Spatially Resolved Modeling of Optical Albedos for a Sample of Six Hot Jupiters

Abstract

Optical secondary eclipse measurements made by Kepler reveal a diverse set of geometric albedos for hot Jupiters with equilibrium temperatures between 1550 − 1700 K. The presence or absence of high altitude condensates, such as Mg 2 SiO 4 , Fe, Al 2 O 3 , and TiO 2 , can significantly alter optical albedos, but these clouds are expected to be confined to localized regions in the atmospheres of these tidally locked planets. Here, we present 3D general circulation models and corresponding cloud and albedo maps for six hot Jupiters with measured optical albedos in this temperature range. We find that the observed optical albedos of K2-31b and K2-107b are best matched by either cloud free models or models with relatively compact cloud layers, while Kepler-8b and Kepler-17b's optical albedos can be matched by moderately extended (f sed = 0.1) parametric cloud models. HATS-11b has a high optical albedo, corresponding to models with bright Mg 2 SiO 4 clouds extending to very low pressures (f sed = 0.03). We are unable to reproduce Kepler-7b's high albedo, as our models predict that the dayside will be dominated by dark Al 2 O 3 clouds at most longitudes. We compare our parametric cloud model with a two-zone microphysical cloud model (CARMA). We find that even after accounting for the 3D thermal structure, no single cloud model can explain the full range of observed albedos within the sample. We conclude that a better knowledge of the vertical mixing profiles, cloud radiative feedback, cloud condensate properties, and atmospheric metallicities is needed in order to explain the unexpected diversity of albedos in this temperature range.

Constraints onTESSalbedos for five hot Jupiters

“…The geometric albedo upper limits which we derive in this work for WASP-18 b, WASP-36 b, WASP-43 b, WASP-50 b, and WASP-51 b all lie below 0.45. This is consistent with previous works which find that hot Jupiters typically have low albedos (e.g., Heng & Demory 2013;Esteves et al 2015;Mallonn et al 2019;Brandeker et al 2022), though higher optical albedos have also been measured in some cases (e.g., Esteves et al 2015;Niraula et al 2018;Wong et al 2020b;Adams et al 2021;Heng et al 2021). The geometric albedos shown in Fig.…”

Cloudy and Cloud-free Thermal Phase Curves with PICASO: Applications to WASP-43b

“…We expand this tool by adding a pipeline to map 3D GCM output onto a radiative transfer scheme that enables the computation of thermal phase curves, with the capability of including scattering by a diversity of cloud species. While PICASO has been previously used for 1D (Fraine et al 2021) and 3D reflected light (Adams et al 2021), and thermal emission spectroscopy in 1D (Tang et al 2021), the work presented here represents the first test of the 3D thermal component of the code. Here we focus on thermal phase curves, but our methodology can also be easily applied to phase-resolved observations in reflected light.…”

“…Virga calculates cloud particle size distribution and the effect of vertical mixing and sedimentation on cloud shapes and locations using the methodology described in Ackerman & Marley (2001) (see e.g., Morley et al 2012, for an application to brown dwarfs), for each condensate independently, and ignoring any microphysical interactions between clouds. In order to compute cloud profiles, Virga takes as input temperature and K zz profiles, estimated by assuming K zz = ω(z)L(z), where ω(z) is the globally averaged root-mean-square vertical velocity at a given pressure level, taken from the MITgcm, and the mixing length L(z) is approximated as the atmospheric scale height H(z) (Adams et al 2021). Limitations of this approach are discussed in Parmentier et al (2013), Zhang & Showman (2018), Komacek et al (2019), andMenou (2019).…”

“…For its phasedependent reflected light component, PICASO chooses 10 Gauss and 10 Chebyshev angles as a default (e.g. Adams et al 2021). In order to determine the default number of angles we ran simulations for angular resolutions of 10×10, 15×15 and 20×20.…”

“…Clouds can interact with the outgoing thermal emission, thus altering the dynamics, chemistry, and thermal structure of the atmosphere through radiative feedback (e.g., Heng While the formation and transport of clouds is certainly a coupled process between advection, radiation, and chemistry (Marley 2008), Venot et al (2020) highlight how heavily dependent the amplitude and spatial distribution of cloud heating is on the model used (e.g., Lee et al 2017;Roman & Rauscher 2017, 2019Roman et al 2021;Lines et al 2018Lines et al , 2019. The use of postprocessed clouds allows for greater flexibility and fast estimates of cloud properties, species, sedimentation parameters, and their effect on the thermal emission without the need to run computationally expensive coupled models (Venot et al 2020;Adams et al 2021). However, the lack of cloud radiative feedback might alter our conclusions about the best fitting cloudy models.…”