Progress in Seasonal Forecasting at NCEP

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Presentation transcript:

Progress in Seasonal Forecasting at NCEP M C Progress in Seasonal Forecasting at NCEP Hindcast Skill in the New Coupled NCEP Ocean-Atmosphere Model MJO Forecast Experiments

E M C Hindcast Skill in the New Coupled NCEP Ocean-Atmosphere Model Suranjana Saha, Wanqiu Wang, Hua-Lu Pan and the NCEP/EMC Climate and Weather Modeling Branch Environmental Modeling Center, NCEP/NWS/NOAA Special Acknowledgements : Sudhir Nadiga, Jiande Wang, Qin Zhang, Shrinivas Moorthi, Huug van den Dool

Introduction A new global coupled atmosphere-ocean model has recently been developed at NCEP/EMC. Components a) the T62/64-layer version of the current NCEP atmospheric GFS (Global Forecast System) model and b) the 40-level GFDL Modular Ocean Model (version 3) Note: Direct coupling with no flux correction This model will replace the current operational NCEP coupled model (CMP14) for SST prediction in 2004.

AMIP run: Rotated EOF (Nov-Mar) Z200 NCEP Reanalysis AMIP

NCEP Global Ocean Data Assimilation System (GODAS) Implemented September 2003 Real time global ocean data base ARGO (1000 reports/month), altimeter, XBTs, buoys, SST Community access to ocean data Standardized formats with embedded QC meta data Global ocean data assimilation system Salinity analysis (improved use of altimeter observations) Upgraded GFDL-MOM ocean model (MOM-3) Prepare for GODAE

Coupled Model Simulation 38 Year Mean SST Bias

Observed Coupled Red: monthly bias

Composite Warm and Cold Events Events exceed ERSST variance by 1.0 SD (warm) 0.75 SD (cold) Heavy black line is mean - 36 mo +36 mo Peak

SST Climatology on Equator Red: coupled model

Hindcast Skill Assessment 5-member ensemble over 22 years from 1981-2002 January and April initial conditions Other months to follow 9 month runs Initial atmospheric states 0000 GMT 19, 20, 21, 22, and 23 for each month Reanalysis-2 archive . Initial ocean states NCEP GODAS (Global Ocean Data Assimilation System) 0000 GMT 21st of each month Same for all runs GODAS operational September 2003

Hindcast Skill Assessment (cont) So far 220 runs have been made Hindcast skill Estimated after doing a bias correction for each year Uses model climatology based on the other years Anomaly correlation skill score for Nino 3.4 region SST prediction Skill maps Global SST U.S. temperature and precipitation. Comparisons with CMP14 and CASST

Ensemble Mean CASST CMP14 April IC

CASST Ensemble Mean January IC CMP14

Observed 6 Month Lead (November) from April IC SST anomaly for 1981-2002 Note Amplitudes

Observed 6 Month Lead (August) from January IC SST anomaly for 1981-2002 Note Amplitudes

Seasonally (3 month) Averaged SST Anomaly Correlation Hindcast Seasonally (3 month) Averaged SST Anomaly Correlation April IC Note: large & persistent skill in tropics

SST Anomaly Correlation Hindcast Monthly Averaged SST Anomaly Correlation April IC June-September Left: New Coupled System Right: CMP14

SST Anomaly Correlation Hindcast Monthly Averaged SST Anomaly Correlation April IC October-January Left: New Coupled System Right: CMP14

Seasonally (3 month) Averaged SST Anomaly Correlation Hindcast Seasonally (3 month) Averaged SST Anomaly Correlation January IC Note: large & persistent skill in tropics

SST Anomaly Correlation Hindcast Seasonally Averaged SST Anomaly Correlation January IC Left: New Coupled System Right: CMP14

U. S. Surface Temperature Hindcast 3 month Averaged U. S. Surface Temperature Anomaly Correlation April IC Note: areas of persistent skill > 60% at up to 6 month lead

U. S. Surface Temperature Hindcast Skill 3 Month Averages April IC Comparison with CPC CCA Method Note: Coupled System skill Has different geographical Distribution than CCA

U. S. Surface Temperature Hindcast Skill 3 Month Averages January IC Comparison with CPC CCA Method Note: Coupled System skill Has different geographical Distribution than CCA

Note: areas of persistent skill > 60% at up to 6 month lead Hindcast 3 month Averaged U. S. Precipitation Anomaly Correlation April IC Note: areas of persistent skill > 60% at up to 6 month lead

Note: Coupled System skill U. S. Precipitation Hindcast Skill 3 Month Averages April IC Comparison with CPC CCA Method Note: Coupled System skill complementary to CCA

Note: Coupled System skill U. S. Precipitation Hindcast Skill 3 Month Averages January IC Comparison with CPC CCA Method Note: Coupled System skill complementary to CCA

MJO Forecasts (W. Wang) Experiments damp: GFS03 with damped SST anomalies clim: GFS03 with climatological SSTs amip: GFS03 with observed SSTs coup: CFS03 with MOM3 ocean analysis All forecasts to 45 days Composite results

(Max pos. ampl. Over IO) (Decay) (Initiation) (Max pos. ampl. Over WPAC) Phase 3 (Max pos. ampl. Over IO) Phase 2 Phase 4 Phase 1 (Decay) (Initiation)

Note: coupling necessary for propagation in Phases 1-3 Days 1-30 Observed SST Expt. Damped Climo AMIP Coupled Note: coupling necessary for propagation in Phases 1-3

Summary and Conclusions CFS03 hindcast skill for January and April initial conditions (1981-2002 ) have been evaluated For April, the SST AC skill over Nino 3.4 is better than CMP14 and CASST at all leads For January, the SST AC skill over Nino-3.4 is better than CMP14 and CASST for all leads, except lead 2

Summary and Conclusions (cont) Ensemble mean forecasts for U.S. temperature and precipitation show comparable skill to CPC’s CCA method. This skill may be complementary to CCA as it manifests itself in different geographical areas and can be used in CPC’s operational seasonal consolidated forecast. Hindcasts for the rest of the calendar months are being performed Implementation is being planned for late 2004

Backup Slides

New Climate Positions at NCEP/EMC UCAR Visiting Scientist Position at NCEP/EMC Work with NCEP Coupled Model http://www.earthworks-jobs.com/climate/ucar3101.html NCEP Climate Team Leader (GS-15) Coordinate development activities with community Provide strategic guidance on NCEP’s Climate Numerical Modeling activities Participate actively in development activities with EMC staff

RMS Error April

RMS Error January