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

Abstract

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.