Articles | Volume 14, issue 3
https://doi.org/10.5194/tc-14-811-2020
© Author(s) 2020. 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-14-811-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
05 Mar 2020
Research article | Highlight paper |
| 05 Mar 2020
| 05 Mar 2020
Assimilation of surface observations in a transient marine ice sheet model using an ensemble Kalman filter
Fabien Gillet-Chaulet
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, CNRS, IRD, IGE, 38000 Grenoble, France
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Transport processes like rocks carried by ice flow and damage evolution – a proxy for crevasses – are key in ice sheet modeling and should occur without diffusion. Yet, standard numerical methods often blur these features. We explore a particle-based Semi-Lagrangian approach, comparing it to a Discontinuous Galerkin method, and show it can accurately simulate such transport when run at high enough resolution.
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Ice calving is a recent inclusion in ice sheet models and there has not been a systematic test of how well it has been implemented. We show that models can accurately represent a given rate of ice calving, properties at the ice front evolve smoothly during calving, and that there are no consistent differences in ice behaviour between various approaches to representing calving in numerical ice models. This gives us confidence in their ability for use in sea level rise prediction simulations.
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How the Antarctic ice sheet responds to rapid ocean warming remains uncertain. To explore this, we used a coupled ocean–ice sheet model. We compared simulations under present-day conditions with simulations under strong future warming for 50 years, then returned to today’s climate. The ocean warmed quickly but recovered within decades. In contrast, several ice streams continued to retreat for centuries. This shows that even brief warming could cause lasting ice loss and sea-level rise.
Heiko Goelzer, Constantijn J. Berends, Fredrik Boberg, Gael Durand, Tamsin L. Edwards, Xavier Fettweis, Fabien Gillet-Chaulet, Quentin Glaude, Philippe Huybrechts, Sébastien Le clec'h, Ruth Mottram, Brice Noël, Martin Olesen, Charlotte Rahlves, Jeremy Rohmer, Michiel van den Broeke, and Roderik S. W. van de Wal
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We present an ensemble of ice sheet model projections for the Greenland ice sheet. The focus is on providing projections that improve our understanding of the range future sea-level rise and the inherent uncertainties over the next 100 to 300 years. Compared to earlier work we more fully account for some of the uncertainties in sea-level projections. We include a wider range of climate model output, more climate change scenarios and we extend projections schematically up to year 2300.
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Antarctica holds enough ice to raise sea levels by many meters, but its future is uncertain. Warm ocean water melts ice shelves from below, letting inland ice flow faster into the sea. By 2300, Antarctica could add 0.6–4.4 m to sea levels. Our study identifies two key factors—how strongly shelves melt and how the ice responds. These explain much of the range, and refining them in models may improve future predictions.
Davor Dundovic, Joseph G. Wallwork, Stephan C. Kramer, Fabien Gillet-Chaulet, Regine Hock, and Matthew D. Piggott
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Accurate numerical studies of glaciers often require high-resolution simulations, which often prove too demanding even for modern computers. In this paper we develop a method that identifies whether different parts of a glacier require high or low resolution based on its physical features, such as its thickness and velocity. We show that by doing so we can achieve a more optimal simulation accuracy for the available computing resources compared to uniform-resolution simulations.
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Internal climate variability, resulting from processes intrinsic to the climate system, modulates the Antarctic response to climate change by delaying or offsetting its effects. Using climate and ice-sheet models, we highlight that irreducible internal climate variability significantly enlarges the likely range of Antarctic contribution to sea-level rise until 2100. Thus, we recommend considering internal climate variability as a source of uncertainty for future ice-sheet projections.
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Emily A. Hill, Benoît Urruty, Ronja Reese, Julius Garbe, Olivier Gagliardini, Gaël Durand, Fabien Gillet-Chaulet, G. Hilmar Gudmundsson, Ricarda Winkelmann, Mondher Chekki, David Chandler, and Petra M. Langebroek
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The grounding lines of the Antarctic Ice Sheet could enter phases of irreversible retreat or advance. We use three ice sheet models to show that the present-day locations of Antarctic grounding lines are reversible with respect to a small perturbation away from their current position. This indicates that present-day retreat of the grounding lines is not yet irreversible or self-enhancing.
Ronja Reese, Julius Garbe, Emily A. Hill, Benoît Urruty, Kaitlin A. Naughten, Olivier Gagliardini, Gaël Durand, Fabien Gillet-Chaulet, G. Hilmar Gudmundsson, David Chandler, Petra M. Langebroek, and Ricarda Winkelmann
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Along the edges of the Greenland Ice Sheet surface melt lubricates the bed and causes large seasonal fluctuations in ice speeds during summer. Accurately understanding how these ice speed changes occur is difficult due to the inaccessibility of the glacier bed. We show that by using surface velocity maps with high temporal resolution and numerical modelling we can infer the basal conditions that control seasonal fluctuations in ice speed and gain insight into seasonal dynamics over large areas.
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Short summary
Short summary
Transport processes like rocks carried by ice flow and damage evolution – a proxy for crevasses – are key in ice sheet modeling and should occur without diffusion. Yet, standard numerical methods often blur these features. We explore a particle-based Semi-Lagrangian approach, comparing it to a Discontinuous Galerkin method, and show it can accurately simulate such transport when run at high enough resolution.
James R. Jordan, Frank Pattyn, Daniel Abele, Torsten Albrecht, Jorge Alvarez-Solas, Jowan M. Barnes, Tijn Berends, Jorge A. Bernales, Javier Blasco, Gong Cheng, Youngmin Choi, Stephen L. Cornford, Cruz Garcia-Molina, Fabien Gillet-Chaulet, G. Hilmar Gudmundsson, Angelika Humbert, Gunter R. Leguy, William H. Lipscomb, Marisa Montoya, Daniel Moreno-Parada, Mathieu Morlighem, Tyler Pelle, Alexander Robinson, Martin Rückamp, Hélène Seroussi, Yanmei Tian, Luisa Wagner, Roderick S. W. van de Wal, Liyun Zhao, and Thomas Zwinger
EGUsphere, https://doi.org/10.5194/egusphere-2026-1962, https://doi.org/10.5194/egusphere-2026-1962, 2026
Short summary
Short summary
Ice calving is a recent inclusion in ice sheet models and there has not been a systematic test of how well it has been implemented. We show that models can accurately represent a given rate of ice calving, properties at the ice front evolve smoothly during calving, and that there are no consistent differences in ice behaviour between various approaches to representing calving in numerical ice models. This gives us confidence in their ability for use in sea level rise prediction simulations.
Pierre Mathiot, Nicolas C. Jourdain, Benoit Urruty, Fabien Gillet-Chaulet, Olivier Gagliardini, and Gaël Durand
EGUsphere, https://doi.org/10.5194/egusphere-2026-927, https://doi.org/10.5194/egusphere-2026-927, 2026
Short summary
Short summary
How the Antarctic ice sheet responds to rapid ocean warming remains uncertain. To explore this, we used a coupled ocean–ice sheet model. We compared simulations under present-day conditions with simulations under strong future warming for 50 years, then returned to today’s climate. The ocean warmed quickly but recovered within decades. In contrast, several ice streams continued to retreat for centuries. This shows that even brief warming could cause lasting ice loss and sea-level rise.
Heiko Goelzer, Constantijn J. Berends, Fredrik Boberg, Gael Durand, Tamsin L. Edwards, Xavier Fettweis, Fabien Gillet-Chaulet, Quentin Glaude, Philippe Huybrechts, Sébastien Le clec'h, Ruth Mottram, Brice Noël, Martin Olesen, Charlotte Rahlves, Jeremy Rohmer, Michiel van den Broeke, and Roderik S. W. van de Wal
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Short summary
Short summary
We present an ensemble of ice sheet model projections for the Greenland ice sheet. The focus is on providing projections that improve our understanding of the range future sea-level rise and the inherent uncertainties over the next 100 to 300 years. Compared to earlier work we more fully account for some of the uncertainties in sea-level projections. We include a wider range of climate model output, more climate change scenarios and we extend projections schematically up to year 2300.
Johanna Beckmann, Ronja Reese, Felicity S. McCormack, Sue Cook, Lawrence Bird, Dawid Gwyther, Daniel Richards, Matthias Scheiter, Yu Wang, Hélène Seroussi, Ayako Abe‐Ouchi, Torsten Albrecht, Jorge Alvarez‐Solas, Xylar S. Asay‐Davis, Jean‐Baptiste Barre, Constantijn J. Berends, Jorge Bernales, Javier Blasco, Justine Caillet, David M. Chandler, Violaine Coulon, Richard Cullather, Christophe Dumas, Benjamin K. Galton‐Fenzi, Julius Garbe, Fabien Gillet‐Chaulet, Rupert Gladstone, Heiko Goelzer, Nicholas R. Golledge, Ralf Greve, G. Hilmar Gudmundsson, Holly Kyeore Han, Trevor R. Hillebrand, Matthew J. Hoffman, Philippe Huybrechts, Nicolas C. Jourdain, Ann Kristin Klose, Petra M. Langebroek, Gunter R. Leguy, William H. Lipscomb, Daniel P. Lowry, Pierre Mathiot, Marisa Montoya, Mathieu Morlighem, Sophie Nowicki, Frank Pattyn, Antony J. Payne, Tyler Pelle, Aurélien Quiquet, Alexander Robinson, Leopekka Saraste, Erika G. Simon, Sainan Sun, Jake P. Twarog, Luke D. Trusel, Benoit Urruty, Jonas Van Breedam, Roderik S. W. van de Wal, Chen Zhao, and Thomas Zwinger
EGUsphere, https://doi.org/10.5194/egusphere-2025-4069, https://doi.org/10.5194/egusphere-2025-4069, 2025
Short summary
Short summary
Antarctica holds enough ice to raise sea levels by many meters, but its future is uncertain. Warm ocean water melts ice shelves from below, letting inland ice flow faster into the sea. By 2300, Antarctica could add 0.6–4.4 m to sea levels. Our study identifies two key factors—how strongly shelves melt and how the ice responds. These explain much of the range, and refining them in models may improve future predictions.
Davor Dundovic, Joseph G. Wallwork, Stephan C. Kramer, Fabien Gillet-Chaulet, Regine Hock, and Matthew D. Piggott
Geosci. Model Dev., 18, 4023–4044, https://doi.org/10.5194/gmd-18-4023-2025, https://doi.org/10.5194/gmd-18-4023-2025, 2025
Short summary
Short summary
Accurate numerical studies of glaciers often require high-resolution simulations, which often prove too demanding even for modern computers. In this paper we develop a method that identifies whether different parts of a glacier require high or low resolution based on its physical features, such as its thickness and velocity. We show that by doing so we can achieve a more optimal simulation accuracy for the available computing resources compared to uniform-resolution simulations.
Justine Caillet, Nicolas C. Jourdain, Pierre Mathiot, Fabien Gillet-Chaulet, Benoit Urruty, Clara Burgard, Charles Amory, Mondher Chekki, and Christoph Kittel
Earth Syst. Dynam., 16, 293–315, https://doi.org/10.5194/esd-16-293-2025, https://doi.org/10.5194/esd-16-293-2025, 2025
Short summary
Short summary
Internal climate variability, resulting from processes intrinsic to the climate system, modulates the Antarctic response to climate change by delaying or offsetting its effects. Using climate and ice-sheet models, we highlight that irreducible internal climate variability significantly enlarges the likely range of Antarctic contribution to sea-level rise until 2100. Thus, we recommend considering internal climate variability as a source of uncertainty for future ice-sheet projections.
Eliot Jager, Fabien Gillet-Chaulet, Nicolas Champollion, Romain Millan, Heiko Goelzer, and Jérémie Mouginot
The Cryosphere, 18, 5519–5550, https://doi.org/10.5194/tc-18-5519-2024, https://doi.org/10.5194/tc-18-5519-2024, 2024
Short summary
Short summary
Inspired by a previous intercomparison framework, our study better constrains uncertainties in glacier evolution using an innovative method to validate Bayesian calibration. Upernavik Isstrøm, one of Greenland's largest glaciers, has lost significant mass since 1985. By integrating observational data, climate models, human emissions, and internal model parameters, we project its evolution until 2100. We show that future human emissions are the main source of uncertainty in 2100, making up half.
Emily A. Hill, Benoît Urruty, Ronja Reese, Julius Garbe, Olivier Gagliardini, Gaël Durand, Fabien Gillet-Chaulet, G. Hilmar Gudmundsson, Ricarda Winkelmann, Mondher Chekki, David Chandler, and Petra M. Langebroek
The Cryosphere, 17, 3739–3759, https://doi.org/10.5194/tc-17-3739-2023, https://doi.org/10.5194/tc-17-3739-2023, 2023
Short summary
Short summary
The grounding lines of the Antarctic Ice Sheet could enter phases of irreversible retreat or advance. We use three ice sheet models to show that the present-day locations of Antarctic grounding lines are reversible with respect to a small perturbation away from their current position. This indicates that present-day retreat of the grounding lines is not yet irreversible or self-enhancing.
Ronja Reese, Julius Garbe, Emily A. Hill, Benoît Urruty, Kaitlin A. Naughten, Olivier Gagliardini, Gaël Durand, Fabien Gillet-Chaulet, G. Hilmar Gudmundsson, David Chandler, Petra M. Langebroek, and Ricarda Winkelmann
The Cryosphere, 17, 3761–3783, https://doi.org/10.5194/tc-17-3761-2023, https://doi.org/10.5194/tc-17-3761-2023, 2023
Short summary
Short summary
We use an ice sheet model to test where current climate conditions in Antarctica might lead. We find that present-day ocean and atmosphere conditions might commit an irreversible collapse of parts of West Antarctica which evolves over centuries to millennia. Importantly, this collapse is not irreversible yet.
Anna Derkacheva, Fabien Gillet-Chaulet, Jeremie Mouginot, Eliot Jager, Nathan Maier, and Samuel Cook
The Cryosphere, 15, 5675–5704, https://doi.org/10.5194/tc-15-5675-2021, https://doi.org/10.5194/tc-15-5675-2021, 2021
Short summary
Short summary
Along the edges of the Greenland Ice Sheet surface melt lubricates the bed and causes large seasonal fluctuations in ice speeds during summer. Accurately understanding how these ice speed changes occur is difficult due to the inaccessibility of the glacier bed. We show that by using surface velocity maps with high temporal resolution and numerical modelling we can infer the basal conditions that control seasonal fluctuations in ice speed and gain insight into seasonal dynamics over large areas.
Cited articles
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, https://doi.org/10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2, 1999. a
Bannister, R. N.: A review of operational methods of variational and
ensemble-variational data assimilation: Ensemble-variational Data
Assimilation, Q. J. Roy. Meteorol. Soc., 143,
607–633, https://doi.org/10.1002/qj.2982, 2017. a, b, c
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive Sampling with
the Ensemble Transform Kalman Filter. Part I: Theoretical
Aspects, Mon. Weather Rev., 129, 420–436,
https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2, 2001. a, b
Bonan, B., Nichols, N. K., Baines, M. J., and Partridge, D.: Data assimilation for moving mesh methods with an application to ice sheet modelling, Nonlin. Processes Geophys., 24, 515–534, https://doi.org/10.5194/npg-24-515-2017, 2017. a, b
Borstad, C. P., Rignot, E., Mouginot, J., and Schodlok, M. P.: Creep deformation and buttressing capacity of damaged ice shelves: theory and application to Larsen C ice shelf, The Cryosphere, 7, 1931–1947, https://doi.org/10.5194/tc-7-1931-2013, 2013. a
Brinkerhoff, D.J., Aschwanden, A., and Truffer, M.: Bayesian Inference of Subglacial Topography Using Mass Conservation. Front. Earth Sci. 4, 1–15, https://doi.org/10.3389/feart.2016.00008, 2016 a
Brondex, J., Gagliardini, O., Gillet-Chaulet, F., and Durand, G.: Sensitivity
of grounding line dynamics to the choice of the friction law, J.
Glaciol., 63, 854–866, https://doi.org/10.1017/jog.2017.51,
2017. a, b
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, b, c, d
Burgers, G., van Leeuwen, P. J., and Evensen, G.: Analysis scheme in the ensemble Kalman filter, Mon. Weather Rev., 126, 1719–1724, https://doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2, 1998. a
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in the geosciences: An overview of methods, issues, and perspectives, WIREs Clim Change, 9, e535, https://doi.org/10.1002/wcc.535, 2018. a, b, c
Church, J. A., Clark, P. U., Cazenave, A., Gregory, J. M., Jevrejeva, S.,
Levermann, A., Merrifield, M. A., Milne, G. A., Nerem, R. S., Nunn, P. D.,
Payne, A. J., Pfeffer, W. T., Stammer, D., and Unnikrishnan, A. S.: Sea level
change, in: Climate Change 2013: The Physical Science Basis.
Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2013. a
Cohn, S. E., da Silva, A., Guo, J., Sienkiewicz, M., and Lamich, D.: Assessing
the Effects of Data Selection with the DAO Physical-Space
Statistical Analysis System, Mon. Weather Rev., 126, 2913–2926,
https://doi.org/10.1175/1520-0493(1998)126<2913:ATEODS>2.0.CO;2,
1998. a
Cosme, E., Verron, J., Brasseur, P., Blum, J., and Auroux, D.: Smoothing problems in a Bayesian framework and their linear Gaussian solutions, Mon. Weather Rev., 140, 683–695, 2012. a
Dai, C. and Howat, I. M.: Measuring Lava Flows With ArcticDEM:
Application to the 2012–2013 Eruption of Tolbachik, Kamchatka,
Geophys. Res. Lett., 44, 12133–12140, https://doi.org/10.1002/2017GL075920,
2017. a
Dee, D. P.: Bias and data assimilation, Q. J. Roy. Meteorol. Soc., 131, 3323–3343, https://doi.org/10.1256/qj.05.137, 2005. a
Durand, G. and Pattyn, F.: Reducing uncertainties in projections of Antarctic ice mass loss, The Cryosphere, 9, 2043–2055, https://doi.org/10.5194/tc-9-2043-2015, 2015. a
Durand, G., Gagliardini, O., Zwinger, T., Le Meur, E., 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, b
Durand, G., Gagliardini, O., Favier, L., Zwinger, T., and Meur, E. L.: Impact
of bedrock description on modeling ice sheet dynamics, Geophys. Res.
Lett., 38, L20501, https://doi.org/10.1029/2011GL048892,
2011. a, b, c
Enderlin, E. M., Howat, I. M., and Vieli, A.: High sensitivity of tidewater outlet glacier dynamics to shape, The Cryosphere, 7, 1007–1015, https://doi.org/10.5194/tc-7-1007-2013, 2013. a
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics, J.
Geophys. Res., 99, 10143, https://doi.org/10.1029/94JC00572,
1994. a
Evensen, G. and van Leeuwen, P. J.: An ensemble Kalman smoother for nonlinear dynamics, Mon. Weather Rev., 128, 1852–1867, 2000. a
Favier, L., Durand, G., Cornford, S. L., Gudmundsson, G. H., Gagliardini, O.,
Gillet-Chaulet, F., Zwinger, T., Payne, A. J., and Le Brocq, A. M.: Retreat
of Pine Island Glacier controlled by marine ice-sheet instability,
Nat. Clim. Change, 4, 117–121, https://doi.org/10.1038/nclimate2094, 2014. a
Fournier, A., Fussell, D., and Carpenter, L.: Computer rendering of stochastic models, Commun. Assoc. Cornput. Mach., 25, 371–384, 1982. 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, b
Gagliardini, O., Cohen, D., Råback, P., and Zwinger, T.: Finite-element
modeling of subglacial cavities and related friction law, J.
Geophys. Res., 112, F02027, https://doi.org/10.1029/2006JF000576,
2007. a
Gagliardini, O., Zwinger, T., Gillet-Chaulet, F., Durand, G., Favier, L., de Fleurian, B., Greve, R., Malinen, M., Martín, C., Råback, P., Ruokolainen, J., Sacchettini, M., Schäfer, M., Seddik, H., and Thies, J.: Capabilities and performance of Elmer/Ice, a new-generation ice sheet model, Geosci. Model Dev., 6, 1299–1318, https://doi.org/10.5194/gmd-6-1299-2013, 2013. a, b
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and
three dimensions, Q. J. Roy. Meteorol. Soc., 125,
723–757, https://doi.org/10.1002/qj.49712555417, 1999. a, b
Gillet-Chaulet, F., Hindmarsh, R. C. A., Corr, H. F. J., King, E. C., and
Jenkins, A.: In-situ quantification of ice rheology and direct measurement of
the Raymond Effect at Summit, Greenland using a phase-sensitive
radar, Geophys. Res. Lett., 38, L24503,
https://doi.org/10.1029/2011GL049843,
2011. a
Gillet-Chaulet, F., Gagliardini, O., Seddik, H., Nodet, M., Durand, G., Ritz, C., Zwinger, T., Greve, R., and Vaughan, D. G.: Greenland ice sheet contribution to sea-level rise from a new-generation ice-sheet model, The Cryosphere, 6, 1561–1576, https://doi.org/10.5194/tc-6-1561-2012, 2012. 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, 2016GL069937,
https://doi.org/10.1002/2016GL069937,
2016. a
Gladstone, R. M., Payne, A. J., and Cornford, S. L.: Resolution requirements
for grounding-line modelling: sensitivity to basal drag and ice-shelf
buttressing, Ann. Glaciol., 53, 97–105, https://doi.org/10.3189/2012AoG60A148,
2012. a
Goldberg, D. N. and Heimbach, P.: Parameter and state estimation with a time-dependent adjoint marine ice sheet model, The Cryosphere, 7, 1659–1678, https://doi.org/10.5194/tc-7-1659-2013, 2013. a, b, c
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, b, c
Goldberg, D. N., Narayanan, S. H. K., Hascoet, L., and Utke, J.: An optimized treatment for algorithmic differentiation of an important glaciological fixed-point problem, Geosci. Model Dev., 9, 1891–1904, https://doi.org/10.5194/gmd-9-1891-2016, 2016. a
Graham, F. S., Roberts, J. L., Galton-Fenzi, B. K., Young, D., Blankenship, D., and Siegert, M. J.: A high-resolution synthetic bed elevation grid of the Antarctic continent, Earth Syst. Sci. Data, 9, 267–279, https://doi.org/10.5194/essd-9-267-2017, 2017. a
Griggs, J. and Bamber, J.: Antarctic ice-shelf thickness from satellite radar
altimetry, J. Glaciol., 57, 485–498,
https://doi.org/10.3189/002214311796905659,
2011. a
Gudmundsson, G. H.: Analytical solutions for the surface response to small amplitude perturbations in boundary data in the shallow-ice-stream approximation, The Cryosphere, 2, 77–93, https://doi.org/10.5194/tc-2-77-2008, 2008. a
Gudmundsson, G. H. and Raymond, M.: On the limit to resolution and information on basal properties obtainable from surface data on ice streams, The Cryosphere, 2, 167–178, https://doi.org/10.5194/tc-2-167-2008, 2008. a, b
Hamill, T. M., Whitaker, J. S., and Snyder, C.: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter, Mon. Weather Rev., 129, 2776–2790, https://doi.org/10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2, 2001. a
Hascoët, L. and Morlighem, M.: Source-to-source adjoint Algorithmic
Differentiation of an ice sheet model written in C, Opt. Method. Softw., 33, 829–843, https://doi.org/10.1080/10556788.2017.1396600,
2018. a
Hendricks Franssen, H. J., Kaiser, H. P., Kuhlmann, U., Bauser, G., Stauffer, F., Müller, R., and Kinzelbach, W.: Operational real‐time modeling with ensemble Kalman filter of variably saturated subsurface flow including stream‐aquifer interaction and parameter updating, Water Resour. Res., 47, W02532, https://doi.org/10.1029/2010WR009480, 2011. a
Houtekamer, P. L. and Mitchell, H. L.: Data assimilation using an ensemble Kalman filter technique, Mon. Weather Rev., 126, 796–811, https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2, 1998. a
Houtekamer, P. L. and Mitchell, H. L.: A Sequential Ensemble Kalman
Filter for Atmospheric Data Assimilation, Mon. Weather Rev.,
129, 123–137, https://doi.org/10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2,
2001. a, b, c
Houtekamer, P. L., Mitchell, H. L., and Deng, X.: Model Error
Representation in an Operational Ensemble Kalman Filter, Mon.
Weather Rev., 137, 2126–2143, https://doi.org/10.1175/2008MWR2737.1,
2009. a
Houtekamer, P. L., Deng, X., Mitchell, H. L., Baek, S., and Gagnon, N.: Higher Resolution in an Operational Ensemble Kalman Filter, Mon. Weather Rev., 142, 1143–1162, https://doi.org/10.1175/MWR-D-13-00138.1, 2014. a
Jenkins, A., Shoosmith, D., Dutrieux, P., Jacobs, S., Kim, T. W., Lee, S. H.,
Ha, H. K., and Stammerjohn, S.: West Antarctic Ice Sheet retreat in the Amundsen Sea driven by decadal oceanic variability, Nat. Geosci., 11, 733–738, https://doi.org/10.1038/s41561-018-0207-4, 2018. a
Joughin, I., Smith, B. E., and Holland, D. M.: Sensitivity of 21st century sea
level to ocean-induced thinning of Pine Island Glacier, Antarctica,
Geophys. Res. Lett., 37, L20502, https://doi.org/10.1029/2010GL044819, 2010. a, b
Joughin, I., Alley, R. B., and Holland, D. M.: Ice-Sheet Response to
Oceanic Forcing, Science, 338, 1172–1176, https://doi.org/10.1126/science.1226481,
2012. a
Joughin, I., Smith, B. E., and Howat, I.: Greenland Ice Mapping Project: ice flow velocity variation at sub-monthly to decadal timescales, The Cryosphere, 12, 2211–2227, https://doi.org/10.5194/tc-12-2211-2018, 2018. a, b
Kalnay, E., Li, H., Miyoshi, T., Yang, S.-C., and Ballabrera-Poy, J.:
4-D-Var or ensemble Kalman filter?, Tellus A: Dynam. Meteorol.
Oceanogr., 59, 758–773, https://doi.org/10.1111/j.1600-0870.2007.00261.x,
2007. a
Kirchgessner, P., Nerger, L., and Bunse-Gerstner, A.: On the Choice of an Optimal Localization Radius in Ensemble Kalman Filter Methods, Mon. Weather Rev., 142, 2165–2175, https://doi.org/10.1175/MWR-D-13-00246.1, 2014 a, b
Kyrke-Smith, T. M., Gudmundsson, G. H., and Farrell, P. E.:
Can Seismic Observations of Bed Conditions on Ice Streams Help Constrain Parameters in Ice Flow Models?,
J. Geophys. Res.-Earth Surf., 122, 2269–2282, https://doi.org/10.1002/2017JF004373, 2017 a
Larour, E., Morlighem, H. S. A. M., and Rignot, E.: Continental scale, high
order, high spatial resolution, ice sheet modeling using the Ice Sheet
System Model (ISSM), J. Geophys. Res., 117, F01022,
https://doi.org/10.1029/2011JF002140,
2012. a
Larour, E., Utke, J., Csatho, B., Schenk, A., Seroussi, H., Morlighem, M., Rignot, E., Schlegel, N., and Khazendar, A.: Inferred basal friction and surface mass balance of the Northeast Greenland Ice Stream using data assimilation of ICESat (Ice Cloud and land Elevation Satellite) surface altimetry and ISSM (Ice Sheet System Model), The Cryosphere, 8, 2335–2351, https://doi.org/10.5194/tc-8-2335-2014, 2014. a
Larour, E., Utke, J., Bovin, A., Morlighem, M., and Perez, G.: An approach to computing discrete adjoints for MPI-parallelized models applied to Ice Sheet System Model 4.11, Geosci. Model Dev., 9, 3907–3918, https://doi.org/10.5194/gmd-9-3907-2016, 2016. a
Li, Z. and Navon, I. M.: Optimality of variational data assimilation and its relationship with the Kalman filter and smoother, Q. J. Roy. Meteorol. Soc., 127, 661–683, https://doi.org/10.1002/qj.49712757220, 2001 a, b
MacAyeal, D. R.: Large-scale ice flow over a viscous basal sediment: Theory
and application to ice stream B, Antarctica, J. Geophys.
Res.-Solid Earth, 94, 4071–4087, https://doi.org/10.1029/JB094iB04p04071,
1989. a
Mitchell, H. L., Houtekamer, P. L., and Pellerin, G.: Ensemble size,balance, and model-error representation in an Ensemble Kalman Filter, Mon. Weather Rev., 130, 2791–2808, https://doi.org/10.1175/1520-0493(2002)130<2791:ESBAME>2.0.CO;2, 2002.. a
Morlighem, M., Rignot, E., Seroussi, H., Larour, E., Dhia, H. B., 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
Morlighem, M., Williams, C. N., Rignot, E., An, L., Arndt, J. E., Bamber,
J. L., Catania, G., Chauché, N., Dowdeswell, J. A., Dorschel, B., Fenty, I.,
Hogan, K., Howat, I., Hubbard, A., Jakobsson, M., Jordan, T. M., Kjeldsen,
K. K., Millan, R., Mayer, L., Mouginot, J., Noël, B. P. Y., O'Cofaigh, C.,
Palmer, S., Rysgaard, S., Seroussi, H., Siegert, M. J., Slabon, P., Straneo,
F., van den Broeke, M. R., Weinrebe, W., Wood, M., and Zinglersen, K. B.:
BedMachine v3: Complete Bed Topography and Ocean Bathymetry
Mapping of Greenland From Multibeam Echo Sounding Combined
With Mass Conservation, Geophys.
Res. Lett., 44, 11051–11061, https://doi.org/10.1002/2017GL074954,
2017. a
Mosbeux, C., Gillet-Chaulet, F., and Gagliardini, O.: Comparison of adjoint and nudging methods to initialise ice sheet model basal conditions, Geosci. Model Dev., 9, 2549–2562, https://doi.org/10.5194/gmd-9-2549-2016, 2016. a
Mouginot, J., Scheuchl, B., and Rignot, E.: Mapping of Ice Motion in
Antarctica Using Synthetic-Aperture Radar Data, Remote Sens.,
4, 2753–2767, https://doi.org/10.3390/rs4092753,
2012. a, b
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., 2017, 9, https://doi.org/10.3390/rs9040364, 2017. a
Murray, T.: Assessing the paradigm shift: deformable glacier beds, Quaternary
Sci. Rev., 16, 996–1016,
1997. a
Nerger, L., Hiller, W., and Schröter, J.: A comparison of error subspace Kalman filters,
Tellus A: Dynam. Meteorol. Oceanogr., 57, 715–735, https://doi.org/10.3402/tellusa.v57i5.14732 a
Nerger, L., Hiller, W., and Schröter, J.: PDAF – The Parallel Data
Assimilation Framework: Experiences with Kalman Filtering, in: Use of High Performance Computing in Meteorology, 63–83, WORLD
SCIENTIFIC, Reading, UK, https://doi.org/10.1142/9789812701831_0006,
2005. a, b
Nerger, L., Danilov, S., Hiller, W., and Schröter, J.: Using sea-level data to constrain a finite-element primitive-equation ocean model with a local SEIK filter, Ocean Dynam., 56, 634–649, https://doi.org/10.1007/s10236-006-0083-0,
2006. a
Nerger, L., Schulte, S., and Bunse-Gerstner, A.: On the influence of model nonlinearity and localization on ensemble Kalman smoothing, Q. J. Roy. Meteorol. Soc., 140, 2249–2259, https://doi.org/10.1002/qj.2293, 2014. a, b
Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J., Corazza, M., Kalnay, E., Patil, D. J., and Yorke, A.: A local ensemble Kalman filter for atmospheric data assimilation, Tellus A, 56, 415–428, https://doi.org/10.1111/j.1600-0870.2004.00076.x, 2004. a
Pattyn, F., Huyghe, A., De Brabander, S., and De Smedt, B.: Role of transition
zones in marine ice sheet dynamics, J. Geophys. Res., 111, F02004,
https://doi.org/10.1029/2005JF000394,2006. a
Pattyn, F., Schoof, C., Perichon, L., Hindmarsh, R. C. A., Bueler, E., de Fleurian, B., Durand, G., Gagliardini, O., Gladstone, R., Goldberg, D., Gudmundsson, G. H., Huybrechts, P., Lee, V., Nick, F. M., Payne, A. J., Pollard, D., Rybak, O., Saito, F., and Vieli, A.: Results of the Marine Ice Sheet Model Intercomparison Project, MISMIP, The Cryosphere, 6, 573–588, https://doi.org/10.5194/tc-6-573-2012, 2012. a, b
Pattyn, F., Perichon, L., Durand, G., Favier, L., Gagliardini, O., Hindmarsh,
R. C., Zwinger, T., Albrecht, T., Cornford, S., Docquier, D., FüRst, J. J.,
Goldberg, D., Gudmundsson, G. H., Humbert, A., Hütten, M., Huybrechts, P.,
Jouvet, G., Kleiner, T., Larour, E., Martin, D., Morlighem, M., Payne, A. J.,
Pollard, D., RüCkamp, M., Rybak, O., Seroussi, H., Thoma, M., and Wilkens,
N.: Grounding-line migration in plan-view marine ice-sheet models: results of
the ice2sea MISMIP3d intercomparison, J. Glaciol., 59, 410–422,
https://doi.org/10.3189/2013JoG12J129, 2013. a, b
Pebesma, E. J. and Wesseling, C. G.: Gstat: a program for geostatistical
modelling, prediction and simulation, Comput. Geosci., 24, 17–31,
https://doi.org/10.1016/S0098-3004(97)00082-4,
1998. a, b
Pham, D. T., Verron, J., and Christine Roubaud, M.: A singular evolutive
extended Kalman filter for data assimilation in oceanography, J.
Mar. Sys., 16, 323–340, https://doi.org/10.1016/S0924-7963(97)00109-7,
1998. a, b, c, d
Pimienta, P., Duval, P., and Lipenkov, V. Y.: Mechanical behavior of
anisotropic polar ice, Internationnal Association of Hydrological Sciences Publication 170 (Symposium at Vancouver 1987 – Physical Basis of Ice Sheet Modelling), 57–66, 1987 a
Pralong, M. R. and Gudmundsson, G. H.: Bayesian estimation of basal conditions on Rutford Ice Stream, West Antarctica, from surface data, J. Glaciol., 57, 315–324, https://doi.org/10.3189/002214311796406004, 2011 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
Sakov, P. and Bertino, L.: Relation between two common localisation methods for the EnKF, Comput. Geosci. 15, 225–237, https://doi.org/10.1007/s10596-010-9202-6, 2011. a
Sakov, P., Counillon, F., Bertino, L., Lisaeter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012. a
Scambos, T., Bell, R., Alley, R., Anandakrishnan, S., Bromwich, D., Brunt, K.,
Christianson, K., Creyts, T., Das, S., DeConto, R., Dutrieux, P., Fricker,
H., Holland, D., MacGregor, J., Medley, B., Nicolas, J., Pollard, D.,
Siegfried, M., Smith, A., Steig, E., Trusel, L., Vaughan, D., and Yager, P.: How much, how fast?: A science review and outlook for research on the instability of Antarctica's Thwaites Glacier in the 21st century, Global Planet. Change, 153, 16–34,
https://doi.org/10.1016/j.gloplacha.2017.04.008, 2017.
a, b
Schoof, C.: The effect of cavitation on glacier sliding,
Proc. R. Soc. A, 461, 609–627, https://doi.org/10.1098/rspa.2004.1350,
2005. a
Schoof, C.: Marine ice-sheet dynamics. Part 1. The case of rapid sliding,
J. Fluid Mech., 573, 27, https://doi.org/10.1017/S0022112006003570,
2007. a, b
Schulson, E. M. and Duval, P.: Creep and Fracture of Ice, Cambridge
University Press, 2009. a
Seroussi, H., Morlighem, M., Rignot, E., Larour, E., Aubry, D., Dhia, H. B.,
and Kristensen, S. S.: Ice flux divergence anomalies on 79north Glacier,
Greenland, Geophys. Res. Lett., 38, L09501, https://doi.org/10.1029/2011GL047338,
2011. a
Seroussi, H., Morlighem, M., Larour, E., Rignot, E., and Khazendar, A.: Hydrostatic grounding line parameterization in ice sheet models, The Cryosphere, 8, 2075–2087, https://doi.org/10.5194/tc-8-2075-2014, 2014. a, b
Sun, L., Seidou, O., Nistor, I., and Liu, K.: Review of the Kalman-type hydrological data assimilation, Hydrol. Sci. J., 61, 2348–2366, https://doi.org/10.1080/02626667.2015.1127376, 2016 a
Tandeo, P., Ailliot, P., Bocquet, M., Carrassi, A. Miyoshi, T., Pulido, M., and Zhen, Y.: A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation, Mon. Weather Rev., submitted, 2020 a
Tsai, V. C., Stewart, A. L., and Thompson, A. F.: Marine ice-sheet profiles and
stability under Coulomb basal conditions, J. Glaciol., 61, 205–215,
https://doi.org/10.3189/2015JoG14J221, 2015. a
Tulaczyk, S., Kamb, W. B., and Engelhardt, H. F.: Basal mechanics of Ice
Stream B, West Antarctica 1. Till mechanics, J. Geophys. Res., 105, 463–481, 2000. a
Van Liefferinge, B. and Pattyn, F.: Using ice-flow models to evaluate potential sites of million year-old ice in Antarctica, Clim. Past, 9, 2335–2345, https://doi.org/10.5194/cp-9-2335-2013, 2013. a
Vetra-Carvalho, S., van Leeuwen, P. J., Nerger, L., Barth, A., Altaf, M. U.,
Brasseur, P., Kirchgessner, P., and Beckers, J.-M.: State-of-the-art
stochastic data assimilation methods for high-dimensional non-Gaussian
problems, Tellus A: Dynam. Meteorol. Oceanogr., 70, 1445364,
https://doi.org/10.1080/16000870.2018.1445364,
2018. a, b, c
Vieli, A. and Payne, A. J.: Application of control methods for modelling the
flow of Pine Island Glacier, West Antarctica, Ann. Glaciol.,
36, 197–204,
2003. a
Vieli, A. and Payne, A. J.: Assessing the ability of numerical ice sheet models
to simulate grounding line migration, J. Geophys. Res.-Earth
Surf., 110, F01003, https://doi.org/10.1029/2004JF000202,
2005. a
Short summary
Marine-based sectors of the Antarctic Ice Sheet are increasingly contributing to sea-level rise. The basal conditions exert an important control on the ice dynamics. For obvious reasons of inaccessibility, they are an important source of uncertainties in numerical ice flow models used for sea-level projections. Here we assess the performance of an ensemble Kalman filter for the assimilation of transient observations of surface elevation and velocities in a marine ice sheet model.
Marine-based sectors of the Antarctic Ice Sheet are increasingly contributing to sea-level rise....
