Dynamical Prediction of Indian
Monsoon Rainfall and the Role of
Indian Ocean
K. Krishna Kumar
CIRES Visiting Fellow, University of Colorado, Boulder
kkrishna@colorado.edu
Martin P. Hoerling
Climate Diagnostics Center, Boulder
and
Balaji Rajagopalan
University of Colorado, Boulder
IRI, 5 May, 2004
Current Practices of Dynamical
Monsoon Rainfall Prediction
 2-tiered approach wherein SSTs are
predicted first using a coupled model and
then the AGCMs are forced using these SST
fields
 Use persistent SSTs to run AGCMs
 Dynamical Downscaling using Regional Climate
Models taking lateral boundary values from
AGCM Simulations
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Skills of the Present Generation of AGCMs
(Reproduced from the IRI Website)
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We set out to examine the skills of monsoon
rainfall in detail by involving long simulations
made using observed SSTs with a suite of
multi-model, multi-member ensemble runs.
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Research Questions..?
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How skillful are the AGCMs in simulating Monsoon
Rainfall over the Indian region?
Is specifying SSTs a constraint on realistic monsoon
simulations?
How sensitive are monsoon simulations to initial
conditions?
What is the impact of coupling on Monsoon-ENSO
relationships?
Are the ENSO related western Indian Ocean SSTs acting
as negative feed-back on Monsoon-ENSO relations?
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Details of AGCMs Used
S.No.
Model
Resolution
Ens. Size
Run Length
1
ECHAM4
2.8x2.8
24
1950-2002
2
ECHAM3
2.8x2.8
10
1950-1999
3
GFDL
2.5x2.0
10
1951-2002
4
NASA
2.8x2.8
9
1950-2002
5
ECPC
1.8x1.8
7
1950-2001
6
MRF (NCEP)
2.8x2.8
13
1951-1994
7
ARPEGE
2.8x2.8
8
1948-1997
8
CCM3
2.8x2.8
12
1950-1999
9
CAM2
2.8x2.8
15
1950-2001
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Simulation of Tropical Rain bands during DJF in AGCMs
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Simulation of Tropical Rain bands during JJA in AGCMs
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Climatology of Monsoon Rainfall
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Monsoon-ENSO Relation in AGCM Simulations
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Impact of Initial Conditions on Monsoon Simulations
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ENSO Warm-Cold Composites of Precipitation and Temperature
in CAM2 (uncoupled) and Observations
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Monsoon-ENSO Teleconnections: Coupled vs. Uncoupled Models
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GOGA: Obs SSTs globally
DTEPOGA: Obs SSTs in
Deep Tropical East
Pacific and Climatological
SSTs elsewhere
DTEPOGA_MLM: Same as
DTEPOGA but a Mixed
Layer Model used in the
Indian Ocean
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Progressive Improvement
in Monsoon Rainfall
Simulation Skills:
1.
Un-coupled AMIP
2.
Un-coupled AMIP only
in
eastern tropical
Pacific and
Climatological
SSTs elsewhere
3.
AMIP in the Pacific
and Mixed
Layer Model in
the Indian
Ocean
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Summary
 The skills of current generation AGCMs in simulating monsoon
rainfall in India even when forced with observed SSTs are very
low.
 However, there appears to be much higher predictive potential as
evidenced by the large PERPROG skills.
 No clear hint of higher skills either for models with better
monsoon climatology or when multi-model-super ensembles are
involved.
 Specification of SSTs in the Indian Ocean appears to be the main
reason for the low-skills.
 An interactive ocean-atmosphere in the Indian Ocean (using even a
simple mixed layer ocean model) produces more realistic monsoon
simulations compared to specifying actual or climatological SSTs.
 General belief that the ENSO related SSTs in the Indian Ocean
(particularly the western Indian Ocean and the Arabian Sea) might
act as a negative feedback on Monsoon-ENSO teleconnections
appears to be wrong based on the above observations.
 In general the monsoon-ENSO links are much stronger in fully
coupled models compared to the AGCMs forced with
observed/predicted SSTs.
 The 2-tiered approach currently
being pursued in seasonal
IRI, 5 May, 2004
forecasting needs immediate revision to achieve higher forecast
The Climatic Impacts on Indian
Agriculture
K. Krishna Kumar
K. Rupa Kumar, R.G. Ashrit, N.R. Deshpande and
James Hansen (IRI, New York)
Indian Institute of Tropical Meteorology, Pune,
India
(krishna@tropmet.res.in)
IRI, 5 May, 2004
Objectives
• To generate data on all-India and state-level Agricultural
•
•
Indices
To Identify Crops and Regions in India having strong
Climatic Signal which can be used for Developing various
Climate Applications initiatives/programs involving
National and Multi-national Institutions and Individual
Scientists
Establishing Climate Signal in various Agricultural Indices
has implications for Climate Change Impact Assessment
Studies as well
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Agriculture Facts
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India lives mainly in its villages, 600,000 of them
Roughly 65% of the population is rural
India’s growth in per capita food production during 1979-92
was about 1.6% per annum – the highest in the world during
this period
Agriculture provides livelihood to about 65% of the labor force
Agriculture contributes nearly 29% to the GDP
In terms of fertilizer consumption, India ranks 4th in the world
About 43% of India’s geographical area is used for agriculture
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Area
under
crop
(mn. hec)
Irrigated
area under
crop
(mn. hec.)
Irrigated
area as %
of total
area under
crops
121
45
37
Rice
43
19
45
Wheat
23
19
84
Nonfoodgrains
61
19
31
Groundnut
9
2
20
Cotton
7
3
33
Sugarcane
4
3
86
183
64
35
IRRIGATION
Crop
Foodgrains
Total
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DATA
Production/Area/ Yield
• Total foodgrains
• Kharif/Rabi Rice
• Winter Wheat
• Groundnut
• Sorghum
• Cereals
• Oilseeds
• Pulses
• Sugarcane
Source
• Agricultural Situation in
India
• India Data Base
Organizations
• Center for Monitoring
Indian Economy
• Dept. of Agriculture and
Cooperation, Ministry of
Agriculture, Govt. of India
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Crop Areas
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Monsoon Variability
Factors
Features
Intraseasonal
Interannual
Decadal/Century
Millennia &
longer
Onset/withdrawal;
Active and breakmonsoon phases;
30-50 day
oscillations;
severe rainstorms
Droughts and
floods
Changes in the
frequency of
droughts and floods
Changes in the
areal extents of
monsoons
Atmospheric
variability;
tropicalmidlatitude
interactions;
Soil moisture;
Sea surface
temperatures
Atmospheric
interactions;
El Niño/
Southern
Oscillation;
Top layers of
tropical oceans;
Snow cover;
Land surface
characteristics
Monsoon circulation
variations;
Deep ocean changes;
Greenhouse gases
increase;
Human activities;
Biospheric changes;
Volcanic dust
Global climate
excursions;
Ice ages;
Warm epochs;
Sun-earth
geometry
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All-India Summer Monsoon Rainfall (1871-2001)
(Based on IITM Homogeneous Monthly Rainfall Data Set)
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JJA-1
SON-1
DJF-1
MAM
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Regional Climate Signal in
Indian Agriculture Indices
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Area under
Major Food
Crops in
India and %
Irrigated
during 19501998
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Total Foodgrain
Production in
India and its
Relation to
Indian Rainfall
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Kharif Rice
Production
in India and
its Relation
to Indian
Rainfall
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Total Wheat
Production
in India and
its Relation
to Indian
Rainfall
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Kharif
Groundnut
Production
and its
relation to
Indian
Rainfall
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Total
Sorghum
Production
and its
relation to
Indian
Rainfall
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Global Climate Signal in Indian
Agriculture
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Summary
Most rainfed crops show statistically significant
relation with Regional and Global Climatic
Factors, the exception being Sorghum.
 Wheat and Sugarcane, the two most irrigated
crops, do not show any climatic signal.
 Groundnut and Kharif (Summer) Rice in India
show very strong regional and global climatic
signals and should be targeted for climate
application as well as climate change impact
assessment studies.

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Predicted and Observed Monsoon Rainfall 2002
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Schematic view of sea surface temperature and tropical rainfall in the
the equatorial Pacific Ocean during normal, El Niño, and La Niña
conditions
.
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Global Impacts
of ENSO
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Low-frequency co-variability of Monsoon
Rainfall and ENSO
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Difference in the Composites of Winter (Prior to Monsoon) Surface Air
Temperatures over the Eurasian Region during El Nino Events pre1980 and post-1980 periods
(1981-97) – (1951-80)
El Ninos
Diff. Climatologies of these Periods
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Relation between Indian Monsoon Rainfall and
200 (A,B) and the Composites of 200 for El Nino
Events pre-,post-80’s (C,D)
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Sea Surface Temp Anomalies in 1982 & 1997
Monsoon Rainfall: -13%
JJA 82
SON 82
Monsoon Rainfall: +2%
JJA 97
SON 97
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Sea Surface Temp Anomalies: 1987 & 2002
Monsoon Rainfall: -18%
JJA 87
SON 87
Monsoon Rainfall: -19%
JJA 02
SON 02
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Precipitation Anomalies: JJA
Monsoon Rainfall: -13%
1982
Monsoon Rainfall: -18%
1987
Monsoon Rainfall: +2%
Monsoon Rainfall: -19%
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1997
2002
Precipitation Anomaly in NE Australia (DJF) and NE Brazil
(JFM) in 1988 and 1998
1988
1998
1988
1998
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Surface Temp Anomaly over North
America: DJF
1983
1998
1988
2003
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Precipitation/Forecasts of SST and Precipitation in JAS 2002 by Different GCMs
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SST Patterns Used for ENSO Experiments
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SST Patterns Used for Indian Ocean
Experiments
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C
A
A
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Response of Indian Monsoon Rainfall to Different
El Nino Related SST Patterns
Model
Monsoon
Rainfall
ENSO - CTL
NINODLNINO
ENSOGWENSO
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