Caio A. S. Coelho Centro de Previs ã o de Tempo e Estudos Clim á ticos (CPTEC)

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

2. History of EUROBRISA Forecast Assimilation Data Assimilation • • 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

3. The EUROBRISA Projectkey Idea:To improve seasonal forecasts in S. America:a region where there is seasonal forecast skill and useful value. http://www.cptec.inpe.br/~caio/EUROBRISA/index.html • 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) Affiliated institutions

4. Planned activities Climate prediction research and development • 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 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 Empirical model Predictor: Atlantic and Pacific SST Predictands: Precipitation and temperature

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

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

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

9. Brier Score decomposition uncertainty reliability resolution

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

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

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. South box: NDJ rainfall anomaly Multi-model - - - Observation Forecast Forecast assimilation (Coelho et al. 2006b) • Forecast assimilation improves skill substantially

14. North box: NDJ rainfall anomaly Multi-model - - - Observation Forecast Forecast assimilation (Coelho et al. 2006b) • Forecast assimilation improved skill marginally

15. River flow predictions (NDJ) Annual cycle (Coelho et al. 2006b) • Harder to downscale river flow than rainfall

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.