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EUROBRISA: Improving South American Seasonal Forecasts

EUROBRISA is a Brazilian initiative aimed at strengthening collaboration and improving seasonal forecasts in South America. The project involves developing real-time forecast products for non-profitable governmental use, such as reservoir management, hydropower production, agriculture, and health. It also focuses on climate prediction research and development, downscaling of forecasts for impact studies, and seasonal predictability studies. The project has shown improved skill and reliability in tropical South America.

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EUROBRISA: Improving South American Seasonal Forecasts

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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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