Forecasting the impact of Climate Change on the Meteorological Parameters Using GCMs Output with the Help of Artificial Neural Network (Case Study: Shiraz Synoptic Station)

With the development of technology and the industrialization of human societies, the increase of greenhouse gases, the occurrence of climate changes on the surface of the earth and its harmful effects (floods and droughts) on human life, and resources have been confirmed. There, obtaining information about the possible effect of climate change on meteorological parameters is of particular importance and necessity. In this study, an attempt was made to determine the potential effect of climate change on the meteorological parameters of Shiraz the synoptic station in a comprehensive way by using the evaluation of General Circulation Models (12 models) and downscaling of their output (with the help of Multilayer Perceptron Neural Network method). The evaluation results (based on MSE, RMSE, and R) in the base period (1986-2005) proved the superiority of the CanESM2 and HadGEM2CC models. As a result, under the two RCP4.5 and RCP8.5 scenarios, HadGEM2CC outcomes during 2045-2026 and 2046-2065 showed a decrease in precipitation (11-19 and 21-36%, respectively). Also, it depicted an increase in minimum temperature (0.4-1 and 0.7-2°C), an increase in maximum temperature (0.5-1 and 0.9-1.8°C), and an increase in solar radiation (0.35-0.7 and 0.6-1.1 kWh per m2 per day). The HadGEM2CC showed a decrease in precipitation (7-16 and 16-35 %, respectively), an increase in minimum temperature (0.3-0.9 and 0.7-1.7°C), in maximum temperature (0.4-1.1 and 0.9-1.8°C) and in solar radiation (0.3-0.8 and 0.8-1.3 kWh per m2 per day).