Abstract
Abstract. This study introduces an observation-based dust identification approach and applies it to reconstruct long-term dust climatology in the western United States. Long-term dust climatology is important for quantifying the effects of atmospheric aerosols on regional and global climate. Although many routine aerosol monitoring networks exist, it is often difficult to obtain dust records from these networks, because these monitors are either deployed far away from dust active regions (most likely collocated with dense population) or contaminated by anthropogenic sources and other natural sources, such as wildfires and vegetation detritus. Here we propose an approach to identify local dust events relying solely on aerosol mass and composition from general-purpose aerosol measurements. Through analyzing the chemical and physical characteristics of aerosol observations during satellite-detected dust episodes, we select five indicators to be used to identify local dust records: (1) high PM10 concentrations; (2) low PM2.5/PM10 ratio; (3) higher concentrations and percentage of crustal elements; (4) lower percentage of anthropogenic pollutants; and (5) low enrichment factors of anthropogenic elements. After establishing these identification criteria, we conduct hierarchical cluster analysis for all validated aerosol measurement data over 68 IMPROVE sites in the western United States. A total of 182 local dust events were identified over 30 of the 68 locations from 2000 to 2007. These locations are either close to the four US Deserts, namely the Great Basin Desert, the Mojave Desert, the Sonoran Desert, and the Chihuahuan Desert, or in the high wind power region (Colorado). During the eight-year study period, the total number of dust events displays an interesting four-year activity cycle (one in 2000–2003 and the other in 2004–2007). The years of 2003, 2002 and 2007 are the three most active dust periods, with 46, 31 and 24 recorded dust events, respectively, while the years of 2000, 2004 and 2005 are the calmest periods, all with single digit dust records. Among these deserts, the Chihuahuan Desert (59 cases) and the Sonoran Desert (62 cases) are by far the most active source regions. In general, the Chihuahuan Desert dominates dust activities in the first half of the eight-year period while the Sonoran Desert in the second half. The monthly frequency of dust events shows a peak from March to July and a second peak in autumn from September to November. The large quantity of dust events occurring in summertime also suggests the prevailing impact of windblown dust across the year. This seasonal variation is consistent with previous model simulations over the United States.
Highlights
Due to its various effects on air quality and climate (Intergovernment Panel on Climate Change, IPCC, 2007), dust aerosol lifted from disturbed soil has been extensively studied through ground observation, remote sensing and model simulations (Gillette and Passi, 1988; Gong et al, 2003; Reid et al, 2003; Zhang et al, 2003)
Based on the observations above, we propose the following five indicators to be used to identify local dust records in the subsequent hierarchical cluster analysis: (1) high PM10 concentrations; (2) low ratio of PM2.5 to PM10; (3) higher concentrations and percentage of crustal elements; (4) lower percentage of anthropogenic pollutants; and (5) low enrichment factors of anthropogenic elements
The revised Ganor method demonstrates dust identifying capability between the two simplified methods. These simplified methods show varying effectiveness to identify local dust events, it should be pointed out that chemical fingerprint is still needed to assure the origin of measured aerosols
Summary
Due to its various effects on air quality and climate (Intergovernment Panel on Climate Change, IPCC, 2007), dust aerosol lifted from disturbed soil has been extensively studied through ground observation, remote sensing and model simulations (Gillette and Passi, 1988; Gong et al, 2003; Reid et al, 2003; Zhang et al, 2003). For both remote sensing and modeling studies, ground measurements are critically important for verifying derived results. The incapability of utilizing such a large set of data results is a missed opportunity to gain insight into dust activities from the perspective of “ground truth”
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