Wind energy is a virtually carbon-free and pollution-free electricity source, with global wind resources greatly exceeding electricity demand. Accordingly, the installed capacity of wind turbines grew at an annualized rate of >20% from 2000 to 2019 and is projected to increase by a further 50% by the end of 2023. In this Review, we describe the factors that dictate the wind resource magnitude and variability and illustrate the tools and techniques that are being used to make projections of wind resources and wind turbine operating conditions. Natural variability due to the action of internal climate modes appears to dominate over global-warming-induced non-stationarity over most areas with large wind energy installations or potential. However, there is evidence for increased wind energy resources by the end of the current century in northern Europe and the US Southern Great Plains. New technology trends are changing the sensitivity of wind energy to global climate non-stationarity and, thus, present new challenges and opportunities for innovative research. The evolution of climate modelling to increasingly address mesoscale processes is providing improved projections of both wind resources and wind turbine operating conditions, and will contribute to continued reductions in the levelized cost of energy from wind power generation.
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Wiser, R. et al. in IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation (eds Edenhofer, O. et al.) 535 (Cambridge Univ. Press, 2012). A comprehensive analysis of the technical potential wind resource, the role of wind energy in climate change mitigation and wind energy technologies.
Eurek, K. et al. An improved global wind resource estimate for integrated assessment models. Energy Econ. 64, 552–567 (2017).
Marvel, K., Kravitz, B. & Caldeira, K. Geophysical limits to global wind power. Nat. Clim. Change 3, 118–121 (2013).
Possner, A. & Caldeira, K. Geophysical potential for wind energy over the open oceans. Proc. Natl Acad. Sci. USA 114, 11338–11343 (2017).
Jung, C., Schindler, D. & Laible, J. National and global wind resource assessment under six wind turbine installation scenarios. Energy Convers. Manag. 156, 403–415 (2018).
International Energy Agency. Global energy & CO2 status (IEA, 2019).
International Energy Agency. Key world energy statistics 2019 (IEA, 2019).
Global Wind Energy Council. Global wind report 2018 (GWEC, 2019).
Veers, P. et al. Grand challenges in the science of wind energy. Science 366, eaau2027 (2019). Emphasizes important trends within the wind energy industry and highlights resulting key research avenues.
Wiser, R. et al. Expert elicitation survey on future wind energy costs. Nat. Energy 1, 16135 (2016).
Global Wind Energy Council. Global wind report: annual market update 2017 (GWEC, 2018).
Smoucha, E. A., Fitzpatrick, K., Buckingham, S. & Knox, O. G. Life cycle analysis of the embodied carbon emissions from 14 wind turbines with rated powers between 50KW and 3.4Mw. J. Fundam. Renew. Energy Appl. 6, 1000211 (2016).
Barthelmie, R. J. & Pryor, S. C. Potential contribution of wind energy to climate change mitigation. Nat. Clim. Change 4, 684–688 (2014). Quantifies the potential role of wind energy to climate change mitigation.
Wiser, R. et al. Long-term implications of sustained wind power growth in the United States: Potential benefits and secondary impacts. Appl. Energy 179, 146–158 (2016).
Global Wind Energy Council. Global wind energy outlook 2016 (GWEC, 2016).
Intergovernmental Panel on Climate Change. Climate Change 2013. The physical science basis (Cambridge Univ. Press, 2013).
Pryor, S. C. & Barthelmie, R. J. Climate change impacts on wind energy: A review. Renew. Sustain. Energy Rev. 14, 430–437 (2010). A comprehensive review on the topic of climate change impacts on the wind energy industry.
Pryor, S. C. & Barthelmie, R. J. Assessing the vulnerability of wind energy to climate change and extreme events. Clim. Change 121, 79–91 (2013). Addresses climate change impacts on the conditions in which WTs operate.
Manwell, J. F., McGowan, J. G. & Rogers, A. L. Wind Energy Explained: Theory, Design and Application (Wiley, 2010).
Stull, R. B. An Introduction to Boundary Layer Meteorology (Springer, 2012).
Irwin, J. S. A theoretical variation of the wind profile power-law exponent as a function of surface roughness and stability. Atmos. Environ. 13, 191–194 (1979).
Barthelmie, R. J., Shepherd, T. J., Aird, J. A. & Pryor, S. C. Power and wind shear implications of large wind turbine scenarios in the US Central Plains. Energies 13, 4269 (2020).
Gryning, S.-E., Batchvarova, E., Brümmer, B., Jørgensen, H. & Larsen, S. On the extension of the wind profile over homogeneous terrain beyond the surface boundary layer. Bound. Layer Meteorol. 124, 251–268 (2007).
Nunalee, C. G. & Basu, S. Mesoscale modeling of coastal low-level jets: implications for offshore wind resource estimation. Wind Energy 17, 1199–1216 (2014).
Pryor, S. C., Shepherd, T. J. & Barthelmie, R. J. Interannual variability of wind climates and wind turbine annual energy production. Wind Energy Sci. 3, 651–665 (2018).
Pryor, S. C., Nielsen, M., Barthelmie, R. J. & Mann, J. Can satellite sampling of offshore wind speeds realistically represent wind speed distributions? Part II: Quantifying uncertainties associated with sampling strategy and distribution fitting methods. J. Appl. Meteorol. 43, 739–750 (2004).
Mann, J., Kristensen, L. & Jensen, N. O. in Bridge Aerodynamics (eds Larsen, A. & Esdahl, S.) 49–56 (Balkema, 1998).
Ramon, J., Lledó, L., Torralba, V., Soret, A. & Doblas-Reyes, F. J. What global reanalysis best represents near-surface winds? Q. J. R. Meteorol. Soc. 145, 3236–3251 (2019).
Olauson, J. ERA5: The new champion of wind power modelling? Renew. Energy 126, 322–331 (2018).
Garcia-Heller, V., Espinasa, R. & Paredes, S. Forecast study of the supply curve of solar and wind technologies in Argentina, Brazil, Chile and Mexico. Renew. Energy 93, 168–179 (2016).
Bianchi, E., Solarte, A. & Guozden, T. Spatiotemporal variability of the wind power resource in Argentina and Uruguay. Wind Energy 22, 1086–1100 (2019).
Pryor, S. C., Conrick, R., Miller, C., Tytell, J. & Barthelmie, R. J. Intense and extreme wind speeds observed by anemometer and seismic networks: An eastern US case study. J. Appl. Meteorol. Climatol. 53, 2417–2429 (2014).
Pryor, S. C., Barthelmie, R. J. & Schoof, J. T. Inter-annual variability of wind indices across Europe. Wind Energy 9, 27–38 (2006).
Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I. & Wernli, H. Balancing Europe’s wind-power output through spatial deployment informed by weather regimes. Nat. Clim. Change 7, 557–562 (2017).
Reichenberg, L., Wojciechowski, A., Hedenus, F. & Johnsson, F. Geographic aggregation of wind power-an optimization methodology for avoiding low outputs. Wind Energy 20, 19–32 (2017).
International Electrotechnical Commission. IEC 61400-1:2019. Wind energy generation systems – Part 1: design requirements (IEC, 2019).
International Electrotechnical Commission. IEC 61400-3-1:2019. Wind energy generation systems. Part 3-1: design requirements for fixed offshore wind turbines (IEC, 2019).
Johansson, V. et al. Value of wind power–implications from specific power. Energy 126, 352–360 (2017).
Jackson, K., Van Dam, C. & Yen-Nakafuji, D. Wind turbine generator trends for site-specific tailoring. Wind Energy 8, 443–455 (2005).
Hansen, M. O. L. Aerodynamics of Wind Turbines (Routledge, 2015).
Knutson, T. et al. Tropical cyclones and climate change assessment: Part I: Detection and attribution. Bull. Am. Meteorol. Soc. 100, 1987–2007 (2019).
Romero, R. & Emanuel, K. Climate change and hurricane-like extratropical cyclones: projections for North Atlantic polar lows and medicanes based on CMIP5 models. J. Clim. 30, 279–299 (2017).
Davis, N., Hahmann, A. N., Clausen, N. E. & Zagar, M. Forecast of icing events at a wind farm in Sweden. J. Appl. Meteorol. Climatol. 53, 262–281 (2014).
Sabatier, J., Lanusse, P., Feytout, B. & Gracia, S. in Informatics in Control, Automation and Robotics Vol. 495 (eds Gusikhin, O. & Madani, K.) 641–663 (Springer, 2020).
Letson, F. W., Barthelmie, R. J. & Pryor, S. C. RADAR-derived precipitation climatology for wind turbine blade leading edge erosion. Wind Energy Sci. 5, 331–347 (2020).
Herring, R., Dyer, K., Martin, F. & Ward, C. The increasing importance of leading edge erosion and a review of existing protection solutions. Renew. Sustain. Energy Rev. 115, 109382 (2019).
Letson, F., Shepherd, T. J., Barthelmie, R. J. & Pryor, S. C. WRF modelling of deep convection and hail for wind power applications. J. App. Meteorol. Climatol. https://doi.org/10.1175/JAMC-D-20-0033.1 (2020).
Feingold, G., Koren, I., Wang, H., Xue, H. & Brewer, W. A. Precipitation-generated oscillations in open cellular cloud fields. Nature 466, 849–852 (2010).
Pleskachevsky, A. L., Lehner, S. & Rosenthal, W. Storm observations by remote sensing and influences of gustiness on ocean waves and on generation of rogue waves. Ocean Dyn. 62, 1335–1351 (2012).
Larsén, X. G., Vincent, C. & Larsen, S. Spectral structure of mesoscale winds over the water. Q. J. R. Meteorol. Soc. 139, 685–700 (2013).
Passon, P. & Branner, K. Condensation of long-term wave climates for the fatigue design of hydrodynamically sensitive offshore wind turbine support structures. Ships Offshore Struct. 11, 142–166 (2016).
Young, I., Vinoth, J., Zieger, S. & Babanin, A. V. Investigation of trends in extreme value wave height and wind speed. J. Geophys. Res. Oceans 117, C00J06 (2012).
Sun, C. & Jahangiri, V. Fatigue damage mitigation of offshore wind turbines under real wind and wave conditions. Eng. Struct. 178, 472–483 (2019).
Trenberth, K. E., Dai, A., Rasmussen, R. M. & Parsons, D. B. The changing character of precipitation. Bull. Am. Meteorol. Soc. 84, 1205–1217 (2003).
Ma, J., Xie, S.-P. & Kosaka, Y. Mechanisms for tropical tropospheric circulation change in response to global warming. J. Clim. 25, 2979–2994 (2012).
Shaw, T. et al. Storm track processes and the opposing influences of climate change. Nat. Geosci. 9, 656–664 (2016).
Bengtsson, L., Hodges, K. & Roeckner, E. Storm tracks and climate change. J. Clim. 19, 3518–3543 (2006).
Catto, J. L. et al. The future of midlatitude cyclones. Curr. Clim. Change Rep. 5, 407–420 (2019).
O’Gorman, P. A. Understanding the varied response of the extratropical storm tracks to climate change. Proc. Natl Acad. Sci. USA 107, 19176–19180 (2011).
McCabe, G., Clark, M. & Serreze, M. Trends in Northern Hemisphere surface cyclone frequency and intensity. J. Clim. 14, 2763–2768 (2001).
Pryor, S. C. & Hahmann, A. N. in Oxford Research Encyclopedias: Climate Science (ed. von Storch, H.) (Oxford Univ. Press, 2019).
Jung, C., Taubert, D. & Schindler, D. The temporal variability of global wind energy–Long-term trends and inter-annual variability. Energy Convers. Manag. 188, 462–472 (2019).
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).
Moemken, J., Reyers, M., Buldmann, B. & Pinto, J. G. Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe. Tellus A 68, 29199 (2016).
Higuchi, K., Huang, J. & Shabbar, A. A wavelet characterization of the North Atlantic oscillation variation and its relationship to the North Atlantic sea surface temperature. Int. J. Climatol. 19, 1119–1129 (1999).
Schwing, F. B., Jiang, J. & Mendelssohn, R. Coherency of multi-scale abrupt changes between the NAO, NPI, and PDO. Geophys. Res. Lett. 30, 1406 (2003).
Schoof, J. T. & Pryor, S. C. Assessing the fidelity of AOGCM-simulated relationships between large-scale modes of climate variability and wind speeds. J. Geophys. Res. 119, 9719–9734 (2014).
Sandeep, S., Ajayamohan, R., Boos, W. R., Sabin, T. & Praveen, V. Decline and poleward shift in Indian summer monsoon synoptic activity in a warming climate. Proc. Natl Acad. Sci. USA 115, 2681–2686 (2018).
Vautard, R., Cattiaux, J., Yiou, P., Thepaut, J. N. & Ciais, P. Northern hemisphere atmospheric stilling partly attributed to an increase in surface roughness. Nat. Geosci. 3, 756–761 (2010).
Pryor, S. C. & Ledolter, J. Addendum to “Wind speed trends over the contiguous United States”. J. Geophys. Res. Atmos. 115, D10103 (2010).
Pryor, S. C. et al. Wind speed trends over the contiguous United States. J. Geophys. Res. Atmos. 114, D14105 (2009).
Wu, J., Zha, J. L., Zhao, D. M. & Yang, Q. D. Changes in terrestrial near-surface wind speed and their possible causes: an overview. Clim. Dyn. 51, 2039–2078 (2018).
Chen, L., Pryor, S. C., Wang, H. & Zhang, R. Distribution and variation of the surface sensible heat flux over the central and eastern Tibetan Plateau: comparison of station observations and multireanalysis products. J. Geophys. Res. Atmos. 124, 6191–6206 (2019).
Zeng, Z. et al. A reversal in global terrestrial stilling and its implications for wind energy production. Nat. Clim. Change 9, 979–985 (2019). Reports evidence for the presence of low-frequency variability in near-surface wind speeds.
Poan, E., Gachon, P., Laprise, R., Aider, R. & Dueymes, G. Investigating added value of regional climate modeling in North American winter storm track simulations. Clim. Dyn. 50, 1799–1818 (2018).
Trzeciak, T. M., Knippertz, P., Pirret, J. S. & Williams, K. D. Can we trust climate models to realistically represent severe European windstorms? Clim. Dyn. 46, 3431–3451 (2016).
Hodges, K. I., Lee, R. W. & Bengtsson, L. A comparison of extratropical cyclones in recent reanalyses ERA-Interim, NASA MERRA, NCEP CFSR, and JRA-25. J. Clim. 24, 4888–4906 (2011).
Pryor, S. C., Schoof, J. T. & Barthelmie, R. J. Winds of change?: Projections of near-surface winds under climate change scenarios. Geophys. Res. Lett. 33, L11702 (2006).
Pryor, S. C. & Barthelmie, R. J. in Climate Change in the Midwest: Impacts, Risks, Vulnerability, and Adaptation Ch. 16 (ed. Pryor, S.C.) 213–229 (Indiana Univ. Press, 2013).
Pryor, S. C. & Barthelmie, R. J. Assessing climate change impacts on the near-term stability of the wind energy resource over the USA. Proc. Natl Acad. Sci. USA 108, 8167–8171 (2011).
Winterfeldt, J. & Weisse, R. Assessment of value added for surface marine wind speed obtained from two regional climate models. Mon. Weather Rev. 137, 2955–2965 (2009).
Pryor, S. C., Barthelmie, R. J. & Schoof, J. T. Past and future wind climates over the contiguous USA based on the North American Regional Climate Change Assessment Program model suite. J. Geophys. Res. 117, D19119 (2012).
Larsen, X. G., Mann, J., Berg, J., Gottel, H. & Jacob, D. Wind climate from the regional climate model REMO. Wind Energy 13, 279–296 (2010).
Pryor, S. C., Nikulin, G. & Jones, C. Influence of spatial resolution on regional climate model derived wind climates. J. Geophys. Res. Atmos. 117, D03117 (2012).
Kusiak, A. Share data on wind energy: giving researchers access to information on turbine performance would allow wind farms to be optimized through data mining. Nature 529, 19–22 (2016).
Holt, E. & Wang, J. Trends in wind speed at wind turbine height of 80 m over the contiguous United States using the North American Regional Reanalysis (NARR). J. Appl. Meteorol. Climatol. 51, 2188–2202 (2012).
McVicar, T. R. et al. Wind speed climatology and trends for Australia, 1975–2006: Capturing the stilling phenomenon and comparison with near-surface reanalysis output. Geophys. Res. Lett. 35, L20403 (2008).
Chen, L., Li, D. & Pryor, S. C. Wind speed trends over China: quantifying the magnitude and assessing causality. Int. J. Climatol. 33, 2579–2590 (2013).
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 (2013).
Pryor, S. C. et al. Analyses of possible changes in intense and extreme wind speeds over northern Europe under climate change scenarios. Clim. Dyn. 38, 189–208 (2012).
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).
Hdidouan, D. & Staffell, I. The impact of climate change on the levelised cost of wind energy. Renew. Energy 101, 575–592 (2017).
Tobin, I. et al. Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections. Clim. Change 128, 99–112 (2015). Includes one of the most comprehensive RCM ensembles and a focus on medium-term projections of wind resources.
Pryor, S. C., Shepherd, T. J., Bukovsky, M. & Barthelmie, R. J. Assessing the stability of wind resource and operating conditions. J. Phys. Conf. Ser. 1452, 012084 (2020).
Greene, J. S., Chatelain, M., Morrissey, M. & Stadler, S. Projected future wind speed and wind power density trends over the Western US High Plains. Atmos. Clim. Sci. 2, 32–40 (2012).
Ruffato-Ferreira, V. et al. A foundation for the strategic long-term planning of the renewable energy sector in Brazil: hydroelectricity and wind energy in the face of climate change scenarios. Renew. Sustain. Energy Rev. 72, 1124–1137 (2017).
de Jong, P. et al. Estimating the impact of climate change on wind and solar energy in Brazil using a South American regional climate model. Renew. Energy 141, 390–401 (2019).
Kulkarni, S. & Huang, H. P. Changes in surface wind speed over North America from CMIP5 model projections and implications for wind energy. Adv. Meteorol. 2014, 292768 (2014).
Wiser, R. H. & Bolinger, M. Benchmarking anticipated wind project lifetimes: results from a survey of U.S. Wind industry professionals. Electricity Markets & Policy https://emp.lbl.gov/publications/benchmarking-anticipated-wind-project (2019).
Pryor, S. C. & Barthelmie, R. J. Hybrid downscaling of wind climates over the eastern USA. Environ. Res. Lett. 9, 024013 (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).
Carvalho, D., Rocha, A., Gomez-Gesteira, M. & Santos, C. S. Potential impacts of climate change on European wind energy resource under the CMIP5 future climate projections. Renew. Energy 101, 29–40 (2017).
Sakaguchi, K. et al. Exploring a multiresolution approach using AMIP simulations. J. Clim. 28, 5549–5574 (2015).
Giorgetta, M. A. et al. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst. 5, 572–597 (2013).
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).
Lindvall, J., Svensson, G. & Caballero, R. The impact of changes in parameterizations of surface drag and vertical diffusion on the large-scale circulation in the Community Atmosphere Model (CAM5). Clim. Dyn. 48, 3741–3758 (2017).
Larsén, X. G., Ott, S., Badger, J., Hahmann, A. N. & Mann, J. Recipes for correcting the impact of effective mesoscale resolution on the estimation of extreme winds. J. Appl. Meteorol. Climatol. 51, 521–533 (2012).
Lombardo, F. T. Improved extreme wind speed estimation for wind engineering applications. J. Wind Eng. Ind. Aerodyn. 104–106, 278–284 (2012).
Cook, N. J. Confidence limits for extreme wind speeds in mixed climates. J. Wind Eng. Ind. Aerodyn. 92, 41–51 (2004).
Weisse, R. & von Storch, H. in Marine Climate and Climate Change: Storms, Wind Waves and Storm Surges Ch. 5 165–203 (Springer, 2010).
Lombardo, F. T. & Ayyub, B. Approach to estimating near-surface extreme wind speeds with climate change considerations. J. Risk Uncertain. Eng. Syst. A 3, A4017001 (2017).
Kumar, D., Mishra, V. & Ganguly, A. R. Evaluating wind extremes in CMIP5 climate models. Clim. Dyn. 45, 441–453 (2015).
Born, K., Ludwig, P. & Pinto, J. G. Wind gust estimation for Mid-European winter storms: towards a probabilistic view. Tellus A 64, 17471 (2012).
Cheng, C. S., Lopes, E., Fu, C. & Huang, Z. Possible impacts of climate change on wind gusts under downscaled future climate conditions: updated for Canada. J. Clim. 27, 1255–1270 (2014).
Prein, A. F. et al. A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges. Rev. Geophys. 53, 323–361 (2015). Emphasizes key trends towards higher-fidelity, high-resolution simulations.
Orwig, K. D. & Schroeder, J. L. Near-surface wind characteristics of extreme thunderstorm outflows. J. Wind Eng. Ind. Aerodyn. 95, 565–584 (2007).
Hoogewind, K. A., Baldwin, M. E. & Trapp, R. J. The impact of climate change on hazardous convective weather in the United States: insight from high-resolution dynamical downscaling. J. Clim. 30, 10081–10100 (2017).
Di, Y., Lu, J., Xu, X., Feng, T. & Li, L. A response characteristics study of widespread power grid icing to El Nino. Math. Probl. Eng. 2019, 6589410 (2019).
Yu, Y., Ren, Z., Gao, F. & Meng, X. Changes in surface icing duration over north china during 1961–2015. Atmos. Sci. Lett. 19, e827 (2018).
Clausen, N. E. et al. in Impacts of Climate Change on Renewable Energy Sources (ed Fenger, J.) 105–128 (Nordic Council of Ministers, 2007).
Zhang, Y., Chen, W. & Riseborough, D. W. Transient projections of permafrost distribution in Canada during the 21st century under scenarios of climate change. Glob. Planet. Change 60, 443–456 (2008).
Zheng, M., Yang, Z. J., Yang, S. & Still, B. Modeling and mitigation of excessive dynamic responses of wind turbines founded in warm permafrost. Eng. Struct. 148, 36–46 (2017).
Cheng, G. Permafrost studies in the Qinghai–Tibet Plateau for road construction. J. Cold Reg. Eng. 19, 19–29 (2005).
Vanem, E. Non-stationary extreme value models to account for trends and shifts in the extreme wave climate due to climate change. Appl. Ocean Res. 52, 201–211 (2015).
Shope, J. B., Storlazzi, C. D., Erikson, L. H. & Hegermiller, C. A. Changes to extreme wave climates of islands within the Western Tropical Pacific throughout the 21st century under RCP 4.5 and RCP 8.5, with implications for island vulnerability and sustainability. Glob. Planet. Change 141, 25–38 (2016).
Dvorak, P. A look at repowering older wind farms to 2020. Windpower Engineering & Development https://www.windpowerengineering.com/look-repowering-older-wind-farms-2020/ (2018).
International Energy Agency. Wind technology collaboration programme: annual report 2017 (IEA, 2018).
Pryor, S. C., Barthelme, R. J. & Shepherd, T. 20% of US electricity from wind will have limited impacts on system efficiency and regional climate. Sci. Rep. 10, 541 (2020).
American Wind Energy Association. Wind industry annual market report, year ending 2017. AWEA https://www.awea.org/resources/publications-and-reports/market-reports/2017-u-s-wind-industry-market-reports (2018).
Dalla Riva, A. et al. IEA wind TCP Task 26 – Wind technology, cost, and performance trends in Denmark, Germany, Ireland, Norway, Sweden, the European Union, and the United States: 2008–2016 (National Renewable Energy Laboratory, 2019).
Wind Europe. Wind energy in Europe in 2018 (Wind Europe, 2019).
National Renewable Energy Laboratory. Annual technology baseline: electricity. NREL https://www.nrel.gov/analysis/data-tech-baseline.html (2019).
Musial, W., Beiter, J., Spitsen, P., Nunemaker, J. & Gevorgian, V. 2018 offshore wind technologies market report (Department of Energy, 2019).
Horn, J. T., Krokstad, J. R. & Leira, B. J. Impact of model uncertainties on the fatigue reliability of offshore wind turbines. Mar. Struct. 64, 174–185 (2019).
Luengo, J., Negro, V., García-Barba, J., López-Gutiérrez, J.-S. & Esteban, M. D. New detected uncertainties in the design of foundations for offshore wind turbines. Renew. Energy 131, 667–677 (2019).
Igwemezie, V., Mehmanparast, A. & Kolios, A. Current trend in offshore wind energy sector and material requirements for fatigue resistance improvement in large wind turbine support structures – A review. Renew. Sustain. Energy Rev. 101, 181–196 (2019).
Dai, K. S. et al. Nonlinear response history analysis and collapse mode study of a wind turbine tower subjected to tropical cyclonic winds. Wind Struct. 25, 79–100 (2017).
Larsen, X. G. et al. Estimation of offshore extreme wind from wind-wave coupled modeling. Wind Energy 22, 1043–1057 (2019).
Du, J. T., Bolanos, R. & Larsen, X. G. The use of a wave boundary layer model in SWAN. J. Geophys. Res. Oceans 122, 42–62 (2017).
Pryor, S. C., Shepherd, T., Volker, P., Hahmann, A. & Barthelmie, R. J. “Wind theft” from onshore wind turbine arrays: Sensitivity to wind farm parameterization and resolution. J. Appl. Meteorol. Climatol. 59, 153–174 (2020).
Jiménez, P. A., Navarro, J., Palomares, A. M. & Dudhia, J. Mesoscale modeling of offshore wind turbine wakes at the wind farm resolving scale: a composite-based analysis with the Weather Research and Forecasting model over Horns Rev. Wind Energy 18, 559–566 (2015).
Wilczak, J. et al. The Wind Forecast Improvement Project (WFIP): A public–private partnership addressing wind energy forecast needs. Bull. Am. Meteorol. Soc. 96, 1699–1718 (2015).
Gutowski, W. J. Jr. et al. The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information. Bull. Am. Meteorol. Soc. 101, E664–E683 (2020).
Kendon, E. J. et al. Do convection-permitting regional climate models improve projections of future precipitation change? Bull. Am. Meteorol. Soc. 98, 79–93 (2017).
Bukovsky, M. S., Gochis, D. J. & Mearns, L. O. Towards assessing NARCCAP regional climate model credibility for the North American monsoon: Current climate simulations. J. Clim. 26, 8802–8826 (2013).
Haarsma, R. J. et al. High resolution model intercomparison project (HighResMIP v1. 0) for CMIP6. Geosci. Model Dev. 9, 4185–4208 (2016).
Stevens, B. et al. DYAMOND: The DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains. Prog. Earth Planet. Sci. 6, 61 (2019).
Hagos, S., Leung, L. R., Zhao, C., Feng, Z. & Sakaguchi, K. How do microphysical processes influence large-scale precipitation variability and extremes? Geophys. Res. Lett. 45, 1661–1667 (2018).
Yang, Z. et al. Modeling analysis of the swell and wind-sea climate in the Salish Sea. Estuarine Coast. Shelf Sci. 224, 289–300 (2019).
Coppola, E. et al. A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean. Clim. Dyn. 55, 3–34 (2020).
Roberts, M. et al. The benefits of global high resolution for climate simulation: process understanding and the enabling of stakeholder decisions at the regional scale. Bull. Am. Meteorol. Soc. 99, 2341–2359 (2018).
von Trentini, F., Leduc, M. & Ludwig, R. Assessing natural variability in RCM signals: comparison of a multi model EURO-CORDEX ensemble with a 50-member single model large ensemble. Clim. Dyn. 53, 1963–1979 (2019).
Marbaix, P., Gallée, H., Brasseur, O. & van Ypersele, J.-P. Lateral boundary conditions in regional climate models: a detailed study of the relaxation procedure. Mon. Weather Rev. 131, 461–479 (2003).
Giorgi, F. Thirty years of regional climate modeling: Where are we and where are we going next? J. Geophys. Res. Atmos. 124, 5696–5723 (2019). Summarizes the history of, challenges to and new prospects for regional climate modelling.
Gao, J. & O’Neill, B. C. Data-driven spatial modeling of global long-term urban land development: The SELECT model. Environ. Model. Softw. 119, 458–471 (2019).
Hamlington, B. D., Hamlington, P. E., Collins, S. G., Alexander, S. R. & Kim, K. Y. Effects of climate oscillations on wind resource variability in the United States. Geophys. Res. Lett. 42, 145–152 (2015).
Lledo, L., Bellprat, O., Doblas-Reyes, F. J. & Soret, A. Investigating the effects of Pacific sea surface temperatures on the wind drought of 2015 over the United States. J. Geophys. Res. Atmos. 123, 4837–4849 (2018).
Greene, J. S., Chatelain, M., Morrissey, M. & Stadler, S. Estimated changes in wind speed and wind power density over the western High Plains, 1971–2000. Theor. Appl. Climatol. 109, 507–518 (2012).
Klink, K. Atmospheric circulation effects on wind speed variability at turbine height. J. Appl. Meteorol. Climatol. 46, 445–456 (2007).
Clifton, A. & Lundquist, J. K. Data clustering reveals climate impacts on local wind phenomena. J. Appl. Meteorol. Climatol. 51, 1547–1557 (2012).
George, S. S. & Wolfe, S. A. El Niño stills winter winds across the southern Canadian Prairies. Geophys. Res. Lett. 36, L23806 (2009).
Torres Silva dos Santos, A., E Silva, S. & Moisés, C. Seasonality, interannual variability, and linear tendency of wind speeds in the Northeast Brazil from 1986 to 2011. Sci. World J. 2013, 490857 (2013).
Bianchi, E., Solarte, A. & Guozden, T. M. Large scale climate drivers for wind resource in Southern South America. Renew. Energy 114, 708–715 (2017).
Watts, D., Duran, P. & Flores, Y. How does El Nino Southern Oscillation impact the wind resource in Chile? A techno-economical assessment of the influence of El Nino and La Nina on the wind power. Renew. Energy 103, 128–142 (2017).
Ravestein, P., van der Schrier, G., Haarsma, R., Scheele, R. & van den Broek, M. Vulnerability of European intermittent renewable energy supply to climate change and climate variability. Renew. Sustain. Energy Rev. 97, 497–508 (2018).
Francois, B. Influence of winter North-Atlantic oscillation on climate-related-energy penetration in Europe. Renew. Energy 99, 602–613 (2016).
Kriesche, P. & Schlosser, C. A. The association of the North Atlantic and the Arctic Oscillation on wind energy resource over Europe and its intermittency. Energy Procedia 52, 270–277 (2014).
Jerez, S. et al. The impact of the North Atlantic Oscillation on renewable energy resources in southwestern Europe. J. Appl. Meteorol. Climatol. 52, 2204–2225 (2013).
Brayshaw, D. J., Troccoli, A., Fordham, R. & Methven, J. The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: A case study over the UK. Renew. Energy 36, 2087–2096 (2011).
Earl, N., Dorling, S., Hewston, R. & von Glasow, R. 1980–2010 variability in UK surface wind climate. J. Clim. 26, 1172–1191 (2013).
Iversen, E. C. & Burningham, H. Relationship between NAO and wind climate over Norway. Clim. Res. 63, 115–134 (2015).
Commin, A. N. et al. The influence of the North Atlantic Oscillation on diverse renewable generation in Scotland. Appl. Energy 205, 855–867 (2017).
Albani, A., Ibrahim, M. Z. & Yong, K. H. Influence of the ENSO and monsoonal season on long-term wind energy potential in Malaysia. Energies 11, 2965 (2018).
Dunning, C., Turner, A. & Brayshaw, D. The impact of monsoon intraseasonal variability on renewable power generation in India. Environ. Res. Lett. 10, 064002 (2015).
Haupt, S. E. et al. A method to assess the wind and solar resource and to quantify interannual variability over the United States under current and projected future climate. J. Appl. Meteorol. Climatol. 55, 345–363 (2016).
Rasmussen, D., Holloway, T. & Nemet, G. Opportunities and challenges in assessing climate change impacts on wind energy — a critical comparison of wind speed projections in California. Environ. Res. Lett. 6, 024008 (2011).
Karnauskas, K. B., Lundquist, J. K. & Zhang, L. Southward shift of the global wind energy resource under high carbon dioxide emissions. Nat. Geosci. 11, 38–43 (2018).
Daines, J. T., Monahan, A. H. & Curry, C. L. Model-based projections and uncertainties of near-surface wind climate in western Canada. J. Appl. Meteorol. Climatol. 55, 2229–2245 (2016).
Gross, M. & Magar, V. Offshore wind energy climate projection using UPSCALE climate data under the RCP8.5 emission scenario. PLoS ONE 11, e0165423 (2016).
Devis, A., Van Lipzig, N. P. M. & Demuzere, M. Should future wind speed changes be taken into account in wind farm development? Environ. Res. Lett. 13, 064012 (2018).
Reyers, M., Pinto, J. G. & Moemken, J. Statistical-dynamical downscaling for wind energy potentials: evaluation and applications to decadal hindcasts and climate change projections. Int. J. Climatol. 35, 229–244 (2015).
Davy, R., Gnatiuk, N., Pettersson, L. & Bobylev, L. Climate change impacts on wind energy potential in the European domain with a focus on the Black Sea. Renew. Sustain. Energy Rev. 81, 1652–1659 (2018).
Chen, L., Pryor, S. C. & Li, D. Assessing the performance of Intergovernmental Panel on Climate Change AR5 climate models in simulating and projecting wind speeds over China. J. Geophys. Res. Atmos. 117, D24102 (2012).
Xiong, Y. J., Xin, X. G. & Kou, X. X. Simulation and projection of near-surface wind speeds in China by BCC-CSM models. J. Meteorol. Res. 33, 149–158 (2019).
Evans, J. P., Kay, M., Prasad, A. & Pitman, A. The resilience of Australian wind energy to climate change. Environ. Res. Lett. 13, 024014 (2018).
Ohba, M. The impact of global warming on wind energy resources and ramp events in Japan. Atmosphere 10, 265 (2019).
Kamranzad, B. & Mori, N. Future wind and wave climate projections in the Indian Ocean based on a super-high-resolution MRI-AGCM3.2S model projection. Clim. Dyn. 53, 2391–2410 (2019).
Lawrence, D. M. et al. Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Model. Earth Syst. 3, M03001 (2011).
Bogenschutz, P. A. et al. The path to CAM6: coupled simulations with CAM5.4 and CAM5.5. Geosci. Model. Dev. 11, 235–255 (2018).
Zhao, C. et al. Exploring the impacts of physics and resolution on aqua-planet simulations from a nonhydrostatic global variable-resolution modeling framework. J. Adv. Model. Earth Syst. 8, 1751–1768 (2016).
Wilks, D. S. Statistical Methods in the Atmospheric Sciences Vol. 100 (Academic, 2011).
This work was supported by the U.S. Department of Energy (DoE) (DE-SC0016438 and DE-SC0016605). The research used computing resources from the NCAR-CISL programme (UCOR0020) and the National Science Foundation’s Extreme Science and Engineering Discovery Environment (XSEDE) (allocation award to S.C.P. is TG-ATM170024). The authors express their appreciation to N. Davis of DTU Wind Energy for providing access to a digital form of the Global Wind Atlas mean wind speeds shown in Fig. 1a.
The authors declare no competing interests.
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Pryor, S.C., Barthelmie, R.J., Bukovsky, M.S. et al. Climate change impacts on wind power generation. Nat Rev Earth Environ 1, 627–643 (2020). https://doi.org/10.1038/s43017-020-0101-7