Editorial Type:
Article
Article Type:
Research Article

Seasonal Tropical Cyclone Predictions Using Optimized Combinations of ENSO Regions: Application to the Coral Sea Basin

Hamish A. Ramsay School of Earth, Atmosphere and Environment and ARC Centre of Excellence for Climate System Science, Monash University, Clayton, Victoria, Australia

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Michael B. Richman School of Meteorology and Cooperative Institute for Meteorological Studies, University of Oklahoma, Norman, Oklahoma

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Lance M. Leslie School of Meteorology and Cooperative Institute for Meteorological Studies, University of Oklahoma, Norman, Oklahoma

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Print Publication:
15 Nov 2014
DOI:
https://doi.org/10.1175/JCLI-D-14-00017.1
Page(s):
8527–8542
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Abstract

This study examines combining ENSO sea surface temperature (SST) regions for seasonal prediction of Coral Sea tropical cyclone (TC) frequency. The Coral Sea averages ~4 TCs per season, but is characterized by strong interannual variability, with 1–9 TCs per season, over the period 1977–2012. A wavelet analysis confirms that ENSO is a key contributor to Coral Sea TC count (TCC) variability. Motivated by the impact of El Niño Modoki on regional climate anomalies, a suite of 38 linear models is constructed and assessed on its ability to predict Coral Sea seasonal TCC. Seasonal predictions of TCC are generated by a leave-one-out cross validation (LOOCV). An important finding is that models made up of multiple tropical Pacific SST regions, such as those that comprise the El Niño Modoki Index (EMI) or the Trans-Niño Index (TNI), perform considerably better than models comprising only single regions, such as Niño-3.4 or Niño-4. Moreover, enhanced (suppressed) TC activity is expected in the Coral Sea when the central Pacific is anomalously cool (warm) and the eastern and western Pacific are anomalously warm (cool) during austral winter. The best cross-validated model has persistent and statistically significantly high correlations with TCC (r > 0.5) at lead times of ~6 months prior to the mean onset of the Coral Sea TC season, whereas correlations based heavily on the widely used Niño-3.4 region are not statistically significant or meaningful (r = 0.09) for the same lead times. Of the 38 models assessed, several optimized forms of the EMI and of the TNI perform best.

Corresponding author address: Hamish Ramsay, School of Earth, Atmosphere and Environment, Monash University, Clayton, Victoria 3800, Australia. E-mail: hamish.ramsay@monash.edu

Abstract

This study examines combining ENSO sea surface temperature (SST) regions for seasonal prediction of Coral Sea tropical cyclone (TC) frequency. The Coral Sea averages ~4 TCs per season, but is characterized by strong interannual variability, with 1–9 TCs per season, over the period 1977–2012. A wavelet analysis confirms that ENSO is a key contributor to Coral Sea TC count (TCC) variability. Motivated by the impact of El Niño Modoki on regional climate anomalies, a suite of 38 linear models is constructed and assessed on its ability to predict Coral Sea seasonal TCC. Seasonal predictions of TCC are generated by a leave-one-out cross validation (LOOCV). An important finding is that models made up of multiple tropical Pacific SST regions, such as those that comprise the El Niño Modoki Index (EMI) or the Trans-Niño Index (TNI), perform considerably better than models comprising only single regions, such as Niño-3.4 or Niño-4. Moreover, enhanced (suppressed) TC activity is expected in the Coral Sea when the central Pacific is anomalously cool (warm) and the eastern and western Pacific are anomalously warm (cool) during austral winter. The best cross-validated model has persistent and statistically significantly high correlations with TCC (r > 0.5) at lead times of ~6 months prior to the mean onset of the Coral Sea TC season, whereas correlations based heavily on the widely used Niño-3.4 region are not statistically significant or meaningful (r = 0.09) for the same lead times. Of the 38 models assessed, several optimized forms of the EMI and of the TNI perform best.

Corresponding author address: Hamish Ramsay, School of Earth, Atmosphere and Environment, Monash University, Clayton, Victoria 3800, Australia. E-mail: hamish.ramsay@monash.edu
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