Caio A. S. Coelho, David B. Stephenson
Dept. of Meteorology, Univ. of Reading
RG6 6BB, Reading, UK
Francisco J. Doblas-Reyes, Magdalena Balmaseda
ECMWF, Shinfield Park
RG2 9AX, Reading, UK
Alexander Guetter
Instituto Tecnologico SIMEPAR, Centro Politecnico da UFPR
Jardim das Americas, Caixa Postal 19100, CEP 81531-990, Curitiba, PR, Brazil
Geert Jan van Oldenborgh
Royal Dutch Meteorological Institute
P.O. Box 201, 3730 AE De Bilt, The Netherlands
This study addresses three issues: spatial downscaling, calibration, and combination
of seasonal predictions produced by different coupled ocean-atmosphere climate models.
It examines the feasibility of using a Bayesian procedure for producing combined,
well-calibrated downscaled seasonal rainfall forecasts for two regions in South
America and river flow forecasts for the Paran river in the south of Brazil and
the Tocantins river in the north of Brazil. These forecasts are important for national
electricity generation management and planning. A Bayesian procedure, referred to here as
forecast assimilation, is used to combine and calibrate the rainfall predictions
produced by three climate models. Forecast assimilation is able to improve the skill
of 3-month lead November-December-January multi-model rainfall predictions over the
two South American regions. Improvements are noted in forecast seasonal mean values
and uncertainty estimates. River flow forecasts are less skilful than rainfall
forecasts. This is partially because natural river flow is a derived quantity that
is sensitive to hydrological as well as meteorological processes, and to human
intervention in the form of reservoir management.