Spectral scaling technique to determine extreme Carrington-level geomagnetically induced currents effects

Space weather events produce variations in the electric current in the Earth's magnetosphere and ionosphere. From these high-altitude atmospheric regions, resulting geomagnetically induced currents (GICs) can lead to fluctuations in ground currents that affect the electric power grid and potentially overload transformers during extreme storms. The most extreme geomagnetic storm on record, known as the 1859 Carrington event, was so intense that ground-based magnetometers were saturated at high magnetic latitudes. The most reliable, unsaturated observation is the hour resolution data from the Colaba Magnetic Observatory in India. However, higher-frequency components-fluctuations at second through minute time cadence-to the magnetic field can play a significant role in GIC-related effects. We present a new method for scaling higher-frequency observations to create a realistic Carrington-like event magnetic field model, using modern magnetometer observations. Using the magnetic field model and ground conductivity models, we produce an electric field model. This method can be applied to create similar magnetic and electric field models for studies of GIC effects on power grids. Plain Language Summary Space weather storms can result in geomagnetically induced currents that can disrupt electric power grids, in the most extreme cases causing blackouts. These extreme storms are rare, but we have on record a case of the 1859 Carrington event which estimates show that if it were to occur today would cause a severe threat to electric power systems. The only reliable magnetic field data from this storm was from an Indian station nearer the equator. Since the effects to power grids are more extreme at higher latitudes, we developed a technique to use magnetic field data from more recent storms along with the 1859 Indian station data to scale the expected magnetic field and electric field from an extreme storm to any latitude. This technique can lead power grid operators to better estimate the effects on their local grids.