1. CliPAS Bin Wang Multi-Model Ensemble Seasonal Prediction System Development IPRC, University of Hawaii, USA APCC International Research Project

2. CliPAS This Year Achievements A coordinated research community is extended consisting of twelve institutions and a large group of leading scientist in the field of climate prediction from USA, Korea, Japan, Australia, and China. The second CliPAS project meeting was successfully held at University of Hawaii on 9-11 th January, 2006. 24-year (1981-2004) MME hindcast experimental dataset are produced for 4 seasons. The dataset consists of 6 one-tier and 7 two-tier model systems (for 4 seasons from 8 models and 2 seasons from 5 models). Scientific achievements are made on seasonal climate prediction and predictability. Metrics for validating hindcast has been designed.

3. CliPAS The Current Status of HFP Production Two-Tier systems CGCM AGCM NASA 2 seasons CFS (NCEP) 4 seasons SNU 4 seasons FSU 2 seasons GFDL 2 seasons ECHAM(UH) 2 seasons CAM2 (UH) 4 seasons SNU/KMA 4 seasons Statistical- Dynamical SST prediction (SNU) One-Tier systems SINTEX-F 4 seasons UH Hybrid 2 seasons IAP 4 seasons GFDL 4 seasons *NCEP 4 seasons * NCEP two-tier prediction was forced by CFS SST prediction

4. CliPAS InstituteAGCMResolutionOGCMResolution Ensemble Member Reference FRCGC ECHAM4T106 L19OPA 8.22 o cos(lat)x2 o lon L319Luo et al. (2005) GFDL R30R30L14R30R30 L1810Delworth et al. (2002) NASA NSIPP1 2 o lat x 2.5 o lon L34 Poseidon V4 1/3 o lat x 5/8 o lon L273Vintzileos et al. (2005) NCEP GFST62 L64MOM31/3 o lat x 1 o lon L4015Saha et al. (2005) SNU T42 L21MOM2.21/3 o lat x 1 o lon L326Kug et al. (2005) UH ECHAM4T31 L19UH Ocean1 o lat x 2 o lon L210Fu and Wang (2001) APCC/CliPAS Tier-1 Models Model Descriptions of CliPAS System InstituteAGCMResolution Ensemble Member SST BCReference FSU FSUGCMT63 L2710 SNU SST forecast Cocke, S. and T.E. LaRow (2000) GFDL AM22 o lat x 2.5 o lon L2410 SNU SST forecastAnderson et al. (2004) IAP LASG2.8 o lat x 2.8 o lon L266 SNU SST forecastWang et al. (2004) NCEP GFST62 L6415 CFS SST forecastKanamitsu et al. (2002) SNU/KMA GCPST63 L216 SNU SST forecastKang et al. (2004) UH CAM2T42 L2610 SNU SST forecastLiu et al. (2005) UH ECHAM4T31 L1910 SNU SST forecastRoeckner et al. (1996) APCC/CliPAS Tier-2 Models

5. CliPAS Scientific Achievements Part I. Assessment of the current status of the climate model’s performances Part II. Improvement of the MME techniques Part III. Predictability of coupled GCM forecast Part IV. Intraseasonal Prediction and Predictability  Current status of simulating Long-term mean and annual cycle of precipitation  Current Skills of MME one-month lead seasonal forecast: NINO 3.4 SST, Rainfall, temperature  Impacts of systematic errors on ENSO and Tropical Precipitation  Predictability of coupled model for precipitation, ENSO, Asian-Australian Monsoon

6. CliPAS Background: Climate Prediction Theories and Reviews Charney and Shukla (1977, 1981), Lorenz (1982) Palmer (1993), Palmer and Shukla (2000), Palmer and Hagedorn (2006), Kang and Shukla (2006), Waliser (2006) Break through in ENSO forecast: Cane and Zebiak (1985) Statistical approaches : Barnston 1994), Hastenrass (1995) Two-tier : AGCM forced by predicted SST Bengtsson et al. (1993), Barnet et al. (1994), Levezey et al. (1996), Wang et al. (2005), One-Tier : Coupled A-OGCM: Ji et al. (1996), Stockdale et al. (1998) MME: Krishnamurti et al. (1999, 2006), Doblas-Reyers et al. (2000) Dynamical vs statistical prediction : Oldenborgh et al. (2003 for ECMWF system), Saha et al. (2006 for NCEP system) Milestones Projects for MME Prediction PROVOST (EU), DSP (USA), SMIP (WCRP), CTB (USA), DEMETER (EU), CliPAS (APCC) Operational MME prediction ECMWF, IRI, APCC

7. CliPAS Part I. Assessment of the current status of the climate model’s performances Annual Cycle and its Linkage with Seasonal Prediction skill  Annual Cycle and its Linkage with Seasonal Prediction skill  Monsoon Domain and Rainy Season Evolution over Asian Sub-monsoon Regions Sub-monsoon Regions  Prediction Skills of NINO 3.4 SST  Prediction Skills of Temperature and Precipitation  Impact of Model Systematic Error for SST and Precipitation  One-Tier vs Two-Tier MME prediction

8. CliPAS Annual Mean Precipitation The models’ performance in simulating and forecasting seasonal mean states is closely related to the models’ capability in predicting seasonal anomalies. Performance on Annual Cycle and its Linkage with Seasonal Prediction skill Performance on Annual mean & Annual Cycle Linkage to Seasonal prediction skill Pattern Correlation Skill over the Global Tropics (0-360E, 30S-30N) Precipitation

9. CliPAS Monsoon Domain and Rainy Season evolution over Asian Sub-monsoon Regions [5-30N, 60-105E] [5-20N, 105-160E] [20-45N, 110-140E]

10. CliPAS Prediction Skills of NINO 3.4 SST Forecast lead month Anomaly Correlation Tier-1 MME Dynamic-Statistical Model Persistence Feb May Aug Nov El Nino Growth La Nina Growth El Nino Decay La Nina Decay Normal Seasonal Initial Conditions ENSO Phase of Initial month Tier-1 MME Forecast Overall Skill

11. CliPAS Temporal Correlation Skill of 2m Air Temperature Performance of MMEs in Hindcast Global Temperature JJA DJF  MME seasonal prediction with 1-month lead time using 17 climate models which participate in CliPAS and DEMETER

12. CliPAS Precipitation Dry Wet Dry Wet Dry Wet * Impact of El-Nino on Global Climate from NOAA (based on Ropelewski and Halpert (1987), Halpert and Ropelewski (1992), and Rasmusson and Carpenter (1982) Temporal Correlation Skill of Precipitation Performance of MMEs in Hindcast Global Precipitation

13. CliPAS Impact of Model Systematic Error 1 st mode SEOF of SST (Low frequency mode) Obs. long run 1 st month 9 th month 5 th month NCEP CFS JJA SINTEX-F MAM Forecast lead month Correlation Temporal correlation coeff. of PC time series with observation Pattern correlation coeff. of eigenvector with free coupled run SINTEX-F NCEP CFS SINTEX-F NCEP CFS With increase of the lead month, the forecast ENSO mode progressively approaches to the model intrinsic mode in free coupled run and departs from the observed.

14. CliPAS Impact of Model Systematic Error Pattern Correlation Skill for the first two AC modes JJAS minus DJFM mean Precipitation The first Annual Cycle mode

15. CliPAS Part II. Improvement of the MME techniques MME Effectiveness  MME Effectiveness  Optimal MME Technique  Deterministic vs Probabilistic Forecast

16. CliPAS Multi-Model Ensemble (MME)  JJA precipitation over Indo- Pacific Region [40-280E, 30S-30N]  MME is produced using 17 climate models which participate in CliPAS and DEMETER.  JJA precipitation over Indo- Pacific Region [40-280E, 30S-30N]  MME is produced using 17 climate models which participate in CliPAS and DEMETER. Forecast Skill of JJA Precipitation Optimal Selection of a Subgroup of Models Example: East Asian Domain [105-145E, 20-45N] The best MME skill is obtained using 4 models.

17. CliPAS Optimal MME Technique Temporal Correlation Skill of MMEs using 15 models Temporal Correlation Skill as a Function of number of models Over the globe [0-360E, 60S-60N] * The MME3.1 is based on a new statistical downscaling method, which is named stepwise pattern projection model (SPPM), combined with prior procedure of predictor selection and posterior procedure of multi-model average with equal weight. (a) Simple composite(b) Superensemble using SVD (c) MME using SPPM1(d) MME using SPPM2

18. CliPAS Optimal MME Technique Temporal Correlation Skill of MMEs using 15 models Temporal Correlation Skill as a Function of number of models Over the globe [0-360E, 60S-60N] * The MME3.1 is based on a new statistical downscaling method, which is named stepwise pattern projection model (SPPM), combined with prior procedure of predictor selection and posterior procedure of multi-model average with equal weight. (MME-S)

19. CliPAS Deterministic vs Probabilistic Forecast JJA DJF * Temporal correlation: Contour (0.5, 0.7) * Area under ROC curve is the averaged value for that of three categorical events, contour (0.65, 0.75) Temporal Correlation SkillArea under ROC curve (Aroc) for three categorical events correlation Aroc

20. CliPAS Part III. Predictability of Coupled GCM Forecast ENSO Predictability and How to Improve it  ENSO Predictability and How to Improve it  Precipitation Over Global Tropics  A-AM Monsoon Predictability

21. CliPAS ENSO Predictability and How to Improve it (a) NCEP CFS (c) ECMWF (d) UKMO (b) SINTEX-F Forecast Error of Ensemble Mean Lorenz Curve of Ensemble Mean Mean of Forecast Error of Each Member Mean of Lorenz Curve of Each member Forecast Error of Each Member Lorenz Curve of Each Member Forecast error: skill of “current” forecast. Lorenz curve: upper bound of predictability, the growth of initial error defined as the difference between two forecasts valid at the same time (Lorenz 1982) Forecast error: skill of “current” forecast. Lorenz curve: upper bound of predictability, the growth of initial error defined as the difference between two forecasts valid at the same time (Lorenz 1982) Forecast lead month RMS error Lorenz Curve of Ensemble Mean is not growing Initial error growth is saturated within first two months followed by an level-off. Most significant improvement of ENSO prediction can be obtained by reducing the forecast error in the first month.

22. CliPAS Predictability in Couple Model MME SEOF Modes for Precipitation over Global Tropics [0-360E, 30S-40N] How many modes are predictable? variance ratio 10 15 20 25 30 40 50 60 70 80 % variance First Four: 59.3%

23. CliPAS Asian-Australian Monsoon Predictability S-EOF of Seasonal Mean Precipitation Anomalies The First Mode: 30%The Second Mode: 13% Forecast Skills of the Leading Modes of AA-M

24. CliPAS Part IV. Intraseasonal Prediction and Predictability The Current Status of ISO Prediction  The Current Status of ISO Prediction  Potential Predictability  Effect of Air-Sea Coupling

25. CliPAS Pattern Corr of climatological Summer Mean Prcp. vs. ISO activity (40-180,20S-30N) Models which represent the pattern of climatological mean state reasonably well (bad) can also represent the pattern of ISO activity well (bad). Proper simulation of mean basic state is crucial to the simulation of the intensity of intraseasonal variations and vice versa. Averaged Pattern Correlation for 21 years / 60E-150E, EQ-25N ISO Prediction

26. CliPAS Signal To Noise Ratio at Indian Ocean and Western Pacific IO : 60E-100E, 10S-20N WP: 120E-140E, EQ-20N CERF ECMW INGV LODY MAXP METF UKMO SNU-T1 NCEP-T1 SNU – T2 FSU – T2 UHCAM2-T2 ISO Potential Predictability

27. CliPAS ISO Potential Predictability Signal CPL Forecast Error ATM Forecast Error Air-Sea Coupling Extends the Predictability of Monsoon Intraseasonal Oscillation ATM: 17 days, CPL: 24 days Fu et al. 2006

28. CliPAS Prediction Strategy: One-Tier vs Two-Tier  Consistency between hindcast and forecast is very important.  One-Tier prediction has better skill than two-tier prediction. These models have no skills in the heavily precipitating summer monsoon regions. Coupled atmosphere-ocean models, on the other hand, can produce qualitatively correct local lead/lag SST-rainfall correlations, enhance the ENSO-monsoon connection, and provide improved skill in summer monsoon precipitation.

29. CliPAS Precipitation Prediction Strategy: SNU One-Tier vs Two-Tier

30. CliPAS One-Tier vs Two-Tier MME Prediction A-AM RegionENSO Region It is documented that the prediction skill of tier-1 systems is superior to the tier-2 seasonal prediction system in boreal summer over both A-AM and ENSO regions.

31. CliPAS Paper Preparation (1) Bin Wang, J. Shukla, In-Sik Kang, June-Yi Lee, C.-K Park, E. K. Jin, J.-S. Kug, P. Liu, X. Fu, J. Schemm, A. Kumar, J.-J. Luo, J. Kinter, B. Kirtman, T. Krishnamurti, S. Cocke, N. C. Lau, T. Rosati, W. Stern, M. Suarez, S. Schubert, W. Lau, W.-T. Yun, and T. Yamagata: Multi-model ensemble dynamic seasonal prediction of APCC/CliPAS and DEMETER. Will be submitted to Journal of Climate (2) June-Yi Lee, Bin Wang, In-Sik Kang, Jong-Seong Kug, J. Shukla, E. K. Jin, C.-K. Park, J. Schemm, A. Kumar, J.-J. Luo, J. Kinter, B. Kirtman, T. Krishnamurti, S. Cocke, N. C. Lau, T. Rosati, W. Stern, M. Suarez, S. Schubert, W. Lau, W.-T. Yun, and T. Yamagata: Performance of climate prediction models on annual modes of precipitation and its linkage with seasonal prediction. Will be submitted to Journal of Climate (3) Bin Wang, June-Yi Lee, In-Sik Kang, Jong-Seong Kug, J. Shukla, C.-K. Park, J.-J. Luo, and J. Schemm: Interannual variability of Asian-Australian monsoon in observation and multi-model ensemble seasonal prediction. Will be submitted to Journal of Climate (4) Bin Wang and Qinghua Ding: The global monsoon: Major modes of annual variation in tropical precipitation and circulation. Will be submitted to Journal of Climate (5) Jong-Seong Kug and In-Sik Kang, 2006: Seasonal climate prediction with SNU tier-one and tier-two systems. submitted to Climate Dynamics (6) June-Yi Lee, Bin Wang, A. Kumar, In-Sik Kang, Jong-Seong Kug, J. Shukla, E. K. Jin, C.-K. Park, J. Schemm,, J.-J. Luo, J. Kinter, B. Kirtman, T. Krishnamurti, S. Cocke, N. C. Lau, T. Rosati, W. Stern, M. Suarez, S. Schubert, W. Lau, W.-T. Yun, and T. Yamagata: Forecast skill comparison between one-tier and two-tier multi-model ensemble prediction. Will be submitted to Journal of Climate

32. CliPAS Paper Preparation (7) H.-M. Kim, I.-S. Kang and coauthors: Simulation of intraseasonal variability and its predictability in climate prediction models. Will be submitted to Journal of Climate (8) E. K. Jin, J. L. Kinter, J. Shukla, B. Kirtman, B. Wang, J.-Y. Lee, I.-S. Kang, J.-S. Kug, C.-K. Park, J. Schemm, A. Kumar, J.-J. Luo, T. Krishnamurti, S. Cocke, N. C. Lau, T. Rosati, W. Stern, M. Suarez, S. Schubert, W. Lau, T. Yamagata, and W.-T. Yun: Impact of model systematic errors on CGCM forecast skills. Will be submitted to Journal of Climate (9) E. K. Jin, J. L. Kinter III, and B. Wang: Current status of ENSO prediction skill in coupled ocean- atmosphere model. Will be submitted to Journal of Climate (10) E. K. Jin, J. L. Kinter III, and B. Wang: Predictability of coupled GCM forecasts: Error growth and its implication on seasonal forecast. Will be submitted to Journal of Climate

33. CliPAS Conclusions (1) 1. MME prediction beats any individual model. The highest skill may be achievable by an optimal choice of a subgroup of models, drawing upon individual models’ skills and their mutual independence. 2. Correlation skill of the CGCM MME forecast of NINO3.4 SST reaches 0.86 at a 6-month lead. The forecast skills depend strongly on the phase of ENSO, the initial time (season), and the strength of ENSO. 3. MME prediction of air temperature is considerably superior to the persistence skill in the warm pool oceans. The precipitation skill is better than what the empirical relationships indicated, especially in the tropical Pacific in JJA and East Asian monsoon region during DJF. 4. Precipitation predictability in the coupled climate models can be quantified by the fractional variance of the “predictable” leading modes. The MME’s prediction skill primarily comes from these predictable modes.

34. CliPAS Conclusions (2) 5. Most significant improvement of ENSO prediction can be achieved by reducing the forecast error in the first month. 6. Coupled model MME captures the first two leading modes of AA-M variability better than those by the reanalyses (ERA 40 and NCEP-2). 7. Model errors, such as biases in the amplitude, spectral peak, and phase locking to the annual cycle, are factors of degrading forecast skills especially at long lead times. 8. Seasonal prediction skills are positively correlated to their performance on both the annual mean and annual cycle of the coupled model. 9. Atmosphere–ocean coupling can extend the intraseasonal predictability by about a week.

35. CliPAS Challenges and Recommendations 1. Rainfall forecasts in A-AM region remains moderate. Over land is particularly poor. There is an urgent need to determine to what extent the intrinsic internal variability of monsoon limits its predictability. 2 Poor performance over land monsoon region may be partially due to poor quality of the land surface initial conditions and the models’ deficiencies in the representation of atmosphere-land interaction. Global land surface data assimilation is an urgent need. Need to determine to what extent improved land processes can contribute to improved predictive skill. 3. The MME can only capture a moderate portion of the precipitation variability. Improvement of the MME skill relies on good models. Improvement of models is essential and remains a long-term goal. 4. Continuing improvement to the models’ representation of the slow coupled dynamics (e.g., properties of ENSO mode) is essential for improving ENSO and long-lead seasonal predictions. Correction of systematic errors also holds a key. 6. The accuracy and consistency of the initial conditions of the coupled ocean-atmosphere system is important for improving short-lead seasonal prediction. 7. The notion that the summer monsoon can be modeled and predicted by prescribing the lower boundary conditions is questionable. Need to reshape our strategy in validating models and predicting summer monsoon rainfall.

36. CliPAS Any Questions and Comments?