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dc.contributor.authorLobeto Alonso, Hector
dc.contributor.authorMenéndez García, Melisa 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2024-06-14T11:57:54Z
dc.date.available2024-06-14T11:57:54Z
dc.date.issued2024-04-12
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/10902/33113
dc.description.abstractThis study assesses the variability of coastal extreme sea levels globally by utilizing nearly three decades of along-track, multi-mission satellite altimetry data. An altimetry-based global coastal database of the non-tidal residual sea level component has been produced. The climate variability of extremes is modeled through a parametric, non-stationary statistical model. This model captures intra-annual, inter-annual and long-term variations in non-tidal residual return levels. Comparisons with tide gauge data demonstrate the ability of altimetry data to capture the variability of coastal extreme sea levels. Our findings reveal a greater complexity in the monthly variability patterns of non-tidal residual extremes in tropical latitudes, often exhibiting multiple storm periods, contrasting with coasts in extratropical latitudes, which are mostly controlled by a winter-summer pattern. This study also highlights the significant influence of established climate circulation patterns on sea level extremes. The positive phase of the Arctic Oscillation pattern leads to increases of over 25% in non-tidal residual return levels in Northwestern Europe with respect to a neutral phase. Furthermore, return levels in the western coast of Central America could be 50% higher during El Niño compared to La Niña. Our results show a robust increasing trend in non-tidal residual return levels along most global coastlines. A comparative analysis shows that variations during the 1995-2020 period were primarily driven by intra-annual variations.es_ES
dc.description.sponsorshipThis research was funded by the European Commission through the project CoCliCo (101003598, Call: H2020-LC-CLA-2020-2), and the ThinkInAzul programme, with funding from European Union NextGenera-tionEU/PRTR-C17.I1 and the Comunidad de Cantabria.es_ES
dc.format.extent25 p.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rights© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licensees_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceRemote Sensing, 2024, 16(8), 1355es_ES
dc.subject.otherExtreme sea levelses_ES
dc.subject.otherSatellite altimetryes_ES
dc.subject.otherClimate variabilityes_ES
dc.titleVariability assessment of global extreme coastal sea levels using altimetry dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsopenAccesses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/101003598/EU/COASTAL CLIMATE CORE SERVICES/CoCliCo/es_ES
dc.identifier.DOI10.3390/rs16081355
dc.type.versionpublishedVersiones_ES


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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licenseExcepto si se señala otra cosa, la licencia del ítem se describe como © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license