This is the third post in a series about geomagnetic storms as a global catastrophic risk. A paper covering the material in this series was just released.
My last post examined the strength of certain major geomagnetic storms that occurred before the advent of the modern electrical grid, as well as a solar event in 2012 that could have caused a major storm on earth if it had happened a few weeks earlier or later. I concluded that the observed worst cases over the last 150+ years are probably not more than twice as intense as the major storms that have happened since modern grids were built, notably the storms of 1982, 1989, and 2003.
But that analysis was in a sense informal. Using a branch of statistics called Extreme Value Theory (EVT), we can look more systematically at what the historical record tells us about the future. The method is not magic—it cannot reliably divine the scale of a 1000-year storm from 10 years of data—but through the familiar language of probability and confidence intervals it can discipline extrapolations with appropriate doses of uncertainty. This post brings EVT to geomagnetic storms.
For intuition about EVT, consider the storm-time disturbance (Dst) index introduced in the last post, which represents the average depression in the magnetic field near the equator. You can download the Dst for every hour since 1957: half a million data points and counting. I took this data set, split it randomly into some 5,000 groups of 100 data points, averaged each group, and then graphed the results. Knowing nothing about the distribution of hourly Dst—does it follow a bell curve, or have two humps, or drop sharply below some threshold, or a have long left tail?—I was nevertheless confident that the averages of random groups of 100 values would approximate a bell curve. A cornerstone of statistics, the Central Limit Theorem, says this will happen. Whenever you hear a pollster quote margins of error, she is banking on the Central Limit Theorem.
