Abstract
Abstract. The current study investigates the diurnal cycle of dust and dust mixture loading across the global tropics, subtropics, and mid-latitudes by analyzing aerosol extinction and typing profiles observed by the Cloud-Aerosol Transport System (CATS) lidar aboard the International Space Station. According to the comparison with ground-based and other satellite observations, CATS aerosol and dust and dust mixture loading observations exhibit reasonable quality but significant day–night inconsistency. To account for this day–night inconsistency in CATS data quality, the diurnal variability in dust and dust mixture characteristics is currently examined separately for daytime and nighttime periods. Based on an analysis of variance (ANOVA) analytical framework, pronounced diurnal variations in dust and dust mixture loading are generally uncovered during daytime periods and over terrestrial areas. The current study identifies statistically significant diurnal variability in dust and dust mixture loading over key dust sources, including the Bodélé Depression, the West African El Djouf, Rub' al-Khali desert, and western and southern North America, confirming the previous observation-based findings regarding the diurnal cycle of dust emission and underlying meteorological processes in these regions. Significant seasonal and diurnal variability in dust and dust mixture is identified over the Iraqi and Thar deserts. The identified significant diurnal cycles in dust mixture loading over the vegetated regions in the Amazon and tropical southern Africa are hypothesized to be driven by enhanced dust emission due to wildfires.
Highlights
Dust mobilization and concentration exhibit substantial diurnal variability around the globe (Knippertz and Stuut, 2014), contributing to the radiative (DeMott et al, 2010; Tegen and Lacis, 1996), biogeochemical (Okin et al, 2004), and societal (Al-Hurban and Al-Ostad, 2010; Furman, 2003) impacts of mineral dust on the Earth system
As an initial assessment of the potential quality inconsistency between daytime and nighttime Cloud-Aerosol Transport System (CATS) data, total aerosol optical depth (AOD) from CATS is evaluated against Aerosol Robotic Network (AERONET), and the agreement between CATS and AERONET AOD – in terms of root-meansquare error (RMSE), percentage of error within error range [%EE, namely ± (0.03 + 10 % of AERONET AOD)], correlation (R), and mean bias – is compared among daytime and nighttime collocated observations at each AERONET site
The mean biases are similar between the daytime and nighttime samples, the significantly different correlation, RMSE, and %EE prevent a direct comparison between daytime and nighttime AOD or dust aerosol optical depth (DAOD) values
Summary
Dust mobilization and concentration exhibit substantial diurnal variability around the globe (Knippertz and Stuut, 2014), contributing to the radiative (DeMott et al, 2010; Tegen and Lacis, 1996), biogeochemical (Okin et al, 2004), and societal (Al-Hurban and Al-Ostad, 2010; Furman, 2003) impacts of mineral dust on the Earth system. Over the Iraqi desert in the Middle East, summertime dust activation is primarily driven by the strong, persistent shamal wind, which peaks around local noon with an intensified low-level temperature gradient (Yu et al, 2016) Beyond these studies that focused on a specific dust source region, there has been limited global analysis of the observed diurnal variability in dust mobilization and concentration, with the exception of some modeling studies (e.g., Yue et al, 2009). Lee et al (2019) did not perform a formal significance test of the aerosol diurnal cycle, leading to a potential overinterpretation over sparsely sampled regions This initial global assessment of aerosol diurnal variability using CATS observations motivates a more sophisticated investigation that, first, accounts for potential day–night data inconsistency and, second, explicitly quantifies the significance of the diurnal variability in dust. The methods, results, and conclusions/discussion are provided in Sects. 2, 3, and 4, respectively
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