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Climate classifications: the value of unsupervised clustering

2012

Conference Paper

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Classifying the land surface according to di erent climate zones is often a prerequisite for global diagnostic or predictive modelling studies. Classical classifications such as the prominent K¨oppen–Geiger (KG) approach rely on heuristic decision rules. Although these heuristics may transport some process understanding, such a discretization may appear “arbitrary” from a data oriented perspective. In this contribution we compare the precision of a KG classification to an unsupervised classification (k-means clustering). Generally speaking, we revisit the problem of “climate classification” by investigating the inherent patterns in multiple data streams in a purely data driven way. One question is whether we can reproduce the KG boundaries by exploring di erent combinations of climate and remotely sensed vegetation variables. In this context we also investigate whether climate and vegetation variables build similar clusters. In terms of statistical performances, k-means clearly outperforms classical climate classifications. However, a subsequent stability analysis only reveals a meaningful number of clusters if both climate and vegetation data are considered in the analysis. This is a setback for the hope to explain vegetation by means of climate alone. Clearly, classification schemes like K¨oppen-Geiger will play an important role in the future. However, future developments in this area need to be assessed based on data driven approaches.

Author(s): Zscheischler, J. and Mahecha, MD. and Harmeling, S.
Book Title: Proceedings of the International Conference on Computational Science
Volume: 9
Pages: 897-906
Year: 2012
Month: June
Day: 0

Series: Procedia Computer Science
Editors: H. Ali, Y. Shi, D. Khazanchi, M. Lees, G.D. van Albada, J. Dongarra, P.M.A. Sloot, J. Dongarra
Publisher: Elsevier

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Address: Amsterdam, Netherlands
DOI: 10.1016/j.procs.2012.04.096
Event Name: ICCS 2012
Event Place: Omaha, NE, USA

Links: Web

BibTex

@inproceedings{ZscheischlerMH2012,
  title = {Climate classifications: the value of unsupervised clustering},
  author = {Zscheischler, J. and Mahecha, MD. and Harmeling, S.},
  booktitle = {Proceedings of the International Conference on Computational Science },
  volume = {9},
  pages = {897-906},
  series = {Procedia Computer Science},
  editors = {H. Ali, Y. Shi, D. Khazanchi, M. Lees, G.D. van Albada, J. Dongarra, P.M.A. Sloot, J. Dongarra},
  publisher = {Elsevier},
  address = {Amsterdam, Netherlands},
  month = jun,
  year = {2012},
  month_numeric = {6}
}