Assigning relative weights to regional climate models: Exploring the concept

An important new development within the European ENSEMBLES project has been to explore
performance-based weighting of regional climate models (RCMs). Until now, even though no
weighting has been applied in multi-RCM analyses, one could claim that an assumption of "equal
weight" was implicitly adopted. At the same time, it is evident that different RCMs to some degree
generate different results, for example for various types of extremes, and these results need to be
combined when using the full ensemble. It is not straight-forward to construct, assign and combine
metrics of model performance. Rather, there is a considerable degree of subjectivity both in the
choice of metrics and on how these may be combined into weights. Here we explore the
applicability of combining a set of six specifically designed RCM performance metrics to produce
one aggregated model weight with the purpose of combining climate change information from the
range of RCMs used within ENSEMBLES. These metrics capture aspects of model performance in
reproducing large scale circulation patterns, meso-scale signals, daily temperature and precipitation
distributions and extremes, trends and the annual cycle. We examine different aggregation
procedures that generate different inter-model spreads of weights. The use of model weights is
sensitive to the aggregation procedure and shows different sensitivities to the selected metrics.
Generally, however, we do not find compelling evidence of an improved description of mean
climate states using performance-based weights in comparison to the use of equal weights. We
suggest that model weighting adds another level of uncertainty to the generation of ensemble based
climate projections, which should be suitably explored, although our results indicate that this
uncertainty likely remains relatively small for the weighting procedures examined.