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
Sukun Cheng, Justin Stopa, Fabrice Ardhuin, and Hayley H. Shen
The Cryosphere, 14, 2053–2069, https://doi.org/10.5194/tc-14-2053-2020, https://doi.org/10.5194/tc-14-2053-2020, 2020
Short summary
Short summary
Wave states in ice in polar oceans are mostly studied near the ice edge. However, observations in the internal ice field, where ice morphology is very different from the ice edge, are rare. Recently derived wave data from satellite imagery are easier and cheaper than field studies and provide large coverage. This work presents a way of using these data to have a close view of some key features in the wave propagation over hundreds of kilometers and calibrate models for predicting wave decay.
Agnieszka Herman, Sukun Cheng, and Hayley H. Shen
The Cryosphere, 13, 2887–2900, https://doi.org/10.5194/tc-13-2887-2019, https://doi.org/10.5194/tc-13-2887-2019, 2019
Short summary
Short summary
Sea ice interactions with waves are extensively studied in recent years, but mechanisms leading to wave energy attenuation in sea ice remain poorly understood. Close to the ice edge, processes contributing to dissipation include collisions between ice floes and turbulence generated under the ice due to velocity differences between ice and water. This paper analyses details of those processes both theoretically and by means of a numerical model.
Agnieszka Herman, Sukun Cheng, and Hayley H. Shen
The Cryosphere, 13, 2901–2914, https://doi.org/10.5194/tc-13-2901-2019, https://doi.org/10.5194/tc-13-2901-2019, 2019
Short summary
Short summary
Sea ice interactions with waves are extensively studied in recent years, but mechanisms leading to wave energy attenuation in sea ice remain poorly understood. One of the reasons limiting progress in modelling is a lack of observational data for model validation. The paper presents an analysis of laboratory observations of waves propagating in colliding ice floes. We show that wave attenuation is sensitive to floe size and wave period. A numerical model is calibrated to reproduce this behaviour.
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
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.
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
Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore
State Planet Discuss., https://doi.org/10.5194/sp-2024-20, https://doi.org/10.5194/sp-2024-20, 2024
Preprint under review for SP
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, as well as 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.
Laurent Bertino, Patrick Heimbach, Ed Blockley, and Einar Ólason
State Planet Discuss., https://doi.org/10.5194/sp-2024-24, https://doi.org/10.5194/sp-2024-24, 2024
Preprint under review for SP
Short summary
Short summary
Forecasts of sea ice are in high demand in the polar regions, 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 days ahead – and an outlook of their upcoming developments.
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.
Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino
EGUsphere, https://doi.org/10.5194/egusphere-2024-1896, https://doi.org/10.5194/egusphere-2024-1896, 2024
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 2011–2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes on sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.
Lilian Garcia-Oliva, Alberto Carrassi, and François Counillon
EGUsphere, https://doi.org/10.5194/egusphere-2024-1843, https://doi.org/10.5194/egusphere-2024-1843, 2024
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.
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.
Jozef Skakala, David Ford, Keith Haines, Amos Lawless, Matthew Martin, Philip Browne, Marcin Chrust, Stefano Ciavatta, Alison Fowler, Daniel Lea, Matthew Palmer, Andrea Rochner, Jennifer Waters, Hao Zuo, Mike Bell, Davi Carneiro, Yumeng Chen, Susan Kay, Dale Partridge, Martin Price, Richard Renshaw, Georgy Shapiro, and James While
EGUsphere, https://doi.org/10.5194/egusphere-2024-1737, https://doi.org/10.5194/egusphere-2024-1737, 2024
Short summary
Short summary
In this paper we review marine data assimilation (MDA) in the UK, its stakeholders, needs, past and present developments in different areas of UK MDA, and offer a vision for their longer future. The specific areas covered are ocean physics and sea ice, marine biogeochemistry, coupled MDA, MDA informing observing network design and MDA theory. We also discuss future vision for MDA resources: observations, software, hardware and people skills.
Yumeng Chen, Lars Nerger, and Amos S. Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-1078, https://doi.org/10.5194/egusphere-2024-1078, 2024
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.
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.
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.
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
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.
Amy Solomon, Céline Heuzé, Benjamin Rabe, Sheldon Bacon, Laurent Bertino, Patrick Heimbach, Jun Inoue, Doroteaciro Iovino, Ruth Mottram, Xiangdong Zhang, Yevgeny Aksenov, Ronan McAdam, An Nguyen, Roshin P. Raj, and Han Tang
Ocean Sci., 17, 1081–1102, https://doi.org/10.5194/os-17-1081-2021, https://doi.org/10.5194/os-17-1081-2021, 2021
Short summary
Short summary
Freshwater in the Arctic Ocean plays a critical role in the global climate system by impacting ocean circulations, stratification, mixing, and emergent regimes. In this review paper we assess how Arctic Ocean freshwater changed in the 2010s relative to the 2000s. Estimates from observations and reanalyses show a qualitative stabilization in the 2010s due to a compensation between a freshening of the Beaufort Gyre and a reduction in freshwater in the Amerasian and Eurasian basins.
Timothy Williams, Anton Korosov, Pierre Rampal, and Einar Ólason
The Cryosphere, 15, 3207–3227, https://doi.org/10.5194/tc-15-3207-2021, https://doi.org/10.5194/tc-15-3207-2021, 2021
Short summary
Short summary
neXtSIM (neXt-generation Sea Ice Model) includes a novel and extremely realistic way of modelling sea ice dynamics – i.e. how the sea ice moves and deforms in response to the drag from winds and ocean currents. It has been developed over the last few years for a variety of applications, but this paper represents its first demonstration in a forecast context. We present results for the time period from November 2018 to June 2020 and show that it agrees well with satellite observations.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
Short summary
Short summary
Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Yumeng Chen, Konrad Simon, and Jörn Behrens
Geosci. Model Dev., 14, 2289–2316, https://doi.org/10.5194/gmd-14-2289-2021, https://doi.org/10.5194/gmd-14-2289-2021, 2021
Short summary
Short summary
Mesh adaptivity can reduce overall model error by only refining meshes in specific areas where it us necessary in the runtime. Here we suggest a way to integrate mesh adaptivity into an existing Earth system model, ECHAM6, without having to redesign the implementation from scratch. We show that while the additional computational effort is manageable, the error can be reduced compared to a low-resolution standard model using an idealized test and relatively realistic dust transport tests.
Sourav Chatterjee, Roshin P. Raj, Laurent Bertino, Sebastian H. Mernild, Meethale Puthukkottu Subeesh, Nuncio Murukesh, and Muthalagu Ravichandran
The Cryosphere, 15, 1307–1319, https://doi.org/10.5194/tc-15-1307-2021, https://doi.org/10.5194/tc-15-1307-2021, 2021
Short summary
Short summary
Sea ice in the Greenland Sea (GS) is important for its climatic (fresh water), economical (shipping), and ecological contribution (light availability). The study proposes a mechanism through which sea ice concentration in GS is partly governed by the atmospheric and ocean circulation in the region. The mechanism proposed in this study can be useful for assessing the sea ice variability and its future projection in the GS.
Einar Ólason, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 15, 1053–1064, https://doi.org/10.5194/tc-15-1053-2021, https://doi.org/10.5194/tc-15-1053-2021, 2021
Short summary
Short summary
We analyse the fractal properties observed in the pattern of the long, narrow openings that form in Arctic sea ice known as leads. We use statistical tools to explore the fractal properties of the lead fraction observed in satellite data and show that our sea-ice model neXtSIM displays the same behaviour. Building on this result we then show that the pattern of heat loss from ocean to atmosphere in the model displays similar fractal properties, stemming from the fractal properties of the leads.
Guillaume Boutin, Timothy Williams, Pierre Rampal, Einar Olason, and Camille Lique
The Cryosphere, 15, 431–457, https://doi.org/10.5194/tc-15-431-2021, https://doi.org/10.5194/tc-15-431-2021, 2021
Short summary
Short summary
In this study, we investigate the interactions of surface ocean waves with sea ice. We focus on the evolution of sea ice after it has been fragmented by the waves. Fragmented sea ice is expected to experience less resistance to deformation. We reproduce this evolution using a new coupling framework between a wave model and the recently developed sea ice model neXtSIM. We find that waves can significantly increase the mobility of compact sea ice over wide areas in the wake of storm events.
Sukun Cheng, Justin Stopa, Fabrice Ardhuin, and Hayley H. Shen
The Cryosphere, 14, 2053–2069, https://doi.org/10.5194/tc-14-2053-2020, https://doi.org/10.5194/tc-14-2053-2020, 2020
Short summary
Short summary
Wave states in ice in polar oceans are mostly studied near the ice edge. However, observations in the internal ice field, where ice morphology is very different from the ice edge, are rare. Recently derived wave data from satellite imagery are easier and cheaper than field studies and provide large coverage. This work presents a way of using these data to have a close view of some key features in the wave propagation over hundreds of kilometers and calibrate models for predicting wave decay.
Colin Grudzien, Marc Bocquet, and Alberto Carrassi
Geosci. Model Dev., 13, 1903–1924, https://doi.org/10.5194/gmd-13-1903-2020, https://doi.org/10.5194/gmd-13-1903-2020, 2020
Short summary
Short summary
All scales of a dynamical physical process cannot be resolved accurately in a multiscale, geophysical model. The behavior of unresolved scales of motion are often parametrized by a random process to emulate their effects on the dynamically resolved variables, and this results in a random–dynamical model. We study how the choice of a numerical discretization of such a system affects the model forecast and estimation statistics, when the random–dynamical model is unbiased in its parametrization.
Roshin P. Raj, Sourav Chatterjee, Laurent Bertino, Antonio Turiel, and Marcos Portabella
Ocean Sci., 15, 1729–1744, https://doi.org/10.5194/os-15-1729-2019, https://doi.org/10.5194/os-15-1729-2019, 2019
Short summary
Short summary
In this study we investigated the variability of the Arctic Front (AF), an important biologically productive region in the Norwegian Sea, using a suite of satellite data, atmospheric reanalysis and a regional coupled ocean–sea ice data assimilation system. We show evidence of the two-way interaction between the atmosphere and the ocean at the AF. The North Atlantic Oscillation is found to influence the strength of the AF and may have a profound influence on the region's biological productivity.
Francine Schevenhoven, Frank Selten, Alberto Carrassi, and Noel Keenlyside
Earth Syst. Dynam., 10, 789–807, https://doi.org/10.5194/esd-10-789-2019, https://doi.org/10.5194/esd-10-789-2019, 2019
Short summary
Short summary
Weather and climate predictions potentially improve by dynamically combining different models into a
supermodel. A crucial step is to train the supermodel on the basis of observations. Here, we apply two different training methods to the global atmosphere–ocean–land model SPEEDO. We demonstrate that both training methods yield climate and weather predictions of superior quality compared to the individual models. Supermodel predictions can also outperform the commonly used multi-model mean.
Agnieszka Herman, Sukun Cheng, and Hayley H. Shen
The Cryosphere, 13, 2887–2900, https://doi.org/10.5194/tc-13-2887-2019, https://doi.org/10.5194/tc-13-2887-2019, 2019
Short summary
Short summary
Sea ice interactions with waves are extensively studied in recent years, but mechanisms leading to wave energy attenuation in sea ice remain poorly understood. Close to the ice edge, processes contributing to dissipation include collisions between ice floes and turbulence generated under the ice due to velocity differences between ice and water. This paper analyses details of those processes both theoretically and by means of a numerical model.
Agnieszka Herman, Sukun Cheng, and Hayley H. Shen
The Cryosphere, 13, 2901–2914, https://doi.org/10.5194/tc-13-2901-2019, https://doi.org/10.5194/tc-13-2901-2019, 2019
Short summary
Short summary
Sea ice interactions with waves are extensively studied in recent years, but mechanisms leading to wave energy attenuation in sea ice remain poorly understood. One of the reasons limiting progress in modelling is a lack of observational data for model validation. The paper presents an analysis of laboratory observations of waves propagating in colliding ice floes. We show that wave attenuation is sensitive to floe size and wave period. A numerical model is calibrated to reproduce this behaviour.
Pierre Rampal, Véronique Dansereau, Einar Olason, Sylvain Bouillon, Timothy Williams, Anton Korosov, and Abdoulaye Samaké
The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, https://doi.org/10.5194/tc-13-2457-2019, 2019
Short summary
Short summary
In this article, we look at how the Arctic sea ice cover, as a solid body, behaves on different temporal and spatial scales. We show that the numerical model neXtSIM uses a new approach to simulate the mechanics of sea ice and reproduce the characteristics of how sea ice deforms, as observed by satellite. We discuss the importance of this model performance in the context of simulating climate processes taking place in polar regions, like the exchange of energy between the ocean and atmosphere.
Jiping Xie, Roshin P. Raj, Laurent Bertino, Annette Samuelsen, and Tsuyoshi Wakamatsu
Ocean Sci., 15, 1191–1206, https://doi.org/10.5194/os-15-1191-2019, https://doi.org/10.5194/os-15-1191-2019, 2019
Short summary
Short summary
Two gridded sea surface salinity (SSS) products have been derived from the European Space Agency’s Soil Moisture and Ocean Salinity mission. The uncertainties of these two products in the Arctic are quantified against two SSS products in the Copernicus Marine Environment Monitoring Services, two climatologies, and other in situ data. The results compared with independent in situ data clearly show a common challenge for the six SSS products to represent central Arctic freshwater masses (<24 psu).
Ali Aydoğdu, Alberto Carrassi, Colin T. Guider, Chris K. R. T Jones, and Pierre Rampal
Nonlin. Processes Geophys., 26, 175–193, https://doi.org/10.5194/npg-26-175-2019, https://doi.org/10.5194/npg-26-175-2019, 2019
Short summary
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.
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019, https://doi.org/10.5194/npg-26-143-2019, 2019
Short summary
Short summary
This paper describes an innovative way to use data assimilation to infer the dynamics of a physical system from its observation only. The method can operate with noisy and partial observation of the physical system. It acts as a deep learning technique specialised to dynamical models without the need for machine learning tools. The method is successfully tested on chaotic dynamical systems: the Lorenz-63, Lorenz-96, and Kuramoto–Sivashinski models and a two-scale Lorenz model.
Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-136, https://doi.org/10.5194/gmd-2019-136, 2019
Revised manuscript not accepted
Short summary
Short summary
We explore the possibility of combining data assimilation with machine learning. We introduce a new hybrid method for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting its future states. Numerical experiments have been carried out using the chaotic Lorenz 96 model, proving both the convergence of the hybrid method and its statistical skills including short-term forecasting and emulation of the long-term dynamics.
Jiping Xie, François Counillon, and Laurent Bertino
The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, https://doi.org/10.5194/tc-12-3671-2018, 2018
Short summary
Short summary
We use the winter sea-ice thickness dataset CS2SMOS merged from two satellites SMOS and CryoSat-2 for assimilation into an ice–ocean reanalysis of the Arctic, complemented by several other ocean and sea-ice measurements, using an Ensemble Kalman Filter. The errors of sea-ice thickness are reduced by 28% and the improvements persists through the summer when observations are unavailable. Improvements of ice drift are however limited to the Central Arctic.
Ali Aydoğdu, Nadia Pinardi, Emin Özsoy, Gokhan Danabasoglu, Özgür Gürses, and Alicia Karspeck
Ocean Sci., 14, 999–1019, https://doi.org/10.5194/os-14-999-2018, https://doi.org/10.5194/os-14-999-2018, 2018
Short summary
Short summary
A 6-year simulation of the Turkish Straits System is presented. The simulation is performed by a model using unstructured triangular mesh and realistic atmospheric forcing. The dynamics and circulation of the Marmara Sea are analysed and the mean state of the system is discussed on annual averages. Volume fluxes computed throughout the simulation are presented and the response of the model to severe storms is shown. Finally, it was possible to assess the kinetic energy budget in the Marmara Sea.
Colin Grudzien, Alberto Carrassi, and Marc Bocquet
Nonlin. Processes Geophys., 25, 633–648, https://doi.org/10.5194/npg-25-633-2018, https://doi.org/10.5194/npg-25-633-2018, 2018
Short summary
Short summary
Using the framework Lyapunov vectors, we analyze the asymptotic properties of ensemble based Kalman filters and how these are influenced by dynamical chaos, especially in the context of random model errors and small ensemble sizes. Particularly, we show a novel derivation of the evolution of forecast uncertainty for ensemble-based Kalman filters with weakly-nonlinear error growth, and discuss its impact for filter design in geophysical models.
Ali Aydoğdu, Timothy J. Hoar, Tomislava Vukicevic, Jeffrey L. Anderson, Nadia Pinardi, Alicia Karspeck, Jonathan Hendricks, Nancy Collins, Francesca Macchia, and Emin Özsoy
Nonlin. Processes Geophys., 25, 537–551, https://doi.org/10.5194/npg-25-537-2018, https://doi.org/10.5194/npg-25-537-2018, 2018
Short summary
Short summary
This study presents, to our knowledge, the first data assimilation experiments in the Sea of Marmara. We propose a FerryBox network for monitoring the state of the sea and show that assimilation of the temperature and salinity improves the forecasts in the basin. The flow of the Bosphorus helps to propagate the error reduction. The study can be taken as a step towards a marine forecasting system in the Sea of Marmara that will help to improve the forecasts in the adjacent Black and Aegean seas.
Anton Andreevich Korosov, Pierre Rampal, Leif Toudal Pedersen, Roberto Saldo, Yufang Ye, Georg Heygster, Thomas Lavergne, Signe Aaboe, and Fanny Girard-Ardhuin
The Cryosphere, 12, 2073–2085, https://doi.org/10.5194/tc-12-2073-2018, https://doi.org/10.5194/tc-12-2073-2018, 2018
Short summary
Short summary
A new algorithm for estimating sea ice age in the Arctic is presented. The algorithm accounts for motion, deformation, melting and freezing of sea ice and uses daily sea ice drift and sea ice concentration products. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel or, in other words, to provide a frequency distribution of the ice age. Multi-year ice concentration can be computed as a sum of all ice fractions older than 1 year.
Takuya Nakanowatari, Jun Inoue, Kazutoshi Sato, Laurent Bertino, Jiping Xie, Mio Matsueda, Akio Yamagami, Takeshi Sugimura, Hironori Yabuki, and Natsuhiko Otsuka
The Cryosphere, 12, 2005–2020, https://doi.org/10.5194/tc-12-2005-2018, https://doi.org/10.5194/tc-12-2005-2018, 2018
Short summary
Short summary
Medium-range predictability of early summer sea ice thickness in the East Siberian Sea was examined, based on TOPAZ4 forecast data. Statistical examination indicates that the estimate drops abruptly at 4 days, which is related to dynamical process controlled by synoptic-scale atmospheric fluctuations such as an Arctic cyclone. For longer lead times (> 4 days), the thermodynamic melting process takes over, which represents most of the remaining prediction.
Fabrice Ardhuin, Yevgueny Aksenov, Alvise Benetazzo, Laurent Bertino, Peter Brandt, Eric Caubet, Bertrand Chapron, Fabrice Collard, Sophie Cravatte, Jean-Marc Delouis, Frederic Dias, Gérald Dibarboure, Lucile Gaultier, Johnny Johannessen, Anton Korosov, Georgy Manucharyan, Dimitris Menemenlis, Melisa Menendez, Goulven Monnier, Alexis Mouche, Frédéric Nouguier, George Nurser, Pierre Rampal, Ad Reniers, Ernesto Rodriguez, Justin Stopa, Céline Tison, Clément Ubelmann, Erik van Sebille, and Jiping Xie
Ocean Sci., 14, 337–354, https://doi.org/10.5194/os-14-337-2018, https://doi.org/10.5194/os-14-337-2018, 2018
Short summary
Short summary
The Sea surface KInematics Multiscale (SKIM) monitoring mission is a proposal for a future satellite that is designed to measure ocean currents and waves. Using a Doppler radar, the accurate measurement of currents requires the removal of the mean velocity due to ocean wave motions. This paper describes the main processing steps needed to produce currents and wave data from the radar measurements. With this technique, SKIM can provide unprecedented coverage and resolution, over the global ocean.
Matthias Rabatel, Pierre Rampal, Alberto Carrassi, Laurent Bertino, and Christopher K. R. T. Jones
The Cryosphere, 12, 935–953, https://doi.org/10.5194/tc-12-935-2018, https://doi.org/10.5194/tc-12-935-2018, 2018
Short summary
Short summary
Large deviations still exist between sea ice forecasts and observations because of both missing physics in models and uncertainties on model inputs. We investigate how the new sea ice model neXtSIM is sensitive to uncertainties in the winds. We highlight and quantify the role of the internal forces in the ice on this sensitivity and show that neXtSIM is better at predicting sea ice drift than a free-drift (without internal forces) ice model and is a skilful tool for search and rescue operations.
Kristoffer Aalstad, Sebastian Westermann, Thomas Vikhamar Schuler, Julia Boike, and Laurent Bertino
The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, https://doi.org/10.5194/tc-12-247-2018, 2018
Short summary
Short summary
We demonstrate how snow cover data from satellites can be used to constrain estimates of snow distributions at sites in the Arctic. In this effort, we make use of data assimilation to combine the information contained in the snow cover data with a simple snow model. By comparing our snow distribution estimates to independent observations, we find that this method performs favorably. Being modular, this method could be applied to other areas as a component of a larger reanalysis system.
Timothy D. Williams, Pierre Rampal, and Sylvain Bouillon
The Cryosphere, 11, 2117–2135, https://doi.org/10.5194/tc-11-2117-2017, https://doi.org/10.5194/tc-11-2117-2017, 2017
Short summary
Short summary
As the Arctic sea ice extent drops, more ship traffic seeks to take advantage of this, and a need for better wave and sea ice forecasts arises. One aspect of this is the location of the sea ice edge. The waves here can be quite large, but they die away as they travel into the ice. This causes momentum to be transferred from the waves to the ice, causing ice drift. However, our study found that the effect of the wind drag had more impact on the ice edge position than the waves.
Jiping Xie, Laurent Bertino, François Counillon, Knut A. Lisæter, and Pavel Sakov
Ocean Sci., 13, 123–144, https://doi.org/10.5194/os-13-123-2017, https://doi.org/10.5194/os-13-123-2017, 2017
Short summary
Short summary
The Arctic Ocean plays an important role in the global climate system, but the concerned interpretation about its changes is severely hampered by the sparseness of the observations of sea ice and ocean. The focus of this study is to provide a quantitative assessment of the performance of the TOPAZ4 reanalysis for ocean and sea ice variables in the pan-Arctic region (north of 63 °N) in order to guide the user through its skills and limitations.
Jiping Xie, François Counillon, Laurent Bertino, Xiangshan Tian-Kunze, and Lars Kaleschke
The Cryosphere, 10, 2745–2761, https://doi.org/10.5194/tc-10-2745-2016, https://doi.org/10.5194/tc-10-2745-2016, 2016
Short summary
Short summary
As a potentially operational daily product, the SMOS-Ice can improve the statements of sea ice thickness and concentration. In this study, focusing on the SMOS-Ice data assimilated into the TOPAZ system, the quantitative evaluation for the impacts and the concerned comparison with the present observation system are valuable to understand the further improvement of the accuracy of operational ocean forecasting system.
Kirill Khvorostovsky and Pierre Rampal
The Cryosphere, 10, 2329–2346, https://doi.org/10.5194/tc-10-2329-2016, https://doi.org/10.5194/tc-10-2329-2016, 2016
Short summary
Short summary
We analyse two methods of freeboard retrieval from ICESat satellite data that were used to derive the two widely used Arctic sea ice thickness products. We show that although different factors result in significant local differences between freeboards, they roughly compensate each other with respect to overall freeboard estimation. Thus the difference found between the sea ice thickness datasets should be attributed to different parameters used in the freeboard-to-thickness conversion.
Pierre Rampal, Sylvain Bouillon, Jon Bergh, and Einar Ólason
The Cryosphere, 10, 1513–1527, https://doi.org/10.5194/tc-10-1513-2016, https://doi.org/10.5194/tc-10-1513-2016, 2016
Short summary
Short summary
Due to the increasing activity in Arctic, sea-ice–ocean models are now frequently used to produce operational forecasts, for oil spill trajectory modelling and to assist in offshore operations planning. In this study we evaluate the performance of two models with respect to their capability to reproduce observed sea ice diffusion properties by using metrics based on Lagrangian statistics. This paper presents a new and useful evaluation metric for current coupled sea ice–ocean models.
Pierre Rampal, Sylvain Bouillon, Einar Ólason, and Mathieu Morlighem
The Cryosphere, 10, 1055–1073, https://doi.org/10.5194/tc-10-1055-2016, https://doi.org/10.5194/tc-10-1055-2016, 2016
Short summary
Short summary
The Arctic sea ice cover has changed drastically over the last decades and undergone a shift in its dynamical regime, as seen by the increase of extreme fracturing events and the acceleration of sea ice drift. In this paper we present a new sea ice model, neXtSIM, that is capable of simulating both sea ice drift and deformation as observed from satellites, with similar spatial and temporal scaling properties. At the same time, the model reproduces sea ice area, extent, and volume correctly.
Natalia Ivanova, Pierre Rampal, and Sylvain Bouillon
The Cryosphere, 10, 585–595, https://doi.org/10.5194/tc-10-585-2016, https://doi.org/10.5194/tc-10-585-2016, 2016
Short summary
Short summary
Accurate observations of lead fraction are of high importance for model evaluation and/or assimilation into models. In this work, consistent quantitative error estimation of an existing lead fraction data set obtained from passive microwave observations is completed using Synthetic Aperture Radar data. A significant bias in the data set is found, and possible improvement in the methodology is suggested, so that the pixel-wise error is reduced by a factor of 2 on average.
S. Bouillon and P. Rampal
The Cryosphere, 9, 663–673, https://doi.org/10.5194/tc-9-663-2015, https://doi.org/10.5194/tc-9-663-2015, 2015
Short summary
Short summary
We present a new method to compute sea ice deformation fields from satellite-derived motion. The method particularly reduces the artificial noise that arises along discontinuities in the sea ice motion field. We estimate that this artificial noise may cause an overestimation of about 60% of sea ice opening and closing. The constant overestimation of the opening and closing could have led in previous studies to a large overestimation of freezing in leads, salt rejection and sea ice ridging.
M. Zygmuntowska, P. Rampal, N. Ivanova, and L. H. Smedsrud
The Cryosphere, 8, 705–720, https://doi.org/10.5194/tc-8-705-2014, https://doi.org/10.5194/tc-8-705-2014, 2014
Related subject area
Discipline: Sea ice | Subject: Data Assimilation
Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model
Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model
Towards improving short-term sea ice predictability using deformation observations
Bounded and categorized: targeting data assimilation for sea ice fractional coverage and non-negative quantities in a single column multi-category sea ice model
Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates
The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system
Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)
A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations
Estimating parameters in a sea ice model using an ensemble Kalman filter
Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system
Estimation of sea ice parameters from sea ice model with assimilated ice concentration and SST
Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis
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.
Keguang Wang, Alfatih Ali, and Caixin Wang
The Cryosphere, 17, 4487–4510, https://doi.org/10.5194/tc-17-4487-2023, https://doi.org/10.5194/tc-17-4487-2023, 2023
Short summary
Short summary
A simple, efficient. and accurate data assimilation method, local analytical optimal nudging (LAON), is introduced to assimilate high-resolution sea ice concentration in a pan-Arctic high-resolution coupled ocean and sea ice model. The method provides a new vision by nudging the model evolution to the optimal estimate forwardly, continuously, and smoothly. This method is applicable to the general nudging theory and applications in physics, Earth science, psychology, and behavior sciences.
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.
Molly Wieringa, Christopher Riedel, Jeffrey Anderson, and Cecilia Bitz
EGUsphere, https://doi.org/10.5194/egusphere-2023-2016, https://doi.org/10.5194/egusphere-2023-2016, 2023
Short summary
Short summary
Statistically combining models and observations with data assimilation (DA) can improve sea ice forecasts but must address several challenges, including irregularity in ice thickness and coverage over the ocean. Using a sea ice column model, we show that novel, bounds-aware DA methods outperform traditional methods for sea ice. Additionally, thickness observations at sub-grid scales improve modeled ice estimates of both thick and thin ice, a finding relevant for realistic forecasting efforts.
Imke Sievers, Till A. S. Rasmussen, and Lars Stenseng
The Cryosphere, 17, 3721–3738, https://doi.org/10.5194/tc-17-3721-2023, https://doi.org/10.5194/tc-17-3721-2023, 2023
Short summary
Short summary
The satellite CryoSat-2 measures freeboard (FB), which is used to derive sea ice thickness (SIT) under the assumption of hydrostatic balance. This SIT comes with large uncertainties due to errors in the observed FB, sea ice density, snow density and snow thickness. This study presents a new method to derive SIT by assimilating the FB into the sea ice model, evaluates the resulting SIT against in situ observations and compares the results to the CryoSat-2-derived SIT without FB assimilation.
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Andrew Ridout, and Lars Nerger
The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, https://doi.org/10.5194/tc-17-2509-2023, 2023
Short summary
Short summary
Observations show that the Arctic sea ice cover has reduced over the last 40 years. This study uses ensemble-based data assimilation in a stand-alone sea ice model to investigate the impacts of assimilating three different kinds of sea ice observation, including the novel assimilation of sea ice thickness distribution. We show that assimilating ice thickness distribution has a positive impact on thickness and volume estimates within the ice pack, especially for very thick ice.
Emma K. Fiedler, Matthew J. Martin, Ed Blockley, Davi Mignac, Nicolas Fournier, Andy Ridout, Andrew Shepherd, and Rachel Tilling
The Cryosphere, 16, 61–85, https://doi.org/10.5194/tc-16-61-2022, https://doi.org/10.5194/tc-16-61-2022, 2022
Short summary
Short summary
Sea ice thickness (SIT) observations derived from CryoSat-2 satellite measurements have been successfully used to initialise an ocean and sea ice forecasting model (FOAM). Other centres have previously used gridded and averaged SIT observations for this purpose, but we demonstrate here for the first time that SIT measurements along the satellite orbit track can be used. Validation of the resulting modelled SIT demonstrates improvements in the model performance compared to a control.
William Gregory, Isobel R. Lawrence, and Michel Tsamados
The Cryosphere, 15, 2857–2871, https://doi.org/10.5194/tc-15-2857-2021, https://doi.org/10.5194/tc-15-2857-2021, 2021
Short summary
Short summary
Satellite measurements of radar freeboard allow us to compute the thickness of sea ice from space; however attaining measurements across the entire Arctic basin typically takes up to 30 d. Here we present a statistical method which allows us to combine observations from three separate satellites to generate daily estimates of radar freeboard across the Arctic Basin. This helps us understand how sea ice thickness is changing on shorter timescales and what may be causing these changes.
Yong-Fei Zhang, Cecilia M. Bitz, Jeffrey L. Anderson, Nancy S. Collins, Timothy J. Hoar, Kevin D. Raeder, and Edward Blanchard-Wrigglesworth
The Cryosphere, 15, 1277–1284, https://doi.org/10.5194/tc-15-1277-2021, https://doi.org/10.5194/tc-15-1277-2021, 2021
Short summary
Short summary
Sea ice models suffer from large uncertainties arising from multiple sources, among which parametric uncertainty is highly under-investigated. We select a key ice albedo parameter and update it by assimilating either sea ice concentration or thickness observations. We found that the sea ice albedo parameter is improved by data assimilation, especially by assimilating sea ice thickness observations. The improved parameter can further benefit the forecast of sea ice after data assimilation stops.
Sindre Fritzner, Rune Graversen, Kai H. Christensen, Philip Rostosky, and Keguang Wang
The Cryosphere, 13, 491–509, https://doi.org/10.5194/tc-13-491-2019, https://doi.org/10.5194/tc-13-491-2019, 2019
Short summary
Short summary
In this work, a coupled ocean and sea-ice ensemble-based assimilation system is used to assess the impact of different observations on the assimilation system. The focus of this study is on sea-ice observations, including the use of satellite observations of sea-ice concentration, sea-ice thickness and snow depth for assimilation. The study showed that assimilation of sea-ice thickness in addition to sea-ice concentration has a large positive impact on the coupled model.
Siva Prasad, Igor Zakharov, Peter McGuire, Desmond Power, and Martin Richard
The Cryosphere, 12, 3949–3965, https://doi.org/10.5194/tc-12-3949-2018, https://doi.org/10.5194/tc-12-3949-2018, 2018
Short summary
Short summary
A numerical sea ice model, CICE, was used along with data assimilation to derive sea ice parameters in the region of Baffin Bay, Hudson Bay and Labrador Sea. The modelled ice parameters were compared with parameters estimated from remote-sensing data. The ice concentration, thickness and freeboard estimates from the model assimilated with both ice concentration and SST were found to be within the uncertainty of the observations except during March.
Jiping Xie, François Counillon, and Laurent Bertino
The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, https://doi.org/10.5194/tc-12-3671-2018, 2018
Short summary
Short summary
We use the winter sea-ice thickness dataset CS2SMOS merged from two satellites SMOS and CryoSat-2 for assimilation into an ice–ocean reanalysis of the Arctic, complemented by several other ocean and sea-ice measurements, using an Ensemble Kalman Filter. The errors of sea-ice thickness are reduced by 28% and the improvements persists through the summer when observations are unavailable. Improvements of ice drift are however limited to the Central Arctic.
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...
