Toward improved global ocean heat content uncertainty quantification by modeling vertical spatio-temporal dependence
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Abstract: Estimating ocean heat content (OHC) with reliable uncertainties is critical for understanding 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. We use Argo profile 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 vertical sections and mapped each separately, which complicates the estimation of uncertainties when the maps are summed to get total OHC. In this work, we consider the case of two vertical sections 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% 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 both on a regional and global scale.