Maryam Asadi; Arash Malekian; Ali Salajegheh
Abstract
GCM models are widely used to assess climate change on a global scale, but outputs of these models are not sufficient and accurate to assess climate change at local and regional levels. Therefore, in this study, SDSM model was used for micro-scaling of CanESM2 model data and climate conditions of Semirom ...
Read More
GCM models are widely used to assess climate change on a global scale, but outputs of these models are not sufficient and accurate to assess climate change at local and regional levels. Therefore, in this study, SDSM model was used for micro-scaling of CanESM2 model data and climate conditions of Semirom region based on three scenarios of RCP2.6, RCP4.5 and RCP8.5 in the period 2020 to 2100. The results of model evaluation based on NCEP database showed that the model was more accurate in estimating and predicting temperature data especially mean temperature. Comparison of observation and simulated data of temperature and precipitation of GCMs in the baseline period (1980 to 2005) based on NCEP predictor variables showed the mean correlation of precipitation data of 0.52, mean temperature of 0.88, maximum temperature of 0.80 and minimum temperature of 0.70 for validation and verification periods. The results of the estimation of precipitation variations in different scenarios also predicted a decrease of at least 7.24% and a maximum of 18.55% for the time period of 2020 to 2100 compared to the baseline period (1980-2005). The results of precipitation prediction also show the changes of precipitation pattern. The comparison of the scenarios also shows that the RCP2.6 scenario as the most optimistic scenario has the least rainfall while the RCP8.5 scenario predicts the highest rainfall reduction. Examination of the predicted changes in temperature also shows an increase for the mean, minimum and maximum temperatures,
