The impact of April sea-ice thickness (SIT) initialization on the predictability of September sea-ice extent (SIE) is investigated based on a series of perfect model ensemble experiments using the MIROC5.2 climate model. Ensembles with April SIT initialization can accurately predict the September SIE for greater lead times than in cases without the initialization – up to 2 years ahead. The persistence of SIT correctly initialized in April contributes to the skilful prediction of SIE in the first September. On the other hand, errors in the initialization of SIT in April cause errors in the predicted sea-ice concentration and thickness in the Pacific sector from July to September and consequently influence the predictive skill with respect to SIE in September. The present study suggests that initialization of the April SIT in the Pacific sector significantly improves the accuracy of the September SIE forecasts by decreasing the errors in sea-ice fields from July to September.

Under adaptive learning, recursive algorithms are proposed to represent how agents update their beliefs over time. For applied purposes, these algorithms require initial estimates of agents perceived law of motion. Obtaining appropriate initial estimates can become prohibitive within the usual data availability restrictions of macroeconomics. To circumvent this issue, we propose a new smoothing-based initialization routine that optimizes the use of a training sample of data to obtain initials consistent with the statistical properties of the learning algorithm. Our method is generically formulated to cover different specifications of the learning mechanism, such as the least-squares and the stochastic gradient algorithms. Using simulations, we show that our method is able to speed up the convergence of initial estimates in exchange for a higher computational cost.