Temperature inversions in China derived from sounding data from 1976 to 2015

Abstract

Temperature inversions inhibit the transfer of momentum, heat and moisture in the atmosphere and have led to severe air pollution in China. This study investigated the spatiotemporal variation in temperature inversions in China using sounding data for the past four decades. Surface-based inversion, elevated inversion, and both in one sounding dataset were analysed. Statistical analyses of inversion parameters included frequency, strength and depth. The annual frequency of total inversions showed no significant increasing or decreasing trend with mean values of 0.78, 0.33, 0.24, 0.28, 0.5 and 0.36 at six stations representing different climate zones—Beijing, Harbin, Haikou, Shaowu, Ruoqiang, and Xining, respectively. The annual inversion strength and depth showed downward trends. Monthly variation in inversion frequency and strength differed among stations. The weakest surface-based inversion was found in summer at Beijing and Harbin with mean values of 1 and 1.3 °C, respectively; the strongest surface-based inversion was found in winter with respective mean values of 3.5 and 3.6 °C. Higher surface temperature in summer and subsidence aloft in winter may explain the monthly variation in inversion depth with a minimum in summer, with mean values of 165, 334, 135, 267, 363 and 420 m, and a maximum in winter, with mean values of 250, 646, 140, 591, 806 and 664 m, at the six respective stations. Total inversion was least frequent in southwestern China (mean 0.15), surface-based inversion was most frequent in the north (mean 0.78), and elevated inversion was most frequent in the southeast (mean 0.42). The strongest, deepest surface-based inversion dominated in the north (mean 3.4 °C and 398 m). Elevated inversion strength did not significantly differ among regions (mean 2.5 °C). The deepest elevated inversion dominated in the southeast (mean 654 m). Future efforts should focus on the interactions between aerosols and temperature inversions and accurate model simulations of temperature inversions.

Keywords

• temperature inversion
• inversion frequency
• inversion strength
• inversion depth