PhD Thesis Defense: Ian Raphael

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"How’s it growing? Tools for observing snow and sea ice in a changing Arctic Ocean"

Abstract

September Arctic sea ice extent has diminished by roughly 50% percent in the 45 years since satellite observations began. The Arctic Ocean may experience ice-free summers within the next decade, with implications for habitat, resource extraction, geopolitics, and local and global climate change. 

To predict how Arctic sea ice will change in the future, we need to understand its behavior in the present. In situ sea ice mass balance measurements (snow depth, ice growth, surface melt, and bottom melt) are essential for studying the processes driving rapid changes in the ice pack, and for validating remote sensing measurements and climate models. 

Here, we evaluate sea ice mass balance observations from the 2019-2020 MOSAiC expedition in the central Arctic, highlighting significant changes in sea ice growth and melt processes over the past several decades. The results indicate that snow depth and its heterogeneity are powerful controls on winter ice growth in the younger, thinner ice pack of the modern Arctic. Yet, we lack the precise, spatially dense snow depth measurements needed to fully understand and model the role of snow in the sea ice system. 

We developed a distributed, autonomous snow depth observation system that is ~95% less expensive than existing systems to remedy this gap. Finally, while this system is a leap forward in low-cost snow observation, budget and resource constraints continue to limit the scope of autonomous snow sampling efforts. We conducted a study to investigate how sample size and arrangement influence parameter estimation errors in order to determine efficient snow sampling strategies. We found that the current practice of using a single autonomous station to estimate mean snow depth produces high estimation error, while increasing the sample size to just 16 stations decreases estimation uncertainty for the mean to roughly ±0.02 m, and enables standard deviation estimation to the same uncertainty. This uncertainty metric represents a ~50% improvement over using a single station and is adequate for most use-cases. 

Collectively, this thesis provides an updated assessment of Arctic sea ice mass balance and provides a new toolset for obtaining urgently needed observations of snow on Arctic sea ice.

Thesis Committee:

• Professor Donald Perovich (Chair)
• Professor Christopher Polashenski
• Professor Robert Hawley
• Professor Marcel Nicolaus (Alfred Wegener Institute, External)

Contact

For more information, contact Thayer Registrar at thayer.registrar@dartmouth.edu.