Morning dust events observed in the Syria–Solis–Thaumasia region in the southern hemisphere of Mars

Morning dust events observed in the Syria–Solis–Thaumasia region in the southern hemisphere of Mars | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Morning dust events observed in the Syria–Solis–Thaumasia region in the southern hemisphere of Mars Sho Okuno, Kazunori Ogohara This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9099304/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract We investigated the local-time dependence of dust event formation in the Syria–Solis–Thaumasia (SST) region of Mars using 3,298 images acquired by the Emirates eXploration Imager (EXI) from L s = 7° in MY 36 to L s = 43° in MY 37. Through visual inspection of paired images separated by several hours, we determined formation times for small-scale dust events and distinguished them from water ice clouds using 320 nm water ice optical depth retrievals. Dust events occurred throughout the year but exhibited clear seasonal differences in their preferred formation times. During the southern winter, dust events were more likely to form in the afternoon, whereas during the southern spring and summer, several events initiated in the morning, particularly within the mountain ranges surrounding the SST region. Accounting for seasonal variations in observation opportunities, we found that morning dust lifting dominates in the southern spring and summer, while afternoon formation dominates in the southern winter. These results suggest that different mechanisms operate seasonally: morning events may be associated with the breakdown of nocturnal low‑level jets, while topographically driven slope winds also likely enhance dust lifting within the SST mountains. This study highlights the unique capability of the non–sun-synchronous EMM orbit to constrain Martian dust storm formation timing. Mars atmosphere dust storm diurnal variation Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Most of Martian local dust storms have been observed in mid-latitudes, where the westerly jets are located and transient eddies pass through during the seasons except for summer. This is well demonstrated in the Mars Dust Activity Database (MDAD, Battalio and Wang 2021) created from Mars Daily Global Maps (MDGMs, Wang and Richardson 2015). Battalio and Wang (2021) clearly showed that sequential dust storms tended to be observed in mid-latitudes of both hemispheres concentrating around the three storm zones. Non-sequential dust storms, whose lifetime is shorter than sequential one’s, showed the same regional characteristics as that of sequential dust storms. Another outstanding region accompanied with a lot of dust storms was the corridor from Acidalia Planitia to Valles Mariners, where cross-equatorial flushing dust storms have been observed many times (Wang et al. 2003). Battalio and Wang (2019) focused on the southern part of the corridor, the Aonia-Solis-Valles Marineris (ASV, Fig. 1 a) region located in lower latitudes than the center latitude of the southern mid-latitude dust storms, and suggested contributions of atmospheric phenomena related to the baroclinic instability to dust storms observed there during the southern spring. Around the northwestern edge of ASV, several non-sequential dust storms were detected while sequential ones were not. Non-sequential local dust storms were also observed in the same region by Emirates eXploration Imager (EXI, Jones et al., 2021) onboard the Emirates Mars Mission (EMM, Amiri et al., 2022). Guha et al. (2024) reported the spatial, seasonal and local time distributions of dust storms in MY 36 observed by EXI, showing several local dust storms that formed in the Syria-Solis-Thaumasia Planum region (SST for brevity, Fig. 1 ), the western part of ASV. While they demonstrated the latitude-season distributions of all observed dust storms along with information on local times of the dust storms’ initiation, peak, and end phases, they did not clearly mention local time of the dust storm formation in SST. Then, we extracted dust storms observed in SST from the dust storm database that Guha (2023) released to the public, and visualized the local times and locations where EXI first spotted the dust storms. Figure 1 b shows that half of the 14 dust storms detected in SST by Guha et al. (2024) seemed to form before noon. This is an interesting result, as the most promising process responsible for the development of small-scale dust events has been thought to be deep convection peaking in the early afternoon (Spiga et al. 2013; Heavens et al. 2019). What should be noted here is that Fig. 1 b does not necessarily mean the local times of the dust storm formations but that the dust storms had already existed at least at the local times because they may have started to form before the locations of the dust storms got visible from EXI. If EXI had observed the dust-free state just a few hours before the first detections of the dust storms, it could determine the local time of the dust storm formations. However, it was not explicitly stated how many such cases were included in their database. In this study, therefore, we visually reinspect images of SST observed by EXI during almost the same time period as the period Guha et al. (2024) investigated and reveal how many dust events, including small-scale dust-related phenomena, start to form in the morning near SST. If numerous dust events forming in the morning are observed, it would be necessary to devise some mechanism capable of generating strong winds near the surface during the morning rather than the afternoon. The data used in the present study and how the formation local time is determined are described in the next section. The spatial distribution and formation local time of dust events we spotted are shown for each season in Section 3. The potential processes for producing strong surface winds in the morning are suggested in Section 4. 2. Materials and Methods 2.1 Data We used calibrated radiance data, so-called Level 2a, of the three bands (320, 546, and 635 nm) observed by EXI in mode XOS1 (Jones et al. 2021), which were acquired from the EMM Science Data Center. Datasets of water ice optical depth (IOD) retrieved from the 320 m band images of the Level 2a data, called Level 3, were also used for distinguishing dust and water ice cloud (Wolff et al. 2022). Based on the method described by Ogohara et al. (2012), global latitude-longitude images with a spatial resolution of 0.1 ° \(\times\) 0.1 ° were generated from Levels 2a and 3 data for all wavelengths, while the Level 2a data for 546 nm observed in XOS1 has lower spatial resolution than the data for the other two wavelengths. Image subsets of SST such as Fig. 1 b were extracted from the global longitude-latitude images. The method for photometric correction proposed by Wang and Richardson (2015) was adapted to the image subsets to generate the false-color images presented in this study. Although it was developed for Mars Color Imager onboard Mars Reconnaissance Orbiter (MRO/MARCI), it is sufficiently applicable for the purpose of this study: to homogenize brightness differences due to incidence angle and improve the visibility of the local patterns inherent in dust events. We regarded the regions where incidence and emission angles are less than 88 ° and 80 ° , respectively, as being visible because incidence angles close to 90 ° lead to brightness saturation after the photometric correction and large emission angles result in the severe degradation of spatial resolution. The total number of 3,298 image subsets for each wavelength covering the period from \({L}_{\text{s}}={7}^{\circ}\) in MY 36 through \({L}_{\text{s}}={43}^{\circ}\) in MY 37 were visually inspected while the whole area of SST was not always visible in the image subsets. Data around \({L}_{\text{s}}={110}^{\circ}\) in MY 36 were missing due to a combination of solar conjunction and spacecraft safing events (Wolff et al. 2022). 2.2 Definition of the formation local time As mentioned in Section 1, to determine the formation time of a dust event, a pair of images is required: the image where the dust storm was first observed and an image taken a few hours earlier, before the dust storm occurred. Figure 2 shows an example of image sequences we visually checked, in which one of the local dust storms listed by Guha (2023) is clearly seen (Figs. 2 c and 2 d). Centroid longitude and latitude recorded in their database are shown by light blue circles, while they appear slightly offset from the center of the dust storm covering most of the Valles Marineris. What is more important is that seeds of the dust storm, which did not appear in the horizontal distribution of IOD shown in Fig. 2 f, had already formed about three hours before the first record of the dust storm in their database (blue circles in Fig. 2 b). Because the dust storm seeds are not observed in Fig. 2 a, they should have started to form at time between Figs. 2 a and 2 b. The formation time of this dust storm can be reasonably assumed to be the midpoint between the times the two images were observed. On the other hand, Fig. 2 b shows a tiny dust event depicted by a yellow circle in Sinai Planum, which still remained in Fig. 2 d, albeit as a faint remnant. Figure 2 f shows clouds around the same region as the yellow circle in Fig. 2 b, yet this tiny event appears just as clearly in Fig. 2 b as the dust event indicated by the blue circle. If this were purely cloud, it would appear in Fig. 2 b with a color very similar to the water ice clouds distributed in the high-latitude region of Figs. 2 a-d, and it should appear in Fig. 2 f with the same clarity as the water ice clouds distributed in the high-latitude region of Figs. 2 e-h. Therefore, this tiny event is thought to consist of dust or a mixture of dust and water ice. The point where this event was observed was out of the visible area in Fig. 2 a. Therefore, the formation time of the tiny event cannot be estimated in the same manner as the formation time of the event indicated by the blue circles in Fig. 2 b and the light blue circle in Fig. 2 c. In this case, it is conservative and reasonable to consider the time when this tiny event was first observed as its formation time. Many large dust storms originating further east penetrate into the region of interest in this study because the region overlaps with the western part of ASV (Battalio and Wang 2019). Although these dust storms exhibit distinct textures, they are too large relative to the region shown in Fig. 1 b to allow precise determination of their formation locations and times. Therefore, this study did not include such dust events observed near the eastern edge of this region, which are so large that they extend beyond the boundaries of Fig. 1 b. 3. Results 3.1 Seasonal and regional characteristics Figure 3 a shows the seasonal variation in frequency of dust events detected in SST by the authors’ visual inspection. The blue bars (called Type 1 in Fig. 3 a) indicate dust events whose formation time can be determined based on a pair of the images as shown by the blue circles in Fig. 2 b and the orange bars accumulated on the blue bars (Type 2) mean dust events whose formation time cannot be determined (the yellow circle in Fig. 2 b). Small-scale dust events in this region tend to occur more frequently during the southern winter, though the difference between summer and winter is not significant. However, there is a tendency for many dust events to occur in the season before the southern spring equinox. Figure 3 b shows the spatial distribution of dust events including both Types 1 and 2. Although the correlation between formation local times and locations is unclear, dust events forming in the morning appear to be frequently observed within the mountain ranges encircling Syria, Solis, and Thaumasia. Figures 3 c- 3 f divides Fig. 3 b into the four seasons, showing the seasonal, regional and local time variations of dust event formations more clearly. In the southern winter, most dust events tended to form in the afternoon (Fig. 3 c). In the southern spring, however, about half of the detected dust events started to form in the morning within the mountain ranges encircling Syria, Solis, and Thaumasia (Fig. 3 d). Also in the southern summer and autumn, morning dust events were observed within the mountain ranges while the number of the detected dust events is fewer than those in the other seasons (Figs. 3 e and 3 f). Few dust events formed in the morning outside the mountain ranges (Fig. 3 b). 3.2 Local time of dust event formations The number of observation opportunities per local time varies over time. This is because the length of daylight varies seasonally, and due to the spacecraft's orbital inclination of 25 ° (Amiri et al. 2022), when the spacecraft is positioned over the northern hemisphere of Mars, the emission angle in SST tends to be larger. We determined that areas where the incidence angle exceeds 88 ° or the emission angle exceeds 80 ° are not visible and excluded them from visual inspection (Section 2.1). Therefore, the trends in formation local time across the four seasons shown in Fig. 3 may contain biases that result from the frequency of observation opportunities. When comparing the probability of dust event formation during a particular local time range with that during another local time range, what we need for reducing such biases is not the number of images where the area within the local time range is visible, but the number of image pairs including the local time range which enable us to judge whether a dust event started to form between the two images or nothing occurred. Following the procedure described below, we counted the number of opportunities to observe whether atmospheric conditions changed during the local time range or not, corresponding to the number of such image pairs. First, we divided the region shown in Fig. 1 b into six areas along the longitude direction, labeling them sequentially from 1 to 6 starting from the west. The i -th area is denoted as LB i ( i = 1, 2, …, 6) in this section and corresponds to the longitudinal band from \({240}^{\circ}\text{E}+\left(i-1\right)\times{15}^{\circ}\) to \({240}^{\circ}\text{E}+i\times{15}^{\circ}\) . Next, if more than 50% of LB i is visible in an image subset observed at time t based on the criteria described in Section 2.1, the local time of LB i at time t shall be defined as the local time at the center longitude of LB i at that time, denoted as \({t}_{\text{l}\text{o}\text{c}\text{a}\text{l}}(i,t)\) . If it is not visible, \({t}_{\text{l}\text{o}\text{c}\text{a}\text{l}}(i,t)\) is undefined and set to an appropriate missing value. Finally, when a pair of images taken at two consecutive times ( t and t + Δ t , where Δ t is variable case by case) on a single sol is available, comparing the LB i in these two images allows observation of changes in atmospheric conditions at local time: $$\begin{array}{c}{\varvec{t}}_{\mathbf{l}\mathbf{o}\mathbf{c}\mathbf{a}\mathbf{l}}\left(\varvec{i},\varvec{t}+\frac{\varvec{\Delta}\varvec{t}}{2}\right)=\frac{{\varvec{t}}_{\mathbf{l}\mathbf{o}\mathbf{c}\mathbf{a}\mathbf{l}}\left(\varvec{i},\varvec{t}\right)+{\varvec{t}}_{\mathbf{l}\mathbf{o}\mathbf{c}\mathbf{a}\mathbf{l}}\left(\varvec{i},\varvec{t}+\varvec{\Delta}\varvec{t}\right)}{2},\left(1\right)\end{array}$$ which is undefined if either or both of the two terms on the right-hand side is undefined. In this way, we can obtain the frequency distribution of local times at which dust event formation can potentially be observed in SST for each season. Figure 4 a shows the frequency distributions of t local defined by Eq. (1) and the local times of the dust event formations observed over the entire period of interest, encompassing all LBs. For example, in Fig. 4 a, it can be seen that changes in atmospheric conditions at local times ranging from LT0730 to LT1030 were observable in SST over 1,000 times in total and dust event formations were confirmed in about 12 of these approximately 1,000 times. The ratio of the number of detected dust event formations to the total number of observation opportunities (~ 12/1000) represents the proportion of cases in the sample where dust events form during that local time bin. The population proportion, which is statistically estimated from the sample proportion, represents the probability of dust event formation during that local time bin. The Jeffreys confidence intervals of the population dust event formation probability are shown with error bars in the figure. Jeffreys interval is considered more accurate when the population proportion is small and the lower bound is guaranteed to be non-negative (Brown et al. 2001). Comparing the five local time bins in this figure indicates that dust events are not particularly likely to occur at specific local times. On the other hand, Figs. 4 b- 4 e show the same figures as Fig. 4 a during the four different seasons, respectively. During the southern autumn (Fig. 4 e), the probability of dust event formation appears independent of local time. Judging by the Jeffreys 68% confidence interval, one could just barely say there is a significant difference, but the Jeffreys 95% confidence intervals overlap considerably. During the other seasons (Figs. 4 b, 4 c, and 4 d), a local time dependence seems to exist for dust event formation. Moreover, in the southern winter (Fig. 4 b), dust events are more likely to form in the afternoon, while in the southern spring and summer (Figs. 4 c and 4 d), the probability of dust event formation is highest in the morning. The 95% confidence intervals of LT0900 and LT1500 in Fig. 4 b hardly overlap. While not to the same extent, the confidence intervals for LT0900 and the subsequent two intervals in Fig. 4 c appear relatively well separated. Although the formation of dust events observed is less frequent in the southern summer, the longer visible hours and greater number of observation opportunities mean that the morning and afternoon 95% confidence intervals are relatively well separated (Fig. 4 d), similar to the southern spring. Therefore, Fig. 4 statistically indicates that dust events tend to initiate in the afternoon during the southern winter and in the morning during the southern spring and summer. This result probably means that the atmospheric phenomena responsible for dust lifting in SST are different between these seasons. 4. Discussions There have been few studies revealing dependence of dust event formation on local time because Mars Global Surveyor (MGS) and MRO were sun-synchronous polar orbiters. Among the other spacecrafts, one that can constrain the local time of dust event formations is EMM. In some cases, the local times of the dust storm formations we determined were earlier than those recorded by Guha et al. (2024) as shown in Fig. 2 . In other cases, they misidentified solar reflection on the ground surface or a water ice cloud as a dust storm. However, the local times of the dust event formations we recorded for this study did not conflict with Guha et al. (2024), as the dust events were observed at various local times, including during the morning. Mars Express (MEx) has also observed Mars from an orbit that allows it to constrain the local time of dust storms. Kazama et al. (2025) retrieved dust optical depth using 2.77 \(\mu\) m CO 2 band absorption from spectroscopic data measured by OMEGA onboard MEx and detected 146 local dust storms. While dependency of the dust storms on local time they revealed for L s = 0 ° –180 ° seems to be qualitatively consistent with our result shown in Fig. 4 b, that for L s = 180 ° –360 ° seems to contradict Fig. 4 d. However, these 146 local dust storms were detected across a fairly broad region spanning low to mid-latitudes. Furthermore, since OMEGA could rarely observe the same region again within a few hours, the local times they recorded for the dust storms were not the local times of their formation, but simply the local times when they were present. Therefore, we cannot straightforwardly compare Fig. 4 with the results of Kazama et al. (2025). The most plausible dust event formation mechanism is likely strong winds associated with deep convection during the day. Several numerical simulations have suggested that dust devils and dust storms that develop rapidly during the day are associated with deep convection within the Martian convective boundary layer (e.g., Toigo et al. 2003; Rafkin 2009; Spiga et al. 2013; Nishizawa et al. 2016). However, this cannot explain why the probability of dust event formation is higher in the morning than in the afternoon during the southern spring. It is known that dust storms with distinct textures observed at mid-latitudes in both hemispheres occur in association with mid-latitude extratropical cyclones (Wang et al. 2005, 2011; Ogohara 2025). Yet, if such synoptic-scale transient eddies are the most important factor for dust events in SST, their formation should not depend on local time. Meanwhile, in the Sahara Desert on Earth, dust storms frequently occur in the morning (Schepanski et al. 2009; Knippertz and Todd 2012). The breakdown of the nocturnal low-level jet (LLJ) is considered a leading mechanism for generating strong winds near the desert surface during the morning hours, sufficient to trigger dust storms ahead of the more active convection that occurs in the afternoon (Schepanski et al. 2009). During the night, atmospheric layers above near-surface air layers are frictionally decoupled so that the decoupled layers are not influenced by surface friction because of the high static stability in the near-surface layers. The disappearance of surface friction generally leads to the acceleration of these decoupled layers, sometimes even causing them to exceed the geostrophic wind speed through inertial oscillation, and forms the nocturnal LLJ (van de Wiel et al. 2010). After sunrise, as convective turbulence grows with the onset of solar heating, the decoupled layers become frictionally coupled again to the surface and the momentum of LLJ is mixed down, generating strong winds capable of lifting dust near the surface. The Coriolis parameter f and therefore the inertial period around SST have a different sign but similar absolute values to those of the Saharan Desert region (~ 20 ° N) where high dust activity was detected in the morning, respectively, because the angular velocities of Earth and Mars are nearly the same. Therefore, similar to the Saharan Desert, dust events may occur in SST during the morning due to the breakdown of the LLJ. Another important mechanism for generating strong winds near the surface is likely the local circulation associated with the terrain specific to SST. As indicated in Fig. 3 , most dust events forming in the morning originated inside the mountain ranges surrounding Syria, Solis, and Thaumasia. Slope wind circulation caused by such distinctive terrain may be related to the morning dust events in the SST. 5. Conclusion The present study has revealed local time dependence of dust event formation in the Syria-Solis-Thaumasia region (Fig. 1 ). The data collected over about 1 MY by EXI was checked visually, and the formation local times of dust events were carefully determined. The results showed that among dust events in SST, those forming in the morning tended to be observed particularly during the southern spring and summer, while those forming in the afternoon were dominant in the southern winter. Moreover, the morning dust events were concentrated inside the mountain ranges encircling Syria, Solis, and Thaumasia (Fig. 3 ). Carefully counting the number of observation opportunities meeting conditions sufficient to determine the formation time of dust events statistically revealed that during the southern spring and summer, dust events were significantly more likely to form in the morning than in the afternoon (Figs. 4 c and 4 d). Conversely, during the southern winter, the probability of dust event formation was higher in the afternoon as shown in Fig. 4 b. We can infer that the breakdown of the nocturnal LLJ –– a process that has been considered a leading mechanism for triggering morning dust storms in the Sahara Desert –– is the most plausible explanation for dust event formations in the morning in SST, given that the absolute values of the inertial periods in these two regions are similar. However, the finding that most dust events occurring in the morning were concentrated inside the mountain ranges encircling the SST suggests that atmospheric phenomena such as slope wind circulation associated with the region's characteristic topography may influence the formation of dust events. Coupling with thermal tides and synoptic-scale transient eddies will also need to be considered in the future. Abbreviations ASV: Aonia–Solis–Valles Marineris region EMM: Emirates Mars Mission EXI: Emirates eXploration Imager IOD: water ice optical depth LLJ: low level jet Ls: solar longitude MARCI: Mars Color Imager MDAD: Mars Dust Activity Database MDGM: Mars Daily Global Weather Map MEx: Mars Express MGS: Mars Global Surveyor MRO: Mars Reconnaissance Orbiter MY: Mars Year SST: Syria–Solis–Thaumasia region Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The products of Levels 2a and 3 of EXI are available from the EMM Science Data Center at https://sdc.emiratesmarsmission.ae. The image files where we detected dust events can be uniquely determined by the file names listed in Additional file 1. The topography data shown in Figure 1 were included in the Mars Climate Database 5.0 (Millour et al. 2018). Competing interests The authors declare that they have no competing interest. Funding The present study was supported by JSPS KAKENHI to KO (Grant Numbers JP23K03481). Authors' contributions SO visually extracted dust events from the Mars images prepared by KO and completed the list of the dust events. SO also wrote the first draft of this manuscript. KO prepared Mars image data and completed the manuscript. KO also rechecked the visual detection results of dust events by SO, thereby improving the accuracy of the dust event list, and made the figures in the manuscript. Acknowledgements The present study was supported by JSPS KAKENHI to KO (Grant Numbers JP23K03481). Authors' information SO is working at Remote Sensing Technology Center of Japan (RESTEC). References Amiri HES, Brain D, Sharaf O, et al (2022) The Emirates Mars Mission. Space Sci Rev 218:4. https://doi.org/10.1007/s11214-021-00868-x Battalio M, Wang H (2021) The Mars Dust Activity Database (MDAD): A comprehensive statistical study of dust storm sequences. Icarus 354:114059. https://doi.org/10.1016/j.icarus.2020.114059 Battalio M, Wang H (2019) The Aonia-Solis-Valles dust storm track in the southern hemisphere of Mars. Icarus 321:367–378. https://doi.org/10.1016/j.icarus.2018.10.026 Brown LD, Cai TT, Dasgupta A (2001) Interval Estimation for a Binomial Proportion. 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J Geophys Res 110:1–20. https://doi.org/10.1029/2005JE002423 Wolff MJ, Fernando A, Smith MD, et al (2022) Diurnal Variations in the Aphelion Cloud Belt as Observed by the Emirates Exploration Imager (EXI). Geophys Res Lett 49:. https://doi.org/10.1029/2022GL100477 Supplementary Files GraphicalabstractOkunoOgohara.png SupplementaryFileDustEventFormationListOkunoOgohara.csv Additional Files Additional file 1 File format: CSV Title of data: List of EXI images available for this study Description of data: This consists of five columns, Image ID (f635), Image ID (f546), Image ID (f320), solar longitude, Mars Year. SupplementaryFileImageFileListOkunoOgohara.csv Additional file 2 File format: CSV Title of data: List of dust event formations detected from images listed in Additional file 1 (Data Set S1). Description of data: This consists of eight columns, Image ID (f635), solar longitude, local time at 285 ° E, longitude of dust event, latitude of dust event, local time of dust event formation, Type. The “Type” column is defined in the caption of Figure 3. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 11 Mar, 2026 First submitted to journal 08 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9099304","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618592413,"identity":"dac698b8-8b14-4278-b3ef-ec3c17e96a00","order_by":0,"name":"Sho Okuno","email":"","orcid":"","institution":"Kyoto Sangyo University: Kyoto Sangyo Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Sho","middleName":"","lastName":"Okuno","suffix":""},{"id":618592414,"identity":"707729eb-bb6e-422c-a074-2ffbec903d4e","order_by":1,"name":"Kazunori Ogohara","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYHACNhAhB8TMKMLM2NQiazGGKjIgXktiA7oWnICff/GxBz/32KX3tx9gNuap+CPHwH/GgOFHDQO7OQ4tkjOepRv2PEvOnXEmgTmZ54yBMYNEjgFjzzEGZssG7FoMbpwxk+A5wJy7QYKB+TBvm0Hi/hs8Bgy8DQzMBgdwa5H8c6A+3QCmpQHoMMa/+LSc7zGT5jlwOAGkJRmshSHHgBmfLZIz2NKkZQ4cN5xxJrHZcM4ZY6Bf0goOyxyTwOkXfv7DxyTfHKiW528/fFjiTYUcMMQOb3z4psYmGVeIMUgkwFiMDUw8UCbQSRLJOKOIH8nFjD+QJOyIiNVRMApGwSgYGQAAp1VP5bFUjHUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-7666-4442","institution":"Kyoto Sangyo University: Kyoto Sangyo Daigaku","correspondingAuthor":true,"prefix":"","firstName":"Kazunori","middleName":"","lastName":"Ogohara","suffix":""}],"badges":[],"createdAt":"2026-03-12 02:43:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9099304/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9099304/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106869222,"identity":"6b53a09a-b33d-4d3f-8c52-73a22fe41bd8","added_by":"auto","created_at":"2026-04-14 09:37:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":256266,"visible":true,"origin":"","legend":"\u003cp\u003eTopographic features in the Syria–Solis–Thaumasia region (SST). (a) Global topography map provided in Mars Climate Database (Millour et al. 2018). A thin-lined square indicates ASV (Battalio and Wang 2019) and a thick-lined square indicates SST. (b) Topography of SST with locations (circles) and local times (colors of the circles) of dust storms detected in SST by Guha et al. (2024).\u003c/p\u003e","description":"","filename":"Fig01SSTtopography.png","url":"https://assets-eu.researchsquare.com/files/rs-9099304/v1/80d83fbe64f6946f99304e17.png"},{"id":106869226,"identity":"b658205e-49b2-44a0-adae-18c450967ffc","added_by":"auto","created_at":"2026-04-14 09:37:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":567528,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of false-color images and water ice distributions in SST starting from \u003cem\u003eL\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 145.8\u003csup\u003e°\u003c/sup\u003e of MY 36. (a-d) A false-color image sequence of SST showing the formation and development of several dust events. Local time at the bottom of each panel indicates that at the center longitude, 285\u003csup\u003e°\u003c/sup\u003eE. The date displayed above Panel (a) is the observation date of the 635 nm band image composed of Panel (a). The light blue circles show one of dust storms listed by Guha (2023). The blue circles show the seeds of the dust storm we detected, whose formation local time can be straightforwardly determined. The yellow one indicates the case where the formation time of a dust event cannot be determined. (e-h) A sequence of the horizontal distributions of IOD retrieved from 320 nm band images, enabling us to distinguish dust and water ice cloud. The time stamps of Panels (e-h) are the same as those of the 320 nm band images composed of Panels (a-d), respectively. Data around 305\u003csup\u003e°\u003c/sup\u003eE–30\u003csup\u003e°\u003c/sup\u003eS are missing in the Level 3 product of EXI.\u003c/p\u003e","description":"","filename":"Fig02emmeximmxl5a20211221T2332400150xos1f635fv0601.png","url":"https://assets-eu.researchsquare.com/files/rs-9099304/v1/5b09fd9bcdc9ef17604040ee.png"},{"id":106869223,"identity":"e102d5b9-7d53-4c8d-a7e6-058658aeb026","added_by":"auto","created_at":"2026-04-14 09:37:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":632135,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal and spatial distributions of dust event formations detected in SST. (a) The accumulated histogram of frequency of dust event formations for several seasonal bins. Type 1 indicates dust events whose formation time could be determined by comparing a pair of images. Type 2 indicates dust events whose formation time could not be determined, but whose presence was confirmed in the latter of the two images. (b) Locations (X marks) and local times (colors of the marks) of the dust event formations detected over the period of interest superposed on the topography of SST. (c-f) Panel (b) is divided into the four seasons, (c) the southern winter, (d) the southern spring, (e) the southern summer, and (f) the southern autumn.\u003c/p\u003e","description":"","filename":"Fig03spatialdist.png","url":"https://assets-eu.researchsquare.com/files/rs-9099304/v1/b51e21a3ff9eff54237b865c.png"},{"id":106869225,"identity":"32b0ff73-e186-4169-b85d-f1f06c72be44","added_by":"auto","created_at":"2026-04-14 09:37:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":81109,"visible":true,"origin":"","legend":"\u003cp\u003eLocal time variations in dust event formation probability. (a) The blue and orange bars represent the number of observation opportunities for dust event formation at each local time bin and the number of times dust event formation was actually confirmed, respectively. For each local time bin, the black dot shows the ratio of the orange bar to the blue bar (the right axis), which means the proportion of cases in the sample where dust events form during the local time bin. The error bars indicate the Jeffreys confidence intervals for the population probability of dust event formation during the time bin. The black error bar represents the 95% confidence interval, while the red error bar represents the 68.3% confidence interval, equivalent to 1σ. (b-e) The same panels as Panel (a) for (b) the southern winter, (c) the southern spring, (d) the southern summer, and (e) the southern autumn. The upper bounds of the confidence intervals for local times of sunrise and sunset tend to overshoot due to the small number of observation opportunities. Therefore, comparison with these local time bins makes little sense.\u003c/p\u003e","description":"","filename":"Fig04localtimehistJeffreys.png","url":"https://assets-eu.researchsquare.com/files/rs-9099304/v1/432aff87815417ab32707dbd.png"},{"id":106961073,"identity":"6f9230f7-aa0a-4ed7-829a-2986c76fb6e3","added_by":"auto","created_at":"2026-04-15 09:24:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2163430,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9099304/v1/cb7c9b3d-23f3-4529-8218-87927e2c62b1.pdf"},{"id":106869221,"identity":"c48d6c20-f493-4f54-8855-7c759b63e1c1","added_by":"auto","created_at":"2026-04-14 09:37:29","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":323369,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalabstractOkunoOgohara.png","url":"https://assets-eu.researchsquare.com/files/rs-9099304/v1/57c439fd767dec8432a520d5.png"},{"id":106869219,"identity":"9768c5c5-f29a-4df0-9e44-ad15b7683038","added_by":"auto","created_at":"2026-04-14 09:37:29","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5894,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional Files\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAdditional file 1\u003c/li\u003e\n \u003cli\u003eFile format: CSV\u003c/li\u003e\n \u003cli\u003eTitle of data: List of EXI images available for this study\u003c/li\u003e\n \u003cli\u003eDescription of data: This consists of five columns, Image ID (f635), Image ID (f546), Image ID (f320), solar longitude, Mars Year.\u003c/li\u003e\n\u003c/ul\u003e","description":"","filename":"SupplementaryFileDustEventFormationListOkunoOgohara.csv","url":"https://assets-eu.researchsquare.com/files/rs-9099304/v1/c042e1fc7ce53a59020d778f.csv"},{"id":106869220,"identity":"2e0aab71-cc44-42fb-975b-205189f025b9","added_by":"auto","created_at":"2026-04-14 09:37:29","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":589096,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eFile format: CSV\u003c/li\u003e\n \u003cli\u003eTitle of data: List of dust event formations detected from images listed in Additional file 1 (Data Set S1).\u003c/li\u003e\n \u003cli\u003eDescription of data: This consists of eight columns, Image ID (f635), solar longitude, local time at 285\u003csup\u003e°\u003c/sup\u003eE, longitude of dust event, latitude of dust event, local time of dust event formation, Type. The “Type” column is defined in the caption of Figure 3.\u003c/li\u003e\n\u003c/ul\u003e","description":"","filename":"SupplementaryFileImageFileListOkunoOgohara.csv","url":"https://assets-eu.researchsquare.com/files/rs-9099304/v1/fefbf658f0d760781ec3c978.csv"}],"financialInterests":"","formattedTitle":"Morning dust events observed in the Syria–Solis–Thaumasia region in the southern hemisphere of Mars","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMost of Martian local dust storms have been observed in mid-latitudes, where the westerly jets are located and transient eddies pass through during the seasons except for summer. This is well demonstrated in the Mars Dust Activity Database (MDAD, Battalio and Wang 2021) created from Mars Daily Global Maps (MDGMs, Wang and Richardson 2015). Battalio and Wang (2021) clearly showed that sequential dust storms tended to be observed in mid-latitudes of both hemispheres concentrating around the three storm zones. Non-sequential dust storms, whose lifetime is shorter than sequential one\u0026rsquo;s, showed the same regional characteristics as that of sequential dust storms. Another outstanding region accompanied with a lot of dust storms was the corridor from Acidalia Planitia to Valles Mariners, where cross-equatorial flushing dust storms have been observed many times (Wang et al. 2003). Battalio and Wang (2019) focused on the southern part of the corridor, the Aonia-Solis-Valles Marineris (ASV, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) region located in lower latitudes than the center latitude of the southern mid-latitude dust storms, and suggested contributions of atmospheric phenomena related to the baroclinic instability to dust storms observed there during the southern spring. Around the northwestern edge of ASV, several non-sequential dust storms were detected while sequential ones were not.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNon-sequential local dust storms were also observed in the same region by Emirates eXploration Imager (EXI, Jones et al., 2021) onboard the Emirates Mars Mission (EMM, Amiri et al., 2022). Guha et al. (2024) reported the spatial, seasonal and local time distributions of dust storms in MY 36 observed by EXI, showing several local dust storms that formed in the Syria-Solis-Thaumasia Planum region (SST for brevity, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the western part of ASV. While they demonstrated the latitude-season distributions of all observed dust storms along with information on local times of the dust storms\u0026rsquo; initiation, peak, and end phases, they did not clearly mention local time of the dust storm formation in SST. Then, we extracted dust storms observed in SST from the dust storm database that Guha (2023) released to the public, and visualized the local times and locations where EXI first spotted the dust storms. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb shows that half of the 14 dust storms detected in SST by Guha et al. (2024) seemed to form before noon. This is an interesting result, as the most promising process responsible for the development of small-scale dust events has been thought to be deep convection peaking in the early afternoon (Spiga et al. 2013; Heavens et al. 2019). What should be noted here is that Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb does not necessarily mean the local times of the dust storm formations but that the dust storms had already existed at least at the local times because they may have started to form before the locations of the dust storms got visible from EXI. If EXI had observed the dust-free state just a few hours before the first detections of the dust storms, it could determine the local time of the dust storm formations. However, it was not explicitly stated how many such cases were included in their database. In this study, therefore, we visually reinspect images of SST observed by EXI during almost the same time period as the period Guha et al. (2024) investigated and reveal how many dust events, including small-scale dust-related phenomena, start to form in the morning near SST. If numerous dust events forming in the morning are observed, it would be necessary to devise some mechanism capable of generating strong winds near the surface during the morning rather than the afternoon. The data used in the present study and how the formation local time is determined are described in the next section. The spatial distribution and formation local time of dust events we spotted are shown for each season in Section 3. The potential processes for producing strong surface winds in the morning are suggested in Section 4.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data\u003c/h2\u003e \u003cp\u003eWe used calibrated radiance data, so-called Level 2a, of the three bands (320, 546, and 635 nm) observed by EXI in mode XOS1 (Jones et al. 2021), which were acquired from the EMM Science Data Center. Datasets of water ice optical depth (IOD) retrieved from the 320 m band images of the Level 2a data, called Level 3, were also used for distinguishing dust and water ice cloud (Wolff et al. 2022). Based on the method described by Ogohara et al. (2012), global latitude-longitude images with a spatial resolution of 0.1\u003csup\u003e\u0026deg;\u003c/sup\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e 0.1\u003csup\u003e\u0026deg;\u003c/sup\u003e were generated from Levels 2a and 3 data for all wavelengths, while the Level 2a data for 546 nm observed in XOS1 has lower spatial resolution than the data for the other two wavelengths. Image subsets of SST such as Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb were extracted from the global longitude-latitude images. The method for photometric correction proposed by Wang and Richardson (2015) was adapted to the image subsets to generate the false-color images presented in this study. Although it was developed for Mars Color Imager onboard Mars Reconnaissance Orbiter (MRO/MARCI), it is sufficiently applicable for the purpose of this study: to homogenize brightness differences due to incidence angle and improve the visibility of the local patterns inherent in dust events. We regarded the regions where incidence and emission angles are less than 88\u003csup\u003e\u0026deg;\u003c/sup\u003e and 80\u003csup\u003e\u0026deg;\u003c/sup\u003e, respectively, as being visible because incidence angles close to 90\u003csup\u003e\u0026deg;\u003c/sup\u003e lead to brightness saturation after the photometric correction and large emission angles result in the severe degradation of spatial resolution. The total number of 3,298 image subsets for each wavelength covering the period from \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({L}_{\\text{s}}={7}^{\\circ}\\)\u003c/span\u003e\u003c/span\u003e in MY 36 through \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({L}_{\\text{s}}={43}^{\\circ}\\)\u003c/span\u003e\u003c/span\u003e in MY 37 were visually inspected while the whole area of SST was not always visible in the image subsets. Data around \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({L}_{\\text{s}}={110}^{\\circ}\\)\u003c/span\u003e\u003c/span\u003e in MY 36 were missing due to a combination of solar conjunction and spacecraft safing events (Wolff et al. 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Definition of the formation local time\u003c/h2\u003e \u003cp\u003eAs mentioned in Section 1, to determine the formation time of a dust event, a pair of images is required: the image where the dust storm was first observed and an image taken a few hours earlier, before the dust storm occurred. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows an example of image sequences we visually checked, in which one of the local dust storms listed by Guha (2023) is clearly seen (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Centroid longitude and latitude recorded in their database are shown by light blue circles, while they appear slightly offset from the center of the dust storm covering most of the Valles Marineris. What is more important is that seeds of the dust storm, which did not appear in the horizontal distribution of IOD shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef, had already formed about three hours before the first record of the dust storm in their database (blue circles in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Because the dust storm seeds are not observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, they should have started to form at time between Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb. The formation time of this dust storm can be reasonably assumed to be the midpoint between the times the two images were observed. On the other hand, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb shows a tiny dust event depicted by a yellow circle in Sinai Planum, which still remained in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, albeit as a faint remnant. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef shows clouds around the same region as the yellow circle in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, yet this tiny event appears just as clearly in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb as the dust event indicated by the blue circle. If this were purely cloud, it would appear in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb with a color very similar to the water ice clouds distributed in the high-latitude region of Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-d, and it should appear in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef with the same clarity as the water ice clouds distributed in the high-latitude region of Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee-h. Therefore, this tiny event is thought to consist of dust or a mixture of dust and water ice. The point where this event was observed was out of the visible area in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. Therefore, the formation time of the tiny event cannot be estimated in the same manner as the formation time of the event indicated by the blue circles in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and the light blue circle in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec. In this case, it is conservative and reasonable to consider the time when this tiny event was first observed as its formation time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMany large dust storms originating further east penetrate into the region of interest in this study because the region overlaps with the western part of ASV (Battalio and Wang 2019). Although these dust storms exhibit distinct textures, they are too large relative to the region shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb to allow precise determination of their formation locations and times. Therefore, this study did not include such dust events observed near the eastern edge of this region, which are so large that they extend beyond the boundaries of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Seasonal and regional characteristics\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea shows the seasonal variation in frequency of dust events detected in SST by the authors\u0026rsquo; visual inspection. The blue bars (called Type 1 in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) indicate dust events whose formation time can be determined based on a pair of the images as shown by the blue circles in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and the orange bars accumulated on the blue bars (Type 2) mean dust events whose formation time cannot be determined (the yellow circle in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Small-scale dust events in this region tend to occur more frequently during the southern winter, though the difference between summer and winter is not significant. However, there is a tendency for many dust events to occur in the season before the southern spring equinox. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb shows the spatial distribution of dust events including both Types 1 and 2. Although the correlation between formation local times and locations is unclear, dust events forming in the morning appear to be frequently observed within the mountain ranges encircling Syria, Solis, and Thaumasia. Figures\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef divides Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb into the four seasons, showing the seasonal, regional and local time variations of dust event formations more clearly. In the southern winter, most dust events tended to form in the afternoon (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). In the southern spring, however, about half of the detected dust events started to form in the morning within the mountain ranges encircling Syria, Solis, and Thaumasia (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Also in the southern summer and autumn, morning dust events were observed within the mountain ranges while the number of the detected dust events is fewer than those in the other seasons (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Few dust events formed in the morning outside the mountain ranges (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Local time of dust event formations\u003c/h2\u003e \u003cp\u003eThe number of observation opportunities per local time varies over time. This is because the length of daylight varies seasonally, and due to the spacecraft's orbital inclination of 25\u003csup\u003e\u0026deg;\u003c/sup\u003e (Amiri et al. 2022), when the spacecraft is positioned over the northern hemisphere of Mars, the emission angle in SST tends to be larger. We determined that areas where the incidence angle exceeds 88\u003csup\u003e\u0026deg;\u003c/sup\u003e or the emission angle exceeds 80\u003csup\u003e\u0026deg;\u003c/sup\u003e are not visible and excluded them from visual inspection (Section 2.1). Therefore, the trends in formation local time across the four seasons shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e may contain biases that result from the frequency of observation opportunities. When comparing the probability of dust event formation during a particular local time range with that during another local time range, what we need for reducing such biases is not the number of images where the area within the local time range is visible, but the number of image pairs including the local time range which enable us to judge whether a dust event started to form between the two images or nothing occurred. Following the procedure described below, we counted the number of opportunities to observe whether atmospheric conditions changed during the local time range or not, corresponding to the number of such image pairs. First, we divided the region shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb into six areas along the longitude direction, labeling them sequentially from 1 to 6 starting from the west. The \u003cem\u003ei\u003c/em\u003e-th area is denoted as LB\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003ei\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, 2, \u0026hellip;, 6) in this section and corresponds to the longitudinal band from \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({240}^{\\circ}\\text{E}+\\left(i-1\\right)\\times{15}^{\\circ}\\)\u003c/span\u003e\u003c/span\u003e to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({240}^{\\circ}\\text{E}+i\\times{15}^{\\circ}\\)\u003c/span\u003e\u003c/span\u003e. Next, if more than 50% of LB\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is visible in an image subset observed at time \u003cem\u003et\u003c/em\u003e based on the criteria described in Section 2.1, the local time of LB\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e at time \u003cem\u003et\u003c/em\u003e shall be defined as the local time at the center longitude of LB\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e at that time, denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({t}_{\\text{l}\\text{o}\\text{c}\\text{a}\\text{l}}(i,t)\\)\u003c/span\u003e\u003c/span\u003e. If it is not visible, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({t}_{\\text{l}\\text{o}\\text{c}\\text{a}\\text{l}}(i,t)\\)\u003c/span\u003e\u003c/span\u003e is undefined and set to an appropriate missing value. Finally, when a pair of images taken at two consecutive times (\u003cem\u003et\u003c/em\u003e and \u003cem\u003et\u003c/em\u003e\u0026thinsp;+\u0026thinsp;Δ\u003cem\u003et\u003c/em\u003e, where Δ\u003cem\u003et\u003c/em\u003e is variable case by case) on a single sol is available, comparing the LB\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e in these two images allows observation of changes in atmospheric conditions at local time:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{\\varvec{t}}_{\\mathbf{l}\\mathbf{o}\\mathbf{c}\\mathbf{a}\\mathbf{l}}\\left(\\varvec{i},\\varvec{t}+\\frac{\\varvec{\\Delta}\\varvec{t}}{2}\\right)=\\frac{{\\varvec{t}}_{\\mathbf{l}\\mathbf{o}\\mathbf{c}\\mathbf{a}\\mathbf{l}}\\left(\\varvec{i},\\varvec{t}\\right)+{\\varvec{t}}_{\\mathbf{l}\\mathbf{o}\\mathbf{c}\\mathbf{a}\\mathbf{l}}\\left(\\varvec{i},\\varvec{t}+\\varvec{\\Delta}\\varvec{t}\\right)}{2},\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhich is undefined if either or both of the two terms on the right-hand side is undefined. In this way, we can obtain the frequency distribution of local times at which dust event formation can potentially be observed in SST for each season.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea shows the frequency distributions of \u003cem\u003et\u003c/em\u003e\u003csub\u003elocal\u003c/sub\u003e defined by Eq.\u0026nbsp;(1) and the local times of the dust event formations observed over the entire period of interest, encompassing all LBs. For example, in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, it can be seen that changes in atmospheric conditions at local times ranging from LT0730 to LT1030 were observable in SST over 1,000 times in total and dust event formations were confirmed in about 12 of these approximately 1,000 times. The ratio of the number of detected dust event formations to the total number of observation opportunities (~\u0026thinsp;12/1000) represents the proportion of cases in the sample where dust events form during that local time bin. The population proportion, which is statistically estimated from the sample proportion, represents the probability of dust event formation during that local time bin. The Jeffreys confidence intervals of the population dust event formation probability are shown with error bars in the figure. Jeffreys interval is considered more accurate when the population proportion is small and the lower bound is guaranteed to be non-negative (Brown et al. 2001). Comparing the five local time bins in this figure indicates that dust events are not particularly likely to occur at specific local times. On the other hand, Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee show the same figures as Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea during the four different seasons, respectively. During the southern autumn (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee), the probability of dust event formation appears independent of local time. Judging by the Jeffreys 68% confidence interval, one could just barely say there is a significant difference, but the Jeffreys 95% confidence intervals overlap considerably. During the other seasons (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), a local time dependence seems to exist for dust event formation. Moreover, in the southern winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), dust events are more likely to form in the afternoon, while in the southern spring and summer (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), the probability of dust event formation is highest in the morning. The 95% confidence intervals of LT0900 and LT1500 in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb hardly overlap. While not to the same extent, the confidence intervals for LT0900 and the subsequent two intervals in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec appear relatively well separated. Although the formation of dust events observed is less frequent in the southern summer, the longer visible hours and greater number of observation opportunities mean that the morning and afternoon 95% confidence intervals are relatively well separated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), similar to the southern spring. Therefore, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e statistically indicates that dust events tend to initiate in the afternoon during the southern winter and in the morning during the southern spring and summer. This result probably means that the atmospheric phenomena responsible for dust lifting in SST are different between these seasons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eThere have been few studies revealing dependence of dust event formation on local time because Mars Global Surveyor (MGS) and MRO were sun-synchronous polar orbiters. Among the other spacecrafts, one that can constrain the local time of dust event formations is EMM. In some cases, the local times of the dust storm formations we determined were earlier than those recorded by Guha et al. (2024) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In other cases, they misidentified solar reflection on the ground surface or a water ice cloud as a dust storm. However, the local times of the dust event formations we recorded for this study did not conflict with Guha et al. (2024), as the dust events were observed at various local times, including during the morning. Mars Express (MEx) has also observed Mars from an orbit that allows it to constrain the local time of dust storms. Kazama et al. (2025) retrieved dust optical depth using 2.77 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu\\)\u003c/span\u003e\u003c/span\u003em CO\u003csub\u003e2\u003c/sub\u003e band absorption from spectroscopic data measured by OMEGA onboard MEx and detected 146 local dust storms. While dependency of the dust storms on local time they revealed for \u003cem\u003eL\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0\u003csup\u003e\u0026deg;\u003c/sup\u003e\u0026ndash;180\u003csup\u003e\u0026deg;\u003c/sup\u003e seems to be qualitatively consistent with our result shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, that for \u003cem\u003eL\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 180\u003csup\u003e\u0026deg;\u003c/sup\u003e\u0026ndash;360\u003csup\u003e\u0026deg;\u003c/sup\u003e seems to contradict Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed. However, these 146 local dust storms were detected across a fairly broad region spanning low to mid-latitudes. Furthermore, since OMEGA could rarely observe the same region again within a few hours, the local times they recorded for the dust storms were not the local times of their formation, but simply the local times when they were present. Therefore, we cannot straightforwardly compare Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e with the results of Kazama et al. (2025).\u003c/p\u003e \u003cp\u003eThe most plausible dust event formation mechanism is likely strong winds associated with deep convection during the day. Several numerical simulations have suggested that dust devils and dust storms that develop rapidly during the day are associated with deep convection within the Martian convective boundary layer (e.g., Toigo et al. 2003; Rafkin 2009; Spiga et al. 2013; Nishizawa et al. 2016). However, this cannot explain why the probability of dust event formation is higher in the morning than in the afternoon during the southern spring. It is known that dust storms with distinct textures observed at mid-latitudes in both hemispheres occur in association with mid-latitude extratropical cyclones (Wang et al. 2005, 2011; Ogohara 2025). Yet, if such synoptic-scale transient eddies are the most important factor for dust events in SST, their formation should not depend on local time. Meanwhile, in the Sahara Desert on Earth, dust storms frequently occur in the morning (Schepanski et al. 2009; Knippertz and Todd 2012). The breakdown of the nocturnal low-level jet (LLJ) is considered a leading mechanism for generating strong winds near the desert surface during the morning hours, sufficient to trigger dust storms ahead of the more active convection that occurs in the afternoon (Schepanski et al. 2009). During the night, atmospheric layers above near-surface air layers are frictionally decoupled so that the decoupled layers are not influenced by surface friction because of the high static stability in the near-surface layers. The disappearance of surface friction generally leads to the acceleration of these decoupled layers, sometimes even causing them to exceed the geostrophic wind speed through inertial oscillation, and forms the nocturnal LLJ (van de Wiel et al. 2010). After sunrise, as convective turbulence grows with the onset of solar heating, the decoupled layers become frictionally coupled again to the surface and the momentum of LLJ is mixed down, generating strong winds capable of lifting dust near the surface. The Coriolis parameter \u003cem\u003ef\u003c/em\u003e and therefore the inertial period around SST have a different sign but similar absolute values to those of the Saharan Desert region (~\u0026thinsp;20\u003csup\u003e\u0026deg;\u003c/sup\u003eN) where high dust activity was detected in the morning, respectively, because the angular velocities of Earth and Mars are nearly the same. Therefore, similar to the Saharan Desert, dust events may occur in SST during the morning due to the breakdown of the LLJ. Another important mechanism for generating strong winds near the surface is likely the local circulation associated with the terrain specific to SST. As indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, most dust events forming in the morning originated inside the mountain ranges surrounding Syria, Solis, and Thaumasia. Slope wind circulation caused by such distinctive terrain may be related to the morning dust events in the SST.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe present study has revealed local time dependence of dust event formation in the Syria-Solis-Thaumasia region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The data collected over about 1 MY by EXI was checked visually, and the formation local times of dust events were carefully determined. The results showed that among dust events in SST, those forming in the morning tended to be observed particularly during the southern spring and summer, while those forming in the afternoon were dominant in the southern winter. Moreover, the morning dust events were concentrated inside the mountain ranges encircling Syria, Solis, and Thaumasia (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Carefully counting the number of observation opportunities meeting conditions sufficient to determine the formation time of dust events statistically revealed that during the southern spring and summer, dust events were significantly more likely to form in the morning than in the afternoon (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Conversely, during the southern winter, the probability of dust event formation was higher in the afternoon as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb. We can infer that the breakdown of the nocturnal LLJ \u0026ndash;\u0026ndash; a process that has been considered a leading mechanism for triggering morning dust storms in the Sahara Desert \u0026ndash;\u0026ndash; is the most plausible explanation for dust event formations in the morning in SST, given that the absolute values of the inertial periods in these two regions are similar. However, the finding that most dust events occurring in the morning were concentrated inside the mountain ranges encircling the SST suggests that atmospheric phenomena such as slope wind circulation associated with the region's characteristic topography may influence the formation of dust events. Coupling with thermal tides and synoptic-scale transient eddies will also need to be considered in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eASV: Aonia\u0026ndash;Solis\u0026ndash;Valles Marineris region\u003c/p\u003e\n\u003cp\u003eEMM: Emirates Mars Mission\u003c/p\u003e\n\u003cp\u003eEXI: Emirates eXploration Imager\u003c/p\u003e\n\u003cp\u003eIOD: water ice optical depth\u003c/p\u003e\n\u003cp\u003eLLJ: low level jet\u003c/p\u003e\n\u003cp\u003eLs: solar longitude\u003c/p\u003e\n\u003cp\u003eMARCI: Mars Color Imager\u003c/p\u003e\n\u003cp\u003eMDAD: Mars Dust Activity Database\u003c/p\u003e\n\u003cp\u003eMDGM: Mars Daily Global Weather Map\u003c/p\u003e\n\u003cp\u003eMEx: Mars Express\u003c/p\u003e\n\u003cp\u003eMGS: Mars Global Surveyor\u003c/p\u003e\n\u003cp\u003eMRO: Mars Reconnaissance Orbiter\u003c/p\u003e\n\u003cp\u003eMY: Mars Year\u003c/p\u003e\n\u003cp\u003eSST: Syria\u0026ndash;Solis\u0026ndash;Thaumasia region\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNot applicable.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNot applicable.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe products of Levels 2a and 3 of EXI are available from the EMM Science Data Center at https://sdc.emiratesmarsmission.ae. The image files where we detected dust events can be uniquely determined by the file names listed in Additional file 1. The topography data shown in Figure 1 were included in the Mars Climate Database 5.0 (Millour et al. 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by JSPS KAKENHI to KO (Grant Numbers JP23K03481).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSO visually extracted dust events from the Mars images prepared by KO and completed the list of the dust events. SO also wrote the first draft of this manuscript. KO prepared Mars image data and completed the manuscript. KO also rechecked the visual detection results of dust events by SO, thereby improving the accuracy of the dust event list, and made the figures in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by JSPS KAKENHI to KO (Grant Numbers JP23K03481).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSO is working at Remote Sensing Technology Center of Japan (RESTEC).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmiri HES, Brain D, Sharaf O, et al (2022) The Emirates Mars Mission. Space Sci Rev 218:4. https://doi.org/10.1007/s11214-021-00868-x\u003c/li\u003e\n\u003cli\u003eBattalio M, Wang H (2021) The Mars Dust Activity Database (MDAD): A comprehensive statistical study of dust storm sequences. Icarus 354:114059. https://doi.org/10.1016/j.icarus.2020.114059\u003c/li\u003e\n\u003cli\u003eBattalio M, Wang H (2019) The Aonia-Solis-Valles dust storm track in the southern hemisphere of Mars. Icarus 321:367\u0026ndash;378. https://doi.org/10.1016/j.icarus.2018.10.026\u003c/li\u003e\n\u003cli\u003eBrown LD, Cai TT, Dasgupta A (2001) Interval Estimation for a Binomial Proportion. Statistical Science 16:101\u0026ndash;133\u003c/li\u003e\n\u003cli\u003eGuha BK (2023) Supporting data set for \u0026ldquo;Seasonal and diurnal variations of dust storms in Martian Year 36 based on the EMM-EXI database\u0026rdquo; [Dataset]\u003c/li\u003e\n\u003cli\u003eGuha BK, Gebhardt C, Young RMB, et al (2024) Seasonal and Diurnal Variations of Dust Storms in Martian Year 36 Based on the EMM-EXI Database. J Geophys Res Planets 129:. https://doi.org/10.1029/2023JE008156\u003c/li\u003e\n\u003cli\u003eHeavens NG, Kass DM, Shirley JH, et al (2019) An observational overview of dusty deep convection in Martian dust storms. J Atmos Sci 76:3299\u0026ndash;3326. https://doi.org/10.1175/JAS-D-19-0042.1\u003c/li\u003e\n\u003cli\u003eJones AR, Wolff M, Alshamsi M, et al (2021) The Emirates Exploration Imager (EXI) Instrument on the Emirates Mars Mission (EMM) Hope Mission. Space Sci Rev 217:81. https://doi.org/10.1007/s11214-021-00852-5\u003c/li\u003e\n\u003cli\u003eKazama A, Aoki S, Leseigneur Y, et al (2025) A Statistical Study of Local Dust Storm Occurrences on Mars Using the 2.77 \u0026mu;m CO2 Band Observed by OMEGA/Mars Express. J Geophys Res Planets 130:. https://doi.org/10.1029/2025JE008987\u003c/li\u003e\n\u003cli\u003eKnippertz P, Todd MC (2012) MINERAL DUST AEROSOLS OVER THE SAHARA : METEOROLOGICAL CONTROLS ON EMISSION AND TRANSPORT AND IMPLICATIONS FOR MODELING. Reviews of Geophysics 50:1\u0026ndash;28. https://doi.org/10.1029/2011RG000362.1.INTRODUCTION\u003c/li\u003e\n\u003cli\u003eMillour E, Forget F, Spiga A, et al (2018) The Mars Climate Database (Version 5 . 3 ). In: Scientific Workshop: \u0026ldquo;From Mars Express to ExoMars.\u0026rdquo; Madrid, pp 27\u0026ndash;28\u003c/li\u003e\n\u003cli\u003eNishizawa S, Odaka M, Takahashi YO, et al (2016) Martian dust devil statistics from high-resolution large-eddy simulations. Geophys Res Lett 43:4180\u0026ndash;4188. https://doi.org/10.1002/2016GL068896\u003c/li\u003e\n\u003cli\u003eOgohara K (2025) Martian Local Dust Storms Associated With Extratropical Cyclones in Arcadia Planitia. J Geophys Res Planets 130:. https://doi.org/10.1029/2024JE008455\u003c/li\u003e\n\u003cli\u003eOgohara K, Kouyama T, Yamamoto H, et al (2012) Automated cloud tracking system for the Akatsuki Venus Climate Orbiter data. Icarus 217:661\u0026ndash;668. https://doi.org/10.1016/j.icarus.2011.05.017\u003c/li\u003e\n\u003cli\u003eRafkin SCR (2009) A positive radiative-dynamic feedback mechanism for the maintenance and growth of Martian dust storms. J Geophys Res 114:1\u0026ndash;18. https://doi.org/10.1029/2008JE003217\u003c/li\u003e\n\u003cli\u003eSchepanski K, Tegen I, Todd MC, et al (2009) Meteorological processes forcing Saharan dust emission inferred from MSG-SEVIRI observations of subdaily dust source activation and numerical models. J Geophys Res 114:1\u0026ndash;18. https://doi.org/10.1029/2008JD010325\u003c/li\u003e\n\u003cli\u003eSpiga A, Faure J, Madeleine J-B, et al (2013) Rocket dust storms and detached dust layers in the Martian atmosphere. J Geophys Res Planets 118:746\u0026ndash;767. https://doi.org/10.1002/jgre.20046\u003c/li\u003e\n\u003cli\u003eToigo AD, Richardson MI, Eswald SP, Gierasch PJ (2003) Numerical simulation of Martian dust devils. 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Icarus 215:242\u0026ndash;252. https://doi.org/10.1016/j.icarus.2011.06.029\u003c/li\u003e\n\u003cli\u003eWang H, Zurek RW, Richardson MI (2005) Relationship between frontal dust storms and transient eddy activity in the northern hemisphere of Mars as observed by Mars Global Surveyor. J Geophys Res 110:1\u0026ndash;20. https://doi.org/10.1029/2005JE002423\u003c/li\u003e\n\u003cli\u003eWolff MJ, Fernando A, Smith MD, et al (2022) Diurnal Variations in the Aphelion Cloud Belt as Observed by the Emirates Exploration Imager (EXI). Geophys Res Lett 49:. https://doi.org/10.1029/2022GL100477\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"earth-planets-and-space","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epsp","sideBox":"Learn more about [Earth, Planets and Space](http://earth-planets-space.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/epsp/default.aspx","title":"Earth, Planets and Space","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mars atmosphere, dust storm, diurnal variation","lastPublishedDoi":"10.21203/rs.3.rs-9099304/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9099304/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe investigated the local-time dependence of dust event formation in the Syria\u0026ndash;Solis\u0026ndash;Thaumasia (SST) region of Mars using 3,298 images acquired by the Emirates eXploration Imager (EXI) from \u003cem\u003eL\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 7\u0026deg; in MY 36 to \u003cem\u003eL\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 43\u0026deg; in MY 37. Through visual inspection of paired images separated by several hours, we determined formation times for small-scale dust events and distinguished them from water ice clouds using 320 nm water ice optical depth retrievals. Dust events occurred throughout the year but exhibited clear seasonal differences in their preferred formation times. During the southern winter, dust events were more likely to form in the afternoon, whereas during the southern spring and summer, several events initiated in the morning, particularly within the mountain ranges surrounding the SST region. Accounting for seasonal variations in observation opportunities, we found that morning dust lifting dominates in the southern spring and summer, while afternoon formation dominates in the southern winter. These results suggest that different mechanisms operate seasonally: morning events may be associated with the breakdown of nocturnal low‑level jets, while topographically driven slope winds also likely enhance dust lifting within the SST mountains. This study highlights the unique capability of the non\u0026ndash;sun-synchronous EMM orbit to constrain Martian dust storm formation timing.\u003c/p\u003e","manuscriptTitle":"Morning dust events observed in the Syria–Solis–Thaumasia region in the southern hemisphere of Mars","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 09:37:24","doi":"10.21203/rs.3.rs-9099304/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-07T07:14:46+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T04:16:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-12T02:42:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Earth, Planets and Space","date":"2026-03-09T00:38:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"earth-planets-and-space","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epsp","sideBox":"Learn more about [Earth, Planets and Space](http://earth-planets-space.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/epsp/default.aspx","title":"Earth, Planets and Space","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eec44279-2bb5-4237-a1d1-23b9774d1e17","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T09:37:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 09:37:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9099304","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9099304","identity":"rs-9099304","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}