Articles | Volume 9, issue 7
https://doi.org/10.5194/gmd-9-2301-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-2301-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model evaluation paper
| 
06 Jul 2016
Model evaluation paper |
| 06 Jul 2016
Performance and applicability of a 2.5-D ice-flow model in the vicinity of a dome
Olivier Passalacqua
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, LGGE, 38401 Grenoble, France
CNRS, LGGE, 38041 Grenoble, France
Olivier Gagliardini
Univ. Grenoble Alpes, LGGE, 38401 Grenoble, France
CNRS, LGGE, 38041 Grenoble, France
Frédéric Parrenin
Univ. Grenoble Alpes, LGGE, 38401 Grenoble, France
CNRS, LGGE, 38041 Grenoble, France
Scott Polar Research Institute, University of Cambridge, Cambridge, UK
Fabien Gillet-Chaulet
Univ. Grenoble Alpes, LGGE, 38401 Grenoble, France
CNRS, LGGE, 38041 Grenoble, France
Catherine Ritz
Univ. Grenoble Alpes, LGGE, 38401 Grenoble, France
CNRS, LGGE, 38041 Grenoble, France
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Jinhwa Shin, Christoph Nehrbass-Ahles, Roberto Grilli, Jai Chowdhry Beeman, Frédéric Parrenin, Grégory Teste, Amaelle Landais, Loïc Schmidely, Lucas Silva, Jochen Schmitt, Bernhard Bereiter, Thomas F. Stocker, Hubertus Fischer, and Jérôme Chappellaz
Clim. Past, 16, 2203–2219, https://doi.org/10.5194/cp-16-2203-2020, https://doi.org/10.5194/cp-16-2203-2020, 2020
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Vincent Peyaud, Coline Bouchayer, Olivier Gagliardini, Christian Vincent, Fabien Gillet-Chaulet, Delphine Six, and Olivier Laarman
The Cryosphere, 14, 3979–3994, https://doi.org/10.5194/tc-14-3979-2020, https://doi.org/10.5194/tc-14-3979-2020, 2020
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Alexander Robinson, Jorge Alvarez-Solas, Marisa Montoya, Heiko Goelzer, Ralf Greve, and Catherine Ritz
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Samuel J. Cook, Poul Christoffersen, Joe Todd, Donald Slater, and Nolwenn Chauché
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Suzanne L. Bevan, Adrian J. Luckman, Douglas I. Benn, Tom Cowton, and Joe Todd
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The Cryosphere, 13, 1681–1694, https://doi.org/10.5194/tc-13-1681-2019, https://doi.org/10.5194/tc-13-1681-2019, 2019
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Lionel Favier, Nicolas C. Jourdain, Adrian Jenkins, Nacho Merino, Gaël Durand, Olivier Gagliardini, Fabien Gillet-Chaulet, and Pierre Mathiot
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The melting at the base of floating ice shelves is the main driver of the Antarctic ice sheet current retreat. Here, we use an ideal set-up to assess a wide range of melting parameterisations depending on oceanic properties with regard to a new ocean–ice-sheet coupled model, published here for the first time. A parameterisation that depends quadratically on thermal forcing in both a local and a non-local way yields the best results and needs to be further assessed with more realistic set-ups.
Jai Chowdhry Beeman, Léa Gest, Frédéric Parrenin, Dominique Raynaud, Tyler J. Fudge, Christo Buizert, and Edward J. Brook
Clim. Past, 15, 913–926, https://doi.org/10.5194/cp-15-913-2019, https://doi.org/10.5194/cp-15-913-2019, 2019
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Atmospheric CO2 was likely an important amplifier of global-scale orbitally-driven warming during the last deglaciation. However, the mechanisms responsible for the rise in CO2, and the coherent rise in Antarctic isotopic temperature records, are under debate. Using a stochastic method, we detect variable lags between coherent changes in Antarctic temperature and CO2. This implies that the climate mechanisms linking the two records changed or experienced modulations during the deglaciation.
Sébastien Le clec'h, Sylvie Charbit, Aurélien Quiquet, Xavier Fettweis, Christophe Dumas, Masa Kageyama, Coraline Wyard, and Catherine Ritz
The Cryosphere, 13, 373–395, https://doi.org/10.5194/tc-13-373-2019, https://doi.org/10.5194/tc-13-373-2019, 2019
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Quantifying the future contribution of the Greenland ice sheet (GrIS) to sea-level rise in response to atmospheric changes is important but remains challenging. For the first time a full representation of the feedbacks between a GrIS model and a regional atmospheric model was implemented. The authors highlight the fundamental need for representing the GrIS topography change feedbacks with respect to the atmospheric component face to the strong impact on the projected sea-level rise.
Julien Brondex, Fabien Gillet-Chaulet, and Olivier Gagliardini
The Cryosphere, 13, 177–195, https://doi.org/10.5194/tc-13-177-2019, https://doi.org/10.5194/tc-13-177-2019, 2019
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Here, we apply a synthetic perturbation to the most active drainage basin of Antarctica and show that centennial mass loss projections obtained through ice flow models depend strongly on the implemented friction law, i.e. the mathematical relationship between basal drag and sliding velocities. In particular, the commonly used Weertman law considerably underestimates the sea-level contribution of this basin in comparison to two water pressure-dependent laws which rely on stronger physical bases.
Aurélien Quiquet, Christophe Dumas, Catherine Ritz, Vincent Peyaud, and Didier M. Roche
Geosci. Model Dev., 11, 5003–5025, https://doi.org/10.5194/gmd-11-5003-2018, https://doi.org/10.5194/gmd-11-5003-2018, 2018
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This paper presents the GRISLI (Grenoble ice sheet and land ice) model in its newest revision. We present the recent model improvements from its original version (Ritz et al., 2001), together with a discussion of the model performance in reproducing the present-day Antarctic ice sheet geometry and the grounding line advances and retreats during the last 400 000 years. We show that GRISLI is a computationally cheap model, able to reproduce the large-scale behaviour of ice sheets.
Marianne Haseloff, Christian Schoof, and Olivier Gagliardini
The Cryosphere, 12, 2545–2568, https://doi.org/10.5194/tc-12-2545-2018, https://doi.org/10.5194/tc-12-2545-2018, 2018
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The widths of the Siple Coast ice streams evolve on decadal to centennial timescales. We investigate how the rate of thermally driven ice stream widening depends on heat dissipation in the ice stream margin and at the bed, and on the inflow of cold ice from the ice ridge. As determining the migration rate requires resolving heat transfer processes on very small scales, we derive a parametrization of the migration rate in terms of parameters that are available from large-scale model outputs.
Olivier Passalacqua, Marie Cavitte, Olivier Gagliardini, Fabien Gillet-Chaulet, Frédéric Parrenin, Catherine Ritz, and Duncan Young
The Cryosphere, 12, 2167–2174, https://doi.org/10.5194/tc-12-2167-2018, https://doi.org/10.5194/tc-12-2167-2018, 2018
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Locating a suitable drill site is a key step in the Antarctic oldest-ice challenge. Here we have conducted a 3-D ice flow simulation in the region of Dome C using a refined bedrock description. Five selection criteria are computed that together provide an objective overview on the local ice flow conditions. We delineate kilometer-scale favorable areas that overlap with the ones recently proposed by another group. We propose a few drill sites that should be surveyed during the next field seasons.
Heiko Goelzer, Sophie Nowicki, Tamsin Edwards, Matthew Beckley, Ayako Abe-Ouchi, Andy Aschwanden, Reinhard Calov, Olivier Gagliardini, Fabien Gillet-Chaulet, Nicholas R. Golledge, Jonathan Gregory, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Joseph H. Kennedy, Eric Larour, William H. Lipscomb, Sébastien Le clec'h, Victoria Lee, Mathieu Morlighem, Frank Pattyn, Antony J. Payne, Christian Rodehacke, Martin Rückamp, Fuyuki Saito, Nicole Schlegel, Helene Seroussi, Andrew Shepherd, Sainan Sun, Roderik van de Wal, and Florian A. Ziemen
The Cryosphere, 12, 1433–1460, https://doi.org/10.5194/tc-12-1433-2018, https://doi.org/10.5194/tc-12-1433-2018, 2018
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We have compared a wide spectrum of different initialisation techniques used in the ice sheet modelling community to define the modelled present-day Greenland ice sheet state as a starting point for physically based future-sea-level-change projections. Compared to earlier community-wide comparisons, we find better agreement across different models, which implies overall improvement of our understanding of what is needed to produce such initial states.
Marie G. P. Cavitte, Frédéric Parrenin, Catherine Ritz, Duncan A. Young, Brice Van Liefferinge, Donald D. Blankenship, Massimo Frezzotti, and Jason L. Roberts
The Cryosphere, 12, 1401–1414, https://doi.org/10.5194/tc-12-1401-2018, https://doi.org/10.5194/tc-12-1401-2018, 2018
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We reconstruct the pattern of surface accumulation in the region around Dome C, East Antarctica, over the last 73 kyr. We use internal isochrones interpreted from ice-penetrating radar surveys and a 1-D ice flow model to invert for time-averaged and paleo-accumulation rates. We observe that surface accumulation patterns are stable through the last 73 kyr, consistent with current observed regional precipitation gradients and consistent interactions between prevailing winds and surface slope.
Frédéric Parrenin, Marie G. P. Cavitte, Donald D. Blankenship, Jérôme Chappellaz, Hubertus Fischer, Olivier Gagliardini, Valérie Masson-Delmotte, Olivier Passalacqua, Catherine Ritz, Jason Roberts, Martin J. Siegert, and Duncan A. Young
The Cryosphere, 11, 2427–2437, https://doi.org/10.5194/tc-11-2427-2017, https://doi.org/10.5194/tc-11-2427-2017, 2017
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The oldest dated deep ice core drilled in Antarctica has been retrieved at EPICA Dome C (EDC), reaching ~ 800 000 years. Obtaining an older palaeoclimatic record from Antarctica is one of the greatest challenges of the ice core community. Here, we estimate the age of basal ice in the Dome C area. We find that old ice (> 1.5 Myr) likely exists in two regions a few tens of kilometres away from EDC:
Little Dome C Patchand
North Patch.
Olivier Passalacqua, Catherine Ritz, Frédéric Parrenin, Stefano Urbini, and Massimo Frezzotti
The Cryosphere, 11, 2231–2246, https://doi.org/10.5194/tc-11-2231-2017, https://doi.org/10.5194/tc-11-2231-2017, 2017
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As the Dome C region is a key area for oldest-ice research, we need to better constrain the geothermal flux (GF) so that past basal melt rates are well constrained. Our inverse heat model significantly reduces the confidence intervals of the GF regional field around Dome C, which ranges from 48 to 60 mW m−2. Radar echoes need to be interpreted knowing the time lag of the climate signal to reach the bed. Several old-ice targets are confirmed and a new one is suggested, in which the GF is very low.
Duncan A. Young, Jason L. Roberts, Catherine Ritz, Massimo Frezzotti, Enrica Quartini, Marie G. P. Cavitte, Carly R. Tozer, Daniel Steinhage, Stefano Urbini, Hugh F. J. Corr, Tas van Ommen, and Donald D. Blankenship
The Cryosphere, 11, 1897–1911, https://doi.org/10.5194/tc-11-1897-2017, https://doi.org/10.5194/tc-11-1897-2017, 2017
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To find records of the greenhouse gases found in key periods of climate transition, we need to find sites of unmelted old ice at the base of the Antarctic ice sheet for ice core retrieval. A joint US–Australian–EU team performed a high-resolution survey of such a site (1 km line spacing) near Concordia Station in East Antarctica, using airborne ice-penetrating radar. We found promising targets in rough subglacial terrain, surrounded by subglacial lakes restricted below a minimum hydraulic head.
Léa Gest, Frédéric Parrenin, Jai Chowdhry Beeman, Dominique Raynaud, Tyler J. Fudge, Christo Buizert, and Edward J. Brook
Clim. Past Discuss., https://doi.org/10.5194/cp-2017-71, https://doi.org/10.5194/cp-2017-71, 2017
Revised manuscript has not been submitted
Short summary
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In this manuscript, we place the atmospheric CO2 and Antarctic temperature records onto a common age scale during the last deglaciation. Moreover, we evaluate the phase relationship between those two records in order to discuss possible climatic and carbon cycle scenarios. Indeed, this phase relationship is central to determine the role of the former in past (and therefore future) climatic variations. This scientific problem was even discussed by some policy makers (e.g., in the USA senate).
Rupert Michael Gladstone, Roland Charles Warner, Benjamin Keith Galton-Fenzi, Olivier Gagliardini, Thomas Zwinger, and Ralf Greve
The Cryosphere, 11, 319–329, https://doi.org/10.5194/tc-11-319-2017, https://doi.org/10.5194/tc-11-319-2017, 2017
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Computer models are used to simulate the behaviour of glaciers and ice sheets. It has been found that such models are required to be run at very high resolution (which means high computational expense) in order to accurately represent the evolution of marine ice sheets (ice sheets resting on bedrock below sea level), in certain situations which depend on sub-glacial physical processes.
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
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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.
Cyrille Mosbeux, Fabien Gillet-Chaulet, and Olivier Gagliardini
Geosci. Model Dev., 9, 2549–2562, https://doi.org/10.5194/gmd-9-2549-2016, https://doi.org/10.5194/gmd-9-2549-2016, 2016
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Model projections of ice sheet contribution to 21st century sea level rise are greatly affected by initial conditions. Solutions have been developed to infer the friction of the ice on its bedrock using observed surface velocities. A drawback of these methods is that remaining uncertainties, especially in the bedrock elevation, lead to non-physical ice flux divergence anomalies. Here, we compare two different solutions able to infer both bedrock friction and elevation with good performance.
T. M. Jordan, J. L. Bamber, C. N. Williams, J. D. Paden, M. J. Siegert, P. Huybrechts, O. Gagliardini, and F. Gillet-Chaulet
The Cryosphere, 10, 1547–1570, https://doi.org/10.5194/tc-10-1547-2016, https://doi.org/10.5194/tc-10-1547-2016, 2016
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Ice penetrating radar enables determination of the basal properties of ice sheets. Existing algorithms assume stationarity in the attenuation rate, which is not justifiable at an ice sheet scale. We introduce the first ice-sheet-wide algorithm for radar attenuation that incorporates spatial variability, using the temperature field from a numerical model as an initial guess. The study is a step toward ice-sheet-wide data products for basal properties and evaluation of model temperature fields.
Lucie Bazin, Amaelle Landais, Emilie Capron, Valérie Masson-Delmotte, Catherine Ritz, Ghislain Picard, Jean Jouzel, Marie Dumont, Markus Leuenberger, and Frédéric Prié
Clim. Past, 12, 729–748, https://doi.org/10.5194/cp-12-729-2016, https://doi.org/10.5194/cp-12-729-2016, 2016
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We present new measurements of δO2⁄N2 and δ18Oatm performed on well-conserved ice from EDC covering MIS5 and between 380 and 800 ka. The combination of the observation of a 100 ka periodicity in the new δO2⁄N2 record with a MIS5 multi-site multi-proxy study has revealed a potential influence of local climatic parameters on δO2⁄N2. Moreover, we propose that the varying delay between d18Oatm and precession for the last 800 ka is affected by the occurrence of ice sheet discharge events.
Shin'ya Nakano, Kazue Suzuki, Kenji Kawamura, Frédéric Parrenin, and Tomoyuki Higuchi
Nonlin. Processes Geophys., 23, 31–44, https://doi.org/10.5194/npg-23-31-2016, https://doi.org/10.5194/npg-23-31-2016, 2016
Short summary
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This paper proposes a technique for dating an ice core. The proposed technique employs a hybrid method combining the sequential Monte Carlo method and the Markov chain Monte Carlo method, which is referred to as the particle Markov chain Monte Carlo method. The sequential Monte Carlo method, which is also known as the particle filter, is widely used for nonlinear time-series analysis. This paper demonstrates the usefulness of the approach in time-series analysis for dating an ice core.
O. Gagliardini, J. Brondex, F. Gillet-Chaulet, L. Tavard, V. Peyaud, and G. Durand
The Cryosphere, 10, 307–312, https://doi.org/10.5194/tc-10-307-2016, https://doi.org/10.5194/tc-10-307-2016, 2016
Short summary
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In this paper it is shown that the sensitivity to the mesh resolution is not
improved for a vanishing friction at the grounding line (GL). For a discontinuous friction at the GL, we further show that the results are moreover very sensitive to the way the friction is interpolated in the close vicinity of the GL. In the light of these new insights, new results for the MISMIP3d experiments obtained for higher resolutions than previously published are made available for future comparisons.
P. Beghin, S. Charbit, C. Dumas, M. Kageyama, and C. Ritz
Clim. Past, 11, 1467–1490, https://doi.org/10.5194/cp-11-1467-2015, https://doi.org/10.5194/cp-11-1467-2015, 2015
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The present study investigates the potential impact of the North American ice sheet on the surface mass balance of the Eurasian ice sheet through changes in the past glacial atmospheric circulation. Using an atmospheric circulation model and an ice-sheet model, we show that the albedo of the American ice sheet favors the growth of the Eurasian ice sheet, whereas the topography of the American ice sheet leads to more ablation over North Eurasia, and therefore to a smaller Eurasian ice sheet.
S. Fujita, F. Parrenin, M. Severi, H. Motoyama, and E. W. Wolff
Clim. Past, 11, 1395–1416, https://doi.org/10.5194/cp-11-1395-2015, https://doi.org/10.5194/cp-11-1395-2015, 2015
J. J. Fürst, G. Durand, F. Gillet-Chaulet, N. Merino, L. Tavard, J. Mouginot, N. Gourmelen, and O. Gagliardini
The Cryosphere, 9, 1427–1443, https://doi.org/10.5194/tc-9-1427-2015, https://doi.org/10.5194/tc-9-1427-2015, 2015
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We present a comprehensive high-resolution assimilation of Antarctic surface velocities with a flow model. The inferred velocities are in very good agreement with observations, even when compared to recent studies on individual shelves. This quality allows to identify a pattern in the velocity mismatch that points at pinning points not present in the input geometry. We identify seven potential pinning points around Antarctica, for now uncharted, providing prominent resistance to the ice flow.
B. Lemieux-Dudon, L. Bazin, A. Landais, H. Toyé Mahamadou Kele, M. Guillevic, P. Kindler, F. Parrenin, and P. Martinerie
Clim. Past, 11, 959–978, https://doi.org/10.5194/cp-11-959-2015, https://doi.org/10.5194/cp-11-959-2015, 2015
F. Parrenin, L. Bazin, E. Capron, A. Landais, B. Lemieux-Dudon, and V. Masson-Delmotte
Geosci. Model Dev., 8, 1473–1492, https://doi.org/10.5194/gmd-8-1473-2015, https://doi.org/10.5194/gmd-8-1473-2015, 2015
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This manuscript describes a probabilistic model which aims at optimizing the chronology of ice cores by combining different sources of information.
F. Parrenin, S. Fujita, A. Abe-Ouchi, K. Kawamura, V. Masson-Delmotte, H. Motoyama, F. Saito, M. Severi, B. Stenni, R. Uemura, and E. Wolff
Clim. Past Discuss., https://doi.org/10.5194/cpd-11-377-2015, https://doi.org/10.5194/cpd-11-377-2015, 2015
Revised manuscript has not been submitted
J. C. Ryan, A. L. Hubbard, J. E. Box, J. Todd, P. Christoffersen, J. R. Carr, T. O. Holt, and N. Snooke
The Cryosphere, 9, 1–11, https://doi.org/10.5194/tc-9-1-2015, https://doi.org/10.5194/tc-9-1-2015, 2015
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An unmanned aerial vehicle (UAV) equipped with a commercial digital camera enabled us to obtain high-resolution digital images of the calving front of Store glacier, Greenland. The three sorties flown enabled key glaciological parameters to be quantified in sufficient detail to reveal that the terminus of Store glacier is a complex system with large variations in crevasse patterns surface velocities, calving processes, surface elevations and front positions at a daily and seasonal timescale.
J. Todd and P. Christoffersen
The Cryosphere, 8, 2353–2365, https://doi.org/10.5194/tc-8-2353-2014, https://doi.org/10.5194/tc-8-2353-2014, 2014
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Many iceberg-calving glaciers in Greenland have recently been observed to accelerate and retreat, prompting fears about their future stability in the face of climate change. We present results from a flow modelling study of Store Glacier, West Greenland, which suggest that glacier geometry may play an important role in determining calving glacier stability. Store Glacier flows into a narrow, shallow fjord and our model suggests this may make it insensitive to future ocean warming.
J. Krug, J. Weiss, O. Gagliardini, and G. Durand
The Cryosphere, 8, 2101–2117, https://doi.org/10.5194/tc-8-2101-2014, https://doi.org/10.5194/tc-8-2101-2014, 2014
M. Schäfer, F. Gillet-Chaulet, R. Gladstone, R. Pettersson, V. A. Pohjola, T. Strozzi, and T. Zwinger
The Cryosphere, 8, 1951–1973, https://doi.org/10.5194/tc-8-1951-2014, https://doi.org/10.5194/tc-8-1951-2014, 2014
E. Le Meur, M. Sacchettini, S. Garambois, E. Berthier, A. S. Drouet, G. Durand, D. Young, J. S. Greenbaum, J. W. Holt, D. D. Blankenship, E. Rignot, J. Mouginot, Y. Gim, D. Kirchner, B. de Fleurian, O. Gagliardini, and F. Gillet-Chaulet
The Cryosphere, 8, 1331–1346, https://doi.org/10.5194/tc-8-1331-2014, https://doi.org/10.5194/tc-8-1331-2014, 2014
D. M. Roche, C. Dumas, M. Bügelmayer, S. Charbit, and C. Ritz
Geosci. Model Dev., 7, 1377–1394, https://doi.org/10.5194/gmd-7-1377-2014, https://doi.org/10.5194/gmd-7-1377-2014, 2014
B. Bonan, M. Nodet, C. Ritz, and V. Peyaud
Nonlin. Processes Geophys., 21, 569–582, https://doi.org/10.5194/npg-21-569-2014, https://doi.org/10.5194/npg-21-569-2014, 2014
P. Beghin, S. Charbit, C. Dumas, M. Kageyama, D. M. Roche, and C. Ritz
Clim. Past, 10, 345–358, https://doi.org/10.5194/cp-10-345-2014, https://doi.org/10.5194/cp-10-345-2014, 2014
J. Krug, J. Weiss, O. Gagliardini, and G. Durand
The Cryosphere Discuss., https://doi.org/10.5194/tcd-8-1111-2014, https://doi.org/10.5194/tcd-8-1111-2014, 2014
Preprint withdrawn
F. Colleoni, S. Masina, A. Cherchi, A. Navarra, C. Ritz, V. Peyaud, and B. Otto-Bliesner
Clim. Past, 10, 269–291, https://doi.org/10.5194/cp-10-269-2014, https://doi.org/10.5194/cp-10-269-2014, 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, 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
A. Legchenko, C. Vincent, J. M. Baltassat, J. F. Girard, E. Thibert, O. Gagliardini, M. Descloitres, A. Gilbert, S. Garambois, A. Chevalier, and H. Guyard
The Cryosphere, 8, 155–166, https://doi.org/10.5194/tc-8-155-2014, https://doi.org/10.5194/tc-8-155-2014, 2014
B. de Fleurian, O. Gagliardini, T. Zwinger, G. Durand, E. Le Meur, D. Mair, and P. Råback
The Cryosphere, 8, 137–153, https://doi.org/10.5194/tc-8-137-2014, https://doi.org/10.5194/tc-8-137-2014, 2014
H. Fischer, J. Severinghaus, E. Brook, E. Wolff, M. Albert, O. Alemany, R. Arthern, C. Bentley, D. Blankenship, J. Chappellaz, T. Creyts, D. Dahl-Jensen, M. Dinn, M. Frezzotti, S. Fujita, H. Gallee, R. Hindmarsh, D. Hudspeth, G. Jugie, K. Kawamura, V. Lipenkov, H. Miller, R. Mulvaney, F. Parrenin, F. Pattyn, C. Ritz, J. Schwander, D. Steinhage, T. van Ommen, and F. Wilhelms
Clim. Past, 9, 2489–2505, https://doi.org/10.5194/cp-9-2489-2013, https://doi.org/10.5194/cp-9-2489-2013, 2013
O. Gagliardini, T. Zwinger, F. Gillet-Chaulet, G. Durand, L. Favier, B. de Fleurian, R. Greve, M. Malinen, C. Martín, P. Råback, J. Ruokolainen, M. Sacchettini, M. Schäfer, H. Seddik, and J. Thies
Geosci. Model Dev., 6, 1299–1318, https://doi.org/10.5194/gmd-6-1299-2013, https://doi.org/10.5194/gmd-6-1299-2013, 2013
L. Bazin, A. Landais, B. Lemieux-Dudon, H. Toyé Mahamadou Kele, D. Veres, F. Parrenin, P. Martinerie, C. Ritz, E. Capron, V. Lipenkov, M.-F. Loutre, D. Raynaud, B. Vinther, A. Svensson, S. O. Rasmussen, M. Severi, T. Blunier, M. Leuenberger, H. Fischer, V. Masson-Delmotte, J. Chappellaz, and E. Wolff
Clim. Past, 9, 1715–1731, https://doi.org/10.5194/cp-9-1715-2013, https://doi.org/10.5194/cp-9-1715-2013, 2013
D. Veres, L. Bazin, A. Landais, H. Toyé Mahamadou Kele, B. Lemieux-Dudon, F. Parrenin, P. Martinerie, E. Blayo, T. Blunier, E. Capron, J. Chappellaz, S. O. Rasmussen, M. Severi, A. Svensson, B. Vinther, and E. W. Wolff
Clim. Past, 9, 1733–1748, https://doi.org/10.5194/cp-9-1733-2013, https://doi.org/10.5194/cp-9-1733-2013, 2013
E. Capron, A. Landais, D. Buiron, A. Cauquoin, J. Chappellaz, M. Debret, J. Jouzel, M. Leuenberger, P. Martinerie, V. Masson-Delmotte, R. Mulvaney, F. Parrenin, and F. Prié
Clim. Past, 9, 983–999, https://doi.org/10.5194/cp-9-983-2013, https://doi.org/10.5194/cp-9-983-2013, 2013
S. Charbit, C. Dumas, M. Kageyama, D. M. Roche, and C. Ritz
The Cryosphere, 7, 681–698, https://doi.org/10.5194/tc-7-681-2013, https://doi.org/10.5194/tc-7-681-2013, 2013
A. Svensson, M. Bigler, T. Blunier, H. B. Clausen, D. Dahl-Jensen, H. Fischer, S. Fujita, K. Goto-Azuma, S. J. Johnsen, K. Kawamura, S. Kipfstuhl, M. Kohno, F. Parrenin, T. Popp, S. O. Rasmussen, J. Schwander, I. Seierstad, M. Severi, J. P. Steffensen, R. Udisti, R. Uemura, P. Vallelonga, B. M. Vinther, A. Wegner, F. Wilhelms, and M. Winstrup
Clim. Past, 9, 749–766, https://doi.org/10.5194/cp-9-749-2013, https://doi.org/10.5194/cp-9-749-2013, 2013
A. S. Drouet, D. Docquier, G. Durand, R. Hindmarsh, F. Pattyn, O. Gagliardini, and T. Zwinger
The Cryosphere, 7, 395–406, https://doi.org/10.5194/tc-7-395-2013, https://doi.org/10.5194/tc-7-395-2013, 2013
A. Quiquet, C. Ritz, H. J. Punge, and D. Salas y Mélia
Clim. Past, 9, 353–366, https://doi.org/10.5194/cp-9-353-2013, https://doi.org/10.5194/cp-9-353-2013, 2013
F. Gillet-Chaulet, O. Gagliardini, H. Seddik, M. Nodet, G. Durand, C. Ritz, T. Zwinger, R. Greve, and D. G. Vaughan
The Cryosphere, 6, 1561–1576, https://doi.org/10.5194/tc-6-1561-2012, https://doi.org/10.5194/tc-6-1561-2012, 2012
G. H. Gudmundsson, J. Krug, G. Durand, L. Favier, and O. Gagliardini
The Cryosphere, 6, 1497–1505, https://doi.org/10.5194/tc-6-1497-2012, https://doi.org/10.5194/tc-6-1497-2012, 2012
F. Parrenin and D. Paillard
Clim. Past, 8, 2031–2037, https://doi.org/10.5194/cp-8-2031-2012, https://doi.org/10.5194/cp-8-2031-2012, 2012
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SNICAR-ADv3: a community tool for modeling spectral snow albedo
STEMMUS-UEB v1.0.0: integrated modeling of snowpack and soil water and energy transfer with three complexity levels of soil physical processes
A versatile method for computing optimized snow albedo from spectrally fixed radiative variables: VALHALLA v1.0
Ice Algae Model Intercomparison Project phase 2 (IAMIP2)
A Gaussian process emulator for simulating ice sheet–climate interactions on a multi-million-year timescale: CLISEMv1.0
SITool (v1.0) – a new evaluation tool for large-scale sea ice simulations: application to CMIP6 OMIP
fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models
Development of adjoint-based ocean state estimation for the Amundsen and Bellingshausen seas and ice shelf cavities using MITgcm–ECCO (66j)
Sensitivity of Northern Hemisphere climate to ice–ocean interface heat flux parameterizations
icepack: a new glacier flow modeling package in Python, version 1.0
Benefits of sea ice initialization for the interannual-to-decadal climate prediction skill in the Arctic in EC-Earth3
Coupling framework (1.0) for the PISM (1.1.4) ice sheet model and the MOM5 (5.1.0) ocean model via the PICO ice shelf cavity model in an Antarctic domain
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth Clare Hunke, and Stefan Rethmeier
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-40, https://doi.org/10.5194/gmd-2024-40, 2024
Revised manuscript accepted for GMD
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Earth system models (ESM) today strive towards better quality based on improved resolution and improved physics. One of the limiting factors is the super computers at hand and how to utilize these. This study focus on a refactorization of one part of a sea ice model (CICE), namely the dynamics. The study shows that the performance can be significantly reduced, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
Geosci. Model Dev., 17, 1903–1929, https://doi.org/10.5194/gmd-17-1903-2024, https://doi.org/10.5194/gmd-17-1903-2024, 2024
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In this paper, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature, and melt while preserving a reasonable computation time.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
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Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Matthias Rauter and Julia Kowalski
EGUsphere, https://doi.org/10.5194/egusphere-2024-210, https://doi.org/10.5194/egusphere-2024-210, 2024
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Snow avalanches can form large powder clouds that substantially exceed the velocity and reach of the dense core. Only a few and complex models exist to simulate this phenomenon, and the respective hazard is hard to predict. This work provides a novel flow model that focuses on simple relations while still encapsulating the significant behaviour. The model is applied to reconstruct two catastrophic powder snow avalanche events in Austria.
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024, https://doi.org/10.5194/gmd-17-1041-2024, 2024
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The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
Anjali Sandip, Ludovic Räss, and Mathieu Morlighem
Geosci. Model Dev., 17, 899–909, https://doi.org/10.5194/gmd-17-899-2024, https://doi.org/10.5194/gmd-17-899-2024, 2024
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We solve momentum balance for unstructured meshes to predict ice flow for real glaciers using a pseudo-transient method on graphics processing units (GPUs) and compare it to a standard central processing unit (CPU) implementation. We justify the GPU implementation by applying the price-to-performance metric for up to million-grid-point spatial resolutions. This study represents a first step toward leveraging GPU processing power, enabling more accurate polar ice discharge predictions.
Iain Wheel, Douglas I. Benn, Anna J. Crawford, Joe A. Todd, and Thomas Zwinger
EGUsphere, https://doi.org/10.5194/egusphere-2023-2778, https://doi.org/10.5194/egusphere-2023-2778, 2024
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Calving, the detachment of large icebergs from glaciers, is one of the largest uncertainties in future sea level rise projections. This process is poorly understood and there is an absence of detailed models capable of simulating calving. A new 3D calving model has been developed to better understand calving at glaciers where detailed modelling was previously limited. Importantly, the new model is very flexible. By allowing for unrestricted calving geometries, it can be applied at any location.
Julien Brondex, Kévin Fourteau, Marie Dumont, Pascal Hagenmuller, Neige Calonne, François Tuzet, and Henning Löwe
Geosci. Model Dev., 16, 7075–7106, https://doi.org/10.5194/gmd-16-7075-2023, https://doi.org/10.5194/gmd-16-7075-2023, 2023
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Vapor diffusion is one of the main processes governing snowpack evolution, and it must be accounted for in models. Recent attempts to represent vapor diffusion in numerical models have faced several difficulties regarding computational cost and mass and energy conservation. Here, we develop our own finite-element software to explore numerical approaches and enable us to overcome these difficulties. We illustrate the capability of these approaches on established numerical benchmarks.
Matthias Tonnel, Anna Wirbel, Felix Oesterle, and Jan-Thomas Fischer
Geosci. Model Dev., 16, 7013–7035, https://doi.org/10.5194/gmd-16-7013-2023, https://doi.org/10.5194/gmd-16-7013-2023, 2023
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Avaframe - the open avalanche framework - provides open-source tools to simulate and investigate snow avalanches. It is utilized for multiple purposes, the two main applications being hazard mapping and scientific research of snow processes. We present the theory, conversion to a computer model, and testing for one of the core modules used for simulations of a particular type of avalanche, the so-called dense-flow avalanches. Tests check and confirm the applicability of the utilized method.
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, https://doi.org/10.5194/gmd-16-6671-2023, 2023
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We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823, https://doi.org/10.5194/gmd-16-5803-2023, https://doi.org/10.5194/gmd-16-5803-2023, 2023
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We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Kevin Hank, Lev Tarasov, and Elisa Mantelli
Geosci. Model Dev., 16, 5627–5652, https://doi.org/10.5194/gmd-16-5627-2023, https://doi.org/10.5194/gmd-16-5627-2023, 2023
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Physically meaningful modeling of geophysical system instabilities is numerically challenging, given the potential effects of purely numerical artifacts. Here we explore the sensitivity of ice stream surge activation to numerical and physical model aspects. We find that surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions and are significantly affected by the incorporation of bed thermal and sub-glacial hydrology models.
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, and Angelika Humbert
Geosci. Model Dev., 16, 5305–5322, https://doi.org/10.5194/gmd-16-5305-2023, https://doi.org/10.5194/gmd-16-5305-2023, 2023
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Water underneath ice sheets affects the motion of glaciers. This study presents a newly developed code, CUAS-MPI, that simulates subglacial hydrology. It is designed for supercomputers and is hence a parallelized code. We measure the performance of this code for simulations of the entire Greenland Ice Sheet and find that the code works efficiently. Moreover, we validated the code to ensure the correctness of the solution. CUAS-MPI opens new possibilities for simulations of ice sheet hydrology.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
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Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
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Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Eric Keenan, Nander Wever, Jan T. M. Lenaerts, and Brooke Medley
Geosci. Model Dev., 16, 3203–3219, https://doi.org/10.5194/gmd-16-3203-2023, https://doi.org/10.5194/gmd-16-3203-2023, 2023
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Ice sheets gain mass via snowfall. However, snowfall is redistributed by the wind, resulting in accumulation differences of up to a factor of 5 over distances as short as 5 km. These differences complicate estimates of ice sheet contribution to sea level rise. For this reason, we have developed a new model for estimating wind-driven snow redistribution on ice sheets. We show that, over Pine Island Glacier in West Antarctica, the model improves estimates of snow accumulation variability.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-298, https://doi.org/10.5194/gmd-2022-298, 2023
Revised manuscript accepted for GMD
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We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better quality maps. The correction can then be extended backwards and forwards in time for periods when better quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the last 60 years at a resolution of one day and one kilometre. This is the first time that such a dataset has been produced.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
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The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Alex S. Gardner, Nicole-Jeanne Schlegel, and Eric Larour
Geosci. Model Dev., 16, 2277–2302, https://doi.org/10.5194/gmd-16-2277-2023, https://doi.org/10.5194/gmd-16-2277-2023, 2023
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This is the first description of the open-source Glacier Energy and Mass Balance (GEMB) model. GEMB models the ice sheet and glacier surface–atmospheric energy and mass exchange, as well as the firn state. The model is evaluated against the current state of the art and in situ observations and is shown to perform well.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
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State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Varun Sharma, Franziska Gerber, and Michael Lehning
Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, https://doi.org/10.5194/gmd-16-719-2023, 2023
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Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere.
Anne M. Felden, Daniel F. Martin, and Esmond G. Ng
Geosci. Model Dev., 16, 407–425, https://doi.org/10.5194/gmd-16-407-2023, https://doi.org/10.5194/gmd-16-407-2023, 2023
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We present and validate a novel subglacial hydrology model, SUHMO, based on an adaptive mesh refinement framework. We propose the addition of a pseudo-diffusion to recover the wall melting in channels. Computational performance analysis demonstrates the efficiency of adaptive mesh refinement on large-scale hydrologic problems. The adaptive mesh refinement approach will eventually enable better ice bed boundary conditions for ice sheet simulations at a reasonable computational cost.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
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Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
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We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
Max Brils, Peter Kuipers Munneke, Willem Jan van de Berg, and Michiel van den Broeke
Geosci. Model Dev., 15, 7121–7138, https://doi.org/10.5194/gmd-15-7121-2022, https://doi.org/10.5194/gmd-15-7121-2022, 2022
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Firn covers the Greenland ice sheet (GrIS) and can temporarily prevent mass loss. Here, we present the latest version of our firn model, IMAU-FDM, with an application to the GrIS. We improved the density of fallen snow, the firn densification rate and the firn's thermal conductivity. This leads to a higher air content and 10 m temperatures. Furthermore we investigate three case studies and find that the updated model shows greater variability and an increased sensitivity in surface elevation.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
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This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Constantijn J. Berends, Heiko Goelzer, Thomas J. Reerink, Lennert B. Stap, and Roderik S. W. van de Wal
Geosci. Model Dev., 15, 5667–5688, https://doi.org/10.5194/gmd-15-5667-2022, https://doi.org/10.5194/gmd-15-5667-2022, 2022
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The rate at which marine ice sheets such as the West Antarctic ice sheet will retreat in a warming climate and ocean is still uncertain. Numerical ice-sheet models, which solve the physical equations that describe the way glaciers and ice sheets deform and flow, have been substantially improved in recent years. Here we present the results of several years of work on IMAU-ICE, an ice-sheet model of intermediate complexity, which can be used to study ice sheets of both the past and the future.
Abby C. Lute, John Abatzoglou, and Timothy Link
Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, https://doi.org/10.5194/gmd-15-5045-2022, 2022
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We developed a snow model that can be used to quantify snowpack over large areas with a high degree of spatial detail. We ran the model over the western United States, creating a snow and climate dataset for three time periods. Compared to observations of snowpack, the model captured the key aspects of snow across time and space. The model and dataset will be useful in understanding historical and future changes in snowpack, with relevance to water resources, agriculture, and ecosystems.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Edoardo Cremonese, Umberto Morra di Cella, Sara Ratto, and Hervé Stevenin
Geosci. Model Dev., 15, 4853–4879, https://doi.org/10.5194/gmd-15-4853-2022, https://doi.org/10.5194/gmd-15-4853-2022, 2022
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Knowing in real time how much snow and glacier ice has accumulated across the landscape has significant implications for water-resource management and flood control. This paper presents a computer model – S3M – allowing scientists and decision makers to predict snow and ice accumulation during winter and the subsequent melt during spring and summer. S3M has been employed for real-world flood forecasting since the early 2000s but is here being made open source for the first time.
Adrian K. Turner, William H. Lipscomb, Elizabeth C. Hunke, Douglas W. Jacobsen, Nicole Jeffery, Darren Engwirda, Todd D. Ringler, and Jonathan D. Wolfe
Geosci. Model Dev., 15, 3721–3751, https://doi.org/10.5194/gmd-15-3721-2022, https://doi.org/10.5194/gmd-15-3721-2022, 2022
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We present the dynamical core of the MPAS-Seaice model, which uses a mesh consisting of a Voronoi tessellation with polygonal cells. Such a mesh allows variable mesh resolution in different parts of the domain and the focusing of computational resources in regions of interest. We describe the velocity solver and tracer transport schemes used and examine errors generated by the model in both idealized and realistic test cases and examine the computational efficiency of the model.
Noah D. Smith, Eleanor J. Burke, Kjetil Schanke Aas, Inge H. J. Althuizen, Julia Boike, Casper Tai Christiansen, Bernd Etzelmüller, Thomas Friborg, Hanna Lee, Heather Rumbold, Rachael H. Turton, Sebastian Westermann, and Sarah E. Chadburn
Geosci. Model Dev., 15, 3603–3639, https://doi.org/10.5194/gmd-15-3603-2022, https://doi.org/10.5194/gmd-15-3603-2022, 2022
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The Arctic has large areas of small mounds that are caused by ice lifting up the soil. Snow blown by wind gathers in hollows next to these mounds, insulating them in winter. The hollows tend to be wetter, and thus the soil absorbs more heat in summer. The warm wet soil in the hollows decomposes, releasing methane. We have made a model of this, and we have tested how it behaves and whether it looks like sites in Scandinavia and Siberia. Sometimes we get more methane than a model without mounds.
Adrian K. Turner, Kara J. Peterson, and Dan Bolintineanu
Geosci. Model Dev., 15, 1953–1970, https://doi.org/10.5194/gmd-15-1953-2022, https://doi.org/10.5194/gmd-15-1953-2022, 2022
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We developed a technique to remap sea ice tracer quantities between circular discrete element distributions. This is needed for a global discrete element method sea ice model being developed jointly by Los Alamos National Laboratory and Sandia National Laboratories that has the potential to better utilize newer supercomputers with graphics processing units and better represent sea ice dynamics. This new remapping technique ameliorates the effect of element distortion created by sea ice ridging.
Zhen Yin, Chen Zuo, Emma J. MacKie, and Jef Caers
Geosci. Model Dev., 15, 1477–1497, https://doi.org/10.5194/gmd-15-1477-2022, https://doi.org/10.5194/gmd-15-1477-2022, 2022
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We provide a multiple-point geostatistics approach to probabilistically learn from training images to fill large-scale irregular geophysical data gaps. With a repository of global topographic training images, our approach models high-resolution basal topography and quantifies the geospatial uncertainty. It generated high-resolution topographic realizations to investigate the impact of basal topographic uncertainty on critical subglacial hydrological flow patterns associated with ice velocity.
Yu Yan, Wei Gu, Andrea M. U. Gierisch, Yingjun Xu, and Petteri Uotila
Geosci. Model Dev., 15, 1269–1288, https://doi.org/10.5194/gmd-15-1269-2022, https://doi.org/10.5194/gmd-15-1269-2022, 2022
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In this study, we developed NEMO-Bohai, an ocean–ice model for the Bohai Sea, China. This study presented the scientific design and technical choices of the parameterizations for the NEMO-Bohai model. The model was calibrated and evaluated with in situ and satellite observations of ocean and sea ice. NEMO-Bohai is intended to be a valuable tool for long-term ocean and ice simulations and climate change studies.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
Geosci. Model Dev., 15, 1155–1176, https://doi.org/10.5194/gmd-15-1155-2022, https://doi.org/10.5194/gmd-15-1155-2022, 2022
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We present an improved coupled modeling system for Arctic sea ice prediction. We perform Arctic sea ice prediction experiments with improved/updated physical parameterizations, which show better skill in predicting sea ice state as well as atmospheric and oceanic state in the Arctic compared with its predecessor. The improved model also shows extended predictive skill of Arctic sea ice after the summer season. This provides an added value of this prediction system for decision-making.
Christopher Horvat and Lettie A. Roach
Geosci. Model Dev., 15, 803–814, https://doi.org/10.5194/gmd-15-803-2022, https://doi.org/10.5194/gmd-15-803-2022, 2022
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Sea ice is a composite of individual pieces, called floes, ranging in horizontal size from meters to kilometers. Variations in sea ice geometry are often forced by ocean waves, a process that is an important target of global climate models as it affects the rate of sea ice melting. Yet directly simulating these interactions is computationally expensive. We present a neural-network-based model of wave–ice fracture that allows models to incorporate their effect without added computational cost.
Ole Richter, David E. Gwyther, Benjamin K. Galton-Fenzi, and Kaitlin A. Naughten
Geosci. Model Dev., 15, 617–647, https://doi.org/10.5194/gmd-15-617-2022, https://doi.org/10.5194/gmd-15-617-2022, 2022
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Here we present an improved model of the Antarctic continental shelf ocean and demonstrate that it is capable of reproducing present-day conditions. The improvements are fundamental and regard the inclusion of tides and ocean eddies. We conclude that the model is well suited to gain new insights into processes that are important for Antarctic ice sheet retreat and global ocean changes. Hence, the model will ultimately help to improve projections of sea level rise and climate change.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
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We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Lianyu Yu, Yijian Zeng, and Zhongbo Su
Geosci. Model Dev., 14, 7345–7376, https://doi.org/10.5194/gmd-14-7345-2021, https://doi.org/10.5194/gmd-14-7345-2021, 2021
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We developed an integrated soil–snow–atmosphere model (STEMMUS-UEB) dedicated to the physical description of snow and soil processes with various complexities. With STEMMUS-UEB, we demonstrated that the snowpack affects not only the soil surface moisture conditions (in the liquid and ice phase) and energy-related states (albedo, LE) but also the subsurface soil water and vapor transfer, which contributes to a better understanding of the hydrothermal implications of the snowpack in cold regions.
Florent Veillon, Marie Dumont, Charles Amory, and Mathieu Fructus
Geosci. Model Dev., 14, 7329–7343, https://doi.org/10.5194/gmd-14-7329-2021, https://doi.org/10.5194/gmd-14-7329-2021, 2021
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In climate models, the snow albedo scheme generally calculates only a narrowband or broadband albedo. Therefore, we have developed the VALHALLA method to optimize snow spectral albedo calculations through the determination of spectrally fixed radiative variables. The development of VALHALLA v1.0 with the use of the snow albedo model TARTES and the spectral irradiance model SBDART indicates a considerable reduction in calculation time while maintaining an adequate accuracy of albedo values.
Hakase Hayashida, Meibing Jin, Nadja S. Steiner, Neil C. Swart, Eiji Watanabe, Russell Fiedler, Andrew McC. Hogg, Andrew E. Kiss, Richard J. Matear, and Peter G. Strutton
Geosci. Model Dev., 14, 6847–6861, https://doi.org/10.5194/gmd-14-6847-2021, https://doi.org/10.5194/gmd-14-6847-2021, 2021
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Ice algae are tiny plants like phytoplankton but they grow within sea ice. In polar regions, both phytoplankton and ice algae are the foundation of marine ecosystems and play an important role in taking up carbon dioxide in the atmosphere. However, state-of-the-art climate models typically do not include ice algae, and therefore their role in the climate system remains unclear. This project aims to address this knowledge gap by coordinating a set of experiments using sea-ice–ocean models.
Jonas Van Breedam, Philippe Huybrechts, and Michel Crucifix
Geosci. Model Dev., 14, 6373–6401, https://doi.org/10.5194/gmd-14-6373-2021, https://doi.org/10.5194/gmd-14-6373-2021, 2021
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Ice sheets are an important component of the climate system and interact with the atmosphere through albedo variations and changes in the surface height. On very long timescales, it is impossible to directly couple ice sheet models with climate models and other techniques have to be used. Here we present a novel coupling method between ice sheets and the atmosphere by making use of an emulator to simulate ice sheet–climate interactions for several million years.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
Geosci. Model Dev., 14, 6331–6354, https://doi.org/10.5194/gmd-14-6331-2021, https://doi.org/10.5194/gmd-14-6331-2021, 2021
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This study introduces a new Sea Ice Evaluation Tool (SITool) to evaluate the model skills on the bipolar sea ice simulations by providing performance metrics and diagnostics. SITool is applied to evaluate the CMIP6 OMIP simulations. By changing the atmospheric forcing from CORE-II to JRA55-do data, many aspects of sea ice simulations are improved. SITool will be useful for helping teams managing various versions of a sea ice model or tracking the time evolution of model performance.
Conrad P. Koziol, Joe A. Todd, Daniel N. Goldberg, and James R. Maddison
Geosci. Model Dev., 14, 5843–5861, https://doi.org/10.5194/gmd-14-5843-2021, https://doi.org/10.5194/gmd-14-5843-2021, 2021
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Sea level change due to the loss of ice sheets presents great risk for coastal communities. Models are used to forecast ice loss, but their evolution depends strongly on properties which are hidden from observation and must be inferred from satellite observations. Common methods for doing so do not allow for quantification of the uncertainty inherent or how it will affect forecasts. We provide a framework for quantifying how this
initialization uncertaintyaffects ice loss forecasts.
Yoshihiro Nakayama, Dimitris Menemenlis, Ou Wang, Hong Zhang, Ian Fenty, and An T. Nguyen
Geosci. Model Dev., 14, 4909–4924, https://doi.org/10.5194/gmd-14-4909-2021, https://doi.org/10.5194/gmd-14-4909-2021, 2021
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High ice shelf melting in the Amundsen Sea has attracted many observational campaigns in the past decade. One method to combine observations with numerical models is the adjoint method. After 20 iterations, the cost function, defined as a sum of the weighted model–data difference, is reduced by 65 % by adjusting initial conditions, atmospheric forcing, and vertical diffusivity. This study demonstrates adjoint-method optimization with explicit representation of ice shelf cavity circulation.
Xiaoxu Shi, Dirk Notz, Jiping Liu, Hu Yang, and Gerrit Lohmann
Geosci. Model Dev., 14, 4891–4908, https://doi.org/10.5194/gmd-14-4891-2021, https://doi.org/10.5194/gmd-14-4891-2021, 2021
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The ice–ocean heat flux is one of the key elements controlling sea ice changes. It motivates our study, which aims to examine the responses of modeled climate to three ice–ocean heat flux parameterizations, including two old approaches that assume one-way heat transport and a new one describing a double-diffusive ice–ocean heat exchange. The results show pronounced differences in the modeled sea ice, ocean, and atmosphere states for the latter as compared to the former two parameterizations.
Daniel R. Shapero, Jessica A. Badgeley, Andrew O. Hoffman, and Ian R. Joughin
Geosci. Model Dev., 14, 4593–4616, https://doi.org/10.5194/gmd-14-4593-2021, https://doi.org/10.5194/gmd-14-4593-2021, 2021
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This paper describes a new software package called "icepack" for modeling the flow of ice sheets and glaciers. Glaciologists use tools like icepack to better understand how ice sheets flow, what role they have played in shaping Earth's climate, and how much sea level rise we can expect in the coming decades to centuries. The icepack package includes several innovations to help researchers describe and solve interesting glaciological problems and to experiment with the underlying model physics.
Tian Tian, Shuting Yang, Mehdi Pasha Karami, François Massonnet, Tim Kruschke, and Torben Koenigk
Geosci. Model Dev., 14, 4283–4305, https://doi.org/10.5194/gmd-14-4283-2021, https://doi.org/10.5194/gmd-14-4283-2021, 2021
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Three decadal prediction experiments with EC-Earth3 are performed to investigate the impact of ocean, sea ice concentration and thickness initialization, respectively. We find that the persistence of perennial thick ice in the central Arctic can affect the sea ice predictability in its adjacent waters via advection process or wind, despite those regions being seasonally ice free during two recent decades. This has implications for the coming decades as the thinning of Arctic sea ice continues.
Moritz Kreuzer, Ronja Reese, Willem Nicholas Huiskamp, Stefan Petri, Torsten Albrecht, Georg Feulner, and Ricarda Winkelmann
Geosci. Model Dev., 14, 3697–3714, https://doi.org/10.5194/gmd-14-3697-2021, https://doi.org/10.5194/gmd-14-3697-2021, 2021
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We present the technical implementation of a coarse-resolution coupling between an ice sheet model and an ocean model that allows one to simulate ice–ocean interactions at timescales from centuries to millennia. As ice shelf cavities cannot be resolved in the ocean model at coarse resolution, we bridge the gap using an sub-shelf cavity module. It is shown that the framework is computationally efficient, conserves mass and energy, and can produce a stable coupled state under present-day forcing.
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Short summary
In ice-flow modelling, computing in 3-D requires a lot of resources, but 2-D models lack physical likelihood when the flow is diverging. That is why 2-D models accounting for the divergence, so-called 2.5-D models, are an interesting trade-off. However, the applicability of these 2.5-D models has never been systematically examined. We show that these models are ineffective in the case of highly diverging flows, but also for varying temperature, which was not suspected.
In ice-flow modelling, computing in 3-D requires a lot of resources, but 2-D models lack...
