Articles | Volume 18, issue 11
https://doi.org/10.5194/tc-18-5207-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-5207-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Nov 2024
Research article |
| 15 Nov 2024
| 15 Nov 2024
Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt
Applied Mathematics Group, Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA
Matthew J. Hoffman
Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, Los Alamos, NM, USA
Nathan M. Urban
Applied Mathematics Group, Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA
Trevor Hillebrand
Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, Los Alamos, NM, USA
Mauro Perego
Center for Computing Research, Sandia National Laboratories, Albuquerque, NM, USA
Stephen Price
Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, Los Alamos, NM, USA
John D. Jakeman
Center for Computing Research, Sandia National Laboratories, Albuquerque, NM, USA
Related authors
No articles found.
Mira Berdahl, Gunter R. Leguy, William H. Lipscomb, Bette L. Otto-Bliesner, Esther C. Brady, Robert A. Tomas, Nathan M. Urban, Ian Miller, Harriet Morgan, and Eric J. Steig
Clim. Past, 20, 2349–2371, https://doi.org/10.5194/cp-20-2349-2024, https://doi.org/10.5194/cp-20-2349-2024, 2024
Short summary
Short summary
Studying climate conditions near the Antarctic ice sheet (AIS) during Earth’s past warm periods informs us about how global warming may influence AIS ice loss. Using a global climate model, we investigate climate conditions near the AIS during the Last Interglacial (129 to 116 kyr ago), a period with warmer global temperatures and higher sea level than today. We identify the orbital and freshwater forcings that could cause ice loss and probe the mechanisms that lead to warmer climate conditions.
Irena Vaňková, Xylar Asay-Davis, Carolyn Branecky Begeman, Darin Comeau, Alexander Hager, Matthew Hoffman, Stephen F. Price, and Jonathan Wolfe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2297, https://doi.org/10.5194/egusphere-2024-2297, 2024
Short summary
Short summary
We study the effect of subglacial discharge on basal melting for Antarctic Ice Shelves. We find that the results from previous studies of vertical ice fronts and two-dimensional ice tongues do not translate to the rotating ice-shelf framework. The melt rate dependence on discharge is stronger in the rotating framework. Further, there is a substantial melt-rate sensitivity to the location of the discharge along the grounding line relative to the directionality of the Coriolis force.
John D. Jakeman, Mauro Perego, D. Thomas Seidl, Tucker A. Hartland, Trevor R. Hillebrand, Matthew J. Hoffman, and Stephen F. Price
EGUsphere, https://doi.org/10.5194/egusphere-2024-2209, https://doi.org/10.5194/egusphere-2024-2209, 2024
Short summary
Short summary
This study investigated the computational benefits of using multiple models of varying cost and accuracy to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario. Despite some models being incapable of capturing the local features of the ice flow fields, using multiple models reduced the error in the estimated statistics by over an order of magnitude when compared to an approach that only used a single accurate model.
Matthew J. Hoffman, Carolyn Branecky Begeman, Xylar S. Asay-Davis, Darin Comeau, Alice Barthel, Stephen F. Price, and Jonathan D. Wolfe
The Cryosphere, 18, 2917–2937, https://doi.org/10.5194/tc-18-2917-2024, https://doi.org/10.5194/tc-18-2917-2024, 2024
Short summary
Short summary
The Filchner–Ronne Ice Shelf in Antarctica is susceptible to the intrusion of deep, warm ocean water that could increase the melting at the ice-shelf base by a factor of 10. We show that representing this potential melt regime switch in a low-resolution climate model requires careful treatment of iceberg melting and ocean mixing. We also demonstrate a possible ice-shelf melt domino effect where increased melting of nearby ice shelves can lead to the melt regime switch at Filchner–Ronne.
Hélène Seroussi, Vincent Verjans, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Peter Van Katwyk, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, https://doi.org/10.5194/tc-17-5197-2023, 2023
Short summary
Short summary
Mass loss from Antarctica is a key contributor to sea level rise over the 21st century, and the associated uncertainty dominates sea level projections. We highlight here the Antarctic glaciers showing the largest changes and quantify the main sources of uncertainty in their future evolution using an ensemble of ice flow models. We show that on top of Pine Island and Thwaites glaciers, Totten and Moscow University glaciers show rapid changes and a strong sensitivity to warmer ocean conditions.
Hyein Jeong, Adrian K. Turner, Andrew F. Roberts, Milena Veneziani, Stephen F. Price, Xylar S. Asay-Davis, Luke P. Van Roekel, Wuyin Lin, Peter M. Caldwell, Hyo-Seok Park, Jonathan D. Wolfe, and Azamat Mametjanov
The Cryosphere, 17, 2681–2700, https://doi.org/10.5194/tc-17-2681-2023, https://doi.org/10.5194/tc-17-2681-2023, 2023
Short summary
Short summary
We find that E3SM-HR reproduces the main features of the Antarctic coastal polynyas. Despite the high amount of coastal sea ice production, the densest water masses are formed in the open ocean. Biases related to the lack of dense water formation are associated with overly strong atmospheric polar easterlies. Our results indicate that the large-scale polar atmospheric circulation must be accurately simulated in models to properly reproduce Antarctic dense water formation.
Mira Berdahl, Gunter Leguy, William H. Lipscomb, Nathan M. Urban, and Matthew J. Hoffman
The Cryosphere, 17, 1513–1543, https://doi.org/10.5194/tc-17-1513-2023, https://doi.org/10.5194/tc-17-1513-2023, 2023
Short summary
Short summary
Contributions to future sea level from the Antarctic Ice Sheet remain poorly constrained. One reason is that ice sheet model initialization methods can have significant impacts on how the ice sheet responds to future forcings. We investigate the impacts of two key parameters used during model initialization. We find that these parameter choices alone can impact multi-century sea level rise by up to 2 m, emphasizing the need to carefully consider these choices for sea level rise predictions.
Trevor R. Hillebrand, Matthew J. Hoffman, Mauro Perego, Stephen F. Price, and Ian M. Howat
The Cryosphere, 16, 4679–4700, https://doi.org/10.5194/tc-16-4679-2022, https://doi.org/10.5194/tc-16-4679-2022, 2022
Short summary
Short summary
We estimate that Humboldt Glacier, northern Greenland, will contribute 5.2–8.7 mm to global sea level in 2007–2100, using an ensemble of model simulations constrained by observations of glacier retreat and speedup. This is a significant fraction of the 40–140 mm from the whole Greenland Ice Sheet predicted by the recent ISMIP6 multi-model ensemble, suggesting that calibrating models against observed velocity changes could result in higher estimates of 21st century sea-level rise from Greenland.
Alexander O. Hager, Matthew J. Hoffman, Stephen F. Price, and Dustin M. Schroeder
The Cryosphere, 16, 3575–3599, https://doi.org/10.5194/tc-16-3575-2022, https://doi.org/10.5194/tc-16-3575-2022, 2022
Short summary
Short summary
The presence of water beneath glaciers is a control on glacier speed and ocean-caused melting, yet it has been unclear whether sizable volumes of water can exist beneath Antarctic glaciers or how this water may flow along the glacier bed. We use computer simulations, supported by observations, to show that enough water exists at the base of Thwaites Glacier, Antarctica, to form "rivers" beneath the glacier. These rivers likely moderate glacier speed and may influence its rate of retreat.
Trevor R. Hillebrand, John O. Stone, Michelle Koutnik, Courtney King, Howard Conway, Brenda Hall, Keir Nichols, Brent Goehring, and Mette K. Gillespie
The Cryosphere, 15, 3329–3354, https://doi.org/10.5194/tc-15-3329-2021, https://doi.org/10.5194/tc-15-3329-2021, 2021
Short summary
Short summary
We present chronologies from Darwin and Hatherton glaciers to better constrain ice sheet retreat during the last deglaciation in the Ross Sector of Antarctica. We use a glacier flowband model and an ensemble of 3D ice sheet model simulations to show that (i) the whole glacier system likely thinned steadily from about 9–3 ka, and (ii) the grounding line likely reached the Darwin–Hatherton Glacier System at about 3 ka, which is ≥3.8 kyr later than was suggested by previous reconstructions.
Mira Berdahl, Gunter Leguy, William H. Lipscomb, and Nathan M. Urban
The Cryosphere, 15, 2683–2699, https://doi.org/10.5194/tc-15-2683-2021, https://doi.org/10.5194/tc-15-2683-2021, 2021
Short summary
Short summary
Antarctic ice shelves are vulnerable to warming ocean temperatures and have already begun thinning in response to increased basal melt rates. Sea level is expected to rise due to Antarctic contributions, but uncertainties in rise amount and timing remain largely unquantified. To facilitate uncertainty quantification, we use a high-resolution ice sheet model to build, test, and validate an ice sheet emulator and generate probabilistic sea level rise estimates for 100 and 200 years in the future.
Tong Zhang, Stephen F. Price, Matthew J. Hoffman, Mauro Perego, and Xylar Asay-Davis
The Cryosphere, 14, 3407–3424, https://doi.org/10.5194/tc-14-3407-2020, https://doi.org/10.5194/tc-14-3407-2020, 2020
Hélène Seroussi, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, https://doi.org/10.5194/tc-14-3033-2020, 2020
Short summary
Short summary
The Antarctic ice sheet has been losing mass over at least the past 3 decades in response to changes in atmospheric and oceanic conditions. This study presents an ensemble of model simulations of the Antarctic evolution over the 2015–2100 period based on various ice sheet models, climate forcings and emission scenarios. Results suggest that the West Antarctic ice sheet will continue losing a large amount of ice, while the East Antarctic ice sheet could experience increased snow accumulation.
Adam M. Schneider, Charles S. Zender, and Stephen F. Price
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-247, https://doi.org/10.5194/gmd-2020-247, 2020
Preprint withdrawn
Short summary
Short summary
We enhance the Energy Exascale Earth System Model's land
component (ELM) to better represent multi-year snow (firn) on ice sheets. Our
developments reveal ELM deficiencies regarding firn density, a fundamental
property in glaciology. To improve firn density profiles, we fine tune
ELM's snowpack parameters using statistical modeling. Our findings demonstrate
how ELM can simulate both seasonal snow and firn on ice sheets and advance a
broader effort to better predict sea level rise.
Anders Levermann, Ricarda Winkelmann, Torsten Albrecht, Heiko Goelzer, Nicholas R. Golledge, Ralf Greve, Philippe Huybrechts, Jim Jordan, Gunter Leguy, Daniel Martin, Mathieu Morlighem, Frank Pattyn, David Pollard, Aurelien Quiquet, Christian Rodehacke, Helene Seroussi, Johannes Sutter, Tong Zhang, Jonas Van Breedam, Reinhard Calov, Robert DeConto, Christophe Dumas, Julius Garbe, G. Hilmar Gudmundsson, Matthew J. Hoffman, Angelika Humbert, Thomas Kleiner, William H. Lipscomb, Malte Meinshausen, Esmond Ng, Sophie M. J. Nowicki, Mauro Perego, Stephen F. Price, Fuyuki Saito, Nicole-Jeanne Schlegel, Sainan Sun, and Roderik S. W. van de Wal
Earth Syst. Dynam., 11, 35–76, https://doi.org/10.5194/esd-11-35-2020, https://doi.org/10.5194/esd-11-35-2020, 2020
Short summary
Short summary
We provide an estimate of the future sea level contribution of Antarctica from basal ice shelf melting up to the year 2100. The full uncertainty range in the warming-related forcing of basal melt is estimated and applied to 16 state-of-the-art ice sheet models using a linear response theory approach. The sea level contribution we obtain is very likely below 61 cm under unmitigated climate change until 2100 (RCP8.5) and very likely below 40 cm if the Paris Climate Agreement is kept.
Hélène Seroussi, Sophie Nowicki, Erika Simon, Ayako Abe-Ouchi, Torsten Albrecht, Julien Brondex, Stephen Cornford, Christophe Dumas, Fabien Gillet-Chaulet, Heiko Goelzer, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Thomas Kleiner, Eric Larour, Gunter Leguy, William H. Lipscomb, Daniel Lowry, Matthias Mengel, Mathieu Morlighem, Frank Pattyn, Anthony J. Payne, David Pollard, Stephen F. Price, Aurélien Quiquet, Thomas J. Reerink, Ronja Reese, Christian B. Rodehacke, Nicole-Jeanne Schlegel, Andrew Shepherd, Sainan Sun, Johannes Sutter, Jonas Van Breedam, Roderik S. W. van de Wal, Ricarda Winkelmann, and Tong Zhang
The Cryosphere, 13, 1441–1471, https://doi.org/10.5194/tc-13-1441-2019, https://doi.org/10.5194/tc-13-1441-2019, 2019
Short summary
Short summary
We compare a wide range of Antarctic ice sheet simulations with varying initialization techniques and model parameters to understand the role they play on the projected evolution of this ice sheet under simple scenarios. Results are improved compared to previous assessments and show that continued improvements in the representation of the floating ice around Antarctica are critical to reduce the uncertainty in the future ice sheet contribution to sea level rise.
Katherine J. Evans, Joseph H. Kennedy, Dan Lu, Mary M. Forrester, Stephen Price, Jeremy Fyke, Andrew R. Bennett, Matthew J. Hoffman, Irina Tezaur, Charles S. Zender, and Miren Vizcaíno
Geosci. Model Dev., 12, 1067–1086, https://doi.org/10.5194/gmd-12-1067-2019, https://doi.org/10.5194/gmd-12-1067-2019, 2019
Short summary
Short summary
A robust validation of ice sheet models is presented using LIVVkit, version 2.1. It targets ice sheet and coupled Earth system models, and handles datasets and operations that require high-performance computing and storage. We apply LIVVkit to a Greenland ice sheet simulation to show the degree to which it captures the surface mass balance. LIVVkit identifies a positive bias due to insufficient melting compared to observations that is focused largely around Greenland's southwest region.
William H. Lipscomb, Stephen F. Price, Matthew J. Hoffman, Gunter R. Leguy, Andrew R. Bennett, Sarah L. Bradley, Katherine J. Evans, Jeremy G. Fyke, Joseph H. Kennedy, Mauro Perego, Douglas M. Ranken, William J. Sacks, Andrew G. Salinger, Lauren J. Vargo, and Patrick H. Worley
Geosci. Model Dev., 12, 387–424, https://doi.org/10.5194/gmd-12-387-2019, https://doi.org/10.5194/gmd-12-387-2019, 2019
Short summary
Short summary
This paper describes the Community Ice Sheet Model (CISM) version 2.1. CISM solves equations for ice flow, heat conduction, surface melting, and other processes such as basal sliding and iceberg calving. It can be used for ice-sheet-only simulations or as the ice sheet component of the Community Earth System Model. Model solutions have been verified for standard test problems. CISM can efficiently simulate the whole Greenland ice sheet, with results that are broadly consistent with observations.
Matthew J. Hoffman, Mauro Perego, Stephen F. Price, William H. Lipscomb, Tong Zhang, Douglas Jacobsen, Irina Tezaur, Andrew G. Salinger, Raymond Tuminaro, and Luca Bertagna
Geosci. Model Dev., 11, 3747–3780, https://doi.org/10.5194/gmd-11-3747-2018, https://doi.org/10.5194/gmd-11-3747-2018, 2018
Short summary
Short summary
MPAS-Albany Land Ice (MALI) is a new variable-resolution land ice model that uses unstructured grids on a plane or sphere. MALI is built for Earth system modeling on high-performance computing platforms using existing software libraries. MALI simulates the evolution of ice thickness, velocity, and temperature, and it includes schemes for simulating iceberg calving and the flow of water beneath ice sheets and its effect on ice sliding. The model is demonstrated for the Antarctic ice sheet.
Tong Zhang, Stephen Price, Lili Ju, Wei Leng, Julien Brondex, Gaël Durand, and Olivier Gagliardini
The Cryosphere, 11, 179–190, https://doi.org/10.5194/tc-11-179-2017, https://doi.org/10.5194/tc-11-179-2017, 2017
Short summary
Short summary
Stokes-flow models are the highest-fidelity representation of the equations governing ice sheet flow and they are often treated as the standard against which other models are compared in model benchmark activities. We compare two different Stokes models applied to a canonical set of idealized marine ice sheet experiments and demonstrate that the solutions converge with increasing grid resolution. This provides confidence in the use of Stokes models for generating test case solution metrics.
Stephen F. Price, Matthew J. Hoffman, Jennifer A. Bonin, Ian M. Howat, Thomas Neumann, Jack Saba, Irina Tezaur, Jeffrey Guerber, Don P. Chambers, Katherine J. Evans, Joseph H. Kennedy, Jan Lenaerts, William H. Lipscomb, Mauro Perego, Andrew G. Salinger, Raymond S. Tuminaro, Michiel R. van den Broeke, and Sophie M. J. Nowicki
Geosci. Model Dev., 10, 255–270, https://doi.org/10.5194/gmd-10-255-2017, https://doi.org/10.5194/gmd-10-255-2017, 2017
Short summary
Short summary
We introduce the Cryospheric Model Comparison Tool (CmCt) and propose qualitative and quantitative metrics for evaluating ice sheet model simulations against observations. Greenland simulations using the Community Ice Sheet Model are compared to gravimetry and altimetry observations from 2003 to 2013. We show that the CmCt can be used to score simulations of increasing complexity relative to observations of dynamic change in Greenland over the past decade.
S. de la Peña, I. M. Howat, P. W. Nienow, M. R. van den Broeke, E. Mosley-Thompson, S. F. Price, D. Mair, B. Noël, and A. J. Sole
The Cryosphere, 9, 1203–1211, https://doi.org/10.5194/tc-9-1203-2015, https://doi.org/10.5194/tc-9-1203-2015, 2015
Short summary
Short summary
This paper presents an assessment of changes in the near-surface structure of the accumulation zone of the Greenland Ice Sheet caused by an increase of melt at higher elevations in the last decade, especially during the unusually warm years of 2010 and 2012. The increase in melt and firn densification complicate the interpretation of changes in the ice volume, and the observed increase in firn ice content may reduce the important meltwater buffering capacity of the Greenland Ice Sheet.
I. K. Tezaur, M. Perego, A. G. Salinger, R. S. Tuminaro, and S. F. Price
Geosci. Model Dev., 8, 1197–1220, https://doi.org/10.5194/gmd-8-1197-2015, https://doi.org/10.5194/gmd-8-1197-2015, 2015
Short summary
Short summary
In this manuscript, we discuss the development and validation of a new momentum balance solver for modeling the flow of glaciers and ice sheets based on the 1st-order Stokes equations. We demonstrate the numerical convergence of our solver (with respect to computational mesh spacing), its flexibility (with respect to both the choice of mesh and finite element type), and its computational performance (robustness and scalability when applied to both idealized and realistic ice sheet simulations).
M. P. Lüthi, C. Ryser, L. C. Andrews, G. A. Catania, M. Funk, R. L. Hawley, M. J. Hoffman, and T. A. Neumann
The Cryosphere, 9, 245–253, https://doi.org/10.5194/tc-9-245-2015, https://doi.org/10.5194/tc-9-245-2015, 2015
Short summary
Short summary
We analyze the thermal structure of the Greenland Ice Sheet with a heat flow model. New borehole measurements indicate that more heat is stored within the ice than would be expected from heat diffusion alone. We conclude that temperate paleo-firn and cyro-hydrologic warming are essential processes that explain the measurements.
T. L. Edwards, X. Fettweis, O. Gagliardini, F. Gillet-Chaulet, H. Goelzer, J. M. Gregory, M. Hoffman, P. Huybrechts, A. J. Payne, M. Perego, S. Price, A. Quiquet, and C. Ritz
The Cryosphere, 8, 181–194, https://doi.org/10.5194/tc-8-181-2014, https://doi.org/10.5194/tc-8-181-2014, 2014
T. L. Edwards, X. Fettweis, O. Gagliardini, F. Gillet-Chaulet, H. Goelzer, J. M. Gregory, M. Hoffman, P. Huybrechts, A. J. Payne, M. Perego, S. Price, A. Quiquet, and C. Ritz
The Cryosphere, 8, 195–208, https://doi.org/10.5194/tc-8-195-2014, https://doi.org/10.5194/tc-8-195-2014, 2014
B. F. Morriss, R. L. Hawley, J. W. Chipman, L. C. Andrews, G. A. Catania, M. J. Hoffman, M. P. Lüthi, and T. A. Neumann
The Cryosphere, 7, 1869–1877, https://doi.org/10.5194/tc-7-1869-2013, https://doi.org/10.5194/tc-7-1869-2013, 2013
W. Leng, L. Ju, M. Gunzburger, and S. Price
The Cryosphere, 7, 19–29, https://doi.org/10.5194/tc-7-19-2013, https://doi.org/10.5194/tc-7-19-2013, 2013
Related subject area
Discipline: Ice sheets | Subject: Ice Sheets
Reconstructing dynamics of the Baltic Ice Stream Complex during deglaciation of the Last Scandinavian Ice Sheet
The influence of firn-layer material properties on surface crevasse propagation in glaciers and ice shelves
Assessing the potential for ice flow piracy between the Totten and Vanderford glaciers, East Antarctica
Stagnant ice and age modelling in the Dome C region, Antarctica
Polar firn properties in Greenland and Antarctica and related effects on microwave brightness temperatures
A model of the weathering crust and microbial activity on an ice-sheet surface
PISM-LakeCC: Implementing an adaptive proglacial lake boundary in an ice sheet model
Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections
Brief communication: On calculating the sea-level contribution in marine ice-sheet models
A simple stress-based cliff-calving law
Scaling of instability timescales of Antarctic outlet glaciers based on one-dimensional similitude analysis
A statistical fracture model for Antarctic ice shelves and glaciers
Modelled fracture and calving on the Totten Ice Shelf
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.
Theo Clayton, Ravindra Duddu, Tim Hageman, and Emilio Martinez-Paneda
EGUsphere, https://doi.org/10.5194/egusphere-2024-660, https://doi.org/10.5194/egusphere-2024-660, 2024
Short summary
Short summary
We develop and validate new analytical solutions that quantitatively consider how the properties of ice vary along the depth of ice shelves and can be readily used in existing ice sheet models. Depth-varying firn properties are found to have a profound impact on ice sheet fracture and calving events. Our results show that grounded glaciers are less vulnerable than previously anticipated while floating ice shelves are significantly more vulnerable to fracture and calving.
Felicity S. McCormack, Jason L. Roberts, Bernd Kulessa, Alan Aitken, Christine F. Dow, Lawrence Bird, Benjamin K. Galton-Fenzi, Katharina Hochmuth, Richard S. Jones, Andrew N. Mackintosh, and Koi McArthur
The Cryosphere, 17, 4549–4569, https://doi.org/10.5194/tc-17-4549-2023, https://doi.org/10.5194/tc-17-4549-2023, 2023
Short summary
Short summary
Changes in Antarctic surface elevation can cause changes in ice and basal water flow, impacting how much ice enters the ocean. We find that ice and basal water flow could divert from the Totten to the Vanderford Glacier, East Antarctica, under only small changes in the surface elevation, with implications for estimates of ice loss from this region. Further studies are needed to determine when this could occur and if similar diversions could occur elsewhere in Antarctica due to climate change.
Ailsa Chung, Frédéric Parrenin, Daniel Steinhage, Robert Mulvaney, Carlos Martín, Marie G. P. Cavitte, David A. Lilien, Veit Helm, Drew Taylor, Prasad Gogineni, Catherine Ritz, Massimo Frezzotti, Charles O'Neill, Heinrich Miller, Dorthe Dahl-Jensen, and Olaf Eisen
The Cryosphere, 17, 3461–3483, https://doi.org/10.5194/tc-17-3461-2023, https://doi.org/10.5194/tc-17-3461-2023, 2023
Short summary
Short summary
We combined a numerical model with radar measurements in order to determine the age of ice in the Dome C region of Antarctica. Our results show that at the current ice core drilling sites on Little Dome C, the maximum age of the ice is almost 1.5 Ma. We also highlight a new potential drill site called North Patch with ice up to 2 Ma. Finally, we explore the nature of a stagnant ice layer at the base of the ice sheet which has been independently observed and modelled but is not well understood.
Haokui Xu, Brooke Medley, Leung Tsang, Joel T. Johnson, Kenneth C. Jezek, Macro Brogioni, and Lars Kaleschke
The Cryosphere, 17, 2793–2809, https://doi.org/10.5194/tc-17-2793-2023, https://doi.org/10.5194/tc-17-2793-2023, 2023
Short summary
Short summary
The density profile of polar ice sheets is a major unknown in estimating the mass loss using lidar tomography methods. In this paper, we show that combing the active radar data and passive radiometer data can provide an estimation of density properties using the new model we implemented in this paper. The new model includes the short and long timescale variations in the firn and also the refrozen layers which are not included in the previous modeling work.
Tilly Woods and Ian J. Hewitt
The Cryosphere, 17, 1967–1987, https://doi.org/10.5194/tc-17-1967-2023, https://doi.org/10.5194/tc-17-1967-2023, 2023
Short summary
Short summary
Solar radiation causes melting at and just below the surface of the Greenland ice sheet, forming a porous surface layer known as the weathering crust. The weathering crust is home to many microbes, and the growth of these microbes is linked to the melting of the weathering crust and vice versa. We use a mathematical model to investigate what controls the size and structure of the weathering crust, the number of microbes within it, and its sensitivity to climate change.
Sebastian Hinck, Evan J. Gowan, Xu Zhang, and Gerrit Lohmann
The Cryosphere, 16, 941–965, https://doi.org/10.5194/tc-16-941-2022, https://doi.org/10.5194/tc-16-941-2022, 2022
Short summary
Short summary
Proglacial lakes were pervasive along the retreating continental ice margins after the Last Glacial Maximum. Similarly to the marine ice boundary, interactions at the ice-lake interface impact ice sheet dynamics and mass balance. Previous numerical ice sheet modeling studies did not include a dynamical lake boundary. We describe the implementation of an adaptive lake boundary condition in PISM and apply the model to the glacial retreat of the Laurentide Ice Sheet.
Heiko Goelzer, Brice P. Y. Noël, Tamsin L. Edwards, Xavier Fettweis, Jonathan M. Gregory, William H. Lipscomb, Roderik S. W. van de Wal, and Michiel R. van den Broeke
The Cryosphere, 14, 1747–1762, https://doi.org/10.5194/tc-14-1747-2020, https://doi.org/10.5194/tc-14-1747-2020, 2020
Short summary
Short summary
Future sea-level change projections with process-based ice sheet models are typically driven with surface mass balance forcing derived from climate models. In this work we address the problems arising from a mismatch of the modelled ice sheet geometry with the one used by the climate model. The proposed remapping method reproduces the original forcing data closely when applied to the original geometry and produces a physically meaningful forcing when applied to different modelled geometries.
Heiko Goelzer, Violaine Coulon, Frank Pattyn, Bas de Boer, and Roderik van de Wal
The Cryosphere, 14, 833–840, https://doi.org/10.5194/tc-14-833-2020, https://doi.org/10.5194/tc-14-833-2020, 2020
Short summary
Short summary
In our ice-sheet modelling experience and from exchange with colleagues in different groups, we found that it is not always clear how to calculate the sea-level contribution from a marine ice-sheet model. This goes hand in hand with a lack of documentation and transparency in the published literature on how the sea-level contribution is estimated in different models. With this brief communication, we hope to stimulate awareness and discussion in the community to improve on this situation.
Tanja Schlemm and Anders Levermann
The Cryosphere, 13, 2475–2488, https://doi.org/10.5194/tc-13-2475-2019, https://doi.org/10.5194/tc-13-2475-2019, 2019
Short summary
Short summary
We provide a simple stress-based parameterization for cliff calving of ice sheets. According to the resulting increasing dependence of the calving rate on ice thickness, the parameterization might lead to a runaway ice loss in large parts of Greenland and Antarctica.
Anders Levermann and Johannes Feldmann
The Cryosphere, 13, 1621–1633, https://doi.org/10.5194/tc-13-1621-2019, https://doi.org/10.5194/tc-13-1621-2019, 2019
Short summary
Short summary
Using scaling analysis we propose that the currently observed marine ice-sheet instability in the Amundsen Sea sector might be faster than all other potential instabilities in Antarctica.
Veronika Emetc, Paul Tregoning, Mathieu Morlighem, Chris Borstad, and Malcolm Sambridge
The Cryosphere, 12, 3187–3213, https://doi.org/10.5194/tc-12-3187-2018, https://doi.org/10.5194/tc-12-3187-2018, 2018
Short summary
Short summary
The paper includes a model that can be used to predict zones of fracture formation in both floating and grounded ice in Antarctica. We used observations and a statistics-based model to predict fractures in most ice shelves in Antarctica as an alternative to the damage-based approach. We can predict the location of observed fractures with an average success rate of 84% for grounded ice and 61% for floating ice and mean overestimation error of 26% and 20%, respectively.
Sue Cook, Jan Åström, Thomas Zwinger, Benjamin Keith Galton-Fenzi, Jamin Stevens Greenbaum, and Richard Coleman
The Cryosphere, 12, 2401–2411, https://doi.org/10.5194/tc-12-2401-2018, https://doi.org/10.5194/tc-12-2401-2018, 2018
Short summary
Short summary
The growth of fractures on Antarctic ice shelves is important because it controls the amount of ice lost as icebergs. We use a model constructed of multiple interconnected blocks to predict the locations where fractures will form on the Totten Ice Shelf in East Antarctica. The results show that iceberg calving is controlled not only by fractures forming near the front of the ice shelf but also by fractures which formed many kilometres upstream.
Cited articles
Alevropoulos-Borrill, A. V., Nias, I. J., Payne, A. J., Golledge, N. R., and Bingham, R. J.: Ocean-forced evolution of the Amundsen Sea catchment, West Antarctica, by 2100, The Cryosphere, 14, 1245–1258, https://doi.org/10.5194/tc-14-1245-2020, 2020. a
Andreasen, J. R., Hogg, A. E., and Selley, H. L.: Change in Antarctic ice shelf area from 2009 to 2019, The Cryosphere, 17, 2059–2072, https://doi.org/10.5194/tc-17-2059-2023, 2023. a, b, c, d
Aschwanden, A. and Brinkerhoff, D. J.: Calibrated Mass Loss Predictions for the Greenland Ice Sheet, Geophys. Res. Lett., 49, e2022GL099058, https://doi.org/10.1029/2022GL099058, 2022. a
Bassis, J. and Ma, Y.: Evolution of Basal Crevasses Links Ice Shelf Stability to Ocean Forcing, Earth Planet. Sc. Lett., 409, 203–211, https://doi.org/10.1016/j.epsl.2014.11.003, 2015. a, b
Bassis, J. and Walker, C.: Upper and Lower Limits on the Stability of Calving Glaciers from the Yield Strength Envelope of Ice, P. Roy. Soc. A-Math. Phy., 468, 913–931, https://doi.org/10.1098/rspa.2011.0422, 2012. a, b
Bassis, J. N., Crawford, A., Kachuck, S., Benn, D., Walker, C., Millstein, J., Duddu, R., Astrom, J., Fricker, H., and Luckman, A.: Stability of Ice Shelves and Ice Cliffs in a Changing Climate, Annu. Rev. Earth Pl. Sc., 52, 221–247, https://doi.org/10.1146/annurev-earth-040522-122817, 2024. a
Berdahl, M., Leguy, G., Lipscomb, W. H., and Urban, N. M.: Statistical emulation of a perturbed basal melt ensemble of an ice sheet model to better quantify Antarctic sea level rise uncertainties, The Cryosphere, 15, 2683–2699, https://doi.org/10.5194/tc-15-2683-2021, 2021. a, b
Berdahl, M., Leguy, G., Lipscomb, W. H., Urban, N. M., and Hoffman, M. J.: Exploring ice sheet model sensitivity to ocean thermal forcing and basal sliding using the Community Ice Sheet Model (CISM), The Cryosphere, 17, 1513–1543, https://doi.org/10.5194/tc-17-1513-2023, 2023. a, b
Bevan, S., Cornford, S., Gilbert, L., Otosaka, I., Martin, D., and Surawy-Stepney, T.: Amundsen Sea Embayment Ice-Sheet Mass-Loss Predictions to 2050 Calibrated Using Observations of Velocity and Elevation Change, J. Glaciol., 1–11, https://doi.org/10.1017/jog.2023.57, online first, 2023. a
Book, C., Hoffman, M. J., Kachuck, S. B., Hillebrand, T. R., Price, S., Perego, M., and Bassis, J.: Stabilizing effect of bedrock uplift on retreat of Thwaites Glacier, Antarctica, at centennial timescales, Earth Planet. Sc. Lett., 597, 117798, https://doi.org/10.1016/j.epsl.2022.117798, 2022. a
Box, G. E. P. and Wilson, K. B.: On the Experimental Attainment of Optimum Conditions, J. Roy. Stat. Soc. B Met., 13, 1–38, https://doi.org/10.1111/j.2517-6161.1951.tb00067.x, 1951. a
Brinkerhoff, D.: Variational inference at glacier scale, J. Comput. Phys., 459, 111095, https://doi.org/10.1016/j.jcp.2022.111095, 2022. a
Bulthuis, K., Arnst, M., Sun, S., and Pattyn, F.: Uncertainty quantification of the multi-centennial response of the Antarctic ice sheet to climate change, The Cryosphere, 13, 1349–1380, https://doi.org/10.5194/tc-13-1349-2019, 2019. a, b
Chang, W., Konomi, B. A., Karagiannis, G., Guan, Y., and Haran, M.: Ice model calibration using semicontinuous spatial data, Ann. Appl. Stat., 16, 1937–1961, https://doi.org/10.1214/21-aoas1577, 2022. a
Choi, Y., Morlighem, M., Rignot, E., Mouginot, J., and Wood, M.: Modeling the Response of Nioghalvfjerdsfjorden and Zachariae Isstrøm Glaciers, Greenland, to Ocean Forcing Over the Next Century, Geophys. Res. Lett., 44, 11071–11079, https://doi.org/10.1002/2017GL075174, 2017. a
Coulon, V., Bulthuis, K., Whitehouse, P. L., Sun, S., Haubner, K., Zipf, L., and Pattyn, F.: Contrasting Response of West and East Antarctic Ice Sheets to Glacial Isostatic Adjustment, J. Geophys. Res.-Earth, 126, e2020JF006003, https://doi.org/10.1029/2020JF006003, 2021. a
DeConto, R. and Pollard, D.: Contribution of Antarctica to Past and Future Sea-Level Rise, Nature, 531, 591–597, https://doi.org/10.1038/nature17145, 2016. a, b, c
Dinniman, M., Asay-Davis, X., Galton-Fenzi, B., Holland, P., Jenkins, A., and Timmermann, R.: Modeling Ice Shelf/Ocean Interaction in Antarctica: A Review, Oceanography, 29, 144–153, https://doi.org/10.5670/oceanog.2016.106, 2016. a
Edwards, T. L., Brandon, M. A., Durand, G., Edwards, N. R., Golledge, N. R., Holden, P. B., Nias, I. J., Payne, A. J., Ritz, C., and Wernecke, A.: Revisiting Antarctic ice loss due to marine ice-cliff instability, Nature, 566, 58–64, https://doi.org/10.1038/s41586-019-0901-4, 2019. a
Edwards, T. L., Nowicki, S., Marzeion, B., Hock, R., Goelzer, H., Seroussi, H., Jourdain, N. C., Slater, D. A., Turner, F. E., Smith, C. J., McKenna, C. M., Simon, E., Abe-Ouchi, A., Gregory, J. M., Larour, E., Lipscomb, W. H., Payne, A. J., Shepherd, A., Agosta, C., Alexander, P., Albrecht, T., Anderson, B., Asay-Davis, X., Aschwanden, A., Barthel, A., Bliss, A., Calov, R., Chambers, C., Champollion, N., Choi, Y., Cullather, R., Cuzzone, J., Dumas, C., Felikson, D., Fettweis, X., Fujita, K., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huss, M., Huybrechts, P., Immerzeel, W., Kleiner, T., Kraaijenbrink, P., Le clec’h, S., Lee, V., Leguy, G. R., Little, C. M., Lowry, D. P., Malles, J.-H., Martin, D.F., Maussion, F., Morlighem, M., O’Neill, J. F., Nias, I., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Radić, V., Reese, R., Rounce, D. R., Rückamp, M., Sakai, A., Shafer, C., Schlegel, N.-J., Shannon, S., Smith, R. S., Straneo, F., Sun, S., Tarasov, L., Trusel, L.D., Van Breedam, J., van de Wal, R., van den Broeke, M., Winkelmann, R., Zekollari, H., Zhao, C., Zhang, T., and Zwinger, T.: Projected land ice contributions to twenty-first-century sea level rise, Nature, 593, 74–82, https://doi.org/10.1038/s41586-021-03302-y, 2021. a
Felikson, D., Nowicki, S., Nias, I., Csatho, B., Schenk, A., Croteau, M. J., and Loomis, B.: Choice of observation type affects Bayesian calibration of Greenland Ice Sheet model simulations, The Cryosphere, 17, 4661–4673, https://doi.org/10.5194/tc-17-4661-2023, 2023. a
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. a
Fricker, H. A., Young, N. W., Allison, I., and Coleman, R.: Iceberg calving from the Amery Ice Shelf, East Antarctica, Ann. Glaciol., 34, 241–246, https://doi.org/10.3189/172756402781817581, 2002. a, b, c, d
Fürst, J. J., Durand, G., Gillet-Chaulet, F., Tavard, L., Rankl, M., Braun, M., and Gagliardini, O.: The safety band of Antarctic ice shelves, Nat. Clim. Change, 6, 479–482, https://doi.org/10.1038/nclimate2912, 2016. a
Gilford, D., Ashe, E., DeConto, R., Kopp, R., Pollard, D., and Rovere, A.: Could the Last Interglacial Constrain Projections of Future Antarctic Ice Mass Loss and Sea-Level Rise?, J. Geophys. Res.-Earth, 125, e2019JF005418, https://doi.org/10.1029/2019JF005418, 2020. a, b
Gillet-Chaulet, F., Durand, G., Gagliardini, O., Mosbeux, C., Mouginot, J., Rémy, F., and Ritz, C.: Assimilation of Surface Velocities Acquired between 1996 and 2010 to Constrain the Form of the Basal Friction Law under Pine Island Glacier, Geophys. Res. Lett., 43, 10311–10321, https://doi.org/10.1002/2016GL069937, 2016. a
Glen, J. W.: The creep of polycrystalline ice, P. Roy. Soc. Lond. A-Mat., 228, 519–538, https://doi.org/10.1098/rspa.1955.0066, 1955. a
Goelzer, H., Coulon, V., Pattyn, F., de Boer, B., and van de Wal, R.: Brief communication: On calculating the sea-level contribution in marine ice-sheet models, The Cryosphere, 14, 833–840, https://doi.org/10.5194/tc-14-833-2020, 2020. a
Gomez, N., Mitrovica, J. X., Huybers, P., and Clark, P. U.: Sea level as a stabilizing factor for marine-ice-sheet grounding lines, Nat. Geosci., 3, 850–853, https://doi.org/10.1038/ngeo1012, 2010. a
Gramacy, R. B.: Surrogates: Gaussian process modeling, design, and optimization for the applied sciences, CRC Press, https://doi.org/10.1201/9780367815493, 2020. a, b
Greene, C. A., Gardner, A. S., Schlegel, N.-J., and Fraser, A. D.: Antarctic calving loss rivals ice-shelf thinning, Nature, 609, 948–953, https://doi.org/10.1038/s41586-022-05037-w, 2022. a, b, c
Gudmundsson, G. H., Paolo, F. S., Adusumilli, S., and Fricker, H. A.: Instantaneous Antarctic ice sheet mass loss driven by thinning ice shelves, Geophys. Res. Lett., 46, 13903–13909, https://doi.org/10.1029/2019GL085027, 2019. a
Hager, A. O., Hoffman, M. J., Price, S. F., and Schroeder, D. M.: Persistent, extensive channelized drainage modeled beneath Thwaites Glacier, West Antarctica, The Cryosphere, 16, 3575–3599, https://doi.org/10.5194/tc-16-3575-2022, 2022. a
Haid, V., Timmermann, R., Gürses, Ö., and Hellmer, H. H.: On the drivers of regime shifts in the Antarctic marginal seas, exemplified by the Weddell Sea, Ocean Sci., 19, 1529–1544, https://doi.org/10.5194/os-19-1529-2023, 2023. a
Hartland, T., Stadler, G., Perego, M., Liegeois, K., and Petra, N.: Hierarchical off-diagonal low-rank approximation of Hessians in inverse problems, with application to ice sheet model initialization, Inverse Probl., 39, 085006, https://doi.org/10.1088/1361-6420/acd719, 2023. a
Hazel, J. E. and Stewart, A. L.: Bistability of the Filchner-Ronne Ice Shelf Cavity Circulation and Basal Melt, J. Geophys. Res.-Oceans, 125, e2019JC015848, https://doi.org/10.1029/2019JC015848, 2020. a
Higdon, D., Gattiker, J., Williams, B., and Rightley, M.: Computer model calibration using high-dimensional output, J. Am. Stat. Assoc., 103, 570–583, https://doi.org/10.1198/016214507000000888, 2008. a, b
Hill, E. A., Rosier, S. H. R., Gudmundsson, G. H., and Collins, M.: Quantifying the potential future contribution to global mean sea level from the Filchner–Ronne basin, Antarctica, The Cryosphere, 15, 4675–4702, https://doi.org/10.5194/tc-15-4675-2021, 2021. a
Hillebrand, T. R., Hoffman, M. J., Perego, M., Price, S. F., and Howat, I. M.: The contribution of Humboldt Glacier, northern Greenland, to sea-level rise through 2100 constrained by recent observations of speedup and retreat, The Cryosphere, 16, 4679–4700, https://doi.org/10.5194/tc-16-4679-2022, 2022. a, b, c
Hoffman, M. and Gelman, A.: The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo, J. Mach. Learn. Res., 15, 1593–1623, 2014. a
Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T., Jacobsen, D., Tezaur, I., Salinger, A. G., Tuminaro, R., and Bertagna, L.: MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids, Geosci. Model Dev., 11, 3747–3780, https://doi.org/10.5194/gmd-11-3747-2018, 2018. a, b
Isaac, T., Petra, N., Stadler, G., and Ghattas, O.: Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet, J. Comput. Phys., 296, 348–368, https://doi.org/10.1016/j.jcp.2015.04.047, 2015. a
Jantre, S., Hoffman, M., Urban, N., Trevor, H., Perego, M., Price, S., and Jakeman, J.: Model data for probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under high ice-shelf basal melt conditions, Zenodo [code and data set], https://doi.org/10.5281/zenodo.11166628, 2024. a
Johnson, A., Aschwanden, A., Albrecht, T., and Hock, R.: Range of 21st Century Ice Mass Changes in the Filchner-Ronne Region of Antarctica, J. Glaciol., 69, 1203–1213, https://doi.org/10.1017/jog.2023.10, 2023. a
Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020. a, b, c, d
Kachuck, S. B., Whitcomb, M., Bassis, J. N., Martin, D. F., and Price, S. F.: Simulating Ice-Shelf Extent Using Damage Mechanics, J. Glaciol., 68, 987–998, https://doi.org/10.1017/jog.2022.12, 2022. a, b
Kennedy, M. C. and O'Hagan, A.: Bayesian calibration of computer models, J. Roy. Stat. Soc. B, 63, 425–464, https://doi.org/10.1111/1467-9868.00294, 2001. a, b
King, M., Coleman, R., Freemantle, A.-J., Fricker, H., Hurd, R., Legrésy, B., Padman, L., and Warner, R.: A 4-decade record of elevation change of the Amery Ice Shelf, East Antarctica, J. Geophys. Res.-Earth, 114, F01010, https://doi.org/10.1029/2008JF001094, 2009. a
Konrad, H., Shepherd, A., Gilbert, L., Hogg, A. E., McMillan, M., Muir, A., and Slater, T.: Net retreat of Antarctic glacier grounding lines, Nat. Geosci., 11, 258–262, https://doi.org/10.1038/s41561-018-0082-z, 2018. a, b, c
Le Brocq, A. M., Payne, A. J., and Vieli, A.: An improved Antarctic dataset for high resolution numerical ice sheet models (ALBMAP v1), Earth Syst. Sci. Data, 2, 247–260, https://doi.org/10.5194/essd-2-247-2010, 2010. a
Lee, B. S., Haran, M., Fuller, R. W., Pollard, D., and Keller, K.: A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet, Ann. Appl. Stat., 14, 605–634, https://doi.org/10.1214/19-aoas1305, 2020. a
Lenaerts, J. T. M., van den Broeke, M. R., van de Berg, W. J., van Meijgaard, E., and Munneke, P. K.: A new, high-resolution surface mass balance map of Antarctica (1979–2010) based on regional atmospheric climate modeling, Geophys. Res. Lett., 39, 1–5, https://doi.org/10.1029/2011GL050713, 2012. a
Lhermitte, S., Sun, S., Shuman, C., Wouters, B., Pattyn, F., Wuite, J., Berthier, E., and Nagler, T.: Damage Accelerates Ice Shelf Instability and Mass Loss in Amundsen Sea Embayment, P. Natl. Acad. Sci. USA, 117, 24735–24741, https://doi.org/10.1073/pnas.1912890117, 2020. a
Liu, C., Wang, Z., Cheng, C., Xia, R., Li, B., and Xie, Z.: Modeling modified Circumpolar Deep Water intrusions onto the Prydz Bay continental shelf, East Antarctica, J. Geophys. Res.-Oceans, 122, 5198–5217, https://doi.org/10.1002/2016JC012336, 2017. a
Loeppky, J. L., Sacks, J., and Welch, W. J.: Choosing the sample size of a computer experiment: A practical guide, Technometrics, 51, 366–376, 2009. a
McKay, M. D., Beckman, R. J., and Conover, W. J.: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 42, 55–61, https://doi.org/10.1080/00401706.2000.10485979, 2000. a
Morlighem, M.: MEaSUREs BedMachine Antarctica, Version 3, NASA National Snow and Ice Data Center Distributed Active Archive Center, https://doi.org/10.5067/FPSU0V1MWUB6, 2022. a, b
Morlighem, M., Bondzio, J., Seroussi, H., Rignot, E., Larour, E., Humbert, A., and Rebuffi, S.: Modeling of Store Gletscher's calving dynamics, West Greenland, in response to ocean thermal forcing, Geophys. Res. Lett., 43, 2659–2666, https://doi.org/10.1002/2016GL067695, 2016. a, b
Morlighem, M., Rignot, E., Binder, T., Blankenship, D., Drews, R., Eagles, G., Eisen, O., Ferraccioli, F., Forsberg, R., Fretwell, P., Goel, V., Greenbaum, J. S., Gudmundsson, H., Guo, J., Helm,V., Hofstede, C., Howat, I., Humbert, A., Jokat, W., Karlsson, N.B., Lee, W.S., Matsuoka, K., Millan, R., Mouginot, J., Paden, J., Pattyn, F., Roberts, J., Rosier, S., Ruppel, A., Seroussi, H., Smith, E. C., Steinhage, D., Sun, B., van den Broeke, M. R., van Ommen, T. D., van Wessem, M., and Young, D. A.: Deep Glacial Troughs and Stabilizing Ridges Unveiled beneath the Margins of the Antarctic Ice Sheet, Nat. Geosci., 13, 132–137, https://doi.org/10.1038/s41561-019-0510-8, 2020. a, b, c
Mouginot, J., Rignot, E., Scheuchl, B., and Millan, R.: Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data, Remote Sens.-Basel, 9, 364, https://doi.org/10.3390/rs9040364, 2017. a
Nias, I. J., Cornford, S. L., and Payne, A. J.: New Mass-Conserving Bedrock Topography for Pine Island Glacier Impacts Simulated Decadal Rates of Mass Loss, Geophys. Res. Lett., 45, 3173–3181, https://doi.org/10.1002/2017GL076493, 2018. a
Nias, I. J., Cornford, S. L., Edwards, T. L., Gourmelen, N., and Payne, A. J.: Assessing Uncertainty in the Dynamical Ice Response to Ocean Warming in the Amundsen Sea Embayment, West Antarctica, Geophys. Res. Lett., 46, 11253–11260, https://doi.org/10.1029/2019GL084941, 2019. a
Nias, I. J., Nowicki, S., Felikson, D., and Loomis, B.: Modeling the Greenland Ice Sheet's Committed Contribution to Sea Level During the 21st Century, J. Geophys. Res.-Earth, 128, e2022JF006914, https://doi.org/10.1029/2022JF006914, 2023. a
Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A., Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M., Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C. M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R. S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.: Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models, The Cryosphere, 14, 2331–2368, https://doi.org/10.5194/tc-14-2331-2020, 2020. a, b
Nye, J. F.: The distribution of stress and velocity in glaciers and ice-sheets, P. Roy. Soc. Lond. A-Mat., 239, 113–133, https://doi.org/10.1098/rspa.1957.0026, 1957. a
Perego, M., Price, S., and Stadler, G.: Optimal Initial Conditions for Coupling Ice Sheet Models to Earth System Models, J. Geophys. Res.-Earth, 119, 1894–1917, https://doi.org/10.1002/2014JF003181, 2014. a, b, c
Petra, N., Martin, J., Stadler, G., and Ghattas, O.: A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems, Part II: Stochastic Newton MCMC with Application to Ice Sheet Flow Inverse Problems, SIAM J. Sci. Comput., 36, A1525–A1555, https://doi.org/10.1137/130934805, 2014. a
Petrovic, J.: Review mechanical properties of ice and snow, J. Mater. Sci., 38, 1–6, https://doi.org/10.1023/a:1021134128038, 2003. a
Pollard, D., DeConto, R. M., and Alley, R. B.: Potential Antarctic Ice Sheet Retreat Driven by Hydrofracturing and Ice Cliff Failure, Earth Planet. Sc. Lett., 412, 112–121, https://doi.org/10.1016/j.epsl.2014.12.035, 2015. a, b, c
Reese, W., Hart, J., van Bloemen Waanders, B., Perego, M., Jakeman, J. D., and Saibaba, A. K.: Hyper-differential sensitivity analysis in the context of Bayesian inference applied to ice-sheet problems, Int. J. Uncertain. Quan., 14, 1–20, https://doi.org/10.1615/int.j.uncertaintyquantification.2023047605, 2024. a
Riel, B. and Minchew, B.: Variational inference of ice shelf rheology with physics-informed machine learning, J. Glaciol., 69, 1167–1186, https://doi.org/10.1017/jog.2023.8, 2023. a
Rignot, E., Jacobs, S., Mouginot, J., and Scheuchl, B.: Ice-Shelf Melting Around Antarctica, Science, 341, 266–270, https://doi.org/10.1126/science.1235798, 2013. a
Rignot, E., Xu, Y., Menemenlis, D., Mouginot, J., Scheuchl, B., Li, X., Morlighem, M., Seroussi, H., den Broeke, M. v., Fenty, I., Cai, C., and de Fleurian, B.: Modeling of ocean-induced ice melt rates of five west Greenland glaciers over the past two decades, Geophys. Res. Lett., 43, 6374–6382, https://doi.org/10.1002/2016GL068784, 2016. a
Rignot, E., Mouginot, J., Scheuchl, B., van den Broeke, M., van Wessem, M. J., and Morlighem, M.: Four decades of Antarctic Ice Sheet mass balance from 1979–2017, P. Natl. Acad. Sci. USA, 116, 1095–1103, https://doi.org/10.1073/pnas.1812883116, 2019. a, b
Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P.: Design and analysis of computer experiments, Stat. Sci., 4, 409–423, https://doi.org/10.1214/ss/1177012413, 1989. a
Salter, J. and Williamson, D.: Efficient calibration for high-dimensional computer model output using basis methods, Int. J. Uncertain. Quan., 12, 47–69, https://doi.org/10.1615/int.j.uncertaintyquantification.2022039747, 2022. a
Salter, J., Williamson, D., Scinocca, J., and Kharin, V.: Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases, J. Am. Stat. Assoc., 114, 1800–1814, https://doi.org/10.1080/01621459.2018.1514306, 2019. a
Scambos, T., Fricker, H. A., Liu, C.-C., Bohlander, J., Fastook, J., Sargent, A., Massom, R., and Wu, A.-M.: Ice shelf disintegration by plate bending and hydro-fracture: Satellite observations and model results of the 2008 Wilkins ice shelf break-ups, Earth Planet. Sc. Lett., 280, 51–60, https://doi.org/10.1016/j.epsl.2008.12.027, 2009. a
Sellar, A. A., Walton, J., Jones, C. G., Wood, R., Abraham, N. L., Andrejczuk, M., Andrews, M. B., Andrews, T., Archibald, A. T., de Mora, L., Dyson, H., Elkington, M., Ellis, R., Florek, P., Good, P., Gohar, L., Haddad, S., Hardiman, S. C., Hogan, E., Iwi, A., Jones, C. D., Johnson, B., Kelley, D. I., Kettleborough, J., Knight, J. R., Köhler, M. O., Kuhlbrodt, T., Liddicoat, S., Linova-Pavlova, I., Mizielinski, M. S., Morgenstern, O., Mulcahy, J., Neininger, E., O'Connor, F. M., Petrie, R., Ridley, J., Rioual, J.-C., Roberts, M., Robertson, E., Rumbold, S., Seddon, J., Shepherd, H., Shim, S., Stephens, A., Teixiera, J. C., Tang, Y., Williams, J., Wiltshire, A., and Griffiths, P. T.: Implementation of U. K. Earth System Models for CMIP6, J. Adv. Model. Earth Sy., 12, e2019MS001946, https://doi.org/10.1029/2019MS001946, 2020. a
Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020. a, b, c
Seroussi, H., Verjans, V., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., Van Katwyk, P., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: Insights into the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty, The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, 2023. a, b, c, d, e, f
Seroussi, H., Pelle, T., Lipscomb, W. H., Abe-Ouchi, A., Albrecht, T., Alvarez-Solas, J., Asay-Davis, X., Barre, J.-B., Berends, C. J., Bernales, J., Blasco, J., Caillet, J., Chandler, D. M., Coulon, V., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Garbe, J., Gillet-Chaulet, F., Gladstone, R., Goelzer, H., Golledge, N., Greve, R., Gudmundsson, G. H., Han, H. K., Hillebrand, T. R., Hoffman, M. J., Huybrechts, P., Jourdain, N. C., Klose, A. K., Langebroek, P. M., Leguy, G. R., Lowry, D. P., Mathiot, P.,Montoya, M., Morlighem, M., Nowicki, S., Pattyn, F., Payne, A. J., Quiquet, A., Reese, R., Robinson, A., Saraste, L., Simon, E. G., Sun, S., Twarog, J. P., Trusel, L. D., Urruty, B., Van Breedam, J., van de Wal, R. S. W., Wang, Y., Zhao, C., and Zwinger, T.: Evolution of the Antarctic Ice Sheet over the next three centuries from an ISMIP6 model ensemble, Earths Future, 12, e2024EF004561, https://doi.org/10.1029/2024EF004561, 2024. a, b, c, d, e
Shapiro, N. M. and Ritzwoller, M. H.: Inferring Surface Heat Flux Distributions Guided by a Global Seismic Model: Particular Application to Antarctica, Earth Planet. Sc. Lett., 223, 213–224, https://doi.org/10.1016/j.epsl.2004.04.011, 2004. a
Shlens, J.: A tutorial on principal component analysis, arXiv [preprint], https://doi.org/10.48550/arXiv.1404.1100, 2014. a
Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., Fettweis, X., and Nowicki, S.: Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution, The Cryosphere, 14, 985–1008, https://doi.org/10.5194/tc-14-985-2020, 2020. a, b
Sobol', I. M.: On the distribution of points in a cube and the approximate evaluation of integrals, USSR Comp. Math. Math.+, 7, 86–112, https://doi.org/10.1016/0041-5553(67)90144-9, 1967. a
Sobol', I. M.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Math. Comput. Simulat., 55, 271–280, 2001. a
Štrumbelj, E. and Kononenko, I.: Explaining prediction models and individual predictions with feature contributions, Knowl. Inf. Syst., 41, 647–665, 2014. a
Ultee, L., Meyer, C., and Minchew, B.: Tensile strength of glacial ice deduced from observations of the 2015 eastern Skaftá cauldron collapse, Vatnajökull ice cap, Iceland, J. Glaciol., 66, 1024–1033, https://doi.org/10.1017/jog.2020.65, 2020. a, b
Urban, N. M. and Fricker, T. E.: A comparison of Latin hypercube and grid ensemble designs for the multivariate emulation of an Earth system model, Comput. Geosci., 36, 746–755, https://doi.org/10.1016/j.cageo.2009.11.004, 2010. a
van den Broeke, M.: RACMO2.3p1 annual surface mass balance Antarctica (1979–2014), PANGAEA – Data Publisher for Earth & Environmental Science, https://doi.org/10.1594/PANGAEA.896940, 2019. a
Van Katwyk, P., Fox-Kemper, B., Seroussi, H., Nowicki, S., and Bergen, K. J.: A Variational LSTM Emulator of Sea Level Contribution From the Antarctic Ice Sheet, J. Adv. Model. Earth Sy., 15, e2023MS003899, https://doi.org/10.1029/2023ms003899, 2023. a
Van Wessem, J., Reijmer, C., Morlighem, M., Mouginot, J., Rignot, E., Medley, B., Joughin, I., Wouters, B., Depoorter, M., Bamber, J., Lenaerts, J., Van De Berg, W., Van Den Broeke, M., and Van Meijgaard, E.: Improved Representation of East Antarctic Surface Mass Balance in a Regional Atmospheric Climate Model, J. Glaciol., 60, 761–770, https://doi.org/10.3189/2014JoG14J051, 2014. a
Weertman, J.: On the sliding of glaciers, J. Glaciol., 3, 33–38, https://doi.org/10.3189/S0022143000024709, 1957. a
Wernecke, A., Edwards, T. L., Nias, I. J., Holden, P. B., and Edwards, N. R.: Spatial probabilistic calibration of a high-resolution Amundsen Sea Embayment ice sheet model with satellite altimeter data, The Cryosphere, 14, 1459–1474, https://doi.org/10.5194/tc-14-1459-2020, 2020. a
Whitehouse, P. L., Gomez, N., King, M. A., and Wiens, D. A.: Solid Earth change and the evolution of the Antarctic Ice Sheet, Nat. Commun., 10, 503, https://doi.org/10.1038/s41467-018-08068-y, 2019. a
Wilkinson, R. D.: Bayesian calibration of expensive multivariate computer experiments, in: Large-scale inverse problems and quantification of uncertainty, John Wiley & Sons, Ltd, 195–215, ISBN 9780470685853, https://doi.org/10.1002/9780470685853.ch10, 2010. a
Wilner, J. A., Morlighem, M., and Cheng, G.: Evaluation of four calving laws for Antarctic ice shelves, The Cryosphere, 17, 4889–4901, https://doi.org/10.5194/tc-17-4889-2023, 2023. a
Zhang, T., Price, S. F., Hoffman, M. J., Perego, M., and Asay-Davis, X.: Diagnosing the sensitivity of grounding-line flux to changes in sub-ice-shelf melting, The Cryosphere, 14, 3407–3424, https://doi.org/10.5194/tc-14-3407-2020, 2020. a
Short summary
We investigate potential sea-level rise from Antarctica's Lambert Glacier, once considered stable but now at risk due to projected ocean warming by 2100. Using statistical methods and limited supercomputer simulations, we calibrated our ice-sheet model using three observables. We find that, under high greenhouse gas emissions, glacier retreat could raise sea levels by 46–133 mm by 2300. This study highlights the need for better observations to reduce uncertainty in ice-sheet model projections.
We investigate potential sea-level rise from Antarctica's Lambert Glacier, once considered...
