Articles | Volume 26, issue 3
https://doi.org/10.5194/npg-26-175-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-26-175-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jul 2019
Research article |
| 24 Jul 2019
| 24 Jul 2019
Data assimilation using adaptive, non-conservative, moving mesh models
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Alberto Carrassi
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Geophysical Institute, University of Bergen, Norway
Colin T. Guider
Department of Mathematics, University of North Carolina, Chapel Hill, USA
Chris K. R. T Jones
Department of Mathematics, University of North Carolina, Chapel Hill, USA
Pierre Rampal
Nansen Environmental and Remote Sensing Center, Bergen, Norway
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Paolo Oddo, Mario Adani, Francesco Carere, Andrea Cipollone, Anna Chiara Goglio, Eric Jansen, Ali Aydogdu, Francesca Mele, Italo Epicoco, Jenny Pistoia, Emanuela Clementi, Nadia Pinardi, and Simona Masina
Geosci. Model Dev., 19, 423–445, https://doi.org/10.5194/gmd-19-423-2026, https://doi.org/10.5194/gmd-19-423-2026, 2026
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This study present a data assimilation system that combines ocean observational data with ocean model results to better understand the ocean and predict its future state. The method uses a three dimensional incremental variational approach focusing on the physical relationships between all the state vector variables errors. Testing in the Mediterranean Sea showed that a complex sea level operator based on a barotropic model works best.
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Sea level trends are analysed in the western and eastern Mediterranean Sea using satellite and in-situ data. We found that sea level rise is not constant but varies over decades and differs between regions. These changes are influenced by temperature, salinity, and local land movements. Understanding this variability helps improve coastal planning and prepares communities for changing sea levels in the near future.
Leonardo Lima, Rafael Menezes, Ehsan Sadighrad, Ronan McAdam, Filipe Costa, Pietro Miraglio, Mehmet Ilicak, Eric Jansen, Ali Aydogdu, Francesco Maicu, and Emanuela Clementi
State Planet Discuss., https://doi.org/10.5194/sp-2025-6, https://doi.org/10.5194/sp-2025-6, 2025
Revised manuscript accepted for SP
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As climate change intensifies, marine heatwaves (MHWs), periods of unusually high ocean temperatures, threaten ecosystems and communities. This study analyzes MHWs in the Mediterranean and Black Seas, examining surface temperatures and ocean heat content in the upper 0–40 meters to capture subsurface warming. Findings highlight the need to monitor both surface and subsurface conditions for a comprehensive understanding and better management considering ongoing warming trends in both seas.
Anna Teruzzi, Ali Aydogdu, Carolina Amadio, Emanuela Clementi, Simone Colella, Valeria Di Biagio, Massimiliano Drudi, Claudia Fanelli, Laura Feudale, Alessandro Grandi, Pietro Miraglio, Andrea Pisano, Jenny Pistoia, Marco Reale, Stefano Salon, Gianluca Volpe, and Gianpiero Cossarini
State Planet, 4-osr8, 15, https://doi.org/10.5194/sp-4-osr8-15-2024, https://doi.org/10.5194/sp-4-osr8-15-2024, 2024
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A noticeable cold spell occurred in Eastern Europe at the beginning of 2022 and was the main driver of intense deep-water formation and the associated transport of nutrients to the surface. Southeast of Crete, the availability of both light and nutrients in the surface layer stimulated an anomalous phytoplankton bloom. In the area, chlorophyll concentration (a proxy for bloom intensity) and primary production were considerably higher than usual, suggesting possible impacts on fishery catches.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Ali Aydogdu, Lluis Castrillo, Daniele Ciani, Andrea Cipollone, Emanuela Clementi, Gianpiero Cossarini, Alvaro de Pascual-Collar, Vincenzo De Toma, Marion Gehlen, Rianne Giesen, Marie Drevillon, Claudia Fanelli, Kevin Hodges, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Priidik Lagemaa, Vidar Lien, Leonardo Lima, Vladyslav Lyubartsev, Ilja Maljutenko, Simona Masina, Ronan McAdam, Pietro Miraglio, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Urmas Raudsepp, Roshin Raj, Ad Stoffelen, Simon Van Gennip, Pierre Veillard, and Chunxue Yang
State Planet, 4-osr8, 2, https://doi.org/10.5194/sp-4-osr8-2-2024, https://doi.org/10.5194/sp-4-osr8-2-2024, 2024
Giovanni Coppini, Emanuela Clementi, Gianpiero Cossarini, Stefano Salon, Gerasimos Korres, Michalis Ravdas, Rita Lecci, Jenny Pistoia, Anna Chiara Goglio, Massimiliano Drudi, Alessandro Grandi, Ali Aydogdu, Romain Escudier, Andrea Cipollone, Vladyslav Lyubartsev, Antonio Mariani, Sergio Cretì, Francesco Palermo, Matteo Scuro, Simona Masina, Nadia Pinardi, Antonio Navarra, Damiano Delrosso, Anna Teruzzi, Valeria Di Biagio, Giorgio Bolzon, Laura Feudale, Gianluca Coidessa, Carolina Amadio, Alberto Brosich, Arnau Miró, Eva Alvarez, Paolo Lazzari, Cosimo Solidoro, Charikleia Oikonomou, and Anna Zacharioudaki
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The paper presents the Mediterranean Forecasting System evolution and performance developed in the framework of the Copernicus Marine Service.
Ali Aydogdu, Pietro Miraglio, Romain Escudier, Emanuela Clementi, and Simona Masina
State Planet, 1-osr7, 6, https://doi.org/10.5194/sp-1-osr7-6-2023, https://doi.org/10.5194/sp-1-osr7-6-2023, 2023
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This paper investigates the salt content, salinity anomaly and trend in the Mediterranean Sea using observational and reanalysis products. The salt content increases overall, while negative salinity anomalies appear in the western basin, especially around the upwelling regions. There is a large spread in the salinity estimates that is reduced with the emergence of the Argo profilers.
Andrea Cipollone, Deep Sankar Banerjee, Doroteaciro Iovino, Ali Aydogdu, and Simona Masina
Ocean Sci., 19, 1375–1392, https://doi.org/10.5194/os-19-1375-2023, https://doi.org/10.5194/os-19-1375-2023, 2023
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Sea-ice volume is characterized by low predictability compared to the sea ice area or the extent. A joint initialization of the thickness and concentration using satellite data could improve the predictive power, although it is still absent in the present global analysis–reanalysis systems. This study shows a scheme to correct the two features together that can be easily extended to include ocean variables. The impact of such a joint initialization is shown and compared among different set-ups.
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.
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
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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.
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
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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.
Paolo Oddo, Mario Adani, Francesco Carere, Andrea Cipollone, Anna Chiara Goglio, Eric Jansen, Ali Aydogdu, Francesca Mele, Italo Epicoco, Jenny Pistoia, Emanuela Clementi, Nadia Pinardi, and Simona Masina
Geosci. Model Dev., 19, 423–445, https://doi.org/10.5194/gmd-19-423-2026, https://doi.org/10.5194/gmd-19-423-2026, 2026
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This study present a data assimilation system that combines ocean observational data with ocean model results to better understand the ocean and predict its future state. The method uses a three dimensional incremental variational approach focusing on the physical relationships between all the state vector variables errors. Testing in the Mediterranean Sea showed that a complex sea level operator based on a barotropic model works best.
Ieuan Higgs, Ross Bannister, Jozef Skákala, Alberto Carrassi, and Stefano Ciavatta
Biogeosciences, 23, 315–344, https://doi.org/10.5194/bg-23-315-2026, https://doi.org/10.5194/bg-23-315-2026, 2026
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We explored how machine learning can improve computer models that simulate ocean ecosystems. These models help us understand how the ocean works, but they often struggle due to limited observations and complex processes. Our approach uses machine learning to better connect the parts of the system we can observe with those we cannot. This leads to more accurate and efficient predictions, offering a promising way to improve future ocean monitoring and forecasting tools.
Lilian Garcia-Oliva, Alberto Carrassi, and François Counillon
Nonlin. Processes Geophys., 32, 439–456, https://doi.org/10.5194/npg-32-439-2025, https://doi.org/10.5194/npg-32-439-2025, 2025
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We used a simple coupled model and a data assimilation method to find the correct initialisation for climate predictions. We aim to clarify when weakly or strongly coupled data assimilation (WCDA or SCDA) is best, depending on the system's dynamical characteristics (spatio-temporal) and data coverage. We found that WCDA is better in full data coverage. When we have a partially observed system, SCDA is better. This result depends on the temporal and spatial scale of the observed quantity.
Leonardo Lima, Rafael Menezes, Ehsan Sadighrad, Ronan McAdam, Filipe Costa, Pietro Miraglio, Mehmet Ilicak, Eric Jansen, Ali Aydogdu, Francesco Maicu, and Emanuela Clementi
State Planet Discuss., https://doi.org/10.5194/sp-2025-6, https://doi.org/10.5194/sp-2025-6, 2025
Revised manuscript accepted for SP
Short summary
Short summary
As climate change intensifies, marine heatwaves (MHWs), periods of unusually high ocean temperatures, threaten ecosystems and communities. This study analyzes MHWs in the Mediterranean and Black Seas, examining surface temperatures and ocean heat content in the upper 0–40 meters to capture subsurface warming. Findings highlight the need to monitor both surface and subsurface conditions for a comprehensive understanding and better management considering ongoing warming trends in both seas.
Federica Borile, Vladyslav Lyubartsev, Begoña Pérez Gómez, Jue Lin-Ye, Anna Mangilli, Ali Aydogdu, Emanuela Clementi, and Paolo Oddo
State Planet Discuss., https://doi.org/10.5194/sp-2025-2, https://doi.org/10.5194/sp-2025-2, 2025
Revised manuscript accepted for SP
Short summary
Short summary
Sea level trends are analysed in the western and eastern Mediterranean Sea using satellite and in-situ data. We found that sea level rise is not constant but varies over decades and differs between regions. These changes are influenced by temperature, salinity, and local land movements. Understanding this variability helps improve coastal planning and prepares communities for changing sea levels in the near future.
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
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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.
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.
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
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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.
Anna Teruzzi, Ali Aydogdu, Carolina Amadio, Emanuela Clementi, Simone Colella, Valeria Di Biagio, Massimiliano Drudi, Claudia Fanelli, Laura Feudale, Alessandro Grandi, Pietro Miraglio, Andrea Pisano, Jenny Pistoia, Marco Reale, Stefano Salon, Gianluca Volpe, and Gianpiero Cossarini
State Planet, 4-osr8, 15, https://doi.org/10.5194/sp-4-osr8-15-2024, https://doi.org/10.5194/sp-4-osr8-15-2024, 2024
Short summary
Short summary
A noticeable cold spell occurred in Eastern Europe at the beginning of 2022 and was the main driver of intense deep-water formation and the associated transport of nutrients to the surface. Southeast of Crete, the availability of both light and nutrients in the surface layer stimulated an anomalous phytoplankton bloom. In the area, chlorophyll concentration (a proxy for bloom intensity) and primary production were considerably higher than usual, suggesting possible impacts on fishery catches.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Ali Aydogdu, Lluis Castrillo, Daniele Ciani, Andrea Cipollone, Emanuela Clementi, Gianpiero Cossarini, Alvaro de Pascual-Collar, Vincenzo De Toma, Marion Gehlen, Rianne Giesen, Marie Drevillon, Claudia Fanelli, Kevin Hodges, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Priidik Lagemaa, Vidar Lien, Leonardo Lima, Vladyslav Lyubartsev, Ilja Maljutenko, Simona Masina, Ronan McAdam, Pietro Miraglio, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Urmas Raudsepp, Roshin Raj, Ad Stoffelen, Simon Van Gennip, Pierre Veillard, and Chunxue Yang
State Planet, 4-osr8, 2, https://doi.org/10.5194/sp-4-osr8-2-2024, https://doi.org/10.5194/sp-4-osr8-2-2024, 2024
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.
Ieuan Higgs, Jozef Skákala, Ross Bannister, Alberto Carrassi, and Stefano Ciavatta
Biogeosciences, 21, 731–746, https://doi.org/10.5194/bg-21-731-2024, https://doi.org/10.5194/bg-21-731-2024, 2024
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A complex network is a way of representing which parts of a system are connected to other parts. We have constructed a complex network based on an ecosystem–ocean model. From this, we can identify patterns in the structure and areas of similar behaviour. This can help to understand how natural, or human-made, changes will affect the shelf sea ecosystem, and it can be used in multiple future applications such as improving modelling, data assimilation, or machine learning.
Giovanni Coppini, Emanuela Clementi, Gianpiero Cossarini, Stefano Salon, Gerasimos Korres, Michalis Ravdas, Rita Lecci, Jenny Pistoia, Anna Chiara Goglio, Massimiliano Drudi, Alessandro Grandi, Ali Aydogdu, Romain Escudier, Andrea Cipollone, Vladyslav Lyubartsev, Antonio Mariani, Sergio Cretì, Francesco Palermo, Matteo Scuro, Simona Masina, Nadia Pinardi, Antonio Navarra, Damiano Delrosso, Anna Teruzzi, Valeria Di Biagio, Giorgio Bolzon, Laura Feudale, Gianluca Coidessa, Carolina Amadio, Alberto Brosich, Arnau Miró, Eva Alvarez, Paolo Lazzari, Cosimo Solidoro, Charikleia Oikonomou, and Anna Zacharioudaki
Ocean Sci., 19, 1483–1516, https://doi.org/10.5194/os-19-1483-2023, https://doi.org/10.5194/os-19-1483-2023, 2023
Short summary
Short summary
The paper presents the Mediterranean Forecasting System evolution and performance developed in the framework of the Copernicus Marine Service.
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.
Ali Aydogdu, Pietro Miraglio, Romain Escudier, Emanuela Clementi, and Simona Masina
State Planet, 1-osr7, 6, https://doi.org/10.5194/sp-1-osr7-6-2023, https://doi.org/10.5194/sp-1-osr7-6-2023, 2023
Short summary
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This paper investigates the salt content, salinity anomaly and trend in the Mediterranean Sea using observational and reanalysis products. The salt content increases overall, while negative salinity anomalies appear in the western basin, especially around the upwelling regions. There is a large spread in the salinity estimates that is reduced with the emergence of the Argo profilers.
Andrea Cipollone, Deep Sankar Banerjee, Doroteaciro Iovino, Ali Aydogdu, and Simona Masina
Ocean Sci., 19, 1375–1392, https://doi.org/10.5194/os-19-1375-2023, https://doi.org/10.5194/os-19-1375-2023, 2023
Short summary
Short summary
Sea-ice volume is characterized by low predictability compared to the sea ice area or the extent. A joint initialization of the thickness and concentration using satellite data could improve the predictive power, although it is still absent in the present global analysis–reanalysis systems. This study shows a scheme to correct the two features together that can be easily extended to include ocean variables. The impact of such a joint initialization is shown and compared among different set-ups.
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, and Véronique Dansereau
The Cryosphere, 17, 2965–2991, https://doi.org/10.5194/tc-17-2965-2023, https://doi.org/10.5194/tc-17-2965-2023, 2023
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We combine deep learning with a regional sea-ice model to correct model errors in the sea-ice dynamics of low-resolution forecasts towards high-resolution simulations. The combined model improves the forecast by up to 75 % and thereby surpasses the performance of persistence. As the error connection can additionally be used to analyse the shortcomings of the forecasts, this study highlights the potential of combined modelling for short-term sea-ice forecasting.
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.
Francine Schevenhoven and Alberto Carrassi
Geosci. Model Dev., 15, 3831–3844, https://doi.org/10.5194/gmd-15-3831-2022, https://doi.org/10.5194/gmd-15-3831-2022, 2022
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In this study, we present a novel formulation to build a dynamical combination of models, the so-called supermodel, which needs to be trained based on data. Previously, we assumed complete and noise-free observations. Here, we move towards a realistic scenario and develop adaptations to the training methods in order to cope with sparse and noisy observations. The results are very promising and shed light on how to apply the method with state of the art general circulation models.
Yumeng Chen, Alberto Carrassi, and Valerio Lucarini
Nonlin. Processes Geophys., 28, 633–649, https://doi.org/10.5194/npg-28-633-2021, https://doi.org/10.5194/npg-28-633-2021, 2021
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Chaotic dynamical systems are sensitive to the initial conditions, which are crucial for climate forecast. These properties are often used to inform the design of data assimilation (DA), a method used to estimate the exact initial conditions. However, obtaining the instability properties is burdensome for complex problems, both numerically and analytically. Here, we suggest a different viewpoint. We show that the skill of DA can be used to infer the instability properties of a dynamical system.
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
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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
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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
Alharbi, A. and Naire, S.: An adaptive moving mesh method for thin film flow
equations with surface tension, J. Comput. Appl. Math., 319, 365–384, https://doi.org/10.1016/j.cam.2017.01.019, 2017. a
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the
nonlinear filtering problem to produce ensemble assimilations and forecasts,
Mon. Weather Rev., 127, 2741–2758, 1999. a
Apte, A. and Jones, C. K. R. T.: The impact of nonlinearity in Lagrangian data assimilation, Nonlin. Processes Geophys., 20, 329–341, https://doi.org/10.5194/npg-20-329-2013, 2013. a
Asch, M., Bocquet, M., and Nodet, M.: Data Assimilation: Methods,
Algorithms, and Applications, Fundamentals of Algorithms, SIAM,
Philadelphia, ISBN 978-1-611974-53-9, 2016. a
Babus̆ka, I. and Aziz, A.: On the Angle Condition in the Finite Element
Method, SIAM J. Numer. Anal., 13, 214–226,
https://doi.org/10.1137/0713021, 1976. a
Baines, M. J., Hubbard, M. E., and Jimack, P. K.: Velocity-Based Moving Mesh
Methods for Nonlinear Partial Differential Equations, Commun. Comput. Phys., 10, 509–576, https://doi.org/10.4208/cicp.201010.040511a, 2011. a, b
Berger, M. J. and Oliger, J.: Adaptive mesh refinement for hyperbolic partial
differential equations, J. Comput. Phys., 53, 484–512,
https://doi.org/10.1016/0021-9991(84)90073-1, 1984. a
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive sampling with
the ensemble transform Kalman filter. Part I: Theoretical aspects, Mon.
Weather Rev., 129, 420–436, 2001. a
Bocquet, M. and Carrassi, A.: Four-dimensional ensemble variational data
assimilation and the unstable subspace, Tellus A, 69, 1304504,
https://doi.org/10.1080/16000870.2017.1304504, 2017. a
Bonan, B., Nichols, N. K., Baines, M. J., and Partridge, D.: Data assimilation for moving mesh methods with an application to ice sheet modelling, Nonlin. Processes Geophys., 24, 515–534, https://doi.org/10.5194/npg-24-515-2017, 2017. a, b, c
Bouillon, S. and Rampal, P.: Presentation of the dynamical core of neXtSIM, a
new sea ice model, Ocean Model., 91, 23–37,
https://doi.org/10.1016/j.ocemod.2015.04.005, 2015. a
Bouillon, S., Rampal, P., and Olason, E.: Sea Ice Modelling and Forecasting,
in: New Frontiers in Operational Oceanography, edited by: Chassignet, E. P.,
Pascual, A., Tintoré, J., and Verron, J., 15, 423–444,
GODAE OceanView, https://doi.org/10.17125/gov2018, 2018. a
Budhiraja, A., Friedlander, E., Guider, C., Jones, C., and Maclean, J.: Assimilating data into models, in: Handbook of Environmental and Ecological Statistics, edited by: Gelfand, A. E., Fuentes, M., Hoeting, J. A., and Smith, R. L., ISBN 9781498752022, CRC Press, 2018. a
Burgers, G., Jan van Leeuwen, P., and Evensen, G.: Analysis scheme in the
ensemble Kalman filter, Mon. Weather Rev., 126, 1719–1724,
https://doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2, 1998. a, b
Burgers, J.: A Mathematical Model Illustrating the Theory of Turbulence, vol. 1
of Advances in Applied Mechanics, 171–199, Elsevier,
https://doi.org/10.1016/S0065-2156(08)70100-5, 1948. a, b
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in
the geosciences: An overview of methods, issues, and perspectives, Wiley
Interdisciplinary Reviews: Climate Change, 9, e535, https://doi.org/10.1002/wcc.535,
2018. a, b, c, d
Cohn, S. E.: Dynamics of short-term univariate forecast error covariances,
Mon. Weather Rev., 121, 3123–3149, 1993. a
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., 213–230,
Springer, Berlin, 2008. a
Compère, G., Remacle, J.-F., Jansson, J., and Hoffman, J.: A mesh
adaptation framework for dealing with large deforming meshes, Int. J. Numer.
Meth. Engng., 82, 843–867, https://doi.org/10.1002/nme.2788, 2009. a
Dansereau, V., Weiss, J., Saramito, P., and Lattes, P.: A Maxwell elasto-brittle rheology for sea ice modelling, The Cryosphere, 10, 1339–1359, https://doi.org/10.5194/tc-10-1339-2016, 2016. a
Davies, D. R., Wilson, C. R., and Kramer, S. C.: Fluidity: A fully unstructured
anisotropic adaptive mesh computational modeling framework for geodynamics,
Geochem. Geophy. Geosy., 12,
6, https://doi.org/10.1029/2011GC003551, 2011. a, b
Du, J., Zhu, J., Fang, F., Pain, C., and Navon, I.: Ensemble data assimilation
applied to an adaptive mesh ocean model, Int. J. Numer. Meth. Fl., 82, 997–1009, https://doi.org/10.1002/fld.4247, 2016. a, b, c, d
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics, J.
Geophys. Res.-Oceans, 99, 10143–10162, https://doi.org/10.1029/94JC00572,
1994. a
Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical
implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003. a, b
Fang, F., Piggott, M., Pain, C., Gorman, G., and Goddard, A.: An adaptive mesh
adjoint data assimilation method, Ocean Model., 15, 39–55,
https://doi.org/10.1016/j.ocemod.2006.02.002,
2006. a
Farrell, P., Piggott, M., Pain, C., Gorman, G., and Wilson, C.: Conservative
interpolation between unstructured meshes via supermesh construction,
Comput. Meth. Appl. Mech. Eng., 198, 2632–2642,
https://doi.org/10.1016/j.cma.2009.03.004, 2009. a
Houtekamer, P. L. and Zhang, F.: Review of the Ensemble Kalman Filter for
Atmospheric Data Assimilation, Mon. Weather Rev., 144, 4489–4532,
https://doi.org/10.1175/MWR-D-15-0440.1, 2016. a, b
Huang, W., Zheng, L., and Zhan, X.: Adaptive moving mesh methods for simulating
one-dimensional groundwater problems with sharp moving fronts, International
J. Numer. Meth. Eng., 54, 1579–1603,
https://doi.org/10.1002/nme.482, 2002. a
Jain, P. K., Mandli, K., Hoteit, I., Knio, O., and Dawson, C.: Dynamically
adaptive data-driven simulation of extreme hydrological flows, Ocean Model., 122, 85–103, https://doi.org/10.1016/j.ocemod.2017.12.004, 2018. a
Kalman, R. E.: A new approach to linear filtering and prediction problems, J.
Fluid. Eng., 82, 35–45, 1960. a
Kuznetsov, L., Ide, K., and Jones, C.: A method for assimilation of Lagrangian
data, Mon. Weather Rev., 131, 2247–2260, 2003. a
Maddison, J., Marshall, D., Pain, C., and Piggott, M.: Accurate representation
of geostrophic and hydrostatic balance in unstructured mesh finite element
ocean modelling, Ocean Model., 39, 248–261,
https://doi.org/10.1016/j.ocemod.2011.04.009, 2011. a
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. a
Nodet, M.: Variational assimilation of Lagrangian data in oceanography, Inverse
problems, 22, 245,
https://doi.org/10.1088/0266-5611/22/1/014, 2006. a
Pain, C., Piggott, M., Goddard, A., Fang, F., Gorman, G., Marshall, D., Eaton,
M., Power, P., and de Oliveira, C.: Three-dimensional unstructured mesh ocean
modelling, Ocean Model., 10, 5–33, https://doi.org/10.1016/j.ocemod.2004.07.005,
2005.
a
Pannekoucke, O., Bocquet, M., and Ménard, R.: Parametric covariance dynamics for the nonlinear diffusive Burgers equation, Nonlin. Processes Geophys., 25, 481–495, https://doi.org/10.5194/npg-25-481-2018, 2018. a
Partridge, D.: Numerical modelling of glaciers: moving meshes and data
assimilation, PhD thesis, University of Reading, 2013. a
Raanes, P. N., Bocquet, M., and Carrassi, A.: Adaptive covariance inflation in
the ensemble Kalman filter by Gaussian scale mixtures, Q. J. Roy. Meteor. Soc.,
145, 53–75, https://doi.org/10.1002/qj.3386, 2019. 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
Rampal, P., Weiss, J., Marsan, D., Lindsay, R., and Stern, H.: Scaling
properties of sea ice deformation from buoy dispersion analysis, J.
Geophys. Res.-Oceans, 113, C03002, https://doi.org/10.1029/2007JC004143,
2008. a
Rampal, P., Bouillon, S., Ólason, E., and Morlighem, M.: neXtSIM: a new Lagrangian sea ice model, The Cryosphere, 10, 1055–1073, https://doi.org/10.5194/tc-10-1055-2016, 2016. a, b, c
Rampal, P., Dansereau, V., Olason, E., Bouillon, S., Williams, T., and Samaké, A.: On the multi-fractal scaling properties of sea ice deformation, The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-290, in review, 2019. a
Sakov, P. and Oke, P. R.: A deterministic formulation of the ensemble Kalman
filter: an alternative to ensemble square root filters, Tellus A, 60,
361–371, 2008. a
Saksono, P. H., Dettmer, W. G., and Perić, D.: An adaptive remeshing
strategy for flows with moving boundaries and fluid-structure interaction,
Int. J. Numer. Meth. Eng., 71, 1009–1050,
https://doi.org/10.1002/nme.1971,
2007. a
Slivinski, L., Spiller, E., Apte, A., and Sandstede, B.: A hybrid
particle–ensemble Kalman filter for Lagrangian data assimilation, Mon. Weather Rev., 143, 195–211, 2015. a
Talagrand, O.: Assimilation of Observations, an Introduction (Special Issue –
Data Assimilation in Meteology and Oceanography: Theory and Practice), J.
Meteorol. Soc. Jpn., 75, 191–209, 1997. a
Verlaan, M. and Heemink, A. W.: Nonlinearity in Data Assimilation Applications:
A Practical Method for Analysis, Mon. Weather Rev., 129, 1578–1589,
https://doi.org/10.1175/1520-0493(2001)129<1578:NIDAAA>2.0.CO;2, 2001. a
Weller, H., Ringler, T., Piggott, M., and Wood, N.: Challenges Facing Adaptive
Mesh Modeling of the Atmosphere and Ocean, B. Am.
Meteorol. Soc., 91, 105–108, https://doi.org/10.1175/2009BAMS2907.1,
2010. a
Short summary
Computational models involving adaptive meshes can both evolve dynamically and be remeshed. Remeshing means that the state vector dimension changes in time and across ensemble members, making the ensemble Kalman filter (EnKF) unsuitable for assimilation of observational data. We develop a modification in which analysis is performed on a fixed uniform grid onto which the ensemble is mapped, with resolution relating to the remeshing criteria. The approach is successfully tested on two 1-D models.
Computational models involving adaptive meshes can both evolve dynamically and be remeshed....
