Troubles in Mars Ionosphere Modeling

Abstract

The Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft has acquired comprehensive measurements of the properties that determine the density and composition of the ionosphere of Mars, namely solar irradiance, thermospheric density and composition, and electron temperature. Ionospheric models that use these measured properties as inputs ought, in principle, to accurately reproduce corresponding MAVEN measurements of ionospheric density and composition. Here we test whether or not several Mars ionosphere models do so. We focus on the optically thin portion of the photochemical equilibrium region of the dayside ionosphere, 160–200 km altitude, where ionospheric modeling is most straight‐forward. The predictions of Fox et al. (2021, https://doi.org/10.1016/j.icarus.2020.114186) clearly delivered better model‐data agreement than the other models considered herein, although the model of Mayyasi et al. (2019, https://doi.org/10.1029/2019ja026481) also performed well. For the other models assessed herein, model‐data discrepancies in ion densities of at least a factor of two were present in at least one major ion species.

Climatology of Martian Ionospheric Disturbances Deduced From MAVEN Data in 2014–2023

“…As $${{\mathrm{O}}_{2}}^{+}$$ is the dominant ion species of the Martian ionosphere (e.g., Withers, 2023, Figure 1), Jiang et al. (2021) and Basuvaraj et al.…”

Bridging Model–Data Discrepancies in Mars’ Dayside Ionosphere: Exploring Varying Reaction Rate Coefficients

“…Recently, Withers (2023) summarized the current status of the extensive modeling of the Martian ionosphere (how the results of models compare to measurements by NGIMS) and pointed out outstanding questions that remain to be answered. Summaries were provided of recent ionospheric models, which range from simple schemes including only a few key reactions (e.g., Xu et al 2018;Yoshida et al 2021;Hensley et al 2022) to complex self-sustained models that may include hundreds of chemical reactions and different transport terms (e.g., Mayyasi et al 2019;Stone et al 2020;Mukundan et al 2020;Fox et al 2021;Wu et al 2021).…”