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Peer-reviewedCC BYNature

Regression to the mean can explain saturation of geomagnetic storms

  • Nithin Sivadas1,2
  • David Gary Sibeck3
  • Varsha Subramanyan4
  • Maria‐Theresia Walach5
  • Dogacan Su Ozturk6
  • Banafsheh Ferdousi7
  • Bayane Michotte de Welle3
7 affiliations
  1. 1Department of Physics, The Catholic University of America, Washington, DC, USA. [email protected]
  2. 2NASA Goddard Space Flight Center, Greenbelt, MD, USA. [email protected]
  3. 3NASA Goddard Space Flight Center, Greenbelt, MD, USA
  4. 4Department of Physics, University of Illinois Urbana-Champaign, Champaign, IL, USA
  5. 5School of Physics and Astronomy, Lancaster University, Lancaster, UK
  6. 6Department of Physics, Geophysical Institute, University of Alaska, Fairbanks, AK, USA
  7. 7Air Force Research Laboratory, Albuquerque, NM, USA
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Abstract

Extreme space weather events on Earth occur during intervals of strong solar wind driving 1 . The solar wind drives plasma convection and currents in the near-Earth space environment 2 . For low values of the driver, the Earth’s response is linear, estimated by parameters such as the polar cap index based on ground magnetometer activity 3 . Curiously, for extreme solar wind driving, the Earth’s response appears not to increase beyond a saturation limit 4 . Theorists have advanced a host of explanations for this saturation effect, but there is no consensus 5 . Here we demonstrate that this saturation is a manifestation of the regression to the mean effect 6 arising from random uncertainty in the timing and magnitude of solar wind measurements. Our results reveal that data analysis underpinning the saturation theories is nonlinearly biased, thereby challenging the validity of the theories. Correcting for the uncertainties reveals that the Earth’s response to solar wind driving is linear throughout, and that the impact of extreme geomagnetic storms can be twice as large as previously thought. We show that regression to the mean is a fundamental property of the relationship between measurement and the truth, where the truth corresponding to the measurement is closer to the mean. This effect is particularly pronounced for uncertain measurements of extreme values and is likely to manifest across various fields, from extreme climate studies to chronic medical pain.

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Cite

BibTeXRIS
BibTeX
@article{sivadas2026regression,
  title = {{Regression to the mean can explain saturation of geomagnetic storms}},
  author = {Nithin Sivadas and David Gary Sibeck and Varsha Subramanyan and Maria‐Theresia Walach and Dogacan Su Ozturk and Banafsheh Ferdousi and Bayane Michotte de Welle},
  journal = {Nature},
  year = {2026},
  month = jul,
  doi = {10.1038/s41586-026-10757-4},
  url = {https://doi.org/10.1038/s41586-026-10757-4},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Regression to the mean can explain saturation of geomagnetic storms
AU  - Nithin Sivadas
AU  - David Gary Sibeck
AU  - Varsha Subramanyan
AU  - Maria‐Theresia Walach
AU  - Dogacan Su Ozturk
AU  - Banafsheh Ferdousi
AU  - Bayane Michotte de Welle
PY  - 2026
DA  - 2026/07/15
JO  - Nature
DO  - 10.1038/s41586-026-10757-4
UR  - https://doi.org/10.1038/s41586-026-10757-4
AB  - Extreme space weather events on Earth occur during intervals of strong solar wind driving 1 . The solar wind drives plasma convection and currents in the near-Earth space environment 2 . For low values of the driver, the Earth’s response is linear, estimated by parameters such as the polar cap index based on ground magnetometer activity 3 . Curiously, for extreme solar wind driving, the Earth’s response appears not to increase beyond a saturation limit 4 . Theorists have advanced a host of explanations for this saturation effect, but there is no consensus 5 . Here we demonstrate that this saturation is a manifestation of the regression to the mean effect 6 arising from random uncertainty in the timing and magnitude of solar wind measurements. Our results reveal that data analysis underpinning the saturation theories is nonlinearly biased, thereby challenging the validity of the theories. Correcting for the uncertainties reveals that the Earth’s response to solar wind driving is linear throughout, and that the impact of extreme geomagnetic storms can be twice as large as previously thought. We show that regression to the mean is a fundamental property of the relationship between measurement and the truth, where the truth corresponding to the measurement is closer to the mean. This effect is particularly pronounced for uncertain measurements of extreme values and is likely to manifest across various fields, from extreme climate studies to chronic medical pain.
SN  - 0028-0836
SN  - 1476-4687
ER  -