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

Daniel Farinotti*, Douglas J. Brinkerhoff, Garry K.C. Clarke, Johannes J. Fürst, Holger Frey, Prateek Gantayat, Fabien Gillet-Chaulet, Claire Girard, Matthias Huss, Paul W. Leclercq, Andreas Linsbauer, Horst Machguth, Fabien Maussion, Mathieu Morlighem, Cyrille Mosbeux, Ankur Pandit, Andrea Portmann, Antoine Rabatel, Raaj Ramsankaran, Thomas J. ReerinkOlivier Sanchez, Peter A. Stentoft, Sangita Singh Kumari, Ward J.J. Van Pelt, Brian Anderson, Toby Benham, Daniel Binder, Julian A. Dowdeswell, Andrea Fischer, Kay Helfricht, Stanislav Kutuzov, Ivan Lavrentiev, Robert McNabb, G. Hilmar Gudmundsson, Huilin Li, Liss M. Andreassen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

93 Citations (Scopus)
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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.

Original languageEnglish
Pages (from-to)949-970
Number of pages22
JournalCryosphere
Volume11
Issue number2
DOIs
Publication statusPublished - 18 Apr 2017
Externally publishedYes

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