Comparison of satellite based observations of Saharan dust source areas

Schepanski, Kerstin; Tegen, Ina; Macke, Andreas

doi:10.1016/j.rse.2012.03.019

Cited by 177 publications

(190 citation statements)

References 51 publications

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“…Thereby, knowledge on the surface characteristics is essential in order to determine spatial and temporal variabilities in dust emission fluxes. Recent studies illustrate the relevance of alluvial sediment deposits acting as dust source and emphasize the potential of this source type to modulate the interannual variability in atmospheric dust loading due to interannually changing sediment variability [14,15]. Furthermore, alluvial dust sources potentially link the atmospheric dust life cycle to the atmospheric water cycle as precipitation drives the formation of fresh alluvial deposits.…”

Section: Mineral Dustmentioning

confidence: 99%

Climate Feedback on Aerosol Emission and Atmospheric Concentrations

Tegen

Schepanski

2018

Curr Clim Change Rep

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Purpose of Review Climate factors may considerably impact on natural aerosol emissions and atmospheric distributions. The interdependencies of processes within the aerosol-climate system may thus cause climate feedbacks that need to be understood. Recent findings on various major climate impacts on aerosol distributions are summarized in this review. Recent Findings While generally atmospheric aerosol distributions are influenced by changes in precipitation, atmospheric mixing, and ventilation due to circulation changes, emissions from natural aerosol sources strongly depend on climate factors like wind speed, temperature, and vegetation. Aerosol sources affected by climate are desert sources of mineral dust, marine aerosol sources, and vegetation sources of biomass burning aerosol and biogenic volatile organic gases that are precursors for secondary aerosol formation. Different climate impacts on aerosol distributions may offset each other. Summary In regions where anthropogenic aerosol loads decrease, the impacts of climate on natural aerosol variabilities will increase. Detailed knowledge of processes controlling aerosol concentrations is required for credible future projections of aerosol distributions.

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Section: Mineral Dustmentioning

confidence: 99%

Climate Feedback on Aerosol Emission and Atmospheric Concentrations

Tegen

Schepanski

2018

Curr Clim Change Rep

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“…It should be mentioned that all of these dust source identification methods are based on satellite remote sensing that needs to be independently verified using ground observations. For instance, Schepanski et al (2007Schepanski et al ( , 2012) combined a back-tracking method with high temporal satellite aerosol data (15-min Aerosol Index (AI) from the Ozone Monitoring Instrument (OMI)) to identify dust sources over the Saharan region. They found that the spatial distribution of dust source areas inferred from OMI 15-min AI is distinctly different from that by using the daily MODIS Deep Blue aerosol data (Schepanski et al, 2012).…”

Section: Q Tong Et Al: Long-term Dust Climatology In the Western Usmentioning

confidence: 99%

Long-term dust climatology in the western United States reconstructed from routine aerosol ground monitoring

Tong

Wang

et al. 2012

Atmos. Chem. Phys.

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Abstract. This study introduces an observation-based dust identification approach and applies it to reconstruct longterm dust climatology in the western United States. Longterm 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 PM 10 concentrations; (2) low PM 2.5 /PM 10 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- 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.

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“…Passive sensors have the advantage of providing daily data on the state of the atmosphere with good spatial and temporal coverage. The satellite products have improved our knowledge of the source regions and dust transport pathways in recent years (Engelstaedter et al, 2006;Schepanski et al, 2007Schepanski et al, , 2009bSchepanski et al, , 2012. However, studies of their spatial and temporal variability are mainly based on indices such as the aerosol optical depth (AOD) or the aerosol index (AI), which provide vertically integrated information on the atmospheric aerosol contents (passive space-derived observations: Cakmur et al, 2001;Chiapello and Moulin, 2002;Kaufman et al, 2005;Engelstaedter et al, 2006;Schepanski et al, 2009b).…”

Section: Introductionmentioning

confidence: 99%