February

1992

S.-F.

Time

and

Space

Tian

and

Structurein Summer

T. Yasunariof Interannual

RainfallBy

585

Shao-Fenover

Variations

China

Tian

Environmental Research Center, University of Tsukuba, Ibaraki 305, Japanand
Tetsuzo

Yasunari

Institute of Geoscience, University of Tsukuba, Ibaraki 305, Japan(Manuscript received 15 July 1991, in revised form 10 December 1991)

Abstract

The time and space structure of interannual fluctuations of summer rainfall (May to September)
for the period from 1951 to 1990 over China is described. First, a harmonic analysis is applied to thesummer rainfall series. The variation with periods of two to four years (hereafter, refered to 2-4 year
component) accounts for more than 40 percent of the total variance for all stations, and more than 70
percent of the total variance for 61 percent of stations. The three-year period seems most prevailent
among this period band.
An EOF (Empirical Orthogonal Function) technique is applied to the 2-4 year period series. The
first two EOF modes account for 12.8 and 10.7 percent of the total variance, respectively. EOF
mode 1 reveals the seesaw between the Yangtze River valley and the Northern part of China. The
spatial pattern of EOF mode 2 is more complicated, but it may be characterized by the oscillation
between the Mei-yu region including the Yangtze River valley, and the rest of the country. Although
the contribution proportion of the two principle modes is not high, composite maps show that theypresent the situations (before applying the EOF analysis) well. These two EOF modes exhibit large
amplitude modulations. The amplitudes or the squares of the time coefficients tend to become large
or small alternatively.

Correlation and composite analyses show that EOF mode 1 correlates with the Indian summer
monsoon and the SOI (Southern Oscillation Index). This result agrees with that of preceding studies
and suggests that the summer rainfall over China is associated with the ENSO (El Nino and Southern
Oscillation) events on the time-scale of two to four years. EOF mode 1 seems to precede the anomalyof the SOI. This result supports the previous proposals that the ENSO signals in the eastern equatorial
Pacific may originate in the central Asia or the Indian Ocean region. EOF mode 2 seems not to berelated to the Indian monsoon and the ENSO.

1. Introduction

Previous observational studies have described interannual fluctuations in rainfall in China (for example, see Wang and Zhao, 1981). Xu and Dong (1982)
found a quasi-three-year period oscillation in the
precipitation over the western part of China. Furthermore, Zhao (1986) demonstrated that a quasibiennial oscillation prevails in the eastern part ofChina, while a quasi-three-year oscillation prevails
in the western part. Many researches indicated that
there exist some links between several indices of atmospheric circulation and interannual variations of(c)1992,MeteorologicalSociety of Japan

rainfall over some regions in China. For instance,
Sha and Li (1979) found that fluctuations in rainfall
over the lower Yangtze River valley are influenced
by the subtropical high and SST (Sea Surface Temperature) in the Pacific Ocean. Recently, the influences of ENSO (El Nino and Southern Oscillation)
on rainfalls over China have been noted. Huang and
Wu (1987) found that rainfall over China is significantly influenced by the SST in the eastern equatorial Pacific. Wang and Li (1990) demonstrated
that the precipitation fluctuation over the semi-arid
region of northern China exhibits a 2-3 year oscillation and is significantly linked with the ENSO
events. However, there have been fewer investiga455

586

JournalFig.

1. Geographicallatitudeby

distribution

1 degreelongitudeof theof theMeteorologicalobservation

Societystations.

of Japan

Altitudes

Vol.

averaged

on

a box

of

70, No.

1

1 degreeare superposed.

tions so far on the interannual variations over thewhole of China. The purpose of the present study
is to acquire a basic understanding of the time andspace structure of the interannual variations of therainfall over the whole of China. The associations
with the ENSO and the Indian summer monsoon
are also discussed.
Since a considerable amount of rainfall is concentrated in the Asian summer monsoon period, thatis, in the summer half of the year in China (Xu etal., 1983; Yoshino and Chiba, 1984), we discuss only
the summer monsoon rainfall. In the present study
the summer rainfall is defined as the total amount of
rainfall during the five months from May to September.
2. Data

The monthly
precipitation
datasetfor 160 stations compiledby the State Meteorological
Bureauof China is used in this study.
The dataset
covers a periodof 40 years from 1951 to 1985.
The
geographicaldistributionof the stations
is shown in
Fig. 1. Generally,
the observation
station network isdense in the eastern part but it is sparse in the western part.
Only a few stations
are located over the
TibetanPlateau.
Figure 2 shows the distributionof the mean over the 40 years of summer
rainfall.
Much summer
rainfall occurs in the southeast
part
and gradually
decreases
toward the northwest
part.
Figure 3 shows the ratio of the standard
deviation
to the mean, which is nearly the reversed patternofFig. 2. The year-to-year
variability
is larger in thenorthwest
part and smallerin the southeastpart.
On the northernfoot of the Tibetan
Plateauthe456

Fig. 2. Spatial distribution of the long-termmean of summer rainfall (in mm).

standard

deviation

year-to-year
east part.
3. Harmonicis comparable

variability

is smaller

to the mean.

Thealso in the north-analysis

We assume that the series of theat each station
is describedby:summerrainfall

Where p0 is the long-term mean, t the time in years,
N the data length in years, (equals 40 years), pk(t)the k-th harmonic component, given by:

February

Fig.

1992

S.-F.

3. Spatial
standard
mer

distribution
deviationof theto the

ratio

mean

Tian

of thefor sum-

rainfall.

pk (t) = ak cos kt + bk sin kt,
where ak, bk, are the coefficientsfor the k-th harmonic component.
Since our interest
is mainly invariations
on the interannual
time scale, we eliminate variations
longer than 10 years by subtracting
the long-termmean and the first four harmoniccomponents.
Then the variations
withthan 10 years are describedby:periods

shorter

To examine how the rainfall belts oscillate spatially,
we use the relative values or the normalized series
X(t) instead of the absolute rainfall series P(t):xk(t)=pk(t)/√σσ is thetotalvariance

of

P(t):

If we round the period T/k for the k-th harmoniccomponent to the nearest integer number and reconstruct components for periods with two, three, fourand five to nine years by summing up the harmoniccomponents with the same integer period band, respectively, then:andT. Yasunari

587

Figure 4 shows spatial distributions of the variances for X2(t), X3 (t) and X4 (t) at each station.
The most outstanding feature may be that the variance contributed by the three-year-period component is much larger than the other two period bands
for most stations. The variance of the two-yearperiod variation is relatively larger in the northwest
part, the Yangtze River valley, the southeast coastal
region and the southwest part, but it seems to besmaller in the northeast part, the upper and the
middle reaches of the Yellow River valley. From Fig.
4b, and we can see that the three-year-period component is the most predominant in the Yellow Rivervalley, the northeast part, and the southwest part.
Larger variances can also be found in regions to thesouth of the Yangtze River. For four-year periodvariations, relatively large variance appears in theregion from the northeast part to the lower Yellow
River, regions between the upper Yellow River andthe upper Yangtze River, and the northwest part ofChina.
The predominance of the three-year and the twoyear periods is similar to the result of the spectral
analysis by Zhao (1986). He also concluded that periods of 21.2-29.2 months and 31.4-39.0 months are
predominant in the monthly precipitation of China.
There are, however, some differences between his result and ours. In his study the magnitudes of thetwo oscillations were nearly comparable, while, inours, the amplitudes or variances of three-year period are generally larger than for the two-year period. In the northeast of China the quasi-biennial
oscillation is dominant in his analysis, but the variation of three-year period is more remarkable in ours.
This inconsistency may be due to the differences inthe datasets. The datasets used in both studies arefundamentally the same, but ours is 10 years longer
than his. Furthermore, we adopted summer rainfall
here, while, in his analysis, the monthly mean was
used.
The

variancesperiodsof twoaccounted
to

four

for

by

yearsΣ4m=2Xm(t)components
and fivewithtonine years X5-9(t) are plotted in Fig. 5 (Hereafterreferred to 2-4-year and 5-9-year components). The
2-4 year component accounts for more than 40 percent of the total variance in all stations, and morethan 70 percent of the total variance for 61 percent
of stations. Therefore the 2-4 year component is
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Journalof theMeteorologicalSocietyof JapanVol. 70, No. 1

Fig. 4. Spatial distributions of variance contributed by each period band (in percentage), (a) for two years,(b) for three years and (c) for four years.

458

February

S.-F.

1992

Tian

and

T. Yasunari589

Fig. 5. Same as Fig. 2 excepting (a) for the 2-4 year period band, (b) for the 5-9 year band.

considered
as representativeof the variationsof theof summer rainfall on the interannual
time-scale
and
we only discuss it below.
4. Time-spacestructure
riod oscillationof the2-4-yearpe-

To explore the time-space structure of the predominant mode of interannual rainfall fluctuation
over the whole of the country, an EOF (Empirical
Orthogonal Function) analysis is applied to the reconstructed series of the 2-4-year component. Here,the correlation matrix is used for calculating the
eigenvalues and eigenvectors.
The spatial distributions of amplitudes of the firsttwo EOF modes are indicated in Fig. 6. The firsttwo EOF modes account for 12.8 and 10.7 percentof the total variance, respectively. Considering thefact that theoretically we should use 160 EOF components to account for the total variance, and wehave a very small observation number (40 obser-

vations) compared with the variable number (160
variables or stations), the proportions contributedby the first two modes are not so small. Figure 6ashows that a broad area with large positive deviation extends from north China to the northwest
part in EOF mode 1. A narrow band with •negative deviation is elongated along the Yangtze River.
The region to the south of the Yangtze River exhibits a moderately positive deviation. A negative deviation area also appears in northeast China.
This mode corresponds to a situation when thetime coefficient is positive/negative then the convection is suppressed/enhanced over the Yangtze
River Valley, and the most northeast part and enhanced/suppressed over the rest. The spatial pattern of this mode closely resembles that of Huang
and Wu (1987) but with the reversed sign; however,they were correlating the summer rainfall with the
SST in the eastern equatorial Pacific.
Compared with EOF mode 1, the spatial pattern
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1

Fig. 6. Spatial pattern or amplitude distribution at each station for (a) EOF mode 1 and (b) EOF mode 2
in relative units. This Unit, when multiplied with the that of time coefficients in Fig. 7, gives the ratio
to the standard deviation of the 2-4 year series. Open and black squares indicate positive and negativevalues, respectively.

of EOF mode 2 (Fig. 6b) is more complicated. Thismode, however, seems to show a seesaw between the
Mei-yu region broadly distributed in the Yangtze
River valley and the rest of the country. When thetime coefficient is positive/negative then the convection is enhanced/suppressed over the Mei-yu region
but suppressed/enhanced over the rest.
The time series, or time coefficients, of the firsttwo EOF modes are shown in Fig. 7. Both modes
show great amplitude modulations. EOF mode 1
shows a larger amplitude during the period from thelate 1950s to late 1960s and the end of the 1980s,
while smaller in the early 1950s and during the period from the end of the 1960s to the middle 1980s.
EOF mode 2 has its larger amplitudes during thefirst half of the 1950s and the period from the late1960s to the beginning of the 1980s, and its smaller
460

amplitudes in the late 1950s and 1980s. This relativities are better seen from Fig. 8. When the amplitude, or the square of the time coefficient, of mode
1 is larger, that of mode 2 is smaller and vice versa,
excepting for the years, e.g. 1963, 1968, 1972, 1973,
1980 and 1981. In other words, the two modes tend
to prevail alternatively.
In order to examine how the dominant EOF
modes represent the observed anomalies we produced the composite of normalized, trend-removed
summer rainfall X (t) for the two modes (Fig. 9). Before producing the differential composite for the positive and negative time coefficients, we have composited the positive and negative cases separately. Theyshow nearly the same patterns but with reversed
signs (not shown). We notice that the composite
patterns resemble those of the two EOF modes. This

February

1992

S.-F.

Tian

and

T. Yasunari

Fig.

591

8. Squaremodes

Fig. 7. Time series or time coefficients for
EOF modes 1(a) and 2(b).

result suggests that the two EOF modes reflect thesituations before applying the EOF analysis well.
Considering together the relationship between theamplitudes of the two EOF modes shown in Fig. 8,
we can conclude that the two modes prevail alternatively, rather than appear simultaneously.
Since the summer rainfall in China is basicallysubject to the Asian monsoon circulation system,
we compared the time series of the principal modes
derived above to the Indian monsoon rainfall index (IMR). The IMR is adopted from Parthasarathy(1987) and is defined as the area-averaged total precipitation from June to September over the wholeof India (for details see Parthasarathy and Mooley, 1978). Fluctuations with periods longer than
10 years involved in the IMR are removed and theseries obtained is then normalized. The time seriesof the IMR with a period of two to four years is prepared by the same procedure as for the China summer rainfall. Figure 10 shows the series of the IMR
with the time series of EOF modes 1(a) and 2(b) superposed. EOF mode 1 is generally correlated wellwith the Indian monsoon rainfall, while EOF inode2 does not exhibit a consistent relation. To examine this further we calculated the lag correlation between the IMR and the time coefficientsof the two
EOF modes. It is difficult to estimate the signif-of the

1 and

time

coefficientsof EOF2.

icance level because the both series are performed
with the same filter and the effective freedom number can not easily be decided. However, from Table 1 we can see that EOF mode 1 is most strongly
correlated with the IMR simultaneously with a correlation coefficient of 0.45. Negative correlations arealso found when EOF mode 1 preceeds or lags the
IMR by one year, but not equally. This result is explained by the fact that the periodicity of two to four
years is dominant, but it is not exactly two years.
Compared with EOF mode 1, the correlations between EOF mode 2 and the IMR are much smaller,
and barely reaches -0.29 when the EOF mode 2 lags
the IMR by one year. This result suggests that EOF
mode 1 is associated with the Indian monsoon, while
EOF mode 2 does not have a consistent relationshipwith it.
Furthermore, the relationships of the two principle modes to ENSO are also examined. We compared the time series of the two modes with that of
the SOI (Southern Oscillation Index), an indicatorof ENSO. Here the SOI is defined as the differenceof the monthly mean of the SLP (Sea Level Pressure) at Tahiti and Darwin. The time series of the
SOI used here is also normalized and performed bya band-pass filter, which is developed by Murakami(1979), with 0.5 response at 18 and 50 months, and
1.0 response at 30 months. Figure 11a shows thatmode 1 exhibits a good correspondence with the
SOI. Mode 1 tends to reach the extremes several
months to a half year in advance of the SOI. From
Fig. 11b, however, mode 2 does not show a consistent relationship with the SOI. To further comfirm
this relationship, we composite the anomaly of the
SOI of each three successive years referenced to thetime series of the two EOF modes. The SOI reaches
its maximum in September of the reference year,
lagging the EOF mode 1 by a month (shown by thesolid line) and reaches its minimum in the following January (shown by the dashed line), lagged the
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Journalof theMeteorologicalSocietyof JapanVol. 70, No.

1Fig. 9. Differential composite of the normalized, trend-removed summer rainfall X (t) or the mean of all
X(t) + and -X(t)-.
X(t)+ and X(t)- denote the values of X(t) when the time coefficientof EOF
modes 1(a) or 2(b) is above +1.0 and below -1.0, respectively. Open and black squares indicate positive
and negative values, respectively.
EOF mode 1 by a half year (Fig. 12). The composite of the SOI shows a nearly inverse anomaly
when the time coefficient of EOF mode 1 takes
the opposite signs. This result is essentially consistent with the finding by Huang and Wu (1987).
They reported that in the summers of the developing stage of El Nino, floods occur in the Yangtze
River and Huaihe River valleys, and droughts occur
in the Yellow River valley. Additionally, by comparing the spatial patterns, we can conclude that our
EOF mode 1 is basically identical with the correlation pattern of Huang and Wu (1987), and it isassociated with the ENSO events. It is noteworthy
that the composite of the SOI shows reversed signs
in the previous and the following years referenced to
the years when the EOF mode 1 reaches is extremes.
This fact is also explained by the periodicity of two
1462

to four years.
However,
althoughthe composite
ofthe SOI by the EOF mode 2 reaches
its maximumof 0.32 in the Januaryof the previous
year, falls andthen turns rapidly negative,no significant
difference
is found when the EOF mode 2 changes its sign. It
seems that the EOF mode 2 does not have a consistent relationshipwith the SOI.
5. Discussion

As described above, our EOF mode 1 is essentially identical with the correlation pattern of Huang
and Wu (1987), and is associated with the El Nino
events. From the present and previous studies there
is no doubt that the Asian monsoon circulation including the Indian summer monsoon, the summer
rainfall over China, and the ENSO events are coupled on the 2-4 year time scale. From the analy-

February

1992

S.-F.

Tian

and

T. Yasunari

Table
the

593

1. Lagtime

2. Lags

correlations

betweenIMR

andcoefficientsof EOF

modes

1 and

-1

indicate

that

the

IMR

coefficients,

re-

and

+1

preceeds
and lags the time
spectively,by one year.

Fig. 10. Time sequences of the Indian monsoon rainfall index (measured by the standard deviation) with that of (a) EOF mode
1 and (b) EOF mode 2 superimposed.

sis of the global SLP using a complex EOF technique, Barnett (1985) proposed that large SLP signals associated with the Southern Oscillation andmonsoon may have their origin in a SLP/snow feedback loop that acts over the region from Siberia to
India. Yasunari (1987) also found that the significant ENSO-associated signals in the SLP first appear over the Indian Ocean, and seem to originate
from central Asia or Eurasia. Recently, from extensive comparison of the Indian monsoon, SWT(Sea Water Temperature) in the Western Pacific and
the SOI, Yasunari (1990) suggested that the Asianmonsoon may have an active role rather than a passive role in the interannual variability, including the
ENSO events, of the coupled atmosphere/ocean system over the tropical Pacific. Huang and Wu (1987)
emphasized the influence of the ENSO events in the
explanation of the association between the summer
rainfall over China and ENSO events. We also consider that the Asian monsoon circulation is coupledwith the ENSO, but it may be more rational to consider that anomalies in the Asian monsoon circulation lead the ENSO events and may be a trigger forthem. In fact, Huang and Wu (1987) also indicatedthat the summer rainfall is strongly correlated with

Fig. 11. Same as Fig. 9 excepting for the SOI(measured by the standard deviation).

the SST in the eastern equatorial Pacific in the developing stage of the ENSO events. Further evidence may be found from the result of Meehl (1987).
He introduced a concept of a strong/weak monsoon year, and showed a dynamically coupled oceanatmosphere system in the Indian-Pacific region to
be involved in producing Southern Oscillation-typesignals in the atmosphere and ocean in these setsof years, with extremes in the system being manifested as Warm and Cold events. In other words,
after a strong/weak monsoon, a Cold/Warm event
may have a large chance of subequently occurring,but this does not always happen. It is consideredthat in strong Indian monsoon years, Hadley circu463

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Journalof theMeteorologicalSocietyof JapanVol. 70, No. 1

to the increased autumn typhoon visits. Further
analysis based on shorter monthly data is necessary
to confirm it.
EOF mode 2, however, does not show a consistent
relationship with the Indian monsoon and the ENSO
event. Further analyses are necessary to clarify thephysical background when this mode is dominant.
At least, we have been successful in exploring a significant mode which seems not be associated with
the ENSO events. However, this mode cannot be
deduced by the correlation analysis method or thecomposite technique by referring to the the ENSO
events.
The rainbelt appears in the southeast China in
May and migrates northward gradually, reaches tothe north and northeast part in late July or earlyAugust and then retreats (e. g. see Xu et al., 1983).
There is no doubt that different spatial patterns of
the summer monsoon rainfall are associated withdifferences in the seasonal march of the rainbelt fromyear to year. It remains an unsolved question as to
how the seasonal march of the rainbelt, including
the intraseasonal oscillation, changes from year toyear. We will discuss this problem in a later paper.
6. Conclusions

Fig. 12. Composite of SOI by the time coefficients of EOF modes 1(a) and 2(b) (Measured by the standard deviation). Solidand dashed lines are the composites when
the time coefficient is above +1.0 and below -1.0, respectively. The Black thickbar indicates the referenced summer rainfall period in China. YEAR (-1), YEAR(0) and YEAR (+1) denote the previous, referenced and following years, respectively.

lation over the western Pacific sector is stronger thannormal because of the enhanced
convection
over thewesternequatorial
Pacific.
This condition
inducesthe rainbelt
over the Yangtze
River Valley to shiftnorthward.
As a result, the Yangtze River Valley region undergoes
a drought,
and most of north China,
and the coastal region of the southeast
part undergo
a wet condition.
This situation
corresponds
to thepositive extremesof the EOF mode 1 and possibly
is followed by a cold event.
The opposite
situation
takes place when the time coefficientof the EOF
mode 1 reaches
the negative
extreme.The
positiveanomalyover the southeast
part maybe partly due
464Time and space structure of interannual fluctuations of summer rainfall was described by applyinga harmonic analysis technique and an EOF (Empirical Orthogonal Function) to the summer rainfall(May to September) series of 40 years from 1951to
1990for 160 stations in China. Additionally, the relationship of two principal EOF modes to the Indiansummer monsoon rainfall and the SOI (Southern Oscillation Index) were discussed. The main results ofthis study are summarized as follows:
1. The harmonic analysis indicates that variation
with periods of two to four years (the 2-4 component) accounts for more than 40 percent of
the total variance at all stations, and more than70 percent of the total variance for 61 percent
of stations. The three-year period prevails mostamong this period band.
2. EOF mode 1, which accounts
for 12.8 percent of
the total variance,
reveals the seesaw between
the Yangtze River valley and the Northern
partof China.
3. EOF mode 2 which, accounts
for 10.7 percentof the total variance,
seems more complicated.
However,
it may be characterized
by the oscillation between
the Mei-yu region including
theYangtze
River valley and the rest of the country.
4. These two EOF modes exhibit large amplitude
modulations.
The amplitudes
or the squaresof the time coefficientstend to become
largealternatively.

February

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and

5. EOF mode 1 shows good correspondence with
the Indian summer monsoon and the SOI(Southern Oscillation Index). This result agrees
with that of preceding studies and suggests thatthe summer rainfall over China is associatedwith ENSO events on a time-scale of 2-4 years(e. g., Huang and Wu, 1987).
6. Furthermore, EOF mode 1 seems to precede theanomaly of the SOI. This supports the previous
proposals (Barnett, 1985; Yasunari, 1987) that
the ENSO signals in the eastern equatorial Pacific may originate in central Asia or the Indian
Ocean region.
7. EOF mode 2 seems not to be related to theIndian monsoon or the ENSO.
Acknowledgements
The authors
would like to thank Miss A. Yatagai
for the assistancein preparing
the figures.
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Barnett,

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1985:

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Tokyo,

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Sci., 42, 478-501.
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Meehl, G. A., 1987: The annual cycle and interannual
variability in the tropical Pacific and Indian Ocean
regions. Mon. Wea. Rev., 115, 27-50.
Murakami, M., 1979: Large-scale aspects of deep convective activity over the GATE area. Mon. Wea. Rev.,
107, 994-1013.
Parthasarathy, B., 1987: Droughts/floods n the summer
monsoon season over different meteorological subdi-

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for theperiod1871-1984.

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7, 57-70.

Parthasarathy,tures

B. andof a longmonsoonMooleyhomogeneous

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Wea.

D. A.,

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Sha W.-Y. and K.-R. Li, 1979: Relationship among subtropical high and Mei-yu in the lower Changjang valley, sea surface temperatures of Pacific Ocean. Collected Geographical papers, 11, 126-137.
Wang W.-C. and K. Li, 1990: Precipitation fluctuation
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Xu G. and A. Dong, 1982: The quasi-three year periodof precipitation in the west of China. Plateau Meteorology, 1, 11-17 (in Chinese).
Xu G., M. Li and Z. Zhang, 1983: Seasonal variationof the rainbelts in China. Scientia Atmospherica
Sinica, 7(3), 312-318 (in Chinese).
Yasunari, T., 1985: Zonally propagation modes of
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Yasunari, T., 1987:
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Yoshino, M. and M. Chiba, 1984: Division of China by
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中国夏期降水量の年々変動の時空間構造田少奮(筑 波 大 学 水 理 実 験 セ ン タ ー)安成哲三
(筑波 大学 地球 科 学系)

160地 点 の40年間(1951‑90年)の

夏 期(5‑9月)の

降 水 量 を 用 い て 、 中 国 夏 期 降 水 量 の 年 々変 動 の 時

空 間 構 造 を調 べ た 。
まず 、 調 和 解 析 を 行 な い 、 各 地 点 に お け る降 水 量 の 年 々 変 動 の 周 期 性 を調 べ た 。 そ の 結 果 、10年
年 々 変 動 の 分 散 は、 す べ て の 地 点 に お い て40%以
動 に よ っ て 説 明 で き る。 特 に3年
次 に 、EOF解析 を2‑4年

上 、61%の地 点 に お い て70%以

上 が2‑4年

以下 の周 期 を もつ 変周 期 が 最 も顕 著 で あ る こ とが 分 か っ た。

周 期 の 変 動 に 適 応 した 。 第1モ

ー ドの 空 間 分 布 は揚 子 江 流 域 と中 国 北 部 と の振465

Journal596

動 の 特 徴 を 示 して い る 。 第2モ
流 域 を 含 む 広 い 梅 雨(Mei‑yu)地of the Meteorologicalー ドの 空 間 分 布 は 、 第1モ

Vol.70,No.1

ー ドに 比 べ て や や 複 雑 で あ る が 、 お も に揚 子 江ー ドの 寄 与 率 は そ れ ぞ れ12.8%と10.7%で

析 を行 う前 の 降 水 量 の 変 動 を よ く表 して い る こ とが 分 か っ た 。

の モ ー ド と夏 期 イ ン ドモ ン ス ー ン そ し てENSOと

夏 期 イ ン ドモ ン ス ー ン降 水 量 、ENSOの

指 標 で あ るSOI変

見 られ る。 こ の こ と は 、ENSOにの 関 係 を調 べ た 。 第1モ

ー ドは

動 に 関 連 して い る こ とが 分 か っ た 。 第1モ

ドは イ ン ドモ ン ス ー ン と 同 時 相 関 が 最 も高 い 。 ま た 、 第1モ

ー ドはSOIをー

数 ヶ月 先 行 し て い る 傾 向 が

関 連 した 偏 差 が 中央 ア ジ ア、 イ ン ド洋 域 で 最 も早 く出現 す る と い う い く

つ か の従 来 の研 究 結 果 を支 持 して い る。 第2モ ー ドは 、 イ ン ドモ ン ス ー ンやENSOと466

あ り、 そ れ析 を 行 う前 の 降 水 量 の 合 成 図 と そ れ ぞ れ の モ ー ドの 空 間 分 布 が よ く似 て い る こ とか ら、 両 モ ー ド と もEOF解さ ら に 、 上 述 の2つof Japan域 と他 の 地 域 との 振 動 を 示 して い る。 二 つ の モ ー ド と も大 き く振 幅 変 調

し、 交 互 に 消 長 す る傾 向 を 示 して い る。 第1、2モ
ほ ど高 くな い が 、EOF解Society

よ い対 応 は見 られ な い。