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Weakening of Jupiter's main auroral emission during January 2014

2016, Badman, S.V., Bonfond, B., Fujimoto, M., Gray, R.L., Kasaba, Y., Kasahara, S., Kimura, T., Melin, H., Nichols, J.D., Steffl, A.J., Tao, C., Tsuchiya, F., Yamazaki, A., Yoneda, M., Yoshikawa, I., Yoshioka, K.

In January 2014 Jupiter's FUV main auroral oval decreased its emitted power by 70% and shifted equatorward by ∼1°. Intense, low-latitude features were also detected. The decrease in emitted power is attributed to a decrease in auroral current density rather than electron energy. This could be caused by a decrease in the source electron density, an order of magnitude increase in the source electron thermal energy, or a combination of these. Both can be explained either by expansion of the magnetosphere or by an increase in the inward transport of hot plasma through the middle magnetosphere and its interchange with cold flux tubes moving outward. In the latter case the hot plasma could have increased the electron temperature in the source region and produced the intense, low-latitude features, while the increased cold plasma transport rate produced the shift of the main oval.

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Correlation-Based characterisation of time-Varying dynamical complexity in the Earth's magnetosphere

2013, Donner, R.V., Balasis, G.

The dynamical behaviour of the magnetosphere is known to be a sensitive indicator for the response of the system to solar wind coupling. Since the solar activity commonly displays very interesting non-stationary and multi-scale dynamics, the magnetospheric response also exhibits a high degree of dynamical complexity associated with fundamentally different characteristics during periods of quiescence and magnetic storms. The resulting temporal complexity profile has been explored using several approaches from applied statistics, dynamical systems theory and statistical mechanics. Here, we propose an alternative way of looking at time-varying dynamical complexity of nonlinear geophysical time series utilising subtle but significant changes in the linear autocorrelation structure of the recorded data. Our approach is demonstrated to sensitively trace the dynamic signatures associated with intense magnetic storms, and to display reasonable skills in distinguishing between quiescence and storm periods. The potentials and methodological limitations of this new viewpoint are discussed in some detail.