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.
1. Introduction
The Asian summer monsoon is an active component of the northern hemisphere
summer circulation. On interannual 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 interannual 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 9-member 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 horizontal 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 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.
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).
Table 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.
Year
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Ensemble
S.D. (cm)
1987
1982
1980
1992
1990
1989
1991
1993
1983
1985
1986
1979
1984
1981
1988
Ensemble
mean
rainfall(cm)
7.34
76.62
6.49
75.15
6.28
80.99
6.11
76.22
5.45
77.45
5.42
78.22
5.37
79.89
5.09
74.82
4.49
75.48
4.27
74.9
4.27
77.09
3.95
82.84
3.45
82.41
3.28
79.8
2.47
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
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.
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.
Table 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
1998
Ensemble April SST
Member ß anomaly
1999
2000
May SST
anomaly
April SST
anomaly
May SST
anomaly
April SST
anomaly
May SST
anomaly
1
-12.7
-8.8
5.4
0.0
8.1
0.6
2
-4.4
8.2
5.8
1.6
10.3
0.4
3
-14.8
1.3
3.4
9.1
9.2
0.4
4
-16.4
-9.5
1.0
7.1
6.2
-1.6
5
2.6
-14.7
-0.9
12.3
7.1
-1.2
6
-14.7
-10.0
-1.1
6.2
1.7
3.0
Mean
-10.1
-5.6
2.3
6.0
7.1
0.3
S.D
6.8
7.8
2.8
4.2
2.8
1.5
Observed
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:
1. 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.
2. 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.
3. 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.
4. 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.
5. Real time experimental seasonal prediction with persisted SST anomaly and 6 member
ensembles show only limited skill.
6. 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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