Strong biases in retrieved atmospheric composition caused by day-night chemical heterogeneities

Abstract

Most planets currently amenable to transit spectroscopy are close enough to their host stars to exhibit a relatively strong day to night temperature gradient. For hot planets this leads to a chemical composition dichotomy between the two hemispheres. In the extreme case of ultra-hot Jupiters, some species, such as molecular hydrogen and water, are strongly dissociated on the day side while others, such as carbon monoxide, are not. However, most current retrieval algorithms rely on 1D forward models that are unable to reproduce this effect. We thus investigate how the 3D structure of the atmosphere biases the abundances retrieved using commonly used algorithms. We study the case of Wasp-121b as a prototypical ultra-hot Jupiter. We use the simulations of this planet performed with the Substellar and Planetary Atmospheric Radiation and Circulation global climate model and generate transmission spectra that fully account for the 3D structure of the atmosphere with Pytmosph3R. These spectra are then analyzed using the TauREx retrieval code. We find that the ultra-hot Jupiter transmission spectra exhibit muted H2O features that originate on the night side where the temperature, hence the scale-height, is smaller than on the day side. However, the spectral features of molecules present on the day side are boosted by both its high temperature and low mean molecular weight. As a result, the retrieved parameters are strongly biased compared to the ground truth. In particular the [CO]/[H2O] is overestimated by one to three orders of magnitude. This must be kept in mind when using the retrieval analysis to infer the C/O of a planet's atmosphere. We also discuss whether indicators can allow us to infer the 3D structure of an observed atmosphere. Finally, we show that Wide Field Camera 3 from Hubble Space Telescope transmission data of Wasp-121b are compatible with the day-night thermal and compositional dichotomy predicted by models.