Articles | Volume 12, issue 8
https://doi.org/10.5194/gmd-12-3745-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-12-3745-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
27 Aug 2019
Model evaluation paper |
| 27 Aug 2019
| 27 Aug 2019
On the discretization of the ice thickness distribution in the NEMO3.6-LIM3 global ocean–sea ice model
François Massonnet
CORRESPONDING AUTHOR
Georges Lemaître Centre for Earth and Climate Research, Earth and
Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Antoine Barthélemy
Georges Lemaître Centre for Earth and Climate Research, Earth and
Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Koffi Worou
Georges Lemaître Centre for Earth and Climate Research, Earth and
Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Thierry Fichefet
Georges Lemaître Centre for Earth and Climate Research, Earth and
Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Martin Vancoppenolle
Sorbonne Universités (UPMC Paris 6), LOCEAN-IPSL, CNRS/IRD/MNHN, Paris, France
Clément Rousset
Sorbonne Universités (UPMC Paris 6), LOCEAN-IPSL, CNRS/IRD/MNHN, Paris, France
Eduardo Moreno-Chamarro
Barcelona Supercomputing Center (Centro Nacional de Supercomputación), Nexus II-Planta 1 C/ Jordi Girona, 29,
Barcelona, Spain
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Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
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We study the parameters involved in the turbulent kinetic energy mixed layer penetration scheme of the NEMO model in Arctic sea ice-covered regions. This evaluation reveals the impact of these parameters on mixed layer depth, sea surface temperature and salinity, and ocean stratification. Our findings also demonstrate considerable impacts on sea ice thickness and sea ice concentration, emphasizing the importance of accurate ocean mixing representation in understanding Arctic climate dynamics.
Steve Delhaye, Rym Msadek, Thierry Fichefet, François Massonnet, and Laurent Terray
EGUsphere, https://doi.org/10.5194/egusphere-2023-1748, https://doi.org/10.5194/egusphere-2023-1748, 2023
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The climate impact of Arctic sea ice loss may depend on the region of sea ice loss and the methodology used to study this impact. This study uses two approaches across seven climate models to investigate the winter atmospheric circulation response to regional sea ice loss. Our findings indicate a consistent atmospheric circulation response to pan-Arctic sea ice loss in most models and across both approaches. In contrast, more uncertainty emerges in the responses linked to regional sea ice loss.
Mukesh Gupta, Leandro Ponsoni, Jean Sterlin, François Massonnet, and Thierry Fichefet
EGUsphere, https://doi.org/10.5194/egusphere-2023-1560, https://doi.org/10.5194/egusphere-2023-1560, 2023
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We explored the relationship of Arctic September minimum sea ice extent with mid-summer melt pond area fraction, under the present-day climate. We confirm through the advanced numerical modelling, with an explicit melt pond scheme in the global climate model, EC-EARTH3, that melt pond fraction in mid-summer (June–July, not May) shows a strong relationship with the Arctic September sea ice extent. Satellite-based inferences validated our findings of this association.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
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This study provides clues on how improved atmospheric reanalysis products influence sea ice simulations in ocean–sea ice models. The summer ice concentration simulation in both hemispheres can be improved with changed surface heat fluxes. The winter Antarctic ice concentration and the Arctic drift speed near the ice edge and the ice velocity direction simulations are improved with changed wind stress. The radiation fluxes and winds in atmospheric reanalyses are crucial for sea ice simulations.
Bianca Mezzina, Hugues Goosse, François Klein, Antoine Barthélemy, and François Massonnet
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-45, https://doi.org/10.5194/tc-2023-45, 2023
Revised manuscript under review for TC
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We analyze years with extraordinary low sea ice extent in Antarctica during summer, the latest of which was the all-time record in 2022. We highlight common aspects among these events, such as the fact that the exceptional melting usually occurs in two key regions and that it is related to winds with similar direction. We also investigate whether the summer conditions are preceded by an unusual state of the sea ice during the previous winter, as well as the physical processes involved.
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The Cryosphere, 17, 407–425, https://doi.org/10.5194/tc-17-407-2023, https://doi.org/10.5194/tc-17-407-2023, 2023
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Using idealized sensitivity experiments with a regional atmosphere–ocean–sea ice model, we show that sea ice advance is constrained by initial conditions in March and the retreat season is influenced by the magnitude of several physical processes, in particular by the ice–albedo feedback and ice transport. Atmospheric feedbacks amplify the response of the winter ice extent to perturbations, while some negative feedbacks related to heat conduction fluxes act on the ice volume.
Steve Delhaye, Thierry Fichefet, François Massonnet, David Docquier, Rym Msadek, Svenya Chripko, Christopher Roberts, Sarah Keeley, and Retish Senan
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It is unclear how the atmosphere will respond to a retreat of summer Arctic sea ice. Much attention has been paid so far to weather extremes at mid-latitude and in winter. Here we focus on the changes in extremes in surface air temperature and precipitation over the Arctic regions in summer during and following abrupt sea ice retreats. We find that Arctic sea ice loss clearly shifts the extremes in surface air temperature and precipitation over terrestrial regions surrounding the Arctic Ocean.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
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The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Charles Pelletier, Thierry Fichefet, Hugues Goosse, Konstanze Haubner, Samuel Helsen, Pierre-Vincent Huot, Christoph Kittel, François Klein, Sébastien Le clec'h, Nicole P. M. van Lipzig, Sylvain Marchi, François Massonnet, Pierre Mathiot, Ehsan Moravveji, Eduardo Moreno-Chamarro, Pablo Ortega, Frank Pattyn, Niels Souverijns, Guillian Van Achter, Sam Vanden Broucke, Alexander Vanhulle, Deborah Verfaillie, and Lars Zipf
Geosci. Model Dev., 15, 553–594, https://doi.org/10.5194/gmd-15-553-2022, https://doi.org/10.5194/gmd-15-553-2022, 2022
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We present PARASO, a circumpolar model for simulating the Antarctic climate. PARASO features five distinct models, each covering different Earth system subcomponents (ice sheet, atmosphere, land, sea ice, ocean). In this technical article, we describe how this tool has been developed, with a focus on the
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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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Ann Keen, Ed Blockley, David A. Bailey, Jens Boldingh Debernard, Mitchell Bushuk, Steve Delhaye, David Docquier, Daniel Feltham, François Massonnet, Siobhan O'Farrell, Leandro Ponsoni, José M. Rodriguez, David Schroeder, Neil Swart, Takahiro Toyoda, Hiroyuki Tsujino, Martin Vancoppenolle, and Klaus Wyser
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Qian Shi, Qinghua Yang, Longjiang Mu, Jinfei Wang, François Massonnet, and Matthew R. Mazloff
The Cryosphere, 15, 31–47, https://doi.org/10.5194/tc-15-31-2021, https://doi.org/10.5194/tc-15-31-2021, 2021
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The ice thickness from four state-of-the-art reanalyses (GECCO2, SOSE, NEMO-EnKF and GIOMAS) are evaluated against that from remote sensing and in situ observations in the Weddell Sea, Antarctica. Most of the reanalyses can reproduce ice thickness in the central and eastern Weddell Sea but failed to capture the thick and deformed ice in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis in Antarctic climate research.
Guillian Van Achter, Leandro Ponsoni, François Massonnet, Thierry Fichefet, and Vincent Legat
The Cryosphere, 14, 3479–3486, https://doi.org/10.5194/tc-14-3479-2020, https://doi.org/10.5194/tc-14-3479-2020, 2020
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We document the spatio-temporal internal variability of Arctic sea ice thickness and its changes under anthropogenic forcing, which is key to understanding, and eventually predicting, the evolution of sea ice in response to climate change.
The patterns of sea ice thickness variability remain more or less stable during pre-industrial, historical and future periods, despite non-stationarity on short timescales. These patterns start to change once Arctic summer ice-free events occur, after 2050.
Eduardo Moreno-Chamarro, Pablo Ortega, and François Massonnet
Geosci. Model Dev., 13, 4773–4787, https://doi.org/10.5194/gmd-13-4773-2020, https://doi.org/10.5194/gmd-13-4773-2020, 2020
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Climate models need to capture sea ice complexity to represent it realistically. Here we assess how distributing sea ice in discrete thickness categories impacts how sea ice variability is simulated in the NEMO3.6–LIM3 model. Simulations and satellite observations are compared by using k-means clustering of sea ice concentration in winter and summer between 1979 and 2014 at both poles. Little improvements in the modeled sea ice lead us to recommend using the standard number of five categories.
Veronika Eyring, Lisa Bock, Axel Lauer, Mattia Righi, Manuel Schlund, Bouwe Andela, Enrico Arnone, Omar Bellprat, Björn Brötz, Louis-Philippe Caron, Nuno Carvalhais, Irene Cionni, Nicola Cortesi, Bas Crezee, Edouard L. Davin, Paolo Davini, Kevin Debeire, Lee de Mora, Clara Deser, David Docquier, Paul Earnshaw, Carsten Ehbrecht, Bettina K. Gier, Nube Gonzalez-Reviriego, Paul Goodman, Stefan Hagemann, Steven Hardiman, Birgit Hassler, Alasdair Hunter, Christopher Kadow, Stephan Kindermann, Sujan Koirala, Nikolay Koldunov, Quentin Lejeune, Valerio Lembo, Tomas Lovato, Valerio Lucarini, François Massonnet, Benjamin Müller, Amarjiit Pandde, Núria Pérez-Zanón, Adam Phillips, Valeriu Predoi, Joellen Russell, Alistair Sellar, Federico Serva, Tobias Stacke, Ranjini Swaminathan, Verónica Torralba, Javier Vegas-Regidor, Jost von Hardenberg, Katja Weigel, and Klaus Zimmermann
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility.
Leandro Ponsoni, François Massonnet, David Docquier, Guillian Van Achter, and Thierry Fichefet
The Cryosphere, 14, 2409–2428, https://doi.org/10.5194/tc-14-2409-2020, https://doi.org/10.5194/tc-14-2409-2020, 2020
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The continuous melting of the Arctic sea ice observed in the last decades has a significant impact at global and regional scales. To understand the amplitude and consequences of this impact, the monitoring of the total sea ice volume is crucial. However, in situ monitoring in such a harsh environment is hard to perform and far too expensive. This study shows that four well-placed sampling locations are sufficient to explain about 70 % of the inter-annual changes in the pan-Arctic sea ice volume.
François Massonnet, Martin Ménégoz, Mario Acosta, Xavier Yepes-Arbós, Eleftheria Exarchou, and Francisco J. Doblas-Reyes
Geosci. Model Dev., 13, 1165–1178, https://doi.org/10.5194/gmd-13-1165-2020, https://doi.org/10.5194/gmd-13-1165-2020, 2020
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Earth system models (ESMs) are one of the cornerstones of modern climate science. They are usually run on high-performance computers (HPCs). Whether the choice of HPC can affect the model results is a question of importance for optimizing the design of scientific studies. Here, we introduce a protocol for testing the replicability of the EC-Earth3 ESM across different HPCs. We find the simulation results to be replicable only if specific precautions are taken at the compilation stage.
Leandro Ponsoni, François Massonnet, Thierry Fichefet, Matthieu Chevallier, and David Docquier
The Cryosphere, 13, 521–543, https://doi.org/10.5194/tc-13-521-2019, https://doi.org/10.5194/tc-13-521-2019, 2019
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The Arctic is a main component of the Earth's climate system. It is fundamental to understand the behavior of Arctic sea ice coverage over time and in space due to many factors, e.g., shipping lanes, the travel and tourism industry, hunting and fishing activities, mineral resource extraction, and the potential impact on the weather in midlatitude regions. In this work we use observations and results from models to understand how variations in the sea ice thickness change over time and in space.
Marion Lebrun, Martin Vancoppenolle, Gurvan Madec, and François Massonnet
The Cryosphere, 13, 79–96, https://doi.org/10.5194/tc-13-79-2019, https://doi.org/10.5194/tc-13-79-2019, 2019
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The present analysis shows that the increase in the Arctic ice-free season duration will be asymmetrical, with later autumn freeze-up contributing about twice as much as earlier spring retreat. This feature is robustly found in a hierarchy of climate models and is consistent with a simple mechanism: solar energy is absorbed more efficiently than it can be released in non-solar form and should emerge out of variability within the next few decades.
David Docquier, François Massonnet, Antoine Barthélemy, Neil F. Tandon, Olivier Lecomte, and Thierry Fichefet
The Cryosphere, 11, 2829–2846, https://doi.org/10.5194/tc-11-2829-2017, https://doi.org/10.5194/tc-11-2829-2017, 2017
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Our study provides a new way to evaluate the performance of a climate model regarding the interplay between sea ice motion, area and thickness in the Arctic against different observation datasets. We show that the NEMO-LIM model is good in that respect and that the relationships between the different sea ice variables are complex. The metrics we developed can be used in the framework of the Coupled Model Intercomparison Project 6 (CMIP6), which will feed the next IPCC report.
Dirk Notz, Alexandra Jahn, Marika Holland, Elizabeth Hunke, François Massonnet, Julienne Stroeve, Bruno Tremblay, and Martin Vancoppenolle
Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, https://doi.org/10.5194/gmd-9-3427-2016, 2016
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The large-scale evolution of sea ice is both an indicator and a driver of climate changes. Hence, a realistic simulation of sea ice is key for a realistic simulation of the climate system of our planet. To assess and to improve the realism of sea-ice simulations, we present here a new protocol for climate-model output that allows for an in-depth analysis of the simulated evolution of sea ice.
P. J. Hezel, T. Fichefet, and F. Massonnet
The Cryosphere, 8, 1195–1204, https://doi.org/10.5194/tc-8-1195-2014, https://doi.org/10.5194/tc-8-1195-2014, 2014
V. Zunz, H. Goosse, and F. Massonnet
The Cryosphere, 7, 451–468, https://doi.org/10.5194/tc-7-451-2013, https://doi.org/10.5194/tc-7-451-2013, 2013
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-49, https://doi.org/10.5194/gmd-2024-49, 2024
Preprint under review for GMD
Short summary
Short summary
We study the parameters involved in the turbulent kinetic energy mixed layer penetration scheme of the NEMO model in Arctic sea ice-covered regions. This evaluation reveals the impact of these parameters on mixed layer depth, sea surface temperature and salinity, and ocean stratification. Our findings also demonstrate considerable impacts on sea ice thickness and sea ice concentration, emphasizing the importance of accurate ocean mixing representation in understanding Arctic climate dynamics.
Steve Delhaye, Rym Msadek, Thierry Fichefet, François Massonnet, and Laurent Terray
EGUsphere, https://doi.org/10.5194/egusphere-2023-1748, https://doi.org/10.5194/egusphere-2023-1748, 2023
Short summary
Short summary
The climate impact of Arctic sea ice loss may depend on the region of sea ice loss and the methodology used to study this impact. This study uses two approaches across seven climate models to investigate the winter atmospheric circulation response to regional sea ice loss. Our findings indicate a consistent atmospheric circulation response to pan-Arctic sea ice loss in most models and across both approaches. In contrast, more uncertainty emerges in the responses linked to regional sea ice loss.
Max Thomas, Briana Cate, Jack Garnett, Inga J. Smith, Martin Vancoppenolle, and Crispin Halsall
The Cryosphere, 17, 3193–3201, https://doi.org/10.5194/tc-17-3193-2023, https://doi.org/10.5194/tc-17-3193-2023, 2023
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A recent study showed that pollutants can be enriched in growing sea ice beyond what we would expect from a perfectly dissolved chemical. We hypothesise that this effect is caused by the specific properties of the pollutants working in combination with fluid moving through the sea ice. To test our hypothesis, we replicate this behaviour in a sea-ice model and show that this type of modelling can be applied to predicting the transport of chemicals with complex behaviour in sea ice.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
EGUsphere, https://doi.org/10.5194/egusphere-2023-1731, https://doi.org/10.5194/egusphere-2023-1731, 2023
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This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used for UK contributions to CMIP7. Documentation of physical science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details of how SI3 was adapted to work with Met Office coupling methodology, along with thorough documentation of coupling processes in the model.
Mukesh Gupta, Leandro Ponsoni, Jean Sterlin, François Massonnet, and Thierry Fichefet
EGUsphere, https://doi.org/10.5194/egusphere-2023-1560, https://doi.org/10.5194/egusphere-2023-1560, 2023
Preprint archived
Short summary
Short summary
We explored the relationship of Arctic September minimum sea ice extent with mid-summer melt pond area fraction, under the present-day climate. We confirm through the advanced numerical modelling, with an explicit melt pond scheme in the global climate model, EC-EARTH3, that melt pond fraction in mid-summer (June–July, not May) shows a strong relationship with the Arctic September sea ice extent. Satellite-based inferences validated our findings of this association.
Katherine Hutchinson, Julie Deshayes, Christian Éthé, Clément Rousset, Casimir de Lavergne, Martin Vancoppenolle, Nicolas C. Jourdain, and Pierre Mathiot
Geosci. Model Dev., 16, 3629–3650, https://doi.org/10.5194/gmd-16-3629-2023, https://doi.org/10.5194/gmd-16-3629-2023, 2023
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Bottom Water constitutes the lower half of the ocean’s overturning system and is primarily formed in the Weddell and Ross Sea in the Antarctic due to interactions between the atmosphere, ocean, sea ice and ice shelves. Here we use a global ocean 1° resolution model with explicit representation of the three large ice shelves important for the formation of the parent waters of Bottom Water. We find doing so reduces salt biases, improves water mass realism and gives realistic ice shelf melt rates.
Koffi Worou, Thierry Fichefet, and Hugues Goosse
Weather Clim. Dynam., 4, 511–530, https://doi.org/10.5194/wcd-4-511-2023, https://doi.org/10.5194/wcd-4-511-2023, 2023
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The Atlantic equatorial mode (AEM) of variability is partly responsible for the year-to-year rainfall variability over the Guinea coast. We used the current climate models to explore the present-day and future links between the AEM and the extreme rainfall indices over the Guinea coast. Under future global warming, the total variability of the extreme rainfall indices increases over the Guinea coast. However, the future impact of the AEM on extreme rainfall events decreases over the region.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
The Cryosphere, 17, 1935–1965, https://doi.org/10.5194/tc-17-1935-2023, https://doi.org/10.5194/tc-17-1935-2023, 2023
Short summary
Short summary
This study provides clues on how improved atmospheric reanalysis products influence sea ice simulations in ocean–sea ice models. The summer ice concentration simulation in both hemispheres can be improved with changed surface heat fluxes. The winter Antarctic ice concentration and the Arctic drift speed near the ice edge and the ice velocity direction simulations are improved with changed wind stress. The radiation fluxes and winds in atmospheric reanalyses are crucial for sea ice simulations.
Bianca Mezzina, Hugues Goosse, François Klein, Antoine Barthélemy, and François Massonnet
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-45, https://doi.org/10.5194/tc-2023-45, 2023
Revised manuscript under review for TC
Short summary
Short summary
We analyze years with extraordinary low sea ice extent in Antarctica during summer, the latest of which was the all-time record in 2022. We highlight common aspects among these events, such as the fact that the exceptional melting usually occurs in two key regions and that it is related to winds with similar direction. We also investigate whether the summer conditions are preceded by an unusual state of the sea ice during the previous winter, as well as the physical processes involved.
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.
Hugues Goosse, Sofia Allende Contador, Cecilia M. Bitz, Edward Blanchard-Wrigglesworth, Clare Eayrs, Thierry Fichefet, Kenza Himmich, Pierre-Vincent Huot, François Klein, Sylvain Marchi, François Massonnet, Bianca Mezzina, Charles Pelletier, Lettie Roach, Martin Vancoppenolle, and Nicole P. M. van Lipzig
The Cryosphere, 17, 407–425, https://doi.org/10.5194/tc-17-407-2023, https://doi.org/10.5194/tc-17-407-2023, 2023
Short summary
Short summary
Using idealized sensitivity experiments with a regional atmosphere–ocean–sea ice model, we show that sea ice advance is constrained by initial conditions in March and the retreat season is influenced by the magnitude of several physical processes, in particular by the ice–albedo feedback and ice transport. Atmospheric feedbacks amplify the response of the winter ice extent to perturbations, while some negative feedbacks related to heat conduction fluxes act on the ice volume.
Guillian Van Achter, Thierry Fichefet, Hugues Goosse, and Eduardo Moreno-Chamarro
The Cryosphere, 16, 4745–4761, https://doi.org/10.5194/tc-16-4745-2022, https://doi.org/10.5194/tc-16-4745-2022, 2022
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We investigate the changes in ocean–ice interactions in the Totten Glacier area between the last decades (1995–2014) and the end of the 21st century (2081–2100) under warmer climate conditions. By the end of the 21st century, the sea ice is strongly reduced, and the ocean circulation close to the coast is accelerated. Our research highlights the importance of including representations of fast ice to simulate realistic ice shelf melt rate increase in East Antarctica under warming conditions.
Steve Delhaye, Thierry Fichefet, François Massonnet, David Docquier, Rym Msadek, Svenya Chripko, Christopher Roberts, Sarah Keeley, and Retish Senan
Weather Clim. Dynam., 3, 555–573, https://doi.org/10.5194/wcd-3-555-2022, https://doi.org/10.5194/wcd-3-555-2022, 2022
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It is unclear how the atmosphere will respond to a retreat of summer Arctic sea ice. Much attention has been paid so far to weather extremes at mid-latitude and in winter. Here we focus on the changes in extremes in surface air temperature and precipitation over the Arctic regions in summer during and following abrupt sea ice retreats. We find that Arctic sea ice loss clearly shifts the extremes in surface air temperature and precipitation over terrestrial regions surrounding the Arctic Ocean.
Sam White, Eduardo Moreno-Chamarro, Davide Zanchettin, Heli Huhtamaa, Dagomar Degroot, Markus Stoffel, and Christophe Corona
Clim. Past, 18, 739–757, https://doi.org/10.5194/cp-18-739-2022, https://doi.org/10.5194/cp-18-739-2022, 2022
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This study examines whether the 1600 Huaynaputina volcano eruption triggered persistent cooling in the North Atlantic. It compares previous paleoclimate simulations with new climate reconstructions from natural proxies and historical documents and finds that the reconstructions are consistent with, but do not support, an eruption trigger for persistent cooling. The study also analyzes societal impacts of climatic change in ca. 1600 and the use of historical observations in model–data comparison.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
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The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Koffi Worou, Hugues Goosse, Thierry Fichefet, and Fred Kucharski
Earth Syst. Dynam., 13, 231–249, https://doi.org/10.5194/esd-13-231-2022, https://doi.org/10.5194/esd-13-231-2022, 2022
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Over the Guinea Coast, the increased rainfall associated with warm phases of the Atlantic Niño is reasonably well simulated by 24 climate models out of 31, for the present-day conditions. In a warmer climate, general circulation models project a gradual decrease with time of the rainfall magnitude associated with the Atlantic Niño for the 2015–2039, 2040–2069 and 2070–2099 periods. There is a higher confidence in these changes over the equatorial Atlantic than over the Guinea Coast.
Charles Pelletier, Thierry Fichefet, Hugues Goosse, Konstanze Haubner, Samuel Helsen, Pierre-Vincent Huot, Christoph Kittel, François Klein, Sébastien Le clec'h, Nicole P. M. van Lipzig, Sylvain Marchi, François Massonnet, Pierre Mathiot, Ehsan Moravveji, Eduardo Moreno-Chamarro, Pablo Ortega, Frank Pattyn, Niels Souverijns, Guillian Van Achter, Sam Vanden Broucke, Alexander Vanhulle, Deborah Verfaillie, and Lars Zipf
Geosci. Model Dev., 15, 553–594, https://doi.org/10.5194/gmd-15-553-2022, https://doi.org/10.5194/gmd-15-553-2022, 2022
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We present PARASO, a circumpolar model for simulating the Antarctic climate. PARASO features five distinct models, each covering different Earth system subcomponents (ice sheet, atmosphere, land, sea ice, ocean). In this technical article, we describe how this tool has been developed, with a focus on the
coupling interfacesrepresenting the feedbacks between the distinct models used for contribution. PARASO is stable and ready to use but is still characterized by significant biases.
Eduardo Moreno-Chamarro, Louis-Philippe Caron, Saskia Loosveldt Tomas, Javier Vegas-Regidor, Oliver Gutjahr, Marie-Pierre Moine, Dian Putrasahan, Christopher D. Roberts, Malcolm J. Roberts, Retish Senan, Laurent Terray, Etienne Tourigny, and Pier Luigi Vidale
Geosci. Model Dev., 15, 269–289, https://doi.org/10.5194/gmd-15-269-2022, https://doi.org/10.5194/gmd-15-269-2022, 2022
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Climate models do not fully reproduce observations: they show differences (biases) in regional temperature, precipitation, or cloud cover. Reducing model biases is important to increase our confidence in their ability to reproduce present and future climate changes. Model realism is set by its resolution: the finer it is, the more physical processes and interactions it can resolve. We here show that increasing resolution of up to ~ 25 km can help reduce model biases but not remove them entirely.
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.
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.
Christoph Kittel, Charles Amory, Cécile Agosta, Nicolas C. Jourdain, Stefan Hofer, Alison Delhasse, Sébastien Doutreloup, Pierre-Vincent Huot, Charlotte Lang, Thierry Fichefet, and Xavier Fettweis
The Cryosphere, 15, 1215–1236, https://doi.org/10.5194/tc-15-1215-2021, https://doi.org/10.5194/tc-15-1215-2021, 2021
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The future surface mass balance (SMB) of the Antarctic ice sheet (AIS) will influence the ice dynamics and the contribution of the ice sheet to the sea level rise. We investigate the AIS sensitivity to different warmings using physical and statistical downscaling of CMIP5 and CMIP6 models. Our results highlight a contrasting effect between the grounded ice sheet (where the SMB is projected to increase) and ice shelves (where the future SMB depends on the emission scenario).
Ann Keen, Ed Blockley, David A. Bailey, Jens Boldingh Debernard, Mitchell Bushuk, Steve Delhaye, David Docquier, Daniel Feltham, François Massonnet, Siobhan O'Farrell, Leandro Ponsoni, José M. Rodriguez, David Schroeder, Neil Swart, Takahiro Toyoda, Hiroyuki Tsujino, Martin Vancoppenolle, and Klaus Wyser
The Cryosphere, 15, 951–982, https://doi.org/10.5194/tc-15-951-2021, https://doi.org/10.5194/tc-15-951-2021, 2021
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We compare the mass budget of the Arctic sea ice in a number of the latest climate models. New output has been defined that allows us to compare the processes of sea ice growth and loss in a more detailed way than has previously been possible. We find that that the models are strikingly similar in terms of the major processes causing the annual growth and loss of Arctic sea ice and that the budget terms respond in a broadly consistent way as the climate warms during the 21st century.
Roberto Bilbao, Simon Wild, Pablo Ortega, Juan Acosta-Navarro, Thomas Arsouze, Pierre-Antoine Bretonnière, Louis-Philippe Caron, Miguel Castrillo, Rubén Cruz-García, Ivana Cvijanovic, Francisco Javier Doblas-Reyes, Markus Donat, Emanuel Dutra, Pablo Echevarría, An-Chi Ho, Saskia Loosveldt-Tomas, Eduardo Moreno-Chamarro, Núria Pérez-Zanon, Arthur Ramos, Yohan Ruprich-Robert, Valentina Sicardi, Etienne Tourigny, and Javier Vegas-Regidor
Earth Syst. Dynam., 12, 173–196, https://doi.org/10.5194/esd-12-173-2021, https://doi.org/10.5194/esd-12-173-2021, 2021
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This paper presents and evaluates a set of retrospective decadal predictions with the EC-Earth3 climate model. These experiments successfully predict past changes in surface air temperature but show poor predictive capacity in the subpolar North Atlantic, a well-known source region of decadal climate variability. The poor predictive capacity is linked to an initial shock affecting the Atlantic Ocean circulation, ultimately due to a suboptimal representation of the Labrador Sea density.
Qian Shi, Qinghua Yang, Longjiang Mu, Jinfei Wang, François Massonnet, and Matthew R. Mazloff
The Cryosphere, 15, 31–47, https://doi.org/10.5194/tc-15-31-2021, https://doi.org/10.5194/tc-15-31-2021, 2021
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The ice thickness from four state-of-the-art reanalyses (GECCO2, SOSE, NEMO-EnKF and GIOMAS) are evaluated against that from remote sensing and in situ observations in the Weddell Sea, Antarctica. Most of the reanalyses can reproduce ice thickness in the central and eastern Weddell Sea but failed to capture the thick and deformed ice in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis in Antarctic climate research.
Guillian Van Achter, Leandro Ponsoni, François Massonnet, Thierry Fichefet, and Vincent Legat
The Cryosphere, 14, 3479–3486, https://doi.org/10.5194/tc-14-3479-2020, https://doi.org/10.5194/tc-14-3479-2020, 2020
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We document the spatio-temporal internal variability of Arctic sea ice thickness and its changes under anthropogenic forcing, which is key to understanding, and eventually predicting, the evolution of sea ice in response to climate change.
The patterns of sea ice thickness variability remain more or less stable during pre-industrial, historical and future periods, despite non-stationarity on short timescales. These patterns start to change once Arctic summer ice-free events occur, after 2050.
Eduardo Moreno-Chamarro, Pablo Ortega, and François Massonnet
Geosci. Model Dev., 13, 4773–4787, https://doi.org/10.5194/gmd-13-4773-2020, https://doi.org/10.5194/gmd-13-4773-2020, 2020
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Climate models need to capture sea ice complexity to represent it realistically. Here we assess how distributing sea ice in discrete thickness categories impacts how sea ice variability is simulated in the NEMO3.6–LIM3 model. Simulations and satellite observations are compared by using k-means clustering of sea ice concentration in winter and summer between 1979 and 2014 at both poles. Little improvements in the modeled sea ice lead us to recommend using the standard number of five categories.
Veronika Eyring, Lisa Bock, Axel Lauer, Mattia Righi, Manuel Schlund, Bouwe Andela, Enrico Arnone, Omar Bellprat, Björn Brötz, Louis-Philippe Caron, Nuno Carvalhais, Irene Cionni, Nicola Cortesi, Bas Crezee, Edouard L. Davin, Paolo Davini, Kevin Debeire, Lee de Mora, Clara Deser, David Docquier, Paul Earnshaw, Carsten Ehbrecht, Bettina K. Gier, Nube Gonzalez-Reviriego, Paul Goodman, Stefan Hagemann, Steven Hardiman, Birgit Hassler, Alasdair Hunter, Christopher Kadow, Stephan Kindermann, Sujan Koirala, Nikolay Koldunov, Quentin Lejeune, Valerio Lembo, Tomas Lovato, Valerio Lucarini, François Massonnet, Benjamin Müller, Amarjiit Pandde, Núria Pérez-Zanón, Adam Phillips, Valeriu Predoi, Joellen Russell, Alistair Sellar, Federico Serva, Tobias Stacke, Ranjini Swaminathan, Verónica Torralba, Javier Vegas-Regidor, Jost von Hardenberg, Katja Weigel, and Klaus Zimmermann
Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, https://doi.org/10.5194/gmd-13-3383-2020, 2020
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility.
Leandro Ponsoni, François Massonnet, David Docquier, Guillian Van Achter, and Thierry Fichefet
The Cryosphere, 14, 2409–2428, https://doi.org/10.5194/tc-14-2409-2020, https://doi.org/10.5194/tc-14-2409-2020, 2020
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The continuous melting of the Arctic sea ice observed in the last decades has a significant impact at global and regional scales. To understand the amplitude and consequences of this impact, the monitoring of the total sea ice volume is crucial. However, in situ monitoring in such a harsh environment is hard to perform and far too expensive. This study shows that four well-placed sampling locations are sufficient to explain about 70 % of the inter-annual changes in the pan-Arctic sea ice volume.
François Massonnet, Martin Ménégoz, Mario Acosta, Xavier Yepes-Arbós, Eleftheria Exarchou, and Francisco J. Doblas-Reyes
Geosci. Model Dev., 13, 1165–1178, https://doi.org/10.5194/gmd-13-1165-2020, https://doi.org/10.5194/gmd-13-1165-2020, 2020
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Earth system models (ESMs) are one of the cornerstones of modern climate science. They are usually run on high-performance computers (HPCs). Whether the choice of HPC can affect the model results is a question of importance for optimizing the design of scientific studies. Here, we introduce a protocol for testing the replicability of the EC-Earth3 ESM across different HPCs. We find the simulation results to be replicable only if specific precautions are taken at the compilation stage.
Guillaume Boutin, Camille Lique, Fabrice Ardhuin, Clément Rousset, Claude Talandier, Mickael Accensi, and Fanny Girard-Ardhuin
The Cryosphere, 14, 709–735, https://doi.org/10.5194/tc-14-709-2020, https://doi.org/10.5194/tc-14-709-2020, 2020
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We investigate the interactions of surface ocean waves with sea ice taking place at the interface between the compact sea ice cover and the open ocean. We use a newly developed coupling framework between a wave and an ocean–sea ice numerical model. Our results show how the push on sea ice exerted by waves changes the amount and the location of sea ice melting, with a strong impact on the ocean surface properties close to the ice edge.
Maria Pyrina, Eduardo Moreno-Chamarro, Sebastian Wagner, and Eduardo Zorita
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2019-50, https://doi.org/10.5194/esd-2019-50, 2019
Revised manuscript not accepted
Renaud Person, Olivier Aumont, Gurvan Madec, Martin Vancoppenolle, Laurent Bopp, and Nacho Merino
Biogeosciences, 16, 3583–3603, https://doi.org/10.5194/bg-16-3583-2019, https://doi.org/10.5194/bg-16-3583-2019, 2019
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The Antarctic Ice Sheet is considered a possibly important but largely overlooked source of iron (Fe). Here we explore its fertilization capacity by evaluating the response of marine biogeochemistry to Fe release from icebergs and ice shelves in a global ocean model. Large regional impacts are simulated, leading to only modest primary production and carbon export increases at the scale of the Southern Ocean. Large uncertainties are due to low observational constraints on modeling choices.
Christoph Heinze, Veronika Eyring, Pierre Friedlingstein, Colin Jones, Yves Balkanski, William Collins, Thierry Fichefet, Shuang Gao, Alex Hall, Detelina Ivanova, Wolfgang Knorr, Reto Knutti, Alexander Löw, Michael Ponater, Martin G. Schultz, Michael Schulz, Pier Siebesma, Joao Teixeira, George Tselioudis, and Martin Vancoppenolle
Earth Syst. Dynam., 10, 379–452, https://doi.org/10.5194/esd-10-379-2019, https://doi.org/10.5194/esd-10-379-2019, 2019
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Earth system models for producing climate projections under given forcings include additional processes and feedbacks that traditional physical climate models do not consider. We present an overview of climate feedbacks for key Earth system components and discuss the evaluation of these feedbacks. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research.
Leandro Ponsoni, François Massonnet, Thierry Fichefet, Matthieu Chevallier, and David Docquier
The Cryosphere, 13, 521–543, https://doi.org/10.5194/tc-13-521-2019, https://doi.org/10.5194/tc-13-521-2019, 2019
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The Arctic is a main component of the Earth's climate system. It is fundamental to understand the behavior of Arctic sea ice coverage over time and in space due to many factors, e.g., shipping lanes, the travel and tourism industry, hunting and fishing activities, mineral resource extraction, and the potential impact on the weather in midlatitude regions. In this work we use observations and results from models to understand how variations in the sea ice thickness change over time and in space.
Marion Lebrun, Martin Vancoppenolle, Gurvan Madec, and François Massonnet
The Cryosphere, 13, 79–96, https://doi.org/10.5194/tc-13-79-2019, https://doi.org/10.5194/tc-13-79-2019, 2019
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The present analysis shows that the increase in the Arctic ice-free season duration will be asymmetrical, with later autumn freeze-up contributing about twice as much as earlier spring retreat. This feature is robustly found in a hierarchy of climate models and is consistent with a simple mechanism: solar energy is absorbed more efficiently than it can be released in non-solar form and should emerge out of variability within the next few decades.
Christoph Kittel, Charles Amory, Cécile Agosta, Alison Delhasse, Sébastien Doutreloup, Pierre-Vincent Huot, Coraline Wyard, Thierry Fichefet, and Xavier Fettweis
The Cryosphere, 12, 3827–3839, https://doi.org/10.5194/tc-12-3827-2018, https://doi.org/10.5194/tc-12-3827-2018, 2018
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Regional climate models (RCMs) used to estimate the surface mass balance (SMB) of Antarctica depend on boundary forcing fields including sea surface conditions. Here, we assess the sensitivity of the Antarctic SMB to perturbations in sea surface conditions with the RCM MAR using unchanged atmospheric conditions. Significant SMB anomalies are found for SSC perturbations in the range of CMIP5 global climate model biases.
David Docquier, François Massonnet, Antoine Barthélemy, Neil F. Tandon, Olivier Lecomte, and Thierry Fichefet
The Cryosphere, 11, 2829–2846, https://doi.org/10.5194/tc-11-2829-2017, https://doi.org/10.5194/tc-11-2829-2017, 2017
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Our study provides a new way to evaluate the performance of a climate model regarding the interplay between sea ice motion, area and thickness in the Arctic against different observation datasets. We show that the NEMO-LIM model is good in that respect and that the relationships between the different sea ice variables are complex. The metrics we developed can be used in the framework of the Coupled Model Intercomparison Project 6 (CMIP6), which will feed the next IPCC report.
Petteri Uotila, Doroteaciro Iovino, Martin Vancoppenolle, Mikko Lensu, and Clement Rousset
Geosci. Model Dev., 10, 1009–1031, https://doi.org/10.5194/gmd-10-1009-2017, https://doi.org/10.5194/gmd-10-1009-2017, 2017
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We performed ocean model simulations with new and old sea-ice components. Sea ice improved in the new model compared to the earlier one due to better model physics. In the ocean, the largest differences are confined close to the surface within and near the sea-ice zone. The global ocean circulation slowly deviates between the simulations due to dissimilar sea ice in the deep water formation regions, such as the North Atlantic and Antarctic.
Heiko Goelzer, Philippe Huybrechts, Marie-France Loutre, and Thierry Fichefet
Clim. Past, 12, 2195–2213, https://doi.org/10.5194/cp-12-2195-2016, https://doi.org/10.5194/cp-12-2195-2016, 2016
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We simulate the climate, ice sheet, and sea-level evolution during the Last Interglacial (~ 130 to 115 kyr BP), the most recent warm period in Earth’s history. Our Earth system model includes components representing the atmosphere, the ocean and sea ice, the terrestrial biosphere, and the Greenland and Antarctic ice sheets. Our simulation is in good agreement with available data reconstructions and gives important insights into the dominant mechanisms that caused ice sheet changes in the past.
Dirk Notz, Alexandra Jahn, Marika Holland, Elizabeth Hunke, François Massonnet, Julienne Stroeve, Bruno Tremblay, and Martin Vancoppenolle
Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, https://doi.org/10.5194/gmd-9-3427-2016, 2016
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The large-scale evolution of sea ice is both an indicator and a driver of climate changes. Hence, a realistic simulation of sea ice is key for a realistic simulation of the climate system of our planet. To assess and to improve the realism of sea-ice simulations, we present here a new protocol for climate-model output that allows for an in-depth analysis of the simulated evolution of sea ice.
Heiko Goelzer, Philippe Huybrechts, Marie-France Loutre, and Thierry Fichefet
Clim. Past, 12, 1721–1737, https://doi.org/10.5194/cp-12-1721-2016, https://doi.org/10.5194/cp-12-1721-2016, 2016
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We have modelled the climate evolution from 135 to 120 kyr BP with an Earth system model to study the onset of the Last Interglacial warm period. Ice sheet changes and associated freshwater fluxes in both hemispheres constitute an important forcing in the simulations. Freshwater fluxes from the melting Antarctic ice sheet are found to lead to an oceanic cold event in the Southern Ocean as evidenced in some ocean sediment cores, which may be used to constrain the timing of ice sheet retreat.
J. L. Lieser, M. A. J. Curran, A. R. Bowie, A. T. Davidson, S. J. Doust, A. D. Fraser, B. K. Galton-Fenzi, R. A. Massom, K. M. Meiners, J. Melbourne-Thomas, P. A. Reid, P. G. Strutton, T. R. Vance, M. Vancoppenolle, K. J. Westwood, and S. W. Wright
The Cryosphere Discuss., https://doi.org/10.5194/tcd-9-6187-2015, https://doi.org/10.5194/tcd-9-6187-2015, 2015
Revised manuscript has not been submitted
C. Rousset, M. Vancoppenolle, G. Madec, T. Fichefet, S. Flavoni, A. Barthélemy, R. Benshila, J. Chanut, C. Levy, S. Masson, and F. Vivier
Geosci. Model Dev., 8, 2991–3005, https://doi.org/10.5194/gmd-8-2991-2015, https://doi.org/10.5194/gmd-8-2991-2015, 2015
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LIM3.6 presented in this paper is the last release of the Louvain-la-Neuve sea ice model, and will be used for the next climate model intercomparison project (CMIP6). The model's robustness, versatility and sophistication have been improved.
M. F. Loutre, T. Fichefet, H. Goosse, P. Huybrechts, H. Goelzer, and E. Capron
Clim. Past, 10, 1541–1565, https://doi.org/10.5194/cp-10-1541-2014, https://doi.org/10.5194/cp-10-1541-2014, 2014
P. J. Hezel, T. Fichefet, and F. Massonnet
The Cryosphere, 8, 1195–1204, https://doi.org/10.5194/tc-8-1195-2014, https://doi.org/10.5194/tc-8-1195-2014, 2014
M. Vancoppenolle, D. Notz, F. Vivier, J. Tison, B. Delille, G. Carnat, J. Zhou, F. Jardon, P. Griewank, A. Lourenço, and T. Haskell
The Cryosphere Discuss., https://doi.org/10.5194/tcd-7-3209-2013, https://doi.org/10.5194/tcd-7-3209-2013, 2013
Revised manuscript not accepted
V. Zunz, H. Goosse, and F. Massonnet
The Cryosphere, 7, 451–468, https://doi.org/10.5194/tc-7-451-2013, https://doi.org/10.5194/tc-7-451-2013, 2013
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SnowClim v1.0: high-resolution snow model and data for the western United States
Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt
MPAS-Seaice (v1.0.0): sea-ice dynamics on unstructured Voronoi meshes
Explicitly modelling microtopography in permafrost landscapes in a land surface model (JULES vn5.4_microtopography)
Geometric remapping of particle distributions in the Discrete Element Model for Sea Ice (DEMSI v0.0)
Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1)
NEMO-Bohai 1.0: a high-resolution ocean and sea ice modelling system for the Bohai Sea, China
An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018
WIFF1.0: a hybrid machine-learning-based parameterization of wave-induced sea ice floe fracture
The Whole Antarctic Ocean Model (WAOM v1.0): development and evaluation
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
Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adélie Land, East Antarctica
Assessment of numerical schemes for transient, finite-element ice flow models using ISSM v4.18
The Utrecht Finite Volume Ice-Sheet Model: UFEMISM (version 1.0)
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.
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.
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.
Charles Amory, Christoph Kittel, Louis Le Toumelin, Cécile Agosta, Alison Delhasse, Vincent Favier, and Xavier Fettweis
Geosci. Model Dev., 14, 3487–3510, https://doi.org/10.5194/gmd-14-3487-2021, https://doi.org/10.5194/gmd-14-3487-2021, 2021
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This paper presents recent developments in the drifting-snow scheme of the regional climate model MAR and its application to simulate drifting snow and the surface mass balance of Adélie Land in East Antarctica. The model is extensively described and evaluated against a multi-year drifting-snow dataset and surface mass balance estimates available in the area. The model sensitivity to input parameters and improvements over a previously published version are also assessed.
Thiago Dias dos Santos, Mathieu Morlighem, and Hélène Seroussi
Geosci. Model Dev., 14, 2545–2573, https://doi.org/10.5194/gmd-14-2545-2021, https://doi.org/10.5194/gmd-14-2545-2021, 2021
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Numerical models are routinely used to understand the past and future behavior of ice sheets in response to climate evolution. As is always the case with numerical modeling, one needs to minimize biases and numerical artifacts due to the choice of numerical scheme employed in such models. Here, we assess different numerical schemes in time-dependent simulations of ice sheets. We also introduce a new parameterization for the driving stress, the force that drives the ice sheet flow.
Constantijn J. Berends, Heiko Goelzer, and Roderik S. W. van de Wal
Geosci. Model Dev., 14, 2443–2470, https://doi.org/10.5194/gmd-14-2443-2021, https://doi.org/10.5194/gmd-14-2443-2021, 2021
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The largest uncertainty in projections of sea-level rise comes from ice-sheet retreat. To better understand how these ice sheets respond to the changing climate, ice-sheet models are used, which must be able to reproduce both their present and past evolution. We have created a model that is fast enough to simulate an ice sheet at a high resolution over the course of an entire 120 000-year glacial cycle. This allows us to study processes that cannot be captured by lower-resolution models.
Cited articles
Barthélemy, A., Goosse, H., Fichefet, T., and Lecomte, O.: On the sensitivity
of Antarctic sea ice model biases to atmospheric forcing uncertainties, Clim.
Dynam., 51, 1585–1603, https://doi.org/10.1007/s00382-017-3972-7,
2017. a, b, c
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic model of
sea ice, J. Geophys. Res., 104, 15669–15677, https://doi.org/10.1029/1999JC900100,
1999. a
Bitz, C. M., Holland, M. M., Weaver, A. J., and Eby, M.: Simulating the
ice-thickness distribution in a coupled climate model, J. Geophys. Res., 106,
2441–2463, https://doi.org/10.1029/1999JC000113,
2001. a, b, c, d
Bouillon, S., Fichefet, T., Legat, V., and Madec, G.: The
elastic-viscous-plastic method revisited, Ocean Modell., 71, 2–12,
https://doi.org/10.1016/j.ocemod.2013.05.013,
2013. a
Brodeau, L., Barnier, B., Treguier, A.-M., Penduff, T., and Gulev, S.: An
ERA40-based atmospheric forcing for global ocean circulation models, Ocean
Modell., 31, 88–104, https://doi.org/10.1016/j.ocemod.2009.10.005,
2010. a
Chevallier, M., Smith, G. C., Dupont, F., Lemieux, J.-F., Forget, G., Fujii,
Y., Hernandez, F., Msadek, R., Peterson, K. A., Storto, A., Toyoda, T.,
Valdivieso, M., Vernieres, G., Zuo, H., Balmaseda, M., Chang, Y.-S., Ferry,
N., Garric, G., Haines, K., Keeley, S., Kovach, R. M., Kuragano, T., Masina,
S., Tang, Y., Tsujino, H., and Wang, X.: Intercomparison of the Arctic sea
ice cover in global ocean–sea ice reanalyses from the ORA-IP project, Clim.
Dynam., SI, 1–30, https://doi.org/10.1007/s00382-016-2985-y, 2016. a
Dai, A. and Trenberth, K. E.: Estimates of Freshwater Discharge from
Continents: Latitudinal and Seasonal Variations, J. Hydrometeor., 3,
660–687, https://doi.org/10.1175/1525-7541(2002)003<0660:EOFDFC>2.0.CO;2,
2002. a
Depoorter, M. A., Bamber, J. L., Griggs, J. A., Lenaerts, J. T. M., Ligtenberg,
S. R. M., van den Broeke, M. R., and Moholdt, G.: Calving fluxes and basal
melt rates of Antarctic ice shelves, Nature, 502, 89–92,
https://doi.org/10.1038/nature12567,
2013. a
Dussin, R., Barnier, B., Brodeau, L., and Molines, J.-M.: The making of the
DRAKKAR Forcing Set DFS5, Drakkar/myocean report 01-04-16, Laboratoire de
Glaciologie et de Géophysique de l'Environnement, Université de Grenoble,
Grenoble, France,
available at: https://www.drakkar-ocean.eu/forcing-the-ocean (last access: 19 August 2019), 2016. a
Eicken, H.: Tracer studies of pathways and rates of meltwater transport through
Arctic summer sea ice, J. Geophys. Res., 107, 8046,
https://doi.org/10.1029/2000jc000583,
2002. a
EUMETSAT: Global sea ice concentration reprocessing dataset 1978–2015 (v1.2),
Ocean and Sea Ice Satellite Application
Facility, Norwegian and Danish Meteorological Institutes,
availabel at: http://osisaf.met.no/p/ice/ (last access: 19 August 2019), 2015. a
Goosse, H., Arzel, O., Bitz, C. M., de Montety, A., and Vancoppenolle, M.:
Increased variability of the Arctic summer ice extent in a warmer climate,
Geophys. Res. Lett., 36, L23702, https://doi.org/10.1029/2009gl040546,
2009. a
Goosse, H., Kay, J. E., Armour, K. C., Bodas-Salcedo, A., Chepfer, H.,
Docquier, D., Jonko, A., Kushner, P. J., Lecomte, O., Massonnet, F., Park,
H.-S., Pithan, F., Svensson, G., and Vancoppenolle, M.: Quantifying climate
feedbacks in polar regions, Nat. Commun., 9, 1,
https://doi.org/10.1038/s41467-018-04173-0,
2018. a
Hamilton, L. C. and Stroeve, J.: 400 predictions: the SEARCH Sea Ice Outlook
2008–2015, Polar Geography, 39, 274–287,
https://doi.org/10.1080/1088937x.2016.1234518,
2016. a
Holland, M. M., Bitz, C. M., Hunke, E. C., Lipscomb, W. H., and Schramm, J. L.:
Influence of the Sea Ice Thickness Distribution on Polar Climate in CCSM3, J.
Climate, 19, 2398–2414, https://doi.org/10.1175/JCLI3751.1,
2006a. a, b
Holland, M. M., Bitz, C. M., and Tremblay, B.: Future abrupt reductions in the
summer Arctic sea ice, Geophys. Res. Lett., 33, L23503,
https://doi.org/10.1029/2006GL028024,
2006b. a, b, c
Holland, M. M., Serreze, M. C., and Stroeve, J.: The sea ice mass budget of the
Arctic and its future change as simulated by coupled climate models, Clim.
Dynam., 34, 185–200, https://doi.org/10.1007/s00382-008-0493-4,
2008. a
Hunke, E. C.: Thickness sensitivities in the CICE sea ice model, Ocean Modell.,
34, 137–149, https://doi.org/10.1016/j.ocemod.2010.05.004,
2010. a
Hunke, E. C.: Sea ice volume and age: Sensitivity to physical parameterizations
and thickness resolution in the CICE sea ice model, Ocean Modell., 82,
45–59, https://doi.org/10.1016/j.ocemod.2014.08.001,
2014. a, b, c, d
Large, W. G. and Yeager, S. G.: Diurnal to decadal global forcing for ocean and
sea-ice models: The data sets and flux climatologies, Tech. rep., National
Center for Atmospheric Research, Boulder, Colorado, USA, 2004. a
Lipscomb, W. H.: Remapping the thickness distribution in sea ice models, J.
Geophys. Res., 106, 13989–14000, https://doi.org/10.1029/2000JC000518,
2001. a, b, c
Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E.,
Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R.,
Hamilton, M., and Seidov, D.: World Ocean Atlas 2013, Volume 1: Temperature, eduted by:
Levitus, S. and Mishonov, A., NOAA atlas nesdis,
National Centers for Environmental Information, 73, 40 pp.,
available at: https://www.nodc.noaa.gov/OC5/woa13/ (last access: 19 August 2019), 2013. a
Madec, G.: NEMO ocean engine, Note du Pôle de modélisation 27, Institut
Pierre-Simon Laplace, France, iSSN No 1288-1619, 2008. a
Massonnet, F., Fichefet, T., Goosse, H., Vancoppenolle, M., Mathiot, P., and König Beatty, C.: On the influence of model physics on simulations of Arctic and Antarctic sea ice, The Cryosphere, 5, 687–699, https://doi.org/10.5194/tc-5-687-2011, 2011. a, b
Massonnet, F., Fichefet, T., Goosse, H., Bitz, C. M., Philippon-Berthier, G., Holland, M. M., and Barriat, P.-Y.: Constraining projections of summer Arctic sea ice, The Cryosphere, 6, 1383-1394, https://doi.org/10.5194/tc-6-1383-2012, 2012. a, b
Massonnet, F., Vancoppenolle, M., Goosse, H., Docquier, D., Fichefet, T., and
Blanchard-Wrigglesworth, E.: Arctic sea-ice change tied to its mean state
through thermodynamic processes, Nat. Clim. Change, 8, 599–603,
https://doi.org/10.1038/s41558-018-0204-z, 2018. a, b
Massonnet, F., Barthélemy, A., Worou, K., Fichefet, T., Vancoppenolle, M., Rousset, C., and Moreno-Chamarro, E.: fmassonn/paper-itd-seaice: Accepted paper (Version 1.2.0), Zenodo, https://doi.org/10.5281/zenodo.3345604, 2019. a
Maykut, G. A.: Large-scale heat exchange and ice production in the central
Arctic, J. Geophys. Res., 87, 7971–7984, https://doi.org/10.1029/JC087iC10p07971,
1982. a
Maykut, G. A. and McPhee, M. G.: Solar heating of the Arctic mixed layer, J.
Geophys. Res., 100, 24691–24703, https://doi.org/10.1029/95JC02554,
1995. a
Maykut, G. A. and Untersteiner, N.: Some results from a time-dependent
thermodynamic model of sea ice, J. Geophys. Res., 76,
1550–1575, https://doi.org/10.1029/jc076i006p01550,
1971. a, b
Meier, W. N. and Stewart, J. S.: Assessing uncertainties in sea ice extent
climate indicators, Environ. Res. Lett., 14, 35005,
https://doi.org/10.1088/1748-9326/aaf52c,
2018. a
Merino, N., Le Sommer, J., Durand, G., Jourdain, N. C., Madec, G., Mathiot, P.,
and Tournadre, J.: Antarctic icebergs melt over the Southern Ocean:
Climatology and impact on sea ice, Ocean Modell., 104, 99–110,
https://doi.org/10.1016/j.ocemod.2016.05.001,
2016. a
Notz, D.: How well must climate models agree with observations?, Philosophical
Transactions of the Royal Society A: Mathematical, Phys. Eng.
Scie., 373, 20140164, https://doi.org/10.1098/rsta.2014.0164,
2015. a
Olonscheck, D. and Notz, D.: Consistently Estimating Internal Climate
Variability from Climate Model Simulations, J. Climate, 30,
9555–9573, https://doi.org/10.1175/jcli-d-16-0428.1,
2017. a
Rabatel, M., Rampal, P., Carrassi, A., Bertino, L., and Jones, C. K. R. T.: Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic, The Cryosphere, 12, 935–953, https://doi.org/10.5194/tc-12-935-2018, 2018.
a
Rousset, C., Vancoppenolle, M., Madec, G., Fichefet, T., Flavoni, S., Barthélemy, A., Benshila, R., Chanut, J., Levy, C., Masson, S., and Vivier, F.: The Louvain-La-Neuve sea ice model LIM3.6: global and regional capabilities, Geosci. Model Dev., 8, 2991–3005, https://doi.org/10.5194/gmd-8-2991-2015, 2015. a, b
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res., 116, C00D06,
https://doi.org/10.1029/2011JC007084,
2011. a, b, c, d
Thorndike, A. S., Rothrock, D. A., Maykut, G. A., and Colony, R.: The Thickness
Distribution of Sea Ice, J. Geophys. Res., 80, 4501–4513,
https://doi.org/10.1029/JC080i033p04501,
1975. a, b, c
Ungermann, M., Tremblay, L. B., Martin, T., and Losch, M.: Impact of the ice
strength formulation on the performance of a sea ice thickness distribution
model in the Arctic, J. Geophys. Res.-Oceans, 122, 2090–2107,
https://doi.org/10.1002/2016JC012128,
2017. a, b, c
Uotila, P., Iovino, D., Vancoppenolle, M., Lensu, M., and Rousset, C.: Comparing sea ice, hydrography and circulation between NEMO3.6 LIM3 and LIM2, Geosci. Model Dev., 10, 1009–1031, https://doi.org/10.5194/gmd-10-1009-2017, 2017. a, b
Urrego-Blanco, J. R., Urban, N. M., Hunke, E. C., Turner, A. K., and Jeffery,
N.: Uncertainty quantification and global sensitivity analysis of the Los
Alamos sea ice model, J. Geophys. Res.-Oceans, 121,
2709–2732, https://doi.org/10.1002/2015jc011558,
2016. a
Vancoppenolle, M., Fichefet, T., and Goosse, H.: Simulating the mass balance
and salinity of Arctic and Antarctic sea ice. 2. Importance of sea ice
salinity variations, Ocean Modell., 27, 54–69,
https://doi.org/10.1016/j.ocemod.2008.11.003,
2009a. a
Vancoppenolle, M., Fichefet, T., Goosse, H., Bouillon, S., Madec, G., and
Morales Maqueda, M. A.: Simulating the mass balance and salinity of Arctic
and Antarctic sea ice. 1. Model description and validation, Ocean Modell.,
27, 33–53, https://doi.org/10.1016/j.ocemod.2008.10.005,
2009b. a
Williams, G., Maksym, T., Wilkinson, J., Kunz, C., Murphy, C., Kimball, P., and
Singh, H.: Thick and deformed Antarctic sea ice mapped with autonomous
underwater vehicles, Nat. Geosci., 8, 61–67, https://doi.org/10.1038/ngeo2299,
2014. a
Zhang, J. and Rothrock, D. A.: Modeling Global Sea Ice with a Thickness and
Enthalpy Distribution Model in Generalized Curvilinear Coordinates, Mon. Weather
Rev., 131, 845–861, https://doi.org/10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2,
2003. a, b, c
Zweng, M., Reagan, J., Antonov, J., Locarnini, R., Mishonov, A., Boyer, T.,
Garcia, H., Baranova, O., Johnson, D., Seidov, D., and Biddle, M.: World Ocean
Atlas 2013, Volume 2: Salinity, edited by: Levitus, S. and Mishonov, A.,
NOAA atlas nesdism National Centers for Environmental Information, 74, 39 pp.,
available at: https://www.nodc.noaa.gov/OC5/woa13/ (last access: 19 August 2019), 2013. a, b
Short summary
Sea ice thickness varies considerably on spatial scales of several meters. However, contemporary climate models cannot resolve such scales yet. This is why sea ice models used in climate models include an ice thickness distribution (ITD) to account for this unresolved variability. Here, we explore with the ocean–sea ice model NEMO3.6-LIM3 the sensitivity of simulated mean Arctic and Antarctic sea ice states to the way the ITD is discretized.
Sea ice thickness varies considerably on spatial scales of several meters. However, contemporary...
Special issue