Uncertainty analysis of temperature and precipitation variation influenced by climate change (Case Study: Southern Khorasan Province)

For survey of climate change effects In this research fifteen GCMs models are used. By using downscaling method of LARS-WG large scale projections of GCMs output subgrided from high resolutions to local coordinate. For this aim, observation data (1990-2010) of synoptic stations in province are collected and was assumed as base period. Trend was fulfilled by Man-Kendal as well as uncertainty was carried out by bootstrapping function. Annual simulations of rainfall and temperature were used as entrance to Bootstrap. Confidence interval for each station was determined in 0.09 levels. Results about performance of GCMs showed that almost all models haven’t high ability to simulation of behavior of precipitation pattern. However performance of these models for simulation of variation of least and most temperature was very good. Results of trend analysis for stations and province showed that decrease of rainfall and increase of average temperature. By comparison of variation of temperature in future than historical period it is founded that minimum and maximum temperature will have 0.6 decrease and 2 increase respectively. also uncertainty analysis showed that there are significant sources of uncertainty in simulation of meteorological components. Also annual precipitation variations in future are more severe than historical period.