The El Niño-Southern Oscillation (ENSO) influences climate variability across the globe. ENSO is highly predictable on seasonal timescales and therefore its teleconnections are a source of extratropical forecast skill. To fully harness this predictability, teleconnections must be represented accurately in seasonal forecasts. We find that a multimodel ensemble from five seasonal forecast systems can successfully capture the spatial structure of the late winter (JFM) El Niño teleconnection to the North Atlantic via North America, but the simulated amplitude is half of that observed. We find that weak amplitude teleconnections exist in all five models and throughout the troposphere, and that the La Niña teleconnection is also weak. We find evidence that the tropical forcing of the teleconnection is not underestimated and instead, deficiencies are likely to emerge in the extratropics. We investigate the impact of underestimated teleconnection strength on North Atlantic winter predictability, including its relevance to the signal-to-noise paradox.

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.