Interannual variability of Indian summer monsoon rainfall onset date at local scale

Interannual Variability of Indian Summer Monsoon arising from Interactionsbetween Seasonal Mean and Intraseasonal Oscillations

E. SUHAS, J. M. NEENA, AND B. N. GOSWAMI

Indian Institute of Tropical Meteorology, Pashan, Pune, India

(Manuscript received 29 July 2011, in final form 29 November 2011)

ABSTRACT

A significant fraction of interannual variability (IAV) of the Indian summer monsoon (ISM) is known to be

governed by ‘‘internal’’ dynamics arising from interactions between high-frequency fluctuations and the

annual cycle. While several studies indicate that monsoon intraseasonal oscillations (MISOs) are at the heart

of such internal IAV of the monsoon, the exact mechanism through which MISOs influence the seasonal mean

monsoon IAV has remained elusive so far. Here it is proposed that exchange of kinetic energy (KE) between

the seasonal mean and MISOs provides a conceptual framework for understanding the role of intraseasonal

oscillations (ISOs) in causing IAV and interdecadal variability (IDV) of the ISM. The rate of KE exchange

between seasonal mean and ISOs is calculated in frequency domain for each Northern Hemispheric summer

season over the ISM domain, using 44 yr of the 40-yr ECMWF Re-Analysis (ERA-40) data. The seasonal

mean KE and the rate of KE exchange between seasonal mean and ISO shows a significant relationship at

both the 850- and 200-hPa pressure levels. Since the rate of KE exchange between seasonal mean and ISO is

found to be independent of known external forcing, the variability in seasonal mean KE arising from this

exchange process can be considered as an internal component explaining about 20% of IAV and about 50%

of IDV. Contrary to the many modeling studies attributing the weakening of tropical circulation to the sta-

bilization of the atmosphere by global warming, this paper provides an alternative view that internal dynamics

arising from scale interactions might be playing a significant role in determining the decreasing strength of the

monsoon circulation.

1. Introduction

The dependence of agriculture, drinking water, and

energy production on the Indian summer monsoon (ISM)

rainfall makes it the lifeline for a large fraction of the

world’s population. The economy, life, and property in

the region are vulnerable to significant variability of the

ISM on intraseasonal, interannual, and interdecadal time

scales (Webster et al. 1998; Krishnamurthy and Goswami

2000; Goswami et al. 2006b). Hence, predicting the sea-

sonal mean ISM rainfall is of great socioeconomic im-

portance and has been attempted for many decades,

albeit with limited success (Gadgil et al. 2005; Kang and

Shukla 2006). Recognizing the fact that the tropical

climate is largely determined by changes in slowly varying

boundary forcing, a physical basis for prediction of sea-

sonal mean monsoon rainfall was proposed by Charney

and Shukla (1981). This concept, supported by a large

number of modeling studies (e.g., Shukla 1998), estab-

lished the basis for seasonal prediction in the tropics.

Such slowly varying forcing can be considered ‘‘external’’

for the atmosphere, and may be predictable as it arises

from the slow oscillations of the coupled climate system.

Some of the major external forcings—the El Nino–

Southern Oscillation (ENSO), Indian Ocean dipole

(IOD), Atlantic multidecadal oscillation (AMO), and

Eurasian snow cover—are known to influence the in-

terannual variability (IAV) and interdecadal variabil-

ity (IDV) of the Indian monsoon and have been well

documented. Initially identified from correlations, the

physical mechanisms and different pathways through

which these phenomena influence the Indian monsoon

have been further elucidated through diagnostic stud-

ies and model simulations (Rasmusson and Carpenter

1983; Shukla and Paolino 1983; Webster et al. 1998;

Krishnamurthy and Shukla 2008; Goswami et al. 2006a;

Krishnamurthy and Goswami 2000; Krishna Kumar

et al. 2006; Hahn and Shukla 1976).

Corresponding author address: E. Suhas, Indian Institute of

Tropical Meteorology, Dr. Homi Bhaba Road, Pashan, Pune

411008, India.

E-mail: [email protected]

VOLUME 69 J O U R N A L O F T H E A T M O S P H E R I C S C I E N C E S JUNE 2012

DOI: 10.1175/JAS-D-11-0211.1

� 2012 American Meteorological Society 1761

While some fraction of the IAV of the seasonal mean

may be governed by such predictable external forcing,

a fraction of the IAV of the seasonal mean may arise

from interactions among different scales of motion, in-

teraction between seasonal mean and ISOs, interaction

between organized convection and large-scale circula-

tion, interaction between flow and topography, and so

on. The contribution to the IAV of seasonal mean

arising from these processes is collectively called the

‘‘internal’’ component. It has been argued that the IAV

of seasonal mean caused by the internal component is

less predictable (Goswami 1998). Hence, the potential

predictability of the seasonal mean would depend on the

relative contribution of the external and internal com-

ponents to the IAV. Recent studies using both obser-

vational analysis and model simulations (Ajaya Mohan

and Goswami 2003; Kang et al. 2004; Goswami and

Xavier 2005; Krishna Kumar et al.2005) show that the

Asian monsoon region is an exception in the tropics

where the potential predictability is relatively low, un-

derpinning the role of internal variability compared to

external variability over this region. The seminal role of

internal variability in determining the seasonal mean

may be one reason for the current poor skill of almost all

models in predicting the seasonal mean ISM rainfall

(Kang and Shukla 2006). A clear understanding of the

mechanism responsible for the internal IAV over the

ISM domain may help us develop better initialization

techniques for improving the seasonal forecast.

The intraseasonal oscillations are quasi-periodic fluc-

tuations of atmospheric origin, which play a major role

in determining the amplitude of seasonal mean of in-

dividual summer seasons by modulating the strength

and duration of active/break spells of the ISM through

the northward-propagating 30–60-day mode and the

westward-propagating 10–20-day mode (Goswami 2005,

and references therein). The ISOs are also found to exert

an indirect control on the seasonal mean by modulating

the synoptic-scale activity through clustering of lows and

depressions along the monsoon trough (Goswami et al.

2003). Hence, the IAV of monsoons arising from in-

teraction between seasonal mean and ISOs may be

a major source for internal IAV. The internal IAV has

received much attention recently and a few studies have

proposed mechanisms for the generation of internal IAV

involving summer ISOs (Sperber et al. 2000; Goswami

and Ajaya Mohan 2001; Goswami et al. 2006b; Hoyos and

Webster 2007). Since the dominant modes of IAV of the

seasonal mean and the ISOs share a common spatial

pattern, it is hypothesized that a shift in the probability

density function of ISOs can give rise to some internal

IAV (Ferranti et al. 1997; Sperber et al. 2000; Goswami

and Ajaya Mohan 2001). It has also been argued that

nonlinear interaction between the ISOs and the annual

cycle could give rise to a biennial internal variability of

the monsoon (Goswami 1995; Goswami et al. 2006b).

While these studies demonstrated the proof of the con-

cept using simple heuristic models, the quantitative con-

tribution of such interactions to the observed IAV could

not be estimated. Several studies showed the existence

of significant correlation between the seasonal mean

and the ISO over the ISM domain, indicating a physical

relationship between these two scales (Lawrence and

Webster 2001; Qi et al. 2008; Fujinami et al. 2011).

Corroborative evidence in this direction is also obtained

by estimating the correlation between area-averaged

variance of ISO and seasonal mean rainfall over India for

104 yr (correlation coefficient 5 0.53). The correlation is

much higher during the recent 54-yr period (correlation

coefficient 5 0.64) (Fig. 1a). Although all these studies

provide circumstantial evidence for the link between

ISOs and seasonal mean over the ISM domain, no clear

picture of how ISOs lead to internal IAV has emerged so

far. Thus, a physical mechanism through which the ISOs

influences the seasonal mean and introduces internal

IAV still remains an open question.

Recently, Qi et al. (2008) made an attempt to quantify

the role of ISO perturbations in determining the strength

of the mean zonal wind, and thereby the strength of ISM,

through a dynamical framework. The relationship be-

tween the mean zonal wind tendency and the eddy

momentum transport was derived from the primitive

equations of motion. It is shown that the eddy momentum

transport can significantly affect the westerly tendency

during strong and weak ISO years. However, since the

eddy momentum transport term includes variability on

all time scales, the role of ISO feedback in affecting the

strength of the mean westerly is not clear. Moreover, the

seasonal mean variability of ISM rainfall is more strongly

related to the strength of the winds over the low-level

jet (LLJ) and the upper-level tropical easterly jet (TEJ)

regions (Figs. 1b,c). Besides, not only the zonal wind

but also the meridional wind component is significant.

Figure 2a shows the seasonal mean rainfall averaged over

the Indian landmass (6.58–27.58N, 72.58–85.58E) and the

seasonal mean kinetic energy (KE) at 850 hPa averaged

over the LLJ core region (08–208N, 508–708E). A strong

correlation of 0.61 significant at the 95% confidence level

establishes the connection between the seasonal mean

rainfall and the cross-equatorial flow. Figure 2b shows the

seasonal mean rainfall averaged over the landmass and

the seasonal mean KE at 200 hPa averaged over the TEJ

region (08–158N, 508–808E). The moderately strong cor-

relation coefficient (0.52, significant at the 95% confidence

level) suggests a significant relationship between the

strength of upper-level monsoon circulation and seasonal

1762 J O U R N A L O F T H E A T M O S P H E R I C S C I E N C E S VOLUME 69

mean monsoon rainfall. The strong relationship between

the IAV of KE of LLJ and that of TEJ and monsoon

rainfall implies that better insight into the IAV of mon-

soon rainfall can be obtained through understanding

the mechanisms of IAV of LLJ KE and TEJ KE. To

quantify the role of interaction between seasonal mean

and ISOs in causing the IAV and IDV of the seasonal

mean, the analysis scheme presented by Qi et al. (2008)

is reframed using spectral energetic analysis. In the

present study, we investigate how scale interactions be-

tween the seasonal mean monsoon and the ISOs can give

rise to variability of kinetic energy of the monsoon flow

on interannual and interdecadal time scales.

The data used in this study are described in section 2.

A dynamical framework for the ISO–seasonal mean

interaction in terms of atmospheric energetics is given in

section 3. Details of the computation of KE exchange

between the seasonal mean and ISO is presented in

section 4. Section 5 describes the IAV and IDV of the

exchange of KE between the seasonal mean and ISOs,

and its relationship with the IAV and IDV of seasonal

mean. The issue of sensitivity of temporal window length

on the calculation of seasonal mean–ISO interaction is

addressed in section 6. The dependence of the KE ex-

change on the reanalysis data used is discussed in section 7.

Section 8 summarizes the important results.

2. Data

Our primary analysis is based on 44 yr (1958–2001) of

the 40-yr European Centre for Medium-Range Weather

Forecasts (ECMWF) Re-Analysis (ERA-40) daily wind

data (Uppala et al. 2005) at the 850- and 200-hPa pres-

sure levels for the ISM period [i.e., June–September

(JJAS)]. The consistency of the analysis is tested with

National Centers for Environmental Prediction (NCEP)–

National Center for Atmospheric Research (NCAR)

reanalysis daily wind data (Kalnay et al. 1996). We have

also used high-resolution 18 3 18 daily rainfall data

available for the period 1901–2004 (Rajeevan et al. 2008).

Monthly Nino-3.4 data available from 1871 to 2007 are

downloaded from the NCAR Earth System Laboratory

(http://www.cgd.ucar.edu/cas/catalog/climind/TNI_N34/)

and the JJAS mean was calculated. The Pacific de-

cadal oscillation (PDO) index is downloaded (from

FIG. 1. (a) Seasonal mean rainfall (mm day21, solid line) and

JJAS ISO (10–90-day bandpass filtered rainfall, dashed line) vari-

ance averaged over the region 10.58–27.58N, 72.58–85.58E from

1901 to 2004 (data source: India Meteorological Department

(IMD) rainfall data; Rajeevan et al. 2008). (b),(c) Climatological

seasonal mean (JJAS) kinetic energy per unit mass (m2 s22) cal-

culated for the period 1958–2001 using the ERA-40 data (Uppala

et al. 2005) at (b) 200 and (c) 850 hPa. The domain of analysis used

in the study at both levels is highlighted with a rectangular box.

FIG. 2. Seasonal mean (JJAS) rainfall (mm day21, solid line) and

seasonal mean kinetic energy per unit mass (m2 s22) calculated

using ERA-40 data (dashed line) (a) at 850 hPa averaged over

08–208N, 508–708E and (b) at 200 hPa averaged over 08–158N,

508–808E. The corresponding correlation coefficients (CC) are

given at the upper-left corner of each panel.

JUNE 2012 S U H A S E T A L . 1763

http://www.atmos.washington.edu/;mantua/; Mantua

et al. 1997), as is the dipole mode index (DMI) data

(representative of IOD; see http://www.jamstec.go.jp/

frsgc/research/d1/iod/). A North Atlantic SST index is

calculated by area averaging the anomalies of Kaplan

SST V2 data, provided by the National Oceanic and

Atmospheric Administration Office of Oceanic and

Atmospheric Research, Earth System Research Labo-

ratory, Physical Science Division (NOAA/OAR/ESRL

PSD) at Boulder, Colorado (see http://www.esrl.noaa.

gov/psd/) over the region 08–708N, 908W–208E. An AMO

index is calculated by applying 11-yr running mean on the

JJAS mean of the North Atlantic SST index (Goswami

et al. 2006a).

3. Dynamical framework for understanding theISO–seasonal mean interaction

The dynamic effect of transient eddies on the time

mean flow received considerable attention during the

1970s and 1980s (Holopainen 1978a; Hoskins et al. 1983;

Hoskins and Pearce 1983). Most of these studies focused

on the influence of transient disturbances on the local

time mean flow over the midlatitude regions in terms of

large-scale Reynolds stresses in a quasigeostrophic (QG)

framework. The atmospheric flow at any time period can

be partitioned as the sum of time mean and transient

fluctuations, and this concept was used by Holopainen

(1978b) to derive the growth or decay of time mean KE

using the primitive equations of motion. The rate of

change of time mean KE is the resultant of various

physical processes. The abbreviated representation of

the equation for the rate of change of KE is given below

(the complete forms of the equations are given in the

appendix):

d(K)/dt 5 BC 1 CAK 1 WF 1 D, (1)

where K represents the time mean KE, BC represents

the barotropic energy conversion, CAK stands for con-

version of available potential energy (APE) to KE, and

WF and D indicate the wave energy flux and dissipation

due to friction, respectively. The term CAK acts as the

source for KE through conversion from APE generated

by the diabatic heat sources in the tropics. The wave

energy flux term includes the advection of KE as well as

the work done by pressure. The BC term involves eddy–

time mean interaction. The mechanism for the growth or

decay of KE in the time mean scale can be summarized

as follows.

The net radiation balance and the resultant equator-

to-pole temperature gradient maintain a thermally

direct large-scale circulation (Hadley circulation) in the

tropics (Held and Hou 1980; Schneider and Lindzen

1977). Such a circulation in the presence of abundant

moisture favors large-scale deep convection in the tropics.

Deep convective activity generates APE that gets con-

verted to KE in various scales. The time mean may gain

KE through this direct conversion. Besides, the KE of

the time mean scale over a local domain may result from

barotropic energy conversion involving eddy–time mean

interactions and from advection of KE by time mean and

eddies. This is the basic principle behind the analysis

used in this study.

This framework has been widely used by many studies

to quantify the effect of transient eddies over differ-

ent tropical regions (Lau and Lau 1992; Maloney and

Hartmann 2001; Seiki and Takayabu 2007; Serra et al.

2008). The growth of barotropic eddy KE through eddy–

mean flow interaction over the eastern and western Pa-

cific was studied by Maloney and Hartmann (2001), and

this study noted the role of the Madden–Julian oscillation

(MJO) in making favorable conditions for the formation

of tropical cyclones over the Pacific. The eddy momen-

tum transport term used in the study by Qi et al. (2008)

can be viewed as an approximate form of the term BC.

The interaction between transient eddies and time mean

depends on the averaging period. The transient fluctua-

tions include a collection of time scales whose maxima

and minima depend on the time resolution and length of

the averaging period. The goal of the present study is to

quantify the feedback between the ISO and seasonal

mean time scale over the ISM domain. Such a feedback

involves interactions between the time mean and multi-

ple frequencies in the ISO scale. Hence in the place of

eddy–time mean interaction framework, we study the

scale interactions in the spectral framework.

The spectral energetic analysis method was originally

introduced by Saltzman (1957) in the wavenumber do-

main for estimating the eddy KE contribution coming

from different spatial scales. This formulation led to

several case studies examining the role of KE ex-

change between zonal mean and waves, mostly the role

of planetary-scale waves in the extreme behavior of the

monsoon (Kanamitsu et al. 1972; Awade et al. 1982).

However, the monsoon ISO is defined as the fluctuations

whose time scale is greater than synoptic scale but less

than the seasonal cycle; the energetic analysis in the

wavenumber domain is not suitable for extracting the

information about interactions between time mean and

monsoon ISO over a region. Hayashi (1980) modified

Saltzman’s scheme using a cross-spectral technique, and

it can be used for studying the interactions between

different scales of motion and the spectral transfer of

energy in both wavenumber and frequency space.


Hayashi’s analysis scheme decomposes the KE equation

in the frequency domain, and the energy conversions

among different time scales can be studied through

spectral windows of different frequency ranges. A com-

prehensive study of the spectral energetics of the atmo-

spheric circulation in the frequency domain was carried

out by Sheng and Hayashi (1990). Since then, this method

has been extensively used for quantifying the interactions

among different time scales ranging from synoptic to in-

terannual over different tropical domains (Krishnamurti

et al. 2000, 2003; Krishnamurti and Chakraborty 2005;

Neena and Goswami 2010). In this study, we adopt an

energetic analysis in the frequency domain to gain insight

into the mechanism of interaction between ISO and

seasonal mean of the ISM and the IAV caused by this

process.

4. Kinetic energy exchange between seasonalmean and ISO

The growth or decay of KE in the frequency domain

can be derived by applying Fourier analysis in time on

the zonal and meridional momentum equations in

spherical coordinate and by solving the resultant equa-

tions with the continuity equation. Analogous to Eq. (1)

in the time domain, the time rate of change of KE in the

frequency domain can be represented by Eq. (2):

›Kn

›t5 h(Km, Kp) �Kni 1 hK �Kni 1 hAn �Kni 1 Fn.

(2)

The rate of KE of a given scale n Kn involves four

different physical processes. The first process represents

the transfer of KE to the scale of frequency n from

a couple of other frequencies m and p that are governed

by a trigonometric selection rule, namely n 5 m 1 p or

n 5 jm 2 pj for an exchange. The second process is the

growth (or decay) of KE of the given frequency n when

it interacts with K. The third process is the growth (or

decay) of KE of a given frequency from the eddy

available potential energy An at the same scale. The last

term Fn represents the net loss or gain of KE of fre-

quency n by friction.

We adopt the method proposed by Hayashi (1980) for

calculating the exchange of KE between the seasonal

mean and individual frequencies. A positive (negative)

sign for (K, Kn) implies that the seasonal mean loses

(gains) energy to (from) the frequency n. The terms

involved in the exchange calculation at a particular

pressure level are given as follows:

hK �Kni 5 2

�›u

›xPn(u, u) 1

›y

›xPn(u, y)

�|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

A

2

�›u

›yPn(u, y) 1

›y

›yPn(y, y)

�|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

B

2tanu

a[uPn(u, y) 2 yPn(u, u)]|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

C

2

�›(uKn)

›x1

›(yKn)

›y1

›(vKn)

›p

�|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

D

2

�›u

›pPn(u, v) 1

›y

›pPn(y, v)

�|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

E

, (3)

where u, y, and v respectively represent the zonal, me-

ridional, and vertical wind components in pressure co-

ordinates and u, y, and v are their seasonal mean

counterparts. Also, Pn and Kn are the cross-spectral co-

efficient of frequency n and KE of nth frequency, re-

spectively; u is the latitude and a is the radius of the earth.

It can be inferred from Eq. (3) that the calculation of

the rate of KE exchange between seasonal mean and

any scale n involves five components (A–E). First, two

terms involves the horizontal gradient of seasonal mean

u wind, seasonal mean y wind, and the cross-spectral

coefficients. The third term represents the product of the

curvature term, seasonal mean u and y winds, and the

cross-spectral coefficients. The fourth term is the con-

vergence of KE of scale n due to seasonal mean wind.

The last term (E) implies the baroclinic processes through

which the scale n and the time mean interact. Since the

order of magnitude of the terms involving the vertical

derivative is very small, it is neglected. This formulation

can be used for examining the rate of KE exchanges

among different scales over a local domain, which would

be helpful in understanding the fundamental dynamics of

interactions between different scales over the region.

The rate of KE exchange between seasonal mean and

each frequency during the 1958–2001 summer seasons is

calculated using JJAS daily wind data of length 122 days.

With 122-day data for each season, 61 frequencies are

resolved. Grouping those frequencies that come under

the ISO scale (10–60-day periodicity, 2–12 harmonics),

the sum of KE exchange by all of these frequencies

with the seasonal mean is considered as the net KE ex-

change by the ISOs with the seasonal mean. The har-

monics representing 122-day periodicity is not considered

as part of ISO since it is close to the seasonal cycle. The

JUNE 2012 S U H A S E T A L . 1765

rate of KE exchange between the seasonal mean and

ISO for 44 summer seasons is calculated using ERA-40

wind data at 200 hPa over the region 08–158N, 508–

808E, where a significant relationship exists between the

wind strength and the seasonal mean Indian monsoon

rainfall. Figure 3a shows the IAV of rate of KE ex-

change between the seasonal mean and ISO (thick black

line) at 200 hPa and the contributions of different terms

in Eq. (3) to this exchange. While during most years the

ISOs take energy from the seasonal mean, the net ex-

change also exhibits considerable IAV. The conver-

gence of ISO KE due to seasonal mean zonal wind

dominates the net rate of KE exchange at 200 hPa. It

explains about 43% of the total variability. Similar to the

net rate of KE exchange, the convergence of ISO KE

due to seasonal mean u wind also gives energy to the

ISO scale. No notable trend is observed in this exchange.

The convergence of ISO KE due to meridional wind is

always negative, which means that energy is transferred

to the seasonal mean scale. A decreasing trend is ob-

served in this exchange, which implies that the seasonal

mean is losing more energy to the ISO in recent decades.

The sum of the first, second, third, and 2d(V0Kn)/dy

terms cancel out each other, making the effective con-

tribution of these terms nearly zero and leaving the last

term [2d(U0Kn)/dy] to dominate the net KE exchange.

The same analysis is repeated at 850 hPa over the LLJ

core region that shows a strong relationship with the

Indian monsoon rainfall. Figure 3b shows the net rate of

KE exchange between seasonal mean (thick black line)

and ISO and the contributions from different terms in

Eq. (3) to this exchange. One interesting fact to be noted

in Figs. 3a and 3b is the opposite sign of the 2d(U0Kn)/dx

and 2d(V0Kn)/dy components in the upper and lower

atmosphere. It may be related to the opposing directions

of circulation in the lower and upper levels. If the rate of

KE exchange between seasonal mean and ISO were

significant to make changes in the amplitude of seasonal

mean KE, it would be reflected in the variability of

seasonal mean KE and there would be a significant

phase relationship between seasonal mean KE and the

rate of KE exchange between seasonal mean and ISO.

In the next section, evidences for significant internal

interannual and interdecadal variability are presented.

5. Internal interannual and interdecadal variabilityof the ISM

To quantify the variability of the ISM IAV associated

with the central Pacific SST anomaly, seasonal mean KE

at 200 hPa over the TEJ region is correlated with the

seasonal mean Nino-3.4 index for the 1958–2001 period.

The Nino-3.4 index represents the area-averaged SST

anomaly over the region 58S–58N, 1208–1708W. The cor-

relation coefficient between the two is 20.53, significant

at the 95% confidence level. Thus, the ENSO can explain

about 30% of the IAV of seasonal mean ISM (as repre-

sented by the KE of TEJ).

Although the ENSO is a dominant driver of IAV of

most of the climate systems around the globe, about

70% of the IAV of the ISM arises from processes other

than the ENSO. External forcing such as IOD and the

North Atlantic SST anomaly could together explain

another 30% of the ISM IAV (Table 1). Hence, it is

suspected that the internal processes can account for

some of the unexplained variability. The rate of KE ex-

change between the seasonal mean and ISO (10–60 day)

at 200 hPa is calculated over the same region for the same

time period using ERA-40 data. Figure 4a shows the IAV

of seasonal mean KE at 200 hPa and the rate of KE ex-

change between seasonal mean and ISO over the TEJ

region. It can be inferred from the figure that almost all

of the years during which the seasonal mean KE shows

a local maximum, ISO supplies energy to the seasonal

FIG. 3. Different components of rate of kinetic energy exchange

per unit mass between the seasonal mean and 10–60-day time scale

(31025 W kg21) (a) at 200 hPa over the region 08–158N, 508–808E

and (b) at 850 hPa over the region 08–208N, 508–708E. The solid

black line represents the net exchange, the dotted line with squares

represents the sum of first three terms of Eq. (3), the dashed line

with open circles represents the exchange due to the 2d(U0Kn)/dx

term, and the dot-dashed line with solid circles represents the ex-

change due to the 2d(V0Kn)/dy term.


mean. At the same time, whenever the seasonal mean

KE is low, either the ISO takes energy away from the

seasonal mean or contributes little to the seasonal mean.

Thus, it clearly demonstrates that internal processes

(i.e., energy exchange among scales) can significantly

affect the amplitude of seasonal mean and thereby cause

its variability on interannual time scale (Hoyos and

Webster 2007). The correlation coefficient between the

KE of seasonal mean and the KE exchange between ISO

and seasonal mean is 20.42, significant at the 95% con-

fidence level. Despite exhibiting a significant relationship

with the seasonal mean KE, the ENSO and other sources

of external forcing do not exhibit any relationship with

the energy exchange processes, indicating that the pro-

cess is purely internal (Table 1). The ENSO effect is

removed from the seasonal mean KE by regressing

it with the Nino-3.4 index. The ENSO removed sea-

sonal mean KE shows enhanced relationship (correla-

tion coefficient 5 20.48) with the energy exchange. Hence,

it may be concluded that the internal process can explain

about 20% of the IAV of seasonal mean of the ISM.

Since the South Asian monsoon is a result of baroclinic

response of the atmosphere to deep tropospheric con-

vective heating, the lower-level circulation is expected to

be related to the upper-level circulation. Hence, a similar

relationship can be expected to exist in the lower atmo-

sphere between the KE of seasonal mean and its ex-

change with the ISO. The seasonal mean KE at 850 hPa

over the LLJ core region and the rate of KE exchange

between the seasonal mean and ISO are calculated for

the 1958–2001 period using ERA-40 data. Figure 4b

corroborates the finding at 200 hPa. It shows a corre-

lation coefficient of 20.28. Even though it is not as

strong as that in the upper atmosphere, the relationship

improves (correlation coefficient 5 20.31) when the

ENSO effect is removed from the seasonal mean KE by

linearly regressing it with the Nino-3.4 index (Table 1).

TABLE 1. Correlations at 850 and 200 hPa on interannual and interdecadal time scales. For the interannual time scale a correlation

coefficient greater than 0.25 is significant at 95% confidence level for 42 degrees of freedom. For the interdecadal time scale the signif-

icance is also estimated at 95% confidence level. The significant correlations are in boldface.

Correlation coefficient

Interannual time scale Interdecadal time scale

850 hPa 200 hPa 850 hPa 200 hPa

Seasonal mean KE and seasonal mean rainfall 0.61 0.52 — —

Seasonal mean KE and exchange 20.28 20.425 20.65 20.76

Seasonal mean KE and Nino-3.4 20.33 20.53 0.32 0.49

Exchange and Nino-3.4 20.095 0.03 0.04 20.6

ENSO removed seasonal mean KE and exchange 20.31 20.48 20.65 20.71Partial correlation coefficient between seasonal mean KE

and exchange excluding the influence of Nino-3.4

20.33 20.483 20.70 20.67

Seasonal mean KE and IOD 20.0574 20.39 0.43 0.169

Exchange and IOD 0.0405 20.0623 20.31 20.34

Seasonal mean KE and North Atlantic SST 0.127 0.39 — —

Exchange and North Atlantic SST 20.114 20.107 — —

Seasonal mean KE and AMO — — 0.41 0.79AMO removed seasonal mean KE and exchange — — 20.81 20.734

Seasonal mean KE and PDO — — 0.46 0.36

Exchange and PDO — — 20.47 20.4

Partial correlation coefficient between seasonal mean KE

and exchange excluding the influence of PDO

— — 20.55 20.72

FIG. 4. Seasonal mean kinetic energy per unit mass (m2 s22, solid

line) and the rate of kinetic energy (per unit mass) exchange be-

tween the seasonal mean and 10–60-day time scale (31025 W kg21,

dashed line) calculated using ERA-40 data (a) at 200 hPa over

the region 08–158N, 508E–808E and (b) at 850 hPa over the region

08–208N, 508–708E. The corresponding correlation coefficients (CC)

are given at the upper-left corner of each panel.

JUNE 2012 S U H A S E T A L . 1767

The moderate relationship between seasonal mean KE

and the energy exchange process can be understood by

considering the fact that the 850-hPa atmosphere is close

enough to come under the influence of the planetary

boundary layer and scale separation in the noisy atmo-

sphere is difficult. In addition to that, the friction term

and boundary fluxes are neglected while calculating the

exchange over the ISM domain both in the upper and

lower atmosphere. The boundary flux terms become

zero when the equation is integrated over the globe, but

it is nonzero if the calculation is restricted to a small

area. The relationship between the seasonal mean KE at

850 hPa and the ENSO is also weak (it shows only

a moderate correlation coefficient of 20.33). Similar to

the upper atmosphere, the exchange process shows no

relationship with the ENSO. The exchange process and

its phase relationship with the seasonal mean KE at

850 hPa validate our finding that the scale interaction is

purely internal, and it can explain considerable amount of

IAV of the ISM.

Since the KE exchange between ISO and seasonal

mean is strongly related to the seasonal mean KE, any

long-term tendency of this exchange may lead to longer-

term variability of the ISM. The internal IDV of the ISM

can be brought out by applying an 11-yr running mean

on both the seasonal mean KE and the exchange. The

running mean is the simplest available low-pass filter

that removes fluctuation whose periodicity is less than its

window size by preserving the low-frequency oscillation.

However, when computing the correlation between two

time series subject to low-frequency filtering, one has to

be cautious about the reduced number of degrees of

freedom. Filtering has the effect of smoothing the time

series and thereby increasing the autocorrelations be-

tween the members of the time series. Here, following

Chen (1982), we estimate the effective time between in-

dependent members t, and the effective degrees of free-

dom, using the autoregressive properties of both time

series. All the correlations on interdecadal time scale

discussed in the following sections are statistically

checked for significance at 95% confidence level, using

the corresponding effective degrees of freedom com-

puted by this method.

Figure 5a shows the 11-yr running mean of the sea-

sonal mean KE and the exchange at 200 hPa calculated

using ERA-40. The low-frequency seasonal mean KE

exhibits a linear decreasing trend with a multidecadal

variability embedded in it. The low-frequency rate of

KE exchange between the seasonal mean and ISO is

always positive except for some part of 1960s, and it

shows a linear increasing trend and multidecadal vari-

ability. The seasonal mean KE and the exchange are

strongly linked with a correlation coefficient of 20.71

(Table 1). The relationship is unaffected whether the

linear trend is removed from both time series or not.

Hence, the internal processes can explain nearly half

of the interdecadal ISM variability. The weakening of

upper-level ISM strength and its multidecadal variability

can be explained in terms of energy exchange between

the seasonal mean and ISO. During the initial periods,

when seasonal mean KE was high, ISO supplied KE to

the seasonal mean but later the seasonal mean started

FIG. 5. Low-pass filtered seasonal mean KE per unit mass (m2 s22, solid line) and the rate of KE (per unit mass) exchange between the

seasonal mean and 10–60-day time scale (31025 W kg21, dashed line) at 200 hPa calculated using ERA-40 data. (a) Unaltered variability;

(b) variability without ENSO; (c) variability without PDO; and (d) variability without AMO. Variability associated with ENSO, PDO,

and AMO is removed from both seasonal mean KE and seasonal mean and 10–60-day KE exchange. The corresponding correlation

coefficients (CC) are given at the upper-left corner of each panel.


losing energy to the ISO. The fluctuations in the rate of

KE exchange can account for the local maxima and

minima in the seasonal mean KE. It can be deduced that

ISO takes more and more energy from the seasonal

mean and makes it lose its strength. Still, the question of

modulation of interdecadal energy exchange process by

external forcing may arise. The IDV of ENSO, IOD,

PDO, and AMO was brought out by applying a 11-yr

running mean on the JJAS mean Nino-3.4, IOD, PDO,

and North Atlantic SST anomaly indices, respectively.

Unlike the case of the interannual time scale, on the in-

terdecadal scale the exchange process and external forc-

ings are linked (Table 1). Figures 5b–d show the ENSO-,

PDO-, and AMO-removed interdecadal seasonal mean

KE and the rate of KE exchange between seasonal mean

and ISO. The removal of external forcings not only pre-

served the linear trend and multidecadal variability in the

seasonal mean KE and the exchange process but also

improved the correlation coefficient between them

(Table 1). It may be cautioned that in our analysis we

removed only the linear relationship between the ex-

change and external forcing. There may still be some

variability associated with complex nonlinear interaction

among the different external forcing components.

The internal interdecadal variability is further exam-

ined at the lower level by applying an 11-yr running

mean on seasonal mean KE and the energy exchange

process at 850 hPa. Contrary to the observation of linear

decreasing trend in the upper-atmospheric ISM strength,

lower-atmospheric seasonal mean KE exhibits a linear

increasing trend on interdecadal time scales (Fig. 6). It

also shows a multidecadal variability. The exchange

process is always negative; that is, the ISO always supplies

energy to the seasonal mean. Analogous to the upper

atmosphere, the long-term behavior and fluctuations

can be explained in terms of energy exchange. The 11-yr

running mean smooths out the high-frequency fluctua-

tions and reveals a strong link between strength of the

ISM and the energy exchange process on the interdecadal

time scale. The relationship is unaltered whether the

known quantifiable external forcing signal is removed

or not. Consistent with the upper-atmospheric obser-

vation, the internal process can explain about 50% of

the interdecadal variability of the ISM. In the next sec-

tion, we discuss the sensitivity of our results to the tem-

poral window length.

6. Temporal window length and the estimate oftime mean–ISO interaction

Since the number of resolvable harmonics of a time

series depends on the length of the time series, it is

possible that the estimate of seasonal mean–ISO energy

exchange may be influenced by the temporal window

length. If the energy exchange is very much sensitive to

the temporal window length, the estimation of IAV of

seasonal mean due to the seasonal mean–ISO energy

exchange may lead to unrealistic conclusions. To check

the robustness of our results, we repeated our calculation

by extending the length of the time series from 122 to 150

days, considering the data from 17 May to 14 October.

The newly constructed time series not only brings in more

harmonics in the ISO time scale but also extends the

largest resolvable time scale from 60 to 75 days. Figure 7

compares the seasonal mean–ISO energy exchange esti-

mated using 122- and 150-day windows at the 200- and

850-hPa pressure levels. Consistency in the estimation of

energy exchange is observed at both levels at a correla-

tion of 0.88 and 0.83, respectively (Figs. 7a,b), and it is

reflected in the correlation coefficient of IAV of seasonal

mean KE and the energy exchange between seasonal

mean and 10–75-day KE (20.47 at 200 hPa and 20.41 at

850 hPa). The inclusion of the 75-day scale and higher

time scale resolution in the estimation of IAV of the

seasonal mean due to feedback between seasonal mean

and ISO brings about only a slight change, and the dis-

crepancy is not significant.

7. Reanalysis data and rate of KE exchangecalculation

The reliability of our results depends upon how well

ERA-40 data capture the behavior of the true atmo-

sphere. To quantify the interannual and interdecadal

variability, long datasets are required. The changes in

observing systems at the beginning of satellite era after

the mid-1970s always raise a concern about the use of

reanalysis products for analyzing long-term trends and

multidecadal variability (Bengtsson et al. 2004). The

consistency of our results is checked by repeating the

analysis at 200 hPa with another independent dataset,

FIG. 6. Low-pass filtered seasonal mean kinetic energy per unit

mass (m2 s22, solid line) and the rate of kinetic energy (per unit

mass) exchange between seasonal mean and 10–60-day time scale

(31025 W kg21, dashed line) at 850 hPa calculated using ERA-40

data. The correlation coefficient (CC) is given at the upper-left

corner of the figure.

JUNE 2012 S U H A S E T A L . 1769

NCEP–NCAR reanalysis data, for the 1958–2001 JJAS

months, and it is presented in Fig. 8. Consistent with the

result obtained from the ERA-40 data, it also shows

a moderate correlation of 20.42, which is statistically

significant at the 95% confidence level.

Although the amount of variability explained by the

internal process is the same for both reanalysis products,

the decreasing trend of seasonal mean KE at 200 hPa

was more pronounced in the ERA-40 dataset. In addi-

tion, the nature of variability of the rate of KE exchange

was different for both datasets. It can be noted from

Figs. 9a and 9b that although the interannual seasonal

mean KEs are in phase, NCEP–NCAR data overestimate

the seasonal mean KE at 200 and 850 hPa. To examine

this discrepancy, the seasonal mean KE is disintegrated

into zonal and meridional components at 850 and

200 hPa and are presented in Fig. 10. It shows that there

are differences in both amplitude and phases of meridi-

onal component of the seasonal mean KE at both levels.

Also, there is no phase relationship between the zonal

and meridional components of the seasonal mean KE of

NCEP–NCAR dataset. For example, at 200 hPa, sea-

sonal mean U2/2 is decreasing but the seasonal mean V2/2

shows a steady linear increasing trend. The cross-spectral

method of estimation of KE exchange involves products

of spectral coefficient and seasonal mean wind com-

ponents. Hence, the difference in the phases of wind

components can cause a discrepancy in the exchange

estimation. The difference in the meridional KE be-

tween the two reanalyses may be related to the fact that

the tropical divergent component of the wind is rather

weak in the NCEP–NCAR reanalysis (Annamalai et al.

1999). Because of the biases in the meridional component

of NCEP–NCAR reanalysis wind data, throughout our

study we have used ERA-40 data, which show a coherent

behavior of zonal and meridional wind at both 850 and

200 hPa.

8. Summary and conclusions

In this study, we have introduced a conceptual

framework for quantifying the role of ISO in causing

FIG. 7. Rate of KE per unit mass (31025 W kg21) exchange

between seasonal mean and 10–60-day time scale (solid line) and

rate of KE per unit mass (31025 W kg21) exchange between

seasonal mean and 10–75-day time scale (dashed line) calculated

using ERA-40 data (a) at 200 hPa over the region 08–158N, 508–808E

and (b) at 850 hPa over the region 08–208N, 508–708E. The corre-

sponding correlation coefficients (CC) are given at the upper-left

corner of each panel.

FIG. 8. Seasonal mean kinetic energy per unit mass (m2 s22, solid

line) and the rate of kinetic energy (per unit mass) exchange be-

tween seasonal mean and 10–60-day time scale (31025 W kg21,

dashed line) at 200 hPa calculated using NCEP–NCAR reanalysis

data from 1958 to 2001. The correlation coefficient (CC) is given at

the upper-left corner of the figure.

FIG. 9. Seasonal mean (JJAS) kinetic energy per unit mass

(m2 s22) calculated using NCEP–NCAR reanalysis (solid line) and

ERA-40 data (dashed line) at (a) 200 hPa averaged over 08–158N,

508–808E and (b) 850 hPa averaged over 08–208N, 508–708E.


interannual and interdecadal variability of the seasonal

mean ISM. Although there have been some studies that

indicated evidence for such an interaction over the ISM

domain, such as the correlation between seasonal mean

and ISO variance, a clear mechanism through which

such an interaction takes place had remained elusive

(Goswami and Ajaya Mohan 2001; Lawrence and

Webster 2001; Qi et al. 2008). As the ISOs represent

a dominant mode of summer monsoon variability, we

hypothesize that the ISOs play a significant role in

modulating the seasonal mean through scale interaction

and causing internally generated interannual and inter-

decadal variability of the seasonal mean.

A cross-spectral method of estimation of KE ex-

change in the frequency domain (Hayashi 1980) is em-

ployed for quantifying the interannual and interdecadal

seasonal mean–ISO interaction. The analysis is done

using ERA-40 wind data at the 850- and 200-hPa pres-

sure levels over the region where the seasonal mean KE

shows a significant relationship with the seasonal mean

ISM rainfall. It is found from the energy exchange cal-

culation in both the upper and lower atmosphere that

the amplitude and phases of the net rate of KE exchange

between seasonal mean and ISO is determined by the

convergence of ISO KE by seasonal mean zonal wind.

The internal interannual and interdecadal variabilities

were quantified by correlating the seasonal mean KE

phase variability with the rate of KE exchange between

the seasonal mean and ISO at upper and lower levels. It

is found that the long-term trend and much of the vari-

ability of the seasonal mean KE could be related to the

exchange of energy between seasonal mean and ISO.

The issue of sensitivity to choice of temporal window

length on the energy exchange is addressed separately

by extending the length of the time series. The KE en-

ergy exchange process at 200 hPa is less noisy and rel-

atively free of boundary layer turbulence, hence its

variability with the seasonal mean KE is considered for

the quantification of internal variability of the ISM

seasonal mean. Although contaminated by friction and

boundary flux terms, the lower-level atmosphere also

shows a statistically significant relationship, which is also

independent of any known and quantifiable external

boundary forcing. On the whole, the influence of ex-

ternal forcing on the exchange process is negligible on

interannual time scale, while on interdecadal time scale

external forcing does have some contribution. The in-

ternal IAV of the ISM could independently account for

approximately 20% of the ISM seasonal mean vari-

ability. It is shown that in both the upper and lower at-

mosphere the energy exchange process could explain

about 50% of the variability of the ISM seasonal mean

on an interdecadal time scale. This conclusion was

drawn on the assumption that the relationship between

the exchange and the different external forcing compo-

nents is linear. There may still exist some unaccounted

external variability arising from nonlinearity and this may

be a caveat in our analysis. Another issue that demands

FIG. 10. (a),(c) Seasonal mean (JJAS) U2/2 (m2 s22) calculated using NCEP–NCAR reanalysis (solid line) and ERA-40 data (dashed

line), at (a) 200 and (c) 850 hPa. (b),(d) Seasonal mean (JJAS) V2/2 (m2 s22) calculated using NCEP–NCAR reanalysis (solid line) and

ERA-40 data (dashed line) at (b) 200 and (d) 850 hPa.

JUNE 2012 S U H A S E T A L . 1771

a closer examination is the weakening of the relationship

between seasonal mean and ISOs after the mid-1970s. At

present, it is not clear whether this difference is due to the

reported mid-1970s climate regime shift or is simply an

artifact of reanalysis dataset.

The weakening of the monsoon circulation on inter-

decadal time scales as evidenced by a decreasing trend of

seasonal mean KE of the TEJ is often attributed to global

warming (Vecchi et al. 2006; Vecchi and Soden 2007).

Based on a modeling study, Chou and Chen (2010) pro-

posed that increase in global temperature would cause

an uplifting of the tropopause, which may favor deeper

convection and eventually lead to an increase in the sta-

bility and weakening of the tropical circulation. However,

we speculate that this decreasing trend of seasonal mean

KE could be partially explained through an internal dy-

namical process where the mean is losing increasingly

more energy to the ISOs through scale interactions.

Concerns about the usage of reanalysis data for the

study of multidecadal variability and long-term trend

are also discussed separately. It is found that the zonal

component of KE is slightly overestimated in NCEP–

NCAR reanalysis but is well correlated on interannual

time scale with that in the ERA-40 dataset. However,

there are some notable differences in the meridional

component of KE in the two products. Significant re-

lationships between the ERA-40 wind data and rain

gauge data add further confidence in using it. It is already

known that the ISO can influence the synoptic activity

through clustering of lows and depression along the

monsoon trough (Goswami et al. 2003). In the present

study, we demonstrate the role of ISO in modulating

the ISM seasonal mean on interannual and interdecadal

time scale. This emphasizes on the need for further

research in improving the simulation of MISO in order

to improve the prediction of monsoon on short, me-

dium, and longer time scales, since it not only affects

active and break spells but also affects the variability in

other scales.

Acknowledgments. The Indian Institute of Tropical

Meteorology is funded by the Ministry of Earth Sci-

ences, Government of India. SE and NJM are grateful to

CSIR for a Fellowship.

APPENDIX

Derivation of Rate of Change of Time MeanKinetic Energy

The rate of change of time mean kinetic energy in the

transient eddy–time mean framework is represented by

Eq. (1). The equation is derived in Cartesian coordinates

from the momentum equations and the continuity

equation by assuming that the large-scale atmosphere

is in the state of hydrostatic balance. Variant forms of

Eq. (1) were used by different researchers (Holopainen

1978b; Lau and Lau 1992; Maloney and Hartmann

2001, Serra et al. 2008) to quantify the eddy–time mean

interactions. Here we again present Eq. (1):

d(K)/dt 5 BC 1 CAK 1 WF 1 D.

Variables with an overbar represent the time mean and

the primed quantities represent the transient eddies. The

expansion of the components of Eq. (1) is given below:

BC 5 u9u9›u

›x1 u9y9

›u

›y1

›y

›x

� �1 u9y9

›y

›y, (A1)

CAK 5 2av, (A2)

WF 5 $ � [fV 1 k � V 1 (V � V9)V9], (A3)

D 5 V � f , (A4)

where V represents the horizontal velocity and u, y, and

v respectively represent the zonal, meridional, and ver-

tical components of velocity. Also, k stands for kinetic

energy per unit mass [i.e., (u 3 u 1 y 3 y)/2], F and a

represent the geopotential height and specific volume,

respectively, and f stands for friction.

REFERENCES

Ajaya Mohan, R. S., and B. N. Goswami, 2003: Potential pre-

dictability of the Asian summer monsoon on monthly and sea-

sonal time scales. Meteor. Atmos. Phys., 84, 83–100, doi:10.1007/

s00703-002-0576-4.

Annamalai, H., J. Slingo, K. R. Sperber, and K. Hodges, 1999: The

mean evolution and variability of the Asian summer monsoon:

Comparison of ECMWF and NCEP–NCAR analysis. Mon.

Wea. Rev., 127, 1157–1186.

Awade, S. T., M. Y. Totagi, and S. M. Bawiskar, 1982: Wave to

wave and wave to zonal mean flow kinetic energy exchanges

during contrasting monsoon years. Pure Appl. Geophys., 120,

463–482.

Bengtsson, L., S. Hagemann, and K. I. Hodges, 2004: Can climate

trends be calculated from reanalysis data? J. Geophys. Res.,

109, D11111, doi:10.1029/2004JD004536.

Charney, J. G., and J. Shukla, 1981: Predictability of monsoons.

Monsoon Dynamics, J. Lighthill and R. P. Pearce, Eds., Cam-

bridge University Press, 99–109.

Chen, W. Y., 1982: Fluctuations in Northern Hemisphere 700-mb

height field associated with the Southern Oscillation. Mon.

Wea. Rev., 110, 808–823.

Chou, C., and C.-A. Chen, 2010: Depth of convection and the

weakening of tropical circulation in global warming. J. Cli-

mate, 23, 3019–3030.


Ferranti, L., J. M. Slingo, T. N. Palmer, and B. J. Hoskins, 1997:

Relations between interannual and intraseasonal monsoon var-

iability as diagnosed from AMIP integrations. Quart. J. Roy.

Meteor. Soc., 123, 1323–1357.

Fujinami, H., and Coauthors, 2011: Characteristic intraseasonal

oscillation of rainfall and its effect on interannual variability

over Bangladesh during boreal summer. Int. J. Climatol., 31,

1192–1204, doi:10.1002/joc.2146.

Gadgil, S., M. Rajeevan, and R. Nanjundiah, 2005: Monsoon

prediction? Why yet another failure? Curr. Sci., 88, 1389–

1400.

Goswami, B. N., 1995: A multiscale interaction model for the origin

of the tropospheric QBO. J. Climate, 8, 524–534.

——, 1998: Interannual variation of Indian summer monsoon in

a GCM: External conditions versus internal feedbacks. J. Cli-

mate, 11, 501–522.

——, 2005: South Asian monsoon. Intraseasonal Variability of the

Atmosphere–Ocean Climate System, W. K. M. Lau and D. E.

Waliser, Eds., Springer, 19–61.

——, and R. S. Ajaya Mohan, 2001: Intraseasonal oscillations

and inter-annual variability of the Indian summer monsoon.

J. Climate, 14, 1180–1198.

——, and P. K. Xavier, 2005: Dynamics of ‘‘internal’’ interannual

variability of Indian summer monsoon in a GCM. J. Geophys.

Res., 110, D24104, doi:10.1029/2005JD006042.

——, R. S. Ajaya Mohan, P. K. Xavier, and D. Sengupta, 2003:

Clustering of low pressure systems during the Indian summer

monsoon by intraseasonal oscillations. Geophys. Res. Lett., 30,

1431, doi:10.1029/2002GL016734.

——, M. S. Madhusoodanan, C. P. Neema, and D. Sengupta, 2006a:

A physical mechanism for North Atlantic SST influence on the

Indian summer monsoon. Geophys. Res. Lett., 33, L02706,

doi:10.1029/2005GL024803.

——, G. Wu, and T. Yasunari, 2006b: Annual cycle, intraseasonal

oscillations and roadblock to seasonal predictability of the

Asian summer monsoon. J. Climate, 19, 5078–5099.

Hahn, D. J., and J. Shukla, 1976: An apparent relationship between

Eurasian snow cover and Indian monsoon rainfall. J. Atmos.

Sci., 33, 2461–2462.

Hayashi, Y., 1980: Estimation of nonlinear energy transfer spectra

by the cross-spectral method. J. Atmos. Sci., 37, 299–307.

Held, I. M., and A. Y. Hou, 1980: Nonlinear axially symmetric

circulations in a nearly inviscid atmosphere. J. Atmos. Sci., 37,

515–533.

Holopainen, E. O., 1978a: On the dynamical forcing of the long-

term mean flow by the large-scale Reynolds’ stresses in the

atmosphere. J. Atmos. Sci., 35, 1596–1604.

——, 1978b: A diagnostic study on the kinetic energy balance of the

long-term mean flow and the associated transient fluctuations

in the atmosphere. Geophysica, 15, 125–145.

Hoskins, B. J., and R. P. Pearce, 1983: Large-Scale Dynamical

Processes in the Atmosphere. Academic Press, 397 pp.

——, I. N. James, and G. H. White, 1983: The shape, propaga-

tion, and mean flow interaction of large-scale weather systems.

J. Atmos. Sci., 40, 1595–1612.

Hoyos, C. D., and P. J. Webster, 2007: The role of intraseasonal

variability in the nature of Asian monsoon precipitation.

J. Climate, 20, 4402–4424.

Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Re-

analysis Project. Bull. Amer. Meteor. Soc., 77, 437–471.

Kanamitsu, M., T. N. Krishnamurti, and C. Depradine, 1972: On

scale interactions in the tropics during northern summer.

J. Atmos. Sci., 29, 698–706.

Kang, I.-S., and J. Shukla, 2006: Dynamic seasonal prediction and

predictability. The Asian Monsoon, Springer, 585–612.

——, J. Y. Lee, and C. K. Park, 2004: Potential predictability of

summer mean precipitation in a dynamical seasonal prediction

system with systematic error correction. J. Climate, 17, 834–

844.

Krishna Kumar, K., M. Hoerling, and B. Rajagopalan, 2005: Ad-

vancing dynamical prediction of Indian monsoon rainfall.

Geophys. Res. Lett., 32, L08704, doi:10.1029/2004GL021979.

——, B. Rajagopalan, M. Hoerling, G. Bates, and M. Cane, 2006:

Unraveling the mystery of Indian monsoon failure during

El Nino. Science, 314, 115–119, doi:10.1126/science.1131152.

Krishnamurthy, V., and B. N. Goswami, 2000: Indian monsoon–

ENSO relationship on interdecadal time scales. J. Climate, 13,

579–595.

——, and J. Shukla, 2008: Seasonal persistence and propagation of

intraseasonal patterns over the Indian monsoon region. Cli-

mate Dyn., 30, 353–369.

Krishnamurti, T. N., and D. R. Chakraborty, 2005: The dynamics of

phase locking. J. Atmos. Sci., 62, 2952–2964.

——, D. Bachiochi, T. Larow, B. Jha, M. Tewari, D. R. Chakraborty,

R. Correa-Torres, and D. Oosterhof, 2000: Coupled atmosphere–

ocean modeling of the El Nino of 1997/98. J. Climate, 13,

2428–2459.

——, D. R. Chakraborty, N. Cubukcu, L. Stefanova, and T. S. V.

Vijaya Kumar, 2003: A mechanism of the Madden–Julian

oscillation based on interactions in the frequency domain.

Quart. J. Roy. Meteor. Soc., 129, 2559–2590.

Lau, K.-H., and N.-C. Lau, 1992: The energetic and propagation

dynamics of tropical summer time synoptic-scale disturbances.

Mon. Wea. Rev., 120, 2523–2539.

Lawrence, D. M., and P. J. Webster, 2001: Interannual variations of

the intraseasonal oscillation in the South Asian summer

monsoon region. J. Climate, 14, 2910–2922.

Maloney, E. D., and D. L. Hartmann, 2001: The Madden–Julian

oscillation, barotropic dynamics, and North Pacific tropical

cyclone formation. Part I: Observations. J. Atmos. Sci., 58,

2545–2558.

Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C.

Francis, 1997: A Pacific interdecadal climate oscillation with

impacts on salmon production. Bull. Amer. Meteor. Soc., 78,

1069–1079.

Neena, J. M., and B. N. Goswami, 2010: Extension of potential

predictability of Indian summer monsoon dry and wet spells in

recent decades. Quart. J. Roy. Meteor. Soc., 136, 583–592.

Qi, Y., R. Zhang, T. Li, and M. Wen, 2008: Interactions between

the summer mean monsoon and the intraseasonal oscillation

in the Indian monsoon region. Geophys. Res. Lett., 35, L17704,

doi:10.1029/2008GL034517.

Rajeevan, M., J. Bhate, and A. K. Jaswal, 2008: Analysis of vari-

ability and trends of extreme rainfall events over India using

104 years of gridded daily rainfall data. Geophys. Res. Lett., 35,

L18707, doi:10.1029/2008GL035143.

Rasmusson, E. M., and T. H. Carpenter, 1983: The relationship

between eastern equatorial Pacific sea surface temperatures and

rainfall over India and Sri Lanka. Mon. Wea. Rev., 111, 517–528.

Saltzman, B., 1957: Equations governing the energetics of the

larger scales of atmospheric turbulence in the domain of wave

number. J. Atmos. Sci., 14, 513–523.

Schneider, E. K., and R. S. Lindzen, 1977: Axially symmetric

steady-state models of the basic state for instability and cli-

mate studies. Part I. Linearized calculations. J. Atmos. Sci., 34,

263–279.

JUNE 2012 S U H A S E T A L . 1773

Seiki, A., and Y. N. Takayabu, 2007: Westerly wind bursts and their

relationship with intraseasonal variations and ENSO. Part II:

Energetics over the western and central Pacific. Mon. Wea.

Rev., 135, 3346–3361.

Serra, Y. L., G. N. Kiladis, and M. F. Cronin, 2008: Horizontal and

vertical structure of easterly waves in the Pacific ITCZ.

J. Atmos. Sci., 65, 1266–1284.

Sheng, J., and Y. Hayashi, 1990: Estimation of atmospheric ener-

getics in the frequency domain during the FGGE year. J. At-

mos. Sci., 47, 1255–1268.

Shukla, J., 1998: Predictability in the midst of chaos: A scientific

basis for climate forecasting. Science, 282, 728–731.

——, and D. Paolino, 1983: The Southern Oscillation and the long-

range forecasting of the summer monsoon rainfall over India.

Mon. Wea. Rev., 111, 1830–1837.

Sperber, K. R., J. M. Slingo, and H. Annamalai, 2000: Predictability

and the relationship between subseasonal and interannual

variability during the Asian summer monsoon. Quart. J. Roy.

Meteor. Soc., 126, 2545–2574.

Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis.

Quart. J. Roy. Meteor. Soc., 131, 2961–3012.

Vecchi, G. A., and B. J. Soden, 2007: Global warming and the

weakening of the tropical circulation. J. Climate, 20, 4316–4340.

——, ——, A. T. Wittenberg, I. M. Held, A. Leetmaa, and M. J.

Harrison, 2006: Weakening of tropical Pacific atmospheric

circulation due to anthropogenic forcing. Nature, 441, 73–76.

Webster, P. J., V. O. Magana, T. N. Palmer, J. Shukla, R. A. Tomas,

M. Yanai, and T. Yasunari, 1998: Monsoons: Processes, pre-

dictability, and the prospects for prediction. J. Geophys. Res.,

103, 14 451–14 510.


Interannual variability of Indian summer monsoon rainfall onset date at local scale

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