The following is a thought piece written by Julie Lundquist, the Bloomberg Distinguished Professor of Atmospheric Science and Wind Energy and a core researcher with the Ralph O’Connor Sustainable Energy Institute (ROSEI), that originally appeared in Nature Energy. In the piece, Lundquist argues that wind variability, turbine wake effects, and extreme events are not just challenges but opportunities to deepen our understanding of the atmosphere and enhance the resilience of wind energy systems.

Julie Lundquist

Wind power is a central pillar of the global energy transition. It offers large-scale electricity that cuts emissions while strengthening energy security. As countries move away from fossil fuels, wind, together with solar and storage, will shape cleaner and more resilient power systems.

In the past decade, wind energy has matured into a mainstream, cost-competitive source of electricity. Global capacity grew from 63.5 GW in 2015 to 1,136 GW in 2024. Onshore projects are among the least expensive and fastest-to-build, making wind the dominant form of new generation capacity in many regions.

Beyond economics, wind also contributes to energy security, job creation, and industrial growth. Yet shifting policies, particularly in the United States, create an unstable business environment. While these political headwinds are significant, bridging some key scientific knowledge gaps can accelerate progress toward a more reliable and resilient wind energy future.

Three key research areas drive advances in wind energy efficiency and resilience. First, variability, natural fluctuations in wind speed across timescales, challenges grid integration and long-term planning. Deeper understanding of variability will improve both grid integration and forecasting. Second, wakes, zones of slower, more turbulent wind downwind of turbines, reduce power generation by 10–40% and increase structural loads. Advancing knowledge of atmospheric turbulence can enhance reliability and reduce costs. Third, extreme weather events, though rare, pose potentially catastrophic risks to infrastructure. Investigating them is essential to building more resilient wind energy systems.

Variability is an enduring challenge. While fluctuations in wind speed and resulting power generation complicate the task of balancing supply and demand, electricity demand itself is variable. Advances in forecasting using improved models and higher-resolution data now help grid operators anticipate renewable generation. At the same time, increased energy storage, expanded transmission networks and hybrid energy systems that combine wind with storage help smooth fluctuations and reduce negative pricing during periods of surplus generation.

At the heart of managing variability lies the ability to accurately predict wind conditions across timescales. Short-term forecasts increasingly benefit from fine-scale observations and models that capture local weather processes driving wind fluctuations, while seasonal planning draws on a growing understanding of climate variability and change. Managing variability requires observations of wind, turbulence, and stratification at turbine heights (50–300 m) and models that capture regional atmospheric processes. Current climate models, with their coarse resolution, cannot yet provide reliable projections at this scale. Advances in observational networks, data assimilation, high-resolution modelling, and artificial intelligence are closing the gap between weather and climate scales, bringing more reliable wind forecasts within reach.

As deployment intensifies, wake interactions between farms emerge as a critical frontier for energy planning. In addition to raising costs and undermining reliability, wakes may also induce environmental impacts. On land, wake-induced air mixing could impact agriculture, though observations show minimal effects. Offshore, wakes spark concerns about impacts on sea surface temperature and upwelling of nutrient-rich waters. These concerns resurface in debates over social acceptance.

Wake behaviour varies strongly with atmospheric stability: wakes dissipate quickly in unstable conditions but can extend tens of kilometres in stable conditions, underscoring the need for accurate atmospheric characterization. Advances in wake prediction, modelling, and control, like active wake steering that optimizes yaw to reduce downwind losses, are transforming how farms operate. Coordinated research and policy frameworks that account for these dynamic wake impacts across national boundaries will enable more efficient growth of wind energy worldwide.

As wind energy expands into regions influenced by powerful storms, researchers develop new strategies to enhance turbine resilience under extreme conditions. Offshore projects along the US East Coast and East Asia offer opportunities to study how tropical cyclones interact with wind energy systems. These events induce rapid shifts in wind speed and direction, intense turbulence across the rotor disk, and powerful wave–structure interactions. Understanding these events will drive innovation in turbine design, control strategies, and system reliability.

Knowledge of these offshore conditions will advance rapidly in coming years. Multi-scale modelling, nesting high-resolution large-eddy simulations within larger-scale forecasts, is beginning to reveal complex structures of cyclones in unprecedented detail. Emerging measurement campaigns address long-standing data gaps in offshore flows at turbine heights. At the same time, we must gain new insights into coupled atmosphere–ocean dynamics to guide development of more resilient turbine structures, optimized siting strategies, and insurance mechanisms. Continued and sustained investment in observing and simulating extreme events will strengthen the foundations for dependable offshore wind energy future.

The past decade has seen remarkable progress. Remote sensing, particularly lidar, has transformed wind characterization. Lidars onshore and offshore now provide high-resolution data at turbine-relevant heights, improving resource assessment and monitoring. Simulation capabilities also advanced. Large-eddy simulations resolve turbulent flows at scales of metres, and, when coupled with numerical weather prediction, capture realistic and evolving inflow. Graphics processing unit-based computing accelerates this progress, enabling finer-resolution predictions with reduced energy demands. Emerging approaches using artificial intelligence and machine learning hold promise, though their ability to capture spatial complexity and extremes remains unproven. Together, these advances mark progress from idealized flows to real-world complexity.

Looking ahead, closer integration of observations and models, powered by data assimilation and artificial intelligence, will further enhance our ability to predict variability, manage wakes, and anticipate extremes. International collaboration and open data sharing are already accelerating discovery and innovation. To fully realize this scientific promise, progress in policy, planning, and public engagement must advance in parallel to encourage rapid, responsible deployment and equitable access to clean energy benefits. Slow permitting, fragmented trade rules, supply chain disruptions, manufacturing capacity gaps, and the turbine upscaling trap (where the drive for ever-larger designs outpaces standards, learning curves, and supply chains) could constrain growth. At the same time, social resistance fuelled by disinformation can undermine public trust even as science demonstrates manageability of any environmental and societal trade-offs.

The coming decade offers extraordinary opportunity. If scientific insight into atmospheric complexity, technological innovation, and forward-thinking policy reform can converge, wind energy can realize its potential as a cornerstone of the renewable, resilient, and reliable energy system for a more sustainable future.