Carbon dioxide COSUB2SUB is a key chemical species that is found in a wide range of planetary atmospheres. In the context of exoplanets COSUB2SUB is an indicator of the metal enrichment that is elements heavier than helium also called metallicitySUP13SUP and thus the formation processes of the primary atmospheres of hot gas giantsSUP46SUP. It is also one of the most promising species to detect in the secondary atmospheres of terrestrial exoplanetsSUP79SUP. Previous photometric measurements of transiting planets with the Spitzer Space Telescope have given hints of the presence of COSUB2SUB but have not yielded definitive detections owing to the lack of unambiguous spectroscopic identificationSUP1012SUP. Here we present the detection of COSUB2SUB in the atmosphere of the gas giant exoplanet WASP39b from transmission spectroscopy observations obtained with JWST as part of the Early Release Science programmeSUP1314SUP. The data used in this study span 3.05.5 micrometres in wavelength and show a prominent COSUB2SUB absorption feature at 4.3 micrometres 26sigma significance. The overall spectrum is well matched by onedimensional tentimes solar metallicity models that assume radiativeconvectivethermochemical equilibrium and have moderate cloud opacity. These models predict that the atmosphere should have water carbon monoxide and hydrogen sulfide in addition to COSUB2SUB but little methane. Furthermore we also tentatively detect a small absorption feature near 4.0 micrometres that is not reproduced by these models.
['Astrophysics - Earth and Planetary Astrophysics']
Identification of carbon dioxide in an exoplanet atmosphere
{'Identification of carbon dioxide in an exoplanet atmosphere': 'JWST Transiting Exoplanet Community Early Release Science Team *  \nCarbon dioxide (CO 2 ) is a key chemical species that is found in a wide range of planetary atmospheres. In the context of exoplanets, CO 2 is an indicator of the metal enrichment (i.e., elements heavier than helium, also called \'metallicity\') 1-3 , and thus formation processes of the primary atmospheres of hot gas giants 4-6 . It is also one of the most promising species to detect  in  the  secondary  atmospheres  of  terrestrial  exoplanets 7-9 .  Previous  photometric measurements of transiting planets with the Spitzer Space Telescope have given hints of the presence of CO 2 but have not yielded definitive detections due to the lack of unambiguous spectroscopic identification 10-12 . Here we present the detection of CO 2 in the atmosphere of the gas giant exoplanet WASP-39b from transmission spectroscopy observations obtained with JWST as part of the Early Release Science Program (ERS) 13,14 . The data used in this study span 3.0 - 5.5 µm in wavelength and show a prominent CO 2 absorption feature at 4.3 µm (26σ significance). The overall spectrum is well matched by one-dimensional, 10x solar metallicity models that assume radiative-convective-thermochemical equilibrium and have moderate  cloud  opacity.  These  models  predict  that  the  atmosphere  should  have  water, carbon monoxide, and hydrogen sulfide in addition to CO 2 , but little methane. Furthermore, we also tentatively detect a small absorption feature near 4.0 µm that is not reproduced by these models.  \nWASP-39b is a hot (planetary equilibrium temperature of 1170 K assuming zero albedo and full heat redistribution), transiting exoplanet that orbits a G7-type star with a period of 4.055 days 15 . The planet has approximately the same mass as Saturn ( M = 0.28 M Jup ) but is ~50% larger ( R = 1.28 R Jup ), likely due to the high level of irradiation it receives from its host star 16-18 . We chose this planet for the JWST ERS transmission spectroscopy observations because analyses of existing space- and ground-based data detected large spectral features and showed that there was minimal contamination  of  the  planetary  signal  from  stellar  activity 10,19-21 .  The  main  spectral  features previously  detected  were  confidently  attributed  to  sodium,  potassium,  and  water  vapor absorption 10,19-20 ,  while  CO 2 was  suggested  to  explain  the  deep  transit  at  4.5  µm  seen  with Spitzer 10 .  \nAtmospheric metallicity has long been thought to be a diagnostic of the relative accretion of solids and gas during the formation of gas giant planets, both of which bring heavy elements to the hydrogen-dominated envelope and visible atmosphere 4-6 .  The  metallicity  of  WASP-39b\'s  host  \nstar, which is a proxy for the metal enrichment of the protoplanetary disk that the planet formed in,  is  approximately  solar 15,22-24 .  Therefore,  the  planet  mass  -  atmospheric  metallicity  trend observed in the solar system giants 25,26 predicts that it has an enhancement of ~10x solar (like that of Saturn, Ref. [27]). Additionally, interior structure models that match WASP-39b\'s low density predict a 95th percentile upper limit for the atmospheric metallicity of 55x solar, under the limiting assumption that the planet has no heavy element core and that all the metals are evenly distributed throughout the envelope 28 .  \nDespite  having  some  of  the  highest  signal-to-noise  detections  of  spectral  features  in  its transmission spectrum, modeling of the existing data for WASP-39b has resulted in metallicity estimates ranging across five orders of magnitude, from 0.003x to 300x solar 10, 29-33 .  The wide range of values stems from the data being of insufficient quality to break the degeneracy between clouds  and  metallicity  in  transmission  spectra  models 34 ,  as  well  as  uncertainty  over  the interpretation of the photometric measurements by the Spitzer Space Telescope at 3.6 and 4.5 µm. Thus, spectroscopic data with greater precision, finer spectral channels, and wider wavelength coverage  were  needed  to  better  constrain  the  metallicity  of  this  (and  other)  giant  exoplanet atmospheres. .  \nThe first JWST ERS observation of WASP-39b was obtained using the Near Infrared Spectrograph (NIRSpec) 35,36 on July 10, 2022, between 15:24 and 23:37 UTC. We used the Bright Object Time Series (BOTS) mode with the 1.6\' x 1.6\' fixed slit aperture and the PRISM disperser to capture spectra between 0.5 and 5.5 µm. The data were recorded using the SUB512 subarray with five groups per integration and the NRSRAPID readout pattern, which gave integration times of 1.38 s. NIRSpec obtained a total of 21,500 integrations over 8.23 hours of observations centered on the 2.8 hour transit duration of WASP-39b.  \nThe count rate in the PRISM mode varies significantly over the bandpass due to the spectral energy distribution of the star and the wavelength-dependency of the spectrograph dispersion. Therefore, the  observations  were  designed to saturate at shorter wavelengths in order to obtain sufficient signal-to-noise ratio at the longer wavelengths in the bandpass for first-of-its kind spectroscopy of transiting exoplanets. Wavelengths between 0.71 and 2.09 µm have at least one group saturated in the pixel at the center of the spectral trace. We concentrate here on the analysis of the data longward of 3.0 µm that are not impacted by saturation to investigate the spectrum overlapping with the previous 3.6 µm and 4.5 µm Spitzer photometric measurements. The subset of the PRISM data described herein has a native spectral resolving power ( R = 𝝀 /Δ 𝝀 , where 𝝀 is wavelength) of 100 350. For this study, we binned the data to lower resolving powers (values range from 60 to 200 depending on wavelength and reduction). The binning is done at the light curve level before the fitting of the transit depths that constitute the transmission spectrum. Analyses of JWST/NIRSpec transit observations obtained during commissioning have shown that similar levels of binning as  \nwe use here results in minimal systematics 37 . An analysis of the complete PRISM dataset at full resolution including recovery of the saturated part of the spectrum is ongoing.  \nFigure 1: JWST NIRSpec time-series data for WASP-39b. a) Spectroscopic light curves for WASP-39b\'s transit with a spectral resolving power of 20 and a time cadence of 1 minute (data are binned and offset vertically for display purposes only). An exoplanet light curve model was fitted to the data using a quadratic limb darkening law with an exponential ramp and a quadratic function of time removed. b) Residuals of the binned light curve after subtracting the transit model scaled up by a factor of five to show the structure. The RMS of the residuals are given in units of ppm. The numbers in brackets are the ratio of the RMS to the predicted photon-limited noise.  \n<!-- image -->  \nWe  reduced  the  NIRSpec  PRISM  data  for  WASP-39b  using  the  JWST  Science  Calibration Pipeline along with customized routines to minimize noise in the time series spectra (see Methods). We performed four different reductions of the transmission spectrum starting from the uncalibrated data 21, 38-40 . Figure 1 shows derived spectroscopic transit light curves from one of the reductions.  \nWe confirm  with  our  analysis  of  the  WASP-39b  data  that  NIRSpec  transit  observations  at  a resolving power of 60 - 200 are nearly free of systematics. We achieved close to photon-noiselimited measurements in the spectroscopic light curves after trimming the first 10 minutes of data and removing a linear trend in time with an average rate of ~190 ppm/hour across the bandpass. We  also  obtained  similar  results  by  fitting  the  full  time-series  with  a  downward  trending exponential ramp (timescale ~100 minutes) combined with a quadratic function of time. The lack of large systematics in these data stands in contrast to previous transit spectroscopy observations with space- or ground-based telescopes 41 .  \nFigure 2: Independent reductions of the WASP-39b transmission spectrum. The JWST data (small colored points) are compared to Spitzer\'s two broadband photometric measurements (grey circles and corresponding sensitivity curves labeled \'IRAC1\' and \'IRAC2\'). The axis on the right shows equivalent scale heights (750 - 1000 km) in WASP-39b\'s atmosphere; for plotting purposes we assume that one scale height corresponds to 800 km. The JWST data are consistent with the Spitzer points (within 2σ) when integrated over the broad bandpasses (indicated by the horizontal lines). The relative transit depths between the 3.6 and 4.5 µm channels are also consistent within 2σ between independent reductions of the JWST data, with most of the deviation coming from the 3.6 µm bandpass. Vertical error bars indicate 1σ uncertainties.  \n<!-- image -->  \nThe transmission spectra derived from the different reductions, shown in Figure 2, have excellent agreement. They all exhibit a large feature at 4.3 µm, as well as a smaller feature near 4.0 µm (discussed below). Detailed modeling of the FIREFLy-reduced data yields a statistical significance  \nof 26σ for the large feature (see the Methods). We attribute this feature to CO 2 absorption based on  a  comparison  of  the  resolved  band  shape  to  theoretical  models  and  the  spectra  of  brown dwarfs 42 . Figure 2 also includes Spitzer\'s two broadband photometric measurements 10 , which are consistent with the JWST data to better than 2σ after integrating the transmission spectrum over the  Spitzer  bandpasses.  We  also  see  good  agreement  (better  than  2σ  for  all  reductions)  in  the relative transit depths between the 3.6 and 4.5 µm channels. The comparison shown in Figure 2 demonstrates both the consistency in the derived spectra from multiple, independent analyses and the reliability of the previous Spitzer measurements.  \nFigure  3:  Interpretation  of  WASP-39b\'s  transmission  spectrum. The  top  panel  shows  a comparison of the FIREFLy reduction to the best-fit ScCHIMERA theoretical model binned to the resolution of the data (blue curve, see Methods). The key parameters of the model are 10x solar metallicity, carbon-to-oxygen ratio of 0.35, and cloud opacity of 7x10 -3 cm 2 /g. The impact of the opacity sources expected from thermochemical equilibrium over the full bandpass are indicated by removing the opacity contribution from individual gasses one at a time. As in Figure 2, the axis on the right shows equivalent scale heights in WASP-39b\'s atmosphere. The bottom panel shows the  molecular  absorption  cross-sections  for  each  gas  in  the  best-fit  model.  The  model  is  well matched  to  the  data  ( 𝜒 ! /𝑁 "#$# =1.3),  suggesting  that  our  assumptions  broadly  capture  the important physics and chemistry in WASP-39b\'s atmosphere. However, there is a feature near 4.0 µm that cannot be reproduced by the models used here. The strong CO 2 absorption (4.1 - 4.6 µm) and  the  apparent  lack  of  methane  (3.0  -  3.5  µm)  is  what  drives  the  solution  to  an  elevated atmospheric  metal  enrichment,  ruling  out  previous  low  metallicity  estimates 29-31 .  The  other reductions and models give similar results.  \n<!-- image -->  \nWe  compared  the  data  to  a  suite  of  one-dimensional  atmospheric  structure  and  transmission spectrum models to constrain the composition of WASP-39b\'s atmosphere. These models assume radiative-convective-thermochemical  equilibrium,  and  they  adopt  a  scaled  solar  abundance pattern.  We  calculated  planet-specific  grids  of  these  models  over  a  range  of  atmospheric metallicities, carbon-to-oxygen ratios, and cloud properties using four different codes. These grids of self-consistent model transmission spectra were then fitted to the FIREFLy-reduced data (the fit results are independent of which data set we use) while also adjusting for a reference radius at 1 bar. The results are illustrated in Figure 3; see the Methods for further details.  \nUnder  similar  assumptions,  all  four  model  grids  are  able  to  match  the  dominant  spectral morphologies - namely the strong CO 2 feature between 4.1 and 4.6 µm and the rise in transit depth blueward of 3.6 µm due to water vapor (a species that had been detected previously at shorter wavelengths, Ref. [10]). More subtle modulations over the whole bandpass are potentially due to contributions from clouds, carbon monoxide, and hydrogen sulfide, though the degree to which the two gas species contribute is unknown pending further study.  \nSeveral  models  for  warm  gas  giant  atmospheres  predict  that  the  CO 2 abundance  scales quadratically with atmospheric metallicity, becoming detectable at 4.3 µm for metallicities above that  of  the  Sun 1-3 .  The  representative  best-fit  model  shown  in  Figure  3  is  consistent  with  this scenario. It has a 10x solar metal enrichment and a slightly sub-solar carbon-to-oxygen ratio (0.35, compared  to  the  solar  value  of  0.55;  Ref.  [43]).  The  moderate  contribution  of  cloud  opacity predicted  by  the  best-fit  model  is  consistent  with  interpretations  of  previous  population-level studies  of  planets  that  have  similar  temperatures  and  gravities  as  WASP-39b 44,45 .  It  is  also consistent with the predictions of aerosol microphysics and global circulation models of hot giant planets 46-48 .  \nIn addition to the large CO 2 feature, we also identify a smaller spectral feature near 4.0 µm that is not matched by our thermochemical equilibrium models (see Figure 3). This feature is present in all  four  independent  reductions  and  has  a  significance  of  2σ  (see  the  Methods).  Further  data analysis  and  modeling  including  non-equilibrium  chemistry  are  needed  to  fully  assess  the robustness of this feature and to identify the chemical species that gives rise to it. Additional JWST ERS observations  of  WASP-39b  that  will  use  the  G395H  grating  on  NIRSpec  also  have  the potential to confirm the 4.0 µm feature and resolve it in greater detail.  \nThe grid fits explored here favor lower metallicities than Refs. [10, 21], and higher metallicities than Ref [31], even though the Spitzer data that their studies included are consistent with our JWST data. The higher precision and more resolved measurement of the CO 2 feature enabled by JWST pulls the models of Refs. [10, 21] to lower metallicity and increased cloudiness. Nevertheless, it is not  possible  to  obtain  a  robust  confidence  interval  on  this  inference  without  more  rigorous Bayesian analyses, which is left to future work (see Methods). Continued modeling of WASP-39b  \nwill also be aided by the future measurements of the planet\'s transmission spectrum from 0.5 - 5.5 µm that are also being obtained by this Early Release Science program. The final transmission spectrum will ultimately have higher spectral resolution than the data presented here (>4x over most of the bandpass) and will be validated using multiple JWST instruments.  \n- 1. Lodders, K., & Fegley, B. Atmospheric Chemistry in Giant Planets, Brown Dwarfs, and Low-Mass Dwarf Stars. I. Carbon, Nitrogen, and Oxygen. Icarus , 155 , 393 (2002).\n- 2. Zahnle, K., Marley, M. S., Freedman, R. S., Lodders, K., & Fortney, J. J. Atmospheric Sulfur Photochemistry on Hot Jupiters. Astrophys. J. Letters 701 , L20-L24 (2009).\n- 3. Moses, J. I., et al. Compositional Diversity in the Atmospheres of Hot Neptunes, with Application to GJ 436b. Astrophys. J. 777 , 34 (2013).\n- 4. Pollack, J. B., Hubickyj, O., Bodenheimer, P., Lissauer, J. J., Podolak, M., & Greenzweig, Y. Formation of the Giant Planets by Concurrent Accretion of Solids and Gas. Icarus 124 , 62-85 (1996).\n- 5. Fortney, J. J., Mordasini, C., Nettelmann, N., Kempton, E. M.-R., Greene, T. P., & Zahnle, K. A Framework for Characterizing the Atmospheres of Low-mass Low-density Transiting Planets. Astrophys. J. 775 , 80 (2013).\n- 6. Venturini, J., Alibert, Y., & Benz, W. Planet formation with envelope enrichment: new insights on planetary diversity. Astron. & Astrophys. 596 , A90 (2016).\n- 7. Kaltenegger, L., & Traub, W. A. Transits of Earth-like Planets. Astrophys. J. 698 , 519527 (2009).\n- 8. Deming, D., et al. Discovery and Characterization of Transiting Super Earths Using an All-Sky Transit Survey and Follow-up by the James Webb Space Telescope. Publ. Astron. Soc. Pac. 121 , 952 (2009).\n- 9. Lustig-Yaeger, J., Meadows, V. S., & Lincowski, A. P. The Detectability and Characterization of the TRAPPIST-1 Exoplanet Atmospheres with JWST. Astron. J. 158 , 27 (2019).\n- 10. Wakeford, H. R., et al. The Complete Transmission Spectrum of WASP-39b with a Precise Water Constraint. Astron. J. 155, 29 (2018).\n- 11. Spake, J. J., et al. Abundance measurements of H 2 O and carbon-bearing species in the atmosphere of WASP-127b confirm its supersolar metallicity. Mon. Not. R. Astron. Soc. 500 , 4042-4064 (2021).  \n- 12. Alderson, L., et al. A comprehensive analysis of WASP-17b\'s transmission spectrum from space-based observations. Mon. Not. R. Astron. Soc. 512 , 4185-4209 (2022).\n- 13. Stevenson, K. B., et al. Transiting Exoplanet Studies and Community Targets for JWST\'s Early Release Science Program. Publ. Astron. Soc. Pac. 128 , 094401 (2016).\n- 14. Bean, J. L., et al. The Transiting Exoplanet Community Early Release Science Program for JWST. Publ. Astron. Soc. Pac. 130 , 114402 (2018).\n- 15. Faedi, F., et al. WASP-39b: a highly inflated Saturn-mass planet orbiting a late G-type star, Astron. & Astrophys. 531 , A40 (2011).\n- 16. Laughlin, G., Crismani, M., & Adams, F. C. On the Anomalous Radii of the Transiting Extrasolar Planets. Astrophys. J. Letters 729 , L7 (2011).\n- 17. Thorngren, D. P., & Fortney, J. J. Bayesian Analysis of Hot-Jupiter Radius Anomalies: Evidence for Ohmic Dissipation? Astron. J. 155 , 214 (2018).\n- 18. Sarkis, P., Mordasini, C., Henning, T., Marleau, G. D., & Mollière, P. Evidence of three mechanisms explaining the radius anomaly of hot Jupiters. Astron. & Astrophys. 645 , A79 (2021).\n- 19. Fischer, P. D., et al. HST Hot-Jupiter Transmission Spectral Survey: Clear Skies for Cool Saturn WASP-39b. Astrophys. J. 827 , 19 (2016).\n- 20. Nikolov, N., et al. VLT FORS2 Comparative Transmission Spectroscopy: Detection of Na in the Atmosphere of WASP-39b from the Ground. Astrophys. J. 832 , 191 (2016).\n- 21. Kirk, J., et al. LRG-BEASTS: Transmission Spectroscopy and Retrieval Analysis of the Highly Inflated Saturn-mass Planet WASP-39b. Astron. J. 158 , 144 (2019).\n- 22. Mancini, L., et al. The GAPS programme with HARPS-N at TNG. XVI. Measurement of the Rossiter-McLaughlin effect of transiting planetary systems HAT-P-3, HAT-P-12, HAT-P-22, WASP-39, and WASP-60. Astron. & Astrophys. 613 , A41 (2018).\n- 23. Biazzo, K., et al. The GAPS Programme with HARPS-N at TNG. XXXV. Fundamental properties of transiting exoplanet host stars. arXiv e-prints, arXiv:2205.15796 (2022).\n- 24. Polanski, A. S., Crossfield, I. J. M., Howard, A. W., Isaacson, H., & Rice, M. Chemical Abundances for 25 JWST Exoplanet Host Stars with KeckSpec. arXiv e-prints, arXiv:2207.13662 (2022).\n- 25. Lodders K, & Fegley B. The planetary scientist\'s companion. Oxford University Press (1998).  \n- 26. Kreidberg, L., et al. A Precise Water Abundance Measurement for the Hot Jupiter WASP-43b. Astrophys. J. Letters 793 , L27 (2014).\n- 27. Atreya, S. K., et al. The Origin and Evolution of Saturn: A Post-Cassini Perspective. arXiv e-prints , arXiv:2205.06914 (2022).\n- 28. Thorngren, D., & Fortney, J. J. Connecting Giant Planet Atmosphere and Interior Modeling: Constraints on Atmospheric Metal Enrichment. Astrophys. J. Letters 874 , L31 (2019).\n- 29. Tsiaras, A., et al. A Population Study of Gaseous Exoplanets. Astron. J. 155 , 156 (2018).\n- 30. Fisher, C., & Heng, K. Retrieval analysis of 38 WFC3 transmission spectra and resolution of the normalization degeneracy. Mon. Not. R. Astron. Soc. 481 , 4698-4727 (2018).\n- 31. Pinhas, A., Madhusudhan, N., Gandhi, S., & MacDonald, R. H 2 O abundances and cloud properties in ten hot giant exoplanets. Mon. Not. R. Astron. Soc. 482 , 1485-1498 (2019).\n- 32. Welbanks, L., Madhusudhan, N., Allard, N. F., Hubeny, I., Spiegelman, F., & Leininger, T. Mass-Metallicity Trends in Transiting Exoplanets from Atmospheric Abundances of H 2 O, Na, and K. Astrophys. J. Letters 887 , L20 (2019).\n- 33. Min, M., Ormel, C. W., Chubb, K., Helling, C., & Kawashima, Y. The ARCiS framework for exoplanet atmospheres. Modelling philosophy and retrieval. Astron. & Astrophys. 642 , A28 (2020).\n- 34. Benneke,  B.,  &  Seager,  S.  How  to  Distinguish  between  Cloudy  Mini-Neptunes  and Water/Volatile-dominated Super-Earths. Astrophys. J. 778 , 153 (2013).\n- 35. Jakobsen, P., et al. The Near-Infrared Spectrograph (NIRSpec) on the James Webb Space Telescope. I. Overview of the instrument and its capabilities. Astron. & Astrophys. 661 , A80 (2022).\n- 36. Birkmann, S. M., et al. The Near-Infrared Spectrograph (NIRSpec) on the James Webb Space Telescope. IV. Capabilities and predicted performance for exoplanet characterization. Astron. & Astrophys. 661 , A83 (2022).\n- 37. Rigby, J., et al. Characterization of JWST science performance from commissioning. arXiv e-prints , arXiv:2207.05632 (2022).\n- 38. Rustamkulov, Z., Sing, D. K., Liu, R., & Wang, A. Analysis of a JWST NIRSpec Lab Time Series: Characterizing Systematics, Recovering Exoplanet Transit Spectroscopy, and Constraining a Noise Floor. Astrophys. J. Letters 928 , L7 (2022).  \n- 39. Bell, T. J., et al. Eureka!: An End-to-End Pipeline for JWST Time-Series Observations. arXiv e-prints , arXiv:2207.03585 (2022).\n- 40. Glidic, K., Schlawin, E., Wiser, L., Zhou, Y., Deming, D., & Line, M. Atmospheric Characterization of Hot Jupiter CoRoT-1 b Using the Wide Field Camera 3 on the Hubble Space Telescope. Astron. J. 164 , 19 (2022).\n- 41. Beichman, C., et al. Observations of Transiting Exoplanets with the James Webb Space Telescope (JWST). Publ. Astron. Soc. Pac. 126 , 1134 (2014).\n- 42. Yamamura, I., Tsuji, T., & Tanabé, T. AKARI Observations of Brown Dwarfs. I. CO and CO 2 Bands in the Near-infrared Spectra. Astrophys. J. 722 , 682-698 (2010).\n- 43. Lodders, K., Palme, H., & Gail, H.-P. Abundances of the Elements in the Solar System. Landolt-Bornstein Series 4B, 712 (2009).\n- 44. Stevenson, K. B. Quantifying and Predicting the Presence of Clouds in Exoplanet Atmospheres. Astrophys. J. Letters 817 , L16 (2016).\n- 45. Fu, G., Deming, D., Knutson, H., Madhusudhan, N., Mandell, A., & Fraine, J. Statistical Analysis of Hubble/WFC3 Transit Spectroscopy of Extrasolar Planets. Astrophys. J. Letters 847 , L22 (2017).\n- 46. Parmentier, V., Fortney, J. J., Showman, A. P., Morley, C., & Marley, M. S. Transitions in the Cloud Composition of Hot Jupiters. Astrophys. J. 828 , 22 (2016).\n- 47. Kataria, T., et al. The Atmospheric Circulation of a Nine-hot-Jupiter Sample: Probing Circulation and Chemistry over a Wide Phase Space. Astrophys. J. 821 , 9 (2016).\n- 48. Gao, P., et al. Aerosol composition of hot giant exoplanets dominated by silicates and hydrocarbon hazes. Nat. Astron. 4 , 951 (2020).', 'Data reduction': 'We reduced the JWST NIRSpec PRISM data for WASP-39b using four separate pipelines to confirm that the results did not depend on the specifics of the analyses, as was sometimes the case for  results  from  the  Spitzer  Space  Telescope  (e.g.,  Ref  [49]).  The  descriptions  below  refer  to calibration pipelines and other software whose code and citations appear in the Code Availability section, below.', 'tshirt pipeline': "We used the Time Series Helper and Integration Reduction Tool 40 (tshirt) to extract light curves of the spectrum. This pipeline modifies the JWST Calibration pipeline steps to improve the precision  of  the  reduction.  tshirt  has  been  used  to  successfully  analyze  the  JWST  transit observations of HAT-P-14b that were obtained during commissioning with NIRCam 37 . First, we used an updated bias frame from commissioning program 1130 observation 29 and ran the JWST Calibration  pipeline  until  the  reference  pixels  step.  We  then  applied  a  correction  for  1/f-noise which varies  for  odd  and  even  rows  and  for  each  column.  We  use  background  pixels  for  the calibration since reference pixels are not available in this subarray. We skipped the jump and dark subtraction steps because they were seen to add noise to the light curves. tshirt fits the profile of the spectrum with splines and rejects outlier pixels that are more than 50 sigma from the spline fits.  We  used  covariance-weighted extraction 50 with  an  assumed  pixel  correlation  of  0.08.  For spectral extraction, we used a background region no closer than 7 pixels on either side of the source and an extraction region width of 16 pixels. The scatter in the light curve was consistent with the theoretical limit of photon and read noise over short timescales.  \nWe fit the light curves with a second-order (quadratic) polynomial baseline, uninformative quadratic limb darkening priors, and an exponential startup ramp with 10σ clipping of outliers. To begin, we fit the white light curve with priors on the transit center, inclination, and period from Ref [22]. We also used the a / R * from  Ref  [22]  but  widened  the  uncertainty  on  this  parameter because the enforced prior resulted in significant residuals. Next, we fit each spectroscopic light curve individually with the orbital parameters fixed at the value from the white light posterior medians. We modeled the light curves using the 'exoplanet' code 51 and the pymc3 52 sampler. We evaluated the wavelengths using the JWST Calibration pipeline at pixel row 16 (Y=16) from the world coordinate solution. This uses an instrument model and could not be verified due to a lack of strong stellar absorption features at the NIRSpec resolution. All the other reductions adopted this wavelength calibration. As shown in Figure 1, the standard deviation in the out-of-transit light curve approaches the theoretical limit of photon and read noise at short wavelengths, but is 20% to 40% higher at longer wavelengths, which may be related to uncorrected 1/f-noise.", 'Eureka! pipeline': "Eureka! 39 is a data reduction and analysis pipeline for time-series observations with JWST or HST. Its modular, multi-stage design provides flexibility and ease of comparison at any step, starting from uncalibrated FITS files and resulting in precise transmission or emission spectra. Eureka! has been used to successfully analyze the JWST transit observations of HAT-P-14b that were obtained during commissioning with NIRCam 37 .  \nWe began the data reduction process using the UNCAL files available from the MAST archive. The first stage of the Eureka! pipeline is primarily a wrapper for Stage 1 of the JWST Calibration  pipeline,  which  converts  groups  to  slopes.  For  this  dataset,  we  skipped  the  jump detection step as it led to a large fraction of detector pixels being incorrectly flagged as outliers. We did, however, search for and flag outliers at multiple points in subsequent stages. We also  \nmanually updated the bad-pixel map to include identified hot pixels on the detector that were not provided in the current (July 2022) full-detector STScI data quality map. As part of Eureka!, we performed a custom background subtraction at the group level prior to Stage 1 ramp fitting to account for 1/f-noise introduced during detector readout. We set the top and bottom six rows of the  detector  as  our  background  region  and  flagged  pixels  deemed  outliers  at  >3σ.  We  then subtracted the mean flux per pixel column and repeated this for each group and integration in the observation. Similar to Stage 1, the second stage of the Eureka! pipeline is a wrapper for Stage 2 of the JWST Calibration pipeline, which calibrates the 2D time series of fitted slopes. Here, we skipped the flux calibration step, thus leaving the data in units of DN/s.  \nFor Stage 3, we performed background subtraction and optimal extraction of the stellar spectrum  for  each  integration  with  Eureka!.  We  only  used  pixels  14  to  495  in  the  dispersion direction of the 512x32-pixel subarray, as NIRSpec's throughput is negligible beyond this range. We also masked pixels that have a non-zero data quality flag to avoid any impact of outlier pixels on the extracted spectra or background subtraction. The position of the source on the detector along the cross-dispersion dimension is located by fitting a Gaussian to the pixel values summed over all  detector  columns.  For  each  pixel,  we  examined  its  flux  variation  in  time  and  performed  a double-iteration,  10σ  outlier  rejection  test.  We  then  executed  a  second  column-by-column background subtraction, this time at the integration level, using pixels located at least 8 pixels away from the source position to compute the mean background per column. Performing this additional background subtraction reduced the number of outliers in the measured light curves and accounted for the residual background and/or noise introduced during the ramp fitting procedure. As with Stage 1, we exclude 3σ outliers from our background region. We adopted an aperture halfwidth of 7 pixels for our optimal spectral extraction step, constructing the profile from the median frame. At the end of this stage, we obtained a time series of 1D spectra.  \nFor  the  remaining  stages,  we  used  multiple  pipelines  (Eureka! 39 and  ExoTEP 53-55 )  to generate  and  fit  the  light  curves.  We  first  generated  median-normalized  light  curves  at  the instrument's native resolution (i.e., from each detector column) using our Stage 3 outputs. We then clipped additional outliers in time for the white and spectroscopic light curves. For this step, we first  rejected  integrations  that  were  more  than  3σ  outliers  for  the  source  position  in  the  crossdispersion  direction,  the  width  of  the  fitted  Gaussian  to  the  spatial  profile,  or  the  drift  in  the dispersion direction. Next, we produced a median-filtered version of the light curve and clipped out 3σ outliers in flux. We jointly fit astrophysical and systematics model parameters to the white and  individual  spectroscopic  light  curves.  Our  astrophysical  transit  model  used  the  batman package 56 with  uniform  priors,  fitting  for  the  following  astrophysical  parameters:  the  two coefficients of a stellar quadratic limb-darkening law, impact parameter, semi-major axis, transit time, and the planet-to-star radius ratio ( a / R * ) in each of the wavelength channels. While the limbdarkening coefficients and planet-to-star radius ratio were fit independently in each spectroscopic channel,  we  used  the  best-fitting  value  of  the  planet's  impact  parameter,  semi-major  axis,  and transit time from a white light curve fit as a fixed value in the wavelength-dependent fits. For the systematics model, we assumed a linear trend in time for each wavelength channel, fitting for both  \nthe slope and y-intercept. Last, we fit a single-point scatter to each light curve, which illustrates the level of additional noise required for our joint model to reach a reduced chi-squared of unity. The white light curve residuals have an RMS of 3013 ppm and the spectroscopic light curves above 3 µm have a median RMS of 5779 ppm. Similar to the reduction shown in Figure 1, both pipelines reach near photon noise. The Eureka! and ExoTEP transmission spectra appear nearly identical; therefore, only one (Eureka!) is shown in Figure 2.", 'Tiberius pipeline': "We built  upon  the  pipeline  developed  for  the  analysis  of  LRG-BEASTS  data 21,57,58 to provide an independent reduction of the data. We began with the outputs of the JWST Calibration Stage 1 pipeline with the jump step correction turned off. We created bad-pixel and cosmic-ray masks  by  identifying  5σ  outliers  in  running  medians  operating  along  pixel  rows  and  along individual pixels in time. Prior to tracing the spectra, we interpolated each column of the detector onto a finer grid, 10x the initial spatial resolution, in order to improve the extraction of flux at the sub-pixel level. We used a 4th-order polynomial to trace the spectra and a 4-pixel-wide aperture. To remove the 1/f-noise, we fit a linear polynomial to 21 background pixels along each column in the  cross-dispersion  direction.  Next,  to  correct  for  shifts  in  the  dispersion  direction,  we  crosscorrelated each stellar spectrum with the first spectrum of the observation to account for very small (0.003 - 0.005) sub-pixel shifts. Our white light curve spans a wavelength range of 0.518-5.348 μm after masking saturated pixels, and our 147 spectroscopic light curves used 3-pixel-wide bins across this same wavelength range. We masked frames 20751-20765 due to a high gain antenna move that led to increased noise in the light curves.  \nWe fit our light curves with a combination of a quadratically limb-darkened transit model (through batman 56 ) with a linear-in-time polynomial. We began by fitting the white light curve to derive the system parameters: inclination, i , time of mid-transit, T C , the semi-major axis scaled to the stellar radius, a/R * , and the linear limb darkening coefficient, u 1 . We placed wide boundaries on the parameter values only to prevent unphysical values. In practice, the parameter values did not  get  close  to  the  boundaries.  We  fixed  the  planet's  orbital  period  to  4.0552941  days  and eccentricity  to  0  from  Ref  [22].  We  fixed  the  quadratic  coefficient, u 2 ,  to  theoretical  values determined by Exo-TiC-LD 59,60 with 3D stellar models 61 ,  and fit for u 1 .  We used a LevenbergMarquardt  algorithm  to  fit  our  light  curves,  rescaled  our  photometric  uncertainties  to  give  a reduced 𝜒 2 =1 for our best-fit model and then re-ran the fits. For the spectroscopic light curves, the system parameters ( i, T C , a/R * ) were held fixed to the best-fit values found from the white light curve. The white light curve residuals had an RMS of 2761 ppm and the spectroscopic light curve residuals had a median RMS of 6731 ppm. In both cases, the variance of the residuals scales upon binning as expected for Poisson noise.", 'FIREFLy pipeline': 'We also reduced the data using the Fast InfraRed Exoplanet Fitting Lyghtcurve (FIREFLy) reduction  routines 38 . These  routines  utilize  the  JWST  Calibration  pipeline  with  custom  \nmodifications. This pipeline has been used to successfully analyze the JWST transit observations of HAT-P-14b that were obtained during commissioning with NIRSpec G395 37 . We removed 1/fnoise (see Ref [36]) at the group level, as the 1/f-noise changes from group to group. We also skipped the jump step and instead flagged and removed cosmic rays, bad pixels, hot pixels, and other outliers using median filtering of the data both spatially and in time, flagging pixels using a 5σ outlier threshold algorithm. The time series of 2D spectra were aligned using cross-correlation and interpolation, with the time series spectra exhibiting an RMS jitter of 0.005 pixels in the Xaxis direction and 0.0026 pixels in the Y-axis direction. We found a small inverse ramp in the light curves, which settled down after the first 2000 exposures, which we discarded. We fit the light curves  with  the  batman 56 transit  model  along  with  a  linear  baseline  and  a  second-order  jitter detrending polynomial of X and Y detector position as described by Ref [38], which are present in the  spectrophotometry  at  the  53+/-2  ppm  level  in  the  x-direction  and  140+/-3  ppm  in  the  ydirection. We applied a fixed quadratic limb darkening law using the 3D models 61 computed using the methods of Ref [62] from ExoTiC-LD 59,60 .  In  fitting the 3 to 5.5 μm white light curve, we allowed the semi-major axis in units of stellar radii a/R* , inclination i , and central transit time T 0 to  freely  vary  along  with  the  transit  depth  and  systematics  model.  We  used  the  Markov  chain Monte Carlo sampling routine EMCEE 63 to find the best-fit parameters and measure the posterior distribution. We find the 3-5.5 μm white light curve has a transit depth of 2.1368+/-0.0014% and achieves 808 ppm scatter in the residuals. This is within 6% of the expected noise limit of 758 ppm as calculated by the JWST Calibration pipeline, with the scatter of the residuals decreasing to below 40 ppm upon binning with no detectable red noise. We fit each spectroscopic light curve shown in Figure 2 with the same astrophysical and systematic models as the white light curve, except fixing the system parameters ( a/R * , i , T 0 ). The transmission spectral light-curve residuals for each bin are typically within 5% of pipeline error or better, also with no detectable red noise.', 'Data-Model Comparison': 'We compared the extracted transmission spectral data to a suite of 1D self-consistent radiativeconvective-thermochemical equilibrium model atmospheres (see e.g., Refs [64, 65] for a general description of such models) described below. In short, all models are able to fit the 3-5.5 μm spectra consistently (with 𝜒 2 /𝑁 "#$# < 1.4) with a 10x solar metal enrichment and varying grey cloud opacity for their single best estimate. Comparisons of the model fits from each grid are shown in ED Figure 1. For additional parameters within the grid (e.g., C/O and heat redistribution), there  is  some  discrepancy  between  each  model  grid\'s  single  best  estimate  values.  Additional Bayesian  analyses  are  needed  to  rigorously  quantify  confidence  intervals  on  atmospheric properties of interest, which is beyond the scope of this work. Future works will focus on modeling that includes the effects of disequilibrium chemistry, aerosol microphysics, and three-dimensional circulation effects. We assumed the following parameters in the modeling: stellar T eff = 5512 K, stellar radius = 0.932 R Sun , planet mass = 0.281 M Jup , planet radius = 1.279 R Jup , and planet orbital semi-major axis = 0.04828 AU.  \nED Figure 1: Comparison of transmission spectrum modeling results from different codes for  WASP-39b. Despite  different  radiative-convective  equilibrium  and  chemical  solvers, treatments of clouds, grid spacing, and grid-fitting approaches, all four grids arrive at the same 10x solar metallicity point solution. Additionally, all four provide an acceptable fit to the data, with best fitting 𝜒 ! /𝑁 "#$# < 1.4.  \n<!-- image -->', 'ScCHIMERA': 'This  framework  was  first  described  in  Refs.  [66,  67],  with  the  most  recent  updates, methods, and opacity sources described in Ref. [68]. We compute the converged atmospheric structure (temperature-pressure and thermochemical equilibrium gas mixing ratio profiles) over a grid  of  atmospheric  metallicity  ([M/H],  where  brackets  indicate  log 10 enrichment  relative  to solar 43 )  spaced at 0.25 dex intervals between 0 and 2.25 (1 to 175 times solar) and carbon-tooxygen  ratio  (C/O)  at  values  0.2,  0.35,  0.55,  0.7,  0.75,  0.8.  We  assume  full  day-to-night temperature redistribution 69 as planets in this temperature regime are unlikely to possess strong day-to-night  temperature  contrast 70,71 .  We  then  compute  transmission  spectra 72,73 from  these converged  atmospheric  structures.  To  match  the  models  to  the  data,  the  DYNESTY 74 fitting routine  is  used  to  search  for  the  optimal  [M/H]  and  C/O  (via  nearest  neighbor)  while  also simultaneously adjusting the 1 bar planetary radius (which controls the absolute transit depth) and an opaque, grey, uniformly vertically distributed, cloud opacity ( 𝜅 %&" ). The optimal model resulting from this process is [M/H] = +1.0, C/O = 0.35, and log 10 𝜅 cld = -2.15 cm 2 /g. The metallicity and cloud opacity are primarily driven by the strength of the 4.3 µm CO 2 feature and lack of CH 4 absorption near 3.3 µm. This result is what is shown in the main text (see Figure 3), which also illustrates the relative contribution of the key opacity sources (H 2 O 75,76 , CO 77,78 , CO 2 79,80 , H 2 S 78,81 , and  CH 4 78,82 )  to  the  overall  spectral  shape.  ED  Figure  2  shows  the  atmospheric  structure (temperature profile and gas mixing ratio profiles) for this best fit model.  \nED Figure 2: Atmospheric structure arising from the best fit model . The thick red curve (and corresponding top x-axis) shows the resulting 1D radiative-convective equilibrium temperature profile. The dashed lines (and bottom x-axis) show the vertical gas mixing ratio profiles under the assumption of thermochemical equilibrium. These abundances, along with the absorption crosssections shown in the bottom panel of Figure 3, are what control the relative contributions of each gaseous opacity to the total transmission spectrum.  \n<!-- image -->', 'PICASO': 'The core 1D radiative-convective model is based upon the legacy \'Extrasolar Giant Planet (EGP)\'  code  described  in  Refs.  [69,  80,  83]  and  since  updated  and  modernized  within  the PICASO 84 framework described in Mukherjee et al., submitted (PICASO 3.0). The PICASO 3.0 model uses gaseous opacities created from the references listed in Ref [80]. The grid of PICASO models contains metallicity points at 0.1, 0.3, 1, 3, 10, 30, 50, and 100x solar; C/O at 0.23, 0.46, 0.69, and 0.92; and also assumes full day-night heat redistribution. The clouds are modeled using the  Virga 85 implementation  of  the  Eddysed 86 framework,  which  requires  a  vertical  mixing coefficient (constant with altitude; log 10 K zz = 5, 7, 9, and 11 [cgs units]) and a vertically-constant sedimentation parameter ( f sed = 0.6, 1, 3, 6, and 10), with optical/material properties for clouds thought to exist at WASP-39b\'s pressures and temperatures (Na 2 S, MnS, and MgSiO 3 ). The f sed parameter controls the vertical extent of the cloud, and K zz and  f sed together  control  the  mean droplet  sizes  with  altitude  in  the  atmosphere.  A  chi-square  grid  search  along  the  described dimensions  is  performed  to  identify  the  best  fit.  Within  this  grid,  the  nominal  best  fit ( 𝜒 2 /𝑁 "#$# =1.34) is 10x solar metallicity, a sub-solar C/O (0.23), with an extended large droplet  \ncloud ( f sed = 0.6, log 10 K zz = 9) that produces a grey continuum over these wavelengths, consistent with the ScCHIMERA results above.', 'ATMO': 'The ATMO radiative-convective-thermochemical equilibrium solver is described in Refs [87-90].  This  grid  consists  of  model  transmission  spectra  for  four  different  day-night  energy redistribution  factors  (0.25,  0.5,  0.75,  1.0,  where  0.5  is  \'full\',  and  1.0  is  \'dayside-only\'),  six metallicities (0.1, 1, 10, 50, 100, 200 times solar), six C/O ratios (0.35, 0.55, 0.70, 0.75, 1.0, 1.5), two haze factors (no haze and 10 times multi-gas Rayleigh Scattering) and four grey cloud factors (no cloud, 0.5, 1, and 5 times the strength of H 2 Rayleigh Scattering at 350 nm between 1 and 50 mbar pressure levels). Each model transmission spectrum from the grid is binned to the same resolution as that of the observations to compute 𝜒 2 , with a (wavelength-independent) transit depth offset as the free parameter. Within this grid, we find a best-fit model ( 𝜒 2 /𝑁 "#$# = 1.39) spectrum arising from a redistribution factor of 0.75 (slightly hotter than a full day-night redistribution would produce), a metallicity of 10x solar, a super-solar C/O ratio of 0.7, a haze factor of 10, and a cloud factor of 5.', 'PHOENIX': 'This  model  originates  from  the  PHOENIX  stellar  atmosphere  code 91 adapted  for exoplanets 92 with additional modeling and opacity updates described in Refs [93,94]. The model grid  is  computed  for  an  array  of  irradiation  temperatures  (920,  1020,  1120,  and  1220  K), metallicities (0.1, 1, 10, and 100x solar), C/O (0.3, 0.54, 0.7, and 1.0) , and includes a sampling of opaque, grey clouds at specified cloud-top pressures. The nominal best-fit model ( 𝜒 2 /𝑁 "#$# = 1.32) from this grid setup results in a 10x solar metallicity, subsolar C/O (0.3) atmosphere with a cloudtop pressure of 0.3 mbar.', 'Quantifying Feature Detection Significance': "We quantified the detection significance 95 of  CO 2 with the following steps: 1) the best-fit grid model without CO 2 (i.e., the 'no CO 2 ' black curve shown in Figure 3) is first subtracted from the data, leaving behind a strong residual feature due to CO 2 (ED Figure 3). The peak per-spectral-bin mean SNR of this residual feature is ~10σ. To utilize the full line/band shape we then fit the residual peak with (1) a four-parameter Gaussian model (centroid, amplitude, width, and vertical offset), shown as red curves in ED Figure 3, and (2) a 'no feature' constant using a nested sampling routine 74 . The Bayesian evidence between the Gaussian model and constant model were then used to compute a Bayes factor, B ,  and corresponding detection significance 96 .  For the CO 2 residual feature, ln( B ) is 340.5, which equates to a 26.2σ detection. From this analysis, we conclude that the CO 2 feature is robustly detected.  \nUpon inspecting Figures 2 and 3 in the main text, there appears to be a feature near 4.0 µm (just short of the major CO 2 feature). We repeated the same analysis as above, but instead compared the  \nBayesian evidence from a 2-component Gaussian model fit (to accommodate for both the CO 2 feature and the unknown absorber) to that of the single component Gaussian model fit above. Upon doing so, we find ln( B ) = 0.98 which equates to a 2σ significance. Restricting the prior range for the second Gaussian to be localized near the 4 µm feature boosts the significance to 2.3σ. Future analyses  will  focus  on  the  nature  of  this  feature  and  more  rigorous  quantification  via  nested Bayesian model comparison within atmospheric retrieval frameworks (e.g., Ref [34]).  \nED Figure 3: Assessment of the strength of spectral features for WASP-39b. Residual features (blue data points) after subtracting the continuum best model (black 'no CO 2 ' model curve in Figure 3). A best-fitting ensemble of a 2-component Gaussian model to both the CO 2 feature and the unknown absorber feature (~4 µm) is shown in red.  \n<!-- image -->", 'Data Availability': 'The data used in this paper are associated with JWST program ERS 1366 (observation #4) and are available from the Mikulski Archive for Space Telescopes (https://mast.stsci.edu). Science data processing version (SDP\\_VER) 2022\\_2a generated the uncalibrated data that we downloaded from MAST.  We  used  JWST  calibration  software  version  (CAL\\_VER)  1.5.3  with  modifications described in the text. We used calibration reference data from context (CRDS\\_CTX) 0916, except  \nas  noted  in  the  text.  All  the  data  and  models  presented  in  this  publication  can  be  found  at https://doi.10.5281/zenodo.6959427.', 'Code Availability': 'The codes used in this publication to extract, reduce and analyse the data are as follows; STScI JWST Calibration pipeline 37 (https://github.com/spacetelescope/jwst), tshirt 40 , Eureka! 39 (https://eurekadocs.readthedocs.io/en/latest/), Tiberius 21,56,57 , FIREFLy 38 . In addition, these made use of Exoplanet 51 (https://docs.exoplanet.codes/en/latest/),Pymc3 52 (https://docs.pymc.io/en/v3/index.html), ExoTEP 53-55 , Batman 56 (http://lkreidberg.github.io/batman/docs/html/index.html), ExoTiC-ISM 59 (https://github.com/Exo-TiC/ExoTiC-ISM), ExoTiC-LD 60 (https://exoticld.readthedocs.io/en/latest/), Emcee 63 (https://emcee.readthedocs.io/en/stable/), Dynesty 74 (https://dynesty.readthedocs.io/en/stable/index.html), and chromatic (https://zkbt.github.io/chromatic/), each of which use the standard python libraries scipy 97 , numpy 98 , astropy 99,100 , and matplotlib 101 . The atmospheric models used to fit the data can be found at PICASO 84 (https://natashabatalha.github.io/picaso/), Virga 85 (https://natashabatalha.github.io/virga/), ScCHIMERA 68 (https://github.com/mrline/CHIMERA), 87-90 93  \nATMO , and PHOENIX .', 'Methods References': "- 49. Himes, M. D. & Harrington, J. On the Dayside Atmosphere of WASP-12b. Astrophys. J. 931 , 86 (2022).\n- 50. Schlawin, E., et al. JWST Noise Floor. II. Systematic Error Sources in JWST NIRCam Time Series. Astron. J. 161 , 115 (2021).\n- 51. Foreman-Mackey, D., et al. A. exoplanet: Gradient-based probabilistic inference for exoplanet data & other astronomical time series. The Journal of Open Source Software 6 , 3285 (2021).\n- 52. Salvatier, J., Wiecki, T. V., & Fonnesbeck, C. PyMC3: Python probabilistic programming framework. Astrophysics Source Code Library ascl:1610.016 (2016).\n- 53. Benneke, B., et al. Spitzer Observations Confirm and Rescue the Habitable-zone SuperEarth K2-18b for Future Characterization. Astrophys. J. 834 , 187 (2017).\n- 54. Benneke, B., et al. Water Vapor and Clouds on the Habitable-zone Sub-Neptune Exoplanet K2-18b. Astrophys. J. Letters 887 , L14 (2019).\n- 55. Benneke, B., et al. A sub-Neptune exoplanet with a low-metallicity methane-depleted atmosphere and Mie-scattering clouds. Nat. Astron. 3 , 813 (2019).  \n- 56. Kreidberg, L. batman: BAsic Transit Model cAlculatioN in Python. Publ. Astron. Soc. Pac. 127 , 1161 (2015).\n- 57. Kirk, J., et al. LRG-BEASTS III: ground-based transmission spectrum of the gas giant orbiting the cool dwarf WASP-80. Mon. Not. R. Astron. Soc. 474 , 876 (2018).\n- 58. Kirk, J., et al. C. ACCESS and LRG-BEASTS: A Precise New Optical Transmission Spectrum of the Ultrahot Jupiter WASP-103b. Astronom. J. 162 , 34 (2021).\n- 59. Laginja, I., & Wakeford, H. ExoTiC-ISM: A Python package for marginalised exoplanet transit parameters across a grid of systematic instrument models. The Journal of Open Source Software 5 , 2281 (2020).\n- 60. Wakeford, H.R., & Grant, D., Exo-TiC/ExoTiC-LD: ExoTiC-LD v2.1 Zenodo Release, doi:10.5281/zenodo.6809899 (2022).\n- 61. Magic, Z., Chiavassa, A., Collet, R., & Asplund, M. The Stagger-grid: A grid of 3D stellar atmosphere models. IV. Limb darkening coefficients. Astron. & Astrophys. 573 , A90 (2015).\n- 62. Sing, D. K. Stellar limb-darkening coefficients for CoRot and Kepler. Astron. & Astrophys. 510 , A21 (2010).\n- 63. Foreman-Mackey, D., et al. emcee v3: A Python ensemble sampling toolkit for affineinvariant MCMC. The Journal of Open Source Software 4 , 1864 (2019).\n- 64. Marley, M. S., & Robinson, T. D. On the Cool Side: Modeling the Atmospheres of Brown Dwarfs and Giant Planets. Ann. Rev. Astron. & Astrophys. 53 , 279 (2015).\n- 65. Hubeny, I. Model atmospheres of sub-stellar mass objects. Mon. Not. R. Astron. Soc. 469 , 841-869 (2017).\n- 66. Piskorz, D., et al. Ground- and Space-based Detection of the Thermal Emission Spectrum of the Transiting Hot Jupiter KELT-2Ab. Astron. J. 156 , 133 (2018).\n- 67. Arcangeli, J., et al. H -Opacity and Water Dissociation in the Dayside Atmosphere of the Very Hot Gas Giant WASP-18b. Astrophys. J. Letters 855 , L30 (2018).\n- 68. Mansfield, M., et al. A unique hot Jupiter spectral sequence with evidence for compositional diversity. Nat. Astron. 5 , 1224-1232 (2021).\n- 69. Fortney, J. J., Marley, M. S., Lodders, K., Saumon, D., & Freedman, R. Comparative Planetary Atmospheres: Models of TrES-1 and HD 209458b. Astrophys. J. Letters 627 , L69-L72 (2005).  \n- 70. Perez-Becker, D., & Showman, A. P. Atmospheric Heat Redistribution on Hot Jupiters. Astrophys. J. 776 , 134 (2013).\n- 71. Zhang, X. Atmospheric regimes and trends on exoplanets and brown dwarfs. Research in Astronomy and Astrophysics 20 , 099 (2020).\n- 72. Mai, C., & Line, M. R. Exploring Exoplanet Cloud Assumptions in JWST Transmission Spectra. Astrophys. J. 883 , 144 (2019).\n- 73. Iyer, A. R., & Line, M. R. The Influence of Stellar Contamination on the Interpretation of Near-infrared Transmission Spectra of Sub-Neptune Worlds around M-dwarfs. Astrophys. J. 889 , 78 (2020).\n- 74. Speagle, J. S. DYNESTY: a dynamic nested sampling package for estimating Bayesian posteriors and evidences. Mon. Not. R. Astron. Soc. 493 , 3132 (2020).\n- 75. Polyansky, O. L., Kyuberis, A. A., Zobov, N. F., Tennyson, J., Yurchenko, S. N., & Lodi, L. ExoMol molecular line lists XXX: a complete high-accuracy line list for water. Mon. Not. R. Astron. Soc. 480 , 2597 (2018).\n- 76. Gharib-Nezhad, E., Iyer, A. R., Line, M. R., Freedman, R. S., Marley, M. S., & Batalha, N. E. EXOPLINES: Molecular Absorption Cross-section Database for Brown Dwarf and Giant Exoplanet Atmospheres. Astrophys. J. Supp. 254 , 34 (2021).\n- 77. Li, G., Gordon, I. E., Rothman, L. S., Tan, Y., Hu, S.-M., Kassi, S., Campargue, A., & Medvedev, E. S. Rovibrational Line Lists for Nine Isotopologues of the CO Molecule in the X 1 Σ + Ground Electronic State. Astrophys. J. Supp. 216 , 15 (2015).\n- 78. Grimm, S. L., et al. HELIOS-K 2.0 Opacity Calculator and Open-source Opacity Database for Exoplanetary Atmospheres. Astrophys. J. Supp. 253 , 30 (2021).\n- 79. Huang, X., Gamache, R.R, Freedman, R.S, Schwenke, D.W., & Lee, T.J. Reliable infrared line lists for 13 CO2 isotopologues up to E'=18,000cm-1 and 1500K, with line shape parameters, Jour. Quant. Spect. Rad. Trans. 147 , 134 (2014)\n- 80. Marley, M. S., et al. The Sonora Brown Dwarf Atmosphere and Evolution Models. I. Model Description and Application to Cloudless Atmospheres in Rainout Chemical Equilibrium. Astrophys. J. 920 , 85 (2021).\n- 81. Azzam, A. A. A., Tennyson, J., Yurchenko, S. N., & Naumenko, O. V. ExoMol molecular line lists - XVI. The rotation-vibration spectrum of hot H 2 S. Mon. Not. R. Astron. Soc. 460 , 4063 (2016).  \n- 82. Hargreaves, R. J., Gordon, I. E., Rey, M., Nikitin, A. V., Tyuterev, V. G., Kochanov, R. V., & Rothman, L. S. An Accurate, Extensive, and Practical Line List of Methane for the HITEMP Database. Astrophys. J. Supp. 247 , 55 (2020).\n- 83. Marley, M.S., & McKay, C. P. Thermal Structure of Uranus' Atmosphere. Icarus . 138 , 2 (1999).\n- 84. Batalha, N. E., Marley, M. S., Lewis, N. K., & Fortney, J. J. Exoplanet Reflected-light Spectroscopy with PICASO. Astrophys. J. 878 , 70 (2019).\n- 85. Rooney, C. M., Batalha, N. E., Gao, P., & Marley, M. S. A New Sedimentation Model for Greater Cloud Diversity in Giant Exoplanets and Brown Dwarfs. Astrophys. J. 925 , 33 (2022).\n- 86. Ackerman, A. S., & Marley, M. S. Precipitating Condensation Clouds in Substellar Atmospheres. Astrophys. J. 556 , 872 (2001).\n- 87. Tremblin, P., et al. Fingering Convection and Cloudless Models for Cool Brown Dwarf Atmospheres. Astrophys. J. 804 , L17 (2015).\n- 88. Drummond, B., et al. The effects of consistent chemical kinetics calculations on the pressure-temperature profiles and emission spectra of hot Jupiters. Astron. & Astrophys. 594 , A69 (2016).\n- 89. Goyal, J. M., Mayne, N., Sing, D. K., Drummond, B., Tremblin, P., Amundsen, D. S., Evans, T., Carter, A. L., Spake, J., Baraffe, I., Nikolov, N., Manners, J., Chabrier, G., & Hebrard, E. A library of ATMO forward model transmission spectra for hot Jupiter exoplanets. Month. Not. Roy. Astron. Soc. 474 , 5158 (2018).\n- 90. Goyal, J. M., et al. A library of self-consistent simulated exoplanet atmospheres. Month. Not. Roy. Astron. Soc. 498 , 4680 (2020).\n- 91. Hauschildt, P. H., Allard, F., & Baron, E. The NextGen Model Atmosphere Grid for 3000<=T<=10,000 K. Astrophys. J. 512 , 377 (1999).\n- 92. Barman, T. S., Hauschildt, P. H., & Allard, F. Irradiated Planets. Astrophys. J. 556 , 885 (2001).\n- 93. Lothringer, J. D., & Barman, T. The PHOENIX Exoplanet Retrieval Algorithm and Using H -Opacity as a Probe in Ultrahot Jupiters. Astron. J. 159 , 289 (2020).\n- 94. Lothringer, J. D., et al. New Window into Planet Formation and Migration: Refractoryto-Volatile Elemental Ratios in Ultra-hot Jupiters. Astrophys. J. 914 , 12 (2021).  \n- 95. Gregory, P. C. Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with 'Mathematica' Support. Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with 'Mathematica' Support. Edited by P. C. Gregory. ISBN 0 521 84150 X (hardback); QA279.5.G74 2005 519.5'42 -- dc22; 200445930. Published by Cambridge University Press (2005).\n- 96. Trotta, R. Bayes in the sky: Bayesian inference and model selection in cosmology. Contemporary Physics 49 , 71 (2008).\n- 97. Virtanen, P., et al., SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods 17, 261 (2020).\n- 98. Harris, C. R., et al., Array programming with NumPy. Nature 585, 357 (2020).\n- 99. Astropy Collaboration, et al., Astropy: A community Python package for astronomy. Astronomy and Astrophysics 558, A33 (2013).\n- 100. Astropy Collaboration, et al., The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package. The Astronomical Journal 156, 123 (2018).\n- 101. Hunter, J. D. Matplotlib: A 2D Graphics Environment. Computing in Science and Engineering 9, 90 (2007).  \nAcknowledgements This work is based on observations made with the NASA/ESA/CSA James Webb Space Telescope. The data were obtained from the Mikulski Archive for Space Telescopes at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-03127 for JWST. These observations are associated with program JWST-ERS-01366. Support for program JWST-ERS-01366 was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-03127. The results reported herein benefited during the design phase from collaborations and/or information exchange within NASA's Nexus for Exoplanet System Science (NExSS) research coordination network sponsored by NASA's Science Mission Directorate.  \nAuthor contributions All members of the JWST Transiting Exoplanet Community Early Release Science Team played a significant role in one or more of the following: development of the original proposal, management of the project, definition of the target list and observation plan, analysis of the data, theoretical modeling, and preparation of this manuscript. Some specific contributions are listed as follows. NMB, JLB, and KBS provided overall program leadership and management. DS, EK, HW, IC, JLB, KBS, LK, MLM, MRL, NMB, VP, and ZBT made significant contributions to  \nthe design of the program. KBS generated the observing plan with input from the team. EvSc, NE, and TGB provided instrument expertise. BB, EK, HW, IC, JLB, LK, MLM, MRL, NMB, and ZBT led or co-led working groups and/or contributed to significant strategic planning efforts like the design and implementation of the pre-launch Data Challenges. AC, DS, EvSc, NE, NG, TGP, VP generated simulated data for pre-launch testing of methods. AC, AF, CP, EA, EvSc, JaK, LA, TJB, and ZR contributed to the development of data analysis pipelines and/or provided the data analysis products  used  in  this  analysis.  Reduced  the  data,  modeled  the  light  curves,  and  produced  the planetary spectrum. JG, JoL, KO, MRL, NEB, SaMu, and SEM generated theoretical model grids for comparison with data. AC, DS, EvSc, HW, JaK, JF, JG, JLB, JoL, KBS, KO, MRL, NEB, NMB, SaMu, SEM, ZBT, and ZR contributed significantly to the writing of this manuscript.  EA, EvSc, MRL, and ZBT generated figures for this manuscript.  \nCompeting interests The authors declare no competing interests.", 'Additional information none Supplementary information none': "Correspondence  and  requests  for  materials should  be  addressed  to  Natalie  Batalha (Natalie.Batalha@ucsc.edu).  \nThe JWST Transiting Exoplanet Community Early Release Science Team:  \nEva-Maria Ahrer 1,2 , Lili Alderson 3 , Natalie M. Batalha 4 , Natasha E . Batalha 5 , Jacob L. Bean 6 , Thomas G. Beatty 7 , Taylor J. Bell 8 , Bj ö rn Benneke 9 , Zachory K. Berta-Thompson 10 , Aarynn L. Carter 4 , Ian J. M. Crossfield 11 , N é stor Espinoza 12,13 , Adina D. Feinstein 6,14 , Jonathan J. Fortney 4 , Neale P. Gibson 15 , Jayesh M. Goyal 16 , Eliza M.-R. Kempton 17 , James Kirk 18 , Laura Kreidberg 19 , Mercedes L ó pez-Morales 18 , Michael R. Line 20 , Joshua D. Lothringer 21 , Sarah E. Moran 22 , Sagnick Mukherjee 4 , Kazumasa Ohno 4 , Vivien Parmentier 23,24 , Caroline Piaulet 9 , Zafar Rustamkulov 25 , Everett Schlawin 26 , David K. Sing 25,13 , Kevin B. Stevenson 26 , Hannah R. Wakeford 3 , Natalie H. Allen 13,14 , Stephan M. Birkmann 27 , Jonathan Brande 11 , Nicolas Crouzet 28 , Patricio E. Cubillos 29,30 , Mario Damiano 31 , Jean-Michel Désert 32 , Peter Gao 33 , Joseph Harrington 34 , Renyu Hu 31,35 , Sarah Kendrew 27 , Heather A. Knutson 35 , Pierre-Olivier Lagage 36 , J é r é my Leconte 37 , Monika Lendl 38 , Ryan J. MacDonald 39 , E. M. May 26 , Yamila Miguel 28,40 , Karan Molaverdikhani 41,42,19 , Julianne I. Moses 43 , Catriona Anne Murray 10 , Molly Nehring 10 , Nikolay K. Nikolov 12 , D. J. M. Petit dit de la Roche 38 , Michael Radica 9 , Pierre-Alexis Roy 9 , Keivan G. Stassun 44 , Jake Taylor 9 , William C. Waalke s 10 , Patcharapol Wachiraphan 10 , Luis Welbanks 20,45 , Peter J. Wheatley 2,1 , Keshav Aggarwal 46 , Munazza K. Alam 33 , Agnibha Banerjee 47 , Joanna K. Barstow 47 , Jasmina Blecic 48 , S.L. Casewell 49 , Quentin Changeat 50 , K. L. Chubb 51 , Knicole D. Col ó n 52 , Louis-Philippe Coulombe 9 , Tansu Daylan 53,54 , Miguel de ValBorro 55 , Leen Decin 56 , Leonardo A. Dos Santos 12 , Laura Flagg 39 , Kevin France 57 , Guangwei  \nFu 17,13 , A. Garc í a Mu ñ oz 36 , John E. Gizis 58 , Ana Glidden 59,60 , David Grant 3 , Kevin Heng 61 , Thomas Henning 19 , Yu-Cian Hong 39 , Julie Inglis 35 , Nicolas Iro 62 , Tiffany Kataria 31 , Thaddeus D. Komacek 17 , Jessica E. Krick 63 , Elspeth K.H. Lee 64 , Nikole K. Lewis 39 , J orge Lillo-Box 65 , Jacob Lustig-Yaeger 26 , Luigi Mancini 66,19,67 , Avi M. Mandell 52 , Megan Mansfield 26,45 , Mark S. Marley 22 , Thomas Mikal-Evans 19 , Giuseppe Morello 68,69,70 , Matthew C. Nixon 71 , Kevin Ortiz Ceballos 18 , Anjali A. A. Piette 33 , Diana Powell 18 , Benjamin V. Rackham 59,60,72 , Lakeisha RamosRosado 13 , Emily Rauscher 73 , Seth Redfield 74 , Laura K. Rogers 71 , Michael T. Roman 49,75 , Gael M. Roudier 31 , Nicholas Scarsdale 4 , Evgenya L. Shkolnik 20 , John Southworth 76 , Jessica J. Spake 35 , Maria E Steinrueck 19 , Xianyu Tan 23 , Johanna K. Teske 33 , Pascal Tremblin 77 , Shang-Min Tsai 23 , Gregory S. Tucker 78 , Jake D. Turner 39,46 , Jeff A. Valenti 12 , Olivia Venot 79 , Ingo P. Waldmann 50 , Nicole L. Wallack 35 , Xi Zhang 80 , Sebastian Zieba 19,28  \n- 1 Department of Physics, University of Warwick, Coventry, UK\n- 2 Centre for Exoplanets and Habitability, University of Warwick, Coventry, UK  \n- 4 Department of Astronomy and Astrophysics, University of California, Santa Cruz, Santa Cruz, CA , USA\n- 5 NASA Ames Research Center, Moffett Field, CA, USA\n- 6 Department of Astronomy & Astrophysics, University of Chicago, Chicago, IL, USA  \n- 10 Department of Astrophysical and Planetary Sciences, University of Colorado, Boulder, CO, USA\n- 11 Department of Physics & Astronomy, University of Kansas, Lawrence, KS, USA  \n- 25 Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA\n- 26 Steward Observatory, University of Arizona, Tucson, AZ, USA\n- 27 Johns Hopkins APL, Laurel, MD, USA\n- 28 European Space Agency, Space Telescope Science Institute, Baltimore , MD, USA\n- 29 Leiden Observatory, University of Leiden, Leiden, The Netherlands\n- 30 INAF - Osservatorio Astrofisico di Torino, Pino Torinese, Italy\n- 31 Space Research Institute, Austrian Academy of Sciences, Graz, Austria\n- 32 Astrophysics Section, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA\n- 33 Anton Pannekoek Institute for Astronomy, University of Amsterdam, Amsterdam, The Netherlands\n- 34 Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC, USA\n- 35 Planetary Sciences Group, Department of Physics and Florida Space Institute, University of Central Florida, Orlando, Florida, USA\n- 36 Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA\n- 37 Universi té Paris-Saclay, Universit é Paris Cit é , Gif-sur-Yvette, France\n- 38 Laboratoire d' A strophysique de Bordeaux, Univ ersité de Bordeaux, Pessac, France\n- 39 D é partement d'Astronomie, Universit é de Gen è ve, Sauverny, Switzerland"}