Ice-sheet models typically treat glacier ice as a viscous material whose flow depends on temperature and stress. However, real glaciers and ice sheets are often heavily crevassed and fractured. Damage caused by crevasses can weaken the ice and alter its effective viscosity, potentially affecting ice-flow speeds and the evolution of ice sheets.

A major challenge in glaciology is developing realistic ways to represent fractures and damage within large-scale ice-flow models. While individual crevasses can be observed from satellites and aircraft, their cumulative effect on ice-sheet dynamics remains poorly quantified.

Projects under this umbrella aim to improve our understanding of how crevasses influence ice flow and how damage can be represented in continuum ice-sheet models.

Possible project directions include:

1. Modelling the evolution of ice damage

Develop a model describing the sources and sinks of a damage-density field. Damage may be generated by tensile stresses and crevasse formation, while healing processes may reduce damage over time. The model should account for advection by ice flow and be calibrated against observations of crevasse density and ice velocity. Depending on the project scope, machine-learning approaches could be explored as an alternative or complement to physically based parameterizations.

2. Quantifying the flow impact of crevassing

How much does crevassing weaken glacier ice?

This project would investigate how newly formed crevasses affect the effective viscosity of glacier ice. A suitable case study could be a surging glacier or another region that experienced substantial changes in both flow speed and crevasse density. By inverting a two-dimensional ice-flow model for effective viscosity before and after the onset of intense crevassing, the weakening effect of fractures can be quantified and related to observed changes in crevasse density.

Possible methods

  • Ice-flow modelling and inverse methods.
  • Continuum mechanics and damage mechanics.
  • Remote sensing of crevasses and glacier velocity.
  • Statistical and machine-learning approaches.
  • Analysis of satellite and airborne observations.

Background and relevant resources