| Climate change is considered one of the most vital issues of our time. This research has been carried out to develop climate change scenarios on a much finer spatial and temporal scale that can be used to address impacts on transportation infrastructure due to climate change. Dynamic downscaling techniques using a meso-scale weather prediction model has been used, where finer resolution climate information is derived from coarser resolution Global Climate Model (GCM) output. In this study, Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) meso-scale model, known as MM5 is used for dynamic downscaling. A coupled land surface model NOAH 1 is used with MM5.; National Centers for Environmental Prediction (NCEP) reanalysis data has been used to validate the model. Surface temperature and dew point temperature have been derived from a validation run. This run is compared with measured station data where correlation coefficients were found to be 0.885 and 0.801, respectively. Through good agreement with observations in present climate, MM5 was extended to investigate downscaling from climate change scenarios.; Canadian Center for Climate Modeling and Analysis (CCCma) data, after necessary transformation, is used as the boundary condition driving the down-scaling process for years representing 1978 (1xCO2) and 2044 (2xCO 2). (Abstract shortened by UMI.); 1NOAH, the Community Land Surface Model developed jointly by National Centers for Environmental Prediction (NCEP), Oregon State University (Dept of Atmospheric Sciences), Air Force (both AFWA and AFRL---formerly AFGL, PL), and Hydrologic Research Lab---NWS (now Office of Hydrologic Dev---OHD). |
