the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 May 2024
Multi-physics ensemble modelling of Arctic tundra snowpack properties
Abstract. Sophisticated snowpack models such as Crocus and SNOWPACK struggle to properly simulate profiles of density and specific surface area (SSA) within Arctic snowpacks due to an underestimation of wind-induced compaction, misrepresentation of basal vegetation influencing compaction and metamorphism, and omission of water vapour flux transport. To improve the simulation of profiles of density and SSA, parameterisations of snow physical processes that consider the effect of high wind speeds, the presence of basal vegetation and alternate thermal conductivity formulations were implemented into an ensemble version of the Soil, Vegetation and Snow version 2 (SVS2-Crocus) land surface model, creating Arctic SVS2-Crocus. The ensemble versions of default and Arctic SVS2-Crocus were driven with in-situ meteorological data and evaluated using measurements of snowpack properties (SWE, depth, density and SSA) at Trail Valley Creek (TVC), Northwest Territories, Canada over 32-years (1991–2023). Results show that both default and Arctic SVS2-Crocus can simulate the correct magnitude of SWE (RMSE for both ensembles: 55 kg m-2) and snow depth (default RMSE: 0.22 m; Arctic RMSE: 0.18 m) at TVC in comparison to measurements. Wind-induced compaction within Arctic SVS2-Crocus effectively compacts the surface layers of the snowpack, increasing the density, and reducing the RMSE by 41 % (176 kg m-3 to 103 kg m-3). Parameterisations of basal vegetation are less effective in reducing compaction of basal snow layers (default RMSE: 67 kg m-3; Arctic RMSE: 65 kg m-3), reaffirming the need to consider water vapour flux transport for simulation of low-density basal layers. The top 100 ensemble members of Arctic SVS2-Crocus produced lower continuous ranked probability scores (CRPS) than default SVS2-Crocus when simulating snow density profiles. The top performing members of the Arctic SVS2-Crocus ensemble featured modifications that raise wind speeds to increase compaction in snow surface layers and prevent snowdrift and increase viscosity in basal layers. Selecting these process representations in Arctic SVS2-Crocus will improve simulation of snow density profiles, which is crucial for many applications.
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RC1: 'Comment on egusphere-2024-1237', Anonymous Referee #1, 24 Jun 2024
General comments
The paper presents a new version of the model Crocus tuned for Arctic snowpack, so that the effect of wind and vegetation on snow compaction and snow drift as well as the thermal conductivity of snow are modified. Different parameterizations of these processes are implemented. Ensemble simulations with the standard Crocus and the Arctic Crocus are compared to field measurements at a study site in Northwest Territories, Canada, over a 32-years period. Results are compared in terms of snow depth, snow water equivalent, and snow density and specific surface area of the wind slab layer and of the depth hoar layer. This paper highlights that the modification related to the wind effect (stronger effect of wind on snow compaction) improve the simulations of the arctic snowpack. The modification related to the vegetation effect (lower compaction and less snow drift in the near-basal snow trapped in vegetation) does not lead to significance improvements.
The presented study will be largely beneficial for the snow community as it offers a first alternative to model arctic snowpack based on an “alpine-based” snow cover model. The model evaluation is based on a long-term dataset and, for the first time, on ensemble simulations, which allow robust comparisons and the assessment of different parameterizations for snow compaction, snow drift and snow heat conduction. The authors should be acknowledged for the field work involved to get the presented evaluation dataset. The paper is well written and well structured. Some elements in the state-of-the-art in the introduction are missing and should be included so that the motivation for the present work and contribution would appear even better (see the specific comment below). I warmly recommend this paper for publication.
The font size on the figures (legends, axis labels, etc) throughout the paper should be increased to make them readable in print (including Figure 9).
The author report that the vegetation effect does not allow for improved simulations compared to the standard crocus, and that this highlights the need to account for water vapor transport in snow. However, looking at the density profiles (fig 4 and 6), simulations do show a drop of density at the base. This drop seems of the same order as the ones reported in measurements, when observed. As you mentioned, the definition of what is the wind slab and what is the depth hoar could impact the conclusion of the statistical analysis. The simulated density drop indeed seems to impact less than the 40 to 70 % of the profile, as indicated as the range of DHF reported from the snow pits. An idea could be to compare bulk density of only the first 10 cm (or any other relevant value that for sure describe only depth hoar, even if not part of the depth hoar layer is not included). Essentially, my question is to what extent the error in defining the depth hoar boundaries could have an impact on the conclusion regarding the proposed vegetation parameterizations. Some more comments could be done on that in the discussion.
Specific comments
Introduction
The second paragraph of the introduction (lines 49 – 67) focuses on the limitations of arctic snow modeling due to misrepresentation of some physical processes, not suited for the Arctic. This part provides a state of the art which is not fully convincing because some information are lacking and/or disorderly. For example, it is not mentioned what is the current issues regarding wind parameterization (is it too weak or too strong?), how is modeled the vegetation (is it accounted at all?) or why should we consider a different thermal conductivity. The process of water vapor transport is well described but is is actually not essential as it is not addressed in this paper (not modeled). Most of the missing information are provided latter in the paper but should be described already in the state of the art so that the reader understands the motivation for the work done. This is why I suggest that, for each of the processes addressed in this paper, which are the effect of wind, the effect of vegetation, and the snow thermal conductivity, you check that the following information are provided in a well-structured way:
- description of the physical processes and the consequences on snow and what is different in the arctic
- how it is modeled or not in “standard” snow model (crocus, snowpack) and what are the errors done by applying it to the arctic (quantify if possible)
- if any, what are the modifications for the arctic already presented and how do they improve the situation, what is left to be addressed (contributions of this paper).
The third paragraph of the introduction (line 69 -83) focuses on another issue on arctic snow modeling, which, this time, concerns the model evaluation method. If this is correct, I suggest to change the first sentence of this paragraph so that this new topic is introduced. The sentence in the current version of the paper refers to issues related to physical processes, so to the topic of to the previous paragraph. It could start as “Another limitation in Arctic snow modeling concerns the method for model evaluation. Indeed, previous evaluation of simulated arctic snow density (e.g. Gouttevin …) neglect uncertainties that arise from ...”
Line 56: “strong temperature gradients generate water vapour flux transport that redistributes mass from the bottom to the top of the snowpack, leading to the formation of low-density basal depth hoar layers (Domine et al., 2016b; Fourteau et al., 2021)”. Citations here should refer to observation, Fourteau et al 2021 did modeling, if my not mistaking. Citation of the experimental work of Weise’s thesis (in Chap.5) and Bouvet et al. 2023 could be included.
Line 65: Is “Domine et al 2016b and Domine et al. 2019” the reference papers to describe wind effect on arctic snow? Papers that describe this process should be given here (maybe the references provdided line 210).
Line 65 “Attempts to account for missing processes that specifically impact Arctic snowpack properties have been made by implementing simplified adaptations to existing snow physical processes (Gouttevin et al., 2018; Royer et al., 2021; Barrere et al., 2017; Lackner et al., 2022).” This sentence should be more explicit or deleted here.
Line 87: It should be mentioned in this paragraph if the parameterizations implemented in crocus are new or from the literature.
Line 103: how is the topography of the study site?
Line 223: “Following the approach of Gouttevin et al. (2018), Royer et al. (2021) and Domine et al. (2016a) deactivated wind compaction and increased η under a set vegetation height.” → What was the improvement of this modification.
Line 225: “Both options” → It is not clear which options are meant here.
Line 259: “… and are applied to the normalized profiles of simulated density and SSA” → describe what are the normalized profiles.
Line 260: “Vegetation in the base of an Arctic snowpack makes density and IceCube measurements difficult meaning measurements do not always reach the base of the snowpack for evaluation of simulated basal layer density and SSA.” → to be reformulated “which might impact the evaluation of simulated basal layer density and SSA.”
Line 270: The start of the paragraph should introduce what do we look at now, instead of going directly into details. I would suggest something like “Over the years 1991-2023, different result can be found in the evolution of snow depth and SWE over the course of the winter between the model and the measurements. Over estimation, good agreement and under estimation can be observed depending on the year considered, as illustrated in Figure 2. These biases can be explained … ect”.
Line 277: “In this case, a snow drift in the SR50 footprint can lead to exaggerated differences between simulated and measured snow depth.” → Is this feature was observed during the field campaign or is it an hypothesis?
Line 287: Here the data that we look at now should be introduced first, otherwise it is unclear. Such as “We look now at the statistical scores when comparing model and measurements over the entire 1991-2023 period at the time of the snow course measurement, i.e. around the peak SWE accumulation.”
Line 289: “Deeper snow depths are simulated by default SVS2-Crocus (default mean: 0.54 m; Arctic mean: 0.47 m) due to the Wind Effect modifications applied to Arctic SVS2-Crocus resulting in increased density in the surface layers of the snowpack, leading to higher bulk density (default mean: 239 kg m-3; Arctic mean: 278 kg m-3; Table 1, Appendix B3) and shallower snow depths.” → This sentence should be reformulated.
Section 2.4
The first paragraph is made of an overview of the snowpack structure at TVC. Then details on the dataset are provided and seem out of context. They seem to come to early in the result, before an overview or a general description of the dataset is provided (following paragraphs), which make it difficult to follow. I would suggest to move them lower down in the paper.
Line 232: “November 2018 shows less variability and range in snow density than other snow seasons (Fig. 4) as the snowpack was shallow and metamorphism in basal layers and compaction in surface layers had little time to affect the density.” → unclear. Do you compare snow in November with snow in later months? in early season, snow had less time to evolve? Or does the comment refer to the year 2018 which was special? Again, this specific comment is hard to follow, as the reader is not yet familiar with the data / figures, which are actually described lower down.
Line 235: “Heightened variability in the density of the top 20% of the January 2019, March 2019 and March 2022 snowpacks was more pronounced than in other winter seasons due to the timing of sampling relative to a fresh snowfall event”. → unclear. Do you mean that the variability observed in snow density at the top 20% at these dates could have been introduced by a changing snowpack during the measurements, as they were performed during a snow fall.
In the paragraph line 357 – 365, the rather sharp drop of density reported in the simulated profiles with the arctic version of crocus needs to be described in the text, as it seems an important feature of the modeling. It differs strongly with the standard crocus. The fact that this sharp drop comes from the modified parameterizations of the vegetation effect (if I’m not mistaken) could also be pointed out.
Line 373: “density” → mean density
Line 398: “The Arctic SVS2-Crocus ensemble was however more skilled at capturing the variability in measurements” → It is not clear to me if this comment is relevant. In which way the arctic crocus would be more tuned / suited to capture spatial variability. Which introduced parameterizations would allow for that. If so, it should be described here.
Line 427 : “As these modifications were developed to consider Arctic processes, it is likely that they are better at simulating physical processes that occur in the Arctic environment over the default parameterisations, leading to lower CRPS scores.” → to be deleted, it was shown and quantified in the sentence above.
Line 446: “Snowdrift parameterisations implemented into Arctic SVS2-Crocus modify the microstructure of snow grains during blowing snow events, which occur frequently at TVC.” → should this sentence be in the discussion?
Line 504: “For the same reason, basal densities using default SVS2-Crocus may be underestimated.” → please provide more explanation of this link.
Line 520 : “The Basal vegetation effect is …” → “In this case the Basal vegetation effect is …”
Conclusion:
I would suggest to include a comment on the potential to improve arctic model evaluation by using dataset that allow to better capture snow properties at and near the base, especially to evaluate the parameterizations of the vegetation effect. Your study pointed out the challenge (but the need) of having density and SSA data near the ground due to the measurement method used.
Reference
Weise 2017, PhD thesis, Time-lapse tomography of mass fluxes and microstructural changes in snow.
Bouvet, L., Calonne, N., Flin, F., and Geindreau, C.: Heterogeneous grain growth and vertical mass transfer within a snow layer under a temperature gradient, The Cryosphere, 17, 3553–3573, https://doi.org/10.5194/tc-17-3553-2023, 2023.
Citation: https://doi.org/10.5194/egusphere-2024-1237-RC1 - AC1: 'Reply on RC1', Georgina Woolley, 16 Aug 2024
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RC2: 'Comment on egusphere-2024-1237', Anonymous Referee #2, 13 Aug 2024
General comments
In this paper an ensemble of simulations made with the snow model CROCUS coupled to the land surface model SVS2 is modified to account for different representations of physical processes specific to the Arctic snowpack such as the influence of wind and basal vegetation on the snow microstructure as well as formulations of thermal conductivity. Improvements compared to the an ensemble default version are discussed and quantified using an impressive set of observations including 32 years of measured SWE and snow depth, together with profiles of density and SSA for 6 winters at one site in the Canadian Arctic. Particular attention is paid to the representation of surface surface high-density wind slab snow layers and basal low-density depth hoar layers. While the former are better reproduced by parameterizing wind-induced compaction, the model still struggles to capture the latter.
The document is pleasant to read and easy to follow. The methodology is well documented and the results are exhaustively described. This is a very interesting contribution to the snow modeling community, not least by offering new perspectives on model evaluation based on a set of simulations and snow density and SSA profiles observed at different times over the course of a winter. Although this is not (and should be) clearly stated in the article, these observations are in fact provided publicly on a public repository. Overall, this article is well suited to The Cryosphere. I'm happy to say that I don't have much to add to the already comprehensive RC1 report, which provides some good leads for improvement, with the exception of the few minor comments below, after which I recommend publication without further iteration.
Specific comments
In the introduction, you mention the problems of site specific calibration of parameter choices, which you avoid here by working with a set of parameters calibrated for different sites. But that doesn't prevent your conclusions from being site specific. This is more of a suggestion than a comment, but other observations are available at other Arctic sites, for instance in Vargel et al. (2020), which would make it easy to elaborate on this point.
L109: How is the data filtered? Is the method known?
L137: What is this most suitable option based on?
Figs. 4,5,6,7,8: Increase label size and legend and explain what is normalized depth in the text for better readability. Wouldn't it be more practical to display the observations and the model on the same graph?
Reference
C. Vargel, A. Royer, O. St-Jean-Rondeau, G. Picard, A. Roy, V. Sasseville, A. Langlois, Arctic and Subarctic snow microstructure analysis for microwave brightness temperature simulations, Remote Sensing of Environment, 241, 111754, doi: 10.1016/j.rse.2020.111754, 2020
Citation: https://doi.org/10.5194/egusphere-2024-1237-RC2 - AC2: 'Reply on RC2', Georgina Woolley, 16 Aug 2024
Status: closed
-
RC1: 'Comment on egusphere-2024-1237', Anonymous Referee #1, 24 Jun 2024
General comments
The paper presents a new version of the model Crocus tuned for Arctic snowpack, so that the effect of wind and vegetation on snow compaction and snow drift as well as the thermal conductivity of snow are modified. Different parameterizations of these processes are implemented. Ensemble simulations with the standard Crocus and the Arctic Crocus are compared to field measurements at a study site in Northwest Territories, Canada, over a 32-years period. Results are compared in terms of snow depth, snow water equivalent, and snow density and specific surface area of the wind slab layer and of the depth hoar layer. This paper highlights that the modification related to the wind effect (stronger effect of wind on snow compaction) improve the simulations of the arctic snowpack. The modification related to the vegetation effect (lower compaction and less snow drift in the near-basal snow trapped in vegetation) does not lead to significance improvements.
The presented study will be largely beneficial for the snow community as it offers a first alternative to model arctic snowpack based on an “alpine-based” snow cover model. The model evaluation is based on a long-term dataset and, for the first time, on ensemble simulations, which allow robust comparisons and the assessment of different parameterizations for snow compaction, snow drift and snow heat conduction. The authors should be acknowledged for the field work involved to get the presented evaluation dataset. The paper is well written and well structured. Some elements in the state-of-the-art in the introduction are missing and should be included so that the motivation for the present work and contribution would appear even better (see the specific comment below). I warmly recommend this paper for publication.
The font size on the figures (legends, axis labels, etc) throughout the paper should be increased to make them readable in print (including Figure 9).
The author report that the vegetation effect does not allow for improved simulations compared to the standard crocus, and that this highlights the need to account for water vapor transport in snow. However, looking at the density profiles (fig 4 and 6), simulations do show a drop of density at the base. This drop seems of the same order as the ones reported in measurements, when observed. As you mentioned, the definition of what is the wind slab and what is the depth hoar could impact the conclusion of the statistical analysis. The simulated density drop indeed seems to impact less than the 40 to 70 % of the profile, as indicated as the range of DHF reported from the snow pits. An idea could be to compare bulk density of only the first 10 cm (or any other relevant value that for sure describe only depth hoar, even if not part of the depth hoar layer is not included). Essentially, my question is to what extent the error in defining the depth hoar boundaries could have an impact on the conclusion regarding the proposed vegetation parameterizations. Some more comments could be done on that in the discussion.
Specific comments
Introduction
The second paragraph of the introduction (lines 49 – 67) focuses on the limitations of arctic snow modeling due to misrepresentation of some physical processes, not suited for the Arctic. This part provides a state of the art which is not fully convincing because some information are lacking and/or disorderly. For example, it is not mentioned what is the current issues regarding wind parameterization (is it too weak or too strong?), how is modeled the vegetation (is it accounted at all?) or why should we consider a different thermal conductivity. The process of water vapor transport is well described but is is actually not essential as it is not addressed in this paper (not modeled). Most of the missing information are provided latter in the paper but should be described already in the state of the art so that the reader understands the motivation for the work done. This is why I suggest that, for each of the processes addressed in this paper, which are the effect of wind, the effect of vegetation, and the snow thermal conductivity, you check that the following information are provided in a well-structured way:
- description of the physical processes and the consequences on snow and what is different in the arctic
- how it is modeled or not in “standard” snow model (crocus, snowpack) and what are the errors done by applying it to the arctic (quantify if possible)
- if any, what are the modifications for the arctic already presented and how do they improve the situation, what is left to be addressed (contributions of this paper).
The third paragraph of the introduction (line 69 -83) focuses on another issue on arctic snow modeling, which, this time, concerns the model evaluation method. If this is correct, I suggest to change the first sentence of this paragraph so that this new topic is introduced. The sentence in the current version of the paper refers to issues related to physical processes, so to the topic of to the previous paragraph. It could start as “Another limitation in Arctic snow modeling concerns the method for model evaluation. Indeed, previous evaluation of simulated arctic snow density (e.g. Gouttevin …) neglect uncertainties that arise from ...”
Line 56: “strong temperature gradients generate water vapour flux transport that redistributes mass from the bottom to the top of the snowpack, leading to the formation of low-density basal depth hoar layers (Domine et al., 2016b; Fourteau et al., 2021)”. Citations here should refer to observation, Fourteau et al 2021 did modeling, if my not mistaking. Citation of the experimental work of Weise’s thesis (in Chap.5) and Bouvet et al. 2023 could be included.
Line 65: Is “Domine et al 2016b and Domine et al. 2019” the reference papers to describe wind effect on arctic snow? Papers that describe this process should be given here (maybe the references provdided line 210).
Line 65 “Attempts to account for missing processes that specifically impact Arctic snowpack properties have been made by implementing simplified adaptations to existing snow physical processes (Gouttevin et al., 2018; Royer et al., 2021; Barrere et al., 2017; Lackner et al., 2022).” This sentence should be more explicit or deleted here.
Line 87: It should be mentioned in this paragraph if the parameterizations implemented in crocus are new or from the literature.
Line 103: how is the topography of the study site?
Line 223: “Following the approach of Gouttevin et al. (2018), Royer et al. (2021) and Domine et al. (2016a) deactivated wind compaction and increased η under a set vegetation height.” → What was the improvement of this modification.
Line 225: “Both options” → It is not clear which options are meant here.
Line 259: “… and are applied to the normalized profiles of simulated density and SSA” → describe what are the normalized profiles.
Line 260: “Vegetation in the base of an Arctic snowpack makes density and IceCube measurements difficult meaning measurements do not always reach the base of the snowpack for evaluation of simulated basal layer density and SSA.” → to be reformulated “which might impact the evaluation of simulated basal layer density and SSA.”
Line 270: The start of the paragraph should introduce what do we look at now, instead of going directly into details. I would suggest something like “Over the years 1991-2023, different result can be found in the evolution of snow depth and SWE over the course of the winter between the model and the measurements. Over estimation, good agreement and under estimation can be observed depending on the year considered, as illustrated in Figure 2. These biases can be explained … ect”.
Line 277: “In this case, a snow drift in the SR50 footprint can lead to exaggerated differences between simulated and measured snow depth.” → Is this feature was observed during the field campaign or is it an hypothesis?
Line 287: Here the data that we look at now should be introduced first, otherwise it is unclear. Such as “We look now at the statistical scores when comparing model and measurements over the entire 1991-2023 period at the time of the snow course measurement, i.e. around the peak SWE accumulation.”
Line 289: “Deeper snow depths are simulated by default SVS2-Crocus (default mean: 0.54 m; Arctic mean: 0.47 m) due to the Wind Effect modifications applied to Arctic SVS2-Crocus resulting in increased density in the surface layers of the snowpack, leading to higher bulk density (default mean: 239 kg m-3; Arctic mean: 278 kg m-3; Table 1, Appendix B3) and shallower snow depths.” → This sentence should be reformulated.
Section 2.4
The first paragraph is made of an overview of the snowpack structure at TVC. Then details on the dataset are provided and seem out of context. They seem to come to early in the result, before an overview or a general description of the dataset is provided (following paragraphs), which make it difficult to follow. I would suggest to move them lower down in the paper.
Line 232: “November 2018 shows less variability and range in snow density than other snow seasons (Fig. 4) as the snowpack was shallow and metamorphism in basal layers and compaction in surface layers had little time to affect the density.” → unclear. Do you compare snow in November with snow in later months? in early season, snow had less time to evolve? Or does the comment refer to the year 2018 which was special? Again, this specific comment is hard to follow, as the reader is not yet familiar with the data / figures, which are actually described lower down.
Line 235: “Heightened variability in the density of the top 20% of the January 2019, March 2019 and March 2022 snowpacks was more pronounced than in other winter seasons due to the timing of sampling relative to a fresh snowfall event”. → unclear. Do you mean that the variability observed in snow density at the top 20% at these dates could have been introduced by a changing snowpack during the measurements, as they were performed during a snow fall.
In the paragraph line 357 – 365, the rather sharp drop of density reported in the simulated profiles with the arctic version of crocus needs to be described in the text, as it seems an important feature of the modeling. It differs strongly with the standard crocus. The fact that this sharp drop comes from the modified parameterizations of the vegetation effect (if I’m not mistaken) could also be pointed out.
Line 373: “density” → mean density
Line 398: “The Arctic SVS2-Crocus ensemble was however more skilled at capturing the variability in measurements” → It is not clear to me if this comment is relevant. In which way the arctic crocus would be more tuned / suited to capture spatial variability. Which introduced parameterizations would allow for that. If so, it should be described here.
Line 427 : “As these modifications were developed to consider Arctic processes, it is likely that they are better at simulating physical processes that occur in the Arctic environment over the default parameterisations, leading to lower CRPS scores.” → to be deleted, it was shown and quantified in the sentence above.
Line 446: “Snowdrift parameterisations implemented into Arctic SVS2-Crocus modify the microstructure of snow grains during blowing snow events, which occur frequently at TVC.” → should this sentence be in the discussion?
Line 504: “For the same reason, basal densities using default SVS2-Crocus may be underestimated.” → please provide more explanation of this link.
Line 520 : “The Basal vegetation effect is …” → “In this case the Basal vegetation effect is …”
Conclusion:
I would suggest to include a comment on the potential to improve arctic model evaluation by using dataset that allow to better capture snow properties at and near the base, especially to evaluate the parameterizations of the vegetation effect. Your study pointed out the challenge (but the need) of having density and SSA data near the ground due to the measurement method used.
Reference
Weise 2017, PhD thesis, Time-lapse tomography of mass fluxes and microstructural changes in snow.
Bouvet, L., Calonne, N., Flin, F., and Geindreau, C.: Heterogeneous grain growth and vertical mass transfer within a snow layer under a temperature gradient, The Cryosphere, 17, 3553–3573, https://doi.org/10.5194/tc-17-3553-2023, 2023.
Citation: https://doi.org/10.5194/egusphere-2024-1237-RC1 - AC1: 'Reply on RC1', Georgina Woolley, 16 Aug 2024
-
RC2: 'Comment on egusphere-2024-1237', Anonymous Referee #2, 13 Aug 2024
General comments
In this paper an ensemble of simulations made with the snow model CROCUS coupled to the land surface model SVS2 is modified to account for different representations of physical processes specific to the Arctic snowpack such as the influence of wind and basal vegetation on the snow microstructure as well as formulations of thermal conductivity. Improvements compared to the an ensemble default version are discussed and quantified using an impressive set of observations including 32 years of measured SWE and snow depth, together with profiles of density and SSA for 6 winters at one site in the Canadian Arctic. Particular attention is paid to the representation of surface surface high-density wind slab snow layers and basal low-density depth hoar layers. While the former are better reproduced by parameterizing wind-induced compaction, the model still struggles to capture the latter.
The document is pleasant to read and easy to follow. The methodology is well documented and the results are exhaustively described. This is a very interesting contribution to the snow modeling community, not least by offering new perspectives on model evaluation based on a set of simulations and snow density and SSA profiles observed at different times over the course of a winter. Although this is not (and should be) clearly stated in the article, these observations are in fact provided publicly on a public repository. Overall, this article is well suited to The Cryosphere. I'm happy to say that I don't have much to add to the already comprehensive RC1 report, which provides some good leads for improvement, with the exception of the few minor comments below, after which I recommend publication without further iteration.
Specific comments
In the introduction, you mention the problems of site specific calibration of parameter choices, which you avoid here by working with a set of parameters calibrated for different sites. But that doesn't prevent your conclusions from being site specific. This is more of a suggestion than a comment, but other observations are available at other Arctic sites, for instance in Vargel et al. (2020), which would make it easy to elaborate on this point.
L109: How is the data filtered? Is the method known?
L137: What is this most suitable option based on?
Figs. 4,5,6,7,8: Increase label size and legend and explain what is normalized depth in the text for better readability. Wouldn't it be more practical to display the observations and the model on the same graph?
Reference
C. Vargel, A. Royer, O. St-Jean-Rondeau, G. Picard, A. Roy, V. Sasseville, A. Langlois, Arctic and Subarctic snow microstructure analysis for microwave brightness temperature simulations, Remote Sensing of Environment, 241, 111754, doi: 10.1016/j.rse.2020.111754, 2020
Citation: https://doi.org/10.5194/egusphere-2024-1237-RC2 - AC2: 'Reply on RC2', Georgina Woolley, 16 Aug 2024
Data sets
Arctic_SVS2-Crocus Georgina Woolley https://github.com/georginawoolley/Arctic_SVS2-Crocus.git
Arctic SVS2-Crocus Ensemble Output Georgina Woolley https://doi.org/10.6084/m9.figshare.25639215.v2
Model code and software
MESH_SVS/Arctic_Mods1 Georgina Woolley and Vincent Vionnet https://github.com/VVionnet/MESH_SVS/tree/Arctic_Mods1
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