INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. 30: 1114–1120 (2010)
Published online 19 May 2009 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/joc.1955

A multi-time scale Australian monsoon index
Yoshiyuki Kajikawa,a * Bin Wanga,b and Jing Yangc
a International Pacific Research Center, University of Hawaii at Manoa, Honolulu, Hawaiib Department of Meteorology and International Pacific Research Center, University of Hawaii at Manoa, Honolulu, Hawaiic State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics,

Chinese Academy of Sciences, Beijing, China

ABSTRACT: A broad-scale Australian monsoon index (AUSMI) describing multi-time scale variations is defined by using
850 hPa zonal wind averaged over the area (5 ° S–15 ° S, 110 ° E–130 ° E). This circulation index reflects monsoonal rainfall
variability over Northern Australia and maritime continent. The index can be used to depict the seasonal cycle (for instancethe onset) and measure the intraseasonal, interannual, and interdecadal variations of the Australian monsoon. The interannual
variation of the Australian monsoon onset determined by the AUSMI agrees well with that derived from the rainfall andwinds at Darwin in the previous studies. We found a significant anti-correlation between the monsoon onset date and theseasonal (DJF) mean AUSMI anomalies; namely an early onset is accompanied by a strong Australian summer monsoon
and vice versa. These interannual variations are also strongly associated with El Niño-Southern Oscillation (ENSO). In
contrast, the retreat dates are not significantly different between the strong and weak Australian summer monsoon years.
The AUSMI is useful in monitoring the weather and climate variations of the Australian monsoon and validating theperformance of climate models. Copyright  2009 Royal Meteorological Society
KEY WORDS

Australian monsoon; multi-scale; circulation index; monsoon onset

Received 11 September 2007; Revised 3 April 2009; Accepted 18 April 20091.

Introduction

The Australian monsoon is one of the energetic components of the Asian–Australian monsoon system anda major heat source of atmospheric general circulation.
The intraseasonal to interannual variability of Australianmonsoon is connected to global tropical and subtropical climate variability. The onset of Australian summer
monsoon and its interannul variation are an important
subject of monsoon studies, which have been investigated in many previous studies (Troup, 1961; Nicholls
et al., 1982; Holland, 1986; Drosdowsky, 1996; Suppiah,
2004; Kim et al., 2006). The primary data analysed in
majority of these previous studies are wind and rainfall
records at Darwin (12 ° S, 130 ° E). Few studies have discussed interannual and interdecadal variability of broadscale Australian summer monsoon due to limited satellite
and objective analysis data. It is therefore meaningful
to discuss the broad scale Australian monsoon seasonal
cycle and climate variability from a broad scale circulation perspective with a simple yet effective index. Suchan approach has provided useful insights to understandthe dynamic structure and teleconnection for Indian summer monsoon and the western North Pacific monsoon(Webster and Yang, 1992; Wang and Fan, 1999; Wanget al., 2001).

* Correspondence to: Yoshiyuki Kajikawa, POST Bldg 401, 1680 East
West Rd., Honolulu, HI 96822, USA. E-mail: ykaji@hawaii.edu
Copyright  2009 Royal Meteorological Society

To facilitate monitoring of intraseasonal to interannual
variations as well as daily fluctuations of Australian summer monsoon, it is advantageous to use an objective andeffective circulation index. Use of the circulation index
has advantages compared to that of rainfall index. Use ofbroad scale wind variation can (1) avoid random noise
associated with local rainfall variability (2) better represent large-scale Australian summer monsoon (3) obtaindataset readily for real-time monitoring (4) establish ahistorical record with reanalysis dataset, and (5) facilitate
a validation of monsoon performance in GCM simulationand forecast.
Wang et al. (2004a) defined an Australian summer
monsoon index by using 850 hPa zonal wind anomalies averaged over the region (EQ-10 ° S, 120 ° E–150 ° E)
following traditional notion used by Australian meteorologists. It can capture seasonal cycle well but not
the interannual variation of the Australian summer monsoon rainfall, because of lack of significant correlation
with the rainfall over the Australian monsoon region(Figure 1(b)). Webster (2006) also defined a vertical wind
shear index between 850 hPa and 200 hPa zonal wind
averaged over the same region as Wang et al. (2004a),
which strongly depends on zonal wind at 200 hPa. This
index has some positive correlation with Australian monsoon rainfall anomalies (Figure 1(c)); but the variability
centre is different from the desired monsoonal rainfall
area (Figure 1(a)). Besides, seasonal variation is not predominant in the 200 hPa zonal wind over that region.

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A MULTI-TIME SCALE AUSTRALIAN MONSOON INDEX(a)

The interannual variability of the onset date and seasonal mean strength of the Australian summer monsoon are investigated by using the new AUSMI in Section 4. Conclusion and discussions are given in the final
section.

2.

(b)

(c)

Datasets

The datasets employed in this study include the daily
outgoing longwave radiation (OLR) data interpolatedby NOAA-CIRES Climate Diagnostic Center (Liebmann and Smith, 1996) for the period 1979–2006, thepentad mean Global Precipitation Climatology Project(GPCP) satellite-gauge combined grid precipitation data
(Huffman et al., 1997) for the period 1979–2006,
and the daily NCEP reanalysis data provided by the
NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, fromtheir Web site at http://www.cdc.noaa.gov/, for the period
1948–2006 (Kalnay et al., 1996). These datasets are
acquired as a regular array with a 2.5° × 2.5° grid. We
also used the monthly mean Nino3 SST index and southern oscillation index (SOI) from the Climate Prediction
Center in NCEP.

3.

Defining an Australian monsoon index

A monsoon circulation index should reflect seasonal cycle
and interannual variability of monsoonal rainfall. The
difference of climatological seasonal mean precipitation
between austral summer (December–February: DJF) andwinter (June–August: JJA) is shown in Figure 1(a).
Large seasonal difference of rainfall is seen over the
Figure 1. (a) Difference of climatological seasonal mean precipita- maritime continent and Northern Australia as well as overtion between austral summer (December–February: DJF) and winter the Indian subcontinent, the Bay of Bengal, the Indochina(June–August: JJA). (b) Correlation coefficient between DJF mean
peninsula, and the South China Sea. The Australian
850 hPa zonal wind averaged in EQ-10 ° S, 120 ° E-150 ° E, and DJF
mean GPCP rainfall. (c) Correlation coefficients between the DJF summer monsoon rainfall is roughly located in the region
mean zonal wind shear (850hPa–200hPa) averaged in EQ-10 ° S, (7.5 ° S–17.5 ° S, 120 ° E–150 ° E,) as indicated by the box
120 ° E–150 ° E and DJF mean GPCP rainfall. Box shows monsoonal shown in Figure 1(a). In order to identify the region
rainfall area. Correlations in (b) and (c) that are significant at the 99% where the interannual variability of lower zonal wind isconfidence level are shaded.
best related to the Australian monsoon rainfall anomalies,
we plot the correlation coefficient of 850 hPa zonal wind
Also there is a discrepancy between the decreasing trends field with respect to the DJF mean Australian monsoon
in this index and the interdecadal variations of the rainfall rainfall averaged over the boxed region in Figure 2(a).
The enhancement of the monsoon rainfall is associatedover the Australian continent as Webster (2006) noted.
The purpose of this study is to examine the possi- with westerly wind anomalies over the area 5 ° S–15 ° S,
bility of redefining and improving the Australian mon- 100 ° E–130 ° E, especially along 10 ° S, and easterly windsoon index, which can quantify the variability on multi- anomalies over the southwest of the monsoonal rainfall
time scales of both the circulation and rainfall over regions. These zonal wind anomalies, which are parts
the Australian monsoon region. Such an index will of the cyclonic circulation anomalies, essentially reflect
be useful for real-time monitoring of monsoon events the Rossby wave response forced by the latent heatsuch as its onset and for investigating intraseasonal to released in the monsoon rainfall (Gill, 1980). Figure 2(b)interdecadal variability of the Australian monsoon strength. shows the difference of climatological seasonal mean
After data description in the next section, we define zonal wind at 850 hPa between austral summer andan improved broad scale Australian monsoon index winter. The large seasonal variation in 850 hPa zonal(AUSMI) and confirm its applicability to multi-time wind field is located over the maritime continent aroundscale from intraseasonal to interdecadal in Section 3. 5 ° S–15 ° S and expanded in the zonal direction from the
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Y. KAJIKAWA ET AL.

(a)

(b)

Figure 2. (a) Correlation coefficient between DJF mean anomalous
850 hPa zonal wind and the Australian monsoon rainfall averaged over
the region (7.5 ° S–17.5 ° S, 120 ° E–150 ° E; shown by the box in the
Figure). Correlations that are significant at the 99% confidence levelare shaded. (b) Difference of climatological seasonal mean zonal windat 850 hPa between austral summer (December–February: DJF) andwinter (June–August: JJA).

equatorial Indian Ocean to the dateline. On the basis ofthese results, we defined the AUSMI as 850 hPa zonal
wind averaged over the area (5 ° S–15 ° S, 10 ° E–130 ° E),
which can reflect the broad scale Australian monsoon
in terms of both the seasonal cycle and interannual
variability.
To confirm the adequacy of the AUSMI in describing interannual variability, the correlation coefficients
between the AUSMI and OLR (and GPCP rainfall)
were calculated by using DJF seasonal mean anomaly
data (Figure 3(a) and (b)). Significant correlations arewidely seen in the maritime continent and Northern
Australia. As such, the AUSMI can capture well the(a)interannual variability of large-scale monsoon rainfall.
The AUSMI can also faithfully represent the relationshipbetween Australian summer monsoon and El Niño Southern Oscillation (ENSO), as well interdecadal changes
in the Australian monsoon. Figure 4 shows the timeseries of normalized seasonal (DJF) mean AUSMI andNino3 SST anomaly. The interannual variability of the
AUSMI is negatively correlated with the Nino3 SST
index with a simultaneous correlation coefficient of−0.51 and an especially negative correlation (−0.65) forthe period as ENSO itself was intensified after 1965;
these correlations are statistically significant at 99% confidence level. This negative correlation suggests that aweak (strong) Australian monsoon is associated withwarm (cold) SST anomalies in the Eastern Pacific. Thisagrees with the previous results (Holland, 1986; Kullgren and Kim, 2006). Thus, the AUSMI well represents
the interannual variability of the Australian monsoonal
rainfall.
The AUSMI can also describe large intraseasonal variability of the Australian summer monsoon. Figure 5(a)and (b) shows the correlation coefficient between the
AUSMI and OLR (and GPCP rainfall) computed by using
pentad mean anomalies during austral summer (fromDecember to February). Statistically significant correlations are found over the maritime and Australian continents in both OLR and rainfall fields. Therefore, the
AUSMI represents not only interannual variability andannual cycle but also intraseasonal variability of the Australian monsoon rainfall. Moreover, the interdecadal variability of the Australian summer monsoon strength can
be clearly seen from the 11-year running mean AUSMIin Figure 4 (thick line), which shows weak monsoon
during 1950–1970 and 1980–1995 and strong monsoon
during 1970–1980 and after 1995. There is no significant trend in the AUSMI. The phase of this interdecadal
variability is consistent with the results found by Lavery et al. (1997) and Suppiah (2004), which showed theinterdecadal variability of the rainfall over the northern part of Australia by using rain gauge data. Thus,
the AUSMI can reflect the rainfall variations over the(b)

Figure 3. Correlation coefficients between the DJF mean AUSMI (850 hPa zonal wind averaged in 5 ° S–15 ° S, 110 ° E–130 ° E; the box in Figures)and the DJF mean (a) OLR and (b) GPCP rainfall anomalies at each grid. Correlations in (a) and (b) that are significant at the 99% confidence
level are shaded.
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A MULTI-TIME SCALE AUSTRALIAN MONSOON INDEX

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Figure 4. Time series of the normalized DJF mean AUSMI (850 hPa zonal wind averaged in 5 ° S–15 ° S, 110 ° E–130 ° E) (bar) with 11-year
running mean of the AUSMI (thick line), and normalized Nino3 SST anomaly (dashed line).
(a)

(b)

Figure 5. Correlation coefficients between the pentad mean AUSMI (850 hPa zonal wind averaged in 5 ° S–15 ° S, 110 ° E–130 ° E; the box in
Figures) and the pentad mean (a) OLR and (b) GPCP rainfall anomalies during austral summer (DJF). Correlations in (a) and (b) that are
significant at the 99% confidence level are shaded.

Australian monsoon region not only on seasonal, intraseasonal, and interannual time scales but also on interdecadal
variability.

4. Onset of the Australian summer monsoon
and its relation with the seasonal mean rainfall
The climatological daily mean AUSMI and pentad
mean GPCP rainfalls over the Australian monsoon area
(7.5 ° S–17.5 ° S, 120 ° E–150 ° E; shown by the box in
the Figure 1(a)) averaged for the period 1979–2006
are shown in Figure 6. Seasonal evolution of the climatological mean AUSMI is characterized by a clear
annual reversal between easterly (winter) and westerly(summer). The gradual transition from easterly to westerly occurs during the beginning of December; the westerly abruptly turns to easterly during the early mid March.
It corresponds well to the seasonal cycle of Australianmonsoon rainfall. It has been well known that the Australian summer monsoon in each year starts abruptly witha large-scale disturbance associated with a wet phase of
the 40-50-day intraseasonal oscillation. According to thecomposite study, the peak-to-peak amplitude of zonal
wind is about 10 m s−1 during 5–10 days around the
Copyright  2009 Royal Meteorological Society

onset date (Hendon and Liebmann, 1990). However, theclimatological onset is gradual from easterly to westerly
in Figure 6, suggesting that the Australian summer monsoon onset has large interannual variability and they are
not phase locked to the annual cycle. In contrast, theabrupt retreat of the climatological mean Australian summer monsoon occurring clearly in the middle of Marchsuggests that the retreat is more tightly phase-locked tothe annual cycle around the middle of March (Wang andXu, 1997).
The Australian summer monsoon onset generally concurs with the transition of lower troposphere zonal windfrom easterly to westerly (Holland, 1986; McBride, 1987;
Hendon and Liebmann, 1990; Drosdowsky, 1996; Kimet al., 2006). Here, similar to the South China Sea
monsoon onset date defined by Wang et al. (2004b),
the onset date of the Australian summer monsoon
in each year is defined as follows. The onset day
should be the first day after 1 November that satisfies the following criteria: (1) on the onset day andduring the 5 days after the onset day the averaged
AUSMI must be greater than 0 (meaning that thewesterly is steadily established) (2) in the subsequent
four pentads, the AUSMI must be positive in at least
three pentads and (3) the accumulative four-pentad mean
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Y. KAJIKAWA ET AL.

Figure 6. The climatological daily mean AUSMI (in units of m s−1 ) and the pentad mean GPCP rainfall (in units of mm day−1 ) over theAustralian monsoon (see the box in Figure 1(a)). The climatology was derived using data from 1979 to 2006.

Figure 7. Time series of the Australian summer monsoon onset date (solid line) and the normalized DJF seasonal mean AUSMI (bar). See
Section 4 in the text for the definition of the AUSMI and the Australian monsoon onset date. Unit of the AUSMI is m s−1 .

AUSMI >1 m s−1 (meaning a persistent seasonal transition).
The time series of the onset date and the seasonal (DJF)
mean AUSMI are shown in Figure 7. The climatologicalmean onset date defined by the AUSMI is December 16
and the standard deviation is about 15 days. The mean
onset date in this study differs from that in the previous
studies obtained by using station data at Darwin (9 days
earlier than that in Drosdowsky (1996), and 5 days earlier than that in Hendon and Liebmann (1990)), whereas
the interannual standard deviation is close to the previous studies; 15 days for Drosdowsky (1996) and 16 daysfor Hendon and Liebmann (1990). However, the present
definition of monsoon onset date is still reasonable interms of its interannual variation because the onset datedefined by the AUSMI has a significant correlation withthe onset date defined in the previous studies: 0.54 with
Drosdowsky (1996) and 0.59 with Hendon and Liebmann(1990); these correlations are statistically significant at
99% confidence level. One of the possible reasons that
Copyright  2009 Royal Meteorological Society

the onset dates in this study are earlier than previous
studies could be due to the fact that the westerly anomalies associated with eastward propagating intraseasonal
disturbance might take several days to travel from the
AUSMI region to Darwin. The interannual variabilityof the onset date is well correlated with the SOI during November and December with the same correlation
coefficient of 0.48. This result is consistent with previous studies with the SOI (Nicholls et al., 1982; Holland,
1986; Drosdowsky, 1996). More importantly, there is asignificant negative correlation between the onset dateand seasonal mean strength of the broad scale Australianmonsoon (−0.47); namely an early (late) onset is accompanied by a strong (weak) Australian summer monsoon
in terms of seasonal mean strength. This relationship was
also evident even in the period when the ENSO was inactive before 1965, although this relationship was not strong
during some period like in late 1980s.
In order to clarify the relationship discussed above,
we selected 10 strong and 12 weak Australian summer
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Figure 8. Time series of composite AUSMI averaged for 10 strong Australian summer monsoon years (solid line) and 12 weak Australiansummer monsoon years (dotted line). See the text for the details of strong and weak monsoon years. Unit of the AUSMI is m s−1 .

monsoon years with the criteria that the AUSMI during
DJF is above (strong) and below (weak) one standard
deviation, respectively (Figure 4). The Strong monsoon
years are 1949, 1950, 1951, 1968, 1974, 1976, 1991,
1997, 1999 and 2001; whereas weak monsoon years are
1952, 1954, 1958, 1959, 1961, 1964, 1965, 1983, 1988,
1990, 1998 and 2005. Figure 8 shows the time series ofcomposite daily AUSMI in strong and weak monsoon
years. The onset date in strong monsoon years (around
1 December) is earlier than that in the weak monsoon
years (around 22 December). The largest difference of
the AUSMI seen in February is remarkable. In terms of
the retreat of the Australian monsoon, it is also interestingthat there is no significant difference between strong andweak monsoon years. This indicates that the signal of
the interannual variability of the Australian monsoon isclear only in the onset phase and mature phase of theAustralian summer monsoon. In addition, the precursor
signal starts from one month before onset date (during
November).

5.

Summary and discussions

On the basis of analysis of the action centres for boththe seasonal and interannual variations, we proposed an
improved AUSMI which is defined as 850 hPa zonal
wind averaged over the area (5 ° S–15 ° S, 110 ° E–130 ° E).
This circulation index represents well the monsoonal
rainfall over the maritime continent and northern partof the Australia on not only seasonal and interannual
time scales but also on the intraseasonal and interdecadal
time scales. The interannual variability of the seasonalmean AUSMI is associated with the ENSO: weak (strong)
Australian summer monsoon in El Niño (La Niña) year.
The interdecadal variability of the AUSMI is consistent
with the previous studies that used rain gauge data over
the Australian continent (Lavery et al., 1997; Suppiah,2004). The Australian summer monsoon onset date is
Copyright  2009 Royal Meteorological Society

also defined by using daily and pentad mean AUSMI.
The interannual variability of this monsoon onset dateis well correlated with Nino3 SST anomalies during
November and December. It is also found that an early(late) onset is accompanied by a strong (weak) Australiansummer monsoon. In contrast, the monsoon retreat inboth strong and weak Australian summer monsoon years
occurs around the middle of March. This suggests that
the Australian monsoon retreat tends to be phase-locked
to the annual cycle. In other words, the Australianmonsoon retreat has less interannual variability, whereas
the monsoon onset has large interannual variability.
This feature is different from the western North Pacificmonsoon, whose stepwise onset has been discussed as aresult from seasonal phase locking of the intraseasonalvariability (Nakazawa, 1992; Ueda et al., 1995; Wu andWang, 2001).
The seasonality of the El Niño evolution is essential
for understanding the relationship between the onset
date and the seasonal mean of the Australian summer
monsoon. In general, the El Niño has developed during
boreal summer and autumn, the pre-Australian monsoon
season (Harrison and Larkin, 1998). It has been suggested
that the ENSO could impact on the Australian monsoon
onset date through anomalous Walker circulation and
SST anomalies around the Australian continent (Nicholls
et al., 1982; Holland, 1986; Drosdowsky, 1996). As theclimatological mean Australian summer monsoon starts
around middle of December and the standard deviationof the onset date is about 15 days, the interannual
variation of the onset date can significantly affect the
duration of rainy season thereby the total amount ofsummer monsoon rainfall. As this relationship is still
significant during an inactive ENSO period, the onset
date may provide a useful short-term precursor forthe seasonal (DJF) mean Australian summer monsoon
strength. The El Niño events usually decay during
boreal spring, concurring with the Australian summer
monsoon retreat. Why the Australian summer monsoon
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Y. KAJIKAWA ET AL.

retreat has moderate interannual variability and tends
phase-locked to the annual cycle remains elusive. Thismay be associated with timing of the El Niño decay
as a phase lock system on annual cycle (Torrence
and Webster, 1998) and with the consecutive stepwise
western North Pacific monsoon onset.
The new AUSMI is useful in representing multitime scale variability of the Australian monsoon on
intraseasonal to interdecadal time scales. It is worthwhile
to use this index for analysing and monitoring theAustralian monsoon variations. The monsoon onset over
the South China Sea is associated with the activity of theintraseasonal variability during early summer (Kajikawa
and Yasunari, 2005). Thus, the relationship between
Australian monsoon onset/retreat and the intraseasonalvariability is another interesting issue for future studiesof the Asian–Australian monsoon system.
Acknowledgements
The authors are grateful to Prof. Nigel Tapper and anonymous reviewers for their useful and critical comments.
The authors acknowledge support from the International
Pacific Research Center (IPRC). This study was supported by the Climate Dynamics Program of the National
Science Foundation and the NOAA OGP through CLIVAR/Pacific Program. This paper is IPRC contribution
number 608 and SOEST contribution number 7744.
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