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Arctic sea ice extent (SIE) has drawn increasing attention from scientists in recent years because of its fast decline in the Boreal summer and early fall. The measurement of SIE is derived from remote sensing data and is both a lagged and leading indicator of climate change. To characterize at a local level the decline in SIE, we use remote-sensing data at 25 km resolution to fit a spatio-temporal logistic autoregressive model of the sea-ice evolution in the Arctic region. The model incorporates last year's ice/water binary observations at nearby grid cells in an autoregressive manner with autoregressive coefficients that vary both in space and time. Using the model-based estimates of ice/water probabilities in the Arctic region, we propose several graphical summaries to visualize the spatio-temporal changes in Arctic sea ice beyond what can be visualized with the single time series of SIE. In ever-higher latitude bands, we observe a consistently declining temporal trend of sea ice in the early fall. We also observe a clear decline in and contraction of the sea ice's distribution between 70 ∘ N – 75 ∘ N , and of most concern is that this may reflect the future behavior of sea ice at ever-higher latitudes under climate change. [ABSTRACT FROM AUTHOR]

Arctic sea ice extent has been of considerable interest to scientists in recent years, mainly due to its decreasing temporal trend over the past 20 years. In this article, we propose a hierarchical spatio‐temporal generalized linear model for binary Arctic sea‐ice‐extent data, where statistical dependencies in the data are modeled through a latent spatio‐temporal linear 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 proposed to estimate model parameters, and an empirical–hierarchical‐modeling approach is applied to obtain the predictive distribution of the latent spatio‐temporal process. We illustrate the accuracy of the parameter estimation through a simulation study. The hierarchical model is applied to spatial Arctic sea‐ice‐extent data in the month of September for 20 years in the recent past, where several posterior summaries are obtained to detect the changes of Arctic sea ice cover. In particular, we consider a time series of latent 2 × 2 tables to infer the spatial changes of Arctic sea ice over time. [ABSTRACT FROM AUTHOR]