Assessing Future Wind Energy Potential under Climate Change: The Critical Role of Multi-Model Ensembles in Robustness Assessment

TL;DR

This study uses a large ensemble of climate models and an event-based analysis to assess future wind energy potential in Europe under climate change, emphasizing the importance of ensemble diversity for reliable projections.

Contribution

It introduces a comprehensive multi-model ensemble approach combined with an event-based framework and formal uncertainty quantification for wind energy assessment under climate change.

Findings

Limited sub-ensembles can produce misleading projections.

Ensemble diversity is crucial for robust wind energy forecasts.

Operationally critical wind episodes are effectively captured.

Abstract

Accurate projections of wind energy potential under climate change are critical for effective long-term energy planning. While previous studies have highlighted the value of multi-model ensembles, they often fall short in capturing the full spectrum of uncertainties and temporal dynamics relevant to wind resource reliability. This paper presents one of the most comprehensive assessments to date, leveraging a large ensemble of 21 high-resolution RCM-GCM combinations from the EURO-CORDEX initiative to evaluate future wind energy conditions across Europe under the RCP8.5 scenario. Moving beyond mean values, we incorporate a novel event-based framework to analyze persistent high- and low-wind episodes using ERA5-derived percentile thresholds -- capturing operationally critical conditions that influence turbine performance and grid stability. To ensure statistical rigor, we apply the IPCC AR6 `Approach C' for robustness assessment, distinguishing climate signals from internal variability and quantifying model agreement. Crucially, we demonstrate that projections based on limited sub-ensembles can lead to contradictory or misleading conclusions, underscoring the essential role of ensemble diversity. The combination of spatial granularity, temporal detail, and formal uncertainty quantification makes this study a significant advancement in climate-informed wind energy research and a valuable tool for resilient energy system design.