Exploring bounded nonparametric ensemble filter impacts on sea ice Data Assimilation

Compared to the other ensemble Kalman filter algorithms, the bounded RHF pulls the ensemble closer to the true value and respects the bounds. Most notably during winter when sea ice concentration is near its upper bound of one, the bounded RHF provides updates in the observation space that are more uniformly distributed around zero compared to the other algorithms. One common finding among all ensemble Kalman filter algorithms tested is the overdispersive nature of sea ice thickness.

This was linked back to the method used to create the initial ensemble spread for our free forecasts in our OSSEs. Improving our ability to assimilate sea ice observations within our coupled Earth system modeling frameworks will help improve future projections of the climate and processes related to the cryosphere.