Predicting of Temperature Parameters under the CanEMS2 Model (Case Study: Lar Synoptic Station)

Among the major challenges facing agriculture and water resources, one can mention the phenomenon of climate change and its impacts. The Global Criculation Models (GCMs) can provide the best information about the response to increasing the concentration of greenhouse gases. Since the output of these models does not have sufficient spatial and temporal accuracy to study the effects of climate change on hydrological systems, it is necessary to provide output data for small-scale general turning models. In this study, the SDSM statistical magnitudes and for the climate change assessment, the CanESM2 model reported in the IPCC's based on three scenarios RCP2.6, RCP4.5 and RCP8.5 is used. The daily minimum temperature and maximum temperature of the land synoptic station is Fars province, and the temperature is predicted for the three periods 2040-2011 (2020S), 2070-2070 (2050S) and 2099-2071 (2080S). The results of the research showed that the SDSM model is well advanced to minimize and maximize temperature, and is a suitable model for exponential smoothness of climate parameters of temperature for the Lar synoptic station. According to the results of the CanESM2 model, in both three scenarios (RCP2.6, RCP4.5, RCP8.5) and all three periods (2020S, 2050S, 2080S), the minimum and maximum temperature has been increased, so that the increase for the minimum temperature and the maximum period (2020S) was between 1.12 to 1.29 and 2.01 to 2.30 celsius degrees, and the increase for the minimum and maximum temperature (2050S) was between 1.47 to 2.35 and 2.61 to 4.14 celsius degrees and the temperature rise (2080S) relative to the base period for minimum and maximum temperatures is between 1.46 to 3.13 and 2.7 to 5.94 celsius degrees.