08 Jul 2024
 | 08 Jul 2024
Status: this preprint is open for discussion.

Sea ice in the Baltic Sea during 1993/94–2020/21 ice seasons from satellite observations and model reanalysis

Shakti Singh, Ilja Maljutenko, and Rivo Uiboupin

Abstract. This study investigates the sea ice characteristics of the Baltic Sea using Copernicus satellite and model reanalysis data products from 1993 onwards. Our primary focus is on assessing the performance of the latest Copernicus model reanalysis product in estimating ice season evolution compared to the satellite dataset. Firstly, the model reanalysis dataset is bias-corrected for further analysis. While the model estimates an earlier start to the ice season, it generally matches satellite data regarding the season’s end. Additionally, we find that the model tends to overestimate ice thickness compared to ice chart-based data. Across the Baltic Sea, declining trends for the sea ice are observed. The sea ice statistics during the recent period (2007–2021) show decreased sea ice fraction and thickness. The decrease in the sea ice thickness is over 50 % in some areas during the melting phase. Trend analysis in the study reveals a uniform pattern towards shorter ice seasons (most prominent being in Bothnian Bay with a range of approximately 1–3 days/year of decline in ice season), reduced sea ice extent (SIE) and reduced mean ice thickness (reaching up to -0.4 cm/year).

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Shakti Singh, Ilja Maljutenko, and Rivo Uiboupin

Status: open (until 19 Aug 2024)

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Shakti Singh, Ilja Maljutenko, and Rivo Uiboupin
Shakti Singh, Ilja Maljutenko, and Rivo Uiboupin


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Short summary
The sea ice statistics study highlights the bias in model estimations compared to satellite data and provides a simple approach to minimise that. During the study period, the model estimates sea ice forming slightly earlier but aligns well with the satellite data for ice season's end. Rapid decrease in the sea ice parameters is observed across the Baltic Sea, especially the ice thickness in the Bothnian Bay sub-basin. These statistics could be crucial for regional adaptation strategies.