ACCUMULATION MODE FROM CLOUD PROCESSING
J.G. HUDSON and S.R. NOBLE
Desert Research Institute, Department of Atmospheric Science,
Reno, Nevada, United States.
Keywords: AEROSOL, ACCUMULATION MODE, CLOUD PROCESSING.
INTRODUCTION
Surface aerosol size distributions from a Scanning Mobility Particle Sizer (SMPS) and remote sensing by
a ceilometer (CEIL), Total Sky Imager (TSI) and pyranometer gridded network (PGN) were used to
investigate cloud processing at the Oklahoma Department of Energy Southern Great Plains Atmospheric
Radiation Measurement site in May, 2003 (Gasparini et al., 2006). Chemical transformations within cloud
droplets, coalescence among droplets and Brownian capture of interstitial material by cloud droplets make
bimodal aerosol because these processes increase material within droplets. When droplets evaporate, as
they usually do, their residuals are larger than particles that did not nucleate cloud droplets. This resulting
bimodality is characteristic of cloud processing. This is usually manifested in a size gap because critical
supersaturation, Sc, is generally inversely related to size. These two modes are then separated by a
concentration minimum at a size referred to as the “Hoppel minimum.” These modes are cloud processed
(accumulation) and cloud unprocessed (Aitken) (e.g., Hudson et al., 2015; hereafter H15). Particles that
remain within the Aitken mode after cloud processing are unaltered, but the Aitken mode is altered by
removal of larger or more cloud-active (low Sc) particles. Accumulation mode particles are not created by
cloud processing; they are rather transferred from the Aitken mode.
While much discussion on cloud processing has focused on marine clouds and particles, cloud
processing has also been observed in continental environments; i.e., central USA (Hoffmann, 1993);
Germany (Birmili et al., 2001); and Nordic background (Tunved et al., 2003). All of these studies cited
cloud processing as the reason for aerosol bimodality.
METHODS
Instead of the subjective categorization of aerosol modality used by H15 we now use the concentrations
within the two modes; Aitken (unprocessed), Nu and accumulation (processed), Np. Nu-Np is then an
objective measure of particle modality. Higher values indicate unimodal aerosol while lower Nu-Np
indicate bimodal aerosol. Nu-Np can be normalized by dividing by Nu+Np.
FINDINGS
Figure 1A and B show high correlations between mean hourly diameters of the two modes and Hoppel
minima, which can only result from a process that simultaneously affects both modes. Other processes are
responsible for some of the particles within the two modes. But no other processes are known to
simultaneously affect both modes in this coordinated fashion. Non-cloud processes would impart separate
effects to each mode. Figure 1C demonstrates the large size separation between the modes and of the two
modes from Hoppel minima between them.
Correlation coefficients, R, of aerosol modalities with cloud base altitude (CBA) and cloud fraction
(CF) are presented in Fig. 2. Lower CBA and higher CF should enhance surface aerosol bimodality.
Since bimodality is denoted by lower values it ought to positively correlate with CBA and negatively
correlate with CF. The CF R signs are therefore reversed/negated in order to positively demonstrate cloud
processing. Time differences between the instantaneous remote cloud measurements and subsequent
mean diameter accumulation mode (nm)
160
140
Aitken mode (nm)
200
300
400
500
600
A
C
R2 = 0.512
120
200
100
80
accumulation
Aitken
Hoppel
R2 = 0.697
60
40
150
accumulation (nm)
20
B
600
R2 = 0.807
500
100
diameter (nm)
100
400
300
R2 = 0.752
200
100
50
100
200
300
400
500
600
mean diameter Hoppel minima (nm)
0
3
6
9
12
15
18
21
24
local time (hr)
Figure 1. Relationships between hourly mean diameters of (a) processed/accumulation mode and unprocessed/Aitken
mode, (b) Hoppel minima and accumulation mode. Blue coefficients of determination exclude the 3 outlying data,
which reduces data from 428 to 425. All two-tailed probabilities are < 10-8. (c) Mean diurnal trends for mean
diameters of SMPS modes and Hoppel minima between modes.
0.40
R
0.30
0.20
CEIL CBA
CEIL CF
TSI CF
PGN CF
0.10
0.00
-0.10
-5
0
5
10
aerosol lag (hr)
15
20
Figure 2. Correlation coefficients (R) between hour averages of SMPS modality with CEIL cloud base altitude
(CBA) and cloud fraction (CF) and TSI and PGN CF against time lag of aerosol after cloud. Sign reversal for CF Rs.
surface aerosol responses after advection of cloud-altitude aerosol are accounted for by time-lagging the
aerosol measurements. This provides R of cloud measurements with later surface aerosol measurements
(positive lags). R is low for negative (aerosol prior to the specific cloud measurements) or zero lag
(simultaneous aerosol and cloud measurements) and positive for small positive aerosol lags. Further
aerosol lags show declining R. Air motion variations during the 21-day measurement period evoke timelag variations that spread positive R over several lag hours. Plots similar to Fig. 2 for each day of this
project showed higher Rs as revealed by the mean Rs in Table 1. Even these higher Rs are downgraded by
variations of aerosol lags within some of these 24-hour periods. R values for each of the three different
“periods” of Table 1 (21 days, mean of daily means and with 5-hour running means) are in the order of the
sky coverage of the three instruments. CEIL with just a vertical line of coverage always has the lowest Rs
while PGN with the largest sky coverage (nearly equal to Oklahoma) always has the highest Rs.
Mean lags of the various days are inversely related to wind speeds both at the surface and aloft; higher
winds make shorter aerosol modality lags to cloud variations. Mean daily aerosol lags are also inversely
related to mean daily CF of all 3 instruments in the order of their sky coverage; i.e., PGN has the highest
such R. Thus greater CF makes shorter lags. Double regression of PGN CF and wind speed predicts
nearly half of the variations of mean daily lags of surface aerosol modality to cloud changes.
Inst
CEIL
TSI
PGN
CEIL
CEIL
TSI
TSI
PGN
PGN
Period
21 days
21 days
21 days
mean of 20 daily means
mean of 20 daily means; 5-hour running mean
mean of 21 daily means
mean of 21 daily means; 5-hour running mean
mean of 21 daily means
mean of 21 daily means; 5-hour running mean
N
383
274
244
19.3
18.4
12.0
12.8
11.5
8.4
R
0.23
0.38
0.44
0.51
0.67
0.73
0.79
0.82
0.88
lag
7
7
8
9.45
10.6
13.3
9.8
10.8
10.7
P2
3.81(-6)
1.00(-8)
1.00(-8)
8.80(-2)
3.37(-2)
3.81(-2)
4.33(-2)
2.43(-2)
1.49(-2)
Table 1. First 3 rows characterize peak Rs in Fig. 3. N is the number of lag is the hour of peak R. P2 is the twotailed probability. Next 6 rows characterize mean values of peak Rs of daily regressions. Rows 5, 7 and 9 use 5hour running means of the cloud measurements.
mean modality
0.3
0.2
cf > 0.8
cf 0
cba < 2 km
cba > 8 km
0.1
0.0
CEIL CBA
A
CEIL CF
B
0.6
0.4
0.2
TSI CF
C
4000
F
E
-3
Np (cm )
3500
PGN CF
D
cf > 0.8
cf 0
accumulation
3000
2500
-3
0.0
cf > 0.8
cf < 0.4
cf > 0.9
cf < 0.1
Nu (cm )
mean modality
-0.1
Aitken
cf > 0.8
cf 0
2000
1500
-5
0
5
10
aerosol lag (hours)
15
20 -5
0
5
10
aerosol lag (hours)
15
20
Figure 3. (a-d) Mean normalized aerosol modality against time after clouds within the extreme quartile ranges for (a)
CEIL CBA, (b) CEIL CF, (c) PGN CF, and (d) TSI CF. (e-f) As Fig. 3B (CEIL CF) but Nu and Np separate and not
normalized. (a) Np (accumulation); (b) Nu (Aitken). Green lines mark the time of the cloud measurements.
Figure 3A-D displays mean values of aerosol modality as a function of aerosol measurement time lags
of specific cloud measurements. For all panels of Fig. 3 the cloud measurements were partitioned into
four groups that represent various degrees of potential cloud effects on particles. For the sake of clarity
and simplicity only the two extreme quartiles are displayed in each panel. Lowest CBA and greatest CF
groups, which should have the greatest potentials toward particle bimodality, are shown in black circles.
Highest CBA and smallest CF are the red squares. In panels A-D the black data shifts toward lower
modalities (more bimodal) from zero cloud hour toward the hours of maximum Rs in the corresponding
plots of Fig. 2. In panels A-D the red data shifts toward higher modalities (more unimodal).
Figure 3E-F demonstrates that the diverse aerosol responses to CF apply to both modes of the particle
size distributions in manners consistent with cloud processing. In Fig. 3E the black plot shows Np
(accumulation mode) increasing in response to the greatest CF quartile whereas the red plot shows Np
decreasing in response to small CF. By contrast, the black plot for Nu (Aitken mode) in the F panel
decreases in response to greater CF whereas the red plot shows Nu increases in response to lower CF.
Figure 4 shows that the mean sizes of both aerosol modes and Hoppel minima respond similarly to
cloudiness. Higher CF (and lower CBA; not shown) tend to reduce the mean modal sizes in Fig. 4A
190
54
accumulation
Aitken
Hoppel
180
52
50
B
accumulation
Aitken
Hoppel
190
48
54
52
50
cf 0
180
48
105
46
-5
0
5
10
15
20
110
100
90
100
95
90
85
Hoppel diameter (nm)
cf > 0.8
120
Hoppel diameter (nm)
56
200
Aitken diameter (nm)
accumulation diameter (nm) accumulation diameter (nm)
58
A
Aitken diameter (nm)
whereas lower CF (and higher CBA; not shown) lead to greater mean modal particle sizes. Since cloud
processing would move the larger of the Aitken particles to the accumulation mode this would reduce the
mean size of the remaining yet unprocessed Aitken mode. Promotion of these marginal particles would
occur to the smaller sizes of the accumulation mode compared to the preexisting accumulation mode
particles. This then reduces the mean size of the accumulation mode. Apparently any tendency of the
preexisting accumulation mode particles to grow larger by further cloud processing is less than the effect
of newly promoted Aitken particles to the low end of the accumulation mode. The opposite size
tendencies for CF = 0 happens when air that has seen less cloud processing advects into the area when
clouds disappear (Fig. 4B). The fact that the size tendencies are the same for both modes and Hoppel
minima is consistent with Fig 1A.
25
aerosol lag (hr)
Figure 4. As Fig. 3 except showing mean diameters of the two modes and Hoppel minima lagged response to
extreme CEIL CF.
CONCLUSIONS
In central Oklahoma clouds produce bimodal aerosol spectra even at the surface. This means that most of
the accumulation mode results from cloud processing of aerosol.
ACKNOWLEDGEMENTS
This research was supported by DOE grants DE-FG02-05ED63999 and DOE DE-SC0009162.
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