Constraining Antarctic polynya formation and sea ice and snow evolution using autonomous observations and modeling

Abstract

This dissertation focuses on resolving key uncertainties in Southern Ocean sea ice and snow processes using under-ice autonomous ocean observations and modeling techniques in a Lagrangian, or flow-following, framework. After an introduction to the region and relevant processes (Chapter 1), the first portion of this work (Chapter 2) investigates the periodic appearance of large sea ice openings offshore of Antarctica, known as open-ocean polynyas. The rarity of these intermittent events in the 50-year satellite record has prevented oceanographers from pinpointing the factors that initiate polynyas and fully characterizing the vigorous cycle of ocean mixing and heat exchange believed to sustain them. Fortuitously, two Argo profiling floats, which are free-drifting robotic instruments that can collect ocean measurements beneath sea ice, were present during unexpected polynya events that occurred over Maud Rise in the Weddell Sea in 2016 and 2017. By placing their ocean measurements in context of meteorological data and past hydrographic and satellite records, we conclude that these sea ice openings were preconditioned by reduced upper-ocean salinity stratification and triggered by storms. Identifying links between these conditions and fluctuations in the primary mode of Southern Hemisphere climate variability, the Southern Annular Mode, yields a robust explanation for why polynyas have appeared at this location in some years but not others. These first in situ ocean observations also confirm that the anomalous openings were maintained by deep convective mixing, as long suspected. Antarctic sea ice thickness and overlying snow depth are important climate variables due to their strong influence on freshwater fluxes, ocean-atmosphere heat exchange, and momentum transfer. Yet monitoring their evolution from satellites has proven challenging, partly due to a sparsity of in situ measurements for validation purposes. The next portion of this work (Chapter 3) presents a newly developed numerical model that reconstructs the daily evolution of snow deposited on Antarctic sea ice along satellite-observed Lagrangian ice drift trajectories. Atmospheric reanalysis input data and parameterizations of key snow accumulation, erosion, and transformation processes are calibrated using autonomous snow buoy measurements. The resulting model reconstruction from 2003 to 2024 offers constraints on the annual mass budget of snow intercepted by sea ice in the Southern Ocean, including the magnitude and timing of freshwater release to the ocean. This represents a substantially larger flux than previously diagnosed, with implications for water mass transformation and vertical mixing. Snow-ice formation is inferred by comparing the simulated snow accumulation with satellite-observed snow depths, and trends in the reconstruction estimates are assessed. In the third portion of this work (Chapter 4), Antarctic sea ice formation and melt rates are directly estimated by calculating mixed layer salinity budgets along the wintertime drift trajectories of over 300 under-ice Argo profiling floats in the Southern Ocean. All except one budget term can be constrained using the float measurements and auxiliary data sources, leaving sea ice-induced fluxes from brine rejection during ice formation and freshwater release during ice melt to be inferred as the large budget residual. The seasonal cycle of sea ice exhibits a pronounced asymmetry with a prolonged net growth phase that slows in mid-winter before the initiation of rapid melt in fall. A circumpolar climatology of sea ice growth and melt rates within the Antarctic seasonal ice zone show net annual sea ice production near the Antarctic continent that switches to net annual melt at around 65°S. However, sea ice freezing and melt rates estimated from the float observations are found to be highly sensitive to uncertainties in the magnitude and timing of freshwater fluxes from snow. This float-based methodology highlights the potential to reconstruct climatological Antarctic sea ice thickness using autonomous ocean measurements. The final portion of this dissertation (Chapter 5) highlights a retrospective study on an undergraduate Python programming and ocean data analysis course that was co-developed and taught remotely in 2020 with another graduate student. Our teaching integrated evidence-based teaching practices—a flipped structure, activities infused with active learning, an individualized final research project, and efforts to center accessibility. A mixed-methods approach is used to evaluate the efficacy of the instructional design using data from surveys, online teaching platforms, student work, assessments, and a focus group. We find that the course elements bolstered student engagement and learning, allowing students with less or no prior coding experience to achieve similar success as peers with more experience.