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Pawel Netzel and Tomasz Stepinski

Abstract

Climate and climate change are among the scientific topics most widely recognized by the public. Thus, climatologists seek out effective ways of communicating results of their research to various constituencies—a task made difficult by the complexity of the concept of climate. The current standard for communicated variability of climate on the global scale is a map based on the Köppen-Geiger classification (KGC) of climates, and maps of change in average annual temperatures and total annual precipitation for communicating climate change. The ClimateEx (Climate Explorer) project (http://sil.uc.edu/webapps/climateex/) communicates spatial variability and temporal change of global climate in a novel way by using the data science concept of similarity-based query. ClimateEx is implemented as a web-based visual spatial search tool. Users select a location (query), and ClimatEx returns a similarity map that visually communicates locations of places in the world having climates similar to the climate at a query location. ClimateEx can also inform about magnitude of temporal climate change by calculating a global map of local magnitudes of climate change. It offers personalized means of communicating climate heterogeneity and conveying magnitude of climate change in a single map. It has the advantage of relating climate to a user’s own experience, and is well-suited for communicating character of global climate to specialists and nonspecialists alike.

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Pawel Netzel and Tomasz Stepinski

Abstract

Classifying the land surface into climate types provides means of diagnosing relations between Earth’s physical and biological systems and the climate. Global climate classifications are also used to visualize climate change. Clustering climate datasets provides a natural approach to climate classification, but the rule-based Köppen–Geiger classification (KGC) is the one most widely used. Here, a comprehensive approach to the clustering-based classification of climates is presented. Local climate is defined as a multivariate time series of mean monthly climatic variables and the authors propose to use dynamic time warping (DTW) as a measure of dissimilarity between local climates. Also discussed are the choice of climatic variables, the importance of their proper normalization, and the advantage of using distance-based clustering algorithms. Using the WorldClim global climate dataset and different combinations of clustering parameters, 32 different clustering-based classifications are calculated. These classifications are compared between themselves and to the KGC using the information-theoretic V measure. It is found that the best classifications are obtained using three climate variables (temperature, precipitation, and temperature range), a data normalization that takes into account the skewed distribution of precipitation values, and the partitioning around medoids clustering algorithm. Two such classifications are compared in detail between each other and to the KGC. About half of the climate types found by clustering can be matched to the familiar KGC classes, but the rest differ in their climatic character and spatial distribution. Finally, it is demonstrated that clustering-based classification results in climate types that are internally more homogeneous and externally more distinct than climate types in the KGC.

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