Interannual Seesaw between the Somali and the Australian Cross-Equatorial Flows and itsConnection to the East Asian Summer Monsoon

CHEN LI

College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, and Climate Change Research Center and

Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

SHUANGLIN LI

Climate Change Research Center and Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of

Sciences, and State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

(Manuscript received 17 May 2013, in final form 23 January 2014)

ABSTRACT

The correlations among the summer, low-level, cross-equatorial flows (CEFs) over the Indian–west Pacific

Ocean region on the interannual time scale are investigated by using both the NCEP–NCAR reanalysis and

40-yr ECMWF Re-Analysis (ERA-40) datasets. A significant negative correlation (seesaw) has been illus-

trated between the Somali CEF and the three CEFs north ofAustralia (the SouthChina Sea, Celebes Sea, and

NewGuinea; they are referred to in combination as the Australian CEF). A seesaw index is thus defined with

a higher (lower) value representing an intensified (weakened) Somali CEF but a weakened (intensified)

Australian CEF. The connection of the seesaw with the East Asian summer monsoon (EASM) is then in-

vestigated. The results suggest that an enhanced seesaw corresponds to an intensified EASM with more

rainfall in north China, theYellowRiver valley, and the upper reach of theYangtzeRiver. The seesaw reflects

the opposite covariability between the two atmospheric action centers in the Southern Hemisphere, Mas-

carene subtropical high, and Australian subtropical high. Whether the seesaw–EASM connection is influ-

enced by El Ni~no–Southern Oscillation (ENSO) or the Indian Ocean SST dipole mode (IOD) is analyzed.

The results remain unchanged when the ENSO- or IOD-related signals are excluded, although ENSO exerts

a significant influence. This implies an additional predictability for the EASM from the CEF seesaw.

1. Introduction

Cross-equatorial flows (CEFs) play a key role in mass,

moisture, and energy transport between the hemispheres

(Tao et al. 1962; Findlater 1966; Wang and Li 1982;

Wang and Xue 2003). A total of five branches of CEFs

are seen over the tropical Indian–western Pacific Ocean

in boreal summer, including the Somali CEF, Bay of

Bengal (BOB) CEF, South China Sea (SCS) CEF,

Celebes Sea CEF, and the New Guinea CEF (Li and

Lou 1987; Shi et al. 2001; Peng and Jiang 2003; Shi et al.

2007; Li andHu 2011). The latter three are located in the

north of Australia and are referred to in combination as

the Australian CEF. All the CEFs serve as an important

member of theAsia–Pacific summermonsoons, including

the Indian summer monsoon (ISM), the East Asian

summermonsoon (EASM), or the western North Pacific

summer monsoon (WNPSM) (Zhu et al. 1986; Tao and

Chen 1987; Wang and Ho 2002).

Numerous investigators have studied the importance

of individual CEFs for the EASM. In comparison to the

ISM, for which the Somali CEF plays the dominant role

formoisture transport (Saha andBavadekar 1973; Cadet

and Reverdin 1981), the amount of rainfall (Ramesh

Kumar et al. 1999; Halpern and Woiceshyn 2001), and

even the intraseasonal active–break phase of the mon-

soon (Joseph and Sijikumar 2004; Cadet and Greco

1987), these CEFs play various roles for the EASM (Tao

and Chen 1987). The Somali CEF is not dominant and

plays just a minor role in determining the EASM rainfall

(Simmonds et al. 1999). Instead, the Australian CEF is

more important. The stronger Australian CEF favors

weakening of the EASM along with an eastward and

northward movement of the western Pacific subtropical

Corresponding author address: Dr. Shuanglin Li, NZC/IAP/

CAS, P. O. Box 9804, Beijing 100029, China.

E-mail: shuanglin.li@mail.iap.ac.cn

3966 JOURNAL OF CL IMATE VOLUME 27

DOI: 10.1175/JCLI-D-13-00288.1

� 2014 American Meteorological Society

high (WPSH), a key member of the EASM system (Liu

et al. 2009). Besides, the SCS CEF is positively corre-

lated with the tropical component of the EASM, but is

oppositely correlated with the ISM (Mao et al. 1990).

The importance of the CEFs for various regional rain-

falls in China was also studied. Han (2002) suggested

that 1) the intensified Somali CEF favors above-normal

rainfall in northern China but deficiency in the Yangtze–

Huaihe River basin, 2) the BOB CEF is negatively re-

lated with rainfall in the middle and lower reaches of the

Yangtze River, and 3) the Australian CEF is negatively

correlated with rainfall in the region in between the

middle reaches of the Yellow River and the Yangtze

River but positively with rainfall in southern China.

A similar result was obtained by Zhu (2012).

It is not of muchmeaning to understand the individual

role of the CEFs for predicting the EASM, because the

CEFs are not independent on but interact with each

other (Zhang 2001; Wang and Yang 2008). Cong et al.

(2007) found a strong negative correlation between the

Somali and SCS CEFs, and the negative correlation

varies at the decadal time scale. Recently, Zhu (2012)

displayed two interdecadal shifts in both the Somali and

Australian CEF, and they occurred almost simulta-

neously: one around the late 1970s and the other around

the late 1990s. This suggests coherence between them on

a decadal time scale.

The present study aims to investigate the linkage be-

tween the CEFs on the interannual time scale, as well as

its connection to the EASM. Details about the dataset

used are described in section 2. The results about the

linkage between the CEFs are given in section 3. Be-

cause a seesaw connection is found between the Somali

and Australian CEF, section 4 discusses the connection

of the seesaw with the EASM. Considering the potential

influence of SST, section 5 analyzes the roles of El Ni~no–

Southern Oscillation (ENSO) and the tropical Indian

Ocean (IO) SST dipole (IOD) in shaping the seesaw

connection. A summary and discussion are given in the

last section.

2. Datasets and methods

Various datasets for atmospheric circulation, convec-

tion, rainfall, and sea surface temperature (SST) are uti-

lized. Two atmospheric circulation reanalysis datasets are

used: the National Centers for Environmental Prediction–

National Center for Atmospheric Research (NCEP–

NCAR)monthly-mean reanalysis (Kalnay et al. 1996) and

the 40-yr European Centre for Medium-Range Weather

Forecasts (ECMWF) Re-Analysis (ERA-40) (Uppala

et al. 2005). The National Oceanic and Atmospheric Ad-

ministration (NOAA) interpolated outgoing longwave

radiation (OLR) data are used for understanding convec-

tion activity (Gruber and Krueger 1984). Two monthly-

mean precipitation datasets, one from the Global

Precipitation Climatology Project (GPCP, version 2) for

1979–2013 (Adler et al. 2003) and the other from the

mainland China 160-station precipitation for 1951–2011

provided by China Meteorological Administration, are

used to analyze the CEFs connection with East Asian

rainfall. The GPCP precipitation product is used due to

its better agreement with that recorded in China in

comparison with other global rainfall datasets (Ma et al.

2009). The distribution of the 160 stations in China is

displayed together with the composite rainfall anomaly

in the next section (see Fig. 5e below). The NCEP–

NCAR reanalysis, ERA-40, OLR, and GPCP data are

on a 2.58 3 2.58 grid. The Ni~no-3.4 index is adapted from

NOAA’s Climate Prediction Center (CPC). The SST

dataset is from the Kaplan extended SST (version 2) at

a 58 3 58 resolution (Kaplan et al. 1998).

Since this study focuses on the interannual time scale,

a linear detrended processing is first applied to the above

datasets prior to all the analyses. Then the fast Fourier

transform (FFT) filter is used to remove the components

beyond the interannual time scale (i.e., greater than 10 yr).

This study stresses the interannual time scale in compar-

ison with most of the previous studies in which unfiltered

original data are used (e.g., Cong et al. 2007). The basic

methods used include composite and correlation analyses.

A Student’s t test is applied for checking the significance

of the results. Because several various indices are adapted

or defined in the study, Table 1 lists a brief introduction to

them for the convenience of reference.

3. The correlations between the CEFs

Figure 1 (upper panels) compares the vertical distri-

bution of the meridional wind component across the

equator (averaged 2.58S–2.58N) over the Indian–western

Pacific Ocean between the two reanalyses. Consistent

in both reanalyses, there are five branches of cross-

equatorial southerlies located near Somalia (37.58–62.58E,hereafter Somali CEF), the Bay of Bengal (82.58–908E),the South China Sea (102.58–1108E), the Celebes Sea of

the western Pacific (WP; 122.58–1308E), andNewGuinea

(NG; 147.58–152.58E). The locations of the five branches

exhibit a considerable consistence in both datasets, albeit

with slightly stronger amplitudes in ERA-40. Previous

studies have revealed that the latter three of the above

five branches, north of Australia, are highly correlated

with each other and may be an integral split by local to-

pographies in the Maritime Continent. Correlation ana-

lyses are conducted for both reanalyses (see the appendix),

and the results validate the substantial linkage among

1 JUNE 2014 L I AND L I 3967

them. Thus, they are combined and referred to as the

Australian CEF in the below analyses.

To obtain a direct vision at the CEFs, Fig. 1 (bottom

panels) displays the spatial distribution of horizontal

winds at the surface. Visually substantial cross-equatorial

flows exist in the above-mentioned five regions (marked

by five small boxes), and a substantial similarity is seen

in both reanalyses. Previous studies suggested differences

between the two reanalyses. Particularly, ERA-40 is more

rational in describing the atmosphericmass flux between

the hemispheres (Zhao and Li 2006). However no sig-

nificant difference between the CEFs is seen here. One

possible explanation is that both the datasets have been

preprocessed by detrending and filtering, and the in-

ternal decadal gap in part responsible for their differ-

ence has been diminished.

Unlike the Somali CEF which has a maximum speed

core around the 850-hPa level, both the BOB and the

Australian CEF have a maximum at a relatively lower

level near 925 hPa. Thus, the meridional wind compo-

nent at 850 hPa is used to measure the strength of So-

mali CEF, while that at 925 hPa is used for the BOB

and Australian CEF. Such a selection of vertical levels

is slightly different from most of the previous studies

(e.g., Cong et al. 2007; Zhu 2012), in which all the CEFs

are defined on the same level, 925 hPa. An index is

defined as the mean of the meridional wind compo-

nents averaged over their individual regions shown in

Table 2 for each CEF. The Australian CEF index is

defined as the mean over the three regions north of

Australia (Figs. 1b,d).

Figure 2 displays the evolution of the CEF index

anomalies in the past decades together with the clima-

tological mean and standard deviation of the three CEFs

(Somali, BOB, and Australian) in the two datasets. The

strength of the CEFs exhibits an increase from the west

to the east in terms of their locations. The standard de-

viation of the CEF index for the Somali, BOB, and

Australian CEFs in theNCEP–NCAR reanalysis is 0.52,

0.52, and 0.91 individually, which exhibits an overall

increase eastward.A similar feature is also seen in ERA-

40 with the values of 0.36, 0.55, and 0.81, respectively.

Visually, there exists substantial decadal variability in

the CEFs. For example, for the Somali CEF there are

high values around the late 1950s and early 1980s but

lower values during the 1970s and the late 1990s.

To check the interannual linkage between the CEFs,

we filtered their individual decadal component and cal-

culated their correlation coefficients by using the data in

the period of 1970–2011. Only the data after year 1970

are used, because they are more creditable due to the

application of satellite observations since then (Kistler

et al. 2001). From Table 3, a negative correlation of the

Australian CEF with the Somali CEF is significant in

both datasets, suggesting a seesaw linkage between

them. Besides, neither the Somali CEF nor the Aus-

tralian CEF is significantly correlated with the BOB. In

other words, the BOB CEF may vary independently

relative to the Somali or the Australian CEF.

To shed light on the variation of the above connec-

tion between the CEFs, Fig. 3 displays year-to-year

evolution of the interannual correlation calculated in

TABLE 1. Description of various indices used in this study.

Index Description Details Reference

Seesaw Seesaw index describing the opposite

covariation between the Somali and

Australian CEFs

The difference between the standardized mean

strength of the Somali CEF and Australian

CEF with the same weight.

Herein

Ni~no-3.4 El Ni~no index Area-averaged SST anomalies in the Ni~no-3.4

region (58S–58N,1708–1208W).

Available online (http://www.

esrl.noaa.gov/psd/data/

correlation/nina34.data)

DMI The dipolar mode index of the

tropical Indian Ocean SST

The difference in SST anomaly between the

tropical western IO (108S–108N, 508–708E) andthe southeastern IO (108S–equator, 908–1108E).

Saji et al. (1999)

MPCOI Maritime Continent–Pacific

convection oscillation index

The time series of the first leading EOF mode of

interannual convection variation over the

domain (208S–308N, 308E–1708W).

Li et al. (2013)

IPCOI Indo-Pacific convection oscillation

index

As above, but for the second leading EOF mode. Li et al. (2013)

EASMI East Asian summer monsoon index Difference of 850-hPa u wind component in

(22.58–32.58N, 1108–1408E) minus that in

(58–158N, 908–1308E).

Wang et al. (2008)

Area-averaged dynamical normalized seasonality

(DNS) at 850 hPa within the East Asian

monsoon domain (108–408N, 1108–1408E).

Li and Zeng (2002)

3968 JOURNAL OF CL IMATE VOLUME 27

an 11-yr running window in both datasets. First the

correlations are qualitatively consistent in two re-

analyses. One significant negative correlation between

the Somali and Australian CEFs exists in both re-

analyses, albeit it is slightly stronger in ERA-40. The

correlation between the Somali and the BOB CEFs is

insignificant in both reanalyses. One inconsistence be-

tween the two datasets is a positive correlation prior

to the mid-1960s in the NCEP–NCAR reanalysis, but

it may be unrealistic due to the poor data quality dur-

ing that time. Besides, decadal variation in the corre-

lations is evident, although a discussion about it is

beyond the scope of this study.

In view of the substantial negative correlation be-

tween the Somali and Australian CEFs, a seesaw index

is defined by using the standardized mean strength of

the Somali CEF minus that of the Australian CEF with

the same weight for the two CEFs (Table 1). A higher

(lower) seesaw index indicates the intensified (weak-

ened) Somali CEF but weakened (intensified) Austra-

lian CEF. Figure 4a displays the seasonal cycle of the

seesaw index. One may see that it peaks in summer

[June–August (JJA)] and becomes opposite in winter

along with seasonal annual cycle.

How the seesaw is connected to EASM and the lower-

boundary forcing are investigated in the next sections.

FIG. 1. (a),(c) Vertical distribution of climatological summer (JJA) seasonal meridional wind component near the

equator (m s21) and (b),(d) horizontal distribution of surface winds over the Asian–Pacific monsoonal region. The

climatology is calculated as the mean during 1958–2001. (left) Calculated based on the NCEP–NCAR reanalysis.

(right) Calculated based on ERA-40. The horizontal solid lines in (a) and (c) stand for the vertical levels and the five

rectangular boxes in (b) and (d) stand for the regions that are used to calculate the Somali, BOB, and threeAustralian

CEFs, respectively.

TABLE 2. The geographic regions used to measure the strength of individual CEFs. SCS,WP, and NG indicate the three branches north

of Australia, that is, the South China Sea CEF, the Celebes Sea CEF, and the NewGuinea CEF, respectively. The mean of them are used

to calculate the Australian CEF index.

Somali BOB

Australian

SCS WP NG

Latitude 2.58S–2.58N 2.58S–2.58N 2.58S–2.58N 2.58S–2.58N 2.58S–2.58NLongitude 37.58–62.58E 82.58–908E 102.58–1108E 122.58–1308E 147.58–152.58EVertical level 850 hPa 925 hPa 925 hPa 925 hPa 925 hPa

1 JUNE 2014 L I AND L I 3969

Because the seesaw peaks in boreal summer, all the

below analyses are for the summer season (JJA) except

for when specially clarified. Just the NCEP–NCAR re-

analysis is used hereinafter in view of its longer record,

although ERA-40 is more rational in describing atmo-

spheric mass flux between the hemispheres (Zhao andLi

2006).

4. Connection to the East Asian summer climate

a. Rainfall and convection

Figure 5a shows the correlation of the Somali–

Australian CEF seesaw index with rainfall in mainland

China, which is derived from the gauged rainfall dataset

at 160 stations within the country. Significantly positive

correlations appear in the upper reach of the Yangtze

River, Yellow River valley, and north China, while neg-

ative correlations are in the southern coast. The maxi-

mum positive correlation coefficient is 0.56 and the mean

is 0.34, which both are significant at the 95% level. Fur-

thermore, a statistics suggests that 51 (29) stations have

the correlation significant above the 90% (95%) level,

and a total of 10 stations have a positive coefficient over

0.4. This indicates an intensified EASM corresponding

to a higher seesaw index.

To further check the robustness of connection be-

tween the seesaw and rainfall, a composite analysis is

conducted. Based on the criterion that the seesaw index

is greater (or smaller) than one positive (sign reversed)

standard deviation, the 5 yr with the highest seesaw in-

dex (1973, 1984, 1989, 1998, and 2010) and the 5 yr with

the lowest seesaw index (1972, 1979, 1982, 1987, and

1997) are identified for the period since 1970. Figure 6

(left panels) displays a comparison of the composite

rainfall derived from the GPCP dataset. During the high

seesaw index years, there is more rainfall in north China,

but a deficiency in the southern coast, in agreement with

the correlation above based on the station data (Fig. 5a).

We also used the CPCMerged Analysis of Precipitation

(CMAP) rainfall dataset (Xie and Arkin 1997) and ob-

tained a similar result except for a slightly weakened

significance in China (not shown). The consistency re-

vealed in these different datasets suggests that the con-

nection between the seesaw and East China rainfall is

robust. Besides, above-normal (below normal) rainfall

is located over the Maritime Continent, BOB, and the

Indian Peninsula, along with decreased rainfall over the

central to eastern tropical Pacific (Fig. 6c). Thus, there

FIG. 2. A comparison of year-to-year evolution of CEF index anomaly. The panels from top to bottom are the

Somali, BOB, and Australian CEFs, individually. (left) Calculated from the NCEP–NCAR reanalysis, and (right)

calculated from ERA-40. The dashed lines are the values after one 11-yr running mean. The numbers on the upper-

left corner in every panel indicate the std dev and the climatological average of the CEF index (m s21), respectively.

TABLE 3. Correlation coefficient between the Somali, BOB, and

the Australian CEFs calculated for the period 1970–2011. The

bottom-left part is calculated from the NCEP–NCAR reanalysis,

while the top-right part is from ERA-40. The boldface font in-

dicates significance at the 95% level (with the correlation co-

efficient greater than 0.3 or less than 20.3 for the NCEP–NCAR

reanalysis, greater than 0.35 or less than 20.35 for ERA-40). The

values in brackets represent the results when the ENSO-related

signals are removed.

Somali BOB Australian

Somali 0.11 (0.13) 20.62 (20.47)

BOB 20.03 (0.02) 20.08 (20.10)

Australian 20.53 (20.32) 0.13 (0.07)

3970 JOURNAL OF CL IMATE VOLUME 27

exist broad rainfall anomalies over the tropical Indian–

Pacific Ocean and the Asian monsoon area linked to

the seesaw.

The above rainfall anomalies are also reflected in

convection seen in the OLR (right panels, Fig. 6). Cli-

matologically there exist two convection activity centers

(with smaller OLR), one locating over BOB and the

other over the Philippines Seas. During the high seesaw

index years, the convections over BOB and the Mari-

time Continent are stronger along with weakening over

the equatorial central-eastern Pacific relative to the low

seesaw index years (cf. Figs. 6d and 6e). This is partic-

ularly clear in their difference (Fig. 6f).

The above opposite connection in convections over the

Maritime Continent and the equatorial central-eastern

Pacific is reminiscent of the two major convection see-

saws over the Indo-Pacific region, theMaritimeContinent–

Pacific convection oscillation (MPCO) and the Indo-Pacific

convection oscillation (IPCO), found recently by Li et al.

(2013). How the present CEF seesaw is connected with

the MPCO and the IPCO is intriguing but unclear.

Following Li et al. (2013), the MPCO and IPCO indices

(MPCOI and IPCOI) are defined by the time series of

the first and second leading EOF modes, respectively

(Table 1). Figure 7 displays a comparison of the present

CEF seesaw index with the MPCO and the IPCO in-

dices. The CEF seesaw shows a high correlation with

the MPCOI with a coefficient of 0.76, but no significant

correlation with the IPCOI (with a coefficient of 0.16).

This suggests that the CEF seesaw in part may be driven

by theMPCO, but the underlying mechanism is unclear.

Significant convection anomalies also exist over the

southern subtropics and the equatorial Atlantic and

Amazon. This suggests that the convection anomalies

linked to the seesaw are not local or regional but global.

The seesaw-related EASM and tropical convection

connection is in agreement with Wang et al. (2001),

which illustrated that the two major convection centers

FIG. 3. The interannual correlation between the Somali, BOB,

the Australian CEFs in an 11-yr running window. The dashed lines

represent statistical significance at the 95% confidence level.

FIG. 4. (a) The seasonal cycle of the seesaw index and summer

(JJA) seesaw’s lead–lag correlation with the (b) Ni~no-3.4 index and

(c) Indian Ocean DMI. The horizontal dashed lines represent the

correlation coefficient significant at the 95% confidence level, that

in (a) is for the correlation between the Somali and Australian

CEFs.

1 JUNE 2014 L I AND L I 3971

exhibit distinct difference when the ISM and the

WNPSM is stronger or weaker.

b. Atmospheric circulation

In this subsection, the associated atmospheric circu-

lation will be analyzed. Figure 8 shows the composite

sea level pressure (SLP) and 850-hPa horizontal winds.

For SLP, in both the high and low seesaw index years

there are two regions with greater values in the southern

subtropics, with centers locating over Australia and the

Mascarene Islands individually. They reflect the two sub-

systems of Asian summer monsoon; the Australian high

and the Mascarene high (Tao et al. 1962). In the high

seesaw index years, the Australian high is weakened,

meanwhile the Mascarene high is intensified, and vice ver-

sa. This is clearer from the composite difference (Fig. 8c).

There are positive values in the southwest to the Mas-

carene Islands but negative values over Australia. This

suggests that the seesaw reflects the opposite variation

of the two highs, one intensifying and shifting to the

west and the other weakening to some extent. That the

Somali and Australian CEFs are connected to the two

highs has been noted previously (Wang andLi 1982; Xue

et al. 2003). Besides, the most significant negative values

FIG. 5. Correlation of mainland China summer precipitation derived from the 160-station-gauged precipitation

dataset with (a) the Somali–Australian CEF seesaw index, (b) simultaneous Ni~no-3.4 index, (c) Somali–Australian

CEF seesaw index after theENSO signals are removed, and (d)Ni~no-3.4 index in the previouswinter [D(21)JF]. The

bigger and smaller black (gray) dots indicate a positive (negative) correlation significant at the 95% and 90% level,

respectively. (e) Displays the distribution of 160 stations in China.

3972 JOURNAL OF CL IMATE VOLUME 27

are over the tropical Indian Ocean and Maritime Con-

tinent as well as tropical Africa. Substantial negative

anomalies are also seen over the Arab and India Pen-

insulas, which are consistent with the intensified ISM. In

addition, there are positive anomalies over the subtrop-

ical western Pacific and eastern China. They reflect the

enhancement of the WPSH, in agreement with the en-

hanced EASM.

The above features are further seen in 850-hPa winds

(Fig. 8f). There are anticyclonic and cyclonic anomalies

over the locations of theMascarene andAustralian highs,

respectively, along with the intensified southwesterly near

the Somali coast and anomalous northwesterly on the

east coast of Australia. Besides, there are intensified

easterly trade winds in the tropical western Pacific and

enhanced westerly in the Arabian Sea. These are cor-

respondent to the intensified ISM and EASM.

Many previous studies investigated the correlation of

the individual CEFs with the WPSH (e.g., Xue et al.

2003). Cong et al. (2007) suggested that the strength of

the Somali CEF is well connected with the east–westward

movement of the WPSH, while the SCS CEF is better

related to the south–north fluctuation of the WPSH. Here

we analyze the connection of the seesaw to the WPSH.

Figure 9 (left panels) illustrates that during the high seesaw

index years the WPSH is intensified with a westward

extension (Fig. 9a). Besides another subtropical high, the

Iran high, is also intensified. Opposite changes can be

overall seen during the low seesaw index years (Fig. 9b). In

addition to the subtropical anomalies, significant nega-

tive anomalies emerge over the tropical Indian Ocean.

The South Asia high (SAH) in the upper troposphere

(;100 hPa) is another important member of the EASM

system. The SAH is strengthened (weakened) and ex-

pands farther (shrinks) longitudinally during the high

(low) seesaw index years (Figs. 9d,e,f). These changes

are consistent with the enhancement (weakening) of the

ISM or EASM. There is a similar height increase over

the southern subtropics along with a decrease over the

wide tropical region.

FIG. 6. Composite of rainfall (mm day21) derived from the (left) GPCP dataset in the (a) high and (b) low Somali–

Australian CEF seesaw index years and (c) their difference. (d)–(f) As in (a)–(c), but for the OLR (W m22). In (c)

and (f), positive, negative, and zero contours are drawn with solid, dashed, and thick lines, and the intervals of 1 and

10 and shading represent the significance over the 90% and 95% levels, respectively.

1 JUNE 2014 L I AND L I 3973

The connection of the seesaw with monsoonal circu-

lation systems is additionally seen from its correlation

with two EASM indices proposed by Li and Zeng (2002)

and Wang et al. (2008), respectively. Table 1 (the last

row) lists the detailed description of the indices. Albeit

an intraseasonal difference, significant correlation is seen

in July with the coefficient of 20.58 and 0.48, respec-

tively, for the period from year 1970 to 2011. This op-

posite sign in correlation coefficient for the two EASM

indices reflects a consistency considering that they are

intrinsically anticorrelated (Wang et al. 2008).

The above results suggest that the enhancement of

the EASM and ISM linked to the Somali–Australian

CEF seesaw is exhibited consistently in many aspects,

not only in rainfall but also in monsoonal circulation

systems or monsoonal indices. Thus, the linkage is ro-

bust. Previous studies (e.g., Simmonds et al. 1999; Liu

et al. 2009) suggest that both the Somali and Australian

CEFs could affect the East Asian monsoon. We calcu-

lated the individual correlation of summer rainfall in

mainland China with the Somali and Australian CEF as

well as the seesaw index, respectively. The results (not

shown) suggest a somewhat opposite connection to rain-

fall in northernChina between theCEFs, in that a stronger

Somali CEF contributes to intensified rainfall, while

a stronger Australian CEF is associated with weakened

rainfall. But their signals are not opposite in coastal

southeastern China. This illustrates that their effects are

FIG. 7. A comparison of the time series of the seesaw index,

MPCOI, and IPCOI from 1970 to 2011. C. C. stands for the cor-

relation coefficient with the seesaw index.

FIG. 8. As in Fig. 6, but for the (left) sea level pressure (hPa) and (right) wind vector at 850 hPa (m s21) derived

from the NCEP–NCAR reanalysis. (a),(b) Shading represents values greater than 1020 hPa, which are used to in-

dicate the Mascarene and Australian subtropical highs. (c),(f) Shading represents significance over 95% level.

Contour interval is 0.5 hPa in (c).

3974 JOURNAL OF CL IMATE VOLUME 27

not elusive and distinguishable from each other. Fur-

ther, we calculated the station number and found more

stations have significant correlation with the seesaw in-

dex than either CEF. Thus, it may bear moremeaning to

incorporate the CEF seesaw as a predictor rather than

the individual CEFs.

5. Connection with SST

a. Connection with ENSO

Previous studies suggested various connection of

CEFs to ENSO (Wang et al. 2001; Zhu and Chen 2002;

Chen et al. 2005). This connection is also reflected in

ENSO’s influence on the Mascarene and Australian

highs. One preliminary result is that the Somali CEF

tends to be weakened (strengthened), while the Aus-

tralian CEF intensifies (weakens) during a warm (cold)

ENSO episode. Zhu (2012) also illustrated that both the

Somali and Australian CEFs are oppositely correlated

with SST in the central-eastern tropical Pacific. Besides

ENSO, the CEFs’ connections to SST in other oceans

were also studied. Zhu (2012) found a negative corre-

lation between the Somali CEF and the SST in the

western tropical Indian Ocean and Arabian Sea. How

the seesaw is connected to SST, that is, whether the

connection is consistent with that derived from the in-

dividual CEFs, is intriguing.

First we compare composite SST anomaly (SSTA) prior

to and following the summer seesaw and its evolution

(Fig. 10). In the high seesaw index years, La Ni~na–like

SSTA emerge simultaneously in the tropical central-

eastern Pacific along with positive SSTA in the western

Pacific and negative SSTA in the Indian Ocean. From

the preceding winter to the following spring, SSTA

features an evolution frommature El Ni~no tomature La

Ni~na and then decaying La Ni~na. In contrast, in the

low seesaw index years an overall opposite SSTA evo-

lution is seen. This suggests that the seesaw is closely

linked to ENSO.

To further examine the seesaw–ENSO relationship,

we calculated the seesaw’s lead–lag correlation with the

FIG. 9. As in Fig. 6, but for the geopotential heights at (left) 500 and (right) 100 hPa (gpdm). (a),(b) The shading

indicates the climatological contours at 586 gpdm that are used to describe the climatological-mean location of the

subtropical high over the western Pacific. (d),(e) Shading indicates the contours at 1680 gpdm used to describe the

South Asian high, and (c),(f) the shading represents significance over the 95% level (gpdm). Contour interval is 0.5

and 1.0 in (c) and (f), respectively.

1 JUNE 2014 L I AND L I 3975

FIG. 10. (a)–(f) Composite SSTA from the preceding winter to the following spring for the high seesaw index years.

(g)–(i) As in (a)–(f), but for the low seesaw index years. Contour interval is 0.258C. The solid contour indicates 0.

Shading indicates significance at the 95% level.

3976 JOURNAL OF CL IMATE VOLUME 27

Ni~no-3.4 index. The 40-yr data series from 1970 to 2011

is used, although it may not be long enough to sample

the intrinsic variability of ENSO (Wittenberg 2009). From

Fig. 4b, a significant negative correlation (nearly20.76)

is seen persisting into the following winter [December–

February (DJF)(11)]. This suggests that a strong (weak)

summer seesaw coincides with the developing phase

of cold (warm) ENSO episodes as revealed in Fig. 10.

However, it does not imply definitely that the seesaw

leads ENSO, because themagnitude of the correlation is

smaller than the ENSO’s lagged autocorrelation, which

has a value of 0.87 from the developing summer (JJA) to

the peak winter [DJF(11)].

During the high (low) seesaw index summer, the

weakened (strengthened) Australian CEF (Fig. 8) tends

to decrease (increase) evaporation over the tropical east-

ern Indian Ocean and the Maritime Continent (Fig. 11a),

accounting for the warming (cooling) of SST therein

(Figs. 10c,i). This results in strengthening (weakening)

of the Walker cell (Fig. 11b) along with easterly (west-

erly) anomalies near the surface. Then, cooler (warmer)

SSTA in the tropical central-eastern Pacific can persist

into the following winter (Figs. 10d,e,j,k).

The seesaw–ENSO connection is consistent with the

ENSO–East Asian summer rainfall. Summer rainfall in

the Yangtze River valley and northeast China is nega-

tively correlated with the simultaneous summer Ni~no-

3.4 SSTA, but positively correlated with Ni~no-3.4 SSTA

in the preceding winter (Figs. 5b,d). Thus, there is one

correspondence among the summer seesaw, East Asian

summer rainfall, the preceding winter El Ni~no, and the

simultaneous La Ni~na.

Now that the seesaw is closely connected to ENSO,

one intriguing question is whether the seesaw is just one

phenomenon forced by ENSO. In other words, whether

there exist seesaw signals independent of ENSO. To

shed light on this, we had the ENSO-related signals re-

moved and recalculated the correlation between the

CEFs. The procedure to remove theENSO-related signals

is as in Li et al. (2006). First, the ENSO-induced anomaly

is determined by regressing the time series of the inter-

annual meridional wind component at each grid against

the Ni~no-3.4 index time series. As a result, the ENSO

signal for a particular month is a product of the ENSO-

associated anomaly pattern and theENSO index for that

month. Last, the original interannual wind component

minus the ENSO signal yields the ENSO-removed wind

component. It should be noted that such a process just

removes the linear signals. The values in brackets in

Table 3 illustrate that the Somali–Australian CEF see-

saw correlation becomes weaker but still pronounced.

Thus, the seesaw may exist independent of ENSO.

Figure 12 displays the year-to-year evolution of the

interannual correlation between the CEFs calculated in

an 11-yr running window after the ENSO-related signals

are removed. Obviously, similar features to Fig. 3 are

seen except for slightly smaller values. A pronounced

shift around 1965 is still there. When the ENSO-related

signals are removed (in both the precipitation dataset

and seesaw index), the correlation between the seesaw

and the summer rainfall derived from the Chinese

160-station precipitation dataset (Fig. 5c) exhibits a

substantial resemblance to that in the original datasets

(Fig. 5a). This indicates that the connection of the see-

saw with the EASM may exist independently, albeit

ENSO modulates the strength of the correlation.

The independence of the seesaw–EASM is also re-

flected in themonsoonal circulation systems.After ENSO-

related signals are removed, the composite SLP (Fig. 13a)

still displays negative anomalies over the eastern Indian

Ocean and north of Australia, corresponding to the

weakened Australian high. The positive anomaly in the

subtropical southwestern Indian Ocean reflects the west-

ward shift and intensification of theMascarene high, albeit

being less significant. Also the opposite covariability be-

tween the Mascarene high and Australian high is seen

linked to the seesaw. Similarly, at the 850-hPa wind the

enhanced Somali CEF and weakened trade winds in the

eastern Indian Ocean and southern tropical western Pa-

cific north of Australia, along with the intensified easterly

FIG. 11. (a) As in Fig. 6c, but for evaporation flux derived from ERA-40 (contour interval is 0.1mmday21), and

(b) the Walker cell, anomaly indicated with the mean vector streamline (y, v) along the 2.58S–2.58N averaged

longitudinal–vertical section. Here y is the zonal component of horizontal wind (m s21), while v is pressure vertical

velocity and scaled by 1000 (Pa s21).

1 JUNE 2014 L I AND L I 3977

in the northern subtropical western Pacific, are still prom-

inent, although the significant domain is much smaller.

This means that the intensified WPSH and ISM are still

correspondentwith the seesaw, evenwithoutENSO.Thus,

the interannual seesaw between the Somali andAustralian

CEFs during boreal summer may be atmospherically and

intrinsically variable within the Asian summer monsoon

system, albeit the ENSO events tend to intensify it.

b. Connection with IOD

The SSTA associated with the CEF seesaw (Figs.

10c–e,i–k) exhibits a dipolar structure in the tropical

Indian Ocean, reminiscent of the well-known tropical

IOD (Saji et al. 1999; Ashok et al. 2004). The IOD has

been known to have a substantial influence on EASM

(e.g., Yuan et al. 2008; Ding et al. 2010). Also, the lower-

level wind anomaly linked to the seesaw (Figs. 8d–f) is

very similar to the effect of the IOD as shown by Ashok

et al. (2004, their Fig. 8) and Ummenhofer et al. (2011,

their Fig. 3). The IOD and ENSO are synchronously

correlated (e.g., Behera et al. 2006), but often considered

to be independent modes of variability, although this is

still a matter of debates. How the seesaw is connected to

the IOD is thus an important issue.

We calculated the lead–lag correlation of the CEF

seesaw with the dipole mode index (DMI; Table 1) re-

flecting the IOD and found the maximum negative

correlation (20.74) when the summer seesaw leads the

IOD by one or two seasons (Fig. 4c). The magnitude of

this correlation is close to that of the DMI’s lead–lag

auto correlation from summer to fall (0.75). Thus,

a negative IOD tends to coincide with the summer CEF

seesaw. This may be physically reasonable. During

summer with a higher seesaw index, the intensified

southwesterly along the Somali coast causes an increase

of evaporation (Fig. 11a) along with the intensified up-

welling of cool water and the deepened thermocline

(e.g., Izumo et al. 2008), which accounts for the cooling

of SST in the western tropical Indian Ocean there.

Meanwhile, weakened evaporation is seen in the eastern

tropical Indian Ocean and offshore Sumatra (Fig. 11a)

along with the intensified atmospheric ascent over the

Maritime Continent (Fig. 11b). They together cause

a negative-phase IOD in SST. Similar to its connection

with ENSO, the correlation of the seesaw with the IOD

is less significant when the IOD leads.

That the seesaw may exist independently is also seen

when the IOD-related signals are removed. A similar

method to the above to remove the ENSO signals is used

to remove the IOD-associated variability. Calculations

suggest that the negative correlation between the Somali

and Australian CEFs remains unchanged (20.41) after

the IOD-associated signals are removed.

The above analyses suggest that the summer seesaw

should not be explained just as one passive atmospheric

response to the ENSO- or IOD-related SSTA. Instead,

FIG. 12. As in Fig. 3, but with the ENSO signals removed when

calculating the CEF index.

FIG. 13. As in Figs. 8c and 8f, but with ENSO-related signals removed.

3978 JOURNAL OF CL IMATE VOLUME 27

it may contribute to the formation of the IOD in the

following fall. In view of its close connection with the

EASM, the seesaw thus provides an additional bench-

mark for understanding the EASM.

6. Summary and discussions

In this study we first investigated the interannual

correlation between the Somali, Bay of Bengal (BOB),

and Australian cross-equatorial flows (CEFs), all of

which are closely linked to the East Asian summer

monsoon. No significant correlation was seen between

the BOB CEF and the other two, but a significant neg-

ative correlation was illustrated between the Somali

and Australian CEFs. The substantially negative corre-

lation between the Somali andAustralian CEFs suggests

a seesaw oscillation. Thus, a seesaw index is defined with

a greater (smaller) value indicating an intensified (weak-

ened) Somali CEF but a weakened (intensified) Austra-

lian CEF. Then the connection of the seesaw with Asian

summer monsoon is analyzed. The results suggest a sub-

stantial correlation, with a stronger seesaw corresponding

to an intensified ISM and EASM and vice versa. Particu-

larly, the seesaw essentially reflects the opposite fluctua-

tion between the two permanent action centers in the

Southern Hemisphere, the Mascarene high and the Aus-

tralian high. During the higher (lower) seesaw index years,

the Mascarene high moves westward (eastward), along

with increased (decreased) convection in India.Meanwhile,

the Australian high weakens (strengthens), along with

the easterly (westerly) anomalies prevailing over the

tropical western Pacific to the South China Sea. This

favors enhancing the EASM. Subsequently the seesaw’s

connection with ENSO and the Indian Ocean SST dipole

(IOD) is explored. The results suggest that the higher

(lower) seesaw tends to happen in the developing phase

of La Ni~na (El Ni~no) and the negative IOD episode.

When ENSO- or IOD-related signals are removed, both

the seesaw itself and the seesaw–EASM connection are

still significant. Thus, the CEF seesaw should not be

explained as one passive response to ENSO or the IOD.

Instead it contributes to the formation of the IOD.

Many previous studies have addressed the connection

of various CEFs to the Asian summer monsoon, but pri-

marily focus on individual CEFs. Here we illustrated that

the CEFs are not all independent of each other and pro-

posed a seesaw index to describe the opposite covariability

between the Somali and Australian CEFs. This considers

the effect of the combined CEFs rather than their in-

dividual one and thus bears more meaning. The present

study suggests that the seesaw–EASM connection is

closer than the ENSO–EASM connection, and the see-

saw is also connected to the Maritime Continent–Pacific

convection oscillation (MPCO) found recently by Li

et al. (2013). Thus, the seesaw and its close connection

to Asian summer monsoon may be intrinsic variability

within the Asian Monsoon system. This contributes to

understanding the interannual variability of summer

rainfall in the Asian monsoonal region and provides

additional insights into the EASM’s predictability.

It is well known that simulating and predicting the

EASM is a big challenge even in current state-of-the-art

atmospheric or climate system models. That the mod-

eledmonsoon does not reproduce the realistic one is one

potential factor responsible for the biases. Now that the

seesaw and seesaw–EASM connection are intrinsic vari-

ability, this provides one benchmark for understanding

the models’ performance.

It should be pointed out that the present study only

considers the interannual correlations between CEFs.

Substantial decadal variations in this connection can

also be seen (Figs. 3, 12). Thus, more comprehensive

studies are needed to investigate their correlation in

various time scales.

Finally, one may have noted a quasi-biennial oscilla-

tion signal in the CEF seesaw index (Fig. 7). One calcu-

lation of its power spectral confirms this signal (Fig. 14).

How the CEF seesaw is connected to the tropospheric

biennial oscillation (TBO) (e.g., Meehl and Arblaster

2002; Chang and Li 2000) is an intriguing issue and de-

serves an in-depth investigation, since the TBO was

found to relate to the interaction between South Asian

and Indo-Australian monsoons.

Acknowledgments. The authors thank three anony-

mous reviewers for constructive suggestions, which led

to a significant improvement of the manuscript. This

study is jointly supported by the strategic project of the

FIG. 14. The power spectral distribution of the CEF seesaw in-

dex. The values above the dashed line are significant at the 95%

level. The unit for frequency is yr21, and the vertical coordinate is

percentage of explained variance ratio.

1 JUNE 2014 L I AND L I 3979

Chinese Academy of Science (XDA11010401), the Na-

tional Key Basic Research and Development (973)

Program of China (2012CB417403), and the Special

Research Fund for Public Welfare of China Meteoro-

logical Administration (GYHY201006022).

APPENDIX

Correlation Analyses between the CEFsNorth of Australia

There are three branches of cross-equatorial flows

north of Australia, which are located in the South China

Sea (102.58–1108E), the Celebes Sea of the western Pa-

cific (122.58–1308E), and near New Guinea (147.58–152.58E), respectively (Fig. 1). One calculation of their

correlations is conducted from both the NCEP–NCAR

and the ERA-40 reanalyses (Table A1). All the corre-

lations among them and their individual correlations

with their combination, the Australian CEF, are over

0.6, significant at the 99% level. This indicates that they

are not independent of each other, but an integral split

by local topographies in the Maritime Continent.

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