Quantifying the differences in transmission and emission spectra for hot irradiated gaseous exoplanet atmospheres: A comparison of 1D and 3D modeling using JWST

TL;DR

The paper assesses how 1D and 3D atmospheric models for a hot Jupiter differ in their transmission and emission spectra, using HD 189733b as a case study and JWST capabilities. It couples 1D RCE and 3D GCM frameworks with both equilibrium and disequilibrium chemistry, and computes spectra via petitRADTRANS and gCMCRT, followed by PANDEXO-based JWST noise simulations to estimate observability. Key findings show that 3D models generally yield weaker spectral features and that JWST can distinguish 1D from 3D spectra for major molecular bands; extended upper-atmosphere extensions further modulate spectral contrasts and SNR, with CH$_4$, CO/CO$_2$, H$_2$O, and NH$_3$ acting as primary discriminants. Overall, the results underscore the necessity of 3D modeling for accurate interpretation of exoplanet atmospheres and provide practical SNR-based observing-time guidance for distinguishing model dimensionality with JWST.

Abstract

Modeling the atmospheres of exoplanets is fundamental to understanding their atmospheric physics and chemical processes. While one-dimensional (1D) atmospheric models with 1D radiative transfer (RT) have been widely used, advances in three-dimensional (3D) general circulation models (GCMs) and 3D RT methods now allow quantitative comparisons of these approaches. With the precision and sensitivity of JWST, such differences can be observationally tested. This study investigates the spectral variations produced by 1D and 3D models and estimates the JWST observing time or number of transits needed to distinguish them. Using HD 189733b as a case study, three sets of simulations were performed: 1D atmospheric models with 1D RT and 3D GCM models coupled with both 1D and 3D RT. An inherent limitation of our study is that the temperature-pressure (T-P) profiles derived from the 3D GCM extend only to the high-pressure regions. The simulations incorporated both equilibrium and disequilibrium chemistry. Significant spectral discrepancies were found, with 3D models generally showing weaker features. Using a JWST noise simulator, the signal-to-noise ratio (SNR) for detecting these differences was calculated. For transmission spectra, the SNR ranged from 2.04-7.68 (equilibrium) and 1.66-7.04 (disequilibrium), while for emission spectra it ranged from 5.90-34.52 (equilibrium) and 7.11-36.93 (disequilibrium). To test the limitations of the 3D GCM, we extended the atmosphere to lower pressures using an isothermal T-P profile and found wavelength-dependent variations in both the spectra and the SNR. These results show that JWST can distinguish 1D from 3D model spectra for major molecular features, underscoring the importance of 3D modeling in interpreting exoplanetary atmospheres.

Figures (12)

• Figure 1: Flowchart illustrating our methodological pipeline, which couples atmospheric, chemical, and radiative transfer codes for each type of simulation. The color codes are as follows: A1 and A2 in orange, B1 and B2 in red, transmission cases C1 and C2 in blue, and emission cases C1 and C2 in light purple. These cases are discussed in Table \ref{['table:modelcodes']}.
• Figure 2: Comparison of T-P profiles at different latitudes and longitudes of the planet. The subplot shows the color coding of the T-P profiles with respect to latitude and longitude. The point (0,0) corresponds to the substellar point.
• Figure 3: Horizontal distribution of temperature on the planet at a pressure level of 0.1 bar. Arrows represent the wind speed and direction at every 10° latitude and longitude point, relative to the THOR grid system.
• Figure 4: Same as Figure \ref{['fig:1mbar']}, but at a pressure level of 30 bar.
• Figure 5: This figure compares the 1D radiative-convective equilibrium profile (solid blue) with the median globally averaged 3D temperature-pressure (T-P) profile (solid orange), the median east terminator average (solid green), the median west terminator average (solid red), and the median dayside average (solid purple). The dashed lines represent the eddy diffusion coefficient ($K_{ZZ}$) for each respective case.