• Aceituno, P., 1988: On the functioning of the Southern Oscillation in the South American sector. Part I: Surface climate. Mon. Wea. Rev., 116 , 505524.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4 , 11471167.

    • Search Google Scholar
    • Export Citation
  • Arnell, N. W., D. A. Hudson, and R. G. Jones, 2003: Climate change scenarios from a regional climate model: Estimating change in runoff in southern Africa. J. Geophys. Res., 108 .4519, doi:10.1029/2002JD002782.

    • Search Google Scholar
    • Export Citation
  • Chen, F., and Coauthors, 1996: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101D , 72517268.

    • Search Google Scholar
    • Export Citation
  • Departamento de Geofísica, cited. 2005: Boletin Climático. Departamento de Geofísica, Universidad de Chile. [Available online at http://met.dgf.uchile.cl/clima/.].

  • Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46 , 30773107.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1993: A nonhydrostatic version of the Penn State/NCAR mesoscale model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev., 121 , 14931513.

    • Search Google Scholar
    • Export Citation
  • Fuenzalida, H., 1982: A country of extreme climate. Chile: Essence and Evolution (in Spanish), H. Garcia, Ed., Instituto de Estudios Regionales, Universidad de Chile, 27–35.

    • Search Google Scholar
    • Export Citation
  • García-Huidobro, T., F. Marshall, and J. Bell, 2001: A risk assessment of potential crop losses due to ambient SO2 in the central regions of Chile. Atmos. Environ., 35 , 49034915.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., X. Bi, and J. Pal, 2004: Means, trends and interannual variability in a regional climate change experiment over Europe. Part I: Present day climate (1961–1990). Climate Dyn., 22 , 733756.

    • Search Google Scholar
    • Export Citation
  • Grell, G., L. Schade, R. Knoche, A. Pfeiffer, and J. Egger, 2000: Nonhydrostatic climate simulations of precipitation over complex terrain. J. Geophys. Res., 105 , 2959529608.

    • Search Google Scholar
    • Export Citation
  • Hack, J. J., B. A. Boville, B. P. Briegleb, J. T. Kiehl, P. J. Rasch, and D. L. Williamson, 1993: Description of the NCAR Community Climate Model (CCM2). NCAR Tech. Note NCAR/TN-382+STR, 108 pp.

  • Instituto Nacional de Estadísticas, cited. 2005: CHILE: Censo de poblacion y vivienda 2002. Instituto Nacional de Estadísticas, Santiago, Chile. [Available online at http://www.ine.cl/redatam/i-redatam.htm/.].

  • Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165–170.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77 , 437471.

  • Miller, A., 1976: The climate of Chile. Climates of Central and South America, W. Schwerdtfeger, Ed., Elsevier, 113–145.

  • Montecinos, A., and P. Aceituno, 2003: Seasonality of the ENSO-related rainfall variability in central Chile and associated circulation anomalies. J. Climate, 16 , 281296.

    • Search Google Scholar
    • Export Citation
  • Pizarro, J. G., and A. Montecinos, 2000: Cutoff cyclones off the subtropical coast of Chile. Preprints, Sixth Int. Conf. on Southern Hemisphere Meteorology and Oceanography, Santiago, Chile, Amer. Meteor. Soc., 278–279.

  • Rutllant, J., and H. Fuenzalida, 1991: Synoptic aspects of the central Chile rainfall variability associated with the Southern Oscillation. Int. J. Climatol., 11 , 6376.

    • Search Google Scholar
    • Export Citation
  • Seth, A., and M. Rojas, 2003: Simulation and sensitivity in a nested modeling system for tropical South America. Part I: Reanalyses boundary forcing. J. Climate, 16 , 24372453.

    • Search Google Scholar
    • Export Citation
  • Small, E. E., F. Giorgi, and L. C. Sloan, 1999: Regional climate model simulation of precipitation in central Asia: Mean and interannual variability. J. Geophys. Res., 104 , 65636582.

    • Search Google Scholar
    • Export Citation
  • Troen, I., and L. Mahrt, 1986: A simple model of the atmospheric boundary layer: Sensitivity to surface evaporation. Bound.-Layer Meteor., 37 , 129148.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 93 46 2
PDF Downloads 70 34 2

Multiply Nested Regional Climate Simulation for Southern South America: Sensitivity to Model Resolution

View More View Less
  • 1 Department of Geophysics, University of Chile, Santiago, Chile
Restricted access

Abstract

Results are reported from two 5-month-long simulations for southern South America using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). The periods of simulation correspond to May–September 1997 and 1998, which were anomalously wet and dry winters for central Chile, respectively. The model setup includes triply nested, two-way-interacting domains centered over the eastern South Pacific and the western coast of southern South America, with horizontal grid intervals of 135, 45, and 15 km. Boundary conditions are provided from NCEP–NCAR reanalyzed fields. The analysis focuses on two subregions of central Chile (30°–41°S). Region 1 (32°–35°S), which is where the observed interannual precipitation differences are largest, is topographically very complex, with a mean height of the Andes Cordillera around 4500 m. Region 2 (35°–39°S) has relatively smooth terrain, as the mean height of the Andes drops to 3000 m. Station precipitation and temperature data are used for model validation. The model exhibits a negative temperature bias (from 2° to 5°C), as well as a positive precipitation bias (40%–80%). This precipitation bias can be partially explained by a positive moisture bias over the ocean in the model. In addition, these biases are highly correlated to the representation of terrain and station elevation in the model. The highest-resolution domain has the smallest precipitation bias for low-elevation stations, but a large positive bias at high altitudes (up to 300%). It also has a better representation of the spatial distribution of the precipitation, especially in region 1, where topography has a larger impact on the precipitation. Overall, the model domain with highest resolution best reproduces the observed precipitation and temperature, as well as the interannual differences. However, this study also shows that large improvements in the simulations of the surface variables are obtained when downscaling from 135 to 45 km, but much smaller improvements are found when downscaling from 45 to 15 km. These simulations represent the first effort in simulating seasonal precipitation in this topographically complex region of the Southern Hemisphere.

Corresponding author address: Maisa Rojas, Dept. of Geophysics, University of Chile, Blanco Encalada 2002, Santiago, Chile. Email: maisa@dgf.uchile.cl

Abstract

Results are reported from two 5-month-long simulations for southern South America using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). The periods of simulation correspond to May–September 1997 and 1998, which were anomalously wet and dry winters for central Chile, respectively. The model setup includes triply nested, two-way-interacting domains centered over the eastern South Pacific and the western coast of southern South America, with horizontal grid intervals of 135, 45, and 15 km. Boundary conditions are provided from NCEP–NCAR reanalyzed fields. The analysis focuses on two subregions of central Chile (30°–41°S). Region 1 (32°–35°S), which is where the observed interannual precipitation differences are largest, is topographically very complex, with a mean height of the Andes Cordillera around 4500 m. Region 2 (35°–39°S) has relatively smooth terrain, as the mean height of the Andes drops to 3000 m. Station precipitation and temperature data are used for model validation. The model exhibits a negative temperature bias (from 2° to 5°C), as well as a positive precipitation bias (40%–80%). This precipitation bias can be partially explained by a positive moisture bias over the ocean in the model. In addition, these biases are highly correlated to the representation of terrain and station elevation in the model. The highest-resolution domain has the smallest precipitation bias for low-elevation stations, but a large positive bias at high altitudes (up to 300%). It also has a better representation of the spatial distribution of the precipitation, especially in region 1, where topography has a larger impact on the precipitation. Overall, the model domain with highest resolution best reproduces the observed precipitation and temperature, as well as the interannual differences. However, this study also shows that large improvements in the simulations of the surface variables are obtained when downscaling from 135 to 45 km, but much smaller improvements are found when downscaling from 45 to 15 km. These simulations represent the first effort in simulating seasonal precipitation in this topographically complex region of the Southern Hemisphere.

Corresponding author address: Maisa Rojas, Dept. of Geophysics, University of Chile, Blanco Encalada 2002, Santiago, Chile. Email: maisa@dgf.uchile.cl

Save