Articles | Volume 17, issue 4
https://doi.org/10.5194/tc-17-1735-2023
© Author(s) 2023. 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-17-1735-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Apr 2023
Research article |
| 25 Apr 2023
| 25 Apr 2023
Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020
Nansen Environmental and Remote Sensing Center, 5007 Bergen, Norway
Navigation College, Dalian Maritime University, Dalian 116026, China
Centre for Ports and Maritime Safety, Dalian Maritime University, Dalian 116026, China
Yumeng Chen
Department of Meteorology and National Centre for Earth Observation, University of Reading, Reading RG6 6AH, UK
Ali Aydoğdu
Ocean Modelling and Data Assimilation Division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici – CMCC, Bologna, Italy
Laurent Bertino
Nansen Environmental and Remote Sensing Center, 5007 Bergen, Norway
Alberto Carrassi
Department of Meteorology and National Centre for Earth Observation, University of Reading, Reading RG6 6AH, UK
Department of Physics and Astronomy “Augusto Righi”, University of Bologna, Bologna, Italy
Pierre Rampal
CNRS, Institut de Géophysique de l’Environnement, Grenoble 38058, France
Nansen Environmental and Remote Sensing Center, 5007 Bergen, Norway
Christopher K. R. T. Jones
Department of Mathematics, University of North Carolina, Chapel Hill, NC, USA
Related authors
No articles found.
Julia Thomas, Hendrik Reich, Thorsten Steinert, Gernot Geppert, Klaus Stephan, Anselm Erdmann, Philipp Gasch, Maxime Hervo, Jan Keller, Alberto Carrassi, Peter Knippertz, and Annika Oertel
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-352, https://doi.org/10.5194/essd-2026-352, 2026
Preprint under review for ESSD
Short summary
Short summary
We present a convective-scale reanalysis of the 'Swabian MOSES 2023' campaign that assimilates 3-months of additional field campaign observations in southwest Germany. It is computed with a 1-hourly assimilation timestep on a 2.2 km grid centred over Germany in moderately complex terrain. In addition to all operationally available observations, it incorporates 6 additional observation types. Among these is an unprecedented number of vertical wind profiles retrieved from 12 Doppler wind lidars.
Lohenn Fiol, Stephanie Leroux, Pierre Rampal, and Jean-Michel Brankart
The Cryosphere, 20, 4655–4680, https://doi.org/10.5194/tc-20-4655-2026, https://doi.org/10.5194/tc-20-4655-2026, 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.
Yumeng Chen, Dale Partridge, and Lars Nerger
Geosci. Model Dev., 19, 7589–7613, https://doi.org/10.5194/gmd-19-7589-2026, https://doi.org/10.5194/gmd-19-7589-2026, 2026
Short summary
Short summary
Operational marine ecosystem forecasts traditionally rely on combining phytoplankton chlorophyll observations with model forecasts. However, using our newly developed ensemble data assimilation system, we demonstrate that assimilating phytoplankton carbon data leads to more balanced phytoplankton estimates and adjusts estimates of global ocean carbon.
Filipe Costa, Ehsan Sadighrad, Leonardo Lima, Ali Aydoğdu, Mehmet Ilicak, Diana Azevedo, and Emanuela Clementi
State Planet Discuss., https://doi.org/10.5194/sp-2026-17, https://doi.org/10.5194/sp-2026-17, 2026
Preprint under review for SP
Short summary
Short summary
Freshwater is essential to the Black Sea, supporting marine ecosystems, coastal management, and climate services. This study presents the first long-term assessment of freshwater storage changes using a high-resolution regional dataset. We show that freshwater has declined mainly because of increasing evaporation and decreasing river discharge. This new indicator supports long-term monitoring of environmental change in the Black Sea.
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
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.
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
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.
Lars Nerger, Yumeng Chen, Armin Corbin, and Johannes Keller
EGUsphere, https://doi.org/10.5194/egusphere-2026-2412, https://doi.org/10.5194/egusphere-2026-2412, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Data Assimilation combines information from numerical models and real-world observations. The complexity of models and observations necessitates advanced software to apply data assimilation with good efficiency. We discuss the relevant features of one such software, the open-source framework PDAF, which is already a widely used software for data assimilation. PDAF allows users to create their own efficient data assimilation system for a model of their choice as we demonstrate with an example.
Achref Othmani, Annette Samuelsen, Jiping Xie, Laurent Bertino, Fabio Mangini, and Roshin Pappukutty Raj
EGUsphere, https://doi.org/10.5194/egusphere-2026-1520, https://doi.org/10.5194/egusphere-2026-1520, 2026
Short summary
Short summary
We developed a high-resolution (6–10 km) HYCOM-CICE configuration to better understand ocean and sea ice conditions in the Arctic and North Atlantic from 2009 to 2019. By comparing the model with available datasets, we found it reliably captures major patterns and seasonal changes. This can support forecasting, helping improve environmental monitoring and decision-making in regions sensitive to climate change.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Julien Brajard, and Laurent Bertino
The Cryosphere, 19, 5613–5637, https://doi.org/10.5194/tc-19-5613-2025, https://doi.org/10.5194/tc-19-5613-2025, 2025
Short summary
Short summary
This paper presents a four-dimensional variational data assimilation system based on a neural network emulator for sea-ice thickness, learned from neXtSIM (neXt generation Sea Ice Model) simulation outputs. Testing with simulated and real observation retrievals, the system improves forecasts and bias error, performing comparably to operational methods, demonstrating the promise of sea-ice data-driven data assimilation systems.
Yumeng Chen, Lars Nerger, and Amos S. Lawless
Geosci. Model Dev., 18, 8235–8252, https://doi.org/10.5194/gmd-18-8235-2025, https://doi.org/10.5194/gmd-18-8235-2025, 2025
Short summary
Short summary
In this paper, we present pyPDAF, a Python interface to the parallel data assimilation framework (PDAF) allowing for coupling with Python-based models. We demonstrate the capability and efficiency of pyPDAF under a coupled data assimilation setup.
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
Short summary
Short summary
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.
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.
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.
Roshin P. Raj, Vidar S. Lien, Sourav Chatterjee, Saradhy Surendran, Antonio Bonaduce, and Laurent Bertino
State Planet Discuss., https://doi.org/10.5194/sp-2025-18, https://doi.org/10.5194/sp-2025-18, 2025
Revised manuscript accepted for SP
Short summary
Short summary
Dense water formed and exiting from the Barents Sea constitutes an important part of the global ocean circulation. Considering the significant impact of salinity changes in dense water formation, we investigate the salinity changes in the Barents Sea during the past 3 decades. Our results highlight the recent freshening and its drivers in the northern and southern Barents Sea and show its impact on the density of the waters exiting the Barents Sea.
Jozef Skákala, David Ford, Keith Haines, Amos Lawless, Matthew J. Martin, Philip Browne, Marcin Chrust, Stefano Ciavatta, Alison Fowler, Daniel Lea, Matthew Palmer, Andrea Rochner, Jennifer Waters, Hao Zuo, Deep S. Banerjee, Mike Bell, Davi M. Carneiro, Yumeng Chen, Susan Kay, Dale Partridge, Martin Price, Richard Renshaw, Georgy Shapiro, and James While
Ocean Sci., 21, 1709–1734, https://doi.org/10.5194/os-21-1709-2025, https://doi.org/10.5194/os-21-1709-2025, 2025
Short summary
Short summary
UK marine data assimilation (MDA) involves a closely collaborating research community. In this paper, we offer both an overview of the state of the art and a vision for the future across all of the main areas of UK MDA, ranging from physics to biogeochemistry to coupled DA. We discuss the current UK MDA stakeholder applications, highlight theoretical developments needed to advance our systems, and reflect upon upcoming opportunities with respect to hardware and observational missions.
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.
Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore
State Planet, 5-opsr, 9, https://doi.org/10.5194/sp-5-opsr-9-2025, https://doi.org/10.5194/sp-5-opsr-9-2025, 2025
Short summary
Short summary
Observations of the ocean from satellites and platforms in the ocean are combined with information from computer models to produce predictions of how the ocean temperature, salinity, and currents will evolve over the coming days and weeks and to describe how the ocean has evolved in the past. This paper summarises the methods used to produce these ocean forecasts at various centres around the world and outlines the practical considerations for implementing such forecasting systems.
Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino
The Cryosphere, 19, 731–752, https://doi.org/10.5194/tc-19-731-2025, https://doi.org/10.5194/tc-19-731-2025, 2025
Short summary
Short summary
This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a combination of machine learning and data assimilation. By training a machine learning model on data from 2011 to 2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes in sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.
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.
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
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.
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.
Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller
The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024, https://doi.org/10.5194/tc-18-2161-2024, 2024
Short summary
Short summary
Sea ice forecasts are operationally produced using physically based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique using machine learning in order to improve the skill of short-term (up to 10 d) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows for the reduction of errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
Marina Durán Moro, Ann Kristin Sperrevik, Thomas Lavergne, Laurent Bertino, Yvonne Gusdal, Silje Christine Iversen, and Jozef Rusin
The Cryosphere, 18, 1597–1619, https://doi.org/10.5194/tc-18-1597-2024, https://doi.org/10.5194/tc-18-1597-2024, 2024
Short summary
Short summary
Individual satellite passes instead of daily means of sea ice concentration are used to correct the sea ice model forecast in the Barents Sea. The use of passes provides a significantly larger improvement of the forecasts even after a 7 d period due to the more precise information on temporal and spatial variability contained in the passes. One major advantage of the use of satellite passes is that there is no need to wait for the daily mean availability in order to update the forecast.
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
Short summary
Short summary
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
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.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
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
Short summary
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.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
Short summary
Short summary
Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
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
Short summary
Short summary
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
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.
Jiping Xie, Roshin P. Raj, Laurent Bertino, Justino Martínez, Carolina Gabarró, and Rafael Catany
Ocean Sci., 19, 269–287, https://doi.org/10.5194/os-19-269-2023, https://doi.org/10.5194/os-19-269-2023, 2023
Short summary
Short summary
Sea ice melt, together with other freshwater sources, has effects on the Arctic environment. Sea surface salinity (SSS) plays a key role in representing water mixing. Recently the satellite SSS from SMOS was developed in the Arctic region. In this study, we first evaluate the impact of assimilating these satellite data in an Arctic reanalysis system. It shows that SSS errors are reduced by 10–50 % depending on areas, encouraging its use in a long-time reanalysis to monitor the Arctic water cycle.
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.
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
Short summary
Short summary
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.
Fabio Mangini, Léon Chafik, Antonio Bonaduce, Laurent Bertino, and Jan Even Ø. Nilsen
Ocean Sci., 18, 331–359, https://doi.org/10.5194/os-18-331-2022, https://doi.org/10.5194/os-18-331-2022, 2022
Short summary
Short summary
We validate the recent ALES-reprocessed coastal satellite altimetry dataset along the Norwegian coast between 2003 and 2018. We find that coastal altimetry and conventional altimetry products perform similarly along the Norwegian coast. However, the agreement with tide gauges slightly increases in terms of trends when we use the ALES coastal altimetry data. We then use the ALES dataset and hydrographic stations to explore the steric contribution to the Norwegian sea-level anomaly.
Justino Martínez, Carolina Gabarró, Antonio Turiel, Verónica González-Gambau, Marta Umbert, Nina Hoareau, Cristina González-Haro, Estrella Olmedo, Manuel Arias, Rafael Catany, Laurent Bertino, Roshin P. Raj, Jiping Xie, Roberto Sabia, and Diego Fernández
Earth Syst. Sci. Data, 14, 307–323, https://doi.org/10.5194/essd-14-307-2022, https://doi.org/10.5194/essd-14-307-2022, 2022
Short summary
Short summary
Measuring salinity from space is challenging since the sensitivity of the brightness temperature to sea surface salinity is low, but the retrieval of SSS in cold waters is even more challenging. In 2019, the ESA launched a specific initiative called Arctic+Salinity to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product.
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
Short summary
Short summary
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.
Cited articles
Alam, J. M. and Lin, J. C.: Toward a Fully Lagrangian Atmospheric Modeling
System, Mon. Weather Rev., 136, 4653–4667,
https://doi.org/10.1175/2008MWR2515.1, 2008. a
Allard, R. A., Farrell, S. L., Hebert, D. A., Johnston, W. F., Li, L., Kurtz,
N. T., Phelps, M. W., Posey, P. G., Tilling, R., Ridout, A., and Wallcraft,
A. J.: Utilizing CryoSat-2 sea ice thickness to initialize a coupled
ice-ocean modeling system, Adv. Space Res., 62, 1265–1280,
https://doi.org/10.1016/j.asr.2017.12.030, 2018. a
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and
Avellano, A.: The data assimilation research testbed: A community facility,
B. Am. Meteorol. Soc., 90, 1283–1296, 2009. a
Anderson, J. L.: Exploring the need for localization in ensemble data
assimilation using a hierarchical ensemble filter, Physica D, 230, 99–111, 2007. 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
Aydoğdu, A., Carrassi, A., Guider, C. T., Jones, C. K. R. T., and Rampal, P.:
Data assimilation using adaptive, non-conservative, moving mesh models,
Nonlinear Proc. Geoph., 26, 175–193,
https://doi.org/10.5194/npg-26-175-2019, 2019. a, b, c, d
Bernard, B., Madec, G., Penduff, T., Molines, J.-M., Treguier, A.-M.,
Le Sommer, J., Beckmann, A., Biastoch, A., Böning, C., Dengg, J., and Derval, C.:
Impact of partial steps and momentum advection schemes in a global ocean
circulation model at eddy-permitting resolution, Ocean Dynam., 56,
543–567, https://doi.org/10.1007/s10236-006-0082-1, 2006. a
Bertino, L. and Holland, M. M.: Coupled ice-ocean modeling and predictions,
J. Mar. Res., 75, 839–875, 2017. a
Bingham, E. C.: Fluidity and plasticity, Vol. 2, Wexford College Pr, ISBN 9781427614629, 1922. a
Blanchard-Wrigglesworth, E. and Bitz, C. M.: Characteristics of Arctic sea-ice
thickness variability in GCMs, J. Climate, 27, 8244–8258, 2014. 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
Bouchat, A., Hutter, N., Chanut, J., Dupont, F., Dukhovskoy, D., Garric, G.,
Lee, Y. J., Lemieux, J.-F., Lique, C., Losch, M., and Maslowski, W.: Sea Ice Rheology
Experiment (SIREx): 1. Scaling and Statistical Properties of Sea-Ice
Deformation Fields, J. Geophys. Res.-Oceans, 127,
e2021JC017667, https://doi.org/10.1029/2021JC017667, 2022. a
Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.:
EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded
Data Sets, ISPRS Int. J. Geo-Inf., 1, 32–45,
https://doi.org/10.3390/ijgi1010032, 2012. a
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in
the geosciences: An overview of methods, issues, and perspectives, WIREs
Clim. Change, 9, e535, https://doi.org/10.1002/wcc.535, 2018. a, b
Carrassi, A., Bocquet, M., Demaeyer, J., Grudzien, C., Raanes, P., and
Vannitsem, S.: Data Assimilation for Chaotic Dynamics, in: Data Assimilation
for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV), edited by:
Park, S. K. and Xu, L., Springer International Publishing, Cham,
1–42, 2022. 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
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
Farrell, P. and Maddison, J.: Conservative interpolation between volume meshes
by local Galerkin projection, Comput. Method. Appl. M., 200, 89–100, 2011. a
Fritzner, S., Graversen, R., Christensen, K. H., Rostosky, P., and Wang, K.: Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system, The Cryosphere, 13, 491–509, https://doi.org/10.5194/tc-13-491-2019, 2019. a, b, c
Goessling, H. F. and Jung, T.: A probabilistic verification score for contours:
Methodology and application to Arctic ice-edge forecasts, Q. J. Roy. Meteor. Soc., 144, 735–743, https://doi.org/10.1002/qj.3242,
2018. a
Goessling, H. F., Tietsche, S., Day, J. J., Hawkins, E., and Jung, T.:
Predictability of the Arctic sea ice edge, Geophys. Res. Lett., 43,
1642–1650, https://doi.org/10.1002/2015GL067232, 2016. a
He, H., Lei, L., Whitaker, J. S., and Tan, Z.-M.: Impacts of Assimilation
Frequency on Ensemble Kalman Filter Data Assimilation and Imbalances, J. Adv. Model. Earth Sy., 12, e2020MS002187,
https://doi.org/10.1029/2020MS002187, 2020. a
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 4.1 la-cc-06-012, T-3 Fluid Dynamics Group, Los Alamos National
Laboratory, 675, 500, 2010. a
Hutter, N., Bouchat, A., Dupont, F., Dukhovskoy, D., Koldunov, N., Lee, Y. J.,
Lemieux, J.-F., Lique, C., Losch, M., Maslowski, W., and Myers, P. G.: Sea Ice Rheology
Experiment (SIREx): 2. Evaluating Linear Kinematic Features in
High-Resolution Sea Ice Simulations, J. Geophys. Res.-Oceans,
127, e2021JC017666, https://doi.org/10.1029/2021JC017666, 2022. a
Jain, P. K., Mandli, K., Hoteit, I., Knio, O., and Dawson, C.: Dynamically
adaptive data-driven simulation of extreme hydrological flows, Ocean
Modell., 122, 85–103, 2018. a
Janjić, T., Bormann, N., Bocquet, M., Carton, J., Cohn, S., Dance, S.,
Losa, S., Nichols, N., Potthast, R., Waller, J., and Weston, P.: On the
representation error in data assimilation, Q. J. Roy. Meteor. Soc., 144, 1257–1278, 2018. a
Kaleschke, L., Tian-Kunze, X., Maaß, N., Beitsch, A., Wernecke, A., Miernecki,
M., Müller, G., Fock, B. H., Gierisch, A. M., Schlünzen, K. H., Pohlmann,
T., Dobrynin, M., Hendricks, S., Asseng, J., Gerdes, R., Jochmann, P.,
Reimer, N., Holfort, J., Melsheimer, C., Heygster, G., Spreen, G., Gerland,
S., King, J., Skou, N., Søbjærg, S. S., Haas, C., Richter, F., and Casal,
T.: SMOS sea ice product: Operational application and validation in the
Barents Sea marginal ice zone, Remote Sens. Environ., 180, 264–273,
https://doi.org/10.1016/j.rse.2016.03.009, 2016. a
Kimmritz, M., Counillon, F., Bitz, C., Massonnet, F., Bethke, I., and Gao, Y.:
Optimising assimilation of sea ice concentration in an Earth system model
with a multicategory sea ice model, Tellus A, 70, 1–23, https://doi.org/10.1080/16000870.2018.1435945, 2018. a, b, c
Kimmritz, M., Counillon, F., Smedsrud, L. H., Bethke, I., Keenlyside, N.,
Ogawa, F., and Wang, Y.: Impact of Ocean and Sea Ice Initialisation On
Seasonal Prediction Skill in the Arctic, J. Adv. Model. Earth Sy., 11, 4147–4166, https://doi.org/10.1029/2019MS001825,
2019. a
Lange, H. and Craig, G. C.: The Impact of Data Assimilation Length Scales on
Analysis and Prediction of Convective Storms, Mon. Weather Rev., 142,
3781–3808, https://doi.org/10.1175/MWR-D-13-00304.1, 2014. a
Lavergne, T., Eastwood, S., Teffah, Z., Schyberg, H., and Breivik, L.-A.: Sea
ice motion from low-resolution satellite sensors: An alternative method and
its validation in the Arctic, J. Geophys. Res.-Oceans, 115, C10032,
https://doi.org/10.1029/2009JC005958, 2010. a
Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R., Cullen,
R., Kwok, R., Schweiger, A., Zhang, J., Haas, C., Hendricks, S., Krishfield,
R., Kurtz, N., Farrell, S., and Davidson, M.: CryoSat-2 estimates of Arctic
sea ice thickness and volume, Geophys. Res. Lett., 40, 732–737,
https://doi.org/10.1002/grl.50193, 2013. a
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,
https://doi.org/10.1002/2014jc010678, 2015. a
Lisæter, K. A., Rosanova, J., and Evensen, G.: Assimilation of ice
concentration in a coupled ice–ocean model, using the Ensemble Kalman
filter, Ocean Dynam., 53, 368–388, https://doi.org/10.1007/s10236-003-0049-4, 2003. a, b, c
Massonnet, F., Fichefet, T., and Goosse, H.: Prospects for improved seasonal
Arctic sea ice predictions from multivariate data assimilation, Ocean
Modell., 88, 16–25, https://doi.org/10.1016/j.ocemod.2014.12.013, 2015. a, b, c
Mathiot, P., König Beatty, C., Fichefet, T., Goosse, H., Massonnet, F., and Vancoppenolle, M.: Better constraints on the sea-ice state using global sea-ice data assimilation, Geosci. Model Dev., 5, 1501–1515, https://doi.org/10.5194/gmd-5-1501-2012, 2012. a
Meier, W. N.: Losing Arctic sea ice: observations of the recent decline and the
long-term context, Chap. 11, John Wiley & Sons, Ltd,
290–303, https://doi.org/10.1002/9781118778371.ch11, 2017. a
Mu, L., Yang, Q., Losch, M., Losa, S. N., Ricker, R., Nerger, L., and Liang,
X.: Improving sea ice thickness estimates by assimilating CryoSat-2 and SMOS
sea ice thickness data simultaneously, Q. J. Roy. Meteor. Soc., 144, 529–538, 2018. a
Mu, L., Nerger, L., Tang, Q., Loza, S. N., Sidorenko, D., Wang, Q., Semmler,
T., Zampieri, L., Losch, M., and Goessling, H. F.: Toward a Data Assimilation
System for Seamless Sea Ice Prediction Based on the AWI Climate Model,
J. Adv. Model. Earth Sy., 12, e2019MS001937,
https://doi.org/10.1029/2019MS001937, 2020. a
National Geophysical Data Center, NESDIS, NOAA, U.S. Department of Commerce:
ETOPO2, Global 2 Arc-minute Ocean Depth and Land Elevation from the US
National Geophysical Data Center (NGDC),
https://doi.org/10.5065/D6668B75, 2001. a
Olason, E., Boutin, G., Korosov, A., Rampal, P., Williams, T., Kimmritz, M.,
Dansereau, V., and Samaké, A.: A New Brittle Rheology and Numerical
Framework for Large-Scale Sea-Ice Models, J. Adv. Model. Earth Sy., 14, e2021MS002685, https://doi.org/10.1029/2021MS002685, 2022. a, b, c
OSI-SAF: Global Sea Ice Concentration (SSMIS), EUMETSAT [data set], https://osi-saf.eumetsat.int/products/osi-401-b, last access: 24 April 2023a. a
OSI-SAF: Global Low Resolution Sea Ice Drift, EUMETSAT [data set], https://osi-saf.eumetsat.int/products/osi-405-c, last access: 24 April 2023b. a
Owens, R. G. and Hewson, T.: ECMWF Forecast User Guide, https://doi.org/10.21957/m1cs7h,
2018 (data available at: https://apps.ecmwf.int/archive-catalogue/?type=fc&class=od&stream=oper&expver=1, last access: April 2023). a, b
Penny, S. G. and Hamill, T. M.: Coupled data assimilation for integrated earth
system analysis and prediction, B. Am. Meteorol. Soc., 98, 169–172, 2017. a
Posey, P. G., Metzger, E. J., Wallcraft, A. J., Hebert, D. A., Allard, R. A., Smedstad, O. M., Phelps, M. W., Fetterer, F., Stewart, J. S., Meier, W. N., and Helfrich, S. R.: Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems, The Cryosphere, 9, 1735–1745, https://doi.org/10.5194/tc-9-1735-2015, 2015. a
Rampal, P., Bouillon, S., Bergh, J., and Ólason, E.: Arctic sea-ice diffusion from observed and simulated Lagrangian trajectories, The Cryosphere, 10, 1513–1527, https://doi.org/10.5194/tc-10-1513-2016, 2016a. 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, 2016b. a, b
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas, C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, 2017. a
Sakov, P.: EnKF-C user guide, arXiv [preprint],
https://doi.org/10.48550/arXiv.1410.1233, 2014. 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, https://doi.org/10.1111/j.1600-0870.2007.00299.x, 2008. a, b, c
Sakov, P. and Sandery, P.: An adaptive quality control procedure for data
assimilation, Tellus A, 69, 1318031,
https://doi.org/10.1080/16000870.2017.1318031, 2017. a
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 (data available at: https://resources.marine.copernicus.eu/product-detail/ARCTIC_ANALYSIS_FORECAST_PHYS_002_001_a/INFORMATION, last access: April 2023). a, b, c, d, e, f, g, h
Sampson, C., Carrassi, A., Aydoğdu, A., and Jones, C. K.: Ensemble Kalman
filter for nonconservative moving mesh solvers with a joint physics and mesh
location update, Q. J. Roy. Meteor. Soc., 147,
1539–1561, https://doi.org/10.1002/qj.3980, 2021. a
Sandery, P. A., O’Kane, T. J., Kitsios, V., and Sakov, P.: Climate Model
State Estimation Using Variants of EnKF Coupled Data Assimilation, Mon. Weather Rev., 148, 2411–2431, https://doi.org/10.1175/MWR-D-18-0443.1, 2020. a
Scheffler, G., Carrassi, A., Ruiz, J., and Pulido, M.: Dynamical effects of
inflation in ensemble-based data assimilation under the presence of model
error, Q. J. Roy. Meteor. Soc., 148,
2368–2383, 2022. a
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling system
(ROMS): a split-explicit, free-surface, topography-following-coordinate
oceanic model, Ocean Modell., 9, 347–404, 2005. a
Stark, J. D., Donlon, C. J., Martin, M. J., and McCulloch, M. E.: OSTIA : An
operational, high resolution, real time, global sea surface temperature
analysis system, in: OCEANS 2007 – Europe, 1–4,
https://doi.org/10.1109/OCEANSE.2007.4302251, 2007. a
Tietsche, S., Notz, D., Jungclaus, J. H., and Marotzke, J.: Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects, Ocean Sci., 9, 19–36, https://doi.org/10.5194/os-9-19-2013, 2013. a
Tonboe, R., Lavelle, J., Pfeiffer, R., and Howe, E.: Product
User Manual for OSI SAF Global Sea Ice Concentration, 1.6, OSI-401-b
& EUMETSAT, Danish Meteorological Institute, Denmark,
https://osisaf-hl.met.no/sites/osisaf-hl/files/user_manuals/osisaf_cdop3_ss2_pum_ice-conc_v1p6.pdf (last access: April 2023), 2017. a
Wagner, P. M., Hughes, N., Bourbonnais, P., Stroeve, J., Rabenstein, L., Bhatt,
U., Little, J., Wiggins, H., and Fleming, A.: Sea-ice information and
forecast needs for industry maritime stakeholders, Polar Geogr., 43,
160–187, https://doi.org/10.1080/1088937X.2020.1766592, 2020. a
Wang, Y., Counillon, F., and Bertino, L.: Alleviating the bias induced by the
linear analysis update with an isopycnal ocean model, Q. J. Roy. Meteor. Soc., 142, 1064–1074, https://doi.org/10.1002/qj.2709, 2016. a
Xie, J., Bertino, L., Counillon, F., Lisæter, K. A., and Sakov, P.: Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013, Ocean Sci., 13, 123–144, https://doi.org/10.5194/os-13-123-2017, 2017. a
Xie, J., Counillon, F., and Bertino, L.: Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis, The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, 2018. a, b, c, d
Zhang, Y.-F., Bitz, C. M., Anderson, J. L., Collins, N., Hendricks, J., Hoar,
T., Raeder, K., and Massonnet, F.: Insights on Sea Ice Data Assimilation from
Perfect Model Observing System Simulation Experiments, J. Climate,
31, 5911–5926, https://doi.org/10.1175/JCLI-D-17-0904.1, 2018. a, b, c, d
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(5104 KB) - Full-text XML
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.
This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data...
