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arxiv: 2606.12116 · v1 · pith:7VWRZTZ4new · submitted 2026-06-10 · 🌌 astro-ph.EP

Reinterpreting the JWST Observations of 55 Cancri e with a Non-Grey General Circulation Model

Ruizhi Zhan , Daniel D.B. Koll This is my paper

Pith reviewed 2026-06-27 08:12 UTC · model grok-4.3

classification 🌌 astro-ph.EP
keywords 55 Cancri eJWSTexoplanet atmospheresgeneral circulation modelsCO2thermal emission spectraatmospheric variability
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The pith

3D models favor thick CO2-rich atmospheres for 55 Cancri e over thin CO models

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

The paper applies non-grey general circulation models to reinterpret JWST observations of 55 Cancri e. It demonstrates that only atmospheres with pressures of at least 10 bars and CO2 mixing ratios above 1 percent match the observed spectra, in contrast to earlier one-dimensional model results. The simulations show that atmospheric dynamics cannot account for the planet's variable eclipse depths. This points to a formation history involving greater retention of volatiles compared to Earth and Venus. The work emphasizes the value of three-dimensional modeling for understanding extreme exoplanet environments.

Core claim

Our best-fit simulations match the JWST spectra well, favoring an atmosphere that is both thick (≥ 10 bar) and CO₂-rich (>1% CO₂ volume mixing ratio), while ruling out thin (< 10 bar) and pure-CO/CO₂-poor atmospheres previously proposed based on 1D models. We also find large-scale atmospheric dynamics is insufficient to explain the observed variability. A thick, CO₂-rich atmosphere implies that 55 Cancri e likely formed with significantly more volatiles than Earth and Venus. In addition, a thick atmosphere makes it unlikely that the planet's variability is caused by transient outgassing, favoring other variability mechanisms such as clouds.

What carries the argument

Cloud-free non-grey general circulation model using custom correlated-k coefficients derived from the ExoMol database to compute radiative transfer and atmospheric circulation self-consistently

Load-bearing premise

The custom correlated-k coefficients accurately represent gas opacities at the extreme temperatures of 55 Cancri e and the assumption of no clouds does not affect the conclusions about spectral fits or variability

What would settle it

New spectra that match thin atmosphere predictions more closely than thick CO2-rich models, or direct evidence of surface pressure below 10 bar, would falsify the preference for thick CO2-rich conditions

Figures

Figures reproduced from arXiv: 2606.12116 by Daniel D.B. Koll, Ruizhi Zhan.

Figure 1
Figure 1. Figure 1: Long-term mean air temperature and horizontal wind (panel a), zonal mean zonal wind (panel b), and vertical temperature profiles (panel c). From left to right: a thin, CO2-poor atmosphere (ps = 0.01 bar, CO2 VMR=10−8 ); a thick, pure CO2 atmosphere (ps = 10 bar); and a thick, pure H2O atmosphere (ps = 10 bar). (a) The thin, CO2-poor and thick, pure CO2 cases are plotted at the half-surface-pressure level; … view at source ↗
Figure 2
Figure 2. Figure 2: Simulated thermal emission spectra of 55 Cancri e versus JWST data from Hu et al. (2024). The JWST data favor thick (≥ 10 bar) and CO2-rich (≥ 10−2 CO2 by volume mixing ratio) atmospheres. Panel a shows the best-fit spectra for five model categories: pure CO2 (100 bar; log(psurf (Pa)) = 7), CO2-CO (100 bar, 1% CO2 VMR; log(psurf (Pa)) = 7, log(CO2 VMR) = -2), CO2-N2 mixed (100 bar, 10% CO2 VMR; log(psurf (… view at source ↗
Figure 3
Figure 3. Figure 3: Broadband and MIRI band heat redistribution factors (f). These factors are derived via Eq. 1 using brightness temperatures calculated from observer-projected fluxes Fp (Zhan et al. 2024, Eq. 14). Comparing with [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Time variability due to large-scale atmospheric dynamics is much weaker than observations (Patel et al. 2024). Panel a shows the time series of brightness temperature at 2.1 µm and 4.5 µm. Panel b shows the variability of eclipse spectra and the comparison with observations. Panel a and b shows the simulations with the largest variability in the 4.5 µm bandpass in each scenario. Note that this selection di… view at source ↗
read the original abstract

Recent observations of 55 Cancri e suggest an atmosphere rich in CO or CO$_2$ (Hu et al. 2024); other observations indicate the planet's eclipse depth is highly variable (e.g. Patel et al. 2024). So far, these observations have only been interpreted using 1D models without self-consistent heat redistribution, as the planet's extreme temperatures make it inaccessible to most 3D models. Here we perform cloud-free GCM simulations of 55 Cancri e using custom correlated-$k$ coefficients developed from the ExoMol database. Our best-fit simulations match the JWST spectra from Hu et al. (2024) well, favoring an atmosphere that is both thick ($\ge$ 10 bar) and CO$_2$-rich ($>1\%$ CO$_2$ volume mixing ratio), while ruling out thin ($<$ 10 bar) and pure-CO/CO$_2$-poor atmosphere, which were previously proposed based on 1D models (Hu et al. 2024; Zilinskas et al. 2025). We also find large-scale atmospheric dynamics, i.e. weather, is insufficient to explain the observed variability. A thick, CO$_2$-rich atmosphere implies that 55 Cancri e likely formed with significantly more volatiles than Earth and Venus. In addition, a thick atmosphere makes it unlikely that the planet's variability is caused by transient outgassing (Heng 2023), favoring other variability mechanisms (e.g. clouds). Our work provides model constraints for upcoming JWST observations of 55 Cancri e, and highlights the importance of interpreting thermal emission observations with self-consistent 3D models.

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

3 major / 2 minor

Summary. The paper performs cloud-free, non-grey GCM simulations of the ultra-hot super-Earth 55 Cancri e using custom correlated-k coefficients derived from the ExoMol database. It reports that the best-fit models with surface pressures ≥10 bar and CO₂ volume mixing ratios >1% reproduce the JWST thermal emission spectra of Hu et al. (2024), while thin (<10 bar) and CO-dominated or CO₂-poor atmospheres are ruled out; it further concludes that atmospheric dynamics cannot account for the observed eclipse-depth variability.

Significance. If the opacity tables and quantitative fits hold, the work would be significant for demonstrating that self-consistent 3D heat redistribution alters the interpretation of JWST data relative to prior 1D models, yielding constraints on atmospheric thickness and composition that bear on formation volatile budgets and variability mechanisms. The explicit use of 3D dynamics rather than parameterized redistribution is a methodological strength.

major comments (3)
  1. [Abstract, results] Abstract and results sections: the claim that best-fit simulations 'match the JWST spectra well' and rule out thin/pure-CO cases is presented without any quantitative fit statistics (χ², reduced χ², residual rms, or direct comparison to the 1D model fits of Hu et al. 2024). This absence prevents assessment of whether the 3D models provide a statistically meaningful improvement or robust exclusion of the previously proposed parameter space.
  2. [Methods] Methods (correlated-k construction): the custom correlated-k coefficients developed from ExoMol are not validated against line-by-line calculations or independent opacity databases at the relevant temperatures (∼1500–3000 K) and pressures. Because these tables directly determine the spectral shape and absolute flux levels in the 4–5 µm and 10–12 µm windows used to favor CO₂-rich over CO-dominated compositions, the lack of such benchmarks is load-bearing for the central claim that thin and CO-poor atmospheres are ruled out.
  3. [Results] Results (parameter exploration): the manuscript does not report the grid or sampling strategy used to explore the two free parameters (surface pressure and CO₂ VMR), nor does it provide posterior uncertainties or degeneracy analysis. Without these details it is unclear whether the ≥10 bar, >1% CO₂ preference is unique or sensitive to post-hoc choices.
minor comments (2)
  1. [Methods] Notation for the correlated-k tables and the specific ExoMol line lists employed should be stated explicitly in the methods to allow reproducibility.
  2. [Figures] Figure captions should include the exact JWST data points (wavelength bins and error bars) overlaid on the model spectra for direct visual comparison.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thoughtful and constructive review. We address each major comment below, indicating revisions that will be incorporated into the revised manuscript to strengthen the quantitative support for our conclusions.

read point-by-point responses

  1. Referee: [Abstract, results] Abstract and results sections: the claim that best-fit simulations 'match the JWST spectra well' and rule out thin/pure-CO cases is presented without any quantitative fit statistics (χ², reduced χ², residual rms, or direct comparison to the 1D model fits of Hu et al. 2024). This absence prevents assessment of whether the 3D models provide a statistically meaningful improvement or robust exclusion of the previously proposed parameter space.

    Authors: We agree that the absence of quantitative fit statistics limits the ability to assess the robustness of our conclusions. In the revised manuscript we will add χ² and reduced-χ² values for the best-fit 3D models against the Hu et al. (2024) JWST data, along with residual rms values and a side-by-side comparison to the 1D model fits reported in that work. These additions will enable a direct statistical evaluation of whether the self-consistent 3D heat redistribution improves the fit or strengthens the exclusion of thin and CO-dominated cases. revision: yes

  2. Referee: [Methods] Methods (correlated-k construction): the custom correlated-k coefficients developed from the ExoMol are not validated against line-by-line calculations or independent opacity databases at the relevant temperatures (∼1500–3000 K) and pressures. Because these tables directly determine the spectral shape and absolute flux levels in the 4–5 µm and 10–12 µm windows used to favor CO₂-rich over CO-dominated compositions, the lack of such benchmarks is load-bearing for the central claim that thin and CO-poor atmospheres are ruled out.

    Authors: We recognize that validation of the correlated-k tables is essential for the reliability of the spectral windows that distinguish CO₂-rich from CO-dominated compositions. In the revised methods section (or a new appendix) we will include direct comparisons of our correlated-k opacities against line-by-line calculations performed with the ExoMol line lists at representative temperatures (1500–3000 K) and pressures spanning the model domain. These benchmarks will be shown for the key 4–5 µm and 10–12 µm regions to confirm that the tables accurately reproduce the relevant absorption features. revision: yes

  3. Referee: [Results] Results (parameter exploration): the manuscript does not report the grid or sampling strategy used to explore the two free parameters (surface pressure and CO₂ VMR), nor does it provide posterior uncertainties or degeneracy analysis. Without these details it is unclear whether the ≥10 bar, >1% CO₂ preference is unique or sensitive to post-hoc choices.

    Authors: We will expand the results section to document the full parameter grid explored, including the discrete values and ranges sampled for surface pressure and CO₂ volume mixing ratio. We will also report the goodness-of-fit metrics across the grid and provide a brief discussion of parameter degeneracies and the robustness of the ≥10 bar, >1% CO₂ preference. Where feasible, we will include simple uncertainty estimates derived from the model ensemble. revision: yes

Circularity Check

0 steps flagged

No circularity: claims rest on forward GCM runs vs external JWST data

full rationale

The paper derives its conclusions by running cloud-free GCM simulations with custom correlated-k tables (developed from ExoMol) and directly comparing the resulting spectra to independent JWST observations reported in Hu et al. (2024). No equation defines a target quantity in terms of itself, no fitted parameter is relabeled as a prediction, and no load-bearing premise reduces to a self-citation chain. The central result (thick, CO2-rich atmosphere preferred; thin and CO-dominated cases ruled out) is obtained by matching an external dataset rather than by construction from the model's inputs.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

Abstract-only review limits visibility into parameters and assumptions. The model relies on custom k-coefficients and the cloud-free condition; surface pressure and CO2 mixing ratio appear to be varied to achieve the best fit.

free parameters (2)
  • surface pressure
    Varied to test thick (>=10 bar) vs thin (<10 bar) cases; value chosen to match spectra.
  • CO2 volume mixing ratio
    Varied to test >1% CO2-rich vs CO/CO2-poor cases; value chosen to match spectra.
axioms (2)
  • domain assumption Custom correlated-k coefficients from ExoMol accurately capture opacities at extreme temperatures
    Invoked to justify the radiative transfer in the GCM.
  • domain assumption Cloud-free conditions do not change the spectral fit or variability conclusions
    Stated as the modeling choice for all simulations.

pith-pipeline@v0.9.1-grok · 5850 in / 1560 out tokens · 17075 ms · 2026-06-27T08:12:26.955787+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

65 extracted references · 61 canonical work pages · 1 internal anchor

  1. [1]

    2016, in SF2A-2016: Proceedings of the Annual meeting of the French Society of Astronomy and Astrophysics, ed

    Allard, F. 2016, in SF2A-2016: Proceedings of the Annual meeting of the French Society of Astronomy and Astrophysics, ed. C. Reylé, J. Richard, L. Cambrésy, & others, Lyon, France, 223–227

    2016

  2. [2]

    Mayne, N. J. 2017, Astronomy & Astrophysics, 598, A97, doi: 10.1051/0004-6361/201629322

    work page doi:10.1051/0004-6361/201629322 2017

  3. [3]

    S., Mayne, N

    Amundsen, D. S., Mayne, N. J., Baraffe, I., et al. 2016, Astronomy & Astrophysics, 595, A36, doi: 10.1051/0004-6361/201629183

    work page doi:10.1051/0004-6361/201629183 2016

  4. [4]

    2017, The Astronomical Journal, 154, 232, doi: 10.3847/1538-3881/aa9278 A vice, G., Parai, R., Jacobson, S., et al

    Angelo, I., & Hu, R. 2017, The Astronomical Journal, 154, 232, doi: 10.3847/1538-3881/aa9278 A vice, G., Parai, R., Jacobson, S., et al. 2022, Space Science Reviews, 218, 60, doi: 10.1007/s11214-022-00929-9

    work page doi:10.3847/1538-3881/aa9278 2017

  5. [5]

    2018, Astronomy & Astrophysics, 619, A1, doi: 10.1051/0004-6361/201833154

    Bourrier, V., Dumusque, X., Dorn, C., et al. 2018, Astronomy & Astrophysics, 619, A1, doi: 10.1051/0004-6361/201833154

    work page doi:10.1051/0004-6361/201833154 2018

  6. [6]

    A., Mayne, N

    Christie, D. A., Mayne, N. J., Lines, S., et al. 2021, Monthly Notices of the Royal Astronomical Society, 506, 4500, doi: 10.1093/mnras/stab2027

    work page doi:10.1093/mnras/stab2027 2021

  7. [7]

    Crossfield, I. J. M. 2012, Astronomy & Astrophysics, 545, A97, doi: 10.1051/0004-6361/201219826

    work page doi:10.1051/0004-6361/201219826 2012

  8. [8]

    , keywords =

    Deibert, E. K., De Mooij, E. J. W., Jayawardhana, R., et al. 2021, The Astronomical Journal, 161, 209, doi: 10.3847/1538-3881/abe768

    work page doi:10.3847/1538-3881/abe768 2021

  9. [9]

    2016a, Monthly Notices of the Royal Astronomical Society, 455, 2018, doi: 10.1093/mnras/stv2239

    Demory, B.-O., Gillon, M., Madhusudhan, N., & Queloz, D. 2016a, Monthly Notices of the Royal Astronomical Society, 455, 2018, doi: 10.1093/mnras/stv2239

    work page doi:10.1093/mnras/stv2239 2018

  10. [10]

    2016b, Nature, 532, 207, doi: 10.1038/nature17169

    Demory, B.-O., Gillon, M., de Wit, J., et al. 2016b, Nature, 532, 207, doi: 10.1038/nature17169

    work page doi:10.1038/nature17169

  11. [11]

    2023, Astronomy & Astrophysics, 669, A64, doi: 10.1051/0004-6361/202244894

    Demory, B.-O., Sulis, S., Meier Valdés, E., et al. 2023, Astronomy & Astrophysics, 669, A64, doi: 10.1051/0004-6361/202244894

    work page doi:10.1051/0004-6361/202244894 2023

  12. [12]

    Elsasser, W. M. 1942, Heat Transfer by Infrared Radiation in the Atmosphere, Harvard Meteorological Studies No. 6 (Cambridge, MA: Harvard University, Blue Hill Meteorological Observatory)

    1942

  13. [13]

    2020, The Astrophysical Journal, 898, 160, doi: 10.3847/1538-4357/ab9cba 15

    Essack, Z., Seager, S., & Pajusalu, M. 2020, The Astrophysical Journal, 898, 160, doi: 10.3847/1538-4357/ab9cba 15

    work page doi:10.3847/1538-4357/ab9cba 2020

  14. [14]

    Magma Ocean Waves and Thermal Variability on Lava Worlds

    Farhat, M., & Chiang, E. 2026, Magma Ocean Waves and Thermal Variability on Lava Worlds, arXiv, doi: 10.48550/arXiv.2601.07080

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2601.07080 2026

  15. [15]

    J., & Tennyson, J

    Faure, A., Wiesenfeld, L., Drouin, B. J., & Tennyson, J. 2013, Journal of Quantitative Spectroscopy and Radiative Transfer, 116, 79, doi: 10.1016/j.jqsrt.2012.09.015

    work page doi:10.1016/j.jqsrt.2012.09.015 2013

  16. [16]

    Fegley, B., Lodders, K., & Jacobson, N. S. 2020, Geochemistry, 80, 125594, doi: 10.1016/j.chemer.2019.125594

    work page doi:10.1016/j.chemer.2019.125594 2020

  17. [17]

    A., Cottrell, E., Hauri, E., Lee, K

    Fischer, R. A., Cottrell, E., Hauri, E., Lee, K. K. M., & Le Voyer, M. 2020, Proceedings of the National Academy of Sciences, 117, 8743, doi: 10.1073/pnas.1919930117

    work page doi:10.1073/pnas.1919930117 2020

  18. [18]

    Fu, Q., & Liou, K. N. 1992, Journal of the Atmospheric Sciences, 49, 2139, doi: 10.1175/1520- 0469(1992)049<2139:OTCDMF>2.0.CO;2

    work page doi:10.1175/1520- 1992

  19. [19]

    R., & Orphanos, N

    Gamache, R. R., & Orphanos, N. G. 2023, Journal of Physical and Chemical Reference Data, 52, 023101, doi: 10.1063/5.0137083

    work page doi:10.1063/5.0137083 2023

  20. [20]

    , keywords =

    Gordon, I. E., Rothman, L. S., Hill, C., et al. 2017, Journal of Quantitative Spectroscopy and Radiative Transfer, 203, 3, doi: 10.1016/j.jqsrt.2017.06.038

    work page doi:10.1016/j.jqsrt.2017.06.038 2017

  21. [21]

    R., Tennyson, J., & Yurchenko, S

    Guest, E. R., Tennyson, J., & Yurchenko, S. N. 2024, Journal of Molecular Spectroscopy, 401, 111901, doi: 10.1016/j.jms.2024.111901

    work page doi:10.1016/j.jms.2024.111901 2024

  22. [22]

    D., Way, M

    Guzewich, S. D., Way, M. J., Aleinov, I., et al. 2021, Journal of Geophysical Research: Planets, 126, e2021JE006825, doi: 10.1029/2021JE006825

    work page doi:10.1029/2021je006825 2021

  23. [23]

    Halliday, A. N. 2013, Geochimica et Cosmochimica Acta, 105, 146, doi: 10.1016/j.gca.2012.11.015

    work page doi:10.1016/j.gca.2012.11.015 2013

  24. [24]

    Pierrehumbert, R

    Hammond, M., & T. Pierrehumbert, R. 2017, The Astrophysical Journal, 849, 152, doi: 10.3847/1538-4357/aa9328

    work page doi:10.3847/1538-4357/aa9328 2017

  25. [25]

    2023, ApJL, 956, L20, doi: 10.3847/2041-8213/acfe05

    Heng, K. 2023, The Astrophysical Journal Letters, 956, L20, doi: 10.3847/2041-8213/acfe05

    work page doi:10.3847/2041-8213/acfe05 2023

  26. [26]

    2024, Nature, 630, 609, doi: 10.1038/s41586-024-07432-x

    Hu, R., Bello-Arufe, A., Zhang, M., et al. 2024, Nature, 630, 609, doi: 10.1038/s41586-024-07432-x

    work page doi:10.1038/s41586-024-07432-x 2024

  27. [27]

    Jans, E. R. 2024, Journal of Quantitative Spectroscopy and Radiative Transfer, 312, 108809, doi: 10.1016/j.jqsrt.2023.108809

    work page doi:10.1016/j.jqsrt.2023.108809 2024

  28. [28]

    D., Coy, B

    Ji, X., Chatterjee, R. D., Coy, B. P., & Kite, E. S. 2025, The Cosmic Shoreline Revisited: A Metric for Atmospheric Retention Informed by Hydrodynamic Escape, arXiv, doi: 10.48550/arXiv.2504.19872

    work page doi:10.48550/arxiv.2504.19872 2025

  29. [29]

    Jindal, A., Mooij, E. J. W. d., Jayawardhana, R., et al. 2020, The Astronomical Journal, 160, 101, doi: 10.3847/1538-3881/aba1eb

    work page doi:10.3847/1538-3881/aba1eb 2020

  30. [30]

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

    Karman, T., Gordon, I. E., van der A voird, A., et al. 2019, Icarus, 328, 160, doi: 10.1016/j.icarus.2019.02.034

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

  31. [31]

    K., Sander, R., & Sörensen, R

    Keller-Rudek, H., Moortgat, G. K., Sander, R., & Sörensen, R. 2013, Earth System Science Data, 5, 365, doi: 10.5194/essd-5-365-2013

    work page doi:10.5194/essd-5-365-2013 2013

  32. [32]

    2020, Research Notes of the AAS, 4, 170, doi: 10.3847/2515-5172/abbc0f

    Kipping, D., & Jansen, T. 2020, Research Notes of the AAS, 4, 170, doi: 10.3847/2515-5172/abbc0f

    work page doi:10.3847/2515-5172/abbc0f 2020

  33. [33]

    Koll, D. D. B. 2022, The Astrophysical Journal, 924, 134, doi: 10.3847/1538-4357/ac3b48

    work page doi:10.3847/1538-4357/ac3b48 2022

  34. [34]

    Koll, D. D. B., & Abbot, D. S. 2015, The Astrophysical Journal, 802, 21, doi: 10.1088/0004-637X/802/1/21

    work page doi:10.1088/0004-637x/802/1/21 2015

  35. [35]

    T., Colyer, G

    Lewis, N. T., Colyer, G. J., & Read, P. L. 2021, Journal of the Atmospheric Sciences, 78, 1245, doi: 10.1175/JAS-D-20-0326.1

    work page doi:10.1175/jas-d-20-0326.1 2021

  36. [36]

    E., Rothman L

    Li, G., Gordon, I. E., Rothman, L. S., et al. 2015, The Astrophysical Journal Supplement Series, 216, 15, doi: 10.1088/0067-0049/216/1/15

    work page doi:10.1088/0067-0049/216/1/15 2015

  37. [37]

    Loftus, K., Luo, Y., Fan, B., & Kite, E. S. 2024, Extreme Weather Variability on Hot Rocky Exoplanet 55 Cancri e Explained by Magma Temperature-Cloud Feedback, arXiv. http://arxiv.org/abs/2409.16270 Lécuyer, C., Simon, L., & Guyot, F. 2000, Earth and Planetary Science Letters, 181, 33, doi: 10.1016/S0012-821X(00)00195-3

    work page doi:10.1016/s0012-821x(00)00195-3 2024

  38. [38]

    G., Bower, D

    Meier, T. G., Bower, D. J., Lichtenberg, T., Hammond, M., & Tackley, P. J. 2023, Astronomy & Astrophysics, 678, A29, doi: 10.1051/0004-6361/202346950

    work page doi:10.1051/0004-6361/202346950 2023

  39. [39]

    J., Dang, L., Gass, A., Cowan, N

    Mercier, S. J., Dang, L., Gass, A., Cowan, N. B., & Bell, T. J. 2022, The Astronomical Journal, 164, 204, doi: 10.3847/1538-3881/ac8f22

    work page doi:10.3847/1538-3881/ac8f22 2022

  40. [40]

    A., Brandeker, A., Kitzmann, D., et al

    Patel, J. A., Brandeker, A., Kitzmann, D., et al. 2024, JWST reveals a rapid and strong day side variability of 55 Cancri e, arXiv. http://arxiv.org/abs/2407.12898

    arXiv 2024

  41. [41]

    Penn, J., & Vallis, G. K. 2018, The Astrophysical Journal, 868, 147, doi: 10.3847/1538-4357/aaeb20

    work page doi:10.3847/1538-4357/aaeb20 2018

  42. [42]

    L., Kyuberis A

    Polyansky, O. L., Kyuberis, A. A., Zobov, N. F., et al. 2018, Monthly Notices of the Royal Astronomical Society, 480, 2597, doi: 10.1093/mnras/sty1877

    work page doi:10.1093/mnras/sty1877 2018

  43. [43]

    Gamache, R. R. 1992, Journal of Quantitative Spectroscopy and Radiative Transfer, 48, 537, doi: 10.1016/0022-4073(92)90119-O

    work page doi:10.1016/0022-4073(92)90119-o 1992

  44. [44]

    2009, The Astrophysical Journal, 703, 1884, doi: 10.1088/0004-637X/703/2/1884

    Seager, S., & Deming, D. 2009, The Astrophysical Journal, 703, 1884, doi: 10.1088/0004-637X/703/2/1884

    work page doi:10.1088/0004-637x/703/2/1884 2009

  45. [45]

    J., Hansen, B

    Seager, S., Richardson, L. J., Hansen, B. M. S., et al. 2005, The Astrophysical Journal, 632, 1122, doi: 10.1086/444411

    work page doi:10.1086/444411 2005

  46. [46]

    D., & Forget, F

    Selsis, F., Wordsworth, R. D., & Forget, F. 2011, Astronomy and Astrophysics, 532, A1, doi: 10.1051/0004-6361/201116654

    work page doi:10.1051/0004-6361/201116654 2011

  47. [47]

    Shemansky, D. E. 1969, The Journal of Chemical Physics, 51, 689, doi: 10.1063/1.1672058 16

    work page doi:10.1063/1.1672058 1969

  48. [48]

    P., & Kaspi, Y

    Showman, A. P., & Kaspi, Y. 2013, The Astrophysical Journal, 776, 85, doi: 10.1088/0004-637X/776/2/85

    work page doi:10.1088/0004-637x/776/2/85 2013

  49. [49]

    N., & Yachmenev, A

    Somogyi, W., Yurchenko, S. N., & Yachmenev, A. 2021, The Journal of Chemical Physics, 155, 214303, doi: 10.1063/5.0063256

    work page doi:10.1063/5.0063256 2021

  50. [50]

    1994, Journal Of The Brazilian Chemical Society, 5, 59, doi: 10.5935/0103-5053.19940010

    Souza, A., & Srivastava, S. 1994, Journal Of The Brazilian Chemical Society, 5, 59, doi: 10.5935/0103-5053.19940010

    work page doi:10.5935/0103-5053.19940010 1994

  51. [51]

    Tennyson, J., & Yurchenko, S. N. 2018, Atoms, 6, 26, doi: 10.3390/atoms6020026

    work page doi:10.3390/atoms6020026 2018

  52. [52]

    I., & Vallis, G

    Thomson, S. I., & Vallis, G. K. 2019a, Atmosphere, 10, 803, doi: 10.3390/atmos10120803 —. 2019b, Quarterly Journal of the Royal Meteorological Society, 145, 2627, doi: 10.1002/qj.3582

    work page doi:10.3390/atmos10120803

  53. [53]

    2009, The Astrophysical Journal, 703, 905, doi: 10.1088/0004-637X/703/1/905

    Tian, F. 2009, The Astrophysical Journal, 703, 905, doi: 10.1088/0004-637X/703/1/905

    work page doi:10.1088/0004-637x/703/1/905 2009

  54. [54]

    P., et al

    Tsiaras, A., Rocchetto, M., Waldmann, I. P., et al. 2016, The Astrophysical Journal, 820, 99, doi: 10.3847/0004-637X/820/2/99

    work page doi:10.3847/0004-637x/820/2/99 2016

  55. [55]

    J., Sergeev, D

    Turbet, M., Fauchez, T. J., Sergeev, D. E., et al. 2022, The Planetary Science Journal, 3, 211, doi: 10.3847/PSJ/ac6cf0 Valdés, E. A. M., Morris, B. M., Wells, R. D., Schanche, N., & Demory, B.-O. 2022, Astronomy & Astrophysics, 663, A95, doi: 10.1051/0004-6361/202243768

    work page doi:10.3847/psj/ac6cf0 2022

  56. [56]

    K., Colyer, G., Geen, R., et al

    Vallis, G. K., Colyer, G., Geen, R., et al. 2018, Geoscientific Model Development, 11, 843, doi: 10.5194/gmd-11-843-2018

    work page doi:10.5194/gmd-11-843-2018 2018

  57. [57]

    J., Aleinov, I., Amundsen, D

    Way, M. J., Aleinov, I., Amundsen, D. S., et al. 2017, The Astrophysical Journal Supplement Series, 231, 12, doi: 10.3847/1538-4365/aa7a06

    work page doi:10.3847/1538-4365/aa7a06 2017

  58. [58]

    Western, C. M. 2017, Journal of Quantitative Spectroscopy and Radiative Transfer, 186, 221, doi: 10.1016/j.jqsrt.2016.04.010

    work page doi:10.1016/j.jqsrt.2016.04.010 2017

  59. [59]

    M., Carter-Blatchford, L., Crozet, P., et al

    Western, C. M., Carter-Blatchford, L., Crozet, P., et al. 2018, Journal of Quantitative Spectroscopy and Radiative Transfer, 219, 127, doi: 10.1016/j.jqsrt.2018.07.017

    work page doi:10.1016/j.jqsrt.2018.07.017 2018

  60. [60]

    N., Al-Refaie, A

    Yurchenko, S. N., Al-Refaie, A. F., & Tennyson, J. 2018, Astronomy & Astrophysics, 614, A131, doi: 10.1051/0004-6361/201732531

    work page doi:10.1051/0004-6361/201732531 2018

  61. [61]

    N., Tennyson, J., Syme, A.-M., et al

    Yurchenko, S. N., Tennyson, J., Syme, A.-M., et al. 2022, Monthly Notices of the Royal Astronomical Society, 510, 903, doi: 10.1093/mnras/stab3267

    work page doi:10.1093/mnras/stab3267 2022

  62. [62]

    G., Welch, R

    Zdunkowski, W. G., Welch, R. M., & Korb, G. 1980, Contributions to Atmospheric Physics, 53, 147

    1980

  63. [63]

    Zhan, R., Koll, D. D. B., & Ding, F. 2024, The Astrophysical Journal, 971, 125, doi: 10.3847/1538-4357/ad54c1

    work page doi:10.3847/1538-4357/ad54c1 2024

  64. [64]

    Zhang, X., & Showman, A. P. 2017, The Astrophysical Journal, 836, 73, doi: 10.3847/1538-4357/836/1/73

    work page doi:10.3847/1538-4357/836/1/73 2017

  65. [65]

    v., Zieba, S., et al

    Zilinskas, M., Buchem, C. v., Zieba, S., et al. 2025, Characterising the Atmosphere of 55 Cancri e: 1D Forward Model Grid for Current and Future JWST Observations, arXiv, doi: 10.48550/arXiv.2503.15844

    work page doi:10.48550/arxiv.2503.15844 2025