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 JWST 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. SIMP 0136 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 JWST/NIRSpec PRISM time-series spectroscopy to investigate the drivers of its variability using PCA. Two principal components are sufficient to reduce the residual spectra to the propagated noise floor, indicating that they capture the detectable coherent spectroscopic variability. The leading principal component captures broadband variability consistent with temperature changes, while the second traces chromatic variability linked to vertical cloud structure. The dominance of two components implies that the spectra can be described as mixtures of three distinct atmospheric states, whose relative contributions we mapped 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 principal components capture a large fraction of the model variance, demonstrating that the same physical processes that govern SIMP-0136's observed variability also capture much of the model grid's variation. Our results establish PCA as a computationally efficient, physically interpretable framework for analysing JWST time-resolved spectroscopy of substellar atmospheres.