Cirrus clouds cover a large fraction of tropical latitudes and play an
important role in Earth's radiation budget. Their optical properties,
altitude, vertical and horizontal coverage control their radiative forcing,
and hence detailed cirrus measurements at different geographical locations
are of utmost importance. Studies reporting cirrus properties over tropical
rain forests like the Amazon, however, are scarce. Studies with satellite
profilers do not give information on the diurnal cycle, and the satellite
imagers do not report on the cloud vertical structure. At the same time,
ground-based lidar studies are restricted to a few case studies. In this
paper, we derive the first comprehensive statistics of optical and
geometrical properties of upper-tropospheric cirrus clouds in Amazonia. We
used 1 year (July 2011 to June 2012) of ground-based lidar atmospheric
observations north of Manaus, Brazil. This dataset was processed by an
automatic cloud detection and optical properties retrieval algorithm.
Upper-tropospheric cirrus clouds were observed more frequently than reported
previously for tropical regions. The frequency of occurrence was found to be
as high as 88 % during the wet season and not lower than 50 % during
the dry season. The diurnal cycle shows a minimum around local noon and
maximum during late afternoon, associated with the diurnal cycle of
precipitation. The mean values of cirrus cloud top and base heights, cloud
thickness, and cloud optical depth were 14.3
Clouds cover on average about 50 % of the Earth's surface (Mace et al., 2007) and cirrus alone cover 16.7 % (Sassen et al., 2008), with higher fractions occurring in the tropics (Sassen et al., 2009). Hence cirrus are important to understand current climate and to predict future climate (Wylie et al., 2005; Stubenrauch et al., 2006; Nazaryan et al., 2008). Several studies emphasize the important role that cirrus clouds play in the Earth's radiation budget (i.e., Liou, 1986; Lynch et al., 2002; Yang et al., 2010a; Campbell et al., 2016). Their role is twofold. First, cirrus clouds increase warming by trapping a portion of infrared radiation emitted by the Earth/atmosphere system. Second, cirrus clouds cool the atmosphere by reflecting part of the incoming solar radiation back into space. The contribution of each effect and the net effect on the radiative forcing depends strongly on cirrus cloud optical properties, altitude, vertical and horizontal coverage (Liou, 1986; Kienast-Sjögren et al., 2016). Therefore, understanding their properties is critical to determining their impact on planetary albedo and greenhouse effects (Barja and Antuña, 2011; Boucher et al., 2013). Also, tropical cirrus clouds could influence the vertical distribution of radiative heating in the tropical tropopause layer (e.g., Yang et al., 2010b; Lin et al., 2013). Noticeably, it has been shown that an accurate representation of cirrus vertical structure in cloud radiative studies improves the results of these calculations (Khvorostyanov and Sassen, 2002; Hogan and Kew, 2005; Barja and Antuña, 2011). Recent research also shows that an increase of stratospheric water vapor is linked mainly to the occurrence of cirrus clouds in the tropical tropopause layer (TTL) (Randel and Jensen, 2013). Finally, measurements of the properties of cirrus clouds at different geographical locations are of utmost importance, potentially allowing for improvements in numerical model parameterizations and, thus, reducing the uncertainties in climatic studies.
Summary of some recent cirrus cloud studies based on at least a few
months of ground-based lidar observations in the tropics and midlatitudes.
The first columns show the period of study and laser wavelength (nm) for each
site location, for which more than one study might be available. The cirrus
characteristics are those reported by the different authors, which might
include: base and top height (km), thickness (km), base and top temperature
(
Ground-based lidars are an indispensable tool for monitoring cirrus clouds, particularly those cirrus clouds with very low optical depth, which are undetectable for cloud radars (Comstock et al., 2002) or for passive instruments (e.g., Ackerman et al., 2008). For this reason, several studies with ground-based lidars have reported the characteristics of cirrus clouds around the globe during the last decade. There are some long-term studies reporting climatologies at midlatitudes (e.g., Sassen and Campbell, 2001; Goldfarb et al., 2001; Giannakaki et al., 2007; Hoareau et al., 2013; Kienast-Sjögren et al., 2016) and tropical regions (e.g., Comstock et al., 2002; Cadet et al., 2003; Antuña and Barja, 2006; Seifert et al., 2007; Thorsen et al., 2011; Pandit et al., 2015). Table 1 shows an overview of these studies with different values for cirrus clouds characteristics in diverse geographical regions. There are also some short-term reports on cirrus clouds characteristics during measurement campaigns at midlatitudes (e.g., Immler and Schrems, 2002a) and tropical latitudes (Immler and Schrems, 2002b; Pace et al., 2003, and references therein). Additionally, satellite-based lidar measurements have been used to investigate the global distribution of cirrus characteristics (e.g., Nazaryan et al., 2008; Sassen et al., 2008, 2009; Wang and Dessler, 2012; Jian et al., 2015). Characteristics of tropical and subtropical cirrus clouds have similar geometrical values and they occur at higher altitudes than those at midlatitudes. However, the frequencies of occurrence of cirrus cloud types differ significantly between different locations.
Reports on cirrus cloud measurement over tropical rain forests like in Amazonia are scarce. Just a few global studies with satellite instruments include these regions, and they do not provide information on the diurnal cycle. There are also a few studies focused on deep convection in Amazonia that report cirrus clouds (e.g., Machado et al., 2002; Hong et al., 2005; Wendisch et al., 2016), but no lidar measurements were used. Baars et al. (2012) focused on aerosol observations with a ground-based Raman lidar, and thus report only one cirrus cloud case that was observed between 12 and 16 km height on 11 September 2008 during an 11-month measurement period in 2008. Barbosa et al. (2014) describe a week of cirrus cloud measurements performed from 30 August to 6 September 2011 during an intensive campaign for calibration of the water vapor channel of the UV Raman lidar, which is also used in this study. Cirrus clouds during that period were present in 60 % of the measurements. Average base and top heights were 11.5 and 13.4 km, respectively, and average maximum backscatter occurred at 12.8 km. Most of the time, two layers of cirrus clouds were present.
Satellite-based map (Google Earth) showing the location of the lidar
site (ACONVEX T0e; 2.89
From the above discussion, the importance of continuous and long-term observations of tropical cirrus clouds is evident. In the present study, we use 1 year of ground-based lidar measurements (July 2011 to June 2012) at Manaus, Brazil to investigate the seasonal and daily cycles of geometrical (cloud top and base altitude) and optical (cloud optical depth (COD) and lidar ratio) properties of cirrus over a tropical rain forest site. In Sect. 2, a description of the Raman lidar system, dataset, processing algorithms, and site are given. The results and discussion are presented in Sect. 3. We close this paper with concluding remarks in Sect. 4.
The ACONVEX (Aerosols, Clouds, cONVection EXperiment) or T0e (nomenclature of
the GoAmazon2014/15 experiment, Martin et al., 2016) site is located up-wind
from Manaus-AM, Brazil, at 2.89
As with most tropical continental sites, the diurnal cycle of precipitation is strong with a late afternoon peak (Adams et al., 2013). The precise definition of the climatological seasons varies among authors (e.g., Machado et al., 2004; Arraut et al., 2012; Tanaka et al., 2014); however, deep convection is a characteristic of the region all year. For our site and period of study, we considered a wet (January–April), dry (June–September), and transition (March, October–December) season respectively. Convection is more active during the wet season, when the Intertropical Convergence Zone (ITCZ) influences the region. As the ITCZ moves northward during the dry months, convective activity decreases.
The lidar system (LR-102-U-400/HP, manufactured by Raymetrics Advanced Lidar
Systems) operates in the ultraviolet (UV) at 355 nm. Three channels detect
the elastically backscattered light at 355 nm as well as the Raman-scattered
light of nitrogen (387 nm) and water vapor (408 nm), simultaneously in
analog and photon-counting modes. The system is tilted by 5
The lidar dataset used in the present study comprises measurements recorded between July 2011 and June 2012, which were temporally averaged into 5 min profiles (3000 laser shoots at 10 Hz). A total of 36 597 profiles were analyzed corresponding roughly to one-third of the maximum possible number of profiles during 1 year.
For the long-term analysis, winds were obtained from the ERA-Interim
reanalysis (Dee et al., 2011)
of European Centre for Midrange Weather Forecast (ECMWF) with spatial
resolution of 0.75
We used an automatic algorithm for the detection of the cloud base, the cloud
top, and the maximum backscattering heights, based on Barja and Aroche (2001).
The algorithm is explained in detail in Barbosa et al. (2014) and is in here
only described briefly. Basically, it assumes a monotonically decreasing
intensity of the lidar signal with altitude in a clear atmosphere and
searches for significant abrupt changes. These abrupt changes are marked as a
possible cloud base. Examining the signal noise, each true cloud base is
discriminated. Then, the lowest altitude above cloud base with signal lower
than that at cloud base and corresponding to a molecular gaseous atmosphere
is determined as the cloud top. When more than one layer is present in the
same profile, and their top and base are separated more than 500 m, they are
considered as individual clouds. Figure S2 in the Supplement gives an example
of cloud detection. Barbosa et al. (2014) also provide information on the
discrimination of false positives and the distinguishing of aerosols from
thin cloud layers. After obtaining the base, top, and maximum backscatter
heights, the corresponding cloud boundary temperatures are obtained from the
nearest radiosonde. A detected high cloud is classified as a cirrus cloud if
the cloud top temperature is lower than
In a simplified manner, the frequency of occurrence would just be the ratio of the number of profiles with cirrus clouds to the total number of profiles. However, while one might be sure when a cirrus cloud was detected in a given profile, there is no certainty of its presence when the profile has a low signal-to-noise ratio or when there is no measurement available. Sampling cirrus clouds with a ground-based profiling instrument can be problematic, particularly for the calculation of the temporal frequency of occurrence, due to the obscuration by lower clouds, or availability of measurements, which might introduce sampling biases (Thorsen et al., 2011).
Summary of column-integrated statistics for the total time of
observation, as well as for the wet, transition, and dry seasons. Frequency of
occurrence is calculated using a conditional sampling to avoid biases
(Sect. 2.4). Mean cirrus cloud properties
and standard deviation of the sample (in parentheses) are shown. The standard
deviations of the mean were calculated and used to determine if seasonal
differences (wet–dry) of the mean values are statistically significant to
the 95 % confidence level (indicated as *) using a two-sample
To avoid these sampling issues, we use an approach similar to the conditional sampling proposed by Thorsen et al. (2011) and Protat et al. (2014). First, we recognize that the presence of cirrus clouds is rather independent of low-level liquid water clouds that can fully attenuate the laser beam, and independent of instrumental issues that might restrict measurement time. Hence, the best estimate of the true frequency of occurrence is the ratio of the number of profiles with cirrus, by the number of profiles where cirrus could have been detected.
These qualifying profiles are identified as follows. The noise in each
clear-sky bin follows a Poisson distribution and is evaluated as the square
root of the signal. The signal-to-noise ratio (SNR) is defined as the background corrected signal
divided by the noise, similar to Heese et al. (2010). Profiles are selected if a clear-sky SNR higher
than 1.0 is found at 16 km, for 7.5 m vertical resolution. Note that this
is not the SNR of the cirrus cloud ((cirrus
From analysis of the available profiles, 16 025 were found to satisfy these criteria (see Table 2). July, August, and September, the driest months, show the highest fraction of profiles with good SNR, while the wettest months have the lowest fraction of lidar profiles with good SNR (see Fig. S1). To avoid introducing biases from the different sample sizes in different months, the frequency of occurrence for the year is calculated as the average frequency of occurrence for each season. The frequency for each season, in turn, is calculated from the frequency of each month. Finally, the frequency for each month is calculated by averaging over the mean diurnal cycles (i.e., mean of hourly means), because there are more profiles with good SNR during night compared to daytime.
Attenuation of the lidar signal by cirrus clouds can be obtained using the
ratio of the range-corrected signal at the top and at the cloud base as
described in Young (1995):
The backscattering coefficients of cirrus clouds were determined by the
Fernald–Klett–Sasano method (Fernald et al., 1972; Klett, 1981; Sasano and
Nakane, 1984) for each 5 min averaged profile having cloud and satisfying
the conditions discussed in the previous section. For retrieving extinction,
the Klett method requires a predetermined value for the layer-mean lidar
ratio (LR), which is the ratio between the extinction and backscattering
coefficients. Then, integrating the extinction coefficient from the cloud
base to cloud top, the cirrus cloud optical depth is obtained (
Monthly frequency of occurrence of cirrus clouds from July 2011 to
June 2012 (blue line) with the associated statistical error (black).
Accumulated (light green) and climatological (dark green) rainfall, shown on
the right axis, were obtained from the TRMM 3B42 version 7 dataset averaged
over an area of 10
The Klett method assumes single scattering, but eventually the received
photons could have been scattered by other particles multiple times before
reaching the telescope. This effect, named multiple scattering, increases the
apparent laser transmittance and decreases the corresponding extinction
coefficient values. Inversion of uncorrected signals could bias the
extinction, and hence the COD and LR, typically by 5–30 % (Thorsen and
Fu, 2015). This is particularly important at UV wavelengths, for which a much
stronger forward scattering and therefore larger amounts of multiple
scattering occur compared to the visible or infrared wavelengths. For this
reason, we refrain from applying empirical correction formulas (e.g.,
Eq. 10 in Chen et al., 2002), and instead perform a full treatment of
multiple scattering following the model of Hogan (2008). The correction is
found iteratively, similar to Seifert et al. (2007) and Kienast-Sjögren
et al. (2016). The forward model is initialized with the originally
retrieved, uncorrected extinction profile, and the model output is used to
correct the extinction profile iteratively, until it converges. In our case,
we assumed the effective radius of ice crystals to vary with temperature
according to a climatology of aircraft measurements of tropical cirrus data
(Krämer et al., 2016a, b), which includes the recent ACRIDICON field
campaign with the German aircraft HALO in the Amazon region (Wendisch et al.,
2016). The full treatment corrects the retrieved LR by about 40 %, from
Summary of layer statistics for the total time of observation, as
well as for the wet, transition, and dry seasons. Mean cirrus cloud properties
and standard deviation of the sample (in parentheses) are shown. The standard
deviations of the mean were calculated and used to determine if seasonal
differences (wet–dry) are statistically significant to the 95 %
confidence level (indicated as *) using a two-sample
A total of 11 252 lidar profiles were recorded with the presence of cirrus clouds, yielding an average temporal frequency of cirrus cloud occurrence of 73.8 % from July 2011 to June 2012. Figure 2 shows the monthly frequency of cirrus cloud occurrence, with statistical error, and precipitation in central Amazonia. There is a well-defined seasonal cycle, with maximum values from November to April, reaching 88.1 % during the wet season, and a minimum value in August during the dry season (59.2 %), but with frequencies not lower than 50 % (see Table 2). Moreover, the mean monthly cirrus cloud frequency follows the same seasonal cycle as accumulated precipitation, which responds to the seasonal changes of the ITCZ, and is higher from January to April and lower from June to September (Machado et al., 2002, 2014). Mean cirrus frequencies during the wet months are higher by a statistically significant amount than during dry months (notice the small standard deviation of the mean despite the high variability). This result and the lack of the other possible formation mechanisms proposed in the literature (Sassen, 2002) suggest that deep convection is the main formation mechanism for cirrus clouds in central Amazonia. Deep convective clouds generate cirrus clouds when winds in the upper troposphere remove ice crystals of the top of the large convective column, generating anvil clouds. Anvil clouds remain even after the deep convective cloud dissipates and persists from 0.5 to 3.0 days (Seifert et al., 2007).
Panels
To further investigate the role of deep convection as the main local
formation mechanism, the high-altitude circulation and spatial distribution
of precipitation were studied. The mean wind field at 150 hPa, approximately
the mean cirrus top-cloud altitude (14.3 km; see Table 3), and accumulated
precipitation are shown in Fig. 3. The study period was divided into wet
(January, February, March, and April), dry (June, July, August, and September)
and transition (May, October, November, and December) periods, based on
accumulated precipitation. During the wet months, the South American monsoon
is prevalent, and associated rain amounts range from 8 to 14 mm day
The backward trajectories also reveal that the high-altitude circulation is
quite variable. Indeed, many backward trajectories do not follow the average
wind pattern and seem to point in the opposite direction of precipitation,
particularly during the dry season. One should note, however, that central
Amazonia still receives about 100 mm month
The diurnal cycle of cirrus cloud frequency, shown in Fig. 4, also has a
close relation with the convective cycle. The frequency of occurrence, for
the overall period or any season, exhibits a minimum between 10:00 and 14:00
local time (LT). Maximum values are found between 17:00 and 18:00 LT, in the
late afternoon, when values are slightly higher than in the morning. This
diurnal variation follows the diurnal cycle of convection documented in the
literature (e.g., Machado et al., 2002; Silva et al., 2011; Adams et al.,
2013), as also shown in Fig. 4 as the diurnal cycle of precipitation averaged
over an area of 2
Panel
To verify that the lower cirrus cloud cover around noon was not related to a
decrease in SNR and, hence, a decrease in detection efficiency, we analyzed
the frequency of occurrence for different cirrus types (following Sassen and
Cho, 1992). Opaque (COD
Table 2 shows column-integrated statistics of the properties of cirrus clouds
during the 1-year observational period, also distinguished by season.
Column-integrated COD varies from 0.25
Panels show the normalized histograms of
As cirrus at different altitudes might have different origins or
microphysical properties, it is more important to analyze the statistics
based on each layer detected, as shown in Table 3. The overall mean value for
the cloud layer base altitude is 12.9
The geometrical characteristics of the detected cirrus clouds were further
examined by means of normalized histograms. Figure 5 shows the results for
cloud base and top height, thickness, and cloud optical depth. Histograms for
the wet and dry season months reveal differences. The cloud base distribution
(Fig. 5a) is wider during the wet season. There are relatively more cirrus
layers with cloud base below 12 km and above 16.5 km during the wet than
during the dry season. Particularly, there is a peak centered at 16.5 km
during wet months, which does not exist during the dry season months. The
distribution of geometrical thickness (Fig. 5b) shows more cirrus layers
thicker than 2 km (and less thinner than that) in the wet season. The
normalized histogram of COD (Fig. 5d) shows relatively more cirrus layers
with COD
Comstock et al. (2002) proposed two different types of cirrus clouds at Nauru in the tropical western Pacific with oceanic conditions: one type (laminar thin cirrus) with cloud base altitudes above 15 km and the other (geometrically thicker and more structured cirrus) with base altitudes below this height, with different characteristics. Liu and Zipser (2005) used the TRMM precipitation radar (PR) dataset to trace the deep convection and precipitation throughout the tropical zone, including oceans and continents. The authors showed that only 1.38 and 0.1 % of tropical convective systems, and consequently their generated cirrus clouds, reached 14 and 16.8 km of altitude, respectively.
Considering these previous results, we suggest that the highest peak in wet months in cloud top distribution originates from convection penetrating the tropopause, located at about 15.9–16.5 km, while the lowest peak is the ceiling of most tropical convection. The single peak observed during the dry months, in turn, originates from cirrus clouds transported by large distances. Clouds generated by convective systems can persist in the atmosphere from hours to days if they are slowly lifted (Ackerman et al., 1988; Seifert et al., 2007). Clouds that ascended and are horizontally transported by long distances are, in general, optically and geometrically thinner and found at higher altitudes in the troposphere. This also explains why the geometrical thicknesses and optical depth are lower during the dry season months.
Two-dimensional histograms of cirrus frequency of occurrence with altitude as a function of optical depth during the wet (top) and dry (bottom) season months are shown on the left. The same is shown on the right but integrated for SVC, thin, and opaque cirrus cloud optical depths.
To investigate whether the higher cirrus layers were indeed geometrically and optically thinner, a more in-depth analysis of the vertical distribution was performed. Figure 6 shows two-dimensional histograms of cloud optical depth and cirrus occurrence vertical distribution for the wet season months (top) and dry season months (bottom). The right panels show the vertical distribution of the frequency of occurrence for the three cirrus categories. During the wet months, there is more dispersion (wider range of COD for a fixed altitude, and vice versa) than in the dry months, which we speculate might be associated with the well-documented variability in the intensity of deep convection in Amazonia (Machado et al., 2002; Adams et al., 2009, 2013, 2015). Indeed, it is only during the wet season that a significant fraction of cirrus is found above 16 km height, and they have a COD ranging from 0.001 to 0.02. Moreover, while the distribution of opaque cirrus peaks at 12 km height in both seasons, thin cirrus and SVC shows a bimodal distribution only in the wet season, with the highest maxima above 14 and 16 km respectively. This is presumably associated with the overshooting convection discussed above, which occurs mostly during the wet season (Liu and Zipser, 2005). Moreover, ice detrainment directly into the tropical tropopause layer (TTL) is one of the main mechanisms of TTL cirrus formation; the other is in situ formation by supersaturation promoted by mesoscale uplift (Cziczo et al., 2013), which can occur above tropical convective systems (Garret et al., 2004), a very common feature of the Amazon hydrological cycle.
To investigate the role of the tropopause capping on the cirrus vertical
development, its altitude was calculated from the ERA-Interim dataset for the
observation time of each cirrus profile (see Sect. 2.2 and Fig. S3a and b).
The tropopause mean altitudes during the wet, transition, and dry periods are
16.5
Normalized histograms of the distance of the tropopause to the
cirrus base and top are shown for overall period (black) and each season
(colors). Negative values mean that clouds are below tropopause. The average
tropopause altitude was 16.2
The classification of cirrus clouds following Sassen and Cho (1992) shows
that 41.6 % of the cirrus clouds measured in our experimental site are
subvisible (
This large fraction of optically thin and subvisible cirrus clouds over
Amazonia present a challenge for using passive remote sensing from space,
such as MODIS. As mentioned by Ackerman et al. (2010), thin cirrus clouds are
difficult to detect because of insufficient contrast with the surface
radiance. MODIS only detects cirrus with optical depth typically higher than
0.2 (Ackerman et al., 2008). Therefore, the MODIS's cloud-mask does not
include 71 % of cirrus clouds over Amazonia, and likewise, their
estimation of aerosol optical depth might be contaminated with these thin
cirrus. Aerosol optical depth measurements from AERONET can also be
contaminated with thin cirrus clouds. Chew et al. (2011), for instance,
estimated that the fraction of contaminated measurements of AERONET aerosol optical depth in
Singapore (1.5
The different types of cirrus clouds measured in central Amazonia, with different formation mechanisms, optical depths, and altitude ranges are expect to be composed of ice crystals of different shapes. One way to gain information on the crystal habits is to compute the lidar ratio (Sassen et al., 1989). As explained in Sect. 2, we are able to estimate the average lidar ratio for the detected cirrus cloud layers in each profile using an interactive approach instead of explicitly calculating the extinction from the Raman signal, which would be available only during nighttime.
Average values are given in Table 3 for all cirrus, and for each category. A
mean value of 23.9
Although the mean LR for all seasons and categories are similar, their statistical distribution might yet reveal differences. Figure 8 shows the histograms of lidar ratio corrected for multiple-scattering for the different seasons (top) and for the different categories (bottom). For all seasons, the most frequent lidar ratios are between 18 and 28 sr. There are notable differences only for different cirrus categories. The opaque cirrus distribution has a peak at 25 sr, while thin cirrus has its peak at about 21 sr, and SVC at about 15 sr, with a secondary peak at 44 sr.
Normalized histograms of the lidar ratio, already corrected for
multiple scattering, for the different seasons
As cirrus microphysical properties are expected to depend on altitude (e.g.,
Goldfarb et al., 2001), we examine the dependence of the lidar ratios with
the cirrus cloud top temperature (Fig. 9). The plots show the mean, the
median, and the interquartile distance. A slight increase in the lidar ratio
values from 20 to 28 sr for a decrease in temperature from
Dependence of the corrected lidar ratio with cloud-top temperature
is shown for the wet (blue) and dry (red) seasons. The markers give mean and
standard deviation of the mean. The continuous and dashed lines give median
and interquartile distance. Temperature is divided into 2.5
One year of ground-based lidar measurements collected between July 2011 and June 2012 were used to investigate the geometrical and optical properties of cirrus clouds in central Amazonia. An algorithm was developed to search through this dataset with high vertical and temporal resolution and to automatically find clouds, calculate particle backscatter, and derive optical depth and lidar ratio. The frequency of cirrus cloud occurrence during the observation period was 73.8 %, which is higher than reported previously in the literature for other tropical regions. Cirrus frequency reached 88.1 % during the wet months (January, February, March, and April), but decreased to 59.2 % during the dry months (June, July, August, and September). Analysis of high-level circulation and precipitation during the wet months indicate that nearby deep convection was likely the main source of these cirrus, whilst during the dry period, there was a mixture of locally produced and transported clouds. Moreover, we found that the diurnal cycle of the frequency of occurrence of opaque and thin cirrus shows a minimum around 12:00 LT and a maximum around 18:00 LT, following the diurnal cycle of the precipitation for both seasons.
The geometrical and optical characteristics of cirrus clouds measured in the
present study were consistent with other reports from tropical regions. The
mean values were 12.9
By simultaneously analyzing cloud altitude and COD, it was found that cirrus clouds observed during the dry season months are optically thinner and lower in altitude than those during the wet period. The vertical distribution of frequency of occurrence is mono-modal, and 13 % of the observed cirrus had top within the TTL. During the wet season months, there is a wider range of COD for a fixed altitude, and vice versa, which is associated with the variability in the intensity of deep convection in Amazonia. The vertical distribution of the frequency of occurrence of the detected clouds shows a bimodal distribution for thin and SVC, and 19 % of the observed cirrus had top within the TTL, which are likely associated to slow mesoscale uplifting or to the remnants of overshooting convection.
For the first time, the lidar ratio of cirrus clouds was obtained for the
Amazon region. The mean lidar ratio, corrected for multiple scattering, was
23.6
For access to the lidar dataset used in this study, please contact the
corresponding author. Other used datasets are publicly available online:
ERA-Interim reanalysis data from the ECMWF webpage
(
The authors declare that they have no conflict of interest.
We thank our colleague David K. Adams from UNAM and two reviewers for reading the manuscript and giving valuable comments. We thank Martina Krämer for sharing the aircraft data on tropical cirrus. Diego A. Gouveia acknowledges the support of the CNPq fellowship program. Boris Barja acknowledges the financial support of CAPES project A016_2013 on the program Science without Frontiers and the SAVERNET project. Henrique M. J. Barbosa and Paulo Artaxo acknowledge the financial support from FAPESP Research Program on Global Climate Change under research grants 2008/58100-1, 2009/15235-8, 2012/16100-1, 2013/50510-5, and 2013/05014-0. Maintenance and operation of the instruments at the experimental site would not have been possible without the institutional support from EMBRAPA. We thank INPA, The Brazilian Institute for Research in Amazonia, and the LBA Central office for logistical support. Special thanks to Marcelo Rossi, Victor Souza, and Jocivaldo Souza at Embrapa, and to Ruth Araujo, Roberta Souza, Bruno Takeshi, and Glauber Cirino from LBA. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model used in this publication. Edited by: G. Vaughan Reviewed by: J. R. Campbell and one anonymous referee