iri, 5 may, 2004 dynamical prediction of indian monsoon rainfall and the role of indian ocean k....
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Dynamical Prediction of Indian Dynamical Prediction of Indian Monsoon Rainfall and the Role ofMonsoon Rainfall and the Role of
Indian OceanIndian Ocean
K. Krishna KumarK. Krishna KumarCIRES Visiting Fellow, University of Colorado, BoulderCIRES Visiting Fellow, University of Colorado, Boulder
[email protected] [email protected]
Martin P. HoerlingMartin P. HoerlingClimate Diagnostics Center, BoulderClimate Diagnostics Center, Boulder
andandBalaji RajagopalanBalaji Rajagopalan
University of Colorado, BoulderUniversity of Colorado, Boulder
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Current Practices of Dynamical Current Practices of Dynamical Monsoon Rainfall PredictionMonsoon Rainfall Prediction
2-tiered approach wherein SSTs are 2-tiered approach wherein SSTs are predicted first using a coupled model and predicted first using a coupled model and then the AGCMs are forced using these then the AGCMs are forced using these SST fieldsSST fields
Use persistent SSTs to run AGCMsUse persistent SSTs to run AGCMs Dynamical Downscaling using Regional Dynamical Downscaling using Regional
Climate Models taking lateral boundary Climate Models taking lateral boundary values from AGCM Simulationsvalues from AGCM Simulations
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Skills of the Present Generation of AGCMsSkills of the Present Generation of AGCMs(Reproduced from the IRI Website)(Reproduced from the IRI Website)
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We set out to examine the skills of We set out to examine the skills of monsoon rainfall in detail by involving long monsoon rainfall in detail by involving long simulations made using observed SSTs simulations made using observed SSTs with a suite of multi-model, multi-member with a suite of multi-model, multi-member ensemble runs.ensemble runs.
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Research Questions..?Research Questions..? How skillful are the AGCMs in simulating Monsoon How skillful are the AGCMs in simulating Monsoon
Rainfall over the Indian region?Rainfall over the Indian region? Is specifying SSTs a constraint on realistic Is specifying SSTs a constraint on realistic
monsoon simulations?monsoon simulations? How sensitive are monsoon simulations to initial How sensitive are monsoon simulations to initial
conditions?conditions? What is the impact of coupling on Monsoon-ENSO What is the impact of coupling on Monsoon-ENSO
relationships?relationships? Are the ENSO related western Indian Ocean SSTs Are the ENSO related western Indian Ocean SSTs
acting as negative feed-back on Monsoon-ENSO acting as negative feed-back on Monsoon-ENSO relations?relations?
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Details of AGCMs UsedDetails of AGCMs Used
S.No.S.No. ModelModel ResolutionResolution Ens. SizeEns. Size Run LengthRun Length
11 ECHAM4ECHAM4 2.8x2.82.8x2.8 2424 1950-20021950-2002
22 ECHAM3ECHAM3 2.8x2.82.8x2.8 1010 1950-19991950-1999
33 GFDLGFDL 2.5x2.02.5x2.0 1010 1951-20021951-2002
44 NASANASA 2.8x2.82.8x2.8 99 1950-20021950-2002
55 ECPCECPC 1.8x1.81.8x1.8 77 1950-20011950-2001
66 MRF (NCEP)MRF (NCEP) 2.8x2.82.8x2.8 1313 1951-19941951-1994
77 ARPEGEARPEGE 2.8x2.82.8x2.8 88 1948-19971948-1997
88 CCM3CCM3 2.8x2.82.8x2.8 1212 1950-19991950-1999
99 CAM2CAM2 2.8x2.82.8x2.8 1515 1950-20011950-2001
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Simulation of Tropical Rain bands during DJF in Simulation of Tropical Rain bands during DJF in AGCMsAGCMs
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Simulation of Tropical Rain bands during JJA in Simulation of Tropical Rain bands during JJA in AGCMsAGCMs
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Climatology of Monsoon RainfallClimatology of Monsoon Rainfall
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Monsoon-ENSO Relation in AGCM Monsoon-ENSO Relation in AGCM SimulationsSimulations
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Impact of Initial Conditions on Monsoon SimulationsImpact of Initial Conditions on Monsoon Simulations
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ENSO Warm-Cold Composites of Precipitation and ENSO Warm-Cold Composites of Precipitation and TemperatureTemperature
in CAM2 (uncoupled) and Observationsin CAM2 (uncoupled) and Observations
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Monsoon-ENSO Teleconnections: Coupled vs. Uncoupled Monsoon-ENSO Teleconnections: Coupled vs. Uncoupled ModelsModels
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
GOGA: Obs SSTs GOGA: Obs SSTs globallyglobally
DTEPOGA: Obs SSTs in DTEPOGA: Obs SSTs in Deep Tropical East Deep Tropical East Pacific and Pacific and Climatological SSTs Climatological SSTs elsewhereelsewhere
DTEPOGA_MLM: Same DTEPOGA_MLM: Same as DTEPOGA but a as DTEPOGA but a Mixed Layer Model Mixed Layer Model used in the Indian used in the Indian OceanOcean
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Progressive Progressive Improvement in Improvement in Monsoon Rainfall Monsoon Rainfall Simulation Skills:Simulation Skills: 1. 1. Un-coupled AMIP Un-coupled AMIP
2.2. Un-coupled AMIP Un-coupled AMIP only in only in eastern eastern tropical Pacific and tropical Pacific and Climatological Climatological SSTs SSTs elsewhere elsewhere
3.3. AMIP in the Pacific AMIP in the Pacific and Mixed and Mixed Layer Model Layer Model in the Indian in the Indian OceanOcean
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SummarySummary The skills of current generation AGCMs in simulating monsoon rainfall in India The skills of current generation AGCMs in simulating monsoon rainfall in India
even when forced with observed SSTs are very low.even when forced with observed SSTs are very low. However, there appears to be much higher predictive potential as evidenced by However, there appears to be much higher predictive potential as evidenced by
the large PERPROG skills.the large PERPROG skills. No clear hint of higher skills either for models with better monsoon climatology or No clear hint of higher skills either for models with better monsoon climatology or
when multi-model-super ensembles are involved.when multi-model-super ensembles are involved. Specification of SSTs in the Indian Ocean appears to be the main reason for the Specification of SSTs in the Indian Ocean appears to be the main reason for the
low-skills.low-skills. An interactive ocean-atmosphere in the Indian Ocean (using even a simple mixed An interactive ocean-atmosphere in the Indian Ocean (using even a simple mixed
layer ocean model) produces more realistic monsoon simulations compared to layer ocean model) produces more realistic monsoon simulations compared to specifying actual or climatological SSTs.specifying actual or climatological SSTs.
General belief that the ENSO related SSTs in the General belief that the ENSO related SSTs in the Indian Ocean Indian Ocean (particularly the (particularly the western Indian Ocean and the Arabian Sea) might act as a negative feedback on 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 Monsoon-ENSO teleconnections appears to be wrong based on the above observations.observations.
In general the monsoon-ENSO links are much stronger in fully coupled models In general the monsoon-ENSO links are much stronger in fully coupled models compared to the AGCMs forced with observed/predicted SSTs.compared to the AGCMs forced with observed/predicted SSTs.
The 2-tiered approach currently being pursued in seasonal forecasting needs The 2-tiered approach currently being pursued in seasonal forecasting needs immediate revision to achieve higher forecast skills for the Indian region. We immediate revision to achieve higher forecast skills for the Indian region. We also believe, this might be true for some other countries located in the warm pool also believe, this might be true for some other countries located in the warm pool region in the west Pacific and the Indian Ocean.region in the west Pacific and the Indian Ocean.
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The Climatic Impacts on The Climatic Impacts on Indian AgricultureIndian Agriculture
K. Krishna KumarK. Krishna KumarK. Rupa Kumar, R.G. Ashrit, N.R. Deshpande K. Rupa Kumar, R.G. Ashrit, N.R. Deshpande
and James Hansen (IRI, New York)and James Hansen (IRI, New York)
Indian Institute of Tropical Meteorology, Indian Institute of Tropical Meteorology, Pune, IndiaPune, India
(([email protected]@tropmet.res.in))
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ObjectivesObjectives
• To generate data on all-India and state-level To generate data on all-India and state-level Agricultural IndicesAgricultural Indices
• To Identify Crops and Regions in India having To Identify Crops and Regions in India having strong Climatic Signal which can be used for strong Climatic Signal which can be used for Developing various Climate Applications Developing various Climate Applications initiatives/programs involving National and Multi-initiatives/programs involving National and Multi-national Institutions and Individual Scientistsnational Institutions and Individual Scientists
• Establishing Climate Signal in various Agricultural Establishing Climate Signal in various Agricultural Indices has implications for Climate Change Indices has implications for Climate Change Impact Assessment Studies as wellImpact Assessment Studies as well
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Agriculture Agriculture FactsFacts
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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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
IRRIGATIONIRRIGATION CropCropArea Area under under crop crop (mn. (mn. hec)hec)
Irrigated Irrigated area area
under under crop crop (mn. hec.)(mn. hec.)
Irrigated Irrigated area as % area as % of total of total
area area under under cropscrops
FoodgrainsFoodgrains 121121 4545 3737
RiceRice 4343 1919 4545
WheatWheat 2323 1919 8484
Non-Non-foodgrainsfoodgrains
6161 1919 3131
GroundnutGroundnut 99 22 2020
CottonCotton 77 33 3333
SugarcaneSugarcane 44 33 8686
TotalTotal 183183 6464 3535
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DATADATA
Production/Area/ YieldProduction/Area/ Yield• Total foodgrains• Kharif/Rabi Rice• Winter Wheat• Groundnut• Sorghum• Cereals• Oilseeds• Pulses• Sugarcane
SourceSource
• Agricultural Situation in Agricultural Situation in IndiaIndia
• India Data BaseIndia Data Base
OrganizationsOrganizations
• Center for Monitoring Center for Monitoring Indian EconomyIndian Economy
• Dept. of Agriculture and Dept. of Agriculture and Cooperation, Ministry of Cooperation, Ministry of Agriculture, Govt. of Agriculture, Govt. of IndiaIndia
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Crop AreasCrop Areas
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Monsoon Monsoon VariabilityVariability
Intraseasonal Interannual Decadal/Century Millennia & longer
Onset/withdrawal; Active and break-monsoon 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;tropical-midlatitude 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
Factors
Factors
Features
Features
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All-India Summer Monsoon Rainfall (1871-2001)All-India Summer Monsoon Rainfall (1871-2001)(Based on IITM Homogeneous Monthly Rainfall Data Set)(Based on IITM Homogeneous Monthly Rainfall Data Set)
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JJA-1JJA-1 SON-1SON-1
DJF-1DJF-1 MAMMAM
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Regional Climate Signal in Regional Climate Signal in Indian Agriculture IndicesIndian Agriculture Indices
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Area under Area under Major Food Major Food Crops in Crops in India and % India and % Irrigated Irrigated during during 1950-19981950-1998
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Total Total Foodgrain Foodgrain Production in Production in India and its India and its Relation to Relation to Indian Indian RainfallRainfall
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Kharif Rice Kharif Rice Production Production in India in India and its and its Relation to Relation to Indian Indian RainfallRainfall
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Total Total Wheat Wheat Production Production in India in India and its and its Relation to Relation to Indian Indian RainfallRainfall
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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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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Global Climate Signal in Global Climate Signal in Indian AgricultureIndian Agriculture
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
SummarySummary 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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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Predicted and Observed Monsoon Rainfall 2002Predicted and Observed Monsoon Rainfall 2002
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Global Impacts Global Impacts of ENSOof ENSO
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IRI, 5 May, 2004IRI, 5 May, 2004
Low-frequency co-variability of Monsoon Low-frequency co-variability of Monsoon Rainfall and ENSORainfall and ENSO
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004
Difference in the Composites of Winter (Prior to Monsoon) Surface Air Difference in the Composites of Winter (Prior to Monsoon) Surface Air Temperatures over the Eurasian Region during El Nino Events pre-Temperatures over the Eurasian Region during El Nino Events pre-
1980 and post-1980 periods1980 and post-1980 periods
(1981-97) – (1951-80)(1981-97) – (1951-80)El NinosEl Ninos
Diff. Climatologies of these PeriodsDiff. Climatologies of these Periods
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IRI, 5 May, 2004IRI, 5 May, 2004
Relation between Indian Monsoon Rainfall and Relation between Indian Monsoon Rainfall and 200 (A,B) and the Composites of 200 (A,B) and the Composites of 200 for El Nino 200 for El Nino
Events pre-,post-80’s (C,D)Events pre-,post-80’s (C,D)
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Sea Surface Temp Anomalies in 1982 & Sea Surface Temp Anomalies in 1982 & 19971997
JJA 82 SON 82
JJA 97 SON 97
Monsoon Rainfall: -13%
Monsoon Rainfall: +2%
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Sea Surface Temp Anomalies: 1987 & Sea Surface Temp Anomalies: 1987 & 20022002
JJA 87 SON 87
JJA 02 SON 02
Monsoon Rainfall: -18%
Monsoon Rainfall: -19%
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Precipitation Anomalies: JJAPrecipitation Anomalies: JJA
1982 1997
1987 2002
Monsoon Rainfall: -13% Monsoon Rainfall: +2%
Monsoon Rainfall: -18% Monsoon Rainfall: -19%
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Precipitation Anomaly in NE Australia (DJF) and NE Precipitation Anomaly in NE Australia (DJF) and NE Brazil (JFM) in 1988 and 1998Brazil (JFM) in 1988 and 1998
1988 1998
1988 1998
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Surface Temp Anomaly over North Surface Temp Anomaly over North America: DJFAmerica: DJF
1983 1998
1988 2003
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
Precipitation/Forecasts of SST and Precipitation in JAS 2002 by Different GCMs
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IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
SST Patterns Used for ENSO SST Patterns Used for ENSO ExperimentsExperiments
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004
SST Patterns Used for Indian Ocean SST Patterns Used for Indian Ocean ExperimentsExperiments
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IRI, 5 May, 2004IRI, 5 May, 2004
AA
AA
CC
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IRI, 5 May, 2004IRI, 5 May, 2004
Response of Indian Monsoon Rainfall to DifferentResponse of Indian Monsoon Rainfall to DifferentEl Nino Related SST PatternsEl Nino Related SST Patterns
Model Model MonsooMonsoon n RainfallRainfall
ENSO - ENSO - CTLCTL
NINODL-NINODL-NINONINO
ENSOGW-ENSOGW-ENSOENSO
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IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004
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IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004IRI, 5 May, 2004