- Bayesian Anal.
- Volume 15, Number 2 (2020), 605-631.
Bayesian Inference of Spatio-Temporal Changes of Arctic Sea Ice
Arctic sea ice extent has drawn increasing interest and alarm from geoscientists, owing to its rapid decline. In this article, we propose a Bayesian spatio-temporal hierarchical statistical model for binary Arctic sea ice data over two decades, where a latent dynamic spatio-temporal Gaussian process is used to model the data-dependence through a logit link function. Our ultimate goal is to perform inference on the dynamic spatial behavior of Arctic sea ice over a period of two decades. Physically motivated covariates are assessed using autologistic diagnostics. Our Bayesian spatio-temporal model shows how parameter uncertainty in such a complex hierarchical model can influence spatio-temporal prediction. The posterior distributions of new summary statistics are proposed to detect the changing patterns of Arctic sea ice over two decades since 1997.
The acknowledgements section was corrected on 26 May 2020 by adding more details to Bohai Zhang’s research support.
Bayesian Anal., Volume 15, Number 2 (2020), 605-631.
First available in Project Euclid: 14 May 2020
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Zhang, Bohai; Cressie, Noel. Bayesian Inference of Spatio-Temporal Changes of Arctic Sea Ice. Bayesian Anal. 15 (2020), no. 2, 605--631. doi:10.1214/20-BA1209. https://projecteuclid.org/euclid.ba/1589421852
- Bayesian spatio-temporal modeling of Arctic sea ice extent. Supplementary Material. The Supplementary Material contains a simulation study that compares the inference performance of the empirical hierarchical model (EHM) and the Bayesian hierarchical model (BHM) in Section S1; a detailed discussion of the dynamics of sea ice in different regions of the Arctic in Section S2; details of the MCMC sampling algorithm and convergence diagnostics for the Arctic sea-ice-extent data in Section S3; the classification accuracy of the Bayesian inference in Section S4; and visualization of the ice-to-water and water-to-ice transition probabilities in Section S5.