<p>Starting in September 2018, a daily repeating extremely elongated cloud was observed extending from the Mars Arsia Mons volcano. We study this Arsia Mons Elongated Cloud (AMEC) using images from VMC, HRSC, and OMEGA on board Mars Express, IUVS on MAVEN, and MARCI on MRO. We study the daily cycle of this cloud, showing how the morphology and other parameters of the cloud evolved with local time. The cloud expands every morning from the western slope of the volcano, at a westward velocity of around 150m/s, and an altitude of around 30-40km over the local surface. Starting around 2.5 hours after sunrise (8.2 Local True Solar Time, LTST), the formation of the cloud resumes, and the existing cloud keeps moving westward, so it detaches from the volcano, until it evaporates in the following hours. At this time, the cloud has expanded to a length of around 1500km. Short time later, a new local cloud appears on the western slope of the volcano, starting around 9.5 LTST, and grows during the morning.</p><p>This daily cycle repeated regularly for at least 90 sols in 2018, around Southern Solstice (Ls 240-300) in Martian Year (MY) 34. According with these and previous &#160;MEx/VMC observations, this elongated cloud is a seasonal phenomenon occurring around Southern Solstice every Martian Year. We study the interannual variability of this cloud, the influence of the Global Dust Storms in 2018 on the cloud&#8217;s properties (S&#225;nchez-Lavega et al., Geophys. Res. Lett. 46, 2019), and its validity as a proxy for the global state of the Martian atmosphere (S&#225;nchez-Lavega et al., J. Geophys. Res., 123, 3020, 2018). We discuss the physical mechanisms behind the formation of this peculiar cloud in Mars.</p>
<p>The wide field of view of the Visual Monitoring Camera (VMC) onboard Mars Express, together with the polar orbit of the spacecraft, make VMC very suitable to monitor polar phenomena on Mars<sup>1</sup>. During Martian Years 34 and 35, Martian polar regions were imaged regularly by VMC, and in this work we use this set of images to analyze the evolution of both north and south polar ice caps. We determine the limits of the ice cap at different longitudes and the total area covered by ice as the season evolves, and we analyze the possible influence of the Global Dust Storm in the evolution of the ice caps regression curves. Finally, we describe a number of mid-scale atmospheric features that develop at the edge of the polar caps.</p><p><sup>1</sup> Hernandez-Bernal et al. &#8221;The 2018 Martian Global Dust Storm Over the South Polar Region Studied With MEx/VMC&#8221; Geophys. Res. Lett. 46, pp 10330-10337 (2019)</p>
<p>During twilight, when the sun is below the horizon, clouds and atmospheric dust can still be illuminated by the sun if they are high enough over the surface, as they escape the shadow of the planet. Observations of this direct illumination can be used to determine the minimum altitude for elevated features. While this is a rare phenomenon on Earth, it is a common occurrence on Mars, where clouds and aerosols can usually reach high altitudes in the mesosphere. Twilight clouds were first observed on Mars by ground-based telescopes, and then by cameras on the Viking orbiters. However, in the last few decades of Mars exploration, most orbiters have been placed in sun-synchronous orbits centered around the afternoon [1], and thus few observations of the twilight have been collected.</p> <p>Mars Express (MEX) is an excellent platform for the study of twilight clouds due to its non-sun-synchronous orbit, The Visual Monitoring Camera (VMC) [2] onboard MEX hasas a wide Field of View (FOV) that allows regular observations of the full disk of Mars (usually including the region in dawn) from the apocenter of the MEX orbit [3]. The current VMC dataset includes ~50 000 images distributed across ~3000 observations (each observation consisting of several consecutive images), covering 8 Martian Years. We have developed a pipeline to perform a the systematic study of twilight clouds in VMC images. As VMC is a broad-band imager, it is not yet possible to properly study the composition of clouds, as the camera cannot distinguish spectroscopically between water ice, dust or CO2. Therefore for simplicity, we use the word &#8220;cloud&#8221; to refer to these elevated features, regardless of their actual composition.</p> <p>We show some examples of twilight clouds in Figure 1. We find three main groups of twilight clouds in a belt around 45&#186;S. These three groups correspond to the regions of Terra Cimmeria, Terra Sirenum, and Aonia Terra, where the clouds appear mostly in the southern winter. Additionally, some of the highest clouds we find correspond to these regions. It is worth noting that an extremely high altitude plume was observed, also at dawn, and around the southern winter solstice, over Terra Cimmeria [6].</p> <p>Whilst we find that these twilight clouds are more common during the Aphelion Cloud Belt season (ACB)[4], the latitudinal distribution of clouds differs from that of the ACB, as the biggest concentration of twilight clouds happens in a belt around 45&#186;S, while the ACB is more or less symmetrical around the equator [5]. This may be explained by the fact that VMC observations are mostly in the early morning, while most ACB studies have been focused on the afternoon [1]. Our results are biased by the fact that we only detect isolated clouds at some altitude over the surface, and that might also be part of the explanation for this discrepancy.</p> <p><img src="data:image/png;base64, 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
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.