DOI: 10.1007/s00382-014-2076-x
Scopus记录号: 2-s2.0-84919921998
论文题名: A generalized conditional heteroscedastic model for temperature downscaling
作者: Modarres R. ; Ouarda T.B.M.J.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2014
卷: 43, 期: 2017-09-10 起始页码: 2629
结束页码: 2649
语种: 英语
英文关键词: Conditional correlation
; Conditional covariance
; DCC
; Diagonal VECH
; Multivariate GARCH
; Nonlinearity
; Stationarity
; Temperature
英文摘要: This study describes a method for deriving the time varying second order moment, or heteroscedasticity, of local daily temperature and its association to large Coupled Canadian General Circulation Models predictors. This is carried out by applying a multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) approach to construct the conditional variance–covariance structure between General Circulation Models (GCMs) predictors and maximum and minimum temperature time series during 1980–2000. Two MGARCH specifications namely diagonal VECH and dynamic conditional correlation (DCC) are applied and 25 GCM predictors were selected for a bivariate temperature heteroscedastic modeling. It is observed that the conditional covariance between predictors and temperature is not very strong and mostly depends on the interaction between the random process governing temporal variation of predictors and predictants. The DCC model reveals a time varying conditional correlation between GCM predictors and temperature time series. No remarkable increasing or decreasing change is observed for correlation coefficients between GCM predictors and observed temperature during 1980–2000 while weak winter–summer seasonality is clear for both conditional covariance and correlation. Furthermore, the stationarity and nonlinearity Kwiatkowski–Phillips–Schmidt–Shin (KPSS) and Brock–Dechert–Scheinkman (BDS) tests showed that GCM predictors, temperature and their conditional correlation time series are nonlinear but stationary during 1980–2000 according to BDS and KPSS test results. However, the degree of nonlinearity of temperature time series is higher than most of the GCM predictors. © 2014, Springer-Verlag Berlin Heidelberg. 资助项目: NSERC, Canada Research Chairs
; Canada Research Chairs
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/54639
Appears in Collections: 过去全球变化的重建
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作者单位: Hydroclimate Modeling Group, INRS-ETE, 490 De La Couronne, Quebec, QC, Canada; Institute Center for Water Advanced Technology and Environmental Research (iWATER), Masdar Institute of Science and Technology, P.O. Box 54224, Abu Dhabi, United Arab Emirates
Recommended Citation:
Modarres R.,Ouarda T.B.M.J.. A generalized conditional heteroscedastic model for temperature downscaling[J]. Climate Dynamics,2014-01-01,43(2017-09-10)