Streamed climate information from high-resolution global climate models for the renewable energy sector
Authors: Lacima-Nadolnik Aleksander , Grayson Katherine , Versteeg Gert , Roura-Adserias Francesc , Soret Albert , Doblas-Reyes Francisco J. 
Published: July 2024

The transition from a fossil fuel-based to a renewable-based energy system has become a reality in recent years. As the pace of climate change accelerates, the need for decarbonisation has provided the necessary momentum for the expansion of the renewable energy sector, heavily driven by the growth in wind and solar energy production (IEA, 2024). At the same time, the transition towards forms of renewable energy with highly variable production entails new challenges for the energy system, potentially endangering supply security and grid stability (Johnson et al., 2020). Existing tools and datasets often overlook the impact of both climate and climate change on renewable resources, particularly on wind energy, which is highly sensitive to internal variability and extreme weather events (Pryor &amp; Barthelmie, 2010). These knowledge gaps require new tools, including climate information from high-resolution global climate models (GCMs), which can accurately estimate spatiotemporal changes in wind resources (e.g., mean state, frequency of extreme events) under current and future climate conditions.In this work, we aim to show how high-frequency (i.e., hourly) climate data from km-scale GCMs (Rackow et al., 2024), in contrast to state-of-the-art models (e.g., CMIP, CORDEX), can be transformed into regional and local climate information tailored towards the needs of the wind energy sector (e.g., capacity factor and energy production estimates, long-term changes in wind speed distributions, frequency of high and low wind events, heating and cooling degree days), aiding stakeholders in their decision-making process. The unprecedented volumes of data generated by these high-resolution projections pose a challenge to traditional storage methods. Data streaming offers an adequate solution to this challenge by deriving statistical summaries of climate data as the model progresses (Grayson et al., 2024). The implementation of a streaming environment allows to estimate relevant user-tailored indicators, as well as other types of climate information, without the need to permanently store the complete model output. By directly simulating wind components at turbine hub height, removing the need for vertical interpolation, and through enhanced horizontal resolution and increased temporal frequency, high-resolution GCMs represent a step forward in assisting adaptation measures against the impacts of climate change.&#160;IEA (2024), Renewables 2023, IEA, Paris https://www.iea.org/reports/renewables-2023, Licence: CC BY 4.0Johnson, S. C., et al. (2020). Understanding the impact of non-synchronous wind and solar generation on grid stability and identifying mitigation pathways. Applied Energy, 262(January), 114492. https://doi.org/10.1016/j.apenergy.2020.114492Pryor, S. C., &amp; Barthelmie, R. J. (2010). Climate change impacts on wind energy: A review. Renewable and Sustainable Energy Reviews, 14(1), 430&#8211;437. https://doi.org/10.1016/j.rser.2009.07.028Rackow, T., et al.: Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5/NEMOv3.4, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-913 , 2024.Grayson, K., et al.: Statistical summaries for streamed data from climate simulations. Geoscientific Model Development (submitted), 2024