Articles | Volume 29, issue 1
Nonlin. Processes Geophys., 29, 1–15, 2022
https://doi.org/10.5194/npg-29-1-2022
Nonlin. Processes Geophys., 29, 1–15, 2022
https://doi.org/10.5194/npg-29-1-2022

Research article 10 Jan 2022

Research article | 10 Jan 2022

A waveform skewness index for measuring time series nonlinearity and its applications to the ENSO–Indian monsoon relationship

Justin Schulte et al.

Data sets

Climate Timeseries NOAA/OAR/ESRL PSD https://www.psl.noaa.gov/gcos_wgsp/Timeseries/

Homogeneous Indian Monthly Rainfall Data Sets (1871-2016) Indian Institute of Tropical Meteorology https://www.tropmet.res.in/DataArchival-51-Page

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Short summary
The skewness of a time series is commonly used to quantify the extent to which positive (negative) deviations from the mean are larger than negative (positive) ones. However, in some cases, traditional skewness may not provide reliable information about time series skewness, motivating the development of a waveform skewness index in this paper. The waveform skewness index is used to show that changes in the relationship strength between climate time series could arise from changes in skewness.