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Statistical significance of climate sensitivity predictors obtained by data mining
Author(s) -
Caldwell Peter M.,
Bretherton Christopher S.,
Zelinka Mark D.,
Klein Stephen A.,
Santer Benjamin D.,
Sanderson Benjamin M.
Publication year - 2014
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2014gl059205
Subject(s) - focus (optics) , sensitivity (control systems) , statistical hypothesis testing , computer science , data mining , field (mathematics) , econometrics , statistics , climatology , mathematics , geology , engineering , physics , electronic engineering , pure mathematics , optics
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.