 Climate model evaluation can have many different goals. A basic motivation is simply to learn about how climate models relate to the climate system. Borrowing the language of Giere (2004), we might say that a goal is to learn about the respects in which and degrees to which climate models are similar to the climate system (Lloyd 2010). A climate scientist might investigate, for example, whether a particular GCM exhibits a seasonal cycle in temperature for North America that roughly matches that which currently occurs in the real system. Evaluation efforts can also be directed at understanding similarities at the level of the processes and mechanisms underlying behaviors. In this regard, Lloyd (2009, 2010, 2015) points to fit between model results and observations on a variety of metrics, independent support for model components, and robustness of findings as considerations that are relevant. A second common goal of climate model evaluation, closely related to the first, is to learn whether a model is adequate for particular purposes (Parker 2009; Baumberger et al. 2017). As discussed in  Section 4.3,  scientists use climate models for various purposes: to characterize climate system properties (e.g., internal variability), to make projections of future climate change under particular scenarios, and so on. Many evaluation activities are intended to inform conclusions about climate models’ adequacy for these purposes and, ipso facto, about the levels of confidence that can be had in the models’ estimates of internal variability, their projections of future climate change, etc.