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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2018-157
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-157
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 14 Aug 2018

Methods for assessment of models | 14 Aug 2018

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Geoscientific Model Development (GMD).

Similarities within a multi-model ensemble: functional data analysis framework

Eva Holtanová1, Thomas Mendlik2, Jan Koláček3, Ivanka Horová3, and Jiří Mikšovský1 Eva Holtanová et al.
  • 1Department of Atmospheric Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, Prague, 180 00, Czech Republic
  • 2Wegener Center for Climate Studies, University of Graz, Brandhofgasse 5/1, Graz, 8010, Austria
  • 3Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Kotlářská 267/2, 611 37, Brno, Czech Republi

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 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 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.

Eva Holtanová et al.
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Eva Holtanová et al.
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Short summary
We presents a methodology framework for the analysis of climate model uncertainty based on functional data analysis approach, an emerging statistical field. The novel method investigates the multi-model spread taking into account the behaviour of entire simulated climatic time series, encompassing both past and future periods. Further, we introduce an innovative way of visualizing climate model similarities based on a network spatialization algorithm which enables an unambiguous interpretation.
We presents a methodology framework for the analysis of climate model uncertainty based on...
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