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Ocean-bottom pressure gauges are widely used for tsunami observations due to their established accuracy and stability. Recent advancements reveal that the magnetic field fluctuates when a large tsunami passes over the ocean, suggesting potential alternatives to pressure gauges in the form of ocean-bottom electromagnetometers (OBEMs). This article offers a comprehensive synthesis of recent findings concerning tsunami magnetic fields and their utility in tsunami source estimation. In addition, we scrutinize the effectiveness of tsunami observations employing OBEMs. Despite the promise of electromagnetometers, it is worth noting that the background noise inherent in electromagnetic observations tends to be approximately 10 times greater than that of pressure observations within the critical tsunami frequency bands. The Earth's magnetic field sporadically disrupts tsunami magnetic fields, presenting a potential limitation to the utility of electromagnetometers in tsunami detection when compared with pressure gauges. Nevertheless, our investigation underscores the potential of electromagnetic observations in detecting tsunamis propagating over the ocean at magnitudes of a few centimetres. An invaluable advantage of electromagnetometers over pressure monitoring lies in their capability to observe tsunami velocity fields, suggesting a promising avenue for further research and development in tsunami observation technology. This article is part of the theme issue 'Magnetometric remote sensing of Earth and planetary oceans'. [ABSTRACT FROM AUTHOR]

A seafloor geomagnetic observatory in the northwest Pacific has provided very long vector geomagnetic time-series. It was found that the time-series include significant magnetic signals generated by a few giant tsunami events including the 2011 Tohoku Tsunami. Here we report that the tsunami-generated magnetic fields consist of the weak but first arriving field, and the strong but second arriving field—similar to the P- and S-waves in seismology. The latter field is a result of coupling between horizontal particle motions of the conductive seawater and the vertical component of the background geomagnetic main field, which have been studied well so far. On the other hand, the former field stems from coupling between vertical particle motions and the horizontal component of the geomagnetic main field parallel to tsunami propagation direction. The former field has been paid less attention because horizontal particle motions are dominant in the Earth's oceans. It, however, was shown that not only the latter but also the former field is significant especially around the magnetic equator where the vertical component of the background magnetic field vanishes. This implies that global tsunami early warning using tsunami-generated magnetic fields is possible even in the absence of the background vertical geomagnetic component. [ABSTRACT FROM AUTHOR]

We have submitted a secular variation (SV) candidate model for the thirteenth generation of International Geomagnetic Reference Field model (IGRF-13) using a data assimilation scheme and a magnetohydrodynamic (MHD) dynamo simulation code. This is the first contribution to the IGRF community from research groups in Japan. A geomagnetic field model derived from magnetic observatory hourly means, and CHAMP and Swarm-A satellite data, has been used as input data to the assimilation scheme. We adopt an ensemble-based assimilation scheme, called four-dimensional ensemble-based variational method (4DEnVar), which linearizes outputs of MHD dynamo simulation with respect to the deviation from a dynamo state vector at an initial condition. The data vector for the assimilation consists of the poloidal scalar potential of the geomagnetic field at the core surface and flow velocity field slightly below the core surface. Dimensionless time of numerical geodynamo is adjusted to the actual time by comparison of secular variation time scales. For SV prediction, we first generate an ensemble of dynamo simulation results from a free dynamo run. We then assimilate the ensemble to the data with a 10-year assimilation window through iterations, and finally forecast future SV by the weighted sum of the future extension parts of the ensemble members. Hindcast of the method for the assimilation window from 2004.50 to 2014.25 confirms that the linear approximation holds for 10-year assimilation window with our iterative ensemble renewal method. We demonstrate that the forecast performance of our data assimilation and forecast scheme is comparable with that of IGRF-12 by comparing data misfits 4.5 years after the release epoch. For estimation of our IGRF-13SV candidate model, we set assimilation window from 2009.50 to 2019.50. We generate our final SV candidate model by linear fitting for the weighted sum of the ensemble MHD dynamo simulation members from 2019.50 to 2025.00. We derive errors of our SV candidate model by one standard deviation of SV histograms based on all the ensemble members. [ABSTRACT FROM AUTHOR]