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Summer and Winter Seasonal Prediction over CONUS with RSM Regional Climate model

Summer and Winter Seasonal Prediction over CONUS with RSM Regional Climate model. Jun Wang and Henry Juang EMC/NCEP. This work is sponsored by the GAPP program of NOAA/OAR/NCPO. Contents. NCEP RSM RCM run Summer seasonal prediction of CFS and RSM RCM

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Summer and Winter Seasonal Prediction over CONUS with RSM Regional Climate model

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  1. Summer and Winter Seasonal Prediction over CONUS with RSM Regional Climate model Jun Wang and Henry Juang EMC/NCEP This work is sponsored by the GAPP program of NOAA/OAR/NCPO

  2. Contents • NCEP RSM RCM run • Summer seasonal prediction of CFS and RSM RCM • Winter seasonal prediction of CFS and RSM RCM • Statistical score • Summary

  3. RSM Regional Climate Model • Model: Nested GSM+RSM GSM: GFS2003 T62L28 RSM: RSM2004 50km, 28 levels • Domain: CONUS • Hindcast: * A 3-member ensemble of 1 month lead forecast from 1982-2001 * Initial Condition: Last day of previous month and the first two days of current month * Boundary Condition: predicted SST from CFS, RSM gets boundary condition from GSM

  4. RSM RCM domain

  5. Summer 1999 Casea strong U.S. monsoon event • Climatology: 3 member ensemble from 1982 to 2001 • Observed and predicted anomalies: Sst Precip 2 meter Temp Surface Latent hear flux 200hpa hgt 500hpa hgt

  6. SST: Clim and Anomaly CFS NARR

  7. JJA Monthly Mean Precip CFS RSM RCM NARR

  8. JJA Monthly Mean TMP2m GR2 NARR CFS RSM RCM

  9. JJA Monthly Mean LHTFL CFS RSM RCM NARR

  10. JJA Monthly Mean 200mb HGT RSM RCM NARR CFS

  11. JJA Monthly Mean 500mb HGT CFS RSM RCM NARR

  12. Winter 1983 case • Climatology • Observed and predicted anomalies Sst Precip 2 meter Temp 200hpa HGT 500hpa HGT

  13. SST Climatology and anomaly CFS NARR

  14. JFM monthly mean Precip CFS RSM RCM NARR

  15. JFM monthly mean TMP2m NARR CFS RSM RCM

  16. JFM monthly mean 200hpa HGT RSM RCM CFS NARR

  17. JFM monthly mean 200hpa HGT RSM RCM NARR CFS

  18. Statistical Score • Full field correlation H500 Precip • Anomaly correlation H500 Precip

  19. One month lead RSM RCM Full-field Corr

  20. One month lead RSM RCM Anomaly Corr

  21. Summary • Both CFS and RSM RCM show some skills in winter ENSO event and in monsoon event. • The large scale features of the forecast from RSM RCM is consist with those features from CFS prediction. • The forecast RSM catches more realistic small scale details. The interactions between small scale phenomena and large skill phenomena change the seasonal climate prediction, the RSM RCM creates a value –added products based on CFS.

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