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A reversal in global terrestrial stilling and its implications for wind energy production

Abstract

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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Fig. 1: Turning point for mean global surface wind speed.
Fig. 2: The decadal variations in wind speed can be reconstructed by ocean/atmosphere oscillations.
Fig. 3: Mechanisms for the decadal variation in wind speed.
Fig. 4: Implications of the recent reversal in global terrestrial stilling for the wind energy industry.

Data availability

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.2.0.2.2017f) 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.

Code availability

The programs used to generate all the results are MATLAB (R2014a) and ArcGIS (10.4). Analysis scripts are available at https://doi.org/10.6084/m9.figshare.9917246.v2. The code to produce the wind records are available in Supplementary Data 1 and 2.

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Acknowledgements

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.

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Z.Z. and E.F.W. designed the research. Z.Z. and L.Y. performed the analysis. Z.Z., A.D.Z. and T.S. wrote the draft. All authors contributed to the interpretation of the results and the writing of the paper.

Corresponding author

Correspondence to Zhenzhong Zeng.

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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.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–25 and Tables 1–3.

Supplementary Dataset 1

Continuous wind records from the GSOD database, including station information, wind speeds and the code for processing the original GSOD database.

Supplementary Dataset 2

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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