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
Related authors
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
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We developed a high-resolution (6–10 km) HYCOM-CICE configuration to better understand ocean and sea ice conditions in the Arctic and North Atlantic from 2009 to 2019. By comparing the model with available datasets, we found it reliably captures major patterns and seasonal changes. This can support forecasting, helping improve environmental monitoring and decision-making in regions sensitive to climate change.
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
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
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.
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
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We developed a high-resolution (6–10 km) HYCOM-CICE configuration to better understand ocean and sea ice conditions in the Arctic and North Atlantic from 2009 to 2019. By comparing the model with available datasets, we found it reliably captures major patterns and seasonal changes. This can support forecasting, helping improve environmental monitoring and decision-making in regions sensitive to climate change.
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
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
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
Ocean Sci., 22, 1073–1084, https://doi.org/10.5194/os-22-1073-2026, https://doi.org/10.5194/os-22-1073-2026, 2026
Short summary
Short summary
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.
Solène Jousset, Sandrine Mulet, Eric Greiner, John Wilkin, Lien Vidar, Léon Chafik, Roshin Raj, Antonio Bonaduce, Nicolas Picot, and Gérald Dibarboure
Earth Syst. Sci. Data, 18, 2285–2303, https://doi.org/10.5194/essd-18-2285-2026, https://doi.org/10.5194/essd-18-2285-2026, 2026
Short summary
Short summary
Satellite altimetry has revolutionized ocean observation, making it possible to track sea level with very good spatio-temporal coverage. However, only sea level anomalies are retrieved; to monitor the entire ocean signal, mean dynamic topography (MDT) must be added to these anomalies. In this study, an evaluation of the CNES-CLS22 MDT shows significant improvements in the Arctic. Over the globe, this new solution represents an incremental update to previous CNES-CLS18 MDT.
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Julien Brajard, and Laurent Bertino
The Cryosphere, 19, 5613–5637, https://doi.org/10.5194/tc-19-5613-2025, https://doi.org/10.5194/tc-19-5613-2025, 2025
Short summary
Short summary
This paper presents a four-dimensional variational data assimilation system based on a neural network emulator for sea-ice thickness, learned from neXtSIM (neXt generation Sea Ice Model) simulation outputs. Testing with simulated and real observation retrievals, the system improves forecasts and bias error, performing comparably to operational methods, demonstrating the promise of sea-ice data-driven data assimilation systems.
Roshin P. Raj, Vidar S. Lien, Sourav Chatterjee, Saradhy Surendran, Antonio Bonaduce, and Laurent Bertino
State Planet Discuss., https://doi.org/10.5194/sp-2025-18, https://doi.org/10.5194/sp-2025-18, 2025
Preprint under review for SP
Short summary
Short summary
Dense water formed and exiting from the Barents Sea constitutes an important part of the global ocean circulation. Considering the significant impact of salinity changes in dense water formation, we investigate the salinity changes in the Barents Sea during the past 3 decades. Our results highlight the recent freshening and its drivers in the northern and southern Barents Sea and show its impact on the density of the waters exiting the Barents Sea.
Laurent Bertino, Patrick Heimbach, Ed Blockley, and Einar Ólason
State Planet, 5-opsr, 14, https://doi.org/10.5194/sp-5-opsr-14-2025, https://doi.org/10.5194/sp-5-opsr-14-2025, 2025
Short summary
Short summary
Forecasts of sea ice are in high demand in the polar regions, and they are also quickly improving and becoming more easily accessible to non-experts. We provide here a brief status of the short-term forecasting services – typically 10 d ahead – and an outlook of their upcoming developments.
Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore
State Planet, 5-opsr, 9, https://doi.org/10.5194/sp-5-opsr-9-2025, https://doi.org/10.5194/sp-5-opsr-9-2025, 2025
Short summary
Short summary
Observations of the ocean from satellites and platforms in the ocean are combined with information from computer models to produce predictions of how the ocean temperature, salinity, and currents will evolve over the coming days and weeks and to describe how the ocean has evolved in the past. This paper summarises the methods used to produce these ocean forecasts at various centres around the world and outlines the practical considerations for implementing such forecasting systems.
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
The Cryosphere, 19, 731–752, https://doi.org/10.5194/tc-19-731-2025, https://doi.org/10.5194/tc-19-731-2025, 2025
Short summary
Short summary
This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a combination of machine learning and data assimilation. By training a machine learning model on data from 2011 to 2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes in sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.
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
State Planet, 3-slre1, 3, https://doi.org/10.5194/sp-3-slre1-3-2024, https://doi.org/10.5194/sp-3-slre1-3-2024, 2024
Short summary
Short summary
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.
Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-2476, https://doi.org/10.5194/egusphere-2024-2476, 2024
Preprint archived
Short summary
Short summary
The formation and evolution of sea ice melt ponds (ponds of melted water) are complex, insufficiently understood and represented in models with considerable uncertainty. These uncertain representations are not traditionally included in climate models potentially causing the known underestimation of sea ice loss in climate models. Our work creates the first observationally based machine learning model of melt ponds that is also a ready and viable candidate to be included in climate models.
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.
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
Short summary
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.
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.
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
Short summary
Short summary
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
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.
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.
Cited articles
Abulaitijiang, A., Andersen, O. B., and Stenseng, L.: Coastal sea level from inland CryoSat-2 interferometric SAR altimetry, Geophys. Res. Lett., 42, 1841–1847, https://doi.org/10.1002/2015GL063131, 2015.
Aure, J. and Østensen, Ø.: Hydrographic normals and long-term variations in Norwegian coastal waters, Fisken og Havet, 6, 75 pp., https://resources.marine.copernicus.eu/product-download/SEALEVEL_GLO_PHY_L3_REP_OBSERVATIONS_008_062 (last access: 2 September 2021), 1993.
Bartlett, M. S.: Some Aspects of the Time-Correlation Problem in Regard to Tests of Significance, J. R. Stat. Soc., 98, 536–543, https://doi.org/10.2307/2342284, 1935.
Benveniste, J., Birol, F., Calafat, F., Cazenave, A., Dieng, H., Gouzenes, Y., Legeais, J. F., Léger, F., Niño, F., Passaro, M., Schwatke, C., and Shaw, A.: Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002–2018, Sci. Data, 7, 1–17, https://doi.org/10.1038/s41597-020-00694-w, 2020.
Bonaduce, A., Pinardi, N., Oddo, P., Spada, G., and Larnicol, G.: Sea-level variability in the Mediterranean Sea from altimetry and tide gauges, Clim. Dynam., 47, 2851–2866, https://doi.org/10.1007/s00382-016-3001-2, 2016.
Breili, K., Simpson, M. J. R., and Nilsen, J. E. Ø.: Observed sea-level changes along the Norwegian coast, J. Mar. Sci. Eng., 5, 1–19, https://doi.org/10.3390/jmse5030029, 2017.
Carrère, L. and Lyard, F.: Modeling the barotropic response of the global ocean to atmospheric wind and pressure forcing – Comparisons with observations, Geophys. Res. Lett., 30, 1275, https://doi.org/10.1029/2002GL016473, 2003.
Cazenave, A., Palanisamy, H., and Ablain, M.: Contemporary sea level changes from satellite altimetry: What have we learned? What are the new challenges?, Adv. Space Res., 62, 1639–1653, https://doi.org/10.1016/j.asr.2018.07.017, 2018.
Chafik, L., Nilsson, J., Skagseth, Ø., and Lundberg, P.: On the flow of Atlantic water and temperature anomalies in the Nordic Seas toward the Arctic Ocean, J. Geophys. Res.-Oceans, 120, 7897–7918, https://doi.org/10.1002/2015JC011012, 2015.
Chafik, L., Nilsen, J. E. Ø., and Dangendorf, S.: Impact of North Atlantic teleconnection patterns on northern European sea level, J. Mar. Sci. Eng., 5, 1–23, https://doi.org/10.3390/jmse5030043, 2017.
Chafik, L., Nilsen, J. E. Ø., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1–9, https://doi.org/10.1038/s41598-018-37603-6, 2019.
Cipollini, P., Benveniste, J., Bouffard, J., Emery, W.,
Fenoglio-Marc, L., Gommenginger, C., Griffin, D., Høyer, J.,
Kuparov, A., Madsen, K., Mercier, F., Miller, L., Pascual, A.,
Ravichandran, M., Shillington, F., Snaith, H., Sturb, P. T.,
Vandemark, D., Vignudelli, S., Wilkin, J., Woodworth, P., and
Zavala-Garay, J.: The Role of Altimetry in Coastal Observing
Systems, in: Proceedings of OceanObs'09: sustained ocean
observations and information for society, vol. 2, edited by: Hall,
J., Harrison, D. E., and Stammer, D., 21–25 September 2009, Venice, Italy,
European Space Agency, WPP-306, 181–191, https://doi.org/10.5270/oceanobs09.cwp.16, 2010.
Cipollini, P., Benveniste, J., Birol, F., Joana Fernandes, M., Obligis, E., Passaro, M., Ted Strub, P., Valladeau, G., Vignudelli, S., and Wilkin, J.: Satellite altimetry in coastal regions, in: Satellite Altimetry Over Oceans and Land Surfaces, Surv. Geophys., 38, 33–57, https://doi.org/10.1201/9781315151779, 2017.
Frederikse, T., Jevrejeva, S., Riva, R. E. M., and Dangendorf, S.: A consistent sea-level reconstruction and its budget on basin and global scales over 1958–2014, J. Climate, 31, 1267–1280, https://doi.org/10.1175/JCLI-D-17-0502.1, 2018.
Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., and Wu, Y. H.: The causes of sea-level rise since 1900, Nature, 584, 393–397, https://doi.org/10.1038/s41586-020-2591-3, 2020.
Gill, A. E. and Niiler, P. P.: The theory of the seasonal variability in the ocean, Deep Sea Res. Oceanogr. Abstr., 20, 141–177, https://doi.org/10.1016/0011-7471(73)90049-1, 1973.
Gómez-Enri, J., Vignudelli, S., Quartly, G. D., Gommenginger, C. P., Cipollini, P., Challenor, P. G., and Benveniste, J.: Modeling Envisat RA-2 waveforms in the coastal zone: Case study of calm water contamination, IEEE Geosci. Remote S., 7, 474–478, https://doi.org/10.1109/LGRS.2009.2039193, 2010.
Hermans, T. H. J., Gregory, J. M., Palmer, M. D., Ringer, M. A., Katsman, C. A., and Slangen, A. B. A.: Projecting Global Mean Sea-Level Change Using CMIP6 Models, Geophys. Res. Lett., 48, e2020GL092064, https://doi.org/10.1029/2020GL092064, 2021.
Ji, M., Reynolds, R. W., and Behringer, D. W.: Use of TOPEX/Poseidon sea level data for Ocean analyses and ENSO prediction: Some early results, J. Climate, 13, 216–231, https://doi.org/10.1175/1520-0442(2000)013<0216:UOTPSL>2.0.CO;2, 2000.
Lichter, M., Vafeidis, A. T., Nicholls, R. J., and Kaiser, G.: Exploring data-related uncertainties in analyses of land area and population in the “Low-Elevation Coastal Zone” (LECZ), J. Coastal Res., 27, 757–768, https://doi.org/10.2112/JCOASTRES-D-10-00072.1, 2011.
Liebmann, B., Dole, R. M., Jones, C., Bladé, I., and Allured, D.: Influence of choice of time period on global surface temperature trend estimates, B. Am. Meteorol. Soc., 91, 1485–1491, https://doi.org/10.1175/2010BAMS3030.1, 2010.
Madsen, K. S., Høyer, J. L., Suursaar, Ü., She, J., and Knudsen, P.: Sea Level Trends and Variability of the Baltic Sea From 2D Statistical Reconstruction and Altimetry, Front. Earth Sci., 7, 243, https://doi.org/10.3389/feart.2019.00243, 2019.
Nerem, R. S., Chambers, D. P., Choe, C., and Mitchum, G. T.: Estimating Mean Sea Level Change from the TOPEX and Jason Altimeter Missions, Mar. Geod., 33, 435–446, https://doi.org/10.1080/01490419.2010.491031, 2010.
Nicholls, R. J.: Planning for the Impacts of Sea Level Rise, Oceanography, 24, 144–157, 2011.
Oelsmann, J., Passaro, M., Dettmering, D., Schwatke, C., Sánchez, L., and Seitz, F.: The zone of influence: matching sea level variability from coastal altimetry and tide gauges for vertical land motion estimation, Ocean Sci., 17, 35–57, https://doi.org/10.5194/os-17-35-2021, 2021.
Passaro, M., Cipollini, P., Vignudelli, S., Quartly, G. D., and Snaith, H. M.: ALES: A multi-mission adaptive subwaveform retracker for coastal and open ocean altimetry, Remote Sens. Environ., 145, 173–189, https://doi.org/10.1016/j.rse.2014.02.008, 2014 (https://openadb.dgfi.tum.de, last access: 22 July 2020).
Passaro, M., Cipollini, P., and Benveniste, J.: Annual sea level variability of the coastal ocean: The Baltic Sea-North Sea transition zone, J. Geophys. Res.-Oceans, 120, 3061–3078, https://doi.org/10.1002/2014JC010510, 2015 (https://openadb.dgfi.tum.de, last access: 22 July 2020).
Passaro, M., Dinardo, S., Quartly, G. D., Snaith, H. M., Benveniste, J., Cipollini, P., and Lucas, B.: Cross-calibrating ALES Envisat and CryoSat-2 Delay-Doppler: A coastal altimetry study in the Indonesian Seas, Adv. Space Res., 58, 289–303, https://doi.org/10.1016/j.asr.2016.04.011, 2016.
Passaro, M., Smith, W., Schwatke, C., Piccioni, G., and Dettmering, D.: Validation of a global dataset based on subwaveform retracking: improving the precision of pulse-limited satellite altimetry, OSTST Meeting 2017, 23–27 October 2017, Miami, USA, 2017 (https://openadb.dgfi.tum.de, last access: 22 July 2020).
Passaro, M., Rose, S. K., Andersen, O. B., Boergens, E., Calafat, F. M., Dettmering, D., and Benveniste, J.: ALES+: Adapting a homogenous ocean retracker for satellite altimetry to sea ice leads, coastal and inland waters, Remote Sens. Environ., 211, 456–471, https://doi.org/10.1016/j.rse.2018.02.074, 2018.
Passaro, M., Müller, F. L., Oelsmann, J., Rautiainen, L., Dettmering, D., Hart-Davis, M. G., Abulaitijiang, A., Andersen, O. B., Høyer, J. L., Madsen, K. S., Ringgaard, I. M., Särkkä, J., Scarrott, R., Schwatke, C., Seitz, F., Tuomi, L., Restano, M., and Benveniste, J.: Absolute Baltic Sea Level Trends in the Satellite Altimetry Era: A Revisit, Front. Mar. Sci., 8, 647607, https://doi.org/10.3389/fmars.2021.647607, 2021.
Picaut, J., Hackert, E., Busalacchi, A. J., Murtugudde, R., and Lagerloef, G. S. E.: Mechanisms of the 1997–1998 El Niño–La Niña, as inferred from space-based observations, J. Geophys. Res., 107, 3037, https://doi.org/10.1029/2001jc000850, 2002.
Raj, R. P., Andersen, O. B., Johannessen, J. A., Gutknecht, B. D., Chatterjee, S., Rose, S. K., Bonaduce, A., Horwath, M., Ranndal, H., Richter, K., Palanisamy, H., Ludwigsen, C. A., Bertino, L., Nilsen, J. E. Ø., Knudsen, P., Hogg, A., Cazenave, A., and Benveniste, J.: Arctic sea level budget assessment during the grace/argo time period, Remote Sens., 12, 2837, https://doi.org/10.3390/rs12172837, 2020.
Richter, K., Nilsen, J. E. Ø., and Drange, H.: Contributions to sea level variability along the Norwegian coast for 1960–2010, J. Geophys. Res., 117, C05038, https://doi.org/10.1029/2011JC007826, 2012.
Richter, K., Meyssignac, B., Slangen, A. B. A., Melet, A., Church, J. A., Fettweis, X., Marzeion, B., Agosta, C., Ligtenberg, S. R. M., Spada, G., Palmer, M. D., Roberts, C. D., and Champollion, N.: Detecting a forced signal in satellite-era sea-level change, Environ. Res. Lett., 15, 094079, https://doi.org/10.1088/1748-9326/ab986e, 2020.
Rose, S. K., Andersen, O. B., Passaro, M., Ludwigsen, C. A., and Schwatke, C.: Arctic ocean sea level record from the complete radar altimetry era: 1991–2018, Remote Sens., 11, 1672, https://doi.org/10.3390/rs11141672, 2019.
Siegismund, F., Johannessen, J., Drange, H., Mork, K. A., and Korablev, A.: Steric height variability in the Nordic Seas, J. Geophys. Res., 112, C12010, https://doi.org/10.1029/2007JC004221, 2007.
Simpson, M. J. R., Nilsen, J. E. Ø., Ravndal, O. R., Breili, K., Sande, H., Kierulf, H. P., Steffen, H., Jansen, E., Carson, M., and Vestøl, O.: Sea Level Change for Norway Past and Present Observations and Projections to 2100, Tech. rep., Norwegian Centre for Climate Services report 1/2015, ISSN 2387–3025, 156 pp., 2015.
Simpson, M. J. R., Ravndal, O. R., Sande, H., Nilsen, J. E. Ø., Kierulf, H. P., Vestøl, O., and Steffen, H.: Projected 21st century sea-level changes, observed sea level extremes, and sea level allowances for Norway, J. Mar. Sci. Eng., 5, 36, https://doi.org/10.3390/jmse5030036, 2017.
Stammer, D., Cazenave, A., Ponte, R. M., and Tamisiea, M. E.: Causes for contemporary regional sea level changes, Annu. Rev. Mar. Sci., 5, 21–46, https://doi.org/10.1146/annurev-marine-121211-172406, 2013.
Volkov, D. L. and Pujol, M. I.: Quality assessment of a satellite altimetry data product in the Nordic, Barents, and Kara seas, J. Geophys. Res., 117, C03025, https://doi.org/10.1029/2011JC007557, 2012.
Wackernagel, H.: Multivariate Geostatistics, 3rd edn., Springer, Berlin, Heidelberg, 388 pp., https://doi.org/10.1007/BF02769635, 2003.
Woodworth, P. L.: A note on the nodal tide in sea level records, J. Coastal Res., 28, 316–323, https://doi.org/10.2112/JCOASTRES-D-11A-00023.1, 2012.
Xu, X.-Y., Xu, K., Xu, Y., and Shi, L.-W.: Coastal Altimetry: A Promising Technology for the Coastal Oceanography Community, in: Estuaries and Coastal Zones – Dynamics and Response to Environmental Changes, Surv. Geophys., 40, 1351–1397, https://doi.org/10.5772/intechopen.89373, 2019.
Zhang, Z., Lu, Y., and Hsu, H.: Detecting ocean currents from satellite altimetry, satellite gravity and ocean data, in Dynamic Planet, International Association of Geodesy Symposia, edited by: Tregoning, P. and Rizos, C., Springer, Berlin, https://doi.org/10.1007/978-3-540-49350-1_3, 2007.
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...
