Weather and climate model data play an increasingly vital role in assessing climate risks within energy system operations and planning. The reliability of these assessments heavily depends on the quality of the input meteorological data, particularly in accurately representing extreme events that challenge the resilience of energy systems.
Climate models provide one of the most significant advantages in modern energy planning—the ability to generate large samples of data. These samples are critical for understanding long-term natural variability, such as interannual and interdecadal fluctuations, and for characterizing rare but impactful extreme weather events. As renewable energy becomes a cornerstone of Europe’s energy mix, understanding how climate change influences high-impact weather events is essential for ensuring the resilience of power systems.
Rare events like low-likelihood, high-impact weather phenomena are notoriously difficult to capture with observational data or reanalysis-based datasets. This is primarily due to the relatively short period of the historical record, spanning a few-to-several decades at most, depending on the dataset selected (Bloomfield et al., 2018; Kay et al., 2023; Wohland et al., 2019). In contrast, outputs from climate models can be used to create powerful simulations of recent ‘near-present’ and ‘decades-ahead future’ climate conditions containing many hundreds of relevant weather-years. This potentially provides a much richer dataset to examine the characteristics of rare and extreme conditions, but require careful evaluation of the model’s performance to ensure the relevant meteorology is well-represented.
An ongoing project at the University of Reading, in collaboration with the Electric Power Research Institute (EPRI), USA, seeks to examine the quality of ‘energy’ data produced using climate model output available on the Copernicus Climate Change Service (C3S). There, nationally-aggregated European wind power, solar power and demand are estimated from high-resolution EURO-CORDEX regional climate model outputs. The data include projections under multiple greenhouse gas emissions scenarios, offering insights that align with European energy initiatives like ENTSO’s Pan-European Climate Database (Bartok et al., 2019; Dubus et al., 2023).
We analysed representation of Dunkelflaute events (periods of calm and cloudy weather typically associated with increased power supply stress) over Europe using wind and solar capacity factors from two tailored climate products: the recent “C3S-Energy” datasets and one of its predecessors, ECEM. We examined the representation of these events in model-derived energy datasets.
Our Preliminary findings reveal significant differences in how Dunkelflaute events are represented across the C3S-Energy and ECEM datasets. In particular, while the overall seasonal evolution of Dunkelflaute occurrence appears to be well represented (compared to their respective reanalyses), there are noticeable differences in winter-time Dunkelflaute frequency across many areas of Europe with the climate models typically simulating fewer Dunkelflautes in the northern part of the region and more frequent events in the south (potentially up to a few 10’s of percent depending on country and area). These findings underscore the importance of cautious interpretation when utilizing climate model-derived energy datasets. While these datasets offer unprecedented opportunities for exploring climate risk in energy systems, careful validation and contextual understanding are necessary to ensure their effective application.
Figure 1: Percentage difference in Dunkelflaute events (lasting at least 2 or more consecutive days) between C3S and ECEM datasets derived from EURO-CORDEX regional climate models (original GCMs: CNRM, EC-Earth, MPI, and IPSL). The percentage difference is calculated as (GCM - Reference Reanalysis Data), where ERA5 is the reference for C3S, and ERA-Interim is the reference for ECEM.
Conclusion: Opportunities and Cautions
The integration of datasets like ECEM and C3S-Energy into energy system planning is a transformative step towards addressing climate risks. By leveraging high-resolution climate model outputs tailored for the energy sector, stakeholders can better prepare for renewable energy droughts and other climate-induced challenges. However, the observed discrepancies in the representation of Dunkelflaute events highlight the need for continuous improvement in climate modeling and rigorous validation against real-world data.
As the energy sector continues to transition towards renewable sources, these tools will remain indispensable. Yet, their utility must be complemented by ongoing research, collaboration, and a commitment to refining the models that underpin our understanding of climate risks.
References
Bartok, B. et al. (2019). A climate projection dataset tailored for the European energy sector. Climate services. 16, p. 100138.
Bloomfield, H. et al. (2018). A critical assessment of the long-term changes in the wintertime surface Arctic Oscillation and Northern Hemisphere storminess in the ERA20C reanalysis. Environmental Research Letters. 13.9, p. 094004.
Dubus, L. et al. (2023). C3S Energy: A climate service for the provision of power supply and demand indicators for Europe based on the ERA5 reanalysis and ENTSO-E data. Meteorological Applications. 30.5, e2145.
Kay, G. et al. (2023). Variability in North Sea wind energy and the potential for prolonged winter wind drought. Atmospheric Science Letters, e1158.
Wohland, J. et al. (2019). Inconsistent wind speed trends in current twentieth century reanalyses. Journal of Geophysical Research: Atmospheres. 124.4, pp. 1931–1940.
