Author ORCID Identifier

https://orcid.org/0000-0003-2557-7327

Date of Award

Spring 6-15-2025

Document Type

Thesis (Ph.D.)

Department or Program

Engineering Sciences

First Advisor

Donald K. Perovich

Second Advisor

Christopher M. Polashenski

Third Advisor

Robert L. Hawley

Abstract

September Arctic sea ice extent has diminished by roughly 50% 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 accumulation, ice growth, snow and ice 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. Our 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 fill 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 depth 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 estimation error on the order of ±0.10 m. 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 critical insight into processes dictating ice mass balance in the contemporary Arctic, and delivers a new toolkit for obtaining urgently needed observations of snow on Arctic sea ice.

Comments

This form provided only three advisor fields. Marcel Nicolaus is the remaining member of my advisory committee.

Original Citation

Chapter 2 of this thesis was published as a journal article in Elementa. Chapter 3 is currently under review at The Cryosphere. Citations are given below.

Raphael, IA, Perovich, DK, Polashenski, CM, Clemens-Sewall, D, Itkin, P, Lei, R, Nicolaus, M, Regnery, J, Smith, MM, Webster, M, Jaggi, M. 2024. Sea ice mass balance during the MOSAiC drift experiment: Results from manual ice and snow thickness gauges. Elementa: Science of the Anthropocene 12(1). DOI: https://doi.org/10.1525/elementa.2023.00040

Raphael, I. A., Perovich, D. K., Polashenski, C. M., and Hawley, R. L.: A low-cost, autonomous system for distributed snow depth measurements on sea ice, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-187, 2025.

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