Why spring thunderstorms are getting fiercer ✎

Original: "Trends in the extremes of environments associated with severe US thunderstorms"
arXiv:1901.10960 · 2019-01-30 · arXiv non-exclusive · independent analysis · 1 min
Scientists measured the 'fuel' for severe thunderstorms over the US for 37 years and found it's increasing in spring.

A thunderstorm is like a pot of boiling water: for steam to escape, you need enough heat and moisture. The atmosphere has its own measures for this heat. One shows how strongly air wants to rise. Another shows how much wind shear increases with height. If both measures are high, a thunderstorm can become very severe.

Scientists combined these two measures into one overall index to make it easier to compare different days and years. They called it PROD.

Researchers took weather data spanning 37 years and, for each month, found the day with the highest values of these measures. Then they looked at how these record-breaking days changed from year to year. It turned out that in April, May, and August, they are becoming increasingly dangerous. This is especially noticeable in areas where thunderstorms were already frequent.

February tells a different story. Here, the strength of thunderstorms is influenced by water temperature in the tropical Pacific Ocean. When the water is cooler than usual, the risk of severe thunderstorms is higher. This wasn't as obvious before because researchers looked at average values rather than the most extreme ones. Now it's clear: the springtime 'fuel' for thunderstorms is on the rise, and this needs to be considered when planning protection against severe weather.

The maximum annual increase in PROD in April is about 3% relative to the 1979 level. For comparison, that is like adding about 26 meters to the height of Mount Everest every year.

PROD = \sqrt{CAPE \times SRH}
The square root of the product of convective available potential energy and storm-relative helicity; high values indicate a high risk of severe thunderstorms.
G(x) = \exp\left[-\left(1 + \xi \frac{x - \mu}{\sigma}\right)^{-1/\xi}\right]
Distribution of block maxima; the shape parameter ξ determines the tail type (Fréchet, Gumbel, Weibull).
\mu(t) = \mu_0 + \mu_1 t
Linear dependence of the GEV location parameter on time t; μ₁ indicates the rate of change of extremes.
Scientists
Johannes Rydberg
Concepts
El Niño spring predictability barrier tropical cyclone Westerly wind burst ENSO North Equatorial Countercurrent interannual variability Warm water volume NOAA CPC La Niña seasonal cycle Multivariate ENSO Index Oceanic Niño index relative ONI North Atlantic Oscillation seasonal forcing Southern Oscillation equatorial wave propagation natural variability NOAA PMEL / TAO