X. Wu, K. A. Balmes , Q. Fu Article · 2021. 1. 25. · Corresponding author: K. A. Balmes,...
Transcript of X. Wu, K. A. Balmes , Q. Fu Article · 2021. 1. 25. · Corresponding author: K. A. Balmes,...
manuscript submitted to JGR: Atmospheres
Aerosol direct radiative effects at the ARM SGP and1
TWP Sites: Clear skies2
X. Wu, K. A. Balmes∗, Q. Fu3
Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA4
Key Points:5
• Clear-sky aerosol direct radiative effects were estimated based on multi-year ground-6
based observations at the ARM SGP and TWP sites7
• Modeled clear-sky surface downwelling shortwave fluxes using observed inputs agreed8
well with surface radiometer observations9
• The annual mean clear-sky aerosol direct radiative effects at the top of the atmo-10
sphere are -3.00 W m−2 at SGP and -2.82 W m−2 at TWP11
Corresponding author: K. A. Balmes, [email protected]
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This article has been accepted for publication and undergone full peer review but has not been throughthe copyediting, typesetting, pagination and proofreading process, which may lead to differences betweenthis version and the Version of Record. Please cite this article as doi: 10.1029/2020JD033663.
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Abstract12
The clear-sky aerosol direct radiative effect (DRE) was estimated at the Atmospheric13
Radiation Measurement (ARM) Southern Great Plains (SGP) and Tropical Western Pa-14
cific (TWP) sites. The NASA Langley Fu-Liou radiation model was used with observed15
inputs including aerosol vertical extinction profile from the Raman lidar; spectral aerosol16
optical depth (AOD), single-scattering albedo and asymmetry factor from Aerosol Robotic17
Network (AERONET); temperature and water vapor profiles from radiosondes; and sur-18
face shortwave spectral albedo from radiometers. A radiative closure experiment was con-19
ducted for clear-sky conditions. The mean differences of modeled and observed surface20
downwelling shortwave total fluxes were 1 W m−2 at SGP and 2 W m−2 at TWP, which21
are within observational uncertainty. At SGP, the estimated annual mean clear-sky aerosol22
DRE is -3.00 W m−2 at the top of atmosphere (TOA) and -6.85 W m−2 at the surface.23
The strongest aerosol DRE of -4.81 (-10.77) W m−2 at the TOA (surface) are in the sum-24
mer when AODs are largest. The weakest aerosol DRE of -1.28 (-2.77) W m−2 at the25
TOA (surface) are in November-January when AODs and single-scattering albedos are26
lowest. At TWP, the annual mean clear-sky DRE is -2.82 W m−2 at the TOA and -10.3427
W m−2 at the surface. The strongest aerosol DRE of -5.95 (-22.20) W m−2 at the TOA28
(surface) are in November (October) due to the biomass burning season’s peak. The weak-29
est aerosol DRE of -0.96 (-4.16) W m−2 at the TOA (surface) are in March (April) when30
AODs are smallest.31
1 Introduction32
Aerosols directly modulate the radiative energy budget by scattering and absorb-33
ing radiation, which is referred to as the aerosol direct radiative effect (DRE). More re-34
cently, the DRE is also referred to by the Intergovernmental Panel on Climate Change35
(IPCC) as the radiative effect due to aerosol-radiation interactions (REari) (Boucher et36
al., 2013). The DRE varies with aerosol optical properties (e.g., aerosol extinction pro-37
file, optical depth, single-scattering albedo, and asymmetry factor), solar zenith angle38
and environmental characteristics (e.g., clouds, surface albedo, and relative humidity).39
Aerosol DREs occur under both clear-sky and cloudy conditions (J. M. Haywood40
& Shine, 1997; Chand et al., 2009; Peters et al., 2011; De Graaf et al., 2012; Vuolo et41
al., 2014). The importance of the latter has been underscored by a continuum transi-42
tion in aerosol and cloud optical depth going from clear-sky to cloudy conditions (Calbo43
et al., 2017; Balmes & Fu, 2018). Furthermore, the impact of clouds on the aerosol DRE44
caused by absorbing aerosols can switch the cooling to a warming effect over the regions45
with a low surface albedo (Chand et al., 2009; Keil & Haywood, 2003; Podgorny & Ra-46
manathan, 2001). The quantification of aerosol DRE requires both aerosol and cloud op-47
tical depths and their individual vertical extinction distributions in the same atmospheres48
(Liao & Seinfeld, 1998; J. M. Haywood & Shine, 1997; Podgorny & Ramanathan, 2001;49
Keil & Haywood, 2003). This study, however, solely focuses on the aerosol DRE in the50
absence of clouds (i.e., clear-sky). This is the first part of our effort to quantify aerosol51
DREs at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP)52
and the Tropical Western Pacific (TWP) sites under all-sky conditions based on the ARM53
ground-based aerosol, cloud, radiation, and atmospheric state measurements, which has54
not been done before.55
Clear-sky global-mean estimates of the shortwave (SW) aerosol DRE at the top of56
the atmosphere (TOA) range between -2 to -7 W m−2 (e.g., Henderson et al., 2013; Ma-57
tus et al., 2015, 2019; Myhre et al., 2007; Reddy et al., 2005; Yu et al., 2006; Christo-58
pher & Zhang, 2002; Remer & Kaufman, 2006; T. X. Zhao et al., 2008; T. X.-P. Zhao59
et al., 2011; Thomas et al., 2013; Boucher et al., 2013). However, many of these stud-60
ies are limited to satellite observations with passive sensors (e.g., Myhre et al., 2007; Christo-61
pher & Zhang, 2002; Remer & Kaufman, 2006; T. X. Zhao et al., 2008) or may use in-62
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struments that do not detect all radiatively significant aerosols (e.g., CALIPSO) (Thorsen63
& Fu, 2015b; Thorsen et al., 2017). In contrast, the high-quality ground-based obser-64
vations at the ARM sites allow the opportunity to quantify the aerosol DRE with all aerosol,65
cloud, radiation and atmospheric state quantities available from observations. The es-66
timated clear-sky aerosol DREs at the ARM SGP and TWP sites should be useful to67
compare and validate the global estimates over these two regions.68
The ARM ground-based observations provide reliable information on the aerosol69
and environmental conditions necessary to determine the aerosol DRE. Paired with a70
radiative transfer model, the regional aerosol DRE can be determined by considering an71
atmosphere with and without aerosols. The ground-based Raman lidars (RL) (Ferrare72
et al., 2006; Goldsmith et al., 1998; Newsom, 2009) at the ARM sites provide aerosol ver-73
tical detection and extinction profile (Balmes et al., 2019; Balmes & Fu, 2018; Thorsen74
& Fu, 2015a; Thorsen et al., 2015; Thorsen & Fu, 2015b; Thorsen et al., 2017). In ad-75
dition, the Aerosol Robotic Network (AERONET) operates collocated Cimel sun pho-76
tometers, which provides aerosol optical depth, single-scattering albedo and asymme-77
try factor at several wavelengths (Holben et al., 1998; Giles et al., 2019). The synergy78
of aerosol observations can then be inputted into the NASA Langley Fu-Liou radiative79
transfer (RT) model (Fu & Liou, 1992, 1993; Fu, 1996; Fu et al., 1998; F. G. Rose & Char-80
lock, 2002; F. Rose et al., 2006) to determine the clear-sky aerosol DRE.81
In this study, the observations from ground-based ARM RLs, AERONET sun pho-82
tometers, radiosondes, and spectral radiometers along with the RT model are utilized83
to investigate the clear-sky aerosol DRE at the ARM SGP and TWP sites. The surface84
SW downwelling fluxes are compared between surface radiometer observations and the85
RT model for clear-sky conditions. The daily mean, monthly mean and seasonal cycle86
of the clear-sky aerosol DRE is quantified.87
Section 2 presents the aerosol optical properties from the RL and AERONET and88
the extension of spectral aerosol optical properties outside the observational spectra range.89
Section 3 describes the RT model and other datasets used in this study. Section 4 shows90
the radiative closure experiment that compares simulated surface downwelling SW fluxes91
using observed inputs with surface radiometer observations. Section 5 details the clear-92
sky aerosol DREs from the daily to seasonal scale at the SGP and TWP sites. Section93
6 quantifies the uncertainties associated with the estimated aerosol DREs and simulated94
downward SW fluxes. Section 7 summarizes and discusses all findings presented and pro-95
vide some conclusions.96
2 Aerosol Optical Properties97
The ARM RL operates at 355 nm. The vertically resolved profiles of aerosol ex-98
tinction are retrieved using the feature detection and extinction (RL-FEX) retrieval al-99
gorithm (Balmes et al., 2019; Balmes & Fu, 2018; Thorsen et al., 2015; Thorsen & Fu,100
2015a; Thorsen et al., 2017). The RL AOD at 355 nm is obtained by vertically integrat-101
ing all aerosol extinction within a profile. In this study, we use the RL data with 10-minute102
temporal resolution and 30 meter vertical resolution from 1 August 2008 to 31 August103
2016 at the SGP site (36.6◦N, 97.49◦W) and from 15 December 2010 to 1 January 2015104
at the TWP site (12.4◦S, 130.89◦E). Lidar retrievals near the surface are difficult due105
to incomplete overlap of the emitted laser beam and the receiver’s field of view (Thorsen106
et al., 2015; Thorsen & Fu, 2015a). To avoid unphysical aerosol extinction near the sur-107
face, the extinction for the first two vertical bins from the surface to 60 m was obtained108
by extrapolating the extinctions from the two vertical bins between 60 to 120 m.109
Figure 1 shows the monthly climatology of the aerosol extinction profile from the110
RL over SGP and TWP. During the winter months at SGP, the aerosols remain quite111
low to the surface but during the other seasons the aerosols can reach quite high in the112
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Figure 1. Monthly climatology of the aerosol extinction profiles for each month from the Ra-
man lidar at 355 nm at the (top) Southern Great Plains (SGP) site from August 2008 to August
2016 and at the (bottom) Tropical Western Pacific (TWP) site from December 2010 to January
2015. The mean is denoted as a line and the shading refers to the 25th to 75th percentiles.
troposphere (Turner et al., 2001), especially from May to September, corresponding to113
larger monthly-mean AOD (Fig. 6). At TWP, the aerosols can be distributed high from114
September to January, especially in October with the largest monthly-mean AOD as-115
sociated with the biomass burning (Fig. 6).116
We also use the Aerosol Robotic Network (AERONET) Version 3 (V3) Level 2 AEROSOL117
OPTICAL DEPTH and Level 1.5 AEROSOL INVERSIONS datasets for spectral AOD,118
column-mean single-scattering albedo, and asymmetry factor (Giles et al., 2019). The119
aerosol optical properties from the AERONET are only available under clear-sky con-120
ditions. The AEROSOL OPTICAL DEPTH dataset provides AOD at 7 wavelengths (340,121
380, 440, 500, 675, 870, and 1020 nm) at the SGP site and 8 wavelengths (340, 380, 440,122
500, 675, 870, 1020, and 1640 nm) at the TWP site. The AEROSOL INVERSIONS dataset123
provides aerosol single-scattering albedo and asymmetry factor at 4 wavelengths (440,124
675, 870, and 1020 nm).125
2.1 Comparison of AOD between the RL and AERONET126
In order to compare the AOD at the same wavelength, the AERONET AOD at 355127
nm is derived by AOD(λ) = cλ−α, where λ is the wavelength and constants c and α (i.e.,128
the Angstrom exponent) are determined by the two AERONET AODs at the two clos-129
est wavelengths (i.e., 340 and 380 nm). AERONET AODs with an Angstrom exponent130
within ±3 standard deviations of the mean are used, following the screening threshold131
of AERONET data quality control program (Smirnov et al., 2000). The RL and AERONET132
AODs are collocated in time for comparison. The RL temporal resolution is 10 minutes,133
therefore, all AERONET AODs within each 10-minute time period are averaged. Only134
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Figure 2. Comparison of aerosol optical depth (AOD) at 355 nm between the Raman lidar
(RL) and Aerosol Robotic Network (AERONET) for collocated times at the (a) Southern Great
Plains (SGP) site from August 2008 to August 2016 and the (b) Tropical Western Pacific (TWP)
site from December 2010 to January 2015. The colors represent the number of occurrences di-
vided by the total number of observations. The 1-to-1 line is shown in dashed black. The sample
size (N), the correlation coefficient (r), the mean relative difference of the RL AOD compared
to the AERONET AOD (MD), and the mean RL (AODRL) and AERONET (AODAERONET )
AODs are also given.
transparent RL profiles are considered in this comparison (i.e., the lidar beam does not135
fully attenuate). We also only consider AODs less than 1 as a quality control.136
Figure 2 shows the comparison of RL and AERONET AOD for all collocated times.137
The RL (AERONET) mean AOD is 0.19 (0.19) at the SGP site and 0.23 (0.24) at the138
TWP site. The relative differences are -3.9% and -0.7% at SGP and TWP, respectively.139
The RL and AERONET AODs were compared in Thorsen and Fu (2015a) and they found140
a better agreement at SGP (-0.3%). This better agreement is due to the time period con-141
sidered (i.e., August 2008 through July 2013), which is related to a cancellation of a pos-142
itive and negative AOD difference over time. The mean RL AOD is smaller than the AERONET143
AOD prior to 2011, but larger after 2011.144
Of all collocated data, 22% and 61% of the RL profiles detect clouds at the SGP145
and TWP sites, respectively. Therefore, there are times when the AERONET determines146
the sky to be clear but the RL profile detects clouds. In order to investigate the impact147
of clouds on the RL and AERONET comparison, we also compare the RL and AERONET148
AODs separately for cases when the RL detects no clouds and when the RL detects clouds149
(Fig. 3). The mean AODs from both AERONET and RL under cloudy conditions at SGP150
are higher than those under cloud-free conditions, by 15.4% and 20.0%, respectively, which151
does not show the role of cloud contamination but suggests that the AOD could be higher152
when clouds are present. At the TWP site, the percentage differences between cloudy153
and cloud-free conditions are smaller for both AERONET (4.8%) and the RL (0.8%) but154
indicate potential small cloud contamination in the AERONET AOD due to high thin155
cirrus clouds (Smirnov et al., 2000; Huang et al., 2011; Chew et al., 2011) that occur fre-156
quently in the tropics (e.g., Fu et al., 2007; Balmes & Fu, 2018; Balmes et al., 2019; Thorsen157
& Fu, 2015a).158
In estimating aerosol DREs in this study, we use the aerosol extinction coefficient159
profile from the RL at 355 nm, scaled to the AERONET AOD. Below we describe the160
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Figure 3. Mean aerosol optical depth (AOD) at 355 nm of Aerosol Robotic Network
(AERONET; blue) and Raman lidar (RL; orange) for all collocated times (AOD355nm), collo-
cated times when the RL detects no clouds (AOD355nm,RL no cloud) and collocated times when
the RL detects clouds (AOD355nm,RL cloud) at the (a) Southern Great Plains (SGP) site from
August 2008 to August 2016 and the (b) Tropical Western Pacific (TWP) site from December
2010 to January 2015.
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Figure 4. Spectral distribution of (a) aerosol optical depth (AOD), (b) aerosol column single-
scattering albedo and (c) aerosol column asymmetry factor at the Southern Great Plains (SGP)
site from August 2008 to August 2016. The means of AERONET observations are denoted by
red solid lines and the shading refers to the 25th percentile through the 75th percentile. The
AOD at wavelengths outside the AERONET spectral range is extrapolated using the power law
with the AERONET-derived Angstrom exponent, as shown by the dotted red line with shading
in (a). The single-scattering albedo and asymmetry factor outside the AERONET spectral range
are obtained by a combination of 84% urban aerosol type plus 16% sulfate droplets, as shown as
solid black lines in (b) and (c), which fit the observed means best.
spectral aerosol optical depth, single-scattering albedo and asymmetry factor in the en-161
tire solar spectrum, constrained by the observations, which are required by the radia-162
tive transfer model to estimate aerosol DREs (Michalsky et al., 2006; Magi et al., 2008).163
2.2 Spectral aerosol optical depth, single-scattering albedo and asym-164
metry factor165
The AOD at any wavelength within the AERONET spectral range (i.e., 340 nm166
to 1020 nm at SGP and 340 nm to 1640 nm at TWP) is obtained by the logarithmic in-167
terpolation using the AERONET-observed AODs at the two nearest wavelengths for each168
AERONET observation in time. At a wavelength outside the AERONET spectral range169
(i.e., less than 340 nm and greater than 1020 nm at SGP and less than 340 nm and greater170
than 1640 nm at TWP), the AOD is obtained from a power law with the Angstrom ex-171
ponent derived by considering the AERONET-observed AODs at all wavelengths. Fig-172
ures 4a and 5a show the spectral distributions of the mean AOD and its 25th to 75th173
percentile range at the SGP and TWP, respectively.174
In order to mitigate the wavelength limitation of measurements (Magi et al., 2007),175
we also leverage AERONET observations to specify the spectral dependence of single-176
scattering albedo and asymmetry factor required by the radiative transfer model. The177
single-scattering albedo and asymmetry factor at a wavelength within the AERONET178
spectral range between 440 and 1020 nm are obtained using the linear interpolation with179
the observations at the two closest wavelengths. The AERONET retrievals of single-scattering180
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Figure 5. The same as Fig. 4 except at the Tropical Western Pacific (TWP) site from Decem-
ber 2010 to January 2015, and that the single-scattering albedo and asymmetry factor outside
the AERONET spectral range are obtained by a combination of 87% water soluble aerosols and
13% soot aerosols.
albedo and asymmetry factor are not available as often as the AERONET AOD obser-181
vations. When the AERONET AOD is available but the single-scattering albedo and asym-182
metry factor are not, the single-scattering albedo and asymmetry factor observation clos-183
est in time are used. At SGP, 50% of the single-scattering albedo and asymmetry fac-184
tor observations are within ∼25 minutes of the AOD observations and 90% are within185
∼1.5 days. At TWP, 50% of the single-scattering albedo and asymmetry factor obser-186
vations are within 15 minutes of the AOD observations and 90% are within 1.5 hours.187
The observed mean single-scattering albedo and asymmetry factor between 440 and 1020188
nm are shown in Figures 4b&c and 5b&c at SGP and TWP, respectively. Since the ob-189
servations of single-scattering albedo and asymmetry factor are column-mean values, we190
neglect their vertical dependences in this study.191
We extend the single-scattering albedo and asymmetry factor at the wavelengths192
outside the observational range by using aerosol optical properties of common aerosol193
types (D’Almeida et al., 1991; Hess et al., 1998). They include the spectral extinction194
coefficient, single-scattering albedo, and asymmetry factor for various relative humidi-195
ties. Briefly, the spectral optical properties profiles for the common aerosol types are first196
derived for the given relative humidity profile (see 3.1) with the extinction scaled by the197
RL extinction vertical distribution. The column-mean single-scattering albedo and asym-198
metry factor are then derived. We identify the combination of two aerosol types to best199
fit the AERONET-observed spectral single-scattering albedo averaged for all data col-200
located with the RL observations. Five common aerosol types are considered, which are201
continental, urban, water soluble, soot, and sulfate droplets (D’Almeida et al., 1991; Hess202
et al., 1998). It is found that the best combinations of two aerosol types are 84% urban203
and 16% sulfate droplets at SGP and 87% water soluble and 13% soot at TWP. The per-204
centage represents the fraction of the AOD at 355 nm from the two aerosol types. Fur-205
ther details on the fitting methodology are provided in Appendix A. The single-scattering206
albedo and asymmetry factor at the wavelengths outside the observational wavelength207
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range are then obtained from the best-fit aerosol type combination, which are shown in208
Figs. 4b&c and 5b&c.209
Figure 6 shows the monthly climatology of the AOD, Angstrom exponent, single-210
scattering albedo, and asymmetry factor at 500 nm from observations over the SGP and211
TWP sites. The AOD monthly climatology is constructed from the AERONET AOD212
at 500 nm and the RL AOD scaled by a monthly mean scaling factor between the AERONET213
and RL to increase the sample size. The Angstrom exponent is derived considering all214
AERONET wavelengths. The single-scattering albedo and asymmetry factor are from215
AERONET observations, which are linearly interpolated to 500 nm. A large monthly216
mean AOD is seen over SGP in the summer with a value of about 0.2, but over TWP217
in the spring with a value of 0.2 to 0.35. The monthly mean Angstrom exponent at SGP218
is between about 1.1 and 1.5 with smaller values in the spring. The mean Angstrom ex-219
ponent at TWP is smaller than one from December to May but is ∼1.2 from June to Novem-220
ber, indicating smaller aerosol particle sizes related to biomass burning (see the discus-221
sion below). The single-scattering albedo at TWP has a large seasonal cycle, ranging from222
∼0.83 in June to ∼0.97 in January.223
The seasonal cycle for the dry season (April-November) at TWP is characterized224
by a range of single-scattering albedos and AOD due to the biomass burning. At the start225
of the biomass burning season (June), the AOD is smaller with smaller single-scattering226
albedos (i.e., higher absorption). The wildfires are closest to Darwin in June (Beringer227
et al., 1995; Carr et al., 2005; Haverd et al., 2013), which corresponds to a larger frac-228
tion of the smoke comprising of fresh smoke and consequently the lowest single-scattering229
albedos (Eck et al., 2003; Carr et al., 2005; Qin & Mitchell, 2009). However, the biomass230
burning is at the peak of area burned for Australia in October when the AOD is the largest,231
but the fires are more dispersed (Kanniah et al., 2010). Therefore, a portion of the biomass232
burning aerosols are aged, which results in higher single-scattering albedo (i.e., lower ab-233
sorption) compared to the fresh smoke’s lower single-scattering albedo.234
3 Radiative Transfer Model and Other Data Used235
The NASA Langley Fu-Liou radiative transfer model (Fu & Liou, 1992, 1993; Fu,236
1996; Fu et al., 1998; F. G. Rose & Charlock, 2002; F. Rose et al., 2006) is used in this237
study. This model employs the delta-four stream scheme for radiative transfer and the238
correlated k-distribution method for non-gray gaseous absorption for 18 SW bands and239
12 longwave bands. Herein we only consider the SW components since we are interested240
in the aerosol DRE and its LW component is often considerably smaller in magnitude241
for the aerosol types taken into account here (e.g., Reddy et al., 2005). The SW upwelling242
and downwelling fluxes at the TOA and surface from the model are used. The total down-243
welling surface SW flux (F ↓total) is further separated into the direct (F ↓direct) and diffuse244
(F ↓diffuse) components in the radiative closure experiment.245
The input for the radiative transfer model is all based on observations. In addi-246
tion to aerosol optical properties (Section 2), other observational data including atmo-247
spheric temperatures and composition concentrations and surface albedo for the radia-248
tive transfer model input are briefly described below. Also described are the SW sur-249
face downwelling direct (F ↓direct) and diffuse (F ↓diffuse) fluxes from the radiometers which250
are used in the radiative closure experiment.251
3.1 Atmospheric temperature and water vapor profiles and other gaseous252
concentrations253
Temperature and relative humidity profiles were from radiosondes, which were in-254
terpolated to match RL-FEX vertical and temporal resolution (i.e., 10 min/30 m) for255
the retrieval processing (Thorsen et al., 2015; Thorsen & Fu, 2015a). Therefore, tem-256
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Figure 6. Seasonal cycle of (a)&(b) AOD, (c)&(d) Angstrom exponent, (e)&(f) single-
scattering albedo, and (g)&(h) asymmetry factor over the (left) Southern Great Plains (SGP)
site from August 2008 to August 2016 and at the (right) Tropical Western Pacific (TWP) site
from December 2010 to January 2015. The AOD, single-scattering albedo and asymmetry fac-
tor are for 500 nm. The monthly means are denoted as dots and the shading refers to the 25th
percentile through the 75th percentile. Annual mean values are given in each plot.
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perature and water vapor profiles are available for all RL profiles. The microwave radiome-257
ter (MWR) provides integrated water vapor measurements every 20 seconds (Turner et258
al., 2007; Morris, 2019), which for the 2-minute interval centered on the RL time are used259
to scale water vapor profiles. Water vapor profiles have an impact on solar radiation not260
only through the absorption but also through aerosol single-scattering albedo and asym-261
metry factor that change with the relative humidity.262
The temperature and water vapor profiles are smoothed by using a running aver-263
age of ±300 m (i.e., average of 20 vertical bins). The smoothed temperature and water264
vapor profiles extend from the surface until 16 km at SGP and 18 km at TWP, which265
are the highest heights for the RL retrievals. For the radiative transfer model input, the266
smoothed temperature and water vapor profiles are extended to 1 hPa using the seasonal267
climatology profiles over the SGP and TWP sites as derived from the European Cen-268
tre for Medium-Range Weather Forecasts (ECMWF) reanalysis product, ERA-Interim269
(Dee et al., 2011), which are blended with the observed profile following Yang et al. (2008).270
The ERA-Interim seasonal climatology profiles of ozone are used. The concentra-271
tions of CO2, N2O, CH4, and CFCs are the corresponding annual global mean concen-272
trations from the National Oceanic and Atmospheric Administration (NOAA) Earth Sys-273
tem Research Laboratory (ESRL) observations.274
3.2 Surface albedo275
The aerosol DRE is affected by the surface albedo (Yu et al., 2006; Chand et al.,276
2009; Keil & Haywood, 2003; Podgorny & Ramanathan, 2001). We use the MODIS MCD43C1277
Version 6 Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model278
Parameters dataset (Schaaf et al., 2002; Roesch et al., 2004), which has a resolution of279
0.05◦x0.05◦. The monthly mean surface albedo from 7 spectral bands are used, which280
are linearly interpolated to model wavelengths. The spectral surface albedo for model281
wavelengths outside the observational range use the value from the closest observational282
wavelength. The black-sky surface albedo is calculated considering the solar zenith an-283
gle dependence (Schaaf et al., 2002). The total albedo is obtained by combining the black-284
sky and white-sky albedos weighted with the diffuse fraction that is initially guessed from285
an empirical equation as a function of the solar zenith angle (Roesch et al., 2004). The286
radiative transfer model is then run to determine the diffuse fraction for the observed287
atmospheric conditions including aerosols. The spectral surface albedo is determined based288
on the recalculated diffuse fraction. The monthly mean climatology MODIS spectral sur-289
face albedos at a solar zenith angle of 60◦ are plotted in Figs. 7b-e.290
At the SGP site, we also consider the ARM Surface Spectral Albedo (SURFSPECALB)291
value-added product (McFarlane et al., 2011) to derive the spectral surface albedo in each292
SW band in the radiative transfer model (Fig. 7a). The SURFSPECALB product con-293
structs the spectral surface albedo by combining observations from the multifilter radiome-294
ters (MFRs) and the multifilter rotating shadowband radiometers (MFRSRs). We equally295
weight the 10-m and 25-m tower spectral surface albedo (Kassianov et al., 2014; Mlawer296
& Turner, 2016) and then group by the solar zenith angle to maintain the solar zenith297
angle dependence of the surface albedo. The SURFSPECALB product is not available298
at the TWP site.299
The ground-based observation of the spectral surface albedo should be more ac-300
curate than satellite observations but might be less representative for a region. We uti-301
lize the satellite surface albedo observations in deriving the aerosol DRE that would be302
more representative for a region, which also facilitates the comparison of the aerosol DRE303
between SGP and TWP. However, we also calculate the aerosol DRE using the ground-304
based albedo observations and compare the results with those using the satellite surface305
albedo observations. In general, the ground-based surface albedos are larger than those306
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from the satellite (Fig. 7). The dependence of surface albedo on the solar zenith angle307
was explicitly considered in deriving the aerosol DRE.308
3.3 Surface shortwave fluxes309
The ARM QCRAD value-added product provides data quality-controlled surface310
flux measurements at the ARM sites (Long & Shi, 2006, 2008). The QCRAD data prod-311
uct combines radiometer observations and a data quality analysis to determine the best-312
estimate of the surface fluxes. The F ↓total is obtained as the summation of the downward313
direct (F ↓direct) and diffuse (F ↓diffuse) fluxes at the horizontal surface. The simulated down-314
welling surface SW fluxes are compared with observed downwelling surface SW fluxes315
to test the radiative closure under clear-sky conditions.316
4 Radiative Closure Experiment317
We compare simulated surface solar broadband radiative fluxes including down-318
welling direct, diffuse and total fluxes with ground-based observations at the SGP and319
TWP sites to test and validate the radiative transfer model with the observed inputs.320
This radiative flux closure method has been extensively used to quantify the retrieval/observation/model321
uncertainty in derived radiative fluxes (e.g., Mace et al., 2006; Michalsky et al., 2006;322
Mather et al., 2007; Miller & Slingo, 2007; Comstock et al., 2013; Shupe et al., 2015; Mc-323
Comiskey & Ferrare, 2016). Only clear-sky conditions when collocated AERONET and324
RL observations are available and both detect no clouds are considered. The inputs for325
the radiative transfer model include the instantaneous atmospheric temperature and wa-326
ter vapor and aerosol optical properties and monthly mean spectral surface albedo, as327
described in Sections 2 and 3.328
To avoid possible cloud contamination or other instrument issues, we applied a thresh-329
old by comparing the model fluxes to the observation after doubling and halving the AOD.330
After doubling the AOD, if the modeled F ↓direct (F ↓diffuse) flux was larger (smaller) than331
the observed F ↓direct (F ↓diffuse) flux, the profile was no longer considered. After halving332
the AOD, if the modeled F ↓direct (F ↓diffuse) flux was smaller (larger) than the observed333
F ↓direct (F ↓diffuse) flux, the clear-sky profile was no longer considered. These thresholds334
exclude 20.3% and 7.0% of the clear-sky profiles at SGP and TWP sites, respectively.335
Figure 8 shows the comparison of simulated and observed downwelling surface SW fluxes336
at the SGP and TWP sites.337
At the SGP site, the mean differences in F ↓total, F↓direct and F ↓diffuse are 1.18, 1.24,338
and -0.05 W m−2, respectively, corresponding to relative differences of ∼0.2%, 0.3%, and339
-0.1%. The correlation coefficients between modeled fluxes and observations are greater340
than 0.99 for F ↓total and F ↓direct, and 0.98 for F ↓diffuse. Noting the measurement uncer-341
tainty of ±2% for the F ↓total and ±3% for the F ↓diffuse in the 95% confidence interval (Michalsky342
& Long, 2016), the simulated F ↓total and F ↓diffuse agree with observations within instru-343
ment uncertainty.344
At the TWP site, the mean differences in F ↓total, F↓direct and F ↓diffuse are 2.38, 2.19,345
and 0.19 W m−2, respectively, corresponding to relative differences of ∼0.5%, 0.5%, and346
0.2%. Similar to the SGP site, the differences are within the measurement uncertainty.347
The correlation coefficients between model and observations are larger than 0.99 for F ↓total348
and F ↓direct, and 0.98 for the F ↓diffuse.349
The overall excellent agreement between modeled and observed SW downwelling350
surface fluxes validates the radiative transfer model along with the observed inputs used351
in the simulations. We will discuss the impact of the uncertainties associated with the352
model inputs on the simulated surface downwelling SW fluxes in Section 6.353
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Figure 7. The spectral (a) surface (SFC) albedo from the ARM observation at the South-
ern Great Plains (SGP) site, (b)&(d) black-sky surface albedo and (c)&(e) white-sky surface
albedo from MODIS at the SGP and Tropical Western Pacific (TWP) sites, respectively, for a
solar zenith angle of 60◦. The lines represent the monthly mean climatology for December (black
solid), January (black dashed), February (black dotted), March (blue solid), April (blue dashed),
May (blue dotted), June (red solid), July (red dashed), August (red dotted), September (brown
solid), October (brown dashed), and November (brown dotted). The observed spectral surface
albedos are interpolated to model wavelengths.
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Figure 8. Downwelling surface shortwave (SW) fluxes for (a, d) the total downwelling surface
SW fluxes, F ↓total, (b, e) the direct downwelling surface SW fluxes, F ↓direct, and (d, f) the diffuse
downwelling surface SW fluxes, F ↓diffuse, from the model versus observations at the Southern
Great Plains (SGP) site (top row) and the Tropical Western Pacific (TWP) site (bottom row).
Clear-sky profiles of collocated Raman lidar (RL) and Aerosol Robotic Network (AERONET)
observations are considered from August 2008 to August 2016 for SGP site and from December
2010 to January 2015 for TWP site. The regression lines are shown as solid black lines. The
mean difference (diff) and correlation coefficient (r) between model and observation are given in
the top of each plot. Mean value of observations (obs) is shown in lower-right corner of each plot.
The sample size is given in the lower-right corner of each plot in the left column.
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5 Clear-sky aerosol direct radiative effect354
The instantaneous aerosol DRE at the top of the atmosphere (TOA) and surface355
(SFC) is defined as:356
357
DRE(TOA) = [F ↓(TOA)− F ↑(TOA)]aerosol − [F ↓(TOA)− F ↑(TOA)]no aerosol,358
(1)359
and360
361
DRE(SFC) = [F ↓(SFC)− F ↑(SFC)]aerosol − [F ↓(SFC)− F ↑(SFC)]no aerosol,362
(2)363
where F ↓ is the downward flux and F ↑ is the upward flux. The aerosol DRE is derived364
for a given atmospheric profile by running the radiative transfer model with and with-365
out aerosols. We consider the daily, monthly and annual mean aerosol DREs.366
The daily mean aerosol DRE, DRE, can be expressed as:367
368
DRE = 1tday
∫ sunsetsunrise
DRE(t)dt,369
(3)370
where DRE(t) is the instantaneous DRE at given time t and tday is the time in a day371
(i.e., 86400 seconds). To adequately capture the diurnal variation of the solar zenith an-372
gle and its effect on the DRE, a time step of 30 minutes is used (Yu et al., 2004, 2006).373
The difference between the daily insolation calculated using a time step of 30 minute and374
the reference value is only ∼0.25 W m−2 (less than 0.1%). The sunrise and sunset are375
determined with 1 second resolution for each day so that the first and last time steps could376
be slightly less than 30 minutes. The time step is centered on the hour mark (e.g., 0:00)377
and half hour mark (e.g., 0:30) to align with the observation’s time steps. The solar zenith378
angle is determined by considering the effects of refraction (Vignola et al., 2012). The379
monthly mean is the average of daily means in each month (e.g., all daily means in Jan-380
uary 2011). Monthly mean climatology is derived by averaging all monthly means for381
a given month in multiple years, and the annual mean climatology is the average of the382
twelve monthly-mean climatology. Because of large gaps in data availability at TWP site,383
we only derive the monthly and annual mean climatology there. At the TWP site, the384
monthly mean climatology is derived by averaging all available daily means for a given385
month regardless of the year.386
5.1 Daily- and monthly-mean time series of clear-sky aerosol DREs at387
SGP388
To determine the daily mean clear-sky aerosol DRE, the instantaneous clear-sky389
aerosol DRE is calculated at each 30-minute time step. However, observations are not390
always available at each time step for every day of the 8-year time period considered at391
SGP (e.g., during the cloudy periods). Therefore, we find the closest clear-sky observa-392
tions in time to fill in time steps without observations. We refer to this method as the393
fill-in method. Only collocated RL and AERONET observations under clear-sky con-394
ditions (see the clear-sky threshold in Section 4) are considered. The collocated RL/AERONET395
observation along with the corresponding atmospheric profile which is closest to the time396
step within one week can be used. If the closest collocated RL/AERONET observation397
is beyond 1 week but the closest clear-sky RL observation is within one week, we use the398
closest RL observations (i.e., RL AOD scaled by the monthly mean scale factor between399
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the RL AOD and AERONET AOD, and aerosol vertical distribution and correspond-400
ing atmospheric profile) and the AERONET monthly mean spectral AOD, single-scattering401
albedo and asymmetry factor. For a time step that is not within 1 week of the closest402
RL clear-sky observation, we use monthly mean climatology aerosol optical properties403
from the RL and AERONET with the closest radiosonde observations. Each time step’s404
AOD is scaled such that the monthly mean AOD from the fill-in method matches the405
monthly mean AOD from observations.406
At SGP, there are 75169 time steps of daylight with 30-minute resolution for the407
time period considered (i.e., August 2008 to August 2016). Of the 75169 time steps, 12.0%408
are directly collocated RL/AERONET observations, 74.2% are within 1 week of a col-409
located RL/AERONET observation, 11.1% are within 1 week of a RL only clear-sky ob-410
servation, and only 2.7% are run using monthly mean climatology aerosol properties. When411
the closest collocated RL/AERONET observation is utilized, 69.4% of time steps are within412
∼1 day and 90% are within ∼3 days. When the closest RL only clear-sky observation413
is utilized, 50% of time steps are within 1.3 hours and 90% are within ∼1 day.414
The time series of the daily mean clear-sky aerosol DRE at the TOA and the sur-415
face are shown in Fig. 9. At the TOA, the daily mean aerosol DRE at SGP is generally416
negative with the largest negative forcing of -14.82 W m−2. The positive TOA aerosol417
DRE is also observed, which occurs on 39 days of the total 2953 days (1.3%) with a max-418
imum value of 2.04 W m−2. The positive TOA aerosol DRE is related to the aerosols419
with stronger absorption (i.e., lower single-scattering albedo) for given surface albedo420
(J. M. Haywood & Shine, 1995; J. Haywood & Boucher, 2000; Yu et al., 2006). At the421
surface, the daily mean aerosol DRE is always negative, ranging from -0.59 to -32.16 W422
m−2. The time series of the monthly mean aerosol DRE for the TOA and the surface423
are also shown in Fig. 9, which ranges from -0.66 to -7.02 W m−2 at the TOA and from424
-1.88 to -14.60 W m−2 at the surface. The aerosol DRE is typically more negative in the425
summer and less negative in the winter, which largely follows the seasonal cycle of AOD426
and daytime fraction (i.e., larger AODs and daytime fractions in the summer). The sea-427
sonal cycle of the aerosol DRE will be explored further in the next section.428
The sensitivity of the DREs to a time step of 30 minutes was tested against a time429
step of 10 minutes. The annual mean differed by 0.01 W m−2 at the TOA and the sur-430
face, which is ∼0.2% of the aerosol DREs. The slightly larger in magnitude aerosol DRE431
for 30 minutes compared to 10 minutes is the result of less resolution at sunrise and sun-432
set, which overestimates the aerosol DRE. However, the difference is very small.433
5.2 Seasonal cycle and annual mean climatology of clear-sky aerosol DRE434
at SGP and TWP435
We examine the seasonal cycle of aerosol DRE by presenting its monthly mean cli-436
matology. The seasonal cycle of the aerosol DRE at SGP is shown in Figs. 10a&c. Over-437
all, the seasonal cycle in the aerosol DRE largely follows the AOD seasonal cycle (see438
Fig. 6), which is further enhanced by the solar insolation seasonal cycle. The strongest439
aerosol DREs of -4.81 W m−2 at the TOA and -10.77 W m−2 at the surface occur in the440
summer, which coincide with the largest AODs. The weakest aerosol DREs are in November-441
January with -1.28 W m−2 at the TOA and -2.77 W m−2 at the surface. The weakest442
aerosol DREs corresponds to smaller AODs and lower single-scattering albedos (Fig. 6).443
The lower single-scattering albedo could be related to nearby agriculture and transporta-444
tion or atmospheric flow patterns (Sheridan et al., 2001). The annual mean clear-sky aerosol445
DRE climatology is -3.00 and -6.85 W m−2 at the TOA and surface, respectively, over446
the SGP site.447
Sherman and McComiskey (2018) determined the clear-sky aerosol DRE for a south-448
east United States site with aerosol and surface properties similar to those at SGP. They449
found a mean TOA aerosol DRE of -1 to -2 W m−2 in the winter and -5 to -6 W m−2450
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Figure 9. Time series of the clear-sky aerosol direct radiative effect (DRE) at (a) the top
of the atmosphere (TOA) and (b) surface (SFC) at the Southern Great Plains (SGP) site from
August 2008 to August 2016. Results are shown for the daily mean aerosol DRE (black) and the
monthly mean aerosol DRE (red).
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Figure 10. Seasonal cycle of aerosol direct radiative effect (DRE) at the (top) top of the
atmosphere (TOA) and (bottom) surface (SFC) at the (left) Southern Great Plains (SGP) site
from August 2008 to August 2016 and at the (right) Tropical Western Pacific (TWP) site from
December 2010 to January 2015. The mean and median are denoted as dots and open circles,
respectively. Shading corresponds to the 25th percentile through the 75th percentile of all avail-
able daily-mean values for a given month. The annual mean climatology values are given in the
bottom left.
in the summer with a mean surface aerosol DRE of -2 to -3 W m−2 in the winter and451
-10 W m−2 in the summer. Their seasonal cycle and magnitude of the aerosol DREs are452
comparable to those presented here (Figs. 10a&c).453
We also calculated the aerosol DRE using the ARM ground-based spectral surface454
albedo. The annual mean clear-sky aerosol DRE is -2.75 and -6.64 W m−2 at the TOA455
and surface, respectively. The weaker aerosol DRE from the ground-based spectral sur-456
face albedo is due to the typically larger surface albedo from the ground-based obser-457
vation compared to MODIS.458
In contrast to SGP, data availability is sparser at TWP. Therefore, the fill-in method459
utilized at SGP cannot be applied to create a daily mean aerosol DRE for each day of460
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the time period considered. Instead, we employ a different method to derive the monthly461
mean climatology of the aerosol DRE (i.e., the aerosol DRE seasonal cycle). In this method,462
a daily mean aerosol DRE is obtained for each RL clear-sky observation by running the463
radiative transfer model with a diurnal cycle of solar zenith angles representative of the464
observation’s month from sunrise to sunset at each 30-minute time step with the same465
aerosol and atmospheric state properties. When the RL is collocated with the AERONET,466
the RL extinction profiles scaled to the AERONET AOD and the spectral AERONET467
AOD, single scattering albedo and asymmetry factor observations are used. When the468
AERONET is not available, the RL extinction profile scaled by a monthly mean clima-469
tology scale factor based on AERONET AOD as well as the monthly mean climatology470
spectral AOD, single-scattering albedo and asymmetry factor are used.471
The representative diurnal cycle of the solar zenith angle for a given month is de-472
termined by first calculating the daily insolation for each day in that month, which is473
then averaged to yield a monthly mean daily insolation. The day in the month with a474
daily insolation closest to the monthly mean daily insolation is found. The diurnal cy-475
cle of the solar zenith angle for this day is used with the solar insolation scaled such that476
it equals the monthly mean daily insolation. We refer to this method as the monthly di-477
urnal integration method.478
To demonstrate this method, consider the example of July 2011 at TWP. The monthly479
mean daily insolation is 333.80 W m−2 and the day with the closest daily insolation is480
July 17th (332.43 W m−2). The solar flux at the TOA normal to the beam on July 17th481
is 1317.47 W m−2, which is scaled to 1318.93 W m−2 by a ratio of 333.80/332.43. For482
each available clear-sky observation at TWP in July 2011, the daily mean aerosol DRE483
is obtained for July 17th with slightly modified daily insolation.484
Results from the monthly diurnal integration method and the fill-in method agree485
well at SGP in the derived monthly and annual mean climatology of aerosol DRE. For486
example, the annual mean climatology of the aerosol DRE agrees within 0.04 W m−2487
(1.5%) at the TOA and 0.14 W m−2 (2.0%) at the surface over SGP. The agreement be-488
tween the two methods provides confidence that the monthly diurnal integration method489
can be employed to derive the annual mean climatology of the aerosol DRE at TWP where490
sparser data availability prohibits use of the fill-in method.491
The seasonal cycle of the aerosol DRE at TWP is shown in Figs. 10b&d. The aerosol492
DRE is strongest in November at the TOA (-5.95 W m−2), and in October at the sur-493
face (-22.20 W m−2). The strongest aerosol DRE at the TOA corresponds to a larger494
monthly mean AOD with higher single-scattering albedo compared to other months with495
larger monthly mean AODs (Fig. 6) (Bouya et al., 2010). The strongest aerosol DRE496
at the surface in October corresponds to the largest monthly mean AOD. The weakest497
aerosol DRE of -0.96 (-4.16) W m−2 is in March (April) at the TOA (surface) corresponds498
to the months with the smallest monthly mean AODs (Fig. 6).499
Luhar et al. (2008) investigated the TOA aerosol DRE in northern Australia where500
the TWP site is located for 1 dry season (April-November 2004). They found a seasonal501
mean of -1.8 W m−2 with a peak of -4 W m−2 in November. The mean and peak val-502
ues of the aerosol DRE are comparable to those presented here (Fig. 10b).503
In addition to the aerosol DRE at the TOA and the surface, we also examine the504
aerosol effect on the radiative heating profile, which is the difference in the daily mean505
radiative heating rate profiles with and without considering the aerosols. Figure 11 shows506
the monthly climatology of aerosol radiative heating rate profile over SGP and TWP.507
It is very similar to the aerosol extinction profile (Fig.1), as expected. The heating is pre-508
dominantly near the surface at SGP. The heating extends higher into the atmosphere509
from May to September at SGP and from September to January at TWP.510
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Figure 11. Monthly climatology of the aerosol radiative heating rate profiles for each month
at the (top) Southern Great Plains (SGP) site from August 2008 to August 2016 and at the (bot-
tom) Tropical Western Pacific (TWP) site from December 2010 to January 2015. The mean is
denoted as a line and the shading refers to the 25th to 75th percentiles.
6 Uncertainty Estimates511
Understanding the uncertainty in estimated aerosol DRF is essential to quantify-512
ing changes in Earth’s radiation budget due to aerosols (e.g., McComiskey et al., 2008).513
Here we quantify the uncertainty in the estimated clear-sky aerosol DRE as well as the514
simulated downwelling SW fluxes used in the radiative closure experiment. Both mea-515
surement and methodology uncertainties are considered.516
6.1 Uncertainty in estimated aerosol DRE517
For the uncertainty estimates, monthly mean climatology of the radiative trans-518
fer model inputs including aerosol optical properties, atmospheric profiles and surface519
albedo, were used to calculate the monthly mean aerosol DREs. The perturbation due520
to measurement and methodology uncertainties was then applied to assess how the cal-521
culated monthly mean and annual mean aerosol DREs changed as a result to quantify522
the aerosol DRE uncertainty. For simplicity, the monthly diurnal integration method is523
applied to one profile for each of the 12 months. The obtained annual mean aerosol DREs524
agree with those presented in Section 5 within 0.05 W m−2 (1.6%) for SGP and 0.03 W525
m−2 (1.2%) for TWP at the TOA, and within 0.01 W m−2 (0.1%) for SGP and 0.20 W526
m−2 (2.0%) for TWP at the surface. Note that the method described here is to quan-527
tify the aerosol DRE uncertainty instead of the aerosol DRE itself.528
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6.1.1 Measurement uncertainty529
The monthly and annual mean aerosol DRE uncertainties due to measurement un-530
certainty from the AOD, single-scattering albedo, asymmetry factor and surface albedo531
are quantified. The annual-mean results are summarized in Table 1.532
For AOD, the uncertainty used is ±0.01 for AOD at 500 nm and the same relative533
uncertainty is applied to the AOD at other wavelengths. This uncertainty is similar to534
AERONET’s AOD uncertainty (Giles et al., 2019). However, this uncertainty in the AOD535
may be an overestimate since the mean difference in the AOD at 355 nm between RL536
and AERONET is 0.01 or less (Fig. 3). The annual mean aerosol DRE uncertainty due537
to AOD is about ±6-8% at the TOA and the surface.538
For the single-scattering albedo and asymmetry factor, the AERONET Aerosol In-539
version Uncertainty for Level 2 Products is used. The data product uses the assumed540
one sigma biases for AOD, radiometric calibration, solar irradiance spectrum and sur-541
face reflectance to get the inversion results for each set of measurement. For all avail-542
able profiles, we derive the mean uncertainty estimate for each observational wavelength543
(440, 675, 870, and 1020 nm). The uncertainty estimate is then added and subtracted544
from each monthly mean climatology single-scattering albedo and asymmetry factor to545
determine the change in the aerosol DRE as a result. For wavelengths less than or greater546
than the observational range, the mean uncertainty estimate considering all observational547
wavelengths is considered. The largest uncertainty in derived aerosol DREs at both SGP548
and TWP at the TOA is due to the single-scattering albedo measurement uncertainty,549
which is consistent with McComiskey et al. (2008). In particular, the relative uncertainty550
in the annual mean aerosol DRE at the TOA at TWP is ±20.7%.551
For the surface albedo, the uncertainty considered is a relative uncertainty of ±10%552
(Mira et al., 2015; Berg et al., 2020). This results in a relative uncertainty in the annual553
mean aerosol DRE of 7-10% at the TOA and 2-3% at the surface. The differences in aerosol554
DRE between using the ARM and MODIS surface albedo at the SGP are similar to those555
due to the surface albedo uncertainties (Table 1).556
The total aerosol DRE uncertainty due to measurement uncertainties (∆DREmeasurement)557
is determined by adding the aerosol DRE measurement uncertainty due to the AOD (∆DREAOD),558
single-scattering albedo (∆DREω), asymmetry factor (∆DREg), and surface albedo (∆DREαs)559
in quadrature:560
561
∆DREmeasurement =√
(∆DREAOD)2 + (∆DREω)2 + (∆DREg)2 + (∆DREαs)2.562
(4)563
The monthly aerosol DRE ±∆DREmeasurement is shown in Fig. 11. The relative564
uncertainty is about 10-30% for each month. At SGP, the largest absolute aerosol DRE565
measurement uncertainty is in June for both the TOA and the surface, and the small-566
est is in January (December) for the TOA (surface). At TWP, the largest absolute aerosol567
DRE uncertainty is in October and the smallest at TWP is in April for both the TOA568
and the surface.569
6.1.2 Methodology uncertainty570
Observed aerosol spectral optical properties from the AERONET do not cover the571
entire solar spectra. Here we quantify the uncertainty due to the methodology used to572
extend the spectral aerosol optical properties outside the AERONET spectral range. The573
results are summarized in Table 2.574
For the AOD, the Angstrom exponent derived from AERONET observations is used575
to extend the AOD outside the AERONET spectra. We use three combinations of stan-576
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Figure 12. Seasonal cycle of aerosol direct radiative effect (DRE) along with the uncertainty
estimate (vertical line) due to aerosol optical properties and surface albedo measurement un-
certainty at the (left) Southern Great Plains (SGP) and the (right) Tropical Western Pacific
(TWP) sites for the (top) top of the atmosphere (TOA) and the (bottom) surface (SFC). The
monthly mean aerosol DRE is the same as those in Fig. 10. The annual mean values (calculated
as the average of monthly means) are given in the bottom with the range due to measurement
uncertainty in the parentheses.
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Table 1. The uncertainty (W m−2) in annual mean aerosol direct radiative effect (DRE) at the
top of atmosphere (TOA) and surface (SFC) over the (left) Southern Great Plains (SGP) and
the (right) Tropical Western Pacific (TWP) sites due to measurement uncertainty of the aerosol
optical depth (AOD), column-mean single-scattering albedo (ω), column-mean asymmetry factor
(g), surface albedo (αs), and all of them together. The relative difference is in the parentheses.
TOA DRE uncertainty [W m−2] SGP TWP
AOD ±0.24 (∓8.3%) ±0.17 (∓6.0%)ω ±0.45 (∓15.2%) ±0.58 (∓20.7%)g ±0.17 (∓5.9%) ±0.15 (∓5.6%)αs ±0.22 (∓7.4%) ±0.28 (∓10.0%)total ±0.58 (∓19.4%) ±0.68 (∓24.1%)
SFC DRE uncertainty [W m−2] SGP TWP
AOD ±0.57 (∓8.3%) ±0.65 (∓6.1%)ω ±0.78 (∓11.3%) ±1.13 (∓10.7%)g ±0.18 (∓2.7%) ±0.17 (∓1.6%)αs ±0.18 (∓2.6%) ±0.21 (∓2.0%)total ±1.00 (∓14.6%) ±1.33 (∓12.9%)
dard aerosol types (Appendix A) that provide the spectral AOD for the entire solar spec-577
tra to determine the uncertainty due to this extension method. Overall, the error in the578
annual mean aerosol DRE is less than 1% at the TOA and 5% at the surface, which is579
all related to the wavelengths greater than the AERONET spectral interval since the lower580
wavelengths are well constrained as the smallest wavelength of observed AODs is 340 nm.581
We conclude that the Angstrom exponent derived from the AERONET spectral range582
can be used to extend the AOD outside the range well to determine the aerosol DRE.583
For the single-scattering albedo, a combination of two standard aerosol types, which584
is found to best fit the AERONET-observed spectral single-scattering albedo over each585
site, is used to obtain the spectral single-scattering albedo outside the AERONET spec-586
tral range (Section 2.2, Appendix A). Two additional combinations over each site are587
found, which show similar fits to the AERONET observations (Appendix A). The an-588
nual mean aerosol DRE uncertainty due to the extension methodology is determined by589
comparing the aerosol DRE using the first fit with the second and third fits by keeping590
the spectral single-scattering albedo in the AERONET spectral range the same. The un-591
certainty in the annual mean aerosol DRE is less than 6% at the TOA, which is primar-592
ily related to longer wavelengths, and smaller than 2% at the surface with similar con-593
tributions from shorter and longer wavelengths (Table 2).594
The asymmetry factor spectral distribution uncertainty is determined in the same595
manner as the single-scattering albedo by comparing the aerosol DRE using the fit 1 asym-596
metry factor to those using the fit 2 and fit 3 asymmetry factors. The uncertainty in the597
annual mean aerosol DRE is less than 9% at the TOA and smaller than 4% at the sur-598
face (Table 2). In contrast to the AOD and single-scattering albedo, the annual mean599
aerosol DRE uncertainty due to the asymmetry factor for wavelengths less than the AERONET600
spectral range exceeds the uncertainty for the wavelengths greater. An additional asym-601
metry factor observation in this spectral range (i.e., λ < 440 nm) would improve aerosol602
DRE estimates, especially at the TOA. By comparing Tables 2 and 1, it is interesting603
to note that errors in aerosol DRE due to the methodology are smaller than those due604
to measurement uncertainties, except for the asymmetry factor.605
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In this study, we explicitly consider the solar zenith angle dependence of the sur-606
face albedo. We quantify the effect on the aerosol DRE of neglecting the solar zenith an-607
gle dependence of the black-sky surface albedo. When using the black-sky surface albedo608
at the solar zenith angle corresponding to local solar noon (as provided by MODIS), the609
TOA aerosol DRE difference is -0.16 W m−2 (5.5%) and -0.12 W m−2 (4.3%) for SGP610
and TWP, respectively. At the surface, the aerosol DRE difference is -0.14 W m−2 (2.1%)611
and -0.10 W m−2 (1.0%) for SGP and TWP, respectively. By neglecting the solar zenith612
angle dependence of the surface albedo, the aerosol DRE is overestimated as the black-613
sky surface albedo increases with increasing solar zenith angle. This highlights the im-614
portance of considering the solar zenith angle dependence for the surface albedo in es-615
timating the aerosol DRE.616
6.2 Uncertainty in simulated downwelling SW fluxes617
We quantify the uncertainty in the simulated downwelling SW fluxes including F↓total,618
F↓direct and F↓diffuse that were used in the radiative closure experiment (Section 4). The619
motivation is to examine how much difference between simulated and observed down-620
welling SW fluxes can be explained by the radiative transfer model input uncertainty621
related to both measurement and methodology used. A similar procedure as in Section622
6.1 is used except that the simulation was performed for all instantaneous profiles used623
in the radiative closure experiment. The uncertainty in simulated downwelling SW fluxes624
due to measurement uncertainty of aerosol optical depth, single-scattering albedo, asym-625
metry factor, and surface albedo is summarized in Table 3.626
Similar to aerosol DRE, the largest error in the F↓total is due to the single-scattering627
albedo measurement uncertainty. Note that the effect of the AOD uncertainty on sim-628
ulated F↓direct and F↓diffuse have opposite signs, leading to a small difference in F↓total.629
The uncertainties in single-scattering albedo, asymmetry factor, and surface albedo af-630
fect the simulated F↓total only through the F↓diffuse.631
At SGP, the differences between simulated and observed downwelling SW fluxes632
(Section 4) are within 1σ of the uncertainty associated with the aerosol optical proper-633
ties and surface albedo observations for the F↓total (1.18 vs. 2.49 W m−2), F↓direct (1.24634
vs. 4.98 W m−2) and F↓diffuse (-0.05 vs. 3.98 W m−2). At TWP, the measurement un-635
certainty due to aerosol optical properties and surface albedo can also explain the dif-636
ferences between simulations and observations in F↓total (2.38 vs. 2.92 W m−2), F↓direct637
(2.19 vs. 4.29 W m−2) and F↓diffuse (0.19 vs. 3.50 W m−2). The uncertainty in F↓direct638
(Table 3) as compared with the difference between simulated and observed F↓direct in-639
dicates that the uncertainties in the AOD might be overestimated by a factor of 2.640
The uncertainty in the simulated downwelling SW fluxes due to the methodology641
extending the spectral distributions of aerosol optical properties is also quantified, which642
is generally less than ∼0.7 W m−2 at SGP site and less than ∼0.3 W m−2 at TWP site643
(not shown). This indicates that the uncertainty in specifying the aerosol optical prop-644
erties at wavelengths outside the observational spectral range is small, despite the range645
of values (Figs. A1-A2).646
7 Conclusions647
The clear-sky aerosol DREs were estimated at the ARM SGP site for 8 years and648
at the TWP site for 4 years. The NASA Langley Fu-Liou radiative transfer model was649
used with observed inputs including aerosol vertical extinction profile from the Raman650
lidar; spectral aerosol optical depth (AOD), single-scattering albedo and asymmetry fac-651
tor from AERONET; temperature and water vapor profiles from radiosondes; and sur-652
face shortwave spectral albedo from radiometers. A clear-sky radiative closure experi-653
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Table
2.
The
unce
rtain
ty(W
m−2)
inth
eannual
mea
naer
oso
ldir
ect
radia
tive
effec
t(D
RE
)due
toth
em
ethodolo
gy
that
exte
nds
the
spec
tral
dis
trib
uti
on
of
aer
oso
lopti
cal
dep
th(A
OD
),co
lum
n-m
ean
single
-sca
tter
ing
alb
edo
(ω),
and
colu
mn-m
ean
asy
mm
etry
fact
or
(g),
toth
ew
avel
ength
souts
ide
the
AE
RO
NE
Tsp
ec-
tral
range,
over
the
(lef
t)South
ern
Gre
at
Pla
ins
(SG
P)
and
the
(rig
ht)
Tro
pic
al
Wes
tern
Paci
fic
(TW
P)
site
sat
the
top
of
the
atm
osp
her
e(T
OA
)and
surf
ace
(SF
C).
The
annual
mea
naer
oso
lD
RE
unce
rtain
tyis
furt
her
separa
ted
into
those
for
wav
elen
gth
sle
ssth
an
and
gre
ate
rth
an
the
AE
RO
NE
Tsp
ectr
al
range.
The
rela
tive
diff
eren
ces
are
inth
epare
nth
eses
.T
he
unce
rtain
tyquanti
fica
tion
isbase
don
the
thre
eco
mbin
ati
ons
of
standard
aer
oso
lty
pes
(i.e
.,fit
1,
fit
2,
and
fit
3),
whic
hb
est
fit
the
AE
RO
NE
T-o
bse
rved
spec
tral
single
-sca
tter
ing
alb
edo. S
GP
TW
PT
OA
DR
Eu
nce
rtai
nty
[Wm−
2]
tota
lλ<
ob
s.λ>
ob
s.to
tal
λ<
ob
s.λ>
ob
s.
AODλ(fi
t1)
-0.0
1(0
.4%
)0.0
0(0
.0%
)-0
.01
(0.4
%)
-0.0
1(0
.4%
)0.0
0(0
.0%
)-0
.01
(0.5
%)
AODλ(fi
t2)
0.02
(-0.6
%)
0.0
0(0
.0%
)0.0
1(-
0.4
%)
-0.0
1(0
.5%
)0.0
0(0
.0%
)-0
.01
(0.4
%)
AODλ(fi
t3)
-0.0
3(1
.1%
)0.0
0(0
.0%
)-0
.03
(1.1
%)
-0.0
1(0
.5%
)0.0
0(0
.0%
)-0
.01
(0.4
%)
ωλ(fi
t2)
-0.1
8(5
.9%
)0.0
6(-
2.1
%)
-0.2
4(7
.9%
)0.0
7(-
2.4
%)
0.0
2(-
0.9
%)
0.0
4(-
1.6
%)
ωλ(fi
t3)
-0.0
8(2
.8%
)-0
.03
(0.9
%)
-0.0
6(2
.0%
)0.0
9(-
3.1
%)
0.0
2(-
0.8
%)
0.0
6(-
2.3
%)
gλ(fi
t2)
0.25
(-8.4
%)
0.1
9(-
6.2
%)
0.0
7(-
2.2
%)
-0.0
8(2
.9%
)-0
.08
(2.9
%)
0.0
0(0
.0%
)gλ(fi
t3)
0.01
(-0.5
%)
0.0
4(-
1.2
%)
-0.0
2(0
.7%
)-0
.09
(3.0
%)
-0.0
8(2
.9%
)0.0
0(0
.1%
)
SG
PT
WP
SF
CD
RE
un
cert
ainty
[Wm−
2]
tota
lλ<
ob
s.λ>
ob
s.to
tal
λ<
ob
s.λ>
ob
s.
AODλ(fi
t1)
0.09
(-1.3
%)
0.0
0(0
.0%
)0.0
9(-
1.2
%)
0.2
2(-
2.2
%)
0.0
0(0
.0%
)0.2
2(-
2.1
%)
AODλ(fi
t2)
-0.1
9(2
.8%
)0.0
0(0
.0%
)-0
.20
(2.9
%)
0.1
8(-
1.7
%)
0.0
0(0
.0%
)0.1
7(-
1.6
%)
AODλ(fi
t3)
0.29
(-4.2
%)
0.0
0(0
.0%
)0.2
9(-
4.2
%)
0.1
8(-
1.7
%)
0.0
0(0
.0%
)0.1
7(-
1.6
%)
ωλ(fi
t2)
0.06
(-0.9
%)
-0.1
5(2
.2%
)0.2
1(-
3.1
%)
-0.1
6(1
.6%
)-0
.10
(0.9
%)
-0.0
6(0
.6%
)ωλ(fi
t3)
0.14
(-2.0
%)
0.0
7(-
1.1
%)
0.0
6(-
0.9
%)
-0.1
8(1
.8%
)-0
.10
(0.9
%)
-0.0
9(0
.8%
)
gλ(fi
t2)
0.26
(-3.7
%)
0.1
9(-
2.8
%)
0.0
6(-
0.9
%)
-0.0
9(0
.9%
)-0
.09
(0.9
%)
0.0
0(0
.0%
)gλ(fi
t3)
0.02
(-0.3
%)
0.0
4(-
0.6
%)
-0.0
2(0
.3%
)-0
.09
(0.9
%)
-0.0
9(0
.9%
)0.0
0(0
.0%
)
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Table 3. The uncertainty (in W m−2) in simulated downwelling SW surface (SFC) fluxes
including the total and direct and diffuse components due to measurement uncertainty of the
aerosol optical depth (AOD), column-mean single-scattering albedo (ω), column-mean asymme-
try factor (g), and surface albedo (αs), and all of them together, over the (left) Southern Great
Plains (SGP) and the (right) Tropical Western Pacific (TWP) sites. The ±(∓) sign before the
simulated downwelling SW fluxes refers to a positive (negative) change corresponding to the
positive change in aerosol optical properties.
SGP TWP
F↓SFC,simulated uncertainty [W m−2] total direct diffuse total direct diffuse
±AOD ∓1.52 ∓4.98 ±3.46 ∓1.71 ∓4.29 ±2.58±ω ±1.86 - ±1.86 ±2.31 - ±2.31±g ±0.44 - ±0.44 ±0.35 - ±0.35±αs ±0.47 - ±0.47 ±0.37 - ±0.37total ±2.49 ±4.98 ±3.98 ±2.92 ±4.29 ±3.50
ment was performed for clear-sky conditions by comparing simulated F ↓total, F↓direct, and654
F ↓diffuse with observations.655
At SGP, the mean differences between simulated and observed SW downwelling sur-656
face fluxes were 1.2 W m−2 (0.2%) for F ↓total, 1.2 W m−2 (0.3%) for F ↓direct, and -0.1 W657
m−2 (-0.1%) for F ↓diffuse. At TWP, the mean differences between simulated and observed658
SW downwelling surface fluxes were 2.4 W m−2 (0.5%) for F ↓total, 2.2 W m−2 (0.5%) for659
F ↓direct, and 0.2 W m−2 (0.2%) for F ↓diffuse. The correlation coefficients between the sim-660
ulated and observed fluxes were greater than 0.99 for F ↓total and F ↓direct, and ∼0.98 for661
the F ↓diffuse.662
At SGP, the daily mean aerosol DRE varied from 2.04 to -14.82 W m−2 at the TOA663
and from -0.59 to -32.16 W m−2 at the surface. The monthly mean aerosol DRE ranged664
from -0.66 to -7.02 W m−2 at the TOA and -1.88 to -14.60 W m−2 at the surface. The665
annual mean aerosol DRE was -3.00 W m−2 at the TOA and -6.85 W m−2 at the sur-666
face. The seasonal cycle of the aerosol DRE is influenced by the seasonal cycle of AOD667
as well as by the solar insolation seasonal cycle. The strongest aerosol DREs are in the668
summer (June-August) with -4.81 W m−2 at the TOA and -10.77 W m−2 at the sur-669
face, when the largest AODs are observed. The weakest aerosol DREs are in late fall into670
winter (November-January) with -1.28 W m−2 at the TOA and -2.77 W m−2 at the sur-671
face. The weakest aerosol DREs coincide with smaller AODs and lower single-scattering672
albedos, which both weaken the aerosol DRE. The lower single-scattering albedos could673
be due to the nearby agriculture and transportation or atmospheric flow patterns that674
transport more absorbing aerosols over the SGP site.675
At TWP, we did not derive the time series of the daily aerosol DRE due to sparse676
data availability. Instead we quantified the annual mean and seasonal cycle of the aerosol677
DRE there. The annual mean aerosol DRE was -2.82 W m−2 at the TOA and -10.34 W678
m−2 at the surface. The seasonal cycle of the aerosol DRE at TWP is influenced by the679
wet and dry season. The marine influences during the wet season lead to lower AODs680
and higher single-scattering albedos, as indicated by smaller aerosol DREs during December-681
April. The dry season is characterized by the biomass burning, which corresponds to larger682
AODs but also a range of lower single-scattering albedos depending on the intensity and683
the proximity of the wildfires. The aerosol DRE is strongest in November at the TOA684
(-5.95 W m−2) and in October at the surface (-22.20 W m−2). The strongest aerosol DRE685
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at TOA coincides with larger AODs and weakened absorption (i.e., higher single-scattering686
albedo) compared to other months with large AODs. At the surface, the strongest aerosol687
DRE corresponds to the largest monthly mean AOD when Australia’s biomass burning688
season is at its peak. The weakest aerosol DRE at the TOA (surface) of -0.96 (-4.16) W689
m−2 is in March (April) when the smallest AODs are observed.690
The uncertainty of estimated aerosol DREs is quantified. By considering the un-691
certainty in aerosol optical properties measurements as well as surface albedo measure-692
ments, the derived annual mean TOA aerosol DRE ranges from -2.42 to -3.58 (-2.14 to693
-3.50) W m−2 at SGP (TWP) while the annual mean aerosol DRE at the surface ranges694
from -5.85 to -7.84 (-9.00 to -11.67) W m−2 at SGP (TWP). The uncertainty of estimated695
aerosol DREs due to the methodology extending the spectral distribution of aerosol op-696
tical properties beyond the observed spectral range is also determined, which is 10% or697
less. The uncertainty in simulated downwelling surface fluxes is also quantified. It is shown698
that the difference between simulated and observed fluxes are within the 68% confidence699
interval (i.e., 1σ) of the uncertainty related to the observations of aerosol optical depth,700
single-scattering albedo, asymmetry factor, and surface albedo, for F ↓total, F↓direct, and701
F ↓diffuse at SGP and TWP.702
This study shows the regional aerosol direct radiative effect under clear-sky con-703
ditions at two locations by combining ground-based observations and RT modeling. The704
different aerosol properties observed at both sites led to absolute and seasonal contrasts705
in the clear-sky aerosol direct radiative effect. The range of the clear-sky aerosol direct706
radiative effect highlights the need to understand the global aerosol direct radiative ef-707
fect under a range of aerosol conditions as well as under cloudy-skies.708
Acknowledgments709
This study is supported by the U.S. Department of Energy Office of Science (BER) un-710
der Grant DE-SC0020135. The Raman lidar (RL-FEX), surface flux (QCRAD), surface711
spectral albedo (SURFSPECALB), radiosonde (sondewnpn) and microwave radiome-712
ter (mwrlos) data products can be downloaded from the ARM data archive (www.arm.gov/data).713
The AERONET datasets can be downloaded from the AERONET site (aeronet.gsfc.nasa.gov/new web/data.html).714
The MODIS spectral surface albedo data can be downloaded from NASA’s EARTHDATA715
website (earthdata.nasa.gov). The annual global mean trace gas concentrations can be716
downloaded from NOAA ESRL’s website717
(www.esrl.noaa.gov/gmd/aggi/aggi.html). The reanalysis data product (ERA-Interim)718
can be downloaded from the the ECMWF’s website (www.ecmwf.int/en/forecasts/datasets/reanalysis-719
datasets/era-interim). Solar zenith angle and Earth-Sun distance calculations were per-720
formed using code from GitHub721
(www.github.com/klapo/solargeo/blob/master/solargeo/sunae.pyx). We thank L. D. Rhi-722
imaki for useful discussions on surface radiometers observations. We also thank R. Wa-723
gener, L. Gregory and L. L. Ma for clarifying information on the AERONET data. X.724
Wu and K. A. Balmes are co-first authors of this paper.725
Appendix A Fitting observations to model single-scattering albedo and726
asymmetry factor727
For the spectral distribution of single-scattering albedo and asymmetry factor for728
model wavelengths outside the observational range (i.e., below 467.6 nm and above 960.9729
nm), a combination of two common aerosol types with known spectral aerosol optical730
properties is determined to best fit the observations. Five common aerosol types (D’Almeida731
et al., 1991; Hess et al., 1998) are utilized, which are continental, urban, water soluble,732
soot, and sulfate droplet aerosols. A combination of two aerosol types could be, e.g., con-733
tinental and soot.734
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The vertical profile of aerosol extinction from two aerosol types can be considered735
as:736
737
βλ(z) = βλ,1(z) + βλ,2(z),738
(A1)739
where βλ(z) is the total aerosol extinction profile at wavelength λ, βλ,1(z) are the aerosol740
extinction profile for aerosol types 1 and 2, respectively. The volume extinction coeffi-741
cient (βλ) is a product of the aerosol number concentration (N) and the extinction cross742
section (σ) (i.e., βλ=Nσ). If the fraction of the total number concentration for aerosol743
type 1 is f , the vertical profile of aerosol extinction due to two aerosol types can then744
be written as:745
746
βλ(z) = fN(z)σλ,1(z) + (1− f)N(z)σλ,2(z),747
(A2)748
where N(z) is the vertical profile of aerosol number concentration, and σλ,1(z) and σλ,2(z)749
are the vertical profiles of extinction cross section for aerosol types 1 and type 2, respec-750
tively.751
The single-scattering albedo profile, ωλ(z), is752
753
ωλ(z) =fN(z)σλ,1(z)ωλ,1(z)+(1−f)N(z)σλ,2(z)ωλ,2(z)
βλ(z) ,754
(A3)755
where ωλ,1(z) and ωλ,2(z) are the single-scattering albedo vertical profiles of aerosol types756
1 and 2, respectively. The column-mean single-scattering albedo, ωλ, which is what the757
AERONET observes, is758
759
ωλ =
∑fN(z)σλ,1(z)ωλ,1(z)∆z+(1−f)N(z)σλ,2(z)ωλ,2(z)∆z∑
βλ(z)∆z,760
(A4)761
where ∆z is the vertical interval.762
The aerosol vertical extinction profile from the RL, the column-mean single-scattering763
albedos at 440, 675, 870 and 1020 nm from AERONET, and relative humidity from ra-764
diosondes for all collocated RL and AERONET profiles are employed. The 10 combi-765
nations of 2 aerosol types from the 5 common aerosol types are used for the vertical pro-766
files of single-scattering albedo (i.e., ωλ,1 and ωλ,2) and extinction cross section (i.e., σλ,1767
and σλ,2) that are a function of relative humidity. The N(z) is constrained by the RL768
aerosol vertical extinction profile. The fraction of aerosol type 1, f , for each combina-769
tion is determined by the following. The spectral mean squared error in single-scattering770
albedo, weighted by the AOD at each wavelength is εi for a given profile, i, which is ex-771
pressed as:772
773
εi =
∑λ
(ωi,λ(f)−ωi,λ(AERONET ))2AODi,λ∑λAODi,λ
,774
(A5)775
where ωi,λ(f) is the column single-scattering albedo at wavelength λ for a given f , and776
ωi,λ(AERONET ) is the observed column single-scattering albedo. The εw, the average777
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of εi weighted by the RL AOD, AODi,355nm, is obtained by considering all profiles such778
that:779
780
εw =
∑iεiAODi,355nm∑iAODi,355nm
.781
(A6)782
The f is determined by minimizing εw for each of the 10 combinations.783
For SGP, the top aerosol type combination is the urban and sulfate droplets aerosols784
(f=0.99). For TWP, it is water soluble and soot (f=0.36). The top aerosol type com-785
bination can also be understood in terms of the percentage contributions to AOD from786
aerosol types 1 and 2, which can be calculated using Eqs. A1 and A2. We first obtain787
the percentage of AOD from aerosol type 1, e.g., for each profile, then the averaged per-788
centage of AOD for all profiles weighted by the RL AOD. In terms of the AOD contri-789
bution, the top aerosol type combinations at SGP is 84% urban and 16% sulfate droplets790
and at TWP is 87% water soluble and 13% soot. The best fits are applied to extend the791
spectral distribution of single-scattering albedo outside the observational spectral range792
for the radiative transfer model simulations.793
The asymmetry factor outside the observational spectral range is obtained from:794
795
gλ(z) =fN(z)σλ,1(z)ωλ,1(z)gλ,1(z)+(1−f)N(z)σλ,2(z)ωλ,2(z)gλ,2(z)
fN(z)σλ,1(z)ωλ,1(z)+(1−f)N(z)σλ,2(z)ωλ,2(z) ,796
(A7)797
where gλ(z) is the vertical profile of asymmetry factor at wavelength λ and gλ,1(z) and798
gλ,2(z) are the vertical profiles of asymmetry factor for aerosol types 1 and 2, respectively.799
We also consider the second and third best combinations at SGP and TWP to de-800
termine a range of possibility for the spectral distribution of single-scattering albedo and801
asymmetry factor outside the observational range. At SGP, the second and third aerosol802
type combinations are 89% sulfate droplets and 11% soot, and 97% water soluble and803
3% soot, respectively. At TWP, they are 88% continental and 12% soot, and 91% ur-804
ban and 9% soot. The second and third combinations are utilized for determining method-805
ology uncertainty for the aerosol DRE and the radiative closure experiment (Section 6).806
Figures A1 and A2 show the spectral distribution of the AOD, column-mean single-scattering807
albedo and column-mean asymmetry factor for the top three best fits at SGP and TWP.808
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