Identifying key convection-sensitive oceanic regions to weaken the ENSO spring predictability barrier

Z Zepeng Mei (Institute for Cross-Straits Integrated Development, Key Laboratory of Humid Subtropical Eco-geographical Process (Ministry of Education), School of Geographical Sciences, Fujian Normal University) S Shuheng Lin (Institute for Cross-Straits Integrated Development, Key Laboratory of Humid Subtropical Eco-geographical Process (Ministry of Education), School of Geographical Sciences, Fujian Normal University) K Keyan Fang (Institute for Cross-Straits Integrated Development, Key Laboratory of Humid Subtropical Eco-geographical Process (Ministry of Education), School of Geographical Sciences, Fujian Normal University) W Wanru Tang (Institute for Cross-Straits Integrated Development, Key Laboratory of Humid Subtropical Eco-geographical Process (Ministry of Education), School of Geographical Sciences, Fujian Normal University) F Feifei Zhou (Institute for Cross-Straits Integrated Development, Key Laboratory of Humid Subtropical Eco-geographical Process (Ministry of Education), School of Geographical Sciences, Fujian Normal University) F Fei Liu S Sen Zhao H Hao Wu J Jinbao Li (Department of Geography, University of Hong Kong) Z Zheng Zhao T Tinghai Ou (Regional Climate Group, Department of Earth Sciences, University of Gothenburg) X Xiaoxun Xie (State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences) D Deliang Chen

Abstract

The El Niño–Southern Oscillation (ENSO) exhibits its weakest predictability during boreal spring, a phenomenon known as the Spring Predictability Barrier (SPB). The SPB arises from weak air–sea coupling that limits the growth and persistence of the ENSO signal. Improving springtime prediction therefore requires identifying oceanic regions most relevant for convection variability. Here, we introduce a Sea Surface Temperature Range Index (SRI), which quantifies the spatial extent of Sea surface temperatures favorable for convection. Using SRI, we show that regions exceeding 26 °C in the east-central Pacific and 28.5 °C in the eastern Atlantic during spring are critical for initiating persistent intense convection. The expansion of these convection-sensitive areas strengthens the Bjerknes feedback by modulating the Walker circulation, providing an effective predictor of ENSO evolution. We further develop a Long Short-Term Memory deep learning model incorporating SRI, which achieves higher predictive skill than the average of dynamical and statistical models, especially for multiyear La Niña events. These results underscore the central role of convection-sensitive oceanic regions in alleviating the SPB.

Article Details

Volume / Issue Vol. 123, Issue 12
Published March 24, 2026
ISSN 0027-8424
Publisher National Academy of Sciences

Authors (13)

Z

Zepeng Mei

Institute for Cross-Straits Integrated Development, Key Laboratory of Humid Subtropical Eco-geographical Process (Ministry of Education), School of Geographical Sciences, Fujian Normal University

S

Shuheng Lin

Institute for Cross-Straits Integrated Development, Key Laboratory of Humid Subtropical Eco-geographical Process (Ministry of Education), School of Geographical Sciences, Fujian Normal University

K

Keyan Fang

Institute for Cross-Straits Integrated Development, Key Laboratory of Humid Subtropical Eco-geographical Process (Ministry of Education), School of Geographical Sciences, Fujian Normal University

W

Wanru Tang

Institute for Cross-Straits Integrated Development, Key Laboratory of Humid Subtropical Eco-geographical Process (Ministry of Education), School of Geographical Sciences, Fujian Normal University

F

Feifei Zhou

Institute for Cross-Straits Integrated Development, Key Laboratory of Humid Subtropical Eco-geographical Process (Ministry of Education), School of Geographical Sciences, Fujian Normal University

F

Fei Liu

S

Sen Zhao

H

Hao Wu

J

Jinbao Li

Department of Geography, University of Hong Kong

Z

Zheng Zhao

T

Tinghai Ou

Regional Climate Group, Department of Earth Sciences, University of Gothenburg

X

Xiaoxun Xie

State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences

D

Deliang Chen