Articles | Volume 20, issue 8
https://doi.org/10.5194/tc-20-4655-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-4655-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Aug 2026
Research article |
| 24 Aug 2026
| 24 Aug 2026
Arctic sea ice predictability on daily-to-weekly timescales: sensitivity to initial positional errors under different rheology formulations
DATLAS, Grenoble, France
Univ. Grenoble Alpes, CNRS, INRAE, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Stephanie Leroux
DATLAS, Grenoble, France
Pierre Rampal
Univ. Grenoble Alpes, CNRS, INRAE, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Jean-Michel Brankart
Univ. Grenoble Alpes, CNRS, INRAE, IRD, Grenoble INP, IGE, 38000 Grenoble, France
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Alejandro Blazquez, Benoit Meyssignac, Robin Fraudeau, Michael Ablain, Jonathan Bamber, Antonio Bonaduce, Marie Bouih, Anny Cazenave, Thorben Döhne, Ines Dussaillant, Ramiro Ferrari, Martin Horwath, Nicolas Kolodziejczyk, Hugo Lecomte, Stephanie Leroux, William Llovel, Daniele Melini, Erwan Oulhen, Thierry Penduff, Roshin P. Raj, Giorgio Spada, Marius Schlaak, Papasarafianou Stamatia, Andrea Storto, Chunxue Yang, and Sarah Connors
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-627, https://doi.org/10.5194/essd-2026-627, 2026
Preprint under review for ESSD
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Closing the sea‑level budget on annual and longer time scales is a cornerstone of physical oceanography because sea‑level rise is one of the best indicators of climate change, and a closed budget shows we have identified and quantified all major drivers. We examined it from 1993 to 2022, finding an accelerated rise that matched ice melt and warm water until 2015. Afterwards an unexplained gap appears. Better deep‑ocean observations and refined gravity processing are needed to close the budget.
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
Geosci. Model Dev., 19, 6467–6496, https://doi.org/10.5194/gmd-19-6467-2026, https://doi.org/10.5194/gmd-19-6467-2026, 2026
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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.
Jean-Michel Brankart, Damien Héron, Lisa Weiss, Thierry Penduff, and Pierre Brasseur
EGUsphere, https://doi.org/10.5194/egusphere-2026-3237, https://doi.org/10.5194/egusphere-2026-3237, 2026
This preprint is open for discussion and under review for Ocean Science (OS).
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In this paper, a method is proposed to estimate directly the low-frequency component of the ocean variability from native observations using statistics from a prior long-term ensemble simulation. The approach is illustrated by the reconstruction of the low-frequency variability of the Mediterranean sea level. The resulting ensemble estimate is assessed against independent observations, showing good reliability.
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).
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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.
Damien Héron, Thierry Penduff, Jean-Michel Brankart, Pierre Brasseur, Samuel Somot, Robin Waldman, and Romain Pennel
Ocean Sci., 22, 531–547, https://doi.org/10.5194/os-22-531-2026, https://doi.org/10.5194/os-22-531-2026, 2026
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Our study used realistic ocean simulations to determine how much of the Mediterranean’s circulation is due to natural randomness rather than atmospheric forcing. We found that spontaneous ocean variability is strong in several regions and can persist for years or even decades. This randomness influences how well models and observations can capture the Mediterranean’s response to climate change.
Loïc Macé, Luc Vandenbulcke, Jean-Michel Brankart, Jean-François Grailet, Pierre Brasseur, and Marilaure Grégoire
EGUsphere, https://doi.org/10.5194/egusphere-2025-4973, https://doi.org/10.5194/egusphere-2025-4973, 2025
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In this paper, we propose a three-stream radiative transfer model and present a use case for the Black Sea. The model is able to simulate in-water irradiance and sea surface reflectance in a wide spectral range. When coupled with an ecosystem model, the simulated irradiances can be used to update water temperature and drive primary production in a consistent way. A stochastic version of this model is also proposed to inform on uncertainties in the optical properties of seawater.
Loïc Macé, Luc Vandenbulcke, Jean-Michel Brankart, Pierre Brasseur, and Marilaure Grégoire
Biogeosciences, 22, 3747–3768, https://doi.org/10.5194/bg-22-3747-2025, https://doi.org/10.5194/bg-22-3747-2025, 2025
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The representation of light propagation in seawater is critical for modelling marine biogeochemistry. We analyse results from a radiative transfer model that accounts for the absorption and scattering of light in the ocean with their respective uncertainties. We compare these results with in situ and remote-sensed data. Our analysis highlights the benefits of accounting for model uncertainties while using advanced representations of light in modelling frameworks.
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
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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.
Olivier Narinc, Thierry Penduff, Guillaume Maze, Stéphanie Leroux, and Jean-Marc Molines
Ocean Sci., 20, 1351–1365, https://doi.org/10.5194/os-20-1351-2024, https://doi.org/10.5194/os-20-1351-2024, 2024
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This study examines how the ocean's chaotic variability and atmospheric fluctuations affect yearly changes in North Atlantic Subtropical Mode Water (STMW) properties, using an ensemble of realistic ocean simulations. Results show that while yearly changes in STMW properties are mostly paced by the atmosphere, a notable part of these changes are random in phase. This study also illustrates the value of ensemble simulations over single runs in understanding oceanic fluctuations and their causes.
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
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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
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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.
Mikhail Popov, Jean-Michel Brankart, Arthur Capet, Emmanuel Cosme, and Pierre Brasseur
Ocean Sci., 20, 155–180, https://doi.org/10.5194/os-20-155-2024, https://doi.org/10.5194/os-20-155-2024, 2024
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This study contributes to the development of methods to estimate targeted ocean ecosystem indicators, including their uncertainty, in the framework of the Copernicus Marine Service. A simplified approach is introduced to perform a 4D ensemble analysis and forecast, directly targeting selected biogeochemical variables and indicators (phenology, trophic efficiency, downward flux of organic matter). Care is taken to present the methods and discuss the reliability of the solution proposed.
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
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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
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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
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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
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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.
Stephanie Leroux, Jean-Michel Brankart, Aurélie Albert, Laurent Brodeau, Jean-Marc Molines, Quentin Jamet, Julien Le Sommer, Thierry Penduff, and Pierre Brasseur
Ocean Sci., 18, 1619–1644, https://doi.org/10.5194/os-18-1619-2022, https://doi.org/10.5194/os-18-1619-2022, 2022
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The goal of the study is to evaluate the predictability of the ocean circulation
at a kilometric scale, in order to anticipate the requirements of the future operational forecasting systems. For that purpose, ensemble experiments have been performed with a regional model for the Western Mediterranean (at 1/60° horizontal resolution). From these ensemble experiments, we show that it is possible to compute targeted predictability scores, which depend on initial and model uncertainties.
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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.
We examine how uncertainty in the initial position of sea ice features (leads, ridges), affects...
