Seasonal Prediction of Arctic Sea Ice Extent from a Coupled Dynamical Forecast System

While fully coupled atmosphere-ocean models have been used to study the seasonal predictability of sea ice variations within the context of models' own variability, their capability in predicting the observed sea ice at the seasonal time scales is not well assessed. In this study, sea ice predictions from the recently developed NCEP Climate Forecast System, version 2 (CFSv2), a fully coupled atmosphere-ocean model including an interactive dynamical sea ice component, are analyzed. The focus of the analysis is the performance of CFSv2 in reproducing observed Northern Hemisphere sea ice extent (SIE). The SIE climatology, long-term trend, interannual variability, and predictability are assessed. CFSv2 contains systematic biases that are dependent more on the forecast target month than the initial month, with a positive SIE bias for the forecast for January-September and a negative SIE bias for the forecast for October-December. A large source of seasonal prediction skill is from the long-term trend, which is underestimated in the CFSv2. Prediction skill of interannual SIE anomalies is found to be primarily within the first three target months and is largest in the summer and early fall. The performance of the prediction of sea ice interannual variations varies from year to year and is found to be related to initial sea ice thickness. Potential predictability based on the forecast ensemble, its dependence on model deficiencies, and implications of the results from this study for improvements in the seasonal sea ice prediction are discussed.