Stochastic superparameterization, a stochastic parameterization framework based on a multiscale formalism, is developed for mesoscale eddy parameterization in coarse-resolution ocean modeling. The framework of stochastic superparameterization is reviewed and several configurations are implemented and tested in a quasigeostrophic channel model – an idealized representation of the Antarctic Circumpolar Current. Five versions of the Gent–McWilliams (GM) parameterization are also implemented and tested for comparison. Skill is measured using the time-mean and temporal variability separately, and in combination using the relative entropy in the single-point statistics. Among all the models, those with the more accurate mean state have the less accurate variability, and vice versa. Stochastic superparameterization results in improved climate fidelity in comparison with GM parameterizations as measured by the relative entropy. In particular, configurations of stochastic superparameterization that include stochastic Reynolds stress terms in the coarse model equations, corresponding to kinetic energy backscatter, perform better than models that only include isopycnal height smoothing.