INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. (2008)
Published online in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/joc.1683

Interannual variability of atmospheric water balance overSouth Peninsular India and Sri Lanka during northeast
monsoon season
Venkatraman Prasannaa * and Tetsuzo Yasunarib
a Graduate School of Environmental Studies, Nagoya University, Nagoya, Aichi, 464-8601, Japanb Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Aichi, 464-8601, Japan

ABSTRACT: In this study we have investigated atmospheric water balance over South Peninsular India and Sri Lanka
during the months October to December (OND) using computed moisture convergence (C) and residual evaporation (E)from National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis
data and Global Precipitation and Climatology Project (GPCP) precipitation data. The interannual signatures of OND
precipitation, moisture convergence and evaporation over the South Peninsular India and Sri Lanka have been captured.
The spatial and temporal characteristics of the hydrological cycle and the contribution of evaporation (E) and convergence(C) to precipitation (P ) are discussed in detail. Over the South Peninsular India and Sri Lanka, evaporation (E) dominates
during the entire monsoon months (OND). However, the interannual variability of precipitation over the domain is notnecessarily influenced by the same criteria which influences the mean seasonal precipitation. The moisture from the Indian
Ocean (IO) sector also modulates the precipitation over this region on a year-to-year basis. It has been noted that thepositive northeast monsoon rainfall (NEMR) is associated with El Nino coupled with IO dipole, but negative NEMR isweakly associated only with La Nina over South Peninsular India and Sri Lanka. There also exists a significant landatmospheric interaction over the region in modulating the hydrological cycle on a year-to-year basis. Copyright  2008
Royal Meteorological Society
KEY WORDS

atmospheric water balance; interannual variability; Indian Ocean Dipole

Received 20 June 2007; Revised 27 September 2007; Accepted 23 December 2007

1.

Introduction

Two vital monsoon systems prevail over the Indian subcontinent, namely, the summer monsoon [also known as
southwest (SW) monsoon] and northeast (NE) monsoon(retreating monsoon). The SW monsoon has been studied
widely compared to the NE monsoon. Monsoon is forced
primarily due to the seasonal shifting of thermally produced planetary belts of pressure and winds under continental influences. The heating over oceans and extensive
cooling over the continental areas facilitate the northeastward flow of winds over the Bay of Bengal (BOB)
and thus, the moisture carried from the BOB creates afavourable condition for NE monsoon to develop over
the South Peninsular India and Sri Lanka during October
to December (OND). Post-monsoon season or simply thenortheast monsoon rainfall (NEMR) season (OND) overSouth Peninsular India and Sri Lanka is the major rainfall season over this region (Dhar and Rakhecha, 1983),
which helps agricultural production in this region (Kumar
et al., 2007). NEMR received very less attention in terms* Correspondence to: Venkatraman Prasanna, Graduate School ofEnvironmental Studies, Nagoya University, Nagoya, Aichi, 464-8601,
Japan. E-mail: Prasanna@hyarc.nagoya-u.ac.jp
Copyright  2008 Royal Meteorological Societyof studies in interannual variability and its teleconnections (Singh and Sontakke, 1999; Kripalani and Kumar,
2004; Kumar et al., 2007) and over the Sri Lankan region
Suppiah (1989, 1996, 1997).
While most parts of India receive nearly the entire
portion of their annual rainfall from the southwest
monsoon rainfall (SWMR), the Southeast Peninsular
India falls under the rain shadow region during thesummer season due to the presence of Western Ghats.
This region therefore critically depends on the NEMR to
supplement the inadequate precipitation during SWMR.
Over the southeastern tip of the Indian Peninsula andneighbouring Sri Lanka, nearly 50% of the annual rainfall
is received during the NEMR season. Hence, this study
focusses on the NEMR variability.
The impact of El Nino/Southern Oscillation (ENSO)
on the SWMR has been explored intensively (Rasmusson
and Carpenter, 1983; Krishna Kumar et al., 1999; Ailikun
and Yasunari, 2001; Chang et al., 2001; Kawamura et al.,2005). The El Nino years are generally associated withbelow-normal SWMR rainfall (Rasmusson and Carpenter, 1983; Yasunari, 1990) and above-normal NEMR rainfall (Suppiah, 1996, 1997). Recently Kumar et al. (2007)
studied the ENSO–NEMR relationship prior to 1976 andthe later periods and concluded that the ENSO–NEMR

V. PRASANNA AND T. YASUNARI

relationship has strengthened considerably in the recent
decades.
Earlier studies have pointed out that a unique oceanatmosphere mode exists in the Indian Ocean (IO), withanomalous warm sea surface temperatures (SSTs) over
the western IO and anomalous cold SSTs in the eastern IO(Saji et al., 1999). This mode induces an unusual rainfall
distribution in the surrounding areas, and is termed as the
Indian Ocean dipole zonal mode (IODZM) (Hastenrath
et al., 1993; Webster et al., 1999). This dipole mode modulates the Maha (September and October) rainfall over Sri
Lanka (Zubair et al., 2003). Similar study on the NEMR
over South Peninsular India (Kripalani and Kumar, 2004)
had revealed that NEMR and the Indian Ocean dipole
mode (IODM) are directly related, suggesting that thepositive dipole phase enhances the NE monsoon activity
and the negative dipole phase suppresses the NE monsoon activity. In viewing the previous studies on NEMR
variability, only ocean-atmospheric coupling has beenstressed in detail, whereas land-atmospheric interactions
remain unknown; to study such a role the atmosphericwater budget can provide some lead.
To understand mechanisms other than ocean-atmospheric interaction over this region, we have tried
to exploit the advantages of atmospheric water balancemethod over South Peninsular India and Sri Lanka. A
few earlier global water budget studies have shown theimportance of water budget analysis (Oki et al., 1995;
Trenberth and Guillemot, 1998; Trenberth, 1999). Whatcan be gained by budget analysis? Through assessing
convergence (C) and evaporation (E), we can understand the hydro meteorological aspect of precipitation (P )dynamics.
Objectively reanalysed, gridded four-dimensional
datasets [National Centers for Environmental Prediction(NCEP), ERA-40 and JRA reanalysis] give us an 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 (P ) to close the reanalyses water
budget, it provides an opportunity to obtain less observed
variable evaporation (E) at each grid point on regionaland global scale through the balance. Since the reanalysis simulated evaporation is heavily model-dependent andthe simulated precipitation is constrained by the model’s
parameterization schemes, we have tried to utilize theadvantage of monthly-observed precipitation (P ) to findthe evaporation as a residual.
The main motivation of the present study is to understand the nature of the large-scale water balance overSouth Peninsular India and Sri Lanka during NEMR season (OND) and to understand the mechanism behind
year-to-year precipitation variability over this region. For
this purpose, the space–time characteristics of monthly
and NEMR (OND) interannual variations of precipitation(P ), convergence of water vapour flux (C) and evaporation (E) during the period, starting from 1979 up to 2005
have been studied in detail. Particular attention is paid tounderstand where the precipitation (P ) comes, i.e. from
Copyright  2008 Royal Meteorological Societylarge-scale moisture convergence (C) due to atmosphericcirculation and/or from evaporation (E) from the surface since both ocean-atmosphere interactions and landatmosphere interactions are fundamental in understanding
the variability of precipitation over this region. Anotheraspect is how P , C and E are modified on interannual
time scale by the slowly varying boundary conditions andhow they are related this is also explored in detail.
The structure of this article is as follows. Section 2
describes the datasets and the computational procedures
used in this study. In Section 3, we discuss the seasonality of NE monsoon over South Peninsular India and
Sri Lanka. In Section 4, we document the basic featuresof the monthly annual cycle of water balance components (E, P , C) and seasonal march of precipitation overthe selected domain and Section 5 presents a discussionof the interannual variability of precipitation and atmospheric water balance components. Section 6 discusses
NEMR index with various climatic indices. Section 7
discusses the teleconnection of NEMR with various climate anomalies and the aspect of large-scale anomalies ofatmospheric water balance for excess and deficient monsoon phases, IODM phases and Nino-3.4 anomaly phases.
In Section 8, the main conclusions and discussions are
addressed.

2.

Data and method of analysis

The period of analysis covers NEMR monsoon seasonof OND from 1979 up to 2005. The data products
used are Global Precipitation and Climatology Project(GPCP) precipitation (Adler et al., 2003), Variability
Analysis of Surface Climate Observations (VASClimO)
land gridded precipitation dataset 1950–2000 (Becket al., 2005). Daily upper level winds (u, v), specifichumidity (q), geo-potential height (z), surface winds(u, v), surface level specific humidity (q) and sea levelpressure (SLP) obtained from NCEP/National Center
for Atmospheric Research (NCAR) reanalysis dataset(Kalnay et al., 1996) and the monthly SST for the period
1982–2005 from NOAA (NOAA optimum interpolated
SST dataset) (Reynolds et al., 2002).
The atmospheric water budget equation can be writtenas (Peixoto and Oort, 1992),<∂W/∂t > + < ∇ · Q >=< E – P > . . . . . . . . . (1)where (P ) is precipitation, (E) is evaporation, and angled
brackets denote the area average, precipitable watercontent (W ), vertically integrated moisture flux vector(Q) and its divergence (∇ · Q).
On longer timescales like monthly or seasonal, undernear equilibrium conditions, the time change of local
available precipitable water content is negligible compared to the variations of large-scale convergence andevaporation (Oki et al., 1995; Trenberth, 1999). We
approximate,<∂W/∂t >∼ 0 . . . . . . . . . . . . . . . . . . . . .

(2)Int. J. Climatol. (2008)
DOI: 10.1002/joc

ATMOSPHERIC WATER BALANCE OVER SOUTH PENINSULAR INDIA AND SRI LANKA

50% over Southeast Peninsular India and Sri Lanka(Figure 2(c)).

Therefore we can approximately write,
P ∼ C + E........................

(3)
3.

Vertically integrated moisture flux vector (Q) is givenby,Ps
Q = 1/g P t∫ qvdp . . . . . . . . . . . . . . . . . .
(4)
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 fromground level (surface pressure level) to 300 hPa for the
standard atmospheric pressure level (1000, 925, 850, 700,
600, 500, 400 and 300). We can neglect pressure levels
above 300 hPa, as the specific humidity above this levelis negligible. The moisture flux divergence which is thesecond term in the left-hand side of Equation (1), and thevertically integrated moisture fluxes are computed using
the linear grids.
Water budget calculations and data analyses were
performed for the 27-year period from January 1979
through December 2005 as the reliable precipitation dataover both land and ocean are available from 1979. The
total vertically integrated moisture convergence is computed for the above period from NCEP/NCAR reanalysis
datasets. Evaporation is obtained as a residual from theprecipitation and convergence. The mean monthly contribution of evaporation (E) or convergence (C) to precipitation (P ) and the year-to-year change in evaporation(E) or convergence (C) to precipitation (P ) areused to understand the seasonal and interannual variability of NEMR (OND) season over South Peninsular India
and Sri Lanka. Mathematically, divergence is a positive
quantity and convergence is a negative quantity, but inthis article, we have defined the computed convergence
as positive values and divergence as negative values.
The study area over South Peninsular India and Sri
Lanka is shown in Figure 1. During the summer monsoon season (JJA), the south peninsular region receives
less rainfall compared to the rest of India due to thepresence of Western Ghats (Figure 2(a)); the region is arain shadow region for summer monsoon. Considerable
amount of mean seasonal precipitation over this region
is evident during NEMR season (OND) (Figure 2(b)).
The annual contribution of OND precipitation exceeds

Seasonality over Peninsular India and Sri Lanka

To clearly distinguish the season over South Peninsular
India and Sri Lanka, the monthly mean moisture flux vectors along 80E are shown in Figure 3(a) and the spatial
mean pattern of moisture convergence and moisture flux
vectors during OND season are shown in Figure 3(b).
We can clearly notice the reversal of moisture transport
vectors from southwesterlies to northeasterlies around
10N–12N during October (Figure 3(a)). Though themean precipitation during September–November (SON)is higher than OND, the OND has been chosen as NEMR
season by considering the wind field. The percentage contribution of annual precipitation exceeds 50% over southeastern coastal Peninsular India during OND and also themoisture transport vectors become completely northeasterlies during OND over this region.
4. Monthly annual cycle of atmospheric water
budget over the selected domain
The monthly mean annual cycle of precipitation (P ),evaporation (E) and convergence (C) over the selecteddomain containing both South Peninsular India and
Sri Lanka are shown in Figure 4(a) and (b). Before
explaining the relative contribution of (C) or (E) to
precipitation (P ) on monthly scale, let us briefly explain
the seasonal march of precipitation over the domain.
4.1. Monthly evolution of precipitation over the PEN
and SL domain
To examine the monthly precipitation evolution for (PENand SL) the domain averaged monthly mean precipitation
for the period 1979–2005 is plotted in Figure 4(a) fromthe GPCP dataset. The monthly annual rainfall pattern has
a double peak, namely, first one at June and another one
at October. The NEMR monsoon (OND) over this region
exhibits a very short rainy season and relatively low magnitude of precipitation unlike the SW monsoon over theentire south Asian region. The precipitation amount gradually increases from March and attains a peak in themonth of June and sustains the peak till July and slowly
falls in the successive months, but does not fall completely; the second peak pattern appears in October. This

Figure 1. Maps showing selected domains for analysis in this study.
Copyright  2008 Royal Meteorological SocietyInt. J. Climatol. (2008)
DOI: 10.1002/joc

V. PRASANNA AND T. YASUNARI

state of Tamil Nadu (Dhar and Rakhecha, 1983; Kumar
et al., 2007). Nearly 50% of annual precipitation over thisregion is realized during the OND period.
4.2. Atmospheric water budget during NEMR season
Rainfall over the domain (Figure 4(b)) during the NEMR
months (OND) are favoured by higher evaporation
throughout the season shown in the box as (E).E ishigher than P during pre-monsoon months and winter.
Evaporation almost equals precipitation throughout therainy season (E ∼ P ) (Figure 4(b)).
Over this region, convergence (C) becomes positivefrom June to November season. Convergence shows
only one peak during July unlike precipitation during
June and October for SW and NE monsoon months
respectively. The double peak in the convergence is
not very clear (Figure 4(b)), which may be due to theprevalence of weak convergence over Peninsular India.
Strong convergence is seen only over Sri Lanka and thesoutheastern tip of Peninsular India implies that the mean
evaporation during OND over South Peninsular India
must be stronger than Sri Lanka.
Most of the earlier studies have shown that the ocean
acts as a source of moisture during the NEMR monsoon
months for this domain (Zubair et al., 2003; Kripalani
and Kumar, 2004). Here, we have found that evaporation(E) is higher than convergence (C) in magnitude during
OND and E contribution to precipitation (P ) during the
OND months is also significant. Thus, E contribution
to precipitation P exceeds C contribution to P during
OND months and the evaporation from the land surface
also aids precipitation during the NEMR season.
5. Interannual variation of atmospheric water
budget over Peninsular India and Sri Lanka

Figure 2. Climatological mean precipitation from VASClimO dataset
for the period 1979–2000. (a) JJAS mean precipitation (mm/day)(b) Climatological mean OND precipitation (mm/day) (c) Percentage
of OND precipitation contribution to annual mean precipitation.

is because of the precipitation from NE monsoon, mainly
due to reversal of winds from southwesterly to northeasterly. The moisture carried from the BOB throughnortheasterly winds during OND brings precipitation overSouth Peninsular India and Sri Lanka. The NE monsoon
precipitation (OND) is especially significant over the
Copyright  2008 Royal Meteorological Society

To study the precipitation variability on interannual timescale, the interannual variations of atmospheric water
budget components are plotted for OND over the domain.
The correlation coefficient (CC) between precipitationand moisture convergence are used to understand therelationship between P and C on the interannual timescale and similarly, for evaporation.
The contribution of evaporation to mean seasonal
precipitation is high over South Peninsular India and the
Sri Lankan region during NEMR monsoon period andalso for interannual variability, the correlation between
P and C is 0.73 (exceeds 95% significant level) andthe correlation between P and E is 0.84 (exceeds
95% significant level) (Figure 4(c)); thus the contributionof evaporation during the monsoon months to the yearto-year variability of precipitation has been brought
out through the atmospheric water budget clearly, i.e.
(E>C). The correlation between precipitation and theresidual evaporation at 0.84 suggests that the land surfacehydrological process are also important to understand the
P variability on interannual timescales over this region.
One important point to be noted here is that the CC
between P and C as well as P and E are highlyInt. J. Climatol. (2008)
DOI: 10.1002/joc

ATMOSPHERIC WATER BALANCE OVER SOUTH PENINSULAR INDIA AND SRI LANKA

Figure 3. Maps of mean moisture convergence and moisture flux over the domain. (a) Monthly mean moisture flux for different latitudes along
80E (b) spatial pattern of moisture convergence and flux (OND). Positive values are darkly shaded and negative values are lightly shaded. (Units
for convergence: mm/day, moisture flux: kg m−1 s−1 ).

significant (significant above 95% level). Thus the Pvariability over this domain during NEMR season OND
is not only dependent on the large-scale convergence C,
but also on E. Strong land-atmospheric interactions do
co-exist along with ocean-atmospheric interactions overthis region during OND.

6. Averaged NEMR monsoon index versusIOD and
NINO-3.4 indices
To identify the significant influence of various climate anomalies to NEMR variability (dominant climate
anomalies like El Nino/La Nina and IOD anomalies), theNEMR precipitation index has been created using theobserved GPCP precipitation over the selected domain
and this index is used for discussing the strength ofthe monsoon over South Peninsular India and Sri Lanka
during OND months as shown in Figure 5. Based on
Copyright  2008 Royal Meteorological Society

the available observed GPCP rainfall data, a time seriesof NEMR over South Peninsular India and Sri Lanka
has been developed. Similarly, an index to quantify the
IODM, exhibiting warm (cool) waters over the equatorial
western (southeastern) IO and Nino-3.4 anomalies has
been developed using (NOAA-IO) optimum interpolated
SST data for the period (1982–2005).
The NEMR, IOD (Difference of SST between western Indian Ocean (WIO) (10S–10N, 55E–70E) andSoutheast Indian Ocean (SEIO) (10S–0N, 90E–120E)
and NINO-3.4 (5S–5N, 190E–240E) SST anomalies
are standardized with their respective standard deviations (SDs) and plotted. We have considered OND season to represent the NEMR, whereas the IOD, El Nino
and La Nina phases are represented by SON season,
since the IOD peaks in the months of SON. Saji andYamagata (2003) have shown that the largest combined
contribution of ENSO and IOD to the zonal and meridional wind variability over the IO is during SON aroundInt. J. Climatol. (2008)
DOI: 10.1002/joc

V. PRASANNA AND T. YASUNARI
(a)

(b)

7

P/C/E (mm/day)

6

6

GPCP

5

NCEP

4

4

E

2
3
0

2
1

-2

0

2

4

6
8
Month

10

12

2

4

6
8
Month

10

12

(c)
20

P/C/E (mm/day)

15

NCEP [CC : P vs C : 0.73][CC : P vs E : 0.84]

10501980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
Year
GPCP Rain
Convergence
Evapotation

Figure 4. Observed monthly mean annual cycle of P , C and E over the selected domain (a) GPCP precipitation; (b) P , C, E (dominance ofevaporation is shown as E in box) and (c) interannual variability of (OND) P , C and E over the selected domain (value of correlation coefficient
between P , C and P , E are shown in graph).

Figure 5. Standardised anomalies of NEMR over the selected domain (PEN & SL) during OND, IOD Indices during SON and NINO-3.4 SST
anomalies during SON from 1982-2000. (Filled bars denote rainfall departures and dashed line denote IOD index and solid line denote Nino-3.4
SST anomalies. Horizontal dashed lines indicate departures of +/−0.5 standardised anomaly for NEMR, NINO 3.4 and +/−0.5 degree SST
anomalies for IOD index).
Copyright  2008 Royal Meteorological SocietyInt. J. Climatol. (2008)
DOI: 10.1002/joc

ATMOSPHERIC WATER BALANCE OVER SOUTH PENINSULAR INDIA AND SRI LANKA

45% between 10S and 10N. The CC between NEMR
and IOD is found to be less than the significant level(0.24), whereas the CC between NEMR and NINO3.4 anomalies are significant (0.46*) at 95% significant
level, which is also evident from the recent studies thatthe impact of ENSO on NEMR is strengthening in the
recent decades (Kumar et al., 2007). The excess/deficient
NEMR, IOD and NINO-3.4 positive/negative phase are
obtained by applying a criterion that the standardized
anomalies exceeds ±0.5 SD.
7. Teleconnections and large-scale anomalies of (P ),(C) and (E) among anomalous NEMR, NINO-3.4
and Indian Ocean dipole mode years
In this section, we discuss the various teleconnectionsof NEMR and the differences in atmospheric circulation
for anomalous NEMR monsoon years and IOD positive–negative phase years over the selected domain.
To show the teleconnection patterns of NEMR, the(NOAA-OI) optimum interpolated global SST(1982–2005) for OND is regressed with the selecteddomain’s OND GPCP precipitation (Figure 6(b)). A significant positive correlation (significant at 95%) over the
Nino-3.4 regions and over BOB near Myanmar coast andSouth China Sea, a less than significant negative correlation over the SEIO over the coast of Sumatra are noticed
and no correlations are observed with IOD.
The regressed pattern of precipitation reveals a significant negative relationship with the Indo-China Peninsula precipitation during NEMR monsoon (OND) season (Figure 6(a)). Figure 6(c) shows that the convergenceover IO is significantly associated with the domain precipitation, and the moisture flux vectors largely originate
from the SEIO region. Figure 6(d) shows that evaporation also plays an active role in modulating the interannual variability of NEMR precipitation over the PEN &SL domain.
In Figures 7 and 8, anomalies of precipitation (P ),large-scale convergence (C), moisture transport vectors
and evaporation (E) are plotted for anomalous NEMR
monsoon years, IOD positive–negative phase years andFigure 6. Regression maps based on PEN and SL domain GPCP precipitation. Top panel: (a) GPCP Precipitation, (b) OND SST (domain
encompassing PEN & SL is shown by a box). Lower panel: (c) NCEP moisture convergence and moisture flux transport, (d) NCEP evaporation.
(95% significant levels are shaded and 90% are just contoured). (Shades indicate regression coefficient; units for precipitation, convergence andevaporation: mm/day, SST:° C, moisture flux: kg m−1 s−1 ).
Copyright  2008 Royal Meteorological SocietyInt. J. Climatol. (2008)
DOI: 10.1002/joc

Figure 7. Standardized anomaly maps based on excess and Indian Ocean dipole positive mode phase and NINO-3.4 positive anomaly phase. Top panel: GPCP precipitation for (a) excess year (OND), (b) IOD
positive phase, (c) NINO-3.4 positive phase. Middle panel: Moisture convergence and moisture flux transport for (d) excess year, (e) IOD positive phase, (f)NINO-3.4 positive phase. Bottom panel: Evaporationfor (g) excess year, (h) positive phase, (i) NINO-3.4 positive phase. (Positive anomalies are darkly shaded and negative anomalies are lightly shaded). (Units for precipitation, convergence and evaporation:
mm/day, moisture flux: kg m−1 s−1 ).

V. PRASANNA AND T. YASUNARI

Copyright  2008 Royal Meteorological SocietyInt. J. Climatol. (2008)
DOI: 10.1002/joc

Figure 8. Standardized anomaly maps based on deficient and Indian Ocean dipole negative mode phase and NINO-3.4 negative anomaly phase. Top panel: GPCP Precipitation for (a) deficient year (OND),(b) IOD negative phase, (c) NINO-3.4 negative phase. Middle panel: Moisture convergence and moisture flux transport for (d) excess year, (e) IOD negative phase, (f) NINO-3.4 negative phase. Bottom panel:
Evaporation for (g) deficient year, (h) negative phase, (i) NINO-3.4 negative phase. (Positive anomalies are darkly shaded and negative anomalies are lightly shaded). (Units for precipitation, convergence andevaporation: mm/day, moisture flux: kg m−1 s−1 ).

ATMOSPHERIC WATER BALANCE OVER SOUTH PENINSULAR INDIA AND SRI LANKA

Copyright  2008 Royal Meteorological SocietyInt. J. Climatol. (2008)
DOI: 10.1002/joc

V. PRASANNA AND T. YASUNARI

Table I. Selected cases of excess NEMR, deficient NEMR, IOD positive, negative phases and El Nino, La Nina phases.
Excess NEMR
1993
1994
1997
2002
2005
–
–
–

Deficient NEMR

IOD positive

IOD negative

El NinoLa Nina

1983
1984
1986
1988
1989
1990
1995
2000

1982
1991
1994
1997
2002
–
–
–

1984
1990
1992
1993
1996
1998
2005
–

1982
1986
1987
1991
1994
1997
2002
2004

1983
1984
1985
1988
1995
1998
1999
2000

Data period: 1979–2005

also Nino-3.4 anomaly phase years in order to understandthe physical mechanism or dynamics through which P
variability over the domain occur.
The excess or deficient years are obtained by applying
the criterion that the standardized anomalies exceeds±0.5 SD. Similarly the IOD and NINO-3.4 phases arealso selected by applying the same criterion.
Five excess years, eight deficient years, five IOD
positive phases, seven IOD negative phase, eight El
Nino phases and eight La Nina phases were selectedfrom (1982–2005) 24 years dataset for analysis as shownin Table I. The excess and deficient years composite
anomaly from the seasonal mean climatology are plotted
to understand the difference between different phases andtheir mechanism.
The dynamics of the NEMR–IOD relationship are
examined using the P , C and E by constructing standardized anomaly composite patterns for each phase.
The excess precipitation composite anomaly pattern(Figure 7(a)) is generally caused by enhanced convergence and enhanced moisture transport from the southeast Indian Ocean and the south Bay of Bengal towards
south peninsular India and Sri Lanka (Figure 7(d)). The
evaporation composite anomaly is also positive duringexcess years (Figure 7(g)). The precipitation anomaly in(Figure 7(a)) is similar to the positive IOD phase composite anomaly pattern (Figure 7(b)) and also similar to the
El-Nina composite anomaly pattern (Figure 7(c)). Similarly, the convergence composite anomalies (Figure 7(d))
over the equatorial IO resembles IOD phase (Figure 7(e))
as well as El Nino phase (Figure 7(f)), except a slighthigher divergence over BOB region in both IOD andEl Nino phases. Evaporation anomaly over the domain
is slightly positive during the excess years (Figure 7(g))
compared to IOD positive phase (Figure 7(h)) and El
Nino phase (Figure 7(i)). Thus the overall features ofexcess NEMR seems to be influenced by the combined
effect of IOD positive and El Nino phases. So we can saythat positive NEMR is approximately equal to the effects
of positive IOD phase and El Nino phase.
Standardized composite anomaly for deficient NEMR,
IOD negative and La Nina phases are shown in Figure 8.
The deficient NEMR precipitation composite anomaly
pattern (Figure 8(a)) shows very strong deficient precipitation, associated with strong divergence and enhanced
Copyright  2008 Royal Meteorological Society

anti-cyclonic moisture transport over the Arabian Sea andSouth Peninsular India and Sri Lanka and the adjacentoceanic regions (Figure 8(d)). The evaporation composite anomaly is also negative during the deficient years(Figure 8(g)). The precipitation anomaly over the domain
during deficient years (Figure 8(a)) is not similar to thenegative IOD phase composite precipitation anomaly pattern (Figure 8(b)), but has some similarity to the La Nina
composite precipitation anomaly pattern (Figure 8(c)),
still the precipitation anomaly during deficient years isquite stronger compared to La Nina composite precipitation anomaly pattern. The IOD negative phase induces
precipitation anomaly over the equatorial IO, but has little impact on South Peninsular India and Sri Lanka as
we can notice positive precipitation anomaly over South
Peninsular India and adjacent region during IOD negative phase (Figure 8(b)). Similarly, the convergence composite anomaly for negative IOD phase (Figure 8(e)) is
confined to the equatorial IO, convergence over SEIO
and BOB and divergence over equatorial WIO, which
does not resemble the deficient composite anomaly phase(Figure 8(d)) as well as La Nina phase (Figure 8(f)). The
composite convergence anomaly pattern during the deficient phase (Figure 8(d)) and La Nina anomaly pattern(Figure 8(f)) are quite similar. And also the evaporation
composite anomaly for deficient years (Figure 8(g)) issimilar to La Nina anomaly years (Figure 8(i)) and does
not compare with negative IOD years (Figure 8(h)). Thus
the overall features of deficient NEMR seems to be influenced only by La Nina phase. So we can say, negative
NEMR is approximately equal to the effects of the La
Nina phase.
The asymmetrical influence of IODM on the NEMR
is also evident in the composite anomaly maps of SST.
Figure 9(a)–(c) shows the SST composite anomaly for
NEMR excess years, IOD positive phase and El Ninophase respectively. The patterns more or less agree with
one another, except that the IOD phase anomaly composite shows enhanced east–west dipole mode (Figure 9(b)).
Both in excess NEMR and El Nino phases, the east–west
dipole is subdued (lower magnitude of anomalies oneither side). So we can say that the excess NEMR
monsoon is favoured by colder SST anomalies over thetropical SEIO and warmer SST anomalies over tropicalWIO (a typical IOD positive phase) associated with the El
Int. J. Climatol. (2008)
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ATMOSPHERIC WATER BALANCE OVER SOUTH PENINSULAR INDIA AND SRI LANKA

Figure 9. NOAA composite SST anomaly maps based on NEMR excess
years, Indian Ocean dipole positive mode phase and NINO-3.4 positive
anomaly phase.(a) for excess years (OND), (b) for IOD positive
phase, (c) for NINO-3.4 positive phase. (Positive anomalies are darkly
shaded and negative anomalies are lightly shaded). (Units for SST
anomaly: ° C).

Nino conditions over tropical Pacific Ocean. Moreover,
the SST anomalies over the BOB is also important as
this basin is responsible for generating tropical cyclones,
which significantly affect the precipitation over NEMR
domain during OND (Kumar et al., 2007).
In contrast, Figure 10(a)–(c) shows the SST composite
anomaly for NEMR deficient years, IOD negative phaseand La Nina phase respectively. In the deficient composite years, the SST pattern over the tropical SEIO exhibits
a warmer SST anomaly and colder anomaly over the tropical WIO (Figure 10(a)), but we can notice a large poolof negative anomaly over BOB; this pattern is absent
in the IOD negative phase (Figure 10(b)). The negative
Copyright  2008 Royal Meteorological Society

Figure 10. NOAA composite SST anomaly maps based on NEMR deficient years, Indian Ocean dipole negative mode phase and NINO-3.4
negative anomaly phase.(a) deficient years (OND), (b) IOD negative
phase, (c) NINO-3.4 negative phase. (Positive anomalies are darkly
shaded and negative anomalies are lightly shaded). (Units for SST
anomaly: ° C).

anomaly over the BOB basin is not favourable for theformation of tropical cyclones, which in turn affect the
precipitation over the NEMR domain considerably. The
SST composite patterns resemble the La Nina phase quite
well (Figure 10(c)), though the pool of SST anomaly is
confined to the eastern part of the BOB basin. In the IOD
negative phase SST composite pattern (Figure 10(b)),
only the east–west SST difference is enhanced, but it
is quite different from the La Nina and NEMR deficient
years composite patterns.
Therefore, the precipitation (P ), SST, convergence(C), evaporation (E) and moisture circulation anomalies associated with the positive IOD and excess NEMR
are consistent, whereas the precipitation (P ), SST,
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V. PRASANNA AND T. YASUNARI

convergence (C), evaporation (E) and moisture circulation anomalies associated with the negative IOD and
deficient NEMR are inconsistent. This result suggests
evidence of the asymmetrical influence of IODM on the
NEMR during OND rainfall over South Peninsular India
and Sri Lanka. Moreover, El Nino/La Nina has (basically) symmetrical influence, unlike IOD. IOD influenceis confined only to positive NEMR.
Apart from the ocean-atmospheric interactions, we
notice some land-atmosphere interactions too. Recently,
by using ensembles of GCM models Koster et al. (2004)
showed that soil moisture hot spots exist over somerelatively dry monsoon regions. The land-atmosphericinteractions over these domains are crucial in understanding the soil moisture impact on precipitation. They haveshown that over the transition zones between wet anddry climates, soil moisture anomalies are translated into
precipitation anomalies. In dry climates, evaporation rates
are sensitive to soil moisture, but are also generally smalland small evaporation rates should have a limited ability to affect precipitation. Only in the transition zones
between wet and dry climates, the atmosphere is conducive for generation of precipitation where evaporation
is suitably high but still sensitive to soil moisture, so
we can expect soil moisture to influence precipitation(in particular, where boundary layer moisture can trigger moist convection). The excess evaporation during
the entire season OND as observed in Figure 3(b) may
support such a hypothesis based on GCM results. We
may notice significant and strong regression coefficientof evaporation also over land apart from adjacent oceanic
regions in the domain (large-scale residual evaporation
regressed with the domain precipitation) (Figure 6(d)).
But the validity of such hypothesized processes requiresa much more detailed study. Thus, the positive (+NEMR)
case is favoured by both evaporation from land surface and enhanced evaporation from positive SST (+SST)
over BOB, while during negative (–NEMR), large-scale
downward motion due to La Nina inhibits higher evaporation over land and adjacent oceans.

8.

Conclusions and discussion

We investigated the interannual variability of atmosphericwater balance components over South Peninsular India
and Sri Lanka during NEMR (OND) season and summarize our results as follows:
1. Asymmetrical influence of IODM on the NE monsoon
rainfall anomaly during OND over South Peninsular
India and Sri Lanka.
The precipitation (P ), SST, convergence (C), evaporation (E) and moisture circulation anomalies associatedwith a positive IOD and NEMR excess years are consistent, whereas the precipitation (P ), SST, convergence(C), evaporation (E) and moisture circulation anomalies
associated with negative IOD and NEMR deficient years
are inconsistent. This result suggests that the influence
Copyright  2008 Royal Meteorological Societyof IODM on the NEMR is not symmetrical, whereas an
asymmetrical influence of IODM on the NEMR is evident.
2. Apart from strong ocean-atmospheric interactions,some land-atmospheric interactions do co-exist overthis domain.
The contribution of evaporation (E) is higher for seasonal mean precipitation (P ) over the domain; the interannual variability of precipitation (P ) is determined not
only by evaporation (E), but also by the large-scaleconvergence (C).
The interannual variability of precipitation over South
Peninsular India and Sri Lanka is mainly influenced byboth evaporation over land and adjacent oceans and largescale moisture convergence from the south IO region isevident from the water budget analysis. However, thelarge-scale circulation anomalies can modulate evaporation over the domain and subsequently modulate precipitation. Mainly, evaporation over the adjacent oceanic
regions acts as the source of moisture for precipitation,
still evaporation over land is also considerably high. Landsurface processes over the domain during NEMR monsoon season may also play a role in modulating precipitation anomalies. However, one has to keep in mind thatthe grid resolution of the model (reanalysis) is relatively
poor and the calculated C and observed P may haveerrors, but still the relative role of land surface hydrological processes cannot be ruled out and further studies are
required to understand the role of land surface processes
in modulating the precipitation anomalies over this region
during NEMR (OND) season. The major findings of thisstudy are also briefly summarized in Table II.
Therefore, we discuss that the land, the atmosphereand the ocean interactively modulate the NEMR overSouth Peninsular India and Sri Lanka during OND. So
far, the existence of the hot spots of land-atmosphereinteractions of significant coupling strength in relatively
dry monsoon regions during summer monsoon (JJAS)
season are known from an ensemble of GCM studies(e.g. Koster et al., 2004). From our study, we have foundthat the land surface processes may also contribute toprecipitation anomalies over South Peninsular India and
Sri Lanka during (OND) NE monsoon season.
Table II. Summary of major findings from the study.
NEMR
variability

NEMR
positive

IOD
conditions

Nino 3.4
conditions

Positive

Negative

El
Nino

La
Nina

Yes

–

Yes

–

i.e. NEMR positive ∼ IOD positive + El Nino
NEMR
negative

–

No

–

Yes

i.e. NEMR negative ∼ La Nina (only)
Int. J. Climatol. (2008)
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ATMOSPHERIC WATER BALANCE OVER SOUTH PENINSULAR INDIA AND SRI LANKA

Acknowledgement
We would like to thank Drs Fujinami, Hori, Takahashi, Abe, Ichikawa and Mr Kanamori for their helpful comments during this research work. We also
would like to thank two anonymous Referees for theirencouraging comments. VP was supported by Monbukagakusho scholarship from the ministry of Education, Culture, Sports, Science and Technology. We
also acknowledge many centres for providing datasets.
NCEP/NCAR Reanalysis and NOAA OI SST V2 data
provided by 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. The diagrams used for thisstudy have been prepared using the free software packages like GrADS and XMGRACE and computational
work done on the Fedora Core 2 operating system environment.
References
Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P, Janowiak J,
Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J,
Arkin P, Nelkin E. 2003. The Version 2 Global Precipitation
Climatology Project (GPCP) Monthly Precipitation Analysis(1979–Present). Journal of Hydrometeorology 4: 1147–1167.
Ailikun B, Yasunari T. 2001. ENSO and Asian summer monsoon:
persistence and transitivity in the seasonal march. Journal of the
Meteorological Society of Japan 79: 145–159.
Beck C, Grieser J, Rudolf B. 2005. A new monthly precipitation
climatology for the global land areas for the period 1951 to 2000.
Climate Status Report 2004: 181–190.
Chang CP, Harr P, Ju J. 2001. Possible roles of Atlantic circulations on
the weakening Indian monsoon rainfall-ENSO relationship. Journalof Climate 14: 2376–2380.
Dhar ON, Rakhecha PR. 1983. Forecasting northeast monsoon rainfall
over Tamil Nadu, India. Monthly Weather Review 111: 109–112.
Hastenrath S, Nicklis A, Greischar L. 1993. Atmospheric hydrosphericmechanisms of climate anomalies in the western equatorial Indian
Ocean. Journal of Geophysical Research 98: 20,219–20,235.
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L,
Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M,
Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C,
Wang J, Leetmaa A, Reynolds R, Jenne Roy, Joseph Dennis. 1996.
The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of theAmerican Meteorological Society 77(3): 437–471.

Copyright  2008 Royal Meteorological Society

Kawamura R, Uemura K, Suppiah R. 2005. On the recent change of
the Indian summer monsoon-ENSO relationship. SOLA 1: 201–204.
Koster RD, GLACE Team. 2004. Regions of strong coupling between
soil moisture and precipitation. Science 305: 1138–1140.
Kripalani RH, Kumar P. 2004. Northeast monsoon rainfall variability
over south peninsular India vis-a-vis the Indian Ocean dipole mode.
International Journal of Climatology 24: 1267–1282.
Krishna Kumar K, Rajagopalan B, Cane MA. 1999. On the weakening
relationship between the Indian monsoon and ENSO. Science 284:
2156–2159.
Oki T, Musiake K, Matsuyama H, Masuda K. 1995. Global atmospheric water balance and runoff from large river basins. Hydrological Processes 9: 655–678.
Kumar P, Rupa Kumar K, Rajeevan M, Sahai AK. 2007. On the recent
strengthening of the relationship between ENSO and northeast
monsoon rainfall over South Asia. Climate Dynamics 28: 649–660.
Peixoto JP, Oort AH. 1992. Physics of Climate. American Institute ofPhysics, Springer Verlag; 520, ISBN: 0883187124.
Rasmusson EM, Carpenter TH. 1983. The relationship between easternequatorial Pacific sea surface temperatures and rainfall over India and
Srilanka, Monthly Weather Review 111: 517–528.
Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W. 2002. An
improved in situ and satellite SST analysis for climate. Journal of
Climate 15: 1609–1625.
Saji NH, Yamagata T. 2003. Possible impacts of Indian Ocean dipole
mode events on global climate. Climate Research 25: 151–169.
Saji NH, Goswami BN, Vinayachandran PN, Yamagata T. 1999. A
dipole mode in the tropical Indian Ocean. Nature 401: 360–363.
Singh N, Sontakke NA. 1999. On the variability and prediction of
post-monsoon season rainfall over India. International Journal of
Climatology 19: 309–339.
Suppiah R. 1989. Relationship between the southern oscillation andthe rainfall of Sri Lanka. International Journal of Climatology 9:
601–618.
Suppiah R. 1996. Spatial and temporal variations in the relationshipbetween southern oscillation phenomenon and the rainfall of Sri
Lanka. International Journal of Climatology 16: 1391–1407.
Suppiah R. 1997. Extremes of southern oscillation phenomenon andthe rainfall of Sri Lanka. International Journal of Climatology 17:
87–101.
Trenberth KE. 1999. Atmospheric moisture recycling: role of advection
and local evaporation. Journal of Climate 12: 1368–1381.
Trenberth KE, Guillemot CJ. 1998. Evaluation of the atmosphericmoisture and hydrological cycle in the NCEP/NCAR reanalyses.
Climate Dynamics 14: 213–231.
Webster PJ, Moore AM, Loschnigg JP, Leben RR. 1999. Coupled
ocean-atmosphere dynamics in the Indian Ocean during 1997–98.
Nature 401: 356–360.
Yasunari T. 1990. Impact of Indian monsoon on the coupledatmosphere/ocean systems in the tropical pacific. Meteorology andAtmospheric Physics 44: 29–41.
Zubair L, Rao SA, Yamagata T. 2003. Modulation of Sri Lankan Maha
rainfall by the Indian Ocean dipole. Geophysical Research Letters
30(2): 35–1 –35–4.

Int. J. Climatol. (2008)
DOI: 10.1002/joc