 Parameterization also gives rise to fuzzy modularity within component models of GCMs/ESMs: which parameterizations work best with a given climate model depends to some extent on how other sub-grid processes have already been represented and the errors that those other representations introduce (see also Lenhard &amp; Winsberg 2010 on “generative entrenchment”). Even the best-available parameterizations, however, usually have significant limitations. Indeed, uncertainty about how to adequately parameterize sub-grid processes remains a major source of uncertainty in climate modeling. Differences in parameterizations—especially for cloud processes—account for much of the spread among simulations of the climate system’s response to increasing greenhouse gas concentrations (Flato et al. 2013: 743). This is one motivation for a new approach to parameterization: super-parameterization involves explicitly simulating a sub-grid process in a simplified way, by coupling a 1-D or 2-D model of the process (e.g., cloud formation) to each GCM/ESM grid point (Randall et al. 2013; Gramelsberger 2010). This is a multi-scale modeling approach that requires significant additional computing power, relative to traditional parameterization.