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Atmos. Chem. Phys., 24, 6455–6476, 2024
https://doi.org/10.5194/acp-24-6455-2024
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the Creative Commons Attribution 4.0 License.
Research article
Aerosol-induced closure of marine cloud cells: enhanced
effects in the presence of precipitation
Matthew W. Christensen, Peng Wu, Adam C. Varble, Heng Xiao, and Jerome D. Fast
Atmospheric Science and Global Change Division, Pacific Northwest National Laboratory, Richland,
Washington, WA 99354, USA
Correspondence: Matthew W. Christensen (matt.christensen@pnnl.gov)
Received: 23 October 2023 – Discussion started: 1 November 2023
Revised: 9 April 2024 – Accepted: 15 April 2024 – Published: 3 June 2024
Abstract. The Weather Research Forecasting (WRF) version 4.3 model is configured within a Lagrangian
framework to quantify the impact of aerosols on evolving cloud fields. Kilometer-scale simulations utilizing
meteorological boundary conditions are based on 10 case study days offering diverse meteorology during the
Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA). Measurements from aircraft, the
ground-based Atmosphere Radiation Measurement (ARM) site at Graciosa Island in the Azores, and A-Train
and geostationary satellites are utilized for validation, demonstrating good agreement with the WRF-simulated
cloud and aerosol properties. Higher aerosol concentration leads to suppressed drizzle and increased cloud water
content in all case study days. These changes lead to larger radiative cooling rates at cloud top, enhanced vertical
velocity variance, and increased vertical and horizontal wind speed near the base of the lower-tropospheric inver-
sion. As a result, marine cloud cell area expands, narrowing the gap between shallow clouds and increasing cloud
optical thickness, liquid water content, and the top-of-atmosphere outgoing shortwave flux. While similar aerosol
effects are observed in lightly to non-raining clouds, they tend to be smaller by comparison. These simulations
show a relationship between cloud cell area expansion and the radiative adjustments caused by liquid water path
and cloud fraction changes. The adjustments are positive and scale as 74 % and 51 %, respectively, relative to the
Twomey effect. While higher-resolution large-eddy simulations may provide improved representation of cloud-
top mixing processes, these results emphasize the importance of addressing mesoscale cloud-state transitions in
the quantification of aerosol radiative forcing that cannot be attained from traditional climate models.
1 Introduction
The surface temperature of Earth is kept cooler by the pres-
ence of low-level clouds, in particular stratocumulus. It has
been estimated that a mere increase of about 4 % in their
global coverage would be enough to offset the radiative
warming due to a doubling of atmospheric carbon dioxide
(Randall et al., 1984). Aerosols, commonly emitted along-
side greenhouse gases, have the potential to decrease cloud
droplet size and create more numerous droplets that effec-
tively suppress precipitation and moisten the boundary layer
(Albrecht, 1989). This process can increase the vertical and
horizontal extents of clouds as well as their lifetime (Al-
brecht, 1989; Pincus and Baker, 1994; Bretherton et al.,
2007; Christensen et al., 2020). However, an increase in
aerosol concentration can also result in cloud desiccation due
to enhanced cloud-top entrainment caused by more effective
evaporation in polluted clouds (Ackerman et al., 2004; Small
et al., 2009) or through reduced cloud droplet sedimentation
(Bretherton et al., 2007). These processes can even mod-
ify the cellular structure of clouds through changing cloud
fraction (Rosenfeld et al., 2006). However, the strength and
sign of the cloud radiative effect depends on a multitude of
meteorological factors such as lower-tropospheric stability
and humidity, precipitation state (Chen et al., 2014), and the
timescale for which clouds have been polluted (Wang and
Feingold, 2009). These complex relationships result in poor
understanding and large uncertainty in estimates of rapid
cloud adjustments to changes in aerosol concentration (Bel-
Published by Copernicus Publications on behalf of the European Geosciences Union.
6456 M. W. Christensen et al.: Lagrangian WRF ACI
louin et al., 2020), the so-called aerosol–cloud lifetime ef-
fect (Albrecht, 1989). It is critical to quantify and resolve
process-scale cloud physics impacting rapid adjustments in
order to improve estimates of aerosol radiative forcing at
global scales (Seinfeld et al., 2016).
A preponderance of evidence linking aerosol and cloud
radiative effects to the mesoscale structure of clouds has
been growing in the literature over the past couple of
decades (Rosenfeld et al., 2006; Wood, 2012; Christensen
and Stephens, 2012; Eastman et al., 2021). Stratocumulus
can exhibit cellular structures which appear closed or open
with hexagonal-like or honeycomb shapes that organize on
scales ranging from 10–50 km (Wood, 2005). The impact
of aerosol on precipitation, as proposed by Rosenfeld et al.
(2006), can reverse the direction of the wind flow through
the vertical extent of the marine boundary layer, doubling
cloud cover and converting cloud structure from open to
closed cells. Eastman et al. (2021) observed that stronger sur-
face winds and lower cloud droplet concentrations are typi-
cal prior to the transition of closed to open cells. Weather
Research and Forecasting (WRF) model simulations from
Zhou et al. (2018) indicate that moisture stratification and
precipitation tend to increase horizontal cloud scales by en-
hancing updraft buoyancy via increased latent heating. Ad-
ditionally, longwave radiative cooling near cloud top plays
a crucial role in increasing horizontal cloud scales, and sub-
cloud moist cold pools tend to respond to, rather than de-
termine, mesoscale variability. A Lagrangian framework has
been shown to be effective in capturing upstream condition-
ing on developing clouds (Lewis et al., 2023), as well as be-
ing used to quantify cloud lifetime and tracking changes in
cloud microphysics associated with changes in aerosol con-
centration and meteorological conditions (Christensen et al.,
2020, 2023).
The shortwave cloud radiative effect of transforming open
to closed stratocumulus cells was estimated to be as large as
109 W m−2in a composite of 50 case studies from Moder-
ate Resolution Imaging Spectroradiometer (MODIS) obser-
vations from Goren and Rosenfeld (2014). Goren and Rosen-
feld (2014) decomposed the aerosol indirect effect into the
Twomey effect (the enhancement in shortwave cloud albedo
caused by increasing cloud droplet concentration for fixed
changes in liquid water path) and rapid adjustments contain-
ing liquid water path and cloud fraction changes. These were
estimated to be approximately 26 %, 32%, and 42 %, respec-
tively. Here, we also quantify cloud water path and fraction
adjustments but use a regression technique following Quaas
et al. (2008) applied to kilometer-scale WRF model sim-
ulations of marine stratocumulus. We utilize a Lagrangian
framework to capture the evolution of low-level clouds and
examine how their cellular patterns change over time in order
to answer the following research questions:
–To what extent does a change in aerosol concentration
modify the area and spacing between cloud cells?
–How does the aerosol indirect radiative effect vary over
diverse meteorological conditions?
–How does changing planetary boundary layer (PBL)
and microphysics schemes affect the aerosol indirect ef-
fect?
–How do liquid water path and cloud fraction adjust-
ments compare to the Twomey effect?
To answer these questions we first describe the details
of the datasets used in this study (Sect. 2), set up several
case study experiments in WRF that utilize a Lagrangian
framework (Sect. 3), and conclude with an assessment of the
aerosol radiative forcing (Sects. 4 and 5).
2 Observational data
The U.S. Department of Energy Atmosphere Radiation Mea-
surement (ARM) program has been providing continuous
measurements of cloud properties at Graciosa Island in the
Azores for over a decade. This location is ideal for study-
ing mesoscale structure (Jensen et al., 2021), turbulence
(Ghate and Cadeddu, 2019), and aerosol–cloud interactions
(ACIs) (Zheng et al., 2022b; Christensen et al., 2023; Varble
et al., 2023) in marine stratocumulus clouds. Ground-based
measurements from ARM, aircraft measurements from the
Aerosol and Cloud Experiments in the Eastern North Atlantic
(ACE-ENA; Wang et al., 2022), and satellite observations
from geostationary and polar orbits are used to evaluate WRF
simulations of boundary layer clouds passing over Graciosa
Island.
Significant progress in the process-scale understanding of
aerosol–cloud interactions, facilitated by observational data
from Graciosa Island, reveals that the seasonal cycle plays
a significant role in aerosol activation. During winter, when
the clouds are more decoupled and connected to stronger
updrafts compared to summertime conditions (Wang et al.,
2022; Zheng et al., 2022a), a higher fraction of accumula-
tion mode particles tends to be activated. Despite higher ac-
tivated aerosol fractions in winter, droplet number concen-
trations are lower due to less available aerosol compared to
summer conditions (Wang et al., 2022). Large-eddy simula-
tions (LESs) using the WRF model with spectral bin micro-
physics and dynamical downscaling from a 19 km horizon-
tal resolution to a 300 m grid spacing (Wang et al., 2020)
demonstrated that imposing aerosol plumes at observed air-
craft heights significantly reduces the effective droplet radius
(ACIr=∂ln(Re)
∂ln(NCCN)≈ −0.11) and increases the liquid water
path (ACIl≈ +0.14). These cloud microphysical changes
may modify the dynamics in the planetary boundary layer
differently between seasons. Consequently, our work focuses
on characterizing the cloud fraction response from numer-
ous summer and winter case studies provided by ACE-ENA,
conducting an in depth investigation into mesoscale struc-
tural changes in clouds, and bridging the gap between cloud
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M. W. Christensen et al.: Lagrangian WRF ACI 6457
morphological changes and aerosol radiative forcing in low
clouds.
2.1 Ground-based observations from ENA
Rain rate is retrieved using a laser optical OTT particle size
and velocity (PARSIVEL-2) disdrometer, which measures
the instantaneous rainfall rate by quantifying the water flux
from drops in 32 size bins (0 to 25 mm) and 32 fall velocity
bins (0.2 to 20 ms−1) falling to the surface. The retrieval has
a 6 % absolute bias with respect to reference gauges over a
1 min sampling interval (Tokay et al., 2014), as provided in
the LDQUANTS value-added product (Hardin et al., 2020).
Cloud-top height and low-level cloud fraction are esti-
mated from the active remote sensing of clouds (ARSCL)
product (O’Connor et al., 2004; Kollias et al., 2016; Cloth-
iaux et al., 2001), which combines vertically pointing Ka-
band radar and lidar data to produce high-resolution time–
height cross sections of cloud boundaries.
Bottom-of-atmosphere shortwave and longwave radiative
fluxes are provided by the ARM best-estimate cloud radi-
ation dataset (ARMBECLDRAD; Xie et al., 2010; Atmo-
spheric Radiation Measurement user facility, 2014) in hourly
intervals using measurements from an infrared radiation sta-
tion. Temperature and specific humidity profiles containing
266 altitude levels are provided every minute by the Inter-
polated Sounding (INTERPSONDE; Troyan, 2013) product
that combines observations from radiosondes, the microwave
radiometer (MWR), and surface meteorological instruments.
The effective radius of cloud droplets and optical depth in
single-layer overcast liquid-only clouds is determined using
the multifilter rotating shadowband radiometer (MFRSR) at a
wavelength of 415 nm (Turner et al., 2021). The retrieval pro-
cess relies on the algorithm developed by Min and Harrison
(1996) for atmospheric radiative transfer. If the MWR suc-
cessfully retrieves liquid water path, then the effective radius
is calculated based on the MWR and MFRSR data. However,
if this information is not available, we exclude it (occurring
in less than 30 % of cases) from the analysis to avoid using
fixed effective radius replacement values of 8 µm in the ARM
product.
2.2 ACE-ENA flights
The ARM Aerial Facility Gulfstream-159 (G-1) research air-
craft flew from Terceira Island in the Azores during two in-
tensive operational periods (IOPs) that occurred from June
to July 2017 and January to February 2018 during ACE-
ENA. Deployments during both seasons are used to evalu-
ate the vertical profile of the bulk liquid water content mea-
sured by the multi-element water content system (WCM-
2000; Matthews and Mei, 2017). The multi-element wa-
ter content measuring system utilizes a scoop-shaped sen-
sor to measure total water content, capturing both liquid-
and ice-phase hydrometeors. It incorporates two heated wire
elements (021-wire and 083-wire), exposed directly to the
airstream, along with a reference element exposed to the air-
flow but not to condensed water. Following the approach of
Miller et al. (2022), we adopt the WCM-2000 system due
to its favorable agreement in liquid water content measure-
ments compared to the Fast Cloud Droplet Probe and two-
dimensional stereo particle imaging probe measurement sys-
tems. The condensation particle counter (CPC) measures the
number concentration of aerosols from 10 nm to 3 µm un-
der kinetic mode. Aerosol concentration uncertainties are ap-
proximately 15 % (Fan and Pekour, 2018). The cloud con-
densation nuclei (CCN) concentration is obtained from the
CCN-200 particle counter on board the G-1 aircraft provid-
ing CCN at approximately 0.2 % supersaturation every sec-
ond (i.e., N_CCN_1 as discussed in Uin and Mei, 2019). To
compare aerosol properties in clear-sky conditions with the
WRF model, we select aircraft samples within a 1° ×1° re-
gion centered around the ARM site at 13:00 UTC ±1.5 h and
below 2 km altitude, excluding those with cloud water con-
tent.
2.3 Satellite observations
Cloud-top effective droplet radius (Re) and cloud optical
thickness (τc) are retrieved from the 1.6, 2.1, and 3.7 µm
channels; cloud-top temperature, pressure, and height are
retrieved from longer-wavelength thermal channels on the
Moderate Resolution Imaging Spectroradiometer (MODIS)
instrument. These data, retrieved from the collection 6.1
cloud product (Platnick et al., 2017b), are at a 1 km pixel-
scale resolution at nadir from satellites Terra and Aqua, pass-
ing over the region at approximately 10:30 and 13:30 LT (lo-
cal time), respectively. Of the three spectral channels used
for Reretrievals, the sensitivity of the 3.7 µm channel is
weighted closest to the cloud top, primarily due to the rel-
atively strong absorption of water vapor at this wavelength
(Platnick, 2000). Because errors in the adiabatic droplet num-
ber concentrations using the 3.7 µm channel are considerably
smaller than in the other bands (Grosvenor et al., 2018), we
choose to use it for this study.
Imagery from the Geostationary Operational Environmen-
tal Satellite (GOES) Advanced Baseline Imager (ABI) of the
National Oceanic and Atmospheric Administration (NOAA)
GOES-R series satellite (Pinker et al., 2022) is utilized to
aid in visualizing the evolving characteristics of mesoscale
cloud structures along Lagrangian trajectories. Full-disk im-
ages covering the entire region are made available every
15 min. These images have spatial resolutions of 0.5 km at
nadir for the 0.64 µm visible channel and 2 km for the 3.9
and 11 µm channels.
The Clouds and the Earth’s Radiant Energy System
(CERES) Synoptic (SYN1deg-1Hour) edition 4.1 product
(Rutan et al., 2015) provides similar cloud-top retrievals to
MODIS using similar algorithms (e.g., the MODIS collec-
tion 5 product), as well as top- and bottom-of-atmosphere
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6458 M. W. Christensen et al.: Lagrangian WRF ACI
Figure 1. Flow chart depicting the methodology for studying
aerosol–cloud interactions in a Lagrangian framework using the
WRF model.
shortwave and longwave radiative fluxes that are gridded
globally at 1° ×1° every hour through combining multi-
spectral retrievals from a network of 16 geostationary satel-
lites as well as the CERES instruments on Terra, Aqua, and
Suomi National Polar-orbiting Partnership.
3 Methodology
Figure 1 depicts a four-step procedure used to initialize and
run the Weather Research and Forecasting (WRF) version 4.3
model (Skamarock et al., 2021) in a Lagrangian framework.
This technique uses an inner nest that moves through the
WRF model (outer domain) at specified time steps. First,
the Hybrid Single-Particle Lagrangian Integrated Trajectory
(HYSPLIT; Stein et al., 2015) version 5 model is used to
calculate a 6 h back and a 6 h forward trajectory using the
Modern-Era Retrospective analysis for Research and Appli-
cations, version 2 (MERRA-2; Gelaro et al., 2017), reanal-
ysis meteorological data. Trajectories are calculated from
the middle of the planetary boundary layer (determined in
HYSPLIT). This height has been shown to be representa-
tive for tracking the general flow of boundary layer clouds
over the ocean (Christensen et al., 2020; Kazil et al., 2021;
Christensen et al., 2023). Back trajectories are initialized at
the Graciosa Island ARM site at 10:00 LT before the Terra
(morning at 10:30) and Aqua (afternoon at 13:30) MODIS
overpass times. Forward and backward trajectories are ini-
tialized at Graciosa Island and run for 6 h. These trajectories
are combined to form a 12 h trajectory starting from the tail
of the back trajectory and ending at the tail of the forward
trajectory. This method ensures that the air mass transits over
the ARM site.
3.1 WRF modeling
Nested simulations are performed using the WRF model
(Fig. 1 box 2). The outer (static) domain is 12° ×12° and
is centered over the ARM site on Graciosa Island. The re-
gion is large enough to span the entire length of the back
and forward Lagrangian trajectories. The outer domain has a
horizontal grid spacing of 4 km and a vertical grid that is log-
stretched where the spacing is approximately 50 m near the
surface and increases to 150 m throughout the PBL to the top
of the model at 20 km. The model time step is 10s. The outer
domain is used to characterize the large-scale meteorological
flow and boundary conditions for the inner domain.
The inner domain allows for convection-permitting scales
and moves along the HYSPLIT trajectory using the multi-
incremental 4D-Var system which allows for translating
(moving) nests within WRF (similar to vortex tracking
for hurricanes as described in Zhang et al., 2014). WRF
was compiled using preset moves to permit higher-spatial-
resolution simulations within the inner domain, which
is computationally more efficient than running a high-
resolution simulation across the entire outer domain. The
inner domain translates in time (across the outer domain)
according to the pre-computed locations using HYSPLIT.
Given the spatial scales of typical cellular maritime cloud
organization (30 to 40 km; Wood, 2005), the inner domain is
spatially large enough to capture the largest scales of vari-
ability spanning approximately 200 ×300 km2with a hori-
zontal grid spacing that is 5 times finer than the outer nest
(800 m) with the same vertical resolution.
Boundary conditions are initialized and updated every
6 h during simulation using reanalysis data from MERRA-2
which are spatially gridded at 0.5° resolution with 72 verti-
cal levels and provided every 6 h. We have tested WRF using
other meteorological datasets (see Fig. S1 in the Supplement
for details) and find that the choice of the reanalysis prod-
uct does not significantly alter the results. To coincide with
earlier work (Christensen et al., 2023) we use MERRA-2 to
drive the WRF boundary conditions for this study.
We use a 6 h spin-up period to allow sufficient time for
the cloud properties to reach steady state. After this pe-
riod, the inner two-way nest begins to move within the
WRF model according to the HYSPLIT trajectory com-
puted using the same reanalysis product as that used to drive
the WRF model. The simulations are performed with the
aerosol-aware Thompson bulk microphysical parameteriza-
tion scheme (Thompson and Eidhammer, 2014) with explicit
cloud droplet nucleation treatment following Köhler activa-
tion theory. Look-up tables generated from parcel modeling
are used to provide the cloud droplet number concentration
based on predicted temperature, vertical velocity, number of
hygroscopic aerosol particles also referred to as “number of
water friendly aerosols” (NWFA), and predetermined values
of the hygroscopicity parameter and aerosol mean radius.
Aerosol sensitivity experiments follow the same approach as
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M. W. Christensen et al.: Lagrangian WRF ACI 6459
described in Thompson and Eidhammer (2014) in which the
input mass mixing ratio of each aerosol species (dust, sea
salt, black and organic carbon, and sulfate aerosols) is ob-
tained from the Goddard Chemistry Aerosol Radiation and
Transport (GOCART) model and is converted to NWFA con-
centration using assumed lognormal distributions with char-
acteristic diameters and geometric standard deviations taken
from Chin et al. (2002) (their Table 2). Next, we modify
the NWFA concentration profile climatology averaged over
7 years using the following scale factors: 0.01 (pristine),
0.1 (clean), 1.0 (control), and 10.0 (polluted) for each ex-
periment (Fig. 1 box 3). Note that no changes are made to
the assumed aerosol chemical species composition, hygro-
scopicity parameter (0.4 in experiments performed in this re-
search), and aerosol mean radius (0.04 µm). These scale fac-
tors significantly affect the NWFA concentration as shown in
Fig. S2. Lower condensation particle concentrations (CPCs)
in cloud-free air sampled by the aircraft suggest that the con-
trol simulation of NWFA may be more polluted than the ob-
servations on this particular day and across seasons (Fig. S3).
However, the CPC and NWFA serve as a rough comparison
as the characteristics (namely the size distribution and hygro-
scopicity) of these two quantities may differ. As discussed
later, cloud droplet number concentrations are also affected
by NWFA with median values broadly approaching 20, 50,
250, and 450 cm−3for our pristine (N1), clean (N2), control
(N3), and polluted (N4) aerosol experiments, respectively.
The Level-3 Mellor–Yamada–Nakanishi–Niino (MYNN3)
PBL scheme (Nakanishi and Niino, 2009) predicts turbu-
lent kinetic energy (TKE) and other second-order moments
within the PBL. The Rapid Radiative Transfer Model for
GCMs (RRTMG) specifies the size of hydrometers and uti-
lizes the correlated-kapproach to calculate fluxes and heating
rates accurately (Iacono et al., 2008) and efficiently through
its use of a Monte Carlo independent column approximation
technique (Pincus et al., 2003). The simulations utilize the
Noah land surface model (Barlage et al., 2010) as well as the
Tiedtke cumulus scheme (Zhang et al., 2011).
Model evaluation (Fig. 1 box 4) is carried out using out-
put from the WRF-Solar model (Jimenez et al., 2016), which
passes the effective radius of cloud particles from the micro-
physics to the radiation parameterization scheme (Thomp-
son and Eidhammer, 2014), impacting cloud albedo and en-
abling quantification of the aerosol indirect effect (Thomp-
son et al., 2016). WRF-Solar includes a solar diagnostics
package that outputs several two-dimensional variables, in-
cluding cloud fraction, liquid effective droplet radius, opti-
cal thickness, and liquid water path. The liquid water path
is computed from the effective radius and optical thickness
quantities, i.e., LWP=2
3τcRewhere τcis the cloud optical
thickness, and Reis the effective droplet radius (Stephens,
1978). These quantities (that are weighted towards the cloud
top) have been shown to be comparable with MODIS ob-
servations (Otkin and Greenwald, 2008). A summary of the
model setup is listed in Table 1.
Table 1. WRF model schemes used to study aerosol–cloud inter-
actions. Values for the coinciding names denote the option number
used in WRF.
WRF scheme Value Name
microphysics 28 Thompson (aerosol-aware)
radiation 4 RRTMG
cumulus 6 Tiedtke
pbl 6 MYNN3
sfclay_physics 2 Eta similarity
surface physics 2 Noah land
3.2 Case studies
Figure 2 shows our selected case studies. Days are selected
based on the following criteria: (1) a dearth of high-level
cloud over the trajectory for optimal comparison with satel-
lite retrievals; (2) aircraft measurements coinciding with in-
tensive operation periods (IOPs) 1 (25 June–25 July 2017)
and 2 (1–25 February 2018); and (3) diverse meteorological
conditions to study the impacts of precipitation, atmospheric
stability, and free-tropospheric humidity states on aerosol–
cloud interactions. Across the experiments, the height of the
PBL top varied from 600 to 1710 m, and the surface air tem-
perature varied from 13 to 22 °C as determined by meteoro-
logical soundings averaged over the entire day. Daily total
accumulated precipitation from the disdrometer varied from
0 to 4 mm. A wide range of cloud patterns were observed in-
cluding disorganized (small, isolated clouds or clouds with
no discernible pattern), homogeneous (solid cloud deck with
no discernible pattern), closed cells (cells filled with cloud),
and open cells (cells where the center is devoid of cloud).
These classifications are broadly inferred using the defini-
tions described in Wood and Hartmann (2006). Table 2 lists
key quantities of interest for the cases displayed in Fig. 2. It
is noteworthy to mention that, while we aim to select cases
which did not have ice cloud in the observations, the WRF
model sometimes simulated them above the boundary layer
(18 July 2017) and within the boundary layer during two of
the wintertime IOP case studies (24 and 25 January 2018).
Potential impacts of simulated ice cloud on the analysis are
discussed in subsequent sections.
3.3 Lagrangian framework and dataset integration
Figure 3 shows the evolution of shallow clouds in the La-
grangian trajectory for the lightly drizzling day of 18 July
2017. This case study forms the backbone for many of the
inter-comparisons made throughout this work due to the dis-
tinct closed-cell features and persistence of the stratocumu-
lus cloud deck throughout the day. Satellite retrievals from
GOES and MODIS are aggregated over a 1° ×1° region (yel-
low box) during each time interval (15 min) along the tra-
jectory. CERES gridded data are interpolated in space and
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6460 M. W. Christensen et al.: Lagrangian WRF ACI
Figure 2. Case studies during summer (a–e) and winter (f–j) ACE-ENA IOP periods. Panels show GOES visible images at 13:00 UTC
displayed over 4° ×4° regions centered over Graciosa Island (yellow star denotes the ARM site). Aircraft flight positions are shown as white
lines. Vertically pointing Ka-band reflectivity at the ARM site is displayed over a 24h period for the corresponding days.
time to the same trajectory grid box. WRF simulations at
roughly kilometer scale are aggregated over the same region
and timescale as the Lagrangian trajectory. Both the obser-
vations and simulations show persistent closed-cell clouds
throughout the day. These clouds produce very light drizzle
as indicated by the Ka-band radar (Fig. 2e) and disdrometer
measurements at Graciosa Island (Table 2). An evident wake
island effect is observed and simulated in the downstream
region from the Azores. In general, the low-level flow and
horizontal displacements of the clouds are well captured us-
ing the Lagrangian framework as depicted in Movie S1 in the
Supplement.
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M. W. Christensen et al.: Lagrangian WRF ACI 6461
Table 2. Case studies from ACE-ENA IOP periods used to simulate stratocumulus clouds in WRF. Surface temperature (Ts), lower-
tropospheric static stability (LTS), free-tropospheric entraining relative humidity at 850 hPa (FTH), PBL height (determined from the tem-
perature and humidity sounding), cloud-base height (determined from the ceilometer), and daily integrated rainfall determined from ARM
distrometer observations. Dominant cloud types following the classification of Wood and Hartmann (2006) based on satellite imagery in-
spection are listed.
TsLTS FTH PBL Cloud base Rainfall Cloud type Precipitation
(°C) (K) height height (mm)
(m) (m)
IOP 1
30 June 2017 20.0 20.0 36 890 950 0 disorganized non-raining
6 July 2017 21.5 20.2 26 1410 1107 0.05 homogeneous light rain
12 July 2017 22.0 17.2 72 1130 325 0.34 homogeneous moderate rain
15 July 2017 16.0 22.0 60 1530 850 3.9 homogeneous heavy rain
18 July 2017 22.0 18.2 63 950 682 0.02 closed cells non-raining with overlying cloud layers
IOP 2
19 January 2018 16.5 16.0 52 950 816 0.2 open cells rain
24 January 2018 14.0 18.0 32 1710 1411 0.03 open cells light rain with ice
25 January 2018 13.0 19.7 21 1510 1302 0.14 closed cells drizzle with ice
29 January 2018 15.0 18.1 50 1200 1062 0 disorganized non-raining
1 February 2018 15.0 17.8 41 600 565 0.19 disorganized drizzle
Figure 3. GOES 11 µm thermal infrared image on 18 July 2017 at 19:00 UTC is centered over Graciosa Island with positions of the HYSPLIT
trajectory computed using ERA5 reanalysis (blue line) (a). The yellow box denotes a 1° ×1° region that moves along the center of the
trajectory. Visible imagery at 0.64 µm reflectance over a larger 2° ×2° region is shown at discrete times (10:00, 13:00, and 16:00 UTC; b,
c,d, respectively). WRF simulations of the brightness temperature at 11 µm and normalized shortwave albedo are displayed at the same
times (e, f, g, h). The black line denotes aircraft observations from the ACE-ENA campaign.
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6462 M. W. Christensen et al.: Lagrangian WRF ACI
4 Results
In the first part of the analysis we quantify the effect
of aerosol changes on the mesoscale structure of clouds
(i.e., size and distance between cloud cells) and associated
radiative impacts from an ensemble of 40 WRF simulations
spanning 10 different case studies with four varying aerosol
concentrations (a set of four, for each case study day) offer-
ing diverse meteorology and cloud types. This particular set
of simulations uses MYNN3 and Thompson (aerosol-aware)
PBL and microphysics schemes, respectively. In the second
part (Sect. 4.3.2), we assess and quantify variations in the
aerosol indirect effect on case study day 18 July by employ-
ing different PBL and microphysical scheme choices across
26 WRF experiments.
4.1 Impact of aerosol on the mesoscale structure of
clouds
Cloud objects are detected using a watershed technique, fol-
lowing the methodology described in Wu and Ovchinnikov
(2022). Because the standard WRF model output does not
include simulated channel reflectances for MODIS, compar-
isons are made based on the LWP. The only difference be-
tween Wu and Ovchinnikov (2022) and our study is that
we use LWP instead of the MODIS reflectance. As LWP
scales well with the visible cloud albedo (Stephens, 1978),
the replacement of LWP for visible reflectance is suitable
after thresholds have been linearly scaled. Moments of the
LWP distributions have been used for cloud classification of
marine stratocumulus in several studies (e.g., see Wood and
Hartmann, 2006; Zheng et al., 2018). The segmentation pro-
cedure initially smooths the LWP field to remove random
field variations while preserving object boundaries using a
two-dimensional Gaussian filter with a kernel standard de-
viation of 250 g m−2. Next, cloud objects are detected using
a watershed technique. A centroid is assigned to each cloud
object based on the distribution of cloudy pixels with LWP
greater than 100 g m−2. Cloud objects are formed if a com-
mon interface is shared. An edge weight is computed, and if
the area-weighted mean difference between pixels along the
interface is smaller than 4 g m−2, the two objects are merged
and a new centroid is assigned to the object.
To determine the spacing between cloudy object centers,
we compute the distance of each cloud object centroid to all
other centroids and select the minimum distance (i.e., Dc).
Due to variable sizes of the cloud objects, we also compute
the distance of all edge pixels of an object to all of the edge
pixels of all other objects and select the minimum distance
(De). This latter method provides an estimate of the closest
distance between neighboring cloud object boundaries, thus
removing the effect of cloud fraction on distances between
clouds that is not accounted for with cloud object centroids.
Cloud objects are identified in WRF (Fig. 4b) every 15 min
along the trajectory and in MODIS at the Terra and Aqua
Figure 4. Liquid water path and watershed regions for WRF (a, b)
and MODIS (c, d) on 18 July 2017 at 13:00 UTC. White stars indi-
cate object centroid locations.
overpass times (Fig. 4d). Cloud area ranges from about 1–
500 km2in WRF and MODIS (Fig. S4a). The majority are at
scales less than about 10 km, a result similarly found in Wu
and Ovchinnikov (2022) and Wood and Hartmann (2006),
based on power spectral analysis of the spatial variance in
LWP. The distance between cloud object centroids is similar
between MODIS and WRF with a mean value of approxi-
mately 12.1 km and median value of roughly 10.7 km for this
particular case study (Fig. S4b).
The size and spacing between cloud objects is to some
extent dictated by the background aerosol concentration. Fi-
gure 5a and b show that the average cell area and spacing be-
tween object centroids increases as the background aerosol
concentration increases. The distance between cloud edges
decreases as the aerosol concentration increases (Fig. 5c).
This is evident when comparing “snapshots” of the pristine
and polluted experiments taken at the same time (Fig. 5d–
e). The cloud objects are spreading away from each other,
but they are also becoming larger and filling the gaps be-
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M. W. Christensen et al.: Lagrangian WRF ACI 6463
tween clouds as aerosol loading increases. Similar behavior
is found on 15 July 2017 (as depicted in Fig. S5) and gener-
ally across all case studies (discussed in Sect. 4.3).
To characterize uncertainty and determine whether this re-
lationship is robust, a sensitivity test of the segmentation al-
gorithm is performed over a range of minimum LWP thresh-
olds for defining cloud object edges spanning 1 to 500 g m−2.
Figure S6 shows that the area of the cloud objects becomes
larger with increasing aerosol concentration. This response
is robust across the full range of LWP threshold values.
The largest sensitivity of this relationship occurs around
200 g m−2. This unique threshold LWP value is also a turn-
ing point for which further increases in LWP decrease the
number of detected cloud objects, which impacts cell separa-
tion distance. Furthermore, the cloud fraction is larger under
polluted conditions, and this relationship is robust for each
minimum LWP threshold value (Fig. S6d). As 100 g m−2
forms roughly the midpoint value we select this represen-
tative threshold for segmenting clouds in this analysis.
4.2 Aerosol–cloud interactions
Two case studies, one with lightly precipitating clouds and
another with heavier precipitating clouds are examined in de-
tail during the summertime IOP period for quantifying the
effects of aerosol on precipitating and lightly precipitating
clouds.
4.2.1 Lightly precipitating clouds
On 18 July 2017 closed-cell-type clouds were found in the
vicinity of the Azores. The clouds produced a light amount
of precipitation where only approximately 0.02 mm was
recorded in the distrometer measurements from ARM. Air-
craft measurements of the cloud water content on this day
fit within the range of variability simulated for clouds in the
WRF model (Fig. 6a). Cloud tops from the aircraft mea-
surements imply that WRF simulates a slightly deeper than
observed boundary layer by approximately 200 m. We find
reasonable agreement between MODIS, CERES, and ARM
datasets with the WRF simulations (Fig. 7). Cloud optical
depth and radiative fluxes tend to agree more closely with
the clean and control WRF experiments. The agreement not
being closest to the control experiment may be indicative of
the following issues: (1) a bias in the climatological aerosol
concentrations (being too high), (2) the Thompson scheme
may be nucleating too many aerosols, or (3) scavenging rates
are not large enough. Despite these differences, the chosen
schemes resolve essential characteristics of a realistic bound-
ary layer based on the reasonable agreement in the cloud-
relevant properties.
Rainwater mixing ratio, also forming closer to cloud top
in the cleaner experiments, decreases by up to an order of
magnitude as background aerosol concentration increases
(Fig. 6b). A modest increase in cloud water content and cloud
water mixing ratio is found in the more polluted simulations
throughout all levels in the cloud. This result is consistent
with the indirect effect using the Thompson microphysics
scheme described in Thompson and Eidhammer (2014). An
increase in aerosol concentration also results in smaller cloud
droplet effective radius (Fig. 7a), larger cloud optical thick-
ness, larger liquid water path, and larger droplet concentra-
tion (Fig. 7b, c, d); cloud-top quantities are obtained from
WRF-Solar. The more polluted aerosol experiments with op-
tically thicker clouds result in more reflected solar radiation
at the top of the atmosphere and less incoming solar radiation
at the bottom of the atmosphere despite having slightly lower
cloud tops. The slightly elevated cloud tops in the more pris-
tine simulation also have elevated cloud bases and are more
decoupled from surface moisture. Nonetheless, all simulated
cloud-top heights are within the range of variability in the
ARM and satellite observations.
Cloud properties tend to vary over the course of the tra-
jectory with increasing cloud optical thickness, liquid water
path, and cloud-top height. This is accompanied by an in-
crease in sea surface temperature and more unstable bound-
ary layer conditions along with rising lifted condensation
level and decreasing free-tropospheric humidity (Fig. S7). A
deepening boundary layer is expected given the warming sea
surface temperature (Eastman et al., 2016), but despite the
changing meteorological conditions over the trajectories, the
cloud alterations attributed to changes in aerosol loading re-
main systematic throughout the 12 h period.
4.2.2 Precipitating clouds
In comparison to the previous case study, the boundary layer
on 15 July is about 750 m deeper and the accumulated rainfall
is significantly larger: 3.9 mm. Much like the previous light-
drizzle case study, the properties of precipitating clouds on
15 July 2017 also broadly fit within the range of variability
in cloud water content as measured by aircraft observations
(Fig. 8) and LWP by satellite and ARM retrievals (Fig. S8c).
Simulated cloud-top height tends to be higher than the obser-
vations during the afternoon hours. Figure 2d reveals more
vigorous clouds, as indicated by relatively large radar re-
flectivities during early morning and late afternoon periods
outside the trajectory time frame. This difference could con-
tribute to the observed mismatch between simulated and ac-
tual cloud-top heights. Despite this, in the control simulation,
peak cloud water contents are approximately 40 % larger, and
peak rainwater mixing ratios are about 90 % larger on 15 July
(precipitating case study) compared to 18 July (drizzling case
study). Furthermore, the cloud water content increase due
to increasing aerosol concentration is significantly larger on
15 July compared to 18 July.
Simulations with elevated concentrations of aerosols have
larger cloud-top shortwave and longwave radiative cooling
rates. The net radiative cooling rate decreases from approx-
imately −10 K d−1in the clean simulations to −30 K d−1in
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6464 M. W. Christensen et al.: Lagrangian WRF ACI
Figure 5. Time series of the average (a) cloud object area, (b) minimum distance between cloud centroids, and (c) minimum distance between
cloud edges over each 15 min time interval detected for pristine (blue), clean (orange), control (green), and polluted (red) experiments on
18 July 2017. MODIS averages (star) and standard deviations (vertical lines) are displayed. Images of the LWP at 13:00 UTC is displayed
for the clean (d) and polluted experiments (e).
Figure 6. (a) Vertical profile of the total water content measured
by the G-1 aircraft in the WCM-2000 data product during the ACE-
ENA flight (stars) on 18 July 2017 is averaged over an hour across
the domain from 13:00–14:00 UTC. The flight path is illustrated in
Fig. 3 and simulated for pristine (N1; blue), clean (N2; orange),
control (N3; green), and polluted (N4; red) experiments. Addition-
ally, the simulated vertical profile of cloud (solid) and rain (dotted)
water mixing ratios is averaged over the domain for each aerosol
experiment.
the more polluted simulations (Fig. 9). Mean vertical and
horizontal wind velocity near the cloud top also tends to
be larger in the more polluted simulations. Vertical velocity
variance and turbulence throughout the boundary layer tend
to be larger in the more polluted simulations. Vertical profile
shapes of these quantities are similar, albeit less pronounced,
on 18 July (Fig. S9). Stronger updrafts in the more polluted
simulations where radiative cooling rates are larger coincide
with larger lateral displacements near the base of the inver-
sion and may be partially responsible for causing the signifi-
cant widening of the clouds and increased cloud fraction.
To account for the turbulence of the convective eddies at
800 m grid spacing (in the so-called “gray zone” where ed-
dies in the PBL are partially resolved; Shin and Dudhia,
2016), TKE is also provided using the three-dimensional-
resolved winds (Fig. 9i) following the equation TKE =
1
2(u02+v02+w02), where u02,v02, and w02are the variances of
the winds computed over 3.2 ×3.2 km2regions. In our sim-
ulations, the resolved TKE is not very sensitive to changes
in the averaging scale in which the 3.2, 6.4, and 12.8 km
scales show similar magnitude within the cloud layer. While
the TKE computed using the resolved winds shows a relative
increase near cloud top hinting at a better connection to the
cloud-top radiative flux profile compared to the subgrid TKE
output from MYNN3, this is a relatively weak relationship
compared to large-eddy simulations of stratocumulus (e.g.,
see McMichael et al., 2019), which may suggest further re-
finement is needed in connecting these processes within the
MYNN3 eddy-diffusivity mass flux (EDMF) scheme (Olson
et al., 2019). Possible implications of the relatively weak
mixing on the liquid water path and cloud fraction adjust-
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M. W. Christensen et al.: Lagrangian WRF ACI 6465
Figure 7. (a) Droplet effective radius (Re), (b) cloud optical thickness (τc) retrieved from the 3.7 µm band, (c) liquid water path (LWP),
(d) droplet concentration (Nd) computed from Reand τc,(e) liquid cloud fraction (Cf), (f) cloud-top height (CTH), (g) top-of-atmosphere
outgoing shortwave radiative flux (F↑
SW), and (h) bottom-of-atmosphere incoming shortwave flux (F↓
SW) for pristine (blue), clean (orange),
control (green), and polluted (red) WRF simulations. WRF-Solar was used for comparison with the satellite retrievals. ARM (brown diamond)
retrievals are provided at all time steps and at the time when the trajectory passes over the ARM site (larger brown diamond), and MODIS
retrievals from satellites Terra (red star) and Aqua (blue star) are provided when available along the trajectory on 18 July 2017. Hourly
retrievals of the cloud fraction and radiative fluxes are provided by CERES. Note that aside from when time to ENA equals 0, the ARM
measurements do not coincide with the trajectory location and are merely used to show Eulerian variability.
Figure 8. Same as Fig. 6 except for case study 15 July 2017.
ments are discussed in further detail in the Conclusions sec-
tion.
Additional tests are carried out at 1 km horizontal grid
spacing to determine the relative roles of cooling caused by
rain drop evaporation (by setting the temperature and mois-
ture tendencies caused by changes in rain mass evaporation
in the Thompson microphysics scheme to zero), cloud radia-
tive effect (setting icloud=0 in the namelist file), and the
cumulus scheme (by turning it off) on the results. Rain evap-
oration below cloud base stabilizes the atmosphere, produc-
ing decoupling and less turbulence (Wood, 2012). However,
Fig. S10 shows that turning off rain droplet evaporation re-
sults in only a small relative change in cloud and rain mixing
ratios, radiative cooling, and turbulence. Turning off the radi-
ation to the clouds significantly decreases turbulent mixing,
cloud-top height, and rainwater mixing ratio. Similarly, turn-
ing off the cumulus scheme significantly decreases cloud and
rainwater mixing ratio and radiative cooling rates.
Figure S11 illustrates the impact of sensitivity experiments
on aerosol effects on cloud properties. In general, an increase
in aerosol concentration enhances cloud fraction, liquid wa-
ter path, and cloud area extent as aerosol loading increases.
Turning off cloud interactions with radiation removes the ef-
fects of changes in cloud radiative heating and cooling, but
clouds still expand (albeit less so) simply due to precipitation
suppression by aerosols. This may indicate that low-clouds
expand due to precipitation suppression through reducing the
magnitude of the primary cloud sink; the next changes in ra-
diative effects cause further increases in cloud fraction (ap-
proximately 100 % more based on Fig. S11). This cloud ra-
diative feedback suggests a notable contribution to promot-
ing the initial cloud expansion via precipitation suppression
by aerosol. For removal of rain evaporation, the precipitation
effect on PBL turbulence is turned off. While this no longer
conserves energy (which is unavoidable in such sensitivity
tests), we continue to simulate strong cloud expansion due to
increased aerosol concentration, and this is largely due to the
suppression of precipitation and growth of the cloud.
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6466 M. W. Christensen et al.: Lagrangian WRF ACI
Figure 9. Vertical profile of the mean (a) shortwave and (b) longwave radiative heating rate (SWHR and LWHR), (c) mean vertical velocity
(W), (d) mean horizontal wind speed (Ws), (e) vertical velocity variance (w02), (h) subgrid-scale (SGS) turbulent kinetic energy (TKE) from
MYNN3, and (i) resolved TKE computed from the three-dimensional wind variances calculated from 3.2 ×3.2 km2regions averaged over
the domain for pristine (blue), unpolluted (orange), control (green), and polluted (red) WRF simulations on 15 July 2017 at 13:00 UTC. Gray
shading indicates the boundaries of the cloud layer for the control experiment.
4.3 Aerosol–cloud interactions across 10 case studies
A suite of aerosol experiments spanning 10 case studies with
varying meteorological conditions provides 40 WRF simula-
tion experiments to examine aerosol indirect radiative effect
across a range of meteorological and cloud conditions. These
case studies are summarized in Table 2.
4.3.1 Aerosol indirect radiative effect
The aerosol indirect radiative effect is calculated from the
change in the top-of-atmosphere outgoing shortwave radia-
tive flux caused by a change in Ndand can be written as
REaci = −F↓φatm
fcαc(1−αc)
3Nd
1+5
2
1lnL
1lnNd
+3(αc−αsfc)
αc(1−αc)
1lnfc
1lnNd!1Nd,(1)
where F↓is the top-of-atmosphere (TOA) incoming solar
radiation, φatm is the transfer function that accounts for the
transmissivity (reflection and absorption) of the non-cloudy
air above the surface and takes an average value of 0.7 (Di-
amond et al., 2020), fcis the cloud cover fraction, αcis the
cloud albedo, Ndis the droplet concentration, Lis the liquid
water path, and αsfc is the surface albedo. The full derivation,
based on Quaas et al. (2008) and Christensen et al. (2023), is
described in Sect. S2 in the Supplement.
Quantities in Eq. (1) are obtained in hourly intervals over a
1° ×1° domain moving along the trajectory. The 1symbols
denote differences between aerosol experiments of varying
aerosol concentrations. There are six possible pairs which
include, polluted −control 1(N4 −N3), polluted −clean
1(N4−N2), polluted −pristine 1(N4−N1), control −clean
1(N3 −N2), control −pristine 1(N3 −N1), and clean −
pristine 1(N2 −N1). F↓is the daily-mean solar insolation,
fcαc(1−αc)
3Ndand 3(αc−αsfc)
αc(1−αc)are computed from mean quantities
of the paired aerosol experiments, and 1Ndrepresents the
mean difference in cloud droplet number concentration be-
tween paired aerosol experiments. By using a wide range
of aerosol concentrations we aim to capture variability in
ACI but acknowledge that nonlinearity in the relationship be-
tween cloud variables with Ndmay be missed from the use
of only four aerosol experiments.
Figure 10 shows the relationship of key variables as they
change in response to increasing background aerosol con-
centrations in the WRF model. In most cases, there is good
agreement in the sign of the response across diverse case
studies. An increase in aerosol concentration enhances the
top-of-atmosphere reflected sunlight, cloud fraction, liquid
water path, cloud optical thickness, and cloud object area.
A robust decrease in droplet effective radius is also evident.
While responses are mostly consistent, the magnitude can
vary substantially. Cases where significant precipitation oc-
curs (15 July and 25 January) exhibit the largest increases in
liquid water path, cloud optical thickness, and cloud object
area. Days having light rain (18, 6, and 12 July) or no mea-
surable rain (30 June, 24 January) have significantly weaker
responses by comparison. Figure 11a and b show the ef-
fect of precipitation on the liquid water path and cloud frac-
tion aerosol adjustments. While there is some scatter across
experiments, this result generally agrees with Chen et al.
(2014); an increase in aerosol concentration has a stronger
radiative effect on precipitating clouds compared to non-
precipitating clouds due to the suppression of precipitation
causing cloud water to increase. While drizzle suppression
reduces scavenging of cloud droplets and goes into spreading
the cloud vertically, the horizontal spreading of the clouds
through increased cloud object area is highly significant.
The Twomey radiative effect estimated as
−13.7 ±9.3 W m−2with a range extending from −4.1
to −29.9 W m−2across case studies. This estimate is based
on the daily-mean solar insolation, which at this location can
vary significantly between winter and summer IOPs. Note
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M. W. Christensen et al.: Lagrangian WRF ACI 6467
Figure 10. Value of the slope in the natural log change in a given variable (χ) with respect to the natural log change in cloud droplet number
concentration (Nd) computed from four aerosol WRF experiments in 10 different case studies (6, 12, 15, and 18 July 2017; 30 June 2017; 19,
24, 25, and 29 January 2018; 1 February 2018) represented at 13:00 UTC. χvariables shown are the top-of-atmosphere outgoing shortwave
flux (F↑
SW), liquid cloud fraction (Cf), liquid water path (LWP), effective droplet radius (Re), cloud optical thickness (τc), cloud geometrical
thickness (H), cloud object area extent (A), and distance between cloud object centroids (Dc). Multiplication of −1 on Reand Dcwas
carried out to make all quantities positive across the bar chart. Uncertainties are represented by the 1σerror in the regression fit between
quantities.
that this estimate is the radiative effect, not the radiative
forcing, and hence does not include the changes in aerosol
concentration attributed to anthropogenic sources (i.e., the
present-day minus pre-industrial values). The radiative
effect is estimated from six different aerosol experiment
pairs (discussed above) that have a wide range of aerosol
concentrations (as shown in Figs. S2 and S3). The cloud
properties and radiative effects associated with each case
study are listed in Tables S1–S10. The quantifications of the
modeled sensitivity in the cloud radiative effect to changes
in cloud droplet concentration are similar to those found
in satellite observations of ship tracks (Christensen and
Stephens, 2012; Goren and Rosenfeld, 2014).
To make the results more intuitive, Table 3 lists the ratios
of the liquid water path and cloud fraction adjustments scaled
by the Twomey effect. These enhancements range from 10 %
to 150 % for the LWPadj, a result that is similar to that found
across multiple GCM experiments in Gryspeerdt et al. (2020)
and in the observations of Goren and Rosenfeld (2014). Con-
sequently, our findings approach the upper limits of these
adjustments possibly due to a weak connection between en-
trainment mixing and cloud-top radiation from the use of
kilometer-scale models (discussed further in the Conclusions
section). During both IOPs we find that the largest indirect ra-
diative effects tend to coincide with the largest daily precip-
itation rates (Fig. 11c). These cases are also consistent with
those which show the largest cell area growth as a function
of aerosol loading (Fig. 10).
The cloud object area expansion relationship is not as
strong during the wintertime IOP. Figure S12 reveals the
presence of ice on 24 and 25 January 2018, and intriguingly,
the Twomey effect and rapid adjustments exhibit compara-
ble agreement in these cases, as seen in the warm cloud
case study days (Fig. 10). Although the Thompson mi-
crophysics scheme considers ice multiplication from rime
splinters through the Hallett–Mossop process (Hallett and
Mossop, 1974), a phenomenon known to lead to cloud mor-
phology breakup and alteration, accompanied by enhanced
precipitation (Abel et al., 2017; Eirund et al., 2019), we have
not altered ice-friendly nuclei concentrations in this study.
Modifying such concentrations could offer additional in-
sights into aerosol–ice-cloud interactions in future research.
4.3.2 Impact of changing PBL and microphysics
schemes
We devise a set of sensitivity experiments where the micro-
physics and PBL schemes are varied to assess the uncer-
tainty of modeling boundary layer clouds and ACI. These
simulations use the double-moment Morrison microphysics
(Morrison et al., 2005) scheme with fixed cloud droplet num-
ber concentration. For the Morrison scheme, we used fixed
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6468 M. W. Christensen et al.: Lagrangian WRF ACI
Table 3. Twomey radiative effects, along with the liquid water path and cloud fraction adjustments relative to the Twomey effect (calculated
using Eq. 1), are listed. Mean values across all case studies and excluding 25 January due to excessive aerosol-induced cloud growth are
included in the last two rows.
Case Twomey LWPadj
Twomey
Cf adj
Twomey
(W m−2)
15 July 2017 −29.9 1.48 0.53
18 July 2017 −27.5 0.41 0.05
6 July 2017 −5.6 0.11 0.66
30 June 2017 −14.8 0.14 0.24
12 July 2017 −24.0 0.48 0.20
19 January 2018 −10.0 1.58 0.64
24 January 2018 −4.7 0.37 0.01
25 January 2018 −4.1 0.67 3.90
29 January 2018 −7.9 1.39 1.29
1 February 2018 −8.5 0.55 0.54
Mean (all) −13.7 ±9.3 0.72 ±0.53 0.81 ±1.09
Mean (excluding 25 January) −14.7 ±9.2 0.72 ±0.56 0.46 ±0.37
Figure 11. Scatter plot of the (a) change in liquid water path ( 1ln L
1lnNd), (b) change in cellular cloud area as a function of Nd, and (c) normal-
ized indirect radiative effect which constitutes the Twomey +LWPadj.+CFadj.as a function of daily accumulated rain rate from ARM for
simulations ±3 h from the time the trajectory intersects Graciosa Island for each case study day, designated by a different symbol as shown
in the legend.
droplet number concentrations with values of 20, 80, 320,
and 1020 cm−3for our pristine (N1), clean (N2), control
(N3), and polluted (N4) aerosol experiments, respectively.
Values for the more polluted runs were increased to coincide
with the scale factors used in the Thompson (aerosol-aware)
scheme for simulating similar values of the cloud droplet
number concentrations. The additional PBL schemes for test-
ing use the nonlocal Yonsei University (YSU; Hong et al.,
2006) or local Mellor–Yamada–Janjic (MYJ; Mellor and Ya-
mada, 1982) closure flux models. These schemes have differ-
ences in vertical mixing strength which affect entrainment of
dry air from above the PBL and can impact cloud properties
differently depending on the scheme chosen (Hu et al., 2010).
A summary of each sensitivity experiment is listed in Table 4.
Note that running the Morrison microphysics scheme with
fixed droplet number concentration does not allow for a full
positive aerosol–cloud–precipitation feedback cycle as sim-
ulated in some LESs (e.g., Yamaguchi et al., 2017). This has
been shown to have a significant influence on the mesoscale
structure of clouds, and hence cloud fraction (Goren et al.,
Table 4. WRF model setup for control and sensitivity experiments.
Values in parenthesis denote the option number used in WRF. Ex-
perimental setup primarily used for analysis of detailed aerosol–
cloud interaction experiments is listed in bold.
Experiment name Microphysics PBL
THA_YSU Thompson (28) YSU (1)
THA_MYJ Thompson (28) MYJ (2)
THA_MYN Thompson (28) MYNN3 (6)
MOR_YSU Morrison (10) YSU (1)
MOR_MYJ Morrison (10) MYJ (2)
MOR_MYN Morrison (10) MYNN3 (6)
2019; Diamond et al., 2022), potentially having a significant
impact on the net radiative effect in this sensitivity study.
Figure 12 shows a comparison of WRF-simulated LWP
with satellite observations from several different micro-
physics and PBL schemes for the 18 July 2017 case study.
All schemes simulate boundary layer cloud in a similar
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M. W. Christensen et al.: Lagrangian WRF ACI 6469
geographic region as that observed by satellites. However,
some of the WRF schemes underpredict LWP and cloud-
top heights, in particular the YSU and MYJ PBL schemes
(as shown in Fig. S13) with respect to MODIS. The MYJ
scheme tends to produce smaller cloud cells containing
much smaller liquid water paths compared to the YSU and
MYNN3 schemes. In general, most of the simulations re-
produce the vertical profiles of temperature, humidity, wind
speed, and wind direction compared to ARM radiosonde
measurements (Fig. S14) but with a slightly elevated cap-
ping inversion and dew point temperature. Overall, we find
the best agreement with the Thompson and MYNN3 PBL
schemes regarding how close the cloud and atmospheric state
compare to the observations.
To test the impact of using different schemes in WRF on
the aerosol indirect effect, four aerosol experiments are car-
ried out for each model configuration, yielding a total of 24
simulations to quantify the range of variability in aerosol in-
direct effect for the case study occurring on 18 July 2017.
Due to computational constraints, we ran these simulations
at a lower spatial resolution (3 km grid spacing for the in-
ner nest) using 99 vertical levels. Here we exclude cloud
area changes in the analysis due to the poorer ability of the
model to simulate these structures at lower resolution and fo-
cus more on the microphysical changes across these model
configurations instead.
Figure S15 shows the aerosol perturbations of various
cloud properties for each of the six WRF configurations. Like
before, all simulations show that an increase in aerosol con-
centration results in an increase in the reflected solar radi-
ation, a reduction in cloud droplet effective radius, and an
increase in cloud optical depth. The lower simulation reso-
lutions produce similar sensitivities compared to the higher-
resolution simulation runs. For example, 1lnτc/1ln Ndfor
the higher-resolution run is 0.55 ±0.12 and 0.48 ±0.15 for
the lower-resolution run. It is noteworthy that the liquid water
path and cloud thickness responses are negative in some of
the configurations; however, a ttest indicates that there is not
a significant difference from zero. The variations (σREaci =
σerr
REaci , estimated from the standard error, σerr, computed from
the standard deviation normalized by the square root of the
10 cases divided by the total indirect effect) across experi-
ments is approximately ±30 %. Given this range of variabil-
ity in the indirect effect, we infer the microphysical cloud
responses are robust across a wide range of possible model
configurations. Thus, variations larger than this level in anal-
yses of the 10 case studies with the Thompson and MYNN3
schemes are likely to be more related to meteorological and
cloud-state modulations as opposed to these particular cho-
sen WRF schemes.
5 Conclusions
We devised a series of realistic WRF simulations using
boundary conditions from MERRA-2 reanalysis to simu-
late PBL clouds as they pass over Graciosa Island in the
Azores during the ACE-ENA field campaign. Kilometer-
scale simulations were carried out within an inner nest that
moves along the Lagrangian flow of the PBL, making higher-
resolution simulations computationally feasible for studying
aerosol–cloud interactions. The Lagrangian framework al-
lows for the analysis of an evolving cloud field over time,
although, for relatively short timescales like those used here,
the aerosol responses were roughly consistent along the
length of trajectories. Cloud water content, temperature, hu-
midity, and wind profiles were in the range of acceptable
uncertainty as determined by comparison with aircraft ob-
servations and radiosonde measurements from Graciosa Is-
land. WRF-simulated cloud microphysical properties and ra-
diative fluxes were generally in closer agreement to the clean
(not control) experiments. This result suggests that the base-
line NWFA concentrations are biased high at Graciosa Is-
land. With the exception of WRF simulating higher cloud
tops during the afternoon compared to MODIS and ARM,
the simulated cloud and radiative properties in general tend
to fit within the range of observed uncertainty.
With these simulations, we addressed the following re-
search questions.
–To what extent does a change in aerosol concentration
modify the area and spacing between cloud cells? An
increase in aerosol concentration suppresses precipita-
tion, causing liquid water content and liquid water path
to increase throughout the PBL. Through applying the
cloud segmentation watershed algorithm developed by
Wu and Ovchinnikov (2022), we find that cloud water
mass is redistributed through the PBL horizontally and
in some cases vertically through the expansion of the
clouds. This is accompanied by a decrease in clear skies
between clouds. The suppression of drizzle through an
increase in aerosol concentration results in more cloud
water. The cloud-top radiative cooling rate, turbulent ki-
netic energy, and vertical velocity variance increased
in strength under polluted conditions. Larger horizon-
tal winds near the cloud tops were typically found in
the simulations with more aerosol. Through this pro-
cess, the additional water (not lost through drizzle) in
polluted clouds is redistributed both vertically as well
as horizontally. This results in the expansion of cloud
cells.
–How does the aerosol indirect radiative effect vary
over diverse meteorological conditions? The sign of
the aerosol indirect radiative effect is robust across all
case study days. They all exhibit liquid water path and
cloud fraction increases with increasing aerosol concen-
tration, a similar result also found in the WRF simu-
https://doi.org/10.5194/acp-24-6455-2024 Atmos. Chem. Phys., 24, 6455–6476, 2024
6470 M. W. Christensen et al.: Lagrangian WRF ACI
Figure 12. WRF control experiment using combinations of the Thompson (THA), Morrison (MOR), Yonsei University (YSU), Mellor–
Yamada–Janjic (MYJ), and Mellor–Yamada–Nakanishi–Niino (MYN) boundary layer schemes. Spatial distributions of liquid water path
(LWP) is shown for (a) MODIS retrieved using the 3.7 µm channel at 14:40 UTC, (b) WRF inner domain at 13:00UTC, and (c) a histogram
of LWP for experiment combination with means and standard deviations displayed.
lations of Zheng et al. (2022a). As found in previous
studies (e.g., Chen et al., 2014), the strength of the ra-
diative effect is strongly tied to the occurrence of precip-
itation. We find that the cloud area expansion is greater
in environments that support deeper boundary layers
with heavier precipitation and the magnitude is gener-
ally smaller in case studies with less background pre-
cipitation.
–How does changing PBL and microphysics schemes af-
fect the aerosol indirect effect? A set of six WRF config-
urations using three different PBL and two different mi-
crophysics schemes revealed robust cloud responses to
changes in aerosol concentration. The range of variabil-
ity in total indirect effect across configurations was ap-
proximately 30 %. We conclude that the choice of valid
WRF schemes plays less of a role in the indirect effect
(at least from these configurations for one case study)
than the impact of precipitation on aerosol–cloud inter-
actions where the variations are larger across the 10 case
studies.
–How do liquid water path and cloud fraction adjust-
ments compare to the Twomey effect? Aerosol radiative
effects were decomposed into contributions from the
Twomey effect and liquid water path and cloud fraction
adjustments. The liquid water path and cloud fraction
adjustments scale as 74 % and 51 % increases relative to
the Twomey effect, respectively. These adjustments are
largest where an increase in aerosol can have a larger
impact on drizzle suppression and cloud water path en-
hancement. Our simulation estimates of the scaled ad-
justments are larger but within the range of uncertainty
estimated from satellite observations (Goren and Rosen-
feld, 2014).
As computation power increases, kilometer-scale mod-
els employed with PBL schemes (similar to ours) will in-
creasingly be used to quantify aerosol–cloud interactions at
global scales with increasing complexity (Terai et al., 2020).
Kilometer-scale models have been shown to successfully
simulate the properties and mesoscale structure of stratocu-
mulus. Chen et al. (2022) used WRF with 1 km grid spac-
ing to simulate the roll structure and transition of stratocu-
mulus and cloud streets by gradients in sea surface tem-
perature. Saffin et al. (2023) utilized the Met Office Uni-
fied Model to simulate cloud transitions observed during
the ATOMIC field campaign at similar scales. This transi-
tion shows the development of small shallow clouds into
larger flower-type clouds with detrainment, triggered by in-
creased mesoscale organization over several tens of kilome-
ters. Beucher et al. (2022) utilized the French convection-
permitting model AROME-OM at kilometer scales, success-
fully simulating four primary mesoscale patterns observed
during the EUREC4A campaign. Despite the success of sim-
ulating the realism of the mesoscale structure of marine stra-
tocumulus, further refinement may continue to be needed to
enhance connections between radiation, microphysics, and
planetary boundary layer schemes for adequately simulating
the complexity of aerosol–cloud interactions.
In all 10 case studies, LWP adjustments were positive.
This result remained consistent even when different PBL and
microphysics schemes were employed. Despite the diversity
in meteorological conditions, we were unable to simulate the
negative LWP responses sometimes reported in LES stud-
ies, albeit using different boundary conditions (Ackerman
et al., 2004; Seifert et al., 2015). Negative LWP responses
have been documented in satellite observations of ship tracks
(Christensen and Stephens, 2012), downstream from vol-
canic aerosol emissions (Malavelle et al., 2017; Toll et al.,
2017), and more broadly in non-precipitating clouds, partic-
ularly in the presence of excessive dry air conditions above
the marine boundary layer (Chen et al., 2014). Our findings
generally align with positive LWP responses also identified
Atmos. Chem. Phys., 24, 6455–6476, 2024 https://doi.org/10.5194/acp-24-6455-2024
M. W. Christensen et al.: Lagrangian WRF ACI 6471
in WRF LESs from the same region used in Wang et al.
(2020). Although, the absence of a negative LWP response
in our study may be attributed to a variety of processes. First,
uncertainties in the autoconversion rate (a tunable parame-
ter that affects the formation rate of raindrops) may lead to
a positive LWP response as droplet number concentrations
increase if this rate is underestimated (Mülmenstädt et al.,
2020; Christensen et al., 2023). Second, if the sedimentation
and entrainment rates are not strong enough in the model,
the entrainment of overlying air may not be effective at the
removal of cloud and rainwater (Bretherton et al., 2007).
While the MYNN3 PBL scheme parameterizes entrain-
ment mixing reasonably well in the gray zone (Ching et
al., 2014), poorly resolved sub-kilometer scales can result
in weaker increases in liquid water path with aerosols due
to fewer precipitating clouds and a weaker LWP increase
in non-raining clouds (Terai et al., 2020) within multi-scale
climate models. Generally, these kilometer-scale resolutions
are well-suited for resolving the cumulus outflow, but they
may still be too course to resolve updrafts well (Atlas et al.,
2022). Ghonima et al. (2017) evaluated the MYNN3 scheme
and other turbulence parameterization schemes using single-
column model experiments showing that entrainment flux
tendencies in stratocumulus tend to be underestimated com-
pared to LESs, resulting in cooler, moister stratocumulus-
topped boundary layers. This discrepancy may imply a defi-
ciency in representing strong turbulent mixing near the cloud
top in our simulations. However, our simulations show an
enhanced peak in the resolved TKE near the top of the stra-
tocumulus cloud layer (Fig. 9i). Also, when radiation is de-
activated, TKE is much smaller and the cloud layer becomes
significantly shallower (Fig. S10), highlighting the role of
radiative processes in driving stronger TKE throughout the
boundary layer. WRF version 4.2 introduced scale aware-
ness, dynamically adjusting parameterized turbulent kinetic
energy as resolution decreases and thus offering a more ex-
plicit representation of turbulent processes at finer scales
(Olson et al., 2019). Subgrid-scale clouds produced by the
MYNN3-EDMF (Sect. 3) are coupled to the longwave and
shortwave radiation schemes (namelist parameter icloud_bl
is set to 1). Despite these couplings, uncertainties may per-
sist due to relatively coarse vertical resolution (compared to
LES) and the ability to capture nonlocal production of TKE
associated with cloud-top radiative cooling. Alternative ap-
proaches, such as explicit entrainment or employing the mass
flux method for downdrafts, may offer improved parameteri-
zation of destabilized parcels in stratocumulus environments
(Olson et al., 2019). The impacts of model caveats like these
on cloud cell expansion due to increased aerosol concentra-
tion should be explored in subsequent research with higher-
resolution models including LESs where the cloud-top en-
trainment interface can be modeled at finer spatial scale res-
olutions. Nevertheless, our model setup shows evidence that
radiative cooling drives stronger turbulence in the marine
boundary layer, but it remains crucial to constrain such pa-
rameters based on observations (Suzuki and Stephens, 2009;
Golaz et al., 2013; Christensen et al., 2023; Varble et al.,
2023), where possible, to enhance model development and
our understanding of aerosol–cloud interactions and radia-
tive forcing.
Overall, these WRF simulations suggest that an increase
in aerosol concentration may result in significantly more ra-
diative cooling than would otherwise be predicted by the
Twomey effect at the relatively short spatiotemporal scales
(300 km over 12 h) considered here. We find generally that
aerosols expand the area of stratocumulus cells and increase
liquid water path and cloud fraction. These relationships be-
come enhanced in the presence of precipitation. Given the
link between these radiative impacts and the nature of the
mesoscale organization of clouds and their sensitivity to
aerosol, it may be prudent to resolve these radiative effects
in larger-scale climate models for improved assessments of
climate change.
Code and data availability. Aircraft measurements of cloud wa-
ter content (https://doi.org/10.5439/1465759, Matthews and Mei,
2017), condensation particle counter (https://doi.org/10.5439/
1440985; Fan and Pekour, 2018), and cloud condensation nu-
clei concentration (https://doi.org/10.2172/1562677, Uin and Mei,
2019) as well as ground-based surface rain rates (https://doi.
org/10.2172/1808573, Hardin et al., 2020), cloud geometri-
cal properties (https://doi.org/10.1175/1520-0426(2004)021<0777:
ATFAOC> 2.0.CO;2, O’Connor et al., 2004; https://doi.org/10.
1175/AMSMONOGRAPHSD-15-0037.1, Kollias et al., 2016;
https://doi.org/10.2172/1808567, Clothiaux et al., 2001), radia-
tive fluxes (https://doi.org/10.1175/2009BAMS2891.1, Xie et al.,
2010), meteorological profiles (https://doi.org/10.2172/1226794,
Troyan, 2013), and cloud optical properties (https://www.arm.gov/
capabilities/vaps/mfrsrcldod, Turner et al., 2021) are obtained from
ARM and available at https://www.arm.gov/data/. The CERES
SYN edition 4.1 product is available at https://doi.org/10.1175/
JTECH-D-14-00165.1 (Rutan et al., 2015). MODIS collection 6
products are available at https://doi.org/10.5067/MODIS/MYD06_
L2.061 (Platnick et al., 2017a). MERRA-2 data were obtained from
https://doi.org/10.1175/JCLI-D-16-0609.1 (Randles et al., 2017).
The HYSPLIT trajectory code is available at https://www.ready.
noaa.gov/HYSPLIT.php (NOAA, 2023). An archive of the WRF
namelist.input and trajectory files for each case study day is pro-
vided in the Supplement. The last access for all data and code avail-
ability websites is 10 October 2023.
Supplement. Movie S1 related to this article is also available in
the Supplement. The supplement related to this article is available
online at: https://doi.org/10.5194/acp-24-6455-2024-supplement.
Author contributions. MWC wrote the manuscript and devel-
oped the Lagrangian trajectory approach and analysis. PW guided
the implementation of the cloud segmentation algorithm. Research
https://doi.org/10.5194/acp-24-6455-2024 Atmos. Chem. Phys., 24, 6455–6476, 2024
6472 M. W. Christensen et al.: Lagrangian WRF ACI
and development ideas, as well as writing and editing, were con-
tributed by PW, ACV, HX, and JDF.
Competing interests. At least one of the (co-)authors is a mem-
ber of the editorial board of Atmospheric Chemistry and Physics.
The peer-review process was guided by an independent editor, and
the authors also have no other competing interests to declare.
Disclaimer. Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims made in the text, pub-
lished maps, institutional affiliations, or any other geographical rep-
resentation in this paper. While Copernicus Publications makes ev-
ery effort to include appropriate place names, the final responsibility
lies with the authors.
Acknowledgements. We would like to thank the reviewers,
Michael Diamond and an anonymous reviewer, as well as the ed-
itor, Tim Garrett, for comments that improved the manuscript. We
would also like to thank Yuwei Zhang for valuable feedback and
assistance in compiling and running the WRF model. Observations
from the ENA site and ACE-ENA campaign are supported by the
Atmospheric Radiation Measurement (ARM) Climate Research Fa-
cility.
Financial support. This research has been supported by the At-
mospheric System Research (ASR) program as part of the U.S. De-
partment of Energy, Office of Science, Office of Biological and En-
vironmental Research under Pacific Northwest National Laboratory
(PNNL) project 57131. PNNL is operated for the U.S. Department
of Energy by Battelle Memorial Institute under contract DE-A06-
76RLO 1830.
Review statement. This paper was edited by Timothy Garrett and
reviewed by Michael Diamond and one anonymous referee.
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