Preprints
https://doi.org/10.5194/tc-2022-92
https://doi.org/10.5194/tc-2022-92
 
02 Jun 2022
02 Jun 2022
Status: this preprint is currently under review for the journal TC.

Reconstruction of Arctic sea ice thickness and its impact on sea ice forecasting in the melting season

Lu Yang1, Hongli Fu2, Xiaofan Luo1, Shaoqing Zhang3, and Xuefeng Zhang1 Lu Yang et al.
  • 1School of Marine Science and Technology, Tianjin University, Tianjin, 300072, China
  • 2Key Laboratory of Marine Environmental Information Technology, National Marine Data and Information Service, Ministry of Natural Resources, Tianjin, 300171, China
  • 3Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao, 266100, China

Abstract. Generally, the sea ice prediction skills can be improved via assimilating available observations of the sea ice concentration (SIC) and the sea ice thickness (SIT) into a numerical forecast model to update the initial fields of the model. However, due to the lack of SIT satellite observations in the melting season, only SIC fields in the forecast model can be directly updated, which will bring about the dynamical mismatch between SIC and SIT to affect the model prediction accuracy. In order to solve this problem, a statistically based bivariate regression model of SIT, named as BRMT, is tentatively established based on the grid reanalysis data of SIC and SIT, to reconstruct the daily Arctic sea ice thickness data. Both BRMT-constructed SIT and several popular reanalysis datasets are compared to each other and validated based on available SIT observations in situ. Results show that BRMT can effectively reproduce the spatial and temporal changes of ice thickness in the melting season, and BRMT-constructed SIT is more accurate in capturing the change trend of ice thickness over a period of time, also the reconstructed SIT of one-year ice and multi-year ice types in the central Arctic and E Greenland Sea are closer to the observations. Further, as SIT from BRMT and SIC from satellite remote sensing are jointly assimilated into the ice-sea coupled numerical model, the prediction accuracy of SIC and SIT in the Arctic melting season is significantly improved, especially the SIC in the marginal ice zone and SIT in the central Arctic.

Lu Yang et al.

Status: open (until 28 Jul 2022)

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Lu Yang et al.

Lu Yang et al.

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Short summary
During the melting season in Arctic, sea ice thickness is difficult to detect directly by the satellite remote sensing. A bivariate regression model is put forward in this study to construct sea ice thickness. Comparisons with observations show that the new sea ice thickness data has some advantages over other data sets. The experiment shows that the model is expected to provide an available data for improving the forecast accuracy of sea ice variables in the Arctic sea ice melting season.