Statistical significance of climate sensitivity predictors obtained by data mining

By Peter M. Caldwell, Chris Bretherton, M. Zelinka, Stephen A. Klein, Benjamin D. Santer, B. Sanderson. 2014

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Citations in the corpus

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, neutral): “, [14], [15], [16]).”
Machine learning and artificial intelligence to aid climate change research and preparedness (2019, methodology): “Although the method has received some scepticism, such as by Caldwell et al (2014), a review by Hall et al (2019) notes thatmany have physical justification, whilst other robust regressions are worthy of investigation to search formechanistic understanding.”
A Representative Democracy to Reduce Interdependency in a Multimodel Ensemble (2015, methodology): “With a truncation length of 9, which we used for the bulk of this study, the resulting distance matrix remains highly correlated to the full-ﬁeld distance matrix, but the inﬂuence of covariant ﬁelds and models is reduced [see Caldwell et al. (2014) for an extensive discussion of these issues].”
Evaluating Emergent Constraints on Equilibrium Climate Sensitivity (2018, background): “One problem with emergent constraints is that large intermodel correlations between current-climate and future-climate quantities are expected by chance in multimodel databases (Masson and Knutti 2013; Caldwell et al. 2014).”
Emergent Constraints for Cloud Feedbacks (2015, unknown): “For example, there may be redundancies among some predictors [8], and in this regard, one might expect the constraints of Qu et al.”
A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014, background): “In a relationship mining application, 34 scientists attempted to automatically identify the various climate variables that are currently observable and are good predictors of simulated increases in global temperatures if CO 2 levels in the atmosphere were to double (known as equilibrium climate…”
Causes of Higher Climate Sensitivity in CMIP6 Models (2020, methodology): “To avoid overstating statistical significance (Caldwell et al., 2014), we average results from all models from single modeling centers as a last step before performing all significance tests.”
Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6? (2020, background): “As reported by Caldwell et al. (2014), the large amount of data provided by modern ESMs can generate spurious correlations of variables between past climate and ECS just by chance, especially when only a small number of climate models is considered.”