DOI: 10.1007/s00382-016-3079-6
Scopus记录号: 2-s2.0-84962670533
论文题名: A new statistical approach to climate change detection and attribution
作者: Ribes A. ; Zwiers F.W. ; Azaïs J.-M. ; Naveau P.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2017
卷: 48, 期: 2017-01-02 起始页码: 367
结束页码: 386
语种: 英语
英文关键词: Attribution
; Climate change
; Detection
; Optimal fingerprint
英文摘要: We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly. As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns. We introduce estimation and testing procedures based on likelihood maximization, and show that climate modelling uncertainty can easily be accounted for. Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity. Our approach is based on the “models are statistically indistinguishable from the truth” paradigm, where the difference between any given model and the truth has the same distribution as the difference between any pair of models, but other choices might also be considered. The properties of this approach are illustrated and discussed based on synthetic data. Lastly, the method is applied to the linear trend in global mean temperature over the period 1951–2010. Consistent with the last IPCC assessment report, we find that most of the observed warming over this period (+0.65 K) is attributable to anthropogenic forcings (+0.67 ± 0.12 K, 90 % confidence range), with a very limited contribution from natural forcings (- 0.01 ± 0.02 K). © 2016, Springer-Verlag Berlin Heidelberg. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53390
Appears in Collections: 过去全球变化的重建
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作者单位: CNRM, Météo France/CNRS, 42 avenue Gaspard Coriolis, Toulouse, France; Pacific Climate Impacts Consortium, University of Victoria, Victoria, BC, Canada; IMT, University of Toulouse, 118 route de Narbonne, Toulouse Cedex 9, France; Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRSUVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
Recommended Citation:
Ribes A.,Zwiers F.W.,Azaïs J.-M.,et al. A new statistical approach to climate change detection and attribution[J]. Climate Dynamics,2017-01-01,48(2017-01-02)