Influence of Reconstruction of Arctic Sea Ice Thickness on the Ice-ocean
Coupled Forecast in Ice Melting Season
Abstract
Generally, the sea ice prediction skills can be improved by assimilating
the observed sea ice data into a numerical forecast model to update the
initial fields of the model. Meanwhile, it is necessary to assimilate
sea ice thickness (SIT) while assimilating sea ice concentration (SIC)
to keep the two harmony in assimilation. However, due to the lack of the
SIT from satellite remote sensing observation, it cannot meet the
bivariate assimilation requirement in Arctic melting season. In order to
solve this problem, an easy-to-implement bivariate regression mode of
SIT is tentatively established based on the grid reanalysis data of SIC
and SIT, through which the SIT field is statistically constructed. Then,
the ice-ocean coupled numerical forecast experiment is carried out in
which both the observed SIC and the constructed SIT are jointly
assimilated using the spatial multi-scale recursive filter (SMRF)
method. Results show the joint assimilation of SIC and the constructed
SIT can greatly improve the forecast accuracy of sea ice elements
especially in the multi-year ice region of Arctic center, where the
average absolute error between the SIT forecast and in situ observations
is about 0.14 m. Further, effects of the bivariate assimilation on the
ocean elements are also deeply investigated in melting season. The
higher forecast skill of sea surface temperature and drift flow can be
obtained via the bivariate assimilation scheme considering the ice-ocean
coupled dynamics and the feedback process between them.