Journal of the Meteorological Society of Japan, Vol. 87, No. 2, pp. 263--287, 2009.

263

DOI:10.2151/jmsj.87.263

Time-Space Characteristics of Seasonal and Interannual Variations of Atmospheric Water
Balance over South Asia

Venkatraman PRASANNA
Graduate School of Environmental Studies, Nagoya University, Nagoya, Japanand
Tetsuzo YASUNARI
Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan(Manuscript received 5 February 2007, in ﬁnal form 18 December 2008)

Abstract
The atmospheric water balance over di¤erent domains within the South Asian monsoon region has beenstudied using moisture convergence ðCÞ computed from JRA-25, ERA-40 and NCEP/NCAR reanalysis datasets,
GPCP precipitation data ðPÞ and evaporation data ðEÞ as a residual of these two parameters. The seasonal climatology of P, C, and E for the selected regions shows generally large contribution of E to P. The inter-annual
characteristics of P, C and E over selected key domains within the South Asian monsoon region have also been
examined for both the early (June and July: JJ) and late summer (August and September: AS) monsoon periods
from 1979 to 2000. The spatial and temporal characteristics of the hydrological cycle and the contribution of E
and C to P are discussed in detail. One important aspect on the seasonal timescale is that from the dry regions in
the northwest to the central and the wettest northeast regions, the monthly variations of E or C are large during
the monsoon months speciﬁc to those regions. However, the interannual variability of P over each domain is notnecessarily inﬂuenced by the same criteria like C or E, which inﬂuences the mean seasonal precipitation. It is also
evident that the structure of variability for early (JJ) and late (AS) summer precipitation is di¤erent over theSouth Asian monsoon region. Over northwest India E is dominant on the seasonal timescale, but C contributes
higher to interannual variability of P. On the other hand, over central India C is dominant during early summer(JJ) on the seasonal timescale, but E contributes higher to P variability on the interannual timescale, and duringlate summer (AS) E is dominant on the seasonal timescale, but C contributes higher to P variability on the interannual timescale. Over northeast India, C is dominant on the seasonal timescale, but E contributes higher to interannual variability of P. The importance of land-atmosphere interaction over each domain is discussed. The
regionality in the mechanism of precipitation generation and its contribution to the India summer monsoon
precipitation variability are also discussed in detail. The role of evaporation variability of precipitation isstronger over the Bay of Bengal sector and the role of convergence on the interannual variability of precipitationis stronger over the Arabian Sea sector.

1. Introduction
Corresponding author: Venkatraman Prasanna, Graduate School of Environmental Studies, Nagoya University, Nagoya, Aichi 464-8601, Japan.
E-mail: prasa_armala@yahoo.com
6 2009, Meteorological Society of JapanThe motivation of the present study is to examine
the characteristic nature of the large-scale waterbalance over South Asia on seasonal and interannual timescales, to facilitate the understanding ofhydro-meteorological processes in di¤erent regional

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Journal of the Meteorological Society of Japandomains in the South Asian monsoon climate. For
this purpose, the space-time characteristics of seasonal and interannual variations of precipitation
ðPÞ, moisture convergence ðCÞ and evaporation
ðEÞ during the period, starting from 1979 up to2000 have been studied in detail. A particular attention has been paid to understand where the precipitation ðPÞ comes from, i.e., from large-scalemoisture convergence ðCÞ due to atmosphericcirculation, and/or from evaporation ðEÞ from thesurface.
Why is the atmospheric water balance analysis
required here? Because the Indian summer monsoon or the South Asian monsoon has long been
conventionally studied as the rainfall averaged
over the entire country (India) and summed overfour months June, July, August and September(JJAS) as a single seasonal mean index or simply
denoted by All India Summer Monsoon Rainfall(AISMR) for characterizing wet or dry condition.
The analysis based on water balance helps us toclearly establish the di¤erences within the season
and among the regions over the South Asian monsoon domain. Moreover, the origin of P, relationship of P with C and E, and its variability on seasonal and interannual timescales can be captured
clearly using the atmospheric water balance method.
Following the seminal works on global water
budget studies (Trenberth 1991, 1999; Oki et al.
1995; Trenberth and Guillemot 1998), we have attempted to understand the atmospheric water balance over South Asia. The objectively reanalysed
gridded four-dimensional datasets (like JRA-25,
ERA-40 and NCEP/NCAR) give us a great opportunity to study water balance on global and regional scales. Though there are physical constraints
in the closed water budget calculation and it may
be imperfect to use observed precipitation ðPÞ toclose the reanalyses water budget, it provides anopportunity to estimate the less observed variableevaporation ðEÞ at each grid point on regional andglobal scales through the balance. Since the evaporation simulated by the reanalyses is heavily model
dependent and the simulated precipitation is constrained by the model’s parameterization schemes,
we have tried to utilize the advantage of monthly
observed precipitation ðPÞ to ﬁnd the evaporation
ðEÞ as a residual.
There are a few previous regional studies whichused observed precipitation to obtain atmosphericwater balance (Oki et al. 1995; Fukutomi et al.
2003; Marengo 2005). The evaporation obtained asVol. 87, No. 2

a residual from the water balance method su¤ers
from substantial errors; the accuracy of E depends
on the accuracy of observed P as well as the accuracy of computed C. Oki et al. (1995) applied theglobal atmospheric water balance analysis to study
the water balance over the Chao Phraya River basin
and estimated a much better evapotranspiration on
monthly timescales than the evaporation ðEÞ obtained by the ECMWF operational model, using
the same assimilated datasets and observed valuesof precipitation over the basin catchment area. Fukutomi et al. (2003) studied the interannual variability of summer water balance over three majorriver basins (Lena, Yennisey and Ob) using observed (CMAP) precipitation, runo¤ datasets andconvergence computed from the NCEP-II reanalysis dataset in the water budget equation. Marengo(2005) studied the spatio-temporal variability of
the Amazon River basin water budget using the
NCEP/NCAR reanalysis and observed gridded
gauge precipitation dataset and compared it withother datasets like GPCP, CRU. In this paper, we
try to exploit the advantages of the atmosphericwater budget analysis for understanding precipitation generation over di¤erent sub-domains within
the South Asian monsoon region using the GPCP
dataset and major reanalysis datasets.
In terms of the interannual variability of monsoon rainfall, Ailikun and Yasunari (2001) notedthat the June (J) and July, August, September(JAS) rainfalls were characteristically di¤erent
from each other over the Indian monsoon domain.
They noticed that the early period (June) wasstrongly inﬂuenced by the anomalous state of
ENSO in the previous winter, whereas the mid-lateperiod (July-August-September) was related to theanomalous state of ENSO in the following winterrather than the previous winter. Kawamura et al.
(2005) emphasized that, the monsoon-ENSO relationship was strong in late summer (AS) over central and northwest India before the 1970s, but inthe recent decades it had shifted to early summer(JJ) over northeast India and argued that thechange in the monsoon-ENSO relationship basedon AISMR represented a change in the dominanceof a spatial correlation pattern, from the northwest
to the northeast after the late 1970s. Thus, from thepapers of Ailikun and Yasunari (2001) as well as
those of Kawamura et al. (2005), the early and latesummer precipitation over India is characteristically di¤erent. Therefore, the summer monsoon period (JJAS) has been judiciously divided into early

April 2009

V. PRASANNA and T. YASUNARI

summer (JJ) and late summer (AS) for assessing theinterannual variability of precipitation and atmospheric water budget components in the summer
monsoon season. In this paper, we basically stress
the time-space characteristics of water balancecomponents on a seasonal timescale as well as theirvariability on an interannual timescale. Further we
discuss the regional contrast of the water budget
components and also how the budget components
control the precipitation generation and its variability over each domain.
The structure of this paper is as follows. Section
2 describes the datasets and the computational procedures used in this study. In any analysis of geophysical parameters, the accuracy of the desired results depends on the method employed and theaccuracy of the datasets used in the analysis, hencein this section the data products used and the methods employed for the interpretation of the results
have been discussed in brief. Section 3 discusses
the spatial correlations of AISMR with monsoon
rainfall (JJAS), early summer rainfall (JJ) and latesummer rainfall (AS) periods. Section 4 presents a
discussion of the spatial di¤erences between wet
and dry years. In Section 5, we document the basic
features of the monthly annual cycle of water balance components (P, C and E) and the seasonal
march of precipitation over the selected domains.
Section 6 presents the interannual variability of precipitation and atmospheric water balance components. Section 7 discusses the aspect of the regionalvariability of atmospheric water balance. In Section
8 the important discussions are addressed. Section 9
presents the main conclusions.
2. Data and method of analysis
The precipitation data used are Global Precipitation and Climatology Project (GPCP) Precipitation(Adler et al. 2003), VASClimO (Variability Analysis of Surface Climate Observations), the land
gridded precipitation dataset (Beck et al. 2005), themonthly all India summer monsoon rainfall index(Parthasarthy et al. 1995). The atmospheric water
budget equation can be written as (Peixoto andOort 1992),
hqW =qti þ h‘  Qi ¼ hE  Pi;

ð1Þ

where P is precipitation, E is evaporation, and theangled brackets denote the area average. Precipitable water content is W , vertically integrated moisture ﬂux vector is given by Q, and its divergence isgiven by ‘  Q.

265

On longer timescales like monthly or seasonal,
under near equilibrium conditions, the time changeof locally available precipitable water content is
negligible compared to the variations of large-scaleconvergence and evaporation (Oki et al. 1995;
Trenberth 1999). We approximate,
hqW =qti @ 0:

ð2Þ

Therefore we can approximately write,
P @ C þ E;

ð3Þ

where C ¼ h‘  Qi.
Vertically integrated moisture ﬂux vector ðQÞ isgiven by,ð Ps
Q ¼ 1=g

qv dp;

ð4Þ

Pt

where q is the speciﬁc humidity, v is the horizontal
wind vector, Ps is the pressure at surface level and
Pt is the pressure at the top of the atmosphere, g is
gravitational acceleration. Vertical integration is
performed from the ground (surface pressure level)
to 300 hPa for all the standard atmospheric pressure levels (1000, 925, 850, 700, 600, 500, 400,
300 hPa). We can neglect pressure levels above
300 hPa, as the speciﬁc humidity above this level isnegligible. The moisture ﬂux divergence that is thesecond term in the left-hand side of the equation(1) and the vertically integrated moisture ﬂuxes arecomputed using the linear grids. Caution has been
exercised, while interpreting the results on the signof convergence and divergence. In this paper, convergence is shown as positive values and divergence
as negative values.
The moisture convergence data are computed forevery six hours from six hourly upper-level winds
ðu; vÞ, speciﬁc humidity ðqÞ, geo-potential heightðzÞ, surface winds ðu; vÞ, surface-level speciﬁc humidity ðqÞ and sea-level pressure ðPsÞ obtained
from the Japanese 25-year Reanalysis (JRA-25) dataset (Onogi et al. 2007) and calculations are also
performed using the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis dataset (Kalnay et al. 1996) and the European Center
for Medium-Range Weather Forecasts (ECMWF)
40-year Reanalysis (ERA-40) dataset (Uppala et al.
2005). The computed six hourly moisture convergence datasets are averaged into monthly means.
The monthly residual evaporation dataset ðEÞ is266

Journal of the Meteorological Society of Japanobtained from the monthly GPCP precipitation dataset ðPÞ and the computed monthly moisture convergence dataset ðCÞ.
Water budget calculations and data analyses
were performed for the 22-year period from January 1979 through December 2000, as the reliable
precipitation data over both land and ocean wereavailable from 1979. We have utilized GPCP precipitation estimate for both land and ocean as theprecipitation estimate over land areas are quite realistic in GPCP. The advantage of the GPCP dataset over other land-based datasets is the blending ofvarious observational datasets like gauge precipitation, infrared precipitation estimates and precipitation estimates from microwave sensors (Adler et al.
2003). The VASClimO dataset and the GPCP dataset showed a similar correlation pattern with the
AISMR index over the South Asian land grid
points (Fig. 2), making the GPCP dataset a reliabledataset for this study.
The mean JJAS precipitation over South Asia isshown in Fig. 1a. The study area over the South
Asian monsoon region has been selected as six sub
domains, Northwest India (NWI), Central India(CEN), Northeast India (NEI), Peninsular India(PEN), the Arabian Sea (ARS) and the Bay of Bengal (BOB) (Fig. 1b), but detailed discussions are restricted to only a few major sub domains like thedriest (NWI), the wettest (NEI), transition between
dry and wet (CEN) and ocean (BOB) are arbitrarily selected and analyzed for space-time featuresin detail. On monthly time scales, we can approximate P @ C þ E, by neglecting local time change.
E is obtained as a residual from P and C, i.e.,
E @ P  C. The mean monthly contribution of
evaporation ðEÞ or convergence ðCÞ to precipitation ðPÞ and the year-to-year change in evaporationðDEÞ or convergence ðDCÞ to precipitation ðDPÞare used to understand the seasonal and interannual variability respectively over di¤erent domainsof the South Asian monsoon region.
Generally, we adopted our results from the JRA25 reanalysis dataset for discussion and compared
our results with the ERA-40 reanalysis dataset and
NCEP/NCAR reanalysis dataset wherever necessary. The JRA-25 reanalysis dataset has a highspatial resolution compared to the NCEP/NCAR
reanalysis dataset and a comparable resolution to
the ERA-40 reanalysis dataset. Moreover, goodcorrelations between P and C are noted over CEN
where both P and C are expected to be the most reliable because of high density of the original station

Vol. 87, No. 2

Fig. 1. a) Climatological JJAS mean precipitation from GPCP (mm day1 ) for the period 1979–2000. b) Selected boxes overSouth Asia for analysis of atmosphericwater balance. NWI: North West India,
NEI: North East India, CEN: Central
India, PEN: Peninsular India, ARS: Arabian Sea, BOB: Bay of Bengal.

data (Table 1). Di¤erences in the vertically integrated precipitable water among NCEP/NCAR
and ERA-40 and SSM/I datasets have alreadybeen discussed in detail and substantial problems
have been found in the low latitudes in the ERA40 dataset (Trenberth et al. 2005). Moreover,
Graversen et al. (2007) have also noted atmosphericmass transport inconsistencies in the ERA-40 reanalysis. Spurious mass ﬂuxes lead to considerable
errors when zonally and vertically integrated quantities are considered. A detailed study on the atmospheric hydrological cycle in the ERA-40 datasets
has been dealt with by Hagemann et al. (2005) andthey found considerable bias over land and oceans.
It is demonstrated that the correlation between P
and C are the most reliable over the CEN domain
in the JRA-25 reanalysis. Therefore, we assumeApril 2009

V. PRASANNA and T. YASUNARI

267

Table 1. Correlation between domain averaged GPCP Precipitation ðPÞ versus domain averaged vertically integrated
moisture convergence ðCÞ (values are shown without brackets) and residual evaporation ðEÞ (values are shown withbrackets) from the three major reanalyses over the South Asian monsoon region. Bold numerals denote correlation
signiﬁcant at 95% level. The higher correlations are shown by larger numerals. Data Period: 1979–2000.
GPCP P domain
Reanalysis C ðEÞ

Months

NWI

CEN

NEI

PEN

BOB

ARS

JRA-25 C ðEÞ

JJ

0.86
(0.05)
0.81(0.22)

0.83(0.41)
0.76(0.08)

0.49(0.63)
0.54(0.59)

0.46(0.21)
0.73(0.32)

C0.47(0.51)
0(0.58)

0.42(0.31)
0.55(0.22)0.56(0.07)
0.78(0.01)

0.68(0.65)
0.68(0.28)

0.66(0.55)
0.35(0.69)

0.62(0.45)
0.77(0.73)

0.26(0.58)
0.57(0.11)

0.78(0.34)
0.69(0.33)

0.56(0.75)
0.75(0.79)

0.64(0.78)
0.74(0.38)

0.58(0.81)
0.4(0.78)

0.56(0.81)
0.77(0.53)

0.3(0.59)
0.02(0.72)

0.63(0.76)
0.79(0.17)AS
ERA-40 C ðEÞ

JJAS

NCEP/NCAR C ðEÞ

JJ
ASthat the JRA-25 dataset could be an appropriate
dataset for this study, as our intension is not tostudy the di¤erences arising from the di¤erent reanalyses in this paper. Basically, the di¤erence
among the reanalysis datasets arises in the representation of surface to upper level humidity. The representation of wind vectors among the reanalysis
datasets is quite good (Figure not shown).
3. Monsoon indices
The AISMR index has been a widely used index
for discussing the strength of the South Asian summer monsoon and Indian monsoon in particular(Parthasarathy et al. 1995). To examine the di¤erences in the representation of AISMR in the earlyand late summer and the associated regional di¤erences, the AISMR index has been correlated withthe spatial precipitation for the entire summer,
early summer and late summer seasons. The correlation between the AISMR index and land gridded
precipitation (VASClimO) for the entire summer(JJAS), early summer (JJ) and late summer (AS)seasons are shown in Fig. 2. The highly signiﬁcantcorrelations are seen over the northwest and central
Indian regions during JJAS (Fig. 2a). Similarly thesigniﬁcant correlations are also conﬁned to northwest India and central India during the earlysummer (JJ) (Fig. 2b). During the late summer
monsoon period (AS), the signiﬁcant positive correlations are again conﬁned to central and western
India and a few pockets of signiﬁcant negative cor-

relation are seen over the Bangladesh region (Fig.
2c). These results suggest that the late summer
regional rainfall (AS) over northeast India has
a non-negligible negative correlation with the
AISMR variability. The basic purpose of preparing
the spatial correlation map between summer monsoon precipitation and the AISMR index is to highlight the regional di¤erences in the precipitation
variability among the selected sub-domains on interannual timescales.
4. Di¤erence in large-scale anomalies of P and C
between wet and dry years
AISMR from June to September for the period
1979–2000 is shown in Fig. 3. AISMR has been
considered to be stable with less trend and large
interannual variability compared to other regionsof tropics (Pant and Rupakumar, 1997). The interannual precipitation anomalies are shown as
standardised anomalies of JJAS from 1979 to
2000. The AISMR mean is given by 830 mm forthe 22-year period (1979–2000) and the interannual
standard deviation of AISMR for the 22-year period is given by 75.4 mm. The wet and dry years
are identiﬁed by a departure of more than one standard deviation from the mean precipitation for therespective years. The wet years are identiﬁed as
1983, 1988, 1990, 1994 and the dry years are identiﬁed as 1979, 1982, 1986 and 1987 respectively.
To study the spatial pattern of anomalies of thesummer monsoon precipitation over the whole of268

Journal of the Meteorological Society of Japan

Fig. 2. Correlation between AISMR index(JJAS) and land gridded precipitation(VASClimO) for the period 1979–2000. a)
AISMR vs. JJAS precipitation b) AISMR
vs. early summer precipitation (JJ) c)
AISMR vs. late summer precipitation(AS). Shaded values are signiﬁcant at 95%
level, while shaded bar indicates correlation coe‰cient.

Vol. 87, No. 2

India, the composite map for wet and dry years areprepared (wet-dry) from the interannual standard
deviations of AISMR (JJAS) (Fig. 3). The composite di¤erence (wet-dry) is made from four anomalous wet years and four anomalous dry years during
1979–2000. Figure 4 represents the composite difference of JJAS (Fig. 4a), JJ (Fig. 4b) and AS(Fig. 4c) between the wet and dry years of AISMR.
We notice large positive anomalies of precipitation (Fig. 4a) over the most part of the Indian region for the entire monsoon period (JJAS) and asmall pocket of negative anomaly over northeast
India. A similar composite only for the (JJ) earlymonsoon period is also made and large positive
anomalies of precipitation envelopes the entire Indian region (Fig. 4b). During the late summer monsoon season (AS), however, the precipitation anomaly becomes negative (large) over the northeast
Indian regions (Fig. 4c). Thus, positive rainfall
anomalies over the entire subcontinent during theearly summer (JJ) do not persist until late summer(AS) even during wet years, but large negative rainfall anomalies appear in late summer (AS) over thenortheast Indian regions. Such appearance of large
negative rainfall anomalies over the northeast Indian regions in the wet years of AISMR over themost part of the subcontinent, establishes the inverse relationship that exists between the northeast
Indian regions and the rest of the subcontinent particularly for late summer (AS) months. A signiﬁcant test of di¤erence (two tailed t-test) has been
performed for early and late summer precipitation(Figs. 4d and 4e). The regions of signiﬁcant di¤erence are conﬁned to high precipitation regions ofnortheast and west coasts of India and also northwestern regions for both early and late summer precipitation (Figs. 4d and 4e).
The overall spatial patterns of the entire monsoon season (JJAS) precipitation anomalies do not
represent the di¤erent situations of the early monsoon season (JJ) and the late monsoon season(AS), as the entire monsoon season (JJAS) based
anomalies for the di¤erent composite (wet-dry) cannot capture the large di¤erences between the earlymonsoon season (JJ) and the late monsoon season(AS). Thus instead of using the entire monsoon season (JJAS) as one season for interannual studies,
we can split it into early (JJ) and late (AS) monsoon seasons. The atmospheric water budget on
monthly, seasonal, interannual time scales and theassociated regionality is presented in the following
sections.

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V. PRASANNA and T. YASUNARI

269

Fig. 3. All India Summer Monsoon Rainfall (AISMR) from 1979–2000. JJAS precipitation mean and standard deviations are given inside the box. Circles denote wet years and squares denote dry years.

5. Monthly annual cycle and interannual variationof moisture convergence among reanalysis
datasets
To elucidate how we exploited the advantages of
all the major reanalysis datasets, we here try toshow the agreement or departure among datasets
over di¤erent domains. The monthly mean cycle ofcomputed convergence ðCÞ and the interannual
variability of computed convergence ðDCÞ from all
the three major reanalysis datasets over the selecteddomains are shown by Figs. 5 and 6 respectively.
The similarity in the monthly cycle and the di¤erences among the reanalyses over each domain areevident from Fig. 5. Generally, the reanalysis results are in good agreement over (CEN) the landdomain, but large discrepancies are noted over thesouth peninsular Indian domain and oceans. The
computed convergences ðCÞ over CEN and NEIfrom the three reanalysis data show quite good
agreement compared to those of other domains.
Over most of the domains the computed convergence ðCÞ from JRA-25 reanalysis exceeds both
ERA-40 and NCEP/NCAR. JRA-25 has a uniqueadvantage because of its relatively high resolution
for water budget analysis and also good correlations between P and C are noted over the CEN
domain (Table 1) and JRA-25 is not signiﬁcantly
inferior to other datasets. So, we believe that theJRA-25 dataset could also be a better dataset forregions with high orography (like NEI) due to theuse of a high resolution model and as well as improvements in data assimilation while generating
the reanalysis dataset compared to the other two
reanalysis datasets. Figure 6 shows the interannual
variability of computed convergence ðDCÞ over
NWI, CEN and NEI. The computed convergenceðCÞ over NWI has large discrepancies among

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Journal of the Meteorological Society of Japan

Vol. 87, No. 2

Fig. 4. Di¤erence between Wet and Dry years [First panel] for entire summer (JJAS), a) GPCP Precipitation.
[Second Panel] b) GPCP Precipitation for early summer (JJ), c) GPCP Precipitation for late summer (AS).
[Third Panel] d) t-test for GPCP precipitation (JJ), e) t-test for GPCP precipitation (AS). Units for precipitation: mm/day, t-test values above 3.71 (99% level) and above 2.48 (95% level), are heavy and lightshaded, respectively in d) and e).

April 2009

V. PRASANNA and T. YASUNARI

Fig. 5. Observed monthly mean cycle of convergence ðCÞ over di¤erent domains of South Asia in three major reanalysis (JRA-25, ERA-40 and NCEP/NCAR) datasets.

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Vol. 87, No. 2

Fig. 6. Interannual variations of convergence ðCÞ over di¤erent domains of South Asia in three major reanalysis (JRA-25, ERA-40 and NCEP/NCAR) datasets. [Top Panel] for NWI domain a) early summer (JJ)
and b) late summer (AS), [Middle Panel] for CEN domain c) early summer (JJ) and d) late summer (AS).
[Bottom Panel] for NEI domain e) early summer (JJ) and f ) late summer (AS).

April 2009

V. PRASANNA and T. YASUNARI

reanalysis datasets, while the DC from the JRA-25
and NCEP/NCAR results are comparable to eachother in their magnitudes in early summer and latesummer (Figs. 6a and 6b). Over CEN domain all
the reanalysis results have comparable magnitude
of C (Fig. 5c) and DC (Figs. 6c and 6d). Whereas
over the NEI domain, the early summer shows
large deviations of C and DC (Fig. 6e), the deviations of C and DC are less during late summer(Fig. 6f ).
5.1

Monthly annual cycle of atmospheric water
budgets
The monthly mean annual cycle of P, C and E
over the six sub domains using the JRA-25 reanalysis results are shown in Fig. 7. Before explaining
the relative contribution of C or E to precipitation
ðPÞ on a monthly scale, let us brieﬂy explain theseasonal march of precipitation over the di¤erent
domains. The rainy seasonal months are identiﬁed
for each sub domain when the observed precipitation exceeds 3 mm day1 and are demarcated byvertical dotted lines.
a. Seasonal march of P
Figure 7a shows P, C and E for the northwest Indian (NWI) domain, the driest region of
the Indian subcontinent. The summer monsoon
rains start from June, and precipitation attains thepeak in July. The month of July gets a decent
5 mm day1 of precipitation. Though the summer
monsoon continues till the end of September, theprecipitation amount is very low. We can see a sudden increase of precipitation as the summer monsoon month arrives and during the pre-monsoon
months the precipitation amount is nearly 0 mm
day1 .
In contrast, over northeast India (NEI) (Fig. 7b),
the wettest region of the Indian subcontinent, precipitation amount gradually increases from Apriland extends till the end of September, i.e., the rainy
period over NEI is longer in the subcontinent ofaround 6 months duration, but considerable
amount of precipitation is realized within the period of the summer monsoon (JJAS).
Over central India (CEN) (Fig. 7c), the precipitation increases suddenly between the months of May
and June, from around 1 mm day1 to 5 mm day1 .
Precipitation reaches its peak during July (8 mmday1 ) and the peak values are maintained until
August and then precipitation falls gradually thereafter, but signiﬁcant precipitation is conﬁned to thefour months of the summer monsoon (JJAS).

273

Peninsular India (PEN) (Fig. 7d) exhibits a longsummer monsoon season; relatively low magnitudeof precipitation compared to that in the NEI region. The precipitation amount gradually increases
from May above 3 mm day1 and attains thepeak in the month of June and sustains the peaktill July and slowly falls in the successive months,
but does not fall completely and the second peaklike pattern appears near October–November. Thisis because of the precipitation from the northeast
monsoon, mainly due to reversal of winds fromsouthwesterly to northeasterly. The moisture carried from the Bay of Bengal through northeasterly
during October–December brings about precipitation over peninsular India as the northeast
monsoon. The northeast monsoon precipitation(October–December) is especially signiﬁcant overthe state of Tamil Nadu in southeast India (Dharand Rakhecha, 1983).
Over the Arabian Sea (ARS) (Fig. 7e), the active
summer monsoon precipitation period is very shortcompared to that of other domains. The precipitation peak is seen during June, around 5 mm day1 .
Over the Bay of Bengal (BOB) (Fig. 7f ), the seasonal march is quite similar to the Peninsular India(PEN) region; the precipitation is prolonged fora longer duration, starting from May till November. The precipitation peak during June around
8 mm day1 gets sustained in its vigour until
October with the precipitation amount roughly
5 mm day1 , and a small peak-like groove appears
in November due to the northeast monsoon activity
over the Bay of Bengal.
b. Seasonal contribution of C and E to P
Over the NWI region, the precipitation during
the summer monsoon months (JJA) is predominantly due to high evaporation (Fig. 7a), where E
exceeds C throughout the year ðP @ EÞ. The higher
evaporation over the NWI domain is brought out
in all the reanalysis results. The magnitude of residual E is higher in the ERA-40 and NCEP/NCAR
over NWI i.e., ðP < EÞ (Figures not shown).
Over the NEI region (Fig. 7b), the heavy precipitation during summer monsoon months (JJAS) is
mainly due to the inﬂuence of active moisture convergence, i.e., ðC > EÞ and ðP @ CÞ during theentire summer monsoon months (JJAS), whichslightly di¤ers with the ERA-40 reanalysis but the
NCEP/NCAR reanalysis reveals C slightly exceeds
E, i.e., ðC @ EÞ during the peak summer monsoon
months (Figures not shown).

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Fig. 7. Monthly mean annual cycle of P, C and E over di¤erent domains of South Asia using JRA-25 dataset. Dominance of convergence or evaporation is shown as C or E in boxes. Vertical dotted lines delineate
the rainy months from the rest of the months where dominance of E or C is valid. Box in the left top shows
the relationship among P, C, and E during the summer monsoon season over the respective domains.

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V. PRASANNA and T. YASUNARI

Over the CEN region, the monsoon rains arecontributed by both moisture convergence andevaporation during the summer monsoon months(Fig. 7c) with the early summer period (JJ) dominated by convergence followed by evaporation in
the late summer period (AS). During September,rainfall over central India is very much inﬂuenced
by higher evaporation, i.e., ðC > EÞ during earlysummer and ðE > CÞ during late summer (Fig. 7c).
The dominance of convergence during early summer and evaporation during late summer has beenbrought out by the other two reanalysis datasets
too (Figures not shown). The result suggests thatthe moisture availability during the later periodof the monsoon months may aid in higher evaporation during the latter months and thus the structure of the precipitation generation during earlyand late summer monsoon months is di¤erent overthis region.
Rainfall over the PEN region (Fig. 7d) during
summer monsoon months is favoured by strong
convergence throughout the summer monsoon season, whereas, the evaporation is strong during the
NE monsoon season (OND).
Over the ARS region, convergence is higher(shown as the box [C]) (Fig. 7e) during the summer
monsoon season (JJAS) of the year, i.e., ðC > EÞ
during summer monsoon months.
Over the BOB region, convergence is dominant
throughout the JJAS season (Fig. 7f ). Convergenceapproximately equals precipitation, i.e., ðPÞ @ ðCÞ
during summer monsoon months, which has beenbrought out with the NCEP/NCAR reanalysis
data too (Figure not shown); on the other hand, in
ERA-40 (Figure not shown), evaporation is higher
than convergence in magnitude during JJAS, suggesting both evaporation and convergence may
contribute to precipitation during the summer monsoon months. During the NE monsoon season(OND), the evaporation is strong over BOB, suggesting that convergence is stronger only during
the summer monsoon season (JJAS).
6. Interannual variation of atmospheric water
budgets
To study the precipitation variability on the interannual time scale, the interannual variations ofwater budget components are plotted for both earlyand late summer for each domain. The correlationcoe‰cient (CC) between DP and DC, DP and DE
are used to understand the relationship of thesethree components in the interannual variability.

275

The CC’s are examined to know how they are related and how they di¤er from the early to latesummer period. Since E is obtained as a residualfrom P and C, E contains the P signature when C
does not vary, so care has been taken while interpreting the correlation between P and E. For understanding the interannual variability of P, C andE over the land domains like NWI, NEI and CEN,
only land grid points are considered for area average, but for the domains like PEN, BOB and ARS,
the area average of the box is considered.
Though the contribution of evaporation to themean seasonal precipitation is high over the northwest Indian region both in the early and late summer monsoon period, the correlation between DP
and DC exceeds 95% signiﬁcant level for early andlate summer, respectively (Figs. 8a and 8b). The CC
results from ERA-40 and NCEP/NCAR also support the conclusion from JRA-25 (Table 1). Therefore, DC contributes higher to the year-to-year variability of DP over the NWI monsoon region.
The NEI monsoon region shows correlation coe‰cients between DP and DC at just above 95% of
signiﬁcant level for early and late summer respectively. Between DP and DE, the correlation coe‰cients are above 95% of signiﬁcant level for earlyand late summer respectively (Figs. 8e and 8f ).
These results suggest that evaporation also partly
play a role in modulating the precipitation anomalies, which are well supported by both the ERA-40
and NCEP/NCAR results (Table 1).
Similarly, over the CEN region precipitationðDPÞ is highly correlated with convergence ðDCÞ(above 95% of signiﬁcant level) for both the earlyand late summer monsoon periods (Figs. 8c and8d) and the CC between DP and DE in CEN considerably changes from JJ (signiﬁcant) to AS (lesssigniﬁcant) suggesting that the climatology is
C > E in the early summer and E > C in the latesummer, while the interannual variability for respective seasons are reversed, i.e., DE contributes
higher to DP for early summer and DC contributes
higher to DP for late summer respectively. This has
been reinstated in all the reanalysis results, i.e., theCC between DP and DE in CEN for ERA-40
changes from JJ (signiﬁcant) to AS (less signiﬁcant)(Table 1) and also CC changes from JJ (signiﬁcant)to AS (less signiﬁcant) in the NCEP/NCAR results(Table 1). CC between DP and DE is above 95% of
signiﬁcant level during early summer in all the reanalysis results, but DE looses its signiﬁcance during late summer.

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Fig. 8. Interannual variations of P, C and E over di¤erent domains of South Asia from JRA-25 dataset.
[Top Panel] for NWI domain a) early summer (JJ) and b) late summer (AS), [Middle Panel] for CEN domain c) early summer (JJ) and d) late summer (AS). [Bottom Panel] for NEI domain e) early summer (JJ)
and f ) late summer (AS). (Value of correlation coe‰cient between P and C & P and E are shown in eachgraph) [95% signiﬁcant level is þ=0.42]

April 2009

V. PRASANNA and T. YASUNARI

In contrast, over the BOB monsoon region,
though the active convergence during the monsoon
months contributes to higher mean seasonal precipitation, the year-to-year variability of precipitation
is not completely dependent on moisture convergence (similar to the NEI domain), which is evident
from the poor positive correlation (negative correlation for early summer) between precipitationðDPÞ and convergence ðDCÞ for early and late summer, respectively (Figs. 9a and 9b) and between DP
and DE during JJ and AS (above 95% of signiﬁcantlevel), respectively (Figs. 9a and 9b). These results
suggest that evaporation plays a key role in modulating the precipitation anomalies over BOB.
Over the PEN monsoon domain, the correlation
between DP and DC (Figs. 9c and 9d) is above 95%
signiﬁcant level for early and late summer, respectively, and correlation between DP and DE are lesssigniﬁcant. The results from ERA-40 and NCEP/
NCAR also support the conclusion from JRA-25(Table 1). Therefore, DC contributes higher to theyear-to-year variability of P over the PEN domain.
Similarly, over the ARS monsoon domain, thecorrelation between DP and DC are above 95% signiﬁcant level for early and late summer, respectively, and correlation between DP and DE is lesssigniﬁcant (Figs. 9e and 9f ). Therefore, DC plays amajor role in the year-to-year variability of P over
the ARS monsoon region. The results from ERA40 and NCEP/NCAR also support the conclusionfrom JRA-25 (Table 1).
Otherwise in brief words, according to the analysis of JRA25, signals in DP are dominated by DC
in NWI, PEN, ARS; DE in BOB; both DC & DE(with nearly equal weights) in NEI; mainly DC,
but also DE in JJ, in CEN. With di¤erent sets ofreanalysis results, following di¤erent features areadded: DE also important in PEN (both ERA and
NCEP); DC also important in BOB in AS (ERA);DE also important in NWI (NCEP); DE also important in ARS in JJ (NCEP).
7. Regional variability in atmospheric water budget
The Indian summer monsoon exhibits a large variety of spatial and temporal variability. In this section we discuss the characteristics of atmosphericcirculation, moisture transport responsible for precipitation generation during early and late summer
among the selected domains. Precipitation ðPÞ,large-scale convergence ðCÞ, winds, moisture transport vectors and evaporation ðEÞ are regressed withthe area averaged GPCP precipitation index over277

each domain in order to understand the structureof P variability, over the respective domains. A
test of local statistical signiﬁcance for spatial correlation coe‰cient was performed using standard
t-test for plotting above 95% signiﬁcant level.
Figures 10a and 10b show the GPCP precipitation and 850 hpa winds regressed with the NWI domain averaged GPCP precipitation for early andlate summer, respectively. During early summer(JJ) (Fig. 10a), the regression pattern reveals thatthe precipitation anomalies over NWI are strongly
associated with local P and moderately associatedwith P along the west coast, Arabian Sea and the
Bay of Bengal region, whereas during late summer(AS) (Fig. 10b) the precipitation anomalies haveprofound e¤ect only over the west coast and northwest of India. The precipitation during early summer (JJ) are generally caused by the disturbancesfrom the Arabian sea sector and the strong convergence over the Arabian sea sector and the positive
precipitation anomalies over NWI are coupledwith the cyclonic circulations over the Arabian sea
and the land regions, especially over central India(Figs. 10a and 10c). During late summer (AS), thecyclonic circulation is conﬁned to the Arabian Sea
sector and the regression coe‰cients are conﬁned
only to northwest India. Therefore, the positive
precipitation anomalies over NWI for late summer(AS) are coupled with cyclonic circulation over the
Arabian Sea only and strong moisture transport
vectors predominantly originate from the Arabian
Sea sector (Figs. 10b and 10d). From the regressed
pattern of E, one can notice that there is no role ofevaporation during early summer and late summer
over NWI to P variability (Figs. 10e and 10f ).
Therefore, DC plays a major role in the interannual
variability P over northwest India in early summer(JJ) and also in late summer (AS).
Figures 11a and 11b show the regressed GPCP
precipitation and 850 hpa winds against the CEN
domain averaged GPCP precipitation for early andlate summer, respectively. During early summer(JJ) (Fig. 11a), the regression pattern reveals thatthe highly correlated precipitation area is eastnorthwest aligned, whereas during late summer(AS) (Fig. 11b), the highly correlated precipitation
area is west-central aligned, and we can also notice
CEN precipitation negatively associated withnortheast India precipitation in the late summer
months (AS). The regression map of large-scaleconvergence and moisture transport for early summer (JJ) is shown in Fig. 11c. Strong convergenceJournal of the Meteorological Society of Japan

278

Fig. 9.

As in Fig. 8, but for the domains BOB, PEN and ARS.

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V. PRASANNA and T. YASUNARI

Fig. 10. Regression maps based on Northwest India (NWI) domain averaged GPCP precipitation for JRA25 dataset. [Top panel] GPCP Precipitation and 850 hpa winds, a) for early summer (JJ) b) for late summer. [Middle Panel] Moisture Convergence and moisture ﬂux transport, c) for early summer d) for latesummer. [Bottom Panel] Evaporation, e) for early summer f ) for late summer. Domain for precipitationindex is shown by a box in the Bottom panel. Shaded values and thick arrows are signiﬁcant at 95% level.
Shaded bar indicates regression coe‰cient. Units for precipitation & convergence: mm/day, winds: m s1 ,moisture ﬂux: kg m1 s1 .

279

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Journal of the Meteorological Society of Japan

Fig. 11.

As in Fig. 10, but for Central India (CEN) domain.

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April 2009

V. PRASANNA and T. YASUNARI

from the head Bay of Bengal sector is associatedwith the precipitation variability over the CEN region and the large scale cyclonic circulation is basically from the Bay of Bengal sector in early summer (Figs. 11a and 11c). During the late monsoon
season (AS), the large-scale cyclonic circulation
over the northwest and central parts of India enveloping peninsular India is strongly associated with
precipitation variability over the CEN region.
Active convergence over the Arabian Sea and cyclonic moisture circulation favour positive precipitation anomalies over central India (Figs. 11b and11d). A similar picture evolves in the ERA-40 dataset (Figures not shown) for early and late summers
and also in the NCEP/NCAR reanalysis in bothsummers (Figures not shown). Regressed patternof evaporation reveals a signiﬁcant but weak association of evaporation over south of central India
both in early summer and in late summer (Figs.
11e and 11f ). Thus, we can say that both DC and
DE during early (JJ) summer months and only DC
during late (AS) summer months play a role in theinterannual variability of P over central India. We
can also notice large negative regression values ofE with the CEN domain precipitation over the
Arabian Sea, which are unrealistic values, due tothe overestimation of C over these grids (Figs. 11eand 11f ).
GPCP precipitation and 850 hpa winds regressedwith the NEI domain averaged GPCP precipitationfor early summer (JJ) is shown in Fig. 12a and thatfor late summer (AS) is shown in Fig. 12b. During
early summer, the NEI precipitation is largely associated with the NEI domain itself. Strong association over southeast of the domain and weak association with the central domain are evident from theregression map. During late summer (AS), the positive association is strong only over the southeast of
the domain (stronger over the oceans), but no signiﬁcant correlations are noticed over the west andnortheast areas of the domain box (unlike in thecase of early summer) and a signiﬁcant negative association with NWI precipitation and the southwest
coast of India are also seen (Fig. 12b). Large-scaleconvergence ðCÞ and moisture transport regressedwith the NEI domain averaged GPCP ðPÞ for earlysummer (JJ) is shown in Fig. 12c. We notice thatthe convergence along the foot hills of Himalayas(northwest of the domain) is regressed positively
with the precipitation index of the NEI domain.
Though regression is weak over the domain box,
the positive precipitation anomalies are coupled281

with the cyclonic circulations from the Bay of Bengal and the upper reaches of the climatological position of the monsoon trough, which is similar to
the typical condition of monsoon break, where dry
condition prevails over most part of the Indian subcontinent. The movement of the low level trough(monsoon trough) to foothills of the Himalayas
causes break condition over the subcontinent (Raghavan 1973, Krishnamurti and Bhalme, 1976) andthe northeast Indian (NEI) regions receive above
normal rainfall. A typical break-like circulation
brings positive precipitation anomalies over northeast India (NEI) during early summer (JJ) (Figs.
12a and 12c).
During the late monsoon season (AS) the cyclonic circulation is conﬁned only to northeast
India, strong divergent ﬂux is noticed over thesouth Bay of Bengal and large-scale divergent ﬂux
encompasses most part of the Indian subcontinent
showing a typical dry condition over the entire subcontinent. The positive precipitation anomalies
over NEI are favoured by localized convergenceand moisture transport from the northern Bay of
Bengal (Figs. 12b and 12d). A similar picture is evidenced in the ERA-40 results and in the NCEP/
NCAR reanalysis results, which suggest that precipitation over the NEI domain is inversely related
to the land domains like NWI, CEN and PEN.
However, one has to keep in mind that the representation of orography over this region in the reanalysis model is relatively poor due to the complex
nature of the terrain, which may a¤ect the calculated convergence ðCÞ and the observed P may
also have some errors. Both in early and late summer, evaporation also plays a key role in modulating the precipitation anomalies over NEI (Figs. 12eand 12f ). A similar result can be seen in the ERA40 dataset (Figures not shown) and the NCEP/
NCAR dataset (Figures not shown).
Figures 13a and 13b show the GPCP precipitation and 850 hpa winds regressed with the BOB domain averaged GPCP precipitation for early andlate summer, respectively. During early summer(JJ) (Fig. 13a), the regression pattern reveals thatthe precipitation anomalies over BOB are strongly
associated with P over the head Bay of Bengal andalong the Burma coast and no association with P
over the central and south of the domain, whereas
during late summer (AS) (Fig. 13b) the precipitation anomalies have profound e¤ect on the entire
BOB domain. The precipitation during early summer (JJ) is generally caused by the excessive evapo-

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282

Fig. 12.

As in Fig. 10, but for Northeast India (NEI) domain.

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V. PRASANNA and T. YASUNARI

Fig. 13.

As in Fig. 10, but for the Bay of Bengal (BOB) domain.

283

284

Journal of the Meteorological Society of Japanration from the Bay of Bengal and strong divergence over the head Bay of Bengal transports moisture to the land region north of it, but strong divergence prevails over south peninsular India (Figs.
13a and 13c). During late summer (AS), the cyclonic circulation conﬁned to the northern part ofthe Bay of Bengal sector and the strong moisturetransport from Arabian sea coupled with higher
evaporation over Bay of Bengal sector contributes
to positive precipitation anomalies over the Bay of
Bengal (Figs. 13b and 13d). From the regressed
pattern of (E), the role evaporation is evident inboth early and late summers over BOB to P variability (Figs. 13e and 13f ).
8. Discussions
Thus the atmospheric water balance analysis hasrevealed that the early summer (JJ) and the latesummer (AS) precipitation anomalies over South
Asia show di¤erent features. The interannual variability of precipitation over central India andnorthwest India during early and late summers
represents the interannual variability of precipitation over the entire Indian subcontinent. From theregression patterns of P, C and E, the precipitation
variability especially over NEI is negatively related
to other domains like NWI, CEN and PEN overthe subcontinent during the late summer season(AS), as can be seen from the fact that the regression pattern of moisture convergence and moisture
ﬂux vector for NWI and CEN (Figs. 10d and 11d)
are quite similar, whereas over NEI it is di¤erent(Fig. 12d). The interannual variability of precipitation over northwest India is inﬂuenced by largescale moisture convergence for both early and latesummers and the interannual variability of precipitation over central India is inﬂuenced by both thelarge-scale moisture convergence and the local
evaporation for both early and late summers. However, over northeast India the surface conditions
during early and late summers may also partly
play a role in modulating the precipitation anomalies, since the convergence is poorly correlated with
precipitation in all the reanalysis datasets and the
CC between P and E is strong and a moderateamount of evaporation is also found over the land.
However, one has to keep in mind that the representation of orography over this region in the reanalysis model is relatively poor due to the complex
nature of the terrain, which may a¤ect the calculated C and the observed P may also have someerrors, but the relative role of land surface hydro-Vol. 87, No. 2

logical processes can not be ruled out and further
studies are required to understand the role of land
surface processes in modulating the precipitation
anomalies over the NEI region.
A probable interaction between land and atmosphere over the CEN and NEI domains cannot be
ruled out during the summer monsoon season due
to the role of evaporation over these regions.
Though evaporation is obtained as a residual fromobserved P and calculated C, we believe that theevaporation obtained as a residual from the waterbalance can at least partly explain the landatmospheric interaction with some limitations better than evaporation obtained directly from reanalysis datasets. Recently, using ensembles of GCM
models, Koster et al. (2005) showed that soil moisture hot spots (the regions where precipitation issensitive to soil moisture anomaly) exist oversome relatively dry monsoon regions. The landatmospheric interaction over the domains (CEN,NEI) is crucial in understanding the soil moisture
impact on precipitation. The stronger role of evaporation ðDEÞ to precipitation ðDPÞ as observed during early summer over CEN and during late summer over NEI shows some surface hydrologicalprocesses responsible for precipitation anomalies
over these regions. It is also consistent with the previous studies such as those of Yoshimura et al.
(2004) that the origin of land evaporation (around36%) is responsible for precipitation over the Calcutta region (NEI domain) during July 1998 using
coloured moisture analysis. As the evaporation isvery much controlled by the amount of precipitation over land and the accuracy of precipitation is
also important in this method, so further studies
are needed to conﬁrm such interactions. Also,
Roads and Betts (2000) have studied the surface
water and energy budgets for Mississippi Riverbasin using NCEP/NCAR and ECMWF reanalyses, highlighted the limitations of both the reanalyses in depicting the surface water and energy
budgets and recommended that further developments be needed in the current models.
The atmospheric water balance analysis based on
the JRA-25 reanalysis dataset is reliable as the P
and C correlations are the highest over land domains (especially over CEN) among the reanalysis
datasets. Contrastive features are evident between
land and oceanic regions. Especially the BOB domain precipitation is very much controlled byhigher evaporation and so, the SST over the BOB
domain is crucial for controlling precipitation vari-

April 2009

V. PRASANNA and T. YASUNARI

ability over not only BOB, in addition to the inﬂuence on the majority of land domains. Warmer(Colder) SST’s over BOB can alter the convergenceand evaporation through stronger (weaker) convection over BOB and thereby it can inﬂuence theother domains substantially.
During early summer season (JJ), the precipitation over BOB is signiﬁcantly correlated with thesouth-westerlies of 850 hpa wind circulation andvertically integrated moisture transport over the
CEN domain (Figs. 13a and 13c) and during lateseason (AS), the precipitation over BOB is signiﬁcantly correlated with the south-westerlies of
850 hpa wind circulation and vertically integrated
moisture transport over the PEN domain (Figs.
13b and 13d) shows that the precipitation variability over the BOB region very much controls theprecipitation variability over the domains like
CEN and PEN. The reliability of precipitation
over the oceans (BOB) and the calculated convergence ðCÞ over the oceans (BOB) are, however,less reliable due to lack of su‰cient observations.
Caution may be needed for P and C correlation
over BOB, since those from the three reanalyses
data show considerably large di¤erences (as shownin Table 1). As we can say, P is a more frequently
observed meteorological variable compared to C
and E. Among P, C and E, E is the less observed
variable and to some extent C can be deduced tosome accuracy, therefore a good correlation between P and C indicates the reliability of the data
to some extent. However, the results from theJRA-25 reanalysis dataset particularly for land domains are consistent in the water balance studies
over South Asia.
9. Conclusions
We investigated the space-time characteristics ofatmospheric water balance components over South
Asia in detail using selected sub domains from theavailable model and observed datasets and summarize our results as follows.
a) JJAS based precipitation anomalies only partially explain the interannual variations of Indian monsoon rainfall, since the large timespace di¤erences are noticed between earlysummer (JJ) and late summer (AS) anomalies.
Therefore, the sub-seasonal (JJ and AS) based
water balance analysis helped us to explain thedi¤erence between the precipitation anomaliesof sub-seasons and over the di¤erent spatial domains of South Asia.

285b) The contribution of convergence and evaporation to the seasonal mean precipitation over the
CEN domain for early summer (JJ) and latesummer (AS) are di¤erent, suggesting that themechanism of precipitation generation during
early and late summers are basically di¤erent.
The di¤erent structure of P variability over
CEN for the early and late monsoon season implies that the modulating component of precipitation for early and late summers is not thesame. The changes in large-scale convergencea¤ect P variability in the late summer season
compared to the early season. Similarly the surface condition during the early summer season
very much a¤ect the P variability compared tolate summer over the CEN domain.
c) The interannual variability of precipitation over
the whole Indian subcontinent measured by
AISMR is closely related mainly to the precipitation variability over northwest India and central India. Though the regional di¤erences of the
Indian monsoon rainfall variabilities have been
discussed in many preceding papers, in thispaper we have shown the regional di¤erences in
the Indian monsoon rainfall variabilities and explained the variabilities with P, C and E relationship using the atmospheric water balancemethod. During early summer, the large-scaleconvergence over the Bay of Bengal sector andduring late summer the large-scale convergenceover the Arabian Sea sector have the potentialof modulating the monsoon for early and latesummer, respectively, on interannual timescales.
d) The precipitation variability over northwest andcentral India is quite di¤erent from that overnortheast India. It is evident from the atmospheric water balance that the processes causing
anomalies over NEI is di¤erent from those over
CEN, NWI. Therefore, the northeast India(NEI) regional monsoon variability is independent of the interannual variability over the domains like NWI, CEN and PEN of the Indian
subcontinent. There exists a strong inverse relationship between the NEI domain and the domains like NWI, CEN and PEN over the Indian
subcontinent during the late summer period.
e) On the interannual time scales, the NEI domain
precipitation anomalies are not directly inﬂuenced by large-scale convergence both in earlyand late summers, whereas the evaporation is
likely to play at least partly a role in modulating
the anomalies over NEI. These results suggest

Journal of the Meteorological Society of Japan

286

that some surface hydrological processes are responsible for modulating the monsoon over the
NEI domain during the summer season. Still,this is a tentative result, as the accuracy of computed C and observed P over this region has
known limitations. Thus by dividing the summer monsoon season (JJAS) into early summer(JJ) and late summer (AS), the interannual variability of water budget components for the earlyand late summer monsoon period have revealeddi¤erent characteristics of E, P and C relationships among di¤erent sub domains.
Acknowledgment
Authors would like to thank Drs. Fujinami,
Hori, Takahashi, Abe, Ichikawa and Mr. Kanamori for their helpful comments during this research work. Authors would also like to thankDrs. K. Onogi, N. Yamazaki, K. Takahashi and
Kamahori of JMA for their helpful comments. VP
was supported by Monbukagakusho scholarshipfrom the ministry of Education, Culture, Sports,
Science and Technology. Authors would also liketo thank the three anonymous reviewers for theirhelpful comments, which helped us immensely in
improving the paper. We also acknowledge manycenters for providing datasets. JRA-25 dataset obtained from the website (http://jra.kishou.go.jp/)archived by JMA, CRIEP. ERA-40 dataset obtained from the website (http://data.ecmwf.int/
data/). NCEP/NCAR reanalysis data obtained
from the NOAA/OAR/ESRL PSD, Boulder,
Colorado, USA, from their web site at (http://
www.cdc.noaa.gov/) and VASClimO dataset from(http://www.deutscherwetterdienst.de/). VASClimO
is an activity of the German Weather Service(http://www.dwd.de/de/FundE/Klima/KLIS/prod/
KSB/archiv.htm). Monthly AISMR index is obtained from (www.tropmet.res.in). The diagrams
used for this study have been prepared using thefree software packages like GrADS, XMGRACE
and computational work done on the Fedora Core
2 operating system environment.
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