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https://doi.org/10.5194/egusphere-2026-242
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2026-242
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
13 Feb 2026
| 13 Feb 2026
Status: this preprint is open for discussion and under review for The Cryosphere (TC).
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.
How to cite. Tang, B., Xiao, P., Zhang, X., Liu, H., Liu, Y., Pellikka, P., Jia, Y., and Wu, J.: Impacts of Snowpack Insulation on Winter Ecosystem Respiration: A Synergistic Analysis in the Northern Hemisphere, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-242, 2026.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Status: open (until 20 May 2026)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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- RC1: 'Comment on egusphere-2026-242', Tao Che, 15 Mar 2026 reply
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RC2: 'Comment on egusphere-2026-242', Anonymous Referee #2, 06 May 2026
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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
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Latest update: 10 May 2026
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu, China
Pengfeng Xiao
CORRESPONDING AUTHOR
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu, China
Xueliang Zhang
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu, China
Hao Liu
College of Meteorology and Oceanography, National University of Defense Technology, Changsha China
Yantao Liu
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu, China
Petri Pellikka
Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China
Yumeng Jia
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu, China
Jinhang Wu
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu, China
Short summary
Our study reveals how snowpack changes affect winter ecosystem respiration across the northern hemisphere through snowpack insulation. The effects of snowpack (depth, density, and duration) on winter ecosystem respiration are more crucial and widespread than climate factors. We found examining these factors in isolation could considerably underestimate the impact of snowpack on winter ecosystem respiration. Our findings provide insights for improving climate-carbon feedback predictions.
Our study reveals how snowpack changes affect winter ecosystem respiration across the northern...
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.