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Abstract. The EU Copernicus Climate Change Service (C3S) European Climatic Energy Mixes (ECEM) has produced, in close collaboration with prospective users, a proof-of-concept climate service, or Demonstrator, designed to enable the energy industry and policy makers assess how well different energy supply mixes in Europe will meet demand, over different time horizons (from seasonal to long-term decadal planning), focusing on the role climate has on the mixes. The concept of C3S ECEM, its methodology and some results are presented here. The first part focuses on the construction of reference data sets for climate variables based on the ERA-Interim reanalysis. Subsequently, energy variables were created by transforming the bias-adjusted climate variables using a combination of statistical and physically-based models. A comprehensive set of measured energy supply and demand data was also collected, in order to assess the robustness of the conversion to energy variables. Climate and energy data have been produced both for the historical period (1979–2016) and for future projections (from 1981 to 2100, to also include a past reference period, but focusing on the 30 year period 2035–2065). The skill of current seasonal forecast systems for climate and energy variables has also been assessed. The C3S ECEM project was designed to provide ample opportunities for stakeholders to convey their needs and expectations, and assist in the development of a suitable Demonstrator. This is the tool that collects the output produced by C3S ECEM and presents it in a user-friendly and interactive format, and it therefore constitutes the essence of the C3S ECEM proof-of-concept climate service.

Abstract:The EDF group is the biggest French electric power producer and distributor. Its activities are greatly related to weather and climate. In particular, optimal management of the hydroelectric power production system requires a good forecast of water resources, from several days to several months in advance. Currently, only climatology at the seasonal timescale is used for operational production management. Seasonal probabilistic forecasts would improve watershed management at some months' lead-time if they are skilful enough. For this, two main problems have to be addressed: first, direct precipitation forecasts at this timescale have little, but positive, skill over Europe; second, the spatial scales of seasonal forecasting models are not adequate to predict local precipitation at the river basin scale. This study aims to evaluate the quality of seasonal forecasts of precipitation for 48 catchments in southern France. These are obtained by spatially downscaling global scale seasonal forecasts of geopotential height at 850 hPa. The method used is based on singular value decomposition and multiple linear regression. The statistical downscaling model is calculated from 45 years of observed local precipitation in the watersheds and geopotential fields from ERA40 re-analysis data. The statistical model is then applied to the seasonal hindcasts from the DEMETER project. Two main results arise from this work. First, we show that it is possible to obtain useful and valuable information for EDF at the local scale from global seasonal averaged information. Second, we find that only a probabilistic multi-model ensemble forecast approach provides useful information for EDF catchments, even with quite low skill, and that a deterministic approach, using only the ensemble mean of the forecasts, is not better than a forecast based on climatology. It has, nevertheless, to be pointed out that for operational purposes, being able to know that a forecast for a given location or date is not reliable is, in itself, valuable information.