The organized nonlinear dynamics of the magnetosphere

Abstract

The linear prediction filters computed by Bargatze et al. [1985] have resulted in a turning point in the study of solar wind‐magnetosphere coupling. The evolution of the filters with varying activity provides a clear demonstration that the coupling is nonlinear. The filters have thus brought about the end of one era of linear correlative studies and the beginning of a new era of nonlinear dynamical studies. Two separate, but complementary, approaches have emerged in these dynamical studies, analogue modeling and data‐based phase space reconstruction. The reconstruction research has evolved from the original autonomous method studies, which were not conclusive, to the more recent input‐output studies that are more appropriate for the solar wind‐driven magnetosphere and have produced more reliable results. At present it appears that the modeling and reconstruction approaches may be merged in future attempts to produce analogue models directly from the results of the input‐output data‐based methods. If this can be accomplished, it will constitute a major step forward toward the goal of a low‐dimensional analogue model of the magnetospheric dynamics derived directly from data and interpreted in terms of magnetospheric physics. These developments are reviewed in three sections: autonomous data analysis methods, analogue models, and input‐output data analysis methods. The introduction provides sufficient information to read each of these sections independently.

Substorms: A global instability of the magnetosphere‐ionosphere system

“…Moreover, it is normally the case that solar wind driving (i.e., energy loading) occurs even as magnetotail energy unloading progresses. This leads to important nonlinear interactions [e.g., Klimas et al, 1996]. Thus, while Akasofu [1994] has cautioned against too much acceptance of the reconnection paradigm of substorms, it seems that a properly balanced view of the "driven-unloading" debate has generally emerged.…”

Intermittency and multifractional Brownian character of geomagnetic time series

“…Its complex dynamics, which results from the superposition of the internal dynamics and the external driving due to the changes of solar wind conditions, has been widely investigated during the past two decades. Several studies have been devoted to examine the occurrence of chaos, turbulence and criticality during geomagnetic storms and substorms, which are the most important manifestations of the magnetospheric activity (e.g., Klimas et al, 1996Klimas et al, , 2000Consolini and Chang, 2001;Dobias and Wanliss, 2009). These studies have clearly evidenced that the magnetospheric evolution is characterized by a near-criticality dynamics, which manifests both in the scale-invariant nature of magnetotail relaxation events and fractal properties of geomagnetic time series.…”

Support vector machine as an efficient tool for high‐dimensional data processing: Application to substorm forecasting

“…This hypothesis is supported by different approaches including autonomous data analysis methods, low-dimensional input-output analogue models [Sharma, 1995;Klimas et al, 1996], and the selforganized criticality approach [Chang, 1999].…”

“…Also, the nonlinear data-driven models of geomagnetic activity have been shown to be superior to the linear ones [e.g., Klimas et al, 1996] Forecasting accuracy can be improved by using more detailed solar wind and interplanetary magnetic field (IMF) data. Simplifying feature extraction based on a priori assumptions can lead to the loss of important information and can result in less accurate forecasting models.…”