1132

Journalof theMeteorologicalSocietyof JapanVol. 60, No. 5

Fluctuationsof Global Surface Pressure Patterns during the
Past 100 Years and Their Relation to the Asian Monsoon
Part I. Northern Summer (July)
By Tan Chang*

and Tetsuzo

Yasunari**

The Center for Southeast Asian Studies, Kyoto University, Kyoto 606, Japan(Manuscript received 29 March 1982, in revised form 21 June 1982)
Abstract
In order to detect the fluctuations of the global circulation patterns and their relation
to the Asian summer monsoon, an empirical orthogonal function (EOF) analysis was applied
to the July monthly mean smoothed surface pressure data for 106 years (1871-1976) covering
most of the globe. The 1st component (31% of the total variance) shows a contrasting
spatial pattern between the Southern Hemispherethrough the equatorialzone and theNorthern Hemisphere mid-latitudes.
This mode may represent the major re-distribution of
the global-scale pressure patterns from the "Little Ice Age" to the recent warmer period.
The 2nd component (19% of the total variance) reflects a pressure seesaw between thesubtropical oceans and the Afro-Eurasian continents, which seems to be largely responsible
for the strength of the Asian monsoon. These two dominant modes seem to correspondwell with the long-term global-scale SST change. The 3rd component (14% of the total
variance) represents a north-south (or east-west) shift of the main centers of action, andis also closely connected with the monsoon circulation.
The EOF analysis of the originalyear-to-year data has revealed that the mode of the Southern Oscillation is also dominant
as a fluctuation with periods less than 10 years, which is well correlated with the shorterperiod fluctuations of the monsoon.

1.

Introduction

Studies of climatic change during the instrumental-record
period have been proceeded
mostly by using the surface
temperaturein theNorthern
Hemisphere(e.g., Budyko, 1969; Mitchell, 1961; Yamamotoand Hoshiai,1980).
However, the surface pressure data also provides
another source of informationon the long-term
fluctuationof the global climate during the past
100 years.
In particular,changes in the globalscale circulation
patterns
from the "Little Ice
Age" in the 19th century up to the present will
be revealed through the analysis of the surface

examined dominant spatial patterns of the circulation variability over the Northern Hemisphereand their changes during the past 70 years (18991969). He found, especially in the case ofJanuary, that the features on the major centersof action occurred in the early to mid-1920's
and the early to mid-1950's.
Kidson (1975) made a principal component
analysis of the global surface pressure data inassociation with the "Southern Oscillation (S.O.)",and found that the S.O. is not a phenomenon
in a limited area in the southern tropics but apressure data.
By using eigenvector
analysis of mean Januaryand July surface pressure data, Kutzbach(1970)

global-scale pressure oscillation including thenorthern and southern high latitudes, though theperiod of the analysis was only 10 years (19511960).
Independently,
thelong-range
forecasting

Present affiliation:
Department*of Geophysics,
Beijing University,
Beijing, People's Republic of China**Institute of Geoscience, the University of Tsukuba,
Tsukuba, Ibaraki 305, Japan

group of Beijing University have compiled theglobal monthly surface pressure data covering
the period from 1871 to 1976. They adopted
the data from various historical maps as wellas the World Weather Records of the U.S.

October

1982

Tan

Chang

and

Weather Bureau and the Monthly Climatic Datafor the World of NOAA for the recent years.
By using this data set for January and July,
Wang (1964, 1965) deduced some large-scale
circulation changes between the end of the 19th
century (1871-1900) and the middle of this century (1931-1960). For example, he noticed thatduring the 90 years the sub-polar low belt andthe subtropical high belt in January shifted equatorward and that the zonal index has decreased
nearly monotonously in the Northern Hemisphere. He also came to the opinion that thedata set may have no serious errors for the discussion of the long-term fluctuations since thetotal air mass was proved to be conserved overthe globe with a fairly good approximation.
In this paper, we will attempt to verify thedominant time-space scales and spatial structuresof the long-term fluctuation of the global surface
circulation regimes in the northern summer andtheir association with the Asian summer monsoon activity for the period of 106 years from
1871 to 1976. Through the analysis of thisperiod, we also see the changes of the general
Table

Fig.

1 July mean surface
interval
is 2mb.

1

Listpressure

T. Yasunaricirculation
regime from the last stage of the"Little Ice Age" to the recent warmer conditions
2,

.

Data

The monthly
mean surface pressure
data ofJuly (and April) for the period of 106 years(1871-76)
was adopted, which is the same data
set as Wang (1965)'s except for the updated
16
years (1961-76).
The original source of this data
set are listed in Table 1. The data was digitizedfrom the analyzed maps for each grid point of
10* longitude-latitudeand covers nearly-global
area from 50N* to 40*S at all longitudes except
the Atlantic
Ocean sector (0*-60*W).
Then,because of the limitation of memory capacity of
our computer,the data were rearranged
to gridpoint
networkof eachlatitude(100 paints)by

30*10*
averagingpoints along the latitudes.
face map and the network
shown in Fig. 1.

The July meanof the grid-points

3.

function

EmpiricalToof original

map

1133

reducedata

and network

orthogonalthedominant

longitudeeach three

analysis

spatialsources.

of the grid-points.

surareA contour

patterns

of

1134

Journalof theMeteorologicalpressure changes and their temporal variations,empirical orthogonal function (EOF) analysis
was adopted. This method for application toclimatological problem has been discussed bymany authors (e.g., Kutzbach, 1970; Sellers,
1968 etc.). Before applying EOF analysis, thepressure values were normalized by dividing theanomaly values (the deviation from the longterm mean) by the standard deviation:
P'(i, t)=(P(i, t)-P(i,t))/*iwhere P(i, t) is the original pressure value ofthe i-th grid point for the t-th year, *i is thestandard deviation, and P'(i, t) is the normalized
data for P(i, t). As is well known, the varianceof the pressure is generally far larger in thehigh latitudes than in the tropical latitudes.
However, it does not necessarily mean that thepressure fluctuations in the tropics are less important than those in the higher latitudes. Normalization is preferred as it seems to exhibit thepatterns of change more significantly in manycases (Willett, 1965). As our interest in thepresent study is in global-scale patterns of change
including the tropical areas, the analysis using
the normalized data may be more suitable thanthat by the original data.
Then, the normalized anomaly pressure P(i, t)
was expanded to EOFs as follows:
P'(i, t)=Fm(i)*m(t)(1)

Societyof JapanVol. 60, No. 5

amine the relationship between the dominantpatterns of pressure and the monsoon activity
over India.
As will be shown later, there is a gap of
dominant time scales at around 10-year period
in the time series of *i(t) for the first three components (i.e., i=1*3).
That is, the time scaleof less than 10 years is dominant in the shortterm fluctuations, while in the long-term fluctuations the dominant time scales are far larger
than 10 years. Therefore, to discuss the longterm and the short-term fluctuations separately,
the EOF analysis was applied to the 11-year
running mean data, as well as the original yearto-year data.
4.

Results and discussion

4-1. Long-term fluctuations
The results for the 11-year running mean data
for the period from 1876 to 1971 are examined
here. The variances and cumulative variances
explained by the first 10 components are presented in Table 2. Although the total numberof the components is 100, the concentration ofvariance to the first 10 components (about 95 %of the total variance) is quite large, with thefirst three components explaining 63% of thetotal variance.
Hereafter, discussion will be made mainly on
the first three components, since there is a gap
where Fm(i) is the space coefficient of the i-th of the variance value between the third and thestation and *m(t) is the time coefficient of the fourth component.
Moreover, the spatial pattime t for the m-th component.
Equation (1) terns from the fourth component show confuswas obtained under the condition of orthogonal- ing features with locally-distributed large positive
ity of time functions for an arbitrary pair of or negative spatial coefficients. Therefore, thecomponents:
first three components may represent the dominant modes of global-scale pressure changes.
The first component accounts for about 31%
where *i corresponds with the variance of *i(t), of the total variance. Its spatial pattern (thewhich is equal to the i-th largest eigenvalue for distribution of the spatial coefficients) generally
the correlation matrix of P'(i, t). While the EOF shows a contrast between the Southern Hemianalysis was performed on the whole grid-point sphere through the northern tropics and the midnetwork (m=100),
calculations of EOFs were dle latitudes of the Northern Hemisphere, asalso made on the data adding the long-term referred to in Fig. 2(a). The area from themonsoon rainfall over India, which is quoted Aleutian islands toward Japan also shows thefrom Parthasarathy and Mooley (1978), to ex- same phase as the Southern Hemisphere.
Table

2

Variancesandcumulative

EOF'sof the11-yearmovingvariancessmoothedofsurfacethefirstpressure.

10

components

for

the

October

1982

Tan

Chang

and

T. Yasunari1135

Fig. 2 (a) Spatial pattern of the first component deduced by the EOF analysis of
the 11-year moving smoothed pressure. Dashed contours are negative values.
Units are 0.2. (b) Time coefficients of the first component.
The time coefficients (Fig. 2(b)) show a monotonous decreasing
trend from 1870's to 1970's.
This implies that at the end of the 19th century
the surface pressure
over the Southern
Hemisphere especiallyover the Southernocean area
and the ITCZ
area indicatedrelativelyhighvalues while the northern
Pacific high was relatively weak or shifted southward,
and that thistendencyof the anomalies
was gradually
reversed toward
the present age. The monsoon
trough over India has gradually been deepened
through the period.
Interestingly,
the spatial pattern of this component
suggeststhat the overall
featureofchange of climatic zone from the 19th century
to the present was quite different between
theSouthern
and the Northern
Hemisphere.
Thatis, in the Northern
Hemispherethe north- or
southward
extensionof the dominant centers ofaction (e.g., the north Pacific high and the monsoon trough) was apparent, while in the Southern
Hemispherethroughthe equatorialzone thepressure values of the whole area changed nearly-simultaneously
without change of the locationof the centers of action.

The second component (19% of the total variance) and its time coefficients are shown in Fig.
3(a) and (b), respectively. The centers of positive anomalies are found over the central northPacific and the southern Pacific near Australia
and South America, and those of the negative
anomalies lie over central Asia, central Africa,
north America and Arafura Sea. In other words,
the spatial pattern of this component seems torepresent the pressure contrast between the equatorial and monsoon trough zone and the subtropical high areas if we refer to the mean map(Fig. 1). Or rather, if we note the major positive and negative anomalies, this pattern seems
to reflect a pressure see saw between the subtropical Pacific Ocean and the Afro-Eurasiancontinent. The time coefficients (Fig. 3(b)) showthat the maxima of the anomaly pressure gradient from the ocean to the continent area (or,from the subtropical high area to the equatorial
and monsoon trough area) appear in 1880's and1940's to 50's and the minima in 1900's to1920's and 1960's to 70's.
Compared to the first and second component,
the third component (14% of the total variance)

1136

Journalof theMeteorologicalSocietyof JapanFig. 3 (a) Same as Fig. 2(a) but for the second component.
but for the second component.
shows a more complicatedspatialpatternasshown in Fig. 4(a).
It is especiallynoted thata large positive-negative
anomaly
contrast exists
along the peripheryof east, southeastand south
Asia. However, if we take account of the dominant trough-ridge
system appearing
in the meanmap (Fig. 1), it becomeseasier to understandthe spatial pattern in this figure. Namely,
thelarge positive anomaly area over Asian continent
throughIndonesian
regionmay represent
theoscillation of the monsoon trough and the ITCZ
over the western Pacific and the negative area
over east Asia and Arabian Sea may be responsible for the north-southward
shift of the monsoon trough and the extent or rereat of the northPacific high, respectively.
As well as the second component,
this patternseems to be related to the monsoon activity over
Asia. Since the subtropicalhigh are located at
the border
betweenthe negativeand positive
anomalies in the southern
subtropics,
this component also represents the north-southward(eastwestward) shift of the southern Pacific (southern
Indian Ocean) high.
Time coefficients of this component(Fig. 4(b))
show a maximum around the 1900's, a minimumfrom the 1920's through1930's,andan increasing trend thereafter.

Vol. 60,

No. 5(b) Same as Fig. 2(b)To clarify the relationship between each modeof the pressure variation described above andthe Indian summer monsoon activity, a joint
EOF analysis was made adding the monsoon
rainfall data over India as the 101st data, which
was compiled by Parthasarathy
and Mooley(1978). They deduced a long homogeneous monsoon rainfall (June to September) over the wholeof India by using about 2,000-3,000 raingage
station data. A part of the series from 1871 to1976 is shown in Fig. 5.
The smoothed rainfall data was used as wellas the smoothed pressure data by 11-year moving average. Factor loadings defined as lm(j)=Fm(j)*m1/2,
were calculated, which are considered to be the measures of correlation of thej-th data with the m-th component.
The values of factor loading of the rainfall
data for the 1st to the 5th component (i.e.,m(101), m=1*5)
are shown in Table 3. Theylshow relatively large negative or positive correlations for the second and the third component.
This implies that for the second component the
maximum (minimum) rainfall occurs when thepressure gradient from central Asia to the northPacific is maximum (minimum), and for thethird component the maximum (minimum) rainfall occurs when the pressure gradient from

October

1982

Tan

Chang

and

T. YasunariFig. 4 (a) Same as Fig. 2(a) but for the third component.
but for the third component.

1137(b) Same as Fig. 2(b)Fig. 5 Long-term series of monsoon rainfall over the whole of Inida.
Mean value is shown with the dashed line. (after Parthasarathy
and Mooley, 1978)

southern India to the Mimalayan regions is
maximum (minimum). These relations may beinterpreted as an active (weak) monsoon condition corresponding to a strong (weak) east-west
and monsoon circulation over Asia through thesurrounding oceans. Thus, there is little doubtthat the relatively weak monsoon period overIndia from around 1900 to 1920's and the strong
monsoon period from 1930 to 1960 are combined with the surface pressure changes appearing in the 2nd and/or the 3rd component.
Moreover, if we take account of nearly the sametime scale of temporal fluctuation (about 70-

Table

3 Factor
rainfallforloadings
the firstof smoothed
monsoon5 components.

year period from peak to peak) and the phaselag of about a quarterof the period between
these two components,
we are tempted
to say
that
these
two
components
exhibitdifferent
phases of one dominant mode of the global-scale
circulations.
Here, we discuss some plausible
relations be-

1138

Journalof theMeteorologicaltween the dominant modes of pressure patterns
and the sea surface temperature (SST). Veryrecently, Paltridge and Woodruff (1981) have revealed changes in global surface temperaturefrom 1880 to 1977 derived from historical
records of SST as well as screen temperature
data over land. They showed for the northernsummer a large increasing linear trend in theSouthern Hemisphere, while a linear trend issomewhat flat in the Northern Hemisphere. They
also found an apparent minimum somewhere between 1900 and 1925 and a maximum somewhere between 1945 to 1970 both in summer
and winter of the two hemispheres. Their main
results are shown in Fig. 6. It is of interest tonote that the first component of pressure patternapproximately corresponds with the linear trendof temperature since the increasing of temperature and decreasing of pressure as a whole in
the Southern Hemisphere can be explained as athermally-induced balance between surface tem-

Fig. 6 Pentad average surface temperaturedeviation of each season (Dec.-Feb. andJune-Aug.)for each hemisphereas afunctionof time (after Paltridge andWoodruff, 1981).

SocietyofJapan

Vol. 60, No. 5

perature and pressure. In the Northern Hemisphere, on the contrary, the flat trend of temperature may be accounted for by the overall
effect of the spatial distribution of positive andnegative anomalies of pressure which occupy
roughly the same areas each other. In addition,
Hastenrath and Wendland (1979) have shown aprominent increasing trend of SST from 1920's
to 1950's over the equatorial eastern Pacific near
central America, which agrees well with thegeneral decreasing trend of pressure corresponding with the anomalies of the first component
over there.
The second component of pressure seems tocorrespondwith the deviations of temperature from the linear trend. The minimum oftime coefficients around 1900's to 1920's andthe maximum around 1940's to 1950's are ingood agreement with those in temperature. During 1900's through 1920's, for example, theminimum SST may account for the maximum
pressure over the equatorial and tropical zone
(note the negative areas in Fig. 3(a)), which implies weak ITCZ and monsoon. The large areasof minimum pressure over the northern Pacificand the southern Pacific near Australia (notethe positive areas in Fig. 3(a)) imply weak subtropical highs over the Pacific. During 1940's
to 1950's the condition is just opposite. It is
presumed, therefore, a large positive (or negative) pressure anomalies over the subtropicaloceans are resulted from the dynamical effect of
the strong (or weak) Hadley and monsoon circulation rather than the thermal effect of theanomalous SST there. In Fig. 3(a) the positive
areas covers even over the equatorial central
Pacific. However, this may be due to the rather
coarse resolution of grid-point network (10*30*), and so, relatively narrow band of negative
anomalies corresponding to the ITCZ there may
be masked by the large positive areas to thenorth and the south of it. In fact, the spatial
pattern of the second component for April (Fig.
7) shows an apparent negative anomalies overthe ITCZ and the positive anomalies over thesubtropical oceans. (The second component for
April is considered to derive from the samemode as the 2nd component for July since thetime coefficients for each month have shownnearly the same tendency each other.)
The direct comparison between the results
here and those by Kutzbach (1970) is not possible, since the area and the period analyzed

October

1982

Tan

Fig. 7

Chang

and T. YasunariSame as Fig. 2(a) but for the second component

1139

of April.

Fig. 8 (a) Spatial pattern of the third component deduced by the EOF analysis of
the original year-to-year pressure.
(b) Time coefficients of the third component.

are different each other. However, the 2nd component for July in Kutzbach (1970) seems to be
identified with the 2nd component here becauseof the similarity of the spatial pattern and thetime coefficient series in the common area andperiod.
4-2. Short-term fluctuations
Short-term fluctuations are also investigatedby using the original year-to-year data from
1871 to 1976.
The first (15% of the total variance) andsecond (9.9%) component show nearly the same
spatial patterns as those for the 11-year moving
smoothed data. The time coefficients also exhibit similar tendencies to Fig. 2(b) and 3(b) but
with shorter-period fluctuations (data not shown).
So, these two components are suggested to befundamentally the same ones as those discussed
in section 4-1.
The spatial pattern of the third component(8% of the total variance) and its time coefficients are shown in Fig. 8(a) and (b), respectively. Fig. 8(a) shows a contrasting pattern

1140

JournaloftheMeteorologicalbetween the equatorial eastern south-Pacific andthe southern Indian Ocean. This can be identified as the pattern of the "Southern Oscillation"
as already examined by many authors (e.g.,
Walker and Bliss, 1932; Troup, 1965; Kidson,
1975 etc.). In particular, the correlation pattern
for the northern summer obtained by Troup(1965) resembles quite well with the present
spatial pattern (Fig. 8(a)). Compared to theother seasons, the center of action in the eastern
hemisphere shifts from the Indonesian region to
the subtropics over the eastern Indian Ocean.
If we take account of the mean map (Fig. 1),
this pattern may imply that in the strong S.O.
year (years of minimum time coefficients) the
southern Pacific high is stronger and expands
more northward than in the weak S.O. year
(year of maximum time coefficients) while theMascarene (or Madagascar) high in the southern
Indian Ocean expands more eastward in the
weak S.O. year. On the other hand, the meridional pressure gradient over central India seems
to be steeper in the strong S.O. year than thatin the weak S.O. year. These features over India
through the Indian Ocean may affect the strengthof the cross-equatorial flow off the Somali coastfor each year.
EOF analysis on the combined data with theyear-to-year surface pressure and Indian monsoon rainfall data (Fig. 5) has revealed that thiscomponent (S.O. mode) is correlated most distinctly with the Indian monsoon rainfall asshown with the factor loading values in Table 4.
That is, the maximum (or minimum) monsoon
rainfall over India occurs when the pressure overthe eastern south Pacific is at the maximum (orminimum) phase. It is true that most of theextremely wet or dry years in Fig. 5 (for example, 1899, 1917, 1918, 1942, 1951, 1965,
1972, 1975) correspond with the years of extremely large positive (or negative) value in Fig.
8(b). The high correlation between the monsoon
rainfall over India and the S.O. was formerly
suggested by Walker and Bliss (1932), and has
recently proved statistically by some authors(eg., Khandekar, 1979; Pant and Parthasarathy,Societyof JapanVol. 60, No. 5

1981 etc.).
Cross spectral
analysis between
the time coefficients and the rainfall (Fig. 5) showed that
the S.O. and the Indian
monsoonrainfallishighly correlated
in the period rangeof 6-8years, 3-4 years and about 2 years.
The cospectrum, coherence and the phase lag are shownin Fig. 9. The dominant time scales of the S.O.
shown here are in good agreementwith those
detected by Berlage (1961).
5.

Conclusion

andremarks

By using nearly-global
July mean surface pressure data covering the period of 106 years (18711976), the fluctuationsof the global circulation
patterns and their relation to the Asian summer
monsoon were examined.
To examine the long-term
fluctuationof thesurface circulation patterns, empirical orthogonal
function (EOF) analysis was applied to the 11year runningmean surface pressure data.
The
spatialpatternof the first component(whichoccupies 31% of the total variance) shows a contrast between the Southern
Hemispherethroughthe equatorialzone and the Northern
Hemisphere mid-latitudes.
The time coefficients show
a linearly-decreasing
trend, which represents
theglobal-scale
air mass movementfrom the Southern Hemisphereto the Northern
Hemisphere

Table 4 Factor loadings of the original yearto-year monsoon rainfall for the first 5
components.
Fig. 9 Cospectrum, coherence and phase lagof the time coefficients (Fig. 8(b)) to theIndian summer monsoon rainfall (Fig. 5).

October

1982

Tan

Chang

and

mid-latitudes during the past 100 years. This
component may be taken as part of the major
re-distribution of the hemispheric pressure patterns from the "Little Ice Age" to the present
warmer period, associated with the apparent increasing of SST in the Southern Hemisphere.
The spatial pattern of the second component(19% of the total variance) shows that the pressure anomalies of the subtropical ocean area isnegatively correlated with those of the continental area, especially of Eurasia and Africa.
The maxima of the anomalous pressure gradientfrom the ocean to the continent appeared in
1880's and 1940's through1950's, and theminima in 1900's through 1920's and 1960's
through 1970's. This pattern seems to be largely
responsible for the active (or weak) Asian monsoon conditions, since the maximum (or minimum) pressure gradient may be well correspondent with the maximum (or minimum) strengthof the Hadley and monsoon circulations. Actually, the time coefficients of this component
are highly correlated with the monsoon rainfall
over India (Parthasarathy and Mooley, 1978).
This component seems to correlate with the longterm SST change over the tropical and equatorial zone with the time scale of about 70 years
or so. The third component is also closely associated with the monsoon rainfall. The spatial
pattern indicates the north-southward(or eastwestward) shift of the main centers of action,such as the north Pacific high, the south Pacifichigh and the southern Indian Ocean high. A
similarity of the time coefficients with somephase lag between the second and the third
components suggests that these two components
represent different phases of one dominant modeof the global-scale surface circulation change.
Through the analysis based on the originalyear-to-year data, the mode of the "Southern
Oscillation" was also confirmed as a short-term
fluctuation with a periodicity of less than 10
years. This mode appeared in the third component. (The first and the second component
show fundamentally the same patterns as thosededuced from the 11-year running mean data.)
The spatial pattern show a contrast of pressureanomaly between the southern Pacific and thesouthern Indian Ocean. The time coefficients ofthis component shows relatively high correlation
with the year-to-year fluctuation of the monsoon
rainfall over India. Spectral analysis showed thatthe dominant time scales of this mode are about

T. Yasunari1141

2 years, 3-4 years and 6-8 years.
Thus, we may conclude that the global-scale
circulation changes relating to the Indian summer monsoon show quite different features between the long-term and short-term fluctuations.
Naturally, different physical processes should betaken into account for the fluctuations of these
two modes. Recently, not a few studies havebeen devoted to the short-term fluctuations especially in connection with the S.O. As for the
long-term fluctuations, however, it has been suggested that the large-scale SST changes andrelated long-term fluctuation of ocean currents
in the equatorial zone and the Southern Hemisphere should be noted more as main controlling
factors.
A similar analysis on the northern winterseason (January) is also being made and theresults will be reported soon.
Acknowledgements
We are greatly acknowledged to the longrange forecasting group of Beijing University forproviding us the surface pressure data. We arealso indebted to Prof. T. Watabe, Prof. S. Ichimura and Prof. Y. Takaya of Kyoto University
for the encouragement of our co-operative work.
References
Berlage, H. P., 1961: Variations in the general atmospheric and hydrospheric circulation of periods of a few years duration affected by variations of solar activity. Ann. New York Acad.
Sci., 95, 354-367.
Budyko, M. I., 1969: The effect of solar radiation
variations on the climate of the earth. Tellus,
21, 611-619.
Hastenrath, S. and W. M. Wendland, 1979: On the
secular variation of storms in the tropical northAtlantic and eastern Pacific. Tellus, 31, 28-38.
Khandekar, M. L., 1979: Climatic teleconnections
from the equatorial Pacific to the Indian monsoon-analysis and implications. Arch. Met.
Geoph. Biokl., Ser. A, 28, 159-168.
Kidson, J. W., 1975: Tropical eigenvector analysis
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Kutzbach, J. E., 1970: Large-scale features ofmonthly mean Northern Hemisphere anomaly
maps of sea-level pressure. Mon. Wea. Rev., 98,
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Mitchell, J. M., 1961: Recent secular changes ofglobal temperature. Ann. New York Acad. Sci.,
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Paltridge, G. and S. Woodruff, 1981: Changes in1142

JournaloftheMeteorologicalglobal surface temperaturefrom 1880 to 1977
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1981:
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aspects of an association between the Southern
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過 去100年

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Vol. 60, No. 5

Walker, G. T. and E. W. Bliss, 1932: World Weather
V. Mem. Roy. Meteor. Soc., 4, 32, 53-84.
Wang, Shao-Wu., 1964: The oscillations of atmospheric circulation in the recent 90 years (PartI). Acta Met. Sinica, 34, 486-506 (in Chinese).
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194-202.

に お け る全球 規 模 の 地 上 気 圧 パ タ ー ンの
変 動 と ア ジア の モ ン ス ー ン
I:北張半球夏季(7月)

鐔*〓 安 成 哲 三**
京都大学東南アジア研究センター

全 球 規 模 で の大 気 循 環 パ タ ー ンの長 周 期 変 動 を,過 去106年を 用 い て,経 験 的 直 交 函 数(EOF)解
11年 移 動 平 均 値 につ い て のEOF解間(1871‑1976)の月 平 均 地 上 気 圧 資 料(7月)析 の手 法 で 調 べ,同 時に 夏 季 ア ジ アモ ンス ーン との 関 連 も考 察 した。
析 の 結 果,第1主成分(総 分 散 の31%)は,南

半 球 か ら赤 道 に か け て の 地

域 と,北 半 球 中緯 度 の あ い だ の気 圧 の コン トラス トを示 し,19世

紀 末 か ら最 近 に か け て,両 地 域 間 で気 圧 偏 差 が

逆 転 す る 現 象 に対 応 して い る。 第2主 成 分(総 分 散 の19%)は,太平 洋〓 イ ン ド洋 の 亜 熱 帯 高 圧 域 と ア ジ ア〓 ア

フ リカ両 大 陸 の 間 の気 圧 振 動 を示 す モ ー ドで あ り,イ ン ド〓モ ンス ー ン降 水 量 の長 周 期 変 動(約70年
ール)と
and

の時 間 ス ケよ く相 関 して い る。 これ らふ た つ の 主 成分 の 時 間 変 動 は,全 球 規 模 で の海 水 温 の長 周 期 変 動(Paltridge

Woodruff,1981)にな活 動 中 心 の 南北(東の値 のEOF解して お り,イ

非 常 に よ く対 応 し て い る こ とも 明 らか とな った 。 第3主 成 分(総 分 散 の14%)は,主西)の 偏 位 を代 表 す る モ ー ドで あ り,こ の成 分 も モン ス ー ン降 水 量 と の相 関 が 高 い。 年々析 の結 果,10年

以下 の 短 周 期 変 動 と して は,南 方振 動(Southern

Oscillation)のン ド〓モ ンス ー ン降 水量 の年 々変 動 と強 く関 連 して い る こ と も明 らか とな った。

*現在所 属:北 京大学地球物理系**現在所 属:筑 波大学地球科学系

モ ー ドが 卓 越