Storm reports show an upward trend in the power of tornadoes. Quantifying the magnitude of the increase is difficult given diurnal and seasonal influences on tornadoes embedded within natural variations and made worse by changes in practices for rating damage. Here the authors solve this problem by fitting a statistical model to a metric of tornado power during the period 1994-2016. They find an increase of 5.5% [(4.6, 6.5%), 95% CI] per year in power controlling for the diurnal cycle, seasonality, natural climate variability, and the switch to a new damage scale. A portion of the trend is attributed to long-term changes in convective storm environments involving dynamic and thermodynamic variables and their interactions. Increasing power is occurring in environments where the effect of convective available potential energy is enhanced by increasing vertical wind shear. However, a majority of the trend is not attributable to changes in storm environments. Plain Language SummaryThere is a clear upward trend in tornado power over the past few decades that amounts to 5.5% per year controlling for time of day, time of year, natural variability, and the switch to a new damage rating scale. Part of the trend can be attributed to long-term changes in convective storm environments involving dynamic and thermodynamic variables and their interactions.

Studies show an increasing tendency for tornadoes in the United States to occur in larger outbreaks. To shed light on the reason for this, the authors use a regression model to quantify the relationship between convective environmental variables and accumulated tornado power (ATP). They consider only days with many tornadoes that occur as part of an outbreak. Results show an average upward trend in ATP at 5% ([2.5%, 12%], 95% uncertainty interval) per year. ATP increases by 125% for every 10 mÁs −1 increase in bulk shear (on average)and by 33% for every 1,000 JÁkg −1 increase in convective available potential energy holding the other variables constant. Changes in bulk shear, which has the largest effect on ATP, might help explain the documented changes in tornado activity. K E Y W O R D S accumulated tornado power, atmospheric environments, regression model, tornado outbreaks

Empirical studies have led to improvements in evaluating and quantifying the tornado threat. However, more work is needed to put the research onto a solid statistical foundation. Here the authors begin to build this foundation by introducing and then demonstrating a statistical model to estimate damage rating (enhanced Fujita scale) probabilities. A goal is to alert researchers to available statistical technology for improving severe weather warnings. The model is cumulative logistic regression and the parameters are determined using Bayesian inference. The model is demonstrated by estimating damage rating probabilities from values of known environmental factors on days with many tornadoes in the United States. Controlling for distance to nearest town/city, which serves as a proxy variable for damage target density, the model quantifies the chance that a particular tornado will be assigned any damage rating given specific environmental conditions. Under otherwise average conditions, the model estimates a 65% chance that a tornado occurring in a city or town will be rated EF0 when bulk shear (1000–500-hPa layer) is weak (10 m s−1). This probability drops to 38% when the bulk shear is strong (40 m s−1). The model quantifies the corresponding increases in the chance of the same tornado receiving higher damage ratings. Quantifying changes to the probability distribution on the ordered damage rating categories is a natural application of cumulative logistic regression.

Storm reports show an upward trend in the power of tornadoes. Quantifying the magnitude of the increase is difficult given diurnal and seasonal influences on tornadoes embedded within natural variations and made worse by changes in practices for rating damage. Here the authors solve this problem by fitting a statistical model to a metric of tornado power during the period 1994–2016. They find an increase of 5.5% [(4.6, 6.5%), 95% CI] per year in power controlling for the diurnal cycle, seasonality, natural climate variability, and the switch to a new damage scale. A portion of the trend is attributed to long‐term changes in convective storm environments involving dynamic and thermodynamic variables and their interactions. Increasing power is occurring in environments where the effect of convective available potential energy is enhanced by increasing vertical wind shear. However, a majority of the trend is not attributable to changes in storm environments.

Environmental variables are routinely used in estimating when and where tornadoes are likely to occur, but more work is needed to understand how tornado and casualty counts of severe weather outbreak vary with the larger scale environmental factors. Here the authors demonstrate a method to quantify ‘outbreak’-level tornado and casualty counts with respect to variations in large-scale environmental factors. They do this by fitting negative binomial regression models to cluster-level environmental data to estimate the number of tornadoes and the number of casualties on days with at least ten tornadoes. Results show that a 1000 J kg−1 increase in CAPE corresponds to a 5% increase in the number of tornadoes and a 28% increase in the number of casualties, conditional on at least ten tornadoes, and holding the other variables constant. Further, results show that a 10 m s−1 increase in deep-layer bulk shear corresponds to a 13% increase in tornadoes and a 98% increase in casualties, conditional on at least ten tornadoes, and holding the other variables constant. The casualty-count model quantifies the decline in the number of casualties per year and indicates that outbreaks have a larger impact in the Southeast than elsewhere after controlling for population and geographic area.