A Dual-Core Model for ENSO Diversity: Unifying Model Hierarchies for Realistic Simulations

A Dual-Core Model for ENSO Diversity: Unifying Model Hierarchies for Realistic Simulations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Dual-Core Model for ENSO Diversity: Unifying Model Hierarchies for Realistic Simulations Xiang-Hui Fang, Jinyu Wang, Nan Chen, Mu Mu, Bo Qin, Chaopeng Ji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6258537/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jul, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted 11 You are reading this latest preprint version Abstract Despite advances in climate modeling, simulating the El Niño-Southern Oscillation (ENSO) remains challenging due to its spatiotemporal diversity and complexity. To address this, we build upon existing model hierarchies to develop a new unified modeling platform, which provides practical, scalable, and accurate tools for advancing ENSO research. Within this framework, we introduce a dual-core ENSO model (DCM) that integrates two widely used ENSO modeling approaches: a linear stochastic model confined to the equator and a nonlinear intermediate model extending off-equator. The stochastic model ensures computational efficiency and statistical accuracy. It captures essential ENSO characteristics and reproduces the observed non-Gaussian statistics. Meanwhile, the nonlinear model assimilates pseudo-observations from the stochastic model while resolving key air-sea interactions, such as feedback balances and spatial patterns of sea surface temperature anomalies (SSTA) during El Niño peaks and improving western-central Pacific SSTA magnitudes and spatial accuracy. The DCM effectively captures the realistic dynamical and statistical features of the ENSO diversity and complexity. Notably, the computational efficiency of the DCM facilitates a rapid generation of extended ENSO datasets, overcoming observational limitations. The outcome facilitates the analysis of long-term variations, advancing our understanding of ENSO and many other climate phenomena. Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics Earth and environmental sciences/Climate sciences/Ocean sciences/Physical oceanography Full Text Additional Declarations No competing interests reported. Supplementary Files NPJSupplementary.pdf Cite Share Download PDF Status: Published Journal Publication published 14 Jul, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted Editorial decision: Revision requested 22 Apr, 2025 Reviews received at journal 16 Apr, 2025 Reviews received at journal 16 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviewers agreed at journal 04 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviewers agreed at journal 20 Mar, 2025 Reviewers invited by journal 20 Mar, 2025 Editor assigned by journal 20 Mar, 2025 Submission checks completed at journal 20 Mar, 2025 First submitted to journal 19 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6258537","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":435127596,"identity":"25d29b66-410b-48e2-9788-858dc658ef87","order_by":0,"name":"Xiang-Hui 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