HIERARCHICAL BAYESIAN MODELING OF OCEAN HEAT CONTENT AND ITS UNCERTAINTY

Samuel Baugh, Karen McKinnon

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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 hierarchical Bayesian Gaussian process model that uses kernel convolutions with cylindrical distances to allow for spatial nonstationarity in all model parameters 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 interpolation and from estimating the mean field and other parameters. Through cross-validation, we show that our model outperforms an out-of-the-box approach as well as other simpler models. The code for performing this analysis is provided as the R package BayesianOHC.

Original languageEnglish (US)
Pages (from-to)2603-2625
Number of pages23
JournalAnnals of Applied Statistics
Volume16
Issue number4
DOIs
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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