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Influence of upper ocean on Indian summer monsoon rainfall: studies by observation and NCEP climate forecast system (CFSv2)
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  • 作者:Hemantkumar S. Chaudhari ; Samir Pokhrel ; H. Rahman…
  • 刊名:Theoretical and Applied Climatology
  • 出版年:2016
  • 出版时间:August 2016
  • 年:2016
  • 卷:125
  • 期:3-4
  • 页码:413-426
  • 全文大小:5,114 KB
  • 刊物类别:Earth and Environmental Science
  • 刊物主题:Earth sciences
    Meteorology and Climatology
    Atmospheric Protection, Air Quality Control and Air Pollution
    Climate Change
    Waste Water Technology, Water Pollution Control, Water Management and Aquatic Pollution
  • 出版者:Springer Wien
  • ISSN:1434-4483
  • 卷排序:125
文摘
This study explores the role played by ocean processes in influencing Indian summer monsoon rainfall (ISMR) and compares the observed findings with National Centers for Environmental Prediction (NCEP)-coupled model Climate Forecast System, version 2 (CFSv2). The excess and deficit ISMR clearly brings out the distinct signatures in sea surface height (SSH) anomaly, thermocline and mixed layer depth over north Indian Ocean. CFSv2 is successful in simulating SSH anomalies, especially over Arabian Sea and Bay of Bengal region. CFSv2 captures observed findings of SSH anomalies during flood and drought (e.g., Rossby wave propagation which reaches western Bay of Bengal (BoB) during flood years, Rossby wave propagation which did not reach western BoB during drought). It highlights the ability of CFSv2 to simulate the basic ocean processes which governs the SSH variability. These differences are basically generated by upwelling and downwelling caused by the equatorial and coastal Kelvin and Rossby waves, thereby causing difference in SSH anomaly and thermocline, and subsequently modifying the convection centers, which dictates precipitation over the Indian subcontinent region. Since the observed SSH anomaly and thermal structure show distinct characteristic features with respect to strong and weak ISMR variability, the assimilation of real ocean data in terms of satellite products (like SSHA from AVISO/SARAL) bestow great promise for the future improvement.

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