Open Access
June 2020 Bayesian Inference of Spatio-Temporal Changes of Arctic Sea Ice
Bohai Zhang, Noel Cressie
Bayesian Anal. 15(2): 605-631 (June 2020). DOI: 10.1214/20-BA1209


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.


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The acknowledgements section was corrected on 26 May 2020 by adding more details to Bohai Zhang’s research support.


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Bohai Zhang. Noel Cressie. "Bayesian Inference of Spatio-Temporal Changes of Arctic Sea Ice." Bayesian Anal. 15 (2) 605 - 631, June 2020.


Published: June 2020
First available in Project Euclid: 14 May 2020

MathSciNet: MR4097811
Digital Object Identifier: 10.1214/20-BA1209

Primary: 60G15 , 62F15 , 62J12 , 62P12

Keywords: Binary data , forecasting , hierarchical statistical model , latent Gaussian process , MCMC

Vol.15 • No. 2 • June 2020
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