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Further Study on Identifying Anomalous Large-Scale Rainfall Regimes in Phase Space∗ REN Hongli1,3 (
), GAO Li2† (
), ZHANG Peiqun1 (
), and LI Weijing1 (
)
1 Laboratory for Climate Studies of China Meteorological Administration, National Climate Centre, Beijing 100081 2 National Meteorological Centre, Beijing 100081 3 College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 (Received October 11, 2005)
ABSTRACT Using a 40-yr daily precipitation dataset including 134 stations from 1962 to 2001, the large-scale distribution patterns of precipitation anomalies over East China are investigated in the present paper. In the phase space spanned by the first 8 EOFs generated from the 20-day low-pass filtered data, the six rainfall regimes (RRs) are identified by applying a cluster analysis method, namely, the northeastern China regime, Yellow River regime, Qinling Mountain-Huaihe River regime, Yangtze River with its south regime, South China regime, and rainless regime. Analyses show that the new RRs exhibit good persistence and evident physical sense, and excellently represent both of countrywide and regional features, which also demonstrate the inhomogeneity of multi-dimensional phase space. Furthermore, it is more important that the new RRs can describe intraseasonal dynamic characteristics of large-scale rainfall anomalies, which is the most significant difference between the new RRs and the conventional seasonal mean rainfall patterns. On the other hand, the climatic characteristics of daily distributions of the RRs events, as well as the 40-year panorama of the RRs occurring are also investigated, which further document rationality and objectivity of the RRs with intraseasonal variability, and are likely to present more helpful information for short-term climate prediction, compared with other previous classical rainfall patterns. Key words: phase space, rainfall regime, summer rainfall, cluster analysis
1. Introduction Many studies have shown that the anomalous distributions of China summer rainfall are characterized by evident spatial patterns which are called rainfall patterns (RPs) (Liao and Chen, 1981; Deng et al., 1989; Li et al., 1992; Zhao and Zhang, 1993; Chen and Wu, 1994; Wang and Wu, 1996; Wang et al., 1998; Xiong and Chen, 2001). At present, three traditional RPs, namely the north pattern, middle pattern, and south pattern, have been employed in operational short-term climate prediction (Chen and Zhao, 2000; Wang, 2001). These RPs, originated from monthly or seasonal mean anomalous precipitation, primarily reflect the interannual variability of summer or floodseason rainfall, and still statically describe the whole seasonal rainfall variations, which do not represent the features of the continuous evolution of intraseasonal
anomalous rainfall. In practice, the experiences in operational prediction show that the rainfall anomalies in summertime are usually made up of some large-scale low-frequency rainfall processes, and exhibit evident spatio-temporal low-frequency variations, which are quite important information for monthly and seasonal prediction. Consequently, the main idea in the present paper is to further understand the spatio-temporal characteristics of low-frequency rainfall in summertime, and to examine the anomalous large-scale rainfall regimes (RRs) on intraseasonal timescale. In previous work, we have put forward the idea of identifying anomalous large-scale RRs in phase space, and pointed out that the local extrema of observed probability density in phase space just are corresponding to the anomalous large-scale RRs (Ren et al., 2004). The experiments for identifying RRs have been carried out by estimating 2-dimensional probability
∗ Supported jointly by the Ministry of Science and Technology of China under Grant No. 2002ccd00100 and the National Natural Science Foundation of China under Grant No. 40375025. † corresponding author:
[email protected]
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density functions (PDFs) on EOF phase plane and determining the local extrema of PDFs, and primarily documented the inhomogeneity of phase space and the rationality of such classification. However, as two EOFs can only account for small variances, it will be quite necessary to increase the dimensionality of phase space. Thus we will identify RRs in higher phase space in this paper. In fact, we have only possessed so limited observed samples that significant PDFs statistically in high-dimensional space cannot be exactly estimated, and it is also very difficult to determine the local extrema of high-dimension PDFs. Thus we should avoid estimating PDFs and consider to directly identify the main gathering regions which are actually corresponding to the local extrema of PDFs, by employing cluster analysis method. Moreover, based on such attempt that the anomalous large-scale RRs are identified in multi-dimensional phase space, we may modify the dimensionality of phase space by changing the number of EOFs introduced in classification in order to extract the main spatio-temporal information of rainfall variations. 2. Data The precipitation data used in the present study are the daily observations over 40 yr from January 1962 to December 2001 from the National Meteorological Information Center of China Meteorological Administration. Similarly to the previous work (Ren et al., 2004), 160 basic stations have been selected for this paper. Differently, at first, as the stations over the Qinghai-Tibetan Plateau and northern desert are very sparse and inhomogeneous, only 134 stations east of 101◦ E are utilized for the classification in eastern China. Secondly, to comprehensively examine the variation features of flood-season RRs in China, we take daily data from May to September for each year, and form a 40 yr dataset with 6120 samples. Furthermore, we remove the seasonal cycle averaged over 40 yr from original data series and define precipitation anomalies (denoted as UNF). In terms of the experiments of classification on 2-D phase plane (Ren et al., 2004), we have found out that the RRs based on 20-day low-pass filter data have evidently longer persistence
than that on 10-day low-pass filter data and can more reflect the characteristics of the large-scale intraseasonal variations of RRs. Therefore, in this paper, we will utilize the standardized version of the 20-day lowpass filter data (denoted as LF20) for identifying RRs in order to eliminate climatological differences between precipitation anomalies of different regions. 3. EOF decomposition We will reduce degrees of freedom of LF20 by constructing EOF phase space (Mo and Ghil, 1988; Zhu et al., 2001; Ren et al., 2004) in the present paper, which can decrease the dimensionality of LF20 and extract the information of primary modes of precipitation anomalies. As an intercomparsion, the EOF decomposition is applied to the UNF and LF20, respectively, and statistical test has shown that all the given EOFs are significant(see Table 1). Table 1. Percentage and accumulated percentage variances (AP, %) associated with the EOFs of two datasets EOF 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 25 30
UNF Percentage 5.12 4.31 3.72 3.35 2.75 2.56 2.41 2.23 1.98 1.85 1.71 1.62 1.60 1.44 1.38 1.09 0.91 0.81
AP 5.11 9.42 13.13 16.49 19.24 21.80 24.21 26.44 28.42 30.26 31.97 33.59 35.19 36.63 38.01 43.92 48.73 52.97
LF20 Percentage 8.90 6.94 6.04 4.90 3.55 3.16 2.71 2.54 2.28 2.03 1.98 1.76 1.66 1.54 1.44 1.02 0.88 0.76
AP 8.90 15.84 21.88 26.78 30.33 33.48 36.19 38.73 41.01 43.04 45.01 46.78 48.44 49.99 51.42 57.10 61.74 65.74
It can be seen that the EOFs’ percentage variances associated with the two kinds of datasets decrease slowly from the first mode to the latter, respectively, and no any single mode is dominant. Furthermore, the percentage variances contributed by the principal EOFs of the LF20 data are higher than those of the UNF as persistent and large-scale low-frequency information including intraseasonal
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oscillation has been held in this dataset. Figure 1 presents the first two EOFs modes of LF20. In the EOF1 (Fig.1a), an out-of-phase oscillation exists between the whole northern and southern regions delimited by the Yangtze River. EOF2 (Fig.1b) is characterized by an obvious big positive anomalous center over the Yangtze-Huaihe River Valley and negative anomalies on both sides. The above two EOF modes reflect the climatological differences between the north and south, and are basically accordant with the first two modes of 160 basic stations only with a few differences for EOF2. As usual, the EOF analysis is quite sensitive to spatio-temporal domain of data, thus it is only employed to span the phase space in the current paper in order to extract the principal information of anomalous rainfall variations, whereas the actual situation of some certain EOF is not especially considered. At present, we will examine the features of RRs in the phase space spanned by primary EOFs. 4. Identifying RRs Due to the evident inhomogeneous distribution of state points in phase space, if the PDFs of state points can be exactly estimated, we just may directly identify RRs similarly to the previous work (Ren et al., 2004). But actually, we have only possessed so limited observations that significant PDFs statistically in
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high-dimension space cannot be exactly estimated. On the other hand, different from the visual and intuitionistic problems of 2-D phase plane, it is very difficult to identify the local extrema of high-dimension PDFs. We can clearly see that there exists a very big gap between statistical feasibility and theoretical expectation by estimating multi-dimensional PDFs in phase space for identifying RRs. Consequently, in this paper, to avoid estimating PDFs of state points in EOF phase space, we will employ a cluster analysis method (Mo and Ghil, 1988) and directly identify the principal gathering regions, which are actually corresponding to the local extrema of multi-dimensional PDFs. 4.1 Cluster analysis Cluster analysis has been widely applied to weather and climatology diagnosis and is a relatively mature method in theory. By utilizing “distance” or similarity relation in phase space, we may directly classify state points corresponding to observed samples in order to avoid the difficulty of estimating PDFs. Thus, a cluster analysis method (Mo and Ghil, 1988) is employed for classifications in terms of “distance” between state points in multi-dimensional phase space. In the process of clustering, we expect to utilize the observed samples dominated by the leading EOFs which stand for more large-scale information of rainfall anomalies, and to remove those governed by the EOFs with minor percentage variances. Thus, for
Fig.1. Spatial patterns of (a) EOF1 and (b) EOF2 of the LF20 (Interval of contours is 0.04).
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every sample, we will firstly examine the signal-noise (S/N) ratio between the variance of the first m principal components (PCs)(physical “signal”) and that of the rest (“noise”), where m is unknown. Secondly, the samples (denoted as SNR), whose corresponding S/N ratio is greater than 1, will be used in the cluster analysis. In terms of Table 1, m is taken as 20 here because the accumulated percentage variance of the first 8 EOFs reaches about 40 percent. We select the correlation coefficient p between state vectors in phase space as the clustering index of this cluster analysis method. Clustering criteria prescribed here are that p between state points belonging to the same cluster is greater than r1 , and p between state points corresponding to different clusters is smaller than r2 , where r1 and r2 are given parameters. On the one hand, we
expect to get the stablest RRs by adjusting r1 and r2 . On the other hand, the number of EOFs introduced is also tried to modify. It has shown from many attempts that when dimensionality of phase space increases, the number of classifications will rise, but some of them are instable, whereas when dimensionality of phase space decreases, some features cannot be displayed as information introduced reduces. Thus two aspects including stability and number minimization have been significantly considered in cluster analysis. Comprehensively compared the results of experiments, six principal clusters in the 8-D phase space, namely the RRs that are in turn denoted as R1 , R2 ,· · · , R6 , respectively, are determined under the condition of r1 =0.68 and r2 =0.20. Table 2 gives the clustering information of the RRs.
Table 2. Information of RRs identified in the 8-dimensional phase space of the LF20 Number of events with given duration days Regimes
Sample num.
Event num.
1 2 3 4 5 6 Sum
251 538 366 446 518 703 2822
45 84 76 67 83 111 466
1 8 5 10 4 5 10 42
2 7 7 10 4 9 9 46
3 2 11 11 10 16 14 64
It can be seen from Table 2 that the number of samples falling into the RRs is near to half of the total, and each RR occurs 1-3 times in each year with an average duration Td , defined as the days covered by an RR event, where the event is defined as the days from the beginning to the ending of RR of about 6 days. If the events shorter than 5 days are not taken into account, Td will near to 9 days (about 8.81 days). The average interval Ti , defined as the days from the ending of certain RR to the beginning of next RR is approximately 7 days. Furthermore, we can see that the number of the events whose duration is not shorter than 5 days is beyond half of the total. It is also noted that the persistent events, lasting for more than 10 days and possibly corresponding to the large-scale rainfall anomalies associated with famous drought and flooding events in history, occupy a considerable proportion of the total. Moreover, the above six RRs do
4 8 8 9 8 10 11 54
5 1 11 10 6 8 15 51
6 3 10 5 7 9 7 41
7 3 4 8 5 2 6 28
8 1 7 3 7 4 6 28
9 3 4 4 2 3 10 26
>10 9 17 6 14 17 23 86
Td
Ti
5.58 6.40 4.82 6.66 6.24 6.33 Avg. 6.01
8.51 6.19 6.64 6.49 6.93 6.76 6.92
not temporally overlap each other, and that is to say, any sample only belongs to single RR, which is decided by this cluster analysis method quantitatively. 4.2 Composites of RRs Every state point in phase space is corresponding to an observed sample and represents a distributed map of anomalous precipitation in actual physical space. Thus, the composite maps of six RRs in physical space, as shown in Fig.2, are obtained by arithmetically averaging their samples, respectively. Statistical u-test is also operated to these composite results, in which provided that variances are known, it is examined whether there exist significant differences between every composite value and time series at this point. R1 : Northeastern China regime (Fig.2a). This regime occurs about half times of R2 and R5 . There exist the significant positive anomalies over the whole
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Fig.2. Composite maps of the six RRs of the LF20: (a) Northeastern China regime; (b) Yellow River regime, (c) Qinling Mountain-Huaihe River regime; (d) Yangtze River with its south regime; (e) South China regime; and (f) rainless regime. The areas over the 99% confidence level of u-test are shaded. Interval of contours is 0.3.
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Northeast China and eastern North China with a center located in the mid-southern Northeast China, whereas negative anomalies lie over the Yangtze River Valley. Such features are similar to EOF1 of Tian and Yasunari (1992), but which reflects interannual variability on 2-4-year scale and central position and intensity are also different. Moreover, Northeast-North China pattern by Wang et al. (1998) is characterized by relatively small-range positive anomalies located in the south of Northeast China. R2 : Yellow River regime (Fig.2b). Similarly to the north pattern by Liao and Chen (1981), the northwest pattern by Wang et al. (1998), the north and south of Yellow-Hetao patterns by Cui et al. (2000), and the NHR regime by Ren et al. (2004), this regime exhibits a north-south out-of-phase seesaw. In Fig.2b, significant positive anomalies cover the Yellow River Valley north of 35◦ N with a center located from northern North China to eastern Northwest China, and relatively weak negative anomalies lie over the Yangtze River Valley and its south. R3 : Qinling Mountain- Huaihe River regime (Fig. 2c). This regime is very similar to the middle pattern by Liao and Chen (1981) and the BYH regime by Ren et al. (2004). We can see that in Fig.2c, significant positive anomalies lie between the Yellow River Valley and the Yangtze River Valley with a center along Qinling Mountain-Huaihe River, and negative anomalies lie on the south of the Yangtze River Valley. Similarly, the Yellow-Huaihe River Valley pattern by Wang et al. (1998) has northward positive anomaly distribution. R4 : Yangtze River with its south regime (Fig.2d). With similar features to the Jiangnan rainfall pattern by Cui et al. (2000) from the REOF analysis and the SYR regime by Ren et al. (2004), this regime has significant positive anomalies over the whole Yangtze River Valley and the Jiangnan region except southern South China, and negative anomalies over the most of northern regions with weak intensity. Such characteristics can be seen in many literatures (e.g., Deng et al., 1989; Li et al., 1992; Tian and Yasunari, 1992; Chen and Wu, 1994; Wang and Wu, 1996; Wang et al., 1998; Cui et al., 2000; Xiong and Chen, 2001). R5 : South China regime (Fig.2e). Frequency of
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this regime is slightly less than that of R2 . In Fig.2e, it can be seen that significant positive anomalies cover the whole South China and negative anomalies are located on the north of the Yangtze River, which are analogical to the Jiangnan pattern by Wang et al. (1998) and the SC regime by Ren et al. (2004). In terms of previous study (Chen and Zhao, 2000), the conventional III RP (namely the south pattern) may be further split into the two subpatterns in which summer rainbelts are located over the Jiangnan region and South China, respectively. Evidently, our two RRs including the Yangtze River with its south regime and South China regime actualize such quantitative splitting, and occur with almost identical frequency. R6 : Rainless regime (Fig.2f). This regime contains the most samples and events, and is characterized by significant negative anomalies over the most areas in China except a southwest positive corner, which often occurs during the decay period of summer monsoon or the heavy drought years in history such as 1965, 1972, and 2000. Wang et al. (1998) introduced the rainless pattern to investigate summer rainfall anomalies in eastern China. However, this pattern needs to be presented with the REOF analysis and also needs to be subjectively identified, where does not appear the large-range significant negative anomalies like Fig.2f. It can be seen that 6 RRs comprehensively reflect the large-scale distributed features of rainfall anomalies over eastern China, and describe often-appearing low-frequency anomalous rainfall regimes. Compared with the RRs from identification on 2-D phase plane (Ren et al., 2004), they have the clearer characteristics of spatial load which are not only located over the eastern regions from North to South China. As the classification of RRs is operated in a phase space spanned by 8 EOFs with 40 percent of total variances, there newly appear the northeastern China regime and rainless regime. As an intercomparsion, the some RPs by Wang et al. (1998), which are close to the RRs in the present paper, are classified by combining the REOF analysis with subjective method, where the spatial ranges and features of the northeastern China and rainless regimes are not quite evident and
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the statistical significances of the RPs still need to be tested. We have actually examined the global similarities between different distributions of rainfall anomalies by carrying out the clustering in phase space and classifying high-correlation samples into the same cluster. On the other hand, as the geographical meanings implied in the RRs’ names, there exists a significant dominant center in every RR, which can also highlight the local features of rainfall anomalies. Thus we can see that the new RRs exhibit evident physical sense, and represent both of countrywide and regional features. 5. Features of RRs distribution We compare the five RRs identified on 2-D plane by using LF20 (Ren et al., 2004) with the RRs in this paper, and find that the NHR, BYH, SYR, and SC regimes have similar spatial features to the Yellow River, Qinling Mountain-Huaihe River, Yangtze River with its south, and South China regimes, respectively. To further contrast their positions on phase plane, Fig.3 gives 2-D PDFs corresponding to the first two EOFs and positions of six RRs in the present paper. It can be seen that there do not exist very evident local extrema of PDFs, which may increase dif-
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ficulty of 2-D classification. The positions of six RRs are quite dispersive, but three RRs are located in the third quadrant, which shows that EOFs 3–8 exert an important action. In other words, the northeastern China and rainless regimes cannot be identified only by 2-D PDFs. Moreover, compared the corresponding relationship of positions on phase plane for two types of classifications, as spatial features of EOF2 in Fig.1 are almost completely contrary to those of literature by Ren et al. (2004), the 2-D positions of South China and Yellow River regimes are nearly symmetrical with respect to the EOF1-axis with those of SC and NHR regimes. But the corresponding relationships, such as between the BYH and Qinling Mountain-Huaihe River regime, as well as between the SYR and Yangtze River with its south regimes, are quite good, which shows that introducing EOFs 3–8 has an important effect on classification. To examine the intraseasonal distributed features of the RRs, Fig.4 presents the daily climate statistic of the number of each RR occurrence. It can be clearly seen that the climatic distributions of RRs-occurrence periods exhibit evident differences from each other. Both the northeastern China regime in July and August, and the Yellow River regime in July-September, are northward geographically and mostly occur during
Fig.3. Estimated probability density functions of the LF20 in 2-D phase space for (a) all of the samples and (b) the SNR data of the first 20 EOFs. Symbols “•” stand for positions of the six RRs. Interval of contours is 0.03.
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Fig.4. Number of daily 40 yr accumulation associated with the six RRs of the LF20 from May to September.
the period of summer monsoon rainfall. The Qinling Mountain-Huaihe River regime spreads around from May to the third dekad of July and the second and third dekads of September, whereas the Yangtze River with its south regime mainly appears in the first dekad of May and from June to the middle dekad of July, and South China regime basically in May-July. All the three regimes lie in East Asian monsoon region and the appearances of anomalous rainfall regimes are incompletely accordant with climatological distributions of summer monsoon rainbelts due to interactions between mid-high-latitude and low-latitude systems. The rainless regime mostly appears in May, June, and September, and indicates the most areas of China, which will become more when the Yellow River regime occurs fewer, and vice versa. Moreover, many R3 –R6 events appear in May, which shows that we should take into account May in the study of flood-season rainfall variations, and there is also similar situation in September. Thus it can be seen that the intraseasonal distributions of rainfall anomalies are climatologically featured by large variations, and any rainfall regime does not last for the whole flood-season. Consequently, when the mechanism of rainfall anomalies is investigated, we should keep our eyes on the largescale anomalous rainfall processes. It will be more reasonable to examine the climatological features of intraseasonal distributions of RRs. Table 3 gives the distributions of the six new RRs during 1962–2001, and comparisons with the two conventional RPs (Liao and Chen, 1981; Wang et al., 1998). It can be found in Table 3 that there exist several RR events in each year, which reflects intraseasonal variations of anomalous rainfall and fully demon-
strates summer rainfall anomalies made up of several large-scale low-frequency rainfall processes. Further, we can see that the number of the events lasting for no shorter than 5 days occupy a considerable proportion of the total, and the extreme events lasting for more than 10 days almost occur in every year except years 1987, 1990, and 1992 . These long-lived persistent events possibly dominate the features of rainfall patterns during the whole flood season. In fact, the holistic rainfall pattern in every flood season should be corresponding to some long-lived RR event appearing in this year, which can be satisfied in most years and shows that such rainfall patterns in these years physically objectively exist. However, there still exist few years in which the above corresponding relationship is satisfied. For example, the flood season in 1975 has been judged as the convectional II RP (Chen and Zhao, 2000) and the Yangtze River pattern (Wang et al., 1998), but actually there occur South China regime, the Yellow River and rainless regimes, which shows that the rainy situation over the whole Yangtze-Huaihe River Valley only is mathematically generated by seasonal average. Thus, there exists the localization in regarding the whole flood season as a rainfall pattern, and it is quite necessary to identify intraseasonal rainfall regimes. Moreover, Table 3 also indicates that all the RRs are characterized by obvious interannual and interdecadal variabilities besides intraseasonal variability, which will be further investigated in future work. 6. Summary As we have known, there exist evident lowfrequency variations in flood-season rainfall in China,
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Table 3. Distribution of the six new RRs in 40 years Year 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
6 new rainfall regimes 4555454512 1 2 ¯ ¯ 43 2 32 65132 ¯ ¯ ¯ 63 2 65 43231 2 223 ¯ ¯ ¯ 65565 6 3564 ¯ 6 6 365 42 626 ¯ ¯¯ ¯ 42316 64 3 2 6 2363 ¯ ¯ ¯ ¯ 66516 5 546362 36 ¯ ¯ 54566 44141 62 ¯ ¯ ¯ 453 6 5 42 6514 ¯ ¯ 236 43 2 655 2 ¯ ¯ ¯ 53 6 36 5 2 31 ¯ 54 53421 612343 2 ¯ ¯ ¯ 54316 54535613 ¯ ¯ ¯ 4565632521623 ¯ ¯ 56555 2523462 ¯ ¯ ¯ 4 5 15412 1246626 5 ¯ ¯ 35162 622662 616 ¯ 6 361233264356 ¯ 5632132643166 ¯ ¯ ¯ 6 5 621 5623265 ¯ ¯ ¯ 654 6 324 ¯ ¯ 412434 4 2 ¯ 625 32 1653 ¯ 3 233 611525 ¯ 6263121456636 53323626561 6312 462 2214 56 ¯ ¯ ¯ 645 3 236 4244 2 ¯ 363433215 225 1 4 6 3 23 3 1 6 56 ¯ ¯ 34615 22622 525 44 2436 5 ¯ 16634 5 2 15 156 ¯ ¯ 46 4 4521 3 2 6 ¯ ¯ ¯¯ ¯ ¯ 3653634 1 156 636 ¯ ¯ 66 4 65654 6 5 ¯ ¯ 1324321432666 ¯ ¯ 6456 4345 4562 ¯ ¯ 6434 5 352445 6 3 ¯ ¯ ¯ 46 6 55 252 5 6 2 ¯ ¯¯ ¯
3 traditional patterns
Wang et al. (1998)
II II I II I I III III III II II I III II I I I II III I II III II I III III I II II II I III I I III III III III II III
5 3 1 3 1 2 5 4 5 3 6 1 6 4 2 5 6 2 4 2 3 4 3 1 4 4 2 4 6 4 2 5 1 1 4 5 4 4 3 6
Note: The Arabic numerals 1-6 denote the sequence numbers of RRs or RPs. Boldfaces denote the events with duration >5 days. The events with duration > 10 days are underlined.
so when examining the distributions of large-scale anomalous rainfall, we should not only aim at monthly and seasonal mean rainfall patterns, but also investigate intraseasonal recurrent persistent large-scale anomalous rainfall regimes. Based on identifying RRs by estimating 2-D PDFs of phase plane previously,
the six intraseasonal RRs, namely the northeastern China regime, Yellow River regime, Qinling MountainHuaihe River regime, Yangtze River with its south regime, South China regime, and rainless regime, are classified by applying a cluster analysis method on the phase space spanned by the first eight principal
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EOFs. Analyses show that the new RRs exhibit evident physical sense and good persistence, and are superior to those identified by 2-D PDFs, which excellently represent both of countrywide and regional features, and also demonstrate the inhomogeneity of multi-dimensional phase space. Statistical results have also shown that the new RRs, appearing by turns in season, can reflect the lowfrequency evolutive features of large-scale anomalous rainfall, and possess behaviors different from summer monsoon rainbelts, which is very evident over the rainy southeastern China. Thus, further investigating features of intraseasonal distributions of RRs, such as relationship between the Yellow River and rainless regimes, may increase our understanding on the characteristics of spatio-temporal variations of summer low-frequency rainfall. Moreover, the occurrence situations of large-scale anomalous RRs in history have been further examined, which can document the objectivity and rationality of classifying new RRs by contrasting with previous RPs. It should be noted that the purpose of classifying intraseasonal largescale anomalous RRs in this paper just is to further understand the spatio-temporal characteristics of lowfrequency rainfall in summertime. But before the RRs are practically applied, there still exist many problems not answered. For instance, we yet do not know the physical process of generating the RRs as well as the relationships between the RRs and anomalous external forcing, which need to be studied by lots of cases and composite analyses and will also be our future work.
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nese J. Atmos. Sci., 24 (4), 519-526. (in Chinese) Deng Aijun, Tao Shiyan, and Chen Lieting, 1989: EOF analysis of flood-season rainfall in China. Chinese J. Atmos. Sci., 13 (3), 289-295. (in Chinese) Li Weijing, Chou Jifan, and Li Liansheng, 1992: Study on spatio-temporal evolution features of flood-season rainfall anomalies in China. Researches on Longrange Weather Forecasting and the Sun-Earth Relationship. Zhang Jijia and Huang Ronghui, Ed. China Oceanological Press, 53-61. (in Chinese) Liao Quansun and Chen Guiying, 1981: The west circulation in Northern Hemisphere and summer rainfall in China. Corpus of long-range weather forecasting, Edition Group of Symposium of Mid-long Range Forecasting Experience, Ed. China Meteorological Press, 103-114. (in Chinese) Mo, K. C., and M. Ghil, 1988: Cluster analysis of multipleplanetary flow regimes. J. Geophys. Res., 93 D, 10927-10952. Ren Hongli, Gao Li, Zhang Peiqun et al., 2004: Primary study on identifying anomalous large-scale rainfall regimes in phase space. Acta Meteorologica Sinica, 62 (4), 459-467. (in Chinese) Tian, S. F., and T. Yasunari, 1992: Time and space structure of interannual variations in summer rainfall over China. J. Meteor. Soc. Japan, 70, 585-595. Wang Shaowu, 2001: Advance in modern climatological studies. China Meteorological Press, 141-158. (in Chinese) Wang Shaowu, Ye Jinlin, Gong Daoyi et al., 1998: Study on the patterns of summer rainfall in eastern China. J. Appl. Meteo. Sci., 9 (supplement), 65-74. (in Chinese) Wang Xiaochun and Wu Guoxiong, 1996: Regional characteristics of summer precipitation anomalies over China identified in a spatial uniform network. Acta Meteorologica Sinica, 54 (3), 324-332. (in Chinese) Xiong Minquan and Chen Juying, 2001: Precipitation distribution pattern of China in July and its cause. Meteorological Monthly, 27 (6), 43-46. (in Chinese) Zhao Hanguang and Zhang Xiangong, 1993: The climatic classification of summer rain belt in eastern China and their circulation features. Meteorological Monthly, 19 (9), 3-8. (in Chinese) Zhu Baozhen, Zhang Ruixue, and Lin Xuechun, 2001: A preliminary study of the Meiyu long-range processes in EOF phase space. Chinese J. Atmos. Sci., 25 (6), 817-826. (in Chinese)
