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International Workshop on Monthly-to-Seasonal Climate Prediction

International Workshop on Monthly-to-Seasonal Climate Prediction National Taiwan Normal Univ., 25-26 October 2003. Evaluation of the APCN Multi-Model Ensemble Prediction System. AP EC C limate N etwork. Woo-Sung Lee / APCN Secretariat. Contents. Objectives

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International Workshop on Monthly-to-Seasonal Climate Prediction

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  1. International Workshop on Monthly-to-Seasonal Climate Prediction National Taiwan Normal Univ., 25-26 October 2003 Evaluation of the APCN Multi-Model Ensemble Prediction System APECClimate Network Woo-Sung Lee / APCN Secretariat

  2. Contents • Objectives • Participating Models • Verification • - Climatology • - Variability • - Predictability • Summary APECClimate Network

  3. Objectives • Is multi-model ensemble prediction superior to single model prediction? • Where do we stand? Multi-modelEnsemble Reduce bias in model formulation Reduce bias in Initial condition APECClimate Network Performance of the APCN MME System in terms of: • climatology • variability • Predictability

  4. Participating Models APECClimate Network

  5. Climatology: JJA mean Precipitation Composite (global mean: 2.79) APECClimate Network OBS (global mean: 2.77)

  6. Climatology: JJA mean Precipitation Composite-OBS APECClimate Network

  7. Climatology: JJA mean 850hPa Temperature Composite (global mean: 9.14) APECClimate Network OBS (global mean: 9.16)

  8. Climatology: JJA mean 850hPa Temperature_Eddy Composite APECClimate Network OBS

  9. Climatology: JJA mean 850hPa Temperature Composite-OBS APECClimate Network

  10. Climatology: JJA mean Temperature & Precipitation Global Mean OBS APECClimate Network Comp JJA 1 CWB2 GCPS3 GDAPS4 IAP5 JMA 6 METRI7 MGO8 NCC9 NCEP10 NSIPP

  11. I IV II III Climatology: JJA mean Temperature & Precipitation I. Asian Monsoon Region II. Indian Monsoon Region APECClimate Network III. Western North Pacific Region IV. East Asian Monsoon Region 1 CWB2 GCPS3 GDAPS4 IAP5 JMA 6 METRI7 MGO8 NCC9 NCEP10 NSIPP

  12. Corr. RMSE SD Variability: Space-Time Variability (Global) 850hPa Temperature Precipitation APECClimate Network 1 CWB2 GCPS3 GDAPS4 IAP5 JMA 6 METRI7 MGO8 NCC9 NCEP10 NSIPP

  13. Variability: Space-Time Variability (Asian Monsoon Region) 850hPa Temperature Precipitation APECClimate Network 1 CWB2 GCPS3 GDAPS4 IAP5 JMA 6 METRI7 MGO8 NCC9 NCEP10 NSIPP

  14. Variability: Annual Cycle (Global) Composite APECClimate Network First Harmonic Of Precipitation CMAP January April July October

  15. Variability: Annual Cycle(Asian Monsoon Region) APECClimate Network COMP OBS

  16. Variability: Inter-annual Variability (Equator) Precipitation Anomaly (mm/day) at Equator OBS Comp CWB GCPS GDAPS JMA MGO NSIPP APECClimate Network

  17. Variability: Inter-annual Variability (Asian Monsoon Region) Empirical Orthogonal Function(1st Mode) COMP(30.8%) CWB(40.8%) GCPS(42.5%) OBS(24.4%) APECClimate Network NCEP(49.9%) MGO(28.8%) JMA(22.0%) GDAPS(24.9%) NSIPP(34.3%) Pricipal component(1st Mode)

  18. Multi-Model Ensemble Technique MME ISimple composite MME IISingular Value Decomposition MME III Composite after statistical downscaling bias correction (Coupled Pattern Projection Method). APECClimate Network

  19. Pattern Correlation: 1979-1999 Hindcast, 2002 JJA forecast Precipitation (Global Mean) APECClimate Network 850hPa Temperature (Global)

  20. Pattern Correlation: 1979-1999 Hindcast, 2002 JJA forecast Precipitation (Asian Monsoon Region) APECClimate Network 850hPa Temperature (Asian Monsoon Region)

  21. 2002 Summer Forecast Precipitation Anomaly (JJA mean) MME2(0.41) MME3(0.46) MME1(0.42) APECClimate Network CWB(0.06) OBS GCPS(0.21) NSIPP(0.35) GDAPS(0.07) NCEP(0.41)

  22. 2002 Summer Forecast 850hPa Temperature Anomaly (JJA mean) MME2(0.40) MME3(0.51) MME1(0.40) APECClimate Network CWB(0.1) OBS GCPS(0.39) NSIPP(0.33) GDAPS(0.12) NCEP(0.25)

  23. Summary • Composite of the APCN participating models reproduces major features of the observation, while there is a considerable diversity among the models. • In the global sense, APCN MME system provides superior performance to any single model prediction in term of climatology, variability and predictability. • Statistical bias correct of individual models prior to multi-model ensemble(MME3) enhances predictability compare to simple model composite(MME1) or SVD superensemble(MME2) • However, most of the models show significant deficiency in simulating regional climate over the Asian monsoon region. Thus the MMEs are relatively not effective. Model physics needs to be improved for better Asian monsoon prediction. APECClimate Network

  24. Predictability Predictability according to participating model numbers Global mean Precipitation APECClimate Network

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