(NEXSTAR) — While La Niña has decided to stick around for a rare, triple-dip winter, we may soon be saying goodbye to the climate pattern that’s been with us, on and off, since 2020.
An updated outlook released by the Climate Prediction Center Thursday confirms La Niña is expected to continue through winter, as has been expected for several months. That tends to bring a dryer winter to the southern half of the U.S. and a wetter winter to the northern half.
And in Northeast Ohio, a wetter winter this year likely means a snowier winter, said FOX 8 meteorologist Dontaé Jones. This year’s weather patterns suggest Northeast Ohio could have winter temperatures that are at or below the average, “which would mean that our wetter La Niña pattern could trend toward snow.
“So thus, we could see a colder and snowier winter.”
But as we transition from winter to spring, climate models are starting to indicate La Niña may fade away. That doesn’t mean we’ll switch into El Niño right away. Meteorologists say we’ll likely end up in an “ENSO-neutral” setup, which means neither La Niña nor El Niño conditions are present.
When the change will happen isn’t yet clear. Models give La Niña about a 50-50 chance of lasting from January to March 2023, according to the Climate Prediction Center. Deeper into spring, chances for an ENSO-neutral situation increase.
The latest outlook gives ENSO-neutral a 71% chance of winning out between February and April 2023.
Whether we’re in a La Niña year, an El Niño year, or neither depends on the temperatures of the Pacific Ocean’s surface and the atmosphere above it. Those variations end up impacting our weather here on land.
La Niña years are typically characterized by stronger hurricane seasons in the Atlantic, drought conditions in the Southern U.S., and cold, rainy winters in the Pacific Northwest.
El Niño is usually associated with the opposite: warm and dry weather in the northern U.S., and extra rain to the south.
ENSO-neutral patterns are neither. They are characterized by a return to the long-term average.