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.
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Global Wind Report—Annual Market Update 2014 (Global Wind Energy Council, 2015).
Synthesis Report, Aggregate Effect of the Intended Nationally Determined Contributions: An Update (United Nations Framework Convention on Climate Change, 2016).
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).
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).
Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F.) Ch. 12 (IPCC, Cambridge Univ. Press, Cambridge, 2013).
Klink, K. Climatological mean and interannual variance of United States surface wind speed, direction and velocity. Int. J. Climatol. 19, 471–488 (1999).
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).
Klink, K. Trends and interannual variability of wind speed distributions in Minnesota. J. Clim. 15, 3311–3317 (2002).
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).
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).
St. George, S. & Wolfe, S. A. El Niño stills winter winds across the southern Canadian Prairies. Geophys. Res. Lett. 36, L23806 (2009).
Pryor, S. C. et al. Wind speed trends over the contiguous United States. J. Geophys. Res. Atmos. 114, D14105 (2009).
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).
Clifton, A. & Lundquist, J. K. Data clustering reveals climate impacts on local wind phenomena. J. Appl. Meteorol. Climatol. 51, 1547–1557 (2012).
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).
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).
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).
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).
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).
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).
Ren, D. Effects of global warming on wind energy availability. J. Renew. Sustain. Energy 2, 052301 (2010).
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).
Kumar, D., Mishra, V. & Ganguly, A. R. Evaluating wind extremes in CMIP5 climate models. Clim. Dyn. 45, 441–453 (2014).
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).
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).
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).
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).
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).
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).
Jager, D. & Andreas, A. NREL National Wind Technology Center (NWTC): M2 Tower; Boulder, Colorado (Data) (National Renewable Energy Laboratory, 1996).
Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996).
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).
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).
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).
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).
Vanderwende, B. J. & Lundquist, J. K. The modification of wind turbine performance by statistically distinct atmospheric regimes. Environ. Res. Lett. 7, 034035 (2012).
Archer, C. L. & Jacobson, M. Z. Evaluation of global wind power. J. Geophys. Res. 110, D12110 (2005).
Lu, X., McElroy, M. B. & Kiviluoma, J. Global potential for wind-generated electricity. Proc. Natl Acad. Sci. USA 106, 10933–10938 (2009).
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).
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).
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).
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).
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).
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).
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).
Fitch, A. C., Olson, J. B. & Lundquist, J. K. Parameterization of wind farms in climate models. J. Clim. 26, 6439–6458 (2013).
Vautard, R. et al. Regional climate model simulations indicate limited climatic impacts by operational and planned European wind farms. Nat. Commun. 5, 3196 (2014).
Fitch, A. C. Climate impacts of large-scale wind farms as parameterized in a global climate model. J. Clim. 28, 6160–6180 (2015).
Tobin, I. et al. Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections. Clim. Change 128, 99–112 (2014).
Wallace, J. M. & Hobbs, P. V. Atmospheric Science: An Introductory Survey (Academic, 2006).
IEC 61400-12-1. Wind Turbines - Part 12-1: Power Performance Measurements of Electricity Producing Wind Turbines (International Electrotechnical Commission, 2005).
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 (cmip5.whoi.edu). 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.
The authors declare no competing financial interests.
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Karnauskas, K.B., Lundquist, J.K. & Zhang, L. Southward shift of the global wind energy resource under high carbon dioxide emissions. Nature Geosci 11, 38–43 (2018). https://doi.org/10.1038/s41561-017-0029-9
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