Journal of Geographical Sciences ›› 2018, Vol. 28 ›› Issue (6): 802-818.doi: 10.1007/s11442-018-1506-9

• Research Articles • Previous Articles     Next Articles

Investigating long-term trends of climate change and their spatial variations caused by regional and local environments through data mining

Yichun XIE1,2(), Yang ZHANG3, Hai LAN4, Lishen MAO1, Shi ZENG5, Yulu CHEN1   

  1. 1. Institute for Geospatial Research and Education, Eastern Michigan University, Ypsilanti, Michigan 48197, USA
    2. Guangzhou Institute of Geography, Guangzhou 510070, China
    3. Department of Computer Science, Indiana University, Bloomington, Indiana 47405, USA
    4. Department of Computer Science, New York University, NY 10012, USA
    5. Center for Advanced Spatial Analysis, University College London, London WC1E 6BT, UK
  • Received:2017-08-23 Accepted:2017-12-10 Online:2018-06-20 Published:2018-06-20
  • About author:

    Author: Xie Yichun (1956-), PhD and Professor, specialized in urban modelling, ecological modelling and environmental modelling. E-mail:

  • Supported by:
    Guangdong Innovative and Entrepreneurial Research Team Program, No.2016ZT06D336;GDAS Special Project of Science and Technology Development, No.2017GDASCX-0101


Climate change is a global phenomenon but is modified by regional and local environmental conditions. Moreover, climate change exhibits remarkable cyclical oscillations and disturbances, which often mask and distort the long-term trends of climate change we would like to identify. Inspired by recent advancements in data mining, we experimented with empirical mode decomposition (EMD) technique to extract long-term change trends from climate data. We applied GIS elevation model to construct 3D EMD trend surface to visualize spatial variations of climate change over regions and biomes. We then computed various time-series similarity measures and plot them to examine spatial patterns across meteorological stations. We conducted a case study in Inner Mongolia based on daily records of precipitation and temperature at 45 meteorological stations from 1959 to 2010. The EMD curves effectively illustrated the long-term trends of climate change. The EMD 3D surfaces revealed regional variations of climate change, while the EMD similarity plots disclosed cross-station deviations. In brief, the change trends of temperature were significantly different from those of precipitation. Noticeable regional patterns and local disturbances of the changes in both temperature and precipitation were identified. The trends of change were modified by regional and local topographies and land covers.

Key words: climate change, empirical mode decomposition, Inner Mongolia, similarity plot, trend surface