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).