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Bin Wang

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

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Bin Wang

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

  2. This Year Achievements A coordinated research community is extendedconsisting 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 9-11th 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). Metrics for validating hindcasthas been designed. Scientific achievementsare made on seasonal climate prediction and predictability.

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

  4. Model Descriptions of CliPAS System APCC/CliPAS Tier-1 Models APCC/CliPAS Tier-2 Models

  5. Scientific Achievements Part I. Assessment of the current status of the climate model’s performances • 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 Part II. Improvement of the MME techniques Part III. Predictability of coupled GCM forecast • Predictability of coupled model for precipitation, ENSO, Asian-Australian Monsoon Part IV. Intraseasonal Prediction and Predictability

  6. 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) Milestones Break through in ENSO forecast: Cane and Zebiak (1985) Statistical approaches:Barnston 1994), Hastenrass (1995) Two-tier: AGCM forced by predicted SSTBengtsson 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) Projects for MME Prediction PROVOST (EU), DSP (USA), SMIP (WCRP), CTB (USA), DEMETER (EU), CliPAS (APCC) Operational MME prediction ECMWF, IRI , APCC

  7. Part I. Assessment of the current status of the climate model’s performances • Annual Cycle and its Linkage with Seasonal Prediction skill • Monsoon Domain and Rainy Season Evolution over Asian 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. Performance on Annual mean & Annual Cycle Linkage to Seasonal prediction skill Pattern Correlation Skill over the Global Tropics (0-360E, 30S-30N) Precipitation Performance on Annual Cycle and its Linkage with Seasonal Prediction skill Annual Mean Precipitation The models’ performance in simulating and forecasting seasonal mean states is closely related to the models’ capability in predicting seasonal anomalies.

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

  10. Prediction Skills of NINO 3.4 SST Overall Skill Tier-1 MME Dynamic-Statistical Model Persistence Anomaly Correlation < 13 Tier-1 Models > Forecast lead month Tier-1 MME Forecast ENSO Phase of Initial month Seasonal Initial Conditions El Nino Growth La Nina Growth El Nino Decay La Nina Decay Normal Feb May Aug Nov

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

  12. Precipitation Wet Dry Dry Dry Wet Dry Dry Wet Dry Wet Dry Wet Wet Dry 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) Performance of MMEs in Hindcast Global Precipitation Temporal Correlation Skill of Precipitation

  13. Impact of Model Systematic Error 1st mode SEOF of SST (Low frequency mode) 1st month 5th month 9th month Obs. long run SINTEX-F MAM NCEP CFS JJA Temporal correlation coeff. of PC time series withobservation Pattern correlation coeff. of eigenvector withfree coupled run SINTEX-F NCEP CFS SINTEX-F NCEP CFS Correlation • 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. Forecast lead month

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

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

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

  17. Optimal MME Technique Temporal Correlation Skill of MMEs using 15 models Temporal Correlation Skill as a Function of number of models (a) Simple composite (b) Superensemble using SVD Over the globe [0-360E, 60S-60N] (c) MME using SPPM1 (d) MME using SPPM2 * 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.

  18. 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] (MME-S) * 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.

  19. Deterministic vs Probabilistic Forecast Temporal Correlation Skill Area under ROC curve (Aroc) for three categorical events JJA DJF correlation Aroc * 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)

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

  21. ENSO Predictability and How to Improve it • 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. (b) SINTEX-F (a) NCEP CFS RMS error (d) UKMO (c) ECMWF 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 lead month • 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)

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

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

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

  25. ISO Prediction Averaged Pattern Correlation for 21 years / 60E-150E, EQ-25N 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.

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

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

  28. 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. Prediction Strategy: SNU One-Tier vs Two-Tier Precipitation

  30. One-Tier vs Two-Tier MME Prediction A-AM Region ENSO 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. 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. 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. 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. 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. 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. Thank You ! Any Questions and Comments?

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