 Scientists and philosophers have raised a number of criticisms of methods that infer probabilities from ensemble results. As implied above, some lines of criticism cite evidence that the statistical assumptions underlying the studies are not met. Others argue that uncertainty about future climate change is deeper than precise probabilities imply, either because of challenges associated with quantifying structural model uncertainty (e.g., Stainforth et al. 2007;  Hillerbrand 2014; Parker 2014a) or, relatedly, because the possible future states of the climate system (i.e., the outcome space) is itself uncertain (Katzav et al. 2012). In other words, these critics contend that probabilistic uncertainty estimates have a false precision and, in that sense, are misleading about the actual state of knowledge. Frigg et al. (2013, 2014, 2015a) argue that, for PPE studies like UKCP09, such probabilities are also likely to be misleading in a different sense, namely, because they differ markedly from the probabilities that would be obtained from a model with no structural error. They illustrate that, for nonlinear systems, even a small amount of structural model error can give rise to very misleading probabilities—a consequence they dub the “hawkmoth effect” (taking inspiration from the “butterfly effect”, which relates to initial condition error; see also Mayo-Wilson 2015, Lawhead forthcoming). Winsberg and Goodwin (2016), however, contend that more work is needed to establish that the hawkmoth effect is as devastating for probabilistic climate projection as Frigg et al. claim (see also Goodwin &amp; Winsberg 2016).