Effects of a fully 3D atmospheric structure on exoplanet transmission spectra: retrieval biases due to day-night temperature gradients

Abstract

Transmission spectroscopy provides us with information on the atmospheric properties at the limb, which is often intuitively assumed to be a narrow annulus around the planet. Consequently, studies have focused on the effect of atmospheric horizontal heterogeneities along the limb. Here we demonstrate that the region probed in transmission -- the limb -- extends significantly toward the day and night sides of the planet. We show that the strong day-night thermal and compositional gradients expected on synchronous exoplanets create sufficient heterogeneities across the limb to result in important systematic effects on the spectrum and bias its interpretation. To quantify these effects, we developed a 3D radiative transfer model able to generate transmission spectra of atmospheres based on 3D atmospheric structures. We first produce synthetic JWST observations from a simulation of GJ 1214b and demonstrate the necessity of a real 3D approach to model data for such precise observatories. Second, we investigate how day-night temperature gradients cause a systematic bias in retrieval analysis performed with 1D forward models. For that purpose we synthesize a large set of forward spectra for prototypical HD 209458 b and GJ 1214 b type planets varying the temperatures of the day and night sides as well as the width of the transition region. We then perform typical retrieval analyses and compare the retrieved parameters to the ground truth of the input model. This study reveals systematic biases on the retrieved temperature (found to be higher than the terminator temperature) and abundances. This is due to the fact that the hotter dayside is more extended vertically and screens the nightside. These biases are difficult to detect as the 1D profiles used in the retrieval are found to provide an excellent match to the observed spectra. This needs to be kept in mind when interpreting real data.