ENSO Prediction Skill in the NCEP CFS

1. CTB Joint Seminar Series ENSO Prediction Skill in the NCEP CFS Renguang Wu Center for Ocean-Land-Atmosphere Studies February 3, 2010, NCEP Coauthors: Ben P. Kirtman (RSMAS, University of Miami) Huug van den Dool (CPC, NCEP, NOAA) 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. May Dec The seasonality of forecast skill (NINO3 SST) Initial Month Jan Kirtman et al. 2001 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: The phase transition of ENSO (Torrence and Webster’98; Clarke and van Gorder’99; Burgers et al.’05) The seasonal change in the ENSO variance (Xue et al.’94; Balmaseda et al.’95; Schneider et al.’03) The seasonal change in the S/N ratio (Webster’95; Torrence and Webster’98) Biennial oscillation/component (Clarke and van Gorden’99; Yu’05) 5

6. ENSO: tropical Pacific air-sea interaction heating winds SST ocean waves thermocline 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) 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 May Yu and Kao’07 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. Phase relationship: Background WP wind EP SST 4-5 Observations 1 EP d20 WEP:130-170E,5S-5N CFS [lead3] 13

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

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

16. short-lead forecast Correlation Skill Dec SST Dec. Jan d20 Dec taux 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 Target Month Initial Month NINO3.4 d20 WEP taux 18

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

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

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

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

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

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

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

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

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

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

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

33. 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

34. SSTA Observations txA Regression wrt Dec NINO3.4 SST CFS ensm July CFS ensm December 34

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

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

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

38. Prediction skill: El Nino vs La Nina 38

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

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

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

42. Prediction skill: Relation to noise rmsIA 42

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

44. 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