Articles | Volume 19, issue 11
https://doi.org/10.5194/tc-19-5871-2025
© Author(s) 2025. 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-19-5871-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Recent history and future demise of Jostedalsbreen, the largest ice cap in mainland Europe
Henning Åkesson
CORRESPONDING AUTHOR
Department of Geosciences, University of Oslo, Oslo, Norway
Kamilla Hauknes Sjursen
Department of Civil Engineering and Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
Thomas Vikhamar Schuler
Department of Geosciences, University of Oslo, Oslo, Norway
Thorben Dunse
Department of Civil Engineering and Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
Liss Marie Andreassen
Norwegian Water Resources and Energy Directorate (NVE), Oslo, Norway
Mette Kusk Gillespie
VIA University College, Nørre Nissum, Denmark
Benjamin Aubrey Robson
Department of Earth Science, University of Bergen, Bergen, Norway
Thomas Schellenberger
Department of Geosciences, University of Oslo, Oslo, Norway
Jacob Clement Yde
Department of Civil Engineering and Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
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Jamie Barnett, Felicity A. Holmes, Joshua Cuzzone, Henning Åkesson, Mathieu Morlighem, Matt O'Regan, Johan Nilsson, Nina Kirchner, and Martin Jakobsson
The Cryosphere, 19, 3631–3653, https://doi.org/10.5194/tc-19-3631-2025, https://doi.org/10.5194/tc-19-3631-2025, 2025
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Understanding how ice sheets have changed in the past can allow us to make better predictions for the future. By running a state-of-the-art model of Ryder Glacier, North Greenland, over the past 12 000 years we find that both a warming atmosphere and the ocean play a key role in the evolution of the glacier. Our conclusions stress that accurately quantifying the ice sheet’s interactions with the ocean is required to predict future changes and reliable sea level rise estimates.
Felicity A. Holmes, Jamie Barnett, Henning Åkesson, Mathieu Morlighem, Johan Nilsson, Nina Kirchner, and Martin Jakobsson
The Cryosphere, 19, 2695–2714, https://doi.org/10.5194/tc-19-2695-2025, https://doi.org/10.5194/tc-19-2695-2025, 2025
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Northern Greenland contains some of the ice sheet's last remaining glaciers with floating ice tongues. One of these is Ryder Glacier, which has been relatively stable in recent decades, in contrast to nearby glaciers. Here, we use a computer model to simulate Ryder Glacier until 2300 under both a low- and a high-emissions scenario. Very high levels of surface melt under a high-emissions future lead to a sea level rise contribution that is an order of magnitude higher than under a low-emissions future.
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
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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.
Kamilla Hauknes Sjursen, Jordi Bolibar, Marijn van der Meer, Liss Marie Andreassen, Julian Peter Biesheuvel, Thorben Dunse, Matthias Huss, Fabien Maussion, David R. Rounce, and Brandon Tober
The Cryosphere, 19, 5801–5826, https://doi.org/10.5194/tc-19-5801-2025, https://doi.org/10.5194/tc-19-5801-2025, 2025
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Understanding glacier mass changes is crucial for assessing freshwater availability in many regions of the world. We present the Mass Balance Machine, a machine learning model that learns from sparse measurements of glacier mass change to make predictions on unmonitored glaciers. Using data from Norway, we show that the model provides accurate estimates of mass changes at different spatiotemporal scales. Our findings show that machine learning can be a valuable tool to improve such predictions.
Tazio Strozzi, Erik Schytt Mannerfelt, Oliver Cartus, Maurizio Santoro, Thomas Schellenberger, and Andreas Kääb
EGUsphere, https://doi.org/10.5194/egusphere-2025-5011, https://doi.org/10.5194/egusphere-2025-5011, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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By analysing 30 years of satellite SAR data, we have found that the number of glacier surges over Svalbard has tripled since 2015. We show that this increase is unlikely to be explained solely by improvements in data quality or by random fluctuations in surge frequency, suggesting that this trend is caused by an external forcing mechanism. Given our incomplete understanding of surge initiation, the cause of the observed threefold increase remains however uncertain.
Julien Vollering, Naomi Gatis, Mette Kusk Gillespie, Karl-Kristian Muggerud, Sigurd Daniel Nerhus, Knut Rydgren, and Mikko Sparf
SOIL, 11, 763–791, https://doi.org/10.5194/soil-11-763-2025, https://doi.org/10.5194/soil-11-763-2025, 2025
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Peat depth is crucial to peatland management but often unknown. We used machine learning to map peat depth in two Norwegian landscapes, based on terrain and remotely sensed radiation. We found that terrain, especially elevation and valley bottom flatness, predicted peat depth better than radiation. Our approach improved existing maps but struggled to identify very deep peat, demonstrating that it can support regional planning but not replace field measurements for local carbon stock assessments.
Jamie Barnett, Felicity A. Holmes, Joshua Cuzzone, Henning Åkesson, Mathieu Morlighem, Matt O'Regan, Johan Nilsson, Nina Kirchner, and Martin Jakobsson
The Cryosphere, 19, 3631–3653, https://doi.org/10.5194/tc-19-3631-2025, https://doi.org/10.5194/tc-19-3631-2025, 2025
Short summary
Short summary
Understanding how ice sheets have changed in the past can allow us to make better predictions for the future. By running a state-of-the-art model of Ryder Glacier, North Greenland, over the past 12 000 years we find that both a warming atmosphere and the ocean play a key role in the evolution of the glacier. Our conclusions stress that accurately quantifying the ice sheet’s interactions with the ocean is required to predict future changes and reliable sea level rise estimates.
Felicity A. Holmes, Jamie Barnett, Henning Åkesson, Mathieu Morlighem, Johan Nilsson, Nina Kirchner, and Martin Jakobsson
The Cryosphere, 19, 2695–2714, https://doi.org/10.5194/tc-19-2695-2025, https://doi.org/10.5194/tc-19-2695-2025, 2025
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Northern Greenland contains some of the ice sheet's last remaining glaciers with floating ice tongues. One of these is Ryder Glacier, which has been relatively stable in recent decades, in contrast to nearby glaciers. Here, we use a computer model to simulate Ryder Glacier until 2300 under both a low- and a high-emissions scenario. Very high levels of surface melt under a high-emissions future lead to a sea level rise contribution that is an order of magnitude higher than under a low-emissions future.
Diego Cusicanqui, Pascal Lacroix, Xavier Bodin, Benjamin Aubrey Robson, Andreas Kääb, and Shelley MacDonell
The Cryosphere, 19, 2559–2581, https://doi.org/10.5194/tc-19-2559-2025, https://doi.org/10.5194/tc-19-2559-2025, 2025
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This study presents a robust methodological approach to detect and analyse rock glacier kinematics using Landsat 7/Landsat 8 imagery. In the semiarid Andes, 382 landforms were monitored, showing an average velocity of 0.37 ± 0.07 m yr⁻¹ over 24 years, with rock glaciers moving 23 % faster. Results demonstrate the feasibility of using medium-resolution optical imagery, combined with radar interferometry, to monitor rock glacier kinematics with widely available satellite datasets.
Thomas James Barnes, Thomas Vikhamar Schuler, Karianne Staalesen Lilleøren, and Louise Steffensen Schmidt
EGUsphere, https://doi.org/10.5194/egusphere-2025-108, https://doi.org/10.5194/egusphere-2025-108, 2025
Preprint archived
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Ribbed moraines are a common, but poorly understood landform within formerly glaciated regions. There are many competing theories for their formation. As such, this paper addresses some of these theories by taking modelled ice conditions and physical characteristics of the landscapes in which they form and, then comparing them to the location of ribbed moraines. Using this we can identify conditions where ribbed moraines are more often present, and therefore we identify the most likely theories.
Marijn van der Meer, Harry Zekollari, Matthias Huss, Jordi Bolibar, Kamilla Hauknes Sjursen, and Daniel Farinotti
The Cryosphere, 19, 805–826, https://doi.org/10.5194/tc-19-805-2025, https://doi.org/10.5194/tc-19-805-2025, 2025
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Glacier retreat poses big challenges, making understanding how climate affects glaciers vital. But glacier measurements worldwide are limited. We created a simple machine-learning model called miniML-MB, which estimates annual changes in glacier mass in the Swiss Alps. As input, miniML-MB uses two climate variables: average temperature (May–Aug) and total precipitation (Oct–Feb). Our model can accurately predict glacier mass from 1961 to 2021 but struggles for extreme years (2022 and 2023).
Mette K. Gillespie, Liss M. Andreassen, Matthias Huss, Simon de Villiers, Kamilla H. Sjursen, Jostein Aasen, Jostein Bakke, Jan M. Cederstrøm, Hallgeir Elvehøy, Bjarne Kjøllmoen, Even Loe, Marte Meland, Kjetil Melvold, Sigurd D. Nerhus, Torgeir O. Røthe, Eivind W. N. Støren, Kåre Øst, and Jacob C. Yde
Earth Syst. Sci. Data, 16, 5799–5825, https://doi.org/10.5194/essd-16-5799-2024, https://doi.org/10.5194/essd-16-5799-2024, 2024
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We present an extensive ice thickness dataset from Jostedalsbreen ice cap that will serve as a baseline for future studies of regional climate-induced change. Results show that Jostedalsbreen currently (~2020) has a maximum ice thickness of ~630 m, a mean ice thickness of 154 ± 22 m and an ice volume of 70.6 ±10.2 km3. Ice of less than 50 m thickness covers two narrow regions of Jostedalsbreen, and the ice cap is likely to separate into three parts in a warming climate.
Etienne Berthier, Jérôme Lebreton, Delphine Fontannaz, Steven Hosford, Joaquín Muñoz-Cobo Belart, Fanny Brun, Liss M. Andreassen, Brian Menounos, and Charlotte Blondel
The Cryosphere, 18, 5551–5571, https://doi.org/10.5194/tc-18-5551-2024, https://doi.org/10.5194/tc-18-5551-2024, 2024
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Repeat elevation measurements are crucial for monitoring glacier health and to understand how glaciers affect river flows and sea level. Until recently, high-resolution elevation data were mostly available for polar regions and High Mountain Asia. Our project, the Pléiades Glacier Observatory, now provides high-resolution topographies of 140 glacier sites worldwide. This is a novel and open dataset to monitor the impact of climate change on glaciers at high resolution and accuracy.
Livia Piermattei, Michael Zemp, Christian Sommer, Fanny Brun, Matthias H. Braun, Liss M. Andreassen, Joaquín M. C. Belart, Etienne Berthier, Atanu Bhattacharya, Laura Boehm Vock, Tobias Bolch, Amaury Dehecq, Inés Dussaillant, Daniel Falaschi, Caitlyn Florentine, Dana Floricioiu, Christian Ginzler, Gregoire Guillet, Romain Hugonnet, Matthias Huss, Andreas Kääb, Owen King, Christoph Klug, Friedrich Knuth, Lukas Krieger, Jeff La Frenierre, Robert McNabb, Christopher McNeil, Rainer Prinz, Louis Sass, Thorsten Seehaus, David Shean, Désirée Treichler, Anja Wendt, and Ruitang Yang
The Cryosphere, 18, 3195–3230, https://doi.org/10.5194/tc-18-3195-2024, https://doi.org/10.5194/tc-18-3195-2024, 2024
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Satellites have made it possible to observe glacier elevation changes from all around the world. In the present study, we compared the results produced from two different types of satellite data between different research groups and against validation measurements from aeroplanes. We found a large spread between individual results but showed that the group ensemble can be used to reliably estimate glacier elevation changes and related errors from satellite data.
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
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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.
Thomas J. Barnes, Thomas V. Schuler, Simon Filhol, and Karianne S. Lilleøren
Earth Surf. Dynam., 12, 801–818, https://doi.org/10.5194/esurf-12-801-2024, https://doi.org/10.5194/esurf-12-801-2024, 2024
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In this paper, we use machine learning to automatically outline landforms based on their characteristics. We test several methods to identify the most accurate and then proceed to develop the most accurate to improve its accuracy further. We manage to outline landforms with 65 %–75 % accuracy, at a resolution of 10 m, thanks to high-quality/high-resolution elevation data. We find that it is possible to run this method at a country scale to quickly produce landform inventories for future studies.
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
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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.
Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Erin Emily Thomas, and Sebastian Westermann
The Cryosphere, 17, 2941–2963, https://doi.org/10.5194/tc-17-2941-2023, https://doi.org/10.5194/tc-17-2941-2023, 2023
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Here, we present high-resolution simulations of glacier mass balance (the gain and loss of ice over a year) and runoff on Svalbard from 1991–2022, one of the fastest warming regions in the Arctic. The simulations are created using the CryoGrid community model. We find a small overall loss of mass over the simulation period of −0.08 m yr−1 but with no statistically significant trend. The average runoff was found to be 41 Gt yr−1, with a significant increasing trend of 6.3 Gt per decade.
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
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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.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
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The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Anirudha Mahagaonkar, Geir Moholdt, and Thomas V. Schuler
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-4, https://doi.org/10.5194/tc-2023-4, 2023
Revised manuscript not accepted
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Surface meltwater lakes along the margins of the Antarctic Ice Sheet can be important for ice shelf dynamics and stability. We used optical satellite imagery to study seasonal evolution of meltwater lakes in Dronning Maud Land. We found large interannual variability in lake extents, but with consistent seasonal patterns. Although correlation with summer air temperature was strong locally, other climatic and environmental factors need to be considered to explain the large regional variability.
Adam Emmer, Simon K. Allen, Mark Carey, Holger Frey, Christian Huggel, Oliver Korup, Martin Mergili, Ashim Sattar, Georg Veh, Thomas Y. Chen, Simon J. Cook, Mariana Correas-Gonzalez, Soumik Das, Alejandro Diaz Moreno, Fabian Drenkhan, Melanie Fischer, Walter W. Immerzeel, Eñaut Izagirre, Ramesh Chandra Joshi, Ioannis Kougkoulos, Riamsara Kuyakanon Knapp, Dongfeng Li, Ulfat Majeed, Stephanie Matti, Holly Moulton, Faezeh Nick, Valentine Piroton, Irfan Rashid, Masoom Reza, Anderson Ribeiro de Figueiredo, Christian Riveros, Finu Shrestha, Milan Shrestha, Jakob Steiner, Noah Walker-Crawford, Joanne L. Wood, and Jacob C. Yde
Nat. Hazards Earth Syst. Sci., 22, 3041–3061, https://doi.org/10.5194/nhess-22-3041-2022, https://doi.org/10.5194/nhess-22-3041-2022, 2022
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Glacial lake outburst floods (GLOFs) have attracted increased research attention recently. In this work, we review GLOF research papers published between 2017 and 2021 and complement the analysis with research community insights gained from the 2021 GLOF conference we organized. The transdisciplinary character of the conference together with broad geographical coverage allowed us to identify progress, trends and challenges in GLOF research and outline future research needs and directions.
Jonathan P. Conway, Jakob Abermann, Liss M. Andreassen, Mohd Farooq Azam, Nicolas J. Cullen, Noel Fitzpatrick, Rianne H. Giesen, Kirsty Langley, Shelley MacDonell, Thomas Mölg, Valentina Radić, Carleen H. Reijmer, and Jean-Emmanuel Sicart
The Cryosphere, 16, 3331–3356, https://doi.org/10.5194/tc-16-3331-2022, https://doi.org/10.5194/tc-16-3331-2022, 2022
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We used data from automatic weather stations on 16 glaciers to show how clouds influence glacier melt in different climates around the world. We found surface melt was always more frequent when it was cloudy but was not universally faster or slower than under clear-sky conditions. Also, air temperature was related to clouds in opposite ways in different climates – warmer with clouds in cold climates and vice versa. These results will help us improve how we model past and future glacier melt.
L. Abad, D. Hölbling, Z. Dabiri, and B. A. Robson
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W1-2022, 5–11, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-5-2022, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-5-2022, 2022
Benjamin Aubrey Robson, Shelley MacDonell, Álvaro Ayala, Tobias Bolch, Pål Ringkjøb Nielsen, and Sebastián Vivero
The Cryosphere, 16, 647–665, https://doi.org/10.5194/tc-16-647-2022, https://doi.org/10.5194/tc-16-647-2022, 2022
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This work uses satellite and aerial data to study glaciers and rock glacier changes in La Laguna catchment within the semi-arid Andes of Chile, where ice melt is an important factor in river flow. The results show the rate of ice loss of Tapado Glacier has been increasing since the 1950s, which possibly relates to a dryer, warmer climate over the previous decades. Several rock glaciers show high surface velocities and elevation changes between 2012 and 2020, indicating they may be ice-rich.
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
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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.
Tazio Strozzi, Andreas Wiesmann, Andreas Kääb, Thomas Schellenberger, and Frank Paul
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-44, https://doi.org/10.5194/essd-2022-44, 2022
Revised manuscript not accepted
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Knowledge on surface velocity of glaciers and ice caps contributes to a better understanding of a wide range of processes related to glacier dynamics, mass change and response to climate. Based on the release of historical satellite radar data from various space agencies we compiled nearly complete mosaics of winter ice surface velocities for the 1990's over the Eastern Arctic. Compared to the present state, we observe a general increase of ice velocities along with a retreat of glacier fronts.
Thorben Dunse, Kaixing Dong, Kjetil Schanke Aas, and Leif Christian Stige
Biogeosciences, 19, 271–294, https://doi.org/10.5194/bg-19-271-2022, https://doi.org/10.5194/bg-19-271-2022, 2022
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We investigate the effect of glacier meltwater on phytoplankton dynamics in Svalbard. Phytoplankton forms the basis of the marine food web, and its seasonal dynamics depend on the availability of light and nutrients, both of which are affected by glacier runoff. We use satellite ocean color, an indicator of phytoplankton biomass, and glacier mass balance modeling to find that the overall effect of glacier runoff on marine productivity is positive within the major fjord systems of Svalbard.
Iwo Wieczorek, Mateusz Czesław Strzelecki, Łukasz Stachnik, Jacob Clement Yde, and Jakub Małecki
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-364, https://doi.org/10.5194/tc-2021-364, 2022
Manuscript not accepted for further review
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Glacial lakes development around the World has been observed since the end of the Little Ice Age. The whole process is especially rapid in Arctic region what shows last researches. One of the last regions which still has not been covered by data about changes of glacial lakes is the Svalbard Archipelago (Norway). We used remote sensing materials and methods to provide information's about changes of glacial lakes and to show major activity of glacial lakes outburst floods.
Trevor R. Hillebrand, John O. Stone, Michelle Koutnik, Courtney King, Howard Conway, Brenda Hall, Keir Nichols, Brent Goehring, and Mette K. Gillespie
The Cryosphere, 15, 3329–3354, https://doi.org/10.5194/tc-15-3329-2021, https://doi.org/10.5194/tc-15-3329-2021, 2021
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We present chronologies from Darwin and Hatherton glaciers to better constrain ice sheet retreat during the last deglaciation in the Ross Sector of Antarctica. We use a glacier flowband model and an ensemble of 3D ice sheet model simulations to show that (i) the whole glacier system likely thinned steadily from about 9–3 ka, and (ii) the grounding line likely reached the Darwin–Hatherton Glacier System at about 3 ka, which is ≥3.8 kyr later than was suggested by previous reconstructions.
Chloé Scholzen, Thomas V. Schuler, and Adrien Gilbert
The Cryosphere, 15, 2719–2738, https://doi.org/10.5194/tc-15-2719-2021, https://doi.org/10.5194/tc-15-2719-2021, 2021
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We use a two-dimensional model of water flow below the glaciers in Kongsfjord, Svalbard, to investigate how different processes of surface-to-bed meltwater transfer affect subglacial hydraulic conditions. The latter are important for the sliding motion of glaciers, which in some cases exhibit huge variations. Our findings indicate that the glaciers in our study area undergo substantial sliding because water is poorly evacuated from their base, with limited influence from the surface hydrology.
Juditha Undine Schmidt, Bernd Etzelmüller, Thomas Vikhamar Schuler, Florence Magnin, Julia Boike, Moritz Langer, and Sebastian Westermann
The Cryosphere, 15, 2491–2509, https://doi.org/10.5194/tc-15-2491-2021, https://doi.org/10.5194/tc-15-2491-2021, 2021
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This study presents rock surface temperatures (RSTs) of steep high-Arctic rock walls on Svalbard from 2016 to 2020. The field data show that coastal cliffs are characterized by warmer RSTs than inland locations during winter seasons. By running model simulations, we analyze factors leading to that effect, calculate the surface energy balance and simulate different future scenarios. Both field data and model results can contribute to a further understanding of RST in high-Arctic rock walls.
Elena Barbaro, Krystyna Koziol, Mats P. Björkman, Carmen P. Vega, Christian Zdanowicz, Tonu Martma, Jean-Charles Gallet, Daniel Kępski, Catherine Larose, Bartłomiej Luks, Florian Tolle, Thomas V. Schuler, Aleksander Uszczyk, and Andrea Spolaor
Atmos. Chem. Phys., 21, 3163–3180, https://doi.org/10.5194/acp-21-3163-2021, https://doi.org/10.5194/acp-21-3163-2021, 2021
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This paper shows the most comprehensive seasonal snow chemistry survey to date, carried out in April 2016 across 22 sites on 7 glaciers across Svalbard. The dataset consists of the concentration, mass loading, spatial and altitudinal distribution of major ion species (Ca2+, K+,
Na2+, Mg2+,
NH4+, SO42−,
Br−, Cl− and
NO3−), together with its stable oxygen and hydrogen isotope composition (δ18O and
δ2H) in the snowpack. This study was part of the larger Community Coordinated Snow Study in Svalbard.
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
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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.
Andreas Alexander, Jaroslav Obu, Thomas V. Schuler, Andreas Kääb, and Hanne H. Christiansen
The Cryosphere, 14, 4217–4231, https://doi.org/10.5194/tc-14-4217-2020, https://doi.org/10.5194/tc-14-4217-2020, 2020
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In this study we present subglacial air, ice and sediment temperatures from within the basal drainage systems of two cold-based glaciers on Svalbard during late spring and the summer melt season. We put the data into the context of air temperature and rainfall at the glacier surface and show the importance of surface events on the subglacial thermal regime and erosion around basal drainage channels. Observed vertical erosion rates thereby reachup to 0.9 m d−1.
Ethan Welty, Michael Zemp, Francisco Navarro, Matthias Huss, Johannes J. Fürst, Isabelle Gärtner-Roer, Johannes Landmann, Horst Machguth, Kathrin Naegeli, Liss M. Andreassen, Daniel Farinotti, Huilin Li, and GlaThiDa Contributors
Earth Syst. Sci. Data, 12, 3039–3055, https://doi.org/10.5194/essd-12-3039-2020, https://doi.org/10.5194/essd-12-3039-2020, 2020
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Knowing the thickness of glacier ice is critical for predicting the rate of glacier loss and the myriad downstream impacts. To facilitate forecasts of future change, we have added 3 million measurements to our worldwide database of glacier thickness: 14 % of global glacier area is now within 1 km of a thickness measurement (up from 6 %). To make it easier to update and monitor the quality of our database, we have used automated tools to check and track changes to the data over time.
Cited articles
Adhikari, S. and Marshall, S. J.: Glacier volume-area relation for high-order mechanics and transient glacier states, Geophysical Research Letters, 39, https://doi.org/10.1029/2012GL052712, 2012. a, b, c
Åkesson, H. and Sjursen, K. H.: Modelled future evolution of Jostedalsbreen ice cap, Norway [fata set], in: The Cryosphere, Zenodo, https://doi.org/10.5281/zenodo.17472491, 2025. a, b, c
Åkesson, H., Morlighem, M., Nisancioglu, K. H., Svendsen, J. I., and Mangerud, J.: Atmosphere-driven ice sheet mass loss paced by topography: Insights from modelling the south-western Scandinavian Ice Sheet, Quaternary Science Reviews, 195, 32–47, https://doi.org/10.1016/j.quascirev.2018.07.004, 2018. a
Åkesson, H., Morlighem, M., O’Regan, M., and Jakobsson, M.: Future Projections of Petermann Glacier Under Ocean Warming Depend Strongly on Friction Law, Journal of Geophysical Research: Earth Surface, 126, https://doi.org/10.1029/2020JF005921, 2021. a
Andreassen, L. M. and Elvehøy, H.: Jostedalsbreen data, Norwegian Nasjonalt Vitenarkiv (NVA) [data set], https://doi.org/10.58059/yhwr-rx55, 2023. a
Andreassen, L., Huss, M., Melvold, K., Elvehøy, H., and Winsvold, S.: Ice thickness measurements and volume estimates for glaciers in Norway, Journal of Glaciology, 61, 763–775, https://doi.org/10.3189/2015JoG14J161, 2015. a
Andreassen, L. M., Elvehøy, H., Kjøllmoen, B., Engeset, R. V., and Haakensen, N.: Glacier mass-balance and length variation in Norway, Annals of Glaciology, 42, 317–325, https://doi.org/10.3189/172756405781812826, 2005. a
Andreassen, L. M., Elvehøy, H., Kjøllmoen, B., and Belart, J. M.: Glacier change in Norway since the 1960s – an overview of mass balance, area, length and surface elevation changes, Journal of Glaciology, 66, 313–328, https://doi.org/10.1017/jog.2020.10, 2020. a
Andreassen, L. M., Robson, B. A., Sjursen, K. H., Elvehøy, H., Kjøllmoen, B., and Carrivick, J. L.: Spatio-temporal variability in geometry and geodetic mass balance of Jostedalsbreen ice cap, Norway, Annals of Glaciology, 1–18, https://doi.org/10.1017/aog.2023.70, 2023. a, b, c, d, e, f, g, h, i, j, k, l, m
ArcGIS Development Team: Esri Inc.: ArcGIS Pro (Version 3.4), https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview (last access: 25 August 2025), 2025. a
Aschwanden, A., Aðalgeirsdóttir, G., and Khroulev, C.: Hindcasting to measure ice sheet model sensitivity to initial states, The Cryosphere, 7, 1083–1093, https://doi.org/10.5194/tc-7-1083-2013, 2013. a
Aðalgeirsdóttir, G., Gudmundsson, G. H., and Björnsson, H.: Volume sensitivity of Vatnajökull Ice Cap, Iceland, to perturbations in equilibrium line altitude, Journal of Geophysical Research: Earth Surface, 110, https://doi.org/10.1029/2005JF000289, 2005. a
Ađalgeirsdóttir, G., Aschwanden, A., Khroulev, C., Boberg, F., Mottram, R., Lucas-Picher, P., and Christensen, J.: Role of model initialization for projections of 21st-century Greenland ice sheet mass loss, Journal of Glaciology, 60, 782–794, https://doi.org/10.3189/2014JoG13J202, 2014. a
Bickerton, R. W. and Matthews, J. A.: ‘Little ice age’ variations of outlet glaciers from the jostedalsbreen ice-cap, Southern Norway: A regional lichenometric-dating study of ice-marginal moraine sequences and their climatic significance, Journal of Quaternary Science, 8, 45–66, https://doi.org/10.1002/jqs.3390080105, 1993. a
Blatter, H.: Velocity and stress fields in grounded glaciers: a simple algorithm for including deviatoric stress gradients, Journal of Glaciology, 41, 333–344, https://doi.org/10.3189/S002214300001621X, 1995. a
Box, J. E., Hubbard, A., Bahr, D. B., Colgan, W. T., Fettweis, X., Mankoff, K. D., Wehrlé, A., Noël, B., van den Broeke, M. R., Wouters, B., Bjørk, A. A., and Fausto, R. S.: Greenland ice sheet climate disequilibrium and committed sea-level rise, Nature Climate Change, 12, 808–813, https://doi.org/10.1038/s41558-022-01441-2, 2022. a
Brondex, J., Gillet-Chaulet, F., and Gagliardini, O.: Sensitivity of centennial mass loss projections of the Amundsen basin to the friction law, The Cryosphere, 13, 177–195, https://doi.org/10.5194/tc-13-177-2019, 2019. a
Budd, W. F., Keage, P. L., and Blundy, N. A.: Empirical Studies of Ice Sliding, Journal of Glaciology, 23, 157–170, https://doi.org/10.3189/S0022143000029804, 1979. a
Carrivick, J. L., Andreassen, L. M., Nesje, A., and Yde, J. C.: A reconstruction of Jostedalsbreen during the Little Ice Age and geometric changes to outlet glaciers since then, Quaternary Science Reviews, 284, 107501, https://doi.org/10.1016/j.quascirev.2022.107501, 2022. a, b
Compagno, L., Zekollari, H., Huss, M., and Farinotti, D.: Limited impact of climate forcing products on future glacier evolution in Scandinavia and Iceland, Journal of Glaciology, 67, 727–743, https://doi.org/10.1017/jog.2021.24, 2021. a, b
Dannevig, H. and Rusdal, T.: Caring for melting glaciers, Tourism Geographies, 25, 1679–1695, https://doi.org/10.1080/14616688.2023.2278762, 2023. a
Davies, B., McNabb, R., Bendle, J., Carrivick, J., Ely, J., Holt, T., Markle, B., McNeil, C., Nicholson, L., and Pelto, M.: Accelerating glacier volume loss on Juneau Icefield driven by hypsometry and melt-accelerating feedbacks, Nature Communications, 15, 5099, https://doi.org/10.1038/s41467-024-49269-y, 2024. a
de Fleurian, B., Davy, R., and Langebroek, P. M.: Impact of runoff temporal distribution on ice dynamics, The Cryosphere, 16, 2265–2283, https://doi.org/10.5194/tc-16-2265-2022, 2022. a
Ekblom Johansson, F., Bakke, J., Støren, E. N., Gillespie, M. K., and Laumann, T.: Mapping of the Subglacial Topography of Folgefonna Ice Cap in Western Norway – Consequences for Ice Retreat Patterns and Hydrological Changes, Frontiers in Earth Science, 10, https://doi.org/10.3389/feart.2022.886361, 2022. a, b, c, d, e, f
Elsberg, D. H., Harrison, W. D., Echelmeyer, K. A., and Krimmel, R. M.: Quantifying the effects of climate and surface change on glacier mass balance, Journal of Glaciology, 47, 649–658, https://doi.org/10.3189/172756501781831783, 2001. a, b
Erikstad, L. and Sollid, J. L.: Neoglaciation in South Norway using lichenometric methods, Norsk Geografisk Tidsskrift – Norwegian Journal of Geography, 40, 85–105, https://doi.org/10.1080/00291958608552159, 1986. a
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, Nature Geoscience, 12, 168–173, https://doi.org/10.1038/s41561-019-0300-3, 2019. a, b
Frank, T. and van Pelt, W. J. J.: Ice volume and thickness of all Scandinavian glaciers and ice caps, Journal of Glaciology, 1–14, https://doi.org/10.1017/jog.2024.25, 2024. a, b
Friedl, P., Seehaus, T., and Braun, M.: Global time series and temporal mosaics of glacier surface velocities derived from Sentinel-1 data, Earth Syst. Sci. Data, 13, 4653–4675, https://doi.org/10.5194/essd-13-4653-2021, 2021. a
Gabbi, J., Carenzo, M., Pellicciotti, F., Bauder, A., and Funk, M.: A comparison of empirical and physically based glacier surface melt models for long-term simulations of glacier response, Journal of Glaciology, 60, 1140–1154, https://doi.org/10.3189/2014JoG14J011, 2014. a
GAMMA AG: GAMMA Remote Sensing Research and Consulting AG (Version 20160625), https://www.gamma-rs.ch/gamma-software/gamma-software (last access: 4 September 2025), 2016. a
Garbe, J., Albrecht, T., Levermann, A., Donges, J. F., and Winkelmann, R.: The hysteresis of the Antarctic Ice Sheet, Nature, 585, 538–544, https://doi.org/10.1038/s41586-020-2727-5,, 2020. a
Gilbert, A., Flowers, G. E., Miller, G. H., Rabus, B. T., Van Wychen, W., Gardner, A. S., and Copland, L.: Sensitivity of Barnes Ice Cap, Baffin Island, Canada, to climate state and internal dynamics: Barnes Ice Cap Stability, Journal of Geophysical Research: Earth Surface, 121, 1516–1539, https://doi.org/10.1002/2016JF003839, 2016. a
Gillespie, M. K., Andreassen, L. M., Huss, M., de Villiers, S., Sjursen, K. H., Aasen, J., Bakke, J., Cederstrøm, J. M., Elvehøy, H., Kjøllmoen, B., Loe, E., Meland, M., Melvold, K., Nerhus, S. D., Røthe, T. O., Støren, E. W. N., Øst, K., and Yde, J. C.: Ice thickness and bed topography of Jostedalsbreen ice cap, Norway, Earth Syst. Sci. Data, 16, 5799–5825, https://doi.org/10.5194/essd-16-5799-2024, 2024a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t
Gillespie, M. K., Andreassen, L. M., Huss, M., de Villiers, S., Sjursen, K. H., Aasen, J., Bakke, J., Cederstrøm, J. M., Elvehøy, H., Kjøllmoen, B., Loe, E., Meland, M., Melvold, K., Nerhus, S. D., Røthe, T. O., Støren, E. W. N., Øst, K., and Yde, J. C.: Jostedalsbreen ice thickness and bed to- pography, Norwegian Nasjonalt Vitenarkiv (NVA) [data set], https://doi.org/10.58059/yhwr-rx55, 2024b. a
Gillet-Chaulet, F., Gagliardini, O., Seddik, H., Nodet, M., Durand, G., Ritz, C., Zwinger, T., Greve, R., and Vaughan, D. G.: Greenland ice sheet contribution to sea-level rise from a new-generation ice-sheet model, The Cryosphere, 6, 1561–1576, https://doi.org/10.5194/tc-6-1561-2012, 2012. a
Gjerde, M., Hoel, O. L., and Nesje, A.: The “Little Ice Age” advance of Nigardsbreen, Norway: A cross-disciplinary revision of the chronological framework, The Holocene, 09596836231185830, https://doi.org/10.1177/09596836231185830, 2023. a
Goldberg, D. N., Heimbach, P., Joughin, I., and Smith, B.: Committed retreat of Smith, Pope, and Kohler Glaciers over the next 30 years inferred by transient model calibration, The Cryosphere, 9, 2429–2446, https://doi.org/10.5194/tc-9-2429-2015, 2015. a
Gulbrandsen, T.: WF-1833 SOGNDAL-JOSTEDALSBREEN-GEIRANGER 1966. Rapport bildematcing av – Historiske ortofoto, Tech. rep., Hexagon, Fredrikstad, Norway, 2022. a
Hacker, B. R., Andersen, T. B., Johnston, S., Kylander-Clark, A. R. C., Peterman, E. M., Walsh, E. O., and Young, D.: High-temperature deformation during continental-margin subduction & exhumation: The ultrahigh-pressure Western Gneiss Region of Norway, Tectonophysics, 480, 149–171, https://doi.org/10.1016/j.tecto.2009.08.012, 2010. a
Hanssen-Bauer, I., Førland, E., Haddeland, I., Hisdal, H., Mayer, S., Nesje, A., Nilsen, J., Sandven, S. A., Sandø, A., Sorteberg, A., and Ådlandsvi, B.: Climate in Norway 2100, Norwegian Center for Climate Services (NCCS) Report 1/2017, Norwegian Environment Agency (Miljødirektoratet), https://www.miljodirektoratet.no/globalassets/publikasjoner/m741/m741.pdf (last access: 3 October 2025), 2017. a, b
Harrison, W. D., Elsberg, D. H., Echelmeyer, K. A., and Krimmel, R. M.: On the characterization of glacier response by a single time-scale, Journal of Glaciology, 47, 659–664, https://doi.org/10.3189/172756501781831837, 2001. a, b
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 Hock, R.: A new model for global glacier change and sea-level rise, Frontiers in Earth Sciences, 3, https://doi.org/10.3389/feart.2015.00054, 2015. a
Huss, M. and Hock, R.: Global-scale hydrological response to future glacier mass loss, Nature Climate Change, 8, 135–140, https://doi.org/10.1038/s41558-017-0049-x, 2018. a
Huss, M., Hock, R., Bauder, A., and Funk, M.: Conventional versus reference-surface mass balance, Journal of Glaciology, 58, 278–286, https://doi.org/10.3189/2012JoG11J216, 2012. a, b, c, d
Huss, M., Bookhagen, B., Huggel, C., Jacobsen, D., Bradley, R., Clague, J., Vuille, M., Buytaert, W., Cayan, D., Greenwood, G., Mark, B., Milner, A., Weingartner, R., and Winder, M.: Toward mountains without permanent snow and ice, Earth's Future, 5, 418–435, https://doi.org/10.1002/2016EF000514, 2017. a
Hutchinson, M. F., Xu, T., Stein, J. A., et al.: Recent progress in the ANUDEM elevation gridding procedure, Geomorphometry, 2011, 19–22, 2011. a
Hutter, K.: Theoretical glaciology: material science of ice and the mechanics of glaciers and ice sheets, vol. 1, Springer, https://doi.org/10.1007/978-94-015-1167-4, 1983. a
Iken, A.: The Effect of the Subglacial Water Pressure on the Sliding Velocity of a Glacier in an Idealized Numerical Model, Journal of Glaciology, 27, 407–421, https://doi.org/10.3189/S0022143000011448, 1981. a
Ismail, M. F., Bogacki, W., Disse, M., Schäfer, M., and Kirschbauer, L.: Estimating degree-day factors of snow based on energy flux components, The Cryosphere, 17, 211–231, https://doi.org/10.5194/tc-17-211-2023, 2023. a
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O., Bouwer, L., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., and Yiou, P.: EURO-CORDEX: New high-resolution climate change projections for European impact research, Regional Environmental Change, 14, https://doi.org/10.1007/s10113-013-0499-2, 2014. a, b, c
Jostedalsbreen nasjonalparkstyre: Besøksstrategi Jostedalsbreen nasjonalpark 2021–2027, Tech. rep., https://www.nasjonalparkstyre.no/uploads/files_jostedalsbreen/Besoksstrategi-Jostedalsbreen-2021-2027-godkjent.pdf (last access: 3 October 2025), 2021. a
Jouvet, G. and Cordonnier, G.: Ice-flow model emulator based on physics-informed deep learning, Journal of Glaciology, 1–15, https://doi.org/10.1017/jog.2023.73, 2023. a
Jouvet, G., Huss, M., Blatter, H., Picasso, M., and Rappaz, J.: Numerical simulation of Rhonegletscher from 1874 to 2100, Journal of Computational Physics, 228, 6426–6439, https://doi.org/10.1016/j.jcp.2009.05.033, 2009. a
Jouvet, G., Huss, M., Funk, M., and Blatter, H.: Modelling the retreat of Grosser Aletschgletscher, Switzerland, in a changing climate, Journal of Glaciology, 57, 1033–1045, https://doi.org/10.3189/002214311798843359, 2011. a
Jouvet, G., Cordonnier, G., Kim, B., Lüthi, M., Vieli, A., and Aschwanden, A.: Deep learning speeds up ice flow modelling by several orders of magnitude, Journal of Glaciology, 1–14, https://doi.org/10.1017/jog.2021.120, 2021. a
Jóhannesson, T., Raymond, C., and Waddington, E.: Time–Scale for Adjustment of Glaciers to Changes in Mass Balance, Journal of Glaciology, 35, 355–369, https://doi.org/10.3189/S002214300000928X, 1989. a, b
Jóhannesson, T., Sigurdsson, O., Laumann, T., and Kennett, M.: Degree-day glacier mass-balance modelling with applications to glaciers in Iceland, Norway and Greenland, Journal of Glaciology, 41, 345–358, https://doi.org/10.3189/S0022143000016221, 1995. a
Ketzler, G., Römer, W., and Beylich, A. A.: The Climate of Norway, Springer International Publishing, Cham, 7–29, ISBN 978-3-030-52563-7, https://doi.org/10.1007/978-3-030-52563-7_2, 2021. a
Larour, E., Seroussi, H., Morlighem, M., and Rignot, E.: Continental scale, high order, high spatial resolution, ice sheet modeling using the Ice Sheet System Model (ISSM): ICE SHEET SYSTEM MODEL, Journal of Geophysical Research: Earth Surface, 117, https://doi.org/10.1029/2011JF002140, 2012. a
Laumann, T. and Nesje, A.: The impact of climate change on future frontal variations of Briksdalsbreen, western Norway, Journal of Glaciology, 55, 789–796, https://doi.org/10.3189/002214309790152366, 2009. a, b, c
Laumann, T. and Nesje, A.: Spørteggbreen, western Norway, in the past, present and future: Simulations with a two-dimensional dynamical glacier model, The Holocene, 24, 842–852, https://doi.org/10.1177/0959683614530446, 2014. a, b
Le Meur, E. and Vincent, C.: A two-dimensional shallow ice-flow model of Glacier de Saint-Sorlin, France, Journal of Glaciology, 49, 527–538, https://doi.org/10.3189/172756503781830421, 2003. a
Le Meur, E., Gagliardini, O., Zwinger, T., and Ruokolainen, J.: Glacier flow modelling: a comparison of the Shallow Ice Approximation and the full-Stokes solution, Comptes Rendus. Physique, 5, 709–722, https://doi.org/10.1016/j.crhy.2004.10.001, 2004. a
Leysinger Vieli, G. J.-M. C. and Gudmundsson, G. H.: On estimating length fluctuations of glaciers caused by changes in climatic forcing, Journal of Geophysical Research: Earth Surface, 109, https://doi.org/10.1029/2003JF000027, 2004. a
Lussana, C.: seNorge observational gridded datasets, seNorge_2018, versions 21.09 and 21.10, METreport 07/2021, The Norwegian Meteorological Institute (MET Norway), Oslo, Norway, https://doi.org/10.13140/RG.2.2.36336.38400, 2021. a
Lussana, C., Tveito, O. E., Dobler, A., and Tunheim, K.: seNorge_2018, daily precipitation, and temperature datasets over Norway, Earth Syst. Sci. Data, 11, 1531–1551, https://doi.org/10.5194/essd-11-1531-2019, 2019. a, b, c
Marshall, S. J.: Simulation of Vatnajökull ice cap dynamics, Journal of Geophysical Research, 110, F03009, https://doi.org/10.1029/2004JF000262, 2005. a
Marzeion, B., Jarosch, A. H., and Hofer, M.: Past and future sea-level change from the surface mass balance of glaciers, The Cryosphere, 6, 1295–1322, https://doi.org/10.5194/tc-6-1295-2012, 2012. 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
Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geosci. Model Dev., 13, 3571–3605, https://doi.org/10.5194/gmd-13-3571-2020, 2020. a, b
Millan, R., Mouginot, J., and Rabatel, A.: Ice velocity and thickness of the world's glaciers, Theia [data set], https://doi.org/10.6096/1007, 2021. a
Milner, A. M., Khamis, K., Battin, T. J., Brittain, J. E., Barrand, N. E., Füreder, L., Cauvy-Fraunié, S., Gíslason, G. M., Jacobsen, D., Hannah, D. M., Hodson, A. J., Hood, E., Lencioni, V., Ólafsson, J. S., Robinson, C. T., Tranter, M., and Brown, L. E.: Glacier shrinkage driving global changes in downstream systems, Proceedings of the National Academy of Sciences, 114, 9770–9778, https://doi.org/10.1073/pnas.1619807114, 2017. a
Mohr, M.: New Routines for Gridding of Temperature and Precipitation Observations for “seNorge. no”, Tech. Rep. 08/2008, Norwegian Meteorological Institute, Oslo, Norway, https://www.researchgate.net/publication/228610451_New_Routines_for_Gridding_of_Temperature_and_Precipitation_Observations_for_seNorge_no (last access: 7 October 2025), 2008. a, b
Morlighem, M., Rignot, E., Seroussi, H., Larour, E., Ben Dhia, H., and Aubry, D.: Spatial patterns of basal drag inferred using control methods from a full-Stokes and simpler models for Pine Island Glacier, West Antarctica, Geophysical Research Letters, 37, https://doi.org/10.1029/2010GL043853, 2010. a, b
Nesje, A. and Matthews, J. A.: The Briksdalsbre Event: A winter precipitation-induced decadal-scale glacial advance in southern Norway in the 1990s and its implications, The Holocene, 22, 249–261, https://doi.org/10.1177/0959683611414938, 2012. a
Nesje, A., Lie, O., and Dahl, S. O.: Is the North Atlantic Oscillation reflected in Scandinavian glacier mass balance records?, Journal of Quaternary Science, 15, 587–601, https://doi.org/10.1002/1099-1417(200009)15:6<587::AID-JQS533>3.0.CO;2-2, 2000. a
Noël, B., van de Berg, W. J., Machguth, H., Lhermitte, S., Howat, I., Fettweis, X., and van den Broeke, M. R.: A daily, 1 km resolution data set of downscaled Greenland ice sheet surface mass balance (1958–2015), The Cryosphere, 10, 2361–2377, https://doi.org/10.5194/tc-10-2361-2016, 2016. a
Oerlemans, J.: Some basic experiments with a vertically-integrated ice sheet model, Tellus, 33, 1–11, https://doi.org/10.1111/j.2153-3490.1981.tb01726.x, 1981. a
Pattyn, F.: A new three-dimensional higher-order thermomechanical ice sheet model: Basic sensitivity, ice stream development, and ice flow across subglacial lakes, Journal of Geophysical Research: Solid Earth, 108, https://doi.org/10.1029/2002JB002329, 2003. a
Paul, F., Andreassen, L. M., and Winsvold, S. H.: A new glacier inventory for the Jostedalsbreen region, Norway, from Landsat TM scenes of 2006 and changes since 1966, Annals of Glaciology, 52, 153–162, https://doi.org/10.3189/172756411799096169, 2011. 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
QGIS Development Team: QGIS Geographic Information System, QGIS Association, https://www.qgis.org (last access: 3 October 2025), 2024. a
Réveillet, M., Six, D., Vincent, C., Rabatel, A., Dumont, M., Lafaysse, M., Morin, S., Vionnet, V., and Litt, M.: Relative performance of empirical and physical models in assessing the seasonal and annual glacier surface mass balance of Saint-Sorlin Glacier (French Alps), The Cryosphere, 12, 1367–1386, https://doi.org/10.5194/tc-12-1367-2018, 2018. a
Robson, B. A., Andreassen, L. M., and Åkesson, H.: Jostedalsbreen ice-surface topography 1966, Zenodo [data set], https://doi.org/10.5281/zenodo.17425322, 2025. 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, c, d
Santos, T. D. D., Morlighem, M., Simões, J. C., and Devloo, P. R. B.: Sensitivity analysis of a King George Island outlet glacier, South Shetlands, Antarctica, Anais da Academia Brasileira de Ciências, 95, e20210560, https://doi.org/10.1590/0001-3765202320210560, 2023. a
Schaffer, N., Copland, L., Zdanowicz, C., and Hock, R.: Modeling the surface mass balance of Penny Ice Cap, Baffin Island, 1959–2099, Annals of Glaciology, 64, 330–342, https://doi.org/10.1017/aog.2023.68, 2023. a
Schellenberger, T. and Åkesson, H.: Ice-surface velocity in 1996 of Jostedalsbreen ice cap, Norway from European Remote Sensing satellite (ERS-1/-2) data, Zenodo [data set], https://doi.org/10.5281/zenodo.17541329, 2025. a
Schmidt, L. S., Ađalgeirsdóttir, G., Pálsson, F., Langen, P. L., Guđmundsson, S., and Björnsson, H.: Dynamic simulations of Vatnajökull ice cap from 1980 to 2300, Journal of Glaciology, 66, 97–112, https://doi.org/10.1017/jog.2019.90, 2020. a, b, c
Seier, G., Abermann, J., Andreassen, L. M., Carrivick, J. L., Kielland, P. H., Löffler, K., Nesje, A., Robson, B. A., Røthe, T. O., Scheiber, T., Winkler, S., and Yde, J. C.: Glacier thinning, recession and advance, and the associated evolution of a glacial lake between 1966 and 2021 at Austerdalsbreen, western Norway, Land Degradation & Development, 35, 394–414, https://doi.org/10.1002/ldr.4923, 2024. a
Sjursen, K. H., Dunse, T., Tambue, A., Schuler, T. V., and Andreassen, L. M.: Bayesian parameter estimation in glacier mass-balance modelling using observations with distinct temporal resolutions and uncertainties, Journal of Glaciology, 1–20, https://doi.org/10.1017/jog.2023.62, 2023. a, b
Sjursen, K. H., Dunse, T., Schuler, T. V., Andreassen, L. M., and Åkesson, H.: Spatiotemporal mass balance variability of Jostedalsbreen Ice Cap, Norway, revealed by a temperature-index model using Bayesian inference, Annals of Glaciology, 66, 1–18, https://doi.org/10.1017/aog.2024.41, 2025. a, b, c, d, e, f, g, h, i, j, k, l
Troch, M., Åkesson, H., Cuzzone, J. K., and Bertrand, S.: Precipitation drives western Patagonian glacier variability and may curb future ice mass loss, Scientific Reports, 14, 26744, https://doi.org/10.1038/s41598-024-77486-4, 2024. a, b
Tvede, A. M. and Liestøl, O.: Blomsterskardbreen, Folgefonni, mass balance and recent fluctuations, Norsk Polarinstitutt Årbok, 1976. a
Tvede, A. M.: Floods Caused by a Glacier-Dammed Lake at the Folgefonni Ice Cap, Norway, Annals of Glaciology, 13, 262–264, https://doi.org/10.3189/S0260305500008016, 1989. a
United Nations Environment Programme: Emissions Gap Report 2024: No more hot air … please! With a massive gap between rhetoric and reality, countries draft new climate commitments., Tech. rep., Nairobi, https://doi.org/10.59117/20.500.11822/46404, 2024. a
van Pelt, W. J. J., Schuler, T. V., Pohjola, V. A., and Pettersson, R.: Accelerating future mass loss of Svalbard glaciers from a multi-model ensemble, Journal of Glaciology, 67, 485–499, https://doi.org/10.1017/jog.2021.2, 2021. a
Van Tricht, L. and Huybrechts, P.: Modelling the historical and future evolution of six ice masses in the Tien Shan, Central Asia, using a 3D ice-flow model, The Cryosphere, 17, 4463–4485, https://doi.org/10.5194/tc-17-4463-2023, 2023. a
Verfaillie, D., Charton, J., Schimmelpfennig, I., Stroebele, Z., Jomelli, V., Bétard, F., Favier, V., Cavero, J., Berthier, E., Goosse, H., Rinterknecht, V., Legentil, C., Charrassin, R., Aumaître, G., Bourlès, D. L., and Keddadouche, K.: Evolution of the Cook Ice Cap (Kerguelen Islands) between the last centuries and 2100 ce based on cosmogenic dating and glacio-climatic modelling, Antarctic Science, 33, 301–317, https://doi.org/10.1017/S0954102021000080, 2021. a
Wangensteen, B., Tønsberg, O. M., Kääb, A., Eiken, T., and Hagen, J. O.: Surface Elevation Change and High Resolution Surface Velocities for Advancing Outlets of Jostedalsbreen, Geografiska Annaler: Series A, Physical Geography, 88, 55–74, https://doi.org/10.1111/j.0435-3676.2006.00283.x, 2006. a
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
Winsvold, S. H., Andreassen, L. M., and Kienholz, C.: Glacier area and length changes in Norway from repeat inventories, The Cryosphere, 8, 1885–1903, https://doi.org/10.5194/tc-8-1885-2014, 2014. a
Wong, W. K., Haddeland, I., Lawrence, D., and Beldring, S.: Gridded 1 × 1 km climate and hydrological projections for Norway, NVE report 59/2016, Norwegian Water Resources and Energy Directorate (NVE), Oslo, Norway, https://publikasjoner.nve.no/rapport/2016/rapport2016_59.pdf (last access: 10 November 2025), 2016. a, b, c, d, e
Zekollari, H., Huybrechts, P., Noël, B., van de Berg, W. J., and van den Broeke, M. R.: Sensitivity, stability and future evolution of the world's northernmost ice cap, Hans Tausen Iskappe (Greenland), The Cryosphere, 11, 805–825, https://doi.org/10.5194/tc-11-805-2017, 2017. a, b, c
Zekollari, H., Huss, M., Farinotti, D., and Lhermitte, S.: Ice-Dynamical Glacier Evolution Modeling – A Review, Reviews of Geophysics, 60, e2021RG000754, https://doi.org/10.1029/2021RG000754, 2022. a, b, c
Zemp, M. and Haeberli, W.: Glaciers and ice caps. Part I: Global overview and outlook. Part II: Glacier changes around the world, UNEP, Nairobi, https://doi.org/10.5167/uzh-40427, 2007. a
Ziemen, F. A., Hock, R., Aschwanden, A., Khroulev, C., Kienholz, C., Melkonian, A., and Zhang, J.: Modeling the evolution of the Juneau Icefield between 1971 and 2100 using the Parallel Ice Sheet Model (PISM), Journal of Glaciology, 62, 199–214, https://doi.org/10.1017/jog.2016.13, 2016. a, b, c
Co-editor-in-chief
This study demonstrates that The Jostedalsbreen ice cap, which is the largest ice cap on the European mainland (458 km2 in 2019), is now in a mode of irreversable mass loss. The ice cap may lose up to 74% of its present-day volume until 2100, depending on future greenhouse gas emissions, which will have direct consequences for human and social interests.
This study demonstrates that The Jostedalsbreen ice cap, which is the largest ice cap on the...
Short summary
We model the historical and future evolution of the Jostedalsbreen ice cap in Norway, projecting substantial and largely irreversible mass loss for the 21st century, and that the ice cap will split into three parts. Further mass loss is in the pipeline, with a disappearance during the 22nd century under high emissions. Our study demonstrates an approach to model complex ice masses, highlights uncertainties due to precipitation, and calls for further research on long-term future glacier change.
We model the historical and future evolution of the Jostedalsbreen ice cap in Norway, projecting...