Assessment of climate change using SDSM downscaling Model (A case study: West of Iran)

Climate change impacts are very dependent on regional geographic features, local climate variability and socio-economic status. Therefore, impact assessment researches on climate change must be launched at the local or at the regional level so that the evaluation of consequences can take place. Climate scenarios are produced by Global Circulation Models for the entire Globe with spatial resolutions of several hundred kilometers. For this reason, downscaling methods are used to bridge the gap between the large-scale climate scenarios and the fine scale where local impacts happen. In order to overcome limited computing power and for catchments with limited data, statistical downscaling is the most feasible approach in obtaining climate data for future impact investigations. So a decision support model named SDSM was used to downscale the data. Model errors and uncertainties were estimated using non-parametric statistical methods at the 95% confidence interval for precipitation, maximum temperature and minimum temperature for the mean and variance for a single site in Kermanshah in the western part of Iran. The comparison between the observed dataset and the simulations showed that the SDSM model was able to better represent the minimum and maximum temperature while for precipitation simulations are slightly under-estimated but still acceptable according to statistical tools. It is also presented simulations for the A2 SRES scenario for the 2041-2069 periods showing that the method can produce similar general tendencies.