The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2
along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var …

Derived from two complementary satellites, CryoSat‐2 and Soil Moisture and Ocean Salinity
(SMOS), sea ice thickness (SIT) data are assimilated into the Met Office's global ocean–sea …

Interest in seasonal predictions of Arctic sea ice has been increasing in recent years owing,
primarily, to the sharp reduction in Arctic sea-ice cover observed over the last few decades …

Exploiting the complementary character of CryoSat‐2 and Soil Moisture and Ocean Salinity
satellite sea ice thickness products, daily Arctic sea ice thickness estimates from October …

Rapidly shrinking Arctic sea ice has had substantial impacts on the Earth system. Therefore,
reliably estimating the Arctic sea-ice thickness (SIT) using a combination of available …

The impact of assimilating satellite-retrieved Arctic sea ice thickness (SIT) on simulating sea
ice concentration (SIC) climatology in CICE5 is examined using a data assimilation (DA) …

Accurately forecasting the sea-ice thickness (SIT) in the Arctic is a major challenge. The new
SIT product (referred to as CS2SMOS) merges measurements from the CryoSat-2 and …

Twin 5-month seasonal forecast experiments are performed to predict the September 2018
mean and minimum ice extent using the fully coupled Navy Earth System Prediction …

Operational forecasting systems routinely assimilate daily means of sea ice concentration
(SIC) from microwave radiometers in order to improve the accuracy of the forecasts …

The current GFDL seasonal prediction system achieved retrospective sea ice extent (SIE)
skill without direct sea ice data assimilation. Here we develop sea ice data assimilation …