Toward improved ocean heat content mapping and uncertainty quantification by modeling vertical spatio-temporal dependence
Date:
Abstract: Estimating ocean heat content 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. Current methods for ocean heat content (OHC) mapping from Argo profiles include partitioning the ocean into at least two vertical sections due to limitations in data availability for deeper layers. However, in order to paint a more complete picture of vertically integrated OHC uncertainties, we need to consider the spatio-temporal correlation between the vertical sections. In this work, we consider two vertical sections and propose an improved mapping and uncertainty quantification method using bivariate locally stationary Gaussian processes and local conditional simulations to account for the correlation between the sections. We find that modeling this effect results in improved OHC anomaly mapping and a 15% reduction of global OHC anomaly uncertainties in comparison to mapping the two layers separately. The estimated uncertainties are useful to investigate the statistical significance of OHC anomalies both on a regional and global scale. Our model also provides interpretable cross-correlation estimates that can shed insights on the processes that are relevant for regional OHC changes.