Spatio-temporal Local Interpolation of Global Ocean Heat Transport using Argo Floats: A Debiased Latent Gaussian Process Approach
Beomjo Park, Mikael Kuusela, Donata Giglio, Alison Gray

TL;DR
This paper introduces a novel statistical framework using latent Gaussian processes and an EM algorithm to accurately interpolate and analyze global ocean heat transport from Argo float data, addressing data incompleteness and complex dynamics.
Contribution
It develops a debiased, spatio-temporal Gaussian process model with a two-stage fitting procedure for improved ocean heat transport estimation from in-situ data.
Findings
Provides realistic subsurface heat transport estimates.
Validates estimates against satellite data.
Captures key climate phenomena like El Niño.
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 spatio-temporal dynamics, as well as from potential model misspecification. We present a comprehensive spatio-temporal 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 under-specified mean field model with a debiasing procedure. This approach provides data-driven global ocean heat…
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Taxonomy
TopicsAtmospheric and Environmental Gas Dynamics · Gaussian Processes and Bayesian Inference · Oceanographic and Atmospheric Processes
