the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatiotemporal variation in the specific surface area of surface snow measured along the traverse route from the coast to Dome Fuji, Antarctica
Abstract. To better understand the surface properties of the Antarctic ice sheet, we measured the specific surface area (SSA) of surface snow during two round-trip traverses between a coastal base near Syowa Station, located 15 km inland from the nearest coast, and Dome Fuji, located 1066 km inland, in East Antarctica from November 2021 to January 2022. Using a handheld integrating sphere snow grain sizer (HISSGraS), which directly measures snow surface without sampling, we collected 215 sets of SSA data, each set comprising measurements from 10 surfaces along a 20 m transect. The measured SSA shows no elevation or temperature dependence between 15 and 500 km from the coast (elevation: 615–3000 m), with a mean and standard deviation of 25 ± 9 m2 kg−1. Beyond this range, SSA increases toward the interior, reaching 45 ± 11 m2 kg−1 between 800 and 1066 km from the coast (3600–3800 m). SSA shows significant variability depending on surface morphologies and short-term meteorological events. For example, (i) glazed surfaces formed by an accumulation hiatus in katabatic wind areas show low SSA (19 ± 4 m2 kg−1), decreasing the mean SSA and increasing SSA variability. (ii) Freshly deposited snow shows high SSA (60–110 m2 kg−1), but the snow deposition is inhibited by snow drifting at wind speeds above 5 m s−1. Our analyses clarified that temperature-dependent snow metamorphism, snowfall frequency, and wind-driven inhibition of snow deposition play crucial roles in the spatial variation of surface snow SSA in the Antarctic inland. The extensive dataset will enable the validation of satellite-derived and model-simulated SSA variations across Antarctica.
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RC1: 'Comment on egusphere-2024-769', Martin Schneebeli, 24 Apr 2024
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The authors describe and discuss detailed measurements of surface snow SSA in East Antarctica. The methods are described in detail and reproducible. This dataset may serve as ground truth validation for optical remote sensing. The sensor measures the surface (a few millimetres of the snowpack). The repeat measurements on the 1000 km traverses with very high spatial resolution illustrate the complexity of the snow surface in Antarctica.
The surface reflectivity is certainly the most important factor in albedo calculations. However, the very small penetration depth of light at 1320 nm into snow is also a limitation in the albedo calculation, as the visible and short near-infrared wavelength backscattering also occurs deeper into the snowpack. This should be clarified in more detail in this paper and discussed.
I was also missing measurements of any snow impurities. Known to be small, they may not be negligible, especially in the coastal region, and could contribute to an altered albedo.
The manuscript is a very important contribution to a better understanding of the antarctic snowpack and its interaction with short-wave radiation.
For directional reflectance calculations, the snow particle shape is important. Robledano et al. 2023 demonstrate that snow grain shape is an important factor in BRDF-calculations, which are necessary for satellite albedo retrievals. Could the author also record the snow particle shape together with their instrumental observations?
The authors mention the potential of these measurements to be used for satellite remote sensing validation. However, there are no temporally coincident optical satellite passes mentioned, so will the large spatial and temporal variability of the surface snow SSA allow later for a direct comparison?
The authors nicely illustrate the observed trends and spatial variability in Fig. 7. In table 2, it would be interesting to see if the differences between the Regions are significant.
There is always a temperature gradient in the snowpack in Antarctica, so isothermal metamorphism is not relevant. The reviewer suggests that more weight is put on temperature gradients and not absolute temperature.
Title: "Spatiotemporal" implicitly suggests that the measurements are during an entire year. I suggest as title "Spatial variation ... during austral summer"
25: "enable the validation ..." this is not shown in greater detail in the paper
43 ff: The isothermal case does not exist in Antarctica, and can be deleted here (or then the newer papers by Kaempfer et al, Calonne et al must be cited and commented.
54: the sentence "In addition ..." is not relevant in the context of this paper
75: ASSSAP was mainly used in Antarctica: I suggest to delete "alpine"
176: "shares the same measurement principle ..." replace by " shares a similar measurement principle ..."
203: "... with the accurate ..." there is quite a large measurement uncertainty by the methane absorption method, give precision in text
249: You could mention here that the measurements are during austral summer, and not spring, autumn and winter
323: meteorological events are by definition short-term. Delete "short-term".
431: I think it's interesting that glazed surfaces do not always lead to a very high variability?
453: the magnitude and frequency of temperature gradients are much more important than absolute temperature
599: "under high pressure." there could also be clear days without high atmospheric pressure.
Robledano, A., Picard, G., Dumont, M., Flin, F., Arnaud, L., and Libois, Q.: Unraveling the optical shape of snow, Nat Commun, 14, 3955, https://doi.org/10.1038/s41467-023-39671-3, 2023.
Citation: https://doi.org/10.5194/egusphere-2024-769-RC1
Data sets
Specific surface area of surface snow along a traverse route between a coastal base near Syowa Station and Dome Fuji in East Antarctica from November 2021 to January 2022 Ryo Inoue, Teruo Aoki, Shuji Fujita, Shun Tsutaki, Hideaki Motoyama, Fumio Nakazawa, and Kenji Kawamura https://ads.nipr.ac.jp/dataset/A20240308-001
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