Decadal predictions of wind and solar power indicators to support the renewable energy sector

Renewable energy production is strongly influenced by weather and climate states, making the energy sector highly sensitive to climate variability from seasonal to decadal timescales. Decadal climate predictions, which forecast climate variability over the next 1–10 years, are therefore promising tools for optimising renewable energy deployment. For example, reliable long-term forecasts can support the identification of the most suitable locations for wind farms and solar plants, helping to stabilize energy production and reduce climate-related risks.

This study assesses the predictive skill of decadal climate predictions for energy-relevant climate impact indicators, focusing on forecast years 1-3 over Western Europe. Climate indicators are used to quantify the impact of climate variability on energy production, which is ultimately the most useful information for the energy industry.  

The calculation of the indicators requires different climate variables and temporal resolutions depending on the energy source. For solar energy, daily mean values of near-surface air temperature (TAS), surface solar radiation (RSDS), and surface wind speed (SFCWIND) are used. For wind energy, 6-hourly SFCWIND is required. The indicators are computed using a multi-model ensemble from climate forecast systems participating in the Decadal Climate Prediction Project (DCPP), which is part of the Coupled Model Intercomparison Project Phase 6 (CMIP6). To evaluate the forecast quality of the indicators, the ERA5 reanalysis is used as the reference dataset during the period 1961-2019. Skill is evaluated against ERA5 and compared with non-initialized historical forcing simulations produced with the same models to quantify the added value of decadal initialization.

Three indicators are considered: photovoltaic potential (PVpot) for solar energy, capacity factor (CF) for wind energy, and the number of effective days (Neff) for both renewable energy resources. PVpot quantifies photovoltaic performance relative to nominal capacity and is derived from RSDS, TAS, and SFCWIND. Wind CF represents the ratio between actual and maximum possible energy production and depends on SFCWIND and turbine characteristics. Neff is defined as the number of days meeting efficiency-related thresholds for each resource, based on radiation and temperature constraints for solar PV technology and wind-speed limits associated with CF ≥ 25% and turbine cut-out for wind energy. By expressing production in terms of effective days, the Neff indicator enables anticipating periods when both renewable energy resources are simultaneously scarce, as well as a consistent cross-resources comparison between them.

Results show higher and more seasonally dependent skill for PVpot than for wind CF, with Neff skill varying across regions and seasons. Decadal initialization generally enhances skill in regions where historical simulations already exhibit predictability, while limited additional skill is introduced elsewhere, suggesting that initialization primarily amplifies existing sources of predictability rather than introducing entirely new skill. These results highlight the potential of tailored climate impact indicators to bridge decadal climate prediction science and renewable-energy applications.