Theoretical and Applied Climatology https://doi.org/10.1007/s00704-017-2346-8
ORIGINAL PAPER
Spatiotemporal variation of upper-air and surface wind speed and its influencing factors in northwestern China during 1980–2012 Jili Zheng 1 & Baofu Li 1
&
Yaning Chen 2 & Zhongsheng Chen 3 & Lishu Lian 1
Received: 12 April 2017 / Accepted: 5 December 2017 # Springer-Verlag GmbH Austria, part of Springer Nature 2017
Abstract Scientists generally believe that human activities and atmospheric circulations have important effects on wind speed changes; however, the main driving factors of wind speed at different times and in different areas are far from clear. Here, based on monthly wind speed data in the northwestern China during 1980–2012, we analyzed the spatiotemporal variations in wind speed and their relationship with atmospheric circulations. The results showed that (1) annual surface wind speed (SWS) displayed a decreasing trend (− 0.032 m/s/10a) during 1980–2012; SWS increased only in winter. In contrast, annual upper-air wind speed (UWS) exhibited an increasing trend; UWS presented a declining trend only in summer. (2) Human activities were likely the major cause of decrease in SWS, whereas the enhancement of the Asian Meridional Circulation (AMC, R = 0.50, P < 0.01) was an important factor for the increase in SWS in winter. (3) Annual UWS exhibited an increasing trend (0.176 m/s/10a) in the troposphere. This trend was closely related to the weakening of the Eurasian Zonal Circulation (EZC, R = − 0.52, P < 0.01) and the enhancement of the Siberian High (SH, R = 0.48, P < 0.01) during the winter, whereas the enhancement of the Eurasian Zonal Circulation (EZC, R = − 0.60, P < 0.001) in summer was the prime reason for the decrease in UWS in summer. (4) UWS increased significantly (0.38 m/s/10a) in the lower stratosphere, and this trend was mainly associated with the weakening of the Pacific Decadal Oscillation (PDO, R = − 0.40, P < 0.05).
1 Introduction A series of phenomena, such as accelerated urbanization and increased industrial pollution, has led to changes in the global climate system during the twenty-first century, which are mainly reflected in the obvious changes in climatic factors, such as wind speed, temperatures, and precipitation. Wind speed is an important parameter that reflects atmospheric dynamic characteristics and climate variability, and its changes are closely associated with the circulation of energy and material within the atmosphere (Zhang et al. 2009).
* Baofu Li
[email protected] 1
College of Geography and Tourism, Qufu Normal University, Rizhao 276826, China
2
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
3
College of Land and Resources, China West Normal University, Nanchong 637002, China
Consequently, studying the characteristics of spatiotemporal changes in the wind speed of a typical region helps to deepen the understanding of regional climate variability and improve climate analysis and prediction capabilities. Recently, the characteristics of wind speed variations in different regions and their driving factors have been reported by many scientists (Ding et al. 2014; Dong et al. 2014; Helbig et al. 2017; Huang et al. 2011; Ni et al. 2015; Wei 2015; Xie et al. 2015; Yao et al. 2009). Previous studies have revealed that reductions in SWS values are caused by changes in atmospheric circulation systems and human activities (Ding et al. 2014; Dong et al. 2014; Hang et al. 2011; Yao et al. 2009). Atmospheric circulation patterns are the primary driving factors of regional climate changes, and the variations and adjustments in atmospheric circulation patterns are key factors leading to regional climate variability within the context of global change (Jiang et al. 2007). On the other hand, the impacts of human activities are mainly reflected in the development of urbanization, the underlying surface changes, and changes in the observed environment (Xie et al. 2015). For example, the increasing quantity, density, and height of buildings have resulted in increased roughness of the
J. Zheng et al.
underlying surface. The friction barrier effect consumes the kinetic energy of the horizontal movement of the air, thus reducing the mean SWS (Zhou and Yu 1988). Zhang et al. (2009) noted that the reduction in average SWS in China may be driven by changes in large-scale atmospheric circulation patterns, which are more likely to be closely related to the influence of human factors, for instance, changes in the observed environment near stations and urbanization. In addition, Jiang et al. (2013) found that changes in wind speed measuring instruments around meteorological stations and the development of urbanization had some influence on the changes in SWS values, but they were not the main reasons for the significant decrease in SWS values. The changes in atmospheric circulation patterns and climate warming were possible driving factors of the reduction in SWS values. Obviously, it is an indisputable fact that changes in wind speed are influenced by atmospheric circulation patterns and human activities, but controversies about the main driving factors of wind speed changes still exist. In addition, the driving factors of wind speed changes may not be consistent at different times or indifferent atmospheric layers (i.e., at the surface and in the upper air). Accordingly, we aim here to explore the direct causes of the observed changes in SWS and UWS values in the ARNC. Li et al. (2012) found that the Siberian High (SH) had a strong association with winter temperatures in the ARNC. Li et al. (2016) also noted that changes in precipitation in the ARNC were significantly affected by the Western Pacific Subtropical High (WPSH) and the North America Subtropical High (NASH). Ji (2009) showed that the positive feedback processes between Eurasian atmospheric circulation patterns and continental temperatures at midhigh latitudes may be the mechanisms responsible for accelerated warming at mid-high latitudes on the Eurasian continent. Chen et al. (2014) believed that changes in the Tibetan Plateau Index_B (TPI_B) represented an important explanatory factor for the abrupt changes in extreme temperature and precipitation in the ARNC. Therefore, we speculate that atmospheric circulation patterns, especially the Siberian High (SH), the Western Pacific Subtropical High (WPSH), the North America Subtropical High (NASH), the Eurasian Meridional Circulation (EMC), the Eurasian Zonal Circulation (EZC), the Asian Meridional Circulation (AMC), and the Tibetan Plateau Index_B (TPI_B), have a vital effect on wind speed changes in the ARNC. The SH is a cold or very cold dry air mass that forms in the Mongolian-Siberian region. It has an immense influence on the weather patterns in most parts of the Northern Hemisphere (Gong and Ho 2002). The intensity index of the WPSH is defined as the encoding sum of the mean height value from 588 dagpm (the encoding of 588,
589, and 590 dagpm represents 1, 2, and 3, respectively, and so on) in the Western Pacific on monthly mean circulation maps at a height of 500 hPa along 10° N and 110° E– 180° E (Zhao 1999). Similarly, according to the above definition, we calculated the intensity index of the NASH (110° W–60° W) (Zhao 1999). The EMC (EZC) is defined by mean meridional (zonal) air transport in Eurasia per unit time at the 500 hPa height from 45°–65° N to 0°–150° E (Zhao 1999). The AMC (60° E–150° E) refers to the movement of air within the meridional section of the Asian region, which is used to describe the flow and exchanges in air in the north-south direction of the atmospheric circulation patterns (Zhao 1999). TPI_B is defined as the cumulative value of all geopotential height values at 500 hPa after removing the hundreds in the area ranging from 30° N–40° N to 75° E–105° E (unit: geopotential meter) (Chen et al. 2014). In addition to the above circulation patterns, this paper also analyzes the relationships between other atmospheric circulation patterns which may have an effect on the wind speed changes and wind speed in the ARNC (Guo et al. 2008; Lee and Zhang 2011; Ren and Yu 2008; Shen et al. 2012; Thompson and Wallace 1998; Yang and Yang 2006; Zhang et al. 1985; Zhang 2013). These circulation patterns mainly include the Arctic Oscillation (AO), the Southern Oscillation (SO), the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), the Northern Hemisphere Polar Area (NHPA), the South China Sea Subtropical High (SCSSH), the Northern Hemisphere Subtropical High (NHSH), and the Atlantic Subtropical High (ASH). The AO comprehensively reflects the basic state of the mid-high latitude atmospheric circulation in the Northern Hemisphere, and it can describe the intensity and location of the mid-latitudes westerly belt (Thompson and Wallace 1998). Moreover, there are periodic oscillations on different scales in precipitation and temperature in the northwest China (Guo et al. 2008). The SO has a significant impact on global climate, and it is closely related to the precipitation that falls during the flood season in the eastern part of northwest China (Ren and Yu 2008). The NAO plays an important role in the Northern Hemisphere climate system, and it is negatively correlated with drought conditions, resulting in a decrease in drought in the northwest China (Lee and Zhang 2011). The PDO is an upper-air pressure stream that appears in the Pacific and alternates between Bwarm phases^ and Bcold phases,^ each lasting 20 to 30 years (Yang and Yang 2006). The NHPA has a strong association with temperatures (Shen et al. 2012; Zhang et al. 1985; Zhang 2013). Similar to the intensity index of the WPSH, we computed these intensity indexes as follows: SCSSH (100° E–120° E), NHSH (5° E–360), and ASH (55° W–25° W) (Zhao 1999).
Spatiotemporal variation of upper-air and surface wind speed and its influencing factors in northwestern...
2 Materials and methods
2.3 Methods
2.1 Study area
The Mann-Kendall non-parametric trend testis used to identify the significance of trends in wind speed. The Mann-Kendall trend test makes use of linear fitting to derive trend equations and quantitatively analyzes the trend characteristics over a certain period of time so that the sequence is represented by an increasing trend or a decreasing trend (Li and Ma 2014), which can often be used in tests of significance of the trends in climate time series (Chen et al. 2015; Zhao et al. 2013). The seasons are defined as follows. Winter extends from December to February, spring extends from March to May, summer extends from June to August, and autumn extends from September to November. The data were standardized to reduce the differences in amplitude of the original data and eliminate the influence of their dimensions. For the sequence x1, x2, …xn, the following formula is given:
The ARNC is generally bounded by 73°–106° E and 35°–50° N, including the whole territory of Xinjiang Uygur Autonomous Region, the Hexi Corridor of Gansu Province, the Alashan Plateau of Inner Mongolia, and Ningxia Hui Autonomous Region to the west of Ningxia section along the Yellow River, of which the land area accounts for approximately one fourth of China’s total area (Chen et al. 2015). The area is located in the hinterland; its weather conditions are perennially controlled by continental air masses and experiences a temperate continental climate. The climate of this region is dry and rainless. This region also has complex terrain, with interspersed plateaus and basins.
2.2 Data The UWS data were obtained from the National Meteorological Administration of China (NMAC), which received the real-time sounding data through the Global Telecommunication System (GTS). These data represented the monthly average wind speed between 1980 and 2012 observed in the ARNC using radiosondes. According to the principles of meteorological data integrity and uniform spatial distribution, 14 radiosonde stations in the ARNC were selected in the end, and 10 standard pressure layers (850, 700, 500, 400, 300, 250, 200, 150, 100, and 50 hPa) were selected for each radiosonde station. To compare with the UWS changes, SWS data (derived from the NMAC) were taken from ground meteorological stations that correspond to the radiosonde stations. These sites cover essentially the entire study area (Fig. 1).
Fig. 1 Distribution of the 14 radiosonde stations and ground meteorological stations in the ARNC
yi ¼
xi −x s
ð1Þ
where 1 n x ¼ ∑ xi ; s ¼ n i¼1
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 1 n ∑ xi −x n−1 i¼1
ð2Þ
then, in the new sequence y1, y2, …yn, mean value is 0, the variance is 1, and the values are dimensionless. We employed correlation analysis in multivariate statistical analysis, a non-parametric test, to detect the correlation between SWS and UWS values and its association with atmospheric circulation patterns. Furthermore, to determine if a particular seasonal wind speed change plays a stronger role in terms of the yearly
J. Zheng et al.
change, we also calculated the seasonal importance (Li et al. 2016). The formula can be represented as follows: ps;1980−1989 −ps;1990−2012 100% I s ¼ ∑ps;1980−1989 −ps;1990−2012
ð3Þ
s∈fspring;summer;autumn;winterg
where Is is the importance of each season’s wind speed variation to the annual wind speed variation; ps;1980−1989 and ps;1990−2012 are the mean wind speeds of during each season of the year from 1980 to 1989 and from 1990 to 2012, respectively (Li et al. 2016).
3 Results and analysis 3.1 Time variation characteristics 3.1.1 Year change The annual average SWS was relatively small, only 2.24 m/s, in the ARNC during the period 1980–2012; the wind speed then rose gradually with increasing altitude and reached its maximum (30.17 m/s) at 200 hPa, where it was equivalent to 15 times the SWS; the wind speed gradually decreased with increasing height from 200 to 50 hPa. The wind speed decreased to 12.89 m/s at 50 hPa, which was approximately six times the SWS. Over the investigated period, the surface and 850 hPa wind speeds existed obviously declining trends in the ARNC, but the trends were not significant in those places (Table 1). The wind speeds were basically stable at 700–300 hPa, whereas the wind speeds above 250 hPa showed a distinctly increasing trend. In particular, the trends in wind speeds at 150 and 100 hPa were significant (P < 0.05). This indicates that the increasing tendency of wind speed becomes more and more distinct with increasing altitude in the ARNC. To further analyze the features of spatiotemporal variations in UWS values and their differences, the vertical profile of the upper air is divided into three layers: the midlower troposphere, the upper troposphere, and the lower stratosphere. The mid-lower troposphere includes the 850, 700, 500, and 400 hPa pressure layers; the upper Table 1
troposphere includes the 300, 250, 200, and 150 hPa pressure layers; and the lower stratosphere includes the 100 and 50 hPa pressure layers. SWS values presented a declining trend in the ARNC from 1980 to 2012, while the UWS in each layer displayed an increasing trend (Fig. 2). The rates of change of wind speeds at the surface, in the mid-lower troposphere, and the upper troposphere were − 0.032 m/s/10a, 0.042 m/s/10a, and 0.31 m/s/10a, respectively, and these rates did not pass the 0.05 significance level test. This result demonstrates that the surface and tropospheric wind speeds did not change substantially over the past 30 years. On the other hand, the UWS in the lower stratosphere increased by 0.38 m/s/10a, and this trend was significant at the 0.05 level. In addition, the SWS showed a downward trend before 1992 and then increased, which may be related to the change of wind speed observation methods and gale frequency in the area (Chen et al. 2010; Liu et al. 2016). UWS dropped to a lower value in 1997 (especially at the upper troposphere), which was consistent with the characteristics of UWS changes in China (Zhang et al. 2009). In summary, the increases in UWS in the upper troposphere through the lower stratosphere were noticeably larger than those that occurred in the mid-lower troposphere in the ARNC over the past 30 years.
3.1.2 Monthly and seasonal changes The monthly mean SWS values display a single peak structure (Fig. 3). Of these values, the average SWS values during March to May (spring) are larger, reaching 2.73 m/s. The peak value appears in April, when the average SWS reaches 2.94 m/s, which is closely related to the many springs and storms that occur in this region (Hang et al. 2011); the minimum SWS occurs in January during the winter. The highest UWS of the upper troposphere appears in September (31.39 m/s), while the lowest UWS occurs in May (26.26 m/s). The change in UWS values in the lower stratosphere is the largest and expresses a BUshaped^ structure, and the lowest UWS appears in July, during the summer, whereas the highest UWS occurs in December, during the winter. This situation, however, is obviously different from the characteristics of the monthly variation of the SWS. The variation range in monthly UWS values in the mid-lower troposphere is relatively small.
Change trend test results of annual speed wind of surface and ten pressure levels in the ARNC between 1980 and 2012
Index
Surface
850 hPa
700 hPa
500 hPa
400 hPa
300 hPa
250 hPa
200 hPa
150 hPa
100 hPa
50 hPa
Z Trend (m/s/10a)
− 1.47 − 0.03
− 1.26 − 0.02
0.57 0.05
− 0.09 0.06
− 0.02 0.08
0.51 0.14
0.79 0.20
1.41 0.38
2.12* 0.53
2.59* 0.46
1.69 0.30
*Significant at P < 0.05
Spatiotemporal variation of upper-air and surface wind speed and its influencing factors in northwestern... 3.0
13 Mid-lower troposphere
2.5
m/s
12 11
2.0
Wind speed
m/s
Surface
Wind speed
Fig. 2 Changes in annual SWS and UWS values in the ARNC between 1980 and 2012
y = -0.0032x + 2.30
10 y = 0.0042x + 10.83 9 1980 1984 1988 1992 1996 2000 2004 2008 2012 year
1.5 1980 1984 1988 1992 1996 2000 2004 2008 2012 year
32 Upper troposphere
20
26 24
18 16 y = 0.0378x + 17.34
y = 0.0311x + 27.40
22
1980 1984 1988 1992 1996 2000 2004 2008 2012 year
14 1980 1984 1988 1992 1996 2000 2004 2008 2012 year
tendencies of UWS values in spring (0.26 m/s/10a), autumn (0.09 m/s/10a), and winter (1.16 m/s/10a) are insignificant. In the lower stratosphere, the rate of change in UWS values in winter is the largest, and it is 1.11 m/s/ 10a (P < 0.05). The rates of change in UWS are smaller in all of the other seasons; moreover, the variation trends are not significant. The results above demonstrate that the SWS values display increasing trend only in winter, and the rate of change during each individual season is relatively small. The UWS values exhibit an obvious declining tendency in summer, whereas the UWS values display increasing trends in all of the other seasons; there is a distinct seasonal difference. Among them, the rates of increase in UWS values in winter are the largest, followed by the spring, whereas the rate of increase in UWS values in every layer in autumn is relatively small.
The seasonal change trend analysis reflects the seasonal differences in SWS and UWS values, and it can also contribute to revealing the relative changes in the surface layer and the different upper-air layers during the same season (Table 2). At the surface, only the winter wind speed shows no obvious increasing trend (the corresponding rate is 0.01 m/s/10a), and the decreasing trend in wind speed is significant only in spring (P < 0.05), when the rate is 0.07 m/s/10a. In the mid-lower troposphere, the rate of increase of UWS values (0.31 m/s/10a) is largest in winter, but it is insignificant at the 0.05 level; this rate is followed by summer, when the rate is − 0.24 m/s/10a (P < 0.05), whereas the rates of change in the spring and autumn are smaller. In the upper troposphere, only the summer UWS values presents a significant (P < 0.05) decreasing trend, with a rate of − 0.34 m/s/10a, while the increasing Mid-lower troposphere
Fig. 3 Monthly variations in SWS and UWS values in the ARNC between 1980 and 2012
Lower stratosphere
m/s
28
Wind speed
Wind speed
m/s
30
Upper troposphere
Lower stratosphere
Surface 3.5
32
Wind speed
m/s
29
3.0
26 2.5
23 20
2.0
17 14
1.5
11 1.0
8 1
2
3
4
5
6 7 Month
8
9
10
11
12
J. Zheng et al. Table 2 2012
Seasonal trends in SWS and UWS values between 1980 and
Layer Surface
Mid-lower troposphere
Season
Z
Trend (m/s/10a)
Spring
− 2.22*
− 0.07
Summer
− 1.41
− 0.06
Autumn Winter
− 0.50 0.48
− 0.03 0.01
Spring Summer Autumn
Upper troposphere
Lower stratosphere
Winter Spring
0.08 − 2.93* 0.05 1.07 0.60
0.04 − 0.24 0.01 0.31 0.26
Summer
− 2.09*
− 0.34
Autumn
0.02
Winter Spring Summer
1.94 1.10 − 1.01
Autumn
0.02
0.06
Winter
2.15*
1.11
0.09 1.16 0. 29 − 0. 08
*Significant at P < 0.05
3.2 The spatial distribution of annual change characteristics The annual changes in SWS and UWS values displayed obvious spatial differences in the ARNC during the period 1980–2012 (Fig. 4). The linear rates of change of SWS
values vary between 0.19 and − 0.25 m/s/10a; the areas with significant (P < 0.05) changes are mainly concentrated in Southern Xinjiang. SWS values show increasing trends at 36% of the stations, and these stations are mainly concentrated in the southern part of Southern Xinjiang and some areas of Northern Xinjiang, such as Kashgar (0.12 m/ s/10a) and Hotan (0.19 m/s/10a). While the decreasing trends in SWS values in the Hexi Corridor and the Tianshan area are distinct, the rates of decline in SWS values are larger in Charkhlik (− 0.25 m/s/10a) and Humul (− 0.22 m/s/10a). In the mid-lower troposphere, the UWS values exhibit decreasing trends in the areas of Minqin, Kashgar, Kuqa, and Hotan, and other regions where increasing trends were noted. The rates of change range between 0.25 and − 0.06 m/ s/10a, and the trends of change are all not significant. The UWS changes are more obvious in Altay (0.25 m/s/10a) of the northern regions and Ejin (0.14 m/s/10a) and Mazongshan (0.12 m/s/10a) of the eastern regions, and the rates of change are all less than 0.1 m/s/10a for all the others. On the whole, the rates of change of UWS values are larger and are primarily increasing trends in Northern Xinjiang and the Hexi Corridor, whereas the rates of change of UWS values are lower and mainly decreasing in Southern Xinjiang. In the upper troposphere, the UWS values exhibit a decreasing tendency only in Minqin. All of the other stations display increasing trends, and the rates of change vary from 0.54 to − 0.01 m/s/10a. The rates of change of the UWS values are smaller in Minqin (− 0.01 m/s/10a) and
Fig. 4 Distribution of the trends in the annual SWS and UWS values in the ARNC between 1980 and 2012 (a surface; b mid-lower troposphere; c upper troposphere; d lower stratosphere)
Spatiotemporal variation of upper-air and surface wind speed and its influencing factors in northwestern...
3.4 Driving factors of the observed wind speed changes
Jiuquan (0.07 m/s/10a) of the eastern areas. Those of the other areas are larger, especially Altay (the highest rate is 0.54 m/s/10a) of the northern areas and Hotan (0.51 m/s/ 10a) and Charkhlik (0.48 m/s/10a) of the southern regions. However, only the wind speed in Altay region increases significantly (P < 0.05). The UWS values measured at each station in the lower stratosphere all show increasing trend, the rates of change are between 0.53 and 0.27 m/s/10a and tend to decrease from east to west. For example, the increasing trends are both significant (P < 0.05) at locations such as Mazongshan (0.50 m/s/10a), Jiuquan (0.53 m/s/10a), and Ejin (0.52 m/s/ 10a) of the eastern regions, while the lowest rate of change of the UWS values occurs at Tacheng in the northwest area (0.27 m/s/10a).
The importance of the mean SWS change for spring and summer to the yearly change was substantial for the period 1980–2012, and these seasons displayed rates of 38 and 36%, respectively. This rate is followed by autumn’s 19%, and the importance of winter was smaller, only 7% (Fig. 5). These results indicate that the changes in SWS in spring and summer are the main causes of the annual change. Within the upper air, the importance values of UWS changes during summer and winter to the yearly change are larger in the troposphere, where they reach 44 and 48%, respectively, whereas the importance of spring and autumn are only 1 and 7%, respectively. The importance of UWS changes during the winter is largest in the lower stratosphere, where it reaches 71%; it is followed by spring, for which the corresponding value is 23%; the importance values of summer and autumn are smaller. As can be seen, investigating the main driving factors of UWS changes in winter and summer is important for analyzing the main reasons for the UWS change in the troposphere, whereas the UWS changes in winter are the main driving force of the annual change in the stratosphere. The correlations between SWS changes in spring and summer and the atmospheric circulation patterns are not significant (Table 4), indicating that the influence of atmospheric circulation patterns on SWS changes are weaker in the ARNC. Although the correlations between the autumn and winter and the individual atmospheric circulation patterns, such as the Asian Meridional Circulation (AMC) and the Arctic Oscillation (AO), are higher at the surface, the importance values of the SWS changes for autumn and winter to the yearly changes are smaller.
3.3 Pearson’s correlation between SWS and UWS values The correlations between SWS and UWS values are weaker and not significant at the 0.05 level (Table 3). The UWS at 850 hPa are only significantly correlated (P < 0.05) with the UWS at 700 and 500 hPa. The UWS at 700 hPa shows an insignificant correlation (P > 0.05) only with the UWS at 50 hPa. The UWS at 500 hPa and the UWS in each layer are all significantly positively correlated (P < 0.05). The 400, 300, and 250 hPa levels all display strong and significant positive correlations with the 700–100 hPa levels (P < 0.01). The UWS in the 700–100 hPa levels are all positively correlated with one another, and these correlations are strong (P < 0.01). On the one hand, these results provide an indication that the UWS is less affected by the SWS. On the other hand, it shows that the UWS changes in each layer are closely related, especially at 700–100 hPa.
Table 3
Surface 850 700 500 400 300 250 200 150 100 50
Pearson’s correlation coefficients of SWS and UWS values in the ARNC between 1980 and 2012 Surface
850
700
500
400
300
250
200
150
100
50
1 0.32 0.192 0.088 0.089 0.076 0.051 0.013 − 0.028 − 0.063 − 0.095
0.32 1 0.564** 0.390* 0.343 0.273 0.255 0.214 0.171 0.105 0.04
0.192 0.564** 1 0.844** 0.743** 0.677** 0.654** 0.616** 0.568** 0.577** 0.321
0.088 0.390* 0.844** 1 0.973** 0.926** 0.889** 0.822** 0.758** 0.721** 0.348*
0.089 0.343 0.743** 0.973** 1 0.983** 0.954** 0.889** 0.823** 0.747** 0.32
0.076 0.273 0.677** 0.926** 0.983** 1 0.988** 0.937** 0.874** 0.772** 0.304
0.051 0.255 0.654** 0.889** 0.954** 0.988** 1 0.975** 0.919** 0.819** 0.34
0.013 0.214 0.616** 0.822** 0.889** 0.937** 0.975** 1 0.979** 0.893** 0.404*
− 0.028 0.171 0.568** 0.758** 0.823** 0.874** 0.919** 0.979** 7 0.945** 0.474**
− 0.063 0.105 0.577** 0.721** 0.747** 0.772** 0.819** 0.893** 0.945** 1 0.665**
− 0.095 0.04 0.321 0.348* 0.32 0.304 0.34 0.404* 0.474** 0.665** 1
*Significant at P < 0.05, **significant at P < 0.01
J. Zheng et al. 75 Seasonal importance (%)
Surface
Troposphere
Lower stratosphere
60 45 30 15 0
Spring
Summer
Autumn
Winter
Season
Fig. 5 Seasonal importance values of changes in SWS and UWS values during the period 1990–2012
The correlation between the summer UWS in the troposphere and the Eurasian Zonal Circulation (EZC) is the highest, with a coefficient of − 0.60 (P < 0.001), followed by the correlation with the Northern Hemisphere Polar Area (NHPA), for which the coefficient is 0.37 (P < 0.05). The correlations with the other circulation patterns are not significant. On the other hand, the correlations between the UWS in winter and the Eurasian Zonal Circulation (EZC) and the Siberian High (SH) are stronger in the troposphere, and the correlation coefficients reach to − 0.52 and 0.48 (P < 0.01), respectively, followed by the Southern Oscillation (SO), the Arctic Oscillation (AO), and the North Atlantic Oscillation (NAO), for which the correlations are all significant at the 0.05 level. The Table 4 2012
Pearson’s correlation coefficients between the wind speed of each layer and atmospheric circulation patterns in the ARNC between 1980 and Surface
ASH SCSSH AMC SO NHPA NASH NHSH WPSH EZC SH AO NAO PDO TPI_B EMC
correlations with the other atmospheric circulation patterns are all weaker. The results above show that the tropospheric wind speed changes are mainly affected by the Eurasian Zonal Circulation (EZC) and the Siberian High (SH). At the same time, the tropospheric wind speeds in summer and winter show all significant negative correlations with the Eurasian Zonal Circulation (EZC) in Fig. 6, whereas the winter UWS in the troposphere presents an obvious synchronization with the Siberian High (SH). The correlation between the winter UWS and the Pacific Decadal Oscillation (PDO) is the strongest in the lower stratosphere, for which the coefficient is − 0.40 (P < 0.05). This relationship is followed by the correlation with the Southern Oscillation (SO), for which the coefficient is 0.35 (P < 0.05). The correlations with the other atmospheric circulation patterns are not significant. This indicates that the Pacific Decadal Oscillation (PDO) has an important impact on the UWS of the lower stratosphere. The clearly negative correlation between the winter UWS and the Pacific Decadal Oscillation (PDO) in the lower stratosphere is displayed in Fig. 6. Moreover, there are significant (P < 0.05) negative correlations between the UWS in the summer and autumn and the Eurasian Zonal Circulation (EZC) in the lower stratosphere. This shows that the Eurasian Zonal Circulation (EZC) exerts some influence on the UWS in summer and autumn in the lower stratosphere, but the importance values of UWS changes for summer and autumn to the yearly change were smaller for the period 1990–2012.
Troposphere
Lower stratosphere
Spring
Summer
Autumn
Winter
Spring
Summer
Autumn
Winter
Spring
Summer
Autumn
Winter
0.12 0.19 0.02 − 0.02 0.24 0.07 0.08 0.06 0.04 − 0.24 − 0.31 − 0.28 0.29 0.12 0.03
− 0.32 − 0.24 0.06 − 0.21 0.03 − 0.09 − 0.18 − 0.29 − 0.11 − 0.06 − 0.15 − 0.05 0.14 − 0.17 0.15
− 0.08 0.10 0.04 0.03 0.27 0.06 0.03 0.01 − 0.27 0.07 − 0.45** − 0.34 0.05 − 0.20 − 0.01
− 0.03 0.15 0.50** 0.09 0.18 − 0.06 0.07 0.09 − 0.18 0.27 − 0.39* − 0.14 − 0.10 0.14 0.34
0.30 0.27 − 0.11 0.20 0.08 0.24 0.30 0.20 − 0.39* 0.38* − 0.37* − 0.13 0.06 0.19 − 0.27
− 0.14 − 0.04 − 0.15 0.00 0.37* − 0.19 − 0.10 0.06 − 0.60*** − 0.01 − 0.09 0.04 0.20 − 0.16 − 0.02
− 0.13 − 0.31 − 0.17 0.33 0.01 − 0.23 − 0.26 − 0.28 − 0.53** 0.21 − 0.20 0.00 − 0.20 − 0.14 − 0.12
0.01 − 0.26 0.12 0.35* 0.09 − 0.11 − 0.17 − 0.18 − 0.52** 0.48** − 0.42* − 0.38* − 0.15 0.26 0.08
0.09 0.14 − 0.14 0.16 − 0.05 0.15 0.19 0.11 − 0.17 0.10 − 0.22 0.15 − 0.02 0.03 − 0.15
0.02 0.35* − 0.15 0.01 0.16 0.13 0.17 0.36* − 0.50** − 0.18 − 0.15 0.08 0.49** 0.12 − 0.03
− 0.02 − 0.24 − 0.16 0.32 − 0.09 − 0.12 − 0.13 − 0.20 − 0.40* − 0.03 − 0.13 0.17 − 0.21 0.07 − 0.17
− 0.05 − 0.20 0.06 0.35* − 0.14 − 0.12 − 0.10 − 0.11 − 0.10 0.16 − 0.14 − 0.25 − 0.40* 0.23 0.05
*Significant at P < 0.05, **significant at P < 0.01, ***significant at P < 0.001
Spatiotemporal variation of upper-air and surface wind speed and its influencing factors in northwestern... 3
3 Troposphere Winter
Troposphere Summer 2 Standardized values
2 Standardized values
Fig. 6 Correlations between the winter and summer UWS changes in the troposphere and winter UWS changes in the stratosphere and atmospheric circulation patterns in the ARNC between 1980 and 2012
1 0 -1 -2 EZC Wind speed -3 1980 1984 1988 1992 1996 2000 2004 2008 2012 Year
-1 -2 EZC
Wind speed
1980 1984 1988 1992 1996 2000 2004 2008 2012 Year
3 Troposphere Winter Standardized values
Standardized values
0
-3
3 2
1
1 0 -1
2
Lower stratosphere Winter
1 0 -1 -2
-2 SH
Wind speed
-3 1980 1984 1988 1992 1996 2000 2004 2008 2012 Year
4 Discussions In recent years, many scientists have focused on climate variability and its driving factors, especially the effects of human activities on climate variability (Li et al. 2014). Nevertheless, the impact of atmospheric circulation patterns and other natural factors on climate variability should not be overlooked (Li et al. 2012). Accordingly, this paper studied the characteristics of spatiotemporal changes in SWS and UWS values and their relationships with atmospheric circulation patterns in the ARNC. Since the 1950s, annual average SWS have declined in most parts of the country (Chen 2013). This study found that the annual SWS values exhibited a decreasing trend in the ARNC, which was consistent with the results of previous research (Ding et al. 2014). Zhang et al. (2009) showed that annual mean UWS for China displayed a decreasing tendency in the troposphere from 1980 to 2006, whereas the annual UWS values exhibited an increasing trend in the lower stratosphere. This is slightly different from the conclusions of this study. The two main reasons are as follows. First, there are some spatial differences in UWS changes in China. Second, inconsistencies in the time period examined in different studies will lead to different trends. Zhang et al. (2009) noted that the decrease in SWS values in China was mainly related to human factors, such as changes in the observed environment and urbanization. Jiang et al. (2013) found that changes in atmospheric circulation patterns and climate warming were possible causes of the SWS
PDO Wind speed -3 1980 1984 1988 1992 1996 2000 2004 2008 2012 Year
reductions. This study found that the importance values of average SWS changes for the spring and summer to the yearly changes was relatively large, but the correlations with the atmospheric circulation patterns were not all significant; although autumn and winter had significant correlations with the individual atmospheric circulation patterns, their importance values were smaller. Accordingly, we speculated that the changes in the spring, summer, and annual mean SWS in the ARNC were mainly related to human activities, whereas the autumn and winter SWS changes were affected obviously by atmospheric circulation patterns. Additionally, only the SWS values in winter presented a slight increasing trend in the ARNC, and the SWS values in winter and the AMC displayed the closest relationship (R = 0.50, P < 0.01), which suggested that the fluctuations of the AMC was an important reason for the increase in SWS values in winter. In general, human activities are likely the main driving factors of decreases in SWS values in the ARNC. Meanwhile, this research suggested that the correlations between the changes in UWS and SWS values were weaker, which indirectly indicates that human activities had little effect on UWS values, and atmospheric circulation fluctuations were likely the primary reason for UWS changes. Moreover, this study identified a highly significant negative correlation between summer UWS in the troposphere and the EZC in the ARNC (R = − 0.60, P < 0.01). The major reason was that the summer climate was greatly affected by WPSH in the ARNC (Jiang et al. 2007), whereas the wind direction of the EZC was mainly westerly, which was opposite to the wind direction of
J. Zheng et al.
the WPSH (which features easterly winds). As a result, the EZC was enhanced in summer, resulting in a decrease in the summer UWS values, whereas opposite behavior was noted during the winter in the troposphere in the ARNC. Li et al. (2012) noted that the more rapid increase in winter temperatures in the ARNC was mainly impacted by the SH. This research discovered that there was a significant positive correlation between the winter UWS of the troposphere and the SH in the ARNC (R = 0.48, P < 0.01), which suggested that SH had a vital effect on the winter UWS of the troposphere in the ARNC. The strong SH throughout the region prevailed in northerly winds during the winter, so the extent of the impact on the north must be greater than that on the south in the ARNC. Thus, the gradual enhancement of the SH caused a gradual increase in tropospheric wind speed in the ARNC from 1980 to 2012, and the increased extent of UWS in the north was higher than the south. The rate of increase in UWS values was higher in places such as the Altay region of Northern Xinjiang. The importance of UWS changes for winter to the yearly change was highest (71%) in the lower stratosphere. This research found that the correlation between winter UWS and the PDO was strongest in the lower stratosphere (R = − 0.40, P < 0.05). In 2000, the entire globe entered a cold phase of the PDO; thus, hurricanes would become increasingly strong (Yang and Yang 2006), which may be the cause of the increases in winter UWS values in the lower stratosphere. Moreover, the increase in UWS values may also be related to the rise of greenhouse gases (Xie et al. 2015). The longwave radiation emitted by the stratosphere (due to greenhouse gases) was considerably more than the infrared longwave radiation it absorbs from the troposphere atmosphere, and the increase in greenhouse gases would lead to warming of the ground and the troposphere, thereby causing the stratosphere to cool (Hu et al. 2009; Shen et al. 2013). When the stratosphere became cool, the north-south temperature difference increases, enhancing the pressure gradient force; thus, the wind speed rose (Guo et al. 2014; Wang and Ren 2005; Wu et al. 2013). Additionally, the funneling effect of the Hexi Corridor enhanced the wind speed of gales (Dong et al. 2014), so the wind speed changes were larger in the eastern portion of the ARNC. There was an anticyclone circulation in the north of the Tibetan Plateau and a cyclone circulation in the south of the Tibetan Plateau (Wu et al. 2007). This would cause a moisture increase from the Caspian Sea into the ARNC (Bothe et al. 2012), and daily precipitation extremes may increase in the southern ARNC (Chen et al. 2014). But the correlation between wind speed and the Tibetan Plateau Circulation was not significant in this study. Summer NAO activity caused Rossby wave activity anomalies along West Asia-Xinjiang jet stream by mid-high latitudes stationary wave activity variations, so summer rainfall anomalies in Xinjiang were affected (Yang
and Zhang 2008). But this paper found that this circulation had little effect on the wind speed in the ARNC. This paper analyzed the characteristics of spatiotemporal changes in SWS and UWS values in the ARNC and provided a preliminary analysis of the relationships between wind speed and atmospheric circulation patterns. The results have significance as scientific guidance for clarifying the characteristics of regional climate variability and its driving factors. However, the mechanism influencing wind speed changes in the ARNC is complex, and our future work will search for deeper linkages among climate elements and the impact of human activities on climatic factors.
5 Conclusions Over the past 30 years, the annual average SWS values showed a decreasing trend, whereas the annual mean UWS values showed all increasing trends. There were obvious seasonal differences in the changes in SWS and UWS values in the ARNC. The SWS values presented an increasing trend only in winter, and UWS values showed clear decreasing tendencies only in summer. The spatial variations in annual mean wind speed displayed significant regional differences in the ARNC. The more obvious areas of SWS variation were mainly concentrated along the southern edge of the Tarim Basin, whereas the changes in SWS values in the remainder of the ARNC were smaller. The rates of increase of UWS in the northern and eastern regions were obviously larger than those seen in the other regions in the mid-lower troposphere. Except that the UWS values exhibited a decreasing trend in Minqin, the rate of increase in UWS values at every station was generally larger in the upper troposphere. The rates of change gradually decreased from east to west in the lower stratosphere. There were no significant correlations between the SWS and UWS values. UWS at 850 hPa was positively correlated with UWS in each individual layer. For the 700–100 hPa levels, the UWS values for the different layers were both positively correlated with one another, and the correlations were larger (P < 0.01). The UWS at 50 hPa was the negative correlation only with SWS. The importance values of SWS for spring and summer to the yearly change were higher, but there were no significant correlations between SWS and atmospheric circulation patterns in spring and summer. The tropospheric wind speed change had a close relationship with the EZC and SH. While the annual mean UWS changes in the lower stratosphere were mainly caused by the changes in winter UWS (the importance reached 71%), it was negatively correlated with the PDO (R = − 0.40, P < 0.05).
Spatiotemporal variation of upper-air and surface wind speed and its influencing factors in northwestern... Acknowledgments We are grateful to Dr. Dominique Ruffieux and anonymous reviewers for their helpful comments on improving the manuscript. Funding information The research is supported by the National Natural Science Foundation of China (41501211).
References Bothe O, Fraedrich K, Zhu XH (2012) Precipitation climate of Central Asia and the large-scale atmospheric circulation. Theor Appl Climatol 108(3–4):345–354. https://doi.org/10.1007/s00704-0110537-2 Chen L (2013) Changes and their impact factors of wind speed (energy) over China under the background of climate warming (in Chinese). Nanjing Unive Infor Sci Tech, Nanjing Chen HW, Xin Y, Chen PX, Li YP et al (2010) Variation tendency of the extreme value of wind speeds and gale frequency over the windy regions in Xinjiang (in Chinese). Clim Environ Res 15(4):479–490 Chen YN, Deng HJ, Li BF, Li Z et al (2014) Abrupt change of temperature and precipitation extremes in the arid region of Northwest China. Quat Int 336(12):35–43. https://doi.org/10.1016/j.quaint. 2013.12.057 Chen ZS, Chen YN, Xu JH, Bai L (2015) Upper-air temperature change trends above arid region of Northwest China during 1960-2009. Theor Appl Climatol 120(1–2):239–248. https://doi.org/10.1007/ s00704-014-1166-3 Ding CX, Yan JP, Li MM (2014) Climate change and response characteristics to wind speed in northwestern China (in Chinese). Bull Soil Water Conserv 34(4):134–137 Dong AX, Hu WC, Zhang Y, Liu DX (2014) Study of the relationship between gale and the peculiar terrain in the Hexi Corridor (in Chinese). J Glaciol Geocryol 36(2):347–351 Gong DY, Ho CH (2002) The Siberian High and climate change over middle to high latitude Asia. Theor Appl Climatol 72(1–2):1–9. https://doi.org/10.1007/s007040200008 Guo Q, Sun WG, Cheng BY (2008) Cross-wavelet analysis of the Arctic Oscillation and climate change in Northwest China (in Chinese). J Nanjing Inst Meteorol 31(6):811–818 Guo FX, Liu B, Bai L, Gong JQ et al (2014) Numerical simulation study on the effect of middle- and low-level horizontal wind speed on lighting and precipitation (in Chinese). Plateau Meteorol 33(4): 1135–1145 Helbig N, Mott R, Herwijnen AV, Winstral A et al (2017) Parameterizing surface wind speed over complex topography. J Geophys Res 122(2):651–667. https://doi.org/10.1002/2016JD025593 Hu YY, Ding F, Xia Y (2009) Stratospheric climate trends under conditions of global climate changes (in Chinese). Adv Earth Sci 24(3): 242–251 Huang XY, Zhang MJ, Wang SJ, Xin H et al (2011) Characteristics of variation in sunshine duration and wind speed in the last 50 years in northwest China (in Chinese). J Nat Resour 26(5):825–836 Ji MX (2009) Response and interact between atmospheric circulation and temperature of Eurasian (in Chinese). Lanzhou Univ, Lanzhou Jiang Y, Luo Y,Zhao ZC, Tao SW (2007) Analysis on the change of wind speed over China in past 50 years (in Chinese). Paper presented at climate change annual meeting of Chinese Meteorol Society in 2007, Nanjing Jiang C, Wang F, Liu YX, Mu XM et al (2013) Spatial-temporal variation and mutation of wind speed in the northern and southern regions of the Qinling mountains (in Chinese). Sci Geogr Sin 33(2):244–250 Lee HF, Zhang DD (2011) Relationship between NAO and drought disasters in northwestern China in the last millennium. J Arid Environ 75(11):1114–1120. https://doi.org/10.1016/j.jaridenv.2011.06.008
Li QH, Ma QX (2014) Extreme precipitation features of arid regions in northwest of China (in Chinese). Sci Geogr Sin 34(9):1134–1138 Li BF, Chen YN, Shi X (2012) Why does the temperature rise faster in the arid region of northwest China? J Geophys Res 117(D16):16115. https://doi.org/10.1029/2012JD017953 Li B, Su HB, Chen F, Li HB et al (2014) Separation of the impact of climate change and human activity on streamflow in the upper and middle reaches of the Taoer River, northeastern China. Theor Appl Climatol 118(1):271–283. https://doi.org/10.1007/s00704-0131032-8 Li BF, Chen YN, Chen ZS, Xiong HG et al (2016) Why does precipitation in northwest China show a significant increasing trend from 1960 to 2010? Atmos Res 167:275–284. https://doi.org/10.1016/j. atmosres.2015.08.017 Liu QJ, Wu SL, Luo L, Dou XJ et al (2016) On spatial and temporal trends of average wind speed in Hotan region in recent 54 years (in Chinese). J Southwest China Norm Univ 41(4):45–51 Ni X, Zhang Q, Ma D, Wu L et al (2015) Climatology and trends of tropical cyclone high wind in mainland China: 1959–2011. J Geophys Res 120(24):024031. https://doi.org/10.1002/ 2015JD024031. Ren GC, Yu MX (2008) Analysis on relationships between Southern Oscillation and tropical sea temperature and precipitation in flood season in eastern of northwestern China (in Chinese). J Shaanxi Meteorol 2008(1):15–18 Shen BZ, Lian Y, Zhang SX, Li SF (2012) Impacts of Arctic Oscillation and polar vortex anomalies on winter temperature over Eurasian Continent (in Chinese). Adv Clim Chang Res 8(6):434–439 Shen WF, Miao QL, Wei TX, Kong CC (2013) Analysis of temperature variation in recent 130 years in central Asia (in Chinese). Arid Meteorol 31(1):32–36 Thompson DWJ, Wallace JM (1998) The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys Res Lett 25(9):1297–1300 Wang Y, Ren GY (2005) Change in free atmospheric temperature over China during 1961-2004 (in Chinese). Clim Environ Res 10(4):780– 789 Wei C (2015) Forecasting surface wind speeds over offshore islands near Taiwan during tropical cyclones: comparisons of data-driven algorithms and parametric wind representations. J Geophys Res 120(5): 1826–1847. https://doi.org/10.1002/2014JD022568 Wu GX, Liu YM, Wang TM, Wan RJ et al (2007) The influence of mechanical and thermal forcing by the Tibetan Plateau on Asian climate. J Hydrometeorol 8(4):205–208 Wu BG, Wang ZY, Xie YY, Zhang HS (2013) Turbulent statistic characteristics of wind, temperature and humidity during autumn fog in urban area of Tianjin (in Chinese). Plateau Meteorol 32(5):1360– 1367 Xie JF, Liu YY, Li YF (2015) Variations and potential causes of surface and free atmospheric wind velocities in Jilin (in Chinese). Plateau Meteorol 34(5):1424–1434 Yang XX, Yang DH (2006) Hurricane and other’s disasters in the cold period of pacific decade oscillation (in Chinese). Marin Forec 23(3): 30–35 Yang LM, Zhang QY (2008) Effects of the North Atlantic Oscillation on the summer rainfall an omalies in Xinjiang (in Chinese). Chin J Atmos Sci 32(5):1187–1196 Yao YB, Xiao GJ, Wang RY, Zhang XY (2009) Climatic changes of semi-arid region over the northwest China in recent 50a (in Chinese). Arid Land Geo 32(2):159–165 Zhang JW (2013) The new features of the polar vortex and its impaction winter temperature of China (in Chinese). Nanjing Unive Infor Sci Tech, Nanjing Zhang SQ, Yu TJ, Li FY, Wang XM et al (1985) The seasonal variations of area and intensity of polar vortex in northern hemisphere and
J. Zheng et al. relationship with temperature in northeast China (in Chinese). China J Atmos Sci 9(2):68–75 Zhang AY, Ren GY, Guo J, Wang Y (2009) Change trend analyzes on upper-air wind speed over china in past 30 years (in Chinese). Plateau Meteorol 28(3):680–687 Zhao ZG (1999) The summer drought and flood and environment field in China (in Chinese). Meteorological Press, Beijing
Zhao BF, Xu JH, Chen ZS, Bai L et al (2013) Air temperature change in the southern Tarim River Basin, China, 1964-2011. Sci World J 2013(5):894851–894858. https://doi.org/10.1155/2013/894851 Zhou SZ, Yu BX (1988) Shanghai urban influences on wind velocity (in Chinese). J East China Norm Univ 1988(3):67–76
