role of stochastic forcing in enso variability in a coupled gcm
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Role of Stochastic Forcing in ENSO variability in a coupled GCM. Atul Kapur Chidong Zhang Javier Zavala-Garay. Acknowledgements: Ben Kirtman, Amy Clement. Introduction. Stochastic Forcing (SF) Atmospheric variability uncoupled to the ocean. Atmosphere. Coupled Dynamics. Uncoupled (SF). - PowerPoint PPT PresentationTRANSCRIPT
Role of Stochastic Forcing inENSO variability in a coupled GCM
Atul KapurChidong Zhang
Javier Zavala-Garay
Acknowledgements: Ben Kirtman, Amy Clement
• Stochastic Forcing (SF)– Atmospheric variability uncoupled to the ocean
Annual Cycle
• Extent to which the ENSO in CGCMs is driven by SF• Contributions of Madden Julian Oscillation (MJO) and non-MJO• Dynamical regime of underlying coupled system – Stable or
Unstable
Introduction
ProcedureENSO
SFSF SFSF
ENSO
CZZ model
ENSO ENSO
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Model and Data
Variant of Zebiak and Cane (1987) model• Chaos switched off (Mantua and Battisti 1995)
• Admits daily SF: Decorrelation time of tropical weather ~ 3-8 days
Bureau of Meteorology Research Center (BMRC) CGCM (Zhong et al. 2004)
• A 163-year run• Realistic ENSO (Wu et al. 2002) and intraseasonal
variability (Zhang et al. 2006)
NCEP-2 Reanalysis (1979-2007)
CZZ model
ProcedureENSO
SFSF SFSF
ENSO
CZZ model
ENSO ENSO
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Stochastic Forcing• Statistical model of u10 anomalies predicted by SST anomalies
u10 = A sst + uResidual
• Wavenumber frequency spectra:
Zonal wavenumber
Period
Inter-annual
Intra-seasonal
Coupled Residual MJO
Caveats: Linear, Contemporaneous, Additive(CGCM)
(Hilbert EOF)
ProcedureENSO
SFSF SFSF
ENSO
CZZ model
ENSO ENSO
Extract Extract
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Simulations using NCEP-2 SFPower Spectra
• CZZ model able to reproduce spectrum• ENSO statistics better for MJO than non-MJO forcing• CZZ model performs best in weakly stable regime
CZZ95 % confid
NCEP-2
0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0
Freq (cycles/yr)
Pow
er *
freq
Simulations using NCEP-2 SFSeasonal Variance
CZZ warmNCEP-2 warm
CZZ coldNCEP-2 cold
• Warm phase better simulated than cold phase in terms of seasonal variance
3
2
1
0
-1
-2J F M A M J J A S O N D
Normalizedvariance
Simulations using NCEP-2 SFSeasonal Autocorrelation
Total MJO Non-MJO
Lag (month)
D
O
A
J
A
F
Startingmonth
ProcedureENSO
SFSF SFSF
ENSO
CZZ model
ENSO ENSO
Extract Extract
CompareCompare CompareCompareRole of SF
Simulations using CGCM SFPower Spectrum
• SF is able to reproduce even local peaks in power spectrum• Results using MJO compare better to “truth” than non-MJO
CZZ95 % confid
CGCM
Simulations using CGCM SFSeasonal Variance
Total SF MJO Non-MJO CZZ warmCGCM warm
CZZ coldCGCM cold
• SF unable to reproduce the seasonal variance of ENSO exhibited by the BMRC CGCM
• Contribution of non-MJO appears to be higher than MJO
Norm.variance
Simulations using CGCM SFSeasonal Autocorrelation
Total MJO Non-MJO
D
O
A
J
A
F
Startingmonth
Lag (month)
ProcedureENSO
SFSF SFSF
ENSO
CZZ model
ENSO ENSO
Extract Extract
CompareCompare CompareCompareRole of SF
Conclusions• Role of SF in BMRC CGCM ENSO– At least the warm phase can be reasonably
simulated using SF– MJO contribution is higher than non-MJO– Underlying dynamical state of coupled system
appears to be weakly stable– Seasonality of ENSO cannot be reproduced by SF
• Procedure can be implemented on any CGCM– Even on runs with long temporal span