Articles | Volume 11, issue 3
https://doi.org/10.5194/tc-11-1417-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-11-1417-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
22 Jun 2017
Research article |
| 22 Jun 2017
Reanalysis of a 10-year record (2004–2013) of seasonal mass balances at Langenferner/Vedretta Lunga, Ortler Alps, Italy
Stephan Peter Galos
CORRESPONDING AUTHOR
Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
Christoph Klug
Institute of Geography, University of Innsbruck, Innsbruck, Austria
Fabien Maussion
Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
Federico Covi
Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
Geophysical Institute, University of Alaska, Fairbanks, USA
Lindsey Nicholson
Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
Lorenzo Rieg
Institute of Geography, University of Innsbruck, Innsbruck, Austria
Wolfgang Gurgiser
Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
Thomas Mölg
Climate System Research Group, Institute of Geography, Friedrich Alexander University Erlangen-Nürnberg (FAU), Erlangen-Nürnberg, Germany
Georg Kaser
Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
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Jakob Steiner, William Armstrong, Will Kochtitzky, Robert McNabb, Rodrigo Aguayo, Tobias Bolch, Fabien Maussion, Vibhor Agarwal, Iestyn Barr, Nathaniel R. Baurley, Mike Cloutier, Katelyn DeWater, Frank Donachie, Yoann Drocourt, Siddhi Garg, Gunjan Joshi, Byron Guzman, Stanislav Kutuzov, Thomas Loriaux, Caleb Mathias, Brian Menounos, Evan Miles, Aleksandra Osika, Kaleigh Potter, Adina Racoviteanu, Brianna Rick, Miles Sterner, Guy D. Tallentire, Levan Tielidze, Rebecca White, Kunpeng Wu, and Whyjay Zheng
Earth Syst. Sci. Data, 18, 1665–1681, https://doi.org/10.5194/essd-18-1665-2026, https://doi.org/10.5194/essd-18-1665-2026, 2026
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Many mountain glaciers around the world flow into lakes – exactly how many however, has never been mapped. Across a large team of experts we have now identified all glaciers that end in lakes. Only about 1% do so, but they are generally larger than those which end on land. This is important to understand, as lakes can influence the behaviour of glacier ice, including how fast it disappears. This new dataset allows us to better model glaciers at a global scale, accounting for the effect of lakes.
Patrick Schmitt, Fabien Maussion, Daniel N. Goldberg, and Philipp Gregor
Geosci. Model Dev., 19, 1301–1319, https://doi.org/10.5194/gmd-19-1301-2026, https://doi.org/10.5194/gmd-19-1301-2026, 2026
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To improve large-scale understanding of glaciers, we developed a new data assimilation method that integrates available observations in a dynamically consistent way, while taking their timestamps into account. It is designed to flexibly include new glacier data as it becomes available. We tested the method with idealized experiments and found promising results in terms of accuracy and efficiency, showing strong potential for real-world applications.
Jan Niklas Richter, Anselm Arndt, Nikolina Ban, Nicolas Gampierakis, Fabien Maussion, Rainer Prinz, Matthias Scheiter, Nikolaus Umlauf, and Lindsey Nicholson
EGUsphere, https://doi.org/10.5194/egusphere-2025-6249, https://doi.org/10.5194/egusphere-2025-6249, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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We developed a multi-objective Bayesian framework to calibrate a surface energy balance (SEB) model using only publicly available satellite data and climate simulations. The framework constrains model parameters well but also reveals biases in the forcing, affecting the SEB and the simulated mass balance and snowlines. The results indicate that an uncertainty-aware model chain can help identify model errors and provides a first step towards physical glacier modelling without in-situ data.
Manuel Saigger, Brigitta Goger, and Thomas Mölg
EGUsphere, https://doi.org/10.5194/egusphere-2025-5608, https://doi.org/10.5194/egusphere-2025-5608, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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We present a new model to predict near-surface winds and wind-driven transport of snow in mountain environments at high horizontal resolution. With its deep-learning based design, it is several orders of magnitude less computationally expensive compared to traditional numerical methods, while being applicable over a wide range of topographic settings and atmospheric conditions. A first application case study in the European Alps showed good agreement with numerical simulations and observations.
David Ibel, Thomas Mölg, and Christian Sommer
The Cryosphere, 19, 6629–6637, https://doi.org/10.5194/tc-19-6629-2025, https://doi.org/10.5194/tc-19-6629-2025, 2025
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As the majority of (tropical) glaciers retreat on a global scale, we analysed area changes of the Puncak Jaya glaciers in South East Asia on West Papua, Indonesia, using high resolution optical satellite imagery, supported by historical glacier accounts. The results show a decrease of total glacier surface area by more than 99 % since 1850 and by ~65 % since the last survey in 2018, with glacier area (in 2024) amounting to 0.165 km2 ± 5 %. Puncak Jaya glaciers will likely disappear around 2030.
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.
Larissa Nora van der Laan, Anouk Vlug, Adam A. Scaife, Fabien Maussion, and Kristian Förster
The Cryosphere, 19, 3879–3896, https://doi.org/10.5194/tc-19-3879-2025, https://doi.org/10.5194/tc-19-3879-2025, 2025
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Usually, glacier models are supplied with climate information from long (e.g., 100-year) simulations by global climate models. In this paper, we test the feasibility of supplying glacier models with shorter simulations to get more accurate information on 5–10-year timescales. Reliable information on these timescales is very important, especially for water management experts, to know how much meltwater to expect, affecting rivers, agriculture and drinking water.
Calvin Beck and Lindsey Nicholson
The Cryosphere, 19, 2715–2731, https://doi.org/10.5194/tc-19-2715-2025, https://doi.org/10.5194/tc-19-2715-2025, 2025
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A glacier's debris cover strongly modifies its mass balance in contrast to a clean-ice glacier. A key parameter for calculating sub-debris melt is the thermal diffusivity of the debris layer. Conway and Rasmussen (2000) present a method to estimate this value based on simple heat diffusion principles. Our analysis shows that the selected temporal and spatial sampling intervals affect the estimated value of thermal diffusivity, resulting in glacier melt being systematically underestimated.
Lorenz Hänchen, Emily Potter, Cornelia Klein, Pierluigi Calanca, Fabien Maussion, Wolfgang Gurgiser, and Georg Wohlfahrt
Hydrol. Earth Syst. Sci., 29, 2727–2747, https://doi.org/10.5194/hess-29-2727-2025, https://doi.org/10.5194/hess-29-2727-2025, 2025
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In semi-arid regions, the timing and duration of the rainy season are crucial for agriculture. This study introduces a new framework for improving estimations of the onset and end of the rainy season by testing how well they fit local vegetation data. We improve the performance of existing methods and present a new one with higher performance. Our findings can help us to make informed decisions about water usage, and the framework can be applied to other regions as well.
Torsten Kanzow, Angelika Humbert, Thomas Mölg, Mirko Scheinert, Matthias Braun, Hans Burchard, Francesca Doglioni, Philipp Hochreuther, Martin Horwath, Oliver Huhn, Maria Kappelsberger, Jürgen Kusche, Erik Loebel, Katrina Lutz, Ben Marzeion, Rebecca McPherson, Mahdi Mohammadi-Aragh, Marco Möller, Carolyne Pickler, Markus Reinert, Monika Rhein, Martin Rückamp, Janin Schaffer, Muhammad Shafeeque, Sophie Stolzenberger, Ralph Timmermann, Jenny Turton, Claudia Wekerle, and Ole Zeising
The Cryosphere, 19, 1789–1824, https://doi.org/10.5194/tc-19-1789-2025, https://doi.org/10.5194/tc-19-1789-2025, 2025
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The Greenland Ice Sheet represents the second-largest contributor to global sea-level rise. We quantify atmosphere, ice and ocean processes related to the mass balance of glaciers in northeast Greenland, focusing on Greenland’s largest floating ice tongue, the 79° N Glacier. We find that together, the different in situ and remote sensing observations and model simulations reveal a consistent picture of a coupled atmosphere–ice sheet–ocean system that has entered a phase of major change.
Finn Wimberly, Lizz Ultee, Lilian Schuster, Matthias Huss, David R. Rounce, Fabien Maussion, Sloan Coats, Jonathan Mackay, and Erik Holmgren
The Cryosphere, 19, 1491–1511, https://doi.org/10.5194/tc-19-1491-2025, https://doi.org/10.5194/tc-19-1491-2025, 2025
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Glacier models have historically been used to understand glacier melt’s contribution to sea level rise. The capacity to project seasonal glacier runoff is a relatively recent development for these models. In this study we provide the first model intercomparison of runoff projections for the glacier evolution models capable of simulating future runoff globally. We compare model projections from 2000 to 2100 for all major river basins larger than 3000 km2 with over 30 km2 of initial glacier cover.
Brigitta Goger, Ivana Stiperski, Matthis Ouy, and Lindsey Nicholson
Weather Clim. Dynam., 6, 345–367, https://doi.org/10.5194/wcd-6-345-2025, https://doi.org/10.5194/wcd-6-345-2025, 2025
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We study with numerical simulations whether changing glacier ice surfaces impacts the atmospheric boundary layer structure over a glacier. Under north-westerly flow, a gravity wave forms over the glacier valley. When the surrounding upstream glaciers are removed, the gravity wave is weakened and breaks earlier. This leads to stronger turbulent mixing over the remaining glacier and to higher temperatures. We suggest that glaciers influence each other and should be studied as a connected system.
Lea Hartl, Patrick Schmitt, Lilian Schuster, Kay Helfricht, Jakob Abermann, and Fabien Maussion
The Cryosphere, 19, 1431–1452, https://doi.org/10.5194/tc-19-1431-2025, https://doi.org/10.5194/tc-19-1431-2025, 2025
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We use regional observations of glacier area and volume change to inform glacier evolution modeling in the Ötztal and Stubai range (Austrian Alps) until 2100 in different climate scenarios. Glaciers in the region lost 23 % of their volume between 2006 and 2017. Under current warming trajectories, glacier loss in the region is expected to be near-total by 2075. We show that integrating regional calibration and validation data in glacier models is important to improve confidence in projections.
Rodrigo Aguayo, Fabien Maussion, Lilian Schuster, Marius Schaefer, Alexis Caro, Patrick Schmitt, Jonathan Mackay, Lizz Ultee, Jorge Leon-Muñoz, and Mauricio Aguayo
The Cryosphere, 18, 5383–5406, https://doi.org/10.5194/tc-18-5383-2024, https://doi.org/10.5194/tc-18-5383-2024, 2024
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Predicting how much water will come from glaciers in the future is a complex task, and there are many factors that make it uncertain. Using a glacier model, we explored 1920 scenarios for each glacier in the Patagonian Andes. We found that the choice of the historical climate data was the most important factor, while other factors such as different data sources, climate models and emission scenarios played a smaller role.
Harry Zekollari, Matthias Huss, Lilian Schuster, Fabien Maussion, David R. Rounce, Rodrigo Aguayo, Nicolas Champollion, Loris Compagno, Romain Hugonnet, Ben Marzeion, Seyedhamidreza Mojtabavi, and Daniel Farinotti
The Cryosphere, 18, 5045–5066, https://doi.org/10.5194/tc-18-5045-2024, https://doi.org/10.5194/tc-18-5045-2024, 2024
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Glaciers are major contributors to sea-level rise and act as key water resources. Here, we model the global evolution of glaciers under the latest generation of climate scenarios. We show that the type of observations used for model calibration can strongly affect the projections at the local scale. Our newly projected 21st century global mass loss is higher than the current community estimate as reported in the latest Intergovernmental Panel on Climate Change (IPCC) report.
Thomas Mölg, Jan C. Schubert, Annette Debel, Steffen Höhnle, Kathy Steppe, Sibille Wehrmann, and Achim Bräuning
Geosci. Commun., 7, 215–225, https://doi.org/10.5194/gc-7-215-2024, https://doi.org/10.5194/gc-7-215-2024, 2024
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We examine the understanding of weather and climate impacts on forest health in high school students. Climate physics, tree ring science, and educational research collaborate to provide an online platform that captures the students’ observations, showing they translate the measured weather and basic tree responses well. However, students hardly ever detect the causal connections. This result will help refine future classroom concepts and public climate change communication on changing forests.
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.
Sarah Hanus, Lilian Schuster, Peter Burek, Fabien Maussion, Yoshihide Wada, and Daniel Viviroli
Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, https://doi.org/10.5194/gmd-17-5123-2024, 2024
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This study presents a coupling of the large-scale glacier model OGGM and the hydrological model CWatM. Projected future increase in discharge is less strong while future decrease in discharge is stronger when glacier runoff is explicitly included in the large-scale hydrological model. This is because glacier runoff is projected to decrease in nearly all basins. We conclude that an improved glacier representation can prevent underestimating future discharge changes in large river basins.
Marin Kneib, Amaury Dehecq, Fanny Brun, Fatima Karbou, Laurane Charrier, Silvan Leinss, Patrick Wagnon, and Fabien Maussion
The Cryosphere, 18, 2809–2830, https://doi.org/10.5194/tc-18-2809-2024, https://doi.org/10.5194/tc-18-2809-2024, 2024
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Avalanches are important for the mass balance of mountain glaciers, but few data exist on where and when they occur and which glaciers they affect the most. We developed an approach to map avalanches over large glaciated areas and long periods of time using satellite radar data. The application of this method to various regions in the Alps and High Mountain Asia reveals the variability of avalanches on these glaciers and provides key data to better represent these processes in glacier models.
Annelies Voordendag, Brigitta Goger, Rainer Prinz, Tobias Sauter, Thomas Mölg, Manuel Saigger, and Georg Kaser
The Cryosphere, 18, 849–868, https://doi.org/10.5194/tc-18-849-2024, https://doi.org/10.5194/tc-18-849-2024, 2024
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Wind-driven snow redistribution affects glacier mass balance. A case study of Hintereisferner glacier in Austria used high-resolution observations and simulations to model snow redistribution. Simulations matched observations, showing the potential of the model for studying snow redistribution on other mountain glaciers.
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, https://doi.org/10.5194/gmd-16-6671-2023, 2023
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We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Annelies Voordendag, Rainer Prinz, Lilian Schuster, and Georg Kaser
The Cryosphere, 17, 3661–3665, https://doi.org/10.5194/tc-17-3661-2023, https://doi.org/10.5194/tc-17-3661-2023, 2023
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The Glacier Loss Day (GLD) is the day on which all mass gained from the accumulation period is lost, and the glacier loses mass irrecoverably for the rest of the mass balance year. In 2022, the GLD was already reached on 23 June at Hintereisferner (Austria), and this led to a record-breaking mass loss. We introduce the GLD as a gross yet expressive indicator of the glacier’s imbalance with a persistently warming climate.
Lea Hartl, Thomas Zieher, Magnus Bremer, Martin Stocker-Waldhuber, Vivien Zahs, Bernhard Höfle, Christoph Klug, and Alessandro Cicoira
Earth Surf. Dynam., 11, 117–147, https://doi.org/10.5194/esurf-11-117-2023, https://doi.org/10.5194/esurf-11-117-2023, 2023
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The rock glacier in Äußeres Hochebenkar (Austria) moved faster in 2021–2022 than it has in about 70 years of monitoring. It is currently destabilizing. Using a combination of different data types and methods, we show that there have been two cycles of destabilization at Hochebenkar and provide a detailed analysis of velocity and surface changes. Because our time series are very long and show repeated destabilization, this helps us better understand the processes of rock glacier destabilization.
Nidheesh Gangadharan, Hugues Goosse, David Parkes, Heiko Goelzer, Fabien Maussion, and Ben Marzeion
Earth Syst. Dynam., 13, 1417–1435, https://doi.org/10.5194/esd-13-1417-2022, https://doi.org/10.5194/esd-13-1417-2022, 2022
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We describe the contributions of ocean thermal expansion and land-ice melting (ice sheets and glaciers) to global-mean sea-level (GMSL) changes in the Common Era. The mass contributions are the major sources of GMSL changes in the pre-industrial Common Era and glaciers are the largest contributor. The paper also describes the current state of climate modelling, uncertainties and knowledge gaps along with the potential implications of the past variabilities in the contemporary sea-level rise.
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.
A. B. Voordendag, B. Goger, C. Klug, R. Prinz, M. Rutzinger, and G. Kaser
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1093–1099, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1093-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1093-2022, 2022
Lorenz Hänchen, Cornelia Klein, Fabien Maussion, Wolfgang Gurgiser, Pierluigi Calanca, and Georg Wohlfahrt
Earth Syst. Dynam., 13, 595–611, https://doi.org/10.5194/esd-13-595-2022, https://doi.org/10.5194/esd-13-595-2022, 2022
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To date, farmers' perceptions of hydrological changes do not match analysis of meteorological data. In contrast to rainfall data, we find greening of vegetation, indicating increased water availability in the past decades. The start of the season is highly variable, making farmers' perceptions comprehensible. We show that the El Niño–Southern Oscillation has complex effects on vegetation seasonality but does not drive the greening we observe. Improved onset forecasts could help local farmers.
Adina E. Racoviteanu, Lindsey Nicholson, and Neil F. Glasser
The Cryosphere, 15, 4557–4588, https://doi.org/10.5194/tc-15-4557-2021, https://doi.org/10.5194/tc-15-4557-2021, 2021
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Supraglacial debris cover comprises ponds, exposed ice cliffs, debris material and vegetation. Understanding these features is important for glacier hydrology and related hazards. We use linear spectral unmixing of satellite data to assess the composition of map supraglacial debris across the Himalaya range in 2015. One of the highlights of this study is the automated mapping of supraglacial ponds, which complements and expands the existing supraglacial debris and lake databases.
A. B. Voordendag, B. Goger, C. Klug, R. Prinz, M. Rutzinger, and G. Kaser
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 153–160, https://doi.org/10.5194/isprs-annals-V-2-2021-153-2021, https://doi.org/10.5194/isprs-annals-V-2-2021-153-2021, 2021
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