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1 Department of Environmental Engineering, Technical University of Denmark 2 Urban Water Engineering, Department of Environmental Engineering, Technical University of Denmark 3 Water Resources Engineering, Department of Environmental Engineering, Technical University of Denmark 4 DHI Denmark
Outputs from climate models are the primary data source in climate change impact studies. However, their interpretation is not straightforward. In recent years, several methods have been developed in order to quantify the uncertainty in climate projections. One of the common assumptions in almost all these methods is that the climate models are independent. This study addresses the validity of this assumption for two ensembles of regional climate models (RCMs) from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project based on the land cells covering Denmark. Daily precipitation indices from an ensemble of RCMs driven by the 40-yrECMWFRe-Analysis (ERA-40) and an ensemble of the same RCMs driven by different general circulation models (GCMs) are analyzed. Two different methods are used to estimate the amount of independent information in the ensembles. These are based on different statistical properties of a measure of climate model error. Additionally, a hierarchical cluster analysis is carried out. Regardless of the method used, the effective number of RCMs is smaller than the total number of RCMs. The estimated effective number of RCMs varies depending on the method and precipitation index considered. The results also show that the main cause of interdependency in the ensemble is the use of the same RCMdriven by different GCMs. This study shows that the precipitation outputs from the RCMs in the ENSEMBLES project cannot be considered independent. If the interdependency between RCMs is not taken into account, the uncertainty in theRCMsimulations of current regional climatemay be underestimated. This will in turn lead to an underestimation of the uncertainty in future precipitation projections. © 2013 American Meteorological Society.
Journal of Climate, 2013, Vol 26, Issue 20, p. 7912-7928
Climate models; Cluster analysis; Hierarchical systems; Climate change
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