 Constructing a parameterization is akin to an engineering problem, where the goal is to find an “adequate substitute” (Katzav 2013a) for the explicit simulation of a sub-grid process, using a limited set of ingredients. This is a challenging task. For given values of the grid-scale variables, there are usually many possible realizations of the sub-grid conditions. The standard approach to parameterization has been a deterministic one, aiming to estimate the contribution that the sub-grid process would make on average, over many possible realizations that are physically consistent with a given set of grid-scale conditions (McFarlane 2011). An alternative approach gaining popularity is stochastic parameterization, which aims to estimate the contribution made by a single, randomly-selected member of the set of realizations physically consistent with the grid-scale conditions (Berner et al. 2017). Typically, parameterizations of either type are informed by physical understanding but also incorporate elements derived at least in part from observations (see also Sundberg 2007; Guillemot 2010); this is why parameterizations are often described as “semi-empirical”. Because climate models incorporate both accepted physical theory and such semi-empirical, engineered elements, they are sometimes described as having a hybrid realist-instrumentalist status (Parker 2006; Katzav 2013a; see also Goodwin 2015).