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
Related authors
Tancrède P. M. Leger, Christopher D. Clark, Carla Huynh, Sharman Jones, Jeremy C. Ely, Sarah L. Bradley, Christiaan Diemont, and Anna L. C. Hughes
Clim. Past, 20, 701–755, https://doi.org/10.5194/cp-20-701-2024, https://doi.org/10.5194/cp-20-701-2024, 2024
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Ryan N. Ing, Jeremy C. Ely, Julie M. Jones, and Bethan J. Davies
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-33, https://doi.org/10.5194/tc-2023-33, 2023
Preprint withdrawn
Short summary
Short summary
Many of the glaciers in Alaska are losing ice, contributing to sea-level rise. Here, we study the inputs and outputs for the Juneau Icefield. We first model the historical changes to snowfall and melt, constraining our model with observations. We then project future changes to the icefield, which show that icefield-wide loss of ice is likely. Losses are driven by rising temperatures, and less snowfall. The exposure of ice, and the break-up of glaciers due to thinning may accelerate ice loss.
Peter A. Tuckett, Jeremy C. Ely, Andrew J. Sole, James M. Lea, Stephen J. Livingstone, Julie M. Jones, and J. Melchior van Wessem
The Cryosphere, 15, 5785–5804, https://doi.org/10.5194/tc-15-5785-2021, https://doi.org/10.5194/tc-15-5785-2021, 2021
Short summary
Short summary
Lakes form on the surface of the Antarctic Ice Sheet during the summer. These lakes can generate further melt, break up floating ice shelves and alter ice dynamics. Here, we describe a new automated method for mapping surface lakes and apply our technique to the Amery Ice Shelf between 2005 and 2020. Lake area is highly variable between years, driven by large-scale climate patterns. This technique will help us understand the role of Antarctic surface lakes in our warming world.
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
Short summary
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.
Izabela Szuman, Jakub Z. Kalita, Christiaan R. Diemont, Stephen J. Livingstone, Chris D. Clark, and Martin Margold
The Cryosphere, 18, 2407–2428, https://doi.org/10.5194/tc-18-2407-2024, https://doi.org/10.5194/tc-18-2407-2024, 2024
Short summary
Short summary
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.
Tancrède P. M. Leger, Christopher D. Clark, Carla Huynh, Sharman Jones, Jeremy C. Ely, Sarah L. Bradley, Christiaan Diemont, and Anna L. C. Hughes
Clim. Past, 20, 701–755, https://doi.org/10.5194/cp-20-701-2024, https://doi.org/10.5194/cp-20-701-2024, 2024
Short summary
Short summary
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-137, https://doi.org/10.5194/egusphere-2024-137, 2024
Short summary
Short summary
The retreat of the northwestern Laurentide Ice Sheet allows us to investigate how the ice drainage network evolves over millennial timescales and understand the influence of climate forcing, glacial lakes, and the underlying geology on the rate of deglaciation. We reconstruct the changes in ice flow at 500-year intervals and identify rapid reorganisations of the drainage network, including variations in ice streaming that we link to climatically-driven changes in the ice sheet surface slope.
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
Short summary
Short summary
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.
Ryan N. Ing, Jeremy C. Ely, Julie M. Jones, and Bethan J. Davies
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-33, https://doi.org/10.5194/tc-2023-33, 2023
Preprint withdrawn
Short summary
Short summary
Many of the glaciers in Alaska are losing ice, contributing to sea-level rise. Here, we study the inputs and outputs for the Juneau Icefield. We first model the historical changes to snowfall and melt, constraining our model with observations. We then project future changes to the icefield, which show that icefield-wide loss of ice is likely. Losses are driven by rising temperatures, and less snowfall. The exposure of ice, and the break-up of glaciers due to thinning may accelerate ice loss.
Camilla M. Rootes and Christopher D. Clark
E&G Quaternary Sci. J., 71, 111–122, https://doi.org/10.5194/egqsj-71-111-2022, https://doi.org/10.5194/egqsj-71-111-2022, 2022
Short summary
Short summary
Glacial trimlines are visible breaks in vegetation or landforms that mark the former extent of glaciers. They are often observed as faint lines running across valley sides and are useful for mapping the three-dimensional shape of former glaciers or for assessing by how much present-day glaciers have thinned and retreated. Here we present the first application of a new trimline classification scheme to a case study location in central western Spitsbergen, Svalbard.
Peter A. Tuckett, Jeremy C. Ely, Andrew J. Sole, James M. Lea, Stephen J. Livingstone, Julie M. Jones, and J. Melchior van Wessem
The Cryosphere, 15, 5785–5804, https://doi.org/10.5194/tc-15-5785-2021, https://doi.org/10.5194/tc-15-5785-2021, 2021
Short summary
Short summary
Lakes form on the surface of the Antarctic Ice Sheet during the summer. These lakes can generate further melt, break up floating ice shelves and alter ice dynamics. Here, we describe a new automated method for mapping surface lakes and apply our technique to the Amery Ice Shelf between 2005 and 2020. Lake area is highly variable between years, driven by large-scale climate patterns. This technique will help us understand the role of Antarctic surface lakes in our warming world.
Rachel K. Smedley, David Small, Richard S. Jones, Stephen Brough, Jennifer Bradley, and Geraint T. H. Jenkins
Geochronology, 3, 525–543, https://doi.org/10.5194/gchron-3-525-2021, https://doi.org/10.5194/gchron-3-525-2021, 2021
Short summary
Short summary
We apply new rock luminescence techniques to a well-constrained scenario of the Beinn Alligin rock avalanche, NW Scotland. We measure accurate erosion rates consistent with independently derived rates and reveal a transient state of erosion over the last ~4000 years in the wet, temperate climate of NW Scotland. This study shows that the new luminescence erosion-meter has huge potential for inferring erosion rates on sub-millennial scales, which is currently impossible with existing techniques.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Related subject area
Cryosphere
SnowQM 1.0: a fast R package for bias-correcting spatial fields of snow water equivalent using quantile mapping
Simulation of snow albedo and solar irradiance profile with the Two-streAm Radiative TransfEr in Snow (TARTES) v2.0 model
Evaluation of MITgcm-based ocean reanalyses for the Southern Ocean
Improvements in the land surface configuration to better simulate seasonal snow cover in the European Alps with the CNRM-AROME (cycle 46) convection-permitting regional climate model
A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting
A global–land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: formulation and evaluation at instrumented sites
Design and performance of ELSA v2.0: an isochronal model for ice-sheet layer tracing
Southern Ocean Ice Prediction System version 1.0 (SOIPS v1.0): description of the system and evaluation of synoptic-scale sea ice forecasts
Lagrangian tracking of sea ice in Community Ice CodE (CICE; version 5)
openAMUNDSEN v1.0: an open-source snow-hydrological model for mountain regions
OpenFOAM-avalanche 2312: depth-integrated models beyond dense-flow avalanches
Refactoring the elastic–viscous–plastic solver from the sea ice model CICE v6.5.1 for improved performance
Tuning parameters of a sea ice model using machine learning
A new 3D full-Stokes calving algorithm within Elmer/Ice (v9.0)
Towards deep learning solutions for classification of automated snow height measurements (CleanSnow v1.0.0)
Clustering simulated snow profiles to form avalanche forecast regions
Quantitative Sub-Ice and Marine Tracing of Antarctic Sediment Provenance (TASP v1.0)
Simulations of Snow Physicochemical Properties in Northern China using WRF-Chem
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
The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere
Glacier Energy and Mass Balance (GEMB): a model of firn processes for cryosphere research
Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean
Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling
SUHMO: an adaptive mesh refinement SUbglacial Hydrology MOdel v1.0
Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau
The Multiple Snow Data Assimilation System (MuSA v1.0)
The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)
Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G
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
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
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 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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Anton Korosov, Yue Ying, and Einar Olason
EGUsphere, https://doi.org/10.5194/egusphere-2024-2527, https://doi.org/10.5194/egusphere-2024-2527, 2024
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
EGUsphere, https://doi.org/10.5194/egusphere-2024-1752, https://doi.org/10.5194/egusphere-2024-1752, 2024
Short summary
Short summary
Accurately measuring snow height is key for modeling approaches in climate sciences, snow hydrology and avalanche forecasting. Erroneous snow height measurements often occur when the snow height is low or changes, for instance, during a snowfall in the 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 into a data pipeline for snow modeling.
Simon Horton, Florian Herla, and Pascal Haegeli
EGUsphere, https://doi.org/10.5194/egusphere-2024-1609, https://doi.org/10.5194/egusphere-2024-1609, 2024
Short summary
Short summary
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, location, and time. Tested in the Columbia Mountains during winter 2022–23, it accurately matched real forecast regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
Jim Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin Siegert, and Liam Holder
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-104, https://doi.org/10.5194/gmd-2024-104, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
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, plus 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.
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-37, https://doi.org/10.5194/gmd-2024-37, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We employed the WRF-Chem model to parameterize atmospheric nitrate deposition in snow and evaluated its performance in simulating snow cover, snow depth, and concentrations of black carbon (BC), 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.
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.
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.
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
Short summary
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.
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.
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...
