Harnessing Subseasonal-to-Seasonal Predictability for Wind Energy in Kenya

Understanding how climate variability influences wind-power resources is critical for energy security, particularly in countries such as Kenya where reliance on weather-sensitive generation is increasing. Using ERA5 reanalysis, climate-mode indices, site observations where available and modelled wind-power potential, we quantify how three major tropical climate modes relevant to subseasonal-to-seasonal (S2S) wind-power variability, the Madden–Julian Oscillation (MJO), El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), influence Kenya's wind-energy system. Results show that MJO Phases 2–4 generally suppress wind-energy potential across Kenya, while Phases 6–8 enhance it. ENSO influences are weaker and more seasonally selective, with El Niño favouring suppressed wind-energy potential during October–November–December and La Niña favouring enhancement during April–May–June. The IOD imprint is strongest in October–November–December, when positive IOD conditions weaken wind-energy potential across key inland regions and negative IOD conditions strengthen it. Using a perfect-forecast framework that assumes known MJO, ENSO and IOD states, with forecast skill evaluated through leave-one-out cross-validation, we quantify the potential value of climate-mode information for wind-power prediction. We show that conditioning on the MJO provides the largest single-mode improvements in deterministic and probabilistic wind-power forecast skill relative to monthly climatology. Conditioning on ENSO and the IOD yields smaller, season-dependent skill gains, with the strongest improvements occurring in October–November–December. Combined MJO–ENSO–IOD conditioning gives the largest overall skill improvement. Taken together, these results identify a clear hierarchy of predictability: MJO information provides the strongest subseasonal contribution to wind-power prediction, while ENSO and IOD provide broader seasonal context and smaller refinements. This hierarchy supports the development of climate-mode-informed “windows of risk and opportunity” that translate S2S climate information into practical services for anticipatory, risk-informed wind-energy operations and planning in Kenya, with potential relevance for wider East Africa.