This study proposes an integrated seasonal forecasting system for producing improved and well-calibrated probabilistic rainfall forecasts for South America. The proposed system has two components: a) an empirical model that uses Pacific and Atlantic sea surface temperature anomalies as predictor for South American rainfall; and b) a multi-model system composed of three European coupled ocean-atmosphere models. Three-month lead austral summer rainfall predictions produced by the components of the system are integrated (i.e. combined and calibrated) using a Bayesian forecast assimilation procedure. The objective calibration and combination of empirical and multi-model coupled predictions makes this a first step towards an integrated forecasting system for issuing South American seasonal forecasts. The skill of empirical, coupled multi-model and integrated forecasts obtained with forecast assimilation is assessed and compared. This comparison reveals that the simple multi-model ensemble of the current generation of coupled models has comparable level of skill to that obtained using a simplified empirical approach. As for most regions of the globe, seasonal forecast skill for South America is low. However, when empirical and coupled multi-model predictions are combined and calibrated using forecast assimilation, more skillful integrated forecasts are obtained than with either empirical or coupled multi-model predictions alone. Both the reliability and resolution of the probability forecasts have been improved by forecast assimilation in several regions of South America. The tropics and the area of southern Brazil, Uruguay, Paraguay and northern Argentina have been found to be the two most predictable regions of South America during the austral summer. Skillful South American rainfall forecasts are generally only possible during El Niño or La Niña years rather than in neutral years.
BT - ECMWF Technical Memoranda CY - Shinfield Park, Reading DA - 06/2005 DO - 10.21957/allssbsgm LA - eng M1 - 461 N2 -This study proposes an integrated seasonal forecasting system for producing improved and well-calibrated probabilistic rainfall forecasts for South America. The proposed system has two components: a) an empirical model that uses Pacific and Atlantic sea surface temperature anomalies as predictor for South American rainfall; and b) a multi-model system composed of three European coupled ocean-atmosphere models. Three-month lead austral summer rainfall predictions produced by the components of the system are integrated (i.e. combined and calibrated) using a Bayesian forecast assimilation procedure. The objective calibration and combination of empirical and multi-model coupled predictions makes this a first step towards an integrated forecasting system for issuing South American seasonal forecasts. The skill of empirical, coupled multi-model and integrated forecasts obtained with forecast assimilation is assessed and compared. This comparison reveals that the simple multi-model ensemble of the current generation of coupled models has comparable level of skill to that obtained using a simplified empirical approach. As for most regions of the globe, seasonal forecast skill for South America is low. However, when empirical and coupled multi-model predictions are combined and calibrated using forecast assimilation, more skillful integrated forecasts are obtained than with either empirical or coupled multi-model predictions alone. Both the reliability and resolution of the probability forecasts have been improved by forecast assimilation in several regions of South America. The tropics and the area of southern Brazil, Uruguay, Paraguay and northern Argentina have been found to be the two most predictable regions of South America during the austral summer. Skillful South American rainfall forecasts are generally only possible during El Niño or La Niña years rather than in neutral years.
PB - ECMWF PP - Shinfield Park, Reading PY - 2005 EP - 26 T2 - ECMWF Technical Memoranda TI - Towards an integrated seasonal forecasting system for South America. UR - https://www.ecmwf.int/node/8744 ER -