Preprints
https://doi.org/10.5194/egusphere-2024-357
https://doi.org/10.5194/egusphere-2024-357
08 Mar 2024
 | 08 Mar 2024
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

A comprehensive insight into trajectory climatology and spatiotemporal distribution of dust aerosols in China

Lu Yang, Lu She, Yahui Che, Jiayu Zhang, Zixian Feng, and Chen Yan

Abstract. Airborne dust aerosols impact negatively the climate, ecosystems, air quality, and human health. To mitigate these impacts, it is crucial to identify their three–dimensional spatiotemporal distribution, transport pathways and driving factors. In this study, the three–dimensional spatiotemporal variations and distribution of dust aerosols in China from 2007 to 2021 were first analyzed using multiple dust datasets, including Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA–2) dust aerosol optical depth (DAOD) data, ultraviolet aerosol index (UVAI) data from the Ozone Monitoring Instrument (OMI), and the Vertical Feature Mask (VFM) product of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). Also, the transport pathways and potential source regions for dust haze in principle provincial capital cities of West and North China in spring were identified using the Hybrid Single–Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Potential Source Contribution Function (PSCF). Additionally, DAOD variations over different land cover types and the impacts of meteorological driving factors were discussed with geographic detectors. Results indicate that: (1) The multi–year average of DAOD in China from 2007 to 2021 was 0.076. A mutation in annual average DAOD occurred between 2010 and 2011, with an insignificant increasing trend between 2007–2010, a downward trend between 2011–2021 and a significant downward trend between 2014–2017. (2) The Taklamakan Desert exhibited the highest DAOD for the entire China during spring throughout the years, with DAOD values ranging from 0.4 to 0.6 and UVAI values exceeding 2.0. The highest frequency of dust occurrence in the northwest and northern regions is at an altitude of 2–4 km in spring and summer, and of 0–2 km in autumn and winter, while it is at an altitude of 4–6 km for the Qinghai–Tibet region. (3) The dust transport routes for the provincial capital cities can be primarily divided into: western, northwestern, northern, southwestern, and local. Northwest cities are notably affected by dust from surrounding deserts. Dust originating from the Qaidam Basin and the Hexi Corridor can be carried further downwind to inland cities, such as Xining and Lanzhou. A dust backflow was found in Beijing and Tianjin. Moreover, the discussion revealed that barren and cropland had the highest DAOD and additionally precipitation, evaporation, and soil moisture were identified as the strongest driving factors affecting dust aerosol variations. The combination effect of precipitation and temperature had the highest explanatory power, ranging from 0.72–0.84, followed by 0.75–0.81 for precipitation and U10m wind speed and 0.67–0.75 for temperature and evaporation.

Lu Yang, Lu She, Yahui Che, Jiayu Zhang, Zixian Feng, and Chen Yan

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-357', Anonymous Referee #1, 10 Apr 2024 reply
Lu Yang, Lu She, Yahui Che, Jiayu Zhang, Zixian Feng, and Chen Yan
Lu Yang, Lu She, Yahui Che, Jiayu Zhang, Zixian Feng, and Chen Yan

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Short summary
A five–years trajectory climatology of dust trajectory from 2017–2021 is established for some cities in China using the HYSPLIT model, to deeply understand the transport patterns and potential sources of dust events in these urban areas. The study also offers the long term three–dimensional spatiotemporal distribution of dust aerosols from 2007 to 2021 based on multi–source data, including OMI, CALIPSO and MERRA–2, providing insights into dust activities in China and the three sub–regions.