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August 17, 2025The cryosphereOpen Access

Investigating the impact of reanalysis snow input on an observationally calibrated snow-on-sea-ice reconstruction

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The calibration approach reconciles snow depths across models, yet leads to discrepancies in snow densities due to parameter sensitivity and uncertainty.
JRA-55 exhibits the largest deviation from prior values during calibration, indicating substantial uncertainty in its snowfall estimates.
Using a Markov chain Monte Carlo method, the study calibrates parameters in the NESOSIM model with various reanalysis inputs for improved accuracy.
Trends in snow depth differ regionally, underscoring the significance of choice in reanalysis input for estimates in the central Arctic Ocean.

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Analysis demonstrates snow depth reconciliation in Arctic models, highlighting sensitivity to input choice.

Cabaj et al. (2025) studied this question.

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