Arctic sea-ice extent has been of considerable interest to scientists in recent years, mainly due to its decreasing trend over the past 20 years. In this talk, I propose a hierarchical spatio-temporal generalized linear model (GLM) for binary Arctic-sea-ice data, where data dependencies are introduced through a latent, dynamic, spatio-temporal mixed-effects model. By using a fixed number of spatial basis functions, the resulting model achieves both dimension reduction and non-stationarity for spatial fields at different time points. An EM algorithm is used to estimate model parameters, and an MCMC algorithm is developed to obtain the predictive distribution of the latent spatio-temporal process. The methodology is applied to spatial, binary, Arctic-sea-ice data for each September over the past 20 years, and several posterior summaries are computed to detect changes of Arctic sea-ice cover. The fully Bayesian version is under development awill be discussed.[-]