A Stochastic Statistical Model for U.S. Outbreak-Level Tornado Occurrence Based on the Large-Scale Environment

Abstract

Abstract Tornado outbreaks—when multiple tornadoes occur within a short period of time—are rare yet impactful events. Here we developed a two-part stochastic tornado outbreak index for the contiguous United States (CONUS). The first component produces a probability map for outbreak tornado occurrence based on spatially resolved values of convective precipitation, storm relative helicity (SRH), and convective available potential energy. The second part of the index provides a probability distribution for the total number of tornadoes given the outbreak tornado probability map. Together these two components allow stochastic simulation of location and number of tornadoes that is consistent with environmental conditions. Storm report data from the Storm Prediction Center for the 1979–2021 period are used to train the model and evaluate its performance. In the first component, the probability of an outbreak-level tornado is most sensitive to SRH changes. In the second component, the total number of CONUS tornadoes depends on the sum and gridpoint maximum of the probability map. Overall, the tornado outbreak index represents the climatology, seasonal cycle, and interannual variability of tornado outbreak activity well, particularly over regions and seasons when tornado outbreaks occur most often. We found that El Niño–Southern Oscillation (ENSO) modulates the tornado outbreak index such that La Niña is associated with enhanced U.S. tornado outbreak activity over the Ohio River Valley and Tennessee River Valley regions during January–March, similar to the behavior seen in storm report data. We also found an upward trend in U.S. tornado outbreak activity during winter and spring for the 1979–2021 period using both observations and the index. Significance Statement Tornado outbreaks are when multiple tornadoes happen in a short time span. Because of the rare, sporadic nature of tornadoes, it can be challenging to use observational tornado reports directly to assess how climate affects tornado and tornado outbreak activity. Here, we developed a statistical model that produces a U.S. map of the likelihood that an outbreak-level tornado would occur based on environmental conditions. In addition, using that likelihood map, the model predicts a range of how many tornadoes could occur in these events. We found that “storm relative helicity” (a proxy for potential rotation in a storm’s updraft) is especially important for predicting outbreak tornado likelihood, and the sum and maximum value of the likelihood map is important for predicting total numbers for an event. Overall, this model can represent the typical behavior and fluctuations in tornado outbreak activity well. Both the tornado outbreak model and observations agree that the state of sea surface temperature in the tropical Pacific (El Niño–Southern Oscillation) is linked to tornado outbreak activity over the Ohio River Valley and Tennessee River Valley in winter through early spring and that there are upward trends in tornado outbreak activity.