Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-7121-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7121-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
21 Sep 2022
Development and technical paper |
| 21 Sep 2022
| 21 Sep 2022
Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G
Max Brils
CORRESPONDING AUTHOR
Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
Peter Kuipers Munneke
Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
Willem Jan van de Berg
Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
Michiel van den Broeke
Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
Related authors
Baptiste Vandecrux, Robert S. Fausto, Jason E. Box, Federico Covi, Regine Hock, Åsa K. Rennermalm, Achim Heilig, Jakob Abermann, Dirk van As, Elisa Bjerre, Xavier Fettweis, Paul C. J. P. Smeets, Peter Kuipers Munneke, Michiel R. van den Broeke, Max Brils, Peter L. Langen, Ruth Mottram, and Andreas P. Ahlstrøm
The Cryosphere, 18, 609–631, https://doi.org/10.5194/tc-18-609-2024, https://doi.org/10.5194/tc-18-609-2024, 2024
Short summary
Short summary
How fast is the Greenland ice sheet warming? In this study, we compiled 4500+ temperature measurements at 10 m below the ice sheet surface (T10m) from 1912 to 2022. We trained a machine learning model on these data and reconstructed T10m for the ice sheet during 1950–2022. After a slight cooling during 1950–1985, the ice sheet warmed at a rate of 0.7 °C per decade until 2022. Climate models showed mixed results compared to our observations and underestimated the warming in key regions.
Sanne B. M. Veldhuijsen, Willem Jan van de Berg, Max Brils, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere, 17, 1675–1696, https://doi.org/10.5194/tc-17-1675-2023, https://doi.org/10.5194/tc-17-1675-2023, 2023
Short summary
Short summary
Firn is the transition of snow to glacier ice and covers 99 % of the Antarctic ice sheet. Knowledge about the firn layer and its variability is important, as it impacts satellite-based estimates of ice sheet mass change. Also, firn contains pores in which nearly all of the surface melt is retained. Here, we improve a semi-empirical firn model and simulate the firn characteristics for the period 1979–2020. We evaluate the performance with field and satellite measures and test its sensitivity.
Baptiste Vandecrux, Robert S. Fausto, Jason E. Box, Federico Covi, Regine Hock, Åsa K. Rennermalm, Achim Heilig, Jakob Abermann, Dirk van As, Elisa Bjerre, Xavier Fettweis, Paul C. J. P. Smeets, Peter Kuipers Munneke, Michiel R. van den Broeke, Max Brils, Peter L. Langen, Ruth Mottram, and Andreas P. Ahlstrøm
The Cryosphere, 18, 609–631, https://doi.org/10.5194/tc-18-609-2024, https://doi.org/10.5194/tc-18-609-2024, 2024
Short summary
Short summary
How fast is the Greenland ice sheet warming? In this study, we compiled 4500+ temperature measurements at 10 m below the ice sheet surface (T10m) from 1912 to 2022. We trained a machine learning model on these data and reconstructed T10m for the ice sheet during 1950–2022. After a slight cooling during 1950–1985, the ice sheet warmed at a rate of 0.7 °C per decade until 2022. Climate models showed mixed results compared to our observations and underestimated the warming in key regions.
Srinidhi Gadde and Willem Jan van de Berg
EGUsphere, https://doi.org/10.5194/egusphere-2024-116, https://doi.org/10.5194/egusphere-2024-116, 2024
Short summary
Short summary
Blowing snow sublimation represents the major loss term in the mass balance of Antarctica. In this study, we update the representation of blowing snow in the regional climate model RACMO. With the updates, results compare well with the observations from East Antarctica. Also, continent wide variation of blowing snow compares well with the satellite observations. Hence, the updates provide a clear step forward in producing physically sound and reliable estimate of the mass balance of Antarctica.
Sanne B. M. Veldhuijsen, Willem Jan van de Berg, Peter Kuipers Munneke, and Michiel R. van den Broeke
EGUsphere, https://doi.org/10.5194/egusphere-2023-2237, https://doi.org/10.5194/egusphere-2023-2237, 2023
Short summary
Short summary
We use the IMAU firn densification model to simulate 21st century evolution of Antarctic firn air content. Our results underline the different response of low- and high-accumulation ice shelves to atmospheric warming, showing increased ice slab formation on low-accumulation ice shelves, thereby enhancing depletion of firn air content accessible for meltwater.
Maria T. Kappelsberger, Martin Horwath, Eric Buchta, Matthias O. Willen, Ludwig Schröder, Sanne B. M. Veldhuijsen, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-140, https://doi.org/10.5194/tc-2023-140, 2023
Revised manuscript under review for TC
Short summary
Short summary
The interannual variations in the height of the Antarctic Ice Sheet (AIS) are mainly due to natural variations in snowfall. Precise knowledge of these variations is important for the detection of any long-term climatic trends in AIS surface elevation. We present a new product that spatially resolves these height variations over the period 1992–2017. The product combines the strengths of atmospheric modeling results and satellite altimetry measurements.
Lena G. Buth, Valeria Di Biase, Peter Kuipers Munneke, Stef Lhermitte, Sanne B. M. Veldhuijsen, Sophie de Roda Husman, Michiel R. van den Broeke, and Bert Wouters
EGUsphere, https://doi.org/10.5194/egusphere-2023-2000, https://doi.org/10.5194/egusphere-2023-2000, 2023
Short summary
Short summary
Liquid meltwater which is stored in air bubbles in the compacted snow near the surface of Antarctica can affect ice shelf stability. In order to detect the presence of such firn aquifers over large scales, satellite remote sensing is needed. In this paper, we present our new detection method using radar satellite data as well as the results for the whole Antarctic Peninsula. Firn aquifers are found in the north and northwest of the peninsula, in agreement with locations predicted by models.
Inès N. Otosaka, Andrew Shepherd, Erik R. Ivins, Nicole-Jeanne Schlegel, Charles Amory, Michiel R. van den Broeke, Martin Horwath, Ian Joughin, Michalea D. King, Gerhard Krinner, Sophie Nowicki, Anthony J. Payne, Eric Rignot, Ted Scambos, Karen M. Simon, Benjamin E. Smith, Louise S. Sørensen, Isabella Velicogna, Pippa L. Whitehouse, Geruo A, Cécile Agosta, Andreas P. Ahlstrøm, Alejandro Blazquez, William Colgan, Marcus E. Engdahl, Xavier Fettweis, Rene Forsberg, Hubert Gallée, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian C. Gunter, Christopher Harig, Veit Helm, Shfaqat Abbas Khan, Christoph Kittel, Hannes Konrad, Peter L. Langen, Benoit S. Lecavalier, Chia-Chun Liang, Bryant D. Loomis, Malcolm McMillan, Daniele Melini, Sebastian H. Mernild, Ruth Mottram, Jeremie Mouginot, Johan Nilsson, Brice Noël, Mark E. Pattle, William R. Peltier, Nadege Pie, Mònica Roca, Ingo Sasgen, Himanshu V. Save, Ki-Weon Seo, Bernd Scheuchl, Ernst J. O. Schrama, Ludwig Schröder, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler C. Sutterley, Bramha Dutt Vishwakarma, Jan Melchior van Wessem, David Wiese, Wouter van der Wal, and Bert Wouters
Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, https://doi.org/10.5194/essd-15-1597-2023, 2023
Short summary
Short summary
By measuring changes in the volume, gravitational attraction, and ice flow of Greenland and Antarctica from space, we can monitor their mass gain and loss over time. Here, we present a new record of the Earth’s polar ice sheet mass balance produced by aggregating 50 satellite-based estimates of ice sheet mass change. This new assessment shows that the ice sheets have lost (7.5 x 1012) t of ice between 1992 and 2020, contributing 21 mm to sea level rise.
Sanne B. M. Veldhuijsen, Willem Jan van de Berg, Max Brils, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere, 17, 1675–1696, https://doi.org/10.5194/tc-17-1675-2023, https://doi.org/10.5194/tc-17-1675-2023, 2023
Short summary
Short summary
Firn is the transition of snow to glacier ice and covers 99 % of the Antarctic ice sheet. Knowledge about the firn layer and its variability is important, as it impacts satellite-based estimates of ice sheet mass change. Also, firn contains pores in which nearly all of the surface melt is retained. Here, we improve a semi-empirical firn model and simulate the firn characteristics for the period 1979–2020. We evaluate the performance with field and satellite measures and test its sensitivity.
Marte G. Hofsteenge, Nicolas J. Cullen, Carleen H. Reijmer, Michiel van den Broeke, Marwan Katurji, and John F. Orwin
The Cryosphere, 16, 5041–5059, https://doi.org/10.5194/tc-16-5041-2022, https://doi.org/10.5194/tc-16-5041-2022, 2022
Short summary
Short summary
In the McMurdo Dry Valleys (MDV), foehn winds can impact glacial meltwater production and the fragile ecosystem that depends on it. We study these dry and warm winds at Joyce Glacier and show they are caused by a different mechanism than that found for nearby valleys, demonstrating the complex interaction of large-scale winds with the mountains in the MDV. We find that foehn winds increase sublimation of ice, increase heating from the atmosphere, and increase the occurrence and rates of melt.
Raf M. Antwerpen, Marco Tedesco, Xavier Fettweis, Patrick Alexander, and Willem Jan van de Berg
The Cryosphere, 16, 4185–4199, https://doi.org/10.5194/tc-16-4185-2022, https://doi.org/10.5194/tc-16-4185-2022, 2022
Short summary
Short summary
The ice on Greenland has been melting more rapidly over the last few years. Most of this melt comes from the exposure of ice when the overlying snow melts. This ice is darker than snow and absorbs more sunlight, leading to more melt. It remains challenging to accurately simulate the brightness of the ice. We show that the color of ice simulated by Modèle Atmosphérique Régional (MAR) is too bright. We then show that this means that MAR may underestimate how fast the Greenland ice is melting.
Lena G. Buth, Bert Wouters, Sanne B. M. Veldhuijsen, Stef Lhermitte, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-127, https://doi.org/10.5194/tc-2022-127, 2022
Manuscript not accepted for further review
Short summary
Short summary
Liquid meltwater which is stored in air bubbles in the compacted snow near the surface of Antarctica can affect ice shelf stability. In order to detect the presence of such firn aquifers over large scales, satellite remote sensing is needed. In this paper, we present our new detection method using radar satellite data as well as the results for the whole Antarctic Peninsula. Firn aquifers are found in the north and northwest of the peninsula, in agreement with locations predicted by models.
Tiago Silva, Jakob Abermann, Brice Noël, Sonika Shahi, Willem Jan van de Berg, and Wolfgang Schöner
The Cryosphere, 16, 3375–3391, https://doi.org/10.5194/tc-16-3375-2022, https://doi.org/10.5194/tc-16-3375-2022, 2022
Short summary
Short summary
To overcome internal climate variability, this study uses k-means clustering to combine NAO, GBI and IWV over the Greenland Ice Sheet (GrIS) and names the approach as the North Atlantic influence on Greenland (NAG). With the support of a polar-adapted RCM, spatio-temporal changes on SEB components within NAG phases are investigated. We report atmospheric warming and moistening across all NAG phases as well as large-scale and regional-scale contributions to GrIS mass loss and their interactions.
Christiaan T. van Dalum, Willem Jan van de Berg, and Michiel R. van den Broeke
The Cryosphere, 16, 1071–1089, https://doi.org/10.5194/tc-16-1071-2022, https://doi.org/10.5194/tc-16-1071-2022, 2022
Short summary
Short summary
In this study, we improve the regional climate model RACMO2 and investigate the climate of Antarctica. We have implemented a new radiative transfer and snow albedo scheme and do several sensitivity experiments. When fully tuned, the results compare well with observations and snow temperature profiles improve. Moreover, small changes in the albedo and the investigated processes can lead to a strong overestimation of melt, locally leading to runoff and a reduced surface mass balance.
Zhongyang Hu, Peter Kuipers Munneke, Stef Lhermitte, Maaike Izeboud, and Michiel van den Broeke
The Cryosphere, 15, 5639–5658, https://doi.org/10.5194/tc-15-5639-2021, https://doi.org/10.5194/tc-15-5639-2021, 2021
Short summary
Short summary
Antarctica is shrinking, and part of the mass loss is caused by higher temperatures leading to more snowmelt. We use computer models to estimate the amount of melt, but this can be inaccurate – specifically in the areas with the most melt. This is because the model cannot account for small, darker areas like rocks or darker ice. Thus, we trained a computer using artificial intelligence and satellite images that showed these darker areas. The model computed an improved estimate of melt.
Kenneth D. Mankoff, Xavier Fettweis, Peter L. Langen, Martin Stendel, Kristian K. Kjeldsen, Nanna B. Karlsson, Brice Noël, Michiel R. van den Broeke, Anne Solgaard, William Colgan, Jason E. Box, Sebastian B. Simonsen, Michalea D. King, Andreas P. Ahlstrøm, Signe Bech Andersen, and Robert S. Fausto
Earth Syst. Sci. Data, 13, 5001–5025, https://doi.org/10.5194/essd-13-5001-2021, https://doi.org/10.5194/essd-13-5001-2021, 2021
Short summary
Short summary
We estimate the daily mass balance and its components (surface, marine, and basal mass balance) for the Greenland ice sheet. Our time series begins in 1840 and has annual resolution through 1985 and then daily from 1986 through next week. Results are operational (updated daily) and provided for the entire ice sheet or by commonly used regions or sectors. This is the first input–output mass balance estimate to include the basal mass balance.
Ruth Mottram, Nicolaj Hansen, Christoph Kittel, J. Melchior van Wessem, Cécile Agosta, Charles Amory, Fredrik Boberg, Willem Jan van de Berg, Xavier Fettweis, Alexandra Gossart, Nicole P. M. van Lipzig, Erik van Meijgaard, Andrew Orr, Tony Phillips, Stuart Webster, Sebastian B. Simonsen, and Niels Souverijns
The Cryosphere, 15, 3751–3784, https://doi.org/10.5194/tc-15-3751-2021, https://doi.org/10.5194/tc-15-3751-2021, 2021
Short summary
Short summary
We compare the calculated surface mass budget (SMB) of Antarctica in five different regional climate models. On average ~ 2000 Gt of snow accumulates annually, but different models vary by ~ 10 %, a difference equivalent to ± 0.5 mm of global sea level rise. All models reproduce observed weather, but there are large differences in regional patterns of snowfall, especially in areas with very few observations, giving greater uncertainty in Antarctic mass budget than previously identified.
Maurice van Tiggelen, Paul C. J. P. Smeets, Carleen H. Reijmer, Bert Wouters, Jakob F. Steiner, Emile J. Nieuwstraten, Walter W. Immerzeel, and Michiel R. van den Broeke
The Cryosphere, 15, 2601–2621, https://doi.org/10.5194/tc-15-2601-2021, https://doi.org/10.5194/tc-15-2601-2021, 2021
Short summary
Short summary
We developed a method to estimate the aerodynamic properties of the Greenland Ice Sheet surface using either UAV or ICESat-2 elevation data. We show that this new method is able to reproduce the important spatiotemporal variability in surface aerodynamic roughness, measured by the field observations. The new maps of surface roughness can be used in atmospheric models to improve simulations of surface turbulent heat fluxes and therefore surface energy and mass balance over rough ice worldwide.
Christiaan T. van Dalum, Willem Jan van de Berg, and Michiel R. van den Broeke
The Cryosphere, 15, 1823–1844, https://doi.org/10.5194/tc-15-1823-2021, https://doi.org/10.5194/tc-15-1823-2021, 2021
Short summary
Short summary
Absorption of solar radiation is often limited to the surface in regional climate models. Therefore, we have implemented a new radiative transfer scheme in the model RACMO2, which allows for internal heating and improves the surface reflectivity. Here, we evaluate its impact on the surface mass and energy budget and (sub)surface temperature, by using observations and the previous model version for the Greenland ice sheet. New results match better with observations and introduce subsurface melt.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Baojuan Huai, Michiel R. van den Broeke, and Carleen H. Reijmer
The Cryosphere, 14, 4181–4199, https://doi.org/10.5194/tc-14-4181-2020, https://doi.org/10.5194/tc-14-4181-2020, 2020
Short summary
Short summary
This study presents the surface energy balance (SEB) of the Greenland Ice Sheet (GrIS) using a SEB model forced with observations from automatic weather stations (AWSs). We correlate ERA5 with AWSs to show a significant positive correlation of GrIS summer surface temperature and melt with the Greenland Blocking Index and weaker and opposite correlations with the North Atlantic Oscillation. This analysis may help explain melting patterns in the GrIS with respect to circulation anomalies.
Jenny V. Turton, Amélie Kirchgaessner, Andrew N. Ross, John C. King, and Peter Kuipers Munneke
The Cryosphere, 14, 4165–4180, https://doi.org/10.5194/tc-14-4165-2020, https://doi.org/10.5194/tc-14-4165-2020, 2020
Short summary
Short summary
Föhn winds are warm and dry downslope winds in the lee of a mountain range, such as the Antarctic Peninsula. Föhn winds heat the ice of the Larsen C Ice Shelf at the base of the mountains and promote more melting than during non-föhn periods in spring, summer and autumn in both model output and observations. Especially in spring, when they are most frequent, föhn winds can extend the melt season by over a month and cause a similar magnitude of melting to that observed in summer.
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
Short summary
Short summary
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.
Baptiste Vandecrux, Ruth Mottram, Peter L. Langen, Robert S. Fausto, Martin Olesen, C. Max Stevens, Vincent Verjans, Amber Leeson, Stefan Ligtenberg, Peter Kuipers Munneke, Sergey Marchenko, Ward van Pelt, Colin R. Meyer, Sebastian B. Simonsen, Achim Heilig, Samira Samimi, Shawn Marshall, Horst Machguth, Michael MacFerrin, Masashi Niwano, Olivia Miller, Clifford I. Voss, and Jason E. Box
The Cryosphere, 14, 3785–3810, https://doi.org/10.5194/tc-14-3785-2020, https://doi.org/10.5194/tc-14-3785-2020, 2020
Short summary
Short summary
In the vast interior of the Greenland ice sheet, snow accumulates into a thick and porous layer called firn. Each summer, the firn retains part of the meltwater generated at the surface and buffers sea-level rise. In this study, we compare nine firn models traditionally used to quantify this retention at four sites and evaluate their performance against a set of in situ observations. We highlight limitations of certain model designs and give perspectives for future model development.
Christiaan T. van Dalum, Willem Jan van de Berg, Stef Lhermitte, and Michiel R. van den Broeke
The Cryosphere, 14, 3645–3662, https://doi.org/10.5194/tc-14-3645-2020, https://doi.org/10.5194/tc-14-3645-2020, 2020
Short summary
Short summary
The reflectivity of sunlight, which is also known as albedo, is often inadequately modeled in regional climate models. Therefore, we have implemented a new snow and ice albedo scheme in the regional climate model RACMO2. In this study, we evaluate a new RACMO2 version for the Greenland ice sheet by using observations and the previous model version. RACMO2 output compares well with observations, and by including new processes we improve the ability of RACMO2 to make future climate projections.
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
Short summary
Short summary
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.
Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andrew Shepherd, Erika Simon, Cécile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald A. Slater, Robin S. Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke
The Cryosphere, 14, 3071–3096, https://doi.org/10.5194/tc-14-3071-2020, https://doi.org/10.5194/tc-14-3071-2020, 2020
Short summary
Short summary
In this paper we use a large ensemble of Greenland ice sheet models forced by six different global climate models to project ice sheet changes and sea-level rise contributions over the 21st century.
The results for two different greenhouse gas concentration scenarios indicate that the Greenland ice sheet will continue to lose mass until 2100, with contributions to sea-level rise of 90 ± 50 mm and 32 ± 17 mm for the high (RCP8.5) and low (RCP2.6) scenario, respectively.
Vincent Verjans, Amber A. Leeson, Christopher Nemeth, C. Max Stevens, Peter Kuipers Munneke, Brice Noël, and Jan Melchior van Wessem
The Cryosphere, 14, 3017–3032, https://doi.org/10.5194/tc-14-3017-2020, https://doi.org/10.5194/tc-14-3017-2020, 2020
Short summary
Short summary
Ice sheets are covered by a firn layer, which is the transition stage between fresh snow and ice. Accurate modelling of firn density properties is important in many glaciological aspects. Current models show disagreements, are mostly calibrated to match specific observations of firn density and lack thorough uncertainty analysis. We use a novel calibration method for firn models based on a Bayesian statistical framework, which results in improved model accuracy and in uncertainty evaluation.
Sophie Nowicki, Heiko Goelzer, Hélène Seroussi, Anthony J. Payne, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Patrick Alexander, Xylar S. Asay-Davis, Alice Barthel, Thomas J. Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan M. Gregory, Tore Hattermann, Nicolas C. Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher M. Little, Mathieu Morlighem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin S. Smith, Fiammetta Straneo, Luke D. Trusel, Michiel R. van den Broeke, and Roderik van de Wal
The Cryosphere, 14, 2331–2368, https://doi.org/10.5194/tc-14-2331-2020, https://doi.org/10.5194/tc-14-2331-2020, 2020
Short summary
Short summary
This paper describes the experimental protocol for ice sheet models taking part in the Ice Sheet Model Intercomparion Project for CMIP6 (ISMIP6) and presents an overview of the atmospheric and oceanic datasets to be used for the simulations. The ISMIP6 framework allows for exploring the uncertainty in 21st century sea level change from the Greenland and Antarctic ice sheets.
Michael Kern, Robert Cullen, Bruno Berruti, Jerome Bouffard, Tania Casal, Mark R. Drinkwater, Antonio Gabriele, Arnaud Lecuyot, Michael Ludwig, Rolv Midthassel, Ignacio Navas Traver, Tommaso Parrinello, Gerhard Ressler, Erik Andersson, Cristina Martin-Puig, Ole Andersen, Annett Bartsch, Sinead Farrell, Sara Fleury, Simon Gascoin, Amandine Guillot, Angelika Humbert, Eero Rinne, Andrew Shepherd, Michiel R. van den Broeke, and John Yackel
The Cryosphere, 14, 2235–2251, https://doi.org/10.5194/tc-14-2235-2020, https://doi.org/10.5194/tc-14-2235-2020, 2020
Short summary
Short summary
The Copernicus Polar Ice and Snow Topography Altimeter will provide high-resolution sea ice thickness and land ice elevation measurements and the capability to determine the properties of snow cover on ice to serve operational products and services of direct relevance to the polar regions. This paper describes the mission objectives, identifies the key contributions the CRISTAL mission will make, and presents a concept – as far as it is already defined – for the mission payload.
Willem Jan van de Berg, Erik van Meijgaard, and Lambertus H. van Ulft
The Cryosphere, 14, 1809–1827, https://doi.org/10.5194/tc-14-1809-2020, https://doi.org/10.5194/tc-14-1809-2020, 2020
Short summary
Short summary
In times of increasing computer power, atmospheric models that estimate the surface mass balance of the Greenland can be run with increasing resolution. However, at which resolution is the error no longer determined by the lacking resolution but by model shortcomings? In this manuscript we show that for the majority of the southern part of the Greenland Ice Sheet, our study area, a model resolution of 20 km is sufficient although finer model resolutions are still beneficial.
Heiko Goelzer, Brice P. Y. Noël, Tamsin L. Edwards, Xavier Fettweis, Jonathan M. Gregory, William H. Lipscomb, Roderik S. W. van de Wal, and Michiel R. van den Broeke
The Cryosphere, 14, 1747–1762, https://doi.org/10.5194/tc-14-1747-2020, https://doi.org/10.5194/tc-14-1747-2020, 2020
Short summary
Short summary
Future sea-level change projections with process-based ice sheet models are typically driven with surface mass balance forcing derived from climate models. In this work we address the problems arising from a mismatch of the modelled ice sheet geometry with the one used by the climate model. The proposed remapping method reproduces the original forcing data closely when applied to the original geometry and produces a physically meaningful forcing when applied to different modelled geometries.
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
Short summary
Short summary
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.
Matthias O. Willen, Martin Horwath, Ludwig Schröder, Andreas Groh, Stefan R. M. Ligtenberg, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere, 14, 349–366, https://doi.org/10.5194/tc-14-349-2020, https://doi.org/10.5194/tc-14-349-2020, 2020
Christiaan T. van Dalum, Willem Jan van de Berg, Quentin Libois, Ghislain Picard, and Michiel R. van den Broeke
Geosci. Model Dev., 12, 5157–5175, https://doi.org/10.5194/gmd-12-5157-2019, https://doi.org/10.5194/gmd-12-5157-2019, 2019
Short summary
Short summary
Climate models are often limited to relatively simple snow albedo schemes. Therefore, we have developed the SNOWBAL module to couple a climate model with a physically based wavelength dependent snow albedo model. Using SNOWBAL v1.2 to couple the snow albedo model TARTES with the regional climate model RACMO2 indicates a potential performance gain for the Greenland ice sheet.
Vincent Verjans, Amber A. Leeson, C. Max Stevens, Michael MacFerrin, Brice Noël, and Michiel R. van den Broeke
The Cryosphere, 13, 1819–1842, https://doi.org/10.5194/tc-13-1819-2019, https://doi.org/10.5194/tc-13-1819-2019, 2019
Short summary
Short summary
Firn models rely on empirical approaches for representing the percolation and refreezing of meltwater through the firn column. We develop liquid water schemes of different levels of complexity for firn models and compare their performances with respect to observations of density profiles from Greenland. Our results demonstrate that physically advanced water schemes do not lead to better agreement with density observations. Uncertainties in other processes contribute more to model discrepancy.
Tyler C. Sutterley, Thorsten Markus, Thomas A. Neumann, Michiel van den Broeke, J. Melchior van Wessem, and Stefan R. M. Ligtenberg
The Cryosphere, 13, 1801–1817, https://doi.org/10.5194/tc-13-1801-2019, https://doi.org/10.5194/tc-13-1801-2019, 2019
Short summary
Short summary
Most of the Antarctic ice sheet is fringed by ice shelves, floating extensions of ice that help to modulate the flow of the glaciers that float into them. We use airborne laser altimetry data to measure changes in ice thickness of ice shelves around West Antarctica and the Antarctic Peninsula. Each of our target ice shelves is susceptible to short-term changes in ice thickness. The method developed here provides a framework for processing NASA ICESat-2 data over ice shelves.
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
Short summary
Short summary
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.
Constantijn L. Jakobs, Carleen H. Reijmer, Peter Kuipers Munneke, Gert König-Langlo, and Michiel R. van den Broeke
The Cryosphere, 13, 1473–1485, https://doi.org/10.5194/tc-13-1473-2019, https://doi.org/10.5194/tc-13-1473-2019, 2019
Short summary
Short summary
We use 24 years of observations at Neumayer Station, East Antarctica, to calculate the surface energy balance and the associated surface melt, which we find to be mainly driven by the absorption of solar radiation. Meltwater can refreeze in the subsurface snow layers, thereby decreasing the surface albedo and hence allowing for more absorption of solar radiation. By implementing an albedo parameterisation, we show that this feedback accounts for a threefold increase in surface melt at Neumayer.
Ludwig Schröder, Martin Horwath, Reinhard Dietrich, Veit Helm, Michiel R. van den Broeke, and Stefan R. M. Ligtenberg
The Cryosphere, 13, 427–449, https://doi.org/10.5194/tc-13-427-2019, https://doi.org/10.5194/tc-13-427-2019, 2019
Short summary
Short summary
We developed an approach to combine measurements of seven satellite altimetry missions over the Antarctic Ice Sheet. Our resulting monthly grids of elevation changes between 1978 and 2017 provide unprecedented details of the long-term and interannual variation. Derived mass changes agree well with contemporaneous data of surface mass balance and satellite gravimetry and show which regions were responsible for the significant accelerations of mass loss in recent years.
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
Short summary
Short summary
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.
Michalea D. King, Ian M. Howat, Seongsu Jeong, Myoung J. Noh, Bert Wouters, Brice Noël, and Michiel R. van den Broeke
The Cryosphere, 12, 3813–3825, https://doi.org/10.5194/tc-12-3813-2018, https://doi.org/10.5194/tc-12-3813-2018, 2018
Short summary
Short summary
We derive the first continuous record of total ice discharged from all large Greenland outlet glaciers over the 2000–2016 period, resolving a distinct pattern of seasonal variability. We compare these results to glacier retreat and meltwater runoff and find that while runoff has a limited impact on ice discharge in summer, long-term changes in discharge are highly correlated to retreat. These results help to better understand Greenland outlet glacier sensitivity over a range of timescales.
Nicole-Jeanne Schlegel, Helene Seroussi, Michael P. Schodlok, Eric Y. Larour, Carmen Boening, Daniel Limonadi, Michael M. Watkins, Mathieu Morlighem, and Michiel R. van den Broeke
The Cryosphere, 12, 3511–3534, https://doi.org/10.5194/tc-12-3511-2018, https://doi.org/10.5194/tc-12-3511-2018, 2018
Short summary
Short summary
Using NASA supercomputers and a novel framework, in which Sandia National Laboratories' statistical software is embedded in the Jet Propulsion Laboratory's ice sheet model, we run a range of 100-year warming scenarios for Antarctica. We find that 1.2 m of sea level contribution is achievable, but not likely. Also, we find that bedrock topography beneath the ice drives potential for regional sea level contribution, highlighting the need for accurate bedrock mapping of the ice sheet interior.
Jiangjun Ran, Miren Vizcaino, Pavel Ditmar, Michiel R. van den Broeke, Twila Moon, Christian R. Steger, Ellyn M. Enderlin, Bert Wouters, Brice Noël, Catharina H. Reijmer, Roland Klees, Min Zhong, Lin Liu, and Xavier Fettweis
The Cryosphere, 12, 2981–2999, https://doi.org/10.5194/tc-12-2981-2018, https://doi.org/10.5194/tc-12-2981-2018, 2018
Short summary
Short summary
To accurately predict future sea level rise, the mechanisms driving the observed mass loss must be better understood. Here, we combine data from the satellite gravimetry, surface mass balance, and ice discharge to analyze the mass budget of Greenland at various temporal scales. This study, for the first time, suggests the existence of a substantial meltwater storage during summer, with a peak value of 80–120 Gt in July. We highlight its importance for understanding ice sheet mass variability
Rajashree Tri Datta, Marco Tedesco, Cecile Agosta, Xavier Fettweis, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere, 12, 2901–2922, https://doi.org/10.5194/tc-12-2901-2018, https://doi.org/10.5194/tc-12-2901-2018, 2018
Short summary
Short summary
Surface melting on the East Antarctic Peninsula (East AP) has been linked to ice shelf collapse, including the Larsen A (1995) and Larsen B (2002) ice shelves. Regional climate models (RCMs) are a valuable tool to understand how wind patterns and general warming can impact the stability of ice shelves through surface melt. Here, we evaluate one such RCM (Modèle Atmosphérique Régionale) over the East AP, including the remaining Larsen C ice shelf, by comparing it to satellite and ground data.
Stefan R. M. Ligtenberg, Peter Kuipers Munneke, Brice P. Y. Noël, and Michiel R. van den Broeke
The Cryosphere, 12, 1643–1649, https://doi.org/10.5194/tc-12-1643-2018, https://doi.org/10.5194/tc-12-1643-2018, 2018
Short summary
Short summary
Firn is the transitional product between fresh snow and glacier ice, and a 10-100 m thick layer covers the Greenland ice sheet. It has the capacity to store meltwater and thereby mitigate runoff to the ocean. Using a model and improved atmospheric forcing, we simulate firn density and temperature that agrees well with observations from firn cores. Especially in the regions with substantial melt, and therefore the most sensitive to a warming climate, the results improved significantly.
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
Short summary
Short summary
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.
Helmut Rott, Wael Abdel Jaber, Jan Wuite, Stefan Scheiblauer, Dana Floricioiu, Jan Melchior van Wessem, Thomas Nagler, Nuno Miranda, and Michiel R. van den Broeke
The Cryosphere, 12, 1273–1291, https://doi.org/10.5194/tc-12-1273-2018, https://doi.org/10.5194/tc-12-1273-2018, 2018
Short summary
Short summary
We analysed volume change, mass balance and ice flow of glaciers draining into the Larsen A and Larsen B embayments on the Antarctic Peninsula for 2011 to 2013 and 2013 to 2016. The mass balance is based on elevation change measured by the radar satellite mission TanDEM-X and on the mass budget method. The glaciers show continuing losses in ice mass, which is a response to ice shelf break-up. After 2013 the downwasting of glaciers slowed down, coinciding with years of persistent sea ice cover.
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
Short summary
Short summary
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.
Alex S. Gardner, Geir Moholdt, Ted Scambos, Mark Fahnstock, Stefan Ligtenberg, Michiel van den Broeke, and Johan Nilsson
The Cryosphere, 12, 521–547, https://doi.org/10.5194/tc-12-521-2018, https://doi.org/10.5194/tc-12-521-2018, 2018
Short summary
Short summary
We map present-day Antarctic surface velocities from Landsat imagery and compare to earlier estimates from radar. Flow accelerations across the grounding lines of West Antarctica's Amundsen Sea Embayment, Getz Ice Shelf and the western Antarctic Peninsula, account for 89 % of the observed increase in ice discharge. In contrast, glaciers draining the East Antarctic have been remarkably stable. Our work suggests that patterns of mass loss are part of a longer-term phase of enhanced flow.
Suzanne L. Bevan, Adrian Luckman, Bryn Hubbard, Bernd Kulessa, David Ashmore, Peter Kuipers Munneke, Martin O'Leary, Adam Booth, Heidi Sevestre, and Daniel McGrath
The Cryosphere, 11, 2743–2753, https://doi.org/10.5194/tc-11-2743-2017, https://doi.org/10.5194/tc-11-2743-2017, 2017
Short summary
Short summary
Five 90 m boreholes drilled into an Antarctic Peninsula ice shelf show units of ice that are denser than expected and must have formed from refrozen surface melt which has been buried and transported downstream. We used surface flow speeds and snow accumulation rates to work out where and when these units formed. Results show that, as well as recent surface melt, a period of strong melt occurred during the 18th century. Surface melt is thought to be a factor in causing recent ice-shelf break-up.
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
Short summary
Short summary
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.
Christian R. Steger, Carleen H. Reijmer, and Michiel R. van den Broeke
The Cryosphere, 11, 2507–2526, https://doi.org/10.5194/tc-11-2507-2017, https://doi.org/10.5194/tc-11-2507-2017, 2017
Short summary
Short summary
Mass loss from the Greenland Ice Sheet, which contributes to sea level rise, is currently dominated by surface melt and run-off. The relation between these two variables is rather uncertain due to the firn layer’s potential to buffer melt in solid (refreezing) or liquid (firn aquifer) form. To address this uncertainty, we analyse output of a numerical firn model run over 1960–2014. Results show a spatially variable response of the ice sheet to increasing melt and an upward migration of aquifers.
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
Short summary
Short summary
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.
Riccardo E. M. Riva, Thomas Frederikse, Matt A. King, Ben Marzeion, and Michiel R. van den Broeke
The Cryosphere, 11, 1327–1332, https://doi.org/10.5194/tc-11-1327-2017, https://doi.org/10.5194/tc-11-1327-2017, 2017
Short summary
Short summary
The reduction of ice masses stored on land has made an important contribution to sea-level rise over the last century, as well as changed the Earth's shape. We model the solid-earth response to ice mass changes and find significant vertical deformation signals over large continental areas. We show how deformation rates have varied strongly throughout the last century, which affects the interpretation and extrapolation of recent observations of vertical land motion and sea-level change.
Harry Zekollari, Philippe Huybrechts, Brice Noël, Willem Jan van de Berg, and Michiel R. van den Broeke
The Cryosphere, 11, 805–825, https://doi.org/10.5194/tc-11-805-2017, https://doi.org/10.5194/tc-11-805-2017, 2017
Short summary
Short summary
In this study the dynamics of the world’s northernmost ice cap are investigated with a 3-D ice flow model. Under 1961–1990 climatic conditions
an ice cap similar to the observed one is obtained, with comparable geometry and surface velocities. The southern part of the ice cap is very unstable,
and under early-21st-century climatic conditions this part of the ice cap fully disappears. In a projected warmer and wetter climate the ice cap will at
first steepen, before eventually disappearing.
Stephen F. Price, Matthew J. Hoffman, Jennifer A. Bonin, Ian M. Howat, Thomas Neumann, Jack Saba, Irina Tezaur, Jeffrey Guerber, Don P. Chambers, Katherine J. Evans, Joseph H. Kennedy, Jan Lenaerts, William H. Lipscomb, Mauro Perego, Andrew G. Salinger, Raymond S. Tuminaro, Michiel R. van den Broeke, and Sophie M. J. Nowicki
Geosci. Model Dev., 10, 255–270, https://doi.org/10.5194/gmd-10-255-2017, https://doi.org/10.5194/gmd-10-255-2017, 2017
Short summary
Short summary
We introduce the Cryospheric Model Comparison Tool (CmCt) and propose qualitative and quantitative metrics for evaluating ice sheet model simulations against observations. Greenland simulations using the Community Ice Sheet Model are compared to gravimetry and altimetry observations from 2003 to 2013. We show that the CmCt can be used to score simulations of increasing complexity relative to observations of dynamic change in Greenland over the past decade.
Thomas J. Reerink, Willem Jan van de Berg, and Roderik S. W. van de Wal
Geosci. Model Dev., 9, 4111–4132, https://doi.org/10.5194/gmd-9-4111-2016, https://doi.org/10.5194/gmd-9-4111-2016, 2016
Short summary
Short summary
Ice sheets are part of the climate system and interact with the atmosphere and the ocean. OBLIMAP is a powerful tool to map climate fields between GCMs and ISMs (ice sheet models), which run on grids that differ in curvature, resolution and extent. OBLIMAP uses optimal aligned oblique projections, which minimize area distortions. OBLIMAP 2.0 allows for high-frequency embedded coupling and masked mapping. A fast search strategy realizes a huge performance gain and enables high-resolution mapping.
Brice Noël, Willem Jan van de Berg, Horst Machguth, Stef Lhermitte, Ian Howat, Xavier Fettweis, and Michiel R. van den Broeke
The Cryosphere, 10, 2361–2377, https://doi.org/10.5194/tc-10-2361-2016, https://doi.org/10.5194/tc-10-2361-2016, 2016
Short summary
Short summary
We present a 1 km resolution data set (1958–2015) of daily Greenland ice sheet surface mass balance (SMB), statistically downscaled from the data of RACMO2.3 at 11 km using elevation dependence, precipitation and bare ice albedo corrections. The data set resolves Greenland narrow ablation zones and local outlet glaciers, and shows more realistic SMB patterns, owing to enhanced runoff at the ice sheet margins. An evaluation of the product against SMB measurements shows improved agreement.
Nicole-Jeanne Schlegel, David N. Wiese, Eric Y. Larour, Michael M. Watkins, Jason E. Box, Xavier Fettweis, and Michiel R. van den Broeke
The Cryosphere, 10, 1965–1989, https://doi.org/10.5194/tc-10-1965-2016, https://doi.org/10.5194/tc-10-1965-2016, 2016
Short summary
Short summary
We investigate Greenland Ice Sheet mass change from 2003–2012 by comparing observations from GRACE with state-of-the-art atmospheric and ice sheet model simulations. We find that the largest discrepancies (in the northwest and southeast) are likely controlled by errors in modeled surface climate as well as ice–ocean interaction and hydrological processes (not included in the models). Models should consider such processes at monthly to seasonal resolutions in order to improve future projections.
Michiel R. van den Broeke, Ellyn M. Enderlin, Ian M. Howat, Peter Kuipers Munneke, Brice P. Y. Noël, Willem Jan van de Berg, Erik van Meijgaard, and Bert Wouters
The Cryosphere, 10, 1933–1946, https://doi.org/10.5194/tc-10-1933-2016, https://doi.org/10.5194/tc-10-1933-2016, 2016
Short summary
Short summary
We present recent (1958–2015) mass balance time series for the Greenland ice sheet. We show that recent mass loss is caused by a combination of increased surface meltwater runoff and solid ice discharge. Most meltwater above 2000 m a.s.l. refreezes in the cold firn and does not leave the ice sheet, but this goes at the expense of firn heating and densifying. In spite of a temporary rebound in 2013, it appears that the ice sheet remains in a state of persistent mass loss.
Zheng Xu, Ernst J. O. Schrama, Wouter van der Wal, Michiel van den Broeke, and Ellyn M. Enderlin
The Cryosphere, 10, 895–912, https://doi.org/10.5194/tc-10-895-2016, https://doi.org/10.5194/tc-10-895-2016, 2016
Short summary
Short summary
In this paper, we compare the regional mass changes of the Greenland ice sheet between the solutions based on GRACE data and input/output method. Differences are found in some regions and indicate errors in those solutions. Therefore we improve our GRACE and IOM solutions by applying a simulation. We show the improved regional mass changes approximations are more consistent in regions. The remaining difference in the northwester Greenland is due to the underestimated uncertainty in IOM solution.
Wenshan Wang, Charles S. Zender, Dirk van As, Paul C. J. P. Smeets, and Michiel R. van den Broeke
The Cryosphere, 10, 727–741, https://doi.org/10.5194/tc-10-727-2016, https://doi.org/10.5194/tc-10-727-2016, 2016
Short summary
Short summary
We identify and correct station-tilt-induced biases in insolation observed by automatic weather stations on the Greenland Ice Sheet. Without tilt correction, only 40 % of clear days have the correct solar noon time (±0.5 h). The largest hourly bias exceeds 20 %. We estimate the tilt angles based on solar geometric relationship between insolation observed on horizontal surfaces and that on tilted surfaces, and produce shortwave radiation and albedo that agree better with independent data sets.
Ioana S. Muresan, Shfaqat A. Khan, Andy Aschwanden, Constantine Khroulev, Tonie Van Dam, Jonathan Bamber, Michiel R. van den Broeke, Bert Wouters, Peter Kuipers Munneke, and Kurt H. Kjær
The Cryosphere, 10, 597–611, https://doi.org/10.5194/tc-10-597-2016, https://doi.org/10.5194/tc-10-597-2016, 2016
Short summary
Short summary
We use a regional 3-D outlet glacier model to simulate the behaviour of Jakobshavn Isbræ (JI) during 1990–2014. The model simulates two major accelerations in 1998 and 2003 that are consistent with observations. We find that most of the JI retreat during the simulated period is driven by the ocean parametrization used, and the glacier's subsequent response, which is largely governed by bed geometry. The study shows progress in modelling the temporal variability of the flow at JI.
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
Short summary
Short summary
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.
J. M. van Wessem, S. R. M. Ligtenberg, C. H. Reijmer, W. J. van de Berg, M. R. van den Broeke, N. E. Barrand, E. R. Thomas, J. Turner, J. Wuite, T. A. Scambos, and E. van Meijgaard
The Cryosphere, 10, 271–285, https://doi.org/10.5194/tc-10-271-2016, https://doi.org/10.5194/tc-10-271-2016, 2016
Short summary
Short summary
This study presents the first high-resolution (5.5 km) modelled estimate of surface mass balance (SMB) over the period 1979–2014 for the Antarctic Peninsula (AP). Precipitation (snowfall and rain) largely determines the SMB, and is exceptionally high over the western mountain slopes, with annual values > 4 m water equivalent. Snowmelt is widespread over the AP, but only runs off into the ocean at some locations: the Larsen B,C, and Wilkins ice shelves, and along the north-western mountains.
C. Charalampidis, D. van As, J. E. Box, M. R. van den Broeke, W. T. Colgan, S. H. Doyle, A. L. Hubbard, M. MacFerrin, H. Machguth, and C. J. P. P. Smeets
The Cryosphere, 9, 2163–2181, https://doi.org/10.5194/tc-9-2163-2015, https://doi.org/10.5194/tc-9-2163-2015, 2015
P. Kuipers Munneke, S. R. M. Ligtenberg, B. P. Y. Noël, I. M. Howat, J. E. Box, E. Mosley-Thompson, J. R. McConnell, K. Steffen, J. T. Harper, S. B. Das, and M. R. van den Broeke
The Cryosphere, 9, 2009–2025, https://doi.org/10.5194/tc-9-2009-2015, https://doi.org/10.5194/tc-9-2009-2015, 2015
Short summary
Short summary
The snow layer on top of the Greenland Ice Sheet is changing: it is thickening in the high and cold interior due to increased snowfall, while it is thinning around the margins. The marginal thinning is caused by compaction, and by more melt.
This knowledge is important: there are satellites that measure volume change of the ice sheet. It can be caused by increased ice discharge, or by compaction of the snow layer. Here, we quantify the latter, so that we can translate volume to mass change.
B. Noël, W. J. van de Berg, E. van Meijgaard, P. Kuipers Munneke, R. S. W. van de Wal, and M. R. van den Broeke
The Cryosphere, 9, 1831–1844, https://doi.org/10.5194/tc-9-1831-2015, https://doi.org/10.5194/tc-9-1831-2015, 2015
Short summary
Short summary
We compare Greenland Ice Sheet surface mass balance (SMB) from the updated polar version of the regional climate model RACMO2.3 and the previous version 2.1. RACMO2.3 has an adjusted rainfall-to-snowfall conversion favouring summer snowfall over rainfall. Enhanced summer snowfall reduce melt rates in the ablation zone by covering dark ice with highly reflective fresh snow. This improves the modelled SMB-elevation gradient and surface energy balance compared to observations in west Greenland.
S. L. Cornford, D. F. Martin, A. J. Payne, E. G. Ng, A. M. Le Brocq, R. M. Gladstone, T. L. Edwards, S. R. Shannon, C. Agosta, M. R. van den Broeke, H. H. Hellmer, G. Krinner, S. R. M. Ligtenberg, R. Timmermann, and D. G. Vaughan
The Cryosphere, 9, 1579–1600, https://doi.org/10.5194/tc-9-1579-2015, https://doi.org/10.5194/tc-9-1579-2015, 2015
Short summary
Short summary
We used a high-resolution ice sheet model capable of resolving grounding line dynamics (BISICLES) to compute responses of the major West Antarctic ice streams to projections of ocean and atmospheric warming. This is computationally demanding, and although other groups have considered parts of West Antarctica, we think this is the first calculation for the whole region at the sub-kilometer resolution that we show is required.
S. de la Peña, I. M. Howat, P. W. Nienow, M. R. van den Broeke, E. Mosley-Thompson, S. F. Price, D. Mair, B. Noël, and A. J. Sole
The Cryosphere, 9, 1203–1211, https://doi.org/10.5194/tc-9-1203-2015, https://doi.org/10.5194/tc-9-1203-2015, 2015
Short summary
Short summary
This paper presents an assessment of changes in the near-surface structure of the accumulation zone of the Greenland Ice Sheet caused by an increase of melt at higher elevations in the last decade, especially during the unusually warm years of 2010 and 2012. The increase in melt and firn densification complicate the interpretation of changes in the ice volume, and the observed increase in firn ice content may reduce the important meltwater buffering capacity of the Greenland Ice Sheet.
R. S. W. van de Wal, C. J. P. P. Smeets, W. Boot, M. Stoffelen, R. van Kampen, S. H. Doyle, F. Wilhelms, M. R. van den Broeke, C. H. Reijmer, J. Oerlemans, and A. Hubbard
The Cryosphere, 9, 603–611, https://doi.org/10.5194/tc-9-603-2015, https://doi.org/10.5194/tc-9-603-2015, 2015
Short summary
Short summary
This paper addresses the feedback between ice flow and melt rates. Using 20 years of data covering the whole ablation area, we show that there is not a strong positive correlation between annual ice velocities and melt rates. Rapid variations around the equilibrium line indicate the possibility of rapid variations high on the ice sheet.
P. M. Alexander, M. Tedesco, X. Fettweis, R. S. W. van de Wal, C. J. P. P. Smeets, and M. R. van den Broeke
The Cryosphere, 8, 2293–2312, https://doi.org/10.5194/tc-8-2293-2014, https://doi.org/10.5194/tc-8-2293-2014, 2014
B. Noël, X. Fettweis, W. J. van de Berg, M. R. van den Broeke, and M. Erpicum
The Cryosphere, 8, 1871–1883, https://doi.org/10.5194/tc-8-1871-2014, https://doi.org/10.5194/tc-8-1871-2014, 2014
S. R. M. Ligtenberg, P. Kuipers Munneke, and M. R. van den Broeke
The Cryosphere, 8, 1711–1723, https://doi.org/10.5194/tc-8-1711-2014, https://doi.org/10.5194/tc-8-1711-2014, 2014
S. A. Khan, K. K. Kjeldsen, K. H. Kjær, S. Bevan, A. Luckman, A. Aschwanden, A. A. Bjørk, N. J. Korsgaard, J. E. Box, M. van den Broeke, T. M. van Dam, and A. Fitzner
The Cryosphere, 8, 1497–1507, https://doi.org/10.5194/tc-8-1497-2014, https://doi.org/10.5194/tc-8-1497-2014, 2014
H. Fréville, E. Brun, G. Picard, N. Tatarinova, L. Arnaud, C. Lanconelli, C. Reijmer, and M. van den Broeke
The Cryosphere, 8, 1361–1373, https://doi.org/10.5194/tc-8-1361-2014, https://doi.org/10.5194/tc-8-1361-2014, 2014
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
I. Sasgen, H. Konrad, E. R. Ivins, M. R. Van den Broeke, J. L. Bamber, Z. Martinec, and V. Klemann
The Cryosphere, 7, 1499–1512, https://doi.org/10.5194/tc-7-1499-2013, https://doi.org/10.5194/tc-7-1499-2013, 2013
A. K. Rennermalm, L. C. Smith, V. W. Chu, J. E. Box, R. R. Forster, M. R. Van den Broeke, D. Van As, and S. E. Moustafa
The Cryosphere, 7, 1433–1445, https://doi.org/10.5194/tc-7-1433-2013, https://doi.org/10.5194/tc-7-1433-2013, 2013
M. M. Helsen, W. J. van de Berg, R. S. W. van de Wal, M. R. van den Broeke, and J. Oerlemans
Clim. Past, 9, 1773–1788, https://doi.org/10.5194/cp-9-1773-2013, https://doi.org/10.5194/cp-9-1773-2013, 2013
I. Joughin, S. B. Das, G. E. Flowers, M. D. Behn, R. B. Alley, M. A. King, B. E. Smith, J. L. Bamber, M. R. van den Broeke, and J. H. van Angelen
The Cryosphere, 7, 1185–1192, https://doi.org/10.5194/tc-7-1185-2013, https://doi.org/10.5194/tc-7-1185-2013, 2013
W. J. van de Berg, M. R. van den Broeke, E. van Meijgaard, and F. Kaspar
Clim. Past, 9, 1589–1600, https://doi.org/10.5194/cp-9-1589-2013, https://doi.org/10.5194/cp-9-1589-2013, 2013
C. L. Vernon, J. L. Bamber, J. E. Box, M. R. van den Broeke, X. Fettweis, E. Hanna, and P. Huybrechts
The Cryosphere, 7, 599–614, https://doi.org/10.5194/tc-7-599-2013, https://doi.org/10.5194/tc-7-599-2013, 2013
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
M. M. Helsen, R. S. W. van de Wal, M. R. van den Broeke, W. J. van de Berg, and J. Oerlemans
The Cryosphere, 6, 255–272, https://doi.org/10.5194/tc-6-255-2012, https://doi.org/10.5194/tc-6-255-2012, 2012
M. R. van den Broeke, C. J. P. P. Smeets, and R. S. W. van de Wal
The Cryosphere, 5, 377–390, https://doi.org/10.5194/tc-5-377-2011, https://doi.org/10.5194/tc-5-377-2011, 2011
M. van den Broeke, P. Smeets, J. Ettema, C. van der Veen, R. van de Wal, and J. Oerlemans
The Cryosphere, 2, 179–189, https://doi.org/10.5194/tc-2-179-2008, https://doi.org/10.5194/tc-2-179-2008, 2008
Related subject area
Cryosphere
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)
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
A wind-driven snow redistribution module for Alpine3D v3.3.0: adaptations designed for downscaling ice sheet surface mass balance
SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping
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)
Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.
Benchmarking the vertically integrated ice-sheet model IMAU-ICE (version 2.0)
SnowClim v1.0: high-resolution snow model and data for the western United States
Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt
MPAS-Seaice (v1.0.0): sea-ice dynamics on unstructured Voronoi meshes
Explicitly modelling microtopography in permafrost landscapes in a land surface model (JULES vn5.4_microtopography)
Geometric remapping of particle distributions in the Discrete Element Model for Sea Ice (DEMSI v0.0)
Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1)
NEMO-Bohai 1.0: a high-resolution ocean and sea ice modelling system for the Bohai Sea, China
An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018
WIFF1.0: a hybrid machine-learning-based parameterization of wave-induced sea ice floe fracture
The Whole Antarctic Ocean Model (WAOM v1.0): development and evaluation
SNICAR-ADv3: a community tool for modeling spectral snow albedo
STEMMUS-UEB v1.0.0: integrated modeling of snowpack and soil water and energy transfer with three complexity levels of soil physical processes
A versatile method for computing optimized snow albedo from spectrally fixed radiative variables: VALHALLA v1.0
Ice Algae Model Intercomparison Project phase 2 (IAMIP2)
A Gaussian process emulator for simulating ice sheet–climate interactions on a multi-million-year timescale: CLISEMv1.0
SITool (v1.0) – a new evaluation tool for large-scale sea ice simulations: application to CMIP6 OMIP
fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models
Development of adjoint-based ocean state estimation for the Amundsen and Bellingshausen seas and ice shelf cavities using MITgcm–ECCO (66j)
Sensitivity of Northern Hemisphere climate to ice–ocean interface heat flux parameterizations
icepack: a new glacier flow modeling package in Python, version 1.0
Benefits of sea ice initialization for the interannual-to-decadal climate prediction skill in the Arctic in EC-Earth3
Coupling framework (1.0) for the PISM (1.1.4) ice sheet model and the MOM5 (5.1.0) ocean model via the PICO ice shelf cavity model in an Antarctic domain
Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adélie Land, East Antarctica
Assessment of numerical schemes for transient, finite-element ice flow models using ISSM v4.18
The Utrecht Finite Volume Ice-Sheet Model: UFEMISM (version 1.0)
PERICLIMv1.0: a model deriving palaeo-air temperatures from thaw depth in past permafrost regions
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
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
Short summary
Short summary
Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Eric Keenan, Nander Wever, Jan T. M. Lenaerts, and Brooke Medley
Geosci. Model Dev., 16, 3203–3219, https://doi.org/10.5194/gmd-16-3203-2023, https://doi.org/10.5194/gmd-16-3203-2023, 2023
Short summary
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.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-298, https://doi.org/10.5194/gmd-2022-298, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
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 last 60 years at a resolution of one day and one kilometre. This is the first time that such a dataset has been produced.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
Short summary
Short summary
This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Constantijn J. Berends, Heiko Goelzer, Thomas J. Reerink, Lennert B. Stap, and Roderik S. W. van de Wal
Geosci. Model Dev., 15, 5667–5688, https://doi.org/10.5194/gmd-15-5667-2022, https://doi.org/10.5194/gmd-15-5667-2022, 2022
Short summary
Short summary
The rate at which marine ice sheets such as the West Antarctic ice sheet will retreat in a warming climate and ocean is still uncertain. Numerical ice-sheet models, which solve the physical equations that describe the way glaciers and ice sheets deform and flow, have been substantially improved in recent years. Here we present the results of several years of work on IMAU-ICE, an ice-sheet model of intermediate complexity, which can be used to study ice sheets of both the past and the future.
Abby C. Lute, John Abatzoglou, and Timothy Link
Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, https://doi.org/10.5194/gmd-15-5045-2022, 2022
Short summary
Short summary
We developed a snow model that can be used to quantify snowpack over large areas with a high degree of spatial detail. We ran the model over the western United States, creating a snow and climate dataset for three time periods. Compared to observations of snowpack, the model captured the key aspects of snow across time and space. The model and dataset will be useful in understanding historical and future changes in snowpack, with relevance to water resources, agriculture, and ecosystems.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Edoardo Cremonese, Umberto Morra di Cella, Sara Ratto, and Hervé Stevenin
Geosci. Model Dev., 15, 4853–4879, https://doi.org/10.5194/gmd-15-4853-2022, https://doi.org/10.5194/gmd-15-4853-2022, 2022
Short summary
Short summary
Knowing in real time how much snow and glacier ice has accumulated across the landscape has significant implications for water-resource management and flood control. This paper presents a computer model – S3M – allowing scientists and decision makers to predict snow and ice accumulation during winter and the subsequent melt during spring and summer. S3M has been employed for real-world flood forecasting since the early 2000s but is here being made open source for the first time.
Adrian K. Turner, William H. Lipscomb, Elizabeth C. Hunke, Douglas W. Jacobsen, Nicole Jeffery, Darren Engwirda, Todd D. Ringler, and Jonathan D. Wolfe
Geosci. Model Dev., 15, 3721–3751, https://doi.org/10.5194/gmd-15-3721-2022, https://doi.org/10.5194/gmd-15-3721-2022, 2022
Short summary
Short summary
We present the dynamical core of the MPAS-Seaice model, which uses a mesh consisting of a Voronoi tessellation with polygonal cells. Such a mesh allows variable mesh resolution in different parts of the domain and the focusing of computational resources in regions of interest. We describe the velocity solver and tracer transport schemes used and examine errors generated by the model in both idealized and realistic test cases and examine the computational efficiency of the model.
Noah D. Smith, Eleanor J. Burke, Kjetil Schanke Aas, Inge H. J. Althuizen, Julia Boike, Casper Tai Christiansen, Bernd Etzelmüller, Thomas Friborg, Hanna Lee, Heather Rumbold, Rachael H. Turton, Sebastian Westermann, and Sarah E. Chadburn
Geosci. Model Dev., 15, 3603–3639, https://doi.org/10.5194/gmd-15-3603-2022, https://doi.org/10.5194/gmd-15-3603-2022, 2022
Short summary
Short summary
The Arctic has large areas of small mounds that are caused by ice lifting up the soil. Snow blown by wind gathers in hollows next to these mounds, insulating them in winter. The hollows tend to be wetter, and thus the soil absorbs more heat in summer. The warm wet soil in the hollows decomposes, releasing methane. We have made a model of this, and we have tested how it behaves and whether it looks like sites in Scandinavia and Siberia. Sometimes we get more methane than a model without mounds.
Adrian K. Turner, Kara J. Peterson, and Dan Bolintineanu
Geosci. Model Dev., 15, 1953–1970, https://doi.org/10.5194/gmd-15-1953-2022, https://doi.org/10.5194/gmd-15-1953-2022, 2022
Short summary
Short summary
We developed a technique to remap sea ice tracer quantities between circular discrete element distributions. This is needed for a global discrete element method sea ice model being developed jointly by Los Alamos National Laboratory and Sandia National Laboratories that has the potential to better utilize newer supercomputers with graphics processing units and better represent sea ice dynamics. This new remapping technique ameliorates the effect of element distortion created by sea ice ridging.
Zhen Yin, Chen Zuo, Emma J. MacKie, and Jef Caers
Geosci. Model Dev., 15, 1477–1497, https://doi.org/10.5194/gmd-15-1477-2022, https://doi.org/10.5194/gmd-15-1477-2022, 2022
Short summary
Short summary
We provide a multiple-point geostatistics approach to probabilistically learn from training images to fill large-scale irregular geophysical data gaps. With a repository of global topographic training images, our approach models high-resolution basal topography and quantifies the geospatial uncertainty. It generated high-resolution topographic realizations to investigate the impact of basal topographic uncertainty on critical subglacial hydrological flow patterns associated with ice velocity.
Yu Yan, Wei Gu, Andrea M. U. Gierisch, Yingjun Xu, and Petteri Uotila
Geosci. Model Dev., 15, 1269–1288, https://doi.org/10.5194/gmd-15-1269-2022, https://doi.org/10.5194/gmd-15-1269-2022, 2022
Short summary
Short summary
In this study, we developed NEMO-Bohai, an ocean–ice model for the Bohai Sea, China. This study presented the scientific design and technical choices of the parameterizations for the NEMO-Bohai model. The model was calibrated and evaluated with in situ and satellite observations of ocean and sea ice. NEMO-Bohai is intended to be a valuable tool for long-term ocean and ice simulations and climate change studies.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
Geosci. Model Dev., 15, 1155–1176, https://doi.org/10.5194/gmd-15-1155-2022, https://doi.org/10.5194/gmd-15-1155-2022, 2022
Short summary
Short summary
We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also shows extended predictive skill of Arctic sea ice after the summer season. This provides an added value of this prediction system for decision-making.
Christopher Horvat and Lettie A. Roach
Geosci. Model Dev., 15, 803–814, https://doi.org/10.5194/gmd-15-803-2022, https://doi.org/10.5194/gmd-15-803-2022, 2022
Short summary
Short summary
Sea ice is a composite of individual pieces, called floes, ranging in horizontal size from meters to kilometers. Variations in sea ice geometry are often forced by ocean waves, a process that is an important target of global climate models as it affects the rate of sea ice melting. Yet directly simulating these interactions is computationally expensive. We present a neural-network-based model of wave–ice fracture that allows models to incorporate their effect without added computational cost.
Ole Richter, David E. Gwyther, Benjamin K. Galton-Fenzi, and Kaitlin A. Naughten
Geosci. Model Dev., 15, 617–647, https://doi.org/10.5194/gmd-15-617-2022, https://doi.org/10.5194/gmd-15-617-2022, 2022
Short summary
Short summary
Here we present an improved model of the Antarctic continental shelf ocean and demonstrate that it is capable of reproducing present-day conditions. The improvements are fundamental and regard the inclusion of tides and ocean eddies. We conclude that the model is well suited to gain new insights into processes that are important for Antarctic ice sheet retreat and global ocean changes. Hence, the model will ultimately help to improve projections of sea level rise and climate change.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
Short summary
Short summary
We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Lianyu Yu, Yijian Zeng, and Zhongbo Su
Geosci. Model Dev., 14, 7345–7376, https://doi.org/10.5194/gmd-14-7345-2021, https://doi.org/10.5194/gmd-14-7345-2021, 2021
Short summary
Short summary
We developed an integrated soil–snow–atmosphere model (STEMMUS-UEB) dedicated to the physical description of snow and soil processes with various complexities. With STEMMUS-UEB, we demonstrated that the snowpack affects not only the soil surface moisture conditions (in the liquid and ice phase) and energy-related states (albedo, LE) but also the subsurface soil water and vapor transfer, which contributes to a better understanding of the hydrothermal implications of the snowpack in cold regions.
Florent Veillon, Marie Dumont, Charles Amory, and Mathieu Fructus
Geosci. Model Dev., 14, 7329–7343, https://doi.org/10.5194/gmd-14-7329-2021, https://doi.org/10.5194/gmd-14-7329-2021, 2021
Short summary
Short summary
In climate models, the snow albedo scheme generally calculates only a narrowband or broadband albedo. Therefore, we have developed the VALHALLA method to optimize snow spectral albedo calculations through the determination of spectrally fixed radiative variables. The development of VALHALLA v1.0 with the use of the snow albedo model TARTES and the spectral irradiance model SBDART indicates a considerable reduction in calculation time while maintaining an adequate accuracy of albedo values.
Hakase Hayashida, Meibing Jin, Nadja S. Steiner, Neil C. Swart, Eiji Watanabe, Russell Fiedler, Andrew McC. Hogg, Andrew E. Kiss, Richard J. Matear, and Peter G. Strutton
Geosci. Model Dev., 14, 6847–6861, https://doi.org/10.5194/gmd-14-6847-2021, https://doi.org/10.5194/gmd-14-6847-2021, 2021
Short summary
Short summary
Ice algae are tiny plants like phytoplankton but they grow within sea ice. In polar regions, both phytoplankton and ice algae are the foundation of marine ecosystems and play an important role in taking up carbon dioxide in the atmosphere. However, state-of-the-art climate models typically do not include ice algae, and therefore their role in the climate system remains unclear. This project aims to address this knowledge gap by coordinating a set of experiments using sea-ice–ocean models.
Jonas Van Breedam, Philippe Huybrechts, and Michel Crucifix
Geosci. Model Dev., 14, 6373–6401, https://doi.org/10.5194/gmd-14-6373-2021, https://doi.org/10.5194/gmd-14-6373-2021, 2021
Short summary
Short summary
Ice sheets are an important component of the climate system and interact with the atmosphere through albedo variations and changes in the surface height. On very long timescales, it is impossible to directly couple ice sheet models with climate models and other techniques have to be used. Here we present a novel coupling method between ice sheets and the atmosphere by making use of an emulator to simulate ice sheet–climate interactions for several million years.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
Geosci. Model Dev., 14, 6331–6354, https://doi.org/10.5194/gmd-14-6331-2021, https://doi.org/10.5194/gmd-14-6331-2021, 2021
Short summary
Short summary
This study introduces a new Sea Ice Evaluation Tool (SITool) to evaluate the model skills on the bipolar sea ice simulations by providing performance metrics and diagnostics. SITool is applied to evaluate the CMIP6 OMIP simulations. By changing the atmospheric forcing from CORE-II to JRA55-do data, many aspects of sea ice simulations are improved. SITool will be useful for helping teams managing various versions of a sea ice model or tracking the time evolution of model performance.
Conrad P. Koziol, Joe A. Todd, Daniel N. Goldberg, and James R. Maddison
Geosci. Model Dev., 14, 5843–5861, https://doi.org/10.5194/gmd-14-5843-2021, https://doi.org/10.5194/gmd-14-5843-2021, 2021
Short summary
Short summary
Sea level change due to the loss of ice sheets presents great risk for coastal communities. Models are used to forecast ice loss, but their evolution depends strongly on properties which are hidden from observation and must be inferred from satellite observations. Common methods for doing so do not allow for quantification of the uncertainty inherent or how it will affect forecasts. We provide a framework for quantifying how this
initialization uncertaintyaffects ice loss forecasts.
Yoshihiro Nakayama, Dimitris Menemenlis, Ou Wang, Hong Zhang, Ian Fenty, and An T. Nguyen
Geosci. Model Dev., 14, 4909–4924, https://doi.org/10.5194/gmd-14-4909-2021, https://doi.org/10.5194/gmd-14-4909-2021, 2021
Short summary
Short summary
High ice shelf melting in the Amundsen Sea has attracted many observational campaigns in the past decade. One method to combine observations with numerical models is the adjoint method. After 20 iterations, the cost function, defined as a sum of the weighted model–data difference, is reduced by 65 % by adjusting initial conditions, atmospheric forcing, and vertical diffusivity. This study demonstrates adjoint-method optimization with explicit representation of ice shelf cavity circulation.
Xiaoxu Shi, Dirk Notz, Jiping Liu, Hu Yang, and Gerrit Lohmann
Geosci. Model Dev., 14, 4891–4908, https://doi.org/10.5194/gmd-14-4891-2021, https://doi.org/10.5194/gmd-14-4891-2021, 2021
Short summary
Short summary
The ice–ocean heat flux is one of the key elements controlling sea ice changes. It motivates our study, which aims to examine the responses of modeled climate to three ice–ocean heat flux parameterizations, including two old approaches that assume one-way heat transport and a new one describing a double-diffusive ice–ocean heat exchange. The results show pronounced differences in the modeled sea ice, ocean, and atmosphere states for the latter as compared to the former two parameterizations.
Daniel R. Shapero, Jessica A. Badgeley, Andrew O. Hoffman, and Ian R. Joughin
Geosci. Model Dev., 14, 4593–4616, https://doi.org/10.5194/gmd-14-4593-2021, https://doi.org/10.5194/gmd-14-4593-2021, 2021
Short summary
Short summary
This paper describes a new software package called "icepack" for modeling the flow of ice sheets and glaciers. Glaciologists use tools like icepack to better understand how ice sheets flow, what role they have played in shaping Earth's climate, and how much sea level rise we can expect in the coming decades to centuries. The icepack package includes several innovations to help researchers describe and solve interesting glaciological problems and to experiment with the underlying model physics.
Tian Tian, Shuting Yang, Mehdi Pasha Karami, François Massonnet, Tim Kruschke, and Torben Koenigk
Geosci. Model Dev., 14, 4283–4305, https://doi.org/10.5194/gmd-14-4283-2021, https://doi.org/10.5194/gmd-14-4283-2021, 2021
Short summary
Short summary
Three decadal prediction experiments with EC-Earth3 are performed to investigate the impact of ocean, sea ice concentration and thickness initialization, respectively. We find that the persistence of perennial thick ice in the central Arctic can affect the sea ice predictability in its adjacent waters via advection process or wind, despite those regions being seasonally ice free during two recent decades. This has implications for the coming decades as the thinning of Arctic sea ice continues.
Moritz Kreuzer, Ronja Reese, Willem Nicholas Huiskamp, Stefan Petri, Torsten Albrecht, Georg Feulner, and Ricarda Winkelmann
Geosci. Model Dev., 14, 3697–3714, https://doi.org/10.5194/gmd-14-3697-2021, https://doi.org/10.5194/gmd-14-3697-2021, 2021
Short summary
Short summary
We present the technical implementation of a coarse-resolution coupling between an ice sheet model and an ocean model that allows one to simulate ice–ocean interactions at timescales from centuries to millennia. As ice shelf cavities cannot be resolved in the ocean model at coarse resolution, we bridge the gap using an sub-shelf cavity module. It is shown that the framework is computationally efficient, conserves mass and energy, and can produce a stable coupled state under present-day forcing.
Charles Amory, Christoph Kittel, Louis Le Toumelin, Cécile Agosta, Alison Delhasse, Vincent Favier, and Xavier Fettweis
Geosci. Model Dev., 14, 3487–3510, https://doi.org/10.5194/gmd-14-3487-2021, https://doi.org/10.5194/gmd-14-3487-2021, 2021
Short summary
Short summary
This paper presents recent developments in the drifting-snow scheme of the regional climate model MAR and its application to simulate drifting snow and the surface mass balance of Adélie Land in East Antarctica. The model is extensively described and evaluated against a multi-year drifting-snow dataset and surface mass balance estimates available in the area. The model sensitivity to input parameters and improvements over a previously published version are also assessed.
Thiago Dias dos Santos, Mathieu Morlighem, and Hélène Seroussi
Geosci. Model Dev., 14, 2545–2573, https://doi.org/10.5194/gmd-14-2545-2021, https://doi.org/10.5194/gmd-14-2545-2021, 2021
Short summary
Short summary
Numerical models are routinely used to understand the past and future behavior of ice sheets in response to climate evolution. As is always the case with numerical modeling, one needs to minimize biases and numerical artifacts due to the choice of numerical scheme employed in such models. Here, we assess different numerical schemes in time-dependent simulations of ice sheets. We also introduce a new parameterization for the driving stress, the force that drives the ice sheet flow.
Constantijn J. Berends, Heiko Goelzer, and Roderik S. W. van de Wal
Geosci. Model Dev., 14, 2443–2470, https://doi.org/10.5194/gmd-14-2443-2021, https://doi.org/10.5194/gmd-14-2443-2021, 2021
Short summary
Short summary
The largest uncertainty in projections of sea-level rise comes from ice-sheet retreat. To better understand how these ice sheets respond to the changing climate, ice-sheet models are used, which must be able to reproduce both their present and past evolution. We have created a model that is fast enough to simulate an ice sheet at a high resolution over the course of an entire 120 000-year glacial cycle. This allows us to study processes that cannot be captured by lower-resolution models.
Tomáš Uxa, Marek Křížek, and Filip Hrbáček
Geosci. Model Dev., 14, 1865–1884, https://doi.org/10.5194/gmd-14-1865-2021, https://doi.org/10.5194/gmd-14-1865-2021, 2021
Short summary
Short summary
We present a simple model that derives palaeo-air temperature characteristics related to the palaeo-active-layer thickness, which can be recognized using many relict periglacial features found in past permafrost regions. Its evaluation against modern temperature records and an experimental palaeo-air temperature reconstruction showed relatively high model accuracy, which suggests that it could become a useful tool for reconstructing Quaternary palaeo-environments.
Cited articles
Albert, M. R. and Shultz, E. F.: Snow and firn properties and air-snow
transport processes at Summit, Greenland, Atmos. Environ., 36,
2789–2797, https://doi.org/10.1016/S1352-2310(02)00119-X, 2002. a
Arthern, R. J., Vaughan, D. G., Rankin, A. M., Mulvaney, R., and Thomas, E. R.:
In situ measurements of Antarctic snow compaction compared with predictions
of models, J. Geophys. Res.-Earth Surf., 115, 1–12,
https://doi.org/10.1029/2009JF001306, 2010. a, b
Ashmore, D. W., Mair, D. W., and Burgess, D. O.: Meltwater percolation,
impermeable layer formation and runoff buffering on Devon Ice Cap, Canada,
J. Glaciol., 66, 61–73, https://doi.org/10.1017/jog.2019.80, 2019. a
Banta, J. R. and McConnell, J. R.: Annual accumulation over recent centuries
at four sites in central Greenland, J. Geophys. Res.-Atmos., 112, 1–9, https://doi.org/10.1029/2006JD007887, 2007. a
Bezeau, P., Sharp, M., Burgess, D., and Gascon, G.: Firn profile changes in
response to extreme 21st-century melting at Devon Ice Cap, Nunavut, Canada,
J. Glaciol., 59, 981–991, https://doi.org/10.3189/2013JoG12J208, 2013. a
Box, J. E., Cressie, N., Bromwich, D. H., Jung, J. H., Van den Broeke, M.,
Van Angelen, J. H., Forster, R. R., Miège, C., Mosley-Thompson, E.,
Vinther, B., and Mcconnell, J. R.: Greenland ice sheet mass balance
reconstruction. Part I: Net snow accumulation (1600–2009), J.
Climate, 26, 3919–3934, https://doi.org/10.1175/JCLI-D-12-00373.1, 2013. a
Braithwaite, R., Pfeffer, W., Blatter, H., and Humphrey, N.: Meltwater
refreezing in the accumulation area of the Greenland ice sheet:
Pâkitsoq, summer 1991, Rapport Grønlands Geologiske
Undersøgelse, 155, 13–17, https://doi.org/10.34194/rapggu.v155.8172, 1992. a
Brandt, R. E. and Warren, S. G.: Solar-heating rates and temperature profiles
in Antarctic snow and ice, J. Glaciol., 39, 99–110,
https://doi.org/10.1017/S0022143000015756, 1993. a
Brils, M., Kuipers Munneke, P., Van de Berg, W. J., and Van den Broeke,
M.: IMAU-FDM v1 GDM release, Zenodo [code], https://doi.org/10.5281/ZENODO.5172513, 2021. a
Brun, E., David, P., Sudul, M., and Brunot, G.: A numerical model to simulate
snow-cover stratigraphy for operational avalanche forecasting, J. Glaciol., 38, 13–22, https://doi.org/10.3189/s0022143000009552, 1992. a
Calonne, N., Milliancourt, L., Burr, A., Philip, A., Martin, C. L., Flin, F.,
and Geindreau, C.: Thermal Conductivity of Snow, Firn, and Porous Ice From
3‐D Image‐Based Computations, Geophys. Res. Lett., 46,
13079–13089, https://doi.org/10.1029/2019GL085228, 2019. a, b, c
Charalampidis, C., van As, D., Box, J. E., van den Broeke, M. R., Colgan, W. T., Doyle, S. H., Hubbard, A. L., MacFerrin, M., Machguth, H., and Smeets, C. J. P. P.: Changing surface–atmosphere energy exchange and refreezing capacity of the lower accumulation area, West Greenland, The Cryosphere, 9, 2163–2181, https://doi.org/10.5194/tc-9-2163-2015, 2015. a
Coble, R. L.: Diffusion models for hot pressing with surface energy and
pressure effects as driving forces, J. Appl. Phys., 41,
4798–4807, https://doi.org/10.1063/1.1658543, 1970. a
Coléou, C. and Lesaffre, B.: Irreducible water saturation in snow:
experimental results in a cold laboratory, Ann. Glaciol., 26, 64–68,
https://doi.org/10.3189/1998aog26-1-64-68, 1998. a
Cuffey, K. and Paterson, W.: The physics of glaciers, vol. 57, fourth edn.,
https://doi.org/10.3189/002214311796405906, 2011. a, b
Enderlin, E. M., Howat, I. M., Jeong, S., Noh, M.-J., Van Angelen, J. H., and
van den Broeke, M. R.: An improved mass budget for the Greenland ice sheet,
Geophys. Prospec., 41, 6413–6419, https://doi.org/10.1002/2014GL061184.Received,
2014. a
Fausto, R. S., Box, J. E., Vandecrux, B., van As, D., Steffen, K., MacFerrin,
M. J., Machguth, H., Colgan, W., Koenig, L. S., McGrath, D., Charalampidis,
C., and Braithwaite, R. J.: A Snow Density Dataset for Improving Surface
Boundary Conditions in Greenland Ice Sheet Firn Modeling, Front. Earth
Sci., 6, 40–49, https://doi.org/10.3389/feart.2018.00051, 2018. a, b, c, d, e, f, g, h, i
Forster, R. R., Box, J. E., van den Broeke, M. R., Miège, C., Burgess,
E. W., van Angelen, J. H., Lenaerts, J. T. M., Koenig, L. S., Paden, J.,
Lewis, C., Gogineni, S. P., Leuschen, C., and McConnell, J. R.: Extensive
liquid meltwater storage in firn within the Greenland ice sheet, Nat.
Geosci., 7, 95–98, https://doi.org/10.1038/ngeo2043, 2014. a, b
Gascon, G., Sharp, M., Burgess, D., Bezeau, P., and Bush, A. B.: Changes in
accumulation-area firn stratigraphy and meltwater flow during a period of
climate warming: Devon Ice Cap, Nunavut, Canada, J. Geophys. Res.-Earth Surf., 118, 2380–2391, https://doi.org/10.1002/2013JF002838, 2013. a
Hanna, E., McConnell, J., Das, S., Cappelen, J., and Stephens, A.: Observed
and modeled Greenland Ice Sheet snow accumulation, 1958-2003, and links with
regional climate forcing, J. Climate, 19, 344–358,
https://doi.org/10.1175/JCLI3615.1, 2006. a, b
Hawley, R. L., Neumann, T. A., Stevens, C. M., Brunt, K. M., and Sutterley,
T. C.: Greenland Ice Sheet Elevation Change: Direct Observation of Process
and Attribution at Summit, Geophys. Res. Lett., 47, 1–8,
https://doi.org/10.1029/2020GL088864, 2020. a, b
Heilig, A., Eisen, O., MacFerrin, M., Tedesco, M., and Fettweis, X.: Seasonal monitoring of melt and accumulation within the deep percolation zone of the Greenland Ice Sheet and comparison with simulations of regional climate modeling, The Cryosphere, 12, 1851–1866, https://doi.org/10.5194/tc-12-1851-2018, 2018. a, b
Heilig, A., Eisen, O., Schneebeli, M., MacFerrin, M., Stevens, C. M., Vandecrux, B., and Steffen, K.: Relating regional and point measurements of accumulation in southwest Greenland, The Cryosphere, 14, 385–402, https://doi.org/10.5194/tc-14-385-2020, 2020. a
Helsen, M. M., Van den Broeke, M. R., Van De Wal, R. S., Van De Berg,
W. J., Van Meijgaard, E., Davis, C. H., Li, Y., and Goodwin, I.: Elevation
changes in antarctica mainly determined by accumulation variability,
Science, 320, 1626–1629, https://doi.org/10.1126/science.1153894, 2008. a
Herron, M. M. and Langway, C. C.: Firn densification: an empirical model.,
J. Glaciol., 25, 373–385, https://doi.org/10.1017/S0022143000015239, 1980. a
Horlings, A. N., Christianson, K., Holschuh, N., Stevens, C. M., and
Waddington, E. D.: Effect of horizontal divergence on estimates of firn-air
content, J. Glaciol., 67, 287–296, https://doi.org/10.1017/jog.2020.105,
2021. a
Keenan, E., Wever, N., Dattler, M., Lenaerts, J. T. M., Medley, B., Kuipers Munneke, P., and Reijmer, C.: Physics-based SNOWPACK model improves representation of near-surface Antarctic snow and firn density, The Cryosphere, 15, 1065–1085, https://doi.org/10.5194/tc-15-1065-2021, 2021. a, b
Koenig, L., Box, J., and Kurtz, N.: Improving surface mass balance over ice
sheets and snow depth on sea ice, Eos, 94, 100, https://doi.org/10.1002/2013EO100006,
2013. a
Koenig, L. S., Miège, C., Forster, R. R., and Brucker, L.: Initial in
situ measurements of perennial meltwater storage in the Greenland firn
aquifer, Geophys. Res. Lett., 41, 81–85,
https://doi.org/10.1002/2013GL058083, 2014. a, b, c
Kuipers Munneke, P., Ligtenberg, S. R., Suder, E. A., and Van den Broeke,
M. R.: A model study of the response of dry and wet firn to climate change,
Ann. Glaciol., 56, 1–8, https://doi.org/10.3189/2015AoG70A994,
2015a. a, b
Kuipers Munneke, P., Ligtenberg, S. R. M., Noël, B. P. Y., Howat, I. M., Box, J. E., Mosley-Thompson, E., McConnell, J. R., Steffen, K., Harper, J. T., Das, S. B., and van den Broeke, M. R.: Elevation change of the Greenland Ice Sheet due to surface mass balance and firn processes, 1960–2014, The Cryosphere, 9, 2009–2025, https://doi.org/10.5194/tc-9-2009-2015, 2015b. a, b, c, d, e, f, g, h, i, j, k
Ligtenberg, S. R. M., Helsen, M. M., and van den Broeke, M. R.: An improved semi-empirical model for the densification of Antarctic firn, The Cryosphere, 5, 809–819, https://doi.org/10.5194/tc-5-809-2011, 2011. a, b
Ligtenberg, S. R. M., Kuipers Munneke, P., Noël, B. P. Y., and van den Broeke, M. R.: Brief communication: Improved simulation of the present-day Greenland firn layer (1960–2016), The Cryosphere, 12, 1643–1649, https://doi.org/10.5194/tc-12-1643-2018, 2018. a, b, c, d
Lundin, J. M., Stevens, C. M., Arthern, R., Buizert, C., Orsi, A., Ligtenberg,
S. R., Simonsen, S. B., Cummings, E., Essery, R., Leahy, W., Harris, P.,
Helsen, M. M., and Waddington, E. D.: Firn Model Intercomparison Experiment
(FirnMICE), J. Glaciol., 63, 401–422, https://doi.org/10.1017/jog.2016.114,
2017. a
MacFerrin, M., Machguth, H., van As, D., Charalampidis, C., Stevens, C. M.,
Heilig, A., Vandecrux, B., Langen, P. L., Mottram, R., Fettweis, X., den
Broeke, M. R., Pfeffer, W. T., Moussavi, M. S., and Abdalati, W.: Rapid
expansion of Greenland's low-permeability ice slabs, Nature, 573, 403–407,
https://doi.org/10.1038/s41586-019-1550-3, 2019. a
Machguth, H., MacFerrin, M., Van As, D., Box, J. E., Charalampidis, C.,
Colgan, W., Fausto, R. S., Meijer, H. A. J., Mosley-Thompson, E., and van de
Wal, R. S. W.: Greenland meltwater storage in firn limited by near-surface
ice formation, Nat. Clim. Change, 6, 390–393,
https://doi.org/10.1038/nclimate2899, 2016. a
Magnusson, J., Wever, N., Essery, R., Helbig, N., Winstral, A., and Jonas, T.:
Evaluating snow models with varying process representations for hydrological
applications, Water Resour. Res., 51, 2707–2723,
https://doi.org/10.1002/2014WR016498, 2015. a
McConnell, J. R., Mosley-Thompson, E., Bromwich, D. H., Bales, R. C., and Kyne,
J. D.: Interannual variations of snow accumulation on the Greenland Ice
Sheet (1985-1996): New observations versus model predictions, J.
Geophys. Res.-Atmos., 105, 4039–4046,
https://doi.org/10.1029/1999JD901049, 2000. a
McMillan, M., Leeson, A., Shepherd, A., Briggs, K., Armitage, T. W., Hogg, A.,
Kuipers Munneke, P., van den Broeke, M., Noël, B., van de Berg,
W. J., Ligtenberg, S., Horwath, M., Groh, A., Muir, A., and Gilbert, L.: A
high-resolution record of Greenland mass balance, Geophys. Res. Lett., 43, 7002–7010, https://doi.org/10.1002/2016GL069666, 2016. a, b
Miège, C., Forster, R. R., Brucker, L., Koenig, L. S., Solomon, D. K.,
Paden, J. D., Box, J. E., Burgess, E. W., Miller, J. Z., McNerney, L.,
Brautigam, N., Fausto, R. S., and Gogineni, S.: Spatial extent and temporal
variability of Greenland firn aquifers detected by ground and airborne
radars, J. Geophys. Res.-Earth Surf., 121, 2381–2398,
https://doi.org/10.1002/2016JF003869, 2016. a
Miller, O., Solomon, D. K., Miège, C., Koenig, L., Forster, R., Schmerr,
N., Ligtenberg, S. R., Legchenko, A., Voss, C. I., Montgomery, L., and
McConnell, J. R.: Hydrology of a Perennial Firn Aquifer in Southeast
Greenland: An Overview Driven by Field Data, Water Resour. Res., 56, 4,
https://doi.org/10.1029/2019WR026348, 2020. a
Montgomery, L., Koenig, L., and Alexander, P.: The SUMup dataset: compiled measurements of surface mass balance components over ice sheets and sea ice with analysis over Greenland, Earth Syst. Sci. Data, 10, 1959–1985, https://doi.org/10.5194/essd-10-1959-2018, 2018. a, b
Montgomery, L. N., Schmerr, N., Burdick, S., Forster, R. R., Koenig, L.,
Legchenko, A., Ligtenberg, S., Miège, C., Miller, O. L., and Solomon,
D. K.: Investigation of firn aquifer structure in southeastern Greenland
using active source seismology, Front. Earth Sci., 5, 1–12,
https://doi.org/10.3389/feart.2017.00010, 2017. a
Mosley-Thompson, E., McConnell, J. R., Bales, R. C., Li, Z., Lin, P. N.,
Steffen, K., Thompson, L. G., Edwards, R., and Bathke, D.: Local to
regional-scale variability of annual net accumulation on the Greenland ice
sheet from PARCA cores, J. Geophys. Res.-Atmos., 106,
33839–33851, https://doi.org/10.1029/2001JD900067, 2001. a
Mouginot, J., Rignot, E., Bjørk, A. A., van den Broeke, M., Millan, R.,
Morlighem, M., Noël, B., Scheuchl, B., and Wood, M.: Forty-six years
of Greenland Ice Sheet mass balance from 1972 to 2018, P.
Natl. Acad. Sci. USA, 116,
9239–9244, https://doi.org/10.1073/pnas.1904242116, 2019. a
Nghiem, S. V., Hall, D. K., Mote, T. L., Tedesco, M., Albert, M. R., Keegan,
K., Shuman, C. A., DiGirolamo, N. E., and Neumann, G.: The extreme melt
across the Greenland ice sheet in 2012, Geophys. Res. Lett., 39,
6–11, https://doi.org/10.1029/2012GL053611, 2012. a
Noël, B., van de Berg, W. J., van Meijgaard, E., Kuipers Munneke, P., van de Wal, R. S. W., and van den Broeke, M. R.: Evaluation of the updated regional climate model RACMO2.3: summer snowfall impact on the Greenland Ice Sheet, The Cryosphere, 9, 1831–1844, https://doi.org/10.5194/tc-9-1831-2015, 2015. a
Noël, B., Van De Berg, W. J., Lhermitte, S., Wouters, B., Machguth, H.,
Howat, I., Citterio, M., Moholdt, G., Lenaerts, J. T. M., and Van Den
Broeke, M. R.: A tipping point in refreezing accelerates mass loss of
Greenland's glaciers and ice caps, Nat. Commun., 8, 14730,
https://doi.org/10.1038/ncomms14730, 2017. a
Noël, B., van de Berg, W. J., Lhermitte, S., Wouters, B., Schaffer, N.,
and van den Broeke, M. R.: Six decades of glacial mass loss in the Canadian
arctic archipelago, J. Geophys. Res.-Earth Surf., 123,
1430–1449, https://doi.org/10.1029/2017JF004304, 2018a. a
Noël, B., van de Berg, W. J., van Wessem, J. M., van Meijgaard, E., van As, D., Lenaerts, J. T. M., Lhermitte, S., Kuipers Munneke, P., Smeets, C. J. P. P., van Ulft, L. H., van de Wal, R. S. W., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 1: Greenland (1958–2016), The Cryosphere, 12, 811–831, https://doi.org/10.5194/tc-12-811-2018, 2018b. a, b
Noël, B., van de Berg, W. J., Lhermitte, S., and van den Broeke, M. R.:
Rapid ablation zone expansion amplifies north Greenland mass loss, Sci.
Adv., 5, 2–11, https://doi.org/10.1126/sciadv.aaw0123, 2019. a
Noël, B., Jakobs, C. L., van Pelt, W. J., Lhermitte, S., Wouters, B.,
Kohler, J., Hagen, J. O., Luks, B., Reijmer, C. H., van de Berg, W. J., and
van den Broeke, M. R.: Low elevation of Svalbard glaciers drives high mass
loss variability, Nat. Commun., 11, 4597,
https://doi.org/10.1038/s41467-020-18356-1, 2020. a
Orsi, A. J., Kawamura, K., Masson-Delmotte, V., Fettweis, X., Box, J. E.,
Dahl-Jensen, D., Clow, G. D., Landais, A., and Severinghaus, J. P.: The
recent warming trend in North Greenland, Geophys. Res. Lett., 44,
6235–6243, https://doi.org/10.1002/2016GL072212, 2017. a
Pfeffer, W. T., Meier, M. F., and Illangasekare, T. H.: Retention of Greenland
runoff by refreezing: implications for projected future sea level change,
J. Geophys. Res., 96, 22117, https://doi.org/10.1029/91jc02502, 1991. a
Polashenski, C., Courville, Z., Benson, C., Wagner, A., Chen, J., Wong, G.,
Hawley, R., and Hall, D.: Observations of pronounced Greenland ice sheet
firn warming and implications for runoff production, Geophys. Res. Lett., 41, 4238–4246, https://doi.org/10.1002/2014GL059806, 2014. a, b
Reid, R. C., Sherwood, T. K., and Street, R. E.: The Properties of Gases and
Liquids, Physics Today, 12, 38–40, https://doi.org/10.1063/1.3060771, 1959. a
Rennermalm, Å. K., Hock, R., Covi, F., Xiao, J., Corti, G., Kingslake, J.,
Leidman, S. Z., Miège, C., MacFerrin, M., MacHguth, H., Osterberg, E.,
Kameda, T., and McConnell, J. R.: Shallow firn cores 1989-2019 in southwest
Greenland's percolation zone reveal decreasing density and ice layer
thickness after 2012, J. Glaciol., 68, 431–442,
https://doi.org/10.1017/jog.2021.102, 2022. a
Robin, G.: Norwegian-British-Swedish Antarctic Expedition, 1949–52, Polar Record,
6, 608–616, https://doi.org/10.1017/S0032247400047665, 1953. a
Sasgen, I., Wouters, B., Gardner, A. S., King, M. D., Tedesco, M., Landerer,
F. W., Dahle, C., Save, H., and Fettweis, X.: Return to rapid ice loss in
Greenland and record loss in 2019 detected by the GRACE-FO satellites,
Commun. Earth Environ., 1, 8, https://doi.org/10.1038/s43247-020-0010-1,
2020. a
Shepherd, A., Ivins, E., Rignot, E., Smith, B., van den Broeke, M., Velicogna,
I., Whitehouse, P., Briggs, K., Joughin, I., Krinner, G., Nowicki, S., Payne,
T., Scambos, T., Schlegel, N., A, G., Agosta, C., Ahlstrøm, A., Babonis,
G., Barletta, V. R., Bjørk, A. A., Blazquez, A., Bonin, J., Colgan, W.,
Csatho, B., Cullather, R., Engdahl, M. E., Felikson, D., Fettweis, X.,
Forsberg, R., Hogg, A. E., Gallee, H., Gardner, A., Gilbert, L., Gourmelen,
N., Groh, A., Gunter, B., Hanna, E., Harig, C., Helm, V., Horvath, A.,
Horwath, M., Khan, S., Kjeldsen, K. K., Konrad, H., Langen, P. L.,
Lecavalier, B., Loomis, B., Luthcke, S., McMillan, M., Melini, D., Mernild,
S., Mohajerani, Y., Moore, P., Mottram, R., Mouginot, J., Moyano, G., Muir,
A., Nagler, T., Nield, G., Nilsson, J., Noël, B., Otosaka, I., Pattle,
M. E., Peltier, W. R., Pie, N., Rietbroek, R., Rott, H., Sandberg
Sørensen, L., Sasgen, I., Save, H., Scheuchl, B., Schrama, E.,
Schröder, L., Seo, K. W., Simonsen, S. B., Slater, T., Spada, G.,
Sutterley, T., Talpe, M., Tarasov, L., van de Berg, W. J., van der Wal, W.,
van Wessem, M., Vishwakarma, B. D., Wiese, D., Wilton, D., Wagner, T.,
Wouters, B., and Wuite, J.: Mass balance of the Greenland Ice Sheet from
1992 to 2018, Nature, 579, 233–239, https://doi.org/10.1038/s41586-019-1855-2, 2020. a
Sørensen, L. S., Simonsen, S. B., Nielsen, K., Lucas-Picher, P., Spada, G., Adalgeirsdottir, G., Forsberg, R., and Hvidberg, C. S.: Mass balance of the Greenland ice sheet (2003–2008) from ICESat data – the impact of interpolation, sampling and firn density, The Cryosphere, 5, 173–186, https://doi.org/10.5194/tc-5-173-2011, 2011. a
Steffen, C., Box, J., and Abdalati, W.: Greenland Climate Network: GC-Net, US
Army Cold Regions Reattach and Engineering (CRREL), CRREL Special Report, pp.
98–103,
https://books.google.com/books?hl=en&lr=&id=m4zuBvzFn60C&oi=fnd&pg=PA98&dq=Steffen,+C.,+Box,+J.,+and+Abdalati,+W.:+Greenland+Climate+Network:+GC-Net.,+CRREL+Special+Report+on+Glaciers,+Ice+Sheets+and+Volcanoes,+trib.+to+M.+Meier,+96,+98-103,+1996.&ots=qqJ (last access: 1 September 2021),
1996. a
Steger, C. R., Reijmer, C. H., and van den Broeke, M. R.: The modelled liquid water balance of the Greenland Ice Sheet, The Cryosphere, 11, 2507–2526, https://doi.org/10.5194/tc-11-2507-2017, 2017a. a
Steger, C. R., Reijmer, C. H., van den Broeke, M. R., Wever, N., Forster,
R. R., Koenig, L. S., Munneke, P. K., Lehning, M., Lhermitte, S., Ligtenberg,
S. R., Miège, C., and Noël, B. P.: Firn meltwater retention on
the greenland ice sheet: A model comparison, Front. Earth Sci., 5, 103–118,
https://doi.org/10.3389/feart.2017.00003, 2017b. a, b
Stevens, C. M., Verjans, V., Lundin, J. M. D., Kahle, E. C., Horlings, A. N., Horlings, B. I., and Waddington, E. D.: The Community Firn Model (CFM) v1.0, Geosci. Model Dev., 13, 4355–4377, https://doi.org/10.5194/gmd-13-4355-2020, 2020. a
van Dalum, C. T., van de Berg, W. J., Lhermitte, S., and van den Broeke, M. R.: Evaluation of a new snow albedo scheme for the Greenland ice sheet in the Regional Atmospheric Climate Model (RACMO2), The Cryosphere, 14, 3645–3662, https://doi.org/10.5194/tc-14-3645-2020, 2020. a
Vandecrux, B.: Firn temperatures and measurement depths at nine Greenland
Climate Network (GC-Net) weather stations, 1998–2017, Arctic Data Center [data set],
https://doi.org/10.18739/A2833N00P, 2020.
a
Vandecrux, B., MacFerrin, M., Machguth, H., Colgan, W. T., van As, D., Heilig, A., Stevens, C. M., Charalampidis, C., Fausto, R. S., Morris, E. M., Mosley-Thompson, E., Koenig, L., Montgomery, L. N., Miège, C., Simonsen, S. B., Ingeman-Nielsen, T., and Box, J. E.: Firn data compilation reveals widespread decrease of firn air content in western Greenland, The Cryosphere, 13, 845–859, https://doi.org/10.5194/tc-13-845-2019, 2019. a, b
Vandecrux, B., Fausto, R. S., van As, D., Colgan, W., Langen, P. L., Haubner,
K., Ingeman-Nielsen, T., Heilig, A., Stevens, C. M., MacFerrin, M., Niwano,
M., Steffen, K., and Box, J.: Firn cold content evolution at nine sites on
the Greenland ice sheet between 1998 and 2017, J. Glaciol., 66,
1–12, https://doi.org/10.1017/jog.2020.30, 2020a. a
Vandecrux, B., Mottram, R., Langen, P. L., Fausto, R. S., Olesen, M., Stevens, C. M., Verjans, V., Leeson, A., Ligtenberg, S., Kuipers Munneke, P., Marchenko, S., van Pelt, W., Meyer, C. R., Simonsen, S. B., Heilig, A., Samimi, S., Marshall, S., Machguth, H., MacFerrin, M., Niwano, M., Miller, O., Voss, C. I., and Box, J. E.: The firn meltwater Retention Model Intercomparison Project (RetMIP): evaluation of nine firn models at four weather station sites on the Greenland ice sheet, The Cryosphere, 14, 3785–3810, https://doi.org/10.5194/tc-14-3785-2020, 2020b. a, b
van den Broeke, M., Smeets, P., Ettema, J., van der Veen, C., van de Wal, R., and Oerlemans, J.: Partitioning of melt energy and meltwater fluxes in the ablation zone of the west Greenland ice sheet, The Cryosphere, 2, 179–189, https://doi.org/10.5194/tc-2-179-2008, 2008. a
Van Den Broeke, M., Bamber, J., Ettema, J., Rignot, E., Schrama, E., Van
Berg, W. J. D., Van Meijgaard, E., Velicogna, I., and Wouters, B.:
Partitioning recent Greenland mass loss, Science, 326, 984–986,
https://doi.org/10.1126/science.1178176, 2009. a
van den Broeke, M. R., Enderlin, E. M., Howat, I. M., Kuipers Munneke, P., Noël, B. P. Y., van de Berg, W. J., van Meijgaard, E., and Wouters, B.: On the recent contribution of the Greenland ice sheet to sea level change, The Cryosphere, 10, 1933–1946, https://doi.org/10.5194/tc-10-1933-2016, 2016. a, b
van Wessem, J. M., Steger, C. R., Wever, N., and van den Broeke, M. R.: An exploratory modelling study of perennial firn aquifers in the Antarctic Peninsula for the period 1979–2016, The Cryosphere, 15, 695–714, https://doi.org/10.5194/tc-15-695-2021, 2021. a
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012. a
Zwally, H. J., Jun, L., Jay Zwally, H., and Jun, L.: Seasonal and
interannual variations of firn densification and ice-sheet surface elevation
at the Greenland summit, J. Glaciol., 48, 199–207,
https://doi.org/10.3189/172756502781831403, 2002. a
Short summary
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.
Firn covers the Greenland ice sheet (GrIS) and can temporarily prevent mass loss. Here, we...
