Solar system

Abstract:

IntroductionThe presence of endogenic hotspots provides a measure of the level of geologic activity of icy moons, since they are indicative of ongoing resurfacing processes. However, to avoid misinterpreting thermal abnormalities, it is first necessary to understand passive thermal emission that is governed by the physical structure of materials. Two important thermophysical properties in such analysis are bolometric Bond albedo and thermal inertia: if diurnal temperature variations can be accurately modeled by adjusting these two parameters, a passive rather than endogenic origin is possible. In this work, we aim to constrain the thermal inertia and Bond albedo across the surfaces of Europa, Ganymede, and Callisto using brightness temperature observations recorded by the Galileo Photopolarimeter-Radiometer (PPR) instrument [1]. By presenting estimated values and their uncertainties for these thermophysical properties, we prepare for future thermal measurements carried out by both Europa Clipper and JUICE.MethodsWe first take average diurnal temperatures of the surfaces of Europa, Ganymede, and Callisto using brightness temperatures recorded by PPR. In this analysis, we use 29 datasets for Europa, notably increased beyond previous efforts [2], comparable to [3].  With less observations available, only 7 datasets each were used for both Ganymede and Callisto. By translating the observed radiance into brightness temperatures, the variation in temperature with local time is determined for latitude and longitude bins across each of the three moons. These diurnal curves are compared to those predicted by a 1-D thermal model [4] to determine what thermal inertias and Bond albedos can fit the data within a reduced chi-squared cut-off of χ2red ≤1.0. This analysis is used to extensively quantify the uncertainty of the two thermophysical parameters derived from PPR data.ResultsEuropa: Ensuring closed upper and lower limits within our χ2red cut-off, we map albedo and thermal inertia for 33% and 24% of Europa's surface area into 6°x6° latitude/longitude bins (Fig. 1, left). We find a range of 0.375-0.75 for albedo and 20-110 J m-2 K-1 s-1/2 (MKS) for thermal inertia, agreeing with [2] and [3]. Our uncertainty analysis indicates well-constrained estimates for albedo, with the average higher and lower ranges overlapping within their uncertainties: Ahigh = 0.11 ± 0.06 and Alow = 0.18 ± 0.12. Uncertainties for thermal inertia remain poorly constrained, with average higher and lower ranges being ­Γhigh = 103(+153/-103) MKS and Γ­low = 17 ± 9 MKS.Ganymede: Due to less available surface coverage, Ganymede’s surface is divided into 18°x6° longitude/latitude bins in order to meet the diurnal fitting routine requirements (Fig. 1, right). Preliminary results indicate the Bond albedo remains nearly uniform, with an average of 0.42 ± 0.07 across the surface and agreeing well with previous work [6]. Specifically, our χ2red-deduced higher and lower uncertainty ranges of albedo are: Ahigh = 0.10 ± 0.06 and Alow = 0.14 ± 0.08. No apparent distinct surface variations in thermal inertia are as of yet discerned, with global values distributed across a range of 20-70 MKS. Upper limit thermal inertia estimates align with [6] within χ2red-deduced uncertainty ranges of ­Γhigh = 57 ± 35 MKS and Γ­low = 25 ± 11 MKS.Callisto: With less PPR coverage available, Callisto fits are performed across grouped hemispheric regions, where the Jovian and anti-Jovian hemispheres are analyzed separately. Each hemisphere is divided into 10° latitude strips. Preliminary results for thermal inertia and Bond albedo indicate an overall agreement with lower-bound estimations from previous literature [5,6]. Further constraints are expected to be obtained at the time of the conference.ConclusionThese results give an indication of the albedo and thermal inertia variation across Europa, Ganymede and Callisto. They aid in preparing for the arrival of Europa Clipper and JUICE to the Jupiter system by improving estimates for passive surface thermal properties and providing uncertainties of their values. This will enable future work to help discern what temperatures may lie above those expected from passive emission alone, providing a critical first step in the search for endogenic heating anomalies. Future work aims to refine the Ganymede and Callisto calculations using a two-component (ice and non-ice) analysis, and characterize the causes behind thermal inertia variations by modeling microphysical ice states for the three Galilean moons.Fig. 1: Thermal Inertia (top) and Bond albedo (bottom) map of Europa (left) and preliminary results for Ganymede (right).[1] Russell, E.E. et al., 1992. Space Sci Rev 60, 531-563.; [2] Rathbun, J.A. et al., 2010. Icarus 210, 763-769.; [3] Lange, L. et al., 2026. arXiv:2604.14374; [4] Lyster, D. et al., 2025. pp. EPSC-DPS2025-1479; [5] Meyer, C. et al., 2026. Planet. Sci. J. 7, 10; [6] Spencer, J.R. et al., 1989. Icarus 78(2), 337-354.

Abstract:

Radiative transfer analyses of Uranus and Neptune spectra have revealed a cloud layer at pressures greater than ~2 bar (1,2), with H₂S gas detected above it on both planets (3,4), suggesting H₂S ice as its main constituent. However, the properties of these clouds and their dependence on the deep atmospheric composition remain poorly constrained.We present an extended version of a one-dimensional cloud microphysics model [5,6] previously applied to simulate CH₄ and H₂S clouds in the Ice Giants (7,8). The model now incorporates NH₄SH chemistry, extending the simulation domain to ~50 bar. Since NH₃ reacts with H₂S to form NH₄SH at depth, the deep N/S ratio controls how much H₂S is available to condense at higher altitudes. We explore how the deep NH₃ and H₂S abundances, together with the vertical mixing profile, determine the properties of the H₂S cloud layer, including its base pressure, total opacity, and particle size distribution.Preliminary results and the implications of this work for the interpretation of current and future observations of Uranus and Neptune will be discussed.References: [1] P. G. Irwin, et al., JGR: Planets, 127, e2022JE007189. [2] L. Sromovsky, et al., Icarus,Volume 317, (2019) [3] P. G. Irwin, et al., Nature Astronomy 2, 420 (2018). [4] P. G. Irwin, et al., Icarus 321, 550 (2019). ). [5] P. Rannou, et al., Science 311, 201 (2006). [6] F. Montmessin, et al., JGR: Planets 107, 4 (2002). [7] D. Toledo, et al., A&A, 694, A81 (2025). [8] D. Toledo, et al.,: Microphysical Modeling of Hydrogen Sulfide Clouds in the Atmospheres of the Ice Giants, EPSC-DPS Joint Meeting 2025,  https://doi.org/10.5194/epsc-dps2025-1456, 2025.

Abstract:

Introduction: Titan’s atmosphere has a north-south haze dichotomy (Fig.1), with an unexpectedly sharp boundary near the equator (e.g., Lorenz et al. 1997; Roos-Serote 2005). The boundary does not sit exactly at the equator, nor does it remain stationary throughout Titan’s year (R. Lorenz 1999; Roman et al. 2009; Kutsop et al. 2022; Vashist et al. 2023; Snell and Banfield 2024). Instead, the boundary migrates in latitude seasonally and is seen to disappear post-equinox then reappear with the dichotomy reversed shortly after. The sharpness of the boundary suggests that there is limited horizontal mixing over the equator. This, in addition to the boundary’s seasonal migration, makes Titan’s equator a dynamically intriguing region. Previously, dynamics in Titan’s stratosphere have been constrained observationally using the thermal wind relation (Sharkey et al. 2021;  Achterberg 2023;  Wright et al. 2025), but this equation breaks down at low latitudes. We instead map infrared-active trace species in Titan’s stratosphere to inspect the dynamics in Titan’s equatorial region.Data & Method: We use infrared spectra acquired by Cassini’s Composite Infrared Spectrometer (CIRS) instrument from 70 fly-bys of Titan spanning the entire 13-year mission. CIRS had an adjustable spectral resolution, typically observing at FWHM~0.5, 2.5, or 14.5 cm-1. We use CIRS FP3/4 observations acquired at a low spectral resolution (FWHM~14.5 cm-1), which achieved the best combination of seasonal and spatial coverage with high spatial resolution, allowing us to discern compositional variations over finer length-scales than in previous studies (Teanby et al. 2006; Teanby et al. 2010). Wright et al. 2024 showed that these data can be reliably forward-modelled despite having subtle and often blended spectral peaks. We use archNEMESIS (Alday et al. 2025) – an open-source Python package based on the NEMESIS (Irwin et al. 2008) radiative transfer and retrieval code – to fit CIRS FP3/4 spectra. We fit CIRS FP4 spectra by retrieving continuous temperature profiles and fit mid-IR spectra from 600-1100 cm-1 by scaling vertical profiles of gas volume mixing ratio.Results: We present maps of the variation in abundances of HCN, C2H2, C2H6, C3H4, C4H2, CO2 in Titan’s stratosphere (~5 mbar pressure) with the highest resolution mapping achieved to date, covering 40oS to 40oN throughout 2004—2017. Many species are seen to have a rapid change in abundance over the equator, with HCN exhibiting the steepest latitudinal gradient (e.g., Fig.2). The improved spatial resolution achieved here allows us to track the migration of the compositional gradient over time. We find that it follows a similar migration to Titan’s north-south haze boundary during the Cassini mission. Haze and HCN distributions appear to behave similarly at the equator, suggesting that the boundary is induced by dynamics, rather than by chemistry or microphysical processes.In addition, we use the meridional composition gradient to predict the tilt offset of Titan’s stratosphere, following the method of (Teanby et al. 2010). We do this over the full 13-year Cassini mission to inspect the seasonal evolution of Titan’s tilted stratosphere. This is compared to the tilt evolution inferred from temperature (Wright et al. 2025) and from images (Snell and Banfield 2024).Fig 1. Titan’s north-south albedo asymmetry. Infrared image taken in 2007 by Cassini’s Imaging Science Subsystem (ISS) Narrow-Angled Camera (NAC) using a 890 nm filter.Fig 2. Retrieved HCN volume mixing ratio (VMR) in Titan’s equatorial region, at 5 mbar. Example from observations taken during 2008. Different colours identify different observation sequences. The steepest gradient is seen to be ~5oS at this time (dashed line, shaded region is the uncertainty).ReferencesAchterberg, R. K. 2023. The Planetary Science Journal 4 (8): 140. https://doi.org/10.3847/PSJ/acebea.Alday, J., J. Penn, P. Irwin, J. Mason, J. Yang, and J. Dobinson. 2025. Journal of Open Research Software  13: 10. https://doi.org/10.5334/jors.554.Irwin, P. G. J., N. A. Teanby, R. de Kok, et al. 2008. Journal of Quantitative Spectroscopy and Radiative Transfer 109 (6): 1136–50. https://doi.org/10.1016/j.jqsrt.2007.11.006.Kutsop, N. W., A. G. Hayes, P. M. Corlies, et al. 2022. The Planetary Science Journal 3 (5): 114. https://doi.org/10.3847/PSJ/ac582d.Lorenz, R. 1999. Icarus 142 (2): 391–401. https://doi.org/10.1006/icar.1999.6225.Lorenz, R. D., P. H. Smith, M. T. Lemmon, E. Karkoschka, G. W. Lockwood, and J. Caldwell. 1997. Icarus 127 (1): 173–89. https://doi.org/10.1006/icar.1997.5687.Roman, M. T., R. A. West, D. J. Banfield, et al. 2009. Icarus 203 (1): 242–49. https://doi.org/10.1016/j.icarus.2009.04.021.Roos-Serote, M. 2005. Space Science Reviews 116 (1–2): 201–10. https://doi.org/10.1007/s11214-005-1956-0.Sharkey, J., N. A. Teanby, Melody Sylvestre, et al. 2021. Icarus 354 (January): 114030. https://doi.org/10.1016/j.icarus.2020.114030.Snell, C., and D. Banfield. 2024. The Planetary Science Journal 5 (1): 12. https://doi.org/10.3847/PSJ/ad0bec.Teanby, N. A., P. G. J. Irwin, and R. de Kok. 2010. Planetary and Space Science 58 (5): 792–800. https://doi.org/10.1016/j.pss.2009.12.005.Teanby, N., P. Irwin, R. Dekok, et al. 2006. Icarus 181 (1): 243–55. https://doi.org/10.1016/j.icarus.2005.11.008.Vashist, A. S., M. F. Heslar, J. W. Barnes, C. Hennen, and R. D. Lorenz. 2023. The Planetary Science Journal 4 (6): 118. https://doi.org/10.3847/PSJ/acdd05.Wright, L., N. A. Teanby, P. G. J. Irwin, and C. A. Nixon. 2024. Experimental Astronomy 57 (2): 15. https://doi.org/10.1007/s10686-024-09934-y.Wright, L, N. A. Teanby, P. G. J. Irwin, et al. 2025. The Planetary Science Journal 6 (5): 114. https://doi.org/10.3847/PSJ/adcab3.

Abstract:

Hydration on the lunar surface has been widely identified in orbital datasets (e.g., M³, LCROSS, LAMP), yet the physical form, abundance, and spatial distribution of lunar volatiles remain poorly constrained. Interpretation is complicated by fine-grained regolith, which modifies local thermophysical conditions, obscures underlying volatiles, and alters diagnostic spectral features through scattering and photometric effects. These uncertainties are particularly significant for permanently shadowed regions (PSRs) and high latitudes , where temperatures below ~120 K may preserve water-ice over geological timescales and where several upcoming missions (e.g., Chang’e-7, PROSPECT, CLPS payloads, LEAP) aim to investigate insitu volatiles.We present the development of the Polar Analogue of Dust Overlying Regolith–Ice (PANDOR-I), a demountable laboratory vacuum chamber designed to simulate lunar polar conditions for infrared studies of water-ice and regolith mixtures. The system is engineered to operate under high vacuum and cryogenic conditions (~10⁻⁶ mbar; ≤120 K) and supports variable illumination geometries relevant to polar environments. PANDOR-I operates in two configurations: (1) coupled to a Bruker Vertex 70v FTIR spectrometer for laboratory reflectance measurements across 1.8–20 µm, and (2) integrated with existing flight-instrument thermal-vacuum facilities to enable direct observations by flight-ready infrared instruments.As an initial experimental phase prior to full cryogenic integration, the FTIR sample compartment has been isolated using KBr windows to enable controlled low-pressure (~0.2 mbar) reflectance measurements of hydrated and anhydrous regolith analogue configurations. These preliminary experiments investigate how dust layering, grain size, regolith maturity, composition, ice abundance, and mixing state influence the spectral expression of hydration features, with emphasis on the ~3 µm O–H stretching region and the diagnostic ~6 µm H–O–H bending mode of molecular water. Laboratory spectra will additionally be compared with Mie–Hapke forward models to examine band depth suppression, spectral mixing behaviour, and detectability thresholds under dusty polar conditions.This work reviews the laboratory framework for constraining infrared water-ice detection limits under mission-relevant lunar conditions and provides initial calibration datasets relevant to upcoming orbital and surface investigations of lunar polar volatiles.1. Honniball, C.I., Lucey, P.G., Hayne, P.O., Little, R.C., Greenhagen, B.T., Malespin, C. and Orlando, T.M., 2021. Molecular water detected on the sunlit Moon by SOFIA. Nature Astronomy, 5(2), pp.121–127. https://doi.org/10.1038/s41550-020-01222-x2. Saal, A.E., Hauri, E.H., Cascio, M.L., Van Orman, J.A., Rutherford, M.C. and Cooper, R.F., 2008. Volatile content of lunar volcanic glasses and the presence of water in the Moon’s interior. Nature, 454(7201), pp.192–195. https://doi.org/10.1038/nature070473. Buffo, J.J., Shepherd, J.D., Xu, J., Whisner, C., Devore, E., Shay, P. and Crites, S.T., 2025. Quantifying Regolith Cover Effects on 3 and 6 µm Water Ice Bands. 56th Lunar and Planetary Science Conference, Abstract 2152.4. Pieters, C.M., Goswami, J.N., Clark, R.N., Annadurai, M., Boardman, J., Buratti, B., Cheek, L., Dhingra, D.K., Green, R.O., Head, J.W., Hiesinger, H., Hypki, A., Isaacson, P., Jolliff, B.L., Klima, R.L., Kramer, G., Kumar, S., Lawrence, S.J., LeCorre, L., Li, S., Malaret, E., Mustard, J.F., Petro, N.E., Robinson, M.S., Samuelson, J., Sundaram, C.N. and Taylor, L.A., 2009. Character and spatial distribution of OH/H₂O on the surface of the Moon seen by M³ on Chandrayaan-1. Science, 326(5952), pp.568–572. https://doi.org/10.1126/science.11786585. McCord, T.B., Taylor, L.A., Combe, J.P., Klima, R.L., Tighe, R., Murray, K., Hayne, P.O., Clark, R.N., Pieters, C.M., Sunshine, J.M., Mellon, M.T., Hargraves, R.B., Dyar, M.D., Bussey, D.B.J., Paige, D.A. and Orlando, T.M., 2011. Sources and processes responsible for OH/H₂O in lunar soil. Journal of Geophysical Research: Planets, 116(E10). https://doi.org/10.1029/2010JE0037116. Ehlmann, B.L., Calvin, W.M., Bowles, N.E., Donaldson Hanna, K.L., Green, R.O., Greenhagen, B.T. and Shirley, K.A., 2022. Lunar Trailblazer: A pathfinding mission for lunar water and the lunar surface composition. IEEE Aerospace and Electronic Systems Magazine, 37(11), pp.6–22. https://doi.org/10.1109/AERO53065.2022.98436637. Bowles, N.E., Thomas, I.R., Calcutt, S.B., Donaldson Hanna, K.L., Ehlmann, B.L., Greenhagen, B.T. and Shirley, K.A., 2020. Lunar Thermal Mapper: Characterising the lunar surface in the mid-infrared. 51st Lunar and Planetary Science Conference, Abstract 1380.8. Colaprete, A., Schultz, P., Heldmann, J., Wooden, D., Ennico, K., Hermalyn, B., Marshall, W., Ricco, A., Shirley, M., Vergoz, J. and Yeomans, D., 2010. Detection of water in the LCROSS ejecta plume. Science, 330(6003), pp.463–468. https://doi.org/10.1126/science.11869869. Ogishima, A., Saiki, K., Okubo, A. and Sasaki, S., 2021. Development of a laboratory apparatus to reproduce lunar polar surface environment and measurements of reflectance spectra of frost on the regolith. Icarus, 358, 114192. https://doi.org/10.1016/j.icarus.2020.114192

Abstract:

Despite constituting a tiny fraction of the Mars atmosphere, trace gases can play an important role in controlling atmospheric chemical cycling. However, some discrepancies between observed distributions of trace gases and modelled values indicate that there are ongoing processes in Mars’ atmosphere that are not fully understood.Hydrogen chloride (HCl) was the first new gas detected by the ExoMars Trace Gas Orbiter (TGO)[1,2], with observations from the Atmospheric Chemistry Suite (ACS) and Nadir and Occultation for Mars Discovery (NOMAD) instruments showing a strong seasonal variation over the last several Mars years. With a few exceptions, detections almost exclusively occur during the second half of the year (at solar longitudes between 180° and 360°). This is during southern hemisphere spring and summer, when temperatures, atmospheric dust content, and water vapour concentrations are higher, and ozone concentrations are low. Previous modelling has shown that heterogeneous chemical reactions involving dust or ice aerosols play a key role in controlling this seasonal pattern[3,4,5,6].Here we use the Mars Planetary Climate Model[7,8], a 3-D global circulation model with photochemistry, to investigate some potential sources and sinks of HCl in the Martian atmosphere, which could account for the seasonal variation in measurements and the observed correlations and anticorrelations with other atmospheric factors. Firstly, we examine the indirect effect of heterogeneous chemistry of OH and HO2 interacting with ice. We compare the effect of two different heterogeneous chemical schemes[9,10] on HCl distributions via their effect on other oxidative species such as O and O3. Secondly, we investigate the isolated effects of two further heterogeneous pathways involving water ice—one HCl source and one sink. Specifically, we model the uptake of HCl onto water ice, which has been studied before, and its subsequent release back to the atmosphere during ice sublimation, which has not been included in previous models.Our preliminary results (e.g., Figure 1) show that the latter cycling could account for some of the seasonality of the HCl observations. HCl concentrations remain close to the ground during the first half of the year, and then increase at higher altitudes during the second half of the year, where they could be detected by TGO's instruments. Even without the addition of other HCl sources and sinks in the model, we expect this pattern to be repeated over multiple Mars years, reproducing at least part of the annual appearance and disappearance of HCl through the recycling of chlorine.Continuing to reconcile models and observations of the cycling of HCl and other trace gases is important for achieving a more complete understanding of atmospheric processes operating on Mars today, as well as those that have played a key role over Mars’ history.Figure 1: Preliminary model results showing seasonal distributions of HCl over more than one Mars year. Each panel shows zonally-averaged HCl volume mixing ratios with altitude and latitude, and there are 30° of solar longitude between each panel. The black contour indicates a mixing ratio of 0.5 ppbv, which is the detection limit for TGO ACS.[1] Korablev O. I. et al. (2021). Sci. Adv., 7, eabe4386. [2] Olsen K. S. et al. (2021). Astron. Astrophys., 647, A161. [3] Benne, B., et al. (2025). Astron. Astrophys., 699, A362. [4] Rajendran, K. et al. (2025). JGR: Planets 130(3), p.e2024JE008537. [5] Streeter, P. M. et al. (2025). GRL 52(6), p.e2024GL111059. [6] Taysum, B. M. et al. (2024). Astron. Astrophys., 687, A191. [7] Forget, F., et al. (1999). JGR: Planets 104, E10. [8] Lefèvre, F., et al. (2004). JGR: Planets 109, E7. [9] Brown M. A. J. et al. (2022). JGR: Planets, 127, p.e2022JE007346. [10] Lefèvre, F., et al. (2021) JGR: Planets, 126, p.e2021JE006838.