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 APEC Climate Network Woo-Sung Lee / APCN Secretariat

2. APEC Climate Network  Objectives Objectives Objectives  Participating Models Participating Models Participating Models  Verification Verification Verification - Climatology - Climatology - Variability - Variability - Predictability - Predictability  Summary Summary Summary Contents

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

4. Member Economies AcronymOrganization Model Resolution Seasonal Prediction DataHindcast Data 200120022003SMIPAMIP China NCC National Climate Center /China Meteorological Administration T63L16 ⓅⓅⓉⒽ IAPInstitute of Atmospheric Physics 4   5  L2 ⓅⓉⒽ Chinese Taipei CWBCentral Weather BureauT42L18 ⓅⓅⓉⒽ 1979-1999 JapanJMAJapan Meteorological AgencyT63L40 ⓅⓉⒽ 1979-1999 Korea GDAPS/KMAKorea Meteorological AdministrationT106L21 ⓅⓅⓉⒽ 1979-19991979-1995 GCPS/KMAKorea Meteorological AdministrationT63L21 ⓅⓅⓉⒽ 1979-19991979-1998 METRI/KMA Meteorological Research Institute / Korea Meteorological Administration 4   5  L17 ⓅⓅⓉⒽ RussiaMGOMain Geophysical ObservatoryT42L14 ⓅⓅⓉⒽ 1979-1999 USA NCEP Climate Prediction Center /National Centers for Environmental Prediction T63L17 ⓅⓅⓉⒽ 1979-1999 (7 months) NSIPP/NASA National Aeronautics and Space Administration 2   2.5  L34 ⓅⓅⓉⒽ 1979-2000 VariableNameReference Precipitation CMAP/NCEP Xie and Arkin, 1997 850hPa Temperature NCEP/NCAR reanalysis Kalnay et al., 1996 500hPa GPH Participating Models APEC Climate Network

5. Composite (global mean: 2.79) OBS (global mean: 2.77) Individual Model Global Mean Bias 3.250.48 3.190.42 2.910.14 2.810.04 2.72-0.05 2.66-0.11 2.49-0.28 2.32-0.45 Composite2.790.02 Climatology: JJA mean Precipitation APEC Climate Network

6. Composite-OBS Climatology: JJA mean Precipitation APEC Climate Network

7. Composite (global mean: 9.14) OBS (global mean: 9.16) Individual Model Global Mean Bias 11.091.93 10.030.87 9.07-0.09 9.07-0.09 8.58-0.58 8.42-0.74 7.73-1.43 Composit e 9.14-0.02 Climatology: JJA mean 850hPa Temperature APEC Climate Network

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

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

10. Global Mean 1 CWB 2 GCPS 3 GDAPS 4 IAP 5 JMA 6 METRI 7 MGO 8 NCC 9 NCEP 10 NSIPP JJA OBS Comp Climatology: JJA mean Temperature & Precipitation APEC Climate Network

11. 1 CWB 2 GCPS 3 GDAPS 4 IAP 5 JMA 6 METRI 7 MGO 8 NCC 9 NCEP 10 NSIPP Climatology: JJA mean Temperature & Precipitation APEC Climate Network I. Asian Monsoon Region II. Indian Monsoon Region III IV II I III. Western North Pacific Region IV. East Asian Monsoon Region

12. Precipitation 1 CWB 2 GCPS 3 GDAPS 4 IAP 5 JMA 6 METRI 7 MGO 8 NCC 9 NCEP 10 NSIPP 850hPa Temperature Variability: Space-Time Variability (Global) APEC Climate Network SD RMSE Corr.

13. Precipitation 1 CWB 2 GCPS 3 GDAPS 4 IAP 5 JMA 6 METRI 7 MGO 8 NCC 9 NCEP 10 NSIPP 850hPa Temperature Variability: Space-Time Variability (Asian Monsoon Region) APEC Climate Network

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

15. COM P OBS Variability: Annual Cycle (Asian Monsoon Region) APEC Climate Network

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

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

18. MME I MME I Simple composite MME II MME II Singular Value Decomposition MME III MME III Composite after statistical downscaling bias correction (Coupled Pattern Projection Method). Used Model CWB, NSIPP GCPS, NCEP, JMA, GDAPS Period 21-year hindcasts from 1979 to 1999 2001/2002/2003 summer forecasts Variable Precipitation, 850hPa Temperature Multi-Model Ensemble Technique APEC Climate Network

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

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

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

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

23.  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. Summary APEC Climate Network

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