Articles | Volume 16, issue 22
https://doi.org/10.5194/gmd-16-6671-2023
© 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/gmd-16-6671-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Universal differential equations for glacier ice flow modelling
Institute for Marine and Atmospheric research Utrecht, Utrecht University, Utrecht, the Netherlands
Faculty of Civil Engineering and Geosciences, Technische Universiteit Delft, Delft, the Netherlands
Facundo Sapienza
CORRESPONDING AUTHOR
Department of Statistics, University of California, Berkeley, CA, USA
Fabien Maussion
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol, UK
Redouane Lguensat
Institut Pierre-Simon Laplace, IRD, Sorbonne Université, Paris, France
Bert Wouters
Institute for Marine and Atmospheric research Utrecht, Utrecht University, Utrecht, the Netherlands
Faculty of Civil Engineering and Geosciences, Technische Universiteit Delft, Delft, the Netherlands
Fernando Pérez
Department of Statistics, University of California, Berkeley, CA, USA
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Jordi Bolibar, Antoine Rabatel, Isabelle Gouttevin, and Clovis Galiez
Earth Syst. Sci. Data, 12, 1973–1983, https://doi.org/10.5194/essd-12-1973-2020, https://doi.org/10.5194/essd-12-1973-2020, 2020
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We present a dataset of annual glacier mass changes for all the 661 glaciers in the French Alps for the 1967–2015 period, reconstructed using deep learning (i.e. artificial intelligence). We estimate an average annual mass loss of –0.69 ± 0.21 m w.e., the highest being in the Chablais, Ubaye and Champsaur massifs and the lowest in the Mont Blanc, Oisans and Haute Tarentaise ranges. This dataset can be of interest to hydrology and ecology studies on glacierized catchments in the French Alps.
Patrick Schmitt, Fabien Maussion, Daniel N. Goldberg, and Philipp Gregor
EGUsphere, https://doi.org/10.5194/egusphere-2025-3401, https://doi.org/10.5194/egusphere-2025-3401, 2025
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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.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-2900, https://doi.org/10.5194/egusphere-2025-2900, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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We produce annual maps of Antarctic surface melt volumes from 2012 to 2021 using satellite microwave data. We detect melting days from thresholds on the satellite signal and then use actual melt measurements from weather stations to convert those signals into water‑equivalent volumes. Our maps capture known melt hotspots and show slightly lower totals than climate models. This dataset supports climate and ice‑shelf studies.
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-315, https://doi.org/10.5194/essd-2025-315, 2025
Preprint under review for ESSD
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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.
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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Thirza Feenstra, Miren Vizcaino, Bert Wouters, Michele Petrini, Raymond Sellevold, and Katherine Thayer-Calder
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We present the first evaluation of Greenland ice sheet (GrIS) and climate feedbacks with a CMIP model. Under 4×CO2 forcing, lower elevations reduce GrIS summer blocking and incoming solar radiation and increase precipitation. Simulated increases of near-surface summer temperature are much lower than the 6 K km-1 lapse rate that is commonly used in non-coupled simulations. CO2 reduction to pre-industrial (PI) halts GrIS mass loss regardless of higher global warming and albedo than PI control.
Matthias O. Willen, Bert Wouters, Taco Broerse, Eric Buchta, and Veit Helm
The Cryosphere, 19, 2213–2227, https://doi.org/10.5194/tc-19-2213-2025, https://doi.org/10.5194/tc-19-2213-2025, 2025
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Collapse of the West Antarctic Ice Sheet in the Amundsen Sea Embayment is likely in the near future. Vertical uplift of bedrock due to glacial isostatic adjustment stabilizes the ice sheet and may delay its collapse. So far, only spatially and temporally sparse GPS measurements have been able to observe this bedrock motion. We have combined satellite data and quantified a region-wide bedrock motion that independently matches GPS measurements. This can improve ice sheet predictions.
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.
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-1206, https://doi.org/10.5194/egusphere-2025-1206, 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.
Ann-Sofie P. Zinck, Bert Wouters, Franka Jesse, and Stef Lhermitte
EGUsphere, https://doi.org/10.5194/egusphere-2025-573, https://doi.org/10.5194/egusphere-2025-573, 2025
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Ocean-driven basal melting of ice shelves can carve channels into the ice shelf base. These channels represent potential weak areas of the ice shelf. On George VI Ice shelf we discover a new channel which onset coincides with the 2015 El-Nino Southern Oscillation event. Since the channel has developed rapidly and is located within a highly channelized area close to the ice shelf front it poses a potential thread of ice shelf retreat.
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EGUsphere, https://doi.org/10.5194/egusphere-2024-4040, https://doi.org/10.5194/egusphere-2024-4040, 2025
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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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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.
Weiran Li, Stef Lhermitte, Bert Wouters, Cornelis Slobbe, Max Brils, and Xavier Fettweis
EGUsphere, https://doi.org/10.5194/egusphere-2024-3251, https://doi.org/10.5194/egusphere-2024-3251, 2024
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Due to the melt events in recent decades, the snow condition over Greenland has been changed. To observe this, we use a parameter (leading edge width; LeW) derived from satellite altimetry, and analyse its spatial and temporal variations. By comparing the LeW variations with modelled firn parameters, we concluded that the 2012 melt event has a long-lasting impact on the volume scattering of Greenland firn. This impact cannot fully recover due to the recent and more frequent melt events.
Julius Sommer, Maaike Izeboud, Sophie de Roda Husman, Bert Wouters, and Stef Lhermitte
EGUsphere, https://doi.org/10.5194/egusphere-2024-3105, https://doi.org/10.5194/egusphere-2024-3105, 2024
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Ice shelves, the floating extensions of Antarctica’s ice sheet, play a crucial role in preventing mass ice loss, and understanding their stability is crucial. If surface meltwater lakes drain rapidly through fractures, the ice shelf can destabilize. We analyzed satellite images of three years from the Shackleton Ice Shelf and found that lake drainages occurred in areas where damage is present and developing, and coincided with rising tides, offering insights into the drivers of this process.
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.
Larissa van der Laan, Anouk Vlug, Adam A. Scaife, Fabien Maussion, and Kristian Förster
EGUsphere, https://doi.org/10.5194/egusphere-2024-387, https://doi.org/10.5194/egusphere-2024-387, 2024
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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 time scales. Reliable information on these time scales is very important, especially for water management experts to know how much meltwater to expect, for rivers, agriculture and drinking water.
Lena G. Buth, Valeria Di Biase, Peter Kuipers Munneke, Stef Lhermitte, Sanne B. M. Veldhuijsen, Sophie de Roda Husman, Michiel R. van den Broeke, and Bert Wouters
EGUsphere, https://doi.org/10.5194/egusphere-2023-2000, https://doi.org/10.5194/egusphere-2023-2000, 2023
Preprint archived
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Liquid meltwater which is stored in air bubbles in the compacted snow near the surface of Antarctica can affect ice shelf stability. In order to detect the presence of such firn aquifers over large scales, satellite remote sensing is needed. In this paper, we present our new detection method using radar satellite data as well as the results for the whole Antarctic Peninsula. Firn aquifers are found in the north and northwest of the peninsula, in agreement with locations predicted by models.
Whyjay Zheng, Shashank Bhushan, Maximillian Van Wyk De Vries, William Kochtitzky, David Shean, Luke Copland, Christine Dow, Renette Jones-Ivey, and Fernando Pérez
The Cryosphere, 17, 4063–4078, https://doi.org/10.5194/tc-17-4063-2023, https://doi.org/10.5194/tc-17-4063-2023, 2023
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We design and propose a method that can evaluate the quality of glacier velocity maps. The method includes two numbers that we can calculate for each velocity map. Based on statistics and ice flow physics, velocity maps with numbers close to the recommended values are considered to have good quality. We test the method using the data from Kaskawulsh Glacier, Canada, and release an open-sourced software tool called GLAcier Feature Tracking testkit (GLAFT) to help users assess their velocity maps.
Ann-Sofie Priergaard Zinck, Bert Wouters, Erwin Lambert, and Stef Lhermitte
The Cryosphere, 17, 3785–3801, https://doi.org/10.5194/tc-17-3785-2023, https://doi.org/10.5194/tc-17-3785-2023, 2023
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The ice shelves in Antarctica are melting from below, which puts their stability at risk. Therefore, it is important to observe how much and where they are melting. In this study we use high-resolution satellite imagery to derive 50 m resolution basal melt rates of the Dotson Ice Shelf. With the high resolution of our product we are able to uncover small-scale features which may in the future help us to understand the state and fate of the Antarctic ice shelves and their (in)stability.
Inès N. Otosaka, Andrew Shepherd, Erik R. Ivins, Nicole-Jeanne Schlegel, Charles Amory, Michiel R. van den Broeke, Martin Horwath, Ian Joughin, Michalea D. King, Gerhard Krinner, Sophie Nowicki, Anthony J. Payne, Eric Rignot, Ted Scambos, Karen M. Simon, Benjamin E. Smith, Louise S. Sørensen, Isabella Velicogna, Pippa L. Whitehouse, Geruo A, Cécile Agosta, Andreas P. Ahlstrøm, Alejandro Blazquez, William Colgan, Marcus E. Engdahl, Xavier Fettweis, Rene Forsberg, Hubert Gallée, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian C. Gunter, Christopher Harig, Veit Helm, Shfaqat Abbas Khan, Christoph Kittel, Hannes Konrad, Peter L. Langen, Benoit S. Lecavalier, Chia-Chun Liang, Bryant D. Loomis, Malcolm McMillan, Daniele Melini, Sebastian H. Mernild, Ruth Mottram, Jeremie Mouginot, Johan Nilsson, Brice Noël, Mark E. Pattle, William R. Peltier, Nadege Pie, Mònica Roca, Ingo Sasgen, Himanshu V. Save, Ki-Weon Seo, Bernd Scheuchl, Ernst J. O. Schrama, Ludwig Schröder, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler C. Sutterley, Bramha Dutt Vishwakarma, Jan Melchior van Wessem, David Wiese, Wouter van der Wal, and Bert Wouters
Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, https://doi.org/10.5194/essd-15-1597-2023, 2023
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By measuring changes in the volume, gravitational attraction, and ice flow of Greenland and Antarctica from space, we can monitor their mass gain and loss over time. Here, we present a new record of the Earth’s polar ice sheet mass balance produced by aggregating 50 satellite-based estimates of ice sheet mass change. This new assessment shows that the ice sheets have lost (7.5 x 1012) t of ice between 1992 and 2020, contributing 21 mm to sea level rise.
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.
Lena G. Buth, Bert Wouters, Sanne B. M. Veldhuijsen, Stef Lhermitte, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-127, https://doi.org/10.5194/tc-2022-127, 2022
Manuscript not accepted for further review
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Liquid meltwater which is stored in air bubbles in the compacted snow near the surface of Antarctica can affect ice shelf stability. In order to detect the presence of such firn aquifers over large scales, satellite remote sensing is needed. In this paper, we present our new detection method using radar satellite data as well as the results for the whole Antarctic Peninsula. Firn aquifers are found in the north and northwest of the peninsula, in agreement with locations predicted by models.
Bas Altena, Andreas Kääb, and Bert Wouters
The Cryosphere, 16, 2285–2300, https://doi.org/10.5194/tc-16-2285-2022, https://doi.org/10.5194/tc-16-2285-2022, 2022
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Repeat overflights of satellites are used to estimate surface displacements. However, such products lack a simple error description for individual measurements, but variation in precision occurs, since the calculation is based on the similarity of texture. Fortunately, variation in precision manifests itself in the correlation peak, which is used for the displacement calculation. This spread is used to make a connection to measurement precision, which can be of great use for model inversion.
F. Dahle, J. Tanke, B. Wouters, and R. Lindenbergh
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 237–244, https://doi.org/10.5194/isprs-annals-V-2-2022-237-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-237-2022, 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.
Jan Bouke Pronk, Tobias Bolch, Owen King, Bert Wouters, and Douglas I. Benn
The Cryosphere, 15, 5577–5599, https://doi.org/10.5194/tc-15-5577-2021, https://doi.org/10.5194/tc-15-5577-2021, 2021
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About 10 % of Himalayan glaciers flow directly into lakes. This study finds, using satellite imagery, that such glaciers show higher flow velocities than glaciers without ice–lake contact. In particular near the glacier tongue the impact of a lake on the glacier flow can be dramatic. The development of current and new meltwater bodies will influence the flow of an increasing number of Himalayan glaciers in the future, a scenario not currently considered in regional ice loss projections.
Rajashree Tri Datta and Bert Wouters
The Cryosphere, 15, 5115–5132, https://doi.org/10.5194/tc-15-5115-2021, https://doi.org/10.5194/tc-15-5115-2021, 2021
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The ICESat-2 laser altimeter can detect the surface and bottom of a supraglacial lake. We introduce the Watta algorithm, automatically calculating lake surface, corrected bottom, and (sub-)surface ice at high resolution adapting to signal strength. ICESat-2 depths constrain full lake depths of 46 lakes over Jakobshavn glacier using multiple sources of imagery, including very high-resolution Planet imagery, used for the first time to extract supraglacial lake depths empirically using ICESat-2.
Maurice van Tiggelen, Paul C. J. P. Smeets, Carleen H. Reijmer, Bert Wouters, Jakob F. Steiner, Emile J. Nieuwstraten, Walter W. Immerzeel, and Michiel R. van den Broeke
The Cryosphere, 15, 2601–2621, https://doi.org/10.5194/tc-15-2601-2021, https://doi.org/10.5194/tc-15-2601-2021, 2021
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We developed a method to estimate the aerodynamic properties of the Greenland Ice Sheet surface using either UAV or ICESat-2 elevation data. We show that this new method is able to reproduce the important spatiotemporal variability in surface aerodynamic roughness, measured by the field observations. The new maps of surface roughness can be used in atmospheric models to improve simulations of surface turbulent heat fluxes and therefore surface energy and mass balance over rough ice worldwide.
Lilian Schuster, Fabien Maussion, Lukas Langhamer, and Gina E. Moseley
Weather Clim. Dynam., 2, 1–17, https://doi.org/10.5194/wcd-2-1-2021, https://doi.org/10.5194/wcd-2-1-2021, 2021
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Precipitation and moisture sources over an arid region in northeast Greenland are investigated from 1979 to 2017 by a Lagrangian moisture source diagnostic driven by reanalysis data. Dominant winter moisture sources are the North Atlantic above 45° N. In summer local and north Eurasian continental sources dominate. In positive phases of the North Atlantic Oscillation, evaporation and moisture transport from the Norwegian Sea are stronger, resulting in more precipitation.
Xavier Fettweis, Stefan Hofer, Uta Krebs-Kanzow, Charles Amory, Teruo Aoki, Constantijn J. Berends, Andreas Born, Jason E. Box, Alison Delhasse, Koji Fujita, Paul Gierz, Heiko Goelzer, Edward Hanna, Akihiro Hashimoto, Philippe Huybrechts, Marie-Luise Kapsch, Michalea D. King, Christoph Kittel, Charlotte Lang, Peter L. Langen, Jan T. M. Lenaerts, Glen E. Liston, Gerrit Lohmann, Sebastian H. Mernild, Uwe Mikolajewicz, Kameswarrao Modali, Ruth H. Mottram, Masashi Niwano, Brice Noël, Jonathan C. Ryan, Amy Smith, Jan Streffing, Marco Tedesco, Willem Jan van de Berg, Michiel van den Broeke, Roderik S. W. van de Wal, Leo van Kampenhout, David Wilton, Bert Wouters, Florian Ziemen, and Tobias Zolles
The Cryosphere, 14, 3935–3958, https://doi.org/10.5194/tc-14-3935-2020, https://doi.org/10.5194/tc-14-3935-2020, 2020
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We evaluated simulated Greenland Ice Sheet surface mass balance from 5 kinds of models. While the most complex (but expensive to compute) models remain the best, the faster/simpler models also compare reliably with observations and have biases of the same order as the regional models. Discrepancies in the trend over 2000–2012, however, suggest that large uncertainties remain in the modelled future SMB changes as they are highly impacted by the meltwater runoff biases over the current climate.
Jordi Bolibar, Antoine Rabatel, Isabelle Gouttevin, and Clovis Galiez
Earth Syst. Sci. Data, 12, 1973–1983, https://doi.org/10.5194/essd-12-1973-2020, https://doi.org/10.5194/essd-12-1973-2020, 2020
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We present a dataset of annual glacier mass changes for all the 661 glaciers in the French Alps for the 1967–2015 period, reconstructed using deep learning (i.e. artificial intelligence). We estimate an average annual mass loss of –0.69 ± 0.21 m w.e., the highest being in the Chablais, Ubaye and Champsaur massifs and the lowest in the Mont Blanc, Oisans and Haute Tarentaise ranges. This dataset can be of interest to hydrology and ecology studies on glacierized catchments in the French Alps.
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, arXiv [preprint], https://doi.org/10.48550/arXiv.1603.04467, 2016. a
Abernathey, R. P., Augspurger, T., Banihirwe, A., Blackmon-Luca, C. C., Crone, T. J., Gentemann, C. L., Hamman, J. J., Henderson, N., Lepore, C., McCaie, T. A., Robinson, N. H., and Signell, R. P.: Cloud-Native Repositories for Big Scientific Data, Comput. Sci. Eng., 23, 26–35, https://doi.org/10.1109/MCSE.2021.3059437, 2021. a
Anilkumar, R., Bharti, R., Chutia, D., and Aggarwal, S. P.: Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques, The Cryosphere, 17, 2811–2828, https://doi.org/10.5194/tc-17-2811-2023, 2023. a
Arakawa, A. and Lamb, V. R.: Computational design of the basic dynamical processes of the UCLA general circulation model, General circulation models of the atmosphere, 17, 173–265, 1977. a
Arendt, A. A., Hamman, J., Rocklin, M., Tan, A., Fatland, D. R., Joughin, J., Gutmann, E. D., Setiawan, L., and Henderson, S. T.: Pangeo: Community tools for analysis of Earth Science Data in the Cloud, in: AGU Fall Meeting Abstracts, vol. 2018, IN54A–05, 2018. a
Arthern, R. J. and Gudmundsson, G. H.: Initialization of ice-sheet forecasts viewed as an inverse Robin problem, J. Glaciol., 56, 527–533, https://doi.org/10.3189/002214310792447699, 2010. a
Baumhoer, C. A., Dietz, A. J., Kneisel, C., and Kuenzer, C.: Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning, Remote Sens., 11, 2529, https://doi.org/10.3390/rs11212529, 2019. a
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V. B.: Julia: A Fresh Approach to Numerical Computing, SIAM Review, 59, 65–98, https://doi.org/10.1137/141000671, 2017. a, b
Bolibar, J. and Sapienza, F.: ODINN-SciML/ODINN.jl: v0.2.0, Zenodo [code], https://doi.org/10.5281/zenodo.8033313, 2023. a, b, c, d
Bolibar, J., Rabatel, A., Gouttevin, I., and Galiez, C.: A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015, Earth Syst. Sci. Data, 12, 1973–1983, https://doi.org/10.5194/essd-12-1973-2020, 2020a. a
Bolibar, J., Rabatel, A., Gouttevin, I., Galiez, C., Condom, T., and Sauquet, E.: Deep learning applied to glacier evolution modelling, The Cryosphere, 14, 565–584, https://doi.org/10.5194/tc-14-565-2020, 2020b. a
Bolibar, J., Rabatel, A., Gouttevin, I., Zekollari, H., and Galiez, C.: Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning, Nat. Commun., 13, 409, https://doi.org/10.1038/s41467-022-28033-0, 2022. a, b
Brinkerhoff, D., Aschwanden, A., and Fahnestock, M.: Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference, J. Glaciol., 67, 385–403, https://doi.org/10.1017/jog.2020.112, 2021. a
Brinkerhoff, D. J., Meyer, C. R., Bueler, E., Truffer, M., and Bartholomaus, T. C.: Inversion of a glacier hydrology model, Ann. Glaciol., 57, 84–95, https://doi.org/10.1017/aog.2016.3, 2016. a
Chen, R. T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D.: Neural Ordinary Differential Equations, arXiv [preprint], https://doi.org/10.48550/arXiv.1806.07366, 2019. a, b, c, d
Consortium, Randolph Glacier Inventory: Randolph Glacier Inventory 6.0, Consortium, RGI [data set], https://doi.org/10.7265/N5-RGI-60, 2017. a, b
Creswell, R., Shepherd, K. M., Lambert, B., Mirams, G. R., Lei, C. L., Tavener, S., Robinson, M., and Gavaghan, D. J.: Understanding the impact of numerical solvers on inference for differential equation models, arXiv [preprint], https://doi.org/10.48550/arXiv.2307.00749, 2023. 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, Nat. Geosci., 12, 168–173, https://doi.org/10.1038/s41561-019-0300-3, 2019. a
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The shuttle radar topography mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007. a
Fowler, A. and Ng, F.: Glaciers and Ice Sheets in the climate system: The Karthaus summer school lecture notes, Springer, Nature, https://doi.org/10.1007/978-3-030-42584-5, 2020. a, b
Gentemann, C. L., Holdgraf, C., Abernathey, R., Crichton, D., Colliander, J., Kearns, E. J., Panda, Y., and Signell, R. P.: Science Storms the Cloud, AGU Advances, 2, 2, https://doi.org/10.1029/2020av000354, 2021. a
GlaThiDa Consortium: Glacier Thickness Database 3.1.0, World Glacier Monitoring Service [data set], Zurich, Switzerland, https://doi.org/10.5904/wgms-glathida-2020-10, 2019. 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
Granger, B. E. and Pérez, F.: Jupyter: Thinking and Storytelling With Code and Data, Comput. Sci. Eng., 23, 7–14, https://doi.org/10.1109/MCSE.2021.3059263, 2021. a
Griewank, A. and Walther, A.: Evaluating Derivatives, Society for Industrial and Applied Mathematics, 2nd Edn., https://doi.org/10.1137/1.9780898717761, 2008. a
Guidicelli, M., Huss, M., Gabella, M., and Salzmann, N.: Spatio-temporal reconstruction of winter glacier mass balance in the Alps, Scandinavia, Central Asia and western Canada (1981–2019) using climate reanalyses and machine learning, The Cryosphere, 17, 977–1002, https://doi.org/10.5194/tc-17-977-2023, 2023. a
Halfar, P.: On the dynamics of the ice sheets, J. Geophys. Res.-Oceans, 86, 11065–11072, https://doi.org/10.1029/jc086ic11p11065, 1981. a
Hock, R.: Temperature index melt modelling in mountain areas, J. Hydrol., 282, 104–115, https://doi.org/10.1016/S0022-1694(03)00257-9, 2003. a
Hock, R., Maussion, F., Marzeion, B., and Nowicki, S.: What is the global glacier ice volume outside the ice sheets?, J. Glaciol., 69, 204–210, https://doi.org/10.1017/jog.2023.1, 2023. a
Hoyer, S. and Hamman, J. J.: xarray: N-D labeled Arrays and Datasets in Python, J. Open Res. Softw., 5, 10, https://doi.org/10.5334/jors.148, 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.: A globally complete, spatially and temporally resolved estimate of glacier mass change: 2000 to 2019, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20908, https://doi.org/10.5194/egusphere-egu2020-20908, 2020. a, b
Huss, M. and Hock, R.: A new model for global glacier change and sea-level rise, Front. Earth Sci., 3, https://doi.org/10.3389/feart.2015.00054, 2015. a
Hutter, K.: Theoretical Glaciology, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-015-1167-4, 1983. a
Imhof, M. A.: Combined climate-ice flow modelling of the Alpine ice field during the Last Glacial Maximum, VAW-Mitteilungen, Doctoral thesis, 152 pp., https://doi.org/10.3929/ethz-b-000471073, 2021. a
Innes, M., Saba, E., Fischer, K., Gandhi, D., Rudilosso, M. C., Joy, N. M., Karmali, T., Pal, A., and Shah, V.: Fashionable Modelling with Flux, CoRR, ArXiv [preprint], https://doi.org/10.48550/arXiv.1811.01457, 2018. a
Innes, M., Edelman, A., Fischer, K., Rackauckas, C., Saba, E., Shah, V. B., and Tebbutt, W.: A Differentiable Programming System to Bridge Machine Learning and Scientific Computing, arXiv [preprint], https://doi.org/10.48550/arXiv.1907.07587, 2019. a
Jouvet, G.: Inversion of a Stokes glacier flow model emulated by deep learning, J. Glaciol., 69, 13–26, https://doi.org/10.1017/jog.2022.41, 2023. 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, J. Glaciol., 68, 651–664, https://doi.org/10.1017/jog.2021.120, 2021. a, b, c
Kidger, P.: On Neural Differential Equations, arXiv [preprint], https://doi.org/10.48550/arXiv.2202.02435, 2022. a
Lange, S.: WFDE5 over land merged with ERA5 over the ocean (W5E5), GFZ Data Services [data set], https://doi.org/10.5880/PIK.2019.023, 2019. a, b
Leong, W. J. and Horgan, H. J.: DeepBedMap: a deep neural network for resolving the bed topography of Antarctica, The Cryosphere, 14, 3687–3705, https://doi.org/10.5194/tc-14-3687-2020, 2020. a
Lguensat, R., Sommer, J. L., Metref, S., Cosme, E., and Fablet, R.: Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models, arXiv: [preprint], https://doi.org/10.48550/arXiv.1911.08856, 2019. a
Ma, Y., Dixit, V., Innes, M., Guo, X., and Rackauckas, C.: A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions, arXiv [preprint], https://doi.org/10.48550/arXiv.1812.01892, 2021. a
MacAyeal, D. R.: A tutorial on the use of control methods in ice-sheet modeling, J. Glaciol., 39, 91–98, https://doi.org/10.3189/S0022143000015744, 1993. 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
Maussion, F., Rothenpieler, T., Dusch, M., Schmitt, P., Vlug, A., Schuster, L., Champollion, N., Li, F., Marzeion, B., Oberrauch, M., Eis, J., Landmann, J., Jarosch, A., Fischer, A., luzpaz, Hanus, S., Rounce, D., Castellani, M., Bartholomew, S. L., Minallah, S., bowenbelongstonature, Merrill, C., Otto, D., Loibl, D., Ultee, L., Thompson, S., anton ub, Gregor, P., and zhaohongyu: OGGM/oggm: v1.6.0, Zenodo [code], https://doi.org/10.5281/zenodo.7718476, 2023. a
Mesnard, O. and Barba, L. A.: Reproducible Workflow on a Public Cloud for Computational Fluid Dynamics, Comput. Sci. Eng., 22, 102–116, https://doi.org/10.1109/mcse.2019.2941702, 2020. a
Millan, R., Mouginot, J., Rabatel, A., and Morlighem, M.: Ice velocity and thickness of the world’s glaciers, Nat. Geosci., 15, 124–129, https://doi.org/10.1038/s41561-021-00885-z, 2022. a, b, c
Mogensen, P. K. and Riseth, A. N.: Optim: A mathematical optimization package for Julia, J. Open Source Softw., 3, 615, https://doi.org/10.21105/joss.00615, 2018. a
Mohajerani, Y., Wood, M., Velicogna, I., and Rignot, E.: Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study, Remote Sens., 11, 74, https://doi.org/10.3390/rs11010074, 2019. a
Moses, W. S., Churavy, V., Paehler, L., Hückelheim, J., Narayanan, S. H. K., Schanen, M., and Doerfert, J.: Reverse-mode automatic differentiation and optimization of GPU kernels via Enzyme, in: Proceedings of the international conference for high performance computing, networking, storage and analysis, pp. 1–16, 2021. a
Nanni, U., Scherler, D., Ayoub, F., Millan, R., Herman, F., and Avouac, J.-P.: Climatic control on seasonal variations in mountain glacier surface velocity, The Cryosphere, 17, 1567–1583, https://doi.org/10.5194/tc-17-1567-2023, 2023. a
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S.: PyTorch: An Imperative Style, High-Performance Deep Learning Library, in: Advances in Neural Information Processing Systems 32, edited by: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F. D., Fox, E., and Garnett, R., Curran Associates, Inc., 8026–8037, http://papers.nips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (last access: 13 November 2023), 2019. a
Project Jupyter: Binder 2.0 – Reproducible, interactive, sharable environments for science at scale, in: Proceedings of the 17th Python in Science Conference, edited by: Akici, F., Lippa, D., Niederhut, D., and Pacer, M., 113–120, https://doi.org/10.25080/Majora-4af1f417-011, 2018. a
Rackauckas, C. and Nie, Q.: DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia, J. Open Res. Softw., 5, 15, https://doi.org/10.5334/jors.151, 2017. a, b, c, d
Rackauckas, C., Innes, M., Ma, Y., Bettencourt, J., White, L., and Dixit, V.: DiffEqFlux.jl – A Julia Library for Neural Differential Equations, arXiv [preprint], https://doi.org/10.48550/arXiv.1902.02376, 2019. a, b
Raissi, M., Perdikaris, P., and Karniadakis, G. E.: Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations, arXiv [preprint], https://doi.org/10.48550/arXiv.1711.10561, 2017. a
Ramsay, J. and Hooker, G.: Dynamic Data Analysis, Modeling Data with Differential Equations, Springer New York, NY, https://doi.org/10.1007/978-1-4939-7190-9, 2017. a, b
Ranocha, H., Dalcin, L., Parsani, M., and Ketcheson, D. I.: Optimized Runge-Kutta Methods with Automatic Step Size Control for Compressible Computational Fluid Dynamics, Commun. Appl. Math. Comput., 4, 1191–1228, https://doi.org/10.1007/s42967-021-00159-w, 2022. a, b
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid processes in climate models, P. Natl. Acad. Sci. USA, 115, 9684–9689, https://doi.org/10.1073/pnas.1810286115, 2018. a
Riel, B., Minchew, B., and Bischoff, T.: Data-Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics-Informed Neural Networks: Case Study on Rutford Ice Stream, Antarctica, J. Adv. Model. Earth Sy., 13, e2021MS00221, https://doi.org/10.1029/2021MS002621, 2021. a
Schanen, M., Narayanan, S. H. K., Williamson, S., Churavy, V., Moses, W. S., and Paehler, L.: Transparent Checkpointing for Automatic Differentiation of Program Loops Through Expression Transformations, in: Computational Science – ICCS 2023, edited by: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V. V., Dongarra, J. J., and Sloot, P. M., Springer Nature Switzerland, Cham, 483–497, ISBN 978-3-031-36024-4, 2023. a, b
Strauss, R. R., Bishnu, S., and Petersen, M. R.: Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1), EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-57, 2023. a
Thomas, K., Benjamin, R.-K., Fernando, P., Brian, G., Matthias, B., Jonathan, F., Kyle, K., Jessica, H., Jason, G., Sylvain, C., Paul, I., Damián, A., Safia, A., Carol, W., and Jupyter development team: Jupyter Notebooks – a publishing format for reproducible computational workflows, Stand Alone, Positioning and Power in Academic Publishing: Players, Agents and Agendas, IOS Press, 87–90, https://doi.org/10.3233/978-1-61499-649-1-87, 2016. a, b
Wang, Y., Lai, C.-Y., and Cowen-Breen, C.: Discovering the rheology of Antarctic Ice Shelves via physics-informed deep learning, Research Square [preprint], https://doi.org/10.21203/rs.3.rs-2135795/v1, 2022. a
Zdeborová, L.: Understanding deep learning is also a job for physicists, Nature Physics, 16, 602–604, https://doi.org/10.1038/s41567-020-0929-2, 2020. a
Zekollari, H., Huss, M., and Farinotti, D.: Modelling the future evolution of glaciers in the European Alps under the EURO-CORDEX RCM ensemble, The Cryosphere, 13, 1125–1146, https://doi.org/10.5194/tc-13-1125-2019, 2019. a
Zhao, C., Gladstone, R. M., Warner, R. C., King, M. A., Zwinger, T., and Morlighem, M.: Basal friction of Fleming Glacier, Antarctica – Part 1: Sensitivity of inversion to temperature and bedrock uncertainty, The Cryosphere, 12, 2637–2652, https://doi.org/10.5194/tc-12-2637-2018, 2018. a
Executive editor
The integration of neural networks into PDE solvers to simulate systems for which the PDE models are incomplete is a key advance at the cutting edge of geoscientific modelling. The approach presented here is applicable far beyond the realm of ice modelling, and will be of interest to model developers and users across geoscience and beyond.
The integration of neural networks into PDE solvers to simulate systems for which the PDE models...
Short summary
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.
We developed a new modelling framework combining numerical methods with machine learning. Using...