Introduction
As nations in southeast Asia have increased bio- and fossil fuel combustion
in recent decades, corresponding increases in atmospheric aerosol pollution
have been seen over the region e.g.,. The high levels of
anthropogenic emissions combine with the seasonal monsoon cycle
to cause frequent episodes of heavy air pollution
over the northern Indian Ocean, especially in the so-called winter monsoon
season (November through March) when the low-level atmospheric flow is
northerly to northeasterly, following the
temperature gradient from the colder subcontinent to the warmer ocean
(Fig. ).
In addition to their direct effects on the climate (i.e., heating or cooling), aerosols are also known to affect clouds by
three primary mechanisms: cloud brightening (e.g., ; the first indirect effect), precipitation suppression
(e.g., ; the second indirect effect), and radiative (the so-called semi-direct) effects, which may either enhance
or diminish cloud cover based on the cloud type and relative position of the aerosol layer e.g.,.
It is important to note that in addition to the often opposing signs of each of these effects, aerosol–cloud interactions
have been shown to be highly dependent on the regime (i.e., the typical meteorological conditions, cloud types, and location)
in which they are found . That is, the expression of any or multiple aerosol–cloud effects will
be dependent on the conditions under which they are expressed and thus may vary from one region to another even when
considering superficially similar clouds. In situ observations of all types of clouds are thus critical to understanding
the full range of indirect effects influencing the Earth's atmosphere.
The current study builds upon a long history of aerosol
studies in the northern Indian Ocean, starting with the Indian Ocean Experiment
(INDOEX), a collaborative multi-platform experiment in 1998–1999 involving scientists from several international organizations
and led by the Scripps Institution of Oceanography .
In INDOEX, simultaneous multi-platform
measurements were made in the Indian Ocean with the goal of observationally
constraining direct and indirect effects of aerosols in the region, in
particular the atmospheric heating and surface cooling caused by the
presence of black carbon (BC) aerosols within the atmospheric column.
The intensive field operations allowed scientists to, for the first
time, quantify the direct radiative effects of absorbing aerosols
originating in southeast Asia and to contrast the highly polluted
conditions north of the Intertropical Convergence Zone (ITCZ) with pristine Southern Hemisphere
conditions e.g.,. INDOEX thus set
the stage for later work in the region investigating the effects of
absorbing aerosols within the atmospheric column.
Map of the study location highlighting the Maldives Climate
Observatory at Hanimaadhoo (MCOH). The overlay is a NASA MODIS
satellite image of the region, showing an aerosol plume coming off
the subcontinent. The presence of absorbing aerosols in the plume
is evident from its greyish color. Predominant low-level flow
during winter months is indicated by the
arrows.
The 2006 Maldives Autonomous unmanned aerial vehicle Campaign (MAC)
investigated the role of absorbing aerosols in the Indian Ocean, and their
effects on clouds, using lightweight unmanned aerial vehicles (UAVs) with
miniaturized radiation, aerosol, and cloud instrumentation as payload
. The UAVs
were flown stacked one on top of the other and, with their upward- and
downward-looking instrumentation operating simultaneously, directly measured
the amount of radiation absorbed within an aerosol layer
. The Cloud, Aerosol, Radiative forcing, Dynamics
EXperiment (CARDEX) follows on from these previous studies using UAVs and
ground measurements and for the first time incorporates measurements of
turbulent kinetic energy and latent heat fluxes for a greater focus on how
thermodynamic factors and atmospheric dynamics may influence aerosol effects
on clouds.
Between 16 February and 30 March 2012, CARDEX was conducted on Hanimaadhoo
Island, Maldives (Fig. ), led by scientists from the Scripps
Institution of Oceanography in San Diego, California, and including
collaborators from the Desert Research Institute in Reno, Nevada; Stockholm
University in Stockholm, Sweden; the Max Planck Institute for Chemistry in
Mainz, Germany; and Argonne National Laboratory in Argonne, Illinois. The
Maldives Climate Observatory at Hanimaadhoo (MCOH) has been making continuous
measurements of aerosol, radiation, and meteorological parameters on
Hanimaadhoo Island since October 2004 . During the
CARDEX campaign, measurements from small aircraft were supplemented with the
continuous ground measurements at MCOH, including additional instruments
exclusive to the CARDEX period: a mini-micropulse lidar (MPL) to measure
cloud base height (zcb), boundary layer height (zPBL), and the altitude of elevated
aerosol plumes; a fast-response water vapor sensor and gust probe (identical
to those on the aircraft) to measure turbulent kinetic energy and latent
energy fluxes (LEF); and a microwave radiometer (MWR) to measure total-column
precipitable water vapor (PWV) and cloud liquid water path (LWP). CARDEX was
designed to observe the atmosphere at the end of the so-called dry season
(winter monsoon), a time when atmospheric flow over the Maldives is
predominantly from the highly polluted Indian subcontinent with little wet
removal due to rainfall. As the atmosphere is heavily influenced by
anthropogenic pollution during this dry season, the data presented here are
valuable for a broader understanding of potential aerosol effects on
atmospheric conditions.
Here we present new observations of the dry-season climatology of this trade
cumulus regime, including cloud, aerosol, and meteorological properties, as
observed during CARDEX. In Sect. , we describe
characteristics of the full CARDEX data set and two distinct classes of
atmospheric properties (“wet” and “dry” regimes) and examine the
differing conditions which are responsible for each.
Section then focuses on cases within the dry regime to
describe the systematic distinctions observed between low- and high-pollution
cases as well as observed aerosol–cloud correlations. These pollution case
studies allow insight into the mechanisms governing the observed differences
in cloud properties. We then offer a brief discussion of some potential
causal factors of the observed correlations, including the role of aerosol in
modifying atmospheric humidity and the potential implications for the
understanding of aerosol effects on clouds.
Methods
In the following sections, unless otherwise stated, aerosol
conditions are determined using the aerosol number
concentration measured by the condensation particle counter (CPC)
instrument at MCOH (Fig. ). Other aerosol
metrics used are aerosol optical depth (AOD) measured by the
MCOH AErosol RObotic NETwork (AERONET) sun photometer, satellite-based AOD from the MODerate resolution Imaging Spectroradiometer
(MODIS) instruments on board NASA's Terra and Aqua satellites, and BC concentration measured by an airborne or ground-based
aethalometer.
Time series showing the dynamic range of precipitable water
vapor (MWR PWV in kg m-2, upper panel) and surface aerosol
concentration (CPC number concentration in cm-3, lower
panel) observed during CARDEX. The colors correspond to the regimes
described in the text: upper panel shows wet (blue) and dry (black)
conditions, and lower panel shows low-pollution (green) and high-pollution (red) conditions. Overlaid vertical lines indicate UAV
flight times for the aerosol and radiation (MAC4, magenta), flux (MAC5,
blue), and cloud microphysics (MAC6, cyan) planes, showing the wide
range of conditions which were sampled.
The cloud liquid water path (LWP) given here is the average-peak value
(the mean of all cloud retrievals within 100 gm-2 of the
peak cloud value) for each cloud event (Fig. ). This
definition preserves the peak LWP as a characteristic of the cloud
while accounting for instrument noise and variability
within the cloud. Further discussion of identification and processing
of cloud “events” is given in Appendix .
Three UAVs were flown during CARDEX. MAC4, MAC5, and MAC6 flew the aerosol
and radiation, water vapor flux, and cloud microphysics payloads,
respectively. A more detailed description of each payload may be found in
, , ,
, and .
A complete description of the permanent MCOH instrumentation and data
used in this paper has been given in .
Additional information on the CARDEX-specific instrumentation used,
including the lidar and the microwave radiometer and the methodology for processing these data, may be found in the
Appendix .
Atmospheric regime as indicated by total-column water vapor content
The high variability in total-column atmospheric water vapor observed during
CARDEX (between 20 and 60 kgm-2, Fig. )
allows one to categorize the observations as either wet (here defined as
total-column PWV>40 kgm-2; blue in
Fig. ) or dry (total-column
PWV<40 kgm-2; black in Fig. ).
This distinction is significant in the context of later analysis
(Sect. ); first we describe the notable differences
observed between these two regimes.
Daily-averaged aerosol and water vapor conditions during CARDEX,
indicating days of low (CPC<1000 cm-3), high
(CPC>1500 cm-3), or intermediate or transitioning
pollution conditions (1000<CPC<1500 cm-3). A “dry”
classification indicates that total-column precipitable water vapor was less
than 40 kgm-2, and “wet” indicates PWV that was greater than
40 kgm-2. “Borderline/transition” indicates that the daily
average was within 40±1 kgm-2 or that the PWV shifted
significantly between dry or wet conditions over the course of the 24 h
period (midnight to midnight, MVT). There were 30 dry and 8 wet days during
this period, corresponding to 37 dry- and 13 wet-condition flights. Flights
on borderline/transition days may still be classified as wet or dry based on
average values measured around the flight time
(Table ). Note that no water vapor data were
available on 28 February, though they seem likely to be wet given the
conditions of the previous and following days. All flights are visualized in
Fig. .
Water vapor
Aerosol
Dates
Wet
low pollution
16–17 March
Wet
middle/transition
13–15, 29 March
Wet
high pollution
27, (28), 29 February
Dry
low pollution
4–6, 10–11 March
Dry
middle/transition
7, 9, 22–24 March
Dry
high pollution
16–26 February;
2–3, 8, 19–21,
25–27 March
Borderline/transition
low pollution
12 March
Borderline/transition
middle/transition
18 March
Borderline/transition
high pollution
1, 28 March
In this analysis, vapor conditions are identified primarily using the MWR
total-column PWV, chosen for its high temporal resolution. Using the good
agreement between the MWR and AERONET column PWV, the CARDEX flight days
before MWR operations began on 6 March are additionally classified.
Daily-averaged PWV conditions for the entire CARDEX period are given in
Table , and classifications for each UAV flight
are given in Table .
Observed distinctions between dry and wet atmospheric conditions
Table shows the differences in observed MCOH
surface parameters for wet vs. dry conditions at 1 min resolution. There are some prominent differences between the two
populations: on average, dry cases correspond to higher wind speed in
both north–south and east–west directions, as well as lower surface
pressures; as may be expected, the surface humidity is greater
for wet cases, and wet days also exhibit greater variability in cloud
LWP.
There were no significant differences in observed average aerosol
amount (CPC number concentration or AERONET column AOD), cloud base or
boundary layer height, or surface fluxes between the two populations
when considering the variability of the observations. The frequency distributions of these parameters are visualized in
Appendix Figs. and .
CARDEX flights and corresponding surface CPC and total-column PWV
conditions for the aerosol and radiation (MAC4), flux (MAC5), and cloud
microphysics (MAC6) planes, indicating high (H), medium (M), or low (L)
pollution and wet (W) or dry (D) total-column water vapor conditions.
Conditions are determined by ±2 hourly averages around the flight time (given below in MVT),
except for PWV before 5 March, which is determined by average
AERONET-retrieved PWV. Note that there was no AERONET retrieval on 28
February and the CPC had a loss of data on 24 March (although the longer time
series suggests a middle-level aerosol amount during the missing period).
Date
MAC4
MAC5
MAC6
Flight
Flight
Flight
time
time
time
23 Feb
12:30
H, D
24 Feb
12:51
H, D
27 Feb
10:00
H, W
28 Feb
09:00
H, NA
14:56
H, NA
12:00
H, NA
29 Feb
14:53
H, W
09:30
H, W
2 Mar
08:30
H, D
13:29
H, D
3 Mar
12:36
H, D
10:55
H, D
4 Mar
12:30
L, D
09:03
L, D
9 Mar
07:00
M, D
12:00
M, D
10 Mar
10:30
L, D
13:22
L, D
08:30
L, D
11 Mar
09:45
L, D
13:09
L, D
14:30
L, D
17:27
L, D
13 Mar
15:15
M, W
10:14
M, W
14 Mar
12:03
M, W
08:30
M, W
15 Mar
13:30
M, W
10:47
M, W
15:30
M, W
17:07
M, W
17 Mar
12:00
M, W
18 Mar
13:59
M, D
11:00
M, D
19 Mar
15:51
H, D
11:00
H, D
15:30
H, D
20 Mar
14:30
H, D
12:23
H, D
09:45
H, D
14:30
H, D
21 Mar
13:30
M, D
14:18
M, D
23 Mar
08:30
M, D
12:58
M, D
08:30
M, W
24 Mar
09:00
(M), D
13:32
(M), D
25 Mar
09:30
H, D
14:02
H, D
12:00
H, D
26 Mar
09:23
M, D
12:45
H, D
Average surface values, standard deviations, and 10th and 90th percentile ranges observed for wet
vs. dry conditions during CARDEX. Note the highly non-normal distributions of many of these parameters. With the
exception of LEF and cloud values, these are calculated from the minute-averaged values for which
PWV<40 or PWV>40 kgm-2. The LWP and cloud base heights shown are the more meaningful
averages over cloud events only; boundary layer height additionally follows this definition to illustrate the position of cloud
relative to the boundary layer. Eddy covariance calculations require a 30 min averaging period; additionally, eddy covariance
fluxes were unresolvable during nighttime due to the low wind speeds. Thus, the values of LEF below are for 30 min averaged
daytime fluxes (06:00–18:00 MVT) only. The corresponding 24 h values are 74.8±54.3 (6.0–137.3) and 67.6±64.1
(3.4–133.7) Wm-2 for dry and wet conditions, respectively. Lifting condensation level is calculated from the
approximation given in .
Dry conditions
Wet conditions
(PWV<40 kgm-2)
(PWV>40 kgm-2)
Mean
1σ
10–90 percentiles
Mean
1σ
10–90 percentiles
Number of cloud events
267
363
Cloud LWP (gm-2)
147.0
105.3
96.3–187.2
204.2
271.4
79.9–435.2
PWV (kgm-2)
31.4
4.6
25.0–37.9
47.8
5.5
41.0–56.5
CPC (cm-3)
1360
352
789–1797
1218
338
778–1621
AOD500
0.48
0.17
0.26–0.66
0.43
0.23
0.20–0.73
Wind speed (ms-1)
2.2
1.2
0.8–4.0
1.6
0.9
0.6–2.8
Surface temperature (∘C)
28.6
1.0
27.4–30.1
28.8
1.1
27.5–30.4
Surface pressure (hPa)
1008.2
1.9
1005.6–1010.7
1009.4
1.5
1007.4–1011.6
Relative humidity (%)
75.6
5.3
68.5–82.3
77.9
4.8
71.7–84.2
Specific humidity (gkg-1)
18.5
1.3
16.3–20.1
19.2
0.9
18.1–20.1
Boundary layer height (m)
895
193
674–1109
841
163
637–1071
Cloud base height (m)
849
252
583–1208
804
371
462–1448
Lifting condensation level (m)
629
137
454–812
570
127
405–731
Latent energy flux (Wm-2)
79.8
56.2
11.4–148.9
70.6
64.2
6.9–135.4
The vertical profiles from the MAC4 aircraft under wet (dark blue) and
dry (cyan, black) conditions are shown in Fig. . First, it is notable that in both categories, the UAV profiles indicate
large variability in aerosol throughout the atmospheric column
(i.e., both boundary layer aerosol and free troposphere aerosol) in
terms of CPC number concentration as well as the aethalometer black carbon
concentrations measured by the aircraft. Other measured values from
MCOH (Fig. , Table )
show only slight differences between the two populations; in
particular, this is true for the average LWP and surface flux values,
although the variability in observed LWP is more than a factor of 2
larger for the wet cases. The measured cloud base heights also show
greater variability under these wet conditions.
Liquid water path measured by the MWR operated during CARDEX.
Cyan points indicate cloud-flagged values, and the inset illustrates
an example of cloud events, as described in
Appendix Sect. .
There is on average slightly lower boundary layer humidity for the dry
flight days compared with wet days, but the most notable difference
between the two populations is in the atmospheric temperature and
humidity vertical structure. While the dry days have a very well-defined
boundary layer top between roughly 1000 and 1500 m, as
indicated by a strong observed temperature inversion and a sharp
decrease in relative humidity, the wet days do not.
Thus, the most significant distinction in the atmospheric structure of the two populations is in the conditions at
the top of and above the boundary layer, namely the lack of temperature inversion and greater atmospheric humidity at
higher elevations for the wet cases. This conclusion is
additionally supported by the ECMWF reanalysis over MCOH (Appendix Fig. a and b).
Average surface values for low, medium, and high pollution for dry
conditions (Cases L, M, and H, respectively). The numbers in parentheses
indicate 1 standard deviation of the minute-averaged values for which
PWV<40 kgm-2 and CPC<1000 cm-3 (low pollution),
1000<CPC<1500 cm-3 (medium pollution), or
CPC>1500 cm-3 (high pollution). Due to the non-normal
distributions of many of these parameters, the 10th and 90th percentile
ranges are additionally shown (second line). LWP and cloud base height are
the averages over cloud events only, as is boundary layer height, to
illustrate the position of cloud relative to the boundary layer. Lifting
condensation level is calculated from the approximation given in
. Eddy covariance calculations require a 30 min averaging
period; additionally, eddy covariance fluxes were unresolvable during
nighttime due to the low wind speeds. Thus, the values of LEF below are for
30 min averaged daytime fluxes (06:00–18:00 MVT) only. The corresponding
24 h values are 98.5±63.4 (37.4–169.3), 70.4±51.5 (5.2–127.8),
and 61.0±42.1 (3.3–113.1) Wm-2 for Cases L, M, and H,
respectively.
Case L
Case M
Case H
low, dry
med, dry
high, dry
Number of cloud events
45
129
89
Cloud LWP (gm-2)
97.5 (19.7)
145 (22.3)
175 (29.2)
75.0–121.8
105.2–163.8
109.0–293.6
PWV (kgm-2)
29.4 (4.2)
31.9 (4.9)
31.2 (4.2)
23.5–34.5
25.4–38.9
26.0–37.0
CPC (cm-3)
767.7 (118.9)
1319.9 (136.9)
1673.9 (169.8)
596–944
1138–1487
1512–1926
AOD500
0.38 (0.28)
0.47 (0.13)
0.50 (0.06)
0.14–0.82
0.26–0.64
0.45–0.56
Wind speed (ms-1)
2.86 (1.20)
2.31 (1.31)
1.84 (1.01)
1.43–4.56
0.77–4.25
0.59–3.17
Surface temperature (∘C)
27.97 (0.88)
28.64 (0.89)
28.80 (1.00)
26.84–29.02
27.67–30.07
27.65–30.26
Surface pressure (hPa)
1006.5 (1.3)
1008.0 (1.8)
1009.0 (1.7)
1004.9–1008.4
1005.4–1010.3
1006.8–1011.3
Relative humidity (%)
69.7 (4.2)
76.4 (4.2)
77.4 (4.6)
63.0–76.7
70.4–81.2
71.3–83.5
Specific humidity (gkg-1)
16.4 (1.2)
18.7 (0.9)
19.1 (0.9)
15.1–18.3
17.6–19.8
17.9–20.3
Boundary layer height (m)
1270 (173)
912 (161)
784 (84)
1009–1460
667–1054
669–863
Cloud base height (m)
1159 (165)
848 (268)
820 (203)
882–1290
595–1288
590–1077
Lifting condensation level (m)
775 (139)
608 (110)
583 (122)
597–952
481–765
423–746
Latent energy flux (Wm-2)
113.9 (66.4)
74.3 (54.4)
64.6 (40.6)
55.7–193.9
5.5–149.4
12.7–113.1
Aerosol, temperature, and relative humidity vertical profiles
from the MAC4 aircraft for individual wet (dark blue) and dry (cyan)
flights, as indicated by Table . The thin
lines indicate individual profiles, and the thick lines indicate the
ensemble mean. For visual clarity, the ensemble mean of the dry cases is
shown in black, while the individual profiles are in cyan. Black carbon retrievals are shown as discrete
circles as they required a period of level flight to obtain an accurate
reading. There were 12 dry and 5 wet flights with this aircraft;
a description of the flight conditions and times may be found in
Table . Note that the strong temperature
inversion on dry days is most evident in the individual profiles
rather than the means, as the latter tends to average out the
inversion due to differing boundary layer heights. The average
values of LWP, zcb, and LEF are measured at MCOH from the MWR, MPL, and gust probe instrumentation, respectively, and are also shown in Table .
Note that the
atmospheric moisture described here is given as relative humidity
(RH), as this metric was directly measured by the aircraft.
Although an increase in temperature would produce a decrease in RH for a fixed specific humidity (q), in our cases
the measured RH is seen to be consistent with q calculated incorporating changes in temperature.
It is worth noting that during CARDEX, the lidar- and
aircraft-measured cloud base heights were generally close in altitude
to the inversion (Fig. ). While many of these
clouds likely penetrated at least partway through the top of the
temperature inversion, rather than being capped by it, the strength of
the observed inversion may help explain the relatively thin clouds in
CARDEX as compared with previous works. (A summary of observations from historical trade cumulus studies may be found in Appendix
Fig. and Table .)
NOAA HYSPLIT 7-day back trajectories arriving at 07:00 UTC
(12:00 MVT) for (a) 10 March 2012, a typical dry day,
and (b) 14 March 2012, a typical wet day.
Visualization from the HYSPLIT-WEB tool
(http://ready.arl.noaa.gov/HYSPLIT.php).
Large-scale contrasts between high and low water vapor conditions
In exploring the mechanisms contributing to this wet versus dry distinction,
we compare the air mass back trajectories from the National Oceanic and
Atmospheric Administration's HYbrid Single-Particle Lagrangian Integrated
Trajectory (NOAA HYSPLIT) model for each case (Fig. ). This
analysis shows that while there is large variability in lower-level flow for
both wet and dry cases, there are consistent differences in the upper-level
flow of each case. On extremely dry days (Fig. a), the back
trajectories indicate that upper-level atmospheric flow originates over the
Indian subcontinent, traveling in an anticyclonic motion before arriving at
MCOH as northeasterlies. During the 7-day air mass history, the air was
continuously descending to the 2–3 km range. In contrast, for
high-PWV conditions (Fig. b), upper-level air masses are
easterly, approaching from the Bay of Bengal and Indonesia, and the
2–3 km air over MCOH has ascended from the boundary layer to the
free troposphere within 4 days of observation. These results are consistent
with the aircraft measurement results (Fig. ): the
primary distinction between wet and dry cases is in the upper-level air mass
conditions. In wet cases, this air originates from a more moist (low-level)
environment and is transported aloft, while in dry cases it originates from
a drier (upper-level) environment and is brought to lower altitude due to
strong subsidence in the atmosphere above the boundary layer. The large-scale
meteorological reanalysis from ECMWF is also consistent with this
interpretation, suggesting that stronger subsidence and a corresponding
increase in low-level divergence are present in the dry cases
(Fig. c and d). The origin of low-level air again
showed no correlation with the wet and dry distinction.
The different characteristics of wet vs. dry cases are thus primarily
attributable to differences in the large-scale advection which brings
air masses to MCOH, as is evident in the CARDEX observations, the air
mass back trajectories, and large-scale reanalysis. This difference
in origin corresponds to greater variability in the clouds formed
during wet conditions; when considering only the dry cases with a narrower range of variability in LWP, we
are able to detect a statistically significant correlation between aerosol and cloud variability.
We hypothesize that the greater variability of LWP
is a result of unconstrained vertical development of the clouds which form under more humid conditions; as greater humidity tends to increase cloud thickness,
greater upper-level humidity may feed cloud development that is decoupled from boundary layer conditions.
The variability within the dry cases is the focus of the following
sections.
Characterization of observed high- vs. low-pollution conditions during CARDEX
Analysis of the meteorological conditions observed during CARDEX
indicated that there was no correlation between cloud liquid water and
any measured surface parameter for the CARDEX data set as a whole.
High variability is also present in the relationship between the
measured cloud liquid water and surface aerosol concentration
(Fig. a).
However, when the data are filtered to take into account meteorology, there is
a positive correlation between LWP and aerosol which is significantly greater than
0 (Spearman ρ=0.48; Pearson R=0.42, both at the 95 % confidence level)
for the dry (PWV<40 kgm-2) cases only (Fig. b).
Note that for the Pearson correlation analysis we have taken the logarithmic transform of the LWP as these data exhibit a
lognormal rather than normal distribution; the nonparametric Spearman coefficient is insensitive to the logarithmic transform.
It is notable that this positive
correlation is the opposite of the expected sign of the cloud burnoff effect, despite
the presence of significant absorbing aerosol in the region; it is also not indicative
of a constant LWP as may be expected in a traditional analysis of the
first indirect effect.
Cloud average-peak liquid water path vs. aerosol
concentration, for all clouds (top; wet in blue, dry in black) and
only dry condition clouds (bottom). Note the logarithmic scaling on
the y axis. The red line indicates the linear best fit between
CPC aerosol number concentration and log(LWP).
In the following section we focus on these dry cases, which correspond to
a more well-defined, structured boundary layer as described above. In this
analysis, we use all low- or high-pollution dry days which had reanalysis and
satellite data available (Table ); observations
from the UAVs are necessarily limited to the subset of these days when a UAV
was flown (Table ). “Low pollution” cases are
defined as having surface CPC measurements less than 1000 cm-3 (9
flights over 5 days), and “high pollution” cases are defined as having
surface CPC greater than 1500 cm-3 (17 flights over 20 days). For
simplicity, in the following sections these are referred to as Case L and
Case H. The “moderately polluted” cases
(1000<CPC<1500 cm-3) are excluded from the figures in
order to bring focus to the high- and low-pollution contrast; however,
Table shows that these observations consistently fall
between Case L and Case H (e.g., LWP, zPBL, LEF and in many cases are in fact closer to Case H values
(e.g., lifting condensation level, zcb, humidity).
This holds true for the UAV vertical profiles (T, RH, aerosol) as well.
In situ measurements of surface and boundary layer characteristics
The summary of the mean values for each pollution case is illustrated in
Fig. , with values given in
Table . Frequency distributions of significant parameters
are shown in Fig. . As expected, the more polluted dry
cases show a higher average cloud LWP; these cases also correspond to lower
surface wind speed and lower surface specific and relative humidities,
although the total-column PWV did not show a statistically significant
difference. Perhaps most strikingly, Case H shows smaller surface latent heat
flux when compared with Case L, indicating that the higher observed
atmospheric humidity is not due to increased surface evaporation. While this
is in large part due to the lower observed wind speed in Case H, the lower
surface fluxes during high-aerosol conditions may partially be a result of
surface dimming due to increased atmospheric absorption by black carbon and
other absorbing aerosols
.
Characteristics of Case L vs. Case H conditions. By
definition Case H has higher surface aerosol concentration; as
expected, this is also true for AERONET-measured column AOD. Case H
also sees higher humidity (RH), lower surface vapor fluxes (LEF), slightly higher average surface temperatures (Tsfc), and lower
wind speed (v) and, as shown by Fig. , has
greater average cloud LWP. The lidar retrievals of cloud base and
boundary layer height and the calculated LCL height are
systematically lower for more polluted conditions.
The UAV flight data
offer further valuable insights into the possible
mechanisms behind the observed increase in polluted LWP.
Figure shows the observed Case L
and Case H flight profiles from the aerosol-radiation UAV.
Note that Case H is uniformly
more polluted (as measured by both the CPC and aethalometer) through
the lower atmosphere up to about 2 km, at which point average
pollution decreases for both cases. This is true for all cases except
for one Case L flight which sampled an elevated aerosol plume. Case H
exhibits warmer temperatures throughout the atmospheric column, with
the maximum mean difference between the two cases occurring around the
temperature inversion or cloud layer altitude (due to systematic
differences in inversion height for Case L vs. H).
Note that while Fig. and Table show that the mean temperature measured
directly at the surface was not statistically different between the two categories, this is not inconsistent with the
aircraft observations, which show a smaller
difference between the two cases near the surface compared with higher altitudes.
The more polluted
cases rather uniformly have higher boundary layer relative humidity
and substantially higher free troposphere relative humidity.
The brief exception to this is around 800 m, where the humidity of Case L is greater than that of Case H; this
corresponds to differences in the average altitude of the sub-cloud mixed layer between the two cases, which is higher in altitude for Case L.
Case H again has higher RH above the inversion, which may
partly facilitate the correspondingly larger average cloud water
content in this case, similar to the hypothesized mechanism behind the variability in cloud liquid water for the wet vs. dry
cases as discussed in Sect. , though to a lesser degree.
Frequency distributions of surface air temperature and
relative humidity (minute data from MCOH), cloud base height
(cloud-averaged data from MPL), and cloud liquid water path
(cloud-averaged data from MWR) for low- vs. high-pollution cases.
It is clear from these figures that higher-pollution days are
correlated with both higher water vapor content and higher
temperatures in the entire atmospheric column, particularly around the
temperature inversion (∼800–1500 m), which is itself
stronger in Case L. The average profiles of equivalent potential temperature in Fig. d
provide further insight into the differences in thermodynamic
structure between each case. The profiles
show θe to be constant within the mixed layer,
while the saturation equivalent potential temperature (θe*, dashed line)
decreases with height to the lifting condensation level (LCL). The layer of saturation, indicated
by values of θe equal to those of θe*, is
significantly greater in vertical extent for the high-pollution cases
(approximately 200 m thick), whereas the low-pollution
profiles barely reach saturation before the temperature inversion.
Above this layer is a sharp increase in θe* following the
inversion, coincident with a sudden decrease in θe due to the
sudden decrease in humidity at the top of the boundary layer. Note that
the intersection of θe and θe* is also
lower in altitude for Case H, corresponding to the lower zcb. The increase
in θe* across the boundary layer top is much greater for Case L than Case H, indicating that the high-pollution cases are less
stably stratified. This, in addition to the greater latent potential
energy of these more moist parcels, may result in Case H clouds more
frequently achieving convection up through the temperature inversion,
resulting in thicker (and thus higher LWP) clouds.
Profiles of (a) aerosol, (b) temperature, (c) relative humidity, and (d) equivalent
potential temperature θe from MAC4 for low- (Case L; green) and high- (Case H; red) pollution cases. Thin lines indicate the individual
flights, and the thick line shows the mean of each case. There were
three and five flights with this aircraft, sampling low- and high-pollution
dry conditions, respectively. In the left panel, CPC-measured aerosol
concentration (cm-3) is indicated by lines, while BC retrievals
(ngm-3), which required a period of level flight to
obtain an accurate measurement, are indicated by colored circles.
Case H has significantly higher aerosol concentration at all
altitudes, although this does not universally show an elevated
aerosol plume. This case is coincident with warmer atmospheric
temperatures and higher humidity at all altitudes. The saturation equivalent potential temperature θe* is
shown as dashed lines in (d). Note that due to missing pressure data in two of the MAC4 flights,
the calculated variables θe and θe* were determined using two less flights compared
with (a)–(c).
Altitude of cloud retrievals by MAC6 under low- (green) and
high- (red) pollution cases, for five high-pollution and two low-pollution
flights. Note that this figure shows the height at which the
aircraft penetrated the clouds rather than cloud base or top height;
however, the observations are consistent with overall lower cloud
heights under polluted conditions.
We explore the dependence of LWP on meteorological factors through a
calculation of the adiabatic cloud LWC (liquid water content, described in
more detail in Appendix Sect. ) and conclude that an increase
in LWP of the magnitude seen in the observations is likely attributable to a
physical thickening of the cloud resulting from the lower cloud base;
additionally, only the increased atmospheric humidity under polluted
conditions, rather than increased temperature, could result in this lower
zcb. That cloud bases are lower for the more polluted case is
further corroborated by the measured lidar cloud base heights
(Fig. ), which indicated lower average zcb
for highly polluted cases, and by the UAV flight data
(Fig. ), which indicated systematically lower cloud
penetrations for high-pollution cases. Although this is not a definitive
indication that the cloud bases themselves were lower, as the plane
penetrated clouds at a variety of altitudes of undetermined distance above
zcb, it is nonetheless consistent with lower cloud bases in
Case H. While it was not possible to directly measure cloud top heights
during CARDEX, a statistical analysis of cloud tops in the region from the
CALIPSO satellite , found higher cloud tops associated
with higher pollution levels, which also supports the conclusion of
physically thicker polluted clouds.
These multiple data sets paint a consistent picture of the systematic differences between
low- and high-pollution cases both at the surface and throughout the
atmospheric column. A more polluted atmosphere is observed to be
simultaneously warmer, more humid, and more convectively unstable, producing
physically thicker, higher-LWP clouds. Further examination of these
conditions (Appendix Sect. ) indicates that only the observed
changes in humidity (rather than changes in temperature) would be able to
account for differences in cloud height of the magnitude of those observed
between low- and high-pollution conditions. We now turn to a larger-scale
analysis to further explore the causes of these observed correlations.
Large-scale variability between low- and high-pollution cases
While thus far we have presented aerosol in terms of the surface particle
number concentration measured at MCOH, in the following large-scale analysis
we use the satellite-retrieved AOD as a metric of pollution level to allow
for analysis on a larger scale.
The evolution of the difference in high- minus low-pollution
conditions for
MODIS AOD (top row) and ECMWF temperature (middle row) and relative
humidity (bottom row) at 1000 hPa (approximately
75 m) for dry days as identified in
Table . The 1-day lag between maximum relative humidity and the maxima in both AOD and temperature is evident in the
day-to-day progression. Average Case L and Case H conditions
overlaid with wind fields are shown in
Figs. –.
Regional aerosol patterns
Figure (top row) shows the
difference in mean MODIS AOD over the CARDEX region for the average of
Case H-L days. That is, H-L is taken as the mean of all high-pollution (dry) days minus the mean of all low-pollution (dry) days
during the CARDEX period (Table ). From
left to right, the top row panels show the difference between average
AOD for 3, 2, 1, and 0 days preceding high-pollution minus
low-pollution conditions (as measured at MCOH).
The separate average Case L and Case H values from Fig. with overlaid 1000 hPa
wind fields are shown in Appendix Figs. , , and .
It is evident from this large-scale perspective that the pollution
level classifications as determined by the conditions over MCOH are
not necessarily representative of the region as a whole. Indeed, the absolute values of
MODIS AOD over the broader CARDEX region for the mean of all Case L days
shows that high aerosol concentrations are present elsewhere in the northern Indian Ocean at the same time as low-aerosol conditions
dominate at MCOH (Fig. ).
This is particularly true over the Indian subcontinent, where H-L is negative (i.e., the AOD
for Case L is significantly higher in magnitude than for Case H).
In Case H and in particular the H-L case, it is clear that the air mass of
high AOD approaches MCOH from the north-northwest rather than the
east-northeast, corresponding to the 1000 hPa wind field rather than
to winds higher in the troposphere and thus indicating that lower-level
transport is primarily responsible for the high-pollution conditions at MCOH.
Elevated plumes, which approach MCOH from the northeast, are not the major
contributor to aerosol loading on these days. It is also notable that the
high aerosol concentration air mass can be seen to dissipate over the 4-day
period, indicating a concentrated source and subsequent dispersion of
polluted air throughout the region as the plume ages. ECMWF divergence fields
(Fig. ) indicate that there is greater
low-level divergence (at the 1000 hPa level) for the low-pollution
cases. Although this divergence may act to dilute the polluted air mass, the
MODIS AOD shown here suggests that dilution is not the dominant factor
distinguishing the two cases. Rather, polluted air is prevented from arriving
at MCOH during the low-pollution cases due to the differences in advection
patterns.
Correlation between large-scale aerosol and temperature
Figure (middle row) shows the
H-L mean difference for the ECMWF 1000 hPa temperature
field. Similar to the patterns in the MODIS AOD, the high
temperatures in Case H are seen to be concentrated in a region which
approaches MCOH from the north and then dissipates somewhat over the 4 days in question as the polluted air mass is advected southward.
The remarkable spatial coincidence of temperature with the maximum AOD over all 3 days is strongly suggestive of heating of
the air mass due to absorbing aerosol, likely occurring since before
the air mass leaves the subcontinent. The rate of aerosol heating was
estimated by to be on the order of
0.5 Kday-1 for similar BC concentrations over the same
region. The positive temperature anomaly is strongest at the surface;
it is similar but weaker in the 900 hPa field and nonexistent
at 800 hPa.
Correlation between AOD and 1000 hPa temperature for
days leading up to high- (left) or low-pollution (right) events.
The bottom row indicates the average of the days classified as
a particular pollution event, while the middle and upper rows
indicate the averages of the previous 1 and 2 days, respectively.
Hatching indicates a statistical significance at the 95 %
level.
The analysis of Fig. suggests that
regions of high temperature are coincident with higher aerosol. We further
explore this relationship with Fig. , which
shows the correlation between AOD and T over the region. For both pollution
cases, Fig. shows a substantial region of
statistically significant correlation (95 % level, indicated by hatching)
between AOD and T. These correlation coefficients (and those in
Fig. ) were determined by calculating the
Pearson correlation R between AOD and T for all days in question (i.e.,
all H days or all L days) for each individual 1∘×1∘
(latitude, longitude) point. Finally,
points were only classified as “significant” if there were no more than
10 % of MODIS retrievals missing. While both Case L and Case H are shown
for comparison, it should be noted that due to fewer Case L days being
observed (Table ), the correlations for Case H
(left panel) should be considered more robust. Analysis for all days
indicates a similar pattern to Case H, although weaker in magnitude.
The region of high positive and significant correlation for Case H
is present over a broad extent of
the Arabian Sea (the low-level source region to MCOH).
The correlation weakens in both magnitude and area of significance
between Day H-2 and Day H, which further suggests a dispersion of the
polluted air mass with time,
consistent with the above
interpretation of Fig. .
Case L
shows a smaller region of positive correlation concentrated to the north in
the Arabian Sea, suggesting that while high pollution and temperature are again coincident, the polluted air mass simply
is not advected in the direction of MCOH in these cases.
That is, in the so-called low-pollution cases (as
defined by conditions at MCOH), the high-pollution, high-temperature
air mass remains concentrated to the north rather than spreading –
and dispersing – southward towards MCOH. Indeed, the difference
between regionally averaged AOD for the two cases over the region is
only 0.05, a factor of 2–3 smaller than the maximum H-L difference.
The smaller region of significant negative correlation to the east of the
subcontinent (particularly evident in Case H) may be explained by
low-atmosphere or surface dimming due to an elevated aerosol plume rather
than the high boundary layer aerosol responsible for the positive correlation
to the northwest; at higher altitudes, for example at 875 hPa
(z≈1250 m), the AOD and temperature T875 show a strong
positive correlation over this region. Elevated aerosol plumes are generally
seen to approach MCOH from this direction, following the upper-level wind
field, consistent with the findings of .
Correlations between aerosol, cloud water content, and atmospheric humidity
The bottom row of Fig. shows the mean
H-L relative humidity for the larger region surrounding MCOH. Again, there
is a notable difference between Case H and Case L: the H-L field indicates
that Case H corresponds to an air mass of high RH approaching MCOH over the 4
days prior to the given event. However, in contrast to the top two rows, the
region of highest RH difference is seen to lag the high-AOD and
high-temperature region by roughly 1 day and develops rather than disperses
with time. That is, the region of higher RH is seen to be relatively small at
-3 days, and subsequently strengthens in magnitude and spatial extent –
approximately coincident in location with the high-AOD and high-temperature
air mass – in the time leading up to the day in question. This lagged
intensification of RH over the 4-day period suggests that some effect within
the polluted air mass may be acting to increase its moisture content even as
the air mass disperses. This effect is not seen in higher-altitude RH fields.
Correlation between MODIS AOD and ECMWF 1000 hPa
relative humidity for days leading up to high- (left) or
low-pollution (right) events. The bottom row indicates the average
of the days classified as a particular pollution event, while the
middle and upper rows indicate the average of the previous 1 and 2 days, respectively.
Hatching indicates a statistical significance at the 95 %
level.
The correlation between AOD and RH
(Fig. ) exhibits a similar high–low
contrast to that observed in the correlations between aerosol and
temperature (Fig. ): Case H has
a weaker correlation over a larger region, whereas Case L is
concentrated in a smaller, more highly correlated region. However,
this relationship differs significantly from the temperature plots in
that instead of dispersing in the 1–2 days prior to Case H, the
correlation between AOD and RH is seen to strengthen during this
period.
While not the only factor, the increased
humidity shown in Figs. through and the bottom row of Fig.
may to a degree contribute
to the observed increase in
cloud LWP.
As was discussed in Sect. , this hypothesis is supported by calculations
attributing the increase in LWP to the lowering of cloud base heights (Figs. and ) resulting
from increased atmospheric humidity.
The atmospheric profiles
of equivalent potential temperature
(Fig. ) also indicate
that under highly polluted
conditions, rising air parcels reach saturation at a lower altitude
and the atmosphere exhibits a thicker saturated layer compared with
the low-pollution conditions, further supporting the conclusion that
the atmosphere is more humid and cloud bases are lower for high-pollution conditions. The large-scale picture shown by Figs. and
indicates that, in contrast to the high-temperature condition, this high-humidity condition develops along with the polluted air mass, rather than exiting the
continent as simultaneously warm, humid, and polluted.
The question then becomes the following: what may be causing this higher-humidity condition to develop within a polluted air mass? We now explore some potential causal
mechanisms by which aerosol may affect atmospheric humidity and, by extension, cloud properties.
Discussion of potential humidification mechanisms
As shown above, there is substantial evidence of a positive correlation between observed aerosol amount and atmospheric humidity. While the present
observations are not sufficient to determine the causal mechanism, we are able to briefly explore some possibilities which present interesting avenues
for further study.
We have previously eliminated sea surface evaporation and decreased
cloud formation as the primary causes of the observed higher humidity,
due to the flux and LWP measurements already described. We may
additionally neglect precipitation in this case, as drizzle was not
observed in these clouds even under low-pollution conditions. This
leaves large-scale factors (e.g., advection of warm, humid, and polluted air masses), local top-of-boundary-layer fluxes, or
possible aerosol-induced effects as potential contributing factors to
the observed higher relative humidity.
To assess the possible influence of large-scale meteorological conditions on humidity, we
examine HYSPLIT back trajectories for any systematic differences in the origin or evolution of the air masses for each case. These
show the upper-level flow
approaching from the northeast
over the subcontinent, consistent with the results
shown in Sect. (Fig. b).
The near-surface flow originates generally from the north or northwest for both cases; although
low-pollution conditions exhibit less extended
back trajectories (i.e., lower wind speed above the boundary layer),
they come from generally the same direction.
We thus found no clear meteorological distinction (in terms of humidity level or origin) between the two cases which might explain
the difference between their boundary layer conditions.
While meteorological conditions may be a potential causal factor of the
observed correlation between aerosol and cloud properties
e.g.,, the present
observations are not sufficient to definitively establish or discard
this hypothesis. Further study of the large-scale context is
necessary to more fully explore the potential meteorological
influences on the low- or high-pollution distinction and on the
aerosol–humidity relationship.
Another possible mechanism to explain the high humidity relates to the
temperature–aerosol relationship. While the observed development of the
AOD–T relationship (Figs.
and ) is consistent with that of aerosol
heating of the air mass , there are two possible
interpretations of how this may relate to the development of high-humidity
conditions. First, the humidification of the boundary layer may be a result
of the meteorological history of the air mass coincident with aerosol
conditions e.g.,; second, aerosol conditions
may be directly or indirectly increasing the boundary layer humidity. As
shown above, the first interpretation is not supported by the present study,
though a more complete analysis is necessary. Regarding the second
possibility, aerosol heating may suppress turbulent mixing and stabilize the
boundary layer, lowering boundary layer height and inducing higher relative
humidity as the polluted plume ages. Alternately, the presence of aerosol
heating within the more polluted air mass may be altering the mesoscale
circulation to bring more moist air to the region. Again, further study is
needed to establish the plausibility of these potential causal mechanisms and
to determine whether meteorological or aerosol mechanisms may be primarily
responsible for the observed correlations. Regardless of their mechanism,
these correlations must be considered in such studies of aerosol–cloud
interactions, as secondary changes in atmospheric properties – either
directly by aerosol effects or coincident with high-pollution conditions –
may alter the effective magnitude of indirect effects. As one example, the
first indirect effect relies on the assumption that the amount of liquid
water in a cloud is unchanged for clean vs. aerosol-perturbed cases. As cloud
albedo is a direct function of cloud liquid water, any coincident changes
observed in cloud liquid water content should be considered as this may alter
the expected magnitude of the aerosol-induced cloud-albedo effect. These
observed correlations require further exploration in future research.
Conclusions
Here we have presented new results on the characterization of trade cumulus
clouds and the dry-season cloud climatology in the northern Indian Ocean
using combined ground station observations, vertical atmospheric profiles
from UAVs, and large-scale satellite data and meteorological reanalysis. We
describe the general characteristics of the atmosphere in the region and
illustrate the existence of two separate climatologies based on the water
vapor conditions in the atmospheric column, which result in different
populations of clouds forming: “dry” conditions result in clouds which tend
to be constrained by a well-defined boundary layer topped by an inversion,
whereas the clouds forming under “wet” conditions exhibit more
unconstrained and varied development fed by the availability of more humid
upper-layer air. When the data are analyzed
according to this climatological separation to filter out the large natural variability of high-vapor
conditions, we observe a distinct positive correlation between aerosol
concentration and cloud liquid water. Highly polluted conditions (with a high
concentration of absorbing aerosol) are found to be systematically warmer and
more humid, as seen by the ground, aircraft, and large-scale analyses. From
the in situ aircraft and remotely sensed ground observations, we observe a
lower boundary layer height under polluted cases, resulting in a lower cloud
base which is responsible for the greater cloud liquid water. The observed
increase in RH was the only potential factor which could account for the
magnitude of the observed difference in cloud LWP which results from this
lower cloud base. The large-scale analysis indicates that highly polluted air
masses exiting the subcontinent are also warmer initially, while
high-humidity conditions develop along with the air mass as it ages.
While the strong correlation between aerosol and temperature is likely
attributable to aerosol heating of the air mass e.g.,, with the given observations we are unable to
definitively determine a causal mechanism responsible for the observed
correlation between aerosol and humidity. Possible mechanisms which may
result in these correlations include meteorological or aerosol-driven
factors, though at this stage we were not able to attribute the observed
differences to differences in large-scale advection patterns. There remains
the possibility that aerosol effects may be driving the observed lagged
humidification of the boundary layer, either by influencing the mesoscale
circulation or stabilizing the boundary layer locally; this is an intriguing
avenue for further study.
Understanding the consequences of aerosol–cloud
interactions in this region requires an understanding of how
variations in atmospheric conditions such as temperature and humidity
may impact cloud dynamics and water content. Additionally, future
research aiming at understanding aerosol–cloud interactions as
a whole, and the effects of aerosols influencing atmospheric dynamics
specifically, should incorporate both local observations of the
instantaneous vertical structure and motion of the atmosphere and large-scale observations to understand the air mass history and the
potential influence of meteorology on these effects.