Precipitation division and climate shift in China from 1960 to 2000

Transcription

1 Theor. Appl. Climatol. (2008) 93: 1 17 DOI /s Printed in The Netherlands Monsoon and Environment Research Group, School of Physics, Peking University, Beijing, China Precipitation division and climate shift in China from 1960 to 2000 W. H. Qian, A. Qin With 10 Figures Received 8 June 2005; Accepted 14 May 2007; Published online 9 November 2007 # Springer-Verlag 2007 Summary Precipitation division and climate shift in China were analysed using daily precipitation data from 486 stations for the period A modified hierarchical clustering method was used to divide China into sub-regions that have a coherent annual cycle, interannual evolutions and longterm trends. A division with 20 grouping areas can well describe the anomalous regional features seasonally, and a division with 40 grouping areas can reach a relatively stable level, theoretically for the annual and summer precipitation in mainland China. Rapid changes or interdecadal transition points of the regional precipitation series are detected by a multiple timescale t-test method. For mean annual precipitation, the interdecadal shift from a low to a high level is found to occur around 1987 in the Xinjiang region and around 1983 in Northeast China. The transition points of increasing summer rainfall in the eastern Tibetan Plateau and the lower Yangtze River are centred closely about Changes in the double-decadal precipitation are also detected for the two periods of and An increasing trend in annual precipitation is mainly found in the lower Yangtze River valley, the Xinjiang region, and Northeast China. Decreasing annual precipitation is mainly situated from the middle Yangtze River to the Yellow River. In summer and winter, the spatial pattern of precipitation anomalies shows two pairs of contrasting shifts with negative departures in North China and southern coastal China, Present address: Meteorological Bureau of Shanxi Province, Taiyuan , China Correspondence: Dr. Weihong Qian, Department of Atmospheric Sciences, Peking University, Beijing , China, qianwh@pku.edu.cn while positive departures are found in the middle and lower Yangtze River valley and Northeast China. This pattern of regional precipitation shift in China is associated with southerly monsoon flow in eastern China and westerly flow in northwestern China. 1. Introduction Regional precipitation variability is one of the major features of China s climatology. Seasonally, the rainbelt moves gradually from south to north as the monsoon advances, bringing hot and humid climate conditions to eastern China from May to August, and then retreats southward from late summer to autumn (Tao and Chen 1987; Ding 1992; Lau and Yang 1997). This seasonal advance and retreat of precipitation is not observed in western China. High variability in regional precipitation causes consecutive floods in some years, but droughts in others and also exhibits high spatial variability. Increasing occurrences of flood events have been reported along the Yangtze River associated with enhanced precipitation since 1990, including flooding events in 1991, 1996, 1998 and In the northern parts of China, however, summer precipitation has shown a decreasing trend during the same time period (Hu and Feng 2001; Huang et al. 2003).

2 2 W. H. Qian, A. Qin Many recent studies have found long-term changes in mean annual precipitation in Northwest China (Shi et al. 2003) and in summer (June, July and August) precipitation over eastern China in recent years (e.g., Weng et al. 1999; Gong and Ho 2002). Some of these studies have shown the presence of interdecadal variations in precipitation and suggested various mechanisms related to large-scale circulation changes in the East Asian summer monsoon system (Fu et al. 2004; Huang et al. 2004; Li et al. 2004; Wang et al. 2004; Yang and Lau 2004). These large-scale circulation changes may affect convective activities that determine the intensity and frequency of rainfall events. In Qian and Lin (2005), we found a decreasing trend in precipitation intensity and frequency from Northeast China to North China and the upper Yangtze River valley, but an increasing trend in Xinjiang and Southeast China. These regional patterns in precipitation are likely to be caused by the spatial configuration of large-scale circulation systems at seasonal to interannual timescales. Documenting and understanding the homogeneity of regional climate and climate change will provide useful information for dividing the country in to different climate zones or areas. In China, there has been much emphasis on the study of regional precipitation variations such as dry wet transitions in the Yangtze River valley or in North China (Yellow River valley) associated with physical mechanisms such as El Ni~no-Southern Oscillation (ENSO), Arctic Oscillation (AO), Antarctic Oscillation (AAO), snow cover and others in recent years, but the size of the domain or how many stations can well represent their regional climate changes are dependent on individual authors. As we know, consistent climate change in a region should be directly controlled by a unique circulation system and topography. Climate division can subdivide China into regions that have a coherent annual cycle, and interannual evolutions and long-term trends, and finally one can well find their physical linkages with circulation systems. Climate division or regionalisation has been developed and applied to long-term climate variations in India (Gadgil and Joshi 1983), West Africa (Anyadike 1987), Australia (Russell and Moore 1970), and the U.S. (Fowell and Fovell 1993). In China, climate division was first documented by Zhu (1931) and Tu (1936) with limited data available from a few sites. Various climate areas were identified at that time, including tropical monsoon climate, inland monsoon climate, oceanic monsoon climate, extra-tropical inland climate and extra-tropical plateau climate (Yao 1951). China Meteorological Center (1979) divided China s domain into nine climate zones, 18 climate regions and 53 climate sub-regions based on mean annual temperature and dryness index for Three summer rainfall patterns are often used in China for routine precipitation forecasting, including northern China, southern China and along the Yangtze River valley. By using a new division of the rotated empirical orthogonal function (REOF) analysis, Wang et al. (1998) provided six patterns of summer rainfall anomaly based on 111 stations in eastern China (east of 105 E and south of 47.5 N). Previous divisions considered regional similarity or consistency from precipitation or dry-wet index, atmospheric circulation and agriculture resources from a few sites. As high spatial resolution precipitation and temperature data are available for the last half century in China, more detailed and elaborate divisions are needed for climate analysis and model downscaling forecast. This paper explores the spatial and temporal precipitation variations in China using a modified hierarchal clustering method, which identifies the similarities of temporal variability within a spatial domain and dissimilarities between the domains. In this study, daily precipitation data from 486 stations derived from the National Climate Center of China and covering most of China, but with only a few stations in the Tibetan Plateau, were used. Although daily precipitation data are available from 1951, there are many unreliable and missing data at most stations before Consequently, our analysis was focused on the 41-year period of Quality control was based on the daily values of temperature and precipitation from individual stations, as described in Feng et al. (2004) and further used by Qian and Lin (2005) in a study on regional trends in recent precipitation indices in China. The remainder of this paper first describes the hierarchical clustering method and its comparison to the empirical orthogonal function (EOF) analysis in Sect. 2. The clustering number and clustering

3 Precipitation division and climate shift in China 3 feature of annual cycle precipitation are illustrated in Sect. 3. Regional variations of annual and summer precipitation are shown in Sects. 4 and 5. Interdecadal climate shifts based on precipitation data are depicted in Sect. 6. Conclusions and discussion are given in Sect Clustering method description The spatial grouping of observation sites is a common practice in climatology. In general, such grouping provides a convenient way to summarise climatic data in a concise manner and can often be justified by the relatively high spatial correlation of variables such as precipitation and temperature. Despite the widespread use of spatial grouping in climatology, the stratification of observation sites is generally subjective rather than being determined by spatial climate variations. The grouping of climate stations is referred to as climate division. In general, the spatial climate homogeneity was only an indirect consideration in the division. For most cases, the climate division boundaries reflect drainage basins (for precipitation) and crop reporting districts (for temperature). Given the long records of data summarised by climate division, these climate groupings have formed the basis of numerous studies with applications ranging from climate forecasting (Huang and Van den Dool 1993; Hanssen et al. 2005) and monitoring (Aguado et al. 1992; DeGaetano 2001) to analyses of climate trends and variability based on instrumental data (Fowell and Fovell 1993; Kunkel et al. 1999) and palaeoclimatic data (Cook et al. 1999). Different clustering methods were used in constructing the climate divisions. The aim is that these methods identify the similarities of temporal variability within a spatial domain and dissimilarities between the domains. Since the goal of the clustering method is to minimise the number of climatic groupings while maintaining some minimum degree of statistical similarity, it is possible to compare the clustering number that results from the different highest correction threshold and produces a relatively stable number of final clusters. Fowell and Fovell (1993) developed a regionalisation for climate classification in the U.S. based on monthly mean temperature and precipitation accumulations from 344 climate divisions. Finally, they proposed three candidate clustering levels that divided the country into 25, 14 or 8 regions. They also pointed out that there were many shortcomings associated with the clustering, such as those biases from the particular clustering method used, the spatial distribution of data, and the correlation between variables used to define the regions, and they also question is how many regions of the division are the best in the U.S. To meet the similarities of temporal variability within a spatial domain and dissimilarities between the domains, some purely statistical methods and the EOF analysis have been used in climate divisions. DeGaetano (2001) applied a hybrid clustering approach in the spatial grouping of climate stations in the United States. The statistical basis for grouping stations is the degree of rank-order correlation between stations. This statistical correlation is based on the nonparametric ranks of the data rather than the data values. Stations were grouped based on the correlations between all station pairs and the straightline geographic distance between sites using the complete-linkage method of cluster analysis. This method is characterized by between-station correlation in excess of some preset threshold for all station pairs and the groups are formed to minimize the geographic distance between stations. This is different from conventional cluster analysis, which seeks to form groups such that withincluster similarity is maximised, while betweencluster similarity is minimised. Besides being the purely statistical method, the EOF has been widely applied in atmospheric sciences as a convenient method for documenting spatial and temporal variability. As this method splits the spatial-temporal field into a set of orthogonal modes, it is also a powerful approach to analyse the characteristics of the spatial patterns and their temporal variations in the original field. If the modes are ordered, each successive mode explains the maximum amount of the remaining variance in the original field. Therefore, EOF provides the most efficient way to compress the spatial-temporal field in both space and time. An et al. (1995) applied the EOF in identifying two rainfall regions (the Yangtze River valley and Southeast China) using 160-station rainfall data in China for the period , where interannual variability is large but relatively homoge-

4 4 W. H. Qian, A. Qin neous. The contribution of variance of the EOF depends on the first several eigenvectors. Usually, the first several EOF modes explain about 60% 80% of the total variance of annual precipitation time series, so that it is not enough to meet a higher request for the number of division. Previous studies applied EOF analysis to highlight potential physical mechanisms associated with climate variability, such as the tropical Indian Ocean dipole (Saji et al. 1999). Recently, Dommenget and Latif (2002) indicated that caution should be noted when trying to interpret these statistically derived modes and their significance based on simple EOF analysis. An inherent limitation of the simple EOF analysis can be clearly noted from several examples showed by Dommenget and Latif (2002). From the physical view, the cluster method is better than EOF because the orthogonality may limit the kind of spatial domain in the EOF analysis. In order to simplify the physical mechanisms underlying the derived patterns, or to seek physical modes, a REOF approach has been developed (Richmann 1986). REOF supplies a new set of modes by rotating the vector space of the initial EOFs and improves the physical interpretation of the original field. It has been used in meteorological studies since the 1980s (Richmann 1986). Zhu (2003) applied the REOF on dryness= wetness series from 1470 to 1999 at 100 selected stations over the eastern part of China. The dryness=wetness division was made based on seven REOF modes and time series of each mode for the last 530 years were identified according to the total variance of original time series. The statistical clustering method is also better than REOF because the clustering method can be arbitrary in terms of how many regions one wants to identify. Another ambiguity comes from how to classify individual site when it is located in a crossing boundary of two REOF modes. From these descriptions, it is noted that the ultimate number and pattern of clusters is strongly influenced by the choice of correlation threshold for statistical method and the total variance of the first leading mode for the REOF analysis. A better and modified hierarchical clustering method should include the following aspects: the physical consistency can be understood for a pattern; the clustering number has an upper limitation depending on original time series; the groups are formed to minimise the geographic distance between stations; and the cluster analysis satisfies that within-cluster similarity is maximised, while between-cluster similarity is minimised. The hierarchical cluster analysis can be divided as coherence method and decomposition method. For the decomposition method, the largest group consists of all stations and then it decomposes up to the last step with every station as a group. The coherence method, however, is a reverse procedure with that every station consists of a group at the first step and then a larger group is combined from some smaller groups up to the largest group including all stations. This clustering procedure depends on distances between groups and their correlations. This paper uses the coherence method. The differences of hierarchical cluster analysis mainly depend on the distance measurements of two groups and their merger linkage in the clustering procedure. The methods used in measuring distance include Euclidean distance, Cosine similarity measure, Chi-square measure, Pearson correlation measure and so on. The clustering procedure includes within-group linkage, median clustering and centroid clustering. By using the statistical package for social science, the within-group linkage and the distance measure of Pearson correlation with the minimised mean within-group distance and the maximised mean between-group distance are used in this clustering procedure. The four-step procedure consists of (1) each station as an individual group, (2) merging a new group combined from those stations with the highest correlation and the shortest distance, (3) calculating mean series from the new group to form a new individual series and repeating (2), and (4) repeating (3) up to the expected number of groups reached. The correlation was calculated based on the following equation r ðx;yþ ¼ Xn ðz xi z yi =ðn 1ÞÞ ð1þ i¼1 where z xi and z yi are the standardisation twogroup series, i ¼ 1,..., n is the series length. 3. Clustering analysis To consider the modified hierarchical clustering method mentioned above, the clustering proce-

5 Precipitation division and climate shift in China 5 Fig. 1. Within-group correlation (dashed line), between-group correlation (dot-dashed line) and their difference (solid line) for (a) climatological annual precipitation cycle division, (b) interannual precipitation division and (c) summer precipitation division. The number of divisions is from 2 to 100 dure should be introduced. In this division, 486 stations covering most of China was used. Data were homogeneous to meet the division in many parts of China except in the Tibet Plateau. For the seasonal or the climatological annual cycle cluster, calendar time series from each station were calculated based on the 41-year ( ) daily precipitation series. The climatological annual cycle cluster is useful for forecasters to understand the climate difference of rainy season onset in various regions. In this case, the maximum number of division is 486 based on all stations but a question that should be answered is, what is the optimal division number? The best number should be >2 areas and <486 areas. Finally, we give a statistical hierarchical clustering procedure from groups (areas) 100 to 2 and calculate the mean within-group and betweengroup correlations for each linkage. A threshold range can be determined and compared according to the difference (r) of mean within-group and between-group correlations. Figure 1 shows the clustering number of climatological annual cycle precipitation, interannual precipitation, and interannual summer precipitation. From Fig. 1a, the within-group correlation averaged from all groups increased gradually as we move from 2 to 100 groups, while the between-group correlation averaged from all groups decreased gradually. The difference of the within-group correlation minus the betweengroup correlation also increased gradually and reached a stable maximum correlation of about 0.38 when the number of groups reached 18. For the interannual precipitation groups, the goal is to find that the anomalous precipitation, such as above normal or below normal precipitation, should be closely matched to a nearby station. In Fig. 1b, the within-group correlation averaged from all groups increased gradually as we move from 2 to 100 groups, while the between-group correlation averaged from all groups decreased rapidly as we move from 2 to 100 groups. The difference of the within-group correlation minus the between-group correlation also increased rapidly from 0.4 for 2 groups and to 0.3 for 20 groups. For the interannual precipitation, 20 groups can well describe the anomalous regional features in mainland China and 40 groups reached a relatively stable level. Correlation coefficients of the interannual summer precipitation grouping as the number of groups changed (Fig. 1c) show similar changes as in Fig. 1b. Areas of 40 groups also reached a relatively stable level. The division process of the climatological annual cycle of precipitation from 2-area division to 21-area division is shown in Fig. 2. It should be noted that this is a presentation of the reverse process as described in the previous section with clustering number. For the 2-area clustering, one area is located in Southeast China, and the rest of China as another area (Fig. 2a). For the 3-area clustering, the region in Southeast China as in 2-area clustering is divided into two separate regions: near the southern coast and in the south of the lower Yangtze River (Fig. 2b). For the 5-area clustering, one new area is added in the north

6 6 W. H. Qian, A. Qin Fig. 2. Division process of climatological annual precipitation cycle from (a) 2-area division, (b) 3-area division, (c) 5-area division, (d) 9-area division, and (e) 22-area division. In (e), heavy solid lines are the division shown in (d) and the large dots presented in 22 areas indicate the sub-regional center. Sign indicates that these stations should be neglected in grouping since they have very low correlations with the nearby grouping stations (outliers). Sign y denotes the dry area in Northwest China and several dots in the plateau. The same number from (a) to(d) indicates that those stations are divided in a region and another is found in the delta of the Yangtze River (Fig. 2c). In Fig. 2d, the 9-area clustering generated five other regions in Southwest China (numbered 7 ), North China (numbered 1 ), the eastern part of Northeast China (numbered 6 ), Northwest China (numbered 8 ), and the northwest Xinjiang region (numbered 9 ). Several outliers (marked as ) were identified in Northwest China, which show very low correlations with the nearby grouping stations. Sign y denotes the dry area in Northwest China and several dots in the plateau. Finally, the 22- area as shown in Fig. 2e generated four and three sub-regions in the Xinjiang region and Northeast China, respectively. Also, there are some additional sub-regions along the Yangtze River and in North China. As an example, the precipitation-area similarity from the climatological annual cycle is clearly observed in Fig. 2d. Seasonally, several precipitation rainbelts oriented from west to east are obviously located in the eastern part (105 E) of China and gradually moved from south to north, accompanying changes in the circulation system and frontal zone. This feature has been observed in Fig. 2c but there is a special area in

7 Precipitation division and climate shift in China 7 Fig. 3. Climatological annual cycle of precipitation rate (mm=day) in all 9 areas over the year (day 1 365). The short-dashed horizontal lines mark the 4 mm=day precipitation. The numbers in the squares indicate the calendar date when the climatological precipitation reaches its peak. The x-axis is the calendar date (day) of the year the delta region of the lower Yangtze River. This implies that there are differences in the circulation systems affecting different regions along the Yangtze River. In central west China (areas g and f ), the regional similarity of annual cycle precipitation and the differences to the eastern part are also clear. The time series of the annual cycle of precipitation amounts over the year from nine areas as identified in Fig. 2d show various timing and magnitude of peak precipitation (Fig. 3). For a discussion of rainy season length it is necessary to develop criteria to define the beginning and end of the monsoon rainy season. Several precipitation values have been used in previous studies to define the start of the rainy season, including 4mm=day (Qian and Lee 2000), 5 mm=day (Wang and Lin 2002) and 6 mm=day (Qian et al. 2002a). After comparing the climatological isochrones of 4 mm=day, 5 mm=day and 7 mm=day precipitation relative to the calendar date from 1st January it was found that the 4 mm=day isochrone is a better criterion to indicate the northernmost boundary of summer monsoon in China.

8 8 W. H. Qian, A. Qin The northernmost boundary of summer monsoon that can divide monsoon and non-monsoon regions in China is situated basically along the semi-arid zone in Northwest China. Along the southeast coast (Region a ), there is a bimodal seasonal structure of precipitation, with maximum rainfall occurring in late May to early June (around Julian day 159). This peak indicates the summer monsoon rainfall in this area. The second peak is in late July and early August (Julian day 212) caused by typhoon precipitation. In this region, the precipitation rate reaches 4 mm=day in late March and ends in September. The criterion of 6 mm=day rainfall can reliably separate two periods of precipitation in the southeast coast region. Over the south of the middle and low reaches of the Yangtze River (Region b ), there is a peak in seasonal precipitation in mid-june (Julian day 171). This peak is called the Jiangnan Meiyu ( plum rain in the south of the Yangtze River). Area c is basically located along the middle and low reaches of the Yangtze River with a peak starting in late June and early July (Julian day 179). This period is the Yangtze River Meiyu from the middle and low reaches of the Yangtze River to Japan (Qian et al. 2002a). Area d mainly covers North China with peak precipitation in late July and early August (Julian day 206). This peak indicates that summer monsoon precipitation has experienced the 4-step extension northward with separated peaks in late May, mid-june, late June and late July. Area e is located in the eastern part of Northeast China, where the seasonal process of precipitation is the same as in North China with peak precipitation in late July and early August (Julian day 212). There are similar profiles of seasonal precipitation in the two Regions f (Southwest China) and g (Northwest China), but only precipitation in Region f can reach the 4 mm=day rate after mid-may. The summer monsoon is clear from mid-may to late September in Southwest China (Region f ) with the first peak on the 181st day after the rapid increase in daily precipitation. The profile of the annual cycle of precipitation in the Xinjiang Region ( h ) is basically similar to that in e and f but is located in the non-monsoon region. Finally, a small region i along the east coast south of the Yangtze River also shows a bimodal seasonal structure of precipitation: the first largest peak in late June (Julian day 172) during the Meiyu rainy season along the Yangtze River valley and the second wet period starting from mid-august caused by typhoons in the Shanghai region. In between these two peaks there is a relatively dry period with a daily precipitation rate less than 4mm=day. The difference between the annual cycle of precipitation along the east coast south of Yangtze River and its two nearby areas further west can be seen clearly from the time sequence of precipitation intensity. The bimodal seasonal evolution of precipitation is located predominantly in the southeast coast of China, with a second precipitation peak from tropical storms. 4. Regional variation of annual precipitation We used similar methods as shown in Fig. 2 to divide China into 5 and 38 regions based on Fig. 4. Five areas (a) and 38 areas (b) are distinguished based on the analysis of total 41-year mean annual precipitation. In (a), sign indicates outliers that should be neglected in grouping due to their very low correlations with the nearby grouping stations. In (b), heavy solid lines are the division shown in (a) and the large dots indicate various areas. The same number in (a) indicates that those stations are divided in a region

9 Precipitation division and climate shift in China 9 mean annual precipitation data from Five areas include Regions a in southern China, b in central China, c in central North China, d in Northeast China, and e in the Xinjiang region (Fig. 4a). These five division areas, although approximate, show regional differences of precipitation anomalies. According to Fig. 1b, the division into 38 regions is mostly needed for the mean annual precipitation (Fig. 4b). The denseness of division areas is higher mainly in Southwest China. The detailed evolution of longterm annual precipitation should be detected based on this division. A trend analysis is used in each series of groups. A linear function of time t can be written as Y ¼ at þ b ð2þ where a and b are two constants based on the group s series. In order to find whether there are long-term fluctuations in the group s series, a cubic function of time t can be written as Y ¼ b 0 þ b 1 t þ b 2 t 2 þ b 3 t 3 ð3þ where b 0, b 1, b 2 and b 3 are three constants based on the group s series. Figure 5 shows the time series of averaged precipitation for each of the five areas with different long-term trends in mean annual precipitation during the 41-year period. In southern China (Fig. 5a), the increasing trend of annual precipitation shows a rate of 30.7 mm=decade. A rapid increase can be observed from the late 1980s to the late 1990s. There is no obvious long-term trend of annual precipitation in central China (Fig. 5b). Unusually, above-normal precipitation is only found in 1964 and there are many years of below-normal annual precipitation after The year 1998 is the second highest above-normal year of precipitation and is accom- Fig. 5. Area-averaged annual precipitation series (mm) in five areas. The five dashed lines from top to bottom in each panel indicate the times of the standard deviation, such as þ1:5 for the first upper dashed line, þ1:0 for the second dashed line, 0 for the third dashed line, and 1:0 and 1:5 for the last two dashed lines, respectively. The trend for each regional series is indicated by a solid line. The curved line indicates the fit to a cubic function. Trends of panels (a), (c) and (e) reach the 0.05 significance level

10 10 W. H. Qian, A. Qin panied by a weakening East Asian summer monsoon and strong El Ni~no event. The decreasing trend with a rate of 11.6 mm=decade is observed in central North China (Fig. 5c). Three years of above-normal precipitation are observed in the 1960s. The year 1997 experienced the most severe drought in the last 40 years in central North China. In that year, the highest record of no-flow days (226 days), no-flow times (13), noflow months (11) and no-flow length (700 km) in the lower reaches of the Yellow River coincided with the low annual precipitation in central North China (Zhao et al. 2002). The year 1997 was also a strong dipole year in the Indian Ocean (Saji et al. 1999) and this event accompanied a circulation anomaly in the East Asian monsoon region (Qian et al. 2002b). The decreasing trend with a rate of 5.8 mm=decade is also found in Northeast China, but it appears to show an oscillation in annual precipitation (Fig. 5d). Before 1982, there was a significant decreasing trend. Large fluctuations of annual precipitation can be clearly observed after 1982 in Northeast China. The increasing trend at a rate of 7.9 mm=decade is observed in the Xinjiang region with a particular transition year in 1987 (Fig. 5e). From five annual precipitation curves in Fig. 5, a contrasting trend can be noted from the two adjacent regions in central North China and the Xinjing region. This difference will be discussed from the circulation changes in the last section. Usually, the running t-test method is used to find the transition or rapid-change point of a long time series (Gu and Philander 1995; Frich et al. Fig. 6. The results of the t-test calculation with different timescales from 5 to 20 years. (a) southern China, (b) central China, (c) central North China, (d) Northeast China and (e) the Xinjiang region. Values 0.01 or 0.05 indicate significance level for the t-test. Positive values (or arrows) denote the series transition from a below-normal to above-normal precipitation, and negative values denote the reverse transition

11 Precipitation division and climate shift in China ). Let the annual precipitation series be fy i g, i ¼ 1, 2,..., n (n ¼ 41) and the two sub-series are fy i1 g, fy i2 g, i 1 ¼ 1, 2,..., m 1, i 2 ¼ 1, 2,..., m 2, m 1 þ m 2 n. Thenwehavethet-test formula y 2 y 1 t ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffir ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; ð4þ ðm 1 1Þs þðm 2 1Þs 2 1 þ 1 m 1 þ m 2 2 m 1 m 2 which estimates whether the two series of fy i1 g and fy i2 g are significantly different. Here, y 1, s 1 and y 2, s 2 are the mean values and standard deviations of the two respective series. The number of degrees of freedom is ¼ m 1 þ m 2 2. In this paper, a multiple timescale method that is similar to wavelet analysis was used in the t- test for all precipitation series. The timescales are from 5 to 20 years based on the total 41-year series. For example, if the timescale is 10 years, the first step is taking the 10 precipitation series from 1960 to 1969 to form a set of series and the next 10 precipitation series (from 1970 to 1979) to form another set of series. The two data series are used to calculate the t-test and estimate whether a rapid transition occurred between the years 1969 and The second step is taking the 10-year precipitation series from 1961 to 1970 to form a set of series and the 10 series from 1971 to 1980 to form another set of series. The t-testisusedonthetwodataseriesfrom the second step. After many steps, the last step is taking the 10-year precipitation series from 1981 to 1990 to form a set of series and the last 10-year series (from 1991 to 2000) to form another set of series. The t-test is used on the last two data series. For the timescale of 20 years, only two steps are calculated with four sets of series, and , and and The results of the t-test calculation with the different timescales from 5 to 20 years are shown in Fig. 6. The longer timescale signals are important for finding the climate transition. In southern China (Region a ), the 6 8 year timescale s transition from lower annual precipitation to higher precipitation occurred in 1992 with a 0.05 level of significance. In Northeast China ( d ), the annual precipitation transition from a lower level to a higher level occurred in 1983 at timescales from 8 to 15 years with a 0.01 level of significance. In the Xinjiang region ( e ), the precipitation decrease appeared in 1973, while the increase transition occurred in 1987 at timescales of 8 15 years with a 0.01 level of significance. This result of interdecadal precipitation transition in the Xinjiang region is the same as that in Shi et al. (2003). 5. Regional variation of summer precipitation In China, the summer precipitation in June, July and August makes a large contribution to total annual precipitation. Using the methods described in Fig. 1c 11 regions for summer precipitation (Fig. 7a) were identified. These regions include South China ( a ), Southwest China ( b ), along the Yangtze River ( c and d ), central part of China ( e ), the eastern Tibetan Plateau ( f ), Northwest China ( g ), North Fig. 7. As in Fig. 4 except for summer precipitation in (a) 9 areas and (b) 40 areas based on the analysis of the total 41-year data series. a South China, b Southwest China, c Jiang-huai River, d lower Yangtze River, e central part of China, f the eastern Tibetan Plateau, g Northwest China, h North China, j Northeast China, i southern Xinjiang region, and k northern Xinjiang region. The same number in (a) indicates that those stations are divided in a region

12 12 W. H. Qian, A. Qin: Precipitation division and climate shift in China Fig. 8. Area-averaged summer (June, July and August) precipitation series in 11 areas. Trends of all panels, except panels (g) and (k), reach the 0.05 significance level China ( h ), Northeast China ( j ) and the Xinjiang region ( i and k ). There are some regional variations in summer precipitation in these regions for the 41-year period. The 40- region division (Fig. 7b) is similar to that based on annual precipitation (Fig. 4b). During the last 41-year period, increasing trends of summer precipitation appear mainly in the lower Yangtze River (69.1 mm=decade), the Jiang-huai River (27.2 mm=decade), the central part of China (14.9 mm=decade), South China (11.7 mm=decade), Northeast China and the Xinjiang region. Decreasing trends in summer precipitation with rates of 22.9 mm=decade, 3.9 mm=decade, 4.5 mm=decade, and 10.7 mm=decade are found in North China, Northwest China, the eastern Tibetan Plateau, and Southwest China, respectively. Decreasing trends occur from Southwest China to North China along the northernmost boundary of the East Asian summer monsoon. Two areas with the strongest contrasting trends are observed in the lower Yangtze River (positive trend) and North China (negative trend). The rapid decadal change in summer precipitation can also be observed in some areas such as in South China, Northeast China and the Xinjiang region. The results of the t-test calculation with different timescales of 5 20 years for a total of 11 summer precipitation regions are shown in Fig. 9. In the area of South China ( a ), the transition of precipitation decrease was observed in 1977 with a 0.05 level of significance, and this was followed by an increase transition starting in 1990 with a 0.01 level of significance. The decrease transition in Southwest China started in 1974 at 8 10 year timescales ( b ). Increasing transitions in precipitation trend were observed

13 Fig. 9. As in Fig. 6 except for the summer (JJA) precipitation in 11 areas

14 14 W. H. Qian, A. Qin in the Jiang-huai River ( c and d ), but three rapid increases at three separate timescales can be observed clearly in the lower Yangtze River ( d ) with a 0.01 level of significance. The rapid increase in precipitation in the central part of China ( e ) and the eastern Tibetan Plateau ( f ) occurred in 1979 with a 0.01 level of significance. In the northern Xinjiang region ( k ), a rapid decrease in summer precipitation is first observed in 1973 with a 0.05 level of significance, and a rapid increase is centered about 1987 with a 0.01 level of significance. In Northeast China ( j ), the rapid increase in summer precipitation is centered at 1981 and 1984 with the 0.01 level of significance. The summer rainfall transition for the interdecadal timescale in the northern part of Xinjiang is also supported by the result of Shi et al. (2003), but the domains are different. In the work of Gong and Ho (2002), the summer precipitation shift over the Yangtze River valley was found in the late 1970s. Figure 9d indicates that the interdecadal transition only appears in the lower Yangtze River or in the delta area of the lower Yangtze River at timescales of more than 15 years since Interdecadal climate shift From the temporal trend analyses of annual and summer precipitation in different regions of China, it appears that precipitation shows an interdecadal shift around We divided the entire time period into two 20-year intervals from 1961 to 1980 and from 1981 to The regional Fig. 10. Precipitation difference (in mm) of two 20-year periods averaged for minus Large triangles! (or ~ ) and small triangles! (or ~ ) indicate that the t-test reaches the 0.01 or 0.05 significance levels, respectively. The triangles ~ and! denote a series transition from a below-normal to above-normal precipitation and from an above-normal to below-normal precipitation, respectively

15 Precipitation division and climate shift in China 15 difference in averaged precipitation during these two 20-year periods is apparent for different seasons and annually (Fig. 10). In spring, precipitation shows a decrease of mm in the Yangtze River valley and an increase of 40 mm along the southeast coast. In summer, precipitation increases up to >120 mm mainly along the Yangtze River and in Northeast China, but decreases of mm in North China, Southeast and Southwest China. In autumn, precipitation appears to decrease everywhere in China with a range of 40 to 80 mm. In winter, precipitation decreases in Southeast China and North China, while increases are found in the middle reaches of the Yangtze River. A common feature in spring and autumn is decreasing precipitation in central China, while in summer and winter decreasing precipitation in North China. The change in the spatial pattern of annual precipitation is similar to that in summer. Increasing annual precipitation was located mainly in the middle-lower reaches of the Yangtze River valley (Fig. 10e). Decreasing annual precipitation after 1980 occurs mainly in the Yellow River and the upper Yangtze River. 7. Discussion and conclusions Precipitation division and climate shift in China from 1960 to 2000 were analysed in this paper. The major conclusions and discussions are given as follows: (1) We divided China into different numbers of divisions (from 5 to 40) using modified hierarchical cluster analysis, based on the variability of daily, seasonal and annual precipitation. The dates of peak precipitation vary with climate division. For example, the bimodal structures of the annual cycle of precipitation are observed in the southeast coast. The first peak occurs in late May and June, called Maiyu ( plum rain during summer monsoon season), and the second peak is due to typhoon activity in late July. Other areas only show a single peak in the annual cycle of precipitation. (2) During the 41-year period analysed, annual precipitation increases in southern China at 30.7 mm=decade and in the Xinjiang region at 7.9 mm=decade, while decreases in central North China at 11.6 mm=decade. Summer precipitation shows increases mainly in the lower Yangtze River valley (69.1 mm=decade), the Jiang-huai River valley (27.2 mm= decade), the central part of China (14.9 mm= decade) and South China (11.7 mm=decade). Summer precipitation decreases at rates of 22.9 mm=decade in North China, 3.9 mm= decade in Northwest China, 4.5 mm=decade on the eastern Tibetan Plateau, and 10.7 mm=decade in Southwest China. Two areas with the strongest contrasting trends are in the lower Yangtze River (positive trend) and North China (negative trend). (3) The multiple timescale t-test method was applied to detect the interdecadal transition of the regional precipitation time series. For the mean annual precipitation time series, we found an abrupt positive shift in the Xinjiang region at about 1987 and in Northeast China at about For the summer precipitation time series, a positive shift around 1979 occurred in four regions at a timescale of >10 years, that is, the lower Yangtze River valley, the eastern Tibetan Plateau, Northeast China and the northern part of the Xinjiang region. (4) Clear multi-decadal and regional shifts in mean precipitation occurred during the two periods of and An interdecadal decrease in mean annual precipitation occurred along the Yellow River, North China, and Southwest China, while an increase was apparent in the lower Yangtze River valley and Northeast China. In summer, a pattern of precipitation anomaly shows two pairs of contrasting shifts with negative areas in North China and southern coastal China, while positive areas in the middle and low Yangtze River valley and Northeast China. The similar pattern of precipitation anomaly is also found in winter but significance is weaker than in summer. This paper only reveals some of the spatial and temporal characteristics of precipitation variations in China. It would also be very interesting to investigate the relationship between the spatial-temporal precipitation trends and atmospheric circulation or external forcing (Yang and Lau 2004). As we mentioned earlier, climate in China is strongly influenced by various circulation sys-

16 16 W. H. Qian, A. Qin tems. The Tibetan Plateau is located in the southwest part of China, while westerly flows influence its northern part and southerly monsoon flows influence its eastern part. In the summer half of the year, southerly moisture transport is dominant over eastern China. The yearly variations of southerly moisture transport can indicate the intensity changes of the summer monsoon rainfall in China. Interdecadal variations of moisture transport for the two periods and were identified in our recent study (Qian et al. 2006). During the first period ( ) the moisture transport was strong in eastern China and it reached the northern boundary of China. During the late period ( ) it did not reach the northern part of North China and the northern part of Northeast China. In the late period the flows with more water vapour transport were dominant from the lower Yangtze River to South Korea, while a ridge of flow was located in North China. This tendency of southerly monsoon flow can be used to explain the annual mean or summer mean drying trend in the northern part of China and the wetting trend centred along the Yangtze River. The westerly flow and its moisture transport were increasing during the late period in far northwest China. This is consistent with the trend of annual and summer precipitation increases in the Xinjiang region. Changes in the intensity of the westerly flow in the Xinjiang region and monsoon flow in eastern China may be supported by thermal contrasts such as caused by temperature variability at the interannual and interdecadal timescales in high latitudes, Tibetan Plateau and nearby oceans. Some anomalous events, such as in 1998 with the heavy flood in the Yangtze River valley and the strong El Ni~no in the eastern equatorial Pacific, and the regional long-term precipitation transition in the late 1970s accompanying the warming in the Asian high latitudes and the increased frequency of warming events in the tropical oceans, may have a causal linkage. These causes should be further investigated by climate modelling. Acknowledgments We thank several reviewers and the editor for helpful comments and suggestions that have improved the manuscript. We also thank Dr. Z. C. Yu from Lehigh University for useful comments and improving the revision. This research was supported by the National Natural Science Foundation of China (Grants and ) and the National Basic Research Program of China (No. 2006CB400504). References Aguado E, Cayan D, Riddle L, Roos M (1992) Climatic fluctuations and the timing of west coast streamflow. J Climate 5: An S, Wang SW, Wang WC (1995) A comparison between observed and GCM-simulated summer monsoon characteristics over China. J Climate 8: Anyadike RNC (1987) A multivariate classification and regionalization of west African climates. J Climatol 7: China Meteorological Center (1979) Climatological atlas of the People s Republic of China. China Atlas Press, Beijing, China, pp Cook ER, Meko DM, Stahle DW, Cleaveland MK (1999) Drought reconstructions for the continental United States. J Climate 12: DeGaetano AT (2001) Spatial grouping of United States climate stations using a hybrid clustering approach. Int J Climatol 21: Ding YH (1992) Summer monsoon precipitation in China. J Meteor Soc Japan 70: Dommenget D, Latif M (2002) A cautionary note on the interpretation of EOFs. J Climate 15: Feng S, Hu SQ, Qian WH (2004) Quality control of daily meteorological data in China, : a new dataset. Int J Climatol 24: Fowell RG, Fovell MC (1993) Climate zones of the conterminous United States defined using cluster analysis. J Climate 6: Frich P, Alexander LV, Della-Marta P, Gleason B, Haylock MK, Tank AMG, Peterson T (2002) Observed coherent changes in climatic extremes during the second half of the twentieth century. Climate Res 19: Fu GB, Chen SL, Liu CM, et al (2004) Hydro-climatic trends of the Yellow River basin for the last 50 years. Climatic Change 65(1 2): Gadgil S, Joshi NV (1983) Climate clusters of the Indian region. J Climatol 3: Gong DY, Ho CH (2002) Summer precipitation shift over Yangtze River valley in the late 1970s. Geophys Res Lett 29(10) (doi: =2001GL014523) Gu DF, Philander SGH (1995) Secular changes of annual and interannual variability in the tropics during the past century. J Climate 8: Hanssen BI, Achberger C, Benestad RE, Chen D, Forland EJ (2005) Downscaling of climate scenarios over Scandinavia. Climate Res 29: Hu Q, Feng S (2001) A southward migration of centennialscale variations of drought=flood in eastern China and the western United States. J Climate 15: Huang J, Van den Dool HM (1993) Monthly precipitationtemperature relations and temperature prediction over the United States. J Climate 6:

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