Andrew E. Mercer

This study supports efforts directed toward research on large‐scale atmospheric patterns and on the variability of tornado outbreaks. Specifically, we applied rotated principal component analysis to identify synoptic‐scale patterns of 500‐hPa geopotential height associated with tornado outbreaks in the United States. We created a database of historic tornado outbreaks using kernel density estimation on events composed of at least seven tornadoes of magnitude (E)F2 or higher (major tornado outbreaks) that occurred in May from 1950 to 2019 (91 events). Results of the analysis show that the first three principal components explained the majority, that is, 74% of the total variation. Based on the analysis of congruence coefficients, the Promax oblique transformation was chosen as the most representative rotation in portraying physically meaningful modes and resulted in three main atmospheric patterns. Two of these, to the best of our knowledge, have not been yet identified as the most representative of tornado outbreaks in any previous studies. Additionally, results suggest that although synoptic patterns associated with major May tornado outbreaks remain the same over time, partial variability in their geographic location exhibits some cyclical behaviour, especially on decadal and multidecadal scales. Identifying these patterns can serve as a first step in determining how they may change under anthropogenic climate change in the future.