Solar system

Abstract:

The origin and vertical distribution of Jupiter’s red coloration remain uncertain, despite multiple proposed aerosol models. Laboratory work (Carlson et al., 2016) showed that photolyzed ammonia and acetylene can form a red compound consistent with Jupiter’s colours, motivating the “universal chromophore” hypothesis (Sromovsky et al., 2017), and the “Crème Brûlée” model (Baines et al., 2019), which places a thin absorber above the ammonia clouds. Later HST and VLT studies (Pérez‑Hoyos et al., 2020; Braude et al., 2020) suggested a more vertically extended, less blue‑absorbing material, while recent analyses of the Great Red Spot and Oval BA indicate the presence of two distinct colouring agents: a universal‑chromophore absorber and a deeper UV‑absorbing aerosol (Anguiano‑Arteaga et al., 2021, 2023). These findings highlight persistent ambiguity in Jovian aerosol composition and structure.To investigate this, we analysed 2019 Jupiter observations from CARMENES (The Calar Alto High-Resolution search for M dwarfs with Exoearths with Near-infrared and optical Échelle Spectrographs), (0.52–1.71 μm). Since no calibration star was available, we calibrated the spectra using Saturn’s B ring and Cassini/VIMS reflectivity (Cuzzi et al., 2009), achieving agreement with published Jupiter spectra to within 10% (Clark, R.N., McCord, T.B., 1979; Mendikoa, I., et al., 2017; Irwin, P.G., et al., 2018).Using 64 VIS–NIR observation pairs, we performed a Minnaert limb‑darkening analysis and generated synthetic spectra for five regions. These were used in NEMESIS retrievals with three aerosol models. Across all models, the highest‑altitude aerosol layer dominated the spectral behaviour, with particle size, cloud‑base abundance, and pressure level strongly influencing the fits. Model B (Braude et al., 2020) produced the lowest χ²/Nfree values, but no model fully reproduced the observations, likely due to the limited wavelength range, which lacks constraints on deeper clouds.The models diverged in retrieved particle sizes and cloud‑base pressures, with several results, such as extremely small tropospheric particles or overly large stratospheric particles, indicating physical inconsistencies. Model A’s tropospheric haze base aligns with Galileo probe measurements (Sromovsky and Fry, 2002); Model C retrieves a cloud base level near the NH₄SH level predicted by Atreya (1998), deeper than CIRS detections (Matcheva et al.,2005) but within the range of Baines et al. (2019), with implausible particle sizes.Overall, the study shows that CARMENES can deliver high‑quality, flux‑calibrated planetary spectra, but also that broader spectral coverage is essential to resolve Jupiter’s chromophore composition and aerosol vertical structure. Figure 1: Location of the spectra used to perform the Minnaert limb-darkening approximation for each region considered in this study. Red for EZ, yellow for NEB, green for SEB, pink for SEB transition and blue for NEB transition. The Jupiter AGC image represented corresponds only to the spectra of the EZ whose longitude was closest to 0º. Figure 2: Comparison between observed and modelled spectra and residuals for EZ using model B]{Comparison between observed (blue) and modelled (red) spectra (left column) and comparison between differences (red) and a priori errors (black) (right column) for the EZ using model B, with the grey shaded areas corresponding to telluric absorption. The top row corresponds to nadir (incidence and emission angle = 0º) and the bottom row to limb (incidence and emission angle = 61.45º). Figure 3: Comparison between the a priori aerosol vertical profiles and the retrieved profiles for every region for models A and B. We compare the optical depth/atm at 0.90 μm of model B with all three aerosol populations considered and model A's stratospheric and tropospheric hazes. The horizontal dashed line corresponds to 0.15 atm, separating model B's deep cloud layer from the haze. References:Carlson, R. W., et al. (2016). Chromophores from photolyzed ammonia reacting with acetylene: Application to Jupiter's Great Red Spot. Icarus, 274, 106–115.Sromovsky, L. A., et al. (2017). A possibly universal red chromophore for modeling color variations on Jupiter. Icarus, 291, 232–244.Baines, K. H., et al. (2019). The visual spectrum of Jupiter's Great Red Spot accurately modelled with aerosols produced by photolyzed ammonia reacting with acetylene. Icarus, 330, 217–229.Pérez-Hoyos, S., et al. (2020). Color and aerosol changes in Jupiter after a North temperate belt disturbance. Icarus, 132, 114021.Braude, A. S., et al. (2020). Colour and tropospheric cloud structure of Jupiter from MUSE/VLT: Retrieving a universal chromophore. Icarus, 338, 113589.Anguiano-Arteaga, A., et al. (2021). Vertical Distribution of Aerosols and Hazes Over Jupiter's Great Red Spot and Its Surroundings in 2016 From HST/WFC3 Imaging. Journal of Geophysical Research: Planets, 126, e2021JE006996.Anguiano-Arteaga, A., et al. (2023). Temporal variations in vertical cloud structure of Jupiter's Great Red Spot, its surroundings and Oval BA from HST/WFC3 imaging. Journal of Geophysical Research: Planets, 128, e2022JE007427.Irwin, P., et al. (2008). The NEMESIS planetary atmosphere radiative transfer and retrieval tool. J. Quant. Spectrosc. Radiat. Transf., 109, 1136–1150.Rodgers CD. (2000). Inverse methods for atmospheric sounding: theory and practice. Singapore: World Scientific.Cuzzi, J., et al., 2009. Ring Particle Composition and Size Distribution. Springer Netherlands, Dordrecht. pp. 459–509.Clark, R.N., McCord, T.B., 1979. Jupiter and Saturn: Near-infrared spectral albedos. Icarus 40, 180–188.Mendikoa, I., et al., 2017. Temporal and spatial variations of the absolute reflectivity of Jupiter and Saturn from 0.38 to 1.7 𝜇m with planetcam-upv/ehu. A&A 607, A72.Irwin, P.G., et al., 2018. Analysis of gaseous ammonia (NH3) absorption in the visible spectrum of Jupiter. Icarus 302, 426–436.Matcheva, K.I., Conrath, B.J., Gierasch, P.J., Flasar, F.M., 2005. The cloud structure of the jovian atmosphere as seen by the Cassini/CIRS experiment. Icarus 179(2), 432–448.Sromovsky, L., Fry, P., 2002. Jupiter’s cloud structure as constrained by Galileo probe and HST observations. Icarus 157 (2), 373–400.

Abstract:

Introduction: Thermal infrared emissivity measurements of asteroid regolith analogs are challenging owing to atmospheric water vapor absorption, sample heating requirements, and the need for controlled atmospheric conditions [1], yet they provide fundamental constraints on surface thermal properties that cannot be obtained from reflectance spectroscopy alone [1]. While diffuse reflectance measurements have demonstrated that minimal fine dust coverage can dominate spectral signatures [2], spacecraft-based thermal emission instruments like the OSIRIS-REx Thermal Emission Spectrometer (OTES) observe different physical processes related to thermal emission rather than scattered light [3]. The disconnect between laboratory studies and spacecraft observations has thus limited our ability to interpret thermal infrared spectra of asteroid surfaces. Previous work using Space Resource Technology's CI simulant showed that 7-10 wt% fine dust coverage could impose fine-dominated reflectance features on coarse substrates [2], but the corresponding thermal emission properties remained uncharacterized. To bridge this gap, we conducted systematic thermal emissivity measurements of layered CI simulant materials using Oxford’s PASCALE instrument [4] under nitrogen atmosphere, constraining how dust deposition mechanisms affect the thermal emission processes observed by spacecraft instruments at airless bodies like asteroid (101955) Bennu. Methods: We measured thermal emission of layered CI simulant [5] samples using PASCALE under nitrogen atmosphere across 2000-400 cm⁻¹ (5-25 µm), eliminating atmospheric water vapor interference. Six layering configurations were tested, using 10 wt% fines (5% emissivity variations from unity), while the fluffy group shows more subdued but consistent spectral signatures. All method-dependent variations exceed the 2% measurement precision, demonstrating that dust deposition mechanism leaves diagnostic thermal emission signatures that can distinguish (and potentially identify) natural surface processes on airless body surfaces. Discussion: The separation between fluffy and compact layering methods demonstrates that thermal emission spectroscopy can distinguish surface formation processes on airless bodies. These results provide constraints missing from reflectance-only studies, by characterizing thermal emission properties relevant to spacecraft observations like OTES. The ability to spectrally distinguish between natural deposition processes offers new frameworks for understanding regolith evolution and thermophysical properties on asteroid surfaces. Summary: This study establishes thermal emissivity as a diagnostic tool for identifying dust deposition mechanisms on asteroid surfaces, demonstrating that layering processes leave distinct spectral signatures. References: [1] Salisbury et al. (1991) Icarus 92, 280-297. [2] Belhadfa et al. (2026) MaPs, In Prep. [3] Christensen P. R. et al. (2018) Space Science Reviews (Vol. 214, Issue 5). [4] Donaldson Hanna et al. (2019) Icarus 319, 701-723. [5] Landsman Z. et al. (2020) EPSC.  

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.