Coupled Radiative-Dynamical GCM Simulations of Hot Jupiters

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The stellar flux incident on hot Jupiters is expected to drive an atmospheric circulation that shapes the day-night temperature difference, infrared lightcurve, spectra, albedo, and atmospheric composition. Recent Spitzer lightcurve observations show that on some hot Jupiters, including HD189733b and HD209458b, the circulation efficiently homogenizes the temperature, whereas other planets such as Ups And b may exhibit large day-night temperature differences. Moreover, Spitzer infrared photometry and spectra constrain the vertical temperature structure in the atmosphere, which may deviate strongly from radiative equilibrium. Several groups have investigated the atmospheric circulation with a variety of 2D and 3D models (Showman and Guillot 2002; Cho et al. 2003, 2006; Langton and Laughlin 2007; Cooper and Showman 2005, 2006; Dobbs-Dixon and Lin 2007). However, all of these models drive the dynamics with simplified heating/cooling schemes that preclude robust predictions for the 3D temperature patterns, spectra, and lightcurves. Here, we present the first simulations of cloud-free hot Jupiters from a 3D general circulation model (GCM) that couples the atmospheric dynamics to a realistic representation of radiative transfer. For the dynamics, we adopt the MITgcm, which is a state-of-the-art circulation model that solves the 3D primitive equations of meteorology. Our radiation model is that of Marley and McKay (1999), which solves the two-stream radiative-transfer equations using the correlated-k method for the opacities; this radiative-transfer model has been extensively applied to brown dwarfs and extrasolar planets by Marley, Fortney, and collaborators. By coupling these components, the GCM provides a much more realistic representation of the radiative-dynamical interaction than possible with previous models. Here, we will present simulations of HD209458b and HD189733b, compare the predicted temperatures, spectra, and lightcurves with existing data, and make predictions for future observations.

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