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...
Published in: | Bayesian Analysis |
---|---|
Main Authors: | , |
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 |
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. |
---|