Aircraft observations of meteorological, trace gas, and aerosol properties
were made between May and September 2013 in the southeastern United States
(US). Regionally representative aggregate vertical profiles of median and
interdecile ranges of the measured parameters were constructed from 37
individual aircraft profiles made in the afternoon when a well-mixed boundary
layer with typical fair-weather cumulus was present (Wagner et al., 2015). We
use these 0–4 km aggregate profiles and a simple model to calculate the
sensitivity of aerosol optical depth (AOD) to changes in dry aerosol mass,
relative humidity, mixed-layer height, the central diameter and width of the
particle size distribution, hygroscopicity, and dry and wet refractive index,
while holding the other parameters constant. The calculated sensitivity is a
result of both the intrinsic sensitivity and the observed range of variation
in these parameters. These observationally based sensitivity studies indicate
that the relationship between AOD and dry aerosol mass in these conditions in
the southeastern US can be highly variable and is especially sensitive to
relative humidity (RH). For example, calculated AOD ranged from 0.137 to
0.305 as the RH was varied between the 10th and 90th percentile profiles with
dry aerosol mass held constant. Calculated AOD was somewhat less sensitive to
aerosol hygroscopicity, mean size, and geometric standard deviation,
Aerosols in the atmosphere scatter and absorb solar radiation and alter the
earth's energy balance. The magnitude and variation in this aerosol direct
radiative effect has large uncertainties that are being addressed by numerous
observational and modeling studies. Key to these investigations, measurements
of AOD, the vertically integrated aerosol extinction coefficient
(
Aerosol optical depth is dependent upon several aerosol characteristics in
addition to mass, the parameter that is often of interest. Many particles are
composed of compounds that can take up water with increasing atmospheric
relative humidity (RH). This hygroscopic water uptake changes particle size
and refractive index and can lead to dramatic changes in the extinction as a
function of RH, even when dry aerosol mass is constant. Since atmospheric RH
is highly variable temporally, horizontally, and especially vertically,
aerosol water plays an important role in establishing the relationship
between ambient extinction (or AOD) and dry aerosol mass. As van Donkelaar et
al. (2015) succinctly state, “the relationship between AOD and
[ground-level] PM
Globally averaged, dust, sea salt, biomass burning, and anthropogenic aerosols dominate AOD (e.g., Boucher et al., 2013; Jacobson, 2001). Between and within each of these aerosol categories there are substantial variations in particle diameter and shape, hygroscopicity, size distribution width, mixing state, and refractive index, as well as in the vertical distribution of these properties. Because of these confounding influences, the relationship between AOD and dry aerosol mass is expected to vary in different regions and seasons.
Several studies have examined the relationship between detailed aerosol
characteristics and the direct aerosol radiative effect, ambient extinction,
or AOD. Hegg et al. (1993) examined the sensitivity of ambient extinction to
particle diameter and refractive index. They found that extinction was
particularly sensitive to the initial dry size of the aerosol prior to
hygroscopic growth. McComiskey et al. (2008) evaluated in detail how aerosol
intensive properties affected the top of the atmosphere and surface radiation for
a wide range of aerosol types, finding the greatest sensitivity to the
aerosol single-scattering albedo. Magi et al. (2005) used airborne in situ
measurements in the eastern US to estimate the contribution of dry
particulate constituents and aerosol water to AOD. Koloutsou-Vakakis et
al. (1998) found that aerosol composition and hygroscopicity were important
in relating aerosol mass concentration measurements to ambient scattering.
Using airborne and remote sensing measurements, Crumeyrolle et al. (2014)
showed a strong relationship between AOD and surface and in situ aerosol mass
concentrations in the eastern US. Ziemba et al. (2013) report good closure
between remotely sensed profiles of aerosol extinction and in situ
measurements taken in the eastern US when aerosol hygroscopic growth was
taken into account. Esteve et al. (2012) found that uncertainty in
hygroscopic growth was likely the largest contributor of discrepancies
between AOD determined from remote sensing and from in situ measurements.
Esteve et al. (2016) used measurements and a radiative transfer model to
determine that the aerosol direct radiative effect in western Europe in
spring was moderately sensitive to the size distribution of the aerosol and
less so to the refractive index of the particles. Several global modeling
studies have found strong sensitivities of the direct aerosol radiative
effect to particle size, composition, and hygroscopicity (e.g., Adams et al.,
2001; Boucher and Anderson, 1995; Nemesure et al., 1995; Pilinis et al.,
1995). Adams et al. (2001) used global model simulations to demonstrate that
the water content of the aerosol, especially for RH
In this study we focus on the relationship between measured aerosol
properties and calculated AOD for a specific aerosol type, the
submicron-dominated mixed organic–sulfate aerosol typical of moderately
polluted and background continental air. This type of aerosol is found in
several regions globally, including southern Africa, Eurasia, and South
America (e.g., Vakkari et al., 2013). A companion paper (Part 1; Brock et
al., 2016) uses detailed in situ airborne measurements of dry aerosol
composition, dry size distribution and change in optical extinction as a
function of relative humidity,
In this analysis (Part 2), the
We analyze vertical profiles derived from airborne, in situ measurements from
the May–July 2013 Southeastern Nexus of Air Quality and Climate (SENEX) and
the portions of the August–September 2013 Study of Emissions and Atmospheric
Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC
Measurements were made in summer during periods when the NASA DC-8
(SEAC
From this complex vertical structure we wish to calculate representative
vertical profiles of aerosol and meteorological parameters. However, direct
altitude-based averaging of the individual profiles would combine air from
the well-mixed layer, the transition layer, and the free troposphere because
the heights of these layers varied from profile to profile. To avoid this
problem, Wagner et al. (2015) defined a normalized altitude,
Ambient extinction must be estimated from measurements that are made inside
the aircraft cabin under different thermodynamic conditions than the
atmosphere. As described in Brock et al. (2016), the hygroscopic growth
parameter
The 37 individual profiles meeting the criteria described in Sect. 2.2 were
combined into an aggregate profile following Eq. (1), from which 10th
percentile, median (50th percentile), and 90th percentile values were
calculated. Because the distribution of most parameters was not Gaussian,
percentile values are used to represent the range of observed variability.
Median values of STP-corrected dry aerosol extinction decreased from
Aggregate vertical profiles of
Aerosol optical depth was calculated between the surface and the top of the
profile by integrating ambient extinction from the surface upward (Fig. 1d).
The extinction within the well-mixed layer was extrapolated to the surface
for each individual profile. Wagner et al. (2015) show that measurements of
extinction at the Centreville, Alabama, surface site during the SENEX time
period agreed with values measured at the lowest altitude of the aircraft,
supporting such extrapolation. The median AOD at 532 nm was 0.19. This value
is similar to values of AOD at 532 nm of 0.19 and 0.17 at the AERONET
(Holben et al., 2001) locations of Centreville, Alabama (
As discussed in Wagner et al. (2015), air in the transition, or cloud, layer is depleted in short-lived gas-phase tracers such as isoprene. This depletion in isoprene suggests that air parcel transport between the surface and the transition layer is slow and/or intermittent, and is probably associated with cloud outflow. The transition layer is likely composed of a combination of a residual well-mixed layer from the previous day, air that has been lifted through cloud convection above the current day's well-mixed layer, and free-tropospheric air mixed from above. Because of this relative isolation, the aerosol in the transition layer aloft may be different than that measured at the surface. In cases where the contribution of the transition layer aerosol extinction to AOD is substantial, this segregation between the transition layer and the surface adds uncertainty to efforts to directly relate remotely sensed AOD measurements to surface values, for example for epidemiological studies that use satellite-based AOD measurements as proxies for surface aerosol concentration (e.g., Crumeyrolle et al., 2014; Engel-Cox et al., 2004; Kim et al., 2015; Kloog et al., 2011; van Donkelaar et al., 2015). Ultimately the transition layer and well-mixed layer aerosols are coupled through dry and moist convection, but the observed isoprene depletion in the transition layer suggests a substantial temporal lag in the response of that layer to changes to the aerosol in the well-mixed layer.
Atmospheric AOD measurements from the
Atlanta, Georgia, and Centreville, Alabama, AERONET sunphotometer network
sites using Level 2.0 data (Holben
et al., 2001). The median and interquartile range for the SENEX and
SEAC
To evaluate the importance of the transition layer to AOD, the contribution
of it and the well-mixed layer to total column AOD was examined for each
altitude-normalized profile that penetrated both layers. The AOD within the
well-mixed and transition layers was then calculated and compared with the
total integrated AOD from the profile. The fractional contribution of the
free-troposphere layer to the total AOD was not calculated because only 5 of
the 37 profiles penetrated far enough into the free troposphere to reasonably
estimate the AOD from this layer. Histograms of the total AOD and the
fractional contribution of the well-mixed and transition layers (Fig. 3) show
that both layers contributed substantially to the column AOD. The mean
fractional contributions of the well-mixed and transition layers to total AOD
were 0.56 and 0.43, respectively, while the median fractional contributions
were 0.54 and 0.43, respectively. These results demonstrate that the
transition layer, which is not in immediate contact with the surface,
contributed nearly half of the integrated AOD in the southeastern US during
the SENEX and SEAC
As described in Sect. 3.1, the aggregation of individual vertical profiles results in a single vertical profile and interdecile range that represents typical midday conditions in the summertime in the southeastern US. This aggregate profile and variability range is used to estimate the sensitivity of the relationship between AOD and dry mass to changes in measured parameters that affect AOD. These sensitivity calculations indicate which parameters are most important to accurately relate AOD and non-water aerosol mass in this region and season.
For the sensitivity calculations we use a single-mode lognormal model to
describe the size distribution of the optically active accumulation mode
aerosol. The geometric mean diameter
AOD calculated from sensitivity tests.
To evaluate the sensitivity of AOD to dry aerosol mass, the AMS mass
concentration profiles were calculated and the number of particles in the
model size distribution were varied to match the mass concentration. Since
Note that these sensitivity tests do not account for co-variance of parameters that might be expected in the atmosphere. For example, larger dry particle diameters might be associated with a more sulfate-rich, more hygroscopic aerosol. The sensitivity evaluations simply describe the first-order response of AOD to changes in the interdecile range of a single parameter, with all other dry parameters being held constant using the median profile for each. More sophisticated model simulations, for example using a large eddy simulation model with aerosol input parameters constrained by observations, could be used to further investigate these sensitivities and the couplings between parameters.
The median AOD calculated from the lognormal size distribution profile was
0.18, similar to the value of 0.19 directly determined from the in situ
measurements of aerosol extinction. As expected, AOD was linearly sensitive
to variations in aerosol mass (Fig. 4, Table 1). Aerosol optical depth was
also highly sensitive to RH as it varied between the 10th and 90th percentile
profile, with a variation in AOD of
Range in AOD at a wavelength of 532 nm due to variations in
measured parameters. AOD values integrated from profiles using a model
aerosol size distribution and the 10th and 90th percentile range of observed
aerosol and meteorological parameters (bars) and from the median profile
(black circles). Numerical values show the extinction-weighted 10th–90th
percentile range of the indicated parameter. The “
Aerosol optical depth was less sensitive to
Variation in the ambient refractive index profile, which is dominated by the
addition of water, had a smaller effect on AOD, as did the variation in the
hygroscopicity parameter
An additional calculation was made to evaluate the change in AOD due to the
choice of hygroscopicity model, e.g.,
A final test was made of the sensitivity of AOD to variations in the thickness of the well-mixed layer under conditions of total columnar aerosol mass loading (i.e., constant sources and sinks). This test was made because regional-scale models often have difficulty simulating the height of the well-mixed layer (e.g., Kim et al., 2015; Scarino et al., 2014). If the aerosol were dry, variations in boundary layer height would not affect AOD much, because the increasing height of well-mixed layer would be compensated for by dilution of the aerosol (assuming the air being mixed in during mixed-layer growth does not contribute to extinction within the layer). However, as the well-mixed layer increases in height, the temperature in the upper part of the layer decreases with the lapse rate, causing an increase in RH. Thus, for the same columnar dry aerosol mass loading, a growing well-mixed layer might increase AOD. Compensating for this increased aerosol water is a reduction in ambient aerosol concentration, and hence extinction, due to decreasing mean air density as the layer grows in altitude.
We simulate this effect with a simple model constrained by our observations.
An aerosol was assumed to be perfectly mixed within the well-mixed layer,
with a resulting dry extinction that decreased as atmospheric density
decreased with altitude. The dry extinction at the bottom of the well-mixed
layer was the median value at the lowest layer of the aggregate profile
(Fig. 1a). Ambient extinction at each level in the well-mixed layer was
calculated using Eq. (2), a fixed value of
There has been considerable research on the effects of aerosol optical,
microphysical, and chemical properties on aerosol extinction and AOD based on
in situ measurements, laboratory studies, and modeling. However, few studies
have systematically investigated the sensitivity of AOD to variations in the
aerosol and meteorological parameters such as RH. Hegg et al. (1993) examined
the sensitivity of ambient extinction to particle diameter and refractive
index. Hegg et al. found that, as the dry aerosol humidified and grew,
variations in the dry mass median diameter relative to the extinction
efficiency curve produced substantial
Analysis of data from NASA's Deriving Information on Surface Conditions from
Column and Vertically Resolved Observations Relevant to Air Quality
(DISCOVER-AQ) airborne program has shown a strong relationship between AOD
and surface and in situ aerosol mass concentrations in the eastern US
(Crumeyrolle et al., 2014). Ziemba et al. (2013) found that aerosol water
(using the
The sensitivities of AOD to RH, to the mean diameter and width of the size distribution, and to the hygroscopicity model indicate the need for a more systematic investigation. Numerical models that incorporate aerosol radiative forcing need to be constrained by observations similar to those reported here in other types of environments, especially the dust, sea salt, biomass burning, and heavily polluted cases that globally dominate aerosol direct radiative effects (Jacobson, 2001; Kahn, 2011). One effort, Systematic Aircraft Measurements to Characterize Aerosol Air Masses (SAM-CAAM), has been proposed to make repeated measurements of critical in situ and remotely sensed parameters in a wide range of air mass types across the globe (Kahn, 2013). A comprehensive observational program such as SAM-CAAM could help disentangle the relationship between in situ aerosol and meteorological properties and AOD in different air masses, and, coupled with model and measurement refinement, reduce uncertainty in direct aerosol radiative effects.
All authors contributed measurements and/or analyses for this manuscript. Charles A. Brock prepared the manuscript with substantial contributions from Nicholas L. Wagner, Timothy D. Gordon, Jose L. Jimenez, Pedo Campuzano-Jost, Ann M. Middlebrook, and Daniel M. Murphy.
This work was supported in part by NOAA's Health of the Atmosphere and
Atmospheric Chemistry, Carbon Cycle, and Climate Programs.
Pedro Campuzano-Jost, Douglas A. Day, and Jose L. Jimenez were supported by
NASA award NNX12AC03G/NNX15AH33A and NSF award AGS-1243354.
Annmarie G. Carlton was supported by NSF award AGS-1242155. We thank
Gary Gimmestad and Brad Gingrey for their effort in establishing and
maintaining the Georgia Tech and SEARCH-Centreville AERONET sites,
respectively.