1. Multi-Perturbation Methods for Ensemble Prediction of the MJO Multi-Perturbation Methods for Ensemble Prediction of the MJO Seoul National University A paper appearing in Climate Dynamics (2013) In-Sik Kang and Pyong-Hwa Jang

2. Post Processing Predictability Research Prediction Initialization Atmosphere Land Ocean CGCM Ocean Model Atmospheric Model Intraseasonal Prediction System 2

3. Boreal WinterBoreal Summer ABOMECGFDLNCEPCMCCECMWFJMA Ensemble number 10 55156 Ref. CLIVAR/ISVHE Intraseasonal Variability Hindcast Experiment Correlation 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Correlation 5 10 15 20 25 30 35 Leadtime (DAY) RMM index 3

4. SNU CGCM MJO simulation Initialization/Perturbation methods MJO Ensemble Prediction Results CONTENTS 4

5. ModelDescriptionReferences AGCM SNU AGCM T42, 21 levels (2.8125X2.8125) SAS cumulus convection 2-stream k-distribution radiation Bonan (1996) land surface Kim (1999) Kang et al. (2002) Kang et al. (2004) Kim et al. (2003) Lee et al. (2003) OGCM SNU OGCM MOM2.2 + Mixed Layer Model 1/3 o lat. x 1 o lon. over tropics (10S-10N), Vertical 32 levels Noh and Kim (1999) Noh et al. (2003a) Noh et al (2003b) Kim et al. (2004) Noh et al. (2004) Noh et al. (2005) CGCM SNU CGCM SNU AGCM + MOM2.2 -1-day interval exchange - Ocean : SST - Atmosphere : Heat, Salt, Momentum Flux - No Flux Correction is applied Vertical Eddy Viscosity: Vertical Eddy Diffusivity: : empirical Constant where : TKE l : the length scale of turbulence Mixed Layer Model Coupling Strategy SNU CGCM 5

6. CNTL CGCM Bias Annual mean SST Convective momentum transport Diurnal coupling Tokioka constraint (alpha=0.1) Auto conversion time scale (3200s) Improved CGCM Bias Annual mean SST Improvement, Ham et al. (2012) Climate Dynamics 6

7. Performance of SNU CGCM ver.2 7 Power Spectrum (averaged 15S-15N), summer (May-Oct) OLR, OBSOLR, SNUCGCM U850, OBSU850, SNUCGCM

8. Performance of SNU CGCM 8 Phase composites of velocity potential at 200 hPa OBSSNU CGCM

9. SNU CGCM V.2 SNU AGCM + MOM2.2 T42, 20 levels resolution Coupled GCM GODAS - SST & salinity Nudging (relaxation time: 5 day) ERA interim - U, V, T, Q, Ps Nudging (relaxation time: 6hr) Ocean Atmosphere Initialization + Data for Nudging Perturbation method Lagged average forecast (LAF) Breeding Method Empirical Singular Vector Intra-seasonal Prediction System 9

10. ATM: ERA interim (T,U,V,Q,Ps) OCN: GODAS (temperature, salinity) ATM: ERA interim (T,U,V,Q,Ps) OCN: GODAS (temperature, salinity) Nudging InitializationPrediction LAF Breeding 4 ensemble members (6hr lag) breeding interval : 1 day, 3 days, 5 days BD (+) perturbation BD (-) perturbation 3(breeding intervals) × 2(mirror images) = 6 ensemble members Rescaling factor (percentage) : 10% ESV ESV (+) perturbation ESV (-) perturbation 2 ensemble members 10

11. Performance of SNU CGCM 11 EV of the combined EOF of 20-100 day filtered summer (May-Oct) OLR U200 U850

12. Boreal Summer season 12

13. STD of VP200 perturbation (b) Bred Perturbation 3% (a) Bred Perturbation 0.3% (c) Bred Perturbation 10% - Characteristics of Bred perturbations Initial Perturbation - Breeding 13

14. Bred Vector 1day Bred Vector 5day Unit : ×10 5 (m²/s) Bred Vector 3day 1st mode 2nd mode 1st mode 2nd mode 1st mode 2nd mode Initial Perturbation - Breeding 14 - Characteristics of Bred perturbations EOF of perturbations

15. Bred Vector mode1 Bred Vector mode2 Breeding rescaling factor : 10% Breeding interval : 5 day -EOF modes of VP200 perturbation -Winter (X 1e+6) Characteristics of Bred perturbation To be update 15

16. Final VP200 anomaly is on the east of initial VP200 anomaly Eastward propagating mode Observation Model Initial Perturbation - ESV - Singular mode 16

17. Initial Perturbation - ESV - Singular mode of ESV 17 5lag 10lag 15lag 20lag

18. Initial Perturbation - EOF of initial perturbation 18

19. Total 360 summer cases  Include all MJO phase Summer : 12 cases × 18 cases/year × 20 years = 4320 predictions 19 - Outline 1 May 11 May 21 Oct 45 Days Integration whole SUMMER Ensemble Prediction

20. - Correlation skill of Real-time Multivariate MJO (RMM) Index 20 LAF [4] BD1day+ BD3day [4] BD1day [2] BD1day+ BD3day+BD5day [6] BD5day [2] BD3day [2] Ensemble Prediction * The parenthesis refers ensemble members Correlation Coefficient Lead Days

21. - Correlation skill of Real-time Multivariate MJO (RMM) Index 21 Ensemble Prediction * The parenthesis refers ensemble members ESV [2] LAF [4]

22. 22 - Correlation skill of Real-time Multivariate MJO Index LAF (4) ESV (2) ALL (12) BD1day+BD3day+ BD5day(6) : 4 ensemble members with 6 hours lag intervals Multi-Perturbation Ensemble * The parenthesis refers ensemble members

23. 23 - Correlation skill of RMM Index for each MJO phase Multi-Perturbation Ensemble * The parenthesis refers ensemble members

24. (a) 5 day (b) 10 day (c) 15 day 24 - Correlation Skill of U850 for lead times MP (12)= LAF (4) + BRED (6) + ESV (2) Multi-Perturbation Ensemble

25. Summary 1. Various perturbation methods (LAF, Breeding, ESV) produce different unstable modes and different perturbations. 2. MJO predictability is not sensitive to the perturbation methods. 3. Multi-perturbation ensemble prediction system is slightly better than that of any perturbation method. 25

26. Thank You 26

27. Multi-Perturbation Ensemble - The ensemble spread of 200-hPa zonal wind averaged 15°S-15°N LAF BD ESV 27

28. Initial Perturbation 28 - Variance of initial perturbation of VP200 Unit : ×10 5 (m²/s) (a) LAF(b) BD (c) ESV

29. 29 - Correlation skill of RMM Index for each MJO amplitude Multi-Perturbation Ensemble * The parenthesis refers ensemble members MP predictions LAF predictions

30. Boreal Winter season 30

31. Inter-comparison of models * The parenthesis refers ensemble members LAF (4) Intra-Seasonal Prediction – Results - Correlation skill of Real-time Multivariate MJO Index To be update 31