The dramatic climate warming in the Qaidam ... - Wiley Online Library

INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 34: 1524–1537 (2014) Published online 4 July 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.3781

The dramatic climate warming in the Qaidam Basin, northeastern Tibetan Plateau, during 1961–2010 Xuejia Wang,a,b Meixue Yang,a,* Xiaowen Liang,a,b Guojin Pang,b,c Guoning Wan,a Xiaolei Chena,b and Xiaoqing Luoa,b a

State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China b University of Chinese Academy of Sciences, Beijing 100049, China c Key Laboratory of Desert and Desertification, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China

ABSTRACT: On the basis of meteorological station records during 1961–2010, we investigate the variations of temperature and precipitation in the Qaidam Basin. Results show that climate warming is significant in the region of Qaidam Basin over the past 50 years, with an average warming rate of 0.53 ◦ C 10a−1 . The largest and smallest warming rate happened at Mangya station (0.89 ◦ C 10a−1 ) and Lenghu station (0.24 ◦ C 10a−1 ), respectively. Seasonal warming was greatest in winter at eight meteorological stations, ranging from 0.43 ◦ C 10a−1 (Lenghu station) to 1.01 ◦ C 10a−1 (Delingha station). Since 1961, the annual precipitation has increased with a rate of 7.38 mm 10a−1 . Seasonal precipitation mainly increased in summer (4.02 mm 10a−1 ). The maximum precipitation increase occurred at Delingha station (25.09 mm 10a−1 ) and the minimum at Lenghu station (0.10 mm 10a−1 ). The elevation dependency of warming trends is unremarkable because most of the stations are located at lower altitudes. It is suggested that sunshine duration is related to the tendencies of temperature increase at different stations. Pollution emissions from industrial processes (i.e. brown clouds) and urbanization are the main factors contributing to the warming climate. Furthermore, the predominant weakening of zonal wind speed over the Tibetan Plateau resulted from the global warming also contributes to the climate warming in the Qaidam Basin. Consequently, the warming rate in the Qaidam Basin is much higher than in other regions over the Tibetan Plateau. The Qaidam Basin is thus considered to be the most susceptible region with the most significant warming in the Tibetan Plateau. KEY WORDS

Tibetan Plateau; Qaidam Basin; climate warming; sunshine duration; urbanization

Received 6 February 2013; Revised 29 May 2013; Accepted 6 June 2013

1.

Introduction

The Qinghai-Xizang (Tibetan) Plateau, with an average of 4000 m a.s.l., is so called as the ‘Third Pole’ (Qiu, 2008). Owing to its thermal and dynamical effects, the Tibetan Plateau profoundly impacts the climate conditions in its surroundings and even the global climate conditions (e.g. Rangarajan, 1963; Ye and Gao, 1979; Jiang et al., 2008; Xu et al., 2008; Wu et al., 2012; Yao et al., 2012). Since the mid-1950s, most areas of the Tibetan Plateau have experienced significant warming (e.g. Liu and Chen, 2000; Wu et al., 2005; Wang et al., 2008; You et al., 2008; Li et al., 2010a; Xie et al., 2010). The warming rate from 1955 to 1996 was 0.16 ◦ C decade−1 , especially in winter (0.32 ◦ C decade−1 ). Compared with the Northern Hemisphere and the same latitudinal zone in the same period, the warming occurred earlier in the Tibetan Plateau (Liu and Chen, 2000). From 1960 to 2007, the * Correspondence to: M. Yang, State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China. E-mail: [email protected]

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annual surface temperature increased by 1.8 ◦ C with an increasing rate of 0.36 ◦ C 10a−1 (Wang et al., 2008). Warming rate varies with seasons. During 1961–2007, the warming rate in spring, summer, autumn and winter was 0.25, 0.26, 0.38, and 0.59 ◦ C decade−1 , respectively (Li et al., 2010a). Extreme temperature indices showed statistically significant increasing trends in the Tibetan Plateau during 1961–2005 (You et al., 2008). Greater warming trends were found in monthly minimum temperature (0.41 ◦ C decade−1 ) than in maximum temperature (0.18 ◦ C decade−1 ) during 1961–2003 (Liu et al., 2006). Also a negative trend in diurnal temperature range was observed across the Tibetan Plateau (Xie et al., 2010). In recent years, climate warming in the Tibetan Plateau was attributed to the increase of anthropogenic-induced greenhouse gases (i.e. CO2 ), changes of cloud cover, Asian brown cloud and urbanization (e.g. Liu et al., 1998; Chen et al., 2003; Duan et al., 2006; Ramanathan et al., 2007; You et al., 2009). Previous studies showed that alpine ecosystems in the Tibetan Plateau were extremely susceptible to the climate change. The climate warming resulted in the extensive glacier retreating, permafrost degradation, vegetation cover changes,

THE DRAMATIC CLIMATE WARMING IN THE QAIDAM BASIN DURING 1961–2010

postponed phonological phenomena and ecological environment deterioration (e.g. Yao et al., 2007; Cui and Graf, 2009; Yang et al., 2010; Yu et al., 2010). Regional climate response to the global change is asynchronous due to the differences of climate background, regional features and climate driving forces (Ren et al., 2005; Hansen et al., 2012). Generally, the climate in midand high latitude is more sensitive to the global change, and the warming amplitude is even greater (Solomon et al., 2007). Therefore, detailed investigation of the climate changes in those regions would be meaningful to a better understanding of the global change. Studies demonstrated that the northern Tibetan Plateau has experienced the most significant warming since the 1960s, and the warming rate is larger than any other areas in the Tibetan Plateau (Duan et al., 2006; Li et al., 2010b; Li et al., 2010a; Guo and Wang, 2011). Why did the northern Tibetan Plateau experience such a dramatic warming? Is the warming a certain phenomenon? Studies thereof remain under-researched, with a recent report by Guo and Wang (2011) that attributed the significant warming to the pronounced stratospheric ozone depletion. The intrinsic mechanism of climate warming of the Qaidam Basin is particularly needed because it would furnish us with insights into further understanding of the ongoing global change. In the paper, initially, we used the meteorological station data sets in the Qaidam Basin to examine the temperature and precipitation changes, aiming at further understand the characteristics of regional climate change. Then based on the sunshine hours, statistical yearbooks and wind speed, we discussed the reasons resulting in the spatial difference in each station. Finally, using the NCEP/NCAR reanalysis data, we analysed the climate background-induced significant climate changes of the Qaidam Basin. In Sections 2 and 3, a brief description of general situation of study area, data and methods are provided, respectively. The climate changes in the Qaidam Basin are revealed in Section 4. The possible causes of the significant warming in the Qaidam Basin are discussed in Section 5; Section 6 presents our conclusions.

2. General situation of study area The Qaidam Basin (90◦ 16 E–99◦ 16 E, 35◦ 00–39◦ 20 N), located in the northeastern Tibetan Plateau, belongs to the Qinghai Province (Figure 1). It is a plateau-type basin with a length of 800 km from east to west and a width of 350 km from south to north. The area is around 28 × 104 km2 . It ranks as one of the three largest inland basins. The altitude in west of the basin is higher than in east. It is wider in west than in east. The Qaidam Basin is a closed inland basin enclosed by high mountains. It borders on the Kunlun Mountains on the south, the Qilian Mountains on the north, the Altyn Mountain on the northwest and the Riyue Mountain on the east. The climate in the basin belongs to the plateau continental climate and is dry and rainless throughout the year. Climatic records  2013 Royal Meteorological Society

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from eight meteorological stations listed in Table 1 show an annual mean precipitation of 81.74 mm and an annual mean temperature of 3.55 ◦ C from 1961 to 2010.

3.

Data and methods

The meteorological data sets are derived from the National Climate Center, China Meteorological Administration, consisting of records from eight meteorological stations distributed within the Qaidam Basin with the records longer than 50 years (1961–2010) (Figure 1). Monthly temperature, monthly precipitation, monthly wind speed and sunshine hours are used in our analysis. The identifier, station name, longitude and latitude, altitude and the starting years of the eight meteorological stations are listed in Table 1. In addition, the dominant land cover of each station is introduced in the last column of Table 1. The data set was obtained from the Environmental and Ecological Science Data Center for West China with 1 km spatial resolution (http://westdc.westgis.ac.cn). The China Meteorological Administration already has done the preliminary quality control for the conventional near surface meteorological observation data sets. Among the eight meteorological stations, the data from April to December of 1974 at Xiaozaohuo station were missing. The missing data were interpolated using the nearest two available data by linear interpolation. Data homogeneity was assessed using the RHtest software (available at the web sitehttp://cccma.seos.uvic.ca/ETCCDI for downloading), and the assessment results indicated that there is no relocation for the eight stations, which is consistent with You et al. (2008). The data homogeneity method is described in detail by Wang (2003). The average series (such as temperature, precipitation, sunshine hours and wind speed) of the Qaidam Basin over the past 50 years was derived from the simple arithmetic average of the eight meteorological stations. NCEP/NCAR Reanalysis Products-1 data were produced by the National Centers for Environmental prediction (NCEP) in collaboration with the National Centre for Atmospheric Research (NCAR) (Kalnay et al., 1996). Chen et al. (2012) confirmed that the NCEP/NCAR reanalysis is better than the ERA-40 in reproducing the observed wind speeds, seasonality and temporal trends over China. The monthly zonal, meridional and vertical wind speeds data sets are available from the data. Horizontal resolution is 2.5◦ × 2.5◦ , and the time span is ranging from 1948 to present. The zonal, meridional and vertical wind speeds consist of 19 standard pressure levels. We thus employed 500 hPa pressure level to represent the lower free atmosphere above the Tibetan Plateau. To investigate the linear trend of variables, the below linear regression equation was used: yx = a + bx + ex y x refers to the temperature or precipitation at time x (in years), e x denotes the bias between observational data and linear regression line. The regression coefficients a Int. J. Climatol. 34: 1524–1537 (2014)

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Figure 1. Sketch map of the study area.

Table 1. The summary of the stations. ID

Station name First Latitude, Longitude, Elevation Annual mean Annual mean Land cover year N E (m) temperature (◦ C) precipitation (mm)

51886 Mangya 1958 52602 Lenghu 1956 52707 Xiaozaohuo 1960 52713 Dachaidan 1956 52737 Delingha 1955 52818 Geermu 1955 52825 Nuomuhong 1956 52836 Dulan 1954

38◦ 15 38◦ 45 36◦ 48 37◦ 51 37◦ 22 36◦ 25 36◦ 26 36◦ 18

90◦ 51 93◦ 20 93◦ 41 95◦ 22 97◦ 22 94◦ 54 96◦ 25 98◦ 06

2944.8 2770.0 2767.0 3173.2 2981.5 2807.6 2790.4 3191.4

2.59 2.95 3.67 1.93 3.94 5.19 4.94 3.18

48.63 16.37 28.02 89.52 179.61 43.31 46.86 201.61

Gobi desert Low coverage grassland Low coverage grassland Barren Urban and built-up lands Urban and built-up lands Barren Plain dryland

The data from April to December of 1974 at Xiaozaohuo station were missing.


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(the intercept) and b (the slope) are obtained by least square method. A student t-test was used to test the statistical significance of the trend. The below statistic parameter was used in t-test: 1/2 t = r (n − 2) / 1 − r 2 where n and r refer to the number of years and the correlation coefficient between x and y x , respectively. Here the larger absolute value of |r|, the more close linear relationship between x and y x . The correlation coefficient needs to be tested to determine whether the variable trend is significant. In this paper the |r| should be greater than 0.27 at a confidence level of α = 0.05, indicating that the linear trend exceeds the 95% confidence level. When confidence level is α = 0.01, |r| > 0.451, indicating that the linear trend exceeds the 99.9% confidence level. In addition, in our paper, spring includes March, April and May; summer includes June, July and August; autumn is September, October, and November; and winter includes December, January and February.

4. The climate change in the Qaidam Basin 4.1. Temperature variations Table 2 shows decadal statistics of annual and seasonal mean temperatures in the Qaidam Basin. The annual and seasonal mean temperatures increased, with an exception in spring of the 1980s with a relatively low temperature. Compared with the 1960s, annual mean temperature in the 2000s has increased by 2.21 ◦ C. However, winter temperature has increased by 3.30 ◦ C. The annual and seasonal temperatures within 30-year average from 1981 to 2010 were higher than within 30-year average from 1961 to 1990 and 1971 to 2000 by 0.80 ◦ C to 1.36 ◦ C and 0.41 ◦ C to 0.64 ◦ C, respectively (Table 2). In contrast, winter experienced a rapid temperature increase than other seasons. Figure 2 shows the variations of annual mean, and spring, summer, autumn and winter temperatures. It demonstrates that the temperature increased with fluctuations from 1961 to 2010. The average temperature increase rate is 0.53 ◦ C 10a−1 in the Qaidam Basin, which exceeded the 99.9% confidence level. Annual mean temperature has increased by around 2.65 ◦ C during the past 50 years. In 1998, temperature is relatively high and reached up to 4.98 ◦ C. Other researches also demonstrated that 1998 is the warmest year during the 1990s in China (Li, 1999; Chen et al., 2004). The year 2006

is the warmest (5.28 ◦ C) in the past 50 years. Actually, 2006 is the warmest year since 1951 in China (National Climate Center/CMA, 2007). Thus it can be seen that the higher temperature in the years 1998 and 2006 in China also appeared in the Qaidam Basin. The temperature in spring, summer, autumn and winter increases at a rate of 0.39, 0.43, 0.57 and 0.74 ◦ C 10a−1 , respectively. All of these rates exceed the 99.9% level of confidence, indicating that annual mean and each season’s warming are remarkable. Relatively speaking, the most significant warming rate occurred in winter, which agrees with Chen et al. (2010) and Li et al. (2010a), whereas the least significant warming rate occurred in spring. The warming rate in winter is almost twice higher than in spring. Likewise, the temperature of eight meteorological stations shown in Figures S1–S8 gradually increased with time. In order to examine the temporal-spatial differences of the temperature changes in the Qaidam Basin, the values of temperature increasing rate at eight meteorological stations are also plotted in Figure 3, showing that the temperatures in each season increased significantly. The increasing rates in spring range from 0.07 ◦ C 10a−1 (Lenghu station, did not exceed the 95% confidence level) to 0.79 ◦ C 10a−1 (Mangya station) (Figure 3(b)). In summer, the warming tendencies are from 0.18 ◦ C 10a−1 (Dulan station) to 0.92 ◦ C 10a−1 (Mangya station) (Figure 3(c)). In autumn, the warming rates range from 0.27 ◦ C 10a−1 (Lenghu station) to 0.95 ◦ C 10a−1 (Mangya station) (Figure 3(d)). However, the most pronounced warming in winter occurred at Delingha station with a maximum value of 1.01 ◦ C decade−1 . The least warming in winter occurred at Lenghu station with a rate of 0.43 ◦ C 10a−1 . At Mangya station, the temperature increasing rate is 0.93 ◦ C 10a−1 in winter (Figure 3(e)). Thus all these findings demonstrate the recent significant warming in the Qaidam Basin. Among the eight meteorological stations, Mangya station, located in the northwestern Qaidam Basin, experienced the most significant warming with the annual warming rate of 0.89 ◦ C 10a−1 . The minimum annual warming rate is at Lenghu station (0.24 ◦ C 10a−1 ) (Figure 3(a)). 4.2.

Precipitation variations

The decadal statistics (Table 3) shows that, even though the precipitation in the 1990s is relatively low, the precipitation increased during the past 50 years (Figure 4). Comparing to the 1960s, the annual precipitation in the 2000s has increased by 47% (32.33 mm) with the largest (65%) in autumn. The annual and summer precipitation

Table 2. Decadal statistics of annual and seasonal mean temperatures in the Qaidam Basin (◦ C). Years Annual Spring Summer Autumn Winter

1961–1970 2.48 4.18 14.87 1.91 −11.08

1971–1980

1981–1990

1991–2000

2001–2010

1961–1990

1971–2000

1981–2010

3.15 4.56 15.02 2.69 −9.69

3.39 4.38 15.23 3.04 −9.18

4.02 5.36 16.00 3.61 −8.90

4.69 5.78 16.60 4.22 −7.78

3.01 4.38 15.04 2.55 −9.98

3.52 4.77 15.42 3.12 −9.26

4.03 5.18 15.94 3.63 −8.62


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Figure 2. Variations of annual and seasonal mean temperature (1961–2010). Dashed lines denote their linear variation rates.

increased during the past 50 years. Maximum precipitation in annual, summer and autumn occurred in the 2000s and minimum precipitation occurred in the 1960s except in spring and autumn. Compared with the period of 1961 to 1990 and 1971 to 2000, the 30-year annual mean precipitation in 1981–2010 has increased by 11.69 mm (15.3%) and 8.74 mm (11%), respectively. The variations of precipitation in spring, autumn and winter are complicated, even though they exhibited an increasing trend (Figure 4). Annual precipitation in the Qaidam Basin increased with a rate of 7.38 mm 10a−1 , which passed the 95% confidence level test. The increasing rates of precipitation in spring, summer, autumn and winter are 1.67, 4.02, 1.27 and 0.42 mm 10a−1 , respectively. Except in spring, the increasing rates in other seasons exceeded the 95% confidence level and the rate in summer exceeded the 99% level. However, it should be noted that the absolute value of precipitation in the study area is quite low. In  2013 Royal Meteorological Society

spring and autumn, the annual mean precipitation is less than 16 mm and in winter it is less than 4 mm (Table 3). Similarly, spatial distributions of annual and seasonal precipitation trend magnitudes in the Qaidam Basin at eight meteorological stations are also plotted in Figure 5 (also shown in Figures S9–S17). Figure 5(a) shows that the annual precipitation of four meteorological stations increased significantly. The increasing rates range from 2.03 mm 10a−1 (Xiaozaohuo station) to 25.09 mm 10a−1 (Delingha station). At the other four stations, even though the precipitation has an increasing trend, they did not pass the 95% confidence level. There are two stations with significant increase of precipitation for spring (Figure 5(b)). The increasing trends in summer are most evident (Figure 5(c)) and the increasing amplitudes are also large, ranging from 3.53 mm 10a−1 (Nuomuhong station) to 14.42 mm 10a−1 (Delingha station). However, at Mangya and Lenghu stations, the precipitation in Int. J. Climatol. 34: 1524–1537 (2014)

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Figure 3. Spatial distributions of annual and seasonal mean temperature trend magnitudes in the Qaidam Basin at eight meteorological stations (1961–2010).

Table 3. Decadal statistics of the annual and seasonal mean precipitation in the Qaidam Basin (mm). Years Annual Spring Summer Autumn Winter

1961–1970

1971–1980

1981–1990

1991–2000

2001–2010

1961–1990

1971–2000

1981–2010

68.82 15.92 39.43 10.64 2.84

74.94 8.53 52.42 10.18 3.85

86.11 21.49 52.05 8.76 3.80

77.68 12.64 51.99 8.09 4.88

101.16 21.44 57.89 17.59 4.23

76.62 15.31 47.97 9.86 3.49

79.58 14.22 52.15 9.01 4.18

88.31 18.53 53.98 11.48 4.30


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Figure 4. The annual and seasonal precipitation variations in the Qaidam Basin (1961–2010). Dashed lines denote their linear variation rates.

summer even has decreased. In autumn and winter, the precipitation at Mangya, Lenghu and Delingha stations increased significantly (Figure 5(c) and (d)). Overall, the increase of precipitation in the Qaidam Basin mainly occurred in summer, which is consistent with previous studies of Shi et al. (2005) and Chen et al. (2010). The maximum precipitation increase occurred at Delingha station (25.09 mm 10a−1 ). The minimum precipitation increase occurred at Lenghu station (0.10 mm 10a−1 ). The climate condition in the Qaidam Basin has become warmer and wetter during the past decades (Shi et al., 2005; Li et al., 2010a). 5. The possible causes of the significant warming in the Qaidam Basin The IPCC AR4 (2007) pointed out that the global annual mean temperature has increased by 0.74 ◦ C ± 0.18 ◦ C  2013 Royal Meteorological Society

within the 100 years from 1906 to 2005. However, the annual mean temperature in China has increased by 1.1 ◦ C during the past 50 years (1951–2001) (Ding et al., 2007). Liu and Chen (2000) argued that the linear rate of temperature increase over the Tibetan Plateau during the period 1955–1996 was about 0.16 ◦ C decade−1 for the annual mean and the increasing rate doubled in winter (0.32 ◦ C 10a−1 ). In the Himalayas region of the southern Tibetan Plateau, the warming trend from 1961 to 2010 approached to 0.38 ◦ C 10a−1 (Zhang et al., 2012). Chen et al. (2009) confirmed that annual mean temperature increased obviously in various regions of Qinghai in the last 46 years, especially in the Qaidam Basin. Figure 3c of Jiang et al. (2010) showed that the region with the most obvious warming trend is near the Qaidam Basin during 1954–2004. Results from Li et al. (2010a) and Guo and Wang (2011) demonstrated that the northern Tibetan Plateau has experienced the most significant Int. J. Climatol. 34: 1524–1537 (2014)


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Figure 5. Spatial distributions of annual and seasonal precipitation trend magnitudes in the Qaidam Basin at eight meteorological stations (1961–2010). The red cycles represent increasing trends exceed the 95% confidence level.

warming. This region also exhibited the greatest and most significant decrease in the frequency of extremely low temperature events (Li et al., 2010a). These results highlighted that the amplitude of temperature increase in the Qaidam Basin is not only higher than that in global average but also higher than that in China. Why is the warming so outstanding in the Qaidam Basin? Is it mainly caused by local factors or by the results of the response to global warming?  2013 Royal Meteorological Society

5.1.

Impact of local factors

Liu and Chen (2000) suggested that the warming rate increases with the elevation. However, some researchers argued that there were no simple linear relationships between the temperature and elevation (Pepin and Lundquist, 2008; You et al., 2010). The elevations of eight meteorological stations in the Qaidam Basin range from 2770 to 3191.4 m a.s.l., with an average elevation of 2928.2 m. There exist negative relationships between the Int. J. Climatol. 34: 1524–1537 (2014)

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Figure 6. The scattering plot of the warming rate and the elevation during 1961–2010. Solid lines represent the regression lines.

temperature increased rates with the elevation (Figure 6). However, such negative relationship did not exceed the 95% confidence level. As a fact, we have to note that the highest elevation is 3191.4 m (Dulan station). Obviously there is no relationship between the warming rate and the elevation. The sunshine durations in the Qaidam Basin decreased significantly in all four seasons (statistical significance >95%), especially in summer (y = −0.64x + 304.35, R 2 = 0.35) and autumn (y = −0.26x + 272.22, R 2 = 0.15) (Figure is not shown). The annual mean sunshine hours present different degrees of reduction at eight stations. In particular, Lenghu station has reduced by about 19.9 d during 1961–2010, with a decreasing rate of about 95.5 h per 10 years (see Table 4). Broadly used as a surrogate  2013 Royal Meteorological Society

for solar radiation, the sunshine duration is closely associated with the atmospheric heating. In principle, a shorter sunshine duration may result in a lower air temperature, further a smaller warming trend, and vice verse. As aforementioned, seasonal warming mainly occurred in autumn and winter. Also the sunshine durations at Lenghu station decreased, in part, because lower clouds in the atmosphere resulted from increased precipitation in the two seasons (Figure 5). There is an inverse relationship between sunshine duration and temperature at the station. The correlation coefficient between temperature and sunshine hours was found to be equal to −0.46 in autumn and −0.54 in winter for the whole time series (1961–2010). Combined with the striking reduced sunshine durations of other seasons, the annual mean Int. J. Climatol. 34: 1524–1537 (2014)


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Table 4. Trends of annual and seasonal mean sunshine duration of eight stations in the Qaidam Basin during the 1961–2010 period. Station name

Spring

Summer

Autumn

Winter

Annual

Mangya Lenghu Xiaozaohuo Dachaidan Delingha Geermu Nuomuhong Dulan

−3.41 −6.29 −2.65 0.29 −2.51 1.10 −3.19 −2.48

−8.89 −10.13 −5.25 −2.82 −8.59 −2.94 −7.97 −4.86

−2.85 −7.42 −2.49 −0.63 −2.07 −1.13 −3.96 −0.36

−2.26 −8.21 −2.63 −0.26 −0.14 −1.28 −3.02 −1.89

−5.16 −9.55 −3.85 −1.05 −4.02 −1.25 −5.45 −2.88

The bold values indicate significant at the 95% confidence level.

warming trend at Lenghu station is not so big, even the smallest. Compared with other seasons, the warming magnitude of Delingha is also the smallest in summer because of the decreased sunshine duration. This phenomenon could also be seen in summer at Dulan station. Solar dimming may have profoundly counterbalanced the increase in downward longwave energy from the enhanced greenhouse effect, and may have been effective in masking greenhouse warming (Wild et al., 2005, 2007). Therefore, the large differences in warming rates among the stations are mainly affected by the sunshine durations. Besides, urbanization also significantly impacted on the local temperature (Zhou and Zhang, 1985; Ren et al., 2008; Zeng et al., 2009). The urbanization process has changed the surface conditions, which resulted in the decreasing of albedo. The heating systems and industrial activities also enforced the effect of heat island. The development of industrialization can help to promote urbanization process. Mangya, a western industrial hub in China, enjoys the reputation of ‘oil city and asbestos kingdom’ for all kinds of rich mineral resources. Therefore, the exploitation of mineral resources consumes large quantities of power. The industrial electricity consumption was up to 2.4 × 104 kW h in Mangya area in 2010 (2.0, 2.7, 3.0, 0.6 and 12.3 × 104 kW h were in Dachaidan, Lenghu, Delingha, Dulan and Geermu, respectively). The added value of the industry was 1.53 billion yuan in 2006, 6.87 times of that in 2002. The added value of the secondary industry increased from 0.69 million in 1997 to 14.7 billion in 2010, and it expanded from 6.0 million to 2.4 billion for that of tertiary industry (Statistics Bureau of Qinghai Province, 2007, 2011). However, air pollution emissions are inevitable in the process of industrialization. The asbestos dust concentration increased year by year: 316.57 mg m−3 in 1987, 431.85 mg m−3 in 1988, 817.14 mg m−3 in 1990 and 691.40 mg m−3 in 1991. In 2008, the main pollution emissions included 351.59 tons of chemical oxygen demand (COD), 48.17 tons of ammonia nitrogen, 1026.89 tons of SO2 , 147.14 tons of soot and 158 tons of industrial dust (Compilation Committee of Local Chronicles of Haixi Mongolian and Tibetan Autonomous Prefecture, 2009). Although the emissions of pollution here are lower than in eastern  2013 Royal Meteorological Society

China, the natural vegetation is assumed to absorb little of them due to the vastness of the Gobi Desert around Mangya. Brown clouds, mainly produced by biomass burning, fossil fuel consumption and these pollutants, could enhance lower atmospheric solar heating by about 50%. Furthermore numerical simulations also manifested that regional lower atmospheric warming are contributed as much as the observed increase in anthropogenic greenhouse gas concentrations by brown clouds (Ramanathan et al., 2007). Heat released from industrial activities, vehicles and residents’ lives (243 d year−1 for residential heating) thus can generate very strong heat island effect. Meanwhile, Mangya is low in the middle but high in other sides. The most significant decreased wind speed (−0.60 m s−1 10a−1 , as shown in Figure S17) is not sufficient to disperse clouds, aerosols and other air pollutants. This can explain significant reduced sunshine durations. We speculate that this heat could not be spread out, leading to a greater warming in Mangya. Population increase is one of the consequences during urbanization. The populations in Geermu and Delingha cities are 0.057 million and 0.049 million in 1982 (Statistics Bureau of Qinghai Province, 1991), respectively. Till the year 2010, the population almost doubled in Delingha city and reached 0.078 million, and it tripled in Geermu city and reached 0.215 million (Statistics Bureau of Qinghai Province, 2011). Actually, Easterling et al. (1997) defined the urban as a city with 50 000 or more population whether a station is in an urban or non-urban environment. Population increase would result in the city expanding and rapid increased energy consumption. This would eventually cause the changes of the heat condition of the suburb and impact the local temperature records. Therefore, the dramatic warming rates in winter at Geermu and Delingha stations may be related to urbanization. 5.2.

Response to the global warming

Duan and Wu (2008) have studied the variations of temperature from troposphere to stratosphere (1980–2003) and results showed that temperatures in the upper troposphere and lower stratosphere were decreasing. However, temperature in the middle- and lower troposphere was increasing. The wind speed in troposphere over the Int. J. Climatol. 34: 1524–1537 (2014)

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Figure 7. The changes of the zonal, merdional and vertical wind speed at 500 hPa over the Tibetan Plateau. Dashed lines denote their linear variation rates.

Tibetan Plateau was weakening, while it was strengthening in stratosphere. The maximum decrease of the wind speed in the troposphere occurred in summer with a rate of −1.2 m s−1 10a−1 . Therefore, it was inferred that the weakening of wind speed near surface was associated with global warming. Figure S18 shows the wind speeds in the Qaidam Basin during 1961–2010, which are averaged from eight meteorological stations. The wind speeds in four seasons (spring, summer, autumn and winter) increased till the 1970s. Then the wind speeds decreased significantly. The average decreasing rate is 0.23 m s−1 10a−1 . Duan and Wu (2009) also pointed out that, as the climate warming, the geopotential height elevated in warming regions but dropped in cooling regions. Such a change in geopotential height over Asian continent would enhance the Mongolia high in spring and summer and weaken the Eastern Asian trough in autumn and winter. Therefore, the meridional pressure gradient and the zonal geostrophic wind would be weakened. Meanwhile, the weakening of the wind speed in mid- and lower troposphere over the Tibetan Plateau likely has something to do with the large-scale atmospheric circulations. There is a narrow but strong westerly belt with large horizontal and vertical shears in the upper troposphere and lower stratosphere over subtropical East Asia (Duan and Wu, 2009). In order to investigate the changes of the westerly, the average zonal, merdional

Figure 8. The spatial–temporal distribution of the linear changes of the zonal wind speed at 500 hPa over the Tibetan Plateau during the period from 1961 to 2010 for the (a) spring, (b) summer, (c) autumn and (d) winter (m s−1 10a−1 ). The grey line represents the boundary of the Tibetan Plateau. The shade areas indicate significant at the 95% confidence level. The green boxes in each panel denote the Qaidam Basin. The black spots represent meteorological stations.


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and vertical wind speeds at 500 hPa over the Tibetan Plateau were calculated and discussed (Figure 7). Please note that the air in 500 hPa represents the low level free air on the Tibetan Plateau. We found that the average wind speed in latitude has decreased significantly with a rate of −0.22 m s−1 10a−1 (passed the 95% confidence level test). Compared to the significant decreasing trend in the zonal wind, the changes of the meridional and vertical wind speed at 500 hPa over the same region are relatively small. The weakening of zonal wind speed is likely to be the main reason caused the weakening of wind speed on the Tibetan Plateau. Nevertheless, is there any impact of the change of the zonal wind speed on the significant warming in the Qaidam Basin? For this reason, the spatial–temporal distributions of the linear trends of the zonal wind speed at 500 hPa are plotted (Figure 8). The zonal wind speed varies with seasons. It can be found that there is a weakening centre from north to south running through the Tibetan Plateau in all seasons. During spring, summer and autumn, the weakening centre is roughly located in the region of 90–105◦ E. The wind speed decrease rate is −0.5 m s−1 10a−1 in spring and autumn. The strongest decrease in wind speed occurred in summer with a rate of −0.8 m s−1 10a−1 . The changes of the wind speed in spring, summer and autumn have a relatively large contribution on the weakening of wind speed in the Qaidam Basin. Therefore, the weakening of wind speed caused by the global warming rises temperature significantly. The global climate change has a pronounced impact on the climate in the Qaidam Basin. Also the abrupt changes of mean temperature began in the Qaidan Basin (1973), which occurred earlier than the Tibetan Plateau (Ma and Li, 2003). Therefore, it becomes the most susceptible region with the most significant climate change over the whole Tibetan Plateau.

6. Conclusions The variations of temperature and precipitation in the Qaidam Basin were investigated using meteorological station data over the period 1961–2010. Temperature in the Qaidam Basin is increasing with a stepped trend. The temperature within the 30-year average from 1981 to 2010 is 0.80 to 1.36 ◦ C and 0.41 ◦ C to 0.64 ◦ C higher than that from 1961 to 1990 and 1971 to 2000, respectively. The average warming rate from 1961 to 2010 is 0.53 ◦ C 10a−1 . Warming is the most significant in winter with an average rate of 0.74 ◦ C 10a−1 (0.43 to 1.01 ◦ C 10a−1 ) and the smallest in spring (0.39 ◦ C 10a−1 ). On average, the most significant warming occurred at Mangya station and the smallest at Lenghu station. Compared with the precipitation from 1961 to 1990 and 1971 to 2000, the precipitation within the 30 year from 1981 to 2010 has increased by 11.69 mm (15.3%) and 8.74 mm (11%). The annual precipitation increased with a rate of 7.38 mm 10a−1 from 1961 to 2010. The maximum  2013 Royal Meteorological Society

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increase of the precipitation occurred in summer with a rate of 4.02 mm 10a−1 . The maximum and minimum precipitation increase occurred at Delingha station and Lenghu station, respectively. Altitude has no significant impact on temperature increasing because most of the stations located at lower altitudes. Local factor, such as the sunshine duration, affects the warming trends of different stations, especially for Lenghu station. The decreased sunshine durations have profoundly counterbalanced the warming from the enhanced greenhouse effect. The air pollution emissions from industrial processes, namely brown clouds, enhance lower atmospheric solar heating to lead much great warming. Besides, the urbanization also has contributed to the climate warming and this is especially the case in winter. The geopotential height changes as a result of global climate warming. It further leads to meridional pressure gradient and a subsequently weakened zonal geostrophic wind. Compared to the no significant decreasing trend in meridional and vertical wind, the average zonal wind speed presents significant decrease trend with a rate of −0.22 m s−1 10a−1 over the Tibetan Plateau. Moreover, the weakening centre is primarily located in the Qaidam Basin. The weakening of zonal wind speed on Tibetan Plateau contributes to the climate warming in the Qaidam Basin. Therefore, the Qaidam Basin becomes the most susceptible region with the most significant climate change for the whole Tibetan Plateau. The average warming rate in the Qaidam Basin is 0.53 ◦ C 10a−1 during the past 50 years. Especially at Mangya station, the climate is undergoing dramatically warming (0.89 ◦ C 10a−1 ). Such a high rate has a large contribution on the average rate in the Qaidam Basin. Therefore, in climate change studies, how to reasonably select meteorological stations is worth taking into account. If more stations such as Mangya with higher warming rate were selected, the conclusion of significant warming climate could be obtained. Otherwise, if such meteorological stations with higher warming rate are rejected or meteorological stations with lower warming (even cooling) rate are chosen, the conclusion would be quite different.

Acknowledgements This work was sponsored jointly by the National Key Basic Research Program of China (2010CB951404), the One Hundred Talent Program of the Chinese Academy of Science (29O827B11), the National Natural Science Foundation of China (41075007), the “Strategic Priority Research Program (B)” of the Chinese Academy of Sciences (XDB03030204), and the State Key Laboratory of Cryospheric Sciences (SKLCS-ZZ-2012-02-03), CAREERI, CAS. We are also indebted to the reviewers for helpful comments and criticisms of the initial draft of this paper. Int. J. Climatol. 34: 1524–1537 (2014)

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