Preprints
https://doi.org/10.5194/egusphere-2024-3402
https://doi.org/10.5194/egusphere-2024-3402
05 Dec 2024
 | 05 Dec 2024
Status: this preprint is open for discussion.

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

Abstract. Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) observations span an entire annual cycle of Arctic snow and sea ice cover. However, the measurements of atmospheric and ocean forcing, as well as distributed measurements of snow and ice properties were occasionally interrupted for logistical reasons. The most prolonged interruption happened during onset of the summer melt season. Here we introduce and apply a novel data-model fusion system that can assimilate relevant observational data in a collection of modeling tools (SnowModel-LG and HIGHTSI) to provide continuous high temporal resolution (3-hourly) time series of snow and sea ice parameters over the entire annual cycle. We used this system to analyze differences between the three main ice types found in the MOSAiC Central Observatory: relatively deformed second year ice, second year ice with extensive smooth refrozen melt pond surfaces, and first year ice. Since SnowModel-LG and HIGHTSI were used in a 1-D configuration, we used a sea ice dynamics term D to parameterize the redistribution of snow to newly created ridges and leads. D correlated highly with the sea ice deformation (R2=59 %, N=33) in the vicinity of the observatory and was at times as high as 10 % of all winter snowfall. In addition, we show, in separate simulations for level ice, that snow bedforms with thin snow in the bedform troughs largely control the ice growth. Here, mean snow depth minus one standard deviation was required to simulate realistic sea ice thickness using HIGHTSI; we surmise that this is accounting for the control of relatively thin snow on local ice growth. Despite different initial sea ice thickness and freeze-up dates, sea ice thickness of level ice across all ice types became similar by early winter. Our simulations suggest that the mean (spatially distributed) MOSAiC snow melt onset began in late May, but was interrupted by a snowfall event and was delayed by 3 weeks until mid June. The level ice started to melt in the last week of June. Depending on the sea ice topography, the ice was snow-free by late June and early July.

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Polona Itkin and Glen E. Liston

Status: open (until 16 Jan 2025)

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Polona Itkin and Glen E. Liston
Polona Itkin and Glen E. Liston

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Short summary
The MOSAiC project 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 three ice types snow redistribution correlated with sea ice deformation and level ice thickness was governed by the thinnest fraction of snow cover.