Framework for extracting and refining energy systems-tailored timeseries at high spatial and temporal resolution from EURO-CORDEX climate projections

Climate changes affect many dimensions of human life on Earth, and even the production and consumption of energy are inevitably touched. As energy systems modelers commonly use historical weather timeseries among the useful data needed by design and operation models, this work introduces a systematic framework for the transformation of EURO-CORDEX long-term climate projections into hourly timeseries, regarding energy-relevant variables which are air temperature, solar radiation, wind speed, and precipitation. This kind of data requires to be provided with very high spatial resolution and temporal frequency, given the local weather effects to be considered and the hourly power flows to be simulated. Thus, an open-source Python code has been developed: it accesses the Copernicus Climate Data Store; downloads future climate projections from a selected climate model, following a defined emission scenario; allows the extraction of specific areas and the spatial aggregation of data; operates a quantile based bias adjustment, calibrating outputs with reference to ERA5 reanalysis as reliable historical database, but at the same time carefully preserving the projected climate change signal; disaggregates the values to hourly frequency. Systematic tests on the proposed outputs have been performed against reanalysis data, regarding residual biases, consistency of projected climate anomalies, and ability to identify energy system’s critical stress occurrences, for providing a quantitative and critical evaluation toward possible improvements. Whereas for temperature and wind speed the work is sufficiently accurate and valuable, solar irradiance presents a substantial underestimation in the daily peak values, and precipitation is intentionally left uncorrected as the implemented bias reduction method was not effective. In any case, the present framework constitutes a practical tool for energy system modelers who want to include and explore the variability of projected climate changes in their analysis, and a solid methodological basis for further research regarding the climate-energy nexus.