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Regional Climate Model Simulation of U.S.–Mexico Summer Precipitation Using the Optimal Ensemble of Two Cumulus Parameterizations

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  • 1 Illinois State Water Survey, University of Illinois at Urbana–Champaign, Champaign, Illinois
  • | 2 NOAA/ESRL, and CIRES, University of Colorado, Boulder, Colorado
  • | 3 NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
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Abstract

The fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) simulations of U.S.–Mexico summer precipitation are quite sensitive to the choice of Grell or Kain–Fritsch convective parameterization. An ensemble based on these two parameterizations provides superior performance because distinct regions exist where each scheme complementarily captures certain observed signals. For the interannual anomaly, the ensemble provides the most significant improvement over the Rockies, Great Plains, and North American monsoon region. For the climate mean, the ensemble has the greatest impact on skill over the southeast United States and North American monsoon region, where CMM5 biases associated with the individual schemes are of opposite sign. Results are very sensitive to the specific methods used to generate the ensemble. While equal weighting of individual solutions provides a more skillful result overall, considerable further improvement is achieved when the weighting of individual solutions is optimized as a function of location.

Corresponding author address: Dr. Xin-Zhong Liang, Illinois State Water Survey, University of Illinois at Urbana–Champaign, 2204 Griffith Drive, Champaign, IL 61820-7495. Email: xliang@uiuc.edu

Abstract

The fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) simulations of U.S.–Mexico summer precipitation are quite sensitive to the choice of Grell or Kain–Fritsch convective parameterization. An ensemble based on these two parameterizations provides superior performance because distinct regions exist where each scheme complementarily captures certain observed signals. For the interannual anomaly, the ensemble provides the most significant improvement over the Rockies, Great Plains, and North American monsoon region. For the climate mean, the ensemble has the greatest impact on skill over the southeast United States and North American monsoon region, where CMM5 biases associated with the individual schemes are of opposite sign. Results are very sensitive to the specific methods used to generate the ensemble. While equal weighting of individual solutions provides a more skillful result overall, considerable further improvement is achieved when the weighting of individual solutions is optimized as a function of location.

Corresponding author address: Dr. Xin-Zhong Liang, Illinois State Water Survey, University of Illinois at Urbana–Champaign, 2204 Griffith Drive, Champaign, IL 61820-7495. Email: xliang@uiuc.edu

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