Radar Sounding Analysis of Mountain Glaciers in Alaska: Revealing Ice Thickness, Subglacial Topography, and Geologic Structure
Bibliographic record
Abstract
Alaskan mountain glaciers (grouped together herein with those of Yukon and British Columbia, Canada) are melting at high rates and contributing disproportionately to global sea level rise, outpacing all glaciated regions on Earth aside from the Antarctic and Greenland ice sheets. Model projections indicate that Alaska will continue to lead glacier melting through the end of the century, contributing at least one third of a millimeter per year to global sea level rise by that time. As ice thickness measurements are only available for <2% of the world’s glaciers, projections are critically based on globally derived ice thickness models which are known to have significant uncertainties. In this dissertation we present investigations aimed at better constraining the ice thickness and subglacial topography of mountain glaciers across Alaska. We employ ice penetrating radar as our primary tool in addressing these objectives. Chapter 1 introduces the decline of mountain glaciers in Alaska and the use of ice penetrating radar to fill large data gaps and reduce uncertainty in modeled future glacier evolutions. Chapter 2 presents an analysis of NASA Operation IceBridge radar sounding data acquired over Malaspina Glacier, the world’s largest piedmont type glacier. Detailed radar mapping of the glacier’s bed reveals that Malaspina is almost entirely grounded below sea level and that distinct channels cut the bed. We show that the morphology of channels mapped at the bed of Malaspina is influenced by tectonic faults which actively accommodate convergence of the Yakutat terrane to North America. Chapter 3 explores the entirety of NASA’s Operation IceBridge radar sounding data from Alaska, providing an overview of the most extensive inventory of Alaska ice thickness measurements to date. Airborne radar sounding reveals that many of Alaska’s land- and lake-terminating glaciers have overdeepened termini – providing insight on the potential growth of new and existing proglacial lakes and associated natural hazards. Gridded measurements of ice thickness and bed elevation are presented for Bering Glacier, revealing additional tectonic structure of the Saint Elias orogen. In Chapter 4, we present a surface-based ice penetrating radar survey from Ruth Glacier on the southern flank of the Alaska Range. While we map bed depths in the amphitheater of Ruth Glacier, interpretable bed returns were not observed down-glacier within the “Great Gorge.” Downstream thickness was therefore derived through mass conservation by combining radar-derived measurements of ice thickness with satellite-derived surface velocities. This study highlights techniques to constrain ice fluxes of glaciers for which direct measurements may be unfeasible due to the glacier’s geometry. Finally, in Chapter 5 we summarize our findings and discuss the implications of this research, as well as future research opportunities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".