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.

Many human activities rely on accurate knowledge of the sea surface dynamics. This is especially true during storm events, when wave-current interactions might represent a leading order process of the upper ocean. In this study, we assess and analyse the impact of including three wave-dependent processes in the ocean momentum equation of the Met Office North West European Shelf (NWS) ocean-wave forecasting system on the accuracy of the simulated surface circulation. The analysis is conducted using ocean currents and Stokes drift data produced by different implementations of the coupled forecasting systems to simulate the trajectories of surface (iSphere) and 15 m drogued (SVP) drifters affected by four storms selected from winter 2016. Ocean and wave simulations differ only in the degree of coupling and the skills of the Lagrangian simulations are evaluated by comparing model results against the observed drifter tracks. Results show that, during extreme events, ocean-wave coupling improves the accuracy of the surface dynamics by 4%. Improvements are larger for ocean currents on the shelf (8%) than in the open ocean (4%): this is thought to be due to the synergy between strong tidal currents and more mature decaying waves. We found that the Coriolis-Stokes forcing is the dominant wave-current interaction for both type of drifters; for iSpheres the secondary wave effect is the wave-modulated water-side stress while for SVPs the wave-dependent sea surface roughness is more important. Our results indicate that coupled ocean-wave systems may play a key role for improving the accuracy of particle transport simulations.