The Effect of Clouds as an Additional Opacity Source on the Inferred Metallicity of Giant Exoplanets

TL;DR

This study investigates how clouds affect the inferred metallicity of giant exoplanets by coupling atmospheric, interior, and evolution models, revealing that clouds can significantly alter metallicity estimates.

Contribution

It introduces a coupled modeling approach to quantify the impact of cloud opacity and depth on metallicity inference in irradiated giant exoplanets.

Findings

Deep-seated, optically thick clouds can increase inferred metallicity by up to 50%.

Optically thick, high clouds have negligible influence on metallicity estimates.

Cloud effects vary with planetary conditions, affecting thermal profiles and interior composition estimates.

Abstract

Atmospheres regulate the planetary heat loss and therefore influence planetary thermal evolution. Uncertainty in a giant planet's thermal state contributes to the uncertainty in the inferred abundance of heavy elements it contains. Within an analytic atmosphere model, we here investigate the influence that different cloud opacities and cloud depths can have on the metallicity of irradiated extrasolar gas giants, which is inferred from interior models. In this work, the link between inferred metallicity and assumed cloud properties is the thermal profile of atmosphere and interior. Therefore, we perform coupled atmosphere, interior, and evolution calculations. The atmosphere model includes clouds in a much simplified manner; it includes long-wave absorption but neglects shortwave scattering. Within that model, we show that optically thick, high clouds have negligible influence, whereas deep-seated, optically very thick clouds can lead to warmer deep tropospheres and therefore higher bulk heavy element mass estimates. For the young hot Jupiter WASP-10b, we find a possible enhancement in inferred metallicity of up to 10% due to possible silicate clouds at $\sim$0.3 bar. For WASP-39b, whose observationally derived metallicity is higher than predicted by cloudless models, we find an enhancement by at most 50%. However, further work on cloud properties and their self-consistent coupling to the atmospheric structure is needed in order to reduce uncertainties in the choice of model parameter values, in particular of cloud opacities.