Climatic Change

, Volume 99, Issue 1–2, pp 93–118 | Cite as

Spatiotemporal change in China’s climatic growing season: 1955–2000

  • Binhui Liu
  • Mark Henderson
  • Yandong Zhang
  • Ming Xu


The timing, length, and thermal intensity of the climatic growing season in China show statistically significant changes over the period of 1955 to 2000. Nationally, the average start of the growing season has shifted 4.6–5.5 days earlier while the average end has moved 1.8–3.7 days later, increasing the length of the growing season by 6.9–8.7 days depending on the base temperature chosen. The thermal intensity of the growing season has increased by 74.9–196.8 growing degree-days, depending on the base temperature selected. The spatial characteristics of the change in the timing and length of the growing season differ from the geographical pattern of change in temperatures over this period; but the spatial characteristics of change in growing degree-days does resemble the pattern for temperatures, with higher rates in northern regions. Nationally, two distinct regimes are evident over time: an initial period where growing season indicators fluctuate near a base period average, and a second period of rapidly increasing growing season length and thermal intensity. Growing degree-days are highly correlated with March-to-November mean air temperatures in all climatic regions of China; the length of the growing season is likewise highly correlated with March-to-November mean air temperatures except in east, southeast and southwest China at base temperature of 0°C and southeast China at base temperature of 5°C. The growing season start date appears to have the greater influence on the length of the growing season. In China, warmer growing seasons are also likely to be longer growing seasons.


Tibetan Plateau Normalize Difference Vegetation Index Base Temperature North China Plain Urbanization Effect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Binhui Liu
    • 1
  • Mark Henderson
    • 2
  • Yandong Zhang
    • 1
  • Ming Xu
    • 3
    • 4
  1. 1.College of ForestryThe Northeast Forestry UniversityHarbinChina
  2. 2.Public Policy ProgramMills CollegeOaklandUSA
  3. 3.Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  4. 4.Center for Remote Sensing and Spatial Analysis, Department of Ecology, Evolution and Natural ResourcesRutgers UniversityNew BrunswickUSA

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