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Rethinking Indian monsoon rainfall prediction in the context of recent global warming

Author

Listed:
  • Bin Wang

    (University of Hawaii at Manoa
    Earth System Modeling Center, Nanjing University of Information Science and Technology)

  • Baoqiang Xiang

    (NOAA/Geophysical Fluid Dynamics Laboratory
    University Corporation for Atmospheric Research)

  • Juan Li

    (University of Hawaii at Manoa)

  • Peter J. Webster

    (Earth and Atmospheric Sciences, Georgia Institute of Technology)

  • Madhavan N. Rajeevan

    (Earth System Science Organization, Ministry of Earth Sciences)

  • Jian Liu

    (Key Laboratories for Virtual Geographic Environment and Numerical Simulation of Large Scale Complex System, School of Geography Science, Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Kyung-Ja Ha

    (Pusan National University)

Abstract

Prediction of Indian summer monsoon rainfall (ISMR) is at the heart of tropical climate prediction. Despite enormous progress having been made in predicting ISMR since 1886, the operational forecasts during recent decades (1989–2012) have little skill. Here we show, with both dynamical and physical–empirical models, that this recent failure is largely due to the models’ inability to capture new predictability sources emerging during recent global warming, that is, the development of the central-Pacific El Nino-Southern Oscillation (CP–ENSO), the rapid deepening of the Asian Low and the strengthening of North and South Pacific Highs during boreal spring. A physical–empirical model that captures these new predictors can produce an independent forecast skill of 0.51 for 1989–2012 and a 92-year retrospective forecast skill of 0.64 for 1921–2012. The recent low skills of the dynamical models are attributed to deficiencies in capturing the developing CP–ENSO and anomalous Asian Low. The results reveal a considerable gap between ISMR prediction skill and predictability.

Suggested Citation

  • Bin Wang & Baoqiang Xiang & Juan Li & Peter J. Webster & Madhavan N. Rajeevan & Jian Liu & Kyung-Ja Ha, 2015. "Rethinking Indian monsoon rainfall prediction in the context of recent global warming," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms8154
    DOI: 10.1038/ncomms8154
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    Cited by:

    1. Alireza Farrokhi & Saeed Farzin & Sayed-Farhad Mousavi, 2020. "A New Framework for Evaluation of Rainfall Temporal Variability through Principal Component Analysis, Hybrid Adaptive Neuro-Fuzzy Inference System, and Innovative Trend Analysis Methodology," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3363-3385, August.
    2. Tuantuan Zhang & Xingwen Jiang & Song Yang & Junwen Chen & Zhenning Li, 2022. "A predictable prospect of the South Asian summer monsoon," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Mehebub Sahana & Sufia Rehman & Raihan Ahmed & Haroon Sajjad, 2021. "Analyzing climate variability and its effects in Sundarban Biosphere Reserve, India: reaffirmation from local communities," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(2), pages 2465-2492, February.

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