Solar eruptions and other types of space weather effects can pose a hazard to the high voltage power grids via geomagnetically induced currents (GICs). In worst cases, they can even cause large-scale power outages. GICs are a complex phenomenon, closely related to the time derivative of the geomagnetic field. However, the behavior of the time derivative is chaotic and has proven to be tricky to predict. In our study, we look at the dynamics of the geomagnetic field during active space weather. We try to characterize the magnetic field behavior, to better understand the drivers behind strong GIC events. We use geomagnetic data from the IMAGE (International Monitor for Auroral Geomagnetic Effect) magnetometer network between 1996 and 2018. The measured geomagnetic field is primarily produced by currents in the ionosphere and magnetosphere, and secondarily by currents in the conducting ground. We use the separated magnetic field in our analysis. The separation of the field means that the measured magnetic field is computationally divided into external and internal parts corresponding to the ionospheric and telluric origin, respectively. We study the yearly directional distributions of the baseline subtracted, separated horizontal geomagnetic field,

Space weather, eventually produced by eruptive phenomena in the Sun, can have harmful effects on Earth via, for example,

Even though the phenomenon of GIC has been studied for decades, we still do not have a complete understanding of the physics behind GIC events due to their complexity. To eventually forecast GIC events, we first need to understand the magnetic field dynamics behind them.
The magnetic field that we can measure on the Earth's surface is primarily produced by

GIC is driven by the ground electric fields. These fields are associated with the time derivative of the geomagnetic field,

Several studies have been done focusing on

Our group is approaching the problem of GIC from a slightly different perspective than previous studies. Many GIC studies based on the time derivative of the ground magnetic field, e.g.,

We use 10 s data from the IMAGE (International Monitor for Auroral Geomagnetic Effects) magnetometer network between 1996–2018. Locations of the IMAGE magnetometers at the beginning of 2017 are presented in Fig.

In this study, we use magnetic data separated into external and internal parts, as was done by

IMAGE station locations and name abbreviations in 2017

A schematic of the quantity

The measured, baseline-subtracted, horizontal magnetic field vector is given as a time series,

The quantities

Is there yearly variation in directional distributions of

How large is the geographic variability in these directional distributions and

Are there differences between the external and internal

What is the dependence of

Does the activity level, represented by

Definitions for quantities used in this study.

We also look at the mean horizontal magnetic field directions at stations. Since we are dealing with circular data, we have to take additional measures to get a meaningful average direction. The directional distribution of the time derivative is bimodal, i.e., the values are clustered around two opposite directions (mainly north and south). The following method is used in the case of

First, we construct a histogram of eight bins of the directional values. The bins are (1) [0, 45)

Different quantities related to the horizontal magnetic field at Tromsø station during 1 h of the Halloween event on 30 October 2003. Panels from the top are (1) magnitude of the horizontal magnetic field,

We first look at the magnetic field behavior during a single space weather event. Figure

Next, we examine directional distributions of the separated magnetic field at the IMAGE stations. Figure

We repeat similar analysis on the time derivative of the external and internal field (Fig.

As for the internal

The data from stations in Germany and Poland are available, but they were not included in these plots due to very limited amount of data points fitting the criterion (

Directional distribution of external

Directional distribution of external

Number of 10 s data points in 2017 fitting the criterion

The directional distributions of

The external and internal

Plots of the external

Mean, standard deviation, and minimum and maximum number of stations used in SECS analysis per year. Numbers are calculated from daily values.

Directional distribution of

Directional distribution of

MLT distribution of the number of events (

Figure

Figure

No clear yearly trend is visible. The mean directions are strictly southward at KIL, SOD and OUJ for both external and internal parts of

Mean directions,

Standard deviations of

Standard deviations of

Examples of

Standard deviation of

Mean values (black markers) and standard deviation (bars) of relative change in amplitude,

We also studied how the time,

Figures

For

Also considering the mean value, mean (

Examples of distribution histograms at Kiruna (KIR) for different values of

Figure

Finally, we look at how the field strength changes over a period

Effect of a smaller threshold value for the time derivative was also studied. The other threshold that we used is 0.5

In this analysis, we studied the directional distributions and change in the direction of the separated horizontal magnetic field and its time derivative. The separation was done to better understand the dynamics behind large GIC events. Previous studies have shown that

The separation of the geomagnetic field can be done using several different methods, and each of them has their own advantages and disadvantages

However, the internal part of the separated field has been shown to follow the well known structure of the ground conductivity

The majority of the events chosen with the derivative criterion have a clear southward distribution of

We also noticed clear differences between magnetometer stations located at similar latitudes with

One of the main new discoveries in this research was the asymptotic value and characteristic time scale of the derivative vector. The asymptotic values of the standard deviations of

Standard deviation,

This value is close to the asymptotic values we got for the standard deviation of

Our analysis and that of

Also,

In addition to the change in direction, we also looked at how the amplitude of total

In the last part of our study, we tested a smaller threshold value for the horizontal time derivative. This smaller limit seems to have no major impact on the main results, i.e., the characteristic time scale of the derivative vector or the relative change in amplitude. Plots, using the smaller threshold value for the standard deviation of

As shown in previous studies, the temporal behavior of GIC typically follows

Forecast of

Concerning the ground magnetic field obtained from simulations, we can suggest a simple diagnostic test. Perform a similar analysis for the simulated

Besides first principle simulations, empirical methods are also popular, but they too face problems with

In this study, we first looked at directional distributions of

Mainly southward orientations with both

Clear, station-specific differences in the directional distribution of

There is little variation in the directional distributions and mean directions between years. However,

In the last part of our analysis, we studied the directional change of

During this analysis, we also discovered a curious feature in Kevo (KEV) internal

Yearly directional distributions of

Monthly directional distributions of internal

We also tested a smaller threshold value for the time derivative, 0.5

Histograms of

Standard deviation of

Mean values and standard deviation of

IMAGE data used in this study are available at the website:

MK prepared most of the material and wrote the text with contributions from AV and LJ. SK provided help and ideas with data analysis and interpreting the results. AV and LJ provided expertise on the theoretical discussion.

The contact author has declared that none of the authors has any competing interests.

Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We thank Elena Marshalko for providing useful feedback for the article. We also thank Academy of Finland for funding this project (grant nos. 314670 and 339329). Finally, we thank the institutes that maintain the IMAGE Magnetometer Array: Tromsø Geophysical Observatory of UiT the Arctic University of Norway (Norway), Finnish Meteorological Institute (Finland), Institute of Geophysics Polish Academy of Sciences (Poland), German Research Centre for Geosciences (GFZ, Germany), Geological Survey of Sweden (Sweden), Swedish Institute of Space Physics (Sweden), Sodankylä Geophysical Observatory of the University of Oulu (Finland) and Polar Geophysical Institute (Russia).

This research has been supported by the Academy of Finland (grant nos. 314670 and 339329).Open-access funding was provided by the Helsinki University Library.

This paper was edited by Dalia Buresova and reviewed by two anonymous referees.