Climate Classifications: the Value of Unsupervised Clustering

Classifying the land surface according to different climate zones is often a prerequisite for global diagnostic or predictive modelling studies. Classical classications such as the prominent K̈oppen–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 classication to an unsupervised classication (k-means clustering). Generally speaking, we revisit the problem of “climate classication” 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 different 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 classications. 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, classication schemes like K̈oppen-Geiger will play an important role in the future. However, future developments in this area need to be assessed based on data driven approaches