Articles | Volume 16, issue 2
https://doi.org/10.5194/esd-16-513-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/esd-16-513-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change
Optimization and Uncertainty Quantification, Sandia National Laboratories, Albuquerque, NM 87123, USA
Mauro Perego
Scientific Machine Learning, Sandia National Laboratories, Albuquerque, NM 87123, USA
D. Thomas Seidl
Scientific Machine Learning, Sandia National Laboratories, Albuquerque, NM 87123, USA
Tucker A. Hartland
Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
Trevor R. Hillebrand
Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, Los Alamos, NM 87544, USA
Matthew J. Hoffman
Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, Los Alamos, NM 87544, USA
Stephen F. Price
Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, Los Alamos, NM 87544, USA
Related authors
Sanket Jantre, Matthew J. Hoffman, Nathan M. Urban, Trevor Hillebrand, Mauro Perego, Stephen Price, and John D. Jakeman
The Cryosphere, 18, 5207–5238, https://doi.org/10.5194/tc-18-5207-2024, https://doi.org/10.5194/tc-18-5207-2024, 2024
Short summary
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.
Irena Vaňková, Xylar Asay-Davis, Carolyn Branecky Begeman, Darin Comeau, Alexander Hager, Matthew Hoffman, Stephen F. Price, and Jonathan Wolfe
The Cryosphere, 19, 507–523, https://doi.org/10.5194/tc-19-507-2025, https://doi.org/10.5194/tc-19-507-2025, 2025
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.
Tim Hill, Derek Bingham, Gwenn E. Flowers, and Matthew J. Hoffman
EGUsphere, https://doi.org/10.22541/essoar.172736254.41350153/v2, https://doi.org/10.22541/essoar.172736254.41350153/v2, 2024
Short summary
Short summary
Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train fast statistical models called Gaussian Process emulators to accelerate subglacial drainage modelling by ~1000 times. We use the fast emulator predictions to show that three of the model parameters are responsible for >90 % of the variance in model outputs. The fast GP emulators will enable future uncertainty quantification and calibration of these models.
Sanket Jantre, Matthew J. Hoffman, Nathan M. Urban, Trevor Hillebrand, Mauro Perego, Stephen Price, and John D. Jakeman
The Cryosphere, 18, 5207–5238, https://doi.org/10.5194/tc-18-5207-2024, https://doi.org/10.5194/tc-18-5207-2024, 2024
Short summary
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.
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.
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
Cited articles
Adalgeirsdottir, G., Aschwanden, A., Khroulev, C., Boberg, F., Mottram, R., Lucas-Picher, P., and Christensen, J.: Role of model initialization for projections of 21st-century Greenland ice sheet mass loss, J. Glaciol., 60, 782–794, https://doi.org/10.3189/2014JoG13J202, 2014. a
Åkesson, H., Morlighem, M., O'Regan, M., and Jakobsson, M.: Future projections of Petermann Glacier under ocean warming depend strongly on friction law, J. Geophys. Res.-Earth, 126, e2020JF005921, https://doi.org/10.1029/2020JF005921, 2021. a
Alnæs, M., Blechta, J., Hake, J., Johansson, A., Kehlet, B., Logg, A., Richardson, C., Ring, J., Rognes, M. E., and Wells, G. N.: The FEniCS project version 1.5, Archive of Numerical Software, 3, https://doi.org/10.11588/ans.2015.100.20553, 2015. a, b
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, b
Bakker, A. M. R., Wong, T. E., Ruckert, K. L., and Keller, K.: Sea-level projections representing the deeply uncertain contribution of the West Antarctic ice sheet, Scientific Reports, 7, 3880, https://doi.org/10.1038/s41598-017-04134-5, 2017. a
Balay, S., Gropp, W. D., McInnes, L. C., and Smith, B. F.: Efficient Management of Parallelism in Object Oriented Numerical Software Libraries, in: Modern Software Tools in Scientific Computing, edited by: Arge, E., Bruaset, A. M., and Langtangen, H. P., Birkhäuser Press, https://doi.org/10.1007/978-1-4612-1986-6_8, 1997. a
Barnes, J. M., Dias dos Santos, T., Goldberg, D., Gudmundsson, G. H., Morlighem, M., and De Rydt, J.: The transferability of adjoint inversion products between different ice flow models, The Cryosphere, 15, 1975–2000, https://doi.org/10.5194/tc-15-1975-2021, 2021. a, b
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
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., 69, 1729–1739, https://doi.org/10.1017/jog.2023.57, 2023. a
Bochev, P., Ridzal, D., D’Elia, M., Perego, M., and Peterson, K.: Optimization-based, property-preserving finite element methods for scalar advection equations and their connection to Algebraic Flux Correction, Comput. Method. Appl. M., 367, 112982, https://doi.org/10.1016/j.cma.2020.112982, 2020. a
Bomarito, G., Leser, P., Warner, J., and Leser, W.: On the optimization of approximate control variates with parametrically defined estimators, J. Comput. Phys., 451, 110882, https://doi.org/10.1016/j.jcp.2021.110882, 2022. a, b, c, d
Brinkerhoff, D., Aschwanden, A., and Fahnestock, M.: Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference, J. Glaciol., 67, 385–403, https://doi.org/10.1017/jog.2020.112, 2021. a
Brinkerhoff, D. J.: Variational inference at glacier scale, J. Comput. Phys., 459, 111095, https://doi.org/10.1016/j.jcp.2022.111095, 2022. a
Brondex, J., Gillet-Chaulet, F., and Gagliardini, O.: Sensitivity of centennial mass loss projections of the Amundsen basin to the friction law, The Cryosphere, 13, 177–195, https://doi.org/10.5194/tc-13-177-2019, 2019. a
Bui-Thanh, T., Ghattas, O., Martin, J., and Stadler, G.: A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion, SIAM J. Sci. Comput., 35, A2494–A2523, https://doi.org/10.1137/12089586X, 2013. a, b, c, d
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
Carr, J. R., Hill, E. A., and Gudmundsson, G. H.: Sensitivity to forecast surface mass balance outweighs sensitivity to basal sliding descriptions for 21st century mass loss from three major Greenland outlet glaciers, The Cryosphere, 18, 2719–2737, https://doi.org/10.5194/tc-18-2719-2024, 2024. a
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, https://doi.org/10.1016/j.jcp.2012.08.037, 2013. a
Cuffey, K. and Paterson, W.: The Physics of Glaciers, Butterworth-Heinneman, Amsterdam, 4th edn., ISBN 9781493300761, 2010. a
DeConto, R. M., Pollard, D., Alley, R. B., Velicogna, I., Gasson, E., Gomez, N., Sadai, S., Condron, A., Gilford, D. M., Ashe, E. L., Kopp, R. E., Li, D., and Dutton, A.: The Paris Climate Agreement and future sea-level rise from Antarctica, Nature, 593, 83–89, 2021. a
Dias dos Santos, T., Morlighem, M., and Brinkerhoff, D.: A new vertically integrated MOno-Layer Higher-Order (MOLHO) ice flow model, The Cryosphere, 16, 179–195, https://doi.org/10.5194/tc-16-179-2022, 2022. a, b, c, d
Dixon, T. O., Warner, J. E., Bomarito, G. F., and Gorodetsky, A. A.: Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates, SIAM/ASA Journal on Uncertainty Quantification, 12, 1005–1049, https://doi.org/10.1137/23M1607994, 2023. a, b
Dukowicz, J. K., Price, S. F., and Lipscomb, W. H.: Consistent approximations and boundary conditions for ice-sheet dynamics from a principle of least action, J. Glaciol., 56, 480–496, https://doi.org/10.3189/002214310792447851, 2010. a, b
Durand, G., Gagliardini, O., Zwinger, T., Meur, E. L., and Hindmarsh, R. C.: Full Stokes modeling of marine ice sheets: influence of the grid size, Ann. Glaciol., 50, 109–114, https://doi.org/10.3189/172756409789624283, 2009. 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, b
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, b, c
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
Giles, M. B.: Multilevel Monte Carlo methods, Acta Numer., 24, 259–328, https://doi.org/10.1017/S096249291500001X, 2015. a, b, c
Goelzer, H., Nowicki, S., Edwards, T., Beckley, M., Abe-Ouchi, A., Aschwanden, A., Calov, R., Gagliardini, O., Gillet-Chaulet, F., Golledge, N. R., Gregory, J., Greve, R., Humbert, A., Huybrechts, P., Kennedy, J. H., Larour, E., Lipscomb, W. H., Le clec'h, S., Lee, V., Morlighem, M., Pattyn, F., Payne, A. J., Rodehacke, C., Rückamp, M., Saito, F., Schlegel, N., Seroussi, H., Shepherd, A., Sun, S., van de Wal, R., and Ziemen, F. A.: Design and results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison, The Cryosphere, 12, 1433–1460, https://doi.org/10.5194/tc-12-1433-2018, 2018. a
Goldberg, D. N., Heimbach, P., Joughin, I., and Smith, B.: Committed retreat of Smith, Pope, and Kohler Glaciers over the next 30 years inferred by transient model calibration, The Cryosphere, 9, 2429–2446, https://doi.org/10.5194/tc-9-2429-2015, 2015. a
Gorodetsky, A., Geraci, G., Eldred, M., and Jakeman, J.: A generalized approximate control variate framework for multifidelity uncertainty quantification, J. Comput. Phys., 408, 109257, https://doi.org/10.1016/j.jcp.2020.109257, 2020. a, b, c, d
Gruber, A., Gunzburger, M., Ju, L., Lan, R., and Wang, Z.: Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling, Geosci. Model Dev., 16, 1213–1229, https://doi.org/10.5194/gmd-16-1213-2023, 2023. a
Halko, N., Martinsson, P. G., and Tropp, J. A.: Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions, SIAM Rev., 53, 217–288, https://doi.org/10.1137/090771806, 2011. a, b
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, b, c
He, Q., Perego, M., Howard, A. A., Karniadakis, G. E., and Stinis, P.: A hybrid deep neural operator/finite element method for ice-sheet modeling, J. Comput. Phys., 492, 112428, https://doi.org/10.1016/j.jcp.2023.112428, 2023. 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, d, e
Hoffman, M. D. and Gelman, A.: The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo, J. Mach. Learn. Res., 15, 1593–1623, http://jmlr.org/papers/v15/hoffman14a.html (last access: 3 March 2025), 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, c
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, b, c, d, e, f, g, h, i
Jakeman, J.: PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling, Environ. Modell. Softw., 170, 105825, https://doi.org/10.1016/j.envsoft.2023.105825, 2023 (software available at: https://github.com/sandialabs/pyapprox, last access: 3 March 2025). a, b, c, d
Jakeman, J. D., Kouri, D. P., and Huerta, J. G.: Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk, Reliab. Eng. Syst. Safe., 221, 108280, https://doi.org/10.1016/j.ress.2021.108280, 2022. a
Jantre, S., Hoffman, M. J., Urban, N. M., Hillebrand, T., Perego, M., Price, S., and Jakeman, J. D.: Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt, The Cryosphere, 18, 5207–5238, https://doi.org/10.5194/tc-18-5207-2024, 2024. a, b, c, d, e, f, g
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, b
Joughin, I., Smith, B. E., and Schoof, C. G.: Regularized Coulomb friction laws for ice sheet sliding: Application to Pine Island Glacier, Antarctica, Geophys. Res. Lett., 46, 4764–4771, 2019. a
Jouvet, G.: Mechanical error estimators for shallow ice flow models, J. Fluid Mech., 807, 40–61, https://doi.org/10.1017/jfm.2016.593, 2016. a
Jouvet, G., Cordonnier, G., Kim, B., Lüthi, M., Vieli, A., and Aschwanden, A.: Deep learning speeds up ice flow modelling by several orders of magnitude, J. Glaciol., 68, 651–664, https://doi.org/10.1017/jog.2021.120, 2021. a
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G. A.: Challenges in Combining Projections from Multiple Climate Models, J. Climate, 23, 2739–2758, https://doi.org/10.1175/2009JCLI3361.1, 2010. a
Koziol, C. P., Todd, J. A., Goldberg, D. N., and Maddison, J. R.: fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models, Geosci. Model Dev., 14, 5843–5861, https://doi.org/10.5194/gmd-14-5843-2021, 2021. a, b
Liegeois, K., Perego, M., and Hartland, T.: PyAlbany: A Python interface to the C++ multiphysics solver Albany, J. Comput. Appl. Math., 425, 115037, https://doi.org/10.1016/j.cam.2022.115037, 2023. a
Lowery, M., Turnage, J., Morrow, Z., Jakeman, J., Narayan, A., Zhe, S., and Shankar, V.: Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning, arXiv [preprint], https://doi.org/10.48550/arXiv.2407.00809, 2024. a
MacAyeal, D. R.: A tutorial on the use of control methods in ice-sheet modeling, J. Glaciol., 39, 91–98, https://doi.org/10.3189/S0022143000015744, 1993. a
Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S., Pean, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J., Maycock, T., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B. (Eds.): Ocean, cryosphere and sea level change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1211–1362, https://doi.org/10.1017/9781009157896.011, 2021. a, b
Moon, T., Fisher, M., Stafford, T., and Harden, L.: QGreenland (3.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.12823307, 2023. a
Morland, L. W. and Johnson, I. R.: Steady Motion of Ice Sheets, J. Glaciol., 25, 229–246, https://doi.org/10.3189/S0022143000010467, 1980. a, b, c
Morlighem, M., Rignot, E., Seroussi, H., Larour, E., Ben Dhia, H., and Aubry, D.: Spatial patterns of basal drag inferred using control methods from a full-Stokes and simpler models for Pine Island Glacier, West Antarctica, Geophys. Res. Lett., 37, L14502, https://doi.org/10.1029/2010GL043853, 2010. a
Nias, I., Cornford, S., and Payne, A.: New mass-conserving bedrock topography for Pine Island Glacier impacts simulated decadal rates of mass loss, Geophys. Res. Lett., 45, 3173–3181, 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
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
Pattyn, F.: A new three-dimensional higher-order thermomechanical ice sheet model: Basic sensitivity, ice stream development, and ice flow across subglacial lakes, J. Geophys. Res.-Sol. Ea., 108, 2382, https://doi.org/10.1029/2002JB002329, 2003. a, b
Peherstorfer, B.: Multifidelity Monte Carlo Estimation with Adaptive Low-Fidelity Models, SIAM/ASA Journal on Uncertainty Quantification, 7, 579–603, https://doi.org/10.1137/17M1159208, 2019. a
Peherstorfer, B. and Willcox, K.: Data-driven operator inference for nonintrusive projection-based model reduction, Comput. Method. Appl. M., 306, 196–215, https://doi.org/10.1016/j.cma.2016.03.025, 2016. a
Peherstorfer, B., Willcox, K., and Gunzburger, M.: Optimal Model Management for Multifidelity Monte Carlo Estimation, SIAM J. Sci. Comput., 38, A3163–A3194, https://doi.org/10.1137/15M1046472, 2016. a, b, c
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., Zhu, H., Stadler, G., Hughes, T. J., and Ghattas, O.: An inexact Gauss-Newton method for inversion of basal sliding and rheology parameters in a nonlinear Stokes ice sheet model, J. Glaciol., 58, 889–903, https://doi.org/10.3189/2012JoG11J182, 2012. a
Reuter, B. W., Geraci, G., and Wildey, T.: Analysis of the Challenges in Developing Sample-Based Multifidelity Estimators for Nondeterministic Models, Int. J. Uncertain. Quan., 14, 1–30, https://doi.org/10.1615/Int.J.UncertaintyQuantification.2024050125, 2024. a
Ritz, C., Edwards, T. L., Durand, G., Payne, A. J., Peyaud, V., and Hindmarsh, R. C. A.: Potential sea-level rise from Antarctic ice-sheet instability constrained by observations, Nature, 528, 115–118, https://doi.org/10.1038/nature16147, 2015. a, b, c, d
Rockafellar, R. T. and Uryasev, S.: The fundamental risk quadrangle in risk management, optimization and statistical estimation, Surveys in Operations Research and Management Science, 18, 33–53, https://doi.org/10.1016/j.sorms.2013.03.001, 2013. a
Schaden, D. and Ullmann, E.: On Multilevel Best Linear Unbiased Estimators, SIAM/ASA Journal on Uncertainty Quantification, 8, 601–635, https://doi.org/10.1137/19M1263534, 2020. a, b
Schlegel, N.-J., Seroussi, H., Schodlok, M. P., Larour, E. Y., Boening, C., Limonadi, D., Watkins, M. M., Morlighem, M., and van den Broeke, M. R.: Exploration of Antarctic Ice Sheet 100-year contribution to sea level rise and associated model uncertainties using the ISSM framework, The Cryosphere, 12, 3511–3534, https://doi.org/10.5194/tc-12-3511-2018, 2018. a, b, c, d
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
Stuart, A. M.: Inverse problems: A Bayesian perspective, Acta Numer., 19, 451–559, https://doi.org/10.1017/S0962492910000061, 2010. a
Tezaur, I., Peterson, K., Powell, A., Jakeman, J., and Roesler, E.: Global Sensitivity Analysis Using the Ultra-Low Resolution Energy Exascale Earth System Model, J. Adv. Model. Earth Sy., 14, e2021MS002831, https://doi.org/10.1029/2021MS002831, 2021. 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
Verjans, V., Robel, A. A., Seroussi, H., Ultee, L., and Thompson, A. F.: The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0), Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, 2022. a
Weis, M., Greve, R., and Hutter, K.: Theory of shallow ice shelves, Continuum Mech. Therm., 11, 15–50, https://doi.org/10.1007/s001610050102, 1999. a, b
Yoo, M., Gopalan, G., Hoffman, M. J., Coulson, S., Han, H. K., Wikle, C. K., and Hillebrand, T.: Uncertainty-enabled machine learning for emulation of regional sea-level change caused by the Antarctic Ice Sheet, arXiv [preprint], https://doi.org/10.48550/arXiv.2406.17729, 2024. a
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.
This study investigated the computational benefits of using multiple models of varying cost and...
Altmetrics
Final-revised paper
Preprint