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 in enso variability in a coupled gcm

Role of Stochastic Forcing inENSO variability in a coupled GCM

Atul Kapur

Chidong Zhang

Javier Zavala-Garay

Acknowledgements: Ben Kirtman, Amy Clement


Introduction

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

Procedure

CGCM

Reanalysis

ENSO

ENSO

Role of SF

Compare

Compare

ENSO

ENSO

CZZ model

Extract

Extract

SF

SF


Model and data

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


Procedure1

Procedure

CGCM

Reanalysis

ENSO

ENSO

Role of SF

Compare

Compare

ENSO

ENSO

CZZ model

Extract

Extract

SF

SF


Stochastic forcing

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


Procedure2

Procedure

CGCM

Reanalysis

ENSO

ENSO

Role of SF

Compare

Compare

ENSO

ENSO

CZZ model

Extract

Extract

SF

SF


Simulations using ncep 2 sf power spectra

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 sf seasonal variance

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 sf seasonal autocorrelation

Simulations using NCEP-2 SFSeasonal Autocorrelation

D

O

A

J

A

F

Starting

month

Lag (month)

Total

MJO

Non-MJO


Procedure3

Procedure

CGCM

Reanalysis

ENSO

ENSO

Role of SF

Compare

Compare

ENSO

ENSO

CZZ model

Extract

Extract

SF

SF


Simulations using cgcm sf power spectrum

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 sf seasonal variance

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 sf seasonal autocorrelation

Simulations using CGCM SFSeasonal Autocorrelation

D

O

A

J

A

F

Starting

month

Lag (month)

Total

MJO

Non-MJO


Procedure4

Procedure

CGCM

Reanalysis

ENSO

ENSO

Role of SF

Compare

Compare

ENSO

ENSO

CZZ model

Extract

Extract

SF

SF


Conclusions

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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