Stratified statistical models of North Atlantic basin-wide and regional tropical cyclone counts

Authors

  • Michael E. Kozar,

    Corresponding author
    1. Department of Meteorology, Pennsylvania State University, University Park, Pennsylvania, USA
    2. Now at Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida, USA
    • Corresponding author: M. E. Kozar, Center for Ocean-Atmospheric Prediction Studies, Florida State University, 200 RM Johnson Bldg., 2035 E. Paul Dirac Dr., Tallahassee, FL 32306-2840, USA. (mkozar@coaps.fsu.edu)

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  • Michael E. Mann,

    1. Department of Meteorology, Pennsylvania State University, University Park, Pennsylvania, USA
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  • Suzana J. Camargo,

    1. Lamont-Doherty Earth Observatory, Earth Institute at Columbia University, Palisades, New York, USA
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  • James P. Kossin,

    1. National Climatic Data Center, NOAA, Asheville, North Carolina, USA
    2. Also at Cooperative Institute for Meteorological Satellite Studies, Madison, Wisconsin, USA
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  • Jenni L. Evans

    1. Department of Meteorology, Pennsylvania State University, University Park, Pennsylvania, USA
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Abstract

[1] Using the historical Atlantic tropical cyclone record, this study examines the empirical relationships between climate state variables and Atlantic tropical cyclone counts. The state variables considered as predictors include indices of the El Niño/Southern Oscillation and Northern Atlantic Oscillation, and both “local” and “relative” measures of Main Development Region sea surface temperature. Other predictors considered include indices measuring the Atlantic Meridional Mode and the West African monsoon. Using all of the potential predictors in a forward stepwise Poisson regression, we examine the relationships between tropical cyclone counts and climate state variables. As a further extension on past studies, both basin-wide named storm counts and cluster analysis time series representing distinct flavors of tropical cyclones, are modeled. A wide variety of cross validation metrics reveal that basin-wide counts or sums over appropriately chosen clusters may be more skillfully modeled than the individual cluster series. Ultimately, the most skillful models typically share three predictors: indices for the main development region sea surface temperatures, the El Niño/Southern Oscillation, and the North Atlantic Oscillation.

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