Articles | Volume 19, issue 2
https://doi.org/10.5194/os-19-499-2023
© 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-19-499-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Apr 2023
Research article |
| 21 Apr 2023
| 21 Apr 2023
Improving statistical projections of ocean dynamic sea-level change using pattern recognition techniques
Víctor Malagón-Santos
CORRESPONDING AUTHOR
NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine & Delta Systems, P.O. Box 140, 4400 AC Yerseke, the Netherlands
Aimée B. A. Slangen
NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine & Delta Systems, P.O. Box 140, 4400 AC Yerseke, the Netherlands
Tim H. J. Hermans
NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine & Delta Systems, P.O. Box 140, 4400 AC Yerseke, the Netherlands
University of Utrecht, Institute for Marine and Atmospheric Research Utrecht (IMAU), Utrecht, the Netherlands
Sönke Dangendorf
Department of River–Coastal Science and Engineering, Tulane University, New Orleans, USA
Marta Marcos
Mediterranean Institute for Advanced Studies (IMEDEA), Spanish National Research Council–University of Balearic Islands (CSIC-UIB), Esporles, Spain
Nicola Maher
Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder, CO, USA
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
Max Planck Institute for Meteorology, Hamburg, Germany
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We present an application of multivariate statistical models to assess compound flooding events in a managed reservoir. Data (from a previous study) were obtained from a physical-based hydrological model driven by a regional climate model large ensemble, providing a time series expanding up to 800 years in length that ensures stable statistics. The length of the data set allows for a sensitivity assessment of the proposed statistical framework to natural climate variability.
Simon Treu, Sanne Muis, Sönke Dangendorf, Thomas Wahl, Julius Oelsmann, Stefanie Heinicke, Katja Frieler, and Matthias Mengel
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Andrew D. King, Tilo Ziehn, Matthew Chamberlain, Alexander R. Borowiak, Josephine R. Brown, Liam Cassidy, Andrea J. Dittus, Michael Grose, Nicola Maher, Seungmok Paik, Sarah E. Perkins-Kirkpatrick, and Aditya Sengupta
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Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
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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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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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Tony E. Wong, Alexander M. R. Bakker, Kelsey Ruckert, Patrick Applegate, Aimée B. A. Slangen, and Klaus Keller
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Alejandra R. Enríquez, Marta Marcos, Amaya Álvarez-Ellacuría, Alejandro Orfila, and Damià Gomis
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A. B. A. Slangen, R. S. W. van de Wal, Y. Wada, and L. L. A. Vermeersen
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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: Numerical Models | Properties and processes: Sea level | Depth range: Surface | Geographical range: All Geographic Regions | Challenges: Oceans and climate
Attributing decadal climate variability in coastal sea-level trends
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The transient sensitivity of sea level rise
Sam Royston, Rory J. Bingham, and Jonathan L. Bamber
Ocean Sci., 18, 1093–1107, https://doi.org/10.5194/os-18-1093-2022, https://doi.org/10.5194/os-18-1093-2022, 2022
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Patrick Wagner, Markus Scheinert, and Claus W. Böning
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Aslak Grinsted and Jens Hesselbjerg Christensen
Ocean Sci., 17, 181–186, https://doi.org/10.5194/os-17-181-2021, https://doi.org/10.5194/os-17-181-2021, 2021
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As we warm our planet, oceans expand, ice on land melts, and sea levels rise. On century timescales, we find that the sea level response to warming can be characterized by a single metric: the transient sea level sensitivity. Historical sea level exhibits substantially higher sensitivity than model-based estimates of future climates in authoritative climate assessments, implying recent projections could well underestimate the likely sea level rise by the end of this century.
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Short summary
Climate change will alter heat and freshwater fluxes as well as ocean circulation, driving local changes in sea level. This sea-level change component is known as ocean dynamic sea level (DSL), and it is usually projected using computationally expensive global climate models. Statistical models are a cheaper alternative for projecting DSL but may contain significant errors. Here, we partly remove those errors (driven by internal climate variability) by using pattern recognition techniques.
Climate change will alter heat and freshwater fluxes as well as ocean circulation, driving local...
