Introduction
Clouds are major contributors to global reflectivity (Trenberth et al.,
2009). Thus, changes in cloud albedo, coverage and lifetime have a large
impact on the Earth's radiation budget. Additionally, changes in
precipitation patterns may have a large impact on agriculture, the
environment and human well-being.
The influence of aerosol on clouds and its contribution to cloud radiative
forcing has become a theme of much debate in the scientific community
(Boucher et al., 2013). The processes involved in cloud development, aerosol
and cloud lifecycles, and cloud radiative responses are complex and not well
represented in global climate models (GCMs). Microphysical responses
associated with aerosol effects on cloud albedo tend to be described as a
sequence of more aerosol resulting in more cloud condensation nuclei (CCN),
and all else equal, smaller cloud drops and a more reflective cloud (Twomey,
1974, 1977). However, aerosol, dynamics and macroscopic cloud properties are
interconnected, and may result in mutually compensating effects and
adjustments that are not fully understood (Stevens and Feingold, 2009). For
example, smaller drops may suppress precipitation and increase cloudiness
(Albrecht, 1989) or, by enhancing entrainment and evaporation, decrease
cloud amount (Wang et al., 2003; Ackerman et al., 2004; Small et al., 2009).
Absorbing aerosol could also modify the atmospheric temperature profile and
stability, and reduce cloud amount via the semi-direct effect (e.g., Koren
et al., 2008; Huang et al., 2009).
Therefore, cloud microphysical variations do not necessarily manifest as
changes in cloud albedo and radiative forcing (Han et al., 1998). The
influence of meteorological drivers and thermodynamic conditions (e.g.,
atmospheric stability and humidity) on aerosol–cloud interaction assessments
is increasingly being brought into focus (e.g., Kaufman, et al., 2005;
Engström and Eckman, 2010; Koren et al., 2012; Chen et al., 2014, 2015). However, untangling the cloud microphysical effects from
dynamics and isolating their contributions to the radiative balance still
remains a big challenge. Direct, independent and collocated measurements of
each pertinent variable are required for understanding the impact of the
anthropogenic aerosol on the cloud radiative effect (McComiskey and
Feingold, 2012). Evidence for anthropogenic aerosol influence on cloud
droplet number concentration and effective radius is commonly noted in in situ
airborne measurements (e.g., Warner and Twomey, 1967; Eagan et al., 1974;
Ackerman et al., 2000; Twohy et al., 2005). Over the past 2 decades,
satellite remote sensing has been widely used to study aerosol–cloud
interactions over large areas (e.g., Nakajima et al., 2001; Bréon et
al., 2002; Quaas et al., 2008; Costantino and Bréon, 2010), usually
showing weaker responses than airborne-based studies. Space-borne
assessments of aerosol–cloud interactions face many challenges, such as
cloud contamination of the aerosol measurement, aerosol humidification
effects near clouds, and the difficulty in obtaining collocated aerosol and
cloud measurements. Different observational scales and platforms result in
large variations in the aerosol–cloud interaction assessments (McComiskey
and Feingold, 2012).
The Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM)
Program continuously operates permanent and mobile facilities that allow
monitoring and study of the atmosphere at different sites. The unrivaled
combination of in situ and ground-based remote sensing instruments provides
collocated and simultaneous measurements of different cloud, aerosol and
meteorological properties. ARM ground-based instrumentation has been
previously used to study aerosol–cloud interactions at several sites around
the world (e.g., Feingold et al., 2003; Kim et al., 2003, 2008; Garrett et al.,
2004; McComiskey et al., 2009). These studies focused on
the microphysical aspect of aerosol–cloud interaction, analyzing a handful,
to months, to up to 3 years of measurements. The ARM Program has been
operating at the Southern Great Plains (SGP), Oklahoma, for more than 2
decades (since 1992). The availability of such a large and comprehensive
data set provides an excellent opportunity to pursue a long-term study of the
effects of aerosol and meteorology on clouds.
In this work, 14 years of ARM ground-based measurements at the SGP were
analyzed to investigate the effects of aerosol and meteorological drivers
(such as capping inversion strength, surface–boundary layer coupling and
turbulence) on clouds. Instead of quantifying the usual metrics for
microphysical response to an aerosol perturbation, we focus on the analysis
of aerosol associations with cloud macroscopic variables and radiative
properties. These quantities are more closely related to the cloud radiative
effect and therefore represent a pragmatic pathway towards quantification.
The structure of the paper is as follows: Sect. 2 describes the
methodology. A climatology of low, warm, non-precipitating clouds at the SGP
is then presented (Sect. 3.1). Some simple approximations are used to
illustrate the theoretical basis behind the data analysis (Sect. 3.2). A
broad statistical analysis of more than a decade of coincident ground-based
measurements of cloud radiative properties and their relationship with
meteorology and aerosol concentration is shown (Sect. 3.3). Two
interesting cases are selected and studied more deeply to improve our
understanding of the problem (Sect. 3.4). Common features observed in the
case studies are further explored (Sect. 3.5). We summarize our results in
Sect. 4.
Methodology
Coincident ground-based remote sensing and in situ measurements of clouds,
aerosol and meteorological properties from Atmospheric Radiation Measurement
(ARM) deployments at the SGP, central facility, near
Lamont, Oklahoma (36.61∘ N, 97.48∘ W), were
used. The period of data analysis ranges from 1997 to 2010 and includes all
available data that present coincident measurements of the variables
considered, subject to the restrictions described below.
The Active Remotely Sensed Cloud Locations (ARSCL) Value-Added Product
(Clothiaux et al., 2000) was used to select low, warm, non-precipitating
clouds from the full 14 years of data. This product combines measurements
from a Ka-band cloud radar (35 GHz or 8.6 mm wavelength), a ceilometer at a
wavelength of 910 nm and a micropulse lidar (MPL) at 532 nm to provide,
among other variables, best estimates of cloud boundaries at 10 s
resolution. To avoid ice, the cloud base height hCB was limited between
300 and 2000 m and the cloud top hCT was limited to 3000 m. Cases that
presented more than one layer of cloud were excluded from the analysis.
Drizzle was mostly avoided by limiting the maximum column radar reflectivity
(Z) to less than -17 dBZ (Frisch et al., 1995).
Surface broadband shortwave radiative fluxes were used to obtain cloud
optical depth τc, (a parameter closely related to cloud albedo,
Ac), cloud fraction fc, and the instantaneous relative cloud radiative effect, using the
Radiative Flux Analysis (RFA) evaluation product
(Barnard and Long, 2004; Long and Ackerman, 2000; Long and Shi, 2006;
Long et al., 2006). Overcast conditions (fc > 0.9 on the
scale of hundreds of meters) and solar zenith angle smaller than 80∘
are required to retrieve τc. Parameters Ac and
fc were simultaneously retrieved using piecewise polynomial fits to
functions of shortwave upward and downward radiation fluxes (Liu et al.,
2011; Xie and Liu, 2013). rCRE, a non-dimensional measure of instantaneous
cloud radiative forcing, or cloud radiative effect (Betts and Viterbo, 2005)
is defined as
rCRE=1-FalldnFclrdn,
where Falldn and Fclrdn are the broadband all-sky and
clear-sky surface downwelling shortwave radiative fluxes (from 0.3 to 3.0 µm), respectively. The use of downwelling fluxes as opposed to net
fluxes minimizes the effects of surface albedo on rCRE (Vavrus, 2006).
The aerosol index Ai was calculated from the surface scattering
coefficient at 550 nm (σ550 nm) multiplied by the
Ångström exponent (Å) and used as a proxy for CCN concentration
(Nakajima et al., 2001)
Ai=σ550 nmÅ,
where Å and σ550 nm were measured by a 3-channel nephelometer (at
450, 550 and 700 nm) at 1 min resolution (Sheridan et al., 2001). An
impactor at the inlet connected to the nephelometer alternates the cut size
from 1 to 10 µm every 6 min. Only measurements obtained at the
1 µm size cut were selected. The data were interpolated to 1 min
resolution, when necessary. The decision to use surface measurements is not
only pragmatic (they are available) but also supported by the result that at SGP
the relationship between surface aerosol measurements and cloud level
aerosol measurements has been shown to be uncorrelated with the degree of
boundary layer vertical mixing (Delle Monache et al., 2004). Their work shows
that, at SGP, extensive and intensive aerosol properties measured at the
surface and within the atmospheric boundary layer are well-correlated.
Therefore, surface-based measurements of aerosol properties are
representative of the air within the atmospheric boundary layer. They also
show that this finding does not depend on the mixing state of the
atmosphere. Another proxy for CCN was also used and showed similar results
to those obtained using Ai (see Fig. S1 in the Supplement).
Liquid water path (LWP) retrievals from a 2-channel (23.8 and 31.4 GHz)
microwave radiometer (MWR) at 20 s resolution (Turner et al., 2007a) were
used. Two different LWP ranges were selected. In the first part of this work
(Sect. 3.3), our goal is to understand how several different properties
impact rCRE. For this part of the study, the LWP is limited between 30 and 250 g m-2,
allowing us to include cloud types ranging from low liquid water
clouds (Vogelmann et al., 2012; Turner et al., 2007b), some of which are
likely broken, to thicker, possibly drizzling clouds. The lower limit was
set taking into account the large uncertainty in the MWR retrieval for low
LWP. For the remaining analysis LWP was further restricted from 50 to 150 g m-2. The larger restriction to the upper range was applied to minimize
contributions from precipitating events. The increased lower limit avoids
very thin or broken clouds where the uncertainty in measuring LWP is high
(Turner et al., 2007b).
Turbulence, via its influence on supersaturation, plays an important role in
determining the number concentration of aerosol particles that are activated
to become cloud droplets (e.g., Twomey, 1959; Feingold et al., 2003). The
vertical component of the turbulent kinetic energy provides an estimate of
the strength of the turbulent fluxes acting at cloud base. Doppler radar
vertical velocities were used to calculate a proxy for turbulence given by
w′2=[w-w0]2, where w is the Doppler radar vertical velocity at
the cloud base, and w0 is the average vertical velocity at the cloud
base centered ±30 min around each measurement.
The decoupling index Di is an indicator of how well-mixed the atmosphere
is, and therefore how well ground-based measurements of conserved variables
and aerosol properties represent the same at cloud base:
Di=hCB-LCLhCB,
where the lifting condensation level (LCL) is calculated using ground-based
meteorological measurements of surface pressure, vapor mixing ratio and
temperature. As the Di retrieval depends on hCB it can only be
calculated in the presence of a cloud. This means that Di does not
necessarily reflect the mean mixing state, unless fc is high. In broken-cloud scenes, a cloud element may be well coupled, whereas the average for
the entire boundary layer may be poorly coupled. This should be kept in mind
in subsequent discussion.
The lower tropospheric stability (LTS), given by the difference between
potential temperatures at 700 hPa and at the surface, was also analyzed.
This variable is related to the strength of the capping inversion. Studies
show that LTS correlates well with the fc of low stratiform clouds
(Klein and Hartmann, 1993; Chen et al., 2014). The potential temperatures
were obtained from the merged sounding value-added product (Troyan, 2012),
version 1. This product combines radiosondes, MWRs, surface measurements and
the European Centre for Medium Range Weather Forecast (ECMWF) model output
to provide several relevant meteorological parameters at 1 min resolution,
at 266 pressure levels, up to 20 km.
List of the measurements, retrievals and ARM instruments at
the Southern Great Plains used in this study.
Instrument
Resolution in the original data set
Measurement/retrieval
Millimeter wavelength cloud radar (MMCR)
10 s
Column maximum reflectivity (Zmax)
Ceilometer/micropulse lidar (MPL)
10 s
Cloud base height (hCB)
MMCR/MPL
10 s
Cloud top height (hCT)
MMCR + ceilometer
10 s
Doppler vertical velocity at hCB (w)
Microwave radiometer (MWR)
20 s
Liquid water path (LWP)
Broadband radiometers
1 min
Relative cloud radiative effect (rCRE)
Cloud optical depth (τc)
Cloud fraction (fc)
Cloud albedo (Ac)
Nephelometer
1 min
Scattering at 550 nm (σ550 nm)
Ångström exponent (Å)
Meteorological station (MET)
1 min
Lifting condensation level (LCL)
Radiosondes + MET + MWR + models
1 min
Lower tropospheric stability (LTS)
A summary of the instruments, the temporal resolution in the original data
set, measurements and retrievals used in this work is shown in Table 1. All
of the relevant variables were averaged (or interpolated, in case of
Ai) to 1 min resolution for the analyses presented here.
Results
Database characterization
Statistical distributions of (a) liquid water path (LWP),
(b) cloud fraction (fc), (c) rCRE, (d) cloud albedo (Ac), (e) cloud optical
depth (τc), (f) cloud thickness, (g) cloud base height
(hCB), (h) cloud top height
(hCT), (i) aerosol index
(Ai), (j) w′2=[w-w0]2, (k) decoupling index (Di) and (l) lower
tropospheric stability (LTS).
A statistical analysis of the data set used in this study is performed.
Relative frequency histograms show the distribution of some of the key
properties that satisfy the selection criteria explained in the previous
section (Fig. 1). Red bars represent the distribution obtained when LWP is
limited between 30 and 250 g m-2; the blue bars are obtained by
limiting LWP between 50 and 150 g m-2. The mean (dot), median (cross)
and standard deviation (vertical lines) are shown above each distribution.
The data set represents about 66 000 valid observations for the first
criterion (red) and about 39 000 for the second criterion (blue). Due to the
long duration of this study period, these distributions can be regarded as
representative of low-level, warm, non-precipitating clouds at the SGP for
the selection criteria stated above.
Figure 1a shows that the data are dominated by clouds with lower LWP, with
the number of observations decreasing as LWP increases. The more restrictive
LWP limit (blue bars) shows a higher relative frequency than the less
restrictive limit (red bars), due to the smaller number of observations. The
non-cloud properties are barely affected by changing the LWP limits. For
Ai, Di, LTS and w′2 (Fig. 1i–l) the red and blue distributions
are essentially the same. On the other hand, the distributions of most of
the cloud properties are modified depending on the LWP limit considered.
Ac, cloud thickness, τc, rCRE and fc show a narrower
distribution when the LWP range is restricted (Fig. 1c–f), indicating that
these variables are closely related to LWP (Turner el al., 2007b).
Due to our selection criteria (low, warm, non-precipitating clouds), most of
the data represent stratiform clouds, characterized by high fc. Figure 1b shows that about 92 % of the observations were acquired in overcast
conditions (fc greater than 0.9). The number of broken-cloud
observations (fc < 0.9) is about 6800 and 3300 for the less and
more restrictive LWP ranges, respectively. The fraction of data points with
fc > 0.99 is 79 %, for LWP between 50 and 150 g m-2
and 75 % for LWP between 30 and 250 g m-2.
To a good approximation, rCRE is directly proportional to both Ac and
fc (Xie and Liu, 2013):
rCRE∼fcAc.
As most of the observations were obtained in overcast conditions (Fig. 1b),
rCRE in this study is mostly determined by Ac, and therefore the shapes
of the distributions of rCRE and Ac (Fig. 1c–d) are very similar
(slightly negatively skewed). Due to the polynomial criterion used to
calculate Ac, about 0.5 % of the observations resulted in Ac= 0. The median values obtained for rCRE, Ac and τc (Fig. 1c–e)
were about 0.68, 0.62 and 17, respectively, for the more restrictive LWP
range, and about 2 to 3 % smaller when the LWP restriction was relaxed.
As expected, the Ai distribution (Fig. 1i) is positively skewed
indicating the predominance of clean cases (low Ai) over polluted cases.
The distribution of the turbulence proxy (w′2) peaks at 0 and rapidly
decreases as w′2 increases. This is due to the small number of cumulus
observations in the database, which are usually associated with higher
turbulent fluxes. For about one-third of the observations, w′2 is greater
than 0.1.
Most of the selected clouds can be classified as thin clouds (Fig. 1f).
About 54 % of the observations correspond to clouds thinner than 500 m,
with cloud thickness peaking at about 300 m. Almost 70 % of the cases
correspond to clouds with hCB lower than 1 km, and for more than 82 %
of the cases, hCT is lower than 2 km.
By definition (Eq. 3) a value of Di= 0 represents a well-mixed
boundary layer, whereas values greater than 0 represent progressively more
decoupled boundary layers and therefore progressively weaker vertical
mixing. The median of the Di distribution (Fig. 1k) is about 0.37, and
about 31 % of the observations show significant decoupling with Di
larger than 0.5. The few cases of negative Di shown in this distribution
are most likely attributed to incorrect retrievals of the hCB. The LTS
distribution (Fig. 1l) is roughly symmetrical and varies between 9 and 20 K,
within 1 standard deviation. These LTS values are smaller than a
previously published long-term evaluation (2001–2010) that reported a
mean value of 20.81 K for stratiform clouds at SGP (Ghate et al., 2015),
based on 83 radiosonde soundings obtained between 2001 and 2010, for both,
nighttime and daytime. A low bias in the LTS from the merged sonde product
can be expected because of the inherent smoothing of the merged soundings
used in this work.
Notwithstanding the important role of fc in cloud radiative
effect (Eq. 4), the predominance of high fc in this data set
shifts our attention in the following analysis to the relationships amongst
rCRE, Ac, τc, LWP and Ai.
Theoretical basis
For high fc conditions, cloud liquid water is an important driver
of variability in cloud radiative effect because it is so tightly correlated
with τc and Ac (e.g., Han et al., 1998; Kim et al., 2003; Chen
et al., 2014). Thus, we are particularly interested in the relationship
between rCRE and LWP and, by contrast, the relationship between rCRE and
aerosol. To give us some insight into the expected behavior of this
function, a simple theoretical relation is derived.
The rCRE (Eq. 1), can be expressed as
rCRE=1-T,
where T is the total cloud transmissivity.
Considering conservative cloud scattering (i.e., no absorption), T is
obtained using a two-stream radiative transfer approximation (Bohren, 1987)
given by
T=2cosθ02+(1-g)τccosθ0,
where g represents the asymmetry parameter of the cloud droplets and θ0 is the solar zenith angle. This same two-stream approximation yields
Ac=(1-g)τccosθ02+(1-g)τccosθ0.
Replacing T (Eq. 6) in Eq. (5) and performing some algebraic
manipulations, the rCRE can be expressed as a function of τc:
rCRE=1+2cosθ0(1-g)τc-1.
Equation (8) shows that, for fixed illumination angle and cloud scattering
geometry, rCRE increases with τc.
Theoretical approximations of (a) rCRE as a function of
LWP, and (b) cloud radiative susceptibility to
Nd as a function of rCRE for
different droplet concentrations:
Nd=200 cm-3 (blue),
Nd = 500 cm-3 (red) and
Nd = 1000 cm-3 (green).
In the adiabatic regime, τc relates to cloud droplet concentration
(Nd) and LWP through (Boers and Mitchell, 1994)
τc=cT,pNd13LWP56,
where c(T,p) is a known function of temperature T and pressure p. According to Eq. (9),
the LWP contribution to τc is, in a relative sense, 2.5 times
larger than that of Nd. The same can be shown to be true for
sub-adiabatic clouds (Boers and Mitchell, 1994). Note that in presenting
these equations with respect to Nd we inherently assume a
proportionality between Nd and aerosol concentration Na (or proxy
such as Ai). If τc were to be cast in terms of Na, the
power-law dependence of τc on Na would be less than one-third.
Because of the uncertainty in the relationship between Nd and Na, we
use Nd to simplify the theoretical arguments.
τc (and therefore Ac) thus subsumes both the amount of
condensed water (a macroscale property) and drops (or aerosol)
concentration (a microphysical property). Thus, the extent to which the rCRE
dependence on LWP differs for different aerosol concentrations is an
expression of the importance of the aerosol in driving rCRE.
Using Eqs. (8) and (9), rCRE can be expressed as a function of LWP and
Nd. The radiative susceptibility of a cloud to changes in Nd is
given by
drCREdNd=rCRE(1-rCRE)3NdLWP.
Figure 2 shows examples of the theoretical relationships between rCRE and
LWP, and between cloud radiative susceptibility and rCRE for different
Nd: 200 cm-3 (blue), 500 cm-3 (red) and 1000 cm-3
(green). The mean solar zenith angle (θ0) observed at SGP
(θ0= 45∘) was used, and we assumed g= 0.86, T= 300 K and p= 1000 mb.
Figure 2a shows that for lower LWP values rCRE increases rapidly with
increasing LWP. The rate of increase decreases with a progressive increase in
LWP until the curve begins to saturate. In this example, the saturation
begins for rCRE between around 0.7 to 0.8. Complete saturation does not
occur at rCRE = 1 due to the diffuse component of the all-sky downwelling
shortwave radiation flux. For a very optically thick cloud the direct beam
is extinguished but the diffuse component is equal to the total radiation,
assuring that the total radiation transmission does not vanish. Therefore,
total radiation extinction does not occur as quickly as might be expected.
We also observe a slight increase in rCRE with increasing Nd. The rCRE
is more sensitive to changes in Nd at moderate LWP values (between 50
and 100 g m-2). Also, for a fixed LWP, the difference between the rCRE
obtained for Nd= 200 cm-3 and Nd= 500 cm-3 is
larger than the rCRE difference obtained using the larger Nd (Nd= 500 cm-3 and Nd= 1000 cm-3). The maximum radiative
susceptibility occurs at rCRE = 0.5, and is higher for smaller Nd (Fig. 2b). This is consistent with previous results that predict that
cleaner clouds are more susceptible to Ac changes than polluted clouds
(Platnick and Twomey, 1994). The same authors also report that Ac
sensitivity to Nd is a maximum when Ac is 0.5, which is consistent
with the larger separation between the curves in the moderate LWP range and
for rCRE = 0.5.
Broad statistical analysis of the observations
To understand how the cloud radiative effect responds to changes in
different parameters, a broad statistical analysis of the long-term data set
obtained at SGP was undertaken. As LWP largely dominates rCRE (Eqs. 8 and 9,
Fig. 2), the data were binned by rCRE and LWP. The bin sizes were 0.02 for
rCRE and 5 g m-2 for LWP. For each bin the average of several
different variables (Ai, Di, fc, LTS, τc and
w′2) was calculated. This procedure allows us to isolate the LWP
contribution to rCRE and to observe the associations of other properties
with rCRE in the third (colored) dimension. To reduce variability due to
poor sampling statistics, we require at least 15 points in each two-dimensional (2-D) bin. To
observe the general trend of rCRE with LWP and the other variables, for this
analysis, the broader LWP range was used. Solar zenith angle (θ0) was limited to 80 degrees to avoid errors in cloud properties
retrieved from the shortwave broadband radiative fluxes. The joint frequency
distribution of rCRE and LWP for this data set is shown in the Supplement
(Fig. S2).
Relative cloud radiative effect as a function of liquid
water path colored by (a) aerosol index, (b) cloud optical depth, (c) w′2, (d) decoupling index, (e) cloud
fraction and (f) lower tropospheric stability.
Figure 3 shows that rCRE presents a clear increasing tendency with LWP, in
agreement with the theoretical two-stream approximation shown in Fig. 2.
The distribution of LWP (Fig. 1a) indicates that the number of observations
decreases with increasing LWP. The larger number of observations at lower
LWP results in a larger vertical rCRE spread for the low LWP values,
compared to the high LWP. Several factors contribute to the variation of
rCRE observed for a fixed LWP. According to Eq. (8), rCRE increases
with θ0. Therefore, for a fixed LWP,
differences in rCRE can be associated with different times of the day, and
day of the year. Some rCRE differences could be related to the relatively
small number of broken-cloud events that (i) reduce rCRE due to the smaller
fc associated with this cloud type; and (ii) introduce the possibility
of 3-D radiative effects (e.g., Wen et al., 2007) or other
retrieval errors, and therefore deviations from the simple two-stream model
approximations that form the basis of the rCRE analysis. This further
contributes to the vertical spread of points at low LWP.
For the liquid clouds that meet our analysis criteria, two different cloud
types are identified: (i) broken-cumulus clouds characterized by lower mean
fc and higher w′2, and (ii) stratiform clouds associated with higher
fc and lower w′2. As most broken cumuli are concentrated in the
lowest LWP range (usually LWP < 100 g m-2) and have lower
fc, they generally present smaller rCREs than stratiform clouds (Eq. 4).
There are exceptions where lower fc in the lowest LWP range present
higher rCRE. This may be due to the deviation from the two-stream model
because of 3-D radiative effects, or some aerosol-related
effect on the cloud properties. Since broken cumuli are associated with
local convection, it is expected that this type of cloud exhibits a higher
local coupling with the surface, and therefore a smaller Di, as observed
in Fig. 3d. On the other hand, the stratiform clouds at SGP tend to be
associated with deeper boundary layers, therefore leading to higher
decoupling between the surface and the atmosphere. Stratiform clouds are
also controlled by large-scale subsidence and exhibit a higher LTS than
broken cumuli (Fig. 3f). The joint probability distribution function of
Di and fc shows that low fc cases are generally only observed
when Di is low (Fig. 4), with the exception of a few spurious data
points.
Joint probability distribution function of
Di and
fc obtained from 14 years of
observations at SGP.
Figure 3b shows the strong dependence of τc on LWP, in agreement
with Eq. (9). The dependence of rCRE on τc is also easily
identified. As τc is only retrieved for fc > 0.9,
low rCRE values do not appear in Fig. 3b. For a fixed LWP, rCRE exhibits a
weak trend with Ai (Fig. 3a). When LWP is smaller than about 100 g m-2, this trend seems to occur in both directions, indicating that both
high and low rCRE can be observed in more polluted conditions. One could
infer that the positive trend is due to cloud microphysical changes caused
by higher aerosol loading, while the negative trend could be due to the
semi-direct effect of aerosol on clouds. We found no evidence of significant
aerosol absorption for these cases. Meteorology also impacts the
system and influences the rCRE. For example, different cloud dynamics could
be linked to both changes in rCRE and in aerosol concentration. To
understand the role that meteorology plays on the rCRE, some dynamical
indices are now considered.
Higher turbulence facilitates more efficient droplet activation. Therefore,
considering that for a constant LWP variation in Ac is due to changes
in Nd, it is expected that more turbulence would result in more
droplets and higher cloud radiative effect (Feingold et al., 2003). However,
Fig. 3c shows that for a fixed LWP there is a weak dependence of rCRE on
w′2, with higher rCRE usually occurring for weaker turbulence. This result confirms that in most cases the rCRE is more dependent on macroscale cloud properties, such as LWP and fc, than on cloud
microphysics. For example, in most cases higher turbulence is associated
with broken cumuli that present lower fc, and therefore lower rCRE.
The correlation coefficients between the mean fc, LTS and Di (Fig. 3d–f) were calculated. The correlation between fc and Di (ρfc,Di= 0.72) is larger than the correlation between fc and LTS
(ρfc,LTS= 0.55). The correlation between LTS and Di is
also positive, with ρLTS,Di= 0.54. As previously mentioned,
LTS and fc are expected to correlate well for low stratiform clouds.
However, as the data in Fig. 3 also include some broken clouds, ρfc,LTS is not as high as in previous assessments that only analyzed
stratiform clouds (e.g., Klein and Hartmann, 1993; Wood and Bretherton, 2006).
We hypothesize that the stronger ρfc,Di compared to ρfc,LTS is a consequence of two factors: (i) Di is calculated for
each cloud element and is therefore closely connected to the local cloud
conditions, and (ii) LTS is based on the potential temperature at 700 hPa,
which may not always be relevant to the local cloud conditions.
Both meteorological indices used in the analysis, LTS and Di, as well as
fc (Fig. 3d–f), impart a less ambiguous signal in rCRE than does
Ai (Fig. 3a). Figure 3d–f show that, on average, the rCRE is larger for
less coupled atmospheric conditions, higher LTS and higher fc,
associated with solid stratiform clouds. Figure 3e shows considerable
fc changes that dominate rCRE variability at low LWP. These results
confirm that, in most cases, the cloud radiative effect is more closely
related to cloud macroscopic variables than to cloud microphysics. At low
LWP and higher rCRE, we find lower cloud fractions, which could indeed
indicate the predominance of a microphysical effect. Some higher turbulence
values are found here along with moderate aerosol index, but unfortunately
those data are somewhat ambiguous and may suffer from 3-D
radiative effects or other retrieval error.
The analysis performed in Fig. 3 provides useful information regarding how
rCRE relates to macroscopic cloud properties, aerosol and meteorological
indices. However, as observed in Eq. (8), rCRE also depends on
θ0. In fact, rCRE varies slowly with θ0 for lower θ0 values, but shows a strong dependence on
θ0 for higher angles. This intrinsic dependence of rCRE on
θ0 does not allow us to isolate the effects on rCRE due solely
to other properties from the effects caused by solar illumination angle. To
reduce this influence, only cases where cosθ0≥0.6 were considered for further analysis. This limit was selected such as
to maximize the amount of data analyzed and at the same time, minimize the
effects of θ0 on rCRE. Figure 5 shows rCRE as a function of
LWP and the same variables analyzed in Fig. 3, when cosθ0≥0.6. We note a priori that this filter preferentially removes early
morning and late afternoon data, with more data loss in the wintertime.
Whereas 18 % of the observations in Fig. 3 were obtained during
wintertime, due to the larger θ0 restriction, for Fig. 5 this
number is reduced to only 2 %.
Relative cloud radiative effect as a function of liquid
water path colored by (a) aerosol index, (b) cloud optical depth, (c) w′2, (d) decoupling
index, (e) cloud fraction and (f) lower tropospheric stability for cosθ0≥0.6.
Figure 5 shows that the general trends of rCRE with these variables do not
change significantly for aerosol and τc, when θ0 is
limited. However, for Di, fc, w′2, and LTS the rCRE trends at
fixed LWP are reduced compared to Fig. 3. One of the explanations for this
behavior is that, as these variables have a marked diurnal cycle, limiting
θ0 significantly reduces their variability. For example, higher
Di values are usually observed during early morning and late afternoon.
Therefore, when only low θ0 values are considered, these higher
Di observations will not appear as frequently in the data set. On the
other hand, as higher LWP values are associated with higher fc, higher
Di and lower w′2 values, high rCRE values will likely be observed
when these macroscopic properties and thermodynamic conditions are met. The
diurnal cycle of Di will be further discussed in Sect. 3.5. Besides
these factors, as the data set is dominated by fc ∼ 1, for
a fixed LWP and low θ0, differences in rCRE should be dominated
by microphysical influences. However, with the convolution of fc and
aerosol it is hard to definitively untangle these effects.
Cloud albedo was also analyzed as a function of LWP and the six other
variables analyzed in Figs. 3 and 5. However, as rCRE is directly
proportional to the product of Ac and fc (Eq. 4) and most of the
observations are concentrated at the same cloud fraction bin (Fig. 1b), the
results obtained for Ac are very similar to the ones obtained for rCRE
and are therefore not shown here. To isolate the effects of fc and
Ac on rCRE, the variation of Ac with five key variables (LWP,
Ai, w′2, Di and LTS) for completely overcast conditions
(fc= 1) was analyzed (Fig. 6). For this analysis only cases observed
when cosθ0≥0.6 were considered. The joint
distribution of these variables for this more restrictive data set is shown
in the Supplement (Fig. S3). Figure 6 shows that, for this more restrictive
range of θ0 and fc= 1, Ac does not show strong,
systematic variations with any of these variables. For low LWP, there is a
small number of points with high Ai and high Ac, which could be
related to microphysical processes. It also seems that lower LWP values,
associated with higher Ac are largely observed when stability is higher
(high LTS), except where aerosol concentrations are highest. To fully
address the impact of these variables on Ac would require further
detailed analysis of the high-resolution data, rather than a broad
statistical analysis, which is deferred to future work.
Cloud albedo as a function of liquid water path colored by
(a) aerosol index, (b) w′2, (c) decoupling index and (d) lower tropospheric stability, for completely
overcast conditions (fc=1) for cosθ0≥0.6.
Since high fc scenes dominate the data (Fig. 1b) and LWP plays a central
role in cloud radiative responses, we attempted to identify and compare the
signals due to LWP with those due to aerosol on rCRE. Daily correlations
between rCRE and these two key variables (Ai and LWP) were analyzed. For
this analysis, the LWP range was restricted to avoid drizzle and uncertain
retrievals, as explained in Sect. 3.2. Cases that had less than 25 points
per day were excluded from this analysis. In the original database, 1093 days fit the low, warm, non-precipitating clouds criteria. After selecting
cases that satisfied the minimum requisite number of points per day, low
θ0 (cosθ0≥0.6), and had
non-missing coincident retrievals of rCRE, LWP and Ai, only 111 days
remained. The histograms of the distribution of the correlations between
rCRE and Ai (ρrCRE,Ai) and rCRE and LWP (ρrCRE,LWP) are shown in Fig. 7.
Figure 7a corroborates Fig. 3a, showing that rCRE and Ai can either be
positively or negatively correlated. The proportion of negatively and
positively correlated cases is roughly 50/50 for ρrCRE,Ai. On the other hand, rCRE and LWP show a much higher positive
correlation than rCRE and Ai (Fig. 7b). The histograms show that ρrCRE,Ai is on average -0.01 ± 0.03 while ρrCRE,LWP was on average 0.56 ± 0.02. For about 98 %
of the cases rCRE and LWP are positively correlated. Therefore, we can infer
that LWP clearly dominates the cloud radiative effect, while the aerosol
signal on rCRE is ambiguous.
Case studies
The results shown in the previous sections provide broad insight into the
general macroscopic behavior observed for warm clouds at SGP and the
potential role of aerosol in driving this behavior. For a deeper
understanding of the processes related to those long-term trends, some cases
were further analyzed. 2 days that presented relatively high positive or
negative correlations between rCRE and Ai were selected and investigated
further. The selected case studies have a long time series, with at least 6 h of rCRE retrievals, in addition to continuous measurements of relevant
properties, providing a good sample of observations.
Case study 1: positive correlation between rCRE and
Ai
Daily distribution of the (a) correlation between the
relative cloud radiative effect (rCRE) and aerosol index
(Ai) and (b) the correlation between
rCRE and liquid water path (LWP) for cosθ0≥0.6.
Figure 8 shows the time series of several relevant measurements, such as
τc, LWP, rCRE, Ai and Di, for 9 January 2006. The
time series of the vertical profile of radar Z is also shown.
Since the rCRE can only be measured during sunlit periods (θ0<80∘), this analysis focuses on that period. Due to the
detection of multiple layers of clouds after 20:00 UTC, the plots are
restricted to the period from 12:00 to 20:00 UTC (06:00 to 14:00 LT). The correlation
between rCRE and Ai for this day is positive and about 0.75.
The radar reflectivity indicates that this case represents a solid
stratiform cloud that begins to develop with the boundary layer at
∼ 12:00 UTC (Fig. 8b). hCT peaks around 1 km and remains
constant after 16:00 UTC. Note that according to the radar reflectivity it is
highly unlikely that this day was affected by precipitation.
The strong positive correlation between rCRE, τc and LWP is also
noted (Fig. 8a). As previously pointed out these three variables are closely
related (Eqs. 8 and 9). On that day, radiometric measurements were only
available after ∼ 14:00 UTC, so rCRE and τc were only
retrieved after that time.
The increase in the incoming solar radiation absorbed by the atmosphere and
reaching the surface, warms the atmosphere. The LCL increases with time
until it stabilizes at 600 m around 18:00 UTC. The diurnal cycle of shortwave
radiation affects the coupling between the surface and the boundary layer
leading to more coupled conditions in the afternoon (Fig. 8d). The relation
between Di and solar radiation is further explored in Sect. 3.4.2 and
3.5.
After about 16:00 UTC both Ai and LWP, decrease (Fig. 8a). The mechanisms
that lead to the decreases are most likely associated with entrainment and
drying as the boundary layer deepens. (The relative humidity RH time series shows that surface RH decreases with time,
until about 18:00 UTC, when it stabilizes at about 0.7.) Dilution due to the increase in the boundary layer
depth likely explains the drop in surface aerosol concentration and decrease
in Ai.
Next, we aim to understand how the co-variability between LWP and Ai
could be linked to the response of rCRE to these two variables. Figure 9a–c
show the correlations between rCRE and Ai (ρrCRE,Ai), rCRE
and LWP (ρrCRE,LWP) and LWP and Ai (ρLWP,Ai) for
the selected day. Only points that have coincident measurements of all three
variables – rCRE, LWP and Ai – are used. The number of valid points is
329.
Time series of (a) rCRE, cloud optical depth and LWP, (b) vertical profile of radar reflectivity, (c) aerosol
index and (d) decoupling
index for 9 January 2006.
For this day, all correlations are positive, with ρrCRE,Ai= 0.75,
ρrCRE,LWP= 0.82 and ρLWP,Ai= 0.50. The
results and theory shown in Sect. 3.2 and 3.3, indicate that the changes
in LWP drive changes in rCRE. However, microphysical responses also need to
be considered. For a vertically homogeneous cloud, droplet effective radius (re)
can be calculated
as a function of LWP and the τc (Stephens, 1978).
re=1.5LWPρwτc,
where LWP is given in g m-2, re is given in µm and ρw is the density of liquid water in g cm-3.
For a cloud with constant LWP, a measure of the strength of aerosol–cloud
interaction (α) can be obtained from the relative change between
re and Ai:
α=-∂lnre∂lnAiLWP.
According to this definition, α is expected to be positive and vary
between 0 and 0.33, with a typical value of 0.23 (Feingold et al., 2001;
McComiskey and Feingold, 2012).
Correlation between (a) rCRE and
Ai, (b) rCRE and LWP, (c) LWP and
Ai and (d) effective radius as a
function of Ai grouped by LWP for 9 January 2006.
To assess the microphysical effect of aerosols on clouds, re was
calculated using Eq. (11) and plotted as a function of Ai. In an
attempt to isolate the aerosol effects on re, the data set was divided
into three LWP bins. For each bin, the linear regression between the
logarithm of re and logarithm of Ai was obtained. The slope of each
linear fit provides the parameter α (Fig. 9d).
For this case, re varied between 2 and 7 µm and α is
positive, as expected. The values obtained for α are within the
expected range, except for the higher LWP category (Fig. 9d). However, there
is a large variability in the magnitude of α. For the highest LWP
range, α is about twice the value obtained for the mid-range LWP.
The question remains whether the positive correlation between rCRE and
Ai is a result of the positive correlation between rCRE and LWP observed
on that and many days in this data set (Fig. 3) – i.e., a macrophysical
response – or whether it is due to the negative correlation between
re and Ai – i.e., a microphysical response. This single case study
suggests that both contributions are possible, but raises concerns about
being too reliant on the microphysical response as an indicator of
aerosol-related rCRE.
Case study 2: negative correlation between rCRE and
Ai
A case that shows a high negative correlation between rCRE and Ai, 26 April 2006, was also selected and analyzed in detail. Similar to the
previous case, Fig. 10 shows the time series of some of the relevant
measurements and retrievals for this day. As the cloud completely vanished
during late afternoon the analysis time frame was once again restricted to
between 12:00 and 20:00 UTC. The radar profile is shown from earlier in the day
(05:00 UTC and on), as some drizzle was detected during nighttime. The drizzle may
have scavenged the aerosol particles and could explain the low Ai values
shown in Fig. 10c, through ∼ 14:50 UTC. The red line
indicates daytime in Fig. 10b.
Time series of (a) rCRE, cloud optical depth and LWP, (b) radar reflectivity, (c) aerosol
index and (d) decoupling index for 26 April 2006.
Once again, a strong positive correlation between rCRE, τc and
LWP is observed.
The evolution of Di is similar to the previous case, indicating that for
both days the coupling between atmosphere and surface is driven by the
diurnal cycle of radiation, rather than by other variables. This day was
much warmer than the previous case and presented higher LCL values and lower
surface RH. The surface temperature differences between the two days varied
from 6 to 10 K during the period analyzed.
Correlation between (a) rCRE and
Ai, (b) rCRE and LWP, (c) LWP and
Ai and (d) effective radius as a
function of Ai grouped by LWP for 26 April 2006.
The temporal evolution of LWP and the vertical profile of reflectivity for
26 April 2006 (Fig. 10b–c) indicate that at about 14:00 UTC the stratiform
cloud begins to dissipate, transitioning to broken cumuli after
∼ 17:00 UTC. The decrease in both LWP and fc after 14:00 UTC
coincides with an increase in Ai. One hypothesis to explain this
behavior is that boundary layer deepening and entrainment drying reduce
cloud amount as the day progresses. Di decreases because when clouds do
form (a prerequisite for calculating Di) the local coupling is
relatively strong. The increase in Ai from a low post-drizzle clean
atmosphere could be a result of a combination of surface sources, transport
and entrainment of free tropospheric air. It is also possible that cloud
breakup may be caused by the aerosol semi-direct effect; however, Ai was
lower on this day and the analysis of the Ångström exponent and
single scattering albedo (SSA) indicate that there are no significant
differences in aerosol intensive properties (and thus, perhaps in aerosol
type) between this and the previous case. The mean Ångström exponent
at 1 µm cut size for case 2 was 2.274 ± 0.010, while in
the previous case it was 2.107 ± 0.008. The mean SSA was
0.9721 ± 0.0012 and 0.9826 ± 0.0004, for case
2 and case 1, respectively. The difference in the uncertainty indicates that
for case 2, both the Ångström exponent and SSA fluctuate more.
Finally, while one might want to invoke a role for the increasing aerosol
evaporating smaller droplets more efficiently, which in turn would decrease
fc (Small et al., 2009), these aerosol loadings are relatively low
and,
as already discussed in Sect. 3.3, many other dynamical features influence
fc and cloud development, especially during the daytime.
The correlations between rCRE, LWP and Ai for case 2 are shown in
Fig. 11a–c. The microphysical effect of aerosol on drop size is shown in
Fig. 11d. The number of valid points for this study case is 204.
The correlation between rCRE and Ai is negative and equal to -0.65 for
this case. The correlation between rCRE and LWP is 0.64, smaller than in the
previous case study, but still positive, as expected. Figure 11c shows that
for case 2, LWP and Ai are negatively correlated with ρLWP,Ai
= -0.44.
The re retrievals indicate that the sizes of most of the droplets
analyzed in this case fall in the same range as the previous case study
(between 3 and 10 µm). Here, however, α is negative (Fig. 11d),
for which there is no physical explanation given the stratification by LWP
and our expectation that drop size decreases with an increasing number of
CCN for the same amount of condensed water (Twomey, 1977). This unexpected
behavior could derive from a combination of factors: uncertainty in
measurements, uncertainty in linear fits and possibly the rather broad LWP
binning, among others. Given the unphysical re response to increasing
aerosol, the positive correlation between rCRE and LWP, and the overwhelming
contribution of macroscopic and dynamical variables to the cloud system
compared to the aerosol signal discussed in Sect. 3.3, the results
indicate that the observed negative correlation between rCRE and Ai is
most likely due to the fact that LWP and aerosol are negatively correlated,
presumably due to independent factors.
Most techniques employed to retrieve τc using ground-based
instruments rely on overcast conditions (e.g., Barnard et al., 2008; Min and
Harrison, 1996). The technique of Xie and Liu (2013) can be used to retrieve
τc for lower cloud coverage. In Figs. 9d and 11d, re was
calculated using retrievals of τc from a broadband radiometer
(RFA) following Barnard and Long (2004). Additionally, two other methods
were used to retrieve τc and re for the case studies
highlighted above: the Multifilter Rotating Shadowband Radiometer (MFRSR;
Turner and Min, 2004) and broadband radiometer retrievals by Xie and Liu (2013). Effective radii re, determined from the measured LWP and each of
the τc retrievals, were used to obtain the aerosol–cloud
interaction (α) slope (Table 2). Retrievals acquired when θ0>70∘ were excluded from this analysis as
the measurements are less reliable at higher solar zenith angles and the
retrievals diverged greatly at high θ0 in some cases. The
different methodologies used to retrieve τc result in different
α, and, for some cases, even the sign of the slopes disagree. The
difference observed for αRFA estimates shown in Table 2,
compared to Figs. 9 and 11, is due to the restriction of collocation of
data points among the three data sets and the θ0<70∘ threshold.
Slopes α and their uncertainty obtained using different τc retrievals: from the Radiative Flux Analysis (RFA), using the Xie and Liu technique (2013, XL) and using MFRSR measurements.
Coincident retrievals of τc from each retrieval acquired when
θ0 < 70∘ for each day were used to calculate
α.
LWP (g m-2)
αRFA
αXL
αMFRSR
Case study 1
50–75
0.27 ± 0.09
0.32 ± 0.09
0.23 ± 0.07
75–100
0.26 ± 0.07
-0.03 ± 0.08
0.25 ± 0.06
100–150
0.73 ± 0.26
0.58 ± 0.30
0.70 ± 0.24
Case study 2
50–75
-0.01 ± 0.09
0.31 ± 0.07
0.10 ± 0.06
75–100
-0.09 ± 0.04
0.25 ± 0.04
0.07 ± 0.03
100–150
-0.23 ± 0.04
0.11 ± 0.02
-0.03 ± 0.02
Mean diurnal cycle of the decoupling index
(Di) obtained using 14 years of
retrievals at the SGP. Error bars indicate the standard deviation of the
mean for each time bin.
As emphasized above, this comparison raises concerns about reliance on
α alone to quantify aerosol-related rCRE in terms of microphysical
metrics. The requirement of binning by LWP leaves low statistics for
calculating slopes in each bin and uncertainties in the slopes are high.
Given the low statistics, differences in the retrievals can result in the
large differences in α seen here, including changes in sign. These
microphysical measures are useful for detecting aerosol effects on cloud
properties, but are best used in conjunction with other measurements to
fully understand the relevant physical processes. Using these measures for
quantification of the aerosol indirect effect (the aerosol induced cloud
radiative effect), especially in case studies where statistics are low, can
be misleading. Studies that provide larger statistics may produce more
meaningful quantifications (e.g., McComiskey et al., 2009), but will still
contain biases inherent in any retrievals used to provide input properties
to the calculation.
Further generalizations
The diurnal cycles of the Di, shown in two case studies of Sect. 3.4,
were very similar, with higher Di in the morning and lower Di around
20:00 UTC (Figs. 8d and 10d). To verify if this trend is generally observed,
the complete time series obtained during this 14 year study was used. The
data set was divided into 0.5 h bins and the mean diurnal cycle of
Di during daytime was analyzed (Fig. 12).
Figure 12 shows that the temporal evolution of Di is strongly linked to
the diurnal cycle of solar radiation. On average, the atmosphere is highly
decoupled in the morning. As the sun rises, the surface gets warmer, and
solar energy is transferred from the surface to the atmosphere, favoring
more coupled conditions (lower Di). The higher coupling between the
surface and the atmosphere increases turbulence. As the incoming solar
radiation during the afternoon decreases, the atmosphere gradually cools.
After ∼ 20:00 UTC, the boundary layer collapses leading to less
coupled conditions in the late afternoon.
The results shown in the previous section also indicate that, for these two
case studies, the correlation between rCRE and Ai has the same sign as
the correlation between LWP and Ai (Figs. 9 and 11). For the first case
study, ρrCRE,Ai and ρLWP,Ai are positive, while for the
second case study both correlations are negative. This suggests that the
sign of ρrCRE,Ai is mainly determined by ρLWP,Ai. We
now test the validity of this hypothesis and if this statement can be
expanded for the whole data set. For each day the correlation between rCRE
and Ai (ρrCRE,Ai) and between LWP and Ai (ρLWP,Ai) were calculated. Figure 13 shows the results obtained for
these correlations, where each point represents 1 day. This was done for
the 111 days that had coincident measurements of the three variables
(Ai, LWP, and rCRE) at low θ0. An orthogonal linear fit of
the observations was performed.
Correlation between rCRE and
Ai(ρrCRE,Ai) vs. the correlation between LWP and
Ai(ρLWP,Ai) for cosθ0≥0.6.
Figure 13 shows that this statement can be generalized. Usually, if
Ai and LWP are positively (negatively) correlated, the correlation
between rCRE and Ai is positive (negative). This relationship was
further analyzed as a function of several variables (Ai, LWP, Di,
τc, wind direction, wind speed, surface RH, w′2), none of
which significantly influenced the results. Considering all the days
analyzed, the correlation between ρrCRE,Ai and ρLWP,Ai
is 0.71. Even when θ0 is not restricted, and therefore
variations in θ0 might obscure this relationship, the
correlation between ρrCRE,Ai and ρLWP,Ai is 0.54. This
result suggests that the aerosol signal observed in rCRE based on daily
correlations may often be a misinterpretation of the positive relationship
between rCRE and LWP. Once again, for the data set analyzed, which consists
overwhelmingly of high fc events, the cloud radiative effect appears to
be predominantly driven by macroscopic variables rather than by
microphysical responses.
Given the uncertainty in calculations of α (Table 2) the current work
sounds a cautionary note regarding placing too much emphasis on
microphysical metrics. This does not exclude the possibility of an aerosol
influence on the cloud radiative effect but suggests that careful analysis
should be done to quantify macrophysical relationships, such as those shown
here. Moreover, consideration of the co-variability in aerosol and cloud
macroscopic quantities (LWP in particular) has a strong influence on the
detectability of aerosol-induced rCRE and therefore deserves attention
(George and Wood, 2010; Feingold et al., 2016).
Summary and conclusions
A comprehensive study was performed to understand the relative effects of
aerosols, macroscopic cloud properties and meteorological drivers on the
radiative effect of low-level clouds. In all, 14 years of coincident
ground-based clouds, aerosol and meteorological measurements over the SGP
were analyzed. The impact of different physical properties on the
instantaneous cloud radiative effect was studied. The data set was divided
into rCRE and LWP bins and the mean values of properties such as fc,
τc, Di, LTS, Ai and turbulence were analyzed. Most of the
data are characterized by high fc so that rCRE is predominantly a
function of Ac (Eq. 4), which is in turn a strong function of LWP, and
to a lesser extent drop concentration (Eqs. 7 and 9). Whereas a strong
dependence of rCRE on LWP is clearly identified, the average over the whole
data set shows an ambiguous influence of aerosol on rCRE. For low LWP,
polluted conditions are associated with both high and low rCRE.
Since LWP is such a key driver of rCRE, the impact of the aerosol and of LWP
on the cloud radiative effect were compared by assessing the daily
correlations between rCRE and Ai and rCRE and LWP. While the daily
distribution of ρrCRE,LWP shows a clear positive signal, the daily
distribution of ρrCRE,Ai is centered around 0, confirming the
previous statement that high aerosol concentrations can be associated with
both higher and lower rCRE.
Case studies that showed both positive and negative correlations between
rCRE and Ai were further investigated. For these 2 selected days, rCRE
was positively (negatively) correlated with Ai when Ai and LWP were
positively (negatively) correlated. This behavior can be generalized to the
other days analyzed. The case studies also show that microphysical metrics
to estimate aerosol–cloud interaction (Eq. 10) are very uncertain and
reliance on these estimates to quantify aerosol-related rCRE can be
misleading.
The diurnal cycle of Di over the SGP is strongly driven by the diurnal
cycle of solar radiation. Both, LTS and Di are highly correlated with
fc however ρfc,Di is larger than ρfc,LTS. This is
because LTS and fc are tightly related for stratiform cloud, but less so
for broken clouds. On the other hand, Di represents both cloud types
well because it is calculated for individual cloud elements. Stratiform
clouds are usually observed early in the morning, when the boundary layer is
less coupled due to the smaller sensible heat flux. As the surface warms up,
turbulence and therefore surface-atmosphere coupling increases, and broken
cumuli that have smaller fc are formed.
The results presented here indicate that to first order, macroscopic
variables such as cloud condensate and fc rather than cloud microphysics
are the properties that most determine the cloud radiative effect. Clearly
the aerosol can play a role by modifying drop size and influencing how LWP
manifests in τc and Ac. However, while LWP and fc present
a clear signature on rCRE, the aerosol signal is barely distinguishable. The
aerosol signal is also difficult to quantify because of the uncertainty in
calculation of the metrics derived from different methods (Table 2, Figs. 9d
and 11d) and platforms (McComiskey and Feingold, 2012). Future studies that
focus on understanding the role of dynamics and other meteorological drivers
that potentially alter the macroscopic cloud properties will be reported on
in the near future.