Climate simulations for the North Atlantic and Europe for recent and future conditions simulated with the regionally coupled ROM model are analyzed and compared to the results from the MPI‐ESM. The ROM simulations also include a biogeochemistry and ocean tides. For recent climate conditions, ROM generally improves the simulations compared to the driving model MPI‐ESM. Reduced oceanic biases in the Northern Atlantic are found, as well as a better simulation of the atmospheric circulation, notably storm tracks and blocking. Regarding future climate projections for the 21st century following the RCP 4.5 and 8.5 scenarios, MPI‐ESM and ROM largely agree qualitatively on the climate change signal over Europe. However, many important differences are identified. For example, ROM shows an SST cooling in the Subpolar Gyre, which is not present in MPI‐ESM. Under the RCP8.5 scenario, ROM Arctic sea ice cover is thinner and reaches the seasonally ice‐free state by 2055, well before MPI‐ESM. This shows the decisive importance of higher ocean resolution and regional coupling for determining the regional responses to global warming trends. Regarding biogeochemistry, both ROM and MPI‐ESM simulate a widespread decline in winter nutrient concentration in the North Atlantic of up to ~35%. On the other hand, the phytoplankton spring bloom in the Arctic and in the North‐Western Atlantic starts earlier, and the yearly primary production is enhanced in the Arctic in the late 21st century. These results clearly demonstrate the added value of ROM to determine more detailed and more reliable climate projections at the regional scale.
Decadal variability in the North Atlantic has been associated in the literature with a tripolar pattern of sea surface temperature (SST) anomalies that show one sign in the western midlatitudinal North Atlantic and the opposite in the subpolar and tropical North Atlantic. The present analysis of observed SST from 1870 to 2009 leads to the dissection of the SST tripole into two components, each with a different time scale in the decadal band and different teleconnections in the Atlantic basin; while the subpolar and tropical poles present quasi-decadal variations with a period of about 9 years, essentially uncorrelated with other parts of the basin, the center of action in the western midlatitudes is characterized by a longer time scale of about 14 years and significant correlations with the tropical South Atlantic and the Norwegian and North Sea(s). The 9-yr period variations are associated with an atmospheric configuration resembling the east Atlantic pattern, whereas the 14-yr period fluctuations seem to be related to the North Atlantic Oscillation pattern. Each component also bears a different relationship with the decadal variability in the Pacific Ocean.
Offshore wind energy is a promising resource for renewable energy development. Reanalysed wind data are unmatched by other wind data sources in providing a long-term assessment of wind power potential. In this study, 10 of the selected offshore locations close to the Moroccan coast were used to evaluate the ERA5 wind reanalysis dataset against the IFREMER-blended observational dataset covering the years 1993–2016. The ERA5 wind data’s capacity to represent wind variability in the area was confirmed by the results of the statistical methodologies used. All the reanalysed data scored better at capturing the observed wind variability at the southern sites than at the northern ones, where the wind variability was more complex. In a long-term evaluation from 1981 to 2020, the wind power potential in the Moroccan Atlantic coast was found to be very stable except in the northern sites and between Agadir and Bou Arich. Seven of the 10 sites considered were ranked as promising sites for offshore wind power generation, with wind power densities above 420 W/m2 at 100 m in height. Additionally, the change in signs in the variability toward the middle of the ERA5 record, which was seen at all locations and was also evident in the observations, did not significantly affect the yearly wind power density. However, the seasonal distribution of the latter was modified according to the local features of the seasonal variability.