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Dynamical MJO Hindcast Experiments: Sensitivity to Initial Conditions and Air-Sea Coupling

Dynamical MJO Hindcast Experiments: Sensitivity to Initial Conditions and Air-Sea Coupling. Yehui Chang, Siegfried Schubert, Max Suarez Global Modeling and Assimilation Office NASA/Goddard Space Flight Center and Duane Waliser Jet Propulsion Laboratory, California Institute of Technology

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Dynamical MJO Hindcast Experiments: Sensitivity to Initial Conditions and Air-Sea Coupling

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  1. Dynamical MJO Hindcast Experiments: Sensitivity toInitial Conditions and Air-Sea Coupling Yehui Chang, Siegfried Schubert, Max Suarez Global Modeling and Assimilation Office NASA/Goddard Space Flight Center and Duane Waliser Jet Propulsion Laboratory, California Institute of Technology October 21, 2008

  2. Outline • Measure the capacity of GEOS-5 to predict the MJO. • Focus on the reasons for the temporal changes in MJO prediction skills. • The experiments were for the period of 1979, Nov 1996 - Aug 1997 and 2002 where there were strong intraseasonal activities.

  3. AGCM Finite-volume dynamical core (S.J. Lin) Moist physics (J. Bacmeister, S. Moorthi and M. Suarez) Physics integrated under the Earth System Modeling Framework (ESMF) Generalized vertical coord to 0.01 hPa Catchment land surface model (R. Koster) Prescribed aerosols (P. Colarco) Interactive ozone Prescribed SST, sea-ice 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z 03Z Initial States for Corrector Reanalysis (MERRA) Reanalysis Tendencies for Corrector Background (model forecast) Assimilated analysis (Application of IAU) Corrector Segment (1- and 3-hrly products) GEOS-5 Atmospheric and Coupled Model Replay System CGCM • GFDL ocean model (MOM4) • A replay of the atmospheric data analysis in the CGCM. • Has the potential to massively reduce initialization shocks Replay • Apply Incremental Analysis Increments (IAU) to reduce shock of data insertion (Bloom et al.) • IAU gradually forces the model integration throughout the 6 hour analysis period Total “observed change” Model predicted change Correction from DAS Analysis Reanalysis • MERRA • NCEP, JRA-25, ERA

  4. Dynamical MJO Hindcast Experiments: Sensitivity to Initial Conditions * Scout: coarse resolution, precursor of MERRA system* AGCM: replay Scout data to generate initial states • Models AGCM: GEOS-5 at 2X2.5X72 • Replay runs (Scout data) AGCM replay run: 1979, 1996-1997, 2002 • 35-day Hindcasts AGCM (1034 cases): 1979, Nov1996-Aug1997, 2002 ICs: daily 21z in 1979, 1996-1997, 2002 from replay runs

  5. Analysis tool • Use Scout 200mb velocity potential from 1979, Nov 1996 – Aug 1997 and 2002. • Band pass 20-90 days between 30S – 30N. • EOF analysis. Hindcasts and verifications will be projected to two leading complex EOFs.

  6. Dynamical MJO Hindcast Experiments: Sensitivity to Initial Conditions (Q IAU) * Scout: coarse resolution, precursor of MERRA system* AGCM: replay Scout data to generate initial states • Models AGCM: GEOS5at 2X2.5X72 • Replay runs (Scout data) AGCM replay run: 1979, 1996-1997, 2002 AGCM replay run (w/o Q IAU): 2002 • 35-day Hindcasts AGCM (1034 cases): 1979, Nov1996-Aug1997, 2002 AGCM ( 365 cases): 2002 (degraded Q in ICs) ICs: daily 21z in 1979, 1996-1997, 2002 from replay runs

  7. Dynamical MJO Hindcast Experiments:Sensitivity to Initial Conditions and Air-Sea Coupling • Models AGCM: GEOS-5 at 2X2.5X72 GFDL MOM4 : 1/3x1 in tropics; 1x1 in extratropics; 50 layers Coupling every 30 minutes Sea ice extent taken as observed climatology • Replay runs (Scout data) AGCM replay run: 1979, 1996-1997, 2002 AGCM replay run (w/o Q IAU): 2002 CGCM replay run: 1979-2005 replay Scout data to initialize ocean and to generate the initial states • 35-day Hindcasts AGCM (1034 cases): 1979, Nov1996-Aug1997, 2002 AGCM ( 365 cases): 2002 (degraded Q ICs) CGCM ( 669 cases): Nov1996-Aug1997, 2002 ICs: daily 21z in 1979, 1996-1997, 2002 from replay runs

  8. ICs lead to low forecasting skill May 14, 1979 Apr 3, 1997 Jun 27, 1979 May 3, 1997 Aug 3, 1979 May 6, 2002 Nov 22, 1979

  9. ICs lead to low forecasting skill: cases 1 & 2

  10. ICs lead to low forecasting skill: case 3

  11. ICs lead to high forecasting skill Apr 19, 1979 Feb 4, 1997 Apr 24, 2002 Jun 8, 1979 May 15,1997 Jun 9, 2002

  12. ICs lead to high forecasting skill: cases 1 & 2

  13. ICs lead to high forecasting skill: case 3

  14. Concluding Remarks: • There are changes in the MJO forecasting skills between 1979 and 2002. Higher forecasting skills in 2002 appear to be resulting from the better moisture observations from the satellite. • Both AGCM and CGCM predicted realistic MJO signals. Coupling tends to improve forecasting skills. Interactive air-sea coupling is essential in maintaining the intensity of the MJO in GEOS-5 coupled model. • GEOS-5 exhibits the different forecasting skills on different phases of the MJO. It has less strength to develop the dry phase of the MJO over the Indian Ocean. Skills are enhanced when intended to develop the wet phase. • The magnitudes of the surface latent heat fluxes in AGCM are much weaker than those in the coupled system. The phasing of the AGCM respond to the prescribed MJO SST anomalies and the associated surface flux anomalies are incorrectly simulated. This could be due to inadequacies in the model parameterizations. However, by forcing the atmospheric model with prescribed SST, it may be inherently difficult for AGCM to simulate realistic MJO. • The study suggests that the following additional factors for improving MJO forecasts and simulation may be required: a) accurate initial conditions (e.g. wet and dry phases of an MJO over Indian Ocean); b) coupled to an ocean model to allow for air-sea interactions and realistic surface heat and moisture fluxes; c) improve the model physical package.

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