Bounded and categorized: Targeting data assimilation for sea ice fractional coverage and nonnegative quantities in a single-column multi-category sea ice model

In many applications, DA is implemented to constrain a modeled estimate of a state with observations. The sea ice DA application is complicated by the wide range of spatiotemporal scales over which key sea ice variables evolve, a variety of physical bounds on those variables, and the particular construction of modern complex sea ice models.

By coupling a single-column sea ice model (Icepack) to the Data Assimilation Research Testbed (DART) in a series of observing system simulation experiments (OSSEs), the grid-cell-level response of a complex sea ice model to a range of ensemble Kalman DA methods designed to address the aforementioned complications is explored. The impact on the modeled ice thickness distribution and the bounded nature of both state and prognostic variables in the sea ice model are of particular interest, as these problems are under-examined.