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Early warning of the Indian Ocean Dipole using climate network analysis.
Lu, Zhenghui; Dong, Wenjie; Lu, Bo; Yuan, Naiming; Ma, Zhuguo; Bogachev, Mikhail I; Kurths, Juergen.
Afiliação
  • Lu Z; CAS Key Laboratory of Regional Climate Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
  • Dong W; School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China.
  • Lu B; Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai 519082, China.
  • Yuan N; Innovation Group of Earth System Model, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China.
  • Ma Z; Key Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081, China.
  • Bogachev MI; School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China.
  • Kurths J; Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai 519082, China.
Proc Natl Acad Sci U S A ; 119(11): e2109089119, 2022 03 15.
Article em En | MEDLINE | ID: mdl-35254900
ABSTRACT
SignificanceThe Indian Ocean Dipole (IOD), an air-sea coupled phenomenon over the tropical Indian Ocean, has substantial impacts on the climate, ecosystems, and society. Due to the winter predictability barrier, however, a reliable prediction of the IOD has been limited to 3 or 4 mo in advance. Our work approaches this problem from a new data-driven perspective the climate network analysis. Using this network-based method, an efficient early warning signal for the IOD event was revealed in boreal winter. Our approach can correctly predict the IOD events one calendar year in advance (from December of the previous year) with a hit rate of higher than 70%, which strongly outperforms current dynamic models.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Clima / Natureza / Modelos Teóricos Tipo de estudo: Prognostic_studies Idioma: En Revista: Proc Natl Acad Sci U S A Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Clima / Natureza / Modelos Teóricos Tipo de estudo: Prognostic_studies Idioma: En Revista: Proc Natl Acad Sci U S A Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China