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

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

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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 (1 sigma 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:	Journal Article
Publication:	CRYOSPHERE
Additional Information:	Copy right for this article belongs to the COPERNICUS GESELLSCHAFT MBH, BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY
Department/Centre:	Division of Mechanical Sciences > Divecha Centre for Climate Change
Date Deposited:	25 May 2017 10:02
Last Modified:	01 Mar 2019 09:50
URI:	http://eprints.iisc.ac.in/id/eprint/57058

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