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A predictable prospect of the South Asian summer monsoon.
Zhang, Tuantuan; Jiang, Xingwen; Yang, Song; Chen, Junwen; Li, Zhenning.
Afiliación
  • Zhang T; School of Atmospheric Sciences, Sun Yat-sen University, Southern Laboratory of Ocean Science and Engineering (Zhuhai), Zhuhai, Guangdong, 519082, China.
  • Jiang X; Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Zhuhai, Guangdong, 519082, China.
  • Yang S; Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Institute of Plateau Meteorology, China Meteorological Administration, Chengdu, Sichuan, 610072, China. xingwen.jiang@yahoo.com.
  • Chen J; School of Atmospheric Sciences, Sun Yat-sen University, Southern Laboratory of Ocean Science and Engineering (Zhuhai), Zhuhai, Guangdong, 519082, China.
  • Li Z; Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Zhuhai, Guangdong, 519082, China.
Nat Commun ; 13(1): 7080, 2022 11 18.
Article en En | MEDLINE | ID: mdl-36400782
ABSTRACT
Prediction of the South Asian summer monsoon (SASM) has remained a challenge for both scientific research and operational climate prediction for decades. By identifying two dominant modes of the SASM, here we show that the unsatisfactory prediction may be due to the fact that the existing SASM indices are mostly related to the less predictable second mode. The first mode, in fact, is highly predictable. It is physically linked to the variation of the Indian monsoon trough coupled with large rainfall anomalies over core monsoon zone and the northern Bay of Bengal. An index is constructed as a physical proxy of this first mode, which can be well predicted one season in advance, with an overall skill of 0.698 for 1979-2020. This result suggests a predictable prospect of the SASM, and we recommend the new index for real-time monitoring and prediction of the SASM.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Clima / Tormentas Ciclónicas Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Clima / Tormentas Ciclónicas Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2022 Tipo del documento: Article País de afiliación: China
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