Articles | Volume 15, issue 16
https://doi.org/10.5194/gmd-15-6429-2022
© Author(s) 2022. 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-15-6429-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
29 Aug 2022
Model description paper |
| 29 Aug 2022
| 29 Aug 2022
Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.
Océane Hames
CORRESPONDING AUTHOR
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
CRYOS, School of Architecture, Civil and Environmental Engineering, EPFL, Lausanne, Switzerland
Mahdi Jafari
CRYOS, School of Architecture, Civil and Environmental Engineering, EPFL, Lausanne, Switzerland
David Nicholas Wagner
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
CRYOS, School of Architecture, Civil and Environmental Engineering, EPFL, Lausanne, Switzerland
Ian Raphael
Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA
David Clemens-Sewall
Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA
Chris Polashenski
Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA
USACE-CRREL Alaska Projects Office, Fairbanks, Alaska, USA
Matthew D. Shupe
NOAA Physical Science Laboratory, Boulder, Colorado, USA
Cooperative Institute for the Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA
Martin Schneebeli
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Michael Lehning
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
CRYOS, School of Architecture, Civil and Environmental Engineering, EPFL, Lausanne, Switzerland
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Patrick C. Taylor, Armin Sorooshian, Rei Ueyama, Sebastian Schmidt, Ihab Abboud, Quincy Allison, Kevin Barry, Sebastian Becker, Holly A. Bender, Joseph R. Bennett, James B. Blair, Niklas Bohn, Linette Boisvert, Matthew D. Brown, Roelof Bruintjes, Anthony Bucholtz, Megan Buzanowicz, Brian Cairns, Filippo Calì Quaglia, Eduard Chemyakin, Bo Chen, Gao Chen, Hong Chen, Yu-Wen Chen, Zezhen Cheng, Swarup China, Dan Chirica, Yonghoon Choi, Peter Colarco, Brian Collister, Ewan Crosbie, Maurice J. Cross, Janet Daniels, Paul DeMott, Joshua P. DiGangi, Alcide Giorgio di Sarra, Glenn S. Diskin, Erica K. Dolinar, Eva-Lou Edwards, Samuel Ephraim, Nikolaos Evangeliou, Romanos Foskinis, Francesca Gallo, Lan Gao, José Luis Gómez-Amo, Daisy Gonzalez, Christine Groot Zwaaftink, Pawan Gupta, Ivan Heckman, Michael Hendrickson, Miguel Ricardo A. Hilario, Ken Hirata, Michelle Hofton, Andrew L. Holen, Ulas Im, Alia L. Khan, Ralph Kahn, Alexei V. Korolev, Sonia Kreidenweis, Thomas Krumpen, Nathan Kurtz, Leslie Lait, Bradley Lamkin, Jack Landy, Nurun Nahar Lata, Paul Lawson, Samuel LeBlanc, Sean Leavor, Jing Li, Thorsten Markus, Hal Maring, Andreas H. Massling, Camille Mavis, Flynn McGinnity, Kerry Meyer, Gabriel Mojica, Richard H. Moore, Parker Morris, Giovanni Muscari, Vikas Nataraja, Amin R. Nehrir, Athanasios Nenes, Edward P. Nowottnick, Matteo Ottaviani, Chelsea Parker, Ryan Patnaude, Michael Perez, Russell J. Perkins, Colten Peterson, Alek Petty, Stevie Phothisane, Chris Polashenski, Kerri A. Pratt, Kayla M. Preisler, John Prytherch, David Rabine, Jens Redemann, Ju-Mee Ryoo, Joseph S. Schlosser, Vanessa Selimovic, Michael A. Shook, Morgan Silverman, Henrik Skov, Alexander Smirnov, Cassidy Soloff, Amy Solomon, Snorre Stamnes, Azusa Takeishi, David R. Thompson, K. Lee Thornhill, Rachel Tilling, Michael Tjernström, Monica Tosco, Pedro C. Valdelomar, David Van Gilst, Jian Wang, Zhien Wang, Andrzej Wasilewski, Manfred Wendisch, Brent Wilder, Edward L. Winstead, Albert Wu, Peng Xian, Lauren M. Zamora, Jiaoshi Zhang, Lei Zhang, Lu Zhang, Luke Ziemba, and Paquita Zuidema
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-241, https://doi.org/10.5194/essd-2026-241, 2026
Preprint under review for ESSD
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This article summarizes the data sets collected during the Arctic Radiation Cloud aerosol Sea ice Interaction eXperiment (ARCSIX), NASA's most comprehensive Arctic field campaign to date. The overarching goal was to quantify the contributions of surface, clouds, aerosol particles, and precipitation to summer sea ice melt. This paper describes the ARCSIX implementation including the flight strategy, instruments, complementary data sets, access, and usage details.
Francesca Carletti, Nora Helbig, Nander Wever, Loïc Brouet, Mathias Bavay, Benjamin Walter, and Michael Lehning
EGUsphere, https://doi.org/10.5194/egusphere-2026-3049, https://doi.org/10.5194/egusphere-2026-3049, 2026
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Melting alpine snowfields develop cup-shaped hollows that reduce how much sunlight the snow reflects, accelerating melt. Over three seasons in the Swiss Alps, we monitored how these hollows form and grow with a laser scanner. Two processes reduce reflectivity simultaneously: hollows trap light through internal reflections, and meltwater washes dark particles into them. Because they are difficult to separate, surface roughness alone emerges as a reliable proxy for overall reflectivity loss.
Joseph Hung, Penny M. Rowe, Christopher J. Cox, Emily M. McCullough, Liam Kroll, Raia Ottenheimer, Matthew D. Shupe, Von P. Walden, and Kimberly Strong
EGUsphere, https://doi.org/10.5194/egusphere-2026-1906, https://doi.org/10.5194/egusphere-2026-1906, 2026
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Climate models struggle to accurately represent polar regions largely due to challenges in modelling clouds. In this article, we sampled clouds at Arctic and Antarctic field sites, and found that Antarctic clouds generally featured lower temperatures, smaller liquid droplets, and are thinner because they have a higher proportion of ice than Arctic clouds. This knowledge improves our ability to monitor remote regions, and serves to improve our understanding of cloud processes.
Kara Hartig, John J. Cassano, Matthew D. Shupe, and Amy Solomon
EGUsphere, https://doi.org/10.5194/egusphere-2026-2426, https://doi.org/10.5194/egusphere-2026-2426, 2026
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Liquid-containing Arctic clouds have a strong influence on surface temperature and sea ice extent in winter, but how weather conditions impact cloud cover remains a mystery. This study uses observations from northern Alaska to investigate this relationship. We find that, contrary to expectations, the amount of cloud liquid is remarkably insensitive to temperature, moisture, wind direction, and the large-scale atmospheric flow.
Mahdi Jafari and Michael Lehning
The Cryosphere, 20, 2439–2467, https://doi.org/10.5194/tc-20-2439-2026, https://doi.org/10.5194/tc-20-2439-2026, 2026
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We studied how air moves within snow in Arctic regions and how this affects the snow's structure. Using a new method that links two computer models, we found that cold weather can trigger air movement inside the snow, creating vertical channels and changing the snow's density and temperature. These changes are not captured by traditional models, so our work helps improve how snow and climate processes are simulated in cold environments.
Samuele Viaro, Armin Sigmund, Evan Thomas, and Michael Lehning
EGUsphere, https://doi.org/10.5194/egusphere-2026-2132, https://doi.org/10.5194/egusphere-2026-2132, 2026
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Our research focuses in understanding snow processes at the surface-atmosphere interface, and their influence at larger scales. The use of our advanced numerical model, which includes blowing snow equations, proved to be valuable in predicting the observed ice crystal concentration number at an alpine site. We further demonstrate the effect of blowing snow particles in precipitation and erosion-deposition patterns. Finally, secondary ice production mechanisms are also treated.
Anne Sledd, Michael R. Gallagher, Matthew D. Shupe, Christopher J. Cox, Robert Hawley, Michael S. Town, Heather Guy, Hans-Peter Marshall, Ryan R. Neely III, Claire Pettersen, Von P. Walden, Catherine Hebson, Andrew Martin, Erik Olson, and Derek Pickell
EGUsphere, https://doi.org/10.5194/egusphere-2026-1842, https://doi.org/10.5194/egusphere-2026-1842, 2026
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Understanding energetic processes at the surface is important for understanding how the Greenland ice sheet melts. However, most temperature observations of firn are insufficient for studying energetic processes near the surface where melt happens. In this work we present new ground observations of high-resolution temperature profiles in the top meter of firn near the southwest coast of Greenland. Using these observations we characterize how temperature and energy vary during the melt season.
Jean Lac, Hélène Chepfer, Matthew D. Shupe, and Hannes Griesche
Atmos. Chem. Phys., 26, 4189–4213, https://doi.org/10.5194/acp-26-4189-2026, https://doi.org/10.5194/acp-26-4189-2026, 2026
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Satellite observations show that Arctic spring experiences a rapid increase in liquid-containing clouds over sea ice. Our study shows that this transition is mostly driven by warmer temperatures in early spring than in late spring, favoring more liquid clouds formation, rather than a limited moisture source in early spring. It suggests that, in the future, this transition is likely to occur earlier in spring considering the rapid Arctic warming.
Pradeep K. Aggarwal, Courtney Schumacher, Frederick J. Longstaffe, Aaron Funk, and Matthew D. Shupe
EGUsphere, https://doi.org/10.5194/egusphere-2026-687, https://doi.org/10.5194/egusphere-2026-687, 2026
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Riming, where supercooled liquid freezes directly on ice particles in mixed-phase clouds, is an efficient process of precipitation formation. We show that the relative composition of oxygen and hydrogen isotopes or d-excess is lower in rimed ice than the liquid, contrary to the existing view. This result will help better understand past climates from ice cores and help improve climate models by providing spatial and temporal constraints on the fraction of precipitation that formed by riming.
Peggy Achtert, Torsten Seelig, Gabriella Wallentin, Luisa Ickes, Matthew D. Shupe, Corinna Hoose, and Matthias Tesche
Atmos. Chem. Phys., 26, 3049–3068, https://doi.org/10.5194/acp-26-3049-2026, https://doi.org/10.5194/acp-26-3049-2026, 2026
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We quantify the occurrence of single- and multi-layer clouds in the Arctic based on combining soundings with cloud-radar observations. We also assess the rate of ice-crystal seeding in multi-layer cloud systems as this is an important initiator of glaciation in super-cooled liquid cloud layers. We find an abundance of multi-layer clouds in the Arctic with seeding in about half to two thirds of cases in which the gap between upper and lower layers ranges between 100 and 1000 m.
Brandon Jacobus Adrianus van Schaik, Alice Juliette Baruzier, Clément Loyer, Hendrik Huwald, and Michael Lehning
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-271, https://doi.org/10.5194/wes-2025-271, 2025
Preprint under review for WES
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We created a new way to estimate how much energy a wind turbine can produce by using the full change of wind with height instead of a single measurement. Testing it on turbines in the Swiss Alps showed that it predicts energy production more accurately and smoothly than current industry methods. It performs as well as advanced models but is much faster, helping guide future wind projects in complex landscapes.
Polona Itkin, Evgenii Salganik, Dmitry V. Divine, Arttu Jutila, Christian Katlein, Mara Neudert, Ian A. Raphael, and Robert Ricker
EGUsphere, https://doi.org/10.5194/egusphere-2025-6081, https://doi.org/10.5194/egusphere-2025-6081, 2025
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We studied how Arctic ice ridges grow and change through winter and spring using a sensor that measures ice thickness without drilling. By comparing these measurements with data from aircraft, underwater surveys, and field work, we show that the sensor can detect both the full ridge thickness and its solid inner core. Our results reveal slow winter strengthening of ridges and highlight this method's value for understanding ice conditions and ecosystems.
Ian A. Raphael, Donald K. Perovich, Christopher M. Polashenski, and Robert L. Hawley
The Cryosphere, 19, 6059–6076, https://doi.org/10.5194/tc-19-6059-2025, https://doi.org/10.5194/tc-19-6059-2025, 2025
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Snow plays competing roles in the sea ice cycle by reflecting sunlight during summer (reducing melt) and insulating the ice from the cold atmosphere during winter (reducing growth). Observing where, when, and how much snow accumulates on sea ice is thus central to understanding the Arctic. Here, we describe a new snow depth observation system that is substantially cheaper and lighter than existing tools and present a study demonstrating its potential to improve snow measurements on sea ice.
Yubing Cheng, Bin Cheng, Roberta Pirazzini, Amy R. Macfarlane, Timo Vihma, Wolfgang Dorn, Ruzica Dadic, Martin Schneebeli, Stefanie Arndt, and Annette Rinke
The Cryosphere, 19, 6001–6021, https://doi.org/10.5194/tc-19-6001-2025, https://doi.org/10.5194/tc-19-6001-2025, 2025
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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.
Francesca Carletti, Carlo Marin, Chiara Ghielmini, Mathias Bavay, and Michael Lehning
The Cryosphere, 19, 5579–5612, https://doi.org/10.5194/tc-19-5579-2025, https://doi.org/10.5194/tc-19-5579-2025, 2025
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This work presents the first high-resolution dataset of wet-snow properties for satellite applications. With it, we validate links between Sentinel-1 backscatter and snowmelt stages and investigate scattering mechanisms through a radiative transfer model. We disclose the influence of liquid water content and surface roughness at different melting stages and address future challenges, such as capturing large-scale scattering mechanisms and enhancing radiative transfer modules for wet snow.
Manfred Wendisch, Benjamin Kirbus, Davide Ori, Matthew D. Shupe, Susanne Crewell, Harald Sodemann, and Vera Schemann
Atmos. Chem. Phys., 25, 15047–15076, https://doi.org/10.5194/acp-25-15047-2025, https://doi.org/10.5194/acp-25-15047-2025, 2025
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Aircraft observations of air parcels moving into and out of the Arctic are reported. From the data, heating and cooling as well as drying and moistening of the air masses along their way into and out of the Arctic could be measured for the first time. These data are used to evaluate if weather prediction models are able to accurately represent these air mass transformations. This work helps to model the future Arctic climate changes, which may have an impact for mid-latitude weather as well.
Hongxiang Yu, Li Guang, Benjamin Walter, Jianping Huang, Ning Huang, and Michael Lehning
The Cryosphere, 19, 5389–5402, https://doi.org/10.5194/tc-19-5389-2025, https://doi.org/10.5194/tc-19-5389-2025, 2025
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Cornices are overhanging snow accumulations that form on mountain crests. Using wind-tunnel experiments and high-speed photography, the study tracks particle trajectories around cornices and finds distinct differences between edge and surface deposition. A static adhesion model for edge deposition was developed and validated to predict how particle size and shape affect adhesion. The work clarifies the microdynamics of early cornice growth and provides a foundation for avalanche modeling.
Tiziana Lazzarina Zendrini, Luca Carturan, Michael Lehning, Mathias Bavay, Federico Cazorzi, Paolo Gabrielli, Nander Wever, and Giancarlo Dalla Fontana
EGUsphere, https://doi.org/10.5194/egusphere-2025-5186, https://doi.org/10.5194/egusphere-2025-5186, 2025
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By combining in situ mass balance data with a physics‐based snow model at Mt. Ortles, in the Italian Alps, we investigate snow accumulation, erosion and melt processes, and their sensitivity to air temperature. We found that wind erosion is currently the major ablation process at this high-elevation site, whereas melt plays a minor role. Quickly rising air temperature is affecting this partitioning and suggests a future shift from an erosion-dominated to a melt-dominated mass balance regime.
Lexie Goldberger, Maxwell Levin, Carlandra Harris, Andrew Geiss, Matthew D. Shupe, and Damao Zhang
Atmos. Meas. Tech., 18, 5393–5414, https://doi.org/10.5194/amt-18-5393-2025, https://doi.org/10.5194/amt-18-5393-2025, 2025
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This study leverages machine learning models to classify cloud thermodynamic phases using multi-sensor remote sensing data collected at the Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. We evaluate model performance, feature importance, and application of the model to another observatory and quantify how the models respond to instrument outages.
Elizaveta Sharaborova, Michael Lehning, Nander Wever, Marcia Phillips, and Hendrik Huwald
The Cryosphere, 19, 4277–4301, https://doi.org/10.5194/tc-19-4277-2025, https://doi.org/10.5194/tc-19-4277-2025, 2025
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Global warming provokes permafrost to thaw, damaging landscapes and infrastructure. This study explores methods to slow this thawing at an alpine site. We investigate different methods based on passive and active cooling. The best approach mixes both methods and manages heat flow, potentially allowing excess energy to be used locally.
Yugo Kanaya, Roberto Sommariva, Alfonso Saiz-Lopez, Andrea Mazzeo, Theodore K. Koenig, Kaori Kawana, James E. Johnson, Aurélie Colomb, Pierre Tulet, Suzie Molloy, Ian E. Galbally, Rainer Volkamer, Anoop Mahajan, John W. Halfacre, Paul B. Shepson, Julia Schmale, Hélène Angot, Byron Blomquist, Matthew D. Shupe, Detlev Helmig, Junsu Gil, Meehye Lee, Sean C. Coburn, Ivan Ortega, Gao Chen, James Lee, Kenneth C. Aikin, David D. Parrish, John S. Holloway, Thomas B. Ryerson, Ilana B. Pollack, Eric J. Williams, Brian M. Lerner, Andrew J. Weinheimer, Teresa Campos, Frank M. Flocke, J. Ryan Spackman, Ilann Bourgeois, Jeff Peischl, Chelsea R. Thompson, Ralf M. Staebler, Amir A. Aliabadi, Wanmin Gong, Roeland Van Malderen, Anne M. Thompson, Ryan M. Stauffer, Debra E. Kollonige, Juan Carlos Gómez Martin, Masatomo Fujiwara, Katie Read, Matthew Rowlinson, Keiichi Sato, Junichi Kurokawa, Yoko Iwamoto, Fumikazu Taketani, Hisahiro Takashima, Mónica Navarro-Comas, Marios Panagi, and Martin G. Schultz
Earth Syst. Sci. Data, 17, 4901–4932, https://doi.org/10.5194/essd-17-4901-2025, https://doi.org/10.5194/essd-17-4901-2025, 2025
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The first comprehensive dataset of tropospheric ozone over oceans/polar regions is presented, including 77 ship/buoy and 48 aircraft campaign observations (1977–2022, 0–5000 m altitude), supplemented by ozonesonde and surface data. Air masses isolated from land for 72+ hours are systematically selected as essentially oceanic. Among the 11 global regions, they show daytime decreases of 11–16 % in the tropics, while near-zero depletions are rare, unlike in the Arctic, implying different mechanisms.
Christopher J. Cox, Janet M. Intrieri, Brian J. Butterworth, Gijs de Boer, Michael R. Gallagher, Jonathan Hamilton, Erik Hulm, Tilden Meyers, Sara M. Morris, Jackson Osborn, P. Ola G. Persson, Benjamin Schmatz, Matthew D. Shupe, and James M. Wilczak
Earth Syst. Sci. Data, 17, 1481–1499, https://doi.org/10.5194/essd-17-1481-2025, https://doi.org/10.5194/essd-17-1481-2025, 2025
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Snow is an essential water resource in the intermountain western United States, and predictions are made using models. We made observations to validate, constrain, and develop the models. The data are from the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign in Colorado's East River valley, 2021–2023. The measurements include meteorology and variables that quantify energy transfer between the atmosphere and surface. The data are available publicly.
Carola Barrientos-Velasco, Christopher J. Cox, Hartwig Deneke, J. Brant Dodson, Anja Hünerbein, Matthew D. Shupe, Patrick C. Taylor, and Andreas Macke
Atmos. Chem. Phys., 25, 3929–3960, https://doi.org/10.5194/acp-25-3929-2025, https://doi.org/10.5194/acp-25-3929-2025, 2025
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Understanding how clouds affect the climate, especially in the Arctic, is crucial. This study used data from the largest polar expedition in history, MOSAiC, and the CERES satellite to analyse the impact of clouds on radiation. Simulations showed accurate results, aligning with observations. Over the year, clouds caused the atmospheric surface system to lose 5.2 W m−² of radiative energy to space, while the surface gained 25 W m−² and the atmosphere cooled by 30.2 W m−².
Benjamin Heutte, Nora Bergner, Hélène Angot, Jakob B. Pernov, Lubna Dada, Jessica A. Mirrielees, Ivo Beck, Andrea Baccarini, Matthew Boyer, Jessie M. Creamean, Kaspar R. Daellenbach, Imad El Haddad, Markus M. Frey, Silvia Henning, Tiia Laurila, Vaios Moschos, Tuukka Petäjä, Kerri A. Pratt, Lauriane L. J. Quéléver, Matthew D. Shupe, Paul Zieger, Tuija Jokinen, and Julia Schmale
Atmos. Chem. Phys., 25, 2207–2241, https://doi.org/10.5194/acp-25-2207-2025, https://doi.org/10.5194/acp-25-2207-2025, 2025
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Limited aerosol measurements in the central Arctic hinder our understanding of aerosol–climate interactions in the region. Our year-long observations of aerosol physicochemical properties during the MOSAiC expedition reveal strong seasonal variations in aerosol chemical composition, where the short-term variability is heavily affected by storms in the Arctic. Local wind-generated particles are shown to be an important source of cloud seeds, especially in autumn.
Madison M. Smith, Niels Fuchs, Evgenii Salganik, Donald K. Perovich, Ian Raphael, Mats A. Granskog, Kirstin Schulz, Matthew D. Shupe, and Melinda Webster
The Cryosphere, 19, 619–644, https://doi.org/10.5194/tc-19-619-2025, https://doi.org/10.5194/tc-19-619-2025, 2025
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The fate of freshwater from Arctic sea ice and snowmelt impacts interactions of the atmosphere, sea ice, and ocean. We complete a comprehensive analysis of datasets from a 2020 central Arctic field campaign to understand the drivers of the sea ice freshwater budget and the fate of this water. Over half of the freshwater comes from surface melt, and a majority fraction is incorporated into the ocean. Results suggest that the representation of melt ponds is a key area for future development.
Johanna Tjernström, Michael Gallagher, Jareth Holt, Gunilla Svensson, Matthew D. Shupe, Jonathan J. Day, Lara Ferrighi, Siri Jodha Khalsa, Leslie M. Hartten, Ewan O'Connor, Zen Mariani, and Øystein Godøy
EGUsphere, https://doi.org/10.5194/egusphere-2024-2088, https://doi.org/10.5194/egusphere-2024-2088, 2024
Preprint archived
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The value of numerical weather predictions can be enhanced in several ways, one is to improve the representations of small-scale processes in models. To understand what needs to be improved, the model results need to be evaluated. Following standardized principles, a file format has been defined to be as similar as possible for both observational and model data. Python packages and toolkits are presented as a community resource in the production of the files and evaluation analysis.
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.
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.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, Nander Wever, Adrien Michel, Michael Lehning, and Pierre-Erik Isabelle
The Cryosphere, 18, 2783–2807, https://doi.org/10.5194/tc-18-2783-2024, https://doi.org/10.5194/tc-18-2783-2024, 2024
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Observations over several winters at two boreal sites in eastern Canada show that rain-on-snow (ROS) events lead to the formation of melt–freeze layers and that preferential flow is an important water transport mechanism in the sub-canopy snowpack. Simulations with SNOWPACK generally show good agreement with observations, except for the reproduction of melt–freeze layers. This was improved by simulating intercepted snow microstructure evolution, which also modulates ROS-induced runoff.
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
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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.
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.
Michael Lonardi, Elisa F. Akansu, André Ehrlich, Mauro Mazzola, Christian Pilz, Matthew D. Shupe, Holger Siebert, and Manfred Wendisch
Atmos. Chem. Phys., 24, 1961–1978, https://doi.org/10.5194/acp-24-1961-2024, https://doi.org/10.5194/acp-24-1961-2024, 2024
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Profiles of thermal-infrared irradiance were measured at two Arctic sites. The presence or lack of clouds influences the vertical structure of these observations. In particular, the cloud top region is a source of radiative energy that can promote cooling and mixing in the cloud layer. Simulations are used to further characterize how the amount of water in the cloud modifies this forcing. A case study additionally showcases the evolution of the radiation profiles in a dynamic atmosphere.
Maximilian Maahn, Dmitri Moisseev, Isabelle Steinke, Nina Maherndl, and Matthew D. Shupe
Atmos. Meas. Tech., 17, 899–919, https://doi.org/10.5194/amt-17-899-2024, https://doi.org/10.5194/amt-17-899-2024, 2024
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The open-source Video In Situ Snowfall Sensor (VISSS) is a novel instrument for characterizing particle shape, size, and sedimentation velocity in snowfall. It combines a large observation volume with relatively high resolution and a design that limits wind perturbations. The open-source nature of the VISSS hardware and software invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.
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).
Olivia Linke, Johannes Quaas, Finja Baumer, Sebastian Becker, Jan Chylik, Sandro Dahlke, André Ehrlich, Dörthe Handorf, Christoph Jacobi, Heike Kalesse-Los, Luca Lelli, Sina Mehrdad, Roel A. J. Neggers, Johannes Riebold, Pablo Saavedra Garfias, Niklas Schnierstein, Matthew D. Shupe, Chris Smith, Gunnar Spreen, Baptiste Verneuil, Kameswara S. Vinjamuri, Marco Vountas, and Manfred Wendisch
Atmos. Chem. Phys., 23, 9963–9992, https://doi.org/10.5194/acp-23-9963-2023, https://doi.org/10.5194/acp-23-9963-2023, 2023
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Lapse rate feedback (LRF) is a major driver of the Arctic amplification (AA) of climate change. It arises because the warming is stronger at the surface than aloft. Several processes can affect the LRF in the Arctic, such as the omnipresent temperature inversion. Here, we compare multimodel climate simulations to Arctic-based observations from a large research consortium to broaden our understanding of these processes, find synergy among them, and constrain the Arctic LRF and AA.
Dylan Reynolds, Ethan Gutmann, Bert Kruyt, Michael Haugeneder, Tobias Jonas, Franziska Gerber, Michael Lehning, and Rebecca Mott
Geosci. Model Dev., 16, 5049–5068, https://doi.org/10.5194/gmd-16-5049-2023, https://doi.org/10.5194/gmd-16-5049-2023, 2023
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The challenge of running geophysical models is often compounded by the question of where to obtain appropriate data to give as input to a model. Here we present the HICAR model, a simplified atmospheric model capable of running at spatial resolutions of hectometers for long time series or over large domains. This makes physically consistent atmospheric data available at the spatial and temporal scales needed for some terrestrial modeling applications, for example seasonal snow forecasting.
Manfred Wendisch, Johannes Stapf, Sebastian Becker, André Ehrlich, Evelyn Jäkel, Marcus Klingebiel, Christof Lüpkes, Michael Schäfer, and Matthew D. Shupe
Atmos. Chem. Phys., 23, 9647–9667, https://doi.org/10.5194/acp-23-9647-2023, https://doi.org/10.5194/acp-23-9647-2023, 2023
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Atmospheric radiation measurements have been conducted during two field campaigns using research aircraft. The data are analyzed to see if the near-surface air in the Arctic is warmed or cooled if warm–humid air masses from the south enter the Arctic or cold–dry air moves from the north from the Arctic to mid-latitude areas. It is important to study these processes and to check if climate models represent them well. Otherwise it is not possible to reliably forecast the future Arctic climate.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
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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.
Shijie Peng, Qinghua Yang, Matthew D. Shupe, Xingya Xi, Bo Han, Dake Chen, Sandro Dahlke, and Changwei Liu
Atmos. Chem. Phys., 23, 8683–8703, https://doi.org/10.5194/acp-23-8683-2023, https://doi.org/10.5194/acp-23-8683-2023, 2023
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Due to a lack of observations, the structure of the Arctic atmospheric boundary layer (ABL) remains to be further explored. By analyzing a year-round radiosonde dataset collected over the Arctic sea-ice surface, we found the annual cycle of the ABL height (ABLH) is primarily controlled by the evolution of ABL thermal structure, and the surface conditions also show a high correlation with ABLH variation. In addition, the Arctic ABLH is found to be decreased in summer compared with 20 years ago.
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.
Kameswara S. Vinjamuri, Marco Vountas, Luca Lelli, Martin Stengel, Matthew D. Shupe, Kerstin Ebell, and John P. Burrows
Atmos. Meas. Tech., 16, 2903–2918, https://doi.org/10.5194/amt-16-2903-2023, https://doi.org/10.5194/amt-16-2903-2023, 2023
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Clouds play an important role in Arctic amplification. Cloud data from ground-based sites are valuable but cannot represent the whole Arctic. Therefore the use of satellite products is a measure to cover the entire Arctic. However, the quality of such cloud measurements from space is not well known. The paper discusses the differences and commonalities between satellite and ground-based measurements. We conclude that the satellite dataset, with a few exceptions, can be used in the Arctic.
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.
Ulrike Egerer, John J. Cassano, Matthew D. Shupe, Gijs de Boer, Dale Lawrence, Abhiram Doddi, Holger Siebert, Gina Jozef, Radiance Calmer, Jonathan Hamilton, Christian Pilz, and Michael Lonardi
Atmos. Meas. Tech., 16, 2297–2317, https://doi.org/10.5194/amt-16-2297-2023, https://doi.org/10.5194/amt-16-2297-2023, 2023
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This paper describes how measurements from a small uncrewed aircraft system can be used to estimate the vertical turbulent heat energy exchange between different layers in the atmosphere. This is particularly important for the atmosphere in the Arctic, as turbulent exchange in this region is often suppressed but is still important to understand how the atmosphere interacts with sea ice. We present three case studies from the MOSAiC field campaign in Arctic sea ice in 2020.
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.
Felix Pithan, Marylou Athanase, Sandro Dahlke, Antonio Sánchez-Benítez, Matthew D. Shupe, Anne Sledd, Jan Streffing, Gunilla Svensson, and Thomas Jung
Geosci. Model Dev., 16, 1857–1873, https://doi.org/10.5194/gmd-16-1857-2023, https://doi.org/10.5194/gmd-16-1857-2023, 2023
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Evaluating climate models usually requires long observational time series, but we present a method that also works for short field campaigns. We compare climate model output to observations from the MOSAiC expedition in the central Arctic Ocean. All models show how the arrival of a warm air mass warms the Arctic in April 2020, but two models do not show the response of snow temperature to the diurnal cycle. One model has too little liquid water and too much ice in clouds during cold days.
Hongxiang Yu, Guang Li, Benjamin Walter, Michael Lehning, Jie Zhang, and Ning Huang
The Cryosphere, 17, 639–651, https://doi.org/10.5194/tc-17-639-2023, https://doi.org/10.5194/tc-17-639-2023, 2023
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Snow cornices lead to the potential risk of causing snow avalanche hazards, which are still unknown so far. We carried out a wind tunnel experiment in a cold lab to investigate the environmental conditions for snow cornice accretion recorded by a camera. The length growth rate of the cornices reaches a maximum for wind speeds approximately 40 % higher than the threshold wind speed. Experimental results improve our understanding of the cornice formation process.
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.
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.
Francesca Carletti, Adrien Michel, Francesca Casale, Alice Burri, Daniele Bocchiola, Mathias Bavay, and Michael Lehning
Hydrol. Earth Syst. Sci., 26, 3447–3475, https://doi.org/10.5194/hess-26-3447-2022, https://doi.org/10.5194/hess-26-3447-2022, 2022
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High Alpine catchments are dominated by the melting of seasonal snow cover and glaciers, whose amount and seasonality are expected to be modified by climate change. This paper compares the performances of different types of models in reproducing discharge among two catchments under present conditions and climate change. Despite many advantages, the use of simpler models for climate change applications is controversial as they do not fully represent the physics of the involved processes.
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923, https://doi.org/10.5194/amt-15-3893-2022, https://doi.org/10.5194/amt-15-3893-2022, 2022
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We proposed new estimates of the surface longwave (LW) cloud radiative effect (CRE) derived from observations collected by a space-based lidar on board the CALIPSO satellite and radiative transfer computations. Our estimate appropriately captures the surface LW CRE annual variability over bright polar surfaces, and it provides a dataset more than 13 years long.
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.
Joel Fiddes, Kristoffer Aalstad, and Michael Lehning
Geosci. Model Dev., 15, 1753–1768, https://doi.org/10.5194/gmd-15-1753-2022, https://doi.org/10.5194/gmd-15-1753-2022, 2022
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This study describes and evaluates a new downscaling scheme that addresses the need for hillslope-scale atmospheric forcing time series for modelling the local impact of regional climate change on the land surface in mountain areas. The method has a global scope and is able to generate all model forcing variables required for hydrological and land surface modelling. This is important, as impact models require high-resolution forcings such as those generated here to produce meaningful results.
Adrien Michel, Bettina Schaefli, Nander Wever, Harry Zekollari, Michael Lehning, and Hendrik Huwald
Hydrol. Earth Syst. Sci., 26, 1063–1087, https://doi.org/10.5194/hess-26-1063-2022, https://doi.org/10.5194/hess-26-1063-2022, 2022
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This study presents an extensive study of climate change impacts on river temperature in Switzerland. Results show that, even for low-emission scenarios, water temperature increase will lead to adverse effects for both ecosystems and socio-economic sectors throughout the 21st century. For high-emission scenarios, the effect will worsen. This study also shows that water seasonal warming will be different between the Alpine regions and the lowlands. Finally, efficiency of models is assessed.
Michael R. Gallagher, Matthew D. Shupe, Hélène Chepfer, and Tristan L'Ecuyer
The Cryosphere, 16, 435–450, https://doi.org/10.5194/tc-16-435-2022, https://doi.org/10.5194/tc-16-435-2022, 2022
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By using direct observations of snowfall and mass changes, the variability of daily snowfall mass input to the Greenland ice sheet is quantified for the first time. With new methods we conclude that cyclones west of Greenland in summer contribute the most snowfall, with 1.66 Gt per occurrence. These cyclones are contextualized in the broader Greenland climate, and snowfall is validated against mass changes to verify the results. Snowfall and mass change observations are shown to agree well.
Marika M. Holland, David Clemens-Sewall, Laura Landrum, Bonnie Light, Donald Perovich, Chris Polashenski, Madison Smith, and Melinda Webster
The Cryosphere, 15, 4981–4998, https://doi.org/10.5194/tc-15-4981-2021, https://doi.org/10.5194/tc-15-4981-2021, 2021
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As the most reflective and most insulative natural material, snow has important climate effects. For snow on sea ice, its high reflectivity reduces ice melt. However, its high insulating capacity limits ice growth. These counteracting effects make its net influence on sea ice uncertain. We find that with increasing snow, sea ice in both hemispheres is thicker and more extensive. However, the drivers of this response are different in the two hemispheres due to different climate conditions.
Heather Guy, Ian M. Brooks, Ken S. Carslaw, Benjamin J. Murray, Von P. Walden, Matthew D. Shupe, Claire Pettersen, David D. Turner, Christopher J. Cox, William D. Neff, Ralf Bennartz, and Ryan R. Neely III
Atmos. Chem. Phys., 21, 15351–15374, https://doi.org/10.5194/acp-21-15351-2021, https://doi.org/10.5194/acp-21-15351-2021, 2021
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We present the first full year of surface aerosol number concentration measurements from the central Greenland Ice Sheet. Aerosol concentrations here have a distinct seasonal cycle from those at lower-altitude Arctic sites, which is driven by large-scale atmospheric circulation. Our results can be used to help understand the role aerosols might play in Greenland surface melt through the modification of cloud properties. This is crucial in a rapidly changing region where observations are sparse.
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.
Pirmin Philipp Ebner, Franziska Koch, Valentina Premier, Carlo Marin, Florian Hanzer, Carlo Maria Carmagnola, Hugues François, Daniel Günther, Fabiano Monti, Olivier Hargoaa, Ulrich Strasser, Samuel Morin, and Michael Lehning
The Cryosphere, 15, 3949–3973, https://doi.org/10.5194/tc-15-3949-2021, https://doi.org/10.5194/tc-15-3949-2021, 2021
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A service to enable real-time optimization of grooming and snow-making at ski resorts was developed and evaluated using both GNSS-measured snow depth and spaceborne snow maps derived from Copernicus Sentinel-2. The correlation to the ground observation data was high. Potential sources for the overestimation of the snow depth by the simulations are mainly the impact of snow redistribution by skiers, compensation of uneven terrain, or spontaneous local adaptions of the snow management.
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Short summary
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.
This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid...
