Thickness of the Seasonal Deposits at the Martian North Polar Region From Shadow Variations of Fallen Ice Blocks

Abstract

The seasonal deposition and sublimation of CO2 constitute a major element in the Martian volatile cycle. Here, we propose to use the shadow variations of the ice blocks at the foot of the steep scarps of the North Polar Layered Deposits (NPLD) to infer the vertical evolution of the seasonal deposits. We conduct an experiment at a steep scarp centered at (85.0°N, 151.5°E). We assume that no snowfall remains on top of the selected ice blocks, the frost ice layer is homogeneous around the ice blocks and their surroundings, and no significant moating is present. We show that the average thickness of the seasonal deposits due to snowfalls in Mars Year 31 is 0.97 ± 0.13 m at Ls = 350.7° in late winter. The large depth measured makes us wonder if snowfalls are more frequent and violent than previously thought. Meanwhile, we show that the average frost thickness in Mars Year 31 reaches 0.64 ± 0.18 m at Ls = 350.7° in late winter. Combined, the total thickness of the seasonal cover in Mars Year 31 reaches 1.63 ± 0.22 m at Ls = 350.7° in late winter, continuously decreases to 0.45 ± 0.06 m at Ls = 42.8° in middle spring and 0.06 ± 0.05 m at Ls = 69.6° in late spring. These estimates are up to 0.8 m lower than the existing Mars Orbiter Laser Altimeter results during the spring. Meanwhile, we observe that snow in the very early spring of Mars Year 36 can be 0.36 ± 0.13 m thicker than that in Mars Year 31. This study demonstrates the dynamics of the Martian climate and emphasizes the importance of its long‐term monitoring.

Evaluating the Use of Seasonal Surface Displacements and Time‐Variable Gravity to Constrain the Interior of Mars

“…Numerous processes can cause this bias and a discussion of how best these biases have been treated in height variation measurements can be found in . Thus, any prospects, as of the writing of this paper, in resolving the surface displacements using MOLA altimetry are unrealistic, although there is still ongoing research into increasing this precision (Xiao et al, 2024). There are other prospects, however, in using future mission data to detect these displacements (see discussion in Section 4.3).…”