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Changes in large-scale controls of Atlantic tropical cyclone activity with the phases of the Atlantic multidecadal oscillation

Abstract

Atlantic tropical cyclone activity is known to oscillate between multi-annual periods of high and low activity. These changes have been linked to the Atlantic multidecadal oscillation (AMO), a mode of variability in Atlantic sea surface temperature which modifies the large-scale conditions of the tropical Atlantic. Cyclone activity is also modulated at higher frequencies by a series of other climate factors, with some of these influences appearing to be more consistent than others. Using the HURDAT2 database and a second set of tropical cyclone data corrected for possible missing storms in the earlier part of the record, we investigate, through Poisson regressions, the relationship between a series of climate variables and a series of metrics of seasonal Atlantic cyclone activity during both phases of the AMO. We find that, while some influences, such as El Niño Southern oscillation, remain present regardless of the AMO phase, other climate factors show an influence during only one of the two phases. During the negative phase, Sahel precipitation and the North Atlantic oscillation (NAO) are measured to play a role, while during the positive phase, the 11-year solar cycle and dust concentration over the Atlantic appear to be more important. Furthermore, we show that during the negative phase of the AMO, the NAO influences all our measures of tropical cyclone activity, and we go on to provide evidence that this is not simply due to changes in steering current, the mechanism by which the NAO is usually understood to impact Atlantic cyclone activity. Finally, we conclude by demonstrating that our results are robust to the sample size as well as to the choice of the statistical model.

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Notes

  1. 1.

    A short-lived system is one for which the lifetime is shorter than 48 hours.

  2. 2.

    For a full discussion on this issue, we refer the interested reader to Vecchi and Knutson (2008).

  3. 3.

    We are using the latest version of HURDAT2, which was last updated in June 2013 to revise the 1851–1945 hurricane seasons.

  4. 4.

    Following results from the available literature, we performed our analyses using both September SSNs and SSNs averaged over ASO. We found that September SSNs generally returned smaller p values. We thus chose to include only those results obtained using September SSNs.

  5. 5.

    We performed the regressions using a range of ENSO indices and found the Niño3.4 and Niño4 indices to return the smallest p values. Regressions performed using the Southern Oscillation Index (SOI), Niño3 and Niño1+2 indices were, in general, also significant, but returned larger p values. Only the results obtained with the Niño3.4 are shown here.

  6. 6.

    Whereas Fink et al. (2010) used data covering the period 1921–2007, we used data covering 1900–2012. We repeated our analysis using their dataset and the results were not significantly affected.

  7. 7.

    The difference has been constructed using the ten most negative and ten most positive NAO (MJ) years during the period for which NCEP reanalysis is available, 1960–2012. Furthermore, failure to remove the linear trend from the GPI timeseries does not significantly impact the result.

  8. 8.

    The exact p values are given as supplementary information.

  9. 9.

    For the exact p values of each regression, please consult the two Excel files associated with the supplementary information.

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Acknowledgments

The authors would like to thank all the people and organizations who made their data available: Gabriel Vecchi, Amato Evan, the National Hurricane Center, the Climate Research Unit of East Anglia, the Hadley Centre, the Earth System Research Laboratory, the National Climatic Data Center, the Solar Influences Data Analysis Center, the National Geophysical Data Center, ECMWF, the Department of Earth Sciences at the University of Berlin and the Joint Institute for the Study of the Atmosphere and Ocean at the University of Washington. Special thanks to Andreas Fink and Malvin Schneidewind who provided us with additional data, and to Thomas Jagger for some valuable feedback in the earlier stage of this project. We would also like to thank Katherine Barrett for putting her proofreading skills at our disposal and Jean-Philippe Boucher for providing additional comments on the statistics of this paper. We are also most grateful to Chris Landsea and an anonymous reviewer for taking the time to review an earlier version of this document, and for their most helpful comments and suggestions. Finally, Mathieu Boudreault would like to acknowledge support from the Natural Sciences and Engineering Research Council of Canada, and Louis-Philippe Caron would like to acknowledge financial support from the EU-funded SPECS project (Grant # 3038378).

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Caron, LP., Boudreault, M. & Bruyère, C.L. Changes in large-scale controls of Atlantic tropical cyclone activity with the phases of the Atlantic multidecadal oscillation. Clim Dyn 44, 1801–1821 (2015). https://doi.org/10.1007/s00382-014-2186-5

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Keywords

  • Tropical cyclones
  • Atlantic variability
  • Poisson regression
  • Atlantic multi-decadal oscillation