Solar flares are commonly accompanied by coronal mass ejections (CME), and thus CMEs display similar size distributions and waiting time distributions as solar flares do. However, some studies report relatively steep power law slopes with values of , which most likely are caused by a bias due to neglecting background subtraction in GOES data. The datasets from LASCO/SOHO are not affected by this background bias, because the white light background from CMEs appears to be sufficiently faint or nonexisting. Waiting time distributions are sampled from a variety of CME and flare catalogs, such as CDAW, LASCO/SOHO, ARTEMIS, CACTus SEEDS, and CORIMP. These waiting time distributions are found to be consistent with the theoretical prediction of the standard FD-SOC model.

Self-organized criticality (SOC) is a theoretical concept that describes the statistics of nonlinear processes. It is a fundamental principle common to many nonlinear dissipative systems in the universe. Due to its ubiquity, SOC is a law of nature, for which we derive a theoretical framework and specific macroscopic physical models. Introduced by Bak, Tang, and Wiesenfeld in 1987, the SOC concept has been applied to laboratory experiments of sandpiles, to human activities such as population growth, language, economy, traffic jams, or wars, to biophysics, geophysics, magnetospheric physics, solar physics, stellar physics, and to galactic physics and cosmology. From an observational point of view, the hallmark of SOC behavior is the power law shape of occurrence frequency distributions of spatial, temporal, and energy scales, implying scale-free nonlinear processes. Power laws are neither a necessary nor a sufficient condition for SOC behavior, because intermittent turbulence produces power law-like size distributions also. A novel trend that is ongoing in current SOC research is a paradigm shift from “microscopic” scales toward “macroscopic” modeling based on physical scaling laws.

Empirically we find that many phenomena in planetary science reveal power law-like size distributions with a power law slope of for differential size distributions, or for cumulative size distributions. These observational results fully agree with theoretical predictions of the FD-SOC model. These observations include lunar craters, Saturn ring particles, near-Earth objects, Jovian Trojans, asteroids, Neptune Trojans, the Kuijper belt, and extrasolar planets. Mars fluvial systems and dust storms reveal fractal structures. Terrestrial gamma-ray flashes indicate also scale-free power law slopes. It appears that the scale-free behavior of planetary phenomena could result from both accumulation and fragmentation processes.

Solar flare hard X-ray events are produced by the electron thick-target bremsstrahlung process at electron energies of ~20 keV. Large statistical samples of hard X-ray fluxes, fluences, energies, flare durations, and waiting times have been observed with instruments from three different spacecraft (HXRBS/SMM, BATSE/CGRO, and RHESSI) from three different solar cycles and analyzed with different automated event detection methods. Despite of this large variety of data, all datasets reveal self-consistent results, for instance, power law peak fluxes with a slope of , which match the theoretical prediction of the fractal-diffusive SOC model, that is, . Systematic errors and uncertainties of these datasets include insufficient fitting ranges, spacecraft orbital data gaps, finite-size effects, south Atlantic anomaly data gaps, instrumental sensitivity, incomplete samples, thresholded event selection, and background subtraction.

A key result of solar flare statistics is the continuity of size distributions over nine orders of magnitude, consisting of nanoflares, microflares, and large flares, covering a range of ~1024–1033 ergs in energy. The FD-SOC model predicts power law distribution functions with a slope of when the energy of flare events are derived from the flare event 2-D area , but a flatter slope of , if the flare energies are derived from the volume-integrated total flux of the 3-D flare volume. These predictions match the observations of EUV nanoflares and microflares. These scaling laws imply more energy is distributed at large flare sizes , and thus, makes nanoflares less important for coronal heating. Such scaling laws are numerically simulated with cellular automaton codes and are applied to the time evolution of coronal loops, magnetic field line breading, and magnetic reconnection processes.

Among black-hole systems, we find a variety with applications of SOC, such as soft gamma-ray repeaters, magnetars, blazars, black holes in accretion disks, and galactic fast radio bursts. Gamma-ray bursts, soft gamma-ray repeaters, as well as black-hole objects, are found to be self-consistent with the theoretical prediction of the FD-SOC mode. Galactic phenomena that possibly have some characteristics in common with SOC models are: fractal galaxy distributions; cosmic ray energy spectrum; extragalactic fast radio bursts; and extragalactic background fluxes.

Research in “complex physics” or “nonlinear physics” is rapidly expanding across various science disciplines, for example, in mathematics, astrophysics, geophysics, magnetospheric physics, plasma physics, biophysics, and sociophysics. What is common among these science disciplines is the concept of “self-organized criticality systems,” which is presented here in detail for observed astrophysical phenomena, such as solar flares, coronal mass ejections, solar energetic particles, solar wind, stellar flares, magnetospheric events, planetary systems, and galactic and black-hole systems. This book explains fundamental questions: Why do power laws, as hallmarks of self-organized criticality, exist? What power law index is predicted for each astrophysical phenomenon? Which size distributions have universality? What can waiting time distributions tell us about random processes? This book is the first monograph that tests comprehensively astrophysical observations of self-organized criticality systems. The highlight of this book is a paradigm shift from microscopic concepts (such as the traditional cellular automaton algorithms) to macroscopic concepts (formulated in terms of physical scaling laws).