Work

A Comparison of One-dimensional and Three-dimensional Exoplanet Atmosphere Model Grids: ScCHIMERA and the SPARC/MiTgcm

Year: 2026

Type: article

Abstract: Inferring the properties of transiting exoplanet atmospheres relies on comparing models to spectroscopic observations. Atmosphere models, however, make a range of assumptions, from one-dimensional (1D, varying with altitude) radiative-convective equilibrium (RCE) to three-dimensional (3D) global circulation models (GCMs). The goal of this investigation is to determine the causes of differences in dayside thermal emission spectra resulting from 3D-GCMs (using SPARC/MITgcm ) and 1D-RCE models (using ScCHIMERA ). We conduct a one-to-one comparison of 1D-RCE models and 3D-GCMs with the same outgoing bolometric thermal flux over a grid of equilibrium temperatures, gravities, metallicities, and rotation periods. Each 1D-RCE model assumes heat redistribution in the planet’s atmosphere consistent with that in the corresponding 3D-GCM’s photosphere. Comparing corresponding models, the dayside average pressure–temperature (or PT) structures can be broken into four vertical regions, each influencing wavelength-dependent differences in their spectra. Furthermore, the dayside average 3D-GCM PTs for planets with T eq = 1400 K exhibit a temperature inversion, whereas corresponding 1D-RCE models do not. We find that spectral differences between 1D-RCE models and 3D-GCMs with the same parameters decrease for hotter planets because the spectral shapes more closely resemble blackbodies. To a lesser extent, spectral differences increase for planets with longer rotation periods because of smaller day–night temperature contrasts in the photosphere. Finally, we compare spectral differences to realistic observational uncertainties from JWST with the NIRISS SOSS, NIRSpec G395H, and MIRI long-resolution spectroscopy instrument modes. We find that 1D-RCE models and 3D-GCMs with the same parameters can produce dayside spectral differences larger than JWST’s uncertainty, potentially biasing data–model inferences.

Source: The Astrophysical Journal

DOI: 10.3847/1538-4357/ae2b6c

Authors: Lindsey S. Wiser, Alexander Roth, Vivien Parmentier, Michael R. Line