Improved Global Ocean Heat Content Estimation by Modeling Vertical Spatio-Temporal Dependence

2026-07-13

AI Summary

This study addresses the limitations of traditional ocean heat content (OHC) estimation methods, which typically treat depth layers independently and thus struggle to accurately quantify overall uncertainty. Leveraging Argo float data from 2004 to 2022, the authors introduce, for the first time, a bivariate locally stationary Gaussian process to explicitly model the vertical spatiotemporal dependencies between two pressure layers. By integrating this approach with conditional simulation, they achieve a joint estimation of global OHC and its associated uncertainty. Compared to conventional layer-wise methods, the proposed framework reduces uncertainty in OHC anomalies by up to 15%, substantially enhancing the reliability of statistical significance assessments for OHC changes at both regional and global scales.

Abstract

Estimating ocean heat content (OHC) with reliable uncertainties is critical for understanding and monitoring the evolution of Earth's climate, as the ocean has stored most of the energy accumulated in the climate system due to Earth Energy Imbalance. Here, we use Argo profiling float data from 2004-2022 to map OHC. As fewer Argo observations are available deeper in the water column, previous studies have partitioned the ocean into at least two pressure layers and mapped each separately, which complicates the estimation of uncertainties when the maps are summed to get the total OHC. In this work, we consider the case of two pressure layers and propose an improved mapping and uncertainty quantification method using bivariate locally stationary Gaussian processes and conditional simulations to map the two sections jointly while accounting for the correlation between them. We find that modeling this correlation results in improved OHC anomaly mapping and up to a 15 percent reduction of global OHC anomaly uncertainties in comparison to mapping the two layers separately without accounting for their dependence. These estimated uncertainties are essential to analyze the statistical significance of OHC anomalies on both regional and global scales, which we demonstrate using several climatological case studies.

Problem

• Ocean Heat Content
• Uncertainty Quantification
• Vertical Spatio-Temporal Dependence
• Argo Data
• Climate Monitoring

Innovation

• bivariate Gaussian processes
• ocean heat content
• spatio-temporal dependence
• uncertainty quantification
• Argo data