Preprints
https://doi.org/10.5194/egusphere-2026-1440
https://doi.org/10.5194/egusphere-2026-1440
18 Mar 2026
 | 18 Mar 2026
Status: this preprint is open for discussion and under review for The Cryosphere (TC).

Estimation of sea ice air-bubble and brine pocket distribution for scattering and emission model parametrization

Rasmus Tage Tonboe, Vishnu Nandan, Marcus Huntemann, Julienne Stroeve, Randall Scharien, John Yackel, Lars Kaleschke, Hoyeon Shi, and Tânia Casal

Abstract. Scattering and absorption from air bubbles, voids, and brine pockets significantly affect radar and microwave radiometer measurements of sea ice and light propagation through sea ice. However, there are only a limited number of in situ measurements of the size of natural sea ice inclusion and its distribution. Here, we used a high-resolution data set of 90-mm wide and 1-mm thin ice slices of various types of Arctic sea ice to estimate the autocorrelation length and density of the inclusions. The data set was collected during the MOSAiC International Arctic Drift Expedition 2019–20 during the winter months of January and February 2020. Thin ice slices from new ice, first-year ice, level second-year ice, second-year hummocks, and a refrozen melt pond were collected and analyzed. The 50-percentile autocorrelation lengths derived (Lobs), a measure of the size and distribution of inclusions, for new ice and brine ice brine pockets of the first-year have mean values between 0.11 and 0.30 mm and a vertical anisotropy ratio (Lz) and horizontal (Lx) of 1.7–3.0 with respect to the ice surface. The exponential model was fitted to the observed autocorrelation function with a delay between 0–2 Lobs. The exponential correlation lengths derived (Lexp) correspond to the 50 percentile Lobs for the ordinary (horizontal) image samples (Lexpx) and the transposed (vertical) images (Lexpz). For new and first-year ice with varying salinity and brine pocket image density, the autocorrelation length is a very robust measure of the size and distribution of brine pockets. For new ice, we find a Lobsx or Lexpx of 0.18 mm and with a Lz / Lx anisotropy of 2 and for first-year ice, we find a Lobs or Lexp of 0.16 mm with a Lz / Lx anisotropy of 3. The samples from the hummock and the refrozen melt pond had 50 percentile Lobsx and Lobsz air bubble autocorrelation lengths varying in the range [0.22, 0.73] mm and [0.22, 0.74] mm and [0.17, 0.56] mm and [0.17, 0.79] mm, respectively. The very consistent Lobs for new and first-year ice can be used to constrain sea ice microwave emission and scattering models.

Competing interests: My co-authors John Yackel, Vishnu Nandan and Lars Kaleschke are members of “The Cryosphere” editorial board.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Rasmus Tage Tonboe, Vishnu Nandan, Marcus Huntemann, Julienne Stroeve, Randall Scharien, John Yackel, Lars Kaleschke, Hoyeon Shi, and Tânia Casal

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Rasmus Tage Tonboe, Vishnu Nandan, Marcus Huntemann, Julienne Stroeve, Randall Scharien, John Yackel, Lars Kaleschke, Hoyeon Shi, and Tânia Casal
Rasmus Tage Tonboe, Vishnu Nandan, Marcus Huntemann, Julienne Stroeve, Randall Scharien, John Yackel, Lars Kaleschke, Hoyeon Shi, and Tânia Casal

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Short summary
Scattering and absorption from air bubbles, voids, and brine pockets significantly affect radar and microwave radiometer measurements of sea ice and light propagation through sea ice. Here, we used a high-resolution dataset, collected during the 2019–20 MOSAiC expedition, of thin ice slices of various Arctic sea ice types to estimate the autocorrelation length and density of the inclusions. The data can be used to initialize sea ice scattering models and for understanding satellite data.
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