A tool for hierarchical climate regionalization

Earth Science Informatics volume 8, pages949–958(2015)Cite this article

Abstract

Climate regionalization is an important but often under-emphasized step in studies of climate variability. While most investigations of regional climate make at least an implicit attempt to focus on a study region or sub-regions that are climatically coherent in some respect, rigorous climate regionalization––in which the study area is divided on the basis of the most relevant climate metrics and at a resolution most appropriate to the data and the scientific question––has the potential to enhance the precision and explanatory power of climate studies in many cases. To facilitate the application of rigorous regionalization for climate studies, we introduce an improved hierarchical clustering method, describe a new open-source R package designed specifically for climate regionalization, and offer concise suggestions for performing appropriate regionalization. This paper describes the regionalization algorithms and presents a demonstration application in which the R package is used to regionalize Africa on the basis of interannual precipitation variability. Both the proposed methodology and the R package can be used for a broad range of applications and over different areas of the globe.

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References

  1. Argüeso D, Hidalgo-Muñoz J M, Gámiz-Fortis S R, Esteban-Parra M J, Dudhia J, Castro-Díez Y (2011) Evaluation of Wrf parameterizations for climate studies over southern spain using a multistep regionalization. J Clim 24

  2. Baeriswyl P-A, Rebetez M (1997) Regionalization of precipitation in Switzerland by means of principal component analysis. Theor Appl Climatol 58:31–41

    Article  Google Scholar 

  3. Burn DH (1989) Cluster analysis as applied to regional flood frequency. J Water Res Plan Manag 115:567–582

    Article  Google Scholar 

  4. Busuioc A, Chen D, Hellström C (2001) Temporal and spatial variability of precipitation in Sweden and its link with the large-scale atmospheric circulation. Tellus A 53:348–367

    Article  Google Scholar 

  5. Cimiano P, Hotho A, Staab S (2004) Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text. Proceedings of the 16th Eureopean Conference on Artificial Intelligence, Ecai’2004, Including Prestigious Applicants Of Intelligent Systems, Pais 2004

  6. Comrie AC, Glenn EC (1998) Principal components-based regionalization of precipitation regimes across the southwest United States and Northern Mexico, with an application to monsoon precipitation variability. Clim Res 10:201–215

    Article  Google Scholar 

  7. Dezfuli AK (2011) Spatio-temporal variability of seasonal rainfall in western equatorial africa. Theor Appl Climatol 104:57–69

    Article  Google Scholar 

  8. Dezfuli, A K, Nicholson S E (2013) The relationship of rainfall variability in western equatorial africa to the tropical oceans and atmospheric circulation. Part Ii: The boreal autumn. J Clim 26

  9. El-Hamdouchi A, Willett P (1989) Comparison of hierarchic agglomerative clustering methods for document retrieval. Comput J 32:220–227

    Article  Google Scholar 

  10. Fovell RG, Fovell M-YC (1993) Climate zones of the conterminous united states defined using cluster analysis. J Clim 6:2103–2135

    Article  Google Scholar 

  11. Gong X, Richman MB (1995) On the application of cluster analysis to growing season precipitation data in north america east of the rockies. J Clim 8:897–931

    Article  Google Scholar 

  12. Harris I, Jones P, Osborn T, Lister D (2013) Updated high-resolution grids of monthly climatic observations–the Cru Ts3. 10 dataset. Int J Climatol

  13. Isik S, Singh VP (2008) Hydrologic regionalization of watersheds in turkey. J Hydrol Eng 13:824–834

    Article  Google Scholar 

  14. Jain A K, Dubes R C (1988) Algorithms For Clustering Data. Prentice-Hall, Inc.

  15. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. Acm Comput Surv (Csur) 31:264–323

    Article  Google Scholar 

  16. Legendre P, Legendre L (2012) Numerical Ecology. Elsevier

  17. Manning CD, Raghavan P, Schütze H (2008) Introduction to Information Retrieval. Cambridge University Press, Cambridge

    Google Scholar 

  18. Munoz-Diaz D, Rodrigo F S (2004) Spatio-Temporal Patterns of Seasonal Rainfall In Spain (1912–2000) Using Cluster and Principal Component Analysis: Comparison. Annales Geophysicae. Copernicus Gmbh, 1435–1448

  19. Murtagh F (1983) A survey of recent advances in hierarchical clustering algorithms. Comput J 26:354–359

    Article  Google Scholar 

  20. Neuwirth E (2011) Rcolorbrewer: Colorbrewer Palettes. R Package Version, 1.0-5

  21. Nicholson S E, Dezfuli A K (2013) The relationship of rainfall variability in western equatorial africa to the tropical oceans and atmospheric circulation. Part i: the boreal spring. J Clim 26

  22. Nicholson SE, Klotter D, Dezfuli AK (2012) Spatial reconstruction of semi-quantitative precipitation fields over africa during the nineteenth century from documentary evidence and gauge data. Quat Res 78:13–23

    Article  Google Scholar 

  23. Ramachandra Rao A, Srinivas V (2006) Regionalization of watersheds by hybrid-cluster analysis. J Hydrol 318:37–56

    Article  Google Scholar 

  24. Rogers J, Mchugh M (2002) On the separability of the north Atlantic oscillation and arctic oscillation. Clim Dyn 19:599–608

    Article  Google Scholar 

  25. Sokal RR (1958) A statistical method for evaluating systematic relationships. Univ Kans Sci Bull 38:1409–1438

    Google Scholar 

  26. Team R C (2012) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2012

  27. Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244

    Article  Google Scholar 

  28. White D, Richman M, Yarnal B (1991) Climate regionalization and rotation of principal components. Int J Climatol 11:1–25

    Article  Google Scholar 

  29. Wilks D S (2011) Statistical Methods in the Atmospheric Sciences. Academic Press

Download references

Acknowledgments

This study was supported by the Department of Earth and Planetary Sciences, The Johns Hopkins University, and NASA Applied Sciences grant NNX09AT61G.

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Affiliations

  1. Department of Earth and Planetary Sciences, The Johns Hopkins University (JHU), 3400 N. Charles Street, Olin Hall, Baltimore, MD, 21218, USA

    Hamada S. Badr, Benjamin F. Zaitchik & Amin K. Dezfuli

Authors
  1. Hamada S. Badr
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  2. Benjamin F. Zaitchik
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  3. Amin K. Dezfuli
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Corresponding author

Correspondence to Hamada S. Badr.

Additional information

Communicated by: H. A. Babaie

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Badr, H.S., Zaitchik, B.F. & Dezfuli, A.K. A tool for hierarchical climate regionalization. Earth Sci Inform 8, 949–958 (2015). https://doi.org/10.1007/s12145-015-0221-7

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Keywords

  • Climate regionalization
  • Spatio-temporal analysis
  • Africa
  • Precipitation
  • Hierarchical clustering
  • Hybrid clustering
  • Multi-variate clustering
  • Cluster validation