New research offers insights into why climate models often get spring El Niño forecasts wrong

El Niño events have sweeping impacts on global weather, agriculture, and economies, so getting the forecast right matters. A new study published in npj climate and atmospheric science highlights a persistent blind spot: the overconfidence of climate models when predicting El Niño from March through May, a historically tricky time known as the “spring predictability barrier.” Researchers found that while models often issue high-confidence El Niño forecasts in spring, they frequently fail to deliver. The authors, Aaron Levine of the University of Washington and the NOAA Climate Prediction Center’s Michelle L’Heureux and Caihong Wen found that the problem is that these models lean too heavily on tropical Pacific signals. The models can miss key influences from outside the tropics, leading to inaccurate forecasts that can leave decision-makers unprepared.