A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

• DOI: 10.1016/j.cliser.2022.100318
• Publication Date: 2022-08-08T22:38:39Z
• Authors: Philip E. Bett; Hazel E. Thornton; Alberto Troccoli; Matteo De Felice; Emma Suckling; Laurent Dubus; Yves-Marie Saint-...; David J. Brayshaw
• Keywords: bepress|Physical Sciences and Mathematics; [SDE] Environmental Sciences; Renewable energy; bepress|Physical Sciences and Mathematics|Physics; EarthArXiv|Physical Sciences and Mathematics|Environmental Sciences; [SPI] Engineering Sciences [physics]; EarthArXiv|Physical Sciences and Mathematics|Physics; 0207 environmental engineering; bepress|Physical Sciences and Mathematics|Earth Sciences; Wind; 02 engineering and technology; EarthArXiv|Physical Sciences and Mathematics|Earth Sciences; Solar; bepress|Physical Sciences and Mathematics|Statistics and Probability|Applied Statistics; 01 natural sciences; 7. Clean energy; EarthArXiv|Physical Sciences and Mathematics|Statistics and Probability|Probability; Article; bepress|Physical Sciences and Mathematics|Statistics and Probability|Probability; [SPI]Engineering Sciences [physics]; Seasonal forecasting; [SDU.STU.CL] Sciences of the Universe [physics]/Earth Sciences/Climatology; Meteorology. Climatology; bepress|Physical Sciences and Mathematics|Environmental Sciences; bepress|Physical Sciences and Mathematics|Environmental Sciences|Sustainability; 0105 earth and related environmental sciences; Climate services; H1-99; [SDU.OCEAN]Sciences of the Universe [physics]/Ocean; EarthArXiv|Physical Sciences and Mathematics|Environmental Sciences|Sustainability; Atmosphere; [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere; EarthArXiv|Physical Sciences and Mathematics|Statistics and Probability|Applied Statistics; EarthArXiv|Physical Sciences and Mathematics; EarthArXiv|Physical Sciences and Mathematics|Statistics and Probability; Social sciences (General); [MATH.APPL] Mathematics [math]/domain_math.appl; 13. Climate action; [SDE]Environmental Sciences; bepress|Physical Sciences and Mathematics|Statistics and Probability; QC851-999; [MATH.APPL]Mathematics [math]/domain_math.appl

We demonstrate levels of skill for forecasts of seasonal-mean wind speed and solar irradiance in Europe, using seasonal forecast systems available from the Copernicus Climate Change Service (C3S). While skill is patchy, there is potential for the development of climate services for the energy sector. Following previous studies, we show that, where there is skill, a simple linear regression-based method using the hindcast and forecast ensemble means provides a straightforward approach for producing calibrated probabilistic seasonal forecasts. This method extends naturally to using a larger-scale feature of the climate, such as the North Atlantic Oscillation, as the climate model predictor, and we show that this provides opportunities to improve the skill in some cases. We further demonstrate that, on seasonal-average and regional (e.g. national) average scales, wind and solar power generation are highly correlated with single climate variables (wind speed and irradiance). The detailed non-linear transformations from meteorological quantities to energy quantities, which are essential for detailed simulation of power system operations, are usually not necessary when forecasting gross wind or solar generation potential at seasonal-mean regional-mean scales. Together, our results demonstrate that where there is skill in seasonal forecasts of wind speed and irradiance, or a correlated larger-scale climate predictor, skilful forecasts of seasonal mean wind and solar power generation can be made based on the climate variable alone, without requiring complex transformations. This greatly simplifies the process of developing a useful seasonal climate service.