the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of measured and simulated tundra snowpack properties on heat transfer
- 1Department of Geography and Environmental Sciences, Northumbria University, Newcastle upon Tyne, UK
- 2Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
- 3Cold Regions Research Centre, Wilfrid Laurier University, Waterloo, Canada
- 4Département de géographie, Université de Montréal, Canada
- 5School of Geosciences, University of Edinburgh, UK
- 6Department of Geography and Environmental Management, University of Waterloo, Canada
- 1Department of Geography and Environmental Sciences, Northumbria University, Newcastle upon Tyne, UK
- 2Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
- 3Cold Regions Research Centre, Wilfrid Laurier University, Waterloo, Canada
- 4Département de géographie, Université de Montréal, Canada
- 5School of Geosciences, University of Edinburgh, UK
- 6Department of Geography and Environmental Management, University of Waterloo, Canada
Abstract. Snowpack microstructure controls the transfer of heat to, and the temperature of, the underlying soils. In situ measurements of snow and soil properties from four field campaigns during two different winters (March and November 2018, January and March 2019) were compared to an ensemble of CLM5.0 (Community Land Model) simulations, at Trail Valley Creek, Northwest Territories, Canada. Snow MicroPenetrometer profiles allowed snowpack density and thermal conductivity to be derived at higher vertical resolution (1.25 mm) and a larger sample size (n = 1050) compared to traditional snowpit observations (3 cm vertical resolution; n = 115). Comparing measurements with simulations shows CLM overestimated snow thermal conductivity by a factor of 3, leading to a cold bias in wintertime soil temperatures (RMSE = 5.8 °C). Bias-correction of the simulated thermal conductivity (relative to field measurements) improved simulated soil temperatures (RMSE = 2.1 °C). Multiple linear regression shows the required correction factor is strongly related to snow depth (R2 = 0.77, RMSE = 0.066) particularly early in the winter. Furthermore, CLM simulations did not adequately represent the observed high proportions of depth hoar. Addressing uncertainty in simulated snow properties and the corresponding heat flux is important, as wintertime soil temperatures act as a control on subnivean soil respiration, and hence impact Arctic winter carbon fluxes and budgets.
Victoria R. Dutch et al.
Status: final response (author comments only)
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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
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AC1: 'Reply to both reviewer comments', Victoria Dutch, 16 Dec 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
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AC1: 'Reply to both reviewer comments', Victoria Dutch, 16 Dec 2021
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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 checkP4 L124: or less WERE filled (word missing)P4 L 156 : ) missingP5 L 171 : AlongerP5 L 200 : please add the number of the AppendixP6 L 244: ofdepthP7 L 279 : "0.55 ≤ α ≤ 0.3" seems the wrong orderP8 L 315 : Required -> requiredP10 L 398: show-> showsP11 L 415 : A point is missing after A1.P11 L 421: the sentence contains two "is"- AC1: 'Reply to both reviewer comments', Victoria Dutch, 16 Dec 2021
Victoria R. Dutch et al.
Victoria R. Dutch et al.
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