Estimating Parameters in a Sea Ice Model using an Ensemble Kalman Filter

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 compared to requiring the parameter to be uniform everywhere. We compare experiments with both PE and state estimation to experiments with only the latter and found that the benefits of PE mostly occur after the DA 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.