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Jovian chromophore and upper hazes from CARMENES spectra

(2025)

Authors:

José Ribeiro, Pedro Machado, Santiago Pérez-Hoyos, Asier Anguiano-Arteaga, Patrick Irwin

Abstract:

The nature of the red colouration of Jupiter’s belts and some of its major anticyclones is still debated to this day. Sromovsky et al. (2017) proposed the existence of an “universal chromophore” by fitting Cassini/VIMS-V observations. Baines et al. (2019) concluded that this chromophore should be located in a thin layer above the ammonia clouds, giving rise to the so called “Crème Brûlée” model. Both of these works had as a basis the red compound that formed through the reaction of photolyzed ammonia with acetylene as obtained in the laboratory by Carlson et al. (2016).However,  both Pérez-Hoyos et al. (2020) and Braude et al. (2020) found that a less blue and more vertically extended chromophore layer would fit better their HST/ WFC3 North Temperate Belt disturbance observations for the former and latitudinal swath from MUSE/VLT observations for the later, without fully discarding the possible existence of an “universal chromophore”. Recently, analysis of HST/WFC3 images of Jupiter’s Great Red Spot, its surroundings, and, Oval BA by Anguiano-Arteaga et al. (2021,2023) suggest the presence of two distinct colouring aerosols. The first being similar to the “universal chromophore” and the second one being a new UV-absorbing species below the main chromophore layer at tropospheric levels. This highlights the uncertainties on the vertical distribution of aerosols, their properties and their variability.To address this uncertainty, we used new Jupiter spectra obtained with CARMENES (The Calar Alto High-Resolution search for M dwarfs with Exoearths with Near-infrared and optical Échelle Spectrographs) in 2019. This instrument consists of two separated spectrographs with spectral resolutions R = 80,000-100,000, covering the wavelength ranges of 0.52 to 0.96 μm and of 0.96 to 1.71 μm. The original purpose of these observations was to measure winds through the Doppler velocimetry method. We used a downgraded resolution version (R = 173-570) so the observations match the available spectral data for methane, as this resolution is enough for constraining aerosol properties. Due to the original nature of the observations, no calibration star was recorded. In order to achieve flux calibration, we used  2017 observations of Saturn with CARMENES. We employed Saturn’s B ring to obtain the response function of the instrument, since no other sources of calibration are available at the desired resolution or epoch.We used the reflectivity (I/F) spectrum obtained with Cassini/VIMS (Cuzzi et al., 2009) at phase angles less than 3º. We applied the response function to the centre of disc spectrum of Saturn and compared the obtained reflectivity spectrum with results from Clark and McCord (1979) and Mendikoa, et al. (2017). Lastly, we applied the flux calibration to the Jupiter observations and compared them results from Mendikoa, et al., (2017) and Irwin et al. (2018) (Figure 1). All calibrations agree within 10% with MUSE calibration.We were able to perform a Minnaert Limb-darkening approximation and produce 2 synthetic spectra (zenith angle = 0º/61.45º) for five distinct sample areas (EZ (Figure 2), SEB, NEB, transition region from EZ to SEB, and from NEB to NTrZ). We performed retrievals using the same a priori atmospheric parameterization as presented in Braude et al. (2020), Pérez-Hoyos et al. (2020) and Anguiano-Arteaga et al. (2021), comparing the retrieved results of each in order to constrain the uncertainties in the Jovian aerosol scheme. To achieve this, we used the NEMESIS (Nonlinear Optimal Estimator for MultivariatE Spectral analySIS) radiative transfer suite (Irwin et al., 2008). We present here the results of this analysis.Figure 1: Comparison of centre of disk Jupiter spectrum after flux calibration with EZ spectrum from Irwin et al. (2018) and 0º latitude spectrum from Mendikoa et al. (2017).Figure 2: Observation spectra compared to the obtained synthetic spectra after retrieving the atmospheric parameters for the EZ using Braude et al. (2020) model. Top row corresponds to nadir (incidence and emission angle = 0º) and 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 the model from Braude et al. (2020).  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. Karkoschka, E. (1994). Spectrophotometry of the Jovian Planets and Titan at 300- to 1000-nm Wavelength: The Methane Spectrum. Icarus, 111, 1, 174–192. 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
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Jupiter’s auroral stratosphere as revealed by IRTF-TEXES spectroscopy

(2025)

Authors:

James Sinclair, Glenn Orton, Thomas Greathouse, Rohini Giles, Conor Nixon, Vincent Hue, Leigh Fletcher, Patrick Irwin

Abstract:

Jupiter has the strongest planetary magnetic field and the most volcanically active moon (Io) in the solar system.  Magnetospheric dynamics and interactions with the solar wind ultimately drive ions and electrons deep into its neutral atmosphere producing auroral emissions over a large range of the electromagnetic spectrum.  Energy is deposited as deep as the lower stratosphere, which drives atmospheric heating, dynamics and unique chemistry.  Jupiter provides a natural laboratory to study how the external space environment can modulate a planet’s atmosphere and context for the extreme space weather likely experienced by exoplanets orbiting close to their host star.  In this work, we present an analysis of high-resolution mid-infrared spectra recorded in March 2025 by the TEXES (Texas Echelon Cross Echelle Spectrograph, Lacy et al. 2002, PASP 114, 153) instrument on NASA’s IRTF (Infrared Telescope Facility).  As part of a long-term program, spectral scans were performed across high-northern and high-southern latitudes in settings centered at 8.0, 10.53, 12.21 and 13.70 micron in order to target the stratospheric emissions of CH4 (methane), C2H4 (ethylene), C2H6 (ethane) and C2H2 (acetylene), respectively.  Such spectra are inverted using the NEMESIS radiative transfer software (Irwin et al., 2008, JQSRT 109, 1136) to derive spatial variations in the vertical profiles of temperature, C2H2, C2H4 and C2H6 and the vertical location of the hydrocarbon homopause.  We will present these results, in addition to those derived from previous measurements, in order to highlight the thermal, chemical and dynamical evolution of Jupiter’s polar stratosphere.  As part of a new project, TEXES spectra were also recorded in settings centered at 10.95, 11.83 and 13.37 with the goal of detecting CH2CCH2 (allene), C3H6 (propene) and C3H8 (propane).  We will present these spectra to indicate whether these species have been detected.   Detected spectral features will be inverted to derive vertical and spatial variations in its abundance.  In the case of a non-detection, an upper limit would be derived.  The presence or absence of such hydrocarbon species would provide unique insight into how auroral processes modify the chemistry of Jupiter’s stratosphere.
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Lunar Trailblazer: Improving Brightness Temperature Estimation Methods and Applications of Temperature Retrieval for Future Missions

(2025)

Authors:

Fiona Henderson, Namrah Habib, Katherine Shirley, Neil Bowles

Abstract:

Introduction: The Lunar Thermal Mapper (LTM) is a multispectral infrared radiometer, built by the Oxford Physics Instrumentation Group for the Lunar Trailblazer mission; a small satellite launched in February 2025 under NASA’s Small Innovative Missions for Planetary Exploration (SIMPLEx). Trailblazer aims to advance our understanding of the lunar water cycle by mapping surface temperature, water abundance, distribution and form (OH, H2O, ice) and silicate lithology (i.e., Si-O Christiansen spectral feature). LTM was developed to improve upon existing infrared instrumentation in lunar orbit (e.g., Diviner Lunar Radiometer Experiment, hereafter referred to as Diviner) to provide higher resolution temperature estimations and refine interpretations of thermophysical properties at the surface [2, 3]. Accurately determining surface temperatures on airless bodies is essential for deriving emissivity spectral features (such as the Christiansen Feature and Restrahlen bands, which are diagnostic of silicate lithologies) that are representative of the surface. Temperature errors can affect spectral shape, resulting in the misidentification of surface composition [5, 8].  Our team compared six methods for estimating LTM’s brightness temperature (BT), including the temperature retrieval approach used by Diviner, to (1) determine which method provides the most representative surface temperature and (2) assess how variations in BT estimation affect derived emissivity spectral shape. Despite challenges facing the Trailblazer mission, refining methods for BT estimation remains relevant to the planetary community, as future missions continue to depend on infrared instrumentation and accurate BT retrievals for remote compositional interpretation (e.g., LEAP, L-CIRiS, Europa Clipper).   Instrumentation: LTM is a 15-channel infrared imager that covers a range between 6 to 100 µm [2,3]. LTM advances infrared compositional analysis by incorporating eleven narrowband compositional filters across the 6.25 to 10 µm range. This expanded spectral coverage enables more precise characterization of key features, such as the Christiansen Feature, Reststrahlen bands, and transparency features, which are essential for identifying spectral endmembers (Table 1) [2,3].   LTM builds upon Diviner, a nine-channel instrument that has a broad spectral range from 0.3 to 400 µm (Table 1) [1]. Diviner’s three narrowband compositional channels, 7.55–8.05 µm (Channel 3), 8.10–8.40 µm (Channel 4), and 8.38–8.60 µm (Channel 5), are specifically tuned to capture the Christiansen Feature (CF), an emissivity peak that is diagnostic of broad silicate mineralogy and sensitive to variations in silica content [1,4].  Table 1: LTM and Diviner observational parameters.    Methodology: To assess BT and emissivity retrieval techniques for LTM, we measured four lunar analog samples under controlled laboratory conditions to retrieve high-resolution emission spectra. These laboratory spectra were down-sampled to match LTM’s narrowband spectral resolution. Six BT estimation methods were tested to determine how effectively each method preserved laboratory spectral shape and temperature. The following section describes the laboratory setup and the BT estimation methods examined in this study. Laboratory: Using the PASCALE (Planetary Analogue Surface Chamber for Asteroids and Lunar Environments) in conjunction with a Bruker 70V Fourier Transform Infrared (FTIR) spectrometer, we conducted thermal infrared measurements of four volcanic lunar analogue samples; dunite (Twin Sisters -1 and -2), basalt (BIR-1) and rhyolite (RGM-1) under controlled ambient conditions (350 K, 1000 mbar, N2 atmosphere) [4]. The integration of PASCALE with FTIR allows for the acquisition of thermal emission spectra (as opposed to typical laboratory reflectance), offering a more representative analog of data collected by orbiting infrared instrumentation. Spectra were measured across ~6000 to 350 cm⁻¹ at a resolution of 4 cm⁻¹. Quality assurance and calibration procedures followed established protocols outlined in [6,7,8].  BT Estimations: To evaluate BT performance at LTM’s spectral resolution, each sample’s measured radiance was convolved with LTM’s filter response to simulate instrument-resolution radiance. The resulting spectra were converted to BT using the Planck function. Seven distinct methods were applied to the LTM-resolution BT data to determine the maximum BT values for each sample (Table 2). Emissivity was subsequently derived as the ratio between the observed LTM-resolution radiance and an ideal blackbody at the retrieved maximum BT for each method across all samples. The accuracy of the BT estimation methods was assessed by comparing the resulting emissivity spectra and maximum BT values to the full laboratory reference data (350K and full resolution emissivity). Additionally, a focused comparison with Diviner’s BT retrieval method was conducted to identify method-specific discrepancies and evaluate cross-instrument consistency.Table 2: BT estimation methods Results & Discussion: Six BT estimation methods were applied to laboratory emissivity spectra of four lunar analogue samples (dunite, basalt, and rhyolite), as shown in Figure 2. The associated standard errors (SE) for each method are reported in Table 3. Among the tested approaches, four methods (3rd degree polynomial, quadratic, spline and narrowband maximum) showed close agreement with high-resolution laboratory spectra (Figure 2). Temperature variations across compositions were minor, with low SE values (Table 3).  Since the spline fit did not significantly outperform the simpler polynomial or narrowband methods, lower complexity approaches are preferred for LTM temperature retrievals, with a maximum SE of 3.42%.In contrast, due to limited spectral sampling, the Diviner method underestimates surface temperatures by up to 19 K (SE max: 5.55%) in the dunite (TS-2) sample. Expanding this analysis to include a broader range of lithologies or impacted processed samples would help assess whether the Diviner approach (and potential other methods with sparse spectral sampling) introduce systematic shifts in the Christiansen Feature (CF) position or affect the spectral shape relative to more spectrally resolved techniques.  Table 3: BT estimations and associated SE of temperature for dunite (TS-1, TS-2), basalt (BIR-1) and rhyolite (RGM-1). Fig 2: Six BT methods are fitted to laboratory emissivity spectra of four lunar analogues. Conclusion:Comparisons between BT estimation methods indicate the 3rd-degree polynomial, quadratic, and narrowband maximum methods offer the best agreement with laboratory data (SE max: 3.42%). Although Diviner’s method tends to underestimate surface temperatures (up to 19 K), it still preserves spectral shape and wavelength range, supporting the reliability of compositional interpretations. Expanding the dataset to include a broader range of compositions could confirm whether different approaches result in systematic shifts in the Christiansen Feature across different lithologies. This work enhances the accuracy of remote compositional interpretation and supports future exploration on airless bodies.
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Methyl Radical Detected on Titan with JWST/MIRI

(2025)

Authors:

Nicholas Teanby, Conor Nixon, Manuel López-Puertas, Brandon Coy, Véronique Vuitton, Panayotis Lavvas, Lucy Wright, Joshua Ford, Patrick Irwin

Abstract:

Saturn’s largest moon Titan has a nitrogen-methane atmosphere and a rich organic photochemistry. Dissociation of Titan’s molecular methane and nitrogen into N and methyl (CH3) radicals forms the basis of this photochemistry and results in a vast array of hydrocarbon and nitrile species. The abundance of CH3 is thus of critical importance to understanding Titan’s atmospheric chemistry. CH3 is predicted by photochemical models and must be present to explain Titan’s trace gas composition, but has never been directly observed. Cassini’s mass spectrometer was unable to make a detection as the extreme reactivity of radicals results in reactions on the instrument wall (e.g. recombination with H) before detection is possible. Emission features in the infra-red are also very weak, so detection from remote-sensing spectroscopy has previously not been possible. Here we use the very high sensitivity of the James Webb Space Telescope’s (JWST) Mid-InfraRed Instrument (MIRI) to detect emission from CH3 at 16.5 microns. We have used this to validate model predictions that underpin Titan’s rich atmospheric chemistry.JWST/MIRI observations were taken in Medium Resolution Spectroscopy (MRS) mode on 11th July 2023 as part of Guaranteed Time Observation programme 1251 [Nixon et al., 2025]. Observations were reduced using the standard pipeline and combined to give a disc-averaged spectrum (Fig 1). The observed spectrum was compared to a forward model generated with a reference Titan atmosphere using the NEMESIS radiative transfer suite [Irwin et al., 2008]. The reference atmospheric temperature profile was based on observation from Cassini half a Titan year previous, augmented with ground-based measurements from ALMA and in-situ measurements from the Huygens probe (Fig 2a). A baseline atmospheric composition was compiled from Cassini/Huygens measurements [Teanby et al., 2019]. For the CH3 profile, in the absence of measurements, we used the predicted abundance from a photochemical model [Vuitton et al., 2019] (Fig 2a).The abundance profile of CH3 is expected to be extremely steep with very high fractional abundances in the thermosphere (100 ppm at 1000km) and much lower abundances in the stratosphere and mesosphere (1 ppb at 300km). Peak emission under conditions of local thermodynamic equilibrium should originate from the mid-thermosphere at an altitude of ~800km (Fig 2b). However, our analysis shows that non-local thermodynamic equilibrium (non-LTE) emission is expected due to very low thermospheric pressures [Nixon et al., 2025]. This supresses emission below that expected from the Planck function and reduces infra-red emission from thermospheric CH3 to negligible levels. When non-LTE effects are considered, we find that the emission instead originates from the stratopause region (~300km) where CH3 abundances are predicted to be around 1 ppb (Fig 2c).Agreement between forward modelled non-LTE emission using the photochemical model profile and the JWST/MIRI observation match very well (Fig 1) – confirming the model predicted abundances are consistent with conditions in Titan’s middle atmosphere. Our initial results were presented in Nixon et al., (2025). Here we present an updated analysis using improved pipeline processing, more in-depth treatment of the disc-averaged nature of the observation, and provide formal limits on the CH3 abundance profiles. The consistency of our results with predictions from photochemical models gives confidence to current chemical schemes for Titan’s low-order chemistry, which provides a sound basis for a deeper analysis of Titan’s more exotic species such as high-order hydrocarbons and poly-aromatic hydrocarbons.ReferencesIrwin, P.G.J., et al., 2008. The NEMESIS planetary atmosphere radiative transfer and retrieval tool. Journal of Quantitative Spectroscopy and Radiative Transfer 109, 1136–1150.Nixon, C.A., et al., 2025., Titan’s Atmosphere in Late Northern Summer from JWST and Keck Observations. Nature Astronomy, in press.Teanby, N.A., et al., 2019. Seasonal Evolution of Titan’s Stratosphere During the Cassini Mission. Geophysical Research Letters 46, 3079–3089.Vuitton, V., et al., 2019. Simulating the density of organic species in the atmosphere of Titan with a coupled ion-neutral photochemical model. Icarus 324, 120–197.Fig 1: JWST/MIRI disc-average spectrum compared with forward models with and without CH3. The model including CH3 provides a much better fit to the observations.Fig 2: (a) Titan’s atmospheric temperature structure and uncertainty envelope from Nixon et al. (2025), along with photochemical model prediction of the CH3 profile from Vuitton et al. (2019). (b) Contribution functions for LTE case with nominal temperature profile (green), hot temperature limit (red) and cold temperature limit (blue). For LTE, peak emission would be from the thermosphere at ~800km, but this is not realistic. (c) Contribution functions for a more realistic non-LTE emission case peak at ~300km around the mesopause as non-LTE effects suppress emission at very low pressures. Our observations are thus most sensitive to abundances around the stratopause. 
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Microphysical Modeling of Hydrogen Sulfide Clouds in the Atmospheres of the Ice Giants

(2025)

Authors:

Daniel Toledo, Pascal Rannou, Patrick Irwin, Bruno de Batz de Trenquelléon, Michael Roman, Noé Clément, Gwenael Milcareck, Victor Apestigue, Ignacio Arruego, Margarita Yela

Abstract:

Radiative transfer analyses of spectra obtained from Uranus and Neptune have revealed the presence ofa cloud layer at pressures greater than ~2 bar (1,2). The detection of hydrogen sulfide (H₂S) gas abovethis cloud layer on both planets (3,4) suggests that H₂S ice is the most likely main constituent. Thisinterpretation is further supported by the expectation that methane (CH₄) clouds condense at higheraltitudes (5). However, due to their depth and observational limitations, our understanding of theproperties of H₂S clouds on these planets remains very limited.To investigate the properties of H₂S clouds in the atmospheres of Uranus and Neptune, we employed aone-dimensional cloud microphysics model originally developed for Titan and Mars (6,7). The modelincludes nucleation, condensation, evaporation, coagulation, and precipitation processes, and haspreviously been used to simulate haze and CH₄ cloud microphysics in the Ice Giants (5,8,9).Figure 1 shows, as an example, simulated H₂S ice profiles for Uranus using this microphysical model.The vertical transport of H₂S gas is simulated using an eddy diffusion coefficient (Keddʏ), which controlsthe supply of vapor for cloud nucleation and particle growth. We employed the Keddʏ profiles derivedin [10] for H₂S abundances of 10× and 30× solar. Since several cloud microphysical parameters for H₂Sremain uncertain (e.g., the contact parameter), different values are tested in the simulations. In theexample shown, the model indicates cloud bases near 5.3 bar for 10× solar abundance and 6.4 bar for30× solar. Near the cloud base, particle mean radii range from 40 to 55 μm, depending on the assumedcontact parameter and abundance. At higher altitudes, particle sizes decrease; for instance, at ~3 bar,mean radii are around 20 μm. In general, H₂S cloud simulations produce higher opacities than CH₄clouds.In this work, we will present a series of cloud microphysical simulations of H₂S clouds in the Ice Giants.Various cloud properties, such as particle size distributions and precipitation rates, will be constrained.We will also discuss the implications of our results for the atmospheric circulation of these planets andfor the future exploration of Uranus.Figure 1. Vertical distributions of H2S ice (g/m³) for Uranus, simulated for different values of the cloudcontact parameter and deep H2S abundances. These simulations employ the Keddʏ profiles calculated in[10] for the corresponding H2S abundances.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] D. Toledo, et al., A&A, 694, A81 (2025). [6] P. Rannou, et al., Science 311,201 (2006). [7] F. Montmessin, et al., JGR: Planets 107, 4 (2002). [8] D. Toledo, et al., Icarus, 333, 1-11, (2019). [9] D. Toledo, et al., Icarus, Volume 350, (2020). [10] H. Ge, et al., The Planetary ScienceJournal,5, 101(2024). 
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