Estimating global ocean heat content from tidal magnetic satellite observations

2019/05/27 by Christopher Irrgang, Jan Saynisch‐Wagner, Maik Thomas · 1 citation
Earth and Planetary Sciences · #Oceanographic and Atmospheric Processes #Underwater Acoustics Research #Geology and Paleoclimatology Research #Satellite #Abyssal zone #Ocean heat content #Geology #Inversion (geology) #Environmental science #Sea surface temperature #Geophysics #Oceanography #Physics

paper · pdf · doi:10.1038/s41598-019-44397-8

openalex publication_date 2019/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02

Abstract

Ocean tides generate electromagnetic (EM) signals that are emitted into space and can be recorded with low-Earth-orbiting satellites. Observations of oceanic EM signals contain aggregated information about global transports of water, heat, and salinity. We utilize an artificial neural network (ANN) as a non-linear inversion scheme and demonstrate how to infer ocean heat content (OHC) estimates from magnetic signals of the lunar semi-diurnal (M2) tide. The ANN is trained using monthly OHC estimates based on oceanographic in-situ data from 1990-2015 and the corresponding computed tidal magnetic fields at satellite altitude. We show that the ANN can closely recover inter-annual and decadal OHC variations from simulated tidal magnetic signals. Using the trained ANN, we present the first OHC estimates from recently extracted tidal magnetic satellite observations. Such space-borne OHC estimates can complement the already existing in-situ measurements of upper ocean temperature and can also allow insights into abyssal OHC, where in-situ data are still very scarce.

Citations

• The perceptron: A probabilistic model for information storage and organization in the brain.
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Cited by

• Earth's magnetic field [wikipedia]

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