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

被引：175

作者：

Farinotti, Daniel ^{[1
,2
]}

Brinkerhoff, Douglas J. ^{[3
]}

Clarke, Garry K. C. ^{[4
]}

Fuerst, Johannes J. ^{[5
]}

Frey, Holger ^{[6
]}

Gantayat, Prateek ^{[7
]}

Gillet-Chaulet, Fabien ^{[8
]}

Girard, Claire ^{[9
]}

Huss, Matthias ^{[1
,10
]}

Leclercq, Paul W. ^{[11
]}

Linsbauer, Andreas ^{[6
,10
]}

Machguth, Horst ^{[6
,10
]}

Martin, Carlos ^{[12
]}

Maussion, Fabien ^{[13
]}

Morlighem, Mathieu ^{[9
]}

Mosbeux, Cyrille ^{[8
]}

Pandit, Ankur ^{[14
]}

Portmann, Andrea ^{[2
]}

Rabatel, Antoine ^{[8
]}

Ramsankaran, Raaj ^{[14
]}

Reerink, Thomas J. ^{[15
]}

Sanchez, Olivier ^{[8
]}

Stentoft, Peter A. ^{[16
]}

Kumari, Sangita Singh ^{[14
]}

van Pelt, Ward J. J. ^{[17
]}

Anderson, Brian ^{[18
]}

Benham, Toby ^{[19
]}

Binder, Daniel ^{[20
]}

Dowdeswell, Julian A. ^{[19
]}

Fischer, Andrea ^{[21
]}

Helfricht, Kay ^{[21
]}

Kutuzov, Stanislav ^{[22
]}

Lavrentiev, Ivan ^{[22
]}

McNabb, Robert ^{[3
,11
]}

Gudmundsson, G. Hilmar ^{[12
]}

Li, Huilin ^{[23
]}

Andreassen, Liss M. ^{[24
]}

机构：

[1] Swiss Fed Inst Technol, Lab Hydraul Hydrol & Glaciol VAW, Zurich, Switzerland

[2] Swiss Fed Inst Forest Snow & Landscape Res WSL, Birmensdorf, Switzerland

[3] Univ Alaska Fairbanks, Inst Geophys, Fairbanks, AK 99775 USA

[4] Univ British Columbia, Dept Earth Ocean & Atmospher Sci, Vancouver, BC, Canada

[5] Friedrich Alexander Univ Erlangen Nuremberg FAU, Inst Geog, Erlangen, Germany

[6] Univ Zurich, Dept Geog, Zurich, Switzerland

[7] Indian Inst Sci, Divecha Ctr Climate Change, Bangalore, Karnataka, India

[8] Univ Grenoble Alpes, CNRS, IRD, IGE, Grenoble, France

[9] Univ Calif Irvine, Dept Earth Syst Sci, Irvine, CA USA

[10] Univ Fribourg, Dept Geosci, Fribourg, Switzerland

[11] Univ Oslo, Dept Geosci, Oslo, Norway

[12] British Antarctic Survey, Nat Environm Res Council, Cambridge, England

[13] Univ Innsbruck, Inst Atmospher & Cryospher Sci, Innsbruck, Austria

[14] Indian Inst Technol, Dept Civil Engn, Bombay, Maharashtra, India

[15] Univ Utrecht, Inst Marine & Atmospher Res IMAU, Utrecht, Netherlands

[16] Tech Univ Denmark, Arctic Technol Ctr ARTEK, Lyngby, Denmark

[17] Uppsala Univ, Dept Earth Sci, Uppsala, Sweden

[18] Victoria Univ Wellington, Antarctic Res Ctr, Wellington, New Zealand

[19] Univ Cambridge, Scott Polar Res Inst, Cambridge, England

[20] Cent Inst Meteorol & Geodynam ZAMG, Vienna, Austria

[21] Austrian Acad Sci, Inst Interdisciplinary Mt Res, Innsbruck, Austria

[22] Russian Acad Sci, Inst Geog, Lab Glaciol, Moscow, Russia

[23] Chinese Acad Sci, CAREERI, Tian Shan Glaciol Stn, State Key Lab Cryospher Sci, Lanzhou, Peoples R China

[24] Norwegian Water Resources & Energy Directorate NV, Oslo, Norway

来源：

CRYOSPHERE | 2017年 / 11卷 / 02期

基金：

欧洲研究理事会; 英国自然环境研究理事会; 美国国家航空航天局; 瑞士国家科学基金会;

关键词：

MASS-BALANCE; SUBGLACIAL TOPOGRAPHY; COLUMBIA GLACIER; BED TOPOGRAPHY; VOLUME; ALASKA; FLOW; SENSITIVITY; INVENTORY;

D O I：

10.5194/tc-11-949-2017

中图分类号：

P9 [自然地理学];

学科分类号：

0705 ; 070501 ;

摘要：

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.

引用

页码：949 / 970

页数：22

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