Eurasian snow depth in long-term climate reanalyses

Wegmann, Martin; Orsolini, Yvan; Dutra, Emanuel; Bulygina, Olga; Sterin, Alexander; Brönnimann, Stefan

doi:https://doi.org/10.5194/tc-11-923-2017

Articles | Volume 11, issue 2

https://doi.org/10.5194/tc-11-923-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/tc-11-923-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 11, issue 2

Research article

|

13 Apr 2017

Research article |

| 13 Apr 2017

Eurasian snow depth in long-term climate reanalyses

Martin Wegmann, Yvan Orsolini, Emanuel Dutra, Olga Bulygina, Alexander Sterin, and Stefan Brönnimann

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Final revised paper (published on 13 Apr 2017)
Supplement to the final revised paper
Preprint (discussion started on 21 Nov 2016)
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 of "Eurasian snow depth in long-term climate reanalyses" by Wegmann et al.', Anonymous Referee #1, 09 Dec 2016
- AC1: 'Author response to Reviewer #1', Martin Wegmann, 16 Feb 2017
RC2: 'Interactive comment on "Eurasian snow depth in long-term climate reanalyses" by Weggmann et al.', Anonymous Referee #2, 19 Jan 2017
- AC2: 'Author response to Reviewer #2', Martin Wegmann, 16 Feb 2017

Peer-review completion

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

AR by Martin Wegmann on behalf of the Authors (16 Feb 2017) Author's response Manuscript

ED: Referee Nomination & Report Request started (05 Mar 2017) by Martin Schneebeli

RR by Anonymous Referee #2 (15 Mar 2017)

ED: Publish as is (15 Mar 2017) by Martin Schneebeli

AR by Martin Wegmann on behalf of the Authors (22 Mar 2017)

Short summary

We investigate long-term climate reanalyses datasets to infer their quality in reproducing snow depth values compared to in situ measured data from meteorological stations that go back to 1900. We found that the long-term reanalyses do a good job in reproducing snow depths but have some questionable snow states early in the 20th century. Thus, with care, climate reanalyses can be a valuable tool to investigate spatial snow evolution in global warming and climate change studies.