution stretched‐grid general circulation model and data assimilation system with multiple areas of interest: Studying the anomalous regional climate events of 1998

[1] The new stretched-grid (SG) design with multiple (four) areas of interest, one at each global quadrant, is implemented into both a SG general circulation model (GCM) and a SG data assimilation system (DAS). The four areas of interest include the United States/northern Mexico, the El Nino area/central South America, India/China, and the eastern Indian Ocean/Australia. Both SG-GCM and SG-DAS annual (November 1997 to December 1998) integrations are performed with 50-km regional resolution. The efficient regional downscaling to mesoscales is obtained for each of the four areas of interest, while the consistent interactions between regional and global scales and the high quality of global circulation are preserved. This is the advantage of the SG approach. The global variable-resolution DAS incorporating the SG-GCM has been developed and tested as an efficient tool for producing regional analyses and diagnostics with enhanced mesoscale resolution. The anomalous regional climate events of 1998 that occurred over the United States, Mexico, South America, China, India, African Sahel, and Australia are investigated in both simulation and data assimilation modes. The assimilated products are also used, along with gauge precipitation data, for validating the simulation results. The obtained results show that the SG-GCM and SG-DAS are capable of producing realistic high-quality simulated and assimilated products at mesoscale resolution for regional climate studies and applications.

[1]  S. Cohn,et al.  Assessing the Effects of Data Selection with the DAO Physical-Space Statistical Analysis System* , 1998 .

[2]  J. Paegle,et al.  A Variable Resolution Global Model Based upon Fourier and Finite Element Representation , 1989 .

[3]  Ka-Ming Lau,et al.  Impact of orographically induced gravity-wave drag in the GLA GCM , 1996 .

[4]  Tim N. Palmer,et al.  Dynamical Seasonal Prediction , 2000 .

[5]  Richard G. Jones,et al.  Simulation of climate change over Europe using a global variable resolution general circulation model , 1998 .

[6]  Kenneth A. Campana,et al.  An Economical Time–Differencing System for Numerical Weather Prediction , 1978 .

[7]  A. Arakawa,et al.  Vertical Differencing of the Primitive Equations in Sigma Coordinates , 1983 .

[8]  Renato Ramos da Silva,et al.  Project to Intercompare Regional Climate Simulations (PIRCS): Description and initial results , 1999 .

[9]  R. Dickinson,et al.  A regional climate model for the western United States , 1989 .

[10]  R. Sadourny,et al.  Compressible Model Flows on the Sphere. , 1975 .

[11]  S. Schubert,et al.  Boreal winter predictions with the GEOS‐2 GCM: The role of boundary forcing and initial conditions , 2000 .

[12]  M. Suárez,et al.  A proposal for the intercomparison of the dynamical cores of atmospheric general circulation models , 1994 .

[13]  F. Giorgi Simulation of Regional Climate Using a Limited Area Model Nested in a General Circulation Model , 1990 .

[14]  Phillip A. Arkin,et al.  Analyses of Global Monthly Precipitation Using Gauge Observations, Satellite Estimates, and Numerical Model Predictions , 1996 .

[15]  Vivek Hardiker,et al.  A Global Numerical Weather Prediction Model with Variable Resolution , 1997 .

[16]  Michael S. Fox-Rabinovitz,et al.  A Variable-Resolution Stretched-Grid General Circulation Model: Regional Climate Simulation , 2001 .

[17]  B. Hoskins,et al.  Believable scales and parameterizations in a spectral transform model , 1997 .

[18]  Ralph Shapiro,et al.  Smoothing, filtering, and boundary effects , 1970 .

[19]  Georgiy L. Stenchikov,et al.  A Uniform- and Variable-Resolution Stretched-Grid GCM Dynamical Core with Realistic Orography , 2000 .

[20]  S. Moorthi,et al.  Relaxed Arakawa-Schubert - A parameterization of moist convection for general circulation models , 1992 .

[21]  Eugenia Kalnay,et al.  Rules for Interchange of Physical Parameterizations , 1989 .

[22]  E. Lorenz Energy and Numerical Weather Prediction , 1960 .

[23]  Y. Sud,et al.  The roles of dry convection, cloud-radiation feedback processes and the influence of recent improvements in the parameterization of convection in the GLA GCM , 1988 .

[24]  Georgiy L. Stenchikov,et al.  A Finite-Difference GCM Dynamical Core with a Variable-Resolution Stretched Grid , 1997 .

[25]  M. Déqué,et al.  High resolution climate simulation over Europe , 1995 .

[26]  A. Staniforth,et al.  The Operational CMC–MRB Global Environmental Multiscale (GEM) Model. Part I: Design Considerations and Formulation , 1998 .

[27]  A. Arakawa,et al.  A Potential Enstrophy and Energy Conserving Scheme for the Shallow Water Equations , 1981 .

[28]  Roger Daley,et al.  A Baroclinic Finite-Element Model for Regional Forecasting with the Primitive Equations , 1979 .

[29]  Robert Vichnevetsky,et al.  Wave propagation and reflection in irregular grids for hyperbolic equations , 1987 .

[30]  Lawrence L. Takacs,et al.  Data Assimilation Using Incremental Analysis Updates , 1996 .

[31]  Philippe Courtier,et al.  A global numerical weather prediction model with variable resolution: Application to the shallow‐water equations , 1988 .

[32]  M. Suárez,et al.  Regional Climate Simulation with a Variable Resolution Stretched Grid GCM: The Regional Down-Scaling Effects , 1999 .

[33]  P. Bénard,et al.  Introduction of a local mapping factor in the spectral part of the Météo‐France global variable mesh numerical forecast model , 1996 .

[34]  Frederick G. Shuman,et al.  Resuscitation of an integration procedure , 1971 .

[35]  J. Côté Variable resolution techniques for weather prediction , 1997 .

[36]  A. Staniforth,et al.  A Variable-Resolution Finite-Element Technique for Regional Forecasting with the Primitive Equations , 1978 .

[37]  Luc Fillion,et al.  A Variable-Resolution Semi-Lagrangian Finite-Element Global Model of the Shallow-Water Equations , 1993 .

[38]  J. Labraga,et al.  Design of a Nonsingular Level 2.5 Second-Order Closure Model for the Prediction of Atmospheric Turbulence , 1988 .

[39]  R. Sadourny The Dynamics of Finite-Difference Models of the Shallow-Water Equations , 1975 .

[40]  V. Kousky,et al.  Climate Assessment for 1998 , 1999 .

[41]  M. Kanamitsu,et al.  The NMC Nested Regional Spectral Model , 1994 .

[42]  Philip W. Mote,et al.  Numerical modeling of the global atmosphere in the climate system , 2000 .

[43]  A. Staniforth,et al.  Regional modeling: A theoretical discussion , 1997 .

[44]  M. Fox-Rabinovitz Simulation of anomalous regional climate events with a variable‐resolution stretched‐grid GCM , 2000 .