Satellite Remote Sensing of Snow Depth on Antarctic Sea Ice: An Inter-Comparison of Two Empirical Approaches

Kern, Stefan; Özsoy, Burcu

doi:10.3390/rs8060450

Cited by 27 publications

(42 citation statements)

References 39 publications

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“…For these, snow depth is underestimated for deformed sea ice and potentially also for flooded sea ice, i.e., regions of negative sea-ice freeboard. For the snow-depth data set used in this paper, the winter-to-spring snow-depth evolution also does not seem to be correct [34]. A snow depth which is biased low explains the observed overestimation of sea-ice thickness, particularly for spring, and also the observed too large winter-to-spring increase in modal sea-ice thickness.…”

Section: Discussionmentioning

confidence: 77%

“…This seems to be confirmed by the fact that these two approaches provide the largest average modal and mean sea-ice thickness during winter and spring (Table 5). A recent paper by Kern and Ozsoy-Cicek [34] illustrates that AMSR-E snow depth is very likely underestimating actual snow depth especially during spring and is also underestimating the increase in snow depth from winter to spring. In particular, the unrealistically large increase in modal sea-ice thickness between winter and spring derived with SICCI could be explained by these biases in snow depth.…”

Section: Potential Biases Due To Inaccurate Snow Depthmentioning

confidence: 99%

“…The main driver for that assumption was to avoid inclusion of snow-depth information into the sea-ice thickness retrieval. Snow depth on Antarctic sea ice is complex, difficult to obtain, and seems to be unbiased only when retrieved from satellite observations over undeformed seasonal sea ice (see e.g., [34]). …”

Section: Introductionmentioning

confidence: 99%

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Antarctic Sea-Ice Thickness Retrieval from ICESat: Inter-Comparison of Different Approaches

2016

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Accurate circum-Antarctic sea-ice thickness is urgently required to better understand the different sea-ice cover evolution in both polar regions. Satellite radar and laser altimetry are currently the most promising tools for sea-ice thickness retrieval. We present qualitative inter-comparisons of winter and spring circum-Antarctic sea-ice thickness computed with different approaches from Ice Cloud and land Elevation Satellite (ICESat) laser altimeter total (sea ice plus snow) freeboard estimates. We find that approach A, which assumes total freeboard equals snow depth, and approach B, which uses empirical linear relationships between freeboard and thickness, provide the lowest sea-ice thickness and the smallest winter-to-spring increase in seasonal average modal and mean sea-ice thickness: A: 0.0 m and 0.04 m, B: 0.17 and 0.16 m, respectively. Approach C uses contemporary snow depth from satellite microwave radiometry, and we derive comparably large sea-ice thickness. Here we observe an unrealistically large winter-to-spring increase in seasonal average modal and mean sea-ice thickness of 0.68 m and 0.65 m, respectively, which we attribute to biases in the snow depth. We present a conceptually new approach D. It assumes that the two-layer system (sea ice, snow) can be represented by one layer. This layer has a modified density, which takes into account the influence of the snow on sea-ice buoyancy. With approach D we obtain thickness values and a winter-to-spring increase in average modal and mean sea-ice thickness of 0.17 m and 0.23 m, respectively, which lay between those of approaches B and C. We discuss retrieval uncertainty, systematic uncertainty sources, and the impact of grid resolution. We find that sea-ice thickness obtained with approaches C and D agrees best with independent sea-ice thickness information-if we take into account the potential bias of in situ and ship-based observations.

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Section: Discussionmentioning

confidence: 77%

Section: Potential Biases Due To Inaccurate Snow Depthmentioning

confidence: 99%

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Antarctic Sea-Ice Thickness Retrieval from ICESat: Inter-Comparison of Different Approaches

2016

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“…Considering the topmost part of the snow regimes only, the differences between FYS and MYS increase for the mean values of the snow properties, while the SD significantly decrease, especially for the MYS. In this regard, it is expected that the variability of snow properties in the basal snow layer, although the snow is dry, might have a profound impact on microwave emissivity and might well impact snow depth retrieval using satellite microwave radiometry (Kern & Ozsoy‐Çiçek, ; Markus & Cavalieri, ). Our results highlight therefore the importance to distinguish between FYS and MYS, while an additional subdivision of either snow types is not necessary for this application.…”

Section: Discussionmentioning

confidence: 99%

Variability of Winter Snow Properties on Different Spatial Scales in the Weddell Sea

Arndt

Paul

2018

JGR Oceans

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The snow cover on Antarctic sea ice persists during most of the year, contributing significantly to the sea ice mass budget due to comprehensive seasonal transition processes within the snowpack as well as at the snow/ice interface. Consequently, snow on sea ice varies not only in depth but also in particular in its physical characteristics such as snow density and stratigraphy. In order to quantify the heterogeneous nature of the Antarctic snowpack on different spatial scales, that is, small (<10 m), floe‐size (1‐2 km), and regional (seasonal/perennial ice) scales, we present here a case study of snow analyses in the Weddell Sea in austral winter 2013. The resulting high variability of snow parameters in the basal snow layer reveals the need to distinguish between seasonal and perennial ice regimes, when retrieving, for example, snow depth using satellite microwave radiometry. Considering the full vertical snow column, a more detailed distinction of the perennial sea ice regime into, for example, more ice classes is suggested in order to represent the high variability range. For the internal snowpack variability, however, we identify the grain size variability as the main driver, while snow density variations can be neglected. Moving from regional to floe‐size scales, a similar variability range of the studied snow properties is found, suggesting that a large number of snow samples on a few floes is more crucial than covering a large region with fewer floe‐scale measurements. The spatiotemporally heterogeneous variability in snow accumulation, redistribution, and metamorphism is, however, too large to upscale the given findings beyond regional scale.

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“…The annual cycle of processes at the snow/ice interface, as e.g., surface flooding, snow‐ice formation, or superimposed ice formation, can be derived from radar backscatter data [ Haas , ], since comprehensive field observations are not feasible. Recent studies on snow depth and ice thickness observations from radar and passive microwave sensors allow for an additional Antarctic‐wide estimation of sea‐ice freeboard and related quantification of sea‐ice surface flooding [ Kern and Ozsoy‐Çiçek , ; Kern et al ., ]. In contrast, the distinct seasonal cycle of Arctic surface properties and more homogeneous vertical snowpack properties allow the parameterization of Arctic‐wide light transmittance during all seasons [ Arndt and Nicolaus , ].…”

Section: Discussionmentioning

confidence: 99%

Influence of snow depth and surface flooding on light transmission through Antarctic pack ice

et al. 2017

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Snow on sea ice alters the properties of the underlying ice cover as well as associated physical and biological processes at the interfaces between atmosphere, sea ice, and ocean. The Antarctic snow cover persists during most of the year and contributes significantly to the sea‐ice mass due to the widespread surface flooding and related snow‐ice formation. Snow also enhances the sea‐ice surface reflectivity of incoming shortwave radiation and determines therefore the amount of light being reflected, absorbed, and transmitted to the upper ocean. Here, we present results of a case study of spectral solar radiation measurements under Antarctic pack ice with an instrumented Remotely Operated Vehicle in the Weddell Sea in 2013. In order to identify the key variables controlling the spatial distribution of the under‐ice light regime, we exploit under‐ice optical measurements in combination with simultaneous characterization of surface properties, such as sea‐ice thickness and snow depth. Our results reveal that the distribution of flooded and nonflooded sea‐ice areas dominates the spatial scales of under‐ice light variability for areas smaller than 100 m‐by‐100 m. However, the heterogeneous and highly metamorphous snow on Antarctic pack ice obscures a direct correlation between the under‐ice light field and snow depth. Compared to the Arctic, light levels under Antarctic pack ice are extremely low during spring (<0.1%). This is mostly a result of the distinctly different dominant sea ice and snow properties with seasonal snow cover (including strong surface melt and summer melt ponds) in the Arctic and a year‐round snow cover and widespread surface flooding in the Southern Ocean.

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Satellite Remote Sensing of Snow Depth on Antarctic Sea Ice: An Inter-Comparison of Two Empirical Approaches

Cited by 27 publications

References 39 publications

Antarctic Sea-Ice Thickness Retrieval from ICESat: Inter-Comparison of Different Approaches

Antarctic Sea-Ice Thickness Retrieval from ICESat: Inter-Comparison of Different Approaches

Variability of Winter Snow Properties on Different Spatial Scales in the Weddell Sea

Influence of snow depth and surface flooding on light transmission through Antarctic pack ice

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