Articles | Volume 19, issue 1
https://doi.org/10.5194/os-19-17-2023
© 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/os-19-17-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
16 Jan 2023
Research article | Highlight paper |
| 16 Jan 2023
| 16 Jan 2023
Regionalizing the sea-level budget with machine learning techniques
Carolina M. L. Camargo
CORRESPONDING AUTHOR
Department of Estuarine and Delta Systems, NIOZ Royal Netherlands Institute for Sea Research, Yerseke, The Netherlands
Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands
Riccardo E. M. Riva
Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands
Tim H. J. Hermans
Department of Estuarine and Delta Systems, NIOZ Royal Netherlands Institute for Sea Research, Yerseke, The Netherlands
Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands
Eike M. Schütt
Department of Geography, Kiel University, Kiel, Germany
Marta Marcos
Mediterranean Institute for Advanced
Studies (IMEDEA), Spanish National Research Council – University of the Balearic Islands (CSIC-UIB), Esporles, Spain
Ismael Hernandez-Carrasco
Mediterranean Institute for Advanced
Studies (IMEDEA), Spanish National Research Council – University of the Balearic Islands (CSIC-UIB), Esporles, Spain
Aimée B. A. Slangen
Department of Estuarine and Delta Systems, NIOZ Royal Netherlands Institute for Sea Research, Yerseke, The Netherlands
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Carine G. van der Boog, Julie D. Pietrzak, Henk A. Dijkstra, Nils Brüggemann, René M. van Westen, Rebecca K. James, Tjeerd J. Bouma, Riccardo E. M. Riva, D. Cornelis Slobbe, Roland Klees, Marcel Zijlema, and Caroline A. Katsman
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Verónica Morales-Márquez, Alejandro Orfila, Gonzalo Simarro, Lluís Gómez-Pujol, Amaya Álvarez-Ellacuría, Daniel Conti, Álvaro Galán, Andrés F. Osorio, and Marta Marcos
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Ismael Hernández-Carrasco, Lohitzune Solabarrieta, Anna Rubio, Ganix Esnaola, Emma Reyes, and Alejandro Orfila
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Karen M. Simon, Riccardo E. M. Riva, Marcel Kleinherenbrink, and Thomas Frederikse
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Marcel Kleinherenbrink, Riccardo Riva, and Thomas Frederikse
Ocean Sci., 14, 187–204, https://doi.org/10.5194/os-14-187-2018, https://doi.org/10.5194/os-14-187-2018, 2018
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Renske C. de Winter, Thomas J. Reerink, Aimée B. A. Slangen, Hylke de Vries, Tamsin Edwards, and Roderik S. W. van de Wal
Nat. Hazards Earth Syst. Sci., 17, 2125–2141, https://doi.org/10.5194/nhess-17-2125-2017, https://doi.org/10.5194/nhess-17-2125-2017, 2017
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Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, https://doi.org/10.5194/gmd-10-4321-2017, 2017
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Tony E. Wong, Alexander M. R. Bakker, Kelsey Ruckert, Patrick Applegate, Aimée B. A. Slangen, and Klaus Keller
Geosci. Model Dev., 10, 2741–2760, https://doi.org/10.5194/gmd-10-2741-2017, https://doi.org/10.5194/gmd-10-2741-2017, 2017
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We present the Building blocks for Relevant Ice and Climate Knowledge (BRICK) model v0.2. BRICK is a model for hindcasting past and projecting future surface temperature and sea-level rise, resolving the sea-level contributions from glaciers and ice caps, the Greenland and Antarctic ice sheets, and thermal expansion. BRICK is specifically designed to support decision analyses through its transparency, and includes functionality to scale global sea-level estimates to regional projections.
Alejandra R. Enríquez, Marta Marcos, Amaya Álvarez-Ellacuría, Alejandro Orfila, and Damià Gomis
Nat. Hazards Earth Syst. Sci., 17, 1075–1089, https://doi.org/10.5194/nhess-17-1075-2017, https://doi.org/10.5194/nhess-17-1075-2017, 2017
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In this work we assess the impacts in reshaping coastlines as a result of sea level rise and changes in wave climate. The methodology proposed combines two wave models to resolve the wave processes in two micro-tidal sandy beaches in Mallorca island, western Mediterranean. The modelling approach is validated with observations. Our results indicate that the studied beaches would suffer a coastal retreat of between 7 and up to 50 m, equivalent to half of the present-day aerial beach surface.
Riccardo E. M. Riva, Thomas Frederikse, Matt A. King, Ben Marzeion, and Michiel R. van den Broeke
The Cryosphere, 11, 1327–1332, https://doi.org/10.5194/tc-11-1327-2017, https://doi.org/10.5194/tc-11-1327-2017, 2017
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The reduction of ice masses stored on land has made an important contribution to sea-level rise over the last century, as well as changed the Earth's shape. We model the solid-earth response to ice mass changes and find significant vertical deformation signals over large continental areas. We show how deformation rates have varied strongly throughout the last century, which affects the interpretation and extrapolation of recent observations of vertical land motion and sea-level change.
Marcel Kleinherenbrink, Riccardo Riva, and Yu Sun
Ocean Sci., 12, 1179–1203, https://doi.org/10.5194/os-12-1179-2016, https://doi.org/10.5194/os-12-1179-2016, 2016
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Satellite altimetry measures changes in sea level, while satellite gravimetry measures mass changes, and one can infer steric sea level from Argo temperature and salinity profiles. For the first time, it is shown that in most cases the mass and steric components match the total sea level measured by altimetry on a sub-basin scale in terms of trend, annual amplitude and interannual variability. We also find that the choice of gravity field filter is essential to close the budget.
I. Hernández-Carrasco, J. Sudre, V. Garçon, H. Yahia, C. Garbe, A. Paulmier, B. Dewitte, S. Illig, I. Dadou, M. González-Dávila, and J. M. Santana-Casiano
Biogeosciences, 12, 5229–5245, https://doi.org/10.5194/bg-12-5229-2015, https://doi.org/10.5194/bg-12-5229-2015, 2015
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We have reconstructed maps of air-sea CO2 fluxes at high resolution (4 km) in the offshore Benguela region using sea surface temperature and ocean colour data and CarbonTracker CO2 fluxes data at low resolution (110 km).
The inferred representation of pCO2 improves the description provided by CarbonTracker, enhancing small-scale variability.
We find that the resolution, as well as the inferred pCO2 data itself, is closer to in situ measurements of pCO2.
A. B. A. Slangen, R. S. W. van de Wal, Y. Wada, and L. L. A. Vermeersen
Earth Syst. Dynam., 5, 243–255, https://doi.org/10.5194/esd-5-243-2014, https://doi.org/10.5194/esd-5-243-2014, 2014
B. C. Gunter, O. Didova, R. E. M. Riva, S. R. M. Ligtenberg, J. T. M. Lenaerts, M. A. King, M. R. van den Broeke, and T. Urban
The Cryosphere, 8, 743–760, https://doi.org/10.5194/tc-8-743-2014, https://doi.org/10.5194/tc-8-743-2014, 2014
I. Hernández-Carrasco, C. López, A. Orfila, and E. Hernández-García
Nonlin. Processes Geophys., 20, 921–933, https://doi.org/10.5194/npg-20-921-2013, https://doi.org/10.5194/npg-20-921-2013, 2013
A. B. A. Slangen and R. S. W. van de Wal
The Cryosphere, 5, 673–686, https://doi.org/10.5194/tc-5-673-2011, https://doi.org/10.5194/tc-5-673-2011, 2011
Related subject area
Approach: Remote Sensing | Properties and processes: Sea level | Depth range: Surface | Geographical range: All Geographic Regions | Challenges: Oceans and climate
Global submesoscale diagnosis using along-track satellite altimetry
Oscar Vergara, Rosemary Morrow, Marie-Isabelle Pujol, Gérald Dibarboure, and Clément Ubelmann
Ocean Sci., 19, 363–379, https://doi.org/10.5194/os-19-363-2023, https://doi.org/10.5194/os-19-363-2023, 2023
Short summary
Short summary
Recent advances allow us to observe the ocean from space with increasingly higher detail, challenging our knowledge of the ocean's surface height signature. We use a statistical approach to determine the spatial scale at which the sea surface height signal is no longer dominated by geostrophic turbulence but in turn becomes dominated by wave-type motions. This information helps us to better use the data provided by ocean-observing satellites and to gain knowledge on climate-driving processes.
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Co-editor-in-chief
Closing the sea level budget (between observed rise and the sum of its causes) has been a challenge, is an ongoing effort and has primarily concerned the global mean. Here, the authors use machine learning to identify sub-areas with similar trends to close the sea level budget on a regional level, with much reduced errors compared with 1-degree grid points.
Closing the sea level budget (between observed rise and the sum of its causes) has been a...
Short summary
Sea-level change is mainly caused by variations in the ocean’s temperature and salinity and land ice melting. Here, we quantify the contribution of the different drivers to the regional sea-level change. We apply machine learning techniques to identify regions that have similar sea-level variability. These regions reduce the observational uncertainty that has limited the regional sea-level budget so far and highlight how large-scale ocean circulation controls regional sea-level change.
Sea-level change is mainly caused by variations in the ocean’s temperature and salinity and...
