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Potential linkages of precipitation extremes in Beijing-Tianjin-Hebei region, China, with large-scale climate patterns using wavelet-based approaches

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Abstract

In this study, various wavelet analysis methods are used to investigate possible influences of large-scale climate patterns, such as El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Indian Ocean Dipole (IOD), on precipitation extremes over Beijing-Tianjin-Hebei region in China at different time scales. Firstly, the temporal patterns of precipitation extremes are detected by wavelet transform. Significant annual or inter-annual oscillations for the precipitation extremes during 1958–2017, with periodicities of around 0.5–1 year, 1–2 years, and 2–5 years were being found for monthly, seasonal, and annual time series, respectively. Subsequently, wavelet coherence method is used to identify the dominant driving factors of precipitation extremes, with ENSO, IOD, and NAO showing stronger correlations with monthly, seasonal, and annual precipitation extremes, respectively. Meanwhile, partial wavelet coherence analyses indicate that the standalone influences of climate factors may be weak, and the influences seem to be stronger because of their interdependences on other climate indices. Finally, multiple wavelet coherences reveal that variations of precipitation extremes could be better explained by combinations of two or more factors, although the additional explanatory variable may have not a significant increase in percent number of significant coherence.

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Data availability

All the precipitation data can be downloaded from the website of China Meteorological Administration (http://www.data.cma.cn). The NINO3.4, NAO, and PDO can be obtained from NOAA Earth System Research Laboratory’s Physical Sciences Division (http://www.ersl.noaa.gov/psd), and the IOD is obtained from the Japan Agency for Marine-Earth Science and Technology (http://www.jamstec.go.jp)

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Acknowledgments

We would like to acknowledge the China Meteorological Administration (http://www.data.cma.cn), the NOAA Earth System Research Laboratory’s Physical Sciences Division (http://www.ersl.noaa.gov/psd), and the Japan Agency for Marine-Earth Science and Technology (http://www.jamstec.go.jp) for their support of precipitation data and climate indices. We are also thankful to Dr. Hu Wei (The New Zealand Institute for Plant & Food Research Limited) and Ms. Zhao Ruiying (Zhejiang University) for their help in multiple wavelet coherence. The first author is also grateful for the support from the China Scholarship Council.

Funding

This study was supported by the Fundamental Research Funds for the Central Universities (No. 2015XKMS034), the National Key Research & Development Program of China (No. 2017YFC1502701), the National Natural Science Foundation of China (No. 51609242 and No. 51979271), and the China Postdoctoral Science Foundation (No. 2018M632333). This project was also funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. School of Resources and Geosciences, China University of Mining & Technology, Xuzhou, 221116, China

    Xiaomeng Song, Chunhua Zhang, Xianju Zou, Yuchen Mo & Yimin Tian

  2. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, 210029, China

    Xiaomeng Song & Jianyun Zhang

Authors
  1. Xiaomeng Song
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  2. Chunhua Zhang
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  3. Jianyun Zhang
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  4. Xianju Zou
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  5. Yuchen Mo
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Contributions

Conceptualization: X. Song and J. Zhang

Formal analysis: X. Song, C. Zhang, X. Zou

Model calculation: X. Song, C. Zhang, X. Zou, Y. Mo, Y. Tian

Funding: X. Song and J. Zhang

Writing, original draft: X. Song

Writing, review and editing: all the authors

Corresponding author

Correspondence to Xiaomeng Song.

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The authors declare that they have no conflict of interest.

Code availability

The original MATLAB code can be accessed at http://grinsted.github.io/wavelet-coherence (for the wavelet transform coherence), http://www.cityu.edu.hk/gcacic/wavelet/ (for the partial wavelet coherence), and https://doi.org/10.5194/hess-20-3183-2016-supplement (for the multiple wavelet coherence). All the revised code for wavelet analysis can be request to the corresponding author.

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Song, X., Zhang, C., Zhang, J. et al. Potential linkages of precipitation extremes in Beijing-Tianjin-Hebei region, China, with large-scale climate patterns using wavelet-based approaches. Theor Appl Climatol 141, 1251–1269 (2020). https://doi.org/10.1007/s00704-020-03247-8

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