Science seems like a straightforward endeavour. You come up with a hypothesis, collect data to prove or disprove it, and analyze that data to see if the hypothesis is right. But anyone who actually does science will tell you many times it\u2019s not that straightforward. And one of the most common complexities is in data analysis. A new paper suggests that one such complexity, known as regression to the mean, might be causing us to massively underestimate how severe solar storms can truly be.<\/p>\n
The interaction between the solar wind and Earth\u2019s magnetosphere can be thought of as a giant dynamo. The combination of solar wind and magnetic field drives high-speed plasma and electric currents down into the upper atmosphere, particularly around the polar caps (hence the appearance of auroras). Scientists typically track this dynamo effect using the Polar Cap Index (PCI).<\/p>\n
One thing has been clear for decades – for moderate solar activity, there is a very clear linear relationship between solar wind electric fields and the electric response measured on Earth. However, for stronger storms, that relationship breaks down as the Earth\u2019s response seems to \u201csaturate\u201d – i.e. cap out at a certain level below what would otherwise be expected. But we never really understood why.<\/p>\n
Fraser talks about the Carrington event – the strongest solar storm on record.<\/p>\n
According to a new paper by Dr. Nithin Sivadas and their co-authors at NASA\u2019s Goddard Space Flight Center, and published in Nature, that saturation might simply be an illusion because of how we measure the strength of the solar storms. They noted that most solar wind measurements come from satellites like WIND, ACE, or DSCOVR, all of which are located at the L1 Earth-Sun Lagrange point – which means they are 1.5 million km closer to the Sun than Earth is.<\/p>\n
That distance introduces a ton of uncertainty. The timing of wind propagation varies, making timing precision difficult. The wind itself can change in the intervening 1.5 million km, and shock fronts can introduce \u201cheteroskedastic noise\u201d – i.e. random errors that grow larger in extreme events. Those errors all make for a situation where we are measuring a highly uncertain value far upstream, which causes a fundamental rule of statistics to come into play – regression to the mean.<\/p>\n
Essentially, scientists paired extreme measurements at the L1 satellites with the smaller, more average geomagnetic responses they triggered in the magnetosphere, since the \u201ctrue\u201d solar wind hitting Earth\u2019s magnetosphere is much more likely to be less extreme and closer to the mean measured there. This mathematical mismatch – of an extreme event at L1 and a more moderate one at Earth, causes a \u201cnonlinear regression bias\u201d in the data curve, artificially bending it and making Earth\u2019s response appear \u201csaturated\u201d.<\/p>\n
Fraser talks about the danger of solar storms with Dr. Benjamin Pope<\/p>\n
To prove their point, the authors applied a \u201cregression calibration\u201d to offset some of the bias. After doing so, the linear relationship between solar storm strength and Earth\u2019s magnetic response continued linearly, with no clear saturation effect. Put simply, a 1 in 1000 year solar storm event would now be much more likely to cause a massive amount of destruction than we had originally thought.<\/p>\n
But this finding isn\u2019t relevant only to space weather. The authors point out that everything from seismology to medical trials can fall victim to the mean masquerading as a threshold. Machine learning exacerbates the problem, as models trained on uncertain inputs will simply learn the statistical illusion and think it’s a physical reality.<\/p>\n
The immediate takeaway is clear, though – Earth\u2019s powergrid and satellite constellation operators need to consider the possibility of a much more massive electrical spike if a very severe solar storm hits. The natural protection we thought we had with the magnetosphere\u2019s saturation unfortunately appears to be just a statistical illusion.<\/p>\n
Learn More:<\/p>\n
NASA – New NASA Study Says Possibly No Limit to Solar Storm Effects<\/a><\/p>\n N. Sivadas et al. – Regression to the mean can explain saturation of geomagnetic storms<\/a><\/p>\n UT – Powerful Solar Storms Can Change Precipitation for Parts of North America<\/a><\/p>\n