干旱区生态环境知识资源中心(Arid): Quantification of uncertainties in projections of extreme daily precipitation simulated by CMIP6 GCMs over homogeneous regions of India

Arid

DOI	10.1002/joc.8269
	Quantification of uncertainties in projections of extreme daily precipitation simulated by CMIP6 GCMs over homogeneous regions of India
	Nair, Meera M.; Rajesh, A. Naga; Sahai, A. K.; Kumar, T. V. Lakshmi
通讯作者	Rajesh, AN
来源期刊	INTERNATIONAL JOURNAL OF CLIMATOLOGY
ISSN	0899-8418
EISSN	1097-0088
出版年	2023
卷号	43 期号:15 页码:7365-7380
英文摘要	Global climate model (GCM) projections are subject to significant uncertainties. Quantifying uncertainties in climate change projections improves credibility and makes climate data more reliable. This study aims to quantify the uncertainties in projected extreme precipitation during the 21st century over the homogeneous rainfall regions of India simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs. The percentile-based square root error variance (SREV) method estimates model, scenario and ensemble uncertainties in projections of extreme precipitation. The uncertainty is investigated at four thresholds: 95th, 99th, 99.9th and 100th percentiles. The results show that the wet northeast region has a greater SREV, which is consistent with previous studies. At 99th and 99.9th percentiles, relative model SREV is dominant over the northeast (NE) region. However, at the 95th percentile high relative model SREV is found over the northwest (NW) region during southwest (June, July, August and September) and NE (October, November and December) monsoon seasons. Model uncertainty is the main source of uncertainty, followed by scenario and ensemble uncertainties. The study indicates that the arid NW region in India has a higher level of uncertainty than other regions with homogeneous rainfall. These findings will assist policymakers in planning infrastructure development in arid regions of India.
英文关键词	CMIP6 extreme precipitation global circulation models SREV uncertainty
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001079347600001
WOS关键词	TEMPERATURE ; MODEL ; PATTERN ; TRENDS
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396978
推荐引用方式 GB/T 7714	Nair, Meera M.,Rajesh, A. Naga,Sahai, A. K.,et al. Quantification of uncertainties in projections of extreme daily precipitation simulated by CMIP6 GCMs over homogeneous regions of India[J],2023,43(15):7365-7380.
APA	Nair, Meera M.,Rajesh, A. Naga,Sahai, A. K.,&Kumar, T. V. Lakshmi.(2023).Quantification of uncertainties in projections of extreme daily precipitation simulated by CMIP6 GCMs over homogeneous regions of India.INTERNATIONAL JOURNAL OF CLIMATOLOGY,43(15),7365-7380.
MLA	Nair, Meera M.,et al."Quantification of uncertainties in projections of extreme daily precipitation simulated by CMIP6 GCMs over homogeneous regions of India".INTERNATIONAL JOURNAL OF CLIMATOLOGY 43.15(2023):7365-7380.