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The potential habitability of known exoplanets is often categorized by a nominal equilibrium temperature assuming a Bond albedo of either ∼0.3, similar to Earth, or 0. As an indicator of habitability, this leaves much to be desired, because albedos of other planets can be very different, and because surface temperature exceeds equilibrium temperature due to the atmospheric greenhouse effect. We use an ensemble of general circulation model simulations to show that for a range of habitable planets, much of the variability of Bond albedo, equilibrium temperature and even surface temperature can be predicted with useful accuracy from incident stellar flux and stellar temperature, two known parameters for every confirmed exoplanet. Earth's Bond albedo is near the minimum possible for habitable planets orbiting G stars, because of increasing contributions from clouds and sea ice/snow at higher and lower instellations, respectively. For habitable M star planets, Bond albedo is usually lower than Earth's because of near-IR H 2 O absorption, except at high instellation where clouds are important. We apply relationships derived from this behavior to several known exoplanets to derive zeroth-order estimates of their potential habitability. More expansive multivariate statistical models that include currently non-observable parameters show that greenhouse gas variations produce significant variance in albedo and surface temperature, while increasing length of day and land fraction decrease surface temperature; insights for other parameters are limited by our sampling. We discuss how emerging information from global climate models might resolve some degeneracies and help focus scarce observing resources on the most promising planets.

The planetary mass and radius sensitivity of exoplanet discovery capabilities has reached into the terrestrial regime. The focus of such investigations is to search within the Habitable Zone where a modern Earth-like atmosphere may be a viable comparison. However, the detection bias of the transit and radial velocity methods lies close to the host star where the received flux at the planet may push the atmosphere into a runaway greenhouse state. One such exoplanet discovery, Kepler-1649b, receives a similar flux from its star as modern Venus does from the Sun, and so was categorized as a possible exoVenus. Here we discuss the planetary parameters of Kepler-1649b with relation to Venus to establish its potential as a Venus analog. We utilize the general circulation model ROCKE-3D to simulate the evolution of the surface temperature of Kepler-1649b under various assumptions, including relative atmospheric abundances. We show that in all our simulations the atmospheric model rapidly diverges from temperate surface conditions towards a runaway greenhouse with rapidly escalating surface temperatures. We calculate transmission spectra for the evolved atmosphere and discuss these spectra within the context of the James Webb Space Telescope (JWST) Near-Infrared Spectrograph (NIRSpec) capabilities. We thus demonstrate the detectability of the key atmospheric signatures of possible runaway greenhouse transition states and outline the future prospects of characterizing potential Venus analogs.

We demonstrate the effectiveness of a relatively straightforward analysis of the complex 3D Fourier transform of galaxy coordinates derived from redshift surveys. Numerical demonstrations of this approach are carried out on a volume-limited sample of the Sloan Digital Sky Survey redshift survey. The direct unbinned transform yields a complex 3D data cube quite similar to that from the Fast Fourier Transform (FFT) of finely binned galaxy positions. In both cases deconvolution of the sampling window function yields estimates of the true transform. Simple power spectrum estimates from these transforms are roughly consistent with those using more elaborate methods. The complex Fourier transform characterizes spatial distributional properties beyond the power spectrum in a manner different from (and we argue is more easily interpreted than) the conventional multi-point hierarchy. We identify some threads of modern large scale inference methodology that will presumably yield detections in new wider and deeper surveys.