HR: 15:30h
AN: A14A-01 INVITED     [Abstracts]
TI: Multi-Model Ensembling: A Comparison of Approaches
AU: Goddard, L
EM: goddard@iri.columbia.edu
AF: International Research Institute for Climate Prediction The Earth Institute of Columbia University, 61 Route 9W, Palisades, NY 10964 United States
AU: * Robertson, A W
EM: awr@iri.columbia.edu
AF: International Research Institute for Climate Prediction The Earth Institute of Columbia University, 61 Route 9W, Palisades, NY 10964 United States
AU: Mason, S J
EM: simon@iri.columbia.edu
AF: International Research Institute for Climate Prediction The Earth Institute of Columbia University, 61 Route 9W, Palisades, NY 10964 United States
AB: A global climate forecast system should be as accurate as possible with probabilities that are as reliable as possible. Individual general circulation models (GCMs) can differ substantially in their seasonal forecast skill due to differences in parameterizations and even in the structure of the models' dynamical cores. A few regions of high predictability exist, for which most, if not all, models simulate the interannual variability accurately when forced with appropriate surface conditions. For most of the continental areas, however, the variability is not so straightforward or deterministic, and in these cases the details of the GCM formulation greatly influences the model's ability to simulate the characteristics of the variability. No one model is best. Thus the value of combining the predictions from several models is clear. Currently a number of approaches exist for combining probabilistic predictions from GCMs. Many consider some measure of model skill in determining the regional weighting given to a particular GCM. In this presentation, several approaches are compared and contrasted; each constitutes a somewhat different philosophy in model combination. Two of the approaches, straight pooling and prediction convergence, do not consider historical model performance. Two other approaches, Bayesian and canonical variate, do incorporate information regarding past performance of the individual models. All multi-model ensembling (MME) approaches yield an overall improvement on skill relative to the performance of the individual models. Occasionally, the MME even outperforms the best model over a particular region. Although the different MME approaches give seasonal forecasts that are more similar to each other than were the constituent model predictions, considerable differences still remain. Differences in skill are evident in the different MME approaches, with the performance-based approaches generally scoring higher. Differences are found also in the sharpness of the forecast probabilities and the amount of area over which non-climatological probabilities are indicated.
DE: 3319 General circulation
DE: 3337 Numerical modeling and data assimilation
DE: 3339 Ocean/atmosphere interactions (0312, 4504)
DE: 3354 Precipitation (1854)
DE: 3367 Theoretical modeling
SC: Atmospheric Sciences [A]
MN: 2005 Joint Assembly