Articles | Volume 17, issue 9
https://doi.org/10.5194/tc-17-4021-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-4021-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Sep 2023
Research article |
| 18 Sep 2023
| 18 Sep 2023
Reconciling ice dynamics and bed topography with a versatile and fast ice thickness inversion
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Ward J. J. van Pelt
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Jack Kohler
Norwegian Polar Institute, Fram Centre, Tromsø, Norway
Related authors
Zhuo Wang, Neil Ross, Thomas Frank, Jamie Barnett, Ilaria Santin, Martin Houssais, Johanna Dahlkvist, and Nina Kirchner
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-745, https://doi.org/10.5194/essd-2025-745, 2026
Preprint under review for ESSD
Short summary
Short summary
Under global warming, Sweden's remaining glaciers are shrinking rapidly, and all four Swedish reference glaciers (Mårmaglaciären, Storglaciären, Rabots glaciär, and Riukojietna) may disappear within this century. To better project their future evolution, we measured the ice thickness of the four glaciers using radio-echo sounding and mapped the bed topography beneath the ice. These maps provide essential insights into future landscapes, ecosystems, and policymaking.
Ward van Pelt and Thomas Frank
The Cryosphere, 19, 1–17, https://doi.org/10.5194/tc-19-1-2025, https://doi.org/10.5194/tc-19-1-2025, 2025
Short summary
Short summary
Accurate information on the ice thickness of Svalbard's glaciers is important for assessing the contribution to sea level rise in a present and a future climate. However, direct observations of the glacier bed are scarce. Here, we use an inverse approach and high-resolution surface observations to infer basal conditions. We present and analyse the new bed and thickness maps, quantify the ice volume (6800 km3), and compare these against radar data and previous studies.
Thomas Frank, Henning Åkesson, Basile de Fleurian, Mathieu Morlighem, and Kerim H. Nisancioglu
The Cryosphere, 16, 581–601, https://doi.org/10.5194/tc-16-581-2022, https://doi.org/10.5194/tc-16-581-2022, 2022
Short summary
Short summary
The shape of a fjord can promote or inhibit glacier retreat in response to climate change. We conduct experiments with a synthetic setup under idealized conditions in a numerical model to study and quantify the processes involved. We find that friction between ice and fjord is the most important factor and that it is possible to directly link ice discharge and grounding line retreat to fjord topography in a quantitative way.
Zhuo Wang, Neil Ross, Thomas Frank, Jamie Barnett, Ilaria Santin, Martin Houssais, Johanna Dahlkvist, and Nina Kirchner
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-745, https://doi.org/10.5194/essd-2025-745, 2026
Preprint under review for ESSD
Short summary
Short summary
Under global warming, Sweden's remaining glaciers are shrinking rapidly, and all four Swedish reference glaciers (Mårmaglaciären, Storglaciären, Rabots glaciär, and Riukojietna) may disappear within this century. To better project their future evolution, we measured the ice thickness of the four glaciers using radio-echo sounding and mapped the bed topography beneath the ice. These maps provide essential insights into future landscapes, ecosystems, and policymaking.
Tim van den Akker, Ward van Pelt, Rickard Petterson, and Veijo A. Pohjola
The Cryosphere, 19, 1513–1525, https://doi.org/10.5194/tc-19-1513-2025, https://doi.org/10.5194/tc-19-1513-2025, 2025
Short summary
Short summary
Liquid water can persist within old snow on glaciers and ice caps if it can percolate into the snow before it refreezes. Snow is a good insulator, and it is porous where the percolated water can be stored. If this happens, the water piles up and forms a groundwater-like system. Here, we show observations of such a groundwater-like system found in Svalbard. We demonstrate that it behaves like a groundwater system and use that to model the development of the water table from 1957 until the present day.
Ward van Pelt and Thomas Frank
The Cryosphere, 19, 1–17, https://doi.org/10.5194/tc-19-1-2025, https://doi.org/10.5194/tc-19-1-2025, 2025
Short summary
Short summary
Accurate information on the ice thickness of Svalbard's glaciers is important for assessing the contribution to sea level rise in a present and a future climate. However, direct observations of the glacier bed are scarce. Here, we use an inverse approach and high-resolution surface observations to infer basal conditions. We present and analyse the new bed and thickness maps, quantify the ice volume (6800 km3), and compare these against radar data and previous studies.
Coline Bouchayer, Ugo Nanni, Pierre-Marie Lefeuvre, John Hult, Louise Steffensen Schmidt, Jack Kohler, François Renard, and Thomas V. Schuler
The Cryosphere, 18, 2939–2968, https://doi.org/10.5194/tc-18-2939-2024, https://doi.org/10.5194/tc-18-2939-2024, 2024
Short summary
Short summary
We explore the interplay between surface runoff and subglacial conditions. We focus on Kongsvegen glacier in Svalbard. We drilled 350 m down to the glacier base to measure water pressure, till strength, seismic noise, and glacier surface velocity. In the low-melt season, the drainage system adapted gradually, while the high-melt season led to a transient response, exceeding drainage capacity and enhancing sliding. Our findings contribute to discussions on subglacial hydro-mechanical processes.
Andrea Spolaor, Federico Scoto, Catherine Larose, Elena Barbaro, Francois Burgay, Mats P. Bjorkman, David Cappelletti, Federico Dallo, Fabrizio de Blasi, Dmitry Divine, Giuliano Dreossi, Jacopo Gabrieli, Elisabeth Isaksson, Jack Kohler, Tonu Martma, Louise S. Schmidt, Thomas V. Schuler, Barbara Stenni, Clara Turetta, Bartłomiej Luks, Mathieu Casado, and Jean-Charles Gallet
The Cryosphere, 18, 307–320, https://doi.org/10.5194/tc-18-307-2024, https://doi.org/10.5194/tc-18-307-2024, 2024
Short summary
Short summary
We evaluate the impact of the increased snowmelt on the preservation of the oxygen isotope (δ18O) signal in firn records recovered from the top of the Holtedahlfonna ice field located in the Svalbard archipelago. Thanks to a multidisciplinary approach we demonstrate a progressive deterioration of the isotope signal in the firn core. We link the degradation of the δ18O signal to the increased occurrence and intensity of melt events associated with the rapid warming occurring in the archipelago.
Alice C. Frémand, Peter Fretwell, Julien A. Bodart, Hamish D. Pritchard, Alan Aitken, Jonathan L. Bamber, Robin Bell, Cesidio Bianchi, Robert G. Bingham, Donald D. Blankenship, Gino Casassa, Ginny Catania, Knut Christianson, Howard Conway, Hugh F. J. Corr, Xiangbin Cui, Detlef Damaske, Volkmar Damm, Reinhard Drews, Graeme Eagles, Olaf Eisen, Hannes Eisermann, Fausto Ferraccioli, Elena Field, René Forsberg, Steven Franke, Shuji Fujita, Yonggyu Gim, Vikram Goel, Siva Prasad Gogineni, Jamin Greenbaum, Benjamin Hills, Richard C. A. Hindmarsh, Andrew O. Hoffman, Per Holmlund, Nicholas Holschuh, John W. Holt, Annika N. Horlings, Angelika Humbert, Robert W. Jacobel, Daniela Jansen, Adrian Jenkins, Wilfried Jokat, Tom Jordan, Edward King, Jack Kohler, William Krabill, Mette Kusk Gillespie, Kirsty Langley, Joohan Lee, German Leitchenkov, Carlton Leuschen, Bruce Luyendyk, Joseph MacGregor, Emma MacKie, Kenichi Matsuoka, Mathieu Morlighem, Jérémie Mouginot, Frank O. Nitsche, Yoshifumi Nogi, Ole A. Nost, John Paden, Frank Pattyn, Sergey V. Popov, Eric Rignot, David M. Rippin, Andrés Rivera, Jason Roberts, Neil Ross, Anotonia Ruppel, Dustin M. Schroeder, Martin J. Siegert, Andrew M. Smith, Daniel Steinhage, Michael Studinger, Bo Sun, Ignazio Tabacco, Kirsty Tinto, Stefano Urbini, David Vaughan, Brian C. Welch, Douglas S. Wilson, Duncan A. Young, and Achille Zirizzotti
Earth Syst. Sci. Data, 15, 2695–2710, https://doi.org/10.5194/essd-15-2695-2023, https://doi.org/10.5194/essd-15-2695-2023, 2023
Short summary
Short summary
This paper presents the release of over 60 years of ice thickness, bed elevation, and surface elevation data acquired over Antarctica by the international community. These data are a crucial component of the Antarctic Bedmap initiative which aims to produce a new map and datasets of Antarctic ice thickness and bed topography for the international glaciology and geophysical community.
Marlene Kronenberg, Ward van Pelt, Horst Machguth, Joel Fiddes, Martin Hoelzle, and Felix Pertziger
The Cryosphere, 16, 5001–5022, https://doi.org/10.5194/tc-16-5001-2022, https://doi.org/10.5194/tc-16-5001-2022, 2022
Short summary
Short summary
The Pamir Alay is located at the edge of regions with anomalous glacier mass changes. Unique long-term in situ data are available for Abramov Glacier, located in the Pamir Alay. In this study, we use this extraordinary data set in combination with reanalysis data and a coupled surface energy balance–multilayer subsurface model to compute and analyse the distributed climatic mass balance and firn evolution from 1968 to 2020.
Johannes Oerlemans, Jack Kohler, and Adrian Luckman
The Cryosphere, 16, 2115–2126, https://doi.org/10.5194/tc-16-2115-2022, https://doi.org/10.5194/tc-16-2115-2022, 2022
Short summary
Short summary
Tunabreen is a 26 km long tidewater glacier. It is the most frequently surging glacier in Svalbard, with four documented surges in the past 100 years. We have modelled this glacier to find out how it reacts to future climate change. Careful calibration was done against the observed length record for the past 100 years. For a 50 m increase in the equilibrium line altitude (ELA) the length of the glacier will be shortened by 10 km after about 100 years.
Thomas Frank, Henning Åkesson, Basile de Fleurian, Mathieu Morlighem, and Kerim H. Nisancioglu
The Cryosphere, 16, 581–601, https://doi.org/10.5194/tc-16-581-2022, https://doi.org/10.5194/tc-16-581-2022, 2022
Short summary
Short summary
The shape of a fjord can promote or inhibit glacier retreat in response to climate change. We conduct experiments with a synthetic setup under idealized conditions in a numerical model to study and quantify the processes involved. We find that friction between ice and fjord is the most important factor and that it is possible to directly link ice discharge and grounding line retreat to fjord topography in a quantitative way.
Enrico Mattea, Horst Machguth, Marlene Kronenberg, Ward van Pelt, Manuela Bassi, and Martin Hoelzle
The Cryosphere, 15, 3181–3205, https://doi.org/10.5194/tc-15-3181-2021, https://doi.org/10.5194/tc-15-3181-2021, 2021
Short summary
Short summary
In our study we find that climate change is affecting the high-alpine Colle Gnifetti glacier (Swiss–Italian Alps) with an increase in melt amounts and ice temperatures.
In the near future this trend could threaten the viability of the oldest ice core record in the Alps.
To reach our conclusions, for the first time we used the meteorological data of the highest permanent weather station in Europe (Capanna Margherita, 4560 m), together with an advanced numeric simulation of the glacier.
Christian Zdanowicz, Jean-Charles Gallet, Mats P. Björkman, Catherine Larose, Thomas Schuler, Bartłomiej Luks, Krystyna Koziol, Andrea Spolaor, Elena Barbaro, Tõnu Martma, Ward van Pelt, Ulla Wideqvist, and Johan Ström
Atmos. Chem. Phys., 21, 3035–3057, https://doi.org/10.5194/acp-21-3035-2021, https://doi.org/10.5194/acp-21-3035-2021, 2021
Short summary
Short summary
Black carbon (BC) aerosols are soot-like particles which, when transported to the Arctic, darken snow surfaces, thus indirectly affecting climate. Information on BC in Arctic snow is needed to measure their impact and monitor the efficacy of pollution-reduction policies. This paper presents a large new set of BC measurements in snow in Svalbard collected between 2007 and 2018. It describes how BC in snow varies across the archipelago and explores some factors controlling these variations.
Cited articles
Bahr, D. B., Meier, M. F., and Peckham, S. D.: The physical basis of glacier volume-area scaling, J. Geophys. Res.-Sol. Ea., 102, 20355–20362, https://doi.org/10.1029/97JB01696, 1997. a
Blatter, H.: Velocity and stress fields in grounded glaciers: a simple algorithm for including deviatoric stress gradients, J. Glaciol., 41, 333–344, https://doi.org/10.3189/S002214300001621X, 1995. a
Bogorodsky, V. V., Bentley, C. R., and Gudmandsen, P. E.: Radioglaciology, Springer Science & Business 50 Media, ISBN 978-90-277-1893-8, 1985. a
Brinkerhoff, D. J., Aschwanden, A., and Truffer, M.: Bayesian Inference of Subglacial Topography Using Mass Conservation, Front. Earth Sci., 4, 8, https://doi.org/10.3389/feart.2016.00008, 2016. a
Bueler, E. and Brown, J.: Shallow shelf approximation as a “sliding law” in a thermomechanically coupled ice sheet model, J. Geophys. Res.-Earth, 114, F03008, https://doi.org/10.1029/2008JF001179, 2009. a, b, c, d
Chen, J. and Ohmura, A.: Estimation of Alpine glacier water resources and their change since the 1870s, IAHS Publ., 193, 127–135, 1990. a
Christianson, K., Kohler, J., Alley, R. B., Nuth, C., and Van Pelt, W. J.: Dynamic perennial firn aquifer on an Arctic glacier, Geophys. Res. Lett., 42, 1418–1426, 2015. a
Clarke, G. K. C., Anslow, F. S., Jarosch, A. H., Radić, V., Menounos, B., Bolch, T., and Berthier, E.: Ice Volume and Subglacial Topography for Western Canadian Glaciers from Mass Balance Fields, Thinning Rates, and a Bed Stress Model, J. Climate, 26, 4282–4303, https://doi.org/10.1175/JCLI-D-12-00513.1, 2013. a, b
Deschamps-Berger, C., Nuth, C., Pelt, W. V., Berthier, E., Kohler, J., and Altena, B.: Closing the mass budget of a tidewater glacier: the example of Kronebreen, Svalbard, J. Glaciol., 65, 136–148, https://doi.org/10.1017/jog.2018.98, 2019. a, b
Farinotti, D., Brinkerhoff, D. J., Clarke, G. K. C., Fürst, J. J., Frey, H., Gantayat, P., Gillet-Chaulet, F., Girard, C., Huss, M., Leclercq, P. W., Linsbauer, A., Machguth, H., Martin, C., Maussion, F., Morlighem, M., Mosbeux, C., Pandit, A., Portmann, A., Rabatel, A., Ramsankaran, R., Reerink, T. J., Sanchez, O., Stentoft, P. A., Singh Kumari, S., van Pelt, W. J. J., Anderson, B., Benham, T., Binder, D., Dowdeswell, J. A., Fischer, A., Helfricht, K., Kutuzov, S., Lavrentiev, I., McNabb, R., Gudmundsson, G. H., Li, H., and Andreassen, L. M.: How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment, The Cryosphere, 11, 949–970, https://doi.org/10.5194/tc-11-949-2017, 2017. a, b, c, d, e, f, g, h, i, j
Farinotti, D., Huss, M., Fürst, J. J., Landmann, J., Machguth, H., Maussion, F., and Pandit, A.: A consensus estimate for the ice thickness distribution of all glaciers on Earth, Nat. Geosci., 12, 168–173, https://doi.org/10.1038/s41561-019-0300-3, 2019. a, b, c
Farinotti, D., Brinkerhoff, D. J., Fürst, J. J., Gantayat, P., Gillet-Chaulet, F., Huss, M., Leclercq, P. W., Maurer, H., Morlighem, M., Pandit, A., Rabatel, A., Ramsankaran, R., Reerink, T. J., Robo, E., Rouges, E., Tamre, E., van Pelt, W. J. J., Werder, M. A., Azam, M. F., Li, H., and Andreassen, L. M.: Results from the Ice Thickness Models Intercomparison eXperiment Phase 2 (ITMIX2), Front. Earth Sci., 8, 571923, https://doi.org/10.3389/feart.2020.571923, 2021. a, b
Flowers, G. E. and Clarke, G. K. C.: Surface and bed topography of Trapridge Glacier, Yukon Territory, Canada: digital elevation models and derived hydraulic geometry, J. Glaciol., 45, 165–174, https://doi.org/10.3189/S0022143000003142, 1999. a
Fretwell, P., Pritchard, H. D., Vaughan, D. G., Bamber, J. L., Barrand, N. E., Bell, R., Bianchi, C., Bingham, R. G., Blankenship, D. D., Casassa, G., Catania, G., Callens, D., Conway, H., Cook, A. J., Corr, H. F. J., Damaske, D., Damm, V., Ferraccioli, F., Forsberg, R., Fujita, S., Gim, Y., Gogineni, P., Griggs, J. A., Hindmarsh, R. C. A., Holmlund, P., Holt, J. W., Jacobel, R. W., Jenkins, A., Jokat, W., Jordan, T., King, E. C., Kohler, J., Krabill, W., Riger-Kusk, M., Langley, K. A., Leitchenkov, G., Leuschen, C., Luyendyk, B. P., Matsuoka, K., Mouginot, J., Nitsche, F. O., Nogi, Y., Nost, O. A., Popov, S. V., Rignot, E., Rippin, D. M., Rivera, A., Roberts, J., Ross, N., Siegert, M. J., Smith, A. M., Steinhage, D., Studinger, M., Sun, B., Tinto, B. K., Welch, B. C., Wilson, D., Young, D. A., Xiangbin, C., and Zirizzotti, A.: Bedmap2: improved ice bed, surface and thickness datasets for Antarctica, The Cryosphere, 7, 375–393, https://doi.org/10.5194/tc-7-375-2013, 2013. a
Frey, H., Machguth, H., Huss, M., Huggel, C., Bajracharya, S., Bolch, T., Kulkarni, A., Linsbauer, A., Salzmann, N., and Stoffel, M.: Estimating the volume of glaciers in the Himalayan–Karakoram region using different methods, The Cryosphere, 8, 2313–2333, https://doi.org/10.5194/tc-8-2313-2014, 2014. a, b
Fürst, J. J., Gillet-Chaulet, F., Benham, T. J., Dowdeswell, J. A., Grabiec, M., Navarro, F., Pettersson, R., Moholdt, G., Nuth, C., Sass, B., Aas, K., Fettweis, X., Lang, C., Seehaus, T., and Braun, M.: Application of a two-step approach for mapping ice thickness to various glacier types on Svalbard, The Cryosphere, 11, 2003–2032, https://doi.org/10.5194/tc-11-2003-2017, 2017. a
Fürst, J. J., Navarro, F., Gillet-Chaulet, F., Huss, M., Moholdt, G., Fettweis, X., Lang, C., Seehaus, T., Ai, S., Benham, T. J., Benn, D. I., Björnsson, H., Dowdeswell, J. A., Grabiec, M., Kohler, J., Lavrentiev, I., Lindbäck, K., Melvold, K., Pettersson, R., Rippin, D., Saintenoy, A., Sánchez-Gámez, P., Schuler, T. V., Sevestre, H., Vasilenko, E., and Braun, M. H.: The Ice-Free Topography of Svalbard, Geophys. Res. Lett., 45, 11760–11769, https://doi.org/10.1029/2018GL079734, 2018. a, b, c
Gantayat, P., Kulkarni, A. V., and Srinivasan, J.: Estimation of ice thickness using surface velocities and slope: case study at Gangotri Glacier, India, J. Glaciol., 60, 277–282, https://doi.org/10.3189/2014JoG13J078, 2014. a
Glen, J. W.: The creep of polycrystalline ice, P. Roy. Soc. Lond. A Mat., 228, 519–538, 1955. a
Goelzer, H., Robinson, A., Seroussi, H., and van de Wal, R. S.: Recent Progress in Greenland Ice Sheet Modelling, Current Climate Change Reports, 3, 291–302, https://doi.org/10.1007/s40641-017-0073-y, 2017. a
Goldberg, D. N. and Heimbach, P.: Parameter and state estimation with a time-dependent adjoint marine ice sheet model, The Cryosphere, 7, 1659–1678, https://doi.org/10.5194/tc-7-1659-2013, 2013. a
Gudmundsson, G. H.: Transmission of basal variability to a glacier surface, J. Geophys. Res.-Sol. Ea., 108, 2253, https://doi.org/10.1029/2002JB002107, 2003. a, b
Habermann, M., Maxwell, D., and Truffer, M.: Reconstruction of basal properties in ice sheets using iterative inverse methods, J. Glaciol., 58, 795–808, https://doi.org/10.3189/2012JoG11J168, 2012. a, b, c, d
Haeberli, W. and Hoelzle, M.: Application of inventory data for estimating characteristics of and regional climate-change effects on mountain glaciers: a pilot study with the European Alps, Ann. Glaciol., 21, 206–212, https://doi.org/10.3189/S0260305500015834, 1995. a, b
Heining, C.: Velocity field reconstruction in gravity-driven flow over unknown topography, Phys. Fluids, 23, 032101, https://doi.org/10.1063/1.3559144, 2011. a, b
How, P., Benn, D. I., Hulton, N. R. J., Hubbard, B., Luckman, A., Sevestre, H., van Pelt, W. J. J., Lindbäck, K., Kohler, J., and Boot, W.: Rapidly changing subglacial hydrological pathways at a tidewater glacier revealed through simultaneous observations of water pressure, supraglacial lakes, meltwater plumes and surface velocities, The Cryosphere, 11, 2691–2710, https://doi.org/10.5194/tc-11-2691-2017, 2017. a
Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kääb, A.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021. a, b
Huss, M. and Farinotti, D.: Distributed ice thickness and volume of all glaciers around the globe, J. Geophys. Res.-Earth, 117, F04010, https://doi.org/10.1029/2012JF002523, 2012. a, b, c, d
Jouvet, G.: Inversion of a Stokes glacier flow model emulated by deep learning, J. Glaciol., 69, 1–14, https://doi.org/10.1017/jog.2022.41, 2022. a
Kamb, B. and Echelmeyer, K. A.: Stress-Gradient Coupling in Glacier Flow: I. Longitudinal Averaging of the Influence of Ice Thickness and Surface Slope, J. Glaciol., 32, 267–284, https://doi.org/10.3189/S0022143000015604, 1986. a
Köhler, A., Pȩtlicki, M., Lefeuvre, P.-M., Buscaino, G., Nuth, C., and Weidle, C.: Contribution of calving to frontal ablation quantified from seismic and hydroacoustic observations calibrated with lidar volume measurements, The Cryosphere, 13, 3117–3137, https://doi.org/10.5194/tc-13-3117-2019, 2019. a
Langhammer, L., Grab, M., Bauder, A., and Maurer, H.: Glacier thickness estimations of alpine glaciers using data and modeling constraints, The Cryosphere, 13, 2189–2202, https://doi.org/10.5194/tc-13-2189-2019, 2019. a
Le clec'h, S., Quiquet, A., Charbit, S., Dumas, C., Kageyama, M., and Ritz, C.: A rapidly converging initialisation method to simulate the present-day Greenland ice sheet using the GRISLI ice sheet model (version 1.3), Geosci. Model Dev., 12, 2481–2499, https://doi.org/10.5194/gmd-12-2481-2019, 2019. a, b
Li, H., Ng, F., Li, Z., Qin, D., and Cheng, G.: An extended “perfect-plasticity” method for estimating ice thickness along the flow line of mountain glaciers, J. Geophys. Res.-Earth, 117, 1020–1030, https://doi.org/10.1029/2011JF002104, 2012. a, b
Lindbäck, K., Kohler, J., Pettersson, R., Nuth, C., Langley, K., Messerli, A., Vallot, D., Matsuoka, K., and Brandt, O.: Subglacial topography, ice thickness, and bathymetry of Kongsfjorden, northwestern Svalbard, Earth Syst. Sci. Data, 10, 1769–1781, https://doi.org/10.5194/essd-10-1769-2018, 2018a. a, b, c
Lindbäck, K., Kohler, J., Pettersson, R., Nuth, C., Langley, K., Messerli, A., Vallot, D., Matsuoka, K., and Brandt, O.: Subglacial topography, ice thickness, and bathymetry of Kongsfjorden, northwestern Svalbard, Norwegian Polar Institute [data set], https://doi.org/10.21334/npolar.2017.702ca4a7, 2018b. a
Linsbauer, A., Paul, F., Hoelzle, M., Frey, H., and Haeberli, W.: The Swiss Alps without glaciers – a GIS-based modelling approach for reconstruction of glacier beds, Proceedings of Geomorphometry, 243–247, https://doi.org/10.5167/uzh-27834, 2009. a
Linsbauer, A., Paul, F., and Haeberli, W.: Modeling glacier thickness distribution and bed topography over entire mountain ranges with GlabTop: Application of a fast and robust approach, J. Geophys. Res.-Earth, 117, F03007, https://doi.org/10.1029/2011JF002313, 2012. a, b
Luckman, A., Benn, D. I., Cottier, F., Bevan, S., Nilsen, F., and Inall, M.: Calving rates at tidewater glaciers vary strongly with ocean temperature, Nat. Commun., 6, 8566, https://doi.org/10.1038/ncomms9566, 2015. a
Maussion, F., Butenko, A., Champollion, N., Dusch, M., Eis, J., Fourteau, K., Gregor, P., Jarosch, A. H., Landmann, J., Oesterle, F., Recinos, B., Rothenpieler, T., Vlug, A., Wild, C. T., and Marzeion, B.: The Open Global Glacier Model (OGGM) v1.1, Geosci. Model Dev., 12, 909–931, https://doi.org/10.5194/gmd-12-909-2019, 2019. a, b, c, d, e
Michel, L., Picasso, M., Farinotti, D., Bauder, A., Funk, M., and Blatter, H.: Estimating the ice thickness of mountain glaciers with an inverse approach using surface topography and mass-balance, Inverse Probl., 29, 035002, https://doi.org/10.1088/0266-5611/29/3/035002, 2013. a
Millan, R., Mouginot, J., Rabatel, A., and Morlighem, M.: Ice velocity and thickness of the world’s glaciers, Sedoo [data set], https://doi.org/10.6096/1007, 2022b. a
NASA JPL: NASADEM Merged DEM Global 1 arc second V001, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MEASURES/NASADEM/NASADEM_HGT.001, 2020. a
Neven, A., Dall'Alba, V., Juda, P., Straubhaar, J., and Renard, P.: Ice volume and basal topography estimation using geostatistical methods and ground-penetrating radar measurements: application to the Tsanfleuron and Scex Rouge glaciers, Swiss Alps, The Cryosphere, 15, 5169–5186, https://doi.org/10.5194/tc-15-5169-2021, 2021. a
Nye, J. F.: A Method of Calculating the Thicknesses of the Ice-Sheets, Nature, 169, 529–530, https://doi.org/10.1038/169529a0, 1952. a, b, c
Oppenheimer, M., Glavovic, B., Hinkel, J., van de Wal, R., Magnan, A., Abd-Elgawad, A., Cai, R., Cifuentes-Jara, M., DeConto, R., Ghosh, T., Hay, J., Isla, F., Marzeion, B., and Sebesvari, Z.: Sea Level Rise and Implications
for Low-Lying Islands, Coasts and Communities, in: IPCC Special Report on the Ocean and Cryosphere in a Changing
Climate, editeds by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A.,
Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., Cambridge University Press, Cambridge, UK and New
York, NY, USA, 321–445, https://doi.org/10.1017/9781009157964.006, 2019. a
Pattyn, F.: A new three-dimensional higher-order thermomechanical ice sheet model: Basic sensitivity, ice stream development, and ice flow across subglacial lakes, J. Geophys. Res.-Sol. Ea., 108, 2382, https://doi.org/10.1029/2002JB002329, 2003. a
Pattyn, F., Schoof, C., Perichon, L., Hindmarsh, R. C. A., Bueler, E., de Fleurian, B., Durand, G., Gagliardini, O., Gladstone, R., Goldberg, D., Gudmundsson, G. H., Huybrechts, P., Lee, V., Nick, F. M., Payne, A. J., Pollard, D., Rybak, O., Saito, F., and Vieli, A.: Results of the Marine Ice Sheet Model Intercomparison Project, MISMIP, The Cryosphere, 6, 573–588, https://doi.org/10.5194/tc-6-573-2012, 2012. a
Paul, F. and Linsbauer, A.: Modeling of glacier bed topography from glacier outlines, central branch lines, and a DEM, Int. J. Geogr. Inf. Sci., 26, 1173–1190, https://doi.org/10.1080/13658816.2011.627859, 2012. a, b
Pollard, D. and DeConto, R. M.: A simple inverse method for the distribution of basal sliding coefficients under ice sheets, applied to Antarctica, The Cryosphere, 6, 953–971, https://doi.org/10.5194/tc-6-953-2012, 2012. a, b, c, d
Rabatel, A., Sanchez, O., Vincent, C., and Six, D.: Estimation of Glacier Thickness From Surface Mass Balance and Ice Flow Velocities: A Case Study on Argentière Glacier, France, Front. Earth Sci., 6, 112, https://doi.org/10.3389/feart.2018.00112, 2018. a, b
Raymond, M. J. and Gudmundsson, G. H.: On the relationship between surface and basal properties on glaciers, ice sheets, and ice streams, J. Geophys. Res.-Sol. Ea., 110, B08411, https://doi.org/10.1029/2005JB003681, 2005. a, b, c
RGI Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier Outlines: Version 6.0, NSIDC: National Snow and Ice Data Center, Boulder, Colorado USA, https://doi.org/10.7265/N5-RGI-60, 2017. a
Rounce, D. R., Hock, R., Maussion, F., Hugonnet, R., Kochtitzky, W., Huss, M., Berthier, E., Brinkerhoff, D., Compagno, L., Copland, L., Farinotti, D., Menounos, B., and McNabb, R. W.: Global glacier change in the 21st century: Every increase in temperature matters, Science, 379, 78–83, https://doi.org/10.1126/science.abo1324, 2023. a, b
Schellenberger, T., Dunse, T., Kääb, A., Kohler, J., and Reijmer, C. H.: Surface speed and frontal ablation of Kronebreen and Kongsbreen, NW Svalbard, from SAR offset tracking, The Cryosphere, 9, 2339–2355, https://doi.org/10.5194/tc-9-2339-2015, 2015. a, b
Schlegel, N.-J., Larour, E., Seroussi, H., Morlighem, M., and Box, J. E.: Ice discharge uncertainties in Northeast Greenland from boundary conditions and climate forcing of an ice flow model, J. Geophys. Res.-Earth, 120, 29–54, https://doi.org/10.1002/2014JF003359, 2015. a
The PISM authors: PISM, a Parallel Ice Sheet Model, https://www.pism.io (last access: 12 September 2023), 2021. a
Vallot, D., Pettersson, R., Luckman, A., Benn, D. I., Zwinger, T., Pelt, W. J. J. V., Kohler, J., Schäfer, M., Claremar, B., and Hulton, N. R. J.: Basal dynamics of Kronebreen, a fast-flowing tidewater glacier in Svalbard: non-local spatio-temporal response to water input, J. Glaciol., 63, 1012–1024, https://doi.org/10.1017/jog.2017.69, 2017. a, b
Vallot, D., Åström, J., Zwinger, T., Pettersson, R., Everett, A., Benn, D. I., Luckman, A., van Pelt, W. J. J., Nick, F., and Kohler, J.: Effects of undercutting and sliding on calving: a global approach applied to Kronebreen, Svalbard, The Cryosphere, 12, 609–625, https://doi.org/10.5194/tc-12-609-2018, 2018. a
van der Wel, L. G., Streurman, H. J., Isaksson, E., Helsen, M. M., Wal, R. S. W. V. D., Martma, T., Pohjola, V. A., Moore, J. C., and Meijer, H. A. J.: Using high-resolution tritium profiles to quantify the effects of melt on two Spitsbergen ice cores, J. Glaciol., 57, 1087–1097, https://doi.org/10.3189/002214311798843368, 2011. a
van Pelt, W., Pohjola, V., Pettersson, R., Marchenko, S., Kohler, J., Luks, B., Hagen, J. O., Schuler, T. V., Dunse, T., Noël, B., and Reijmer, C.: A long-term dataset of climatic mass balance, snow conditions, and runoff in Svalbard (1957–2018), The Cryosphere, 13, 2259–2280, https://doi.org/10.5194/tc-13-2259-2019, 2019. a
van Pelt, W. J., Pettersson, R., Pohjola, V. A., Marchenko, S., Claremar, B., and Oerlemans, J.: Inverse estimation of snow accumulation along a radar transect on Nordenskiöldbreen, Svalbard, J. Geophys. Res.-Earth, 119, 816–835, https://doi.org/10.1002/2013JF003040, 2014. a
van Pelt, W. J. J. and Kohler, J.: Modelling the long-term mass balance and firn evolution of glaciers around Kongsfjorden, Svalbard, J. Glaciol., 61, 731–744, https://doi.org/10.3189/2015JoG14J223, 2015.
a, b
Welty, E., Zemp, M., Navarro, F., Huss, M., Fürst, J. J., Gärtner-Roer, I., Landmann, J., Machguth, H., Naegeli, K., Andreassen, L. M., Farinotti, D., Li, H., and GlaThiDa Contributors: Worldwide version-controlled database of glacier thickness observations, Earth Syst. Sci. Data, 12, 3039–3055, https://doi.org/10.5194/essd-12-3039-2020, 2020. a
Werder, M. A., Huss, M., Paul, F., Dehecq, A., and Farinotti, D.: A Bayesian ice thickness estimation model for large-scale applications, J. Glaciol., 66, 137–152, https://doi.org/10.1017/jog.2019.93, 2020. a, b
Winkelmann, R., Martin, M. A., Haseloff, M., Albrecht, T., Bueler, E., Khroulev, C., and Levermann, A.: The Potsdam Parallel Ice Sheet Model (PISM-PIK) – Part 1: Model description, The Cryosphere, 5, 715–726, https://doi.org/10.5194/tc-5-715-2011, 2011. a
Zorzut, V., Ruiz, L., Rivera, A., Pitte, P., Villalba, R., and Medrzycka, D.: Slope estimation influences on ice thickness inversion models: a case study for Monte Tronador glaciers, North Patagonian Andes, J. Glaciol., 66, 996–1005, https://doi.org/10.1017/jog.2020.64, 2020. a
Short summary
Since the ice thickness of most glaciers worldwide is unknown, and since it is not feasible to visit every glacier and observe their thickness directly, inverse modelling techniques are needed that can calculate ice thickness from abundant surface observations. Here, we present a new method for doing that. Our methodology relies on modelling the rate of surface elevation change for a given glacier, compare this with observations of the same quantity and change the bed until the two are in line.
Since the ice thickness of most glaciers worldwide is unknown, and since it is not feasible to...
