Real‐time estimation of Arctic sea ice thickness through maximum covariance analysis

A challenge for model-based seasonal predictions of sea ice is an accurate representation of sea ice initial conditions, particularly sparsely observed sea ice thickness (SIT). The Canadian Seasonal to Interannual Prediction System (CanSIPS) currently initializes SIT by nudging simulated values toward a model-based climatology. To improve on this, we use sea ice data from Pan-Arctic Ice Ocean Modeling and Assimilation System to investigate how accurately SIT can be estimated in real time using better observed and physically relevant predictors. We (1) test the skill of several predictors using maximum covariance analysis (MCA), (2) apply an approach which blends sea ice concentration and lagged (4 month averaged) sea level pressure, and (3) compare this method against the current CanSIPS initialization scheme over 1981–2012. The MCA-based statistical model reduces SIT areal mean and temporal mean absolute errors by 48% relative to the current CanSIPS initialization and shows consistent skill estimating ice volume in all months (r = 0.95).