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Diagnosing ENSO and MJO signal in the new NCEP coupled model

Diagnosing ENSO and MJO signal in the new NCEP coupled model. Wanqiu Wang, Suranjana Saha, Hua-Lu Pan Sudhir Nadiga, and Glenn White. Acknowledgements: Dave Behringer, Scott Harper, Qin Zhang, Shrinivas Moorthi and all of the EMC Climate and Weather Modeling Branch. Background.

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Diagnosing ENSO and MJO signal in the new NCEP coupled model

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  1. Diagnosing ENSO and MJO signal in the new NCEP coupled model Wanqiu Wang, Suranjana Saha, Hua-Lu Pan Sudhir Nadiga, and Glenn White Acknowledgements: Dave Behringer, Scott Harper, Qin Zhang, Shrinivas Moorthi and all of the EMC Climate and Weather Modeling Branch.

  2. Background Current NCEP operational coupled model (M. Ji, A. Kumar, A. Leetmaa, 1994) • Old version of NCEP MRF model • Old version of GFDL MOM • Coupling over tropical Pacific • Flux correction at air-sea interface New NCEP Coupled Forecast System Model (CFS03) • NCEP Global Forecast System 2003 • Global GFDL MOM3 • No flux adjustment

  3. Objective • Assess ENSO and MJO simulation by the new NCEP coupled model

  4. Outline • The model • The simulation • Diagnoses • Conclusions

  5. The coupled model (CFS03) • Global Forecast System 2003 (GFS03) • T62 in horizontal; 64 layers in vertical • Recent upgrades in model physics • Solar radiation (Hou, 1996) • cumulus convection (Hong and Pan, 1998) • gravity wave drag (Kim and Arakawa, 1995) • cloud water/ice (Zhao and Carr,1997) 1. Atmospheric component 2. Oceanic component • GFDL MOM3 (Pacanowski and Griffies, 1998) • 1/3°´1° in tropics; 1°´1° in extratropics; 40 layers • Quasi-global domain (74°S to 64°N) • Free surface 3. Coupled model • Once-a-day coupling • Sea ice extent taken as observed climatology

  6. Simulation • Free integration of 32+ years • Initial date: 1 January 2002 • Initial conditions • Atmosphere: NCEP GDAS • Ocean: NCEP GODAS Observations • ERSST: Extended reconstructed SST (Smith and Reynolds, 2003) • R2: NCEP/DOE reanalysis 2 (Kanamitsu et al., 2002) • GODAS: NCEP global ocean data assimilation (Behringer, personal communication)

  7. Diagnoses ENSO variability • Nino3.4 SST • EOF modes of SSH • Composites of Tau, SST, SSH for El Nino events

  8. Nino3.4 SST anomalies (K) Composite

  9. Diagnoses MJO variability The runs • Coupled simulation by CFS03 (21 years) • AMIP simulation by GFS03 for 1982-2002 • Wavenumber-frequency spectra • EOF modes of Precipitation, U850, and U200

  10. Conclusions ENSO Simulation • CFS03 simulates an ENSO with amplitude and periodicity comparable to that observed. But the simulated ENSO appears to be too regular. • CFS03 reproduces the observed seasonality of ENSO variability, although the initial warming from January to May of the simulated El Nino events is somewhat too strong. • Diagnoses of the simulated ENSO suggest that different mechanisms (delayed oscillator, western Pacific oscillator, recharge oscillator, and advective-reflective oscillator) may all contribute to the ENSO variability.

  11. Conclusions MJO Simulation • Compared with GFS03, CFS03 simulates a more realistic MJO • frequency range more narrow and closer to the observed • convection and circulation more coherent • propagation better organized • The MJO in CFS03 is too strong and a little too slow. • Precipitation, solar radiation, and SST in CFS03 are not as well organized as in the analyses • Latent heat flux associated with the MJO in CFS03 is not consistent with that in the reanalysis, possibly due to that the mean surface westerly in the Indian ocean and western Pacific is too weak

  12. PC1 leads PC2 PC2leads PC1

  13. Diagnoses Climatology • Sea surface temperature (SST) • Surface momentum flux (Tau) • Sea surface height (SSH)

  14. SSH and Nino3.4 SST in phase SSH lags Nino3.4 SST by one quarter of the period Consistent with Hasegawa and Hanawa (2003)

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