The JWST weather report: Unravelling the atmospheric variability of isolated worlds using principal component analysis

Brown dwarf variability directly probes atmospheric dynamics beyond the Solar System, and recent time-resolved spectroscopy has opened a new window into these processes. Principal component analysis (PCA) offers a data-driven framework to identify the dominant, independent patterns of spectral variability of variable targets without relying on prior atmospheric assumptions. James Webb Space Telescope (JWST) simp ( ) is a young, T2.5, brown dwarf at the planetary-mass boundary, making it an ideal analogue for directly imaged exoplanets. We analysed one rotation of /NIRSpec PRISM time-series spectroscopy to investigate the drivers of its variability using PCA. Prior analyses of these data revealed wavelength-dependent variability consistent with multi-layer atmospheric structure and thermal variations atop patchy clouds, motivating a complementary, model-independent approach. Our PCA showed that the variability is intrinsically low dimensional. Two principal components are sufficient to reduce the residual spectra to the propagated noise floor, indicating that the detectable coherent spectroscopic variability is captured by the first two components. The leading principal component captures broadband variability consistent with temperature changes, while the second traces chromatic variability linked to vertical cloud structure. The existence of two dominant components implies that the spectra can be described as mixtures of three distinct atmospheric states. We identified these three extreme spectral states from PCA projections and mapped their relative contributions as a function of rotational phase. The observed spectra are described as evolving linear combinations of these states, indicating that the variability arises from the changing visibility of spatially distinct atmospheric regions. By projecting Sonora Diamondback forward models into the same principal component space, we found that the eigenspectra capture a large fraction of the model variance, demonstrating that the same physical processes govern much of the model grid and observed variability. These results show that changes in temperature and cloud vertical structure account for most of the variability in , and establish PCA as a computationally efficient, physically interpretable framework for analysing time-resolved spectroscopy of substellar atmospheres. simp JWST simp JWST