HIERARCHICAL BAYESIAN MODELING OF OCEAN HEAT CONTENT AND ITS UNCERTAINTY

• 成果类型: Article
• 署名作者: Baugh, Samuel; Mckinnon, Karen
• 署名单位: University of California System; University of California Los Angeles; University of California System; University of California Los Angeles
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1605
• 发表日期: 2022
• 页码: 2603-2625
• 关键词: Convolution

摘要

The accurate quantification of changes in the heat content of the world's oceans is crucial for our understanding of the effects of increasing green-house gas concentrations. The Argo program, consisting of Lagrangian floats that measure vertical temperature profiles throughout the global ocean, has provided a wealth of data from which to estimate ocean heat content. How-ever, creating a globally consistent statistical model for ocean heat content remains challenging due to the need for a globally valid covariance model that can capture complex nonstationarity. In this paper, we develop a hierar-chical Bayesian Gaussian process model that uses kernel convolutions with cylindrical distances to allow for spatial nonstationarity in all model param-eters while using a Vecchia process to remain computationally feasible for large spatial datasets. Our approach can produce valid credible intervals for globally integrated quantities that would not be possible using previous ap-proaches. These advantages are demonstrated through the application of the model to Argo data, yielding credible intervals for the spatially varying trend in ocean heat content that accounts for both the uncertainty induced from in-terpolation and from estimating the mean field and other parameters. Through cross-validation, we show that our model outperforms an out-of-the-box ap-proach as well as other simpler models. The code for performing this analysis is provided as the R package BayesianOHC.