A team of Florida State University researchers said it found a way to predict winter weather patterns months before the season begins — a development that could give communities and industries much more time to prepare for extreme cold.
According to EurekAlert, the research, published in the Journal of Geophysical Research: Atmospheres, focused on the stratospheric polar vortex — a ring of powerful winter winds that keeps frigid Arctic air bottled up near the poles.
When that vortex is strong, cold air tends to remain in the Arctic. When it weakens, that air is more likely to move into North America and Eurasia.
Scientists have long understood that the polar vortex plays a major role in shaping winter weather. But predicting its behavior more than a couple of weeks in advance has remained a major challenge.
The team said it tackled that problem by focusing less on day-to-day atmospheric shifts and more on broader climate patterns that develop over the course of a year.
Researchers said they used recurring climate signals, including El Niño-Southern Oscillation patterns, to predict the vortex ahead of winter and then reconstruct its day-to-day behavior — with results that outperformed current forecasting systems.
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Better long-range winter forecasts could help farmers protect crops, utilities prepare for spikes in heating demand, water managers brace for freezes or shifts in supply, and health officials stay ahead of dangerous cold-related risks.
The research also suggests that some unusual weather events may be less random than previously thought.
A large share of subseasonal and seasonal variability may actually be embedded in the climate system's annual cycle.
The researchers said the same approach may sharpen forecasts for climate phenomena such as El Niño and La Niña, which influence everything from rain in the Southern United States to hurricane activity.
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"This work shows that a large portion of subseasonal to seasonal variability is not random," co-author Ming Cai said. Instead, it's tied to the climate system's yearly evolution.
Cai also praised lead author Michael Secor's work as "a significant contribution," saying it approached a long-standing problem from "a fundamentally different perspective."
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