A dual-core model for ENSO diversity: unifying model hierarchies for realistic simulations
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, sc...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-025-01164-z |
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| author | Jinyu Wang Xianghui Fang Nan Chen Bo Qin Mu Mu Chaopeng Ji |
| author_facet | Jinyu Wang Xianghui Fang Nan Chen Bo Qin Mu Mu Chaopeng Ji |
| author_sort | Jinyu Wang |
| collection | DOAJ |
| description | 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 oceanic feedback balances and spatial patterns of sea surface temperature anomalies during El Niño peaks. The DCM effectively captures the realistic dynamical and statistical features of the ENSO diversity and complexity. The computational efficiency of the DCM also facilitates a rapid generation of extended ENSO datasets, overcoming observational limitations. |
| format | Article |
| id | doaj-art-84f66d31d95f46a5afb137e1b609e6ec |
| institution | Kabale University |
| issn | 2397-3722 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Climate and Atmospheric Science |
| spelling | doaj-art-84f66d31d95f46a5afb137e1b609e6ec2025-08-20T03:42:27ZengNature Portfolionpj Climate and Atmospheric Science2397-37222025-07-018111210.1038/s41612-025-01164-zA dual-core model for ENSO diversity: unifying model hierarchies for realistic simulationsJinyu Wang0Xianghui Fang1Nan Chen2Bo Qin3Mu Mu4Chaopeng Ji5Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan UniversityDepartment of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan UniversityDepartment of Mathematics, University of Wisconsin-MadisonDepartment of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan UniversityDepartment of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan UniversityDepartment of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan UniversityAbstract 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 oceanic feedback balances and spatial patterns of sea surface temperature anomalies during El Niño peaks. The DCM effectively captures the realistic dynamical and statistical features of the ENSO diversity and complexity. The computational efficiency of the DCM also facilitates a rapid generation of extended ENSO datasets, overcoming observational limitations.https://doi.org/10.1038/s41612-025-01164-z |
| spellingShingle | Jinyu Wang Xianghui Fang Nan Chen Bo Qin Mu Mu Chaopeng Ji A dual-core model for ENSO diversity: unifying model hierarchies for realistic simulations npj Climate and Atmospheric Science |
| title | A dual-core model for ENSO diversity: unifying model hierarchies for realistic simulations |
| title_full | A dual-core model for ENSO diversity: unifying model hierarchies for realistic simulations |
| title_fullStr | A dual-core model for ENSO diversity: unifying model hierarchies for realistic simulations |
| title_full_unstemmed | A dual-core model for ENSO diversity: unifying model hierarchies for realistic simulations |
| title_short | A dual-core model for ENSO diversity: unifying model hierarchies for realistic simulations |
| title_sort | dual core model for enso diversity unifying model hierarchies for realistic simulations |
| url | https://doi.org/10.1038/s41612-025-01164-z |
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