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Abstract Spatial variation in the intensity of magnetospheric and ionospheric fluctuations during solar storms creates ground-induced currents, of importance in both infrastructure engineering and geophysical science. This activity is presently measured using a network of ground-based magnetometers, typically consisting of extensive installations at established observatory sites. We show that this network can be enhanced by the addition of remote quantum magnetometers which combine high sensitivity with intrinsic calibration. These nodes utilize scalable hardware and run independently of wired communication and power networks. We demonstrate that optically pumped magnetometers, utilizing mass-produced and miniaturized components, offer a single scalable sensor with the sensitivity and stability required for space weather observation. We describe the development and deployment of an off-grid magnetic sensing node, powered by a solar panel, present observed data from periods of low and high geomagnetic activity, and compare it to existing geomagnetic observatories.

Abstract Space weather has the potential to impact ground-based technologies on Earth, affecting many systems including railway signalling. This study uses a recently developed model to analyse the impact of geomagnetically induced currents on railway signalling systems in the United Kingdom during the March 1989 and October 2003 geomagnetic storms. The March 1989 storm is also scaled to estimate a 1-in-100 year and a 1-in-200 year extreme storm. Both the Glasgow to Edinburgh line, and the Preston to Lancaster section of the West Coast Main Line are modelled. No “right side” failures (when unoccupied sections appear occupied) are suggested to have occurred during either storm, and the total number of potential “wrong side” failures (when occupied sections appear clear) is low. However, the modelling indicates “right side” and “wrong side” failures are possible on both routes during the 1-in-100 year and 1-in-200 year extreme storms, with the Glasgow to Edinburgh line showing more total misoperations than the Preston to Lancaster section of the West Coast Main Line. A 1-in-100 year or 1-in-200 year extreme storm would result in misoperations over an extended period of time, with most occurring over a duration of 2–3 h either side of the peak of the storm.

Space weather events can have damaging effects on ground-based infrastructure. Geomagnetically induced currents (GIC) caused by rapid magnetic field fluctuations during geomagnetic storms can negatively affect power networks, railways as well as navigation systems. To reduce such negative impacts, good forecasting capability is essential. In this study we assess the performance of contemporary magnetohydrodynamic (MHD) models in predicting the external-only ground magnetic field perturbations at three United Kingdom observatories during two severe space weather events: September 2017 and March 2015. Simulated magnetic data were acquired via Community Coordinated Modeling Center (CCMC), using the following models: Space Weather Modeling Framework (SWMF), Open Geospace General Circulation Model (Open GGCM) and Lyon–Fedder–Mobarry (LFM) combined with the Rice Convection Model (RCM). All simulations use spacecraft measurements at L1 as their solar wind input in calculating ground perturbations. Qualitative and quantitative comparison between measured and modelled values suggest that the performance of MHD models vary with latitude, the magnetic component and the characteristics of the storm analysed. Most models tend to exaggerate the magnitude of disturbances at lower latitudes but better capture the fluctuations at the highest latitude. For the two storms investigated, the addition of RCM tends to result in overestimation of the amplitude of ground perturbations. The observed data-model discrepancies most likely arise due to the many approximations required in MHD modelling, such as simplified solar wind input or shift in location of the electrojets in the simulated magnetospheric and ionospheric currents. It was found that no model performs consistently better than any other, implying that each simulation forecasts different aspects of ground perturbations with varying level of accuracy. Ultimately, the decision of which model is most suitable depends on specific needs of the potential end user.

Abstract The International Geomagnetic Reference Field (IGRF) is a multi-institute model of the Earth’s magnetic field, compactly described by sets of up to 195 spherical harmonic (Gauss) coefficients to degree and order 13, which allows the continuous evaluation of the field at any location and time on or above the surface. It is developed from satellite and ground-based magnetometer data and describes the large-scale variation of the magnetic field in space and time under quiet conditions. While much effort has been made on improving the forecast of the secular variation of the field over the 5-year intervals between release and renewal, less emphasis has been placed on understanding the spatial errors from a user point of view. In this study, we estimate the large-scale time-invariant spatial uncertainty of the IGRF based on the globally averaged misfit of the model to ground-based measurements at repeat stations and observatories between 1980 and 2021. As the ground measurements are reduced to quiet-time values, the external field is minimized for the purposes of this study. We find the 68.3% confidence interval is 87 nT in the North (X) component, 73 nT in the East (Y) component and 114 nT in vertical (Z) component. Due to the Laplacian distribution of the residuals, the standard deviations are larger at 144, 136 and 293 nT, respectively. Graphical Abstract

Extreme space weather events can have large impacts on ground-based infrastructure important to technology-based societies. Machine learning techniques based on interplanetary observations have proven successful as a tool for forecasting global geomagnetic indices, however, few studies have examined local ground magnetic field perturbations. Nowcast and forecast models which predict the magnitude of the horizontal geomagnetic field, |BH|, and its time derivative, dBHdt, at ground level would be valuable for assessing the potential space weather hazard. We attempt to predict the variation of the magnetic field at the three United Kingdom observatories (Eskdalemuir, Hartland and Lerwick) driven by L1 solar wind parameters. The horizontal magnetic field component and its time derivative are predicted from solar wind plasma and interplanetary magnetic field observations using Long Short-Term Memory (LSTM) networks and hybrid Convolutional Neural Network-LSTM models. A 5-fold grid search cross-validation is used for tuning the hyperparameters in each model. Forecasts were made with 5, 15 and 30-min lead times. Models were trained and validated with geomagnetic storm-only data from 1997 to 2016; their outputs were evaluated with the 7–9th September 2017 storms. The forecast models are only able to predict the directly driven parts of geomagnetic storms (not the substorms) and LSTM models generally perform best. We find the |BH| 15- and 30-min forecasts at Lerwick and Eskdalemuir have some predictive power. The 5-min |BH| forecast as well as all the dBHdt models for Eskdalemuir and all the Hartland models were found to have little or no predictive power. This suggests that the machine learning models have better forecasting power at higher latitude (closer to the auroral zones), where the ground magnetic variation field is larger and during storm onset, which is directly driven by changes in the solar wind.

Abstract Background Migratory animals use information from the Earth’s magnetic field on their journeys. Geomagnetic navigation has been observed across many taxa, but how animals use geomagnetic information to find their way is still relatively unknown. Most migration studies use a static representation of geomagnetic field and do not consider its temporal variation. However, short-term temporal perturbations may affect how animals respond - to understand this phenomenon, we need to obtain fine resolution accurate geomagnetic measurements at the location and time of the animal. Satellite geomagnetic measurements provide a potential to create such accurate measurements, yet have not been used yet for exploration of animal migration. Methods We develop a new tool for data fusion of satellite geomagnetic data (from the European Space Agency’s Swarm constellation) with animal tracking data using a spatio-temporal interpolation approach. We assess accuracy of the fusion through a comparison with calibrated terrestrial measurements from the International Real-time Magnetic Observatory Network (INTERMAGNET). We fit a generalized linear model (GLM) to assess how the absolute error of annotated geomagnetic intensity varies with interpolation parameters and with the local geomagnetic disturbance. Results We find that the average absolute error of intensity is − 21.6 nT (95% CI [− 22.26555, − 20.96664]), which is at the lower range of the intensity that animals can sense. The main predictor of error is the level of geomagnetic disturbance, given by the Kp index (indicating the presence of a geomagnetic storm). Since storm level disturbances are rare, this means that our tool is suitable for studies of animal geomagnetic navigation. Caution should be taken with data obtained during geomagnetically disturbed days due to rapid and localised changes of the field which may not be adequately captured. Conclusions By using our new tool, ecologists will be able to, for the first time, access accurate real-time satellite geomagnetic data at the location and time of each tracked animal, without having to start new tracking studies with specialised magnetic sensors. This opens a new and exciting possibility for large multi-species studies that will search for general migratory responses to geomagnetic cues. The tool therefore has a potential to uncover new knowledge about geomagnetic navigation and help resolve long-standing debates.

Abstract The three candidate models submitted by the British Geological Survey for the 13th generation International Geomagnetic Reference Field are described. These DGRF and IGRF models are derived from vector and scalar magnetic field data from the European Space Agency Swarm satellites and ground observatories, covering the period 2013.9 to 2019.7. The internal field model has time dependence for degrees 1 to 15, represented by order 6 B-splines with knots at six monthly intervals. We also solve for a degree 1 external field time dependence describing annual and semi-annual signals with additional dependence on a bespoke Vector Magnetic Disturbance index. Satellite data are weighted by spatial density, along-track standard deviations, and a larger-scale noise estimator defined in terms of a measure of Local Area Vector Activity at the geographically closest magnetic observatories to the sampled datum. Forecasting of the magnetic field secular variation for 2020–2025 is by advection of the main field using steady core surface flows with steady acceleration applied. We also investigate the performance of the previous generation of candidate secular variation models, for IGRF-12, analysing the agreement of the candidates between 2015 and 2020 with the retrospective IGRF-13. We find that there is no clear distinction between the performance of mathematically and physically extrapolated forecasts in the period 2015–2020. We confirm that the methodology for the BGS IGRF-12 predictions performed well, despite observed secular accelerations that are highlighted by our analysis, and thus justify the methodology used for our IGRF-13 SV candidate.

Abstract We present geomagnetic main field and secular variation time series, at 300 equal-area distributed locations and at 490 km altitude, derived from magnetic field measurements collected by the three Swarm satellites. These Geomagnetic Virtual Observatory (GVO) series provide a convenient means to globally monitor and analyze long-term variations of the geomagnetic field from low-Earth orbit. The series are obtained by robust fits of local Cartesian potential field models to along-track and East–West sums and differences of Swarm satellite data collected within a radius of 700 km of the GVO locations during either 1-monthly or 4-monthly time windows. We describe two GVO data products: (1) ‘Observed Field’ GVO time series, where all observed sources contribute to the estimated values, without any data selection or correction, and (2) ‘Core Field’ GVO time series, where additional data selection is carried out, then de-noising schemes and epoch-by-epoch spherical harmonic analysis are applied to reduce contamination by magnetospheric and ionospheric signals. Secular variation series are provided as annual differences of the Core Field GVOs. We present examples of the resulting Swarm GVO series, assessing their quality through comparisons with ground observatories and geomagnetic field models. In benchmark comparisons with six high-quality mid-to-low latitude ground observatories we find the secular variation of the Core Field GVO field intensities, calculated using annual differences, agrees to an rms of 1.8 nT/yr and 1.2 nT/yr for the 1-monthly and 4-monthly versions, respectively. Regular sampling in space and time, and the availability of data error estimates, makes the GVO series well suited for users wishing to perform data assimilation studies of core dynamics, or to study long-period magnetospheric and ionospheric signals and their induced counterparts. The Swarm GVO time series will be regularly updated, approximately every four months, allowing ready access to the latest secular variation data from the Swarm satellites.

Abstract Earth’s internal magnetic field is generated through motion of the electrically conductive iron-alloy fluid comprising its outer core. Temporal variability of this magnetic field, termed secular variation (SV), results from two processes: one is the interaction between core fluid motion and the magnetic field, the other is magnetic diffusion. As diffusion is widely thought to take place over relatively long, millennial time scales, it is common to disregard it when considering yearly to decadal field changes; in this frozen-flux approximation, core fluid motion may be inferred on the core–mantle boundary (CMB) using observations of SV at Earth’s surface. Such flow models have been used to forecast variation in the magnetic field. However, recent work suggests that diffusion may also contribute significantly to SV on short time scales provided that the radial length scale of the magnetic field structure within the core is sufficiently short. In this work, we introduce a hybrid method to forecast field evolution that considers a model based on both a steady flow and diffusion, in which we adopt a two-step process: first fitting the SV to a steady flow, and then fitting the residual by magnetic diffusion. We assess this approach by hindcasting the evolution for 2010–2015, based on fitting the models to CHAOS-6 using time windows prior to 2010. We find that including diffusion yields a reduction of up to 25% in the global hindcast error at Earth’s surface; at the CMB this error reduction can be in excess of 77%. We show that fitting the model over the shortest window that we consider, 2009–2010, yields the lowest hindcast error. Based on our hindcast tests, we present a candidate model for the SV over 2020–2025 for IGRF-13, fit over the time window 2018.3–2019.3. Our forecasts indicate that over the next decade the axial dipole will continue to decay, reversed-flux patches will increase in both area and intensity, and the north magnetic (dip) pole will continue to migrate towards Siberia.

Abstract By assuming that changes in the magnetic field in the Earth’s outer core are advection-dominated on short timescales, models of the core surface flow can be deduced from secular variation. Such models are known to be under-determined and thus require other assumptions to produce feasible flows. There are regions where poor knowledge of the core flow dynamics gives rise to further uncertainty, such as within the tangent cylinder, and assumptions about the nature of the flow may lead to ambiguous patches, such as if it is assumed to be strongly tangentially geostrophic. We use spherical Slepian functions to spatially and spectrally separate core flow models, confining the flow to either inside or outside these regions of interest. In each region we examine the properties of the flow and analyze its contribution to the overall model. We use three forms of flow model: (a) synthetic models from randomly generated coefficients with blue, red and white energy spectra, (b) a snapshot of a numerical geodynamo simulation and (c) a model inverted from satellite magnetic field measurements. We find that the Slepian decomposition generates unwanted spatial leakage which partially obscures flow in the region of interest, particularly along the boundaries. Possible reasons for this include the use of spherical Slepian functions to decompose a scalar quantity that is then differentiated to give the vector function of interest, and the spectral frequency content of the models. These results will guide subsequent investigation of flow within localized regions, including applying vector Slepian decomposition methods.

Abstract Continuous geomagnetic records of the strength and direction of the Earth's field at the surface extend back to the 1840s. Over the past two centuries, eight observatories have existed in the United Kingdom, which measured the daily field variations using light‐sensitive photographic paper to produce analogue magnetograms. Around 350,000 magnetograms have been digitally photographed at high resolution. However, converting the traces to digital values is difficult and time consuming as the magnetograms can have over‐lapping lines, low quality recordings and obscure metadata for conversion to SI units. We discuss our approach to digitizing the traces from large geomagnetic storms and highlight some of the issues to be aware of when capturing magnetic information from analogue measurements. These include cross‐checking the final digitized values with the recorded hourly mean values from observatory year books and comparing several observatory records for the same storm to catch errors such as sign inversions or incorrect ‘wrap‐around’ of data on the paper records.

The Earth–ionosphere cavity resonator is occupied primarily by the electromagnetic radiation of lightning below 100 Hz. The phenomenon is known as Schumann resonances (SR). SR intensity is an excellent indicator of lightning activity and its distribution on global scales. However, long-term measurements from high latitude SR stations revealed a pronounced in-phase solar cycle modulation of SR intensity seemingly contradicting optical observations of lightning from satellite, which do not show any significant solar cycle variation in the intensity and spatial distribution of lightning activity on the global scale. The solar cycle-modulated local deformation of the Earth–ionosphere cavity by the ionization of energetic electron precipitation (EEP) has been suggested as a possible phenomenon that may account for the observed long-term modulation of SR intensity. Precipitating electrons in the energy range of 1–300 keV can affect the Earth–ionosphere cavity resonator in the altitude range of about 70–110 km and modify the SR intensities. However, until now there was no direct evidence documented in the literature supporting this suggestion. In this paper we present long-term SR intensity records from eight stations, each equipped with a pair of induction coil magnetometers: five high latitude (|lat| > 60°), two mid-high latitude (50° < |lat| < 60°) and one low latitude (|lat| < 30°). These long-term, ground-based SR intensity records are compared on the annual and interannual timescales with the fluxes of precipitating 30–300 keV medium energy electrons provided by the POES NOAA-15 satellite and on the daily timescale with electron precipitation events identified using a SuperDARN radar in Antarctica. The long-term variation of the Earth–ionosphere waveguide’s effective height, as inferred from its cutoff frequency, is independently analyzed based on spectra recorded by the DEMETER satellite. It is shown that to account for all our observations one needs to consider both the effect of solar X-rays and EEP which modify the quality factor of the cavity and deform it dominantly over low- and high latitudes, respectively. Our results suggest that SR measurements should be considered as an alternative tool for collecting information about and thus monitoring changes in the ionization state of the lower ionosphere associated with EEP.