Articles | Volume 18, issue 2
https://doi.org/10.5194/os-18-331-2022
https://doi.org/10.5194/os-18-331-2022
Research article
 | 
18 Mar 2022
Research article |  | 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, Léon Chafik, Antonio Bonaduce, Laurent Bertino, and Jan Even Ø. Nilsen

Related authors

Data assimilation schemes for ocean forecasting: state of the art
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
Numerical models for monitoring and forecasting sea ice: a short description of present status
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
Vertical Crustal Movement along the Coast of South Africa
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
Reconstruction of Arctic sea ice thickness (1992–2010) based on a hybrid machine learning and data assimilation approach
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
Four-dimensional variational data assimilation with a sea-ice thickness emulator
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Julien Brajard, and Laurent Bertino
EGUsphere, https://doi.org/10.5194/egusphere-2024-4028,https://doi.org/10.5194/egusphere-2024-4028, 2025
Short summary

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. 
Download
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.
Share