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 ORCID: https://orcid.org/0000-0002-2661-169X; 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, G. Hilmar ORCID: https://orcid.org/0000-0003-4236-5369; Li, Huilin; 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). 949-970. https://doi.org/10.5194/tc-11-949-2017

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

Abstract/Summary

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:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.5194/tc-11-949-2017
Programmes:	BAS Programmes > BAS Programmes 2015 > Ice Dynamics and Palaeoclimate
ISSN:	1994-0424
Date made live:	24 May 2017 12:57 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/517073

Item Type:

Publication - Article

Digital Object Identifier (DOI):

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

Programmes:

BAS Programmes > BAS Programmes 2015 > Ice Dynamics and Palaeoclimate

ISSN:

1994-0424

Date made live:

24 May 2017 12:57 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/517073