The ECMWF Seasonal Forecast System-3

1. The ECMWF Seasonal Forecast System-3 Magdalena A. Balmaseda Franco Molteni,Tim Stockdale Laura Ferranti, Paco Doblas-Reyes, Frederic Vitart European Centre for Medium-Range Weather Forecasts, Reading, U.K.

2. Overview • Introduction to Seasonal Forecasts • End to End Seasonal Forecasting System • Importance of Ocean Initial Conditions • ECMWF Seasonal forecasting system 3 • Overview • Performance • Web products • Calibration of model output • Multimodel (EUROSIP) • Calibration + Multimodel • Summary

3. End to End Forecasting System atmos obs atmos reanalysis initial conditions Reliable probability forecasts land,snow…? ensemble generation atmos DA AGCM Probabilistic forecast SST analysis calibration OGCM ocean DA initial conditions ocean reanalysis Tailored products sea-ice? ocean obs

4. 1997-1998 El-Niño forecast Forecast Initial Conditions

5. 2007 La Niña Initial Conditions

7. S3 NodataS3 Assim Impact on ECMWF-S3 Forecast Skill In ECMWF S3, ocean Data Assimilation improves forecast skill in the Equatorial Pacific, especially in the Western Part S2S2ic_S3modelS3 The impact of ocean initialization in the prediction of SST is comparable to the impact of atmospheric model cycle

8. The seasonal forecast System-3(implem. March 07) • COUPLED MODEL (IFS + OASIS2 + HOPE) • Recent cycle of atmospheric model (Cy31R1) • Atmospheric resolution TL159 and 62 levels • Time varying greenhouse gasses. • Includes ocean currents in wave model • INITIALIZATION • Includes bias correction in ocean assimilation. • Includes assimilation of salinity and altimeter data. • ERA-40 data used to initialize ocean and atmosphere in hindcasts • Ocean reanalysis back to 1959, using ENACT/ENSEMBLES ocean data • ENSEMBLE GENERATION • Extended range of back integrations: 11 members, 1981-2005. • Revised wind and SST perturbations. • Use EPS Singular Vector perturbations in atmospheric initial conditions. • Forecasts extended to 7 months (to 13 months 4x per year).

9. .. but ensemble spread (dashed lines) is still substantially less than actual forecast error. Rms error / spread in different ECMWF systems Rms error of forecasts has been systematically reduced (solid lines) ….

12. ACC for seasonal-mean (1981-2005) 2m-T: DJF from 1 Nov 2m-T: JJA from 1 May Precip: DJF from 1 Nov Precip: JJA from 1 May • Doblas-Reyes

13. New products in the web:ocean reanalysis http://www.ecmwf.int/products/forecasts/d/charts/ocean/reanalysis/

14. New products from Sys-3: annual-range Nino indices

15. New products from Sys-3: ’tercile summary’

16. Predictability barrier New products from Sys-3: climagrams a) Teleconnection and monsoon indices with verification http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/

17. Climagrams : area-averages of 2mT and rainfall 2m Temperature Amazones Anomaly Correlation Temperature Anomaly Correlation Precipitation

18. Feb/March as a Window of predictability Target month is more predictable Climagrams : area-averages of 2mT and rainfall North-East Brasil Anomaly Correlation Temperature Anomaly Correlation Precipitation

19. Climagrams : area-averages of 2mT and rainfall South America Atlantic Coast Anomaly Correlation Temperature Anomaly Correlation Precipitation

20. Can we reduce the error? How much? (Predictability limit) • Can we increase the spread by improving the ensemble generation? Is the ensemble spread sufficient? Are the forecast reliable? Forecast System is not reliable: RMS > Spread • To calibrate the model output • To sample model error (multi-model): EUROSIP • Both

21. Anomaly correlation of seasonal-mean rainfall • Franco Molteni

22. Can we predict tropical rainfall anomalies?

23. Prediction of All India Rainfall: EOF filtered fc. in JAS CC = .50 • Franco Molteni

24. Prediction of All India Rainfall JJAS CC = .25 JAS CC = .46

25. Prediction of East Africa short rains: OND from Aug. Unfiltered fc. : CC = 0.04 EOF-filt. : CC = 0.42 • Franco Molteni

26. Sampling model error: The Real Time Multimodel RMS error of Nino3 SST anomalies Persistence ECMWF ensemble spread EUROSIP EUROSIP ECMWF-UKMO-MeteoFrance

27. TROPICAL CYCLONES Forecasts starting on 1st June 2005: JASON ECMWF Met Office Obs July-November Meteo-France Multi-model W-Pac E-Pac Atl 1987-2004 2005 • Frederic Vitart

28. MULIMODEL: EUROSIP But sometimes the spread with EUROSIP is too large!! ECMWF MULTI-MODEL

29. Bayesian Calibration of the Nino Indices: • Based on the Forecast Assimilation Framework • It will produce a revised mean and variance • Specific Ingredients: • Take into account that error in the models can be correlated (remove correlation from errors, not from the signal, by doing SVD of error covariance matrix) • Model for the errors: • Given the mean and variance, produce the individual plumes

30. EUROSIP: Bayesian Combination

31. RMS error of Nino3 SST anomalies Bayesian Calibration Persistence ECMWF ensemble spread EUROSIP Sampling model error: The Real Time Multimodel EUROSIP ECMWF-UKMO-MeteoFrance

32. Conclusions • The new ECMWF seasonal forecast system-3 gives improved predictions of tropical/summer variability respect the previous system. • SST predictions are good in the tropical Pacific and eastern Indian Oc., but western Indian Oc. and tropical Atlantic are not better than persistence in NH summer. • Difficulty in getting the correct rainfall variability over land. Predictive skill over land can be improved by exploiting teleconnections (calibration) • The Multi-Model (EUROSIP) provides skilful predictions of tropical storms. In general it improves reliability, but sometimes the spread is too large • Bayesian Calibration can improve the products, but attention should be paid to the estimation of the model error (sensitive to sampling size)

33. Interannual variability of tropical storms in EURO-SIP Forecasts issued in June for the period July-November Correlation: 0.72 RMS error: 2.93 • Frederic Vitart