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Journal of Arid Land  2023, Vol. 15 Issue (3): 274-296    DOI: 10.1007/s40333-023-0050-3
Research article     
Projection of precipitation extremes over South Asia from CMIP6 GCMs
Adnan ABBAS1, Asher S BHATTI2, Safi ULLAH3, Waheed ULLAH1, Muhammad WASEEM4, ZHAO Chengyi1,*(), DOU Xin1, Gohar ALI5
1Land Science Research Center, Nanjing University of Information Science & Technology, Nanjing 210044, China
2Department of Geology, Bacha Khan University, Charsadda 24420, Pakistan
3Department of Atmospheric and Oceanic Sciences/Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
4Center of Excellence in Water Resources, University of Engineering and Technology, Lahore 54890, Pakistan
5Pakistan Meteorological Department, Sector H-8/2, Islamabad 44000, Pakistan
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Abstract  

Extreme precipitation events are one of the most dangerous hydrometeorological disasters, often resulting in significant human and socio-economic losses worldwide. It is therefore important to use current global climate models to project future changes in precipitation extremes. The present study aims to assess the future changes in precipitation extremes over South Asia from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs). The results were derived using the modified Mann-Kendall test, Sen's slope estimator, student's t-test, and probability density function approach. Eight extreme precipitation indices were assessed, including wet days (RR1mm), heavy precipitation days (RR10mm), very heavy precipitation days (RR20mm), severe precipitation days (RR50mm), consecutive wet days (CWD), consecutive dry days (CDD), maximum 5-day precipitation amount (RX5day), and simple daily intensity index (SDII). The future changes were estimated in two time periods for the 21st century (i.e., near future (NF; 2021-2060) and far future (FF; 2061-2100)) under two Shared Socioeconomic Pathway (SSP) scenarios (SSP2-4.5 and SSP5-8.5). The results suggest increases in the frequency and intensity of extreme precipitation indices under the SSP5-8.5 scenario towards the end of the 21st century (2061-2100). Moreover, from the results of multimodel ensemble means (MMEMs), extreme precipitation indices of RR1mm, RR10mm, RR20mm, CWD, and SDII demonstrate remarkable increases in the FF period under the SSP5-8.5 scenario. The spatial distribution of extreme precipitation indices shows intensification over the eastern part of South Asia compared to the western part. The probability density function of extreme precipitation indices suggests a frequent (intense) occurrence of precipitation extremes in the FF period under the SSP5-8.5 scenario, with values up to 35.00 d for RR1mm and 25.00-35.00 d for CWD. The potential impacts of heavy precipitation can pose serious challenges to the study area regarding flooding, soil erosion, water resource management, food security, and agriculture development.



Key wordsprecipitation extremes      extreme precipitation indices      climate change      Coupled Model Intercomparison Project 6 (CMIP6)      Global Climate Model (GCM)      South Asia     
Received: 10 May 2022      Published: 31 March 2023
Corresponding Authors: * ZHAO Chengyi (E-mail: zhaocy@nuist.edu.cn)
About author: First author contact:The first and second authors contributed equally to this work.
Cite this article:

Adnan ABBAS, Asher S BHATTI, Safi ULLAH, Waheed ULLAH, Muhammad WASEEM, ZHAO Chengyi, DOU Xin, Gohar ALI. Projection of precipitation extremes over South Asia from CMIP6 GCMs. Journal of Arid Land, 2023, 15(3): 274-296.

URL:

http://jal.xjegi.com/10.1007/s40333-023-0050-3     OR     http://jal.xjegi.com/Y2023/V15/I3/274

Fig. 1 Spatial distribution of elevation in South Asia and its three subregions (R1, R2, and R3). R1, northern subtropical monsoon subregion; R2, eastern tropical monsoon subregion; R3, westerly subregion.
No. Model Name Country Resolution
1 BCC-CSM2-MR China 1.1°×1.1°
2 CanESM5 Canada 2.8°×2.8°
3 CESM2-WACCM United States of America 1.3°×0.9°
4 CNRM-CM6-1 France 1.4°×1.4°
5 CNRM-ESM2-1 France 1.4°×1.4°
6 IPSL-CM6A-LR France 2.5°×1.3°
7 MIROC6 Japan 1.4°×1.4°
8 MPI-ESM1-2-HR Germany 0.9°×0.9°
9 MRI-ESM2-0 Japan 1.1°×1.1°
10 NESM3 China 1.9°×1.9°
Table 1 Details of the selected Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs)
Index Abbreviation Definition Unit
Wet days RR1mm Annual count of days when precipitation ≥1.00 mm d
Heavy precipitation days RR10mm Annual count of days when precipitation ≥10.00 mm d
Very heavy precipitation days RR20mm Annual count of days when precipitation ≥20.00 mm d
Severe precipitation days RR50mm Annual count of days when precipitation ≥50.00 mm d
Consecutive wet days CWD Annual maximum number of consecutive days with precipitation >1.00 mm d
Consecutive dry days CDD Annual maximum number of consecutive days with precipitation <1.00 mm d
Max 5-day precipitation amount RX5day Annual maximum 5-day consecutive precipitation mm
Simple daily intensity index SDII Mean precipitation amount on wet days mm/d
Table 2 Description of the selected extreme precipitation indices used in this study
Fig. 2 Long-term temporal changes in extreme precipitation indices of CMIP6 GCMs and MMEM in the NF and FF periods under the SSP2-4.5 scenario. CMIP6, Coupled Model Intercomparison Project Phase 6; GCMs, Global Climate Models; MMEM, multi-model ensemble mean; NF, near future (2021-2060); FF, far future (2061-2100); SSP, Shared Socioeconomic Pathway. (a), RR1mm (wet days); (b), RR10mm (heavy precipitation days); (c), RR20mm (very heavy precipitation days); (d), RR50mm (severe precipitation days); (e), CWD (consecutive wet days); (f), CDD (consecutive dry days); (g), RX5day (max 5-day precipitation amount); (h), SDII (simple daily intensity index). The negative values indicate a decreasing trend, while the positive values indicate an increasing trend.
Fig. 3 Long-term temporal changes in extreme precipitation indices of CMIP6 GCMs and MMEM in the NF and FF periods under the SSP5-8.5 scenario. (a), RR1mm; (b), RR10mm; (c), RR20mm; (d), RR50mm; (e), CWD; (f), CDD; (g), RX5day; (h), SDII. The negative values indicate a decreasing trend, while the positive values indicate an increasing trend.
Fig. 4 Spatial distribution of changes in the MMEMs of RR1mm (a and b), RR10mm (c and d), RR20mm (e and f), and RR50mm (g and h) in the NF and FF periods under the SSP2-4.5 scenario. The black dot indicates that the trend is statistically significant at the 5% risk level at each grid point. The negative values indicate a decreasing trend, while the positive values indicate an increasing trend.
Fig. 5 Spatial distribution of changes in the MMEMs of CWD (a and b), CDD (c and d), RX5day (e and f), and SDII (g and h) in the NF and FF periods under the SSP2-4.5 scenario. The black dot indicates that the trend is statistically significant at the 5% risk level at each grid point. The negative values indicate a decreasing trend, while the positive values indicate an increasing trend.
Fig. 6 Spatial distribution of changes in the MMEMs of RR1mm (a and b), RR10mm (c and d), RR20mm (e and f), and RR50mm (g and h) in the NF and FF periods under the SSP5-8.5 scenario. The black dot indicates that the trend is statistically significant at the 0.05 significance level at each grid point. The negative values indicate a decreasing trend, while the positive values indicate an increasing trend.
Fig. 7 Spatial distribution of changes in the MMEMs of CWD (a and b), CDD (c and d), RX5day (e and f), and SDII (g and h) in the NF and FF periods under the SSP5-8.5 scenario. The black dot indicates that the trend is statistically significant at the 0.05 significance level at each grid point. The negative values indicate a decreasing trend, while the positive values indicate an increasing trend.
Fig. 8 Estimated probability distribution functions in MMEMs of extreme precipitation indices in the NF and FF periods under SSP2-4.5 and SSP5-8.5 scenarios. (a), RR1mm; (b), RR10mm; (c), RR20mm; (d), RR50mm; (e), CWD; (f), CDD; (g), RX5Day; (h), SDII. The negative values indicate a decreasing trend, while the positive values indicate an increasing trend.
Fig. 9 Box plots showing the changes in MMEMs of extreme precipitation indices in the NF and FF periods under the SSP2-4.5 and SSP5-8.5 scenarios. (a), RR1mm; (b), RR10mm; (c), RR20mm; (d), RR50mm; (e), CWD; (f), CDD; (g), RX5Day; (h), SDII. The upper and lower limits of the box indicate the 75th and 25th percentile values, respectively; the horizontal line in each box represents the median of the distributions; and the upper and lower whiskers show the 95th and 5th percentile values, respectively. The green short lines indicate outliers.
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