Caio A. S. Coelho Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) Instituto Nacional de Pesquisas Espaciais (INPE) 10 th International Meeting on Statistical Climatology Beijing, August 2007 PLAN OF TALK Introduction Methods Skill assessment Summary Integrated seasonal climate forecasts for South America

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

Empirical model Predictors: Atlantic e Pacific SST Predictand: Precipitation Hindcast period: Integrated forecasts for South America Integrated Coupled modelCountry ECMWFInternational UKMOU.K. Combined and calibrated coupled + empirical forecasts

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

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

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

4. Cross validated skill assessment

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

Empirical Integrated Gerrity score for JJA tercile precip. categories  Best skill in tropical South America  Integrated forecasts have improved skill in tropical and southeast South America ECMWFUKMO Hindcast period: Coupled models with I.C. 1 st May (1-month lead for JJA) Empirical model uses April SST as predictor for JJA precip. Integrated forecasts (coupled + empirical) with forecast assimilation

Empirical Integrated ROC skill score for JJA positive anomalies  Integrated forecasts have improved skill in tropical and southeast South America ECMWFUKMO Hindcast period: Coupled models with I.C. 1 st May (1-month lead for JJA) Empirical model uses April SST as predictor for JJA precip. Integrated forecasts (coupled + empirical) with forecast assimilation

The EUROBRISA Project key Idea: To improve seasonal forecasts in S. America: a region where there is seasonal forecast skill and useful value. 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) Involved institutionsCountryPartners CPTECBrazilCoelho, Cavalcanti, Costa Silva Dias, Pezzi ECMWFEUAnderson, Balmaseda, Doblas-Reyes, Stockdale INMETBrazilMoura Met OfficeUKGraham, Colman Météo FranceFranceDéqué SIMEPARBrazilSilveira UFPRBrazilGuetter Uni. of ExeterUKStephenson Uni. of São PauloBrazilAmbrizzi, Silva Dias CIIFENEcuadorCamacho IRIUSAGoddard UFRGSBrazilBergamaschi Affiliated institutions

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

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, 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, Available from

Forecast assimilation leading mode: SCF 63.8% Observation EmpiricalUKMOECMWF  All models depict ENSO north-south precipitation dipole 1-month lead for JJA

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