Abstract

Over the past two decades, forecasting the El Niño–Southern Oscillation has become increasingly challenging, primarily due to the inherent difficulty in predicting Central Pacific (CP) El Niño events. In this study, we present a detailed analysis by explicitly quantifying the role of the tropical Indian Ocean (IO) on the initial uncertainty of CP-El Niño predictions. We identify optimal observation sites within the tropical IO that significantly reduce this uncertainty and validate their efficacy through observing system simulation experiments within a fully coupled climate prediction system. Our results demonstrate a remarkable achievement in the root mean squared errors of the prediction initial conditions using Community Earth System Model. This study not only enriches our understanding of the interplay between the tropical IO and CP-El Niño but also offers a concrete strategy for enhancing CP-El Niño forecasts, advancing seasonal prediction capabilities, and bolstering global resilience against the escalating frequency of CP-El Niño events.

Details

Title
Quantifying the role of tropical Indian Ocean observations to central Pacific El Niño prediction
Author
Li, Xiaojing 1   VIAFID ORCID Logo  ; Tang, Youmin 2 ; McPhaden, Michael J 3   VIAFID ORCID Logo  ; Zhou, Lei 4 ; Li, Yi 5 ; Song, Xunshu 1   VIAFID ORCID Logo  ; Lian, Tao 1   VIAFID ORCID Logo  ; Dake, Chen 1 

 State Key Laboratory of Satellite Ocean Environment Dynamics , Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, People’s Republic of China 
 Department of Geography, Earth and Environmental Sciences, University of Northern British Columbia , Prince George, Canada; College of Oceanography, Hohai University , Nanjing, People’s Republic of China 
 NOAA/PMEL , Seattle, WA, United States of America 
 School of Oceanography, Shanghai Jiao Tong University , Shanghai, People’s Republic of China 
 College of Oceanography, Hohai University , Nanjing, People’s Republic of China 
First page
074023
Publication year
2025
Publication date
Jul 2025
Publisher
IOP Publishing
e-ISSN
17489326
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3216556236
Copyright
© 2025 The Author(s). Published by IOP Publishing Ltd. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.