Sensitivity of Sea Ice Growth to Snow Properties in Opposing Regions of the Weddell Sea in Late Summer

Arndt, Stefanie

doi:10.1029/2022gl099653

Cited by 6 publications

(4 citation statements)

References 48 publications

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“…Density is currently used to parametrize thermal conductivity because it is a simple, low cost and quick measurement in the field (Orvig, 1970;Yen, 1981;Fukusako, 1990;Radionov et al, 1997;Sturm et al, 1997;Warren et al, 1999;Sturm et al, 2002;Domine et al, 2011;King et al, 2020;Arndt, 2022). However, we are now aware of shortfalls when excluding other necessary textural properties from thermal conductivity parametrizations.…”

Section: Introductionmentioning

confidence: 99%

Thermal Conductivity of Snow on Arctic Sea Ice

Macfarlane¹,

Löwe²,

Gimenes³

et al. 2023

Preprint

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Abstract. Snow significantly impacts the seasonal growth of Arctic sea ice due to its thermally insulating properties. Various measurements and parametrizations of thermal properties exist, but an assessment of the entire seasonal evolution of thermal conductivity and snow resistance is hitherto lacking. Using the comprehensive snow data set from the MOSAiC expedition, we have evaluated for the first time the seasonal evolution of the snow's thermal conductivity and thermal resistance on different ice ages (leads, first and second-year ice) and topographic features (ridges). Combining different measurement parametrizations and assessing the robustness against spatial variability, we infer and quantify a hitherto undocumented feature in the seasonal dynamics of snow on sea ice. We observe an increase in thermal conductivity up to March and a decrease thereafter, both on first-year and second-year ice before the melt period started. Since a similar non-monotonic behaviour is extracted for the snow depth, the thermal resistance of snow on level sea ice remains approximately constant with a value of 515 ± 404 m2 K W−1 on first-year ice and 660 ± 475m2 K W−1 on second-year ice. We found approximately three times higher thermal resistance on ridges (1411 ± 910 m2 K W−1). Our findings are that the micropenetrometer-derived thermal conductivities give accurate values, and confirm that spatial variability of the snow cover is vertically and horizontally large. The implications of our findings for Arctic sea ice are discussed.

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Section: Introductionmentioning

confidence: 99%

Thermal Conductivity of Snow on Arctic Sea Ice

Macfarlane¹,

Löwe²,

Gimenes³

et al. 2023

Preprint

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“…During austral autumn, the signal is stronger in the Weddell Sea than in the other regions. Therefore, we focus on the austral autumn (March‐April‐May) intraseasonal sea ice concentration variability over the Weddell Sea (60°W–0°, 60°S–70°S) where the heat and momentum exchange between the atmosphere and the sea ice is evident (Arndt, 2022; Jena et al., 2022).…”

Section: Resultsmentioning

confidence: 99%

Intraseasonal Sea Ice Concentration Variability Over the Weddell Sea During Austral Autumn

Song

Chen

et al. 2023

Geophysical Research Letters

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The sea ice concentration (SIC) over the Weddell Sea displays obvious intraseasonal variability during austral autumn with a dominant frequency of 5–20 days. The intraseasonal SIC variability is manifested as development of sea ice anomalies from the northeastern Antarctic Peninsula toward the central Weddell Sea. Rossby wave train associated with the internal atmospheric variability featuring circumglobal zonal wavenumber 4 pattern induces the development of anomalous surface winds and sea ice drifting, leading to the SIC anomalies directly. The thermodynamical processes associated with the intraseasonal SIC variability include the air temperature, the moisturizing of the lower troposphere, the latent and sensible heating, and the downwelling longwave radiation, which contribute to the variation of the local greenhouse effect over the Weddell Sea, and thus lead to the sea ice variation. The investigation of the intraseasonal SIC variability helps the understanding of sea‐ice‐atmosphere interaction over the Antarctic region.

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“…Although Snow Buoys have revealed some surface melting of the summer snowpack, only a few buoys in the marginal ice zone experienced strong surface ablation, and complete melt or evaporation of the snow cover was not observed (Nicolaus et al., 2021). However superimposed ice resulting from the trickling down and refreezing of melt water in the snowpack of perennial ice has been widely reported in summer (Arndt, 2022; Haas et al., 2001; Massom et al., 2001), resulting in a reduction of the snow cover. As discussed in Section 2.3, the blowing snow loss term, since its magnitude is calibrated to the in‐situ data, will also incorporate snow loss due to other factors like snow melt.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, dual-frequency satellite altimetry has been used to derive snow depths on sea ice in both polar oceans (Garnier et al, 2021;Guerreiro et al, 2016;Kacimi & Kwok, 2020, 2022Lawrence et al, 2018). While the approach shows promise, there are challenges in determining the depth of radar penetration into the snowpack and the ice freeboard, and in achieving compatibility between measurements acquired from sensors with markedly different spatial sampling (Fons et al, 2021;Giles et al, 2008;Willatt et al, 2010).…”

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confidence: 99%

A Simulation of Snow on Antarctic Sea Ice Based on Satellite Data and Climate Reanalyses

Lawrence,

Ridout,

Shepherd

et al. 2023

JGR Oceans

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Although snow plays an important role in the energy and mass balance of sea ice, it is little studied in the Southern Ocean. We present a Lagrangian model of snow on sea ice, CASSIS, that simulates the daily creation and drift of floes. Drifting floes accumulate snow from the atmosphere and the Antarctic ice sheet, and lose snow to the ocean and snow‐ice formation. The depth of snow on Southern Ocean sea ice increases in all sectors between autumn and spring 1981–2021, reaching 40 cm in much of the Weddell Sea, coastal Amundsen Sea and south east Indian Ocean. The root mean square difference between seasonally‐averaged model and ship‐based snow depths is 13.1 cm, and between modeled and airborne snow depths from Operation IceBridge is 13.5 cm. Our model offers an alternative long‐term snow depth record to that from passive microwave (PM) radiometry, which does not capture the seasonal growth of the snow cover. We find that although the average circumpolar snow layer thickness has increased by 16 mm between 1981 and 2021 (P = 0.004), there has been a decrease of 13 mm in the Southern Pacific Ocean (P = 0.133, but significant in spring and autumn), driven by a reduction of summer sea ice extent in this region. Our model paves the way for improved satellite‐based estimates of Antarctic sea ice thickness.

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Sensitivity of Sea Ice Growth to Snow Properties in Opposing Regions of the Weddell Sea in Late Summer

Cited by 6 publications

References 48 publications

Thermal Conductivity of Snow on Arctic Sea Ice

Thermal Conductivity of Snow on Arctic Sea Ice

Intraseasonal Sea Ice Concentration Variability Over the Weddell Sea During Austral Autumn

A Simulation of Snow on Antarctic Sea Ice Based on Satellite Data and Climate Reanalyses

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