An ENSO-Forecast Independent Statistical Model for the Prediction of Annual Atlantic Tropical Cyclone Frequency in April

Abstract EN

Statistical models for preseason prediction of annual Atlantic tropical cyclone (TC) and hurricane counts generally include El Niño/Southern Oscillation (ENSO) forecasts as a predictor.

As a result, the predictions from such models are often contaminated by the errors in ENSO forecasts.

In this study, it is found that the latent heat flux (LHF) over Eastern Tropical Pacific (ETP, defined as the region 0°–5°N, 115°–125°W) in spring is negatively correlated with the annual Atlantic TC and hurricane counts.

By using stepwise backward elimination regression, it is further shown that the March value of ETP LHF is a better predictor than the spring or summer ENSO index for Atlantic TC counts.

Leave-one-out cross validation indicates that the annual Atlantic TC counts predicted by this ENSO-independent statistical model show a remarkable correlation with the actual TC counts (R=0.72; P value <0.01).

For Atlantic hurricanes, the predictions using March ETP LHF and summer (July–September) ENSO indices show only minor differences except in moderate to strong El Niño years.

Thus, March ETP LHF is an excellent predictor for seasonal Atlantic TC prediction and a viable alternative to using ENSO index for Atlantic hurricane prediction.