1. ENSO Prediction Skill in the NCEP CFS Renguang Wu Center for Ocean-Land-Atmosphere Studies Coauthors: Ben P. Kirtman (RSMAS, University of Miami) Huug van den Dool (CPC, NCEP, NOAA) CTB Joint Seminar Series February 3, 2010, NCEP 1

2. Spring prediction barrier What is the spring prediction barrier? A large drop in the prediction skill of eastern equatorial Pacific SST during boreal spring 2

3. The seasonality of forecast skill (NINO3 SST) May Kirtman et al. 2001 Initial Month Jan Dec Lead time (month) 3

4. Spring Prediction Barrier: Attribution Why is there a spring prediction barrier?. Low variance of Equatorial Pacific SSTAs in boreal spring e.g., Xue et al.’94; Torrence and Webster’98; Clarke and van Gorder’99. The physical argument: weak Walker Cell and minimum zonal pressure and SST gradient (Equatorial Pac) in spring, initial errors project strongly onto ENSO modes, leading to large error growth Issue: Perfect ICs in observations are not necessarily perfect for models used in predictions. The statistical argument: Signal/Noise ratio lowest in spring But, cannot explain the El Nino versus La Nina skill difference 4

5. Spring Persistence Barrier: auto-lag cor drop Reasons: (1)The phase transition of ENSO (Torrence and Webster’98; Clarke and van Gorder’99; Burgers et al.’05) (2)The seasonal change in the ENSO variance (Xue et al.’94; Balmaseda et al.’95; Schneider et al.’03) (3)The seasonal change in the S/N ratio (Webster’95; Torrence and Webster’98) (4)Biennial oscillation/component (Clarke and van Gorden’99; Yu’05) 5

6. ENSO: tropical Pacific air-sea interaction Q: Any prediction/persistence barrier in thermocline and wind? A boreal winter prediction/persistence barrier in the heat content/warm water volume (Balmaseda et al.’95; McPhaden’03) SST heating winds ocean wavesthermocline 6

7. Winter prediction barrier Balmesada et al.’95 r(HC’pred, HC’sim): a large drop in winter > a winter barrier 7

8. Winter persistence barrier McPhaden’03 May Jan 8

9. Winter persistence barrier Yu and Kao’07 May Jan 9

10. Questions. How is the ENSO prediction skill in the CFS? Is there a spring prediction barrier?. Can the CFS capture the persistence barrier?. What are plausible reasons for the drop of skill in spring?. Are these related to the S/N ratio?. Is the prediction skill related to the ENSO phase, initial or current state, different between El Nino and La Nina? 10

11. NCEP CFS 24-year ensemble forecasts CFS (Climate Forecast System) model Atmosphere: NCEP GFS (Global Forecast System) T62 64 sigma levels Ocean: GFDL MOM3 long 1degree, latitude 1/3 degree 10S-10N and 1 degree 30S/30N, 40 levels (27 levels 400m) 15 forecasts (each 9-month length): three groups 1st: 9th,10th,11th,12th,13th (atm) & 11th (ocn, pentad); 2nd: 19th,20th,21th,22th,23th (atm) & 21th (ocn, pentad); 3rd: 29th,30th, 1st, 2nd, 3rd (atm) & 1st (ocn, pentad) 11

12. Measure of the prediction skill & noise. Anomaly correlation coefficient (ACC). Root-mean-square error (Interannual component) (RMSE) Three quantities: NINO3.4 SST, NINO3.4 d20, WEP taux. ACC or RMSE calculated based on ensemble mean or individual members (mean value displayed), similar results. Spread (noise): standard deviation of members with respect to ensemble mean 12

13. Background Phase relationship: EP SST EP d20 WP wind 1 4-5 Observations CFS [lead3] 13 WEP:130-170E,5S-5N

14. Correlation Skill Target Month Saha et al.’06 14

15. Correlation Skill long-lead forecast Dec SST July Dec taux Jan d20 15

16. Correlation Skill short-lead forecast Dec SST Jan d20 Dec taux Dec. 16

17. Is the spring skill drop due to noise? If noise is critical to the low skill, then We would expect to see large noise when the skill drops. Is that so? 17

18. Correlation Skill vs Spread (noise) NINO3.4 SST WEP taux NINO3.4 d20 Target Month Initial Month 18

19. NINO3.4 SST NINO3.4 d20 WEP taux Initial Month Target Month Correlation Skill vs Signal-to-Noise Ratio 19

20. Correlation Skill “perfect model approach” - skill drop due to noise - Target Month Saha et al.’06 20

21. Perfect Model Skill vs Prediction Skill Target Month 21

22. Skill Difference (individual-ensemble) vs Prediction Skill Target Month

23. Plausible Reasons for spring prediction barrier  Noise cannot explain the spring prediction barrier  What are the plausible reasons?  Bias in atmospheric model wind response 22

24. Spring Persistence Barrier Reasons: (1)The phase transition of ENSO (Torrence and Webster’98; Clarke and van Gorder’99; Burgers et al.’05) (2)The seasonal change in the ENSO variance (Xue et al.’94; Balmaseda et al.’95; Schneider et al.’03) (3)The seasonal change in the S/N (Webster’95; Torrence and Webster’98) (4)Biennial oscillation (Clarke and van Gorden’99; Yu’05) 23

25. Persistence Barrier (Auto-lag correlation) Initial Month Target Month Persistence barrier delayed in CFS long-lead forecasts 24 CFS vs OBS NINO3.4 SST NINO3.4 d20 WEP taux

26. Seasonal change: composite anomaly Target Month Initial Month Peak and decay time delayed in CFS long-lead forecasts 25 CFS vs OBS NINO3.4 SST NINO3.4 d20 WEP taux

27. Persistence Barrier vs Anomaly (obs) Initial Month Target Month consistent NINO3.4 SST NINO3.4 d20 WEP taux 26

28. Persistence Barrier vs Anomaly (cfs) Initial Month Target Month consistent NINO3.4 SST NINO3.4 d20 WEP taux 27

29. Seasonal change: variance Initial Month Target Month 28 CFS vs OBS

30. Persistence Barrier vs Variance (obs) Target Month Initial Month NINO3.4 SST NINO3.4 d20 WEP taux 29

31. Persistence Barrier vs Variance (cfs) Target Month Relation is good for SST, but not for d20 and wind NINO3.4 SST NINO3.4 d20 WEP taux Initial Month 30

32. Persistence Barrier vs S/N (cfs) Target Month Initial Month Not explained by seasonal change in S/N ratio NINO3.4 SST NINO3.4 d20 WEP taux 31

33. Persistence Barrier vs Spread (cfs) Target Month Initial Month 32 Auto-lag cor starts to drop before the largest spread WEP taux NINO3.4 d20 NINO3.4 SST

34. Plausible Reasons for spring prediction barrier  Noise cannot explain the spring prediction barrier  What are the plausible reasons?  Bias in atmospheric model wind response 33

35. Regression wrt Dec NINO3.4 SST SSTAObservationstxA CFS ensm July CFS ensm December 34

36. Anomaly (2S-2N): regression wrt DEC NINO3.4 SST CFS ensm December Observations SSTA txA*40 d20A/20 35

37. Anomaly (2S-2N): regression wrt DEC NINO3.4 SST CFS ensm July Observations SSTA txA*40 d20A/20 36

38. Area-mean Anomaly NINO3.4 SST NINO3.4 d20 NINO3.4 taux WEP taux 37

39. Prediction skill: El Nino vs La Nina 38

40. Prediction skill (RMSE): El Nino vs La Nina 39 Target Month Initial Month

41. Prediction skill: Dependence on current state Large RMSE composite Small RMSE composite 40

42. Prediction skill: Dependence on initial state Large RMSE composite Small RMSE composite 41

43. Prediction skill: Dependence on initial state (d20) Large RMSE composite Small RMSE composite

44. Prediction skill: Developing phase vs decaying phase Jin and Kinter 2009

45. Prediction skill: Developing phase vs decaying phase Jin et al. 2008

46. Prediction skill: Developing phase vs decaying phase

47. Prediction skill: Relation to noise 42 rmsIA

48. Spread: El Nino vs La Nina

50. Summary 1.The spring prediction barrier in EEP SST is preceded by a boreal winter prediction barrier in the WEP zonal wind stress 2.The seasonal change in noise cannot entirely explain the spring prediction barrier 3.The prediction barriers could be related to the erroneous atmospheric model wind response to SST anomalies 4.The prediction skill is better for El Nino than for La Nina 43

51. Problems for future studies What contribute to low prediction skill in winter WEP wind stress? Why is the skill lower in La Nina than in El Nino prediction

52. Thanks! Wu, R., B. P. Kirtman, and H. van den Dool, 2009: An analysis of ENSO prediction skill in the CFS retrospective forecasts. J. Climate, 22, 1801-1818. Wu, R., and B. P. Kirtman, 2009: Variability of El Niño- Southern Oscillation-related noise in the equatorial Pacific Ocean. J. Geophys. Res., 114, D23106, doi:10.1029/2009JD012456. 44