Articles | Volume 19, issue 12
https://doi.org/10.5194/tc-19-6907-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
Recent and projected changes in rain-on-snow event characteristics across Svalbard
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- Final revised paper (published on 19 Dec 2025)
- Supplement to the final revised paper
- Preprint (discussion started on 27 May 2025)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
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RC1: 'Comment on egusphere-2025-2099', Anonymous Referee #1, 08 Jul 2025
- AC1: 'Reply on RC1', Hannah Vickers, 14 Sep 2025
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RC2: 'Comment on egusphere-2025-2099', Anonymous Referee #2, 08 Aug 2025
- AC2: 'Reply on RC2', Hannah Vickers, 14 Sep 2025
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) (15 Sep 2025) by Nora Helbig
AR by Hannah Vickers on behalf of the Authors (06 Nov 2025)
Author's response
Author's tracked changes
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ED: Referee Nomination & Report Request started (09 Nov 2025) by Nora Helbig
RR by Anonymous Referee #1 (05 Dec 2025)
ED: Publish subject to minor revisions (review by editor) (07 Dec 2025) by Nora Helbig
AR by Hannah Vickers on behalf of the Authors (09 Dec 2025)
Author's response
Author's tracked changes
EF by Polina Shvedko (10 Dec 2025)
Manuscript
ED: Publish as is (10 Dec 2025) by Nora Helbig
AR by Hannah Vickers on behalf of the Authors (11 Dec 2025)
Manuscript
The manuscript describes a trend analysis study of rain-on-snow (ROS) events in Svalbard. The authors use reanalysis data (CARRA, ERA5) and one dynamically downscaled climate simulation (HCLIM-MPI) for this purpose and found, that ROS events have been increasing in frequency, duration, and intensity in several regions in the past, and are expected to further increase in the future. To motivate this study, the authors provide a brief discussion of the ecological and hydrological consequences of ROS events and propose directions for future research.
Major comments:
A convection permitting RCM (HCLIM) is used for downscaling the single GCM used in this study. The authors argue, that this would lead to superior results compared coarser-scale RCMs (e.g., l74, l363), but miss to demonstrate this claimed added value. One reference is given (Landgren et al., 2025), but this is only a conference abstract with no information content (no results in the abstract). I.e., a basic evaluation and demonstration of added value of the HCLIM is completely missing, which leads to several complications. One of them is, that if no significant added value compared to other RCMs (e.g., from CORDEX-ARC-22) can be demonstrated, the authors could use an ensemble of conventional climate models instead. This would resolve the major weakness of this study (see following comment). Another issue of missing model evaluation is that the authors have to speculate on the reasons for differences between CARRA and HCLIM ROS characteristics. E.g., l312: “The discrepancy in absolute values of the characteristics for the present climate (2000-2020) are likely attributable to the uncertainty in temperature threshold for partitioning rain and snow used in the different datasets”. Couldn’t it also be a simple temperature bias in HCLIM?
My main concern about this manuscript is the use of only one GCM/RCM combination for the analysis of future trends. Particularly, the choice of the GCM can be expected to have large impact on the results. This weakness is clearly identified by the authors (l364), which is good, but at the same time, this is no justification, since other options would be available. E.g., the CORDEX-ARC-22 archive contains 79 simulations (https://esgf-node.ipsl.upmc.fr/search/cordex-ipsl/). The authors have to demonstrate how this single realization be regarded as representative, or at least show whether is a cool/warm and dry/moist realization of expected future climate. Or, preferably, use a comprehensive model ensemble, as it is state-of-the-art.
The method of trend detection is not named nor described. Please give a clear explanation of the statistical method used. This is particularly important, since the analyzed period (1991 – 2023) is rather short for detecting significant trend in time series with large variability. I additionally suggest presenting some time series, in order to allow the reader to get an impression of the variability involved.
l197: the results show a maximum of the trends in November. This would imply, that significant trends could also be present in October, or even earlier in the year. Please expand your evaluation time-window (currently Nov-April) accordingly, in order not to miss significant results.
l258: The future trends feature a maximum in Nov., a minimum in March and another maximum in April. This seems to be counter-intuitive and would deserve some discussion, or ideally an explanation.
Minor and editorial comments:
The introduction features some repetitions (e.g., the fact that ROS impact ecosystems) and would gain from streamlining/shortening.