Abstract
Improvements in air quality and Earth’s climate predictions require improvements of the aerosol speciation in chemical transport models, using observational constraints. Aerosol speciation (e.g., organic aerosols, black carbon, sulfate, nitrate, ammonium, dust or sea salt) is typically determined using in situ instrumentation. Continuous, routine surface network aerosol composition measurements are not uniformly widespread over the globe. Satellites, on the other hand, can provide a maximum coverage of the horizontal and vertical atmosphere but observe aerosol optical properties (and not aerosol speciation) based on remote sensing instrumentation. Combinations of satellite-derived aerosol optical properties can inform on air mass aerosol types (AMTs e.g., clean marine, dust, polluted continental). However, these AMTs are subjectively defined, might often be misclassified and are hard to relate to the critical parameters that need to be refined in models. In this paper, we derive AMTs that are more directly related to sources and hence to speciation. They are defined, characterized, and derived using simultaneous in situ gas-phase, chemical and optical instruments on the same aircraft during the Study of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS, US, summer of 2013). First, we prescribe well-informed AMTs that display distinct aerosol chemical and optical signatures to act as a training AMT dataset. These in situ observations reduce the errors and ambiguities in the selection of the AMT training dataset. We also investigate the relative skill of various combinations of aerosol optical properties to define AMTs and how much these optical properties can capture dominant aerosol speciation. We find distinct optical signatures for biomass burning (from agricultural or wildfires), biogenic and dust-influence AMTs. Useful aerosol optical properties to characterize these signatures are the extinction angstrom exponent (EAE), the single scattering albedo, the difference of single scattering albedo in two wavelengths, the absorption coefficient, the absorption angstrom exponent (AAE), and the real part of the refractive index (RRI). We find that all four AMTs studied when prescribed using mostly airborne in situ gas measurements, can be successfully extracted from at least three combinations of airborne in situ aerosol optical properties (e.g., EAE, AAE and RRI) over the US during SEAC4RS. However, we find that the optically based classifications for BB from agricultural fires and polluted dust include a large percentage of misclassifications that limit the usefulness of results relating to those classes. The technique and results presented in this study are suitable to develop a representative, robust and diverse source-based AMT database. This database could then be used for widespread retrievals of AMTs using existing and future remote sensing suborbital instruments/networks. Ultimately, it has the potential to provide a much broader observational aerosol data set to evaluate chemical transport and air quality models than is currently available by direct in situ measurements. This study illustrates how essential it is to explore existing airborne datasets to bridge chemical and optical signatures of different AMTs, before the implementation of future spaceborne missions (e.g., the next generation of Earth Observing System (EOS) satellites addressing Aerosol, Cloud, Convection and Precipitation (ACCP) designated observables).
Highlights
Aerosols have an important yet uncertain impact on the Earth’s radiation budget (e.g., Boucher et al, 2013) and human health (e.g., US EPA, 2011, 2016; Lim et al, 2012; Lanzi, 2016; Landrigan et al, 2018; Wu et al, 2020)
We derive Optical-based Air Mass Types (AMTs) using the set of aerosol optical properties defined in the second step above, the DO-Class defined in the third step above and the Specified Clustering and Mahalanobis Classification method (SCMC) method for a set of observations that was not included in the training data sets, 5
335 During SEAC4RS, according to Kim et al (2015) and Wagner et al (2015), the campaign-averaged aerosol mass was composed of mostly Organic Aerosol (OA) that is internally mixed with sulfate and nitrate at all altitudes over the southeastern U.S (SEUS) i.e., 55% OA and 25% sulfate mass on average according to ground-based filter-based PM2.5 (Particulate Matter concentration with an aerodynamic diameter smaller than 2.5 μm) speciation measurements from EPA CSN sites
Summary
Aerosols have an important yet uncertain impact on the Earth’s radiation budget (e.g., Boucher et al, 2013) and human health (e.g., US EPA, 2011, 2016; Lim et al, 2012; Lanzi, 2016; Landrigan et al, 2018; Wu et al, 2020). Constraining model-predicted aerosol mass concentrations with passive satellite total column-integrated aerosol 95 properties has been shown to be useful to constrain model-predicted AOD This is the case for the singlechannel visible Aerosol Optical Depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (e.g., Yu et al, 2003; Zhang et al, 2008; Benedetti et al, 2009; Sessions et al, 2015; Buchard et al, 2017; Kumar et al, 2019; Ma et al, 2019). Assimilation of satellite-derived optical properties related to particle size (e.g., Extinction Angstrom Exponent, EAE) and light absorption (e.g., Single Scattering Albedo, SSA) represents a step forward (e.g., Tsikerdekis et al, 2021) Another way to improve estimates of speciated RFari would be to use satellite-derived total column speciated aerosol mass concentration to adjust the mass concentration of individual aerosol masses when applying data 105 assimilation techniques in the model (and potentially the emission/chemistry/transport processes driving them). It is a coarse definition (qualitative) of the aerosol size, shape and color that dominates an air mass (e.g., clean marine, dust, polluted continental, clean continental, polluted dust, smoke, and stratospheric in the case of 115 CALIOP/ CALIPSO, Cloud-Aerosol Lidar with Orthogonal Polarization/ Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (Omar et al, 2009))
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