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. 2019 May 27;9(1):7893.
doi: 10.1038/s41598-019-44397-8.

Estimating global ocean heat content from tidal magnetic satellite observations

Christopher Irrgang  1 , Jan Saynisch  2 , Maik Thomas  2   3
Affiliations

Estimating global ocean heat content from tidal magnetic satellite observations

Christopher Irrgang et al. Sci Rep. .

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Absolute radial component of the periodic M2 tidal magnetic field at a satellite altitude of 430 km above sea surface.
Figure 2
Figure 2
Sketch of a feed-forward artificial neural network (ANN) with one input layer, two hidden layers, and one output layer. Training (or validation) data are successively passed through all neurons of the ANN from the input layer to the output layer. The ANN is trained to estimate the global ocean heat content (output layer) based on the corresponding M2 tidal magnetic field (input layer).
Figure 3
Figure 3
Ensemble (CORA5, JMA, EN4, IAP) mean areal distributions of the upper ocean heat content for the 0–700 m and 700–2000 m ocean layers in 1990 (left column) and corresponding linear ocean heat content trends for the 1990–2015 time period (right column).
Figure 4
Figure 4
Global ocean heat content (OHC) predictions based on simulated M2 tidal magnetic fields. OHC values are shown w.r.t. the 1990 mean OHC. Panels (A and B) show the recovery of highest and lowest OHC trajectory in the 0–700 m ocean layer after training the ANN with the respective remaining three data products. Panels (C and D) show the recovery of the ensemble mean OHC in the 0–700 m and 700–2000 m ocean layers after training the ANN with all four data products. RMS errors (1 ZJ = 1021 J) are given for the offset between the ANN prediction and the validation set.
Figure 5
Figure 5
Global ocean heat content (OHC) predictions based on satellite measurements of the M2 tidal magnetic field from the CM5 and CI products. Panel (E) shows the predictions for the 0–700 m and panel (F) for the 700–2000 m ocean layer. The gray boxes indicate the temporal data coverage of the respective satellite measurements. All OHC values are shown w.r.t. the mean values over the CM5 time period. Note that the ensemble mean and the neural network prediction overlap for the CM5 time period.
See this image and copyright information in PMC

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