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

• Published in: The Cryosphere
• Main Authors: D. Farinotti, D. J. Brinkerhoff, G. K. C. Clarke, J. J. Fürst, H. Frey, P. Gantayat, F. Gillet-Chaulet, C. Girard, M. Huss, P. W. Leclercq, A. Linsbauer, H. Machguth, C. Martin, F. Maussion, M. Morlighem, C. Mosbeux, A. Pandit, A. Portmann, A. Rabatel, R. Ramsankaran, T. J. Reerink, O. Sanchez, P. A. Stentoft, S. Singh Kumari, W. J. J. van Pelt, B. Anderson, T. Benham, D. Binder, J. A. Dowdeswell, A. Fischer, K. Helfricht, S. Kutuzov, I. Lavrentiev, R. McNabb, G. H. Gudmundsson, H. Li, L. M. Andreassen
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2017
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-11-949-2017; https://doaj.org/article/7b92c212db514fef88bb497f2d0997d7

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 σ 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.