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SCIENCE CHINA Earth Sciences, Volume 64 , Issue 7 : 1036-1049(2021) https://doi.org/10.1007/s11430-020-9738-4

Has the stilling of the surface wind speed ended in China?

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  • ReceivedAug 25, 2020
  • AcceptedFeb 7, 2021
  • PublishedJun 9, 2021

Abstract


Funded by

the National Key R&D Program of China(Grant,No.,2016YFA0600404)

the National Natural Science Foundation of China(Grant,Nos.,41705073,41530532,41506040)

and the Jiangsu Collaborative Innovation Center for Climate Change


Acknowledgment

We sincerely thank Dr. Li Zhen and Dr. Tian Qun from Institute of Atmospheric Physics, Chinese Academy of Sciences for providing surface wind speed data in China, and Dr. Zeng Zhenzhong from Southern University of Science and Technology for sharing quality-controlled global GSOD and HadISD surface wind speed data (https://www.nature.com/articles/s41558-019-0622-6#Sec15). This work was supported by the National Key R&D Program of China (Grant No. 2016YFA0600404), the National Natural Science Foundation of China (Grant Nos. 41705073, 41530532, 41506040), and the Jiangsu Collaborative Innovation Center for Climate Change.


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  • Figure 1

    Distribution of stations in four SWS datasets and the spatial distribution of nine regions in China.

  • Figure 2

    Trend change characteristics of annual mean SWS in China based on four SWS datasets. Left column: annual mean SWS (m s−1) in China and the piecewise linear fit, where TP, TP1, and TP2 indicate the turning points (TP), and each gray line (the total number 300) is a piecewise linear fit for a randomly selected subset (40%) of the total stations. Middle column: frequency distribution of the trend coefficients (m s−1 yr−1) in annual mean SWS in each period segmented by turning points identified in 300 resampling results. Right column: Frequency distribution of the estimated turning point (TP, TP1, and TP2) derived from 300 resampling results.

  • Figure 3

    Trend change characteristics of annual mean SWS in China based on the ALL dataset.

  • Figure 4

    Trends (m s−1 yr−1) of annual mean SWS in China during (a) 1979–2016, (b) 1979–1989, (c) 1990–2013, and (d) 2014–2016 based on the ALL dataset. Filled symbols indicate that the trend coefficient passed the 0.05 significance test. Red (blue) values represent the percentages of stations with a positive (negative) trend.

  • Figure 5

    Annual mean SWS (m s−1) of nine regions in China and associated piecewise linear fit (only based on the ALL dataset), where TP, TP1, and TP2 indicate the turning points (TP).

  • Figure 6

    Seasonal mean SWS (m s−1) in China and associated piecewise linear fit, where TP, TP1, and TP2 indicate the turning points (TP), and each gray line (the total number 300) is a piecewise linear fit for a randomly selected subset (40%) of the total stations.

  • Figure 7

    Seasonal mean SWS (m s−1) of nine regions in China from 1971 to 2019 based on CMA, where the vertical dashed line indicates the turning point of the seasonal mean SWS series with the same color. For convenience, annual mean SWS are also drawn in this figure.

  • Figure 8

    (a) Distribution of overlapping stations (171 pairs) between CMA/CMA_Tian and GSOD/HadISD datasets. Filled circles indicate that the distance between a pair of station is less than 1 km and its RMSE is greater than 75th percentile; (b) 1979–2016 RMSE (m s−1) of annual mean SWS of the 171 pairs of stations, and associated piecewise linear fit, where the vertical dashed line and TP indicates the turning point; (c) scatter plots of trend coefficients of annual mean SWS (m s−1 yr−1) in 1979–2016 versus those in 2000–2016.

  • Figure 9

    Comparison of annual mean SWS (m s−1) of 10 pairs of stations (green stations in Figure 8a), where the green line refers to homogenized SWS data obtained from Li et al. (2011), and the two values indicate the distance (km) and RMSE (m s−1) between a pair of station, respectively.

  • Table 1   SWS datasets used in this study

    No.

    Data

    Source

    Number of stations

    Period

    Time resolution

    1

    CMA

    This study

    413

    1971–2019

    Daily

    2

    CMA_Tian

    Tian et al. (2019)

    351

    1979–2016

    Daily

    3

    HadISD

    Zeng et al. (2019)

    255

    1978–2017

    Annual

    4

    GSOD

    Zeng et al. (2019)

    306

    1978–2017

    Annual

    5

    ALL

    This study

    633

    1979–2016

    Annual

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