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Reply to: Landfalling hurricane track modes and decay

Nature volume 606, pages E12–E15 (2022)Cite this article

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The Original Article was published on 22 June 2022

replying to: K. T. F. Chan et al. Nature https://doi.org/10.1038/s41586-022-04791-1 (2022)

We thank Chan et al.1 (hereafter, CZ) for their critique of our study2 (hereafter, LC). In LC, we studied the change in the decay timescale τ of North Atlantic landfalling hurricanes between 1967 and 2018. By analysing the time series of τ and the factors that affect τ, we found that τ has increased by 94% over the past half-century and attributed this increase primarily to the contemporaneous rise in sea surface temperature (SST), with additional contributions from climate-modulated changes in hurricane tracks. We tested the robustness of these findings through several tests2; in response to CZ’s critique, here we conduct further tests. The results remain robust.

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Fig. 1: The change with time in decay timescale τ and effect of SST.
Fig. 2: Analysis of Ao time series. To compute Ao, we use CZ’s definition.

Data availability

The Atlantic HURDAT2 database is available at https://www.nhc.noaa.gov/data/hurdat/hurdat2-1851-2018-120319.txt. The Extended Best Track Dataset is available at https://rammb.cira.colostate.edu/research/tropical_cyclones/tc_extended_best_track_dataset/data/ebtrk_atlc_1988_2018.txt.

References

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Acknowledgements

This work was supported by the Okinawa Institute of Science and Technology Graduate University. We thank P. Shah and M. Howell for helpful feedback.

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Authors and Affiliations

  1. Fluid Mechanics Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan

    Lin Li & Pinaki Chakraborty

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  1. Lin Li
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Contributions

L.L. analysed the data; L.L. and P.C. discussed the results; P.C. wrote the Reply with input from L.L.; P.C. supervised the research.

Corresponding author

Correspondence to Pinaki Chakraborty.

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The authors declare no competing interests.

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Extended data

is available for this paper at https://doi.org/10.1038/s41586-022-04792-0.

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Extended data figures and tables

Extended Data Fig. 1 Two illustrative examples of identifying outliers using DBSCAN (left) and global threshold (right).

The data correspond to the results tabulated in Fig. 1b for resolution = 0.2o (a, b) and to CZ’s analysis for resolution = 0.5o (c, d). DBSCAN needs two parameters: the neighborhood search radius and the minimum number of neighbors required to form a cluster; epsilon and minpts, respectively, in MATLAB nomenclature. We set epsilon, the key parameter of the algorithm, to be the mean distance between pairs of the normalized data points, and minpts to be 1. We analyse the data points in the core cluster (the largest subset of the data, consisting of densely populated points) for computing trends; the data points in the remaining cluster or clusters are the outliers. To compare the results of using DBSCAN with the method used in LC, in the right panels, we show the results of using a global threshold of 2 standard deviations (2σ). The events inside the dotted box are identified as outliers using the 2σ threshold but not using DBSCAN. Note that their values of τ are comparable with the values of τ of other events in its local neighbourhood. While the outliers do not substantially affect the trends in smoothed data, they can substantially skew the trends in unsmoothed, noisy data. After excluding the outliers from CZ’s data for resolution = 0.5o, increase in τ becomes significant (at 95% CI) for season-averaged and event-level data, analogous to the case of CZ’s 66 events discussed in the text.

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Li, L., Chakraborty, P. Reply to: Landfalling hurricane track modes and decay. Nature 606, E12–E15 (2022). https://doi.org/10.1038/s41586-022-04792-0

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