Dynamic Coupling to the Magnetosphere
Part of the
Space Science Series of ISSI
book series (SSSI, volume 15)
This chapter deals with dynamical coupling of the auroral ionosphere to the magnetosphere. As discussed in the previous chapters, many important questions in auroral physics can be described within a quasi-static picture. However, even the most static arcs do dissipate energy and this energy dissipation must be supplied by an external energy source, which ultimately is the solar wind-magnetosphere interaction. The coupling, undoubtedly, is a dynamical phenomenon. Furthermore, most auroral phenomena, at least the physically most interesting ones, are far from static. This is true in particular during times of magnetospheric activity, such as storms and substorms. Although a full treatment of the storm or substorm physics is beyond the scope of this book, we discuss in this chapter how the magnetospheric activity is related to the auroral phenomena. The basic ways how the magnetosphere controls auroral dynamics are:
Formation of auroral plasma sources in the magnetosphere. For example, diffuse aurorae are produced by direct precipitation through processes taking place in the magnetosphere (Lyons et al., 1999a).
Creation of FAC systems which are associated with parallel electric fields and a host of wave-particle interactions able to energize charged particles.
Changes in plasma sheet parameters that influence the regime and efficiency of field-aligned acceleration. For example, changes in electron temperature and density affect the current-voltage relationship (see Section 3.3.1)
Configurational changes which distort the mapping between the magnetosphere and the ionosphere. This causes observable asymmetries in auroral structures and lead to stresses that can cause rapid motions, particle acceleration, etc.
KeywordsSolar Wind Current Sheet Interplanetary Magnetic Field Flux Tube Plasma Sheet
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
© Springer Science+Business Media Dordrecht 2003