Articles | Volume 16, issue 15
https://doi.org/10.5194/gmd-16-4521-2023
© 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-16-4521-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Aug 2023
Development and technical paper |
| 10 Aug 2023
| 10 Aug 2023
Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms
Julia Kaltenborn
CORRESPONDING AUTHOR
WSL Institute for Snow and
Avalanche Research SLF, Flüelastrasse 11, 7260 Davos Dorf, Switzerland
Institute of Cognitive Science, University Osnabrück, Wachsbleiche 27, 49090 Osnabrück, Germany
Mila – Quebec AI Institute, 6666 Rue Saint-Urbain, QC H2S 3H1, Montréal, Canada
School of Computer Science, McGill University, 3480 Rue University, QC H3A 2A7, Montréal, Canada
Amy R. Macfarlane
WSL Institute for Snow and
Avalanche Research SLF, Flüelastrasse 11, 7260 Davos Dorf, Switzerland
Viviane Clay
Institute of Cognitive Science, University Osnabrück, Wachsbleiche 27, 49090 Osnabrück, Germany
Numenta, 889 Winslow Street, Redwood City, CA 94063, USA
WSL Institute for Snow and
Avalanche Research SLF, Flüelastrasse 11, 7260 Davos Dorf, Switzerland
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Yubing Cheng, Bin Cheng, Roberta Pirazzini, Amy R. Macfarlane, Timo Vihma, Wolfgang Dorn, Ruzica Dadic, Martin Schneebeli, Stefanie Arndt, and Annette Rinke
EGUsphere, https://doi.org/10.5194/egusphere-2025-1164, https://doi.org/10.5194/egusphere-2025-1164, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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We study snow density from the MOSAiC expedition. Several snow density schemes were tested and compared with observation. A thermodynamic ice model was employed to assess the impact of snow density and precipitation on the thermal regime of sea ice. The parameterized mean snow densities are consistent with observations. Increased snow density reduces snow and ice temperatures, promoting ice growth, while increased precipitation leads to warmer snow and ice temperatures and reduced ice thickness.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
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Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
Jack C. Landy, Claude de Rijke-Thomas, Carmen Nab, Isobel Lawrence, Isolde A. Glissenaar, Robbie D. C. Mallett, Renée M. Fredensborg Hansen, Alek Petty, Michel Tsamados, Amy R. Macfarlane, and Anne Braakmann-Folgmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2904, https://doi.org/10.5194/egusphere-2024-2904, 2024
This preprint is open for discussion and under review for The Cryosphere (TC).
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In this study we use three satellites to test the planned remote sensing approach of the upcoming mission CRISTAL over sea ice: that its dual radars will accurately measure the heights of the top and base of snow sitting atop floating sea ice floes. Our results suggest that CRISTAL's dual radars won’t necessarily measure the snow top and base under all conditions. We find that accurate height measurements depend much more on surface roughness than on snow properties, as is commonly assumed.
Moein Mellat, Amy R. Macfarlane, Camilla F. Brunello, Martin Werner, Martin Schneebeli, Ruzica Dadic, Stefanie Arndt, Kaisa-Riikka Mustonen, Jeffrey M. Welker, and Hanno Meyer
EGUsphere, https://doi.org/10.5194/egusphere-2024-719, https://doi.org/10.5194/egusphere-2024-719, 2024
Preprint archived
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Our research, utilizing data from the Arctic MOSAiC expedition, reveals how snow on Arctic sea ice changes due to weather conditions. By analyzing snow samples collected over a year, we found differences in snow layers that tell us about their origins and how they've been affected by the environment. We discovered variations in snow and vapour that reflect the influence of weather patterns and surface processes like wind and sublimation.
Amy R. Macfarlane, Henning Löwe, Lucille Gimenes, David N. Wagner, Ruzica Dadic, Rafael Ottersberg, Stefan Hämmerle, and Martin Schneebeli
The Cryosphere, 17, 5417–5434, https://doi.org/10.5194/tc-17-5417-2023, https://doi.org/10.5194/tc-17-5417-2023, 2023
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Snow acts as an insulating blanket on Arctic sea ice, keeping the underlying ice "warm", relative to the atmosphere. Knowing the snow's thermal conductivity is essential for understanding winter ice growth. During the MOSAiC expedition, we measured the thermal conductivity of snow. We found spatial and vertical variability to overpower any temporal variability or dependency on underlying ice type and the thermal resistance to be directly influenced by snow height.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
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The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
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Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
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We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Julia Martin and Martin Schneebeli
The Cryosphere, 17, 1723–1734, https://doi.org/10.5194/tc-17-1723-2023, https://doi.org/10.5194/tc-17-1723-2023, 2023
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The grain size of snow determines how light is reflected and other physical properties. The IceCube measures snow grain size at the specific near-infrared wavelength of 1320 nm. In our study, the preparation of snow samples for the IceCube creates a thin layer of small particles. Comparisons of the grain size with computed tomography, particle counting and numerical simulation confirm the aforementioned observation. We conclude that measurements at this wavelength underestimate the grain size.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
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Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
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This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
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Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Sönke Maus, Martin Schneebeli, and Andreas Wiegmann
The Cryosphere, 15, 4047–4072, https://doi.org/10.5194/tc-15-4047-2021, https://doi.org/10.5194/tc-15-4047-2021, 2021
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As the hydraulic permeability of sea ice is difficult to measure, observations are sparse. The present work presents numerical simulations of the permeability of young sea ice based on a large set of 3D X-ray tomographic images. It extends the relationship between permeability and porosity available so far down to brine porosities near the percolation threshold of a few per cent. Evaluation of pore scales and 3D connectivity provides novel insight into the percolation behaviour of sea ice.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Rasmus Tonboe, Stefan Hendricks, Robert Ricker, James Mead, Robbie Mallett, Marcus Huntemann, Polona Itkin, Martin Schneebeli, Daniela Krampe, Gunnar Spreen, Jeremy Wilkinson, Ilkka Matero, Mario Hoppmann, and Michel Tsamados
The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, https://doi.org/10.5194/tc-14-4405-2020, 2020
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This study provides a first look at the data collected by a new dual-frequency Ka- and Ku-band in situ radar over winter sea ice in the Arctic Ocean. The instrument shows potential for using both bands to retrieve snow depth over sea ice, as well as sensitivity of the measurements to changing snow and atmospheric conditions.
Jacinta Edebeli, Jürg C. Trachsel, Sven E. Avak, Markus Ammann, Martin Schneebeli, Anja Eichler, and Thorsten Bartels-Rausch
Atmos. Chem. Phys., 20, 13443–13454, https://doi.org/10.5194/acp-20-13443-2020, https://doi.org/10.5194/acp-20-13443-2020, 2020
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Earth’s snow cover is very dynamic and can change its physical properties within hours, as is well known by skiers. Snow is also a well-known host of chemical reactions – the products of which impact air composition and quality. Here, we present laboratory experiments that show how the dynamics of snow make snow essentially inert with respect to gas-phase ozone with time despite its content of reactive chemicals. Impacts on polar atmospheric chemistry are discussed.
Neige Calonne, Bettina Richter, Henning Löwe, Cecilia Cetti, Judith ter Schure, Alec Van Herwijnen, Charles Fierz, Matthias Jaggi, and Martin Schneebeli
The Cryosphere, 14, 1829–1848, https://doi.org/10.5194/tc-14-1829-2020, https://doi.org/10.5194/tc-14-1829-2020, 2020
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During winter 2015–2016, the standard program to monitor the structure and stability of the snowpack at Weissflujoch, Swiss Alps, was complemented by additional measurements to compare between various traditional and newly developed techniques. Snow micro-penetrometer measurements allowed monitoring of the evolution of the snowpack's internal structure at a daily resolution throughout the winter. We show the potential of such high-resolution data for detailed evaluations of snowpack models.
Pirmin Philipp Ebner, Aaron Coulin, Joël Borner, Fabian Wolfsperger, Michael Hohl, and Martin Schneebeli
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-56, https://doi.org/10.5194/tc-2020-56, 2020
Revised manuscript not accepted
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These laboratory measurements allow to analyse wet snow and to find the narrow range of the starting point of water percolation in coarse-grained snow. Based on the electrical monitoring a promising perspective for retrieving water content and water distribution in the snowpack is given. The water distribution is analysed using micro-computer tomography to find preferential spots of the accumulated water. These findings are pertinent to the interpretation of the snow melt run-off of spring snow.
Achim Heilig, Olaf Eisen, Martin Schneebeli, Michael MacFerrin, C. Max Stevens, Baptiste Vandecrux, and Konrad Steffen
The Cryosphere, 14, 385–402, https://doi.org/10.5194/tc-14-385-2020, https://doi.org/10.5194/tc-14-385-2020, 2020
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We investigate the spatial representativeness of point observations of snow accumulation in SW Greenland. Such analyses have rarely been conducted but are necessary to link regional-scale observations from, e.g., remote-sensing data to firn cores and snow pits. The presented data reveal a low regional variability in density but snow depth can vary significantly. It is necessary to combine pits with spatial snow depth data to increase the regional representativeness of accumulation observations.
Kévin Fourteau, Patricia Martinerie, Xavier Faïn, Christoph F. Schaller, Rebecca J. Tuckwell, Henning Löwe, Laurent Arnaud, Olivier Magand, Elizabeth R. Thomas, Johannes Freitag, Robert Mulvaney, Martin Schneebeli, and Vladimir Ya. Lipenkov
The Cryosphere, 13, 3383–3403, https://doi.org/10.5194/tc-13-3383-2019, https://doi.org/10.5194/tc-13-3383-2019, 2019
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Understanding gas trapping in polar ice is essential to study the relationship between greenhouse gases and past climates. New data of bubble closure, used in a simple gas-trapping model, show inconsistency with the final air content in ice. This suggests gas trapping is not fully understood. We also use a combination of high-resolution measurements to investigate the effect of polar snow stratification on gas trapping and find that all strata have similar pores, but that some close in advance.
Isabelle Gouttevin, Moritz Langer, Henning Löwe, Julia Boike, Martin Proksch, and Martin Schneebeli
The Cryosphere, 12, 3693–3717, https://doi.org/10.5194/tc-12-3693-2018, https://doi.org/10.5194/tc-12-3693-2018, 2018
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Snow insulates the ground from the cold air in the Arctic winter, majorly affecting permafrost. This insulation depends on snow characteristics and is poorly quantified. Here, we characterize it at a carbon-rich permafrost site, using a recent technique that retrieves the 3-D structure of snow and its thermal properties. We adapt a snowpack model enabling the simulation of this insulation over a whole winter. We estimate that local snow variations induce up to a 6 °C spread in soil temperatures.
Pirmin Philipp Ebner, Hans Christian Steen-Larsen, Barbara Stenni, Martin Schneebeli, and Aldo Steinfeld
The Cryosphere, 11, 1733–1743, https://doi.org/10.5194/tc-11-1733-2017, https://doi.org/10.5194/tc-11-1733-2017, 2017
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Stable water isotopes (δ18O) obtained from snow and ice samples from polar regions are used to reconstruct past climate variability. We present an experimental study on the effect on the snow isotopic composition by airflow through a snowpack in controlled laboratory conditions. The disequilibrium between snow and vapor isotopes changed the isotopic content of the snow. These measurements suggest that metamorphism and its history affect the snow isotopic composition.
Sascha Bellaire, Martin Proksch, Martin Schneebeli, Masashi Niwano, and Konrad Steffen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-55, https://doi.org/10.5194/tc-2017-55, 2017
Preprint withdrawn
Juha Lemmetyinen, Anna Kontu, Jouni Pulliainen, Juho Vehviläinen, Kimmo Rautiainen, Andreas Wiesmann, Christian Mätzler, Charles Werner, Helmut Rott, Thomas Nagler, Martin Schneebeli, Martin Proksch, Dirk Schüttemeyer, Michael Kern, and Malcolm W. J. Davidson
Geosci. Instrum. Method. Data Syst., 5, 403–415, https://doi.org/10.5194/gi-5-403-2016, https://doi.org/10.5194/gi-5-403-2016, 2016
Pascal Hagenmuller, Margret Matzl, Guillaume Chambon, and Martin Schneebeli
The Cryosphere, 10, 1039–1054, https://doi.org/10.5194/tc-10-1039-2016, https://doi.org/10.5194/tc-10-1039-2016, 2016
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The paper focuses on the characterization of snow microstructure with X-ray microtomography, a technique that is progressively becoming the standard for snow characterization. In particular, it rigorously investigates how the image processing algorithms affect the subsequent microstructure characterization in terms of density and specific surface area. From this analysis, practical recommendations concerning the processing X-ray tomographic images of snow are provided.
William Maslanka, Leena Leppänen, Anna Kontu, Mel Sandells, Juha Lemmetyinen, Martin Schneebeli, Martin Proksch, Margret Matzl, Henna-Reetta Hannula, and Robert Gurney
Geosci. Instrum. Method. Data Syst., 5, 85–94, https://doi.org/10.5194/gi-5-85-2016, https://doi.org/10.5194/gi-5-85-2016, 2016
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The paper presents the initial findings of the Arctic Snow Microstructure Experiment in Sodankylä, Finland. The experiment observed the microwave emission of extracted snow slabs on absorbing and reflecting bases. Snow parameters were recorded to simulate the emission upon those bases using two different emission models. The smallest simulation errors were associated with the absorbing base at vertical polarization. The observations will be used for the development of snow emission modelling.
Pirmin Philipp Ebner, Martin Schneebeli, and Aldo Steinfeld
The Cryosphere, 10, 791–797, https://doi.org/10.5194/tc-10-791-2016, https://doi.org/10.5194/tc-10-791-2016, 2016
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Changes of the porous ice structure were observed in a snow sample. Sublimation occurred due to the slight undersaturation of the incoming air into the warmer ice matrix. Diffusion of water vapor opposite to the direction of the temperature gradient counteracted the mass transport of advection. Therefore, the total net ice change was negligible, leading to a constant porosity profile. However, the strong recrystallization of water molecules in snow may impact its isotopic or chemical content.
Martin Proksch, Nick Rutter, Charles Fierz, and Martin Schneebeli
The Cryosphere, 10, 371–384, https://doi.org/10.5194/tc-10-371-2016, https://doi.org/10.5194/tc-10-371-2016, 2016
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Density is a fundamental property of porous media such as snow. During the MicroSnow Davos 2014 workshop, different approaches (box-, wedge- and cylinder-type density cutters, micro-computed tomography) to measure snow density were applied in a controlled laboratory environment and in the field. In general, results suggest that snow densities measured by different methods agree within 9 %. However, the density profiles resolved by the measurement methods differed considerably.
M. Proksch, C. Mätzler, A. Wiesmann, J. Lemmetyinen, M. Schwank, H. Löwe, and M. Schneebeli
Geosci. Model Dev., 8, 2611–2626, https://doi.org/10.5194/gmd-8-2611-2015, https://doi.org/10.5194/gmd-8-2611-2015, 2015
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The measurement of snow properties on global scale relies on microwave remote sensing data. The interpretation of the data is however challenging. Here we introduce MEMLS3&a, an extension of the snow emission model MEMLS, to include a backscatter model for active microwave remote sensing. In MEMLS3&a, snow input parameters can be derived by objective measurement methods, which avoids fitting the scattering efficiency of snow. The model is validated with combined active and passive measurements.
P. P. Ebner, M. Schneebeli, and A. Steinfeld
The Cryosphere, 9, 1363–1371, https://doi.org/10.5194/tc-9-1363-2015, https://doi.org/10.5194/tc-9-1363-2015, 2015
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Time-lapse X-ray microtomography was used to investigate the structural dynamics of isothermal snow metamorphism exposed to an advective airflow and possible effects on natural snowpacks were discussed. The results showed that isothermal advection with saturated air have no influence on the coarsening rate that is typical for isothermal snow metamorphism. It is driven by sublimation-deposition caused by Kelvin effect and is the limiting factor independently of the transport regime in the pores.
J. Schwaab, M. Bavay, E. Davin, F. Hagedorn, F. Hüsler, M. Lehning, M. Schneebeli, E. Thürig, and P. Bebi
Biogeosciences, 12, 467–487, https://doi.org/10.5194/bg-12-467-2015, https://doi.org/10.5194/bg-12-467-2015, 2015
S. Schleef, H. Löwe, and M. Schneebeli
The Cryosphere, 8, 1825–1838, https://doi.org/10.5194/tc-8-1825-2014, https://doi.org/10.5194/tc-8-1825-2014, 2014
P. P. Ebner, S. A. Grimm, M. Schneebeli, and A. Steinfeld
Geosci. Instrum. Method. Data Syst., 3, 179–185, https://doi.org/10.5194/gi-3-179-2014, https://doi.org/10.5194/gi-3-179-2014, 2014
H. Löwe, F. Riche, and M. Schneebeli
The Cryosphere, 7, 1473–1480, https://doi.org/10.5194/tc-7-1473-2013, https://doi.org/10.5194/tc-7-1473-2013, 2013
T. Bartels-Rausch, S. N. Wren, S. Schreiber, F. Riche, M. Schneebeli, and M. Ammann
Atmos. Chem. Phys., 13, 6727–6739, https://doi.org/10.5194/acp-13-6727-2013, https://doi.org/10.5194/acp-13-6727-2013, 2013
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AvaFrame com1DFA (v1.3): a thickness-integrated computational avalanche module – theory, numerics, and testing
Universal differential equations for glacier ice flow modelling
A new model for supraglacial hydrology evolution and drainage for the Greenland Ice Sheet (SHED v1.0)
Modeling sensitivities of thermally and hydraulically driven ice stream surge cycling
A parallel implementation of the confined–unconfined aquifer system model for subglacial hydrology: design, verification, and performance analysis (CUAS-MPI v0.1.0)
An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0
A wind-driven snow redistribution module for Alpine3D v3.3.0: adaptations designed for downscaling ice sheet surface mass balance
The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere
Glacier Energy and Mass Balance (GEMB): a model of firn processes for cryosphere research
Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean
Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling
SUHMO: an adaptive mesh refinement SUbglacial Hydrology MOdel v1.0
Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau
The Multiple Snow Data Assimilation System (MuSA v1.0)
The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)
Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G
Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.
Benchmarking the vertically integrated ice-sheet model IMAU-ICE (version 2.0)
SnowClim v1.0: high-resolution snow model and data for the western United States
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025, https://doi.org/10.5194/gmd-18-1829-2025, 2025
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Accurately measuring snow height is key for modeling approaches in climate science, snow hydrology, and avalanche forecasting. Erroneous snow height measurements often occur when snow height is low or changes, for instance during snowfall in summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep-learning approaches. Our approach can be easily implemented in a data pipeline for snow modeling.
James W. Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin J. Siegert, and Liam Holder
Geosci. Model Dev., 18, 1673–1708, https://doi.org/10.5194/gmd-18-1673-2025, https://doi.org/10.5194/gmd-18-1673-2025, 2025
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Ice sheet models can help predict how Antarctica's ice sheets respond to environmental change, and such models benefit from comparison to geological data. Here, we use an ice sheet model output and other data to predict the erosion of debris and trace its transport to where it is deposited on the ocean floor. This allows the results of ice sheet modelling to be directly and quantitively compared to real-world data, helping to reduce uncertainty regarding Antarctic sea level contribution.
Anton Korosov, Yue Ying, and Einar Ólason
Geosci. Model Dev., 18, 885–904, https://doi.org/10.5194/gmd-18-885-2025, https://doi.org/10.5194/gmd-18-885-2025, 2025
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We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev., 18, 651–670, https://doi.org/10.5194/gmd-18-651-2025, https://doi.org/10.5194/gmd-18-651-2025, 2025
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We employed the WRF-Chem model to parameterize atmospheric nitrate deposition in snow and evaluate its performance in simulating snow cover, snow depth, and concentrations of dust and nitrate using new observations from northern China. The results generally exhibit reasonable agreement with field observations in northern China, demonstrating the model's capability to simulate snow properties, including concentrations of reservoir species.
Simon Horton, Florian Herla, and Pascal Haegeli
Geosci. Model Dev., 18, 193–209, https://doi.org/10.5194/gmd-18-193-2025, https://doi.org/10.5194/gmd-18-193-2025, 2025
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We present a method for avalanche forecasters to analyze patterns in snowpack model simulations. It uses fuzzy clustering to group small regions into larger forecast areas based on snow characteristics, locations, and temporal history. Tested in the Columbia Mountains in two winter seasons, it closely matched real forecast regions regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev., 17, 8969–8988, https://doi.org/10.5194/gmd-17-8969-2024, https://doi.org/10.5194/gmd-17-8969-2024, 2024
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We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better-quality maps. The correction can then be extended backwards and forwards in time for periods when better-quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the past 60 years at a resolution of 1 d and 1 km. This is the first time that such a dataset has been produced.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
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The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow albedo in the solar domain and the profiles of light and energy absorption in a multi-layered snowpack whose physical properties are user defined. It uniquely considers snow grain shape flexibly, based on recent insights showing that snow does not behave as a collection of ice spheres but instead as a random medium. TARTES is user-friendly yet performs comparably to more complex models.
Tim Hill, Derek Bingham, Gwenn E. Flowers, and Matthew J. Hoffman
EGUsphere, https://doi.org/10.22541/essoar.172736254.41350153/v2, https://doi.org/10.22541/essoar.172736254.41350153/v2, 2024
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Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train fast statistical models called Gaussian Process emulators to accelerate subglacial drainage modelling by ~1000 times. We use the fast emulator predictions to show that three of the model parameters are responsible for >90 % of the variance in model outputs. The fast GP emulators will enable future uncertainty quantification and calibration of these models.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Yafei Nie, Ian Fenty, Matthew Mazloff, Armin Köhl, and Dimitris Menemenlis
Geosci. Model Dev., 17, 8613–8638, https://doi.org/10.5194/gmd-17-8613-2024, https://doi.org/10.5194/gmd-17-8613-2024, 2024
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Global- and basin-scale ocean reanalyses are becoming easily accessible. However, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluation. We conduct intercomparison analyses of Massachusetts Institute of Technology General Circulation Model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open-ocean temporal variability and Antarctic continental shelves require improvements.
Diego Monteiro, Cécile Caillaud, Matthieu Lafaysse, Adrien Napoly, Mathieu Fructus, Antoinette Alias, and Samuel Morin
Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, https://doi.org/10.5194/gmd-17-7645-2024, 2024
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Modeling snow cover in climate and weather forecasting models is a challenge even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties when representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration, enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
Alessandro Maissen, Frank Techel, and Michele Volpi
Geosci. Model Dev., 17, 7569–7593, https://doi.org/10.5194/gmd-17-7569-2024, https://doi.org/10.5194/gmd-17-7569-2024, 2024
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By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
EGUsphere, https://doi.org/10.5194/egusphere-2024-2546, https://doi.org/10.5194/egusphere-2024-2546, 2024
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How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
Davor Dundovic, Joseph G. Wallwork, Stephan C. Kramer, Fabien Gillet-Chaulet, Regine Hock, and Matthew D. Piggott
EGUsphere, https://doi.org/10.5194/egusphere-2024-2649, https://doi.org/10.5194/egusphere-2024-2649, 2024
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Accurate numerical studies of glaciers often require high-resolution simulations, which often prove too demanding even for modern computers. In this paper we develop a method that identifies whether different parts of a glacier require high or low resolution based on its physical features, such as its thickness and velocity. We show that by doing so we can achieve a more optimal simulation accuracy for the available computing resources compared to uniform resolution simulations.
Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova
Geosci. Model Dev., 17, 7219–7244, https://doi.org/10.5194/gmd-17-7219-2024, https://doi.org/10.5194/gmd-17-7219-2024, 2024
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We describe a new snow scheme developed for use in global climate models, which simulates the interactions of snowpack with vegetation, atmosphere, and soil. We test the new snow model over a set of sites where in situ observations are available. We find that when compared to a simpler snow model, this model improves predictions of seasonal snow and of soil temperature under the snowpack, important variables for simulating both the hydrological cycle and the global climate system.
Niccolò Maffezzoli, Eric Rignot, Carlo Barbante, Troels Petersen, and Sebastiano Vascon
EGUsphere, https://doi.org/10.5194/egusphere-2024-2455, https://doi.org/10.5194/egusphere-2024-2455, 2024
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In this work we introduces IceBoost, a machine learning framework to model the ice thickness distribution of all the World's glaciers, with greater accuracy than state-of-the-art methods. The model is trained on 3.7 million measurements globally available and provides skillful estimates across all regions. This advancement will help in better assessing future sea level changes, freshwater resources, with significance for both the scientific community and society at large.
Therese Rieckh, Andreas Born, Alexander Robinson, Robert Law, and Gerrit Gülle
Geosci. Model Dev., 17, 6987–7000, https://doi.org/10.5194/gmd-17-6987-2024, https://doi.org/10.5194/gmd-17-6987-2024, 2024
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We present the open-source model ELSA, which simulates the internal age structure of large ice sheets. It creates layers of snow accumulation at fixed times during the simulation, which are used to model the internal stratification of the ice sheet. Together with reconstructed isochrones from radiostratigraphy data, ELSA can be used to assess ice sheet models and to improve their parameterization. ELSA can be used coupled to an ice sheet model or forced with its output.
Fu Zhao, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu
Geosci. Model Dev., 17, 6867–6886, https://doi.org/10.5194/gmd-17-6867-2024, https://doi.org/10.5194/gmd-17-6867-2024, 2024
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In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely the Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea ice‒ocean‒ice shelf coupled model and can assimilate sea ice concentration observations. By assessing the system's performance in sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for the next 7 d and has the potential to guide ship navigation in the Antarctic sea ice zone.
Chenhui Ning, Shiming Xu, Yan Zhang, Xuantong Wang, Zhihao Fan, and Jiping Liu
Geosci. Model Dev., 17, 6847–6866, https://doi.org/10.5194/gmd-17-6847-2024, https://doi.org/10.5194/gmd-17-6847-2024, 2024
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Sea ice models are mainly based on non-moving structured grids, which is different from buoy measurements that follow the ice drift. To facilitate Lagrangian analysis, we introduce online tracking of sea ice in Community Ice CodE (CICE). We validate the sea ice tracking with buoys and evaluate the sea ice deformation in high-resolution simulations, which show multi-fractal characteristics. The source code is openly available and can be used in various scientific and operational applications.
Ulrich Strasser, Michael Warscher, Erwin Rottler, and Florian Hanzer
Geosci. Model Dev., 17, 6775–6797, https://doi.org/10.5194/gmd-17-6775-2024, https://doi.org/10.5194/gmd-17-6775-2024, 2024
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openAMUNDSEN is a fully distributed open-source snow-hydrological model for mountain catchments. It includes process representations of an empirical, semi-empirical, and physical nature. It uses temperature, precipitation, humidity, radiation, and wind speed as forcing data and is computationally efficient, of a modular nature, and easily extendible. The Python code is available on GitHub (https://github.com/openamundsen/openamundsen), including documentation (https://doc.openamundsen.org).
Matthias Rauter and Julia Kowalski
Geosci. Model Dev., 17, 6545–6569, https://doi.org/10.5194/gmd-17-6545-2024, https://doi.org/10.5194/gmd-17-6545-2024, 2024
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Snow avalanches can form large powder clouds that substantially exceed the velocity and reach of the dense core. Only a few complex models exist to simulate this phenomenon, and the respective hazard is hard to predict. This work provides a novel flow model that focuses on simple relations while still encapsulating the significant behaviour. The model is applied to reconstruct two catastrophic powder snow avalanche events in Austria.
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth C. Hunke, and Stefan Rethmeier
Geosci. Model Dev., 17, 6529–6544, https://doi.org/10.5194/gmd-17-6529-2024, https://doi.org/10.5194/gmd-17-6529-2024, 2024
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Earth system models (ESMs) today strive for better quality based on improved resolutions and improved physics. A limiting factor is the supercomputers at hand and how best to utilize them. This study focuses on the refactorization of one part of a sea ice model (CICE), namely the dynamics. It shows that the performance can be significantly improved, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.
Alex Edward West and Edward William Blockley
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-121, https://doi.org/10.5194/gmd-2024-121, 2024
Revised manuscript accepted for GMD
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This study uses ice mass balance buoys – temperature and height-measuring devices frozen into sea ice – to find how well climate models simulate the melt & growth of, and conduction of heat through, Arctic sea ice. This may help understand why models produce varying amounts of sea ice in the present day. We find models tend to show more melt, growth or conduction for a given ice thickness than the buoys, though the difference is smaller for models with more physically realistic thermodynamics.
Iain Wheel, Douglas I. Benn, Anna J. Crawford, Joe Todd, and Thomas Zwinger
Geosci. Model Dev., 17, 5759–5777, https://doi.org/10.5194/gmd-17-5759-2024, https://doi.org/10.5194/gmd-17-5759-2024, 2024
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Calving, the detachment of large icebergs from glaciers, is one of the largest uncertainties in future sea level rise projections. This process is poorly understood, and there is an absence of detailed models capable of simulating calving. A new 3D calving model has been developed to better understand calving at glaciers where detailed modelling was previously limited. Importantly, the new model is very flexible. By allowing for unrestricted calving geometries, it can be applied at any location.
Constantijn J. Berends, Victor Azizi, Jorge Bernales, and Roderik S. W. van de Wal
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-5, https://doi.org/10.5194/gmd-2024-5, 2024
Revised manuscript accepted for GMD
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Ice-sheet models are computer programs that can simulate how the Greenland and Antarctic ice sheets will evolve in the future. The accuracy of these models depends on their resolution: how small the details are that the model can resolve. We have created a model with a variable resolution, which can resolve a lot of detail in areas where lots of changes happen in the ice, and less detail in areas where the ice does not move so much. This makes the model both accurate and fast.
Ole Richter, Ralph Timmermann, G. Hilmar Gudmundsson, and Jan De Rydt
EGUsphere, https://doi.org/10.5194/egusphere-2024-648, https://doi.org/10.5194/egusphere-2024-648, 2024
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The new coupled ice sheet-ocean model addresses challenges related to horizontal resolution through advanced mesh flexibility, enabled by the use of unstructured grids. We describe the new model, verify its functioning in an idealised setting and demonstrate its advantages in a global-ocean/Antarctic ice sheet domain. The results of this study comprise an important step towards improving predictions of the Antarctic contribution to sea level rise over centennial time scales.
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
Geosci. Model Dev., 17, 1903–1929, https://doi.org/10.5194/gmd-17-1903-2024, https://doi.org/10.5194/gmd-17-1903-2024, 2024
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In this paper, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature, and melt while preserving a reasonable computation time.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
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Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024, https://doi.org/10.5194/gmd-17-1041-2024, 2024
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The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
Anjali Sandip, Ludovic Räss, and Mathieu Morlighem
Geosci. Model Dev., 17, 899–909, https://doi.org/10.5194/gmd-17-899-2024, https://doi.org/10.5194/gmd-17-899-2024, 2024
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We solve momentum balance for unstructured meshes to predict ice flow for real glaciers using a pseudo-transient method on graphics processing units (GPUs) and compare it to a standard central processing unit (CPU) implementation. We justify the GPU implementation by applying the price-to-performance metric for up to million-grid-point spatial resolutions. This study represents a first step toward leveraging GPU processing power, enabling more accurate polar ice discharge predictions.
Julien Brondex, Kévin Fourteau, Marie Dumont, Pascal Hagenmuller, Neige Calonne, François Tuzet, and Henning Löwe
Geosci. Model Dev., 16, 7075–7106, https://doi.org/10.5194/gmd-16-7075-2023, https://doi.org/10.5194/gmd-16-7075-2023, 2023
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Vapor diffusion is one of the main processes governing snowpack evolution, and it must be accounted for in models. Recent attempts to represent vapor diffusion in numerical models have faced several difficulties regarding computational cost and mass and energy conservation. Here, we develop our own finite-element software to explore numerical approaches and enable us to overcome these difficulties. We illustrate the capability of these approaches on established numerical benchmarks.
Daniel Moreno-Parada, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
EGUsphere, https://doi.org/10.5194/egusphere-2023-2690, https://doi.org/10.5194/egusphere-2023-2690, 2023
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We introduce Nix, an ice-sheet model designed for understanding how large masses of ice behave. Nix as a computer program that simulates the movement and temperature changes in ice sheets. Nix helps us study how ice sheets respond to changes in the atmosphere and ocean. We found that how fast ice melts under the shelves and how heat is exchanged, play a role in determining the future of ice sheets. Nix is a useful tool for learning more about how climate change affects polar ice sheets.
Matthias Tonnel, Anna Wirbel, Felix Oesterle, and Jan-Thomas Fischer
Geosci. Model Dev., 16, 7013–7035, https://doi.org/10.5194/gmd-16-7013-2023, https://doi.org/10.5194/gmd-16-7013-2023, 2023
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Avaframe - the open avalanche framework - provides open-source tools to simulate and investigate snow avalanches. It is utilized for multiple purposes, the two main applications being hazard mapping and scientific research of snow processes. We present the theory, conversion to a computer model, and testing for one of the core modules used for simulations of a particular type of avalanche, the so-called dense-flow avalanches. Tests check and confirm the applicability of the utilized method.
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.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823, https://doi.org/10.5194/gmd-16-5803-2023, https://doi.org/10.5194/gmd-16-5803-2023, 2023
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We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Kevin Hank, Lev Tarasov, and Elisa Mantelli
Geosci. Model Dev., 16, 5627–5652, https://doi.org/10.5194/gmd-16-5627-2023, https://doi.org/10.5194/gmd-16-5627-2023, 2023
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Physically meaningful modeling of geophysical system instabilities is numerically challenging, given the potential effects of purely numerical artifacts. Here we explore the sensitivity of ice stream surge activation to numerical and physical model aspects. We find that surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions and are significantly affected by the incorporation of bed thermal and sub-glacial hydrology models.
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, and Angelika Humbert
Geosci. Model Dev., 16, 5305–5322, https://doi.org/10.5194/gmd-16-5305-2023, https://doi.org/10.5194/gmd-16-5305-2023, 2023
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Water underneath ice sheets affects the motion of glaciers. This study presents a newly developed code, CUAS-MPI, that simulates subglacial hydrology. It is designed for supercomputers and is hence a parallelized code. We measure the performance of this code for simulations of the entire Greenland Ice Sheet and find that the code works efficiently. Moreover, we validated the code to ensure the correctness of the solution. CUAS-MPI opens new possibilities for simulations of ice sheet hydrology.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
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Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Eric Keenan, Nander Wever, Jan T. M. Lenaerts, and Brooke Medley
Geosci. Model Dev., 16, 3203–3219, https://doi.org/10.5194/gmd-16-3203-2023, https://doi.org/10.5194/gmd-16-3203-2023, 2023
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Ice sheets gain mass via snowfall. However, snowfall is redistributed by the wind, resulting in accumulation differences of up to a factor of 5 over distances as short as 5 km. These differences complicate estimates of ice sheet contribution to sea level rise. For this reason, we have developed a new model for estimating wind-driven snow redistribution on ice sheets. We show that, over Pine Island Glacier in West Antarctica, the model improves estimates of snow accumulation variability.
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.
Alex S. Gardner, Nicole-Jeanne Schlegel, and Eric Larour
Geosci. Model Dev., 16, 2277–2302, https://doi.org/10.5194/gmd-16-2277-2023, https://doi.org/10.5194/gmd-16-2277-2023, 2023
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This is the first description of the open-source Glacier Energy and Mass Balance (GEMB) model. GEMB models the ice sheet and glacier surface–atmospheric energy and mass exchange, as well as the firn state. The model is evaluated against the current state of the art and in situ observations and is shown to perform well.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
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State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Varun Sharma, Franziska Gerber, and Michael Lehning
Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, https://doi.org/10.5194/gmd-16-719-2023, 2023
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Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere.
Anne M. Felden, Daniel F. Martin, and Esmond G. Ng
Geosci. Model Dev., 16, 407–425, https://doi.org/10.5194/gmd-16-407-2023, https://doi.org/10.5194/gmd-16-407-2023, 2023
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We present and validate a novel subglacial hydrology model, SUHMO, based on an adaptive mesh refinement framework. We propose the addition of a pseudo-diffusion to recover the wall melting in channels. Computational performance analysis demonstrates the efficiency of adaptive mesh refinement on large-scale hydrologic problems. The adaptive mesh refinement approach will eventually enable better ice bed boundary conditions for ice sheet simulations at a reasonable computational cost.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
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Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
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We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
Max Brils, Peter Kuipers Munneke, Willem Jan van de Berg, and Michiel van den Broeke
Geosci. Model Dev., 15, 7121–7138, https://doi.org/10.5194/gmd-15-7121-2022, https://doi.org/10.5194/gmd-15-7121-2022, 2022
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Firn covers the Greenland ice sheet (GrIS) and can temporarily prevent mass loss. Here, we present the latest version of our firn model, IMAU-FDM, with an application to the GrIS. We improved the density of fallen snow, the firn densification rate and the firn's thermal conductivity. This leads to a higher air content and 10 m temperatures. Furthermore we investigate three case studies and find that the updated model shows greater variability and an increased sensitivity in surface elevation.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
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This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Constantijn J. Berends, Heiko Goelzer, Thomas J. Reerink, Lennert B. Stap, and Roderik S. W. van de Wal
Geosci. Model Dev., 15, 5667–5688, https://doi.org/10.5194/gmd-15-5667-2022, https://doi.org/10.5194/gmd-15-5667-2022, 2022
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The rate at which marine ice sheets such as the West Antarctic ice sheet will retreat in a warming climate and ocean is still uncertain. Numerical ice-sheet models, which solve the physical equations that describe the way glaciers and ice sheets deform and flow, have been substantially improved in recent years. Here we present the results of several years of work on IMAU-ICE, an ice-sheet model of intermediate complexity, which can be used to study ice sheets of both the past and the future.
Abby C. Lute, John Abatzoglou, and Timothy Link
Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, https://doi.org/10.5194/gmd-15-5045-2022, 2022
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We developed a snow model that can be used to quantify snowpack over large areas with a high degree of spatial detail. We ran the model over the western United States, creating a snow and climate dataset for three time periods. Compared to observations of snowpack, the model captured the key aspects of snow across time and space. The model and dataset will be useful in understanding historical and future changes in snowpack, with relevance to water resources, agriculture, and ecosystems.
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Short summary
Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Snow layer segmentation and snow grain classification are essential diagnostic tasks for...
