4.7 Article

Contributions of SST Anomalies in the Indo-Pacific Ocean to the Interannual Variability of Tropical Cyclone Genesis Frequency over the Western North Pacific

期刊

JOURNAL OF CLIMATE
卷 32, 期 11, 页码 3357-3372

出版社

AMER METEOROLOGICAL SOC
DOI: 10.1175/JCLI-D-18-0439.1

关键词

North Pacific Ocean; Sea surface temperature; Tropical cyclones; Seasonal forecasting; Interannual variability

资金

  1. National Natural Science Foundation of China [41875114, 41875057]
  2. Open Research Fund of the State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences [2017LASW-A01]

向作者/读者索取更多资源

This study attempts to evaluate quantitatively the contributions of sea surface temperature (SST) anomalies in the Indo-Pacific Ocean to the interannual variability of tropical cyclone (TC) genesis frequency (TCGF) over the western North Pacific (WNP). Three SST factors in the Indo-Pacific Ocean are found to play key roles in modulating the interannual variability of WNP TCGF. They are summer SST anomaly in the east Indian Ocean (EIO), the summer El Nino-Southern Oscillation Modoki index (EMI), and the spring SST gradient (SSTG) between the southwestern Pacific and the western Pacific warm pool. Results show that the three factors together can explain 72% of the total variance of WNP TCGF in the typhoon season for the period 1980-2015. Among them, the spring SSTG and the summer EIO contribute predominantly to the interannual variability of TCGF, followed by the summer EMI, with respective contributions being 39%, 38%, and 23%. Further analysis shows that the summer EMI was affected significantly by the spring SSTG and thus had a relatively lower contribution to the TCGF than the spring SSTG. In addition, a statistical model is constructed to predict the WNP TCGF in the typhoon season by a combination of the May EIO and the spring SSTG. The new model can reproduce well the observed WNP TCGF and shows an overall better skill than the ECMWF Seasonal Forecasting System 5 (SEAS5) hindcasts. This statistical model provides a good tool for seasonal prediction of WNP TCGF.

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