PhD Research Project: NERC CENTA - Estimating the risk of Antarctic ice shelf collapse using Bayesi

Location
United Kingdom
Posted
Nov 30, 2016
Closes
Jan 23, 2017
Organization Type
University and College
Hours
Full Time
Details

Ice shelves form the floating extensions of the Antarctic Peninsula ice sheet and play a crucial role in regulating ice sheet flow and global sea- level rise. The presence of an ice shelf provides resistive (back or buttressing) forces, which partly compensate the driving forces of inland ice flowing to the sea1. Since the mid-20th century and during the satellite observational era, several ice shelves in the Antarctic Peninsula have substantially retreated or even catastrophically collapsed2. This has resulted in acceleration of inland ice flow3 by a factor of up to eight4, with some basins still adjusting to pre-collapse velocities some twenty years after disintegration5. Acceleration of inland ice following shelf collapse has resulted in significant contributions to sea- level rise from this region5,6, with future contributions expected to be heavily dependent on the state and fate of the remaining shelves in the peninsula and elsewhere in Antarctica7. Forecasts of future sea-level rise require ice sheet models incorporating realistic predictions of the timing of future ice shelf collapses. Risk estimation of Antarctic ice shelf collapse thus remains a important goal of the cryospheric sciences.

This project will utilise satellite and climate model to construct a statistical model of ice shelf collapse risk. Despite many satellite observations and proxy reconstructions of previous collapse episodes, the complexity of governing processes occuring within ice shelves so far precludes the use of physically-based forecast models. However, the emerging and substantial observational record of ice shelf properties, and surveys of more than half a century of ice shelf collapse episodes2, lend themselves well to combination within a statistical model framework. Bayesian nonparametrics provide a class of data- led statistical models that adapt their complexity to the data itself. This approach incorporates existing observations to model a phenomenon, yet is flexible enough to allow future inclusion of new datasets. This quality is essential to modelling ice shelf collapse risk, where new observation and information are often made available. The project will make use of ice shelf physical properties, environmental conditions and collapse timing histories to estimate the risk of future collapse events. In particular, we will seek to assign probabilities to major collapse events at individual ice shelves over the course of the next 100 years. These probabilities can then be used in physically-based ice sheet models to improve forecasts of the Antarctic ice sheet contribution to sea-level rise.

Methodology:
The project will undertake a variety of state-of- the-art quantitative analyses to assess recent collapse episodes, elicit expert judgemente.g.8 on future ice shelf collapse risk, produce a data-led framework for selection, analysis, archiving and retrieval of environmental and glaciological datasets, and derive probabalistic estimates of ice shelf collapse risk.
The main aim is to combine data observed in the past three decades with expert opinions, coherently through a Bayesian analysis. In order to achieve this goal we will apply a spatio- temporal covariance regression model coupled with Gaussian process priors. This Bayesian nonparametric procedure effectively tunes the complexity of the model to the information present in the data. In addition, it propagates the uncertainty in physical observations and inputs from climate models coherently, and yields risk estimates for future collapse events under various projections, averaging latent observables according to their plausibility.

Funding Notes

In addition to completing an online application form, you will also need to complete and submit the CENTA studentship application form available from www.centa.org.uk.

CENTA studentships are for 3.5 years and are funded by the Natural Environment Research Council (NERC). In addition to the full payment of their tuition fees, successful candidates will receive the following financial support.

Annual stipend, set at £14,296 for 2016/17
Research training support grant (RTSG) of £8,000

CENTA students are required to undertake from 45 days training throughout their PhD including a 10 day placement.

References

1. Cuffey, K.M. & Paterson, W.S.B. (2010) ‘The Physics of Glaciers’ 4th edition, Elsevier. ISBN 978-0-12-369461-4. 2. Cook, A.J. & Vaughan,
D.G. (2010) The Cryosphere, 4, 77-98, doi:10.5194/tc-4-77-2010. 3. De Angelis, H. & Skvarca, P. (2003) Science, 299, 1560, doi:10.1126/science.1077987. 4. Rignot, E. et al. (2004) Geophys. Res. Lett., 31, L18401, doi:10.1029/2004GL020697. 5. Rott, H. et al. (2014) Geophys. Res. Lett., 41, 8123-8129, doi:10.1002/2014GL061613. 6. McMillan, M. et al. (2014) Geophys. Res. Lett., 32, L19604, doi:10.1002/2014GL060111. 7. Barrand, N.E. et al. (2013) J. Glaciol., 59, 215, 397-409, doi:10.3189/2015JoG12J139. 8. Bamber, J.L. & Aspinall, W.P. (2013) Nature Climate Change, doi:10.1038/nclimate1778.