Articles | Volume 18, issue 2
https://doi.org/10.5194/os-18-331-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-18-331-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Mar 2022
Research article |
| 18 Mar 2022
| 18 Mar 2022
Sea-level variability and change along the Norwegian coast between 2003 and 2018 from satellite altimetry, tide gauges, and hydrography
Fabio Mangini
CORRESPONDING AUTHOR
Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
Léon Chafik
Department of Meteorology and Bolin Centre for Climate Research, Stockholm, Sweden
National Oceanography Centre, Southampton, UK
Antonio Bonaduce
Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
Laurent Bertino
Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
Jan Even Ø. Nilsen
Institute of Marine Research and Bjerknes Centre for Climate Research, Bergen, Norway
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Bjorn Backeberg, Jemma Johnson, Natalia Aleksandrova, Cornelis Slobbe, Fabio Mangini, Marcello Passaro, Sanne Muis, Pavel Ditmar, Antonio Bonaduce, Gundula Winter, and Michael Hart-Davis
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Small details in Arctic sea ice thickness, such as ridges, cracks and leads, are difficult to observe with satellites and are rarely represented in climate models, even though they strongly influence sea ice motion and its interaction with the climate system. In this study, we introduce an artificial intelligence method that reconstructs realistic small‑scale ice thickness features from coarse observations. The results show more accurate estimates and physically realistic sea ice patterns.
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Coastal cities face growing flood risk from rising seas and storms. Scientists estimate dangerous water levels using satellite measurements of sea level and computer models of ocean behaviour, but the two are often combined inconsistently. We reviewed this research and found the real challenge is not merging the data, but making sure both methods measure the same thing, the same way. Solving this could lead to more reliable, consistent flood-risk estimates for coastal communities worldwide.
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Michael G. Hart-Davis, Blanca E. Torunski, Antonio Bonaduce, Torbjørn Taskjelle, and Johnny A. Johannessen
EGUsphere, https://doi.org/10.5194/egusphere-2026-5493, https://doi.org/10.5194/egusphere-2026-5493, 2026
This preprint is open for discussion and under review for Ocean Science (OS).
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Alejandro Blazquez, Benoit Meyssignac, Robin Fraudeau, Michael Ablain, Jonathan Bamber, Antonio Bonaduce, Marie Bouih, Anny Cazenave, Thorben Döhne, Ines Dussaillant, Ramiro Ferrari, Martin Horwath, Nicolas Kolodziejczyk, Hugo Lecomte, Stephanie Leroux, William Llovel, Daniele Melini, Erwan Oulhen, Thierry Penduff, Roshin P. Raj, Giorgio Spada, Marius Schlaak, Papasarafianou Stamatia, Andrea Storto, Chunxue Yang, and Sarah Connors
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Closing the sea‑level budget on annual and longer time scales is a cornerstone of physical oceanography because sea‑level rise is one of the best indicators of climate change, and a closed budget shows we have identified and quantified all major drivers. We examined it from 1993 to 2022, finding an accelerated rise that matched ice melt and warm water until 2015. Afterwards an unexplained gap appears. Better deep‑ocean observations and refined gravity processing are needed to close the budget.
Sayantani Ojha, Joakim Kjellsson, Torge Martin, Léon Chafik, Eric Maisonnave, and Wonsun Park
EGUsphere, https://doi.org/10.5194/egusphere-2026-1481, https://doi.org/10.5194/egusphere-2026-1481, 2026
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We study how model resolution affects the climate of the North Atlantic Ocean, with a focus on heat transport. Using a new climate model that improves interactions between the ocean and atmosphere, we compare simulations with different resolutions. Higher ocean resolution improves sea surface temperature and ocean circulation. Increasing both ocean and atmospheric resolution produces a more realistic balance of heat transport due to matching grid resolutions.
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
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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.
Julien Brajard, Anton Korosov, Fabio Mangini, Richard Davy, and Yiguo Wang
EGUsphere, https://doi.org/10.5194/egusphere-2026-2318, https://doi.org/10.5194/egusphere-2026-2318, 2026
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Small details in Arctic sea ice thickness, such as ridges, cracks and leads, are difficult to observe with satellites and are rarely represented in climate models, even though they strongly influence sea ice motion and its interaction with the climate system. In this study, we introduce an artificial intelligence method that reconstructs realistic small‑scale ice thickness features from coarse observations. The results show more accurate estimates and physically realistic sea ice patterns.
Shaun A. Eisner, James A. Carton, Leon Chafik, and Lars H. Smedsrud
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The Barents Sea is a major route for Atlantic Water to enter the Arctic. Cold air cools incoming Atlantic Water before it exits to the Arctic through the St. Anna Trough. We derive the first long-term estimate of the heat leaving the Barents Sea through St. Anna Trough. The heat leaving has increased since 1980, but only by half as much as the increase in heat entering. Finally, we present evidence for a previously proposed "ocean feedback" mechanism to help cool inflowing Atlantic Water.
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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
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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.
Franck Eitel Kemgang Ghomsi, Muharrem Hilmi Erkoç, Roshin P. Raj, Atinç Pirti, Antonio Bonaduce, Babatunde J. Abiodun, and Julienne Stroeve
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-6-2025, 393–397, https://doi.org/10.5194/isprs-archives-XLVIII-M-6-2025-393-2025, https://doi.org/10.5194/isprs-archives-XLVIII-M-6-2025-393-2025, 2025
Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino
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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.
José A. Jiménez, Gundula Winter, Antonio Bonaduce, Michael Depuydt, Giulia Galluccio, Bart van den Hurk, H. E. Markus Meier, Nadia Pinardi, Lavinia G. Pomarico, and Natalia Vazquez Riveiros
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The Knowledge Hub on Sea Level Rise (SLR) has done a scoping study involving stakeholders from government and academia to identify gaps and needs in SLR information, impacts, and policies across Europe. Gaps in regional SLR projections and uncertainties were found, while concerns were raised about shoreline erosion and emerging problems like saltwater intrusion and ineffective adaptation plans. The need for improved communication to make better decisions on SLR adaptation was highlighted.
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Preprint archived
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The formation and evolution of sea ice melt ponds (ponds of melted water) are complex, insufficiently understood and represented in models with considerable uncertainty. These uncertain representations are not traditionally included in climate models potentially causing the known underestimation of sea ice loss in climate models. Our work creates the first observationally based machine learning model of melt ponds that is also a ready and viable candidate to be included in climate models.
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 18, 2381–2406, https://doi.org/10.5194/tc-18-2381-2024, https://doi.org/10.5194/tc-18-2381-2024, 2024
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We explore multivariate state and parameter estimation using a data assimilation approach through idealised simulations in a dynamics-only sea-ice model based on novel rheology. We identify various potential issues that can arise in complex operational sea-ice models when model parameters are estimated. Even though further investigation will be needed for such complex sea-ice models, we show possibilities of improving the observed and the unobserved model state forecast and parameter accuracy.
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
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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
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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.
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
Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, https://doi.org/10.5194/tc-17-1735-2023, 2023
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This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
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
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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.
Martin Horwath, Benjamin D. Gutknecht, Anny Cazenave, Hindumathi Kulaiappan Palanisamy, Florence Marti, Ben Marzeion, Frank Paul, Raymond Le Bris, Anna E. Hogg, Inès Otosaka, Andrew Shepherd, Petra Döll, Denise Cáceres, Hannes Müller Schmied, Johnny A. Johannessen, Jan Even Øie Nilsen, Roshin P. Raj, René Forsberg, Louise Sandberg Sørensen, Valentina R. Barletta, Sebastian B. Simonsen, Per Knudsen, Ole Baltazar Andersen, Heidi Ranndal, Stine K. Rose, Christopher J. Merchant, Claire R. Macintosh, Karina von Schuckmann, Kristin Novotny, Andreas Groh, Marco Restano, and Jérôme Benveniste
Earth Syst. Sci. Data, 14, 411–447, https://doi.org/10.5194/essd-14-411-2022, https://doi.org/10.5194/essd-14-411-2022, 2022
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Global mean sea-level change observed from 1993 to 2016 (mean rate of 3.05 mm yr−1) matches the combined effect of changes in water density (thermal expansion) and ocean mass. Ocean-mass change has been assessed through the contributions from glaciers, ice sheets, and land water storage or directly from satellite data since 2003. Our budget assessments of linear trends and monthly anomalies utilise new datasets and uncertainty characterisations developed within ESA's Climate Change Initiative.
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
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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.
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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.
We validate the recent ALES-reprocessed coastal satellite altimetry dataset along the Norwegian...
