Spectral width of premonsoon and monsoon clouds over Indo-Gangetic valley

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117,, doi: /2011jd016837, 2012 Spectral width of premonsoon and monsoon clouds over Indo-Gangetic valley Thara V. Prabha, 1 S. Patade, 1 G. Pandithurai, 1 A. Khain, 2 D. Axisa, 3 P. Pradeep-Kumar, 4 R. S. Maheshkumar, 1 J. R. Kulkarni, 1 and B. N. Goswami 1 Received 6 September 2011; revised 28 August 2012; accepted 28 August 2012; published 25 October [1] The combined effect of humidity and aerosol on cloud droplet spectral width (s) in continental monsoon clouds is a topic of significant relevance for precipitation and radiation budgets over monsoon regions. The droplet spectral width in polluted, dry premonsoon conditions and moist monsoon conditions observed near the Himalayan Foothills region during Cloud Aerosol Interaction and Precipitation Enhancement EXperiment (CAIPEEX) is the focus of this study. Here s is small in premonsoon clouds developing from dry boundary layers. This is attributed to numerous aerosol particles and the absence/suppression of collision-coalescence during premonsoon. For polluted and dry premonsoon clouds, s is constant with height. In contrast to premonsoon clouds, s in monsoon clouds increases with height irrespective of whether they are polluted or clean. The mean radius of polluted monsoon clouds is half that of clean monsoon clouds. In monsoon clouds, both mean radius and s decreased with total cloud droplet number concentration (CDNC). The spectral widths of premonsoon clouds were independent of total droplet number concentrations, but both s and mean radius decreased with small droplet (diameter < 20 mm) number concentrations in the diluted part of the cloud. Observational evidence is provided for the formation of large droplets in the adiabatic regions of monsoon clouds. The number concentration of small droplets is found to decrease in the diluted cloud volumes that may be characterized by various spectral widths or mean droplet radii. Citation: Prabha, T. V., S. Patade, G. Pandithurai, A. Khain, D. Axisa, P. Pradeep-Kumar, R. S. Maheshkumar, J. R. Kulkarni, and B. N. Goswami (2012), Spectral width of premonsoon and monsoon clouds over Indo-Gangetic valley, J. Geophys. Res., 117,, doi: /2011jd Introduction [2] Aerosol particles (AP) in the atmosphere from both natural and anthropogenic emissions influence the Earth- Atmosphere system through direct and indirect effects. The latter modifies clouds, precipitation and radiation budgets [Ramanathan et al., 2001; Nakajima et al., 2001; Kim et al., 2003; Zhang et al., 2004; Grabowski, 2006; Rosenfeld et al., 2008; Khain 2009]. It is well known that an increase in aerosol loading results in increase in cloud condensation nuclei (CCN) concentrations. This further leads to increases in cloud droplet number concentration (CDNC) and cloud albedo [Twomey, 1977]. The effective radius is related to the mean volume radius and the s of the cloud droplet spectra 1 Indian Institute of Tropical Meteorology, Pune, India. 2 Department of Atmospheric Sciences, Hebrew University of Jerusalem, Jerusalem, Israel. 3 Research Applications Laboratory, NCAR, Boulder, Colorado, USA. 4 Department of Atmospheric and Space Sciences, University of Pune, Pune, India. Corresponding author: T. V. Prabha, Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pashan, Pune , India. (thara@tropmet.res.in; prabha.thara@gmail.com) American Geophysical Union. All Rights Reserved /12/2011JD [Martin et al., 1994; Liu and Daum, 2000], which is related to cloud radiative properties [Liu and Daum, 2002]. Droplet spectral width (the standard deviation of droplet size distribution) depends on many parameters such as aerosol chemical composition, aerosol size distributions, vertical velocity, entrainment and other parameters that determine droplet size distribution [Khain et al., 2000; Yum and Hudson, 2005; Liu et al., 2006a; Peng et al., 2007]. [3] Cloud droplet size distributions exhibit a variety of spectral shapes [Martin et al., 1994; Hudson and Yum 1997; Liu and Daum 2000; Miles et al., 2000]. The addition of anthropogenic aerosol to marine air masses enhances not only the CDNC, but also the spectral dispersion. The increased spectral dispersion has been described to offset the cooling due to the Twomey effect by as much as 10% to 80% [Liu and Daum, 2002; Pandithurai et al., 2012]. [4] According to theoretical predictions, s should decrease with height (or with mean radius) in adiabatic clouds. Various studies [Hudson and Yum, 1997; Miles et al., 2000] illustrate that theoretical predictions give much smaller droplet spectral widths than observations [Politovich, 1993; Martin et al., 1994; Hudson and Yum, 1997]. For a given flight, local droplet concentration varied considerably during the Second Aerosol Characterization Experiment (ACE2). The width of cloud droplet spectra was typically in the range 1of15

2 of 1 to 2 mm [Pawlowska et al., 2006]. The spectral width did not vary systematically between pristine and polluted clouds and a small difference between near-adiabatic and diluted cloud regions was noted in that study. The width of the droplet size distribution and the relative dispersion were similar to that of stratocumulus clouds in the lowest few hundred meters during RICO (Rain In Cumulus over the Ocean) [Arabas et al., 2009], but they were significantly larger in the upper parts of the clouds. Miles et al. [2000] gives a comprehensive list of observations encompassing marine and continental cases. However, there are no studies of dispersion in tropical continental deep convective clouds and especially for highly polluted premonsoon and monsoon conditions. [5] In an attempt to better understand cloud-aerosol interactions, a major experiment named Cloud Aerosol Interaction and Precipitation Enhancement EXperiment (CAIPEEX) is underway in India. Observations of convective clouds, aerosol and CCN were carried out during this experiment, mainly over continental regions using an instrumented aircraft (SOAR Piper Cheyenne II, see Axisa et al. [2005]). The observations during Phase I of the experiment were taken during May September A detailed description of the above project, science plan and instruments mounted on the aircraft can be found at and in Kulkarni et al. [2012]. Premonsoon (March, April and May) and monsoon (June, July, August, and September) clouds develop in different thermodynamic and aerosol conditions. Premonsoon clouds develop in dry and polluted conditions; so that cloud base height exceeds 2 km. Cloud base height for monsoon clouds is well below 2 km as they develop in very humid environments. Though the concentration of aerosol particles (AP) and CCN during monsoon periods is quite high, it is still significantly lower than concentrations during premonsoon periods [Prabha et al., 2011]. Aerosol indirect effect and what fraction of it can be offset by droplet dispersion is quantitatively estimated for warm continental cumulus over the Indian sub-continent using CAIPEEX phase 1 data sets [Pandithurai et al., 2012]. [6] In this study, vertical variations of spectral width in premonsoon and monsoon clouds developing under different thermodynamic and aerosol environments over the Indo- Gangetic Plains (IGP) (21 45 to 31 N latitudes and to E longitudes) are discussed. During the premonsoon, clouds are super-continental microphysically, which means that the cloud droplets are very small and prevent any warm rain. During the CAIPEEX campaign, observations were performed at heights up to 7 km inside deep convective clouds over the foothills of the Himalayas. Such cloud microphysical observations of deep convective clouds over the Indian subcontinent and especially over the highly polluted IGP are carried out for the first time. The remainder of this article is organized as follows. In section 2, data and methods are discussed; detailed descriptions of the cases considered in the present study are described in section 3. Results and their brief discussion are presented in Section 4. Section 5 summarizes the findings. 2. Data and Methods [7] Data for the current study are from CAIPEEX (Phase-I conducted in 2009). Five research flights, each characterized by different aerosol and moisture conditions were analyzed for premonsoon (May 24; PRE1, May 28; PRE2) clouds over Pathankot (32.28 N, E) and monsoon convective clouds (August 23; MON1, August 24; MON2, August 25; MON3) over Bareilly (28.22 N, E). The observations over Pathankot were located close to the Himalayan slopes and the Bareilly observations were in the main Gangetic Valley. [8] The cloud droplet probe (CDP) of Droplet Measurement Technologies (DMT) was used for the measurement of cloud droplet size distributions (DSD) in 30 bins between 2 and 50 mm. Liquid water content (LWC), droplet effective radius, CDNC were also derived from the CDP. Mean radius (r ), spectral width/standard deviation of DSD (s), and relative dispersion (ɛ = r/s) were derived from CDP DSD. The standard deviation of these parameters at 100 m height intervals refers to spatial variation (aircraft may make several point measurements at any specific level). Temperature, relative humidity, and winds were measured by the Aircraft Integrated Meteorological Measurement System (AIMMS). CCN concentration (N CCN using DMT CCN counter) and aerosol concentration (using Passive Cavity Aerosol Spectrometer Probe; PCASP at mm) were also measured. Aerosol data is considered only outside of clouds (CDNC < 10 cm 3 ) and are averaged for every 100 m level. Cloud microphysics data (from the CDP probe) used for the current study is at 1 Hz sampling frequency, i.e., over approximately 100 m of horizontal distance. Cloud microphysics data during ascent and descent through single deep convective clouds are used for the present analysis. In-cloud data is defined by CDNC > 10 cm 3. In the CCN measurements, set supersaturations (SS) of 0.2%, 0.4% and 0.6% are used. Each of these SS corresponds to a specific temperature gradient in the CCN column. During SS cycles with SS = 0.2%, 0.4% and 0.6%, the temperature of the column is changed to adjust for the new SS setting. We remove CCN data when the difference between the top of the column and the base of the column temperature changes by more than 0.1 C per second. This criterion makes sure that the CCN data that we report corresponds to droplets formed at a SS close to that set by the instrument. Little deviation from the set SS was noted and errors on the SS spectrum estimates are less than 5%. 3. Description of Cases [9] CAIPEEX was conducted during the premonsoon, monsoon and the transition period between them. Figure 1a shows averaged CDNC, plotted against water vapor mixing ratio (r) in the mixed layer (the layer between 100 m below cloud base and 100 m above the ground), and aerosol number concentration (N a ) in the mixed layer (ML). Each point in the figure corresponds to a CAIPEEX cloud observation and the flight tracks as indicted in Figure 1b. Three regimes based on the available water vapor in the boundary layer and progression of monsoon are identified (shown with ellipses in Figure 1a). This showed a very dry regime primarily in the premonsoon, a moderately wet transition and wet regime during the monsoon. The premonsoon, CDNC was higher than during the monsoon. The premonsoon regime was characterized by dry conditions and highly polluted conditions (N a > 1000 cm 3 ). Within the monsoon regime, the CDNC increases with increase in r. A few coastal cloud observations 2of15

3 Figure 1. (a) Variation of CDNC with boundary layer water vapor for cloud observations during CAIPEEX Phase I. Labels are marked with date of observation (mmdd; month and date) and grouped (shown with ellipses) to premonsoon, transition and monsoon period. Rectangular boxes show the observations used in this study, with two extreme conditions of water vapor and high pollution. The N a is shown with color and cloud averaged effective radius with size of the symbols as described in the legend. (b) The flight tracks over the Indian region and HYSPLIT back trajectories (at 3, 4 and 6 km levels) over the longitude-height cross section (c) at Pathankot and (d) over Bareilly. Latitude corresponding to trajectory is given in color map. (indicated as 0707 and 0705) during monsoon are also shown in the figure. It may be noted that a few continental observations in the monsoon and premonsoon regime show relatively high droplet number concentrations. These observations were conducted in the Ganges valley (Figure 1b) during the premonsoon (May 24: PRE1 and May 28: PRE2) and active monsoon (Aug 23: MON1, Aug 24: MON2, Aug 25: MON3) periods. The high CDNC monsoon cases are associated with very moist and polluted conditions. [10] The large-scale dynamical conditions under which these clouds developed are investigated with Hybrid Single- Particle Lagrangian Integrated Trajectory (HYSPLIT) model ( 24 h trajectories based on the Global Data Assimilation System (GDAS) data products. It may be noted that HYSPLIT is based on GDAS meteorology input and is used as a tracer model. However, the representation of aerosol and chemistry effects is not part of this analysis. Figures 1c and 1d show longitude-height cross-sections of 24 h back trajectories that are reaching over Pathankot and Bareily, respectively, at three vertical levels (3 km, 4 km and 6 km indicated as A, B, C in the Figures 1c and 1d). Color map indicates latitude of the trajectory location. In the PRE cases, high level back trajectories originated from northern regions that are source areas of dust and biomass burning. Six km level trajectories originated at higher levels and subsidence is noted over the location of aircraft observations. Four km level trajectories originated in the boundary layer, but trajectories again descended. The large-scale dynamical effects as illustrated by trajectories are not favorable for cloud formation in the PRE cases, while trajectories showed oscillatory nature, possibly attributed to the gravity waves and upslope flows due to diurnal heating. 3of15

4 Figure 2. Vertical profile of (a) N a and (b) water vapor. In the MON cases, subsidence is reduced and especially for MON3, the trajectories begin at very low level in the southeastern region and rise to higher elevations. In such a scenario, it is likely that boundary layer aerosols are transported to elevated layers in cloud. [11] Aerosol vertical profiles from the PCASP onboard the CAIPEEX aircraft for five cases are shown in Figure 2a. This figure shows that N a is very high during premonsoon (exceeding 2000 cm 3 ) throughout the lowest 4 km. Cloud microphysical observations on PRE1 and PRE2 were carried out during premonsoon under extremely polluted conditions over Pathankot. Elevated pollution layers reaching 6 km are characteristic of these observations. Reasons for such elevated pollution layers are discussed in Prabha et al. [2012a]. The vertical profile of r derived from AIMMS probe (outside cloud) is shown in Figure 2b. The premonsoon cases showed less than half the boundary layer water vapor content compared to monsoon conditions. Additionally, the upper layer r in the premonsoon conditions was also much less. This means that clouds developed in the very polluted and dry ambient conditions in this region. Boundary layer (BL) N a for these two cases were 2500 cm 3 and 2700 cm 3, respectively (Table 1). Average relative humidity (in the boundary layer) was about 44% and 36%. Average r was about 7.2 gkg 1 and 4.7 gkg 1, respectively. [12] Monsoon cases were cleaner and high N a is noticed primarily below 2 km. It is to be noted that N a decreased drastically above the boundary layer as the monsoon progressed. Average mixed layer relative humidity for these three cases were 72%, 82%, 68%, respectively, and corresponding water vapor mixing ratios were 19 gkg 1,22gkg 1,20gkg 1. For MON1 and MON2, clouds developed above a comparatively high polluted boundary layer. Average boundary layer N a for these two cases were 1689 cm 3 and 2752 cm 3, respectively. For MON3, average relative humidity in the boundary layer was 68%, mixing ratio was 20 gkg 1 and N a was 764 cm 3. Compared to the premonsoon cases with very dry boundary layer, the monsoon cases can be described as having a moist boundary layer and relatively low N a, depending on scavenging by rain. [13] Thermodynamic characteristics of the cloud observations (Table 1) considered in this study were derived from radiosonde observations taken prior to the aircraft flights (at 0600 UTC; 1130 IST). The Convective Available Potential Energy (CAPE) for PRE1 and PRE2 calculated from the radiosonde observations were 1407 Joules kg 1 and 1883 Joules kg 1, respectively, and integrated precipitable water was 2 cm. Lifting condensation level (LCL) temperatures were 6 Cand7 C at pressure levels 679 hpa and 663 hpa for PRE1 and PRE2, respectively. Cloud base heights on PRE1 and PRE2 from aircraft observations were at the altitude of 3403 m, and 4500 m, respectively. Actual time (1130 LST) and location of radiosonde observations were km away from the slopes compared to aircraft observations, which makes observed cloud base height different from that derived from radiosonde observations (3252 m and 3438 m). [14] Radiosonde observations for MON1, MON2 and MON3 from Bareilly showed signatures of monsoon conditions. CAPE for these cases was 3142 Joules kg 1, 3101 Joules kg 1 and 958 Joules kg 1, respectively. Integrated precipitable water Table 1. Boundary Layer Characteristics of Different Flights During CAIPEEX-2009 and Thermodynamic Characteristics of Different Flights From Radiosonde Observations Flight May 24 PRE1 May 28 PRE2 Aug. 23 MON1 Aug. 24 MON2 Aug. 25 MON3 Mixing Ratio (g/kg) Relative Humidity (%) Aerosol Concentration (cm 3 ) N CCN (cm 3 ) at 0.4% SS Constant C (cm 3 ) from CCN spectrum (valid over the range 0.1 to 0.6% SS) Slope of CCN spectrum (k) Sounding CAPE (Joule kg 1 ) Precipitable Water (cm) LCL height (hpa) Temperature (LCL) ( C) Cloud base height (m) From sounding, From aircraft 3252, , , , , 650 TRMM (3B42.007) Rainfall (mm) within an area of 1 1 during the flight of15

5 Figure 3. CCN spectra derived from the subcloud CCN cycles for respective cloud samples. updrafts and downdrafts in the premonsoon case (especially PRE1).It may also be noted that trajectory analysis also showed large scale subsidence in the PRE cases, with more displacement of trajectories in the vertical. PRE cases showed more updrafts. PRE cases are also closer to the topographic barriers than the MON cases and may also be influenced by orographically forced localized circulation patterns with embedded strong vertical motions. CAPE is estimated from radiosonde observations prior to the flight (indicated in Table 1). The calculated value of p maximum updraft using a relationship with CAPE (w max ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð2capeþ [Dutton, 1976] is much higher than the aircraft observations p of updraft velocities. It is to be noted that the formula w max ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð2capeþ can be used only for simplified estimation of maximum updrafts. In MON1, MON2 CAPE is two times larger, so were Wmax. An interesting question is whether high fluctuations of updrafts in PRE cases are caused by aerosol effects, specifically convective invigoration? Indeed, in the for these three cases was 4 cm, 5 cm and 5 cm, respectively. There was no change in the precipitable water for MON2 and MON3 and continuous advection of water vapor was present over the region. LCL temperatures were 22 C, 24 C and 19 C at pressure levels 854 hpa, 900 hpa and 845 hpa for MON1, MON2, and MON3, respectively. Cloud base heights for these cases are located at altitudes of 1180, 735, and 650 m, respectively. 4. Results 4.1. CCN Activation Spectra and Vertical Velocities [15] N CCN measured in the sub cloud layer for four cases with the SS ranging from 0.1 to 0.6 SS are presented in Figure 3. The CCN spectra was found in the SS range of CCN observations are the N CCN measured in the subcloud layer at various SS. Note that in the log-log plot, curves with different values of k are straight lines. The interesting result is that the formula with k = constant leads to a good approximation of activity spectrum within the range % of SS without any tendency of the curve to saturate (i.e., k = const, and does not tend to zero to the end of the supersaturation range). This indicates that the aerosol spectrum contains small CCN. Fine aerosol particles can become CCN and activate at higher SS [Pruppacher and Klett, 1997]. The CCN spectrum (N CCN =CS k ) shows similar C values for the PRE2, MON1 and MON2 cases. The PRE2 case has a slightly higher slope of the CCN spectrum (k) than the MON cases. The N CCN for MON3 is one-third of the other three cases. All monsoon cases show k within a narrow range (0.54 to 0.57). High C and lower k lead to spectral broadening in single cloud parcels and internal mixing may cause spectral broadening for higher k as illustrated by Hudson and Yum [1997]. Oscillations (alternating updraft and downdrafts) were observed in CAIPEEX monsoon cloud observations as well [Prabha et al., 2012b], but was not found to contribute to spectral broadening in PRE clouds. [16] The frequency distribution of vertical velocity observations below (in the sub cloud layer) and inside cloud is presented in Figure 4a. It may be noted that there were strong Figure 4. (a) Frequency distribution of in-cloud (thick lines) subcloud (symbols) vertical velocity and (b) averaged drop spectrum distribution (DSD) for 1000 m above cloud base for all five cases. 5of15

6 Table 2. Averaged Cloud Parameters for Cloud Base +300 m Above a Parameter 24 May PRE1 28 May PRE2 23 August MON1 24 August MON2 25 August MON3 CDNC (cm 3 ) r (mm) s (mm) w+ (ms 1 ) w (ms 1 ) a CDNC is cloud droplet concentration, r is mean radius, s is spectral width of cloud droplet spectra and ɛ is relative dispersion, mean updraft (w+) and downdraft (w ) with spatial spread. PRE we have larger amount of small droplets that may continue growing without sedimentation. The resulting latent heat may lead to increased updrafts in PRE. So, in spite of lower CAPE, and large scale subsidence as indicated by trajectories (Figure 1c), Wmax in PRE is higher because of the possibility of aerosol-induced convective invigoration. Figure 4a also show existence of strong downdrafts in convective clouds, but these downdrafts do not reach lower levels inside the cloud. This may indicate the possibility of internal oscillations in the cloud, which might lead to internal mixing and spectral broadening in polluted clouds as indicated by Hudson and Yum [1997].The proximity to the Himalayan range and thus orographic forcing can affect the intensity of convective scale updrafts and downdrafts. Vertical velocity fluctuations are also introduced as larger scale (>10 km wavelength) gravity wave effects in the presence of clouds [Prabha et al., 2012a]. It may also be noted that below cloud base vertical velocities in the PRE cases showed stronger updrafts and downdrafts. [17] Higher CCN and k in PRE1 along with strong updrafts may indicate a greater spectral broadening as found by [Hudson and Yum, 1997; Miles et al., 2000]. According to the parcel theory, increase in W should lead to decrease in droplet size in updrafts due to more activated droplets competing for condensate. As we see from Figure 4a, W at cloud base in PRE1 is much higher than in MON. So, in spite of similar N CCN, CDNC is higher and DSD is narrower in PRE (Figure 4b). In this case we see a combined dynamical and microphysical effect on DSD. Table 2 shows the average r, CDNC, ɛ, s, updraft and downdraft at the cloud base (at 300 m above cloud base). Table 3 shows these data for the nonprecipitating part (using the criteria of effective radius <12 mm 300 m above cloud base). The CDNC at the cloud base of MON cases are less than half of that in the PRE cases. The r is slightly higher for MON cases. Here s is nearly the same for all cases at the cloud base. However, in the nonprecipitating part of the cloud (Table 3) s in MON cases are almost double that of the PRE cases, which may be due to rapid diffusional growth (like in a marine case where CDNC is lower but droplet spectra is wider). We observe high updrafts and weak downdrafts in PRE1 and MON3, while strong updrafts and downdrafts in PRE2, MON1 and MON2 in the nonprecipitating region. These three cases have high C values and are all polluted cases. [18] Average r (Table 3) for clean monsoon case (e.g., MON3) is about 11 mm to about 5 mm for polluted premonsoon (e.g., PRE1, PRE2) and 6.5 mm for polluted monsoon cases (e.g., MON1, MON2). In the case of polluted clouds, competition between large numbers of CCN for a given amount of water vapor produces droplets of smaller sizes. The CDNC is highest for the PRE cases and is only 118 cm 3 for MON3. The corresponding r and s increased for this case with low CDNC. But in the MON1 and MON2, s is higher than PRE2, and droplets have a higher r. CDNC is greater than in the MON3 clean monsoon case. These observations suggest that in spite of the presence of pollution aerosol, the drop spectrum broadens at a lower height in the MON cases. The increase in r also means that the mass in the smallest droplet diameter bins decreases and in the largest droplet diameter bins increases. This is illustrated with averaging of DSDs. The DSD averaged for regions within cloud 1 km above cloud base is presented in Figure 4b for comparison. It may be noted that PRE cases show narrow DSD with only one mode. Meanwhile all three monsoon cases show multiple modes in the DSD. The monsoon cases show an increase in concentration of large droplets with a decrease in pollution aerosol and an increase in moisture. The bimodal nature of DSD showed clear indications of small mode concentration peak below 20 mm. Such bimodal spectra were simulated in the presence of high relative humidity in the boundary layer by Segal et al. [2003]. Prabha et al. [2011] investigated details of bimodal spectra in monsoon clouds during CAIPEEX. It was reported that the small mode is associated with nucleation of small CCN which are not Table 3. Averaged Cloud Parameters for Non-precipitating Part of Clouds a Parameter 24 May PRE1 28 May PRE2 23 August MON1 24 August MON2 25 August MON3 CDNC (cm 3 ) r (mm) s (mm) w+ (ms 1 ) w (ms 1 ) a CDNC is cloud droplet concentration, r is mean radius, s is spectral width of cloud droplet spectra and ɛ is relative dispersion, mean updraft (w+) and downdraft (w ) with spatial spread. Data presented are averaged within the height of 300 m above cloud bases in non-precipitating part (i.e., r <12mm and T>0 C). 6of15

7 Figure 5. Variation of (a) droplet r, (b) s, (c) relative dispersion ɛ = s/r, (d) droplet number concentration, and (e) LWC corresponding to five cloud samples. activated at cloud base and activate at some distance above cloud base where supersaturation in strong updrafts exceeds that at cloud base. CDNC increases with altitude in regions with strong updrafts. Small droplets were also found in downdrafts (at sub saturated regions) that are caused by partial droplet evaporation. Several theoretical studies have illustrated bimodal DSD due to various reasons. In the last section of this manuscript we investigate how the presence of small droplets (diameter < 20 mm) may change the r and s Vertical Distribution of Cloud Microphysical Parameters [19] The vertical variation of r (Figure 5a), s (Figure 5b), ɛ (Figure 5c) droplet number concentration (Figure 5d) and LWC (Figure 5e) for all five cases are presented for heights above cloud base. r shows a slight variation with height, for PRE1 and PRE2 while for monsoon clouds there is large (2 12 mm) variation with height. s is constant ( 1.5) with height in the premonsoon cases. In the monsoon cases, s increases with height and reaches 5 mm at height above 5 km. The s was similar in all monsoon cases irrespective of polluted or clean condition. Differences among the monsoon cases are mainly in the r with MON3 (clean case) showing higher r than MON1 and MON2 (polluted) at higher elevations (1 km above cloud base). Another important observation is the spatial variation (indicated by error bars calculated as the standard deviation of r and s within 100 m vertical distance). In premonsoon cases, the standard deviation representing the spatial variation of rand s is less and it appears that the r and s does not vary with height. If we use s for a comparison between the cases, it is reasonable to use a constant value of 1.5 for premonsoon cases. For monsoon cases, s increased with height. However, the relative dispersion in the polluted monsoon (MON1, MON2) increased with height. This is due to the fact that in the clean monsoon case (MON3), r was considerably higher than in polluted monsoon cases. [20] It is to be noted that for premonsoon conditions, the available water vapor is low, CCN are high and LWC is low (<0.5 g 3 for premonsoon compared to >1 g 3 for monsoon cases). It is also to be noted that premonsoon clouds are only 2 km deep compared to 7 km deep monsoon clouds. This difference is attributed to the amount of water vapor present. Saturation is reached at a higher altitude during dry conditions, compared to the moist monsoon conditions, which results in drastic differences in the lifting condensation level (LCL) (see Table 1). It may be noted that r in PRE cases were very low and s did not change with height. This shows that there is very little droplet growth with height in these clouds as they are constantly entrained and diluted (as shown in the next section). In Prabha et al. [2011], premonsoon polluted clouds from the peninsular Indian region were analyzed and showed high LWC and droplet growth with height. This seems to be due to the presence of small droplets that do not precipitate, i.e., remaining in the cloud for further condensational growth. This feature was not seen in the PRE clouds analyzed in the present study. The subsidence noted for PRE cases also indicates that the large scale synoptic conditions did not favor cloud development. 7of15

8 Figure 6. Vertical variations (height above mean sea level) of (top) droplet r and (bottom) s of five cloud samples. The symbols are shaded based on the adiabatic fraction (%). [21] As these droplets grow by condensation, entrainment of dry non cloudy air (ambient conditions are drier for premonsoon; Figure 2b) from the surroundings causes depletion of LWC, reduction of CDNC [Warner, 1973], changing DSD, and a reduction in r from homogeneous mixing. Droplet partial evaporation is also possible in downdrafts, by which a decrease in r is possible. Thus small r is due to higher N CCN and then due to entrainment mixing and droplet evaporation. Entrainment and evaporation both contribute to the increase in ɛ. Freud et al. [2008] discussed robustness of effective radius at any specific level and also presented similar range of variations in the r as in the present study, however these changes in r can have variations in the relative dispersion in the clouds with constant s. In monsoon clouds, typical values of s vary from 1.5 mm at the cloud base to 5 mm at the cloud top (7 km above the cloud base). However, for polluted and dry premonsoon clouds s appears to be constant with height. Large horizontal variability of s in monsoon clouds compared to that of premonsoon cases is especially notable. Values of ɛ are about (Figure 5c) similar to the continental stratus cloud observations reported in Miles et al. [2000], which are less than the range of values of ɛ observed during RICO ( ) [Arabas et al., 2009] and higher than the ones reported for ACE-2 ( ) [Pawlowska et al., 2006]. Politovich [1993] reports that for observations inside cumulus clouds, the dispersion was nearly constant with height, and was reported to be between 0.15 and 0.3. In the present study, ɛ increased with height in the polluted monsoon clouds, where s also increases with height. [22] Much higher CDNC are observed in premonsoon convective clouds (Figure 5d) compared to several of the continental and maritime stratus cases presented in Miles et al. [2000] and are comparable to deep convective clouds profiled during LBA-SMOCC (Large-Scale Biosphere-Atmosphere Experiment in Amazonia Smoke, Aerosol, Clouds, Rainfall, and Climate) [Andreae et al., 2004; Freud et al., 2008].Itis also noted that CDNC increased with height especially from 2 to 4 km above cloud base. The question arises why there is increase in CDNC with height. Prabha et al. [2011] attributed this increase to in-cloud nucleation. Martins and Silva Dias [2009] indicated that relative dispersion in the LBA clouds varied between 0.4 and 0.6 as cloud water content increased. LWC in the premonsoon cloud is <1 g 3 while in the monsoon clouds a maximum of 3 g 3 was noted. The LWC varied significantly at any vertical level (Figure 5e), suggesting entrained or mixed cloud parcels Mixing and Entrainment [23] Vertical profiles of r (Figure 6, top) and s (Figure 6, bottom) are presented along with the adiabatic fraction (ADF) for all five cases. Important information from this representation is that droplets with largest r and s have highest ADF, indicating less mixed samples. Cloud volumes with small r (<5 mm) show narrower spectra, compared to that of r > 5mm. In the premonsoon conditions, ADF corresponding to drop r >5mmhas s between 1.2 and 1.8 mm. This indicates that in the premonsoon clouds, s and r are well correlated (correlation coefficient of 0.6) and both are maximum in the less diluted regions of the cloud. The correlation between r and s may also be due to dilution, where r and s decreases. [24] For the monsoon cases (Figure 6), the above picture is somewhat different. r does not change significantly at any specific flight level in polluted monsoon cases, whereas in the clean monsoon (MON3) and in PRE cases, there is significant variation in r at any specific level (see Figure 6 (top) for all levels). r and s are not always maximum when ADF is maximum. The correlation between r and s is above 0.8 in these monsoon cases (larger that in PRE cases) Relationship With CDNC in Diluted and Less Diluted Samples [25] Data is classified by ADF greater than 0.5 and ADF between 0.1 and 0.5. ADF is the degree of cloud dilution due to the entrainment of free atmospheric air from the 8of15

9 Figure 7. Results from five flights as a function of CDNC. First, second, third, and fourth rows show r, s, and LWC, and relative dispersion (ɛ). Left and right panels are for less diluted (ADF > 0.5) and strongly diluted (0.1 < ADF < 0.5) cloud samples. The cloud base data and the lower 500 m of cloud data, cloud top data are screened out. Error bars indicate the variation within each concentration bin. surroundings of the cloud. Cloud base and cloud top data are not used in this analysis. [26] Figure 7 shows r (Figures 7a and 7b), s of cloud droplet spectra (Figures 7c and 7d), LWC (Figures 7e and 7f) and relative dispersion (Figures 7g and 7h) as a function of droplet concentration CDNC. Averaged data for each 100 cm 3 CDNC bin are shown with error bars indicating the standard deviation within each bin. The right panels show less adiabatic (0.1 < ADF < 0.5) and left panels show more adiabatic (ADF > 0.5) cloud samples. This figure combines observations from 1000 m above cloud base to the altitudes below 6.0 km (to exclude data from the cloud base and the cloud top). For monsoon cases it is observed that r (Figure 7a) decreases as CDNC increases in both cases of dilution. It appears that r is much less dependent on CDNC in the premonsoon cases (PRE1 and PRE2) and it never exceeds 6 mm. For the less diluted monsoon cloud samples (Figure 7a), a strong dependence of r on CDNC is observed. Such negative correlation between CDNC and r was also noted in several earlier studies [Hudson and Svensson, 1995; Hudson and Li, 1995; Brenguier et al., 2000;Pawlowska and Brenguier, 2000]. [27] Higher values of s (Figures 7c and 7d) are observed in monsoon clouds (3 5 mm) as compared to premonsoon clouds (1 2 mm), irrespective of dilution. Monsoon conditions show gradual decrease in s with increase in the CDNC for the less diluted parcels (Figure 7c). s is independent of CDNC for both diluted and less diluted premonsoon cloud samples. The near absence of collision-coalescence in premonsoon clouds keeps a constant s. LWC increases with CDNC in the more diluted monsoon cloud parcels (Figure 7f). For the premonsoon clouds, LWC in the less diluted parcels are nearly independent of CDNC. However, in the less diluted monsoon cloud parcels, LWC increases with CDNC. [28] ɛ shown in Figures 7g and 7h ranges from about 0.2 to The overall pattern for ɛ seems to result from strong dependence of r on CDNC combined with weak dependence of s on CDNC. There is a large spread in the relationship between ɛ and CDNC especially for the monsoon cases, depending on whether it is polluted or clean, i.e., ɛ is rather consistent with CDNC. It may be noted that for the premonsoon case, s and r are nearly independent of CDNC. This leads to a nearly constant ɛ of 0.28 for premonsoon clouds, which is independent of CDNC. It may be noted that premonsoon cases are characterized by drier ambient conditions compared to that of monsoon cases. The drier air entrainment leads to a reduction in the CDNC and droplet evaporation at diluted regions of the cloud and a reduction in the LWC in all cases Spectral Broadening and Dispersion [29] Some emphasis should be given to the identification of conditions under which s increases mainly because cloud droplet spectral broadening is critical to precipitation formation. In Figure 8 a comparison of DSD (drop spectrum as a function of time inside the cloud) of the premonsoon cloud on 24 May (PRE2) and the monsoon cloud on 23 August (MON1) are shown. Height of observation and effective radius calculated from these measurements is presented in respective top panels. 9of15

10 Figure 8. Drop spectrum distribution (DSD) in (a) a premonsoon cloud on 24 May and (b) monsoon cloud 23 August. Corresponding observations of height and effective radius are shown in the top panel. Color map indicates the number concentration per micrometer interval per cm 3. Horizontal dashed line shows 24 mm limit. (c) Bimodal DSD from the highlighted region in Figure 5b. Legend show time of observation (UTC), height (m), CDNC (cm 3 ), LWC(gm 3 ), effective radius (mm), vertical velocity (ms 1 ), and adiabatic fraction corresponding to a region approximately m away from the cloud edge. It may be noted that droplet spectra never show any sign of collision and coalescence in the premonsoon cloud (Figure 8a). This indeed is due to the second indirect effect [Albrecht, 1989; Hudson, 1993] where high N CCN leads to formation of small droplets with low collision efficiency. Also, numerous droplets in these clouds cause first indirect effect by increasing albedo. In the monsoon cloud (Figure 8b), we may note several instances with effective radius exceeding mm. Some of these instances are also associated with multiple modes (shown with arrows in Figure 8b) due to the appearance of small (diameter < 20 mm) and large droplets in the cloud. A detailed bimodal DSD and associated cloud microphysical parameters for such a cloud pass m away from the cloud edge is given in Figure 8c. It may be noted that strong updrafts are found to be associated with high CDNC (>400 cm 3 ) and high cloud LWC in these clouds [Prabha et al., 2011]. In small cumulus such as the ones observed in RICO [Gerber et al., 2008], the entrainment 10 of 15

11 Figure 9. Relationship between the (left) r, (middle) spectral width, (right) relative dispersion and minimum diameter of ten largest droplets at the tail of DSD. Cloud base and cloud top data was removed from this analysis. Color map is adiabatic fraction. is observed to be taking place at much smaller spatial scales. The sampling frequency used in CAIPEEX was 1 Hz, which corresponds to a 100 m spatial resolution. Each cloud pass is over distances of km across deep convective clouds. [30] Drop spectral broadening is related to the activation/ formation of droplets at the lower and higher diameter ranges of the DSD. Several theories have been discussed to explain the broadening of the DSD [Beard and Ochs, 1993;Martin et al., 1994;Hudson and Yum, 1997;Pinsky and Khain, 1997; Khain and Pinsky, 1997; Chaumat and Brenguier, 2001; Feingold and Chuang, 2002; Yum and Hudson 2005]. Spectral broadening can occur either by formation of small droplets (drop diameter < 20 mm) at the left part of DSD, or/ and by generation of large droplets by efficient collisions at the right side of DSDs. Entrainment and mixing of relatively dry environmental air with the cloud [Paluch and Knight, 1984; Brenguier and Grabowski, 1993;Lasher-Trapp et al., 2005; Gerber et al., 2008] can also cause spectral broadening. [31] Another possible mechanism of DSD broadening and formation of bi-modal and multimodal DSD is in-cloud nucleation [Prabha et al., 2011] Droplets at the Tails of DSD [32] To investigate the tail of droplet spectra, we first counted the 10 largest droplets at the tail of droplet spectra from 1 Hz data and determined the minimum diameter of the largest 10 droplets (D10). This diameter is used as a parameter characterizing the DSD tail at the higher end of the spectrum. Data considered for this analysis excludes cloud base and cloud top samples. [33] Relationship between D10 and mean droplet diameter is presented in Figure 9 (left) for all the five cases as indicated. Adiabatic fraction is used to shade the symbols in Figure 9. It may be noted that high ADF parcels lie on the top of the diagonals and low ADF parcels on the lower part of the diagonal (Figure 9). Most important result from this analysis is that D10 is highest in the adiabatic cloud parcels. This means that the largest droplets form in adiabatic regions. This observation may support the entity type mixing proposed by Telford et al. [1984] but detailed investigation in this regard will be presented elsewhere [Prabha et al., 2012c]. In PRE cases, r is maximum when D10 is maximum. The decrease in r and D10 in the less adiabatic parcels of PRE cases indicates the possibility of droplet partial evaporation or mixing with non-cloudy air parcels. To investigate this aspect, we have used vertical velocity observed along the flight track from the AIMMS instrument. D10, r, s, ɛ are replotted by changing the color map to vertical velocity (Figure 10). [34] The decrease in r and D10 in the less adiabatic parcels of PRE cases occur concurrently with low ADF and somewhat stronger updrafts. Largest droplets (highest D10), high r and s are noted for adiabatic regions with strong downdrafts (Figures 9 and 10). This may indicate that dry air is entrained into the cloud and CDNC decreases. However, in MON cases, adiabatic parcels are observed to have different ranges of D10 (Figure 9). For a fixed value of D10, r and s are lower for adiabatic parcels as compared to less adiabatic parcels. Sometimes high D10 in adiabatic parcels are also observed with strong updrafts (Figure 10), corresponding to higher values of s (indicated with ellipse in Figure 10 for MON1) and ɛ. Highly diluted parcels were not observed for D10 > 20 mm in MON1. It may be noted that this behavior is also associated with strong updrafts 10 ms 1 (Figure 10). 11 of 15

12 Figure 10. Same as Figure 9 but for different vertical velocities in the color map. [35] Small slope between D10 and mean radius (<0.2 for PRE cases and >0.2 for MON cases) shows that at a given r, droplets in the tail are small, i.e., DSD is narrower in PRE. Figure 9 shows that in parcels with high ADF, the DSD contain a large tail. Among monsoon cases, MON3 is a clean and moist case compared to all other cases. MON3 is more marine like (with comparatively higher droplet concentration than a typical maritime cloud). DSDs in parcels with high ADF in MON3 contain droplets as large as 20 mm in diameter. This supports the formation of raindrops at a lower height in the MON3 case. Another observation is that MON1 and MON2 cases have highly polluted upper layers compared to MON3. The entrained air into the cloud is polluted and some of the aerosol could act as CCNs at high supersaturations (above the cloud base leading to a decrease in r and increase in droplet number concentration in the updrafts). [36] Another important result is noted in Figure 9 (middle), where D10 as a function of s is presented. The main result is that the relationship D10-s is not linear as that of D10-r. A relationship between D10 and ɛ is presented in Figure 9 (right). We see again that at a given relative dispersion, parcels with large ADF contain larger droplets while the ɛ is more variable in the diluted parcels Effect of Small Droplets on s and r [37] The number concentration of smallest droplets (<20 mm in diameter) and the r are related in Figure 11 (left). The adiabatic fraction is used as an indicator for mixing, which is shown with color scale. A similar representation with s is shown in Figure 11 (middle) and with relative dispersion in Figure 11 (right) for all five cases. Figure 11 (left) show that r is maximum in adiabatic core regions. This r maximum is reached at high droplet concentrations with ADF maximum, corresponding to adiabatic core regions, except for a few data points in the polluted monsoon cases (MON1 and MON2). As dilution increases, the small droplet number concentration decreases. Dependencies in Figure 10 are the result of two effects: a) dilution, i.e., mechanical mixing of cloudy and droplet free volumes, leading to a decrease in the concentration of all droplets in the same proportion and b) evaporation of droplets during mixing. Evaporation can both decrease (total evaporation) and increase (partial evaporation of larger droplets) the concentration of small droplets. Since DSDs in PRE cases are narrow and contain only small droplets, dilution (likely in the updrafts) and evaporation (in the subsaturated downdrafts) lead to a marked decrease in r with decreasing ADF. In MON 1 and MON2 DSDs are wide and contain larger drops. [38] For a given ADFr in the adiabatic parcels decreases, as concentration of small droplets increases. This feature is especially pronounced in monsoon clouds. Such dependence of r on concentration of small droplets can be attributed to diffusional growth in ascending parcels. For instance, in adiabatic parcels increase in droplet concentration corresponds to decrease in drop size and in mean drop radius. [39] The premonsoon cases show maximum radius corresponding to highly adiabatic parcels that have maximum small droplet number concentration. Numerous small droplet concentrations indicate that collision efficiencies are reduced and thus raindrop formation is delayed. A similar effect is also illustrated for s with maximum s in the more adiabatic parcels and small values of s in less adiabatic parcels corresponding to a decrease in small droplet concentration (Figure 11, middle), which is less evident in MON2 and MON3. Relative dispersion in the PRE1, PRE2, and MON3 showed a slight increase with decreasing concentrations of small droplets (Figure 11, right). In the case of MON1, relative dispersion is independent of small droplet concentration. 12 of 15

13 Figure 11. Relation between the (left) r, (middle) spectral width, (right) relative dispersion with the smallest droplet (<20 mm) number concentration. The symbols are colored with adiabatic fraction. In addition relative dispersion is highly variable in premonsoon and monsoon clouds. 5. Discussion [40] It appears that moisture and aerosol particle concentration for monsoon and premonsoon cloud samples have great impact on s and r. General characteristics of our analysis of the entrainment effects on droplet growth is consistent with numerical studies such as Hill and Choularton [1986]. According to that study, three factors were important, humidity of the environmental air, N CCN and rate of entrainment. As a parcel ascends, SS initially increases. The droplets inside the parcel experience growth. CCN get activated and grow more rapidly, which then decreases supersaturation. This sequence of events may seem realistic in the PRE cloud observed, where strong vertical velocity is present in the diluted parcels (low ADF). Concentration of small droplets in these highly diluted parcels decreases. The decrease in mean radius and concentration may lead to further increase in supersaturation. Thus the sequence of events in these cloudy parcels may experience nucleation/evaporation in the regions of supersaturation/subsaturation, leading to a near constant s and slight variations in r. [41] It is observed that in polluted premonsoon cases s does not change with height, however mean radii show a slight increase with altitude. The detailed analysis based on adiabatic fraction has shown a reduction in s and r corresponding to a decrease in small droplet concentrations. For polluted, moist monsoon cloud samples r is less than that of clean monsoon cloud samples. In order to parameterize the width of the droplet spectra, it seems that a reasonable approach is to assume a constant s in the case of premonsoon cloud samples. For monsoon cloud samples it is observed that there is an increase in s with height. s was nearly the same in the clean or polluted monsoon cloud samples. In monsoon clouds typical values of s vary from 1 mm at the cloud base to 5 mm at the cloud top. [42] The theoretical predictions of DSD evolution in an ascending adiabatic cloud parcel show that ɛ in clouds with higher concentration of droplets decreases with altitude faster than ɛ for lower droplet concentration [Pinsky et al., 2012; A. P. Khain and M. B. Pinsky, On the theory of cloud droplet diffusion growth. Part 1: Monodisperse spectra, submitted to Journal of the Atmospheric Sciences, 2012]. Thus observations of constant dispersion or increase of the dispersion with height is consistent with Khain and Pinsky [2012]. However, a continuous increase with height of s in monsoon cloud is associated with the presence small droplets in less diluted parts of clouds (in all three cloud samples presented) needs further investigation. [43] In a review article on the droplet growth in warm clouds, Devenish et al. [2012] argues that entrainment and mixing has a very important role in the evolution of droplet spectra. In a recent study, Bewley and Lasher-Trapp [2011] illustrate that variability in the droplet growth histories could result primarily from entrainment and could explain observed droplet width in small cumuli of RICO. They considered the inhomogeneous droplet evaporation in a numerical model. Relevance of such aspects in monsoon clouds are yet to be investigated. Our observations of premonsoon clouds as illustrated above indicate that entrainment and mixing are important in reducing the small droplet number concentration and thus changing both r and s. r changes through two processes, by total or partial evaporation of droplets and by mixing of cloud free air with cloudy air. The 13 of 15