Ocean Temperatures May Hold Key to Predicting Tornado Outbreaks
Tornadoes are one of nature’s most destructive forces. Recent violent and widespread tornado outbreaks in the United States, such as occurred in the spring of 2011, have caused significant loss of life and property. Currently, our capacity to predict tornadoes and other severe weather risks does not extend beyond seven days. Extending severe weather outlooks beyond seven days will assist emergency managers, businesses, and the public prepare the resources needed to prevent economic losses and protect communities. So how can scientists better predict when and where tornadoes are likely to strike, before the tornado season begins?
In a recent paper published in Environmental Research Letters, scientists with NOAA and the University of Miami identified how patterns in the spring phases of the El Niño-Southern Oscillation (ENSO), coupled with variability in North Atlantic sea surface temperatures, could help predict U.S. regional tornado outbreaks.
“This is the first study to show that the most frequently occurring spring sea surface temperature patterns in the tropical Pacific and North Atlantic are linked to distinctive spatial patterns of the probability of U.S. regional tornado outbreaks,” said lead author Sang-Ki Lee, Ph.D., an oceanographer at NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML).
Researchers at AOML and the Cooperative Institute for Marine and Atmospheric Studies (CIMAS) at the University of Miami investigated the spatial patterns of springtime U.S. regional tornado outbreaks from 1950-2014 and their connection to springtime phases of ENSO. ENSO, or the El Niño-La Niña cycle, is a natural climate pattern in the Pacific Ocean. During an El Niño event, warm sea surface temperatures spread across the tropics. In a La Niña year, the opposite happens: Cool sea surface temperatures dominate in the eastern tropical Pacific. These temperature shifts have a ripple effect on large-scale atmospheric processes conducive to tornado outbreaks across the U.S.
The researchers focused on four variations of ENSO events: strong winter events that persist well into spring, and weak events that dissipate soon after their winter peak. They found that weak El Niños led to tornado outbreaks in May throughout the Upper Midwest, while strong El Niños led to outbreaks in February across Central Florida and the Gulf Coast. In contrast, weak La Niñas led to April outbreaks throughout the South, particularly in Oklahoma and Kansas, while strong La Niñas led to April outbreaks along the Ohio Valley and in the Southeast and Upper Midwest.
The results suggest that each of the four dominant spring ENSO variations is linked to distinct and significant U.S. regional patterns of outbreak probability. The strongest tornado connection was with strong, persistent La Niñas, consistent with the Super Outbreak of 1974 and the record-shattering tornado outbreaks of 2011, both of which occurred during strong La Niñas.
The researchers plan to incorporate the spring ENSO state and the North Atlantic sea surface temperature variability into a forecast model to predict which regions are more likely to experience widespread tornado outbreaks. By extending NOAA’s ability to predict tornado outbreaks, federal, state, and local agencies can plan early and pre-position resources to better prepare for emergency response.
It is important to remember that a regional tornado outbreak may occur in any season and almost anywhere in the U.S. regardless of ENSO state. Even during an overall quiet season, one outbreak event could cause significant loss of life and property. Therefore, communities routinely exposed to severe weather systems should be ready for every severe weather season regardless of what a seasonal outlook may predict.
This research was funded in part by the Modeling, Analysis, Predictions, and Projections (MAPP) program, part of NOAA’s Climate Program Office in the Office of Oceanic and Atmospheric Research, and NOAA’s Climate Prediction Center (CPC) and Atlantic Oceanographic and Meteorological Laboratory (AOML), working in collaboration with the Geophysical Fluid Dynamics Laboratory (GFDL).