ENSEMBLE SEASONAL PREDICTION OF INDIAN SUMMER MONSOON WITH
ATMOSPHERIC GENERAL CIRCULATION MODEL
(M.K. Soman)
Climate and Global Modelling Division
Indian Institute of Tropical Meteorology, Pune 411 008, INDIA.
ABSTRACT
The interannual variability of the Indian summer monsoon simulated by ensemble integrations of
Hadley Centre Climate Model is examined to assess the possibility of using the model for
dynamical seasonal forecasting of monsoon rainfall. Seasonal integrations with observed initial
conditions and sea surface temperatures for 15 years are used in the study. Each year, 9 member
ensemble runs starting from 23 May to 31 May initial conditions were made.
The model simulates the mean monsoon circulation and precipitation quite well. However, the
interannual variability is not adequately handled. Taking average of ensembles substantially
reduces the interannual variability of the Indian Monsoon. The Co-efficient of variation (CV) of
monsoon rainfall from a 17-year long integration of the same model with observed SSTs was 7.3%
whereas average rainfall from the seasonal integration with 9 initial conditions gave a CV of 3.4%.
The observed CV of
Indian summer monsoon rainfall is 10%. The standard deviation (SD)
among the members of ensemble of an year varies between 45 to 65 mm (CV= 6-8%). From the
above it is clear that giving a quantitative forecast of areal average monsoon rainfall is difficult.
However, it is seen that if majority of the ensembles indicate either positive or negative departure
from normal, the qualitative forecast can be of some use.
An interesting fact that came out of these analysis is that the members of each ensemble show
much larger variability in El Nino years compared to La Nina years. The larger spread of monsoon
rainfall over India is due to long delays in the monsoon onset over India in some members of the
El Nino ensembles.
Traditionally in India statistical methods are used for operational seasonal forecasting of summer
monsoon rainfall. During the last three years experimental attempts have been made to use
dynamical models for seasonal forecasting. Ensemble integrations with persisted SST anomalies
are carried out. The results of these experiments will be presented.
-21.
Introduction
The Asian summer monsoon is an active component of the northern hemisphere summer
circulation. On inter-annual time scales it is influenced by large scale features such as El
Niño/Southern Oscillation (ENSO), Eurasian snow cover in winter and spring, northern hemisphere
temperature in winter etc. In turn, the variability of the monsoon can substantially modify the
diabatic heating of the tropical atmosphere effecting weather systems elsewhere in the world.
Over India and other countries of south Asia most part of the annual rainfall is received during the
4 months (June to September) of the summer monsoon season. The majority of the people in
these areas depend on agriculture for their livelihood, which depends largely on the monsoon
rainfall. Since the rainfall is highly seasonal, both the time of onset of the monsoon and the total
seasonal rainfall influence agriculture production, hydroelectric power generation, industrial activity
and ultimately the whole economy of these countries.
The monsoon rainfall over India varies from less than 50 cm over north-western parts to more than
200 cm over the west coast and over north-east India. Though rainfall over India has not shown
any decreasing or increasing trend for the last 125 years, it shows considerable interannual
variability. The variability is larger over areas of low rainfall leading to floods and droughts,
adversely affecting the farming activities of the region.
In view of the critical influence of inter-annual variability of monsoon rainfall on the economy, its
seasonal forecasting is of great importance. In India this challenging problem has attracted
meteorologists from the late nineteenth century. Most of the earlier work on the seasonal
forecasting of the Indian monsoon rainfall is based on statistical and empirical techniques. Over
the years several "predictors" have been identified with diagnostic studies of historical data and
these are currently used to issue seasonal forecasts by the India Meteorological Department ( See
Krishna Kumar et al., 1995, for a review).
The mechanisms responsible for interannual variability fall in two categories; (i) the internal
dynamics and (ii) slowly varying boundary conditions such as sea surface temperatures (SST), soil
moisture, snow cover etc. The seasonal mean tropical circulation is thought to be influenced to a
greater extend by the boundary conditions rather than internal dynamics (Charney and Shukla,
1981, Shukla, 1981). There are several studies, both observational and modelling indicating that
the interannual variability of Indian summer monsoon (JJAS) rainfall is linked to the SST variation
in Pacific (Rasmusson and Carpender, 1983; Mooley and Parthasarathy, 1983; Ju and Slingo,
1995; Soman and Slingo, 1997).
The simulation of interannual variability of monsoon is sensitive to small changes in the initial
conditions also. This shows that the mean monsoon circulation may not be entirely forced by
slowly varying boundary conditions but is also governed by internal dynamics. A recent study by
Goswami (1998) indicated that more than half of interannual variability of Indian monsoon rainfall
may be coming from internal dynamics. However, a study by IITM group using Hadley Centre
Climate Model indicated that about 60% of the variability is due to SST forcing.
As part of a European PROVOST project (Prediction Of climate Variations On Seasonal and
interannual Timescales), the UKMO Unified Model at Climate resolution has been integrated in 9member ensembles initialised at 24 hour intervals for four months for each season for the years
1979 -1993. The SST anomalies from UKMO GISST and Reynold's OI data sets were used in all
experiments. All initialisations were at 0000Z finishing on the day prior to the start of the season.
This set of model output present a rare opportunity to examine the interannual variability of the
monsoon in relation to both SSTs and initial conditions using a large number of integrations. The
aim of the study is to assess the possibility of using the UKMO model for dynamical prediction of
Indian summer monsoon rainfall.
2.
Model and simulation details
The Unified Model (UM) uses a grid-point scheme of a regular latitude-longitude grid in the
-3horizontal and a hybrid vertical grid, which is terrain-following near the surface but evolving to
constant pressure surfaces higher p. An efficient split-explicit scheme is used to solve the
equations, which is designed conserve mass, mass weighted potential temperature and moisture
and angular momentum. The model can be integrated with a variety of horizontal and vertical
resolutions. The resolution used in the present study is the standard climate resolution of the
model, i.e.; 96x73 points in horizontal and 19 levels in vertical. The UM is continuously evolving
and the version used here is called HadAM2b (See Hall et al., 1995 for details).
The physical processes represented include: (1) Atmospheric radiation allowing for the effects of
clouds, water vapour, ozone, carbon dioxide and a number of trace gases, (2) Land surface
processes including a multi-layer soil temperature and hydrology scheme, (3) A treatment of the
form drag due to the sub-grid scale variations in orography, (4) Vertical turbulent transport within
the boundary layer based on mixing theory, (5) Large-scale precipitation determined from the
water or ice content of a cloud, (6) The effects of convection through a scheme based on the initial
buoyancy flux of a parcel of air which includes entrainment, detrainment and the evaporation of
falling precipitation. An explicit treatment of convective downdraughts is also included, (7) The
effects of the drag caused by vertically propagating gravity waves is modelled using sub-grid scale
orographic variance and known absorption properties of gravity waves.
The model has been integrated for 130 days starting from initial conditions of 23 May to 31 May (9
members) for the years 1979 to 1993. The initial conditions were derived from ECMWF
reanalyses. Monthly observed SST and Sea-ice values are used as boundary conditions.
3.
Assessment of model performance
In the mean, the PROVOST integrations simulate reasonably the distribution of monsoon wind and
rainfall. However, simulated rainfall is higher and lower tropospheric winds are stronger than the
observed values based on CMAP precipitation (Xie and Arkin, 1996) and ECMWF reanalysis
(ERA) data over the monsoon region. The interannual variability of monsoon rainfall over India
simulated by the model is low compared with observations. Still, during El Niño years the model
tends to simulate lower rainfall over India. In many years the model simulated the correct sign of
the anomalies, but in some years, notably in 1979,1983 and 1993 the model produced large
anomalies the opposite sign.
During the 15 years considered in the study, 3 years (1982, 1987 and 1991) were El Niño years
and 2 years (1984 and 1988) were La Niña years. Composite precipitation and wind charts for
these years show that the model's monsoon response to Pacific SST anomalies is quite different
from that seen in the observation. The major errors in the model are over Peninsular India, the
Bay of Bengal and the west Pacific between lat. 10-20 N. Over all these regions, the model shows
an increase in precipitation instead of decrease seen in CMAP data. In La Niña years the model
shows decrease in rainfall over west coast of India, Bay of Bengal and south-east Asia and west
Pacific north of 10 N. However, the model does show correct rainfall anomalies over north-west
India, the region known to have highest correlation with predictors used in the statistical long
range forecast of monsoon rainfall over India.
Examination of the monthly mean precipitation and lower tropospheric winds shows that the model
qualitatively captures the correct sign of anomalies at the beginning of the season. During June
and July the model simulates the correct rainfall anomaly over Indian land areas. However, by
August the model shows little skill in simulating the sign of the anomaly. The correlation
coefficient (CC) between the monthly observed and simulated rainfall are: June, 0.645; July,
0.617; August, -0.125 and September, -0.215 leading to a CC of 0.05 for the season as a whole.
Taking average of ensembles substantially reduces the interannual variability of the Indian
Monsoon. The Co-efficient of variation (CV) of monsoon rainfall from a 17-year long integration of
the same model with observed SSTs was 7.3% whereas average rainfall from the seasonal
integration with 9 initial conditions gave a CV of 3.4%. The observed CV of Indian summer
monsoon rainfall is 10%. The standard deviation (SD) among the members of ensemble of an
-4year varies between 45 to 65 mm (CV= 6-8%). From the above it is clear that giving a quantitative
forecast of areal average monsoon rainfall is difficult. However, it is seen that if majority of the
ensembles indicate either positive or negative departure from normal, the qualitative forecast can
be of some use.
4.
Ensemble variability
Model simulation shows large variability of monsoon rainfall between the members of the
ensemble for any year. This variability may result from many factors, the main factors for
consideration would be atmospheric features present in the initial conditions which are known to
have some impact on the monsoon such as the phase of tropical Madden-Julian Oscillations
(MJO), typhoon activity in the west Pacific, southward intrusion of westerly troughs over Indian
area, cross-equatorial flow and its link with southern hemisphere mid-latitude circulations and also
errors in initial conditions.
An unexpected point which comes out of the analysis is that the range of monsoon rainfall in the
ensembles is dependent on the phase of the ENSO. During the strong El Niño years (1982,87)
the spread is much larger compared to that during La Niña years (1984,1988). The ensemble
members with lowest rainfall contribute to this larger range in El Niño years. In Table 1 the years
are ranked according to the ensemble standard deviations (S.D.) of the monsoon rainfall over
India. The largest S.D. is in the El Nino year 1987 and the smallest in the La Niña year 1988.
Examination of the rainfall of individual ensemble members reveals that the high S.D. during El
Niño years are due to some members of the ensembles simulating extremely weak monsoons
over India for these years. The ensemble mean rainfall over India is below normal during El Niño
and above normal during La Niña, as in the observations. However, the variability among the
ensembles is much larger during El Ni o years (CV=9.6% and 8.6% for 1987 and 1988) compared
to La Ni a years (3.1% and 4.2% for 1988 and 1984).
To study this difference in behaviour of the model during El Niño and La Niña conditions, the daily
rainfall time series over the Indian peninsula (8-20N, 70-90E) for all members of ensembles from
each year were examined. The most noticeable feature of the rainfall time series from the
ensembles in El Niño years, apart from the general weakness of the monsoon, is the wide spread
of the time of onset of the monsoon over India. In some individual members of the ensembles, the
monsoon rainfall do not occur over India until the end of June while in other members the rain
starts much earlier. During La Niña years the spread in onset dates is much smaller and the
difference between the ensembles are not as large as in El Niño years, resulting in a much smaller
coefficient of variation (CV) of the seasonal rainfall. This shows that the larger spread in the
seasonal rainfall over India during El Niño years is due to large delays in the monsoon onset over
India simulated by some members of the ensembles.
-5Table 1: Relationships between ensemble variability and ensemble mean rainfall and observed
rainfall. Model rainfall is computed over land points between Lat 5-30 N, Long. 65-95 E.
Rank based
on S.D.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Year
1987
1982
1980
1992
1990
1989
1991
1993
1983
1985
1986
1979
1984
1981
1988
Ensemble
S.D. (cm)
7.34
6.49
6.28
6.11
5.45
5.42
5.37
5.09
4.49
4.27
4.27
3.95
3.45
3.28
2.47
Ensemble mean
rainfall(cm)
76.62
75.15
80.99
76.22
77.45
78.22
79.89
74.82
75.48
74.9
77.09
82.84
82.41
79.8
80.67
CV
AISMR
9.58
8.64
7.75
8.01
7.04
6.93
6.72
6.8
5.95
5.7
5.54
4.77
4.18
4.1
3.07
69.74
73.55
88.29
78.5
90.88
86.69
78.47
89.67
95.59
76
74.32
70.8
83.68
85.23
96.16
The reason that only some members of the ensembles fail to produce monsoon onset over India
until the end of June in El Ni o years is not clear. It is well known that El Niño conditions can delay
monsoon onset over India (Joseph et al.,1994). However, in the case of the model simulation, the
delay is much longer than the observed delay, and all the members of the ensembles do not
reproduce this delay. If the delay was purely due to the presence of El Niño, then all the members
should have shown a qualitatively similar response. Conversely, if it is purely due to factors in the
initial conditions then non-El Niño years also should have shown similar behaviour. Since La Niña
conditions favour a strong monsoon, features in the atmosphere present in the initial conditions
which may inhibit the development or northward progression of the monsoon in the model may be
lost quickly and all the ensemble members show similar behaviour. However, it is reasonable to
assume that when El Niño conditions prevail, the inhibiting features in the initial conditions can
persist much longer before the monsoon develop. More studies are required to understand the
interactions in the model between atmospheric features and SST boundary forcings especially
during El Niño years which causes the large delay in the onset of the monsoon over India.
5.
Real Time Experimental Forecast
Dynamical Seasonal forecast of summer monsoon (June-September) 1998, 1999 and 2000 with
HadAM2b model was attempted in research mode. In the absence of an ocean model which can
predict the SSTs, the SST anomalies of the last available month is persisted over the monthly
climatological SSTs of the monsoon months. The India Meteorological Department releases the
seasonal forecast for June to September monsoon rainfall in the last week of May. Two sets of
forecast experiments were carried out; one with April OISST anomalies persisting on monthly
climatological SSTs of May to September (AF) and the other set with May OISST anomalies
persisting on climatological monthly SST of June to September(MF). In both sets, 6 member
ensemble integrations were carried out with initial conditions taken from the model dumps
corresponding to 1 March of the last six years from a 17-years long integration of the same model
forced with climatological SST. The departures of area averaged rainfall over land areas of India
from the model climatology are computed for each member of the ensemble. The model
climatology used is based on a 17-year integration with observed SSTs. The percentage
departures are given in table 2. The observed departures for the years 1998 and 1999 are based
on All India rainfall series prepared by Parthasarathy and group (Parthasarathy et al , 1994). The
departure for year 2000 is from India Meteorological Department estimate from operational data.
-6Table 2: Percentage departure of simulated seasonal rainfall from model climatology (Rainfall
averaged over land points of the region between 5-30o N and 65-95o E ).
Year Þ
Ensemble
Member ß
1
2
3
4
5
6
Mean
S.D
Observed
1998
1999
2000
April SST May SST April SST May SST April SST May SST
anomaly
anomaly
anomaly
anomaly
anomaly
anomaly
-12.7
-8.8
5.4
0.0
8.1
0.6
-4.4
8.2
5.8
1.6
10.3
0.4
-14.8
1.3
3.4
9.1
9.2
0.4
-16.4
-9.5
1.0
7.1
6.2
-1.6
2.6
-14.7
-0.9
12.3
7.1
-1.2
-14.7
-10.0
-1.1
6.2
1.7
3.0
-10.1
-5.6
2.3
6.0
7.1
0.3
6.8
7.8
2.8
4.2
2.8
1.5
0.0
2.0
-7.0
As can be seen from the table, the model has limited skill in predicting the seasonal rainfall over
India. The spread among the members of the ensembles was very high in 1998. Examination of
the spatial distribution of the simulated rainfall has shown that there are systematic errors in the
model simulation of the interannual variability of the monsoon. Attempts are being made to correct
these systematic errors statistically.
6.
Conclusions
The possibility of using UKMO Unified Model for the seasonal prediction of Indian summer
monsoon is assessed. Seasonal integrations with observed initial conditions and sea surface
temperatures for 15 years are used in the study. The main conclusions from the study are:

The UM simulates the mean monsoon circulation and rainfall reasonably well. However,
the interannual variability of the monsoon rainfall is not well handled. The interannual
variability of the simulated ensemble mean rainfall over India is lower compared to
observations.

Although when averaged over the whole of India and the whole of season, the model
reproduces the correct sign of rainfall anomalies, the spatial distribution of the anomalies
over the monsoon region is erroneous during El Niño and La Niña years. The simulated
rainfall differences between El Niño and La Niña years over large areas of the monsoon
region are opposite to that seen from CMAP data. Analysis of monthly rainfall shows that
the spatial distribution of anomalies is correct at the beginning of the monsoon but
changes sign midway through the season.

The members of each ensemble show much larger variability in El Niño years, compared to
La Niña years. The larger spread in the monsoon rainfall over India is due to long delays in
the monsoon onset over India in some members of the El Niño ensembles.

Consistent with the developing El Niño conditions over Pacific Ocean, the real time
forecast for the 1997 monsoon using May initial conditions and SST anomalies indicated
below average rainfall over India.

Real time experimental seasonal prediction with persisted SST anomaly and 6 member
ensembles show only limited skill.

Systematic errors in the simulation of interannual variability of the monsoon need to be
corrected for using the model for seasonal prediction with useful skill. A coupled model
with good SST simulation will also help to improve the forecast.
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