Assimilating synthetic CryoSat sea ice thickness in a coupled ice‐ocean model

Simulated CryoSat ice thickness measurements have been assimilated into a coupled ice‐ocean model to examine the impact in Arctic ocean prediction systems. The model system is based on the HYbrid Coordinate Ocean Model (HYCOM) and the EVP ice rheology, and the data assimilation method is the Ensemble Kalman Filter (EnKF). It is shown how ocean salinity, surface temperature, and ice concentration fields are affected by the ice thickness assimilation, and how these fields are improved relative to a free‐run experiment of the model. The ice thickness assimilation primarily affects the surface properties of the ocean. By running two different assimilation experiments, it is shown how the choice of stochastic forcing is crucial to the performance of the assimilation. Specifically, it is shown how stochastic wind forcing is important to correctly describe model prediction errors, which are important for the data assimilation step. The assimilation experiments illustrate how the ice thickness observations can have a strong impact on the ice thickness estimates of the model system. The manner in which the EnKF forcing is set up is crucial, but with the correct setup, the assimilation of ice thickness measurements could have a beneficial effect on the modeled ice thickness and ocean fields.