Offshore wind energy technology has developed rapidly over the last decade. It is expected to significantly contribute to the further increase of renewable energy in the global energy production in the future. However, even with floating wind turbines, only a fraction of the global offshore wind energy potential can be harvested because grid-connection, moorings, installation and maintenance costs increase tremendously as the distance to shore and the water depth increase. Thus, new technologies enabling harvesting the far offshore wind energy resource are required. To tackle this challenge, mobile energy ship concepts have been proposed. In those concepts, electricity is produced by a water turbine attached underneath the hull of a ship propelled by the wind using sails. It includes an on-board energy storage system since energy ships are not grid-connected. Thus, the ships route schedules could be dynamically optimized taking into account weather forecast in order to maximize their capacity factors (CF). The aim of this study is to investigate how high the capacity factors of energy ships could be when using weather-routing and compare them to that of stationary wind turbines that would be deployed in the same areas. To that end, a modified version of the weather-routing software QtVlm was used. Velocity and power production polar plots of an energy ship that was designed at LHEEA were used as input to QtVlm. Results show that capacity factors over 80% can be achieved with energy ships and stationary offshore wind turbines deployed in the North Atlantic Ocean.

Abstract. Projections of coastal sea level (SL) changes are of great interest for coastal risk assessment and decision making. SL projections are typically produced using global climate models (GCMs), which cannot fully resolve SL changes at the coast due to their coarse resolution and lack of representation of some relevant processes (tides, atmospheric surface pressure forcing, waves). To overcome these limitations and refine projections at regional scales, GCMs can be dynamically downscaled through the implementation of a high-resolution regional climate model (RCM). In this study, we developed the IBI-CCS (Iberian–Biscay–Ireland Climate Change Scenarios) regional ocean model based on a 1/12∘ northeastern Atlantic Nucleus for European Modelling of the Ocean (NEMO) model configuration to dynamically downscale CNRM-CM6-1-HR, a GCM with a 1/4∘ resolution ocean model component participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) by the Centre National de Recherches Météorologiques (CNRM). For a more complete representation of the processes driving coastal SL changes, tides and atmospheric surface pressure forcing are explicitly resolved in IBI-CCS in addition to the ocean general circulation. To limit the propagation of climate drifts and biases from the GCM into the regional simulations, several corrections are applied to the GCM fields used to force the RCM. The regional simulations are performed over the 1950 to 2100 period for two climate change scenarios (SSP1-2.6 and SSP5-8.5). To validate the dynamical downscaling method, the RCM and GCM simulations are compared to reanalyses and observations over the 1993–2014 period for a selection of ocean variables including SL. Results indicate that large-scale performance of IBI-CCS is better than that of the GCM thanks to the corrections applied to the RCM. Extreme SLs are also satisfactorily represented in the IBI-CCS historical simulation. Comparison of the RCM and GCM 21st century projections shows a limited impact of increased resolution (1/4 to 1/12∘) on SL changes. Overall, bias corrections have a moderate impact on projected coastal SL changes, except in the Mediterranean Sea, where GCM biases were substantial.

Abstract. Wind waves and swells are major drivers of coastal environment changes and coastal hazards such as coastal flooding and erosion. Wave characteristics are sensitive to changes in water depth in shallow and intermediate waters. However, wave models used for historical simulations and projections typically do not account for sea level changes whether from tides, storm surges, or long-term sea level rise. In this study, the sensitivity of projected changes in wave characteristics to the sea level changes is investigated along the Atlantic European coastline. For this purpose, a global wave model is dynamically downscaled over the northeastern Atlantic for the 1970–2100 period under the SSP5–8.5 climate change scenario. Twin experiments are performed with or without the inclusion of hourly sea level variations from regional 3D ocean simulations in the regional wave model. The largest impact of sea level changes on waves is located on the wide continental shelf where shallow-water dynamics prevail, especially in macro-tidal areas. For instance, in the Bay of Mont-Saint-Michel in France, due to an average tidal range of 10 m, extreme historical wave heights were found to be up to 1 m higher (+30 %) when sea level variations are included. At the end of the 21st century, extreme significant wave heights are larger by up to +40 % (+60 cm), mainly due to the effect of tides and mean sea level rise. The estimates provided in this study only partially represent the processes responsible for the sea-level–wave non-linear interactions due to model limitations in terms of resolution and the processes included.

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