1. Caio A. S. Coelho e-mail: c.a.d.s.coelho@reading.ac.uk Supervisors: D. B. Stephenson, F. J. Doblas-Reyes (*) Thanks to CAG, S. Pezzulli and M. Balmaseda (*) Department of Meteorology, University of Reading and ECMWF (*) Forecast Assimilation of DEMETER Coupled Model Seasonal Predictions

2. Plan of talk 1.Issues 2.Conceptual framework (“Forecast Assimilation”) 3.DEMETER 4.Examples of application: 0-d, 1-d, 2-d. 5.Conclusions

3. 1. Issues Why do forecasts need it? Which are the best ways to calibrate? How to get good probability estimates? Calibration Combination Why to combine? Should model predictions be selected? How best to combine?

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

5. 3. Multi-model ensemble approach DEMETER Development of a European Multi-Model Ensemble System for Seasonal to Interannual Prediction Solution: Multi-model Ensemble Errors: Model formulation Initial conditions http://www.ecmwf.int/research/demeter

6. DEMETER Multi-model ensemble system 7 coupled global circulation models Hindcast period: 1980-2001 (1959-2001) 9 member ensembles ERA-40 initial conditions SST and wind perturbations 4 start dates per year (Feb, May, Aug and Nov) 6 month hindcasts ModelCountry ECMWFInternational LODYCFrance CNRMFrance CERFACSFrance INGVItaly MPIGermany UKMOU.K.

7. 4. Examples of application Niño-3.4 index (0-d) Equatorial Pacific SST (1-d) South American rainfall (2-d)

8. Example 1: Niño-3.4 forecasts Well-calibrated: Most observations in the 95% prediction interval (P.I.) 95% P.I.

9. ECMWF coupled model ensemble forecasts  Observations not within the 95% prediction interval!  Coupled model forecasts need calibration m=9 DEMETER: 5-month lead

10. Prior: Univariate X and Y Posterior: Likelihood: Bayes’ theorem:

11. Modelling the likelihood p(X|Y) y

12. Combined forecasts  Note: most observations within the 95% prediction interval!

13. All forecasts ForecastMAE (  C) MAESS (%) BSBSS (%) Uncert (  C) Climatol.1.1600.2501.19 Empirical0.53550.05790.61 Coupled0.57510.18290.33 Combined0.31740.04810.32 MAESS = [1- MAE/MAE(clim.)]*100% Empirical Coupled Combined BSS = [1- BS/BS(clim.)]*100%

14. Prior: Likelihood: Posterior: Multivariate X and Y bias Matrices

15. Example 2: Equatorial Pacific SST ForecastBrier Score (BS) BSS (%) Climatol p=0.50.250 Multi-model0.1924 FA 58-010.1731 SST anomalies: Y (°C) Forecast probabilities: p DEMETER: 7 coupled models; 6-month lead BSS = [1- BS/BS(clim.)]*100%

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

17. Brier Score decomposition reliability resolution uncertainty

18. Forecast assimilation improves reliability in the western Pacific Reliability as a function of longitude

19. Resolution as a function of longitude Forecast assimilation improves resolution in the eastern Pacific

20. Why South America? El Niño (DJF) La Niña (DJF) Source: Climate Prediction Center (http://www.cpc.ncep.noaa.gov)  Seasonal climate potentially predictable DEMETER Multi-model Correlation: DJF rainfall

21. Why South American rainfall?  Agriculture  Electricity: More than 90% produced by hydropower stations e.g. Itaipu (Brazil/Paraguay): World largest hydropower plant I nstalled power: 12600 MW 18 generation units (700 MW each) ~25% electricity consumed in Brazil ~95% electricity consumed in Paraguay

22. Itaipu

23. Example 3: South American rainfall anomalies Obs Multi-model Forecast Assimilation (mm/day) DEMETER: 3 coupled models (ECMWF, CNRM, UKMO) 1-month lead Start: Nov DJF ENSO composites: 1959-2001 16 El Nino years 13 La Nina years r=0.51 r=0.28 r=0.97 r=0.82

24. South American DJF rainfall anomalies ObsMulti-model Forecast Assimilation (mm/day) r=-0.09 r=0.32 r=0.59 r=0.56

25. South American DJF rainfall anomalies ObsMulti-model Forecast Assimilation (mm/day) r=0.04 r=0.08 r=0.32 r=0.38

26. Brier Skill Score for S. American rainfall Forecast assimilation improves the Brier Skill Score (BSS) in the tropics

27. Reliability component of the BSS Forecast assimilation improves reliability over many regions

28. Resolution component of the BSS Forecast assimilation improves resolution in the tropics

29. unified framework for the calibration and combination of predictions – “forecast assimilation” improves the skill of probability forecasts Example 1: Ni ñ o-3.4  improved mean forecast value and  prediction uncertainty estimate Example 2: Equatorial Pacific SST  improved reliability (west) and resolution (east) Example 3: South American rainfall  improved reliability and resolution in the tropics  improved reliability over subtropical and central regions 5. Conclusions:

30. Coelho C.A.S. “ Forecast Calibration and Combination: Bayesian Assimilation of Seasonal Climate Predictions ”. PhD Thesis. University of Reading (to be submitted) Coelho C.A.S., D. B. Stephenson, F. J. Doblas-Reyes and M. Balmaseda: “ From Multi-model Ensemble Predictions to Well-calibrated Probability Forecasts: Seasonal Rainfall Forecasts over South America 1959-2001”. CLIVAR Exchanges (submitted). Stephenson, D. B., Coelho, C. A. S., Doblas-Reyes, F.J. and Balmaseda, M. “Forecast Assimilation: A Unified Framework for the Combination of Multi-Model Weather and Climate Predictions.” Tellus A - DEMETER special issue (in press). 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 More information …

31. Reliability diagram (Multi-model) (p i ) (o i ) o

32. Reliability diagram (FA 58-01) o (p i ) (o i )

33. Operational Seasonal forecasts for S. America Coupled models U.S.A: http://iri.columbia.edu Atmospheric models forced by persisted/forecast SSTs Brazil: http://www.cptec.inpe.br Europe: http://www.ecmwf.int U.K: http://www.metoffice.com

34. Mean Anomaly Correlation Coefficient

35. Momentum measure of skewness Measure of asymmetry of the distribution