On the characterization of tidal ocean-dynamo signals in coastal magnetic observatories

Abstract

Periodic tidal ocean currents induce electric currents and, therefore, magnetic field signals that are observable using spaceborne and ground-based observation techniques. In theory, the signals can be used to monitor oceanic temperature and salinity variations. Tidal magnetic field amplitudes and phases have been extracted from magnetometer measurements in the past. However, due to uncertainties caused by a plentitude of influencing factors, the shape and temporal variation of these signals are only known to a limited extent. This study uses past extraction methods to characterize seasonal variations and long-term trends in the ten year magnetometer time series of three coastal island observatories. First, we assess data processing procedures used to prepare ground-based magnetometer observations for tidal ocean dynamo signal extraction to demonstrate that existing approaches, i.e., subtraction of core field models or first-order differencing, are unable to reliably remove low-frequency contributions. We hence propose low-frequency filtering using smoothing splines and demonstrate the advantages over the existing approaches. Second, we determine signal and side peak magnitudes of the M2 tide induced magnetic field signal by spectral analysis of the processed data. We find evidence for seasonal magnetic field signal variations of up to 25% from the annual mean. Third, to characterize the long-term behavior of tidal ocean dynamo signal amplitudes and phases, we apply different signal extraction techniques to identify tidal ocean-dynamo signal amplitudes and phases in sub-series of the ten-year time series with incrementally increasing lengths. The analyses support three main findings: (1) trends cause signal amplitude changes of up to ≈1 nT and phase changes are in the order of O(10∘) within the observation period; (2) at least four years of data are needed to obtain reliable amplitude and phase values with the extraction methods used and (3) signal phases are a less dependent on the chosen extraction method than signal amplitudes.