Emerging climate signals in the Lena River catchment:  a non-parametric statistical approach

Pohl, Eric; Grenier, Christophe; Vrac, Mathieu; Kageyama, Masa

doi:https://doi.org/10.5194/hess-24-2817-2020

Articles | Volume 24, issue 5

https://doi.org/10.5194/hess-24-2817-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-24-2817-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 5

Research article

|

29 May 2020

Research article |

| 29 May 2020

Emerging climate signals in the Lena River catchment: a non-parametric statistical approach

Eric Pohl, Christophe Grenier, Mathieu Vrac, and Masa Kageyama

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Final revised paper (published on 29 May 2020)
Supplement to the final revised paper
Preprint (discussion started on 10 Sep 2019)
Supplement to the preprint

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review', Wolfgang Schwanghart, 04 Oct 2019
- AC1: 'Answer to review', Eric Pohl, 31 Jan 2020
RC2: 'Reviewer comments', Anonymous Referee #2, 24 Oct 2019
- AC2: 'Answer to reviewer 2', Eric Pohl, 31 Jan 2020

Peer-review completion

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

ED: Publish subject to revisions (further review by editor and referees) (07 Feb 2020) by Thomas Kjeldsen

AR by Eric Pohl on behalf of the Authors (20 Mar 2020) Author's response Manuscript

ED: Publish as is (07 Apr 2020) by Thomas Kjeldsen

AR by Eric Pohl on behalf of the Authors (27 Apr 2020) Manuscript

Short summary

Existing approaches to quantify the emergence of climate change require several user choices that make these approaches less objective. We present an approach that uses a minimum number of choices and showcase its application in the extremely sensitive, permafrost-dominated region of eastern Siberia. Designed as a Python toolbox, it allows for incorporating climate model, reanalysis, and in situ data to make use of numerous existing data sources and reduce uncertainties in obtained estimates.