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Comparisons of CCN with Supercooled Clouds

JAMES G. HUDSON, STEPHEN NOBLE, AND VANDANA JHA

Desert Research Institute, Reno, Nevada

(Manuscript received 7 January 2010, in final form 2 April 2010)

ABSTRACT

More than 140 supercooled clouds were compared with corresponding out-of-cloud cloud condensation

nuclei (CCN) measurements. In spite of significant differences in altitude, temperature, distances from cloud

base, updraft velocity (W ), entrainment, and so on, the correlation coefficients (R) between droplet and CCN

concentrations were substantial although not as high as those obtained in warm clouds with less variability of

nonaerosol influences. CCN at slightly lower altitudes than the clouds had higher R values than CCN mea-

sured at thesame altitude.Ice particle concentrations appeared to reducedroplet concentrations andreduce R

between CCN and droplet concentrations, but only above 6-km altitude and for temperatures below 2208C.Although higher CCN concentrations generally resulted in higher droplet concentrations, increases in

dropletconcentrations were generallyless than the increases in CCNconcentrations. This was apparently due

to the expected lower cloud supersaturations (S) when CCN concentrations are higher as was usually the case

at lower altitudes. Cloud supersaturations showed more variability at higher altitudes and often very high

values at higher altitudes. The use of liquid water content rather than droplet concentrations for cloud

threshold resulted in higher R between CCN and droplet concentrations. The same R pattern for cumulative

droplet–CCN concentrations as a function of threshold droplet sizes as that recently uncovered in warm

clouds was found. This showed R changing rapidly from positive values when all cloud droplets were con-

sidered to negative valuesfor slightly largerdroplet size thresholds. After reaching a maximum negative value

at intermediate droplet sizes, R then reversed direction to smaller negative or even positive values for larger

cloud droplet size thresholds.This R pattern of CCN concentrations versus cumulative droplet concentrations

for increasing size thresholds is consistent with adiabatic model predictions and thus suggests even greater

CCN influence on cloud microphysics.

1. Introduction

The indirect aerosol effect (IAE) continues to be the

largest climate uncertainty (Alley et al. 2007). It has been

well established that cloud condensation nuclei (CCN)

concentrations (N CCN) have a large influence on cloud

microphysics (e.g., Squires 1956, 1958; Twomey and

Warner 1967; Yum and Hudson 2002). Sometimes the

aerosol influence is obvious, such as with ship tracks

(Hobbs et al. 2000; Hudson et al. 2000), and sometimesaerosol influence appears to be mitigated by cloud dy-

namics (Baker et al. 1980; Telford and Chai 1980; Telford

and Wagner 1981). Since updraft velocity (W ) at cloud

base interacts with CCN spectra to determine initial cloud

droplet concentrations (N c) (e.g., Twomey 1959, 1977),

W variations can mitigate or obscure CCN influence on

N c (e.g., Peng et al. 2005). The tendency for initial N c to

persist, even as condensation continues as air rises be-

yond cloud base, is because the presence of the initial

droplets inhibits nucleation and growth of new droplets,

which cannot compete for condensation with established

droplets. Thus, most subsequent condensation occurs

only on the droplets that were initially produced. This

can result in constant N c with altitude (e.g., Rogers and

Yau 1989). However, droplet sizes and concentrationscan subsequently be reduced by the evaporation due to

entrainment and mixing. Since these dynamic processes

are generally independent of the aerosol, they could re-

duce N c in a random manner independent of the initial

droplet sizes or concentrations. This could eventually

reduce the influence of the initial aerosol on N c (Kim

et al. 2008). Furthermore, ice particles are expected to

grow at the expense of liquid droplets and this could re-

duce droplet sizes and thus detected N c, which could also

reduce the influence of the aerosol on N c.

Corresponding author address: James G. Hudson, Division of

Atmospheric Science, DRI, 2215 Raggio Pkwy., Reno, NV 89512–

1095.

E-mail: [email protected]

3006 J O U R N A L O F T H E A T M O S P H E R I C S C I E N C E S VOLUME 67

DOI: 10.1175/2010JAS3438.1

Ó 2010 American Meteorological Society

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Therefore, one of the most important and interest-

ing aerosol observations is the comparison of CCN spec-

tral concentrations with initial or adiabatic cloud parcels

that have not had their N c reduced by entrainment (e.g.,

Yum et al. 1998; Hudson and Yum 2001) or ice particles.

When done correctly this can provide a determination

of the initial cloud supersaturation S and thus of which

CCN (according to S) actually produced the cloud drop-

lets. This is a difficult experiment mainly because onlya limited number of clouds or cloud parcels are adiabatic;

that is, their N c is not reduced by entrainment. Moreover,

without vertical profiles through cloud base, which are

seldom available, it is very difficult to be sure or prove

that a given cloud parcel is adiabatic. Therefore, since

most clouds (parcels) are not adiabatic, it is more re-

alistic and climatically significant to determine the rel-

ative influence of the aerosol (CCN) on clouds at large

regardless of their adiabaticity. After all, it is not just the

adiabatic parcels of clouds that determine planetary al-

bedo and precipitation. Just because an aerosol in-

fluence is less obvious does not diminish its importance.Ideally both adiabatic and nonadiabatic parcels would

be sampled and compared with CCN spectra and W .

2. Measurements

We present comparisons of N CCN (Table 1) with N c and

droplet concentrations using various thresholds (Table 2).

Regression correlations are shown and concentration

comparisons are made. These measurements were all

done from the National center for Atmospheric Research

(NCAR) C-130 airplane during the Ice in Layer Clouds

Experiment (ICE-L). Twelve research flights were done

between 7 November and 16 December 2007 over

Colorado and Wyoming. Many of the high-altitude mea-

surements were done in Rocky Mountain wave clouds.

Although temperatures were always subfreezing (Fig. 1f)

there were usually substantial liquid droplets. Cloud

droplet measurements presented here were made with

the Droplet Measurement Technologies (DMT) cloud

droplet probe (CDP) (McFarquhar et al. 2007; Rosenfeld

et al. 2008) (range 2.8–47-mm diameter), which has beendescribed as a miniature forward scattering spectrometer

probe (FSSP). McFarquhar et al. (2007) found that the

CDP concentrations compared well with a CAS probe

(2% disagreement) in no ice conditions and seemed to

be less susceptible to artifacts when ice was present.

Rosenfeld et al. (2008) indicates 1–2-mm CDP size res-

olution. Liquid water content (LWC) was integrated from

the CDP. The CDP operated throughout the final 11

(from 13 November) of the 12 ICE-L research flights.

Larger cloud particles (drops or ice) were measured with

the 2DC probe (range 25–600-mm diameter; Strapp et al.

2001).CCN were measured with the Desert Research In-

stitute (DRI) CCN spectrometer (Hudson 1989). Total

particle concentrations or condensation nuclei (CN) were

measured with a TSI3010 condensation nucleus counter.

Employment of the CCN spectrometer for special sam-

ple processing measurements (Hudson and Da 1996;

Hudson 2007) during some of the flights (especially later

flights) reduced the available ambient measurements.

The cloud data shown are limited to cloud penetrations

for which nearby CCN measurements were available

within 1–5 min (6–30 km). The CCN measurements that

are presented were either at the same altitude as the

TABLE 1. Out-of-cloud average total particle (CN) and cumulative CCN concentrations (cm23 at altitude) at the various S for all

altitudes and for the three altitude bands. Also shown are the total numbers of cloud penetrations and the number of seconds of CCN

measurements. The column labeled CCN is for an uncalibrated S greater than 1.5%; K is the slope of the log–log plot of cumulative CCN

concentrations vs S over the 0.3%–1% range.

Alt Clouds Seconds CN CCN 1.5% 1% 0.6% 0.4% 0.3% K

All 143 12 241 1045 434 289 248 191 154 132 0.50

,3 km 35 2461 3942 1455 931 782 588 468 403 0.483–6 km 87 8210 157 111 88 81 68 53 44 0.49

.6 km 21 1570 126 68 53 49 40 32 26 0.53

TABLE 2. Average cloud microphysics values: number of clouds, number of seconds of cloud measurements, LWC (g m23), mean

diameter (MD,mm) of cloud droplet spectra, and N c cloud droplet concentrations (cm23 at altitudes) using droplet concentration 1 cm23

andvarious LWCthresholds indicatedin squarebrackets. Theclouds, seconds, LWC, andMD arefor cloud thresholdLWC5 0.01 g m23.

Alt Clouds Seconds LWC MD N c (cm23) N c[0.01] N c[0.02] N c[0.05] N c[0.15] N c[0.20]

All 143 11 302 0.11 11.2 102 119 140 159 180 190

,3 km 35 6331 0.17 9.9 177 210 242 270 280 279

3–6 km 87 3697 0.10 11.2 85 98 106 109 119 126.6 km 21 1274 0.07 13.4 48 58 69 74 65 59

SEPTEMBER 2010 H U D S O N E T A L . 3007

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corresponding cloud measurements or at slightly lower

altitudes though not always directly under the corre-

sponding clouds. In some cases two or three different

CCN measurement periods (usually before and after the

cloud measurement) are averaged. More than 3 h of cloud

data (Table 2) in 143 cloud penetrations with an average

duration of 70 s and a median duration of 35 s are pre-

sented. The CCN measurements associated with each

FIG. 1. Mean values as a function of altitude for each of the 143 clouds that meet the

0.01 g m23 LWCthreshold: (a) Cloud droplet concentrations (N c); (b) LWC; (c) nearbyout-of-

cloud CCN concentration at 1% S (N 1%); (d) updraft velocity (W ) within cloud; (e) CN con-

centration (N CN) in the same location as the CCN measurements; and (f) cloud temperature.


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cloud penetration also amounted to more than 3 h

(Table 1). These numbers apply for the lowest cloud

threshold of 0.01 g m23 LWC. An LWC threshold of

0.10 g m23 yielded 2 h of cloud data in 94 penetrations

of average duration 73 s and median duration 37 s(Table 3).

Figure 1 shows the vertical distributions of mean values

for each cloud penetration. All data considered here

are averages of 1-s means. In Figs. 1a, 1b, 1d, and 1f the

only 1-s data that is considered for any of these aver-

ages is that for which CDP LWC exceeded 0.01 g m23,

which is the cloud threshold. Subsequent presentations

with this or other cloud thresholds are done in the same

manner. The CN and CCN measurements are aver-

ages over periods of approximately 20–400 s outside of

clouds. The sample integration time for the CN measure-

ments was one second and it was one to a few seconds forthe CCN measurements. The smaller decrease with al-

titude of N c (Fig. 1a) compared to the N CCN (Fig. 1c) and

CN concentrations (N CN) (Fig. 1e) probably reflects the

lower cloud S at lower altitudes due to greater competition

among droplets. Thus, at lower altitudes N c values are

lower relative to N CN or N CCN. At lower altitudes where

N CCN are higher, N c are closer to N CCN at lower S.

However, some of this difference in vertical gradients

might also be due to coincidence in the CDP that might

occasionally underestimate high N c. Another factor is

that some of the smaller cloud droplets at high concen-

trations were below the threshold diameter of the CDP(2.8 mm). Hudson and Yum (2001) showed that even

some activated cloud droplets can sometimes be missed

by cloud probes. Since most of the clouds that were

penetrated in ICE-L were rather small, especially in

vertical extent, this means that the droplets were small.

Furthermore, the small sizes of the clouds probably made

them more susceptible to entrainment and evaporation,

which makes the droplets more likely to be small and

possibly smaller than the instrument threshold size. The

overall average N c of 119 cm23 using cloud threshold

0.01 g m23 LWC (Table 2) was higher when higher LWC

thresholds were used (Table 2). Overall average N CCN at

1% S (N 1%) was 248 cm23 (Table 1), which seems to

suggest that overall average cloud S was lower than 1%

even for an LWC threshold as high as 0.20 g m23

withaverage N c of 190 cm23. The variability of Figs. 1a, 1c,

and 1e makes these comparisons of overall averages of

limited value.

A cursory examination of Fig. 1, suggests a conve-

nient altitude division at 3 and 6 km. Tables 1 and 2

show how the average concentrations in these three al-

titude bands decrease with altitude. Comparing average

N c with average N CCN within each altitude band sug-

gests cloud S less than 0.3% for the lowest altitudes but

cloud S exceeding 1.5% at the middle and high altitudes.

Average N c are higher for higher LWC thresholds ex-

cept for the two highest LWC thresholds in the highestaltitude band (Table 2, last columns); this is probably

due to the small number of clouds (Table 2, column 1)

and the small number of seconds of data that exceed

these thresholds (Table 3, row 2). Higher LWC thresh-

olds should better represent cloud parcels that are closer

to adiabatic because the higher LWC is probably due to

less entrainment, which usually reduces LWC and N c.

3. Results

Figure 2 compares N c and N 1% for each of the 143

cloud penetrations (LWC . 0.01 g m23). The linear re-gression coefficients for Fig. 2 are shown in Table 3,

column 1; i.e., the zero N 1% intercept of N c, slope, and

correlation coefficient (R). Since these data do not seem

to conform to a linear relationship, the R for a third-

order polynomial fit of N c versus N 1% is also displayed.

Table 3 then also lists these coefficients for plots of N cversus N1% using higher LWC thresholds. The R values

for the second power of N c versus N 1% are not presented

because they show unrealistic decreasing N c for large N 1%.

The right side of Table 3 shows similar corresponding

TABLE 3. Regression coefficients for plots of cloud droplet concentrations against CCN concentrations at 1% S. Each column pertains

to average droplet concentrations in each cloud for the seconds with LWC in excess of the values listed in the first row in g m23. The

intercept, slope, and correlation coefficient (R1) pertain to linear regressions of the data. The number of clouds is the number of data

points considered; R3 values are correlation coefficients for third-order regressions in droplet concentrations vs CCN. The second row

shows the number of cloud penetrations and the third row the total numbers of seconds of those penetrations.

Only below-cloud CCN

LWC 0.01 0.05 0.10 0.15 0.20 0.01 0.05 0.10 0.15 0.20Clouds 143 121 94 74 59 98 80 59 47 37

Seconds 11 302 8755 6894 5471 4252 8094 6284 4973 4014 3171

Intercept 89 98 104 118 123 86 89 92 107 116

Slope 0.12 0.15 0.18 0.18 0.19 0.12 0.16 0.19 0.19 0.20

R1 0.68 0.75 0.78 0.74 0.72 0.75 0.82 0.85 0.80 0.76

R3 0.77 0.80 0.82 0.78 0.74 0.87 0.91 0.90 0.86 0.81


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regression coefficients when data are confined to clouds

where N CCN are from slightly lower altitudes than the

cloud measurements. The number of clouds and seconds

of measurements for each LWC threshold is thus lower.

The R values are significantly higher on the right-hand

side of Table 3 because the CCN that affect N c come into

the clouds from below.

Since there are obvious differences in concentrations

with altitude (e.g., higher concentrations in the bound-

ary layer) and since the relationship between N c and

N CCN also varies with concentration (nonlinear), it seems

that charting this relationship over such wide ranges maynot be of much predictive value. Nonetheless, the re-

lationship indicates the overall importance of N CCN in

determining N c.

Figure 3 shows N c–N 1% relationships within each of

the three altitude bands. Table 4 shows the linear re-

gression coefficients for the three altitude bands using

two LWC thresholds both for all clouds and for only

those with CCN measurements at slightly lower alti-

tudes. For each of the three altitude bands R is lower

than it is when data from all altitudes were considered

(Table 3). This is largely because of the smaller range of

concentrations within each altitude band. The smallerrange is not as obvious for the lowest altitude band where

concentrations were never as low as in the other altitude

bands. Since there is no suggestion of nonlinearity for

the data within each altitude band, higher-order regres-

sions are not considered. The differences between the

regressions for all altitudes and for each altitude band

demonstrate that measurements over wider concentra-

tion ranges tend to produce higher R. This is especially

the case when there are so many other variable factors

involved in determining N c such as W , altitude, cloud-base

temperature, distances between the cloud measurements

and cloud base, and amount of entrainment. Neverthe-less, at least R is positive for all but the highest altitude

band and lowest LWC threshold. At this altitude the

range of variability and number of clouds and seconds is

the lowest. Furthermore, even these R values turn posi-

tive when the three exceptionally low N c (Fig. 3c), all from

the last flight (RF12, lowest three red squares), are re-

moved from consideration (Table 4, row 4). These three

clouds are at the lowest temperatures (upper left corners

of Figs. 1f and 1a) of the 143 clouds presented here and

they had the highest, second-highest, and fourth-highest

2DC probe concentrations (N 2DC larger than 87 mm)of

the 21 clouds above 6-km altitude. For the 0.10 g m23

threshold, the R values in Table 4 are positive for all

altitudes because there are no clouds from the last flight

that meet this threshold.

On the left side of Table 4, the lower altitude bands

have higher R. When only the below-cloud CCN mea-

surements are considered on the right side of Table 4, R

is higher, especially for the mid-altitude band (0.40–0.69

and 0.39–0.69 km) where a large number of the CCN

measurements at the same altitudes as the correspond-

ing clouds were apparently not representative of the

FIG. 2. Mean droplet concentrations (N c) vs nearby out-of-cloud

CCN concentrations at 1% supersaturation for all altitudes for the

143 clouds that meet the 0.01 g m23 LWC threshold. The re-

gression coefficients are in Table 3, column 1. Data are divided into

the three altitude bands listed.

FIG. 3. As in Fig. 2, but for each altitude band: (a),3 km; (b) 3–

6 km; (c) .6 km. In (c) the four red squares are clouds with the

highest N 2DC . 87 mm diameter. Numbers to the right of each

panel are number of clouds.


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CCN concentrations that produced the cloud droplets

(half of these cases were from RF2). The exception to

this is the lowest altitude band and lower LWC threshold

where R for below-cloud CCN is lower than R for all

CCN.

In mixed phase clouds diffusion of water moleculesfrom droplets to ice particles should produce smaller

droplets and thus reductions of droplet concentrations

larger than specific sizes. The presence of ice thus could

disrupt correlations between N CCN and Nc since ice con-

centrations may be independent of N c or N CCN. The 2DC

probe measures cloud particles that are larger than most

cloud droplets and in much lower concentrations than

cloud droplets. At sufficiently cold temperatures many

of these particles are ice particles. When the four clouds

(red squares in Fig. 3c) with the lowest N c (,47 cm23) as

well as the highest N 2DC larger than 87 mm (.12 L21

whereas the next lower N 2DC in this altitude band hadN 2DC of only 6 L21) are removed from consideration, R

goes from 20.01 to 10.49 in Fig. 4a. Similarly Table 4,

row 4 shows R going to 0.47 when just the three lowest

N c clouds are removed from consideration. When the

four clouds with the next higher N 2DC are removed from

consideration in Fig. 4b, R increases to 0.63. Then when

the clouds with the next four higher N 2DC are removed,

leaving the nine clouds with the lowest N 2DC (,0.9 L21)

in Fig. 4c, R goes up to 0.83. This same analysis using

a 0.05 g m23 threshold shows similar results except that

only one of the three 2358C clouds meets this criterion.

These omissions result in a slight positive R of 0.20 for the17 clouds that meet this higher LWC threshold. Similar

increases of positive R are found by peeling away clouds

with next higher N 2DC. The 2DC measurements at high

altitudes suggest that ice crystals are reducing the droplet

concentrations and thus reducing R for N 1%–N c. At the

two lower altitude bands, equal divisions of the clouds

according to N 2DCproduce similar positive R for N 1%–N cthat is, R for N 1%–N c is not related to N 2DC because at

these altitudes and temperatures N 2DC presumably rep-

resents large drops rather than ice particles.

Often droplet number concentration has been used for

cloud threshold (e.g., Hudson and Yum 2001). Table 5

shows regressions using 1 cm23 as the cloud threshold

for N c. This threshold revealed six more clouds and 30%

more seconds of data (unequally distributed among the

altitudes) than the 0.01 g m23

threshold. Table 2 showsthat average N c values using the number concentration

threshold were approximately 15% lower than N c using

the 0.01 g m23 LWC threshold. Except for the highest

altitude band the R values in Table 5 are significantly

lower than corresponding R in Tables 3 and 4, especially

for the lowest altitude band. Table 6 shows little im-

provement of R with higher N c thresholds. These R are

TABLE 4. As in Table 3, but for the three altitude bands. The extra row for 0.01 . 6 km excludes RF12.

Only below-cloud CCN

LWC Alt Clouds Seconds Intercept Slope R Clouds Seconds Intercept Slope R

0.01 ,3 km 35 6331 166 0.06 0.53 25 4758 192 0.04 0.43

3–6 km 87 3697 64 0.41 0.40 62 2783 30 0.78 0.69

.6 km 21 1274 58 20.01 20.01 11 553 52 0.06 0.01

.6 km 18 1226 50 0.36 0.47 9 525 45 0.47 0.67EX12

0.10 ,3 km 33 5090 177 0.12 0.64 23 4014 183 0.12 0.68

3–6 km 50 1309 78 0.39 0.39 31 768 38 0.67 0.69.6 km 11 495 59 0.22 0.31 5 191 59 0.21 0.51

FIG. 4. As in Fig. 3c, but dividing the 21 clouds according to

N 2DC . 87 mm: (a) the 17 clouds with the lowest N 2DC(,7 L21)

(i.e., Fig. 3c without the 4 red squares); (b) the 13 lowest N 2DC(,1.6 L21); (c) the 9 clouds with the lowest N 2DC (,0.9 L21).


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Table 9 shows R of N c with W and the variability of

W as expressed by the standard deviation of W (s w),

which is a measure of turbulence. These R are shownfor two LWC thresholds for the three altitude bands.

The most significant R values are at the lowest altitude

band (,3 km). The relationships for the two highest

R are displayed in Fig. 5. There are high R values also

for s w at the highest altitude band for the lower LWC

threshold, but these are due only to the extremely low N cof the last flight, which as shown earlier resulted from

the action of ice on the droplet concentrations. When

these 2 of 18 and 1 of 9 clouds are removed from con-

sideration in the next row of Table 9, R is negligible. In

all cases the R for W with N c is greater for the higher

LWC threshold. The R values for within-cloud W withN c for each altitude band are similar to the R values

for N 1% with N c in Table 4. The number of clouds is

smaller in some of the rows of Table 9 compared to

corresponding rows of Tables 3 and 4 because of the

elimination of 1-s clouds. However, elimination of these

clouds had miniscule effects on the R values in Tables 3

and 4. However, the W – N c R values for all of the clouds

in Table 9 are considerably lower than the correspond-

ing N 1%–N c R values in Table 3. This all-cloud contrast

in R is due to the aforementioned greater range of N 1%when all clouds are considered. In other words, there

are greater differences in CCN concentrations at the

various altitudes whereas the W values are more similar

at the various altitudes. Thus, it is inappropriate to try to

correlate W or s w with N c over wide differences in al-titude or N CCN or N c.

The location of the W measurements was not ideal but

it is difficult to measure W at the time and location when

and where clouds form and N c is determined. Here we

consider W measurements within cloud or at the location

of the below-cloud CCN measurements as surrogates

for the W that produced the clouds. We are suggesting

that the measured in-cloud or below-cloud W values are

proportional to the W thatoriginally produced the clouds.

The W and s w within clouds are only for those seconds

when LWC exceeds the given values. These had higher

W , which is probably more relevant to or more repre-sentative of W at cloud formation. Median W and the

percentage of clouds with positive average W progres-

sively increased for higher LWC thresholds. Although

mean or median W values were often negative, high N cvalues were mainly confined to clouds with positive

mean W .

Although negative W does not produce clouds, W is

not only an indicator of the dynamic contribution to

the initial N c but also an indication of entrainment (i.e.,

positive W suggests less entrainment that could reduce

N c). Ignoring the negative W values would bias the data.

When we considered only clouds with positive average

TABLE 7. As in Table 4 for only 0.01 g m23 LWC threshold, but for CN and CCN at 0.30% S.

Below-cloud aerosol measurements

Aerosol Alt Clouds Intercept Slope R Clouds Intercept Slope R

CN All 141 99 0.02 0.64 96 97 0.02 0.70

,3 km 33 170 0.01 0.65 62 190 0.01 0.64

3–6 km 88 79 0.12 0.29 62 32 0.42 0.64

.6 km 21 71 20.11 20.37 11 74 20.18 20.45

.6 km 18 72 20.05 20.31 11 60 0.07 0.23

Ex12

0.30% All 139 84 0.27 0.72 97 80 0.28 0.79,3 km 35 152 0.14 0.55 25 180 0.11 0.45

3–6 km 83 62 0.81 0.43 61 30 1.35 0.71

.6 km 21 80 20.85 20.31 11 63 20.33 20.14

.6 km 18 47 0.84 0.48 9 39 1.16 0.77

Ex12

TABLE 8. Number of clouds with approximate Seff values for two LWC (g m23) thresholds and for the three altitude bands.

LWC Alt Clouds ,0.3% 0.3% 0.4% 0.6% 1.0% 1.5% CCN CN .CN

0.01 ,3 km 35 26 3 2 2 1 1

3–6 km 87 6 2 7 6 9 34 9 2 12

.6 km 21 3 2 1 4 6 1 4

0.20 ,3 km 26 17 1 1 2 2 3

3–6 km 30 2 2 2 9 3 3 9.6 km 3 2 1


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W we eliminated half of the clouds and found no dif-

ference in R for N 1%–N c or W –N c. When we limited eachsecond of data to positive W we retained 90% of the

clouds but 50% of the number of seconds and again

found only very modest changes in R. Attempts to use

the product of W and N CCN (Hudson and Noble 2009;

hereafter HN9) resulted in negligible increases in R

over those for N 1%–N c. The product of s w and N CCNresulted in lower R than R for N 1%–N c.

Hudson et al. (2009, hereafter H9) and HN9 showed

how R between N 1% and cumulative droplet concen-

trations larger than specific droplet size thresholds (N t )

varies with the threshold sizes. The value of R was posi-

tive for small size thresholds that included all activatedcloud droplets (N c) because in this cumulative cloud

droplet size range the N t values are directly related to

the concentrations of the nuclei upon which the droplets

formed. These are usually N CCN at high S (e.g., N 1%)but

even for clouds formed at lower S the N CCN(S) are often

in proportion to the N CCN at higher S (i.e., N 1%).

Therefore, R for N t –N CCN at most S (i.e., N 1%) is posi-

tive. At larger droplet size thresholds R was negative

(H9 and HN9). The negative R for N t –N CCN was the

result of competition among droplets for condensed wa-

ter, which restricts cloud S and droplet sizes to a greater

extent when concentrations are higher. The greater re-striction of cloud S and droplet sizes in cases when N CCNis higher produces the negative R for N t with N CCN for

N t above somewhat larger size thresholds. Although

for each CCN spectrum N CCN is always lower at lower S,

N c is always higher for higher N CCN situations compared

to lower N CCN situations even though the latter produce

higher cloud S; this is the case when all other factors are

equal.

Droplet sizes are restricted to the greatest extents

within and near the size range of the mode of the droplet

distribution. This is the phenomenon that turns R for

N t –N 1% from positive when all cloud droplets are con-sidered (i.e., N c–N CCN) to negative R for N t –N 1% for

increasing size thresholds; R thus reaches its maximum

negative absolute value (HN9) slightly beyond the av-

erage mode of the droplet distributions (Fig. 2 of HN9).

Competition for condensate is less intense for droplets

beyond the mode. The smaller droplets within the mode,

though numerous, usually do not have sufficient surface

area to have much influence on the growth of the larger

droplets. Thus, at sizes larger than the mode of the droplet

distributions the concentrations of droplets tend to-

ward being proportional to the concentrations of par-

ticles upon which they condensed (i.e., the larger andmore soluble particles, CCN with low Sc). Often N CCNat various S are in proportion for the various aerosol

distributions. If and when this is the case the R for N 1%with the concentrations of large cloud droplets will tend

toward less negative values (H9) or even positive values

(HN9).

Within cumulative cloud droplet size ranges that are

intermediate between the average droplet mode and both

the small and large sizes there will be conflict between

the positive and negative R tendencies that will result in

FIG. 5. As inFig.3a (altitude, 3 km), but forLWC. 0.1 g m23:

(a) N c is plotted against updraft velocity (W ) measured withinthese

24 clouds; (b) as in (a), but only the 17 clouds with CCN mea-

surements below the clouds are plotted against the standard de-

viations of W (s w) measured at the same place below the clouds.

TABLE 9. As in Table 4, but theregressions are of mean updrafts

(W ) and standard deviations of W (s w) with droplet concentrations

(N c). The below cloud category represents W measurements made

when the CCN were measured only below adjacent clouds but not

necessarily immediately below each cloud.

Below cloud

LWC Alt Clouds R(W ) R(s w

) Clouds R(W ) R(s w

)0.01 All 139 0.16 20.10 96 0.20 20.08

,3 km 34 0.45 0.40 25 0.16 0.45

3–6 km 87 20.01 0.20 62 20.10 0.02.6 km 18 0.03 0.42 9 20.30 0.77

.6 km 16 20.40 0.04 8 0.24 0.01

EX12

0.10 All 84 0.28 20.19 52 0.35 20.03,3 km 24 0.61 0.03 17 0.21 0.76

3–6 km 49 0.27 0.14 30 0.17 0.26

.6 km 11 0.30 20.54 5 20.02 20.62


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intermediate R values. The depth and breadth of the

negative R depends on the degree of competition, the

range of concentration variability, cloud adiabaticity, dy-namics, temperature, distance from cloud base, entrain-

ment, and CCN spectra, among others. The sequence

of R values that has been described would best be ex-

hibited by cloud measurements at similar distances above

cloud bases that are at similar temperatures. That way

the droplet spectra in the various aerosol regimes would

be at similar stages of development. This was apparently

the case for the Rain in Cumulus over the Ocean (RICO)

experiment (H9) where cloud-base temperatures and al-

titudes were similar throughout the project and cloud

measurements were compared within similar altitude

bands. This was apparently also much the case for thePacific Atmospheric Sulfur Experiment (PASE; HN9)

wherein only very shallow clouds were observed. This is

certainly not the case in ICE-L where clouds are con-

sidered over large altitude and temperature ranges with

no discrimination of distances between cloud base and

the cloud measurements. As a result, it seemed less likely

to discover negative R between N c and N CCN in ICE-L.

This was especially so when all altitudes were consid-

ered together. Nonetheless, even when all altitudes were

considered together R displayed the characteristic pro-

gressive decrease with increasing droplet size thresholds

(Fig. 6), similar to the findings of H9 and HN9. But thenegative R in ICE-L has smaller absolute values. How-

ever, the number of data points (94) in ICE-L is much

larger since they are for individual clouds rather than the

smaller number of flight averages in RICO or PASE.

There was much more concentration variability within

flights in ICE-L than there was in RICO or PASE.

Therefore, the significance level of these smaller nega-

tive R values approaches 90% though the coefficient of

determination (R2) suggests modest influence of N CCNon N t variations. But even with the large variations of

other factors that influence N c and N t the effect of theaerosol comes through even for these small negative R

values.

Figure 7a for the 50 clouds within the intermediate

altitude band and LWC.0.10 g m23 yields a maximum

negative R of 0.5, which has a significance level beyond

99.95%. As in the previous studies, this greatest negative

R occurs at sizes just beyond the mode of the average

spectra shown in Fig. 7b. As in RICO the negative R of

N 1%–N t in ICE-L is slightly greater than the positive R

for N 1%–N c; in ICE-L similar positive R values occur at

both ends of the droplet size distribution as was the case

in PASE. This suggests that there is proportionality—among the N CCNs at various Ss among theinput aerosols—

to the various cloud parcels considered here in ICE-L, as

was indicated in PASE (HN9). The fact that R smoothly

changes with size threshold indicates the validity of the

correlations.

Figure 8 demonstrates that the R patterns displayed

in Figs. 6 and 7a and in HN9 and H9 are consistent

with theoretical predictions of droplet spectra. Here the

Robinson (1984) adiabatic droplet growth model is ap-

plied to an observed ICE-L CCN spectrum and two

spectra that are multiples of that spectrum (i.e., con-

centrations at all S are in the same proportions). Allother factors (altitude, W , pressure, and temperature) are

identical for these three predictions. At threshold sizes

below 7 mm N c is proportional to N CCN, between 9 and

12 mm N c is inversely related to N CCN, and beyond

14 mm N c is again positively correlated with N CCN.

4. Discussion

The N c–N CCN R values at the highest altitude band

were very low, mainly because of the influence of ice

FIG. 6. Correlation coefficients (R) of N c with N 1% for various

droplet size thresholds for N c. Thisis for clouds atall altitudes using

an LWC threshold of 0.10 g m23. Thus, 94 clouds (data points) are

considered for each correlation (Table 3, column 3). FIG. 7.(a) Asin Fig.6, but onlythealtitude range of3–6 km; this

means 50 clouds (data points) for each correlation (Table 4, col-

umn3). (b) Mean differential cloud droplet spectra of the 50 clouds

also using the 0.10 g m23 LWC threshold.


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particles on droplet concentrations. The extremely low

N c of less than 10 cm23 with temperatures less than

2358C may not have been liquid droplets because this is

cold enough to produce homogeneous ice (Sassen and

Dodd 1988; Heymsfield and Miloshevich 1993). It is

rather certain that the N 2DC in these three cold clouds

were ice particles, especially since these were the highest

N 2DC of the high-altitude clouds. The low N c in these

clouds may have been liquid or solid: possibly frozendroplets. These low N cs relative to the nearby out-of-

cloud N CNs and N CCNs are commensurate with droplet

concentration reductions due to the presence of ice

particles.

When the wider range of concentrations at all alti-

tudes were considered in Fig. 2 the N c–N CCN regression

appeared to be nonlinear; N c did not keep pace with

N CCN at high concentrations. This rolling off of N c at

high N CCN observed previously by Leaitch et al. (1986)

and Hudson and Yum (2002) is expected because of the

competition among droplets that drives down cloud S

when N CCN is higher so that N c in those cases is pro-portional to N CCN at lower S, which for the same aerosol

is lower than N CCN at higher S. However, as noted

earlier these N c are still higher than the N c for lower

N CCN situations. This was consistent with the compari-

sons of CCN spectral concentrations with N c (Table 8).

These comparisons indicated considerably lower Seff at

lower altitudes where concentrations were higher. At

higher altitudes there was considerably more variability

of Seff and significantly higher Seff . In fact the Seff was so

high in a large proportion of clouds at higher altitudes

that every atmospheric particle seemed to act as a nu-

cleus for cloud droplets. This is especially pertinent since

Seff is an underestimate of initial maximum cloud S that

produced adiabatic N c. The variability of Seff contrib-

uted to the lack of correlation between N c and N CCN at

a fixed S. These observations seem to mitigate the con-

cern that entrainment and/or ice formation had reducedN c significantly below adiabatic values. The latter was

the case for only a few high-altitude clouds with high

N 2DC.

Correlations of N c with within-cloud W were lower

but usually comparable with R for N c–N CCN for the

three altitude bands. Also, R for below-cloud W with N cwas considerably lower than the corresponding R values

for N CCN–N c. Correlations of N c with s w were good only

for the lowest altitude band (,3 km), especially for s w

measured below the corresponding clouds even though

these were not directly below the clouds. Measurement

limitations may have reduced the apparent influence of W on N c; however, accuracy of W for the NCAR C-130

system is purported to be 0.10 m s21 (Lenschow and

Spyers-Duran 1989) and since we are dealing only in

terms of relative values, precision should be closer to

1 cm s21.

The fact that the LWC threshold rather than droplet

concentration threshold provided superior N c–N CCNcorrelations (Tables 3–6) is somewhat contrary to RICO

(H9), where the 1 cm23 threshold produced R values

similar to the LWC thresholds. The similarities of R for

N t –N CCN for the different LWC thresholds in Table 3

are similar to those in H9. As in RICO and PASE thissuggests that entrainment did not disrupt the effect of

CCN on cloud microphysics. This is all the more sig-

nificant for ICE-L where there was so much more vari-

ability in the other factors that influence N c.

Correlations using N CCN at a lower S of 0.3% were

only slightly better than the N 1% correlations, even at

low altitudes where Seff was considerably lower, and

a majority of cases in Table 8 indicated Seff ,0.3%. This

indicated that indeed Seff was an underestimate of the

cloud S that had produced the original N c and thus that

N c at low altitudes was reduced by entrainment. On the

other hand, the R similarities of N c with N CCN at variousS may merely reflect the proportionalities of the CCN

spectra. These results, nevertheless, call for CCN mea-

surements at lower S at the lower altitudes, which might

show better correlations with N c.

The N t –N CCN R pattern was consistent with predic-

tions of droplet spectra for various CCN spectra with the

same shape. However, we confirmed that differences in

CCN spectral shapes also produce differences in droplet

spectral shapes that produce consequent differences in

the pattern of R for N t –N CCN with threshold size. The

FIG. 8. Computer model–predicted droplet concentrations for

CCN spectra that are multiples of each other.


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fact that observed R patterns are similar to these pre-

dicted R patterns suggests that the CCN spectral shapes

were not different enough to disrupt the predicted R

pattern. Further investigations of measured droplet and

CCN spectra and model predictions are warranted be-

cause the results suggest greater influences of CCN on

droplet spectra than just determination of total clouddroplet concentrations—that is, spectral influences (i.e.,

Takahashi and Lee 1978; Takeda and Kuba 1982).

Furthermore, as pointed out by HN9, the lower R values

at intermediate droplet size thresholds do not necessarily

indicate less aerosol influence but are the result of the

conflict between the positive and negative influences on

R (i.e., the direct correlation with aerosol concentrations

and the negative R due to competition among droplets).

The negative values were not as deep in ICE-L as was

the case with the more similar cloud parcels considered

in RICO and PASE.

Although there have been several previous CCN mea-surements in supercooled conditions (Hoppel et al. 1973;

Radke et al. 1984; Hudson and Xie 1998; Yum and

Hudson 2001) and many cloud microphysics measure-

ments in supercooled conditions, there has only been

one previous study that has included both together

(Rosenfeld et al. 2008). The present study is more ex-

tensive and includes a wider range of concentrations and

temperatures. CCN concentrations in ICE-L show alti-

tude differences similar to those observed by Hudson and

Xie (1998) except that the decrease with altitude seems

more gradual in ICE-L, which may be a function of the

different seasons: spring versus fall/winter. The high-altitude N 1% concentrations in the two projects were

generally similar except that the earlier study found more

variability with concentrations of several hundred and

less than 10 cm23. These CCN results in ICE-L indicate

that further analysis is justified. This will focus on lower S

CCN measurements and attempt to isolate more similar

and comparable cloud parcels.

5. Conclusions

In spite of large differences in altitudes, temperatures,

dynamics, adiabaticity, entrainment, etc., total cloud drop-let concentrations (N c) in a variety of supercooled clouds

at various altitudes over Colorado and Wyoming were

correlated with nearby out-of-cloud CCN concentrations

(N CCN). Correlations of N c with N CCN were significantly

higher when restricted to clouds with CCN measurements

at slightly lower altitudes, since the major CCN influence

comes about from air coming into cloud base. Correlation

coefficients (R) were stronger for the larger concentration

ranges encompassed by all altitudes considered together

compared to the smaller ranges of concentrations within

each of three altitude bands. However, the ICE-L N CCN–

N c Rs were not as high as those obtained in warm clouds

where there were smaller variations in other factors that

influenced droplet concentrations.

High-altitude clouds with high ice concentrations had

either negative or negligible N CCN–N c Rs; whereas high-

altitude clouds with lower ice concentrations had pro-gressively higher positive R. Thus, clouds at high altitudes

showed droplet concentration reductions associated with

the presence of ice particles.

Correlations between measured updraft velocities (W )

and N c were comparable with N CCN–N c Rs, especially at

the lowest altitudes. Cloud supersaturations seemed to

follow the expected pattern of being higher when CCN

concentrations were lower. Liquid water content (LWC)

thresholds produced better correlations between CCN

and droplet concentrations than droplet concentration

thresholds. Higher LWC thresholds, which should cor-

respond to more adiabatic (less entrained) clouds showedhigher CCN–droplet correlations.

CCN correlations with cumulative cloud droplet con-

centrations changed from positive to negative and back

to positive with increasing droplet threshold sizes. The

negative correlations at intermediate threshold sizes are

due to competition among cloud droplets for condensed

water, which causes greater limitations to droplet sizes

when concentrations are higher. The positive correlations

for large size thresholds are attributed to proportional

relationships among CCN concentrations at various S

and to the lower concentrations of large cloud drops that

result in less competition for condensate. This correla-tion pattern is consistent with predictions of an adiabatic

model for various N CCN spectra that have identical

shapes. However, since the shapes of CCN spectra are

not always identical, the shapes of subsequent droplet

spectra may consequently differ enough to cause dif-

ferent patterns of correlations between CCN and cu-

mulative droplet concentrations.

Acknowledgments. This work was supported by NSF

Grant ATM-0615414. The National Center for Atmo-

spheric Research provided the C-130 airplane and the

CDP, 260X, and CN measurements. Discussions withDavid Mitchell of DRI were very helpful.

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