1. Caio A. S. Coelho Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) Instituto Nacional de Pesquisas Espaciais (INPE) caio@cptec.inpe.br CPTEC-IRI Workshop, Cachoeira Paulista (Brazil), 8 November 2006 PLAN OF TALK History Aims Planned activities Motivating results Summary EUROBRISA: A EURO-BRazilian Initiative for improving South American seasonal forecasts

2. History of EUROBRISA 2001-2005: PhD work on forecast calibration and combination (Coelho 2005) Developed conceptual framework for forecasting (Bayesian approach named forecast assimilation) Nino index (Coelho et al. 2003, 2004) Equatorial Pacific SST (Stephenson et al. 2005) South American rainfall (Coelho et al. 2006a) Regional rainfall and river flow downscaling (Coelho et al. 2006b) 2005: Preparation, submission and approval of EUROBRISA proposal by ECMWF council 2005/2006: Preparation, submission and approval of young investigator fellowship by FAPESP and start of EUROBRISA Data Assimilation Forecast Assimilation

3. The EUROBRISA Project key Idea: To improve seasonal forecasts in S. America: a region where there is seasonal forecast skill and useful value. Aims Strengthen collaboration and promote exchange of expertise and information between European and S. American seasonal forecasters Produce improved well-calibrated real-time probabilistic seasonal forecasts for South America Develop real-time forecast products for non-profitable governmental use (e.g. reservoir management, hydropower production, and agriculture) Involved institutionsCountryPartners CPTECBrazilCoelho, Cavalcanti, Costa Silva Dias, Pezzi ECMWFEUAnderson, Balmaseda, Doblas-Reyes, Stockdale INMETBrazilMoura, Silveira, Lucio Met OfficeUKGraham, Davey, Colman Météo FranceFranceDéqué UFPRBrazilGuetter Uni. of ReadingUKStephenson, Challinor Uni. of Sao PauloBrazilAmbrizzi, Silva Dias http://www.cptec.inpe.br/~caio/EUROBRISA/index.html CIIFENEcuadorCamacho IRIUSABaethgen UFRGSBrazilBergamaschi Affiliated institutions

4. Planned activities Produce probabilistic forecasts of precip. and temp. with empirical and dynamical coupled models Deliver objectively combined (dynamical + empirical) well-calibrated forecasts Compare skill of empirical, dynamical and combined forecasts using deterministic and probabilistic measures Dynamical and statistical downscaling Seasonal predictability studies Climate prediction research and development Impacts (collaborative work with users) Hydrology: Downscaling of seasonal forecasts for river flow predictions and use in hydrological models Agriculture: Research on the use of seasonal forecasts in agricultural activities; Downscaling of seasonal forecasts for use in crop models

5. EUROBRISA multi-model ensemble system 4 coupled global circulation models + 1 empirical model Coupled ModelCountryHindcast period CPTECBrazil1982-2001 ECMWFInternational1987-2001 Meteo-FranceFrance1993-2001 UKMOU.K.1987-2005 Empirical model Predictor: Atlantic and Pacific SST Predictands: Precipitation and temperature

6. Empirical DEMETER Multi-model (*) Integrated Correlation maps: DJF rainfall anomalies  Comparable level of determinist skill  Better skill in tropical and southeastern South America * ECMWF, Meteo-France, UKMO (1959-2001), I.C. November

7. Mean Anomaly Correlation Coefficient Most skill in ENSO years and forecast assimilation can improve skill Multi-model Integrated Empirical

8. ENS Forecast assimilation improved Brier Skill Score (BSS) in the tropics Brier Skill Score for DJF rainfall Empirical Multi-model Integrated

9. Brier Score decomposition reliability resolution uncertainty

10. Forecast assimilation improved reliability in many regions Reliability component of the BSS Empirical Multi-model Integrated

11. Forecast assimilation improved resolution in the tropics Resolution component of the BSS Empirical Multi-model Integrated

12. Regional rainfall downscaling Multi-model ensemble 3 DEMETER coupled models ECMWF, Meteo-France, UKMO 3-month lead Start: Aug NDJ Period: 1959-2001

13. ForecastCorrelationBrier Score Multi-model0.570.22 FA0.740.17 South box: NDJ rainfall anomaly Multi-model Forecast assimilation  Forecast assimilation improves skill substantially - - - Observation Forecast (Coelho et al. 2006b)

14. Forecast CorrelationBrier Score Multi-model0.620.21 FA0.630.18 - - - Observation  Forecast assimilation improved skill marginally North box: NDJ rainfall anomaly Multi-model Forecast assimilation (Coelho et al. 2006b)

15. ForecastCorrelationBrier Score Parana0.160.25 Tocantins0.290.22 Annual cycle  Harder to downscale river flow than rainfall River flow predictions (NDJ) (Coelho et al. 2006b)

16. Agricultural application (Challinor et al. 2004)

17. EUROBRISA summary Challenging initiative for improving the quality of South American seasonal forecasts Facilitate exchange and transfer of technology, knowledge and expertise between participating institutions Valuable opportunity to: - develop an objectively integrated (i.e. dynamical + empirical) forecasting system for South America - work closely with end-users to evaluate our forecasting system in terms of user variables rather than solely on traditional climate variables Collaborative activities with IRI are of great interest

18. References: 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. 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”. J. Climate, 17, 1504-1516. 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, M. Balmaseda, F. J. Doblas-Reyes and G. J. van Oldenborgh, 2006a: Towards an integrated seasonal forecasting system for South America. J. Climate, 19, 3704-3721. Coelho C.A.S., D. B. Stephenson, F. J. Doblas-Reyes, M. Balmaseda, A. Guetter and G. J. van Oldenborgh, 2006b: A Bayesian approach for multi-model downscaling: Seasonal forecasting of regional rainfall and river flows in South America. Meteorological Applications, 13, 73-82. Stephenson, D. B., Coelho, C. A. S., Doblas-Reyes, F.J. and Balmaseda, M., 2005: “Forecast Assimilation: A Unified Framework for the Combination of Multi-Model Weather and Climate Predictions.” Tellus A, Vol. 57, 253-264. Available from http://www.cptec.inpe.br/~caio Challinor et al.,2004: “Design and optimisation of a large-area process-based model for annual crops”. Agricultural and Forest Meteorology, 124, 99-112.