Multi-model seasonal hindcasts over the Euro-Atlantic: skill scores and dynamic features

By V. Pavan, Francisco J. Doblas-Reyes. 2000

105 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/849f4396bc1aa881b216cb286b87ccd7ef0451ca

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Citations in the corpus

The rationale behind the success of multi-model ensembles in seasonal forecasting — I. Basic concept (2005, methodology): “However, more complex methods of optimally combining the single-model output have been described (Krishnamurti et al., 1999; Pavan and Doblas-Reyes, 2000; Rajagopalan et al., 2002).”
Multimodel Ensembling in Seasonal Climate Forecasting at IRI (2003, result): “This is consistent with findings of others that, following the removal of biases in individual models, multimodel ensembling typically produces skills equal to or just slightly better than the skill of the most skillful AGCM in the set (Pavan and Doblas-Reyes 2000; Palmer et al. 2000; Peng et al. 20”
The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination (2005, methodology): “This approach estimates the best linear unbiased estimator (BLUE) as in Sarda et al. (1996), Pavan and Doblas-Reyes (2000) and Derome et al. (2001).”
DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER) (2004, result): “…results from PROVOST and DSP is that, despite identical SSTs, ensembles showed considerable model-to-model variability in the estimates both of the SST-forced seasonal-mean signal, and the seasonal-mean “noise” generated by internal dynamics (Straus and Shukla 2000; Pavan and Doblas-Reyes 2000).”
Categorical Climate Forecasts through Regularization and Optimal Combination of Multiple GCM Ensembles (2002, methodology): “The models in question are general circulation models that numerically integrate the governing equations of motion using prescribed boundary and initial * International Research Institute Contribution Number IRI-PP/02/ 01.”
Forecast calibration and combination: A simple Bayesian approach for ENSO (2004, methodology): “The inverse regression model is the typical regression model used in previous climate forecasting studies (e.g., Kharin and Zwiers 2002; Pavan and Doblas-Reyes 2000).”
An analysis of multimodel ensemble predictions for seasonal climate anomalies (2002, background): “For this reason the skill scores of DSP are also model dependent [Pavan and Doblas-Reyes, 2000; Shukla et al., 2000].”
Improved Combination of Multiple Atmospheric GCM Ensembles for Seasonal Prediction (2004, background): “Our results demonstrate clear gains in skill by simply pooling together the ensemble hindcasts made with individual GCM ensembles, corroborating previous studies (Fraedrich and Smith 1989; Graham et al. 2000; Palmer et al. 2000; Pavan and Doblas-Reyes 2000; Peng et al. 2002).”