Indian Summer Monsoon Flow

CDC, 10 Nov 2004CDC, 10 Nov 2004CDC, 10 Nov 2004CDC, 10 Nov 2004

Indian Monsoon Variability Indian Monsoon Variability and Predictabilityand Predictability

K. Krishna KumarK. Krishna KumarCIRES Visiting FellowCIRES Visiting [email protected] [email protected]

Collaborators:Collaborators:

Martin P. HoerlingMartin P. HoerlingClimate Diagnostics Center, BoulderClimate Diagnostics Center, Boulder

andandBalaji RajagopalanBalaji Rajagopalan

University of Colorado, BoulderUniversity of Colorado, Boulder


Indian Summer Monsoon FlowIndian Summer Monsoon Flow


Mean Annual Cycle of Mean Annual Cycle of All-India Mean Monthly RainfallAll-India Mean Monthly Rainfall

0

50

100

150

200

250

300

350

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Ra

infa

ll (m

m/m

on

th)


The Stability of the Indian Summer The Stability of the Indian Summer MonsoonMonsoon

0

20

40

60

80

100

120

1810 1830 1850 1870 1890 1910 1930 1950 1970 1990

Years

Ra

infa

ll (c

m)

Mean (R) = 84.8 cmStandard Deviation (S) = 8 cm

R

R+S

R-S


All-India Summer Monsoon Rainfall (1871-2003)All-India Summer Monsoon Rainfall (1871-2003)(Based on IITM Homogeneous Monthly Rainfall Data Set)(Based on IITM Homogeneous Monthly Rainfall Data Set)


Total Total Foodgrain Foodgrain Production in Production in India and its India and its Relation to Relation to Indian Indian RainfallRainfall


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


Empirical/Statistical Monsoon Empirical/Statistical Monsoon PredictionPrediction

Long HistoryLong History

Blanford (1884) Blanford (1884) Himalayan Snow-MonsoonHimalayan Snow-Monsoon

Walker (1918,1924) Walker (1918,1924) Southern Osc. - Southern Osc. - MonsoonMonsoon

Normand (1953) Normand (1953)

And many studies in theAnd many studies in the

recent decades…recent decades…

Sir. Gilbert Walker


All-India Summer Monsoon Rainfall (1871-2003)All-India Summer Monsoon Rainfall (1871-2003)(Based on IITM Homogeneous Monthly Rainfall Data Set)(Based on IITM Homogeneous Monthly Rainfall Data Set)


Composite SSTA of Drought (Composite SSTA of Drought (**) and Normal () and Normal (**) ) Monsoon YearsMonsoon Years

El Nino-DroughtEl Nino-Drought El Nino-NormalEl Nino-Normal

Diff

ere

nce

Diff

ere

nce


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%


Sea Surface Temp Anomalies: 1987 & Sea Surface Temp Anomalies: 1987 & 20022002

JJA 87 SON 87

JJA 02 SON 02




Observed SST, Precipitation, Velocity Potential (200hPa) differences Observed SST, Precipitation, Velocity Potential (200hPa) differences between composites of El Nino/Drought and El Nino/Normal yearsbetween composites of El Nino/Drought and El Nino/Normal years


Idealized AGCM Idealized AGCM Experiments:Experiments:

AGCM: CCM3 (T42, 18 vert AGCM: CCM3 (T42, 18 vert levels)levels)

A 20-member Ensemble with A 20-member Ensemble with different atmospheric initial different atmospheric initial conditions is performed for each conditions is performed for each of the two El Nino flavorsof the two El Nino flavors

Runs initiated from 1Runs initiated from 1stst November and continued for 14 November and continued for 14 months until the end of months until the end of December. The first two-December. The first two-months of simulations months of simulations discarded.discarded.

Climatological SSTs prescribed Climatological SSTs prescribed outside of ENSO regionoutside of ENSO region

Control Expt: 150 year run is Control Expt: 150 year run is made with monthly-evolving made with monthly-evolving climatological SSTs globallyclimatological SSTs globally


CCM3 SST, Precipitation, Velocity Potential (200hPa) differences CCM3 SST, Precipitation, Velocity Potential (200hPa) differences between composites of El Nino/Drought and El Nino/Normal yearsbetween composites of El Nino/Drought and El Nino/Normal years


Dominant EOFs in Tropical Pacific SSTsDominant EOFs in Tropical Pacific SSTs(Jan 1948- Sept 2004)(Jan 1948- Sept 2004)

(Trenberth and Stepaniak, 2001)(Trenberth and Stepaniak, 2001)


Idealized SST Experiments with CCM3Idealized SST Experiments with CCM3

3 sets of 10-member ensemble runs for (1) EOF1 3 sets of 10-member ensemble runs for (1) EOF1

(2) EOF1+EOF2 and (3) EOF1-EOF2 are performed by (2) EOF1+EOF2 and (3) EOF1-EOF2 are performed by ramping the magnitude of SST anomaly patterns from ramping the magnitude of SST anomaly patterns from 0 to 20 to 2σσ at a rate of 0.2 at a rate of 0.2σσ per year (in all 11 years). per year (in all 11 years).


Increasing influence of TNI/EOF2 on the Indian Increasing influence of TNI/EOF2 on the Indian Monsoon in recent decadesMonsoon in recent decades

Relation of NINO Indices and TNI on MonsoonRelation of NINO Indices and TNI on Monsoon

NINO3.4 and TNI: 1871-2000 (Trenberth & Stepaniak, 2001)NINO3.4 and TNI: 1871-2000 (Trenberth & Stepaniak, 2001)

TNITNI

Krishna Kumar et al (Science, 1999)Krishna Kumar et al (Science, 1999)


Surface Temp Anomaly over North Surface Temp Anomaly over North America: DJFAmerica: DJF

1983 1998

1988 2003


Summary so far…Summary so far…

• Stronger El Nino events are necessary for bigger monsoonal Stronger El Nino events are necessary for bigger monsoonal droughts – but not all El Nino events result in droughtsdroughts – but not all El Nino events result in droughts

• The two El Nino flavors that produce drought/normal The two El Nino flavors that produce drought/normal monsoons appear to be linked to the interplay between monsoons appear to be linked to the interplay between EOF1 and EOF2 of Tropical eastern Pacific SSTsEOF1 and EOF2 of Tropical eastern Pacific SSTs

• While there is proven skill in predicting EOF1 at a reasonably While there is proven skill in predicting EOF1 at a reasonably longer lead-time, it is not yet clear if EOF2 has predictability longer lead-time, it is not yet clear if EOF2 has predictability so that it can be utilized in monsoon rainfall predictionso that it can be utilized in monsoon rainfall prediction

• The two El Nino flavors identified here have implications for The two El Nino flavors identified here have implications for tropical-wide seasonal rainfall anomalies as well as for the El tropical-wide seasonal rainfall anomalies as well as for the El Nino related north American teleconnectionsNino related north American teleconnections


Prediction and Predictability of Prediction and Predictability of Indian Monsoon RainfallIndian Monsoon Rainfall


Operational Official Monsoon Forecasts from India Operational Official Monsoon Forecasts from India Meteorological Dept: 1988-2004Meteorological Dept: 1988-2004


Popular Practices of Dynamical Popular 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

DEMETERDEMETER


Predicted and Observed Monsoon Rainfall 2002Predicted and Observed Monsoon Rainfall 2002


Predicted and Observed Monsoon Rainfall 2004Predicted and Observed Monsoon Rainfall 2004


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 (known as AMIP or GOGA) with a suite of (known as AMIP or GOGA) with a suite of multi-model, multi-member ensemble multi-model, multi-member ensemble runs.runs.


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


Annual cycle of Indian Rainfall Annual cycle of Indian Rainfall (8-30N; 70-90E) in AGCMS(8-30N; 70-90E) in AGCMS Land-SeaLand-Sea

Land-onlyLand-only


Questions???Questions???

• How much is the SST driven Predictability How much is the SST driven Predictability in Indian Monsoon Rainfall?in Indian Monsoon Rainfall?

• How sensitive are Indian monsoon rainfall How sensitive are Indian monsoon rainfall simulations to the atmospheric initial simulations to the atmospheric initial conditions?conditions?

• How are the actual skills of model How are the actual skills of model simulated Monsoon Rainfall compared to simulated Monsoon Rainfall compared to obs.?obs.?


‘‘Perfect Prog’ Skills of MonsoonPerfect Prog’ Skills of Monsoon

PERPROGPERPROG

For each of theFor each of theAGCMs, the Monsoon AGCMs, the Monsoon Rainfall (8-30N; 70-Rainfall (8-30N; 70-

90E)90E)simulated in onesimulated in oneensemble run is ensemble run is

correlatedcorrelatedwith the mean ofwith the mean ofRemaining runsRemaining runs


Actual Skill (Corr. bet GCM rain Actual Skill (Corr. bet GCM rain and Obs.)and Obs.)

ObservedObservedIndian MonsoonIndian MonsoonRainfall IndexRainfall Index(IITM) is used(IITM) is usedto compute to compute correlationscorrelations


Impact of Initial Conditions on Monsoon SimulationsImpact of Initial Conditions on Monsoon Simulations


Monsoon-ENSO Relation in AGCM Monsoon-ENSO Relation in AGCM SimulationsSimulations

ObservedObserved


Climatology of Monsoon RainfallClimatology of Monsoon Rainfall


ENSO Warm-Cold Composites of Precipitation and TemperatureENSO Warm-Cold Composites of Precipitation and Temperaturein CCM3 (uncoupled) and Observationsin CCM3 (uncoupled) and Observations


Mixed Layer Model (MLM) ExperimentsMixed Layer Model (MLM) Experiments

• GFDL R30 (14 Vertical levels)GFDL R30 (14 Vertical levels)31-layer Mixed Layer Model (Gasper, 1988; Alexander et al 31-layer Mixed Layer Model (Gasper, 1988; Alexander et al 2000)2000)Observed SSTs prescribed in the region 15S-15N and 172E Observed SSTs prescribed in the region 15S-15N and 172E to South American Coast. In the rest of world oceans (all to South American Coast. In the rest of world oceans (all ice-free), SST changes are predicted by MLMice-free), SST changes are predicted by MLM

16-member simulations made for the period 1950-199916-member simulations made for the period 1950-1999 (for more details see Lau and Nath, 2003)(for more details see Lau and Nath, 2003)

• NCAR CAM2/Slab Ocean (Saravanan)NCAR CAM2/Slab Ocean (Saravanan)The prescription of SSTs is similar to the above experimentThe prescription of SSTs is similar to the above experiment(for more details see Giannini et al 2004)(for more details see Giannini et al 2004)


GFDL R30/MLM (16 Members)GFDL R30/MLM (16 Members)


NCAR CAM2/Slab OceanNCAR CAM2/Slab Ocean


S. No.S. No. ModelModel DescriptionDescription Hindcast PeriodHindcast Period

1.1. ECMWFECMWF AGCM: NWP/IFS (T95)AGCM: NWP/IFS (T95)

OGCM: HOPE (0.3 to 1.4OGCM: HOPE (0.3 to 1.4º)º)1958-20011958-2001

2.2. METEOFRMETEOFR AGCM: ARPEGE (T63)AGCM: ARPEGE (T63)

OGCM: OPA8.1 (0.5 to 2OGCM: OPA8.1 (0.5 to 2º)º)1958-20011958-2001

3.3. LODYCLODYC AGCM: ECMWF IFSAGCM: ECMWF IFS

OGCM: OPA (2OGCM: OPA (2ººx2x2º)º)1974-20011974-2001

4.4. UKMOUKMO (HadCM3)HadAm3 (2.5(HadCM3)HadAm3 (2.5ºx3.75º)ºx3.75º)

Hadley’s Ocean)Hadley’s Ocean)1959-20011959-2001

5.5. MPIMPI AGCM: ECHAM5 (T42)AGCM: ECHAM5 (T42)

OGCM: MPI-OM1 (next generation OGCM: MPI-OM1 (next generation

of HOPE)of HOPE)

1969-20011969-2001

6.6. CERFACSCERFACS AGCM: ARPEGEAGCM: ARPEGE

OGCM: OPA 8.1OGCM: OPA 8.11980-20011980-2001

7.7. INGVINGV AGCM: ECHAM4 (T42)AGCM: ECHAM4 (T42)

OGCM: OPA 8.1OGCM: OPA 8.11973-20011973-2001

DEMETEDEMETE

RR

A 9-member ensemble of 6-monthly forecasts issued 4 times A 9-member ensemble of 6-monthly forecasts issued 4 times a year - Feb, May, August and Novembera year - Feb, May, August and November

CDC, 10 Nov 2004CDC, 10 Nov 2004


DEMETER Predicted and Observed NINO3.4DEMETER Predicted and Observed NINO3.4


SummarySummary The skills of current generation AGCMs in simulating monsoon rainfall in The skills of current generation AGCMs in simulating monsoon rainfall in

India even when forced with observed SSTs are very low.India even when forced with observed SSTs are very low.

However, there appears to be much higher predictive potential as However, there appears to be much higher predictive potential as evidenced by the large PERPROG skills.evidenced by the large PERPROG skills.

No clear hint of higher skills either for models with better monsoon No clear hint of higher skills either for models with better monsoon climatology or when multi-model-super ensembles are involved.climatology or when multi-model-super ensembles are involved.

Specification of SSTs in the Indian Ocean appears to be the main reason for Specification of SSTs in the Indian Ocean appears to be the main reason for the low-skills.the low-skills.

An interactive ocean-atmosphere in the Indian Ocean (using even a simple An interactive ocean-atmosphere in the Indian Ocean (using even a simple mixed layer ocean model) produces more realistic monsoon simulations mixed layer ocean model) produces more realistic monsoon simulations compared to specifying actual or climatological SSTs.compared to specifying actual or climatological SSTs.

The 2-tiered approach currently being pursued in seasonal forecasting The 2-tiered approach currently being pursued in seasonal forecasting needs immediate revision to achieve higher forecast skills for the Indian needs immediate revision to achieve higher forecast skills for the Indian region. We also believe, this might be true for some other countries region. We also believe, this might be true for some other countries located in the warm pool region in the west Pacific and the Indian Ocean.located in the warm pool region in the west Pacific and the Indian Ocean.

Further evaluation of skills and problems in DEMETER are needed.Further evaluation of skills and problems in DEMETER are needed.


CIRES/CUCIRES/CUBalaji / Vijay GuptaBalaji / Vijay GuptaRandyRandyMartyMartyJon, Gary, Taiyi, Jamie, Jean, Lucia, Systems guys…Jon, Gary, Taiyi, Jamie, Jean, Lucia, Systems guys…IITMIITMAll of you…All of you…

CDC, 10 Nov 2004CDC, 10 Nov 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

CDC, 10 Nov 2004CDC, 10 Nov 2004

Low-frequency co-variability of Monsoon Low-frequency co-variability of Monsoon Rainfall and ENSORainfall and ENSO

CDC, 10 Nov 2004CDC, 10 Nov 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


ENSO Warm-Cold Composites of Precipitation and ENSO Warm-Cold Composites of Precipitation and TemperatureTemperature

in CAM2 (uncoupled) and Observationsin CAM2 (uncoupled) and Observations


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


Progressive Progressive Improvement in Improvement in Monsoon Rainfall Monsoon Rainfall Simulation Skills:Simulation Skills: 1. Un-coupled AMIP 1. Un-coupled AMIP

2.Un-coupled AMIP only in 2.Un-coupled AMIP only in eastern tropical Pacific and eastern tropical Pacific and Climatological Climatological SSTs SSTs elsewhere elsewhere

3.AMIP in the Pacific and 3.AMIP in the Pacific and Mixed Mixed Layer Model in the Layer Model in the Indian Indian OceanOcean

Indian Summer Monsoon Flow

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