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

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