Articles | Volume 18, issue 2
https://doi.org/10.5194/tc-18-849-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-849-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Feb 2024
Research article |
| 23 Feb 2024
| 23 Feb 2024
A novel framework to investigate wind-driven snow redistribution over an Alpine glacier: combination of high-resolution terrestrial laser scans and large-eddy simulations
Annelies Voordendag
CORRESPONDING AUTHOR
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, Switzerland
Brigitta Goger
CORRESPONDING AUTHOR
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Center for Climate Systems Modeling, ETH Zurich, Zurich, Switzerland
Rainer Prinz
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Tobias Sauter
Geographisches Institut, Humboldt-Universität zu Berlin, Berlin, Germany
Thomas Mölg
Climate System Research Group, Institute of Geography, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany
Manuel Saigger
Climate System Research Group, Institute of Geography, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany
Georg Kaser
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
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Christoph Klug, Erik Bollmann, Stephan Peter Galos, Lindsey Nicholson, Rainer Prinz, Lorenzo Rieg, Rudolf Sailer, Johann Stötter, and Georg Kaser
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Ulrich Strasser, Thomas Marke, Ludwig Braun, Heidi Escher-Vetter, Irmgard Juen, Michael Kuhn, Fabien Maussion, Christoph Mayer, Lindsey Nicholson, Klaus Niedertscheider, Rudolf Sailer, Johann Stötter, Markus Weber, and Georg Kaser
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Stephan Peter Galos, Christoph Klug, Fabien Maussion, Federico Covi, Lindsey Nicholson, Lorenzo Rieg, Wolfgang Gurgiser, Thomas Mölg, and Georg Kaser
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Kai-Uwe Eiselt, Frank Kaspar, Thomas Mölg, Stefan Krähenmann, Rafael Posada, and Jens O. Riede
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Tobias Sauter and Stephan Peter Galos
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Bernadette Rosati, Martin Gysel, Florian Rubach, Thomas F. Mentel, Brigitta Goger, Laurent Poulain, Patrick Schlag, Pasi Miettinen, Aki Pajunoja, Annele Virtanen, Henk Klein Baltink, J. S. Bas Henzing, Johannes Größ, Gian Paolo Gobbi, Alfred Wiedensohler, Astrid Kiendler-Scharr, Stefano Decesari, Maria Cristina Facchini, Ernest Weingartner, and Urs Baltensperger
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Wolfgang Gurgiser, Irmgard Juen, Katrin Singer, Martina Neuburger, Simone Schauwecker, Marlis Hofer, and Georg Kaser
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R. Prinz, L. I. Nicholson, T. Mölg, W. Gurgiser, and G. Kaser
The Cryosphere, 10, 133–148, https://doi.org/10.5194/tc-10-133-2016, https://doi.org/10.5194/tc-10-133-2016, 2016
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F. Maussion, W. Gurgiser, M. Großhauser, G. Kaser, and B. Marzeion
The Cryosphere, 9, 1663–1683, https://doi.org/10.5194/tc-9-1663-2015, https://doi.org/10.5194/tc-9-1663-2015, 2015
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E. Collier, F. Maussion, L. I. Nicholson, T. Mölg, W. W. Immerzeel, and A. B. G. Bush
The Cryosphere, 9, 1617–1632, https://doi.org/10.5194/tc-9-1617-2015, https://doi.org/10.5194/tc-9-1617-2015, 2015
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We investigate the impact of surface debris on glacier energy and mass fluxes and on atmosphere-glacier feedbacks in the Karakoram range, by including debris in an interactively coupled atmosphere-glacier model. The model is run from 1 May to 1 October 2004, with a simple specification of debris thickness. We find an appreciable reduction in ablation that exceeds 5m w.e. on glacier tongues, as well as significant alterations to near-surface air temperatures and boundary layer dynamics.
M. Hofer, B. Marzeion, and T. Mölg
Geosci. Model Dev., 8, 579–593, https://doi.org/10.5194/gmd-8-579-2015, https://doi.org/10.5194/gmd-8-579-2015, 2015
W. Gurgiser, B. Marzeion, L. Nicholson, M. Ortner, and G. Kaser
The Cryosphere, 7, 1787–1802, https://doi.org/10.5194/tc-7-1787-2013, https://doi.org/10.5194/tc-7-1787-2013, 2013
S. MacDonell, C. Kinnard, T. Mölg, L. Nicholson, and J. Abermann
The Cryosphere, 7, 1513–1526, https://doi.org/10.5194/tc-7-1513-2013, https://doi.org/10.5194/tc-7-1513-2013, 2013
T. Sauter, M. Möller, R. Finkelnburg, M. Grabiec, D. Scherer, and C. Schneider
The Cryosphere, 7, 1287–1301, https://doi.org/10.5194/tc-7-1287-2013, https://doi.org/10.5194/tc-7-1287-2013, 2013
L. I. Nicholson, R. Prinz, T. Mölg, and G. Kaser
The Cryosphere, 7, 1205–1225, https://doi.org/10.5194/tc-7-1205-2013, https://doi.org/10.5194/tc-7-1205-2013, 2013
N. J. Cullen, P. Sirguey, T. Mölg, G. Kaser, M. Winkler, and S. J. Fitzsimons
The Cryosphere, 7, 419–431, https://doi.org/10.5194/tc-7-419-2013, https://doi.org/10.5194/tc-7-419-2013, 2013
D. B. Bahr, W. T. Pfeffer, and G. Kaser
The Cryosphere Discuss., https://doi.org/10.5194/tcd-6-5405-2012, https://doi.org/10.5194/tcd-6-5405-2012, 2012
Revised manuscript not accepted
T. Mölg, F. Maussion, W. Yang, and D. Scherer
The Cryosphere, 6, 1445–1461, https://doi.org/10.5194/tc-6-1445-2012, https://doi.org/10.5194/tc-6-1445-2012, 2012
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Understanding snow bedform formation by adding sintering to a cellular automata model
Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations
Brief communication: Analysis of organic matter in surface snow by PTR-MS – implications for dry deposition dynamics in the Alps
Evaluation of the CloudSat surface snowfall product over Antarctica using ground-based precipitation radars
Sonja Wahl, Benjamin Walter, Franziska Aemisegger, Luca Bianchi, and Michael Lehning
The Cryosphere, 18, 4493–4515, https://doi.org/10.5194/tc-18-4493-2024, https://doi.org/10.5194/tc-18-4493-2024, 2024
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Wind-driven airborne transport of snow is a frequent phenomenon in snow-covered regions and a process difficult to study in the field as it is unfolding over large distances. Thus, we use a ring wind tunnel with infinite fetch positioned in a cold laboratory to study the evolution of the shape and size of airborne snow. With the help of stable water isotope analyses, we identify the hitherto unobserved process of airborne snow metamorphism that leads to snow particle rounding and growth.
Dylan Reynolds, Louis Quéno, Michael Lehning, Mahdi Jafari, Justine Berg, Tobias Jonas, Michael Haugeneder, and Rebecca Mott
The Cryosphere, 18, 4315–4333, https://doi.org/10.5194/tc-18-4315-2024, https://doi.org/10.5194/tc-18-4315-2024, 2024
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Information about atmospheric variables is needed to produce simulations of mountain snowpacks. We present a model that can represent processes that shape mountain snowpack, focusing on the accumulation of snow. Simulations show that this model can simulate the complex path that a snowflake takes towards the ground and that this leads to differences in the distribution of snow by the end of winter. Overall, this model shows promise with regard to improving forecasts of snow in mountains.
Lara Foth, Wolfgang Dorn, Annette Rinke, Evelyn Jäkel, and Hannah Niehaus
The Cryosphere, 18, 4053–4064, https://doi.org/10.5194/tc-18-4053-2024, https://doi.org/10.5194/tc-18-4053-2024, 2024
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It is demonstrated that the explicit consideration of the cloud dependence of the snow surface albedo in a climate model results in a more realistic simulation of the surface albedo during the snowmelt period in late May and June. Although this improvement appears to be relatively insubstantial, it has significant impact on the simulated sea-ice volume and extent in the model due to an amplification of the snow/sea-ice albedo feedback, one of the main contributors to Arctic amplification.
Daniela Brito Melo, Armin Sigmund, and Michael Lehning
The Cryosphere, 18, 1287–1313, https://doi.org/10.5194/tc-18-1287-2024, https://doi.org/10.5194/tc-18-1287-2024, 2024
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Snow saltation – the transport of snow close to the surface – occurs when the wind blows over a snow-covered surface with sufficient strength. This phenomenon is represented in some climate models; however, with limited accuracy. By performing numerical simulations and a detailed analysis of previous works, we show that snow saltation is characterized by two regimes. This is not represented in climate models in a consistent way, which hinders the quantification of snow transport and sublimation.
Christophe Leroy-Dos Santos, Elise Fourré, Cécile Agosta, Mathieu Casado, Alexandre Cauquoin, Martin Werner, Benedicte Minster, Frédéric Prié, Olivier Jossoud, Leila Petit, and Amaëlle Landais
The Cryosphere, 17, 5241–5254, https://doi.org/10.5194/tc-17-5241-2023, https://doi.org/10.5194/tc-17-5241-2023, 2023
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In the face of global warming, understanding the changing water cycle and temperatures in polar regions is crucial. These factors directly impact the balance of ice sheets in the Arctic and Antarctic. By studying the composition of water vapor, we gain insights into climate variations. Our 2-year study at Dumont d’Urville station, Adélie Land, offers valuable data to refine models. Additionally, we demonstrate how modeling aids in interpreting signals from ice core samples in the region.
Alia L. Khan, Peng Xian, and Joshua P. Schwarz
The Cryosphere, 17, 2909–2918, https://doi.org/10.5194/tc-17-2909-2023, https://doi.org/10.5194/tc-17-2909-2023, 2023
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Ice–albedo feedbacks in the ablation region of the Greenland Ice Sheet are difficult to constrain and model. Surface samples were collected across the 2014 summer melt season from different ice surface colors. On average, concentrations were higher in patches that were visibly dark, compared to medium patches and light patches, suggesting that black carbon aggregation contributed to snow aging, and vice versa. High concentrations are likely due to smoke transport from high-latitude wildfires.
Sara Arioli, Ghislain Picard, Laurent Arnaud, and Vincent Favier
The Cryosphere, 17, 2323–2342, https://doi.org/10.5194/tc-17-2323-2023, https://doi.org/10.5194/tc-17-2323-2023, 2023
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To assess the drivers of the snow grain size evolution during snow drift, we exploit a 5-year time series of the snow grain size retrieved from spectral-albedo observations made with a new, autonomous, multi-band radiometer and compare it to observations of snow drift, snowfall and snowmelt at a windy location of coastal Antarctica. Our results highlight the complexity of the grain size evolution in the presence of snow drift and show an overall tendency of snow drift to limit its variations.
Guillaume Gastineau, Claude Frankignoul, Yongqi Gao, Yu-Chiao Liang, Young-Oh Kwon, Annalisa Cherchi, Rohit Ghosh, Elisa Manzini, Daniela Matei, Jennifer Mecking, Lingling Suo, Tian Tian, Shuting Yang, and Ying Zhang
The Cryosphere, 17, 2157–2184, https://doi.org/10.5194/tc-17-2157-2023, https://doi.org/10.5194/tc-17-2157-2023, 2023
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Snow cover variability is important for many human activities. This study aims to understand the main drivers of snow cover in observations and models in order to better understand it and guide the improvement of climate models and forecasting systems. Analyses reveal a dominant role for sea surface temperature in the Pacific. Winter snow cover is also found to have important two-way interactions with the troposphere and stratosphere. No robust influence of the sea ice concentration is found.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, François Anctil, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 127–142, https://doi.org/10.5194/tc-16-127-2022, https://doi.org/10.5194/tc-16-127-2022, 2022
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The surface energy budget is the sum of all incoming and outgoing energy fluxes at the Earth's surface and has a key role in the climate. We measured all these fluxes for an Arctic snowpack and found that most incoming energy from radiation is counterbalanced by thermal radiation and heat convection while sublimation was negligible. Overall, the snow model Crocus was able to simulate the observed energy fluxes well.
Abigail G. Hughes, Sonja Wahl, Tyler R. Jones, Alexandra Zuhr, Maria Hörhold, James W. C. White, and Hans Christian Steen-Larsen
The Cryosphere, 15, 4949–4974, https://doi.org/10.5194/tc-15-4949-2021, https://doi.org/10.5194/tc-15-4949-2021, 2021
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Water isotope records in Greenland and Antarctic ice cores are a valuable proxy for paleoclimate reconstruction and are traditionally thought to primarily reflect precipitation input. However,
post-depositional processes are hypothesized to contribute to the isotope climate signal. In this study we use laboratory experiments, field experiments, and modeling to show that sublimation and vapor–snow isotope exchange can rapidly influence the isotopic composition of the snowpack.
Holt Hancock, Jordy Hendrikx, Markus Eckerstorfer, and Siiri Wickström
The Cryosphere, 15, 3813–3837, https://doi.org/10.5194/tc-15-3813-2021, https://doi.org/10.5194/tc-15-3813-2021, 2021
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We investigate how snow avalanche activity in central Spitsbergen, Svalbard, is broadly controlled by atmospheric circulation. Avalanche activity in this region is generally associated with atmospheric circulation conducive to increased precipitation, wind speeds, and air temperatures near Svalbard during winter storms. Our results help place avalanche activity on Spitsbergen in the wider context of Arctic environmental change and provide a foundation for improved avalanche forecasting here.
Thorsten Bartels-Rausch, Xiangrui Kong, Fabrizio Orlando, Luca Artiglia, Astrid Waldner, Thomas Huthwelker, and Markus Ammann
The Cryosphere, 15, 2001–2020, https://doi.org/10.5194/tc-15-2001-2021, https://doi.org/10.5194/tc-15-2001-2021, 2021
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Chemical reactions in sea salt embedded in coastal polar snow impact the composition and air quality of the atmosphere. Here, we investigate the phase changes of sodium chloride. This is of importance as chemical reactions proceed faster in liquid solutions compared to in solid salt and the precise precipitation temperature of sodium chloride is still under debate. We focus on the upper nanometres of sodium chloride–ice samples because of their role as a reactive interface in the environment.
Dimitri Osmont, Sandra Brugger, Anina Gilgen, Helga Weber, Michael Sigl, Robin L. Modini, Christoph Schwörer, Willy Tinner, Stefan Wunderle, and Margit Schwikowski
The Cryosphere, 14, 3731–3745, https://doi.org/10.5194/tc-14-3731-2020, https://doi.org/10.5194/tc-14-3731-2020, 2020
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In this interdisciplinary case study, we were able to link biomass burning emissions from the June 2017 wildfires in Portugal to their deposition in the snowpack at Jungfraujoch, Swiss Alps. We analysed black carbon and charcoal in the snowpack, calculated backward trajectories, and monitored the fire evolution by remote sensing. Such case studies help to understand the representativity of biomass burning records in ice cores and how biomass burning tracers are archived in the snowpack.
Wenkai Li, Shuzhen Hu, Pang-Chi Hsu, Weidong Guo, and Jiangfeng Wei
The Cryosphere, 14, 3565–3579, https://doi.org/10.5194/tc-14-3565-2020, https://doi.org/10.5194/tc-14-3565-2020, 2020
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Understanding the forecasting skills of the subseasonal-to-seasonal (S2S) model on Tibetan Plateau snow cover (TPSC) is the first step to applying the S2S model to hydrological forecasts over the Tibetan Plateau. This study conducted a multimodel comparison of the TPSC prediction skill to learn about their performance in capturing TPSC variability. S2S models can skillfully forecast TPSC within a lead time of 2 weeks but show limited skill beyond 3 weeks. Systematic biases of TPSC were found.
Nikolas O. Aksamit and John W. Pomeroy
The Cryosphere, 14, 2795–2807, https://doi.org/10.5194/tc-14-2795-2020, https://doi.org/10.5194/tc-14-2795-2020, 2020
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In cold regions, it is increasingly important to quantify the amount of water stored as snow at the end of winter. Current models are inconsistent in their estimates of snow sublimation due to atmospheric turbulence. Specific wind structures have been identified that amplify potential rates of surface and blowing snow sublimation during blowing snow storms. The recurrence of these motions has been modeled by a simple scaling argument that has its foundation in turbulent boundary layer theory.
Andrew J. Schwartz, Hamish A. McGowan, Alison Theobald, and Nik Callow
The Cryosphere, 14, 2755–2774, https://doi.org/10.5194/tc-14-2755-2020, https://doi.org/10.5194/tc-14-2755-2020, 2020
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This study measured energy available for snowmelt during the 2016 and 2017 snow seasons in Kosciuszko National Park, NSW, Australia, and identified common traits for days with similar weather characteristics. The analysis showed that energy available for snowmelt was highest in the days before cold fronts passed through the region due to higher air temperatures. Regardless of differences in daily weather characteristics, solar radiation contributed the highest amount of energy to snowpack melt.
Benjamin Walter, Hendrik Huwald, Josué Gehring, Yves Bühler, and Michael Lehning
The Cryosphere, 14, 1779–1794, https://doi.org/10.5194/tc-14-1779-2020, https://doi.org/10.5194/tc-14-1779-2020, 2020
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We applied a horizontally mounted low-cost precipitation radar to measure velocities, frequency of occurrence, travel distances and turbulence characteristics of blowing snow off a mountain ridge. Our analysis provides a first insight into the potential of radar measurements for determining blowing snow characteristics, improves our understanding of mountain ridge blowing snow events and serves as a valuable data basis for validating coupled numerical weather and snowpack simulations.
Charles Amory and Christoph Kittel
The Cryosphere, 13, 3405–3412, https://doi.org/10.5194/tc-13-3405-2019, https://doi.org/10.5194/tc-13-3405-2019, 2019
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Snow mass fluxes and vertical profiles of relative humidity are used to document concurrent occurrences of drifting snow and near-surface air saturation at a site dominated by katabatic winds in East Antarctica. Despite a high prevalence of drifting snow conditions, we demonstrate that saturation is reached only in the most extreme wind and transport conditions and discuss implications for the understanding of surface mass and atmospheric moisture budgets of the Antarctic ice sheet.
Varun Sharma, Louise Braud, and Michael Lehning
The Cryosphere, 13, 3239–3260, https://doi.org/10.5194/tc-13-3239-2019, https://doi.org/10.5194/tc-13-3239-2019, 2019
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Snow surfaces, under the action of wind, form beautiful shapes such as waves and dunes. This study is the first ever study to simulate these shapes using a state-of-the-art numerical modelling tool. While these beautiful and ephemeral shapes on snow surfaces are fascinating from a purely aesthetic point of view, they are also critical in regulating the transfer of heat and mass between the atmosphere and snowpacks, thus being of huge importance to the Earth system.
Yvan Orsolini, Martin Wegmann, Emanuel Dutra, Boqi Liu, Gianpaolo Balsamo, Kun Yang, Patricia de Rosnay, Congwen Zhu, Wenli Wang, Retish Senan, and Gabriele Arduini
The Cryosphere, 13, 2221–2239, https://doi.org/10.5194/tc-13-2221-2019, https://doi.org/10.5194/tc-13-2221-2019, 2019
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The Tibetan Plateau region exerts a considerable influence on regional climate, yet the snowpack over that region is poorly represented in both climate and forecast models due a large precipitation and snowfall bias. We evaluate the snowpack in state-of-the-art atmospheric reanalyses against in situ observations and satellite remote sensing products. Improved snow initialisation through better use of snow observations in reanalyses may improve medium-range to seasonal weather forecasts.
Dušan Materić, Elke Ludewig, Kangming Xu, Thomas Röckmann, and Rupert Holzinger
The Cryosphere, 13, 297–307, https://doi.org/10.5194/tc-13-297-2019, https://doi.org/10.5194/tc-13-297-2019, 2019
Niels Souverijns, Alexandra Gossart, Stef Lhermitte, Irina V. Gorodetskaya, Jacopo Grazioli, Alexis Berne, Claudio Duran-Alarcon, Brice Boudevillain, Christophe Genthon, Claudio Scarchilli, and Nicole P. M. van Lipzig
The Cryosphere, 12, 3775–3789, https://doi.org/10.5194/tc-12-3775-2018, https://doi.org/10.5194/tc-12-3775-2018, 2018
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Snowfall observations over Antarctica are scarce and currently limited to information from the CloudSat satellite. Here, a first evaluation of the CloudSat snowfall record is performed using observations of ground-based precipitation radars. Results indicate an accurate representation of the snowfall climatology over Antarctica, despite the low overpass frequency of the satellite, outperforming state-of-the-art model estimates. Individual snowfall events are however not well represented.
Cited articles
ACINN: ACINN Stations, Department for Atmospheric and Cryospheric Sciences, Universität Innsbruck [data set], https://acinn-data.uibk.ac.at/pages/station-list.html, last access: 19 February 2024. a
Aksamit, N. O. and Pomeroy, J. W.: Near-surface snow particle dynamics from particle tracking velocimetry and turbulence measurements during alpine blowing snow storms, The Cryosphere, 10, 3043–3062, https://doi.org/10.5194/tc-10-3043-2016, 2016. a
Arduini, G., Balsamo, G., Dutra, E., Day, J. J., Sandu, I., Boussetta, S., and Haiden, T.: Impact of a Multi-Layer Snow Scheme on Near-Surface Weather Forecasts, J. Adv. Model. Earth Sy., 11, 4687–4710, https://doi.org/10.1029/2019MS001725, 2019. a
Baron, M., Haddjeri, A., Lafaysse, M., Le Toumelin, L., Vionnet, V., and Fructus, M.: SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-43, in review, 2023. a
Beniston, M., Farinotti, D., Stoffel, M., Andreassen, L. M., Coppola, E., Eckert, N., Fantini, A., Giacona, F., Hauck, C., Huss, M., Huwald, H., Lehning, M., López-Moreno, J.-I., Magnusson, J., Marty, C., Morán-Tejéda, E., Morin, S., Naaim, M., Provenzale, A., Rabatel, A., Six, D., Stötter, J., Strasser, U., Terzago, S., and Vincent, C.: The European mountain cryosphere: a review of its current state, trends, and future challenges, The Cryosphere, 12, 759–794, https://doi.org/10.5194/tc-12-759-2018, 2018. a
Blümcke, A. and Hess, H.: Untersuchungen am Hintereisferner, Zeitschrift des deutschen und österreichischen Alpenvereins, https://opac.geologie.ac.at/ais312/dokumente/AV_001_2.pdf (last access: 13 February 2024), 1899. a
Cogley, J. G., Hock, R., Rasmussen, L., Arendt, A., Bauder, A., Braithwaite, R., Jansson, P., Kaser, G., Möller, M., Nicholson, L., and Zemp, M.: Glossary of glacier mass balance and related terms, IHP-VII technical documents in hydrology, 86, https://wgms.ch/downloads/Cogley_etal_2011.pdf (last access: 13 February 2024), 2011. a
Colle, B. A., Smith, R. B., and Wesley, D. A.: Theory, Observations, and Predictions of Orographic Precipitation, in: Mountain Weather Research and Forecasting, edited by Chow, F. K., De Wekker, S. F. J., and Snyder, B. J., Springer Atmospheric Sciences, Springer Netherlands, 291–344, ISBN 978-94-007-4097-6, 978-94-007-4098-3, https://doi.org/10.1007/978-94-007-4098-3_6, 2013. a
Colli, M., Lanza, L., Barbera, P. L., and Chan, P.: Measurement accuracy of weighing and tipping-bucket rainfall intensity gauges under dynamic laboratory testing, Atmos. Res., 144, 186–194, https://doi.org/10.1016/j.atmosres.2013.08.007, 2014. a
Copernicus Climate Change Service (C3S): European State of the Climate 2022, https://doi.org/10.24381/GVAF-H066, 2023. a
Cremona, A., Huss, M., Landmann, J. M., Borner, J., and Farinotti, D.: European heat waves 2022: contribution to extreme glacier melt in Switzerland inferred from automated ablation readings, The Cryosphere, 17, 1895–1912, https://doi.org/10.5194/tc-17-1895-2023, 2023. a
Dadic, R., Mott, R., Lehning, M., and Burlando, P.: Wind influence on snow depth distribution and accumulation over glaciers, J. Geophys. Res.-Earth, 115, F01012, https://doi.org/10.1029/2009JF001261, 2010. a, b
Deardorff, J. W.: Stratocumulus-capped mixed layers derived from a three-dimensional model, Bound.-Lay. Meteorol., 18, 495–527, https://doi.org/10.1007/BF00119502, 1980. a
ESA: Land Cover CCI Product User Guide Version 2., ESA [data set], https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 13 February 2024), 2017. a
European Environmental Agency: Copernicus Land Service – Pan-European Component: CORINE Land Cover, EEA [data set], https://doi.org/10.2909/a84ae124-c5c5-4577-8e10-511bfe55cc0d, 2017. a
Fey, C., Schattan, P., Helfricht, K., and Schöber, J.: A compilation of multitemporal TLS snow depth distribution maps at the Weisssee snow research site (Kaunertal, Austria), Water Resour. Res., 55, 5154–5164, https://doi.org/10.1029/2019wr024788, 2019. a
Filhol, S. and Sturm, M.: Snow bedforms: A review, new data, and a formation model, J. Geophys. Res.-Earth, 120, 1645–1669, https://doi.org/10.1002/2015jf003529, 2015. a
Fischer, M., Huss, M., Kummert, M., and Hoelzle, M.: Application and validation of long-range terrestrial laser scanning to monitor the mass balance of very small glaciers in the Swiss Alps, The Cryosphere, 10, 1279–1295, https://doi.org/10.5194/tc-10-1279-2016, 2016. a
Fox-Kemper, B., Hewitt, H., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S., Edwards, T., Golledge, N., Hemer, M., Kopp, R., Krinner, G., Mix, A., Notz, D., Nowicki, S., Nurhati, I., Ruiz, L., Sallée, J.-B., Slangen, A., and Yu, Y.: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, chap. Ocean, Cryosphere and Sea Level Change, Cambridge University Press, 1211–1362, https://doi.org/10.1017/9781009157896.011, 2021. a
Frei, C. and Schär, C.: A precipitation climatology of the Alps from high-resolution rain-gauge observations, Int. J. Climatol., 18, 873–900, https://doi.org/10.1002/(SICI)1097-0088(19980630)18:8<873::AID-JOC255>3.0.CO;2-9, 1998. a
Gabbud, C., Micheletti, N., and Lane, S. N.: Lidar measurement of surface melt for a temperate Alpine glacier at the seasonal and hourly scales, J. Glaciol., 61, 963–974, https://doi.org/10.3189/2015jog14j226, 2015. a
Gerber, F., Lehning, M., Hoch, S. W., and Mott, R.: A close-ridge small-scale atmospheric flow field and its influence on snow accumulation, J. Geophys. Res.-Atmos., 122, 7737–7754, https://doi.org/10.1002/2016JD026258, 2017. a
Gerber, F., Besic, N., Sharma, V., Mott, R., Daniels, M., Gabella, M., Berne, A., Germann, U., and Lehning, M.: Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain, The Cryosphere, 12, 3137–3160, https://doi.org/10.5194/tc-12-3137-2018, 2018. a
Gerber, F., Sharma, V., and Lehning, M.: CRYOWRF–Model Evaluation and the Effect of Blowing Snow on the Antarctic Surface Mass Balance, J. Geophys. Res.-Atmos., 128, e2022JD037744, https://doi.org/10.1029/2022JD037744, 2023. a
Goger, B., Rotach, M. W., Gohm, A., Fuhrer, O., Stiperski, I., and Holtslag, A. A. M.: The Impact of Three-Dimensional Effects on the Simulation of Turbulence Kinetic Energy in a Major Alpine Valley, Bound.-Lay. Meteorol., 168, 1–27, https://doi.org/10.1007/s10546-018-0341-y, 2018. a
Goger, B., Rotach, M. W., Gohm, A., Stiperski, I., Fuhrer, O., and de Morsier, G.: A New Horizontal Length Scale for a Three-Dimensional Turbulence Parameterization in Mesoscale Atmospheric Modeling over Highly Complex Terrain, J. Appl. Meteor. Climatol., 58, 2087–2102, https://doi.org/10.1175/JAMC-D-18-0328.1, 2019. a, b
Goodison, B. E., Louie, P. Y., and Yang, D.: WMO solid precipitation measurement intercomparison, World Meteorological Organization, https://library.wmo.int/idurl/4/28336 (last access: 13 February 2024), 1998. a
Gordon, M., Biswas, S., Taylor, P. A., Hanesiak, J., Albarran‐Melzer, M., and Fargey, S.: Measurements of drifting and blowing snow at Iqaluit, Nunavut, Canada during the star project, Atmos. Ocean, 48, 81–100, https://doi.org/10.3137/AO1105.2010, 2010. a
Gouttevin, I., Vionnet, V., Seity, Y., Boone, A., Lafaysse, M., Deliot, Y., and Merzisen, H.: To the Origin of a Wintertime Screen-Level Temperature Bias at High Altitude in a Kilometric NWP Model, J. Hydrometeorol., 24, 53–71, https://doi.org/10.1175/JHM-D-21-0200.1, 2023. a
Grünewald, T. and Lehning, M.: Altitudinal dependency of snow amounts in two small alpine catchments: can catchment-wide snow amounts be estimated via single snow or precipitation stations?, Ann. Glaciol., 52, 153–158, https://doi.org/10.3189/172756411797252248, 2011. a
Grünewald, T., Bühler, Y., and Lehning, M.: Elevation dependency of mountain snow depth, The Cryosphere, 8, 2381–2394, https://doi.org/10.5194/tc-8-2381-2014, 2014. a
Gugerli, R., Salzmann, N., Huss, M., and Desilets, D.: Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier, The Cryosphere, 13, 3413–3434, https://doi.org/10.5194/tc-13-3413-2019, 2019. a
Guo, Y.-R. and Chen, S.: Terrain and land use for the fifth-generation Penn State/NCAR Mesoscale Modeling System (MM5): Program TERRAIN, Tech. Rep., https://doi.org/10.5065/D68C9T67, 1994. a
Helfricht, K., Kuhn, M., Keuschnig, M., and Heilig, A.: Lidar snow cover studies on glaciers in the Ötztal Alps (Austria): comparison with snow depths calculated from GPR measurements, The Cryosphere, 8, 41–57, https://doi.org/10.5194/tc-8-41-2014, 2014. a
Hock, R., Rasul, G., Adler, C., Cáceres, B., Gruber, S., Hirabayashi, Y., Jackson, M., Kääb, A., Kang, S., Kutuzov, S., Milner, A., Molau, U., Morin, S., Orlove, B., and Steltzer, H.: High Mountain Areas, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N., Cambridge University Press, 131–202, https://doi.org/10.1017/9781009157964.004, 2022. a
Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kääb, A.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021. a
Huss, M. and Hock, R.: Global-scale hydrological response to future glacier mass loss, Nat. Clim. Change, 8, 135–140, https://doi.org/10.1038/s41558-017-0049-x, 2018. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Isotta, F. A., Frei, C., Weilguni, V., Perčec Tadić, M., Lassègues, P., Rudolf, B., Pavan, V., Cacciamani, C., Antolini, G., Ratto, S. M., Munari, M., Micheletti, S., Bonati, V., Lussana, C., Ronchi, C., Panettieri, E., Marigo, G., and Vertačnik, G.: The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data, Int. J. Climatol., 34, 1657–1675, https://doi.org/10.1002/joc.3794, 2013. a
Jiménez, P. A., Dudhia, J., González-Rouco, J. F., Navarro, J., Montávez, J. P., and García-Bustamante, E.: A Revised Scheme for the WRF Surface Layer Formulation, Mon. Weather Rev., 140, 898–918, https://doi.org/10.1175/MWR-D-11-00056.1, 2012. a
Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M., Schweizer, J., and Mauser, W.: Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay, Water Resour. Res., 55, 4465–4487, https://doi.org/10.1029/2018wr024431, 2019. a
Kochanski, K., Anderson, R. S., and Tucker, G. E.: Statistical Classification of Self-Organized Snow Surfaces, Geophys. Res. Lett., 45, 6532–6541, https://doi.org/10.1029/2018gl077616, 2018. a
Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C. B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Ménégoz, M., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M. S., Schaedler, G., Semenov, V., Smirnova, T. G., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., and Zhu, D.: ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, 2018. a
Lehning, M., Bartelt, P., Brown, B., Russi, T., Stöckli, U., and Zimmerli, M.: SNOWPACK model calculations for avalanche warning based upon a new network of weather and snow stations, Cold Reg. Sci. Technol., 30, 145–157, https://doi.org/10.1016/s0165-232x(99)00022-1, 1999. a, b
Machguth, H., Paul, F., Hoelzle, M., and Haeberli, W.: Distributed glacier mass-balance modelling as an important component of modern multi-level glacier monitoring, Ann. Glaciol., 43, 335–343, https://doi.org/10.3189/172756406781812285, 2006. a
Marsh, C. B., Pomeroy, J. W., Spiteri, R. J., and Wheater, H. S.: A Finite Volume Blowing Snow Model for Use With Variable Resolution Meshes, Water Resour. Res., 56, e2019WR025307, https://doi.org/10.1029/2019WR025307, 2020. a
Marzeion, B., Jarosch, A. H., and Hofer, M.: Past and future sea-level change from the surface mass balance of glaciers, The Cryosphere, 6, 1295–1322, https://doi.org/10.5194/tc-6-1295-2012, 2012. a
Menard, C. B., Essery, R., Krinner, G., Arduini, G., Bartlett, P., Boone, A., Brutel-Vuilmet, C., Burke, E., Cuntz, M., Dai, Y., Decharme, B., Dutra, E., Fang, X., Fierz, C., Gusev, Y., Hagemann, S., Haverd, V., Kim, H., Lafaysse, M., Marke, T., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Schädler, G., Semenov, V. A., Smirnova, T., Strasser, U., Swenson, S., Turkov, D., Wever, N., and Yuan, H.: Scientific and Human Errors in a Snow Model Intercomparison, B. Am. Meteorol. Soc., 102, E61–E79, https://doi.org/10.1175/BAMS-D-19-0329.1, 2021. a
Mendoza, P. A., Shaw, T. E., McPhee, J., Musselman, K. N., Revuelto, J., and MacDonell, S.: Spatial Distribution and Scaling Properties of Lidar-Derived Snow Depth in the Extratropical Andes, Water Resour. Res., 56, e2020WR028480, https://doi.org/10.1029/2020wr028480, 2020. a, b
Mott, R. and Lehning, M.: Meteorological Modeling of Very High-Resolution Wind Fields and Snow Deposition for Mountains, J. Hydrometeorol., 11, 934–949, https://doi.org/10.1175/2010jhm1216.1, 2010. a, b
Mott, R., Vionnet, V., and Grünewald, T.: The Seasonal Snow Cover Dynamics: Review on Wind-Driven Coupling Processes, Front. Earth Sci., 6, 197, https://doi.org/10.3389/feart.2018.00197, 2018. a, b
Nakanishi, M. and Niino, H.: Development of an Improved Turbulence Closure Model for the Atmospheric Boundary Layer, J. Meteorol. Soc. Jpn Ser. II, 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011. a, b
Obleitner, F.: Climatological features of glacier and valley winds at the Hintereisferner (Ötztal Alps, Austria), Theor. Appl. Climatol., 49, 225–239, https://doi.org/10.1007/BF00867462, 1994. a
Pomeroy, J. and Male, D.: Steady-state suspension of snow, J. Hydrol., 136, 275–301, 1992. a
Prantl, H., Nicholson, L., Sailer, R., Hanzer, F., Juen, I., and Rastner, P.: Glacier Snowline Determination from Terrestrial Laser Scanning Intensity Data, Geosciences, 7, 60, https://doi.org/10.3390/geosciences7030060, 2017. a
Quéno, L., Mott, R., Morin, P., Cluzet, B., Mazzotti, G., and Jonas, T.: Snow redistribution in an intermediate-complexity snow hydrology modelling framework, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2071, 2023. a
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer, A. P., Black, J., Thériault, J. M., Kucera, P., Gochis, D., Smith, C., Nitu, R., Hall, M., Ikeda, K., and Gutmann, E.: How Well Are We Measuring Snow: The NOAA/FAA/NCAR Winter Precipitation Test Bed, B. Am. Meteorol. Soc., 93, 811–829, https://doi.org/10.1175/bams-d-11-00052.1, 2012. a
RIEGL: RiSCAN PRO, RIEGL Laser Measurement Systems, Horn, Austria, 2.8.0 edn., RIEGL [software], http://www.riegl.com/index.php?id=221 (last access: 13 February 2024), 2019. a
Rotach, M. W., Stiperski, I., Fuhrer, O., Goger, B., Gohm, A., Obleitner, F., Rau, G., Sfyri, E., and Vergeiner, J.: Investigating Exchange Processes over Complex Topography: The Innsbruck Box (i-Box), B. Am. Meteorol. Soc., 98, 787–805, https://doi.org/10.1175/BAMS-D-15-00246.1, 2017. a
Saigger, M.: WRFsnowdrift (v1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.10654680, 2024. a
Saigger, M., Sauter, T., Schmid, C., Collier, E., Goger, B., Kaser, G., Prinz, R., Voordendag, A., and Mölg, T.: Snowdrift scheme in the Weather Research and Forecasting model, ESS Open Archive, https://doi.org/10.22541/essoar.169755043.33054646/v1, 2023. a, b, c
Sauter, T., Arndt, A., and Schneider, C.: COSIPY v1.3 – an open-source coupled snowpack and ice surface energy and mass balance model, Geosci. Model Dev., 13, 5645–5662, https://doi.org/10.5194/gmd-13-5645-2020, 2020. a, b
Schmid, C.: Implementierung eines Schneedriftmoduls in das Weather Research and Forecasting (WRF) Modell und eine erste Evaluation, PhD thesis, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/17236 (last access: 13 February 2024), 2021. a
Schröder, M.: Snow Water Equivalent measured by a Cosmic Ray Neutron Sensor: A Case Study from Hintereisferner, Austria, Master's thesis, Universität Innsbruck, https://ulb-dok.uibk.ac.at/ulbtirolhs/content/titleinfo/9182794 (last access: 13 February 2024), 2023. a
Sharma, V., Gerber, F., and Lehning, M.: Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling, Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, 2023. a, b, c, d
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A Description of the Advanced Research WRF Model Version 4, Tech. Rep., NCAR Technical Note NCAR/TN-556+STR, https://doi.org/10.5065/1dfh-6p97, 2019. a, b
Smith, C. D.: Correcting the wind bias in snowfall measurements made with a Geonor T-200B precipitation gauge and alter wind shield, in: Proceedings of the 14th SMOI, San Antonio, 20, https://www.cmos.ca/uploaded/web/members/Bulletin/Vol_36/b3605.pdf#page=16 (last access: 13 February 2024), 2007. a
Stiperski, I. and Calaf, M.: Generalizing Monin-Obukhov Similarity Theory (1954) for Complex Atmospheric Turbulence, Phys. Rev. Lett., 130, 124001, https://doi.org/10.1103/PhysRevLett.130.124001, 2023. a
Stiperski, I. and Rotach, M. W.: On the Measurement of Turbulence Over Complex Mountainous Terrain, Bound.-Lay. Meteorol., 159, 97–121, https://doi.org/10.1007/s10546-015-0103-z, 2016. a
Stiperski, I., Katul, G. G., and Calaf, M.: Universal Return to Isotropy of Inhomogeneous Atmospheric Boundary Layer Turbulence, Phys. Rev. Lett., 126, 194501, https://doi.org/10.1103/PhysRevLett.126.194501, 2021. a
Strasser, U., Marke, T., Braun, L., Escher-Vetter, H., Juen, I., Kuhn, M., Maussion, F., Mayer, C., Nicholson, L., Niedertscheider, K., Sailer, R., Stötter, J., Weber, M., and Kaser, G.: The Rofental: a high Alpine research basin (1890–3770 m a.s.l.) in the Ötztal Alps (Austria) with over 150 years of hydrometeorological and glaciological observations, Earth Syst. Sci. Data, 10, 151–171, https://doi.org/10.5194/essd-10-151-2018, 2018. a
Sun, S., Jin, J., and Xue, Y.: A simple snow-atmosphere-soil transfer model, J. Geophys. Res.-Atmos., 104, 19587–19597, https://doi.org/10.1029/1999JD900305, 1999. a
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008. a
Thorpe, A. D. and Mason, B. J.: The evaporation of ice spheres and ice crystals, Brit. J. Appl. Phys., 17, 541–548, https://doi.org/10.1088/0508-3443/17/4/316, 1966. a
Umek, L., Gohm, A., Haid, M., Ward, H. C., and Rotach, M. W.: Large eddy simulation of foehn-cold pool interactions in the Inn Valley during PIANO IOP2, Q. J. Roy. Meteor. Soc., 147, 944–982, https://doi.org/10.1002/qj.3954, 2021. a, b
USGS: Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global Dataset, USGS [data set], https://doi.org/10.5066/F7PR7TFT, 2000. a
Vionnet, V., Guyomarc'h, G., Bouvet, F. N., Martin, E., Durand, Y., Bellot, H., Bel, C., and Puglièse, P.: Occurrence of blowing snow events at an alpine site over a 10-year period: Observations and modelling, Adv. Water Resour., 55, 53–63, https://doi.org/10.1016/j.advwatres.2012.05.004, 2013. a, b, c
Vionnet, V., Martin, E., Masson, V., Guyomarc'h, G., Naaim-Bouvet, F., Prokop, A., Durand, Y., and Lac, C.: Simulation of wind-induced snow transport and sublimation in alpine terrain using a fully coupled snowpack/atmosphere model, The Cryosphere, 8, 395–415, https://doi.org/10.5194/tc-8-395-2014, 2014. a
Vionnet, V., Dombrowski-Etchevers, I., Lafaysse, M., Quéno, L., Seity, Y., and Bazile, E.: Numerical Weather Forecasts at Kilometer Scale in the French Alps: Evaluation and Application for Snowpack Modeling, J. Hydrometeor., 17, 2591–2614, https://doi.org/10.1175/JHM-D-15-0241.1, 2016. a
Vionnet, V., Marsh, C. B., Menounos, B., Gascoin, S., Wayand, N. E., Shea, J., Mukherjee, K., and Pomeroy, J. W.: Multi-scale snowdrift-permitting modelling of mountain snowpack, The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, 2021. a
Voordendag, A., Réveillet, M., MacDonell, S., and Lhermitte, S.: Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile, The Cryosphere, 15, 4241–4259, https://doi.org/10.5194/tc-15-4241-2021, 2021a. a
Voordendag, A. B., Goger, B., Klug, C., Prinz, R., Rutzinger, M., and Kaser, G.: Automated and Permanent Long-Range Terrestrial Laser Scanning in a High Mountain Environment: Setup and First Results, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 153–160, https://doi.org/10.5194/isprs-annals-V-2-2021-153-2021, 2021b. a, b, c, d
Voordendag, A., Goger, B., Klug, C., Prinz, R., Rutzinger, M., Sauter, T., and Kaser, G.: Uncertainty assessment of a permanent long-range terrestrial laser scanning system for the quantification of snow dynamics on Hintereisferner (Austria), Front. Earth Sci., 11, https://doi.org/10.3389/feart.2023.1085416, 2023a. a, b, c, d, e
Voordendag, A., Prinz, R., Schuster, L., and Kaser, G.: Brief communication: The Glacier Loss Day as an indicator of a record-breaking negative glacier mass balance in 2022, The Cryosphere, 17, 3661–3665, https://doi.org/10.5194/tc-17-3661-2023, 2023b. a, b
Wagner, J. S., Gohm, A., and Rotach, M. W.: The Impact of Horizontal Model Grid Resolution on the Boundary Layer Structure over an Idealized Valley, Mon. Weather Rev., 142, 3446–3465, https://doi.org/10.1175/MWR-D-14-00002.1, 2014. a
Walter, M. T., McCool, D. K., King, L. G., Molnau, M., and Campbell, G. S.: Simple snowdrift model for distributed hydrological modeling, J. Hydrol. Eng., 9, 280–287, 2004. a
Xu, C., Li, Z., Li, H., Wang, F., and Zhou, P.: Long-range terrestrial laser scanning measurements of annual and intra-annual mass balances for Urumqi Glacier No. 1, eastern Tien Shan, China, The Cryosphere, 13, 2361–2383, https://doi.org/10.5194/tc-13-2361-2019, 2019. a
Zemp, M., Hoelzle, M., and Haeberli, W.: Six decades of glacier mass-balance observations: a review of the worldwide monitoring network, Ann. Glaciol., 50, 101–111, https://doi.org/10.3189/172756409787769591, 2009. a
Short summary
Wind-driven snow redistribution affects glacier mass balance. A case study of Hintereisferner glacier in Austria used high-resolution observations and simulations to model snow redistribution. Simulations matched observations, showing the potential of the model for studying snow redistribution on other mountain glaciers.
Wind-driven snow redistribution affects glacier mass balance. A case study of Hintereisferner...
