Caio A. S. Coelho, D. B. Stephenson, F. J. Doblas-Reyes (*) and M. Balmaseda (*) Department of Meteorology, University of Reading and ECMWF (*) The skill of empirical and combined/calibrated coupled multi-model South American seasonal predictions during ENSO

Aim: To produce improved probability rainfall forecasts for S. America Strategy: Stage 1: Nino-3.4 index, 1 model (Coelho et al. 2003,2004) Stage 2: Equatorial Pac. SST, 7 models (Stephenson et al. 2005) Stage 3: S. American rainfall, 3 models (Coelho et al. 2005a,b)

Plan of talk 1.Issues 2.Conceptual framework (“Forecast Assimilation”) 3.Examples of application: 0-d (Nino-3.4) 1-d (Eq. Pac. SST) 2-d (S. Amer. rainfall) Downscaling 4. Conclusions

1. Issues Why do forecasts need it? How to do it? How to get good probability estimates? Calibration Combination Why to combine? How to combine?

2. Conceptual framework Data Assimilation “Forecast Assimilation”

y Modelling the likelihood p(x|y)

ForecastMAE (  C) MAESS (%) BSBSS (%) Uncert (  C) Climatol Empirical ECMWF Integrated MAESS = [1- MAE/MAE(clim.)]*100% Empirical ECMWF Integrated BSS = [1- BS/BS(clim.)]*100% Example 1: Dec Niño3.4 forecasts (5-month lead)

Example 2: Equatorial Pacific SST ForecastBrier Score (BS) BSS (%) Climatol p= Ensemble (ENS) Integrated (INT) SST anomalies: Y (°C) Forecast probabilities: p DEMETER: 7 coupled models; 6-month lead BSS = [1- BS/BS(clim.)]*100% OBS INT ENS

Brier Score as a function of longitude Forecast assimilation reduces (i.e. improves) the Brier score in the eastern and western equatorial Pacific ENS INT

Example 3: South American rainfall anomalies (mm/day) ENSO composites: El Nino years 13 La Nina years Empirical model (EMP): ASO SST DJF Multi-model ensemble (ENS): 3 DEMETER coupled models ECMWF, Meteo-France, Met Office 1-month lead Start: Nov DJF Integrated (INT) forecast Combines EMP and ENS OBS (El Nino) EMP (El Nino) ENS (El Nino) INT (El Nino) OBS (La Nina) EMP (La Nina) ENS (La Nina) INT (La Nina)

Mean Anomaly Correlation Coefficient (ACC)  Generally low skill (c.f. ACC<0.31)  Larger skill in ENSO years than in neutral years  Calibration and combination improves skill

EMP ENS INT Correlation score for S.American rainfall  Comparable level of deterministic skill  Higher skill in the tropics and southeastern S. America

Brier Skill Score for S. American rainfall Forecast assimilation improves the Brier Skill Score (BSS) in the tropics EMP ENS INTENS

Why has the skill been improved? How well calibrated the forecasts are (reliability) Ability to discriminate between different observed situations (resolution) Forecast skill depends on:

Brier Score decomposition reliability resolution uncertainty

Reliability component of the BSS Forecast assimilation improves reliability over many regions EMPENS INT

Resolution component of the BSS Forecast assimilation improves resolution in the tropics INTENSEMP

Example 4: Downscaling of rainfall anomalies Multi-model ensemble (ENS): 3 DEMETER coupled models ECMWF, Meteo-France, Met Office 1-month lead Start: Nov DJF

ForecastCorrelationBrier Score ENS INT South Box: DJF rainfall anomalies (1-month lead) ENS INT  Forecast assimilation substantially improves forecast skill Observation Forecast

CorrelationBrier Score ENS INT North Box : DJF rainfall anomalies (1-month lead) ENS INT  Forecast assimilation slightly improves forecast skill Observation Forecast

Forecast assimilation improves the skill of probability forecasts South America rainfall example: - empirical and integrated predictions have comparable level of deterministic skill - improved reliability and resolution in the tropics; - improved reliability in subtropical and central regions - higher skill in ENSO years than neutral years - tropical and southeastern South America are the two most predictable regions - first step towards an integrated system for South America 4. Conclusions:

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, F. J. Doblas-Reyes and M. Balmaseda, 2005a: “ From Multi-model Ensemble Predictions to Well-calibrated Probability Forecasts: Seasonal Rainfall Forecasts over South America ”. CLIVAR Exchanges No 32, Vol. 10, No 1, Coelho C.A.S., D. B. Stephenson, M. Balmaseda, F. J. Doblas-Reyes and G. J. van Oldenborgh, 2005b: “Towards an integrated seasonal forecasting system for South America”. Submitted to J. Climate. Stephenson, D. B., C.A.S. Coelho, F. J. Doblas-Reyes, and M. Balmaseda, 2005: “Forecast Assimilation: A Unified Framework for the Combination of Multi-Model Weather and Climate Predictions.” Tellus A, Vol. 57, 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”. Journal of Climate. Vol. 17, No. 7, 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. Available at More information …

Forecast assimilation improves reliability in the western Pacific Reliability as a function of longitude ENS INT

Resolution as a function of longitude Forecast assimilation improves resolution in the eastern Pacific ENS INT