Hindawi Advances in Meteorology Volume 2018, Article ID 5473105, 26 pages https://doi.org/10.1155/2018/5473105
Research Article Temporal Trends and Spatial Patterns of Temperature and Its Extremes over the Beijing-Tianjin Sand Source Region (1960–2014), China
Wei Wei ,1 Baitian Wang ,1 Kebin Zhang ,1 Zhongjie Shi ,2 Genbatu Ge,2 and Xiaohui Yang2
1 College of Water and Soil Conservation, Beijing Forestry University, Beijing 100083, China 2Institute of Desertifcation Studies, Chinese Academy of Forestry, Beijing 100091, China
Correspondence should be addressed to Baitian Wang; [email protected] and Kebin Zhang; [email protected]
Received 9 December 2017; Accepted 24 January 2018; Published 10 July 2018
Academic Editor: Harry D. Kambezidis
Copyright © 2018 Wei Wei et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In order to examine temperature changes and extremes in the Beijing-Tianjin Sand Source Region (BTSSR), ten extreme temperature indices were selected, categorized, and calculated spanning the period 1960–2014, and the spatiotemporal variability and trends of temperature and extremes on multitimescales in the BTSSR were investigated using the Mann-Kendall (M-K) test, Sen’s slope estimator, and linear regression. Results show that mean temperatures have increased and extreme temperature events have become more frequent. Annual temperature has recorded a signifcant increasing trend over the BTSSR, in which 51 stations exhibited signifcant increasing trends (� < 0.05); winter temperature recorded the most signifcant increasing trend in the northwest subregion. All extreme temperature indices showed warming trends at most stations; a higher warming slope in extreme temperature mainly occurred along the northeast border and northwest border and in the central-southern mountain area. As extreme low temperature events decrease, vegetation damage due to freezing temperatures will reduce and low cold-tolerant plants may expand their distribution range northward to revegetate barren areas in the BTSSR. However, in water-limited areas of the BTSSR, increasing temperatures in the growing season may exacerbate stress associated with plants relying on precipitation due to higher temperatures combining with decreasing precipitation.
1. Introduction Global regional-scale temperature changes are currently well documented. For example, Rehman and Al-Hadhrami As highlighted by the IPCC, climate change is the most [8] showed that the annual average maximum temperature important environmental problem and the greatest challenge on the west coast of Saudi Arabia has increased more facing mankind [1]. From 1951 to 2012, global average surface than the average minimum temperature, while Khomsi et temperatures are reported to have risen by 0.72 degrees al. [9] recorded that hot and very hot events during the [1]; as temperatures have increased, extreme climatic events, summer in Morocco (Saf and Marrakech) showed statis- which can be sudden and destructive, have also increased tically signifcant decreasing trends. Te number of very in frequently, and their occurrence directly or indirectly has cold nights recorded annually and during the winter has resulted in prominent social problems and natural disasters signifcantly decreased across the Basilicata region (southern [2], especially in ecologically fragile areas. Recent global Italy) over the period 1951–2010, while the frequency of temperature extremes [3] indicate that the number of cold cold days has shown a weak increase [10]. Extreme tem- days, cold nights, and frost days has decreased while the perature indices have indicated steady warming trends for number of warm days and warm nights has increased [3], arid and semiarid areas of southeastern Kenya [11], while these changes having obvious regional characteristics [4– days with 1-day extreme minimum or maximum apparent 7]. temperature have exhibited an increasing trend across many 2 Advances in Meteorology stations in the United States [12]. Temporal and spatial 2. Study Area distribution of extreme temperatures across China shows thathotextremesindicateanupwardtrend,whilecold Te study area, located at the scope of the phase II project extremes show downward trends; between 1961 and 2011, in the BTSSR, includes six provinces with 138 counties. Tis more frequent extreme temperature events were recorded in area includes the Mu Us Sandland, situated between Yulin city the southern area of the major grain producing region in (Shaanxi province) and Erdos city (Inner Mongolia), and the KubuqiDesert,situatedinthenorthernOrdosPlateau.Te China [13] and some trends of extreme temperature indices in 4 2 ∘ region covers an area of 71.05 × 10 km (approximately 36 ∼ the Songhua River Basin were stronger than those recorded ∘ ∘ ∘ 46 N, 107 ∼119 E; Figure 1). Te area of land desertifcation in the Yangtze River Basin, southwestern China, and the 4 2 TibetanPlateau.Zhongetal.[14]alsofoundthenumberof covers an area of 22.69 × 10 km , exceeding one-third of the warm nights, warm days, and summer days would be more total area of phase II [22]. signifcant at higher latitudes. Sun et al. [15], investigating Te climate in the study region is complex, including extreme temperature events in the Loess Plateau, found temperate semihumid, semiarid, arid, and extremely arid that the trend magnitudes of cold extremes were greater climate types [27]. Annual precipitation across the area varies from 105 to 743 mm, with the majority of precipitation occur- than those of warm extremes and that the growing season ∘ increased and diurnal temperature ranges declined in the ring from June to September. Average temperature is 6.2 C with signifcant regional diferences, such as a maximum region. ∘ temperature of 12.7 CinBeijingandaminimumtemperature Surface temperature changes have been mainly consid- ∘ ered to be due to anthropogenic activities, especially due to of −2.6 C in Arxan, Inner Mongolia. Te average sunshine changes in concentrations of greenhouse gases (GHG) [16, duration is 7.9h/d, and the regional mean wind speed is 17]. In areas surrounding Beijing and Tianjin, a large amount 4.5 m/s. Te average growing season length is 206 days. of land was reclaimed in the 1960s which destroyed the In general, the climate of the BTSSR is dry, windy, and surface vegetation and caused mobile and semimobile sand has a low temperature, with climatic characteristics varying dunes to expand [18]. Since the implementation of the reform signifcantly across the region. and development policy (1979), environmental degradation has continued to intensify in these areas due to over grazing 3. Data and Methods and excessive reclamation of grasslands [19, 20]; as a result, the Beijing, Tianjin, and Hebei areas experienced huge dust 3.1. Data Source, Quality Control, and Homogeneity Test. stormsduringMarchtoAprilintheyear2000.Toimprove Daily temperature data from the BTSSR was provided by the the environment of these areas, the Chinese government China Meteorological Data Network (http://data.cma.cn/). established the sand source control project (phase I)inthe When more than 10% of daily temperature data was missing, Beijing-Tianjin Sand Source Region (BTSSR) in the year or more than three months contained more than 20% missing 2000 [21, 22]. Following the implementation of this project, days, annual temperature records were deemed as missing. To more than half of the land in this region has experienced ensure data integrity and consistency, meteorological stations an increase in vegetation productivity during 2000–2010 lacking more than 75% of a year’s temperature records were [23]. Studies on the characteristics of climate change and omitted; missing days were not superseded by estimated daily the response of vegetation cover to weather changes in temperature values [28, 29]. A total of 53 stations evenly this region have found that NDVI has recorded a slight distributedacrosstheBTSSRwereselectedforthisstudy;data increasing trend in the growing season [24, 25]. In order from these stations spanned the period from 1960 to 2014 to consolidate the results of phase � of the Blown-Sand (Figure 1). Control Project, and to further reduce sandstorm hazards and Quality control of the data was undertaken using the construct the northern ecological barrier, phase II has been RClimDex package developed by Zhang and Yang [30], a implemented, spanning the period 2013–2022. However, with control package that has been previously applied to test continued increasing temperatures, decreasing precipitation, climate data [31]. Te RClimDex package enabled errors in and enlarging of the drought area, it is very difcult to the data to be eliminated, including errors in manual keying, consolidate the achievements of phase � and to implement daily maximum temperatures lower than daily minimum andimprovetheconstructionofphaseII atthesametime temperatures, and any identifed outliers. Daily temperature [23, 26]. Evaluation of extreme climate changes in the BTSSR outliers were identifed by manually examining visual data can identify factors causing regional environmental and graphs and histograms; suspicious outliers were identifed social problems and provide reference for the construction using statistical tests, local knowledge, and comparisons with of phase II to cope with extreme climatic events and to adjacent days or the same day at neighboring stations. Data improve disaster prevention and mitigation work in this thatwasclearlyfawedwereadjustedorremoved. area. Duetorelocationofmeteorologicalstations,instru- Te objectives of this study, therefore, are (1) to under- ment changes, or observing procedures, step changes were take trend analysis of temperature and extremes in the identifed at specifc stations which resulted in observed BTSSR; (2) to investigate the spatial patterns of trends meteorological data to not be fully comparable. To eliminate of temperature and extremes; (3) and to discuss the pos- problems associated with step changes, daily temperature sible impact temperature and extremes have on vegeta- values were used to calculate the monthly temperatures; tion. log transformations were subsequently used to test data Advances in Meteorology 3
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e Beijing-Tianjin Sand Source Region phase I project Altitude (m) Meteorological station High: 3072 River Lake Low: 20 Figure 1: Location of the study area and meteorological stations. Greek capital numbers indicate the subregion division: I is the southwest subregion (SR-I), II is the northwest subregion (SR-II), III is the south-central subregion (SR-III), IV is the east-central subregion (SR-IV), and V is the northeast subregion (SR-V). Source: [27].
homogeneity using the RHtest V4 [32]. Any step changes referred to as project phase I) (2000). To identify the impact identifed using RHtest V4 were analyzed using metadata to of land use/land cover on temperature and extremes, the evaluate the changes. Results from these tests showed that all study period (S: 1960–2014) was divided into three time data from the 53 meteorological stations were acceptable for stages: 1960–1978 (S1) (prior to ROP); 1979–1999 (S2)(ROP use in this investigation. to before project phase I); and 2000–2014 (S3)(Projectphase Afer quality control and homogeneity tests, monthly I). temperature values were used to calculate mean annual Ten core indices for extreme temperature from ETC- temperatures, the standard climatological seasons (spring: CDMI, indices which have been widely applied to evaluate March–May; summer: June–August; autumn: Septem- extreme temperature shifs [33–40], were selected and classi- ber–November; Winter: December–February) and the fed into three categories: cold-related indices, warm-related growing season (GS: April–October). indices, and variability indices (Table 1). All calculations of these indices were undertaken using RClimDex 1.0 sofware 3.2. Calculation of Mean Temperature and Indices. To identify [30]. spatial changes in average temperature and extremes, the study region was divided into fve subregions: the southeast 3.3. Trend Analysis of Mean Temperature and Indices. Trends subregion (SR-I), the northwest subregion (SR-II), the south- in mean temperature and indices were analyzed for the three central subregion (SR-III), the east-central subregion (SR- time stages and for the duration of the study period. Trends IV), and the northeast subregion (SR-V), as per the investi- for individual station data and regional series were estimated; gation of Lu and Wu [27] (Figure 1). Temporally, two time the signifcance of the trends was determined using the cut points were determined as the starting year of reform Mann-Kendall test [41, 42]. Tis test does not assume that and opening policy (ROP) (1979) and the starting year of the data are normally distributed, and it robustly responds to the Blown-Sand Control Project Phase I in the BTSSR (hereafer efects of outliers in the series; this test has been previously 4 Advances in Meteorology C C C C C C C C C ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ Days Days Days Days Days Days Days Units C ∘ 10th percentile 10th percentile 90th percentile 90th percentile 5 < < > > < 10th percentile 90th percentile < > Defnition temperature temperature 6dayswithTG Annual mean temperature when TN when TX Mean temperature in spring Mean temperature in winter Mean temperature in autumn Mean temperature in summer Mean temperature in growing season C and frst span afer July 1 (January 1 in SH) of ∘ 5 Monthly minimum value of daily minimum Monthly maximum value of daily maximum Percentage of days when TX Percentage of days when TN Percentage of days when TX Percentage of days when TN Monthly mean diference between TX and TN > Annualcountofdayswithatleast6consecutivedays Annualcountofdayswithatleast6consecutivedays in SH) count between frst span of at least 6 days with Annual (1st Jan to 31st Dec in NH, 1st July to 30th June TG Cold days Min Tmin Max Tmax Warm days Descriptive name Spring temperature Winter temperature Autumn temperature Summer temperature Growing season length Diurnal temperature range Growing season temperature Cold spell duration indicator Warm spell duration indicator Table 1: Defnitions of temperature and extreme indices used in this study. spring TA Annual temperature winter TG autumn GSL summer TXx TNn DTR CSDI Index � � WSDI TX10p TN10p Cold nights � TX90p � TN90p Warm nights Type Mean temperature Cold-related indices Warm-related indices Variability indices Advances in Meteorology 5
y = 0.0377x + 6.7123 3 y = 0.0340x + 5.2737 3 3 y = 0.0239x + 20.246 R2 = 0.315 (p < 0.01) 2 2 R2 = 0.3279 (p < 0.01) 2 R = 0.512 (p < 0.01) 2 C) C) C) ∘ ∘ ∘ 1 1 1 0 0 −1 0 temperature ( temperature temperature ( temperature temperature ( temperature −1 −1 Anomaly of spring of Anomaly
Anomaly of annual −2 Anomaly of summer −2 −3 −2 1960 1970 1980 1990 2000 2010 201020001990198019701960 201020001990198019701960 (a) (b) (c) y = 0.0274x + 14.669 3 y = 0.0301x + 5.5689 5 y = 0.045x − 11.501 2 R2 = 0.5336 (p < 0.01) R2 = 0.272 (p < 0.01) 4 R2 = 0.237 (p < 0.01) 2 3
C) 1 C) C) ∘ ∘ ∘ 2 1 1 0 0 −1 0 −1 −2 −3 −1 temperature ( temperature temperature ( temperature temperature ( temperature
Anomaly of winter −4
Anomaly of autumn of Anomaly −2 −5
−3 −6 season growing of Anomaly −2 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (d) (e) (f)
Figure 2: Anomaly of regional average temperature curves over the BTSSR, 1960–2014. Te blackline represents the linear trends for (a) annual, (b) spring, (c) summer, (d) autumn, (e) winter, and (f) growing season. Te redline represents the 5-year moving average.
used to test trends in hydrological and meteorological data where �� is a standard normal variable. A positive �� [26, 43, 44]. indicatesanupwardtrendwhileanegative�� indicates a Te Mann-Kendall test, enabling data with missing values downward trend [46]. A two-sided test at the signifcance and values below a detection limit to be analyzed, is one of level of 5% was undertaken in this study, and Sen’s slope the most important and commonly used statistical methods estimator was used to estimate the true slope of an existing to detect trends as nonparametric tests in hydroclimatic time trend (the change per year) [47]. All of the trend calculations series data. Te null hypothesis that �� is not statistically for these indices were undertaken using MAKESENS 1.0 [48]. signifcant or has no signifcant trend will be accepted if Prior to performing the Mann-Kendall test, the trend-free, −�(1−�)/2 ≤�� ≤�(1−�)/2,where±�(1−�)/2 are the standard prewhitening method of Yue et al. [49] was used to limit the normal deviates and � is the signifcance level for the test [45]. efect of serial correlations on the Mann-Kendall test [50, 51]. Kendall’s statistic � assessing the monotonic trend was calculated as 3.4. Spatial Interpolation of Mean Temperature and Extremes �−1 � Trends. Mean temperature and indices trends were spatially �=∑ ∑ sgn (�� −��), interpolated using the spline method. Tis is characterized by �=1 �=�+1 ftting a smooth and continuous surface with the observation (1) � points, and it does not require a preliminary estimate for the � (�−1)(2� + 5) −∑�=1 �� (�� −1)(2�� +5) var (�) = , structure of a temporal variance and statistical hypothesis 18 [52]. In comparison with other methods, previous studies, for example, those using Kriging [53, 54] and those analyzing where �� and �� are sequential data values representing the � � � climatic and meteorological change [55–58], have shown the annual values in years and ,respectively; isthetimelength spline method to be more advantageous around topographic of the dataset; sgn(�) = −1 if �<0,sgn(�) = 0 if �=0,and (�) = 1 �>0� � features. Spatial interpolation with the spline method was sgn if ; � isthenumberoftiesofthe th value completed using ArcGIS sofware (Version 10.2). and � isthenumberoftiedvalues.TeMann-Kendalltest statistic �� was calculated as 4. Results and Discussion �−1 �� = , if �>0, √var (�) 4.1. Mean Temperature Spatiotemporal Trends
�� =0, if �=0, (2) 4.1.1. Annual Temperature. MK test results across the study period for the BTSSR show that annual temperature (TA) �+1 signifcantly increased (� < 0.001). Te TA slope was �� = , if �<0, ∘ √var (�) 0.34 C/decade (Figures 2(a) and 3(a), Table 2), a result which 6 Advances in Meteorology Units C/decade C/decade C/decade C/decade C/decade C/decade C/decade C/decade C/decade ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ days/decade days/decade days/decade days/decade days/decade days/decade days/decade � < 0.05 )trend 7.55% 1.89% 1.89% 1.89% 1.89% 9.43% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 13.21% 15.09% 43.40% 43.40% 1.89% 1.89% 3.77% 3.77% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 81.13% 56.60% 49.06% 66.04% Decreasing (%) %1 %5 %1 %5 %49 %2 7.55 7.55 %1 1.89 3.77 %1 5.66 %43 5.66 %50 0.00 %1 0.00 %53 13.21 %1 13.21 %2 is 0.05 level of signifcance; percentage of stations with a signifcant ( 37.74 18.87 71.70 %0 20.75 %0 20.75 43.40 ∗ 0.00% 0.00% 0.00% 13.21% 98.11% 18.87% 84.91% 52.83% 83.02% 54.72% 79.25% 79.25% 79.25% 96.23% 94.34% 88.68% Increasing (%) 3 0 4 51 51 10 53 53 52 52 52 52 52 52 48 is 0.01 level of signifcance, and ∗∗ ∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗∗ S − 0.96 − 0.80 − 0.17 − 2.10 . (italics) ∗ is 0.001 level of signifcance, 3 S 0.50 0.47 0.00 0.38 − 1.10 0.45 − 1.37 1.13 − 1.02 1.11 − 0.15 0.27 − 0.12 1.74 − 0.12 − 0.14 0.34 � > 0.05 )trend − 0.32 0.24 − 0.35 − 0.73 − 0.95 0.16 48 − 0.81 ∗∗∗ ∗ ∗ ∗∗ ∗ ∗ ∗ ∗ ∗∗ ∗∗ ∗∗ 2 is 2000–2014; S 3 4.41 0.62 3.20 − 0.11 Table 2: Trend slopes for average temperature and extreme temperature indices in the BTSSR during the diferent stages. − 2.19 − 0.73 − 3.40 ∗ 1 1.33 0.64 1.28 is 1979–2000, and S − 0.17 1.61 − 0.17 3.49 − 0.01− 0.01 0.77 0.76 − 1.06 2.77 − 0.23 0.36 − 0.22 − 0.27 3.52 − 0.03 0.64 0.29 0.30 − 0.02 0.47 − 0.24 0.35 − 0.98 − 0.43 2 , and percentage of stations with an insignifcant ( is 1960–1978, S � spring summer autumn winter � 1 TNn CSDI TX90p TXx Index� S � � � � � TN10p TN90p DTR GSL WSDI TX10p 1.38 S (bold) Advances in Meteorology 7
1.5 1.5 1.5 ∗∗ ∗ 1.0 1.0 ∗∗∗ 1.0 ∗∗∗ ∗∗∗ 0.5 0.5 C/decade) C/decade) C/decade) ∘ ∘ 0.5 ∘ 0.0 0.0 0.0 −0.5 −0.5 Slope of spring Slope of annual −0.5 −1.0 summer of Slope −1.0 temperature ( temperature temperature ( temperature −1.0 ( temperature −1.5 −1.5 S1 S2 S3 S S1 S2 S3 S S1 S2 S3 S (a) (b) (c) 1.5 3.0 1.2 ∗∗ ∗ 1.0 1.5 0.6 ∗∗∗ ∗∗∗ ∗∗∗ C/decade) C/decade) C/decade) ∘ ∘ ∘ 0.5 0.0 0.0 0.0 −1.5 −0.6 Slope of winter of Slope
Slope of autumn of Slope −0.5 Slope of growing season growing of Slope temperature ( temperature temperature ( temperature temperature ( temperature −1.0 −3.0 −1.2 S1 S2 S3 S S1 S2 S3 S S1 S2 S3 S (d) (e) (f)
Figure 3: Box-Plot of Slopes for regional average temperature over the BTSSR in diferent stages. (a) Annual, (b) spring, (c) summer, (d) autumn, (e) winter, and (f) growing season. S1 is 1960–1978, S2 is 1979–2000, S3 is 2000–2014, and S is 1960–2014. ∗∗∗is 0.001 level of signifcance, ∗∗ is 0.01 level of signifcance, and ∗ is 0.05 level of signifcance.
1.5 ∗ 1.5 1.5 S1 S2 S3 S S1 S∗2 S3 S S1 S2 S3 S ∗∗ ∗ ∗∗ ∗
∗∗ 1.0 1.0 ∗∗
1.0 ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ 0.5 0.5 ∗∗∗ C/decade) C/decade) 0.5 C/decade) ∘ ∘ ∘ 0.0 0.0 0.0 −0.5 −0.5 −0.5 Slope of spring Slope of Slope of annual of Slope
−1.0 −1.0 summer of Slope −1.0 temperature ( temperature temperature ( temperature −1.5 −1.5 ( temperature −1.5 I I I I I I I I I I I I II II II II II II II II II II II II V V V V V V V V V V V V III III III III III III III III III III III III IV IV IV IV IV IV IV IV IV IV IV IV (a) (b) (c) 1.5 3.0 S1 S2 S3 S S1 S2 S3 S 1.0 S1 S2 S3 S ∗ ∗∗ ∗∗ ∗ ∗ ∗∗ ∗ 1.0 ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ 1.5 0.5 ∗∗∗ ∗∗∗ ∗∗∗
∗∗ ∗∗∗
∗∗ ∗ ∗∗∗ ∗∗∗ C/decade) C/decade) ∗∗∗ ∗∗∗ C/decade) ∘ ∘ 0.5 ∘ 0.0 0.0 0.0 −0.5 −1.5
−0.5 winter of Slope Slope of autumn of Slope −1.0 Slope of growing season growing of Slope temperature ( temperature temperature ( temperature −1.0 ( temperature −3.0 −1.5 I I I I I I I I I I I I II II II II II II II II II II II II V V V V V V V V V V V V III III III III III III III III III III III III IV IV IV IV IV IV IV IV IV IV IV IV (d) (e) (f)
Figure 4: Box-Plot of Slopes for regional average temperature over the fve subregions in diferent stages. (a) Annual, (b) spring, (c) summer, (d) autumn, (e) winter, and (f) growing season. S1 is 1960–1978, S2 is 1979–2000, S3 is 2000–2014, and S is 1960–2014. I is the southwest subregion (SR-I), II is the northwest subregion (SR-II), III is the south-central subregion (SR-III), IV is the east-central subregion (SR-IV), and V is the northeast subregion (SR-V). ∗∗∗is 0.001 level of signifcance, ∗∗ is 0.01 level of signifcance, and ∗ is 0.05 level of signifcance.
coincides with TA change trends identifed in the China insignifcant downward trend in S3 (Figure 3(a), Table 2), territory [59, 60], in regions such as the Yellow River basin, results which may be due to alternating surface albedo due the Yangtze River Basin [61], arid and semiarid regions in to large areas of desertifcation in S2 and rapid revegetation northwest China [62], and those in the monsoon area of activities in S3 [23, 37, 64–70]. eastern China [63]. TA results for the diferent stages showed Te variations in �� trends for the diferent stages in somediferencestothoserecordedfortheentirestudyperiod. the fve subregions are shown in Figure 4(a) and Table 3. TA recorded a slightly downward trend in S1,asignifcantly Results show that �� trends in these subregions signifcantly ∘ upward trend in S2 (with a slope of 0.77 C/decade), and an increased from 1960 to 2014 (� < 0.001); the highest �� 8 Advances in Meteorology C/decade C/decade C/decade C/decade C/decade C/decade C/decade C/decade C/decade ∘ days/decade days/decade days/decade days/decade days/decade days/decade ∗∗ ∗∗ ∗∘ ∗∘ ∗∗∗ ∘ ∗∗ ∗∗ ∗∗∗ ∘ ∗∗∗ ∘ ∗∗∗ ∗∗∗ ∘ ∗∗∗ ∘ ∗∗∗ ∗∗∗ ∘ − 0.81 days/decade 0.37 0.49 1.13 0.88 0.31 1.70 0.41 0.35 3.28 0.37 0.38 − 1.04 − 2.28 − 0.21 ∗ ∗∗ ∗ ∗∗∗ ∗∗ ∗∗ ∗∗∗ ∗∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ 0.87 0.41 0.57 0.34 0.49 − 0.82 1.63 3.08 0.22 0.29 0.30 0.26 − 0.81 − 2.14 − 0.20 ∗∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ S 1.40 1.17 − 1.13 1.80 0.25 0.27 0.29 0.36 0.50 3.40 0.60 0.40 − 0.54 − 0.17 − 2.30 ∗ ∗∗ ∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ 1.00 0.46 − 0.86 0.31 0.41 3.42 2.07 − 1.38 0.38 0.34 0.40 − 0.23 − 2.30 ∗ ∗∗ ∗∗ ∗∗∗ ∗∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ − 0.09 − 0.72 − 0.97 − 1.72 ∗∗ − 1.21 0.45 − 2.13 1.31 1.07 − 0.14 − 0.53 − 0.75 0.19 − 2.92 1.34 − 0.27 0.36 − 0.29 0.31 − 0.20 0.25 0.55 − 0.54 0.11 0.23 0.23 0.05 0.24 − 0.48 1.60 − 0.46 0.23 − 1.14 ∗∗ ∗∗ is 0.05 level of signifcance. − 1.12 − 1.12 − 1.18 − 1.53 − 0.18 − 1.04 − 0.58 − 0.28 − 0.29 − 0.07 0.09 − 0.09 − 2.76 − 2.54 ∗ 3 S − 1.31 − 3.16 − 0.15 − 0.71 − 1.08 − 1.09 − 0.22 − 0.36 − 0.07 − 0.84 0.35 3.23 2.92 − 0.04 − 0.13 − 1.20 − 0.12 − 0.01 − 0.53 0.18 − 0.94 − 0.64 0.39 1.17 1.96 0.81 0.90 0.521.20 0.17 2.16 4.31 − 1.13 − 1.33 − 1.22 − 0.61 − 0.56 0.45 − 0.20 − 0.09 − 0.09 0.08 − 0.40 − 0.04 0.02 ∗ ∗ ∗∗ ∗ ∗ ∗ ∗∗ ∗∗ ∗∗ 1.50 − 0.21 − 0.01 is 0.01 level of signifcance, and 2.05 2.45 0.85 − 1.95 − 0.92 − 2.64 ∗∗ ∗∗ ∗ ∗∗ ∗∗ ∗∗ 0.56 0.91 0.36 0.51 2.96 3.09 − 1.80 − 0.10 0.57 − 0.16 0.22 − 0.83 1.28 2.10 0.68 − 2.65 ∗ ∗ ∗∗ ∗ 2 ∗ ∗∗ ∗∗ ∗∗ ∗∗ S 0.45 0.34 0.49 − 0.18 3.34 4.31 1.34 0.77 − 2.20 − 0.89 − 4.72 ∗ ∗ ∗ ∗ ∗∗ ∗∗ ∗∗ 0.82 0.73 0.49 0.38 0.08 1.37 4.01 − 0.06 − 0.66 3.29 0.58 0.92 − 2.59 − 3.04 is 0.001 level of signifcance, ∗ ∗ ∗ ∗ ∗ ∗∗ ∗∗ − 3.32 − 2.38 − 0.45 − 0.04 C/decade) of mean temperature and extreme temperature indices in the fve subregions during the diferent stages. ∗∗∗ ∘ ∗∗ − 0.11 0.85 − 0.71 3.29 3.69 2.69 1.98 2.03 − 2.07 − 0.92 − 0.08 0.74 − 0.46 5.42 − 0.58 ∗∗ is 2000–2014; 3 − 0.11 0.08 0.53 − 1.55 − 1.07 − 0.35 − 0.02 − 0.28 − 0.20 0.04 0.61 − 0.42 0.11 0.54 1.02 0.33 − 0.54 1 S − 0.16 0.15 0.13 0.64 0.71 0.62 0.56 0.75 0.43 0.33 0.28 0.19 0.08 0.24 − 1.44 − 0.33 − 0.32 Table 3: Trend slopes ( − 0.78 − 0.23 − 0.58 0.11 0.48 3.96 − 0.02 − 0.07 − 0.42 ∗ ∗∗ ∗∗ is 1979–2000, and S − 1.62 − 0.12 − 0.18 0.05 0.03 − 0.35 − 0.03 − 2.64 0.29 − 1.44 − 1.57 2 − 0.60 ∗∗ 0.11 0.54 1.17 0.77 0.42 0.25 1.07 0.00 0.08 − 0.31 − 0.01 − 0.10 0.03 − 0.33 0.20 − 0.55 − 0.93 − 0.07 0.98 0.09 0.05 0.19 1.03 1.26 1.78 − 4.26 5.92 3.56 1.11 3.90 3.39 5.17 3.03 5.64 7.95 0.14 3.52 − 0.09 is 1960–1978, S � spring summer autumn winter � 1 CSDI 0.74 Stage Index� I II III IV V I II III IV V I II III IV V I II III IV V Units TX10p 1.10TNn 1.13TX90p 1.74WSDI 0.58 TXx 1.29 0.33 1.52 � � � � � TN10p TN90p DTR GSL S Advances in Meteorology 9
∘ slope was 0.41 C/decade in the northwest subregion (SR-II) seasonal temperature series showed insignifcant positive while the east-central subregion (SR-IV) recorded the lowest trends for most subregions. Insignifcant decreasing trends ∘ slope (0.30 C/decade) (Figures 3(a) and 4(a), Table 3). Results were recorded in the fve subregions during the winter for all fve subregions recorded a slight fuctuation during months and summer months, while spring temperatures S1 and a signifcant increasing trend in S2,especiallyinthe showed a mixture of slightly negative and positive trends, ∘ northwest subregion (SR-II; 0.92 C/decade), a result which with more negative than positive trends being recorded, and may be attributed to serious steppe degradation in this area slightly increasing trends were found in all fve subregions [71, 72]. During the last decade, �� recorded slightly negative during the autumn months over the last decade (Figures trends, except for the northwest subregion (SR-II). 4(b)–4(e), Table 3). Results for the spatial distribution of �� trends (Fig- Te spatial distribution of seasonal temperature trends ure 5(a)) show that most areas of the BTSSR had positive (Figures 5(b)–5(e)) shows that a positive temperature slope slopes, with the largest occurring in the central-southern was identifed for almost the entire BTSSR in the four mountain area; however, negative slopes were identifed in seasons; the southeastern area of SR-III and the eastern the southeastern region in the Luanhe River Basin. Based area of SR-I were exceptions, these areas recording neg- � � � on data from 51 stations (96.23% of all stations), the spatial ative spring, summer,and autumn slopes and a negative � � distribution of � showed a signifcant increase in the BTSSR autum slope, respectively (Figures 5(b)–5(e)). Most stations (� < 0.01)(Table2),correspondingtotheaverageslopeof exhibited increasing trends, with the lowest winter slope of ∘ ∘ 0.35 C/decade. A strong increasing trend was observed at the 0.01 C/decade (Hequ station) and the highest winter slope of ∘ Wutaishan station and a weak negative trend was found at the 1.51 C/decade (Wutaishan station) being located in SR-I and Chengde station, both meteorological stations being situated SR-III, respectively. In spring, data from 98.11% of stations in the south-central subregion (SR-III). recorded a signifcant increasing trend (Figure 5(b), Table 2). In general, an increasing �� trend in the BTSSR can Insummerandautumn,thenumberofstationsrecording accelerate plant growth by altering plant photosynthesis signifcant positive trends was similar, the trends mainly processes[73,74]andlengtheningthegrowingseason occurring in SR-I, SR-III, and SR-IV.Similarly, the number of [75, 76]. However, increasing �� trends can also increase stations recording negative trends was few (Figures 5(c) and regional evapotranspiration [26] which, in conjunction with 5(d),Table2).Inthewinter,42stationsrecordedsignifcant a decreasing trend in precipitation in the BTSSR [77], will positive trends (79.25% of all stations), these being mainly increasetheriskofdrought[78].Tetradeofofchange located in SR-I, SR-III, and SR-IV,with no station recording a trends of these meteorological factors is currently unclear. negative trend (Figure 5(e), Table 2). Tis result implies that the southern and east-central areas of the BTSSR experienced 4.1.2. Seasonal Temperature. MK test results indicate a warmer climate each year. that the seasonal temperature of the BTSSR signifcantly Te winter warming trends identifed in our study across increased (� < 0.05) during the study period; the lowest theBTSSRaresimilartothosepreviouslyidentifedinEurope ∘ ∘ (0.24 C/decade) and highest (0.45 C/decade) rate of [81], North Asia and North America [82], subarctic [83], temperature rise were recorded in summer and winter, China [84, 85], and other areas across the globe [86]. Winter respectively (Figures 2(b)–2(e) and 3(b)–3(e), Table 2). Tis warming will undoubtedly be benefcial in alleviating vegeta- result was similar to that recorded by Wang et al. [79] for tiondamageduetofreezingtemperatures,tobothnativeand seasonaltemperaturechangesacrossthearidregionof planted vegetation, and it will increase the possibility of using northwestern China. Results for the diferent stages of the low cold-tolerant plants to revegetate the BTSSR. BTSSR showed difering changes. Results during S1 recorded seasonal temperature to have a slightly negative trend, except 4.1.3. Growing Season Temperature. MK test results for grow- � � for winter temperature ( winter). Seasonal temperature in ing season temperature ( �)from1960to2014acrossthe S2 increased, with � recording a signifcant increase BTSSR recorded a signifcant increase (� < 0.001), with winter∘ ∘ (having a slope of 1.28 C/decade) and � having an aslopeof0.27C/decade (Figures 2(f) and 3(f), Table 2). ∘ summer insignifcant increase (with a slope of 0.35 C/decade), results Results during the diferent stages showed �� to record a which were in agreement with seasonal temperature changes slight decrease in S1, a signifcant increase in S2 (with a ∘ across northeastern China [80]. Seasonal temperatures slope of 0.47 C/decade), and an insignifcant decline in S3 during S3 recorded a mixture of slightly negative and positive (Figure 3(f), Table 2). Te overall trend of �� was similar with trends (Table 2). that of ��. Results for seasonal temperature trends across the fve Results for the variations in �� trends for the fve sub- subregions (Figures 4(b)–4(e), Table 3) recorded a signifcant regions in the diferent stages recorded a signifcant increase increase (� < 0.05) during the study period, especially for (� < 0.001; Figure 4(f), Table 3) from 1960 to 2014, especially � ∘ ∘ winter in SR-III (having a slope of 0.50 C/decade) (Figures inSR-V,withaslopeof0.35C/decade (Figures 5(f) and 4(f), 5(b)–5(e)). During S1, insignifcant increasing trends for Table 3). As temperatures increased, the date of initiation � winter were recorded in all subregions, while other seasonal of the growing season advanced and earlier growth resulted temperature series showed a mixture of slightly negative in vegetation covering the land earlier, thus reducing the and positive trends across the fve subregions. During S2, occurrence of dust storms [87]. However, earlier vegetation � showed a signifcant increasing trend in all subregions, growthcouldincreasewaterstressinthereproductivegrowth winter ∘ especially in SR-V (with a slope of 1.50 C/decade), and other period of water-limited vegetation in the BTSSR [24]. �� 10 Advances in Meteorology
0.6 3 V: y = 0.0369x − 0.0711 0.6 2 R2 = 0.4171 (p < 0.01) C) V 0.4 ∘ 1 0 IV 0.2 −1 II 0.4 0.2 ( temperature
Anomaly of annual −2
III −3 0 1960 1970 1980 1990 2000 2010 0.2 0.4 0.4 0.4 0 0.6 I 0.6 1 3 0.8 IV: y = 0.0302x + 5.3145 2 2 0.4 0.2 R = 0.4087 (p < 0.01) C) ∘ 1 0 0 0.2 0.4 0.6 0.8 1 1.2 −1 ∘ Annual temperature ( C/decade) ( temperature −2 −0.07 0.31 –0.00 –0.57 −3 0.00 0.57 –0.21 –1.17 1960 1970 1980 1990 2000 2010 0.21–0.31 3 3 3 I: y = 0.0307x + 7.185 II: y = 0.0405x + 3.3526 III: y = 0.0356x + 6.0841 2 2 2 R2 = 0.4601 (p < 0.01) 2 R = 0.4826 (p < 0.01) 2 R = 0.5397 (p < 0.01) C) C) C) ∘ ∘ ∘ 1 1 1
0 0 0 temperature ( temperature temperature ( temperature
temperature ( temperature −1 −1 −1 Anomaly of annual Anomaly of annual Anomaly of annual Anomaly of annual −2 −2 −2 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (a)
0.6 6 V: y = 0.0412x + 1.3067 4 R2 = 0.2393 (p < 0.01) C) V ∘ 2
0.4 IV 0 II −2 temperature ( temperature
0.2 Anomaly of spring 0.2 0.4 0.4 −4 III 0 0.2 201020001990198019701960 0.4 I 0.2 0.6 1 4 y = 0.0341x + 6.6014 0.4 0.8 1.4 IV: 3 R2 = 0.2102 (p < 0.01) C) 0.2 ∘ 2 1 0 0.4 0.8 1.2 1.4 0 −1 ∘
Spring temperature ( C/decade) ( temperature Anomaly of spring −2 −0.04–0.00 0.41–0.59 −3 0.00–0.31 0.59–1.49 1960 1970 2010200019901980 0.31–0.41 3 4 4 I: y = 0.0364x + 8.8075 y = 0.038x + 4.8935 III: y = 0.0399x + 7.4401 2 II: 3 2 R = 0.3022 (p < 0.01) 3 2 R2 = 0.3461 (p < 0.01) C)
R = 0.235 (p < 0.01) C) C) ∘ ∘ 1 ∘ 2 2 1 1 0 0 0 −1 −1 −1 temperature ( temperature temperature ( temperature
−2 ( temperature Anomaly of spring Anomaly of spring Anomaly of spring −2 −2 −3 −3 −3 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 2010200019901980 (b)
Figure 5: Continued. Advances in Meteorology 11
4 V: y = 0.0306x + 17.568 3 R2 = 0.3325 (p < 0.01) 0.4 C) ∘ 2 V 1 0.4 0.2 0 IV −1
temperature ( temperature −2 II 0.2 summer of Anomaly 0.2 −3 1960 1970 1980 1990 2000 2010
0.4 III 0 0.4 0.2 3 I 1 IV: y = 0.0219x + 20.661 0.2 0.6 2 C) 2 R = 0.2438 (p < 0.01) ∘
1 0.2 0 0.2 0.4 0.6 0.8 1 0 −1 ∘ ( temperature
Summer temperature ( C/decade) summer of Anomaly −0.10–0.00 0.21–0.45 −2 0.00–0.14 0.45–1.07 1960 1970 1980 1990 2000 2010 0.14–0.21 3 I: y = 0.0186x + 21.222 3 II: y = 0.0311x + 19.714 3 III: y = 0.0248x + 20.178 2 2 R2 = 0.2084 (p < 0.01) 2 R2 = 0.3491 (p < 0.01) 2 R = 0.3089 (p < 0.01) C) C) C) ∘ ∘ ∘ 1 1 1 0 0 0 −1 −1 −1 temperature ( temperature
−2 ( temperature temperature ( temperature −2 −2 Anomaly of summer of Anomaly Anomaly of summer of Anomaly Anomaly of summer of Anomaly −3 −3 −3 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (c)
4 V: y = 0.0382x + 0.5082 3 0.6 R2 = 0.2339 (p < 0.01) C) 2 ∘ V 0. 1 4 0 0.2 −1 IV −2 temperature ( temperature
II 0.2 autumn of Anomaly −3 0.4 0.4 −4
III 1960 1970 1980 1990 2000 2010 0.2 0 0.4 3
0 0.6 y = 0.0286x + 5.6733 I 1 IV: 2 0.8 2 0.2 R = 0.2213 (p < 0.01) C) 0.2 ∘ 1 0 0 0 0.2 0.4 0.6 0.8 1 1.2 −1
∘ ( temperature −2
Autumn temperature ( C/decade) autumn of Anomaly −0.18–0.00 0.31–0.48 −3 0.00–0.21 0.48–1.27 1960 1970 1980 1990 2000 2010 0.21–0.31 4 5 4 y = 0.0243x + 7.2882 III: y = 0.0291x + 6.4626 I: 4 II: y = 0.0398x + 3.538 3 2 3 2 R = 0.2151 (p < 0.01) 3 R2 = 0.2706 (p < 0.01) R = 0.2717 (p < 0.01) C) C) C) ∘ ∘ ∘ 2 2 2 1 1 1 0 0 0 −1 −1 −1 −2 temperature ( temperature temperature ( temperature temperature ( temperature Anomaly of autumn of Anomaly
Anomaly of autumn of Anomaly −2 Anomaly of autumn of Anomaly −2 −3 −3 −4 −3 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (d)
Figure 5: Continued. 12 Advances in Meteorology
8 V: y = 0.0371x − 19.79 0.6 6 R2 = 0.0953 (p < 0.05) C) 0.2 V ∘ 4 0.4 0.4 2 0.2 0 IV −2 0.2 II ( temperature
Anomaly of winter of Anomaly −4 0.4 0.8 0.6 0.6 0.4 −6 III 1960 1970 1980 1990 2000 2010 0.6 0.2 0.2 0.4 0.8 0.4 0.8 0.6 6 0 1 IV: y = 0.0407x − 11.752 0.6 I 0.6 4 R2 = 0.1542 (p < 0.01)
0.6 C)
0.4 ∘ 0.4 0.2 2 0 0 0.3 0.6 0.9 1.2 1.5 −2 ∘
temperature ( temperature −4
Winter temperature ( C/decade) winter of Anomaly 0.00 0.42 –0.27 –0.79 −6 0.27–0.31 0.79–1.51 1960 1970 1980 1990 2000 2010 0.31–0.42 6 y = 0.0447x − 8.6436 8 6 y = 0.0496x − 9.7881 I: y = 0.0462x − 14.818 III: 2 6 II: 2 4 R = 0.2443 (p < 0.01) 2 4 R = 0.3315 (p < 0.01)
R = 0.1744 (p < 0.01) C) C) C) 4 ∘ ∘ ∘ 2 2 2 0 0 0 −2 −2 −4 −2 −4 −6 temperature ( temperature −4 temperature ( temperature temperature ( temperature Anomaly of winter of Anomaly Anomaly of winter of Anomaly −6 winter of Anomaly −8 −8 −10 −6 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (e)
0.6 3 V: y = 0.035x + 11.042 0.6 2 0.4 2 C) R = 0.5514 (p < 0.01) V ∘ 0.4 1 0 IV −1
II ( temperature 0.4 0.2 0.4 −2 Anomaly of growing season growing of Anomaly 1960 1970 1980 1990 2000 2010 III 0 0.2 0.4 0.2 3 I 0.8 y = 0.0257x + 14.996 0.4 IV: 2 2
0.2 C) R = 0.4233 (p < 0.01) ∘
0.2 1
000.2 0.4 0.6 .80
∘ −1 Growing season temperature ( C/decade) ( temperature −0.14–0.00 0.30–0.48 −2 0.00–0.13 0.48–0.96 season growing of Anomaly 1960 1970 1980 1990 2000 2010 0.13–0.30 3 3 3 y = 0.0337x + 13.53 y = 0.027x + 14.967 I: y = 0.0233x + 15.937 II: III: 2 2 2 R2 = 0.5624 (p < 0.01) 2 R2 = 0.5018 (p < 0.01) C) R = 0.4416 (p < 0.01) C) C) ∘ ∘ ∘ 1 1 1 0 0 0 −1 −1 −1 temperature ( temperature ( temperature ( temperature −2 −2 −2 Anomaly of growing season growing of Anomaly Anomaly of growing season growing of Anomaly Anomaly of growing season growing of Anomaly 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (f) Figure 5: Spatial patterns of the trend slopes of mean temperature (1960–2014) over the BTSSR and anomaly of subregional indices curves. Te blackline represents linear trends for (a) annual, (b) spring, (c) summer, (d) autumn, (e) winter, and (f) growing season. Te redline represents the 5-year moving average. Advances in Meteorology 13 trends for all fve subregions recorded slight fuctuations Variations in the trends for the extreme cold indices at ∘ during S1.DuringS2,aslopeof0.58C/decade was recorded, the diferent stages in the fve subregions (Figures 8(a)–8(d), indicating a signifcant increasing trend, especially in SR-II. Table 3) show that their frequency and duration for the Tis result was consistent with previous fndings showing majority of the subregions signifcantly decreased from 1960 �� to have signifcantly increased across most regions of to 2014 (� < 0.05). TN10p results in SR-III recording a slope China since 1980 [88]. During the last decade (S3), �� showed of −2.30 days/decade (Figures 8(a) and 9(a), Table 3) and, slightly negative trends, except in SR-II (Figure 4(f), Table 3). for the majority of subregions, TNn signifcantly increased Results for the �� slopeshowedasimilarspatialpattern during the study period (� < 0.05), notably in SR-III (with ∘ with those for �� (Figure 5(f)). A larger positive slope aslopeof0.60C/decade, Figures 8(d) and 9(d), Table 3). was recorded in the central-southern mountain area and DuringtheperiodS1, all subregions showed a mixture of the northeast region, and a negative slope occurred in the negativeandpositivetrends,exceptforTN10pandTX10p. southeastern area of SR-III. Based on data from 50 stations Tese fuctuating trends stabilized during S2 as a signifcant (94.34% of all stations; Table 2), the spatial distribution of warming trend occurred, especially for TN10p in SR-III �� showed a signifcant increase over the BTSSR (�< (with a slope of −4.72 days/decade), and for TNn in SR- ∘ 0.05) (Figure 5(f)), corresponding to an average slope of IV (with a slope of 2.10 C/decade), these results being in ∘ 0.29 C/decade. A strong increasing trend was observed at the accordance with observations over China during 1979–1999 Wutaishan station and a signifcant negative trend was only [95]. During S3,TN10p,TX10p,andCSDIshowednegative found at the Chengde station, both stations being located in trends for most subregions while TNn showed a positive SR-III (Figure 5(f)). trend for most subregions, especially in SR-II (with a slope ∘ Our results showed that a trend of increasing �� can of −0.90 C/decade). enhance plant photosynthetic activities and further produc- Te slope distribution of TN10p showed a spatial disparity tivity without causing water stress [89, 90]. However, in across the BTSSR (Figure 9(a)). A positive slope was identi- water-limited areas of the BTSSR, increasing temperature in fed in the eastern area of SR-I, the northern area of SR-II, the growing season may exacerbate rain-fed plant stress due and the southeastern area of SR-III, this being the area around to high temperatures combined with decreasing precipitation the Chengde station; a negative slope prevailed across the rest [77, 91], especially for vegetation planted (shrubs or trees) of the region where higher negative slopes occurred along as part of the degraded land restoration project in the thenortheastborderandnorthwestborderandinthesouth- BTSSR. central part. Results for the spatial distribution of TN10p, based on data from 43 stations (81.13% of all stations), showed � < 0.05 4.2. Spatiotemporal Trends of Indices a signifcant decrease across the BTSSR ( )(Table2), correspondingtoanaverageslopeof−5.05 days/decade. 4.2.1. Cold-Related Indices. Results for temporal variation A strong decreasing trend was observed at the Wutaishan of cold nights (TN10p), cold days (TX10p), and cold spell station in SR-III and a weak positive trend was identifed at duration indicator (CSDI), representing the frequency and the Sonid Lef station (SR-II; Figure 9(a)). duration of extreme low temperature for the BTSSR, are A negative slope for TX10p was identifed across the shown in Figures 6(a)–6(c). In general, from 1960 to 2014, entire region, with the largest slope occurring in the central- the fuctuations of these indices continuously signifcantly southern mountain area and in the east Siramulen River decreased (� < 0.05), especially TN10p (having a slope of Basin (Figure 9(b)). Data from 56.60% of stations recorded −2.10 days/decade; Figure 7(a), Table 2). Changes observed a signifcant decreasing trend (� < 0.05)withnostations for TN10p are more signifcant than those of TX10p across recording an increasing trend (Table 2). Te Wutaishan the BTSSR (Figures 7(a) and 7(b), Table 2); a reduction in station in SR-III recorded the strongest decreasing trend in TN10pwillresultinareductioninfrostdamageduringthe the BTSSR (Figure 9(b)). night [92, 93]. Te results of our study were in accordance Results for CSDI also recorded a negative slope across with those over the arid region of northwestern China [79] the majority of the region, an exception being in the eastern andinIran[94].Resultsforthediferentstagesshowed parts of SR-I and SR-III where positive slopes occurred that, during S1, the frequency and duration of extreme low (Figure 9(c)). Data from 49.06% of stations recorded a sig- temperature recorded a slightly negative trend, except for nifcant decreasing trend, concentrated in the northern part TX10p. During S2, the frequency and duration of extreme low of the study area (Figure 9(c), Table 2); a strong decreasing temperatures signifcantly decreased, especially TN10p (with trend was observed at the Linhe station (SR-I) and a weak aslopeof−3.40 days/decade), and results during S3 showed positive trend was identifed at the Yuanping station (SR-III; CSDI to signifcantly decrease (a slope of −0.81 days/decade), Figure 9(c)). while TN10p and TX10p insignifcantly decreased (Figures Te slopes for TNn and TN10p recorded similar patterns 7(a)–7(c), Table 2). Figure 6(d) shows the temporal variation (Figure 9(d)), with larger positive slopes being recorded along of Min Tmin (TNn) across the BTSSR. From 1960 to 2014, the northeast-northwest border and in the south-central part, the fuctuations of TNn continuously signifcantly increased, and negative slopes in the eastern and southern areas of SR- ∘ having a slope of 0.47 C/decade (� < 0.01). During the I, the northern area of SR-II, and the eastern area of SR- diferent stages, TNn showed a slightly positive trend during III. Based on data from 28 stations (52.83% of all stations; ∘ S1, a signifcant increase in S2 (with a slope of 1.61 C/decade), Table 2), the spatial distribution of TNn showed a signifcant and an insignifcant increased in S3 (Figure 7(d), Table 2). increase across the BTSSR (� < 0.05), corresponding to an 14 Advances in Meteorology
50 y = −0.2105x + 15.895 40 y = −0.0956x + 12.709 16 y = −0.0803x + 4.5343 2 40 R = 0.6357 (p < 0.05) 30 R2 = 0.2464 (p < 0.05) R2 = 0.2173 (p < 0.05) 30 12 20 20 10 8 10 0 0 4 −10 −10 −20 −20 0 Anomaly of CSDI (d) CSDI of Anomaly Anomaly of TN10p (d) of Anomaly −30 TX10p of (d) Anomaly −30 −4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (a) (b) (c) 8 y = 0.0473x − 27.422 30 y = 0.1737x + 4.6893 36 y = 0.1126x + 6.517 7 2 C) 2 2
∘ 6 R = 0.1936 (p < 0.05) R = 0.5931 (p < 0.05) R = 0.2977 (p < 0.05) 5 20 24 4 3 12 2 10 1 0 0 0 −1 −2 −3 −10 −12
Anomaly of TNn ( TNn of Anomaly −4 Anomaly of TX90p of (d) Anomaly −5 TN90p (d) of Anomaly −20 −24 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (d) (e) (f) y = 0.02x + 34.31 15 y = 0.1107x + 0.0352 4 2 y = −0.0167x + 13.393 2 R2 = 0.0778 (p > 0.05) C) R = 0.2625 (p < 0.05) C) 2 ∘ 3 ∘ R = 0.3724 (p < 0.05) 10 2 1 5 1 0 0 0 −1 Anomaly of TXx of ( Anomaly Anomaly (d) of WSDI −5 −2 ( DTR of Anomaly −1 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (g) (h) (i) 30 y = 0.320x + 197.49 24 R2 = 0.4002 (p < 0.05) 18 12 6 0 −6 −12
Anomaly of GSL (d) GSL of Anomaly −18 −24 1960 1970 1980 1990 2000 2010 (j)
Figure 6: Anomaly of extreme temperature indices curves across the BTSSR (1960–2014). Te black line represents the linear trends for (a) TN10p, (b) TX10p, (c) CSDI, (d) TNn, (e) TN90p, (f) TX90p, (g) WSDI, (h) TXx, (i) DTR, and (j) GSL. Te redline represents the 5-year moving average.
∘ average slope of 0.75 C/decade. A strong increasing trend was 4.2.2. Warm-Related Indices. Te frequency and duration observed at the Wutaishan station (SR-III) and a signifcant of extreme high temperature events across the BTSSR are negative trend was identifed at the Hequ station (SR-I; shown by the results for the temporal variation of warm Figure 9(d)). nights (TN90p), warm days (TX90p), and the warm spell Results for the study period across the BTSSR showed duration indicator (WSDI) (Figures 6(e)–6(g)). In general, a general trend of warming for the cold-related indices, these indices fuctuated continuously, having a signifcant trends which were also identifed in northern Mongolia [96], increasing trend during the study period (� < 0.01). Te South Africa [97], northeast China [98], and globally [99]. results for TN90p (with a slope of 1.74 days/decade; Figures As extreme low temperature events decreased in the BTSSR, 7(e)–7(g), Table 2) were noticeable, these being in accordance vegetation damage due to freezing conditions was mitigated, with previous fndings across China [100]. Results for the thus resulting in an expansion of range northwards of low, diferent stages showed that the frequency and duration of cold-tolerant plants which can result in the enrichment of extreme high temperature recorded an insignifcant negative plant species in this area. trend during S1.ResultsduringS2 only recorded signifcant Advances in Meteorology 15
8 4 8
4 2 4 ∗ ∗ ∗∗ ∗∗∗ 0 ∗∗ 0 ∗ ∗ 0 (days/decade) (days/decade) (days/decade) −4 −2 Slope of CSDI −4 Slope of TX10p of Slope Slope of TN10p of Slope
−8 −4 −8 S1 S2 S3 S S1 S2 S3 S S1 S2 S3 S (a) (b) (c) 6 9 6 ∗ 6 ∗ ∗∗∗ 3 3 3 ∗∗ C/decade) ∘ ∗∗ 0 0 0 −3
(days/decade) −3 (days/decade) Slope of TN90p of Slope −6 TX90p of Slope
Slope of TNn ( TNn of Slope −3 −9 −6 S1 S2 S3 S S1 S2 S3 S S1 S2 S3 S (d) (e) (f)
10 ∗∗ 2 2 1 5 1
∗∗ C/decade) C/decade) ∗∗∗ ∘ ∘ 0 ∗ 0 0 −1 −1
(days/decade) −5 Slope of WSDI of Slope −2 Slope of TXx of Slope ( −10 −3 ( DTR of Slope −2 S1 S2 S3 S S1 S2 S3 S S1 S2 S3 S (g) (h) (i) 15 10 ∗∗∗ 5 0 −5 Slope of GSL of Slope (days/decade) −10 −15 S1 S2 S3 S (j)
Figure 7: Box-Plot of Slopes for extreme temperature indices across the BTSSR in diferent stages. (a) TN10p, (b) TX10p, (c) CSDI, (d) TNn, (e) TN90p, (f) TX90p, (g) WSDI, (h) TXx, (i) DTR, and (j) GSL. S1 is 1960–1978, S2 is 1979–2000, S3 is 2000–2014, and S is 1960–2014. ∗∗∗ is 0.001 level of signifcance, ∗∗ is 0.01 level of signifcance, and ∗ is 0.05 level of signifcance.
increases for TN90p and WSDI; TN90p had the most and duration of extreme high temperature recorded sig- noticeable increase with a slope of 3.49 days/decade; during nifcant increasing trends (1960–2014) for the majority of S3, the frequency and duration of extreme high temperature the subregions, especially TN90p (Figures 10(a)–10(c)). Te decreased (Figures 7(e)–7(g), Table 2). Te temporal vari- change of TN90p is more pronounced than that of TX90p, ation of Max Tmax (TXx) across the BTSSR (Figure 6(h)) a fnding which is in accordance with previous studies [79, from 1960 to 2014 recorded fuctuations to insignifcantly 94].TetemporalvariationofTXxforthefvesubregions ∘ increase, having a slope of 0.16 C/decade. TXx results during (Figure 10(d)) shows an increasing trend for all subregions, ∘ the diferent stages recorded a slightly negative trend in S1, especially SR-V (with a slope of 0.24 C/decade). As daily an insignifcant increase in S2, and an insignifcant decrease maximum temperatures increase, transpiration and plant in S3 (Figure 7(h), Table 2). water stress will be exacerbated which may lead to an increase Variations in extreme warm indices in the fve subre- in water demand for vegetation production [101]. All fve gions (Figures 8(e)–8(h), Table 3) showed that frequency subregions showed that there was a combination of negative 16 Advances in Meteorology
8 4 6 S1 S2 S3 S S1 S2 S3 S S1 S2 S3 S 4 2 3 ∗∗∗ ∗∗∗
∗ ∗∗ ∗∗
∗∗ ∗ ∗∗ ∗∗
∗∗∗ ∗ ∗∗ 0 0 ∗∗ ∗∗ ∗∗ ∗ ∗∗∗ ∗∗ 0 ∗ ∗∗ ∗
∗ ∗∗ −4 ∗ −3 ∗ −2 (days/decade) (days/decade) (days/decade) Slope of CSDI Slope of TX10p of Slope Slope of TN10p of Slope −8 −6 −12 −4 −9 I I I I I I I I I I I I II II II II II II II II II II II II V V V V V V V V V V V V III III III III III III III III III III III III IV IV IV IV IV IV IV IV IV IV IV IV (a) (b) (c) 6 9 ∗ 6 S1 S2 S3 S S1 S2 S3 S S1 S2 S3 S ∗∗ 4 6 ∗ ∗∗∗ ∗∗∗ ∗∗ 3 ∗∗∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗ ∗
∗∗∗ ∗
3 ∗∗ C/decade) ∘
2 ∗∗∗ ∗∗ ∗∗ ∗ 0 0 ∗ 0 −3 −3 (days/decade) (days/decade) Slope of TX90p of Slope −2 TN90p of Slope −6 −4 −9 −6 Slope of TNn ( TNn of Slope I I I I I I I I I I I I II II II II II II II II II II II II V V V V V V V V V V V V III III III III III III III III III III III III IV IV IV IV IV IV IV IV IV IV IV IV (d) (e) (f) 12 2 2 S1 S2 S3 S S1 S2 S3 S S1 S2 S3 S 9 ∗∗ ∗∗ 6 1 ∗∗
∗∗ 0 C/decade) C/decade) ∘ ∗∗∗ ∘ ∗∗∗ ∗∗ ∗∗∗ 3 ∗ ∗∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗∗ ∗∗ ∗∗
∗∗ 0 ∗∗ 0 ∗∗ −2 ∗∗ −3 −1 (days/decade) Slope of WSDI of Slope −6 −9 −4 −2 Slope of TXx of Slope ( Slope of DTR ( DTR of Slope I I I I I I I I I I I I II II II II II II II II II II II II V V V V V V V V V V V V III III III III III III III III III III III III IV IV IV IV IV IV IV IV IV IV IV IV (g) (h) (i) 15 S1 S2 S3 S 10 ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ 5 ∗∗∗ 0 −5 Slope of GSL of Slope (days/decade) −10 −15 I I I I II II II II V V V V III III III III IV IV IV IV (j)
Figure 8: Box-Plot of Slopes for extreme temperature indices across the fve subregions in diferent stages. (a) TN10p, (b) TX10p, (c) CSDI, (d) TNn, (e) TN90p, (f) TX90p, (g) WSDI, (h) TXx, (i) DTR, and (j) GSL. S1 is 1960–1978, S2 is 1979–2000, S3 is 2000–2014, S is 1960–2014. ∗∗∗is 0.001 level of signifcance, ∗∗ is 0.01 level of signifcance, and ∗ is 0.05 level of signifcance.
and positive trends during S1,withmorenegativetrends of the southwest subregion (SR-I) (Figure 10(a)). Based on being identifed. During S2, an increasing trend occurred for data from 47 stations (88.68% of all stations) (Table 2), the the majority of the subregions, with more insignifcant than spatial distribution of TN90p showed a signifcant increase signifcant trends, especially for TN90p in SR-III (with a slope for the BTSSR (� < 0.05), corresponding to an average of 3.34 days/decade). Tese fndings are also in accordance slope of 4.58 days/decade. Signifcant decreasing trends (at with changes of temperature extremes in China as a whole the 99% confdence level) were identifed at the Hequ and [95]. During the last decade (S3), the fve subregions showed Chengde stations (Figure 10(a)), accounting for 3.77% of the a mixture of negative and positive trends, with more negative total stations, and a strong increasing trend was observed at trends recorded than positive (Figures 8(e)–8(h), Table 3). the Wutaishan station (SR-III). Results across the BTSSR showed that positive slopes A positive slope for TX90p was identifed across almost in TN90p occurred across the majority of the region, with theentireBTSSR,theonlyexceptionbeingthesoutheastern the largest trend occurring in the central-southern mountain areaofSR-Iwhereanegativeslopewasrecorded.Te area. However, negative slopes were identifed in the Luanhe larger positive slopes were recorded in the central-southern River Basin of the southeastern region and the east area mountain area, and the number of stations recording positive Advances in Meteorology 17
−12 −9 60 V: y = −0.2281x + 16.032 −6 2 V 40 R = 0.6041 (p < 0.01) 0 −3 −3 20 IV 0 0 −9 II −3 −20 −3 −6
−3 TN10p (d) of Anomaly −40 −6 III −3 0 1960 1970 1980 1990 2000 2010
−6 0 I −6 −9 60 IV: y = −0.2140x + 16.055 2 −3 40 R = 0.6059 (p < 0.01) −3 0 20 0 −3 −6 −9 0
TN10p (days/decade) −20
−9.55–−7.73 −2.63–0.00 TN10p (d) of Anomaly −40 −7.73–−5.00 0.00–2.31 1960 1970 1980 1990 2000 2010 −5.00–−2.63 60 y = −0.1725x + 14.74 60 60 I: II: y = −0.2304x + 16.821 III: y = −0.2262 + 16.351 2 R = 0.4637 (p < 0.01) R2 = 0.6090 (p < 0.01) R2 = 0.5999 (p < 0.01) 30 30 30
0 0 0 Anomaly of TN10p (d) of Anomaly Anomaly of TN10p (d) of Anomaly Anomaly of TN10p (d) of Anomaly −30 −30 −30 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (a)
−2 0 50 V V: y = −0.0807x + 12.087 40 2 30 R = 0.1158 (p > 0.05) 20 −2 −8 IV 10 −4 II 0 −2 −10 −2 −20 III −2 TX10p of (d) Anomaly −30 −4 1960 1970 1980 1990 2000 2010
−4 −6 −8 I 60 IV: y = −0.0819x + 12.304 2 −2 R = 0.1348 (p < 0.05) 30 −10 −8 −6 −4 −2 0 0 TX10p (days/decade) −10.00 −8.33 −2.05 −1.11 – – TX10p of (d) Anomaly −30 −8.33–−3.08 −1.11–−0.70 1960 1970 1980 1990 2000 2010 −3.08–−2.05 60 60 I: y = −0.0967x + 12.923 II: y = −0.0863x + 12.507 50 III: y = −0.1130x + 13.09 2 2 40 2 R = 0.2499 (p < 0.05) R = 0.1796 (p < 0.05) 30 R = 0.2726 (p < 0.05) 30 30 20 10 0 0 0 −10 −20 Anomaly of TX10p of (d) Anomaly TX10p of (d) Anomaly −30 −30 TX10p of (d) Anomaly −30 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (b)
Figure 9: Continued. 18 Advances in Meteorology
−1.5 20 V: y = −0.104x + 5.1131 −1.5 16 R2 = 0.2293 (p < 0.05) −1 V 12 0.5 − 8 IV 4 −0.5 −0.5 −2 − II 2.5 0 0 −2 Anomaly of CSDI (d) CSDI of Anomaly III −4 1960 1970 1980 1990 2000 2010 −0.5 0 −1.5 −0.5−1 −1 24 I 0.5 IV: y = −0.081x + 4.5271 20 −0.5 R2 = 0.1362 (p < 0.05) 16 −0.5 12 −2 −1.5 −1 −0.5 0 8 4 0
CSDI (days/decade) (d) CSDI of Anomaly −4 −2.83–−1.92 −0.90–0.00 1960 1970 1980 1990 2000 2010 −1.92–−1.52 0.00–0.85 −1.52–−0.90 18 I: y = −0.0729x + 4.7329 36 y = −0.1382x + 6.6043 12 y = −0.0538x + 3.289 16 II: III: 2 30 2 2 14 R = 0.1019 (p < 0.05) R = 0.1846 (p < 0.05) 9 R = 0.1645 (p < 0.05) 12 24 10 8 18 6 6 12 4 3 2 6 0 0 −2 0 Anomaly of CSDI (d) CSDI of Anomaly Anomaly of CSDI (d) CSDI of Anomaly −4 (d) CSDI of Anomaly −6 −3 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (c)
10 V: y = 0.0491x − 36.917 C) ∘ R2 = 0.1372 (p < 0.05) 0.8
0.4 V 5 0
0.4 IV 0 0.4 1.2 II 0.4 0.8 0.4 ( TNn of Anomaly −5 0.4 III 0 0.8 0.4 1960 1970 1980 1990 2000 2010 0.8 0.4 1.2 8 0 IV: y = 0.0492x − 27.252 I 0.4
−0.4 C) 2 ∘ 6 R = 0.1467 (p < 0.05) 0 4 2 0 0.4 0.8 1.2 1.6 1.8 0 ∘ −2 TNn ( C/decade) Anomaly of TNn ( TNn of Anomaly −0.65–0.00 0.50–0.69 −4 0.00–0.09 0.69–1.91 1960 1970 1980 1990 2000 2010 0.09–0.50 8 y = 0.0248x − 24.401 8 II: y = 0.0546x − 31.831 8 I: III: y = 0.0603x − 25.274
C) C) 2
6 6 C) ∘ 2 ∘ R = 0.1614 (p < 0.05) R = 0.0383 (p > 0.05) ∘ 6 R2 = 0.2882 (p < 0.05) 4 4 4 2 2 0 0 2 −2 −2 0 −4 −4 −2 Anomaly of TNn ( TNn of Anomaly Anomaly of TNn ( TNn of Anomaly −6 −6 ( TNn of Anomaly −4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (d) Figure 9: Spatial patterns of the trend slopes of cold-related indices across the BTSSR and anomaly of subregional indices curves, 1960–2014. Te black line represents the linear trends for (a) TN10p, (b) TX10p, (c) CSDI, and (d) TNn. Te redline represents the 5-year moving average. Advances in Meteorology 19
8 40 V: y = 0.1696x + 4.9845 2 6 R = 0.5946 (p < 0.01) 6 V 20 6 4 IV 0 II 4 2
6 TN90p (d) of Anomaly 6 2 −20 III 2 1960 1970 1980 1990 2000 2010 2 0 −2 8 2 0 6 6 10 8 I 6 −2 30 IV: y = 0.1628x + 5.2439 4 2 4 R2 = 0.5804 (p < 0.01) 2 20
4 2 10 −2 0 2 4 6 8 10 12 14 0
TN90p (days/decade) −10
−4.32 4.80 TN90p (d) of Anomaly –0.00 –7.91 −20 0.00–1.88 7.91–14.23 1960 1970 1980 1990 2000 2010 1.88–4.80 30 I: y = 0.1596x + 5.2413 36 35 III: y = 0.1804x + 4.1004 II: y = 0.2070x + 3.8483 30 R2 = 0.4514 (p < 0.01) 2 20 24 R2 = 0.6009 (p < 0.01) 25 R = 0.5828 (p < 0.01) 20 10 12 15 10 0 5 0 0 −5 −10 −12 −10
Anomaly of TN90p (d) of Anomaly TN90p (d) of Anomaly TN90p (d) of Anomaly −15 −20 −24 −20 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (a) 40 4 V: y = 0.1129x + 6.6284 30 R2 = 0.2584 (p < 0.05) 20 4 V 10 0 2 IV −10 2 II 4 2 −20 Anomaly of TX90p of (d) Anomaly −30
2 2 III 1960 1970 1980 1990 2000 2010 2 4 4 40 8 6 y = 0.0866x + 7.5891 I IV: 0 30 R2 = 0.2169 (p < 0.05) 20 2 10 0 2 4 1086 12 0 −10 TX90p (days/decade) −20 −1.00 2.00 –0.00 –4.44 −30 0.00 4.44 –0.75 –13.00 1960 1970 1980 1990 2000 2010 0.75–2.00 40 40 50 I: y = 0.1309x + 6.1647 II: y = 0.1070x + 6.7135 III: y = 0.1168 + 5.9582 30 2 30 40 R = 0.247 (p > 0.05) R2 = 0.2355 (p < 0.05) R2 = 0.3181 (p < 0.05) 20 20 30 20 10 10 10 0 0 0 −10 −10 −10 −20 −20 −20 Anomaly of TX90p of (d)Anomaly TX90p of (d) Anomaly Anomaly of TX90p of (d) Anomaly TX90p of (d) Anomaly −30 −30 −30 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (b)
Figure 10: Continued. 20 Advances in Meteorology
15 V: y = 0.0864x − 0.2311 R2 = 0.1715 (p < 0.01) 10
1 V 5 IV II 0
2 1 Anomaly (d) of WSDI −5 III 1 1960 1970 1980 1990 2000 2010 1 1 2 2 1 10 4 y = 0.0569x − 0.1185 I IV: 3 8 2 1 R = 0.2382 (p < 0.01) 1 6 1 4 051 2 3 4 2 0 WSDI (days/decade) −2 0.00 1.46–1.94 Anomaly (d) of WSDI –0.54 −4 0.54–0.94 1.94–5.45 1960 1970 1980 1990 2000 2010 0.94–1.46 24 32 15 y = 0.1341x + 0.6984 II: y = 0.0998x + 0.1625 III: y = 0.1397x − 0.4196 I: 2 2 12 2 18 R = 0.2634 (p < 0.01) 24 R = 0.1445 (p < 0.01) R = 0.1866 (p < 0.01) 9 12 16 6 3 6 8 0 0 0 −3 Anomaly of WSDI (d) WSDI of Anomaly Anomaly of WSDI (d) WSDI of Anomaly Anomaly (d) of WSDI −6 −6 −8 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (c) 8 y = 0.0243x + 33.513 0 6 V: C) ∘ R2 = 0.0621 (p > 0.05) 4 0.2 2 0.4 V 0
IV 0 −2 II 0.4 −4 0.2 0 TXx of ( Anomaly 0.4 −6
0.2 III 1960 1970 1980 1990 2000 2010 0 0.2 6 0.6 1 IV: y = 0.0050x + 35.273
I 0 0.4 2 0 C) R = 0.0164 (p > 0.05) ∘ 4
0 2
0 0.2 0.80.60.4 1.41.21 1.6 0
∘ TXx ( C/decade) −2 −0.09–0.00 0.21–0.38 TXx of ( Anomaly −4 0.00 0.38 –0.09 –1.61 1960 1970 1980 1990 2000 2010 0.09 –0.21 6 6 5 y = 0.0234x + 34.4 III: y = 0.0226x + 33.207 I: y = 0.0112x + 35.051 II: 2 C)
4 2 ∘ R = 0.0962 (p > 0.05)
C) 4 C) 2
∘ R = 0.0815 (p > 0.05) ∘ R = 0.0367 (p > 0.05) 3 3 2 2 1 0 0 0 −1 −2 Anomaly of TXx of ( Anomaly Anomaly of TXx of ( Anomaly
Anomaly of TXx of ( Anomaly −2 −4 −3 −3 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (d)
Figure 10: Spatial patterns of the trend slopes of warm-related indices across the BTSSR and anomaly of subregional indices curves, 1960–2014. Te black line represents the linear trends for (a) TN90p, (b) TX90p, (c) WSDI, and (d) TXx. Te redline represents the 5-year moving average. Advances in Meteorology 21 trends was much larger than those recording negative trends subregions showed negative trends (Figure 8(i), Table 3). (Figure 10(b)). Twenty-nine stations recorded signifcant Higher DTR is propitious to the accumulation of biomass increases (54.72% of the total stations), with the Wutaishan [93, 109], therefore, in the BTSSR, as DTR decreased the station in SR-III recording the strongest increasing trend, and quality of vegetation may decline. only one station recorded a decrease (Table 2; Figure 10(b)). Te slope distribution of DTR shows a spatial disparity WSDI results across the region recorded positive slopes, in the BTSSR (Figure 11(a)). A positive slope was recorded in with the largest being in the central-southern mountain area the north, east, and southwestern areas of the region, and a (Figure 10(c)). Positive trends were signifcant at 42 stations negative slope prevailed in the other areas where higher neg- (79.25%),thesebeingevenlydistributedacrosstheregion; ative slopes occurred along the northeast-northwest border there were no negative trends recorded for this variable and the central-southern mountain area. Based on data from (Figure 10(c), Table 2). Te strongest increasing trend was 35 stations (66.04% of all stations), the spatial distribution observed at the Wutaishan station in SR-III (Figure 10(c)). of DTR showed a signifcant decrease across the BTSSR TXx slope distribution results across the BTSSR showed a (� < 0.05)(Table2),correspondingtoanaverageslopeof ∘ spatial disparity. Negative slopes were identifed in the eastern −0.29 C/decade. Seven stations (13.21% of total stations) had and southern areas of SR-I, in southeastern areas of SR- a signifcant increasing trend at the 95% confdence level. A IV, and in the northern area of SR-V. Positive slopes were strong decreasing trend was observed at the Weixian station present in the other areas of the study region, with the highest (SR-III) and a strong increasing trend was observed at the positive slopes occurring in the central-southern mountain Hequ station (SR-I; Figure 11(a)). area (Figure 10(d)). Te number of stations recording positive Te temporal variation of the growing season length trends was much greater than those recording negative (GSL) across the BTSSR during the study period continuously trends; 10 stations recorded signifcant increases (18.87%), increased signifcantly by 3.20 days/decade from 1960 to 2014 with no signifcant decreases being recorded at any station (� < 0.001) (Figures 6(j) and 7(j), Table 2). Results for the (Table 2). Te strongest increasing trend was recorded at the three stages showed that GSL across the BTSSR showed an Wutaishan station (SR-III; Figure 10(d)). insignifcant positive trend during S1,S2,andS3 (Figure 7(j), Diferences in results between the minimum nighttime Table 2). Te variations in GSL trends for the diferent temperature (TNn) and the maximum daytime temperature stages in the fve subregions showed signifcant increasing (TXx) (Figures 7(d) and 7(h), Table 2) indicated that temper- trends for the majority of the subregions during the study atures across the BTSSR showed a general warming trend, a period, especially for GSL results in SR-II (with a slope of result which is consistent with global increases [102]. Te rate 3.42 days/decade; Table 3, Figures 8(j) and 11(b)). As the of warming of TXx was lower than that of TNn over all of the length of the growing season increased, the potential number subregions (Figures 8(d) and 8(h), Table 3), an occurrence of harvests and seasonal yields for perennial forage crops which is not conducive to daytime vegetation growth; this maybepromotedathighlatitudes[110].Allfvesubregions warmingwillhavetheoppositeefectonnighttimevegetation showed a mixture of negative and positive trends, with more growth by afecting carbon absorption and consumption [103, positive trends recorded during S1.DuringS2,insignifcant 104].Moreover,theincreaseofmaximumdailytemperature increasing trends occurred for all subregions and, during the may inhibit the growth of grass across the BTSSR; an increase last decade (S3), the fve subregions recorded a mixture of of the minimum temperature may promote grassland growth negative and positive trends, with more positive than negative via the compensatory efect of increasing nocturnal respira- trends recorded (Figure 8(j), Table 3). tion [105], as well as reducing desert vegetation productivity Te slope distribution of GSL shows a spatial dispar- by accelerating plant respiration [106], increasing the rate of ity across the BTSSR (Figure 11(b)). Negative slopes were nutrient metabolism, and shortening the duration of grain recorded in the eastern areas of SR-III and positive slopes flling [107, 108]. were recorded for all of the other areas in the region; the highest positive slopes occurred in the central-southern 4.2.3. Variability Indices. Te temporal variation of the diur- mountain area. Te number of stations recording positive nal temperature range (DTR) across the BTSSR showed trends was much greater than those recording negative DTR fuctuations to continuously signifcantly decrease (�< trends; 42 stations recorded signifcant increases (79.25%) ∘ 0.001), with a slope of −0.17 C/decade (Figures 6(i) and and no station recorded a signifcant decrease (Table 2). 7(i), Table 2). During the diferent stages, DTR showed A strong increasing trend was observed at the Wutaishan asignifcantnegativetrendduringS1 (with a slope of station (SR-III) and a negative trend was only found at the ∘ −0.43 C/decade; � < 0.05), and insignifcant decreases Chengde station (SR-III; Figure 11(b)). during S2 and S3 (Figure 7(i), Table 2). DTR trend variations for the diferent stages for the majority of the fve subregions 5. Conclusions (Figure 8(i), Table 3) showed a signifcant decreasing trend across the study period, especially in SR-II which had a slope Based on daily temperature data from 53 meteorological sta- ∘ of −0.23 C/decade (Figures 8(i) and 11(a), Table 3). All fve tions across the Beijing-Tianjin Sand Source Region (BTSSR), subregions showed negative trends during S1; SR-II recorded spanning 1960–2014, annual and seasonal temperature tem- ∘ a more signifcant trend, having a slope of −0.60 C/decade. poral and spatial trends were analyzed, as well as extreme During S2, insignifcant decreasing trends were recorded temperature, using the Mann-Kendall test, Sen’s slope estima- for all subregions, and during the last decade (S3)thefve tor, and linear regression. 22 Advances in Meteorology
3 V: y = −0.021x + 14.277 −0.4
C) 2 ∘ 2 R = 0.308 (p < 0.01)
0 V 1 −0.2 0 −0.2IV −1 II 0 −0.4 −0.2 ( DTR of Anomaly −0.2 −2 −0.4 III 0.2 0 1960 1970 1980 1990 2000 2010
−0.2 0.2 −0.6 −0.4 3 y = −0.0196x + 13.361 0 IV:
C) 2
I 0 ∘ −0.2 0.4 2 R = 0.3605 (p < 0.01)
0.2 0 1
0 0.4 0.2 0 −0.2 −0.4 ∘ −1 DTR ( C/decade) Anomaly of DTR ( DTR of Anomaly −0.61–−0.60 0.00–0.30 −2 −0.60–−0.30 –0.660.30 1960 1970 1980 1990 2000 2010 –0.00−0.30 3 3 3 I: y = −0.0093x + 13.43 II: y = −0.023x + 13.901 III: y = −0.017x + 12.881 C) C) 2 2
C) 2 ∘ ∘ R = 0.5126 (p < 0.01) ∘ 2 R = 0.0528 (p > 0.05) 2 2 R = 0.3175 (p < 0.01)
1 1 1
0 0 0 −1 −1 −1 Anomaly of DTR ( DTR of Anomaly Anomaly of DTR ( DTR of Anomaly Anomaly of DTR ( DTR of Anomaly −2 −2 −2 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (a) 50 V: y = 0.328x + 162.01 40 R2 = 0.2634 (p < 0.01) 30 20 V 4 10 4 0 IV −10 2 II 2 (d) GSL of Anomaly −20
2 −30 III 1960 1970 1980 1990 2000 2010 0 40 2 2 y = 0.3077x + 194.57 8 6 4 IV: 30 2 4 R = 0.2491 (p < 0.01) I 10 4 20 10 2 0 0 2 4 6 8 1210 14 −10
GSL (days/decade) (d) GSL of Anomaly −20 −0.88–0.00 3.00–10.00 −30 0.00 –1.95 10.00–15.14 1960 1970 1980 1990 2000 2010 1.95–3.00 50 40 y = 0.3422x + 183.1 30 II: y = 0.3397x + 201.98 I: y = 0.2917x + 214.62 40 2 III: 30 R = 0.2454 (p < 0.01) 2 R2 = 0.2382 (p < 0.01) 30 20 R = 0.4368 (p < 0.01) 20 20 10 10 10 0 0 −10 0 −10 −20
Anomaly of GSL (d) GSL of Anomaly −10 Anomaly of GSL (d) GSL of Anomaly Anomaly of GSL (d) GSL of Anomaly −20 −30 −40 −30 −20 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 (b)
Figure 11: Spatial patterns of the trend slopes of variability indices over the BTSSR and anomaly of subregional indices curves, 1960–2014. Te black line represents the linear trends for (a) DTR, (b) GSL. Te redline represents 5-year moving average. Advances in Meteorology 23
Resultsfrom1960to2014showedthatalong-term (2015DFR31130), the National Key Research and warming trend is evident in this region which is afecting Development Program of China (2016YFC0500801; weather conditions. Across the whole area, annual tem- 2016YFC0500804; 2016YFC0500908), the Fundamental ∘ perature recorded a signifcant increase of 0.34 C/decade, Research Funds of CAF (CAFYBB2017ZA006), and the with the trend test for annual temperature showing that the National Natural Science Foundation of China (31670715; majority of meteorological stations (96.23% of all stations) 41471029; 41271033; 41371500; 31200350; 41701249). exhibited an increasing trend at the 95% confdence level. 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