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Evaluation and analysis of observed soil temperature data over Northwest China

  • Yuanhao Wang , Zhihuai Jiang , Degang Zhou and Zhiyu Gong EMAIL logo
Published/Copyright: December 23, 2022
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Open Geosciences
From the journal Open Geosciences Volume 14 Issue 1

Abstract

Soil temperature (ST) is an important land surface factor as a predictor of regional climate change. Based on the ST data from 30 climate stations in Northwest China (NWC) during the period 1960–2000, the trends and distribution of STs at 0, 80, 160, and 320 cm depths were investigated, together with observed atmospheric variables, such as air temperature (AT) and precipitation. The differences between the annual average AT and ST at each layer (0, 80, 160, and 320 cm) in each station was no more than 6°C. In different seasons, the differences were even more remarkable than annual values. Trends in some stations varied during the period 1961–2000, with warming trends appearing at some stations after the mid-1980s. Few warming trends appeared in the west part of NWC, while most warming trends appeared in the east part. There were more warming trends at annual time scales than in season timescales. The fewest warming trends were found in winter, but they had the largest magnitude of increase (1.1°C/decade) for surface ST; the largest magnitudes of increase for 80 and 160 cm ST were in summer, with increase of 0.95 and 0.88°C/decade, respectively. The correlation coefficients between ST and AT were positive and statistically significant at the 95% confidence level in most stations except in winter. The correlation values between ST and precipitation were different at different time scales.

Keywords: soil temperature; air temperature; trend; Northwest China

1 Introduction

Soil temperature (ST) data series that date back to the 17th century exist; for example, ST data have been collected from the cellar of the observatory of Paris since 1670 [1]. However, compared with studies of other parameters, studies on ST have been relatively simple and the mechanisms controlling ST remain poorly understood. Tang et al. [2] explored the relationship between ST at depths of 80 cm and the precipitation patterns of the following season. Tang and Gao [3] defined geothermal vortices with ST at depths of 3.2 m, which were good predictors of rainfall anomalies of the same season. Zhao et al. [4] also proposed the winter geothermal field at a depth of 3.2 m as a good predictor of the precipitation field of the following summer. In recent years, ST has attracted increasing attention among researchers. Some studies have highlighted the importance of ST in land–air interactions [5,6].

The ST is a measure of the energy conditions, heat storage, and heat flux in the soil. It is a model parameter in simulations of the heat transfer between the soil layer and the atmosphere [7,8,9,10,11,12,13]. Studies have also examined ST variations [14,15,16,17,18,19,20] and the connection between ST and climate change [19,21,22]. Hu and Feng [23] calculated the soil enthalpy of subsurface soil and pointed out that it can last for 2–3 months. Zhang [11] found that snow thickness and other factors modulate the effect of air temperature (AT) changes on ST. Xue et al. [21] found that surface heating of the spring subsurface influenced the climate of downstream over North America. Recently, the lag influences of ST excited more and more attention [19,24,25].

How does ST change with global warming? By examining ST data from Irkutsk between the 1890s and the 1990s, Zhang et al. [17] found rapid warming trends in ST at depths of 40 cm. Qian et al. [16] investigated the trends in ST across Canada; they reported that the land had warmed faster than the oceans and considered this phenomenon as an indicator of climate change in Canada. Wang et al. [26] pointed out that ST at the surface and shallow depths across China continued to increase during the so-called global warming hiatus.

Northwest China (NWC) is located upstream of the East Asian summer monsoon area and lies far away from the flow of warm and humid air masses. It is classified as an arid/semi-arid region [27,28,29,30,31,32] precipitation is relatively low; sensible heat flux at the surface is high in spring and summer and the variability of this flux influences climate variability [33]. Because of its extreme sensitivity to land surface heat flux in spring, NWC is considered the heat pad of the Eurasian continent. Zhou and Huang [34] found that the difference between skin ST and surface AT in NWC in spring was closely linked to summer precipitation in North China and that this difference. Wang et al. [18] found that thermal change in NWC could have a considerable impact on the East Asian monsoon circulation.

It is important to understand the connection between ST and other climate variables, and the trend in ST. Here, we investigate the relationship between ST and other climate variables at each station and trends in ST to determine the association between ST and other climate variables [35,36]. This study tries to find how historical ST in NWC changed and provides an observation basis for the prediction of summer monsoon changes.

2 Data and methods

2.1 Data

ST data are widely collected in China, and the length of the data varies at different stations. Here, we used the daily mean ST data at depths of 0, 80, 160, and 320 cm provided by the China Meteorological Administration from 1960 to 2000 (Table 1). Most of the stations have missing values, and thus we selected 30 stations (Figure 1) from the dataset for NWC, where the annual precipitation is less than 400 mm. A monthly mean value was obtained if there were more than 25 available values in the month. Daily AT, daily maximum and minimum AT, and daily precipitation were derived from China Meteorological Administration 560-station datasets, and monthly mean values were obtained for each variable.

Table 1

The available length of temperature data for different stations

WMO lon lat ts st80 st160 st320 r t t max t min
51076 88.08 47.73 1960–2000 1980–2000 1980–2000 * 1960–2000
51243 84.85 45.6 * 1960–2000 1960–2000 * 1960–2000
51431 81.33 43.95 1960–2000 1960–2000 * * 1960–2000
51463 87.62 43.78 1960–2000 1960–2000 1960–2000 1960–2000 1960–2000
51573 89.2 42.93 1960–2000 1980–2000 1980–2000 1980–2000 1960–2000
51656 86.13 41.75 1960–2000 1981–2000 1981–2000 1981–2000 1960–2000
51828 79.93 37.13 1960–2000 1981–2000 1981–2000 1981–2000 1960–2000
52203 93.52 42.82 1960–2000 1960–2000 1981–2000 1981–2000 1960–2000
52533 98.48 39.77 1960–2000 1960–2000 1960–2000 1960–2000 1960–2000
52602 93.38 38.83 1980–2000 1980–2000 1980–2000 1980–2000 1960–2000
52652 100.43 38.93 1960–2000 1960–2000 1980–2000 1980–2000 1960–2000
52679 102.67 37.92 1960–2000 1980–2000 1980–2000 1980–2000 1960–2000
52737 97.37 37.37 1960–2000 1981–2000 1981–2000 1981–2000 1960–2000
52754 100.13 37.33 1980–2000 1980–2000 1980–2000 1980–2000 1960–2000
52818 94.9 36.42 1960–2000 1976–2000 1976–2000 1976–2000 1960–2000
52866 101.77 36.62 1960–2000 1960–2000 1966–2000 1966–2000 1960–2000
52984 103.18 35.58 1960–2000 1960–2000 1960–2000 1960–2000 1960–2000
53192 114.95 44.02 1960–2000 1960–2000 * * 1960–2000
53391 114 41.9 1960–2000 1960–2000 * * 1960–2000
53463 111.68 40.82 1960–2000 1960–2000 1960–2000 1960–2000 1960–2000
53480 113.07 41.03 1960–2000 1960–2000 1980–2000 1980–2000 1960–2000
53487 113.33 40.1 1960–2000 1960–2000 1980–2000 1980–2000 1960–2000
53513 107.42 40.75 1960–2000 1980–2000 1980–2000 1980–2000 1960–2000
53602 105.67 38.83 1960–2000 1960–2000 * * 1960–2000
53614 106.22 38.48 1960–2000 1979–2000 1979–2000 1979–2000 1960–2000
53646 109.7 38.23 1960–2000 1960–2000 1980–2000 1980–2000 1960–2000
53817 106.27 36 1960–2000 1960–2000 1978–2000 1978–2000 1960–2000
53845 109.5 36.6 1960–2000 1960–2000 1980–2000 1980–2000 1960–2000
53923 107.63 35.73 1960–2000 1960–2000 1975–2000 1975–2000 1960–2000
56080 102.9 35 1960–2000 1980–2000 1980–2000 1980–2000 1960–2000

Note: The data were collected from http://data.cma.cn, lon is longitude of the station, lat is the latitude of the station, ts is surface ST; st80 is 80 cm ST; st160 is 160 cm ST; st320 is 320 cm ST; r, t, t max, and t min represent precipitation, AT, daily maximum temperature, daily minimum temperature, respectively; * indicates that the time period available is too short.

Figure 1 The 30 stations used in this study on the elevation map.
Figure 1

The 30 stations used in this study on the elevation map.

The surface AT used in this study for Western Asia (WA), the European Union (EU), the United States (US) was collected from the historical surface temperature data set HadCRUT [37].

3 Methods

The Mann–Kendall test was developed by Mann [38] and Kendall [39]. It was originally used to detect trend changes in a sequence of values over time. Now, it has been widely used in the field of geosciences to detect the trend of climate variables [40,41,42,43].

For time series x with n sample sizes, a rank series S k was constructed

(1) S k = ∑ i = 1 k r i   r i = 1 , x i > x j 0 , else , j = 1 , 2 , … , i ,

(2) UF k = S k − E ( S k ) Var ( S k ) , k = 1 , 2 , … , n .

In equation (2), UF1 = 0, E(S k ) and Var(S k ) are the mean and variance of S k , respectively.

(3) E ( S k ) = n ( n + 1 ) 4 Var ( S k ) = n ( n − 1 ) ( 2 n + 5 ) 72 .

The values of X were arranged in reverse chronological order, x n , x n−1, …, x 1. This process was repeated to obtain UB.

(4) UB k = − UF k k = n , n − 1 , … , 1 .

UF is the standard normal distribution, which is in the time series x order x 1, x 2, …, x n . The normal distribution table was consulted at the given significance. If |UF i | > Uα, it indicates that there is an obvious trend change in the sequence. If the significance set to 0.05, then U0.05 = ± 1.96.

The magnitude of the trend was derived by linear regression.

Natural-neighbor interpolation [44] is a fast, robust, and reliable technique for reconstructing a surface from irregularly distributed sample points. It faithfully preserves input data values and produces a continuous surface as its output. It also provides good (though not perfect) continuity for slope. The technique is particularly useful for the kinds of unstructured data commonly encountered in geophysical applications where it yields smooth and visually appealing results.

4 Results

We first detected the distribution of ST in this area. Then, trends at different stations and the relationships between ST and other climate factors were detected.

4.1 The distribution of temperature in different layers

We explored the distribution and variation of ST in different layers. Figures 2–4 show the average temperature distribution maps of different layers (AT, surface ST, 80, 160, and 320 cm soil depth) during 1981–2000. The annual average temperature at each layer was similar to AT, and there was a cryogenic center to the north of the Tibetan Plateau in NWC (Figure 2). ST in NWC was higher in the west and lower in the east, increasing from northeast to southwest. The difference between surface ST and ST at 320 cm is large, about 6°C. The difference between the other layers and AT is within 3°C.

Figure 2 Spatial distribution map of annual average temperature in each layer from 1981 to 2000: (a) AT, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST, and (e) 320 cm ST.
Figure 2

Spatial distribution map of annual average temperature in each layer from 1981 to 2000: (a) AT, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST, and (e) 320 cm ST.

Figure 3 Spatial distribution map of average winter (December [−1], Janurary [0], Februrary [0]) temperature in each layer from 1981 to 2000: (a) AT, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST, and (e) 320 cm ST.
Figure 3

Spatial distribution map of average winter (December [−1], Janurary [0], Februrary [0]) temperature in each layer from 1981 to 2000: (a) AT, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST, and (e) 320 cm ST.

Figure 4 Spatial distribution map of average summer (June, July, and August) temperature in each layer from 1981 to 2000: (a) AT, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST, and (e) 320 cm ST.
Figure 4

Spatial distribution map of average summer (June, July, and August) temperature in each layer from 1981 to 2000: (a) AT, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST, and (e) 320 cm ST.

In winter (December, January, and February), ST of the 320 cm layer was the highest and that at the surface was the lowest. The difference between surface ST and AT was −2–1°C; the 80 cm ST was 5–17°C higher than AT, 160 cm ST was 8–24°C higher than AT, and 320 cm ST was 12–31°C higher than AT (Figure 3).

In spring (March, April, and May), the highest ST was at the surface (here not shown). The differences in surface soil, 80, 160, and 320 cm soil depth temperatures and AT were 0–5, −4–1, −5–0, and −5–2°C, respectively.

In summer (June, July, and August), the temperature of each layer (Figure 4) was similar to that in spring. Surface ST was the highest, with temperatures decreasing with increasing soil depth. The temperature of surface soil was 0–5°C higher than that of AT. The differences between 80, 160, and 320 cm STs and AT were −5–1, −10–1, and −14–5°C, respectively.

In autumn (September, October, and November), the temperature of each layer was the lowest of all seasons (data not shown). Among the soil depths, the temperature of 320 cm soil was the highest. The surface ST varied from AT by about −1–3°C. The differences between 80, 160, and 320 cm STs and AT were 4–8, 5–10, and 2–11°C, respectively.

Although there was little difference between the temperature of each soil layer (no more than 6°C), the temperatures of each layer varied with the seasons; the temperature decreased with soil depth in spring and summer, and it increased with soil depth in autumn and winter. Within a given year, the temperature range was smaller in deeper soil layers than for the surface soil or AT. Sun [45] showed that when solar radiation energy source continuously reaches the ground surface, most of it is absorbed by the soil, except for limited heating of air near the soil. With the increase in surface ST, heat gradually flows into the deeper layers. This process is called the positive exchange. The distribution of temperature in each layer mentioned above is the result of the positive exchange, and the positive exchange period in NWC is spring and summer, while the negative exchange period is in autumn and winter.

If we take Urumqi, for example (Figure 5), with the WMO number of 51,463, located in Xinjiang Province, the temperature difference between different layers was within 6°C. The range of AT and surface ST were larger than that for deep soil layers; the range of AT was about 45°C, while that of 320 cm ST was only about 10°C. The air and surface STs peaked in July, the 80 and 160 cm STs peaked in August, and the 320 cm ST peaked last in October. These results reflect the lag of energy transition. From March to September, the surface ST was warmer than 80 cm ST, indicating that the positive exchange occurred during this period.

Figure 5 (a) Annual temperature series in different soil layers in Urumqi and (b) annual variations of temperature in different layers in Urumqi. st80 is 80 cm ST, st160 is 160 cm ST, and st320 is 320 cm ST.
Figure 5

(a) Annual temperature series in different soil layers in Urumqi and (b) annual variations of temperature in different layers in Urumqi. st80 is 80 cm ST, st160 is 160 cm ST, and st320 is 320 cm ST.

4.2 Trend of temperature in different layers

Our previous study showed a close relationship between ST and the ensuing climate [18]. Therefore, understanding how ST change is very important for studies on the East Asia summer monsoon. We used the Mann–Kendall test to judge the trend of ST at each station during the period 1961–2000. We found that regardless of the season, most of the stations had positive regression coefficients, indicating that in the context of global warming, most stations were demonstrating a warming trend. In NWC, the annual average temperature showed a relatively obvious warming trend in each layer, and this warming trend was more obvious in the east part of NWC (Figure 6). In winter, in the west part of NWC, there were few stations with an increasing temperature trend, but in the deep layer, there were more stations with an increasing temperature trend than in the shallow layer (Figure 7). In the east part of NWC in winter, most stations had an obvious warming trend (Figure 7). The trend of spring temperature (data not shown) in 1981–2000 was basically consistent with the distribution in winter. The areas with warming trends on the surface were mainly in the east, and there were more stations with warming trends in the deep layer than in the shallow layer. The trend map of summer temperature in 1981–2000 was consistent with the distribution in winter and spring (Figure 8). The areas with a warming trend on the surface were mainly in the east and there were more stations with a warming trend in the deep layer than in the shallow layer. In different seasons, more warming trends appeared in the deeper soil layers. Wang et al. [18] found that ST in East China began to rise in the 1980s. Since 1981, most STs showed a positive trend in NWC. Based on these results, we then derived the magnitude of the trend in each station from 1981 to 2000.

Figure 6 Trend in annual average temperature in different layers: (a) AT, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST, and (e) 320 cm ST. The gray and the red solid dots represent warming (cooling) trend exceeding the 95% confidence level, respectively.
Figure 6

Trend in annual average temperature in different layers: (a) AT, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST, and (e) 320 cm ST. The gray and the red solid dots represent warming (cooling) trend exceeding the 95% confidence level, respectively.

Figure 7 Trend in average winter (D(−1)J(0)F(0)) temperature in different layers: (a) AT, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST, and (e) 320 cm ST. The gray and the red solid dots represent warming (cooling) trend exceeding the 95% confidence level, respectively.
Figure 7

Trend in average winter (D(−1)J(0)F(0)) temperature in different layers: (a) AT, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST, and (e) 320 cm ST. The gray and the red solid dots represent warming (cooling) trend exceeding the 95% confidence level, respectively.

Figure 8 Trend in average summer (JJA) temperature in different layers: (a) AT, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST, and (e) 320 cm ST. The red solid dots represent warming trend exceeding the 95% confidence level.
Figure 8

Trend in average summer (JJA) temperature in different layers: (a) AT, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST, and (e) 320 cm ST. The red solid dots represent warming trend exceeding the 95% confidence level.

The trends of the annual average temperature of each layer at different stations from 1981 to 2000 are shown in Table 2. Among them, 24 stations showed a warming trend, with a large range of increase of up to 0.63°C/decade on average. Twenty-one stations also had an obvious increase in surface ST, with a large range of increase. The STs of 80, 160, and 320 cm soil depths increased in 21, 20, and 19 stations, with increases of 0.70, 0.66, and 0.62°C/decade, respectively.

Table 2

Trends of annual average temperature of each layer in different stations from 1981 to 2000 (°C/decade)

WMO Surface st80 st160 st320 Air
51,076 — — — * —
51,243 * — 0.47 * —
51,431 — 0.23 * * —
51,463 — 0.53 0.46 0.39 —
51,573 0.60 0.60 0.70 0.77 —
51,656 0.50 0.88 0.83 0.56 —
51,828 0.49 0.41 0.50 0.42 0.47
52,203 — — 0.40 0.36 0.49
52,533 0.59 0.63 0.61 0.52 0.56
52,602 — — — –0.53 0.43
52,652 0.74 0.71 0.68 0.63 0.63
52,679 0.92 0.79 0.73 0.70 0.58
52,737 0.71 1.03 1.02 0.98 0.64
52,754 0.82 0.65 0.64 0.64 0.52
52,818 0.51 — — — 0.55
52,866 — — — — 0.50
52,984 0.51 — — — 0.55
53,192 0.92 1.15 * * 0.90
53,391 0.80 0.63 * * 0.78
53,463 0.75 0.58 0.59 0.62 0.71
53,480 — 0.98 1.14 1.00 0.78
53,487 0.87 0.63 0.50 0.41 0.72
53,513 1.03 1.00 1.00 0.89 0.79
53,602 0.70 0.66 * * 0.68
53,614 — — — — 0.58
53,646 0.82 0.68 0.54 0.45 0.69
53,817 0.78 0.81 0.78 0.66 0.66
53,845 0.90 — 0.48 0.56 0.78
53,923 0.70 0.52 0.58 0.57 0.68
56,080 0.51 0.58 0.64 0.57 0.56

Note: * indicates that the data sequence is too short to be used; – indicates that there is no obvious change trend; st80 is 80 cm ST; st160 is 160 cm ST; st320 is 320 cm ST.

Eight stations showed increased average winter temperature in the period 1981–2000, with an average increase of 1.20°C/decade (Table 3), while seven stations showed an increase in the average surface ST with a large range of increase. The STs of 80, 160, and 320 cm soil depths increased at 9, 10, and 16 stations with increases of 0.85, 0.61, and 0.63°C/decade, respectively, the range of which was smaller than that of the surface soil. The stations with increasing trends in winter were less than that of the annual average, but the warming trends were higher than that of the annual average. In terms of spring average temperature of each layer (data not shown), 15 stations showed increasing temperature, with a large average range of increase of 0.72°C/decade, and 12 stations also had an obvious increasing trend in surface ST. For soil at 80, 160, and 320 cm soil depths, there were 16, 15, and 16 stations showing a warming trend, with average increases of 0.89, 0.77, and 0.64°C/decade, respectively.

Table 3

Trends of average winter temperature of each layer in different stations from 1981 to 2000 (°C/decade)

WMO Surface st80 st160 st320 Air
51,076 — — — * —
51,243 * — — * —
51,431 — — * * —
51,463 — — — 0.36 —
51,573 1.23 1.33 1.08 0.84 —
51,656 — — — — —
51,828 — — — — —
52,203 — 0.39 0.71 0.87 —
52,533 — — — 0.33 —
52,602 — — –1.65 –1.10 —
52,652 — 0.52 0.59 0.59 —
52,679 0.82 0.69 0.49 0.63 —
52,737 — 0.51 0.52 0.83 —
52,754 — — 0.53 0.66 —
52,818 — –0.66 –0.26 — —
52,866 — — — — —
52,984 — — — — —
53,192 — 2.54 * * —
53,391 1.41 — * * 1.60
53,463 — 0.54 0.53 0.58 1.13
53,480 — 0.83 0.88 0.93 1.38
53,487 1.12 — — — 1.27
53,513 1.28 — — 1.14 1.41
53,602 0.99 — * * 1.04
53,614 — — — — 0.96
53,646 — — — 0.44 —
53,817 — — 0.31 0.55 —
53,845 0.85 — — 0.38 0.83
53,923 — — — 0.44 —
56,080 — 0.33 0.48 0.45 —

Note: * indicates that the data sequence is too short to be used; – indicates that there is no obvious change trend; st80 is 80 cm ST; st160 is 160 cm ST; st320 is 320 cm ST.

Table 4 shows the trend of average summer temperature in the period 1981–2000. There were 14, 18, and 18 stations with an increase in temperature at 80, 160, and 320 cm soil depths and increases of 0.95, 0.88, and 0.72°C/decade, respectively. In autumn (data not shown), when compared with AT and surface ST, there were more stations showing a warming trend for deep STs. There were 17, 17, and 16 stations showing an increase at 80, 160, and 320 cm soil depths and increases of 0.75, 0.76, and 0.73°C/decade, respectively.

Table 4

Trends of average summer temperature of each layer in different stations from 1981 to 2000 (°C/decade)

WMO Surface st80 st160 st320 Air
51,076 — — — * —
51,243 * — 0.65 * —
51,431 — — * * —
51,463 — — — 0.49 —
51,573 — — 0.32 0.66 —
51,656 — 1.27 1.09 0.66 —
51,828 — — 1.13 0.79 —
52,203 — — — — 0.59
52,533 1.25 0.96 0.92 0.70 0.63
52,602 — — 0.69 — —
52,652 0.85 0.72 0.69 0.64 0.73
52,679 0.95 0.83 0.88 0.69 0.57
52,737 — 1.39 1.41 1.29 0.62
52,754 1.14 0.88 0.65 0.66 0.52
52,818 0.80 –– — — 0.57
52,866 — — — — —
52,984 — — — — 0.48
53,192 — — * * —
53,391 — — * * 0.64
53,463 — — — 0.59 0.79
53,480 — 1.05 1.47 1.26 0.77
53,487 1.05 0.79 0.56 0.55 0.78
53,513 0.98 1.78 1.42 0.67 0.65
53,602 — 0.55 * * —
53,614 — — — — —
53,646 1.24 0.93 0.67 0.49 0.85
53,817 — 0.81 1.01 0.76 0.61
53,845 0.84 — 0.90 0.66 0.92
53,923 — 0.76 0.70 0.66 0.69
56,080 — 0.58 0.70 0.69 0.47

Note: * indicates that the data sequence is too short to be used; – indicates that there is no obvious change trend; st80 is 80 cm ST; st160 is 160 cm ST; st320 is 320 cm ST.

Table 5 shows the number of stations with significant trends of temperature change in each layer. The number of stations showing an increase in the annual average temperature warming trend was the highest, and the number of stations with an increasing trend in winter was the lowest; the change trend of surface ST in winter was the largest at up to 1.1°C/decade. In summer, the warming trend of 80 and 160 cm soil depths was the largest (0.95 and 0.88°C/decade, respectively).

Table 5

Number of stations with statistically significant trends in temperature across NWC during the period 1,981–2,000, and the magnitude (°C/decade)

Time period Layer
Air Surface st80 st160 st320
Annual
 N 30 29 30 26 24
 NSPT 24 21 21 20 19
 M 0.63 0.72 0.70 0.66 0.62
Dec, Jan, Feb
 N 30 29 30 26 24
 NSPT 8 7 9 10 16
 M 1.20 1.10 0.85 0.61 0.63
Mar, Apr, May
 N 30 29 30 26 24
 NSPT 15 12 16 15 16
 M 0.72 0.98 0.89 0.77 0.64
Jun, Jul, Aug
 N 30 29 30 26 24
 NSPT 18 9 14 18 18
 M 0.66 1.01 0.95 0.88 0.72
Sep, Oct, Nov
 N 30 29 30 26 24
 NSPT 9 9 17 17 16
 M 0.67 0.72 0.75 0.76 0.73

Note: N, number of stations (out of 30) available for trend analysis; NSPT, number of stations with a significant positive trend; M, mean magnitude of the positive trend (°C/decade). st80 is 80 cm ST; st160 is 160 cm ST; st320 is 320 cm ST.

Generally, in different seasons, there were more stations showing a warming trend in deeper layers than for surface soil and air. The thermal insulation by snow cover appeared to play an important role in the response of STs to climate change and must be accounted for in projecting future soil-related impacts of climate change.

4.3 Linking ST to other climate variables

Because ST is regarded as an indicator of climate, its relationship with other climate factors is very important. The correlation between ST and AT, daily maximum temperature, and daily minimum temperature was statistically significant at 0.05 in most stations. This correlation was relatively poor in winter, especially in the west part of NWC, which might be due to the snow cover in winter. Qian et al. [16] pointed out that more snow on the ground normally results in higher STs in winter because of the thermal insulation effect of snow cover.

We further studied the relationship between the temperature of each layer and the precipitation in the same period. There were negative correlations between surface ST (or AT) and precipitation in many stations, but the correlation of other layers was not significant (Figure 9).

Figure 9 The relationship between annual average temperature of each layer and annual average precipitation of the same station, (a) surface temperature, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST. The downward blue triangles represent negative correlations exceeding 90 or 95% confidence level, respectively.
Figure 9

The relationship between annual average temperature of each layer and annual average precipitation of the same station, (a) surface temperature, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST. The downward blue triangles represent negative correlations exceeding 90 or 95% confidence level, respectively.

Figure 10 shows the distribution of the correlation between the temperature of each layer and the precipitation in winter. The AT and the surface ST were negatively correlated with the precipitation, as a result of the cooling effect of precipitation. In the west part of NWC, 80 cm ST was positively correlated with precipitation, which is related to the insulation effect of the snow cover in the area. In winter, the ST will have relatively upward energy transmission; the upward energy transport of ST of 80 and 160 cm soil depths is blocked by snow cover, and thus the winter ST will be higher with higher snow cover.

Figure 10 The relationship between average winter (D(−1)J(0)F(0)) temperature of each layer and average winter precipitation of the same station: (a) surface temperature, (b) surface ST, (c) 80 cm ST, and (d) 160 cm ST. The upward red (downward blue) triangles represent positive (negative) correlations exceeding 90 or 95% confidence level, respectively.
Figure 10

The relationship between average winter (D(−1)J(0)F(0)) temperature of each layer and average winter precipitation of the same station: (a) surface temperature, (b) surface ST, (c) 80 cm ST, and (d) 160 cm ST. The upward red (downward blue) triangles represent positive (negative) correlations exceeding 90 or 95% confidence level, respectively.

In spring (data not shown), the AT was negatively correlated with precipitation only in the west of NWC, but not in the east. Surface ST was negatively correlated with precipitation in the whole region. ST at 80 and 160 cm soil depth was not correlated with precipitation. The precipitation in this region was low in spring, and the heating effect of solar radiation on the ground in this season was a key factor affecting ST.

The correlation distribution of ST and precipitation in summer is shown in Figure 11. AT, surface ST and 80 cm ST were negatively correlated with precipitation in many stations, which is likely related to the cooling effect of latent heat release caused by precipitation. There was no significant relationship between 160 and 320 cm ST and precipitation.

Figure 11 The relationship between average summer temperature of each layer and average summer (JJA) precipitation of the same station, (a) surface temperature, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST. The upward red (downward blue) triangles represent positive (negative) correlations exceeding 90 or 95% confidence level, respectively.
Figure 11

The relationship between average summer temperature of each layer and average summer (JJA) precipitation of the same station, (a) surface temperature, (b) surface ST, (c) 80 cm ST, (d) 160 cm ST. The upward red (downward blue) triangles represent positive (negative) correlations exceeding 90 or 95% confidence level, respectively.

The annual average and seasonal surface ST was significantly negatively correlated with precipitation at many stations (Figures 9–11). This correlation is due to the heat loss of latent heat released by precipitation in summer, as well as the reflection of solar radiation from snow cover in winter and the energy transmission to snow as mentioned above; the correlation between AT and precipitation was similar to that of surface ST, but the number of stations showing significant correlation was lower than that for soil surface temperature. There was no significant correlation between other layers of ST and precipitation.

In winter, there were significant positive correlations between 80 or 160 cm ST and precipitation, concentrated in the west of the arid/semi-arid area. This might be due to the precipitation in the form of snow cover in the west of the arid/semi-arid area hindered the negative exchange of soil heat, which resulted in higher ST. In summer, there were negative correlations between 80 or 160 cm ST and precipitation, concentrated in the middle and west part of NWC, which might be caused by the cooling effect of latent heat released by summer precipitation.

5 Discussions

In recent years, extreme weather events bring more and more disasters in the context of global warming. Is the variation of ST related to the global warming? We take West Asia, EU, and US as example, calculate the correlation coefficients between regional mean annual ST series at 80 cm in NWC and regional mean annual AT series in WA, EU, US; the correlation coefficients are 0.62, 0.61, 0.48, all exceeding the 95% reliability (Figure 12). In summer, correlation coefficients are 0.62, 0.61, 0.48; it means that the trend of ST is closely related to global warming. The closer the region, the better the correlation coefficient. They will interact with each other through the atmospheric circulation, the mechanism of which needs further studies. In different seasons, the correlation coefficients are also significant.

Figure 12 (a) The regions we take as example: NWC, WA, EU, and the US, and (b) the standardized curves of NWC 80 cm ST, WA AT, EU AT, US AT.
Figure 12

(a) The regions we take as example: NWC, WA, EU, and the US, and (b) the standardized curves of NWC 80 cm ST, WA AT, EU AT, US AT.

In our previous study [19], we found that memories of ST in NWC were longer than 2 months. The relationship between soil temperature and ground surface temperature and how soil exchange with air is very pivotal for finding the mechanism how ST influence climate. In addition, the memory of ST might be the key to detect the connection between ST and atmospheric circulation in the following seasons. Additionally, snow cover is another key factor for climate. Snow change is closely related to ST. How ST influence atmospheric circulation via snow change needs more attention.

Also in recent years, extreme weather events occur frequently. Whether the warmer soil temperature plays an important role on extreme events, and how to quantify the impact is also need future research.

With the lack of the observation data, the results of this study need to be further validated with high-quality reanalysis land data, model simulations also need to be cited for more systematic research. How the variation of ST connects with global warming also needs further study.

6 Conclusions

As an important part of the land surface, ST is the embodiment of soil heat storage, and its effect on climate change cannot be ignored. In this study, the distribution of ST in different seasons was analyzed. Then, the trends in ST and other layers in NWC were explored in detail and found that under the background of global warming, the trend of warming was reflected in each season. The correlation between ST and other climatic factors at the same station in NWC was investigated. The main conclusions were as follows:

The cold center of ST in different layers was located in the middle of NWC to the north of the Tibetan Plateau. The most variation within a given year was observed for surface ST when compared with other soil layers, and the annual average AT was close to that of soil layers (the difference was no more than 6°C), but there were great differences in different seasons. With increasing soil depth in spring and summer, the temperature gradually decreased; with increasing soil depth in autumn and winter, the temperature gradually increased, and the difference between the upper and lower layers was the largest in winter, up to 31°C.

The change in the 80 cm ST at different time scales in different regions was inconsistent, and the trend in ST changed during the period 1961–2000; the increasing trend of temperature in many stations was obvious after the mid-1980s. In winter and summer, the number of stations with a warming trend was low, but the number of stations with an increasing trend in the deep layers was more than that in the surface. In east NWC, most of the stations had an obvious increasing trend of both AT and ST. In each season, more stations had a warming trend in the deep soil than that in the shallow layers. The magnitude of the trend in ST at all levels from 1980 to 2000 was analyzed. The number of stations with an increasing trend was the highest for annual average temperature and the lowest for winter average temperature. The maximum range of increase of surface ST in winter was 1.1°C/decade, and the maximum increase in 80 and 160 cm ST in summer was 0.95 and 0.88°C/decade, respectively. Generally, in different seasons, more warming trends were found in the deeper soil layers than in shallow layers.

ST was strongly positively correlated with daily average temperature, daily maximum temperature, and daily minimum temperature in most stations. Annual average AT and surface ST were significantly negatively correlated with precipitation at many stations; the correlation values between AT and precipitation were similar to those of surface ST, but the number of stations showing a significant correlation was relatively small. In winter, 80 cm ST, 160 cm ST and precipitation were positively correlated in the west of the arid/semi-arid area, while in summer, 80 cm ST, 160 cm ST and precipitation were negatively correlated in the middle and west of NWC.

Generally, the trend of ST in NWC is one of warming within a considerable range. The correlation of ST and other climate factors is vital to the energy exchange of land and air. The positive change or negative change is the main factor influencing the correlation between ST and AT. The latent heat release of the soil surface contributed to the negative correlation between ST and precipitation in summer.

Acknowledgments

This study was funded by the Key Project of Jiangxi Meteorological Bureau and the Second Tibetan Plateau Scientific Expedition and Research Program (Grant No. 2019QZKK0103).

  1. Author contributions: Yuanhao Wang: Conceptualization; Zhihuai Jiang: methodology; Degang Zhou: formal analysis; Zhiyu Gong: investigation; Zhiyu Gong: data curation; Zhihuai Jiang: writing – original draft preparation; Yuanhao Wang: writing – review and editing.

  2. Conflict of interest: The authors declare there are no conflicts of interest regarding the publication of this paper.

  3. Data availability statement: The observed data used in this study were collected from the China Meteorological Data Sharing Service System, China Meteorological Administration (http://data.cma.cn).

References

[1] Dettwiller J. Deep soil temperature trends and urban effects at Paris. J Appl Meteor. 1970;9:178–9.10.1175/1520-0450(1970)009<0178:DSTTAU>2.0.CO;2Search in Google Scholar

[2] Tang M, Zhang J, Wang J, Yang L. Correlation analysis between the average 0.8 m ground temperature anomaly and the precipitation in the next season in China (in Chinese). Acta Meteorologica. 1998;4:99–103.Search in Google Scholar

[3] Tang M, Gao X. Some statistic characteristics of “underground Hot vortex” in China during 1980–1993 (II). Sci China Earth Sci. 1997;40:569–76. 10.1007/bf02877684.Search in Google Scholar

[4] Zhao H, Li D, Guo W. Variation features of winter soil temperature field in the depth of 3.2 m and its relation to summer precipitation field in China (in Chinese. Plateau Meteor. 2001;21(1):52–8.Search in Google Scholar

[5] Holmes TRH, Owe M, De Jeu RAM, Kooi H. Estimating the soil temperature profile from a singledepth observation: A simple empirical heatflow solution. Water Resour Res. 2008;44(2):103–12. 10.1029/2007WR005994.Search in Google Scholar

[6] Bilgili M, Sahin B, Sangun L. Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models. Env Monit Assess. 2013;185:347–58. 10.1007/s10661-012-2557-5.Search in Google Scholar

[7] Deardorff JW. Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation. J Geophys Res. 1978;83:1889–903. 10.1029/JC083iC04p01889.Search in Google Scholar

[8] Kimura F, Shimizu Y. Estimation of sensible and latent heat fluxes from soil surface temperature using a linear air-land heat transfer model. J Appl Meteor. 1994;33(4):477–89.10.1175/1520-0450(1994)033<0477:EOSALH>2.0.CO;2Search in Google Scholar

[9] Wang W. Numerical experiments of the soil temperature and moisture anomalies’ effects on the short term climate (in Chinese). Chin J Atmos Sci. 1991;15(5):115–23.Search in Google Scholar

[10] Lakshmi V. A simple surface temperature assimilation scheme for use in land surface models. Water Resour Res. 2000;36(12):3687–700.10.1029/2000WR900204Search in Google Scholar

[11] Zhang Y. Soil temperature in Canada during the twentieth century: Complex responses to atmospheric climate change. J Geophys Res. 2005;100:D03112. 10.1029/2004JD004910.Search in Google Scholar

[12] Zeng J, Li Z, Chen Q, Bi H. Method for soil moisture and surface temperature estimation in the Tibetan Plateau using spaceborne radiometer observations. IEEE Geosci Remote Sens Lett. 2015;12(1):97–101.10.1109/LGRS.2014.2326890Search in Google Scholar

[13] Zhao C, Chen H, Sun S. Evaluating the capabilities of soil enthalpy, soil moisture and soil temperature in predicting seasonal precipitation. Adv Atmos Sci. 2018;35(4):445–56.10.1007/s00376-017-7006-5Search in Google Scholar

[14] Du J, Li C, Liao J, Pa L, Lu H. Responses of climatic change on soil temperature at shallow layers in Lhasa from 1961 to 2005 (in Chinese). Meteorol Mon. 2007;33(10):61–7.Search in Google Scholar

[15] Yeşilırmak E. Soil temperature trends in Büyük Menderes Basin, Turkey. Meteorol Appl. 2014;21(4):859–66.10.1002/met.1421Search in Google Scholar

[16] Qian BD, Gregorich EG, Gameda S, Hopkins DW, Wang XL. Observed soil temperature trends associated with climate change in Canada. J Geophys Res. 2011;116:D02106. 10.1029/2010jd015012.Search in Google Scholar

[17] Zhang T, Barry RG, Gilichinsky D, Bykhovets SS, Sorokovikov VA, Ye J. An amplified signal of climatic change in soil temperature during the last century at Irkutsk, Russia. Climatic Change. 2001;49(1/2):41–76. 10.1023/A:1010790 203146.Search in Google Scholar

[18] Wang X, Guo W, Zhong Z, Cui X. Long term trends of soil moisture and temperature change in east china in relationship with climate background (in Chinese). Adv Earth Sci. 2009;24(2):181–91.Search in Google Scholar

[19] Wang Y, Chen W, Zhang J, Nath D. Relationship between soil temperature in may over Northwest China and the East Asian summer monsoon precipitation. Acta Meteor Sin. 2013,;27:716–24. 10.1007/s13351-013-0505-0.Search in Google Scholar

[20] Wu Q, Zhang T. Recent permafrost warming on the Qinghai-Tibetan Plateau. J Geophys Res. 2008;113(D13):D13108. 10.1029/2007JD009539.Search in Google Scholar

[21] Xue YK, Vasic R, Janjic Z, Liu YM, Chu PC. The impact of spring subsurface soil temperature anomaly in the western U.S. on North American summer precipitation: A case study using regional climate model downscaling. J Geophys Res. 2012;117:D11103. 10.1029/2012jd017692.Search in Google Scholar

[22] Fang X, Luo S, Lyu S. Observed soil temperature trends associated with climate change in the Tibetan Plateau, 1960–2014. Theor Appl Climatol. 2019;135:169–81. 10.1007/s00704-017-2337-9.Search in Google Scholar

[23] Hu Q, Feng S. A role of the soil enthalpy in land memory. J Clim. 2004;17:3633–43. 10.1175/1520-0442(2004)017<3633:arotse>2.0.co;2.Search in Google Scholar

[24] Yang K, Zhang JY. Spatiotemporal characteristics of soil temperature memory in China from observation. Theor Appl Climatol. 2016;126:739–49. 10.1007/s00704-015-1613-9.Search in Google Scholar

[25] Zhang HX, Yuan NM, Ma ZG, Huang Y. Understanding the soil temperature variability at different depths: Effects of surface air temperature, snow cover, and the soil memory. Adv Atmos Sci. 2021;38(3):493–503. 10.1007/s00376-020-0074-y.Search in Google Scholar

[26] Wang L, Henderson M, Liu B, Shen X, Chen X, Lian L, et al. Maximum and minimum soil surface temperature trends over China, 1965–2014. J Geophys Res – Atmos. 2018;123:2004–16. 10.1002/2017JD027283.Search in Google Scholar

[27] Yatagai A, Yasunari T. Interanual variations of summer precipitation in the arid/semi-arid regions in China and Mongolia: their regionality and relation to the Asian summer monsoon. J Meteor Soc Jpn. 1995;73:909–23.10.2151/jmsj1965.73.5_909Search in Google Scholar

[28] Huang RH, Wei ZG, Zhang Q, Ma YM, Hu ZY, Hou XH, et al. The field experiment on air-land interaction in the arid area of Northwest China (NEWC-LAIEX) and the preliminary scientific achievements of this experiment. International Workshop on the Air-Land Interaction in Arid and Semi-arid Areas and its Impact on Climate (IWALI), Dunhuang. Gansu Province China. 2002 August;17–21:29–41.Search in Google Scholar

[29] Koster RD, Dirmeyer PA, Guo Z, Bonan G, Chan E, Cox P, et al. Regions of strong coupling between soil moisture and precipitation. Science. 2004;305(5687):1138–40. 10.1126/science.1100217.Search in Google Scholar PubMed

[30] Shi Y, Shen Y, Kang E, Li D, Ding Y, Zhang G, et al. Recent and future climate change in northwest china. Climatic Change. 2007;80(3–4):379–93.10.1007/s10584-006-9121-7Search in Google Scholar

[31] Chen F, Huang W, Jin L, Chen J, Wang J. Spatiotemporal precipitation variations in the arid Central Asia in the context of global warming. Sci China:Earth Sci. 2011;54:1812–21. 10.1007/s11430-011-4333-8.Search in Google Scholar

[32] Peng D, Zhou T. Why was the arid and semiarid northwest China getting wetter in the recent decades. J Geophys Res – Atmos. 2017;122:122–9075. 10.1002/2016JD026424.Search in Google Scholar

[33] Chen W, Zhu D, Liu H, Sun S. Land-air interaction over arid/semi-arid areas in China and its impact on the east Asian summer monsoon. Part I: Calibration of the land surface model (BATS) using multicriteria methods. Adv Atmos Sci. 2009;26:1088–98. 10.1007/s00376-009-8187-3.Search in Google Scholar

[34] Zhou LT, Huang RH. Characteristics of interdecadal variability of the difference between surface temperature and surface air temperature in spring in arid and semi-arid region of Northwest China and its impact on summer precipitation in North China (in Chinese). Climatic Env Res. 2006;11(1):1–13.Search in Google Scholar

[35] Zhan M, Xia L, Zhan L, Wang Y. Recognition of changes in air and soil temperatures at a station typical of China’s subtropical monsoon region (1961–2018). Adv Meteorol. 2019;2019:6927045–9. 10.1155/2019/6927045.Search in Google Scholar

[36] Zhan M, Xia L, Zhan L, Wang Y. Evaluation and analysis of soil temperature data over poyang lake basin, China. Adv Meteorol. 2020;2020:8839111. 10.1155/2020/8839111.Search in Google Scholar

[37] Brohan P, Kennedy JJ, Harris I, Tett S, Jones PD. Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850. J Geophys Res. 2006;111:D12106. 10.1029/2005JD006548.Search in Google Scholar

[38] Mann HB. Non-parametric tests against trend. Econome-Trica. 1945;13:245–59.10.2307/1907187Search in Google Scholar

[39] Kendall MG. Rank correlation measures. Charles Griffin. London; 1975.Search in Google Scholar

[40] Shadmani M, Marofi S, Roknian M. Trend analysis in reference evapotranspiration using mann-kendall and spearman’s rho tests in arid regions of Iran. Water Resour Manag. 2012;26:211–24. 10.1007/s11269-011-9913-z.Search in Google Scholar

[41] Ahmad I, Tang D, Wang T, Wang M, Wagan B. Precipitation trends over time using mann-kendall and spearman’s rho tests in swat river basin, Pakistan. Adv Meteorol. 2015;2015:431860–9.10.1155/2015/431860Search in Google Scholar

[42] Radaković MG, Tošić I, Bačević N, Mladjan D, Gavrilov MB, Marković SB. The analysis of aridity in Central Serbia from 1949-2015. Theor Appl Climatol. 2018;133:887–98. 10.1007/s00704-017-2220-8.Search in Google Scholar

[43] Bačević RN, Milentijević MN, Valjarević A, Gicić A, Kićović D, Radaković MG, et al. Spatio-temporal variabilty of air temperatures in Central Serbia from 1949 to 2018. Idȍjárás-Quarterly J Hungarian Meteorological Serv. 2021;125(2):229–53.10.28974/idojaras.2021.2.4Search in Google Scholar

[44] Sibson R. A brief description of natural neighbor interpolation. In Interpolating multivariate data. New York: John Wiley & Sons; 1981. p. 21–36.Search in Google Scholar

[45] Sun S. Physical, biochemical mechanism and parametric model of land surface processes. Beijing: Meteorological Press; 2005.Search in Google Scholar
Received: 2021-10-07
Revised: 2022-04-15
Accepted: 2022-04-27
Published Online: 2022-12-23

© 2022 Yuanhao Wang et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/geo-2022-0376
Keywords for this article
soil temperature; air temperature; trend; Northwest China
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