Combining sparse observations and reanalysis data for refining spatiotemporal variability in near‐surface air temperature lapse rates over China

Abstract

AbstractUneven and sparse surface air temperature (Ta) observations challenge the understanding of the atmosphere‐land interactions and global environmental changes. Lapse rate () is thought of with great potential to obtain accurate long‐term and high‐resolution Ta dataset. However, previous studies on over China were solely based on observed data, which limited our understanding since few stations are distributed in western China. In this study, an improved procedure for estimating by combining sparse observations and newly released ERA5 reanalysis data was proposed. The spatial heterogeneity in over China and its influencing factors (e.g., topography, climate, and vegetation) were then explored. A re‐examination of long‐term spatiotemporal variations in and its potential driving forces over China was also performed at grid scales. The results demonstrated that our estimations reproduced similar patterns to those derived from observations. The subsequent analyses revealed that the annual and seasonal variability in varied substantially among different altitude gradients, land cover types, and climate zones. The vital role played by vegetation covers in regulating local was emphasized in this study, which was poorly assessed previously. Moreover, in low altitude zones showed lower values but strong temporal fluctuations and contributed more to the interannual variability in over China, which could be mainly attributed to human activities. The present findings can provide an alternative method for estimating and be further used to assess regional or global temperature changes.