期刊
SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS
卷 15, 期 1, 页码 99-114出版社
AMER GEOPHYSICAL UNION
DOI: 10.1002/2016SW001469
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资金
- EDF Energy
- Institut Royal Meteorologique de Belgique, Belgium
- Institut de Physique du Globe de Paris, France
- Irish Meteorological Service, Ireland
- British Geological Survey, United Kingdom
- European Community's Seventh Framework Programme (FP7) [260330]
- NERC New Investigators grant [NE/J004693/1]
- Natural Environment Research Council [NE/J004693/1, bgs05003] Funding Source: researchfish
- NERC [NE/J004693/1, bgs05003] Funding Source: UKRI
The measurement and collection of digital magnetic field data in Europe extends back to the 1970s, providing over 30 years of data for the analysis of severe space weather. Although paper records can potentially extend these data sets back by over a century, few digitized records are currently available for use in extreme studies. Therefore, we rely on theoretical arguments and modeling to elucidate the largest likely variations of the magnetic field. We assess the relationship, during the three largest storms in the digital era, between variations in the horizontal magnetic field and the largest measured Dst index to estimate likely magnetic variations for more extreme storms in northern and midlatitude Europe. We examine how geomagnetically induced currents (GIC) flow in the UK and French networks during recent severe storms and analyze the sensitivity of these flows to changes in grid parameters. The maximum GIC computed at any one node in the French and UK grids are 44 A and 208 A, respectively. Sensitivity tests show that while gross changes of the whole network structure, such as disconnecting parts of the network, reduces the mean GIC per node, changes in GIC at individual nodes have distinct behaviors implying that local effects are network dependent and require detailed modeling to sufficiently characterize GIC. In addition, the scale factors we have derived allow GIC results from recent storms to be upscaled to estimate the potential risk to the system from more extreme events, such as the Carrington storm in 1859.
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