Climate projections from multi-model ensembles
are commonly represented by the multi-model mean
(MMM) climate change. As an alternative, various subjectively
formulated schemes for performance-based
weighting of models have been proposed. Here, a more
objective framework for model weighting is developed. A
key ingredient of this scheme is a calibration step quantifying
the relationship between intermodel similarity in
observable climate and intermodel similarity in simulated
climate change. Models that simulate the observable climate
better are only given higher weight where and when
such an intermodel relationship is found, and the difference
in weight between better and worse performing models
increases with the strength of this relationship. The method
is applied to projections of temperature change from the
Third Coupled Model Intercomparison Project. First, crossvalidation
is used to estimate the potential of the method to
improve the accuracy of climate change estimates and to
search for suitable predictor variables. The decrease in
cross-validation error allowed by the weighting is relatively
modest but not negligible, and it could potentially be
increased if better predictor variables were found. Second,
observations are used to weight the models, to study the
differences between the weighted mean and MMM estimates
of twenty-first century temperature change and the
sensitivity of these differences to the predictor variables
and observational data sets used.

Full Article: http://www.springerlink.com/content/f8816x012h8hv609/

Last Updated: 2009-09-18