the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modeling 21st century snow dynamics in Switzerland using high-resolution Climate CH2025 scenarios
Abstract. Snow is a key component of Alpine landscapes, providing numerous ecosystem and economic services that include hydropower production, winter tourism, groundwater recharge, and regulation of stream temperatures, with significant implications for aquatic ecosystems. In a warming climate, expected increases in winter precipitation do not necessarily lead to greater snow accumulation, as rising air temperatures shift precipitation from snowfall to rainfall and reduce the persistence of snow on the ground. This creates a need for future snow projections at locally relevant spatial scales to support adaptation for snow-dependent sectors.
Here, we present daily projections of snow water equivalent (SWE) for Switzerland at 1x1 km² resolution, based on the Climate CH2025 scenarios, which downscaled and bias-adjusted meteorological forcings from an ensemble of EURO-CORDEX regional climate models. SWE was simulated using a spatially distributed temperature-index snow model for 12 climate model chains, selected for their ability to accurately represent atmospheric forcings for modeling snow cover dynamics across Switzerland over the historical reference period (1991–2020). To reduce biases associated with the simplified degree-day-based snowmelt representation used here, simulated SWE was quantile mapped toward SPASS-CLQM, a new gridded climatological snow reference dataset for Switzerland.
We found that the univariate quantile mapping of meteorological forcings in Climate CH2025 models reduced precipitation under sub-freezing conditions, resulting in too little simulated snowfall in many model chains, which then propagated nearly linearly into biases in SWE. Simulated SWE in the final set of 12 models was additionally quantile mapped toward SPASS-CLQM, which substantially reduced SWE biases across elevation bands and was then applied to future simulations. By the end of century (2069–2099), projected SWE showed widespread declines across Switzerland relative to 1991–2020. Percentage September–May mean SWE losses exceeded 80 % below 1000 m a.s.l. for the highest emission pathway (RCP8.5), where snow became increasingly rare. At intermediate elevations (1000–2000 m a.s.l.), mean SWE declines of 50–90 % are projected, with the snowpack becoming increasingly ephemeral. Above 2000 m a.s.l., mean SWE reductions ranged from 20 to 70 %, indicating that even high-elevation seasonal snow storage would reduce substantially. These projections provide a spatially detailed basis for assessing future changes in Alpine snow resources and for informing the management of snow-dependent hydrological, ecological, and economic systems in a rapidly warming climate.
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Status: open (until 17 Aug 2026)
- RC1: 'Comment on egusphere-2026-3478', Anonymous Referee #1, 16 Jul 2026 reply
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RC2: 'Comment on egusphere-2026-3478', Anonymous Referee #2, 20 Jul 2026
reply
The authors provide a useful and well-organized study that fills a genuine gap in the climate impact field across Switzerland, by providing a 1x1 SWE (snow water equivalent) dataset based on the high-end emission scenario. While the study could be considered for publication, in its current form I recommend major revision. Several methodological choices are not explained, backed or justified in enough detail (e.g. the quantile mapping approach used, the GWL block-time-shift method, the degree-day snow model itself), which makes it hard to judge the results in their proper context and would make it difficult/ impossible to reproduce the study (FAIR). In addition, some of the figures could benefit from some adaptations.
Major comments
- Model exclusion (IPSL-WRF) contradicts the historical evaluation. Excluding IPSL-WRF because its future climate signal looks like an outlier in summer precipitation, after selecting models based on historical performance, seems like (and risks) narrowing the projected spread on purpose!? Please justify this with an independent criterion (e.g. from the CH2025 report itself or other studies flagging IPSL-WRF!?), or show results with/without this model to test sensitivity?
- SWE quantile-mapping validation (Section 3.5): stating that the post-QM agreement with SPASS-CLQM (Fig. 9) shows the correction is "reliable" overstates imho what this check can really reliably show. QM guarantees close agreement with the calibration target over the fitting period by construction, regardless of the underlying model's actual skill I would argue and thus this might not be an independent evidence of reliability?
- Which QM variant was used? Neither the meteorological forcing QM nor the SWE correction parts state whether this is standard quantile mapping or for example a trend-preserving variant (e.g. quantile delta mapping). In my opinion, this matters here, since naive QM is known to dampen or distort the projected change signal, and the remarkable numbers (>80% SWE loss) depend on this choice. Please clarify.
- SPASS products. Two versions of the same underlying snow model climatology are used for two different purposes: SPASS-CL for model screening (Section 2.3) and SPASS-CLQM, described as enhanced via data assimilation from 1998 onward, for the actual SWE bias correction. Please clarify why the (implicitly weaker) SPASS-CL is used for screening rather than SPASS-CLQM throughout, and how much the two datasets actually differ (i.e. before SPASS-CLQM's assimilation period begins and after), I think this is worth a supplement figure. If they are nearly identical before 1998, this may not matter much, but this isn't stated.
- Unclear whether RhiresD/TabsD is the actual bias-adjustment target inside CH2025. The paper uses RhiresD/TabsD as an evaluation benchmark (Fig. 3, Fig. 4), however, the reference dataset of CH2025's own QM is not clear, thus I am wondering if this was Rhires/Tabs as well? If it's the same, the "evaluation" of snowfall bias in Section 2.3/3.1 is a bit circular maybe?
- No equations given for any core component. The degree-day melt model, the precipitation-phase partitioning function, the snow-covered-area algorithm, and the QM transfer function are all described only in words (regardless of whether the equations are simple and straightforward). Since it is anyway central, at least the melt equations, the partitioning threshold function, and the QM equation should be given explicitly, even briefly.
- Fig 2 (flowchart) is too coarse. It hides many separate steps: the parallel obs-driven historical run, two different reference datasets (SPASS-CL vs SPASS-CLQM) used at different stages, the two non-criterion exceptions, and the fact there are two separate QM steps (forcing-level and SWE-level). Please expand this into a more complete, detailed flowchart.
- Section 4.3 (GWL mapping) is unclear and partly self-contradicting. It's not clear why the GWL framework "cannot be transferred" to the 12-model subset. Is this a sample-size problem, or because the subset was chosen for historical snow performance and therefore isn't representative of future warming trajectories? The next sentence (RCP8.5 guarantees high GWLs by 2100) doesn't really support the previous claim either. Section 3.4 already gives a concrete reason (the subset misses the warmest spring-warming chains) –> this might be linked here instead of a vague new justification. (See also next point)
- "Block-time-shift" approach is not explained. The term is used with no definition, but what is a "block", what gets "shifted", and why is linking CMIP5-driven RCMs to CMIP6 warming trajectories needed in the first place? Since this underlies the whole GWL framing (and the confusion in the point before), 2-3 explanatory sentences here would help a lot, not just a citation to the external report. (This is also true for many parts of the methodology)
Minor comments
- Section 3.2 (snowfall bias "propagating" into SWE bias): (a) is SPASS-CL really the same experiment as the obs-forced historical run, or a separately built product with its own assumptions? and (b) Fig. 3's snowfall bias looks like an absolute fraction difference (colorbar -0.4 to 0.4) while Fig. 5's SWE bias is explicitly a relative percentage. These aren't the same kind of number, so "larger" isn't really a fair comparison or am I wrong?
- RCM-dominance statement. "RCM parameterization together with downscaling and bias adjustment" lumps the raw dynamical model and the statistical correction applied afterward as one cause, but the paper's own later sections show these can behave very differently. Also, the claim that GCM choice barely matters seems a bit strong when then pointing out that the single best-performing chain is specifically the CCCMA-driven one (so differs significantly from the others!?).
- Fig. 4 needs a sentence of explanation. Since the QM was used, it isn't expected to reproduce the correct joint/conditional precip -temp relationships which leads to the mismatch under sub-zero temperatures and thus could be seen as an expected result of that choice, not an unexplained separate problem!?
- ±10% cutoff. Fig. 6 plots the quantity the selection is based on and marks retained models with an outline, but doesn't show the threshold band itself; the criterion is otherwise only supported by supplementary Fig. S1. Please add the threshold line to Fig. 6, or bring the relevant panel from S1 into the main text.
- Black outline in Figs. 6 & 8 is weak. Retained/excluded is one of the most important parts in both figures, but it's shown with the least visible channel (thin outline) on top of two other categorical channels (color = RCM, shape = GCM). Consider using solid vs. hollow/transparent fill, or two side-by-side panels instead, maybe much easier to read, also in grayscale.
- Fig. 12: I found it hard to see what are now no change and what not considered areas, maybe this could be visually done better!?
- Only RCP8.5 is used throughout. Given the paper's aim of supporting adaptation planning (abstract, conclusions), including at least one more moderate scenario (e.g. RCP4.5) would make the results considerably more useful!? RCP8.5-only risks a bit the framing of the "high-end" outcome as the outcome, especially since the abstract/conclusions don't always make the RCP8.5-only scope clear at first read.
Citation: https://doi.org/10.5194/egusphere-2026-3478-RC2
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General Assessment:
This study evaluates simulations of snowfall fraction and snow water equivalent from 26 Climate CH2025 models across Switzerland over the period 1991–2020, and projects the variations in SWE during 2035-2099 for the RCP8.5 scenario. The overall framework is clear, and the results are generally robust. However, there is a lack of detailed explanations regarding research methods and data. Some suggestions require the authors to give further consideration.
Major Comments:
Minor Comments: