Research Progress and Prospects of Terrestrial Near-Surface Wind Speed Variations in China

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  • Corresponding author: Deming ZHAO, zhaodm@tea.ac.cn
  • Funds:

    Supported by the National Key Research and Development Program of China (2018YFA0606004 and 2016YFA0600403), National Natural Science Foundation of China (42005023, 41875178, 41775087, and 41675149), and the Project funded by China Postdoctoral Science Foundation (2019M660761)

  • doi: 10.1007/s13351-021-0143-x
  • Note: This paper has been peer-reviewed and is just accepted by J. Meteor. Res. Professional editing and proof reading are underway. Please use with caution.

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  • Changes in terrestrial near-surface wind speed (NSWS) are the concentrated embodiment of climate change and anthropogenic activities. Investigating changes and mechanisms of NSWS not only furthers understanding of how the atmosphere changes and improves climate analysis and projection, but also aids the evaluation and application of wind energy. Recent advances in studies of the changes and mechanisms of the NSWS over China are reviewed in this paper. Some results have been achieved in understanding the behaviors of the NSWS changes. NSWS over China have experienced a decrease in the past forty years and a recovery in the recent decade, which exhibited regional and seasonal differences. Understanding on the mechanisms of the NSWS changes have been improved in several aspects: the reduced NSWS over China due to the weakening of the pressure-gradient force (PGF) attributed to variations in large-scale ocean-atmosphere circulations (LOACs) and the increase of surface roughness due to the land use and cover change (LUCC). The main methods that used to analyze the NSWS changes and the corresponding mechanisms are also discussed. However, studies are still lacking on the mechanisms for multi-timescale (seasonal, interannual, decadal, multi-decadal) variations in the NSWS over China, and the contribution to NSWS changes from different forcing factors remains unknown. Finally, some scientific issues regarding our understanding of the NSWS changes are proposed for future investigation.
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  • Fig. 1.  The trends of NSWS among different regions in globe. The study number is shown in Table 2(先于Table 1出现了) in McVicar et al. (2012a). Copied from McVicar et al. (2012a).

    Fig. 2.  (a) Relative change in the magnitude (%) of the annual mean NSWS from 1966-2011. The major river basins (NW: Northwest, YR: Yellow River, HAI: Hai River, LR: Liao River, SHJ: Songhuajiang, SW: Southwest, CJ: Yangtze River, HUAI: Huai River, PR: Pearl River, SE: Southeast) in China are shown. (b) Trends of observational NSWS across different regions in China (m s−1 dec−1). The study number is shown in Table 1. (a) copied from Liu et al. (2014).

    Fig. 3.  Temporal changes in probabilities of six wind grades in observed NSWS across eastern China in 1980-2011 (%). R2 is the correlation coefficient, which passed the significance t-test at the 0.01 level. [Calm: 0-0.2 m s−1, LA: light air (0.3-1.5 m s−1), LB: light breeze (1.6-3.3 m s−1), GB: gentle breeze (3.4-5.4 m s−1), MB: moderate breeze (5.5-7.9 m s−1), WSGE8.0: wind speed greater than or equal to 8.0 m s−1 (≥ 8.0 m s−1)]. Copied from Zha et al. (2016).

    Fig. 4.  Temporal variations and trends (solid lines) in seasonal mean NSWS across China from 1969-2005. Copied from Guo et al. (2011).

    Fig. 5.  Time series of annual mean NSWS and normalized geostrophic wind across (a)-(f) six sub-regions and (g) China from 1970-2017. Copied from Zhang and Wang (2020).

    Fig. 6.  (a) Temporal changes of annual NSWS (m s−1) and temperature (°C) over China from 1956-2004. (b) Relationship between the annual mean NSWS and temperature over China from 1969-2000. (a) and (b) copied from Jiang et al. (2010a) and Xu et al. (2006), respectively.

    Fig. 7.  Normalized linear trends (% dec−1) of (a) and (c) NSWS and (b) and (d) GWS in different periods across China. (a) and (b): 1970-2004; (c) and (d): 1960-2017. The black dots indicate areas that passed the 95% significance level on the Mann-Kendall test. Reprinted from Zhang and Wang (2020).

    Fig. 8.  (a) Temporal changes of NSWSs at 30 large city and 275 non-urban stations in China from 1969-2000, and (b) temporal changes of NSWSs at 174 large city stations and 180 small city stations in China from 1956-2004. (a) copied from Xu et al. (2006) and (b) copied from Jiang et al. (2010a).

    Fig. 9.  (a) Temporal changes in the horizontal pressure-gradient force (PGF), (b) the model wind speed and ERA-Interim 10 m wind speed, (c) the annual mean drag coefficient and urbanization rate, and (d) the difference between the observation and model (SWSD), and the difference between the observation and V10m-ERA (OEWSD). In (a) and (b), the annual mean values are indicated by rectangles and circles. R is the correlation coefficient; Rc is the threshold; P is the significance level. In (d), SWSD and OEWSD are obtained using the FWM and OMR, respectively. Copied from Wu et al. (2016).

    Fig. 10.  Annual mean NSWS from (a) observations, (b) JRA55, (c) MERRA, (d) MERRA2, (e) CFSR, and (f) ERA-Interim from 1980-2017. Reprinted from Zhang and Wang (2020).

    Fig. 11.  (a) Annual mean NSWS in China from individual simulations with nine atmosphere-ocean general circulation models (1850-2005) and the time series of mean wind speed from NCEP-NCAR, NCEP-DOE(无全称), and ERA-40(无全称), and the average of the observations. (b) Time series of the annual mean NSWS in observation (Obs: black line), Beijing Normal University Earth System Model (BNU-ESM: purple line), Norwegian Climate Center’s Earth System Model (NorESM1-M: blue line), and the ensemble of the models, in which the annual mean NSWS is similar for the wind trend (Opt_T_Ens: red line). (a) and (b) copied from Chen et al. (2012), and Jiang et al. (2017), respectively.

    Table 1.  Summary of observed NSWS trends in China, including data for the study regions, periods, the trends (m s-1 dec-1), and the references. BTH: Beijing-Tianjin-Hebei. YRD: Yangtze River Delta. TP: Tibetan Plateau. HRB: Haihe River Basin

    No.Region (Site)PeriodTrendReferenceNo.Region (Site)PeriodTrendReference
    1China (729)1954-2000−0.11Wang et al. (2004)21Southwest China (110)1969-2009−0.24Yang et al. (2012)
    2China (323)1951-2002−0.10Ren et al. (2005)22Southwest China (110)1969-2000−0.37Yang et al. (2012)
    3China (305)1969-2000−0.22Xu et al. (2006)23China (540)1971-2007−0.17Chen et al. (2013)
    4TP (75)1966-2003−0.17Zhang et al. (2007)24China (472)1960-2009−0.10Lin et al. (2013)
    5China (604)1960-1999−0.12Li et al. (2008a)25TP (64)1960-2009−0.06Lin et al. (2013)
    6Western deserts (23)1973-2003−0.29Mahowald et al. (2009)26China (741)1966-2011−0.16Liu et al. (2014)
    7HRB (45)1957-2001−0.14Zheng et al. (2009)27East China (93)1980-2011−0.13Wu et al. (2016)
    8China (317)1956-2005−0.11Cong et al (2009)28East China (93)1980-2011−0.13Zha et al. (2016)
    9China (535)1956-2004−0.12Jiang et al. (2010b)29Xinjiang (10)1984-2013−0.29Liu et al. (2017)
    10Loess Plateau (82)1960-2006−0.14McVicar et al. (2010)30East China (93)1980-2011−0.13Wu et al. (2017)
    11China (595)1961-2008−0.09Yin et al. (2010a)31China (492)1979-2010−0.11Zha et al. (2017a)
    12China (603)1971-2008−0.12Yin et al. (2010b)32China (580)1970-2011−0.15Zha et al. (2017b)
    13TP (71)1980-2005−0.24You et al. (2010)33BTH (154)1978-2014−0.10Zhou et al. (2017)
    14China (597)1961-2007−0.13Fu et al. (2011)34North China (155)1971-2015−0.17Han et al. (2018)
    15China (726)1969-2005−0.18Guo et al. (2011)35YRD(128)1960-2015−0.065Li et al. (2018a)
    16China (518)1960-1991−0.12Liu et al. (2011a)36East China (328)1981-2011−0.09Zha et al. (2019a)
    17China (518)1992-2007−0.07Liu et al. (2011b)37China (524)1958-2015−0.109Zhang et al. (2019a)
    18HRB (34)1950-2007−0.10Tang et al. (2011)38BTH (223)1980-2016−0.25Wang et al. (2020)
    19TP(78)1984-2006−0.30Yang et al. (2011)39China (582)1980-2017−0.10Zhang et al. (2020a)
    20Northeast China (87)1961-2010−0.25Jin et al. (2012)40China (2333)1960-2017−0.15Zhang and Wang (2020)
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    Table 2.  Experiment in DAMIP of CMIP6. Refer to Qian and Zhang (2019)

    ExperimentExperiment descriptionSimulation periodMinimum ensemble member
    CMIP6 historical simulation and SSP2-4.5Historical climate simulation in CMIP6 (1850-2014) and the SSP2-4.5 scenario simulation (2015-2020)1850-20203
    His-natHistorical simulations involving only natural forcing1850-20203
    His-GHGHistorical simulations involving only sufficiently mixed greenhouse gas forcing1850-20203
    Hist-aerHistorical climate simulations involving only anthropogenic aerosol forcing1850-20203
    Hist-solHistorical climate simulations involving only solar radiation forcing1850-20203
    Hist-volcHistorical climate simulation involving only volcanic forcing1850-20203
    His-CO2Historical climate simulation involving only CO21850-20203
    SSP245-aerFuture projections based on the His-aer test accompanied by the aerosol concentration or emission in tropospheric under the SSP2-4.52021-21001
    SSP245-natFuture projections based on the His-nat test accompanied by Solar forcing and volcanic forcing under the SSP2-4.52021-21001
    Download: Download as CSV
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Research Progress and Prospects of Terrestrial Near-Surface Wind Speed Variations in China

    Corresponding author: Deming ZHAO, zhaodm@tea.ac.cn
  • 1. CAS Key Laboratory of Regional Climate and Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
  • 2. Key Laboratory of Atmospheric Environment and Processes in the Boundary Layer over the Low-Latitude Plateau Region, Department of Atmospheric Science, Yunnan University, Kunming 650091
  • 3. Gaochun Meteorological Bureau, Nanjing 211300
Funds: Supported by the National Key Research and Development Program of China (2018YFA0606004 and 2016YFA0600403), National Natural Science Foundation of China (42005023, 41875178, 41775087, and 41675149), and the Project funded by China Postdoctoral Science Foundation (2019M660761)

Abstract: Changes in terrestrial near-surface wind speed (NSWS) are the concentrated embodiment of climate change and anthropogenic activities. Investigating changes and mechanisms of NSWS not only furthers understanding of how the atmosphere changes and improves climate analysis and projection, but also aids the evaluation and application of wind energy. Recent advances in studies of the changes and mechanisms of the NSWS over China are reviewed in this paper. Some results have been achieved in understanding the behaviors of the NSWS changes. NSWS over China have experienced a decrease in the past forty years and a recovery in the recent decade, which exhibited regional and seasonal differences. Understanding on the mechanisms of the NSWS changes have been improved in several aspects: the reduced NSWS over China due to the weakening of the pressure-gradient force (PGF) attributed to variations in large-scale ocean-atmosphere circulations (LOACs) and the increase of surface roughness due to the land use and cover change (LUCC). The main methods that used to analyze the NSWS changes and the corresponding mechanisms are also discussed. However, studies are still lacking on the mechanisms for multi-timescale (seasonal, interannual, decadal, multi-decadal) variations in the NSWS over China, and the contribution to NSWS changes from different forcing factors remains unknown. Finally, some scientific issues regarding our understanding of the NSWS changes are proposed for future investigation.

    • Wind speed is an important parameter when studying atmospheric dynamics and climate change. Investigating near-surface wind speed (NSWS) changes not only facilitates the understanding of atmospheric circulation and improves climate analysis and prediction, but also aids the evaluation and development of wind energy (Zhang et al., 2009). Investigations of the causes of NSWS variations are required urgently to assess directly many long- and short-term societal and economic issues related to NSWS.

      NSWS has a considerable effect on visibility (Wu et al., 2013; Zhang et al., 2015, 2020a). A decrease in NSWS hampers the dispersal of pollution and reduces the height of the atmospheric boundary layer, thereby reducing NSWS further in a negative feedback mechanism (McVicar and Roderick, 2010; Sterk et al., 2015). Wind power is a renewable energy resource that is being used increasingly to provide electricity (Pryor and Barthelmie, 2011). Changes in NSWS directly determine the evaluation and development of wind power. A decrease of 1-5% in NSWS could cause a loss of 1.7-8.6% in wind energy (He et al., 2010). NSWS changes also influence global and regional evapotranspiration and water circulation (Rayner, 2007; Roderick et al., 2007; McVicar et al., 2008; McInnes et al., 2011; Niyogi et al., 2011; McVicar et al., 2012a, b; McMahon et al., 2013; Liu et al., 2014). Results show that 65% of the reduction in evaporation is due to a reduced NSWS from 1960-1991 (Liu et al., 2010, 2011a, b). Accordingly, it is important to study the changes in NSWS because they are related closely to daily human activity.

      Given that increased global temperatures and intensified human activity affect climate, analysis has focused mainly on temperature and precipitation in China, and less on NSWS. The literature to date lacks reviews of comparative analyses of NSWS and perspectives regarding future research on NSWS across China. Consequently, this paper focuses exclusively on changes in NSWS over China. Although the characteristics and causes of changes in NSWS at global scale have been reviewed by Wu et al. (2018a), our research includes the following:

      1)Detailed information on changes in NSWS over China: including, for example, the variations in different NSWS categories, characteristics of NSWS recovery, and projections of NSWS changes.

      2)A discussion of the credibility of datasets and methods used in former studies: including, for example, a quality control and homogenization for observational NSWS, comparisons of changes in NSWS using different datasets and analytical methods.

      3)We propose the remarks for future studies of NSWS changes in China: including, for example, the correction and homogenization of observational wind data should be carried out, the NSWS results from high-resolution regional climate models (RCMs) should be estimate, the contributions of external and natural forces to variation in NSWS must be quantified, and the variations and mechanisms in strong wind and wind gust need to be evaluated and so forth.

      This study should encourage researchers to pay more attention to NSWS field. The paper is organized as follows: Detailed characteristics of changes in NSWS across China are discussed in Section 2, and the possible causes of such changes are summarized in Section 3. A discussion is presented in Section 4, and conclusions and possible future studies are presented in Section 5.

    2.   Characteristics of changes in NSWS across different regions
    • Studies on wind have focused mainly on NSWS, which has decreased at the global scale in the past 40 years (Vautard et al., 2012; Tobin et al., 2014; Berrisford et al., 2015) by 5-15% in the mid-latitudes of the Northern Hemisphere from 1979-2008 (Vautard et al., 2010). The largest decrease was observed in Central Asia (−0.16 m s−1 dec−1), followed by in East Asia (−0.12 m s−1 dec−1) and in Europe (−0.09 m s−1 dec−1), and the smallest decrease was observed in North America (−0.07 m s−1 dec−1) (Vautard et al., 2010). Roderick et al. (2007) referred to this declining trend in NSWS as “stilling”. At a regional scale, a slowdown of NSWS was observed. NSWS in the Great Plains of the United States decreased by nearly 20% during the spring from 1971-2000 (Pryor et al., 2009; Green et al., 2012). The downward trend in NSWS over Australia reached −0.17 m s−1 dec−1 from 1975-2006 (McVicar et al., 2008; Troccoli et al., 2012). In Europe, NSWS in Switzerland decreased by −0.25 and −0.05 m s−1 dec−1 during the winter and summer from 1960-2006, respectively, and the rate of decrease of NSWS increased with increasing altitude (McVicar et al., 2010). A decreasing trend in NSWS was also reported in southern and central France from 1974-2002 (Najac et al., 2011), in Turkey between 1975 and 2006 (Dadaser-Celik and Cengiz, 2014). In addition to the overall reduction in NSWS, the probabilities of the existence of strong winds also showed decreasing trends. For instance, strong winds in the Netherlands decreased by 10% from 1910-2010 (Cusack, 2013). The frequency of extreme winds in Spain and Portugal decreased by 1.5 d y−1 from 1961-2014 (Azorin-Molina et al., 2016). The 90th percentile of NSWS showed a decreasing trend in England from 1980-2010 (Earl et al., 2013). The trends of regional NSWSs during different study periods are summarized in Fig. 1. Most studies present a reduced NSWS at the regional scale, although the study periods and regions are not consistent with one another (McVicar et al., 2012a). The trends in NSWSs are not consistent across different study periods and regions, so the differences of the long-term changes in NSWS over different regions are considerable (Wu et al., 2018a).

      Figure 1.  The trends of NSWS among different regions in globe. The study number is shown in Table 2(先于Table 1出现了) in McVicar et al. (2012a). Copied from McVicar et al. (2012a).

    • A stilling phenomenon has been observed in China (Li et al., 2004; Ren et al., 2005; Xu et al., 2006; Jiang et al., 2010a; Zha et al., 2017a). The relative change in magnitude of the annual mean NSWS decreased by more than 20% in most regions of China from 1966-2011; however, the strong reduction was found over Northwest, Songhuajiang, Yangtze River and the Southeast River basins, which reached up to 80% (Fig. 2a; Liu et al., 2014). The trends of NSWS are sensitive to the time periods (Fu et al., 2011). The magnitudes of decreasing trends in NSWSs vary among different studies because the different periods, datasets, and methodologies are used in various studies. From 1951-2000, NSWS decreased by −0.11 m s−1 dec−1 based on observed values (Wang et al., 2004). For the period 1956-2004, the annual NSWS decreased linearly by −0.12 m s−1 dec−1 (Jiang et al., 2010a). From 1969-2005, the decreasing trend of the annual NSWS was −0.18 m s−1 dec−1 (Guo et al., 2011). Several studies discovered that the decreasing trend in the annual NSWS was −0.07 m s−1 dec−1 in 1960-2007 (Yin et al., 2010a, b; Liu et al., 2011a, b). The results concerning trends in NSWSs over China for different regions and time periods are summarized in Fig. 2b, with detailed information in Table 1. Most studies have found a stilling in many regions of China for the past 40 years, although the magnitudes of the weakening NSWS trends vary among different studies.

      No.Region (Site)PeriodTrendReferenceNo.Region (Site)PeriodTrendReference
      1China (729)1954-2000−0.11Wang et al. (2004)21Southwest China (110)1969-2009−0.24Yang et al. (2012)
      2China (323)1951-2002−0.10Ren et al. (2005)22Southwest China (110)1969-2000−0.37Yang et al. (2012)
      3China (305)1969-2000−0.22Xu et al. (2006)23China (540)1971-2007−0.17Chen et al. (2013)
      4TP (75)1966-2003−0.17Zhang et al. (2007)24China (472)1960-2009−0.10Lin et al. (2013)
      5China (604)1960-1999−0.12Li et al. (2008a)25TP (64)1960-2009−0.06Lin et al. (2013)
      6Western deserts (23)1973-2003−0.29Mahowald et al. (2009)26China (741)1966-2011−0.16Liu et al. (2014)
      7HRB (45)1957-2001−0.14Zheng et al. (2009)27East China (93)1980-2011−0.13Wu et al. (2016)
      8China (317)1956-2005−0.11Cong et al (2009)28East China (93)1980-2011−0.13Zha et al. (2016)
      9China (535)1956-2004−0.12Jiang et al. (2010b)29Xinjiang (10)1984-2013−0.29Liu et al. (2017)
      10Loess Plateau (82)1960-2006−0.14McVicar et al. (2010)30East China (93)1980-2011−0.13Wu et al. (2017)
      11China (595)1961-2008−0.09Yin et al. (2010a)31China (492)1979-2010−0.11Zha et al. (2017a)
      12China (603)1971-2008−0.12Yin et al. (2010b)32China (580)1970-2011−0.15Zha et al. (2017b)
      13TP (71)1980-2005−0.24You et al. (2010)33BTH (154)1978-2014−0.10Zhou et al. (2017)
      14China (597)1961-2007−0.13Fu et al. (2011)34North China (155)1971-2015−0.17Han et al. (2018)
      15China (726)1969-2005−0.18Guo et al. (2011)35YRD(128)1960-2015−0.065Li et al. (2018a)
      16China (518)1960-1991−0.12Liu et al. (2011a)36East China (328)1981-2011−0.09Zha et al. (2019a)
      17China (518)1992-2007−0.07Liu et al. (2011b)37China (524)1958-2015−0.109Zhang et al. (2019a)
      18HRB (34)1950-2007−0.10Tang et al. (2011)38BTH (223)1980-2016−0.25Wang et al. (2020)
      19TP(78)1984-2006−0.30Yang et al. (2011)39China (582)1980-2017−0.10Zhang et al. (2020a)
      20Northeast China (87)1961-2010−0.25Jin et al. (2012)40China (2333)1960-2017−0.15Zhang and Wang (2020)

      Table 1.  Summary of observed NSWS trends in China, including data for the study regions, periods, the trends (m s-1 dec-1), and the references. BTH: Beijing-Tianjin-Hebei. YRD: Yangtze River Delta. TP: Tibetan Plateau. HRB: Haihe River Basin

      Figure 2.  (a) Relative change in the magnitude (%) of the annual mean NSWS from 1966-2011. The major river basins (NW: Northwest, YR: Yellow River, HAI: Hai River, LR: Liao River, SHJ: Songhuajiang, SW: Southwest, CJ: Yangtze River, HUAI: Huai River, PR: Pearl River, SE: Southeast) in China are shown. (b) Trends of observational NSWS across different regions in China (m s−1 dec−1). The study number is shown in Table 1. (a) copied from Liu et al. (2014).

      Some studies have pointed out that the reduction of NSWS is mainly attributed to the reduction of the wind strength (Xu et al., 2006; Guo et al., 2011). For instance, gentle breezes (3.4-5.4 m s−1) and moderate breezes (5.5-7.9 m s−1) were reduced by 2.2% and 1.0% dec−1 in 1981-2011, respectively (Zha et al., 2016). However, the slowdown in NSWS did not occur in weak winds. The probabilities of light air (0.3-1.5 m s−1) and light breeze (1.6-3.3 m s−1) showed increasing trends, at rates of 1.1% dec−1 and 3.1% dec−1, respectively (Fig. 3). Hence, the variations of probabilities for different NSWS categories are inconsistent. A decrease in NSWS does not mean that all wind categories decreased. The average NSWS mainly reflects the seasonal, interannual, and decadal variations, which cannot reveal the detailed characteristics of variations in NSWS ranges. Hence, studies on different wind categories are helpful in understanding the detailed characteristics of changes in NSWS.

      Figure 3.  Temporal changes in probabilities of six wind grades in observed NSWS across eastern China in 1980-2011 (%). R2 is the correlation coefficient, which passed the significance t-test at the 0.01 level. [Calm: 0-0.2 m s−1, LA: light air (0.3-1.5 m s−1), LB: light breeze (1.6-3.3 m s−1), GB: gentle breeze (3.4-5.4 m s−1), MB: moderate breeze (5.5-7.9 m s−1), WSGE8.0: wind speed greater than or equal to 8.0 m s−1 (≥ 8.0 m s−1)]. Copied from Zha et al. (2016).

    • Regional differences of changes in NSWS are considerable (Shi et al., 2015). Large NSWS values were found in coastal regions and northern China, where the mean NSWS exceeded 2.0 m s−1. Small NSWS values were detected in the Tibetan Plateau, the middle reaches of Yangtze River, and southwestern and southeastern China, where the NSWS was lower than 1.8 m s−1 (Jiang et al., 2010a). The spatial pattern of NSWS trends was consistent with that of the mean NSWS, for which a strong decreasing trend was accompanied by a large NSWS and weak decreasing trend was accompanied by a small NSWS. The reduction in NSWS was more significant in northern China than that in southern China (Yin et al., 2010a). The most significant reductions in NSWS were mainly located in northwestern China (McVicar et al., 2010; Lin et al., 2013), followed by in northeastern China, and in the middle and lower reaches of the Yangtze River (Wang et al., 2004). To evaluate the impact on NSWS from climate backgrounds, several climate zones in China have been studied (Bian et al., 2013; Zheng et al., 2013). The results showed that differences of the changes in NSWS were considerable over different zones: the strongest slowdown trend was in the extratropical monsoon climate zone, and the weakest slowdown trend was in the subtropical monsoon climate zone (Zha et al., 2017b). The NSWS in the Tibetan Plateau climate zone had a decreasing trend (Yang et al., 2011; Lin et al., 2013); however, the humidity and elevation of the Tibetan Plateau could influence the NSWS. The decrease of NSWS in the arid areas of the Tibetan Plateau was stronger than in the humid areas (Yang et al., 2011), and which over the high elevation around the Tibetan Plateau was also greater than those over the low elevation (Guo et al., 2017).

      Seasonal differences of changes in NSWS are also considerable (Wang et al., 2004; Jiang et al., 2010a). Across the whole of China from 1951-2000, the most significant decrease in NSWS was observed in winter (−0.14 m s−1 dec−1), followed by spring (−0.12 m s−1 dec−1), autumn (−0.10 m s−1 dec−1), and summer (−0.08 m s−1 dec−1). In Chinese Hexi, the maximum NSWS occurred in summer (Li et al., 2004), whereas it occurred in spring over the Tibetan Plateau (Lin et al., 2013). Although the downward trends in NSWS were pronounced during different seasons, the downward trends in seasonal NSWS began to slow after 1990 (Fig. 4; Guo et al., 2011). NSWS presented seasonal, interannual, decadal, and multi-decadal variations; however, current studies mainly analyze the trend in NSWS, and the spatiotemporal characteristics of NSWS at multiple timescales are rarely investigated. Investigating the multi-timescale variations of NSWS can aid the assessment of many societal and economic issues related to NSWS at different timescales.

      Figure 4.  Temporal variations and trends (solid lines) in seasonal mean NSWS across China from 1969-2005. Copied from Guo et al. (2011).

    • A terrestrial stilling has been revealed in the last several decades, but some studies also discovered a weak increase in NSWS over the past decade (Zeng et al., 2019; Zhang and Wang, 2020). Zeng et al. (2019) termed this increasing trend in NSWS “reversal”. The reversal of terrestrial stilling was also reported in China (Fig. 5; Zhang and Wang, 2020); however, the magnitudes and turning points of stilling in China are different due to differences in study regions, periods, datasets, and methods. Yang et al. (2012) analyzed the spatiotemporal characteristics of NSWSs over southwestern China and found that NSWSs increased after 2000 at rates of 0.60, 0.13, 0.82, and 0.65 m s−1 dec−1 in winter, spring, summer, and autumn, respectively. An increase in NSWS was also observed over northwestern China from 1993-2005, with a trend of 0.04 m s−1 dec−1 (Li et al., 2018b). Zha et al. (2019a) revealed a reversal of stilling in winter over eastern China since 2000 (0.0008 m s−1 dec−1). Potential causes for NSWS recovery include the increasing temperature difference between high and low latitudes (Yang et al., 2012; Li et al., 2018b), increasing sea-level pressure at high latitudes (Zha et al., 2019a), and increasing intensity of the Aleutian low over the North Pacific (Zhang and Wang, 2020). The stilling and reversal could be a cyclical, decadal pattern of NSWS. However, recent studies mainly analyzed the trends in NSWS in China over the past several decades, while the multi-decadal characteristics in NSWS have yet to be revealed. Furthermore, all studies used correlation analysis to discuss the causes of NSWS recovery in the recent decades, and the corresponding mechanisms were not revealed in detail. A reversal in terrestrial stilling and the corresponding mechanisms must be revealed systematically in future.

      Figure 5.  Time series of annual mean NSWS and normalized geostrophic wind across (a)-(f) six sub-regions and (g) China from 1970-2017. Copied from Zhang and Wang (2020).

    • Unlike research on historical NSWS, projections of future changes in NSWS are investigated rarely in China. It is especially important to select credible models to predict future changes in NSWS. To date, however, researchers mainly used the arithmetic-mean ensemble method to project such changes (Jiang et al., 2009, 2010b, 2010c, 2017, 2018; Jiang and Tian, 2013). The errors between observations and different models are different, implying that a model with large errors has the same effect on the ensemble results as a model with small errors based on the arithmetic-mean ensemble method. Therefore, the relative error between observation and model is used as the weight coefficient of models and the researchers found that NSWS increased in the next two decades under Representative Concentration Pathways 4.5 (RCP4.5) and RCP8.5. Furthermore, based on the statistical downscaling method, the weakening trend in NSWS in the RCP8.5 scenario was 3.5 times greater than that in the RCP4.5 scenario (Zha et al., 2020). Similar results are also reported by Jiang et al. (2009, 2017, 2018) and Chen et al. (2012). The abovementioned studies mainly analyzed the performance of models in simulating the long-term trend in NSWS. The capacity of the models to simulate seasonal, interannual, decadal, and multi-decadal variations in NSWS, as well as the causes of the differences in multi-timescale variations in NSWS between models and observations has not yet been examined. Furthermore, the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) do not perform well in reproducing past long-term decreasing trends in NSWS (Chen et al., 2012; Jiang et al., 2018; Zha et al., 2020). What are the main causes that the CMIP cannot effectively capture the significantly decreasing trends in the observed NSWS. These issues should be estimated systematically, which could reduce the uncertainty of projections.

    • Wind power is a virtually carbon-free and pollution-free electricity source, with global wind resources greatly exceeding electricity demand. Hence, the installed capacity of wind turbines grew at an annualized rate of > 20% from 2000-2019 and is projected to increase by a further 50% by the end of 2023 (Pryor et al., 2020). As a response to the development, the wind energy industry and wind power assessment have received much attention in China (Li et al., 2007). The cumulative installed capacity of wind energy amounted to 180.4 GW by the end of 2015; the newly installed capacity reached 30.5 GW, which accounted for about 48.4% of new windmills globally (Zhang et al., 2017a). Accompanied by a reduction in NSWS, the wind energy also significantly decreased across most regions of China. The mean wind energy decreased by −3.84 W m−2 dec−1 due to changes in anthropogenic land-use, which was close to the observed climate change (-4.51 W m−2 dec−1) (Li et al., 2008a). The significant decreases of wind-generated electricity were mainly found in western Inner Mongolia and northern Gansu, which are the two leading wind energy investment areas, with a decrease of −15 ± 7% and −17 ± 8%, respectively (Sherman et al., 2017). It is worth noting that the projected wind power density in northern China could increase by about 0.7% in the middle of the 21st century in the future if greenhouse gas emissions stay at current levels but could drop significantly by about -3.32% at the end of the century. Additionally, seasonal differences of changes in future wind energy are considerable. Winter wind energy is projected to increase significantly, and spring and summer wind energy resources are projected to generally decrease (Chen et al., 2020). Details for the introduction of wind energy can be found in Yang et al. (2017) and Pryor et al. (2020).

    3.   Causes of variations in NSWS across China
    • Changes in NSWS are due to changes in the driving force from LOACs, which are under the global warming backgrounds. Some researchers have noted a relationship between a decrease in NSWS and an increase in air temperature across China (Fig. 6a; Jiang et al., 2010a). The correlation between NSWS and air temperature reached −0.7 (p < 0.01), with a 1°C increase in temperature causing a 0.34 m s−1 decrease in NSWS (Fig. 6b; Xu et al., 2006). Under global warming, the difference in air temperature between land and ocean, and the difference in sea-level pressure between East Asia and Pacific decreased, which induced a decrease in NSWS. Nevertheless, the effects of increase in temperature on the reduction of NSWS were only evident via correlation analysis, and the corresponding mechanisms are yet to be explored in depth. Furthermore, the phase variations between NSWS and temperature are different. Therefore, changes in NSWS cannot be measured using air temperature alone.

      Figure 6.  (a) Temporal changes of annual NSWS (m s−1) and temperature (°C) over China from 1956-2004. (b) Relationship between the annual mean NSWS and temperature over China from 1969-2000. (a) and (b) copied from Jiang et al. (2010a) and Xu et al. (2006), respectively.

      How LOACs influence NSWS can be revealed by the relationship between climate indices and NSWS. Fu et al. (2011) proposed that NSWS variations across China are related to the interdecadal Pacific oscillation (IPO): positive IPO phases are associated with a significant reduction in NSWS, whereas negative IPO phases are usually not accompanied by a reduction in NSWS. Furthermore, the magnitude of the NSWS trend is much larger during a positive IPO phase than during a negative IPO phase. However, the spatial pattern of the differences in NSWS trends between positive and negative IPO phases is different—the positive and negative differences in trend are mainly located in western and eastern China, respectively. A reduced NSWS could also be attributed to a decline in East Asia monsoons (Xu et al., 2006). The decline in the East Asia winter monsoon is associated with global-scale warming and the decline in the East Asia summer monsoon is associated with local cooling over south-central China. Changes in NSWS displayed multi-timescale characteristics, which could be controlled by different LOACs at different timescales. For instance, from 1979-2010, the decadal variability in NSWS over eastern China was controlled mainly by the Pacific decadal oscillation (PDO), with a positive correlation of 0.74, whereas the interannual variability in NSWS was determined by the Northern Hemisphere annular model (NAM), with a negative correlation of −0.40 (Wu et al., 2018b). Moreover, the interannual variations in spring NSWS in the agro-pastoral transitional zone of northern China were related to the sea-surface temperature over the North Atlantic and North Pacific (Hu et al., 2019). Since the interaction and modulation among different LOACs are considerable, it is difficult to isolate and quantify the contributions of different LOACs to NSWS changes and reveal the corresponding mechanisms.

      LOACs can cause variations in the pressure-gradient force (PGF), which is the driving force of NSWS changes, so some studies have compared NSWS with the PGF. The observed NSWS decrease across the Tibetan Plateau from 1980-2005 was caused by a reduced zonal PGF, with a correlation coefficient of 0.88 (p < 0.01). At the same time, both the geostrophic wind speed (GWS) (determined by PGF) at the 850 hPa and the NSWS slowed down in spring and summer over China from 1969-1990. Therefore, some studies proposed that a decreased PGF in the lower troposphere was the leading factor in the decrease of NSWS (You et al., 2010; Guo et al., 2011; Lin et al., 2013). Nevertheless, the changes of PGF at 850 hPa were not consistent with NSWS at near-surface (Wu et al., 2016), and that the decreasing trends in GWS were much weaker than the trends in NSWS (Fig. 7) (Zhang and Wang, 2020). Accordingly, variations in high-level circulation fields cannot be used to determine changes in NSWS due to NSWS being sensitive to regional climate changes and underlying surface characteristics. Moreover, the changes of LOACs are influenced by both natural force and anthropogenic force—how natural force and anthropogenic force affect NSWS is yet to be quantified. The various studies analyzed how LOACs affect NSWS, but the effects of LOACs on NSWS changes also failed to confirm the sources of LOACs and quantify their contributions.

      Figure 7.  Normalized linear trends (% dec−1) of (a) and (c) NSWS and (b) and (d) GWS in different periods across China. (a) and (b): 1970-2004; (c) and (d): 1960-2017. The black dots indicate areas that passed the 95% significance level on the Mann-Kendall test. Reprinted from Zhang and Wang (2020).

    • LUCC in China has been demonstrated significantly (Asselen and Verburg, 2013). From 1990-2010, China lost more than 10% of its arable land and saw its total urban area expand by more than 20% (Liu and Tian, 2010), with approximately 3.18 × 106 hm2 of arable land used for construction (Liu et al., 2014). In particular, the total area of built-up land increased by 1.76 × 106 hm2 before 2000 and 3.76 × 106 hm2 after 2000. Some studies have shown that LUCC influences regional climate change, which cannot be ignored in climatic change studies (Zhao and Zeng, 2002; Gao et al., 2003; Feddema et al., 2005; Han et al., 2016; Zhao and Wu, 2017a).

      To analyze how LUCC, including urbanization, affects NSWS across China, most studies compare the NSWSs of urban stations with those of rural stations [represented by the urban minus rural (UMR) method]. The NSWSs of large city stations were larger than those of 275 non-urban stations in China (Fig. 8a; Xu et al., 2006). Similar results are reported by Jiang et al. (2010a), who compared the NSWSs of 174 large cities with those of 180 small cities from 1956-2004 (Fig. 8b). Because the selection criteria of urban and non-urban stations are different, the estimation results of urbanization influence NSWS are not same. For instance, Guo et al. (2011) discovered that the average NSWS at rural stations was larger than that at urban stations by 0.3 m s−1. Zha et al. (2016) found that the difference in NSWS between small and large cities in China from 1979-2010 was 0.47 m s−1. The urbanization declined NSWS mainly showed that reduced frequency of the days for strong winds (−4.7 d dec−1) (Wang et al., 2020), and that the vertical decline rate in NSWS grew with the development of urbanization (Xu et al., 2009; Zha et al., 2017a).

      Figure 8.  (a) Temporal changes of NSWSs at 30 large city and 275 non-urban stations in China from 1969-2000, and (b) temporal changes of NSWSs at 174 large city stations and 180 small city stations in China from 1956-2004. (a) copied from Xu et al. (2006) and (b) copied from Jiang et al. (2010a).

      How urbanization and other land surface changes influence NSWS can also be examined by comparing the results of observation minus reanalysis (OMR) because the reanalysis datasets were insensitive to changes in the underlying surface (Kalnay and Cai, 2003; Lim et al., 2005). Based on the OMR method, the significant decrease in NSWS over China from 1960-1999 can be attributed to anthropogenic LUCC (Li et al., 2008a). LUCC could have caused a decrease in NSWS by 0.11 m s−1 dec−1 from 1979-2010, with an estimation error of 10% (Zha et al., 2017a). Furthermore, the intensity of the decrease in NSWS is closely related to the rate of urbanization—the stronger the NSWS weakening the faster the urbanization (Li et al., 2018c). Because the coarse resolution of global climate models (GCMs), it is difficult to provide accurate regional climate signals to meet the needs of evaluation (Kirchmeier et al., 2014; Huang et al., 2015). Hence, the OMR method shows considerable uncertainty. Consequently, a downscaling technique, which is an effective method and could be adopted to detected regional- or local-scale climate signals. Wu et al. (2017) used the statistical downscaling method to downscale the European Centre for Medium-Range Weather Forecasts reanalysis from January 1989 onward (extended back to January 1979) (ERA-Interim); they estimated the impacts of LUCC on NSWS over eastern China and suggested that every 10% increase in the urbanization rate caused a 0.12 m s−1 decrease in NSWS.

      In current studies, the effects of LUCC on NSWS reduction across China have been investigated mainly based on correlation analyses. Wu et al. (2016) isolated and quantified the contributions of PGF and LUCC to changes in NSWS and proposed an explanation. From 1980-2011, the actual PGF increased with a mean value of 4.1 × 10−4 N (Fig. 9a). Wind speed (named as model wind speed), influenced by the PGF, increased, and changes in the model wind speed were consistent with the changes in PGF (Fig. 9b). However, long-term changes in model wind speed were not consistent with those in observations. These results imply that the PGF was not the only factor in the reduced NSWS. The drag coefficient increased, and the corresponding long-term changes were consistent with the changes in urbanization rate (Fig. 9c). The estimated result based on FWM(无全称) was similar to that based on OMR (Fig. 9d). Consequently, the reduced NSWS across eastern China could be attributed to the increased drag coefficient induced by LUCC (Wu et al., 2016). Based on FWM, the probability of a NSWS greater than 3.8 m s−1 is only 1.8% when LUCC effects are included; however, the probability is 20.6% when LUCC effects are excluded (Zha et al., 2016). FWM has also been used to analyze the contribution of LUCC to changes in NSWS at the global scale, and which has been considered as a significant method in evaluating LUCC effects (Zhang et al., 2019b).

      Figure 9.  (a) Temporal changes in the horizontal pressure-gradient force (PGF), (b) the model wind speed and ERA-Interim 10 m wind speed, (c) the annual mean drag coefficient and urbanization rate, and (d) the difference between the observation and model (SWSD), and the difference between the observation and V10m-ERA (OEWSD). In (a) and (b), the annual mean values are indicated by rectangles and circles. R is the correlation coefficient; Rc is the threshold; P is the significance level. In (d), SWSD and OEWSD are obtained using the FWM and OMR, respectively. Copied from Wu et al. (2016).

      The deceleration effects of LUCC on NSWS across China have also been calculated by numerical simulations (Zhao and Wu, 2017b). Zhang et al. (2010) simulated the impacts of urban land cover expansion on regional climate and discovered a 50% drop in NSWS over the Yangtze River Delta, especially for the high-density urban area, reaching 1.5 m s−1. Similar results were also found for the Pearl River Delta, with the difference in NSWS between urban and non-urban areas reaching −0.27 m s−1 (Zhang et al., 2015). Wang et al. (2013) also showed that urbanization caused a decrease of approximately 37% in NSWS over the Pearl River Delta. Based on the Weather Research and Forecasting (WRF) model, the ratio of the urbanized area to the Beijing metropolitan area increased from 1.3% to 11.9%, which was speculated to be the cause of a 0.4 m s−1 decrease in the regional mean NSWS (Hou et al., 2013). Based on the WRF model, Zha et al. (2019b) showed that NSWS based on land-use data for the 2010s was lower than that based on land-use data for the 1980s by a difference of −0.17 m s−1; meanwhile, LUCC induced a decrease of 9.0% in the probability of a strong wind. The significant reduction in NSWS over the middle reaches of the Yangtze River Delta was induced by changes in closed shrubland and cropland/natural vegetation mosaic to evergreen broadleaf and deciduous broadleaf forest. The slowdown in NSWS across the Shandong Peninsula, the Beijing-Tianjin-Hebei, and the Pearl River Delta could be attributed to an increase in urban and built-up and a decrease in croplands and cropland/natural vegetation mosaic (Zha et al., 2019b). Anthropogenic activities include not only LUCC but also greenhouse gases (GHGs) (Zhang et al., 2017), anthropogenic aerosols (AAs) (Bichet et al., 2012), and anthropogenic heat release (AHR) (Zhang et al., 2015). Current studies have evaluated the impacts of anthropogenic LUCC on changes of NSWS, but the contributions of GHGs, AAs, and AHR to changes of NSWS are detected rarely.

    • Some studies have reported that the GHGs can alter the thermodynamics and dynamic processes of the atmosphere and induce NSWS variability (Zhang et al., 2017). The AHR reduces boundary layer stability and enhances vertical mixing, which leads to an increase in NSWS (Zhang et al., 2015). Some studies have hypothesized that aerosols could influence changes in NSWS. An increase in air stability due to interactions between aerosols and radiation reduces vertical mixing, which in turn reduces the vertical flux of horizontal momentum. Since winds are generally higher aloft than at the surface, weakened vertical mixing reduces the transfer of fast winds aloft to the surface, slowing surface winds compared with those aloft (Li et al., 2016; Zhao et al., 2016). Quantitatively, Jacobson and Kaufman (2006) proposed that an increase in aerosol particles may reduce NSWS by up to 8% locally in China. However, the real physical mechanism of the AAs affecting NSWS has not yet been revealed systematically.

      The observed tendencies of NSWS are, at least partly, the product of non-climate-related factors, such as inhomogeneities in the NSWS (Liu, 2000; Chen et al., 2012). Long-term wind speed series are subject to inhomogeneities resulting from station relocations, anemometer height changes, instrumentation malfunctions, instrumentation changes, different sampling intervals, and observing environment changes (Azorin-Molina et al., 2014). The trends in NSWS are strongly sensitive to these abovementioned systematic errors (Wan et al., 2010; Li et al., 2011a). Winds generally increase with height, so the higher (lower) the anemometer, the larger (smaller) the wind speed. Furthermore, changes in anemometer location can influence wind speed because NSWS is extremely sensitive to changes in the surface roughness. Unfortunately, the effects of these non-climate-related factors on the slowdown of NSWS across China are rarely estimated.

    4.   Discussion
    • The NSWS datasets were mainly obtained from the China Meteorological Administration (CMA). Station selection, anemometer installation, and the observation process were done in line with the standards of the World Meteorological Organization’s guide to the Global Observing System and the CMA’s technical regulations on weather observation. Several wind speed datasets have been examined and calibrated by the Chinese National Meteorological Information Center. Therefore, many studies have stated that most observed wind speeds should be credible (Xu et al., 2006; Fu et al., 2011; Guo et al., 2011). However, changes in observation instruments were considerable during the period 1967-1970 (Liu, 2000; Fu et al., 2011); the number of stations with measurements for wind speed remained steady after 1970 (Feng et al., 2004). Therefore, to obtain a homogeneous wind speed record at a station, the stations must be selected carefully. For instance, the station must be a standard national meteorological station, which did not relocate in the study period.

      Although the quality of selected stations is higher than non-selected stations, an estimation of significance for the selected stations is necessary because the results are influenced considerably by the inhomogeneity of NSWS. Inhomogeneities in the long-term series of observed wind have been pointed out by many authors; consequently, quality control and homogeneity testing of the observed NSWS is necessary (Alexandersson, 1986; Liu, 2000; He et al., 2012). Quality control involves a high-low extreme check, internal consistency check, spatiotemporal outlier check, and a missing data check (Feng et al., 2004). Primary homogenization methods include the departure accumulating method (Buishand, 1982), the moving t-test (Peterson et al., 1998), the multiple analysis of series for homogenization (Li et al., 2011a), and the standard normal homogeneity test (Wu et al., 2018b; Zhang et al., 2020b), and so forth.

    • The NSWSs in reanalysis products are compared with observations to estimate the reanalysis’ capacities in simulating the climatology and long-term trend of NSWS (Wang et al., 2020; Zhang and Wang, 2020). Regarding climatology, the reanalysis datasets had a larger NSWS than observational datasets, except for the Japanese Meteorological Agency 55-yr Reanalysis (JRA55) (Fig. 10). Reanalysis datasets, such as the Modern-Era Retrospective analysis for Research and Application (MERRA), and its updated version (MERRA2), the NCEP Climate Forecast System Reanalysis (CFSR), and the ERA-Interim, showed a similar spatial pattern for observations; however, JRA55 showed the smallest root-mean square error, followed by CFSR (Chen et al., 2014). For long-term trends, reanalysis products can reproduce NSWS reductions across China, but almost all studies indicate that such reductions in reanalysis datasets are weaker than those in observational datasets (Wu et al., 2016; Zeng et al., 2019; Wang et al., 2020). In the current reanalysis products, almost no reanalysis products assimilate the observational NSWS data in the assimilation system, but for JRA55. The observed NSWS data is assimilated in the JRA55, so which can better capture the decreasing trend of NSWS. Therefore, in order to reduce the uncertainties of reanalysis products in simulating the changes in NSWS, the observed NSWS data must be considered to assimilate in the next generation reanalysis products.

      Figure 10.  Annual mean NSWS from (a) observations, (b) JRA55, (c) MERRA, (d) MERRA2, (e) CFSR, and (f) ERA-Interim from 1980-2017. Reprinted from Zhang and Wang (2020).

      Several studies have compared the performance of the CMIP in simulating historical NSWS across China. Zha et al. (2020) discovered that most CMIP5 models and observations had a negative correlation. The standard deviations of NSWSs in most CMIP5 models were smaller than those of observations, implying that most CMIP5 models underestimated the interannual and decadal changes in NSWS. Coupled Atmosphere-Ocean General Circulation Models also underestimated the interannual and decadal changes in the observed NSWS (Fig. 11a) (Chen et al., 2012). Jiang et al. (2017) evaluated the long-term changes of the CMIP5 models with a good performance and observation during the period 1961-2005 and suggested that CMIP5 models with a good performance underestimated the decreasing trend in NSWS (Fig. 10b). Global reanalysis products are generated at numerical weather prediction centers with advanced data assimilation systems, but most reanalysis products cannot simulate the changes of terrestrial NSWS satisfactorily. Possible reasons why models fail to reproduce the terrestrial stilling include: 1) the considerable decrease in observed NSWS is a manifestation of changes in surface roughness that are not included in the surface boundary conditions used in the climate models; 2) the current models have only a relatively weak capacity for representing some aspects of atmospheric flow; 3) the observed trends are partly the product of non-climate-related factors, such as inhomogeneities in station settings or instrumentation; 4) inappropriate model topography and inaccuracies in the atmospheric boundary layer processes are implemented into data assimilation systems of reanalysis products (Chen et al., 2012; Zeng et al., 2019).

      Figure 11.  (a) Annual mean NSWS in China from individual simulations with nine atmosphere-ocean general circulation models (1850-2005) and the time series of mean wind speed from NCEP-NCAR, NCEP-DOE(无全称), and ERA-40(无全称), and the average of the observations. (b) Time series of the annual mean NSWS in observation (Obs: black line), Beijing Normal University Earth System Model (BNU-ESM: purple line), Norwegian Climate Center’s Earth System Model (NorESM1-M: blue line), and the ensemble of the models, in which the annual mean NSWS is similar for the wind trend (Opt_T_Ens: red line). (a) and (b) copied from Chen et al. (2012), and Jiang et al. (2017), respectively.

      Actually, some studies have discovered that in regions where Atmospheric Model Intercomparison Project simulations [atmospheric simulations forced with only the observed sea surface temperature (SST)] capture terrestrial stilling, global reanalysis products are capable of reproducing stilling. In contrast, for regions in which Atmospheric Model Intercomparison Project simulations do not capture stilling, global reanalysis products fail to reproduce stilling. These results imply that a reanalysis product can effectively simulate the force of SST on the atmosphere, determining its ability to simulate terrestrial stilling. However, NSWS is extremely sensitive to changes in the surface roughness; greater efforts are required to improve surface process parameterization schemes and their connections to ocean-atmosphere circulations in climate models and operational weather data assimilation systems (Zeng et al., 2019).

    • The main statistical and dynamical methods used to investigate the effects of LOACs on changes in NSWS include correlation analysis, forward stepwise regression algorithm (FSRA), and geostrophic wind theory.

      Correlation analysis is the most widely used method, which can qualitatively analyze the relationship between LOACs and NSWS. However, correlation can only analyze the relationship between one ocean-atmosphere oscillation index and NSWS. Changes in NSWS are associated with the combined effects of variations in various LOACs. The levels of interaction and inter-modulation among different LOACs are considerable. Hence, the mechanisms for the LOACs affecting NSWS are difficult to ascertain using correlation analysis.

      FSRA can be used to identify the effects of LOACs on NSWS, can recognize primary climate indices, and has the largest explanatory power for changes in NSWS (Zeng et al., 2019). FSRA is a systematic method for adding predictors from a linear regression model based on statistical significance. The earlier the climate indices enter the model, the larger the explanatory power the climate indices. FSRA also can estimate the relative importance of LOACs to changes in NSWS. Based on FSRA, a reconstructed wind speed can be obtained, including only the effects of LOACs. Comparisons between the reconstructed wind speed and the observed wind speed permit the evaluation of LOAC contributions to changes in NSWS; however, the results depend on selected predictors. Based on different predictors, the estimated results might be different. Furthermore, the physical mechanisms of LOACs affecting NSWS cannot be revealed by FSRA. The use of FSRA have also an important presupposition is that the predictors are independent and the predictand and predictors satisfy the linear relations. However, until now, we cannot guarantee the predictand and predictors satisfy the linear relations. Whether a nonlinear relation between NSWS and LOACs can better express the effects of LOACs on the NSWS changes, which is an interesting issue and need to be further demonstrated in the future.

      Geostrophic wind theory is a mature theory in atmospheric sciences (Wu et al., 2018b; Zhang and Wang, 2020), which can be used to calculate the geostrophic wind ($ {\overrightarrow{V}}_{g} $). $ {\overrightarrow{V}}_{g} $ can be used to describe the quantitative effects of PGF on NSWS because it is a direct function of the PGF ($ f\overrightarrow{k}\times {\overrightarrow{V}}_{g}=\nabla \varPhi $, where $ \overrightarrow{k} $ is the unit vector in the vertical direction, $ f $ is the Coriolis parameter, and $ \nabla \varPhi $ is the PGF)—$ {\overrightarrow{V}}_{g} $ has a similar magnitude to that of actual wind (Guo et al., 2011). The observed NSWS cannot be in a quasi-geostrophic equilibrium state. Therefore, geostrophic wind theory is mainly used at a high level over the mid-latitudes (Guo et al., 2011). The PGF near the surface is not consistent with that at a high level, so the contribution of PGF to changes in NSWS could show a large uncertainty near the surface and low-latitudes based on geostrophic wind theory.

      Methods used to investigate the effects of LUCC on changes in NSWS across China include the UMR, OMR, FWM, and numerical simulations.

      UMR method has been widely used in the current studies (Jiang et al., 2010a; Zha et al., 2016, 2017; Wang et al., 2020) and can be employed when examining the difference in NSWSs between urban and rural stations at any scale. However, economic development is unbalanced in China, with large cities mainly located in eastern China and small cities mainly located in western China. Long-term observations involve very few, strictly rural stations in China. Inhomogeneities in the spatial patterns of large and small cities, and inconsistencies in the selection criteria for urban and rural stations, in different studies, induce large discrepancies in the estimation results. The UMR method cannot determine the effects of LUCC on changes in NSWS in rural stations due to there being no reference standard in existence.

      OMR method can be used to quantify the effects of LUCC on long-term changes in NSWS; the effects of LUCC on changes in NSWS in small cities or rural stations can be estimated. However, it is difficult to provide accurate regional and local climate signals to meet the needs of evaluation due to the coarse resolution of GCMs (Fan et al., 2011, 2013; Huang et al., 2015). Therefore, the estimated results could be questionable when the OMR method is used at regional or local scales. The significance of results based on the OMR method could be improved by increasing the resolution in reanalysis data and using a statistical or dynamical downscaling methods. The statistical and dynamical downscaling methods are the valid methods to diminish the bias in climate prediction versus observation that is induced by the local characteristics missing in the reanalysis products; thus, which can be used to optimize the reanalysis data to reduce the bias to a minimum. Then, the OMR method can be used to compare the difference between observation and downscaled reanalysis data, which could reduce the uncertainty of estimation result based on original OMR method (Wu et al., 2017).

      FWM is a simple dynamical method, which can be used to isolate the effects of PGF and drag force (Wu et al., 2016; Zhang et al., 2019b). However, FWM is difficult to use in regions with complex terrain, and it ignores the effects of turbulent fluxes and horizontal advection. Furthermore, the temporal change terms of zonal and meridional winds are not considered in FWM. These factors combine to affect changes in NSWS. Drag force is considered as a simple linear combination of NSWS and drag coefficient; hence, the influence of the drag force on the results remains uncertain (Wu et al., 2018c). In future, several important parameterization schemes should be introduced to FWM to improve estimation results and isolate the effects of surface drag and turbulent flux.

      Numerical simulations can quantitatively evaluate the contributions of LUCC to changes in NSWS, which can be used to reveal the effects of different land cover categories on changes in NSWS (Hou et al., 2013; Zhang et al., 2015; Zhao and Wu, 2017a, b; Zha et al., 2019b). Different numerical experiments elucidate the mechanisms of LUCC affecting changes in NSWS. Nevertheless, due to limitations in computing resources, the simulation be run based on land types, with coarse resolution. The impacts of various regional and local land type changes on NSWS can, therefore, be ignored. The effects of LUCC mainly present themselves at the decadal scale; thus, numerical experiments must be integrated over a period of a decade or more. Consequently, the economic and time costs are significant when many numerical tests are carried out.

    5.   Conclusions and remarks for future studies
    • In the last several years, significant advances have been made in studies of the changes in NSWS over China. Almost all the studies show that NSWS in China has been reduced significantly over the past 40 years, with marked regional and seasonal characteristics. However, the results are inconsistent among different studies because of differences in study methods, datasets, and periods. Furthermore, a reversal of terrestrial stilling has been found in recent decades across China. The projected NSWS at the decadal scale might decrease in the future, and the downward trend could be strengthened with increasing GHGs emissions. However, the potential mechanisms remain unknown. The causes of the reduction in NSWS are not uniform. Some researchers have proposed that the slowdown of NSWS across China is due to variations in LOACs under global warming (e.g., East Asia monsoons, PDO, NAM), whereas others have suggested the slowdown is due to increasing surface roughness induced by LUCC. Although studies on the behavior of NSWS in a changing climate have made progress in the last few decades, further investigations for understanding the variation in wind speed and its underlying mechanisms are needed—especially investigations that address the following issues:

      Wind observations are extremely sensitive to changes in anemometer height and in the location and exposure of the observing site, especially over complex terrains (Wan et al., 2010). Changes in these factors can cause large discontinuities in the wind data series; however, they are often inevitable, especially during a long period of record in China. Therefore, it is not easy to judge which one is correct, the observation or the model, as observational data may also contain large biases results from insufficient station representativeness. Consequently, correction and homogenization of wind data are imperative for climate studies and other applications, in particular for the assessments of observed wind speed trends, data assimilation, model performance. Some homogenization methods, such as the standard normal homogeneity test, the penalized maximal t test, the penalized maximal F test, the Buishand test, the Pettitt test, the multiple analysis of series for homogenization, and the two-phase regression can be employed to correct the wind data series. Based on the homogenized NSWS series, exploring the multi-timescale changes (seasonal, interannual, decadal, multi-decadal) in NSWS and revealing the key areas of LOACs that significantly affect multi-timescale changes in NSWS across China; meanwhile, quantitatively estimating the contributions of LOACs to these changes.

      The NSWS results from high-resolution RCMs are necessary, which may be a future direction for application of NSWS (e.g., the evaluation and development of wind energy). High-resolution RCMs are increasingly able to simulate offshore winds to accurately depict the influence of complex orography. The NSWS is affected by the thermodynamical and dynamical factors. Increasing the horizontal resolution of models can better present the topography and land-sea contrast, which may improve the performance of models in simulating the NSWS (Yu et al., 2014; Pryor et al., 2020). Actually, surface winds are much better simulated by a regional model at 6-km grid spacing compared to a reanalysis product at ~25-km grid spacing (Pryor et al., 2020). Furthermore, the NSWS simulation also need to consider the assimilation of observed wind data and the improvement in physical processes, because the variations in NSWS could be influenced by cumulus convection, planetary boundary layer, land surface, radiative transfer processes and many others. The assimilation of observed wind data can correct the systematic errors of models and the improvement in physical processes can well capture some refined physical characteristics, especially for the simulation of diurnal cycle of NSWS (Yu et al., 2009, 2010), the improvement in physical parameterization scheme is crucial (Li et al., 2008b, 2011b; Yu et al., 2014). Furthermore, increasingly large ensembles of high-resolution climate simulations could also better sample uncertainties related to internal variability and model structural uncertainty (Pryor et al., 2020). The surface is the primary source of drag. LUCC can change the surface roughness, drag coefficient, albedo, and heat exchange, thereby changing the drag from the perspective of dynamics and thermal force. High-resolution RCMs can better describe complex topography, so which is coupled with a GCM driven by high-precision land-cover data could effectively capture the relative contributions of the total effects, surface friction, and turbulent mixing of LUCC to multi-timescale changes in NSWS.

      Detection and attribution (D&A) of NSWS changes are required. Based on the results of D&A, an evaluation can be conducted of the contributions of natural forcing, anthropogenic forcing, and internal variability to changes in NSWS. To achieve this goal, several CMIP6 projects will prove useful—for instance, the D&A Model Intercomparison Project (DAMIP) and Land-Use Model Intercomparison Project (LUMIP). Comparative analysis of DAMIP and LUMIP can detect the effects of different external forcing and natural forcing on changes in NSWS (Table 2). Based on experiments using His-nat and historical results, natural forcing and anthropogenic forcing can be divided. Based on experiments in His-nat, His-GHG, Hist-aer, and historical results, contributions from natural forcing, GHGs, and aerosols to variation in NSWS can be divided.

      ExperimentExperiment descriptionSimulation periodMinimum ensemble member
      CMIP6 historical simulation and SSP2-4.5Historical climate simulation in CMIP6 (1850-2014) and the SSP2-4.5 scenario simulation (2015-2020)1850-20203
      His-natHistorical simulations involving only natural forcing1850-20203
      His-GHGHistorical simulations involving only sufficiently mixed greenhouse gas forcing1850-20203
      Hist-aerHistorical climate simulations involving only anthropogenic aerosol forcing1850-20203
      Hist-solHistorical climate simulations involving only solar radiation forcing1850-20203
      Hist-volcHistorical climate simulation involving only volcanic forcing1850-20203
      His-CO2Historical climate simulation involving only CO21850-20203
      SSP245-aerFuture projections based on the His-aer test accompanied by the aerosol concentration or emission in tropospheric under the SSP2-4.52021-21001
      SSP245-natFuture projections based on the His-nat test accompanied by Solar forcing and volcanic forcing under the SSP2-4.52021-21001

      Table 2.  Experiment in DAMIP of CMIP6. Refer to Qian and Zhang (2019)

      The projections and corresponding mechanisms of future changes in NSWS over China are lacking. Projections of NSWS in China would aid the evaluation and exploitation of wind energy and exploiting and utilizing wind energy will help China to fulfill the Paris Climate Agreement and its commitment to reduce emissions. Consequently, the multi-timescale variations in NSWS must be projected within different emission scenarios, with statistical and dynamical downscaling methods across China and several representative districts (e.g., Tibetan Plateau, Yangtze River Delta, Pearl River Delta, Beijing-Tianjin-Hebei, the arid and semi-arid regions in the northwest). GCMs with the high horizontal resolution (25-50 km) are being included in CMIP6 under High resolution model intercomparison project (HighResMIP) and those with 5-km grid spacing are being actively developed and tested. They can employ mesoscale and even convection-permitting resolutions in the refined domain while using coarser grid spacing. Hence, projections of NSWS across China and several representative districts could also be estimated based on HighResMIP.

      CO2 emissions increase air temperature but warming displays temporal non-synchronism and spatial non-uniformity, and these characteristics result in spatial differences in temperature changes in the horizontal direction. Accompanied by changes in CO2 emissions, the spatial difference in temperature could be in different degree. Therefore, changes in NSWS are not consistent with warming under different social-development scenarios. Investigating the mechanism of future changes in NSWS is required to estimate the performance of the model in simulating variation in LOACs, and in analyzing the effects of changes in CO2 emission on the primary LOACs, affecting changes in wind speed over China.

      Studies of wind speed changes must cover variation in the daily maximum wind speed (DMWS) and wind gust because the DMWS and wind gust influence soil erosion, pollutant diffusion, and damage to buildings, infrastructure, and crops, with large concomitant economic losses (Lin et al., 2015; Zhang et al., 2017, 2019c). We need to evaluate multi-timescale variation in DMWS and wind gust, then reveal the mechanisms of DMWS and wind gust changes. The occurrence and evolution of DMWS and wind gust are generated within the mean flows; therefore, to explore DMWS variation, one must compare DMWS with the mean wind field. Additionally, some human activities, such as LUCC, AAs, GHGs, and AHR may influence DMWS and wind gust, especially in mega-city clusters. Future work must isolate and quantify the effects of such anthropogenic factors on changes in DMWS and wind gust.

      Acknowledgments. This work is supported by the Program for Special Research Assistant Project of Chinese Academy of Sciences, Program for Key Laboratory in University of Yunnan Province, and the Chinese Jiangsu Collaborative Innovation Center for Climate Change.

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