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 & 17 others 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

Research output: Contribution to journalArticle

55 Citations (Scopus)

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.
LanguageEnglish
Article number11
Pages949-970
Number of pages22
JournalThe Cryosphere
Volume11
Issue number2
DOIs
Publication statusPublished - 18 Apr 2017

Cite this

Farinotti, D., Brinkerhoff, D. J., Clarke, G. K. C., Fürst, J. J., Frey, H., Gantayat, P., ... Andreassen, L. 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. [11]. https://doi.org/10.5194/tc-11-949-2017
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 ; Maussion, Fabien ; Morlighem, Mathieu ; Mosbeux, Cyrille ; Pandit, Ankur ; Portmann, Andrea ; Rabatel, Antoine ; Ramsankaran, Raaj ; Reerink, Thomas J. ; Sanchez, Olivier ; Stentoft, Peter A. ; Kumari, Sangita Singh ; 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 ; Li, Huilin ; Andreassen, Liss M. / How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment. In: The Cryosphere. 2017 ; Vol. 11, No. 2. pp. 949-970.
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Farinotti, D, Brinkerhoff, DJ, Clarke, GKC, Fürst, JJ, Frey, H, Gantayat, P, Gillet-Chaulet, F, Girard, C, Huss, M, Leclercq, PW, Linsbauer, A, Machguth, H, Martin, C, Maussion, F, Morlighem, M, Mosbeux, C, Pandit, A, Portmann, A, Rabatel, A, Ramsankaran, R, Reerink, TJ, Sanchez, O, Stentoft, PA, Kumari, SS, van Pelt, WJJ, Anderson, B, Benham, T, Binder, D, Dowdeswell, JA, Fischer, A, Helfricht, K, Kutuzov, S, Lavrentiev, I, McNabb, R, Gudmundsson, GH, Li, H & Andreassen, LM 2017, 'How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment', The Cryosphere, vol. 11, no. 2, 11, pp. 949-970. https://doi.org/10.5194/tc-11-949-2017

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; Maussion, Fabien; Morlighem, Mathieu; Mosbeux, Cyrille; Pandit, Ankur; Portmann, Andrea; Rabatel, Antoine; Ramsankaran, Raaj; Reerink, Thomas J.; Sanchez, Olivier; Stentoft, Peter A.; Kumari, Sangita Singh; 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; Li, Huilin; Andreassen, Liss M.

In: The Cryosphere, Vol. 11, No. 2, 11, 18.04.2017, p. 949-970.

Research output: Contribution to journalArticle

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T1 - How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment

AU - Farinotti, Daniel

AU - Brinkerhoff, Douglas J.

AU - Clarke, Garry K. C.

AU - Fürst, Johannes J.

AU - Frey, Holger

AU - Gantayat, Prateek

AU - Gillet-Chaulet, Fabien

AU - Girard, Claire

AU - Huss, Matthias

AU - Leclercq, Paul W.

AU - Linsbauer, Andreas

AU - Machguth, Horst

AU - Martin, Carlos

AU - Maussion, Fabien

AU - Morlighem, Mathieu

AU - Mosbeux, Cyrille

AU - Pandit, Ankur

AU - Portmann, Andrea

AU - Rabatel, Antoine

AU - Ramsankaran, Raaj

AU - Reerink, Thomas J.

AU - Sanchez, Olivier

AU - Stentoft, Peter A.

AU - Kumari, Sangita Singh

AU - van Pelt, Ward J. J.

AU - Anderson, Brian

AU - Benham, Toby

AU - Binder, Daniel

AU - Dowdeswell, Julian A.

AU - Fischer, Andrea

AU - Helfricht, Kay

AU - Kutuzov, Stanislav

AU - Lavrentiev, Ivan

AU - McNabb, Robert

AU - Gudmundsson, G. Hilmar

AU - Li, Huilin

AU - Andreassen, Liss M.

PY - 2017/4/18

Y1 - 2017/4/18

N2 - 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.

AB - 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.

U2 - 10.5194/tc-11-949-2017

DO - 10.5194/tc-11-949-2017

M3 - Article

VL - 11

SP - 949

EP - 970

JO - The Cryosphere

T2 - The Cryosphere

JF - The Cryosphere

SN - 1994-0416

IS - 2

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