Articles | Volume 16, issue 11
https://doi.org/10.5194/gmd-16-3203-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3203-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
08 Jun 2023
Development and technical paper |
| 08 Jun 2023
| 08 Jun 2023
A wind-driven snow redistribution module for Alpine3D v3.3.0: adaptations designed for downscaling ice sheet surface mass balance
Eric Keenan
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Jan T. M. Lenaerts
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
Brooke Medley
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
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The Cryosphere, 15, 5187–5203, https://doi.org/10.5194/tc-15-5187-2021, https://doi.org/10.5194/tc-15-5187-2021, 2021
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We present a 20-year, satellite-based record of velocity and thickness change on the Thwaites Eastern Ice Shelf (TEIS), the largest remaining floating extension of Thwaites Glacier (TG). TG holds the single greatest control on sea-level rise over the next few centuries, so it is important to understand changes on the TEIS, which controls much of TG's flow into the ocean. Our results suggest that the TEIS is progressively destabilizing and is likely to disintegrate over the next few decades.
Eric Keenan, Nander Wever, Marissa Dattler, Jan T. M. Lenaerts, Brooke Medley, Peter Kuipers Munneke, and Carleen Reijmer
The Cryosphere, 15, 1065–1085, https://doi.org/10.5194/tc-15-1065-2021, https://doi.org/10.5194/tc-15-1065-2021, 2021
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Snow density is required to convert observed changes in ice sheet volume into mass, which ultimately drives ice sheet contribution to sea level rise. However, snow properties respond dynamically to wind-driven redistribution. Here we include a new wind-driven snow density scheme into an existing snow model. Our results demonstrate an improved representation of snow density when compared to observations and can therefore be used to improve retrievals of ice sheet mass balance.
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The Cryosphere, 19, 713–730, https://doi.org/10.5194/tc-19-713-2025, https://doi.org/10.5194/tc-19-713-2025, 2025
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Traditionally, glaciologists use global navigation satellite systems (GNSSs) to measure the surface elevation and velocity of glaciers to understand processes associated with ice flow. Using the interference of GNSS signals that bounce off of the ice sheet surface, we measure the surface height change near GNSS receivers in the Amundsen Sea Embayment (ASE). From surface height change, we infer daily accumulation rates that we use to understand the drivers of extreme precipitation in the ASE.
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The Cryosphere, 19, 597–618, https://doi.org/10.5194/tc-19-597-2025, https://doi.org/10.5194/tc-19-597-2025, 2025
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We use three sophisticated climate models to examine extreme precipitation in a critical region of West Antarctica. We found that rainfall probably occurred during the two cases we examined and that it was generated by the interaction of air with steep topography. Our results show that kilometre-scale models are useful tools for exploring extreme precipitation in this region and that more observations of rainfall are needed.
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EGUsphere, https://doi.org/10.5194/egusphere-2024-2395, https://doi.org/10.5194/egusphere-2024-2395, 2025
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EGUsphere, https://doi.org/10.5194/egusphere-2024-4174, https://doi.org/10.5194/egusphere-2024-4174, 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 system. The best approach mixes both methods and manages heat flow, potentially allowing excess energy to be used locally.
Marissa E. Dattler, Brooke Medley, and C. Max Stevens
The Cryosphere, 18, 3613–3631, https://doi.org/10.5194/tc-18-3613-2024, https://doi.org/10.5194/tc-18-3613-2024, 2024
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We developed an algorithm based on combining models and satellite observations to identify the presence of surface melt on the Antarctic Ice Sheet. We find that this method works similarly to previous methods by assessing 13 sites and the Larsen C ice shelf. Unlike previous methods, this algorithm is based on physical parameters, and updates to this method could allow the meltwater present on the Antarctic Ice Sheet to be quantified instead of simply detected.
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.
Rajashree Tri Datta, Adam Herrington, Jan T. M. Lenaerts, David P. Schneider, Luke Trusel, Ziqi Yin, and Devon Dunmire
The Cryosphere, 17, 3847–3866, https://doi.org/10.5194/tc-17-3847-2023, https://doi.org/10.5194/tc-17-3847-2023, 2023
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Precipitation over Antarctica is one of the greatest sources of uncertainty in sea level rise estimates. Earth system models (ESMs) are a valuable tool for these estimates but typically run at coarse spatial resolutions. Here, we present an evaluation of the variable-resolution CESM2 (VR-CESM2) for the first time with a grid designed for enhanced spatial resolution over Antarctica to achieve the high resolution of regional climate models while preserving the two-way interactions of ESMs.
Haokui Xu, Brooke Medley, Leung Tsang, Joel T. Johnson, Kenneth C. Jezek, Marco Brogioni, and Lars Kaleschke
The Cryosphere, 17, 2793–2809, https://doi.org/10.5194/tc-17-2793-2023, https://doi.org/10.5194/tc-17-2793-2023, 2023
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Megan Thompson-Munson, Nander Wever, C. Max Stevens, Jan T. M. Lenaerts, and Brooke Medley
The Cryosphere, 17, 2185–2209, https://doi.org/10.5194/tc-17-2185-2023, https://doi.org/10.5194/tc-17-2185-2023, 2023
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To better understand the Greenland Ice Sheet’s firn layer and its ability to buffer sea level rise by storing meltwater, we analyze firn density observations and output from two firn models. We find that both models, one physics-based and one semi-empirical, simulate realistic density and firn air content when compared to observations. The models differ in their representation of firn air content, highlighting the uncertainty in physical processes and the paucity of deep-firn measurements.
Michelle L. Maclennan, Jan T. M. Lenaerts, Christine A. Shields, Andrew O. Hoffman, Nander Wever, Megan Thompson-Munson, Andrew C. Winters, Erin C. Pettit, Theodore A. Scambos, and Jonathan D. Wille
The Cryosphere, 17, 865–881, https://doi.org/10.5194/tc-17-865-2023, https://doi.org/10.5194/tc-17-865-2023, 2023
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Atmospheric rivers are air masses that transport large amounts of moisture and heat towards the poles. Here, we use a combination of weather observations and models to quantify the amount of snowfall caused by atmospheric rivers in West Antarctica which is about 10 % of the total snowfall each year. We then examine a unique event that occurred in early February 2020, when three atmospheric rivers made landfall over West Antarctica in rapid succession, leading to heavy snowfall and surface melt.
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The Cryosphere, 17, 789–808, https://doi.org/10.5194/tc-17-789-2023, https://doi.org/10.5194/tc-17-789-2023, 2023
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The Cryosphere, 16, 4379–4401, https://doi.org/10.5194/tc-16-4379-2022, https://doi.org/10.5194/tc-16-4379-2022, 2022
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Meltwater runoff is one of the main contributors to mass loss on the Greenland Ice Sheet that influences global sea level rise. However, it remains unclear where meltwater runs off and what processes cause this. We measured the velocity of meltwater flow through snow on the ice sheet, which ranged from 0.17–12.8 m h−1 for vertical percolation and from 1.3–15.1 m h−1 for lateral flow. This is an important step towards understanding where, when and why meltwater runoff occurs on the ice sheet.
Devon Dunmire, Jan T. M. Lenaerts, Rajashree Tri Datta, and Tessa Gorte
The Cryosphere, 16, 4163–4184, https://doi.org/10.5194/tc-16-4163-2022, https://doi.org/10.5194/tc-16-4163-2022, 2022
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Earth system models (ESMs) are used to model the climate system and the interactions of its components (atmosphere, ocean, etc.) both historically and into the future under different assumptions of human activity. The representation of Antarctica in ESMs is important because it can inform projections of the ice sheet's contribution to sea level rise. Here, we compare output of Antarctica's surface climate from an ESM with observations to understand strengths and weaknesses within the model.
Brooke Medley, Thomas A. Neumann, H. Jay Zwally, Benjamin E. Smith, and C. Max Stevens
The Cryosphere, 16, 3971–4011, https://doi.org/10.5194/tc-16-3971-2022, https://doi.org/10.5194/tc-16-3971-2022, 2022
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Satellite altimeters measure the height or volume change over Earth's ice sheets, but in order to understand how that change translates into ice mass, we must account for various processes at the surface. Specifically, snowfall events generate large, transient increases in surface height, yet snow fall has a relatively low density, which means much of that height change is composed of air. This air signal must be removed from the observed height changes before we can assess ice mass change.
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.
Karen E. Alley, Christian T. Wild, Adrian Luckman, Ted A. Scambos, Martin Truffer, Erin C. Pettit, Atsuhiro Muto, Bruce Wallin, Marin Klinger, Tyler Sutterley, Sarah F. Child, Cyrus Hulen, Jan T. M. Lenaerts, Michelle Maclennan, Eric Keenan, and Devon Dunmire
The Cryosphere, 15, 5187–5203, https://doi.org/10.5194/tc-15-5187-2021, https://doi.org/10.5194/tc-15-5187-2021, 2021
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We present a 20-year, satellite-based record of velocity and thickness change on the Thwaites Eastern Ice Shelf (TEIS), the largest remaining floating extension of Thwaites Glacier (TG). TG holds the single greatest control on sea-level rise over the next few centuries, so it is important to understand changes on the TEIS, which controls much of TG's flow into the ocean. Our results suggest that the TEIS is progressively destabilizing and is likely to disintegrate over the next few decades.
Madison L. Ghiz, Ryan C. Scott, Andrew M. Vogelmann, Jan T. M. Lenaerts, Matthew Lazzara, and Dan Lubin
The Cryosphere, 15, 3459–3494, https://doi.org/10.5194/tc-15-3459-2021, https://doi.org/10.5194/tc-15-3459-2021, 2021
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We investigate how melt occurs over the vulnerable ice shelves of West Antarctica and determine that the three primary mechanisms can be evaluated using archived numerical weather prediction model data and satellite imagery. We find examples of each mechanism: thermal blanketing by a warm atmosphere, radiative heating by thin clouds, and downslope winds. Our results signify the potential to make a multi-decadal assessment of atmospheric stress on West Antarctic ice shelves in a warming climate.
Devon Dunmire, Alison F. Banwell, Nander Wever, Jan T. M. Lenaerts, and Rajashree Tri Datta
The Cryosphere, 15, 2983–3005, https://doi.org/10.5194/tc-15-2983-2021, https://doi.org/10.5194/tc-15-2983-2021, 2021
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Here, we automatically detect buried lakes (meltwater lakes buried below layers of snow) across the Greenland Ice Sheet, providing insight into a poorly studied meltwater feature. For 2018 and 2019, we compare areal extent of buried lakes. We find greater buried lake extent in 2019, especially in northern Greenland, which we attribute to late-summer surface melt and high autumn temperatures. We also provide evidence that buried lakes form via different processes across Greenland.
Eric Keenan, Nander Wever, Marissa Dattler, Jan T. M. Lenaerts, Brooke Medley, Peter Kuipers Munneke, and Carleen Reijmer
The Cryosphere, 15, 1065–1085, https://doi.org/10.5194/tc-15-1065-2021, https://doi.org/10.5194/tc-15-1065-2021, 2021
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Snow density is required to convert observed changes in ice sheet volume into mass, which ultimately drives ice sheet contribution to sea level rise. However, snow properties respond dynamically to wind-driven redistribution. Here we include a new wind-driven snow density scheme into an existing snow model. Our results demonstrate an improved representation of snow density when compared to observations and can therefore be used to improve retrievals of ice sheet mass balance.
J. Melchior van Wessem, Christian R. Steger, Nander Wever, and Michiel R. van den Broeke
The Cryosphere, 15, 695–714, https://doi.org/10.5194/tc-15-695-2021, https://doi.org/10.5194/tc-15-695-2021, 2021
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This study presents the first modelled estimates of perennial firn aquifers (PFAs) in Antarctica. PFAs are subsurface meltwater bodies that do not refreeze in winter due to the isolating effects of the snow they are buried underneath. They were first identified in Greenland, but conditions for their existence are also present in the Antarctic Peninsula. These PFAs can have important effects on meltwater retention, ice shelf stability, and, consequently, sea level rise.
Tessa Gorte, Jan T. M. Lenaerts, and Brooke Medley
The Cryosphere, 14, 4719–4733, https://doi.org/10.5194/tc-14-4719-2020, https://doi.org/10.5194/tc-14-4719-2020, 2020
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In this paper, we analyze several spatial and temporal criteria to assess the ability of models in the CMIP5 and CMIP6 frameworks to recreate past Antarctic surface mass balance. We then compared a subset of the top performing models to all remaining models to refine future surface mass balance predictions under different forcing scenarios. We found that the top performing models predict lower surface mass balance by 2100, indicating less buffering than otherwise expected of sea level rise.
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.
Marie G. P. Cavitte, Quentin Dalaiden, Hugues Goosse, Jan T. M. Lenaerts, and Elizabeth R. Thomas
The Cryosphere, 14, 4083–4102, https://doi.org/10.5194/tc-14-4083-2020, https://doi.org/10.5194/tc-14-4083-2020, 2020
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Surface mass balance (SMB) and surface air temperature (SAT) are correlated at the regional scale for most of Antarctica, SMB and δ18O. Areas with low/no correlation are where wind processes (foehn, katabatic wind warming, and erosion) are sufficiently active to overwhelm the synoptic-scale snow accumulation. Measured in ice cores, the link between SMB, SAT, and δ18O is much weaker. Random noise can be removed by core record averaging but local processes perturb the correlation systematically.
Xavier Fettweis, Stefan Hofer, Uta Krebs-Kanzow, Charles Amory, Teruo Aoki, Constantijn J. Berends, Andreas Born, Jason E. Box, Alison Delhasse, Koji Fujita, Paul Gierz, Heiko Goelzer, Edward Hanna, Akihiro Hashimoto, Philippe Huybrechts, Marie-Luise Kapsch, Michalea D. King, Christoph Kittel, Charlotte Lang, Peter L. Langen, Jan T. M. Lenaerts, Glen E. Liston, Gerrit Lohmann, Sebastian H. Mernild, Uwe Mikolajewicz, Kameswarrao Modali, Ruth H. Mottram, Masashi Niwano, Brice Noël, Jonathan C. Ryan, Amy Smith, Jan Streffing, Marco Tedesco, Willem Jan van de Berg, Michiel van den Broeke, Roderik S. W. van de Wal, Leo van Kampenhout, David Wilton, Bert Wouters, Florian Ziemen, and Tobias Zolles
The Cryosphere, 14, 3935–3958, https://doi.org/10.5194/tc-14-3935-2020, https://doi.org/10.5194/tc-14-3935-2020, 2020
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We evaluated simulated Greenland Ice Sheet surface mass balance from 5 kinds of models. While the most complex (but expensive to compute) models remain the best, the faster/simpler models also compare reliably with observations and have biases of the same order as the regional models. Discrepancies in the trend over 2000–2012, however, suggest that large uncertainties remain in the modelled future SMB changes as they are highly impacted by the meltwater runoff biases over the current climate.
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.
Thore Kausch, Stef Lhermitte, Jan T. M. Lenaerts, Nander Wever, Mana Inoue, Frank Pattyn, Sainan Sun, Sarah Wauthy, Jean-Louis Tison, and Willem Jan van de Berg
The Cryosphere, 14, 3367–3380, https://doi.org/10.5194/tc-14-3367-2020, https://doi.org/10.5194/tc-14-3367-2020, 2020
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Ice rises are elevated parts of the otherwise flat ice shelf. Here we study the impact of an Antarctic ice rise on the surrounding snow accumulation by combining field data and modeling. Our results show a clear difference in average yearly snow accumulation between the windward side, the leeward side and the peak of the ice rise due to differences in snowfall and wind erosion. This is relevant for the interpretation of ice core records, which are often drilled on the peak of an ice rise.
Michael Studinger, Brooke C. Medley, Kelly M. Brunt, Kimberly A. Casey, Nathan T. Kurtz, Serdar S. Manizade, Thomas A. Neumann, and Thomas B. Overly
The Cryosphere, 14, 3287–3308, https://doi.org/10.5194/tc-14-3287-2020, https://doi.org/10.5194/tc-14-3287-2020, 2020
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We use repeat airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88° S. We find small–scale variability in snow accumulation based on the snow radar subsurface layering, indicating areas of strong wind redistribution are prevalent at 88° S. There is no slope–independent relationship between surface roughness and accumulation.
Jan T. M. Lenaerts, M. Drew Camron, Christopher R. Wyburn-Powell, and Jennifer E. Kay
The Cryosphere, 14, 2253–2265, https://doi.org/10.5194/tc-14-2253-2020, https://doi.org/10.5194/tc-14-2253-2020, 2020
Simone Tilmes, Douglas G. MacMartin, Jan T. M. Lenaerts, Leo van Kampenhout, Laura Muntjewerf, Lili Xia, Cheryl S. Harrison, Kristen M. Krumhardt, Michael J. Mills, Ben Kravitz, and Alan Robock
Earth Syst. Dynam., 11, 579–601, https://doi.org/10.5194/esd-11-579-2020, https://doi.org/10.5194/esd-11-579-2020, 2020
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This paper introduces new geoengineering model experiments as part of a larger model intercomparison effort, using reflective particles to block some of the incoming solar radiation to reach surface temperature targets. Outcomes of these applications are contrasted based on a high greenhouse gas emission pathway and a pathway with strong mitigation and negative emissions after 2040. We compare quantities that matter for societal and ecosystem impacts between the different scenarios.
Brice Noël, Leonardus van Kampenhout, Willem Jan van de Berg, Jan T. M. Lenaerts, Bert Wouters, and Michiel R. van den Broeke
The Cryosphere, 14, 1425–1435, https://doi.org/10.5194/tc-14-1425-2020, https://doi.org/10.5194/tc-14-1425-2020, 2020
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We present a reconstruction of historical (1950–2014) surface mass balance of the Greenland ice sheet using the Community Earth System Model (CESM2; ~111 km) to force a high-resolution regional climate model (RACMO2; ~11 km), which is further refined to 1 km spatial resolution. For the first time, an Earth-system-model-based product, assimilating no observations, can reconstruct realistic historical ice sheet surface mass balance as well as the mass loss acceleration that started in the 1990s.
Quentin Dalaiden, Hugues Goosse, François Klein, Jan T. M. Lenaerts, Max Holloway, Louise Sime, and Elizabeth R. Thomas
The Cryosphere, 14, 1187–1207, https://doi.org/10.5194/tc-14-1187-2020, https://doi.org/10.5194/tc-14-1187-2020, 2020
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Large uncertainties remain in Antarctic surface temperature reconstructions over the last millennium. Here, the analysis of climate model outputs reveals that snow accumulation is a more relevant proxy for surface temperature reconstructions than δ18O. We use this finding in data assimilation experiments to compare to observed surface temperatures. We show that our continental temperature reconstruction outperforms reconstructions based on δ18O, especially for East Antarctica.
Hailong Wang, Jeremy G. Fyke, Jan T. M. Lenaerts, Jesse M. Nusbaumer, Hansi Singh, David Noone, Philip J. Rasch, and Rudong Zhang
The Cryosphere, 14, 429–444, https://doi.org/10.5194/tc-14-429-2020, https://doi.org/10.5194/tc-14-429-2020, 2020
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Using a climate model with unique water source tagging, we found that sea-ice anomalies in the Southern Ocean and accompanying SST changes have a significant influence on Antarctic precipitation and its source attribution through their direct impact on moisture sources and indirect impact on moisture transport. This study also highlights the importance of atmospheric dynamics in affecting the thermodynamic impact of sea-ice anomalies on regional Antarctic precipitation.
Nander Wever, Leonard Rossmann, Nina Maaß, Katherine C. Leonard, Lars Kaleschke, Marcel Nicolaus, and Michael Lehning
Geosci. Model Dev., 13, 99–119, https://doi.org/10.5194/gmd-13-99-2020, https://doi.org/10.5194/gmd-13-99-2020, 2020
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Sea ice is an important component of the global climate system. The presence of a snow layer covering sea ice can impact ice mass and energy budgets. The detailed, physics-based, multi-layer snow model SNOWPACK was modified to simulate the snow–sea-ice system, providing simulations of the snow microstructure, water percolation and flooding, and superimposed ice formation. The model is applied to in situ measurements from snow and ice mass-balance buoys installed in the Antarctic Weddell Sea.
Raymond Sellevold, Leonardus van Kampenhout, Jan T. M. Lenaerts, Brice Noël, William H. Lipscomb, and Miren Vizcaino
The Cryosphere, 13, 3193–3208, https://doi.org/10.5194/tc-13-3193-2019, https://doi.org/10.5194/tc-13-3193-2019, 2019
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We evaluate a downscaling method to calculate ice sheet surface mass balance with global climate models, despite their coarse resolution. We compare it with high-resolution climate modeling. Despite absence of fine-scale simulation of individual energy and mass contributors, the method provides realistic vertical SMB gradients that can be used in forcing of ice sheet models, e.g., for sea level projections. Also, the climate model simulation is improved with the method implemented interactively.
Cécile B. Ménard, Richard Essery, Alan Barr, Paul Bartlett, Jeff Derry, Marie Dumont, Charles Fierz, Hyungjun Kim, Anna Kontu, Yves Lejeune, Danny Marks, Masashi Niwano, Mark Raleigh, Libo Wang, and Nander Wever
Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019, https://doi.org/10.5194/essd-11-865-2019, 2019
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This paper describes long-term meteorological and evaluation datasets from 10 reference sites for use in snow modelling. We demonstrate how data sharing is crucial to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The ease of use, availability, and quality of the datasets will help model developers quantify and reduce model uncertainties and errors.
Leonardus van Kampenhout, Alan M. Rhoades, Adam R. Herrington, Colin M. Zarzycki, Jan T. M. Lenaerts, William J. Sacks, and Michiel R. van den Broeke
The Cryosphere, 13, 1547–1564, https://doi.org/10.5194/tc-13-1547-2019, https://doi.org/10.5194/tc-13-1547-2019, 2019
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A new tool is evaluated in which the climate and surface mass balance (SMB) of the Greenland ice sheet are resolved at 55 and 28 km resolution, while the rest of the globe is modelled at ~110 km. The local refinement of resolution leads to improved accumulation (SMB > 0) compared to observations; however ablation (SMB < 0) is deteriorated in some regions. This is attributed to changes in cloud cover and a reduced effectiveness of a model-specific vertical downscaling technique.
Alexandra Gossart, Stephen P. Palm, Niels Souverijns, Jan T. M. Lenaerts, Irina V. Gorodetskaya, Stef Lhermitte, and Nicole P. M. van Lipzig
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-25, https://doi.org/10.5194/tc-2019-25, 2019
Manuscript not accepted for further review
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Blowing snow measurements are scarce, both in time and space over the Antarctic ice sheet. We compare here CALIPSO satellite blowing snow measurements, to ground-base remote sensing ceilometer retrievals at two coastal stations in East Antarctica. Results indicate that 95 % of the blowing snow occurs under cloudy conditions, and are missed by the satellite. In addition, difficulties arise if comparing point locations to satellite overpasses.
Cécile Agosta, Charles Amory, Christoph Kittel, Anais Orsi, Vincent Favier, Hubert Gallée, Michiel R. van den Broeke, Jan T. M. Lenaerts, Jan Melchior van Wessem, Willem Jan van de Berg, and Xavier Fettweis
The Cryosphere, 13, 281–296, https://doi.org/10.5194/tc-13-281-2019, https://doi.org/10.5194/tc-13-281-2019, 2019
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Antarctic surface mass balance (ASMB), a component of the sea level budget, is commonly estimated through modelling as observations are scarce. The polar-oriented regional climate model MAR performs well in simulating the observed ASMB. MAR and RACMO2 share common biases we relate to drifting snow transport, with a 3 times larger magnitude than in previous estimates. Sublimation of precipitation in the katabatic layer modelled by MAR is of a magnitude similar to an observation-based estimate.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
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This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
Christian Gabriel Sommer, Nander Wever, Charles Fierz, and Michael Lehning
The Cryosphere, 12, 2923–2939, https://doi.org/10.5194/tc-12-2923-2018, https://doi.org/10.5194/tc-12-2923-2018, 2018
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Wind packing is how wind produces hard crusts at the surface of the snowpack. This is relevant for the local mass balance in polar regions. However, not much is known about this process and it is difficult to capture its high spatial and temporal variability. A wind-packing event was measured in Antarctica. It could be quantified how drifting snow leads to wind packing and generates barchan dunes. The documentation of these deposition dynamics is an important step in understanding polar snow.
Jan Melchior van Wessem, Willem Jan van de Berg, Brice P. Y. Noël, Erik van Meijgaard, Charles Amory, Gerit Birnbaum, Constantijn L. Jakobs, Konstantin Krüger, Jan T. M. Lenaerts, Stef Lhermitte, Stefan R. M. Ligtenberg, Brooke Medley, Carleen H. Reijmer, Kristof van Tricht, Luke D. Trusel, Lambertus H. van Ulft, Bert Wouters, Jan Wuite, and Michiel R. van den Broeke
The Cryosphere, 12, 1479–1498, https://doi.org/10.5194/tc-12-1479-2018, https://doi.org/10.5194/tc-12-1479-2018, 2018
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We present a detailed evaluation of the latest version of the regional atmospheric climate model RACMO2.3p2 (1979-2016) over the Antarctic ice sheet. The model successfully reproduces the present-day climate and surface mass balance (SMB) when compared with an extensive set of observations and improves on previous estimates of the Antarctic climate and SMB.
This study shows that the latest version of RACMO2 can be used for high-resolution future projections over the AIS.
Brice Noël, Willem Jan van de Berg, J. Melchior van Wessem, Erik van Meijgaard, Dirk van As, Jan T. M. Lenaerts, Stef Lhermitte, Peter Kuipers Munneke, C. J. P. Paul Smeets, Lambertus H. van Ulft, Roderik S. W. van de Wal, and Michiel R. van den Broeke
The Cryosphere, 12, 811–831, https://doi.org/10.5194/tc-12-811-2018, https://doi.org/10.5194/tc-12-811-2018, 2018
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We present a detailed evaluation of the latest version of the regional climate model RACMO2.3p2 at 11 km resolution (1958–2016) over the Greenland ice sheet (GrIS). The model successfully reproduces the present-day climate and surface mass balance, i.e. snowfall minus meltwater run-off, of the GrIS compared to in situ observations. Since run-off from marginal narrow glaciers is poorly resolved at 11 km, further statistical downscaling to 1 km resolution is required for mass balance studies.
Alexandra Gossart, Niels Souverijns, Irina V. Gorodetskaya, Stef Lhermitte, Jan T. M. Lenaerts, Jan H. Schween, Alexander Mangold, Quentin Laffineur, and Nicole P. M. van Lipzig
The Cryosphere, 11, 2755–2772, https://doi.org/10.5194/tc-11-2755-2017, https://doi.org/10.5194/tc-11-2755-2017, 2017
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Blowing snow plays an important role on local surface mass balance of Antarctica. We present here the blowing snow detection algorithm, to retrieve blowing snow occurrence from the attenuated backscatter signal of ceilometers set up at two station. There is a good correspondence in detection of heavy blowing snow by the algorithm and the visual observations performed at Neumayer station. Moreover, most of the blowing snow occurs during events bringing precipitation from the coast inland.
Jeremy Fyke, Jan T. M. Lenaerts, and Hailong Wang
The Cryosphere, 11, 2595–2609, https://doi.org/10.5194/tc-11-2595-2017, https://doi.org/10.5194/tc-11-2595-2017, 2017
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In this CESM modeling study, we uncover regional relationships in snowfall across Antarctica that are corroborated by regional modeling and ice core records. These relationships are driven by variability in large-scale atmospheric moisture transport and dampen overall Antarctic snowfall variability, with implications for Antarctic-sourced sea level variability and detection of an emergent anthropogenic signal in Antarctic mass trends.
Elizabeth R. Thomas, J. Melchior van Wessem, Jason Roberts, Elisabeth Isaksson, Elisabeth Schlosser, Tyler J. Fudge, Paul Vallelonga, Brooke Medley, Jan Lenaerts, Nancy Bertler, Michiel R. van den Broeke, Daniel A. Dixon, Massimo Frezzotti, Barbara Stenni, Mark Curran, and Alexey A. Ekaykin
Clim. Past, 13, 1491–1513, https://doi.org/10.5194/cp-13-1491-2017, https://doi.org/10.5194/cp-13-1491-2017, 2017
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Regional Antarctic snow accumulation derived from 79 ice core records is evaluated as part of the PAGES Antarctica 2k working group. Our results show that surface mass balance for the total Antarctic ice sheet has increased at a rate of 7 ± 0.13 Gt dec-1 since 1800 AD, representing a net reduction in sea level of ~ 0.02 mm dec-1 since 1800 and ~ 0.04 mm dec-1 since 1900 AD. The largest contribution is from the Antarctic Peninsula.
Peter Kuipers Munneke, Daniel McGrath, Brooke Medley, Adrian Luckman, Suzanne Bevan, Bernd Kulessa, Daniela Jansen, Adam Booth, Paul Smeets, Bryn Hubbard, David Ashmore, Michiel Van den Broeke, Heidi Sevestre, Konrad Steffen, Andrew Shepherd, and Noel Gourmelen
The Cryosphere, 11, 2411–2426, https://doi.org/10.5194/tc-11-2411-2017, https://doi.org/10.5194/tc-11-2411-2017, 2017
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How much snow falls on the Larsen C ice shelf? This is a relevant question, because this ice shelf might collapse sometime this century. To know if and when this could happen, we found out how much snow falls on its surface. This was difficult, because there are only very few measurements. Here, we used data from automatic weather stations, sled-pulled radars, and a climate model to find that melting the annual snowfall produces about 20 cm of water in the NE and over 70 cm in the SW.
Sebastian Würzer, Nander Wever, Roman Juras, Michael Lehning, and Tobias Jonas
Hydrol. Earth Syst. Sci., 21, 1741–1756, https://doi.org/10.5194/hess-21-1741-2017, https://doi.org/10.5194/hess-21-1741-2017, 2017
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We discuss a dual-domain water transport model in a physics-based snowpack model to account for preferential flow (PF) in addition to matrix flow. So far no operationally used snow model has explicitly accounted for PF. The new approach is compared to existing water transport models and validated against in situ data from sprinkling and natural rain-on-snow (ROS) events. Our work demonstrates the benefit of considering PF in modelling hourly snowpack runoff, especially during ROS conditions.
Willem Jan van de Berg and Brooke Medley
The Cryosphere, 10, 459–463, https://doi.org/10.5194/tc-10-459-2016, https://doi.org/10.5194/tc-10-459-2016, 2016
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Regional climate models improve the spatial surface mass balance (SMB) patterns in Antarctica compared to reanalyses, but they deteriorate the representation of interannual variability in SMB. Hence, we implemented additional nudging in our regional climate model RACMO2. Using annual SMB observations of the Twaites drainage basin, Antarctica, we show that this nudging vastly improves the representation of interannual variability without significant deterioration of the modelled mean SMB fields.
B. Medley, I. Joughin, B. E. Smith, S. B. Das, E. J. Steig, H. Conway, S. Gogineni, C. Lewis, A. S. Criscitiello, J. R. McConnell, M. R. van den Broeke, J. T. M. Lenaerts, D. H. Bromwich, J. P. Nicolas, and C. Leuschen
The Cryosphere, 8, 1375–1392, https://doi.org/10.5194/tc-8-1375-2014, https://doi.org/10.5194/tc-8-1375-2014, 2014
J. T. M. Lenaerts, C. J. P. P. Smeets, K. Nishimura, M. Eijkelboom, W. Boot, M. R. van den Broeke, and W. J. van de Berg
The Cryosphere, 8, 801–814, https://doi.org/10.5194/tc-8-801-2014, https://doi.org/10.5194/tc-8-801-2014, 2014
B. C. Gunter, O. Didova, R. E. M. Riva, S. R. M. Ligtenberg, J. T. M. Lenaerts, M. A. King, M. R. van den Broeke, and T. Urban
The Cryosphere, 8, 743–760, https://doi.org/10.5194/tc-8-743-2014, https://doi.org/10.5194/tc-8-743-2014, 2014
J. M. van Wessem, C. H. Reijmer, J. T. M. Lenaerts, W. J. van de Berg, M. R. van den Broeke, and E. van Meijgaard
The Cryosphere, 8, 125–135, https://doi.org/10.5194/tc-8-125-2014, https://doi.org/10.5194/tc-8-125-2014, 2014
X. Fettweis, B. Franco, M. Tedesco, J. H. van Angelen, J. T. M. Lenaerts, M. R. van den Broeke, and H. Gallée
The Cryosphere, 7, 469–489, https://doi.org/10.5194/tc-7-469-2013, https://doi.org/10.5194/tc-7-469-2013, 2013
I. M. Howat, S. de la Peña, J. H. van Angelen, J. T. M. Lenaerts, and M. R. van den Broeke
The Cryosphere, 7, 201–204, https://doi.org/10.5194/tc-7-201-2013, https://doi.org/10.5194/tc-7-201-2013, 2013
Related subject area
Cryosphere
The Utrecht Finite Volume Ice-Sheet Model (UFEMISM) version 2.0 – Part 1: Description and idealised experiments
A Flexible Snow Model (FSM 2.1.1) including a forest canopy
CMIP6 models overestimate sea ice melt, growth and conduction relative to ice mass balance buoy estimates
Coupling framework (1.0) for the Úa (2023b) ice sheet model and the FESOM-1.4 z-coordinate ocean model in an Antarctic domain
A gradient-boosted tree framework to model the ice thickness of the world's glaciers (IceBoost v1.1)
A Python library for solving ice sheet modeling problems using Physics Informed Neural Networks, PINNICLE v1.0
Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2)
Constraining CMIP6 sea ice simulations with ICESat-2
Quantitative sub-ice and marine tracing of Antarctic sediment provenance (TASP v1.0)
Tuning parameters of a sea ice model using machine learning
WRF-Chem simulations of snow nitrate and other physicochemical properties in northern China
A framework for three-dimensional dynamic modeling of mountain glaciers in the Community Ice Sheet Model (CISM v2.2)
Clustering simulated snow profiles to form avalanche forecast regions
SnowQM 1.0: a fast R package for bias-correcting spatial fields of snow water equivalent using quantile mapping
Simulation of snow albedo and solar irradiance profile with the Two-streAm Radiative TransfEr in Snow (TARTES) v2.0 model
Computationally efficient subglacial drainage modelling using Gaussian Process emulators: GlaDS-GP v1.0
Evaluation of MITgcm-based ocean reanalyses for the Southern Ocean
Improvements in the land surface configuration to better simulate seasonal snow cover in the European Alps with the CNRM-AROME (cycle 46) convection-permitting regional climate model
A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting
Anisotropic metric-based mesh adaptation for ice flow modelling in Firedrake
A global–land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: formulation and evaluation at instrumented sites
Design and performance of ELSA v2.0: an isochronal model for ice-sheet layer tracing
Southern Ocean Ice Prediction System version 1.0 (SOIPS v1.0): description of the system and evaluation of synoptic-scale sea ice forecasts
Lagrangian tracking of sea ice in Community Ice CodE (CICE; version 5)
openAMUNDSEN v1.0: an open-source snow-hydrological model for mountain regions
OpenFOAM-avalanche 2312: depth-integrated models beyond dense-flow avalanches
Refactoring the elastic–viscous–plastic solver from the sea ice model CICE v6.5.1 for improved performance
A new 3D full-Stokes calving algorithm within Elmer/Ice (v9.0)
A novel numerical implementation for the surface energy budget of melting snowpacks and glaciers
SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0)
Graphics-processing-unit-accelerated ice flow solver for unstructured meshes using the Shallow-Shelf Approximation (FastIceFlo v1.0.1)
A finite-element framework to explore the numerical solution of the coupled problem of heat conduction, water vapor diffusion, and settlement in dry snow (IvoriFEM v0.1.0)
Description and validation of the ice sheet model Nix v1.0
AvaFrame com1DFA (v1.3): a thickness-integrated computational avalanche module – theory, numerics, and testing
Universal differential equations for glacier ice flow modelling
A new model for supraglacial hydrology evolution and drainage for the Greenland Ice Sheet (SHED v1.0)
Modeling sensitivities of thermally and hydraulically driven ice stream surge cycling
A parallel implementation of the confined–unconfined aquifer system model for subglacial hydrology: design, verification, and performance analysis (CUAS-MPI v0.1.0)
Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms
An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0
The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere
Glacier Energy and Mass Balance (GEMB): a model of firn processes for cryosphere research
Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean
Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling
SUHMO: an adaptive mesh refinement SUbglacial Hydrology MOdel v1.0
Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau
The Multiple Snow Data Assimilation System (MuSA v1.0)
The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)
Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G
Constantijn J. Berends, Victor Azizi, Jorge A. Bernales, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 3635–3659, https://doi.org/10.5194/gmd-18-3635-2025, https://doi.org/10.5194/gmd-18-3635-2025, 2025
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Ice-sheet models are computer programs that can simulate how the Greenland and Antarctic ice sheets will evolve in the future. The accuracy of these models depends on their resolution: how small the details are that the model can resolve. We have created a model with a variable resolution that can resolve a lot of detail in areas where lots of changes happen in the ice and less detail in areas where the ice does not move so much. This makes the model both accurate and fast.
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
Geosci. Model Dev., 18, 3583–3605, https://doi.org/10.5194/gmd-18-3583-2025, https://doi.org/10.5194/gmd-18-3583-2025, 2025
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How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
Alex E. West and Edward W. Blockley
Geosci. Model Dev., 18, 3041–3064, https://doi.org/10.5194/gmd-18-3041-2025, https://doi.org/10.5194/gmd-18-3041-2025, 2025
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This study uses ice mass balance buoys – temperature- and height-measuring devices frozen into sea ice – to find how well climate models simulate (1) melt and growth of Arctic sea ice and (2) conduction of heat through Arctic sea ice. This may help understand why models produce varying amounts of sea ice in the present day. We find that models tend to show more melt, growth or conduction for a given ice thickness than the buoys, although the difference is smaller for models with more physically realistic thermodynamics.
Ole Richter, Ralph Timmermann, G. Hilmar Gudmundsson, and Jan De Rydt
Geosci. Model Dev., 18, 2945–2960, https://doi.org/10.5194/gmd-18-2945-2025, https://doi.org/10.5194/gmd-18-2945-2025, 2025
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The new coupled ice sheet–ocean model addresses challenges related to horizontal resolution through advanced mesh flexibility, enabled by the use of unstructured grids. We describe the new model, verify its functioning in an idealised setting and demonstrate its advantages in a global-ocean–Antarctic ice sheet domain. The results of this study comprise an important step towards improving predictions of the Antarctic contribution to sea level rise over centennial timescales.
Niccolò Maffezzoli, Eric Rignot, Carlo Barbante, Troels Petersen, and Sebastiano Vascon
Geosci. Model Dev., 18, 2545–2568, https://doi.org/10.5194/gmd-18-2545-2025, https://doi.org/10.5194/gmd-18-2545-2025, 2025
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In this work we introduce IceBoost, a machine learning framework to model the ice thickness distribution of all the world's glaciers with greater accuracy than state-of-the-art methods. The model is trained on 3.7 million measurements globally available and provides skilful estimates across all regions. This advancement will help in better assessing future sea level changes and freshwater resources, with significance for both the scientific community and society at large.
Gong Cheng, Mansa Krishna, and Mathieu Morlighem
EGUsphere, https://doi.org/10.5194/egusphere-2025-1188, https://doi.org/10.5194/egusphere-2025-1188, 2025
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Predicting ice sheet contributions to sea level rise is challenging due to limited data and uncertainties in key processes. Traditional models require complex methods that lack flexibility. We developed PINNICLE, an open-source Python library that integrates machine learning with physical laws to improve ice sheet modeling. By combining data and physics, PINNICLE enhances predictions and adaptability, providing a powerful tool for climate research and sea level rise projections.
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025, https://doi.org/10.5194/gmd-18-1829-2025, 2025
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Accurately measuring snow height is key for modeling approaches in climate science, snow hydrology, and avalanche forecasting. Erroneous snow height measurements often occur when snow height is low or changes, for instance during snowfall in summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep-learning approaches. Our approach can be easily implemented in a data pipeline for snow modeling.
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.
James W. Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin J. Siegert, and Liam Holder
Geosci. Model Dev., 18, 1673–1708, https://doi.org/10.5194/gmd-18-1673-2025, https://doi.org/10.5194/gmd-18-1673-2025, 2025
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Ice sheet models can help predict how Antarctica's ice sheets respond to environmental change, and such models benefit from comparison to geological data. Here, we use an ice sheet model output and other data to predict the erosion of debris and trace its transport to where it is deposited on the ocean floor. This allows the results of ice sheet modelling to be directly and quantitively compared to real-world data, helping to reduce uncertainty regarding Antarctic sea level contribution.
Anton Korosov, Yue Ying, and Einar Ólason
Geosci. Model Dev., 18, 885–904, https://doi.org/10.5194/gmd-18-885-2025, https://doi.org/10.5194/gmd-18-885-2025, 2025
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We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev., 18, 651–670, https://doi.org/10.5194/gmd-18-651-2025, https://doi.org/10.5194/gmd-18-651-2025, 2025
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We employed the WRF-Chem model to parameterize atmospheric nitrate deposition in snow and evaluate its performance in simulating snow cover, snow depth, and concentrations of dust and nitrate using new observations from northern China. The results generally exhibit reasonable agreement with field observations in northern China, demonstrating the model's capability to simulate snow properties, including concentrations of reservoir species.
Samar Minallah, William Lipscomb, Gunter Leguy, and Harry Zekollari
EGUsphere, https://doi.org/10.5194/egusphere-2024-4152, https://doi.org/10.5194/egusphere-2024-4152, 2025
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We implemented a new modeling framework within an Earth system model to study the evolution of mountain glaciers under different climate scenarios and applied it to the European Alps. Alpine glaciers will lose a large volume fraction under current temperatures, with near complete ice loss under warmer scenarios. This is the first use of a 3D, higher-order ice flow model for regional-scale glacier simulations that will enable assessments of coupled land ice and Earth system processes.
Simon Horton, Florian Herla, and Pascal Haegeli
Geosci. Model Dev., 18, 193–209, https://doi.org/10.5194/gmd-18-193-2025, https://doi.org/10.5194/gmd-18-193-2025, 2025
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We present a method for avalanche forecasters to analyze patterns in snowpack model simulations. It uses fuzzy clustering to group small regions into larger forecast areas based on snow characteristics, locations, and temporal history. Tested in the Columbia Mountains in two winter seasons, it closely matched real forecast regions regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev., 17, 8969–8988, https://doi.org/10.5194/gmd-17-8969-2024, https://doi.org/10.5194/gmd-17-8969-2024, 2024
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We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better-quality maps. The correction can then be extended backwards and forwards in time for periods when better-quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the past 60 years at a resolution of 1 d and 1 km. This is the first time that such a dataset has been produced.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
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The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow albedo in the solar domain and the profiles of light and energy absorption in a multi-layered snowpack whose physical properties are user defined. It uniquely considers snow grain shape flexibly, based on recent insights showing that snow does not behave as a collection of ice spheres but instead as a random medium. TARTES is user-friendly yet performs comparably to more complex models.
Tim Hill, Derek Bingham, Gwenn E. Flowers, and Matthew J. Hoffman
EGUsphere, https://doi.org/10.22541/essoar.172736254.41350153/v2, https://doi.org/10.22541/essoar.172736254.41350153/v2, 2024
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Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train fast statistical models called Gaussian Process emulators to accelerate subglacial drainage modelling by ~1000 times. We use the fast emulator predictions to show that three of the model parameters are responsible for >90 % of the variance in model outputs. The fast GP emulators will enable future uncertainty quantification and calibration of these models.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Yafei Nie, Ian Fenty, Matthew Mazloff, Armin Köhl, and Dimitris Menemenlis
Geosci. Model Dev., 17, 8613–8638, https://doi.org/10.5194/gmd-17-8613-2024, https://doi.org/10.5194/gmd-17-8613-2024, 2024
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Global- and basin-scale ocean reanalyses are becoming easily accessible. However, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluation. We conduct intercomparison analyses of Massachusetts Institute of Technology General Circulation Model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open-ocean temporal variability and Antarctic continental shelves require improvements.
Diego Monteiro, Cécile Caillaud, Matthieu Lafaysse, Adrien Napoly, Mathieu Fructus, Antoinette Alias, and Samuel Morin
Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, https://doi.org/10.5194/gmd-17-7645-2024, 2024
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Modeling snow cover in climate and weather forecasting models is a challenge even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties when representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration, enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
Alessandro Maissen, Frank Techel, and Michele Volpi
Geosci. Model Dev., 17, 7569–7593, https://doi.org/10.5194/gmd-17-7569-2024, https://doi.org/10.5194/gmd-17-7569-2024, 2024
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By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
Davor Dundovic, Joseph G. Wallwork, Stephan C. Kramer, Fabien Gillet-Chaulet, Regine Hock, and Matthew D. Piggott
EGUsphere, https://doi.org/10.5194/egusphere-2024-2649, https://doi.org/10.5194/egusphere-2024-2649, 2024
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Accurate numerical studies of glaciers often require high-resolution simulations, which often prove too demanding even for modern computers. In this paper we develop a method that identifies whether different parts of a glacier require high or low resolution based on its physical features, such as its thickness and velocity. We show that by doing so we can achieve a more optimal simulation accuracy for the available computing resources compared to uniform resolution simulations.
Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova
Geosci. Model Dev., 17, 7219–7244, https://doi.org/10.5194/gmd-17-7219-2024, https://doi.org/10.5194/gmd-17-7219-2024, 2024
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We describe a new snow scheme developed for use in global climate models, which simulates the interactions of snowpack with vegetation, atmosphere, and soil. We test the new snow model over a set of sites where in situ observations are available. We find that when compared to a simpler snow model, this model improves predictions of seasonal snow and of soil temperature under the snowpack, important variables for simulating both the hydrological cycle and the global climate system.
Therese Rieckh, Andreas Born, Alexander Robinson, Robert Law, and Gerrit Gülle
Geosci. Model Dev., 17, 6987–7000, https://doi.org/10.5194/gmd-17-6987-2024, https://doi.org/10.5194/gmd-17-6987-2024, 2024
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We present the open-source model ELSA, which simulates the internal age structure of large ice sheets. It creates layers of snow accumulation at fixed times during the simulation, which are used to model the internal stratification of the ice sheet. Together with reconstructed isochrones from radiostratigraphy data, ELSA can be used to assess ice sheet models and to improve their parameterization. ELSA can be used coupled to an ice sheet model or forced with its output.
Fu Zhao, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu
Geosci. Model Dev., 17, 6867–6886, https://doi.org/10.5194/gmd-17-6867-2024, https://doi.org/10.5194/gmd-17-6867-2024, 2024
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In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely the Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea ice‒ocean‒ice shelf coupled model and can assimilate sea ice concentration observations. By assessing the system's performance in sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for the next 7 d and has the potential to guide ship navigation in the Antarctic sea ice zone.
Chenhui Ning, Shiming Xu, Yan Zhang, Xuantong Wang, Zhihao Fan, and Jiping Liu
Geosci. Model Dev., 17, 6847–6866, https://doi.org/10.5194/gmd-17-6847-2024, https://doi.org/10.5194/gmd-17-6847-2024, 2024
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Sea ice models are mainly based on non-moving structured grids, which is different from buoy measurements that follow the ice drift. To facilitate Lagrangian analysis, we introduce online tracking of sea ice in Community Ice CodE (CICE). We validate the sea ice tracking with buoys and evaluate the sea ice deformation in high-resolution simulations, which show multi-fractal characteristics. The source code is openly available and can be used in various scientific and operational applications.
Ulrich Strasser, Michael Warscher, Erwin Rottler, and Florian Hanzer
Geosci. Model Dev., 17, 6775–6797, https://doi.org/10.5194/gmd-17-6775-2024, https://doi.org/10.5194/gmd-17-6775-2024, 2024
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openAMUNDSEN is a fully distributed open-source snow-hydrological model for mountain catchments. It includes process representations of an empirical, semi-empirical, and physical nature. It uses temperature, precipitation, humidity, radiation, and wind speed as forcing data and is computationally efficient, of a modular nature, and easily extendible. The Python code is available on GitHub (https://github.com/openamundsen/openamundsen), including documentation (https://doc.openamundsen.org).
Matthias Rauter and Julia Kowalski
Geosci. Model Dev., 17, 6545–6569, https://doi.org/10.5194/gmd-17-6545-2024, https://doi.org/10.5194/gmd-17-6545-2024, 2024
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Snow avalanches can form large powder clouds that substantially exceed the velocity and reach of the dense core. Only a few complex models exist to simulate this phenomenon, and the respective hazard is hard to predict. This work provides a novel flow model that focuses on simple relations while still encapsulating the significant behaviour. The model is applied to reconstruct two catastrophic powder snow avalanche events in Austria.
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth C. Hunke, and Stefan Rethmeier
Geosci. Model Dev., 17, 6529–6544, https://doi.org/10.5194/gmd-17-6529-2024, https://doi.org/10.5194/gmd-17-6529-2024, 2024
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Earth system models (ESMs) today strive for better quality based on improved resolutions and improved physics. A limiting factor is the supercomputers at hand and how best to utilize them. This study focuses on the refactorization of one part of a sea ice model (CICE), namely the dynamics. It shows that the performance can be significantly improved, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.
Iain Wheel, Douglas I. Benn, Anna J. Crawford, Joe Todd, and Thomas Zwinger
Geosci. Model Dev., 17, 5759–5777, https://doi.org/10.5194/gmd-17-5759-2024, https://doi.org/10.5194/gmd-17-5759-2024, 2024
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Calving, the detachment of large icebergs from glaciers, is one of the largest uncertainties in future sea level rise projections. This process is poorly understood, and there is an absence of detailed models capable of simulating calving. A new 3D calving model has been developed to better understand calving at glaciers where detailed modelling was previously limited. Importantly, the new model is very flexible. By allowing for unrestricted calving geometries, it can be applied at any location.
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
Geosci. Model Dev., 17, 1903–1929, https://doi.org/10.5194/gmd-17-1903-2024, https://doi.org/10.5194/gmd-17-1903-2024, 2024
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In this paper, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature, and melt while preserving a reasonable computation time.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
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Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024, https://doi.org/10.5194/gmd-17-1041-2024, 2024
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The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
Anjali Sandip, Ludovic Räss, and Mathieu Morlighem
Geosci. Model Dev., 17, 899–909, https://doi.org/10.5194/gmd-17-899-2024, https://doi.org/10.5194/gmd-17-899-2024, 2024
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We solve momentum balance for unstructured meshes to predict ice flow for real glaciers using a pseudo-transient method on graphics processing units (GPUs) and compare it to a standard central processing unit (CPU) implementation. We justify the GPU implementation by applying the price-to-performance metric for up to million-grid-point spatial resolutions. This study represents a first step toward leveraging GPU processing power, enabling more accurate polar ice discharge predictions.
Julien Brondex, Kévin Fourteau, Marie Dumont, Pascal Hagenmuller, Neige Calonne, François Tuzet, and Henning Löwe
Geosci. Model Dev., 16, 7075–7106, https://doi.org/10.5194/gmd-16-7075-2023, https://doi.org/10.5194/gmd-16-7075-2023, 2023
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Vapor diffusion is one of the main processes governing snowpack evolution, and it must be accounted for in models. Recent attempts to represent vapor diffusion in numerical models have faced several difficulties regarding computational cost and mass and energy conservation. Here, we develop our own finite-element software to explore numerical approaches and enable us to overcome these difficulties. We illustrate the capability of these approaches on established numerical benchmarks.
Daniel Moreno-Parada, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
EGUsphere, https://doi.org/10.5194/egusphere-2023-2690, https://doi.org/10.5194/egusphere-2023-2690, 2023
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We introduce Nix, an ice-sheet model designed for understanding how large masses of ice behave. Nix as a computer program that simulates the movement and temperature changes in ice sheets. Nix helps us study how ice sheets respond to changes in the atmosphere and ocean. We found that how fast ice melts under the shelves and how heat is exchanged, play a role in determining the future of ice sheets. Nix is a useful tool for learning more about how climate change affects polar ice sheets.
Matthias Tonnel, Anna Wirbel, Felix Oesterle, and Jan-Thomas Fischer
Geosci. Model Dev., 16, 7013–7035, https://doi.org/10.5194/gmd-16-7013-2023, https://doi.org/10.5194/gmd-16-7013-2023, 2023
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Avaframe - the open avalanche framework - provides open-source tools to simulate and investigate snow avalanches. It is utilized for multiple purposes, the two main applications being hazard mapping and scientific research of snow processes. We present the theory, conversion to a computer model, and testing for one of the core modules used for simulations of a particular type of avalanche, the so-called dense-flow avalanches. Tests check and confirm the applicability of the utilized method.
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, https://doi.org/10.5194/gmd-16-6671-2023, 2023
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We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823, https://doi.org/10.5194/gmd-16-5803-2023, https://doi.org/10.5194/gmd-16-5803-2023, 2023
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We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Kevin Hank, Lev Tarasov, and Elisa Mantelli
Geosci. Model Dev., 16, 5627–5652, https://doi.org/10.5194/gmd-16-5627-2023, https://doi.org/10.5194/gmd-16-5627-2023, 2023
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Physically meaningful modeling of geophysical system instabilities is numerically challenging, given the potential effects of purely numerical artifacts. Here we explore the sensitivity of ice stream surge activation to numerical and physical model aspects. We find that surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions and are significantly affected by the incorporation of bed thermal and sub-glacial hydrology models.
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, and Angelika Humbert
Geosci. Model Dev., 16, 5305–5322, https://doi.org/10.5194/gmd-16-5305-2023, https://doi.org/10.5194/gmd-16-5305-2023, 2023
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Water underneath ice sheets affects the motion of glaciers. This study presents a newly developed code, CUAS-MPI, that simulates subglacial hydrology. It is designed for supercomputers and is hence a parallelized code. We measure the performance of this code for simulations of the entire Greenland Ice Sheet and find that the code works efficiently. Moreover, we validated the code to ensure the correctness of the solution. CUAS-MPI opens new possibilities for simulations of ice sheet hydrology.
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.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
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Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
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The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Alex S. Gardner, Nicole-Jeanne Schlegel, and Eric Larour
Geosci. Model Dev., 16, 2277–2302, https://doi.org/10.5194/gmd-16-2277-2023, https://doi.org/10.5194/gmd-16-2277-2023, 2023
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This is the first description of the open-source Glacier Energy and Mass Balance (GEMB) model. GEMB models the ice sheet and glacier surface–atmospheric energy and mass exchange, as well as the firn state. The model is evaluated against the current state of the art and in situ observations and is shown to perform well.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
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State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Varun Sharma, Franziska Gerber, and Michael Lehning
Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, https://doi.org/10.5194/gmd-16-719-2023, 2023
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Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere.
Anne M. Felden, Daniel F. Martin, and Esmond G. Ng
Geosci. Model Dev., 16, 407–425, https://doi.org/10.5194/gmd-16-407-2023, https://doi.org/10.5194/gmd-16-407-2023, 2023
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We present and validate a novel subglacial hydrology model, SUHMO, based on an adaptive mesh refinement framework. We propose the addition of a pseudo-diffusion to recover the wall melting in channels. Computational performance analysis demonstrates the efficiency of adaptive mesh refinement on large-scale hydrologic problems. The adaptive mesh refinement approach will eventually enable better ice bed boundary conditions for ice sheet simulations at a reasonable computational cost.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
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Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
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We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
Max Brils, Peter Kuipers Munneke, Willem Jan van de Berg, and Michiel van den Broeke
Geosci. Model Dev., 15, 7121–7138, https://doi.org/10.5194/gmd-15-7121-2022, https://doi.org/10.5194/gmd-15-7121-2022, 2022
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Firn covers the Greenland ice sheet (GrIS) and can temporarily prevent mass loss. Here, we present the latest version of our firn model, IMAU-FDM, with an application to the GrIS. We improved the density of fallen snow, the firn densification rate and the firn's thermal conductivity. This leads to a higher air content and 10 m temperatures. Furthermore we investigate three case studies and find that the updated model shows greater variability and an increased sensitivity in surface elevation.
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Short summary
Ice sheets gain mass via snowfall. However, snowfall is redistributed by the wind, resulting in accumulation differences of up to a factor of 5 over distances as short as 5 km. These differences complicate estimates of ice sheet contribution to sea level rise. For this reason, we have developed a new model for estimating wind-driven snow redistribution on ice sheets. We show that, over Pine Island Glacier in West Antarctica, the model improves estimates of snow accumulation variability.
Ice sheets gain mass via snowfall. However, snowfall is redistributed by the wind, resulting in...
