Intercomparison of Environmental Proxies for Severe Local Storms in Capturing the Spatial Distribution of Tornadoes and Its Variability on Climate Time Scales over North America.

Tornadoes cause numerous deaths and significant property damage each year, yet how tornado activity varies across climate states, particularly under global warming, remains poorly understood. Importantly, severe weather events arise during transient periods of extreme thermodynamic environments whose variability may differ from that of the environmental mean state. This study analyzes the climatological relationships between commonly-used severe weather environmental proxies (the product of convective available potential energy and bulk vertical wind shear, energy-helicity index, and the significant tornado parameter) and tornado density on three dominant timescales of climate forcing: diurnal, seasonal, and interannual. We utilize reanalysis data to calculate the spatial distributions of the mean, median, and a range of extreme percentiles of these proxies across each timescale as well as for the full climatology. We then test the extent to which each measure captures the spatiotemporal variability of tornado density over the continental United States. Initial results indicate that the means of the significant tornado parameter and the product of convective available potential energy and bulk vertical wind shear are comparable to the highest correlated extreme percentiles on the full climatology and on interannual variability, but are outperformed by the highest correlated extreme percentiles in seasonal and diurnal variability. The means of the energy-helicity index are comparable to the highest correlated extreme percentiles on interannual variability, but are outperformed by the highest correlated extreme percentiles in seasonal and diurnal variability as well as the full climatology. This understanding of climatological relationships between tornadoes and the large scale environments can improve prediction of tornado frequency and provides a foundation for understanding how changes in the statistics of large-scale environments may affect tornado activity in a future warmer climate state.