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dc.contributor.authorEspejo Hermosa, Antonioes_ES
dc.contributor.authorCamus Braña, Paulaes_ES
dc.contributor.authorLosada Rodríguez, Iñigo es_ES
dc.contributor.authorMéndez Incera, Fernando Javier es_ES
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2017-02-10T19:10:23Z
dc.date.available2017-02-10T19:10:23Z
dc.date.issued2014-08-05es_ES
dc.identifier.issn0022-3670es_ES
dc.identifier.issn1520-0485es_ES
dc.identifier.otherCT M2010-15009es_ES
dc.identifier.urihttp://hdl.handle.net/10902/10284
dc.description.abstractTraditional approaches for assessing wave climate variability have been broadly focused on aggregated or statistical parameters such as significant wave height, wave energy flux, or mean wave direction. These studies, although revealing the major general modes of wave climate variability and trends, do not take into consideration the complexity of the wind-wave fields. Because ocean waves are the response to both local and remote winds, analyzing the directional full spectra can shed light on atmospheric circulation not only over the immediate ocean region, but also over a broad basin scale. In this work, the authors use a pattern classification approach to explore wave climate variability in the frequency–direction domain. This approach identifies atmospheric circulation patterns of the sea level pressure from the 31-yr long Climate Forecast System Reanalysis (CFSR) and wave spectral patterns of two selected buoys in the North Atlantic, finding one-to-one relations between each synoptic pattern (circulation type) and each spectral wave energy distribution (spectral type). Even in the absence of long-wave records, this method allows for the reconstruction of longterm wave spectra to cover variability at several temporal scales: daily, monthly, seasonal, interannual, decadal, long-term trends, and future climate change projections.es_ES
dc.description.sponsorshipThe authors are grateful to Puertos del Estado (Spanish Ministry of Public Works and Infrastructures) for providing us the instrumental buoy data. This work was partially funded by the project IMAR21 (CT M2010-15009) from the Spanish Government.es_ES
dc.format.extent14 p.es_ES
dc.language.isoenges_ES
dc.publisherAmerican Meteorological Societyes_ES
dc.rights© Copyright 2014 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act September 2010 Page 2 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or copyrights@ametsoc.org.es_ES
dc.sourceJournal of Physical Oceanography 44, 2139–2152es_ES
dc.titleSpectral Ocean Wave Climate Variability Based on Atmospheric Circulation Patternses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttp://journals.ametsoc.org/doi/abs/10.1175/JPO-D-13-0276.1es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1175/JPO-D-13-0276.1es_ES
dc.type.versionpublishedVersiones_ES


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