DOI: 10.1002/2014GL059205
论文题名: Statistical significance of climate sensitivity predictors obtained by data mining
作者: Caldwell P.M. ; Bretherton C.S. ; Zelinka M.D. ; Klein S.A. ; Santer B.D. ; Sanderson B.M.
刊名: Geophysical Research Letters
ISSN: 0094-10310
EISSN: 1944-10041
出版年: 2014
卷: 41, 期: 5 起始页码: 1803
结束页码: 1808
语种: 英语
英文关键词: climate sensitivity
; CMIP
; data mining
; ensemble
; intercomparison
Scopus关键词: Climate change
; Climate sensitivity
; CMIP
; Coupled Model Intercomparison Project
; ensemble
; Intercomparisons
; Predictive relationships
; Significance testing
; Statistical significance
; Data mining
; climate effect
; climate modeling
; climate prediction
; correlation
; data mining
; hypothesis testing
; statistical analysis
; Southern Ocean
英文摘要: Several recent efforts to estimate Earth's equilibrium climate sensitivity (ECS) focus on identifying quantities in the current climate which are skillful predictors of ECS yet can be constrained by observations. This study automates the search for observable predictors using data from phase 5 of the Coupled Model Intercomparison Project. The primary focus of this paper is assessing statistical significance of the resulting predictive relationships. Failure to account for dependence between models, variables, locations, and seasons is shown to yield misleading results. A new technique for testing the field significance of data-mined correlations which avoids these problems is presented. Using this new approach, all 41,741 relationships we tested were found to be explainable by chance. This leads us to conclude that data mining is best used to identify potential relationships which are then validated or discarded using physically based hypothesis testing. Key Points Correlation magnitude is not sufficient proof of predictive skill Significance testing is complicated by model nonindependence in ensembles The best predictors of climate change are related to the Southern Ocean ©2014. American Geophysical Union. All Rights Reserved. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84895643490&doi=10.1002%2f2014GL059205&partnerID=40&md5=f8ca70c87d9de4ef37c49523dcc26a75
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/7574
Appears in Collections: 气候减缓与适应
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作者单位: Lawrence Livermore National Laboratory California, Livermore CA, United States
Recommended Citation:
Caldwell P.M.,Bretherton C.S.,Zelinka M.D.,et al. Statistical significance of climate sensitivity predictors obtained by data mining[J]. Geophysical Research Letters,2014-01-01,41(5).