How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment

Farinotti, Daniel, Brinkerhoff, Douglas J., Clarke, Garry K. C., Fürst, Johannes J., Frey, Holger, Gantayat, Prateek, Gillet-Chaulet, Fabien, Girard, Claire, Huss, Matthias, Leclercq, Paul W., Linsbauer, Andreas, Machguth, Horst, Martin, Carlos, Maussion, Fabien, Morlighem, Mathieu, Mosbeux, Cyrille, Pandit, Ankur, Portmann, Andrea, Rabatel, Antoine, Ramsankaran, RAAJ, Reerink, Thomas J., Sanchez, Olivier, Stentoft, Peter A., Singh Kumari, Sangita, van Pelt, Ward J. J., Anderson, Brian, Benham, Toby, Binder, Daniel, Dowdeswell, Julian A., Fischer, Andrea, Helfricht, Kay, Kutuzov, Stanislav, Lavrentiev, Ivan, McNabb, Robert, Gudmundsson, Hilmar, Li, Huilin and Andreassen, Liss M. (2017) How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment. The Cryosphere, 11 (2). pp. 949-970. ISSN 1994-0424

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Official URL: https://doi.org/10.5194/tc-11-949-2017

Abstract

Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably - locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: on average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations on the order of 10 ± 24% of the mean ice thickness (1σ estimate). Models relying on multiple data sets - such as surface ice velocity fields, surface mass balance, or rates of ice thickness change - showed high sensitivity to input data quality. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches.

Item Type: Article
Subjects: F600 Geology
F700 Ocean Sciences
F800 Physical and Terrestrial Geographical and Environmental Sciences
F900 Others in Physical Sciences
Department: Faculties > Engineering and Environment > Geography and Environmental Sciences
Depositing User: Rachel Branson
Date Deposited: 04 Aug 2020 08:29
Last Modified: 04 Aug 2020 08:30
URI: http://nrl.northumbria.ac.uk/id/eprint/43960

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