The composite (superposed epoch) analysis technique has been frequently employed to examine a hypothesized link between solar activity and the Earth’s atmosphere, often through an investigation of Forbush decrease (Fd) events (sudden high-magnitude decreases in the flux cosmic rays impinging on the upper-atmosphere lasting up to several days). This technique is useful for isolating low-amplitude signals within data where background variability would otherwise obscure detection. The application of composite analyses to investigate the possible impacts of Fd events involves a statistical examination of time-dependent atmospheric responses to Fds often from aerosol and/or cloud datasets. Despite the publication of numerous results within this field, clear conclusions have yet to be drawn and much ambiguity and disagreement still remain. In this paper, we argue that the conflicting findings of composite studies within this field relate to methodological differences in the manner in which the composites have been constructed and analyzed. Working from an example, we show how a composite may be objectively constructed to maximize signal detection, robustly identify statistical significance, and quantify the lower-limit uncertainty related to hypothesis testing. Additionally, we also demonstrate how a seemingly significant false positive may be obtained from non-significant data by minor alterations to methodological approaches.

Based on the results of decadal correlation studies between the International Satellite Cloud Climatology Project–detected cloud anomalies and the galactic cosmic ray (GCR) flux, it has been suggested that a relationship exists between solar activity and cloud cover. If valid, such a relationship could have important implications for scientists’ understanding of recent climate change. In this work, an analysis of the first decade of Moderate Resolution Imaging Spectroradiometer (MODIS)–detected cloud anomalies are presented, and the data at global and local geographical resolutions to total solar irradiance (TSI), GCR variations, and the multivariate El Niño–Southern Oscillation index are compared. The study identifies no statistically significant correlations between cloud anomalies and TSI/GCR variations, and concludes that solar-related variability is not a primary driver of monthly to annual MODIS cloud variability. The authors observe a net increase in cloud detected by MODIS over the past decade of ~0.58%, arising from a combination of a reduction in high- to midlevel cloud (−0.31%) and an increase in low-level cloud (0.89%); these long-term changes may be largely attributed to ENSO-induced cloud variability.