When Spring Arrives, It Becomes Harder to Predict El Nino and La Nina

(Inside Science) -- Each year, when winter ends, meteorologists notice that some of their highly dependable climate models stumble. Supercomputers tasked with predicting the conditions associated with El Nino over the next several months generate less accurate forecasts. That means that their predictions of important weather events, such as a tropical cyclone in Hawaii, become less reliable.

Spring is a particularly tricky time for long-term weather forecasting. From October data, meteorologists can make pretty accurate predictions for the winter. But using April data to predict summer conditions leads to a less accurate forecast, despite the similar time horizon. This phenomenon of inaccuracy in the springtime is named the Spring Predictability Barrier.

However, it’s far from a forecasting meltdown. It’s more of a meteorological glitch, due to the way atypically warm or cold ocean water temperatures can take hold late in the calendar year or early the following year. Meteorologists watching the Pacific Ocean’s temperature, near the equator, can only start seriously issuing long-term forecasts after June.

This combination of volatility and confusion is mostly due to the phenomenon known as El Nino Southern Oscillation, or ENSO. It is a powerful climate force characterized by pressure and temperature differences between the warm western Pacific and the cool eastern Pacific waters around the equator.

Throughout the year, pressure and temperature fluctuate together on the sea’s surface. This drives a continuous clockwise cycle of wind and precipitation that strongly influences regional climates from the United States to Indonesia.

ENSO brews two of the most impactful climate events in the world -- El Nino and La Nina. An El Nino event happens when the water temperature around the equator becomes warmer than usual. An El Nina event arises when the temperature is irregularly cold.

Both of these events have drastic effects on global and local climates. In California’s case, an El Nino event brings rain the state desperately needs. But in Florida’s case, it brings a higher chance of severe weather conditions.

Meteorologists’ ability to predict ENSOs ebbs and flows through various models and methods is crucial to anticipating such events. The predominant measurement used in these models is the average sea surface temperature around the equatorial Pacific. To get this data, researchers look at an area that captures changes in the average temperature of the equatorial Pacific -- the Nino 3.4 Index region.

But sea surface temperature does not dictate changes in ENSO; it’s a proxy for all of the conditions that affect the atmospheric conditions in that area. “If the entire tropical Pacific just warmed up or cooled down, that would not affect the atmospheric circulation, so it really wouldn’t matter. So it’s really not just what’s going on in that box. It’s what that implies for the overall pattern,” said Lisa Goddard, a meteorologist at Columbia University in New York.

A series of buoys bobbing in the Nino 3.4 region record real-time surface and subsurface temperatures. Computers use this data to predict the next month’s water temperature variations, specifically whether the next few month’s temperatures will be colder or warmer than usual.

Unfortunately, during spring, ENSO isn’t that powerful, which allows other climate phenomena to overwhelm forecasts.

Developing an accurate ENSO model is complex. No meteorological variable single-handedly affects local climates, much less global ones. While mechanisms within ENSO are well understood, how these mechanisms interact with external factors is more complicated.

“It’s a long, slow, frustrating slog, but our models are actually getting a lot better,” said Benjamin Kirtman, a meteorologist at the University of Miami.

To overcome the springtime barrier, labs have tried including new measurements in their models. Recently, a Russian lab made better predictions by including pressure conditions from around Hawaii.

But, Goddard said, trying to overcome the forecasting barrier isn’t the biggest springtime meteorological problem. An even bigger challenge is learning what gets ENSO going again. Improving computers’ ability to predict future temperature variability after the spring will likely be crucial to improving the models and their projections.

For Goddard, one of the most appealing theories is that the mechanisms behind the Westerly Wind Burst -- a climate phenomenon associated with the start of an El Nino event -- spur ENSO through an unknown process.

While there is still mystery around the Spring Predictability Barrier, summer and fall forecasts are still there to help inform the public of possible El Nino and La Nina events.