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...

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Bibliographic Details
Published in:Bayesian Analysis
Main Authors: Zhang, Bohai, Cressie, Noel
Format: Text
Language:English
Published: International Society for Bayesian Analysis 2020
Subjects:
Online Access:https://projecteuclid.org/euclid.ba/1589421852
https://doi.org/10.1214/20-BA1209
Description
Summary: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.