Effects of conductivity perturbations in time‐dependent global electric circuit model

Abstract

This paper contributes to the understanding of the influence of conductivity perturbations on the ionospheric potential in the Earth's global electric circuit (GEC). The conductivity perturbations appearing in the middle atmosphere produced by γ ray bursts from magnetars are studied first. The transient response of the ionospheric potential is modeled in this case, and timescales of interest are identified (0.01–10s). In this case modification of ionospheric potential is small. Additionally, the principal effects of topography and reduction of conductivity inside the thundercloud are studied. Both of these factors effectively increase the ionospheric potential for a classic source in the GEC represented by a current dipole leading to formation of two main charge centers of the thunderstorm. On the other hand, for GEC including topography and conductivity reduction in thunderclouds the contribution of sequence of negative cloud‐to‐ground lightning discharges to the ionospheric potential is decreased. Simulation results show a very good agreement with equivalent circuit models for conductivity perturbations with horizontal dimensions exceeding 20 km.

Analysis of the Diurnal Variation of the Global Electric Circuit Obtained From Different Numerical Models

“…For an exponential conductivity profile the GEC models have to converge to the same result as the integral approach (see discussion in Jánský & Pasko, 2015, section 3.1). The integral approach is solved analytically, and 234.8 Ω is obtained.…”

“…The interpretation of the behavior observed in Figure 2 is not straightforward. The conductivity influences ionospheric potential proportionally to ratio of columnar resistance between the bottom negative (BN) and top positive (TP) charge centers, R BN-TP , to the columnar resistance from the earth surface to TIB, R 0-TIB : IE ∼ I s R BN-TP ∕R 0-TIB (e.g., Jánský & Pasko, 2015). The variation of columnar resistances has significant random component due to the distribution of clouds seen in Figure 1b.…”