Characterization of Aerosol Hygroscopicity Over the Northeast Pacific Ocean: Impacts on Prediction of CCN and Stratocumulus Cloud Droplet Number Concentrations

Characterization of Aerosol Hygroscopicity Over the

Northeast Paci

c Ocean: Impacts on Prediction

of CCN and Stratocumulus Cloud Droplet

Number Concentrations

B. C. Schulze

, S. M. Charan

, C. M. Kenseth

, W. Kong

, K. H. Bates

, W. Williams

A. R. Metcalf

, H. H. Jonsson

, R. Woods

, A. Sorooshian

6,7

, R. C. Flagan

8,2

, and

J. H. Seinfeld

8,2

Department of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA, USA,

Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA,

Center for the

Environment, Harvard University, Cambridge, MA, USA,

Department of Environmental Engineering and Earth

Sciences, Clemson University, Anderson, SC, USA,

Naval Postgraduate School, Monterey, CA, USA,

Department of

Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA,

Department of Hydrology and

Atmospheric Sciences, University of Arizona, Tucson, AZ, USA,

Division of Engineering and Applied Science, California

Institute of Technology, Pasadena, CA, USA

Abstract

During the Marine Aerosol Cloud and Wild

re Study (MACAWS) in June and July of 2018,

aerosol composition and cloud condensation nuclei (CCN) properties were measured over the N.E.

Paci

c to characterize the in

uence of aerosol hygroscopicity on predictions of ambient CCN and

stratocumulus cloud droplet number concentrations (CDNC). Three vertical regions were characterized,

corresponding to the marine boundary layer (MBL), an above

‐

cloud organic aerosol layer (AC

‐

OAL), and

the free troposphere (FT) above the AC

‐

OAL. The aerosol hygroscopicity parameter (

) was calculated

from CCN measurements (

CCN

) and bulk aerosol mass spectrometer (AMS) measurements (

AMS

Within the MBL, measured hygroscopicities varied between values typical of both continental environments

(~0.2) and remote marine locations (~0.7). For most

ights, CCN closure was achieved within 20% in the

MBL. For

ve of the seven

ights, assuming a constant aerosol size distribution produced similar or

better CCN closure than assuming a constant

“

marine

”

hygroscopicity (

= 0.72). An aerosol

‐

cloud parcel

model was used to characterize the sensitivity of predicted stratocumulus CDNC to aerosol hygroscopicity,

size distribution properties, and updraft velocity. Average CDNC sensitivity to accumulation mode

aerosol hygroscopicity is 39% as large as the sensitivity to the geometric median diameter in this

environment. Simulations suggest CDNC sensitivity to hygroscopicity is largest in marine stratocumulus

with low updraft velocities (<0.2 m s

−

), where accumulation mode particles are most relevant to CDNC,

and in marine stratocumulus or cumulus with large updraft velocities (>0.6 m s

−

), where hygroscopic

properties of the Aitken mode dominate hygroscopicity sensitivity.

1. Introduction

Marine stratocumulus (MSc) clouds, commonly observed off the Western coasts of North America, South

America, Africa, and Australia, cover nearly one

fth of the Earth's surface and exert a large impact on its

radiative balance (Wood, 2012). These cloud decks are particularly relevant to global climate due to their

high albedo contrast with the underlying ocean and relatively low altitude, resulting in stronger shortwave

ectance than longwave absorption (Brenguier et al., 2000; Randall et al., 1984; Wood, 2012). Previous esti-

mates suggest that a ~12% increase in the albedo of these clouds would produce a negative radiative forcing

equivalent in magnitude to that of doubling atmospheric CO

concentrations (Latham et al., 2008; Stevens &

Brenguier, 2009). Remote sensing, parcel modeling, and large eddy simulation (LES) studies have all estab-

lished that MSc exhibit substantial albedo susceptibility to variations in cloud droplet number concentra-

tions (CDNC) (Berner et al., 2015; Chen et al., 2011; Oreopoulos & Platnick, 2008; Platnick &

Twomey, 1994; Sanchez et al., 2016). Understanding the sensitivity of MSc CDNC to aerosols acting as

This is an open access article under the

terms of the Creative Commons

Attribution

‐

NonCommercial

‐

NoDerivs

License, which permits use and distri-

bution in any medium, provided the

original work is properly cited, the use

is non

‐

commercial and no modi

ca-

tions or adaptations are made.

RESEARCH ARTICLE

10.1029/2020EA001098

Key Points:

•

Aerosol hygroscopicity exhibited

substantial temporal variability in

the MBL

•

Errors in predicted MBL CCN

concentrations produced by

assuming a constant aerosol size

distribution or hygroscopicity are

discussed

•

Sensitivity of simulated CDNC to

hygroscopicity is maximized in

marine clouds with either very weak

or relatively strong updraft velocities

Supporting Information:

•

Supporting Information S1

Correspondence to:

J. H. Seinfeld,

seinfeld@caltech.edu

Citation:

Schulze, B. C., Charan, S. M., Kenseth,

C. M., Kong, W., Bates, K. H., Williams,

W., et al. (2020). Characterization of

aerosol hygroscopicity over the

Northeast Paci

c Ocean: Impacts on

prediction of CCN and stratocumulus

cloud droplet number concentrations.

Earth and Space Science

e2020EA001098. https://doi.org/

10.1029/2020EA001098

Received 10 FEB 2020

Accepted 23 MAY 2020

Accepted article online 3 JUN 2020

SCHULZE ET AL.

1of26

cloud condensation nuclei (CCN) is therefore a critical aspect of reducing uncertainty in climate change pre-

dictions (Seinfeld et al., 2016).

The CDNC and albedo of MSc are substantially in

uenced by the abundance of below

‐

cloud CCN. A recent

satellite analysis suggested that variability in below

‐

cloud CCN concentration may be responsible for ~45%

of the variability in the radiative effect of marine boundary layer clouds (Rosenfeld et al., 2019). This in

ence results from the fact that increased CCN abundance enhances cloud re

ectivity at constant liquid water

path (Twomey, 1977) and has the potential to reduce MSc precipitation rates, increasing cloud lifetime

(Ackerman et al., 1993; Albrecht, 1989; Goren & Rosenfeld, 2012; Rosenfeld, 2006). As a result, a major com-

ponent of the uncertainty in the estimated indirect aerosol forcing has been attributed to the prediction of

below

‐

cloud CCN concentrations (Rosenfeld et al., 2014; Sotiropoulou et al., 2007). While the aerosol size

distribution is generally thought to be the most important determinant of CCN activity (e.g., Dusek

et al., 2006; Ervens et al., 2007; McFiggans et al., 2006; Reutter et al., 2009), particle composition has also

been shown to exert a substantial in

uence (Jimenez et al., 2009; Liu & Wang, 2010; Mei et al., 2013;

Quinn et al., 2008; Sanchez et al., 2016).

The propensity of a given aerosol particle to act as a CCN can be described using Köhler theory

(Köhler, 1936; Seinfeld et al., 2016), provided suf

cient information is known regarding particle size and

solute properties (e.g., molecular weight, solubility, density, and activity). A novel framework,

‐

Köhler the-

ory, condenses these solute characteristics into a single parameter

(the aerosol hygroscopicity) that can be

easily incorporated into large

‐

scale models (Petters & Kreidenweis, 2007). Substantial effort has, therefore,

been devoted to quantifying

values in a multitude of environments (Ervens et al., 2010; Gunthe et al., 2009;

Pringle et al., 2010; Rose et al., 2010; Thalman et al., 2017). While

values characteristic of inorganic aerosol

components are relatively well

‐

established, atmospheric organic aerosol is composed of numerous, highly

diverse organic compounds, complicating representation of organic hygroscopicity using a single parameter

(Kanakidou et al., 2005). Experimental studies have characterized

values of secondary organic aerosol

(SOA) (e.g., Asa

‐

Awuku et al., 2010; Duplissy et al., 2008, 2011; Frosch et al., 2013; Lambe et al., 2011;

Massoli et al., 2010; Zhao et al., 2015), and

eld studies have characterized the typical range of organic

values (

org

) observed in the atmosphere (Chang et al., 2010; Gunthe et al., 2009; Levin et al., 2014; Mei

et al., 2013; Thalman et al., 2017; Wang et al., 2008). Generally, ambient

org

values are found to be 0.1

–

0.2 for aged aerosol and primary marine organics and ~0 for freshly emitted combustion aerosol (e.g., soot)

(Kreidenweis & Asa

‐

Awuku, 2014). A linear relationship has been noted between observed

org

values and

organic aerosol oxygen

‐

carbon (O:C) ratios in both the laboratory and the

eld (Chang et al., 2010; Lambe

et al., 2011; Mei et al., 2013; Wang et al., 2019).

Ambient particle hygroscopicity data have been combined with aerosol size distribution measurements in CCN

closure studies to assess the extent to which Köhler theory can be used to predict ambient CCN concentrations

(e.g., Almeida et al., 2014; Asa

‐

Awuku et al., 2011; Cubison et al., 2008; Medina et al., 2007; McFiggans

et al., 2006; Moore et al., 2012; Ren et al., 2018; VanReken et al., 2003). Analyzing the accuracy of predicted

CCN concentrations can provide insight into the in

uence of speci

c aerosol characteristics on CCN activity

(Bougiatioti et al., 2011; Cubison et al., 2008; Medina et al., 2007; VanReken et al., 2003; Wang et al., 2010).

For instance, size

‐

resolved compositional (i.e., hygroscopicity) data are often required to accurately reproduce

observed CCN concentrations in locations dominated by organic aerosol (Bhattu & Tripathi, 2015; Medina

et al., 2007; Ren et al., 2018), while aerosol mixing state has been shown to strongly impact total CCN concen-

trations in urban environments (Cubison et al., 2008; Ervens et al., 2010; Quinn et al., 2008). By analyzing data

from

ve ambient measurement campaigns, Ervens et al. (2010) found that for aerosol measured farther than a

few tens of kilometers from the emission source, CCN activity could be predicted within a factor of two inde-

pendent of either aerosol mixing state (i.e., internal or external) or organic solubility (i.e., insoluble or slightly

soluble). Wang et al. (2010) further demonstrated that CCN concentrations can often be reproduced within 20%

assuming internal mixing of aerosol components if the overall

of the aerosol population is >0.1. The direct

impact of variability in aerosol hygroscopicity on CCN concentrations is often assessed by assuming an invar-

iant chemical composition, represented as a

xed

, in CCN closure analyses. Field campaigns in continental

environments ranging from polluted megacities to the pristine tropical rainforest have shown that CCN con-

centrations could be reproduced within 20% and 50%, respectively, assuming a constant

= 0.3 (Gunthe

et al., 2009; Rose et al., 2010), a value representative of average continental conditions (Andreae &

Rosenfeld, 2008; Pringle et al., 2010). However, in coastal regions, MBL aerosol can result from a mixture of

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SCHULZE ET AL.

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distinct marine and continental emissions (e.g., Coggon et al., 2014; Mardi et al., 2018; Modini et al., 2015;

Sorooshian et al., 2009), which complicates aerosol representation using regional or global models. CCN closure

analysis can provide insight into the uncertainties in CCN concentrations that may result from inaccurate

model representation of aerosol composition in these environments.

Due to the importance of the persistent stratocumulus cloud decks over the N.E. Paci

c to global climate,

aerosol characteristics in this region have received considerable attention. However, the diverse range of par-

ticle sources, including shipping exhaust (Coggon et al., 2012; Murphy et al., 2009; Prabhakar et al., 2014;

Wonaschütz et al., 2013), primary and secondary natural marine emissions (Modini et al., 2015;

Prabhakar et al., 2014; Sorooshian et al., 2009), anthropogenic and biogenic continental emissions

(Coggon et al., 2014; Hegg et al., 2010; Moore et al., 2012), wild

re plumes (Brioude et al., 2009; Mardi

et al., 2018), and aged aerosol from the Asian continent (Roberts et al., 2006, 2010), combined with strong

temporal and spatial variability due to variable meteorological conditions, has hindered determination of

general characteristics of the marine atmosphere in this location. This complexity is re

ected in the diversity

of hygroscopicity measurements previously reported in the marine boundary layer (MBL) and free tropo-

sphere (FT). For instance, average

values reported from MBL measurements have varied from ~0.2

–

0.3

(Moore et al., 2012; Roberts et al., 2010) to ~0.5

–

0.7 (Royalty et al., 2017; Yakobi

‐

Hancock et al., 2014).

Measurements in the FT, while sparse, have been even more variable (

~0.05

–

1.0) (Roberts et al., 2006,

2010). While these measurements could largely be reconciled assuming various mixtures of continental

(0.27 ± 0.2) and marine (0.72 ± 0.2) aerosol, determining the major emissions sources and meteorological

patterns dictating these changes is important for improving model representation of the region (Pringle

et al., 2010). CCN

‐

based measurements of aerosol hygroscopicity and the resulting information about small

particle composition can be especially useful in this regard, as knowledge of small particle composition can

provide substantial insight into particle sources.

While hygroscopicity and mixing state characterization are important components of understanding the

CCN activity of ambient aerosol, the dynamic processes controlling supersaturation, droplet nucleation,

and droplet growth within clouds lead to nonlinear relationships between aerosol properties and CDNC.

As a result, aerosol

‐

cloud parcel modeling is instrumental to fully understand the role of aerosol hygrosco-

picity and mixing state on CDNC. Reutter et al. (2009) used such a model to distinguish three regimes of

aerosol activation, de

ned as the aerosol

‐

limited, updraft

‐

limited, and transitional regimes, based on the

ratio of updraft velocity to aerosol number concentration at the cloud base. The dependence of CDNC on

aerosol hygroscopicity, while limited relative to other parameters such as particle number concentration

and updraft velocity, was found to vary substantially between regimes. Additional modeling revealed that

CDNC sensitivity to aerosol hygroscopicity is highly dependent on the below

‐

cloud aerosol size distribu-

tion, with sensitivity increasing substantially with smaller median radii (Ward et al., 2010). Sanchez

et al. (2016) concluded that modeled stratocumulus albedo is insensitive to the assumed hygroscopicity

of the organic aerosol fraction; however, the sensitivity of CDNC to bulk hygroscopicity has yet to be fully

evaluated in this environment.

The present study uses measurements of aerosol composition and CCN activity collected during the Marine

Aerosol Cloud and Wild

re Study (MACAWS), combined with an aerosol

‐

cloud parcel model, to gain

insight into near

‐

coastal aerosol hygroscopicity and its in

uence on prediction of CCN and MSc CDNC.

Hygroscopicity measurements are combined with airmass backward trajectories and meteorological para-

meters to attribute observed particle characteristics to distinct sources when possible. CCN closure analyses

are performed to investigate the impact of compositional and mixing state assumptions on CCN predictions.

Finally, aerosol

‐

cloud parcel model simulations constrained with MSc microphysical measurements are

used to directly investigate the sensitivity of stratocumulus CDNC to aerosol hygroscopicity, mixing state,

and size distribution properties.

2. Methodology

2.1. MACAWS Field Mission

The 2018 Marine Aerosol Cloud and Wild

re Study (MACAWS) consisted of 16 research

ights operated out

of the Center for Interdisciplinary Remotely

‐

Piloted Aircraft Studies (CIRPAS) in Marina, California,

during June and July. Measurements were performed on

‐

board the CIRPAS Navy Twin Otter aircraft

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(Coggon et al., 2012, 2014; Russell et al., 2013; Sorooshian et al., 2019; Wang et al., 2016). The scienti

objectives of individual

ights included characterization of marine aerosols and clouds, sampling of

shipping vessel exhaust plumes, and investigation of nearby wild

re emissions. The present study focuses

on seven research

ights primarily aimed at characterization of the relationship between marine aerosol

and the overlying stratocumulus cloud deck. Paths of these seven

ights are depicted in Figure 1. Flight

strategies typically involved a series of level legs at varying altitudes within the MBL and overlying FT.

Slant or spiral soundings were generally performed before and after a series of level legs.

2.2. Twin Otter Instrumentation

The navigational and meteorological instrumentation utilized by the Twin Otter aircraft is described in

detail by Sorooshian et al. (2018). Ambient aerosol was sampled using a forward

‐

facing sub

‐

isokinetic inlet

(Hegg et al., 2005). Aerosol and cloud droplet number concentrations were characterized using a variety of

instruments, including multiple condensation particle counters (CPC, TSI 3010,

> 10 nm; ultra

ne CPC,

TSI UFCPC,

> 3 nm), a passive cavity aerosol spectrometer probe (PCASP,

~0.11

–

3.4

m), and forward

scattering spectrometer probe (FSSP, Particle Measuring Systems [PMS],

~1.6

–

m). Cloud liquid water

content was measured using a PVM

‐

100A probe (Gerber et al., 1994), and a threshold value of 0.02 g m

−

was

used to distinguish in

‐

cloud sampling (Dadashazar et al., 2018; MacDonald et al., 2018).

Cloud condensation nuclei (CCN) number concentrations were measured at four supersaturations (SS)

(0.1%, 0.3%, 0.43%, and 0.57%) using a Droplet Measurement Technologies (DMT) dual

‐

column streamwise

thermal

‐

gradient cloud condensation nuclei counter (CCNC) (Lance et al., 2006; Roberts & Nenes, 2005).

The CCNC operates by applying a linear temperature gradient to a cylindrical sampling tube with continu-

ously wetted walls. As the thermal diffusivity of water vapor exceeds the diffusivity of air, supersaturated

conditions are produced along the sampling column centerline. For this study, activated droplets grown to

sizes larger than 0.75

‐

m diameter were counted and sized by an optical particle counter. The sheath and

sample

ows of each column were maintained at 0.45 and 0.05 L min

−

, respectively. Instrument pressure

was maintained at 750 mb using a

ow ori

ce and active pressure control system at the instrument inlet.

Each column of the CCNC was calibrated using ammonium sulfate particles following standard methods

as described in Rose et al. (2008). Calibrations were performed before and after the campaign, and observed

Figure 1.

(a) Trajectories of the seven MACAWS research

ights analyzed in this study. (b) Relative vertical locations of the marine boundary layer, the

above

‐

cloud organic

‐

aerosol layer (AC

‐

OAL), and the free troposphere.

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deviations in applied SS for a given temperature gradient imply uncertainties of ~6%, similar to the 5% value

typical of

eld campaigns, as reported by Rose et al. (2008).

Aerosol size distributions and number concentrations for

between ~15 and 800 nm were measured with a

custom

‐

built scanning mobility particle sizer (SMPS) consisting of a differential mobility analyzer (DMA,

TSI 3081) coupled to a condensation particle counter (TSI 3010). The DMA is operated in a closed

‐

system

con

guration with a recirculating sheath and excess

ow of 2.67 L min

−

and an aerosol

ow of

0.515 L min

−

. The column voltage was scanned from 15 to 9,850 V over a ~2

‐

min interval.

Aerosol chemical composition was measured using a high

‐

resolution time

‐

ight aerosol mass spectro-

meter (HR

‐

ToF

‐

AMS, Aerodyne Research Inc., hereafter referred to AMS) (DeCarlo et al., 2006).

Incoming air enters the AMS through a 100

‐

m critical ori

ce, after which an aerodynamic lens pro-

duces a particle beam that is accelerated under high vacuum. The particle beam is

ash

‐

vaporized on

a resistively heated surface (600°C), and the resulting gases are ionized by electron impaction

(70 eV). Individual ion identity is determined using a high

‐

resolution time

‐

ight mass spectrometer.

Due to the limited amount of aerosol mass present over the MBL, data were collected in high

‐

sensitivity

‐

mode. The ionization ef

ciency (IE) of the AMS was calibrated using dry, 350

‐

nm ammonium nitrate

particles before each

ight. Data were averaged over 1

‐

min intervals, and all data were analyzed using

standard AMS software (SQUIRREL v1.57 and PIKA v1.16l) within Igor Pro 6.37. The collection ef

ciency (CE) was determined using the composition

‐

dependent calculator within the SQUIRREL and

PIKA software packages (Middlebrook et al., 2012). Elemental H:C and O:C ratios were calculated using

the

“

Improved

‐

Ambient

”

elemental analysis method for AMS mass spectra (Canagaratna et al., 2015).

Positive matrix factorization (PMF) analysis (Paatero & Tapper, 1994) was performed on the

high

‐

resolution AMS mass spectra in order to distinguish major classes and transformation processes

of measured OA. Three factors were extracted, two of which factors correspond to OA subtypes charac-

teristic of the MBL and above

‐

cloud organic aerosol layer (AC

‐

OAL), respectively, and resemble

low

‐

volatility oxygenated organic aerosol (LV

‐

OOA). The third factor, which was rarely observed, is

likely a result of primary anthropogenic emissions and resembles hydrocarbon

‐

like organic aerosol

(HOA). Further discussion of PMF data preparation and factor interpretation is included in the support-

ing information.

2.3. Determination of Aerosol Hygroscopicity

Aerosol hygroscopicity was calculated using two distinct methods based on measurements with the CCNC

and AMS, respectively. Assuming a particle population is internally mixed, the critical activation diameter

(

p,c

) (the diameter at which all larger particles will activate into cloud droplets) produced by a given SS

can be determined by integrating the particle size distribution until the total CN concentration is equivalent

to the measured CCN concentration:

CCN

∫

∞

;

(1)

Knowledge of the critical diameter can then be used to calculate a single parameter representation of aerosol

hygroscopicity from Köhler theory (Petters & Kreidenweis, 2007):

wet

−

;

wet

−

;

−

CCN

ðÞ

exp

wet



(2)

where

is the equilibrium supersaturation,

p,c

is the critical activation diameter,

wet

is the droplet dia-

meter,

is the universal gas constant,

is the absolute temperature,

is the molar density of water,

is the molecular weight of water, and

is the surface tension of the droplet at the point of activation.

Following Rose et al. (2010),

was determined by applying the observed activation diameter and varying

both

wet

and

until

is equivalent to the applied supersaturation of the CCNC and the maximum of a

Köhler curve of CCN activation. The droplet surface tension is assumed equal to that of water for compar-

ison with other studies (Collins et al., 2013; Petters & Kreidenweis, 2007; Roberts et al., 2010;

Yakobi

‐

Hancock et al., 2014). Hygroscopicity values calculated using this method are referred to as

“

CCN

‐

derived.

”

Since the likelihood of particle activation at a given SS tends to be a stronger function

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of size than composition (Dusek et al., 2006),

CCN

values correspond to particles with diameters near the

calculated critical diameter.

A Monte Carlo approach was used to estimate the uncertainty in CCN

‐

derived kappa values (Wang

et al., 2019). A detailed description is provided in the supporting information. For a given measurement

of the aerosol size distribution and CCN number concentration, the distribution of possible

CCN

values

calculated by varying these input parameters (i.e., CCN number concentration and size distribution)

within their respective uncertainties is lognormally distributed. As a result, uncertainties attributed to

CCN

are not symmetric about the geometric mean values. In general, we estimate 1

uncertainties of

+55%/

−

40% for

CCN

calculated at SS = 0.3%, ~+75%/

−

45% at SS = 0.43%, and +100%/

−

50% to values

calculated at SS = 0.57%. Due to the low CCN number concentrations observed at SS = 0.1%

(<100 cm

−

) and possibility of counting unactivated particles (expected to only be a few per cm

−

CCN

at SS = 0.1% are not reported, as small absolute deviations in particle number concentration mea-

sured by the CCNC and DMA due to differential inlet losses could strongly in

uence the resulting

CCN

estimates.

Hygroscopicity estimates can also be made using component volume fractions measured by the HR

‐

ToF

‐

AMS using the following equation (Petters & Kreidenweis, 2007):

AMS

∑

(3)

where

and

represent the volume fraction and hygroscopicity of the

th NR

‐

component, respec-

tively. While this calculation cannot capture the contribution of refractory components (sea salt, mineral

dust, etc.), further analysis suggests their contribution is minor, as discussed in the supporting informa-

tion. Organic aerosol density was assumed to be 1.4 g cm

−

for volume fraction calculations given the

remote nature of the environments sampled and the oxidized character of the measured organic aerosol

(e.g., O:C ratios of MBL and AC

‐

OAL PMF factors were 0.91 and 0.76, respectively) (Hallquist

et al., 2009; Roberts et al., 2010). The hygroscopicity of individual inorganic components is calculated

using





(4)

where

and

are the molar mass and density of water, respectively, and

, and

are the molar

mass, density, and van't Hoff factor of the inorganic component. Inorganic aerosol was dominated by sul-

fate and ammonium. The relative abundances of ammonium sulfate, ammonium bisulfate, and sulfuric

acid were calculated using the molar ratio of ammonium to sulfate (Asa

‐

Awuku et al., 2011; Nenes

et al., 1998). Ammonium sulfate and bisulfate were assigned van't Hoff factors of 2.5, while sulfuric acid

was assigned

= 0.9 to align with previous measurements (Petters & Kreidenweis, 2007). Modifying the

van't Hoff factors of ammonium sulfate and ammonium bisulfate and assumed

of sulfuric acid within

reasonable limits had a negligible in

uence on the presented results. Chloride measured by the AMS

was assumed to represent sodium chloride and was assigned a hygroscopicity of 1.28 (Petters &

Kreidenweis, 2007). AMS

‐

measured nitrate aerosol was assumed to be ammonium nitrate with a hygrosco-

picity of 0.67 (Petters & Kreidenweis, 2007). The hygroscopicity of the organic component (

org

) was

assumed to be either 0 (non

‐

hygroscopic), 0.1 (slightly

‐

hygroscopic), or a function OA composition using

a parameterization based on bulk O:C ratios developed in the literature (Lambe et al., 2011). Comparisons

CCN

and

AMS

values, analysis of PMF factor composition, and evaluation of CCN

‐

closure calculations

are used to evaluate these different

org

estimates.

An uncertainty analysis similar to that described for

CCN

values was performed for

AMS

values and is

described in detail in the supporting information. For median conditions in the MBL and FT, the relative

uncertainty in

AMS

is estimated to be ~10

–

20%, due primarily to uncertainty in the estimated hygroscopicity

of the organic component (

org

). In the AC

‐

OAL, the dominant contribution of organic aerosol increases the

relative uncertainty to ~50%; however, due to the low absolute

AMS

values observed in the AC

‐

OAL, the

absolute uncertainty is only ~0.1 or less.

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2.4. Aerosol

‐

Cloud Parcel Model

The aerosol

‐

cloud parcel model used in this study employs a user

‐

speci

ed updraft velocity to induce adia-

batic cooling of an air parcel, leading to water vapor supersaturation. The predicted parcel supersaturation at

each time step is determined by the relative rates of production through adiabatic cooling and loss through

condensation of water vapor onto activated cloud droplets (Pruppacher & Klett, 1997; Seinfeld et al., 2016).

In the present study, meteorological parameters such as ambient pressure, temperature, and lapse rate are

obtained from MACAWS aircraft measurements and are speci

ed before model execution. The

below

‐

cloud dry size distribution is assumed to contain Aitken and accumulation modes, the characteristics

of which (i.e., number concentration, geometric mean diameter, hygroscopicity) are set by the user. Particles

within each mode can be speci

ed as either internally or externally mixed. Each compositional class, 1

per size mode if internally mixed or 2 per size mode if externally mixed, contains 300 lognormally

spaced bins ranging from 1 nm to 3

m. Droplet activation is assumed to occur when the ambient

supersaturation of the parcel exceeds the critical supersaturation of the particles in a given size bin,

as determined from

‐

Kohler theory (Petters & Kreidenweis, 2007). Following activation, the growth

of individual cloud droplet bins due to water vapor diffusion is explicitly represented. Additional physi-

cal processes such as droplet coagulation, coalescence, and deposition are not included, as previous par-

cel model studies have demonstrated that these processes have little in

uence on model predictions for

typical marine stratocumulus conditions (Sanchez et al., 2016). Model execution proceeds until a

user

‐

speci

ed liquid water content (0.4 g m

−

in this study) has been reached. Activated particle size

bins larger than 1

m are considered cloud droplets; however, using an alternative size threshold of

m or 0.75

m has a negligible in

uence on the results.

2.5. Air Mass Backward Trajectories

Air mass backward trajectories (120 hr) were calculated in the MBL for each

ight using the NOAA

HYSPLIT v4.2 model with the global data assimilation system (GDAS) 1° × 1° meteorological data set

(Draxler & Hess, 1997, 1998; Stein et al., 2015). The higher spatial resolution EDAS 40 km × 40 km meteor-

ological data set was not used due to its limited spatial range over the Paci

c Ocean. The ending altitude of

each trajectory was the approximate midpoint of the MBL during each

ight.

Figure 2.

Vertical pro

les of (a) RH and LWC, (b) CCN and CN concentrations, and (c) non

‐

refractory (NR) PM

component mass loadings for the seven RFs in

Figure 1. Markers represent median values, while horizontal bars span the interquartile range. (d) Vertical contour plot of median size distributio

ns measured

during the seven RFs. The dark grey region in panels a

–

c represents the average stratocumulus cloud depth (avg. cloud top height

≈

570 m; avg. cloud bottom

height

≈

300 m). The lighter grey region represents the standard deviation of cloud top and bottom heights (e.g., avg. cloud top + cloud top height S.D.

≈

680 m).

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3. Results and Discussion

3.1. Aerosol Characteristics Over the N.E. Paci

Results from the seven

ights analyzed in this study are summarized in Figure 2 and Tables 1

–

3. In the sub-

sequent analyses,

“

all

ights

”

refers to these seven. Typical

ight patterns included sampling within the

MBL, FT, and, when present, the above

‐

cloud organic aerosol layer (AC

‐

OAL). The AC

‐

OAL is operationally

ned as the narrow altitude band (generally <200 m) directly above the marine stratocumulus cloud decks

where OA mass loadings were relatively large (>1.5

−

) and a distinct AC

‐

OAL PMF factor contributed

>80% of total OA mass (Figure S6). This region occupies a similar location as the commonly referenced

entrainment interface layer (EIL) above cloud decks (Dadashazar et al., 2018; Wood, 2012), but is de

ned

by the aerosol characteristics described above rather than by turbulence and buoyancy characteristics, as

is common for the EIL (Carman et al., 2012). Median aerosol properties are reported in Tables 1

–

3 for each

of these three regions, while Figure 2 displays vertical pro

les of aerosol and meteorological properties.

Distinct differences in particle properties were observed within each vertical region. Median aerosol number

concentrations observed in the MBL (754 cm

−

) exceeded those in the FT (333 cm

−

), as expected. Observed

particle concentrations were maximized within the AC

‐

OAL (1,662 cm

−

), where intense actinic

uxes and

elevated concentrations of the hydroxyl radical may drive new particle formation (Dadashazar et al., 2018;

Mauldin et al., 1999). For all measured SS > 0.1%, observed CCN concentrations were also largest within

the AC

‐

OAL, rather than the MBL or FT, underscoring the importance of understanding the hygroscopicity

of above

‐

cloud CCN

‐

active particles (Coggon et al., 2014; Sorooshian, Lu, et al., 2007; Sorooshian et al., 2007;

Wang et al., 2008).

Observed aerosol composition in the MBL was relatively evenly divided between organic aerosol (OA) (43%)

and sulfate (SO

) (48%), with a minor contribution from ammonium (NH

) (~10%) and negligible nitrate

(NO

)(

≤

1%). Prabhakar et al. (2014) have demonstrated that nitrate is preferentially distributed in

super

‐

micron particles in this marine environment, in agreement with the minor contribution observed with

the AMS in this study. Using the

“

clean

”

versus

“

perturbed

”

threshold introduced by Coggon et al. (2012) for

this region (where

“

clean

”

is de

ned by aerosol mass concentrations <1

−

), average MBL conditions

were

“

perturbed

”

by shipping vessel emissions or other anthropogenic sources such as continental out

ow.

A distinct, highly oxidized MBL PMF factor was extracted from the data set (Figure S6). The oxidized nature

of the MBL factor (O:C = 0.91) precludes the use of marker ions to distinguish individual sources; however,

potential sources include shipping and biogenic emissions, as well as oxidized continental out

aerosol (Coggon et al., 2012; Hegg et al., 2010; Sorooshian et al., 2009). In the AC

‐

OAL, observed aerosol

composition was dominated by organics (80%), as has been previously reported (Coggon et al., 2014;

Table 1

Median Aerosol Number (N) and Cloud Condensation Nuclei (CCN) Concentrations Measured in the Marine Boundary Layer (MBL), Above

‐

Cloud Organic Aerosol

Layer (AC

‐

OAL), and Free Troposphere (FT)

Location

N (cm

−

)

CCN: 0.1% (cm

−

)

CCN: 0.3% (cm

−

)

CCN: 0.43% (cm

−

)

CCN: 0.57% (cm

−

)

MBL

754 (509

–

978)

75 (33

–

106)

194 (146

–

285)

302 (187

–

410)

410 (229

–

522)

‐

OAL

1,662 (1,303

–

1,959)

58 (41

–

84)

363 (260

–

537)

574 (403

–

876)

781 (539

–

1,051)

333 (296

–

555)

21 (14

–

35)

115 (89

–

145)

144 (102

–

194)

162 (118

–

240)

Note

. Values in parentheses represent the interquartile range. CCN concentrations are provided as a function of the instrument supersaturation (%).

Table 2

Median Mass Loadings of Total Non

‐

Refractory PM1 (NR

‐

), and Organic (Org.), Sulfate (SO

), Ammonium (NH

), and Nitrate (NO

) Aerosol Components in

the Marine Boundary Layer (MBL), Above

‐

Cloud Organic Aerosol Layer (AC

‐

OAL), and Free Troposphere (FT)

Location

‐

(

−

)

Org. (

−

)SO

(

−

)NH

(

−

)NO

(

−

)

MBL

2.8 (2.3

–

2.5)

1.1 (0.8

–

1.4)

1.5 (0.9

–

2.0)

0.2 (0.2

–

0.3)

0.0 (0.0

–

0.1)

‐

OAL

5.5 (4.5

–

7.5)

4.4 (3.2

–

6.1)

0.7 (0.6

–

1.1)

0.2 (0.2

–

0.3)

0.1 (0.0

–

0.1)

1.5 (1.2

–

2.1)

0.7 (0.5

–

1.0)

0.6 (0.4

–

0.7)

0.1 (0.1

–

0.2)

0.0 (0.0

–

0.0)

Note

. Values in parentheses represent the interquartile range.

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