The Southern Ocean is a highly dynamic region of the world's ocean. It is difficult to observe, so oceanographers rely on models. Both models and observations rely on sea surface temperature as a fundamental ocean property for determining heat exchange between sea ice, the atmosphere, and the ocean. Particularly within the Southern Ocean, observations are sparse and limited. Models have similar pitfalls, as they do not resolve the scales necessary for many physical processes like sea ice formation or melt. This is not done to exclude small-scale physics but rather to optimize for computational resources and calculation time. The lack of resolution is recovered by the use of parameterizations. Accurately estimating the spatial distribution of sea surface temperature is needed to inform parameterizations.

I utilize the Surface Underway Measurements Dataset compiled from various research vessels as they sample sea surface temperature in the marginal sea ice zone within the Southern Ocean. This dataset proves useful as it yields results from temporal binning and analysis of the measurements as they relate to processes that occur on the order of one day and 50 kilometers. The analysis presents submesoscale variability in the surface temperature field, indicating complex processes underrepresented within models. Computing statistical equations of this variability allows for the implementation of more advanced parameterizations within models. Suggestions are made on improvement to parameterizations as well as a discussion on possible mechanisms creating elevated horizontal variability of sea surface temperature within the marginal sea ice zone.