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Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble

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Abstract

Extreme precipitation is the leading cause of the flood, soil erosion, and drought with significant socioeconomic impacts on human resources. Therefore, projecting future precipitation changes, especially the intensity of extreme precipitation (IEP) in the future, is very important. This study’s main objective is IEP projection in Iran based on CMIP6 bias-correction (BC) multi-model ensemble (MME). The daily precipitation data of five CMIP6 BC models with 0.5 ͦ horizontal resolution was used under three shared socioeconomic pathways; SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Simple Daily Intensity (SDII) and maximum consecutive 1-day precipitation (RX1day) were used to measure IEP changes. Two Kling Gupta efficiency (KGE) and percent bias (PBIAS) methods evaluate the performance of the models, and the independence weighted mean (IWM) method was applied for ensemble averaging. Among the studied CMIP6 bias-correction models, the IPSL-CM6A-LR model has more underestimation than other models with a KGE score of 0.751 has the lowest performance and the MPI-ESM1-2-HR model (0.768) showed the highest performance. In general, the study results showed the uncertainties in CMIP6 models for precipitation, according to which no single model is reliable even in the BC method. The PBIAS in the region affected by Asian summer monsoon (ASM) in Iran is about 10%, based on which the bias of the mentioned models for monsoon precipitation is high. SDII and RX1day anomalies in all climatic zones of Iran except for the RX1day index in Cfa climatic zones in other zones and scenarios during the two periods 2021-2060 and 2061-2100 are positive. Also, the trend and slope of the IEP are increasing in all zones except for BWh, BWk, and Cfa zones for SSP1-2.6 and SSP3-7.0 scenarios. Investigation of trend IEP changes showed that the maximum of these changes will occur in the BWh climate zone, and the minimum would occur in the cold and mountainous climate zone of Iran (Dsc).

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Acknowledgments

This research was funded by Vice Chancellor for Research of Ferdowsi University of Mashhad, which is hereby acknowledged. We would like to thank the Iran Meteorological Organization (IRIMO) for providing the necessary data and information. We also acknowledge the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) and associated World Climate Research Program (WCRP) for the production of the data.

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(Vice Chancellor for Research of Ferdowsi University of Mashhad).

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  1. Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran

    Azar Zarrin & Abbasali Dadashi-Roudbari

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Conceived and designed the analysis: Azar Zarrin and Abbasali Dadashi-Roudbari. Collected the data: Azar Zarrin and Abbasali Dadashi-Roudbari. Contributed data or analysis tools: Azar Zarrin and Abbasali Dadashi-Roudbari. Performed the analysis: Azar Zarrin and Abbasali Dadashi-Roudbari. Wrote the paper: Abbasali Dadashi-Roudbari. Writing—review and editing: Azar Zarrin. Corresponding author: Azar Zarrin.

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Zarrin, A., Dadashi-Roudbari, A. Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theor Appl Climatol 144, 643–660 (2021). https://doi.org/10.1007/s00704-021-03568-2

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