Abstract. Despite the abundance of available global and regional climate model outputs, their use for evaluation of past and future climate changes is often complicated by substantial differences between individual simulations, and the resulting uncertainties. In this study, we present a methodology framework for the analysis of multi-model ensembles based on functional data analysis approach. A set of two metrics that generalize the concept of similarity based on the behaviour of 15 entire simulated climatic time series, encompassing both past and future periods, is introduced. As far as our knowledge, our method is the first to quantitatively assess similarities between model simulations based on the temporal evolution of simulated values. To evaluate mutual distances of the time series we used two semimetrics based on Euclidean distances between the simulated trajectories and on differences in their first derivatives. Further, we introduce an innovative way of visualizing climate model similarities based on a network spatialization algorithm. Using the layout graphs the data are 20 ordered on a 2-dimensional plane which enables an unambiguous interpretation of the results. The method is demonstrated using two illustrative cases of air temperature over the British Isles and precipitation in central Europe, simulated by an ensemble of EURO-CORDEX regional climate models and their driving global climate models over the 1971-2098 period.In addition to the sample results, interpretational aspects of the applied methodology and its possible extensions are also discussed. 25
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