Spatiotemporal local interpolation of global ocean heat transport using Argo floats: A debiased latent Gaussian process approach

Abstract

The world ocean plays a key role in redistributing heat in the climate system and hence in regulating Earth’s climate. Yet statistical analysis of ocean heat transport suffers from partially incomplete large-scale data intertwined with complex spatiotemporal dynamics as well as from potential model misspecification. We present a comprehensive spatiotemporal statistical framework tailored to interpolating the global ocean heat transport using in situ Argo profiling float measurements. We formalize the statistical challenges using latent local Gaussian process regression accompanied by a two-stage fitting procedure. We introduce an approximate expectation-maximization algorithm to jointly estimate both the mean field and the covariance parameters, and refine the potentially underspecified mean field model with a debiasing procedure. This approach provides data-driven global ocean heat transport fields that vary in both space and time and can provide insights into crucial dynamical phenomena, such as El Niño & La Niña, as well as the global climatological mean heat transport field which by itself is of scientific interest. The proposed framework and the Argo-based estimates are thoroughly validated with state-of-the-art multimission satellite products and shown to yield realistic subsurface ocean heat transport estimates.