Predicting Atlantic Seasonal Hurricane Activity 6–11 Months in Advance

William M. Gray Colorado State University, Fort Collins, Colorado

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Christopher W. Landsea Colorado State University, Fort Collins, Colorado

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Paul W. Mielke Jr. Colorado State University, Fort Collins, Colorado

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Kenneth J. Berry Colorado State University, Fort Collins, Colorado

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Abstract

A surprisingly strong long-range predictive signal exists for Atlantic-basin seasonal tropical cyclone activity. This predictive skill is related to two measures of West African rainfall in the prior year and to the phase of the stratospheric quasi-biennial oscillation of zonal winds at 30 mb and 50 mb, extrapolated ten months into the future. These predictors, both of which are available by 1 December, can be utilized to make skillful forecasts of Atlantic tropical cyclone activity in the following June-November season. Using jackknife methods to provide independent testing of datasets, it is found that these parameters can be used to forecast nearly half of the season-to-season variability for seven indices of Atlantic seasonal tropical cyclone activity as early as late November of the previous year.

Abstract

A surprisingly strong long-range predictive signal exists for Atlantic-basin seasonal tropical cyclone activity. This predictive skill is related to two measures of West African rainfall in the prior year and to the phase of the stratospheric quasi-biennial oscillation of zonal winds at 30 mb and 50 mb, extrapolated ten months into the future. These predictors, both of which are available by 1 December, can be utilized to make skillful forecasts of Atlantic tropical cyclone activity in the following June-November season. Using jackknife methods to provide independent testing of datasets, it is found that these parameters can be used to forecast nearly half of the season-to-season variability for seven indices of Atlantic seasonal tropical cyclone activity as early as late November of the previous year.

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