@Article{CoelhoStepDoblBalm:2006:SkEmCo,
author = "Coelho, Caio Augusto dos Santos and Stephenson, David B. and
Doblas-Reyes, Francisco J. and Balmaseda, Magdalena",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Department
of Meteorology, University of Reading, Reading, UK and {European
Centre for Medium-Range Weather Forecasts (ECMWF)} and {European
Centre for Medium-Range Weather Forecasts (ECMWF)}",
title = "The skill of empirical and combined/calibrated coupled multi-model
south American seasonal predictions during ENSO",
journal = "Advances in Geosciences",
year = "2006",
volume = "06",
number = "SRef-ID: 1680-7359/adgeo/2006-6-51",
pages = "51--55",
month = "Jan.",
keywords = "coupled multi-model, surface temperatures, tropics south Brazil,
Paraguay, Uruguay, Northern Argentina, ENSO.",
abstract = "This study addresses seasonal predictability of South American
rainfall during ENSO. The skill of empirical and coupled
multi-model predictions is assessed and compared. The
empiricalmodel uses the previous season August- September-October
Pacific and Atlantic sea surface temperatures as predictors for
December-January-February rainfall. Coupled multi-model 1-month
lead December-January- February rainfall predictions were obtained
from the Development of a European Multi-model Ensemble systemfor
seasonal to inTERannual prediction (DEMETER) project. Integrated
(i.e. combined and calibrated) forecasts that incorporate
information provided by both the empirical and the coupled
multi-model are produced using a Bayesian procedure. This
procedure is referred to as forecast assimilation. The skill of
the integrated forecasts is compared to the skill of empirical and
coupled multi-model predictions. This comparison reveals that when
seasonally forecasting December- January-February South American
rainfall at 1-month leadtime the current generation of coupled
models have a level of deterministic skill comparable to those
obtained using simplified empirical approaches. However, Bayesian
combined/ calibrated forecasts provide better estimates of
forecast uncertainty than the coupled multi-model. This indicates
that forecast assimilation improves the quality of probabilistic
predictions. The tropics and the area of South Brazil, Paraguay,
Uruguay and Northern Argentina are found to be the two most
predictable regions of South America. ENSO years are more
predictable than neutral years, the latter having nearly null
skill.",
copyholder = "SID/SCD",
issn = "1680-7340 and 1680-7359",
language = "en",
targetfile = "Coelho.The Skill.pdf",
url = "http://www.copernicus.org",
urlaccessdate = "06 maio 2024"
}