1. Caio A. S. Coelho Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) Instituto Nacional de Pesquisas Espaciais (INPE) caio@cptec.inpe.br 1 st EUROBRISA workshop, Paraty, 17-19 March 2008 Deriving South America seasonal rainfall from upper level circulation predictions

2. Conceptual framework Data Assimilation “Forecast Assimilation” Stephenson et al. (2005)

3. Calibration and combination experiment Common hindcast period: 1987-2005 (19 years) y: observed rainfall (GPCP, Adler et al. 2003 ) x: predicted upper level (200 hPa) circulation Use two EUROSIP model predictions: ECMWF System 3 UK Met Office Target season: DJF Start date: November (i.e. 1-month lead predictions for DJF) NCEP/NCAR Reanalysis (Kalnay et al. 1996) is used to verify predicted upper level circulation

4. Upper level circulation u: zonal wind v: meridional wind  : stream function : zonal mean of  : perturbed (eddy) stream function u v

6. Observed perturbed stream function

7. ECMWF perturbed stream function

8. UKMO perturbed stream function

9. Perturbed stream function verification

10. El Niño Observed perturbed stream function anomaly

11. La Niña Observed perturbed stream function anomaly

12. La Niña El Niño Observed perturbed stream function anomaly

13. El Niño ECMWF perturbed stream function anomaly

14. La Niña ECMWF perturbed stream function anomaly

15. El Niño La Niña ECMWF perturbed stream function anomaly

16. El Niño UKMO perturbed stream function anomaly

17. La Niña UKMO perturbed stream function anomaly

18. El Niño La Niña UKMO perturbed stream function anomaly

19. Perturbed stream function anomaly verification

20. Prior: Likelihood: Posterior: Calibration and combination procedure: Forecast Assimilation Matrices Stephenson et al. (2005) Forecast assimilation uses first three leading MCA modes of the matrix Y T X. X: circulation predictions (ECMWF + UKMO) Y: DJF rainfall

21. Forecast Assimilation: First MCA mode

22. Forecast Assimilation: Second MCA mode

23. Forecast Assimilation: Third MCA mode

24. Correlation between predicted and observed anomalies Forecast Assimilation ECMWF UKMO Issued: November, Valid for DJF, Hindcast period: 1987-2005  Upper level circulation derived predictions obtained with forecast assimilation have comparable level of skill to indiv. model predictions

25. ECMWF UKMO Forecast Assimilation Ranked Probability Skill Score (tercile categories) Issued: November, Valid for DJF, Hindcast period: 1987-2005

26. Gerrity Score (tercile categories) ECMWF UKMO Forecast Assimilation Issued: November, Valid for DJF, Hindcast period: 1987-2005

27. ROC Skill Score (positive or negative anomaly) ECMWF UKMO Forecast Assimilation

28. Issued: November, Valid for DJF, Hindcast period: 1987-2005 Reliability diagram (positive or negative anomaly) ECMWFUKMOForecast Assimilation  Forecast assimilation improves prediction reliability

29. ROC plot (positive or negative anomaly) Issued: November, Valid for DJF, Hindcast period: 1987-2005 ECMWFUKMO Forecast Assimilation

30. Summary Forecast assimilation is a useful framework for exploring atmospheric teleconnections in seasonal forecasts ENSO atmospheric teleconnections is the main source of skill for South America rainfall predictions Combined and calibrated circulation derived predictions obtained with forecast assimilation have comparable level of skill to single model rainfall prediction Additional skill improvements can be investigated by including humidity predictions in the forecast assimilation procedure