Clim Dyn (2006) 27:661–675
DOI 10.1007/s00382-006-0163-3

Boreal summer quasi-monthly oscillation in the global tropics
Bin Wang Æ Peter Webster Æ Kazuyoshi Kikuchi Æ
Tetsuzo Yasunari Æ Yanjun Qi

Received: 16 September 2005 / Accepted: 22 May 2006 / Published online: 3 August 2006
 Springer-Verlag 2006

P. Webster
School of Earth & Atmospheric Science& Civil and Environmental Engineering,
Georgia Tech University, Atlanta, GA, USA

During the initiation of the convective anomalies, thestratiform and convective rains have comparable rates;
the prevailing cloud type experiences a trimodal
evolution from shallow to deep convection, and finallyto anvil and extended stratiform clouds. A major
northwest/southeast-slanted rainband forms as theequatorial rainfall anomalies reach Sumatra, and therainband subsequently propagates northeastward into
the west Pacific Ocean. The enhanced precipitation in
the west Pacific then rapidly traverses the Pacific along
the Intertropical Convergence Zone, meanwhile
migrating northward to the Philippine Sea. A seesaw
teleconnection in rainfall anomalies is found between
the southern Bay of Bengal (5–15N, 80–100E) andthe eastern Pacific (5–15N, 85–105W). Local seasurface temperature (SST)-rainfall anomalies display anegative simultaneous correlation in the off-equatorial
regions but a zero correlation (quadrature phase relationship) near the equator. We propose that atmosphere–ocean interaction and the vertical monsoon
easterly shear are important contributors to thenortheastward propagation component of the intraseasonal rainband. The observed evidence presented
here provides critical information for validating thenumerical models, and it supports the self-induction
mechanism theory for maintenance of the boreal
summer ISO.

T. Yasunari
Hydrospheric and Atmospheric Research Center,
Nagoya University, Nagoya, Japan

1 Introduction

Y. Qi
Laboratory of Numerical Modeling for Atmospheric
Sciences and Geophysical Fluid Dynamics (LASG),
Institute of Atmospheric Physics,
Chinese Academy of Science, Beijing, China

During the boreal summer, the equatorial-trapped,
eastward-propagating Madden and Julian (1971, 1972)
Oscillation (MJO) weakens considerably; major centers of intraseasonal variability in precipitation and

Abstract The boreal summer intraseasonal oscillation(ISO) in the global tropics is documented here using a
7-year suite (1998–2004) of satellite measurements. A
composite scenario was made of 28 selected eventswith reference to the oscillation in the eastern equatorial Indian Ocean (EIO), where the oscillation ismost regular and its intensity is indicative of thestrength of the subsequent northward propagation. The
average oscillation period is about 32 days, and thisquasi-monthly oscillation (QMO) is primarily confinedto the tropical Indian and Pacific Oceans. Topics thatwere investigated are the partition of convective versus
stratiform clouds, the vertical structure of precipitation
rates, and the evolution of cloud types during the initialorganization and the development of intraseasonal
convective anomalies in the central Indian Ocean.

B. Wang (&) Æ K. Kikuchi
Department of Meteorology and the International PacificResearch Center (IPRC), University of Hawaii,
1680 East West Road, POST Bldg. 409E,
Honolulu, HI 96822, USAe-mail: wangbin@hawaii.edu

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low-level zonal winds shift to monsoon trough regionsin the Northern Hemisphere (Lau and Chan 1988;
Wang and Rui 1990; Salby and Hendon 1994; KemballCook and Wang 2001, among others). Northward
propagation prevails in the northern Indian Ocean(e.g., Yasunari 1979, 1980; Sikka and Gadgil 1980;
Krishnamurti and Subrahmanyam 1982; Webster 1983;
Goswami and Shukla 1984), while the intraseasonalvariability in the western North Pacific features salient
westward and northwestward propagation (e.g.,
Murakami 1980; Lau and Chan 1986; Chen andMurakami 1988). The complex behavior of boreal
summer intraseasonal oscillation (ISO) has previously
been documented by using the following datasets:
reanalysis products from the National Centers forEnvironmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) and the European
Centre for Medium-Range Weather Forecasts(ECMWF), outgoing longwave radiation (OLR),
rawinsonde, and results from the atmospheric generalcirculation model (GCM) (Hartmann et al. 1992;
Ferranti et al. 1997; Webster et al. 1998; Krishnan et al.
2000; Anamalai and Slingo 2001; Kemball-Cook andWang 2001; Hsu and Weng 2001; Lawrence andWebster 2002).
The lack of accurate observations over the tropicaloceans has been a major impediment in detecting theoscillation’s initiation and evolution processes. Even
using the same data (e.g., OLR), ambiguous results
have been produced by studies that use different
statistical methods. For instance, the composite studyof Kemball-Cook and Wang (2001) found that theinitial negative OLR anomalies starts from the western equatorial Indian Ocean (EIO); however, thecomposite life cycle, based on an MJO index derived
from a multivariate empirical orthogonal function(EOF) analysis, showed that the OLR anomalies startfrom the eastern EIO (Wheeler and Hendon 2004).
And, the regression analysis of Jiang and Li (2005)
suggested the OLR anomalies originating from thewestern coast of Equatorial Africa, whereas Lawrence and Webster (2002) found that some ISOs formin the western EIO while others form in the eastern
EIO.
Using data from the Tropical Rainfall Measuring
Mission (TRMM), Wang et al. (2005)—hereafter referred to as WWT05—found that the antecedents of
the active and break periods of the monsoon emerge inthe western EIO, and the initiation of a new rainyphase is preceded by in situ surface wind convergenceand central EIO warming. Unfortunately, these conclusions were derived based on only 3 years of data,
and the focus of WWT05 was on the Indian monsoon

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only. The present study uses 7 years of TRMM data
and the study’s scope extends beyond the Indian
monsoon region. In this study, we were especiallyinterested in a global picture of the boreal summer ISO
and the intraseasonal linkage between the Indian andPacific Oceans.
Knowledge of the vertical structure of heating
distribution and the cloud’s evolution during theinitial organization and development of intraseasonal
convective anomalies is critical for validating our
ISO simulations of GCMs; this knowledge is vital
because the uncertainties in mathematical descriptions of these interactive parameterizations could
jeopardize the model’s capability to simulate theISO. Johnson et al. (1999) found that MJO involves
trimodal (deep, congestus, shallow) distribution of
convection in the western Pacific (see also Kikuchi
and Takayabu 2004). Increased vertical resolution of
the numerical model undoubtedly more accurately
resolves the melting layer and cumulus congestus,
thus providing a more realistic simulation of the
MJO (Slingo et al. 2003). Unfortunately, little is
known about the vertical-distribution precipitationand evolution of clouds in the ISO generation overthe Indian Ocean. Use of TRMM precipitation radar(PR) data to examine cloud evolution during reinitiation of the boreal summer ISO is a uniqueaspect of the present study.
Section 2 explains data and analysis methods used inthis study. Next is a satellite perspective of the mean
life cycle in terms of (1) the ISO rainfall (Sect. 3) and(2) the sea-surface temperature (SST) anomalies(Sect. 4). Section 5 documents an intraseasonal seesaw
teleconnection between the Indian and the eastern
Pacific monsoons. In Sect. 6, variations in the verticalprofiles of precipitation and cloud types during theinitiation process are presented. Section 7 discusses themechanisms for monsoon ISO, drawing upon the results derived from the present analyses and previous
studies. The final section summarizes our research andfindings and discusses remaining issues.

2 Data and analysis method
The TRMM data provide an unprecedented opportunity for the investigation of the ISO evolution andstructure over those oceanic regions that have been
typically devoid of traditional data. The semi-equatorial orbit of the TRMM satellite allows data be collected at various local times in any given Earth location
between 35S and 35N (Kummerow et al. 1998).
This yields an improved estimate of daily mean

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precipitation rate as well as atmospheric water vapor
and liquid cloud water. The TRMM microwave imager(TMI) can measure a number of variables in thepresence of clouds, including SST, precipitable water,
and precipitation (Wentz and Spencer 1998; Chelton
et al. 2001). The TMI data used in this study is produced by Remote Sensing Systems and sponsored by
the NASA Earth Science REASoN DISCOVER
Project and can be found at http://www.remss.com. The
resolution of the original data was 0.25 · 0.25 degrees
of latitude/longitude from which we made 2 · 2
averaged data for the present study. Since TMI rainfall
data are available only over the ocean, the precipitation datasets TRMM 3B42 and PR2a25 have also beenused. The 3B42 precipitation data blend microwave
measurements, visible and infrared measurements,
and adjust precipitation estimates based on the
Geostationary Operational Environmental Satellite(GOES) precipitation index, yielding precipitation
estimates over both ocean and land areas with a resolution of 2 · 2 degrees of latitude/longitude. The
PR2a25 products are estimated from the TRMM precipitation radar, which provides three-dimensionalrain-rate profiles for each cell (measuring
4 km · 4 km · 250 m) and information about thestratiform and convective rains.
The surface winds and convergence are derived
from the QuikSCAT scatterometer (National Aeronautics and Space Administration 2001). The data were
downloaded from the website of the Air–Sea Interaction & Climate Team, which is a group in the Jet
Propulsion Laboratory in Pasadena, California. The
GCM ECMWF reanalysis product ER40 is used only
to complement the satellite data. The reanalysis data
was downloaded from the following web site: http://
www.ecmwf.int/products/data/archive/descriptions/e4/.
To extract signals of intraseasonal variability, aLanczos band-pass filter was applied to the daily data
(Duchon 1979). A composite method similar to
WWT05 was used to document the mean behavior of
the ISO. Special attention was paid to two important
issues for making an adequate composite: the selectionof the reference time series and the determination of
the composite time interval. We chose a reference timeseries of precipitation averaged in the eastern EIO(5S–5N, 75–100E). This is essentially the same region chosen by Lawrence and Webster (2002) and also
by WWT05. This location sees the largest intraseasonalvariability of rainfall in the tropics, yet most atmospheric GCMs have difficulty reproducing the observed
intraseasonal variability (Waliser et al. 2003). As ISOs
tend to form either in the western and central EIO or
in situ in the eastern EIO, this reference location

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would seem ideal. The intraseasonal variation of the
precipitation in this region is nearly 180 out of phasewith that over the central India and the northern Bay
of Bengal. Therefore, this reference location might also
be representative of the active/break cycles of theSouth Asian monsoon. And, as the boreal summer ISO
expands into the Pacific Ocean, our choice of basereference will facilitate the detection of subsequent
behavior in the Pacific.
A time series of daily precipitation rates averaged
over the reference region is shown in Fig. 1a. The 20–
50 day oscillation component is predominant andgenerally accounts for 60–70% of the total square root
variance in the time series. Thus, the 20–50 day bandpass-filtered component is used to produce composite
scenarios. From 1998 through 2004, each summer
experienced several active break periods between midMay and mid-September, providing a total of 28 cycles
with significant magnitude (a rain rate greater than
5 mm/day) for the peak positive phase and the two
adjacent negative phases. These 28 events were selected for inclusion in constructing a composite; each
event is indicated by a double-sided arrow in Fig. 1a.
Nearly all of these strong cycles are associated witha well-organized northward propagation from theequator to the South Asian region (Fig. 1b). Figure 1b
shows that the northward propagation is more systematic from May to August and is less well defined
during September and October. The ISOs that were
not chosen generally have relatively small amplitudes
and also tend to correspond to ill-organized northward
propagations, suggesting that the ISO strength in theeastern EIO is an indication of the intensity of broadscale northward propagation.
The periods of individual cycles are notably irregular. We defined the oscillation period of an individual
event as the time interval around a selected peak,spanning from the preceding minimum to the ensuingminimum. The periods of 26 out of the 28 individual
cycles range from 24 to 40 days (Fig. 1a). The average
period for the 28 cycles is about 32 days per cycle; thus,
the mean interval between two adjacent phases isabout 4 days. The boreal summer ISO has a shorter
period than the boreal winter MJO.
Since the periods are irregular, the method used toconstruct the composite life cycle was based on eight
consecutive phases in each of the 28 cycles. Phases 1, 3,
5, and 7 correspond to, respectively, the times whenrainfall anomalies are at a minimum, a positive transition (negative-turning-to-positive) phase, a maximum, and a negative transition phase. Thus, Phases 1
and 5 represent the driest and the wettest phases,
respectively, in the eastern EIO. This phase-dependent

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(a)

(b)

TMI rainrate (75-100E,5S-5N)

20-50day 3B42 rainrate(75-100E)
1998

26

37

26

40

39

43

28
2000

31

36

33

32

34

32

24

26

Daily rain rate (blue, mm/day)

20-50day anomalous rain rate (red dashed, mm/day)

1999

32

49

30

27

32

2001

2002

29
2003

28

38

28
2004

30

33

37

29

Fig. 1 a TRMM/TMI precipitation rate averaged over theeastern EIO (5S–5N, 75–100E). Blue represents the 3-day
running mean, and red shows the 20–50 day anomalies; doubleheaded arrows indicate the selected 28 events, along with time

period in days; b time-latitude plot of the 20 to 50-day filtered
TMI 3B42 daily precipitation rate (mm/day–1) averaged between
75 and 100E. The slanted lines indicate the northward
propagation of the ISO’s rainy phasecomposite has advantages over regression analysis withrespect to the reference time series at different fixedlags (days) and also has advantages over other analyses
in which the period is considered as fixed. In the lattermethods, when individual oscillation periods are highly
irregular, the derived composite evolution would mix
up different phases of the oscillation cycles, which isundesirable when detecting transition ISO phases (reinitiation or generation).
Statistical analysis was applied to the composite
fields. Based on the Student t-test, the findings showedthat statistical significant at the 90% confidence level
for (1) the magnitude of anomalous rainfall rate is
greater than 2 mm day–1 and (2) the magnitude ofanomalous SST is greater than 0.1C (refer to thecomposite maps in Fig. 2).

3 A satellite perspective of the boreal summer QMO
rainfall anomalies

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Figure 2 presents a composite eight-phase QMO life
cycle of the TMI precipitation rate and SST anomalies
in the global tropics. The figure shows only the Indianand Pacific sector (2/3 of the tropics) because the TMI
precipitation and SST anomalies over the Atlantic
sector are insignificant, suggesting that the global
QMO is primarily confined to the tropical Indo-Pacificdomain. Major features of positive rainfall anomalies
are highlighted using black solid lines; negative
anomalies are indicated with dashed lines. For convenience, the following description will focus on thepositive (wet) anomalies, but the formation and propagation of dry anomalies follow a similar footprint as

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665

Composite life cycle of QMO rainrate (contour) & SST (shading)Fig. 2 Composite eight-phase life cycle of the boreal summer
QMO. Green denotes positive precipitation rate (TRMM/TMI)
anomalies starting from 2 mm day–1 with contour intervals of
3 mm day–1; lavender shows negative anomalies starting from –2 mm day–1. The major features of positive rainfall anomalies

are highlighted using black solid lines; dashed lines indicate
negative anomalies. Shading represents sea-surface temperature(SST) anomalies in units of degrees Celsius. Only statisticallysignificant rainfall and SST anomalies at 90% confidence levelare shown

the enhanced rainfall anomalies across the Indo-Pacificduring the complete QMO cycle.
Phase 1 features a minimum rainfall in the eastern
EIO and a peak wet monsoon over the head Bay of
Bengal. About 4 days later (Phase 2), a small area oforganized positive rainfall anomalies first emerges in
the western EIO between 60 and 70E, initiating thenext spell of QMO rainfall. In Phase 3, the new rainfall-anomaly area expands eastward along 5S andfurther develops into a heavy precipitation region that
becomes symmetric about the equator in Phase 4.
Phases 1 through 3 can be viewed as the genesis process for a new wet phase of the QMO cycle.
Rainfall anomaly in the eastern EIO reaches a peakduring Phase 5; meanwhile, rainfall has ceased overIndia. Note that at this stage, anomalous convection
over Sumatra weakens considerably. Meanwhile, theanomalously wet region bifurcates poleward, transforming into a V-shaped rainband that tails the main
center of the equatorial convection; this is Phase 6 ofthe life cycle. The V-shaped rainband is highly asymmetric about the equator and is far stronger in the
Northern Hemisphere (NH). Thus, the major rainband
displays a northwest/southeast-tilted structure in theNH, extending from the Arabian Sea to the equatorial
western Pacific (Phases 6 and 7), concurrent with
suppressed convection restarting in the western EIO.
The life cycle continues with the northward andeastward propagation of the enhanced rainband in thenorthern Indian and western Pacific Oceans, from
Phase 6 to the Phase 1 of the next cycle, causing a peak
activity period in the Indian monsoon. Although thestrongest fluctuations associated with the QMO are in
the Indian Ocean and the western North Pacific,
coherent signals are seen to swiftly traverse the North
Pacific along the Intertropical Convergence Zone
(ITCZ) from Phase 8 to the next Phase 1.
The results derived by using 3B42 product are extremely consistent with the results derived from the
TMI data (figure not shown). The major difference
between the 3B42 and TMI data lies in their amplitudes. The amplitude of precipitation derived from
TMI tends to be twice larger than that of 3B42 forsome reasons (e.g., Kummerow et al. 2000).
Figure 3 highlights the life cycle shown in Fig. 2.
The evolutionary cycles of wet and dry QMO anomalies follow similar paths. The evolution of wet phaseincludes (1) the initiation of rainfall anomalies in the

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Fig. 3 Schematic diagram
showing four stages of theboreal summer QMO wet anddry anomalies: (1) initiation,(2) maturity, (3) formation of
the slanted precipitation
band, and (4) northeastward
propagation. Each stage lasts
about 8 days

Schematic evolution of tropical QMO rain anomalies

4
3

1
4

1

2

EIO from 60 to 70E and their subsequent development and eastward propagation along the equator; (2)the formation of a slanted band of rainfall anomalies,
tilted from the maritime continent to India; (3) thenortheastward migration of the rainband in the northern Indian and western North Pacific Oceans, and (4)the rapid spread of these rainfall anomalies along the
ITCZ to the eastern North Pacific (ENP) and concurrent slow northward migration to the Philippine Sea.

4 SST anomalies in association with the intraseasonalrainfall anomalies
A striking feature seen in Fig. 2 is that significant SST
anomalies occur in a systematic manner throughout the
QMO life cycle. For instance, from Phase 3 to 7, warm
SST anomalies are always located northeast of theconvective anomalies; i.e., the rainfall anomaly tends tomove toward the area of ocean surface warming. The
implication of this phase relationship will be further
discussed in Sect. 7.
Due to lack of daily observation of precipitation
over the oceans, the SST-precipitation relationshipassociated with boreal summer ISO have been studied
by using OLR (e.g., Kemball-Cook and Wang 2001;
Stephens et al. 2004) or MSU data (e.g. Fasullo andWebster 2000; Webster et al. 2002) as a proxy to precipitation. The SST data that were used are from
NCEP reanalysis (weekly resolution interpreted todaily). These studies found that intraseasonal SST
anomalies tend to be in quadrature with intraseasonal
convective anomalies (e.g., Fasullo and Webster 2000;
Kemball-Cook and Wang 2001; Webster et al. 2002;
Stephens et al. 2004). At odds with others, Vecchi andHarrison (2002) found a lag of the same length but
opposite in sign, with cold water leading precipitation.
Here, we used the best available satellite observations

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1

3

and further scrutinize the intraseasonal SST-rainfall
relationship.
To see the phase relationship between local SST andrainfall anomalies, we show, in Fig. 4, their lag correlation coefficients computed for three regions of the largest
ISO variability: the eastern EIO, the head Bay of Bengal,
and the Philippine Sea. A positive time lag means that
SST leads rainfall. Over the eastern EIO, the SST andrainfall anomalies exhibit a nearly perfect quadrature
temporal phase relationship, thus the simultaneous correlation coefficient is nearly zero. However, this is nottrue in the off-equatorial monsoon regions, i.e., the Bayof Bengal and the Philippine Sea. In these areas, theocean surface warming tends to lead enhanced convection by about 10 days, but the enhanced rainfall leads
ocean surface cooling by about 5 days. Of note is that thesimultaneous correlation between SST and rainfall
anomalies is not zero but a significant negative value(between –0.2 and –0.3). What is new here is that the
SST-precipitation relation is different between theequatorial and off-equatorial regions.
The intraseasonal SST-rainfall relationship can berealistically reproduced using the coupled atmosphereocean model for boreal winter eastward propagation of
MJO (Waliser et al. 1999) and northward-propagating
monsoon ISO (Fu et al. 2003), but not in the AGCMalone simulations in which the SST and rainfall
anomalies tend to be in phase (Wu et al. 2002; Fu andWang 2004). This suggests that the observed SSTPrecipitation relationship results from atmosphere–
ocean interaction.

5 Intraseasonal seesaw between the Indian and ENP
monsoons
Figure 2 shows that the rainfall anomalies over thesouthern Bay of Bengal (5–15N, 80–100E) tend to be

B. Wang et al.: Boreal summer quasi-monthly oscillation in the global tropics (2006) 27:661–675

Lag correlation (SST leads precipitation)
EIO (5S-5N,80-90E)
BOB (10-20N,90-100E)
PS (10-20N,130-140E)

Lag correlation coefficientFig. 4 Lag correlation
coefficients between SST and
precipitation (TRMM/3B42)
QMO anomalies at three key
locations. A positive lag
indicates that SST leads
precipitation. The
computation periods are fromMay 1 to September 30 overthe central equatorial Indian
Ocean (EIO) (5S–5N, 80–90E) and the Bay of Bengal(10–20N, 90–100E), andfrom June 1 to October 31
over the Philippine Sea (10–20N, 130–140E). The
sample size at each location is
900

Lag (days)out of phase with anomalous rainfall over the ENP(5–15N, 85–105W), a region that has typical monsoon
features (Murakami et al. 1992). Figure 5 shows the lagcorrelation between QMO rainfall anomalies in the tworegions, which confirms the seesaw feature shown in
Fig. 2. The maximum correlation coefficient is about –0.60 for a sample size of 900 occurring at lag –16 day,
implying that the rainfall anomaly in the southern Bay
of Bengal leads the anomaly in the ENP by about
16 days. Meanwhile the minimum concurrent negative
correlation is about –0.70, implying that it is out of phaseby nearly 180. A stronger negative correlation pattern
exists in the 200 hPa-velocity potential field (Fig. 5).

SST leads

WWT05 has shown that during the re-initiation of
new convective anomalies, the EIO is under control oflarge-scale upper-level convergence and subsidence;
when deep convection starts in the central EIO, a
negative OLR anomaly develops locally, and upperlevel divergence occurs later as a result of deep convection rather than because of it (Fig. 4, WWT05).
Using 7-year data, we constructed a composite evolution of 200 hPa-velocity potential anomalies derived
from ECMWF reanalysis along with TRMM 3B42
rainfall anomalies. The derived figure is similar to
Fig. 4 in WWT05 and is therefore not shown here.
However, we want to add two remarks to WWT05.

Bay of Bengal & eastern North Pacific:Rainfall: χ 200

Lag correlation coefficientFig. 5 Lag correlation
coefficients in QMO rainfall
anomalies from TRMM/3B42(solid line) and the 200 hPavelocity potential anomalies(dashed line) between thesouthern Bay of Bengal (5–15N, 80–100E) and theeastern North Pacific (ENP,
5–15N, 85–105W). The
negative lag indicates that the
Bay of Bengal leads ENP

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Lag (days)

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First, different to the MJO, the upper-level velocity
potential throughout the entire QMO cycle shows amarked stationary wavenumber one pattern with two
opposite polarities located at 90E and 90W, respectively. The simultaneous negative correlation between
the southern Bay of Bengal (90E) and the ENP(90W), shown in Fig. 5, reflects this stationary wavenumber-one feature and suggests a stationary pulsationof anomalous Walker circulation on the quasi-monthly
time scale, which is consistent with the negative correlation in the rainfall anomalies between the southern
Bay of Bengal and the ENP. Second, during the persistence of the stationary seesaw pattern, the 200 hPavelocity potential perturbation over the Indian Ocean
experiences a northward propagation from Phases 1 to
3 (and Phases 5 to 7) and a transformation from amonopole structure to a north-south dipole pattern in
the Indian Ocean. From Phases 3 to 4, the upper-level
divergence wave shows a rapid eastward movement
across the Pacific and Atlantic Oceans, leading to an
opposite phase of the seesaw pattern. This reversal of
the anomalous Walker circulation is driven by the rapid development of the strong convection over theeastern EIO and the arrival of dry anomalies in the
ENP after the dry anomalies rapidly traverse thePacific.

6 Changes in the rainfall profile and cloud properties
during the genesis process
Figure 6 shows rain rate profile anomalies during
Phases 1 through 3 in the region of initiation (5S–5N,
60–70E). The anomalies are defined as deviations
from the eight-phase mean profile. The rain rate profile
below 1 km was not shown in order to avoid complications arising from signal clutter. During Phase 1,
there is a slightly positive anomaly increasing downward below 4 km in total rainfall rate, which is almost
solely attributed to convective rain (Fig. 6a). Bothconvective and stratiform types of rain develop rapidly
from Phases 1 to 2. During Phase 2, the amount of
convective rain is about 1.5 times that of stratiform rain
(Fig. 6b). The vertical distributions of rainfall rate forboth types show typical properties that are similar to
those in the previous study using TRMM PR data (Liuand Fu 2001) and that have been explained by previous
investigators (e.g., Houze 1993, Chap. 6). From Phases
2 to 3, the total rainfall rate changes little, but the ratio
between convective and stratiform rain decreases
markedly (Fig. 6c). At this stage, the amount of stratiform rain is about 1.5 times that of convective rain.

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The above results suggest the following sequence ofcloud development during the genesis process of
QMO. In Phase 1, shallow convection develops first,
followed by rapidly deepening convective development
in Phase 2. Finally, in Phase 3, stratiform clouds predominate. This hypothesis is consistent with someprevious studies (Kemball-Cook and Weare 2001; Wu
2003) that focused on different locations (the Maritime
Continent and the western Pacific Ocean, respectively)
and during different seasons (primarily, the boreal
winter). More importantly, the stratiform precipitation
has a rate comparable to that of convective clouds,
which may provide a rigorous test for GCMs.

7 Discussion: mechanisms of boreal summer QMO
7.1 How is the QMO sustained?
Explanations of the initiation and maintenance of theboreal summer ISO have been contentious. WWT05
discussed this issue, and our current discussion here
draws upon that research. Rodwell (1997) proposedthat a dry phase (break) in the summer South Asianmonsoon could be triggered by the injection of highnegative-potential vorticity air coming from Southern
Hemisphere extratropical disturbances and entering a
low-level monsoon flow. However, the extratropical
weather systems of both hemispheres are essentiallychaotic, with time scales spanning days. In the absenceof some selection criteria, it is difficult to attribute therelatively regular low-frequency variations of the
QMO to mid-latitude weather noise. Model studies by
Wang and Xie (1997) suggested that the off-equatorial
Rossby waves could transfer energy to the equatorial
region through basic-state Hadley circulation andcould then invigorate equatorial convective anomalies.
However, the means of transfer were not addressed.
Krishnan et al. (2000) speculated that decoupling of theeastward-propagating equatorial anomaly and thenorthwestward-propagating descending Rossby waves
from the central Bay of Bengal might contribute to thetransition from a wet phase to a break phase in the
Indian summer monsoon. But, the reasons for why
decoupling occurs and how decoupling generates newconvective anomalies were left as unanswered questions.
Stephens et al. (2004) suggested that the hydrological cycle associated with the ISO acts as a self-regulating oscillator. They argued that regulation occurs asa feedback between radiation processes and hydrological processes in the atmosphere. It is argued that

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669(b) Phase 2(c) Phase 3

mm/hour

mm/hour

mm/hour

Height (km)(a) Phase 1

Fig. 6 Composite vertical profiles of precipitation rate anomalies associated with QMO measured by the TRMM precipitation
radar (TRMM/2A25) over the EIO (5S–5N, 60–70E) where

ISO initiates. Each phase spends about 4 days. Red and green
color represent convective and stratiform rain rate, respectively

there are three main phases to self-regulating oscillation: (1) a destabilization phase in which the surface
layer of the ocean heats while the upper atmosphereradiatively cools, (2) a convective phase in which deep
convection grows in the heated ocean region as available potential convective energy is released, and (3) arestoring and stabilizing phase in which SST is cooledby strong winds and reduced radiation, while the upper
atmosphere is radiatively heated, leading to a reduction of convection and a calming of the winds. Stephens et al. (2004) noted the importance of thehydrological cycle in explaining time scales spanning
30–60 days as a natural oscillator through feedbacks
between local thermodynamics and dynamic responses.
Note that their argument does not take into accountthe complex feedback of atmospheric circulation that
occurs in both space and time. Stephens et al. (2004)
also argued that the heating of the ocean during theundisturbed phase is a necessary condition.
The dashed line in Fig. 7 shows the composite life
cycle of the SST and the rainfall anomalies in the
QMO initiation region (5S–5N, 60–70E). The
warming leads rainfall anomalies by about 1.5 phase,
which suggests that sea-surface warming may precondition the initiation. On the other hand, although
weak positive SST anomalies appear over the EIO in
Phase 1 before the occurrence of the new rainfall
anomaly (Fig. 2), the occurrence of the new convective
anomalies (Phase 2) does not take place directly above
the maximum SST anomalies, suggesting that the insitu weak SST warming is not sufficient in the initiationof new convective anomalies.

Complementary to the argument made by Stephenset al. (2004), WWT05 took into consideration the roleof dynamics of the atmospheric circulation. Using
3 years of data (1999–2001), WWT05 showed that the
local moisture convergence in the western-central EIO
occurs in Phase 1 before the re-initiation of the next
QMO cycle. To confirm this finding, we show in Fig. 7
the composite life cycle of the surface convergence in
the initiation region where QuikSCAT data give reliable estimations (dot-and-dash line in Fig. 7). The
surface convergence leads rainfall anomalies by about
one half to one phase, suggesting that the surface
convergence may play a critical role in initiating the
new ISO convection. WWT05 proposed that both thein situ surface wind convergence and the central EIO
warming are induced by the anomalous conditions set
up in the previous cycle; therefore, a self-induction
mechanism is acting in maintaining the Indian monsoon active-break cycle. The present study supports
this hypothesis through the use of an extended dataset
and further suggests that this self-induction mechanism
plays a key role in maintaining the global scale boreal
summer QMO.
Why does this self-induction mechanism occur onlyin the western-central Indian Ocean? The main reason
is that the strongest QMO convective anomalies during the boreal summer is located in the eastern EIO,
which creates the strongest surface divergence/convergence to its west through atmospheric Rossby wave
response and creates SST anomalies to its west when it
moves eastward and encounters the blocking of theSumatra Island. WWT05 showed that the upper-level

123

SST ˚C

7

Sfc. Conv. ( 10 ) s–1Central EIO (5S-5N,60-70E)–1Fig. 7 Composite life cycle ofrainfall (TRMM/TMI),
surface convergence, and SST
anomalies in the initiation
region (5S–5N, 60–70E).
The bar segments represent
90% confidence interval
based on the Student t-test

B. Wang et al.: Boreal summer quasi-monthly oscillation in the global tropics (2006) 27:661–675

Rainfall mm day

670: rainfall: SST: sfc. Conv.

Phase

divergence wave does not lead the development of the
new convection over the central EIO; rather it is a
result of the Indian Ocean ISO convective development. Once the ISO strongly developed in the Indian
Ocean sector, the upper-level circulation anomalies
eventually propagate eastward. The region outside of
the Indian Ocean sector is thus a passive response to
the strong oscillation in the northern Indian Ocean.
That is why the EIO is a key region for the recurrenceof the QMO. The ocean surface warming and surface
convergence that are critical for generation of a new
wet ISO cycle is essentially a result of the dry phase in
the eastern EIO in the previous cycle. The specificphase-locking development of boreal summer QMO
determines the Indian Ocean is conducive for selfinduction.
7.2 Why does the QMO rainband movenortheastward?
The present results show that the northward propagation over the eastern Indian Ocean (Fig. 1b) is a twodimensional manifestation of the northeastward
movement of the slanted rainband (Fig. 2). The twodimensional northward propagation of QMO has
stimulated numerous studies in the last two decades,
producing a large number of hypotheses and theories(for a detailed review, see Wang 2005). Here, we argue
that air–sea interaction may be one of the contributing
factors in the northeastward propagation of the intraseasonal rainband.
How can air–sea interaction enhance northeastward
propagation? As shown in Sect. 4, the air–sea interac-

123tion creates a phase difference between SST andrainfall anomalies. Figure 2 shows clear evidence thatthe organized rainfall anomalies tend to move northeastward toward areas of ocean-surface warming. The
sea-surface warming caused by subdued convection in
the dry phase of QMO may play an important role insteering the QMO convective anomalies toward ananomalously warm ocean surface. First, sea-surface
warming may enhance energy flux into the atmosphere,
which helps to destabilize the atmospheric boundary
layer and build up convective available potential energy for organized deep convection (Stephens et al.
2004). Second, the surface-warming-induced sensible
heat flux would lower surface pressure. This assertion
is supported by the composite life cycle of rainfall
anomalies and surface pressure anomalies, shown in
Fig. 8. A pressure decrease tends to lead the enhanced
rainfall by about one phase in the Bay of Bengal,
where the northward propagation component is
prominent. The positive SST-anomaly-induced lower
surface pressure would, in turn, reinforce the boundary-layer convergence and promote organized deep
convection to the northeast of the convection anomalies.
Another potentially important contributor is thevertical easterly shear in the monsoon region. How canthe easterly vertical shear of the summer mean circulation contribute to northeastward propagation of therainband? Wang and Xie (1997) demonstrated that theslanted rainband forms as a result of the continuing
emanation of moist Rossby waves from the equatorialconvective anomaly (an equatorial Kelvin–Rossby
wave packet). Thus, the rainband has an eastward

B. Wang et al.: Boreal summer quasi-monthly oscillation in the global tropics (2006) 27:661–675

BOB (10-20N,85-95E): rainfall

–1

: Ps

Ps hPaRainfall mm day

Fig. 8 Composite life cycle ofsea-level pressure and rainfall
anomalies (TRMM/TMI) in
the Bay of Bengal (10–20N,
85–95E). The bar segments
represetnt 90% confidence
interval based on the Student
t-test671

Phase

component following its equatorial eastward propagation. The individual moist Rossby wave moves westward; its northward component may be understood interms of the effects of the mean flow on the moist
Rossby waves. The cells embedded in the northwest/
southeast-tilted rainband are convectively interactive
Rossby waves (Wang and Xie 1997). A peculiar featureis that the relative vorticity anomalies associated with
the QMO rainband have a barotropic structure, andthe positive vorticity anomaly is located north of thecorresponding anomalous convection center (Jiang
et al. 2004). The positive vorticity, in turn, induces
boundary-layer convergence, which destabilizes theatmosphere and triggers new convection to the north of
the anomalous convection, thus favoring northward
propagation. The result of Jiang et al. (2004) is derived
for convection anomalies located at 5N. To see
whether or not this is a general case in all phases ofnorthward propagation, we show the meridional vertical profiles of vertical motion and relative-vorticity
anomalies along 90E (Fig. 9). When the maximum
upward motion is located around 5N (Phases 5 and 6),
the positive relative-vorticity anomaly below about
150 hPa is located about 3–5 latitude to the north of
the upward motion, which agrees with Jiang et al.
(2004). However, the spatial phase difference between
positive vorticity and upward motion decreases during
the northward movement of the ISO rainband. Notealso that the relative vorticity anomalies have a dominant barotropic structure but with a slightly northwardtilt.

A key question is, how is the relative vorticity
anomaly generated? Obviously, the barotropic vorticity anomalies cannot be directly forced by the convection, and there must be other reasons for its
existence. Wang (2005) illustrated how a mean zonal
flow with easterly vertical shear can generate such abarotropic vorticity anomaly. The interaction between
barotropic mode and baroclinic mode in the presenceof basic flow easterly vertical shear can be understoodin terms of a two-layer equatorial beta plane model. To
illustrate this mechanism, let us consider a simplified
2-D version of the model of Wang and Xie (1997) inwhich the zonal variations of the basic state anddependent variables are neglected. The vorticity
equations for the barotropic component (denoted by
the subscript ‘+’) is (refer to Wang and Xie for derivation) :
 
@1þ
@x
¼ bvþ  UT
;
@y
@t

ð1Þ

where UT denotes a constant vertical shear of thebasic monsoon zonal flow. Equation 1 indicates thatin the presence of vertical easterly shear (UT < 0 in
p-coordinate), a northward decrease in the perturbation upward motion can generate positive barotropic vorticity to the north of the convection.
Conversely, in the equatorward side it would produce
negative barotropic vorticity. That explains how
the vertical monsoon easterly shear interacting withmoist Rossby waves can generate positive (negative)

123

672
Fig. 9 Composite verticalstructure of the QMO along
90E (averaged between 85
and 95E) from Phases 4 to 7.
Shown are anomalies invertical pressure velocity(arrows) and relative vorticity(contours in units of 10–6 s–1).
The data used here were
derived from ECMWF
reanalysis

B. Wang et al.: Boreal summer quasi-monthly oscillation in the global tropics (2006) 27:661–675

ω ( 0.04 Pa/s) and relative velocity (10 –6 s–1 )phase 4phase 5phase 6phase 7barotropic vorticity to the poleward (equatorward)
side of the convective anomalies. Physically, theeasterly vertical shear of the monsoon mean flow has
a strong equatorward horizontal relative vorticity tothe order of 3 · 10–2 ms–1 hPa–1. The Rossby-waveinduced heating generates a perturbation verticalmotion field that decreases northward from theanomalous rain band center. This vertical-motion
field twists the horizontal vorticity of the mean flow,
generating a positive vertical component of relative
vorticity to the north of the rainfall anomalies. For
the same reason, negative vorticity anomalies develop
to the south of the convection region. Thus, theinteraction between moist Rossby-wave-induced
heating and the vertical shear of the mean monsoon
creates a condition that favors the northward movement of the enhanced rainfall.

123

8 Concluding remarks
With a suite of TRMM satellite measurements from1998 to 2004, we examined the full cycle of the boreal
summer ISO by compositing 28 individual events. Our
major findings are summarized as follows.
(a)

The average oscillation period during boreal
summer over the Indian Ocean sector is about
32 days (Fig. 1). This quasi-monthly oscillation(QMO) is originated and reaches maximum
strength in the EIO. The tropics-wide QMO isprimarily confined to the tropical Indian and Pacific Oceans.
(b) The life cycles of wet QMO anomalies consist offour stages (Figs. 2, 3): (1) the initiation in the
EIO between 60 and 70E and subsequentdevelopment and eastward propagation along the

B. Wang et al.: Boreal summer quasi-monthly oscillation in the global tropics (2006) 27:661–675

equator; (2) the formation of a slanted band of
rainfall anomalies tilted from the maritime continent to India; (3) the northeastward migration of
the rain band in the northern Indian and western
North Pacific Oceans, and (4) the rapid spread ofthese rainfall anomalies along the ITCZ to the
ENP and concurrent slow northward migration to
the Philippine Sea.
(c) A marked east-west seesaw in the rainfall anomalies exist between the southern Bay of Bengal
and the ENP (Fig. 5). In general, the anomalous
ISO convection in the southern Bay of Bengal
leads convection in the ENP by about 16 days.
One may take advantage of this intraseasonalteleconnection to enhance predictability of the
ISO in the ENP.
(d) During the initiation of the convective anomalies,
the stratiform and convective rains have comparable rates; the prevailing cloud type experiencesa trimodal evolution from shallow to deep convection, and finally to anvil and extended stratiform clouds (Fig. 6).
(e) SST-precipitation relation is found different
between the equatorial and off-equatorial regions(Fig. 4). In the equatorial region, the simultaneous correlation between SST and rainfall
anomalies is nearly zero; but in the off-equatorial
region, significant negative correlation is found.
The observed evidence presented here provides
critical information for validating the numerical models. The finding that the stratiform precipitation rate in
the genesis region has the same order of magnitude as
the convective precipitation may have important
implications for numerical modeling of the ISO. The
AGCMs that fail to simulate or that poorly simulate
the ISO might have underestimated the portion ofcondensational heating released by stable precipitation.
The observed simultaneous negative correlation andthe ‘‘asymmetric’’ SST-rainfall correlation in the offequatorial region have important implications inmonsoon climate simulation and prediction. As foundby Wang et al. (2004) in a performance assessment of
11 atmospheric GCMs (AGCMs) that simulate summer monsoon precipitation, all models produce a positive simultaneous SST-rainfall correlation over thePhilippine Sea and South China Sea, which is at odds
with observations. The observations showed a negative
simultaneous SST-precipitation correlation in theseheavy precipitating summer monsoon regions whenmonthly anomalies are considered. The results heresuggest that a negative SST-precipitation correlation

673

seen on monthly mean time scale may result from theasymmetric SST-rainfall relationship on the intraseasonal time scale. Over the off equatorial region, it isseen from Fig. 4 that the simultaneous correlation isnegative, and the negative lag-correlation is stronger
than the positive lead-correlation. The corresponding
monthly mean SST-precipitation correlation is indeed
negative (figure not shown).
Previous studies have termed summer monsoon
oscillation during boreal summer as 30–60 day oscillation, suggesting that the boreal summer ISO has thesame time scale as that of boreal winter MJO—theequatorial trapped mode. Based on most reliable
TRMM precipitation estimation, we found the dominant periodicity over the Indian Ocean as is about
32 days rather than centered on 45 days (Fig. 1). This
periodicity is determined from analysis of individual
events which do not include the entire period of theboreal summer. Thus, discrepancies are expected
between the current analysis and the previous spectral
analysis. Our additional spectral analysis shows thatthe major spectral peak over the eastern EIO is at
30 days and in the ISM regions are in the range of
25–45 days, which supports the conclusion derived
from Fig. 1. However, over the western Pacific, thepeak is shifted to lower frequency around 45 days
(figure not shown), which is in a better agreement with
Chen and Murakami (1988). The QMO lifecycle patterns shown in Figs. 2 and 3, indeed consists of thepreviously reported ‘‘30–60 day’’ (may be more precisely 30–40 day) monsoon trough/ridge extending
from India to the western Pacific (Krishnamurti andSubrahmanyam 1982; Lau and Chan 1986), as well as
the trough-ridge system over the South China Sea(Chen and Murakami 1988). The movement of the
precipitation anomalies suggests that the intraseasonalmodes identified in the previous studies are interconnected as a whole system.
The 7-year summer data are not long, but it allows
for selection of 28 events for the composite study,
which gives a reasonable sampling for deriving mean
behavior of QMO. The results derived from the 7-year
data, however, need to be cautious because the data
are not long enough to reflect possible decadal variations in QMO.
The theoretical analysis in Sect. 7 suggests that
atmosphere-ocean interaction and the vertical monsoon easterly shear are important contributors to thenortheastward propagation of the intraseasonal rain
band. We should also note that the strong monsoon
easterly vertical shear is a result of the land-ocean
thermal contrast and the effects of the Tibetan plateau.
In this sense, the land processes play an indirect but not

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B. Wang et al.: Boreal summer quasi-monthly oscillation in the global tropics (2006) 27:661–675

negligible role in sustaining northward propagation.
The observed evidence presented here supports theself-induction mechanism theory for maintenance ofthe boreal summer ISO (WWT05).
Although this study offers explanations on aspects of
the QMO, it leaves a number of issues unaddressed.
Further clarification is needed about what determines
the speed of the northward propagation component andthe quasi-monthly time scale. Another issue is why theformation of the slanted rainband has preferred alocation near Sumatra. It has been speculated that the
decay of ISO over the maritime continent might involve
multiple factors. For instance, the topographic blockingof the Sumatra Island (a mountain range higher than
2 km) could be destructive to the boundary layer organization of the MJO convection (Wang and Li 1994).
The strong diurnal cycle over the Indonesian region is
also an unfavorable condition because it constantly releases convective energy and might demolish the energy
accumulation needed for development of convection on
the intraseasonal time scale. Another possibility is thedestructive land effects on air–sea interaction that is apositive contributor to the QMO. These possibilities
deserve further investigation with numerical models.
Acknowledgements The authors thank Dr. R. Madden for hiscomments on an earlier version of the manuscript. Dr. Bin Wangacknowledges the support from NSF climate dynamics program(ATM03-29531) and in part by IPRC, which is in part sponsoredby FRCGC.

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