A few researches based on the 10th (90th) percentiles as thresholds had presented to assess moderate extremes in China. However,
there has been very little research reported on the occurrences of high extremes warm days (TX95p and TX99p) and cold nights
(TN05p and TN01p) according to 95th or 99th (5th or 1st) percentiles which has more directly impacts on society and ecosystem
systems. The study showed: (1) the frequencies of TX95p and TX99p averagely increased by 1.80 days
An increase in global average temperature since the mid-20th century has been observed and the warming of the climate is unequivocal (IPCC, 2013). Global warming may have increased the frequency and intensity of the extreme weather and climate events (Alexander et al., 2006; IPCC, 2012; Habeeb et al., 2015). Compared to mean temperature increase, changing in regional temperature extremes, has more directly impacts on society and ecosystem systems (McGregor et al., 2005). Extremely hot summers can drastically reduce agricultural production (Asseng et al., 2011; Farooq et al., 2011), increase energy consumption (Hadley et al., 2006), and lead to hazardous health conditions (Dematte et al., 1998; Pantavou et al., 2011). Thus, understanding and predicting the spatial and temporal variability and trends of extreme weather events is crucial for the protection of socio-economic well-being, and is also crucial for understanding extreme weather events and mitigating its regional impact.
To analyze the variations in extreme climate, Expert Team of Climate Change Detection, Monitoring and Index (ETCCDMI) defined 27 extreme temperature and precipitation indices (Klein Tank et al., 2009). Two main types of extremes indices were developed by the calculation of the number of days in a year exceeding specific thresholds that have fixed values (absolute thresholds) and thresholds that are relative value (percentile thresholds) to a base period climate (Zhang et al., 2011). These indices of the number of days above or below percentile thresholds are more suitable for spatial comparisons of extremes than those based on certain absolute thresholds (Klein Tank et al., 2009). Thus, extreme temperature indexes which based on minimum temperature below the longterm 10th percentile and/or maximum temperature above the longterm 90th percentile were widely used and published in global scale, North America, South America, Europe, Asia and Australia (Alexander et al., 2006; Bonsal et al., 2001; DeGaetano and Allen, 2002; Klein Tank and Können, 2003; Zhou and Ren, 2011; Kothawale et al., 2010; Aguilar et al., 2005; Rusticucci, 2012; Nemec et al., 2013). Most of the researches based on the 10th (90th) percentiles as thresholds set to assess moderate extremes that averagely occur 36.5 times every year (10 percentage of 365 days) rather than high impact, once or twice-in-a-year weather events. Compared to moderate extremes, the high extremes temperature that based on 5th or 1st (95th or 99th) percentiles have higher potential risks on people's health and lifestyles, the economy, society, and the environment. However, there has been very little research reported on the occurrences of high extremes warm days and cold nights according to 5th or 1st (95th or 99th) percentiles.
More than 70 % of the Earth's land area underwent a significant reduction in the number of cool nights but insignificant increase in warm days (Alexander et al., 2006). But in China, regional analyses reported that a significant reduction occurred for cool nights and a significant increase occurred for warm days (You et al., 2013; Liang et al., 2014; Wang et al., 2013; Yu and Li, 2015). Along with these regional analyses in China, the changes are much less spatially coherent even though significant trends are found in more than half of stations in the entire China mainland. Further updated studies need to amplify the spatially heterogeneous of temporal-trends on temperature extremes among different climate regions in China.
In this paper, two questions are studied in terms of temperature extreme: how the temperature extreme trends spatially distributed
in different regions in mainland China; when the change point of temperature extreme trends were happened in the annual
Daily temperature records were provided by the National Meteorological Information Center of China Meteorological Administration (CMA),
including maximum and minimum surface temperature records of China from 1 January 1960 to 31 December 2010. A series of control methods
were employed and the errors were corrected by the National Meteorological Information Center, which includes extreme value control and
consistency check (Liu and Li, 2003; li and Xiong, 2004). Finally, 591 stations which had good quality data were chosen to use to
analyze (Fig. 1). Data homogeneity was tested by the software RHtest V3
(
The spatial heterogeneity of temporal-trends need define the climate zones in China. Based on climate zones of China that calculated by using monthly temperature and precipitation data (Zhang and Yan, 2014), considering the coincidence with administrative division of province, mainland of china was regionalized into 9 climate zones as follows (Fig. 1): Northeast China (NEC: Liaoning, Jilin, Heilongjiang), North China (NC: Beijing, Tianjin, Hebei, Shaanxi, Inner Mongolia), Northwest China (NWC: Shanxi, Gansu, Ningxia), East China (EC: Shanghai, Jiangsu, Zhejiang, Anhui, Shandong), West China (WC: Xinjiang), Southwest China (SWC: Chongqing, Sichuan, Guizhou, Yunnan), South China (SC: Guangdong, Guangxi, Hainan, Fujian), Central China (CC: Jiangxi, Henan, Hunan, Hubei) and Tibetan Plateau (TP: Xizang, Qinghai).
We used 5th (95th) and 1st (99th) percentile were individually chosen to get the thresholds of the 4 indices (Table 1), which is
different with the indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) that used 10th percentile to
define the occurrences of cold nights (days) and warm days (nights). The thresholds for temperature extremes at each station are set at
the 95th and 99th percentiles of daily
Annual time-series of the indices were calculated for each station. Trends in the annual indices were calculated using linear trend estimated for each station, using all available years from 1960 to 2010. Statistical significance of the trends is evaluated at the 5 % level of significance against the null hypothesis.
The possible abrupt changes in trends of the indices have been examined by using Mann–Kendall method and 5 yr moving
This section gives an overview of time series for 4 temperature extreme indices averaged by all the stations in mainland of China
(Fig. 2). Figure 3 summarizes the results of anomalies in annual temperature extreme indices averaged by all the stations in mainland
of China. The warm days (TX95p) and hot days (TX99p) days underwent increase trends in recent 51
This section showed the trends' spatial variations of 4 indices in mainland China (Fig. 4) AND the stations' percentage of trends
and passed the significant test (
TX99p and trends TX95p have similar spatial patterns, although the trend values may be different, and the TX95P and TX99P showed an increasing trend in almost all the regions of mainland but Central China (CC) and its surrounding areas where presented a decreasing trend or insignificant trends (Fig. 4a and b). Except the junction area of North and Northwest China (NC and NWC), the northwest of Southwest China (SWC), and a few sites in northeast of East China (EC), most of the site was to reduce TN05p and TN01p trend (Fig. 4c and d). Both the largest increases in TX95P and TX99P frequency, and biggest decreases in TN05p and TN01p, are mostly located in similar regions in NC, NWC, WC, TP, SWC, SC north of CC, WC, especially in up reaches of Yellow River and Yangtze River, and estuary of Yangtze River.
TX95p and TX99p in more than half of the stations increased significantly, and TN05p and TN01p in more than three-quarters of stations decreased significantly (Table 2). In mainland China, more than half of the stations were significantly increasing trends in TX95p and TX99p (50.42 and 58.21 %, respectively), and 83.76 % stations of TN05p and 76.48 % stations of TN01p showed significantly downward tendencies. The percentages of significantly decrease sites in TX95p and TX99p were 3.38 and 4.74 % respectively, and significantly increase sites in TN05p and TN01p were 0.85 and 1.18 % respectively.
Except for CC where only 8.7 % stations in TX95p and 18.84 % stations in TX99p significantly increased but 11.59 % stations in TX95p and 36.23 % stations in TX99p significantly decreased, 30–75 % stations had significantly increase tendency and less than 10 % stations had significantly decrease trends among the other 8 climate zones (Table 2). More than 80 % stations in TN05p among the other 8 climate zones significantly decrease except for TP where only 39.36 % stations in TN05p significantly declined, and more than 80 % stations in TN01p among the climate zones would significantly decrease if Not including TP, NEC and EC.
The mutation in TX95p and TX99p trends were mainly in 1990s and 2000s (Table 3). The abrupt changes in trends of TX95p early occurred in the 1980s in NEC and CC, and latest occurred in the 2000s in the SWC, SC and EC, and mutations in the 1990s was NC, TP, WC and NWC. The TX99p mutation early occurred in SC and CC in the mid-1980s and latest occurred in the 2000s in the NEC and SWC and EC, and the observed abrupt changes in the 1990s was NC, TP, WC and NWC (Table 3).
All the trends values of TX99p among the climate zones after mutation were bigger than before mutation, but the trends values of TX9p in more than half the number of climate zones (NEC, NC, WC, NWC and EC) after mutation were bigger than before mutation (Table 3).
Except for TP that mutation of TN05p occurred in 2001, other climate zones' mutation of TN05p and TN01p occurred in the 1980s and the end of 1970s (Table 4). The decreasing trends of TN01p among the climate zones after mutation were shrunk than before mutation except for WC, but the decreasing trends of TN05p in more than half the number of climate zones (NC, TP, SC, NWC and EC) after mutation were shrunk than before mutation (Table 4).
The last IPCC report points out that there is an increasing concern about temperature extremes, which are expected to be more
frequent (IPCC, 2012). In mainland China, this study showed that the frequencies of TX95p and TX99p averagely increased by 1.80
days/10a and 0.62 days
This study showed the frequency of warm and hot days was an increasing trend but the cold and frozen days was a decreasing
tendency in almost everywhere except for Central China and its surrounding areas where the warm and hot days tended to
decrease. In Central China,
It is showed that warm days and hot days underwent an increase trend in recent 51 The warm days (cold days) and hot days (frozen days) showed an upward (downward) tendency in most area of China, but
Central China and its surrounding areas showed an decline tendency in warm days and hot days. The trends of warm days and hot days mutations time were in about 1990s or 2000s, but the trends of cold days and hot
days has mutated in the early and mid 1980s. The increasing trend of warm day and hot day is greater after the mutation in most
regions, which indicated that more potential risk of heatwaves in future.
S. Fang. and G. Zhou designed the research. Y. Qi and G. Han carried them out. S. Fang prepared the manuscript with contributions from all co-authors.
This work was supported by the National Natural Science Foundation of China (NSFC) (No. 41375117), the International Science and Technology Partnerships Program of China and Canada (2009DFA91900) and the National Basic Research Program of China funded by MOST China (2010CB951300).
Definition of 4 temperature indices in China.
The percentage of stations which had increase and decrease trends and passed the significant test (
Mutation years, trends before and after the mutation in TX95p and TX99p.
Mutation years, trends before and after the mutation in TN05p and TN01p.
Distribution of the weather stations and climate zones. The up purple curve lines is the Yellow River and the down purple curve lines is Yangtze River. The blue line is used to separate the different climate zones.
Time series of annual occurrences of warm days (TX95p), hot days (TX99p), cold nights (TN05p) and frozen night (TN01p) in
mainland China during 1956–2010.
Anomalies in annual
Trends' spatial distributions of temperature extreme indices among China during 1960–2010: