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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
  • , Carlos Martin
  • , Fabien Maussion
  • , Mathieu Morlighem
  • , Cyrille Mosbeux
  • , Ankur Pandit
  • , Andrea Portmann
  • , Antoine Rabatel
  • , Raaj Ramsankaran
  • Thomas J. Reerink, Olivier 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
  • Swiss Federal Institute of Technology Zurich
  • Swiss Federal Institute for Forest, Snow and Landscape Research
  • University of British Columbia
  • Friedrich-Alexander University Erlangen-Nürnberg
  • University of Zurich
  • Indian Institute of Science Bangalore
  • Université Grenoble Alpes
  • University of California at Irvine
  • University of Fribourg
  • University of Oslo
  • British Antarctic Survey
  • University of Innsbruck
  • Indian Institute of Technology Bombay
  • Utrecht University
  • Technical University of Denmark
  • Uppsala University
  • Victoria University of Wellington
  • University of Cambridge
  • Austrian Academy of Sciences
  • Russian Academy of Sciences
  • University of Alaska Fairbanks
  • Chinese Academy of Sciences
  • Norwegian Water Resources and Energy Directorate

Research output: Contribution to journalArticlepeer-review

226 Scopus citations

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
StatePublished - Apr 18 2017

Funding

Funder number
320816

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