Extreme sea-level projections along European coasts for climate adaptation services

Sea-level rise is one of the most hazardous climate-change impacts and is projected to trigger dramatic increases of coastal flooding frequency in Europe in the current century and beyond. As such, adaptation-related effective decision making relies on the availability of authoritative and locally relevant information on future coastal sea-levels and their extremes, which include uncertainty quantification. However, current available sea-level projections are typically limited by either too low spatial resolution and therefore missing physical processes relevant at the coast, they account for only part of the sea-level signal (e.g. storm surges), and/or are typically limited to the downscaling of a single atmospheric model and therefore offer no quantification of the potentially significant inter-model uncertainty. In response to this knowledge gap, we present a novel extreme sea-level (ESL) projection dataset which focuses on the North-east Atlantic region. The dataset consists of a CMIP6-forced multi-model ensemble of downscaled projections until the end of the century, generated with a regional 3-dimensional ocean model at ~7km resolution. As such, the model captures not only storm-surge and tide induced ESLs, typically captured in barotropic 2-dimensional models, but also accounts for the contribution of circulation and density-driven modulations to extremes. Therefore, the ensemble dataset offers an excellent opportunity to explore ESL drivers at different spatio-temporal scales, their projected future changes, and associated uncertainties. This dataset will help to advance scientific knowledge on climate-change induced coastal flood risk changes, but also to increase confidence in quantitative assessments of impacts of sea-level rise through its contribution to the Coastal Climate Core Service (CoCliCo), a decision-oriented platform which will inform users on present-day and future coastal risks, and which is currently under development as part of a European Unions Horizon 2020 project. The CoCliCo project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 101003598