1. El Niño Forecasting Stephen E. Zebiak International Research Institute for climate prediction The basis for predictability Early predictions New questions raised in the 1990s Beyond El Niño proper – seasonal climate prediction 2002 El Niño Summary and questions for future research

3. Southern Oscillation

5. ENSO wind and SST patterns

6. Strong trade winds Westward currents, upwelling Cold east, warm west Convection, rising motion in west Weak trade winds Eastward currents, suppressed upwelling Warm west and east Enhanced convection, eastward displacement

9. Surface layer  Deep ocean; u=v=w=0 Active layer 50 m 150 m Simplified Ocean and Atmosphere Models Simplified form of equations for conservation of mass, momentum, energy  SSTA Tropopause

11. Model ENSO

13. wind stress - h + h - h Ocean wave dynamics and ENSO

14. Early Forecasting Methods Build-up, then relaxation of trade winds (Wyrtki; diagnostic) Ocean dynamic response to observed wind patterns (Inoue & O’Brien; prognostic, but not coupled) Identification of precursor patterns in sea level pressure, SST, winds from historical observations (Graham, Barnett, …; statistical) Simplified dynamical coupled models (Cane, Zebiak, … ; prognostic)

15. Winds, Heat fluxes Ocean simulation Ocean obs. Ocean analysis t  t +  t SST forcing Atmos. simulation Atmos. obs. Atmos. analysis t  t +  t Data assimilation Initial Conditions, t=t 0 Atmosphere modelOcean model FORECAST Forecast Initialization Procedures

16. Winds, Heat fluxes Ocean simulation Ocean analysis t  t +  t SST forcing Atmos. simulation Atmos. analysis t  t +  t Data assimilation Initial Conditions, t=t 0 Atmosphere modelOcean model FORECAST Forecast Initialization Procedures

18. Correlation Skill for NINO3 forecasts

19. A real-time forecast

20. Retrospective Assessment of ENSO Prediction Skill over the period 1970-1992 Statistical prediction modelsMixed statistical-dynamical models Dynamical coupled models

21. Winds, Heat fluxes Ocean simulation Ocean analysis t  t +  t SST forcing Atmos. simulation Atmos. analysis t  t +  t Data assimilation Initial Conditions, t=t 0 Atmosphere modelOcean model FORECAST Forecast Initialization Procedures

22. NINO3 forecasts initialized each month

23. Revised NINO3 forecasts initialized each month

25. Lamont Model; 1972-1992 validation period

27. Winds, Heat fluxes Ocean simulation Ocean obs. Ocean analysis t  t +  t SST forcing Atmos. simulation Atmos. analysis t  t +  t Data assimilation Initial Conditions, t=t 0 Atmosphere modelOcean model FORECAST Forecast Initialization Procedures

31. NCEP PREDICTION

32. Impact of ocean initialization on NCEP coupled model forecast system skill

34. Zonal Wind and zonal wind anomalies during 1996-97

39. U.S. Precipitation in four El Niño winters

40. PERSISTED GLOBAL SST FORECAST SST TROP. PACIFIC (NCEP dynamical) TROP. ATL, INDIAN (statistical) EXTRATROPICAL (damped persistence) GLOBAL ATMOSPHERIC MODELS 2°- 3° lat-lon 18 -19 vertical layers ECHAM3.6(MPI) ECHAM4.5(MPI) NCEP (MRF9) CCM3.2(NCAR) NSIPP(NASA) COLA2.x AGCM INITIAL CONDITIONS UPDATED ENSEMBLES (10+) WITH OBSERVED SST Forecast SST Ensembles 3/6 Mo. lead Persisted SST Ensembles 3 Mo. lead REGIONAL MODELS HISTORICAL DATA Extended simulations Observations IRI DYNAMICAL CLIMATE FORECAST SYSTEM POST PROCESSING -Statistics -Multimodel Ensembling -graphics

43. Probabilistic Skill Score – IRI Seasonal Temperature Forecasts Probabilistic Skill Score – IRI Seasonal Precipitation Forecasts

44. IRI Global Precipitation Forecasts – Ranked Prob. Skill Score

46. Forecasts for Jun-Jul-Aug 2002 NINO3.4 SST anomalies Dynamical ocean/atm or hybridStatistical models Obs. El Nino yrs

47. IRI ENSO Quick Look

50. Summary There is a physical basis for (limited) predictability of El Niño – relies on “slow” ocean dynamics and strong coupling between ocean and atmosphere in tropical Pacific There are also clear limits to predictability – model errors – effects of lack of observations (e.g., salinity) – unpredictable “noise” Dynamical and statistical models comparable in performance – dynamical methods have more potential for improvement Ensemble-based predictions offer best hope for characterizing real uncertainties Current global seasonal climate forecast performance depends strongly on the state of ENSO – necessarily probabilistic – also depend on other phenomena requiring further study

51. Summary early 2002 situation: critical preconditions for El Niño were in place, but development was considered uncertain – El Nino “watch” issued – consensus of forecasts was “correct” For future progress, we must: – understand the role of “noise” and how to address it in forecasting – reduce systematic errors that limit forecast skill – improve initialization methods for predictions – further develop ensemble methods for probabilistic forecasts