Author ORCID Identifier

https://orcid.org/0000-0002-1202-1541

Date of Award

2024

Document Type

Thesis (Ph.D.)

Department or Program

Engineering Sciences

First Advisor

Ian Baker

Second Advisor

Colin Meyer

Abstract

Glacier ice cores are unparalleled natural archives of past climate, containing ancient air and impurities. Understanding glacier and ice sheet behavior and their impact on impurity records is crucial for interpreting ice core records. H2SO4 is particularly significant, as it provides the strongest evidence for the diffusion of chemical signals and decreases the strength of single crystals and polycrystals of ice. However, its effects on recrystallization, grain growth, and fabric development mechanisms, as well as the effects of grain boundaries on its post-depositional diffusion remain unclear.

This thesis investigates the effects of the microstructural location of H2SO4 on the mechanical properties of polycrystalline ice, as well as its post-depositional diffusion using a series of constant load uniaxial compression tests and constant strain rate shear tests. Materials characterization techniques such as cross-polarized light imaging, electron backscatter diffraction (EBSD), and Rigsby stage fabric analysis were used to study the evolution of grains and grain boundaries, while energy-dispersive spectroscopy (EDS), and Raman spectroscopy were used to characterize H2SO4 migration. Results indicate that low concentrations of H2SO4 delay the onset of recrystallization in compression but accelerate it in shear, enhance grain boundary mobility, and influence fabric development processes in polycrystalline ice. The results also confirm that H2SO4 concentrates at the grain boundaries and triple junctions, creating a liquid layer that may enhance impurity diffusion in veins.

While plastic deformation of polycrystalline ice dominates the lower parts of ice sheets, firn densification dominates the upper parts. The study also investigates the microstructural changes and recrystallization mechanisms during the creep of firn. The results indicate that recrystallization in firn starts in secondary creep by strain-induced boundary migration and nucleation and growth of new grains.

Finally, FirnLearn, a steady-state firn densification model for Antarctica, was developed using deep learning techniques. When compared to a state-of-the-art densification model, FirnLearn demonstrates superior performance in the second stage of densification and excels in regions lacking surface density observations. These results establish deep learning as a promising tool for understanding firn densification, with the potential for further improvement as additional observations and features are incorporated.

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