Theor Appl Climatol (2017) 130:933–943 DOI 10.1007/s00704-016-1934-3
ORIGINAL PAPER
Spatial and temporal changes in daily temperature extremes in China during 1960–2011 Xiangjin Shen 1 & Binhui Liu 2 & Xianguo Lu 1 & Gaohua Fan 1
Received: 21 February 2016 / Accepted: 6 September 2016 / Published online: 12 September 2016 # Springer-Verlag Wien 2016
Abstract Based on daily maximum and minimum temperature data from 437 weather stations over China, this study examined the spatiotemporal change of temperature extremes in China from 1960 to 2011. Results showed a general downward trends in the occurrence of cold days (TX10) and nights (TN10) (base period 1961–1990), but upward tendency on the occurrence of warm days (TX90) and nights (TN90), the temperatures of coldest day (TXn), coldest night (TNn), warmest day (TXx), and warmest night (TNx) in China and most climate regions. At the national scale, TX10 and TN10 have significantly decreased by −1.89 and −4.39 days/decade, and TX90 and TN90 have significantly increased by 2.49 and 4.72 days/decade from 1960 to 2011. The national average trends for TXn, TNn, TXx, and TNx were 0.28, 0.54, 0.17, and 0.27 °C/decade, respectively. The temporal changes of extremes indices showed that changes in cold (warm) relative indices may be primarily related to that of corresponding winter (summer) Tmax and Tmin, respectively. Regionally, the magnitudes of changes in extreme indices decreased from the north to south of China. However, we found significant increase of warm extremes, especially warm days and nights in Southeast China. For most climate regions, the trend magnitudes in warm days/nights were larger than that in cold days/nights, but the trend in coldest temperature was much higher than that in warmest temperature. The trend magnitudes in minimum temperature indices were larger than those
* Xiangjin Shen
[email protected]
1
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2
College of Forestry, Northeast Forestry University, Harbin 150040, China
based on daily maximum temperature, explaining the faster increase of Tmin than Tmax in China.
1 Introduction Climate change is characterized by changes of climatic variables both in mean and extremes values (Toreti and Desiato 2008). In recent years, variations and trends in extreme climate events have received considerable attention as extreme climate events can sometimes be more sensitive to climate change than the mean values (Katz and Brown 1992; Easterling et al. 1997). Extreme climate events have significant impacts on society, economy, and environment (Easterling et al. 2000; Halsnæs et al. 2015). Analysis of climate extremes is important for everyday life and climate change impact studies. In the context of global warming, there have been significant changes of extreme temperature events in most regions of the world, including China (Easterling et al. 2000; Donat et al. 2013). Many studies have investigated temperature extremes in China nationally (Zhai et al. 1999; Zhai and Pan 2003; Qian and Lin 2004; You et al. 2011; Jiang et al. 2012; Zhou and Ren 2012; Wang et al. 2014) and in many parts of China (Yan et al. 2002; Gong et al. 2004; Yang et al. 2008; You et al. 2008; Li et al. 2012; Du et al. 2013; Wang et al. 2013; Song et al. 2014; Wu et al. 2014; Yu and Li 2014; Zhang et al. 2016). However, most of these studies focused on temperature extremes at individual sites (or sometimes gridded values). It is well known that extreme temperature events are generally regional phenomena and they can occur over a long period affecting a large area (Wang et al. 2014). As climate changes have different influences on the geographically diverse region, changes in temperature extremes of China have large regional variations (Wang et al. 2013). Considering huge land area and the
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regional climatic gap in China, the variations in temperature extremes should be investigated at both national and regional scales. In this study, we used daily observation data from 437 weather stations of China during 1960–2011. By analyzing eight indices of extreme temperature, we examined the spatial and temporal changes of temperature extremes on the nationwide scale and in eight climatic regions of China. Comprehensive and detailed analyses of temperature extremes are crucial for assessing climate variation and investigating the influence of extreme temperature in China. Moreover, the study is convenient for quantitative comparison of regional research results, which contribute to better understand the changes of extreme climate.
2 Materials and methods 2.1 Data sources The dataset used in this study are the same as that of Shen et al. (2014). Based on the criteria that no more than 2 % of the data are missing and the series length is no less than 52 years, we finally selected the data of 437 stations for analysis (Fig. 1). In order to analyze and compare the spatial changes of temperature extremes in China, we divided China into eight climatic regions by longitude and latitude, as defined by many previous studies (Shen and Varis 2001; Liu et al. 2005, 2008, 2009, 2011; Shen et al. 2014). The eight climatic regions not only reflect climatic and landform conditions in China (Liu et al. 2011) but also coincide roughly with socioeconomic Fig. 1 Geographical distribution of the 437 weather stations from eight climatic regions in China
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macroregions of China (Qi et al. 2004). In general, temperatures in eight climatic regions increase from north to south, and precipitation increases from west to east (Liu et al. 2011). The annual precipitation is less than 100 mm in Northwest China but more than 1500 mm in Southeast China (Qian et al. 2009). 2.2 Definition of extreme indices We chose eight temperature indices in this study, all of which are commonly used to investigate the changes of temperature extremes (Alexander et al. 2006). Detailed definitions of eight temperature extreme indices are given in Table 1. The eight indices used in this study can be divided into two different categories: (1) relative indices, including the frequency of cold days (TX10), cold nights (TN10), warm days (TX90), and warm nights (TN90); (2) absolute indices, including the temperatures of coldest day (TXn), coldest night (TNn), warmest day (TXx), and warmest night (TNx). Absolute indices are defined as the highest (lowest) Tmax or Tmin within a year, and relative indices are counted as the number of days with temperatures exceeding (not exceeding) the 90th (10th) percentile each year at each station. In this study, the thresholds of relative indices were calculated from reference period 1961– 1990, which is a climate normal period widely used (You et al. 2008, 2011). 2.3 Data processing In order to obtain a homogenized daily dataset, we have made vigorous data assurance assessment to assure the quality and
Spatial and temporal changes in daily temperature Table 1
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Definitions of eight temperature indices used in this study
Index
Descriptive name
Definition
Units
Cold day frequency Cold night frequency Warm day frequency Warm night frequency
Days when Tmax 90th percentile of 1961–1990
Days Days Days Days
The temperature of coldest day The temperature of coldest night The temperature of warmest day The temperature of warmest night
Annual lowest Tmax Annual lowest Tmin Annual highest Tmax Annual highest Tmin
°C °C °C °C
Relative indices TX10 TN10 TX90 TN90 Absolute indices TXn TNn TXx TNx
consistency of the selected data, following the procedure described in our previous paper (Shen et al. 2014). At most weather stations, missing data are inevitable for long-term monitoring. In this study, missing data accounted for less than 0.38 % of the total records from 1960 to 2011, and most temperature data gaps were no more than 3 days. We used a simple linear interpolation algorithm to supply the missing temperature data when the missing data were less than seven consecutive days. But, when the temperature data were missing for more than seven consecutive days, a stepwise regression was used to fill the gaps (Liu et al. 2004; Shen et al. 2014). The stepwise regression was performed per 5 years, with missing stations as the dependent variable and all the stations having no missing data as the variable. We analyzed the spatial and temporal changes of Tmax, Tmin, and temperature extremes in China and eight climatic regions. The regional average variable values were computed based on the Thiessen polygon method (Nicholls 2001). The trends in temperature variables were determined by simple linear regression, and the Mann-Kendall statistical test is used to analyze the significance of trends (Mann 1945; Kendall 1975). In analyzing temporal variation of temperature variables, we applied a nine-point binomial filter for the purpose of smoothing out the year-to-year variations of the time series.
3 Results and discussion 3.1 National and regional change character of Tmax and Tmin In order to investigate the spatial and temporal changes of temperature extremes in relation with Tmax and Tmin, we first analyzed the variations in Tmax and Tmin. The annual and seasonal trends of Tmax and Tmin are shown both nationally and in all eight climatic regions (Table 2). Nationally, annual Tmax and Tmin increased by 0.21 and 0.39 °C/decade from
1960 to 2011, respectively. During the same period, both annual Tmax and Tmin increased significantly in all eight climatic regions in China. These warming trends generally decreased from north to south of China, whereas the increasing rate of Tmax in Tibetan Plateau was larger than that in Northwest China (Table 2). For the annual Tmax, we found the maximum increase in North Central China (0.28 °C/decade) and minimum increase in Southwest China (0.11 °C/decade). By contrast, the increase of annual Tmin was the greatest in Northeast China (0.54 °C/decade) and the lowest in Southwest China (0.18 °C/decade). Seasonally, both Tmax and Tmin increased significantly for China and most climatic regions. In general, increases of Tmax and Tmin were greatest in winter and lowest in summer, but with some differences in eight climatic regions (Table 2). At both national and regional scales, Tmin increased at a faster rate than Tmax, which results in the decrease of diurnal temperature range in China (Liu et al. 2004; Shen et al. 2014). In terms of temporal change, at the national scale, the variations in annual Tmax showed three different regimes: Tmax fluctuated before around 1985, rapidly increased from 1985 to the late 2000s, and then showed no significant change after that (Fig. 2). Turning to the temporal variation in annual Tmin, it showed a slightly increasing trend from the late 1960s to 1985, increased rapidly from 1985 to the late1990s, and reached a stable level after that. The temporal change in Tmax or Tmin showed different patterns in winter and summer (Fig. 2). In winter, Tmax fluctuated before around 1985, rapidly increased from 1985 to 1989, and then showed no significant change after that; Tmin showed a slightly increasing trend from the late 1960s to 1985, increased rapidly from 1985 to 1990, and reached a stable level after that. In summer, both Tmax and Tmin showed no obvious change before around 1992, but increased significantly after that. On a regional basis, regional temporal variations in Tmax and Tmin (not shown) are generally similar to the national patterns of Tmax and Tmin, respectively.
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Shen X. et al. Annual and seasonal trends (°C/decade) of Tmax and Tmin in China and the eight climatic regions
1960–2011
Annual
Winter (DJF)
Spring (MAM)
Summer (JJA)
Autumn (SON)
Tmax
Tmin
Tmax
Tmin
Tmax
Tmin
Tmax
Tmin
Tmax
Tmin
Nationwide Northeast China
0.21** 0.27**
0.39** 0.54**
0.26** 0.32
0.52** 0.67**
0.18** 0.26*
0.36** 0.61**
0.15** 0.26**
0.29** 0.43**
0.25** 0.24*
0.37** 0.45**
North China Plain East China
0.21** 0.15**
0.42** 0.26**
0.31* 0.17
0.60** 0.39**
0.20* 0.30*
0.44** 0.26**
0.15* 0.01
0.27** 0.15**
0.19* 0.19**
0.35** 0.25**
Southeast China North Central China
0.14** 0.28**
0.22** 0.45**
0.16 0.37**
0.34** 0.66**
0.08 0.22**
0.12 0.37**
0.14** 0.18**
0.17** 0.39**
0.18** 0.33**
0.23** 0.39**
Southwest China
0.11**
0.18**
0.16
0.28**
0.04
0.14**
0.08*
0.13**
0.17**
0.17**
Northwest China Tibetan Plateau
0.23** 0.26**
0.47** 0.45**
0.23 0.32**
0.63** 0.56**
0.17 0.17**
0.38** 0.42**
0.18** 0.22**
0.38** 0.37**
0.32** 0.32**
0.51** 0.46**
*P < 0.05; **P < 0.01
3.2 Trends in temperature extremes of China 3.2.1 Cold extremes (TX10, TN10, TXn, TNn) Figure 3 demonstrates the temporal trends in cold extremes across the eight climatic regions of China during 1960–2011. During the study period, the occurrence of cold days (TX10) and cold nights (TN10) has significantly decreased by −1.89 and −4.39 days/decade, respectively, across China (Fig. 3a, b). The magnitude of TN10 decrease was much larger than that of TX10 decrease, which was consistent with some previous studies of China (You et al. 2011; Zhou and Ren 2012; Ren and Zhou 2014). Regionally, similar to the national trend, both the cold days (TX10) and cold nights (TN10) decreased during 1960–2011 for all the climatic regions, with the decline rate of cold days or nights becoming slower from the north to south of China (Fig. 3a, b). However, the decreases of TX10 were not statistically significant at the 95 % confidence level except in North China Plain, North Central China, and the Tibetan Plateau. Although the number of cold days is affected by mean Tmax, the spatial pattern of annual TX10 trends is not like that of annual Tmax, but resembles that of change in winter Tmax. The seasonal changes of TX10 confirmed the largest decreasing rate in winter (−0.67 days/decade) nationwide, with no significant decrease in spring and summer (not shown). Thus, it is concluded that the decline of cold days in China mainly occurred in winter. Nationally, TXn and TNn showed an increase of 0.28 and 0.54 °C/decade, respectively, during 1960– 2011(Fig. 3c, d). Spatially, both TXn and TNn increased from 1960 to 2011 for all the eight climatic regions, with the increase rate also decreasing from the north to south of China. Similar to the TX10, we found that the increase of TXn was only significant in North China
Plain, Southwest China, and the Tibetan Plateau, which was consistent with the pattern of change in winter Tmax during the study period. It is reasonable because TXn mainly occurs in winter and change in mean Tmax has a direct impact on the TXn. Similarly, the spatial characteristics of change in TNn resemble the pattern for winter Tmin with climate region of North Central China being an exception: although the increasing trend of winter Tmin in this region was much larger than that in Southwest China, TNn showed almost the same increasing rate in these two climatic regions. It indicates that some other factors besides average Tmin may also influence the change in TXn. At both national and regional scales, the trend magnitudes in TXn (TNn) were larger than winter Tmax (Tmin), suggesting that extreme cold events in winter are more sensitive to global warming than the mean values in China. 3.2.2 Warm extremes (TX90, TN90, TXx, TNx) From 1960 to 2011, the warm day frequency (TX90) and warm night frequency (TN90) increased by 2.49 and 4.72 days/decade, respectively, across China (Fig. 4a, b). These trends were a little smaller than that of previous studies for China (You et al. 2011; Ren and Zhou 2014), which may be mainly related to different time periods. At the regional scale, both the warm day frequency (TX90) and warm night frequency (TN90) increased significantly from 1990 to 2011 for all the climate regions, except for TX90 in East China where the increase of TX90 was not significant. Following the spatial patterning trends of annual Tmax and Tmin, the increase rates of TX90 and TN90 generally decreased from the north to south of China, except for Southeast
Spatial and temporal changes in daily temperature
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Fig. 2 Time series of national average annual, winter, and summer maximum temperature and minimum temperature over the period
1960–2011. The heavy line is the result of smoothing with a 9-year binomial filter with reflected ends
China and the Tibetan Plateau where the increases of TX90 and TN90 were larger than the north regions (Fig. 4). These abnormal increases in warm days and nights may be related to urbanization effect in Southeast China, where most of China’s urbanization has occurred (Zhou et al. 2004). It is reported that extreme temperatures can be influenced by urbanization (Ren and Zhou 2014), and the influence degree of urbanization is larger for warm extremes (DeGaetano and Allen 2002). This may partly explains the large increase
of warm extremes and moderate decrease of cold extremes in Southeast China. During the study period, the national average trends for TXx and TNx were 0.17 and 0.27 °C/decade, respectively. These trends were larger than that from You et al. (2011) but were basically consistent with the trends reported by Ren and Zhou (2014). In this study, the larger increase of TNx than TXx may be associated with the faster increase of Tmin than Tmax in summer (Table 2). Spatially, both TXx and TNx
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Fig. 3 Temporal trends in cold extremes for eight climatic regions of China during 1960–2011: a TX10 (days/decade), b TN10
(days/decade), c TXn (°C/decade), d TNn (°C/decade). The national average trends are given below each map
increased from 1960 to 2011 for all the climate regions, and the increase rates also decreased from the north to south of China, except for TXx in Southeast China and the Tibetan Plateau (Fig. 4). Compared with TNx increase, the increase of TXx was smaller in each region and it was not statistically significant in East China and Northwest China. The spatial pattern of TXx trends was not like that of summer Tmax, where the nonsignificant change of summer Tmax was only found in East China. It is reported that precipitation could increase the soil moisture content and damp T max through increasing daytime evaporative cooling (Dai et al. 1999; Zhou et al. 2009). In addition, an inverse relationship between temperature extremes and soil moisture during summer is also supported by Durre et al. (2000), who showed that the summer extreme temperatures will show a rapid rise as the soil dries. In our previous study, we found that summer precipitation significantly increased in East China and Northwest China, but showed no significant
change in other climatic regions in the past 50 years (Shen et al. 2014). Thus, the increase of summer precipitation may partly account for the nonsignificant changes of TXx in these two climatic regions. 3.3 Temporal variations in temperature extremes of China 3.3.1 Cold extremes (TX10, TN10, TXn, TNn) Figure 5 shows the national annual series for indices of cold extremes in China during 1960–2011. Nationally, the occurrence of cold days (TX10) fluctuated before the mid-1980s, abruptly decreased from 1985 to 1989, and showed no obvious change after that; the occurrence of cold nights (TN10) showed decline trend from the late 1960s, decreased rapidly from 1985 to 1990, and then reached a stable level. Both the temporal changes of annual TX10 and TN10 were opposite to that of winter Tmax and T min , respectively (Fig. 2).
Spatial and temporal changes in daily temperature
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Fig. 4 Same with Fig. 3 but for warm extremes in China: a TX90 (days/decade), b TN90 (days/decade), c TXx (°C/decade), d TNx (°C/decade). The national average trends are given below each map
The correlation analysis results also showed the largest negative correlation between TX10 (TN10) and winter Tmax (Tmin) in China (Table 3). It suggests that the changes in TX10 and TN10 may be primarily related to temperature changes in winter. For the temperatures of the coldest day (TXn) and coldest night (TNn) of the year, TXn had fluctuant variations before 1985, increased abruptly from 1985 to 1996, and then turned to a slight decreasing trend after 1996; TNn increased continually from the late 1960s to 1996 and showed fluctuant variations after that. Similar to TX10 and TN10, there were largest positive correlation between TXn (TNn) and winter T max (T min ) (Table 3). However, the temporal changes of TXn and TNn were different from that of corresponding mean temperature (winter Tmax and T min), which confirms the nonlinear nature of the relationship between changes of mean temperature and the corresponding changes for extreme temperature events (Mearns et al. 1984).
3.3.2 Warm extremes (TX90, TN90, TXx, TNx) At the national scale, both the warm day frequency (TX90) and warm night frequency (TN90) showed no significant change before 1992, and then began to increase rapidly after that (Fig. 6). The temporal patterns of change in TX90 and TN90 are similar to that for summer Tmax and Tmin (Fig. 2). Seasonally, we found the largest positive correlation between TX90 (TN90) and summer Tmax (Tmin), suggesting that the changes in TX90 or TN90 may be primarily related to temperature changes in summer. For the warmest day temperature (TXx) and warmest night temperature (TNx), they also showed no obvious change before around 1992 but increased abruptly after that. Unlike TXn and TNn, the temporal patterns of TXx and TNx were consistent with the changes in corresponding mean temperatures (summer T m a x and Tmin).
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Fig. 5 National annual series for indices of cold extremes in China: TX10 (days), TN10 (days), TXn (°C), TNn (°C). The heavy line is the result of smoothing with a 9-year binomial filter with reflected ends
3.4 Comparison of warm and cold extremes We compared the magnitudes of trends in cold versus warm indices. For TX10 versus TX90, the national average trend in TX90 (2.49 days/decade) is slightly more than 1.3 times that of TX10 (1.89 days/decade), which is consistent with previous studies (You et al. 2011). Regionally, the trend magnitudes in TX90 were larger than those in TX10 for all the climate
Table 3 Correlation coefficients between extreme indices and corresponding Tmax or Tmin
TX10 TN10 TXn TNn TX90 TN90 TXx TNx
Annual
Winter
Spring
Summer
Autumn
−0.743** −0.864** 0.509** 0.741** 0.782** 0.884** 0.599** 0.786**
−0.959** −0.977** 0.587** 0.820** 0.316* 0.620** 0.246 0.548**
−0.207 −0.624** 0.197 0.550** 0.598** 0.742** 0.425** 0.693**
−0.308* −0.660** 0.277* 0.552** 0.956** 0.983** 0.828** 0.895**
−0.354** −0.572** 0.248 0.513** 0.614** 0.799** 0.422** 0.667**
*P < 0.05; **P < 0.01
regions, except for North China Plain and East China. For TN10 versus TN90, the trend magnitude in TN90 was larger than those in TN10 nationally and for all the climate regions except for Northeast China, North China Plain, and East China. The correlation analysis results also showed larger correlation between warm day/night frequency and corresponding mean temperature than cold day/night frequency (Table 2). In addition, we also found that the trend magnitudes in minimum temperature indices (TN10/TN90) were much larger than those based on maximum temperature (TX10/TX90), indicating the greater warming of temperature extremes in China occurred at night, not in daytime. For warmest temperature versus coldest temperature, the trend in TXn (TNn) was much higher than in TXx (TNx) nationally and for each of the eight climatic regions. It indicates the changes in minimum of daily maximum and minimum temperature have higher trend magnitudes than that in maximum of daily maximum and minimum temperature. Consistent with the results of relative indices, we also found that trends in minimum temperature indices (TNn and TNx) were more rapid than those based on daily maximum temperature (TXn and TXx), which may partly explain the faster increase of Tmin than Tmax in China.
Spatial and temporal changes in daily temperature
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Fig. 6 Same with Fig. 2 but for warm extremes in China: TX90 (days), TN90 (days), TXx (°C), TNx (°C). The heavy line is the result of smoothing with a 9-year binomial filter with reflected ends
4 Conclusions Based on daily maximum temperature (Tmax) and minimum temperature (Tmin) records at 437 meteorological stations over China, we calculated eight indices of extreme temperature to analyze the spatiotemporal variation in temperature extremes of China during 1960–2011. The results showed a general upward tendency on the occurrence of warm days (TX90) and warm nights (TN90), but showed downward trends in the occurrence of cold days (TX10) and cold nights (TX10) in China. In addition, the temperatures of coldest day (TXn), coldest night (TNn), warmest day (TXx), and warmest night (TNx) showed significant increasing trends nationally and for most climate regions. Nationwide, the occurrence of TX10 and TN10 have significantly decreased by −1.89 and −4.39 days/decade respectively, and the occurrence of TX90 and TN90 have increased by 2.49 and 4.72 days/decade, respectively. The national average trends for TXn,TNn, TXx, and TNx were 0.28, 0.54, 0.17, and 0.27 °C/decade, respectively, during 1960–2011. In general, both the decrease rates of two extreme indices (TX10 and TN10) and increase rates of other six extreme indices (TX90, TN90, TXn, TNn, TXx, and TNx) decreased from
the north to south of China. In Southeast China, however, we found abnormal increase of warm extremes, especially warm days and nights. The temporal changes of cold/warm day and night frequency were similar to that of corresponding winter/summer Tmax and Tmin, and temporal patterns of changes in warmest day and night temperature are similar to that for corresponding summer Tmax and Tmin. It seems that changes in cold and warm relative indices may be primarily related to temperature changes in winter and summer, respectively. However, the temporal patterns of coldest day and night temperature were different from the changes in corresponding mean temperatures. It confirms the nonlinear relationship between extreme temperature and corresponding mean temperature, and suggests that some other factors may influence extreme temperature events. For the occurrence of cold extremes versus warm extremes, the trend magnitudes in warm extremes (TX90 /TN90) were larger than those in cold extremes (TX10/TN10) for most climate regions. For coldest versus warmest day/night temperature, the trend in coldest temperature (TXn/TNn) was much higher than that in warmest temperature (TXx/TNx) nationally and for each of the eight climatic regions. In addition, the
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trend magnitudes in minimum temperature indices were larger than those based on daily maximum temperature, respectively, partly explaining the faster increase of Tmin than Tmax in China. Acknowledgments This work was supported by the National Natural Science Foundation of China (Grant No. 41601048).
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