Knowledge of the spatiotemporal coverage of cirrus clouds is
vital in quantifying the radiation budget of the Earth–atmosphere system. In
this paper, we present the diurnal and vertical distributions of the
occurrence of cirrus clouds during different seasons as well as the
interannual variation in the
occurrence of cirrus over Kattankulathur (12.82∘ N,
80.04∘ E) on the west coast of the Bay of Bengal. Long-term
(2016–2018) continuous micropulse lidar (MPL) observations demonstrate
laminar and descending cirrus clouds that occur either as single or
multiple layers. The single-layer cirrus occurrence shows a diurnal pattern
with frequent occurrence in the late evening (∼ 30 %–40 %),
whereas multilayer cirrus clouds occur in the early morning
(∼ 10 %–20 %). For the diurnal pattern in single-layer cirrus
cloud occurrences, convective processes dominate during the pre-monsoon,
southwest (SW) monsoon, and northeast (NE) monsoon seasons, while the freeze-drying
process is favorable during the winter season. However, both convective and
freeze-drying processes are dominant in the diurnal pattern of the
multilayer cirrus occurrences. The occurrence reaches a maximum
(∼ 40 %) during the SW and NE monsoon seasons, and it shows a minimum
(∼ 25 %) during the winter season. The vertical
distributions indicate that the maximum occurrence is confined within the
tropical tropopause layer (TTL) during all seasons. Cirrus cloud
rarely occurs above the tropopause; however, it frequently occurs below the
TTL during all seasons. The vertical extent of the occurrence has a
broader altitudinal coverage (∼ 8–17 km) during December–March
and June–September, while the altitudinal coverage is narrower during April–May (∼ 10–17 km) and October–November (∼ 9–15 km). Cirrus cloud
occurrence also exhibits interannual variations, with higher occurrence
during 2016 compared with 2017 and 2018, in association with the El Niño–Southern
Oscillation (ENSO).
Introduction
Cirrus clouds are the first clouds to interact with solar radiation. They
modify the Earth's radiation budget by reflecting the incoming solar
radiation (albedo effect) and trapping the outgoing longwave radiation
(greenhouse effect). Therefore, the net radiative effects depend on the
macrophysical, microphysical, and optical properties of cirrus clouds
(Lynch, 2002). Tropical deep convective areas are generally capped by cirrus
clouds (Sassen et al., 2009), with the highest fraction of optically thin
cirrus (0.03<τ≤0.3) followed by optically thick
(τ>0.3) and subvisible (τ≤0.3) cirrus.
Optically thin cirrus clouds cause a net positive radiative forcing in the
upper troposphere; however, thick clouds may produce cooling
(Stephens and Webster, 1981; Fu and
Liou, 1993). Overall, when the greenhouse effect dominates, there will be
net warming in the tropical atmosphere, whereas there will be net cooling when albedo (scattering) dominates. However, knowledge of scattering
by nonspherical ice crystals is highly limited (Liou et al., 1986), and the
quantification of the net radiative effect of cirrus clouds on the
atmosphere is highly uncertain. Although cirrus clouds affect the whole
column (Fleming and Cox, 1974), their effect is most pronounced in the
tropical tropopause layer (TTL). Yang et al. (2010) quantified the
radiative impacts of cirrus clouds on the TTL and observed net cloud
radiative heating below ∼ 16 km and mostly cooling above
∼ 17 km. Thus, net heating due to cirrus clouds can strengthen
upwelling and provide an alternative mechanism for
troposphere–stratosphere exchange process
(Corti et al., 2006). Randel and Jensen (2013) reported that the occurrence of TTL cirrus clouds leads to a
significant increase in stratospheric water vapor. Recent studies have suggested
that changes in stratospheric humidity due to cirrus clouds will
substantially impact climate variability compared with those associated with
decadal increases in greenhouse gases (Riese et al., 2012). A
warming anomaly in the vertical temperature gradient can significantly
decrease the cirrus fraction in the TTL, whereas a cooling anomaly increases
it (Tseng and Fu, 2017).
Cirrus clouds over tropical latitudes exhibit a variety of natures. Broadly,
cirrus clouds are observed as laminar or horizontally lying (Jensen et al.,
1996), descending, and ascending (Nair et al., 2012). These
clouds can appear as either single-layer or multilayer cirrus (Li et al.,
2011). The laminar cirrus clouds very close to the cold-point tropopause
(CPT) are generally referred to as “tropopause” or “cold-trap” cirrus (Winker
and Trepte, 1998). They occur as thin–subvisible forms under
cold (-70 to -90 ∘C) and high (15–20 km) tropopause conditions,
which are rarely encountered outside of the tropics. Tropopause cirrus is
composed of relatively small ice crystals (Heymsfield, 1986) due to the
abundant moisture deposited by deep convection near the tropopause. Various
dynamical processes control the generation, growth, maintenance, and decay of
tropopause cirrus clouds (Liou, 1986; Fujiwara et
al., 2009). The descending cirrus cloud layer results from the transport
of condensate from the upper- to mid-tropospheric levels, either due to
gravitational settlement or atmospheric wave propagation (Heymsfield and
Iaquinta, 2000; Mitchell et al., 2008). Over the tropics, the occurrence of
multilayer cirrus clouds is the highest (Nazaryan et al., 2008). These
multilayer cirrus clouds represent breaks in a vertically continuous
cirrus layer due to wind shear (Jakob, 2002).
Globally, cirrus clouds cover about 50 % of the Earth's surface, with a maximum fraction of
coverage over the tropics (Liou, 1986), mainly within
the TTL (Wang et al., 2012). The TTL is a region between convective outflow
(∼ 12 km) and the CPT (∼ 17 km). Studies indicate
that cirrus clouds occur more than ∼ 80 % of the time over
the tropics (e.g., Nazaryan et al., 2008). Cirrus clouds have also
been found to occur above the CPT in the lower stratosphere (Winker and
Trepte, 1998; Pan and Munchak, 2011; Sandhya et al., 2015) as well as below
the TTL (Dowling and Radke, 1990; Nazaryan et al., 2008). The major processes
responsible for the formation of cirrus clouds are due to the convectively
generated remnants of cumulonimbus outflow anvils and are formed in situ
by the condensation and nucleation of aerosols and water vapors
(Jensen et al., 1996;
Lynch, 2002). Cirrus clouds generated in situ form preferably due to
inhomogeneous nucleation, including aerosols acting as ice crystal nuclei and the
homogeneous nucleation of water vapor at an extremely cold temperature of between
-70 and -90 ∘C near the CPT (Cziczo and Froyd, 2014;
Annamalai and Mehta, 2022). Recent studies have suggested that increasing aerosol
concentrations in the tropopause region may significantly affect the cirrus
cloud distribution (Massie et al., 2013; Vernier et al.,
2015).
The occurrence of cirrus clouds maximizes around regions of intense
convective activities (Spinhirne et al., 2005). Cirrus
clouds frequently occur over the convectively active areas primarily
associated with oceanic convection, such as equatorial Africa, South
America, and southern Asia (Dessler et al., 2006). The cirrus
cloud occurrence over these convective regions shows a large seasonal
variation which is found to be associated with the seasonal shift in the
Intertropical Convergence Zone (ITCZ) and midlatitude storm belts
(Wylie et al., 2005; Nazaryan
et al., 2008). Large-scale circulation patterns, like the El Niño–Southern
Oscillation (ENSO), the quasi-biennial oscillation (QBO), and the Brewer–Dobson
circulation (BDC), also significantly affect the temporal variability in the
occurrence of cirrus clouds (Davis et al., 2013; Tseng and Fu, 2017). For
example, the occurrence of cirrus clouds over a tropical station, Gadanki, was
found to be ∼ 44 %
(Pandit et al.,
2015) and ∼ 51 %
(Sunil Kumar et al., 2003) using 16
years (1998–2013) and 3 years (1998–2001) of ground-based lidar
observations, respectively, whereas it was found to be ∼ 30 %–40 % using the
analysis of 2 years of spaceborne lidar data (Sassen et
al., 2008). Such a difference in the occurrence percentage could be due to
interannual variation.
The tropical convection or cloudiness shows a diurnal pattern, with a peak in
the afternoon to early evening over the continents and midnight to early
morning over the oceanic regions (Chen and Houze, 1997; Liu et al., 2008).
Recently, Kottayil et al. (2021) also found deep convection peaks in the
late evening hours over land. In contrast, it peaks in the afternoon
hours over the majority of the oceanic regions. Such a diurnal cycle is
attributed to the diurnal cycle of the net radiative forcing due to
radiative heating and infrared cooling. In addition, the thermal properties
of the land and ocean surfaces also play a significant role in the diurnal
cycle of convective activities. Eriksson et al. (2010) observed substantial
diurnal variation in the upper-tropospheric humidity and ice water content
over tropical land regions. Gupta et al. (2017) investigated the day–night
changes in the vertical distribution of tropical clouds. They reported an
enhancement in the cloud occurrence during nighttime throughout the middle
and upper troposphere.
The diurnal pattern in tropical convection, upper-tropospheric humidity,
and ice water indicates that diurnal variation in cirrus clouds exists.
However, the complete diurnal variation in cirrus clouds is not yet known. Thus, in the present study, we aim to make use of the continuous micropulse lidar (MPL) observations over the period from 2016 to 2018 in order to
disentangle the vertical distribution and diurnal variation of cirrus
cloud occurrence during different seasons as well as the interannual
variations in cirrus
cloud occurrence over a tropical coastal region, Kattankulathur (12.82∘ N,
80.04∘ E). MPL observations indicate that laminar and descending types
of cirrus clouds, either in the form of a single layer or multiple layers,
occur throughout the year over Kattankulathur. Here, our main focus is to
examine the occurrence of the single- and multilayer cirrus clouds
over different timescales. Our analysis also reveals that the seasonal
patterns in the percentage occurrence of cirrus clouds over the NE
monsoon region are different from the SW monsoon region. The main objectives
of the present study are to (i) identify cirrus cloud occurrence and study
the diurnal pattern in the occurrence of the single-layer and multilayer
cirrus clouds, (ii) understand the role of convection and the TTL temperature in
diurnal variations of cirrus cloud occurrence, and (iii) examine the
vertical distributions of cirrus clouds during different seasons and
different years. Details about the MPL data and cirrus cloud
identification are described in Sect. 2. The results and discussions are
illustrated in Sect. 3 followed by a list of conclusions summarized in
Sect. 4.
Dataset and method of analysisMicropulse lidar (MPL)
The MPL is an elastic backscatter, dual-polarization compact lidar system
(miniMPL5231, Sigma Space Corporation, USA) that was installed on the
premises of the SRM Institute of Science and Technology (SRMIST), Kattankulathur
(12.82∘ N, 80.04∘ E; 45 m a.m.s.l., where “a.m.s.l.” denotes meters above mean sea level) on January
2016. The MPL is set up on the roof of the university building, 60 m above
ground level (i.e., 105 m a.m.s.l). We operated the MPL regularly from
15:00 IST on the first day to 11:00 IST on the second day. Operation was stopped
from 11:00 to 15:00 IST in order to avoid exposing the
receiver to direct solar radiation. The MPL is a diode-pumped frequency-doubled solid-state Nd:YAG
laser transmitter at a wavelength of 532 nm with low pulse energy (3–4 µJ)
and a high repletion rate (2500 Hz). The pulse width is set to 200 ns, which
corresponds to a range resolution of 30 m. The telescope is a Galilean-type instrument, and its diameter is 80 mm. We collected lidar data at a 1 min
interval. The MPL provides measurements of the vertical and temporal
distributions of aerosols and clouds in the troposphere, which are
helpful to study the atmospheric boundary layer, properties of aerosols,
the occurrence of clouds, and cloud radiative effects on the atmosphere
(Campbell et al., 2003; Welton and Campbell, 2002). The detailed
specifications of the MPL can be found in Ananthavel et al. (2021a).
A lidar transmits short pulses of laser light into the atmosphere. The
received signals, due to scattering from air molecules and particles as well
as additional signal due to instrumental effects, are expressed as follows:
Pr=Pocτ2Aβ(r)r2exp-2∫0rαrdr,
where Pr is the instantaneously received power (raw signal)
at time t, Po is the transmitted power at the time to, c
is the velocity of light, τ is the pulse duration, and
A is the area of the receiver. β(r) and α(r) are the volume
backscatter and extinction coefficients, respectively. The received signal
is corrected using system-dependent parameters such as dead time, after pulse, overlap corrections, and background noise corrections. The
normalized relative backscatter (NRB) signal is obtained as follows:
NRB=[(Raw×Deadtime)-After pulse-Background]×Range2Overlap×Laserenergy.
Dead time correction is applied to remove the saturation effect due to the
high-count rate. After-pulse correction is required to eliminate the signal due
to the internally scattered light that saturates the detector at the beginning
of each sampling interval, creating a blind zone in the near field. The overlap
correction occurs when the receiver field of view is inside the transmitter,
causing an over-attenuated near-field signal. For our MPL system, the
average overlap range is found to be ∼ 1.1 km. More details
about the calculation of the overlap factor can be found in Ananthavel et
al. (2021b). In the present study, we have used the NRB coefficients,
the signal-to-noise ratio (SNR), and the linear depolarization ratio (LDR) during
2016–2018. The LDR is calculated as follows
(Flynn et al., 2007):
LDR=NRBcrossNRBco/NRBcrossNRBco+1,
where NRBco and NRBcross are the parallel and perpendicular
components measured with linearly polarized and circularly polarized beams,
respectively. The linearly (parallel) and circularly (perpendicular)
polarized components of the backscattered signals are achieved by means of
an actively controlled liquid crystal retarder. The polarizing beam splitter
finally directs the co-polarized and depolarized signals to the detector.
More details about the estimation of the LDR can be found in Flynn et al. (2007).
Radiosonde
This study used upper-air data observed with radiosonde from the Indian
Meteorological Department (IMD) Chennai at Meenambakkam (13.0∘ N,
80.18∘ E; 16 m a.m.s.l.) over the period from 2016 to 2018. IMD
Chennai is located approximately 20.13 km northeast of SRM IST,
Kattankulathur. The daily average data from radiosonde observations at 05:30
and 17:30 IST are used to gather background meteorological information
and identify the TTL. Knowledge of the TTL width is important to understand
the occurrence of cirrus clouds.
Infrared brightness temperature (IRBT) data
To investigate the role of deep convection in the diurnal variations of
cirrus clouds, we used globally merged infrared brightness temperature (IRBT) data obtained from the
National Weather Service Climate Prediction Center of the National Oceanic and Atmospheric Administration (NOAA). IRBT data are a
globally merged, full-resolution (up to ∼ 4 km) IR dataset formed
from the ∼ 11 µm IR channels aboard the GMS-5, GOES-8,
GOES-10, Meteosat-7, and Meteosat-5 geostationary satellites. The IRBT data
are available with a time resolution of 1 h and a spatial resolution of
0.03∘×0.03∘ (latitude × longitude). In this
study, we have averaged the IRBT data over a 0.5∘×0.5∘ (latitude × longitude) grid centered on Kattankulathur.
MethodologyCloud-base and cloud-top altitudes
NRB profiles from the MPL observations are used to identify cirrus cloud
layers based on the differential zero-crossing method (Pal et al., 1992).
This method is based on the fact that the NRB signal intensity generally
decreases monotonically with altitude until a cloud appears. When the signal
encounters a cloud, the NRB signal level begins to increase significantly
due to the larger droplets in clouds compared with ambient air. As a result, using MPL observations, the
cloud-base height of the lowest cloud is directly identified as the change in the slope or gradient (the first derivative) of the
NRB (i.e., dNRB/dz), referred to as the differential
zero-crossing method (Dai et al., 2019; Nair et al., 2012; Platt et al.,
1994; Wu et al., 2015). To identify the base of cirrus clouds over the
tropics, we limit the lowest altitude to 8 km; over this region, cirrus
clouds usually occur at a temperature below -20 ∘C, corresponding
to an altitude above 8 km (Liou, 1986; Lynch, 2002; Pandit et al., 2014).
We have sampled the NRB profiles at a resolution of 30 m, which includes
several random variations arising due to background noise. These small-scale
fluctuations are smoothed by employing a 10-point (width 300 m) running mean
filter to the first derivative of the NRB signal. The 10-point running mean
filter improves the detection of the robust cloud layer and reduces
computation time. The base, top, and peak of cirrus clouds are
identified from the smoothed NRB
signals by the differential zero-crossing method. The NRB signal starts to increase relative to the threshold value
at the cloud base, whereas the NRB signal comes
down to the threshold level at the apparent cloud top. The cloud-base and cloud-top altitudes are only
identified when the NRB gradient increases or decreases, respectively, relative to the
threshold value for at least three consecutive range bins (90 m). The threshold value is taken as the mean plus 2 standard
deviations of the background NRB signal from ambient air over the altitude from
25 to 30 km (see Fig. S1 in the Supplement). To avoid detecting any spurious
layer, we make sure that the NRB signals possess a good SNR value. The level
up to which the NRB signals are more than the 1 standard deviation of the
column-integrated signal is considered a good SNR. In general, a good SNR is
found up to ∼ 15–20 km at nighttime (Ananthavel et al.,
2021a). However, at daytime, a good SNR is only found up to ∼ 4–6 km due to the high solar irradiance, except if
cirrus clouds occur (Fig. S1 in the Supplement). Whenever cirrus
clouds occur during the daytime, the SNR value increases within the cloud
boundaries and satisfies the criteria of a good SNR.
The apparent cloud-top height is detected by searching the first level where
the NRB value is just less than or equal to the NRB value at the cloud base.
In the cases of multiple cirrus clouds, the NRB profile may reveal several
peaks. However, each of these peaks may not really be associated with
separate cloud layers. The distinct multiple cirrus cloud layers are
detected only when they are separated by a clear-air region, i.e., the NRB
signal must drop to the background level. Finally, LDR values were checked for
each cloud layer identified using the differential zero-crossing method and the criteria that the
LDR within the cirrus layer should be at least 0.05 greater than the LDR
from the ambient air outside of the cloud boundaries, with a minimum absolute
value of 0.08 (Sassen and Cho, 1992; Nair et al., 2012).
Cloud optical thickness
Once the cirrus cloud-base and cloud-top altitudes are identified, the cirrus
cloud optical thickness is derived using the two-way transmittance method
(Young, 1995). The optical thickness is half of the logarithm of the ratio
of the NRB signals just below the cloud base to those immediately above the cloud
top. Note that the NRB is the range-corrected signal that has passed through
the system and overlap corrections. The molecular and aerosol contributions
to the altitudinal variation in the NRB are also removed before calculating the
optical thickness. Thus, using the transmittance method, optical thickness is
obtained without inverting the lidar signals and without the requirement for
knowledge of the lidar ratio. We have obtained the altitude profiles of the
molecular coefficient using the monthly mean temperature and pressure
profiles. For the aerosol contribution to the NRB signal, we used the seasonal
mean altitude profiles of the aerosol extinction coefficient derived from MPL
observations over the site during the period from 2016 to 2018. The details of deriving the
extinction coefficients are provided in Ananthavel et al. (2021a) and
Ananthavel et al. (2021b). However, it is to be noted that the molecular and
aerosol contributions to the NRB signal are very small compared with cirrus
clouds occurring above 8 km (Young, 1995; Nair et al., 2012). We have
employed only nighttime profiles with a very good SNR to determine the
optical thickness. Whenever a low SNR is observed, time averaging of the NRB
signals is done to reduce the noise level and improve the SNR before
calculating the optical thickness (Nair et al., 2012).
Percentage of cirrus cloud occurrence
Once the cirrus cloud-base and cloud-top altitudes are identified, the height and
time functions of the monthly percentage occurrence (POC) of cirrus clouds are calculated. For this, we have segregated the total hours of MPL
and cirrus cloud observations between 14:00 IST on the first day and 11:00 IST
on the second day for each month during 2016–2018. We obtained 665 d of
MPL data that were continuously measured for 1 h, i.e., 11 778 h of MPL observations were collected in total. Of the abovementioned 665 d of data, 496 d contained observed cirrus
cloud durations longer than 30 min, i.e., 5002 h
of cirrus cloud observations are found in total. The POC is the ratio of the total
hours of cirrus cloud to the total hours of MPL operation
multiplied by 100. We have counted the total hours of cirrus clouds in
30 m altitude bins from cloud base to cloud top that occur between 8
and 20 km at a 5 min time interval between 14:00 IST on the first day
and 11:00 IST on the second day.
Tropopause parameters
The tropopause parameters, such as the CPT, convective tropopause (COT), and
TTL, are obtained using radiosonde temperature profiles at 05:30 and
17:30 IST over the period from 2016 to 2018. The CPT is defined as the minimum
temperature level in the troposphere (Selkirk et al., 1993), and the COT is
defined as the minimum potential temperature gradient (Mehta et al., 2011).
The region between the COT and CPT levels is called the tropical tropopause
layer (TTL).
Results and discussionBackground meteorological information
Figure 1 shows the monthly mean variation in the temperature, relative
humidity (RH), zonal wind, meridional wind, potential temperature gradient
from the surface to an altitude of 25 km, rainfall, outgoing longwave
radiation (OLR), and the CPT and COT altitudes over IMD Chennai. The monthly mean
CPT and COT altitudes are obtained by averaging the daily data.
Kattankulathur (Chennai) is a coastal station that experiences rainfall
both from the southwest (SW) monsoon during June–July–August–September (JJAS) and the
northeast (NE) monsoon during October–November–December (OND). Thus, the
influence of both of the monsoons results in an abundant supply of moisture
into the upper troposphere, which is favorable for cirrus cloud formation. The
study region is also influenced by the sea breeze that triggers local
convective rainfall (Reddy et al., 2020; Simpson et al., 2007) over Kattankulathur.
The temperature shows substantial seasonal variation at the surface,
within the atmospheric boundary layer (ABL ∼ 2.0 km), and
within the TTL. In the mid-troposphere, seasonal variation in
temperature is very weak (Ananthavel et al., 2021a, b). The surface temperature reaches a minimum (∼ 298.9 ± 0.3 K) during December–January and a maximum (∼ 304.6 ± 0.6 K) during April–May–June. May is the hottest month over
Kattankulathur. The frequent sea breeze during pre-monsoon seasons
(March–April–May; MAM) decreases the temperature around 0.6 km (the average
height of the thermal internal boundary layer – TIBL; Reddy et al., 2020).
The monthly mean temperature in the TTL shows marked seasonal variation
with the minimum CPT temperature (∼ 190 ± 2 K) during October–May, covering the NE monsoon, winter (January–February), and pre-monsoon
seasons and the maximum CPT temperature (∼ 192 ± 2 K) during
June–September covering the SW monsoon season. The monthly mean temperature
shows a marked wider cold-point region between ∼ 16.1 and 18.7 km during October–May, which becomes a narrow region between 16.4 and 16.9 km during June–September. The CPT altitude is higher (∼ 17.5 ± 0.5 km) during the winter season and lower (16.7 ± 0.4 km)
during the SW monsoon season. The COT altitude also shows a strong seasonal
variation with the minimum altitude (∼ 11.2 ± 1.4 km) from
December to April and the maximum altitude (12.2 ± 1.6 km) from May to December. The COT altitude starts to increase in May, during which rainfall
is observed due to isolated convection, mainly thunderstorms. The RH is found to be greater than 70 % within the TIBL throughout the
year. Above the ABL, the mid-troposphere is almost dry, with RH < 20 % from December to April. The atmosphere is moist from May to
mid-November, with RH > 50 %, due to strong convection during the
SW monsoon and NE monsoon seasons. The RH is found to be lowest within the TTL. However, the RH is relatively higher (∼ 60 %) near the
CPT, indicating the uplift of moist air near the CPT due to convection
during the SW monsoon season. Humid air can also be advected to near the CPT
due to the tropical easterly jet (TEJ) during the SW monsoon season. The TEJ
core (zonal wind speed ≤30 m s-1) is located near the CPT during
the abovementioned season (Ratnam et al., 2011; Ramakumar et al., 2010). Over Chennai,
northwesterly winds prevail up to 10 km throughout the
year, except near the surface, with a maximum speed during the SW monsoon season. During the pre-monsoon
season, the wind near the surface is southwesterly due to the frequent
occurrence of the sea breeze (Reddy et al., 2020).
Westerly/southwesterly (easterly/northeasterly) winds prevail during
November–April (June–September) above 10 km. During the SW monsoon season,
the low-level jet (LLJ) stream in the lower troposphere and the TEJ stream in
the upper troposphere dominate. The monthly mean of the potential
temperature gradient shows distinct minima around the convective tropopause.
The temperature gradient starts to increase above the CPT. The CPT altitude
marks the location where the potential temperature gradient drastically
changes from a low to a high value.
Time–height sections of the composite monthly mean (a) temperature,
(b) relative humidity, (c) zonal wind, (d) meridional wind, and (e) potential
temperature gradient superimposed with the monthly mean and standard
deviation of the CPT (black line) and COT (gray line) heights. The monthly
means of the (f) rainfall (cyan) and outgoing longwave radiation (OLR)
(magenta) with their standard deviations are also shown.
Detection of cirrus cloud layers from NRB signals
The identification of cirrus cloud layers is illustrated for different
types of cirrus cloud cases, such as laminar cirrus, descending
cirrus, broad cirrus, and multilayer cirrus, observed on 12 February 2018,
27 May 2016, 3 August 2017, and 26 July 2016, respectively, as shown in
Fig. 2. Figure 2 presents the time–height section of the NRB signals over the
altitude from 0.3 to 22 km observed on 14:00 IST on the first day to 11:00 IST on
the second day; vertical profiles of the NRB signal, dNRBdZ, and the
SNR at 01:30 IST using MPL observations; and the daily mean
temperature, potential temperature, and potential temperature gradient. Note that the NRB
gradient and potential temperature gradient profiles are smoothed by the 10-point running mean to avoid short-scale fluctuations.
Time–height sections of the normalized backscatter (NRB) signals
over the altitude from 0.3 to 22 km observed from 15:00 IST on the first day to 11:00 IST on the second day. The panels show the vertical profiles of the NRB; the gradient of NRB
and SNR signals at 01:30 IST using MPL over Kattankulathur; and the
daily mean temperature, potential temperature, and potential temperature
gradient using radiosonde observation over Meenambakkam for different types
of cirrus cloud cases: (a) laminar cirrus (12 February 2018), (b) descending
cirrus (27 May 2016), (c) broad cirrus (3 August 2017), and (d) multilayer
cirrus (26 July 2016). Red and black dots denote the cirrus cloud top
and base of the cloud layers, respectively. Dashed lines indicate the peak
altitudes of cirrus layers. Dotted horizontal lines indicate the secondary peak associated with the main peak. The cold-point tropopause (CPT)
and convective tropopause (COT) are also shown. Note that a 10-point running
mean has been applied to the NRB gradient and temperature gradient profiles
to smooth out the small-scale fluctuations.
On 12 February 2018 (Fig. 2a), we observed a laminar cirrus cloud
layer between the altitudes of ∼ 11.0 km (cloud base) and 13.5 km
(cloud top) from 18:00 to 08:00 IST. During this day, a typical case for
the winter season, relatively higher aerosol concentrations dominated
within the ABL (∼ 2.0 km). Above it, up to ∼ 3–4 km, a diffuse regime of more aged aerosols (Jacob et al., 2002)
dominated. Above 4 km, aerosol concentrations were extremely low, and the
signal was mainly dominated by molecular scattering and the presence of
clouds. The NRB profile observed at 01:30 IST shows a monotonic
decrease in the signal above the ABL (∼ 2.0 km) to the
cloud-base height (∼ 11.6 km). The first derivative of the NRB
begins to increase at the cloud base. At the cloud peak, dNRBdZ
shows zero crossing, i.e., a change in sign from positive to negative. One can
notice that several zero crossings are present below the cloud base that
arise from inhomogeneity in the background aerosols; however, these
insignificant zero crossings are below the level of the threshold value and
are rejected. Note that the threshold value is taken as the mean plus 2
standard deviations of the background NRB signal from ambient air, as
mentioned earlier, and it is calculated for each profile. The cloud peak at
∼ 12.8 km is identified as the level of zero crossings of
dNRBdZ (where it changes sign from positive to negative). The
apparent cloud-top height is identified at 13.6 km. At the cloud-top height,
the NRB value was found to be ∼ 0.12 counts km2µs-1µJ-1, which is much less than the NRB value of ∼ 0.85 counts km2µs-1µJ-1 at the cloud base. The detected cloud-base and
cloud-top heights from 18:00 to 08:00 IST are superimposed on the
contour plot shown in Fig. 2a. On this day, both subvisible (τ≤0.03) and
optically thin (0.03<τ≤0.3) cirrus clouds are present, with a mean
optical thickness of ∼ 0.05 ± 0.04. Cirrus cloud is
observed near the convective tropopause (COT altitude ∼ 11.7 km). The temperature and potential temperature of the cloud layer are
∼ 216 and 347 K, respectively. The cold-point tropopause
(CPT altitude ∼ 17.3 km) lies much above the cirrus cloud top.
On 27 May 2016 (Fig. 2b), we observed the descending type of cirrus cloud
layer, which descended from an altitude with a cloud base at ∼ 13.5 km
and cloud top at ∼ 15.0 km at 16:00 IST to an altitude with
a cloud base at ∼ 11.0 km and cloud top at ∼ 12.0 km at
08:00 IST. Cirrus clouds, while descending, change their vertical
structure and optical thickness. The NRB profile observed at 01:30 IST shows
high aerosol concentrations up to about 6 km, which is a typical feature during monsoon
seasons over Kattankulathur. During this season, strong convection and
strong southwesterly winds prevail, bringing enormous amounts of
moisture-laden coarse particles from the Arabian Sea and the Bay of Bengal
(Ananthavel et al., 2021a, b). Above about 6 km, the NRB signal decreases until
it encounters the cloud. The identification of the cirrus cloud layer is
similar to that mentioned in the previous case. The heights of the cloud base,
cloud peak, and cloud top are at ∼ 12.2, 12.9, and
∼ 13.9 km, respectively. In this case, the NRB value at the
cloud top is ∼ 0.15 counts km2µs-1µJ-1 less
than at cloud base ∼ 0.21 km2µs-1µJ-1. The
detected cloud-base and cloud-top altitudes from 16:00 to 08:00 IST are
superimposed over the contour plot shown in Fig. 2b. We observed mostly optically thin
(0.03<τ≤0.3) and thick clouds (τ>0.3) with a
mean cloud optical thickness of ∼ 0.13 ± 0.07. In the
beginning, cirrus clouds lie within the TTL (COT altitude
∼ 11.9 km, CPT altitude ∼ 17.1 km); however,
while descending, they fall below the TTL base at ∼ 02:00 IST,
and the layer reaches just below the TTL at 08:00 IST. The mean cloud
temperature before descent is 209 K, whereas it is 232 K after descent.
On 3 August 2017 (Fig. 2c), a broad layer of cirrus clouds was observed
with a cloud base at ∼ 10.5 km and cloud top at ∼ 12.5 km at 15:00 IST and at ∼ 17.0 km at 01:00 IST. On this day,
cirrus clouds appear to present even before 14:00 IST; however, the MPL was
not operating during this time due to high solar radiation, as mentioned
earlier. The subvisible cirrus clouds above 13 km from midnight to early
morning may have formed either due to a decrease in the ambient air temperature
or the reappearance of the existing cirrus with a decrease in the optical
thickness of the lower-level cirrus clouds. It is to be noted that the MPL
signal is unable to penetrate deep enough in the presence of optically
thick cirrus clouds. This is the limitation of the up-looking lidar
and results in the ambiguous detection of the cirrus top, which is then generally referred
to as the “apparent cirrus top” (Pal et al., 1992). However, in cases of
subvisible-cirrus clouds and thin-cirrus clouds, the actual cloud top can be
detected, as observed on 12 February 2018 (Fig. 2a). From the surface to
∼ 3 km, high aerosol concentrations are observed. As this
typical case is observed in the SW monsoon season, increased water vapor and
aerosols are transported to this region due to prevailing strong
southwesterly winds and strong convection, as mentioned in the previous case.
Mid-tropospheric thick clouds and boundary layer clouds substantially
attenuate the lidar signal. The NRB profile observed at 01:30 IST
monotonically decreases above the boundary layer until it encounters
cirrus cloud with a base at ∼ 10.5 km. Unlike previous
cases, in this case, two respective peaks at ∼ 12.0 and
∼ 16.0 km are discernible; however, they do not represent the
multiple cirrus layer. Because there is no clear-sky region between these
peaks, the signal never reaches the background value. The NRB value between
these peaks never became less than the NRB value at the cloud base. Hence,
the cloud-top height is identified at 16.95 km. The value of the NRB at the
cloud top is 0.05 counts km2µs-1µJ-1, which is much
less than the value of 0.15 counts km2µs-1µJ-1 at
cloud base. The detected cloud layers are shown in the contour plot shown in Fig. 2c. Mostly,
optically thin and thick clouds with a mean cloud optical thickness of
∼ 0.12 ± 0.07 are
observed in this case (similar to the previous case). Similar to the previous case, the cirrus cloud layer
was found within the TTL (COT altitude ∼ 10.8 km, CPT altitude
∼ 16.8 km) with the cirrus top during the period from midnight to early morning
nearly coinciding with the CPT. In this case, the CPT is found to be sharper and
colder than the previous cases, with cirrus clouds occurring below the CPT.
The mean temperature of the cloud layer is found to be ∼ 213 K.
On 26 July 2016 (Fig. 2d), multiple cirrus layers were observed from
22:00 to 06:00 IST. In this case, the cirrus layers appear to present
before 15:00 IST on the first day and after 11:00 IST on the second day;
however, the lidar was switched off due to high solar radiation, missing the
complete diurnal feature. The cirrus cloud-base and cloud-top heights are at
∼ 10 and ∼ 12 km, respectively, at 15:00 IST; increase to ∼ 14 km (cloud base) and ∼ 16 km (cloud top) at 20:00 IST; and remain laminar until morning (8:00 IST).
During 22:00–06:00 IST, another cirrus layer with a cloud base
at ∼ 10.0 km and cloud top at ∼ 13.5 km appeared.
About 06:00–08:00 IST, a convective cloud is observed at an altitude of
about 4–6 km. From 08:00 to 11:00 IST, a cirrus layer appears whose cloud base
increases from 8.0 to 11.0 km, and the cloud top increases from 10.0 to 14.0 km. The cirrus clouds observed show a significant variation in the
optical and geometrical thickness related to temperature, moisture, and
convective strength. In this case, we observed a disturbed boundary layer
feature with highly variable aerosol concentrations. The NRB profile
observed at 01:30 IST shows high aerosol concentrations up to
∼ 3.0 km that decrease monotonically above this altitude until
encountering the cirrus cloud base at ∼ 11.0 km. We observed
the two respective peaks at ∼ 11.6 and ∼ 12.8 km for the
lower cirrus layer, similar to the previous case. However, as mentioned
previously, it represents a single cirrus layer. Similarly, the upper cirrus
layer also contains two respective peaks at ∼ 16.0 and 16.8 km;
however, it also represents a single cloud layer. Between the cloud top of the
lower cirrus layer and the cloud base of upper cirrus layer, clear sky is
observed, with signal from ambient air. Thus, these two layers are multiple
cirrus layers, and the detected cloud-base and cloud-top heights are shown in
contour plots shown in Fig. 2d. In this case, optically thin and thick clouds with a mean
optical thickness of ∼ 0.17 ± 0.31 are also observed.
Interestingly, both cirrus layers occur mainly within the TTL (COT altitude
∼ 12.0 km, CPT altitude ∼ 17.1 km). However,
part of the top of the upper cirrus layer appears just above the CPT, and
part of the lower cirrus layer appears below the COT. In this case, the
temperature profile shows a relatively broader tropopause. Within the TTL, the
potential temperature gradient shows significant enhancement. The average
temperature of the lower and upper cirrus layers is ∼ 226
and 197 K, respectively. The upper layer occurs near the tropopause and is
generally referred to as the tropopause cirrus.
Diurnal and day-to-day patterns of cirrus cloud occurrence
We applied the abovementioned zero-crossing method to detect the cirrus
cloud layers for all of the observations over the period from 2016 to 2018. Figure 3
shows the day-to-day variation in the total duration of the first-layer or
single-layer cirrus cloud observations and the total duration of the MPL
observations during 2016–2018. The MPL is operated between 14:00 IST on the first day and 11:00 IST on the second day under clear-sky conditions. The maximum duration of MPL operation
is 20 h; however, on several occasions, MPL operation was stopped due to
technical issues or bad weather conditions. The major data gaps are during February,
June, and December 2016; July, October, and November 2017; and April and September 2018. Note that we have plotted only those days with more than 1 h of
lidar operation and more than 30 min of cirrus cloud observations. Some
noticeable differences can be seen in cirrus cloud occurrence among 2016,
2017, and 2018, indicating robust interannual variation. Cirrus clouds
frequently occur during January–February of 2016 and 2018 compared with 2017.
While cirrus cloud occurrence and duration is
higher during March–April in 2016 and 2017 compared with 2018. During May–June, all of the years
show a relatively long duration of cirrus cloud occurrence. We also checked the occurrence of cirrus clouds persisting for more than 1 d. In total, cirrus clouds were observed on 665 d during 2016–2018. Of these 665 d, cirrus clouds persist for more than 1 d on 93 of the observed days (i.e., 14 %).
The persistence of cirrus clouds for longer than 1 d frequently occurs
from May to August (covering the SW monsoon) and from October to November
(covering the NE monsoon). It is to be noted that cirrus persistence for a
longer duration may have large implications for the TTL region; this will likely be explored in a future study.
The day-to-day total duration of cirrus cloud (single-layer)
occurrence and the total duration of MPL observations during (a) 2016, (b) 2017, and (c) 2018.
Figure 4 shows the timing of the occurrence of single and multiple layers of cirrus
cloud as well as the day-to-day variations in cirrus
cloud during 2016–2018. We
use the term “single” for single-layer clouds and the first layer in the
case of multilayer cirrus clouds. At the same time, “multi” is used to refer
to the second layer of cirrus clouds in the case of multilayer cirrus. The occurrence of a third layer was rare and is not considered in
this study. The purpose of showing different colors on the left side of the
panels in Fig. 4 is to distinguish the day-to-day occurrence of cirrus
clouds. Single-layer cirrus clouds appear to occur throughout the year.
However, multilayer cirrus clouds occur mainly from May to August,
followed by occurrence in October–November. It can be seen that cirrus
clouds sometimes occur throughout the night and sometimes during the early
evening and the early morning. To understand the overall occurrence
frequency, we have calculated the percentage of occurrence between 14:00 IST on the first day and 12:00 IST on the second day at a 2 h interval. On
the right side in the panels of Fig. 4, the
occurrence frequency is displayed at 15:00, 17:00, 19:00 IST, and so on. It should be noted that the occurrence
of cirrus clouds during the daytime is affected by the high solar noise,
which significantly reduces the MPL detection capability with respect to cirrus
clouds, especially the thin and subvisible types. Thus, the occurrence of
cirrus clouds presented from sunrise to 11:00 IST and from 15:00 IST to
sunset may not represent accurate statistics. The overall occurrence of
single-layer cirrus clouds reveals that they occur more frequently during
the evening hours (18:00–20:00 IST). At the same time, multilayer cirrus
clouds occur more frequently during the early morning hours (04:00–06:00 IST). The diurnal occurrence of cirrus clouds shows variation in terms
of the amount and pattern among the years (2016–2018). The occurrence of both
single- and multilayer cirrus clouds was higher during 2016 compared with
2017 and 2018. It should be noted that, to our best knowledge, the diurnal
structure of single- and multilayer cirrus cloud occurrence is being
reported here for the first time: we have not come across any such existing work. Earlier studies have reported the diurnal cycle of
the total cloud fractions using spaceborne observations, which do not
provide information on multilayer cirrus clouds (Noel et al., 2018; Feofilov
Stubenrauch, 2019).
The day-to-day occurrence of cirrus clouds from 14:00 IST on
the first day to 11:00 IST on the second day as well as the overall percentage occurrence for (a) single-layer and (b) multilayer cirrus clouds during 2016. Panels (c) and (d) and panels (e) and (f) are the
same as panels (a) and (b) but were observed during 2017 and 2018, respectively. We use
“single” to refer to the single-layer clouds and the first layer in the case of
multilayer cirrus clouds; “multi” is used to refer to the second
layer of cirrus clouds in the case of multilayer cirrus. Note that the
colors used here have no specific meaning but are simply used to make it
easier to distinguish between different days.
As mentioned in Sect. 1, cirrus clouds are either
convectively generated or formed in situ. Convectively generated cirrus
develops mainly due to the remnants of anvil clouds following deep
convection, whereas cirrus formed in situ originates due to an extremely cold TTL
temperature. In this section, we examine the possible roles of
convection and TTL temperatures on the diurnal variation in cirrus
cloud occurrence during different seasons. The diurnal variation in the
occurrence of single- and multilayer cirrus clouds, the occurrence of an
IRBT of less than 240 and 220 K during different seasons, and the monthly
occurrence of a CPT temperature less than 191 K at 05:30 and 17:30 IST
(evening) are calculated, as shown in Fig. 5. During the winter and SW
monsoon seasons, cirrus clouds occur more frequently in the late evening
(∼ 18:00–20:00 IST). In contrast, cirrus
clouds uniformly occur throughout the night during the pre-monsoon season. For the NE monsoon season,
cirrus clouds occur more frequently during the late evening and early
morning (04:00–06:00 IST). The SW monsoon season also shows a relatively
enhanced occurrence during the early morning hours, similar to the NE monsoon. Multilayer cirrus clouds occur more frequently during the SW monsoon compared with
other seasons, which have similar frequencies. The diurnal variation in the
occurrence of multilayer cirrus shows pronounced maxima in the early morning
hours. Feofilov and Stubenrauch (2019) retrieved the diurnal pattern
of cirrus clouds from two spaceborne infrared sounders: the Atmospheric
Infrared Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer
(IASI). Over our location, they observed that the diurnal cycle is not
pronounced. We have compared our results with the cirrus clouds closest to
12.82∘ N, 80.04∘ E from the publicly available dataset
(10.13140/RG.2.2.13038.15681), as shown in the Supplement (Fig. S2). It
indicates that the diurnal cycle of the total occurrence is less pronounced, which is
consistent with Feofilov and Stubenrauch (2019). The diurnal cycle is
discernible only when the single- and multilayer cirrus are separately
analyzed.
Diurnal variation in the percentage occurrence of (a) single-layer and
(b) multilayer cirrus clouds, (c) an IRBT less than 240 K, and (d) an IRBT less
than 220 K during different seasons over the period from 2016 to 2018. (e) The monthly
percentage occurrence of a CPT temperature less than 191 K at 05:30
and 17:30 IST over the 2016–2018 period.
To understand the role of convection in the occurrence of cirrus
clouds, we obtained the occurrence of an IRBT of less than 240 K,
representing convective clouds with a cloud top above 8 km, and an IRBT of less
than 220 K, representing deep convection with a cloud top above 12 km (Ali
et al., 2020). Convective clouds and deep convection frequently occur
during the SW monsoon season, followed by the NE monsoon and pre-monsoon
seasons. During the winter seasons, convection is rare. During the SW and NE
monsoon seasons, the diurnal pattern of convection is similar. In these
seasons, convection frequently starts in the late morning and remains prevalent
until midnight, with the maximum occurrence from late evening to midnight. Daytime convection during the SW and NE monsoons can be related to the
higher occurrence of cirrus clouds in the late evening hours. At the same
time, the maximum occurrence of deep convection at midnight may be
related to the cirrus occurrence during the early morning hours. During the
pre-monsoon season, convection mainly occurs in the daytime, resulting in
uniform cirrus occurrence throughout the night. However, although there is no
convection during the winter season, cirrus clouds frequently occur during
the late evening, similar to the SW and NE monsoon seasons. To understand
this, we examined the role of freeze-drying processes in the formation of cirrus clouds formed in situ by calculating the occurrence frequency of
a cold-point tropopause (CPT) temperature less than 191 K during the morning
and evening hours. This CPT temperature (<191 K) is believed to be the
threshold value for the freeze-drying of water vapor that can lead to the
formation of cirrus clouds. We observed that the CPT temperature
more frequently becomes colder (than 191 K) from November to May than during the period from June
to October, which may be conducive for the formation of
cirrus clouds generated in situ. Note that a CPT temperature of <191 K more frequently
occurs in the morning than in the evening hours during all seasons. The
frequent occurrence of multilayer cirrus clouds during morning hours can
also be partly related to a CPT temperature <191 K. Thus, the clouds formed in situ (due to a CPT temperature <191 K) may result in
multilayer cirrus at a higher altitude during the SW monsoon season.
Additionally, it is well known that cirrus clouds are closely associated
with turbulence, and turbulence generally becomes stronger during the afternoon and
evening hours (Parameswaran et al., 2004; Mushin et al., 2017). Thus, strong
turbulent transport of aerosols and water vapor near the CPT may result
in the higher occurrence of single-layer cirrus clouds during afternoon or
evening hours. Such cirrus clouds are known to persist for a longer
duration.
To understand the altitudinal distribution of the diurnal variation in
cirrus cloud occurrence during different seasons, we calculated its
percentage occurrence for each month from January to December over the
period from 2016 to 2018, as shown in Fig. 6. We calculated the monthly
POC from 14:00 IST on the first day to 11:00 IST on the second day, covering the entire
night above 8 km. Note that we have shown the plot from 5 to 20 km (Fig. 6). Monthly mean CPT and COT heights and standard deviations obtained using
daily average IMD radiosonde data are also embedded in Fig. 6. As mentioned
earlier, the vertical and diurnal structure of the occurrence is calculated
by taking the ratio of the total number of cirrus clouds observed to the
total number of observations for every 5 min at 30 m altitude intervals in
a given month for the period from 2016 to 2018. Note that the occurrence is only
calculated for MPL operational periods longer than 1 h and cirrus presence
longer than 30 min. The POC shows high temporal and vertical structures of occurrence
during each month. In general, it is found to be between ∼ 10 % and 30 %, with the highest occurrence during May–June and the lowest
occurrence during February–March.
Monthly occurrence of cirrus clouds as a function of time and
altitude from 2016 to 2018. The mean CPT (red line) and COT (white line)
altitudes for the corresponding months are also embedded. The standard
deviation of the CPT altitude at 17:30 and 05:30 IST is also shown.
Vertical dashed lines indicate the sunset and sunrise times.
During the winter season (December–February, DJF), the POC is observed from the evening (∼ 17:00 IST) of the
first day to the morning (∼ 08:00 IST) of the following day
over the altitude range of ∼ 8–17 km.
Noel et al. (2018) also observed diurnal variation in the POC over the
Northern Hemisphere (NH) tropical region during DJF using the Cloud–Aerosol Transport
System (CATS). We have compared the POC obtained using the MPL with Noel et al. (2018), as shown in the Supplement (Fig. S3). The comparison from both
ground-based and spaceborne observations appears consistent. However, the POC value from the point observation may differ from the POC calculated over the
entire NH tropical region (Fig. S2a, c). It is found to be ∼ 10 %–25 %, with the highest POC over the altitude of ∼ 14–16 km,
just below the mean CPT altitude (17.2 km), throughout the night (18:00–06:00 IST). The presence of the highest POC value over such a narrow region indicates the frequent
occurrence of optically thin laminar cirrus
(Sivakumar et al., 2003) due to an increase in
sedimentation with a decrease in the CPT temperature. It can be seen that a
POC very close to the CPT does not occur before the evening hours and after
the morning hours. This could be due to either the dissipation of the optically
very thin cirrus clouds with sunrise or the limited detection
capability of the MPL under solar noise conditions. The decrease in the POC just below the
CPT during the early morning hours supports this contention. It also indicates
that cirrus clouds closer to the CPT are generally optically thin and do not
descend but last longer. Furthermore, a higher POC is also noticed at
altitudes between ∼ 8 and 12 km from ∼ 16:00 to 19:00 and
from ∼ 02:00 to 07:00 IST. Although cirrus clouds are mainly optically
thin during the winter season, cirrus sometimes appears to be descending due to
an increase in the sedimentation load that gets deposited at the COT
altitude and dissipated at lower altitudes due to an increase in temperature
(Nair et al., 2012).
Generally, cirrus clouds are either thin or
subvisible during the winter season (Sivakumar et al., 2003), as this season is free from any deep
convection. Sedimentation occurs due to a decrease in the temperature close
to the tropopause. Our observation indicates that cirrus clouds are not
always laminar and descend even in winter. Such descending cirrus during the
winter season could be due to increases in sedimentation (Nair et al.,
2012). However, the different types of cirrus clouds, such as descending and
laminar cirrus clouds, are outside the scope of this study and will be
reported in a separate study. At the same time, higher POC values near the COT
during the evening and early night hours that last for a shorter duration
appear to be locally generated due to turbulence (Satheesan and Krishna
Murthy, 2002; Mushin et al., 2016), as it is known that turbulence
frequently occurs during nighttime when compared with daytime over Gadanki
(13.45∘ N, 79.2∘ E) close to our station (Chennai) (Mushin et al., 2016).
The POC during March is minimal (10 %–15 %); however, it extends from 8 to 16 km, similar to all of the other months. The POC during March shows two distinct
layers: the first one within the TTL and the second one just below the TTL
base. As mentioned earlier, the second peak in the occurrence of cirrus
clouds could be remnants of the deep convective outflows. Compared with
previous months, the POC significantly improved from April to May, with the
maximum occurrence confined within the TTL.
During June–September, cirrus clouds frequently occur between 14:00 IST
on the first day and 11:00 IST on the second day compared with the rest of the months. The
POC varies between 10 % and 30 % during June, with the highest occurrence
observed at night. The POC is limited to a height of 8–14 km during the
daytime; however, it is in the altitude range of 8–17 km during nighttime.
Note that cirrus clouds at higher altitudes (generally the thin
and subvisible types) may remain undetected due to high solar noise during
daytime. The POC during June–July–August (JJA) over Kattankulathur is found to be consistent
with CATS observations over the NH tropical region (Noel et al., 2018); however, the
magnitude differs, as mentioned earlier (Fig. S2b and d in the Supplement) The
diurnal variation in the temperature in the upper troposphere (Mushin et
al., 2017) seems to be the controlling factor of the higher occurrence and
greater extent of cirrus clouds during nighttime compared with daytime.
The limited vertical extent of the daytime cirrus cloud occurrence could be
due to the limitation of the lidar with respect to detecting cirrus (due to high solar
noise). Note that, during the SW monsoon season, deep convective clouds such
as cumulonimbus clouds frequently occur at the same height (∼ 8–10 km) (Subrahmanyam and Kumar, 2013) at which cirrus clouds
also occur. We have carefully examined the LDR to distinguish cirrus
clouds from the cumulonimbus clouds (that have a high optical thickness) to avoid
including such clouds. Cirrus clouds occur (i) sometimes above the CPT, (ii) several times below the mean TTL, and (iii) frequently as multilayer
cirrus clouds during the SW monsoon season. Multilayer cirrus clouds
occur with the upper layer close to the CPT and the lower layer close to the COT.
During JJA, the upper layer of cirrus clouds mainly occurs after
the sunset and before the sunrise (∼ 17:00–05:00 IST). As
mentioned earlier, the cirrus layer close to the CPT could be generated in situ
due to cold temperatures and the abundance of moisture transported from deep
convection. At the same time, cirrus clouds close to the COT could be due to
remnants of clouds from deep convective outflows.
In contrast, the lower layer of cirrus clouds occurs even before sunset
and after sunrise. This indicates that the upper layer of cirrus clouds or
multiple cirrus clouds would have disappeared after sunrise due to
dissipation by solar heating or would have remained undetected due to high solar noise.
However, the lower-layer cirrus clouds occur throughout the day and night,
indicating the role of high moisture availability during the SW monsoon
season. Compared with the rest of the seasons, the higher POC during the SW
monsoon season mainly appears due to large-scale convection and the
upper-tropospheric anticyclonic circulation
(Parameswaran et al.,
2003; Satheesan and Murthy, 2002).
The vertical structure of the POC has a lesser extent during the transition
(October) from the SW monsoon season to the NE monsoon season. It is
generally confined within the TTL, with fewer or no cirrus clouds above the
CPT. During October, zonal wind in the upper troposphere changes to weak
easterlies (Sunilkumar et
al., 2010), decreasing the moisture due to horizontal transport from the Bay
of Bengal and, thus, significantly reducing the POC. The inadequate supply of moisture
due to the weakening of local convection reduces the sustenance of
cirrus clouds. However, a relatively higher POC during November appears due to
the prevalence of the NE monsoon over Kattankulathur. The convection during
the NE monsoon is not as strong as during the SW monsoon, leading to the formation
of cirrus clouds at relatively lower heights during November.
Overall, the POC varies from 20 % to 40 %, except during the March and
October months with low occurrence. It is worth mentioning here that the
lower POC value is found during the months when the zonal wind
pattern transits from westerly to easterly (easterly to westerly) in the
upper troposphere during March (October) over the Indian monsoon region
(Goswami, 2005). It is well known that the zonal wind shear significantly
changes the TTL temperature and, hence, cirrus cloud occurrence
(Randel et al., 2002) by uplifting the
humidity to the upper troposphere, which provides favorable conditions for
the in situ formation of cirrus clouds (Das
et al., 2011). We also examined the zonal wind shear using radiosonde
observations at IMD Chennai (13.0∘ N, 80.18∘ E).
Figure S4 in the Supplement indicates maximum shear within the 8–16 km altitude range from May–September, which is consistent with the POC. However, it should be noted that large-scale circulations
and wave activities can also modulate TTL clouds (Held and
Hoskins, 1985; Kim et al., 2013). Podglajen et al. (2018) observed that
gravity waves affect the distribution of ice particles and that
cirrus clouds are primarily confined to the altitude region where water
vapor is saturated in association with positive zonal wind anomalies.
The top of cirrus clouds is observed at and above the CPT, especially
during July and August. The occurrence of cirrus in the vicinity of and
above the CPT has significant implications for the vertical and poleward transport of water vapor, resulting in a change in stratospheric ozone
chemistry. Thus, we have segregated cirrus clouds occurring above the
CPT and their corresponding altitude. It is observed that cirrus clouds
frequently occur above the CPT during May (∼ 7 %), July
(∼ 10 %), and August (∼ 9 %), as shown in the
Supplement (Fig. S5). Only 2 %–3 % of cirrus clouds occur above the CPT
during the rest of the months. We also calculated the altitudinal
separation between the CPT and the cirrus cloud top occurring above the CPT for
months with occurrences greater than 5 %. It is observed that the
difference between the cirrus top and CPT altitudes is ∼ 0.35 ± 0.22, 0.28 ± 0.20, and 0.43 ± 0.35 km during May,
July, and August, respectively. The occurrence of the cirrus top above the
CPT indicates the transport of water vapor into the lower stratosphere.
Such water vapor transport by means of the formation of cirrus clouds
can radiatively affect stratospheric chemistry. Our observations
indicate that the cirrus top occurring above the CPT varies between
∼ 0.1 and 0.7 km. It should be noted that the occurrence of
the cirrus top above the CPT is calculated relative to the mean CPT altitude
at 05:30 and 17:30 IST. However, the CPT shows significant diurnal variation,
with amplitude ranging between 0.2 and 0.5 km (Mushin et al., 2017).
Vertical structure of the POC during different seasons
Figure 7 shows the vertical extent of cirrus occurrence; the monthly means
and standard deviations of the altitudes of the cloud base, cloud top, CPT and COT; monthly total observations of the MPL; and the occurrence of
single- and multilayer cirrus clouds. It can be seen that the POC has a greater
vertical extent during January, February, March, June–September, and
December, whereas it had a relatively lesser vertical extent during April–May and
October–November. The POC is lower from December to March and higher from
April–November. The peak of the POC varies during different months. The
monthly variation in the altitude of the POC peak shows a strong
seasonal variation, with higher altitude during the winter season and lower
altitude during the SW monsoon season.
Cirrus clouds occur at higher altitudes from January to April, during
which time the dry season prevails over Kattankulathur. During this time, the
cloud-base and cloud-top altitudes (referred to as CBH and CTH, respectively, in Fig. 7b)
show nearly in-phase variation with the COT altitude. This indicates that the main
convective outflow provides a conducive mechanism for the formation of
cirrus clouds. However, from May to November, Kattankulathur remains wet due
to the frequent rainfall from the SW and NE monsoons. The strong convection
during the SW and NE monsoons pushes the COT to a relatively higher
altitude (Mehta et al., 2011). During this time, the mean cloud base
occurs ∼ 1–2 km lower than the COT. During the
SW and NE monsoon seasons, the higher moisture availability in the
mid-troposphere results in the formation of cirrus clouds at lower levels.
However, during these seasons, multiple cirrus clouds frequently occur. It
can be seen that, although multiple cirrus clouds occur throughout the year,
their frequencies are less than 10 %, except for May–August. The POC for
the multiple cirrus clouds are found to be 19 %, 17 %, 15 %, and
14 % during May, June, July, and August, respectively (Fig. 7d). In the
monthly mean occurrence profiles, the double peak is only discernible during
July–August. This is because both single- and multilayer cirrus clouds have the same
frequency during July–August, whereas the POC values for multilayer cirrus
for other months is much lower than for single-layer cirrus clouds and are not discernible in the
monthly mean occurrence profiles. The peak occurrence of upper-layer
cirrus clouds close to the CPT could be generated in situ due to cold
temperatures and the abundance of moisture transported from deep convection,
whereas the peak occurrence of lower-layer cirrus clouds close to the COT
could be due to the remnants of clouds from deep convective outflows. Both
single- and multilayer cirrus clouds vary in phase with the CPT height and
roughly out of phase with the COT altitude during May–December. Note that, in
total, the MPL is operated more than 746 h every month, with a maximum duration
of ∼ 1393 h during March over the period from 2016 to 2018 (Fig. 7c).
(a) Monthly variation in the altitudinal extent of the POC; (b) monthly mean and standard deviations of the altitudes of the cloud base (CBH), cloud top (CTH), CPT, and COT; (c) monthly total observations of MPL; and (d) the occurrence of the single- and multilayer cirrus clouds over the period from
2016 to 2018. The suffixes “s” and “m” in relation to CBH and CTH denote “single” and “multi”, respectively.
We also obtained the vertical distribution of the POC during different
seasons, such as DJF, MAM, JJA, and SON, as shown in the Supplement (Fig. S6). It can be seen that the detailed vertical structure of the POC observed
during different months, as shown in Fig. 7, is absent when the POC is
calculated with respect to season. The overall POC profile shows that cirrus clouds
occur within the 8–17 km range, with a maximum POC of about 25 % in the altitude
range of 13–15 km (a broad peak). It is observed that the POC gradually
increases from 8.0 km to about ∼ 13–15 km and drastically
decreases above this altitude to about 17 km. The maximum POC is about 2 km below the
mean CPT altitude (∼ 17 km) and 1.0 km above the mean COT
altitude. The peak of the POC is generally confined to the TTL. This is
because the TTL is primarily characterized by very low temperature and a
trace amount of water vapor, such that the moist adiabatic lapse rate
approaches the dry adiabatic lapse rate; thus, the TTL is relatively
stably stratified when compared with the troposphere below it
(Fueglistaler et al., 2009). According to the dehydration
mechanism, during the slow ascent of the TTL air parcel, trace water vapor
gets freeze-dried due to the low temperature, leading to the frequent formation
of cirrus clouds in the TTL
(Jensen
et al., 2013; McFarquhar et al., 2000).
The vertical distribution of the POC during different seasons shows unique
characteristics. First of all, the POC varies significantly among the
seasons, with the minimum occurrence during the winter season and the maximum
occurrence during the other seasons. The maximum POC is found to be
about 19 %, 29 %, 25 %, and 26 % during DJF, MAM, JJA, and SON,
respectively. Second, the peak of the POC occurs at different altitudes
during different seasons. The altitude of the maximum POC is at 15.5 km during
DJF, 15.0 km during MAM, 12.0 and 15.0 km during JJA, and 13.0 km during
SON. Thus, the peak POC is at a higher altitude during the winter and
pre-monsoon seasons and at a lower altitude during the SW and NE monsoon
seasons. Finally, the vertical structures of the POC are unique
during different seasons.
During the winter season, the vertical profile of the POC shows a gradual
increase from 8 to 15.5 km, a drastic decrease up to 17 km, and a few
occurrences above it. The vertical profile of the POC during the pre-monsoon
season shows that the occurrence increases gradually from the altitude above
8 to 15 km and gradually decreases up to 18 km. Unlike the winter season,
the maximum POC during the pre-monsoon season has a relatively sharper peak
that is confined to a narrow altitude region between 13 and 15 km. Although
frequent convection does not occur during the pre-monsoon season, deep or
very deep convection even penetrates the lower stratosphere, especially
during the month of May (Devasthale et al., 2010). The POC at
relatively higher altitudes during the pre-monsoon season could be due to
the occurrence of very deep convection. The vertical distribution of the
POC during the SW monsoon season shows double peaks at altitudes of about
12 and 15 km, unlike the other seasons. Generally, frequent convection,
ranging from shallow to deep convection, occurs during the SW monsoon season
and spurs a large amount of water vapor into the upper troposphere, causing
the frequent occurrence of multiple cirrus clouds in the altitude range between 12
and 15 km. Such multiple layers of cirrus clouds could also be due to
vertically propagating gravity waves (Tsuda et al., 1994;
Murthy et al., 2002). In this season, the POC
gradually increases from 8 to 12 km, shows a broad maximum
characterizing a double peak at 12 and 15 km, and then gradually decreases
above this altitude.
The vertical profile of the POC during the post-monsoon (NE) season shows a
gradual increase from 8 to 13 km (peak altitude cirrus clouds) and then a
gradual decrease to about 17 km. It is interesting to find that the POC shows a
substantial interannual variation, with a higher POC during the year 2016 when
compared with 2017–2018. This higher occurrence of the POC during 2016 is
mainly due to the higher POC observed in the winter and SW monsoon
seasons in 2016. The POC during the pre-monsoon and NE monsoon seasons does not
show intraseasonal variation as large as that observed during the winter and SW
monsoon seasons.
Interannual variation in the POC
As mentioned earlier, the seasonal variation in the POC, with maxima during
the SW monsoon season and minima during the winter season, is well known over
the Asian monsoon region. The SW monsoon, which provides an enormous amount
of water vapor to the upper troposphere, mainly dominates the seasonal
feature of the POC. Here, we have obtained the annual cycles of the POC,
the monthly duration of total hours of lidar observations and cirrus
observations, and the annual cycles of the CPT and COT altitudes for three
years (2016, 2017, and 2018), as shown in Fig. 8. As mentioned earlier, in
total, the lidar was operated for 11 778 h; of these 11 778 h, 5002 h (about 42.5 %) contained cirrus cloud observations over the period from
2016 to 2018. The POC values for the years 2016, 2017, and 2018 are 55.9 %, 38.7 %,
and 36.8 %, respectively. We have listed the POC, CPT altitude, COT
altitude, and TTL thickness (TTLt) during different seasons as well as for
different years (2016, 2017, and 2018), as shown in the Supplement
(Table S1). The years 2017 and 2018 show about the same percentage occurrence
(38 %); however, the POC during 2016 is about 56 %, indicating a large
interannual variation in the POC over Kattankulathur. The POC decreases from
72 % to 46 % during the SW monsoon season from 2016 to 2018. The POC values
during the pre-monsoon and post-monsoon seasons of 2016 were ∼ 55 % and ∼ 48 %, respectively, and these values decreased by
∼ 12 % and ∼ 13.5 % during 2018. It should be
noted that the TTL increased during the pre-monsoon season in
2016–2018. This feature was not seen in other seasons.
Interestingly, the TTL thickness decreases with increasing POC, especially
during the SW monsoon season.
Monthly variation in the POC as a function of altitude during (a) 2016, (b) 2017, and (c) 2018. The monthly mean CPT altitude (red line) and COT altitude
(white line) with their standard deviations. (d–f) Total hours of lidar
operation (black bars) and the available cirrus observations (red bars) for
the corresponding year.
In general, the maximum POC is found to be during February, May–September, and November, whereas the minimum POC is during March and October.
The observed higher POC during May–September and November appears to be related
to the convective activities during the SW and NE monsoons, respectively.
It can also be noticed that the POC is relatively higher during the SW monsoons
in 2016 and 2017 compared with 2018. It is also observed that the POC during
the NE monsoon, especially in November 2017, is higher than in November 2016 and
2018. We have examined the monthly mean variation in the OLR over central
India, throughout Tamil Nadu, and over Kattankulathur (Chennai), as shown in
the Supplement (Fig. S7). It can be seen that OLR <240 W m-2
starts to occur after May and persists until November. However, over central
India, OLR < 240 W m-2 occurs only during June-September. An
abundance of deep convective clouds during the SW monsoon season triggers
the formation of cirrus clouds from the convective outflow anvil clouds. In
addition, the TEJ advects the upper-level moisture from the South China Sea
and the Bay of Bengal, while LLJ brings moisture from the Indian subcontinent that is
favorable for cirrus cloud formation (Subrahmanyam et al., 2016).
The higher POC observed during February may be associated with turbulence, which
is prominent over the altitude range of ∼ 10–18 km during the winter
season (Satheesan and Krishna Murthy, 2002).
The vertical extent of cirrus clouds is highly variable from month to
month, as mentioned earlier. The cirrus cloud top is generally confined
below the CPT altitude except during May–September, when the POC occurs
below the COT altitude. Similar to our results,
Pan
and Munchak (2011) and Pandit et al. (2014) reported a POC above the CPT
altitude. The layers of the maximum POC at ∼ 12 and
∼ 15 km and the minimum POC at ∼ 14 km characterize
the multilayer cirrus clouds during the SW monsoon seasons. Such
multiple cirrus layers are attributed to the nucleation, condensation,
and subsequent freeze-drying of the moisture as a result of the slow updraft
from the COT top (Meenu et al., 2011), the
freeze-drying of the moisture transported at a relatively higher altitude
due to TEJ (Das et al., 2011), and the cold temperature anomaly induced by
wave activities near the tropopause (Kim et al., 2016). Although
Kattankulathur is located in the rain shadow region of the SW monsoon
season, it is located in the vicinity of the TEJ stream associated with
upper-tropospheric circulations that seem to favor the frequent occurrence
of cirrus clouds. The TEJ advects an enormous amount of moisture from the Bay of
Bengal and a supply of moisture from convection results in the formation
of cirrus clouds over a large vertical depth.
It is important to note that the enhanced occurrence of the POC from May to September was significantly reduced in the year 2017 except for the
enhancement from March to April. We also observed enhancement in the POC
immediately above the COT altitude to 14 km during February and November.
The POC is observed to be significantly reduced above the COT altitude in
July. The frequent occurrence of mid-level clouds could have prevented
the lidar signals from detecting cirrus clouds. In contrast to 2016 and
2017, the POC during February, August, and September 2018 was significantly
reduced.
Interestingly, the maximum POC observed at 16 km during April shifts to 9 km
during July. The POC was also found to be just above the CPT altitude during
2016, which was not observed during 2017 and 2018. Such interannual
variation in the POC appears to be related to the El Niño–Southern Oscillation (ENSO)
and the quasi-biennial oscillation (QBO); these are the important factors
influencing the interannual variation in the TTL cloud fractions (Tseng and
Fu, 2017) and the TTL temperatures (Randel and Jensen, 2013). During the
first half of 2016, stronger El Niño conditions were observed, whereas weak La Niña conditions prevailed for the
other half of 2016 and during the second half of 2017 to the
first half of 2018, as shown in the
Supplement (Fig. S7). Westerly winds prevailed from January 2016 to June
2017 and from October to December 2018, whereas easterly winds prevailed from July
2017 to September 2018. These interannual components are known to modulate the
TTL temperatures, thereby affecting the occurrence of cirrus clouds. In the presence
of warm temperature anomalies, cirrus clouds may dissipate, whereas cirrus
clouds may be generated in situ in the presence of cold temperature
anomalies. It should be noted that relatively stronger convection was
observed during the SW monsoons in 2016 and 2017 compared with the SW monsoon
in 2018 over Chennai. In addition, relatively stronger convection was
observed in association with a higher POC during November 2017 compared with
November 2016 and 2018. It appears that the prevalence of convection is
an important factor in such strong interannual variations in the POC over
Kattankulathur (Chennai).
Figure 9 shows the relationship of the POC and convection (OLR) and the POC
anomalies with the ENSO and QBO indices. We observed that the POC is negatively
correlated (r=-0.50) with OLR, indicating the higher occurrence of
cirrus clouds during deeper convection. To understand the interannual
variations in the POC, we have obtained the POC anomalies by subtracting the
annual cycle calculated over the period from 2016 to 2018, as shown in Fig. 9b–c.
Note that ENSO affects the upper-tropospheric temperature with a lag of
4 months (Mehta et al., 2015); thus, we have considered a 4-month lag
in POC anomalies for ENSO (POC anomalies lagging ENSO). It is observed that
the POC anomalies lagged at 4 months are positively correlated (r=0.51)
with the ENSO index (significant at the 95 % confidence level). This indicates
that occurrence is enhanced during El Niño years and decreases during
La Niña years. The POC anomalies are also positively correlated (r=0.50)
with the QBO index (significant at a 95 % confidence level). This indicates
that the POC is enhanced during the westerly phases, whereas it decreases during the
easterly phases. Although the POC shows a strong interannual variation in
connection with the ENSO and QBO indices, we would like to investigate this aspect
in more detail using longer-term data in a future study.
Summary and conclusions
Cirrus clouds play an important role in the Earth–atmosphere radiation
budget due to their greenhouse and albedo effects, which depend on the physical
and optical properties. Therefore, a precise understanding of the physical
properties (e.g., diurnal cycle and vertical extent) of cirrus cloud
occurrence and cloud optical depth is a highly essential input to climate
modeling and prediction. However, to the authors' knowledge, no
such studies (on the diurnal variation in
cirrus clouds) exist over the Indian monsoon region, mainly due to limited long-term continuous
observations.
The time series of (a) the POC and OLR, (b) a 4-month lagged
ENSO index (the Niño 3.4SST anomalies) and POC anomalies, and (c) the QBO
indices (the zonal wind at 50 hPa) and POC anomalies. The correlation
coefficient (r) and the number of months (N) are also shown. An asterisk
indicates that the correlation coefficient is significant at 95 % confidence
level.
Hence, for the first time, our study presents the diurnal variation in
cirrus clouds during different seasons over the NE monsoon region,
Kattankulathur, located near the west coast of the Bay of Bengal. The cirrus
cloud occurrence for the single and multiple layers identified by the
zero-crossing method is evaluated using MPL observations between 14:00 IST on the first day and 11:00 IST on the second day over the period from 2016 to 2018. The main
conclusions from this study are briefly summarized as follows:
Cirrus cloud occurrence shows a unique diurnal structure with a higher
occurrence of single-layer cirrus in the late evening hours (18:00–21:00 IST) controlled by convective processes during the SW and NE monsoon
seasons. For multilayer cirrus, in contrast, the occurrence is higher in
the early morning hours (04:00–05:00 IST), subjugated by both
freeze-drying and deep convection processes.
Deep convection and low tropopause temperatures are crucial for the
enhancement of cirrus cloud occurrences. We calculated the frequency of
occurrence of a brightness temperature <240 K as a proxy for
convection. The results indicate that the convection that occurs more
frequently in the afternoon hours dominates the higher occurrence of evening
single-layer cirrus clouds during the SW and NE monsoon seasons. We also
obtained the frequency of occurrence of a CPT temperature <191 K,
which occurs more frequently during the morning hours throughout the year. This
result indicates that the freeze-drying process is favorable for the higher
occurrence of morning multilayer cirrus clouds at relatively higher
altitudes.
The overall (single and multiple layers together) cirrus cloud occurrence
shows a substantial seasonal variation. The occurrence is ∼ 10 %–25 % during the winter season with the highest occurrence over the
altitude range of ∼ 14–16 km just below the mean CPT altitude (17.2 km). In this season, cirrus clouds are predominantly subvisible and are mostly
generated in situ by the condensation of trace water vapor due to
extremely cold temperatures at higher altitudes.
During the pre-monsoon season (March–May), cirrus cloud occurrence varies
from 10 % to 30 %. The minimum occurrence is observed during March.
During April–May, the occurrence shows a significant enhancement in the
altitude range of 10–17 km, with peak occurrence (20 %–30 %) in the altitude range of
∼ 14–15 km, mainly confined within the TTL. The frequent
thunderstorm activity during April–May appears to be the main reason for
the phenomenal increase in cirrus cloud occurrence in these months.
During the SW monsoon season, cirrus cloud occurrence reaches a maximum
(∼ 40 %), with frequent multilayer cirrus clouds. Cirrus
clouds also occur more frequently below the TTL but rarely above the
CPT. Although Kattankulathur is located in the rain shadow region of the SW
monsoon, convection prevails. However, it is also located in the TEJ core
region, which is favorable for the generation of cirrus
due to wind shear (turbulence). Additionally, the TEJ advects an enormous amount of moisture from the
Bay of Bengal. The supply of moisture from convection results in cirrus
cloud occurrence over a large vertical extent (∼ 8–18 km).
During the NE monsoon seasons, cirrus occurrence has a limited vertical extent.
It is generally confined within the TTL, with fewer or no occurrence of
cirrus clouds above the CPT. Due to the weakening of local convection,
the inadequate moisture supply reduces the sustenance of cirrus clouds
during October. However, a relatively higher occurrence during November
appears due to the prevalence of the NE monsoon over Kattankulathur. The
convection during the NE monsoon is not as strong as that during the SW monsoon, leading
to the formation of cirrus clouds at relatively lower heights during the
former season.
The occurrence shows a distinct interannual variability, with higher
occurrence during the year 2016 compared with 2017 and 2018. The occurrence
during 2016, 2017, and 2018 was found to be ∼ 55.9 %,
38.7 %, and 36.8 %, respectively. We observed the role of ENSO and
QBO in the interannual variations in cirrus clouds. The POC anomalies
are positively correlated with the ENSO index, indicating a higher occurrence
in El Niño years than in La Niña years. The POC anomalies also show a
positive correlation with the QBO index, indicating a higher occurrence in
westerly phases compared with easterly phases.
The present study shows the diurnal cycle of cirrus clouds, which will
be helpful in the assessment of climate models. Although MPL detection is
limited to mostly nocturnal cirrus clouds between 15:00 IST on the first day and
11:00 IST on the second day, it has captured the diurnal pattern in the occurrence
of the single- and multilayer cirrus clouds that show augmentation
during the evening and morning hours. In a future study, we are planning to
explore temporally co-located satellite data simultaneous to MPL observations to unravel the
day–night difference in cirrus cloud occurrence over Kattankulathur and
adjoining regions.
Data availability
The MPL data used in this study are not yet publicly available; however, the
data can be provided upon request to the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-8321-2022-supplement.
Author contributions
SKM was responsible for conceptualizing and supervising the study; carrying out the investigation; and
writing, reviewing, and editing the paper. SA contributed to data curation, carried out the investigation, and prepared the original draft of the paper.
AA was responsible for curating the data,
developing software, and carrying out the investigation. TVRR contributed to data curation and carried out the
investigation.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher' note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work has been fully supported by the Department of Science and Technology,
Government of India – Science and Engineering Research Board (DST-SERB)
project (grant no. EMR/2015/000525). Sanjay Kumar Mehta wishes to thank the Earth Science and Technology
Cell (ESTC), Ministry of Earth Sciences (MoES), for MPL
observations. Saleem Ali is grateful to SERB for providing
a fellowship for this study. Sanjay Kumar Mehta acknowledges MHRD, Government of India, for support within the framework of the Scheme for Promotion of Academic and Research Collaboration
(SPARC) project (grant no.SPARC/2018–2019/P835/SL). The SRM HPCC facility was used to
process the MPL data. The authors thank the reviewers and the handling editor,
Peter Haynes, for their valuable comments and suggestions.
Financial support
This research has been supported by the Science and Engineering Research Board (grant no. EMR/2015/000525).
Review statement
This paper was edited by Peter Haynes and reviewed by Artem Feofilov and one anonymous referee.
ReferencesAli, S., Mehta, S. K., Annamalai, V., Ananthavel, A., and Reddy, R.:
Qualitative observations of the cirrus clouds effect on the thermal
structure of the tropical tropopause, J. Atmos.
Sol.-Terr. Phys., 211, 105440, 10.1016/j.jastp.2020.105440, 2020.Ananthavel, A., Mehta, S. K., Ali, S., Reddy, T. R., Annamalai, V., and Rao,
D. N: Micro Pulse Lidar measurements in coincidence with CALIPSO overpasses:
Comparison of tropospheric aerosols over Kattankulathur (12.82∘ N,
80.04∘ E), Atmos. Pollut. Res., 12, 101082, 10.1016/j.apr.2021.101082, 2021a.Ananthavel, A., Mehta, S. K., Reddy, T. R., Ali, S., and Rao, D. N.:
Vertical distributions and columnar properties of the aerosols during
different seasons over Kattankulathur (12.82∘ N, 80.04∘ E): A
semi-urban tropical coastal station, Atmos. Environ., 256, 118457, 10.1016/j.atmosenv.2021.118457,
2021b.Annamalai, V. and Mehta, S. K.: Extreme variability of the tropical tropopause
over the Indian monsoon region, Clim. Dynam.,
10.1007/s00382-022-06264-7, 2022.Campbell, J. R., Welton, E. J., Spinhirne, J. D., Ji, Q., Tsay, S. C.,
Piketh, S. J., and Holben, B. N.: Micropulse lidar observations of
tropospheric aerosols over northeastern South Africa during the ARREX and
SAFARI 2000 dry season experiments, J. Geophys. Res., 108, 8497,
10.1029/2002JD002563, 2003.
Chen, S. S. and Houze Jr., R. A.:Diurnal variation and life-cycle of
deep convective systems over the tropical Pacific warm pool, Q.
J. Roy. Meteorol. Soc., 123, 357–388, 1997.Corti, T., Luo, B. P., Fu, Q., Vömel, H., and Peter, T.: The impact of
cirrus clouds on tropical troposphere-to-stratosphere transport, Atmos.
Chem. Phys., 6, 2539–2547, 10.5194/acp-6-2539-2006, 2006.Cziczo, D. J. and Froyd, K. D.: Sampling the composition of cirrus ice
residuals, Atmos. Res., 142, 15–31, 10.1016/j.atmosres.2013.06.012,
2014.Dai, G., Wu, S., Song, X., and Liu, L.: Optical and Geometrical Properties of
Cirrus Clouds over the Tibetan Plateau Measured by Lidar and Radiosonde
Sounding at the Summertime in 2014, Remote
Sens., 2019, 302,
10.1051/epjconf/201817605040, 2017.Das, S. K., Chiang, C. W., and Nee, J. B.: Influence of tropical easterly jet
on upper tropical cirrus: An observational study from CALIPSO, Aura-MLS, and
NCEP/NCAR data, J. Geophys. Res.-Atmos., 116, D12, 10.1029/2011JD015923,
2011.Dessler, A. E., Palm, S. P., Hart, W. D., and Spinhirne, J. D.:
Tropopause-level thin cirrus coverage revealed by ICESat/Geoscience Laser
Altimeter System, J. Geophys. Res.-Atmos., 111, 1–10, 10.1029/2005JD006586, 2006.Devasthale, A. and Fueglistaler, S.: A climatological perspective of deep convection penetrating the TTL during the Indian summer monsoon from the AVHRR and MODIS instruments, Atmos. Chem. Phys., 10, 4573–4582, 10.5194/acp-10-4573-2010, 2010.
Davis, S. M., Liang, C. K., and Rosenlof, K. H.: Interannual
variability of tropical tropopause layer clouds, Geophys. Res.
Lett., 40, 2862–2866, 2013.
Dowling, D. R. and Radke, L. F.: A summary of the physical
properties of cirrus clouds, J. Appl. Meteorol. Climatol.,
29, 970–978, 1990.Eriksson, P., Rydberg, B., Johnston, M., Murtagh, D. P., Struthers, H., Ferrachat, S., and Lohmann, U.: Diurnal variations of humidity and ice water content in the tropical upper troposphere, Atmos. Chem. Phys., 10, 11519–11533, 10.5194/acp-10-11519-2010, 2010.Feofilov, A. G. and Stubenrauch, C. J.: Diurnal variation of high-level clouds from the synergy of AIRS and IASI space-borne infrared sounders, Atmos. Chem. Phys., 19, 13957–13972, 10.5194/acp-19-13957-2019, 2019.
Fleming, J. R. and Cox, S. K.: Radiative effects of cirrus clouds, J.
Atmos. Sci., 31, 2182–2188, 1974.
Flynn, C. J., Mendoza, A., Zheng, Y., and Mathur, S.: Novel
polarization-sensitive micropulse lidar measurement technique, Opt.
Exp., 15, 2785–2790, 2007.Fu, Q. and Liou, K. N.: Parameterization of the Radiative Properties of
Cirrus Clouds, J. Atmos. Sci., 50, 2008–2025,
10.1175/1520-0469(1993)050<2008:POTRPO>2.0.CO;2,
1993.Fueglistaler, S., Dessler, A. E., Dunkerton, T. J., Folkins, I., Fu, Q., and
Mote, P. W.: Tropical tropopause layer, Rev. Geophys., 47, 10.1029/2008RG000267, 2009.Fujiwara, M., Iwasaki, S., Shimizu, A., Inai, Y., Shiotani, M., Hasebe, F.,
Matsui, I., Sugimoto, N., Okamoto, H., Nishi, N., Hamada, A., Sakazaki, T.,
and Yoneyama, K.: Cirrus observations in the tropical tropopause layer over
the western Pacific, J. Geophys. Res., 114, D09304,
10.1029/2008JD011040, 2009.Goswami, B. N. and Xavier, P. K.: ENSO control on the south
Asian monsoon through the length of the rainy season, Geophys. Res.
Lett., 32, 10.1029/2005GL023216, 2005.Gouveia, D. A., Barja, B., Barbosa, H. M. J., Seifert, P., Baars, H., and
Pauliquevis, T.: Optical and geometrical properties of cirrus clouds in
Amazonia derived from 1 year of ground-based lidar measurements, Atmos. Chem. Phys., 17,
3619–3636, 10.5194/acp-17-3619-2017, 2017.
Gupta, A. K., Rajeev, K., and Sijikumar, S.: Day-night changes in the
altitude distribution, physical properties and radiative impact of
low-altitude clouds over the stratocumulus-dominated subtropical oceans,
J. Atmos. Sol.-Terr. Phys., 161, 118–126, 2017.Haladay, T. and Stephens, G.: Characteristics of tropical thin cirrus clouds
deduced from joint CloudSat and CALIPSO observations, J. Geophys. Res.-Atmos., 114, 1–13, 10.1029/2008JD010675, 2009.Held, I. M. and Hoskins, B. J.: Large-scale eddies and the general
circulation of the troposphere, in: Advances in geophysics, Elsevier, Vol. 28,
3–31, 10.1016/S0065-2687(08)60218-6, 1985
JCA Marrero, B. B.: Cirrus Clouds Optical Properties Measured With Lidar At
Camagüey, Cuba, Propiedades Ópticas de Nubes Cirros Medidas con
Lidar en Camagüey, Cuba, Opt. Pura Apl., 39, 11–16, 2006.Heymsfield, A. J.: Ice particles observed in a cirriform cloud at
-83 ∘C and implications for polar stratospheric clouds, J. Atmos.
Sci., 43, 851–855, 1986.
Heymsfield, A. J. and Iaquinta, J.: Cirrus crystal terminal velocities, J.
Atmos. Sci., 57, 914–936, 2000.Jakob, C.: Ice clouds in numerical weather prediction models:
Progress, problems, and prospects, Cirrus, Oxford University Press, 10.1093/oso/9780195130720.001.0001, 2002.
Jensen, E. J., Toon, O. B., Pfister, L., and Selkirk, H. B.: Dehydration of
the upper troposphere and lower by subvisible cirrus clouds near the
tropical tropopause, Geophys. Res. Lett., 23, 825–828, 1996.Jensen, E. J., Diskin, G., Lawson, R. P., Lance, S., Bui, T. P., Hlavka, D.,
McGill, M., Pfister, L., Toon, O. B., and Gao, R.: Ice nucleation and
dehydration in the Tropical Tropopause Layer, P. Natl. Acad. Sci. USA,
110, 2041–2046, 10.1073/pnas.1217104110, 2013.Kim, J., Grise, K. M., and Son, S.-W.: Thermal characteristics of the
cold-point tropopause region in CMIP5 models, J. Geophys. Res.-Atmos.,
118, 8827–8841, 10.1002/jgrd.50649, 2013.
Kim, J.-E., Alexander, M. J., Bui, T. P., Dean-Day, J. M., Lawson, R. P.,
Woods, S., Hlavka, D., Pfister, L., and Jensen, E. J.: Ubiquitous influence of waves on tropical high cirrus
clouds, Geophys. Res. Lett., 43, 5895–5901, 2016.Kottayil, A., Satheesan, K., John, V. O., and Antony, R.: Diurnal variation of deep convective clouds over Indian monsoon region and its association with rainfall, Atmos. Res., 255, 105540, 10.1016/j.atmosres.2021.105540, 2021.Kulkarni, P., Ramachandran, S., Bhavani Kumar, Y., Narayana Rao, D., and
Krishnaiah, M.: Features of upper troposphere and lower stratosphere
aerosols observed by lidar over Gadanki, a tropical Indian station, J.
Geophys. Res., 113, D17207, 10.1029/2007JD009411, 2008.
Li, J., Yi, Y., Minnis, P., Huang, J., Yan, H., Ma, Y., and Ayers, J.
K.: Radiative effect differences between multi-layered and single-layer
clouds derived from CERES, CALIPSO, and CloudSat data, J.
Quant. Spectrosc. Ra., 112, 361–375, 2011.Liou, K. N.: Influence of cirrus clouds on weather and climate
processes A global perspective, Mon. Weather Rev., 114, 1167–1199,
10.1175/1520-0493(1986)114<1167:IOCCOW>2.0.CO;2,
1986.Liu, Z., Vaughan, M., Winker, D., Kittaka, C., Getzewich, B., Kuehn, R.,
Omar, A., Powell, K., Trepte, C., and Hostetler, C.: The CALIPSO Lidar Cloud and
Aerosol Discrimination: Version 2 Algorithm and Initial Assessment of
Performance, J. Atmos. Ocean. Technol., 26, 1198–1213,
10.1175/2009JTECHA1229.1, 2009.Lynch, D. K.: Cirrus, Oxford University Press,
https://books.google.co.in/books/about/Cirrus.html?id=58v1fg4xeo8C
(last access: 25 October 2018), 2002.Massie, S. T., Khosravi, R., and Gille, J. C.: A multidecadal study of cirrus
in the tropical tropopause layer, J. Geophys. Res.-Atmos., 118, 7938–7947,
10.1002/jgrd.50596, 2013.McFarquhar, G. M., Heymsfield, A. J., Spinhirne, J., and Hart, B.: Thin and
Subvisual Tropopause Tropical Cirrus: Observations and Radiative Impacts, J.
Atmos. Sci., 57, 1841–1853, 10.1175/1520-0469(2000)057<1841:TASTTC>2.0.CO;2, 2000.Meenu, S., Rajeev, K., and Parameswaran, K.: Regional and vertical
distribution of semitransparent cirrus clouds over the tropical Indian
region derived from CALIPSO data, J. Atmos. Sol.-Terr. Phys., 73,
1967–1979, 10.1016/j.jastp.2011.06.007, 2011.
Mehta, S. K., Venkat Ratnam, M., and Krishna Murthy, B. V.:
Characteristics of the tropical tropopause over different longitudes,
J. Atmos. Sol.-Terr. Phys., 73, 2462–2473, 2011.
Mehta, S. K., Fujiwara, M., Tsuda, T., and Vernier, J. P: Effect of recent
minor volcanic eruptions on temperatures in the upper troposphere and lower
stratosphere, J. Atmos. Sol.-Terr. Phys., 129,
99–110, 2015.Mitchell, D. L., Rasch, P., Ivanova, D., McFarquhar, G., and Nousiainen, T.:
Impact of small ice crystal assumptions on ice sedimentation rates in cirrus
clouds and GCM simulations, Geophys. Res. Lett., 35, 10.1029/2008GL033552, 2008.
Muhsin, M., Sunilkumar, S. V., Ratnam, M. V., Parameswaran, K., Murthy, B.
K., Ramkumar, G., and Rajeev, K.: Diurnal variation of atmospheric stability
and turbulence during different seasons in the troposphere and lower
stratosphere derived from simultaneous radiosonde observations at two
tropical stations, in the Indian Peninsula, Atmos. Res., 180,
12–23, 2016.
Muhsin, M., Sunilkumar, S. V., Venkat Ratnam, M., Krishna Murthy, B. V., and
Parameswaran, K.: Seasonal and diurnal variations of tropical tropopause
layer (TTL) over the Indian Peninsula, J. Geophys. Res.-Atmos., 122, 12–672, 2017.Murthy, B. V. K., Satheesan, K., Parameswaran, K., Sasi, M. N., Ramkumar,
G., Bhavanikumar, Y., Raghunath, K., and Krishniah, M.: Equatorial waves in
temperature in the altitude range 4 to 70 km, Q. J. R. Meteorol. Soc.,
128, 819–837, 10.1256/0035900021643700, 2002.Nair, A. K. M., Rajeev, K., Mishra, M. K., Thampi, B. V., and Parameswaran,
K.: Multiyear lidar observations of the descending nature of tropical cirrus
clouds, J. Geophys. Res.-Atmos., 117, 1–9, 10.1029/2011JD017406,
2012.Nazaryan, H., McCormick, M. P., and Menzel, W. P.: Global characterization of
cirrus clouds using CALIPSO data, J. Geophys. Res.-Atmos., 113, 1–11,
10.1029/2007JD009481, 2008.Noel, V., Chepfer, H., Chiriaco, M., and Yorks, J.: The diurnal cycle of cloud profiles over land and ocean between 51∘ S and 51∘ N, seen by the CATS spaceborne lidar from the International Space Station, Atmos. Chem. Phys., 18, 9457–9473, 10.5194/acp-18-9457-2018, 2018.Pal, S. R., Steinbrecht, W., and Carswell, A. I.: Automated method for lidar
determination of cloud-base height and vertical extent, Appl. Opt., 31,
1488, 10.1364/AO.31.001488, 1992.Pan, L. L. and Munchak, L. A.: Relationship of cloud top to the tropopause
and jet structure from CALIPSO data, J. Geophys. Res.-Atmos., 116,
1–17, 10.1029/2010JD015462, 2011.Pandit, A. K., Gadhavi, H., Ratnam, M. V., Jayaraman, A., Raghunath, K., and
Rao, S. V. B.: Characteristics of cirrus clouds and tropical tropopause
layer: Seasonal variation and long-term trends, J. Atmos. Sol.-Terr.
Phys., 121, 248–256, 10.1016/j.jastp.2014.07.008, 2014.Pandit, A. K., Gadhavi, H. S., Ratnam, M. V., Raghunath, K., Rao, S. V. B.,
and Jayaraman, A.: Long-term trend analysis and climatology of tropical
cirrus clouds using 16 years of lidar data set over Southern India, Atmos.
Chem. Phys., 15, 13833–13848, 10.5194/acp-15-13833-2015, 2015.Parameswaran, K., SunilKumar, S. V., Krishna Murthy, B. V., Satheesan, K.,
Bhavani Kumar, Y., Krishnaiah, M., and Nair, P. R.: Lidar observations of
cirrus cloud near the tropical tropopause: Temporal variations and
association with tropospheric turbulence, Atmos. Res., 69, 29–49,
10.1016/j.atmosres.2003.08.002, 2003.
Parameswaran, K., Sunilkumar, S. V., Murthy, B. K., and Satheesan, K.: Lidar observations of high altitude cirrus clouds
near the tropical tropopause, Adv. Space Res., 34, 845–850, 2004.Platt, C. M., Young, S. A., Carswell, A. I., Pal, S. R., McCormick, M. P.,
Winker, D. M., DelGuasta, M., Stefanutti, L., Eberhard, W. L., Hardesty, M.,
Flamant, P. H., Valentin, R., Forgan, B., Gimmestad, G. G., Jäger, H.,
Khmelevtsov, S. S., Kolev, I., Kaprieolev, B., Lu, D., Sassen, K.,
Shamanaev, V. S., Uchino, O., Mizuno, Y., Wandinger, U., Weitkamp, C.,
Ansmann, A., and Wooldridge, C.: The Experimental Cloud Lidar Pilot Study
(ECLIPS) for Cloud – Radiation Research, Bull. Am. Meteorol. Soc., 75,
1635–1654, 10.1175/1520-0477(1994)075<1635:TECLPS>2.0.CO;2, 1994.Podglajen, A., Plougonven, R., Hertzog, A., and Jensen, E.: Impact of gravity
waves on the motion and distribution of atmospheric ice particles, Atmos.
Chem. Phys., 18, 10799–10823, 10.5194/acp-18-10799-2018, 2018.Ramkumar, T. K., Niranjan Kumar, K., and Mehta, S. K.:
Mesosphere-stratosphere-troposphere radar observations of characteristics of
lower atmospheric high-frequency gravity waves passing through the tropical
easterly jet, J. Geophys. Res.-Atmos., 115, 10.1029/2009JD013733, 2010.
Randel, W. J. and Jensen, E. J.: Physical processes in the tropical
tropopause layer and their roles in a changing climate, Nat. Geosci.,
6, 169–176, 2013.Randel, W. J., Garcia, R. R., and Wu, F.: Time-Dependent Upwelling in the
Tropical Lower Stratosphere Estimated from the Zonal-Mean Momentum Budget,
J. Atmos. Sci., 59, 2141–2152, 10.1175/1520-0469(2002)059<2141:tduitt>2.0.co;2, 2002.
Ratnam, M. V., Raman, M. R., Mehta, S. K., Nath, D., Krishnamurthy, B. V.,
Rajeevan, M., and Rao, D. N.: Sub-daily variations observed in
Tropical Easterly Jet (TEJ) streams, J. Atmos. Sol.-Terr. Phys., 73, 731–740, 2011.
Reddy, T. R., Mehta, S. K., Ananthavel, A., Ali, S., Annamalai, V., and Rao,
D. N.:Seasonal characteristics of sea breeze and thermal internal boundary
layer over Indian east coast region, Meteorol. Atmos. Phys., 133, 217–232, 2020.Riese, M., Ploeger, F., Rap, A., Vogel, B., Konopka, P., Dameris, M., and
Forster, P.: Impact of uncertainties in atmospheric mixing on simulated UTLS
composition and related radiative effects, J. Geophys. Res.-Atmos., 117, 1–10,
10.1029/2012JD017751, 2012.Sandhya, M., Sridharan, S., Indira Devi, M., Niranjan, K., and Jayaraman, A.: A case study of formation and maintenance of a lower stratospheric cirrus cloud over the tropics, Ann. Geophys., 33, 599–608, 10.5194/angeo-33-599-2015, 2015.
Sassen, K. and Byung, S. C.: Subvisual-thin cirrus lidar dataset
for satellite verification and climatological research, J. Appl.
Meteorol. Climatol., 31, 1275–1285, 1992.Sassen, K., Wang, Z., and Liu, D.: Global distribution of cirrus clouds from
CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations
(CALIPSO) measurements, J. Geophys. Res., 113, D00A12,
10.1029/2008JD009972, 2008.Sassen, K., Wang, Z., and Liu, D.: Cirrus clouds and deep convection in the
tropics: Insights from CALIPSO and CloudSat, J. Geophys. Res.-Atmos.,
114, 1–11, 10.1029/2009JD011916, 2009.
Satheesan, K. and Murthy, B. V. K.: Turbulence parameters in the tropical
troposphere and lower stratosphere, J. Geophys. Res.-Atmos., 107, 1–13, 2002.
Selkirk, H. B.: The tropopause cold trap in the Australian monsoon during STEP/AMEX 1987, J. Geophys. Res.-Atmos., 98, 8591–8610, 1993.
Simpson, M., Warrior, H., Raman, S., Aswathanarayana, P. A., Mohanty, U. C., and Suresh, R.: Sea-breeze-initiated rainfall over the east coast of India during the Indian southwest monsoon, Nat. Hazard., 42, 401–413, 2007.Sivakumar, V., Bhavanikumar, Y., Rao, P. B., Mizutani, K., Aoki, T., Yasui,
M., and Itabe, T.: Lidar observed characteristics of the tropical cirrus
clouds, Radio Sci., 38, 10.1029/2002RS002719, 2003.Spinhirne, J. D., Palm, S. P., Hart, W. D., Hlavka, D. L., and Welton, E. J.:
Cloud and aerosol measurements from GLAS: Overview and initial results, Geophys. Res. Lett.,
32, 1–5, 10.1029/2005GL023507, 2005.Stephens, G. L. and Webster, P. J.: Clouds and Climate: Sensitivity of
Simple Systems, J. Atmos. Sci., 38, 235–247,
10.1175/1520-0469(1981)038<0235:CACSOS>2.0.CO;2,
1981.Subrahmanyam, K. V. and Kumar, K. K.: CloudSat observations of cloud-type distribution over the Indian summer monsoon region, Ann. Geophys., 31, 1155–1162, 10.5194/angeo-31-1155-2013, 2013.Sunil Kumar, S. V., Parameswaran, K., and Krishna Murthy, B. V.: Lidar
observations of cirrus cloud near the tropical tropopause: General features,
Atmos. Res., 66, 203–227, 10.1016/S0169-8095(02)00159-X, 2003.Sunilkumar, S. V., Parameswaran, K., Rajeev, K., Krishna Murthy, B. V.,
Meenu, S., Mehta, S. K., and Babu, A.: Semitransparent cirrus clouds in the
tropical tropopause layer during two contrasting seasons, J. Atmos.
Sol.-Terr. Phys., 72, 745–762,
10.1016/j.jastp.2010.03.020, 2010.Tseng, H.-H. and Fu, Q.: Tropical tropopause layer cirrus and its relation
to tropopause, J. Quant. Spectrosc. Radiat. Transf., 188, 118–131,
10.1016/J.JQSRT.2016.05.029, 2017.Tsuda, T., Murayama, Y., Wiryosumarto, H., Harijono, S. W. B., and Kato, S.:
Equatorial waves and diurnal tides, J. Geophys. Res.-Atmos., 99, 10491–10505, 10.1029/94JD00355, 1994.Vernier, J., Fairlie, T. D., Natarajan, M., Wienhold, F. G., Bian, J.,
Martinsson, B. G., Crumeyrolle, S., Thomason, L. W., and Bedka, K. M.:
J. Geophys. Res.-Atmos., 120, 1608–1619,
10.1002/2014JD022372.Received, 2015.Wang, T. and Dessler, A. E.: Analysis of cirrus in the tropical tropopause
layer from CALIPSO and MLS data: A water perspective, , 117,
1–10, 10.1029/2011JD016442, 2012.Welton, E. J., Voss, K. J., Quinn, P. K., Flatau, P. J., Markowicz, K.,
Campbell, J. R., Spinhirne, J. D., Gordon, H. R., and Johnson, J. E.:
Measurements of aerosol vertical profiles and optical properties during
INDOEX 1999 using micropulse lidars, J. Geophys. Res.-Atmos., 107,
1–20, 10.1029/2000JD000038, 2002.Winker, D. M. and Trepte, C. R: Laminar cirrus observed near the tropical
tropopause by LITE, Geophys. Res. Lett., 25, 3351–3354, 1998.
Wu, S., Song, X., Liu, B., Dai, G., Liu, J., Zhang, K., Qin, S., Hua, D.,
Gao, F., and Liu, L.: Mobile multi-wavelength polarization Raman lidar for
water vapor, cloud and aerosol measurement, Opt. Express, 23, 33870,
10.1364/OE.23.033870, 2015.Wylie, D., Jackson, D. L., Menzel, W. P., and Bates, J. J.: Trends in
Global Cloud Cover in Two Decades of HIRS Observations, J. Clim., 18, 3021–3031, 10.1175/JCLI3461.1, 2005.Yang, Q., Fu, Q., and Hu, Y.: Radiative impacts of clouds in the tropical
tropopause layer, J. Geophys. Res.-Atmos., 115, 10.1029/2009JD012393,
2010.
Young, S. A.: Analysis of lidar backscatter profiles in optically thin
clouds, Appl. Optics, 34, 7019–7031, 1995.