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Role of Stochastic Forcing in ENSO variability in a coupled GCM

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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  1. Role of Stochastic Forcing inENSO variability in a coupled GCM Atul Kapur Chidong Zhang Javier Zavala-Garay Acknowledgements: Ben Kirtman, Amy Clement

  2. 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

  3. Procedure CGCM Reanalysis ENSO ENSO Role of SF Compare Compare ENSO ENSO CZZ model Extract Extract SF SF

  4. 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

  5. Procedure CGCM Reanalysis ENSO ENSO Role of SF Compare Compare ENSO ENSO CZZ model Extract Extract SF SF

  6. 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

  7. Procedure CGCM Reanalysis ENSO ENSO Role of SF Compare Compare ENSO ENSO CZZ model Extract Extract SF SF

  8. 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)

  9. 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

  10. Simulations using NCEP-2 SFSeasonal Autocorrelation D O A J A F Starting month Lag (month) Total MJO Non-MJO

  11. Procedure CGCM Reanalysis ENSO ENSO Role of SF Compare Compare ENSO ENSO CZZ model Extract Extract SF SF

  12. 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

  13. 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

  14. Simulations using CGCM SFSeasonal Autocorrelation D O A J A F Starting month Lag (month) Total MJO Non-MJO

  15. Procedure CGCM Reanalysis ENSO ENSO Role of SF Compare Compare ENSO ENSO CZZ model Extract Extract SF SF

  16. 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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