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tokens: inhomogeneities provide a number of testable predictions, such as photometric or radial-velocity flickering of integrated starlight. Stellar brightness varies in a somewhat random fashion, as differently bright granular structures develop across the surface ( Cranmer et al. 2014 ; Ludwig 2006 ). The effects are greater for giant stars, where the small-number statistics of fewer granules across the star induces a greater variability, as observed ( Bastien et al. 2013 , 2016 ; Mathur et al. 2011 ) and as modeled ( Samadi et al. 2013a , b ). For the Sun, with 10 granules across its surface, each with a 20 intensity contrast, the amplitude of variability in integrated sunlight (assuming random variability among the granules) can be expected to be on the order of this contrast divided by the square root of the number of granules, i.e., , measurable with photometric instruments in space. Since the contrast is greater at short optical wavelengths (as expected for a given temperature contrast in the photosphere), also the amplitude of flickering is greater in the blue than in the red. In specific wavelength regions comprising numerous temperature-sensitive spectral lines, such as the -band, the photometric flickering may be expected to be greater but does not yet appear to have been either computed nor observed. An analogous variability in the disk-averaged radial velocity on a level of some m s is a concern in radial-velocity searches for low-mass exoplanets and for the confirmation of exoplanet candidates found from transits ( Cegla et al. 2013 , 2014 ; Dumusque et al. 2011a ). Again, the amplitude of flickering can be expected to increase at shorter wavelengths, where the granular contrast is greater. Further, the astrometric location of the stellar photocenter may wander and is detectable with space astrometry ( Chiavassa et al. 2011a ; Pasquato et al. 2011 ). Other effects include interpretation of stellar p-mode oscillations, where 3D models have to be applied for any more precise representation of stellar near-surface layers, detecting inconsistencies that may indicate which modeling parameters need adjustment ( Baldner Schou 2012 ; Kallinger et al. 2014 ; Piau et al. 2014 ; Schou 2015 ). Fig. 2 Top : build-up of synthetic Fe I lines at stellar disk center ( = cos.0) from a CO BOLD model of a dwarf star with approximate spectral type F7 V (250 K, log [cgs] = 4.5). Thin colored lines are spatial averages over the simulation area for each of 20 temporal snapshots; bold curves are the total averages. All five different line strengths are for 20 nm, eV. The convection patterns especially cause the weaker lines to become asymmetric and wavelength-shifted. Bottom : evolution of the continuum intensity during the simulation sequence for four different center-to-limb positions , 0.87, 0.59, 0.21. For off-center positions, slight differences exist among the four different azimuth angles (, /2, , 3/2 rad). Fig. 3 Spectral line changes across a