Quarterly Journal of the Royal Meteorological Society
Q. J. R. Meteorol. Soc. 139: 434–450, January 2013 B
Contrasting aerosol characteristics and radiative forcing over Hyderabad, India due to seasonal mesoscale and synoptic-scale processes P. R. Sinha,a * U. C. Dumka,a R. K. Manchanda,a D. G. Kaskaoutis,b S. Sreenivasan,a K. Krishna Moorthyc and S. Suresh Babuc a National Balloon Facility, Tata Institute of Fundamental Research, Hyderabad, India Research and Technology Development Centre, Sharda University, Greater Noida, Delhi, India c Space Physics Laboratory, Vikram Sarabhai Space Centre, Trivandrum, India *Correspondence to: P. R. Sinha, Tata Institute of Fundamental Research, National Balloon Facility, P.B. No 05, ECIL POST, X-Road, Hyderabad 500 062, India. E-mail:
[email protected] b
Regular measurements of spectral Aerosol Optical Depth (AOD) at ten wavelengths, obtained from multi-wavelength radiometer (MWR) under cloudless conditions in the outskirts of the tropical urban region of Hyderabad, India for the period January 2008 to December 2009, are examined. In general, high AOD with a coarse-mode abundance is seen during the pre-monsoon (March to May) and summer monsoon ˚ (June to September) with flat AOD spectra and low Angstr¨ om wavelength exponent (α), while in post-monsoon (October–November) and winter (December–February) seasons, fine-mode dominance and steep AOD spectra are the basic features. The aerosol columnar size distribution (CSD) retrieved from the spectral AOD using King’s inversion showed bimodal size distributions for all the seasons, except for the monsoon, with an accumulation-mode radius at 0.12–0.25 µm and a coarsemode one at 0.86–1.30 µm. On the other hand, the CSD during the monsoon follows the power law for fine mode and the unimodal distribution for coarse mode. The fine-mode aerosols during post-monsoon and winter appear to be associated with air masses from continental India, while the coarse-mode particles during pre-monsoon and monsoon with air masses originating from west Asia and western India. The single-scattering albedo (SSA) calculated using the OPAC model varied from 0.83 ± 0.05 in winter to 0.91 ± 0.01 during the monsoon, indicating significant absorption by aerosols due to larger black carbon mixing ratio in winter, whereas a significant contribution of sea-salt in the monsoon season leads to higher SSAs. Aerosol radiative forcing (ARF) calculated using SBDART shows pronounced monthly variability at the surface, top of atmosphere (TOA) and within the atmosphere due to large variations in AOD and SSA. In general, larger negative ARF values at the surface (−65 to −80 W m−2 ) and TOA (∼−17 W m−2 ) are observed during the pre-monsoon and early monsoon, while the atmospheric heating is higher (∼50–70 W m−2 ) during January-April resulting in heating rates c 2012 Royal Meteorological Society of ∼1.6–2.0 K day−1 . Copyright Key Words:
spectral AOD; aerosol size distribution; MWR; radiative forcing; back trajectories; Hyderabad
Received 5 March 2011; Revised 23 February 2012; Accepted 22 March 2012; Published online in Wiley Online Library 7 June 2012 Citation: Sinha PR, Dumka UC, Manchanda RK, Kaskaoutis DG, Sreenivasan S, Krishna Moorthy K, Suresh Babu S. 2013. Contrasting aerosol characteristics and radiative forcing over Hyderabad, India due to seasonal mesoscale and synoptic-scale processes. Q. J. R. Meteorol. Soc. 139: 434–450. DOI:10.1002/qj.1963
c 2012 Royal Meteorological Society Copyright
Aerosol Characteristics Over Hyderabad
1. Introduction Atmospheric aerosols play an important role in the Earth’s radiation budget by exerting direct and indirect radiative forcing of climate (e.g. Rosenfeld et al., 2001; Satheesh and Moorthy, 2005), contributing to significant heating in the troposphere (Gautam et al., 2010; Babu et al., 2011), influencing the general circulation patterns (e.g. Lau et al., 2006), hydrological cycle (Ramanathan et al., 2001a) as well as biochemical cycling (Xin et al., 2005). Because of the variety of sources, their short residence time and the dynamic processes that may alter their properties, aerosols can be divided into several types having different physical, chemical and optical properties (Kaskaoutis et al., 2009). To better understand the effect of aerosols on the Earth–atmosphere system, it is essential to study the optical and physical properties, which are controlled by aerosol size distribution and refractive index. Especially over the Indian subcontinent, aerosols continue to be a growing scientific issue, and several extensive field campaigns (e.g. INDOEX, ARMEX, LC-I, LC-II, ICARB and W-ICARB) using groundbased instrumentation, ship and airborne measurements have been conducted focusing on physical, chemical and optical aerosol properties as well as on their climate implications (Ramanathan et al., 2001b; Ganguly et al., 2005a; Moorthy et al., 2005, 2008, 2010). The aerosols over Hyderabad are both of natural, such as marine particles and mineral or desert dust, and anthropogenic origin, such as industrial emissions, fossil-fuel and bio-fuel combustion, automobile exhausts and biomass burning (e.g. Kaskaoutis et al., 2009). The anthropogenic aerosols originate either from gas-phase reaction of low volatile vapours in the atmosphere or by direct emissions, and are physically and chemically different from those in remote areas and over marine environments (e.g. Latha and Badarinath, 2005). The natural aerosols due to their dominant contribution evidently play a crucial role in the global climate whereas the anthropogenic aerosols play a crucial role in regional scales (Satheesh and Moorthy, 2005). On the other hand, under favourable meteorological conditions different types of aerosols could be advected over a region producing a consequent signature on the columnar aerosol optical depth (AOD) and optical properties (Moorthy et al., 1991, 2007; Kaskaoutis et al., 2010). Therefore, the columnar aerosol properties would be the resultant mixture of different aerosol types and would undergo seasonal changes associated with the synoptic meteorology, consequently impacting the radiative forcing (Kim et al., 2010). Viewed in the light of the above, the environment over Hyderabad constitutes an excellent atmospheric laboratory for examining the optical and microphysical aerosol characteristics, since it is affected by locally produced anthropogenic aerosols and naturally produced particles that travelled large distances before reaching the site. In addition, the seasonally changing air masses and the meteorological parameters strongly affect the aerosol load and properties over the urban site (Kaskaoutis et al., 2009). Therefore, studying aerosol characteristics over Hyderabad on a seasonal basis provides a view of aerosols over central-south India that can be inferred in the framework of the Aerosol Radiative Forcing over India (ARFI) project. Of the several parameters that are important in influencing the radiative impacts of aerosols over a given location, the most important is the spectral AOD, which is a strong c 2012 Royal Meteorological Society Copyright
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function of the abundance, size distribution and complex refractive index of the aerosols present in the vertical column of the atmosphere at that location. The size distribution and chemical composition, as well as their vertical distribution, strongly depend on the aerosol sources and the prevailing meteorology (Rastogi and Sarin, 2005; Ganguly et al., 2006). The main scope of the present study is to analyse the spectral AOD measurements carried out using a multiwavelength solar radiometer (MWR) over the tropical site of Hyderabad, India, during the period January 2008 to December 2009, and examine aerosol characteristics in terms of local, regional and far-off source impacts. Monthly mean columnar size distributions are retrieved by inverting the mean spectral AODs, and the physical properties relevant to radiative forcing are estimated. All the aerosol characteristics are examined in the view of the air-mass trajectories, mesoscale and synoptic-scale atmospheric processes in order to highlight the great seasonality in atmospheric composition over the site. The aerosol radiative forcing (ARF) was calculated over the region on a monthly basis by means of the SBDART model (see section 2). The results of the study are examined with estimations over other locations to delineate the regionally significant features. In contrast to the previous studies conducted over the region during limited time periods and specific events, i.e. dust transport and biomass burning, the present one is the first that analyses the aerosol physical and optical properties as well as climate implications over Hyderabad covering a two-year period, aiming to reveal a view of aerosols over central-south India. 2. Experimental site, database and analysis The urban area of Hyderabad is located between 17.10◦ N and 17.50◦ N latitude and 78.10◦ E and 78.50◦ E longitude (Figure 1) and is considered as one of the highly polluted cities in India (Beegum et al., 2009a). This is a direct result of the growth in population, number of motor vehicles and associated anthropogenic activities that have been observed during the last decades and the widespread industrial activities prevailing in the urban area. Furthermore, particles produced by natural sources are known to be transported over Hyderabad by seasonally changing circulation patterns. These include mineral or desert dust, the mass-burning of the crop residues, as well as forest fires occurring in the dry period of the year (Badarinath et al., 2007a, 2009). The aerosol measurements were carried out in the premises of the Tata Institute of Fundamental Research (TIFR) National Balloon Facility (NBF) campus located at ∼15 km from the urban centre at the northeast edge of the city, more representative of the suburbs and somewhat removed from the strong source regions (Figure 1). The spectral AOD has been estimated using MWR (Moorthy et al., 2007) that performs continuous measurements of directbeam solar irradiance at ten wavelength bands centred at 380, 400, 450, 500, 600, 650, 750, 850, 935 and 1025 nm. The full width at half maximum bandwidth is 5 nm with a band shape factor of 3 ensuring a near-uniform transmittance within the pass-band and a sharp reduction in the transmission beyond. The radiation is passed through field-limiting optics that limit the total field of view of the instrument to < 2o . Measurements were carried out mainly under cloudless sky conditions, or when no clouds were present in the vicinity of the Q. J. R. Meteorol. Soc. 139: 434–450 (2013)
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Figure 1. Terrain of Peninsular India and site map of the measurement location (NBF, TIFR) at the outskirts of Hyderabad.
Sun. More details about instrumentation, method of analysis and uncertainties are discussed elsewhere (e.g. Moorthy et al., 1997; Dumka et al., 2008; Gogoi et al., 2008, 2009) and the AODs retrieved using the MWR are compared with estimates made using several commercial sun-photometers with very good agreement (Kompalli et al., 2010). The spectral AODs were estimated using the Langley plot technique at each of the ten wavelengths. The presence of clouds, mainly in the monsoon, limits the radiation measurements; however, the present study focuses on the monthly averages and seasonal patterns of aerosol properties, and thus a sufficient number of spectral AODs are available for each month, except for the month of July that was omitted from the analysis. The 935 nm channel was used for estimating the columnar water vapour (CWV), along with the window channel of 1025 nm (Nair and Moorthy, 1998); these records were also used to correct for water vapour absorption at 850 nm, which is ∼0.005 (Gogoi et al., 2009). The typical error in the retrieved AOD is ∼0.01 excluding the variance of the Langley fit. The variance of the Langley intercept (typically 5%) along with other uncertainties, i.e. influence of Rayleigh scattering and absorption by ozone and trace gases (Gupta et al., 2003), increases the uncertainty in AOD in the range of 0.02–0.03. The optical depth due to ozone absorption was estimated in the band 450–700 nm using spectral absorption cross-section from MODTRAN (MODerateresolution atmospheric TRANsmission) and the tropical altitude profile of ozone. The uncertainties in the AOD for subtracting the ozone absorption are very small ( 0.92 were considered; thus, the number of available spectra was limited to 316. The spectral AOD values are also related to the aerosol columnar size distribution (CSD) through the Mie integral equation: rb τλ = π r2 Qext (m, r, λ) nc (r) dr, (4) ra
where Qext is the Mie extinction efficiency, which depends on the complex refractive index (m), particle radius (r) and wavelength (λ); nc (r) is the aerosol columnar number (1) ταλ = βλ−α density (in a vertical column per unit cross-section) in the where α is the wavelength exponent, which is a good radius range dr centred at r, while the ra and rb are the indicator of the particle size and fine-mode fraction to lower and upper cut-off radii of the particles, respectively, c 2012 Royal Meteorological Society Copyright
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such that the particles within this range contribute almost totally to the observed AOD spectra. Thus, ra and rb depend on the short and long wavelength limits of the AOD spectra (King, 1982). The ra and rb are selected by evaluating the kernels (integrand of Eq. (4)) for the extreme wavelengths used in the MWR, i.e. from visible (400) to near infrared (1025 nm) for different types of aerosol size distribution, and the best selection of values of ra and rb are found to be 0.05 and 3.0 µm, respectively. The CSD nc (r) is defined such that the number size distribution is height-invariant or averaged over the vertical column (King et al., 1978). nc (r) was retrieved from the spectral AODs by numerical inversion of Eq. (4) using the linear inversion method of King et al. (1978). The wavelengthdependent complex refractive index by Lubin et al. (2002) was used for the present study. The seasonal variation in refractive index is not considered in the inversion since it is based on the measurement of spectral solar radiation rather than the scattered sky radiances, and under such conditions the retrieved columnar size distribution shows a weak dependency on changes in the aerosol refractive index (Gogoi et al., 2009). The spectral AODs are then re-estimated using the direct Mie equation and are compared with the measured ones. The estimated CSDs are accepted only when the re-estimated AODs agree with the measured ones within the measurement errors. As the measurement errors are part of the inputs, the final CSDs are weighted by these errors around the size ranges sensitive to more accurate AOD measurements (King, 1982). In Eq. (4), spherical aerosols are assumed; thus, the presence of non-spherical dust aerosols would modify the results leading to underestimation of the mean and effective radius. More details about the application of the inversion algorithm to MWR data, errors and uncertainties are presented elsewhere (Moorthy et al., 1991; Saha and Moorthy, 2004; Satheesh et al., 2006; Dumka et al., 2009; Gogoi et al., 2009). For describing the radiative transfer properties of aerosols, the area-weighted mean or effective radius (Reff ) constitutes a key parameter. Reff is the radius of an equivalent monodispersive system exhibiting the same total scattering as a polydispersive one, and is defined as the ratio of the third moment to the second moment of the aerosol number size distribution: 3 3 r nc (r) dr . (5) Reff = 2 0.05 r nc (r) dr From the retrieved CSDs the columnar number density (NT ) and the columnar mass loading (mL ) of aerosols were also calculated as: 3 NT = nc (r) dr, (6) 0.05 4π d 3 3 r nc (r) dr, (7) mL = 3 0.05
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The ARF, either at the surface or at the top of atmosphere (TOA), is the change in the net radiative flux due to aerosols. The Santa Barbara Discrete ordinate Atmospheric Radiative Transfer (SBDART) model (Ricchiazzi et al., 1998) was used for estimating ARF over Hyderabad. The input parameters in the model are the AOD, α, single-scattering albedo (SSA), asymmetry parameter (g), columnar water vapour content (WVC) and ozone. The model is based on the discrete ordinate (DISORT) approach for a vertically inhomogeneous, non-isothermal, planeparallel atmosphere. The algorithm is known for its reliability and its computational efficiency for solving the radiative transfer equations and has been extensively used over different locations in India (e.g. Satheesh et al., 2002; Vinoj et al., 2004; Ramachandran et al., 2006; Moorthy et al., 2009; Pathak et al., 2010; Ramachandran and Kedia, 2010; among many others). The ARF calculations were performed in the short-wave (0.3–4.0 µm) solar spectrum, while the diurnally-averaged ARF values are used to compute the monthly means. Regarding the input parameters in SBDART, spectral AOD and α values were obtained from MWR, while the columnar ozone and WVC were taken from Ozone Monitoring Instrument (OMI) and Atmospheric InfraRed Sounder (AIRS), respectively. Furthermore, we have used the MWR spectral AOD and the Black Carbon (BC) measurements, from a seven wavelengths (370, 470, 520, 590, 660, 880 and 950 nm) Aethalometer (model AE42), as inputs to the Optical Properties of Aerosol and Clouds (OPAC) model (Hess et al., 1998) in order to calculate the SSA and asymmetry parameter (g) values on a monthly basis. The OPAC-derived parameters are shown in Table 1. The aerosol optical properties and ARF depend on size, shape and composition of the particles, as well as on ambient relative humidity (RH) that determines the growth rate of aerosols; the measured monthly mean values of RH were used for the ARF calculation. The uncertainty in the ARF computation using this method is about 10% (Bellouin et al., 2004) as ARF depends also on the surface reflectance, which is one of the major contributors to the ARF uncertainties (McComiskey et al., 2008). In order to eliminate such errors, the 8-day (L3 Global 500 m) surface reflectance values over Hyderabad were taken from MODIS at seven wavelengths from visible to infrared. More details about ARF calculations are given in section 4.4. 3. Regional and synoptic meteorology
The annual variation of the meteorological parameters is shown in Figure 2(a)–(c). The mean air temperature is high, ranging from 21◦ C in December to 33◦ C in May, while during the monsoon it is depressed (25–28o C) due to increased cloudiness. The RH exhibits high values (up to 70%) in the monsoon period, while in March–May (also called the dry season) the RH is the lowest (35–40%). −3 The atmospheric pressure presents lower values during the where d is the mean aerosol density defined as 2.2 g cm (Pruppacher and Klett, 1978). Furthermore, the columnar monsoon (not shown), favouring the uplift of moist air amount of accumulation (Na ) and coarse (Nc ) aerosols were masses and release of precipitation. Precipitation is nearly absent in the winter and pre-monsoon. In contrast, in calculated using 0.5 µm as threshold radius: the late monsoon (August–September) it is high, reaching 0.5 470 mm in August 2008, further reducing afterwards. Note Na = nc (r) dr, (8) also the great variability in the rainfall amounts between the 0.05 3 two years, able to cause significant variations in aerosol load nc (r) dr. (9) and properties between the two contrasting monsoon years Nc = 0.5 (Gautam et al., 2009). c 2012 Royal Meteorological Society Copyright
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Figure 2. Annual variation of (a) air temperature, (b) relative humidity and (c) monthly accumulated precipitation over Hyderabad during the period 2008–2009.
The flow chart of the wind speed (WS) and direction is shown in Figure 3 on a seasonal basis. The WS is generally low (1.2 (indicating accumulation-mode dominance) during post-monsoon and winter. Pre-monsoon and early monsoon are the favourable seasons for dust transport exposure from Arabia, Iran, Pakistan and northwestern India (e.g. Badarinath et al., 2007a; Prasad and Singh, 2007), enriching the atmosphere with coarse-mode aerosols and flattening the AOD spectral dependence. In addition, during the monsoon the α values are being mediated by the higher ambient RH, possible cloud contamination and long-range transport of marine Q. J. R. Meteorol. Soc. 139: 434–450 (2013)
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Figure 3. Seasonal variation of the flow chart of wind speed (m s−1 ) and direction over Hyderabad during the period 2008–2009. Winter: December–February, Pre-monsoon: March–May, Monsoon: June–September, Post-monsoon: October–November.
Figure 4. Monthly variation of the spectral AOD over Hyderabad during the period 2008–2009. The gap in the data series is attributed to lack of data in July.
air mixed with dust aerosols in certain circumstances. On the other hand, during winter the dust activity is at its lowest and the reduction in boundary-layer height traps the local aerosols and pollutants, enhancing the presence of fine-mode anthropogenic aerosols. In the post-monsoon, biomass-burning aerosols are produced in northern India due to crop residue burning, and the northerly winds carry them over Hyderabad (Badarinath et al., 2009), increasing the α values. The increased dust activity has a pronounced signature in β values, progressively increasing them from January (0.15) to June (0.39); in the rest of the year β remains close to 0.2. The mean values of α (1.08 ± 0.26) and β (0.24 ± 0.07) show relative dominance of accumulationmode aerosols within a turbid environment. c 2012 Royal Meteorological Society Copyright
In order to study the distribution of the aerosol properties in each season, the percentage frequency of occurrence for AOD500 , α and coefficient a2 are shown in Figure 6(a)–(c), respectively. The significant frequency for high (>0.7) AOD500 values (13.2% in winter, 20.9% during pre-monsoon, 21.4% in monsoon and 15.2% in post-monsoon) indicates that the atmospheric load over Hyderabad is usually high, directly affected by the local emissions and long-range transported aerosols. During winter the AOD distribution is skewed towards lower values (mode at 0.2–0.3), indicating more frequent occurrence of less polluted conditions. However, episodic occurrence of higher AODs between 0.4 and 0.6 and a few cases of AODs >1.0 are seen, which affects the seasonal mean Q. J. R. Meteorol. Soc. 139: 434–450 (2013)
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The frequency distribution of the coefficient a2 is shown in Figure 6(c), revealing large variability of aerosol types over Hyderabad. In general, the negative a2 values are predominant during winter and post-monsoon and the positive a2 in the rest of the year. It should be noted that the seasonal mean values are rather meaningless due to large standard deviations, but are given to provide information only about mean positive or negative values. During winter the majority of cases (53.1%) exhibit negative a2 , while there is a large fraction (26.5%) with a2 values within ± 0.1, suggesting negligible curvature. The wide range of a2 (−3.0 to 3.0) during pre-monsoon justifies the very differing aerosol types and CSDs in this season, also reported by Kaskaoutis et al. (2009). However, the vast majority of the cases (74.5%) exhibit positive curvatures characteristic of size distributions with a coarse-mode dominance; a similar Figure 5. Annual variation of α and β values over Hyderabad during the feature also exists in the monsoon with 75% of positive period 2008–2009. The vertical bars express one standard deviation from values. Therefore, the enhanced presence of dust in late prethe mean. monsoon and early monsoon as well as the strong marine influence during the monsoon has a clear signature in a2 . Similar to winter, post-monsoon presents a larger fraction AOD value (0.48 ± 0.26). During pre-monsoon the AOD (56.5%) of a2 < 0, but with more negative values, which are frequency distribution is progressively increasing up to ∼0.6, much greater for aged smoke aerosols due to particle growth while it drops dramatically for higher values. The seasonal by coagulation (Eck et al., 2003). mean of 0.57 ± 0.19 indicates a large aerosol burden in the atmosphere. The distribution in the monsoon is somewhat 5. Columnar size distribution between those of winter and pre-monsoon, while postmonsoon exhibits a mode value at 0.4–0.5 with larger It is important to understand the distribution of aerosols in frequency for high AODs. The frequency distribution of α400−1025 (Figure 6(b)) the vertical column under different atmospheric conditions reveals a great dispersion of the values in all seasons, and aerosol loading to investigate the aerosol types and thus denoting variability in the aerosol-size distribution their modification processes, e.g. coagulation, growth by and dominant aerosol types. During winter the α values aging and humidification, gas-to-particle conversion and are somewhat skewed towards higher values, while in the air masses with different compositions and histories in the monsoon towards lower. During winter, the mean value atmosphere, which can be inferred from the inversion of of 1.04 ± 0.29 indicates the dominance of sub-micron spectral AODs as it contains an imprint of the aerosol aerosols originating from bio-fuel burning and fossil-fuel CSD. The seasonal mean CSDs were retrieved following combustion sources mainly, while the surface inversion the numerical inversion technique discussed in section 2. traps the pollutants near the ground giving rise to hazy The left panels of Figure 7 show the measured spectral and foggy conditions (Madhavan et al., 2008). On the other AODs and the re-estimated ones from the retrieved CSDs, hand, the significant fraction (33.6%) of α < 0.9 indicates while the right panels present the retrieved CSDs and a well-mixed aerosol burden where the coarse aerosols play fitted log-normal and power-law distributions. The bestan important role. However, the fact that α becomes less fit CSDs are shown by dotted lines and from them than 0.6 only rarely (6%) in winter shows that the influence the rm1 , rm2 , size index and σ were estimated. The CSD of dust aerosols is very limited. During pre-monsoon α exhibits clear bimodal characteristics in winter (Figure 7(a)) ranges from very low (1.5) values, and post-monsoon (Figure 7(d)) with a primary peak at suggesting various aerosol sources, i.e. local emissions, accumulation mode (0.25 µm) and a secondary one at coarse biomass burning, soil dust erosion and/or transported desert mode (0.91 µm and 0.86 µm, respectively). The aerosol dust and marine particles. The lowest seasonal α value number density at accumulation mode is 177 and 131 (0.84 ± 0.49) in the monsoon is attributed to the combined times larger than that at coarse mode for winter and postinfluence of marine air masses (Figure 9) as well as advected monsoon, respectively. During pre-monsoon (Figure 7(b)) dust in this season (Kaskaoutis et al., 2009). During post- and monsoon (Figure 7(c)) the fine mode is not so explicit monsoon, the highest frequency is in the 1.0–1.1 interval and can be detected by the slanting curve towards lower with a similar possibility for higher and lower values. The α radius values. However, the CSDs in these seasons clearly distribution classifies the aerosols into two distinct types: a differ, as these are simulated by log-normal distributions fine mode with α > 1.1 and a coarse mode with α < 0.8. with two modes in pre-monsoon: The former is the result of the influence of crop residue 2 burning in north India, while the latter is attributed to N0i (lnr − lnrmi )2 (10) exp − n(r) = √ air masses coming from the west. The seasonal variation 2σi2 2π σi r i=1 of α is similar to those found over other Indian locations (Singh et al., 2004; Moorthy et al., 2007; Gogoi et al., 2009), highlighting the strong influence of the weather conditions and a combination of power-law (for fine mode) and and changing air masses on aerosol properties over the unimodal (for coarse mode) distributions in the monsoon, which is common in simulating the aerosol number size Indian subcontinent. c 2012 Royal Meteorological Society Copyright
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Figure 6. Percentage (%) frequency of occurrence for (a) AOD500 , (b) α and (c) coefficient a2 for each season over Hyderabad during the period 2008–2009.
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Figure 7. Aerosol CSDs (right panel) retrieved from the inversion of Eq. (4) for each season. The measured and re-estimated spectral AODs used for the CSDs retrievals are shown in the left panels. For each season ((a)–(d)) the fitted curves and the statistical parameters are plotted.
distribution (Gogoi et al., 2009): n(r) = N01 r−ν + √
(lnr − lnrm2 )2 , (11) exp − 2σ22 2π σ2 r N02
where N0i is the columnar aerosol number concentration, rmi and σi are the mode radius and the variance of each mode, respectively, while i = 1 represents the fine and i = 2 the coarse mode. ν is the power-law index (aerosol size index), varying from ∼2 to ∼5 for ambient aerosol distribution and is highly sensitive to biomass-burning and anthropogenic aerosols (Nair et al., 2008); high values of ν indicate the dominance of sub-micron particles. The ν value (4.02) found in the monsoon is comparable with those obtained over AS (3.8 to 4.2) during Integrated Campaign for Aerosols Trace Gases and Radiation Budget (ICARB) (Nair et al., 2008), indicating the significant influence of marine air masses in the CSD. The derived radius for the fine mode (r1 = 0.12 µm) in premonsoon compares well with the five-year (1996–2000) averaged records over coastal India (0.15 µm) and the tropical Indian Ocean (0.12 µm), but higher as compared to the Arabian Sea (0.10 µm) (Ramachandran and Jayaraman, 2002). Moorthy et al. (1991) found bimodal CSDs with coarse mode at 0.8 µm during the monsoon in Trivandrum (south coastal India) due to the influence of marine aerosols. Similarly, Moorthy and Satheesh (2000) reported strong influence of coarse-mode aerosols (r2 = 0.86 µm) in Minicoy island and attributed to sea-spray aerosols. Therefore, it is concluded that the enhanced presence of coarse-mode aerosols in pre-monsoon and monsoon over Hyderabad is closely associated with dust presence or marine aerosols or both. Furthermore, Gogoi et al. (2009) reported c 2012 Royal Meteorological Society Copyright
values for r1 and r2 of 0.11 ± 0.07 µm and 0.99 ± 0.10 µm in northeast India, expressing lesser variability than the present ones. Significant variations are also obtained in the spread of the log-normal fits, indicating large heterogeneities in aerosol sources and their strengths. In all seasons the σ1 is higher than σ2 indicating that the primary mode is broader than the secondary one. Figure 7 reveals that the CSDs during winter and postmonsoon are strongly bimodal whereas in the other two seasons they are different. This indicates well-defined limited aerosol sources in winter and post-monsoon and multiplicity of sources in pre-monsoon and monsoon seasons. This is clearly supported by the back trajectory analysis shown in section 4.3. The bimodal CSDs in winter and post-monsoon result in negative curvature of the lnAOD vs lnλ (Figure 6(c)). However, the bimodal CSD in pre-monsoon and the combination of the powerlaw and unimodal distributions in the monsoon result in positive curvature. The positive curvature is attributed to the increased contribution of the coarse aerosols in the near-infrared rather than that of the fine aerosols at shorter wavelengths (Schuster et al., 2006). Thus, despite the fact that the spectral AOD variation as well as the α values alone cannot give sufficient information about the particle size and CSD (O’Neill et al., 2001), the inversion method of King with the combination of the a2 estimates are valuable tools in revealing the aerosol size distribution and the dominant types, as also revealed over Trivandrum, southernmost India (Beegum et al., 2009b). The annual variation of the derived parameters (Reff , mL , NT , and Nc /Na ) is shown in Figure 8. Reff Q. J. R. Meteorol. Soc. 139: 434–450 (2013)
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Figure 8. Annual variation of the retrieved parameters (Reff , mL , NT , Nc /Na ) from the CSDs in the period 2008–2009.
presents increased values in pre-monsoon, peaking in May (0.64 ± 0.06 µm), as well as a higher value in August (0.72 ± 0.19 µm) that are similar to those found over Kanpur (Dey et al., 2004) during dusty periods. During postmonsoon and winter its values are below 0.45 µm, indicating presence of smaller particles. Progressively increased values for mL are shown till pre-monsoon, while in post-monsoon and winter they remain ∼200 mg m−2 , or even lower. NT exhibits a more complicated pattern, but with higher values in the period March–June. The ratio Nc /Na presents a similar variation as that of Reff , highlighting the stronger presence of coarse-mode particles during the pre-monsoon and monsoon seasons, except for June. It was found that the correlation between Nc /Na and Reff is associated with 77% of the variance, showing that Reff is a good indicator of the coarse-to-fine mode ratio; a similar feature was also reported by Gogoi et al. (2009). 5.1. Air mass trajectories In order to study the relation between the synoptic air masses and aerosol properties over Hyderabad, seven-day isentropic back trajectories at 2000 m above ground level (as 2000 m could be treated as the representative altitude for the aerosol transport in the free atmosphere) are analysed for all the days when MWR data were obtained, using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Rolph, 2003). The air masses are classified into groups following cluster analysis (Gogoi et al., 2009; Vinoj et al., 2010) for each season (Figure 9). During winter, 93% of the air masses originate from northern directions, originating mainly from the Indo-Gangetic Plains (IGP), where intense fog and pollution-haze conditions occur (e.g. Das et al., 2008). The lower altitude trajectories favour the transport of pollution aerosols over Hyderabad during winter; thus the relatively high AODs compared with those from other polluted urban locations (e.g. Cairo) during this season (El-Metwally et al., 2008). However, there is a small possibility for trajectories from western directions, originating mainly from Africa and driven by the western c 2012 Royal Meteorological Society Copyright
synoptic circulation pattern in northern midlatitudes. These trajectories traverse the arid regions of Arabia, Iran and Pakistan, carrying significant amounts of dust over AS and continental India on certain occasions (Badarinath et al., 2010). However, the low frequency of occurrence of such trajectories in winter (7%) does not favour considerable presence of coarse-mode particles, and the CSD is dominated by fine-mode aerosols (Figure 7(a)) either produced locally or transported from IGP. During the pre-monsoon period, the air masses are different, since they originate from different source regions and mainly from southeast Asia. Southeast Asia is known to produce fine-mode aerosols (Moorthy et al., 2003), which may be coated with other types during their long transport. The southwestern air masses from the Arabian Peninsula crossing the AS are rich in coarse-mode aerosols, since the dust activity in west Asia is at its maximum in the pre-monsoon (Prospero et al., 2002). Furthermore, in several cases (29%) the air masses come from arid locations in northwestern India. These continental air masses can also carry biomass-burning aerosols during the forest-fires period in early March–May. This leads to enhanced aerosol loading with increased concentrations of BC (Badarinath et al., 2007b). During the monsoon the air-circulation pattern is driven by the strong southwesterly winds. Although the air trajectories are clearly southwesterly with 100% of the air masses having an oceanic origin, transport of dust aerosols from the arid regions of west Asia occurs at high altitudes (Kaskaoutis et al., 2009). Thus, the marine and/or desert origin of the air masses leads to lower α, positive curvature and large coarse-to-fine mode fraction. Finally, in the transition season of post-monsoon, the air-mass trajectories exhibit a similar pattern to that of winter with low occurrence of air masses from west Asia and Africa (2%). Within the boundary layer, the air masses mainly originate from IGP, capable of transporting anthropogenic pollution and biomass-burning aerosols in certain cases, e.g. during residue crop burning. This contributes to the presence of fine-mode aerosols and higher α values. Q. J. R. Meteorol. Soc. 139: 434–450 (2013)
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Figure 9. Seven-day air-mass back-trajectory groups at Hyderabad using trajectory cluster analysis in the period 2008–2009. The values denote the percentage occurrence for each trajectory group in each season. Table 1. Composition of aerosol types and aerosol optical properties estimated from measured spectral AOD using OPAC model over Hyderabad during 2008–2009. Month
Aerosol component (%) (Vol. mixing ratio)∗ 100
January February March April May June July August September October November December
Aerosol optical properties (550 nm)
waso
inso
Soot
mitr
ssam
sscm
SSA
g
σsca
σabs
σext
47.6 55.5 50.7 51.1 54.6 52.3 – 47.3 50.9 53.1 53.9 50.3
44.6 36.7 40.9 33.1 26.9 38.4 – 31.5 41.7 43.1 40.6 46.1
7.76 5.83 4.62 2.37 2.06 1.19 – 1.91 2.04 3.05 4.91 3.17
0.06 1.89 1.07 12.97 15.82 – – – – 0.73 0.63 0.43
– – 2.63 0.42 0.58 8.07 – 18.79 5.42 – – –
– – – – – 0.11 – 0.48 0.03 – – –
0.779 0.832 0.846 0.892 0.905 0.922 – 0.913 0.902 0.880 0.844 0.871
0.667 0.670 0.676 0.681 0.680 0.685 – 0.704 0.699 0.694 0.689 0.695
0.265 0.358 0.414 0.463 0.422 0.475 – 0.284 0.394 0.368 0.383 0.381
0.075 0.072 0.075 0.056 0.044 0.040 – 0.027 0.043 0.050 0.071 0.056
0.340 0.431 0.490 0.519 0.466 0.515 – 0.311 0.437 0.418 0.454 0.437
waso: water soluble, inso: insoluble, mitr: mineral transported, ssam: sea salt accumulation mode, sscm: sea salt coarse mode, SSA: single-scattering albedo, g: asymmetry factor, σsca : scattering coefficient, σabs : absorption coefficient, σext : extinction coefficient.
5.2.
Single-scattering albedo and aerosol radiative forcing
ARF was also estimated over Hyderabad on a monthly basis for the years 2008–2009 using SBDART from measured and OPAC-calculated aerosol parameters as described in section 2. More specifically, externally mixed aerosol types (i.e. water soluble, insoluble, soot, mineral transported, sea salt) were taken as presented by Hess et al. (1998) for a continental/urban aerosol model, aiming to attain the best fit between measured and modelled spectral AODs. We have chosen the combination of species for the OPAC model depending on air-mass type. The measured BC values were used as input for the soot component in OPAC, and the number concentrations for the other aerosol types were adjusted iteratively until the spectral AODs and ˚ Angstr¨ om wavelength exponent were consistent with the observations (Das and Jayaraman, 2011). However, it is not the best approach to assess the aerosol composition for a c 2012 Royal Meteorological Society Copyright
given location but these are the options when no direct measurements are available and, once expressed with a range of variation, are useful to infer the order of magnitude of the impact to a first degree (Satheesh and Srinivasan, 2005). The volume mixing ratio values for all the aerosol components as well as the aerosol optical properties derived by OPAC are summarized in Table 1. The water-soluble aerosol component, mainly consisting of anthropogenic aerosols (i.e. ammonium, nitrate, chloride and sulphate), is the dominant type with contributions higher than 50% except for two months (January and August). The insoluble aerosols present a lower contribution in pre-monsoon and a higher one during the September–December period, while the sea-salt component (accumulation and coarse-mode) exhibits larger values in the monsoon and is nearly absent in the rest of the year. During the period April–May the mineral transported type presents a contribution of ∼13–16% due to dust transport. On the other hand, Q. J. R. Meteorol. Soc. 139: 434–450 (2013)
Aerosol Characteristics Over Hyderabad
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Table 2. SSA values of the present and other studies over the Indian subcontinent and adjoining oceans. Station
SSA (14.47◦
78.58◦
Hyderabad N, E) Ahmedabad (23.03◦ N, 72.5◦ E) Delhi (28.63◦ N, 77.17◦ E) Kanpur (23.43◦ N, 80.33◦ E) Bangalore (13◦ N, 77◦ E) Chennai (13.04◦ N, 80.17◦ E) TVM (8.55◦ N, 76.97◦ E) Pune (18.53◦ N, 73.85◦ E) Dibrugarh (27.3◦ N, 94.6◦ E) HR (27.59◦ N, 85.31◦ E) MP (29.37◦ N, 79.45◦ E) BoB WCI AS TIO SIO
W
PrM
M
PoM
References
0.83 ± 0.05 0.68 ± 0.04 0.74 ± 0.03 0.89 0.78 0.77 0.77 ± 0.04 0.76 0.77 0.8 ± 0.14 0.90 ± 0.03 0.88 ± 0.05 0.86 ± 0.08 0.89 ± 0.02 0.88 ± 0.11 0.95–0.99
0.88 ± 0.03 0.83 ± 0.08 0.63 ± 0.06 0.94 – – 0.80 ± 0.02 0.80 0.80 – – – – – – –
0.91 ± 0.01 0.93 ± 0.03 0.69 ± 0.07 0.96 – – 0.83 ± 0.02 – 0.80 – – – – – – –
0.86 ± 0.02 0.69 ± 0.04 0.72 ± 0.04 0.91 – – 0.84 ± 0.02 0.77 0.78 – – – – – – –
this study Ramachandran and Kedia (2010) Soni et al. (2010) Singh et al. (2004)∗ Babu et al. (2002) Ramachandran (2005) Babu et al. (2007) Panicker et al. (2010) Pathak et al. (2010) Ramana et al. (2004) Pant et al. (2006) Gogoi et al. (2010) Ramanathan et al. (2001b) Ramanathan et al. (2001b) Ramachandran and Jayaraman (2002) Ramanathan et al. (2001b)
W: Winter, PrM: Pre-monsoon, M: Monsoon, PoM: Post-monsoon ∗ Data reported for the year 2002 at 670 nm, TVM: Thiruvananthapuram, HR: Himalayan region, BoB: Bay of Bengal, AS: Arabian Sea, WCI: West coast of India, MP: Manora Peak, TIO: Tropical Indian Ocean, SIO: Southern Indian Ocean.
the soot component and SSA show a pronounced annual variation, with winter high (low) and monsoon low (high), respectively. During April–September, SSA is found to be higher (> ∼ 0.9) due to increased contribution of watersoluble aerosols, sea salt, mineral transported and lower soot concentrations (Table 1). The water-soluble aerosols may grow hygroscopically in size under high RH and contribute to the higher SSA values (Singh et al., 2004). The SSA value in January 2008–2009 (0.78) is similar to that found in Hyderabad (∼0.79) during the road campaign in February 2004 (Ganguly et al., 2005b). The seasonally averaged SSA values obtained over Hyderabad are summarized with those reported over different locations in the Indian subcontinent and adjoining oceanic regions in Table 2. Except for the small differences depending on location, all the studies agree with the lower SSA in winter and higher in the monsoon, indicating large BC mixing ratio and absorbing nature of aerosols in winter and particles of scattering type in the monsoon. Regarding the other aerosol properties derived from OPAC, the extinction and scattering coefficients increase from winter to pre-monsoon and decrease afterwards closely following the AOD variation, while the absorption coefficient is mainly controlled by SSA. The asymmetry parameter shows slightly higher values in the monsoon, suggesting more forward scattering by coarse particles. Figure 10 shows the monthly mean variation of the AOD500 obtained from both MWR and OPAC in 2008–2009. The AOD500 gradually increases from January to April and then decreases, exhibiting its lowest value in August. This value seems to be very low, but due to extensive cloudiness during this period, the number of clear-sky days is limited and occur mostly after rainfall, thus giving low AODs. The AOD500 variation over Hyderabad is similar to those presented over four Indian cities (Chennai, New Delhi, Mumbai and Kolkata) (Ramachandran, 2007). The OPACestimated AOD and its spectral variation (not shown) are close to the measured ones and within the standard error of the measurements. This gives credit to the optical properties obtained from the OPAC simulations (Table 1). c 2012 Royal Meteorological Society Copyright
Figure 10. Monthly mean MWR AOD500 in comparison with OPACderived AOD500 over Hyderabad for 2008–2009. The vertical bars in the MWR AOD500 denote the standard error of the monthly mean.
The surface albedo is an important parameter for the radiative transfer calculations, since elevated absorbing aerosol layers above highly-reflective surfaces can heat the lower atmosphere more, and change the sign of forcing from cooling to heating (Kinne and Pueschel, 2001). Eight-day MODIS-derived spectral reflectance values over Hyderabad have been used to model the surface reflectance (using a combination of sand and vegetation) required as input to SBDART. The monthly variation of the MODIS reflectance values at seven wavelengths is shown in Figure 11. The surface reflectance presents increasing values with wavelength up to 0.86–1.24 µm and lower values afterwards, while exhibiting a similar annual variation with enhanced values in the monsoon (mainly) and premonsoon (secondarily) and lower in winter. The surface reflectance is well below 0.2 (for short wavelengths) and 0.3 (for long wavelengths). Similar annual variation in the Q. J. R. Meteorol. Soc. 139: 434–450 (2013)
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Figure 11. Annual variation of 8-day MODIS-derived surface reflectance values at seven wavelengths in Hyderabad for 2008–2009. The vertical bars denote the standard deviation of the mean.
Figure 12. Monthly mean short-wave (0.2–4.0 µm) aerosol radiative forcing values (W m−2 ) at top of the atmosphere (TOA), in the atmosphere (ATM) and at the surface (SUR) at Hyderabad for 2008–2009.
MODIS-derived surface reflectance was also observed in Ahmedabad, located in arid northwestern India (Ganguly and Jayaraman, 2006). The monthly ARF at the surface ranges from–80 W m−2 (March) to −33 W m−2 (August), presenting more negative values in pre-monsoon and January (Figure 12). March is characterized by both high AOD (0.55) and soot component (4.62%), thus contributing to significant attenuation (scattering and absorption) of solar radiation at the surface. Similarly to the surface, TOA forcing exhibits more negative values (cooling) in the April–June period (−15 to −17.5 W m−2 ) and lowest forcing in winter. ARF values depend strongly on the scattering and absorbing aerosol properties, which are governed by their size distribution and chemical composition. The aerosols over Hyderabad can significantly heat the atmosphere and, more particularly, the lower troposphere where they are more abundant (Badarinath et al., 2009). The atmospheric heating is controlled by the aerosol load and absorbing capability of aerosols and is ∼55 W m−2 in the period January to April, except for the higher value (70 W m−2 ) in March. The enhanced presence of BC aerosols in winter, associated with the lower boundary-layer height and the absence of precipitation, plays an important role in the large atmospheric forcing values also found over continental India(Ganguly et al., 2005b). In contrast, in the monsoon and post-monsoon the atmospheric forcing is much lower. Several studies have calculated the ARF over continental India and adjoining oceanic regions resulting in significant differences in the regional ARF estimates, mainly due to the large heterogeneity in aerosol properties, mixing processes and types (e.g. Ganguly et al., 2005a; Satheesh et al., 2006; Moorthy et al., 2009; among many others). During the road campaign (February 2004), the ARF over Hyderabad was found to be ∼−40 W m−2 at the surface and ∼−5.5 W m−2 at TOA, higher than those found over remote areas in continental India (Ganguly et al., 2005b; Jayaraman et al., 2006). Regionally averaged net ARF over the Bay of Bengal was in the range of −15 to −24 W m−2 at the surface and −2 to −4 W m−2 at TOA during February 2003 (Vinoj et al., 2004). Kim et al. (2010) calculated a mean ARF of −27.5 ± 9.2 W m−2 at the surface in Gosan, Korea in
the period 2001–2008; the respective mean ARF at TOA was −15.8 ± 4.4 W m−2 , causing an atmospheric heating of 11.7 ± 5.8 W m−2 . These lower ARF values are attributed to the much lower AODs and scattering types of aerosols over the Bay of Bengal and Gosan compared to those found over Hyderabad. Satheesh et al. (2010) found ARF values in the range of −40 W m−2 to −20 W m−2 (at the surface) in Bangalore and atmospheric heating in the range of 20–45 W m−2 . While the annual variation of ARF is somewhat similar over the two sites (Hyderabad and Bangalore) presenting increased ARF in the pre-monsoon, the TOA forcing was positive in Bangalore indicating enhanced presence of more absorbing aerosols. The large differences in ARF between surface and TOA suggest the presence of light-absorbing aerosols, which are consistent with relative low SSA values. The ratio (F) of the surface to the TOA ARF is an important indicator of the aerosol type. The value of F > 3 correspond to strong influence of absorbing aerosols, while values 3, even above 6.9 in the December–March period, indicating a large presence of absorbing aerosols. Podgorny et al. (2000) reported values below 4, while Satheesh and Ramanathan (2000) had values >3 during INDOEX, similar to our values in the monsoon, suggesting the strong influence of the marine air masses (Figure 9). The absolute ARF values are strong functions of the AOD; thus normalizing the ARF with AOD delineates the aerosol efficiency to attenuate the solar radiation and, therefore, is an indicator of the aerosol type and aerosol absorption efficiency. The forcing efficiency at the surface shows, in general, more negative values in winter, while in the atmosphere more positive values for the same period (Table 3), attributed to the absorbing aerosols. At TOA the annual variation of the forcing efficiency is more pronounced, with more negative values (
