Space Science Reviews

, Volume 198, Issue 1–4, pp 217–266 | Cite as

25 Years of Self-organized Criticality: Numerical Detection Methods

  • R. T. James McAteer
  • Markus J. Aschwanden
  • Michaila Dimitropoulou
  • Manolis K. Georgoulis
  • Gunnar Pruessner
  • Laura Morales
  • Jack Ireland
  • Valentyna Abramenko


The detection and characterization of self-organized criticality (SOC), in both real and simulated data, has undergone many significant revisions over the past 25 years. The explosive advances in the many numerical methods available for detecting, discriminating, and ultimately testing, SOC have played a critical role in developing our understanding of how systems experience and exhibit SOC. In this article, methods of detecting SOC are reviewed; from correlations to complexity to critical quantities. A description of the basic autocorrelation method leads into a detailed analysis of application-oriented methods developed in the last 25 years. In the second half of this manuscript space-based, time-based and spatial-temporal methods are reviewed and the prevalence of power laws in nature is described, with an emphasis on event detection and characterization. The search for numerical methods to clearly and unambiguously detect SOC in data often leads us outside the comfort zone of our own disciplines—the answers to these questions are often obtained by studying the advances made in other fields of study. In addition, numerical detection methods often provide the optimum link between simulations and experiments in scientific research. We seek to explore this boundary where the rubber meets the road, to review this expanding field of research of numerical detection of SOC systems over the past 25 years, and to iterate forwards so as to provide some foresight and guidance into developing breakthroughs in this subject over the next quarter of a century.


Self organized criticality Numerical methods 



The author team acknowledges the hospitality and partial support for two workshops on Self-Organized Criticality and Turbulence at the International Space Science Institute (ISSI) at Bern, Switzerland, during October 15–19, 2012, and September 16–20, 2013. One of us (JMA) was partially supported by a National Science Foundation Career award, NSF AGS-1255024, and NASA contracts NNH12CG10C and NNX13AE03G. One of us (MJA) was partially supported by NASA contract NNX11A099G and NASA contract NG04EA00C of the SDO/AIA instrument to LMSAL. One of us (MKG) was partially supported by EU FP7 grant PIRG07-GA-2010-268245. One of us (VIA) was partially supported by NASA LWS NNX11AO73G grant and by the Program of the Presidium of Russian Academy of Sciences No. 21. The authors acknowledge the comprehensive and dedicated work of an anonymous referee.


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • R. T. James McAteer
    • 1
  • Markus J. Aschwanden
    • 2
  • Michaila Dimitropoulou
    • 3
  • Manolis K. Georgoulis
    • 4
  • Gunnar Pruessner
    • 5
  • Laura Morales
    • 6
  • Jack Ireland
    • 7
  • Valentyna Abramenko
    • 8
    • 9
  1. 1.Solar Physics and Space Weather, Department of AstronomyNew Mexico State UniversityLas CrucesUSA
  2. 2.Lockheed Martin, Solar and Astrophysics Laboratory (LMSAL)STAR LabsPalo AltoUSA
  3. 3.Dept. PhysicsKapodistrian University of AthensAthensGreece
  4. 4.Research Center Astronomy and Applied MathematicsAcademy of AthensAthensGreece
  5. 5.Dept. MathematicsImperial College LondonLondonUnited Kingdom
  6. 6.Departamento de Física, Facultad de Ciencias Exactas y Naturales, Instituto de Física Plasmas (CONICET)Universidad de Buenos AiresBuenos AiresArgentina
  7. 7.ADNET Systems, Inc.NASA Goddard Space Flight CenterGreenbeltUSA
  8. 8.Space Weather Prediction Laboratory, Department of Solar PhysicsCentral Astronomical Observatory of Russian Academy of Science at PulkovoSt. PetersburgRussia
  9. 9.Big Bear Solar Observatory of NJITBig Bear CityUSA

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