Date of Award

11-2024

Document Type

Thesis (Master's)

Department or Program

Earth Sciences

First Advisor

Robert L. Hawley

Second Advisor

Erich C. Osterberg

Third Advisor

Jonathan W. Chipman

Abstract

Surface roughness is a critical component of the energy and mass balance of the Greenland Ice Sheet (GrIS). However, roughness is often oversimplified in predictive models due to its inherent scale dependency. Accurate quantification of roughness, including its recent changes and driving factors, is critically important for understanding and predicting GrIS surface dynamics. Using multi- and single-scale methods of roughness analysis, I assess spatiotemporal trends in GrIS meter-scale surface roughness from 2009 to 2019 with Operation IceBridge’s Airborne Topographic Mapper ILATM2 product. Additionally, with data from on-ice automated weather stations, I employ machine learning techniques to identify primary climatic controls on roughness. While no climatic variables emerge as clear controls on roughness, results indicate that roughness behaves differently between high and low elevations: roughness is high and increasing in the ablation zone and is low and decreasing in the accumulation zone. This is likely due to the effects of increased mass loss associated with a warming climate and decreased precipitation over the ice sheet’s interior. Furthermore, I evaluate the performance of an on-ice laser distance meter deployed at Summit in the 2024 field season, finding the laser to have a precision of ± 7 mm, reducing error in the validation process of ICESat-2’s ATLAS laser and providing monthly, in-situ measurements of high-altitude roughness.

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