Abstract
Wave climate characterization at different time scales (long-term historical periods, seasonal prediction, and future projections) is required for a broad number of marine activities. Wave reanalysis databases have become a valuable source of information covering time periods of decades. A weather-type approach is proposed to statistically downscale multivariate wave climate over different time scales from the reanalysis long-term period. The model calibration is performed using historical data of predictor (sea level pressure) and predictand (sea-state parameters) from reanalysis databases. The storm activity responsible for the predominant swell composition of the local wave climate is included in the predictor definition. N-days sea level pressure fields are used as predictor. K-means algorithm with a postorganization in a bidimensional lattice is used to obtain weather patterns. Multivariate hourly sea states are associated with each pattern. The model is applied at two locations on the east coast of the North Atlantic Ocean. The validation proves the model skill to reproduce the seasonal and interannual variability of monthly sea-state parameters. Moreover, the projection of wave climate onto weather types provides a multivariate wave climate characterization with a physically interpretable linkage with atmospheric forcings. The statistical model is applied to reconstruct wave climate in the last twentieth century, to hindcast the last winter, and to project wave climate under climate change scenarios. The statistical approach has been demonstrated to be a useful tool to analyze wave climate at different time scales.
Highlights
Accurate characterization of local wave climate is required in series of sectors such as shipping, offshore industry, marine engineering, and coastal management
We have found that our mean Hs at the two target locations are similar to those from Bertin et al [2013] for the period 1900–2008 (3.2 m at IR and 2.6 m at GA) but differ from those of Wang et al [2012] for the period 1958–2001 (2.9 m at IR and 2.4 m at GA)
A statistical downscaling framework to project wave climate is proposed based on weather typing
Summary
Accurate characterization of local wave climate is required in series of sectors such as shipping, offshore industry, marine engineering, and coastal management. VOS data provide the longest records of independent sea and swell parameters [Gulev and Grigorieva, 2006] and reliable climate variability and trends with less inhomogeneities than wave hindcast [Gulev et al, 2003] Their sampling is insufficient and they require correction algorithms. In the case of sea surface waves, sea level pressure (SLP) fields and the squared SLP gradient fields have demonstrated to be good predictors [Wang et al, 2012; Casas-Prat et al, 2014] This is especially relevant for applications of climate projections, since the SLP variable is supposed to be less biased than wind fields from GCMs [Caires et al, 2006].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
