Articles | Volume 23, issue 3
https://doi.org/10.5194/hess-23-1741-2019
https://doi.org/10.5194/hess-23-1741-2019
Technical note
 | 
27 Mar 2019
Technical note |  | 27 Mar 2019

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

Jan Hnilica, Martin Hanel, and Vladimír Puš

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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
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-7,https://doi.org/10.5194/hess-2018-7, 2018
Manuscript not accepted for further review
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Cited articles

Bárdossy, A. and Pegram, G.: Multiscale spatial recorrelation of RCM precipitation to produce unbiased climate change scenarios over large areas and small, Water Resour. Res., 48, W09502, https://doi.org/10.1029/2011WR011524, 2012. 
Chen, J., Brissette, F. P., and Lucas-Picher, P.: Assessing the limits of bias-correcting climate model outputs for climate change impact studies, J. Geophys. Res.-Atmos., 120, 1123–1136, https://doi.org/10.1002/2014JD022635, 2015. 
Davison, A. C. and Hinkley, D. V.: Bootstrap methods and their application, Cambridge University Press, Cambridge, United Kingdom, 1997. 
Déqué, M.: Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: Model results and statistical correction according to observed values, Global Planet. Change, 57, 16–26, https://doi.org/10.1016/j.gloplacha.2006.11.030, 2007. 
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., and Liebert, J.: HESS Opinions “Should we apply bias correction to global and regional climate model data?”, Hydrol. Earth Syst. Sci., 16, 3391–3404, https://doi.org/10.5194/hess-16-3391-2012, 2012. 
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A statistical significance of changes in correlations of daily precipitation in six RCM simulations is assessed. The effect of outliers is explored and a concept of dependence outliers is presented. We show that correlation estimates can be strongly affected by a few outliers; therefore any statistical correction relying on sample correlation can provide misleading results. An exploratory procedure is proposed to detect and evaluate the dependence outliers in multivariate data.