1 / 30

Predictability and Prediction of Tropical Intra-Seasonal Oscillation (TISO)

Predictability and Prediction of Tropical Intra-Seasonal Oscillation (TISO). Xiouhua (Joshua) Fu. International Pacific Research Center (IPRC) University of Hawaii at Manoa, Honolulu, Hawaii. Collaborators: Bin Wang, Bo Yang, Qing Bao.

lenore
Download Presentation

Predictability and Prediction of Tropical Intra-Seasonal Oscillation (TISO)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Predictability and Prediction of Tropical Intra-Seasonal Oscillation (TISO) Xiouhua (Joshua) Fu International Pacific Research Center (IPRC) University of Hawaii at Manoa, Honolulu, Hawaii Collaborators: Bin Wang, Bo Yang, Qing Bao NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  2. Global Impacts of TISO Boreal-summer ISO The TISO Eastward Boreal-winter MJO NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  3. Intra-Seasonal Variability NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  4. Air-sea Coupling Extends the Predictability of TISO ATM Forecast Error Signal CPL Forecast Error [ATM: 17 days;CPL: 24 days] Fu and Wang et al. 2007, JAS NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  5. Air-Sea Coupling Processes  + Wang and Xie (1998) NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  6. Specific Questions to be Addressed • How will different surface boundary conditions (five different SST settings) affect the TISO predictability (with “perfect” model assumption)? What are the best SST configurations for TISO hindcasts and operational forecasts? •  What is the “practical” predictability of TISO in a dynamical model (IPRC_HcGCM)? Fu et al. (2007), MWR, In press NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  7. IPRC/UH Hybrid coupled GCM (IPRC_HcGCM) • Atmospheric component: • ECHAM-4 T30L19 AGCM • (Roeckner et al. 1996) • Ocean component: • Wang-Li-Fu intermediate upper ocean model (0.5ox0.5o) • (Wang et al. 1995; Fu and Wang 2001) •  Wang, Li, and Chang (1995): upper-ocean thermodynamics • McCreary and Yu (1992): upper-ocean dynamics •  Jin (1997) : mean and ENSO (intermediate fully coupled model) Zebiak and Cane (1987): ENSO (intermediate anomaly coupled model) • Fully coupling without heat flux correction • Coupling region: Tropical Indian and Pacific Oceans (30oS-30oN) • Coupling interval: Once per day Fu et al. 2003; Fu and Wang 2004 (TISO) NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  8. Ensemble Experiments With Five Different SST Settings NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  9. Experimental Design 2 TISO events (boreal-summer) in a coupled control run (Targets) 4 phases for each TISO event (Starting points) 10 ensemble forecasts starting from each phase of selected events under five different SST settings (80 forecasts per SST setting) Data Processing TISO: 20-90-day filtered daily rainfall NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  10. Event-I Event-II Targets Coupled Forecasts (CPL) Ten-ensemble-mean Atmosphere-only Forecasts (ATM) Boreal-summer Rainfall over (65oE-120oE) NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  11. TISOPredictability Measured by ACC ACC (Tier-1/Tier-2) ATM/ATMp: 30 days CPL/ATMd: 42 days Ensemble means NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  12. TISO Forecast Experiments with IPRC_HcGCM An MJO event observed during TOGA-COARE 1993, Jan. 01-Feb. 10 (boreal-winter) A monsoon ISO event 2006, Jun. 11-Jul. 11 (boreal-summer)  Initialized with NCEP reanalysis  100-ensemble forecasts for each event NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  13. MJO Events Observed during TOGA-COARE 1992 1993 VP-200 U-850 OLR Vitart et al. (2007) NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  14. MJO Forecasted by ECMWF Operational Seasonal Forecast System Dec. 31,1992 Feb. 1,1993 VP-200 Vitart et al. (2007) NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  15. MJO Forecasted by the IPRC_HcGCM Rainfall With default cumulus scheme With revised cumulus scheme Observation NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  16. MJO Forecasted by the IPRC_HcGCM OLR (U850 - U200) NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  17. Atmosphere-only Forecast With revised cumulus scheme NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  18. An Experimental Forecast of Monsoon ISO Boreal-summer Rainfall over (65oE-120oE) NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  19. Summary  The TISO predictability in IPRC/UH_HcGCM reaches about 40 days averaged over the Southeast Asia. The predictability in the atmosphere-only model is about 30 days. Interactive air-sea coupling extends the TISO predictability by about 10 days.  Tier-two system could reach similar TISO predictability as tier-one system, suggesting that using observed high-frequency SST for TISO hindcasts and using interactive air-sea coupling and forecasted daily SST for real-time forecastsare good options. • The optimistic side of this TISO forecast experiment suggests that some current dynamical models are ready to carry out intraseasonal forecast and will provide useful information for extended weather forecast. NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  20. Thanks NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  21. Air-sea coupling enhances the northward propagating monsoon ISO (IPRC_HcGCM) (65oE-95oE) Fu et al. 2003, Fu and Wang 2004 NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  22. Coupling also extends the predictability of weather ATM Forecast Error Signal CPL Forecast Error ATM/(Negative): 8 days CPL/(Positive): 16 days (During break-to-active transition) NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  23. Two Methods to Measure the Predictability Ratio of Signal- to- Forecast Error Forecast Time Anomaly Correlation Coefficient (ACC) 1.0 0.5 Forecast Time NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  24. Wheeler-Hendon Phase-space MJO Diagram PC-2 PC-2 PC-1 PC-1 Model Observation NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  25. Filtered rainfall over (80oE–100oE, 5oS-5oN) Phase 3 Phase 2 Phase 4 Fu et al. 2006, JAS Phase 1 NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  26. TISOPredictability Measured by Signal-to-Error Ratio ATM Forecast Error CPL Forecast Error Signal ATM/ATMp: 24 days CPL/ATMd: 34 days Individual ensembles NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  27. TISOPredictability Measured by ACC ACC ATM/ATMp: 21 days CPL/ATMd: 30 days Individual ensembles NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  28. MJO Forecasted by the IPRC_HcGCM OBS Revised Default Atmosphere only NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  29. Signal-to-Error Method Control run Perturbed Forecasts (Signal) L=25 days for TISO (Forecast Error) Waliser et al. (2003) NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

  30. SSTs in Five Experiments Control Mixed-layer “Smoothed” Coupled/Daily Damped persistent NOAA 32nd CDPW, Tallahassee, Oct 22-26, 2007

More Related