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Variability of Midtropospheric Moisture and Its Effect on Cloud-Top Height
Distribution during TOGA COARE*
RANDY G. BROWN
AND
CHIDONG ZHANG1
Department of Atmospheric Sciences and Joint Institute for the Study of the Atmosphere and Ocean,
University of Washington, Seattle, Washington
(Manuscript received 17 May 1996, in final form 15 March 1997)
ABSTRACT
The tropical western Pacific warm pool is often generalized to be a region of heavy precipitation. This concept
is useful in constructing simplified models of the tropical circulation. However, the warm pool region is often
punctuated by periods of little rain. Such drought periods may last up to 10 days over an area of at least 6 3
105 km2. Other common features of the drought periods include an extremely dry midtroposphere, few deep
clouds typically associated with mesoscale convective systems, and a substantial amount of clouds that are too
tall to be categorized as trade cumuli but too short to fall into the category of deep convective clouds. Midtropospheric moisture varies substantially (60% in relative humidity, 4 g kg21 in water vapor mixing ratio) between
rainy and drought periods. The frequency distributions of humidity exhibit bimodal structures at certain levels
above the freezing level. In either rainy or drought periods, or in a long period including both, the time-mean
humidity above the boundary layer deviates substantially from the most frequent profile of humidity, defined
as the relative humidity corresponding to the maximum frequency distribution at each level. Mean soundings,
therefore, do not accurately represent the overall vertical structure of moisture in the warm pool. Calculations
of a simple parcel model demonstrate that the warm pool atmosphere above the boundary layer can be dry
enough to discourage the growth of deep convective clouds by depleting parcel buoyancy through entrainment.
These results were drawn from an analysis of soundings collected during the Intensive Observing Period (1
November 1992–28 February 1993) of the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere
Response Experiment.
1. Introduction
The western Pacific warm pool is home to widespread
deep convection and heavy precipitation. The release of
large amounts of latent heat from deep convection is an
important process of the earth’s climate. The very large
equatorial radius of deformation, however, keeps any
resulting temperature perturbation in the Tropics small,
except on global scales. No similar constraint on moisture variability, on the other hand, is known to exist.
Because deep convective clouds associated with the
western Pacific ‘‘heat source’’ depend on a supply of
moisture from the boundary layer, studies of warm pool
convection have focused on the moisture budget of the
* Joint Institute for the Study of the Atmosphere and Ocean Contribution Number 345.
1 Current affiliation: Rosenstiel School of Marine and Atmospheric Science, Division of Meteorology and Physical Oceanography,
University of Miami, Miami, Florida.
Corresponding author address: Dr. Chidong Zhang, University of
Miami, RSMAS/MPO, 4600 Rickenbacker Causeway, Miami, FL
33149-1031.
E-mail: [email protected]
q 1997 American Meteorological Society
boundary layer, especially the roles played therein by
surface evaporation, convective downdrafts, dry air entrainment from above the boundary layer, and horizontal
moisture convergence. Comparatively little attention
has been given to the variability of moisture above the
boundary layer.
Recently, it has been shown that warm and dry midlevel layers exist in the western Pacific warm pool during even the rainy season and that the presence of these
layers corresponds to periods with noticeably less rainfall; the dry layers, which have a finite width of ;300
km, are associated with differential horizontal advection
of air into the Tropics from higher latitudes (Numaguti
et al. 1995; Sheu and Liu 1995; Mapes and Zuidema
1996). The presence of these extended drought1 periods
during the rainy season leads one to ask what effects
these dry layers have on deep convection. This is an
especially interesting question for it can be shown that
during the drought periods, convection did form, but
deep convection that extends upward to the tropopause
was rare. Clouds only reached intermediate heights,
1
We use the word drought in a relative sense to describe situations
of less rain rather than completely no rain.
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BROWN AND ZHANG
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FIG. 1. TOGA COARE sounding sites. Only the sites from which data are used in this study are marked. Open circles
indicate ISS sites (except R/V Xiangyanghong 5), which are the primary data sources of this study. Dots mark selected
PSS sites from which soundings are used to verify results based on the ISS soundings. LSA stands for Large-Scale Array,
OSA for Outer Sounding Array, and IFA for Intensive Flux Array. EMA sites are in boldface. The location of the IMET
buoy is also shown.
above the tops of trade cumuli but way below the tropopause. These types of convective clouds will hereafter
be termed middle clouds, in comparison with ‘‘deep
clouds’’ and ‘‘low clouds.’’2 The presence of middle
clouds indicates that a lack of deep convection was not
because of the absence of a convective ‘‘trigger,’’ but
presumably because of the absence of some environmental factor that is a necessary (though not sufficient)
condition for the formation of an organized deep convective system. The coexistence of midlevel dry layers
with the absence of organized deep convection and associated high precipitation rates makes the dry midlevel
atmosphere a suspect (Yoneyama and Fujitani 1995;
Mapes and Zuidema 1996; Lucas and Zipser 1996).
The coexistence of midlevel dry air with anomalously
low convective activity has also been noted in other
regions. For example, Fuelberg and Biggar (1994) used
summertime soundings from north Florida to show that
the relative humidity in the layer 500–700 hPa tends to
be 20% greater on days with strong convective activity
than on days with weak convective activity. This result
is consistent with studies of convection over south Florida as well (e.g., Burpee 1979).
The absence of abundant deep clouds, which tends
to moisten the environment by detrainment, cannot explain the midlevel dryness during the drought periods
(Mapes and Zuidema 1996). On the other hand, the
suppression of deep convection in the presence of midlevel dry layers and unstable boundary layer air may
possibly be explained in terms of entrainment of the dry
air by ascending parcels, which tends to dilute the parcel’s buoyancy and thereby result in much lower cloud
2
The deep, middle, and low clouds here are categorized according
to the height of cloud top and should not be confused with the cloud
identification based on the height of cloud base.
tops. This idea, first proposed formally by Stommel
(1947),3 will be discussed in the present study.
Through analyzing Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) (Webster and Lukas 1992)
soundings (section 2), we will first document the general
moisture variability (section 3) and its association with
precipitation (section 4). With a special emphasis on a
10-day drought period and a following rainy period, we
will then examine the variation in cloud-top height in
relation to the moisture variability (section 5). At last,
using a simple parcel model, we will illustrate the possibility that entrainment of dry air by clouds acts to
limit the cloud-top height in a dry environment above
the boundary layer, thereby providing a possible, if only
partial, explanation for the absence of organized deep
convection during the drought periods (section 6). The
limits of our analysis will be discussed (section 7) before
a summary is given (section 8). Our analysis focused
on a 6 3 105 km2 area in the equatorial western Pacific
during the TOGA COARE Intensive Observing Period
(IOP, 1 November 1992–28 February 1993).
2. Data
The main datasets used are TOGA COARE soundings, Japanese Geosynchronous Meteorological Satellite
(GMS) infrared (IR) temperatures, and precipitation
from three different sources. The soundings were from
three land sites (Kavieng, Kapingamarangi, Manus) and
four ship sites (R/V Kexue 1, R/V Moana Wave, R/V
Shiyan 3, and R/V Xiangyanghong 5) (Fig. 1, open
circles). They are all ISS (Integrated Sounding System)
sites (Parsons et al. 1994) except R/V Xiangyanghong
3
See Simpson (1983) for a brief account of early studies on entrainment.
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5.4 For convenience, hereafter we will refer to soundings
from all these sites, including R/V Xiangyanghong 5,
as the ISS soundings and the sounding sites as the ISS
sites. ‘‘Areal means’’ will be used to refer to averages
taken over these sounding sites, which covered an area
of about 6 3 105 km2.
The ISS soundings were launched at 6-h intervals
every day throughout the COARE IOP. The soundings,
processed by the Atmospheric Technology Division
(ATD) of the National Center for Atmospheric Research
(NCAR), were interpolated to 5-hPa vertical resolution;
instrumental errors were partially corrected. The dataset
used in this study is the official release in June 1995.
Because this study relies in part on boundary-layer relative humidity measurements, it is important to note that
a considerable fraction of the COARE ISS soundings
required corrections to the near-surface relative humidity. A more detailed description of these problems can
be obtained through the NCAR ATD office (also see
Miller 1993). For this reason, we have omitted relative
humidity data that were less than 50% between the surface and 980 hPa. In addition, we have omitted data
flagged by NCAR as bad data or as data that required
interpolation across excessively long pressure intervals
(Miller and Riddle 1994). A total of 2482 ISS soundings
have passed our in-house screening.
Soundings from other selected sites (dots in Fig. 1)
were also used in this study for sensitivity tests, but the
results are not shown. They are part of the Priority
Sounding System (PSS, Loehrer et al. 1995). They were
selected either because they belong to an Enhanced
Monitoring Array (EMA) that covered from 1 July 1992
to 30 June 1993 (Biak, Manus, Kavieng, Kapingamarangi, Nauru, and Tarawa) or because they were
launched four times per day during the COARE IOP.5
The cloud-top height distribution was examined using
hourly GMS IR data of 5-km resolution (resampled every 10 km). These data are available for the TOGA
COARE region throughout most of the IOP (Chen et
al. 1995). Because the data were used to determine the
cloud-top height distribution, it is important to note that
the data are of limited resolution and cannot provide an
accurate accounting of the contribution of isolated
clouds with diameters ,1–3 km. A high IR temperature
reading may result from a situation where a single high
cloud is embedded in an otherwise clear-sky pixel area
as well as from a pixel uniformly covered by low clouds.
Also, only the highest clouds can be detected in the
presence of multilayer clouds. In the presence of thin,
high cirrus overlaying thick middle cloud, cloud-top
height for the latter could be overestimated.
Three daily precipitation datasets were used: precip-
Another ISS site (Nauru) was not included because it was far
apart from the others.
5
Most of the rest of the COARE soundings were launched twice
a day.
4
VOLUME 54
itation averaged over the IFA (Intensive Flux Array, Fig.
1) based on MSU (Microwave Sounding Unit) rain estimates (Spencer 1993); ‘‘Version 2’’ rain estimates
from TOGA COARE ship radar observations (Short et
al. 1997), covering an area roughly 400 km (in longitude) 3 300 km (in latitude) within the IFA and centered
at 28S, 1568E; and point measurements of rain from
IMET (Improved Meteorology) buoy deployed at
18459S, 1658E (Weller and Anderson 1996). Because of
the different spatial coverage, large detailed discrepancies exist among the three precipitation time series.
Major rainy and drought periods, however, are detected
by all three estimates. There are not many events of
significant precipitation that were recorded by the IMET
measurements or the ship radar observations but missed
by the MSU estimates (see section 4). This indicates
that precipitation from small isolated thunderstorms,
such as the airmass thunderstorms (Byers and Braham
1949), did not contribute much to the total areal mean
precipitation.
3. Moisture variability
We found that it is very informative and interesting
to examine the moisture variability in the form of the
frequency (or probability) distributions of relative humidity (RH) as functions of pressure (Fig. 2). The distributions were constructed from the 6-hourly ISS
soundings and normalized separately at each pressure
level for each 1% RH bin by the total number of observations at that level. The RH above the freezing level
(roughly 550 hPa) has been computed with respect to
ice. There are cases in which the RH with respect to
ice exceeds 100%, probably associated with cirrus
clouds. Distributions above 250 hPa should be viewed
with caution because of a problem of icing on the instruments (Lin and Johnson 1996). The three panels in
Fig. 2 are for the entire IOP (Fig. 2a), a representative
drought period from IOP day 12 to 21 (12–21 November
1992) (Fig. 2b), and a rainy period extending from IOP
day 39 to 48 (9–18 December 1992) (Fig. 2c). The
drought and rainy periods were selected using the three
precipitation datasets (see section 4 for details).6
The mean RH profile for each period was plotted as
the solid line (the IOP mean is duplicated in the panels
for the rainy and drought periods as the thick solid
lines). All three periods show similar mean structure in
the boundary layer (below 950 hPa), with a peak of
about 80% at the top of the boundary layer. Above the
boundary layer the mean RH decreases with height
much more rapidly during the drought period than dur-
6
Notice that one of the ISS sounding sites (Manus) is not in IFA
(Fig. 1). The drought and rainy periods selected using IFA precipitation data may not apply precisely to this site. Analyses excluding
soundings from this site were conducted but the results show no
difference.
1 DECEMBER 1997
BROWN AND ZHANG
2763
FIG. 2. Probability distribution of relative humidity as a function of pressure for (a) IOP (1 November 1992–28 February 1993), (b) a
drought period (12–21 November 1992), and (c) a rainy period (9–18 December 1992). The distribution at each level is normalized by the
total number of observations available at that level. Relative humidity is computed with respect to ice above the freezing level. Thin solid
lines are time means for respective periods. Dashed lines are the means plus and minus one standard deviation. Thick solid lines in (b) and
(c) are the IOP mean. Triangles indicate the most frequent profiles; see text for more details. The unit of color code is %.
ing the rainy period. Between the top of the boundary
layer and the freezing level, the mean RH is 10%–20%
lower during the drought period than in the rainy period.
The discrepancies between the mean RH for the IOP
and the drought period are much greater than those between the IOP and the rainy period. As will be discussed
later, however, the physical meaning of the time-mean
soundings is questionable.
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FIG. 3. Probability distribution of relative humidity at selected pressure levels (dots) for IOP. Solid lines are smoothed distributions. Vertical solid and dotted lines are IOP means and the means plus/minus one standard deviation.
The variability of the relative humidity generally increases with height, as clearly indicated by the standard
deviations (dashed lines) and by the general spread of
the probability distribution. The largest variability is at
500–300 hPa, where extremely low RH (,20%) can be
commonly found in both drought and rainy periods. In
contrast, a unique, equally dry low troposphere (roughly
850–700 hPa) is found only in the drought period.
An extremely interesting and intriguing feature shown
in Fig. 2a is an indication of bimodality in the distribution of RH at certain levels above the freezing level
for the IOP. One distribution peak (the green/yellow
colors) resides at about 75%–90% and the other one at
10%–40%. It is not unexpected to see broad probability
ranges in the humidity-related variables, considering the
effects on water vapor distribution from advection by
large-scale circulation (e.g., Numaguti et al. 1995;
Mapes and Zuidema 1996) and from cloud detrainment
(e.g., Udelhofen and Hartmann 1995). What is surprising is the low probability (the red color) in between the
two extreme (moister and drier) situations. Figures 2b
and 2c suggest that the distribution peak of high RH
values is mainly contributed by moist soundings from
rainy periods, and the peak of low values by dry soundings from drought periods. The bimodality can be more
clearly seen in Fig. 3, where probability distributions
of RH are plotted at selected pressure levels. It needs
to be determined, however, whether the apparent bimodal structures are artificial due to insufficient sampling in space and/or time, unique to periods with strong
signals of the Madden–Julian oscillation (MJO, Madden
and Julian 1971, 1972), or characteristic of the warm
pool.
The bimodal signals were reproduced using the following datasets: two subsets of soundings formed by
randomly but equally dividing the COARE ISS soundings, daily mean soundings averaged over the ISS sites,
a set of selected Priority Sounding System soundings
that covered a much broader spatial domain than the
ISS soundings (Fig. 1), and a set of soundings from the
Enhanced Monitoring Array (Fig. 1) that covered from
July 1992 through June 1993. The main features shown
in Fig. 2 are not sensitive to whether soundings from
particular sites are included or not. Furthermore, a bimodal structure can also be seen from the probability
distributions of daily and areal mean water vapor mixing
ratio at certain levels (Fig. 4). All of these suggest that
the bimodality is not associated with a specific sampling
procedure but is an indication of an interesting feature
of the large-scale variability in water vapor.
It is equally interesting that the IOP mean RH coincides with local low probability between the two high
distribution peaks at certain levels above the freezing
level (450–200 hPa) (Figs. 2a, 3, and 4). This raises a
serious question: What is the physical implication of a
mean sounding at those levels? Certainly, it does not
represent the soundings that are most likely to be observed. The most observable soundings can be represented by a ‘‘most frequent profile,’’ determined as the
RH corresponding to the highest probability at each level7 (marked with triangles in Fig. 2).
There are large discrepancies between the most frequent profiles and the time-mean soundings, except in
the boundary layer, for all three periods. Between the
boundary layer and the freezing level, the most frequent
profiles are 5%–10% higher than the mean soundings.
The discrepancies between the two become much larger
above the freezing level. In the IOP and the rainy period,
the most frequent profiles are about one standard de-
7
The distribution at each level was first smoothed with a threepoint (1–5–1 weighting) running mean for this purpose.
1 DECEMBER 1997
BROWN AND ZHANG
2765
FIG. 4. Same as Fig. 3 but for water-vapor mixing ratio.
viation higher than the means, in part because the means
are biased toward low values due to a significant number
of extremely low RH soundings. In the dry period, the
most frequent profile can be as much as 40% lower than
the mean sounding above the freezing level. The most
frequent profiles themselves also differ from each other
between different periods (mainly between the drought
period and the other two). This underlines the fact that
moisture stratification above the boundary layer systematically changes between rainy and drought periods.
Apparently, the most frequent profiles are much more
realistic representations than the time-mean soundings
for the overall moisture distributions in either rainy or
drought periods. For example, our current research
shows that subtle but physically significant discrepancies between the longwave radiative cooling rates in
rainy and drought periods can be clearly identified only
if the most frequent profiles, not the mean soundings,
are used in the calculations. Because of the large variability and the bimodality of moisture, however, any
single sounding would fail to accurately represent its
overall structure over a time span covering both rainy
and drought periods. For example, using either the time
mean or the most frequent profile for the TOGA COARE
IOP would result in 20%–30% root-mean-square errors
above the freezing level. A time-mean sounding tends
to misrepresent the reality most of the time by relatively
moderate error margins, while a most frequent profile,
which tends to bias toward rainy periods, as shown in
Fig. 2a, would represent the reality well most of the
time but misrepresent by substantial error margins other
times.
The large variability of RH can be confirmed by the
probability distributions of the water vapor mixing ratio
q (Fig. 4). Many features shown in Fig. 4 are similar
to those in Fig. 3: a bimodal structure at certain levels,
discrepancies between the mean and the most frequent
q, and a wide range in the variability of q, especially
in the low and midtroposphere where long ‘‘dry tails’’
in the distribution curves are seen.
We have also examined the variability of RH in other
drought and rainy periods during the IOP. The two periods chosen in Fig. 2 are typical cases. The existence
of some very moist soundings in the drought period and
some very dry soundings in the rainy period reflects the
situations that are common in the warm pool. Extreme
cases with much drier or moister soundings than what
have been shown can be found, but they are less common.
4. Precipitation
The three daily time series of precipitation are shown
in Fig. 5a. There are several extended drought and rainy
periods during the IOP. A major rainy period, starting
from IOP day 39 (9 December 1992) and lasting for 20
days or so, was preceded by a drought period from days
12 to 34, which was interrupted by an event of intense
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FIG. 5. Time series of MSU daily rain rate (mm day21) for the TOGA COARE IFA (dotted lines) and vertically averaged water vapor
mixing ratio (^q&, g kg21, solid lines) between (a) 1000 and 950 hPa, (b) 950 and 850 hPa, (c) 850 and 550 hPa, and (d) 550 and 200 hPa.
In (a), dots mark daily precipitation measured at the IMET buoy (unit: 0.1 mm day 21) and triangles are radar daily rain estimates (unit: 0.5
mm day21). IOP day 1 corresponds to 1 November 1992 and day 120 corresponds to 28 February 1993.
rainfall centered on day 24. Without this rainy day the
average daily MSU rainfall over the IFA is less than 1
mm day21, with 9 days hardly having measurable arealmean rainfall at all.8 Gutzler et al. (1994) and Chen et
al. (1996) have shown that the drought and rainy periods
were, respectively, associated with passages of convectively suppressed and active phases of the MJO. The
drought and rainy periods chosen for the RH distribution
calculation in the last section were the first halves of
the suppressed and active phases of the MJO.
Bulk properties of the water vapor variability during
the IOP can be described in terms of the daily and areal
mean mixing ratios vertically integrated through four
8
Isolated, local precipitation did occur, as shown by the radar rain
estimates and rain gauge measurements at certain locations.
layers: 1000–950 hPa (the boundary layer), 950–850
hPa (low troposphere above the boundary layer), 850–
550 hPa (midtroposphere below the freezing level), and
550–200 hPa (troposphere above the freezing level).
The vertically averaged mixing ratio in the boundary
layer (^q&950
1000, Fig. 5a) fluctuated on various timescales.
Based on a visual inspection, a period of 20 or 25 days
seems to exist, with corresponding local minima on IOP
days 13, 24, 44, 68, and 91. The most interesting feature,
however, is the sudden decreases and quick recoveries
in ^q&950
1000 associated with almost all the major rain events.
If the drying effect of unsaturated mesoscale downdrafts
penetrating into the boundary layer (Zipser 1969) explains the negative spikes in ^q&950
1000 , then Fig. 5a shows
the cumulative effects of the mesoscale phenomenon on
the large scales. This high-frequency abrupt behavior in
mixing ratio also occurred to a lesser degree in the low
1 DECEMBER 1997
BROWN AND ZHANG
troposphere above the boundary layer (950–850 hPa,
^q&850
950, Fig. 5b) but seemed to disappear gradually at
higher levels. In the midtroposphere below the freezing
level (850–550 hPa, Fig. 5c), the most striking fluctuations in the mixing ratio (^q&850
950 ) were marked by four
events during which ^q&850
suddenly
decreased about 2–
950
4 g kg21 (27%–54% of its mean value and two to four
times of its standard deviation in that layer) in about
five days. These events of low ^q& (the dry events) can
also be observed in other layers, with smaller magnitudes (;1 g kg21). They disappeared much more quickly
in the lower layers but persisted longer in the layer
above the freezing level (550–200 hPa, ^q&250
550 , Fig. 5d).
The variability of ^q&250
was
apparently
short
of high550
frequency fluctuations, with dominant periods of about
10–20 days. These dry events can also be clearly seen
from time series of relative humidity profiles averaged
over the IFA (Lin and Johnson 1996).
The dry events are impressive because of their magnitudes, which were about 6% (surface–950 hPa) to 67%
(550–200 hPa) of the respective means and greater than
the respective standard deviations by a factor of 1.5
(950–850 hPa) to 4 (850–550 hPa). These dry events
appear to be the reason for the dry branches in the
bimodal structure in the probability distributions of RH
and q (Figs. 2–4).
The relationships between ^q& and precipitation are
interesting as well as puzzling. In all layers, ^q& was
high during the rainy periods and all the extremely dry
events occurred during drought periods. The opposite,
however, is not true. It did not always rain when ^q&
was high and all drought days were not low in ^q& (e.g.,
days 30–40), especially in the lower layers. In general,
it appears that ^q& and precipitation share better instantaneous relationships in the higher layers. The crosscorrelation between the two is #0.1 in the two lower
layers and 0.5 in the upper two layers.9 After the daily
time series were applied with a 5-day running mean, the
correlation remains low (#0.25) in the lower layers but
increases slightly (0.65) in the upper layers.
Regardless of whether the correlation between ^q& and
precipitation is high or low, interpreting their local relationships is difficult. While atmospheric humidity may
affect the development of precipitation, ^q& can be modified by the feedback from clouds as well as the atmospheric circulation. In addition to the obvious drying
effect experienced in the boundary layer during major
rainy events (Fig. 5a), moistening by deep clouds may
be part of the reason for the high correlation between
^q&250
550 and precipitation (Fig. 5d). It is unlikely, however,
that the sudden dry events displayed by ^q&550
850 (Fig. 5c)
were caused in any manner directly by precipitationrelated processes. Independent factors, such as horizon-
The 99% significance level is at 0.55 if the degree of freedom is
conservatively estimated as 24 with one independent sample every
5 days.
9
2767
FIG. 6. Normalized cloud-top height distributions based on 2 3 2
pixel-mean IR temperatures from the three island stations (Manus,
Kavieng, and Kapingamarangi) for IOP (solid line), the rainy period
(dashed), and the drought period (dotted).
tal advection, are more plausible mechanisms (e.g., Numaguti et al. 1995; Mapes and Zuidema 1996). On the
other hand, even though the sustained drought periods
cannot be explained by the dryness of the atmosphere,
the cessation of a rainy period may result from a sudden
reduction in ^q&; the two almost always coincided in all
the layers.
We now propose a working hypothesis to guide the
rest of the analysis. An extremely dry event, such as
that which occurred between IOP days 11 and 18 (11–
18 November 1992), is an important factor for a rainy
period to come to an end. This hypothesis can be alternatively stated as: An extremely dry atmosphere prevents middle clouds from growing into the deep ones
that account for a large fraction of rain production in
the Tropics (Houze and Cheng 1977). We believe the
dry events were the cause, not the result, of the end of
the rainy periods for two reasons. First, there was no
other apparent reason for the cessation of the rainy periods. Boundary-layer moisture (^q&950
1000 ) suddenly decreased at the peak of the rainy periods but quickly
recovered even before the end of the rainy periods (Fig.
5a). Vertically integrated boundary-layer equivalent potential temperature ue did not show sudden decreases
during or after the rainy periods (not shown). Wind shear
can be important for convective systems to become organized (e.g., Dudhia and Moncrieff 1987) but is not
known to be a factor that may terminate convection.
This leaves the dry events as the main suspects. Second,
that the dry events quickly diminished while the drought
periods persisted supports the notion that the dry events
were not caused by the lack of precipitating clouds. It
follows that, even if the dry events helped bring forth
the end of the rainy periods, they may not be accountable
for the persistence of the drought periods.
5. Cloud-top height distribution
The drought periods were not cloud free. Figure 6
shows the frequency distributions of cloud-top height,
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FIG. 7. Cloud-top height distributions as functions of time for the first half of the IOP, inferred from 6-h IR temperatures
at the same three island sites as in Fig. 6. Superimposed is daily mean relative humidity (solid line) vertically averaged
between 950 and 550 hPa and averaged over the three sites.
inferred from IR temperatures (Tb), for the IOP and the
same drought and rainy periods chosen for the RH distribution plots in Fig. 2. These distributions were constructed using hourly sampled Tb averaged over four
pixels (each of 10 3 10 km2) centered at each ISS site.
The possible sources of error in estimates of cloud-top
height based on the IR temperatures, mentioned in section 2, should be kept in mind.
The general features of Fig. 6 are a gradual increase
in frequency as Tb increases from its lowest value (indicating the highest cloud top) to 280 K for all three
cases and a well-defined sharp peak centered at 292 K
for the IOP and the drought period. This continuous
increase in the probability distribution, extending from
the deepest to the shallowest possible clouds, indicates
a lack of any single dominant type of cloud in terms of
its height (e.g., Mapes and Houze 1993). The symmetric
shape of the sharp peaks at the high Tb end for the IOP
and the drought period is indicative of the variability
of Tb with clear-sky water vapor. Probability distribution
of clear-sky Tb at the top of the atmosphere based on
calculations of a radiation transfer model using the same
set of soundings exhibits a peak of the same shape (not
shown). The maximum frequency at 292 K, therefore,
corresponds to the most probable clear-sky total water
vapor content.
During the rainy period, the distribution (dashed line)
shows greater occurrence of clouds with Tb below 280
K than during the other two periods. The largest difference occurs at Tb 5 212 K. The lowest Tb is 185 K.
During this period, a portion of the clouds with intermediate heights is growing into even taller clouds later
on, and perhaps a greater portion of such middle clouds
is associated with decaying mesoscale convective systems (e.g., Chen and Houze 1997).
The drought-period distribution (dotted line) is heavily weighted toward clear sky and low clouds with Tb
lying mostly between 280 and 300 K. Clouds with toptemperature Tb less than 210 K are virtually nonexistent
(occurrence frequency less than 1%).10 But clouds do
exist, especially with cloud-top temperatures between
230 and 280 K. Most of these middle clouds did not
grow into deep clouds associated with widespread convective systems with heights comparable to the tropopause and with a noticeable increase in areal-mean precipitation because few such clouds exist during the
drought period. It is possible, and perhaps even probable, that some of the middle clouds are actually remnants of deeper cloud systems that were advected into
the region. However, the maximum cloud-top height
observed is typically accompanied by a continuous distribution of cloud-top heights that extend to the lowest
possible clouds, reflecting an ensemble of growing, mature, and decaying clouds (Chen and Houze 1997). This
can be seen from Figs. 7 and 8, where temporal progressions of the cloud-top height distribution are displayed.
The cloud-top distribution in Fig. 7 was computed by
combining Tb from 10 3 10 pixel arrays centered at
each of the three land sites (Manus, Kavieng, and Kapingamarangi) into 10-K bins and then normalizing the
resulting distribution at each time by the total number
of pixels recorded at that time. In order to show the
progression from the drought to rainy periods, the distribution is calculated for four sounding hours per day
for the first half of the IOP (1 November–31 December
1992). Recall that the drought and rainy periods chosen
for Figs. 2 and 6 are 12–21 November (IOP days 12–
21) and 9–18 December (IOP days 39–48), respectively.
In addition, time series of daily mean RH, vertically
averaged from 950 to 550 hPa at each sounding site and
then averaged over the three sites, is overlaid in Fig. 7
(^RH&, solid line). The ^RH& time series matches well
10
The existence of isolated but deep clouds of subpixel size (K100
km2) should not be ruled out. These clouds are usually short lived
and probably are present only in the local afternoon. They may,
however, produce local rain.
1 DECEMBER 1997
BROWN AND ZHANG
FIG. 8. Cloud-top height distribution inferred from IR temperatures
at Kavieng for the drought period from 12 to 21 November 1992.
Superimposed are (a) daily mean relative humidity (solid line) vertically averaged between 950 and 550 hPa at the same site and (b)
LCL (dots), nonentraining LFC (open triangles), nonentraining LNB
(solid diamonds), entraining LNB with « 5 0.005 (open squares),
and entraining LNB with « 5 0.01 (solid triangles), all calculated
using soundings from the same site.
the ^q& time series covering a larger area (Fig. 5). The
correlation between ^RH& in Fig. 7 and ^q&550
850 in Fig. 5c,
for example, is 0.85.
The rainy period began around IOP day 39 (9 December) and extends to day 50 and beyond. During this
period, there were plenty of high clouds (Tb , 220 K).
During the drought period (days 12–21 and 28–37), in
contrast, the cloud distribution is weighted heavily toward low clouds and clear sky as expected, but there is
clear evidence of middle clouds with cloud-top temperatures of up to 220 K, especially in the first drought
period. All these clouds, including the low and middle
clouds and isolated high clouds that may have not been
properly represented by the IR data, seemed not to produce significant amounts of precipitation over a large
area and a multiple-day time span, even though they
may have produced some isolated rain. If this is indeed
the case, then their direct impact on large-scale dynamics through latent heat release may be small and is in
any case practically immeasurable (e.g., their heating
rates are overwhelmed by radiative cooling rate). However, these clouds might still be playing an important
role in the moisture budget of the troposphere.
The quick recovery in moisture from the dry events
seen in Figs. 5 and 7 remain unexplained. The dry event
of days 11–16, for example, was apparently caused by
horizontal advection from the subtropics into the deep
Tropics (Numaguti 1995; Mapes and Zuidema 1996).
At the end of this dry event, a pattern of such advection,
2769
although weaker, still existed (Numaguti 1995). Meanwhile, a large-scale subsidence dominated the deep troposphere over the IFA (Lin and Johnson 1996) and surface
evaporation was relatively low (Weller and Anderson
1996). All this leaves the moistening effect of the middle
clouds present at that time the most plausible mechanism
for the moisture recovery in the midtroposphere. In
comparison, the dry event of days 29–33 shown in Fig.
7 was quite local (not obvious in the ^q& time series
over the larger domain, Figs. 5b and 5c). This dry event
features a more gradual decrease in ^RH& and a more
sudden recovery at the end. The large-scale subsidence
over the IFA was not greater than that during the previous dry event (Lin and Johnson 1996). In addition to
a possible large-scale circulation, the absence of middle
clouds during this dry event may be a reason for the
delayed recovery of moisture. Notice that the quick recovery at the end of the dry event coincided with the
appearance of middle clouds on day 33.
Figure 8a shows in detail the cloud-top height distribution during the drought period of days 12–21 at
one site (Kavieng), overlaid with the 950–550 hPa layeraveraged relative humidity ^RH& from the same site. The
presence of middle clouds is again clear, with only a
few times appearing to be truly cloud free. The disappearance of high clouds on day 13 and 14 when ^RH&
decreased due to dry-air advection (Numaguti 1995;
Mapes and Zuidema 1996) is clearly shown. Apparently,
this dry-air advection overrode the moistening effect the
low and middle clouds may have had on day 14. The
^RH& recovery on day 15, however, coincided with the
reappearance of middle clouds. Later, ^RH& kept increasing in the presence of more middle and some high
clouds. This local correspondence between ^RH& recovery and the presence of middle clouds is consistent with
what was previously seen for a larger area (Fig. 7). The
above hypothetical descriptions of the cloud-moistening
effect have to be checked against a different set of
soundings. The soundings currently used include those
that may have actually gone through clouds. A set of
‘‘clear-sky soundings’’ will be needed to demonstrate
that the environmental humidity indeed increases in the
presence of middle clouds.
The existence of the abundant middle clouds in the
drought period implies that it is a shortfall of convective
growth and development but not convective initiation
that may account for the lack of very deep and widespread convective clouds. This assertion is supported
by the observations that surface and boundary-layer
conditions in the IFA were in favor of deep convection
during that period. Surface sea and air temperatures
were, respectively, 28.58C and 288C or higher (Weller
and Anderson 1996); vertically integrated boundarylayer ue was no less than 352 K (calculated from the
same set of soundings). What then prevents clouds from
growing deep and developing into mesoscale convective
systems in the drought period? We now return to the
hypothesis made earlier that the dryness of the midtropo-
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JOURNAL OF THE ATMOSPHERIC SCIENCES
sphere above the top of the boundary layer may play
an important role in suppressing the growth of deep
clouds.
6. Role of dry-air entrainment
The possible effects of dry-air entrainment on tropical
convective clouds has attracted research attention for
many years (e.g., Simpson 1983). The basic idea of
entrainment is that dry environmental air is entrained
into the cloud and mixed with the rising air parcels,
leading to a dilution of the parcel buoyancy (Stommel
1947). Applying this idea to this study, we postulate
that such entrainment during the dry events makes middle clouds difficult to grow into deep ones that would
otherwise eventually become widespread, organized,
mesoscale convective systems producing a great amount
of precipitation. To test this hypothesis or to simply
illustrate the possibility, we will now examine how
clouds grow in the same dry environment with and without entrainment.
Entrainment processes have been incorporated into
many one-dimensional cloud models and cumulus parameterization schemes (e.g., Warner 1970; Arakawa
and Schubert 1974; Kain and Fritsch 1990; Raymond
and Blyth 1986; Chen and Frank 1993). Sensitivity tests
have shown that convective mass flux simulated by such
cloud models are significantly reduced above the freezing level by decreases in RH (e.g., Kain and Fritsch
1990). For illustration, we use the simplest model based
on the ‘‘standard’’ parcel theory that can be found in
many textbooks.
The height of clouds depends on a combination of
the boundary-layer ue and the vertical structure of the
environmental temperature profile. To a first approximation, the maximum cloud top can be determined by
raising an air parcel from the boundary layer along a
moist adiabats (determined by the parcel ue) until the
parcel reaches its level of free convection (LFC). The
resulting positive buoyancy above the LFC, measured
in a bulk sense by convective available potential energy
(CAPE), is assumed to accelerate the parcel upward
until it encounters its level of neutral buoyancy (LNB),
which, also commonly known as the equilibrium level,
is defined as the first level above the LFC at which the
virtual temperatures of the environment and the parcel
are equal.
This naive approach to computing LNB as cloud-top
heights is based on a number of simplifications and can
lead to inconclusive results due to calculation problems
as well. For example, one particular problem is the determination of the appropriate level of origin for the
parcel. A recent study by Renno and Williams (1995)
shows that parcels ascending into cumulus clouds originate near the land surface. However, it is not clear that
their results apply equally well to air parcels in maritime
boundary layers for the surface forcing over ocean is
not nearly as strong as over land. It is possible that air
VOLUME 54
parcels ascending into maritime cumulus clouds originate from a variety of levels in the boundary layer,
which, if true, would necessitate averaging boundary
layer properties before CAPE is calculated or computing
CAPE for separate layers and then averaging the various
CAPE’s together (Mapes 1993). There is still no consensus on the origin of air in maritime clouds, and it is
impossible to remove this uncertainty from the calculations. For simplicity, we assume that all the parcels
ascend from 1000 hPa.
We computed LNB and CAPE using both the traditional parcel approach and a simple modification to that
approach to account in a crude way for the effect of
dry-air entrainment on parcel buoyancy. We will refer
to the CAPE and LNB computed using the entraining
parcel model as the ECAPE and ELNB to distinguish
them from the traditional nonentraining CAPE and
LNB. Both CAPE and ECAPE were computed using
the equation:
CAPE 5 R d
E
pLNB
(Tye 2 Typ ) d ln( p),
(1)
pLFC
where Ty e is the environmental virtual temperature from
the sounding, pLFC and pLNB are the pressures at the LFC
and LNB respectively, Rd is the dry gas constant, and
Ty p is the parcel’s virtual temperature. The difference
between the CAPE and ECAPE calculations lies entirely
in the way that Ty p is computed. In the case of nonentraining CAPE, we computed Ty p by lifting an air parcel
from 1000 hPa dry adiabatically until the lifting condensation level (LCL) was reached, and then following
a pseudoadiabat thereafter until the LFC was reached.
For the entraining parcel Ty p was computed in a stepwise
fashion as follows. Starting at the LCL, the parcel was
lifted along a pseudoadiabat until it reached the next
sounding level, at which point any condensed water was
rained out. The parcel was then mixed with a fixed
fraction « of environmental air, and the temperature and
mixing ratio of the resulting mixture determined by iteratively solving equations for the conserved quantities
ue and total water mixing ratio, from which a new parcel
ue was then computed. The value of « undoubtedly depends on a number of factors such as the parcels spatial
size and upward velocity, but for simplicity we assumed
« was constant.
Once Ty p was determined, we computed the LNB for
both ECAPE and CAPE by requiring that Ty p exceed
Ty e by 0.15 K. In other words, the LNB, with and without
entrainment, was determined as the first level above the
LFC for which Ty p . Ty e 1 0.15 K. The physical motivation for doing this is to try to account for the parcel’s
ability to break through weak inversions as a result of
its nonzero upward momentum. We did not include latent heat of fusion in computing Ty p to keep the calculations simple, although it may not always be small.
Figure 8b shows various parcel quantities such as the
LCL, LFC, and LNB with and without entrainment,
1 DECEMBER 1997
BROWN AND ZHANG
superimposed on the same cloud-top distribution for the
dry period of 12–21 November as in Fig. 8a. The LCL,
LFC, and LNB have been plotted in terms of the sounding temperature at the level where they were found instead of in terms of pressure, thereby allowing direct
comparison with the IR temperature. The bin width used
for computing the IR temperature distribution is 10 K
so that the correspondence between a computed LNB
(taken as cloud top) and the maximum in the cloud-top
height distribution is only accurate to within ;5 K. The
LFC (open triangles) was computed with no entrainment
effects. Three different LNB are shown: the nonentraining LNB (solid diamonds) and ELNB for « 5 0.005
(open squares) and 0.01 (solid triangles), respectively.
Two points are immediately clear. First, the variability
of cloud-top height in this 10-day period cannot be accounted for by properties of the air parcels in the boundary layer (in this case, at 1000 hPa); the computed LCL
(dots) does not change much during the period. Second,
the nonentraining LNB (solid diamonds) is consistently
much higher than the observed maximum cloud-top
height during this drought period, with only a few exceptions. In some instances, the difference is dramatic,
as on days 13, 15, and 20. Entrainment obviously reduces the LNB. The larger the entrainment rate («) is,
the greater the reduction of the LNB would be. Although
we choose « 5 0.005 to produce an ELNB (open
squares) that appears to match the observed maximum
cloud-top height, this particular value of « does not bear
any realistic physical meaning because of the simplicity
of the model. Nevertheless, our purpose of demonstrating our hypothesis on entrainment as a viable one is
well served by these calculations.
To further demonstrate the effect of dry-air entrainment, a comparison of CAPE and ECAPE was made
using soundings averaged over ten categories of ^q&. The
ten sounding categories were computed by binning all
the soundings into deciles based on the magnitude of
^q&200
950, with category 1 representing the driest 10% of
soundings and category 10 the moistest 10% of soundings. The traditional CAPE ranges from a low of 600
J kg21 for the driest category to between 1200 and 1650
J kg21 in the remaining categories (Fig. 9). The entraining CAPE (i.e., ECAPE with « 5 0.005), however, is
zero for the driest 10% of soundings and reaches a maximum of only about 460 J kg21. While CAPE generally
increases as ^q&200
950 increases, it is clear that there is considerable variability unrelated to ^q&200
950 . This is not surprising, for CAPE depends primarily on ue or q in the
boundary layer (i.e., ^q&950
1000 ). The correlation between
200
^q&950
1000 and ^q&950 within each of the ten categories is never
more than 0.26, so one would not expect CAPE to increase uniformly with ^q&200
950 . ECAPE, on the other hand,
tends to increase more smoothly as ^q&200
950 increases,
which is a reflection of the decreasing effectiveness of
entrainment at neutralizing parcel buoyancy as the environment becomes more moist.
Figure 9 also shows the values of CAPE and ECAPE
2771
FIG. 9. Nonentraining CAPE, entraining CAPE (ECAPE with « 5
0.005), CAPE, and ECAPE computed up to the freezing level (FLCAPE and FL-ECAPE) calculated using ten category-mean soundings. Each category represents a 10% portion of the total sounding
from the seven sites. Category 1 is the driest and category 10 is the
moistest set of soundings in terms of vertically integrated water vapor
mixing ratio from 950 to 200 hPa.
if the vertical integration of parcel buoyancy is halted
at the freezing level (FL-CAPE and FL-ECAPE). The
large difference between CAPE and FL-CAPE shows
that most of the CAPE comes from the relatively large
difference between Ty e and Ty p above the freezing level.
This is also true for ECAPE, though to a lesser extent.
One implication is that, in cases where entrainment is
important, the large parcel buoyancy above the freezing
level might never be realized if dry-air entrainment
drives the actual LNB downward to the ELNB. It should
again be noted that the large CAPE above the freezing
level is not a result of including latent heat of fusion in
our calculation of Ty p, including latent heat of fusion
would make the contribution to CAPE above the freezing level even higher.
7. Discussion
Our central hypothesis is that dry air above the boundary layer acts to limit the cloud-top heights by entrainment in temporary drought periods within the generally
rainy season of the warm pool. If our hypothesis is
correct, any process that facilitates the moistening of
the atmosphere above the boundary layer would be a
factor in favor of deep convective development. This
amounts in a rough sense to an upward extension of the
view of shallow convection in which shallow nonprecipitating clouds act to increase or maintain the moisture
supply between the top of the boundary layer and the
trade inversion against drying by large-scale subsidence
(Sarachik 1978). The difference in this case is twofold:
the clouds in the warm pool extend through a much
greater depth of the troposphere than do the shallow
nonprecipitating clouds in the tradewind regime and,
based on field observations, these clouds are in all likelihood precipitating (Liu et al. 1995; Rickenbach 1995).
Because the area-averaged precipitation rate from these
clouds is small (e.g., Fig. 4), they may have little impact
in a direct way on the heat budget of the atmosphere.
2772
JOURNAL OF THE ATMOSPHERIC SCIENCES
Their importance probably lies in their ability of blocking incoming solar radiation and moistening the lower
troposphere.
While inclusion of some sort of entrainment process
in the computation of CAPE and LNB might prove useful in assessing the likelihood of deep convection during
dry periods, it is unlikely that it will render significantly
better results during the convectively active periods, at
least when using the simple model as in this study. There
are several reasons for this. First and foremost, the continuous entraining model used in this study is far from
realistic for entrainment by isolated convective clouds
(Warner 1970), let alone cumulonimbus clouds organized into mesoscale convective systems. The only advantage of the continuous entrainment model is strictly
one of simplicity. The simple entrainment model is more
problematic in rainy periods because cloud parcels that
ascend through large convective storms may, in fact, be
protected from direct entrainment of environmental air
by a shroud of cloudy (i.e., saturated) air. In this case,
the simple entraining parcel model would provide an
erroneously low estimate of cloud-top height. This is
especially true given that the soundings used for these
estimates during active periods are often biased toward
the drier clear air between clouds as a result of the
relatively high failure rate of radiosondes launched into
convective clouds. The entrainment of much of the environmental air into convective elements in tropical
squall lines probably occurs in part between the ascending boundary-layer parcels and descending midlevel environmental air that is injected along the leading
edge of the storm, further complicating the entrainment
process. The continuous entrainment model used in this
study should therefore be thought of as only a crude
approximation to the actual entrainment process and as
a tool of elucidation. Most of the deficiencies in our
simple model have been remedied to different degrees
in sophisticated models. The hypothesis on the effects
of entrainment on convective clouds needs to be confirmed by consistent results from different models.
The dry events observed during TOGA COARE are
in part the result of horizontal advection of dry air from
higher latitudes instead of local subsidence drying (Numaguti et al. 1995; Mapes and Zuidema 1996). Thus, a
complete picture of the interaction of tropical deep convection with larger-scale dynamics must account for the
possibility of a ‘‘dry air valve’’ on deep convection
resulting not only from subsidence, but also from the
lateral advection of dry midlevel air. The presence of
drought periods and the effect of the dry air on the
cloud-top distribution may be of some importance to
the convective parameterization problem. This is especially true if the middle clouds are providing a necessary premoistening of the environment before the onset of deep widespread convection. Failure to properly
account for these clouds in a convective parameterization could lead to errors in general circulation model
(GCM) forecasts of the convective variability. Whether
VOLUME 54
such a failure would have severe enough effects to degrade a model’s simulated climate has yet to be explored. Many features (e.g., probability distributions of
the soundings, variability of precipitation, and cloudtop height in relation to humidity distributions) revealed
by our analysis can be used to validate GCM simulations.
8. Summary
Using TOGA COARE soundings, we have demonstrated that, on timescales of up to 10 days, and for
space scales of about 6 3 105 km2, the tropical atmosphere can experience large variability in its moisture
as measured by the relative humidity (Figs. 2 and 3)
and water-vapor mixing ratio (Figs. 4 and 5). The largest
fluctuations in the relative humidity occurred in the troposphere above the freezing level where the relative humidity could vary by as much as 60% between drought
and rainy periods. The large moisture variability primarily resulted from frequent emergence of extremely
dry events in the otherwise moist environment. Because
of the large fluctuations in moisture between the two
extreme stages, tropospheric moisture exhibits a bimodal distribution, with the peaks corresponding to the
maximum distributions in rainy and drought periods,
respectively. As a consequence, using any single sounding to represent the overall vertical moisture structure
in the warm pool atmosphere over a period (such as the
COARE IOP) that includes both rainy and drought episodes would inevitably lead to large biases.
We have also shown that a substantial amount of
clouds found during drought periods are too tall to be
categorized as trade-cumulus-type clouds, but too short
to fall into the deep convective category associated with
the mesoscale convective systems (Figs. 6–8). Those
clouds may play roles in the moisture variability of the
troposphere above the boundary layer. Using a simple
parcel model, we illustrated that the warm pool atmosphere above the boundary layer can be dry enough to
have suppressing effects on deep convection by depleting parcel buoyancy through entrainment (Figs. 8
and 9).
This study has been explorative, qualitative, and
mostly empirical. Extension can be made in many ways.
If dry-air entrainment is indeed a viable mechanism
limiting the growth of deep convective clouds, then a
particularly intriguing question can be asked: Does tropical deep convection regulate itself on large-scales by
inducing the circulation that would advect dry air into
the Tropics from higher latitudes?
Acknowledgments. We would like to thank the NCAR
ATD, especially Erik Miller, for processing the TOGA
COARE sounding data. We would also like to thank
Shuyi S. Chen of the University of Washington for the
GMS data, David A. Short of NASA for the radar precipitation data, Roy W. Spencer of NASA for the MSU
1 DECEMBER 1997
BROWN AND ZHANG
data, and Robert A. Weller of WHIO for the IMET data.
Suggestions and comments made during the course of
the study by Christopher S. Bretherton, Shuyi S. Chen,
Brian E. Mapes, and Bradley F. Smull have been very
beneficial. Careful comments from two anonymous reviewers helped improve the manuscript. K. Dewar provided graphics assistance and G. C. Gudmundson provided editorial assistance. This study was supported by
NSF Grant ATM-9320193.
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