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This work was supported by the Okinawa Institute of Science and Technology Graduate University. We thank P. Shah and M. Howell for helpful feedback.
The authors declare no competing interests.
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