 Finally, it is worth noting that a significant motivation for model evaluation studies is model improvement: model evaluation activities can be directed toward understanding why a model’s results exhibit particular errors, in the hope of improving the model in future. Lenhard and Winsberg (2010), however, suggest that such “analytical understanding” of climate model performance is largely out of reach. They argue that features of climate models, including their complexity, their fuzzy modularity (see  Section 4.2)  and their incorporation of “kludges”—unprincipled fixes applied in model development to make the model as a whole work better—will often make it very difficult to apportion blame for poor simulation performance to different parts of climate models; climate modeling, they argue, faces a particularly challenging variety of confirmational holism (see also Petersen 2000). As empirical support, they point to the limited success of model intercomparison projects (e.g., Taylor et al. 2012) in identifying the sources of disagreement in climate model simulations. However, there have been some notable successes in localizing sources of error and disagreement in climate simulations. For example, differences in the representation of cloud processes have been identified as a major source of disagreement among estimates of climate sensitivity (Flato et al. 2013: 743). Likewise, a growing body of work on “emergent constraints” has uncovered sources of spread in various aspects of climate change projections (see, e.g., Qu &amp; Hall 2007 and subsequent work; Frigg et al. 2015c). Thus, while Lenhard and Winsberg are surely right that it is difficult to achieve analytical understanding of climate model performance, just how limited the prospects are seems an open question.