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代表性论文2-VOCALS MAP 2015

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Meteorol Atmos Phys (2015) 127:625–634
DOI 10.1007/s00703-015-0392-2
ORIGINAL PAPER
Understanding the synoptic variability of stratocumulus cloud
liquid water path over the Southeastern Pacific
Lanxi Min1,3 • Wei Gong1 • Guangyi Liu2 • Qilong Min1,3
Received: 2 April 2015 / Accepted: 2 July 2015 / Published online: 17 July 2015
Springer-Verlag Wien 2015
Abstract The spatial and temporal variance of stratocumulus cloud liquid water path (CLWP) over Southeastern
Pacific has been investigated by combining satellite moderate resolution imaging spectroradiometer cloud products,
CLWP from advanced microwave scanning radiometerEOS observations and NCEP final analysis atmospheric
products with empirical orthogonal function (EOF) Analysis. CLWP variance is the most complicated factor among
three fundamental cloud quantities (the microphysical
cloud droplet concentration, and the macrophysical CLWP
and cloud fractional cover). The results show that EOF/
PC1 of CLWP represents the variation of domain-averaged
CLWP, which is mainly controlled by surface meteorological factors. Sea surface temperature and cold advection
drive the synoptic and seasonal scales of variance of
CLWP, while surface wind speed plays a fundamental role
in stratocumulus cloud formation and daily variance of
CLWP. EOF/PC2 of CLWP describes the spatial variance
of CLWP. This daily spatial variance of CLWP is controlled by the factors of lower tropospheric stability and
cloud top relative humidity, which determine cloud thickness and, consequently, CLWP through thermodynamic
and entrainment processes. Further study indicates a
Responsible Editor: S. Hong.
& Qilong Min
qmin@albany.edu
1
State Key Laboratory of Information Engineering in
Surveying, Mapping and Remote Sensing, Wuhan University,
Wuhan, China
2
Smart Grid Operation Research Center, China Electric Power
Research Institute, Beijing, China
3
Atmospheric Science Research Center, State University of
New York, Albany, NY 12203, USA
twofold interaction of the surface wind speed on stratocumulus CLWP: (1) dynamically through modulation of
surface latent heat and sensible heat fluxes and (2) microphysically through enhanced marine aerosol production.
1 Introduction
Stratocumulus clouds play an important role in the global
radiation budget system. The stratocumulus decks reflect
solar radiation back to space, causing a cooling effect.
Unique climatological conditions over the southeast pacific
(SEP) support the formation of the world’s most persistent
stratocumulus clouds (Bretherton et al. 2004). During
Austral spring (August to October), the Andes forms a
sharp barrier to the warm easterly wind from South
American, helping to maintain strong divergence and
temperature inversion in the southeast pacific region (Xu
et al. 2004). Stronger winds are generated in this process,
bringing cold water to the surface to sustain a lower sea
surface temperature. The combination of cold SST and
subsidence aloft is ideal for the persistence of large-scale
stratocumulus clouds over the southeast pacific of South
America (Wood et al. 2011). Furthermore, an anomalous
shallow meridional circulation developed through the
radiative cooling effect of these clouds enhance the subsidence over the SEP region (Wang et al. 2004a, b, 2005),
helping to maintain the stratocumulus clouds there.
However, the covarying impacts of meteorology and
aerosols on those clouds and the complex interaction
between clouds and the turbulent boundary layer (Lin et al.
2009; Stevens and Feingold 2009) make it hard to understand the SEP stratocumulus clouds, despite their importance (Klein 1997). These clouds are sensitive to their
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environment, especially on timescales relevant to its
physical evolution (Bony and Dufresne 2005). To understand the radiative cooling effect of stratocumulus clouds,
it is important to study both cloud variability and associated microphysical and macrophysical mechanisms. A
better understanding of stratocumulus cloud variability and
associated microphysical and macrophysical mechanism
will lead to improved representation of such clouds and
may help provide constraints for model simulations (Wang
et al. 2004a). In general, there are three fundamental cloud
quantities that contribute to the cloud radiation effect:
microphysical cloud droplet concentration (Nd), macrophysical cloud liquid water path (CLWP) and cloud fraction cover (Fc). These three fundamental cloud quantities
are involved in cloud formation processes that are controlled by meteorological conditions and modulated
microphysically by aerosols (George and Wood 2010).
They also vary substantially at different spatial and temporal scales. Understanding meteorological controls on
these fundamental cloud quantities may offer clues
regarding the key physical processes.
Due to the intrinsic coupling of dynamical and microphysical processes related to cloud formation, cloud cover,
CLWP, and cloud droplet concentration are physically
correlated. When clouds have greater spatial coverage,
CLWP is usually greater (Considine et al. 1997). Also,
cloud droplet growth processes link CLWP to cloud droplet
concentration (Lohmann and Feichter 2005). Understanding the nature of the correlation of cloud microphysics with
macrophysical variables is one simple constraint beyond
the mean state. Cloud cover is the most widely studied
cloud macrophysical variable from observations and from
numerical modeling in the literature. On long time scales,
the cloud fraction is strongly correlated with meteorological predictors, such as lower tropospheric stability (LTS),
sea surface temperature (SST), and atmospheric circulation
(Klein and Hartmann 1993; Norris and Leovy 1994; Stevens and Brenguier 2009; Weare 1994). The seasonal cycle
of cloud cover controlled largely by the seasonal cooling of
the free-tropospheric air temperature instead of the sea
surface temperature. Seasonal variances depend largely on
MBL depth, inversion strength, and cloud top entrainment
(Lin et al. 2009; Wang et al. 2011). On short (subseasonal
and synoptic) timescales, variations in low-cloud amount
are significantly (albeit weakly) correlated with LTS, cold
advection, and relative humidity of the cloud layer (George
and Wood 2010; Klein 1997; Klein et al. 1995; Xu et al.
2005). On the diurnal scale, the decoupling effect plays an
important role in the diurnal cycle of low clouds (Klein
1997) and is driven primarily by cloud solar absorption
(Wood et al. 2002). However, the cloud cover is also a
loosely defined variable, depending on not only the minimal detection limit of instruments and the minimal
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threshold of models but also the domain size. As the key
cloud variable in cloud formation processes, CLWP is less
studied than the cloud cover, partly because its temporal
variance is much quicker and its spatial variance is of much
more complex. Understanding the controls of the high
spatial and temporal variability of cloud liquid path will be
important for correctly representing stratocumulus clouds
in the global climate system and identifying feedback
processes associated with aerosol–cloud interaction. In this
study, therefore, we focus on the variability of CLWP and
investigate possible meteorological and microphysical
controls at synoptic timescale in the Southeastern Pacific
(SEP).
2 Data and methods
2.1 Data
NASA Aqua satellite has visible–infrared moderate resolution imaging spectroradiometer (MODIS) and advanced
microwave scanning radiometer-EOS (AMSR-E) sensors,
providing cloud and aerosol information. We use spatially
griddedLevel-3 Low-Resolution Daily (MYD08_D3)
MODIS Images and AMSR-E Level 3 data over the
oceanic part of a spatial domain of 0–30S and 70–
100W in the SEP at 1 9 1 resolution from September
1 to November 30, 2008. The selection of spatial domain
and time span corresponds to the VAMOS Ocean-CloudAtmosphere-Land Study (VOCAL) field campaign. Not
only are clouds prevalent during Austral spring in the
region, but also satellite remote sensing can be compared
with VOCALS in situ measurements (Min et al. 2012).
Specifically, the daily variance of cloud properties, i.e.,
cloud fraction (CF), CLWP (CLWP), and cloud droplet
concentration (CDN), derived from retrieved cloud
effective radius and cloud optical depth (Szczodrak et al.
2001), are derived from MODIS; and seasonal sea surface
temperature, column cloud water, column water vapor
(CWV), and sea surface wind are retrieved from AMSRE.
To explore the influence of meteorology factors, NCEP
final analysis (FNL) operational global analysis data with
1 9 1 resolution is used in this study (Kalnay et al.
1996). Specifically, temperature, relative humidity, pressure, wind, and vertical velocity at multiple levels at
18:00UTC (close to Aqua overpass time) are taken directly
from NCEP FNL. The LTS is calculated as the potential
temperature difference between 850 and 1000 hPa (Klein
and Hartmann 1993). As the boundary layer is lower in the
SEP, we use the potential temperature at 850 hPa instead of
at 700 hPa, as originally defined in (Klein et al. 1995). We
also derive the temperature advection (TMPADV) using
Understanding the synoptic variability…
627
~sfc rSST from the measurements of SST
the formula V
and sea surface wind speed.
2.2 EOF and DINEOF
The empirical orthogonal function (EOF) is a powerful tool
to analyze the spatial and temporal variance of large-scale
atmospheric fields (Wilks 2006). The EOF method, in
which the original data are transformed into a set of
orthogonal functions or EOFs, provides us an effective way
to study cloud variability: (1) each EOF represents a spatial
pattern, and (2) the corresponding principal component
(PC) is the temporal variation that represents the magnitude
of the EOF over time. We are able to project various
meteorological and other fields on each of the EOFs and
PCs.
Due to a limited instrument swath, some data may be
missing within the analysis domain. To apply the EOF
method to a field with possible missing data, we use the
Data INterpolating Empirical Orthogonal Function
(DINEOF) (Beckers and Rixen 2003), in which the EOF
modes and their relative temporal amplitudes are utilized to
reconstruct the missing data. One advantage of this method
compared to other interpolation methods is that the EOF
analysis result will not be changed after using this DINEOF
method. Also, DINEOF is self-consistent, parameter-free
and does not need any a priori knowledge of the errors. The
DINEOF method was widely used to reconstruct the missing data of the reduced MODIS data set (Mauri et al. 2007).
3 Results
Many dynamical, thermodynamical, and microphysical
processes are involved in the cloud formation, resulting in
the complicated spatial and temporal variability of cloud
quantities. EOF/PC analysis illustrates that liquid water
path exhibits the most variable quantity among three key
cloud quantities. As shown in Fig. 1, the first EOF/PC
explains only 13 % of the variance of the CLWP, and the
first 15 principal components explain 50 % of the variance
CLWP. In contrast to the variance of CLWP, the first EOF/
PC of cloud droplet concentration and cloud fraction can
explain 86 and 22 % variance, respectively. It indicates
that CLWP variance is complicated and influenced by
many factors at different scales. Although it is impossible
to capture the variance of CLWP with a few EOF/PCs, we
only focus on the first two leading EOF/PCs to illustrate
possible controlling factors for simplicity.
Various large-scale meteorological factors, such as sea
surface temperature, surface wind speed, temperature
advection, LTS, relative humidity, influence cloud
Fig. 1 91 eigenvalues derived from 91 days of CLWP over spatial
domain of 0–30S and 70–100W in the SEP from MODIS level 3
data. Value of each eigenvalue represents the weights of each CLWP
EOF/PC component
formation and CLWP variance (Ackerman et al. 2004;
George and Wood 2010; Myers and Norris 2013; Xu et al.
2005). As listed in Table 1, surface level meteorology
factors play a key role in the EOF/PC1 of CLWP variance,
while the cloud layer meteorological factors control the
EOF/PC2 of CLWP variance.
3.1 EOF/PC1 of CLWP
The first component of EOF/PC explains the maximal
amount of variance of CLWP. As shown in Fig. 2, the
spatial pattern of EOF1 closely resembles the three-month
averaged CLWP from AMSR-E, suggesting that the large
variance is located where there is a larger liquid water path.
The near coast region with high anthropogenic aerosol
loading and the southwest corner with a lot of drizzle make
a relatively small contribution to the CLWP EOF1 variance. The temporal variation of its corresponding principal
component (PC1), representing the magnitude of EOF1
over time, is strongly correlated with the variation of the
domain-averaged CLWP (Fig. 3). EOF/PC1 captures the
variance of averaged CLWP in the domain, although it
only explains 13 % of the overall variance of CLWP.
Sea Surface Temperature (SST) is a central determinant
of air–sea interactions and cloud variability. Evaporation
from the oceans is the primary source of water vapor to fuel
cloud growth in the atmosphere. Following the Clausius–
Clapeyron relationship, the warmer the water, the greater is
the evaporation. On the other hand, a decrease in SST
increases the stability of the lower troposphere, leading to
an increase in low-cloud amount. In turn, the increased
cloud amount cools the ocean further to form a positive
feedback (Xu et al. 2005). Certainly, such an effect is scale
dependent.
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Table 1 Correlation coefficients for liquid water path PC1 and PC2 with PC1 of selected cloud properties and macrophysical variables
R
CF
PC1
CDN
PC1
WIND10M
PC1
SST
PC1
CLWP_PC1
0.396
0.365
CLWP_PC2
0.327
-0.541
CF_PC1
1
0.141
-0.263
CDN_PC2
0.141
1
-0.305
0
TMPADV
PC1
0.604
-0.709
0.658
0
-0.152
0.243
0.447
-0.148
0
LTS
PC1
RH850
PC1
RH1000
PC1
VVEL850
PC1
-0.239
-0.148
-0.311
0.581
0.67
0.626
0.23
VVEL1000
PC1
0.616
0.379
0.352
-0.319
-0.221
-0.429
-0.239
-0.261
0.675
0.628
0.226
0.155
0.187
The italicized numbers indicate the confidence level over 99 %
Fig. 2 The comparison
between a EOF1 pattern of
CLWP and b 3-month mean of
CLWP averaged from 91 days
MODIS level 3 daily mean data
during austral spring
Fig. 3 Comparison of 91 PC1 variables of CLWP and daily spatially
averaged CLWP over the same subtropical southeast Pacific region
(0–30S, 70–100W)
Figure 4 shows the temporal variances of SST EOF/PC1
and CLWPEOF/PC1. The variance of SST EOF/PC1is
much smoother than that of CLWPEOF/PC1. However, the
long-term trend of CLWPEOF/PC1 is negatively correlated
with the variance of SST EOF/PC1 (reversed plotted in
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Fig. 4) with a correlation coefficient of 0.71. The strong
negative correlation between the SST and CLWP leading
modes over the 30 9 30 domain represents the SST
control on large scale of both spatial and temporal domain.
Although it is focused on the CLWP variability, the finding
is consistent with previous research indicating that seasonal
to inter-annual variations in low-cloud amount are closely
related to variation in SST (Klein 1997; Klein and Hartmann 1993; Klein et al. 1995; Norris and Leovy 1994;
Weare 1994; Xu et al. 2005).
Temperature advection and stability of the lower
atmosphere are two other large-scale factors that are significantly correlated with stratocumulus cloud cover (Klein
1997; Xu et al. 2005). However, temperature advection but
not LTS (important in the CLWPEOF/PC2, more discussion later) plays an important role in the variance of
CLWPEOF/PC1, as shown in Fig. 5 and listed in Table 1.
The correlation coefficient between CLWPEOF/PC1 and
TMPADVEOF/PC1 is 0.65. As discussed in Xu et al.
(2005), the cold advection increases surface latent and
sensible heat fluxes and destabilizes the low boundary
layer. The stronger overturning and moisture transport into
the cloud layer results in thicker clouds and larger CLWP.
Further inspection into the temporal variation of EOF/PC1s
indicates that unlike the SST, the temperature advection
Understanding the synoptic variability…
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Fig. 4 a Comparison between
PC1 series of CLWP and
reversed SST. Reversed SST
PC1 trend is given to show the
tendency that CLWP varies with
the SST. It shows that when
SST is lower, CLWP tends to be
larger, and vice versa.
b Scatterplot of CLWP PC1 vs.
reversed SST PC1
Fig. 5 Relationship between
PC1s of CLWP and temperature
advection during the 91 days in
austral spring over the southeast
Pacific deck 0–30S,
70–100W
captures some temporal variance at synoptic scale of a few
days.
Surface wind has long been thought as an important
factor in controlling the variance of CLWP (Stevens and
Brenguier 2009). Xu et al. (2005) found that SSM/I CLWP
was correlated significantly with local wind velocity based
on a buoy observation. As surface wind physically links to
SST and TMPADV, the temporal variance of wind speed
EOF/PC1 is significantly correlated with the temporal
variance of CLWP EOF/PC1 with correlation coefficient of
0.60, shown in Fig. 6. We applied a Butterworth filter to
both CLWP and wind EOF/PC1s with cutoff value of
30 days. The filtered CLWP and wind EOF/PC1s are correlated with correlation coefficient of 0.43, indicting the
impact of surface wind on cloud formation and consequently CLWP. Notably, the temporal variation of wind
speed EOF/PC1 is consistent with CLWP EOF/PC1, except
for the period from Oct. 8 (day 38) to Oct. 15 (day 45).
During that period, there was a strong cyclone passage
through this region (Rahn and Garreaud 2010). The
increase of CLWP on Oct. 8 is due to this cyclone,
resulting in the inconsistency between the two.
It is clear that SST, TMPADV, and wind speed are not
mutually independent. Figures 4, 5 and 6 suggest that all
three factors impact on cloud formation and CLWP at
different temporal scales. To further illustrate this, we
composite the daily difference of all four parameters [EOF/
PC1 (n ? 1) - EOF/PC1 (n), where n is the index of the
day]. As shown in Fig. 7, the daily difference of CLWP
EOF/PC1 is significantly correlated with that of wind speed
with correlation coefficient of 0.57. The correlation coefficients are reduced to 0.22 and 0.14 for TMPADV and
SST, respectively. It suggests that although the correlation
coefficient of wind speed EOF/PC1 with CLWP EOF/PC1
is the lowest among the three factors, the wind speed plays
a more fundamental role in stratocumulus cloud formation
and CLWP. SST and cold advection mainly drive the
synoptic or longer-term scales of variance of CLWP, while
surface wind mainly controls the daily variance of CLWP.
3.2 EOF/PC2 of CLWP
The CLWP EOF2 provides detailed spatial pattern of
partial variability of CLWP, shown in Fig. 8a, which is
orthogonal from the CLWP EOF1. The vertical velocity at
850 hPa is a good indicator of the boundary layer turbulent
structure and strength. The spatial pattern of CLWP EOF2
is similar to the pattern of three-month averaged vertical
velocity at 850 hPa (Fig. 8b). Strong upward velocity
results in large variability. Strong subsidence, particularly
at the southeast corner of the domain (the blue region in
Fig. 8b), corresponds to a large positive variance of CLWP
EOF2.
On temporal variance of CLWP EOF2, LTS has the
highest correlation coefficient of 0.67 among all other
large-scale meteorological factors with CLWP EOF/PC2,
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Fig. 6 Relationship between
PC1sof CLWP and wind speed
at 10 m level; the upper panel
for unfiltered data and the
bottom panel for the filtered
data
Fig. 7 Comparison between CLWP PC1 daily difference (EOF/PC1 (n ? 1) - EOF/PC1 (n), where n is the index of the day) and a PC1 of
wind at 10 m daily difference, b PC1 of SST daily difference and c PC1 of cold advection daily difference
as listed in Table 1 and shown in Fig. 9. The temporal
variation of EOF/PC1 of LTS captures the variance of
CLWP EOF/PC2, except for the days when the cyclone
came by. The second highest correlation is between the
EOF/PC1 of relative humidity in the cloud layer (e.g., RH
at 850 hPa) and CLWP EOF/PC2. The correlation coefficient of 0.63 is also above the 99 % confidence level. In
Fig. 10, close inspection of relationship between LTS and
RH at 850 hPa indicates that the two fields are highly anticorrelated on both observed fields and their EOF/PC1s.
Both fields in concert control cloud thickness and consequently CLWP through thermodynamic and entrainment
processes.
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3.3 Overall CLWP variability
Although the first two EOFs/PCs of CLWP explain small
portion of CLWP variance (less than 20 %), the above
analysis does provide the basic understanding of meteorological controls on CLWP variability. Previous studies
have shown that on sub-seasonal timescales, LTS and relative humidity of the cloud layer (cold advection as well)
are significantly correlated with variations in low-cloud
amount (Klein 1997; Klein et al. 1995). However, the
correlations of low cloud amount with meteorological
predictors are substantially weaker than on longer timescales (George and Wood 2010). As for CLWP,
Understanding the synoptic variability…
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Fig. 8 The comparison
between a spatial EOF2 pattern
of CLWP and b 3 months mean
of vertical velocity averaged
from 91 days NCEP re-analysis
during austral spring
Fig. 9 Relationship between
CLWP PC2 and LTS PC1
during the 91 days in austral
spring over the southeast Pacific
deck 0–30S, 70–100W
Fig. 10 Relationship between
CLWP PC2 and RH PC1 at
850 hPa during the 91 days in
austral spring over the southeast
Pacific deck 0–30S, 70–
100W
meteorological conditions at the low boundary of clouds,
such as SST, TMPADV, and wind, control overall CLWP
(EOF1) of stratocumulus clouds. The meteorological factors at the cloud layer, such as LTS and cloud top RH,
influence CLWP spatial distribution (EOF2). Specifically,
as shown in Fig. 11, the overall changes in CLWP from
Sept. 20 to Sept. 27 as a cloud system correspond well with
change in sea surface wind speed except for near coast
regions. Temperature advection seems to play a minor role
in controlling CLWP. The movement from southwest to
northeast, shown in Fig. 11, corresponds well with changes
in LTS and cloud top RH. The major cloud bands with
large CLWP were also located in the regions with large
gradients of LTS. The cloud seems to break into small parts
where the LTS reduced substantially, as weaker LTS promotes decoupling which is favorable for stratocumulus
breaking up into cumulus (Bretherton and Wyant 1997; Dal
Gesso et al. 2011). In the meantime, RHs at the top of those
cloud bands were very high (near 100 %). (Caldwell and
Bretherton 2009) found that the cloud top height exhibits a
larger variation than the cloud base and cloud layer
thickening follows the entrainment pattern. High RH at the
cloud top helps to slow the entrainment and to sustain the
cloud longer with larger CLWP.
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Fig. 11 Corresponding meteorological fields from Sept. 20 to Sept.
27. a liquid water path (gm-2) from MODIS data, b surface wind
speed (m/s) from NCEP data, c cold advection (C day-1) from
NCEP data, d lower tropospheric stability (T) from NCEP data,
e relative humidity at 850 hPa (%) from NCEP data
As discussed previously, at short-time scales, surface
wind speed plays more dominant role in determining
CLWP variation than other factors. One reason is that the
increased wind speed and associated temperature advection
increase surface latent and sensible heat fluxes and destabilize the low boundary layer, resulting in stronger moisture transport into the cloud layer and thicker CLWP (Xu
et al. 2005). However, it is demonstrated from Figs. 4, 5, 6
and 7 that surface wind speed captures much more daily
variance than both temperature advection and sea surface
temperature. (Stevens and Brenguier 2009) indicate that
besides setting the rate of coastal ocean upwelling and
hence surface temperatures, surface wind speed can also
increase mechanical and bio-chemical production of marine aerosol as an compound effect. Former researches have
also shown that the surface wind influences sea salt and
Dimethyl sulfate (DMS) production in the atmosphere,
which can be activated effectively into cloud droplets
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Fig. 12 Relationship between surface wind speed and liquid water path from AMSR-E measurements
(Latham and Smith 1990; O’Dowd et al. 1999). It is highly
possible that the daily correlationship between surface
wind speed and CLWP results from wind-induced marine
aerosol.
Simultaneous retrievals of surface wind and CLWP from
AMSR-E measurements allow us to investigate the relationship of wind and CLWP through the perspective of the
aerosol indirect effect. Cloud formation is affected by both
dynamical and microphysical processes. We segregate the
observed data into three regions: drizzle influenced region,
anthropogenic aerosol influenced region, and normal marine aerosol region (the rest of the remote ocean domain).
The drizzle influenced region is mostly located at the
southwest corner, and the anthropogenic aerosol influenced
region is along the Chile coast, where the CDN is larger
than 100 cm-3. To further limit other meteorological
impacts, we only use the data in which SST is less than
20 C and the total water vapor path is within 20 mm. As
shown in Fig. 12, the slope and correlation coefficient are
substantially different among the three regions. The marine
aerosol region without drizzle has the greatest slope of
0.011 with the highest coefficient of 0.72, exhibiting the
strongest impact of surface wind on CLWP. Although there
is tendency of increasing CLWP with surface wind in the
anthropogenic aerosol region, the slope is about one-third of
that in the marine aerosol region with the weakest coefficient of 0.23. It is understandable that drizzle removes
liquid water from clouds, resulting in that the slope is only
half of that in the marine aerosol region without drizzle.
It is clear that the sea surface wind could impact cloud
development micro-physically. The pattern of relationship
between surface wind speed and CLWP corresponds very
well with the first aerosol indirect effect and second aerosol
indirect effect. More CDN due to enhanced marine aerosols
would increase the CLWP. Most importantly, the anthropogenic aerosols due to the copper smelters near the Chile
coast provide sufficient CCNs to be activated into cloud
droplets, resulting in high CDN and inhibiting the precipitation initiation process (Albrecht 1989). Note that clouds
with drizzle are formed mostly in the remote ocean with
normal marine aerosol region.
4 Conclusions
It is important to understand the fundamental processes of
stratocumulus clouds. The EOF method provides us an
effective way to untangle the varieties of factors, and
separates them into spatial and temporal aspects. In particular, stratocumulus CLWP, the most complex quantity
among all key cloud properties, is influenced by both
microphysical and macrophysical factors from all levels
and each of these factors may be tangled with each other.
The first principal component of CLWP can explain
13 %, while the first 15 principal components explain
50 % of the variance of CLWP. Although explained less
than 20 % of CLWP variance, the EOF analysis over the
first two principal components of CLWP provides insights
into how key meteorological factors influence CLWP.
The overall CLWP (EOF1) of stratocumulus clouds is
controlled by the sea surface processes and associated
factors, such as SST, TMPADV, and wind, while the
meteorological factors at the top of the cloud, such as
LTS and RH, influence the second principal of CLWP
(EOF2) and control the daily spatial movement of CLWP.
SST significantly correlates with the variance of CLWP at
seasonal scale. The correlations of CLWP with LTS, RH,
and TMPADV are significant at sub-seasonal or synaptic
scales. The sea surface wind is highly correlated with
CLWP at daily scale. The influence of surface wind on
CLWP is twofold: (1) dynamically through modulation of
surface latent heat and sensible heat fluxes and (2)
microphysically through enhancement of marine aerosol
production.
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Acknowledgments This work was supported by US NSF under
contract AGS-1138495, by the DOE’s Atmospheric System Research
program (Office of Science, OBER) under contract DE-FG0203ER63531, and by the NOAA Educational Partnership Program with
Minority Serving Institutions (EPP/MSI) under cooperative agreements NA17AE1625 and NA17AE1623, and supported by Science
and Technology Research Foundation of SGCC contract
DZB17201200260.
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