Southward shift of the global wind energy resource under high carbon dioxide emissions

  • Nature Geosciencevolume 11pages3843 (2018)
  • doi:10.1038/s41561-017-0029-9
  • Download Citation


The use of wind energy resource is an integral part of many nations’ strategies towards realizing the carbon emissions reduction targets set forth in the Paris Agreement, and global installed wind power cumulative capacity has grown on average by 22% per year since 2006. However, assessments of wind energy resource are usually based on today’s climate, rather than taking into account that anthropogenic greenhouse gas emissions continue to modify the global atmospheric circulation. Here, we apply an industry wind turbine power curve to simulations of high and low future emissions scenarios in an ensemble of ten fully coupled global climate models to investigate large-scale changes in wind power across the globe. Our calculations reveal decreases in wind power across the Northern Hemisphere mid-latitudes and increases across the tropics and Southern Hemisphere, with substantial regional variations. The changes across the northern mid-latitudes are robust responses over time in both emissions scenarios, whereas the Southern Hemisphere changes appear critically sensitive to each individual emissions scenario. In addition, we find that established features of climate change can explain these patterns: polar amplification is implicated in the northern mid-latitude decrease in wind power, and enhanced land–sea thermal gradients account for the tropical and southern subtropical increases.


While the rapid growth of wind power capacity across the world1 constitutes a component of a broader strategy to reduce the reliance on fossil fuels and mitigate future climate change2, this deployment occurs in the context of today’s climate or that of the very recent past, subject to interannual variations. Specifically, wind farms are being sited, designed and built under the assumption that the amount of power that can be drawn from the wind today will remain approximately constant into the future, within the range of existing interannual variability3. However, climate, including the global atmospheric circulation, is already changing in response to anthropogenic radiative forcing4 and is projected to continue doing so in the coming decades5. Therefore, the economic and engineering calculus of wind resource management should consider projected changes in surface climate.

Several studies have considered evolution in wind energy resources, generally emphasizing local and regional scales. Observational records6,7,8,9,10,11,12,13,14 and reanalysis datasets12,15,16,17 have assessed past variability, in some locations finding relationships with internal modes of climate variability such as El Niño–Southern Oscillation (ENSO)9,10,11,14,15,16 while other locations are more sensitive to changes in surface roughness12,13 or broader pressure gradients14. Considering future developments, global climate model (GCM) simulations have been analysed to understand impacts on wind resources in North America18,19, Europe20, in China21 on the East Indian Coast22 or to explore wind extremes23. Other studies employ dynamic downscaling of GCM results to focus on particular regions24,25,26,27,28. Some researchers emphasize the need to consider multi-model ensembles due to uncertainties in predictions of interannual variability20. Such analyses are critically important at local and regional levels, but together are not sufficient to provide an overall assessment of how climate change will impact the global wind power capacity and thus the extent to which wind energy production can offset fossil fuel combustion to meet the world’s growing energy demand. To date, a global assessment of likely future changes in the wind energy resource has not been made. This study utilizes an ensemble of ten state-of-the-art GCMs from the Coupled Model Intercomparison Project phase 5 (CMIP5)29 to predict the large-scale geographic distribution of future changes in wind energy resource under two future greenhouse gas emissions scenarios (see Methods) and understand the physical mechanisms for such changes.

Using global climate models to assess wind energy

Three characteristics of GCMs render them imperfect tools for assessing wind energy potential; we demonstrate quantitatively that none of them are sufficiently severe to undermine the scientific validity or societal value of such projections derived therefrom. The first and perhaps the most obvious potential limitation of GCMs is their horizontal resolution. The resolution of the atmospheric component of CMIP5 GCMs varies by model and is non-uniform in space, but is typically on the order of 1–2° or ~100 km. To demonstrate that the magnitude and variability of actual monthly total wind power can be reproduced by coarse-resolution gridded data, we compare the power calculated using wind measurements from a tower located at the US National Renewable Energy Laboratory’s (NREL) National Wind Technology Center (NWTC) in Boulder, CO, USA (Tower M2; 105.23° W, 39.90° N)30 to power calculated using winds estimated by the 2.5°-resolution NCEP/NCAR Reanalysis31. The NWTC Tower M2 is located approximately 4 km from mountains and other complex terrain, and flow there is influenced by the complex terrain32,33. We use a reanalysis product, rather than a CMIP5 GCM, to compare with tower measurements because we are comparing time series (that is, actual, historical monthly to interannual variability) of wind estimates, whereas fully coupled GCMs are not temporally synchronous with the real world at such frequencies. Furthermore, the two datasets being compared are independent because wind measurements made by the NWTC towers are not assimilated into the NCEP/NCAR Reanalysis. Despite their coarse resolution (and averaging two adjacent 2.5° × 2.5° grid cells), the gridded data are able to reproduce (r = 0.94) the magnitude and variability of monthly total power inferred from the point measurements made on the tower (Fig. 1a). By proxy, we conclude that the coarse horizontal resolution of the GCMs does not undermine their utility for assessing wind resource variability.

Fig. 1: Isolating the impacts of GCM approximations.
Fig. 1

a, Effect of using coarse gridded (rather than station) W on estimated monthly P. Solid: start with NREL 10-m daily W, extrapolate to 80 m, compute P, compute monthly mean of daily P. Dashed: start with NCEP 10-m daily W, extrapolate to 80 m, compute P, compute monthly mean of daily P. b, Effect of using near-surface (rather than turbine-level) W on estimated monthly P. Solid: start with NREL 80-m hourly W, compute P, compute monthly mean of hourly P. Dashed: start with NREL 10-m hourly W, extrapolate to 80 m, compute P, compute monthly mean of hourly P. c, Effect of using monthly mean (rather than hourly mean) wind speed (W) on estimated monthly power (P). Solid: start with NREL 80-m hourly W, compute P, compute monthly mean of hourly P. Dashed: Start with NREL 80-m hourly W, compute monthly mean of hourly W, compute P. d, Effect of all three approximations simultaneously. Solid: start with NREL 80-m hourly W, compute P, compute monthly mean of hourly P. Dashed: start with NCEP 10-m monthly W, extrapolate to 80 m, compute P. See Supplementary Figs. 1 and 2 for further direct comparisons between W time series from the NREL tower and the NCEP reanalysis, and a map illustrating the location and scale of the two NCEP reanalysis grid cells that are averaged together relative to the point location of the NREL tower.

The second potential limitation of GCMs in the context of wind energy is the fact that wind turbines integrate momentum from wind across the rotor disk34, typically 40–120 m above the surface, whereas the vertical grid structure of GCMs is such that wind information is available only at a height of 10 m and on standard pressure levels (for exmple, 1,000 mbar, 925 mbar, 850 mbar, and so on). To assess the uncertainty introduced by this vertical offset, we first calculate monthly total power based on wind speed measured at a height of 80 m on the M2 tower, and compare that estimate with that estimated using 10-m tower measurements of wind speed extrapolated upward to 80 m using a power law. Although instantaneous wind profiles rarely follow the power law with the same coefficient throughout the diurnal cycle35,36, the power law applies on average, and the monthly total power at 80 m is predicted extremely well (r = 0.98) by the extrapolated 10-m winds (Fig. 1b).

The third potential limitation of GCMs is the provision of monthly mean fields, whereas winds fluctuate at much higher frequencies. To quantify the possible effect of averaging out the high-frequency wind fluctuations, we compare time series of monthly total power based on high-frequency (hourly) wind speed measurements (from the M2 tower) with that based on the same hourly wind speed measurements but averaged across the calendar month prior to computing power. In this case, a systematic bias occurs, such that using monthly averaged wind speed results in an underestimation of monthly total power by 114 kW (48% of average production) on average (Fig. 1c). However, the temporal variability of this offset is relatively small (σ = 43 kW) and the temporal correlation remains very high (r = 0.94). A broader perspective on the potential impact of this approximation is provided by further comparing the global wind power climatology obtained using 6-hourly and monthly mean wind speed fields, both from reanalysis (Supplementary Figs. 3 and 4). Offsets are similar in magnitude and stability to that estimated from the tower measurements, thus temporal correlations between monthly power computed with and without explicitly resolving the high-frequency wind speed variations are extremely high across the globe.

The offset due to using monthly averaged wind speed dominates the exercise wherein all three limitations are considered simultaneously (Fig. 1d) and thus should be borne in mind as a persistent low bias when interpreting absolute wind power values derived from monthly mean GCM output (or any monthly mean data, including measurements). The correlation between monthly total power derived from hourly wind measurements at 80 m on the M2 tower (ideal case) versus monthly mean wind estimates at 10 m from the gridded reanalysis data (most equivalent to a GCM) is 0.92 with a mean offset of 93 ± 44 kW (~39% of average production).

Global wind power projections from CMIP5 simulations

The global distribution of present-day annual mean wind power is well reproduced by the CMIP5 multi-model median of historical simulations (Fig. 2) including the correct location of maxima within the American continents (central US and Patagonia), Africa (Sahara, horn and southern tip), Europe (northwestern and far eastern), Asia (Tibetan Plateau and Inner Mongolia), and Australasia (southwestern Australia and New Zealand)37,38. The remainder of this Article focuses on how and why this distribution is projected to change in response to future greenhouse gas forcing.

Fig. 2: Simulated wind power climatology.
Fig. 2

Multi-model median annual mean wind power (kW) averaged over the historical baseline period (1980–2005) from the ten CMIP5 coupled GCMs used in this study (Supplementary Table 1). Also shown are the 16 regional domains selected for time-series analysis in this study (Supplementary Table 2). For reference, the 250 and 300 kW contours are shown as dashed and solid lines, respectively.

Projected changes in wind power over the coming century reveal strong interhemispheric asymmetry (Fig. 3 and Supplementary Fig. 6). Specifically, reductions in wind power are generally projected broadly across northern mid-latitude regions, while increases are projected across tropical and southern subtropical regions. Particularly notable and robust (in terms of inter-model agreement) reductions are found over the US, the British Isles, northern Middle East, central and northern Asia, and far eastern Asia including the Korean Peninsula and Japan. Increases are found throughout Mexico and Central America, eastern Brazil, southeastern Africa including Madagascar, the southeastern Arabian Peninsula including Yemen and Oman, India, Southeast Asia, and northeastern Australia. Most of the aforementioned increases are only robust under RCP8.5 and for later time periods; see below for further discussion of dependence on emissions and interhemispheric asymmetry.

Fig. 3: Predicted changes in wind power.
Fig. 3

a, Multi-model median change in annual mean wind power (kW) in 2020–2040 relative to baseline from the ten CMIP5 coupled GCMs used in this study under the RCP4.5 forcing scenario. Locations with fewer than 8/10 models agreeing on the sign of change are deemed not robust and left blank (white); yellow indicates that at least 8/10 models agree that the change will be very small (±5 kW or smaller). b, As in a but for the RCP8.5 forcing scenario. c, Zonally integrated changes in power in 2020–2040 (only including robust changes as defined above) in the RCP4.5 (dashed) and RCP8.5 (solid) forcing scenarios; for reference, the zonally integrated annual mean power is shown as a grey line; zonally integrated change profiles are scaled by a factor of 10 for clarity. df, As in ac but for 2080–2100. Also shown in e are the 16 regional domains selected for the time-series analysis in this study (Supplementary Table 2). For reference, the 250 and 300 kW contours are shown in each map as dashed and solid lines, respectively. The corresponding results for the period 2050–2070 are provided in Supplementary Fig. 6.

Changes in wind power vary by region. In the central US, the area-averaged power is projected to decline by 8% (14%) by 2050 (2100) under the RCP4.5 scenario or by 10% (18%) by 2050 (2100) under the RCP8.5 scenario (Fig. 4). An extremely large increase in wind power is projected for eastern Brazil under the RCP8.5 scenario—21% (42%) by 2050 (2100), but those increases are only a quarter as large under the RCP4.5 scenario. Similarly, northeastern Australia is also projected to experience an extremely large increase in wind power by 2100 (41%), but only under the RCP8.5 scenario. Several other key regions around the globe have substantial projected changes in wind energy resource, as shown in Fig. 4. Beyond the robustness of projections in terms of inter-model agreement, the significance of the future changes can be assessed relative to the amplitude of local internal variability (Supplementary Fig. 5). At the near-term time horizon (2020–2040), most of the robust changes are between 50–100% of the baseline variability, with the exception of eastern Europe and northern Asia, where on average the projected changes exceed one standard deviation of the simulated interannual variability. Near the end of the century, nearly all projected changes due to anthropogenic forcing are well in excess of the level of internal variability—in many cases several times greater than baseline interannual variability.

Fig. 4: Evolution of regional wind power over the twenty-first century.
Fig. 4

a, Time series of changes in wind power (per cent of baseline) averaged across each regional domain (Figs. 2 and 3e, and Supplementary Table 2) and from each model over the course of the twenty-first century in the RCP8.5 forcing scenario. For clarity, the seasonal-to-interannual variability is removed from each time series prior to plotting, using a multidecadal low-pass filter (30-year period). The multi-model median, baseline averaged (1980–2005) power (kW) averaged across each domain is given in the upper-left corner of each panel. b, As in a but for the RCP4.5 forcing scenario, with each region collapsed to its multi-model mean, and changes relative to baseline expressed in kW. c, As in b but for the RCP8.5 forcing scenario.

Some of the projected regional changes in wind power noted above have a strong seasonal dependence, while others are manifest throughout the year (Supplementary Fig. 7). For example, the projected decreases in the central US, Japan and the horn of Africa occur primarily during boreal winter, the projected increases in eastern Brazil, Madagascar and the southern Arabian Peninsula are much stronger during austral winter, and the projected increases in southern mainland Africa and Southeast Asia are relatively constant throughout the year.

When integrated zonally (along lines of latitude; Fig. 3), a clear and asymmetric dependence on emissions scenario emerges. The total decrease in power across the northern mid-latitudes (30–65° N) is largely insensitive to emissions scenario. In contrast, the total increase across the southern tropical/subtropical band (10–30° S) only emerges under the RCP8.5 scenario. Thus, in a globally integrated sense, the decrease in the Northern Hemisphere is partially balanced by an increase in the Southern Hemisphere, but only under the high greenhouse gas emissions scenario.

The regional changes in continental wind power described above are linked to large-scale dynamical and thermodynamic mechanisms governing the overall response of the atmospheric general circulation to radiatively forced changes in surface temperature (Fig. 5). However, the mechanisms explaining the changes in the two hemispheres are distinct. The relatively uniform decrease in power across the Northern Hemisphere mid-latitudes on land is consistent with a general reduction in surface wind speed in that latitude band including the ocean (Fig. 5a,b). This general reduction is caused by a strongly reduced meridional temperature gradient due to polar amplification of global warming (Fig. 5d,e), which reduces mid-latitude baroclinicity and hence storm track intensity. It is worth noting, however, that there is a relatively large inter-model spread in the projected response of Northern Hemisphere storm tracks39,40,41, which cannot be fully explained by uncertainties in the projected change in the meridional temperature gradient42. Other factors, such as tropical processes and changes in subtropical static stability may also play a role43. While the reduction of westerly surface flow is strongest over the ocean and is thus not entirely zonally symmetric, it extends readily across the continents within approximately the same latitude band without exception (note that background mean surface wind speeds are also higher over the oceans, so the fractional change in wind speed is roughly zonally symmetric). In contrast, the increase in power in the Southern Hemisphere tropics and subtropics is driven by land–sea warming gradients (or ‘land amplification’). The regions where surface wind speed increases within the latitude band 0–30° S (Fig. 5f) are not only constrained by the continents, but by their associated pressure gradient amplifications (Fig. 5g), which are clearly driven by warming amplifications (Fig. 5h) via thermal low intensification over southern Africa, Australia and northern South America

Fig. 5: Hemispheric-scale drivers of regional wind power changes.
Fig. 5

ae, Projected changes in zonal mean surface wind speed (m s–1; a), surface wind speed (m s–1; b), sea-level pressure (mbar; c), surface temperature (°C; d) and zonal mean surface temperature (°C; e) in 2080–2100 relative to baseline (1980–2005) under the RCP8.5 forcing scenario. Climatological contours are shown on bd for reference. fh, Projected changes in surface wind speed, sea-level pressure and surface temperature, respectively, averaged between 30° S–0° (dashed lines shown on bd). Note that the colour scale in d is shifted such that blue (red) shades represent ΔT SFC less than (greater than) +4 °C—not cooling (warming).

Summary and essential research needs

Results presented herein reveal strong interhemispheric asymmetry of future changes in potential wind power, including decreases across the Northern Hemisphere mid-latitudes and increases across the tropics and subtropics of the Southern Hemisphere. The global distribution of projected regional changes in surface wind speed over land can be mechanistically linked to well-documented and understood large-scale changes in surface temperature and atmospheric circulation. This clearly explicable relationship lends further confidence in the projections of wind power provided here. While the relatively coarse spatial and temporal resolution of the models on which these projections are made may not allow high-fidelity predictions of wind resource that would be required for localized wind farm siting, especially in regions of complex terrain, this investigation does highlight areas around the globe where more refined investigations with downscaling can lend critical insight into the likely impacts of climate change on wind resources. Consideration of future build-out of wind energy resources should also consider climate change impacts on offshore wind energy, as well as the impacts of wind farms on local and regional climate44,45,46,47,48.


Global climate models

This study utilizes an ensemble of ten state-of-the-art global climate models (GCMs) from the Coupled Model Intercomparison Project phase 5 (CMIP5) to calculate the large-scale geographic distribution of future changes in wind energy resource under two future greenhouse gas emissions scenarios, or Representative Concentration Pathways (RCPs). Details of the CMIP5 experimental design are given in ref. 29, and the names and relevant details of the CMIP5 GCMs used in this study are provided in Supplementary Table 1. The GCMs were selected based on if they provided all of the required output variables (listed below) for both of the RCPs (2006–2100) as well as the historical experiment (1850–2006), where the availability of 10-m wind speed was frequently a limiting factor. Nonetheless, the GCMs included in this study represent a broad cross-section of the full CMIP5 ensemble, both in terms of national origin of GCM development and various GCM characteristics such as resolution (Supplementary Table 1).

Wind power calculation

As wind power is not a standard output of any GCM, a method for an offline diagnostic calculation thereof was developed here. The calculation requires four monthly mean fields: 10-m wind speed (W 10), surface pressure (p s), surface temperature (T s) and surface specific humidity (q s). On acquiring all necessary GCM output fields for the historical, RCP4.5 and RCP8.5 simulations, all fields were linearly regridded onto a common 1° by 1° grid and an ocean mask was applied.

Wind power, P, is a function of wind speed, W, at rotor height (taken here as 100 m), thus wind speed as provided at the nominal GCM output level of 10 m (W 10) must first be extrapolated to rotor height (W 100). Furthermore, wind speed must be corrected for air density (as greater density of air imparts a larger force on turbine blades), and density must be corrected for humidity (a relatively minor adjustment). All calculations and corrections are transient in both space and time, and therefore account for the full influence of climate change on wind power (for example, changes in wind speed and air density that may arise due to global warming). The calculations and corrections are made as follows.

The 10-m wind speed fields are extrapolated to 100 m using a power law35 with coefficient 1/7, as dynamic power law coefficients have been shown to only slightly impact wind energy potential49).

W 100 = W 10 100 m 10 m 1 7

Next, dry air density, ρ, is calculated using the ideal gas law

ρ d = p R T

where R = 287.058 J kg–1 K–1. Density is then corrected for humidity50

ρ m = ρ d 1 + q 1 + 1.609 q

Next, wind speed is scaled for density51

W 100 = W 100 ρ m 1.225 1 3

Finally, wind power is calculated using an industry wind turbine power curve

P=f W 100

where f is a function that models the General Electric (GE) 1.5 MW sle turbine (Supplementary Fig. 8). The power curve function f is implemented numerically using the following algorithm.

The polynomial function describing the ramp-up stage is modelled by fitting a curve via least-squares linear regression to the wind speed–power relationship given in the industry table for wind speeds between cut-in and rated power. Graphically, the solution to the full algorithm is virtually indistinguishable from the values given by the industry table (Supplementary Fig. 8).

Data availability

All data used in this study are freely available online. Global climate model output data are available from the Earth System Grid Federation ( Reanalysis data are available from the US National Oceanic and Atmospheric Administration ( Observations from the M2 meteorological tower in Boulder, CO, USA are available from the US National Renewable Energy Laboratory (

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Global Wind Report—Annual Market Update 2014 (Global Wind Energy Council, 2015).

  2. 2.

    Synthesis Report, Aggregate Effect of the Intended Nationally Determined Contributions: An Update (United Nations Framework Convention on Climate Change, 2016).

  3. 3.

    Bodini, N., Lundquist, J. K., Zardi, D. & Handschy, M. Year-to-year correlation, record length, and overconfidence in wind resource assessment. Wind Energy Sci. 1, 115–128 (2016).

  4. 4.

    Hartmann, D. L. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F.) Ch. 2 (IPCC, Cambridge Univ. Press, Cambridge, 2013).

  5. 5.

    Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F.) Ch. 12 (IPCC, Cambridge Univ. Press, Cambridge, 2013).

  6. 6.

    Klink, K. Climatological mean and interannual variance of United States surface wind speed, direction and velocity. Int. J. Climatol. 19, 471–488 (1999).

  7. 7.

    Klink, K. Trends in mean monthly maximum and minimum surface wind speeds in the coterminous United States, 1961 to 1990. Clim. Res. 13, 193–205 (1999).

  8. 8.

    Klink, K. Trends and interannual variability of wind speed distributions in Minnesota. J. Clim. 15, 3311–3317 (2002).

  9. 9.

    Enloe, J., O’Brien, J. J. & Smith, S. R. ENSO impacts on peak wind gusts in the United States. J. Clim. 17, 1728–1737 (2004).

  10. 10.

    Harper, B. R., Katz, R. W. & Harriss, R. C. Statistical methods for quantifying the effect of the El Niño–Southern Oscillation on wind power in the Northern Great Plains of the United States. Wind Eng. 31, 123–137 (2007).

  11. 11.

    St. George, S. & Wolfe, S. A. El Niño stills winter winds across the southern Canadian Prairies. Geophys. Res. Lett. 36, L23806 (2009).

  12. 12.

    Pryor, S. C. et al. Wind speed trends over the contiguous United States. J. Geophys. Res. Atmos. 114, D14105 (2009).

  13. 13.

    Vautard, R., Cattiaux, J., Yiou, P., Thépaut, J.-N. & Ciais, P. Northern Hemisphere atmospheric stilling partly attributed to an increase in surface roughness. Nat. Geosci. 3, 756–761 (2010).

  14. 14.

    Clifton, A. & Lundquist, J. K. Data clustering reveals climate impacts on local wind phenomena. J. Appl. Meteorol. Climatol. 51, 1547–1557 (2012).

  15. 15.

    Li, X., Zhong, S., Bian, X. & Heilman, W. E. Climate and climate variability of the wind power resources in the Great Lakes region of the United States. J. Geophys. Res. Atmos. 115, D18107 (2010).

  16. 16.

    Berg, N., Hall, A., Capps, S. B. & Hughes, M. El Niño-Southern Oscillation impacts on winter winds over Southern California. Clim. Dyn. 40, 109–121 (2012).

  17. 17.

    Bett, P. E., Thornton, H. E. & Clark, R. T. Using the Twentieth Century Reanalysis to assess climate variability for the European wind industry. Theor. Appl. Climatol. 127, 61–80 (2017).

  18. 18.

    Breslow, P. B. & Sailor, D. J. Vulnerability of wind power resources to climate change in the continental United States. Renew. Energy 27, 585–598 (2002).

  19. 19.

    Sailor, D. J., Smith, M. & Hart, M. Climate change implications for wind power resources in the Northwest United States. Renew. Energy 33, 2393–2406 (2008).

  20. 20.

    Reyers, M., Moemken, J. & Pinto, J. G. Future changes of wind energy potentials over Europe in a large CMIP5 multi-model ensemble. Int. J. Climatol. 36, 783–796 (2016).

  21. 21.

    Ren, D. Effects of global warming on wind energy availability. J. Renew. Sustain. Energy 2, 052301 (2010).

  22. 22.

    Kulkarni, S., Deo, M. C. & Ghosh, S. Evaluation of wind extremes and wind potential under changing climate for Indian offshore using ensemble of 10 GCMs. Ocean Coast. Manag. 121, 141–152 (2016).

  23. 23.

    Kumar, D., Mishra, V. & Ganguly, A. R. Evaluating wind extremes in CMIP5 climate models. Clim. Dyn. 45, 441–453 (2014).

  24. 24.

    Pryor, S. C. & Schoof, J. T. Importance of the SRES in projections of climate change impacts on near-surface wind regimes. Meteorol. Z. 19, 267–274 (2010).

  25. 25.

    Pryor, S. C. & Barthelmie, R. J. Assessing climate change impacts on the near-term stability of the wind energy resource over the United States. Proc. Natl Acad. Sci. USA 108, 8167–8171 (2011).

  26. 26.

    Hueging, H., Haas, R., Born, K., Jacob, D. & Pinto, J. G. Regional changes in wind energy potential over Europe using regional climate model ensemble projections. J. Appl. Meteorol. Climatol. 52, 903–917 (2012).

  27. 27.

    Gonçalves-Ageitos, M., Barrera-Escoda, A., Baldasano, J. M. & Cunillera, J. Modelling wind resources in climate change scenarios in complex terrains. Renew. Energy 76, 670–678 (2015).

  28. 28.

    Johnson, D. L. & Erhardt, R. J. Projected impacts of climate change on wind energy density in the United States. Renew. Energy 85, 66–73 (2016).

  29. 29.

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

  30. 30.

    Jager, D. & Andreas, A. NREL National Wind Technology Center (NWTC): M2 Tower; Boulder, Colorado (Data) (National Renewable Energy Laboratory, 1996).

  31. 31.

    Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996).

  32. 32.

    Banta, R. M., Olivier, L. D., Neff, W. D., Levinson, D. H. & Ruffieux, D. Influence of canyon-induced flows on flow and dispersion over adjacent plains. Theor. Appl. Climatol. 52, 27–42 (1995).

  33. 33.

    Aitken, M. L., Banta, R. M., Pichugina, Y. L. & Lundquist, J. K. Quantifying wind turbine wake characteristics from scanning remote sensor data. J. Atmos. Ocean. Technol. 31, 765–787 (2014).

  34. 34.

    Wagner, R., Courtney, M., Gottschall, J. & Lindelöw-Marsden, P. Accounting for the speed shear in wind turbine power performance measurement: accounting for speed shear in power performance measurement. Wind Energy 14, 993–1004 (2011).

  35. 35.

    Walter, K., Weiss, C. C., Swift, A. H. P., Chapman, J. & Kelley, N. D. Speed and direction shear in the stable nocturnal boundary layer. J. Sol. Energy Eng. 131, 011013 (2009).

  36. 36.

    Vanderwende, B. J. & Lundquist, J. K. The modification of wind turbine performance by statistically distinct atmospheric regimes. Environ. Res. Lett. 7, 034035 (2012).

  37. 37.

    Archer, C. L. & Jacobson, M. Z. Evaluation of global wind power. J. Geophys. Res. 110, D12110 (2005).

  38. 38.

    Lu, X., McElroy, M. B. & Kiviluoma, J. Global potential for wind-generated electricity. Proc. Natl Acad. Sci. USA 106, 10933–10938 (2009).

  39. 39.

    Chang, E. K. M., Guo, Y. & Xia, X. CMIP5 multimodel ensemble projection of storm track change under global warming: CMIP5 model-projected storm track change. J. Geophys. Res. Atmos. 117, D23118 (2012).

  40. 40.

    Harvey, B. J., Shaffrey, L. C., Woollings, T. J., Zappa, G. & Hodges, K. I. How large are projected 21st century storm track changes?: Storm track variability and change. Geophys. Res. Lett. 39, L18707 (2012).

  41. 41.

    Zappa, G., Shaffrey, L. C., Hodges, K. I., Sansom, P. G. & Stephenson, D. B. A multimodel assessment of future projections of North Atlantic and European extratropical cyclones in the CMIP5 climate models. J. Clim. 26, 5846–5862 (2013).

  42. 42.

    Harvey, B. J., Shaffrey, L. C. & Woollings, T. J. Equator-to-pole temperature differences and the extra-tropical storm track responses of the CMIP5 climate models. Clim. Dyn. 43, 1171–1182 (2014).

  43. 43.

    Mbengue, C. & Schneider, T. Storm track shifts under climate change: what can be learned from large-scale dry dynamics. J. Clim. 26, 9923–9930 (2013).

  44. 44.

    Fitch, A. C. et al. Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP model. Mon. Weather Rev. 140, 3017–3038 (2012).

  45. 45.

    Fitch, A. C., Lundquist, J. K. & Olson, J. B. Mesoscale influences of wind farms throughout a diurnal cycle. Mon. Weather Rev. 141, 2173–2198 (2013).

  46. 46.

    Fitch, A. C., Olson, J. B. & Lundquist, J. K. Parameterization of wind farms in climate models. J. Clim. 26, 6439–6458 (2013).

  47. 47.

    Vautard, R. et al. Regional climate model simulations indicate limited climatic impacts by operational and planned European wind farms. Nat. Commun. 5, 3196 (2014).

  48. 48.

    Fitch, A. C. Climate impacts of large-scale wind farms as parameterized in a global climate model. J. Clim. 28, 6160–6180 (2015).

  49. 49.

    Tobin, I. et al. Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections. Clim. Change 128, 99–112 (2014).

  50. 50.

    Wallace, J. M. & Hobbs, P. V. Atmospheric Science: An Introductory Survey (Academic, 2006).

  51. 51.

    IEC 61400-12-1. Wind Turbines - Part 12-1: Power Performance Measurements of Electricity Producing Wind Turbines (International Electrotechnical Commission, 2005).

Download references


We acknowledge the WCRP Working Group on Coupled Modelling and US DOE/PCMDI for CMIP, and thank the climate modelling groups (listed in Supplementary Table 1) for producing and making available their model output. We also acknowledge the WHOI CMIP5 Community Storage Server, Woods Hole Oceanographic Institution, Woods Hole, MA, USA ( We express appreciation to the US DOE’s National Renewable Energy Laboratory for sustained observations from the M2 meteorological tower, and to NOAA/OAR/ESRL/PSD in Boulder, CO, USA for archiving NCEP/NCAR Reanalysis fields.

Author information


  1. Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA

    • Kristopher B. Karnauskas
    • , Julie K. Lundquist
    •  & Lei Zhang
  2. Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA

    • Kristopher B. Karnauskas
  3. Renewable and Sustainable Energy Institute, University of Colorado, Boulder, CO, USA

    • Julie K. Lundquist


  1. Search for Kristopher B. Karnauskas in:

  2. Search for Julie K. Lundquist in:

  3. Search for Lei Zhang in:


K.B.K. and J.K.L. conceived the research; J.K.L. provided algorithms for calculating variables related to wind energy; K.B.K. analysed the tower, reanalysis and climate model data; L.Z. assisted with the diagnostic analyses; all authors contributed to the final interpretation and writing of the manuscript with major contributions by K.B.K. and J.K.L.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Kristopher B. Karnauskas.

Supplementary information

  1. Supplementary Information

    Supplementary Tables and Figures