the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Aug 2025
Technical note: Literature based approach to estimate future snow
Abstract. The seasonal snow cover in the European Alps is increasingly threatened by rising temperatures due to climate change. Still, downscaled climate projections are lacking for many regions. To address this gap, we developed a literature-based approach for projecting future snow depths, that is applicable to all locations where historical snow depth data is available.
We harmonized heterogeneous literature on future snow depth and snow water equivalent by translating emission scenarios to corresponding temperature scenarios and standardizing seasonal periods. Then, we parameterized localized reduction curves based on elevation, temperature scenarios and local climatologies, as mean snow cover length and mean maximum snow depth. This method was applied to four measurement stations in Switzerland under a +2 °C temperature scenario, revealing significant declines in snow depth and season length, especially at lower elevations. Validation against published data shows that the approach captures key trends in snow loss, despite the simplification of climate dynamics.
This resource-efficient method provides a practical tool for estimating climate change related snow depth declines in snow dominated regions, which are lacking highly resolved climate projections, and can support decision-makers in developing adaptation strategies for climate-related challenges.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(2354 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2354 KB) - Metadata XML
- BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-3518', J. Ignacio López-Moreno, 27 Aug 2025
- AC2: 'Reply on RC1', Bettina Richter, 31 Oct 2025
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RC2: 'Comment on egusphere-2025-3518', Anonymous Referee #2, 01 Oct 2025
This technical report takes an interesting approach, harmonising multiple manuscript sources under a common framework and synthesising their findings into a unified indicator using various future projection results. It is a technical method of consolidating various types of data into a single metric and yields compelling results. In my opinion, the manuscript is ready for publication as a technical report.
While reviewing this manuscript, I came across several points that I found unclear. I have commented on these below.
minor comments
Lines 45–60, Section 2.1.2 and Figure 1: Please clarify the roles of what is represented as NDJFMA – xxx (e.g., DJF) and NDJFMA-decrease. My understanding is that equation (1) refers to NDJFMA-decrease, while Figure 1 shows NDJFMA – xxx. The decreases such as –25% mentioned in lines 58–60 presumably correspond to NDJFMA-decrease. It seems to me that NDJFMA – xxx and NDJFMA-decrease are conceptually different (the former being adjustments due to different averaging periods, and the latter being the actual future decrease ratio). However, in the current explanation, they appear to be mixed together. Could you please make their distinction more explicit?
Lines 58–60: To which values do the reported decreases of 25% and 20% refer? They do not appear to be within the range shown in Figure 1. Could you please clarify what these percentages are based on?
Lines 84–85: Could you include an illustration of Δb and Δc in Figure 2? It would help readers better understand the concept.
Lines 141–143: I understand that, due to global warming, the snowmelt season begins earlier, as does the peak in snow depth. One point I found questionable is that the dependence of Δb on elevation appears stronger than its dependence on temperature change compared to parameters such as a or Δc. The weak temperature dependence may be due to discontinuous changes; for example, when two peaks exist and the position of the dominant peak shifts. However, the fact that Δb shows stronger elevation dependence than dependence on temperature change raises the question of whether this behaviour is a general characteristic or a result specific to the dataset used. If the latter, the explanatory power of the Δb equation would be reduced. It is important to clarify this point.
Citation: https://doi.org/10.5194/egusphere-2025-3518-RC2 - AC1: 'Reply on RC2', Bettina Richter, 31 Oct 2025
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-3518', J. Ignacio López-Moreno, 27 Aug 2025
I enjoyed reading this note and believe it addresses, in a very smart way, an important issue in comparing previous snow projections: the use of different time horizons, models, emission scenarios, etc. Most of the implications of the assumptions and simplifications are well discussed. The manuscript is well written, and I did not identify any methodological flaws. Therefore, I recommend its publication.
Below, I provide a few minor suggestions and some ideas from my related research, which the authors may consider using to further strengthen the discussion:
-I wonder about the impact of the methodology used in previous studies to perturb observed series with climate projections (e.g., the Delta method on seasonal or monthly bases, quantile perturbation, or directly using simulated climate to drive snow models). Different methods may influence the asymmetry in the start and end of the snow season or other metrics that relate snow changes solely to temperature.
- It is somewhat surprising to me that the changes in the start and end of the snow season appear symmetric. Is the projected temperature increase generally similar for winter and spring? Even if it is, I would expect some patterns related to elevation—for instance, an earlier snowmelt may eliminate periods of very high solar radiation, whereas a later snow onset may have less significant implications for incoming solar radiation and melt dynamics. This is particularly true at higher elevations but not at lower ones.
-One of the strengths of this methodology is its ability to translate different scenarios into temperature changes. This can make it easier to communicate results for policy decisions, as many greenhouse gas emission targets are linked to temperature thresholds (e.g., 1.5 °C). This aspect could be highlighted more explicitly in the discussion, as it helps make the results more accessible to non-scientific audiences.
- Related to the previous point, in recent years I have preferred, instead of simulating future snow conditions for different climate models and emission scenarios, to perform sensitivity analyses (e.g., adding 1–2 °C, or ±5–10 % changes in precipitation; see DOI: 10.1088/1748-9326/abb55f). Then, the changes in T and P can be framed with climate projections for specific regions.. While this approach requires simplifying assumptions about the system, I find it makes the results much easier to compare. It may be interesting to contrast your approach with this type of sensitivity analysis.
- Perhaps Figure 2 could more clearly illustrate how changes in “b” and “c” are derived.
Hoping my comments will result useful,
Best,
J. Ignacio López-Moreno
Citation: https://doi.org/10.5194/egusphere-2025-3518-RC1 - AC2: 'Reply on RC1', Bettina Richter, 31 Oct 2025
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RC2: 'Comment on egusphere-2025-3518', Anonymous Referee #2, 01 Oct 2025
This technical report takes an interesting approach, harmonising multiple manuscript sources under a common framework and synthesising their findings into a unified indicator using various future projection results. It is a technical method of consolidating various types of data into a single metric and yields compelling results. In my opinion, the manuscript is ready for publication as a technical report.
While reviewing this manuscript, I came across several points that I found unclear. I have commented on these below.
minor comments
Lines 45–60, Section 2.1.2 and Figure 1: Please clarify the roles of what is represented as NDJFMA – xxx (e.g., DJF) and NDJFMA-decrease. My understanding is that equation (1) refers to NDJFMA-decrease, while Figure 1 shows NDJFMA – xxx. The decreases such as –25% mentioned in lines 58–60 presumably correspond to NDJFMA-decrease. It seems to me that NDJFMA – xxx and NDJFMA-decrease are conceptually different (the former being adjustments due to different averaging periods, and the latter being the actual future decrease ratio). However, in the current explanation, they appear to be mixed together. Could you please make their distinction more explicit?
Lines 58–60: To which values do the reported decreases of 25% and 20% refer? They do not appear to be within the range shown in Figure 1. Could you please clarify what these percentages are based on?
Lines 84–85: Could you include an illustration of Δb and Δc in Figure 2? It would help readers better understand the concept.
Lines 141–143: I understand that, due to global warming, the snowmelt season begins earlier, as does the peak in snow depth. One point I found questionable is that the dependence of Δb on elevation appears stronger than its dependence on temperature change compared to parameters such as a or Δc. The weak temperature dependence may be due to discontinuous changes; for example, when two peaks exist and the position of the dominant peak shifts. However, the fact that Δb shows stronger elevation dependence than dependence on temperature change raises the question of whether this behaviour is a general characteristic or a result specific to the dataset used. If the latter, the explanatory power of the Δb equation would be reduced. It is important to clarify this point.
Citation: https://doi.org/10.5194/egusphere-2025-3518-RC2 - AC1: 'Reply on RC2', Bettina Richter, 31 Oct 2025
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Christoph Marty
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2354 KB) - Metadata XML
I enjoyed reading this note and believe it addresses, in a very smart way, an important issue in comparing previous snow projections: the use of different time horizons, models, emission scenarios, etc. Most of the implications of the assumptions and simplifications are well discussed. The manuscript is well written, and I did not identify any methodological flaws. Therefore, I recommend its publication.
Below, I provide a few minor suggestions and some ideas from my related research, which the authors may consider using to further strengthen the discussion:
-I wonder about the impact of the methodology used in previous studies to perturb observed series with climate projections (e.g., the Delta method on seasonal or monthly bases, quantile perturbation, or directly using simulated climate to drive snow models). Different methods may influence the asymmetry in the start and end of the snow season or other metrics that relate snow changes solely to temperature.
- It is somewhat surprising to me that the changes in the start and end of the snow season appear symmetric. Is the projected temperature increase generally similar for winter and spring? Even if it is, I would expect some patterns related to elevation—for instance, an earlier snowmelt may eliminate periods of very high solar radiation, whereas a later snow onset may have less significant implications for incoming solar radiation and melt dynamics. This is particularly true at higher elevations but not at lower ones.
-One of the strengths of this methodology is its ability to translate different scenarios into temperature changes. This can make it easier to communicate results for policy decisions, as many greenhouse gas emission targets are linked to temperature thresholds (e.g., 1.5 °C). This aspect could be highlighted more explicitly in the discussion, as it helps make the results more accessible to non-scientific audiences.
- Related to the previous point, in recent years I have preferred, instead of simulating future snow conditions for different climate models and emission scenarios, to perform sensitivity analyses (e.g., adding 1–2 °C, or ±5–10 % changes in precipitation; see DOI: 10.1088/1748-9326/abb55f). Then, the changes in T and P can be framed with climate projections for specific regions.. While this approach requires simplifying assumptions about the system, I find it makes the results much easier to compare. It may be interesting to contrast your approach with this type of sensitivity analysis.
- Perhaps Figure 2 could more clearly illustrate how changes in “b” and “c” are derived.
Hoping my comments will result useful,
Best,
J. Ignacio López-Moreno