Spatiotemporal evolution of aeolian dust in China: An insight into the synoptic records of 1984–2020 and nationwide practices to combat desertification

Abstract

Aeolian dust is closely related to land degradation, desertification, and sand and dust storm weather, and is a significant factor influencing the Earth's biogeochemical cycles. Here we present an investigation of the recent synoptic features of atmospheric dust in China and analyze its spatiotemporal change based upon meteorological observation data and satellite products. It shows that aeolian dust frequency in China has decreased gradually from 1984 to 2020. The outbreak frequency of dust storms declined significantly by 97.7%, followed by severe dust storm, dust in suspension, and blowing dust with 88.8%, 75%, and 64.3% reduction, respectively. The main dust sources influencing China are the Mongolian Gobi Desert, the Taklimakan Desert, the Hexi Corridor, the Alxa Plateau Desert, the Qaidam basin Desert, and the northeast‐southwest stretching semiarid farming‐pasture ecotone. Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) may play an important role in teleconnection with aeolian dust event occurrence in China. Their correlation coefficients are −0.6 and 0.37, respectively. For more than 40 years, China has made huge investments in ecological restoration projects, and enacted national policies and laws to mitigate desertification. Significant environmental improvement has consequently ensued in China in the early 21st century, and the aeolian dust events were reduced to a record minimum in 2020. In contrast to the growing nationwide greenness, China is now facing up to an incremental dust risk generated from extraterritorial source regions such as the Mongolian Gobi Desert and Central Asian desiccated salt lakes.

Effect of Grassland Fires on Dust Storms in Dornod Aimag, Mongolia

“…According to the studies, fires directly damage surface vegetation cover. In dust weather studies, researchers have used groundbased dust observation data to analyze the spatiotemporal patterns of dust weather in different regions [8,[27][28][29][30][31][32]. Other researchers have used remote sensing image inversion data and reanalysis information to analyze the factors influencing dust weather in different areas [16,[33][34][35][36].…”