Articles | Volume 12, issue 3
https://doi.org/10.5194/gmd-12-933-2019
© Author(s) 2019. 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-12-933-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
12 Mar 2019
Methods for assessment of models |
| 12 Mar 2019
| 12 Mar 2019
ATAT 1.1, the Automated Timing Accordance Tool for comparing ice-sheet model output with geochronological data
Department of Geography, University of Sheffield, Sheffield, S10 2TN, UK
Chris D. Clark
Department of Geography, University of Sheffield, Sheffield, S10 2TN, UK
David Small
Department of Geography, Durham University, Durham, DH1 3LE, UK
Richard C. A. Hindmarsh
British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, UK
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This preprint is open for discussion and under review for The Cryosphere (TC).
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This study uses state-of-the-art computer simulations to better understand how the Greenland ice sheet has changed over the past 24,000 years. By comparing model results with geological data, it reveals when and why the ice sheet grew and shrank, helping improve future predictions of sea level rise and climate change.
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Projecting the future evolution of the Greenland Ice Sheet is key. However, it is still under the influence of past climate changes that occurred over thousands of years. This makes calibrating projection models against current knowledge of its past evolution (not yet achieved) important. To help with this, we produced a new Greenland-wide reconstruction of ice sheet extent by gathering all published studies dating its former retreat and by mapping its past margins at the ice sheet scale.
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We use advanced statistical techniques and a simple ice-sheet model to produce an ensemble of plausible 3D shapes of the ice sheet that once stretched across northern Europe during the previous glacial maximum (140,000 years ago). This new reconstruction, equivalent in volume to 48 ± 8 m of global mean sea-level rise, will improve the interpretation of high sea levels recorded from the Last Interglacial period (120 000 years ago) that provide a useful perspective on the future.
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The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-33, https://doi.org/10.5194/tc-2023-33, 2023
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The Cryosphere, 12, 3635–3651, https://doi.org/10.5194/tc-12-3635-2018, https://doi.org/10.5194/tc-12-3635-2018, 2018
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We use the deglaciation of the last British–Irish Ice Sheet as a valuable case to examine the processes of contemporary ice sheet change, using an ice sheet model to simulate the Minch Ice Stream. We find that ice shelves were a control on retreat and that the Minch Ice Stream was vulnerable to the same marine mechanisms which threaten the future of the West Antarctic Ice Sheet. This demonstrates the importance of marine processes when projecting the future of our contemporary ice sheets.
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This study uses state-of-the-art computer simulations to better understand how the Greenland ice sheet has changed over the past 24,000 years. By comparing model results with geological data, it reveals when and why the ice sheet grew and shrank, helping improve future predictions of sea level rise and climate change.
Benjamin J. Stoker, Helen E. Dulfer, Chris R. Stokes, Victoria H. Brown, Christopher D. Clark, Colm Ó Cofaigh, David J. A. Evans, Duane Froese, Sophie L. Norris, and Martin Margold
The Cryosphere, 19, 869–910, https://doi.org/10.5194/tc-19-869-2025, https://doi.org/10.5194/tc-19-869-2025, 2025
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The Cryosphere, 18, 2407–2428, https://doi.org/10.5194/tc-18-2407-2024, https://doi.org/10.5194/tc-18-2407-2024, 2024
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A Baltic-wide glacial landform-based map is presented, filling in a geographical gap in the record that has been speculated about by palaeoglaciologists for over a century. Here we used newly available bathymetric data and provide landform evidence of corridors of fast ice flow that we interpret as ice streams. Where previous ice-sheet-scale investigations inferred a single ice source, our mapping identifies flow and ice margin geometries from both Swedish and Bothnian sources.
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Oliver G. Pollard, Natasha L. M. Barlow, Lauren J. Gregoire, Natalya Gomez, Víctor Cartelle, Jeremy C. Ely, and Lachlan C. Astfalck
The Cryosphere, 17, 4751–4777, https://doi.org/10.5194/tc-17-4751-2023, https://doi.org/10.5194/tc-17-4751-2023, 2023
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We use advanced statistical techniques and a simple ice-sheet model to produce an ensemble of plausible 3D shapes of the ice sheet that once stretched across northern Europe during the previous glacial maximum (140,000 years ago). This new reconstruction, equivalent in volume to 48 ± 8 m of global mean sea-level rise, will improve the interpretation of high sea levels recorded from the Last Interglacial period (120 000 years ago) that provide a useful perspective on the future.
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The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-33, https://doi.org/10.5194/tc-2023-33, 2023
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The Cryosphere, 15, 5785–5804, https://doi.org/10.5194/tc-15-5785-2021, https://doi.org/10.5194/tc-15-5785-2021, 2021
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Izabela Szuman, Jakub Z. Kalita, Marek W. Ewertowski, Chris D. Clark, Stephen J. Livingstone, and Leszek Kasprzak
Earth Syst. Sci. Data, 13, 4635–4651, https://doi.org/10.5194/essd-13-4635-2021, https://doi.org/10.5194/essd-13-4635-2021, 2021
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The Baltic Ice Stream Complex was the most prominent ice stream of the last Scandinavian Ice Sheet, controlling ice sheet drainage and collapse. Our mapping effort, based on a lidar DEM, resulted in a dataset containing 5461 landforms over an area of 65 000 km2, which allows for reconstruction of the last Scandinavian Ice Sheet extent and dynamics from the Middle Weichselian ice sheet advance, 50–30 ka, through the Last Glacial Maximum, 25–21 ka, and Young Baltic advances, 18–15 ka.
Jean Vérité, Édouard Ravier, Olivier Bourgeois, Stéphane Pochat, Thomas Lelandais, Régis Mourgues, Christopher D. Clark, Paul Bessin, David Peigné, and Nigel Atkinson
The Cryosphere, 15, 2889–2916, https://doi.org/10.5194/tc-15-2889-2021, https://doi.org/10.5194/tc-15-2889-2021, 2021
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Subglacial bedforms are commonly used to reconstruct past glacial dynamics and investigate processes occuring at the ice–bed interface. Using analogue modelling and geomorphological mapping, we demonstrate that ridges with undulating crests, known as subglacial ribbed bedforms, are ubiquitous features along ice stream corridors. These bedforms provide a tantalizing glimpse into (1) the former positions of ice stream margins, (2) the ice lobe dynamics and (3) the meltwater drainage efficiency.
Emma L. M. Lewington, Stephen J. Livingstone, Chris D. Clark, Andrew J. Sole, and Robert D. Storrar
The Cryosphere, 14, 2949–2976, https://doi.org/10.5194/tc-14-2949-2020, https://doi.org/10.5194/tc-14-2949-2020, 2020
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We map visible traces of subglacial meltwater flow across Keewatin, Canada. Eskers are commonly observed to form within meltwater corridors up to a few kilometres wide, and we interpret different traces to have formed as part of the same integrated drainage system. In our proposed model, we suggest that eskers record the imprint of a central conduit while meltwater corridors represent the interaction with the surrounding distributed drainage system.
James E. Lee, Edward J. Brook, Nancy A. N. Bertler, Christo Buizert, Troy Baisden, Thomas Blunier, V. Gabriela Ciobanu, Howard Conway, Dorthe Dahl-Jensen, Tyler J. Fudge, Richard Hindmarsh, Elizabeth D. Keller, Frédéric Parrenin, Jeffrey P. Severinghaus, Paul Vallelonga, Edwin D. Waddington, and Mai Winstrup
Clim. Past, 16, 1691–1713, https://doi.org/10.5194/cp-16-1691-2020, https://doi.org/10.5194/cp-16-1691-2020, 2020
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The Roosevelt Island ice core was drilled to investigate climate from the eastern Ross Sea, West Antarctica. We describe the ice age-scale and gas age-scale of the ice core for 0–763 m (83 000 years BP). Old ice near the bottom of the core implies the ice dome existed throughout the last glacial period and that ice streaming was active in the region. Variations in methane, similar to those used as evidence of early human influence on climate, were observed prior to significant human populations.
Stephen J. Livingstone, Emma L. M. Lewington, Chris D. Clark, Robert D. Storrar, Andrew J. Sole, Isabelle McMartin, Nico Dewald, and Felix Ng
The Cryosphere, 14, 1989–2004, https://doi.org/10.5194/tc-14-1989-2020, https://doi.org/10.5194/tc-14-1989-2020, 2020
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We map series of aligned mounds (esker beads) across central Nunavut, Canada. Mounds are interpreted to have formed roughly annually as sediment carried by subglacial rivers is deposited at the ice margin. Chains of mounds are formed as the ice retreats. This high-resolution (annual) record allows us to constrain the pace of ice retreat, sediment fluxes, and the style of drainage through time. In particular, we suggest that eskers in general record a composite signature of ice-marginal drainage.
Mai Winstrup, Paul Vallelonga, Helle A. Kjær, Tyler J. Fudge, James E. Lee, Marie H. Riis, Ross Edwards, Nancy A. N. Bertler, Thomas Blunier, Ed J. Brook, Christo Buizert, Gabriela Ciobanu, Howard Conway, Dorthe Dahl-Jensen, Aja Ellis, B. Daniel Emanuelsson, Richard C. A. Hindmarsh, Elizabeth D. Keller, Andrei V. Kurbatov, Paul A. Mayewski, Peter D. Neff, Rebecca L. Pyne, Marius F. Simonsen, Anders Svensson, Andrea Tuohy, Edwin D. Waddington, and Sarah Wheatley
Clim. Past, 15, 751–779, https://doi.org/10.5194/cp-15-751-2019, https://doi.org/10.5194/cp-15-751-2019, 2019
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We present a 2700-year timescale and snow accumulation history for an ice core from Roosevelt Island, Ross Ice Shelf, Antarctica. We observe a long-term slightly decreasing trend in accumulation during most of the period but a rapid decline since the mid-1960s. The latter is linked to a recent strengthening of the Amundsen Sea Low and the expansion of regional sea ice. The year 1965 CE may thus mark the onset of significant increases in sea-ice extent in the eastern Ross Sea.
Niall Gandy, Lauren J. Gregoire, Jeremy C. Ely, Christopher D. Clark, David M. Hodgson, Victoria Lee, Tom Bradwell, and Ruza F. Ivanovic
The Cryosphere, 12, 3635–3651, https://doi.org/10.5194/tc-12-3635-2018, https://doi.org/10.5194/tc-12-3635-2018, 2018
Short summary
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We use the deglaciation of the last British–Irish Ice Sheet as a valuable case to examine the processes of contemporary ice sheet change, using an ice sheet model to simulate the Minch Ice Stream. We find that ice shelves were a control on retreat and that the Minch Ice Stream was vulnerable to the same marine mechanisms which threaten the future of the West Antarctic Ice Sheet. This demonstrates the importance of marine processes when projecting the future of our contemporary ice sheets.
Thomas Lelandais, Édouard Ravier, Stéphane Pochat, Olivier Bourgeois, Christopher Clark, Régis Mourgues, and Pierre Strzerzynski
The Cryosphere, 12, 2759–2772, https://doi.org/10.5194/tc-12-2759-2018, https://doi.org/10.5194/tc-12-2759-2018, 2018
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Scattered observations suggest that subglacial meltwater routes drive ice stream dynamics and ice sheet stability. We use a new experimental approach to reconcile such observations into a coherent story connecting ice stream life cycles with subglacial hydrology and bed erosion. Results demonstrate that subglacial flooding, drainage reorganization, and valley development can control an ice stream lifespan, thus opening new perspectives on subglacial processes controlling ice sheet instabilities.
Nancy A. N. Bertler, Howard Conway, Dorthe Dahl-Jensen, Daniel B. Emanuelsson, Mai Winstrup, Paul T. Vallelonga, James E. Lee, Ed J. Brook, Jeffrey P. Severinghaus, Taylor J. Fudge, Elizabeth D. Keller, W. Troy Baisden, Richard C. A. Hindmarsh, Peter D. Neff, Thomas Blunier, Ross Edwards, Paul A. Mayewski, Sepp Kipfstuhl, Christo Buizert, Silvia Canessa, Ruzica Dadic, Helle A. Kjær, Andrei Kurbatov, Dongqi Zhang, Edwin D. Waddington, Giovanni Baccolo, Thomas Beers, Hannah J. Brightley, Lionel Carter, David Clemens-Sewall, Viorela G. Ciobanu, Barbara Delmonte, Lukas Eling, Aja Ellis, Shruthi Ganesh, Nicholas R. Golledge, Skylar Haines, Michael Handley, Robert L. Hawley, Chad M. Hogan, Katelyn M. Johnson, Elena Korotkikh, Daniel P. Lowry, Darcy Mandeno, Robert M. McKay, James A. Menking, Timothy R. Naish, Caroline Noerling, Agathe Ollive, Anaïs Orsi, Bernadette C. Proemse, Alexander R. Pyne, Rebecca L. Pyne, James Renwick, Reed P. Scherer, Stefanie Semper, Marius Simonsen, Sharon B. Sneed, Eric J. Steig, Andrea Tuohy, Abhijith Ulayottil Venugopal, Fernando Valero-Delgado, Janani Venkatesh, Feitang Wang, Shimeng Wang, Dominic A. Winski, V. Holly L. Winton, Arran Whiteford, Cunde Xiao, Jiao Yang, and Xin Zhang
Clim. Past, 14, 193–214, https://doi.org/10.5194/cp-14-193-2018, https://doi.org/10.5194/cp-14-193-2018, 2018
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Temperature and snow accumulation records from the annually dated Roosevelt Island Climate Evolution (RICE) ice core show that for the past 2 700 years, the eastern Ross Sea warmed, while the western Ross Sea showed no trend and West Antarctica cooled. From the 17th century onwards, this dipole relationship changed. Now all three regions show concurrent warming, with snow accumulation declining in West Antarctica and the eastern Ross Sea.
Christopher N. Williams, Stephen L. Cornford, Thomas M. Jordan, Julian A. Dowdeswell, Martin J. Siegert, Christopher D. Clark, Darrel A. Swift, Andrew Sole, Ian Fenty, and Jonathan L. Bamber
The Cryosphere, 11, 363–380, https://doi.org/10.5194/tc-11-363-2017, https://doi.org/10.5194/tc-11-363-2017, 2017
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Knowledge of ice sheet bed topography and surrounding sea floor bathymetry is integral to the understanding of ice sheet processes. Existing elevation data products for Greenland underestimate fjord bathymetry due to sparse data availability. We present a new method to create physically based synthetic fjord bathymetry to fill these gaps, greatly improving on previously available datasets. This will assist in future elevation product development until further observations become available.
Stephen J. Livingstone and Chris D. Clark
Earth Surf. Dynam., 4, 567–589, https://doi.org/10.5194/esurf-4-567-2016, https://doi.org/10.5194/esurf-4-567-2016, 2016
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We mapped and analysed nearly 2000 large valleys that were formed by meltwater flowing under a former ice sheet. Our results demonstrate that valleys tend to cluster together in distinctive networks. The valleys themselves are typically < 20 km long, and 0.5–3 km wide, and their morphology is strongly influenced by local bed conditions (e.g. topography) and hydrology. We suggest valleys formed gradually, with secondary contributions from flood drainage of water stored on top of or under the ice.
H. Patton, A. Hubbard, T. Bradwell, N. F. Glasser, M. J. Hambrey, and C. D. Clark
Earth Surf. Dynam., 1, 53–65, https://doi.org/10.5194/esurf-1-53-2013, https://doi.org/10.5194/esurf-1-53-2013, 2013
S. J. Livingstone, C. D. Clark, J. Woodward, and J. Kingslake
The Cryosphere, 7, 1721–1740, https://doi.org/10.5194/tc-7-1721-2013, https://doi.org/10.5194/tc-7-1721-2013, 2013
H. Fischer, J. Severinghaus, E. Brook, E. Wolff, M. Albert, O. Alemany, R. Arthern, C. Bentley, D. Blankenship, J. Chappellaz, T. Creyts, D. Dahl-Jensen, M. Dinn, M. Frezzotti, S. Fujita, H. Gallee, R. Hindmarsh, D. Hudspeth, G. Jugie, K. Kawamura, V. Lipenkov, H. Miller, R. Mulvaney, F. Parrenin, F. Pattyn, C. Ritz, J. Schwander, D. Steinhage, T. van Ommen, and F. Wilhelms
Clim. Past, 9, 2489–2505, https://doi.org/10.5194/cp-9-2489-2013, https://doi.org/10.5194/cp-9-2489-2013, 2013
A. S. Drouet, D. Docquier, G. Durand, R. Hindmarsh, F. Pattyn, O. Gagliardini, and T. Zwinger
The Cryosphere, 7, 395–406, https://doi.org/10.5194/tc-7-395-2013, https://doi.org/10.5194/tc-7-395-2013, 2013
P. Fretwell, H. D. Pritchard, D. G. Vaughan, J. L. Bamber, N. E. Barrand, R. Bell, C. Bianchi, R. G. Bingham, D. D. Blankenship, G. Casassa, G. Catania, D. Callens, H. Conway, A. J. Cook, H. F. J. Corr, D. Damaske, V. Damm, F. Ferraccioli, R. Forsberg, S. Fujita, Y. Gim, P. Gogineni, J. A. Griggs, R. C. A. Hindmarsh, P. Holmlund, J. W. Holt, R. W. Jacobel, A. Jenkins, W. Jokat, T. Jordan, E. C. King, J. Kohler, W. Krabill, M. Riger-Kusk, K. A. Langley, G. Leitchenkov, C. Leuschen, B. P. Luyendyk, K. Matsuoka, J. Mouginot, F. O. Nitsche, Y. Nogi, O. A. Nost, S. V. Popov, E. Rignot, D. M. Rippin, A. Rivera, J. Roberts, N. Ross, M. J. Siegert, A. M. Smith, D. Steinhage, M. Studinger, B. Sun, B. K. Tinto, B. C. Welch, D. Wilson, D. A. Young, C. Xiangbin, and A. Zirizzotti
The Cryosphere, 7, 375–393, https://doi.org/10.5194/tc-7-375-2013, https://doi.org/10.5194/tc-7-375-2013, 2013
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Glacier Energy and Mass Balance (GEMB): a model of firn processes for cryosphere research
Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean
Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling
SUHMO: an adaptive mesh refinement SUbglacial Hydrology MOdel v1.0
Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau
The Multiple Snow Data Assimilation System (MuSA v1.0)
The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)
Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G
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)
Alex E. West and Edward W. Blockley
Geosci. Model Dev., 18, 3041–3064, https://doi.org/10.5194/gmd-18-3041-2025, https://doi.org/10.5194/gmd-18-3041-2025, 2025
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This study uses ice mass balance buoys – temperature- and height-measuring devices frozen into sea ice – to find how well climate models simulate (1) melt and growth of Arctic sea ice and (2) conduction of heat through Arctic sea ice. This may help understand why models produce varying amounts of sea ice in the present day. We find that models tend to show more melt, growth or conduction for a given ice thickness than the buoys, although the difference is smaller for models with more physically realistic thermodynamics.
Ole Richter, Ralph Timmermann, G. Hilmar Gudmundsson, and Jan De Rydt
Geosci. Model Dev., 18, 2945–2960, https://doi.org/10.5194/gmd-18-2945-2025, https://doi.org/10.5194/gmd-18-2945-2025, 2025
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The new coupled ice sheet–ocean model addresses challenges related to horizontal resolution through advanced mesh flexibility, enabled by the use of unstructured grids. We describe the new model, verify its functioning in an idealised setting and demonstrate its advantages in a global-ocean–Antarctic ice sheet domain. The results of this study comprise an important step towards improving predictions of the Antarctic contribution to sea level rise over centennial timescales.
Niccolò Maffezzoli, Eric Rignot, Carlo Barbante, Troels Petersen, and Sebastiano Vascon
Geosci. Model Dev., 18, 2545–2568, https://doi.org/10.5194/gmd-18-2545-2025, https://doi.org/10.5194/gmd-18-2545-2025, 2025
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In this work we introduce IceBoost, a machine learning framework to model the ice thickness distribution of all the world's glaciers with greater accuracy than state-of-the-art methods. The model is trained on 3.7 million measurements globally available and provides skilful estimates across all regions. This advancement will help in better assessing future sea level changes and freshwater resources, with significance for both the scientific community and society at large.
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025, https://doi.org/10.5194/gmd-18-1829-2025, 2025
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Accurately measuring snow height is key for modeling approaches in climate science, snow hydrology, and avalanche forecasting. Erroneous snow height measurements often occur when snow height is low or changes, for instance during snowfall in summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep-learning approaches. Our approach can be easily implemented in a data pipeline for snow modeling.
James W. Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin J. Siegert, and Liam Holder
Geosci. Model Dev., 18, 1673–1708, https://doi.org/10.5194/gmd-18-1673-2025, https://doi.org/10.5194/gmd-18-1673-2025, 2025
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Ice sheet models can help predict how Antarctica's ice sheets respond to environmental change, and such models benefit from comparison to geological data. Here, we use an ice sheet model output and other data to predict the erosion of debris and trace its transport to where it is deposited on the ocean floor. This allows the results of ice sheet modelling to be directly and quantitively compared to real-world data, helping to reduce uncertainty regarding Antarctic sea level contribution.
Anton Korosov, Yue Ying, and Einar Ólason
Geosci. Model Dev., 18, 885–904, https://doi.org/10.5194/gmd-18-885-2025, https://doi.org/10.5194/gmd-18-885-2025, 2025
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We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev., 18, 651–670, https://doi.org/10.5194/gmd-18-651-2025, https://doi.org/10.5194/gmd-18-651-2025, 2025
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We employed the WRF-Chem model to parameterize atmospheric nitrate deposition in snow and evaluate its performance in simulating snow cover, snow depth, and concentrations of dust and nitrate using new observations from northern China. The results generally exhibit reasonable agreement with field observations in northern China, demonstrating the model's capability to simulate snow properties, including concentrations of reservoir species.
Simon Horton, Florian Herla, and Pascal Haegeli
Geosci. Model Dev., 18, 193–209, https://doi.org/10.5194/gmd-18-193-2025, https://doi.org/10.5194/gmd-18-193-2025, 2025
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We present a method for avalanche forecasters to analyze patterns in snowpack model simulations. It uses fuzzy clustering to group small regions into larger forecast areas based on snow characteristics, locations, and temporal history. Tested in the Columbia Mountains in two winter seasons, it closely matched real forecast regions regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev., 17, 8969–8988, https://doi.org/10.5194/gmd-17-8969-2024, https://doi.org/10.5194/gmd-17-8969-2024, 2024
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We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better-quality maps. The correction can then be extended backwards and forwards in time for periods when better-quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the past 60 years at a resolution of 1 d and 1 km. This is the first time that such a dataset has been produced.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
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The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow albedo in the solar domain and the profiles of light and energy absorption in a multi-layered snowpack whose physical properties are user defined. It uniquely considers snow grain shape flexibly, based on recent insights showing that snow does not behave as a collection of ice spheres but instead as a random medium. TARTES is user-friendly yet performs comparably to more complex models.
Tim Hill, Derek Bingham, Gwenn E. Flowers, and Matthew J. Hoffman
EGUsphere, https://doi.org/10.22541/essoar.172736254.41350153/v2, https://doi.org/10.22541/essoar.172736254.41350153/v2, 2024
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Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train fast statistical models called Gaussian Process emulators to accelerate subglacial drainage modelling by ~1000 times. We use the fast emulator predictions to show that three of the model parameters are responsible for >90 % of the variance in model outputs. The fast GP emulators will enable future uncertainty quantification and calibration of these models.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Yafei Nie, Ian Fenty, Matthew Mazloff, Armin Köhl, and Dimitris Menemenlis
Geosci. Model Dev., 17, 8613–8638, https://doi.org/10.5194/gmd-17-8613-2024, https://doi.org/10.5194/gmd-17-8613-2024, 2024
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Global- and basin-scale ocean reanalyses are becoming easily accessible. However, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluation. We conduct intercomparison analyses of Massachusetts Institute of Technology General Circulation Model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open-ocean temporal variability and Antarctic continental shelves require improvements.
Diego Monteiro, Cécile Caillaud, Matthieu Lafaysse, Adrien Napoly, Mathieu Fructus, Antoinette Alias, and Samuel Morin
Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, https://doi.org/10.5194/gmd-17-7645-2024, 2024
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Modeling snow cover in climate and weather forecasting models is a challenge even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties when representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration, enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
Alessandro Maissen, Frank Techel, and Michele Volpi
Geosci. Model Dev., 17, 7569–7593, https://doi.org/10.5194/gmd-17-7569-2024, https://doi.org/10.5194/gmd-17-7569-2024, 2024
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By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
EGUsphere, https://doi.org/10.5194/egusphere-2024-2546, https://doi.org/10.5194/egusphere-2024-2546, 2024
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How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
Davor Dundovic, Joseph G. Wallwork, Stephan C. Kramer, Fabien Gillet-Chaulet, Regine Hock, and Matthew D. Piggott
EGUsphere, https://doi.org/10.5194/egusphere-2024-2649, https://doi.org/10.5194/egusphere-2024-2649, 2024
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Accurate numerical studies of glaciers often require high-resolution simulations, which often prove too demanding even for modern computers. In this paper we develop a method that identifies whether different parts of a glacier require high or low resolution based on its physical features, such as its thickness and velocity. We show that by doing so we can achieve a more optimal simulation accuracy for the available computing resources compared to uniform resolution simulations.
Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova
Geosci. Model Dev., 17, 7219–7244, https://doi.org/10.5194/gmd-17-7219-2024, https://doi.org/10.5194/gmd-17-7219-2024, 2024
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We describe a new snow scheme developed for use in global climate models, which simulates the interactions of snowpack with vegetation, atmosphere, and soil. We test the new snow model over a set of sites where in situ observations are available. We find that when compared to a simpler snow model, this model improves predictions of seasonal snow and of soil temperature under the snowpack, important variables for simulating both the hydrological cycle and the global climate system.
Therese Rieckh, Andreas Born, Alexander Robinson, Robert Law, and Gerrit Gülle
Geosci. Model Dev., 17, 6987–7000, https://doi.org/10.5194/gmd-17-6987-2024, https://doi.org/10.5194/gmd-17-6987-2024, 2024
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We present the open-source model ELSA, which simulates the internal age structure of large ice sheets. It creates layers of snow accumulation at fixed times during the simulation, which are used to model the internal stratification of the ice sheet. Together with reconstructed isochrones from radiostratigraphy data, ELSA can be used to assess ice sheet models and to improve their parameterization. ELSA can be used coupled to an ice sheet model or forced with its output.
Fu Zhao, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu
Geosci. Model Dev., 17, 6867–6886, https://doi.org/10.5194/gmd-17-6867-2024, https://doi.org/10.5194/gmd-17-6867-2024, 2024
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In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely the Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea ice‒ocean‒ice shelf coupled model and can assimilate sea ice concentration observations. By assessing the system's performance in sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for the next 7 d and has the potential to guide ship navigation in the Antarctic sea ice zone.
Chenhui Ning, Shiming Xu, Yan Zhang, Xuantong Wang, Zhihao Fan, and Jiping Liu
Geosci. Model Dev., 17, 6847–6866, https://doi.org/10.5194/gmd-17-6847-2024, https://doi.org/10.5194/gmd-17-6847-2024, 2024
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Sea ice models are mainly based on non-moving structured grids, which is different from buoy measurements that follow the ice drift. To facilitate Lagrangian analysis, we introduce online tracking of sea ice in Community Ice CodE (CICE). We validate the sea ice tracking with buoys and evaluate the sea ice deformation in high-resolution simulations, which show multi-fractal characteristics. The source code is openly available and can be used in various scientific and operational applications.
Ulrich Strasser, Michael Warscher, Erwin Rottler, and Florian Hanzer
Geosci. Model Dev., 17, 6775–6797, https://doi.org/10.5194/gmd-17-6775-2024, https://doi.org/10.5194/gmd-17-6775-2024, 2024
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openAMUNDSEN is a fully distributed open-source snow-hydrological model for mountain catchments. It includes process representations of an empirical, semi-empirical, and physical nature. It uses temperature, precipitation, humidity, radiation, and wind speed as forcing data and is computationally efficient, of a modular nature, and easily extendible. The Python code is available on GitHub (https://github.com/openamundsen/openamundsen), including documentation (https://doc.openamundsen.org).
Matthias Rauter and Julia Kowalski
Geosci. Model Dev., 17, 6545–6569, https://doi.org/10.5194/gmd-17-6545-2024, https://doi.org/10.5194/gmd-17-6545-2024, 2024
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Snow avalanches can form large powder clouds that substantially exceed the velocity and reach of the dense core. Only a few complex models exist to simulate this phenomenon, and the respective hazard is hard to predict. This work provides a novel flow model that focuses on simple relations while still encapsulating the significant behaviour. The model is applied to reconstruct two catastrophic powder snow avalanche events in Austria.
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth C. Hunke, and Stefan Rethmeier
Geosci. Model Dev., 17, 6529–6544, https://doi.org/10.5194/gmd-17-6529-2024, https://doi.org/10.5194/gmd-17-6529-2024, 2024
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Earth system models (ESMs) today strive for better quality based on improved resolutions and improved physics. A limiting factor is the supercomputers at hand and how best to utilize them. This study focuses on the refactorization of one part of a sea ice model (CICE), namely the dynamics. It shows that the performance can be significantly improved, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.
Iain Wheel, Douglas I. Benn, Anna J. Crawford, Joe Todd, and Thomas Zwinger
Geosci. Model Dev., 17, 5759–5777, https://doi.org/10.5194/gmd-17-5759-2024, https://doi.org/10.5194/gmd-17-5759-2024, 2024
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Calving, the detachment of large icebergs from glaciers, is one of the largest uncertainties in future sea level rise projections. This process is poorly understood, and there is an absence of detailed models capable of simulating calving. A new 3D calving model has been developed to better understand calving at glaciers where detailed modelling was previously limited. Importantly, the new model is very flexible. By allowing for unrestricted calving geometries, it can be applied at any location.
Constantijn J. Berends, Victor Azizi, Jorge Bernales, and Roderik S. W. van de Wal
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-5, https://doi.org/10.5194/gmd-2024-5, 2024
Revised manuscript accepted for GMD
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Ice-sheet models are computer programs that can simulate how the Greenland and Antarctic ice sheets will evolve in the future. The accuracy of these models depends on their resolution: how small the details are that the model can resolve. We have created a model with a variable resolution, which can resolve a lot of detail in areas where lots of changes happen in the ice, and less detail in areas where the ice does not move so much. This makes the model both accurate and fast.
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
Geosci. Model Dev., 17, 1903–1929, https://doi.org/10.5194/gmd-17-1903-2024, https://doi.org/10.5194/gmd-17-1903-2024, 2024
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In this paper, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature, and melt while preserving a reasonable computation time.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
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Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024, https://doi.org/10.5194/gmd-17-1041-2024, 2024
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The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
Anjali Sandip, Ludovic Räss, and Mathieu Morlighem
Geosci. Model Dev., 17, 899–909, https://doi.org/10.5194/gmd-17-899-2024, https://doi.org/10.5194/gmd-17-899-2024, 2024
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We solve momentum balance for unstructured meshes to predict ice flow for real glaciers using a pseudo-transient method on graphics processing units (GPUs) and compare it to a standard central processing unit (CPU) implementation. We justify the GPU implementation by applying the price-to-performance metric for up to million-grid-point spatial resolutions. This study represents a first step toward leveraging GPU processing power, enabling more accurate polar ice discharge predictions.
Julien Brondex, Kévin Fourteau, Marie Dumont, Pascal Hagenmuller, Neige Calonne, François Tuzet, and Henning Löwe
Geosci. Model Dev., 16, 7075–7106, https://doi.org/10.5194/gmd-16-7075-2023, https://doi.org/10.5194/gmd-16-7075-2023, 2023
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Vapor diffusion is one of the main processes governing snowpack evolution, and it must be accounted for in models. Recent attempts to represent vapor diffusion in numerical models have faced several difficulties regarding computational cost and mass and energy conservation. Here, we develop our own finite-element software to explore numerical approaches and enable us to overcome these difficulties. We illustrate the capability of these approaches on established numerical benchmarks.
Daniel Moreno-Parada, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
EGUsphere, https://doi.org/10.5194/egusphere-2023-2690, https://doi.org/10.5194/egusphere-2023-2690, 2023
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We introduce Nix, an ice-sheet model designed for understanding how large masses of ice behave. Nix as a computer program that simulates the movement and temperature changes in ice sheets. Nix helps us study how ice sheets respond to changes in the atmosphere and ocean. We found that how fast ice melts under the shelves and how heat is exchanged, play a role in determining the future of ice sheets. Nix is a useful tool for learning more about how climate change affects polar ice sheets.
Matthias Tonnel, Anna Wirbel, Felix Oesterle, and Jan-Thomas Fischer
Geosci. Model Dev., 16, 7013–7035, https://doi.org/10.5194/gmd-16-7013-2023, https://doi.org/10.5194/gmd-16-7013-2023, 2023
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Avaframe - the open avalanche framework - provides open-source tools to simulate and investigate snow avalanches. It is utilized for multiple purposes, the two main applications being hazard mapping and scientific research of snow processes. We present the theory, conversion to a computer model, and testing for one of the core modules used for simulations of a particular type of avalanche, the so-called dense-flow avalanches. Tests check and confirm the applicability of the utilized method.
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, https://doi.org/10.5194/gmd-16-6671-2023, 2023
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We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823, https://doi.org/10.5194/gmd-16-5803-2023, https://doi.org/10.5194/gmd-16-5803-2023, 2023
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We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Kevin Hank, Lev Tarasov, and Elisa Mantelli
Geosci. Model Dev., 16, 5627–5652, https://doi.org/10.5194/gmd-16-5627-2023, https://doi.org/10.5194/gmd-16-5627-2023, 2023
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Physically meaningful modeling of geophysical system instabilities is numerically challenging, given the potential effects of purely numerical artifacts. Here we explore the sensitivity of ice stream surge activation to numerical and physical model aspects. We find that surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions and are significantly affected by the incorporation of bed thermal and sub-glacial hydrology models.
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, and Angelika Humbert
Geosci. Model Dev., 16, 5305–5322, https://doi.org/10.5194/gmd-16-5305-2023, https://doi.org/10.5194/gmd-16-5305-2023, 2023
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Water underneath ice sheets affects the motion of glaciers. This study presents a newly developed code, CUAS-MPI, that simulates subglacial hydrology. It is designed for supercomputers and is hence a parallelized code. We measure the performance of this code for simulations of the entire Greenland Ice Sheet and find that the code works efficiently. Moreover, we validated the code to ensure the correctness of the solution. CUAS-MPI opens new possibilities for simulations of ice sheet hydrology.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
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Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
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Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
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
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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.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
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The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Alex S. Gardner, Nicole-Jeanne Schlegel, and Eric Larour
Geosci. Model Dev., 16, 2277–2302, https://doi.org/10.5194/gmd-16-2277-2023, https://doi.org/10.5194/gmd-16-2277-2023, 2023
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This is the first description of the open-source Glacier Energy and Mass Balance (GEMB) model. GEMB models the ice sheet and glacier surface–atmospheric energy and mass exchange, as well as the firn state. The model is evaluated against the current state of the art and in situ observations and is shown to perform well.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
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State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Varun Sharma, Franziska Gerber, and Michael Lehning
Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, https://doi.org/10.5194/gmd-16-719-2023, 2023
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Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere.
Anne M. Felden, Daniel F. Martin, and Esmond G. Ng
Geosci. Model Dev., 16, 407–425, https://doi.org/10.5194/gmd-16-407-2023, https://doi.org/10.5194/gmd-16-407-2023, 2023
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We present and validate a novel subglacial hydrology model, SUHMO, based on an adaptive mesh refinement framework. We propose the addition of a pseudo-diffusion to recover the wall melting in channels. Computational performance analysis demonstrates the efficiency of adaptive mesh refinement on large-scale hydrologic problems. The adaptive mesh refinement approach will eventually enable better ice bed boundary conditions for ice sheet simulations at a reasonable computational cost.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
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Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
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We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
Max Brils, Peter Kuipers Munneke, Willem Jan van de Berg, and Michiel van den Broeke
Geosci. Model Dev., 15, 7121–7138, https://doi.org/10.5194/gmd-15-7121-2022, https://doi.org/10.5194/gmd-15-7121-2022, 2022
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Firn covers the Greenland ice sheet (GrIS) and can temporarily prevent mass loss. Here, we present the latest version of our firn model, IMAU-FDM, with an application to the GrIS. We improved the density of fallen snow, the firn densification rate and the firn's thermal conductivity. This leads to a higher air content and 10 m temperatures. Furthermore we investigate three case studies and find that the updated model shows greater variability and an increased sensitivity in surface elevation.
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
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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
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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.
Cited articles
Applegate, P. J., Kirchner, N., Stone, E. J., Keller, K., and Greve, R.:
An assessment of key model parametric uncertainties in projections of Greenland
Ice Sheet behavior, The Cryosphere, 6, 589–606, https://doi.org/10.5194/tc-6-589-2012, 2012.
Arnold, J. R. and Libby, W. F.: Radiocarbon dates, Science, 113, 111–120,
1951.
Auriac, A., Whitehouse, P. L., Bentley, M. J., Patton, H., Lloyd, J. M., and
Hubbard, A.: Glacial isostatic adjustment associated with the Barents Sea ice
sheet: a modelling inter-comparison, Quaternary Sci. Rev., 147,
122–135, 2016.
Balco, G.: Contributions and unrealized potential contributions of
cosmogenic-nuclide exposure dating to glacier chronology, 1990–2010,
Quaternary Sci. Rev., 30, 3–27, 2011.
Bamber, J. L. and Aspinall, W. P.: An expert judgement assessment of future
sea level rise from the ice sheets, Nat. Clim. Change, 3, 424–427, 2013.
Bateman, M. D., Evans, D. J., Roberts, D. H., Medialdea, A., Ely, J., and Clark,
C. D.: The timing and consequences of the blockage of the Humber Gap by the
last British-Irish Ice Sheet, Boreas, 47, 41–61, 2018.
Boulton, G. and Hagdorn, M.: Glaciology of the British Isles Ice Sheet during
the last glacial cycle: form, flow, streams and lobes, Quaternary Sci. Rev.,
25, 3359–3390, 2006.
Braconnot, P., Harrison, S. P., Kageyama, M., Bartlein, P. J.,
Masson-Delmotte, V., Abe-Ouchi, A., Otto-Bliesner, B., and Zhao, Y.:
Evaluation of climate models using palaeoclimatic data, Nat. Clim.
Change, 2, 417–424, 2012.
Briggs, R. D. and Tarasov, L.: How to evaluate model-derived deglaciation
chronologies: a case study using Antarctica, Quaternary Sci. Rev., 63,
109–127, 2013.
Briggs, R. D., Pollard, D., and Tarasov, L.: A data-constrained large ensemble
analysis of Antarctic evolution since the Eemian, Quaternary Sci. Rev., 103,
91–115, 2014.
Brown, E. J., Rose, J., Coope, R. G., and Lowe, J. J.: An MIS 3 age organic
deposit from Balglass Burn, central Scotland: palaeoenvironmental
significance and implications for the timing of the onset of the LGM ice
sheet in the vicinity of the British Isles, J. Quaternary Sci., 22,
295–308, 2007.
Bueler, E. and Brown, J.: Shallow shelf approximation as a “sliding law” in
a thermomechanically coupled ice sheet model, J. Geophys. Res.-Earth, 114,
F03008, https://doi.org/10.1029/2008JF001179, 2009.
Bueler, E. D., Lingle, C. S., and Brown, J.: Fast computation of a viscoelastic
deformable Earth model for ice-sheet simulations, Ann. Glaciol., 46,
97–105, 2007.
Calov, R. and Greve, R.: A semi-analytical solution for the positive
degree-day model with stochastic temperature variations, J. Glaciol., 51,
173–175, 2005.
Chiverrell, R. C., Thrasher, I. M., Thomas, G. S., Lang, A., Scourse, J. D., van
Landeghem, K. J., Mccarroll, D., Clark, C. D., Cofaigh, C.Ó., Evans, D.
J., and Ballantyne, C. K.: Bayesian modelling the retreat of the Irish Sea Ice
Stream, J. Quaternary Sci., 28, 200–209, 2013.
Clark, C. D., Hughes, A. L., Greenwood, S. L., Jordan, C., and Sejrup, H. P.:
Pattern and timing of retreat of the last British-Irish Ice Sheet,
Quaternary Sci. Rev., 44, 112–146, 2012.
Clark, C. D., Ely, J. C., Greenwood, S. L., Hughes, A. L. C., Meehan, R.,
Barr, I. D., Bateman, M. D., Bradwell, T., Doole, J., Evans, D. J. A., Joran,
C. J., Monteys, X., Pellicier, X. M., and Sheehy, M.: BRITICE Glacial Map,
version 2: a map and GIS database of glacial landforms of the last
British-Irish Ice Sheet, Boreas, 47, 11–27, 2018.
Collins, M.: Ensembles and probabilities: a new era in the prediction of
climate change, Philos. T. R. Soc. A, 365, 1957–1970, 2007.
Collins, M., Booth, B. B., Bhaskaran, B., Harris, G. R., Murphy, J. M., Sexton,
D. M., and Webb, M. J.: Climate model errors, feedbacks and forcings: a
comparison of perturbed physics and multi-model ensembles, Clim.
Dynam., 36, 1737–1766, 2011.
Cornford, S. L., Martin, D. F., Graves, D. T., Ranken, D. F., Le Brocq, A. M.,
Gladstone, R. M., Payne, A. J., Ng, E. G., and Lipscomb, W. H.: Adaptive mesh,
finite volume modeling of marine ice sheets, J. Comput.
Phys., 232, 529–549, 2013.
DeConto, R. M. and Pollard, D.: Contribution of Antarctica to past and future
sea-level rise, Nature, 531, 591–597, 2016.
Duller, G. A. T.: Single grain optical dating of glacigenic deposits,
Quaternary Geochr., 1, 296–304, 2006.
Dyke, A. S.: An outline of North American deglaciation with emphasis on
central and northern Canada, Dev. Quaternary Sci., 2,
373–424, 2004.
Edwards, T. L., Fettweis, X., Gagliardini, O., Gillet-Chaulet, F., Goelzer,
H., Gregory, J. M., Hoffman, M., Huybrechts, P., Payne, A. J., Perego, M.,
Price, S., Quiquet, A., and Ritz, C.: Effect of uncertainty in surface mass
balance-elevation feedback on projections of the future sea level contribution
of the Greenland ice sheet, The Cryosphere, 8, 195–208,
https://doi.org/10.5194/tc-8-195-2014, 2014.
Ely, J. C., Clark, C. D., Hindmarsh, R. C. A., Hughes, A. L. C., Greenwood, S. L.,
Bradley, S. L., Gasson, E., Gregoire, L., Gandy, N., Stokes, C. R., and Small,
D.: An approach to combining geomorphological and geochronological data with
ice sheet modelling, demonstrated using the last British-Irish Ice Sheet,
J. Quaternary Sci., accepted, 2019a.
Ely, J. C., Clark, C. D., Small, D., and Hindmarsh, R. C. A.: ATAT code and
example, data set, https://doi.org/10.15131/shef.data.7172243, 2019b.
Fowler, A. C.: A sliding law for glaciers of constant viscosity in the
presence of subglacial cavitation, Proc. Roy. Soc.
London A, 407, 147–170, 1986.
Fretwell, P., Pritchard, H. D., Vaughan, D. G., Bamber, J. L., Barrand, N.
E., Bell, R., Bianchi, C., Bingham, R. G., Blankenship, D. D., Casassa, G.,
Catania, G., Callens, D., Conway, H., Cook, A. J., Corr, H. F. J., Damaske,
D., Damm, V., Ferraccioli, F., Forsberg, R., Fujita, S., Gim, Y., Gogineni,
P., Griggs, J. A., Hindmarsh, R. C. A., Holmlund, P., Holt, J. W., Jacobel,
R. W., Jenkins, A., Jokat, W., Jordan, T., King, E. C., Kohler, J., Krabill,
W., Riger-Kusk, M., Langley, K. A., Leitchenkov, G., Leuschen, C., Luyendyk,
B. P., Matsuoka, K., Mouginot, J., Nitsche, F. O., Nogi, Y., Nost, O. A.,
Popov, S. V., Rignot, E., Rippin, D. M., Rivera, A., Roberts, J., Ross, N.,
Siegert, M. J., Smith, A. M., Steinhage, D., Studinger, M., Sun, B., Tinto,
B. K., Welch, B. C., Wilson, D., Young, D. A., Xiangbin, C., and Zirizzotti,
A.: Bedmap2: improved ice bed, surface and thickness datasets for Antarctica,
The Cryosphere, 7, 375–393, https://doi.org/10.5194/tc-7-375-2013, 2013.
Fuchs, M. and Owen, L. A.: Luminescence dating of glacial and associated
sediments: review, recommendations and future directions, Boreas, 37,
636–659, 2008.
Gasson, E., DeConto, R. M., Pollard, D., and Levy, R. H.: Dynamic Antarctic ice
sheet during the early to mid-Miocene, P. Natl. Acad.
Sci. USA, 113, 3459–3464, 2016.
Golledge, N. R., Levy, R. H., McKay, R. M., Fogwill, C. J., White, D. A., Graham,
A. G., Smith, J. A., Hillenbrand, C. D., Licht, K. J., Denton, G. H., and Ackert,
R. P.: Glaciology and geological signature of the Last Glacial Maximum
Antarctic ice sheet, Quaternary Sci. Rev., 78, 225–247, 2013.
Gomez, N., Pollard, D., Mitrovica, J. X., Huybers, P., and Clark, P. U.:
Evolution of a coupled marine ice sheet-sea level model, J. Geophys. Res.,
117, F01013, https://doi.org/10.1029/2011JF002128, 2012.
Gomez, N., Pollard, D., and Mitrovica, J. X.: A 3-D coupled ice sheet–sea
level model applied to Antarctica through the last 40 ky, Earth Planet
Sci. Lett., 384, 88–99, 2013.
Gowan, E. J.: An assessment of the minimum timing of ice free conditions of
the western Laurentide Ice Sheet, Quaternary Sci. Rev., 75, 100–113, 2013.
Gregoire, L. J., Payne, A. J., and Valdes, P. J.: Deglacial rapid sea level rises
caused by ice-sheet saddle collapses, Nature, 487, 219–222, 2012.
Greve, R. and Hutter, K.: Polythermal three-dimensional modelling of the
Greenland ice sheet with varied geothermal heat flux, Ann. Glaciol., 21,
8–12, 1995.
Greve, R., Wyrwoll, K. H., and Eisenhauer, A.: Deglaciation of the Northern
Hemisphere at the onset of the Eemian and Holocene, Ann. Glaciol., 28, 1–8, 1999.
Gudmundsson, G. H.: Ice-shelf buttressing and the stability of marine ice
sheets, The Cryosphere, 7, 647–655, https://doi.org/10.5194/tc-7-647-2013, 2013.
Gudmundsson, G. H., Krug, J., Durand, G., Favier, L., and Gagliardini, O.:
The stability of grounding lines on retrograde slopes, The Cryosphere, 6,
1497–1505, https://doi.org/10.5194/tc-6-1497-2012, 2012.
Heroy, D. C. and Anderson, J. B.: Radiocarbon constraints on Antarctic
Peninsula ice sheet retreat following the Last Glacial Maximum (LGM),
Quaternary Sci. Rev., 26, 3286–3297, 2007.
Heyman, J., Stroeven, A. P., Harbor, J. M., and Caffee, M. W.: Too young or too
old: evaluating cosmogenic exposure dating based on an analysis of compiled
boulder exposure ages, Earth Planet Sci. Lett., 302, 71–80, 2011.
Hindmarsh, R. C.: Consistent generation of ice-streams via thermo-viscous
instabilities modulated by membrane stresses, Geophys. Res. Lett., 36,
L06502, https://doi.org/10.1029/2008GL036877, 2009.
Hubbard, A., Bradwell, T., Golledge, N., Hall, A., Patton, H., Sugden, D.,
Cooper, R., and Stoker, M.: Dynamic cycles, ice streams and their impact on
the extent, chronology and deglaciation of the British–Irish ice sheet,
Quaternary Sci. Rev., 28, 758–776, 2009.
Hughes, A. L., Greenwood, S. L., and Clark, C. D.: Dating constraints on the last
British-Irish Ice Sheet: a map and database, J Maps, 7, 156–184, 2011.
Hughes, A. L., Clark, C. D., and Jordan, C. J.: Flow-pattern evolution of the
last British Ice Sheet, Quaternary Sci. Rev., 89, 148–168, 2014.
Hughes, A. L., Gyllencreutz, R., Lohne, Ø. S., Mangerud, J., Svendsen, J.
I.: DATED-1: compilation of dates and time-slice reconstruction of the
build-up and retreat of the last Eurasian (British-Irish, Scandinavian,
Svalbard-Barents-Kara Seas) Ice Sheets 40–10 ka. Department of Earth
Science, University of Bergen and Bjerknes Centre for Climate Research,
PANGAEA, https://doi.org/10.1594/PANGAEA.848117, 2015.
Hughes, A. L., Gyllencreutz, R., Lohne, Ø. S., Mangerud, J., and Svendsen,
J. I.: The last Eurasian ice sheets–a chronological database and time-slice
reconstruction, DATED-1, Boreas, 45, 1–45, 2016.
Hughes, T. J.: Is the West Antarctic ice sheet disintegrating?, J. Geophys.
Res., 78, 7884–7910, 1973.
Huybrechts, P.: The Antarctic ice sheet during the last glacial-interglacial
cycle: a three-dimensional experiment, Ann. Glaciol., 14, 115–119, 1990.
Imbrie, J., Hays, J. D., Martinson, D. G., McIntyre, A., Mix, A. C., Morley,
J. J., Pisias, N. G., Prell, W. L., and Shackleton, N. J.: The orbital theory of
Pleistocene climate: support from a revised chronology of the marine δ18O record, edited by: Berger, A., Imbrie, J., Hays, H., Kukla, G., and Saltzman, B.,
in: Milankovitch and Climate, Part I. D. Reidel Publishing, Dordrecht,
269–305, 1984.
Johnsen, S. J., Dahl-Jensen, D., Dansgaard, W., and Gundestrup, N.: Greenland
palaeotemperatures derived from GRIP bore hole temperature and ice core
isotope profiles, Tellus B, 47, 624–629, 1995.
Kirchner, N., Hutter, K., Jakobsson, M., and Gyllencreutz, R.: Capabilities
and limitations of numerical ice sheet models: a discussion for
Earth-scientists and modelers, Quaternary Sci. Rev., 30, 3691–3704, 2011.
Kirchner, N., Ahlkrona, J., Gowan, E. J., Lötstedt, P., Lea, J. M.,
Noormets, R., von Sydow, L., Dowdeswell, J. A., and Benham, T.: Shallow ice
approximation, second order shallow ice approximation, and full Stokes
models: A discussion of their roles in palaeo-ice sheet modelling and
development, Quaternary Sci. Rev., 147, 136–147, 2016.
Kleman, J., Hättestrand, C., Stroeven, A. P., Jansson, K. N., De Angelis,
H., and Borgström, I.: Reconstruction of Palaeo-Ice Sheets-Inversion of
their Glacial Geomorphological Record, in: Glacier science
and environmental change, edited by: Knight, P. G., 192–198, 2006.
Larour, E., Seroussi, H., Morlighem, M., and Rignot, E.: Continental scale,
high order, high spatial resolution, ice sheet modeling using the Ice Sheet
System Model (ISSM), J. Geophys. Res.-Earth, 117, F01022,
https://doi.org/10.1029/2011JF002140, 2012.
Libby, W. F., Anderson, E. C., and Arnold, J. R.: Age determination by
radiocarbon content: world-wide assay of natural radiocarbon, Science,
109, 227–228, 1949.
Lingle, C. S. and Clark, J. A.: A numerical model of interactions between a
marine ice sheet and the solid earth: Application to a West Antarctic ice
stream, J. Geophys. Res.-Oceans, 90, 1100–1114, 1985.
Livingstone, S. J., Cofaigh, C. Ó., Stokes, C. R., Hillenbrand, C. D.,
Vieli, A., and Jamieson, S. S.: Antarctic palaeo-ice streams, Earth-Sci. Rev.,
111, 90–128, 2012.
Lorenz, E. N.: Deterministic Nonperiodic Flow, J. Atmos. Sci., 20, 130–141,
1963.
Lowe, J. J. and Walker, M. J.: Radiocarbon Dating the Last Glacial-Interglacial
Transition (Ca. 14–9 14C Ka Bp) in Terrestrial and Marine Records: The Need
for New Quality Assurance Protocols, Radiocarbon, 42, 53–68, 2000.
Lowell, T. V., Fisher, T. G., Hajdas, I., Glover, K., Loope, H., and Henry,
T.: Radiocarbon deglaciation chronology of the Thunder Bay, Ontario area and
implications for ice sheet retreat patterns, Quaternary Sci. Rev., 28,
1597–1607, 2009.
Lukas, S., Spencer, J. Q., Robinson, R. A., and Benn, D. I.: Problems associated
with luminescence dating of Late Quaternary glacial sediments in the NW
Scottish Highlands, Quaternary Geochron., 2, 243–248, 2007.
Mercer, J. H.: West Antarctic ice sheet and CO2 greenhouse effect: a threat of
disaster, Nature, 271, 321–325, 1978.
Murphy, J. M., Sexton, D. M., Barnett, D. N., Jones, G. S., Webb, M. J., Collins,
M., and Stainforth, D. A.: Quantification of modelling uncertainties in a large
ensemble of climate change simulations, Nature, 430, 768–771, 2004.
Napieralski, J., Harbor, J., and Li, Y.: Glacial geomorphology and geographic
information systems, Earth-Sci. Rev., 85, 1–22, 2007.
Ó Cofaigh, C. Ó. and Evans, D. J.: Radiocarbon constraints on the age
of the maximum advance of the British–Irish Ice Sheet in the Celtic Sea,
Quaternary Sci. Rev., 26, 1197–1203, 2007.
Patton, H., Hubbard, A., Andreassen, K., Winsborrow, M., and Stroeven, A. P.:
The build-up, configuration, and dynamical sensitivity of the Eurasian
ice-sheet complex to Late Weichselian climatic and oceanic forcing,
Quaternary Sci. Rev., 153, 97–121, 2016.
Pattyn, F.: Sea-level response to melting of Antarctic ice shelves on
multi-centennial timescales with the fast Elementary Thermomechanical Ice
Sheet model (f.ETISh v1.0), The Cryosphere, 11, 1851–1878,
https://doi.org/10.5194/tc-11-1851-2017, 2017.
Pattyn, F., Perichon, L., Aschwanden, A., Breuer, B., de Smedt, B.,
Gagliardini, O., Gudmundsson, G. H., Hindmarsh, R. C. A., Hubbard, A.,
Johnson, J. V., Kleiner, T., Konovalov, Y., Martin, C., Payne, A. J.,
Pollard, D., Price, S., Rückamp, M., Saito, F., Soucek, O., Sugiyama, S., and
Zwinger, T.: Benchmark experiments for higher-order and full-Stokes ice sheet
models (ISMIP-HOM), The Cryosphere, 2, 95–108,
https://doi.org/10.5194/tc-2-95-2008, 2008.
Pollard, D. and DeConto, R. M.: Modelling West Antarctic ice sheet growth and
collapse through the past five million years, Nature, 458, 329–332,
2009.
Ritz, C., Edwards, T. L., Durand, G., Payne, A. J., Peyaud, V., and Hindmarsh,
R. C.: Potential sea-level rise from Antarctic ice-sheet instability
constrained by observations, Nature, 528, 115–118, 2015.
Robinson, A., Calov, R., and Ganopolski, A.: Greenland ice sheet model
parameters constrained using simulations of the Eemian Interglacial,
Clim. Past, 7, 381–396, https://doi.org/10.5194/cp-7-381-2011, 2011.
Rougier, J.: Probabilistic inference for future climate using an
ensemble of climate model evaluations, Clim. Change, 81, 247–264, 2007.
Rutt, I. C., Hagdorn, M., Hulton, N. R. J., and Payne, A. J.: The Glimmer
community ice sheet model, J. Geophys. Res.-Earth, 114, F02004,
https://doi.org/10.1029/2008JF001015, 2009.
Schoof, C. S.: Ice sheet grounding line dynamics: steady states, stability
and hysteresis, J. Geophys. Res.-Earth Surf., 112, F03S28,
https://doi.org/10.1029/2006JF000664, 2007.
Schoof, C.: Coulomb friction and other sliding laws in a higher-order glacier
flow model, Math. Mod. Meth. Appl. S, 20, 157–189, 2010.
Schoof, C.: Marine ice sheet stability, J. Fluid Mech., 698, 62–72, 2012.
Seddik, H., Greve, R., Zwinger, T., Gillet-Chaulet, F., and Gagliardini, O.:
Simulations of the Greenland ice sheet 100 years into the future with the
full Stokes model Elmer/Ice, J. Glaciol., 58, 427–440, 2012.
Seguinot, J., Rogozhina, I., Stroeven, A. P., Margold, M., and Kleman, J.:
Numerical simulations of the Cordilleran ice sheet through the last glacial
cycle, The Cryosphere, 10, 639–664, https://doi.org/10.5194/tc-10-639-2016, 2016.
Simpson, M. J., Milne, G. A., Huybrechts, P., and Long, A. J.: Calibrating a
glaciological model of the Greenland ice sheet from the Last Glacial Maximum
to present-day using field observations of relative sea level and ice
extent, Quaternary Sci. Rev., 28, 1631–1657, 2009.
Small, D., Clark, C. D., Chiverrell, R. C., Smedley, R. K., Bateman, M. D.,
Duller, G. A., Ely, J. C., Fabel, D., Medialdea, A., and Moreton, S. G.: Devising
quality assurance procedures for assessment of legacy geochronological data
relating to deglaciation of the last British-Irish Ice Sheet, Earth-Sci. Rev.,
164, 232–250, 2017.
Smedley, R. K., Glasser, N. F., and Duller, G. A. T.: Luminescence dating of
glacial advances at Lago Buenos Aires (∼46∘S), Patagonia,
Quaternary Sci. Rev., 134, 59–73, 2016.
Smedley, R. K., Chiverrell, R. C., Ballantyne, C. K., Burke, M. J., Clark, C. D.,
Duller, G. A. T., Fabel, D., McCarroll, D., Scourse, J. D., Small, D., and
Thomas, G. S. P.: Internal dynamics condition centennial-scale oscillations in
marine-based ice-stream retreat, Geology, 45, 787–790, 2017.
Stokes, C. R., Tarasov, L., Blomdin, R., Cronin, T. M., Fisher, T. G.,
Gyllencreutz, R., Hättestrand, C., Heyman, J., Hindmarsh, R. C., Hughes,
A. L., and Jakobsson, M.: On the reconstruction of palaeo-ice sheets: recent
advances and future challenges, Quaternary Sci. Rev., 125, 15–49, 2015.
Tarasov, L. and Peltier, W. R.: A geophysically constrained large ensemble
analysis of the deglacial history of the North American ice-sheet complex,
Quaternary Sci. Rev., 23, 359–388, 2004.
Tarasov, L., Dyke, A. S., Neal, R. M., and Peltier, W. R.: A data-calibrated
distribution of deglacial chronologies for the North American ice complex
from glaciological modeling, Earth Planet Sci. Lett., 315, 30–40, 2012.
Tebaldi, C. and Knutti, R.: The use of the multi-model ensemble in
probabilistic climate projections, Philos. T. R. Soc. A, 365, 2053–2075,
2007.
Tushingham, A. M. and Peltier, W. R.: Validation of the ICE-3G Model of
Würm-Wisconsin Deglaciation using a global data base of relative sea
level histories, J. Geophys. Res.-Solid Earth, 97, 3285–3304, 1992.
Weertman, J.: Stability of the junction of an ice-sheet and an ice-shelf, J.
Glaciol., 13, 3–11, 1974.
Winkelmann, R., Martin, M. A., Haseloff, M., Albrecht, T., Bueler, E.,
Khroulev, C., and Levermann, A.: The Potsdam Parallel Ice Sheet Model
(PISM-PIK) – Part 1: Model description, The Cryosphere, 5, 715–726,
https://doi.org/10.5194/tc-5-715-2011, 2011.
Short summary
During the last 2.6 million years, the Earth's climate has cycled between cold glacials and warm interglacials, causing the growth and retreat of ice sheets. These ice sheets can be independently reconstructed using numerical models or from dated evidence that they leave behind (e.g. sediments, boulders). Here, we present a tool for comparing numerical model simulations with dated ice-sheet material. We demonstrate the utility of this tool by applying it to the last British–Irish ice sheet.
During the last 2.6 million years, the Earth's climate has cycled between cold glacials and warm...
