Improving Regional Climate Modeling of the North American Monsoon Through Physically Consistent Bias Corrected CCSM4 Output

Friday, 19 December 2014
Jon Meyer and Jiming Jin, Utah State University, Logan, UT, United States
The Weather Research and Forecasting (WRF) model was used to simulate a 32-year climatology of the North American Monsoon (NAM) using forcing data provided by 1) the Climate Forecast System Reanalysis (CFSR), and 2) the Community Climate System Model version 4 (CCSM). Systematic biases in the CCSM output such as significant dry biases in the tropics are transmitted into the WRF model through the lateral boundary conditions and degrade the performance of the model when compared to both observations and simulations forced with the CFSR dataset. To improve the ability of CCSM output to appropriately prescribe the NAM, we introduce a process using simple linear regression and the CFSR dataset to perform a mean bias correction that also maintains the physical dependencies across variables. A third NAM climatology was simulated using this bias corrected CCSM output, which showed marked improvement to the NAM precipitation, most notably in the Mexican core of the NAM. Additionally, the climatology of NAM evolutionary characteristics (i.e. onset, intensity, decay) are much better represented in the bias corrected CCSM WRF model than in the original CCSM WRF model, and closely resemble the CFSR simulations. NAM precipitation simulated by each of the three forcing datasets show the bias corrected CCSM simulations produce the most consistent trends when compared to observations, providing confidence for future projections of the NAM.