Jiming Jin, Haifeng Zhuo
Departments of Watershed Sciences and Plants, Soils & Climate, Utah State University
 

This study is intended to generate improved climate projections through statistical downscaling using global climate model output for northwestern China, a region with arid and semiarid climate. Due to global warming, climate experiences unprecedented changes especially in drought prone regions such as northwestern China. Accurate climate predictions for these regions are essential to their well-being. In this study, we use both historical and future temperature and precipitation outputs produced from the Community Climate System Model (CCSM) developed at the National Center for Atmospheric Research.  These modeling results were generated under A1, A2 and B1 carbon emission scenarios at a resolution of 1.5o x 1.5o for the two century-period of 1900-2099. Through statistical regression techniques, these global model outputs were downscaled to a resolution of 0.5o x 0.5o using global precipitation and temperature observations at the same spatial resolution obtained from the University of Delaware.  These downscaled data were analyzed through empirical orthogonal functions to better understand climate changes in our study regions. The downscaling process used in this study not only generated higher spatial resolution climate projections, but it removed climatological biases in the CCSM data as well, resulting in more reliable climate projections for this region. The results from this study gives insight into more informed water resource management for arid and semiarid regions.