the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Nov 2025
Sensitivity of ski resorts in the western US to climate change
Abstract. Winter recreation’s vulnerability to climate change, especially to warming, is widely recognized but few studies report quantitatively on the observed effects of climate change on ski resorts, in part because consistent and available data directly from ski resorts is scarce. Instead, we use proxy data from nearby SNOTEL (snow telemetry) and snow course sites to examine sensitivity of snow depth (HS) and snow water equivalent (SWE) to temperature and precipitation at 41 select ski resorts in Washington, Idaho, Oregon, and California, during the ski season. Multiple regression on climate variables then permits statistical projections of future snow depth from projected changes in temperature and precipitation. We also use projected future SWE from a hydrology model with climate input from CMIP5 models with the RCP4.5 and RCP8.5 scenarios to evaluate future changes in snow depth at the selected ski resorts. While many resorts indeed face substantial declines in ski-season snow depth, many of those in Idaho and a few at high elevation are likely to be minimally affected. Mitigating factors include (a) projected increases in winter precipitation over the Rockies that partly offset the effects of warming; (b) low temperature sensitivity there and over high altitudes; (c) lower observed declines and temperature sensitivity for snow in winter compared with spring; and (d) many ski resorts are located in areas of high snowfall and/or span a considerable range of altitudes.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-5113', Anonymous Referee #1, 19 Dec 2025
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AC1: 'Reply on RC1', Erica Kim, 13 Jan 2026
General comments
The manuscript addresses the vulnerability of ski resorts in the western United States, to climate change by using proxy snow and climate data and projecting future snow depth under climate scenarios. The topic is clearly relevant and timely, as climate impacts on winter tourism have important economic and social implications. However, in its current form, I find it difficult to understand what the study is ultimately useful for and what new scientific insight it provides beyond what has already been established in the literature.
Several previous studies have already investigated essentially the same research question - how unfavorable climate change is for the winter sports industry in the western US: Wobus et al. (2017) and Scott & Steiger (2024). Against this backdrop, the novelty and added value of the present study are unclear.
http://dx.doi.org/10.1016/j.gloenvcha.2017.04.006
https://doi.org/10.1080/13683500.2024.2314700
Major comments
- The manuscript does not clearly articulate how it advances existing knowledge. Given that Wobus et al. (2017) already assessed climate change impacts on western US ski areas using climate projections and Scott & Steiger (2024) provided an updated, tourism-focused vulnerability perspective, the authors need to explicitly state:
- What is new in this study?
- How does it improve upon or differ from prior work in terms of methods, spatial resolution, indicators, or decision relevance?
- Why are the results expected to change our understanding of ski industry vulnerability?
Response: We have rewritten the abstract and added text to the introduction to make clearer that the unique contribution of this paper is to estimate the sensitivity of ski areas to climate fluctuations using observed data, whereas other studies including the two that the reviewer mentioned were solely based on numerical models. Moreover, we incorporated 20 climate projections providing a more robust estimate of change including uncertainties in the climate forcing. In short, we place more emphasis on characterizing the climate context rather than the fiscal or operational consequences for ski resorts. We have changed the title and added text to the conclusions to underscore this difference.
Without a clear positioning relative to existing studies, the manuscript risks being perceived as a partial replication without sufficient added insight.
- The results focus primarily on percentage reductions in snow depth. However, it is unclear how such metrics help answer the central question of ski resort vulnerability.
From an operational perspective, a (e.g.) 20% reduction in snow depth may be:
- Critical if it brings snow conditions below the minimum threshold required for ski operations, or
- Irrelevant if snow depth remains well above that threshold.
Because the manuscript does not define or analyze operational thresholds, the results are difficult to interpret in terms of real-world impacts on ski resorts. As currently presented, the findings remain largely descriptive rather than decision-relevant.
- Snowmaking is not included in the analysis, despite being an operational reality for most ski resorts in the western US. Ignoring snowmaking substantially limits the applicability of the results, especially since:
- Snowmaking can partially offset reduced natural snowfall,
- Temperature constraints on snowmaking are often more important than changes in total snow depth or precipitation, and
- Many existing studies explicitly incorporate snowmaking capacity to assess vulnerability.
The omission should be more clearly justified, and the limitations for interpreting ski industry impacts should be discussed more explicitly.
For many decades, the scientific community and notably the IPCC has recognized that climate risk is a function both of exposure (the physical climate system) and vulnerability (a function of other aspects of the system, including human institutions and behavior). Consistent with many other Cryosphere papers, our focus is on the physical climate system: As stated in the title, the purpose of the study is to quantify the sensitivity of ski resorts, which relates to their exposure to climate change. As the reviewer notes, there are additional considerations to estimate vulnerability, including the operational considerations and the feasibility of snowmaking, which was not the purpose of this analysis. Since every ski area has its own unique and complex operational considerations (eg there often isn’t a fixed snow depth threshold) the approach this reviewer advocates could, in the limit, require co-producing research with each ski resort.
- Only RCP4.5 results are shown. It is common practice in climate impact studies to present a range of plausible climate futures to reflect uncertainty.
I see no clear rationale for focusing on RCP4.5 while effectively ignoring RCP8.5 in the results. At a minimum, the authors should:
- Present results for both scenarios, or
- Provide a strong justification for excluding the higher-emissions pathway.
Without this, the study understates uncertainty.
As we note in a paragraph near the end of the manuscript, RCP8.5 is now viewed as an unlikely scenario; moreover, the differences at mid-century between RCP4.5 and RCP8.5 are not as large as late century. Nonetheless, we have added RCP8.5 results to figure 8.
- The results section is difficult to follow due to:
- An excessive number of numerical values,
- Frequent shifts between different case-study examples
I recommend restructuring the results to emphasize key messages and comparative insights, rather than listing many individual values.
We are puzzled about the comment that our results are too quantitative on the one hand and that we use case studies on the other hand. The figures were carefully chosen to represent, in most cases, all 41 ski resorts, and the results section consists primarily of descriptions of those figures; the number of numbers per paragraph does not seem out of line with other Cryosphere papers. If the reviewer could point out some specific paragraphs that seem too number-heavy we could address the comment. As for the ‘case study example’ point, we are also puzzled: in some instances we point out an outlier in a figure but that hardly constitutes a case study in the conventional meaning.
- An important methodological detail remains unclear:
What was the altitudinal difference between the temperature data used and the actual elevation of the ski resorts?
Specifically:
- Were temperature and precipitation data lapse-rate adjusted?
- How were differences between station elevation and resort base/summit elevations handled?
- Could elevation mismatches bias the estimated temperature sensitivity of snow depth?
Yes. As described on lines 185-188, we adjust for the lapse rate in temperature by using differences in site temperature between the location of the observation and the location of the ski resort to adjust aT (only). We examined the ‘lapse rate’ of precipitation but it is effectively random and we did not make adjustments to aP.
Specific comments:
- 2,l. 46-47 "Similar such studies date to the 1980s in Canada (Harrison et al, 1986) using a threshold snow depth (HS) of 5cm; they projected reductions by 2050 of HS of 40-100% for various ski resorts" -> relevance of this 40y old study? The editor read our original version of the manuscript and rightly noted that we had given short shrift to work that predated studies in the western US which are more recent than those in Canada and Europe. We agree with the editor that some historical context is appropriate.
- 3, l. 89-90 "In short, ski resort revenue is affected directly by skier behavior and only indirectly by a complex set of snow conditions" -> The sentence is correct but also quite banal. Revenue is of course directly affected by skier behavior and not by snow conditions, as the ski resort doesn't sell the snow. This is like "beach tourism revenue is affected by tourist behavior and not by hours of sunshine", not wrong, but also not informative. For researchers outside the field of tourism studies, it may not be obvious that the availability of the primary resource (in this case snow) is not necessarily directly related to revenue; we think that this comment is informative to some readers.
- 4, l. 101-103 research question: "how unfavorable is climate change for the winter sports industry in the western US?" -> this question has been addressed by Wobus et al. (2017) (http://dx.doi.org/10.1016/j.gloenvcha.2017.04.006) and Scott & Steiger (2024) (https://doi.org/10.1080/13683500.2024.2314700). So, what is new about your study? See response above to this point.
- 5, l.149 "average vertical distance 194m" between weather station and ski area. -> 194m is about 1.2°C which is 2 decades of climate change. So, the average uncertainty inhibited in your results due to this average vertical distance is 2 decades of climate change... And much more at sites where the vertical difference is greater than the average. See comment above: we did indeed make adjustments. Also, as Minder et al. (JGR 2010) noted, a more typical wintertime lapse rate in at least the Cascades is 4.5°C/km so ~200m is about 0.9°C, and in any case the uncertainty in the slope aT is what’s relevant not the uncertainty in dT/dz.
- 5, l. 154-155 "Many more California snow courses have data for 1 February (17) than for 1 January (9)." -> I don't understand this. So, the stations you used do not measure continuously, or why do have more stations records on 1 Feb compared to 1 Jan?
Indeed, snow courses are manual measurements that in most of the Western US are taken at no more than monthly intervals and in the case of many California snow courses, are not taken in January. There are no SNOTEL sites close to the California ski resorts. We have added a bit of text in the data section to clarify.
Fig 7: figure not self-explanatory. what is the difference between grey and color?
Apologies, a legend was inadvertently omitted and the caption also failed to describe the colors. The revised version will address these deficiencies.
14, l. 371-372 "it is striking what a wide range of conditions under which the resorts operate" -> Striking and not surprising as ski operations require a certain minimum snow depth threshold but there is no difference in ski operations with a snow depth of 50cm or 300cm. This is also why I don't see the relevance of your results for ski resorts. What does a reduction of 20% of SWE or SH mean? Nothing if the ski resort's SH is still above the minimum threshold.
These are fair points. But also, see comments above - this paper is about sensitivity and uncertainty in the climate domain. One partial remedy might be to retitle the paper as ‘sensitivity of snow at ski resorts’
17, l. 472 "The extreme (and unlikely) emissions scenario RCP8.5" -> why "unlikely"? Please provide a reference
That was just stated a few paragraphs above in the paragraph on RCP8.5: Hausfather and Peters 2020. We will add a phrase pointing to that earlier paragraph.
p- 17, l.483 "A generation ago" -> I don't think that belongs in a scientific publication.
Fair enough; we will remove it.
Citation: https://doi.org/10.5194/egusphere-2025-5113-AC1
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AC1: 'Reply on RC1', Erica Kim, 13 Jan 2026
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RC2: 'Comment on egusphere-2025-5113', Bettina Richter, 29 Dec 2025
General comments:
This manuscript addresses how climate change affects snow depths at 41 ski resorts across the western United States. Two approaches were used to estimate the sensitivity of snow depths to changing temperature and precipitation: First, A combination of observational data (SNOTEL and snow courses) with statistical regression method and second, future SWE output from hydrologic modeling (VIC). Despite this timely and relevant topic, the manuscript has drawbacks, which must be addressed before further revision.
Major:
1. Unclear scientific objective: The manuscript does not clearly state its primary research question and objective. It remains ambiguous whether the study aims to:
- project future snow depth at ski resorts,
- compare two methodological approaches (regression vs. VIC),
- assess the impact of climate change on future ski resorts,
- or analyze snow–climate sensitivity in a more general sense
2. Methodology is hard to follow:
- how are regression parameters derived from observations (a figure would be very helpful for illustration),
- how is VIC used and how it differs conceptually from the regression approach,
- how elevation differences between SNOTEL sites and ski resorts are handled.
3. Unclear relevance to the impact on ski resort:
Despite the title and the introduction, the analysis only presents changes in snow depth and the sensitivity of snow depth to temperature and precipitation, without translating these changes into ski-relevant outcomes. Critical aspects raised in the introduction, such as season length, snow depths in winter months (December and January), thresholds of snow depths relevant for skiing, and economic impacts, but are not addressed in the results. The research question “how unfavorable is climate change for the winter sport industry” is hence not addressed and the actual impact on ski resort viability remains unclear.
4. Missing analyses (elevation, seasonality)
Elevation is repeatedly mentioned as important, but it is not reported in the table, or stated which elevation was used as ski resorts have a large range. Furthermore, the manuscript would highly benefit from analyzing the elevation dependence of projected changes. The winter months December and January are mentioned as being very important, but February is shown and December is not even analyzed.
5. Clarity and precision
- imprecise and sometimes incorrect terminology,
- incorrect figure panel references,
- unclear figure captions and missing legends,
- reliance on supplementary figures that are not shown or explained,
- numerous minor but distracting editorial errors (citations, hyphenation, missing DOIs).
Minor:
- Fig. 1: How many years are the long-term average?
- Fig. 3: Please write units into the axis-labels. X-ticks are too small
- Fig. 3: It would be nice to indicate those regions in Fig. 2, too (California, Cascades, eastern…)
- Fig. 3: Where does the uncertainty for each resort come from? Please explain what is shown in this graph.
- Fig. 3: The text says (L. ) the x-axis is the “climatological mean November-January site temperature”, please add this information in the caption, so it’s more self-explaining. How many years are used for computing the “climatological” Nov-Jan mean temperature? Is it the site temperature of the SNOTEL site or the temperature of the ski resort? Please clarify.
- Fig. 4: Legend for colors missing, consider adding number of ski resort in the y-axis. X-axis label is too small.
- Fig. 4: Please rewrite the caption, so it’s clearer: Projected change of 10 individual climate scenarios (orange triangles), average change of climate scenarios (orange bar?). Bars are the individual contributions of a_T* dT (deep colors) and a_t*dP (light colors to the right, positive contribution). Clarify in the caption.
- Fig. 4: How does this contribution correlate to elevation?
- Fig. 5: What are brown and turquoise arrows? Should the black line not cross the 0/0 point?
- Fig. 5: The caption says that red and green arrows indicate extremes in climate change. When I compare panel a with panel b, the orange arrow in panel a shows an extreme temperature change of less than 3°C (from -5°C to -2.sth) but the right panel indicates extreme changes of 4°C. Please clarify what those arrows mean.
- Fig. 6: There’s a lot of information in here, maybe consider putting this figure in the supplements. It’s also confusing that the order is different to Figure 4 and Figure 7.
- Fig. 7: What are grey bars and colored bars? Is the change in snow depth shown or the mean snow depths for past and future? Color legend missing. Tick-labels are too small.
- Fig. 6 and Fig. 7: Both Figures seem to be very similar to Figure 4 and might not contribute to the clarity of the manuscript. I would consider putting these in the appendix and instead, include one Figure to better illustrate and clarify the methodology. Another interesting investigation is the dependency on elevation, which is not addressed here and would add value to this manuscript.
- Table 1: Why do these values differ from the values shown in Figure 3?
- Table A1: Add elevation to the ski resorts.
- L. 10: at 41 *selected* ski resorts
- L. 11: in Washington, Idaho, Oregon, and California during the ski season (no comma after California?)
- L. 17: low temperature sensitivity there and over high altitudes … (*there* likely refers to Idaho and higher elevations, but please clarify what it is, *over*?)
- L. 49 & L 50: comma missing in citation
- L. 53: Hyphenation of *calcula-tions* is superscript
- L. 91: Hyphenation of *econ-omy* is superscript
- L. 101: observations, is: *just* how unfavorable is: remove *just*
- L. 101: Maybe this question could be more explicit, as it is not clear what the goal is.
- L. 103-110: To address this question, we use a combination of …. : To do what? With your stated research question and the methods, you are using, it is still not clear to me what the goal of this study is. Do you predict future snow depth with two different approaches or are you using them for something else?
- L. 114–126: You are summarizing the focusses of other studies, but not really what the focus of you study is. Is the focus being to report December and January changes of snow depth and SWE changes only, then there where many studies in Europe which describe those seasonal changes and which should be mentioned in the introduction:
- Predict seasonal evolution of snow with climate change, e.g.:
- Marty, C., Schlögl, S., Bavay, M., and Lehning, M.: How much can we save? Impact of different emission scenarios on future snow cover in the Alps, The Cryosphere, 11, 517–529, https://doi.org/10.5194/tc-11-517-2017, 2017.
- Richter, B. and Marty, C.: Technical note: Literature based approach to estimate future snow, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-3518, 2025.
- Changes in winter snow depth (December-February) with climate change:
- Schmucki, E., Marty, C., Fierz, C., Weingartner, R., and Lehning, M.: Impact of climate change in Switzerland on socioeconomic snow indices, Theoretical and Applied Climatology, 127, 875–889, https://doi.org/10.1007/s00704-015-1676-7, 2017
- Predict seasonal evolution of snow with climate change, e.g.:
- L. 132: Could be helpful to mention the ski resort id’s here, instead of the number of ski resorts for each state, e.g. California (35-41).
- L. 147-148: Do you use an average of all stations within 50km? And if there were none within 10km, why did you then only use the closest two? How did you account for the elevation difference, you mention 200m average distance, did you also allow stations, that are outside the elevation ranges of a ski resort?
- L. 152-155: In your objectives (L. 114-126), you stated that December is an important month for ski resorts and there are no studies focusing on snow depth declines for the month of December and January, and here you write that there’s no data available in December. Please consider rewriting your objectives to be more consistent. Furthermore, which temporal resolution do snow observations have, daily, monthly? Why are less data available for January?
- L. 167-190: Please describe those two approaches and their difference more clearly as this paragraph is hard to understand. As I understand, the Climate toolbox was used to retrieve temperature and precipitation and season-to-date values for future, and not future SWE values. The first paragraph mixes SWE values retrieved from VIC with climate data on TA and P. The second paragraph then explains how to retrieve future SWE from observations, which is mixed with retrieving regression parameters from VIC, which is confusing. Please structure this methods part more clearly. Also please give a bit more details on the VIC method.
- L. 178-190: An illustration or an example for a SNOTEL site would help to clarify this methodology. How were regression parameters derived? As I understand, you have measured HS for several days between January and April (daily values?) from around year 2000 until today. Then, for each HS measurement (for example HS=153 cm on 15. January 2023), average season-to-date temperature is documented (here: average temperature between 1. November 2022 and 15. January 2023 is -5°C), similar with precipitation. Then, do you perform a regression between HS and average temperature data points and predict changes in HS using the same regression parameters? You could illustrate this methodology using a scatter plot with HS and temperature and HS and precipitation for one site and showing the regression lines.
- L. 179: What are the “important modification”?
- L. 184: Luce, 2014 is using an interaction term in the regression, why did you neglect this term?
- L. 191: What is MACA?
- L. 217: Precipitation is not shown in Figure 3B but precipitation coefficient! Please be more precise with your terminology.
- L. 219-212: You state that “eastern sites have a weak sensitivity to temperature but are very sensitive to precipitation”. Both regression coefficients seem to have similar magnitudes, a_T ~-1cm/°C and a_P >0.8cm/cm, if they are at all comparable due to different units. I think this statement needs a bit more context, how much precipitation change, vs temperature change is expected until mid-century? Does the positive contribution of precipitation counterbalance the negative contribution of temperature? Figure 4 shows that most purple bars will have less snow in future, which shows that this statement is wrong.
- L. 223: Did you want to refer to Table 1?
- L. 223-229: Please show Figure S1 as it’s hard to follow the paragraph without it.
- L. 231: Which variance, the variance in a_T? Which average? Please be more precise.
- L. 239-249: It seems like mostly the cascades diverge, please discuss that.
- L. 263: the lighter *colors* instead of *components*?
- L. 261: For each *ski resort* instead of *bar*?
- L. 351: Panel b and *d*?
- L. 352: *steep values*, *shallow values*? I think you are referring to the slope of the black lines, however *steep values* are not very scientific expressions. Please explain this, add units to those numbers, maybe explain what numbers imply, e.g. what does a value of 0.1 compared to 0.05 mean for climate change? Also, it would be beneficial if these numbers were also shown in the corresponding Figure for better interpretation.
- L. 353 or 354: there is no black line in panel c!
- L. 355: Why is the line number not aligned with the text?
- Discussion: Many points are discussed which are not relevant to the results shown in this manuscript, e.g. elevation dependence and artificial snow making.
- L. 420: *base* elevation for ski resorts? That’s not mentioned in the methods, please clearly state in the methods section which elevation of ski resorts are used.
- L. 556 The European Mountain cryosphere: a review of its current state, trends, and future challenges.: Authors and Doi missing.
- Bibliography: Please make it consistent, sometime DOI’s are missing, sometimes it’s a link sometimes not. Sometime authors are missing.
Citation: https://doi.org/10.5194/egusphere-2025-5113-RC2 -
AC2: 'Reply on RC2', Erica Kim, 13 Jan 2026
General comments:
This manuscript addresses how climate change affects snow depths at 41 ski resorts across the western United States. Two approaches were used to estimate the sensitivity of snow depths to changing temperature and precipitation: First, A combination of observational data (SNOTEL and snow courses) with statistical regression method and second, future SWE output from hydrologic modeling (VIC). Despite this timely and relevant topic, the manuscript has drawbacks, which must be addressed before further revision.
Major:
- Unclear scientific objective: The manuscript does not clearly state its primary research question and objective. It remains ambiguous whether the study aims to:
- project future snow depth at ski resorts,
- compare two methodological approaches (regression vs. VIC),
- assess the impact of climate change on future ski resorts,
- or analyze snow–climate sensitivity in a more general sense
Both reviewers found the objective unclear; we replicate here the response to reviewer 1:
Response: We have rewritten the abstract and added text to the introduction to make clearer that the unique contribution of this paper is to estimate the sensitivity of ski areas to climate fluctuations using observed data, whereas other studies including the two that the reviewer mentioned were solely based on numerical models. Moreover, we incorporated 20 climate projections providing a more robust estimate of change including uncertainties in the climate forcing. In short, we place more emphasis on characterizing the climate context rather than the fiscal or operational consequences for ski resorts. We have changed the title and added text to the conclusions to underscore this difference.
- Methodology is hard to follow:
- how are regression parameters derived from observations (a figure would be very helpful for illustration),
- how is VIC used and how it differs conceptually from the regression approach,
- how elevation differences between SNOTEL sites and ski resorts are handled.
We can certainly add a figure to illustrate how a scatterplot leads to a regression coefficient (the slope of the best-fit line). VIC is a distributed (gridded) hydrologic model that accepts inputs of daily minimum and maximum temperature and precipitation and computes the water balance, with outputs including snow water equivalent. For the future climate change projections, the climate scenarios are the same as those used for the regression approach. Elevation differences (see above): as described on lines 185-188, we adjust for the lapse rate in temperature by using differences in site temperature between the location of the observation and the location of the ski resort to adjust aT (only). We examined the ‘lapse rate’ of precipitation but it is effectively random and we did not make adjustments to aP.
- Unclear relevance to the impact on ski resort:
Despite the title and the introduction, the analysis only presents changes in snow depth and the sensitivity of snow depth to temperature and precipitation, without translating these changes into ski-relevant outcomes. Critical aspects raised in the introduction, such as season length, snow depths in winter months (December and January), thresholds of snow depths relevant for skiing, and economic impacts, but are not addressed in the results. The research question “how unfavorable is climate change for the winter sport industry” is hence not addressed and the actual impact on ski resort viability remains unclear.
Reviewer 1 made a similar point; here is our response; For many decades, the scientific community and notably the IPCC has recognized that climate risk is a function both of exposure (the physical climate system) and vulnerability (a function of other aspects of the system, including human institutions and behavior). Consistent with many other Cryosphere papers, our focus is on the physical climate system: As stated in the title, the purpose of the study is to quantify the sensitivity of ski resorts, which relates to their exposure to climate change. As the reviewer notes, there are additional considerations to estimate vulnerability, including the operational considerations and the feasibility of snowmaking, which was not the purpose of this analysis. Since every ski area has its own unique and complex operational considerations (eg there often isn’t a fixed snow depth threshold) the approach this reviewer advocates could, in the limit, require co-producing research with each ski resort.
- Missing analyses (elevation, seasonality)
Elevation is repeatedly mentioned as important, but it is not reported in the table, or stated which elevation was used as ski resorts have a large range. Furthermore, the manuscript would highly benefit from analyzing the elevation dependence of projected changes. The winter months December and January are mentioned as being very important, but February is shown and December is not even analyzed.
We will certainly add the elevations in the table, which in hindsight was an obvious omission. Note however that across the wide range of longitudes and latitudes, elevation is a much weaker predictor of sensitivity than site temperature, which is what we use. However, it would be possible to calculate a second snow-depth change at the top of each ski resort in addition to the one at the base, although operationally in most cases (Heavenly and Mammoth being exceptions familiar to us) only the base elevation matters operationally.
The remaining comments below are all easily addressed in revision and we look forward to doing so.
- Clarity and precision
- imprecise and sometimes incorrect terminology,
- incorrect figure panel references,
- unclear figure captions and missing legends,
- reliance on supplementary figures that are not shown or explained,
- numerous minor but distracting editorial errors (citations, hyphenation, missing DOIs).
Minor:
- 1: How many years are the long-term average?
- 3: Please write units into the axis-labels. X-ticks are too small
- 3: It would be nice to indicate those regions in Fig. 2, too (California, Cascades, eastern…)
- 3: Where does the uncertainty for each resort come from? Please explain what is shown in this graph.
- 3: The text says (L. ) the x-axis is the “climatological mean November-January site temperature”, please add this information in the caption, so it’s more self-explaining. How many years are used for computing the “climatological” Nov-Jan mean temperature? Is it the site temperature of the SNOTEL site or the temperature of the ski resort? Please clarify.
- 4: Legend for colors missing, consider adding number of ski resort in the y-axis. X-axis label is too small.
- 4: Please rewrite the caption, so it’s clearer: Projected change of 10 individual climate scenarios (orange triangles), average change of climate scenarios (orange bar?). Bars are the individual contributions of a_T* dT (deep colors) and a_t*dP (light colors to the right, positive contribution). Clarify in the caption.
- 4: How does this contribution correlate to elevation?
- 5: What are brown and turquoise arrows? Should the black line not cross the 0/0 point?
- 5: The caption says that red and green arrows indicate extremes in climate change. When I compare panel a with panel b, the orange arrow in panel a shows an extreme temperature change of less than 3°C (from -5°C to -2.sth) but the right panel indicates extreme changes of 4°C. Please clarify what those arrows mean.
- 6: There’s a lot of information in here, maybe consider putting this figure in the supplements. It’s also confusing that the order is different to Figure 4 and Figure 7.
- 7: What are grey bars and colored bars? Is the change in snow depth shown or the mean snow depths for past and future? Color legend missing. Tick-labels are too small.
- 6 and Fig. 7: Both Figures seem to be very similar to Figure 4 and might not contribute to the clarity of the manuscript. I would consider putting these in the appendix and instead, include one Figure to better illustrate and clarify the methodology. Another interesting investigation is the dependency on elevation, which is not addressed here and would add value to this manuscript.
- Table 1: Why do these values differ from the values shown in Figure 3?
- Table A1: Add elevation to the ski resorts.
- 10: at 41 *selected* ski resorts
- 11: in Washington, Idaho, Oregon, and California during the ski season (no comma after California?)
- 17: low temperature sensitivity there and over high altitudes … (*there* likely refers to Idaho and higher elevations, but please clarify what it is, *over*?)
- 49 & L 50: comma missing in citation
- 53: Hyphenation of *calcula-tions* is superscript
- 91: Hyphenation of *econ-omy* is superscript
- 101: observations, is: *just* how unfavorable is: remove *just*
- 101: Maybe this question could be more explicit, as it is not clear what the goal is.
- 103-110: To address this question, we use a combination of …. : To do what? With your stated research question and the methods, you are using, it is still not clear to me what the goal of this study is. Do you predict future snow depth with two different approaches or are you using them for something else?
- 114–126: You are summarizing the focusses of other studies, but not really what the focus of you study is. Is the focus being to report December and January changes of snow depth and SWE changes only, then there where many studies in Europe which describe those seasonal changes and which should be mentioned in the introduction:
- Predict seasonal evolution of snow with climate change, e.g.:
- Marty, C., Schlögl, S., Bavay, M., and Lehning, M.: How much can we save? Impact of different emission scenarios on future snow cover in the Alps, The Cryosphere, 11, 517–529, https://doi.org/10.5194/tc-11-517-2017, 2017.
- Richter, B. and Marty, C.: Technical note: Literature based approach to estimate future snow, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-3518, 2025.
- Changes in winter snow depth (December-February) with climate change:
- Schmucki, E., Marty, C., Fierz, C., Weingartner, R., and Lehning, M.: Impact of climate change in Switzerland on socioeconomic snow indices, Theoretical and Applied Climatology, 127, 875–889, https://doi.org/10.1007/s00704-015-1676-7, 2017
- 132: Could be helpful to mention the ski resort id’s here, instead of the number of ski resorts for each state, e.g. California (35-41).
- 147-148: Do you use an average of all stations within 50km? And if there were none within 10km, why did you then only use the closest two? How did you account for the elevation difference, you mention 200m average distance, did you also allow stations, that are outside the elevation ranges of a ski resort?
- 152-155: In your objectives (L. 114-126), you stated that December is an important month for ski resorts and there are no studies focusing on snow depth declines for the month of December and January, and here you write that there’s no data available in December. Please consider rewriting your objectives to be more consistent. Furthermore, which temporal resolution do snow observations have, daily, monthly? Why are less data available for January?
- 167-190: Please describe those two approaches and their difference more clearly as this paragraph is hard to understand. As I understand, the Climate toolbox was used to retrieve temperature and precipitation and season-to-date values for future, and not future SWE values. The first paragraph mixes SWE values retrieved from VIC with climate data on TA and P. The second paragraph then explains how to retrieve future SWE from observations, which is mixed with retrieving regression parameters from VIC, which is confusing. Please structure this methods part more clearly. Also please give a bit more details on the VIC method.
- 178-190: An illustration or an example for a SNOTEL site would help to clarify this methodology. How were regression parameters derived? As I understand, you have measured HS for several days between January and April (daily values?) from around year 2000 until today. Then, for each HS measurement (for example HS=153 cm on 15. January 2023), average season-to-date temperature is documented (here: average temperature between 1. November 2022 and 15. January 2023 is -5°C), similar with precipitation. Then, do you perform a regression between HS and average temperature data points and predict changes in HS using the same regression parameters? You could illustrate this methodology using a scatter plot with HS and temperature and HS and precipitation for one site and showing the regression lines.
- 179: What are the “important modification”?
- 184: Luce, 2014 is using an interaction term in the regression, why did you neglect this term?
- 191: What is MACA?
- 217: Precipitation is not shown in Figure 3B but precipitation coefficient! Please be more precise with your terminology.
- 219-212: You state that “eastern sites have a weak sensitivity to temperature but are very sensitive to precipitation”. Both regression coefficients seem to have similar magnitudes, a_T ~-1cm/°C and a_P >0.8cm/cm, if they are at all comparable due to different units. I think this statement needs a bit more context, how much precipitation change, vs temperature change is expected until mid-century? Does the positive contribution of precipitation counterbalance the negative contribution of temperature? Figure 4 shows that most purple bars will have less snow in future, which shows that this statement is wrong.
- 223: Did you want to refer to Table 1?
- 223-229: Please show Figure S1 as it’s hard to follow the paragraph without it.
- 231: Which variance, the variance in a_T? Which average? Please be more precise.
- 239-249: It seems like mostly the cascades diverge, please discuss that.
- 263: the lighter *colors* instead of *components*?
- 261: For each *ski resort* instead of *bar*?
- 351: Panel b and *d*?
- 352: *steep values*, *shallow values*? I think you are referring to the slope of the black lines, however *steep values* are not very scientific expressions. Please explain this, add units to those numbers, maybe explain what numbers imply, e.g. what does a value of 0.1 compared to 0.05 mean for climate change? Also, it would be beneficial if these numbers were also shown in the corresponding Figure for better interpretation.
- 353 or 354: there is no black line in panel c!
- 355: Why is the line number not aligned with the text?
- Discussion: Many points are discussed which are not relevant to the results shown in this manuscript, e.g. elevation dependence and artificial snow making.
- 420: *base* elevation for ski resorts? That’s not mentioned in the methods, please clearly state in the methods section which elevation of ski resorts are used.
- 556 The European Mountain cryosphere: a review of its current state, trends, and future challenges.: Authors and Doi missing.
- Bibliography: Please make it consistent, sometime DOI’s are missing, sometimes it’s a link sometimes not. Sometime authors are missing.
- Predict seasonal evolution of snow with climate change, e.g.:
Citation: https://doi.org/10.5194/egusphere-2025-5113-AC2
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- 1
General comments
The manuscript addresses the vulnerability of ski resorts in the western United States, to climate change by using proxy snow and climate data and projecting future snow depth under climate scenarios. The topic is clearly relevant and timely, as climate impacts on winter tourism have important economic and social implications. However, in its current form, I find it difficult to understand what the study is ultimately useful for and what new scientific insight it provides beyond what has already been established in the literature.
Several previous studies have already investigated essentially the same research question - how unfavorable climate change is for the winter sports industry in the western US: Wobus et al. (2017) and Scott & Steiger (2024). Against this backdrop, the novelty and added value of the present study are unclear.
http://dx.doi.org/10.1016/j.gloenvcha.2017.04.006
https://doi.org/10.1080/13683500.2024.2314700
Major comments
Without a clear positioning relative to existing studies, the manuscript risks being perceived as a partial replication without sufficient added insight.
From an operational perspective, a (e.g.) 20% reduction in snow depth may be:
Because the manuscript does not define or analyze operational thresholds, the results are difficult to interpret in terms of real-world impacts on ski resorts. As currently presented, the findings remain largely descriptive rather than decision-relevant.
The omission should be more clearly justified, and the limitations for interpreting ski industry impacts should be discussed more explicitly.
I see no clear rationale for focusing on RCP4.5 while effectively ignoring RCP8.5 in the results. At a minimum, the authors should:
Without this, the study understates uncertainty.
I recommend restructuring the results to emphasize key messages and comparative insights, rather than listing many individual values.
What was the altitudinal difference between the temperature data used and the actual elevation of the ski resorts?
Specifically:
Specific comments:
Fig 7: figure not self-explanatory. what is the difference between grey and color?
p- 17, l.483 "A generation ago" -> I don't think that belongs in a scientific publication.