气候变化领域集成服务门户(CCPortal): Quantifying uncertainties in temperature projections: A factorial-analysis-based multi-ensemble downscaling (FAMED) method

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DOI	10.1016/j.atmosres.2020.105241
	Quantifying uncertainties in temperature projections: A factorial-analysis-based multi-ensemble downscaling (FAMED) method
	Liu Y.R.; Li Y.P.; Ding Y.K.
发表日期	2021
ISSN	0169-8095
卷号	247
英文摘要	In this study, a factorial-analysis-based multi-ensemble downscaling (FAMED) method is developed through incorporating multi-level factorial analysis and multiple statistical methods within a general framework. FAMED can effectively downscale climate variables as an ensemble from a global scale to a local scale, as well as disclose the individual and interactive effects of global climate model (GCM), emission scenario (ES), statistical downscaling method (SDM) on climate projection responses. Then, FAMED is applied to the City of Nur Sultan (the capital of Kazakhstan) for projecting the future changes of daily maximum, mean and minimum temperatures (Tmax, Tmean and Tmin). The mean, extreme and trend indices of temperature projections under 60 simulation chains with five GCMs, three ESs and four SDMs are examined for the period of 2021–2100. Major findings are: (i) ensemble simulations under the sixty simulation chains show that Nur Sultan would experience a warming trend (0.00046–0.00566 °C/month of Tmax, 0.00049–0.00540 °C/month of Tmean, and 0.00056–0.00542 °C/month of Tmin) in 2021–2100; (ii) comparing with the base period (1979–2004), monthly maximum, mean and minimum temperatures would increase by 3.25–7.41 °C, 1.96–5.87 °C, 2.10–6.00 °C in the future period (2075–2100); (iii) GCM is the main factor affecting the mean values of temperature projections (its contribution >65%), ES is the primary factor for the trend of temperature projections (its contribution >80%), and both GCM and SDM have important effects on the extreme values of temperature projections (the total contribution >70%); (iv) among all GCMs and SDMs, IPSL-CM5A-LR and stepwise cluster analysis (SCA) have the best performances in the model validation, demonstrating that the two tools are applicable to other cities and regions in Central Asia. © 2020 Elsevier B.V.
英文关键词	Climate change; Downscaling; Ensemble simulation; Multi-GCMs; Multi-level factorial analysis; Uncertainty
语种	英语
scopus关键词	Cluster analysis; Uncertainty analysis; Climate projection; Ensemble simulation; Factorial analysis; Global climate model; Interactive effect; Minimum temperatures; Statistical downscaling; Temperature projection; Climate models; air temperature; climate change; climate modeling; downscaling; ensemble forecasting; global climate; uncertainty analysis; Central Asia
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141739
作者单位	Environment and Energy Systems Engineering Research Center, School of Environment, Beijing Normal University, Beijing, 100875, China; Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, SK S4S0A2, Canada
推荐引用方式 GB/T 7714	Liu Y.R.,Li Y.P.,Ding Y.K.. Quantifying uncertainties in temperature projections: A factorial-analysis-based multi-ensemble downscaling (FAMED) method[J],2021,247.
APA	Liu Y.R.,Li Y.P.,&Ding Y.K..(2021).Quantifying uncertainties in temperature projections: A factorial-analysis-based multi-ensemble downscaling (FAMED) method.Atmospheric Research,247.
MLA	Liu Y.R.,et al."Quantifying uncertainties in temperature projections: A factorial-analysis-based multi-ensemble downscaling (FAMED) method".Atmospheric Research 247(2021).