A Bayesian Framework for Multimodel Regression

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Climate model dependence and the replicate Earth paradigm (2013, methodology): “If one method gives a very ill-775 conditioned matrix (a concern of DelSole, 2007) and the other does not, it is possible that the 776 numerical accuracy of the inversions of the matrices might differ significantly.”
Skill improvement from increased ensemble size and model diversity (2014, background): “…generated in model intercomparison projects [Krishnamurti et al., 2000; Pavan and Doblas-Reyes, 2000; Barnston et al., 2003; Tebaldi et al., 2004; Hagedorn et al., 2005; DelSole, 2007; Peña and van den Dool, 2008; Casanova and Ahrens, 2009; van Oldenborgh et al., 2012; DelSole et al., 2013].”
Is unequal weighting significantly better than equal weighting for multi‐model forecasting? (2013, methodology): “In practice, the leastsquares model tends to perform poorly in cross-validation experiments, especially when the number of forecasts being combined is large (Kharin and Zwiers, 2002; DelSole, 2007).”
Skill of Multimodel ENSO Probability Forecasts (2008, methodology): “2, but for independently calibrated methods. rrmmr methodologies since they are not able to use the data to determine when the differences between the models in the historical record are insufficient to weight one model more than another (DelSole 2007).”