Jet stream 'traffic jams' can trigger strange weather patterns

May 25 (UPI) -- Atmospheric scientists at the University of Chicago believe blockages in jet stream, like a traffic jam on a highway, are responsible for extreme weather and unusual weather patterns.

According to their analysis, the jet stream hosts a phenomenon called "blocking." Like a highway, scientists believe the jet stream has a maximum capacity. When it becomes overwhelmed with atmospheric traffic -- too many air masses vying for space -- blockages happen.

In a new study, published this week in the journal Science, researchers claim the phenomenon was responsible for the 2003 European heat wave and California's 2014 drought, as well as the unexpected path of Superstorm Sandy in 2012.

Scientists have known about the phenomenon, but until now, they'd struggled to explain how blockages happen.

"Blocking is notoriously difficult to forecast, in large part because there was no compelling theory about when it forms and why," Noboru Nakamura, a professor of geophysical sciences at Chicago, said in a news release.

While studying the phenomenon, Nakamura and then-graduate student Clare S.Y. Huang realized the math used to measure the jet stream's meander looks familiar. The equations were nearly identical to those developed by engineers a few decades prior to describe traffic jams.

"It turns out the jet stream has a capacity for 'weather traffic,' just as a highway has traffic capacity, and when it is exceeded, blocking manifests as congestion," said Huang.

Slowdowns often occur near highway interchanges or where busy roads converge. Similarly, when different currents converge in the atmosphere, squeezed together by the Earth's contours, including mountains and coastal features, blocking can occur.

Nakamura and Huang tweaked the traffic math and developed a model that can both replicate and predict the phenomenon. While the breakthrough might not immediately improve short-term forecasts, it will help scientists more accurately predict long-term trends.

"It's very difficult to forecast anything until you understand why it's happening, so this mechanistic model should be extremely helpful," Nakamura said.