The School of Earth and Atmospheric Sciences Presents Dr. Ravindra Duddu, Vanderbilt University

The dynamic ice mass loss from the Antarctic and Greenland ice sheets into oceans is one of the greatest sources of uncertainty in predicting future sea level rise. The fracture and detachment of icebergs, that is, calving controls the mass loss from ice sheets, and is intricately linked to climate dynamics through processes such as hydrofracturing. 

It has been hypothesized that hydrofracturing of ice shelves followed by ice cliff failure in Antarctica could contribute to rapid sea level rise over the coming centuries. Therefore, it is important that we improve our understanding of calving and its representation in numerical ice sheet models. 

In contrast to existing empirical calving models, fracture-mechanics-based models assume that calving occurs when the combination of surface and basal crevasses penetrates the entire ice thickness. These models can be classified into three groups: zero stress, linear elastic fracture mechanics (LEFM), and continuum damage mechanics (CDM) models. 

In this presentation, I will present an brief overview of zero stress and LEFM calving models followed by two CDM models for brittle tensile fracture to study the propagation of air-filled and water-filled crevasses. Using idealized simulations studies on rectangular glaciers in two-dimensions (plane strain conditions), I will compare the predicted crevasse depths across different models. I will discuss the agreement between the models in relation to ice rheology, fracture process zone and basal boundary conditions. 

Based on preliminary results, I will argue that floating ice shelves are more vulnerable than grounded glaciers due to the combination of meltwater-induced hydrofracture and plate bending. I will end with some remarks on the limitations of fracture-mechanics-based calving models and directions for future work, namely modeling shear failure of ice cliffs and improving parametrizations of calving in ice sheet models.  

Acknowledgements: This work is funded by prior NSF grant #PLR-1341428 and a current NSF CAREER grant #PLR-1847173.