Theoretical and Applied Climatology

, Volume 109, Issue 3–4, pp 383–395 | Cite as

Dependency of typhoon intensity and genesis locations on El Niño phase and SST shift over the western North Pacific

  • Kyung-Ja Ha
  • Soon-Jo Yoon
  • Kyung-Sook Yun
  • Jong-Seong Kug
  • Yeon-Soo Jang
  • Johnny C. L. Chan
Original Paper

Abstract

The effects of the El Niño-Southern Oscillation (ENSO) phase and the shifting of the ENSO sea surface temperature (SST) on the intensity of tropical cyclones (TC) have been extensively investigated in terms of TC genesis locations in the western North Pacific (WNP). To advance the hypothesis for a relation of genesis location–intensity that the TC formation location hints its intensity, two cases have been compared, which include the phase of the decaying El Niño turning over to La Niña (type I) and the phase that recovers to a neutral condition (type II). In addition, the shift of ENSO SST to the central Pacific warming (CPW) from the East Pacific warming (EPW) has been examined. The genesis potential index (GPI) and the accumulated cyclone energy have been applied to compare the differences between the ENSO phase and the TC formation location. It was apparent that ENSO influences the WNP typhoon formation location depending on the cycle of the ENSO phase. In addition, the typhoon activity was affected by the zonal shift of the El Niño SST. The CPW, which has maximum SST over the central Pacific, tends to have a persistently high GPI over the WNP in September–November and June–August, demonstrating that the formation locations of strong TCs significantly shift southeastward compared with the EPW having SST maximum over the eastern Pacific. CPW years revealed a distinguishable relationship between the TC formation location and the TC between the tropical depression (TD) + tropical storm (TS) and the intense typhoon of category 4 + 5.

Keywords

Tropical Cyclone Western North Pacific Tropical Cyclone Activity Tropical Cyclone Intensity Western North Pacific Subtropical High 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST, no. 2011-0021927). J.-S. Kug is partly supported by KORDI (PE98801, PE98563).

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Kyung-Ja Ha
    • 1
  • Soon-Jo Yoon
    • 1
  • Kyung-Sook Yun
    • 1
  • Jong-Seong Kug
    • 2
  • Yeon-Soo Jang
    • 2
  • Johnny C. L. Chan
    • 3
  1. 1.Division of Earth Environmental SystemPusan National UniversityBusanSouth Korea
  2. 2.Korea Ocean Research and Development InstituteAnsanSouth Korea
  3. 3.School of Energy and EnvironmentCity University of Hong KongHong KongChina

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