Toward skillful forecasting of super El Niño events using a diffusion-based westerly wind burst parameterization

IF 8.5 1区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES
Chaopeng Ji, Mu Mu, Bo Qin, Tao Lian, Shijin Yuan, Jie Feng, Xunshu Song, Yuntao Wei, Guokun Dai, Jinyu Wang, Xianghui Fang
{"title":"Toward skillful forecasting of super El Niño events using a diffusion-based westerly wind burst parameterization","authors":"Chaopeng Ji, Mu Mu, Bo Qin, Tao Lian, Shijin Yuan, Jie Feng, Xunshu Song, Yuntao Wei, Guokun Dai, Jinyu Wang, Xianghui Fang","doi":"10.1038/s41612-025-01158-x","DOIUrl":null,"url":null,"abstract":"<p>Forecasting super El Niño remains challenging, partly due to poor representation of westerly wind bursts (WWBs). We developed an artificial intelligence-based denoising diffusion probabilistic model (DDPM) to skillfully parameterize WWBs, capturing their joint modulation by oceanic and atmospheric processes. The DDPM-based scheme effectively captures observed WWBs’ characteristics (e.g., frequency, intensity, and spatial center). When implemented in the Community Earth System Model, it outperforms both the control (CTRL, without WWBs parameterization) and conventional warm pool eastern edge (WPEE)-dependent parameterization in predicting intensity and seasonal phase-locking for super El Niños (1982/83, 1997/98, 2015/16). This improvement stems from DDPM’s realistic WWBs representation, correcting CTRL and WPEE’s biases of overly weak and westward-shifted winds during El Niño growth. Consequently, DDPM produces more realistic eastern Pacific sea surface temperature anomaly warming patterns. These findings underscore WWB's accuracy as key to super El Niño prediction and demonstrate machine learning’s potential for WWB's parameterization.</p>","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"210 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Climate and Atmospheric Science","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1038/s41612-025-01158-x","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Forecasting super El Niño remains challenging, partly due to poor representation of westerly wind bursts (WWBs). We developed an artificial intelligence-based denoising diffusion probabilistic model (DDPM) to skillfully parameterize WWBs, capturing their joint modulation by oceanic and atmospheric processes. The DDPM-based scheme effectively captures observed WWBs’ characteristics (e.g., frequency, intensity, and spatial center). When implemented in the Community Earth System Model, it outperforms both the control (CTRL, without WWBs parameterization) and conventional warm pool eastern edge (WPEE)-dependent parameterization in predicting intensity and seasonal phase-locking for super El Niños (1982/83, 1997/98, 2015/16). This improvement stems from DDPM’s realistic WWBs representation, correcting CTRL and WPEE’s biases of overly weak and westward-shifted winds during El Niño growth. Consequently, DDPM produces more realistic eastern Pacific sea surface temperature anomaly warming patterns. These findings underscore WWB's accuracy as key to super El Niño prediction and demonstrate machine learning’s potential for WWB's parameterization.

Abstract Image

利用基于扩散的西风爆发参数化技术预测超级El Niño事件
预测超级厄尔Niño仍然具有挑战性,部分原因是西风爆发(WWBs)的代表性不足。我们开发了一种基于人工智能的去噪扩散概率模型(DDPM)来巧妙地参数化水波,捕捉海洋和大气过程对水波的联合调制。基于ddpm的方案有效地捕获了观测到的水波特征(如频率、强度和空间中心)。当在社区地球系统模型中实施时,它在预测超级厄尔Niños的强度和季节锁相方面优于控制(CTRL,不含WWBs参数化)和传统的依赖于暖池东部边缘(WPEE)的参数化(1982/83,1997/98,2015/16)。这种改进源于DDPM对真实的wwb表示,纠正了CTRL和WPEE在El Niño生长期间过于微弱和向西移动的风的偏差。因此,DDPM产生的东太平洋海表温度异常增温型更为真实。这些发现强调了WWB的准确性是超级El Niño预测的关键,并展示了机器学习在WWB参数化方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
npj Climate and Atmospheric Science
npj Climate and Atmospheric Science Earth and Planetary Sciences-Atmospheric Science
CiteScore
8.80
自引率
3.30%
发文量
87
审稿时长
21 weeks
期刊介绍: npj Climate and Atmospheric Science is an open-access journal encompassing the relevant physical, chemical, and biological aspects of atmospheric and climate science. The journal places particular emphasis on regional studies that unveil new insights into specific localities, including examinations of local atmospheric composition, such as aerosols. The range of topics covered by the journal includes climate dynamics, climate variability, weather and climate prediction, climate change, ocean dynamics, weather extremes, air pollution, atmospheric chemistry (including aerosols), the hydrological cycle, and atmosphere–ocean and atmosphere–land interactions. The journal welcomes studies employing a diverse array of methods, including numerical and statistical modeling, the development and application of in situ observational techniques, remote sensing, and the development or evaluation of new reanalyses.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信