Brown dwarfs—substellar bodies more massive than planets but not massive enough to initiate the sustained hydrogen fusion that powers self-luminous stars1,2—are born hot and slowly cool as they age. As they cool below about 2,300 kelvin, liquid or crystalline particles composed of calcium aluminates, silicates and iron condense into atmospheric ‘dust’3,4, which disappears at still cooler temperatures (around 1,300 kelvin)5,6. Models to explain this dust dispersal include both an abrupt sinking of the entire cloud deck into the deep, unobservable atmosphere5,7 and breakup of the cloud into scattered patches6,8 (as seen on Jupiter and Saturn9). However, hitherto observations of brown dwarfs have been limited to globally integrated measurements10, which can reveal surface inhomogeneities but cannot unambiguously resolve surface features11. Here we report a two-dimensional map of a brown dwarf’s surface that allows identification of large-scale bright and dark features, indicative of patchy clouds. Monitoring suggests that the characteristic timescale for the evolution of global weather patterns is approximately one day.
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We thank A. Hatzes for advice on Doppler imaging analysis, J. Smoker for helping to plan and execute our observations and E. Mills for help in designing figures. This work is based on Director’s Discretionary observations made with the European Southern Observatory Telescopes at the Paranal Observatory under programme ID 291.C-5006(A); data are available in the European Southern Observatory Data Archive at http://archive.eso.org/. D.H. received support from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013 Grant Agreement number 247060). M.B., D.H. and F.A. acknowledge support from the French National Research Agency (ANR) through project grant ANR10-BLANC0504-01. E.B. is supported by the Swiss National Science Foundation (SNSF). Atmosphere models have been computed on the Pôle Scientifique de Modélisation Numérique at the Ecole Normale Supérieure (ENS) de Lyon, and at the Gesellschaft für Wissenschaftliche Datenverarbeitung Göttingen in co-operation with the Institut für Astrophysik Göttingen. IRAF (the Image Reduction and Analysis Facility) is distributed by the National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under cooperative agreement with the National Science Foundation. PyRAF (Python-based IRAF) is a product of the Space Telescope Science Institute, which is operated by AURA for NASA. We also thank contributors to SciPy, Matplotlib, AstroPy and the Python programming language.
The authors declare no competing financial interests.
Extended data figures and tables
The red curves (a and c) show the modelled spectra, which mostly overlap the observed spectra (plotted in black). The gaps in the spectra correspond to physical spaces between the four infrared array detectors. The residuals to the fits (b and d) are generally a few per cent, with larger deviations apparent near CO bandheads (for example, at 2.294 µm and 2.323 µm) and strong telluric absorption lines (for example, at 2.290 µm and 2.340 µm).
Extended Data Figure 2 Calibrated spectra of the brown dwarfs, showing the individual calibrated spectra of Luhman 16A (a) and Luhman 16B (b).
The time of each observation is indicated.
Extended Data Figure 3 Luhman 16B shows strong rotationally induced variability (b) whereas Luhman 16A does not (a).
The colour scale indicates the deviations from a uniform line profile as measured relative to the line continuum. Luhman 16B’s variations are dominated by a dark region (diagonal streak, corresponding to a decrease of roughly 4% in equivalent width) that comes into view at 1.5 h heading towards the observer, rotates across the brown dwarf to the receding side, and is again hidden behind the limb at 3 h. Brighter regions are visible at earlier and later times, but are less prominent. No significant spectroscopic variability is apparent for Luhman 16A, and no coherent features are seen beyond Luhman 16B’s projected rotational velocity (enclosed between the vertical dashed lines). All these points indicate that we are detecting intrinsic spectroscopic variability from Luhman 16B.
Extended Data Figure 4 Posterior parameter distributions from our single-spot toy model, showing a large mid-latitude spot.
The inner and outer curves in panels a–c indicate the 68.3% and 95.4% confidence regions. The plot shown assumes i = 30 degrees; smaller inclinations result in a slightly more equatorial spot, but the best-fit values remain within the inner 68.3% confidence regions.
Extended Data Figure 5 Simulated brown dwarf with spots, and the map recovered from Doppler imaging.
a, Simulated variable brown dwarf seen at an inclination of i = 30 degrees. The dark and light mid-latitude spots are, respectively, 40% darker and 10% brighter than the photosphere; the dark streak is 10% darker and the polar spot is 20% brighter. b, Surface map recovered from Doppler imaging, assuming noise levels similar to that seen in our observed data, after tuning the hyperparameter a to minimize spurious features. High-contrast features are recovered: the dark spot is in the correct location and the polar spot is only moderately distorted. The equatorial bright spot is visible in the recovered map, but it cannot be reliably distinguished from image artefacts that preferentially cluster near the equator. The dark stripe is not recovered. Thus our analysis can accurately recover strong features, but data quality precludes us from discerning smaller or fainter features.
Extended Data Figure 6 Simulated brown dwarf with spots and bands, and the map recovered from Doppler imaging.
a, Simulated variable brown dwarf with the same surface features as in Extended Data Fig. 5, but now also exhibiting zonal bands with an amplitude of ±20% of the mean photospheric brightness level. b, Surface map recovered from Doppler imaging under the same assumptions as in Extended Data Fig. 5. High-contrast, non-axisymmetric features are recovered as before, but we cannot recover even prominent global bands with the current precision of our data.
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Crossfield, I., Biller, B., Schlieder, J. et al. A global cloud map of the nearest known brown dwarf. Nature 505, 654–656 (2014). https://doi.org/10.1038/nature12955
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