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The Defining Characteristics of ENSO Extremes and the Strong 2015/2016 El Nino

期刊

REVIEWS OF GEOPHYSICS
卷 55, 期 4, 页码 1079-1129

出版社

AMER GEOPHYSICAL UNION
DOI: 10.1002/2017RG000560

关键词

ENSO; El Nino; La Nina; greenhouse warming; extremes; Pacific

资金

  1. Centre for Southern Hemisphere Ocean Research (CSHOR)
  2. Australian Research Council
  3. Earth Science and Climate Change Hub of the Australian Government's National Environmental Science Programme (NESP)
  4. Earth Science and Climate Change Hub of NESP
  5. CSIRO Office of Chief Executive Science Leader award
  6. NOAA

向作者/读者索取更多资源

The year 2015 was special for climate scientists, particularly for the El Nino Southern Oscillation (ENSO) research community, as a major El Nino finally materialized after a long pause since the 1997/1998 extreme El Nino. It was scientifically exciting since, due to the short observational record, our knowledge of an extreme El Nino has been based only on the 1982/1983 and 1997/1998 events. The 2015/2016 El Nino was marked by many environmental disasters that are consistent with what is expected for an extreme El Nino. Considering the dramatic impacts of extreme El Nino, and the risk of a potential increase in frequency of ENSO extremes under greenhouse warming, it is timely to evaluate how the recent event fits into our understanding of ENSO extremes. Here we provide a review of ENSO, its nature and dynamics, and through analysis of various observed key variables, we outline the processes that characterize its extremes. The 2015/2016 El Nino brings a useful perspective into the state of understanding of these events and highlights areas for future research. While the 2015/2016 El Nino is characteristically distinct from the 1982/1983 and 1997/1998 events, it still can be considered as the first extreme El Nino of the 21st century. Its extremity can be attributed in part to unusually warm condition in 2014 and to long-term background warming. In effect, this study provides a list of physically meaningful indices that are straightforward to compute for identifying and tracking extreme ENSO events in observations and climate models.

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