On the Saturation (or Not) of Geomagnetic Indices

Most geomagnetic indices are associated with processes internal to the magnetosphere-ionosphere system: convection, magnetosphere-ionosphere current systems, particle pressure, ULF wave activity, etc. The saturation (or not) of various geomagnetic indices under various solar-wind driver functions (a.k.a. coupling functions) is explored by examining plots of the various indices as functions of the various driver functions. In comparing an index with a driver function, “saturation” of the index means that the trend of stronger geomagnetic activity with stronger driving weakens in going from mid-range driving to very strong driving. Issues explored are 1) whether the nature of the index matters (i.e., what the index measures and how the index measures it), 2) the relation of index saturation to cross-polar-cap potential saturation, and 3) the role of the choice of the solar-wind driver function. It is found that different geomagnetic indices exhibit different amounts of saturation. For example the SuperMAG auroral-electrojet indices SME, SML, and SMU saturate much less than do the auroral-electrojet indices AE, AL, and AU. Additionally it is found that different driver functions cause an index to show different degrees of saturation. Dividing a solar-wind driver function by the theoretical cross-polar-cap-potential correction (1+Q) often compensates for the saturation of an index, even though that index is associated with internal magnetospheric processes whereas Q is derived for solar-wind processes. There are composite geomagnetic indices E(1) that show no saturation when matched to their composite solar-wind driver functions S(1). When applied to other geomagnetic indices, the composite S(1) driver functions tend to compensate for index saturation at strong driving, but they also tend to introduce a nonlinearity at weak driving.

Magnetospheric Mass Density as Determined by ULF Wave Analysis

The technique to estimate the mass density in the magnetosphere using the physical properties of observed magnetohydrodynamic waves is known as magnetoseismology. This technique is important in magnetospheric research given the difficulty of determining the density using particle experiments. This paper presents a review of magnetoseismic studies based on satellite observations of standing Alfvén waves. The data sources for the studies include AMPTE/CCE, CRRES, GOES, Geotail, THEMIS, Van Allen Probes, and Arase. We describe data analysis and density modeling techniques, major results, and remaining issues in magnetoseismic research.

Quantifying the nonlinear dependence of energetic electron fluxes in the Earth's radiation belts with radial diffusion drivers

In this study, we use mutual information to characterise statistical dependencies of seed and relativistic electron fluxes in the Earth's radiation belts on ultra low frequency (ULF) wave power measured on the ground and at geostationary orbit . The benefit of mutual information, in comparison to measures such as the Pearson correlation, lies in the capacity to distinguish nonlinear dependencies from linear ones. After reviewing the property of mutual information and its relationship with the Pearson correlation for Gaussian bivariates of arbitrary correlation, we present a methodology to quantify and distinguish linear and nonlinear statistical dependencies that can be generalised to a wide range of solar wind drivers and magnetospheric responses. We present an application of the methodology by revisiting the case events studied by Rostoker et al. (1998). Our results corroborate the conclusions of Rostoker et al. (1998) that ULF wave power and relativistic electron fluxes are statistically dependent upon one another. However, we find that observed enhancements in relativistic electron fluxes correlate modestly, both linearly and nonlinearly, with the ULF power spectrum when compared with values found in previous studies (Simms et al., 2014), and with values found between seed electrons and ULF wave power for the same case events. Our results are indicative of the importance in incorporating data analysis tools that can quantify and distinguish between linear and nonlinear interdependencies of various solar wind drivers.