Hierarchical Bayesian Modeling of Ocean Heat Content and its Uncertainty

TL;DR

This paper presents a hierarchical Bayesian Gaussian process model for estimating ocean heat content from Argo data, capturing spatial non-stationarity and providing credible intervals for global trends, outperforming simpler models.

Contribution

The paper introduces a novel hierarchical Bayesian Gaussian process model with kernel convolutions for spatial non-stationarity, suitable for large oceanographic datasets.

Findings

Model produces valid credible intervals for global heat content trends.

Outperforms simpler models and standard approaches in cross-validation.

Provides an R package for implementation.

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 greenhouse 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. However, 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 non-stationarity 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 approaches. 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 out-performs 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.