HR: 10:20h
AN: H32C-01 INVITED    [Abstracts]
TI: Multi-Model Ensembling for Seasonal-to-Interannual Climate Forecasting at the IRI
AU: * Goddard, L
EM: goddard@iri.columbia.edu
AF: International Research Institute for Climate & Society, The Earth Institute at Columbia University, 61 Route 9W, P. O. Box 1000, Palisades, NY 10964, United States
AU: Barnston, A G
EM: tonyb@iri.columbia.edu
AF: International Research Institute for Climate & Society, The Earth Institute at Columbia University, 61 Route 9W, P. O. Box 1000, Palisades, NY 10964, United States
AU: DeWitt, D G
EM: daved@iri.columbia.edu
AF: International Research Institute for Climate & Society, The Earth Institute at Columbia University, 61 Route 9W, P. O. Box 1000, Palisades, NY 10964, United States
AU: Mason, S J
EM: simon@iri.columbia.edu
AF: International Research Institute for Climate & Society, The Earth Institute at Columbia University, 61 Route 9W, P. O. Box 1000, Palisades, NY 10964, United States
AU: Robertson, A W
EM: awr@iri.columbia.edu
AF: International Research Institute for Climate & Society, The Earth Institute at Columbia University, 61 Route 9W, P. O. Box 1000, Palisades, NY 10964, United States
AU: Tippett, M K
EM: tippett@iri.columbia.edu
AF: International Research Institute for Climate & Society, The Earth Institute at Columbia University, 61 Route 9W, P. O. Box 1000, Palisades, NY 10964, United States
AB: Ultimately one wants a climate forecast that is as sharp as possible (i.e. high probabilities for a specific outcome) with probabilities that are as reliable as possible (i.e. true to the observed frequency of that outcome over time). Currently, there are many approaches to combining probabilistic predictions from GCMs. Many, but not all, consider some measure of model skill in determining the regional weighting given to a particular GCM. With reasonable sample sizes, most multi-model ensembling approaches yield an overall improvement on skill relative to the performance of the individual models. Occasionally, the multi-model ensemble even outperforms the best model over a particular region. The simplest approach to model combination is straight averaging, an approach often referred to as "pooling". It is easily shown that overall, pooling leads to more skilful and reliable forecasts than is possible with a single model. More elaborate techniques can involve recalibration of the probability distributions from the individual models, performance-weighting the models, or some combination of both. This talk outlines the evolution of IRI's approach to probabilistic seasonal forecasting from methods that we've tried, what we're using now, and finally the combination of techniques currently being developed as we work towards putting out a more flexible forecast product. Some of the techniques illustrated and evaluated in this talk will be categorical contingency table correction of probabilities, canonical variate ensembling, probabilistic regression methods, Bayesian ensembling, and analytical recalibration of both local and non-local model response. Pooling will serve as the methodological baseline. The relative enhancements in reliability and/or sharpness from the various approaches will be compared. Not all approaches benefit both the sharpness and reliability aspects of forecast quality.
DE: 3245 Probabilistic forecasting (3238)
DE: 3275 Uncertainty quantification (1873)
DE: 3294 Instruments and techniques
DE: 3333 Model calibration (1846)
DE: 3337 Global climate models (1626, 4928)
SC: Hydrology [H]
MN: 2007 Fall Meeting