Impact of measured and simulated tundra snowpack properties on heat transfer

Dutch, Victoria R.; Rutter, Nick; Wake, Leanne; Sandells, Melody; Derksen, Chris; Walker, Branden; Hould Gosselin, Gabriel; Sonnentag, Oliver; Essery, Richard; Kelly, Richard; Marsh, Phillip; King, Joshua; Boike, Julia

doi:https://doi.org/10.5194/tc-16-4201-2022

Articles | Volume 16, issue 10

https://doi.org/10.5194/tc-16-4201-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-16-4201-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 10

Research article

|

11 Oct 2022

Research article |

| 11 Oct 2022

Impact of measured and simulated tundra snowpack properties on heat transfer

Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike

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Final revised paper (published on 11 Oct 2022)
Preprint (discussion started on 08 Oct 2021)

Interactive discussion

Status: closed

RC1:
'Comment on tc-2021-313', Anonymous Referee #1, 17 Nov 2021

Review of “Impact of measured and simulated tundra snowpack properties on heat transfer”, submitted to The Cryosphere, November 2021.

The paper deals on modeling the heat transfer through the snow pack in order to obtain the variability of the soil temperature through two different snow seasons in the Canadian Arctic. The work is well presented and the topic is dealing with critical issue related to snow thermal conductivity. There is no doubt it fits into the scope of TC.

General comments:

Thermal conductivity of snow is critical and still very challenging to access. Field measurements have been proven to be satisfactory to a certain level but the inclusion of theoretical equation (or assumption) into models is not yet actual. While most of the main parameters have been discussed and tested, and appropriate literature cited, in order to explain the differences between measured and modeled snow KT, the hardness of the snow pack has never been mentioned in the work, neither its permeability. For sure hardness is kind of included if we think of the measurement using micropen, but as demonstrated in the work, such measurements had to be tuned to the Arctic snow pack as originally validated for Alpine snow packs. Vapour kinetics transfer are necessary to form depth hoar, but the original structure of the snow on ground will also affects such transfers.

The depth proportion of each different layer is indicated on fig 2 but not discussed, as for example to presence of indurated depth hoar in 2019 and not in 2018. Both seasons had some warming events, which seems to not have affected the soil temperature, but what about the snow pack and the formation of internal, even small, ice layers? Are there any relation between the wind (speed threshold during storms?), the amount of rain/positive degree days and the errors in term of soil temperature? It is also surprising that no wind data are presented in the climatology...

Snow depth has been shown in the work to be the dominant factor for applying the correcting factor, but if one think of snow being deposited as a single homogeneous layer, then only the physical snow parameters are of interest. The amount of snow layer and their internal properties would be then the controlling factor, for example an very hard and dense snow layer or thick ice layer with a low permeability likely to enhance the formation of depth hoar. Such phenomena are likely to be more present in the future as it is expected more extremes events in the Arctic. I do not think it is necessary to change a lot of the work done and am sure all the data are available in the runs done.

A discussion around that topic is to me of interest in order to pave the future work into such topics and go further into the effects of snow heat properties on the soil’s ones, as it is unlikely in the coming years we would be able to develop, and run, a full model with a proper physical scheme for snow thermal conductivity, without considering the validation. It is then today only by using such work presented in this paper that we could be able to better understand the soil-snow-atmosphere feedback in the Arctic.

Minor comments:

Line 117: am not sure a value of 2 for k is what was intended to write

Figures 8 and 9 appear before 5, 6, and 7

Figure 3, November appears before March from left to right

Citation: https://doi.org/10.5194/tc-2021-313-RC1
- AC1: 'Reply to both reviewer comments', Victoria Dutch, 16 Dec 2021
  
  Hi,
  Thank you for taking the time to review and comment on our paper. Please find our response attached.
  
  Citation: https://doi.org/10.5194/tc-2021-313-AC1
RC2:
'Comment on tc-2021-313 by referee#2', Anonymous Referee #2, 26 Nov 2021

Review of Dutch et al. "Impact of measured and simulated tundra snowpack properties on heat transfer"

The paper presents an evaluation of CLM v5 1D "point" snow simulation at an Arctic site, Trail Valley Creek, where intensive snow observations have been carried out for 3 periods over the 2017-2018 and 2018-2019 winters, while in-situ observations provide an adequate forcing for the model. A novelty lies in the use of SMP profiles to infer snow density and then conductivity, allowing for a much higher number of observations than traditionnal snowpit density measurements. The evaluation focuses on snow characteristics and the (un)ability of the model to reproduce key features of the Arctic snowpack on the one side, and on the ground thermal regime on the other side, whereby the Snow Heat Transfer Metric (Slater et al., 2017) is used. A bias correction is proposed to help CLM better capture the insulating properties of the snowpack.

The paper adresses a very relevant topic to assess the role of snow and the impact of climate change and on Arctic ecosystems and the cryosphere, and is well in the scope of The Cryosphere. The methods used are scientifically sound and the results'analysis is relevant. However I have some general comments/questions regarding the methodological choices :

**** General Comments ****

- the CLM snow module seems from scratch unappropriate to simulate the properties of the Arctic snowpack. This finding is clearly not new ; previous studies, notably Barrere et al., 2017, and others well cited by the authors, have highlighted the deficiences of this kind of models in the context of Arctic snow. In the present version of the manuscript, the description of the snow module of CLM is so light, that it is hard to capture the key features and fonctionning of the model. Typically, are the modifications Van Kampenhout et al., 2017, used in the present study ? It is not clear to the reader. As the CLM snow model is at the core of this study, a more enhanced description of the fonctionning of this model is required in the Methods of the paper : presently the line "Each layer is parameterised using layer thickness, temperature and mass of water and ice, as per Anderson (1976), Dai and Zeng (1997) and Jordan (1991)." (L37 p4) is much too vague as each of these publications contain different variants of constitutive equations. In the present form, it is not possible for the reader to understand which one is used for which process/variable. The constitutive equations for the evolution of layer thickness (hence compaction) and ice/water content or density should be explicited or at least explicitely referenced.

- CLM relies on the parametrization of snow thermal conductivity by Jordan, 1991, as recalled in eq 2. However, the authors chose to derive their conductivity profiles from observations using the Calonne and the Sturm parameterization, and not the one by Jordan, which would have allowed a much more direct comparison to CLM results (eg Fig 6). How do you justify this choice, how does the Jordan, 1991 parameterization compare to the others ? Is the effective thermal conductivity mentionned by Jordan et al., 1991 (p18) and accounting for heat transport through conduction and vapor diffusion, effectively used in CLM (and not just the k_snow) ? This should be explicited. The following is more a minor comment : please also beware of the use of the term "effective conductivity". Following Calonne et al., 2011 and in a material science perspective, effective conductivity refers to the conductivity of the ice-air-liquid water mixture that constitutes the snow material, while individual components like ice or air, have just a thermal condictivity. In other studies, and often in land-surface modelling as in Jordan et al., 1991, "effective conductivity" refers to the additional inclusion of latent-heat exchange within the conductivity used in the Fourier heat transfer equation. So the use of Keff L149 p4 is appropiate, while its uses L151 p4 are not.

_ As a general question, is there any perspective to generalize the bias correction factor designed here, and have in the future global CLM runs using this usefull correction ? This would strongly enhance the impact of the work carried out in this study.

**** Minor comments ****

Please find also below a list of more minor comments which I hope will improve the manuscript :

P3 L110-113 : the way it is formulated gives the impression that the snowpack always entailed the 4 disctinct layers mentionned. I imagined that in practise, more complex layerings were sometimes encountered. Maybe just reformulating saying "Stratigraphic information profiled in each snowpit (n = 115) was used to assign

layer types to the measured densities (Fierz et al., 2009), among four different layer types : surface snow, wind slab, indurated hoar and depth hoar. This was made to assess spatial variability in the thickness and properties of different snowpack layers."

P3 L112-119: It is hard to understand the nature and impact of these adjustement without being quite familiar with CLM. I would suggest, if this PTCLM version is not published/referenced anywhere else, to just mention "adjustements" here and maybe detail them a bit more explicitely in an Appendix, so that the curious reader may dig full, explicit information from there.

P4 L126 : was a debiaising of ERA5 or adaptation to the local conditions maybe required ?(see : https://doi.org/10.5194/tc-2021-255 )

P5 L 171 : "slower rate of soil freezing" : a delay is visible, but not so clearly a slower rate. Could you justify this more with the observations ?

P5 L176-178 : "anomalously warm mid-winter air temperatures... had little influence on the soil temperature profile" : the March 2019 warming is visible though on the soil temperatures, isn'it ?

P6 L 211: "Temperature-gradient" metamosphism should be specified here (as wet-snow metamorphism would have different consequences !).

P6, paragraph 3.2 : I think that a line on the increase in density and effective thermal conductivity towards the bottom of the snowpack should be added, to complete the description of the profiles. I noted that possible reasons for this are given in the Discussion, and it could be referred to here.

P6 L237 : "Whilst the absolute number of simulated snow layers is plausible," : these layers have no physical meaning in the model, this is well explained in the methods. Therefore the remark is in my opinion unappropriate, or it should be specified w/r to what this is plausible.

P7 L250: " with the median thermal conductivity using the Calonne approximation still notably lower". The difference between simulations (0.3 Wm-1 K-1) and the median thermal conductivity using the Calonne approximation (0.25 Wm-1 K-1) is not so high, given the uncertainties attached to thermal conductivity estimations, and their range of variations. I suggest to suppress "notably".

P7 L 264 : what is the "maximum duration of simulated snow cover" ? (the period with continuous snow cover on the model ? )

P7 L 266-272: wouldn't it be easier to say that density was multiplied by a corrective factor α prior to the calculation of Keff ?

P7 L 274 : " between the interquartile range of observed values shown in Table 2" :

for a given snow type of for all ? If for all, the range should be specified as it is not in Table 2 if I am not mistaken.

P7 L 284-286 : It is really not clear to me how this linear regression was performed, could you give more details ? (also regarding the temporal sampling used)

P9 L337 and 361 : "vital" seems a bit strong and out of place in this context

P9 L 346-347: maybe also mention here that the insulating effect of snow somehow saturates after a certain snow height has been reached, impliying a stronger sensitivity of soil thermal regime to snow depth in the early winter close to snow onset, when the snow cover is very thin.

P9 L356: Comparing this point simulation with adjusted Keff, to CMIP5 models evaluated globally (if I am not mistaken..) is actually very unfair to the CMIP5 models ! The string differences in setup should be mentionned to balance this statement.

P9 L354-358: in relation to one of my major comments : did you tests other parameterizations than Jordan, 1991, for Keff in CLM, for instance the Sturm ? (I do not know how it positions w/r to the Jordan, but this should be mentionned if likely to also yield improvements).

P9 L363-365: "Larger values of the correction factor are needed to replicate observed soil temperatures later in the winter season, as errors in simulating earlier season snow depth are additive, leading to larger discrepancies for both snow depth and soil temperatures" : Could you specify to which specific Figure or result statement this assessment relate ?

P9 L367 : "This bias compensation between underestimates of snow depth and overestimates of snow thermal conductivity": I had rather say bias compensation between underestimates of snow depth and UNDERestimates of snow thermal conductivity (?) please correct me if I am wrong.

P10 L 380 : " Reducing simulated thermal conductivity by 80 % (α = 0.3) produces changes in soil temperatures approximately equivalent to the impact of changing depth hoar fraction from 0 to 60 % (Zhang et al., 1996), suggesting the inclusion of vapour transport in the snowpack is at least equally important as values of snow thermal conductivity in accurately simulating wintertime soil temperatures. ". The sentence is ambiguous and the message hard to understand (not sureI understood properly). The inclusion of vapour transport in the snowpack will change the thermal conductivity of the snowpack by two ways : i) because this will form depth hoar with lower Keff ; ii) because of vapour transprt induced heat transport. Anycase the inclusion of vapour transport in the snowpack, means different values for snow thermal conductivity. I think the sentence should be rephrased.

P9 L381-384: Honestly, the explicit inclusion of vapour transport within the snowpack in the snow modules of land surface models is a (very) long way from now. I am unaware of very concrete plans in that direction for CLM, but please correct me if they exist. I am much more confident that physically representative approaches, (like Royer et al., 2021), though not physically explicit, will be first used, and this would be the short to mid term perspective.

P11 L448: Figure A2c does not explicitely show that the relationship between FÌ and L is not heteroscedastic; it is rather the combination of all figures from this panel (?)

Fig 1 is hard to read, maybe consider having a grid (making date correspondances easier to follow), use backgroun color instead of the color of very small histograms for precipitation phase ; maybe also enhance y-axes sizes and line width. Plus, the caption contains some errors, please check.

Fig 6 : nice figure !

Fig 8 is mentionned in the text before Fig 7, they should be inverted.

Could you thicken the lines a little bit (should be feasible when reducing the y range) - the figure is quite hard to read.

Fig 9 :
please add the 1:1 line on Fig 9a. The caption should tell what the background color of Fig 9b represents (I assume it is the result of eq 4 ?)

**** Edits ****

P3 L95 : measured BY a SR50a ?

P3 L106 : the position of the coma is weird, please check

P4 L124: or less WERE filled (word missing)

P4 L 156 : ) missing

P5 L 171 : Alonger

P5 L 200 : please add the number of the Appendix

P6 L 244: ofdepth

P7 L 279 : "0.55 ≤ α ≤ 0.3" seems the wrong order

P8 L 315 : Required -> required

P10 L 398: show-> shows

P11 L 415 : A point is missing after A1.

P11 L 421: the sentence contains two "is"

Citation: https://doi.org/10.5194/tc-2021-313-RC2
- AC1: 'Reply to both reviewer comments', Victoria Dutch, 16 Dec 2021
  
  Hi,
  Thank you for taking the time to review and comment on our paper. Please find our response attached.
  
  Citation: https://doi.org/10.5194/tc-2021-313-AC1

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (21 Jan 2022) by Ketil Isaksen

AR by Victoria Dutch on behalf of the Authors (25 Jan 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (06 Feb 2022) by Ketil Isaksen

RR by Florent Dominé (24 Feb 2022)

RR by David Lawrence (23 Mar 2022)

Suggestions for revision or reasons for rejection

This is a solid study that makes use of new detailed high vertical resolution snow thermal conductivity measurements to assess the ability of the Community Land Model to represent the snow thermal conductivity. The authors find, somewhat unsurprisingly, that the lack of a process representation of depth hoar formation in CLM leads to much higher snowpack effective thermal conductivities than are observed at these sites. The paper includes a good discussion of paths forward, including introducing an adjustment factor and utilizing alternative snow thermal conductivity formulations. Overall, I think the authors did a thorough job responding to the comments of the prior reviewers. From my perspective this paper is suitable for publication with minor revisions suggested below.

1. I found typos throughout the revised manuscript. Many of them looked like they arose through accepting track changes wherein periods or other punctuation was lost. I suggest that the authors carefully review the manuscript to correct these typos.

2. The discussion of the snow model in CLM5 is reasonable, but there is no mention of fresh snow density. The formulation of fresh snow density was modified substantially with a new temperature relationship and the addition of wind effects. In other assessment of CLM, we found that these changes resulted in higher and more realistic bulk snow densities (van Kampenhout et al., 2017, Lawrence et al. 2019), though the lack of widespread availability of snow density data makes these assessments more qualitative than quantitative. In any case, the fresh snow density parameterization should be introduced as well, I think, for the sake of completeness in the description of the snow model. I don’t think the full set of equations need to be included, but a description would be helpful. The equations can be found here: https://escomp.github.io/ctsm-docs/versions/release-clm5.0/html/tech_note/Snow_Hydrology/CLM50_Tech_Note_Snow_Hydrology.html#ice-content

3. In a response to a reviewer, the authors note that they are exploring what could be done in terms of changes that would improve the model at the global scale. But, there is no mention of this in the text. As an interested reader, I was very curious about what approach could be taken at global scale, since this bias correction parameter would be difficult to define globally and the Sturm parameterization only really applies for tundra snowpacks. I think it would be helpful for a reader to elaborate just a bit more on the challenges here and to note explicitly that it is under investigation in an ongoing study.

4. If I am understanding correctly (I might not be, in which case the text needs to be modified to make this clearer), the same bias correction factor is applied to all the layers in the simulated snowpack. But, the main limitation of the model is that there is not representation of depth hoar that forms at the base of the snowpack. Would it be possible or advisable to apply a correction factor to just the deepest model snow layer to try to replicate this real world behavior? One reason I ask is that in a study that was just published, we found that winter daily temperature variability is substantially improved in CESM2, where improved is towards less temperature variability. We inferred in that study that this reduction in variability can be largely attributed to the higher snow densities and relatedly higher thermal conductivities that acted to reduce air temperature variability due to the larger effective surface heat sink. The results of your present study imply that the thermal conductivities in CLM5 are too high, thus possibly countering the result of our study on temperature variability. However, if the uppermost snow layers are still actually relatively dense and it is just the deepest snow layer that has low thermal conductivity, it’s possible that this result on temperature variability is still valid. Anyway, not sure that you need to address this comment much or at all in the manuscript, though it might be interesting as a point of reference to mention this paper (it’s another argument for why snow thermal conductivity matters, in addition to the soil respiration that you already mention). The paper is Simpson et al., 2022, JAMES, https://doi.org/10.1029/2021MS002880

5. The Figure 5 figure caption needs more info about what the colors mean. I was able to infer that they indicated the snow layers (nice way to look at the evolution of the snowpack layers in CLM!), but I guess some readers may not be able to recognize that immediately.

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ED: Reconsider after major revisions (further review by editor and referees) (13 Apr 2022) by Ketil Isaksen

AR by Victoria Dutch on behalf of the Authors (07 Jun 2022) Author's response Author's tracked changes Manuscript

ED: Publish subject to revisions (further review by editor and referees) (24 Jun 2022) by Ketil Isaksen

ED: Referee Nomination & Report Request started (02 Aug 2022) by Ketil Isaksen

RR by David Lawrence (08 Aug 2022)

Suggestions for revision or reasons for rejection

The authors have suitably addressed my comments. Clearly, representing snow thermal conductivity realistically in a global land model is a major challenge. The authors highlight CLM deficiencies and identify some potential pathways for improvement. I will admit that I was hoping for a set of recommended changes that were clearer, but I acknowledge that this isn't necessarily possible within the context of this study.

One point that I was drawn to as I read through the responses to reviewers and the revised manuscript is the discussion about which of the tested parameterizations was most appropriate to be considered for future versions of CLM, presuming that the challenge of global parameterization could be overcome. The results imply that the Sturm et al. (1997) parameterization provides the biggest improvement in soil temperatures compared to observations. However, Reviewer #3 believes that this parameterization is wrong. I definitely do not know enough to be able to comment on the relative merits of the various parameterizations, but this does give me pause. However, one thing that occurred to me is that the authors note that the simulated snow depth is biased low in the CLM simulations. This low bias makes it difficult to know what emphasis should be placed on the simulated soil temperature biases in the control and modified versions of the model. It may be beyond the scope of this study, but did the authors consider using this low snow depth bias to try to correct the snowfall rates at these sites so as to drive a more realistic seasonal and end of season snowpack depth. If the snowpack depth was represented more realistically, then the comparison of modeled and observed soil temperatures would be more meaningful. I wonder if the relative impact of the different tested parameterizations would be different under a low snow depth bias. If such a simulation is not easy to do, then I would at least suggest a better discussion of the potential interpretation error that the low snow depth bias could impart.

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RR by Florent Dominé (10 Aug 2022)

Suggestions for revision or reasons for rejection

I believe the Authors have in general reacted constructively to the reviews. The CLM approach to snow modeling is clearly not within my philosophy and I do not think it is on the right path to a predictive general snow model. I however accept the established fact that it is widely used by the community and that this scheme will not be replaced overnight. Modelers have to use what is available. The Authors do their best to adapt the existing tool to their topic and do so with a critical and I feel constructive attitude. Science does not progress by consensus, but rather by confronting different ideas. I am therefore perfectly fine with this research being published. Comments can always be made, but I think 4 reviews is plenty and I will not make any additional scientific comment. I wish the Authors the best of luck with their research.

I would nevertheless like to react to the Authors comment on my “patronising and dismissive language”. First of all, I apologize if what I wrote offended the Authors. This was not my intention, and please note that I signed my review. I seldom turn down review requests and often spend a lot of time writing detailed comments with, I admit, a tone that is not always the most diplomatic, but I do my best to make constructive and helpful comments while of course not always consensual. Making such detailed comments is a way to show respect and interest for the Authors’ work. In return, I do expect Authors to show respect for the Reviewers by submitting fine-tuned papers written with a great attention to detail. It is unfortunately too frequent that papers I review are rough drafts. “We’ll make corrections after the reviews” have I heard too many times from colleagues. No. My patience with this attitude has worn thin and I perhaps now overreact to this. I believe one of the important elements of communication Early Career Researchers must learn is attention to detail and respect for the Reviewer. This is probably patronising, I apologize again.

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ED: Publish subject to technical corrections (08 Sep 2022) by Ketil Isaksen

Dear Victoria Dutch and co-authors,

We have heard back from Reviewer #3 and #4 of your submission entitled “Impact of measured and simulated tundra snowpack properties on heat transfer”. The reviewer #3 recommendation is accepted as is and Reviewer #4 notes that “the authors have suitably addressed his comments” and has provided an upgraded recommendation of Minor Revision.

I am also appreciating Referee #3 apology. In my last inquiry directed to Referee 3 by email I addressed that the tone and language in parts of his review was not in line with a constructive and professional review.

In order not to delay the processing of your manuscript, I have examined your earlier revision and responses from the previous round of review. I find that your aggregate response is satisfactory, as you have addressed most of the reviewer’s concerns and followed most of the suggestions. Given these revisions and responses, together with the improvements you made in preparing the earlier revision, I am pleased to accept your paper for publication in The Cryosphere.

I leave it to you to consider if you in your final version would like to include some minor modifications in the discussion related to Referee #4 final comments regarding which of the tested parameterizations was most appropriate to be considered for future versions of CLM and discussion of the potential interpretation error that the low snow depth bias could impart.

Your paper will likely be of considerable interest to the scientific community. Accordingly, your work on the revisions should turn out to have been worth the effort.

You will be hearing further from the journal concerning next steps in the publication process. In the meantime, congratulations on the acceptance!

Sincerely,
Ketil Isaksen
Editor
The Cryosphere

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AR by Victoria Dutch on behalf of the Authors (16 Sep 2022) Author's response Manuscript

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Short summary

Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.