Assessment of seven CMIP5 model precipitation extremes over Iran based on a satellite‐based climate data set

Abstract

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 × 2°. 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.

Evaluation of precipitation datasets against local observations in southwestern Iran

“…This is especially important in relatively data‐sparse regions such as Iran. Although there have been some studies on the evaluation of precipitation products over Iran's climatic zones (Moazami et al ., ; Katiraie‐Boroujerdy et al ., , , ; Ghajarnia et al ., ; Sharifi et al ., ; Khodadoust Siuki et al ., ; Alijanian et al ., , ; Hosseini‐Moghari et al ., ; Dezfooli et al ., ; Saeidizand et al ., ), comprehensive evaluations of reanalysis, satellite‐based, and interpolated precipitation data are lacking.…”

An assessment of global satellite‐based precipitation datasets in capturing precipitation extremes: A comparison with observed precipitation dataset in India

“…In climate change studies, the accurate future assessment of precipitation extremity using GCM datasets and regional climate model (RCM) datasets can be affected due to their coarser resolutions (Shivam et al, 2019;. The presence of high-resolution precipitation datasets can be utilized to downscale these GCMs and RCMs and the accuracy of forecasted and projected extreme precipitation events can be enhanced (Katiraie-Boroujerdy et al, 2019). In hydrological modelling, precipitation is considered as a base input that governs most of the hydrological processes .…”

Evaluation of ERA5 Precipitation Accuracy Based on Various Time Scales over Iran during 2000–2018

“…Iran is a semi-arid region with an average annual precipitation of 245 mm/y. Desert areas with <50 mm/y and the coastal Caspian Sea area with >1500 mm/y are characterized as the hotspots of dry and wet regions in Iran, respectively [25,26]. Generally, the precipitation characteristics of Iran central and eastern parts are as follows: low annual, severe, and extreme short-term precipitation.…”