Articles | Volume 17, issue 8
https://doi.org/10.5194/tc-17-3617-2023
https://doi.org/10.5194/tc-17-3617-2023
Research article
 | 
28 Aug 2023
Research article |  | 28 Aug 2023

Multi-decadal analysis of past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets

Diego Monteiro and Samuel Morin

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-166', Anonymous Referee #1, 07 Mar 2023
  • RC2: 'Comment on egusphere-2023-166', Anonymous Referee #2, 08 Mar 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (08 Jun 2023) by Patricia de Rosnay
AR by Diego Monteiro on behalf of the Authors (12 Jun 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to revisions (further review by editor and referees) (14 Jun 2023) by Patricia de Rosnay
ED: Referee Nomination & Report Request started (15 Jun 2023) by Patricia de Rosnay
RR by Anonymous Referee #1 (26 Jun 2023)
RR by Anonymous Referee #3 (05 Jul 2023)
ED: Publish subject to minor revisions (review by editor) (06 Jul 2023) by Patricia de Rosnay
AR by Diego Monteiro on behalf of the Authors (08 Jul 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (12 Jul 2023) by Patricia de Rosnay
AR by Diego Monteiro on behalf of the Authors (13 Jul 2023)  Manuscript 
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Short summary
Beyond directly using in situ observations, often sparsely available in mountain regions, climate model simulations and so-called reanalyses are increasingly used for climate change impact studies. Here we evaluate such datasets in the European Alps from 1950 to 2020, with a focus on snow cover information and its main drivers: air temperature and precipitation. In terms of variability and trends, we identify several limitations and provide recommendations for future use of these datasets.