We evaluate for the first time the large-scale influence of the upstream interplanetary magnetic field (IMF) magnitude and orientation on Martian proton aurora. We identify a correlation between proton aurora activity and IMF magnitude that varies seasonally, with highest correlations around Mars southern summer solstice when proton aurora activity reaches an annual high. We identify preferentially increased proton aurora emission enhancements at IMF cone and clock angles consistent with the shape of the Parker spiral at Mars’s heliocentric distance. An increased proton aurora occurrence rate is observed for near-radial IMF orientations, particularly evident when the hydrogen corona and bow shock are annually decreased. Lastly, our results suggest that dayside magnetic reconnection may influence proton aurora, which are observed to have preferentially higher occurrence under strong ±Bz orientations. These findings enhance our understanding of the upstream IMF’s influence on Martian proton aurora as an important driver for auroral brightness and occurrence.

We compare observations of hydrogen (H) and protons associated with Martian proton aurora activity, co-evaluating remote sensing and in situ measurements during these events. Following the currently understood relationship between penetrating protons and H energetic neutral atoms (ENAs) in the formation of proton aurora, we observe an expected correlation between the H Lyman-alpha (Ly-α) emission enhancement (used herein as a proxy for H-ENAs) and penetrating proton flux. However, we observe a notable spread in the trend between these two datasets. We find that this spread is contemporaneous with one of two major impacting events: high dust activity or extreme solar activity. Proton aurora events exhibiting a relative excess in penetrating proton flux compared to Ly-α enhancement tend to correspond with periods of high dust activity. Conversely, proton aurora events exhibiting a relative deficit of penetrating proton flux compared to Ly-α enhancement are qualitatively associated with periods of extreme solar activity. Moreover, we find that the largest proton aurora events occur during concurrent dust storm and solar events, primarily due to the compounding intensified increase in H column density above the bow shock. Finally, we present a simplified empirical estimate for Ly-α emission enhancement during proton aurora events based on observed penetrating proton flux and a knowledge of local dust/solar activity at the time, providing a straightforward method for predicting auroral activity when direct observations are not available. The results of this study advance our understanding of the interconnected relationship between H and protons during Martian proton aurora activity.

Proton aurora are the most commonly observed yet least studied type of aurora at Mars. In order to better understand the physics and driving processes of Martian proton aurora, we undertake a multi-model comparison campaign. We compare results from four different proton/hydrogen precipitation models with unique abilities to represent Martian proton aurora: Jolitz model (3-D Monte Carlo), Kallio model (3-D Monte Carlo), Bisikalo/Shematovich et al. model (1-D kinetic Monte Carlo), and Gronoff et al. model (1-D kinetic). This campaign is divided into two steps: an inter-model comparison and a data-model comparison. The inter-model comparison entails modeling five different representative cases using similar constraints in order to better understand the capabilities and limitations of each of the models. Through this step we find that the two primary variables affecting proton aurora are the incident solar wind particle flux and velocity. In the data-model comparison, we assess the robustness of each model based on its ability to reproduce a MAVEN/IUVS proton aurora observation. All models are able to effectively simulate the data. Variations in modeled intensity and peak altitude can be attributed to differences in model capabilities/solving techniques and input assumptions (e.g., cross sections, 3-D versus 1-D solvers, and implementation of the relevant physics and processes). The good match between the observations and multiple models gives a measure of confidence that the appropriate physical processes and their associated parameters have been correctly identified, and provides insight into the key physics that should be incorporated in future models.