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Aeolian activity, the movement of sand and dust by the wind, is common on Earth and has been observed on other planets [1]. Under the current climatic conditions on Mars, aeolian activity is the primary process of surface modification driven by winds, dust storms and wind vortices. Landed and orbiting cameras show that widespread aeolian activity occurs despite low measured and modelled winds, challenging Earth-based theories [2, 3]. Dust particles enter into long-term suspension forming global dust storms which drastically alter the Martian atmospheric dynamics and present hazards to robotic and human missions.Several models have been proposed on the long-standing conundrum of sediment transport on Mars, however, none of these have been verified on the planet. The outstanding question of what wind shear velocities mobilize sediments on Mars has remained elusive despite multiple spacecrafts carrying wind sensors and studying aeolian activity on finer spatial and temporal scales than can be achieved in orbit. Quantitative examination of aeolian activity under natural Martian surface conditions is imperative in validating transport models.The InSight lander has provided a unique opportunity for monitoring simultaneous coverage of aeolian activity on Mars by combining, for the first time, imaging with atmospheric, seismic and magnetic measurements. Previous studies spanned over just half of the first Martian year, from the end of northern winter to midsummer, and observed minor aeolian activity limited to sporadic grain motion and dust devil tracks [4, 5].In this study, we extend observations of aeolian activity for two Martian years, allowing us to infer the seasonal evolution at the landing site. We report a series of remarkable daytime vortex-induced events with pressure excursions up to 10 Pa, including an investigation of the burst in daytime vortices and emergence of nighttime vortices in northern autumn. Despite our observations reinforcing the quiescent aeolian surface environment at InSight, we observe further evidence and constrain timings of surface track formation, saltation, dust lifting and surface creep of coarser particles both on the surface of Mars and lander elements. Such an investigation was previously impossible due to power constraints allowing only intermittent meteorological measurements in the second year and wind-sensor saturation from energetic close vortex encounters that cause surface changes. Here, we derive estimates of vortex-induced peak wind speeds responsible for grain motion based on strong correlations from the excitation of high-frequency lander resonances sensitive to wind forcing measured continuously by the seismometers [6]. This wealth of data allows us to obtain a unique catalogue of complete wind-induced surface activity at InSight over two Martian years. Our findings provide an insight into the long-standing paradox of aeolian transportation on Mars by quantifying the environmental variables responsible for sand motion which help constrain current threshold and transport models.[1] Hayes (2018) Sci. [2] Kok et al. (2012) RPP [3] Newman et al. (2022) Auth. [4] Charalambous et al. (2021) JGR 126(6) e2020JE006538 [5] Baker et al., (2021) JGR [6] Charalambous et al. (2021) JGR 126(4) e2020JE006514.

Air temperature, ground temperature, pressure, and wind speed and direction data obtained from the APSS (Auxiliary Payload Sensor Suite) and HP3 radiometer (RAD) onboard the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) lander are compared to data from the Mars Regional Atmospheric Modeling System. A full diurnal cycle at four different seasons (Ls 0º, 90º, 180º and 270º) is investigated at the lander location at 4.5° N 135.6° E in Elysium Planitia on Mars (Figure shows comparison results for Ls 180º). This work extends the atmospheric observations perform by [1]. Model results are shown to be in good agreement with observations. The good agreement provides justification for utilizing the model results to investigate the broader meteorological environment of Elysium Planitia in a companion paper. The observed air temperature, pressure and winds are taken at ∼1m above ground, while MRAMS provides those values at the lowest atmospheric model level of ∼14 m. As expected, the MRAMS air temperature values at this height tend to be cooler than the observed in the morning and early afternoon, and then tend to be warmer in the late afternoon and through the night. Also, the difference in height should not have a large impact on wind direction, but modeled wind speeds at ∼14 m are faster than the observed at 1.5 m due to frictional effects. Small discrepancies in ground temperatures could be attribute to a different initialization of thermal inertia, dust and clouds in the model when compared with the data. The diurnal pressure amplitude at Elysium Planitia varies from 2.52% to 4.5% depending on the season. The total amplitude is then considerably smaller compared to Gale crater (up to ∼13%, [2]). [3] attributed the amplification at Gale due to a mesoscale hydrostatic adjustment process in regions of topographic slopes. We also use a Computational Fluid Dynamics (CFD) to study the mechanical disturb of the wind directions due to other instruments onboard the lander and it effect into the wind directions discrepancy between modeling and observations [4]. For low wind speeds (~3.4 m/s), there is an important mechanical contamination in the 330º-30º wind directions range for FM1 and in the 210-330º range for FM2 (Figure bottom left), mostly during nighttime. Figure 1. Observed and modeled diurnal air temperature, ground temperature, pressure, wind speed and wind direction signal at Ls 180. MRAMS are the black dots. InSight data taken within a few sols of the Ls 180 are shown in different colors, which each color representing data from a single sol. CFD results with the mechanical disturb (from the higher value -0- to the lower value -1.2-) of the wind directions due to other instruments onboard the lander for low wind speeds (~3.4 m/s) are shown in the bottom left.

Despite the little insolation received by the ice giant planets, the stormy activity of their atmospheres is intense. Indeed Neptune has the strongest winds in the solar system, reaching 400 m/s in addition to being retrograde. What is the phenomenum responsible for this activity ? Interestingly, unlike the Earth, the species able to condense in the atmospheres of Uranus and Neptune, methane in particular, are heavier than the ambient air, essentially hydrogen. This property makes convection difficult to start [1,2]. Convection in these atmospheres should therefore be a regime of strong intermittence where convective energy can be stored for a long time before being released in short episodes. Our hypothesis is that this regime is at the origin of intense storms. Then, these storms could drive the intense winds observed on a global scale. To study this hypothesis, we use a "cloud-resolving" model. This model is built from a dynamical core (The Weather Research and Forecasting model), that has been initially developed for terrestrial applications and already adapted for simulations on Mars and Venus [3,4], coupled to independent physical parameterizations such as a radiative transfer. The high resolution of the model grid can allow us to highlight dry but also moist atmospheric convection, by resolving cloud formation and dissipation. We will present our implementation of methane cycle in this model and first moist simulations, and compare them with simple dry simulations, in order to reveal the impact of Methane cycle in tropospheric convection on Neptune. (1) Leconte J., Selsis F., Hersant F., Guillot T. (2017) Condensation-inhibited convection in hydrogen-rich atmospheres. stability against double-diffusive processes and thermal profiles for Jupiter, Saturn, Uranus, and Neptune Astron. Astrophys., 598 (2017), p. A98, 10.1051/0004- 361/201629140 (2) Hueso R, Guillot T, and Sánchez-Lavega A. (2020) Convective storms and atmospheric vertical structure in Uranus and Neptune. Phil. Trans. R. Soc. A 378: 20190476. http://dx.doi.org/10.1098/rsta.2019.0476 (3) Spiga, A., and F. Forget (2009), A new model to simulate the Martian mesoscale and microscale atmospheric circulation: Validation and first results, J. Geophys. Res., 114, E02009, doi:10.1029/2008JE003242. (4) Lefèvre, M., A. Spiga, and S. Lebonnois (2017), Three-dimensional turbulence-resolving modeling of the Venusian cloud layer and induced gravity waves, J. Geophys. Res. Planets, 122, 134–149, doi:10.1002/2016JE005146.

Uranus and Neptune have a tropopause, stratosphere and a thermosphere inexplicably too hot. This problem is called "Energy Crisis" [1,2]. Radio occultation measurements [3,4] and UV stellar and solar measurements [5,6] by Voyager 2 revealed that their stratosphere reach values of 150 K and their thermosphere reach values of 800 K. Globally-averaged temperature observations [7,8] have also shown a warmer stratosphere than expected. Current radiative equilibrium models can’t explain the observed temperatures [9,10], one or several unknown heat sources are necessarily responsible of this warm atmosphere. Thus, the nature of this or these heat sources is still an open question. To evaluate the role of several heat sources, we use the radiative-convective model developed by the Laboratoire de Météorologie Dynamique (LMD), previously used for the atmospheres of gas giant planets [11,12]. The model employs the correlated-k formalism to compute gaseous opacities from CH4, C2H2 and C2H6, opacities from collision-induced absorption (H2-H2, H2-He, H2-CH4, He-CH4 and CH4-CH4) assuming a H2 ortho/para fraction at equilibrium. Our model accounts for the internal heat flux, for multiple scattering and Rayleigh scattering. Cloud and aerosols physical properties (exctinction coefficient, single scattering albedo and asymmetry factor) are computed offline assuming Mie scattering, given a particle size distribution and optical constants. In the radiative-convective model, their optical depth is computed for each layer from several entry parameters: the total optical depth (at a reference wavelength), the boundary pressures (bottom and top of the layer), and an aerosol scale height [13]. Three hypotheses were explored separately to induce sufficient heating to warm the atmosphere. We varied the methane mole fraction profile, added an ad hoc aerosol layer, and imposed thermal conduction from the thermosphere. Values of observed methane molar fraction in the uranian stratosphere being scattered [14], a sensitivity study was carried out by varying the methane homopause altitude and the stratospheric molar fraction. As expected, a high molar fraction makes it possible to heat the stratosphere and to obtain a tropopause altitude similar to observations. Nevertheless, all tests failed to fully heat the tropopause. This is also expected, as much less solar photons in the near infrared reach these levels. Concerning thermal conduction from the thermosphere, the heating allows to obtain a temperature gradient in upper stratosphere similar to nominal temperature profile from [7]. However, simulated temperature profile is still colder (see figure 1). Simulations showed that temperature profile of Uranus was very sensitive to the addition of an optically thin aerosols layer confined to a few levels in the stratosphere. To obtain a sufficiently important stratospheric heating, this layer requires that the absorption spectrum is relatively intense in ultraviolet and thermal infrared. This type of spectrum is similar to tholins, black carbon or a carbon-NH3 mixture [15]. The relatively dark spectrum of this layer also induces greenhouse effect in the tropopause. In the case of Neptune, all hypothesis tested couldn’t warm stratosphere. Therefore, other heat sources are needed to explain observations, such as a dynamical forcing in stratosphere (...) and/or a coupling with thermospheric dynamics and/or auroral heating. Figure 1: Temperature profiles from our simulations and from observations [3,4,16] at the same latitude and solar longitude of Voyager 2 measurements. Blue curve corresponds to a simulation without aerosols, orange curve aerosolsmodel from [17] is added, and for green curve, we add a conduction from thermosphere with aerosols. Methane molar fraction profile from [14] is used on our simulations. Acknowledgements G. Milcareck, S. Guerlet and A. Spiga acknowledge funding from Agence Nationale de la Recherche (ANR) project SOUND, ANR-20-CE49-00009-01. References [1] Marley, M. S. and McKay, C. P. (1999). Icarus, 138.[2] Fletcher, L. N., de Pater, I., Orton, G. S., Hofstadter, M. D., Irwin, P. G. J., Roman, M. T., and Toledo, D. (2020). Space Science Reviews, 216.[3] Lindal, G. F., Lyons, J. R., Sweetnam, D. N., Eshleman, V. R., Hinson, D. P., and Tyler, G. L. (1987). Journal of Geophysics Research, 92.[4] Lindal, G. F. (1992). Astronomical Journal, 103.[5] Herbert, F., Sandel, B. R., Yelle, R. V., Holberg, J. B., Broadfoot, A. L., Shemansky, D. E., Atreya, S. K., and Romani, P. N. (1987). Journal of Geophysical Research, 92.[6] Broadfoot, A. L., Atreya, S. K., Bertaux, J. L., Blamont, J. E., Dessler, A. J., Donahue, T. M., Forrester, W. T., Hall, D. T., Herbert, F., Holberg, J. B., Hunten, D. M., Krasnopolsky, V. A., Linick, S., Lunine, J. I., Mcconnell, J. C., Moos, H. W., Sandel, B. R., Schneider, N. M., Shemansky, D. E., Smith, G. R., Strobel, D. F., and Yelle, R. V. (1989). Science, 246.[7] Orton, G. S., Fletcher, L. N., Moses, J. I., Mainzer, A. K., Hines, D., Hammel, H. B., Martin- Torres, F. J., Burgdorf, M., Merlet, C., and Line, M. R. (2014). Icarus, 243.[8] Fletcher, L. N., de Pater, I., Orton, G. S., Hammel, H. B., Sitko, M. L., and Irwin, P. G. J. (2014). Icarus, 231.[9] Greathouse, T. K., Richter, M., Lacy, J., Moses, J., Orton, G., Encrenaz, T., Hammel, H. B., and Jaffe, D. (2011). Icarus, 214.[10] Li, C., Le, T., Zhang, X., and Yung, Y. L. (2018). Journal of Quantitative Spectroscopy and Radiative Transfer, 217.[11] Guerlet, S., Spiga, A., Sylvestre, M., Indurain, M., Fouchet, T., Leconte, J., Millour, E., Wordsworth, R., Capderou, M., Bézard, B., and Forget, F. (2014). Icarus, 238.[12] Guerlet, S., Spiga, A., Delattre, H., and Fouchet, T. (2020). Icarus, 351.[13] Madeleine, J. B., Forget, F., Millour, E., Navarro, T., and Spiga, A. (2012). Geophysical Research Letters, 39.[14] Lellouch, E., Moreno, R., Orton, G. S., Feuchtgruber, H., Cavalié, T., Moses, J. I., Hartogh, P., Jarchow, C., and Sagawa, H. (2015). Astronomy and Astrophysics, 579.[15] Rizk, B. and Hunten, D. M. (1990). Icarus, 88.[16] Lindal, G. F., Lyons, J. R., Sweetnam, D. N., Eshleman, V. R., Hinson, D. P., and Tyler, G. L. (1990). Geophysical Research Letters, 17.[17] Irwin, P. G. J., Teanby, N. A., Fletcher, L. N., Toledo, D., Orton, G. S., Wong, M. H., Roman, M. T., Perez-Hoyos, S., James, A., and Dobinson, J. (2022). arXiv e-prints.

Previous CO2 ice cloud modeling studies [Colaprete et al., 2003, Tobie et al., 2003, Forget et al., 1998, Kuroda et al., 2013, Colaprete et al., 2008] allowed to develop and test ideas on CO2 ice cloud formation; however, the studies were often made in idealized settings and included simplified physics and/or limited model boundaries such as a low model top. Listowski et al. [2014] developed refined CO2 cloud microphysics adapted for the Martian conditions of a near-pure vapor condensing in highly supersaturated conditions [Listowski et al., 2013]. They showed with a one-dimensional model and a temperature structure including tidal and gravity wave effects that an additional source of condensation nuclei (CN) was required in the mesosphere for the formation of observed-like clouds. The model of Listowski et al. [2014] has now been coupled with the LMD Mars GCM (MGCM), jointly developed by several laboratories, including LATMOS, LMD, and IAA. The MGCM is now able to simulate the formation of CO2 ice clouds in the Martian atmosphere taking into account mineral dust particles and water ice crystals as potential condensation nuclei (CN). The first simulations are used for testing the new microphysics and comparing its results to the previous simplified CO2 condensation parameterization. The simulations are compared to the published CO2 ice cloud observations. We are using the most recent version of the MGCM and most of the included processes have been described in Navarro et al. [2014], including water ice cloud microphysics and their radiative effect. The new CO2 ice cloud microphysics follow closely the implementation of water ice cloud microphysics in the MGCM, performed via the so-called modal approach, frequently used in GCMs. This means that the particle size distribution is described assuming a lognormal form for the distribution and its evolution is described through the integral properties (moments) of the distribution. We have performed three simulations, one with the previous, simple parametrization of CO2 condensation, one with the new CO2 cloud microphysics but only including dust particles as CN, and one using both dust and water ice as CN, respectively called PARAM, MPCO2, and MPCO2+H2OCN. The simulations have been run for three Martian years and we take the results from the last simulation year to insure convergence. We use the climatological dust scenario that describes well an average Martian Year without a global dust storm. We use a spatial resolution of 5.6258° longitude x 3.758° latitude, corresponding to a 64 longitude x 48 latitude horizontal grid, and 32 vertical levels. The model top is defined at the pressure level 3x10-3 Pa that corresponds approximately to an altitude of 120 km. The model produces CO2 ice clouds in the polar regions in the troposphere during the winter and in the mesosphere at equatorial altitudes. Figure 1 shows the CO2 ice column density as a function of latitude and solar longitude for the two simulations: MPCO2 and MPCO2+H2OCN with observations from several instruments [Clancy et al., 2007, Montmessin et al., 2006, Määttänen et al., 2010,McConnochie et al., 2010, Scholten et al., 2010, Vincendon et al., 2011, Aoki et al., 2018, Clancy et al., 2019, Jiang et al., 2019, Liuzzi et al., 2021]. The polar clouds are formed in a larger altitude range than in the observations, reaching from the surface up to 40 km altitude. The polar atmosphere is cold and supersaturated in a deeper layer than in the observations, causing the thicker cloud formation that forms earlier and lasts longer. The cause of the colder polar winter troposphere may be the prescribed dust column depth coming from observations: as there are very few observations in the polar night, the column depth has been given a minimum value that might still be too high compared to reality. This larger dust content in the polar night causing a too large radiative cooling may be the reason for the cold temperatures. The snowfall from polar clouds contributes about 10% to the formation of the polar ice caps. Substantial mesospheric clouds only form when the model takes into account CO2 ice nucleation on both water ice crystals and dust grains. The seasonal distribution of the clouds agrees well with observations during most of the mesospheric cloud season, but the pause in cloud formation around the aphelion is more clear-cut than in the observations where cloud formation seems to continue during the whole cloud season, although at a lower frequency during a certain period around the aphelion. The clear pause in cloud formation in the model is due to the rise in mesospheric temperatures that seems to be related to a change in the circulation regime and winds in the mesosphere. The optical thickness of the clouds remains two orders of magnitude below observed values, pointing to a need for an exogenous CN source (meteoritic dust: [Listowski et al., 2014, Plane et al., 2018]). Such a source is being added to the model. Acknowledgements We thank the Agence National de la Recherche for funding (projet MECCOM, ANR18CE310013), the LabEx ESEP, CNES, PNP, and the Spanish Ministerio de Ciencia, Innovación y Universidades, the Agencia Estatal de Investigación and EC FEDER funds under project RTI2018-100920-J-I00, and the State Agency for Research of the Spanish MCIU through the Center of Excellence Severo Ochoa award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709). This work was performed using HPC computing resources from GENCI/CINES (Grant 2021A0100110391), and resources at the ESPRI mesocentre of the IPSL institute.

The Saturn DYNAMICO Global Climate Model (GCM) is a high-resolution, multi-annual numerical simulation of Saturn's atmospheric dynamics [1], combining a radiative-convective equilibrium model [2] and a hydrodynamical solver on an icosahedral grid. The model reproduces well the observed behaviour of jets and eddy-momentum transfer to the mean flow. Vortices arise naturally in the model over time but until now they have not been given direct consideration. Here we investigate the long-term statistical distribution and organization of vortices using (1) a manual visual inspection method and (2) automated techniques that utilise machine learning and analytical calculations as a means of validating the first approach. Manual Detection This vortex detection method is similar to previous observational studies of Jupiter and Saturn [3, 4, 5, 6] and shows how the spatial and temporal distributions of the modelled vortices compares to those observed on Saturn [4, 5, 6], as well as studying the formation conditions and long-term temporal evolution of vortex distributions. With seven simulated model years at ½-degree spatial resolution, we constrain well the size and location of vortices, the horizontal wind field components and the magnitude and sign of horizontal vorticity, enabling direct comparison of the manual and automated methodologies. Automated Detection A convolutional neural network is used to reproduce the manual visual detection method across the entire timeseries using the same assumptions and using the results of the manual study as a training set. We also study the Angular Momentum Eddy Detection Algorithm (AMEDA, [7]) designed for the analysis of terrestrial oceanic eddies. The machine learning study is ongoing and the results of the AMEDA algorithm are largely consistent with the manual approach, meaning that this algorithm can be used in future studies of Jupiter and Saturn DYNAMICO GCM outputs. Figure 1: Vortex count over the entire mature model timeline for the manual (black) and AMEDA (red) approaches. Manual technique measures at each seasonal peak, AMEDA measures at each timestep to create a seasonal average for comparison. AMEDA analysis to be extended to earlier years. Figure 2: Vortex east-west size (left), north-south size (centre) and shape (right), in units of 103km, for the manual (top) and AMEDA (bottom) approaches. Figure 3: Overall distribution of vortices for entire timeseries with the manual (left subplot) and AMEDA (right subplot) approaches. In each subplot: (Left) histogram of total vortex count. (Centre) instantaneous zonal wind speed at the vortex centre with the mean zonal wind profile. (Right) vortex average size and vorticity sign. Figure 4: (Top) Maximum tangential velocity at vortex edge as a function of distance from the vortex centre (scatter), derived from the AMEDA approach, alongside the geostrophic balance condition (black straight line), all vortices are subgeostrophic. (Bottom) the ratio of vortex Vmax and the geostrophic condition as a measure of "vortex geostrophy", with respect to latitude, smaller and higher-latitude vortices tend to be clsoer to the geostrophic condition. Acknowledgements Donnelly and the France authors were supported by Agence Nationale de la Recherche (ANR) and the UK authors acknowledge the Science and Technology Facilities Council (STFC, James) and the European Research Council (ERC, Bardet). References [1] Spiga et al. (2020), Icarus, 335, http://dx.doi.org/10.1016/j.icarus.2019.07.011. [2] Guerlet et al. (2014), Icarus, 238, https://doi.org/10.1016/j.icarus.2014.05.010. [3] Li et al. (2004), Icarus, 172, https://doi.org/10.1016/j.icarus.2003.10.015. [4] Vasavada et al. (2006), J. Geophys. Res. Planets, 111, https://doi.org/10.1029/2005JE002563. [5] Trammell et al. (2014), Icarus, 242, https://doi.org/10.1016/j.icarus.2014.07.019. [6] Trammell et al. (2016), J. Geophys. Res. Planets, 121, https://doi.org/10.1002/2016JE005122. [7] Le Vu et al. (2018), J. Atmos. Ocean. Tech., 35, https://doi.org/10.1175/JTECH-D-17-0010.1

Titan, the largest satellite of Saturn, is known to host a rich and active meteorology, by all standards more dynamic than previously thought before the Cassini-Huygens mission. One key element of the dynamic Titan's atmosphere is the Planetary Boundary Layer (PBL), defined as the lowermost part of the atmosphere directly influenced by the presence of a surface below it. Turbulence in the PBL is a controlling factor for exchanges of momentum, heat, chemical species and aerosols between surface and atmosphere. Following observations by the Cassini-Huygens mission, a debate has arisen on the vertical extent of the PBL mixing on Titan in daytime conditions. Radio-occultations by Voyager and Cassini, as well as insights from dune spacing, indicate that a mixed layer, following the dry adiabatic lapse rate, extends 2 to 4 km above the surface. Conversely, in situ mid-morning measurements on board the equatorial Huygens descent probe suggests a more complex structure, with a clear-cut mixed layer only found to extend 300 m from the surface of Titan, with slope breaks between 2 and 4 km in the vertical temperature profile being related to PBL processes by some studies but not by others. The turbulent dynamics of Titan's PBL have been studied thus far either by Global Climate Models where mixing dynamics is parameterized rather than resolved, or by idealized Large-Eddy Simulations not coupled to realistic Titan physics and limited to the first hundreds meters above the surface. Here, to broaden the knowledge of Titan PBL processes, we present turbulent-resolving Large Eddy Simulations coupled with a realistic Titan radiative transfer and soil model, extended several kilometers above the surface and ran for the full daytime cycle of Titan environmental conditions. Using those new simulations, we are able to reconcile all existing observations in a single scenario of Titan PBL growth in daytime, from a couple hundreds of meters in mid-morning conditions to a 3 km fully-developed mixing layer in early afternoon conditions. We also explored turbulent statistics for Titan's PBL: sensible heat flux, updraft and downdraft intensity, wind gustiness. We also note the spontaneous occurrence of convective vortices (dustless devils) in our Titan Large-Eddy Simulations. Our new simulations for Titan PBL bears the potential to help rethink Titan weather processes close to the surface -- and their link to the aerosol / volatile cycles. Furthermore, basic knowledge on PBL turbulence on Titan is needed in the perspective of the Dragonfly spacecraft which will be directly flying within Titan's PBL, thereby experiencing its turbulent dynamics and carrying out in situ measurements of this turbulence.

A recent work (Hernández-Bernal et al., 2021) described the Arsia Mons Elongated Cloud (AMEC), an impressive orographically generated cloud that appears next to the Arsia Mons volcano on Mars during the early morning on a daily basis in southern spring and summer. The most visually striking characteristic of this cloud is its extremely elongated shape.This spectacular cloud is formed by underlying dynamical and microphysical processes that remain to be elucidated. To that end, we run the LMD (Laboratoire de Météorologie Dynamique) MMM (Mars Mesoscale Model; Spiga and Forget, 2009) for Solar Longitude 270º, with a grid resolution of 10km. The model shows that the interaction of fast transient easterly winds with the summit of Arsia Mons results in strong ascending winds on the western slope of the volcano, seasonally and diurnally coincident with the occurrence of the AMEC according to observations. These ascending winds propagate vertically and result in a temperature drop which takes values of down to -30K in the hygropause (around 45 km over the areoid). This results in extreme relative humidity values and condensation, spatially coincident with what Hernández-Bernal et al. (2021) called the head of the AMEC. We expect advection by easterly winds to produce the particular elongated shape of the AMEC, however the advection of condensed particles is not clearly reproduced by the model.This AMEC study demonstrates that coupling the analysis of mesoscale modeling with imagery monitoring on elongated clouds help to better understand the involved processes to form the cloud. We aim to search for similar mechanisms in other visually resemblant clouds, like those reported by Clancy et al. (2006; 2021), and others observed by the Visual Monitoring Camera onboard Mars Express, among other imagers. In the meantime, the AMEC is expected to appear again in early June 2022 and different instruments are already planning observations.References:Hernández‐Bernal, Jorge, et al. "An extremely elongated cloud over Arsia Mons volcano on Mars: I. Life cycle." Journal of Geophysical Research: Planets 126.3 (2021): e2020JE006517. Spiga, Aymeric, and François Forget. "A new model to simulate the Martian mesoscale and microscale atmospheric circulation: Validation and first results." Journal of Geophysical Research: Planets 114.E2 (2009). Clancy, R. Todd, et al. "Valles Marineris cloud trails." Journal of Geophysical Research: Planets 114.E11 (2009). Clancy, R. Todd, et al. "Mars perihelion cloud trails as revealed by MARCI: Mesoscale topographically focused updrafts and gravity wave forcing of high altitude clouds." Icarus 362 (2021): 114411.

Numerous past observations of Saturn by ground based and space telescopes have monitored the movements of clouds and derived direct measurements of tropospheric wind speeds, giving insights into the tropospheric circulation of the planet. The most remarkable feature is a broad and fast equatorial prograde jet, reaching 400-450 m/s. Saturn's stratospheric dynamics is less well known. At low latitudes, it is characterized by the thermal signature of an equatorial oscillation: the observed thermal structure implies that there is a strong oscillating vertical shear of the zonal winds throughout the stratosphere, however, wind speeds in this region cannot be measured by cloud-tracking techniques and remain unknown.The objective of our study is to measure the stratospheric zonal winds of Saturn and unveil the circulation of this layer by observing it in the submillimeter range with the ALMA interferometer. For this, we observed the spectral lines of HCN at 354 GHz and CO at 345 GHz emitted from the limb of the planet. The pressure level at which we measure the winds is about 0.2 mbar. Thanks to the high spatial and spectral resolution of ALMA observations at 345 GHz, we measured the central frequencies of the emission lines in the whole limb, subtracted the rigid rotation of the planet, and thus derived the Doppler shift due to the atmospheric motions of the probed layer, i.e. the stratospheric winds. The method we used in this study was first developed to observe the stratospheric winds in Jupiter (Cavalié et al. 2021). Saturn's rings have limited our wind observations to latitudes north of 20°S. The zonal winds obtained in the eastern and western limbs are consistent within error bars. We most noticeably detected a very broad eastward jet that spreads from 20°S to 20°N with an average speed of exceeding 250 m/s.

To accurately simulate the climate and the fate of volatiles for thousands to millions of years we must couple physical processes with very different timescale, ranging from clouds microphysics and atmospheric dynamics (represented in the GCM) to the evolution of lakes, glacier accumulation, and subsurface ice evolution. Given the diversity and the complexity of the Martian paleoclimates, we choose to use use an ambitious “asynchronous coupling” between the slow ice and water reservoirs models and the GCM. In practice our innovative Mars evolution model will use a horizontal grid identical to that of the GCM, and include the same representation of the micro-climate on slopes. In our case, we will run the Mars Evolution Model with a timestep of 50 to ~500 years, depending upon the dynamics of the modeled system (smaller timesteps must first be used so that the different volatile reservoirs reach a quasi-equilibrium, then the timestep will depends on the evolution of the forcing, which is slow in the case of obliquity, for instance) . At each timestep, the inputs from the atmosphere (e.g. mean precipitation, sublimation and evaporation, temperatures, dust deposition) will be obtained through a multi-annual run of the Global Climate model using the outcome of the Mars Evolution Model as initial state.First results about evolution of water ice and CO2 ice glacier will be presented.

Our understanding of Venus and Mars climate has been noticeably improved thanks to progress with General Circulation Models (GCM) (e.g., Forget et al. 1999, Lebonnois et al. 2010, Gilli et al. 2021) and increasing measurements, both from space missions and ground-based telescopes. While there are 13 operational missions currently dedicated to Mars, a new era in the exploration of “our sister” planet Venus is coming in the next decades with the selection of 3 missions: DAVINCI and VERITAS by NASA, EnVision by ESA, in addition to the Indian orbiter mission, Shukrayyan-1 (planned for 2025). Nevertheless, our view of the upper layers of those planets (i.e., above approximately 80 km and 60 km on Venus and Mars, respectively) remains incomplete. The observed high variability of those regions (e.g., Gerard et al. 2014, Gonzalez-Galindo et al. 2015) is very challenging to predict by 3D models. Planetary waves (e.g., Kelvin waves) are suggested to play an important role in the variability in the so-called transition region on Venus (between super-rotation and day-to-night circulation) (Navarro et al. 2021) and gravity waves are recognized to produce a significant impact on the thermal tides of Mars (Gilli et al. 2020). In this talk, I will give a brief overview of recent 3D GCM developments done in collaboration with the Institut Pierre-Simon Laplace (IPSL) laboratories in France and the Instituto de Astrofisica de Andalucia (IAA) in Spain, such as the inclusion of a stochastic non-orographic gravity wave parameterization and improvements on the parameterization of non-LTE CO2 heating rates (Martinez et al. 2022, submitted), to provide a more realistic picture of those upper regions of the Venus and Mars atmosphere. References:Forget et al. 1999, JGR, 104, 155-24Lebonnois et al. 2010, JGR-Planets, 115, 6006Gilli et al. 2021, Icarus, Vol. 366, 114432Navarro et al. 2021, Icarus, Vol. 366, 114400Gilli et al. 2020, JGR-Planets, 125-3Gilli et al. 2017, Icarus, Vol.248, 478-498Gerard et al. 2014, Icarus, 236, 92-103Gonzalez-Galindo et al. 2015. JGR-Planets, 120, 2020-2035Martinez et al. 2022, submitted to Icarus Acknowledgments:GG is funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, the Agencia Estatal de Investigación and EC-FEDER funds under project RTI2018-100920-J-I00, and acknowledges financial support from the State Agency for Research of the Spanish MCIU through the “Center of Excellence Severo Ochoa” award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709). This research was also supported by Fundação para a Ciência e a Tecnologia (FCT) through the research grants UIDB/04434/2020, UIDP/04434/2020, P-TUGA PTDC/FIS-AST/29942/2017.

In some high latitude craters, intriguing moraines are interpreted to have been deposited by CO2 ice glaciers, essentially frozen from when the local climate was colder (i.e., when Mars obliquity was low) [1]. This scenario has been little studied, but it suggests that the atmosphere could totally collapse into CO2 glaciers, leaving behind a residual atmosphere of only Ar and N2, but 20 times less dense than today [2]. However, these results are based on a radiative equilibrium that does not take into account all the dynamics of the atmosphere. Such periods of low obliquities generally last tens of thousands of years [3], making a complete simulation with a classical Global Circulation Model impossible. We will present the preliminary results of our climate simulations of Mars at low obliquity based on our new tool which is the Planetary Evolution Model developed at the LMD (Fig 1.). This model allows us to simulate the evolution of the climate, based on the LMD GCM, over long-time steps. Particular attention will be paid to the condensation of the atmosphere in the form of CO2 glaciers, and to the composition of the residual atmosphere.Fig 1. Principles of the Planetary Evolution Model References: [1] Kreslavsky and Head, Carbon dioxide glaciers on Mars: Products of recent low obliquity epochs(?). Icarus, 216:111–115, 2011.[2] Kreslavsky and Head. Mars at very low obliquity: Atmospheric collapse and the fate of volatiles. Geophysical Research Letters, 32(L12202), 2005.[3] Laskar, Correia, Gastineau, Joutel, Levrard, Robutel, Long term evolution and chaotic diffusion of the insolation quantities of Mars. Icarus 170, 343–364, 2004.Acknowledgments:This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 835275).

Since 30 years, an equatorial oscillation of the temperature structure with a quasi-period of 4 years has been discovered in the atmosphere of Jupiter (Orton et al. 1991, Leovy et al. 1991). This phenomenon results in a complex vertical and horizontal structure of prograde and retrograde jets. However, the wind structure of the stratosphere in the equatorial zone of Jupiter has not been measured directly. It has only been inferred in the tropical region from the thermal wind balance using temperatures measured in the jovian stratosphere and the cloud-top wind speeds measured as a initial condition (e.g. Flasar et al. 2004). But temperatures are not constrained between the upper troposphere and the middle stratosphere from observations, limiting thus the accuracy of the thermal wind balance. In this study, we derive self-consistently for the first time the structure of the tropical winds by utilizing wind and temperature observations all performed in the stratosphere. The wind speeds were obtained by Cavalié et al. (2021) at 1 mbar in Jupiter's stratosphere in both the equatorial and tropical regions in March 2017 with ALMA. The stratospheric thermal field was measured a few days before from the equator to the mid-latitudes with Gemini/TEXES (Giles et al. 2020). For the derivation of the wind, we use both the thermal wind equation (Pedlosky 1979) and the equatorial thermal wind equation (Marcus et al. 2019). In this paper, we will present and discuss our results.

Saturn's cloud-top zonal winds have been measured since the Voyager days. Contrary to Jupiter, the jets are mostly prograde and with a noticeable broad super-rotating jet between 35°S and 35°N with peak velocities reaching ~450 m/s between 10°S and 10°N (e.g. Sanchez-Lavega et al. 2000). The Cassini mission revealed, during its Grand Finale, that these winds extend as deep as 8000 km below the clouds (Galanti et al. 2019). Above the tropopause, in the stratosphere, there has been no direct determination of the zonal winds, although thermal wind balance calculations have shown the signature of Saturn's semi-annual oscillation (SAO) in the tropical zone (Fouchet et al. 2008, Guerlet et al. 2011, 2018). These derivations lack an initial condition, in terms of wind speeds, located in the sensitivity zone of the temperature measurements. It thus remains unknown if the SAO jets alternate in direction as a function of altitude. In addition, more and more sophisticated general circulation models are being developed to constrain the dynamics of Saturn's stratosphere (Friedson & Moses 2012, Spiga et al. 2020, Bardet et al. 2021). These models now crucially need observational constraints. We used the Atacama Large Millimeter/submillimeter Array (ALMA) to map Saturn's stratospheric zonal winds. We derive the zonal winds as a function of latitude from the Doppler shifts induced by the winds on the spatially and spectrally resolved spectral lines. In this paper, we will present and discuss our results.

The surface pressure measurements recorded by InSight seem to be the best in terms of precision and stability for comparison with Viking data and to rule on a possible secular climate change. However, the comparison of the measurements between the two consecutive mission years reveals an offset imposed by the sensor temperature. We propose here a correction of this effect by exploiting the MSL REMS data acquired at the same time. The corrected data then follow perfectly the pressure models based on the pressure recorded by MSL during MY 34 and 35 interpolated with the help of the LMD GCM. Further investigations studying the possible difference between Viking 1 and InSight seasonal variations will be presented during the conference. Introduction Remote observations of interannual variations in polar cap coverage suggest that the south polar residual cap (SPRC) might be eroding [1,2,3]. Such a change in the SPRC would raise the surface pressure every year. Previous comparisons of surface pressures recorded by Viking 1 with Phoenix data [4] or MSL REMS measurements [5] report a possible increase of pressure at the surface of Mars within 30 years. However, the conclusions were unsure due to the uncertainty of the data for Phoenix, and the sensitivity of the hydrostatic adjustment imposed by the climbing of MSL on the complex Gale crater topography. In 2018, the InSight lander brought an accurate pressure sensor at Elysium Planitia. The uncertainty due to the long-term instrumental drift and the accuracy of the sensor is negligible compared to the requirement of accuracy to detect this secular climate change [4]. We thus propose to detect any change of CO2 mass in the atmosphere or a change in the seasonality of the polar cap with an accurate comparison between the seasonal pressure variations recorded by Viking 1 and InSight. Thermal Sensitivity of the Pressure SensorA direct comparison between pressure measurements made during the first and second years of the InSight mission shows a strong difference, thus questioning the reliability of the measurements (Figure 1). Considering the large difference between the measured pressure over the two years, and the correlation of some pressure decreases with some drops of the sensor temperature, we believe that the measurements are affected by a possible thermal offset due to a gradient of temperature within the sensor. Figure 1. (a) Uncorrected diurnal-averaged surface pressure recorded by InSight during the first and second years of the mission. (b) Diurnal-averaged pressure sensor temperature. Red dashed lines indicate the correlation between drops in the pressure measurements and drops in the sensor temperature records. Recalibration of the Pressure dataWe propose here an empirical calibration to correct this sensor thermal effect. To remove any meteorological effects (thermal tides, baroclinic waves), the measured pressure P is averaged over 10-sols to give < P >10sols. As reported in [6] who used MSL data, it seems that the pressure recorded at the beginning of the mission (Ls < 70°, MY = 35) could be slightly lower than the pressure recorded at the end of the mission (Ls < 70°, MY = 36), possibly as a result of the MY34 global dust storm. To quantify this difference, we interpolated the MSL REMS data available on the PDS with careful altitude corrections. Neglecting the difference of scale height between MY35 and MY36, one may have: Where: with E the thermal-induced offset on pressure data described previously. Assuming that E can be written as a polynomial function of the sensor temperature, and introducing (2) and (1) yield to a minimization problem that needs to be constrained to avoid any non-physical solution. Figure 1 presents an unexpected increase of uncorrected pressure at Ls~63°, and then a drop, both certainly resulting from a rise of temperature at T = 270K, followed by a decrease of temperature at T = 275K. Based on this observation, we simply assumed that: E(T = 0°C) = 0. With this constraint, the optimization problem can be solved and a first temporary correction is: Applying this correction to InSight measurements following Eq. (2) leads to the results presented in Figure 2. Figure 2. (a) Uncorrected diurnal-averaged surface pressure recorded by InSight during the first and second years of the mission. (b) Diurnal averaged pressure after sensor temperature correction following Eq. (2). Comparison of the Corrected Pressure and MSL MeasurementsTo assess the quality of this correction, we interpolate the MSL Measurements from MY35 et MY34 (assuming that MY34 behaves like MY36 for Ls < 70°) to predict the surface pressure at the InSight landing site. MSL measurements are interpolated to the InSight landing site using the hydrostatic correction presented in [7]. Dynamical effects from MSL to InSight landing site are corrected using GCM runs with MY34 and MY35 as input dust scenarios. The comparison is presented in Figure 3 and reveals a good match between the model and the result obtained after correction. Figure 3. Surface pressure as predicted from the interpolation of MSL REMS measurements, after hydrostatic and dynamic correction (dashed curves) and observations from InSight after thermal correction (crosses). Observations are diurnally-averaged whereas predictions are 10-sols averaged. Comparison of InSight and Viking 1 Surface Pressure The comparison between Viking 1 and InSight data after hydrostatic, dynamical, and temperature-induced offset will be presented during the talk. Interpretations and conclusions on a possible secular climate change will be drawn. References [1] Malin et al., 2001, Science294(5549), 2146–2148 [2] Thomas et al., 2009, Icarus203, 352–375 [3] Blackburn et al., 2010, Space Sci.58, 780–791 [4] Haberle and Kahre, 2010, Mars Int. J. Mars Sci. Explor5., 68–75 [5] Haberle et al., 2014, J. Geophys. Res., Planets119,440–453 [6] de la Torre Juarez et al., 2019, AGU fall meeting abstracts, Vol. 2019, p. P51C-02 [7] Forget et al., 2007, Planets112(E11), 8

Introduction: The Saturn's Semi-Annual Oscillation (SSAO) observed by Cassini is a source of debate within the community, because of its similarities (sometimes conflicting) with both the terrestrial Quasi-Biennial Oscillation (QBO) and the terrestrial Semi-Annual Oscillation (SAO). As the QBO, the downward propagation of the SSAO occurs almost to the tropopause (Schinder et al. 2011). In contrast, the half a Saturn year period of the SSAO is advocated for a seasonal forcing and hints the SAO mechanism driving. Moreover, observation of anomalies in warm temperature and high hydrocarbon concentration at winter tropics is interpreted as the downwelling branch of a meridional stratospheric circulation. Using DYNAMICO-Saturn Global Climate Model (GCM) -- with an higher vertical discretization (96 vertical levels from 3x105 to 10-1 Pa) than previous works (Spiga et al. 2020, Bardet et al. 2021) -- we performed simulations lasting at 13 simulated Saturn years, to study Saturn's stratospheric equatorial oscillation, its inter-hemispheric circulation and the driving mechanism connecting them. Results: Firstly, DYNAMICO-Saturn depicts a stratospheric equatorial oscillation of temperature and zonal wind. The new vertical resolution permits to stabilize more the oscillation periodicity and its eastward phase compared to previous study. The period varies between 0.5 and 1 simulated Saturn years. Indeed, because of irregularity in the waves and eddy-to-mean forcings, the downward propagation is carried out by episodes of descent followed by episodes of stagnation at a given level of pressure. The amplitude of the associated temperature oscillation is under-estimated by 10 K compared to the Cassini observations.Secondly, DYNAMICO-Saturn also models an inter-hemispheric circulation taking place from the summer tropical latitudes to the winter ones, with a strong subsidence between 20 and 40° in the winter hemisphere. The main subsidence branch is located in the same latitude region as temperature and hydrocarbons anomalies observed by Cassini (Guerlet et al. 2009, 2010, Sinclair et al. 2013, Fletcher et al. 2015 and Sylvestre et al. 2015). Furthermore, eddy-to-mean interaction diagnostics show that the phases of Saturn's equatorial oscillation are controlled by the inter-hemispheric circulation. During the solstices, the cross-equatorial drift of the inter-hemispheric circulation, associated to the forcing of the mid-latitude planetary-scale Rossby waves, drive the equatorial zonal wind to westward direction. In contrast, during the equinoctial overturning of the inter-hemispheric circulation, the residual mean circulation is reduced to an unique ascendance at the equator to permit the transport and eastward moment deposition of Kelvin waves from the troposphere.Perspectives: This present modelling study of the dynamics of Saturn's stratosphere confirms the SAO-like character of the Saturn's equatorial oscillation. However, we will also explore the putative part of the QBO-like character of it. We plan to use this new vertical resolution combine to the subgrid-scale gravity wave parameterization.

The NASA InSight lander is a geophysical and meteorological observatory operating on Mars for over a Martian year/two Earth years. Continuous records of seismic, pressure, wind and temperature data over this period have led to significant breakthroughs in determining the planet's structure and climate. With such a wealth of data now received, machine learning offers a nascent tool to extract further information. The seismic data is extremely correlated to the atmospheric conditions. Discerning the coupling between the atmosphere and ground motion is of significant interest and this work aims to predict the ground motion generated by wind and pressure forcing using machine learning techniques. From this prediction we can untangle the various contributions to ground motion, determine atmospheric/ground properties, analyse/discriminate marsquakes and potentially decorrelate waveforms to remove the atmospheric contribution. While a physical model for this atmospheric forcing is desirable, machine learning approaches the problem from an alternative view point where mathematical and algorithmic tools add the necessary complexity for fitting the data. In this way, we may be able to capture detailed variation and inform further modelling efforts.We will detail the initial application of machine learning for predicting the ground motion from the atmospheric data inputs of wind speed, wind direction, pressure and temperature. First though, we will describe the issues that need to be tackled to obtain a good prediction using the InSight data. To illustrate some of some of these problems, consider that glitches are known to occur in the seismic data. They offer a way to detect overfitting as they should not in general be predicted from atmospheric forcing. However, a subset of the glitches are correlated to temperature on top of the fact they are only visible during quiet enough periods, as are marsquakes. Therefore, they are not a normally distributed source of noise or uncorrelated from the input atmospheric data, breaking typical assumptions used for regression. A similar issue is presented by the changing weather conditions throughout a Martian sol, where the time series distribution varies. As a result, prior information on the instrumentation and data qualities is essential for applying the machine learning methods and interpretation of the results.We demonstrate the specifics of the InSight data with respect to 1) how a curve fitting problem can be constructed, 2) the necessary degrees of freedom of the problem, 3) consideration of non-stationary/heteroscedastic errors and 4) the optimisation and machine learning method applied. Current results will be presented from the implementation of random forests and gaussian processes. These results demonstrate a good performance so far for capturing the global variation and we will offer perspectives on how these results can be used and improved.

Introduction The martian atmosphere is mainly composed of CO2 (~ 95 %). The first spectral confirmation of CO2 cloud was acquired by Montmessin et al. [2007] using the 4.26 µm band of the ν3 fundamental asymmetric stretching mode of CO2 from OMEGA data onboard Mars Express. Since then, numerous observational studies have constrained the climatology of CO2 cloud in the martian atmosphere [Määttänen et al., 2013]. There are two different types of clouds involving different formation processes: (i) those located in the troposphere at the winter polar region, and (ii) those located in the mesosphere at low and mid-latitudes during the martian year [Määttänen et al., 2013]. Microphysical processes of formation of theses clouds are still not fully understood. However, modeling studies revealed processes necessary for their formation: the requirement of waves that perturb the atmosphere leading to a temperature below the condensation of CO2 (transient planetary waves for tropospheric clouds [Kuroda et al., 2013], thermal tides [Gonzàlez-Galindo et al., 2011] and gravity waves for mesospheric clouds [Spiga et al., 2012]). We use our microphysical model of CO2 cloud formation to investigate the occurrence of these CO2 cloud by coupling it with the Global Climate Model (GCM) of the Institut Pierre Simon Laplace (IPSL) [Forget et al., 1999]. We focus our efforts on the modeling of the tropospheric clouds during the winter in the polar regions. Model description The microphysical model of CO2 cloud formation of LATMOS includes nucleation on crystals on cloud condensation nuclei (CCN), condensation/sublimation, and sedimentation. Sources of CCN for CO2 are mainly dust particles, secondarily water ice particles. The particle size distribution is described with the moment method allowing to compute the effect of the microphysical processes on the average properties of the distribution. This method is the same as used for water ice clouds microphysics in the MGCM-IPSL [Madeleine et al., 2014, Navarro et al., 2014]. For more details about the microphysical processes of CO2 clouds, we invite the reader to the work of Listowski et al. [2013, 2014]. The MGCM-IPSL is a finite difference model based on the primitive equations of meteorology in σ coordinates [Forget et al., 1999]. The horizontal resolution grid used is 64 x 48, corresponding to 5.6258° longitude by 3.758° latitude, respectively. The top of the atmosphere was extended to ~ 120 km to describe well the processes in the mesosphere. The atmosphere is divided into 32 vertical layers from the surface to the top of the atmosphere. At each call of physical processes (every 15 minutes), the microphysics of CO2 cloud formation is called 50 times leading to a time resolution of 18 seconds to resolve the very rapid microphysical processes. We have used a dust scenario from the MY29. Results We present our first results on 3D modeling CO2 clouds in the Martian atmosphere. Figure 1 shows the zonal mean density column of atmospheric CO2 ice as simulated by the model during a Martian year. The maximum columns of CO2 clouds are found in winter polar regions. In the northern polar region, CO2 ice clouds form from around Ls = 220° at highest northern latitude, reaching the latitude of 50°N around Ls = 270°, and disappear slightly before the northern spring equinox at Ls = 350°. In the southern polar region, CO2 clouds are formed from around Ls = 0° at highest southern latitude, reaching the latitude of 54°S around Ls = 130°, and disappear at Ls = 190°. This result is quite consistent with MCS for MY29 observation which showed CO2 ice cloud up to 68°N (Fig. 1a from Kuroda et al. [2013]) during the period Ls = 255°-285°. Note that their observational data came from MRO-MCS Derived Data Version 2 [Kleinböhl et al., 2009], where the dust profiles retrieved in winter polar regions are likely to be caused by CO2 ice clouds [McCleese et al., 2010]. Our CO2 ice clouds simulated are also qualitatively consistent with those observed from the Mars Orbiter Laser Altimeter (MOLA) (see Figure 2). The temporal distribution of simulated clouds in winter polar regions are in good agreement with MOLA observations. Figure 3 and 4 show the zonal mean CO2 mass mixing ratio in both winter polar regions: between 60°-90°N averaged in time between Ls = 180°-360°, and between 60°-90°S averaged in time between Ls = 0°-180°, respectively. The density of CO2 ice clouds simulated are in agreement with the distribution of winter polar clouds observed by MOLA during the same Ls period (Fig. 8 from Neumann et al. [2003]). The thickness of CO2 saturation layer observed by MCS during the MY29 is around 8 km in the northern polar region between Ls = 180°-360° [Hu et al., 2012], lower than our CO2 ice cloud extension in altitude reaching around 20 km altitude. But the thickness of CO2 saturation layer in the southern polar region observed by MCS for the same year is around 15 km, nearly equal to our top of CO2 ice cloud below 15 km altitude. Figure 1: Zonal mean density column of CO2 ice during one Martian year. Figure 2: Cloud top altitude (km) from MOLA observations binned in 1°x1° latitude-solar longitude grid. Figure 3: Zonal mean CO2 mass mixing ratio in the northern polar region as a function of altitude, averaged between Ls = 180° to 360°. Figure 4: Same as Fig. 3, except for the south pole during Ls = 0° to 180°. Conclusion The microphysical module for CO2 clouds of LATMOS coupled with the Martian Global Climate Model of the Institut Pierre Simon Laplace has reproduced qualitatively the CO2 ice clouds in winter polar regions in the first full-year simulations. Further analysis needs to be done to complete the study of these CO2 ice clouds and compare more quantitatively to the observational data. We will also study more in detail the formation of mesospheric clouds in the martian atmosphere in the near future. Acknowledgments We thank our funders, the Agence National de la Recherche (project MECCOM, ANR-18-CE31-0013), the Laboratoire d'Excellence ESEP, and the French space agency CNES.

On the first hundreds of sols in which the InSight lander operated on the surface of Mars, its instrumentation has proven to be particularly suitable to unveil and understand atmospheric variability at all temporal scales, from the synoptic scale (baroclinic waves) to the sub-hour scale (gravity waves, bores) down to the turbulent scale (vortices, gusts, infrasounds). Recently, the InSight lander achieved a complete Martian year of observations of the atmosphere of Mars -- allowing for the seasonal variability of the Martian atmosphere and its phenomena at all scales to be monitored almost continuously, including during several large dust storms episodes. In this presentation, based on this Martian year of InSight observations, we will review the annual CO2 sublimation / condensation cycle, the variability of large-scale meteorology, the statistics of a year of wind observations -- and insightful comparisons with global climate models, the strong seasonal variability of gravity wave and turbulent activity, including a burst of activity of convective vortices in Mars' southern summer. We will also discuss how the atmosphere influences seismic and magnetic signals captured by InSight -- and the search for Martian infrasound.

Context One-dimensional modelling efforts for icy giant atmospheres have been performed in the past, from pioneering works to more recent comprehensive studies [2]. Circulation patterns in the troposphere and stratosphere inferred from visible, infrared, and microwave observations and models have been discussed in [3], but few fully three-dimensional models of Ice Giants have ever been presented. Among the differences between these studies are the estimated radiative time constants and the consequences for the atmospheric circulation on Uranus and Neptune. For instance, comparing 2018 VLT images to Voyager data, Roman et al. [4] pointed out that the Uranian troposphere underwent almost no changes in terms of thermal structure over the intervening decades, pointing out that either the radiative time constants are much longer than estimated by Li et al. [2], or there is a very efficient energy redistribution by global circulation. Deciphering such questions as well as preparing future observations require a supporting model able to adequately simulate atmospheric structure of these planets. Method Here, following what has been previously done for Saturn [5] and Jupiter [6], 1-D radiative-convective equilibrium modelling – corresponding to a GCM column in the absence of dynamics – is performed with a radiative-transfer code based on a correlated-k method and a two-stream solver. We utilise modern estimates of gaseous opacities, with vertical and horizontal distributions provided by photochemical modelling [7]. We explore the response to seasonal solar forcing along with the sensitivity to aerosols - with optical properties generated based upon Mie theory - which are poorly constrained by observations and therefore a source of uncertainties for radiative models. Full 3-D simulations with dynamics are discussed in a companion abstract [8]. Results First, aerosol-free simulations with planetary-averaged gaseous opacities lead to thermal structures that are globally too cold in the modelled stratospheres (e.g. more than 30K in the Uranian case). This ‘energy crisis’ in the middle atmosphere is an outcome that previous models have already been facing, and various processes have been proposed to solve the enigma, such as gravity waves breaking or radiative processes due to aerosols. Thus, in the present work, we investigate if and how this gap could be closed within our radiative model in the absence of dynamics, and provide some quantitative analysis of this ‘energy crisis’. As photochemical models such as those by Moses et al. [7] point out, there exists considerable leeway in the choice of hydrocarbons profiles (e.g. assuming different eddy diffusion coefficients) to constrain our model that are still consistent with the available data. Furthermore, due to both seasonal photochemistry and atmospheric circulation, there are strong evidence for latitudinal variations of methane in both planets and some theoretical evidence for latitudinal variations of hydrocarbons, at least on Neptune. Hence we prescribe seasonal variations of their abundances within our radiative transfer based on KINETICS [7] outputs. On this basis, we present how the thermal structure and its seasonal patterns are affected. In addition, we explore the parameter space of aerosol properties – within the range of observational constraints - with various sets of synthetic tropospheric clouds (CH4,H2S) and stratospheric haze (optical depth, albedo, particle size distribution, etc.). For the different sets of simulations, we discuss the inferred radiative time constants, how they can be compared to previous work and how this will affect the circulation and constrain energy redistribution once dynamics is activated. Finally, compared to previous works, we go one step further to provide a solid ground upon which the full 3-D circulation model for the Ice Giants can grow. Such work will be useful for interpreting future observations of Uranus and Neptune from the James Webb Space Telescope and new missions to these outer worlds. Further reading [1] Conrath et al., 1990 ; https://doi.org/10.1016/0019-1035(90)90068-K [2] Li et al., 2018 ; https://arxiv.org/pdf/1806.02573.pdf [3] Fletcher et al., 2020 ; https://arxiv.org/pdf/1907.02901.pdf [4] Roman et al., 2020 ; https://arxiv.org/pdf/1911.12830.pdf [5] Guerlet et al., 2014 ; https://doi.org/10.1016/j.icarus.2014.05.010 [6] Guerlet et al., 2019 : https://arxiv.org/pdf/1907.04556.pdf [7] Moses et al., 2018 ; https://arxiv.org/pdf/1803.10338.pdf [8] Milcareck et al., 2020, EPSC 2020 Abstract Book.

Uranus and Neptune’s atmospheres are active worlds, with vigorous meteorological activity and strong zonal winds occurring despite small absorbed solar radiation and internal heat fluxes. A few 3-D General Circulation Models (GCM) of their atmospheres exist in the literature, focusing mostly on understanding their zonal jet structure [1,2] or the evolution of large disturbances [3,4]. Building a complete and realistic GCM is a challenging task, given the long orbital and radiative timescales involved, along with the rather high spatial and temporal resolution needed when solving the atmospheric equations of motion on the rotating sphere. For this reason, existing GCMs include crude representation of radiative transfer (a simple relaxation scheme to an equilibrium temperature profile) and/or neglect seasonal variations. We are currently developing a GCM for Uranus and Neptune’s atmospheres, building on our existing expertise on Jupiter and Saturn GCMs [5,6]. Compared to other existing GCMs for ice giants, our model includes state-of-the art parametrization of radiative transfer. The radiation scheme is a full radiative transfer using correlated-k distributions. Seasonal variations of the incoming solar flux are taken into account. Opacity sources include gaseous opacity from methane, ethane, acetylene, H2-H2, H2-He continua along with opacity from two aerosol layers: one optically thick cloud with a base at the 2-bar level and one optically thin haze layer with a base at 300 mbar. These layers are consistent with the putative H2S and CH4 clouds reported by many observational studies (eg [7,8]). Simulations at radiative-equilibrium are discussed in a companion abstract [9] ; in this one we focus on dynamical aspects. We will present results from first 3D GCM simulations performed at a horizontal resolution up to 256x192 in longitude x latitude (corresponding to 1.4°x0.9°), extending from 3 bars to 0.3 mbar. A broad equatorial retrograde jet develop on both Uranus and Neptune and two prograde jets emerge near 50° latitude in the Neptune simulation. This is in qualitative agreement with the observed zonal wind structure on Neptune, although the zonal jet wind speeds are much smaller than the observed ones. We are able to show that acceleration by eddies is an important contributor to the two prograde jets in the Neptune simulation. However, the Uranus simulation does not exhibit high-latitude prograde jets that have been reported by cloud-tracking observations. In other words, the zonal jet structure currently obtained in our simulations differs significantly between the two planets, which is puzzling and at odds with their qualitatively similar observed zonal wind structures. This might indicate that important processes governing the atmospheric circulation of ice giants is missing in our GCM. Another outcome of these simulations is that all tropospheric zonal jets are slowed down to near zero wind speeds in the lower stratosphere. The reason behind this behaviour is under investigation, as is the associated meridional circulation. Next steps will include the study of the role of Uranus and Neptune respective axial tilts and internal heat fluxes (or lack thereof) on their circulation. Furthermore, our GCM is still lacking important processes, such as latent heat release from water and other condensing species, and is lacking a realistic parametrization for convective processes. This might explain the observation-model mismatches in their zonal wind structure and will be the subject of future developments. [1] Lian and Showman, Icarus, Vol. 207, Issue 1, p. 373-393, 2010. [2] Liu and Schneider, Journal of the Atmospheric Sciences, Vol. 67, issue 11, pp. 3652-3672, 2010. [3] Lebeau and Dowling, Icarus, Vol. 132, Issue 2, pp. 239-265, 1998. [4] Hammel et al., Icarus, Vol. 201, Issue 1, p. 257-271, 2006. [5] Spiga et al., Icarus, Vol. 335, article id. 113377, 2020. [6] Guerlet et al., accepted in Icarus, 2020. [7] Irwin et al., Icarus, Vol. 227, p. 37-48, 2014. [8] Sromovsky et al., Icarus, Vol. 317, p. 266-306, 2018. [9] Vatant d’Ollone et al., EPSC 2020

The Martian atmosphere (from the surface up to the outer layers) is a very dynamic system, quickly responding to strong radiative forcing coming from the absorption of solar radiation from dust particles lofted during dust storms. So far, such dynamical phenomena at short time scales and large spatial scales have been observed mainly from spacecraft in polar or quasi-polar orbits, which cannot provide continuous and simultaneous observations over fixed, large regions. This limitation can be bypassed using spacecraft in equatorial, circular, planet-synchronous (i.e. areostationary) orbit at an altitude of 17,031.5 km above the Martian surface. Besides their possible use as communication relays for ground-based assets, for space weather monitoring (they orbit outside Mars' bow shock), and for the study of surface properties (e.g. thermal inertia and albedo), the unique scientific advantages of areostationary satellites for weather monitoring are comparable to those provided by geostationary satellites. These platforms greatly increase the temporal resolution and coverage of single events, and are ideally suited for data assimilation in global climate models. Thanks to NASA PSDS3 program, we have elaborated a mission concept to put a low-cost, low-weight, ESPA-class SmallSat in areostationary orbit, which is capable of supporting various tank sizes in order to provide a wide range of ΔV for three different Mars arrival scenarios. ExoTerra Resource LLC adapted its "Electrically Propelled Interplanetary CubeSat" bus as part of the mission design. Despite the optimization of the flight trajectories and the use of machine learning algorithms to prioritize data downlink, the conclusions of the concept study clearly point towards the current challenges represented by propulsion, communication, and possibly radiation tolerance for scientific SmallSat missions to Mars. Such conclusions are generally common among all low-cost interplanetary SmallSat concepts. Furthermore, a single areostationary satellite is enough to provide a full-disk view to monitor regional dust storms and water ice clouds at specific locations, but cannot provide the global coverage required to understand extreme phenomena such as Martian planetary-scale dust events. For this reason, we have recently started to study a more advanced mission concept involving the use of at least three areostationary satellites. This new study is carried out in collaboration with the Jet Propulsion Laboratory within the scope of a wider NASA-funded project (PMCS program) looking at a constellation concept. The challenge is to keep the areostationary satellite configuration within the ESPA class limits, in order to take advantage of possible future rideshare opportunities.

To address questions about the driving mechanisms of Saturn's equatorial oscillation, our team at the Laboratoire de Météorologie Dynamique built the DYNAMICO-Saturn Global Climate Model to study tropospheric dynamics, tropospheric waves activity (Spiga et al. 2020) and equatorial stratospheric dynamics (Bardet et al. 2020) of Saturn. Previous studies (Guerlet et al. 2014, Spiga et al. 2020, Cabanes et al. 2020) have shown that our model produces consistent thermal structure and seasonal variability compared to Cassini CIRS measurements, mid-latitude eddy-driven tropospheric eastward and westward jets commensurate to those observed and following the zonostrophic regime, and planetary-scale waves such as Rossby-gravity (Yanai), Rossby and Kelvin waves in the tropical channel. Extending the model top toward the upper stratosphere allowed our model to produce an almost semi-annual equatorial oscillation with opposite eastward and westward phases. Associated temperature anomalies have a similar behavior than the Cassini/CIRS observations, but the amplitude of the temperature oscillation is twice smaller than the observed one. The absence of sub-grid-scale waves in the model produces an imbalance in eastward- and westward-wave forcing on the mean flow and could be an explanation to the irregularity in both the oscillating period and the downward rate propagation of the resolved Saturn equatorial oscillation. To explore the impact of those small-scale waves on the spontaneous equatorial oscillation emerging in the DYNAMICO-Saturn GCM (Bardet et al. 2020), we add a sub-grid-scale non-orographic gravity waves drag parameterization in our model.This parameterization is directly adapted from the stochastic terrestrial model of Lott et al. (2012). This formalism represents a broadband gravity wave spectrum, using the superposition of a large statistical set of monochromatic waves. As the time scale of the life cycles of gravity waves is much longer than the time step of our GCM, our parametrization can launch a few waves whose characteristics are randomly chosen at each time step. This stochastic gravity waves drag parameterization is applied in DYNAMICO-Saturn on all points of the horizontal grid. A key parameter used in the non-orographic gravity waves drag parameterization is the maximum value of the Eliassen-Palm flux. The Eliassen Palm flux represents the momentum carried by waves that could be transferred to the mean flow. This value has never been measured in Saturn's atmosphere and it represents an important degree of freedom in the parameterization of gravity waves. We performed several test simulations, lasting two Saturn years whose initial state is derived from Bardet et al (2020), with an horizontal resolution of 1/2° in longitude/latitude and a vertical resolution ranging between 3 bar to 1 μbar. For these test simulations, the maximum value of the Eliassen-Palm fulx is set to 10-6, 10-5, 10-4 and 10-3 kg m-1 s-2. Preliminary results show that the appropriate value of our main parameter is between 10-5 and 10-4 kg m-1 s-2. Eliassen-Palm flux value of 10-3 kg m-1 s-2 demonstrates a too large impact: the equatorial oscillation is entirely vanished is this configuration. The simulation using the value of 10-6 kg m-1 s-2 is equivalent to the control simulation without the gravity waves drag parameterization. The next step is to test other parameters, as phase velocity of the gravity waves, horizontal wavenumber, to understand how gravity waves impact the equatorial oscillation.

Introduction: Remnants of glacial and periglacial geomorphological features are visible up to mid-latitudes on Mars. Notably, an out-of-equilibrium “latitude-dependant mantle” extends to 30° latitude in both hemispheres [1]. These patches were likely deposited as snowfall in the recent past (less than ~2Myr) in response to climate change driven by shift in obliquity, similar to Earth glacial/interglacial periods [2]. However, martian climate models usually struggle to reproduce environmental conditions required to form LDM under recent paleoclimatic orbital forcing. We present a new set of simulations with refined description of surface and sub-surface water processes and their relevance regarding the deposition and above all stability of large ice patches up to mid-latitudes in both martian hemispheres. Water-ice clouds in recent paleoclimates: As present-day martian atmosphere is extremely dry, water ice clouds have second-order effects in global climate models [6]. Modeling higher obliquity episodes, ie shifts from 25° up to 35°, atmospheric humidity is enhanced by polar warming and water-ice cloud become a key element of martian climate [7,8]. Their radiative effect strongly warms the atmosphere, amplifies meridional circulation and water transport toward tropical latitudes. Previous work showed that radiatively active water-ice clouds allow for the transportation and deposition of water ice patches up to mid-latitudes [7]. However, these deposits can only be perennial under extreme atmospheric dust scenarios, in order to limit summertime sublimation. Frost and ice albedo: Surface water ice has a typical albedo of 0.35 in our present-day Mars model [9]. Mid-latitude ice patches resulting from an intensified water cycle at higher obliquity would rather have a 0.7 albedo, due to less dust content and fresh snowfall. By limiting solar heating, this albedo parametrization favors the persistence of mid-latitude ice throughout summer. Latent heat of sublimation: The latent heat of water sublimation has been neglected in present-day climate models, because of the low amount of water and thus energy flux involved. The sublimation of water was hitherto simply computed using surface temperature and water vapor equilibrium. Similarly to the frost albedo effect, taking into account the latent heat of water becomes relevant when the water cycle, and thus the energy flux involved, is intensified at higher obliquity. It also favors the year-long persistence of mid-latitude ice by adding an energy cost to sublimation, which decreases surface heating. Nudging subsurface thermal inertia: Martian soil’s thermal inertia is driven by the presence of subsurface perennial water ice, regulated by long-term equilibrium with water vapor [10]. This equilibrium is modified with the water cycle at higher obliquity. Instead of waiting for natural equilibrium to occur, we artificially accelerate the relocation of subsurface water ice, and accordingly increase subsurface thermal inertia. Subsurface ice inventory is computed each year using annual mean water vapor, as proposed in [10]. Conclusions with idealized orbital forcing: The recent excursions to 35° obliquity are thought to be the main drivers of martian glaciations. We use our new parametrizations along with idealized orbital forcing, that is a 35° obliquity and a null-excentricity, to show that the effect of water-ice clouds, frost albedo and latent heat of sublimation allow for the preservation of mid-latitude ice deposits when equilibrium is reached. In the last ~2 Myr on Mars, obliquity has reached 35° a dozen times, for approximately 1000 years each time [11]. Under our idealized hypothesis, the accumulation rate is compatible with hundreds of meters thick latitude-dependent mantle of ice-rich deposits. References: [1] Head, J. W., J. F. Mustard, M. A., Kreslavsky, R. E. Milliken, and D. R. Marchant (2003), Nature, 426 (6968), 797–802. [2] Forget, F., Byrne, S., Head, J. W., Mischna, M. A., & Schörghofer, N. (2017), The Atmosphere and Climate of Mars, Haberle et al. Editors, Cambridge University Press [3] Forget, F., Haberle, R. M., Montmessin, F., Levrard, B., & Head, J. W. (2006), 311(5759), 368-371. [4] Levrard, B., Forget, F., Montmessin, F and Laskar, J. (2004), Nature, 431 (7012), 1072-1075. [5] Madeleine, J. B., Forget, F., Head, J. W., Levrard, B., Montmessin, F., & Millour, E. (2009), Icarus, 203(2), 390-405. [6] Madeleine, J. B., Forget, F., Millour, E., Navarro, T., & Spiga, A. (2012), GRL, 39(23).[7] Madeleine, J. B., Head, J. W., Forget, F., Navarro, T., Millour, E., Spiga, A., ... & Dickson, J. L. (2014), GRL, 41(14), 4873-4879. [8] Kahre, M. A., Haberle, R. M., Hollingsworth, J. L. and Wilson, R. J. (2019), Ninth International Conference on Mars, held 22-25 July, 2019 in Pasadena, California. LPI Contribution No. 2089, id.6303. [9] Navarro, T., Madeleine, J. B., Forget, F., Spiga, A., Millour, E., Montmessin, F., & Määttänen, A. (2014), JGR: Planets, 119(7), 1479-1495. [10] Schorghofer, N. (2007). Theory of ground ice stability in sublimation environments. Physical Review E, 75(4), 041201. [11] Laskar, J., Correia, A. C. M., Gastineau, M., Joutel, F., Levrard, B., & Robutel, P. (2004), Icarus, 170(2), 343-364.

Amongst the numerous achievements of the Cassini-Huygens mission, the exploration of the Titan environment has had an unprecedented impact on the knowledge of this planet-like satellite of Saturn. The next milestone is the Dragonfly mission selected for a launch in 2026 and an arrival on Titan in 2034 (Turtle et al. LPSC abstract 2019). Now is the moment to bridge the gap between the end of the Cassini-Huygens measurements and the preparation of Dragonfly operations by prospecting on the work needed to take Titan’s science to the next level prior to DragonFly’s future discoveries. Those first remarks are generic for all possible Titan science; yet those apply particularly to Titan’s atmosphere (Hörst et al. JGR planets 2017). In this piece we address the need for innovative atmospheric modeling for Titan. The present state of the art on Titan’s atmospheric modeling could be summarized as follows. On the one hand, global climate modeling for Titan helped to make sense of Titan’s atmospheric circulation on a planetary scale (Lebonnois et al. Icarus 2012, Lora et al. Icarus 2015). On the other hand, regional climate (“mesoscale”) modeling was employed to understand the dynamics of convective storms made of methane and hydrocarbons (Barth and Rafkin Icarus 2010) and atmospheric dynamics in the vicinity of lakes and seas (Rafkin and Soto, submitted arxiv 1904.00120). The two approaches are complementary. Maybe the best example of this is the demonstration that the direction of equatorial dunes can only be explained when the large-scale winds are combined with convective methane storms able to cause wind maxima close to the surface opposite to the GCM predictions and compliant with observed dune propagation (Charnay et al. Nature Geoscience 2015). At the time of writing, no three-dimensional atmospheric model, suitable for the small scales and including complete physical packages (radiative transfer, cloud microphysics, etc), is available to resolve turbulence, convection and regional winds on Titan. Furthermore, assuming the Rafkin and collaborators’ model will soon be upgraded to those capabilities, having a diverse set of mesoscale models for Titan available is an asset for the community. Why would a regional-scale and turbulence-resolving model for Titan be needed? 1/ To characterize turbulent motions and help to constrain the flight conditions for the Dragonfly mission and, more generally, to understand turbulent motions on Titan. 2/ To resolve the three-dimensional convective dynamics in Titan’s methane and hydrocarbon storms and elucidate how they form, develop and dissipate; to assess the role of those storms in the near-surface exchanges and the organic cycle. 3/ To characterize surface-atmosphere exchange of particles and organic matter, in particular aeolian processes and sediment transport shaping Titan’s surface and dunes as major organic reservoirs; to understand the dynamics and aeolian potential of dust storms and dust devils on Titan. 4/ To elucidate the possibility of mesoscale dynamical features on Titan: slope winds, sea breezes, gravity waves, etc… and how those mesoscale phenomena impact the global dynamics. 5/ To make available a modeling platform used in specific, targeted regions, in which to couple photochemical models of interest for organic matter studies. Our team started to develop such a new atmospheric model for Titan and to propose it as a tool to the Titan community. We base our efforts on similar modeling tools developed for Mars (Spiga and Forget JGR planets 2009 ; Spiga et al. Nature Geoscience 2017) and Venus (Lefèvre et al. JGR planets 2017 & Icarus 2020). Our initial idea for developing mesoscale models is simple: an atmospheric model comprises a dynamical core (hydrodynamical solver for the Navier Stokes equations) coupled with physical packages for sub-grid scale unresolved processes (radiative transfer, cloud microphysics); we take the latter out of a Titan GCM (here, the IPSL Titan GCM) and we borrow the former from the terrestrial mesoscale model Weather Research and Forecast (WRF, Skamarock et al. Journal of Computational Physics 2008). That way, both the Titan GCM providing initial and boundary conditions for wind, temperature, and chemical species and our newly-built Titan mesoscale model share similar physical packages, ensuring optimal consistency in the downscaling approach. With such an admittedly ambitious program, which starting point could we adopt? Our experience with other planets is that the first applications shall be three-dimensional idealized simulations before the complete workflow for fully realistic simulations can be developed. Modeling instabilities caused by wind shear in Titan’s lower stratosphere, to explain the unexpected wind minima experienced by the Huygens probe is a first example (Lefèvre et al. EPSC-DPS abstract 2019). Another possibility explored here is to carry out large-eddy simulations for Titan’s near-surface turbulence. Those simulations are named as such because only the largest turbulent eddies are resolved / predicted by the model’s dynamics. Our prospect is, for instance, to evaluate the ability of dust devils to redistribute organic dust at the surface and in the atmosphere. Preliminary results show horizontally-organized turbulent structures (Figure 1), yet organized swirling motions are not yet reproduced at the time of writing. Figure 1 : Near-surface turbulent wind fields simulated by a prototype large-eddy simulation for Titan, using coupling of the adapted WRF dynamical core with the Titan physics from the IPSL Titan GCM. The development of the model is currently ongoing ; the first test for mesoscale simulations with existing topography (Corlies et al. GRL 2017) will be in the region enclosing the Shangri-La dune field, the Dragonfly provisional landing site (Figure 2). The development of our modeling system makes it easy to update the surface thermophysical properties with new datasets arising for topography, albedo, thermal inertia. Ideas for collaboration are very welcome following the presentation of this abstract. Figure 2 : An example of horizontal domain for mesoscale applications with our model, considered in the region of the Dragonfly landing site.

The InSight mission landed on Mars in November of 2018 and completed installation of a seismometer (SEIS) on the surface about two months later. In addition to SEIS, InSight carries a diverse geophysical observatory including a heat flow and sub-surface physical properties experiment (HP3), a geodesy (planetary rotation dynamics) experiment (RISE), and a suite of environmental sensors measuring the magnetic field and atmospheric temperature, pressure and wind (APSS). For more than a year, SEIS has been providing near-continuous seismic monitoring of Mars, with background noise levels orders of magnitude lower than that achievable on the Earth. Since installation was completed, the SEIS team has identified more than 400 events that we have not attributed to the local environment or spacecraft activity, and dozens that appear to be marsquakes of tectonic origin. We present an overview of observations by the SEIS instrument as well as a summary of other geophysical observations made by InSight during the past year and a half.

Mars atmospheric pressure variations induce ground displacements through elastic deformations. The various sensors of INSIGHT mission were designed in order to be able to understand and correct these ground deformations induced by atmospheric effects. Particular efforts were done on one side to avoid direct pressure and wind effects on the seismometer, and on the other side to have a high performance pressure sensor operating in the same frequency range than the seismometer.As a consequence of the high performances of both instruments, their very efficient protection systems against direct atmospheric disturbances, and the low Mars background seismic noise, INSIGHT mission is opening a new science domain for which the ground displacements can be used to perform atmospheric science.This study presents an analysis of pressure and seismic signals and their relations. After a short description of the pressure and seismic sensors deployed by INSIGHT, we present an analysis of these signals as a function of local time at INSIGHT location.Then, the background and event like coherent signals between Pressure and seismometer sensors are interpreted in terms of various atmospheric excitations and induced ground deformation processes. Different methods to remove the pressure effects recorded by SEIS sensors are presented, and their efficiency is estimated. Finally, we demonstrate that the pressure and ground deformations measurements can be used to decipher between various atmospheric excitation types (meteorological pressure variations, acoustic and gravity waves) and characterize these. Effects of the local sub-surface structure are also suggested by the data analysis.

The InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission landed in western Elysium Planitia on November 26, 2018. Because of its stationary position and a multi-instrument package, InSight offers the unique opportunity of detecting changes induced by aeolian activity and constraining the atmospheric conditions responsible for particle motion.In this work, we present the most significant changes from aeolian activity as detected by the InSight lander during its first 400 Martian days of operations. We will show that particle entrainment by wind activity around InSight is a subtle process and report simultaneous measurements observed across multiple instruments. The changes observed are episodic and are seen correlated with excursions in both seismic and magnetic signals, which will be discussed further. Our observations show that all aeolian movements are consistent with the passage of deep convective vortices between noon to 3 pm local time. These vortices may be the primary initiators for aeolian transportation at InSight, inducing episodic particulate motion of grains up to 3 mm in diameter.

Large-Eddy Simulations (LES) for Mars resolve the Planetary Boundary Layer (PBL) turbulent dynamics by using a very fine horizontal resolution of a few tens of meters. LES modeling is becoming a more and more useful tool to prepare the robotic exploration of Mars by providing means to evaluate the intensity of convective plumes and vortices, horizontal wind gustiness, and turbulent fluctuations of temperature in the Martian PBL. In such context, and given the relative paucity of turbulence-related measurements on Mars, an intercomparison of LES models is a fruitful way to evaluate the models' predictions and to indicate possible areas of improvement. Thus, to prepare the landing of the ExoMars Schiaparelli lander (also named ExoMars Demonstrator Module, EDM), scheduled for October 2016, the results of the Laboratoire de Météorologie Dynamique (LMD) and South-West Research Institute (SwRI) LES models have been compared. The objective of this study is to determine the range of uncertainties, and dispersions, of the two numerical models' predictions, for the critical phase of the spacecraft's descent in the Martian daytime turbulent PBL. First, a strategy is defined to ensure similar radiative forcing in both the LMD and SwRI models. Then, LES are performed over a flat terrain with and without large-scale ambient horizontal wind. The LMD and SwRI Martian LES models predict similar temporal evolution of the PBL and organization in the horizontal and vertical wind fields. However, the convective motions in the daytime PBL are more vigorous by a factor 1.5–2 in SwRI results than in LMD results, independently of the presence or not of ambient horizontal wind. This discrepancy is further investigated through sensitivity studies to surface conditions, ambient wind, and airborne dust loading.