Theor Appl Climatol
DOI 10.1007/s00704-010-0331-6

ORIGINAL PAPER

Simulated changes in the atmospheric water balanceover South Asia in the eight IPCC AR4 coupledclimate models
Venkatraman Prasanna & Tetsuzo Yasunari

Received: 1 April 2009 / Accepted: 19 August 2010
# Springer-Verlag 2010

Abstract This paper evaluates the performance of eightstate-of-art IPCC-AR4 coupled atmosphere-ocean generalcirculation models in their representation of regional
characteristics of atmospheric water balance over South
Asia. The results presented here are the regional climatechange scenarios of atmospheric water balance components, precipitation, moisture convergence and evaporation(P, C and E) up to the end of the twenty-second century
based on IPCC AR4 modelling experiments conducted for(A1B) future greenhouse gas emission scenario. The
AOGCMs, despite their relatively coarse resolution, haveshown a reasonable skill in depicting the hydrological cycle
over the South Asian region. However, considerable biases
do exist with reference to the observed atmospheric waterbalance and also inter-model differences. The monsoon
rainfall and atmospheric water balance changes under A1B
scenario are discussed in detail. Spatial patterns of rainfallchange projections indicate maximum increase over northwest India in most of the models, but changes in theatmospheric water balance are generally widespread overSouth Asia. While the scenarios presented in this study are
indicative of the expected range of rainfall and waterbalance changes, it must be noted that the quantitative
estimates still have large uncertainties associated with them.
V. Prasanna : T. Yasunari
Hydrospheric Atmospheric Research Center (HyARC),
Nagoya University,
Nagoya, Japan
Present Address:
V. Prasanna (*)
International Pacific Research Center, SOEST,
University of Hawaii at Manoa,
Honolulu, Hawaii
e-mail: prasa_arnala@yahoo.com

1 Introduction
The Intergovernmental Panel on Climate Change (IPCC)
scientific assessment reports (IPCC 1996, 2001, 2007;
Christensen et al. 2007) present scientific evidence thathuman activities may already be influencing the climate. If
one wishes to understand, detect and eventually predict thehuman influence on climate, one need to understand thesystem that determines the climate of the earth and of
the processes that lead to climate change. The regional
climatic impacts associated with global climatic change andtheir assessments are gaining increasing importance. This isbecause the agriculture, water resources, ecology etc., arevulnerable to climatic changes on regional scale.
Climate variations and change, caused by external forcings,
may be partly predictable, particularly on the larger (e.g.
continental, global) spatial scales. Because human activities,
such as the emission of greenhouse gases or land-use change,
do result in external forcing, it is believed that the large-scaleaspects of human-induced climate change are also partly
predictable. However, the ability to actually do so is limited
because we cannot accurately predict population change,
economic change, technological development, and otherrelevant characteristics of future human activity. In practice,therefore, one has to rely on carefully constructed scenarios ofhuman behaviour and determine climate projections on thebasis of such scenarios.
Several model intercomparison efforts have been made toresolve the major issues in realistically simulating the Asiansummer monsoon precipitation. The most prominent andrelevant include the Atmospheric Model Intercomparison
Project (Gates et al. 1999; Gadgil and Sajani 1998; Kang etal. 2002; Waliser et al. 2003; Wang et al. 2004) and the
Coupled Model Intercomparison Project (Lambert and Boer
2001; Covey et al. 2003). These studies bring out the

V. Prasanna, T. Yasunaristrengths and shortcomings of both atmospheric and coupledmodels as regarding their ability in simulating both thecirculation and the precipitation on global and regional
scales. However, this paper brings out the strength ofcoupled atmosphere-ocean general circulation models(AOGCMs) in simulating the regional hydrological cycle
over South Asia and uncertainties involved in thesesimulations.
The results of several AOGCM simulations are presented
for the South Asian monsoon region, to provide an idea of
their skill in representing the regional hydrological cycle andtheir future projections. Datasets used in the study are from
IPCC-AR4 simulations of AOGCMs forced with reconstructed greenhouse gas and aerosol concentrations torepresent the present day climate (20C3m) and future
concentrations according to the Special Report on Emission
Scenario (SRES) A1B. The likely future changes in rainfall
and associated hydrological cycle over the South Asian region
are presented. In order to provide the essential background, abrief description of the observed hydrological cycle over theSouth Asian region is presented using observed and reanalysis
datasets before discussing the future scenarios of atmosphericwater balance (AWB) over South Asia.
The water balance method provides an opportunity toeffectively study the hydrological cycle at each grid point on
the regional scale through the balance. The simulated waterbalance components are compared with the observed precipitation and the moisture convergence computed from thereanalysis dataset. Here attempt has been made to utilize thebest available precipitation (P) and convergence (C) computed from the reanalysis dataset. Considering the South Asianmonsoon region in the domain of 0–30N and 65–100E ineach reanalysis and AOGCM, the area averages of precipitation, convergence and evaporation fields are worked out togenerate monthly data for the considered periods of the
AOGCM simulations. These monthly data are then used tocompute the seasonal totals of P, C and evaporation (E).
Taking 1980–1999 as the baseline period, the seasonalquantities are then examined for their likely future changes
into the twenty-first and twenty-second centuries.
Following the seminal works on global water budget
studies (Trenberth 1991; Oki et al. 1995; Trenberth andGuillemot 1998; Trenberth 1999); Prasanna and Yasunari(2008, 2009) studied the water balance over South Asianmonsoon region and found that the role of evaporation is
also crucial for understanding the interannual variability of
precipitation over this region. They have pointed out thatthe contribution of evaporation to precipitation is as large asthat of convergence over both dry and wet regimes. So thecontribution of evaporation cannot be simply neglected
when we consider projections based on AOGCM results.
The structure of this paper is as follows. Section 2
describes the datasets and methodology. Section 3 discusses

the observed water balance over South Asia. Section 4
presents a discussion of the simulated hydrological cycle
over South Asia by AOGCMs. In Section 5, we document
the climate change simulations of water balance over South
Asia in eight AOGCMs. Section 6 presents the discussion
and main conclusions.

2 Datasets and methodology
The transient climate change simulations of eight AOGCMs
have been used in the present study. These climate change
simulations are part of the suite of simulations performed forthe IPCC AR4 scientific assessment report, and the data havebeen obtained from the Program for Climate Model Diagnosis
and Intercomparison (PCMDI) at the Lawrence Livermore
National Laboratory (LLNL), USA. The main centres ofdevelopment of the eight models used in the study, along with
their acronyms are listed in Table 1. The technical details of theabove AOGCMs, including resolution and various physicalparameterization schemes are summarized in the references
quoted in Table 1. The eight models have been used tosimulate global climate representing the present climate(20C3M) and the likely future changes with A1B forcing.
2.1 Model experiments
Scenarios are among the main methods used to address thecomplexity and uncertainty of future challenges associatedwith climate change. The IPCC initiated the development ofgreenhouse gas emission scenarios designed to serve as
inputs to GCMs and facilitate the assessments of climatechange impacts (IPCC 1996). The simulation experiments
used in the present study indicated below are(1) 20C3M Run
The 20C3M run is a control integration, in which theatmospheric forcing in terms of the greenhouse gas
concentration is taken from reconstructed time series ofconcentrations and performed for a period of roughly
150 years, typically 1860–2000. The climatology
constructed is from the last 20 years of the 20C3M
run to represent the present day climate (1980–1999) ineach model.
(2) A1B Scenario
A1B scenario falls in the category of ‘Medium-Highemission scenario’. The scenario family describes a
future world of very rapid economic growth, global
population that peaks in mid-century and declines
thereafter, and the rapid introduction of new and more
efficient technologies, with a substantial reduction inregional differences in per capita income and a balanced
usage of fossil and non-fossil fuels (IPCC 2001).

Simulated changes in the atmospheric water balance
Table 1 Description of eight-coupled climate model simulations used in the study
Model name

IPCC, ID

CentreReference

CCMA
CNRM
GFDL
MPI
MRI
HADCM
HADGEM
INMCM

CGCM3.1 (t47)
CNRM-CM3
GFDL-CM2.0
ECHAM5/MPI-OM
MRI-CGCM2.3.2
UKMO-HadCM3
UKMO-HadGEM1
INM-CM3.0

Canadian Centre for Climate Modelling and Analysis, Canada
Meteo-France/Centre National de Recherches, France
Geophysical Fluid Dynamics Laboratory, USA
Max Planck Institute for Meteorology, Germany
Meteorological Research Institute, Japan
Hadley Centre for Climate, UK
Hadley Centre for Climate, UK
Institute for Numerical Mathematics, Russia

Flato et al. (2000)
Salas-Melia et al. (2005)
Delworth et al. (2006)
Jungclaus et al. (2006)
Yukimoto et al. (2001)
Jones et al. (2004)
Johns et al. (2006); Jones et al. (2004)
Diansky and Volodin (2002)

Monthly data of P and E from AOGCMs (Table 1) areutilized for 20C3M run and A1B scenario. The lengths ofthe data for each model run are 240 months for 20C3M
simulation to correspond with the present climate of 1980–
1999 (i.e. last 20 years of 20C3M run of each model) and
2,400 months simulation of A1B (i.e. 200 years representing
2000–2200). The data have been downloaded from the
PCMDI—LLNL (http://www-pcmdi.llnl.gov/). Due to data
limitations, results are presented only for eight models
for 20C3M and A1B scenarios. The moisture C for eachmodel is obtained from P and E, as the model is balanced
throughout the runs.
Six hourly data from the National Center for Environmental
Prediction-National Center for Atmospheric Research (NCEP/
NCAR) reanalysis dataset (Kalnay et al. 1996) are used to
calculate water budgets for the 20-year period starting fromJanuary 1980 up to December 1999. These data comprise of
upper level winds (u, v), specific humidity (q), geopotential
height (z), surface winds (u, v), surface level specifichumidity (q) and sea level pressure (slp). In addition, GPCP
precipitation estimate for both land and ocean is utilized inthis study, as the precipitation estimates over land areas arequite realistic in GPCP (Adler et al. 2003) due to the merging
of IR and microwave sensor estimates with the gauge
precipitation estimates. Similar calculations are also done
for the 40 years European centre for medium range weather
forecasting (ECMWF) reanalysis (ERA-40) dataset (Uppalaet al. 2005) and 25 years Japanese reanalysis (JRA-25)
dataset (Onogi et al. 2000). But for the sake of brevity weused C computed from NCEP/NCAR reanalysis only forcomparison with models in this study.

3 Observed water balance over South Asia
The most important feature in the global large-scalemeteorology is the hydrological cycle over the tropics,
especially the hydrological cycle over the Asian monsoon

region. A simple schematic diagram of water balance over asmall area on a longer time scale is shown in Fig. 1.
3.1 Water balance components
The atmospheric water budget equation can be written as(Peixoto and Oort 1992),< @W =@t > þ < r : Q >¼< EP >ð1Þ

Where P is precipitation, E is evaporation, W is
precipitable water content, Q is vertically integrated
moisture flux vector, ∇·Q its divergence and angled
brackets denote the area average.
On longer timescales like monthly or seasonal, undernear equilibrium conditions, the time change of local
available precipitable water content is negligible comparedwith the variations of large-scale convergence and evaporation (Oki et al. 1995; Trenberth 1999).
< @W =@t > 0

ð2Þ

We can approximate,
P ¼CþE

ð3Þ

Where,
C = – <∇∙Q>
Vertically integrated moisture flux vector is given by,Z

Q ¼ 1=gPt

Ps

qvdp

ð4Þ

C is computed by performing vertical integration fromground (surface pressure level) to 300 hPa for all thestandard atmospheric pressure levels.
The C in the observation case is obtained from thevertical integration, where as the C in the model is obtained

V. Prasanna, T. YasunariFig. 1 A simple Schematic diagram showing water balance over asmall area on a longer time scale, where P is the precipitation, Q is thevertically integrated moisture flux. E is the evaporation from surface;
W is the precipitable water content. C=– <∇∙Q>

from the atmospheric water balance equation [C=P–E] as
the components are balanced in the coupled model runs.
The sign of convergence and divergence are reversed in thisstudy for convenience. The E in the observation case is
obtained as a residual between observed P and the C
computed from the respective reanalyses, since the P fromGPCP is reliable than the P obtained from the reanalyses.
In principle C has to be calculated on the model time step,
so that using 6-hourly values from the reanalyses leads tosome unknown uncertainty in the absolute values of C. But
we believe that the calculated convergence from 6-hourly datais acceptable to conduct a comparison with coarse resolution
AOGCM results, especially as the large-scale patterns of C
are rather similar in the three reanalyses (see Fig. 2).
3.2 Computed large-scale convergence from reanalyses
The moisture convergence computed from the three major
reanalyses are shown in Fig. 2 for comparison. The large-scaleconvergences computed from different reanalysis dataset
differ considerably due to the differences in the data
assimilated over a specific region and also over data sparse
regions. The data over data sparse region is filled with the first
guess of the model, so that such regions like oceans arecompletely model dependent. The regions with dense observations will have high reliability, for example over continental
regions where observations of upper levels are adequate
compared with areas over oceans. Results from this study andfrom the expert references yielded that ERA-40 has problems
over the regions close to tropics. The ERA-40 has estimatedstrong convergence over oceans and strong divergence overland (Hagemann et al. 2005). Some problems and deficienciesof the column integrated water vapour in NCEP/NCAR are
also highlighted by Trenberth et al. 2005. However the grossfeatures of moisture convergence and monthly annual cycle
are captured well over the South Asian region in all threemajor reanalyses. To reassure our confidence, we comparedcalculated moisture C from all three reanalysis datasets.

Fig. 2 Moisture convergence (mm/day) and moisture flux vectors(kg−1 m−1 s−1) computed for JJAS season from three major reanalyses
for the period 1980–1999

Simulated changes in the atmospheric water balancea)Precipitation

400

and NCEP/NCAR is in between JRA-25 and ERA-40
reanalyses.

P (mm/mon)300

4 Simulation of hydrological cycle over South Asia
by AOGCMs

200

4.1 Observed and simulated precipitation and convergenceclimatology

100
0
J

b)

F M A M JJA S O N D

Convergence

200

C (mm/mon)

150
100
50
0
-50
-100
J

F M A M JJA S O N D

Evaporationc) 200E (mm/mon)

150
100
50
0

J

F M A M J J A S O N D
Month
GPCP
ERA-40

NCEP/NCAR
JRA-25

Fig. 3 Observed annual cycle of atmospheric water balance (P, C andE) (mm/month) over South Asia

3.3 Contribution of C and E to P
Major portion of precipitation over the South Asian monsoon
region is realized within the 4 months JJAS (Fig. 3a),divergence prevails until early May and net divergence
becomes net convergence over the large domain of South
Asia from the month of May (Fig. 3b). The monsoon
rains are contributed equally by C and E during monsoon
months JJAS in the NCEP/NCAR reanalysis, where as
E>C in ERA-40 reanalysis and C>E in JRA-25 reanalysis(Fig. 3b, c). These results show that convergence is verystrong in JRA-25 and divergence is very strong in ERA-40

The large-scale South Asian monsoon precipitation andconvergence patterns in the summer (JJAS), as simulatedby the eight-coupled models, are shown in Figs. 4 and 5,
respectively. For this purpose, the long-term means of
seasonal precipitation and convergence have been computed based on all available observation datasets and 1980–
1999 (20C3M) control simulations. The simulated rainfall
patterns are compared with the observed precipitation of
GPCP (Global Precipitation Climatology Project) (Adler et
al. 2003) for the period 1980–1999. While most models
simulate the general maximum precipitation over west
coasts of India and Bay of Bengal, some of them miss therainfall maximum in the equatorial Indian Ocean. Apartfrom this, in the Indian monsoon context, the observed
maximum rainfall during the monsoon season along thewest coast of India and the North Bay of Bengal andadjoining northeast India is not quite realistically simulated
in many models except GFDL and to some extent in
CCMA, MPI, HADCM and MRI (Fig. 4). This may
possibly be linked to the coarse resolution of the models
as the heavy rainfall over these regions is generally inassociation with the steep orography. Similarly the moisture
convergence pattern is not realistically simulated in manymodels except GFDL and to some extent in CCMA, MPI,HADCM and MRI (Fig. 5).
Most of the models miss the equatorial Indian Ocean
precipitation maximum, except (GFDL) and to some extent
in MRI, CNRM and INMCM (Fig. 4), which is also evident
from the moisture convergence pattern (Fig. 5). Only
GFDL model could reproduce the observed precipitation
pattern and moisture convergence quite well over South
Asia. Moreover, large inter-model differences are notedamong the models.
4.2 Annual cycles of observed and simulated precipitationand convergence
Observed (GPCP) and simulated annual cycles of precipitation are shown in Fig. 6 where the precipitation isaveraged over the South Asian region (land and sea)
comprising 0N–30N and 65E–100E. The simulated precipitation shows remarkably similar patterns to the observed.
Over the course, the precipitation changes from less thanV. Prasanna, T. YasunariFig. 4 Simulations of JJAS mean rainfall (mm/day) in the eight IPCC-AR4 models

50 mm/month during the January–April period to morethan 200 mm/month during the peak summer monsoon
period of June–August. Thus the annual cycle is characterized by a sharp increase from April to June and thereafter agradual decrease from September. An examination of themodel simulations (Fig. 6) suggests that
1. Three models (CCMA, CNRM and MPI) simulate asimilar annual cycle in terms of shape and magnitude toobserved characteristics. However, CNRM simulates
excess precipitation during November and December.
2. Two models (HADGEM and MRI) simulate the shape of
the annual cycle well but underestimate precipitation
amounts, in particular during the spring and the summer
periods.
3. Three models (GFDL, HADCM and INMCM) simulate
peak rainfall a month later than observed, resulting in theunderestimation of rainfall during spring and summer.
The estimated and simulated annual cycles of convergence for the eight models are shown in Fig. 7. The

observed and simulated patterns of convergence resemble
the observed and simulated patterns of precipitation.
1. Three models (CCMA, CNRM and MPI) simulate asimilar annual cycle of convergence in terms of shapeand magnitude to observed characteristics; also CNRM
simulates excess convergence during November–
December.
2. Two models (HADGEM and MRI) simulate theshape of the annual cycle well but underestimate
convergence.
3. Three models (GFDL, HADCM and INMCM) simulate
peak convergence a month later than observed.
The peak values of convergence (Fig. 7) in the model
simulation and observation are roughly around 150 mm/
month, where as the peak values of precipitation during
summer season are roughly around 250 mm/month. Thebalance of roughly about 100 mm/month may come fromthe evaporation through in situ surface hydrologicalprocesses.

Simulated changes in the atmospheric water balance

Fig. 5 Simulations of JJAS mean moisture convergence (mm/day) in the eight IPCC-AR4 models

5 Climate change simulations of water balanceover South Asia in eight AOGCMs
The current generation of climate models reproduces the
major features of the observed distribution of the P and C tosome extent, but there is nevertheless, a considerable scatter
among model results and between simulated and observed
values. The time series of simulated South Asian summer
monsoon rainfall are shown in Fig. 8 for 20C3m and theA1B scenario. The thick lines represent values smoothedwith a 9-year moving average for each model respectively.
As might be expected, in the 20C3M experiments withobserved greenhouse forcing, the simulations of all models
show very little resemblance to the observations of GPCP in
the twentieth century. On the other hand, the transient
simulations with A1B scenarios show marked increase inmonsoon precipitation in the twenty-first and twenty-second
centuries, particularly becoming obvious after the 2050s. As
can be seen from Fig. 8, though all models show increase inprecipitation over South Asia—there is a considerable spreadamong the models in the magnitude of change.

Comparison of AWB between observed and model
simulations in the 20C3M and the subsequent changes in
the future (A1B-20C3M) for JJAS over South Asianmonsoon domain are shown in Fig. 9. All models show ageneral consensus of increase in the precipitation. The
increase in mean precipitation is mainly due to the increasein the projected moisture convergence (Fig. 9) attributed tothe horizontal transport of moisture from the oceans
towards land through advection, which is consistent withearlier studies by Ueda et al. 2006 and Kripalani et al. 2007.
The role of evaporation through insitu surface hydrologicalprocesses cannot be ruled out completely. The projected
increase in the mean precipitation is evident in all the
AOGCM results, while increases are large for moisture
convergence but not for evaporation (Fig. 9).
5.1 Spatial patterns of changes in the atmospheric waterbalance over South Asia
The future rainfall changes on a broad regional scale
described above are examined further to see their spatial

V. Prasanna, T. Yasunari
300
Precipitation (mm/mon)

CCMA

GFDL

MPI

200
150
100
GPCP
20-CENTSRESA1B50
0300

Precipitation (mm/mon)

CNRM

250

J F MAM J J A S O N D J F MAM J J A S O N D
MRI

HADCM

J F MAM J J A S O N D
HADGEM

J F MAM J J A S O N D
INMCM

250
200
150
100
50
0

J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J J A S O N D
Month

J F MAM J J A S O N D

Fig. 6 Annual cycles of observed and simulated rainfall (mm/month) over South Asia in the 20C3M and A1B runs

manifestation over the South Asian region. The A1B forcedchanges in monsoon rainfall are computed for 200-years
period starting 2000 to 2200 for all models and for 20-year
period starting 1980 to 2000 in the 20C3M. The changes
are expressed as percentage departure for the water budget

Convergence (mm/mon)

200

CNRM

GFDL

MPI

150
100
50
0
NCEP/NCAR
20-CENTSRESA1B

-50
-100
200

Convergence (mm/mon)

CCMA

components P, C and E (Figs. 10, 11 and 12) with reference
to the present period (1980–1999) of 20C3M AOGCM
simulations, considered as the baseline climate. As can beseen from Fig. 10, most of the models project an enhanced
precipitation during the monsoon season, particularly overJ F MAM J J A S O N D J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J J A S O N D
MRI

HADCM

HADGEM

INMCM

150
100
50
0
-50
-100

J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J J A S O N D
MonthFig. 7 Annual cycles of observed and simulated moisture convergence (mm/month) over South Asia in the 20C3M and A1B runs

Simulated changes in the atmospheric water balance
Fig. 8 Variation of South
Asian monsoon rainfall(mm/day) simulated in theeight IPCC-AR4 models
from 1980 to 2200

the northwestern parts of India. However, the magnitudes ofprojected change differ considerably from one model to theother. There is very little or no change noted in the
monsoon rainfall over a major part of India. It is important
to note here that the maximum change in rainfall isoccurring over the climatologically low rainfall region ofnorthwest India. The implications of such change over thisregion have to be carefully assessed in future studies.

A1B-20C3M
50

P
C
E

40

30

20

P/C/E (mm/month)Fig. 9 Seasonal mean (JJAS) of
AWB components (P, C and E)(mm/month) for different
models in 20C3M and A1B
Scenario runs. Bottom Panelshows observation and 20C3M
run (1980–1999). Top panel
shows difference between A1B
runs and 20C3M runs. For
observational AWB P is from
GPCP, C is from NCEP/NCAR
and E is a residual from P and C

GFDL and MPI model simulations show general decreasein precipitation over North West India, which is evident fromthe precipitation change (Fig. 10) due the decrease in both C
and E (Figs. 11 and 12). Whereas, the HADCM and
HADGEM model simulations show decrease in precipitation
over South peninsular India (Fig. 10) due to decrease in C
and E (Figs. 11 and 12). Generally increases in the
precipitation over the South Asian region are accompanied

10

0

300

-10
300

250

250

200

200

150

150

100

100

50

50

0

0

OBS

CCMA CNRM

GFDL

MPI
20C3M

MRI

HADCM HADGEM INMCM

V. Prasanna, T. YasunariFig. 10 Spatial patterns ofprojected monsoon rainfallchanges (A1B–20C3M) in
percentage departure

by increases in convergence as well as evaporation over theland area.
The changes in the moisture flux convergence are
examined in detail using 850 hPa level winds and

850 hPa level tropospheric humidity. The 850 hPa windchanges for A1B scenario are shown in percentage
departures from 20C3M (Fig. 13). Most of the models
show decreases in the cross-equatorial flow and subsequentSimulated changes in the atmospheric water balance
Fig. 11 Spatial patterns ofprojected convergence changes(A1B–20C3M) in percentage
departure

V. Prasanna, T. YasunariFig. 12 Spatial patterns ofprojected evaporation changes(A1B–20C3M) in percentage
departure

decrease in the winds over South Asia, which leads todecrease in the wind convergence over the South Asianmonsoon region. Also, the specific humidity changes for
A1B scenario are shown in percentage departures from

20C3M (Fig. 14). The specific humidity increase over South
Asia is consistent in all models. We have also shown the
850-hPa moisture flux change for A1B scenario in total
seven models (for HADCM model the specific humidity

Simulated changes in the atmospheric water balance
Fig. 13 Spatial patterns ofprojected 850 hPa wind changes(A1B–20C3M) in (m/s). Shaded
values are differences in zonal
wind and contour values aredifferences in meridional windfields were not available) in percentage departures from
20C3M (Fig. 15). The increase in moisture convergence
flux can be attributed to the increase in the lower
tropospheric specific humidity, which is evident in allmodels. Therefore P increase in the models is due to theincrease of C due to increase in the lower troposphericprecipitable water component (PW) and not the windconvergence.

V. Prasanna, T. YasunariFig. 14 Spatial patterns ofprojected 850 hPa specifichumidity changes(A1B–20C3M) in percentage
departure (kg/kg)

Simulated changes in the atmospheric water balance
Fig. 15 Spatial patterns ofprojected 850 hPa moisture flux
vector changes (A1B–20C3M)
in (kg−1 m−1 s−1). Shaded values
are differences in zonal moisture
flux and contour values aredifferences in meridional
moisture flux

V. Prasanna, T. YasunariFig. 16 Spatial patterns ofprojected surface skintemperature changes(A1B–20C3M) in °C

The projected surface temperature increase (Fig. 16) islarge over the monsoon region, which increases the water
holding capacity of the atmosphere to greater extent;
whereby the hydrological cycle over the South Asianmonsoon is enhanced in the A1B scenario in the model
projections for the twenty-second century climate simulations. The homogeneous increase of surface temperature
over the tropics in the models may have decreased thecontinental North South temperature gradient over theSouth Asian region, which could be a possible explanation
for the decrease in the wind convergence in the A1B
scenario.
We also investigated soil moisture fields (Fig. 17)
available from five models (CCMA, MRI, HADCM,

HADGEM and INMCM), which show a projected increasein soil moisture over the core Indian monsoon region,
leading to increased precipitation recycling. The importanceof the surface processes are also stressed in previous studies(Douville et al. 2002). We notice large increase in the
projected soil moisture over western parts of India in MRI
and HADGEM models (Fig. 17) compared with other threemodels. The soil moisture increase can be attributed to theincrease in the precipitation due to increase in the lower
tropospheric humidity increase and an enhanced precipitation recycling in these models.
We noticed that CCMA and MRI show a similar spatial
feature in precipitation change while GFDL and MPI is
another group in spatial similarity in precipitation in terms

Simulated changes in the atmospheric water balance
Fig. 17 Spatial patterns ofprojected surface soil moisturechanges (A1B–20C3M) in
percentage departureof future projections in A1B scenario. These two majordifferent patterns appear due to the circulation features in
the models. The divergent circulation over the northwestern
India in the models GFDL and MPI are strong comparedwith other models. The divergent circulation becomes evenstronger in the A1B scenario leading to strong moisturedivergence and reduction in precipitation.
5.2 Scenarios of variability of atmospheric water balancecomponents
Apart from the changes in mean state, the likely changes inthe variability of monsoon rainfall and water balancecomponents in future will have profound impact onvarious facets of human activities as the year to year
variability or otherwise known as the interannual variability of monsoon leads occasionally to large-scale
drought and flood situations. Any increase in thevariability will mean a more frequent occurrence oflarge-scale seasonal anomalies and associated drought
and flood like situations. In view of this, we examined
the likely changes in the variance of P, C, and E in the
A1B scenarios from 2000 to 2200. The standard deviation
is taken here to represent the variance of the P, C, and E
and is computed on a 31-year sliding window during theentire period of simulations. The results are presented inV. Prasanna, T. Yasunari
20

CCMA

30
25
20
15
10
5
0
40

15
10
5

Std. Dev. (mm/mon)Fig. 18 Future changes in thevariance (time series from 2000
to 2200) of AWB components(P, C and E) (mm/month)(standard deviation is taken as ameasure of variability) ofdifferent models for A1B
scenario run. The dark-filled
circles denote 20-year(1980–1999) standard deviationof precipitation for respectivemodels; similarly, open squares
indicate standard deviation of
convergence and open diamondindicate standard deviation of
evaporation in 20C3M run

0
30
25
20
15
10
5
0
50
40
30
20
10
0
30
25
20
15
10
5
0

GFDL

CNRM

MPI

30
20
10
0
40

MRI

HADCM

30
20
10
HADGEM

2010 2040 2070 2100 2130 2160 2190

0
30
25
20
15
10
5
0

INMCM

2010 2040 2070 2100 2130 2160 2190

Central Year of 31yr Sliding WindowP

the form of time series of variance for the threecomponents of water balance (P, C and E) for A1B
simulation in Fig. 18. The standard deviation values forthe 20C3M runs between 1980 and 2000 are also shownin Fig. 18 for comparison of changes in the variance ofeach water balance component (P, C and E). Most of themodels show a general tendency of increased variance inthe later part of twenty-first century. However, only a few
models show such systematic increase in variance into the
future. Therefore, it can be stated that there is as yet noconclusive evidence or a general consensus among models
in the future changes of enhanced variability in monsoon
rainfall.

6 Discussion and conclusions
This paper provides a brief description of the observed
atmospheric water balance over the South Asian summer
monsoon region and then proceeds to evaluate theperformance of eight state-of-art coupled atmosphereocean general circulation models (AOGCMs) in theirrepresentation of regional characteristics of rainfall andatmospheric water balance over the South Asian summer
monsoon region. Based on modelling experiments conducted for A1B scenario, regional climate change projections of rainfall and water balance components up to theCE

end of the twenty-second century have been presented. The
following are the major conclusions of this study:
1. The AOGCMs, despite their relatively coarse resolution, have shown a reasonable skill in depicting theobserved hydrological cycle over the South Asian
region. However, considerable biases do exist withreference to the observed hydrological cycle and alsointer-model differences, which should be taken into
account while interpreting the model projections.
2. Model simulations under scenarios of increased greenhouse gas concentrations indicate marked increase inboth rainfall as well as atmospheric water balancecomponents, particularly becoming obvious after the
2050s. There is considerable inter-model dispersion in
the case of monsoon rainfall projections and atmosphericwater balance.
3. Spatial patterns of rainfall change projections indicate
maximum increase over northwest India, but changes in
the atmospheric water balance components are generally
widespread over the domain.
4. There is no clear evidence of substantial change in thevariability of monsoon rainfall and atmospheric waterbalance over the next century.
5. While the scenarios presented in this study are
indicative of the expected range of rainfall andatmospheric water balance changes, it must be notedSimulated changes in the atmospheric water balancethat the quantitative estimates still have large uncertainties associated with them.
6. Apart from the role of moisture convergence to theprecipitation increase, evaporation too plays a role in
the increase of precipitation through in situ surfacehydrological processes.
The use of the 6-h interval instead of the model time stepfrom the reanalyses raises questions on the quality of the
moisture budget. The analysis is created at every 6 h in
the reanalyses, the model used to create the first guess(6-h forecast) has a time step of 30 min, thus the fluxes
accumulated during the 6-h intervals is best, but the wholecalculations have much errors (analyses has errors, model has
errors). However, the same calculations are done on a numberof different reanalyses (NCEP/NACR, ERA-40, and JRA-25)
to make sure that the best estimate is obtained. The data
quality of the reanalyses, therefore, is guaranteed at this datainput time resolution, i.e. 6-hourly. The 6-hourly includes
basically the diurnal cycle of circulation and convection,
which guarantees fundamental quality of energy and water
budget of the atmosphere.
Acknowledgment Authors would like to acknowledge many modelling centres for providing model simulation for about 200 years and also
would like to acknowledge the PCMDI for archiving and providing thelarge datasets through their website (http://www-pcmdi.llnl.gov/).
Authors would also like to acknowledge comments from anonymous
reviewer for improving the paper. The diagrams used for this study havebeen prepared using the free software packages like GrADS,
XMGRACE, Intel Fortran and computational work done on the Fedora
Core operating system environment.

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