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).

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Role of Stochastic Forcing in ENSO variability in a coupled GCM

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Role of Stochastic Forcing inENSO 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)

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

Ocean


Procedure

CGCM

Reanalysis

ENSO

ENSO

Role of SF

Compare

Compare

ENSO

ENSO

CZZ model

Extract

Extract

SF

SF


Model and Data

  • 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)

CGCM

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

Reanalysis

NCEP-2 Reanalysis (1979-2007)

CZZ model


Procedure

CGCM

Reanalysis

ENSO

ENSO

Role of SF

Compare

Compare

ENSO

ENSO

CZZ model

Extract

Extract

SF

SF


Stochastic Forcing

  • Statistical model of u10 anomalies predicted by SST anomalies

    u10 = A sst + uResidual

  • Wavenumber frequency spectra:

(Hilbert EOF)

(CGCM)

Caveats: Linear, Contemporaneous, Additive

Coupled

Residual

MJO

Inter-

annual

Period

Intra-

seasonal

Zonal wavenumber


Procedure

CGCM

Reanalysis

ENSO

ENSO

Role of SF

Compare

Compare

ENSO

ENSO

CZZ model

Extract

Extract

SF

SF


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

CZZ

95 % confid

NCEP-2

Power * freq

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)


Simulations using NCEP-2 SFSeasonal Variance

3

2

1

0

-1

-2

  • Warm phase better simulated than cold phase in terms of seasonal variance

CZZ warm

NCEP-2 warm

CZZ cold

NCEP-2 cold

Normalized

variance

J F M A M J J A S O N D


Simulations using NCEP-2 SFSeasonal Autocorrelation

D

O

A

J

A

F

Starting

month

Lag (month)

Total

MJO

Non-MJO


Procedure

CGCM

Reanalysis

ENSO

ENSO

Role of SF

Compare

Compare

ENSO

ENSO

CZZ model

Extract

Extract

SF

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

CZZ

95 % confid

CGCM


Simulations using CGCM SFSeasonal Variance

  • SF unable to reproduce the seasonal variance of ENSO exhibited by the BMRC CGCM

  • Contribution of non-MJO appears to be higher than MJO

CZZ warm

CGCM warm

CZZ cold

CGCM cold

Total SF

MJO

Non-MJO

Norm.

variance


Simulations using CGCM SFSeasonal Autocorrelation

D

O

A

J

A

F

Starting

month

Lag (month)

Total

MJO

Non-MJO


Procedure

CGCM

Reanalysis

ENSO

ENSO

Role of SF

Compare

Compare

ENSO

ENSO

CZZ model

Extract

Extract

SF

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


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