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Improving the Prediction of Winter Precipitation and Temperature over the Continental United States: Role of the ENSO State in Developing
 

Summary: Improving the Prediction of Winter Precipitation and Temperature over
the Continental United States: Role of the ENSO State in Developing
Multimodel Combinations
NARESH DEVINENI AND A. SANKARASUBRAMANIAN
Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, North Carolina
(Manuscript received 24 June 2009, in final form 5 November 2009)
ABSTRACT
Recent research into seasonal climate prediction has focused on combining multiple atmospheric general
circulation models (GCMs) to develop multimodel ensembles. A new approach to combining multiple GCMs
is proposed by analyzing the skill levels of candidate models contingent on the relevant predictor(s) state. To
demonstrate this approach, historical simulations of winter (December­February, DJF) precipitation and
temperature from seven GCMs were combined by evaluating their skill--represented by mean square error
(MSE)--over similar predictor (DJF Nin~o-3.4) conditions. The MSE estimates are converted into weights for
each GCM for developing multimodel tercile probabilities. A total of six multimodel schemes are considered
that include combinations based on pooling of ensembles as well as on the long-term skill of the models. To
ensure the improved skill exhibited by the multimodel scheme is statistically significant, rigorous hypothesis
tests were performed comparing the skill of multimodels with each individual model's skill. The multimodel
combination contingent on Nin~o-3.4 shows improved skill particularly for regions whose winter precipitation
and temperature exhibit significant correlation with Nin~o-3.4. Analyses of these weights also show that the
proposed multimodel combination methodology assigns higher weights for GCMs and lesser weights for

  

Source: Arumugam, Sankar - Department of Civil, Construction, and Environmental Engineering, North Carolina State University

 

Collections: Environmental Sciences and Ecology; Engineering