final_program_abstracts[1]

11 IMSC Session Program
Weighting of model results for improving projections of
climate change
Wednesday - Parallel Session 2
Jouni Räisänen
Department of Physics, University of Helsinki, Finland
Climate projections from multi-model ensembles are commonly represented by the
multi-model mean climate change. As an alternative, various subjectively formulated
schemes for performance-based weighting of models have been proposed. Here we
introduce a more objective framework for model weighting. A key ingeredient of this
scheme is a calibration step quantifying the relationship between intermodel similarity
in observed climate and intermodel similarity in simulated climate change. Models
that simulate the observed 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 in the CMIP3 ensemble.
First, cross-validation is used to study 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 found to be
relatively modest, but it might 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 multi-model mean estimates of 21st century temperature
change.
Abstracts 182