Articles | Volume 10, issue 3
https://doi.org/10.5194/tc-10-1055-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-10-1055-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
20 May 2016
Research article |
| 20 May 2016
neXtSIM: a new Lagrangian sea ice model
Nansen Environmental and Remote Sensing Center and Bjerknes Centre
for Climate Research, Bergen, Norway
Sylvain Bouillon
Nansen Environmental and Remote Sensing Center and Bjerknes Centre
for Climate Research, Bergen, Norway
Einar Ólason
Nansen Environmental and Remote Sensing Center and Bjerknes Centre
for Climate Research, Bergen, Norway
Mathieu Morlighem
Department of Earth System Science, University of California, Irvine, California, USA
Related authors
Fabien Salmon, Pierre Rampal, Stéphanie Leroux, Timothy Williams, Einar Ólason, and Nicolas Barral
EGUsphere, https://doi.org/10.5194/egusphere-2026-1869, https://doi.org/10.5194/egusphere-2026-1869, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Accurate modeling of sea ice dynamics is a major challenge for forecasting its future evolution and assessing its impact on climate change. This paper presents the parallelisation of state-of-the art sea-ice dynamics model NeXtSIM. The code was interfaced with a new parallel version of the remeshing library MMG. Validation and performance of the code are discussed. Simulations with a uniform 1km spatial resolution are run, which is unprecedented with this kind of lagrangian sea-ice models.
Lohenn Fiol, Stephanie Leroux, Pierre Rampal, and Jean-Michel Brankart
EGUsphere, https://doi.org/10.5194/egusphere-2025-6379, https://doi.org/10.5194/egusphere-2025-6379, 2026
Short summary
Short summary
We examine how uncertainty in the initial position of sea ice features (leads, ridges), affects daily-to-weekly winter sea-ice forecasts. Using ensemble simulations with a sea ice–ocean model, we compare two formulations of sea ice mechanics. We show that pack-ice dynamics are highly sensitive to this choice: one formulation strongly amplifies small initial errors, while the other damps them. Our results highlight the need for ensemble forecasts to capture uncertainty and risks in the Arctic.
Einar Ólason, Guillaume Boutin, Timothy Williams, Anton Korosov, Heather Regan, Jonathan Rheinlænder, Pierre Rampal, Daniela Flocco, Abdoulaye Samaké, Richard Davy, Timothy Spain, and Sean Chua
EGUsphere, https://doi.org/10.5194/egusphere-2024-3521, https://doi.org/10.5194/egusphere-2024-3521, 2025
Short summary
Short summary
This paper introduces a new version of the neXtSIM sea-ice model. NeXtSIM is unique among sea-ice models in how it represents sea-ice dynamics, focusing on features such as cracks and ridges and how these impact interactions between the atmosphere and ocean where sea ice is present. The new version introduces some physical parameterisations and model options detailed and explained in the paper. Following the paper's publication, the neXtSIM code will be released publicly for the first time.
Rémy Lapere, Louis Marelle, Pierre Rampal, Laurent Brodeau, Christian Melsheimer, Gunnar Spreen, and Jennie L. Thomas
Atmos. Chem. Phys., 24, 12107–12132, https://doi.org/10.5194/acp-24-12107-2024, https://doi.org/10.5194/acp-24-12107-2024, 2024
Short summary
Short summary
Elongated open-water areas in sea ice, called leads, can release marine aerosols into the atmosphere. In the Arctic, this source of atmospheric particles could play an important role for climate. However, the amount, seasonality and spatial distribution of such emissions are all mostly unknown. Here, we propose a first parameterization for sea spray aerosols emitted through leads in sea ice and quantify their impact on aerosol populations in the high Arctic.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 18, 2381–2406, https://doi.org/10.5194/tc-18-2381-2024, https://doi.org/10.5194/tc-18-2381-2024, 2024
Short summary
Short summary
We explore multivariate state and parameter estimation using a data assimilation approach through idealised simulations in a dynamics-only sea-ice model based on novel rheology. We identify various potential issues that can arise in complex operational sea-ice models when model parameters are estimated. Even though further investigation will be needed for such complex sea-ice models, we show possibilities of improving the observed and the unobserved model state forecast and parameter accuracy.
Anton Korosov, Pierre Rampal, Yue Ying, Einar Ólason, and Timothy Williams
The Cryosphere, 17, 4223–4240, https://doi.org/10.5194/tc-17-4223-2023, https://doi.org/10.5194/tc-17-4223-2023, 2023
Short summary
Short summary
It is possible to compute sea ice motion from satellite observations and detect areas where ice converges (moves together), forms ice ridges or diverges (moves apart) and opens leads. However, it is difficult to predict the exact motion of sea ice and position of ice ridges or leads using numerical models. We propose a new method to initialise a numerical model from satellite observations to improve the accuracy of the forecasted position of leads and ridges for safer navigation.
Heather Regan, Pierre Rampal, Einar Ólason, Guillaume Boutin, and Anton Korosov
The Cryosphere, 17, 1873–1893, https://doi.org/10.5194/tc-17-1873-2023, https://doi.org/10.5194/tc-17-1873-2023, 2023
Short summary
Short summary
Multiyear ice (MYI), sea ice that survives the summer, is more resistant to changes than younger ice in the Arctic, so it is a good indicator of sea ice resilience. We use a model with a new way of tracking MYI to assess the contribution of different processes affecting MYI. We find two important years for MYI decline: 2007, when dynamics are important, and 2012, when melt is important. These affect MYI volume and area in different ways, which is important for the interpretation of observations.
Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, https://doi.org/10.5194/tc-17-1735-2023, 2023
Short summary
Short summary
This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
Guillaume Boutin, Einar Ólason, Pierre Rampal, Heather Regan, Camille Lique, Claude Talandier, Laurent Brodeau, and Robert Ricker
The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, https://doi.org/10.5194/tc-17-617-2023, 2023
Short summary
Short summary
Sea ice cover in the Arctic is full of cracks, which we call leads. We suspect that these leads play a role for atmosphere–ocean interactions in polar regions, but their importance remains challenging to estimate. We use a new ocean–sea ice model with an original way of representing sea ice dynamics to estimate their impact on winter sea ice production. This model successfully represents sea ice evolution from 2000 to 2018, and we find that about 30 % of ice production takes place in leads.
Timothy Williams, Anton Korosov, Pierre Rampal, and Einar Ólason
The Cryosphere, 15, 3207–3227, https://doi.org/10.5194/tc-15-3207-2021, https://doi.org/10.5194/tc-15-3207-2021, 2021
Short summary
Short summary
neXtSIM (neXt-generation Sea Ice Model) includes a novel and extremely realistic way of modelling sea ice dynamics – i.e. how the sea ice moves and deforms in response to the drag from winds and ocean currents. It has been developed over the last few years for a variety of applications, but this paper represents its first demonstration in a forecast context. We present results for the time period from November 2018 to June 2020 and show that it agrees well with satellite observations.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
Short summary
Short summary
Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Jamie Barnett, Felicity A. Holmes, Sarah L. Greenwood, Mathieu Morlighem, Nina Kirchner, and Martin Jakobsson
The Cryosphere, 20, 3599–3617, https://doi.org/10.5194/tc-20-3599-2026, https://doi.org/10.5194/tc-20-3599-2026, 2026
Short summary
Short summary
Computer models used to predict future change of the Greenland Ice Sheet are uncertain, especially in how they represent iceberg calving. We compare several calving approaches by testing model results against satellite observations of changes at three unique floating ice shelves in Greenland. We then extend the simulations to the year 2300 to explore future ice loss, finding that warming of the atmosphere or ocean is more important than the choice of calving method.
Mansa Krishna, Gong Cheng, and Mathieu Morlighem
The Cryosphere, 20, 3533–3558, https://doi.org/10.5194/tc-20-3533-2026, https://doi.org/10.5194/tc-20-3533-2026, 2026
Short summary
Short summary
Estimates of the Greenland Ice Sheet’s contribution to sea level rise are affected by uncertainties in the bed topography. Traditional, physics-based methods for inferring the bed elevation are limited to fast-flowing areas of the ice sheet. We use machine learning models informed with two physical laws to infer the bed elevation for different regions in Greenland, showing that this method can be used to infer the bed elevation in slower-moving, sparsely surveyed regions of the ice sheet.
Adam J. Hepburn, Sammie Buzzard, Andrew J. Sole, Stephen J. Livingstone, Felix Ng, Mathieu Morlighem, Elizabeth A. Bagshaw, Caroline Clason, Lisa Craw, Christine Dow, Samuel Doyle, Jonathan Hawkins, Matthew Peacey, and Robert Storrar
EGUsphere, https://doi.org/10.5194/egusphere-2026-2948, https://doi.org/10.5194/egusphere-2026-2948, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Lakes at glacier margins can drain beneath the ice in sudden, devastating, floods called jökulhlaup. We present the first 2D model of jökulhlaup propagation, first applying it to a synthetic glacier, before then assessing its ability to reproduce an observed record of lake fill-drain cycles in Greenland. Our results match a 17-year record of flood timing but underpredicts peak discharge likely because we are missing certain physical processes. Our results hold promise for simulating jökulhlaup.
Daniel Abele, Thomas Kleiner, Yannic Fischler, Benjamin Uekermann, Gerasimos Chourdakis, Mathieu Morlighem, Achim Basermann, Christian Bischof, Hans-Joachim Bungartz, and Angelika Humbert
Geosci. Model Dev., 19, 5019–5039, https://doi.org/10.5194/gmd-19-5019-2026, https://doi.org/10.5194/gmd-19-5019-2026, 2026
Short summary
Short summary
Accurate simulations of ice-sheet evolution require a multi-physics approach that captures the interaction between glaciers and their subglacial environment. For this purpose, we have extended ice sheet and subglacial hydrology models using a coupling library. This simplifies coupled Earth systems simulations by allowing models to interact with minimal effort and computational expense. We have evaluated the solution regarding performance and choice of coupling library.
Fabien Salmon, Pierre Rampal, Stéphanie Leroux, Timothy Williams, Einar Ólason, and Nicolas Barral
EGUsphere, https://doi.org/10.5194/egusphere-2026-1869, https://doi.org/10.5194/egusphere-2026-1869, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Accurate modeling of sea ice dynamics is a major challenge for forecasting its future evolution and assessing its impact on climate change. This paper presents the parallelisation of state-of-the art sea-ice dynamics model NeXtSIM. The code was interfaced with a new parallel version of the remeshing library MMG. Validation and performance of the code are discussed. Simulations with a uniform 1km spatial resolution are run, which is unprecedented with this kind of lagrangian sea-ice models.
James R. Jordan, Frank Pattyn, Daniel Abele, Torsten Albrecht, Jorge Alvarez-Solas, Jowan M. Barnes, Tijn Berends, Jorge A. Bernales, Javier Blasco, Gong Cheng, Youngmin Choi, Stephen L. Cornford, Cruz Garcia-Molina, Fabien Gillet-Chaulet, G. Hilmar Gudmundsson, Angelika Humbert, Gunter R. Leguy, William H. Lipscomb, Marisa Montoya, Daniel Moreno-Parada, Mathieu Morlighem, Tyler Pelle, Alexander Robinson, Martin Rückamp, Hélène Seroussi, Yanmei Tian, Luisa Wagner, Roderick S. W. van de Wal, Liyun Zhao, and Thomas Zwinger
EGUsphere, https://doi.org/10.5194/egusphere-2026-1962, https://doi.org/10.5194/egusphere-2026-1962, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Ice calving is a recent inclusion in ice sheet models and there has not been a systematic test of how well it has been implemented. We show that models can accurately represent a given rate of ice calving, properties at the ice front evolve smoothly during calving, and that there are no consistent differences in ice behaviour between various approaches to representing calving in numerical ice models. This gives us confidence in their ability for use in sea level rise prediction simulations.
Ankit Pramanik, Sarah L. Greenwood, Mathieu Morlighem, Jamie Barnett, Felicity A. Holmes, Richard Gyllencreutz, and Carl Regnéll
EGUsphere, https://doi.org/10.5194/egusphere-2026-172, https://doi.org/10.5194/egusphere-2026-172, 2026
Short summary
Short summary
Ice sheet/glacier retreat is driving expansion of glacial lakes impounded at the ice margin. Using an ice sheet model, we find that lake filling and drainage causes systematic changes to glacier thickness, velocity and mass loss. Rising lake levels dramatically increase mass loss as the ice margin becomes buoyant, while rapid lake drainage can drive crevassing and destabilise the margin. Steady lake drainage, in contrast, has a stabilising effect, and may mitigate mass loss and flood hazards.
Marek Muchow, Einar Ólason, and Arttu Polojärvi
EGUsphere, https://doi.org/10.5194/egusphere-2025-6421, https://doi.org/10.5194/egusphere-2025-6421, 2026
Short summary
Short summary
Sea-ice ridges increase the ice thickness beyond values created by thermodynamic growth. We simulated ridging using a discrete-element-method model and compared the resulting ice thickness changes to commonly used methods in continuum sea-ice modeling. The discrete-element-method simulations have a higher spatial resolution. We observe both triangular and trapezoidal ridges. Both ridge shapes influence the amount of thick ice after deformation, which we describe with an analytical function.
Lohenn Fiol, Stephanie Leroux, Pierre Rampal, and Jean-Michel Brankart
EGUsphere, https://doi.org/10.5194/egusphere-2025-6379, https://doi.org/10.5194/egusphere-2025-6379, 2026
Short summary
Short summary
We examine how uncertainty in the initial position of sea ice features (leads, ridges), affects daily-to-weekly winter sea-ice forecasts. Using ensemble simulations with a sea ice–ocean model, we compare two formulations of sea ice mechanics. We show that pack-ice dynamics are highly sensitive to this choice: one formulation strongly amplifies small initial errors, while the other damps them. Our results highlight the need for ensemble forecasts to capture uncertainty and risks in the Arctic.
Johanna Beckmann, Ronja Reese, Felicity S. McCormack, Sue Cook, Lawrence Bird, Dawid Gwyther, Daniel Richards, Matthias Scheiter, Yu Wang, Hélène Seroussi, Ayako Abe‐Ouchi, Torsten Albrecht, Jorge Alvarez‐Solas, Xylar S. Asay‐Davis, Jean‐Baptiste Barre, Constantijn J. Berends, Jorge Bernales, Javier Blasco, Justine Caillet, David M. Chandler, Violaine Coulon, Richard Cullather, Christophe Dumas, Benjamin K. Galton‐Fenzi, Julius Garbe, Fabien Gillet‐Chaulet, Rupert Gladstone, Heiko Goelzer, Nicholas R. Golledge, Ralf Greve, G. Hilmar Gudmundsson, Holly Kyeore Han, Trevor R. Hillebrand, Matthew J. Hoffman, Philippe Huybrechts, Nicolas C. Jourdain, Ann Kristin Klose, Petra M. Langebroek, Gunter R. Leguy, William H. Lipscomb, Daniel P. Lowry, Pierre Mathiot, Marisa Montoya, Mathieu Morlighem, Sophie Nowicki, Frank Pattyn, Antony J. Payne, Tyler Pelle, Aurélien Quiquet, Alexander Robinson, Leopekka Saraste, Erika G. Simon, Sainan Sun, Jake P. Twarog, Luke D. Trusel, Benoit Urruty, Jonas Van Breedam, Roderik S. W. van de Wal, Chen Zhao, and Thomas Zwinger
EGUsphere, https://doi.org/10.5194/egusphere-2025-4069, https://doi.org/10.5194/egusphere-2025-4069, 2025
Short summary
Short summary
Antarctica holds enough ice to raise sea levels by many meters, but its future is uncertain. Warm ocean water melts ice shelves from below, letting inland ice flow faster into the sea. By 2300, Antarctica could add 0.6–4.4 m to sea levels. Our study identifies two key factors—how strongly shelves melt and how the ice responds. These explain much of the range, and refining them in models may improve future predictions.
Jamie Barnett, Felicity A. Holmes, Joshua Cuzzone, Henning Åkesson, Mathieu Morlighem, Matt O'Regan, Johan Nilsson, Nina Kirchner, and Martin Jakobsson
The Cryosphere, 19, 3631–3653, https://doi.org/10.5194/tc-19-3631-2025, https://doi.org/10.5194/tc-19-3631-2025, 2025
Short summary
Short summary
Understanding how ice sheets have changed in the past can allow us to make better predictions for the future. By running a state-of-the-art model of Ryder Glacier, North Greenland, over the past 12 000 years we find that both a warming atmosphere and the ocean play a key role in the evolution of the glacier. Our conclusions stress that accurately quantifying the ice sheet’s interactions with the ocean is required to predict future changes and reliable sea level rise estimates.
Gong Cheng, Mansa Krishna, and Mathieu Morlighem
Geosci. Model Dev., 18, 5311–5327, https://doi.org/10.5194/gmd-18-5311-2025, https://doi.org/10.5194/gmd-18-5311-2025, 2025
Short summary
Short summary
Predicting ice sheet contributions to sea level rise is challenging due to limited data and uncertainties in key processes. Traditional models require complex methods that lack flexibility. We developed PINNICLE (Physics-Informed Neural Networks for Ice and CLimatE), an open-source Python library that integrates machine learning with physical laws to improve ice sheet modeling. By combining data and physics, PINNICLE enhances predictions and adaptability, providing a powerful tool for climate research and sea level rise projections.
Felicity A. Holmes, Jamie Barnett, Henning Åkesson, Mathieu Morlighem, Johan Nilsson, Nina Kirchner, and Martin Jakobsson
The Cryosphere, 19, 2695–2714, https://doi.org/10.5194/tc-19-2695-2025, https://doi.org/10.5194/tc-19-2695-2025, 2025
Short summary
Short summary
Northern Greenland contains some of the ice sheet's last remaining glaciers with floating ice tongues. One of these is Ryder Glacier, which has been relatively stable in recent decades, in contrast to nearby glaciers. Here, we use a computer model to simulate Ryder Glacier until 2300 under both a low- and a high-emissions scenario. Very high levels of surface melt under a high-emissions future lead to a sea level rise contribution that is an order of magnitude higher than under a low-emissions future.
Younghyun Koo, Gong Cheng, Mathieu Morlighem, and Maryam Rahnemoonfar
The Cryosphere, 19, 2583–2599, https://doi.org/10.5194/tc-19-2583-2025, https://doi.org/10.5194/tc-19-2583-2025, 2025
Short summary
Short summary
Calving, the breaking of ice bodies from the terminus of a glacier, plays an important role in the mass losses of Greenland ice sheets. However, calving parameters have been poorly understood because of the intensive computational demands of traditional numerical models. To address this issue and find the optimal calving parameter that best represents real observations, we develop deep-learning emulators based on graph neural network architectures.
Shfaqat A. Khan, Helene Seroussi, Mathieu Morlighem, William Colgan, Veit Helm, Gong Cheng, Danjal Berg, Valentina R. Barletta, Nicolaj K. Larsen, William Kochtitzky, Michiel van den Broeke, Kurt H. Kjær, Andy Aschwanden, Brice Noël, Jason E. Box, Joseph A. MacGregor, Robert S. Fausto, Kenneth D. Mankoff, Ian M. Howat, Kuba Oniszk, Dominik Fahrner, Anja Løkkegaard, Eigil Y. H. Lippert, Alicia Bråtner, and Javed Hassan
Earth Syst. Sci. Data, 17, 3047–3071, https://doi.org/10.5194/essd-17-3047-2025, https://doi.org/10.5194/essd-17-3047-2025, 2025
Short summary
Short summary
The surface elevation of the Greenland Ice Sheet is changing due to surface mass balance processes and ice dynamics, each exhibiting distinct spatiotemporal patterns. Here, we employ satellite and airborne altimetry data with fine spatial (1 km) and temporal (monthly) resolutions to document this spatiotemporal evolution from 2003 to 2023. This dataset of fine-resolution altimetry data in both space and time will support studies of ice mass loss and be useful for GIS ice sheet modeling.
Laurent Bertino, Patrick Heimbach, Ed Blockley, and Einar Ólason
State Planet, 5-opsr, 14, https://doi.org/10.5194/sp-5-opsr-14-2025, https://doi.org/10.5194/sp-5-opsr-14-2025, 2025
Short summary
Short summary
Forecasts of sea ice are in high demand in the polar regions, and they are also quickly improving and becoming more easily accessible to non-experts. We provide here a brief status of the short-term forecasting services – typically 10 d ahead – and an outlook of their upcoming developments.
Joshua K. Cuzzone, Aaron Barth, Kelsey Barker, and Mathieu Morlighem
The Cryosphere, 19, 1559–1575, https://doi.org/10.5194/tc-19-1559-2025, https://doi.org/10.5194/tc-19-1559-2025, 2025
Short summary
Short summary
We use an ice sheet model to simulate the Last Glacial Maximum conditions of the Laurentide Ice Sheet (LIS) across the northeastern United States. A complex thermal history existed for the LIS that caused high erosion across most of the NE USA but prevented erosion across high-elevation mountain peaks and areas where ice flow was slow. This has implications for geologic studies which rely on the erosional nature of the LIS to reconstruct its glacial history and landscape evolution.
Anton Korosov, Yue Ying, and Einar Ólason
Geosci. Model Dev., 18, 885–904, https://doi.org/10.5194/gmd-18-885-2025, https://doi.org/10.5194/gmd-18-885-2025, 2025
Short summary
Short summary
We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Einar Ólason, Guillaume Boutin, Timothy Williams, Anton Korosov, Heather Regan, Jonathan Rheinlænder, Pierre Rampal, Daniela Flocco, Abdoulaye Samaké, Richard Davy, Timothy Spain, and Sean Chua
EGUsphere, https://doi.org/10.5194/egusphere-2024-3521, https://doi.org/10.5194/egusphere-2024-3521, 2025
Short summary
Short summary
This paper introduces a new version of the neXtSIM sea-ice model. NeXtSIM is unique among sea-ice models in how it represents sea-ice dynamics, focusing on features such as cracks and ridges and how these impact interactions between the atmosphere and ocean where sea ice is present. The new version introduces some physical parameterisations and model options detailed and explained in the paper. Following the paper's publication, the neXtSIM code will be released publicly for the first time.
Francesca Baldacchino, Nicholas R. Golledge, Mathieu Morlighem, Huw Horgan, Alanna V. Alevropoulos-Borrill, Alena Malyarenko, Alexandra Gossart, Daniel P. Lowry, and Laurine van Haastrecht
The Cryosphere, 19, 107–127, https://doi.org/10.5194/tc-19-107-2025, https://doi.org/10.5194/tc-19-107-2025, 2025
Short summary
Short summary
Understanding how the Ross Ice Shelf flow is changing in a warming world is important for predicting ice sheet change. Field measurements show clear intra-annual variations in ice flow; however, it is unclear what mechanisms drive this variability. We show that local perturbations in basal melt can have a significant impact on ice flow speed, but a combination of forcings is likely driving the observed variability in ice flow.
Rémy Lapere, Louis Marelle, Pierre Rampal, Laurent Brodeau, Christian Melsheimer, Gunnar Spreen, and Jennie L. Thomas
Atmos. Chem. Phys., 24, 12107–12132, https://doi.org/10.5194/acp-24-12107-2024, https://doi.org/10.5194/acp-24-12107-2024, 2024
Short summary
Short summary
Elongated open-water areas in sea ice, called leads, can release marine aerosols into the atmosphere. In the Arctic, this source of atmospheric particles could play an important role for climate. However, the amount, seasonality and spatial distribution of such emissions are all mostly unknown. Here, we propose a first parameterization for sea spray aerosols emitted through leads in sea ice and quantify their impact on aerosol populations in the high Arctic.
Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-2476, https://doi.org/10.5194/egusphere-2024-2476, 2024
Preprint archived
Short summary
Short summary
The formation and evolution of sea ice melt ponds (ponds of melted water) are complex, insufficiently understood and represented in models with considerable uncertainty. These uncertain representations are not traditionally included in climate models potentially causing the known underestimation of sea ice loss in climate models. Our work creates the first observationally based machine learning model of melt ponds that is also a ready and viable candidate to be included in climate models.
Gong Cheng, Mathieu Morlighem, and G. Hilmar Gudmundsson
Geosci. Model Dev., 17, 6227–6247, https://doi.org/10.5194/gmd-17-6227-2024, https://doi.org/10.5194/gmd-17-6227-2024, 2024
Short summary
Short summary
We conducted a comprehensive analysis of the stabilization and reinitialization techniques currently employed in ISSM and Úa for solving level-set equations, specifically those related to the dynamic representation of moving ice fronts within numerical ice sheet models. Our results demonstrate that the streamline upwind Petrov–Galerkin (SUPG) method outperforms the other approaches. We found that excessively frequent reinitialization can lead to exceptionally high errors in simulations.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 18, 2381–2406, https://doi.org/10.5194/tc-18-2381-2024, https://doi.org/10.5194/tc-18-2381-2024, 2024
Short summary
Short summary
We explore multivariate state and parameter estimation using a data assimilation approach through idealised simulations in a dynamics-only sea-ice model based on novel rheology. We identify various potential issues that can arise in complex operational sea-ice models when model parameters are estimated. Even though further investigation will be needed for such complex sea-ice models, we show possibilities of improving the observed and the unobserved model state forecast and parameter accuracy.
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Guillaume Boutin, and Einar Ólason
The Cryosphere, 18, 1791–1815, https://doi.org/10.5194/tc-18-1791-2024, https://doi.org/10.5194/tc-18-1791-2024, 2024
Short summary
Short summary
This paper focuses on predicting Arctic-wide sea-ice thickness using surrogate modeling with deep learning. The model has a predictive power of 12 h up to 6 months. For this forecast horizon, persistence and daily climatology are systematically outperformed, a result of learned thermodynamics and advection. Consequently, surrogate modeling with deep learning proves to be effective at capturing the complex behavior of sea ice.
In-Woo Park, Emilia Kyung Jin, Mathieu Morlighem, and Kang-Kun Lee
The Cryosphere, 18, 1139–1155, https://doi.org/10.5194/tc-18-1139-2024, https://doi.org/10.5194/tc-18-1139-2024, 2024
Short summary
Short summary
This study conducted 3D thermodynamic ice sheet model experiments, and modeled temperatures were compared with 15 observed borehole temperature profiles. We found that using incompressibility of ice without sliding agrees well with observed temperature profiles in slow-flow regions, while incorporating sliding in fast-flow regions captures observed temperature profiles. Also, the choice of vertical velocity scheme has a greater impact on the shape of the modeled temperature profile.
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
Short summary
Short summary
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.
Youngmin Choi, Helene Seroussi, Mathieu Morlighem, Nicole-Jeanne Schlegel, and Alex Gardner
The Cryosphere, 17, 5499–5517, https://doi.org/10.5194/tc-17-5499-2023, https://doi.org/10.5194/tc-17-5499-2023, 2023
Short summary
Short summary
Ice sheet models are often initialized using snapshot observations of present-day conditions, but this approach has limitations in capturing the transient evolution of the system. To more accurately represent the accelerating changes in glaciers, we employed time-dependent data assimilation. We found that models calibrated with the transient data better capture past trends and more accurately reproduce changes after the calibration period, even with limited observations.
Hélène Seroussi, Vincent Verjans, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Peter Van Katwyk, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, https://doi.org/10.5194/tc-17-5197-2023, 2023
Short summary
Short summary
Mass loss from Antarctica is a key contributor to sea level rise over the 21st century, and the associated uncertainty dominates sea level projections. We highlight here the Antarctic glaciers showing the largest changes and quantify the main sources of uncertainty in their future evolution using an ensemble of ice flow models. We show that on top of Pine Island and Thwaites glaciers, Totten and Moscow University glaciers show rapid changes and a strong sensitivity to warmer ocean conditions.
Joel A. Wilner, Mathieu Morlighem, and Gong Cheng
The Cryosphere, 17, 4889–4901, https://doi.org/10.5194/tc-17-4889-2023, https://doi.org/10.5194/tc-17-4889-2023, 2023
Short summary
Short summary
We use numerical modeling to study iceberg calving off of ice shelves in Antarctica. We examine four widely used mathematical descriptions of calving (
calving laws), under the assumption that Antarctic ice shelf front positions should be in steady state under the current climate forcing. We quantify how well each of these calving laws replicates the observed front positions. Our results suggest that the eigencalving and von Mises laws are most suitable for Antarctic ice shelves.
Anton Korosov, Pierre Rampal, Yue Ying, Einar Ólason, and Timothy Williams
The Cryosphere, 17, 4223–4240, https://doi.org/10.5194/tc-17-4223-2023, https://doi.org/10.5194/tc-17-4223-2023, 2023
Short summary
Short summary
It is possible to compute sea ice motion from satellite observations and detect areas where ice converges (moves together), forms ice ridges or diverges (moves apart) and opens leads. However, it is difficult to predict the exact motion of sea ice and position of ice ridges or leads using numerical models. We propose a new method to initialise a numerical model from satellite observations to improve the accuracy of the forecasted position of leads and ridges for safer navigation.
Alice C. Frémand, Peter Fretwell, Julien A. Bodart, Hamish D. Pritchard, Alan Aitken, Jonathan L. Bamber, Robin Bell, Cesidio Bianchi, Robert G. Bingham, Donald D. Blankenship, Gino Casassa, Ginny Catania, Knut Christianson, Howard Conway, Hugh F. J. Corr, Xiangbin Cui, Detlef Damaske, Volkmar Damm, Reinhard Drews, Graeme Eagles, Olaf Eisen, Hannes Eisermann, Fausto Ferraccioli, Elena Field, René Forsberg, Steven Franke, Shuji Fujita, Yonggyu Gim, Vikram Goel, Siva Prasad Gogineni, Jamin Greenbaum, Benjamin Hills, Richard C. A. Hindmarsh, Andrew O. Hoffman, Per Holmlund, Nicholas Holschuh, John W. Holt, Annika N. Horlings, Angelika Humbert, Robert W. Jacobel, Daniela Jansen, Adrian Jenkins, Wilfried Jokat, Tom Jordan, Edward King, Jack Kohler, William Krabill, Mette Kusk Gillespie, Kirsty Langley, Joohan Lee, German Leitchenkov, Carlton Leuschen, Bruce Luyendyk, Joseph MacGregor, Emma MacKie, Kenichi Matsuoka, Mathieu Morlighem, Jérémie Mouginot, Frank O. Nitsche, Yoshifumi Nogi, Ole A. Nost, John Paden, Frank Pattyn, Sergey V. Popov, Eric Rignot, David M. Rippin, Andrés Rivera, Jason Roberts, Neil Ross, Anotonia Ruppel, Dustin M. Schroeder, Martin J. Siegert, Andrew M. Smith, Daniel Steinhage, Michael Studinger, Bo Sun, Ignazio Tabacco, Kirsty Tinto, Stefano Urbini, David Vaughan, Brian C. Welch, Douglas S. Wilson, Duncan A. Young, and Achille Zirizzotti
Earth Syst. Sci. Data, 15, 2695–2710, https://doi.org/10.5194/essd-15-2695-2023, https://doi.org/10.5194/essd-15-2695-2023, 2023
Short summary
Short summary
This paper presents the release of over 60 years of ice thickness, bed elevation, and surface elevation data acquired over Antarctica by the international community. These data are a crucial component of the Antarctic Bedmap initiative which aims to produce a new map and datasets of Antarctic ice thickness and bed topography for the international glaciology and geophysical community.
Heather Regan, Pierre Rampal, Einar Ólason, Guillaume Boutin, and Anton Korosov
The Cryosphere, 17, 1873–1893, https://doi.org/10.5194/tc-17-1873-2023, https://doi.org/10.5194/tc-17-1873-2023, 2023
Short summary
Short summary
Multiyear ice (MYI), sea ice that survives the summer, is more resistant to changes than younger ice in the Arctic, so it is a good indicator of sea ice resilience. We use a model with a new way of tracking MYI to assess the contribution of different processes affecting MYI. We find two important years for MYI decline: 2007, when dynamics are important, and 2012, when melt is important. These affect MYI volume and area in different ways, which is important for the interpretation of observations.
Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, https://doi.org/10.5194/tc-17-1735-2023, 2023
Short summary
Short summary
This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
Guillaume Boutin, Einar Ólason, Pierre Rampal, Heather Regan, Camille Lique, Claude Talandier, Laurent Brodeau, and Robert Ricker
The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, https://doi.org/10.5194/tc-17-617-2023, 2023
Short summary
Short summary
Sea ice cover in the Arctic is full of cracks, which we call leads. We suspect that these leads play a role for atmosphere–ocean interactions in polar regions, but their importance remains challenging to estimate. We use a new ocean–sea ice model with an original way of representing sea ice dynamics to estimate their impact on winter sea ice production. This model successfully represents sea ice evolution from 2000 to 2018, and we find that about 30 % of ice production takes place in leads.
Francesca Baldacchino, Mathieu Morlighem, Nicholas R. Golledge, Huw Horgan, and Alena Malyarenko
The Cryosphere, 16, 3723–3738, https://doi.org/10.5194/tc-16-3723-2022, https://doi.org/10.5194/tc-16-3723-2022, 2022
Short summary
Short summary
Understanding how the Ross Ice Shelf will evolve in a warming world is important to the future stability of Antarctica. It remains unclear what changes could drive the largest mass loss in the future and where places are most likely to trigger larger mass losses. Sensitivity maps are modelled showing that the RIS is sensitive to changes in environmental and glaciological controls at regions which are currently experiencing changes. These regions need to be monitored in a warming world.
Joshua K. Cuzzone, Nicolás E. Young, Mathieu Morlighem, Jason P. Briner, and Nicole-Jeanne Schlegel
The Cryosphere, 16, 2355–2372, https://doi.org/10.5194/tc-16-2355-2022, https://doi.org/10.5194/tc-16-2355-2022, 2022
Short summary
Short summary
We use an ice sheet model to determine what influenced the Greenland Ice Sheet to retreat across a portion of southwestern Greenland during the Holocene (about the last 12 000 years). Our simulations, constrained by observations from geologic markers, show that atmospheric warming and ice melt primarily caused the ice sheet to retreat rapidly across this domain. We find, however, that iceberg calving at the interface where the ice meets the ocean significantly influenced ice mass change.
Yannic Fischler, Martin Rückamp, Christian Bischof, Vadym Aizinger, Mathieu Morlighem, and Angelika Humbert
Geosci. Model Dev., 15, 3753–3771, https://doi.org/10.5194/gmd-15-3753-2022, https://doi.org/10.5194/gmd-15-3753-2022, 2022
Short summary
Short summary
Ice sheet models are used to simulate the changes of ice sheets in future but are currently often run in coarse resolution and/or with neglecting important physics to make them affordable in terms of computational costs. We conducted a study simulating the Greenland Ice Sheet in high resolution and adequate physics to test where the ISSM ice sheet code is using most time and what could be done to improve its performance for future computer architectures that allow massive parallel computing.
Thomas Frank, Henning Åkesson, Basile de Fleurian, Mathieu Morlighem, and Kerim H. Nisancioglu
The Cryosphere, 16, 581–601, https://doi.org/10.5194/tc-16-581-2022, https://doi.org/10.5194/tc-16-581-2022, 2022
Short summary
Short summary
The shape of a fjord can promote or inhibit glacier retreat in response to climate change. We conduct experiments with a synthetic setup under idealized conditions in a numerical model to study and quantify the processes involved. We find that friction between ice and fjord is the most important factor and that it is possible to directly link ice discharge and grounding line retreat to fjord topography in a quantitative way.
Thiago Dias dos Santos, Mathieu Morlighem, and Douglas Brinkerhoff
The Cryosphere, 16, 179–195, https://doi.org/10.5194/tc-16-179-2022, https://doi.org/10.5194/tc-16-179-2022, 2022
Short summary
Short summary
Projecting the future evolution of Greenland and Antarctica and their potential contribution to sea level rise often relies on computer simulations carried out by numerical ice sheet models. Here we present a new vertically integrated ice sheet model and assess its performance using different benchmarks. The new model shows results comparable to a three-dimensional model at relatively lower computational cost, suggesting that it is an excellent alternative for long-term simulations.
Matt O'Regan, Thomas M. Cronin, Brendan Reilly, Aage Kristian Olsen Alstrup, Laura Gemery, Anna Golub, Larry A. Mayer, Mathieu Morlighem, Matthias Moros, Ole L. Munk, Johan Nilsson, Christof Pearce, Henrieka Detlef, Christian Stranne, Flor Vermassen, Gabriel West, and Martin Jakobsson
The Cryosphere, 15, 4073–4097, https://doi.org/10.5194/tc-15-4073-2021, https://doi.org/10.5194/tc-15-4073-2021, 2021
Short summary
Short summary
Ryder Glacier is a marine-terminating glacier in north Greenland discharging ice into the Lincoln Sea. Here we use marine sediment cores to reconstruct its retreat and advance behavior through the Holocene. We show that while Sherard Osborn Fjord has a physiography conducive to glacier and ice tongue stability, Ryder still retreated more than 40 km inland from its current position by the Middle Holocene. This highlights the sensitivity of north Greenland's marine glaciers to climate change.
Timothy Williams, Anton Korosov, Pierre Rampal, and Einar Ólason
The Cryosphere, 15, 3207–3227, https://doi.org/10.5194/tc-15-3207-2021, https://doi.org/10.5194/tc-15-3207-2021, 2021
Short summary
Short summary
neXtSIM (neXt-generation Sea Ice Model) includes a novel and extremely realistic way of modelling sea ice dynamics – i.e. how the sea ice moves and deforms in response to the drag from winds and ocean currents. It has been developed over the last few years for a variety of applications, but this paper represents its first demonstration in a forecast context. We present results for the time period from November 2018 to June 2020 and show that it agrees well with satellite observations.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
Short summary
Short summary
Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Cited articles
Amante, C. and Eakins, B. W.: ETOPO1 Global Relief Model converted to PanMap layer format, NOAA – National Geophysical Data Center, https://doi.org/10.1594/PANGAEA.769615, 2009.
Amitrano, D., Grasso, J. R., and Hantz, D.: From diffuse to localised damage through elastic interaction, Geophys. Res. Lett., 26, 2109–2112, 1999.
Batchelor, G. K.: Diffusion in a field of homogeneous turbulence, Math. Proc. Cambridge, 48, 345–362, https://doi.org/10.1017/S0305004100027687, 1952.
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic model of sea ice, J. Geophys. Res., 104, 15669–15678, 1999.
Bouillon, S. and Rampal, P.: On producing sea ice deformation data sets from SAR-derived sea ice motion, The Cryosphere, 9, 663–673, https://doi.org/10.5194/tc-9-663-2015, 2015a.
Bouillon, S. and Rampal, P.: Presentation of the dynamical core of neXtSIM, a new sea ice model, Ocean Model., 91, 23–37, 2015b.
Bromwich, D., Bai, L., Hines, K., Wang, S., Liu, Z., Lin, H., Kuo, Y., and Barlage, M.: Arctic System Reanalysis (ASR) Project. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, https://doi.org/10.5065/D6K072B5, 2012.
Bromwich, D. H., Wilson, A. B., Bai, L., Moore, G. W. K., and Bauer, P.: A comparison of the regional Arctic System Reanalysis and the global ERA-Interim Reanalysis for the Arctic, Q. J. Roy. Meteorol. Soc., 142, 644–658, https://doi.org/10.1002/qj.2527, 2016.
Buck, A. L.: New Equations for Computing Vapor Pressure and Enhancement Factor, J. Appl. Meteorol., 1527–1532, 1981.
Cavalieri, D. J., Parkinson, C. L., Gloersen, P., and Zwally, H.: Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, https://doi.org/10.5067/8GQ8LZQVL0VL, updated yearly, 1996.
Comiso, J. C.: Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMI S, Version 2, https://doi.org/10.5067/J6JQLS9EJ5HU, updated 2015, 2000.
Compère, G., Remacle, J. F., and Marchandise, E.: Transient Mesh Adaptivity with Large Rigid-Body Displacements, in: Proceedings of the 17th International Meshing Roundtable, edited by: Garimella, R., Springer, Berlin, 213–230, 2008.
Compère, G., Remacle, J.-F., Jansson, J., and Hoffman, J.: A mesh adaptation framework for dealing with large deforming meshes, Int. J. Numer. Meth. Eng., 82, 843–867, https://doi.org/10.1002/nme.2788, 2009.
Connolley, W. M., Gregory, J. M., Hunke, E. C., and McLaren, A. J.: On the consistent scaling of terms in the sea-ice dynamics equation, J. Phys. Oceanogr., 34, 1776–1780, 2004.
Coon, M. D., Maykut, G. A., Pritchard, R. S., Rothrock, D. A., and Thorndike, A. S.: Modeling the pack ice as an elastic plastic material, AIDJEX Bull., 24, 1–105, 1974.
Copernicus Marine environment monitoring service: Ocean reanalysis data, available at: http://marine.copernicus.eu/, last access: 15 July 2015.
Dansereau, V., Weiss, J., Saramito, P., and Lattes, P.: A Maxwell-Elasto-Brittle rheology for sea ice modelling, The Cryosphere Discuss., https://doi.org/10.5194/tc-2015-200, in review, 2016.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, 2011.
Ebert, E. E. and Curry, J. A.: An intermediate one-dimensional thermodynamic sea ice model for investigating ice–atmosphere interactions, J. Geophys. Res., 98, 10085–10109, 1993.
EUMETSAT Ocean and Sea Ice Satelitte Application Facility: Global sea ice concentration reprocessing dataset 1978–2015 (v1.2), available at: http://osisaf.met.no, last access: 1 June 2015.
Gill, A. E.: Atmosphere-Ocean Dynamics, International Geophysics Series, Academic Press, London, 1982.
Girard, L., Weiss, J., Molines, J. M., Barnier, B., and Bouillon, S.: Evaluation of high-resolution sea ice models on the basis of statistical and scaling properties of Arctic sea ice drift and deformation, J. Geophys. Res., 114, C08015, https://doi.org/10.1029/2008JC005182, 2009.
Girard, L., Bouillon, S., Weiss, J., Amitrano, D., Fichefet, T., and Legat, V.: A new modelling framework for sea ice mechanics based on elasto-brittle rheology, Ann. Glaciol., 52, 123–132, 2011.
Hecht, F.: BAMG: bidimensional anisotropic mesh generator, INRIA, Rocquencourt, France, 1998.
Herman, A.: Discrete-Element bonded-particle Sea Ice model DESIgn, version 1.3a – model description and implementation, Geosci. Model Dev., 9, 1219–1241, https://doi.org/10.5194/gmd-9-1219-2016, 2016.
Hibler, W.: Ice Dynamics, in: The Geophysics of Sea Ice, edited by: Untersteiner, N., NATO ASI Series, Springer US, New York, 577–640, https://doi.org/10.1007/978-1-4899-5352-0_10, 1986.
Hibler, W. D.: A dynamic thermodynamic sea ice model, J. Phys. Oceanogr., 9, 817–846, 1979.
Hopkins, M. A.: A discrete element Lagrangian sea ice model, Eng. Comput., 21, 409–421, https://doi.org/10.1108/02644400410519857, 2004.
Hunke, E. C. and Dukowicz, J. K.: An elastic-viscous-plastic model for sea ice dynamics, J. Phys. Oceanogr., 27, 1849–1867, 1997.
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott, S.: CICE: the Los Alamos Sea Ice Model Documentation and Software User's Manual Version 5.1LA-CC-06-012, 5.1 Edn., Los Alamos National Laboratory, Los Alamos, 2015.
Integrated Climate Data Center – ICDC: Sea ice concentration datasets, available at: http://icdc.zmaw.de/, last access: 15 July 2015.
Ivanova, N., Johannessen, O. M., Pedersen, L. T., and Tonboe, R. T.: Retrieval of Arctic Sea Ice Parameters by Satellite Passive Microwave Sensors: A Comparison of Eleven Sea Ice Concentration Algorithms, IEEE T. Geosci. Remote, 1–14, 2014.
Jakobsson, M., Macnab, R., Mayer, L., Anderson, R., Edwards, M., Hatzky, J., Schenke, H. W., and Johnson, P.: An improved bathymetric portrayal of the Arctic Ocean: Implications for ocean modeling and geological, geophysical and oceanographic analyses, Geophys. Res. Lett., 35, L07602, https://doi.org/10.1029/2008GL033520, 2008.
Kagan, Y. Y.: Fractal dimension of brittle fracture, J. Nonlinear Sci., 1, 1–16, 1991.
Kagan, Y. Y. and Jackson, D. D.: Long-term earthquake clustering, Geophys. J. Int., 104, 117–133, 1991.
Kaleschke, L., Lüpkes, C., Vihma, T., Haarpaintner, J., Bochert, A., Hartmann, J., and Heygster, G.: SSM/I Sea Ice Remote Sensing for Mesoscale Ocean-Atmosphere Interaction Analysis, Can. J. Remote Sens., 27, 526–537, 2001.
Kauker, F., Kaminski, T., Karcher, M., Giering, R., Gerdes, R., and Voßbeck, M.: Adjoint analysis of the 2007 all time Arctic sea-ice minimum, Geophys. Res. Lett., 36, L03707, https://doi.org/10.1029/2008GL036323, l03707, 2009.
Kimmritz, M., Danilov, S., and Losch, M.: On the convergence of the modified elastic–viscous–plastic method for solving the sea ice momentum equation, J. Comp. Phys., 296, 90–100, https://doi.org/http://dx.doi.org/10.1016/j.jcp.2015.04.051, 2015.
Korsnes, R., Souza, S. R., Donangelo, R., Hansen, A., Paczuski, M., and Sneppen, K.: Scaling in fracture and refreezing of sea ice, Physica A: Statistical Mechanics and its Applications, 331, 291–296, 2004.
Kwok, R., Curlander, J. C., McConnell, R., and Pang, S. S.: An Ice Motion Tracking System at the Alaska SAR Facility, IEEE J. Ocean. Eng., 15, 44–54, 1990.
Kwok, R., Cunningham, G. F., and Pang, S. S.: Fram Strait sea ice outflow, J. Geophys. Res., 109, C01009, https://doi.org/10.1029/2003JC001785 2004.
Larour, E., Seroussi, H., Morlighem, M., and Rignot, E.: Continental scale, high order, high spatial resolution, ice sheet modelling using the Ice Sheet System Model (ISSM), J. Geophys. Res.-Earth, 117, F01022, https://doi.org/10.1029/2011JF002140, 2012.
Lemieux, J. F., Tremblay, B., Sedlacek, J., Tupper, P., Thomas, S., D, H., and Auclair, J. P.: Improving the numerical convergence of viscous-plastic sea ice models with the Jacobian-free Newton-Krylov method, J. Comp. Phys., 229, 2840–2852, 2010.
Lemieux, J.-F., Tremblay, B. L., Dupont, F., Plante, M., Smith, G. C., and Dumont, D.: A basal stress parameterization for modeling landfast ice, J. Geophys. Res. Oceans, 120, 3157–3173, 2015.
Lu, P., Leppäranta, M., Li, Z., and Cheng, B.: A parameterization of the ice-ocean drag coefficient, J. Geophys. Res., 116, C07019, https://doi.org/10.1029/2010JC006878, 2011.
Lüpkes, C., Gryanik, V. M., Hartmann, J., and Andreas, E. L.: A parametrization, based on sea ice morphology, of the neutral atmospheric drag coefficients for weather prediction and climate models, J. Geophys. Res.-Atmos., 117, D13112, https://doi.org/10.1029/2012JD017630, 2012.
Marsan, D. and Weiss, J.: Space/time coupling in brittle deformation at geophysical scales, Earth Planet. Sc. Lett., 296, 353–359, 2010.
Marsan, D., Stern, H. L., Lindsay, R., and Weiss, J.: Scale dependence and localization of the deformation of Arctic sea ice, Phys. Rev. Lett., 93, 178501, https://doi.org/10.1103/PhysRevLett.93.178501, 2004.
Maykut, G. A. and Untersteiner, N.: Some results form time-dependent thermodynamic model of sea ice, J. Geophys. Res., 76, 1550–1575, 1971.
McPhee, M. G.: The Effect of the Oceanic Boundary Layer on the Mean Drift of Pack Ice: Application of a Simple Model, J. Phys. Oceanogr., 9, 388–400, 1979.
National Snow and Ice Data Center – NSIDC: Data Sets for Researchers, available at: https://nsidc.org/data/search/, last access: 15 July 2015.
Notz, D., Haumann, F. A., Haak, H., Jungclaus, J. H., and Marotzke, J.: Arctic sea-ice evolution as modeled by Max Planck Institute for Meteorology's Earth system model, J. Adv. Model. Earth Syst., 5, 173–194, https://doi.org/10.1002/jame.20016, 2013.
Ólason, E. and Notz, D.: Drivers of variability in Arctic sea-ice drift speed, J. Geophys. Res. Oceans, 119, 5755–5775, https://doi.org/10.1002/2014JC009897, 2014.
Polar Meterology Group/Byrd Polar Research Center/The Ohio State University: Arctic Systems Reanalysis Model Output Data. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, available at: http://rda.ucar.edu/datasets/ds631.4/ (last access: 1 January 2015), 2012.
Rabatel, M., Labbé, S., and Weiss, J.: Dynamics of an Assembly of Rigid Ice Floes, J. Geophys. Res. Oceans, 120, 5887–5909, https://doi.org/10.1002/2015JC010909, 2015.
Rampal, P., Weiss, J., Marsan, D., Lindsay, R., and Stern, H. L.: Scaling properties of sea ice deformation from buoy dispersion analysis, J. Geophys. Res., 113, C03002, https://doi.org/10.1029/2007JC004143, 2008.
Rampal, P., Weiss, J., and Marsan, D.: Positive trend in the mean speed and deformation rate of Arctic sea ice, 1979–2007, J. Geophys. Res., 114, C05013, https://doi.org/10.1029/2008JC005066, 2009.
Richardson, L. F. and Stommel, H.: Note on eddy diffusion in the sea, J. Meteorol., 5, 238–240, 1948.
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012.
Sakov, P., Counillon, F., Bertino, L., Finck, N., and Renkl, C.: Quality Information Document For Arctic Physical Reanalysis Product – EU Copernicus Marine Service, Tech. rep., Bergen, 2015.
Schreyer, H. L., Sulsky, D., Munday, L. B., Coon, M., and Kwok, R.: Elastic-decohesive constitutive model for sea ice, J. Geophys. Res., 111, C11S26, https://doi.org/10.1029/2005JC003334, 2006.
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.: Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res. Oceans, 116, C00D06, https://doi.org/10.1029/2011JC007084, c00D06, 2011.
Semtner, A. J.: A model for the thermodynamic growth of sea ice in numerical investigations of climate, J. Phys. Oceanogr., 6, 379–389, 1976.
Semtner, A. J.: On modelling the seasonal thermodynamic cycle of sea ice in studies of climatic change, Climatic Change, 6, 27–37, https://doi.org/10.1007/BF00141666, 1984.
Sirevaag, A., de la Rosa, S., Fer, I., Nicolaus, M., Tjernström, M., and McPhee, M. G.: Mixing, heat fluxes and heat content evolution of the Arctic Ocean mixed layer, Ocean Sci., 7, 335–349, https://doi.org/10.5194/os-7-335-2011, 2011.
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using AMSR-E 89-GHz channels, J. Geophys. Res. Oceans, 113, C02S03, https://doi.org/10.1029/2005JC003384, 2008.
Stern, H. L. and Lindsay, R.: Spatial scaling of Arctic sea ice deformation, J. Geophys. Res., 114, C10017, https://doi.org/10.1029/2009JC005380, 2009.
Sulsky, D., Schreyer, H., Peterson, K., Kwok, R., and Coon, M.: Using the material-point method to model sea ice dynamics, J. Geophys. Res., 112, C02S90, https://doi.org/10.1029/2005JC003329, 2007.
Thorndike, A. S., Rothrock, D. A., Maykut, G. A., and Colony, R.: The thickness distribution of sea ice, J. Geophys. Res., 80, 4501–4513, 1975.
Tremblay, L. B. and Mysak, L. A.: Modeling sea ice as a granular material, including the dilatancy effect, J. Phys. Oceanogr., 27, 2342–2360, 1997.
Tsamados, M., Feltham, D. L., and Wilchinsky, A. V.: Impact of a new anisotropic rheology on simulations of Arctic sea ice, J. Geophys. Res., 118, 91–107, https://doi.org/10.1029/2012JC007990, 2013.
Tsamados, M., Feltham, D. L., Schroeder, D., Flocco, D., Farrell, S. L., Kurtz, N., Laxon, S. W., and Bacon, S.: Impact of Variable Atmospheric and Oceanic Form Drag on Simulations of Arctic Sea Ice, J. Phys. Oceanogr., 44, 1329–1353, https://doi.org/10.1175/JPO-D-13-0215.1, 2014.
Turner, A. K., and Hunke, E. C.: Impacts of a mushy-layer thermodynamic approach in global sea-ice simulations using the CICE sea-ice model, J. Geophys. Res. Oceans, 120, 1253–1275, https://doi.org/10.1002/2014JC010358, 2015.
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 Model., 27, 33–53, https://doi.org/10.1016/j.ocemod.2008.10.005, 2009.
Wang, L. R. and Ikeda, M.: A Lagrangian description of sea ice dynamics using the finite element method, Ocean Model., 7, 21–38, 2004.
Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin, N. N., Aleksandrov, Y. I., and Colony, R.: Snow Depth on Arctic Sea Ice, J. Climate, 12, 1814–1829, https://doi.org/10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2, 1999.
Weiss, J. and Schulson, E. M.: Coulombic faulting from the grain scale to the geophysical scale: Lessons from ice, J. Phys. D Appl. Phys., 42, 214017, https://doi.org/10.1088/0022-3727/42/21/214017, 2009.
Weiss, J., Stern, H. L., and Schulson, E. M.: Sea ice rheology from in-situ, satellite and laboratory observations: Fracture and friction, Earth Planet. Sc. Lett., 255, 1–8, 2007.
Weiss, J., Marsan, D., and Rampal, P.: Space and time scaling laws induced by the multiscale fracturing of the Arctic sea ice cover, in: IUTAM Symposium on Scaling Laws in Ice Mechanics and Ice Dynamics, Springer Netherlands, 101–109, 2009.
Wilchinsky, A. V. and Feltham, D.: Modelling the rheology of sea ice as a collection of diamond-shaped oes, J. Non-Newton Fluid, 138, 22–32, 2006.
Winton, M.: A reformulated three-layer sea ice model, J. Atmos. Ocean. Tech., 17, 525–531, 2000.
Zhang, J. and Rothrock, D.: Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates, Mon. Weather Rev., 131, 845–861, 2003.
Zygmuntowska, M., Rampal, P., Ivanova, N., and Smedsrud, L. H.: Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends, The Cryosphere, 8, 705–720, https://doi.org/10.5194/tc-8-705-2014, 2014.
Short summary
The Arctic sea ice cover has changed drastically over the last decades and undergone a shift in its dynamical regime, as seen by the increase of extreme fracturing events and the acceleration of sea ice drift. In this paper we present a new sea ice model, neXtSIM, that is capable of simulating both sea ice drift and deformation as observed from satellites, with similar spatial and temporal scaling properties. At the same time, the model reproduces sea ice area, extent, and volume correctly.
The Arctic sea ice cover has changed drastically over the last decades and undergone a shift in...
