5B.4 Bias-correction of Global Climate Model output to improve regional climate modeling of the North American Monsoon

Meyer, Jonathan D. D., Jiming Jin, Utah State University, and David J. Gochis, National Center for Atmospheric Research

This project focuses on historical calibration and evaluation of the Weather Research and Forecasting (WRF) model version 3.5 coupled with the Community Land Model version 4.0. For our study, WRF was used as a regional climate model to reproduce the North American Monsoon (NAM) over the 1990’s. Using calibrated model physics, driving WRF with reanalysis data provided by the National Center for Environmental Prediction (NCEP) version 1 data produces realistic NAM precipitation. On the other hand, forcing WRF with global climate model output provided by the Community Climate System Model (CCSM) performs poorly at simulating the NAM due to biases inherent to the CCSM data. In order to generate future predictions of the NAM, improving simulations using CCSM output is crucial. For this reason, we bias-corrected the CCSM data using a simple linear regression technique with NCEP data. When compared to NCEP lateral boundary conditions (LBCs) of temperature, specific humidity, geopotential height, and winds, analysis of mean bias and errors for the corrected CCSM LBCs show large improvements over the original CCSM LBCs. This improvement to CCSM LBCs yields a much better representation of the NAM system and results in much improved precipitation patterns and magnitudes, providing a solid tool for future NAM predictions.