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Coupled Model Inter-comparison Project Database to Calculate Drought Indices for Saudi Arabia: A Preliminary Assessment

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Abstract

The objective of this paper is to present how the Coupled Model Inter-comparison Project phase 3 (CMIP3) multi-model datasets might be used to calculate drought indices for Saudi Arabia. Widely used drought indices such as the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI) are constructed and analyzed using observed rainfall from 27 stations as well as data from best performing CMIP3 models along with other variables for the present climate. Of the 22 CMIP3 models, the Canadian (CC: CCCMA-CGCM3.1) and the Australian (CS: CSIRO-Mk3.0) models were used in estimating the annual rainfall over Saudi Arabia while the German/Korean (MI: MIUB-ECHO-G) and the Japanese (MM: MIROC3.2 and MR: MRI-CGCM2.3.2) models were used in estimating the annual temperature. Results show that the CS model is superior to the other 21 CMIP3 models in calculating both SPI and PDSI. As for drought indices, PDSI (76% and 65% for CS and CC, respectively) performs well in assessing the spatial distribution of drought conditions as well as in determining the number of events (63% and 26% for CS and CC, respectively) within the different drought categories when compared to observations. Therefore, further use of PDSI is recommended for drought diagnosis in future climate for the disaster management purposes for Saudi Arabia, however, the use of the latest climate models datasets e.g. AR5 or AR6 may need further investigation.

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Acknowledgements

The CMIP3 datasets were obtained from the websites. Computation for the work described in this paper was performed using Aziz Supercomputer at King Abdulaziz University’s High Performance Computing Center, Jeddah, Saudi Arabia.

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Correspondence to Mansour Almazroui.

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Almazroui, M., Islam, M.N. Coupled Model Inter-comparison Project Database to Calculate Drought Indices for Saudi Arabia: A Preliminary Assessment. Earth Syst Environ 3, 419–428 (2019). https://doi.org/10.1007/s41748-019-00126-9

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