Multi-Perturbation Methods for Ensemble Prediction of the MJO

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

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

3. RMM index Boreal Winter Boreal Summer 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Correlation Correlation 10 15 20 25 30 35 Leadtime (DAY) Ref. CLIVAR/ISVHEIntraseasonal Variability Hindcast Experiment

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

5. SNU CGCM Coupling Strategy Mixed Layer Model Vertical Eddy Viscosity: • 1-day interval exchange • Ocean : SST • Atmosphere : Heat, Salt, Momentum Flux • No Flux Correction is applied Vertical Eddy Diffusivity: where : empirical Constant : TKE l : the length scale of turbulence

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

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

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

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

10. Initialization Prediction Nudging ATM: ERA interim (T,U,V,Q,Ps) OCN: GODAS (temperature, salinity) 4ensemble members (6hr lag) LAF BD (+) perturbation • 3(breeding intervals) × 2(mirror images) = 6 ensemble members • Rescaling factor (percentage) : 10% Breeding BD (-) perturbation breeding interval : 1 day, 3 days, 5 days ESV (+) perturbation L()= : VP200, U200, U850 : VP200, U200, U850 2ensemble members ESV ESV (-) perturbation

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

12. Boreal Summer season

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

14. Initial Perturbation - Breeding - Characteristics of Bred perturbations Unit : ×105(m²/s) EOF of perturbations Bred Vector 1day Bred Vector 5day Bred Vector 3day 1st mode 1st mode 1st mode 2nd mode 2nd mode 2nd mode

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

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

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

18. Initial Perturbation - EOF of initial perturbation

19. Ensemble Prediction - Outline Total 360 summer cases  Include all MJO phase 45 DaysIntegration Summer : 12< LAF(4) + BD1day(2) + BD3day(2) + BD5day(2) + ESV(2)> cases × 18cases/year × 20years =4320 predictions 1 May 11 May whole SUMMER 21 Oct

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

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

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

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

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

25. Summary1. 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.

26. Thank You

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

28. Initial Perturbation - Variance of initial perturbation of VP200 Unit : ×105(m²/s) (a) LAF (b) BD (c) ESV

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

30. Boreal Winter season

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