Observed Coherent Trends of Surface and Upper-Air Wind Speed overChina since 1960

CHANGGUI LIN

Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research,

Chinese Academy of Sciences, and University of the Chinese Academy of Sciences, Beijing, China

KUN YANG AND JUN QIN

Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan

Plateau Research, Chinese Academy of Sciences, Beijing, China

RONG FU

Department of Geological Sciences, The University of Texas at Austin, Austin, Texas, and Center of Earth

System Science, Tsinghua University, Beijing, China

(Manuscript received 15 February 2012, in final form 8 November 2012)

ABSTRACT

Previous studies indicated that surface wind speed over China declined during past decades, and several

explanations exist in the literature. This study presents long-term (1960–2009) changes of both surface and

upper-air wind speeds over China and addresses observed evidence to interpret these changes. It is found that

surface wind over China underwent a three-phase change over the past 50 yr: (i) it step changed to a strong

wind level at the end of the 1960s, (ii) it declined until the beginning of the 2000s, and (iii) it seemed to be

steady and even recovering during the very recent years. The variability of surface wind speed is greater at

higher elevations and less at lower elevations. In particular, surface wind speed over the elevated Tibetan

Plateau has changed more significantly. Changes in upper-air wind speed observed from rawinsonde are

similar to surface wind changes. The NCEP–NCAR reanalysis indicates that wind speed changes correspond

to changes in geopotential height gradient at 500 hPa. The latter are further correlated with the changes of

latitudinal surface temperature gradient, with a correlation coefficient of 0.88 for the past 50 yr over China.

This strongly suggests that the spatial gradient of surface global warming or cooling may significantly change

surface wind speed at a regional scale through atmospheric thermal adaption. The recovery of wind speed

since the beginning of the 2000s over the Tibetan Plateau might be a precursor of the reversal of wind speed

trends over China, as wind over high elevations can respond more rapidly to the warming gradient and

atmospheric circulation adjustment.

1. Introduction

Widespread surface wind speed declines have been

observed from ground measurements over the past few

decades in many tropical and midlatitude regions in-

cluding China (McVicar et al. 2012, and references

therein). These declines of surface wind speed may con-

tribute to declines of atmospheric potential evapora-

tion, as measured by pan evaporation (Chen et al. 2006;

McVicar et al. 2012; Roderick et al. 2007), and the

weakening trend in atmospheric sensible heat over some

regions (Duan and Wu 2008; Yang et al. 2011a).

Recently, there has been great interest in the causes

of the surface wind decline, as it is a key component of

global climate change. Nevertheless, a widely accepted

explanation has not been reached yet. By investigating

normalized difference vegetation index (NDVI) data,

Vautard et al. (2010) partly attributed the lowering of

Northern Hemisphere atmospheric wind to an increase

of surface roughness, but their study did not consider

some specific cases such as one happening over the el-

evated Tibetan Plateau (TP) where wind speed consid-

erably declined over the last 30 yr (Yang et al. 2011b)

Corresponding author address: Kun Yang, Institute of Tibetan

Plateau Research, Chinese Academy of Sciences, Bldg. 3, Court-

yard 16, Chaoyang District, Beijing 100101, China.

E-mail: yangk@itpcas.ac.cn

1 MAY 2013 L I N ET AL . 2891

DOI: 10.1175/JCLI-D-12-00093.1

� 2013 American Meteorological Society

but the surface roughness change is not so significant. In

Australia, McVicar et al. (2012) found that NDVI is

slightly positively correlated with surface wind speed

trends. The regional cooling in south-central China

caused by air pollution was also speculated to be the

cause of the weakening of the East Asia summer mon-

soon (Xu et al. 2006), which needs a further discussion.

Alternatively, the weakening of atmospheric circulation

under the background of global warming was considered

to be the main factor contributing to the decline of

surface wind in several studies (e.g., Duan andWu 2009;

Guo et al. 2011; Jiang et al. 2010; You et al. 2010; Zhang

et al. 2009b).

These previous studies focused more on the wind

speed changes over the globe and China during the past

decades without including the most recent changes or

addressing their elevation dependence. Calculating the

trends of wind speed with respect to elevation in two

mountainous regions in China and Switzerland, McVicar

et al. (2010) uncovered the phenomenon that surface

winds were declining more rapidly at higher elevations

than lower elevations. Yang et al. (2011b) also pointed

out that wind speed over the Tibetan Plateau during the

last 30 yr declined much more than the one averaged

over China, and therefore there is a need to give a de-

tailed investigation on the elevation dependence of wind

speed change and its cause. If so, this elevation de-

pendencemay imply the relationshipwith upper-air wind

speed, which has not been taken into accounting in pre-

vious studies. Figure 3a of Vautard et al. (2010) showed

decreasing upper-air (850 hPa) wind speed trends over

the past three decades (1979–2008) in Asian regions,

which is in contrast with the increasing trends over

European–American regions. This difference was not

discussed as Vautard et al. (2010) focused primarily on

the European–American regions. Zhang et al. (2009a)

also reported the decreasing upper-air wind speed trends

during 1980–2006 over China, especially in the lower

troposphere. However, a longer-term upper-air wind

speed analysis would be helpful to further explore

whether there is a relationship between the surface wind

change and the upper-air wind change.

In this study, we aim to present a comprehensive

analysis on the trends of wind speed over China during

the past 50 yr. To achieve this, it is analyzed from fol-

lowing several aspects: (i) the observed trends of annual

and seasonal mean surface wind speed; (ii) the spatial

distribution of observed trends in surface wind speed;

(iii) the elevation dependence of observed trends in

surface wind speed; (iv) the trends of observed upper-

air wind speed; and (v) the trends in latitudinal gradients

of 500-hPa geopotential height and surface tempera-

ture. The results are given in section 4. The detailed

description of the datasets and trend-calculating methods

applied in this study is presented in section 2. As the

quality and homogeneity of data are critical for long-term

change analysis, we first present cross-validations to

confirm the reliability of the derived wind speed trends

in section 3, in terms of (i) comparison between In-

tegrated Global Radiosonde Archive (IGRA) 850-hPa

and China Meteorological Administration (CMA) sur-

face wind speed and (ii) comparison between IGRA

850-hPa and CMA surface wind speed. In section 5 of

concern and discussed are the causes of the wind speed

changes over China. Conclusions are given in section 6.

2. Datasets and methods

a. CMA surface routine data

CMAprovides for this study a long-term (from January

1960 to December 2009) routine dataset of surface wind

speed observed at 10 m above the surface. At each CMA

station, surface wind speed was measured 6-hourly [at

0200, 0800, 1400, and 2000 Beijing standard time (BST);

BST 5 UTC 1 8 h]. Figure 1 shows the distribution of

CMA stations and their elevations. The elevations of the

CMA stations vary from sea level up to 4800 m above

mean sea level (MSL). There are a total of 125 stations

with elevations greater than 2000 m MSL located in the

TP area. This region exerts profound influences not only

on the local climate and environment but also on the

global atmospheric circulation through its thermal and

mechanical forcing (Yeh and Gao 1979). Meanwhile, its

environment is sensitive to global climate change (Liu

and Chen 2000). Because of its unique environmental

characteristics and close link to the upper-air atmospheric

circulation, the wind speed changes over this region were

highlighted and discussed herein.

b. IGRA upper-air radiosonde data

The radiosonde data gathered in IGRA are used to

analyze the changes of upper-air wind in this study. The

IGRA archive is integrated from different sources with

a sequence of specialized quality assurance algorithms

(Durre et al. 2006). The filled square symbols in Fig. 1

denote the IGRA (totaling 149) stations in China. These

data are available for 1964–2009 for some stations in

China; nevertheless, data over 1973–90 for many stations

are missing. Hence we focused on 1990 onward. Monthly

averaged values of upper-air wind speed at mandatory

levels of 850, 700, 500, 300, and 200 hPa were used here.

c. Other datasets

To further explore the causations of the derived wind

speed changes, several datasets are used in the study.

2892 JOURNAL OF CL IMATE VOLUME 26

First, the National Centers for Environmental Prediction–

National Center of Atmospheric Research (NCEP–

NCAR) reanalysis (Kalnay et al. 1996) was used.McVicar

et al. (2008) showed that of the three common re-

analysis outputs [NCEP–NCAR,NCEP/U.S.Department

of Energy (DOE), and 40-yr European Centre for

Medium-Range Weather Forecasts (ECMWF) Re-

Analysis (ERA-40)] tested, the NCEP–NCAR reanalysis

was best able to capture observed near-surface wind

speed trends during 1979–2001 for Australia. We

therefore used NCEP–NCAR 500-hPa geopotential

height data to examine whether the changes exist in the

large-scale atmospheric circulation. Second, the Had-

ley Centre and Climate Research Unit (CRU) gridded

near-surface temperature dataset, version 3 (Had-

CRUT3; Brohan et al. 2006; Jones et al. 1999; available

at http://www.cru.uea.ac.uk/cru/data/temperature/),

was used to further explore the changes in latitudinal

gradient of surface warming rate. Third and finally, three

climate indiceswere used: (i)ArcticOscillation (AO); (ii)

North Atlantic Oscillation (NAO); and (iii) Pacific de-

cadal oscillation (PDO). AO is an annular mode of at-

mospheric circulation (Thompson and Wallace 1998).

NAO is the difference of atmospheric pressure at sea

level between the Icelandic low and the Azores high

(Wallace and Gutzler 1981). AO and NAO are the

dominant patterns that affect Northern Hemisphere cli-

mate variability, which are themost prevalent inwinter at

themid–high latitudes. Both indices are available online

(at http://www.cpc.ncep.noaa.gov/). PDO is the leading

principal component of monthly sea surface tempera-

ture (SST) anomalies in the North Pacific Ocean

(poleward of 208N) (Mantua et al. 1997); it is available

online (at http://jisao.washington.edu/pdo/PDO.latest).

The period 1960–2009 was used in this study to match

with the temporal extant of the CMA surface dataset.

These indices are examined in terms of their relationship

with the changes in surface wind speed.

d. Analysis procedures

To account for the irregularly distributed CMA sta-

tions, the area-weighted grid average is preferred in

climate analysis. Duan (2009), however, showed that the

station-averaged surface wind speed exhibits changes

similar to those that are grid averaged, although the two

averages have a systematic difference. We therefore

used the simple station averaging to derive the regional

mean wind speed changes. Based on the CMA surface

wind speed dataset, we calculated the seasonal and an-

nual mean values of wind speed at the four measuring

times of day and their daily means at two spatial scales

(China and TP). The station averages are calculated for

the stations that have ‘‘good data records’’ (defined as

data records that have more than 275 observations each

year). There are 472 such CMA stations in China and 64

such stations in TP. Then, the linear trends of their time

FIG. 1. Distribution of CMA stations. The square markers (filled for stations with IGRA

radiosonde data) in various sizes depict the elevation of each site. The nine regions (TP, A, B,

C, D, E, F, G, andH) are distinguished by different colors. The thick curve encloses the regions

with a height above 2000 m MSL.

1 MAY 2013 L I N ET AL . 2893

series are calculated by fitting a linear regression (ordi-

nary least squares). The trend significance is tested using

Student’s t test at the probability level of P , 0.05. At

each station, the linear trend of surface or upper-air

wind speed is also obtained based on the monthly mean

deseasonalized values. The deseasonalization was per-

formed through the process of removing the seasonal

climatology from the time series.

In view of unrealistic surface wind speed trends cal-

culated at a few stations, we fitted the dependence of

wind speed trend on elevation using a robust linear re-

gression to explore the impact of elevation on surface

wind speed trend. The regression method (available in

MATLAB Statistics Toolbox) employs an iteratively

reweighted least squares algorithm with the weight at

each iteration determined by applying a bisquare func-

tion to the residuals from the previous iteration (Huber

1981; Street et al. 1988). It can therefore minimize the

effect of outliers and/or nonstationary variability com-

pared to the ordinary linear least squares regression.

The wind speed changes are investigated through the

pressure gradient force in the upper air. We defined a

high-latitude band (HL; 458–508N) and a low-latitude

band (LL; 208–258N). The upper-air wind speed over

China, especially the TP region located between latitudes

of 258 and 408N, is largely determined by the pressure

difference (or geopotential height gradient) between

the two bands according to the geostrophic approxi-

mation theory. Using NCEP–NCAR reanalysis data, we

calculated the geopotential height difference between the

two bands (with longitudinal range of 708–1408E) at the500-hPa level as used to denote the geopotential height

gradient. Correspondingly, we also examined the dif-

ference of surface temperature between the two latitude

bands based on the CRU data.

3. Cross-validations of the derived wind speedchanges

The quality and homogeneity of data are critical for

long-term change analysis. Instrumental changes, ob-

servational drift, or other human-induced changes may

yield unreal changes in time series. As far as we know,

only air temperature is homogenized for the CMA sta-

tions, while surface wind speed is more sensitive to local

environment so that its homogenization is more difficult

and not done for the CMA stations. The rawinsondewind

data in IGRA have not yet been adjusted for inhomo-

geneity as well, and therefore one needs to be cautious

when using these data for climate analysis (Durre et al.

2006). For instance, Dai et al. (2011) found that the ra-

diosonde humidity records in IGRA need homogeniza-

tion for its trend analysis, because of ubiquitous and large

discontinuities resulting from changes to instrumentation

and observing practices.

In view of the inhomogeneity problem of the CMA

data, any discussion for a subregion can be uncertain and

risky. So, we calculated the wind speed changes aver-

aged over each of nine regions of China to check the

consistency of wind speed changes in China. Following

Duan (2009), the nine regions of China including the TP

were identified based on the rotated empirical orthog-

onal function analysis and climate features. The stations

within each region are shown in different colors in Fig. 1.

The variations of annual mean surface wind speed in the

nine regions are provided in Fig. 2. It can be found that

the surface wind speeds of all regions other than regions

G and H have the similar changes. Particularly, the step

change around the end of the 1960s is an outstanding

phenomenon and seems like a spurious one. Hereafter,

we confirmed a physical basis for the observed step

change based on independent observations.

a. Comparison between IGRA 850-hPa and CMAsurface wind speed

Because radiosonde data for 1973–90 are unavailable

for most IGRA stations, 1964–72 is used when analyzing

the wind speed step change around the end of the 1960s.

The annual mean wind speed at 850 hPa averaged over

31 IGRA stations with continuous 1964–72 observations

was compared with CMA surface wind speed averaged

at the collocated stations (Fig. 3). They have similar

changes and their correlation coefficient is 0.70 (P ,0.05). The spatial distribution of trends in upper-air wind

speed derived from IGRA changed similarly with the

ones in surface wind speed, as shown in section 4d. As

the two datasets are independent, we tend to believe both

FIG. 2. Annual mean surface wind speeds averaged over nine

regions of China.

2894 JOURNAL OF CL IMATE VOLUME 26

the CMA data and the IGRA wind speed data are

qualified for climate analysis and their trends are quan-

titatively reliable.

b. Comparison between ground–air temperaturegradient and surface wind speed

The surface sensible heat flux is mainly determined by

the ground–air temperature difference (Tg 2 Ta) and

surface wind speed together. When sensible heat flux

dominates the surface energy budget, locally, a high

wind speed usually results in a low value of the tem-

perature difference and vice versa. Therefore, opposite

trends between the surface wind speed and the ground–

air temperature gradient are expected. Figure 4 shows

that this phenomenon of opposing trends is rather ap-

parent in spring over the TP, when the sensible heat flux

is strongest. The correlation coefficient of the two time

series is 20.76 (P , 0.01). This is additional evidence

that the observed wind speed change in Fig. 2 is very

likely real (not instrumental), as the measurements of

ground temperature Tg, air temperature Ta, and surface

wind speed are all independent.

From the above cross-validations, the wind speed step

change around the end of the 1960s and the decline af-

terward are likely real, although artifacts such as instru-

ment updates and station migration may contaminate

the wind data to some degree (Cao and Yang 2012).

4. Results

a. Observed trends of annual and seasonal meansurface wind speed

Figure 5 shows the observed variations of annual mean

surface wind speeds averaged over China and the TP.

Over the past 50 yr, the linear trend in annual mean

surface wind speed is 20.010 6 0.001 m s21 yr21 (error

bar forP, 0.05) for thewhole ofChina and a smaller value

of 20.006 6 0.002 m s21 yr21 (error bar for P , 0.05)

for the entire TP region. However, it can be seen that

the surface wind speed over China did not persistently

decline. There are two transitions in wind speeds,

occurring at the beginning of the 1970s and the 2000s;

particularly, surface wind speeds step changed to strong

ones around the end of the 1960s. These wind speed

transitions are more significant over the TP. Regarding

the annual mean daily wind speed, the difference in

surface wind speed between the average over 1960–68

and the average over 1969–74 is 0.150 m s21 over

China and 0.431 m s21 over the TP. On average, the

magnitude of the daily mean increasing trend during the

first period (1960–73: referred to as P1) is 0.011 60.005 m s21 yr21 (error bar forP, 0.05) for the whole of

China and 0.040 6 0.009 m s21 yr21 (error bar for P ,0.05) for the entire TP region and then weakened during

the second period (1974–2001: referred to as P2) with

a trend slope of20.0186 0.001 m s21 yr21 (error bar for

P , 0.05) for the whole of China and 20.023 60.001 m s21 yr21 (error bar forP, 0.05) for the entire TP

region. Previous studies mainly addressed the wind speed

decline over the second period. During the third period

(2002–09: referred to as P3), there is no significant trend in

the nation-averaged annual mean daily wind speed

(20.0026 0.006 m s21 yr21) other than the one averaged

over the entire TP region (0.027 6 0.013 m s21 yr21).

During P3, the positive trends in wind speeds are de-

tected for 0200 and 0800 BST in the whole of China and

for all times in the entire TP region. In the atmospheric

boundary layer, the surface wind speed usually exhibits

a clear diurnal variation, because convection in the

daytime boundary layer enhances the vertical mixing of

momentum. This phenomenon is particularly prominent

over the TP region, where intense sensible heat flux in

the daytime enhances the development of very deep

FIG. 3. Surface wind speed vs 850-hPa wind speed. Both are

averaged over collocated 31 stations with continuous 1964–72

observations.FIG. 4. Spring surface wind speed vs ground–air temperature

gradient. Both are averaged over 21 stations located in the east TP

(east of 858E), which have continuous good data records (i.e., more

than 300 observations each year) of every observational parameter

over 1960–2006.

1 MAY 2013 L I N ET AL . 2895

boundary layers (e.g., Luo andYanai 1984;Ma et al. 2005;

Yang et al. 2004; Yeh and Gao 1979). However, no ob-

vious diurnal variation exists in the surface wind speed

trends during all the periods over the TP. Changes in

the boundary layer, therefore, should not be a consider-

able cause of wind speed change, which was also pointed

out by Vautard et al. (2010). This implies that the wind

speed change may be beyond the boundary layer scale.

Similar to the annual mean surface winds, seasonal

mean surface wind speeds had transitions at the begin-

ning of the 1970s and the 2000s (not shown). All the four

seasonal mean surface winds consistently step changed

to strong ones during P1 and weakened during P2 sig-

nificantly. During both the strengthening period and the

weakening period, the magnitudes of change for the

seasonal mean surface wind speeds over the TP are

larger than in the whole of China. A positive wind speed

trend in winter [December–February (DJF)] and nega-

tive ones in other seasons are found in the whole of China

duringP3, but noneof them is statistically significant (Fig. 6).

However, the trends in all the seasons other than the spring

are significantly positive during P3 over the TP region.

In a word, during the last 50 yr, there are three periods

of wind speed changes in China. The trends over the TP

are similar to the averaged ones over China but have

larger magnitudes. In particular, the recent recovery

(i.e., positive trend) of wind speed over the TP was

found here, a phenomenon not previously reported in

the literature.

b. Spatial distribution of observed trends in surfacewind speed

Since the changes in surface wind speed over the

whole of China and the TP are somewhat different, their

spatial distribution is examined. Figure 7 shows the spa-

tial distribution of the CMA surface wind speed trends

during the three periods (P1, P2, and P3) defined in

section 4a. Almost all the CMA stations in China ex-

hibited positive trends in surface wind speed during P1,

especially over the TP, where surface wind speeds in-

creased considerably during this period (Fig. 7a). The

averaged trend value is 0.010 m s21 yr21 over the

whole of China versus 0.042 m s21 yr21 over the entire

TP region. During P2, negative trends are dominant,

with values of 20.018 m s21 yr21 over the whole of

China and20.024 m s21 yr21 over the TP (Fig. 7b). The

spatial distribution of surface wind speed trends is

complex during P3 as shown in Fig. 7c, perhaps be-

cause of too short of a period. Nevertheless, surface

wind speeds at a large number of stations, especially

over the TP, turned to increase during P3, although the

wind speeds at the remaining stations still decline. The

FIG. 5. Annual mean surface wind speeds averaged over all CMA stations (left) in China (CN) and (right) in the

TP. Curves with differentmarkers denote the four daily observation hours (0200, 0800, 1400, and 2000BST) and daily

mean (dm). The linear trends (m s21 yr21) during the entire record (ER; 1960–2009), P1 (1960–73), P2 (1974–2001),

and P3 (2002–09) are given in the legend of the figure panels.

2896 JOURNAL OF CL IMATE VOLUME 26

average value during P3 was20.003 m s21 yr21 over the

whole of China versus 0.022 m s21 yr21 over the TP.

c. Elevation dependence of observed trends in surfacewind speed

From the above analyses, changes in surface wind

speed over the TP aremore apparent than those over the

other regions of China. Previous studies (Liu and Chen

2000; Qin et al. 2009) have suggested that the warming

rate over the TP is elevation dependent; we therefore

examined whether the trends in surface wind speed are

elevation dependent.

Figure 8 shows the elevation dependence of surface

wind speed trend during the three periods defined be-

fore. All the P values of regressions during the three

periods are less than 0.001. During P1, the positive slope

value of 0.018 m s21 yr21 km21 (0.7% yr21 km21) and

the near-zero intercept of20.007 m s21 yr21 (0.2% a21)

mean that surface wind speeds increasedmore drastically

at higher elevations than lower elevations (Figs. 8a,b).

In contrast, during P2 the negative slope value of

20.004 m s21 yr21 km21 (20.2% yr21 km21) and the

negative intercept of 20.014 m s21 yr21 (20.6% yr21)

indicate that surface winds declined more intensely at

higher elevations than lower elevations (Figs. 8c,d). Fig-

ures 8e and 8f illustrate that surface winds during P3

strengthened more at high-elevation stations and

weakened more at low-elevation stations, according to

the negative intercept of 20.014 m s21 yr21 (20.4%

yr21) and the positive slope of 0.010 m s21 yr21 km21

(0.4% yr21 km21) of the fitted line. These results suggest

that wind speed transitions occur earlier in high eleva-

tions than in low elevations.

Based on the above analysis, changes in surface wind

speeds are more significant at higher elevations than at

lower elevations. This result is consistent with the find-

ing of McVicar et al. (2010). Surface winds at higher

elevations are more sensitive to the changes in the upper-

air winds, when the momentum is transported from the

free atmosphere to the atmospheric boundary layer. Such

elevation dependence of surface wind speed trends im-

plies coincident changes in upper-air winds.

d. Trends in IGRA rawinsonde upper-air wind speed

At each rawinsonde station, trends in mean upper-air

wind speeds at mandatory pressure levels of 850, 700,

500, 400, 300, and 200 hPa are derived from the IGRA

rawinsonde dataset for the three periods. Figure 9 shows

the spatial distribution of wind speed trends at 500 and

850 hPa, which are chosen to represent the lower free

atmosphere over the TP and the remaining part of China,

respectively. During P1, few (only two) stations in the TP

region are available. Nevertheless, the upper-air winds

speeds exhibited significantly positive trend values at

FIG. 6. As in Fig. 5, but for seasonal [DJF, Mar–May (MAM), June–Aug (JJA), and Sep–Nov (SON)] mean surface

wind speeds.

1 MAY 2013 L I N ET AL . 2897

all the selected levels. During P2, the negative trends

in upper-air wind speeds prevailed under the level of

300 hPa and are significant over the TP at 500 hPa.

During P3, positive trends in upper-air wind speeds in

north China and the TP, and the negative trends in

south China are detected from upper-air wind speeds

above the level of 500 hPa, while the trends at 700 and

850 hPa are more complex.

Therefore, changes in upper-air wind speeds closely

resemble to those in surface wind speeds during the P1

and P2. This resemblance is particularly distinctive

between changes in surface wind speeds and those in

winds at 500 hPa over the TP and between those in

surface wind speeds and those at 850 hPa over non-TP.

Although the spatial distribution of trends in upper-air

wind speeds is not in complete agreement with surface

wind speed trends during P3, the recovery of upper-air

wind speeds is observed over the TP and some non-TP

regions and a similar phenomenon also occurs in sur-

face wind speeds. Since there is an intimate link between

the surface and upper-air winds, changes in atmospheric

circulation may exert more influence on changes in sur-

face wind speeds than local changes in surface rough-

ness, especially for the TP.

e. Trends in latitudinal gradients of 500-hPageopotential height and surface temperature

Changes in geopotential height gradient are inves-

tigated to examine the intimate link between upper-air

and surface winds. Figure 10a shows the temporal vari-

ation of geopotential height difference between the two

latitude bands defined above (LL and HL) over the past

50 yr. The geopotential height gradient strengthened

during P1, weakened during P2, and was enhanced again

during P3. Such variations are synchronous with the ob-

served upper-air and surface wind speed changes. It

qualitatively confirms the coincident changes in regional

atmospheric circulation.

The role of regional surface warming in the changes

of wind speed under the background of global warming

is further explored. Figure 10b shows the difference of

surface temperature between the two latitude bands de-

fined above. Clearly, the interannual variability of the

surface temperature gradient is consistent with the geo-

potential height gradient at 500 hPa (Fig. 10a). Their

correlation coefficient is as high as 0.88.

5. Discussion

The causes of the wind speed changes are currently

not fully understood. Several hypotheses have been

proposed to explain the decline trends in surface wind

speed, such as (i) changes in large-scale atmospheric

circulation (e.g., Duan and Wu 2009; Guo et al. 2011;

Jiang et al. 2010); (ii) increasing surface roughness

(Vautard et al. 2010); (iii) effects of local air pollution

(e.g., Jacobson and Kaufman 2006; Xu et al. 2006); (iv)

influences of urbanization (e.g., Klink 1999; Li et al.

2011); (v) instrumental changes or observational drifts

(e.g., DeGaetano 1998; Thomas and Swail 2011); and

(vi) other possible causes given by McVicar et al. (2012).

However, most of the factors may only explain the

changes in surface wind speed in a local area and/or for

a certain period. We demonstrated a reasonable

FIG. 7. Spatial distribution of CMA surface wind speed trends

during three periods: (a) P1 (1960–73); (b) P2 (1974–2001); and

(c) P3 (2002–09). The positive and negative trends are marked by

regular triangles and inverted triangles, respectively. The filled

triangle indicates that the trend at this station is statistically sig-

nificant (P , 0.05).

2898 JOURNAL OF CL IMATE VOLUME 26

explanation for the nation-wide surface wind speed

changes in China over the last 50 yr.

The impact of instrumental changes or observational

drifts cannot be ignored as it may yield spurious trends.

Nevertheless, according the cross-validations in section 3,

we illustrated that the trends derived from the ob-

served data are not artifacts. The difference between

the trends in urban and rural stations in China is quite

small (Guo et al. 2011; Jiang et al. 2010), so urbanization

should not be a considerable cause of surface wind speed

changes. Direct anthropogenic impact over the TP is

relatively small even though land-use change and in-

dustrialization have occurred over a small fraction of the

TP.Air pollution, therefore, cannot be used to explain the

observed wind speed changes and particularly their ele-

vation dependence. Increasing surface roughness is re-

garded as an important factor, which attributes 20%–60%

of Northern Hemispheric atmospheric stilling (Vautard

et al. 2010). This may explain the contrasting trends

between surface and upper-air wind speed over Europe

and North America. However, this mechanism is not

applicable to China, because the changes in upper-air

wind speed coincided with the changes in surface wind

speed according to the analyses made in section 3. Ad-

ditionally, bare soils and short grasses are the dominant

land-cover types and the surface roughness lengths are

generally small (millimeters to centimeters) over the

TP. The changes in surface roughness are quite in-

significant as trends of NDVI for the TP during 1982–99

are shown to be very small in Beck et al. (2011). Thus, it

is unlikely that the changes of roughness length affect the

surface wind speed so significantly over the plateau.

As shown in the Fig. 10a, the three phases of surface

and upper-air wind speed changes over China corre-

spond well to the changes in the pressure gradient force

and the atmospheric circulation. This substantiates the

FIG. 8. Elevation dependence of CMA surface wind speed trends (slopes in the figure) during the three periods:

(a),(b) P1 (1960–73); (c),(d) P2 (1974–2001); and (e),(f) P3 (2002–09). The unit of elevation dependence for (a),(c),(e)

is m s21 yr21 km21, while for (b),(d),(f) it is % yr21 km21. The filled square markers indicate sites with significant

trend (P , 0.05). The thick black lines show the linear regression between surface wind speed trend and elevation.

1 MAY 2013 L I N ET AL . 2899

explanation that the changes of surface wind speed are

largely induced by the changes in atmospheric circulation

(e.g., Duan and Wu 2009; Guo et al. 2011; Jiang et al.

2010; You et al. 2010; Zhang et al. 2009b). Using a mul-

timodel simulation during 1979–2000, Duan and Wu

(2009) discussed the relationship between the East Asian

subtropical westerly jet and latitudinal temperature

gradient at 500 hPa. In this study, the role of regional

surface warming in the changes of wind speed under the

background of global warming was further explored

(section 4e). The high correlation between the gradient

of NCEP–NCAR 500-hPa geopotential height and the

gradient of CRU surface temperature gradient is not an

accident but has a physical basis. The regional difference

in the surface warming rate may modify the gradient of

geopotential height through thermal adaption, and this

change in free atmospheric circulation will further in-

fluence the surface wind speed change through the

momentum downward transport from free atmosphere

to the atmospheric boundary layer. Therefore, the trends

of the latitudinal gradient in surface temperature during

the three periods approximately correspond to the ones

of surface wind speed over China (see Fig. 5).

Several climate indices are used to characterize the

atmospheric circulation variability. Therefore, it is worth

discussing whether the wind speed change is related to

FIG. 9. Spatial distribution of IGRA upper-air wind speed trends at (a),(c),(e) 500 and (b),(d),(f) 850 hPa during

(a),(b) P1 (1964–72); (c),(d) P2 (1991–2001); and (e),(f) P3 (2002–09). The positive (negative) trends are marked

by circles with (without) a plus sign. The filled circles indicate that the trend at this station is statistically significant

(P , 0.05).

2900 JOURNAL OF CL IMATE VOLUME 26

the indices. As indicated in previous studies (e.g., Gong

and Ho 2003; Gong et al. 2001; Ogi et al. 2003), AO/

NAO variability exerts different impacts on the zonal

mean wind speed at different latitudes of the Northern

Hemisphere. However, both the AO and NAO indices

have increasing (decreasing) trends before (after) 1990

(Figs. 11a,b), and these trends are not in phase with the

changes in wind speeds over China. The PDO can

modulate the impact of ENSO on the East Asian at-

mospheric circulation (e.g., Wang et al. 2008; Yoon and

Yeh 2010), but we found no robust correlation between

the PDO index and changes in wind speeds over China

(Fig. 11c). Therefore, wind speed changes over China

cannot be simply linked with oscillation indices such as

AO/NAO/PDO. Nevertheless, we cannot exclude other

climate indices or circulation patterns that are forcing

the wind speed changes.

6. Conclusions

Based on temporal and spatial analysis of surface wind

speed using the CMA operational meteorological data,

we found that the surface wind speeds in China step

changed to strong ones around the end of the 1960s. The

winds then experienced weakening trend until the

beginning of the 2000s and a possible recovery afterward.

The high-elevation areas such as the Tibetan Plateau

experienced more significant change during both the

step change and decline period. Moreover, synchronous

changes in surface wind and upper-air wind speed were

observed over both the TP and the whole of China.

The deceleration of wind speed over the Tibetan

Plateau since the 1970s was ascribed to the substantial

tropospheric warming in themiddle and high latitudes to

the north of the plateau in two recent studies (e.g., Duan

andWu 2009; Zhang et al. 2009b). Our investigation was

extended for a longer period and a larger region and

supported the basic idea of the previous studies, but note

that here we presented more direct evidence that asso-

ciated wind speed changes with the latitudinal gradient

of the surface temperature (instead of the 500 hPa in the

two earlier studies), which may be easily measured. It is

shown that there is a high correlation coefficient (0.88)

between the latitudinal gradient of the surface temper-

ature and the latitudinal gradient of geopotential height

at 500 hPa in East Asia. The trend of the latter corre-

sponds well to both the weakening and strengthening of

wind speed over the Tibetan Plateau and over the whole

of China. Therefore, the spatial inhomogeneity of global

warming or coolingmay significantly change surfacewind

speeds regionally via thermal adaption. This result

contrasts with those for North America and Europe,

where opposite changes in surface wind and upper-air

wind speed were found and such changes can be partly

explained by the increase of surface roughness lengths

(Vautard et al. 2010). McVicar et al. (2012) note that the

FIG. 10. Interannual variability of (a) the difference of annual

mean NCEP–NCAR reanalysis 500-hPa geopotential height and

(b) the difference of annual mean CRU surface temperature (com-

bined HadCRUT3 temperature anomalies and absolute tempera-

tures for the base period 1961–90) between the low and high latitudes

of China. HL is defined as 458–508N and LL is 208–258N, and the

longitudes of interest are between 708 and 1408E. The differences

between HL and LL are indicated by DGH and DT, respectively.The dashed lines denote the linear trends of DGH (gpm yr21) and

DT (8C yr21) during the three periods. The trend slope values are

given in the figure (i.e., P1, P2, and P3, respectively).

FIG. 11. Annual anomalies of (a) AO, (b) NAO, and (c) PDO

indices during the period of 1960–2009. The plain curves are the

5-yr smoothing averages.

1 MAY 2013 L I N ET AL . 2901

different causes of wind speed trends maybe occurred

at different locations for different periods, and hence

global attribution remains a key scientific challenge.

The intense variability of wind speed over the Tibetan

Plateau is another indicator of the high sensitivity of the

Tibetan Plateau responding to climate change. There-

fore, the recovery of wind speed over the TP since 2002

might imply another transition of the regional atmo-

spheric circulation and be a precursor of the reversal of

wind speed trend over China, noting that both the wind

speed and geopotential height gradient at 500 hPa over

China turned to be increasing during recent years.

Finally, we noticed that the sudden increase of wind

speeds both at the surface and upper troposphere dur-

ing 1969–74 and 2002–09 generally coincided with pe-

riods of strongly negative AO phase. It is necessary to

investigate whether equatorward displacement of jets

occurred over Asian sector and, if so, how such anoma-

lous would contribute to the increase of winds during

those two periods.

Acknowledgments. This work was supported by the

Global Change Program of Ministry of Science and

Technology of China (2010CB951703), the Strategic

Priority Research Program of the Chinese Academy of

Sciences (Grant XDB03030300), and National Natural

Science Foundation of China (Grant 41190083). The

surface station data used in this study were provided by

the Climate Data Center at the CMA National Mete-

orological Information Center. The involvement of Rong

Fu in this work is supported by the Center of Earth Sys-

tem Science at Tsinghua University. We are very grateful

to the anonymous reviewers for their valuable comments

and constructive suggestions.

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