Articles | Volume 11, issue 2
The Cryosphere, 11, 949–970, 2017
The Cryosphere, 11, 949–970, 2017

Research article 18 Apr 2017

Research article | 18 Apr 2017

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

Daniel Farinotti1,2, Douglas J. Brinkerhoff3, Garry K. C. Clarke4, Johannes J. Fürst5, Holger Frey6, Prateek Gantayat7, Fabien Gillet-Chaulet8, Claire Girard9, Matthias Huss1,10, Paul W. Leclercq11, Andreas Linsbauer6,10, Horst Machguth6,10, Carlos Martin12, Fabien Maussion13, Mathieu Morlighem9, Cyrille Mosbeux8, Ankur Pandit14, Andrea Portmann2, Antoine Rabatel8, RAAJ Ramsankaran14, Thomas J. Reerink15, Olivier Sanchez8, Peter A. Stentoft16, Sangita Singh Kumari14, Ward J. J. van Pelt17, Brian Anderson18, Toby Benham19, Daniel Binder20, Julian A. Dowdeswell19, Andrea Fischer21, Kay Helfricht21, Stanislav Kutuzov22, Ivan Lavrentiev22, Robert McNabb3,11, G. Hilmar Gudmundsson12, Huilin Li23, and Liss M. Andreassen24 Daniel Farinotti et al.
  • 1Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
  • 2Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
  • 3Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA
  • 4Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC, Canada
  • 5Institute of Geography, Friedrich Alexander University of Erlangen-Nuremberg (FAU), Erlangen, Germany
  • 6Department of Geography, University of Zurich, Zurich, Switzerland
  • 7Divecha Centre for Climate Change, Indian Institute of Science, Bangalore, India
  • 8Institut des Géosciences de l'Environnement (IGE), Université Grenoble Alpes, CNRS, IRD, Grenoble, France
  • 9Department of Earth System Science, University of California Irvine, Irvine, CA, USA
  • 10Department of Geosciences, University of Fribourg, Fribourg, Switzerland
  • 11Department of Geosciences, University of Oslo, Oslo, Norway
  • 12British Antarctic Survey, Natural Environment Research Council, Cambridge, UK
  • 13Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
  • 14Department of Civil Engineering, Indian Institute of Technology, Bombay, India
  • 15Institute for Marine and Atmospheric Research (IMAU), Utrecht University, Utrecht, the Netherlands
  • 16Arctic Technology Centre ARTEK, Technical University of Denmark, Kongens Lyngby, Denmark
  • 17Department of Earth Sciences, Uppsala University, Uppsala, Sweden
  • 18Antarctic Research Centre, Victoria University of Wellington, Wellington, New Zealand
  • 19Scott Polar Research Institute, University of Cambridge, Cambridge, UK
  • 20Central Institute for Meteorology and Geodynamics (ZAMG), Vienna, Austria
  • 21Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, Innsbruck, Austria
  • 22Laboratory of Glaciology, Institute of Geography, Russian Academy of Science, Moscow, Russia
  • 23State Key Laboratory of Cryospheric Sciences, Tian Shan Glaciological Station, CAREERI, CAS, Lanzhou, China
  • 24Norwegian Water Resources and Energy Directorate (NVE), Oslo, Norway

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.

Short summary
ITMIX – the Ice Thickness Models Intercomparison eXperiment – was the first coordinated performance assessment for models inferring glacier ice thickness from surface characteristics. Considering 17 different models and 21 different test cases, we show that although solutions of individual models can differ considerably, an ensemble average can yield uncertainties in the order of 10 ± 24 % the mean ice thickness. Ways forward for improving such estimates are sketched.