Efficiency Analysis of SDSM in predicting temperature parameters in three different climates (case study: Mashhad, Shiraz and Ramsar)

Statistical downscaling methods to predict climate variables such as temperature, because of the importance of these factors in the planning and environmental management have been used extensively. In this study, the performance of statistical downscaling model (SDSM) is the predicted temperature parameters studied. The data used in this study includes data on the minimum, maximum and average synoptic stations Mashhad, Shiraz and Ramsar data NCEP data model HadCM3 (the third generation model of global climate scenario A2 and B2) for the base period ( 1990-1961 AD). The first 15 years of data (1975-1961) for calibration and the second 15 years (1976-1990) were used to evaluate the performance of the model. To help HadCM3 data (A2) and HadCM3 (B2), temperatures for three terms (2039-2010), (2069-2040) and (2099-2070) predicted and compared with the base period Statistical criteria to evaluate the performance of the model, such as the mean absolute error and root mean square error model results HadCM3, indicated that this model in cold semi-arid to arid region of desert cold of higher performance and acceptable accuracy for predicting the temperature is.The results for scenario A2 B2 represents the temperature rise in the months of June to August for each station.