Wind power, a rapidly growing alternative energy source, has been threatened by reductions in global average surface wind speed, which have been occurring over land since the 1980s, a phenomenon known as global terrestrial stilling. Here, we use wind data from in situ stations worldwide to show that the stilling reversed around 2010 and that global wind speeds over land have recovered. We illustrate that decadal-scale variations of near-surface wind are probably determined by internal decadal ocean–atmosphere oscillations, rather than by vegetation growth and/or urbanization as hypothesized previously. The strengthening has increased potential wind energy by 17 ± 2% for 2010 to 2017, boosting the US wind power capacity factor by ~2.5% and explains half the increase in the US wind capacity factor since 2010. In the longer term, the use of ocean–atmosphere oscillations to anticipate future wind speeds could allow optimization of turbines for expected speeds during their productive life spans.
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The data for quantifying wind speed changes are the Global Surface Summary of the Day database (GSOD, ftp://ftp.ncdc.noaa.gov/pub/data/gsod) and the HadISD (v.126.96.36.1997f) global subdaily database (https://www.metoffice.gov.uk/hadobs/hadisd/). The time series of climate indices describing monthly atmospheric and oceanic phenomena are obtained from the National Oceanic and Atmospheric Administration (https://www.esrl.noaa.gov/psd/data/climateindices/list/). Simulated wind speed changes in Coupled Model Intercomparison Project Phase 5 (CMIP5) are available in the for Climate Model Diagnosis and Intercomparison (https://esgf-node.llnl.gov/projects/cmip5/). Simulated wind speed changes constrained by historical sea surface temperature are provided by the IPSL Dynamic Meteorology Laboratory. Wind records in reanalysis products include the ECMWF ERA-Interim Product (apps.ecmwf.int/datasets/data/interim-full-daily/), the ECMWF ERA5 Product (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means) and the NCEP/NCAR Global Reanalysis Product (http://rda.ucar.edu/datasets/ds090.0/). The processed wind records and the relevant code are available in Supplementary Data 1 and 2 (https://doi.org/10.6084/m9.figshare.9917246.v2). All datasets are also available upon request from Z. Zeng.
Roderick, M. L., Rotstayn, L. D., Farquhar, G. D. & Hobbins, M. T. On the attribution of changing pan evaporation. Geophys. Res. Lett. 34, 1–6 (2007).
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).
Mcvicar, T. R., Roderick, M. L., Donohue, R. J. & van Niel, T. G. Less bluster ahead? Ecohydrological implications of global trends of terrestrial near-surface wind speeds. Ecohydrology 5, 381–388 (2012).
McVicar, T. R. et al. Global review and synthesis of trends in observed terrestrial near-surface wind speeds: implications for evaporation. J. Hydrol. 416–417, 182–205 (2012).
Tian, Q., Huang, G., Hu, K. & Niyogi, D. Observed and global climate model based changes in wind power potential over the northern hemisphere during 1979–2016. Energy 167, 1224–1235 (2019).
Lu, X., McElroy, M. B. & Kiviluoma, J. Global potential for wind-generated electricity. Proc. Natl Acad. Sci. USA 106, 10933–10938 (2009).
Adoption of the Paris Agreement FCCC/CP/2015/L.9/Rev.1 (UNFCCC, 2015).
IPCC Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) (Cambridge Univ. Press, 2014).
Projected Growth Wind Industry Now until 2050 (US Department of Energy, 2018).
Nathan, R. & Muller-landau, H. C. Spatial patterns of seed dispersal, their determinants and consequences for recruitment. Trends Ecol. Evol. 15, 278–285 (2000).
Torralba, V., Doblas-Reyes, F. J. & Gonzalez-Reviriego, N. Uncertainty in recent near-surface wind speed trends: a global reanalysis intercomparison. Environ. Res. Lett. 12, 114019 (2017).
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. Dynam. 51, 2039–2078 (2018).
Nchaba, T., Mpholo, M. & Lennard, C. Long-term austral summer wind speed trends over southern Africa. Int. J. Climatol. 37, 2850–2862 (2017).
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).
Naizghi, M. S. & Ouarda, T. B. Teleconnections and analysis of long-term wind speed variability in the UAE. Int. J. Climatol. 37, 230–248 (2017).
Guo, H., Xu, M. & Hu, Q. Changes in near-surface wind speed in China: 1969-2005. Int. J. Climatol. 31, 349–358 (2011).
Wu, J., Zha, J. L., Zhao, D. M. & Yang, Q. D. Changes of wind speed at different heights over eastern China during 1980–2011. Int. J. Climatol. 38, 4476–4495 (2018).
Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–796 (2016).
Kim, J. C. & Paik, K. Recent recovery of surface wind speed after decadal decrease: a focus on South Korea. Clim. Dynam. 45, 1699–1712 (2015).
Azorin-Molina, C. et al. Homogenization and assessment of observed near-surface wind speed trends over Spain and Portugal, 1961–2011. J. Clim. 27, 3692–3712 (2014).
Tobin, I., Berrisford, P., Dunn, R. J. H., Vautard, R. & McVicar, T. R. in State of the Climate in 2013 (eds Blunden, J. & Arndt, D. T.) S28–S29 (American Meteorological Society, 2014).
Toms, J. D. & Lesperance, M. L. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84, 2034–2041 (2003).
Ryan, S. E. & Porth, L. S. A Tutorial on the Piecewise Regression Approach Applied to Bedload Transport Data (CreateSpace Independent Publishing Platform, 2015).
Dunn, R. J. H., Willett, K. M., Morice, C. P. & Parker, D. E. Pairwise homogeneity assessment of HadISD. Clim. Past 10, 1501–1522 (2014).
Pettitt, A. N. A non-parametric approach to the change-point problem. J. R. Stat. Soc. Ser. C 28, 126–135 (1979).
Zeng, Z. et al. Global terrestrial stilling: does Earth’s greening play a role? Environ. Res. Lett. 13, 124013 (2018).
Draper, N. R. & Smith, H. Applied Regression Analysis 3rd edn (Wiley-Interscience, 1998).
Wang, C. Z. Atlantic climate variability and its associated atmospheric circulation cells. J. Clim. 15, 1516–1536 (2002).
Hurrell, J. W., Kushnir, Y., Ottersen, G. & Visbeck, M. (eds) The North Atlantic Oscillation Climatic Significance and Environmental Impact (American Geophysical Union, 2003).
Zhang, Y., Xie, S.-P., Kosaka, Y. & Yang, J.-C. Pacific decadal oscillation: tropical Pacific forcing versus internal variability. J. Clim. 31, 8265–8279 (2018).
Timmermann, A. et al. El Niño-southern oscillation complexity. Nature 559, 535–545 (2018).
Dee, D. P. et al. The ERA–Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).
Pryor, S. C. et al. Wind speed trends over the contiguous US. J. Geophys. Res. D 114, D14105 (2009).
Wind-turbine-models.com. General Electric GE 2.5-120 https://www.en.wind-turbine-models.com/turbines/310-general-electric-ge-2.5-120 (2018).
Steinman, B. A. et al. Atlantic and Pacific multidecadal oscillations and northern hemisphere temperatures. Science 347, 988–991 (2015).
Tobin, I. et al. Climate change impacts on the power generation potential of European mid-century wind farms scenario. Environ. Res. Lett. 11, 034013 (2016).
US Energy Information Administration. Capacity Factors for Utility Scale Generators Not Primarily Using Fossil Fuels, January 2013–July 2019 https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_6_07_b (2018).
Dell, J. & Klippenstein, M. Wind Power Could Blow Past Hydro’s Capacity Factor by 2020 https://www.greentechmedia.com/articles/read/wind-power-could-blow-past-hydros-capacity-factor-by-2020 (2018).
Wind-turbine-models.com. General Electric GE 1.85-87 https://www.en.wind-turbine-models.com/turbines/745-general-electric-ge-1.85-87 (2018).
Global Wind Report 2018 (Global Wind Energy Council, 2019).
Hughes, G. The Performance of Wind Farms in the United Kingdom and Denmark (Renewable Energy Foundation, 2012).
Morice, C. P., Kennedy, J. J., Rayner, N. A. & Jones, P. D. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 data set. J. Geophys. Res. Atmos. 117, 1–22 (2012).
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C. & Wang, W. An improved in situ and satellite SST analysis for climate. J. Clim. 15, 1609–1625 (2002).
WMO Resolution 40 (Cg-XII). WMO Policy and Practice for the Exchange of Meteorological and Related Data and Products including Guidelines on Relationships in Commercial Meteorological Activities (World Meteorological Organisation, 1996).
Granger, C. W. J. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438 (1969).
Henriksson, S. V. Interannual oscillations and sudden shifts in observed and modeled climate. Atmos. Sci. Lett. 19, e850 (2018).
NCEP/NCAR Global Reanalysis Products, 1948–continuing (NCAR/UCAR Research Data Archive, accessed 10 August 2018); http://rda.ucar.edu/datasets/ds090.0/
ERA-Interim Project (NCAR/UCAR Research Data Archive, accessed 10 August 2018); https://doi.org/10.5065/D6CR5RD9
ERA5: Fifth Generation of ECMWF Atmospheric Reanalyses of the Global Climate (Copernicus Climate Data Store, accessed 25 May 2019); https://cds.climate.copernicus.eu/cdsapp#!/home
Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3G) for the period 1981 to 2011. Remote Sens. 5, 927–948 (2013).
Liu, X. et al. High-resolution multi-temporal mapping of global urban land using landsat images based on the Google Earth engine platform. Remote Sens. Environ. 209, 227–239 (2018).
This study was supported by the Strategic Priority Research Programme of Chinese Academy of Sciences (grant no. XDA20060402), the start-up fund provided by Southern University of Science and Technology (no. 29/Y01296122) and Lamsam–Thailand Sustain Development (no. B0891). L.Z.X.L. was partially supported by the National Key Research and Development Programme of China (grant no. 2018YFC1507704). J.L. was supported by the National Natural Science Foundation of China (grant no. 41625001). P.C. acknowledges support from the European Research Council Synergy project (SyG-2013-610028 IMBALANCE-P) and the ANR CLAND Convergence Institute. C.A.M. was supported by grants no. VR-2017-03780 and RTI2018-095749-A-I00 (MCIU/AEI/FEDER, UE). We thank Della Research Computing in Princeton University for providing computing resources. We thank the US National Climatic Data Center and the UK Met Office Hadley Centre for providing surface wind speed measurements. We also thank the Programme for Climate Model Diagnosis and Intercomparison and the IPSL Dynamic Meteorology Laboratory for providing surface wind speed simulations.
The authors declare no competing interests.
Peer review information Nature Climate Change thanks Sonia Jerez and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–25 and Tables 1–3.
Continuous wind records from the GSOD database, including station information, wind speeds and the code for processing the original GSOD database.
Continuous wind records from the HadISD database, including station information, wind speeds and the code for processing the original HadISD database.
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Zeng, Z., Ziegler, A.D., Searchinger, T. et al. A reversal in global terrestrial stilling and its implications for wind energy production. Nat. Clim. Chang. 9, 979–985 (2019). https://doi.org/10.1038/s41558-019-0622-6
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