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Publication Date
22 January 2021

Explaining the Trends and Variability in the United States Tornado Records Using Climate Teleconnections and Shifts in Observational Practices

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The annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability.


Tornadoes are one of the most devastating, severe weather events in the United States that always pose risks to human life and cause extensive property damage. Improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies. The more we can understand and predict tornado prevalence and occurrence, the more resilience we build to these catastrophic events.


In this study, we build nested models using a hierarchical Bayesian inference framework, which not only allows for a full uncertainty quantification but also its reduction by pooling information across an appropriate classification of states. The hierarchical framework provides an elegant means of propagating the parameter uncertainty through appropriate conditional distributions. The hierarchical model provides pooling of common information and reduces the equivalent number of independent parameters, resulting in lower uncertainty in parameter estimates. Using this holistic modeling framework, for the first time, we explained the factors governing secular trends and cyclical variability in the annual frequency of tornadoes across the major tornado-impacted states in the U.S. The relative contribution of the anthropogenic and climate factors and their primary influence regions are discussed.

Point of Contact
Naresh Devineni
City College of New York (CUNY)
Funding Program Area(s)