On the ability of statistical wind-wave models to capture the variability and long-term trends of the North Atlantic winter wave climate

Martínez-Asensio, Adrián; Marcos, Marta; Tsimplis, Michael N.; Jordà, Gabriel; Feng, Xiangbo; Gomis, Damià. 2016 On the ability of statistical wind-wave models to capture the variability and long-term trends of the North Atlantic winter wave climate. Ocean Modelling, 103. 177-189. https://doi.org/10.1016/j.ocemod.2016.02.006

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© 2016 Elsevier B.V. This is the author’s version of a work that was accepted for publication in Ocean Modelling. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was/will be published in Ocean Modelling doi:10.1016/j.ocemod.2016.02.006
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Official URL: http://dx.doi.org/10.1016/j.ocemod.2016.02.006

Abstract/Summary

A dynamical wind-wave climate simulation covering the North Atlantic Ocean and spanning the whole 21st century under the A1B scenario has been compared with a set of statistical projections using atmospheric variables or large scale climate indices as predictors. As a first step, the performance of all statistical models has been evaluated for the present-day climate; namely they have been compared with a dynamical wind-wave hindcast in terms of winter Significant Wave Height (SWH) trends and variance as well as with altimetry data. For the projections, it has been found that statistical models that use wind speed as independent variable predictor are able to capture a larger fraction of the winter SWH inter-annual variability (68% on average) and of the long term changes projected by the dynamical simulation. Conversely, regression models using climate indices, sea level pressure and/or pressure gradient as predictors, account for a smaller SWH variance (from 2.8% to 33%) and do not reproduce the dynamically projected long term trends over the North Atlantic. Investigating the wind-sea and swell components separately, we have found that the combination of two regression models, one for wind-sea waves and another one for the swell component, can improve significantly the wave field projections obtained from single regression models over the North Atlantic.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.ocemod.2016.02.006
ISSN:	14635003
Additional Keywords:	Wave climate; Climate change; Statistical downscaling; Dynamical downscaling; North Atlantic
Date made live:	03 Jun 2016 13:19 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/513747

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.ocemod.2016.02.006

ISSN:

14635003

Additional Keywords:

Wave climate; Climate change; Statistical downscaling; Dynamical downscaling; North Atlantic

Date made live:

03 Jun 2016 13:19 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/513747