the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Feb 2026
Impacts of Snowpack Insulation on Winter Ecosystem Respiration: A Synergistic Analysis in the Northern Hemisphere
Abstract. Climate-driven snowpack changes across the Northern Hemisphere introduce substantial uncertainty into the global carbon budget, but how winter ecosystem respiration (Recowinter) responds to these changes remains unclear. In this study, we investigated the impact of seasonal snowpack on Recowinter in the Northern Hemisphere (NH, >30° N) from the perspective of snowpack insulation using multi-source datasets. Our analysis revealed that in 30.43 % of NH ecosystems, snowpack (thickness, duration, and density) exerted the most critical impact on respiration, surpassing climatic impacts (by 19.27 %). The positive impact of snowpack on Recowinter operates through snowpack insulation, with a stronger effect in colder regions. Neglecting the various aspects of the snowpack may systematically underestimate the ecological impacts of snowpack. Accurate assessments of snowpack ecological impacts must account for its synergistic aspects. Consequently, ignoring snowpack ecological processes in future prediction models risk misrepresenting winter carbon fluxes and ecosystem responses to climate change. Our study highlight the importance of snowpack on carbon source in winter ecosystems.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-242', Tao Che, 15 Mar 2026
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RC2: 'Comment on egusphere-2026-242', Anonymous Referee #2, 06 May 2026
This study attempted to investigate the impact of snow on winter ecosystem respiration from the perspective of the snow insulation effect. This is an interesting and significant topic given that numerous previous studies payed extensive attention to the effect of snowmelt water in spring and summer seasons. However, I find that the analyses of this study did not provide sufficient evidence to support its topic. Moreover, there are some problems regarding the methods. I list my comments as follow:
- As a reader, what I mainly expect to get from this paper is how snow insulation regulates ecosystem respiration. However, authors only show the statistical relationships between some snow metrics and respiration, and attribute these relationships to the impact of snow insulation effect without any analysis on the underlying physical and biological mechanisms. It is very arbitrary and not convincing. I suggest that authors should define an index to indicate the snow insulation effect, for example, the difference of temperature between land surface and overlying atmosphere over the snow-covered regions. Then, the relationships between snow and respiration should be explained by introducing the snow insulation index.
- From figures 1 and 2, snow variability can account for ecosystem respiration at very limited regions (at most 20% area for SD). In addition, the correlations for both positive and negative values are scattered at any region with no apparent spatial distribution pattern. I thus doubt whether these correlations simply arise by chance. Although authors found a negative correlation between R2 improvement and temperature gradient, this can only indicate that snow is more important in colder regions. We do not yet know how the magnitude and sign of the snow-respiration relationships vary along some variables, such as the gradients of temperature, precipitation, snow volume, and soil moisture, etc. That is to say, we can not understand why this correlation value between snow and respiration appears at this site and can not predict the snow-respiration relationship using snow and some other variables. Revealing the rule of distribution pattern of snow-respiration connection is of great significance for revealing the mechanisms and calibrating Earth system models. Authors should address this issue.
- Although this study used three observational ecosystem respiration datasets, the results were obtained based solely on the ERA5 snow data, which is a reanalysis product. To enhance the reliability of the results, I strongly suggest authors adding additional analyses using observational snow data, such as GlobSnow or SNOW-CCI. At least, multiple reanalysis data (including MERRA2, GLDAS and so on) should be analyzed.
- In Abstract, there are only two so-called key findings (lines 4-6), which is too weak to support the title of the study. In addition, lines 7-9 should not be presented in abstract as the main results, because the study did not analyze any models’ results regarding the effect of the inaccurate snow process on the simulated ecosystem respiration.
- Line 87, both respiration and snow data have high spatial resolution, why did the authors interpolate all the data to a 0.25 grid? As stated in this study and previous literatures, the relationship between snow and respiration shows obvious spatial heterogeneity. The coarse spatial resolution may conceal some important signals. In addition, why did author restrict the study period to 2001-2015? It is too short to investigate the snow-respiration relation.
- Did authors remove the linear trend for all data before analysis?
- I am puzzled by all the “proportion” in the MS. For example, figures 1g, 2, and 3, does it mean the proportion of the grid points of a certain value to the total number of land grid points?
Citation: https://doi.org/10.5194/egusphere-2026-242-RC2 -
RC3: 'Comment on egusphere-2026-242', Anonymous Referee #3, 13 May 2026
This study examines how snowpack affects winter respiration across the Northern Hemisphere. The topic matters and the dataset is impressive, but the analytical framework has a fundamental problem: you're treating snowpack and soil temperature as competing independent drivers when snowpack actually works through soil temperature. This makes your results hard to interpret mechanistically.
Major comments:
- The causal logic is backwards. Your PLSR puts snowpack depth, soil temperature, and climate into one model and asks "which matters most?" But that's not how snowpack works. The real causal chain is: Snowpack → changes soil temperature → affects respiration. It’s not reasonable to ask "is snowpack or soil temperature more important?" when snowpack operates through soil temperature. When your results show snowpack has higher VIP than soil temperature in 30% of the area, what does that actually mean? Either: (a) snowpack strongly affects soil temperature which drives respiration, or (b) snowpack has some direct effect independent of temperature (like blocking gas exchange). You did not distinguish these possibilities. Some mediation analysis could be added. Maybe use R packages like mediation or lavaan to partition snowpack's effect into: indirect (through soil temperature), indirect (through soil moisture), and direct (independent of soil conditions). You already have all the data. Without this, your claim that "snowpack impacts exceed climate impacts" is mechanistically empty.
- The method part includes some content about comparing snowpack, climate, and soil factors. But Figure 2 only shows detailed spatial patterns for the three snowpack variables. Climate and soil just appear as colored categories in panel 2a. A basic questions: In the 11% where climate dominates, which climate variable specifically? What do soil temperature VIP patterns look like? You ran the analysis but didn't report it? Same with Figure 3, you show adding snowpack improves R² by 0.23 on average, but never tell us what the baseline R² was. If it goes from 0.1 to 0.33, which means the whole model is weak. If it goes from 0.6 to 0.83, snowpack is a nice addition. We need that context. Also, Figure 2a only shows pixels where VIP>1 and R²>0.8. About half of your study area is grayed out. What's different about those regions where the model fails?
- Section 3.1: partial correlation controlling for climate (treating climate as a confounder); Section 3.2: PLSR with climate as predictor (treating climate as competitor). These assume different causal structures. You don't explain why you need both or how to reconcile them. Path analysis would handle both issues in one coherent framework.
- You keep saying "snowpack insulation" but never demonstrate it. If that's really the mechanism, show that in regions where snowpack has high VIP: (a) snowpack correlates strongly with soil temperature, AND (b) soil temperature correlates strongly with respiration. You have the data but never test this chain. lines 295-302 say snowpack matters more in cold regions because "respiration is more temperature-sensitive below 0°C" . but do you actually see that in your data? Consider to calculate Q10 values for different temperature zones. The negative correlations in coastal Canada get one vague paragraph (lines 268-275) about "too thick snowpack limits gas exchange." At what threshold? Could it be something else, like unstable snowpack in maritime climates?
- Everything depends on ERA5-Land snowpack (which is modeled, not observed). You mention it overestimates mountain snow depth in one sentence (line 308) but provide no actual validation. Compare to SNOTEL, Russian network, or Scandinavian station data. Show bias by region. Your "insulation" interpretation depends on whether ERA5 gets snow density and thermal conductivity right.
- Section 3.2 is confused. Title says "Impacts of snowpack dynamics" but content is multi-factor comparison. You mix two different analyses: VIP (who's dominant?) and R² improvement (how much does adding snowpack help?). These answer different questions. Your results show this tension: snowpack dominates in 30% of areas but improves R² in 50%. That 20% gap means snowpack helps without being #1. Why not discuss this?
Minor comments:
Line 73: What is the total snowmelt? Is that SWE? How did you calculate it?
Line86: Regarding the soil data, what are the specific soil depths? How did you calculate the mean value using depth weighting? Use one or two sentences to describe them.
Line 135-138: It may be better to move these sentences to the method part.
Lines 188-205: Figure 3 shows every possible combination of snowpack indicators. Just show the progression: baseline → add depth → add density → add duration. Much clearer.
Lines 212-222: Good finding on temperature gradient, but your explanation (line 303) that warm regions are "less constrained by temperature" is unclear. Higher baseline respiration making snow effects relatively smaller would be a better mechanism.
Figure 4d,e: This is actually your clearest result.
The core problem is you're using a prediction model (PLSR) to make causal claims about mechanisms. That doesn't work without proper causal analysis. Fix the logic, complete the reporting, and this could contribute meaningfully to understanding winter carbon dynamics.
Citation: https://doi.org/10.5194/egusphere-2026-242-RC3
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- 1
General comments:
Snow is a crucial component in the land surface energy balance and the water-carbon cycle. Our understanding of the mechanisms by which snowpacks influence ecosystem respiration remains limited. This is partly due to the difficulty in monitoring snowpack properties, and over-simplification of snow processes when investigating the impact of snow on the ecosystem carbon cycle. In this study, the authors investigated the relation between six snow properties and ecosystem respiration using remote sensing data. They identified snowpack depth, snowpack duration, and snowpack density as the most important snow property that influence winter soil respiration, and they suggest that snowpack-respiration relation was regulated by thermal conditions rather than water availability. While previous studies on winter ecosystem respiration have presented observational results, information on broad-scale understanding using remote sensing and modelling has been lacking, making this study significant. The manuscript includes all the essential components. However, I have outlined several methodological concerns, along with several detailed or technical points, that could be addressed to further enhance the overall rigor and presentation of the study.
Major problems：
1) The metrics used for snowpack include snowpack depth and snowpack density, which are interrelated. Given the collinearity among variables, using partial correlation analysis and partial least squares regression models is certainly reasonable. Moreover, statistical approaches should be evaluated for simplifying analysis and enhancing understanding.
2) The author employs snowpack depth and snowpack density to characterize snow. A question arises as to why snowpack water equivalent is not used, considering it can describe both snowpack depth and snowpack density and is commonly utilized in snow-ecology studies (e.g., Wang et al. "Disentangling the mechanisms behind winter snow impact on vegetation activity in northern ecosystems." Global Change Biology 24.4 (2018): 1651-1662).
3) The datasets are described as having been interpolated to a 0.25-degree resolution. This characterization raises two related concerns: (1) The procedure appears to represent aggregation or downsampling rather than interpolation in the strict sense, and it would be important to clarify whether this step was implemented through spatial averaging. (2) If all available 0.25-degree grid cells were assigned equal weights in the statistical analyses, this would introduce a latitudinal bias, disproportionately emphasizing higher latitudes relative to lower ones (by approximately a factor of two). Although the maps visually resemble an equal-area projection or a comparable adjustment, clarification is needed as to whether and how differences in grid-cell area were taken into account in the analyses.
Minor problems:
Line 67: “average … of all nine machine learning methods.” This phrasing may be unclear to readers.
Line 99: what is the difference between PLSR analysis and multiple linear regression?
Line 104: how was VIP calculated?
Line 223: what is the hypothesis that the regulation effect of snow should differ among vegetation types? The rationality of this analysis needs to be explained.