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

Integrated seasonal climate forecasts for South America. 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 Introduction Methods Skill assessment Summary.

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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. Integrated seasonal climate forecasts for South America 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 • Introduction • Methods • Skill assessment • Summary 10th International Meeting on Statistical Climatology Beijing, 20-24 August 2007

  2. 1. Seasonal climate forecasts Forecasts of climate conditions for the next 3-6 months JJA • • • • • • • Nov Oct Sep Aug May Jun Jul 0 1 2 3 4 5 6 1-month lead for JJA Current forecast approaches • Empirical/statistical models • Dynamical atmospheric models • Dynamical coupled (ocean-atmosphere) models

  3. Integrated forecasts for South America Combined and calibrated coupled + empirical forecasts Integrated Empirical model Predictors: Atlantic e Pacific SST Predictand: Precipitation Hindcast period: 1987-2001

  4. 2. The Empirical model Y Z Y|Z ~ N (M (Z - Zo),T) Y: JJA precipitation Z: April sea surface temperature (SST) Model uses first six leading Maximum CovarianceAnalysis (MCA) modes of the matrix YT Z. Coelho et al. (2006)

  5. Empirical model leading mode: SCF 53.7% JJA Precipitation April SST • Tropical Pacific (ENSO) and Atlantic are the main sources of seasonal predictability for South America

  6. 3. Calibration and combination procedure: Forecast Assimilation Stephenson et al. (2005) X: forecasts (coupled + empir.) Y: JJA precipitation Prior: Likelihood: Matrices Posterior: Forecast assimilation uses first three leading MCA modes of the matrix YT X.

  7. 4. Cross validated skill assessment

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

  9. 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 for JJA) • Empirical model uses April SST as predictor for JJA precip. • Integrated forecasts (coupled + empirical) with forecast assimilation • Best skill in tropical South America • Integrated forecasts have improved skill in tropical and southeast South America

  10. 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 for JJA) • Empirical model uses April SST as predictor for JJA precip. • Integrated forecasts (coupled + empirical) with forecast assimilation • Integrated forecasts have improved skill in tropical and southeast South America

  11. 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

  12. 5. Summary • Forecast skill can be improved by calibration and combination • Availability of empirical and dynamical model forecasts provides opportunity to produce objectively integrated (i.e. combined and calibrated) forecasts • Preliminary results on seasonal precipitation are encouraging: • improved skill in tropical and southeast South America • More results will be available at http://www.cptec.inpe.br/~caio/EUROBRISA

  13. References: • 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. • 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

  14. Forecast assimilation leading mode: SCF 63.8% Observation ECMWF UKMO Empirical 1-month lead for JJA • All models depict ENSO north-south precipitation dipole

  15. 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

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