Numerical models of the atmosphere discretize space and time, and are unable to resolve processes smaller than model resolution. As such, the aggregate effects of these sub-grid scale processes must be parameterized when their effects are manifest at resolved grid scale. However, it is known that the enhancement of sea-surface fluxes by sub-grid scale wind variations is difficult to appropriately parameterize deterministically. This limitation can be realized in a numerical model by the use of stochasticity, explicitly accounting for the randomness in how sea surface wind variability enhances sea-surface fluxes. The robustness of stochastically parameterizing sea surface flux enhancement due to wind speed variability is investigated by applying an established statistical model to coarse grained global convection permitting numerical model output from six different numerical models and four different geographical regions, to determine if there exists any sensitivities to region, time period, or model type. The sensitivity of the deterministic part of a surface flux parameterization studied is quantified via correlation, where different ten-day periods have the highest correlations and thus the least sensitivity, followed by differences in numerical models and differences in geographical regions. Results suggest that the choice of cumulus parameterization employed by a numerical model may contribute to statistical model sensitivity and consequent regression fit portability. The robustness of a Gaussian process fit applied to the stochastic part of the sea-surface flux enhancement parameterization reveals spatial non-stationarity, which provides insight into the potential for further improvements to the sea-surface flux parameterization studied. Results suggest that the stochastic parameterization studied is broadly robust, supporting implementation of such sea surface flux parameterization in operational weather and climate models. Results are also used to identify specific methods that may be utilized for improvements of the stochastic parameterization. / Graduate / 2023-01-27