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  • Matters Arising
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Uncertainties in tropical-cyclone translation speed

Nature volume 570, pages E6–E15 (2019)Cite this article

Matters Arising to this article was published on 05 June 2019

The Original Article was published on 06 June 2018

ARISING FROM J. P. Kossin Nature https://www.nature.com/articles/s41586-018-0158-3 (2018)

In the scientific literature there have been suggestions that anthropogenic climate change (ACC) may lead to slower movement of tropical cyclones, potentially resulting in more intense precipitation in their path. Motivated by this, a recent innovative study1 suggested that over about the past 70 years there has been a considerable monotonic slowdown in translational speed in many regions of the world. Here I raise doubt as to the veracity of that finding, because the long-term changes appear to be due primarily to a few abrupt, step-like changes, both natural and artificial, in the early part of the record. This greatly reduces the likelihood that the apparent slowdown is driven primarily by anthropogenic causes.

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Fig. 1: Time series of annually averaged TCS with model fits by region.
Fig. 2: Latitudinal profiles of TCS and change in sampling due to satellites.

Data availability

The tropical-cyclone data analysed in this study were taken from the International Best Track Archive for Climate Stewardship (IBTrACS; https://www.ncdc.noaa.gov/ibtracs/, file ftp://eclipse.ncdc.noaa.gov/pub/ibtracs/v03r10/all/csv/Allstorms.ibtracs_all.v03r10.csv). Indices of a variety of the most commonly cited regional climate modes of variability were obtained from several sources: the Earth System Research Laboratory (ESRL; https://www.esrl.noaa.gov/psd/data/climateindices/), the National Centers for Environmental Information (NCEI; https://www.ncdc.noaa.gov/teleconnections/), the National Center for Atmospheric Research (NCAR; https://climatedataguide.ucar.edu/climate-data/), the University of East Anglia (UEA; https://crudata.uea.ac.uk/cru/data/nao/) and the British Antarctic Survey (BAS; http://www.nerc-bas.ac.uk/public/icd/gjma/newsam.1957.2007.seas.txt).

Code availability

The FORTRAN code used to perform the change-point analyses6 is available from the author on request.

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Acknowledgements

I thank T. Knutson and C. Stock for comments on an earlier version of this manuscript, and J. Kossin for providing information and feedback on the processing of the IBTrACS data.

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Authors and Affiliations

  1. NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA

    John R. Lanzante

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  1. John R. Lanzante
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Correspondence to John R. Lanzante.

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Extended data figures and tables

Extended Data Fig. 1 Trends and significances by scenario and region.

Each bar represents the trend (km h−1 yr−1) corresponding to one of the nine scenarios given in Extended Data Table 4. Since some scenarios are redundant for a given basin, only the first occurrence is plotted. Stippling indicates significance at the 5% level based on a two-tailed Student’s t-test. Each panel corresponds to a different region: GL (a), NH (b), SH (c), NA (d), EP (e), WP (f), NI (g), SP (h) and SI (i).

Extended Data Table 1 Change-point analysis results for the basin TCS time series plotted in Fig. 1
Full size table
Extended Data Table 2 Relative weights based on the Bayesian information criterion
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Extended Data Table 3 Change-point analysis results for regional mode index time series
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Extended Data Table 4 Scenarios for the trend analyses in Extended Data Fig. 1
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Extended Data Table 5 Statistics for trend analysis
Full size table

Supplementary information

Supplementary Information

This file contains the Supplementary Methods, including sections describing: preparation of the storm track data and computation of translational speeds; Bayesian information criterion (BIC) analyses; generation of the curves in Fig. 2 and miscellaneous statistical considerations

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Lanzante, J.R. Uncertainties in tropical-cyclone translation speed. Nature 570, E6–E15 (2019). https://doi.org/10.1038/s41586-019-1223-2

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