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

An intercomparison of multivariate regression methods for the calibration and combination of seasonal climate predictions. Caio A. S. Coelho Centro de Previs ã o de Tempo e Estudos Clim á ticos (CPTEC) Instituto Nacional de Pesquisas Espaciais (INPE) caio.coelho@cptec.inpe.br.

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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. An intercomparison of multivariate regression methods for the calibration and combination of seasonal climate predictions Caio A. S. Coelho Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) Instituto Nacional de Pesquisas Espaciais (INPE) caio.coelho@cptec.inpe.br Thanks to David Stephenson • PLAN OF TALK • Motivation • Datasets and regression methods • Skill assessment • Summary 11th International Meeting on Statistical Climatology Edinburgh, 12-16 July 2010

  2. Framework for calibration and combination of climate predictions Stephenson et al. (2005) Tellus A, 57(3), 253-264 Data Assimilation “Forecast Assimilation”

  3. Multi-model ensemble approach ENSEMBLES ENSEMBLE-based Predictions of Climate Changes and their Impacts Model formulation Errors: Initial conditions Multi-model Ensemble Solution: http://www.ecmwf.int/research/EU_projects/ENSEMBLES/

  4. ENSEMBLES multi-model seasonal predictions 1-month lead precip. predictions for DJF over S. America (i.e. issued in Nov) 9 ens memb. each total: 45 members Hindcast period: 1960-2005 (46 years) http://www.ecmwf.int/research/EU_projects/ENSEMBLES/

  5. Multivariate regression model for thecalibration and combination of climate predictions Y|X ~ N (L (X - Xo),D) Y: DJF precipitation X: DJF precipitation predictions Use PCs of X: Principal component (PC) regression Use Maximum Covariance Analysis (MCA) modes of YT X: MCA regression

  6. Multivariate regression model for thecalibration and combination of climate predictions Y|X ~ N (L (X - Xo),D) Y: DJF precipitation X: DJF precipitation predictions Use PCs of X: Ridge principal component regression

  7. Taking advantage of forecast skill over the Pacificto improve forecasts over land Source: Franco Molteni (ECMWF)

  8. Cross validated skill assessment

  9. Correlation maps: DJF precip. anomalies PC regression Ridge PC regr. MCA regression 6 PCs 3 MCAs All PCs Hindcast period: 1960-2005 (46 years) • Ridge PC shows improved skill in central South America • PC and MCA regression show improved skill in SE South America

  10. Gerrity score for DJF tercile precip. categories PC regression Ridge PC regr. MCA regression 6 PCs 3 MCAs All PCs Hindcast period: 1960-2005 (46 years)

  11. ROC skill score for DJF positive anomalies PC regression Ridge PC regr. MCA regression All PCs 6 PCs 3 MCAs Hindcast period: 1960-2005 (46 years)

  12. Summary • Multivariate regression is a powerful tool for the calibration and combination of multi-model ensemble predictions • Ridge principal component regression allows incorporation of full forecast variability in the calibration and combination procedure (advantage to PC and MCA regression that require truncation) • South American austral summer predictions: • Principal component regression requires retaining more modes to achieve similar level of skill to MCA regression • Over Central South America ridge principal component regression shows improved skill compared to PC and MCA regression • Over SE South America PC and MCA regression show improved skill compared to ridge principal component regression

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