Status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.
Technical note: Changes of cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers
Jan Hnilica,Martin Hanel,and Vladimír Puš
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 the biases and/or changes in dependence structure between variables or within. In the present paper we assess the biases and 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/covariance 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.
How to cite. Hnilica, J., Hanel, M., and Puš, V.: Technical note: Changes of cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2018-7, 2018.
Received: 08 Jan 2018 – Discussion started: 19 Feb 2018
The paper investigates primarily the changes of the cross- and auto-correlation structures of daily precipitation in an ensemble of climate models. The changes vary in a range from −0.08 to 0.08 and individual models differ considerably. The analysis of significance revealed the strong influence of outliers on correlation structures, which can bring severe artefacts into the climate impact studies. An exploratory procedure is proposed to detect the correlation outliers in multi-variate data.
The paper investigates primarily the changes of the cross- and auto-correlation structures of...