loading page

Homogenization of the Daily Land Skin Temperature (LST) over China from 1960 to 2017
  • Dan Wang,
  • Aihui Wang,
  • Xianghui kong
Dan Wang
The Institute of Atmospheric Physics, Chinese Academy of Sciences
Author Profile
Aihui Wang
Institute of Atmospheric Physics

Corresponding Author:wangaihui@mail.iap.ac.cn

Author Profile
Xianghui kong
Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences
Author Profile


Land skin temperature (LST) is one of the most important factors in the land-atmosphere interaction process. Raw measured LSTs may contain biases due to instrument replacement, changes in recording procedures, and other nonclimatic factors. This study attempts to reduce the above biases in raw daily measurements and achieves a homogenized daily LST dataset over China using 2360 stations from 1960 to 2017. The high-quality land surface air temperature (LSAT) dataset is used to correct the LST warming biases in cold months in regions north of 40ºN due to the replacement of observation instruments around 2004. Subsequently, the Multiple Analysis of Series for Homogenization (MASH) method is adopted to detect and then adjust the daily observed LST records. In total, 3.68×103 significant breakpoints in 1.65×106 monthly records are detected. A large number of these significant breakpoints are located over large parts of the Sichuan Basin and southern China. After MASH procedure, LSTs at more than 80% of the breakpoints are adjusted within +/- 0.5 ºC, and 10% of the breakpoints are adjusted over 1.5 ºC. Compared to the raw LST dataset over the whole domain, the homogenization significantly reduces the mean LST magnitude and its interannual variability as well as its linear trend at most stations. Finally, we preliminarily analyze the homogenized LST and find that the annual mean LST averaged across China shows a significant warming trend (0.22 ºC decadal-1). The homogenized LST dataset can be further adopted for a variety of applications (e.g., model evaluation and extreme event characterization).