Projected consecutive dry and wet days in Iran based on CMIP6 bias‐corrected multi‐model ensemble

Document Type : Research Article

Authors

1 Assistant Professor, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran

2 Post-Doc Researcher, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Climate change with changes in precipitation patterns around the world can cause significant changes in the frequency, intensity and duration of precipitation events. In the context of climate change and with the increase of extreme climate events, irreparable consequences are imposed on the environment and the economy. Therefore, it is necessary to have an appropriate understanding of the frequency, intensity and spatial distribution of these extreme events in order to take a fundamental step in preventing damage caused by them. The purpose of this study is to analyze the characteristics of consecutive dry/wet days during 1975-2014 and 2021-2100 based on the output of CMIP6 models. In this regard, the evaluation of CMIP6 models against gauge precipitation data has been done in Iran.
In this study, historical precipitation (1975-2014) and scenarios-based output of CMIP6 models under shared socioeconomic pathways (SSPs) in the two future periods (2021-2060 and 2061-2100) were used. Basic statistics of r, RMSE, MBE and receiver operating characteristic (ROC) were used to validate the precipitation output of selected models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL). Then, consecutive dry and wet days were calculated using the CDD and CWD indices of the Expert team on climate change detection and indices (ETCCDI). After examining each individual model, an ensemble model is applied with independent weighted mean (IWM) method.
The results showed that among the five CMIP6 models, the IPSL-CM6A-LR model has the most underestimation and the UKESM1-0-LL has the most overestimation for Iran precipitation. The average amount of precipitation bias in the whole country for GFDL-ESM4 (2.56), IPSL-CM6A-LR (2.29), MPI-ESM1-2-HR (2.89), MRI-ESM2-0 (2.18), and UKESM1-0-LL (2.53) mm were calculated. The skill score is improved significantly by applying the multi model ensemble (MME). Consecutive dry days in Iran will increase by a maximum of 26.4 days under the SSP5-8.5 scenario in the period 2061-2100 for the Caspian Sea and Lake Urmia basins. In contrast, consecutive wet days will decrease in these two basins.
Validation results for the period of 1975-2014 showed that (compared to observations), CMIP6 models have a high performance in estimating precipitation in Iran. However, despite the uncertainties in precipitation change, the CMIP6 results provide evidence that the anomaly of consecutive dry and wet periods is an indicator for short-term droughts under increasing climate change conditions. Consecutive dry days will increase significantly in the north and northwest of Iran in the future.
The maximum changes related to CDD and CWD indices are observed under SSP5–8.5 scenario, while the lowest frequency for both indices is under SSP1–2.6 scenario. Examination of CDD and CWD anomalies showed that even in the optimistic scenario (SSP1-2.6), drought responses to climate change are significant. Consecutive dry periods are increasing in most of the northern, northwestern and northeastern regions of Iran. It is urgent to consider these changes in the hydrological cycle as a tool to improve water management, especially in the northern and northwestern regions of Iran. Also, in some areas, such as the southeast and the coasts of the Persian Gulf, there is a significant decrease in consecutive dry periods, which indicates an increase in precipitation on a seasonal and inter-annual scale in the future.

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References

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Journal of the Earth and Space Physics
Volume 47, Issue 3
November 2021
Pages 561-578

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