Abstract. Uncertain or inaccurate parameters in sea ice models influence seasonal
predictions and climate change projections in terms of both mean and trend.
We explore the feasibility and benefits of applying an ensemble Kalman
filter (EnKF) to estimate parameters in the Los Alamos sea ice model (CICE).
Parameter estimation (PE) is applied to the highly influential dry snow
grain radius and combined with state estimation in a series of perfect model
observing system simulation experiments (OSSEs). Allowing the parameter to
vary in space improves performance along the sea ice edge but degrades in
the central Arctic compared to requiring the parameter to be uniform
everywhere, suggesting that spatially varying parameters will likely improve
PE performance at local scales and should be considered with caution. We
compare experiments with both PE and state estimation to experiments with
only the latter and have found that the benefits of PE mostly occur after the
data assimilation period, when no observations are available to assimilate
(i.e., the forecast period), which suggests PE's relevance for improving
seasonal predictions of Arctic sea ice.