Assessing the climate sensitivity of wind power resources: Multi scenario-based analysis via bias-corrected CMIP6 scenarios

Abstract

Wind energy is a key pillar of low-carbon transitions, yet wind power density (WPD) is highly sensitive to climate-driven changes in near-surface winds and their seasonality. This study presents projected relative changes (%) in WPD over T & uuml;rkiye for the period 2025-2100 under SSP1-2.6, SSP2-4.5, and SSP5-8.5. After evaluating multiple bias-correction methods against observations, Empirical Quantile Mapping (EQM) was selected as the best-performing approach; therefore, all subsequent analyses use EQM-corrected data. Similarly, although nine CMIP6 (Coupled Model Intercomparison Project Phase 6) global climate models were initially assessed for each SSP, ACCESS-CM2 showed the highest agreement with observations and was thus used for all projections. Monthly and annual WPD changes reveal a pronounced seasonal asymmetry. During winter and late autumn (November-February), relative changes indicate enhanced wind potential in northern and northwestern T & uuml;rkiye, while southern coastal regions tend to experience reductions, forming a recurring north-south dipole. January emerges as the most scenario-sensitive month: under SSP1-2.6 and SSP2-4.5, northern increases coexist with southern decreases, whereas SSP5-8.5 amplifies spatial contrasts rather than producing uniform change. February generally preserves this north-favored pattern, albeit with weaker contrasts. The warm season exhibits the clearest degradation in wind resources. April marks a transition month with widespread negative changes across much of the country. From May through August, persistently negative anomalies dominate large areas under all SSPs, indicating a systematic weakening of late-spring and summer wind potential. September shows limited and spatially heterogeneous recovery, while October and November display a pronounced rebound with widespread positive anomalies, particularly in northern regions, consistent with a return to stronger autumn circulation. Annual changes are comparatively muted, reflecting substantial compensation between cold-season gains and warm-season losses. Overall, the results demonstrate that climate change affects wind energy potential in T & uuml;rkiye primarily through seasonal redistribution and increased intra-annual variability, highlighting the importance of scenario-based, month-resolved assessments rather than reliance on annual mean indicators alone.