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Enhancing Seasonal Climate Forecasts for South America

Explore integrated seasonal climate forecasts for South America presented at the 10th International Meeting on Statistical Climatology in Beijing, August 2007. This research introduces methods, skill assessment, and summarizes seasonal climate forecasts, focusing on JJA precipitation. The study discusses the empirical model, calibration, and combination procedures, and cross-validated skill assessment for tropical and southeast South America. The EUROBRISA project aims to improve seasonal forecasts in South America, promoting collaboration between European and South American seasonal forecasters. Preliminary results show improved forecast skill, particularly in tropical and southeast South America. For more information, visit the provided link.

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Enhancing Seasonal Climate Forecasts for South America

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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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