2026-04-21T01:44:09Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/61762026-03-17T08:58:34Zcom_20.500.11984_473col_20.500.11984_478

00925njm 22002777a 4500 dc Barkanov, Egor author Penalba, Markel author Martinez, Abel author Martinez Perurena, Ander author Zarketa-Astigarraga, Ander author Iglesias, Gregorio author 2024 The design of the different offshore renewable energy (ORE) technologies depends on the characteristics of wind/wave resources. However, these characteristics are not stationary under climate change. In this study the evolution of European offshore wave/wind resources is assessed under mid- and high-emissions scenarios based on the Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) and three forecasting horizons including the near (2023–2032), mid (2041–2050) and long term (2091–2100). The novelties lie in: (i) the concurrent analysis of wind and wave resources, (ii) the use of data with a 3-hour temporal resolution, and (iii) the local-scale statistical analysis. Results show significant variations along the 21st century, with an overall decline in average wind and wave conditions. Importantly, and somewhat counter-intuitively, much of the Atlantic coast of continental Europe experiences increasing extremes in the high-emissions scenario. For the local-scale statistical analysis, the focus is on five wind farm sites, commissioned or in planning. In the high-emissions scenario, the 99th and 99.99th percentiles decrease in three of them and increase in the other two. The combination of decreasing averages and increasing extremes presents the worst-case scenario for ORE technology developers. 0196-8904 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=174466 https://hdl.handle.net/20.500.11984/6176 Offshore renewable energy resource Climate change Shared socioeconomic pathways CMIP6 Global and local scales Statistical analysis Evolution of the European offshore renewable energy resource under multiple climate change scenarios and forecasting horizons via CMIP6