Evaluating the observed variability in hyperspectral Earth‐reflected solar radiance

We explore the potential for directly measured hyperspectral Earth‐reflected solar radiances to provide sufficient information to study changes in Earth’s climate based on the quantified variability of the data using principal component analysis (PCA) and singular spectrum analysis. To do this we used these two multivariate analysis techniques on Earth‐reflected radiances between 300 and 1750 nm measured from space by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) instrument. The spatial and temporal variability of hyperspectral reflected radiances over global, hemispherical, and regional scales was quantified. As few as six components were needed to explain over 99.5% of the variance in all cases studied, with the exception of an Arctic Ocean case in which only four components were needed. Both of these values represent large reductions in dimensionality of the input radiances from 291 spectral bands. PCA facilitated attribution of the dominant spectral patterns extracted to atmospheric and surface variables, including water vapor, clouds, surface albedo, and sea ice. The second most dominant spectral variable, that is, the second principal component, in the Arctic closely resembled sea ice reflectance and followed the temporal behavior of sea ice extent determined from AMSR‐E observations. The extraction of the spectral, spatial, and temporal variability in reflected shortwave hyperspectral radiance using multivariate analysis provides an alternate and complementary approach to inverse methods for applying space‐based observations to climate studies.