Aim:

1. The skill of empirical and combined/calibrated coupled multi-model South American seasonal predictions during ENSO Caio A. S. Coelho, D. B. Stephenson, F. J. Doblas-Reyes (*) and M. Balmaseda (*) Department of Meteorology, University of Reading and ECMWF (*)E-mail: c.a.d.s.coelho@reading.ac.uk

2. Aim: To produce improved probability rainfall forecasts for S. America • Strategy: • Stage 1: Nino-3.4 index, 1 model (Coelho et al. 2003,2004) • Stage 2: Equatorial Pac. SST, 7 models (Stephenson et al. 2005) • Stage 3: S. American rainfall, 3 models (Coelho et al. 2005a,b)

3. Plan of talk • Issues • Conceptual framework (“Forecast Assimilation”) • Examples of application: 0-d (Nino-3.4) • 1-d (Eq. Pac. SST) • 2-d (S. Amer. rainfall) • Downscaling • 4. Conclusions

4. 1. Issues Calibration • Why do forecasts need it? • How to do it? • How to get good probability • estimates? Combination • Why to combine? • How to combine?

5. 2. Conceptual framework “Forecast Assimilation” Data Assimilation

6. Modelling the likelihood p(x|y) y

7. Example 1: Dec Niño3.4 forecasts (5-month lead) Empirical ECMWF Integrated MAESS = [1- MAE/MAE(clim.)]*100% BSS = [1- BS/BS(clim.)]*100%

8. Example 2: Equatorial Pacific SST DEMETER: 7 coupled models; 6-month lead INT ENS OBS OBS BSS = [1- BS/BS(clim.)]*100% Forecast probabilities: p SST anomalies: Y (°C)

9. Brier Score as a function of longitude - - - INT ENS Forecast assimilation reduces (i.e. improves) the Brier score in the eastern and western equatorial Pacific

10. Example 3: South American rainfall anomalies OBS (El Nino) EMP (El Nino) ENS (El Nino) INT (El Nino) ENSO composites: 1959-200116 El Nino years 13 La Nina years •Empirical model (EMP): ASO SST DJF •Multi-model ensemble (ENS): 3 DEMETER coupled models ECMWF, Meteo-France, Met Office 1-month lead Start: Nov DJF •Integrated (INT) forecast Combines EMP and ENS EMP (La Nina) OBS (La Nina) ENS (La Nina) INT (La Nina) (mm/day)

11. Mean Anomaly Correlation Coefficient (ACC) • Generally low skill (c.f. ACC<0.31) • Larger skill in ENSO years than in neutral years • Calibration and combination improves skill

12. Correlation score for S.American rainfall INT EMP ENS • Comparable level of deterministic skill • Higher skill in the tropics and southeastern S. America

13. Brier Skill Score for S. American rainfall ENS EMP ENS INT Forecast assimilation improves the Brier Skill Score (BSS) in the tropics

14. Why has the skill been improved? Forecast skill depends on: • How well calibrated the forecasts are (reliability) • Ability to discriminate between different observed situations (resolution)

15. Brier Score decomposition uncertainty reliability resolution

16. Reliability component of the BSS EMP ENS INT Forecast assimilation improves reliability over many regions

17. Resolution component of the BSS EMP ENS INT Forecast assimilation improves resolution in the tropics

18. Example 4: Downscaling of rainfall anomalies •Multi-model ensemble (ENS): 3 DEMETER coupled models ECMWF, Meteo-France, Met Office 1-month lead Start: Nov DJF

19. South Box: DJF rainfall anomalies (1-month lead) ENS - - - Observation Forecast INT • Forecast assimilation substantially improves forecast skill

20. North Box: DJF rainfall anomalies (1-month lead) ENS - - - Observation Forecast INT • Forecast assimilation slightly improves forecast skill

21. 4. Conclusions: • Forecast assimilation improves the skill of probability forecasts • South America rainfall example: • - empirical and integrated predictions have comparable level of deterministic skill • - improved reliability and resolution in the tropics; • - improved reliability in subtropical and central regions • - higher skill in ENSO years than neutral years • - tropical and southeastern South America are the two most predictable regions • - first step towards an integrated system for South America

22. More information … • Coelho C.A.S., 2005: “Forecast Calibration and Combination: Bayesian Assimilation of Seasonal Climate Predictions”. PhD Thesis. University of Reading, 178 pp. • Coelho C.A.S., D. B. Stephenson, F. J. Doblas-Reyes and M. Balmaseda, 2005a: “From Multi-model Ensemble Predictions to Well-calibrated Probability Forecasts: Seasonal Rainfall Forecasts over South America 1959-2001”.CLIVAR Exchanges No 32, Vol. 10, No 1, 14-20. • Coelho C.A.S., D. B. Stephenson, M. Balmaseda, F. J. Doblas-Reyes and G. J. van Oldenborgh, 2005b: “Towards an integrated seasonal forecasting system for South America”. Submitted to J. Climate. • Stephenson, D. B., C.A.S. Coelho, F. J. Doblas-Reyes, and M. Balmaseda, 2005: • “Forecast Assimilation: A Unified Framework for the Combination of • Multi-Model Weather and Climate Predictions.” Tellus A, Vol. 57, 253-264. • Coelho C.A.S., S. Pezzulli, M. Balmaseda, F. J. Doblas-Reyes and D. B. Stephenson, 2004: “Forecast Calibration and Combination: A Simple Bayesian Approach for ENSO”. Journal of Climate. Vol. 17, No. 7, 1504-1516. • Coelho C.A.S., S. Pezzulli, M. Balmaseda, F. J. Doblas-Reyes and D. B. Stephenson, 2003: “Skill of Coupled Model Seasonal Forecasts: A Bayesian Assessment of ECMWF ENSO Forecasts”. ECMWF Technical Memorandum No. 426, 16pp. Available at http://www.met.rdg.ac.uk/~swr01cac

23. Reliability as a function of longitude Reliability as a function of longitude ENS - - - INT Forecast assimilation improves reliability in the western Pacific

24. Resolution as a function of longitude ENS - - - INT Forecast assimilation improves resolution in the eastern Pacific