An ensemble of high‐resolution regional climate model simulation data is used to examine the impacts of climate change on offshore and onshore wind energy generation in Ireland. Two Representative ...Concentration Pathway (RCP) scenarios (RCP 4.5 and 8.5) are analysed for the mid‐term (2041–2060) and the long‐term (2081–2100) future. Wind energy is projected to decrease (≤2%) overall in future climate scenarios. Changes are evident by mid‐century and are more pronounced by late 21st century, particularly for RCP 8.5 offshore. Seasonally, wind energy is projected to decrease by less than 6% in summer and to increase slightly in winter (up to 1.1%). The distinct changes in different parts of the power curve, presented here for the first time, show a reversed pattern of duration at certain levels of the power curve. In summer, there is an increase of low‐power and a decrease of high‐power generation, whereas during winter, there is a projected increase in the time spent at high power. This could lead to diverse consequences for system operators depending on the season. The impacts of climate change on the duration and frequency of long periods (longer than 24 h) of low‐/high‐power wind energy events in Ireland are also presented. The frequency of low‐power events is projected to increase slightly, especially during summer. Onshore and offshore events are considered separately, demonstrating the complementarity of developing both onshore and offshore wind farms for future energy systems. Regional analysis highlights the benefit of developing a geographically dispersed wind farm network incorporating different local wind conditions.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Attention should always be given to which reanalysis dataset to use when preparing analysis for a project. The accuracies of three reanalysis datasets, two global (ERA5 and MERRA-2) and one ...high-resolution regional reanalysis (MÉRA), are assessed by comparison with observations at seven weather observing stations around Ireland. Skill scores are calculated for the weather variables at these stations that are most relevant to the renewable energy sector: 10 m wind for wind power; surface shortwave radiation (SW) and 2 m temperature for photovoltaic power generation. The choice of which reanalysis dataset to use is important when future planning depends on this data. The newer ERA5 generally outperforms the other two reanalyses. However, this is not always true, and the best performing reanalysis dataset often depends on the variable of interest and location. As errors are significant for these reanalysis datasets, consideration should also be given to datasets specifically tailored to renewable energy resource modelling.
Increased use of solar photovoltaic electricity requires a better understanding of the impact of large‐scale atmospheric teleconnections on incident short wave (SW) solar radiation. Our focus is on ...the relationship between winter (December to February) SW radiation in northwest Europe and the dominant Euro‐Atlantic atmospheric teleconnection patterns using multiple multi‐decadal observational and gridded reanalysis datasets, with a focus on the islands of Ireland and Britain. Our study reveals that the previously reported west–east seesaw in the correlation pattern between the winter North Atlantic Oscillation (NAO) index and winter SW radiation across the United Kingdom is complex, involving several zonal changes in the sign of the NAO–SW correlations (multiple seesaws). By comparison with the NAO, the east Atlantic pattern exerts only a weak control on winter SW radiation across the United Kingdom and Ireland, although in the western part of the Iberian Peninsula and adjacent Atlantic Ocean significant positive correlations occur. High values of the Scandinavian pattern index result in higher than average winter SW radiation in much of northern Europe, although it is evident that some regions (e.g. northeast England, east Scotland and the adjacent North Sea area) exhibit the opposite behaviour. Inter‐seasonal variations in the dominant atmospheric flow and moisture transport directions, steered by large‐scale atmospheric pressure patterns, combined with orographic uplift and rainout effects on the windward side of hills and mountains are interpreted to be the physical drivers of the observed zonal variations and correlation sign reversals between winter SW anomalies and the NAO index.
The zonal correlation patterns between winter short wave solar radiation and the North Atlantic Oscillation (NAO) across Ireland and the United Kingdom are complex but are linked to land surface elevation.
Winter short wave radiation anomalies across Ireland and the United Kingdom in both strongly positive (blue curve) and negative (red curve) NAO winters can be explained by interactions between the dominant NAO‐steered moisture‐bearing winds and local topography (black curve), causing orographic uplift and rainout on the windward side of slopes.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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