Magnetospheric chaos and dynamical complexity response during storm time disturbance

Abstract

Abstract. In this study, we examine the magnetospheric chaos and dynamical complexity response to the disturbance storm time (Dst) and solar wind electric field (VBs) during different categories of geomagnetic storm (minor, moderate and major geomagnetic storm). The time series data of the Dst and VBs are analysed for a period of 9 years using non-linear dynamics tools (maximal Lyapunov exponent, MLE; approximate entropy, ApEn; and delay vector variance, DVV). We found a significant trend between each non-linear parameter and the categories of geomagnetic storm. The MLE and ApEn values of the Dst indicate that chaotic and dynamical complexity responses are high during minor geomagnetic storms, reduce at moderate geomagnetic storms and decline further during major geomagnetic storms. However, the MLE and ApEn values obtained from VBs indicate that chaotic and dynamical complexity responses are high with no significant difference between the periods that are associated with minor, moderate and major geomagnetic storms. The test for non-linearity in the Dst time series during major geomagnetic storm reveals the strongest non-linearity features. Based on these findings, the dynamical features obtained in the VBs as input and Dst as output of the magnetospheric system suggest that the magnetospheric dynamics are non-linear, and the solar wind dynamics are consistently stochastic in nature.

Dynamical Complexity Transitions During High‐Intensity Long Duration Continuous Auroral Activities (HILDCAA) Events: Feature Analysis Based on Neural Network Entropy

“…For instance, Balasis et al. (2006, 2008, 2009), Oludehinwa, Olusola, Bolaji, and Odeyemi (2021), and Oludehinwa, Olusola, Bolaji, Odeyemi, & Njah (2021) found lower entropy/lower complexity around intense magnetic storms, while Michelis and Consolini (2015) found higher entropy/higher complexity around magnetospheric substorms similar to the results presented herein for HILDCAA. So, substorm events and HILDCAA events seem to follow a very similar dynamical behavior.…”

“…Interestingly, the underlying physical processes of the AE signatures as HILDCAA emerges are connected with long‐duration of large amplitude Alfvénic fluctuations, which possess some inherent irregularities in its underlying dynamics. Its influence on the internal dynamics of the magnetosphere would lead to chaotic variation due to the inherent irregularities driven by magnetic reconnections and viscous interactions (Alberti et al., 2020, 2022; Balasis et al., 2009, 2023; Consolini, 2018; Donner et al., 2019; Manshour et al., 2021; Mendes et al., 2017; Ogunsua, 2018; Oludehinwa, Olusola, Bolaji, & Odeyemi, 2021; Oludehinwa, Olusola, Bolaji, Odeyemi, & Njah, 2021; Pavlos, 1994; Pavlos et al., 1992, 1999; Toledo et al., 2021). It is well established that the Earth's magnetosphere is driven by the solar wind and exhibits complex dynamical features especially during severe space weather conditions.…”

Katz Fractal Dimension of Geoelectric Field during Severe Geomagnetic Storms

“…The fractal dimension changeability suggests substantial differences in the level of storm complexity. Comparable results were obtained recently by [24,74], showing that during major geomagnetic storms the strongest nonlinearity features occur. Moreover, there is no unequivocal dependence of the fractal dimension extremes on the maximum value of the Kp-index, as most of the storms shown in Figure 6a-f, were characterized by the maximum value of Kp = 8+.…”

Dynamical Complexity Response in Traveling Ionospheric Disturbances Across Eastern Africa Sector During Geomagnetic Storms Using Neural Network Entropy

“…The degree of disorderliness/irregularities in a dynamical system is defined by the entropy measures and thus describes its dynamical complexity. Notably, the underlying dynamics of the ionosphere as a dynamical system depends on the changes in external influences and its various internal irregularities leading to chaotic behavior and dynamical complexity (Ogunsua, 2018; Ogunsua et al., 2014; Oludehinwa et al., 2021; Papadimitriou et al., 2020; Rabiu et al., 2015; Unnikrishnan et al., 2006; Unnikrishnan & Ravindran, 2010). Based on the dynamical nature of the ionosphere, the occurrence of TIDs possesses some dynamical characteristics that need to be unveiled by determining the degree of dynamical complexity response in the dynamics of the ionosphere.…”