Reconstruction of Arctic Sea Ice Thickness and Its Impact on Sea Ice Forecasting in the Melting Season

Abstract

Abstract Generally, sea ice prediction skills can be improved by assimilating available observations of the sea ice concentration (SIC) and sea ice thickness (SIT) into a numerical forecast model to update the initial conditions. However, due to inadequate daily SIT satellite observations in the Arctic melting season, the SIC fields in forecast models are usually directly updated, which causes mismatch of SIC and SIT in dynamics and affects the model prediction accuracy. In this study, a statistically based bivariate regression model of SIT (BRMT) is tentatively established based on the grid reanalysis data of SIC and SIT to reconstruct daily Arctic SIT data. The results show that the BRMT can reproduce the spatial and temporal changes in the SIT in the melting season and capture the variation trend of SIT in some periods. Compared with the SIT observations from buoy and satellite, the reconstructed SIT shows better performance in the central Arctic than other datasets. Furthermore, when the reconstructed SIT is added to the forecast model with only assimilated SIC, the forecast accuracy of SIC, sea ice extent, and SIT in the Arctic melting season is improved and does not weaken with the increase in the forecast time. Especially in the central Arctic, the average absolute deviation between 24-h SIT forecast results and observations is only 0.16 m. The results indicate great potential for applying the reconstructed SIT to the operational forecast of Arctic sea ice during the melting season in the future. Significance Statement To improve the prediction skills of Arctic sea ice, it is necessary to assimilate the sea ice observation into the dynamic model to generate a more realistic initial prediction field. At present, the observation data of daily sea ice thickness (SIT) during the Arctic melting season are few, which cannot well meet the demand of operational SIT forecast. In this study, a bivariate regression model is put forward to construct SIT using the sea ice concentration (SIC) observation. Benefitting from the joint assimilation of the observed SIC and the reconstructed SIT, the forecast accuracy of sea ice variables is greatly improved. The reconstructed SIT is expected to provide an available dataset for further research on the Arctic sea ice forecast.