| DOI: | 10.1175/JCLI-D-15-0640.1
|
| Scopus记录号: | 2-s2.0-84966350041
|
| 论文题名: | On using a clustering approach for global climate classification |
| 作者: | Netzel P.; Stepinski T.
|
| 刊名: | Journal of Climate
|
| ISSN: | 8948755
|
| 出版年: | 2016
|
| 卷: | 29, 期:9 | | 起始页码: | 3387
|
| 结束页码: | 3401
|
| 语种: | 英语
|
| Scopus关键词: | Classification (of information)
; Climate change
; Earth (planet)
; Information theory
; Classification results
; Climate classification/regimes
; Distance-based clustering algorithm
; Dynamic time warping
; Global climate dataset
; Multivariate time series
; Partitioning around medoids
; Statistical techniques
; Clustering algorithms
; climate change
; climate classification
; cluster analysis
; global climate
; multivariate analysis
; precipitation (climatology)
; time series analysis
; visualization
|
| 英文摘要: | 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. © 2016 American Meteorological Society. |
| Citation statistics: |
|
| 资源类型: | 期刊论文
|
| 标识符: | http://119.78.100.158/handle/2HF3EXSE/49960
|
| Appears in Collections: | 气候变化事实与影响
|
|
There are no files associated with this item.
|
|
作者单位: | Space Informatics Laboratory, Department of Geography, University of Cincinnati, Cincinnati, OH, United States
|
| Recommended Citation: | |
Netzel P.,Stepinski T.. On using a clustering approach for global climate classification[J]. Journal of Climate,2016-01-01,29(9)
|
|
|