Locally stationary Argo ocean heat content estimates: Modeling, validation and uncertainty quantification

2026-06-30

AI Summary

This work proposes an end-to-end spatiotemporal statistical framework for generating globally gridded ocean heat content (OHC) anomaly estimates with reliable uncertainty quantification from Argo float data. The approach models vertically integrated temperature profiles as a spatially and temporally locally stationary Gaussian process, incorporating a data-driven decorrelation scale and a mean field that accounts for climate trends. Spatiotemporally coherent uncertainties are quantified through localized conditional simulation ensembles and pairwise cross-validation. The framework is computationally efficient and scalable, yielding a global OHC product spanning 2004–2022 with fully quantified uncertainties. This product supports diverse downstream climate analyses and has been validated for both accuracy and reliability.

Abstract

Argo profiling floats measure seawater temperature and salinity in the upper 2000 meters of the ocean. These floats are uniquely capable of measuring the global Ocean Heat Content (OHC), a quantity that is of central importance for understanding Earth Energy Imbalance. Yet, producing Argo-based OHC estimates with reliable uncertainties is statistically challenging due to the complex structure and large size of the Argo dataset. Here we present an end-to-end mapping and uncertainty quantification framework for Argo-based OHC estimation using state-of-the-art methods from spatio-temporal statistics. The framework is based on modeling vertically integrated Argo temperature profiles as a locally stationary Gaussian process defined over space and time. This enables us to produce computationally tractable OHC anomaly maps based on data-driven decorrelation scales estimated from the Argo observations. Our modeling choices are validated using statistical cross-validation, which demonstrates the importance of including a climatological time trend in the mean field and accounting for time in the covariance function. We quantify the uncertainty of these maps using local conditional simulation ensembles, a novel approach that leads to principled spatially and temporally correlated uncertainty quantification. A new paired cross-validation technique is presented to validate these uncertainties. The mapping framework is implemented in an open-source codebase that is designed to be modular, reproducible and extensible. To demonstrate the mapping and uncertainty quantification capabilities of this approach, we present new Argo OHC maps with uncertainties for 2004-2022 and report on various downstream climatological estimates and their uncertainties.

Problem

• Ocean Heat Content
• Argo floats
• uncertainty quantification
• spatio-temporal statistics
• locally stationary

Innovation

• locally stationary Gaussian process
• uncertainty quantification
• conditional simulation ensembles
• spatio-temporal statistics
• Argo ocean heat content