Atmospheric controls and long range predictability of directional waves in the United Kingdom & Ireland
|Acceptance Date||2020 IN PRESS|
|Author(s)||Scott T1, Masselink G1, McCarroll Rj1, Castelle B2, Dodet Guillaume3, Saulter A4, Scaife Aa4, 5, Dunstone N4|
|Affiliation(s)||1 : School of Biological and Marine Sciences, University of Plymouth, UK.
2 : University of Bordeaux/CNRS, UMR EPOC, Bordeaux, France
3 : IFREMER, Univ. Brest, CNRS, IRD, Laboratoire d’Océanographie Physique et Spatiale, IUEM, Brest, France
4 : UK Met Office, Exeter, UK
5 : College of Engineering, Mathematics and Physical Sciences, University of Exeter, UK
|Source||Earth's Future (2328-4277) (Wiley) In Press|
Improved understanding of how our coasts will evolve over a range of time scales (years-decades) is critical for effective and sustainable management of coastal infrastructure. Globally, sea-level rise will result in increased erosion, with more frequent and intense coastal flooding. Understanding of current and future coastal evolution requires robust knowledge of the wave climate. This includes spatial, directional and temporal variability, with recent research highlighting the importance of wave climate directionality on coastal morphological response, for example in UK, Australia and California. However, the variability of the inshore directional wave climate has received little attention, and an improved understanding could drive development of skillful seasonal or decadal forecasts of coastal response. We examine inshore wave climate at 63 locations throughout the United Kingdom and Ireland (1980–2017) and show that 73% are directionally bimodal. We find that winter-averaged expressions of six leading atmospheric indices are strongly correlated with both total and directional winter wave power (peak spectral wave direction) at all studied sites. Coastal classification through hierarchical cluster analysis and stepwise multi-linear regression of directional wave correlations with atmospheric indices defined four spatially coherent regions. We show that combinations of indices have significant skill in predicting directional wave climates (r= 0.45–0.8; p<0.05). We demonstrate for the first time the significant explanatory power of leading winter-averaged atmospheric indices for directional wave climates, and show that leading seasonal forecasts of the NAO skillfully predict wave climate in some regions.