Articles | Volume 19, issue 8
https://doi.org/10.5194/tc-19-3033-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-3033-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Aug 2025
Research article |
| 13 Aug 2025
| 13 Aug 2025
Investigating the impact of reanalysis snow input on an observationally calibrated snow-on-sea-ice reconstruction
Climate Research Division, Environment and Climate Change Canada, Toronto, ON M3H 5T4, Canada
Paul J. Kushner
Department of Physics, University of Toronto, Toronto, ON M5S 1A7, Canada
Alek A. Petty
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
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Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal discrepancies among the eight snow depth products are analyzed together with their seasonal variability and long-term trends. These snow products are further compared against various ground-truth observations. More analyses on representation error of sea ice parameters are needed for systematic comparison and fusion of airborne, in situ and remote sensing observations.
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Short summary
The output of snow-on-sea-ice models is influenced by the choice of snowfall input used. We ran such a model with different snowfall inputs and calibrated it to observations, produced a new calibrated snow product, and regionally compared the model outputs to outputs from another snow-on-sea-ice model. The two models agree best on the seasonal cycle of snow in the central Arctic Ocean. Observational comparisons highlight ongoing challenges in estimating the depth and density of snow on Arctic sea ice.
The output of snow-on-sea-ice models is influenced by the choice of snowfall input used. We ran...
