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

Hnilica, Jan; Hanel, Martin; Puš, Vladimír

doi:https://doi.org/10.5194/hess-23-1741-2019

Articles | Volume 23, issue 3

https://doi.org/10.5194/hess-23-1741-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-23-1741-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 3

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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Final revised paper (published on 27 Mar 2019)
Supplement to the final revised paper
Preprint (discussion started on 13 Sep 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Changes of cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers', Geoff Pegram, 11 Oct 2018
- AC1: 'reply to RC1', Jan Hnilica, 23 Oct 2018
SC1: 'review of correlation paper', Ashish Sharma, 26 Oct 2018
- AC3: 'reply to SC1', Jan Hnilica, 29 Nov 2018
RC2: 'correlation biases paper review', Ashish Sharma, 29 Oct 2018
- AC2: 'reply to RC2', Jan Hnilica, 29 Nov 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to minor revisions (further review by editor) (18 Jan 2019) by András Bárdossy

AR by Jan Hnilica on behalf of the Authors (25 Jan 2019) Author's response Manuscript

ED: Publish as is (05 Mar 2019) by András Bárdossy

AR by Jan Hnilica on behalf of the Authors (08 Mar 2019)

Short summary

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.