Changes in Daily Climate Extremes of Observed Temperature and Precipitation in China

ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2013, VOL. 6, NO. 5, 312 319 Changes in Daily Climate Extremes of Observed Temperature and Precipitation in China WANG Ai-Hui and FU Jian-Jian Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Beijing 100029, China Received 14 December 2012; revised 30 January 2013; accepted 1 February 2013; published 16 September 2013 Abstract Daily precipitation for 1960 2011 and maximum/minimum temperature extremes for 1960 2008 recorded at 549 stations in China are utilized to investigate climate extreme variations. A set of indices is derived and analyzed with a main focus on the trends and variabilities of daily extreme occurrences. Results show significant increases in daily extreme warm temperatures and decreases in daily extreme cold temperatures, defined as the number of days in which daily maximum temperature (T max ) and daily minimum temperature (T min ) are greater than the 90th percentile and less than the10th percentile, respectively. Generally, the trend magnitudes are larger in indices derived from T min than those from T max. Trends of percentile-based precipitation indices show distinct spatial patterns with increases in heavy precipitation events, defined as the top 95th percentile of daily precipitation, in western and northeastern China and in the low reaches of the Yangtze River basin region, and slight decreases in other areas. Light precipitation, defined as the tail of the 5th percentile of daily precipitation, however, decreases in most areas. The annual maximum consecutive dry days (CDD) show an increasing trend in southern China and the middle-low reach of the Yellow River basin, while the annual maximum consecutive wet days (CWD) displays a downtrend over most regions except western China. These indices vary significantly with regions and seasons. Overall, occurrences of extreme events in China are more frequent, particularly the night time extreme temperature, and landmasses in China become warmer and wetter. Keywords: climate extremes, temperature, rain, maximum dry/wet days Citation: Wang, A.-H., and J.-J. Fu, 2013: Changes in daily climate extremes of observed temperature and precipitation in China, Atmos. Oceanic Sci. Lett., 6, 312 319, doi: 10.3878/j.issn.1674-2834.12.0106. 1 Introduction In recent decades, climate change detection and attribution studies have focused on extreme meteorological and hydrological events such as floods and droughts (Frich et al., 2002; Wang et al., 2009, 2011), partly because such events have caused increasing losses of human life and social economy. Of all climate change indicators, temperature and precipitation or their derivative quantities are widely used in monitoring and quantifying these events. Research on climate change including observed Corresponding author: WANG Ai-Hui, wangaihui@mail.iap.ac.cn daily temperature and precipitation have been conducted globally (e.g., Frich et al., 2002; Alexander et al., 2006) and regionally (e.g., Zhai et al., 1999, 2005). Changes in extreme temperature are caused by many factors. In addition to natural factors, anthropogenic effects such as urbanization and deforestation significantly affect extreme changes (Kalnay and Cai, 2003; Hu et al., 2010). Air temperature analysis indicates that increases in global average temperature since the mid-20th century are mainly attributed to a faster increase of daily minimum temperature (T min ) than that of daily maximum temperature (T max ; Karl et al., 1993; Easterling et al., 1997; Alexander et al., 2006). The climate in China is strongly affected by the East Asian Summer Monsoon system, and the annual precipitation and temperature display temporal and spatial variability. The annual precipitation in China varies from 25 mm in the northwestern region to more than 2000 mm in the southeast. In recent decades, intensive land use changes, including increases in agricultural activities and rapid expansion of urban areas, led to substantial changes in the extreme climate (Zhou et al., 2004; Hu et al., 2010). Analyzing observed precipitation over 740 stations in China, Zhai et al. (2005) indicated that although few trends in total precipitation occurred in China during 1951 2000, distinctively regional and seasonal patterns were present. Moreover, they detected a positive relationship between total precipitation and precipitation intensity and frequency. Wang and Zhou (2005) determined that the top 5% of heavy precipitation contributed to approximately 40% 50% of total summer rainfall during 1961 2001 in eastern China and an even higher amount in northern and northwestern China. As a result, the increase in heavy precipitation over western China was likely responsible for the increase in mean annual precipitation in recent years. In this study, we analyze the most recently developed homogenized daily T min and T max temperature observation for 1960 2008 and daily precipitation for 1960 2011 over 549 stations in China. The purpose of this study is to further understand the characteristics of extreme climate variations observed during past 50 years both seasonally and regionally, and we focus our analyses on variations of extreme event occurrences. 2 Data T max and T min at 549 stations in China for 1960 2008 and the precipitation amounts at the same locations for

NO. 5 WANG AND FU: CLIMATE EXTREMES IN CHINA 313 1960 2011 were used in this study. The original sources of both precipitation and temperature are the Climate Data Center (CDC) of the National Meteorological Center (NMC) of the China Meteorological Administration (CMA). The temperature datasets have been homogenized to reduce the inconsistence due to the station relocation and other factors (Li and Yan, 2010). Although available data for precipitation was more sporadic spatially and temporally than that for temperature, the data were selected from 750 stations with relatively complete records. The numbers of missing data were less than 10% in all stations; therefore the missing values were ignored in the following analyses. All used datasets were used in previous studies and passed quality control standards (Wang and Zhou, 2005; Zhai et al., 2005). The station locations are shown in Li and Yan (2009). To facilitate regional analyses, we subdivided the entire land mass into seven regions according to climate regimes. The abbreviations of region locations were adopted from Wang (2011), which are denoted as Northeast (NE), North (N), Southeast (SE), West of Northeast (ENW), Southwest (SW), West of Northwest (WNW), and Tibet, respectively. Due to harsh environmental conditions and steep topography, station distributions in western China are very sparse. In particular no stations are present over large areas of the western Tibetan Plateau. To perform model-data comparison and statistical analysis, several previous studies interpolated the station data into a regular grid cell by using a weighted interpolation method (e.g., Alexander et al., 2006), and others conducted analysis over each station location (e.g., Zhai et al., 1999, 2005). The interpolation method inevitably introduced error, particularly over regions with complex topography and sparse stations. Therefore, in the following analyses, we performed all statistical computations at individual stations. 3 Method Climate indices are widely used for detecting trends of climate change. The Expert Team on Climate Change Detection and Indices (ETCCDI) has recommended 27 indices to describe such changes (Easterling et al., 2003). Of all indices, 16 were derived from daily temperature, including T max and T min, and others were obtained from daily precipitation. These indices were generally classified into three categories on the basis of percentile, amount, and duration. The absolute values of temperature and precipitation varied substantially among stations and days. Percentile-based indices were independent of actual quantities and facilities for performing spatial and temporal analyses because the value of the percentile varied between zero and one. In our approach, percentile was computed by using a little different algorithm from that used in the standard ETCCDI package. The procedures of our approach are described subsequently. For daily T max /T min and precipitation amount at each station for each day, the percentile was computed on the basis of annual time series for each variable such as T max /T min for 1960 2008 and precipitation for 1960 2011. At each station, we then normalized the percentile by computing the ratio of the differences between the percentile value at each step and the minimum value derived from the entire time period (e.g., 1960 2008 for temperature) against the differences between the maximum and minimum values over the entire time period. After normalization, the annual time series for the specific station for each day was between zero and one. Moreover, we adopted the following four percentile-based temperature indices from ETCCDI: warm day daily T max are greater than the 90th percentile (TX90); and cold day daily T max is less than the 10th percentile (TX10); warm night daily T min are greater than the 90th percentile (TN90); and cold night daily T max is less than the 10th percentile (TN10). In addition, the following two precipitation indices were derived: heavy precipitation the top 95th percentile of daily precipitation (RR95); and light precipitation the tail of the 5th percentile of daily precipitation (RR05). TX90 (TX10) is defined as the percentile of the daily T max greater (less) than 90 (10) percent; TN90 (TN10) is defined as the percentile of the daily T min greater (less) than 90 (10) percent; and RR95 (RR05) is defined as the percentile of daily precipitation amount greater (less) than 95 (5) percent. For each station in each year or season, we counted the numbers of daily occurrences of each index. Moreover, we computed the following two duration-based indices for precipitation: maximum length of dry days within a year, consecutive dry days (CDD); and maximum length of wet days within a year, consecutive wet days (CWD). 4 Results In this section, the annual variations and linear trends based on the above indices were computed and analyzed to determine for annual and seasonal results over the entire landmass and subregions. For easier understanding, the unit of trends was converted into days per year for the annual time series plots and days per decade for others. In the following contour plots, we used a mapping scheme in which the grid cell value represents the closest stations within a 250 km radius. If no station was available within this threshold, the grid cell values were set as missing, such as those for the western Tibetan Plateau. 4.1 Trends in extreme temperature indices, 1960 2008 Figure 1 shows annual trends in the occurrences of extreme T max and T min during 1960 2008. The trends significant at a 0.05 level were indicated at the station location. Overall, TX90 and TN90 showed uptrends over the entire country, while TX10 and TN10 displayed downtrends over most regions. Significant warm days with uptrends were reported for 56% of the stations. A small region over the low reaches of the Yellow River basin displayed a slight, insignificant downtrend. The TX10 occurrence showed trends opposite those of TX90 over most regions. In particular, approximately 29% stations in northern China showed significant cooling trends. The most prominent warming trend, TN90, occurred at night and accounted for 78% of stations with significant trends. Over some stations, the average occurrences of TN90

314 ATMOSPHERIC AND OCEANIC SCIENCE LETTERS VOL. 6 Figure 1 Annual trends in days/decade for time series of percentile derived from daily temperature extreme indices for 1960 2008: (a) warm day, TX90; (b) cold day, TX10; (c) warm night, TN90; (d) cold night, TN10. Circles indicate the stations with showing trends significant at the 0.05 level (t-test). were more than 20 days per decade. Figure 2 plots the average annual temperature indices and their linear regression lines over seven subregions and the entire country of China. All four indices showed substantial annual variations. TX90 and TN90 generally showed opposite variations. Over most stations, the number of TX10 during pre-mid-1980 was slightly larger than that of TX90. After mid-1980, the conditions reversed totally; in particular, TX90 substantially increased after mid-1990. Similar variations were apparent in both TN90 and TN10. The magnitude of TX90 was greater than that of cold days; however, both values showed high variations. The absolute trend values of night temperature extremes were generally larger than those of day temperature extremes, which implies prominent variation of the night extreme temperature. For example, the downtrends of TX90 and TX10 over the entire country were 0.41 and 0.26 days yr 1, respectively, while those of TN90 and TN10 were approximately 0.72 and 0.74 days yr 1, respectively. Among all regions, the eastern Tibetan Plateau showed the largest uptrends and downtrends of 0.56 and 0.4 days yr 1, respectively). The most remarkable warming trends of TN90, 0.95 days yr 1, occurred at the NE region, while the largest cooling trend of TN10, 0.85 days yr 1, appeared at the WNW region. Zhai and Pan (2003) used similar indices to analyze the daily T max and T min extremes for 1951 1999 on the basis of data from 200 stations and determined that the numbers of warm days in the entire country slightly decreased before mid-1980 and significantly increased after that time. This characteristic was verified by our analysis and appears in the bottom panel of Fig. 2. For further examination of the seasonal variation in these indices, Table 1 lists the annual trend of four extreme temperature indices in winter (December-January- February, DJF) and summer (June-July-August, JJA). Similar to that shown in Fig. 2, both TX90 and TN90 showed significant uptrends for most regions and the entire country, while both TN90 and TN10 displayed significant downtrends, or cooling trends, for all regions. During spring and fall, the trends for the four indices resembled those in DJF and JJA (table not shown). The NE and N regions showed relatively larger trends of TN90 and TN10 in DJF than those in other regions, while TN10 showed the largest downtrends over the WNW region in JJA. These results are generally consistent with previous studies in China (Zhai et al., 1999) and other global results (Alexander et al., 2006) that show the uptrends of

NO. 5 WANG AND FU: CLIMATE EXTREMES IN CHINA 315 Figure 2 Annual variations of the regionally averaged extreme temperature occurrences (days) and their linear regression lines for 1960 2008. Left column shows the occurrences for TX90 (black) and TX10 (red), and the right column shows those for TN90 (black) and TN10 (red). The values in the brackets are parameters for the corresponding regression function. (For example, over northeast regions for TX90, the regression function is y = 0.82x +18.18). The brackets with star symbols indicate trends significant at the 0.05 level. Table 1 Annual trends in days/decade for time series of percentile derived from daily temperature extreme indices averaged over DJF and JJA during 1960 2008. Bold values indicate trend significant at the 0.05 level (t-test). Region DJF JJA TX90 TX10 TN90 TN10 TX90 TX10 TN90 TN10 NE 0.97 1.09 2.22 2.96 1.03 0.79 1.65 2.10 0.92 2.23 2.32 1.11 0.74 1.72 1.69 N 0.83 SE 1.30 0.62 1.21 1.40 1.05 0.66 1.33 1.19 ENW 1.11 0.76 1.59 1.65 1.60 1.37 1.53 1.75 SW 0.67 0.06 1.22 1.20 1.03 0.50 1.55 1.24 WNW 1.31 0.50 1.97 1.54 1.62 0.98 1.85 2.54 Tibet 0.87 0.67 1.88 1.82 0.61 1.64 1.99 2.03 China 1.06 0.66 1.74 1.83 1.21 0.84 1.63 1.67

316 ATMOSPHERIC AND OCEANIC SCIENCE LETTERS VOL. 6 warm nights are major contributors to surface warming recorded during past decades (Karl et al., 1993; Easterling et al., 1997). 4.2 Trends in precipitation indices, 1960 2011 Figure 3 shows the trends in annual extreme precipitation events (RR95 and RR05) and the maximum durations of dry spells (CDD) and wet spells (CWD). RR95 significantly increased over northeastern China and western China and slightly decreased over large areas in eastern China, accounting for 16%. The RR5 significantly decreased over most stations in eastern China and slightly increased over western China, accounting for 27% and 2%, respectively. The spatial distribution of trends in RR95 was mostly consistent with the results of Wang and Zhou (2005) for 1961 2001, in which the extreme was defined as the top 2.5% of daily precipitation. However, Fig. 3a shows uptrends over southern and western Xinjiang province, while Wang and Zhou (2005) showed substantial downtrends over the same areas. The reason for this discrepancy could be attributed to the use of different extreme thresholds and data ranges. Yang et al. (2011) analyzed the station precipitation and discovered a significant increase of daily precipitation in southern Xinjiang province since 2000, which partly explains the aforementioned inconsistency. For frequency of RR05, most stations exhibited declining trends with the exception of those in western China. Considering the information given in Figs. 3a and 3b, we can conclude that the extreme precipitation events tended to more frequent in China during 1960 2011. The trend of CWD over large regions in eastern and southern China declined but slightly increased over western China (Fig. 3c). Significant testing of trends at each station revealed that trends at only 16% stations were significant, and 13% of those showing downtrends were located over southern and eastern China. The changes in CDD (Fig. 3d) exhibit significant uptrends over southern and eastern China and downtrends over western China and the low reaches of the Yangtze River basin. Annual variations of regional averaged indices are presented in Fig. 4. The annual variations of RR95 and RR05 trends varied as 6 20 and 4 14 days/decade in all regions, respectively. The annual variations in RR95 Figure 3 Annual trends in days/decade for time series of percentile derived from daily precipitation indices for 1960 2011: (a) heavy precipitation days (RR95); (b) light precipitation days (RR05) in wet days; (c) maximum consecutive days (CDD); (d) maximum consecutive days (CWD). Circles indicate stations with trends significant at the 0.05 level (t-test).

NO. 5 WANG AND FU: CLIMATE EXTREMES IN CHINA 317 Figure 4 Annual variations of the regionally averaged extreme temperature occurrences (days) and their linear regression lines for 1960 2011. Left column shows annual frequency of heavy precipitation (RR95, black) and light precipitation (RR05, red). Right column shows the annual frequency of the CDD (left y-axis, black) and CWD (right y-axis, red). The brackets with star symbols indicate trends significant at the 0.05 level. trends showed slight uptrends or no trends in all regions, while RR05 displayed slight downtrends. The annual CDD showed increases in trends over most regions expect WNW and the Tibetan Plateau, while CWD over all regions displayed slight downtrends. The east-northwest (ENW) region showed no trend. Over the Tibetan Plateau, however, both trends were negative, which is mainly attributed to missing values in the precipitation records over this region. Table 2 shows annual trends of time series of RR95 and RR05 over various regions for DJF and JJA. The frequencies of RR95 over all regions in DJF and most regions in JJA showed uptrends, with the exception of NE and N, and frequencies of RR05 at most station in both DJF and JJA showed downtrends. Similar to that shown in Fig. 3, the trends over some regions did not pass significance tests. The trends of CDD and CWD varied sig- nificantly among seasons and regions. Overall, the RR95 in the entire country showed significant uptrends in both DJF and JJA of 0.13 and 0.05 days/decade, respectively. The CDD tended to be prolonged, particularly in summer at 0.15 days/decade, and the CWD tended to be shortened at 0.16 days/decade. 5 Conclusions Daily extreme temperatures for 1960 2008 and precipitation for 1960 2011 over 549 stations in China were used to detect extreme climate changes. Four percentilebased temperature extreme indices TX90, TX10, TN90, and TN10 and two percentile-based precipitation indices RR95 and RR05 along with two duration-based indices CDD and CWD were adopted to represent the extreme changes. The observed data were first converted

318 ATMOSPHERIC AND OCEANIC SCIENCE LETTERS VOL. 6 Table 2 Annual trends in days/decade for time series of percentile derived from daily precipitation indices averaged over DJF and JJA during 1960 2011. Bold values indicate trend significant at the 0.05 level (t-test). DJF JJA Region RR95 RR05 CDD CWD RR95 RR05 CDD CWD NE 0.15 0.15 0.38 0.01 0.05 0.17 0.62 0.13 N 0.04 0.04 0.67 0.02 0.04 0.19 0.27 0.11 SE 0.16 0.11 0.31 0.01 0.14 0.09 0.28 0.10 ENW 0.18 0.01 0.46 0.07 0.03 0.12 0.38 0.01 SW 0.05 0.10 0.42 0.05 0.03 0.16 0.21 0.53 WNW 0.27 0.20 0.12 0.16 0.11 0.11 0.22 0.02 Tibet 0.14 0.03 0.42 0.06 0.01 0.06 0.10 0.16 China 0.13 0.06 0.15 0.01 0.05 0.13 0.14 0.16 into percentiles at each station for each day, and the occurrences of extreme events were counted on the basis of percentiles in different spatial (regional and whole landmass) and temporal (seasonal and annual) scales. Trends and annual/seasonal variations of occurrence of extreme climate events for the these indices were computed and analyzed. The occurrences of warm days (TX90) and warm nights (TN90) showed steady inclining trends in China during 1960 2008, while cold days (TX10) and cold nights (TN10) showed opposite tendencies. Time series analyses indicated substantial annual variations in the number of days for these indices over various regions. The increasing trend in TX90 (TN90) was generally larger (smaller) than the decreasing trends in TX10 (TN10). The asymmetry in changes of warm versus cold extremes was consistent those reported in other studies, which is a major characteristic of surface warming during past decades (e.g., Karl et al., 1993; Alexander et al., 2006). Analysis of the precipitation data indicated that increasing trends of RR95 accompanied by decreases in RR05 were common in most regions. Due to lower availability of spatial and temporal data for precipitation than that for temperature, trends of precipitation indices were statistically insignificant over some stations. However, results did indicate distinct spatial patterns between arid and humid regions. The maximum CDD tended to be prolonged over humid regions and shortened over arid regions such as northwestern China. However, the maximum CWD showed opposite distribution trends. Moreover, occurrences of these events varied seasonal and annually over different regions. In addition, results indicated both spatial and temporal complex changes in extreme temperatures and precipitation events, which generally suggest slightly wetter and warmer conditions for the entire country. 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