Much attention has been paid to the effects of global climate change. The IPCC’s fifth assessment reported that the average temperature from 2003 to 2012 was 0.78℃ higher than that from 1950 to 1900 (
Journal of Geographical Sciences, Volume. 30, Issue 4, 657(2020)
Spatio-temporal differentiation of climate warming (1959-2016) in the middle Qinling Mountains of China
Based on air temperature observation data from 32 meteorological stations, temperature changes in the middle Qinling Mountains from 1959 to 2016 were analysed with respect to the north-south, seasonal and altitude differences. Our research mainly showed the following results. The annual temperature (TA) rose approximately 0.26℃/10a within the past 58 years. This warming trend was stronger on the northern slope than on the southern slope, and a warming trend reversal occurred in 1994 on the northern slope, which was three years earlier than on the southern slope. The temperature changes for the four seasons were not synchronized, and the trend in spring contributed the most to the TA trend, followed by winter, autumn, and summer. The temperature difference between summer and winter (TDSW) decreased significantly over the past 58 years. The temperature change in the middle Qinling Mountains was clearly dependent on altitude. With increases in altitude, the TA increased gradually and became stronger while the TDSW decreased gradually and became weaker. Differences in temperature change between the north and south were mainly observed in low-altitude areas. With increase in altitude, the differences gradually tended to disappear.
1 Introduction
Much attention has been paid to the effects of global climate change. The IPCC’s fifth assessment reported that the average temperature from 2003 to 2012 was 0.78℃ higher than that from 1950 to 1900 (
In the 20th century, the rate of climate warming in the European Alps was higher than the global average and the Northern Hemisphere average. As a rule, the increase in low temperatures was greater than that of high temperatures, with the trend of temperature change showing an imbalance between day and night (
The Qinling Mountains, which stretch from east to west across the central part of China, are one of the key areas of global biodiversity and an important north-south boundary in China (
2 Study area
This article takes the section of the Qinling Mountains in Shaanxi Province (middle Qinling Mountains) as the study area (105°30′-110°05′E and 32°40′-34°35′N). This area starts from the Jialing River in the west and is connected to the Funiu Mountains on the east, with the Weihe River as the boundary to the north and the Hanshui River as the boundary to the south. The altitude of the study area is approximately 195-3771.2 m. The northern slope is steep, and the southern slope extends gradually (
Figure 1.
Figure 2.
3 Data and methods
3.1 Data sources
The daily average temperature data used for this study were collected from 32 national meteorological stations in the study area covering a period of 1959 to 2016 and obtained from the Shaanxi Meteorological Bureau. The DEM is obtained from National Geomatics Centre of China, and its spatial resolution is 25 metres.
3.2 Acquiring the spatial temperature datasets
The Thin Plate Smoothing Spline interpolation method, which is built into ANUSPLIN software, uses a smooth parameter balance to optimize the smoothness of the fitting surface and data fidelity and is suitable for complex mountainous areas because the root-mean- square error and other statistical indicators can evaluate the high interpolation accuracy of the method (Xu et al., 2018;
Regional meteorological stations on Mt. Taibai, Shaanxi Province
Regional meteorological stations on Mt. Taibai, Shaanxi Province
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3.3 Analysis of temperature variation trends
A linear trend analysis was used to analyse the spatial variation trend of air temperature. The least square method was used to fit the temperature trend by pixel (Deng et al., 2018), and the formula is as follows.
${{\theta }_{slope}}=\frac{n\times \mathop{\sum }_{i=1}^{n}i\times {{T}_{i~}}-\mathop{\sum }_{i=1}^{n}i\mathop{\sum }_{i=1}^{n}~{{T}_{i}}}{n\times \mathop{\sum }_{i=1}^{n}{{i}^{2}}-{{\left( \mathop{\sum }_{i=1}^{n}i \right)}^{2}}}$
where i is the annual serial number from 1 to 58, Ti is the temperature in year i, and qslope is the change slope. When qslope is greater than 0, then the temperature of the pixel shows an increasing trend over the past 58 years; and when qslope is less than 0, then the temperature of the pixel shows a decreasing trend over the past 58 years.
The Mann-Kendall method was used to test the abrupt change in and significance of the temperature trend over the time series. The M-K test is a non-parametric test recommended by the World Meteorological Organization and is widely used. This method can test the significance of the change trend of a time series as well as the mutation test (Wang et al., 2017). The basis of this test is to provide the statistic “U” and provide the level of significance “a”. For a=0.05 and Ua/2=1.96, if |U|> Ua/2, then there is a significant trend; if U is negative, then there is a downward trend; and if U is positive, then there is an upward trend.
4 Results
4.1 Spatiotemporal variation trends of annual temperature
Figures 3a and 3b show the time regulation of the TA variation trend for the northern and southern slopes of the middle Qinling Mountains from 1959 to 2016.
Figure 3.
4.2 Seasonal changes in temperature
4.2.1 Asynchrony of temperature change across the four seasons
Figure 4.
In addition to the TA change trend, there was also a north-south variation in the temperature among the four seasons.
Figure 5.
4.2.2 Temperature difference between summer and winter showed a decreasing trend
The TDSW also changed noticeably with an unsynchronized change in temperature across the four seasons.
Figure 6.
4.3 Altitude-dependent temperature variation in the middle Qinling Mountains
4.3.1 TA and TDSW change with elevation
Figure 7.
The TDSW trend weakened with an increase in altitude. The TDSW rate of decrease was reduced by 0.008℃/10a per 100 m and 0.009℃/10a per 100 m of increased altitude on the southern slope and northern slope, respectively. In areas of low altitude, the TDSW showed a significant decreasing trend and a significant difference occurred between the north and south, with a stronger descending trend observed for the northern slope than the southern slope. As the altitude increased, the difference in the TDSW trend between the northern and southern slopes gradually decreased, and the decrease in the TDSW trend gradually approached 0℃/10a at altitudes above 3000 m.
4.3.2 Seasonal temperature trend at different altitudes
According to the outline of ecological and environmental protection of the middle Qinling Mountains in Shaanxi Province and considering the obvious influence of human activities on air temperature in low-altitude areas, the middle Qinling Mountains are divided into four categories according to the altitude. The trend of temperature across four seasons in different vertical belts is analysed as shown in
Temperature trend of different vertical belts in four seasons of the middle Qinling Mountains during 1959 to 2016 (℃/10a)
Temperature trend of different vertical belts in four seasons of the middle Qinling Mountains during 1959 to 2016 (℃/10a)
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5 Discussion and conclusions
5.1 Conclusions
Based on our research on temperature changes from different spatial and temporal scales from 1959 to 2016, we found that the middle Qinling Mountains showed a warming trend and that the trend of TA was 0.26℃/10a. Due to the specific topography and geographical location of the middle Qinling Mountains, there was a north-south difference in temperature change. The warming trend of the northern slope was greater than that of the southern slope, with values of 0.29℃/10a and 0.25℃/10a, respectively. The abrupt change in temperature on the northern slope took place earlier than on the northern slope, which occurred in 1994 and 1997, respectively.
Temperature changes for the four seasons were not synchronized in the middle Qinling Mountains and occurred in the order of spring (0.33℃/10a) > winter (0.30℃/10a) > autumn (0.27℃/10a) > summer (0.08℃/10a). Temperature changes in spring had the greatest contribution to the trend of the TA and reached 35.60% on the northern slope and 32.31% on the southern slope. The TDSW showed a significantly decreasing trend at a rate of -0.23℃/10a. Both the northern and the southern slopes exhibited a synchronous mutation in 1976.
Altitude dependence is observed in the trend of temperature change. At a higher altitude, the TA increased to a greater degree and the TDSW decreased at a lower rate. In low-altitude areas, the temperature on the northern slope increased significantly in spring, while in areas of high altitude, the temperature increased significantly in every season. The difference between the north and the south mainly occurred in low-altitude areas, and this difference gradually decreased with altitude.
5.2 Discussion
Mountainous areas are sensitive to climate change, and the middle Qinling Mountains represent the north-south boundary for China’s climate, geographical resources, and environment. Therefore, the difference in climate change between the southern slope and the northern slope of the middle Qinling Mountains also represents climate change characteristics for both the south and the north of China. Under the background of global warming, the temperature of the middle Qinling Mountains increased significantly, the warming trend of the northern slope was stronger than that of the southern slope, and the warming mutation of the northern slope occurred before that of the southern slope. At the same time, the temperature change trend of the four seasons was not synchronized, and the trend of the temperature increase in spring and winter was significantly higher than that in summer. Why do we observe north-south and seasonal differences? Studies have noted that temperature changes are affected by human activities, solar radiation, atmospheric aerosols, ENSO and other factors (
First, the formation of the climate in the middle Qinling Mountains is closely related to two circulation systems. In the south of the middle Qinling Mountains, climate was mainly affected by the East Asian monsoon system and controlled by westerlies in the north (
Second, the middle Qinling Mountains represent a typical forest ecosystem, with lush vegetation in summer and autumn, declining vegetation in winter and spring, and greater vegetation coverage on the southern slope than on the northern slope (
Third, the northern slope of the middle Qinling is near the provincial capital Xi’an. The urban heat island has a certain effect on local microclimates. The warming trend of the middle Qinling Mountains increased with elevation, but the temperature trend of the northern slope (< 600 m) in spring is an exception, which is greater than that of the region at 600-1500 m. Thus, the urban heat island has an obvious effect on climate change.
In addition, the dependence of climate change on altitude is the focus of many scholars (
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Ting ZHAO, Hongying BAI, Yuan YUAN, Chenhui DENG, Guizeng QI, Danping ZHAI. Spatio-temporal differentiation of climate warming (1959-2016) in the middle Qinling Mountains of China[J]. Journal of Geographical Sciences, 2020, 30(4): 657
Received: May. 31, 2019
Accepted: Nov. 8, 2019
Published Online: Sep. 30, 2020
The Author Email: BAI Hongying (hongyingbai@163.com)