Articles | Volume 19, issue 8
https://doi.org/10.5194/tc-19-3033-2025
© Author(s) 2025. 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-19-3033-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Aug 2025
Research article |
| 13 Aug 2025
| 13 Aug 2025
Investigating the impact of reanalysis snow input on an observationally calibrated snow-on-sea-ice reconstruction
Climate Research Division, Environment and Climate Change Canada, Toronto, ON M3H 5T4, Canada
Paul J. Kushner
Department of Physics, University of Toronto, Toronto, ON M5S 1A7, Canada
Alek A. Petty
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
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Stephen Howell, Alex Cabaj, David Babb, Jack Landy, Jackie Dawson, Mallik Mahmud, and Mike Brady
EGUsphere, https://doi.org/10.5194/egusphere-2025-2029, https://doi.org/10.5194/egusphere-2025-2029, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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The Northwest Passage provides a shorter transit route connecting the Atlantic Ocean to the Pacific Ocean but ever-present sea ice has prevented its practical navigation. Sea ice area in the northern route of the Northwest Passage on September 30, 2024 fell to a minimum of 4x103 km2, the lowest ice area observed since 1960. This paper describes the unique processes that contributed to the record low sea ice area in the northern route of the Northwest Passage in 2024.
Alek A. Petty, Nicole Keeney, Alex Cabaj, Paul Kushner, and Marco Bagnardi
The Cryosphere, 17, 127–156, https://doi.org/10.5194/tc-17-127-2023, https://doi.org/10.5194/tc-17-127-2023, 2023
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We present upgrades to winter Arctic sea ice thickness estimates from NASA's ICESat-2. Our new thickness results show better agreement with independent data from ESA's CryoSat-2 compared to our first data release, as well as new, very strong comparisons with data collected by moorings in the Beaufort Sea. We analyse three winters of thickness data across the Arctic, including 50 cm thinning of the multiyear ice over this 3-year period.
Stephen Howell, Alex Cabaj, David Babb, Jack Landy, Jackie Dawson, Mallik Mahmud, and Mike Brady
EGUsphere, https://doi.org/10.5194/egusphere-2025-2029, https://doi.org/10.5194/egusphere-2025-2029, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
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The Northwest Passage provides a shorter transit route connecting the Atlantic Ocean to the Pacific Ocean but ever-present sea ice has prevented its practical navigation. Sea ice area in the northern route of the Northwest Passage on September 30, 2024 fell to a minimum of 4x103 km2, the lowest ice area observed since 1960. This paper describes the unique processes that contributed to the record low sea ice area in the northern route of the Northwest Passage in 2024.
Zhihong Zhuo, Xinyue Wang, Yunqian Zhu, Ewa M. Bednarz, Eric Fleming, Peter R. Colarco, Shingo Watanabe, David Plummer, Georgiy Stenchikov, William Randel, Adam Bourassa, Valentina Aquila, Takashi Sekiya, Mark R. Schoeberl, Simone Tilmes, Wandi Yu, Jun Zhang, Paul J. Kushner, and Francesco S. R. Pausata
EGUsphere, https://doi.org/10.5194/egusphere-2025-1505, https://doi.org/10.5194/egusphere-2025-1505, 2025
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The 2022 Hunga eruption caused unprecedented stratospheric water injection, triggering unique atmospheric impacts. This study combines observations and model simulations, projecting a stratospheric water vapor anomaly lasting 4–7 years, with significant temperature variations and ozone depletion in the upper atmosphere lasting 7–10 years. These findings offer critical insights into the role of stratospheric water vapor in shaping climate and atmospheric chemistry.
Alek Aaron Petty, Christopher Cardinale, and Madison Smith
EGUsphere, https://doi.org/10.5194/egusphere-2025-766, https://doi.org/10.5194/egusphere-2025-766, 2025
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We put global climate models to the test against NASA’s ICESat-2 satellite to see how well they simulate global sea ice cover. By adding fancy laser data from ICESat-2, we can better assess how well the models are performing compared to the standard assessments of sea ice area. Overall the models do a good job but there’s room for improvement, especially across the Southern Ocean. We should think a bit more about sea ice density if we want more reliable freeboard comparisons.
Siqi Liu, Shiming Xu, Wenkai Guo, Yanfei Fan, Lu Zhou, Jack Landy, Malin Johansson, Weixin Zhu, and Alek Petty
EGUsphere, https://doi.org/10.5194/egusphere-2025-1069, https://doi.org/10.5194/egusphere-2025-1069, 2025
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In this study, we explore the potential of using synthetic aperture radars (SAR) to predict the sea ice height measurements by the airborne campaign of Operation IceBridge. In particular, we predict the meter-scale sea ice height with the statistical relationship between the two, overcoming the resolution limitation of SAR images from Sentinel-1 satellites. The prediction and ice drift correction algorithms can be applied to the extrapolation of ICESat-2 measurements in the Arctic region.
Youngmin Choi, Alek Petty, Denis Felikson, and Jonathan Poterjoy
EGUsphere, https://doi.org/10.5194/egusphere-2025-301, https://doi.org/10.5194/egusphere-2025-301, 2025
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In this study, we combined numerical models with satellite data using the ensemble Kalman filter to improve predictions of glacier states and their basal conditions. Simulations showed that adding more data enhances prediction accuracy. We also tested the effect of various data types and found that the high-resolution data improve model performance. This method could inform the design of better observation systems and refine future projections of ice sheet behavior.
Lawrence Mudryk, Colleen Mortimer, Chris Derksen, Aleksandra Elias Chereque, and Paul Kushner
The Cryosphere, 19, 201–218, https://doi.org/10.5194/tc-19-201-2025, https://doi.org/10.5194/tc-19-201-2025, 2025
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We evaluate and rank 23 different datasets on their ability to accurately estimate historical snow amounts. The evaluation uses new a set of surface snow measurements with improved spatial coverage, enabling evaluation across both mountainous and nonmountainous regions. Performance measures vary tremendously across the products: while most perform reasonably in nonmountainous regions, accurate representation of snow amounts in mountainous regions and of historical trends is much more variable.
Aleksandra Elias Chereque, Paul J. Kushner, Lawrence Mudryk, Chris Derksen, and Colleen Mortimer
The Cryosphere, 18, 4955–4969, https://doi.org/10.5194/tc-18-4955-2024, https://doi.org/10.5194/tc-18-4955-2024, 2024
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We look at three commonly used snow depth datasets that are produced through a combination of snow modelling and historical measurements (reanalysis). When compared with each other, these datasets have differences that arise for various reasons. We show that a simple snow model can be used to examine these inconsistencies and highlight issues. This method indicates that one of the complex datasets should be excluded from further studies.
Jack C. Landy, Claude de Rijke-Thomas, Carmen Nab, Isobel Lawrence, Isolde A. Glissenaar, Robbie D. C. Mallett, Renée M. Fredensborg Hansen, Alek Petty, Michel Tsamados, Amy R. Macfarlane, and Anne Braakmann-Folgmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2904, https://doi.org/10.5194/egusphere-2024-2904, 2024
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In this study we use three satellites to test the planned remote sensing approach of the upcoming mission CRISTAL over sea ice: that its dual radars will accurately measure the heights of the top and base of snow sitting atop floating sea ice floes. Our results suggest that CRISTAL's dual radars won’t necessarily measure the snow top and base under all conditions. We find that accurate height measurements depend much more on surface roughness than on snow properties, as is commonly assumed.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
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We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
Alexander Mchedlishvili, Christof Lüpkes, Alek Petty, Michel Tsamados, and Gunnar Spreen
The Cryosphere, 17, 4103–4131, https://doi.org/10.5194/tc-17-4103-2023, https://doi.org/10.5194/tc-17-4103-2023, 2023
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In this study we looked at sea ice–atmosphere drag coefficients, quantities that help with characterizing the friction between the atmosphere and sea ice, and vice versa. Using ICESat-2, a laser altimeter that measures elevation differences by timing how long it takes for photons it sends out to return to itself, we could map the roughness, i.e., how uneven the surface is. From roughness we then estimate drag force, the frictional force between sea ice and the atmosphere, across the Arctic.
Alek A. Petty, Nicole Keeney, Alex Cabaj, Paul Kushner, and Marco Bagnardi
The Cryosphere, 17, 127–156, https://doi.org/10.5194/tc-17-127-2023, https://doi.org/10.5194/tc-17-127-2023, 2023
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We present upgrades to winter Arctic sea ice thickness estimates from NASA's ICESat-2. Our new thickness results show better agreement with independent data from ESA's CryoSat-2 compared to our first data release, as well as new, very strong comparisons with data collected by moorings in the Beaufort Sea. We analyse three winters of thickness data across the Arctic, including 50 cm thinning of the multiyear ice over this 3-year period.
Chih-Chun Chou, Paul J. Kushner, Stéphane Laroche, Zen Mariani, Peter Rodriguez, Stella Melo, and Christopher G. Fletcher
Atmos. Meas. Tech., 15, 4443–4461, https://doi.org/10.5194/amt-15-4443-2022, https://doi.org/10.5194/amt-15-4443-2022, 2022
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Aeolus is the first satellite that provides global wind profile measurements. The mission aims to improve the weather forecasts in the tropics, but also, potentially, in the polar regions. We evaluate the performance of the instrument over the Canadian North and the Arctic by comparing its measured winds in both cloudy and non-cloudy layers to wind data from forecasts, reanalysis, and ground-based instruments. Overall, good agreement was seen, but Aeolus winds have greater dispersion.
Ron Kwok, Alek A. Petty, Marco Bagnardi, Nathan T. Kurtz, Glenn F. Cunningham, Alvaro Ivanoff, and Sahra Kacimi
The Cryosphere, 15, 821–833, https://doi.org/10.5194/tc-15-821-2021, https://doi.org/10.5194/tc-15-821-2021, 2021
Lu Zhou, Julienne Stroeve, Shiming Xu, Alek Petty, Rachel Tilling, Mai Winstrup, Philip Rostosky, Isobel R. Lawrence, Glen E. Liston, Andy Ridout, Michel Tsamados, and Vishnu Nandan
The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, https://doi.org/10.5194/tc-15-345-2021, 2021
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Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal discrepancies among the eight snow depth products are analyzed together with their seasonal variability and long-term trends. These snow products are further compared against various ground-truth observations. More analyses on representation error of sea ice parameters are needed for systematic comparison and fusion of airborne, in situ and remote sensing observations.
Alek A. Petty, Melinda Webster, Linette Boisvert, and Thorsten Markus
Geosci. Model Dev., 11, 4577–4602, https://doi.org/10.5194/gmd-11-4577-2018, https://doi.org/10.5194/gmd-11-4577-2018, 2018
Paul J. Kushner, Lawrence R. Mudryk, William Merryfield, Jaison T. Ambadan, Aaron Berg, Adéline Bichet, Ross Brown, Chris Derksen, Stephen J. Déry, Arlan Dirkson, Greg Flato, Christopher G. Fletcher, John C. Fyfe, Nathan Gillett, Christian Haas, Stephen Howell, Frédéric Laliberté, Kelly McCusker, Michael Sigmond, Reinel Sospedra-Alfonso, Neil F. Tandon, Chad Thackeray, Bruno Tremblay, and Francis W. Zwiers
The Cryosphere, 12, 1137–1156, https://doi.org/10.5194/tc-12-1137-2018, https://doi.org/10.5194/tc-12-1137-2018, 2018
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Here, the Canadian research network CanSISE uses state-of-the-art observations of snow and sea ice to assess how Canada's climate model and climate prediction systems capture variability in snow, sea ice, and related climate parameters. We find that the system performs well, accounting for observational uncertainty (especially for snow), model uncertainty, and chaotic climate variability. Even for variables like sea ice, where improvement is needed, useful prediction tools can be developed.
Lawrence R. Mudryk, Chris Derksen, Stephen Howell, Fred Laliberté, Chad Thackeray, Reinel Sospedra-Alfonso, Vincent Vionnet, Paul J. Kushner, and Ross Brown
The Cryosphere, 12, 1157–1176, https://doi.org/10.5194/tc-12-1157-2018, https://doi.org/10.5194/tc-12-1157-2018, 2018
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This paper presents changes in both snow and sea ice that have occurred over Canada during the recent past and shows climate model estimates for future changes expected to occur by the year 2050. The historical changes of snow and sea ice are generally coherent and consistent with the regional history of temperature and precipitation changes. It is expected that snow and sea ice will continue to decrease in the future, declining by an additional 15–30 % from present day values by the year 2050.
Alek A. Petty, Julienne C. Stroeve, Paul R. Holland, Linette N. Boisvert, Angela C. Bliss, Noriaki Kimura, and Walter N. Meier
The Cryosphere, 12, 433–452, https://doi.org/10.5194/tc-12-433-2018, https://doi.org/10.5194/tc-12-433-2018, 2018
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There was significant scientific and media attention surrounding Arctic sea ice in 2016, due primarily to the record-warm air temperatures and low sea ice conditions observed at the start of the year. Here we quantify and assess the record-low monthly sea ice cover in winter, spring and fall, and the lack of record-low sea ice conditions in summer. We explore the primary drivers of these monthly sea ice states and explore the implications for improved summer sea ice forecasting.
Thomas W. K. Armitage, Sheldon Bacon, Andy L. Ridout, Alek A. Petty, Steven Wolbach, and Michel Tsamados
The Cryosphere, 11, 1767–1780, https://doi.org/10.5194/tc-11-1767-2017, https://doi.org/10.5194/tc-11-1767-2017, 2017
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We present a new 12-year record of geostrophic currents at monthly resolution in the ice-covered and ice-free Arctic Ocean and characterise their seasonal to decadal variability. We also present seasonal climatologies of eddy kinetic energy, and examine the changing location of the Beaufort Gyre. Geostrophic current variability highlights the complex interplay between seasonally varying forcing and sea ice conditions, changing ice–ocean coupling and increasing ocean surface stress in the 2000s.
Alek A. Petty, Michel C. Tsamados, Nathan T. Kurtz, Sinead L. Farrell, Thomas Newman, Jeremy P. Harbeck, Daniel L. Feltham, and Jackie A. Richter-Menge
The Cryosphere, 10, 1161–1179, https://doi.org/10.5194/tc-10-1161-2016, https://doi.org/10.5194/tc-10-1161-2016, 2016
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This study presents an analysis of Arctic sea ice topography using high-resolution, three-dimensional surface elevation data from the Airborne Topographic Mapper (ATM) laser altimeter, flown as part of NASA's Operation IceBridge mission. We describe and implement a newly developed sea ice surface feature-picking algorithm and derive novel information regarding the height, volume and geometry of surface features over the western Arctic sea ice cover.
Related subject area
Discipline: Snow | Subject: Numerical Modelling
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The Cryosphere, 19, 1313–1334, https://doi.org/10.5194/tc-19-1313-2025, https://doi.org/10.5194/tc-19-1313-2025, 2025
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Light-absorbing particle (LAP) deposition on snow leads to a darkening of the snow surface and can thus accelerate snow melt. Understanding the extent to which different types of LAPs contribute to snow melt is important to both predict changes in water availability and improve global climate model predictions. Here, we extend a recently developed snow model to account for the deposition of LAPs in the snowpack and evaluate the effect of snow darkening on accelerating snow melt.
Manon Gaillard, Vincent Vionnet, Matthieu Lafaysse, Marie Dumont, and Paul Ginoux
The Cryosphere, 19, 769–792, https://doi.org/10.5194/tc-19-769-2025, https://doi.org/10.5194/tc-19-769-2025, 2025
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This study presents an efficient method to improve large-scale snow albedo simulations by considering the spatial variability in light-absorbing particles (LAPs) like black carbon and dust. A global climatology of LAP deposition was created and used to optimize a parameter in the Crocus snow model. Testing at 10 global sites improved albedo predictions by 10 % on average and over 25 % in the Arctic. This method can enhance other snow models' predictions without complex simulations.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamond Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
The Cryosphere, 18, 5685–5711, https://doi.org/10.5194/tc-18-5685-2024, https://doi.org/10.5194/tc-18-5685-2024, 2024
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Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of the snow model Crocus (embedded within the Soil, Vegetation, and Snow version 2 land surface model) and evaluated at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and specific surface area featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift, and increase viscosity in basal layers.
Sylvie Charbit, Christophe Dumas, Fabienne Maignan, Catherine Ottlé, Nina Raoult, Xavier Fettweis, and Philippe Conesa
The Cryosphere, 18, 5067–5099, https://doi.org/10.5194/tc-18-5067-2024, https://doi.org/10.5194/tc-18-5067-2024, 2024
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The evolution of the Greenland ice sheet is highly dependent on surface melting and therefore on the processes operating at the snow–atmosphere interface and within the snow cover. Here we present new developments to apply a snow model to the Greenland ice sheet. The performance of this model is analysed in terms of its ability to simulate ablation processes. Our analysis shows that the model performs well when compared with the MAR regional polar atmospheric model.
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.
Giulia Mazzotti, Jari-Pekka Nousu, Vincent Vionnet, Tobias Jonas, Rafife Nheili, and Matthieu Lafaysse
The Cryosphere, 18, 4607–4632, https://doi.org/10.5194/tc-18-4607-2024, https://doi.org/10.5194/tc-18-4607-2024, 2024
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As many boreal and alpine forests have seasonal snow, models are needed to predict forest snow under future environmental conditions. We have created a new forest snow model by combining existing, very detailed model components for the canopy and the snowpack. We applied it to forests in Switzerland and Finland and showed how complex forest cover leads to a snowpack layering that is very variable in space and time because different processes prevail at different locations in the forest.
Clémence Herny, Pascal Hagenmuller, Guillaume Chambon, Isabel Peinke, and Jacques Roulle
The Cryosphere, 18, 3787–3805, https://doi.org/10.5194/tc-18-3787-2024, https://doi.org/10.5194/tc-18-3787-2024, 2024
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This paper presents the evaluation of a numerical discrete element method (DEM) by simulating cone penetration tests in different snow samples. The DEM model demonstrated a good ability to reproduce the measured mechanical behaviour of the snow, namely the force evolution on the cone and the grain displacement field. Systematic sensitivity tests showed that the mechanical response depends not only on the microstructure of the sample but also on the mechanical parameters of grain contacts.
Louis Quéno, Rebecca Mott, Paul Morin, Bertrand Cluzet, Giulia Mazzotti, and Tobias Jonas
The Cryosphere, 18, 3533–3557, https://doi.org/10.5194/tc-18-3533-2024, https://doi.org/10.5194/tc-18-3533-2024, 2024
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Snow redistribution by wind and avalanches strongly influences snow hydrology in mountains. This study presents a novel modelling approach to best represent these processes in an operational context. The evaluation of the simulations against airborne snow depth measurements showed remarkable improvement in the snow distribution in mountains of the eastern Swiss Alps, with a representation of snow accumulation and erosion areas, suggesting promising benefits for operational snow melt forecasts.
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamps-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont
The Cryosphere, 18, 3081–3116, https://doi.org/10.5194/tc-18-3081-2024, https://doi.org/10.5194/tc-18-3081-2024, 2024
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Our study addresses the complex challenge of evaluating distributed alpine snow simulations with snow transport against snow depths from Pléiades stereo imagery and snow melt-out dates from Sentinel-2 and Landsat-8 satellites. Additionally, we disentangle error contributions between blowing snow, precipitation heterogeneity, and unresolved subgrid variability. Snow transport enhances the snow simulations at high elevations, while precipitation biases are the main error source in other areas.
Michael A. Rawlins and Ambarish V. Karmalkar
The Cryosphere, 18, 1033–1052, https://doi.org/10.5194/tc-18-1033-2024, https://doi.org/10.5194/tc-18-1033-2024, 2024
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Flows of water, carbon, and materials by Arctic rivers are being altered by climate warming. We used simulations from a permafrost hydrology model to investigate future changes in quantities influencing river exports. By 2100 Arctic rivers will receive more runoff from the far north where abundant soil carbon can leach in. More water will enter them via subsurface pathways particularly in summer and autumn. An enhanced water cycle and permafrost thaw are changing river flows to coastal areas.
Edoardo Raparelli, Paolo Tuccella, Valentina Colaiuda, and Frank S. Marzano
The Cryosphere, 17, 519–538, https://doi.org/10.5194/tc-17-519-2023, https://doi.org/10.5194/tc-17-519-2023, 2023
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We evaluate the skills of a single-layer (Noah) and a multi-layer (Alpine3D) snow model, forced with the Weather Research and Forecasting model, to reproduce snowpack properties observed in the Italian central Apennines. We found that Alpine3D reproduces the observed snow height and snow water equivalent better than Noah, while no particular model differences emerge on snow cover extent. Finally, we observed that snow settlement is mainly due to densification in Alpine3D and to melting in Noah.
Florian Herla, Pascal Haegeli, and Patrick Mair
The Cryosphere, 16, 3149–3162, https://doi.org/10.5194/tc-16-3149-2022, https://doi.org/10.5194/tc-16-3149-2022, 2022
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We present an averaging algorithm for multidimensional snow stratigraphy profiles that elicits the predominant snow layering among large numbers of profiles and allows for compiling of informative summary statistics and distributions of snowpack layer properties. This creates new opportunities for presenting and analyzing operational snowpack simulations in support of avalanche forecasting and may inspire new ways of processing profiles and time series in other geophysical contexts.
Anne Sophie Daloz, Clemens Schwingshackl, Priscilla Mooney, Susanna Strada, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Michal Belda, Tomas Halenka, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 2403–2419, https://doi.org/10.5194/tc-16-2403-2022, https://doi.org/10.5194/tc-16-2403-2022, 2022
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Snow plays a major role in the regulation of the Earth's surface temperature. Together with climate change, rising temperatures are already altering snow in many ways. In this context, it is crucial to better understand the ability of climate models to represent snow and snow processes. This work focuses on Europe and shows that the melting season in spring still represents a challenge for climate models and that more work is needed to accurately simulate snow–atmosphere interactions.
Konstantin Schürholt, Julia Kowalski, and Henning Löwe
The Cryosphere, 16, 903–923, https://doi.org/10.5194/tc-16-903-2022, https://doi.org/10.5194/tc-16-903-2022, 2022
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This companion paper deals with numerical particularities of partial differential equations underlying 1D snow models. In this first part we neglect mechanical settling and demonstrate that the nonlinear coupling between diffusive transport (heat and vapor), phase changes and ice mass conservation contains a wave instability that may be relevant for weak layer formation. Numerical requirements are discussed in view of the underlying homogenization scheme.
Anna Simson, Henning Löwe, and Julia Kowalski
The Cryosphere, 15, 5423–5445, https://doi.org/10.5194/tc-15-5423-2021, https://doi.org/10.5194/tc-15-5423-2021, 2021
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This companion paper deals with numerical particularities of partial differential equations underlying one-dimensional snow models. In this second part we include mechanical settling and develop a new hybrid (Eulerian–Lagrangian) method for solving the advection-dominated ice mass conservation on a moving mesh alongside Eulerian diffusion (heat and vapor) and phase changes. The scheme facilitates a modular and extendable solver strategy while retaining controls on numerical accuracy.
Thomas Brall, Vladimir Mares, Rolf Bütikofer, and Werner Rühm
The Cryosphere, 15, 4769–4780, https://doi.org/10.5194/tc-15-4769-2021, https://doi.org/10.5194/tc-15-4769-2021, 2021
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Neutrons from secondary cosmic rays, measured at 2660 m a.s.l. at Zugspitze, Germany, are highly affected by the environment, in particular by snow, soil moisture, and mountain shielding. To quantify these effects, computer simulations were carried out, including a sensitivity analysis on snow depth and soil moisture. This provides a possibility for snow depth estimation based on the measured number of secondary neutrons. This method was applied at Zugspitze in 2018.
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mäder, Alec van Herwijnen, Louis Quéno, Yves Bühler, Jeff S. Deems, and Simon Gascoin
The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, https://doi.org/10.5194/tc-15-4607-2021, 2021
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The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Louis Quéno, Charles Fierz, Alec van Herwijnen, Dylan Longridge, and Nander Wever
The Cryosphere, 14, 3449–3464, https://doi.org/10.5194/tc-14-3449-2020, https://doi.org/10.5194/tc-14-3449-2020, 2020
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Deep ice layers may form in the snowpack due to preferential water flow with impacts on the snowpack mechanical, hydrological and thermodynamical properties. We studied their formation and evolution at a high-altitude alpine site, combining a comprehensive observation dataset at a daily frequency (with traditional snowpack observations, penetration resistance and radar measurements) and detailed snowpack modeling, including a new parameterization of ice formation in the 1-D SNOWPACK model.
David M. W. Pritchard, Nathan Forsythe, Greg O'Donnell, Hayley J. Fowler, and Nick Rutter
The Cryosphere, 14, 1225–1244, https://doi.org/10.5194/tc-14-1225-2020, https://doi.org/10.5194/tc-14-1225-2020, 2020
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This study compares different snowpack model configurations applied in the western Himalaya. The results show how even sparse local observations can help to delineate climate input errors from model structure errors, which provides insights into model performance variation. The results also show how interactions between processes affect sensitivities to climate variability in different model configurations, with implications for model selection in climate change projections.
Grégoire Bobillier, Bastian Bergfeld, Achille Capelli, Jürg Dual, Johan Gaume, Alec van Herwijnen, and Jürg Schweizer
The Cryosphere, 14, 39–49, https://doi.org/10.5194/tc-14-39-2020, https://doi.org/10.5194/tc-14-39-2020, 2020
Michael A. Rawlins, Lei Cai, Svetlana L. Stuefer, and Dmitry Nicolsky
The Cryosphere, 13, 3337–3352, https://doi.org/10.5194/tc-13-3337-2019, https://doi.org/10.5194/tc-13-3337-2019, 2019
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We investigate the changing character of runoff, river discharge and other hydrological elements across watershed draining the North Slope of Alaska over the period 1981–2010. Our synthesis of observations and modeling reveals significant increases in the proportion of subsurface runoff and cold season discharge. These and other changes we describe are consistent with warming and thawing permafrost, and have implications for water, carbon and nutrient cycling in coastal environments.
Pierre Spandre, Hugues François, Deborah Verfaillie, Marc Pons, Matthieu Vernay, Matthieu Lafaysse, Emmanuelle George, and Samuel Morin
The Cryosphere, 13, 1325–1347, https://doi.org/10.5194/tc-13-1325-2019, https://doi.org/10.5194/tc-13-1325-2019, 2019
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This study investigates the snow reliability of 175 ski resorts in the Pyrenees (France, Spain and Andorra) and the French Alps under past and future conditions (1950–2100) using state-of-the-art climate projections and snowpack modelling accounting for snow management, i.e. grooming and snowmaking. The snow reliability of ski resorts shows strong elevation and regional differences, and our study quantifies changes in snow reliability induced by snowmaking under various climate scenarios.
Zhengshi Wang and Shuming Jia
The Cryosphere, 12, 3841–3851, https://doi.org/10.5194/tc-12-3841-2018, https://doi.org/10.5194/tc-12-3841-2018, 2018
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Drifting snowstorms that are hundreds of meters in depth are reproduced using a large-eddy simulation model combined with a Lagrangian particle tracking method, which also exhibits obvious spatial structures following large-scale turbulent vortexes. The horizontal snow transport flux at high altitude, previously not observed, actually occupies a significant proportion of the total flux. Thus, previous models may largely underestimate the total mass flux and consequently snow sublimation.
Franziska Gerber, Nikola Besic, Varun Sharma, Rebecca Mott, Megan Daniels, Marco Gabella, Alexis Berne, Urs Germann, and Michael Lehning
The Cryosphere, 12, 3137–3160, https://doi.org/10.5194/tc-12-3137-2018, https://doi.org/10.5194/tc-12-3137-2018, 2018
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A comparison of winter precipitation variability in operational radar measurements and high-resolution simulations reveals that large-scale variability is well captured by the model, depending on the event. Precipitation variability is driven by topography and wind. A good portion of small-scale variability is captured at the highest resolution. This is essential to address small-scale precipitation processes forming the alpine snow seasonal snow cover – an important source of water.
Edward H. Bair, Andre Abreu Calfa, Karl Rittger, and Jeff Dozier
The Cryosphere, 12, 1579–1594, https://doi.org/10.5194/tc-12-1579-2018, https://doi.org/10.5194/tc-12-1579-2018, 2018
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In Afghanistan, almost no snow measurements exist. Operational estimates use measurements from satellites, but all have limitations. We have developed a satellite-based technique called reconstruction that accurately estimates the snowpack retrospectively. To solve the problem of estimating today's snowpack, we used machine learning, trained on our reconstructed snow estimates, using predictors that are available today. Our results show low errors, demonstrating the utility of this approach.
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Macfarlane, A. R., Schneebeli, M., Dadic, R., Wagner, D. N., Arndt, S., Clemens-Sewall, D., Hämmerle, S., Hannula, H.-R., Jaggi, M., Kolabutin, N., Krampe, D., Lehning, M., Matero, I., Nicolaus, M., Oggier, M., Pirazzini, R., Polashenski, C., Raphael, I., Regnery, J., Shimanchuck, E., Smith, M. M., and Tavri, A.: Snowpit raw data collected during the MOSAiC expedition, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.935934, 2021. a, b, c, d, e
Macfarlane, A. R., Schneebeli, M., Dadic, R., Wagner, D. N., Arndt, S., Clemens-Sewall, D., Hämmerle, S., Hannula, H.-R., Jaggi, M., Kolabutin, N., Krampe, D., Lehning, M., Matero, I., Nicolaus, M., Oggier, M., Pirazzini, R., Polashenski, C., Raphael, I., Regnery, J., Shimanchuck, E., Smith, M. M., and Tavri, A.: Snowpit snow density cutter profiles measured during the MOSAiC expedition, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940214, 2022. a, b, c, d, e
Macfarlane, A. R., Schneebeli, M., Dadic, R., Tavri, A., Immerz, A., Polashenski, C., Krampe, D., Clemens-Sewall, D., Wagner, D. N., Perovich, D. K., Henna-Reetta, H., Raphael, I., Matero, I., Regnery, J., Smith, M. M., Nicolaus, M., Jaggi, M., Oggier, M., Webster, M. A., Lehning, M., Kolabutin, N., Itkin, P., Naderpour, R., Pirazzini, R., Hämmerle, S., Arndt, S., and Fons, S.: A Database of Snow on Sea Ice in the Central Arctic Collected during the MOSAiC expedition, Scientific Data, 10, 398, https://doi.org/10.1038/s41597-023-02273-1, 2023. a, b, c, d, e
MacGregor, J. A., Boisvert, L. N., Medley, B., Petty, A. A., Harbeck, J. P., Bell, R. E., Blair, J. B., Blanchard-Wrigglesworth, E., Buckley, E. M., Christoffersen, M. S., Cochran, J. R., Csathó, B. M., De Marco, E. L., Dominguez, R. T., Fahnestock, M. A., Farrell, S. L., Gogineni, S. P., Greenbaum, J. S., Hansen, C. M., Hofton, M. A., Holt, J. W., Jezek, K. C., Koenig, L. S., Kurtz, N. T., Kwok, R., Larsen, C. F., Leuschen, C. J., Locke, C. D., Manizade, S. S., Martin, S., Neumann, T. A., Nowicki, S. M., Paden, J. D., Richter-Menge, J. A., Rignot, E. J., Rodríguez-Morales, F., Siegfried, M. R., Smith, B. E., Sonntag, J. G., Studinger, M., Tinto, K. J., Truffer, M., Wagner, T. P., Woods, J. E., Young, D. A., and Yungel, J. K.: The Scientific Legacy of NASA's Operation IceBridge, Rev. Geophys., 59, e2020RG000712, https://doi.org/10.1029/2020RG000712, 2021. a
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Meier, W. N. and Stewart, J. S.: NSIDC Land, Ocean, Coast, Ice, and Sea Ice Region Masks, NSIDC Special Report No. 25, National Snow and Ice Data Center, Boulder, CO, USA, https://nsidc.org/sites/default/files/documents/technical-reference/nsidc-special-report-25.pdf (last access: 15 June 2024), 2023. a, b
Milani, L. and Wood, N. B.: Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations, Remote Sensing, 13, 2041, https://doi.org/10.3390/rs13112041, 2021. a
Milani, L., Kulie, M. S., Casella, D., Dietrich, S., L'Ecuyer, T. S., Panegrossi, G., Porcù, F., Sanò, P., and Wood, N. B.: CloudSat snowfall estimates over Antarctica and the Southern Ocean: An assessment of independent retrieval methodologies and multi-year snowfall analysis, Atmos. Res., 213, 121–135, https://doi.org/10.1016/j.atmosres.2018.05.015, 2018. a
Mudryk, L. R., Derksen, C., Kushner, P. J., and Brown, R.: Characterization of Northern Hemisphere Snow Water Equivalent Datasets, 1981–2010, J. Climate, 28, 8037–8051, https://doi.org/10.1175/JCLI-D-15-0229.1, 2015. a, b, c
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Short summary
The output of snow-on-sea-ice models is influenced by the choice of snowfall input used. We ran such a model with different snowfall inputs and calibrated it to observations, produced a new calibrated snow product, and regionally compared the model outputs to outputs from another snow-on-sea-ice model. The two models agree best on the seasonal cycle of snow in the central Arctic Ocean. Observational comparisons highlight ongoing challenges in estimating the depth and density of snow on Arctic sea ice.
The output of snow-on-sea-ice models is influenced by the choice of snowfall input used. We ran...
