 Interpretation can be difficult even when there is relatively good fit between modeling results and observational data. One reason is that, to varying degrees, today’s climate models have been tuned to twentieth century observations. Tuning sometimes delivers an improved score on a performance metric by compensating for errors elsewhere in the model (Petersen 2000), and the stability of errors is not guaranteed as external conditions change, e.g., as greenhouse gas concentrations continue to rise (Stainforth et al. 2007; Knutti 2008). Some climate scientists suggest that data used in tuning cannot be used subsequently in model evaluation (e.g., Randall et al. 2007: 596; Flato et al. 2013: 750). Steele and Werndl (2013, 2016), however, argue that in some circumstances it is legitimate to use data for both calibration (i.e., tuning) and confirmation, something they illustrate with both Bayesian and frequentist methodologies. Frisch (2015) advocates what he calls a moderate predictivism in this context: if a climate model is tuned to achieve a good fit with data for a particular variable, this provides less support for the hypothesis that the model can deliver the same performance with respect to other predictions for that variable (e.g., future values), compared to the case when the good fit is achieved without tuning (see also Stainforth et al. 2007; Katzav et al. 2012; Mauritsen et al. 2012; Schmidt &amp; Sherwood 2015). Frisch defends this view on the grounds that successful simulation without tuning gives us more reason to think that a climate model has accurately represented key processes shaping the variable of interest; if we were already confident of the latter, then successful simulation without tuning would not have such an advantage. In any case, to take account of tuning in model evaluation, it is important to know what tuning has been done, and this information is often not readily available (see  Section 4.2).