Journal
GEOPHYSICAL RESEARCH LETTERS
Volume 41, Issue 5, Pages 1803-1808Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1002/2014GL059205
Keywords
data mining; climate sensitivity; CMIP; intercomparison; ensemble
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Funding
- DOE by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
- NASA [NNX09AH73G]
- Office of Science, Biological and Environmental Research, U.S. Department of Energy [DE-FC02-97ER62402]
- National Center for Atmospheric Research
- National Science Foundation
- DOE's Regional and Global Climate Modeling Program
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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
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