Sea ice and snow six years after MOSAiC: bringing data to numerical models

MOSAiC expedition in 2019/2020 collected an unprecedented volume of sea ice and snow data. For example, over 50 autonomous drifters, more than 160 km of sea ice thickness transect lines, nearly 250 snow pits, hundreds of satellite images, and thousands of ship radar images. Studies based on these data have yielded, among others, new data-based estimates of snow thermal conductivity, discovered new freezing mechanisms in the ridges and pinned the limits of sea ice deformation scale invariance. MOSAiC data analyses show that the snow depth and ice thickness was largely ice-age independent and that most of the winter’s snow was trapped in the deformed ice and that the loss of snow to leads was small.

Numerous sea ice deformation events formed the rough topography that trapped the snow throughout the winter. In order to better understand these processes and interactions, drifter, satellite remote sensing, and ship radar data can be used to analyse the deformation processes themselves, including reactivation of the leads and pressure ridges. These dynamics can not be represented in continuum models due to the spatial scale discrepancy. Here we will present how the sea ice deformation data from remote sensing can be used to create ice topography to constrain the snow distribution processes and, with it, the ice growth. We will use the recently developed methodology of data-model fusion by Itkin and Liston (2025, The Cryophere) to quantify the local snow-ice processes on spatial extents relevant also to the continuum models.