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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-468
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Technical note 13 Sep 2018

Technical note | 13 Sep 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Technical note: Changes of cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers

Jan Hnilica1,2, Martin Hanel2, and Vladimír Puš2 Jan Hnilica et al.
  • 1The Czech Academy of Sciences, Institute of Hydrodynamics, Pod Paťankou 5, Prague 6, 166 12, Czech Republic
  • 2Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Prague 6 – Suchdol, 165 21, Czech Republic

Abstract. Simulations of regional or global climate models are often used for climate change impact assessment. To eliminate systematic errors, which are inherent to all climate model simulations, a number of post processing (statistical downscaling) methods have been proposed recently. In addition to basic statistical properties of simulated variables, some of these methods consider also a dependence structure between or within variables. In the present paper we assess the changes in cross- and auto-correlation structures of daily precipitation in six regional climate model simulations. In addition the effect of outliers is explored making distinction between ordinary outliers (i.e. values exceptionally small or large) and dependence outliers (values deviating from dependence structures). It is demonstrated that correlation estimates can be strongly influenced by few outliers even in large data sets. In turn, any statistical downscaling method relying on sample correlation can therefore provide misleading results. An exploratory procedure is proposed to detect the dependence outliers in multi-variate data and to quantify their impact on correlation structures.

Jan Hnilica et al.
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Supporting data and source codes for Hnilica et al. (submitted to HESS) J. Hnilica https://doi.org/10.5281/zenodo.1407993

Jan Hnilica et al.
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Short summary
The changes in correlations of daily precipitation in six RCM simulations are assessed and the effect of outliers (often neglected) is explored. It is demonstrated that correlations can be strongly influenced by few outliers even in large data. Any downscaling method relying on sample correlation can therefore provide misleading results. An exploratory procedure is proposed to detect the outliers and to quantify their impact on correlations.
The changes in correlations of daily precipitation in six RCM simulations are assessed and the...
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