Ionosphere fluctuations and global indices: A scale dependent wavelet-based cross-correlation analysis

Ionosphere corresponds to the ionized upper part of atmosphere. Characterizing Ionosphere variations is of major importance to address both practical (radio-communications and navigation systems) and theoretical (Space–Earth coupling, climatological global change, and human activity impact) issues. Therefore, establishing a statistical description of Ionosphere variations and relating them to potential driving sources such as global solar and geomagnetic activities constitute important stakes. often, Ionosphere variations are described in terms of long-term trends versus short-term fluctuations. To better ground such a separation, instead of performing a classical and arbitrary low-pass versus high-pass filtering operation, it is here, instead, propose to recourse to a scale dependent analysis. It is based on a (continuous) wavelet transform and the wavelet coherence function. Applied to F2-region critical frequency data, locally measured at 11 mid-latitude European stations, as well as to five global solar and geomagnetic indices, these tools show that Ionospheric variations are well described by the superimposition of well-defined long-term cycles with highly correlated fractional Gaussian noise fluctuations. Also, they show that mid-latitude European stations display highly correlated variations even for short-term fluctuations and that, while solar activity mostly drives long-term cycles, short-term fluctuations and scaling properties are essentially controlled by geomagnetic activity.

► Wavelet-based cross-correlation analysis of Ionosphere variability.
► Eleven foF2-measurements and five global indices are used.
► Short-term fluctuations and scaling properties controlled by geomagnetic activity.
► Predictive model based on a scale dependent linear combination of indices and noise.