Hughes, Andrea ; Chaffin, Mike ; Mierkiewicz, Edwin ; Deighan, Justin ; Schneider, Nick ; Jain, Sonal ; Joltiz, Rebecca ; Kallio, Esa ; Gronoff, Guillaume ; Simon Wedlund, Cyril Simon ; Bisikalo, Dimitry ; Shematovich, Valery ; Gerard, Jean-Claude ; Ritter, Birgit ; Halekas, Jasper ; Girazian, Zachary

Talk presented at AGU Fall Meeting 2020 on 2020-12-16

Abstract: Proton aurora, one of three types of Martian aurora (Deighan et al., 2018), are the most commonly observed type of aurora at Mars (Hughes et al., 2019). Many past efforts to model these phenomena have been unable to fully reproduce the observations, suggesting a gap in our understanding of the underlying physics. We present the results of a Martian proton aurora modeling campaign. Through this campaign we aim to gain a better understanding of the physics and driving processes of Martian proton aurora, with particular emphasis on inter-model and data-model comparisons for these events. We compare results from four different kinetic transport models with unique abilities to represent Martian proton aurora. This campaign is divided into two steps: an inter-model comparison step and a data-model comparison step. A radiative transfer model is used to “forward model” the results of each step into observation space. The first step entails modeling five different test cases using similar constraints in order to better understand the capabilities and limitations of each of the models in the study. Through this step we find that the two primary variables affecting proton aurora are the incident solar wind particle flux and velocity. In each of the models, when the particle flux is decreased by an order of magnitude the corresponding volume emission rate (VER) also decreases by an order of magnitude; similarly, doubling the particle velocity leads to an increase in the proton aurora peak VER and a ~5-10km decrease in the proton aurora peak altitude. Further, we find that increasing the neutral atmospheric temperature (and thereby changing the neutral atmospheric density profile) leads to a slight decrease in the VER and a ~5-10km increase in the proton aurora peak altitude. In the second step, we assess the accuracy of each model based on their abilities to reproduce “typical” and “atypical” proton aurora detections from the MAVEN/IUVS dataset (e.g., Hughes et al., 2019). In so doing, we may better constrain the unique physical processes driving proton aurora that are incorporated in each model. The results of this comparative study provide a new understanding of the primary factors influencing variability in Martian proton aurora, as well as the dominant physics that need be incorporated in future models to accurately represent these events.