1. Spatio-temporal Local Interpolation of Global Ocean Heat Transport using Argo Floats: A Debiased Latent Gaussian Process Approach
- Author
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Park, Beomjo, Kuusela, Mikael, Giglio, Donata, and Gray, Alison
- Subjects
Statistics and Probability ,FOS: Computer and information sciences ,Physics - Atmospheric and Oceanic Physics ,Modeling and Simulation ,Atmospheric and Oceanic Physics (physics.ao-ph) ,FOS: Physical sciences ,Applications (stat.AP) ,Statistics, Probability and Uncertainty ,Statistics - Applications ,Physics::Atmospheric and Oceanic Physics - 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 transport fields that vary in both space and time and can provide insights into crucial dynamical phenomena, such as El Ni{\~n}o \& La Ni{\~n}a, 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., Comment: 30 pages, 10 figures with supplementary material 9 pages, 10 figures
- Published
- 2021
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