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).
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) Period Trend Reference No. Region (Site) Period Trend Reference 1 China (729) 1954-2000 −0.11 Wang et al. (2004) 21 Southwest China (110) 1969-2009 −0.24 Yang et al. (2012) 2 China (323) 1951-2002 −0.10 Ren et al. (2005) 22 Southwest China (110) 1969-2000 −0.37 Yang et al. (2012) 3 China (305) 1969-2000 −0.22 Xu et al. (2006) 23 China (540) 1971-2007 −0.17 Chen et al. (2013) 4 TP (75) 1966-2003 −0.17 Zhang et al. (2007) 24 China (472) 1960-2009 −0.10 Lin et al. (2013) 5 China (604) 1960-1999 −0.12 Li et al. (2008a) 25 TP (64) 1960-2009 −0.06 Lin et al. (2013) 6 Western deserts (23) 1973-2003 −0.29 Mahowald et al. (2009) 26 China (741) 1966-2011 −0.16 Liu et al. (2014) 7 HRB (45) 1957-2001 −0.14 Zheng et al. (2009) 27 East China (93) 1980-2011 −0.13 Wu et al. (2016) 8 China (317) 1956-2005 −0.11 Cong et al (2009) 28 East China (93) 1980-2011 −0.13 Zha et al. (2016) 9 China (535) 1956-2004 −0.12 Jiang et al. (2010b) 29 Xinjiang (10) 1984-2013 −0.29 Liu et al. (2017) 10 Loess Plateau (82) 1960-2006 −0.14 McVicar et al. (2010) 30 East China (93) 1980-2011 −0.13 Wu et al. (2017) 11 China (595) 1961-2008 −0.09 Yin et al. (2010a) 31 China (492) 1979-2010 −0.11 Zha et al. (2017a) 12 China (603) 1971-2008 −0.12 Yin et al. (2010b) 32 China (580) 1970-2011 −0.15 Zha et al. (2017b) 13 TP (71) 1980-2005 −0.24 You et al. (2010) 33 BTH (154) 1978-2014 −0.10 Zhou et al. (2017) 14 China (597) 1961-2007 −0.13 Fu et al. (2011) 34 North China (155) 1971-2015 −0.17 Han et al. (2018) 15 China (726) 1969-2005 −0.18 Guo et al. (2011) 35 YRD(128) 1960-2015 −0.065 Li et al. (2018a) 16 China (518) 1960-1991 −0.12 Liu et al. (2011a) 36 East China (328) 1981-2011 −0.09 Zha et al. (2019a) 17 China (518) 1992-2007 −0.07 Liu et al. (2011b) 37 China (524) 1958-2015 −0.109 Zhang et al. (2019a) 18 HRB (34) 1950-2007 −0.10 Tang et al. (2011) 38 BTH (223) 1980-2016 −0.25 Wang et al. (2020) 19 TP(78) 1984-2006 −0.30 Yang et al. (2011) 39 China (582) 1980-2017 −0.10 Zhang et al. (2020a) 20 Northeast China (87) 1961-2010 −0.25 Jin et al. (2012) 40 China (2333) 1960-2017 −0.15 Zhang 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).
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.
Experiment Experiment description Simulation period Minimum ensemble member CMIP6 historical simulation and SSP2-4.5 Historical climate simulation in CMIP6 (1850-2014) and the SSP2-4.5 scenario simulation (2015-2020) 1850-2020 3 His-nat Historical simulations involving only natural forcing 1850-2020 3 His-GHG Historical simulations involving only sufficiently mixed greenhouse gas forcing 1850-2020 3 Hist-aer Historical climate simulations involving only anthropogenic aerosol forcing 1850-2020 3 Hist-sol Historical climate simulations involving only solar radiation forcing 1850-2020 3 Hist-volc Historical climate simulation involving only volcanic forcing 1850-2020 3 His-CO2 Historical climate simulation involving only CO2 1850-2020 3 SSP245-aer Future projections based on the His-aer test accompanied by the aerosol concentration or emission in tropospheric under the SSP2-4.5 2021-2100 1 SSP245-nat Future projections based on the His-nat test accompanied by Solar forcing and volcanic forcing under the SSP2-4.5 2021-2100 1
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.
|No.||Region (Site)||Period||Trend||Reference||No.||Region (Site)||Period||Trend||Reference|
|1||China (729)||1954-2000||−0.11||Wang et al. (2004)||21||Southwest China (110)||1969-2009||−0.24||Yang et al. (2012)|
|2||China (323)||1951-2002||−0.10||Ren et al. (2005)||22||Southwest China (110)||1969-2000||−0.37||Yang et al. (2012)|
|3||China (305)||1969-2000||−0.22||Xu et al. (2006)||23||China (540)||1971-2007||−0.17||Chen et al. (2013)|
|4||TP (75)||1966-2003||−0.17||Zhang et al. (2007)||24||China (472)||1960-2009||−0.10||Lin et al. (2013)|
|5||China (604)||1960-1999||−0.12||Li et al. (2008a)||25||TP (64)||1960-2009||−0.06||Lin et al. (2013)|
|6||Western deserts (23)||1973-2003||−0.29||Mahowald et al. (2009)||26||China (741)||1966-2011||−0.16||Liu et al. (2014)|
|7||HRB (45)||1957-2001||−0.14||Zheng et al. (2009)||27||East China (93)||1980-2011||−0.13||Wu et al. (2016)|
|8||China (317)||1956-2005||−0.11||Cong et al (2009)||28||East China (93)||1980-2011||−0.13||Zha et al. (2016)|
|9||China (535)||1956-2004||−0.12||Jiang et al. (2010b)||29||Xinjiang (10)||1984-2013||−0.29||Liu et al. (2017)|
|10||Loess Plateau (82)||1960-2006||−0.14||McVicar et al. (2010)||30||East China (93)||1980-2011||−0.13||Wu et al. (2017)|
|11||China (595)||1961-2008||−0.09||Yin et al. (2010a)||31||China (492)||1979-2010||−0.11||Zha et al. (2017a)|
|12||China (603)||1971-2008||−0.12||Yin et al. (2010b)||32||China (580)||1970-2011||−0.15||Zha et al. (2017b)|
|13||TP (71)||1980-2005||−0.24||You et al. (2010)||33||BTH (154)||1978-2014||−0.10||Zhou et al. (2017)|
|14||China (597)||1961-2007||−0.13||Fu et al. (2011)||34||North China (155)||1971-2015||−0.17||Han et al. (2018)|
|15||China (726)||1969-2005||−0.18||Guo et al. (2011)||35||YRD(128)||1960-2015||−0.065||Li et al. (2018a)|
|16||China (518)||1960-1991||−0.12||Liu et al. (2011a)||36||East China (328)||1981-2011||−0.09||Zha et al. (2019a)|
|17||China (518)||1992-2007||−0.07||Liu et al. (2011b)||37||China (524)||1958-2015||−0.109||Zhang et al. (2019a)|
|18||HRB (34)||1950-2007||−0.10||Tang et al. (2011)||38||BTH (223)||1980-2016||−0.25||Wang et al. (2020)|
|19||TP(78)||1984-2006||−0.30||Yang et al. (2011)||39||China (582)||1980-2017||−0.10||Zhang et al. (2020a)|
|20||Northeast China (87)||1961-2010||−0.25||Jin et al. (2012)||40||China (2333)||1960-2017||−0.15||Zhang and Wang (2020)|