slow/large-scale variables and accounting for the fast/small-scale processes through surrogate models. In weather/climate models, for example, semi-empirical physics-based parameterizations are often used to represent the effects of processes such as gravity waves and moist convection in the atmosphere or sub-mesoscale eddies in the ocean [@stevens2013climate; @hourdin2017art; @garcia2017modification; @jeevanjee2017perspective; @schneider2017climate]. A more advanced approach is “super-parameterization”, which for example, involves solving the PDEs of moist convection on a high-resolution grid inside each grid point of large-scale atmospheric circulation, whose governing PDEs (the Navier-Stokes equations) are solved on a coarse grid [@khairoutdinov2001cloud]. While computationally more expensive, super-parameterized climate models have been shown to outperform parameterized models in simulating some aspects of climate variability and extremes [@benedict2009structure; @andersen2012moist; @kooperman2018rainfall].

More recently, “inexact computing” has received attention from the weather/climate community. This approach involves reducing the computational cost of each simulation by decreasing the *precision* of some of the less-critical calculations [@palem2014inexactness; @palmer2014climate], thus allowing the saved resources to be *reinvested*, for example, in more simulations (e.g., for probabilistic predictions) and/or higher resolutions for critical processes [@duben2014use; @duben2014benchmark; @thornes2017use; @hatfield2018improving]. One type of inexact computing involves solving the PDEs of some of the processes with single- or half-precision arithmetic, which requires less computing power 