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The golden mean: how a balanced approach to cloud modelling powers atmospheric retrieval

Presentation #627.12 in the session Planetary Atmospheres - Theory.

Published onApr 03, 2024
The golden mean: how a balanced approach to cloud modelling powers atmospheric retrieval

With the launch of JWST, we are embracing an era of high-precision measurements of exoplanet atmospheres’ transmission and emission spectra. In interpreting these data, the presence of clouds plays a crucial role, as clouds obscures the spectral signature and their formation consumes vapor, changing the molecular inventory. Hence, understanding clouds is instrumental to properly characterize exoplanets. However, in retrieval methods, clouds are typically imposed manually through parameters such as the cloud deck pressure, the mixing ratio, sedimentation strength, etc, which is too simplistic. At the other extreme, fully consistent modeling of clouds, which involves following their nucleation and size distribution from first principles, is prohibitively slow. Here, we have developed a streamlined 1D cloud model that solves for the steady state cloud profile, following, but vastly improving on, Ormel & Min (2019). Our condensation model accommodates an arbitrary number of condensate species and operates synergistically in particle collisional growth and particle transport processes (i.e., particle settling and diffusion). A novel relaxation method ensures rapid and robust convergence under arbitrary conditions. The general design of our model enables computation of cloud structures in various planetary types, including hot-Jupiters, super-Earths and self-luminous planets. For hot-Jupiter planets, we find that cloud layers of MgSiO3, Fe and Al2O3 are formed top-down in the atmosphere, in line with the condensation sequence of these species. This result is consistent with more complex cloud models. Cloud particle radii increase with depth, reaching micron sizes in the lower regions of the cloud. Under the condition of efficient diffusion (high Kzz), the formation of clouds might dominate the planet’s transmission spectrum, suppressing vapor molecular lines while concurrently displaying a distinct 10-micron silicate feature. With the computational cost on the order of seconds on a regular PC, the cloud model can easily be embedded into the majority of existing atmosphere retrieval codes.

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