Preprints
https://doi.org/10.5194/tc-2020-52
https://doi.org/10.5194/tc-2020-52
03 Apr 2020
 | 03 Apr 2020
Status: this preprint was under review for the journal TC. A final paper is not foreseen.

Seasonal and interannual variability of sea-ice state variables: Observations and predictions for landfast ice in northern Alaska and Svalbard

Marc Oggier, Hajo Eicken, Meibing Jin, and Knut Høyland

Abstract. Validation of sea-ice models, representation of sea-ice processes in large-scale models, and regional planning around ice use and hazards requires climatological ice property data. We summarize key ice properties, in particular temperature and salinity, representative of broader Arctic conditions, from long-term observations near Utqiaġvik, Alaska and Van Mijen Fjord, Svalbard. Additionally, we simulate salinity and temperature profiles using the Los Alamos sea-ice model (CICE) in stand-alone mode, forced with meteorological data for both locations. We compare observations and model results by aggregating profiles using a degree day model and statistical analysis to create ice property climatologies, which describe the seasonal evolution of sea ice. During the growth season, the CICE model accurately replicates ice property evolution for both salinity (R = 0.7) and temperature (R = 0.9). While the model initiates ice desalination at melt onset, and reproduces the temperature field well through melt (R = 0.9), model salinities later tend towards an asymptotic value of 5 ‰ (R = 0.3). This suggests that the model does not fully capture the desalination processes and their impact on ice physico-chemical properties during the melt season. Overall, the standard deviation of the model remains similar to the natural sea-ice variability throughout the season. Despite mismatches during the melt season, the CICE model shows promise for simulating the seasonal evolution of salinity and temperature profiles, which may serve as proxies for bulk ice properties that constrain transport of heat and mass through sea ice. Our findings highlight the necessity for a large number of observations throughout the year to create an effective model benchmarking dataset.

This preprint has been withdrawn.

Marc Oggier, Hajo Eicken, Meibing Jin, and Knut Høyland

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Marc Oggier, Hajo Eicken, Meibing Jin, and Knut Høyland

Data sets

Van Mijenfjord Ice Core Data 1999-2013 K. V. Høyland, M. Oggier, and A. Ervik, https://doi.org/10.5281/zenodo.3737133

Automated ice mass balance site (SIZONET) H. Eicken https://doi.org/10.18739/a2d08x

Marc Oggier, Hajo Eicken, Meibing Jin, and Knut Høyland

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Latest update: 21 Feb 2024
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This preprint has been withdrawn.