The ARCiS framework for Exoplanet Atmospheres: The Cloud Transport Model. (arXiv:1812.05053v1 [astro-ph.EP])
<a href="http://arxiv.org/find/astro-ph/1/au:+Ormel_C/0/1/0/all/0/1">Chris W. Ormel</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Min_M/0/1/0/all/0/1">Michiel Min</a>
Understanding of clouds is instrumental in interpreting current and future
spectroscopic observations of exoplanets. Modelling clouds consistently is
complex, since it involves many facets of chemistry, nucleation theory,
condensation physics, coagulation, and particle transport. We develop a simple
physical model for cloud formation and transport, efficient and versatile
enough that it can be used in modular fashion for parameter optimization
searches of exoplanet atmosphere spectra. The transport equations are
formulated in 1D, accounting for sedimentation and diffusion. The grain size is
obtained through a moment method. For simplicity, only one cloud species is
considered and the nucleation rate is parametrized. From the resulting physical
profiles we simulate transmission spectra covering the visual to mid-IR
wavelength range. We apply our models towards KCl clouds in the atmosphere of
GJ1214 b and towards MgSiO3 clouds of a canonical hot-Jupiter. We find that
larger cloud diffusivity $K_{zz}$ increases the thickness of the cloud, pushing
the $tau=1$ surface to a lower pressure layer higher in the atmosphere. A
larger nucleation rate also increases the cloud thickness while it suppresses
the grain size. Coagulation is most important at high nuclei injection rates
($dotSigma_n$) and low $K_{zz}$. We find that the investigated combinations
of $K_{zz}$ and $dotSigma_n$ greatly affect the transmission spectra in terms
of the slope at near-IR wavelength (a proxy for grain size), the molecular
features seen at ~1micr (which disappear for thick clouds, high in the
atmosphere), and the 10micr silicate feature, which becomes prominent for
small grains high in the atmosphere. The result of our hybrid approach — aimed
to provide a good balance between physical consistency and computational
efficiency — is ideal towards interpreting (future) spectroscopic observations
of exoplanets.
Understanding of clouds is instrumental in interpreting current and future
spectroscopic observations of exoplanets. Modelling clouds consistently is
complex, since it involves many facets of chemistry, nucleation theory,
condensation physics, coagulation, and particle transport. We develop a simple
physical model for cloud formation and transport, efficient and versatile
enough that it can be used in modular fashion for parameter optimization
searches of exoplanet atmosphere spectra. The transport equations are
formulated in 1D, accounting for sedimentation and diffusion. The grain size is
obtained through a moment method. For simplicity, only one cloud species is
considered and the nucleation rate is parametrized. From the resulting physical
profiles we simulate transmission spectra covering the visual to mid-IR
wavelength range. We apply our models towards KCl clouds in the atmosphere of
GJ1214 b and towards MgSiO3 clouds of a canonical hot-Jupiter. We find that
larger cloud diffusivity $K_{zz}$ increases the thickness of the cloud, pushing
the $tau=1$ surface to a lower pressure layer higher in the atmosphere. A
larger nucleation rate also increases the cloud thickness while it suppresses
the grain size. Coagulation is most important at high nuclei injection rates
($dotSigma_n$) and low $K_{zz}$. We find that the investigated combinations
of $K_{zz}$ and $dotSigma_n$ greatly affect the transmission spectra in terms
of the slope at near-IR wavelength (a proxy for grain size), the molecular
features seen at ~1micr (which disappear for thick clouds, high in the
atmosphere), and the 10micr silicate feature, which becomes prominent for
small grains high in the atmosphere. The result of our hybrid approach — aimed
to provide a good balance between physical consistency and computational
efficiency — is ideal towards interpreting (future) spectroscopic observations
of exoplanets.
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