'Improving Regional Climate Modeling of the North American Monsoon'
Jonathan Meyer (email@example.com), Utah State University; Jiming Jin (firstname.lastname@example.org), USU Dep of PSC; Ripley McCoy (RipleyMcCoy@yahoo.com), USU Dep of PSC
This project investigates the ability of the Weather Research and Forecasting model (WRF) used as a regional climate model (RCM) to simulate summertime precipitation associated with the North American Monsoon (NAM) for the 1990’s. Summertime precipitation associated with the NAM provides more than 50% of the annual precipitation in southern Arizona and New Mexico; an arid and semi-arid region. Changes to the onset, intensity, and duration of the NAM are expected to occur under a global warming background, and an understanding of these changes in summertime precipitation in this region is important. While RCM simulations conducted using observation-based National Center for Environmental Prediction Reanalysis I data (NCEP-R1) produce accurate precipitation patterns, simulations driven with general circulation model (GCM) data provided by the Community Climate System Model (CCSM) performed poorly due to biases in the CCSM forcing data. While this study focuses on historical time periods where observation-based data is available as forcing data, future predictions rely on GCM data to drive the model. To improve the CCSM data’s ability to accurately resolve the NAM, a simple linear regression technique employing the NCEP-R1 data was used to reduce the biases in CCSM temperature, specific humidity, and sea-level pressure. In order to maintain physical consistency among atmospheric variables, physical equations and the bias-corrected variables were used to calculate the remaining dependant variables needed by the WRF model. This study presents an evaluation of simulations driven by 1) NCEP-R1 data, 2) original CCSM data, and 3) bias-corrected CCSM data. This evaluation highlights strengths and weaknesses of the WRF model to resolve various NAM controlling factors using the CCSM forcing data, which gives insight into future predictions of the NAM, using these global model data.