the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Historical snowfall measurements in the Central and Southern Apennine Mountains: climatology, variability and trend
Abstract. This work presents an analysis of historical snow precipitation data collected in the period 1951–2001 in Central and Southern Apennines (Italy), an area scarcely investigated so far. To pursue this aim, we used the monthly observations of the snow cover duration, number of days with snow and total height of new snow collected at 129 stations located between 288 and 1750 m a.s.l.. Such data have been manually digitized from the Hydrological Yearbooks of the Italian National Hydrological and Mareographic Service. The available dataset has been primarily analyzed to build a reference climatology (related to 1971–2000 period) for the considered Apennine region. More specifically, using a methodology based on Principal Component Analysis and k-means clustering, we have identified different modes of spatial variability, mainly depending from the elevation, which reflect different climatic zones. Subsequently, focusing on the number of days with snow and snow cover duration on the ground, we have carried out a linear trend analysis, employing the Theil-Sen estimator and the Mann-Kendall test. An overall negative tendency has been found for both variables. For clusters including only stations above 1000 m a.s.l., a significant (at 95 % confidence level) decreasing trend has been found in winter season (i.e. from December to February): −3.2 [−6.0 to 0.0] days/10 years for snow cover duration and −1.6 [−2.5 to −0.6] days/10 years for number of days with snow. Moreover, in all considered seasons, a clear direct relationship between trend magnitude and elevation has emerged. In addition, using a cross wavelet analysis, we found a close in-phase linkage on decadal time scale between the investigated snow indicators and the Eastern Mediterranean Pattern. For both snow cover duration and number of days with snow, such connection appears to be more relevant in full (i.e. from November to April) and in late (i.e. from February to April) seasons.
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CC1: 'Comment on egusphere-2024-1056', Danilo Godone, 05 Jun 2024
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Dear Authors,
thanks for the interesting work. If I may, I would like to report two papers that are very similar and may provide useful insights:
- https://doi.org/10.1016/j.jhydrol.2022.128532
- http://dx.doi.org/10.1088/1748-9326/acdb88
Moreover, I would like to suggest avoiding the translation "Arm of Carabineers" thus favoring the notation in Italian such as "Carabinieri" or "Carabinieri Corps" which should be the official one.
Citation: https://doi.org/10.5194/egusphere-2024-1056-CC1 -
AC1: 'Reply on CC1', Vincenzo Capozzi, 06 Jun 2024
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Dear Dr. Godone,
thank you very much for the comment and for your interest in our paper.
In the framwork of the review process, we'll take into consideration your suggestions.
Kind regards,
Vincenzo Capozzi
On behalf of all Co-Authors
Citation: https://doi.org/10.5194/egusphere-2024-1056-AC1
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RC1: 'Comment on egusphere-2024-1056', Anonymous Referee #1, 05 Jul 2024
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This paper presents a valuable collation of historical snow records for an understudied region. As such, the authors should fully document and deposit the data in a public repository, in compliance with the Copernicus Publications data policy: https://www.the-cryosphere.net/policies/data_policy.html
The cluster analysis is detailed, but I am not sure that the discussion reveals much more than could have been illustrated by plotting the snow metrics against elevation and examining the outliers. Although the wavelet analysis is rather preliminary and descriptive, it presents results and so should be moved to the Results section.
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The difference between “snow cover duration” and “number of days with snow” is not clear, and is not made clear until line 183. State “number of days with snowfall” throughout.
31
Snowfall is certainly an essential climate variable, but it is not a GCOS Essential Climate Variable distinct from precipitation, so do not use that specific term.
257-300
The description of Climatol tests is barely comprehensible without reading the references.
360
Relationships of PCs to geographical features are stated but not made clear to the reader.
Figure 5
There is some appeal to having elevation on the y-axis, but it would conventional for it to be on the x-axis as the independent variable. This would also better show the overlap in elevation between clusters and the increasing gradient. Rather than the generic x = ay^b, it would be better to show the power fit equations as SCD = az^b.
464
“subset” would be a more widely comprehensible term than “aliquot”.
Figure 11
Does this contradict recovery of NDS in the Southern Apennines cited in the introduction?
593
XX century?
Figures 12 and 13
The captions should state that arrows pointing to the right indicate that signals are in phase.
675
When claiming 90% confidence, it would make more sense to quote the 90% confidence interval.
Citation: https://doi.org/10.5194/egusphere-2024-1056-RC1 -
AC2: 'Reply on RC1', Vincenzo Capozzi, 27 Aug 2024
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Dear Referee, we are grateful for the time dedicated to the revision of our manuscript and for the suggestions, which will help us to improve our paper. Here we provide a point-by-point response to his/her comments. All required changes will be included in the new manuscript version.
You can find our replies in the attached pdf document.
Kind regards,
Vincenzo Capozzi
on behalf of all Co-Authors
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AC2: 'Reply on RC1', Vincenzo Capozzi, 27 Aug 2024
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