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Summer Research Fellowship Programme of India's Science Academies

On the effect of aerosols on cloud microphysical parameters: An observational study

Kamran Ansari

Center for Basic Sciences, Pt. Ravishankar Shukla University, Raipur 492010

Dr. Govindan Pandithurai

Scientist F, Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pashan, Pune 411008

Abstract

Cloud condensation nuclei (CCN) are tiny particles around 0.2 µm or 1/100th size of cloud droplet which give the non-gaseous surface to water vapour for condensation and produced from natural sources like sea salt, small dust, and soil particles, and anthropogenic sources like particulates from fuel combustion and sulfate aerosols. The direct interaction of aerosols in cloud nucleation creates its indirect contribution in the formation of precipitation and due to aerosol-cloud interaction, there is a strong correlation between aerosol and cloud microphysical properties as cloud droplet number concentration (CDNC) and cloud droplet effective diameter (ED). At fixed liquid water content (LWC), more aerosols leads to a higher concentration of relatively smaller cloud droplets which increases the cloud albedo and cloud optical thickness and this effect is termed as the aerosol indirect effect (AIE). In this project, the effect of aerosols in the cloud microphysical properties has been studied over Mahabaleshwar (17.56˚ N, 73.4˚ E; 1348m a.m.s.l.), Western Ghats region in Maharashtra during monsoon season in the year 2017. The aerosol indirect effect is calculated by (i) relative change in CDNC (AIEn) and (ii) relative change in cloud droplet ED (AIEs) with the relative change in CCN concentration for different liquid water contents. This study showed that for constant LWC, droplet ED decreases as CDNC increases. The diurnal variation of CCN concentration showed that the value of CCN concentration was maximum in morning hour (7 to 9’o clock) in each month. From the simultaneous measurement of clouds and aerosol particles, the AIE was calculated and the values of AIEn was about 50% higher than AIE­s. For this study, the measurement of cloud droplet number concentration and droplet effective diameter were taken by ground-based instrument cloud droplet probe (CDP) only during non-rainy cloudy conditions and simultaneous measurement of CCN concentration was taken by CCN counter (CCN-100). CCN concentration at supersaturation level 0.5% was used at 1Hz sampling rate for estimating AIE.

Keywords: aerosol-cloud interaction, aerosol indirect effect, cloud condensation nuclei, cloud droplet number concentration, cloud droplet effective diameter, liquid water content

INTRODUCTION

Atmospheric Aerosols

Atmospheric aerosols are small liquid droplets and solid particles suspended in the atmosphere and these include a broad range of phenomena such as dust, smoke, fog, mist, haze and smog [​​Seinfeld and Pandis, 1998​]. Atmospheric aerosols are generally considered those particles those range of size from a few nanometers (nm) e.g. fine mode particles to tens of micrometers (μm) in diameter e.g. coarse mode particles. Atmospheric aerosols have important local, regional and global significant impacts in the climate. Local impacts include vehicle emissions, firewood burning and industrial procedures that can contribute to urban air pollution [​Fenger, 1999​​; ​Mayer, 1999​] and different possible and negative health effects [​Dockery et al., 1993​​;​Harrison and Yin, 2000​​; ​Pope and Burnett et al., 2002​​]. The impact of gaseous and aerosol particles can speed up the cause of breathing problems and damage the aquatic ecosystem. In regionally, aerosols can be transported through air mass from a polluted area to relatively clean remote area. The concentration and characteristics properties of aerosols are extremely variable over different space and time. Hence, the study of characterizing the population of aerosols is more interested rather than individual particles. The most important characteristics of an aerosol population are the size distribution, chemical composition, and shape of the particles. It is useful to classify aerosols in different categories according to their properties [​​Boucher, 2015​​]. The different possible classification of aerosols are:

  • The aerosols those are directly emitted into the atmosphere as particles are called primary aerosols. It is produced by the incomplete combustion, fly ash from industrial productivity and by the wind effect on the oceanic and terrestrial surface produced sea-salt particle or mineral dust particles. Some aerosols those are not directly emitted into the atmosphere rather produced by the condensation of gas-phase species. An example of a secondary aerosol is sulfate aerosol that is formed from dimethyl sulfide (DMS) emissions by marine phytoplankton and sulfur emissions from fossil fuel burning [​Haywood, J. and Boucher, O. (2000)​]. These gas-phase species, which can undergo several chemical transformations before they condense, are called aerosol precursors [​Boucher, 2015​]. The primary or secondary aerosols are usually identified by the chemical composition of aerosols.
  • According to the origin, aerosols can be classified into natural or anthropogenic. The natural sources of aerosol can be oceans, seas, volcanoes, deserts, soil, vegetation, wildlife. The main anthropogenic sources aerosols are industrial and urban areas, including the traffic, different industrial activities, constructions, and emissions from housing. The biomass burning and the emissions due to various farming activities are also a source of anthropogenic aerosol mainly comes from rural areas.

Particle size classification

One of the most important parameters of the aerosol is particle size for characterizing its behavior, impact and lifetime in the atmosphere. The diameter of atmospheric aerosols spans from few nanometers to 100 μm. The exact size range of diameter of different modes of aerosols varies in different regions. Based on the concentration of number, surface, and volume distributions of aerosol particles was shown in ​​Fig 1​​.

  • Coarse mode: Aerosols with diameter larger than 1.0 μm are classified as coarse mode particles. These particles are mainly introduced directly into the atmosphere from both anthropogenic and natural sources. The bursting of water bubbles in the ocean creates coarse mode particles of sea-salt or marine aerosols, the wind also picks up bigger dust and soil particles. Anthropogenic sources like industrial and agriculture processes also suspend bigger particle into the atmosphere. Due to relatively larger size, make their lifetime shorter in the atmosphere because coarse particles are settled down in sedimentation or raining out in precipitating clouds (wet deposition).
  • Accumulation mode: Particles with diameter between the range of 0.1 – 1.0 μm are considered as accumulation mode. These particles are mainly produced by the growth due to the coagulation and collision of particles with diameter smaller than 0.1 μm and by the condensation of vapors onto the existing particles making them grow in this size range. These particles are mainly introduced into the atmosphere by the incomplete combustion of coal, gasoline, wood and other fuels. These range of particles generally contains the most amount of organic materials and also soluble inorganic like nitrate, sulfate, and ammonium. The removal mechanisms of accumulation mode are least efficient in this regime, they are ultimately removed by the raining out and another form of precipitation (wet deposition).
  • Nucleation mode / Aitken mode: Aitken mode aerosol particles lie between the size range of 0.01 to 0.1 μm diameter, mainly formed from gas to particle conversion and condensation of hot vapors in ambient temperature during combustion processes. The lifetime of these particles is short, they are removed by coagulation with larger particles.
size disribution.png
    Number, surface and volume distribution of typical
    remote continental aerosol [Seinfeld and Pandis, 1998].

    Chemical composition of aerosols

    Aerosols according to their chemical composition can be broadly classified into:

    • Sulfur species: Most sulfate aerosols in the atmosphere are secondary sulfate formed by the oxidation of gaseous precursors (with SO2 and dimethyl sulfide as the main contributors), followed by particle formation through nucleation and condensation processes [Calvo et al. 2013].​
    • Nitrogen species: These aerosols generally have diameters smaller than 2.5 µm. Nitrogen compounds are generally of secondary origin and produce from the reaction of the anthropogenic and natural gaseous precursors.
    • Carbonaceous species: Carbonaceous aerosols are a significant fraction of atmospheric aerosols and comprise a wide range of compounds. The carbon in aerosols can be classified into three groups: a) the group corresponding to carbonates, b) elemental carbon (EC) or black carbon (BC) in terms of light absorption, and c) organic carbon (OC) [​​Calvo et al. 2013​​].

    The role of the atmospheric aerosols in the global climate

    The difference between absorbed solar radiation by the Earth and radiated energy back into space is known as radiative forcing. If Earth receives more solar radiation than radiates into space, called positive radiative forcing which causes warming effect and conversely, negative radiative forcing causes the cooling effect when it loses more energy than receiving from the Sun. Atmospheric aerosols play a crucial role in the radiation budget of the Earth-atmosphere system. Anthropogenic aerosol influences this radiation budget in two different ways.

    First, the property of scattering and absorbing the solar and thermal infrared radiation by aerosol is known as ‘direct effect’ and it alters the radiative balance of the Earth-atmosphere system or planetary albedo. The sulfate and nitrate aerosol particles scatter sunlight back into space which reduces the amount of energy than the planet absorbs, keeping it cooler whereas, the black carbon (BC) and elemental carbon type of aerosol particles lead to heating the lower atmosphere by absorbing the sunlight and thermal infrared radiation. It was proposed that the warming effect from black carbon in aerosols may balance the cooling effect of the sulfate component, the largest single contributor to aerosol cooling [​Jacobson, 2001​].

    Second, aerosols have the ‘indirect effect’ on the climate by changing the cloud microphysical and scattering properties and its lifetime or longevity. Atmospheric aerosols play an important role in the formation of cloud and precipitation by acting as ‘cloud seeds’ or ‘cloud condensation nuclei’. Without the aerosol, clouds would be much less common. The aerosol indirect efffect splits into two effects: As aerosol concentration increases inside the clouds, liquid water gets more surface for growth into the cloud. According to Twomey’s theory [​Twomey, 1974​, ​Twomey, 1977​]. As aerosol concentration increases cause an increase in cloud droplet number and a decrease in droplet size for fixed liquid water content. A cloud with a greater number of smaller droplets has more reflective area for incoming solar radiation than a cloud with lesser larger droplet at fixed liquid water content (​​​​Fig 2​​).

    surface_area_sphere.png
      Smaller water droplets give more surface area. (Adapted from Water and Other Extinguishing Agents (p. 194) by Stefan Särdqvist, 2002, Karlstad, Sweden: Raddningsverket.)

      Therefore, aerosols increase the reflective surface area of cloud droplet by decreasing its size by acting as condensation nuclei and also increases the cloud albedo. This effect is known as ‘first indirect effect’ or ‘Twomey effect’.

      aerosol-indirect-effects.png
        Schematic diagram of the aerosol indirect effects where CDNC means cloud droplet
        number concentration, and LWC means liquid water content [ Haywood, J. and Boucher, O. (2000)].​

        Increasing the amount of smaller droplets decreases the probability or frequency of collision and coalesce into larger water droplets and make larger enough to fall into the ground. So, the cloud with smaller droplets will not precipitate as much and it can retain its water content last longer inside and become larger and more reflective. By affecting the precipitation efficiency, tending to increase the liquid water content, the cloud lifetime [​Albrecht, 1989​], and the cloud thickness [​Pincus and Baker, 1994​]. This effect is known as ‘second indirect effect’.

        IPCC 2013.jpg
          Estimated Radiative forcing in 2011 relative to 1750 and aggregated uncertainties for the main drivers of climate change with the confidence level in the net forcing (VH – very high, H – high, M – medium, L – low, VL – very low) reported by Intergovernmental Panel on Climate Change [​IPCC, 2013​].

          Fig 4​ summarizes the state of scientific knowledge on the effect of main drivers of climate change estimated in 2011 reported by ​IPCC, 2013​ . It is clear that on the context of the ability to quantify the direct effect of aerosol on warming and cooling, the confidence level is high whereas on the indirect effect of aerosol on cloud and precipitation, the confidence level is low and for quantification of radiative effect due to the greenhouse gas, the confidence level is very high due to their long lifetime, over the globe the principal greenhouse gas (like CO2) is well-mixed whereas there is relatively short lifetime of aerosol in the troposphere which results into the temporal and spatial non uniformity in quantifying forcing. Due to the rise of anthropogenic aerosol emissions from recent centuries has likely affected the Earth’s radiation budget through the modification of cloud properties, although to a highly uncertain extent [​IPCC, 2013​] due to a poor understanding of aerosol-cloud interaction and feedbacks involving ice clouds and a general lack of knowledge regarding preindustrial aerosol sources [​Penner et al., 2011​; ​Carslaw et al., 2013​ ; ​Ghan et al., 2016​]. The effects of aerosols represent one of the largest uncertainties in the detection and prediction of climate change [​Haywood, J. and Boucher, O. (2000)​].

          Cloud Condensation Nuclei (CCN)

          A small fraction of atmospheric aerosols which serves as particles give a non-gaseous surface to water vapor and those particles are activated upon which water vapor starts to condense to form water droplet and grow by condensation process to form into cloud droplet at the supersaturation (relative humidity greater than 100%) of around 0.1 – 1%. These activated particles are called ‘cloud condensation nuclei (CCN)’ and these particles are typically 0.2 μm or around 1/100th size of cloud droplet and produced from natural sources like sea salt, small dust, and soil particles, and anthropogenic sources like particulates from fuel combustion and sulfate aerosols. As larger the size of the particle, it will more readily be wetted by water and greater its solubility in water which lowers the supersaturation required for particles to act as a CCN. In the absence of such CCNs, the spontaneous conversion of water vapor into ice crystals or liquid water droplet is needed the conditions with relative humidity much greater than 100%, with respect to a flat surface of H2O.

          Clouds those lie completely below than 0˚C isotherm are considered as ‘warm clouds’ that contains only water droplets. For describing the microphysical property of warm clouds, the important parameters are the amount of liquid water per unit volume of air called liquid water content (lwc) expressed in gm cm-3, the total number of water droplet in per unit volume of air called cloud droplet number concentration (CDNC) measured in number per cubic centimeter and the droplet size distribution. The LWC do not significantly vary over marine cumulus from of continental cumulus clouds but the higher concentration of CCN particles present over continental regime leads to higher CDNC with smaller average droplet size in comparison to marine clouds where the concentration of CCN is lower leads to lower CDNC with the higher and broader average droplet size spectrum. In ​​Fig 5​​, it can be observed that the droplet size spectrum over continental cumulus is much narrower than the marine cumulus clouds and also comparatively lower droplet radius.

          CCN_marine and continental_Hobbs.png
            (a) Percentage of droplet concentration, (b) droplet radius spectrum for marine cumulus and (c) Percentage of droplet concentration, (d) droplet radius spectrum for continental cumulus clouds [ Wallace, J. M., & Hobbs, P. V. (2006)].​

            LITERATURE REVIEW

            From the previous studies, it may be noted that there is a strong correlation of aerosol with cloud microphysics and have shown importance of aerosol-cloud interaction. There are several observational and theoretical studies of aerosol cloud interaction and measurement of aerosol indirect effect (AIE) was carried and however, still because of the estimation, the measurement of AIE remains uncertain [​Menon et al., 2008​]. ​Pandithurai et al., 2012​ reported the aerosol-cloud relationships for warm continental cumuli observed over the Indian subcontinent using Cloud Aerosol Interactions and Precipitation Enhancement Experiment (CAIPEEX) aircraft Campaign data sets and observed the effect of CCN on CDNC and effective radius and also quantitatively measured AIE. The estimation of AIE using droplet concentration, the dispersion effect should be taken into account for correct estimation of AIE [​Liu and Daum, 2002​; ​Anil Kumar, et al, 2016​ ].

            In this project, the monthly variation of cloud microphysical properties like cloud droplet number concentration and effective diameter in different liquid water content from collocated simultaneous measurements from June 2017 – September 2017 over Mahabaleshwar, Western Ghats region in the Indian state of Maharashtra, have been studied. Monthly and diurnal variation of CCN concentration have been observed over this region. The measurement of aerosol indirect effect on number concentration (AIEn) and on droplet surface (AIEs) are also presented.

            METHODOLOGY

            Measurement Site

            Measurements were performed at High Altitude Cloud Physics Laboratory (HACPL) located at Mahabaleshwar (17.56˚ N, 73.4˚ E; 1348m a.m.s.l.) having various ground-based instruments for the observation of aerosol as well as clouds. This observatory is located at Western Ghats mountain range in south west India. Location of observation site is shown in ​Fig 6​.

            Site_map.png
              A map from Google map showing the study location (red
              marker shows location of HACPL).

              During monsoon season the temperature is varied from 17 - 23 ˚C and relative humidity varied between 85 - 100% with an average rainfall of 500 cmyr-1. It is also observed that the site is covered by warm continental clouds most of the time during the summer monsoon season [Anil Kumar, et al, 2016]. The aerosol and CCN concentration measurement shows that the region experiences higher aerosol concentrations during monsoon season in spite of washout/scavenging due to precipitation [Leena et al., 2016].

              Data Used

              Data collected from the observational site during the monsoon season of June – September 2017. For this study, the measurement of cloud droplet number concentration and droplet effective diameter were taken by ground-based instrument cloud droplet probe (CDP) only during non-rainy cloudy conditions and simultaneous measurement of CCN concentration was taken by CCN counter (CCN-100). CDP was operated only during non-rainy cloudy conditions, and some possibilities of even small drizzles were eliminated by measuring the data with rain rates obtained from impact disdrometer. After synchronizing the common data from CDP and CCN counter, about 99 min. data was available for observing the indirect effect of aerosol.

              Cloud Droplet Probe (CDP - 2)

              The Cloud Droplet Probe (CDP) is designed for measuring the cloud droplet number concentration and to measure cloud droplet size distribution (DSD) from 3 μm to 50 μm which is categorized into 30 channels. From this measurement, we can also calculate the various parameters including effective diameter (ED), median volume diameter (MVD) and liquid water content (LWC).

              Theory of operation

              The CDP uses a laser to illuminate the droplet particles and forward-scatter their light. The scattered light is then used to size the water droplets. The intensity of the scattered light depends on the size, shape, and composition of the particles. The CDP accepts and sizes only those particles that pass through a uniform power region of the laser beam for the accurate sizing. This region of the laser is known as depth of field (DOF). The CDP qualifies and sizes only those particles that fall within the depth of field. When particles pass through the laser beam, light scatters in all direction. The CDP collects those forward-scattered photons which comes within a cone of 4˚ - 12˚ from the laser beam and photos within the range of 0˚ - 4˚ range are considered as part of the unobstructed laser and diverted elsewhere so as not burn out the photodetectors (see ​​Fig 7​​). The collected light is then diverted towards the sizer and the qualifier photodetectors. Both photodetectors then convert the photon pulses into electrical pulses. The pulse from the qualifier is then multiplied by two. If the resulting signal exceeds the pulse from the sizer, a DOF flag is set to true, indicating the particle is within the depth of field (see ​​Fig 8​​). As the CDP’s electronics have recorded, amplified, and evaluated the particle signal to see if the particle falls within the depth of field, the analog voltage value is digitized and the peak digital value corresponding to the particle’s size will be categorized into one of 30 bins.

              CDP_theory.png
                Theory of operation of CDP.
                CdP_component.png
                  Important components of CDP.

                  Cloud Condensation Nuclei Counter (CCN - 100)

                  The CCN counter is an instrument for measuring the concentration of aerosol particles that can act as cloud condensation nuclei [​Roberts and Nenes, 2005; ​D. Rose et al., 2007] manufactured by Droplet Measurement Technologies (DMT Inc.). The CCN counter puts the aerosol particles through a column contains thermodynamically unstable supersaturated water vapor than can condense onto the activated aerosol particles. Activated aerosol particles are grown larger in size and then are counted and sized by an optical particle counter (OPC) and hence the activated ambient aerosol particles or CCN particle number concentration is measured as a function of supersaturation. Model CCN – 100 has one column of humidifier column (​​Fig 9​​).

                  CCN counter.png
                    The Cloud Condensation Nuclei Counter (CCN-100).(Adapted from www.dropletmeasurement.com)

                    Theory of operation

                    The CCN counter is a continuous-flow, thermal gradient diffusion chamber for the measurement of aerosols that can function as a CCN [Roberts and Nenes, 2005]. An aerosol sample is drawn into a column of CCN counter with a thermodynamically unstable and supersaturated water vapor condition is created by taking benefit of the diffusion rate difference in between water vapor and heat. The water vapor diffuses with the warm and wet column walls and the temperature of wall along the column is gradually increased to create a quasi-uniform and well-controlled supersaturation condition. The supersaturated water vapor starts to condense on the CCN in sample air for seeking equilibrium and form into the water droplets like a cloud droplet form in the atmosphere. Using the side-scattering technology, an OPC counts and sizes the activated water droplets. In CCN counter, the range of supersaturation can be varied from 0.07% to 2.0% and after humidification, the particle size can be measured in between 0.75 μm - 10 μm. The range of particle number concentration depends on the supersaturation due to the growth rate of activated aerosol particles.

                    For this study, concentration of these activated aerosol particles measured at 0.1, 0.3, 0.5, 0.7 and 1.0% supersaturation. The instrument was configured with sampling rate of 1 Hz and take half an hour for completing the one cycle of supersaturation. When the supersaturation varies from one value to other then it takes some time for stabilizing the temperature. So, the data obtained from each supersaturation were used for observing the diurnal variation of CCN concentration in each month with ignoring first 100 sec. values in each supersaturation data. For estimating the AIE, CCN concentration at 0.5% supersaturation were used.

                    Analysis Procedure

                    According to the Twomey’s theory, increase in the aerosol concentration increases the CDNC with smaller cloud droplet size for a fixed liquid water content. Using the CDP data, AIE was estimated by the power law fit in relative change in droplet effective diameter with change in CDNC (AIEs).

                    AIEs=ΔlogDeffΔlogNcAIE_s=-\frac{\Delta logD_{eff}}{\Delta logN_c}

                    where ΔDeff is the relative change in effective diameter (ED) and ΔNc is the relative change in CDNC.

                    With comparing the cloud microphysical parameters with the CCN concentration AIE was estimated using (i) relative changes in CDNC (AIEn) and (ii) relative changes in droplet effective radius (AIEs) with relative changes in CCN concentration for different cloud liquid water contents (LWCs) [Feingold et al., 2003].

                    AIEn=13ΔlogNcΔlogNCCNAIE_n=\frac{1}{3}\frac{\Delta logN_{c}}{\Delta logN_{CCN}}
                    AIEs=ΔlogDeffΔlogNcAIE_s=-\frac{\Delta logD_{eff}}{\Delta logN_c}

                    where ΔNCCN is the relative change in CCN concentration.

                    RESULTS AND DISCUSSION

                    Variation of Cloud Microphysical Parameters

                    To observe the variation of LWC of non-precipitating clouds during the monsoon months Jun – Sep 2017, the histogram of frequency distribution of LWC was shown in ​​Fig 10​​. It can be seen that the variation of LWC is between 0 to 1.2 gm-3 and with the maximum number of occurrence is between 0.0 to 0.15 gm-3. The number of occurrence beyond 0.70 gm-3 is very less.

                    LWC_CDP.png
                      Frequency distribution of LWC of non-precipitating clouds.

                      Considering this variation of LWC, to understand the relative change in droplet ED with the relative change in CDNC, the AIEs has been calculated in each bin of liquid water content and for that the cloud microphysical parameters CDNC and droplet ED were grouped in 0.05 gm-3 LWC bin and the variation of ED with CDNC were shown in each month (see ​​Fig 11​​). It is observed that the correlation of variation of ED with CDNC is statistically significant in each LWC bin for all the months. For fixed LWC, droplet ED decreases as the CDNC increases. In late monsoon September month, the spectral width of ED is comparatively wider and there is relatively higher droplet concentration of smaller droplets are present. Whereas, in other months, there are more data points with bigger droplet (most particles are larger than 10μm). As the value of LWC increases, more data points with higher CDNC are present. Because the concentration of CCN is proportional to droplet CDNC so for estimating the indirect effect, in each plot power law fit was done in each LWC bin. The power of CDNC (in x-axis) or value of exponent will show the AIEs (see ​​Equation 1​​). It can be seen that for June month value of AIEs is relatively higher compared to another month for most LWC bin with variation of 0.37, 0.36 and 0.35 for LWC bin 0.15-0.20, 0.20-0.25 and 0.25-0.30 gm-3 respectively.

                      Picture1_2.png
                        Scatter plot between cloud droplet number concentration (CDNC) and cloud effective diameter (ED) in six different LWC bin during (a) June, (b) July, (c) August and (d) September 2017. 
                        cdnc vs ed_2.png
                          Scatter plot between cloud droplet number concentration (CDNC) and cloud effective diameter (ED) in six different LWC bin during Jun - Sep 2017.

                          To estimate the AIEs or to observe the variation of droplet ED with CDNC for non – rainy clouds of all monsoon months during 2017, ​​Fig 12​​ shows the scatter plot between droplet ED and CDNC for all four months (Jun – Sep 2017). For each the correlation is statistically significant because linear fit to log-log plot ED and CDNC has good correlation. The value of ED decreases as CDNC increases for each bin and the value of exponent (AIEs) varies as -0.25, -0.24 and -0.22 for LWC bin 0.15-0.20, 0.20-0.25 and 0.25-0.30 gm-3 respectively. ​​Fig 13​​ shows the calculated no. of counts, the value of exponent with correlation for each LWC bin and for each month and with average of all four months.

                          Table 1.png
                            Calculated number of counts, exponent (power of CDNC) and R2 in six different LWC bin for four different months and with average of all four months.
                            AIE.png
                              Variation of calculated AIEs with the LWC.

                              The calculated AIEs as a function of LWC is shown in ​Fig 14​, which shows that the value of AIE is maximum at lower LWC 0.025 gm-3 and then relatively decreases as LWC increase which shows that as LWC increase in water droplets, the solubility of aerosol decrease which offset its effect.

                              lwc vs ED_CDNC.png
                                The contour plot for showing the relationship between cloud microphysical parameters, cloud droplet number concentration (CDNC), droplet effective diameter (ED), and liquid water content (LWC).

                                ​Fig 15​​ shows that the contour plot between the cloud microphysical properties CDNC, droplet ED and LWC and some white patches show that lack of data points in those region of LWC and ED. But overall contour plot shows that for constant LWC, CDNC decreases as droplet ED increase because of sharing same available LWC to more number of droplets leads to reduction in its effective shape and size.

                                Diurnal Variation of CCN Concentration

                                CCN_June.png
                                June
                                  CCN_July.png
                                  July
                                    CCN_Aug.png
                                    August
                                      CCN_Sep.png
                                      September
                                        Diurnal variation of CCN concentration in different supersaturation for four different months.

                                        The diurnal variation of CCN concentration at five supersaturation value of 0.1, 0.3, 0.5, 0.7 and 0.9% for four monsoon months during 2017, is shown in ​​Fig 16a​ - ​​Fig 16d​​. As supersaturation increases that activates more aerosol particles so that for higher supersaturation shows a higher CCN concentration. The CCN concentration was relatively maximum for June month and then comparatively lower CCN concentration was observed for other month data which may be due to washout or scavenging by higher precipitation in those months. Maximum value of CCN concentration for June month showed up to around 9000 cm-3. For all months, CCN concentration shows a strong diurnal variation in which there is strong morning peaks in concentration and in June month, some evening peaks was also observed which is less dominant in other month variation. For morning hour and evening hour peak in CCN, one of possible reason was proposed by ​Leena et al., 2016​ which is may be due to some anthropogenic activities which are irrespective of season and due to the presence of dense vegetation forest. The observational site is surrounded by the dense trees/forest. Hence, the biogenic volatile organic compounds from trees and wet biosphere can contribute for secondary organic aerosol formation which leads to increase the aerosol concentration [​Leena et al., 2016​].

                                        Comparison of CCN Concentration with Cloud Microphysical Parameters

                                        ED vs CDNC_1.png
                                          Variation of CDNC and droplet ED with CCN number concentration at 0.5% supersaturation.

                                          For estimating the effect of aerosol on cloud microphysical parameters, ​​Fig 17​​ shows the variation of droplet ED with CDNC at different bin of constant CCN concentration at LWC bin 0.10 – 0.15 gm-3. It can be seen that for higher value of CCN concentration, the value of CDNC is higher with smaller droplet ED and lower value of CCN concentration, the CDNC is lower with larger droplet ED. This variation shows the aerosol indirect effect on cloud microphysical parameters.

                                          CCN and CDNC.png
                                          CCN concenration vs CDNC
                                            CCN and ED.png
                                            CCN concentration v droplet ED
                                              Scatter plot for showing the variation of (a) cloud droplet number concentration (CDNC) and (b) droplet effective diameter (ED) with cloud condensation nuclei (CCN) concentration measured at 0.5% supersaturation for four different LWC bin.

                                              In this part, data points of CCN concentration (from CCN counter) and cloud microphysical parameter like CDNC and droplet ED (from CDP) were synchronized at the same duration and around 99 min. data were available for analysis in the non-rainy condition during observed months. In low LWC value, the value correlation of CDNC vs CCN (see ​​Fig 18a​​) and droplet ED vs CCN (see ​​Fig 18b​​) was not statistically significant due to lack of common data available from both instruments but in higher LWC, there was a good correlation for some higher LWC.

                                              However, it shows the increment of aerosol particles leads to increases in higher value of CDNC with the lower value of ED due to aerosol indirect effect. The value of exponent of power law fit in ​​Fig 18a​ were 1.38, 1.72 and 1.58 which results into value of AIEn were 0.46, 0.57 and 0.53 (by ​​Equation 2​​) for LWC bin 0.20-0.25, 0.25-0.30 and 0.30-0.35 gm-3 respectively. The value of AIEs (by ​​Equation 3​​) or the value of exponent varied as 0.30, 0.39 and 0.36 for LWC bin 0.20-0.25, 0.25-0.30 and 0.30-0.35 gm-3 respectively. It can be observed that values of AIEn are about 50% higher than AIE­s which shows the overestimate in the AIEn compare to AIEs that may be some other effect like entrainment and dispersion effect.

                                              CONCLUSION

                                              The measured cloud microphysical parameters such as cloud droplet number concentration (CDNC), droplet effective diameter (ED) and liquid water content (LWC) from cloud droplet probe (CDP) explicitly for non–rainy cloudy conditions and simultaneously measured CCN concentration at five different supersaturation conditions during June to September 2017, were utilized in this study. Measurements were done for monsoon clouds over a high-altitude site in the Western Ghats. The variation of droplet ED with CDNC in different LWC bins was observed and the effect of aerosol particles (or CCN) on these microphysical parameters (CDNC and ED) were analyzed. The calculation of aerosol indirect effect (AIEn and AIEs) were done. The main conclusions from this study are listed below:

                                              • During this observational period, the frequency of occurrence of cloud droplets with LWC in between 0.0 to 0.15 gm-3 was maximum (see ​Fig 10​).
                                              • The variation of droplet ED with CDNC in different LWC bin were observed for separately four months and averaged from these months (see ​​Fig 11​​ and ​Fig 12​). It showed that the correlation was statistically significant and the value of droplet ED decreases as CDNC increases for fixed LWC. The same result was also obtained from the contour plot (see ​​Fig 15​) for all constant LWC, ED decreases as CDNC increases.
                                              • The calculated AIEs varied as 0.25, 0.24 and 0.22 for LWC bin 0.15-0.20, 0.20-0.25 and 0.25-0.30 gm-3 respectively. The value of AIEs was maximum at lower LWC 0.025 gm-3 and then relatively decreases as LWC increases (see ​Fig 14​).
                                              • The diurnal variation of CCN concentration at five supersaturation value of 0.1, 0.3, 0.5, 0.7 and 0.9% during four months was observed (see ​​​Fig 16a​​ to ​​​Fig 16d​​​​), which shows the CCN concentration increases as the value of supersaturation increases due to enhanced activation of aerosol particles in higher supersaturation. The CCN concentration was maximum during June month, reached up to around 9000 cm-3 and it was observed that there was a higher peak in CCN concentration during the morning hours (7 to 9’o clock) for all months.
                                              • The relative change in CDNC and ED with the relative change in CCN concentration at 0.5% supersaturation showed that increase in CCN particles increases droplet concentration with smaller in size at fixed LWC (Twomey’s theory) (see ​Fig 18a​ to ​Fig 18b​).
                                              • The calculated AIEn were varied as 0.46, 0.57 and 0.53 and the AIEs were varied as 0.30, 0.39 and 0.36 for LWC bin 0.20-0.25, 0.25-0.30 and 0.30-0.35 gm-3 respectively. The values of AIEn is about 50% higher than AIE­s which shows the overestimate in the AIEn compare to AIEs which due to other effects like entrainment and dispersion effect.

                                              ACKNOWLEDGEMENT

                                              I am greatly thankful to those who rendered their valuable suggestions and help, enabling me to complete my summer project. Firstly, I am very grateful toward the Indian Academy of Sciences and to Dr. C.S. Ravi Kumar Coordinator, Science Education Programme for selecting in Summer Research Fellowship Programme - 2019 and providing me this great opportunity as a summer student in Indian Institute of Tropical Meteorology, Pune. I would like to express my sincere gratitude and thankfulness to my guide Dr. G. Pandithurai, Scientist F, Indian Institute of Tropical Meteorology, Pune, who of his busy schedule, provided me with all the necessary guidance for the successful completion of my project. I am also thankful to Dr. P. P. Leena for her kind help and informative discussion.

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                                              Source

                                              • Fig 2: Water and Other Extinguishing Agents (p. 194) by Stefan Särdqvist, 2002, Karlstad, Sweden: Raddningsverket.
                                              • Fig 9: www.dropletmeasurement.com
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