Forecasting Frost Changes in the City of Kashan based on the Simulation of General Atmospheric Circulation Model
In this study, in order to know the trend of frost phenomenon in Kashan station in the next years, the ice days of this station were modeled. To this end, the comparison and selection of the best fitted model has been investigated by atmospheric circulation models. After collecting daily data of Kashan synoptic station in the period of 1973-2000, according to the data of the general circulation model in the period of 2011-2030 and the selection of the A1 emission scenario, among the general atmospheric circulation models, five applicable models including BCM2, HADCM3, IPCM4, GIAOM, and HADGEM were selected and the climatic data of the station were redistributed using LARS-WG software. In order to create daily data by 2030, the mean and standard deviation of each model were compared with the calculation of bias and absolute error. Considering the lowest absolute error among models, the GIAOM model was selected to predict the minimum and maximum temperature and artificial ice data, as well as to investigate Kashan frost for future climatic intervals. The results of the GIAOM model showed that the highest increase in the minimum and maximum mean temperatures in the future will occur in the summer and autumn seasons and the lowest temperature increase will be in the winter and spring seasons, which indicates that we should be waiting for warmer summers and autumns. The results of frost trends show that the number of days with weak, moderate, and severe frost in the future will be reduced. Meanwhile, the maximum reduction in the number of freezing days will be due to weak freezing. The decrease in the number of freezing days in Kashan station indicates the climatic susceptibility of this region to the global warming consequences and, in fact, shows the sensitivity of the number of freezing days in this region to this phenomenon.
Article Type:
Research/Original Article
Geography and Environmental Planning, Volume:28 Issue: 3, 2018
21 - 36  
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