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

EUROBRISA : A EURO - BR azilian I nitiative for improving S outh A merican seasonal forecasts. Caio A. S. Coelho Centro de Previs ã o de Tempo e Estudos Clim á ticos (CPTEC) Instituto Nacional de Pesquisas Espaciais (INPE) caio@cptec.inpe.br. PLAN OF TALK Aims Activities

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

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  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 • Aims • Activities • Results • Summary ENSEMBLES meeting on seasonal to decadal prediction Barcelona, 7-8 June 2007

  2. 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/ • 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, agriculture and health) Affiliated institutions

  3. EUROBRISA activities Climate prediction research and development • Probabilistic forecasts of precip. and temp. with empirical and dynamical coupled models • Production of objectively combined (dynamical + empirical) well-calibrated integrated forecasts • Skill assessment 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

  4. EUROBRISA multi-model ensemble system 4 coupled global circulation models + 1 empirical model Longest common period: 1987-2001 (CPTEC,ECMWF,UKMO) Empirical model Predictor: Atlantic and Pacific SST Predictands: Precipitation and temperature

  5. Precipitation results for JJA

  6. Correlation maps: JJA precip. anomalies Empirical Integrated ECMWF UKMO • Hindcast period: 1987-2001 • Coupled models with I.C. 1st May (1-month lead) • Empirical model uses April SST as predictor • Integrated forecasts (coupled + empirical) with forecast assimilation • Better skill in tropical South America • Integrated forecasts have improved skill in tropical South America and Southeast Argentina

  7. Gerrity score for JJA tercile precip. categories Empirical ECMWF UKMO Integrated • Hindcast period: 1987-2001 • Coupled models with I.C. 1st May (1-month lead) • Empirical model uses April SST as predictor • Integrated forecasts (coupled + empirical) with forecast assimilation • Better skill in tropical South America • Integrated forecasts have improved skill in tropical South America and Southeast Argentina

  8. ROC skill score for JJA positive anomalies ECMWF UKMO Empirical Integrated • Hindcast period: 1987-2001 • Coupled models with I.C. 1st May (1-month lead) • Empirical model uses April SST as predictor • Integrated forecasts (coupled + empirical) with forecast assimilation • Integrated forecasts have improved skill in tropical South America and Southeast Argentina

  9. Brier skill score for JJA precip. in the upper tercile Empirical Integrated UKMO ECMWF • Hindcast period: 1987-2001 • Coupled models with I.C. 1st May (1-month lead) • Empirical model uses April SST as predictor • Integrated forecasts (coupled + empirical) with forecast assimilation • Integrated forecasts have improved skill in tropical S. America

  10. Reliability component of the Brier skill score for JJA precip. in the upper tercile Empirical Integrated ECMWF UKMO • Hindcast period: 1987-2001 • Coupled models with I.C. 1st May (1-month lead) • Empirical model uses April SST as predictor • Integrated forecasts (coupled + empirical) with forecast assimilation • Integrated forecasts have improved reliability in tropical S. America

  11. Resolution component of the Brier skill score for JJA precip. in the upper tercile ECMWF UKMO Integrated Empirical • Hindcast period: 1987-2001 • Coupled models with I.C. 1st May (1-month lead) • Empirical model uses April SST as predictor • Integrated forecasts (coupled + empirical) with forecast assimilation • Integrated forecasts have improved resolution in north Brazil

  12. Example: JJA 2007 precipitation forecast ECMWF UKMO Integrated Empirical Issued: May 2007 Most likely tercile category forecast: upper tercile (wet conditions) in North South America and lower tercile (dry conditions) in central South America

  13. 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 • Preliminary results on seasonal precipitation are encouraging • More results will be available at http://www.cptec.inpe.br/~caio/EUROBRISA

  14. 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

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