全球变化知识资源中心

The ability of the seven CMIP5 models to simulate extreme precipitation events over Iran was evaluated using the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) data set. The criterion used to select the CMIP5 models was the availability of historical daily precipitation data (PERSIANN-CDR) for the retrospective period 1983-2005, as well as future projections for the three representative concentration pathways emission scenarios (RCP2.6, RCP4.5, and RCP8.5) and spatial resolution higher than 2 x 2 degrees. This is the first study to focus on extreme precipitation climate model simulations over Iran that includes high topography and different climates. The results show that CCSM4 has the highest correlation coefficients (CC = 0.85) and lowest root-mean-square error (RMSE = 73.6 mm) compared to PERSIANN-CDR for the mean annual precipitation. However, HadGEM2-ES shows the best (highest CCs between 0.67-0.79 and almost the lowest root-mean-square errors [RMSEs] compared to PERSIANN-CDR) performance for intensity indices; MIROC5 ranked seventh (least CCs and almost the highest RMSEs) among the selected models. The results show that BCC-CSM1-1-M captures maximum consecutive dry days (CDD) better than the other models. The probability matching method (PMM) is used to bias-correct daily precipitation events from CMIP5 models with respect to the PERSIANN-CDR estimations. All the model performances designed to capture the mean annual precipitation, as well as extreme intensity indices, improved after correction. The ensemble, constructed from the bias-corrected model simulations using multiple linear regression (MLR), has the best performance for simulating the mean annual precipitation and extreme indices (CCs between 0.82 for consecutive wet days [CWD] and 0.93 for the mean annual precipitation) compared to the PERSIANN-CDR estimations. Among the seven selected models, CCSM4 has the highest ranking (CCs between 0.70 for CWD to 0.91 for mean annual precipitation) after bias correction.