Presentations

Abstract

Amid climate change, reliable climate information specifically tailored to the renewable energy sector is essential for the advancement of the future low-carbon economy. Local climate insights at the global scale are key for supporting informed decisions, such as identifying optimal sites for renewable deployment to ensure efficient long-term operation. Nonetheless, state-of-the-art climate data sources, such as CMIP or CORDEX, remain constrained by long update cycles, relatively coarse spatial resolutions and a lack of outputs specifically tailored to support the adaptation needs of climate-dependent sectors. Within the Climate Adaptation Digital Twin (Climate DT) from the Destination Earth (DestinE) initiative, a suite of km-scale atmosphere-ocean coupled simulations has been produced using three state-of-the-art global climate models (GCMs). The Climate DT aims to operationalise the production of climate projections, prioritising accessibility, interactivity and timely availability of the data (i.e., sub-annual updates), while targeting specific climate-dependent sectors. Furthermore, the enhanced resolution of km-scale simulations allows to obtain local estimates of the interannual variability and trends of near-term (i.e., next 30 years) wind resources, removing the reliance on extrapolated trends from historical data. Km-scale simulations provide a notably improved representation of key wind energy metrics in regions with complex topography. Evaluation against global reanalysis products (e.g., ERA5) shows that km-scale simulations capture wind spatiotemporal variability effectively, with reduced biases compared to their coarse-resolution counterparts. We present a novel open-sourced Python tool, Energy Indicators, co-developed with stakeholders from the wind energy sector. It provides essential metrics for the wind energy sector at an unprecedented (5-10 km) horizontal resolution. These include, among others, high and low wind events, capacity factors for various turbine types, and demand metrics such as heating and cooling degree days.