Vertical distribution of aerosols and clouds over north-eastern South Asia: Aerosol-cloud interactions

Atmospheric Environment 215 (2019) 116882

Contents lists available at ScienceDirect

Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Vertical distribution of aerosols and clouds over north-eastern South Asia: Aerosol-cloud interactions

T

Papori Dahutiaa, Binita Pathaka,b,∗, Pradip Kumar Bhuyanb a b

Department of Physics, Dibrugarh University, Dibrugarh, 786004, Assam, India Centre for Atmospheric Studies, Dibrugarh University, Dibrugarh, 786004, Assam, India

G R A P H I C A L A B S T R A C T

The vertical distribution and the subtype of aerosols and clouds have been studied over the north-eastern South Asia for June 2006–May 2017 using CALIPSO data. Distinct seasonal features with multiple elevated aerosol layers up to height ~7 km have been observed during monsoon at some locations due the convective overturning of southwest monsoon Walker and Hadley circulation within this region. Cloud occurrence frequency is maximum during the monsoon season. Aerosols invigorate the warm clouds over this region as a result of aerosol-cloud interaction.

A R T I C LE I N FO

A B S T R A C T

Keywords: Aerosol vertical distribution Aerosol extinction coefficient Elevated aerosol layers Aerosol subtype Cloud subtype Aerosol-cloud interaction

Eleven years of CALIOP and MODIS data are used to investigate the vertical distribution of aerosols and clouds and their possible interactions over the north-eastern South Asia (22–30°N, 88–98°E). Distinct seasonality in the vertical aerosol structure with the presence of elevated aerosol layers (EALs) is observed. The EALs vary from ~1.4 to 4.8 km in post monsoon to ~4.8–7.4 km in monsoon. Strong convective activities mainly in pre-monsoon and monsoon and upper air transportation of aerosols contribute to the formation of EALs. The contribution of polluted dust, polluted continental/smoke and elevated smoke are found to be predominant in the vertical column during pre-monsoon and monsoon. Contrarily, clean continental, clean marine and dusty marine are dominant during winter and post monsoon. Small spherical particles are abundant during winter while in monsoon hygroscopically grown spherical particles predominate. Seasonally, the cloud occurrence frequency (COF) as a function of altitude is maximum during monsoon. An increase in cloud top height (CTH) is observed within this region corresponding to an increase in number of cloud layers, thus revealing invigoration effect. The occurrence of cirrus and deep convective clouds is maximum in monsoon and minimum in the dry season. Significant inhibition/invigoration is observed for mixed-phase/liquid clouds.

1. Introduction Atmospheric ∗

aerosols

influence

the

radiation

budget

and

thermodynamic balance of the globe and consequently the global climate through their direct (absorption and scattering of radiation) and indirect (alteration of cloud properties) effects (Ackerman et al., 2000;

Corresponding author. Department of Physics, Dibrugarh University, Dibrugarh, 786004, Assam, India. E-mail address: [email protected] (B. Pathak).

https://doi.org/10.1016/j.atmosenv.2019.116882 Received 1 February 2019; Received in revised form 31 July 2019; Accepted 2 August 2019 Available online 10 August 2019 1352-2310/ © 2019 Published by Elsevier Ltd.

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Fig. 1. The selected locations within north-eastern South Asia in the present study: Dhaka (23.4°N, 90.2°E, 4 m above mean sea level (AMSL)), Agartala (23.9°N, 91.2°E, 14.9 m AMSL), Dhubri (26°N, 90°E, 28 m AMSL), Guwahati (26.2°N, 91.7°E, 55 m AMSL), Dibrugarh (27.3°N, 94.6°E, 111 m AMSL), Banmauk (24.24°N, 95.51°E, 279 m AMSL), Imphal (24.75°N, 93.92°E, 765 m AMSL), Aizawl (23.7°N, 92.8°E, 1001 m AMSL), Shillong (25.6°N, 91.9°E, 1496 m AMSL), Tawang (27.6°N, 91.9°E, 2668 m AMSL) and Thimphu (27.5°N, 89.6°E, 2737 m AMSL). The seasonal variation of temperature (T) (in °C) (thin lines) and relative humidity (RH, in %) (thick lines) with respect to altitude (km) (AMSL) over the locations along with the regional average (RAVG) are also shown.

status of ACI processes and observed that the understanding of implication of ACI on weather systems and regional/global climate, mechanisms of interaction with each other at different spatio-temporal domains as well as the feedbacks between microphysical and dynamical processes and between local-scale processes and large-scale circulations is still limited. Aerosols are not homogeneously distributed in the vertical column. According to Winker et al. (2010), the vertical structures of aerosols and clouds are the keys to the understanding of the climate system. Improved characterization of vertical and regional distributions of aerosols (both anthropogenic and natural) is crucial for assessment of their role in climate systems (Bourgeois et al., 2015). The aerosols and their properties vary with time, altitude and geographical location due

Haywood and Boucher, 2000 etc). Aerosols affect radiation (aerosolradiation interaction (ARI)) and cloud processes (aerosol-cloud interaction (ACI)) in a complex manner and large uncertainties are still present in the quantification (through estimation of radiative forcing) of these impacts (IPCC, 2013). Efforts have been made in recent past to understand the impact of aerosols on clouds and precipitation (Tao et al., 2012). The effective radiative forcing by the ACI (ERFACI) is −0.45 Wm-2 (with an uncertainty of −1.2-0 Wm-2) which is the single largest uncertainty in global radiative forcing estimates (IPCC, 2013) and the level of scientific understanding is low. The ERFACI can offset the warming due to greenhouse gases considerably and better understanding of this offset is necessary for improved future climate prediction (Huber and Knutti, 2011). Fan et al. (2016) reviewed the present

2

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

2. Approach

to the variety in sources and formation mechanisms (Winker et al., 2010). The variability in aerosol vertical distribution is due to the combined effects of emissions, vertical lifting strength and exchange, atmospheric transport through air mass trajectory as well as their removal process over a region (Winker et al., 2013). Transportation of aerosols through air mass trajectory at high altitudes and piling up of aerosols from the surface to the elevated altitudes due to convective uplifting are the two major processes controlling the aerosol vertical distribution. South Asia, particularly India and the adjoining Sub-Himalayan region is home to variety of aerosols of both natural and anthropogenic origin (e.g. Li et al., 2016) that play a significant role in climate change (Moorthy et al., 2016). Increasing anthropogenic emission due to rapid industrialization and population density coupled with complex orography have contributed to substantial loading of anthropogenic and natural aerosols (e.g. smoke, desert dust, polluted dust) over this region (Bollasina et al., 2011; Kim et al., 2015). This results in a conducive environment for the aerosol-cloud interaction over this region. Several researchers have examined the vertical distribution of aerosols over the Indian subcontinent using active and passive instruments and reported the presence of elevated aerosols layers (EALs) during the pre-monsoon (MAM) and monsoon (JJAS) seasons (Padmakumari et al., 2013; Prijith et al., 2016; Sarangi et al., 2016; Ratnam et al., 2018 etc.). Mao et al. (2018) studied the vertical response of ice cloud to aerosols within the Indian subcontinent during monsoon 2006–2010. Pan et al. (2018) estimated the intrinsic response of clouds to aerosols over South Asia covering the Indian subcontinent by combining CloudSat and CALIPSO measurements made during the period 2006 to 2011. However, while these studies focused on South Asia, the north-eastern part of South Asia, a region covering the northeastern states of India, Bhutan, Myanmar and Bangladesh (22–30°N and 88–98°E) have not been adequately investigated, where level of scientific understanding on ACI is still low. Pathak et al. (2016) had studied the vertical distribution of aerosols over four ARFINET (Aerosol Radiative Forcing over India Network) stations within northeast India. Dahutia et al. (2018) studied the aerosol characteristics and their climatic implications from multiple satellites and reanalysis data over selected locations in this region between 22–30°N and 88–98°E. An increase in the number density of cloud condensation nuclei (CCN) and decrease in cloud effective radius (CER) associated with the increase in anthropogenic aerosol loading have been observed over this region. However, the regional characteristics of clouds and their vertical distribution, as well as their subtypes over this region have not been studied so far. As such, the present study focuses on investigation of the temporal and spatial distribution of aerosols and clouds as well as their subtypes over the north-eastern South Asia using active and passive remote sensing data for the period 2006 to 2017. An attempt has also been made to investigate ACI in terms of thermodynamical effect of aerosols on clouds i.e., invigoration of clouds.

2.1. Study region The region consisting of Bhutan, Myanmar, Bangladesh and the Indian North eastern states: Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura, and Sikkim within the 22–30°N and 88–98°E is designated as north-eastern South Asia in the present study (Fig. 1). This region residing in the foothills of the eastern Himalaya is an integral part of South Asian monsoon. Westerly moving tropical weather system like lows, depressions and cyclonic storms are experienced during pre-monsoon (MAM), monsoon (JJAS) and post monsoon (ON) as this region belongs to the transition zone of tropics and extra-tropics. Extra-tropical easterly moving weather system like western disturbances are also experienced in winter (DJF). The Walker and Hadley circulations result in the overturning circulations over the Brahmaputra river basin during the southwest monsoon (JJAS) (Rahul et al., 2014). Unique topography, exposure to the highly polluted IndoGangetic Plain (IGP) and convectively active nature together result in a complex aerosol environment near the surface at high altitudes over this region. The details of the selected sites (Fig. 1) are presented in the supplementary table (Table S1) and also available in Dahutia et al. (2018). Regionally, vertical temperature (°C) and relative humidity (RH, %) profiles for each location are also presented in Fig. 1. The temperature decreases exponentially with altitude from maximum surface value (~29 °C) during monsoon and minimum surface value (~20 °C) in winter. The peak in RH occurs near the surface in all seasons except for monsoon when it maximizes at ~5 km. 2.2. Data and methodology The Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observation (CALIPSO) carrying the Cloud-Aerosol Lidar Infrared Orthogonal Polarization (CALIOP; Hunt et al., 2009) instrument orbits the earth at 705 km altitude and crosses the equator at 13:30 local solar time with a 16-day repeating cycle (Winker et al., 2009). CALIOP maximum vertical and horizontal resolutions are 30 m and 333 m respectively (Yu et al., 2010). The CALIOP version 4.10 (V4) level 2 data are a standard product with substantial advantages than the previous version (Kim et al., 2018). The aerosol (CAL_LID_L2_05kmAPro-Standard-V4-10) and cloud (CAL_LID_L2_05kmCPro-Standard-V4-10) profiles and aerosolcloud merged layer product (CAL_LID_L2_05kmMLay-Standard-V4-10) are used in the present analyses during June 2006 to May 2017 available at the NASA Langley Atmospheric Science Data Centre (ASDC) (https://eosweb.larc.nasa.gov/project/calipso/calipso_table). Specific information of all the variables and data products used in the present study are provided in Table 1. 2.2.1. Aerosol extinction coefficient and subtypes In CALIOP Level 2 data algorithm a layer is first detected by the Selective Iterated Boundary Locator (SIBYL) algorithm (Vaughan et al., 2009) and then the features (clouds, aerosol, surface and substances, stratospheric layers etc.) are discriminated using the Scene

Table 1 The parameters and data products used in the present study. Sensor

Spatial resolution

Products

Parameters

CALIPSO

5 km × 5 km

5 km Level 2 Aerosol Profile V4.10

MODIS

1° × 1°

5 km Level 2 Cloud Profile V4.10 5 km Level 2 Merged Layer Product V4.10 MYD08_D3_v6.1

ECMWF

0.125° × 0.125°

ECMWF

Aerosol Extinction Coefficient, Aerosol Types, Particulate Depolarization Ratio, Color Ratio Cloud Types Cloud Top Height, Cloud Base Height, Cloud Geometrical Depth Aerosol Optical Depth, Angström Exponent, Cloud Fraction, Cloud Top Temperature, Cloud Top Pressure Convective Available Potential Energy (CAPE)

3

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

spectral behaviour of lidar backscatter (Winker et al., 2010). It overestimates the true base layer height (Winker et al., 2009) as it does not penetrate cloud layers with optical depth 3 (Liu et al., 2005). Only 66% of CALIPSO profiles can reach up to earth's surface and 80% penetrate up to 1.5 km altitude, despite the prevalence of dense clouds at lower altitudes (Winker et al., 2010). Thus, there is a probability of misclassification of the low clouds as middle clouds by CALIPSO (Mace and Zhang, 2014; Pan et al., 2017).

Classification Algorithm (SCA) (Liu et al., 2009, 2019). The retrieved attenuated backscatter and extinction coefficient of those features are calculated by the Hybrid Extinction Retrieval Algorithm (HERA) (Young and Vaughan, 2009; Young et al., 2018). CALIPSO aerosol subtypes were first categorized into six aerosol subtypes by Omar et al. (2009) based on altitude, location, surface type, volume depolarization ratio, and integrated attenuated backscatter measurements. These were further improved with the help of surface type integrated attenuated backscatter, estimated particulate depolarization ratio, layer top and base altitudes by Kim et al. (2018) to define seven aerosol subtypes: clean marine (CM), dust (DS), polluted continental/smoke (PC/S), clean continental (CC), polluted dust (PD), elevated smoke (ES) and dusty marine (DM) within the troposphere.

2.2.4. Convective available potential energy (CAPE) The daily CAPE (J/kg) values in 0.125° × 0.125° resolution from European Centre for Medium-range Weather Forecasting (ECMWF) (http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/) are used to investigate atmospheric stability. The spatio-temporal coverage of CAPE data is kept similar to that of CALIPSO profile data i.e., within 1° × 1° centred at each location and during the CALIPSO overpass days.

2.2.2. Cloud subtypes The International Satellite Cloud Climatology Project (ISCCP)'s cloud classification scheme is based on cloud top pressure (CTP) and cloud optical depth (COD) (Rossow and Schiffer, 1999). Nevertheless, the ISCCP scheme cannot be used directly in CALIPSO for cloud subtyping, as CALIOP lidar beam reaches the base of the cloud with an optical depth less than 3 only (Liu et al., 2005). However, CALIOP uses information of CTP, cloud layer fraction and opacity flag for eight different cloud subtypes (1) low overcast, transparent (viz., transparent stratus, stratocumulus and fog): (LoT) (2) low overcast, opaque (viz., opaque stratus, stratocumulus, and fog), (LoO), (3) transition stratocumulus (TSc), (4) low, broken cumulus (trade cumulus and shallow cumulus) (LBC), (5) transparent altocumulus (AcT), (6) opaque altostratus (opaque altostratus, nimbostratus, altocumulus) (AsO), (7) transparent cirrus (Ci) and (8) deep convective clouds (opaque altostratus, nimbostratus and cumulonimbus) (DCC) (Liu et al., 2005).

2.2.5. Aerosol-cloud interaction (ACI) To study the effect of aerosols on the cloud, the cloud top height (CTH), cloud base height (CBH), cloud geometrical depth (CGD) retrieved from CALIPSO layer data have been investigated over the study region. The daily data of AOD, Ångström exponent (AE), cloud top temperature (CTT), CTP and cloud faction (CF) from Moderate resolution Imaging Spectroradiometer (MODIS) Level 3 collection 6 data of 1° × 1° resolution (Levy et al., 2013) are used to study ACI. The correlation between CCN proxy i.e., the aerosol index (AICCN = AOD × AE; Nakajima et al., 2001) and CTT has been investigated for ACI following Niu and Li (2012). The noise in the data has been removed by discrete wavelet transformation for better representation of results. Clouds are composed of liquid at temperatures above 0 °C, ice below about −38 °C and either or both phases at an intermediate temperature (IPCC, 2013). Following this differentiation, CTT > 0 °C, 0 °C > CTT > −38 °C and CTT < −38 °C correspond to liquid (warm) clouds, mixed-phase clouds and ice clouds respectively. To account for the influence of aerosol on the cloud, the association of AICCN with CTT, CTP and CF for these clouds are investigated.

2.2.3. Uncertainties and limitations of CALIPSO observation In the present study, the CALIPSO data are averaged for 1° × 1° grid centred at each location since the spatial and climatological averaging improves the signal-to-noise ratio in aerosol retrieval (Winker et al., 2010). In order to reduce uncertainties, the quality assurance is obtained using the cloud aerosol discrimination score (CAD_Score), extinction quality control flag of 532 nm (Ext_QC_flag_532) and extinction uncertainty (Winker et al., 2013). The CAD_Score indicating the confidence in the classification of aerosol and cloud layers lie within −20 to −100 for aerosols and 20 to 100 for clouds. The accepted Ext_QC_flag_532 values indicating the type of retrieval performed on each layer and flags are 0, 1, 16 and 18. The samples with extinction uncertainties 99.9 km−1 are excluded (Young et al., 2013). The sensitivity and uncertainty analyses of clustering algorithm applied to aerosol types are quite robust and reproduce more than 94% of classification records applied for a subset of data created arbitrarily (Omar et al., 2005). The main sources of bias between CALIPSO aerosol optical depth (AOD) and other measurements and retrievals arise from (i) aerosol layer detection failures and (ii) inaccurate lidar ratios (Kim et al., 2018). Rogers et al. (2014) found a mean underestimation of 0.02 in CALIOP AOD due to aerosol layer detection failure, when they compared the CALIOP AOD with NASA LaRC airborne High Spectral Resolution Lidar (HSRL). Later, Kim et al. (2017) retrieved global mean AOD of 0.031 for the undetected aerosol layers. Toth et al. (2018) reported that in Level 2 V3 45% of profiles have not detected aerosols. Papagiannopoulos et al. (2016) found a relative difference of 25% on extinction profiles by comparing CALIPSO and EARLINET (European Aerosol Research Lidar Network). The maximum allowed uncertainty in lidar ratio for a given aerosol type is 30% (Omar et al., 2009). CALIPSO observation only at 1:30 p.m./a.m. provide an instantaneous relation between aerosols and clouds and cannot address aerosol effect on cloud life cycle and time dependent mesoscale system (Jiang et al., 2018). CALIOP distinguishes optically thin boundary layer clouds from aerosols and location of the layers together with the

3. Results and Discussion 3.1. Vertical distribution of aerosols and their subtypes: spatial and seasonal variability The aerosols distribution exhibits large variation on daily, seasonal and interannual scales depending on heterogeneity in source, residence time and dependence of sinks on meteorology (IPCC, 2013). The aerosol characteristics over Indian subcontinent including present study region are mainly governed by the synoptic meteorological conditions (Dahutia et al., 2018) associated with air mass circulation pattern (Moorthy et al., 2007; Gogoi et al., 2009; Ramachandran et al., 2013). The variability in EALs indicates vertical heterogeneity in aerosol profiles and is attributed to short-scale weather phenomena, stratified turbulences and long-range transport of aerosols above ABL (Moorthy et al., 2010). To delineate the seasonality and spatial behaviour in aerosol vertical distribution over this region, seasonal averages (day and night) of aerosol extinction coefficient profiles (km−1) at 532 nm and associated aerosol subtypes at eleven selected locations along with the regional average (RAVG) have been investigated. Also, based on the examination of CAPE values during CALIPSO overpass days (Table 2 and Fig. S1) and trajectory analyses (Fig. S2 and Table S2), an attempt has been made to establish the dominant cause of the EALs, i.e., whether convective uplifting or long-range transportation. 3.1.1. Vertical distribution of aerosols during winter (DJF) Aerosol extinction coefficient profiles indicate the presence of EALs extending from ~2.5 km (~0.18 km−1) at Dhubri to ~3.5 km 4

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Table 2 The seasonal range of convective available potential energy (CAPE) (Jkg−1) value and the percentage (%) contribution of CAPE > 500 Jkg-1 during CALIPSO overpass days over the study locations. Locations

RAVG Dhaka Agartala Dhubri Guwahati Dibrugarh Banmauk Imphal Aizawl Shillong Tawang Thimphu

Seasonal CAPE (Jkg−1) value

% contribution of CAPE > 500 Jkg-1

DJF

MAM

JJAS

ON

DJF

MAM

JJAS

ON

3–541 0–844 0–769 0–385 0–606 0–515 0–854 0–629 0–796 0–934 1–539 5–388

3–2249 0–5022 0–4415 0–3815 0–3503 0–1912 0–3508 0–2438 0–3168 0–4055 12–1465 14–1373

117–1695 28–4657 16–3463 115–3092 0–3503 24–2708 0–3658 30–2509 0–2056 0–2928 48–1973 27–1703

3–1322 0–2234 0–2941 0–1670 0–1351 0–1497 0–2557 0–1903 0–2154 0–1411 4–635 9–576

0.3 1.5 0.6 0 0.9 0.9 2.5 1.2 1.3 1.9 0.6 0

68.0 80.7 78.8 50.9 64.8 39.0 63.8 61.0 63.5 72.8 22.2 21.9

72.5 65.1 60.1 58.8 46.9 54.7 84.2 54.7 46.1 50.5 45.5 40.5

16.2 25.9 32.8 14.3 16.5 12.0 48.3 23.7 24.8 21.2 5.1 0.9

(~0.08 km−1) at Tawang with a regional average of ~2.7 km (~0.10 km−1) (Fig. 2a (i)) in winter. The lower regional average CAPE values (~3–541 Jkg-1), indicate the lesser contribution of convective uplifting of aerosols to EALs (Table 2). On the other hand, the air mass trajectories contribution can be dominant emerging mainly at West Asia and west India via the IGP (Fig. S2 (a) and Table S2 (a), also see Pathak et al., 2013, 2016, Gogoi et al., 2009, 2011) which forces the accumulation of aerosols at higher altitudes. As such, the variability in extinction profiles is contributed by different aerosol subtypes originated from various sources (Fig. 2a (ii)). For example, main contribution to the regional atmospheric column is from maritime aerosols (~33.3% clean marine and ~13.6% dusty marine) during winter (Table 3). Trajectories from Bay of Bengal (BoB) in monsoon and pre-monsoon (Pathak et al., 2012) and those from Arabian Sea in post monsoon and winter towards the study region are prominent (Fig. S2 and Table S2), carrying maritime aerosols (clean and dusty marine). These along with clean continental aerosols are considered as background types over the region (Pathak et al., 2012). The elevated smoke (~8.5%) originating from both biomass-burning and fossil fuel burning (Gogoi et al., 2017), polluted dust (~16.6%) and dust (~16.4%) transported from the West Asia and west India via IGP contribute prominently to aerosol loading over the region. The peak in EAL extinction is observed over Dhubri, situated at the western corridor of northeast India through which most of the longrange transported aerosols from west enter the region. Dust (~32.9%), polluted continental/smoke (~22.7%), along with dusty marine (~20.4%) and elevated smoke (~13.7%) (Fig. 2a (ii)) contribute to EALs over Dhubri. Contrarily, high biomass-burning activity and growing urbanization/transportation at Aizawl result in ~42.2% elevated smoke and ~11.6% dust respectively. These along with ~35.2% dusty marine aerosols lead to higher extinction of ~0.17 km−1 at 2.6 km (Fig. 2a (i)). Comparable extinction of ~0.16 km−1 at the same altitude over the metropolitan city Guwahati is attributed to elevated smoke (~23.8%), dusty marine (~21.8%), polluted dust (~21.2%). Lowest EAL extinction of ~0.08 km−1 at 3.5 km is observed at the less populated high altitude location Tawang. This is attributed to transported polluted dust (~38.9%), dust (~23.2%) as well as elevated smoke (~27.7%) (Fig. 2a (ii)).

dust (~38.9%), elevated smoke (~15.2%) and polluted continental/ smoke (~14.3%) (Table 3, Fig. 2b (ii)). Here, the EALs are mostly contributed by elevated smoke (~34.3%), dusty marine (~26.9%), polluted continental/smoke (~17.0%). Polluted dust, a mixture of dust and smoke dominated by the anthropogenic aerosols, is significant over the Indian subcontinent due to industrial pollution (Yu et al., 2010). Pathak et al. (2012) reported presence of 41% urban/industrial or biomass-burning and 15% desert dust over Dibrugarh which are comparable to present values (35% polluted dust, 15% elevated smoke and 14% dust) as presented in Table 3. Dhubri, being dominated by significant convective activities along with long range transportation of aerosols experiences the peak EALs (~0.13 km−1) at a lower altitude (~1.4 km). This is attributed to polluted dust (~30.0%), polluted continental/smoke (~29.8%) and clean marine (~15.7%) aerosols (Fig. 2b). Significant extinction at peak EALs are observed at polluted Dhaka/Agartala (~0.15 km−1 at ~2.8/1.8 km) with dominance of elevated smoke (~41.0%), polluted dust (~26.6%) and dust (~10.4%) at Dhaka and polluted continental/smoke (~38.7%) and polluted dust (~36.1%) at Agartala (Fig. 2b). Extinctions (~0.14 km−1) at ~2.7 km are comparable at the biomass-burning dominated locations of Banmauk, Imphal and Aizawl, where contributions of elevated smoke are ~22.1%, 47.8%, 40.3%, and of polluted dust are ~27.7%, 28.5%, 25.3% (Fig. 2b).

3.1.3. Vertical distribution of aerosols during monsoon (JJAS) The EALs ranging from ~4.8 km (~0.14 km−1) at Thimphu to ~7.4 km (0.12 km−1) at Banmauk exhibiting regional peak at ~5.2 km have been observed in monsoon season (Fig. 2c (i)). Regionally, elevated smoke (~36.1%), polluted dust (~29.2%), clean continental (~19.0%) and dust (~15.7%) are dominating in this season. Regional CAPE values ranging from ~117 Jkg-1 to 1695 Jkg-1 with 72.5% of CAPE > 500 Jkg-1 indicate strong convection (Table 2 and Fig. S1). Further, the low mean sea level pressure (Fig. S3) associated with the atmospheric parameters like vorticity, divergence and wind shear favour aerosol uplifting to the higher altitudes during monsoon over the Indian region (Babu and Sivaprasad, 2014). Transportation of air masses from central India to the BoB traversing the northeast India towards the Himalayas (Lawrence and Lelieveld, 2010) is also evident in this season. The absorption-warming-convection cycle, applicable to any part of the globe can uplift the black carbon (BC) from the surface to the stratosphere by the strong convection during monsoon (Moorthy et al., 2016; as explained in Fig. 7). Elevated BC layer warms the environment through enhanced absorption of solar radiation and thus lowers the environmental lapse rate. This results in a sharp increase in atmospheric stability at that particular height. The stable BC layer is slowly uplifted by strong convective activities during monsoon (Moorthy et al.,

3.1.2. Vertical distribution of aerosols during pre-monsoon (MAM) In the pre-monsoon season, distinct EALs extending from ~1.4 km (~0.13 km−1) at Dhubri to ~5.6 km (0.07 km−1) at Tawang with a regional average of ~2.6 km (~0.11 km−1) are observed (Fig. 2b (i)). Regionally, high CAPE values (3–2249 Jkg-1) with 68% occurrence of CAPE > 500 Jkg-1 indicate the significant contribution of convective activities during this season (Table 2 and Fig. S1). The dominant aerosol subtypes in the total column over the region in this season are polluted 5

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Fig. 2a. The vertical distribution of (i) extinction profiles of aerosols (km−1) and (ii) aerosol subtypes:clean marine, dust, polluted continental/smoke, clean continental, polluted dust, elevated smoke and dusty marine occurrence frequency during winter (DJF) over the study region during June 2006 to May 2017. The vertical bars in the pannel (i) represents the standard errors of each locations and white gap in pannel (ii) indicates the altitude between the sea-level and location's surface level. 6

Atmospheric Environment 215 (2019) 116882

38.9 40.6 33 38.4 42.9 35.2 37.7 37.5 38.4 41.3 40 61.7

25.3 26.7 25.5 23.9 29.3 24.2 29 25.8 17.8 27.9 35.6 23.7

24.4 14.3 11.7 16.4 13.5 13.5 27.4 16.3 24.1 22.8 30.1 51.1

8.5 7.3 3.7 13.9 5.3 3.2 2.5 6 17.7 17 7.4 28.6

15.2 10.9 13.5 11.7 12.8 15.2 11.7 17.5 18.4 19.7 20.2 12.9

20.2 23.6 22 21 25.1 26.7 16.6 27.6 24.7 31.4 30.1 24.4

19 7 9.2 2.3 2.9 2.8 18.8 8.7 16.3 17.6 13.7 8.7

13.6 11.8 10.5 11 10.7 18.1 11.2 20.9 16.8 9.8 3.6 1.5

3.2 4 4 3.1 2 4.7 3.3 4.7 1.8 2.1 1 1.4

1.2 0.9 1.8 1 1 1.8 0.7 1.4 0.8 0.4 0.2 0.4

9.3 11 13.4 2.4 6.2 6.2 5.4 7.7 2.1 7.2 4.3 7.3

2016). The extension of EALs up to 3–5 km and 6–7 km in monsoon season at Guwahati, due to lofting of BC was observed during CAIPEEX by Rahul et al. (2014). They have attributed this to the presence of carbonaceous aerosols (i.e., elevated smoke and polluted dust) at higher altitudes due to the large-scale southwest monsoon Walker and Hadley circulation pattern producing a strong ascending motion. This motion is characterized by the overturning circulation in northeast India extending from the surface to the upper troposphere. The formation of EALs is additionally attributed to the confinement of aerosol due to the faster rebuilding after rain in monsoon (Jai Devi et al., 2011). Significant contribution of elevated smoke (~36.1%), polluted dust (~29.2%), clean continental (~19.0%), and dust (~15.7%) to EALs over this region (Fig. 2c (ii)) is observed. Prominent EALs at different altitudes are observed at Guwahati (5.3 km), Dibrugarh (7 km), Banmauk (7.4 km) and Imphal (7 km). These EALs are mostly contributed by elevated smoke (~55.2% at Guwahati, ~65.6% Imphal and 66.6% Dibrugarh). Contrarily, the EALs at Banmauk are mainly assigned to dust (40.8%) and polluted dust (45.3%). The contribution of polluted dust and elevated smoke to the aerosol column are higher at high altitude locations. For example, polluted dust varies from ~17.8% (Aizawl) to ~35.6% (Tawang). Though located at higher altitude, Tawang is mostly influenced by dust from western India (Fig. S2 (c), Table S2 (c)). On the other hand, a major fraction of air mass trajectories reaching Aizawl originate in the oceanic region (Fig. S2 (c), Table S2 (c)). Thus, contribution of polluted dust is more in Tawang compared to that in Aizawl. Mao et al. (2018) have also reported the frequent occurrence of aerosol layers during monsoon seasons over the Indian subcontinent, with significant contribution of dust (45.4%), polluted dust (35.3%) and smoke (10.0%).

0.8 1.3 1.7 2 0.7 0.7 0.8 1.1 1 1.1 1.3 0.4

3.1.4. Vertical distribution of aerosols during post monsoon (ON) The EALs in post monsoon season are less prominent compared to rest of the seasons and are present at altitudes ranging from ~1.4 km (~0.20 km−1) at Dibrugarh to ~4.8 km (~0.12 km−1) at Shillong while the regional average is ~2.1 km (~0.11 km−1) (Fig. 2d (i)). The convective activities during post monsoon are minimal with lower regional CAPE values (~3–1322 Jkg-1) than in monsoon with only 16% of CAPE > 500 Jkg-1 (Table 2 and Fig. S1). Thus, aerosols mostly accumulate near the surface in absence of strong transportation from remote places (Pathak et al., 2016, Fig. S2 (d), Table S2 (d)). The occasional contribution of convection results in the EALs at the observed altitudes (Fig. 2d (i)). After the wet removal of aerosols in monsoon, aerosol environment is composed primarily of background aerosols. Thus the contribution of background maritime aerosols are prevalent (regionally ~25.8%) with spatial distribution of ~12.9% (Aizawl) to ~52.4% (Dhubri) (Fig. 2d (ii), Table 3). Further, dusty marine contributes ~9.3% regionally and it varies from ~2.1% (Aizawl) to ~13.4% (Agartala) signifying the influence of prevailing background maritime aerosol environment. Despite the dominant contribution of background aerosols, regionally the polluted dust contributed ~24.4% to the columnar aerosol and it lies between ~11.7% (at Agartala) and ~51.1% (at Thimphu) (Table 3). Regionally, EALs are mainly constituted by clean marine (~28.7%), dusty marine (~23.1%) and dust (~13.5%) aerosols. The EALs when formed at lower altitudes (2–2.4 km) are mostly contributed by marine aerosols (~20.9% at Aizawl to ~66.5% at Dhaka) while those at higher altitudes (4.4–4.7 km) are resulted from polluted dust (~38.1% at Tawang to ~77.5% at Shillong). At some of the locations elevated smoke at EALs is also significant (~31.2% at Aizawl to ~62.9% at Dhubri).

6.4 5.1 8.3 7 3.3 6.1 4.5 2.4 1 0.7 0 0 33.3 32.9 41.7 29.2 38.9 31.3 25.7 15.6 13.4 9.2 0 0

3.2. Morphology of elevated aerosols

RAVG Dhaka Agartala Dhubri Guwahati Dibrugarh Banmauk Imphal Aizawl Shillong Tawang Thimphu

6 7.1 6.7 10.7 5.1 9.6 10.9 4 5.4 1.7 0 0

25.8 43.4 44.3 52.4 41.7 41.7 30.5 27 12.9 19.2 0 0

16.4 13.8 15.1 13.2 14.3 18.3 22.4 21.5 21.4 29.2 42.1 25.1

13.7 13.1 17.7 13.5 16 14.3 13.8 13.7 21.4 20.5 27.4 15.9

10 9.2 15.1 13.6 8.5 8.8 9.9 9.9 11.7 9.7 17.9 25.7

16.1 19.4 16.6 20.7 18.9 18.9 10.6 28.7 38.7 29.6 50.6 32.5

9.2 13.2 14.3 9 15.2 5.1 13.3 3.7 8.2 5.9 0 0

14.3 16 17.1 14.4 12.9 11.8 16.5 12.6 10.4 8.7 1.1 0.1

25.8 24 19.4 22.1 23.5 21 25.7 21.5 27.6 18.2 2.2 2.2

4.6 3.6 3.1 3.8 16.1 16.2 6.5 10.5 4.9 2.5 0 0

2.4 5.7 2.6 8.3 3.2 1.5 3.5 2 5.3 3.1 2 2.6

8.7 8.3 8 8.2 8.3 9.2 6.1 10 4.2 6 10.3 8

11.1 10.5 7.9 11.4 9.3 11.4 13.6 11.4 16.4 11.7 14 23.6

16.6 15.3 12.1 15.4 12.4 22.5 21.4 30.3 17.2 25.8 44.9 42.2

ON JJAS MAM DJF ON JJAS DJF

MAM

ON

DJF

MAM

JJAS

ON

DJF

MAM

JJAS

ON

DJF

MAM

JJAS

DJF

MAM

JJAS

ON

DJF

MAM

JJAS

ON

DM ES PD CC PC/S DS CM Locations

Table 3 Seasonally estimated percentage contribution of aerosol types (CM: Clean Marine, DS: Dust, PC/S: Polluted Continental or Smoke, CC: Clean Continental, PD: Polluted Dust, ES: Elevated Smoke and DM: Dusty Marine) over the study locations.

P. Dahutia, et al.

The particle linear depolarization ratio (PDR) is a post-extinction quantity indicative of the shape of the particles derived from the ratio of layer integrated perpendicular and parallel polarization components of particulate backscatter coefficients at a given altitude (Omar et al., 7

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Fig. 2b. Same as Fig. 2a but for pre-monsoon (MAM).

8

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Fig. 2c. Same as Fig. 2a but for monsoon (JJAS).

9

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Fig. 2d. Same as Fig. 2a but for post monsoon (ON).

10

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Fig. 3. Seasonal variation of particulate depolarization ratio (PDR) (represented by square symbols) over the study locations of north-eastern South Asia.

Fig. 4a. Seasonal variation of color ratio over the study locations of north-eastern South Asia. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

11

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

2009). A larger value of PDR signifies higher percentage contribution of non-spherical particles (Murayama et al., 1999). In the present study, the vertical distribution of PDR is homogeneous at all the locations with values lying between 0.1 and 0.2 from surface up to 3 km (Fig. 3). This suggests presence of spherical aerosol particles near the surface during all the seasons. However, non-sphericity of the particles is prominent at higher altitudes during winter and pre-monsoon. Larger values of PDR (up to ~0.5) occur at 4–8 km at Dibrugarh, Guwahati, Imphal, Banmuk, Shillong, Tawang and Thimphu in these seasons. The fluctuations of PDR at 4–8 km in different seasons and locations are associated with various types of aerosols contributing to EALs as discussed in the previous sections. Liu et al. (2017) have reported a homogeneous vertical distribution of PDR over Wuhan, with an average value of 0.18 within 0.3–3 km. Haarig et al. (2016) reported PDR value of 0.25 ± 0.02 in the altitude range 1.8–4.1 km for dust layer. The particle size can be estimated from the integrated attenuated color ratio ( x ′), which is defined as the ratio of the layer mean attenuated backscatter at 1064 nm to that at 532 nm (Liu et al., 2019). Regionally, x ′ is minimum (0.62 ± 0.35) during winter, indicating the presence of smaller and spherical aerosol particles (Fig. 4a and b). This is associated mainly with the presence of background marine aerosols as well as clean continental (Pathak et al., 2016) aerosols. The marine

Fig. 4b. Correlation between color ratio and particulate depolarization ratio over the study locations of north-eastern South Asia. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5. The annual (ANL) and seasonal (DJF, MAM, JJAS, ON) occurrence frequencies of cloud as a function of altitude (km) above mean sea level (AMSL) for regional average RAVG and over the selected eleven locations of the north-eastern South Asia during June 2006 to May 2017 retrieved from CALIPSO satellite. 12

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Fig. 6a. The vertical structure of the occurrence frequencies of cloud subtypes: low overcast (transparent), low overcast (opaque), transition stratocumulus, low broken cumulus, transparent altocumulus, opaque altostratus, transparent cirrus and deep convective clouds during winter (DJF) over the study region. The white gap in the subplots indicate the altitude between the sea-level and location's surface level.

(0.62 ± 0.32) at Dhubri and highest (0.81 ± 0.38) at Tawang. Seasonally, regional x ′ is the highest (0.88 ± 0.29) during monsoon, varying from minimum at Dhubri (0.79 ± 0.33) to maximum at Thimphu (0.88 ± 0.33) and Tawang (0.88 ± 0.31). Generally x ′ of 1064 nm–532 nm band lies in the range 0.4–1 (Sugimoto et al., 2000). Thus it can be inferred that aerosols are of fine mode over this region. Earlier Pathak et al. (2012), have also reported the dominance of fine mode particles over Dibrugarh throughout the year. Higher PDR and x ′ in monsoon (Fig. 4b) are due to the hygroscopic growth of particles in the moisture laden environment.

aerosols are composed of sea salts with size smaller than desert dust (Porter and Clarke, 1997) and spherical fine mode anthropogenic aerosols (Omar et al., 2005). x ′ is lowest at Dhubri (0.49 ± 0.27), which is situated at the western corner of northeast India and influenced by polluted dust (15.4%), elevated smoke (13.9%) and marine (29.2% clean marine + 11% dusty marine) aerosols. Similarly, as all other low altitude western locations are highly influenced by marine aerosols together with the transported aerosols from IGP and other places (Pathak et al., 2012), x ′ remains low throughout the year (Fig. 4a). The maximum value of x ′ obtained at Tawang (0.73 ± 0.41) is due to the higher contribution of dust (42.1%) and polluted dust (44.9%), as discussed in previous section. The dust particles are composed of non-spherical coarse mode dominated mineral dust (Kandler et al., 2011). During post monsoon, regional x ′ attains a value of 0.71 ± 0.38, contributed by marine (25.8% clean + 9.3% dusty) and polluted dust (24.4%). In this season, x ′ is minimum at Dhaka and maximum at Tawang. During winter and post monsoon, spherical and small mode particles are dominant as indicated by the lower PDR and x ′ values (Fig. 4b). On the contrary, the long-range transported aerosols particularly dust added with biomass-burning lead to x ′ (0.76 ± 0.34) regionally during pre-monsoon. During this season, x ′ is lowest

3.3. Vertical structure and types of clouds Pan et al. (2015) defined the cloud vertical structure as the cloud occurrence probability in bins at different heights over a given region. The change in cloud vertical structure directly affects the atmospheric circulation by modifying the radiative cooling profile and atmospheric stability (Wang and Rossow, 1998). The study of cloud vertical structure over a region helps in understanding the influence of radiative effects that provides a better understanding of earth's climate system. Keeping these in view, we have studied the seasonal cloud vertical 13

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Fig. 6b. Same as Fig. 6a, but for pre-monsoon (MAM).

The study of cloud subtypes is important for the understanding of cloud microphysical properties, radiative properties and latent heat released over a region (Subrahmanyam and Kumar, 2013). The vertical distribution of cloud subtypes are investigated based on seasonal occurrence frequency (OF) of each subtype over the study region (Fig. 6a–d). The high level transparent cirrus clouds predominates over with highest OF in monsoon (57.5%) and lowest in winter (43.3%) (Fig. 6a–d, Table S4). The hot and humid environment with strong convective activities (Table 2) in monsoon is associated with observed highest occurrence of transparent cirrus. Spatially, in monsoon maximum transparent cirrus occurs in Tawang (68.6%) and minimum in Dhaka (58%). Cirrus clouds evolve from the detached anvils of DCCs or are generated in situ by synoptic-scale uplifting of humid layers. DCCs associated with tropical convection that play a significant role in precipitation, extends up to ~12 km in all the seasons regionally with highest OF in monsoon (~19.1%) and lowest (~9.9%) in post monsoon season (Table S4) (Fig. 6a–d). These clouds exhibit the highest inhomogeneity and are extremely sensitive to changes in the environmental conditions (i.e., atmospheric instability) (Koren et al., 2010). Thus, spatially, in monsoon DCC's maximum OF is observed in Thimphu (19.1%) followed by Aizawl and Shillong (18.9%). Regionally, the significant amount of DCCs added by complex monsoon system determined by the depression in BoB, location of monsoonal trough and

structure and associated subtypes over the region. The annual and seasonal variations of cloud occurrence frequencies (COF) are obtained by summing up all the cloud subtype occurrences at different altitude bins in the vertical column (Fig. 5). Since the high cloud occurrence is a function of altitude, the peak height for COF increases with increasing altitude (IPCC, 2013). Seasonally, regional single peak COF for all clouds varies from ~39.7% in monsoon (at ~8.4 km) to ~20.7% in winter (at ~5.1 km). This seasonal variability is attributed to the thermal (convective activities and intense solar heating), dynamical as well as microphysical processes in the clouds, which influence the weather and climate (Weare, 2000; IPCC, 2013). The strong convective activities (Table 2 and Fig. S1) added by persistent solar heating during monsoon lead to observed highest COF (45.4%) at 8.4 km for high clouds (Table S3 and Fig. 5). The contribution of the high clouds to total clouds is dominant in all the seasons, maximum being in monsoon (77.1%). Large cloud clusters reported by Lau et al. (2008) along the foothills of the Himalayas near Nepal are related to enhanced convection associated with heavy monsoon rainfall. The frequency of cloud detection above 6 km altitude is > 50% over the Indian subcontinent and the adjoining oceanic region (Wylie and Menzel, 1999). Further, minimal convective activities (Table 2) with lower solar heating/intensity during winter reduces the peak height (5.1 km) of high clouds as well as COF (17.3%) (Table S3). 14

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Fig. 6c. Same as Fig. 6a, but for monsoon (JJAS).

drizzle (Yamaguchi et al., 2017). However, the occurrence of low clouds decreases with increase in altitude of the study locations, being almost absent in Tawang and Thimphu. The low shallow cumulus and stratiform clouds mainly exist over cooler subtropical oceans and are less common over land except at night and winter (IPCC, 2013). It is worthwhile here to recall the limitation of CALIPSO to trace the low level clouds as discussed earlier. However, the prevalence of low clouds as well as DCCs in appreciable fractions over this region (Fig. 6a–d), favours both microphysical and thermodynamical effects of aerosols on ACI.

jet stream strengthened by monsoonal winds, contribute to the high amount of precipitation and have helped this region to become one of the wettest region in the globe. The DCCs are found to exist over a wide range of frequencies from 15% (at Dibrugarh) to 6.1% (in Shillong) during pre-monsoon. According to Hong et al. (2007), the cirriform (cirrus) clouds contribute more than 80% of the high clouds and DCCs contribute less than 20% over the globe. The frequency of DCCs occurrence exceeds ~25% at BoB during monsoon (Roca and Ramanathan, 2000). The radiative effects of DCCs can vary from the cooling of low cumulus to warming of deep convective cells and their anvils. The reduction in relative abundance of low and middle clouds during monsoon plausibly attributed to the evolution of DCCs (Fig. 6c). The midlevel transparent altocumulus is prevalent with maximum OF during winter (~23.4%), spatially varying from ~18.1% at Aizawl to ~28.8% at Dhaka and it's occurrence is minimum in monsoon (10.6%). During the post monsoon season, the contribution of low and middle clouds is higher than that in monsoon due to the limited convective activities (Fig. 6d, Table S4). Further, the transition stratocumulus (5.9%) and low broken cumulus (3.9%) are the main contributors to the low clouds in winter due to the stable and dry atmosphere. The transition stratocumulus, formed by the transition from stratocumulus to cumulus clouds through a slow, multi-day process and caused primarily by dry air entrainment, are associated with overshooting cumulus and

3.4. Aerosol-cloud interactions (ACI) ACI is a process by which aerosols by acting as a cloud condensation nuclei or ice nuclei, affect the evolution of clouds as well as their microphysical properties (IPCC, 2013). Thus, the perturbation in aerosol characteristics as well distribution over a region is bound to affect the regional impact on cloud properties, cloud radiative effect, and precipitation (Fan et al., 2016). For comprehensive understanding of ACI, microphysical factors (effect on cloud droplet size), thermodynamical (invigoration) and dynamical effects of aerosols are important. The estimation of ACI over the globe has been derived by satellite, groundbased measurements (Fan et al., 2016) as well as modeling approaches. 15

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Fig. 6d. Same as Fig. 6a, but for post monsoon (ON).

and CGD (Figs. 7 and 8) as well as CTP, CTT, CF, AICCN (Fig. 9). The regional CTH, CBH, and CGD of single layer clouds are 7.22 km, 5.43 km, and 1.78 km respectively on an annual basis (Fig. 7). The single-layer CTH/CGD maximizes at Shillong (10.02 km/2.29 km) being a cumulonimbus tower. The CTH is lowest at Dibrugarh (6.74 km), while CGD at Thimphu (1.37 km). Being located in the tropical monsoon region, the strong convective activities result in maximum CTH and CDG at Dhaka (13.96 km and 3.40 km) for single-layer cloud followed by Shillong (13.81 km and 3.09 km). Earlier Islam et al. (2005) observed a significant amount of monsoon rainfall (60%) contributed by single-layer cloud over Dhaka. The lifting of southerly flow over the steep southern side of the Meghalaya plateau due to the monsoon trough, together with uniqueness in orography (Prokop and Walanus, 2014) lead to the high CDG and CTH at Shillong. Thus, Shillong experiences very heavy rainfall during monsoon and world's highest rainfall is received at the nearby locations Cherrapunji and Mawsynram. On the other hand, the regional annual CTHs of the uppermost layer in 2-layer (2nd bar), 3-layer (3rd bar), 4-layer (4th bar) clouds are 7.82 km, 8.98 km, and 10.74 km respectively (Fig. 7). An increase in CTH is observed over this region corresponding to the increasing number of cloud layers. This can be explained on the basis of invigoration theory (thermodynamic effect of aerosols on clouds), according to which, more latent heat is released at higher altitudes during

Pan et al. (2018) have quantified intrinsic radiative forcing exerted by aerosols interacting with warm clouds and reported regional inhomogeneity and seasonal variations. Strong ACI through enhancement in the formation and growth of clouds (invigoration) during moist monsoon season was observed, whereas ACI weakens in dry nonmonsoon seasons. Further, gradual decrease in ACI with increasing CBH above ground level for aerosols below 2 km was also reported. They have assigned aerosol vertical distribution and vertical atmospheric upward motion to variation of ACI vertical intensity. The significant negative and positive responses of warm clouds to aerosols were also reported over northern and southern South Asia in non-monsoon seasons. Further, in their extensive study on ACI over the Indian subcontinent, Mao et al. (2018) have found a strong negative response between the ice clouds and aerosols under moist/unstable conditions during monsoon than dry/stable condition. They have found that the COD, CGD and ice water path decreased as a function of aerosol loading. They also observed that ice clouds might decrease in size and become more spherical with increase in AOD. Dahutia et al. (2018) had discussed about the microphysical effect in terms of trend analyses of AICCN and CER and concluded that increasing AICCN with time is contributing to decrease in CER over the same study region. In this study, we investigate the thermodynamic effect of aerosols (affecting cloud invigoration) on ACI using some of the cloud parameters like CTH, CBH 16

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Fig. 7. The average CTH (top of each bar), CBH (bottom of each bar) and CGD (difference between each bar) for 1-layer (1st bar), 2-layer (2nd bar), 3-layer (3rd bar), 4-layer (4th bar) and all-layer (5th bar) indicated by each subsections for annual (ANL) and seasonal (DJF, MAM, JJAS, ON) variation for selected study locations.

years during pre-monsoon (Fig. 8) indicate stronger invigoration effect aided by increasing aerosol loading (Dahutia et al., 2018). However, the invigoration is comparatively weaker in monsoon (than in pre-monsoon) as suggested by simultaneous decreasing trends of CTH and CGD. This thermodynamic effect had been used to explain the effect of aerosols on observed increasing trends of the CTH and CF (Li et al., 2016 and references therein). In dry season, all the three parameters: CTH, CBH as well as CGD exhibit decreasing trends (Fig. 8) revealing minimal invigoration. The correlation between CTT and AICCN for the liquid, mixed-phase and ice clouds (Table 4) are investigated adopting the method described in section 2.2.5. Regionally, significant (p < 0.001) negative correlation is observed between CTT and AICCN for warm (liquid) clouds (Fig. 9, Table 4). This further suggests presence of invigoration for liquid clouds. For mixed phase clouds CTT/CF increases/decreases significantly (95% confidence level) with increasing AICCN due to semi direct effect of dust, polluted dust and elevated smoke as discussed earlier (Section 3.1). Although, mixed phase clouds present the most favourable condition for invigoration (Rosenfeld et al., 2008), in our study inhibition due to the presence of absorbing aerosols is noticed. This is mainly attributed to elevated smoke as well as polluted dust. Further, the dust in South Asia behaves like smoke and suppress convection unlike in South America where dust tends to invigorate convection (Jiang et al., 2018). However, the insignificant negative/positive correlation of AICCN with CTP/CTT for DDC reveals weaker

Fig. 8. Variation of cloud top height (CTH), cloud base height (CBH) and cloud geometrical depth (CGD) during the study period (2006–2017) for pre-monsoon, monsoon and dry seasons.

freezing of smaller numerous number of droplets and the additional latent heat invigorates convection (Rosenfeld et al., 2008; Li et al., 2011). The increasing trends of CTH and CBH as well as CGD over the 17

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Fig. 9. Scatter plots showing the correlation between (i)AICCN (aerosol index as a proxy for cloud condensation nuclei) and cloud top temperature (CTT), (ii) AICCN and cloud-top pressure (CTP) and (iii)AICCN and cloud fraction (CF) for (a) liquid, (b) mixed-phase, (c) ice and (d) deep convective clouds (DCCs) over the study region.

significant negative correlation between the two for mixed phase clouds. Ice clouds are prevalent only during monsoon season when insignificant correlation of AICCN with CF (positive), CTT and CTP (negative) exist. It is hard to disentangle the aerosol impact on invigoration/inhibition of clouds, since these are regulated by meteorological factors as well as aerosol types (Jiang et al., 2018) and their vertical distribution (Pan et al., 2018). Therefore, further study on effect of the aerosol types on ACI is applauded.

Table 4 The correlation between (i) AICCN (aerosol index as a proxy for cloud condensation nuclei) and cloud top temperature (CTT), (ii) AICCN and cloud top pressure (CTP) and (iii) AICCN and cloud fraction (CF) for liquid, mixed-phase, ice and deep convective clouds (DCCs) over the study region. Correlation of

Liquid cloud

Mixed-phase cloud

Ice cloud

DCCs

slope (m) intercept (c) correlation (R) significance (P) slope (m) intercept (c) correlation (R) significance (P) slope (m) intercept (c) correlation (R) significance (P) slope (m) intercept (c) correlation (R) significance (P)

AICCN - CTT

AICCN - CTP

AICCN - CF

−3.79 8.41 −0.97 < 0.0001 6.06 −17.87 0.8 0.001 −1.25 −40.96 −0.24 0.47 0.29 −6.29 0.05 0.87

−71.77 747.29 −0.91 < 0.0001 68.39 434.07 0.77 0.003 7.97 239.86 0.15 0.65 −9.72 573.46 −0.13 0.68

0.31 0.23 0.98 < 0.0001 −0.04 0.77 −0.49 0.1 −0.02 0.95 −0.36 0.76 0.13 0.53 0.83 < 0.0001

4. Conclusion Extensive study on the vertical distributions of both aerosols and clouds along with their subtypes are carried out using CALIPSO observation during 2006–2017 over the north-eastern South Asia. Both CALIPSO and MODIS observations are utilised to investigate the aerosol-cloud interaction (ACI) further. Distinct seasonality in elevated aerosol layers (EALs) are observed throughout the year. These extended up to ~7.4 km in monsoon, associated with strong ascending motions of Walker and Hadley circulations of southwest monsoon. The EALs during the dry season is mainly attributed to long-range transportation of aerosols, primarily dust, polluted dust, and elevated smoke, while those in pre-monsoon and monsoon result from both long-range transportation and pile up of aerosols under strong convective activities. Dust, polluted dust, and elevated smoke aerosols were significantly present in the EALs. In addition, sizable amount of clean continental aerosols are found in the EALs during monsoon. The high clouds: transparent cirrus and deep convective clouds (DCCs) are prevalent in monsoon. The increase in cloud top height (CTH) with number of cloud layers, suggests invigoration effect, which is more in liquid cloud compared to mixed phase clouds or DCCs. ACI is found to be stronger in pre-monsoon (than in monsoon) when cloud condensation

invigoration effect of above mentioned absorbing aerosols, thereby increasing cloud fraction with AICCN. The DCCs, a fraction of mixed phase clouds is a vital contributor to the cloud invigoration effect that enhances convection and precipitation (Altaratz et al., 2014). Thus these clouds are driven by strong convection which fuels ACIs and are the major focus in the ACI studies (Tao et al., 2012). Unlike the present study, Niu and Li (2012) did not find any significant correlation between AOD and CTT over land globally for liquid cloud, but observed a 18

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

nuclei dominates over the region.

Tropical Convergence Zone: implications to regional climate. Tellus 63B, 971–989. https://doi.org/10.1111/j.1600-0889.2011.00580.x. Jiang, J.H., Su, H., Huang, L., Wang, Y., Massie, S., Zhao, B., Omar, A., Wang, Z., 2018. Contrasting effects on deep convective clouds by different types of aerosols. Nat. Commun. https://doi.org/10.1038/s41467-018-06280-4. Kandler, K., Lieke, K., Benker, N., Emmel, C., Küpper, M., Müller-Ebert, D., Ebert, M., Scheuvens, D., Schladitz, A., Schütz, L., Weinbruch, S., 2011. Electron microscopy of particles collected at Praia, Cape Verde, during the Saharan Mineral Dust Experiment: particle chemistry, shape, mixing state and complex refractive index. Tellus B 36 (4), 475–496. https://doi.org/10.1111/j.1600-0889.2011.00550.x. Kim, P.S., Jacob1, D.J., Fisher, J.A., Travis, K., Yu, K., Zhu, L., Yantosca, R.M., Sulprizio, M.P., Jimenez, J.L., Campuzano-Jost, P., Froyd, K.D., Liao, J., Hair, J.W., Fenn, M.A., Butler, C.F., Wagner, N.L., Gordon, T.D., Welti, A., Wennberg, P.O., Crounse, J.D., St Clair, J.M., Teng, A.P., Millet, D.B., Schwarz, J.P., Markovic, M.Z., Perring, A.E., 2015. Sources, seasonality, and trends of southeast US aerosol: an integrated analysis of surface, aircraft, and satellite observations with the GEOS-Chem chemical transport model. Atmos. Chem. Phys. 15, 10411–10433. https://doi.org/10.5194/acp-1510411-2015. Kim, M.H., Omar, A.H., Vaughan, M.A., Winker, D.M., Trepte, C.R., Hu, Y., Liu, Z., Kim, S.W., 2017. Quantifying the low bias of CALIPSO’s column aerosol optical depth due to undetected aerosol layers. J. Geophys. Res. Atmos. 122, 1098–1113. 2017. https://doi.org/10.1002/2016JD025797. Kim, M.H., Omar, A.H., Tackett, J.L., Vaughan, M.A., Winker, D.M., Trepte, C.R., Hu, Y., Liu, Z., Poole, L.R., Pitts, M.C., Kar, J., Magill, B.E., 2018. The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm. Atmos. Measure. Techniques. 11, 6107–6135. https://doi.org/10.5194/amt-11-6107-2018. Koren, I., Feingold, G., Remer, L.A., 2010. The invigoration of deep convective clouds over the Atlantic: aerosol effect, meteorology or retrieval artifact? Atmos. Chem. Phys. 10, 8855–8872. https://doi.org/10.5194/acp-10-8855-2010. Lau, K.M., Ramanathan, V., Wu, G.X., Li, Z., Tsay, S.C., Hsu, C., Sikka, R., Holben, B., Lu, D., Tartari, G., Chin, M., Koudelova, P., Chen, H., Ma, Y., Huang, J., Taniguchi, K., Zhang, R., 2008. The joint aerosol-monsoon experiment: a new challenge for monsoon climate research. Bull. Am. Meteorol. Soc. 89 (3), 369–383. https://doi.org/10. 1175/BAMS-89-3-369. Lawrence, M.G., Lelieveld, J., 2010. Atmospheric pollutant outflow from southern Asia: a review. Atmos. Chem. Phys. 10, 11017–11096. https://doi.org/10.5194/acp-1011017-2010. Levy, R.C., Mattoo, S., Munchak, L.A., Remer, L.A., Sayer, A.M., Patadia, F., Hsu, N.C., 2013. The Collection 6 MODIS aerosol products over land and ocean. Atmos. Measure. Techniques. 6, 989–3034. https://doi.org/10.5194/amt-6-2989-2013. Li, Z., Niu, F., Fan, J., Liu, Y., Rosenfeld, D., Ding, Y., 2011. Long-term impacts of aerosols on the vertical development of clouds and precipitation. Nat. Geosci. 4 (12), 888–894. https://doi.org/10.1038/ngeo1313. Li, Z., Lau, W.K.M., Ramanathan, V., Wu, G., Ding, Y., Manoj, M.G., Liu, J., Qian, Y., Li, J., Zhou, T., Fan, J., Rosenfeld, D., Ming, Y., Wang, Y., Huang, J., Wang, B., Xu, X., Lee, S.-S., Cribb, M., Zhang, F., Yang, X., Zhao, C., Takemura, T., Wang, K., Xia, X., Yin, Y., Zhang, H., Guo, J., Zhai, P.M., Sugimoto, N., Babu, S.S., Brasseur, G.P., 2016. Aerosol and monsoon climate interactions over Asia. Rev. Geophys. 54, 866–929. https://doi. org/10.1002/2015RG000500. Liu, B., Ma, Y., Gong, W., Zhang, M., 2017. Observations of aerosol color ratio and depolarization ratio over Wuhan. Atmos. Pollute. Res. 1113–1122. https://doi.org/10. 1016/j.apr.2017.04.004. Liu, Z., Omar, A., Hu, Y., Vaughan, M., Winker, D., 2005. CALIOP Algorithm Theoretical Basis Document-Part 3: Scene Classification algorithms. Release 1.0, PC-SCI-202 NASA Langley Research Center, Hampton, VA, pp. 56. Available online at: https:// www-calipso.larc.nasa.gov/resources/pdfs/PC-SCI-202_Part3_v1.0.pdf. Liu, Z., Vaughan, M.A., Winker, D.M., Kittaka, C., Getzewich, B.J., Kuehn, R.E., Omar, A., Powell, K., Trepte, C., Hostetler, C.A., 2009. The CALIPSO lidar cloud and aerosol discrimination: version 2 algorithm and initial assessment of performance. J. Atmos. Ocean. Technol. 26, 1198–1213. https://doi.org/10.1175/2009JTECHA1229.1. Liu, Z., Kar, J., Zeng, S., Tackett, J., Vaughan, M., Avery, M., Pelon, J., Getzewich, B., Lee, K.-P., Magill, B., Omar, A., Lucker, P., Trept, C., Winker, D., 2019. Discriminating between clouds and aerosols in the CALIOP version 4.1 data products. Atmos. Measure. Technique. 12, 703–734. https://doi.org/10.5194/amt-12-703-2019. Mace, G.G., Zhang, Q., 2014. The CloudSat radar-lidar geometrical profile product (RLGeoProf): updates, improvements, and selected results. J. Geophys. Res.: Atmosphere 119, 9441–9462. https://doi.org/10.1002/2013JD021374. Mao, F., Pan, Z., Henderson, D.S., Wang, W., Gong, W., 2018. Vertically resolved physical and radiative response of ice clouds to aerosols during the Indian summer monsoon season. Remote Sens. Environ. 216, 171–182. https://doi.org/10.1016/j.rse.2018.06. 027. Moorthy, K.K., Babu, S.S., Satheesh, S.K., 2007. Temporal heterogeneity in aerosol characteristics and the resulting radiative impact at a tropical coastal station - Part 1: microphysical and optical properties. Ann. Geophys. 25, 2293–2308. https://doi.org/ 10.5194/angeo-25-2293-2007. Moorthy, K.K., Beegum, N., Babu, S.S., Smirnov, A., John, S.R., Raghavendra Kumar, K., Narasimhulu, K., Dutt, C.B.S., Nair, V.S., 2010. Optical and physical characteristics of Bay of Bengal aerosols during W-ICARB: spatial and vertical heterogeneities in the marine atmospheric boundary layer and in the vertical column. J. Geophys. Res. 115, D24213. https://doi.org/10.1029/2010JD014094. Moorthy, K.K., Satheesh, S.K., Kotamarthi, V.R., 2016. Evolution of aerosol research in India and the RAWEX–GVAX: an overview. Curr. Sci. 111 (1), 53–75. https://doi.org/ 10.18520/cs/v111/i1/53-75. Murayama, T., Okamoto, H., Kaneyasu, N., Kamataki, H., Miura, K., 1999. Application of lidar depolarization measurement in the atmospheric boundary layer: effects of dust and sea-salt particles. J. Geophys. Res. 104 (D24), 31781–31792. https://doi.org/10.

Acknowledgments Authors are thankful to CALIPSO (https://www-calipso.larc.nasa. gov) and NASA Langley Atmospheric Science Data Centre (ASDC) (https://eosweb.larc.nasa.gov) science team and Giovanni (https:// giovanni.sci.gsfc.nasa.gov/giovanni/) science data team for the data support. PD is grateful to ISRO-GBP-ARFI for providing her with an assistantship. BP is a Junior Associate in the Abdus Salam International Centre for Theoretical Physics, Italy. PKB is a recipient of the Emeritus Fellowship awarded by the University Grants Commission of India. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.116882. REFERCENCES Ackerman, A.S., Toon, O.B., Stevens, D.E., Heymsfield, A.J., Ramanathan, V., Welton, E.J., 2000. Reduction of tropical cloudiness by soot. Science 288, 1042–1047. https://doi.org/10.1126/science.288.5468.1042. Altaratz, O., Koren, I., Remer, L.A., Hirsch, E., 2014. Review: cloud invigoration by aerosols-Coupling between microphysics and dynamics. Atmos. Res. 140–141, 38–60. https://doi.org/10.1016/j.atmosres.2014.01.009. Babu, C.A., Sivaprasad, P., 2014. Variability and mechanisms of vertical distribution of aerosols over the Indian region. Int. J. Remote. Sense. 35 (22), 7691–7705. https:// doi.org/10.1080/01431161.2014.975379. Bollasina, M.A., Ming, Y., Ramaswamy, V., 2011. Anthropogenic aerosols and the weakening of the south Asian summer monsoon. Science 334 (6055), 502–505. https://doi.org/10.1126/science.1204994. Bourgeois, Q., Ekman, A.M.L., Krejci, R., 2015. Aerosol transport over the andes from the amazon basin to the remote pacific ocean: a multiyear CALIOP assessment. J. Geophys. Res.: Atmosphere 120. https://doi.org/10.1002/2015JD023254. Dahutia, P., Pathak, B., Bhuyan, P.K., 2018. Aerosols characteristics, trends and their climatic implications over North-East India and adjoining South-Asia. Int. J. Climatol. 38, 1234–1256. https://doi.org/10.1002/joc5240. Fan, J., Wang, Y., Rosenfeld, D., Liu, X., 2016. Review of aerosol-cloud interactions: mechanisms, significance, and challenges. J. Atmos. Sci. 73, 4221–4252. https://doi. org/10.1175/JAS-D-16-0037.1. Gogoi, M.M., Moorthy, K.K., Babu, S.S., Bhuyan, P.K., 2009. Climatology of columnar aerosol properties and the influence of synoptic conditions: first-time results from the northeastern region of India. J. Geophys. Res. 114, D08202. https://doi.org/10. 1029/2008JD010765. Gogoi, M.M., Pathak, B., Moorthy, K.K., Bhuyan, P.K., Babu, S.S., Bhuyan, K., Kalita, G., 2011. Multi-year investigations of near surface and columnar aerosols over Dibrugarh, north-eastern location of India: heterogeneity in source impacts. Atmos. Environ. 45, 1714–1724. https://doi.org/10.1016/j.atmosenv.2010.12.056. Gogoi, M.M., Babu, S.S., Moorthy, K.K., Bhuyan, P.K., Pathak, B., Subba, T., Chutia, L., Kundu, S.S., Bharali, C., Borgohain, A., Guha, A., De, B.K., Singh, B., Chin, M., 2017. Radiative effects of absorbing aerosols over northeastern India: observations and model simulations. J. Geophys. Res. 122, 1132–1157. https://doi.org/10.1002/ 2016JD025592. Haarig, M., Althausen, D., Ansmann, A., Klepel, A., Baars, H., Engelmann, R., Groß, S., Freudenthaler, V., 2016. Measurement of the linear depolarization ratio of aged dust at three wavelengths (355, 532 and 1064 nm) simultaneously over Barbados. EPJ Web Conf. 18009https://doi.org/10.1051/epjconf/20161 1918009. 2016. Haywood, J., Boucher, O., 2000. Estimates of the direct and indirect radiative forcing due to tropospheric aerosols: a review. Rev. Geophys. 38 (4), 513–543. https://doi.org/ 10.1029/1999RG000078. Hong, G., Yang, P., Gao, B.C., Baum, B.A., Hu, X.A., King, M.D., Platnick, S., 2007. High cloud properties from three years of MODIS terra and aqua collection-4 data over the tropics. J. Appl. Meteorology. Climatology. 46, 1840–1856. https://doi.org/10.1175/ 2007JAMC1583.1. Huber, M., Knutti, R., 2011. Anthropogenic and natural warming inferred from changes in Earth's energy balance. Nat. Geosci. 5 (1), 31–36. https://doi.org/10.1029/ 1999RG000078. Hunt, W.H., Winker, D., Vaughan, M.A., Powell, K.A., Lucker, P.L., Weimer, C., 2009. CALIPSO lidar description and performance assessment. J. Atmos. Ocean. Technol. 26, 1214–1228. https://doi.org/10.1175/2009JTECHA1223.1. Intergovernmental Panel on Climate Change (IPCC), 2013. The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the IPCC, Climate Change 2013. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA 2013. Islam, M.N., Terao, T., Uyeda, H., Hayashi, T., Kikuchi, K., 2005. Spatial and temporal variation of precipitation in and around Bangladesh. J. Meteorol. Soc. Jpn. 83, 21–39. https://doi.org/10.2151/jmsj.83.21. Jai Devi, J., Tripathi, S.N., Gupta, T., Singh, B.N., Gopalakrishnan, V., Dey, S., 2011. Observation-based 3-D view of aerosol radiative properties over Indian Continental

19

Atmospheric Environment 215 (2019) 116882

P. Dahutia, et al.

Roca, R., Ramanathan, V., 2000. Scale dependence of monsoonal convective systems over the Indian ocean. J. Clim. 13, 1286–1298. Rogers, R.R., Vaughan, M.A., Hostetler, C.A., Burton, S.P., Ferrare, R.A., Young, S.A., Hair, J.W., Obland, M.D., Harper, D.B., Cook, A.L., Winker, D.M., 2014. Looking through the haze: evaluating the CALIPSO level 2 aerosol optical depth using airborne high spectral resolution lidar data. Atmos. Measure. Techniques. 7, 4317–4340. https://doi.org/10.5194/amt-7-4317-2014. Rosenfeld, D., Lohmann, U., Raga, G.B., O'Dowd, C.D., Kulmala, M., Fuzzi, S., Reissell, A., Andreae, M.O., 2008. Flood or drought: how do aerosols affect precipitation? Science 321, 1309. https://doi.org/10.1126/science.1160606. Rossow, W.B., Schiffer, R.A., 1999. Advances in understanding clouds from ISCCP. Bull. Am. Meteorol. Soc. 80, 2261–2288. https://doi.org/10.1175/1520-0477(1999). Sarangi, C., Tripathi, S.N., Mishra, A.K., Goel, A., Welton, E.J., 2016. Elevated aerosol layers and their radiative impact over Kanpur during monsoon onset period. J. Geophys. Res.: Atmosphere 121, 7936–7957. https://doi.org/10.1002/ 2015JD024711. Subrahmanyam, K.V., Kumar, K.K., 2013. CloudSat observations of cloud-subtype distribution over the Indian summer monsoon region. Ann. Geophys. 31, 1155–1162. https://doi.org/10.5194/angeo-31-1155-2013. Sugimoto, N., Matsui, I., Liu, X., Shimizu, A., Tamamushi, I., Asai, K., 2000. Observation of aerosols and clouds using a two-wavelength polarization lidar during the Nauru99 experiment. Sea Sky 76, 90–95. Tao, W.K., Chen, J.P., Li, Z., Wang, C., Zhang, C., 2012. Impact of aerosols on convective clouds and precipitation. Rev. Geophys. 50, RG2001. https://doi.org/10.1029/ 2011RG000369. Toth, T.D., Campbell, J.R., Reid, J.S., Tackett, J.L., Vaughan, M.A., Zhang, J., Marquis, J.W., 2018. Minimum aerosol layer detection sensitivities and their subsequent impacts on aerosol optical thickness retrievals in CALIPSO level 2 data products. Atmos. Measure. Technique. 11, 499–514. https://doi.org/10.5194/amt-11-499-2018. Vaughan, M.A., Powell, K.A., Kuehn, R.E., Young, S.A., Winker, D.M., Hostetler, C.A., Hunt, W.H., Liu, Z., McGill, M.J., Getzewich, B.J., 2009. Fully automated detection of cloud and aerosol layers in the CALIPSO lidar measurements. J. Atmos. Ocean. Technol. 26, 2034–2050. 2009. https://doi.org/10.1175/2009JTECHA1228.1. Wang, J., Rossow, W.B., 1998. Effects of cloud vertical structure on atmospheric circulation in the GISS GCM. J. Clim. 11, 3010–3029. Weare, B.C., 2000. Insights into the importance of cloud vertical structure in climate. Geophys. Res. Lett. 27 (6), 907–910. https://doi.org/10.1029/1999GL011214. Winker, D.M., Vaughan, M.A., Omar, A.H., Hu, Y., Powell, K.A., Liu, Z., Hunt, W.H., Young, S.A., 2009. Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Ocean. Technol. 26, 2310–2323. https://doi.org/10.1175/ 2009JTECHA1281.1. Winker, D.M., Pelon, J., Coakley Jr., , Ackerman, J.A., Charlson, S.A., Colarco, R.J., Flamant, P., Fu, Q., Hoff, R.M., Kittaka, C., Kubar, T.L., Le Treut, H., Mccormick, M.P., Mégie, G., Poole, L., Powell, K., Trepte, C., Vaughan, M.A., Wielicki, B.A., 2010. The CALIPSO mission: a global 3D view of aerosols and clouds. Bull. Am. Meteorol. Soc. 91, 1211–1229. https://doi.org/10.1175/2010BAMS3009.1. Winker, D.M., Tackett, J.L., Getzewich, B.J., Liu, Z., Vaughan, M.A., Rogers, R.R., 2013. The global 3-D distribution of tropospheric aerosols as characterized by CALIOP. Atmos. Chem. Phys. 13, 3345–3361. https://doi.org/10.5194/acp-13-3345-2013. Wylie, D.P., Menzel, W.P., 1999. Eight years of high cloud statistics using HIRS. J. Clim. 12, 170–184. Yamaguchi, T., Feingold, G., Kazil, J., 2017. Stratocumulus to cumulus transition by drizzle. J. Adv. Model. Earth Syst. 9, 2333–2349. https://doi.org/10.1002/ 2017MS001104. Young, S.A., Vaughan, M.A., 2009. The retrieval of profiles of particulate extinction from cloud aerosol lidar infrared pathfinder satellite observations (CALIPSO) data: algorithm description. J. Atmos. Ocean. Technol. 26, 1105–1119. https://doi.org/10. 1175/2008JTECHA1221.1. Young, S.A., Vaughan, M.A., Kuehn, R.E., Winker, D.M., 2013. The retrieval of profiles of particulate extinction from cloud-aerosol lidar infrared pathfinder satellite observations (CALIPSO) data: uncertainty and error sensitivity analyses. J. Atmos. Ocean. Technol. 30, 395–428. https://doi.org/10.1175/JTECH-D-12-00046.1. Young, S.A., Vaughan, M.A., Garnier, A., Tackett, J.L., Lambeth, J.D., Powell, K.A., 2018. Extinction and optical depth retrievals for CALIPSO's version 4 data release. Atmos. Measure. Techniques. 11, 5701–5727. https://doi.org/10.5194/amt-11-5701-2018. Yu, H., Chin, M., Winker, D.M., Omar, A.H., Liu, Z., Kittaka, C., Diehl, T., 2010. Global view of aerosol vertical distributions from CALIPSO lidar measurements and GOCART simulations: regional and seasonal variations. J. Geophys. Res. 115, D00H30. https://doi.org/10.1029/2009JD013364.

1029/1999JD900503. Nakajima, T.A., Higurashi, K., Penner, J.E., 2001. A study of correlation between satellitederived cloud and aerosol microphysical parameters. Geophys. Res. Lett. 28, 1171–1174. https://doi.org/10.1029/2000GL012186. Niu, F., Li, Z., 2012. Systematic variations of cloud top temperature and precipitation rate with aerosols over the global tropics. Atmos. Chem. Phys. 12, 8491–8498. www. atmos-chem-phys.net/12/8491/2012/. Omar, A.H., Won, J.G., Winker, D.M., Yoon, S.C., Dubovik, O., McCormick, M.P., 2005. Development of global aerosol models using cluster analysis of Aerosol Robotic Network (AERONET) measurements. J. Geophys. Res. 110, D10S14. https://doi.org/ 10.1029/2004JD004874. Omar, A.H., Winker, D.M., Kittaka, C., Vaughan, M.A., Liu, Z., Hu, Y., Trepte, C.R., Rogers, R.R., Ferrare, R.A., Lee, K.-P., Kuehn, R.E., Hostetler, C.A., 2009. The CALIPSO automated aerosol classification and lidar ratio selection algorithm. J. Atmos. Ocean. Technol. 26, 1994–2013. https://doi.org/10.1175/ 2009JTECHA1231.1. Padmakumari, B., Maheskumar, R.S., Morwal, S.B., Harikishan, G., Konwar, M., Kulkarni, J.R., Goswami, B.N., 2013. Aircraft observations of elevated pollution layers near the foothills of the Himalayas during CAIPEEX-2009. Q. J. R. Meteorol. Soc. 139, 625–638. https://doi.org/10.1002/qj.1989. Pan, Z., Gong, W., Mao, F., Li, F., Wang, W., Li, C., Min, Q., 2015. Macrophysical and optical properties of clouds over East Asia measured by CALIPSO. J. Geophys. Res.: Atmosphere 120 (22), 11653–11668. https://doi.org/10.1002/2015JD023735. Pan, Z., Mao, F., Gong, W., Min, Q., Wang, W., 2017. The warming of Tibetan Plateau enhanced by 3D variation of low-level clouds during daytime. Remote Sens. Environ. 198, 363–368. https://doi.org/10.1016/j.rse.2017.06.024. Pan, Z., Mao, F., Wang, W., Logan, T., Hong, J., 2018. Examining intrinsic aerosol-cloud interactions in South Asia through multiple satellite observations. J. Geophys. Res.: Atmosphere 123. https://doi.org/10.1029/2017JD028232. Papagiannopoulos, N., Mona, L., Alados-Arboledas, L., Amiridis, V., Baars, H., Binietoglou, I., Bortoli, D., D'Amico, G., Giunta, A., Guerrero-Rascado, J.L., Schwarz, A., Pereira, S., Spinelli, N., Wandinger, U., Wang, X., Pappalardo, G., 2016. CALIPSO climatological products: evaluation and suggestions from EARLINET. Atmos. Chem. Phys. 16, 2341–2357. https://doi.org/10.5194/acp-16-2341-2016. Pathak, B., Bhuyan, P.K., Gogoi, M.M., Bhuyan, K., 2012. Seasonal heterogeneity in aerosol types over Dibrugarh-north-eastern India. Atmos. Environ. 47, 307–315. https://doi.org/10.1016/j.atmosenv.2011.10.061. Pathak, B., Bhuyan, P.K., Biswas, J., Takemura, T., 2013. Long term climatology of particulate matter and associated microphysical and optical properties over Dibrugarh, North–East India and inter-comparison with SPRINTARS simulations. Atmos. Environ. 69, 334–344. https://doi.org/10.1016/j.atmosenv.2012.12.032. Pathak, B., Subba, T., Dahutia, P., Bhuyan, P.K., Moorthy, K.K., Gogoi, M.M., Babu, S.S., Chutia, L., Ajay, P., Biswas, J., Bharali, C., Borgohain, A., Dhar, P., Guha, A., De, B.K., Banik, T., Chakraborty, M., Kundu, S.S., Sudhakar, S., Singh, S.B., 2016. Aerosol characteristics in north-east India using ARFINET spectral optical depth measurements. Atmos. Environ. 125, 461–473. https://doi.org/10.1016/j.atmosenv.2015.07. 038. Porter, J.N., Clarke, A.D., 1997. Aerosol size distribution models based on in situ measurements. J. Geophys. Res. 102 (D5), 7936–7957. https://doi.org/10.1029/ 96JD03403. Prijith, S.S., Babu, S.S., Lakshmi, N.B., Satheesh, S.K., Moorthy, K.K., 2016. Meridional gradients in aerosol vertical distribution over Indian Mainland: observations and model simulations. Atmos. Environ. 125B, 37–345. https://doi.org/10.1016/j. atmosenv.2015.10.066. Prokop, P., Walanus, A., 2014. Variation in the orographic extreme rain events over the Meghalaya Hills in northeast India in the two halves of the twentieth century. Theor. Appl. Climatol. https://doi.org/10.1007/s00704-014-1224-x. Rahul, P.R.C., Bhawar, R.L., Ayantika, D.C., Panicker, A.S., Safai, P.D., Tharaprabhakaran, V., Padmakumari, B., Raju, M.P., 2014. Double blanket effect caused by two layers of black carbon aerosols escalates warming in the Brahmaputra River Valley. Sci. Rep. 4, 3670. https://doi.org/10.1038/srep03670. Ramachandran, S., Ghosh, S., Verma, A., Panigrahi, P.K., 2013. Multiscale periodicities in aerosol optical depth over India. Environ. Res. Lett. 8, 014034. https://doi.org/10. 1088/1748-9326/8/1/014034. Ratnam, M.V., Prasad, P., Raman, M.R., Ravikiran, V., Bhaskara Rao, S.V., Krishna Murthy, B.V., Jayaraman, A., 2018. Role of dynamics on the formation and maintenance of the elevated aerosol layer during monsoon season over south-east peninsular India. Atmos. Environ. 188, 43–49. https://doi.org/10.1016/j.atmosenv. 2018.06.023. 2018.

20