Received: 24 May 2017 – Accepted for review: 02 Jun 2017 – Discussion started: 09 Jun 2017
Abstract. Antarctic and Greenland hold more than 99 % of all fresh water on Earth and, therefore, can significantly influence global sea level. Predicting future ice sheet mass balance depends upon ice sheet modelling, but it is limited by knowledge of a number of processes, some of which are still poorly understood. One such process is the calving of the ice shelves, where blocks of ice break off from the ice front. However, large scale ice flow models do not include an accurate representation of this process and the most commonly used damage mechanics and fracture mechanics methods have a large number of uncertainties. Here we present an alternative, statistics-based method to model the most probable zones of nucleation of fractures. We test our theory on all main ice shelf regions in Antarctica, including the Antarctic Peninsula. We can model up to 99 % of observed fractures, with an average rate of 77 % which represents a 50 % improvement over previously used damage-based approaches, thus providing the basis for modelling calving of ice shelves. We found that classifying Antarctic ice shelf regions based on the factors that controlled fracture formation led to grouping of ice shelves/glaciers with similar physical characteristics and geometry.
This preprint has been withdrawn.
How to cite: Emetc, V., Tregoning, P., and Sambridge, M.: A statistical fracture model for Antarctic glaciers, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-98, 2017.
We developed a statistics-based method to identify zones of Antarctic ice shelves that are likely to fracture, with an average accuracy of 77 % when compared to observed fractures identified in optical imagery. We find that we can identify 4 main groups of ice shelf regions having similar characteristics. Our method of identifying fracture regions provides the initial step in the modelling of the propagation of crevasses and can form the basis for modelling ice shelf calving processes.
We developed a statistics-based method to identify zones of Antarctic ice shelves that are...