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Testing for Collective Statistical Significance in Climate Change Detection Studies

  • Radan HuthEmail author
  • Martin Dubrovský
Chapter
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

We examined several approaches to detecting statistical significance of trends defined on a grid, that is, on a regional scale. To this end, we introduced a novel simple procedure of significance testing based on counting signs of local trends (sign test), and comparing it with four other approaches to testing collective significance of trends (counting, extended Kendall, Walker, and FDR tests). Synthetic data were used to construct the null distributions of trend statistics and determine critical values of the tests. The application of the five tests to real datasets reveals that outcomes of the tests may differ even though trends are locally significant at the majority of the grid points.

Keywords

Climate change Significance testing Collective statistical significance Temperature Trend detection 

Notes

Acknowledgements

This study was supported by the Czech Science Foundation, project 16-04676S.

References

  1. 1.
    DelSole, T., Yang, X.S.: Field significance of regression patterns. J. Climate 24, 5094–5107 (2011)CrossRefGoogle Scholar
  2. 2.
    Katz, R.W., Brown, B.G.: The problem of multiplicity in research on teleconnections. Int. J. Climatol. 11, 505–513 (1991)CrossRefGoogle Scholar
  3. 3.
    Khaliq, M.N., Ouarda, T.B.M.J., Gachon, P., Sushama, L., St-Hilaire, A.: Identification of hydrological trends in the presence of serial and cross correlations: a review of selected methods and their application to annual flow regimes of Canadian rivers. J. Hydrol. 368, 117–130 (2009)CrossRefGoogle Scholar
  4. 4.
    Livezey, R.E., Chen, W.Y.: Statistical field significance and its determination by Monte Carlo techniques. Mon. Weather Rev. 111, 46–59 (1983)CrossRefGoogle Scholar
  5. 5.
    Ventura, V., Paciorek, C.J., Risbey, J.S.: Controlling the proportion of falsely rejected hypotheses when conducting multiple tests with climatological data. J. Climate 17, 4343–4356 (2004)CrossRefGoogle Scholar
  6. 6.
    Wilks, D.S.: On “field significance” and the false discovery rate. J. Appl. Meteorol. Climatol. 45, 1181–1189 (2006)CrossRefGoogle Scholar
  7. 7.
    Wilks, D.S.: The stippling shows statistically significant gridpoints. How research results are routinely overstated and overinterpreted, and what to do about it. Bull. Amer. Meteorol. Soc. 97, 2263–2273 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Physical Geography and GeoecologyCharles UniversityPragueCzech Republic
  2. 2.Institute of Atmospheric PhysicsCzech Academy of SciencesPragueCzech Republic

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