pith. sign in

arxiv: 2605.29070 · v1 · pith:HN76UTSWnew · submitted 2026-05-27 · 🌌 astro-ph.EP · astro-ph.IM

Next-generation Exo-REM atmospheric models: application to VHS 1256 b to emulate patchy clouds

Alice Radcliffe , Benjamin Charnay , Anne-Marie Lagrange , Flavien Kiefer , Bruno B\'ezard , Simon Petrus , Paulina Palma-Bifani , Matthieu Ravet
show 2 more authors
J\'er\'emy Leconte Gabriel-Dominique Marleau
This is my paper

Pith reviewed 2026-06-29 09:28 UTC · model grok-4.3

classification 🌌 astro-ph.EP astro-ph.IM
keywords brown dwarf atmospheresexoplanet atmospherespatchy cloudscloud sedimentationVHS 1256 bJWST spectroscopy1D atmosphere modelssilicate absorption
0
0 comments X

The pith

A 60-40 mix of thick and thin clouds reproduces the JWST spectrum of VHS 1256 b, including its strong 10 μm silicate feature.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper upgrades the Exo-REM model with a cloud sedimentation parameter, revised opacities, and tighter convergence rules to generate new grids. It then combines two of these 1D models in different proportions to stand in for patchy cloud cover on the variable brown dwarf VHS 1256 b. The best match occurs at roughly 60 percent thick clouds and 40 percent thin clouds. This approach improves the global fit over any single 1D model and accounts for the observed 10 μm absorption. A sympathetic reader would care because the method lets observers extract cloud structure information from existing spectra without running expensive 3D simulations.

Core claim

New Exo-REM k26 grids add a sedimentation efficiency parameter f_sed that controls cloud opacity, updated molecular line lists, and strict convergence to avoid unstable solutions. When these grids are used in a two-column setup for VHS 1256 b, a 60-40 weighting of thick-cloud and thin-cloud columns produces a superior fit to the full spectrum and specifically recovers the depth of the 10 μm silicate band seen in JWST data.

What carries the argument

The two-column framework that weights pre-computed 1D models with different f_sed values to emulate heterogeneous cloud cover.

If this is right

  • Varying f_sed across the grid reaches the reddest objects on the color-magnitude diagram and reveals a systematic drop in f_sed from L to T spectral types.
  • The same two-column method applied to GJ 504 b revises its effective temperature to 473 K and surface gravity to log g = 4.0.
  • The framework supplies a practical way to model rotational variability in other cloudy substellar objects using existing 1D grids.
  • Updated alkali opacities and corrected CH3D abundances change the predicted spectra of the coolest objects.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same weighting technique could be tested on other JWST targets to see whether 60-40 splits are common or object-specific.
  • If the two-column approximation holds across many objects, observers might infer typical cloud patch sizes from the required column fractions alone.
  • Future work could check whether the best-fit fractions change with viewing angle or time, providing a low-cost proxy for cloud evolution.
  • The approach leaves open whether full 3D models would still be needed for objects with more extreme temperature or composition contrasts.

Load-bearing premise

A linear combination of two separate 1D calculations can capture the radiative effects of real three-dimensional cloud patches.

What would settle it

New phase-resolved spectra that show the 10 μm silicate depth changing independently of the 60-40 weighting, or requiring a third distinct cloud column to fit, would falsify the claim.

Figures

Figures reproduced from arXiv: 2605.29070 by Alice Radcliffe, Anne-Marie Lagrange, Benjamin Charnay, Bruno B\'ezard, Flavien Kiefer, Gabriel-Dominique Marleau, J\'er\'emy leconte, Matthieu Ravet, Paulina Palma-Bifani, Simon Petrus.

Figure 2
Figure 2. Figure 2 [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 1
Figure 1. Figure 1: Top panel: comparison of the new Exo-REM k26 cloudless grid (coloured) to the previous grid (in grey) (Charnay et al. 2018), for models with log g = 4 dex and solar C/O and [M/H]. The difference between both models at the same Teff is shaded in. The most notable changes are due to change in Na and K line profiles and the revised CH3D abundance. Bottom panel: relative residual flux (Fnew − Fold)/Fold × 100.… view at source ↗
Figure 3
Figure 3. Figure 3: Spectra from the R = 500 Exo-REM k26 fsed grid; from almost cloudless (fsed = 9) to extremely optically thick clouds (fsed = 0.5). The 1–2 µm flux decreases with fsed, with the thickest cloud cover dampening spectral features the most. In red is a Sonora Diamondback model with fsed = 1, the lowest value explored in their grid; the corresponding Exo-REM k26 model is shown in blue. The other parameters are f… view at source ↗
Figure 4
Figure 4. Figure 4: CMD with the computed J vs. J − K magnitudes for the Exo￾REM k26 cloudless log g = 5 (in black) and simple cloud microphysics models (for log g = 3, 3.5, 4, 4.5, and 5 in purple, green, pink, light blue, and blue respectively), at constant solar C/O and [M/H]. The simple microphysics grid was computed with a supersaturation parameter s = 0.1. The M, L, and T dwarfs are plotted in black, red and blue dots r… view at source ↗
Figure 5
Figure 5. Figure 5: Evolution of fsed for BDs over the L–T transition, found by computing the magnitudes of Exo-REM k26 spectra of varying fsed and Teff values at log g = 4.5 and solar C/O and [M/H], then interpolating to find the fsed and Teff at the Best et al. (2025) data points. The median over 20 K Teff ranges is shown in black, and the 1σ region is shaded in grey. Sonora Diamondback, a clear trend of increasing fsed fro… view at source ↗
Figure 6
Figure 6. Figure 6: Best fit for GJ 504 b photometry with revised parameters found with Exo-REM k26, posterior distributions of which are provided in Fig. B.4. Plotted in grey are random samples from the family of spectra with a likelihood within 1 σ of the best fit. Top panel: normalised transmission for each filter. Bottom panel: residual flux between the observed and simulated photometry. Through light curve fitting from 3… view at source ↗
Figure 7
Figure 7. Figure 7: and Fig. B.1 respectively; in particular we were able to re￾produce the silicate absorption with, as expected, a low fsed value of 0.82, as well as a super-solar metallicity and a Teff of 1038 K, a temperature lower than previous estimates. With χ 2 red = 1.96, the overall shape of the pseudo-continuum is a good fit and the absorption features match up, but do not completely reflect the depth that is seen … view at source ↗
Figure 8
Figure 8. Figure 8: Top panel: same as [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Final best fit for VHS 1256 b. Top panel: same as [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Vertical cloud structure for both fsed columns used to describe the patchy cloud cover of VHS 1256 b. We show the cumulative forsterite (Mg2SiO4) cloud opacity (from the top layer) at λ = 1 µm and the pressure level corresponding to a cloud opacity of τ = 1 (dashed). to Exo-REM. Grids relying on fixed cloud profiles or simple mi￾crophysics often fail to follow the overall shape of observed spectra, or acc… view at source ↗
Figure 12
Figure 12. Figure 12: Left panel: Photospheric pressures, where most of the thermal emission originates, for each component (atmosphere column) of the spectrum of VHS 1256 b; colour: temperature at the respective pressure. Right panel: P–T profile for the fsed = 0.7 (red) and 2.4 (blue) column. cloudiest of atmospheres. Our results suggest that fsed could vary on BDs along the L–T transition, with the coolest L-dwarfs hav￾ing … view at source ↗
Figure 13
Figure 13. Figure 13: Simplified schematic of a possible cloud configuration of VHS 1256 b at the time the JWST observations were recorded. Here we show a physically plausible configuration of waves and spots that could cause the complex sinusoidal rotationally-modulated variations reported in Zhou et al. (2022). The fsed = 0.7 region would emanate flux from higher altitude and give rise to silicate absorption, while the fsed … view at source ↗
read the original abstract

Condensate clouds are a defining feature of brown dwarf and exoplanet atmospheres, producing a broad range of colours on the CMD and giving rise to spectral features such as the distinct $\sim 10 \mu$m spectral imprint. Cloud cover is likely to be heterogeneous in many objects, with observed rotational variability providing evidence for the presence of thick and thin cloud regions rotating in and out of view. Yet current 1D atmosphere models often fail to reproduce the spectra of highly cloudy substellar objects, especially those with complex cloud structures. We address these limitations by upgrading the Exo-REM atmosphere model, and by devising a more nuanced approach to describe heterogeneous cloud cover with pre-computed 1D grids. We present new Exo-REM grids, hereafter Exo-REM k26, featuring critical updates: (1) the incorporation of a cloud sedimentation parameter, $f_{sed}$, to govern cloud opacity, thereby enabling even the reddest of objects to be accessed on a CMD, revealing a trend of decreasing $f_{sed}$ along the L--T transition (2) the substantial update of molecular opacities and abundances used, including new experimentally validated alkali line lists, and (3) the implementation of strict convergence criteria that entirely avoid unstable model solutions. Correcting an erroneous $\text{CH}_3\text{D}$ abundance leads to spectral changes for low-$T_{eff}$ objects. Applying Exo-REM k26 to the cool GJ 504 b thus leads to a revision of its parameters ($T_{eff} = 473^{+14}_{-12}$ K, $\log g = 4.0 \pm 0.1$ dex). For the variable VHS 1256 b, a two-column framework that emulates cloud heterogeneities achieves an improved global fit over a single 1D model. A ~60-40% split of thick and thin clouds best describes its atmosphere, further confirming the presence of patchy clouds. This reproduces the strong $10 \mu$m silicate absorption in the JWST data of VHS 1256 b.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper presents updates to the Exo-REM atmosphere model (k26 grids) incorporating a cloud sedimentation efficiency parameter f_sed, revised molecular opacities and abundances (including new alkali line lists), and strict convergence criteria to eliminate unstable solutions. It applies the new grids to revise parameters for GJ 504 b and, for VHS 1256 b, implements a two-column framework that linearly combines pre-computed thick- and thin-cloud 1D spectra in a ~60-40% ratio to emulate patchy clouds, claiming an improved global fit that reproduces the strong 10 μm silicate absorption in JWST observations.

Significance. If the central results hold, the work supplies a computationally efficient method for modeling heterogeneous cloud cover in substellar objects using existing 1D grids, which could be adopted for interpreting JWST spectra of variable brown dwarfs and directly imaged exoplanets. The reported trend of decreasing f_sed along the L-T transition from the new grids provides a concrete, testable prediction for cloud evolution. The reproduction of the silicate feature with the patchy-cloud model adds to the observational case for cloud heterogeneity.

major comments (1)
  1. [Application section] Application section (two-column framework for VHS 1256 b): the central claim that a 60-40% thick/thin split 'best describes' the atmosphere and reproduces the 10 μm silicate feature rests on a flux-weighted average of two independent pre-computed 1D Exo-REM k26 spectra. This construction assumes negligible horizontal radiative exchange and no thermal readjustment between columns. Neither assumption is tested against 3D radiative-transfer calculations or a self-consistent multi-column solution with shared T-P structure; if either fails, the inferred patch fraction and the reported improvement over single-column models become non-unique.
minor comments (1)
  1. The abstract states that the two-column model 'achieves an improved global fit' but supplies no quantitative metrics (e.g., χ² values, residual statistics, or degrees of freedom) to support this claim; adding such numbers in the main text would improve clarity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and for recognizing the potential utility of the Exo-REM k26 grids and the two-column emulation approach. We address the single major comment below.

read point-by-point responses

  1. Referee: [Application section] Application section (two-column framework for VHS 1256 b): the central claim that a 60-40% thick/thin split 'best describes' the atmosphere and reproduces the 10 μm silicate feature rests on a flux-weighted average of two independent pre-computed 1D Exo-REM k26 spectra. This construction assumes negligible horizontal radiative exchange and no thermal readjustment between columns. Neither assumption is tested against 3D radiative-transfer calculations or a self-consistent multi-column solution with shared T-P structure; if either fails, the inferred patch fraction and the reported improvement over single-column models become non-unique.

    Authors: We appreciate the referee’s emphasis on the underlying assumptions. The two-column framework is presented in the manuscript as a computationally efficient emulation that linearly combines pre-computed 1D spectra to capture the net effect of thick and thin cloud regions; it is not claimed to be a fully self-consistent multi-column or 3D solution. This style of approximation is already used in the literature to interpret rotational variability and spectral features in brown dwarfs and directly imaged planets. The 60-40% combination demonstrably improves the global fit to VHS 1256 b and reproduces the 10 μm silicate feature, lending empirical support to its practical utility. We agree, however, that the neglect of horizontal radiative transfer and thermal readjustment constitutes a limitation whose impact is not quantified here. In the revised manuscript we will add an explicit paragraph in Section 4.2 stating these assumptions, noting that the derived patch fraction should be regarded as an effective rather than literal value, and outlining why a full 3D treatment lies beyond the present scope. revision: partial

Circularity Check

1 steps flagged

Fitted cloud fractions in two-column model presented as atmospheric description

specific steps
  1. fitted input called prediction [Abstract]
    "A ~60-40% split of thick and thin clouds best describes its atmosphere, further confirming the presence of patchy clouds. This reproduces the strong 10 μm silicate absorption in the JWST data of VHS 1256 b."

    The 60-40% fractions are the free parameters optimized to minimize the difference between the flux-weighted average of two fixed 1D models and the observed spectrum. The statement that this split 'best describes' the atmosphere is therefore the definition of the best-fit solution rather than an independent result or prediction from the model physics.

full rationale

The paper's central application result for VHS 1256 b is the ~60-40% thick/thin cloud split obtained by optimizing weights in a linear combination of two pre-computed 1D Exo-REM k26 models to match the JWST spectrum. This directly matches the 'fitted input called prediction' pattern: the split is the output of the fit, yet is stated as what 'best describes' the atmosphere and 'further confirming' patchy clouds. The model updates themselves (f_sed, opacities, convergence) show no circularity and are independent. No self-citation load-bearing steps or other patterns are present in the provided text.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

Based on abstract only; limited visibility into full derivations. Key fitted elements include f_sed and cloud fractions. The two-column approach rests on the assumption that linear mixing of 1D models captures heterogeneities.

free parameters (2)
  • f_sed
    Sedimentation parameter governing cloud opacity, adjusted to match reddest objects on CMD and L-T transition trend
  • cloud_cover_fraction = 60-40
    Proportion of thick to thin clouds fitted to spectral data of VHS 1256 b
axioms (2)
  • domain assumption Pre-computed 1D model grids can be linearly combined to emulate spatial heterogeneities in cloud cover
    Basis for the two-column framework used for VHS 1256 b
  • domain assumption Updated molecular opacities and abundances (including new alkali line lists) are accurate for the relevant temperature range
    Invoked for the model updates and spectral fits

pith-pipeline@v0.9.1-grok · 5968 in / 1513 out tokens · 38144 ms · 2026-06-29T09:28:27.278368+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

125 extracted references · 116 canonical work pages · 2 internal anchors

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

  3. [3]

    , " * write output.state after.block = add.period write newline

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint doi url journal key month note number organization pages publisher school series title type volume year adsurl label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'be...

  4. [4]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

  5. [5]

    Ackerman, A. S. & Marley, M. S. 2001, http://dx.doi.org/10.1086/321540 magenta ApJ , 556, 872

    work page doi:10.1086/321540 2001

  6. [6]

    D., Zhou, Y., Marleau, G.-D., et al

    Adams, A. D., Zhou, Y., Marleau, G.-D., et al. 2025, http://dx.doi.org/10.3847/1538-3881/ae07d2 magenta AJ , 170, 289

    work page doi:10.3847/1538-3881/ae07d2 2025

  7. [7]

    Philosophical Transactions of the Royal Society of London Series A , keywords =

    Allard, F., Homeier, D., & Freytag, B. 2012, http://dx.doi.org/10.1098/rsta.2011.0269 magenta Phil. Trans. R. Soc. A , 370, 2765

    work page doi:10.1098/rsta.2011.0269 2012

  8. [8]

    F., Allard, F., Hauschildt, P

    Allard, N. F., Allard, F., Hauschildt, P. H., Kielkopf, J. F., & Machin, L. 2003, http://dx.doi.org/10.1051/0004-6361:20031299 magenta A&A , 411, L473

    work page doi:10.1051/0004-6361:20031299 2003

  9. [9]

    Allard, N. F. & Kielkopf, J. F. 2025, http://dx.doi.org/10.1051/0004-6361/202556933 magenta A&A , 703, A71

    work page doi:10.1051/0004-6361/202556933 2025

  10. [10]

    F., Spiegelman, F., & Kielkopf, J

    Allard, N. F., Spiegelman, F., & Kielkopf, J. F. 2016, http://dx.doi.org/10.1051/0004-6361/201628270 magenta A&A , 589, A21

    work page doi:10.1051/0004-6361/201628270 2016

  11. [11]

    F., Spiegelman, F., Leininger, T., & Mollière, P

    Allard, N. F., Spiegelman, F., Leininger, T., & Mollière, P. 2019, http://dx.doi.org/10.1051/0004-6361/201935593 magenta A&A , 628, A120

    work page doi:10.1051/0004-6361/201935593 2019

  12. [12]

    2013, http://dx.doi.org/10.1088/0004-637X/768/2/121 magenta ApJ , 768, 121

    Apai, D., Radigan, J., Buenzli, E., et al. 2013, http://dx.doi.org/10.1088/0004-637X/768/2/121 magenta ApJ , 768, 121

    work page doi:10.1088/0004-637x/768/2/121 2013

  13. [13]

    2009, http://dx.doi.org/10.1088/0004-637X/701/2/1534 magenta ApJ , 701, 1534

    Artigau, E., Bouchard, S., Doyon, R., & Lafrenière, D. 2009, http://dx.doi.org/10.1088/0004-637X/701/2/1534 magenta ApJ , 701, 1534

    work page doi:10.1088/0004-637x/701/2/1534 2009

  14. [14]

    M., & Grevesse, N

    Asplund, M., Amarsi, A. M., & Grevesse, N. 2021, http://dx.doi.org/10.1051/0004-6361/202140445 magenta A&A , 653, A141

    work page internal anchor Pith review doi:10.1051/0004-6361/202140445 2021

  15. [15]

    J., & Scott, P

    Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, http://dx.doi.org/10.1146/annurev.astro.46.060407.145222 magenta ARA&A , 47, 481

    work page doi:10.1146/annurev.astro.46.060407.145222 2009

  16. [16]

    Baraffe, I., Chabrier, G., Allard, F., & Hauschildt, P. H. 2002, http://dx.doi.org/10.1051/0004-6361:20011638 magenta A&A , 382, 563

    work page doi:10.1051/0004-6361:20011638 2002

  17. [17]

    , archivePrefix = "arXiv", eprint =

    Baraffe, I., Homeier, D., Allard, F., & Chabrier, G. 2015, http://dx.doi.org/10.1051/0004-6361/201425481 magenta Astronomy & Astrophysics , 577, A42

    work page internal anchor Pith review doi:10.1051/0004-6361/201425481 2015

  18. [18]

    2015, http://dx.doi.org/10.1051/0004-6361/201526332 magenta A&A , 582, A83

    Baudino, J.-L., Bézard, B., Boccaletti, A., et al. 2015, http://dx.doi.org/10.1051/0004-6361/201526332 magenta A&A , 582, A83

    work page doi:10.1051/0004-6361/201526332 2015

  19. [19]

    Bernath, P. F. 2020, http://dx.doi.org/10.1016/j.jqsrt.2019.106687 magenta Journal of Quant. Spect. and Rad. Trans. , 240, 106687

    work page doi:10.1016/j.jqsrt.2019.106687 2020

  20. [20]

    Best, W. M. J., Dupuy, T. J., Liu, M. C., et al. 2025, https://zenodo.org/records/13993077

    work page arXiv 2025

  21. [21]

    A., Vos, J

    Biller, B. A., Vos, J. M., Zhou, Y., et al. 2024, http://dx.doi.org/10.1093/mnras/stae1602 magenta MNRAS , 532, 2207

    work page doi:10.1093/mnras/stae1602 2024

  22. [22]

    2021, http://dx.doi.org/10.1051/0004-6361/202039072 magenta A&A , 646, A15

    Blain, D., Charnay, B., & Bézard, B. 2021, http://dx.doi.org/10.1051/0004-6361/202039072 magenta A&A , 646, A15

    work page doi:10.1051/0004-6361/202039072 2021

  23. [23]

    2018, http://dx.doi.org/10.1051/0004-6361/201832942 magenta A&A , 618, A63

    Bonnefoy, M., Perraut, K., Lagrange, A.-M., et al. 2018, http://dx.doi.org/10.1051/0004-6361/201832942 magenta A&A , 618, A63

    work page doi:10.1051/0004-6361/201832942 2018

  24. [24]

    P., Zhou, Y., Morley, C

    Bowler, B. P., Zhou, Y., Morley, C. V., et al. 2020, http://dx.doi.org/10.3847/2041-8213/ab8197 magenta ApJ , 893, L30

    work page doi:10.3847/2041-8213/ab8197 2020

  25. [25]

    S., et al

    Buenzli, E., Saumon, D., Marley, M. S., et al. 2015, http://dx.doi.org/10.1088/0004-637X/798/2/127 magenta ApJ , 798, 127

    work page doi:10.1088/0004-637x/798/2/127 2015

  26. [26]

    J., Kirkpatrick, J

    Burrows, A., Burgasser, A. J., Kirkpatrick, J. D., et al. 2002, http://dx.doi.org/10.1086/340584 magenta ApJ , 573, 394

    work page doi:10.1086/340584 2002

  27. [27]

    B., Lunine, J

    Burrows, A., Hubbard, W. B., Lunine, J. I., & Liebert, J. 2001, http://dx.doi.org/10.1103/RevModPhys.73.719 magenta RMP , 73, 719

    work page doi:10.1103/revmodphys.73.719 2001

  28. [28]

    & Sharp, C

    Burrows, A. & Sharp, C. M. 1999, http://dx.doi.org/10.1086/306811 magenta ApJ , 512, 843

    work page doi:10.1086/306811 1999

  29. [29]

    & Volobuyev, M

    Burrows, A. & Volobuyev, M. 2003, http://dx.doi.org/10.1086/345412 magenta ApJ , 583, 985

    work page doi:10.1086/345412 2003

  30. [30]

    2011, http://dx.doi.org/10.1007/s11207-010-9541-4 magenta Sol

    Caffau, E., Ludwig, H.-G., Steffen, M., Freytag, B., & Bonifacio, P. 2011, http://dx.doi.org/10.1007/s11207-010-9541-4 magenta Sol. Phys. , 268, 255

    work page doi:10.1007/s11207-010-9541-4 2011

  31. [31]

    2018, http://dx.doi.org/10.3847/1538-4357/aaac7d magenta ApJ , 854, 172

    Charnay, B., Bézard, B., Baudino, J.-L., et al. 2018, http://dx.doi.org/10.3847/1538-4357/aaac7d magenta ApJ , 854, 172

    work page doi:10.3847/1538-4357/aaac7d 2018

  32. [32]

    A., Yurchenko, S

    Coles, P. A., Yurchenko, S. N., & Tennyson, J. 2019, http://dx.doi.org/10.1093/mnras/stz2778 magenta MNRAS , 490, 4638

    work page doi:10.1093/mnras/stz2778 2019

  33. [33]

    S., Sudarsky, D., Milsom, J

    Cooper, C. S., Sudarsky, D., Milsom, J. A., Lunine, J. I., & Burrows, A. 2003, http://dx.doi.org/10.1086/367763 magenta ApJ , 586, 1320

    work page doi:10.1086/367763 2003

  34. [34]

    2008, http://dx.doi.org/10.1086/526489 magenta ApJ , 678, 1372

    Cushing, M., Marley, M., Saumon, D., et al. 2008, http://dx.doi.org/10.1086/526489 magenta ApJ , 678, 1372

    work page doi:10.1086/526489 2008

  35. [35]

    J., Liu, M

    Dupuy, T. J., Liu, M. C., Evans, E. L., et al. 2023, http://dx.doi.org/10.1093/mnras/stac3557 magenta MNRAS , 519, 1688

    work page doi:10.1093/mnras/stac3557 2023

  36. [36]

    J., Liu, M

    Dupuy, T. J., Liu, M. C., Magnier, E. A., et al. 2020, RNAAS, 4, 54

    2020

  37. [37]

    G., et al

    D’Orazi, V., Desidera, S., Gratton, R. G., et al. 2017, http://dx.doi.org/10.1051/0004-6361/201629283 magenta A&A , 598, A19

    work page doi:10.1051/0004-6361/201629283 2017

  38. [38]

    C., Janson, M., & Calissendorff, P

    Eriksson, S. C., Janson, M., & Calissendorff, P. 2019, http://dx.doi.org/10.1051/0004-6361/201935671 magenta A&A , 629, A145

    work page doi:10.1051/0004-6361/201935671 2019

  39. [39]

    & Chini, R

    Fuhrmann, K. & Chini, R. 2015, http://dx.doi.org/10.1088/0004-637X/806/2/163 magenta ApJ , 806, 163

    work page doi:10.1088/0004-637x/806/2/163 2015

  40. [40]

    & Marty, B

    Füri, E. & Marty, B. 2015, http://dx.doi.org/10.1038/ngeo2451 magenta Nat. Geo , 8, 515

    work page doi:10.1038/ngeo2451 2015

  41. [41]

    Gauza, B., Béjar, V. J. S., Pérez-Garrido, A., et al. 2015, http://dx.doi.org/10.1088/0004-637X/804/2/96 magenta ApJ , 804, 96

    work page doi:10.1088/0004-637x/804/2/96 2015

  42. [42]

    P., Merritt, S., Nugroho, S

    Gibson, N. P., Merritt, S., Nugroho, S. K., et al. 2020, http://dx.doi.org/10.1093/mnras/staa228 magenta MNRAS , 493, 2215

    work page doi:10.1093/mnras/staa228 2020

  43. [43]

    Journal of Quantitative Spectroscopy and Radiative Transfer , author =

    Gordon, I., Rothman, L., Hargreaves, R., et al. 2022, http://dx.doi.org/10.1016/j.jqsrt.2021.107949 magenta Journal of Quant. Spect. and Rad. Trans. , 277, 107949

    work page doi:10.1016/j.jqsrt.2021.107949 2022

  44. [44]

    J., Gordon, I

    Hargreaves, R. J., Gordon, I. E., Rey, M., et al. 2020, http://dx.doi.org/10.3847/1538-4365/ab7a1a magenta ApJ , 247, 55

    work page doi:10.3847/1538-4365/ab7a1a 2020

  45. [45]

    , keywords =

    Harris, G. J., Tennyson, J., Kaminsky, B. M., Pavlenko, Y. V., & Jones, H. R. A. 2006, http://dx.doi.org/10.1111/j.1365-2966.2005.09960.x magenta MNRAS , 367, 400

    work page doi:10.1111/j.1365-2966.2005.09960.x 2006

  46. [46]

    Helling, C., Dehn, M., Woitke, P., & Hauschildt, P. H. 2008, http://dx.doi.org/10.1086/533462 magenta ApJ , 675, L105

    work page doi:10.1086/533462 2008

  47. [47]

    L., Ray, S., et al

    Hinkley, S., Carter, A. L., Ray, S., et al. 2022, http://dx.doi.org/10.1088/1538-3873/ac77bd magenta PASP , 134, 095003

    work page doi:10.1088/1538-3873/ac77bd 2022

  48. [48]

    Hoch, K. K. W., Rowland, M., Petrus, S., et al. 2025, http://dx.doi.org/10.1038/s41586-025-09174-w magenta Nature , 643, 938

    work page doi:10.1038/s41586-025-09174-w 2025

  49. [49]

    D., Kuzuhara, M., et al

    Janson, M., Brandt, T. D., Kuzuhara, M., et al. 2013, http://dx.doi.org/10.1088/2041-8205/778/1/L4 magenta ApJ , 778, L4

    work page doi:10.1088/2041-8205/778/1/l4 2013

  50. [50]

    J., et al

    Karalidi, T., Marley, M., Fortney, J. J., et al. 2021, http://dx.doi.org/10.3847/1538-4357/ac3140 magenta ApJ , 923, 269

    work page doi:10.3847/1538-4357/ac3140 2021

  51. [51]

    E., van der Avoird, A., et al

    Karman, T., Gordon, I. E., van der Avoird, A., et al. 2019, http://dx.doi.org/10.1016/j.icarus.2019.02.034 magenta Icarus , 328, 160

    work page doi:10.1016/j.icarus.2019.02.034 2019

  52. [52]

    Kirkpatrick, J. D. 2005, http://dx.doi.org/10.1146/annurev.astro.42.053102.134017 magenta ARA&A , 43, 195

    work page doi:10.1146/annurev.astro.42.053102.134017 2005

  53. [53]

    M., Rameau, J., Duchêne, G., et al

    Konopacky, Q. M., Rameau, J., Duchêne, G., et al. 2016, http://dx.doi.org/10.3847/2041-8205/829/1/L4 magenta ApJ , 829, L4

    work page doi:10.3847/2041-8205/829/1/l4 2016

  54. [54]

    2013, http://dx.doi.org/10.1088/0004-637X/774/1/11 magenta ApJ , 774, 11

    Kuzuhara, M., Tamura, M., Kudo, T., et al. 2013, http://dx.doi.org/10.1088/0004-637X/774/1/11 magenta ApJ , 774, 11

    work page doi:10.1088/0004-637x/774/1/11 2013

  55. [55]

    Lacis, A. A. & Oinas, V. 1991, http://dx.doi.org/10.1029/90JD01945 magenta JGR: Atmospheres , 96, 9027

    work page doi:10.1029/90jd01945 1991

  56. [56]

    & Burrows, A

    Lacy, B. & Burrows, A. 2023, http://dx.doi.org/10.3847/1538-4357/acc8cb magenta AJ , 950, 8

    work page doi:10.3847/1538-4357/acc8cb 2023

  57. [57]

    2018, http://dx.doi.org/10.3847/2041-8213/aaaa61 magenta ApJ , 853, L30

    Leconte, J. 2018, http://dx.doi.org/10.3847/2041-8213/aaaa61 magenta ApJ , 853, L30

    work page doi:10.3847/2041-8213/aaaa61 2018

  58. [58]

    2021, http://dx.doi.org/10.1051/0004-6361/202039040 magenta A&A , 618, A63

    Leconte, J. 2021, http://dx.doi.org/10.1051/0004-6361/202039040 magenta A&A , 618, A63

    work page doi:10.1051/0004-6361/202039040 2021

  59. [59]

    K., Tremblin, P., Phillips, M

    Leggett, S. K., Tremblin, P., Phillips, M. W., et al. 2021, http://dx.doi.org/10.3847/1538-4357/ac0cfe magenta AJ , 918, 11

    work page doi:10.3847/1538-4357/ac0cfe 2021

  60. [60]

    2010, Solar System Abundances of the Elements, 379--417

    Lodders, K. 2010, Solar System Abundances of the Elements, 379--417

    2010

  61. [61]

    P., et al

    Lueber, A., Heng, K., Bowler, B. P., et al. 2024, http://dx.doi.org/10.1051/0004-6361/202451301 magenta A&A , 690, A357

    work page doi:10.1051/0004-6361/202451301 2024

  62. [62]

    2019, http://dx.doi.org/10.1146/annurev-astro-081817-051846 magenta ARA&A , 57, 617

    Madhusudhan, N. 2019, http://dx.doi.org/10.1146/annurev-astro-081817-051846 magenta ARA&A , 57, 617

    work page doi:10.1146/annurev-astro-081817-051846 2019

  63. [63]

    2011, http://dx.doi.org/10.1088/0004-637X/737/1/34 magenta ApJ , 737, 34

    Madhusudhan, N., Burrows, A., & Currie, T. 2011, http://dx.doi.org/10.1088/0004-637X/737/1/34 magenta ApJ , 737, 34

    work page doi:10.1088/0004-637x/737/1/34 2011

  64. [64]

    2025, A&A, 693, 9

    M \^a lin, M., Boccaletti, A., Perrot, C., et al. 2025, A&A, 693, 9

    2025

  65. [65]

    & Robinson, T

    Marley, M. & Robinson, T. 2015, http://dx.doi.org/10.1146/annurev-astro-082214-122522 magenta ARA&A , 53, 279

    work page doi:10.1146/annurev-astro-082214-122522 2015

  66. [66]

    S., Saumon, D., Cushing, M., et al

    Marley, M. S., Saumon, D., Cushing, M., et al. 2012, http://dx.doi.org/10.1088/0004-637X/754/2/135 magenta ApJ , 754, 135

    work page doi:10.1088/0004-637x/754/2/135 2012

  67. [67]

    The Astrophysical Journal , author =

    Marley, M. S., Saumon, D., & Goldblatt, C. 2010, http://dx.doi.org/10.1088/2041-8205/723/1/L117 magenta ApJ , 723, L117

    work page doi:10.1088/2041-8205/723/1/l117 2010

  68. [68]

    arXiv , author =:2107.07434 , journal =

    Marley, M. S., Saumon, D., Visscher, C., et al. 2021, http://dx.doi.org/10.3847/1538-4357/ac141d magenta ApJ , 920, 85

    work page doi:10.3847/1538-4357/ac141d 2021

  69. [69]

    M., Vos, J

    McCarthy, A. M., Vos, J. M., Muirhead, P. S., et al. 2025, http://dx.doi.org/10.3847/2041-8213/ad9eaf magenta The Astrophysical Journal Letters , 981, L22

    work page doi:10.3847/2041-8213/ad9eaf 2025

  70. [70]

    K., Masseron, T., Hoeijmakers, H

    McKemmish, L. K., Masseron, T., Hoeijmakers, H. J., et al. 2019, http://dx.doi.org/10.1093/mnras/stz1818 magenta MNRAS , 488, 2836

    work page doi:10.1093/mnras/stz1818 2019

  71. [71]

    K., Yurchenko, S

    McKemmish, L. K., Yurchenko, S. N., & Tennyson, J. 2016, http://dx.doi.org/10.1093/mnras/stw1969 magenta MNRAS , 463, 771

    work page doi:10.1093/mnras/stw1969 2016

  72. [72]

    A., Heinze, A., Apai, D., et al

    Metchev, S. A., Heinze, A., Apai, D., et al. 2015, http://dx.doi.org/10.1088/0004-637X/799/2/154 magenta ApJ , 799, 154

    work page doi:10.1088/0004-637x/799/2/154 2015

  73. [73]

    E., Biller, B

    Miles, B. E., Biller, B. A., Patapis, P., et al. 2023, http://dx.doi.org/10.3847/2041-8213/acb04a magenta ApJ , 946, L6

    work page doi:10.3847/2041-8213/acb04a 2023

  74. [74]

    E., Skemer, A

    Miles, B. E., Skemer, A. J., Barman, T. S., Allers, K. N., & Stone, J. M. 2018, http://dx.doi.org/10.3847/1538-4357/aae6cd magenta ApJ , 869, 18

    work page doi:10.3847/1538-4357/aae6cd 2018

  75. [75]

    C., et al

    Mollière, P., Kühnle, H., Matthews, E. C., et al. 2025, http://dx.doi.org/10.1051/0004-6361/202555732 magenta A&A , 703, A79

    work page doi:10.1051/0004-6361/202555732 2025

  76. [76]

    P., van Boekel, R., et al

    Mollière, P., Wardenier, J. P., van Boekel, R., et al. 2019, http://dx.doi.org/10.1051/0004-6361/201935470 magenta A&A , 627, A67

    work page doi:10.1051/0004-6361/201935470 2019

  77. [77]

    and Fortney, Jonathan J

    Morley, C. V., Fortney, J. J., Marley, M. S., et al. 2012, http://dx.doi.org/10.1088/0004-637X/756/2/172 magenta ApJ , 756, 172

    work page doi:10.1088/0004-637x/756/2/172 2012

  78. [78]

    V., Marley, M

    Morley, C. V., Marley, M. S., Fortney, J. J., & Lupu, R. 2014 a , http://dx.doi.org/10.1088/2041-8205/789/1/L14 magenta ApJ , 789, L14

    work page doi:10.1088/2041-8205/789/1/l14 2014

  79. [79]

    V., Marley, M

    Morley, C. V., Marley, M. S., Fortney, J. J., et al. 2014 b , http://dx.doi.org/10.1088/0004-637X/787/1/78 magenta ApJ , 787, 78

    work page doi:10.1088/0004-637x/787/1/78 2014

  80. [80]

    V., et al., 2024, @doi [ ] 10.3847/1538-4357/ad71d5 , https://ui.adsabs.harvard.edu/abs/2024ApJ...975...59M 975, 59

    Morley, C. V., Mukherjee, S., Marley, M. S., et al. 2024, http://dx.doi.org/10.3847/1538-4357/ad71d5 magenta ApJ , 975, 59

    work page doi:10.3847/1538-4357/ad71d5 2024

Showing first 80 references.