Statistical significance of climate sensitivity predictors obtained by data mining

Caldwell, Peter M., Bretherton, Christopher S., Zelinka, Mark D., Klein, Stephen A., Santer, Benjamin D. and Sanderson, Benjamin M. (2014) Statistical significance of climate sensitivity predictors obtained by data mining. Geophysical Research Letters, 41 (5). pp. 1803-1808. ISSN 0094-8276

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Abstract

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

Item Type:	Article
Uncontrolled Keywords:	climate sensitivity,cmip,data mining,ensemble,intercomparison,geophysics,earth and planetary sciences(all) ,/dk/atira/pure/subjectarea/asjc/1900/1908
Faculty \ School:	Faculty of Science > School of Environmental Sciences
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	18 Mar 2026 16:30
Last Modified:	18 Mar 2026 16:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/102441
DOI:	10.1002/2014GL059205

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