Articles | Volume 23, issue 2
https://doi.org/10.5194/nhess-23-587-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-587-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Coastal extreme sea levels in the Caribbean Sea induced by tropical cyclones
Ariadna Martín
Mediterranean Institute for Advanced Studies (UIB-CSIC), Esporlas, Spain
Angel Amores
Mediterranean Institute for Advanced Studies (UIB-CSIC), Esporlas, Spain
Department of Physics, University of Balearic Islands, Palma, Spain
Alejandro Orfila
Mediterranean Institute for Advanced Studies (UIB-CSIC), Esporlas, Spain
Tim Toomey
Mediterranean Institute for Advanced Studies (UIB-CSIC), Esporlas, Spain
Mediterranean Institute for Advanced Studies (UIB-CSIC), Esporlas, Spain
Department of Physics, University of Balearic Islands, Palma, Spain
Related authors
No articles found.
Michael G. Hart-Davis, Roman L. Sulzbach, Stefan A. Talke, Ivan D. Haigh, Marta Marcos, Philip Woodworth, Richard Ray, Ole B. Andersen, Florent Lyard, Ergane Fouchet, Denise Dettmering, Maik Thomas, and Florian Seitz
EGUsphere, https://doi.org/10.5194/egusphere-2026-346, https://doi.org/10.5194/egusphere-2026-346, 2026
This preprint is open for discussion and under review for Ocean Science (OS).
Short summary
Short summary
Ocean tides are a critical component of the global climate system, influencing a wide range of geophysical processes. Tide gauges have been a valuable source to develop the theory of ocean tides and understand their variability. We present updated tidal characteristics from the GESLA-4 global tide gauge dataset. We provide updated and new statistics on tidal properties, intended to be useful to a range of communities, from navigation and fishing communities to ocean scientists and tidal experts.
Angélique Melet, Roderik van de Wal, Angel Amores, Arne Arns, Alisée A. Chaigneau, Irina Dinu, Ivan D. Haigh, Tim H. J. Hermans, Piero Lionello, Marta Marcos, H. E. Markus Meier, Benoit Meyssignac, Matthew D. Palmer, Ronja Reese, Matthew J. R. Simpson, and Aimée B. A. Slangen
State Planet, 3-slre1, 4, https://doi.org/10.5194/sp-3-slre1-4-2024, https://doi.org/10.5194/sp-3-slre1-4-2024, 2024
Short summary
Short summary
The EU Knowledge Hub on Sea Level Rise’s Assessment Report strives to synthesize the current scientific knowledge on sea level rise and its impacts across local, national, and EU scales to support evidence-based policy and decision-making, primarily targeting coastal areas. This paper complements IPCC reports by documenting the state of knowledge of observed and 21st century projected changes in mean and extreme sea levels with more regional information for EU seas as scoped with stakeholders.
Nil Carrion-Bertran, Albert Falqués, Francesca Ribas, Daniel Calvete, Rinse de Swart, Ruth Durán, Candela Marco-Peretó, Marta Marcos, Angel Amores, Tim Toomey, Àngels Fernández-Mora, and Jorge Guillén
Earth Surf. Dynam., 12, 819–839, https://doi.org/10.5194/esurf-12-819-2024, https://doi.org/10.5194/esurf-12-819-2024, 2024
Short summary
Short summary
The sensitivity to the wave and sea-level forcing sources in predicting a 6-month embayed beach evolution is assessed using two different morphodynamic models. After a successful model calibration using in situ data, other sources are applied. The wave source choice is critical: hindcast data provide wrong results due to an angle bias, whilst the correct dynamics are recovered with the wave conditions from an offshore buoy. The use of different sea-level sources gives no significant differences.
Víctor Malagón-Santos, Aimée B. A. Slangen, Tim H. J. Hermans, Sönke Dangendorf, Marta Marcos, and Nicola Maher
Ocean Sci., 19, 499–515, https://doi.org/10.5194/os-19-499-2023, https://doi.org/10.5194/os-19-499-2023, 2023
Short summary
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.
Carolina M. L. Camargo, Riccardo E. M. Riva, Tim H. J. Hermans, Eike M. Schütt, Marta Marcos, Ismael Hernandez-Carrasco, and Aimée B. A. Slangen
Ocean Sci., 19, 17–41, https://doi.org/10.5194/os-19-17-2023, https://doi.org/10.5194/os-19-17-2023, 2023
Short summary
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.
Begoña Pérez Gómez, Ivica Vilibić, Jadranka Šepić, Iva Međugorac, Matjaž Ličer, Laurent Testut, Claire Fraboul, Marta Marcos, Hassen Abdellaoui, Enrique Álvarez Fanjul, Darko Barbalić, Benjamín Casas, Antonio Castaño-Tierno, Srđan Čupić, Aldo Drago, María Angeles Fraile, Daniele A. Galliano, Adam Gauci, Branislav Gloginja, Víctor Martín Guijarro, Maja Jeromel, Marcos Larrad Revuelto, Ayah Lazar, Ibrahim Haktan Keskin, Igor Medvedev, Abdelkader Menassri, Mohamed Aïssa Meslem, Hrvoje Mihanović, Sara Morucci, Dragos Niculescu, José Manuel Quijano de Benito, Josep Pascual, Atanas Palazov, Marco Picone, Fabio Raicich, Mohamed Said, Jordi Salat, Erdinc Sezen, Mehmet Simav, Georgios Sylaios, Elena Tel, Joaquín Tintoré, Klodian Zaimi, and George Zodiatis
Ocean Sci., 18, 997–1053, https://doi.org/10.5194/os-18-997-2022, https://doi.org/10.5194/os-18-997-2022, 2022
Short summary
Short summary
This description and mapping of coastal sea level monitoring networks in the Mediterranean and Black seas reveals the existence of 240 presently operational tide gauges. Information is provided about the type of sensor, time sampling, data availability, and ancillary measurements. An assessment of the fit-for-purpose status of the network is also included, along with recommendations to mitigate existing bottlenecks and improve the network, in a context of sea level rise and increasing extremes.
Cited articles
Amores, A., Marcos, M., Carrió, D. S., and Gómez-Pujol, L.: Coastal impacts of Storm Gloria (January 2020) over the north-western Mediterranean, Nat. Hazards Earth Syst. Sci., 20, 1955–1968, https://doi.org/10.5194/nhess-20-1955-2020, 2020. a
Appendini, C. M., Pedrozo-Acuña, A., Meza-Padilla, R., Torres-Freyermuth,
A., Cerezo-Mota, R., López-González, J., and Ruiz-Salcines, P.: On
the role of climate change on wind waves generated by tropical cyclones in
the Gulf of Mexico, Coast. Eng. J., 59, 1740001,
https://doi.org/10.1142/S0578563417400010, 2017. a
Arenal, I. M., Baños, I. H., Valdés, E. G., Mayo, A. H.,
Rodríguez, O. O. D., Llamo, A. V., and Zas, J. A. R.: The Coastal Flood
Regime around Cuba, the Thermohaline Structure Influence and Its Climate
Tendencies, Environ. Ecol. Res., 4, 37–49, https://doi.org/10.13189/eer.2016.040201, 2016. a
Bertin, X., Li, K., Roland, A., and Bidlot, J.-R.: The contribution of
short-waves in storm surges: Two case studies in the Bay of Biscay,
Cont. Shelf Res., 96, 1–15,
https://doi.org/10.1016/j.csr.2015.01.005, 2015. a
Bloemendaal, N., Haigh, I. D., de Moel, H., Muis, S., Haarsma, R. J., and
Aerts, J. C.: Generation of a global synthetic tropical cyclone hazard
dataset using STORM, Scientific data, 7, 1–12,
https://doi.org/10.1038/s41597-020-0381-2, 2020. a, b, c, d
Boose, E.: A method for reconstructing historical hurricanes, in: Hurricanes and
Typhoons: Past, Present, and Future, Vol. 99, Columbia University Press, 2004. a
Brabson, B. and Palutikof, J.: Tests of the generalized Pareto distribution for
predicting extreme wind speeds, J. Appl. Meteorol., 39,
1627–1640,
https://doi.org/10.1175/1520-0450(2000)039<1627:TOTGPD>2.0.CO;2, 2000. a
Bustos Usta, D. F. and Torres Parra, R. R.: Ocean and atmosphere changes in the
Caribbean Sea during the twenty-first century using CMIP5 models, Ocean
Dynam., 71, 757–777, https://doi.org/10.1007/s10236-021-01462-z,
2021. a
Camus, P., Mendez, F. J., Medina, R., and Cofiño, A. S.: Analysis of
clustering and selection algorithms for the study of multivariate wave
climate, Coast. Eng., 58, 453–462,
https://doi.org/10.1016/j.coastaleng.2011.02.003, 2011. a
Cangialosi, J. P., Latto, A. S., and Berg, R.: Hurricane Irma (AL112017), National Hurricane Center Tropical Cyclone Report,
111, 2018. a
Centre for Research on the Epidemiology of Disasters:
The Human Cost of Disasters. An Overview of the Last 20 Years (2000–2019),
2020. a
Chenoweth, M.: A reassessment of historical Atlantic basin tropical cyclone
activity, 1700–1855, Climatic Change, 76, 169–240,
https://doi.org/10.1007/s10584-005-9005-2, 2006. a
Colbert, A. J., Soden, B. J., Vecchi, G. A., and Kirtman, B. P.: The impact of
anthropogenic climate change on North Atlantic tropical cyclone tracks,
J. Climate, 26, 4088–4095,
https://doi.org/10.1175/JCLI-D-12-00342.1, 2013. a, b
Dullaart, J., Muis, S., Bloemendaal, N., Chertova, M. V., Couasnon, A., and
Aerts, J. C.: Accounting for tropical cyclones more than doubles the global
population exposed to low-probability coastal flooding, Commun. Earth
Environ., 2, 1–11, https://doi.org/10.1038/s43247-021-00204-9,
2021a. a, b
Dullaart, J. C., Muis, S., Bloemendaal, N., Chertova, M. V., Couasnon, A., and
Aerts, J. C.: Accounting for tropical cyclones more than doubles the global
population exposed to low-probability coastal flooding, Commun. Earth
Environ., 2, 1–11, https://doi.org/10.1038/s43247-021-00204-9,
2021b. a
Duvat, V. K., Magnan, A. K., Wise, R. M., Hay, J. E., Fazey, I., Hinkel, J.,
Stojanovic, T., Yamano, H., and Ballu, V.: Trajectories of exposure and
vulnerability of small islands to climate change, WIREs Clim. Change, 8, e478, https://doi.org/10.1002/wcc.478,
2017. a
ECLAC: The impact of hurricane Ivan in the Cayman Islands, Economic Commission
for Latin America and the Caribbean,
https://www.cepal.org/en/publications/25728-impact-hurricane-ivan-cayman-islands (last access: 5 September 2021),
2004. a
Enríquez, A. R., Wahl, T., Marcos, M., and Haigh, I. D.: Spatial Footprints of
Storm Surges Along the Global Coastlines, J. Geophys. Res.-Oceans, 125, e2020JC016367, https://doi.org/10.1029/2020JC016367,
2020. a
GEBCO: General Bathymetric Chart of the Ocean,
https://www.gebco.net/, last access: 9 October 2020. a
GFDRR: Post-Disaster Needs Assessment Hurricane Maria, Global Facility for
Disaster Reduction and Recovery,
https://www.gfdrr.org/sites/default/files/publication/Dominica_mp_012418_web.pdf (last access: 7 September 2021),
2017. a
Giuliani, G. and Peduzzi, P.: The PREVIEW Global Risk Data Platform: a geoportal to serve and share global data on risk to natural hazards, Nat. Hazards Earth Syst. Sci., 11, 53–66, https://doi.org/10.5194/nhess-11-53-2011, 2011. a
Goldenberg, S. B., Landsea, C. W., Mestas-Nuñez, A. M., and Gray, W. M.:
The recent increase in Atlantic hurricane activity: Causes and implications,
Science, 293, 474–479, https://doi.org/10.1126/science.1060040, 2001. a
Gregory, J. M., Griffies, S. M., Hughes, C. W., Lowe, J. A., Church, J. A.,
Fukimori, I., Gomez, N., Kopp, R. E., Landerer, F., Le Cozannet, G., Ponte, R. M., Stammer, D., Tamisiea, M. E., and van de Wal, R. S. W.:
Concepts and terminology for sea level: Mean, variability and change, both
local and global, Surv. Geophys., 40, 1251–1289,
https://doi.org/10.1007/s10712-019-09525-z, 2019. a
Hebert, P. J.: Atlantic hurricane season of 1979, Mon. Weather Rev., 108,
973–990,
https://doi.org/10.1175/1520-0493(1980)108<0973:AHSO>2.0.CO;2, 1980. a
Holland, G. J., Belanger, J. I., and Fritz, A.: A revised model for radial
profiles of hurricane winds, Mon. Weather Rev., 138, 4393–4401,
https://doi.org/10.1175/2010MWR3317.1, 2010. a, b
Kleptsova, O. S., Dijkstra, H. A., van Westen, R. M., van der Boog, C. G.,
Katsman, C. A., James, R. K., Bouma, T. J., Klees, R., Riva, R. E., Slobbe,
D. C., Zijlema, M., and Pietrzak, J. D.: Impacts of Tropical Cyclones on the Caribbean Under Future
Climate Conditions, J. Geophys. Res.-Oceans, 126,
e2020JC016869, https://doi.org/10.1029/2020JC016869, 2021. a, b
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.:
The international best track archive for climate stewardship (IBTrACS)
unifying tropical cyclone data, B. Am. Meteorol.
Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010. a
Knapp, K. R., Diamond, H. J., Kossin, J. P., Kruk, M. C., and Schreck, C.:
International Best Track Archive for Climate Stewardship (IBTrACS) Project, Version 4, NOAA National Centers for Environmental Information, https://doi.org/10.25921/82ty-9e16, 2018. a
Krien, Y., Dudon, B., Roger, J., and Zahibo, N.: Probabilistic hurricane-induced storm surge hazard assessment in Guadeloupe, Lesser Antilles, Nat. Hazards Earth Syst. Sci., 15, 1711–1720, https://doi.org/10.5194/nhess-15-1711-2015, 2015. a, b
Lin, N., Lane, P., Emanuel, K. A., Sullivan, R. M., and Donnelly, J. P.:
Heightened hurricane surge risk in northwest Florida revealed from
climatological-hydrodynamic modeling and paleorecord reconstruction, J. Geophys. Res.-Atmos., 119, 8606–8623,
https://doi.org/10.1002/2014JD021584, 2014. a
Martín, A., Amores, A., Orfila, A., Toomey, T., and Marcos, M.: Return levels of Hs and SSE corresponding to 10, 50, 100, 200 and 500 years along all coastal grid points, along with the data (latitude, longitude, Rmax, minimum pressure and maximum wind speed) for the TC's subsample, and the results (Maximum of SSE and Hs, Median of Tp and Dp) for all simulations, Zenodo [data set], https://doi.org/10.5281/zenodo.7069110, 2023. a
Montoya, R. D., Menendez, M., and Osorio, A. F.: Exploring changes in Caribbean
hurricane-induced wave heights, Ocean Eng., 163, 126–135,
https://doi.org/10.1016/j.oceaneng.2018.05.032, 2018. a
Murakami, H., Li, T., and Hus, P.-C.: Contributing factors to the recent high
level of accumulated cyclone energy (ACE) and power dissipation index (PDI)
in the North Atlantic, J. Climate, 27, 3023–3034,
https://doi.org/10.1175/JCLI-D-13-00394.1, 2014. a
Needham, H. F., Keim, B. D., and Sathiaraj, D.: A review of tropical
cyclone-generated storm surges: Global data sources, observations, and
impacts, Rev. Geophys., 53, 545–591,
https://doi.org/10.1002/2014RG000477, 2015. a
OpenStreetMap contributors: Planet dump,
https://planet.osm.org and https://www.openstreetmap.org (last access: 27 August 2020),
2017. a
Pasch, R. J., Penny, A. B., and Berg, R.: Hurricane Maria (AL152017), National Hurricane Center Tropical Cyclone Report, https://www.nhc.noaa.gov/data/tcr/AL152017_Maria.pdf, last access: 3 February 2023. a
Pillet, V., Duvat, V. K., Krien, Y., Cécé, R., Arnaud, G., and
Pignon-Mussaud, C.: Assessing the impacts of shoreline hardening on beach
response to hurricanes: Saint-Barthélemy, Lesser Antilles, Ocean
Coast. Manage., 174, 71–91,
https://doi.org/10.1016/j.ocecoaman.2019.03.021, 2019a. a
Pillet, V., Duvat, V. K., Krien, Y., Cécé, R., Arnaud, G., and
Pignon-Mussaud, C.: Assessing the impacts of shoreline hardening on beach
response to hurricanes: Saint-Barthélemy, Lesser Antilles, Ocean
Coast. Manage., 174, 71–91,
https://doi.org/10.1016/j.ocecoaman.2019.03.021, 2019b. a
Pond, S. and Pickard, G. L.: Introductory dynamical oceanography, Butterworth-Heinemann, ISBN 9780080570549, 1983. a
Roland, A., Zhang, Y. J., Wang, H. V., Meng, Y., Teng, Y.-C., Maderich, V.,
Brovchenko, I., Dutour-Sikiric, M., and Zanke, U.: A fully coupled 3D
wave-current interaction model on unstructured grids, J. Geophys.
Res.-Oceans, 117, C00J33, https://doi.org/10.1029/2012JC007952, 2012. a
Shewchuk, J. R.: Triangle: Engineering a 2D quality mesh generator and Delaunay
triangulator, in: Workshop on Applied Computational Geometry, edited by: Lin, M. C. and Manocha, D., 203–222,
Springer, https://doi.org/10.1007/BFb0014497, 1996. a
Sitkowski, M., Kossin, J. P., and Rozoff, C. M.: Intensity and structure
changes during hurricane eyewall replacement cycles, Mon. Weather Rev.,
139, 3829–3847, https://doi.org/10.1175/MWR-D-11-00034.1, 2011. a
Toomey, T., Amores, A., Marcos, M., Orfila, A., and Romero, R.: Coastal Hazards
of Tropical-Like Cyclones Over the Mediterranean Sea, J. Geophys.
Res.-Oceans, 127, e2021JC017964,
https://doi.org/10.1029/2021JC017964, 2022. a
Torres, R. R. and Tsimplis, M. N.: Sea level extremes in the Caribbean Sea,
J. Geophys. Res.-Oceans, 119, 4714–4731,
https://doi.org/10.1002/2014JC009929, 2014. a, b, c, d
Willoughby, H. and Black, P.: Hurricane Andrew in Florida: Dynamics of a
disaster, B. Am. Meteorol. Soc., 77, 543–550,
https://doi.org/10.1175/1520-0477(1996)077<0543:HAIFDO>2.0.CO;2, 1996. a
woldometers: Caribbean Population,
https://www.worldometers.info/world-population/caribbean-population, last access: 21 August 2020. a
Woodworth, P. L., Hunter, J. R., Marcos, M., Caldwell, P., Menéndez, M.,
and Haigh, I.: Towards a global higher-frequency sea level dataset,
Geosci. Data J., 3, 50–59, https://doi.org/10.1002/gdj3.42,
2016.
a
Zhang, Y. and Baptista, A. M.: SELFE: A semi-implicit Eulerian–Lagrangian
finite-element model for cross-scale ocean circulation, Ocean Model., 21,
71–96, https://doi.org/10.1016/j.ocemod.2007.11.005, 2008. a
Zhang, Y. J., Ye, F., Stanev, E. V., and Grashorn, S.: Seamless cross-scale
modeling with SCHISM, Ocean Model., 102, 64–81,
https://doi.org/10.1016/j.ocemod.2016.05.002, 2016. a
Short summary
Tropical cyclones (TCs) are among the potentially most hazardous phenomena affecting the coasts of the Caribbean Sea. This work simulates the coastal hazards in terms of sea surface elevation and waves that originate through the passage of these events. A set of 1000 TCs have been simulated, obtained from a set of synthetic cyclones that are consistent with present-day climate. Given the large number of hurricanes used, robust values of extreme sea levels and waves are computed along the coasts.
Tropical cyclones (TCs) are among the potentially most hazardous phenomena affecting the coasts...
Special issue
Altmetrics
Final-revised paper
Preprint