The Performance of the HadGEM2-ES Model in the Evaluation of Seasonal Temperature Anomaly of Iran under RCP scenarios

Global warming is considered as a major challenge for food security and in recent years, it has attracted attentions, especially in arid areas. The Middle East and the Mediterranean are known as sensitive areas to climate changes. It is essential to understand the condition of climate changes, firstly at regional scales and secondly at large scales to adopt appropriate policies. Although the variety of seasonal temperature anomalies by the CMIP5 through the use of statistical downscaling model has not been thoroughly investigated. The aim of this research is to evaluate the diversity of seasonal temperature anomalies which are extracted from RegCM2-ES as the CMIP5 collection model based on the dynamic regression method by CORDEX model for the first time in Iran. In this study, the HadGEM2-ES model has been applied as one of the CMIP5 collection models to investigate the seasonal temperature anomaly by the use of CORDEX dynamic regression downscaling method under the Representative Concentration Pathway (RCP) scenarios. This model was operated for the period (1970-2005) and for a predicted future period (2011-2099) under three RCP scenarios (RCP2.6, RCP4.5, RCP8.5). In order to achieve better evaluation of global warming at the next step, the model data was divided into three time intervals comprises near future (2011-2040), middle future (2041-2070), and far future (2071-2099). The model was validated using data based on 31 synoptic stations and by applying six statistics of deterministic coefficient (R2), mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE), t-Jacovides, and t-Jacovides/R2 ratio. According to statistical results of validation, the HadGem2-ES model proves that it has appropriate ability to reconstruct the seasonal temperature anomalies. Results show that the seasonal temperature anomalies are positive during the study of time intervals under the used RCP scenarios and it is high in winters which is considered as a significant threat to the water resources in Iran. The reconstructed maximum seasonal temperature anomalies indicate that it matchs well in high regions and high latitudes during winters and springs. Subsequently the northwest of Iran is known as the center of maximum temperature anomalies. In summer, the mountains and central Iran indicate the maximum temperature anomalies while in autumn, anomalies are recognized on the coastal regions in the south and east border of Iran. Furthermore, there is evidence for a seasonal response to performance of the model and spatial diversity based on different topography in Iran. Based on the results, it can be concluded that the most seasonal temperature anomalies based on the HadGEM2-ES model and dynamical downscaling CORDEX method is evident in high latitudes and mountainous regions of Iran. There is a different pattern of maximum positive anomalies in southwest, south, southeast, and east of country during autumn. Not only was the temperature anomaly positive in all of seasons but also it was positive in each of scenarios and time periods of study. Consequently, it is a major threat to natural water resources in Iran. The temperature anomaly extracted from HadGEM2-ES Model comparing to the temperature ranges of different models proves that according to global warming, there is a reduction in the cold extreme events in Iran. Moreover, the spatial variation of temperature in Iran depends on the complex environment topography which causes a specific response of the model to surface forcing. It is concluded that the existence of snow is the main reason for increasing the temperature anomaly in the highlands of northwest of Iran.