The thermal conductivity of seasonal snow

Abstract Twenty-seven studies on the thermal conductivity of snow ( K eff ) have been published since 1886. Combined, they comprise 354 values of K eff , and have been used to derive over 13 regression equation and predicting K eff vs density. Due to large (and largely undocumented) differences in m...

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Published in:Journal of Glaciology
Main Authors: Sturm, Matthew, Holmgren, Jon, König, Max, Morris, Kim
Format: Article in Journal/Newspaper
Language:English
Published: Cambridge University Press (CUP) 1997
Subjects:
Online Access:http://dx.doi.org/10.1017/s0022143000002781
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143000002781
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spelling crcambridgeupr:10.1017/s0022143000002781 2024-09-15T18:15:39+00:00 The thermal conductivity of seasonal snow Sturm, Matthew Holmgren, Jon König, Max Morris, Kim 1997 http://dx.doi.org/10.1017/s0022143000002781 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143000002781 en eng Cambridge University Press (CUP) Journal of Glaciology volume 43, issue 143, page 26-41 ISSN 0022-1430 1727-5652 journal-article 1997 crcambridgeupr https://doi.org/10.1017/s0022143000002781 2024-08-28T04:03:44Z Abstract Twenty-seven studies on the thermal conductivity of snow ( K eff ) have been published since 1886. Combined, they comprise 354 values of K eff , and have been used to derive over 13 regression equation and predicting K eff vs density. Due to large (and largely undocumented) differences in measurement methods and accuracy, sample temperature and snow type, it is not possible to know what part of the variability in this data set is the result of snow microstructure. We present a new data set containing 488 measurements for which the temperature, type and measurement accuracy are known. A quadratic equation, where ρ is in g cm −3 , and K eff is in W m −1 K −1 , can be fit to the new data ( R 2 = 0.79). A logarithmic expression, can also be used. The first regression is better when estimating values beyond the limits of the data; the second when estimating values for low-density snow. Within the data set, snow types resulting from kinetic growth show density-independent behavior. Rounded-grain and wind-blown snow show strong density dependence. The new data set has a higher mean value of density but a lower mean value of thermal conductivity than the old set. This shift is attributed to differences in snow types and sample temperatures in the sets. Using both data sets, we show that there are well-defined limits to the geometric configurations that natural seasonal snow can take. Article in Journal/Newspaper Journal of Glaciology Cambridge University Press Journal of Glaciology 43 143 26 41
institution Open Polar
collection Cambridge University Press
op_collection_id crcambridgeupr
language English
description Abstract Twenty-seven studies on the thermal conductivity of snow ( K eff ) have been published since 1886. Combined, they comprise 354 values of K eff , and have been used to derive over 13 regression equation and predicting K eff vs density. Due to large (and largely undocumented) differences in measurement methods and accuracy, sample temperature and snow type, it is not possible to know what part of the variability in this data set is the result of snow microstructure. We present a new data set containing 488 measurements for which the temperature, type and measurement accuracy are known. A quadratic equation, where ρ is in g cm −3 , and K eff is in W m −1 K −1 , can be fit to the new data ( R 2 = 0.79). A logarithmic expression, can also be used. The first regression is better when estimating values beyond the limits of the data; the second when estimating values for low-density snow. Within the data set, snow types resulting from kinetic growth show density-independent behavior. Rounded-grain and wind-blown snow show strong density dependence. The new data set has a higher mean value of density but a lower mean value of thermal conductivity than the old set. This shift is attributed to differences in snow types and sample temperatures in the sets. Using both data sets, we show that there are well-defined limits to the geometric configurations that natural seasonal snow can take.
format Article in Journal/Newspaper
author Sturm, Matthew
Holmgren, Jon
König, Max
Morris, Kim
spellingShingle Sturm, Matthew
Holmgren, Jon
König, Max
Morris, Kim
The thermal conductivity of seasonal snow
author_facet Sturm, Matthew
Holmgren, Jon
König, Max
Morris, Kim
author_sort Sturm, Matthew
title The thermal conductivity of seasonal snow
title_short The thermal conductivity of seasonal snow
title_full The thermal conductivity of seasonal snow
title_fullStr The thermal conductivity of seasonal snow
title_full_unstemmed The thermal conductivity of seasonal snow
title_sort thermal conductivity of seasonal snow
publisher Cambridge University Press (CUP)
publishDate 1997
url http://dx.doi.org/10.1017/s0022143000002781
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143000002781
genre Journal of Glaciology
genre_facet Journal of Glaciology
op_source Journal of Glaciology
volume 43, issue 143, page 26-41
ISSN 0022-1430 1727-5652
op_doi https://doi.org/10.1017/s0022143000002781
container_title Journal of Glaciology
container_volume 43
container_issue 143
container_start_page 26
op_container_end_page 41
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