Comparative Assessment of Downscaling Methods and Application Towards Analysis of Climate Change Impact on Urban Regions
Abstract
Global climate models (GCM) are sophisticated numerical models used to make long term climate projections. However, the resolution of their output is too coarse for climate change related local impact studies on urban regional scales. Downscaling efforts are taken to address this and increase GCM projection resolution. Physical Scaling (SP) downscaling methodology attempts to incorporate the physical basis of dynamical downscaling efforts with the computational efficiency of statistical methods. In this study, North American Regional Reanalysis surface skin temperature and precipitation data for a 1°x1° region centered on Houston, TX are downscaled to a resolution of 500m via SP and Weather Research and Forecasting (WRF) models. SP models are found to significantly and moderately outperform WRF models in terms of surface temperature and precipitation, respectively. SP methodology is then chosen to downscale GCM projections across 44 urban regions within Canada and the USA. Climate change impact is assessed via comparison of change factors between projections for representative concentration pathway (RCP) scenarios 2.6 and 8.5. Nearly half of all regions have significant projected increases in median and variance of surface skin temperature between RCP scenarios. Precipitation change factors vary significantly depending on GCM choice with median annual precipitation change factors of 29mm to 256mm projected by the 2090s in RCP 8.5.