Promises and Prospects for Predicting the South Asian Monsoon - PowerPoint Presentation

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Promises and Prospects for Predicting the South Asian Monsoon

Jim Kinter

COLAGeorge Mason University

National Monsoon MissionPrincipal Investigators MeetingPune, India :: 19 February 2015

Many Thanks To: Rodrigo Bombardi, Paul Dirmeyer, Mike Fennessy, Bohua Huang, Subhadeep Halder, Larry Marx, Ed Schneider, J. Shukla, R. Shukla, Chul-Su Shin, Bohar Singh

Figure credit: National Geographic © 2002

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South Asian Monsoon

One of largest

variations of seasonal to interannual climate observed on Earth Affects lives, property and (largely agrarian) economies of countries inhabited by nearly 25% of human

population …Current state of the science

Rudimentary understanding of monsoon dynamicsExtremely limited ability to predict monsoon variations

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South Asian MonsoonOne of largest variations seasonal to interannual climate observed on Earth Rudimentary understanding of the monsoon remains

Extremely limited ability to predict monsoon variations

Affects lives, property and (largely agrarian) economies of countries inhabited by nearly 20% of human population

… either by monsoon flood …

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…or by monsoon drought

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South Asian Monsoon

One of largest variations seasonal to

interannual climate observed on Earth Affects lives, property and (largely agrarian) economies of countries inhabited by nearly

25% of human populationCurrent state of the scienceRudimentary

understanding of monsoon dynamicsDemonstrable predictability of monsoon seasonal rainfallLimited ability to actually predict monsoon variations

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National Monsoon Mission of India and COLA Joint Research to Enhance Monsoon PredictionOcean-Land-Atmosphere Coupling and Initialization Strategies to Improve CFSv2 and Monsoon PredictionSponsored by Ministry of Earth Sciences, IndiaGoal 1: Improve the coupled ocean-land-atmosphere model (CFSv2) performance.

Goal 2: Improve initialization of ocean and land states in the pre-monsoon season to improve forecasts of onset, monsoon season precipitation.Goal 3: Improve representation of land-atmosphere feedback in monsoon-dominated regions

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Climate Forecast System version 2 (CFSv2) Global coupled model: Atmosphere, Ocean, Land Surface, Sea IceAtmosphere:

based on the NCEP Global Forecast System (GFS) used for global numerical weather predictionspectral discretization at T126 resolution (about 100 km grid spacing)

64 levels in the verticalOcean: Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model version 4 (MOM4) 1/2° horizontal grid spacing; 1/4° meridional grid spacing in the

tropics40 vertical levelsLand Surface: Noah (GFS grid)Sea I

ce: a modified version of the GFDL Sea Ice Simulator (MOM4 grid)

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Climate Forecast System version 2 (CFSv2) Global coupled model: Atmosphere, Ocean, Land Surface, Sea Ice

Atmosphere: based on the NCEP Global Forecast System (GFS

) used for global numerical weather predictionspectral discretization at T126 resolution (about 100 km grid spacing)64 levels in the vertical

Ocean: Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model version 4 (MOM4)

1/2° horizontal grid spacing; 1/4° meridional grid spacing in the tropics40 vertical levelsLand Surface: Noah (GFS grid)Sea Ice:

a modified version of the

GFDL Sea

Ice

Simulator (MOM4 grid)

2011 Operational CFSv2 –

plus COLA changes:

Corrected coding irregularity that produces mismatch in air-sea fluxes

Corrected low

v

alue of sea ice albedo

Testing various changes that can improve overall CGCM performance and especially monsoon prediction

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Role of atmospheric convection

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Convective Cloud Parameterization:The Simplified Arakawa-Schubert (SAS) Scheme

Deep ConvectionTrigger Mechanism:

Level of free convection (LFC) [for parcel with no sub-cloud level entrainment] being within 150-hPa of convection starting point (ΔP < 150)Hong & Pan (1996)

Source: MetEd

http://www.meted.ucar.edu/nwp/pcu2/avncp1.htm

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Evap

Sensible

Heated Condensation Framework (HCF)

Quantifies

how close atmosphere is to moist

convection

Does

not require parcel

selection

Uses

typically

measured quantities (needs only

q

and

θ

profiles)

Is

“conserved”

diurnally

Can

be used any time of year or any time of

day and interpretation stays the same

(

Tawfik

and

Dirmeyer

2014

GRL

)

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Evap

Sensible

Threshold Variables:

BCL =

Buoyant Condensation

Level [m]

θ

BM

=

Buoyant mixing

temperature [K]

Heated Condensation Framework

(

Tawfik

and

Dirmeyer

2014

GRL

)

Convection is initiated when:

PBL

intersects

BCL

θ

2m

reaches

θ

BM

New trigger mechanism for deep convection

Original SAS criterion (

Δ

P < 150

-

hPa)

.OR.

Condensation due to mixing (HCF)

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Experiments:

Seasonal hindcasts

Four members per year (1998 – 2010)

From April to October starting on April 1,2,3, and 4With (HCF) and without (CTRL) the new

triggerAdditional subset tests with new SAS (Han and Pan 2011), shallow convection, and BCL cloud base criterionThanks to Rodrigo BombardiMonthly Accumulated Precip. (mm)OBS

CTL

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Thanks to Rodrigo BombardiJJAS

PrecipitationHCF Trigger:

Small but significant improvement of seasonal total

HCF - CTRL

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Monsoon OnsetImproved seasonal cycle of precipitationImproved rainy season onset dates.

Thanks to Rodrigo Bombardi

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MechanismsThe new trigger is an alternative condition

Better

representation of the background state of convectionSAS triggers more frequently – produces more rainfall overall

Small improvement in the right directionCentral India: 16.5-26.5

oN, 74.5-86.5oE Thanks to Rodrigo Bombardi

l

ess

drizzle

m

ore

m

oderate rain

s

till insufficient

h

eavy rain

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CTRL

HCF

HCF/BCL

See Poster by Singh et al.

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JJAS

Precip

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Land-atmosphere feedbacks

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Hot SpotsMulti-model results indicate that there are geographic “hot spots” where the atmosphere is responsive to the state of the land surface (soil moisture) = potential predictability.There have been many subsequent studies using models, analyses and observations that corroborate and help explain this result.

The

Global Land-Atmosphere Coupling Experiment (GLACE) : A joint GEWEX/CLIVAR modeling study

Koster, et al., 2004: Science, 1138-.

Dirmeyer, et al., 2006: JHM, 1177-.Guo, et al., 2006: JHM, 611-.Koster, et al., 2006: JHM, 590-.Thanks to Paul Dirmeyer

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Spatial Variation of SensitivityOver the extreme north-west (dry), central (humid) and northeast (wet) India, correlation of LHF with SM is positive, but it strongly depends on the initial soil moisture condition and availability of radiation in June.

Difference in volumetric surface soil moisture initial state

Difference in day-1 LHF (Wm-2)

PLEASE SEE POSTER BY HALDER ET AL.

Thanks to Subhadeep Halder

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Soil Moisture Memory (GLDAS-2)

days

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Ocean initialization

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Multiple Ocean Initial Conditions (OICs) for CFSv2 Four different ocean data setsECMWF Combine-NV (NEMO

)NCEP CFSR (Climate Forecast System Reanalysis)ECMWF ORA-S3 (Ocean Reanalysis

System3; HOPE model)NCEP GODAS (Global Ocean data Assimilation System)Variables Monthly mean potential temperature[°C], salinity[g/kg], u[m/s], v[m/s]

Pre-ProcessingFilling up (extrapolation) the land mass to make up potential gaps due to different land-sea masks between each ocean analysis and MOM4 (Note that zonal mean of each variable is initially assigned to grids over the land as an initial guess.)

4. Period: 1979-2009 (Jan. – May)

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OICsInitial month

NEMO

(4mem*30yrs)CFSR(4mem*31yrs)

ORA-S3

(4mem*31yrs)GODAS(4mem*31yrs)May(

5-month lead)

April

(6-month lead)

March

(7-month lead)

February

(8-month lead)

January

(9-month lead)

* 4 members : 1

st

4 days (00Z) of each month using the atmosphere/land surface conditions from CFSR

600 runs

completed

620 runs

completed

620 runs

completed

620 runs completed

CFSv2 Retrospective Forecasts with 4

OICs

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NEMO

CFSR

ORA-S3GODASES_MEAN

JAN ICsFEB ICs

MAR ICsCFSv2 Prediction Skill of NINO3.4 (1982-2008) vs. OISSTThanks to Chul-Su Shin

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NEMOCFSR

ORA-S3

GODASES_MEAN

APR ICsMAY ICs

CFSv2 Prediction Skill of NINO3.4 (1982-2008) vs. OISSTThanks to Chul-Su Shin

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Multiple Ocean Analyses Ensemble Mean CFSv2 Prediction Skill

of NINO3.4 (1982-2008) vs. OISSTThanks to

Chul-Su Shin

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Characterizing monsoon variability w.r.t. enso

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(1979 – 2008; linear trend removed)2 Dominant Modes of JJAS Mean Precip

Thanks to Chul-Su Shin

corresponds to

ENSO onset years

corresponds to ENSO decay years

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CFSv2CMAP

1st

EOF ModeCorrelation (1979-2008)

lead monthNEMO

CFSRORA-S3GODASES_MEANThanks to Chul-Su Shin

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CFSv2CMAP

2nd EOF Mode

Correlation (1979-2008)

lead monthNEMO

CFSRORA-S3GODASES_MEANThanks to Chul-Su Shin

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* Events: ’82-83, ’87-88, ’91-92, ’94-95,

’97-98, ’02-03, ’06-07

Thanks to Chul-Su Shin5-month lead forecast

5-month lead forecast

ObservationsObservationsENSO Warm Composite * JJAS Mean Rainfall Anomaly

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5-month lead forecast Thanks to

Chul-Su Shin5-month

lead forecast ObservationsObservations

ENSO Warm Event (‘82-’83) JJAS Mean Rainfall Anomaly

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Thanks to Chul-Su Shin5-month

lead forecast 5-month

lead forecast ObservationsObservations

ENSO Warm

Event (‘97-’98) JJAS Mean Rainfall Anomaly

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JJAS rainfall (regional) EOF-1

CMAP (27%)

CFSv2 (22%)

Shukla and Huang (2015)

Physical processes associated with the interannual dominant mode of regional and global Asian summer monsoon rainfall in NCEP CFSv2, Climate Dynamics (in review) Thanks to Ravi Shukla

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CMAP

CFSv2

Regression of Indo-Pacific JJAS rainfall against standardized PC1 of JJAS rainfall

in extended Indian monsoon region (R)

Thanks to Ravi Shukla

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Regression of SST against standardized PC1(R) of JJAS

rainfall

MAMJJAS

SON

OBS. SSTCFSv2 SSTThanks to Ravi Shukla

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Regression of JJAS rainfall & SLP against standardized PC1 of

JJAS rainfall in Asian

-Australian monsoon / ENSO region (T)

SLP

CFSv2OBSPrecipitationThanks to Ravi Shukla

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43CFSv2

OBS

R[PC1(R), NINO3.4] = -0.42

R[PC1(T), NINO3.4] = -0.93

R[PC1(R), NINO3.4] = -0.60 R[PC1(T),NINO3.4] = -0.78 PC1(R), PC1(T)

and

NINO3.4

PC1(R)

PC1(T)

NINO3.4

PC1(R)

PC1(T)

NINO3.4

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44OBS

CFSv2

Regression of Z(p) against

standardized PC1(R), PC1(T)

and NINO3.4 PC1(R) -NINO3.4

PC1(T)

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ConclusionsROLE OF CONVECTIONHCF – alternative deep convection trigger condition and cloud base – implemented

and tested with SAS & new-SAS in CFSv2 Better representation of

convection: increased occurrence, total precip amountBetter daily and seasonal mean, seasonal cycle, onset datesLAND-ATMOSPHERE FEEDBACKSTerrestrial leg of

coupled L-A feedback pathway well represented in CFSv2. Relatively weak sensitivity of PBL depth to surface flux variations over eastern part of central India: atmospheric leg not strong

in CFSv2. Need better observations to validate.ROLE OF OCEAN ICsSubstantial dependence on ocean ICs of skill of monsoon rainfall forecastsMulti-analysis initialization results in superior prediction, on averageCHARACTERIZING MONSOON VARIABILITYConsiderable work remains to be done to beat down bias Two main modes of monsoon variability, associated with onset and decay phases of ENSO, are well reproduced and predicted in CFSv2 with most ocean IC data sets Fundamental ENSO-monsoon dynamics imperfect

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Future PlansCFSv2 code improvements and re-forecast experiments:Port to NCAR Yellowstone supercomputer for use by broader group of Monsoon Mission PIsConduct ensemble hindcasts with additional initial condition

dates Conduct 10-day simulations with high frequency output (transpose AMIP) to diagnose and improve model biases in simulating the diurnal cycle of

convectionLand-atmosphere feedbacks:Quantify L-A feedbacks in CFSv2 using coupling metrics (Dirmeyer 2011; Santanello et al. 2009, 2001; Findell and Eltahir 2003, Tawfik and Dirmeyer 2013) Evaluate sensitivity of subseasonal to seasonal IMR to Eurasian spring snow

anomalies and ‘hot-spot’ pre-monsoon soil moisture anomaliesMultiple ocean analysis initialization:Conduct additional multi-ocean analysis initialization hindcast experiments with more recent ODA products.

Assess relative roles of Indian and Pacific Oceans in IMR predictabilityImproved representation of ocean-atmosphere relevant processes:Test new configuration of CFSv2 with the new SAS, new shallow convection, HCF convective trigger and BCL cloud base in re-forecasts and long simulations IMPACTS???

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PublicationsBombardi R. J., E. K. Schneider, L. Marx, S. Halder, B. Singh, A.B. Tawfik, P.A. Dirmeyer, J.L. Kinter III 2015: Improvements in the representation of the Indian Summer Monsoon in the NCEP Climate Forecast System version 2.

Climate Dyn. DOI: 10.1007/s00382-015-2484-6Bombardi and Co-Authors, 2015: Further improvements in the representation of convection in

the NCEP Climate Forecast System version 2. (in preparation)Halder, S. and P. A. Dirmeyer, 2015: Relation of Eurasian snow cover and Indian monsoon rainfall: Delayed hydrological effect. Geophys. Res. Lett. (submitted). Shin, C.-S., B. Huang, J. Zhu, L

. Marx 2015: Predictability of the Asian summer monsoon rainfall associated with ENSO. Climate Dyn. (submitted)  Shukla, R. P. and B. Huang 2015:

Physical processes associated with the interannual dominant mode of regional and global Asian summer monsoon rainfall in NCEP CFSv2. Climate Dyn. (in review)Shukla, R. P. and B. Huang 2015: Interannual variability of the Indian summer monsoon associated with the air-sea feedback in the northern Indian Ocean. Climate Dyn. (in review)Tawfik, A. B., and P. A. Dirmeyer, 2014: A process-based framework for quantifying the atmospheric preconditioning of surface triggered convection. Geophys. Res. Lett., 41, 173-178, doi: 10.1002/2013GL057984.

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JJAS Seasonal PrecipitationThanks to Rodrigo

BombardiStDev

Improvement of seasonal variabilityStandard Deviation

Error(CTRL – TRMM)

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Thanks to Rodrigo

Bombardi

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Slide53

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Thanks to Rodrigo Bombardi

CTRL

NSAS

HCF

HCF/NSAS0.5

0.6

0.7

0.8

0.9

ACC

3.0

RMSE (mm/d)

4.0

5

.0

2.0

Slide55

Temperature

(ubiquitous

cold bias)Specific Humidity(ubiquitous

dry bias)

Kg/kgArabian SeaCentral IndiaBay of Bengal

Thanks to Rodrigo

Bombardi

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56

Climatological JJAS Rainfall & SST

JJAS Rainfall Bias ( CFSv2 – OBS) JJAS SST (Bias)

OBS.

CFSv2

Slide57

57

Climatological JJAS SLP & Wind1000 hPa

JJAS H850 hPa & Wind850 hPa

Mascarene High

OBS

.

CFSv2

OBS

.

CFSv2

Slide58

58CFSv2

OBS.

CFSv2

Climatological JJAS Wind200 hPa and H200 hPa

Climatological JJAS Mean Temp. (500 hPa to 200 hPa)OBS.CFSv2

North

–south gradient of the vertically averaged air temperature between 200 and

500

hPa

over

Indian

subcontinent

region is very important in order to sustain the monsoon

circulation.

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59

Regression of JJAS SLP

& V850 against standardized PC1 of JJAS rainfall anomalies in extended Indian monsoon region

CFSv2

OBS