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This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, represents the first coordinated multivariate ensemble of 21st Century global wind-wave climate projections available (henceforth COWCLIP2.0). COWCLIP2.0 comprises general and extreme statistics of significant wave height (HS), mean wave period (Tm), and mean wave direction (θm) computed over time-slices 1979-2004 and 2081-2100, at different frequency resolutions (monthly, seasonally and annually). The full ensemble comprising 155 global wave climate simulations is obtained from ten CMIP5-based state-of-the-art wave climate studies and provides data derived from alternative wind-wave downscaling methods, and different climate-model forcing and future emissions scenarios. The data has been produced, and processed, under a specific framework for consistency and quality, and follows CMIP5 Data Reference Syntax, Directory structures, and Metadata requirements. Technical comparison of model skill against 26 years of global satellite measurements of significant wave height has been undertaken at global and regional scales. This new dataset provides support for future broad scale coastal hazard and vulnerability assessments and climate adaptation studies in many offshore and coastal engineering applications.

Understanding climate-driven impacts on the multivariate global wind-wave climate is paramount to effective offshore/coastal climate adaptation planning. However, the use of single-method ensembles and variations arising from different methodologies has resulted in unquantified uncertainty amongst existing global wave climate projections. Here, assessing the first coherent, community-driven, multi-method ensemble of global wave climate projections, we demonstrate widespread ocean regions with robust changes in annual mean significant wave height and mean wave period of 5–15% and shifts in mean wave direction of 5–15°, under a high-emission scenario. Approximately 50% of the world’s coastline is at risk from wave climate change, with ~40% revealing robust changes in at least two variables. Furthermore, we find that uncertainty in current projections is dominated by climate model-driven uncertainty, and that single-method modelling studies are unable to capture up to ~50% of the total associated uncertainty.

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In coastal regions, floods can arise through a combination of multiple drivers, including direct surface run- off, river discharge, storm surge and waves. In this study, we analyse compound flood potential in Europe caused by these four main flooding sources using state-of-the-art databases with homogenous forcing (i.e., ERA5). First, we perform an analysis to assess the sensitivity of the compound flooding potential to several factors: 1) sampling method; 2) time window to select the concurrent event of the conditioned driver; 3) dependence metrics; 4) wave-driven sea level definition. We observe higher correlation coefficients using annual maxima than peaks over threshold. Regarding the other factors, our results show similar spatial distributions of the compound flooding potential. Second, the dependence between the pairs of drivers using the Kendall's rank correlation coefficient and the joint occurrence are synthesized for coherent patterns of compound flooding potential using a clustering technique. This quantitative multi-driver assessment not only distinguishes where overall compound flooding potential is the highest, but also discriminates which driver combinations are more likely to contribute to compound flooding. We identify hotspots of compound flooding potential located along the southern coast of the North Atlantic Ocean and the northern coast of the Mediterranean Sea. [ABSTRACT FROM AUTHOR]