Spatiotemporal changes in extreme sea levels along the coasts of the North Atlantic and the Gulf of Mexico

Marcos, M.; Woodworth, P. ORCID: https://orcid.org/0000-0002-6681-239X. 2017 Spatiotemporal changes in extreme sea levels along the coasts of the North Atlantic and the Gulf of Mexico. Journal of Geophysical Research: Oceans, 122 (9). 7031-7048. https://doi.org/10.1002/2017JC013065

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Abstract/Summary

Extreme sea levels along the densely monitored coasts of the North Atlantic Ocean and the Gulf of Mexico have been investigated using high frequency tide gauge measurements in the GESLA-2 data set (www.gesla.org). Our results, based on non-tidal residuals and skew surges in records since 1960, confirm that mean sea level (MSL) is a major, but not a unique, driver of extremes. Regionally-coherent linear trends and correlations with large scale climate patterns are found in extreme events, even after the removal of MSL. A similar conclusion, that MSL is a major but not the only driver of extremes, comes from a small number of long records starting in the mid-19th century. The records show slight increases in the intensity of extreme episodes at centennial time scales, together with multi-decadal variability unrelated to MSL. Objective statistical criteria have been used to investigate whether extreme sea level distributions are stationary or not, resulting in non-stationarity being favoured in many records, with or without accounting for changes in MSL. Extremes have been found to favour a non-Gumbel behaviour at many locations, with implications for the accuracy of return levels for coastal engineering.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1002/2017JC013065
ISSN:	2169-9291
Date made live:	30 Aug 2017 14:27 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/517521

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1002/2017JC013065

ISSN:

2169-9291

Date made live:

30 Aug 2017 14:27 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/517521