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The Annual Cycle, Intraseasonal Oscillations, and Roadblock to Seasonal Predictabilityof the Asian Summer Monsoon
B. N. GOSWAMI
Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore, India

GUOXIONG WULaboratory of Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing, China

T. YASUNARI
Hydrospheric Atmospheric Research Center, Nagoya University, Furo-cho, Nagoya Aichi, Japan(Manuscript received 23 January 2005, in final form 8 November 2005)
ABSTRACT
Factors responsible for limited predictability of the Asian summer monsoon (ASM) are investigatedwithin a conceptual framework for predictability. Predictability of the seasonal mean depends on theinterannual variability (IAV) of the monsoon annual cycle (MAC) and is determined by relative contribution of the predictable “external” component of IAV compared to the unpredictable “internal” IAV.
Contributions of slow processes such as those involving air–sea interactions associated with the El Niño–
Southern Oscillation (ENSO) or local warm ocean–atmosphere interactions in generating IAV of the MAC
are reviewed. Empirical evidence that these air–sea interactions modulate the MAC is presented. Estimatesof internal IAV have been made from observations as well as atmospheric model simulations. In contrast
to a large part of the Tropics where the summer climate is predictable, with the internal variability being
much smaller than the external one, the limited predictability of the Asian monsoon appears to be due tothe fact that the contribution from the external IAV over the region is relatively weak and comparable tothat from internal IAV.
Cause for large internal IAV over the ASM region is investigated, and it is proposed that the internal IAVof the MAC is primarily due to interaction between the MAC and the summer intraseasonal oscillations(ISOs). Two mechanisms through which ISOs lead to internal IAV of the MAC are unraveled. The seasonal
bias of the ISO anomalies can influence the seasonal mean if the spatial structure of the ISO has significant
projection on that of the seasonal mean and if frequency of occurrence of positive and negative phases isunequal. Evidence supporting this is presented. In addition, it is demonstrated that the chaotic summer ISOs
modulated by the annually varying forcing associated with the “slow annual cycle” can lead to IAV of theseasonal mean. Empirical evidence that IAV of ISO activity is related to IAV of the seasonal mean or MAC
is also presented.
Thus, the Asian monsoon would remain a difficult system to predict. To exploit the predictable signal,
however, it is imperative that systematic bias of the models is improved and the space–time structure of thesummer ISOs is simulated accurately.

1. Introduction
A strong annual cycle with rainy summer and dry
winter seasons is a characteristic feature of the Asianmonsoon. As the agrarian economy and the drinking
Corresponding author address: Prof. B. N. Goswami, Centre for
Atmospheric and Oceanic Sciences, Indian Institute of Science,
Bangalore 560012, India.
E-mail: goswamy@caos.iisc.ernet.in© 2006 American Meteorological Society

JCLI3901

water requirements of the region depend critically on
the monsoon rains, agricultural practices and social
events of the people in the region are strongly tied up
with the annual cycle (Zimmermann 1987). Even as thegreen revolution steadily increased food productivity
over the last four decades, a modest decrease in the
monsoon rainfall (e.g., 10% of long-term mean) leads
to a significant decrease in rice production over India(Abrol and Gadgil 1999; Gadgil 1995; Parthasarathy et15 OCTOBER 2006

GOSWAMI ET AL.

al. 1988; Swaminathan 1987; Webster et al. 1998). Theinterannual variability (IAV) of the monsoon rainfall
may have even larger impact on agricultural productivity in the coming years, as the growth rates of agricultural production have decreased in recent years in association with the fatigue of the green revolution (Gadgil et al. 1999). Extremes in monsoon rainfall lead todevastating floods and droughts (Mooley and Shukla
1987; Shukla 1987; Sikka 1999; Webster et al. 1998),
causing enormous economic loss and human misery.
The rainfall within the monsoon season, however, does
not occur as a continuous deluge but is punctuated bymonsoon intraseasonal oscillations (ISOs) that manifest in prolonged spells of dry and wet conditions sometimes lasting for 2–3 weeks. Extended above-normalrain spells represent periods when the monsoon has
been vigorous or “active” while the dry spells represent
periods when the monsoon takes a “break” (Ramamurthy 1969) from its activity and hence are known as active and break conditions, respectively (Gadgil 2003;
Goswami 2005; Webster et al. 1998). Frequency of occurrence of active and break spells within the season
influences the seasonal mean and hence agricultural
production. Knowledge of the active and break spells is
important for sowing, harvesting of crops, and water
management within the season. Furthermore, long
breaks in critical growth periods also lead to a substantially reduced yield of certain crops (Gadgil and Rao2000). Therefore, “long-range” forecasting of seasonalmean monsoon at least one season in advance and “extended range” forecasting of dry and wet spells of thesubseasonal variability 2–3 weeks in advance are ofgreat importance.
For over a century, attempts to predict seasonalmean monsoon rainfall have been made using empiricalmethods (Blanford 1884; Goswami and Srividya 1996;
Gowarikar et al. 1989; Iyengar and Kanth 2004; Sahai etal. 2003; Walker 1923, 1924). The empirical methods
have been useful in providing guidance for seasonal
forecasting and have reasonable skill when the monsoon is close to normal but have little skill in predicting
the extreme events. All empirical models failed to predict the severe drought of the 2002 Indian monsoon.
Dynamical prediction of the seasonal mean using astate-of-the-art climate model, therefore, offers a logical alternative to empirical prediction of the monsoon.
Over the last couple of decades, the climate models
have improved steadily in simulating the mean global
climate (Gates et al. 1999), and a conceptual frameworkfor predicting the tropical climate has also been established (Charney and Shukla 1981; Shukla 1981, 1998).
Following the seminal work of Charney and Shukla(1981), a series of sensitivity studies with climate mod-5079

els (Anderson et al. 1999; Fennessy and Shukla 1994;
Kumar and Hoerling 1995; Lau 1985; Shukla and Wallace 1983) has established that the interannual variability of the tropical climate is largely driven by slowly
varying anomalous boundary conditions (ABCs) and ismuch less sensitive to initial conditions and hence morepredictable compared to the extratropical climate.
While this conclusion is generally true over a large partof the Tropics, it has also come to light over the last
decade that the summer climate over the Asian monsoon region appears to be an exception within the
Tropics, simulation of which is quite sensitive to initialconditions (Brankovic and Palmer 2000; Cherchi andNavarra 2003; Krishnamurthy and Shukla 2000; Sperber and Palmer 1996; Sperber et al. 2001). The current
skill of most atmospheric GCMs with perfect boundary
conditions in predicting Asian summer monsoon(ASM) precipitation is, however, insignificant (e.g.,Kang et al. (2004); Wang et al. (2004)). Sensitivity ofsimulation of the seasonal mean to initial conditions
indicates that the interannual variability of the seasonalmean is governed partly by “internal” low-frequency(LF) variability in addition to contribution from theABCs.
The first Climate Variability and Predictability(CLIVAR) decade has witnessed major advances in understanding several aspects of ASM-related climate
variability. For example, it is recognized that the El
Niño–Southern Oscillation (ENSO) and ASM are notindependent entities but are components of a coupled
ocean–atmosphere oscillation (Loschnigg et al. 2003;
Wu and Kirtman 2004). Better understanding of the
dynamical origin and northward propagation of thesummer ISOs has evolved (Chatterjee and Goswami
2004; Jiang et al. 2004; Wang and Xie 1997) and air–sea
interactions associated with the summer ISOs havebeen unraveled (Fu et al. 2003; Sengupta et al. 2001;
Waliser et al. 2004). Furthermore, it has been demonstrated that extended-range prediction of phases of thesummer ISOs up to three weeks in advance is feasible(Goswami and Xavier 2003; Webster and Hoyos 2004).
In light of these major achievements, the failure in predicting the mean south Asian (SA) summer monsoon
has remained the single major enigma. Why is dynamical prediction of seasonal Asian monsoon so difficult,when the skill of dynamical prediction of seasonal climate over a large part of the Tropics has steadily improved over the last decade and shows good promisefor the future? Is it because the simulation of the mean
Asian summer monsoon by GCMs is still too poor andthe systematic biases are still too large? Or, is there amore fundamental reason for the poor predictability of
the Asian monsoon?

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FIG. 1. A schematic picture of factors contributing to the predictability of the seasonalmean Asian monsoon.

Here, we present a broad framework for understanding the factors limiting the predictability of the seasonalmean monsoon (Fig. 1) and indicate that there is indeed
a fundamental reason for the poor predictability of the
Asian summer monsoon. At the heart of the problem isthe monsoon annual cycle (MAC) mutually interactingwith the monsoon ISOs. Predictability of the seasonalmean monsoon depends on the IAV of the MAC. The
MAC, on the other hand, could be modulated by twoclasses of processes. The slowly varying ABC we referred to earlier in the context of an AGCM is nothing
but a manifestation of slow unstable oscillations of thecoupled ocean–atmosphere system such as the ENSO.
As the spatial scale of these coupled oscillations is large
compared to the spatial scale of the Asian monsoon
system and the temporal scale is much larger than a
year, they essentially modulate the MAC. Other feedbacks within the ocean–atmosphere system such as soil
moisture–radiation feedback (Meehl 1987), Tibetan
Plateau (TP) heating or Eurasian snow cover feedback,etc., that have time scales longer than a year also lead toa slow modulation of the MAC. We refer to these modification of the MAC associated with the slow processes
as “external.” The other process that could also lead to
IAV of the MAC is through nonlinear interaction withthe summer monsoon ISOs. Since ISOs themselvesarise from internal atmospheric dynamics involving
feedback between convection and dynamics, the component of IAV of the MAC governed by ISOs is sensitive to initial conditions. This component of IAV of
the MAC is termed as of internal origin, as it arises
through fast processes. The predictability of the seasonal mean monsoon, therefore, depends on the relative contribution of the predictable external component
and unpredictable internal component to the IAV of
the MAC. The seasonal mean monsoon would havehigh predictability over regions of the Tropics where
the contribution of the external component to IAV of
the MAC is much larger than that from the internal
component. On the other hand, regions of the Tropics
where the contribution of the external component to
IAV of the MAC is comparable to that from the internal component would have poor predictability of theseasonal mean. Two fundamental aspects of the problem are the description of the Asian monsoon as a seasonal cycle and the role of the summer monsoon ISOs.
The important features of MAC are highlighted in section 2. Evidence of IAV of the MAC is shown in section
3, and its association with slowly varying ABC is indicated. How ocean–atmosphere coupling could lead tointerannual variability of the MAC is indicated in thissection. The role of the Tibetan Plateau heating on the15 OCTOBER 2006

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FIG. 2. Seasonal cycle of precipitation (mm day⫺1) and low-levelwinds (m s⫺1) over the Asian monsoon region. (a) Climatologicalmean JJA precipitation from CMAP and 850-hPa winds from
NCEP–NCAR reanalysis. (b) Same as in (a), but for DJF. Scalefor wind vectors is shown at the bottom of each panel. Precipitation more than 4 mm day⫺1 is shaded.

MAC is indicated in section 4. Scale interaction
through which ISOs influence the MAC is described in
section 5, and evidence of how the summer ISOs influence the MAC is presented from observations. Estimates of contribution from internal variability to IAV
of the monsoon and that of predictability are presented
in section 6. Main conclusions are summarized in section 7.

2. The monsoon annual cycle
The annual cycle is embodied in the very definitionof the monsoon, namely, seasonal reversal of winds andprecipitation (Ramage 1971) and has been discussed
extensively (Hastenrath 1996; Ramage 1971; Webster
et al. 1998). Usually, the Asian summer monsoon begins in May and ends in September while the Australiansummer monsoon begins in December and ends in
March. Here, we shall concentrate primarily on the annual cycle of precipitation and low-level winds over the
Asian–Australian monsoon region as shown in Fig. 2.
The amplitude of the climatological mean annual cycle(AC) of precipitation and that of the zonal and meridional winds at 850 hPa are shown in Fig. 3. The largest

FIG. 3. Amplitude of climatological mean AC as defined by thestd dev of climatological monthly means of (a) precipitation (mmday⫺1), (b) zonal wind at 850 hPa (m s⫺1), and (c) meridional
wind at 850 hPa (m s⫺1).

amplitude of the AC of precipitation occurs over the
Asian and the Australian monsoon regions. The largest
amplitude of the AC for zonal winds at low level occur
over the low-level westerly jet (LLJ) region of the central and western Arabian Sea while that for the meridional winds occur over the western equatorial Indian
Ocean (IO) and Somalia.
The AC of the monsoon is a manifestation of theseasonal migration of the zonally oriented belt of precipitation or the intertropical convergence zone (ITCZ;
Gadgil 2003). Meridional migration of the ITCZ overthe Indian monsoon region (70°–90°E) and over the
East Asian (EA) monsoon region (120°–140°E) isshown in Figs. 4a,b using precipitation data from the
Climate Prediction Center (CPC) Merged Analysis ofPrecipitation (CMAP; Xie and Arkin 1996). A characteristic feature of the AC of the Indian monsoon is theonset associated with rapid transition of the high precipitation zone from near the equator to about 15°N

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temperature of the tropospheric layer between 200 and
600 hPa averaged between 30° and 100°E (Fig. 4c). The
meridional temperature gradient reverses sign aroundthe time of onset of the south Asian summer monsoon.
It may be noted that after the onset, the tropospherictemperature in the north may be affected by the convective heating associated with the ITCZ. The reversalof meridional temperature gradient ushering the onsetof the monsoon is, however, not affected by the ITCZ
heating. The heating over the Tibetan Plateau plays acrucial role in this seasonal evolution of the meridional
gradient of heating (Wu and Zhang 1998; Wu et al.
2002; Yanai et al. 1992) and is an important driving
mechanism for the MAC.

Summer ISOs and MAC

FIG. 4. Annual evolution of the ITCZ over, (a) the Indian monsoon region, defined by climatological precipitation (mm day⫺1)averaged between 70° and 90°E and (b) the Australian monsoon
region, defined by climatological precipitation (mm day⫺1) averaged between 120° and 140°E. (c) Annual evolution of climatological mean temperature averaged between 200 and 600 hPa andaveraged over the Asian monsoon region between 30° and 110°E
in °C.

toward the end of May and beginning of June. Other
characteristics are the primary ITCZ over the continental region between 20° and 25°N, the secondary ITCZ
between the equator and 10°S, and withdrawal in late
September (Fig. 4a). During the Australian summer
monsoon (December–March), the ITCZ is active between the equator and 15°S with dry conditions in the
Northern Hemisphere. The onset of the East Asiansummer monsoon (EAM) takes place in several stages(Wu and Wang 2001). The ITCZ is weak during April
and the first onset of the EAM takes place in mid-May
with rapid transition and setting up of the meiyu front.
The second onset of the EAM takes place in early Junewith the southern ITCZ moving rapidly to about 20°N
and setting up dry condition south of the equator (Fig.
4b). The seasonal migration of the ITCZ is a result of
seasonal evolution of the meridional gradient of tropospheric heating illustrated by the evolution of the mean

The monsoon ISOs also play an important role indetermining the climatological MAC (Figs. 3 and 4). It
may be noted from Fig. 4a that the climatological mean
daily precipitation, based on data for more than 25 yr,
still contains some residual intraseasonal variability and“singularity” such as the rapid northward movement of
the rain belt by 10°–15° within a short time during theonset phase and fluctuations within the season with aperiod of about a month. The residual ISO and singularity in the climatological MAC is due to phase lockingof monsoon ISOs with a certain phase of the annual
cycle (Kang et al. 1989; Wang and Xu 1997). Wang and
Xu (1997) and Kang et al. (1999) call them “climatological intraseasonal oscillations” (CISOs). Thus, theMAC contains a “slow annual cycle” primarily due tothe external forcing and a “fast annual cycle” arisingfrom the ISOs (Wang and LinHo 2002). We separate
the slow annual cycle component from the climatological pentad mean CMAP (Xie and Arkin (1996)) usinga harmonic analysis and by constructing a sum of theannual mean and the first three harmonics. The difference between the climatological mean annual cycle andthe slow annual cycle represents the fast annual cycle.
The amplitudes of the slow and the fast components of
the MAC of precipitation are shown in Figs. 5a,b. The
ratio between the two (Fig. 5c) shows that the fast annual cycle contributes between 20% and 40% overmost of the Asian–Australian monsoon region. There
are regions such as the western Indian Ocean and Arabia, south equatorial eastern Indian Ocean, and equatorial western Pacific, where the contribution of the fastannual cycle could be even larger than that of the slow
annual cycle. The contribution to the seasonal evolution of the MAC (Figs. 3a,b) from the slow and fastannual cycles is shown in Fig. 6. The south-to-northtransition of the rain belt associated with the slow annual cycle smoothly follows the evolution of the solar

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FIG. 5. Amplitude of the climatological mean (a) slow annual
cycle, (b) fast annual cycle of precipitation (mm day⫺1), and (c)ratio between the slow and fast annual cycles.

heating over the SA monsoon region (Fig. 6a) but lags
behind it in the EA monsoon region (Fig. 6b) due tothermal inertia of the larger ocean involved in the later
region. Over the SA monsoon region, the main contribution of the fast annual cycle comes during the onset(June) and withdrawal (September) in the form ofnorthward-propagating ISO events (Fig. 6c). Even overthe EA monsoon region, a large contribution of the fastannual cycle is seen at the time of onset in the form ofa northward-propagating ISO event (Fig. 6d). Thus, thesummer ISOs are closely tied up with the MAC andrepresent an important and integral component of the
Asian–Australian monsoon system.

3. IAV of the MAC: Ocean–atmosphere coupling
One example of the way in which ocean–atmosphereinteraction introduces IAV of the MAC is through themutual interaction between the ENSO and the monsoon. This interaction is primarily through the change
in the equatorial Walker circulation influencing the regional Hadley circulation associated with the Asianmonsoon (Goswami 1998; Lau and Nath 2000; Webster

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et al. 1998). Since the pioneering work of Sir Gilbert
Walker (Walker 1924), the influence of the ENSO on
the ASM has been noted (Rasmusson and Carpenter
1983; Shukla 1987; Sikka 1980). As the strong heatsource associated with the ASM could influence theatmospheric circulation in a significant way, it has also
been recognized that it could modify the surface
stresses over the central and western Pacific and influence the strength and evolution of the ENSO (Chung
and Nigam 1999; Kirtman and Shukla 2000; Yasunari1990). These independent studies of ENSO influenceon the ASM and ASM influence on the ENSO indicatethat the ENSO and the ASM are not independent phenomena but part of a coupled ocean–atmosphere oscillation. Led by this recognition several recent studies
with coupled ocean–atmosphere GCMs (Loschnigg etal. 2003; Wu and Kirtman 2004; Yu et al. 2003) investigate IAV of the ASM due to air–sea interaction involving ASM and the ENSO. These studies indicatethat the observed biennial tendency of the ASM may be
a result of such air–sea coupling. Based on analysis ofsimulations of their coupled model, Wu and Kirtman(2004) propose a plausible mechanism through which a
tropical biennial oscillation (TBO) may be generated.
A strong ASM during June–August (JJA) can enhancesurface easterlies in the central equatorial Pacific, induce an eastward-propagating upwelling Kelvin wave,
and give rise to negative SST anomalies in the eastern
Pacific that amplify through air–sea interactions.
Colder SST in the eastern Pacific is also associated withwarmer SST in the western Pacific. A strong ASM also
cools the Indian Ocean through enhanced evaporation
and upwelling. Associated intensification of the Walker
circulation leads to divergence of moisture supply in thewestern Indian Ocean. Reduced moisture supply at low
levels together with upper-level subsidence leads to a
weaker ASM during the next summer. A weak ASM
induces opposite affects and can lead to a stronger
monsoon next year. This indicates that ocean–atmosphere interaction could generate IAV of the ASM via
generation of TBO signal.
This two-way interaction between the monsoon and
ENSO led to floating of the “monsoon year” concept ofclimate year in the Tropics. However, the recent analysis of Ailikun and Yasunari (2001) indicates that thebiennial transition takes place within the summer monsoon season. They found that the variability of the
Asian monsoon in the early summer (June) is associated with the anomalous state of the ENSO in the previous winter while that of the mid–late summer (July–
September) is associated with the anomalous state of
the ENSO in the following winter. Their finding thatthe all-India monsoon rainfall in September is well cor-

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FIG. 6. Contribution of the slow annual cycle to the seasonal evolution of ITCZ (precipitation; mm day⫺1) over (a) the Indian region(70°–90°E) and (b) the Australian monsoon region (120°–140°E). (c), (d) Same as in (a) and (b), respectively, but for contribution fromthe fast annual cycle. Values greater than 6 mm day⫺1 are shaded in (a) and (b).Contour interval is 1 mm day⫺1 in (c) and (d), andminimum contours are ⫹0.5 mm day⫺1 (solid) and ⫺0.5 mm day⫺1 (dashed).

related with that in the following June indicates a continuity of the climate year of the coupled ocean–atmosphere system. The fact that all-India rainfall of June isuncorrelated with that of July–September (JAS) rainfall indicates that the biennial transition takes place
between June and July. Based on these findings, they
propose a slightly modified conceptual model monsoon
year (Yasunari 1991; Yasunari and Seki 1992) for the
coupled monsoon/atmosphere–ocean system as shownin Fig. 7. This schematic diagram depicts sequence two
successive monsoon years, a year starting from themid–late northern summer (JAS) and persists till earlynext summer (June). In the figure, a sequence of a
“weak” monsoon year is shown followed by a “strong”
monsoon year. The east–west Walker circulation is
weakened associated with a weak monsoon and leads tocolder SST in the western Pacific and warmer SST in
the eastern Pacific in the following seasons (an El Niño
condition). The colder SST in the western Pacific is
crucial in maintaining anomalous ENSO state fromwinter until next June actively leading to a weaker
Asian monsoon during early summer. The right side of
the figure depicts the processes in a strong monsoon

year. The strong convective activity during the mid–latesummer leads to a cold condition in the eastern Pacificand a warm condition in the western Pacific during thefollowing winter (a La Niña condition). The La Niña
signal in the western Pacific carried by the ocean until
the next June maintains a strong Asian monsoon in
June.
Thus, there is evidence that air–sea interaction plays
an important role in the observed TBO. However, it is
not clear that air–sea coupling is essential for existenceof the observed TBO. It may be noted that mostcoupled GCMs (CGCMs) that simulate TBO have asignificant systematic bias in simulating the climatological mean AC. Systematic biases in simulating the climatological mean may play a significant role in simulating a TBO in many of these CGCMs. Hence, it is notwell settled that air–sea coupling is essential for the
existence of the TBO. It is possible that a TBO may betriggered by atmospheric internal dynamics but amplified through air–sea coupling. In section 6, we provide
such a possibility as a counterexample.
In addition to the ENSO-related ocean–atmosphereinteraction, local warm-ocean atmosphere interaction

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FIG. 7. Schematic diagram of two successive monsoon years for the coupled ENSO–monsoon
system (from Ailikun and Yasunari 2001, © Meteorological Society of Japan).

over the IO and western North Pacific (WNP) can also
give rise to IAV of the MAC. The recently discovered
Indian Ocean dipole mode (Saji et al. 1999; Webster etal. 1999) is a good example of manifestation of such
air–sea interaction. This mode is not an equatoriallyconfined zonal mode. The SST dipole is coupled withthe south IO anticyclonic anomalies. In the presence ofthe summer monsoon background flow, the ocean tothe east of the anticyclone near Sumatra cools as aresult of coastal upwelling, evaporation, and entrainment. Reduction of convection associated with thecooling excites westward-propagating descending
Rossby waves and reinforces the anticyclone (Li et al.
2003; Wang et al. 2003). However, the role of the IO
dipole mode on IAV of the south Asian summer monsoon is unclear at this moment. Similar warm ocean–
atmosphere interaction involving the WNP anticyclone
leads to IAV of the EAM (Wang et al. 2003).
We provide some empirical evidence that the observed MAC is modulated by the coupled ocean–
atmosphere interaction. In Fig. 8a the amplitude of the
AC of zonal winds from National Centers for Environmental Prediction–National Center for AtmosphericResearch (NCEP–NCAR) reanalysis (Kalnay et al.
1996; Kistler et al. 2001) at 850 hPa averaged over 7°–
17°N, 40°–80°E (U850) is plotted for 54 yr (1948–2002)
together with Niño-3 SST anomalies (SSTAs) during
JJA. The correlation between the AC of U850 and JJA
Niño-3 SSTA is ⫺0.53. This correlation is significant at
better than the 99.9% level and indicates that the AC of
U850 (which is related with the strength of the ASM) is

modulated by the ENSO. In another example, anomalous AC of Indian rainfall is related with JJA Niño-3
SSTA in Fig. 8b over a much longer period between
1871 and 2000. The anomaly of all-India monthly rainfall (Parthasarathy et al. 1994) is obtained as departureof observed monthly means from a climatologicalmonthly annual cycle. Anomalous AC is defined as thedifference between the JJA mean anomaly minus thefollowing DJF mean anomaly. The correlation between
the two is ⫺0.61 for the 130-yr period. The 21-yr sliding
correlation between the anomalous AC of Indian monsoon rainfall and Niño-3 SSTA during JJA (Fig. 8c)indicates that even though the correlation between thetwo has decreased during the recent years it is still
higher than that between JJAS Indian precipitation and
ENSO (Kumar et al. 1999). Thus, the IAV of the AC of
Indian monsoon rainfall is also strongly related with the
ENSO.

4. IAV of the onset of a monsoon: Thermal effectof the Tibetan Plateau
The role of the TP thermal forcing on maintaining
the MAC was mentioned in section 2. The TP thermal
forcing also influences the IAV of the MAC by influencing the seasonal transitions (Hung et al. 2004; Yanai
and Tomita 1998). Li and Yanai (1996) used the difference between 30° and 5°N of the upper-tropospheretemperature to present the meridional land–sea thermal contrast between subtropics and Tropics and to
study the Asian monsoon onset and maintenance. It

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FIG. 8. (a) Amplitude of AC (JJA ⫺ DJF) of zonal winds at 850
hPa in the north central Arabian Sea averaged over 7°–17°N,
40°–80°E from NCEP–NCAR reanalysis between 1948 and2002 (solid; scale on left in m s⫺1) and Niño-3 SSTA during JJA(dotted; scale on right in °C). (b) Anomalous AC of precipitation
over India between 1871 and 2000 and Niño-3 SSTA during JJA(dotted; scale on right in °C). (c) The 21-yr sliding window correlation between the two variables in (b).

may be noted that the reversal of the prevailing winddirection associated with the Asian monsoon onset occurs along the zonal-oriented ridgeline of the tropical–
subtropical anticyclone, which is a narrow band between 30° and 5°N. During the monsoon onset, lowertroposphere westerlies cross the ridgeline and intrude
into the lower-latitude area where easterlies predominate in the winter months, and the ridgeline moves
southward rapidly. Therefore, to reveal the monsoon
onset dynamics, it is insightful to study the thermal contrast across the ridgeline. It may also be noted that theridgeline surface of the subtropical anticyclone can bewell defined by the westerly–easterly boundary (WEB;where u ⫽ 0) that separates the midlatitude westerly
from the tropical easterly. Subject to the thermal windbalance, the WEB always tilts toward the warmer region with increasing height. Therefore, the north–southtemperature gradient reversal accompanied with themonsoon onset is manifested as a rapid change in thetilting of the WEB from southward in winter to northward in summer over the monsoon area. The perpendicular configuration of the WEB surface in this area

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then implies the seasonal transitions between winterand summer. The evolution of the WEB configuration
during the seasonal transition over the Asian monsoon
area is presented in Fig. 9 based on the NCEP–NCAR
reanalysis and averaged between 1980 and 1998 together with outgoing longwave radiation (OLR). In
April, while the whole WEB surface keeps tilting
southward, such tilting becomes less and less over theeastern Bay of Bengal (BOB; Figs. 9a–d). In the firstpentad of May (Fig. 9e), the sector between points D(90°E) and C (103°E) of the WEB surface becomes
perpendicular to the earth’s surface, indicating the occurrence of seasonal transition over the region and onset of the BOB monsoon (Mao et al. 2002a,b). Afterward point C propagates eastward quickly and reaches
the eastern part of the South China Sea (SCS) by the
fourth pentad of May (Fig. 9h). This corresponds to the
SCS monsoon onset. Meanwhile point D moves slowly
westward and reaches the west of the Indian subcontinent by early June, corresponding to the Indian monsoon onset. The above discussion concerns the climatological mean evolution, or MAC, of the Asian monsoon onset. It agrees with the finding of Wu and Zhang(1998) that the Asian monsoon onset composes threeconsecutive stages and the earliest onset occurs overthe eastern BOB. Mao et al. (2002b) showed that thethermal contrast across the WEB is connected to the
TP heating to the north and the thermal status of air
over the Bay of Bengal to the south, with the former
playing more important roles. This then provides the
dynamical background for understanding the significant roles of the Tibetan Plateau heating in the IAV of
the Asian monsoon onset.
The ISOs also play an important role in determining
the IAV of the monsoon onset. In a detailed study of
the Asian monsoon onset in 1989, Wu and Zhang(1998) stressed the importance of ISO in determining
the three-stage feature of the Asian monsoon onset.
They revealed that phase locking of an extratropicaleastward-propagating 2–3-week oscillation in uppertropospheric temperature and the rising phase of thenorthward-propagating divergent summer ISO over the
East Asian monsoon region create the favorable condition for atmospheric overturning to take place overa large area and for the Asian monsoon onset. They
also found that each of the three stages of the Asianmonsoon onset is preceded by a rising surge of thesurface temperature over the Tibetan Plateau. In a follow-up study, Zhang and Wu (1999) employed the European Centre for Medium-Range Weather Forecasts(ECMWF) analysis between 1980 and 1989 to diagnose
the temporal and spatial evolutions of the air temperature, general circulation, and surface heating during the15 OCTOBER 2006

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5087

FIG. 9. The projection of the subtropical anticyclone ridgeline (u ⫽ 0) and OLR (W m⫺2) at the levels
from 850 to 200 hPa during the season transition (from the third pentad of April to the second pentadof June): (a) 11–15 Apr, (b) 16–20 Apr, (c) 21–25 Apr, (d) 26–30 Apr, (e) 1–5 May, (f) 6–10 May,(g) 11–15 May, (h) 16–20 May, (i) 21–25 May, (j) 26–31 May, (k) 1–5 Jun, and (l) 6–10 Jun. Light, heavy,and dark shadings indicate, respectively, the OLR values between 230 and 215, 215 and 200, and less than
200 W m⫺2.

period from late spring to early summer over the Asianmonsoon area. It was shown that each of the sequential
onsets of the Asian monsoon coincides well with anabrupt increase of the air temperature over the Tibetan

Plateau. Using the NCAR–NCEP reanalysis from 1951
to 1996, Zhang et al. (2002) investigated the MAC andIAV of the summer monsoon onset over the Indochina
Peninsula. They confirmed that the monsoon onset is

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characterized by the pronounced northeastward progression of the low-level southwesterlies over the Indian Ocean and the intensification and northward extension of the tropical convection from Sumatra. It coincides with the westward propagation of the ISO
originated from the South China Sea and the western
Pacific. All these studies provide further evidence that
the IAV of the Asian monsoon onset is significantly
affected by the thermal status of the Tibetan Plateau
and modulated by ISO.
As discussed above, the Indian monsoon onset is a
result of the large-scale meridional pressure gradient inwhich the thermal heating over the TP plays a crucialrole (Li and Yanai 1996). Here we present a new thermodynamic definition of the Indian monsoon onset interms of meridional gradient of tropospheric temperature and compare with traditional monsoon onset over
Kerala, India (MOK; Ananthakrishnan and Soman
1988a,b; Ananthakrishnan et al. 1967; Soman andKrishnaKumar 1993). We define a tropospheric temperature (TT) as an average of temperature between
200 and 600 hPa. Using daily NCEP–NCAR reanalysis
data between 1950 and 2002, we calculate TT and define a TTN and TTS as averaged TT over a northern
region (5°–35°N, 40°–100°E) and a southern region(15°S–5°N, 40°–100°E). Climatological evolution of
TTN and TTS over the year is shown in Fig. 10a. We
propose that large-scale Indian summer monsoon onset
takes place when the TTN–TTS becomes positive. This
marks the date when a clear large-scale tropospheric
off-equatorial heat source is set up that drives crossequatorial flow at lower atmosphere (above the boundary layer), makes the zero absolute vorticity line atlower atmosphere move to about 5° north of the equator, and triggers inertial instability (Krishnakumar andLau 1998; Tomas and Webster 1997). The instability
overcomes the inhibition associated with existing subsidence above the boundary layer and realizes the highconvective potential leading to explosive development
of off-equatorial convection, acceleration of low-level
flow, and monsoon onset. It may be noted that theclimatological mean onset date according to this criterion is 29 May (Fig. 10a), which is quite close to theclimatologial mean MOK (2 June). Actual MOK dates
were obtained from Joseph et al. (1994) and supplemented by dates published by the India Meteorological
Department. The first times when TTN–TTS becomes
positive are noted from NCEP–NCAR reanalysis datain terms of Julian days every year and are plottedagainst observed MOK dates in Fig. 10b. Strong correlation between the two (r ⫽ 0.67 for 53 yr) validates our
hypothesis regarding the relationship between MOK
and the change in sign of the meridional tropospheric

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FIG. 10. TTN and TTS are TT averaged over a northern region(15°–35°N, 40°–110°E) and a southern region (10°S–15°N, 40°–
110°E). (a) Climatological evolution of TTN and TTS over theyear calculated from daily NCEP–NCAR reanalysis between 1950
and 2002. (b) Julian day of each year (1950–2002) when TTN
becomes greater than TTS against corresponding MOK. Correlation between the two is shown.

heating gradient. Thus, the thermal status of the Tibetan Plateau during the premonsoon season has significant impact on the interannual variations of the
MAC through changes in the seasonal transitions.
The IAV of the transition of meridional gradient of
TT largely depends on IAV of TTN. Sensible heatingfrom the surface of the Tibetan Plateau (depends on
soil moisture and snow cover, etc.) has been shown toplay an important role on IAV of temperature over thearea and hence of monsoon onset (Li and Yanai 1996;
Ueda and Yasunari 1998; Yanai et al. 1992). Recently,
it has also been shown (Goswami and Xavier 2005) thatboth the onset and the withdrawal of the Indian monsoon could be influenced by the persistent TT anomaly
forced remotely by tropical heating associated with the
ENSO. An El Niño introduces a persistent negative TT

15 OCTOBER 2006

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anomaly over the TTN region and results in a late onset
and early withdrawal of the monsoon. Thus, in addition
to the local processes, the teleconnection associatedwith climate variability such as the ENSO can also influence the gradient of TT and hence the monsoon onset. The newly discovered teleconnection pathway isalso helpful in understanding the physical connection
between North Atlantic SST and the Indian monsoon(Goswami et al. 2006).

5. Scale interaction with monsoon ISO and IAV of
the MACIn addition to the external or slow processes that
influence the MAC, described in the previous two sections, there are some internal or fast processes that
could also influence the MAC and lead to IAV of thesame. In this section, we illustrate one such process anddemonstrate that interaction between the slow component of the climatological AC and the summer monsoon ISOs could impart significant influence on the
MAC and cause appreciable IAV of the MAC. It may
be recalled that the subseasonal variability of the Asiansummer monsoon that shows up as active and breakspells within the monsoon season is a manifestation ofsummer monsoon ISOs. The spatiotemporal structureof the summer monsoon ISOs has been studied extensively (see Goswami 2005, and references therein). Basic characteristics of the summer ISOs are very briefly
summarized here. The temporal scale of the summer
ISOs is larger than the synoptic time scale (⬎10 days)
and smaller than a season (⬍90 days), within which two
dominant time scales are prominent, one with periods
between 10 and 20 days (Chatterjee and Goswami 2004;
Chen and Chen 1993; Krishnamurti and Bhalme 1976)
and another with periods between 30 and 60 days
(Hartmann and Michelson 1989; Krishnamurthy andShukla 2000; Krishnamurti 1985; Krishnamurti andSubrahmanyam 1982; Murakami and Nakazawa 1985;
Sikka and Gadgil 1980; Yasunari 1979, 1980, 1981). The
30–60-day mode during northern summer has a large
spatial scale similar in structure to that of the seasonalmean (JJAS) and leads to strengthening and weakeningof the seasonal mean in its opposite phases (Annamalai
and Slingo 2001; Goswami and Ajayamohan 2001;
Krishnamurthy and Shukla 2000; Webster et al. 1998).
In contrast, the spatial structure of the 10–20-day modeis rather regional in character (Annamalai and Slingo
2001; Goswami and Ajayamohan 2001). While the 30–
60-day mode has a prominent poleward propagation
over the ASM region, the 10–20-day mode is characterized by a westward phase propagation. Basic genesis
and temporal and spatial scale selection appear to be of

5089

internal atmospheric origin involving convective feedbacks (Chatterjee and Goswami 2004; Gadgil and Srinivasan 1990; Goswami and Shukla 1984; Nanjundiah etal. 1992; Wang and Xie 1997; Webster 1983) but aremodified by air–sea interactions (Fu et al. 2003; Rajendran et al. 2004; Sengupta et al. 2001; Waliser et al.
2004).
How do the ISOs influence the seasonal mean and its
IAV? The ISOs could influence the seasonal mean if
two criteria are fulfilled. First, the spatial structure of
the dominant ISO mode should have significant projection on that of the seasonal mean, and second, the frequency of occurrence of the positive and negative
phases should be asymmetric. If the spatial structure of
the dominant ISO mode is similar to that of the seasonal mean and if the ISOs induced IAV of the seasonal mean, the spatial structure of the IAV of theseasonal mean and that of the ISO should also be similar (Palmer 1994). The possibility is tested with precipitation from NCEP–NCAR reanalysis. Precipitation
data from NCEP–NCAR reanalysis between 1948 and2002 are used in order to get enough statistics of interannual variability of the seasonal mean monsoon. Summer ISOs are extracted from 10–90-day filtered precipitation between 1 June and 30 September. A referencetime series is created from 10–90-day filtered precipitation between 1 June and 30 September averaged over
10°–30°N, 70°–90°E. Active (break) days are identified
from a reference time series normalized by its own standard deviation being greater than ⫹1 (⬍⫺1). The spatial structure of the dominant ISO mode is shown in
Fig. 11a, obtained from a composite of active minus
break conditions from all years. The spatial structure of
the IAV of Asian summer monsoon is shown in Fig.
11b, constructed from a composite of eight strong monsoon years minus eight weak monsoon years. The
strong (weak) monsoon years are selected based on
normalized JJAS rainfall averaged over 10°–30°N, 70°–
90°E being greater than ⫹1 (⬍⫺1). The similarity between the two patterns (pattern correlation is 0.74) indicates that the intraseasonal and interannual variations of the summer monsoon are governed by acommon spatial mode of variability. Several earlier
studies (Ajayamohan and Goswami 2000; Fennessy andShukla 1994; Ferranti et al. 1997; Molteni et al. 2003;
Sperber et al. 2001) have also noted that a common
mode of spatial variability governs the interannual andintraseasonal variations of the Indian summer monsoon. We then ask whether the probability of occurrence of the positive and negative phases of intraseasonal variability of precipitation is distinctly different instrong and weak monsoon years. Frequency distributions of 10–90-day filtered precipitation anomalies be-

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JOURNAL OF CLIMATE—SPECIAL SECTION

FIG. 11. (a) Spatial pattern of dominant ISO mode in precipitation (mm day⫺1). Composite of all active minus break conditions, 10–90-day filtered precipitation between 1 Jun and 30 Sep
for the period between 1948 and 2002. (b) Spatial pattern of dominant IAV of Asian monsoon. Composite of strong minus weak
JJAS precipitation based on eight strong (1958, 1959, 1961, 1970,
1978, 1980, 1981, and 1994) and eight weak (1962, 1964, 1965,
1966, 1972, 1974, 1987, and 2002) years.

tween 1 June and 30 September averaged over 10°–30°N, 70°–90°E for eight strong monsoon years and foreight weak monsoon years are shown in Figs. 12a and12b, respectively. The distribution is significantly biased
toward the positive (negative) side of anomalies duringstrong (weak) monsoon years. The difference between
the means of the two distributions is highly significant(at 99.9% confidence level using a two-tailed Student’s
t test). Thus, asymmetry in the frequency of occurrenceof the active and break conditions is associated withstronger or weaker monsoons. This is essentially aquasi-linear mechanism in which the residual of the
ISO anomalies over the season is well correlated withthe seasonal mean. The aperiodicity or the broadband
nature of the spectrum (10–90 days) of the ISOs is cru-

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FIG. 12. Frequency distribution of 10–90-day filtered precipitation anomalies averaged over 10°–30°N, 70°–90°E for (a) eightstrong monsoon years between 1 Jun and 30 Sep normalized by its
own standard deviation. (b) Same as in (a), but for eight weak
monsoon years from NCEP–NCAR reanalysis.

cial for the asymmetry in the frequency of occurrenceof the active/break conditions. The aperiodicity, in turn,arises due to nonlinear interactions among the dominant modes with higher-frequency disturbances. Thus,
this mechanism implicitly involves some nonlinear interactions.
There is no doubt that the probability density function (PDF) of the summer ISOs determines the seasonal mean. However, a major debate continues onwhether the modification of the PDF is entirely by external boundary forcing or partly by internal dynamics(independent of the external forcing). It is recognizedthat even in the absence of external forcing, the seasonal mean could be different from one year to another.
Palmer (1994) first introduced the paradigm that theseasonal mean is determined by the PDF of the ISOs
that are essentially of internal origin, but externalboundary forcing could introduce IAV by modulating
the PDF of the chaotic ISOs. Krishnamurthy andShukla (2000) presented a modified form of the Char-15 OCTOBER 2006

5091

GOSWAMI ET AL.

ney and Shukla (1981) hypothesis for predictability ofseasonal mean monsoon and indicated that the seasonalmean of a given year is a linear combination of theexternally forced seasonally persistent component anda component coming from a shift of the PDF of ISOsdue to internal dynamics. Krishnamurthy and Shukla(2000), through analysis of daily rainfall over India, andSperber et al. (2000), from analysis NCEP–NCAR reanalysis data, examined this hypothesis and arrive at
the conclusion that almost all IAV of the seasonal meanarises from slowly varying external forcing. However,both studies arrive at this conclusion from examinationof the PDF of ISOs after removing the total IAV ofseasonal mean from data. Both external forcing andinternal dynamics introduce a shift of mean in the PDF.
If the total IAV of the seasonal mean is removed fromthe data, it is rather obvious that the PDF of the ISO
anomalies would be Gaussian. Unless a method isfound to separate the contribution to the shift of the
PDF due to external forcing from that due to internal
origin, the question of contribution of pure ISOs to theseasonal mean could not be settled. A conceptual example of how internal dynamics could lead to change in
the PDF of the ISOs and hence that of the seasonalmean is illustrated below.
In addition to the quasi-linear mechanism describedabove, there exists a nonlinear mechanism throughwhich ISOs influence the seasonal mean and its IAV.
Empirical evidence for such a nonlinear mechanism isobtained from the fact that the monsoon ISO activity isrelated to the MAC of precipitation and its IAV. Pentad precipitation from CMAP (Xie and Arkin 1996) for
23 yr (1979–2001) is used for this purpose and summer
ISO activity is defined as the standard deviation of 10–
90-day filtered (using a Lanczos filter) precipitation between 1 June and 30 September. The JJAS seasonalmean precipitation averaged over the region between
0°⫺30°N and 60°–100°E is plotted against summer ISO
activity over the same region in Fig. 13a. Similarly, theamplitude of the AC of precipitation over the region, as
defined by the difference between JJAS and DJF seasonal means, is plotted against summer ISO activity in
Fig. 13b. The seasonal mean as well as the AC of precipitation over the monsoon region are well correlatedwith the amplitude of ISO activity. This result is consistent with the results of Waliser et al. (2004), whofound that the amplitude of the AC of precipitation in
AGCMs is closely related to the amplitude of ISO activity simulated by the AGCMs. This indicates that thenonlinearity of the ISOs is somehow related to the seasonal mean and IAV of the MAC. How does the nonlinearity of the ISOs lead to IAV of the MAC? Here,
we propose that modulation of chaotic summer ISOs by

FIG. 13. Relationship between IAV of ISO activity in precipitation during northern summer (1 Jun–30 Sep) and that of (a)JJAS seasonal mean precipitation and (b) IAV of the AC (JJA
minus DJF) of precipitation. Both quantities are averaged over
0°⫺30°N, 60°–100°E. Data used are CMAP between 1979 and2002. ISO activity is defined as standard deviation of 10–90-day
filtered precipitation. Correlations between the two are indicatedin each panel.

annually varying forcing associated with the slow annual cycle can give rise to IAV of the AC. The feasibility of this hypothesis is demonstrated using a toy
model. The model consists of a simple nonlinear dynamical system forced by annually varying forcing
originally constructed by Lorenz (1984) to describe thegeneral circulation of the atmosphere. The model is
described byẊ ⫽ ⫺Y 2 ⫺ Z 2 ⫺ aX ⫹ aF
Ẏ ⫽ XY ⫺ bXZ ⫺ Y ⫹ GŻ ⫽ bXY ⫹ XZ ⫺ Z,

共1兲

where X may be interpreted as the zonal mean component while Y and Z may be considered as two wave

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FIG. 14. (left) Time series of X and Y for constant forcing F0 ⫽ 7.62, F1 ⫽ 0.0, and G ⫽ 1.18 for a typical
10-yr period from a 200-yr simulation with (right) corresponding spectra. Power spectrum is plotted as
power multiplied by frequency vs period.

components. The terms XY and XZ represent amplification of the waves through interaction with the mean
flow and happen at the expense of the mean flow represented by the terms ⫺Y 2 and ⫺Z 2 in Eq. (1). The
terms ⫺bXZ and bXY represent displacement of the
waves by the mean flow. The linear terms represent
mechanical and thermal damping; F is forcing for thezonally symmetric component like the solar forcing
while G is forcing for the wave component representative of the land–ocean contrast. For fixed values of a, b,
and G, different values of the solar forcing F lead tosolutions of Eq. (1) that vary from periodic solutions
with different periods to chaotic or aperiodic solutions
for some values of F. In order that the nonlinear systemrepresent the observed dominant ISO periods, the timein Eq. (1) has been scaled by a factor C (⫽0.57). The
variables X, Y, and Z; the parameter a; and forcing F
and G are also scaled appropriately. Using unscaled
parameters, a ⫽ 0.25, b ⫽ 4.0, and G ⫽ 1.18, for F ⫽7.62, the solution is quite aperiodic (Figs. 14a,b) withdominant periods between 30 and 70 days (Fig. 14c) in
X and 10 and 20 days in Y (Fig. 14d). The spectrum ofoscillations represented by the model is representativeof that of the monsoon ISO. The parameters werechosen following a method similar to one describedby Lorenz (1984), so that the system has aperiodic solutions. The forcing F is then made to have an annual
cycle like the solar forcing, namely, F ⫽ F 0 ⫹F1 cos(2␲t/T ), with period T as one year. Taking F0 ⫽5.6 and F1 ⫽ 2.5, the equations were integrated formore than 250 yr. Time series for X and Y for a typical
10-yr period and the corresponding spectra are shownin Fig. 15. It is seen that modulation of the intraseasonal
oscillations by the annual cycle has resulted in a strong
quasi-biennial oscillation (QBO) in both X and Y. The
system goes to a regime in one year characterized by a
low mean X and high mean Y, both with very low amplitude of fluctuations, while it goes to another regime

in the next year with high mean X and low mean Y,
both with high-amplitude oscillations. Thus, if the intraseasonal oscillations are vigorous and nonlinear andthe annual cycle of the mean flow is strong, modulationof the ISOs by the annual cycle could result in an internal IAV.
The mechanism through which the modulation of thenonlinear ISOs by the annual cycle forcing leads to
IAV is as follows: The nonlinear system is chaotic only
for some values of the forcing during the AC. For othervalues of the forcing, the system goes through various
periodic regimes. For the forcing corresponding to thechaotic regime (corresponding to northern summer
conditions), the attractor of the nonlinear system is acomplicated “strange attractor” with several different
folds in the phase space. As the forcing changes, theISOs do not get a chance to explore the full phase spaceof the attractor during the summer season and map
only a small part of the attractor. The seasonal mean isdetermined by the higher residence time of the ISOs
during the summer season around one of the basins.
Whether the system would reside more around one or
the other basin depends on the initial condition withwhich it enters the chaotic regime. The initial condition
with which the system enters the chaotic regime thenext year (after coming out of the chaotic regime andafter passing through the periodic regimes for the restof the year) is different from that of the previous year
and preconditions the system to reside around a different basin. This would give rise to a different seasonalmean for the next year. In the simple example, thechaotic regime visits two different basins facilitating aquasi-biennial IAV. If the process described above iscorrect, short-term integrations of Eq. (1) with different
initial conditions should give different seasonal means.
This was tested by integrating Eq. (1) for three months
with parameters a ⫽ 0.25, b ⫽ 4.0, G ⫽ 1.18, and F ⫽7.62, for a large number of different initial conditions. It

15 OCTOBER 2006

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5093

FIG. 15. Same as in Fig. 14, but for an annually varying forcing, F0 ⫽ 5.6, F1 ⫽ 2.5, and G ⫽ 1.18,
that includes the chaotic regime shown in Fig. 17.

is found that for some initial conditions, the system(e.g., X ) goes to a state of high mean with lowamplitude fluctuations while for another set of initialconditions, it goes to a state of low mean with highamplitude fluctuations (figure not shown). Lorenz(1990) showed that a similar mechanism could give rise
to year-to-year variation of extratropical climate where
the flow is chaotic during winter rather than during
summer as in the Tropics. Goswami (1995) investigatedthe origin of the tropospheric QBO using a model similar to Eq. (1) coupled to a low-frequency oscillator(representing the ENSO). The results from the toymodel preset here indicate that the coupling to the lowfrequency oscillator is not crucial and that modulationof the ISO by the annual cycle is sufficient to generatea tropospheric QBO.

6. Contribution of ISOs to internal IAV and theirlimit on monsoon predictability
The preceding discussions lend support to the factthat the monsoon ISO could lead to significant internal
IAV of the seasonal mean monsoon. The importantquestion, therefore, is how much of the observed IAVof the monsoon is governed by the internal component?
Several recent studies have attempted to estimate thepotential predictability of the summer monsoon bymaking estimates of internal variability from modelingstudies (Cherchi and Navarra 2003; Goswami 1998;
Kang et al. 2004; Molteni et al. 2003) and from observations (AjayaMohan and Goswami 2003). Almost allof these studies indicate that the contribution of theinternal component to IAV of the summer monsoon isas large as or larger than that from the external component over the Asian monsoon region. We use zonal
winds at 850 hPa from NCEP–NCAR reanalysis data
for the period between 1979 and 2002 and estimate theinternal variance of the monthly means during northernsummer using the method described in AjayaMohan
and Goswami (2003), and potential predictability is es-

timated as a ratio (⌫) between “total” interannual variance and internal variance and is shown in Fig. 16a. The
predictability over the Asian summer monsoon region
is poor, as the ⌫ ratio is less than or close to 2 over theregion, indicating that the internal variance is comparable to or larger than external variance. A similar ⌫ratio between the total interannual variance of the seasonal mean (JJAS) precipitation and the internal variance simulated by an AGCM [the Laboratoire de Météorologie Dynamique (LMD) GCM; Sadourny and
Laval 1984] from a five-member ensemble simulationof 20-yr duration, all forced by observed SST as aboundary condition but differing only on initial conditions, is shown in Fig. 16b (Xavier et al. 2005, manuscript submitted to J. Climate). This figure also indicates that the ⌫ ratio for precipitation during northernsummer (JJAS) over the Asian monsoon region is lessthan or close to 2. Thus, the Asian summer monsoon isa formidable system to predict as the predictable signal(external variance) is comparable to the unpredictablenoise (internal variance) over this region. The internalvariability of the seasonal mean largely arises partly
from seasonal bias of ISO anomalies and from variability of ISO activity (not shown).

7. Conclusions and discussion
A conceptual framework is presented for examining
the problem of predictability of the Asian summer
monsoon. The framework starts with recognition that
the Asian monsoon is associated with a strong AC andemphasizes that the ISOs are an integral part of thesystem and cannot be isolated from the annual AC. It is
recognized that the climatological mean monsoon annual cycle (MAC) can be viewed in terms of superposition of a “slow annual cycle” and a “fast annual
cycle.” The fast annual cycle arising from the “climatological ISOs” indicates that the ISOs play an importantrole even in defining the climatological mean MAC.
The summer monsoon ISOs are, therefore, an impor-

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FIG. 16. Estimate of predictability for (a) zonal winds at 850 hPa and (b) precipitation during
northern summer (JJAS). (a) Ratio (F ) between total interannual variability of monthly
means during JJAS and internal variability of zonal winds at 850 hPa based on daily NCEP–
NCAR reanalysis data between 1979 and 2002. (b) Ratio (F ) between total interannual
variability of JJAS seasonal mean precipitation from a five-member ensemble of 20-yr simulation of LMD GCM and estimate of internal variability of the seasonal mean.

tant building block of the Asian monsoon system. The
Asian monsoon is viewed as a nonlinearly interacting
system of a MAC and chaotic summer ISOs.
The predictability of seasonal mean monsoon depends on IAV of the MAC. The IAV of the MAC, inturn, is governed by two distinctly different classes ofprocesses. Slowly evolving coupled ocean–atmosphereoscillations modulate the MAC and so does slowly
varying forcing associated with land surface processes
such as TP heating, Eurasian snow cover changes, soil
moisture changes, etc. Local warm ocean–atmosphereinteraction can also give rise to IAV of the MAC. The
IAV arising from these slow processes or “external”
forcing is less sensitive to initial condition and morepredictable. On the other hand, there is a fast process
through which IAV of the MAC could take place. Thisoccurs through nonlinear interaction between the MAC
and chaotic summer monsoon ISOs. As the summer
monsoon ISOs primarily arise from atmospheric internal dynamics and since the interaction through which
this IAV is generated is intrinsically nonlinear, this “internal” part of IAV of the MAC is sensitive to initialcondition and is hence unpredictable. Therefore, thepredictability of the monsoon depends on relative contributions of the external and internal components tothe total IAV of the MAC.

Processes that influence the climatological mean
MAC and its IAV are reviewed. Empirical evidence
that IAV of MAC is linked to air–sea interaction synonymous with ENSO is presented. It is shown that thedominant modes of IAV of the amplitude of the MAC
of precipitation and low-level winds over the IndoPacific basin are closely correlated with ENSO. It isalso shown that the timing of change in the sign of thedifference between TT averaged over a northern box(10°–35°N, 30°–100°E) and a southern box (15°S–10°N,
30°–100°E) from negative to positive is strongly correlated with the date of onset of Indian summer monsoon
over Kerala (MOK). While the meridional temperature
gradient (or the pressure gradient) could be influenced
by the monsoon heating itself after the summer monsoon is set up, the TP heating during the northern
spring (April–May) has a seminal role in determining
the gradient during the preonset time, influencing theonset phase of the MAC.
For over a decade, it has been recognized that thedifficulty in predicting the summer monsoon either
through empirical techniques or dynamical method using climate models is due to the existence of a significant amount of internal IAV of the Asian monsoon.
However, a consensus on what is responsible for thisinternal IAV of the monsoon has not been reached. We

15 OCTOBER 2006

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propose here that the internal IAV of the monsoon is
primarily due to interaction of the MAC and the ISOs.
The ISOs do depend on the background condition set
up by the slow component of the AC (external), but
once activated can influence and change the seasonalmean and hence the MAC. Thus, ISOs represent an
important player through which interaction with the
AC gives rise to IAV of the MAC and sets up a limit topredictability of the monsoon. Consistent with somemodeling studies, empirical evidence is provided showing that ISO activity is related to the amplitude of theannual cycle of precipitation. Using CMAP data during
the period between 1979 and 2002, it is shown that theamplitude of AC of precipitation (JJA–DJF) over the
Asian monsoon region is strongly correlated with ISO
activity (standard deviation of ISO anomalies) during
northern summer (June–September).
Two mechanisms through which ISOs modulate the
MAC are discussed. The first of them works as follows:
The spatial structure of the dominant summer ISO
mode has significant projection on that of the summer
mean and leads to the fact that the interannual andintraseasonal variations of the Asian summer monsoon
are governed by a common mode of spatial variability.
The spatial structure being similar, seasonal mean biasof the ISO anomalies leads to strengthening or weakening of the seasonal mean and hence that of the MAC.
Nonzero seasonal mean bias of ISO anomalies is due tothe fact that frequency distribution of intraseasonal
anomalies during the summer season (June–September) is skewed toward the positive (negative) side during strong (weak) monsoon years.
The summer ISOs also produce IAV of the MAC
through the following nonlinear mechanism: The largescale flow associated with the slow annual cycle acts
like an annually varying forcing for the ISOs. The ISOs
are chaotic (nonlinear) for the forcing corresponding to
the northern summer while they may be periodic (linear) for forcing corresponding to the rest of the year.
The chaotic summer ISOs have a complex attractor
with a number of different basins. The ISOs get to map
only a small part of the attractor as the forcing change
and chaotic regime could not be sustained at the end of
the summer season. Higher residence time around oneof the basins during the summer season determines theseasonal mean. Whether the ISO would reside morearound one or the other basin during the season depends on the initial condition with which it enters the
chaotic regime. The modulation of the chaotic ISOs by
the slow annual cycle can, thus, lead to IAV of theseasonal mean or the MAC. This is essentially thesource of the internal or unpredictable part of IAV of
the monsoon.

5095

It is clear that the asymmetry of the PDF of ISO
anomalies contributes to the anomaly of the seasonalmean. A part of the asymmetry of the PDF may be due
to modulation of the ISOs by external slowly varying
forcing while the remaining part of asymmetry of the
PDF is due to internal dynamics of the ISOs. Becauseof the interacting nature of the MAC, ISOs, and theslowly varying external forcing, it is rather difficult toseparate the contribution from external forcing and internal dynamics to the asymmetry of the ISO PDF fromobservations. It remains to be an unsettled problem.
Two observations (Fig. 17), however, indicate that internal dynamics may play a greater role in the asymmetry of the ISO PDF over the ASM region. First,
summer ISOs seem to be somewhat decoupled from the
ENSO forcing over this region. While the correlation
between IAV of the standard deviation of ISO anomalies during JJAS with Niño-3 SST is quite large over thetropical Pacific, it is nearly zero over the ASM region(Fig. 17a). Second, the frequency distribution of normalized ISO anomalies between 1 June and 30 September averaged over 10°–30°N, 70°–90°E, for four nonENSO strong monsoon years and four non-ENSO
weak monsoon years, is still asymmetric (Fig. 17b, statistically significant). Assuming that non-ENSO strong
or weak seasonal mean monsoon rainfall is essentiallydriven by internal dynamics, Fig. 17b indicates that internal dynamics could produce asymmetry of the ISO
PDF. Various estimates of contribution of internal IAV
to total IAV of the monsoon indicates that up to 50%
or more of the observed IAV of the monsoon over the
Asian monsoon regions could be attributed to internalvariability. The fact that the predictable “signal” of the
Asian monsoon is comparable to the unpredictable internal variability (“noise”) makes it a most difficult system to predict. This results from a combination of twothings. The amplitude of the external component of the
IAV is weak over this region while that of the internal
component generated by the ISOs is relatively large.
Thus, the ISOs are, in a way, responsible for limiting
predictability of the Asian monsoon. It may be noted
here that there are other regions in the Tropics (e.g.,
the equatorial central Pacific) where the external
forced component of IAV is much larger than that frominternal variability and the climate is predictable (consistent with the Charney–Shukla hypothesis).
Such analysis also indicates that there are two important requirements for reasonable success in predicting
the seasonal mean Asian monsoon. First, any model(AGCM or CGCM) attempting to predict the monsoon
must be able to simulate the amplitude and spatial
structure of the observed external component of theIAV (caused by air–sea interactions, TP heating, etc.)

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VOLUME 19

FIG. 17. (a) Correlation coefficient between IAV of standard deviation of 10–90-day filteredprecipitation between 1 Jun and 30 Sep from CMAP (1979–2002) and JJAS mean Niño-3 SST.
(b) Frequency distribution of 10–90-day filtered precipitation anomalies averaged over 10°–30°N, 70°–90°E for four non-ENSO strong monsoon years (1956, 1959, 1961, and 1994),
between 1 Jun and 30 Sep normalized by its own standard deviation. (c) Same as in (b), but
for four non-ENSO weak monsoon years (1968, 1974, 1985, and 2002) from NCEP–NCAR
reanalysis. To get a reasonable number of non-ENSO strong (weak) monsoon years, thecriterion for selection used is for the monsoon index to be greater than ⫹0.5 (less than ⫺0.5).

correctly. Since the amplitude of this signal is rather
small over the Asian monsoon region, a small shift insimulating the spatial pattern could lead to a large percentage error for the simulated variability over the
Asian monsoon region. Similarly, if the amplitude of
the simulated external component by a model is systematically weaker than observed, the internal variability would have an undue advantage in making the monsoon unpredictable. A systematic diagnosis of simulation of this component of variability by the model is,therefore, required before embarking on prediction of
the monsoon. The second requirement is that themodel must be able to simulate the observed spatiotemporal characteristics of the summer ISOs with a certain
degree of fidelity. For example, higher-than-observed
amplitude of simulated summer ISO activity may resultin higher-than-observed amplitude of simulated internal IAV of the seasonal mean and may adversely influence prediction skill. The structure and amplitude of
the summer ISOs in a model depend on the hydrological cycle of the model governed, among others, by parameterization of convection and land surface processes in the model. Therefore, correct simulation of
the summer ISOs involves complex interactions within
the model framework.

It has come to light in the last couple of years thatcertain amounts of air–sea interactions are involved
with the summer ISOs. Therefore, ideally a coupled
ocean–atmosphere model would be desirable for correct simulation of the observed characteristics of thesummer ISOs. However, the coupled models have theirown systematic bias in simulating the mean climate.
The systematic bias of the coupled model could influence the statistics of the simulated ISOs. Therefore, it is
not obvious that a coupled GCM would automatically
improve the simulation of the ISOs. However, air–sea
coupling associated with the ISOs also raises an interesting issue. If the air–sea coupling introduces certain
amount of constraint on the ISOs, would it also introduce constraint on the internal variability and possibly
make it more predictable? Systematic studies bycoupled and uncoupled AGCMs are required to answer
these questions.
Acknowledgments. BNG thanks the Department of
Science and Technology, Government of India, for partial support. He is thankful to M. S. Madhusoodanan,
Prince K. Xavier, and R. Vinay for help in preparing
the manuscript. He acknowledges involvement with the
Indo-France IFCEC project and thanks Dr. J. P. Duvel

15 OCTOBER 2006

GOSWAMI ET AL.

for sharing some results of the LMD model simulations.
Comments by Prof. Bin Wang on an earlier version of
the manuscript are greatly appreciated. We are also
thankful to three anonymous reviewers for their comments that led to improvement of the manuscript.
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