气候变化领域集成服务门户(CCPortal): A novel self-adjusting weight approximation procedure to minimize non-identical seasonal effects in multimodel ensemble for accurate twenty-first century drought assessment

CCPortal

DOI	10.1007/s00477-024-02689-0
	A novel self-adjusting weight approximation procedure to minimize non-identical seasonal effects in multimodel ensemble for accurate twenty-first century drought assessment
	Naz, Rubina; Ali, Zulfiqar
发表日期	2024
ISSN	1436-3240
EISSN	1436-3259
起始页码	38
结束页码	6
卷号	38 期号:6
英文摘要	Inherent biases in numerical simulation models of global climate models (GCMs) reduce the scope for accurately assessing future droughts under multimodel ensemble. The aim of this research is to increase the efficiency of multimodel ensembles. Consequently, this paper introduces a two-way hybrid weighting scheme for ensembling multiple GCMs. The proposed weighting scheme enhances the coherence of the multimodel ensemble of climate model simulations with real observed data and minimizes the impact of non-identical seasonal behavior in climate simulations within the multimodel ensemble. In the application, we consider precipitation data from 18 GCMs of CMIP6 from the Tibet Plateau region. To evaluate the effectiveness of the proposed scheme, we compared the proposed ensemble data with simple model averaged (SMA) ensemble using the Pearson product-moment correlation coefficient (r), root mean squared error (RMSE), and mean absolute error (MAE). Furthermore, we assessed the future characteristics of various drought categories using the steady-state probabilities of a Markov Chain. Small values of errors metircs and high correlation indicate that the proposed hybrid weighting scheme exhibits better performance than the SMA. Under the proposed index, the long term probabilities of various drought classe show variations across different time scales of drought. These results provide perspectives on how the the long term probabilities of various drought categories evolve over time.
英文关键词	Inherent biases; Global climate models (GCMs); Drought; Multimodel ensemble
语种	英语
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources
WOS类目	Engineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources
WOS记录号	WOS:001176587200001
来源期刊	STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/296886
作者单位	University of Punjab
推荐引用方式 GB/T 7714	Naz, Rubina,Ali, Zulfiqar. A novel self-adjusting weight approximation procedure to minimize non-identical seasonal effects in multimodel ensemble for accurate twenty-first century drought assessment[J],2024,38(6).
APA	Naz, Rubina,&Ali, Zulfiqar.(2024).A novel self-adjusting weight approximation procedure to minimize non-identical seasonal effects in multimodel ensemble for accurate twenty-first century drought assessment.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,38(6).
MLA	Naz, Rubina,et al."A novel self-adjusting weight approximation procedure to minimize non-identical seasonal effects in multimodel ensemble for accurate twenty-first century drought assessment".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 38.6(2024).