Combining observational data and numerical models to obtain a seamless high-temporal-resolution seasonal cycle of snow and ice mass balance at the MOSAiC Central Observatory

Itkin, Polona; Liston, Glen E.

doi:https://doi.org/10.5194/tc-19-5111-2025

Articles | Volume 19, issue 10

https://doi.org/10.5194/tc-19-5111-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-19-5111-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 10

Research article

|

27 Oct 2025

Research article |

| 27 Oct 2025

Combining observational data and numerical models to obtain a seamless high-temporal-resolution seasonal cycle of snow and ice mass balance at the MOSAiC Central Observatory

Polona Itkin and Glen E. Liston

Data sets

Combining observational data and numerical simulations for seasonal cycle of snow and ice mass balance at MOSAiC: scripts, input datasets and model output P. Itkin and G. Liston https://doi.org/10.5281/zenodo.15089579

Model code and software

NCAR/Parallel-SnowModel: Parallel-SnowModel R. Mower https://doi.org/10.5281/zenodo.11168392

Short summary

A sea ice drifting observatory in 2019 and 2020 provided a year of observations of Arctic snow and sea ice, though some data were interrupted, especially during summer melt onset. We developed a data–model fusion system to produce continuous, high-resolution time series of snow and sea ice parameters. On all three analyzed ice types, snow redistribution correlated with sea ice deformation, and level ice thickness was governed by the thinnest fraction of snow cover.