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Atmospheric Environment 44 (2010) 5295e5304

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Atmospheric Environment

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Radiative effects of aerosols at an urban location in southern India:Observations versus model

S.K. Satheesh a,b,*, V. Vinoj a, K. Krishna Moorthy c

aCentre for Atmospheric & Oceanic Sciences, Indian Institute of Science, Bangalore 560 012, IndiabDivecha Centre for Climate Change, Indian Institute of Science, Bangalore, Indiac Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram, India

a r t i c l e i n f o

Article history:Received 13 April 2010Received in revised form30 June 2010Accepted 13 July 2010

Keywords:AerosolsRadiative fluxesClimate changeRadiative forcingRadiation budget

* Corresponding author. Centre for AtmosphericInstitute of Science, Bangalore 560 012, India.

E-mail address: satheesh@caos.iisc.ernet.in (S.K. S

1352-2310/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.atmosenv.2010.07.020

a b s t r a c t

The radiative impact of aerosols is one of the largest sources of uncertainty in estimating anthropogenicclimate perturbations. Here we have used independent ground-based radiometer measurements madesimultaneously with comprehensive measurements of aerosol microphysical and optical properties ata highly populated urban site, Bangalore (13.02�N, 77.6�E) in southern India during a dedicated campaignduring winter of 2004 and summer and pre-monsoon season of 2005. We have also used longer termmeasurements carried out at this site to present general features of aerosols over this region. The aerosolradiative impact assessments were made from direct measurements of ground reaching irradiance aswell as by incorporating measured aerosol properties into a radiative transfer model. Large discrepancieswere observed between measured and modeled (using radiative transfer models, which employedmeasured aerosol properties) radiative impacts. It appears that the presence of elevated aerosol layersand (or) inappropriate description of aerosol state of mixing are (is) responsible for the discrepancies. Ona monthly scale reduction of surface irradiance due to the presence of aerosols (estimated using radiativeflux measurements) varies from 30 to 65 W m�2. The lowest values in surface radiative impact wereobserved during June when there is large reduction in aerosol as a consequence of monsoon rainfall.Large increase in aerosol-induced surface radiative impact was observed from winter to summer. Ourinvestigations re-iterate the inadequacy of aerosol measurements at the surface alone and importance ofrepresenting column properties (using vertical profiles) accurately in order to assess aerosol-inducedclimate changes accurately.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Aerosols are tiny suspended particles in the atmosphere thatperturb the Earth’s energy budget directly by scattering andabsorbing radiation (Bohren and Huffman, 1983; Coaklev et al.,1983; Ramanathan et al., 2001, 2007) and indirectly by modifyingthe cloud microphysics and cloud lifetime. The radiative impact ofaerosols remains one of the largest sources of uncertainties inestimating anthropogenic climate perturbations (IPCC, 2007),primarily due to the large heterogeneities in its physical andchemical properties in space and time. Thus region specific inves-tigations using measurements and modeling is the only means toimprove the understanding on aerosols. It has been shown that thepresence of absorbing aerosols can even change the sign of top of

& Oceanic Sciences, Indian

atheesh).

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the atmosphere (TOA) forcing from negative to positive (Coakleyand Cess, 1985; Kim and Ramanathan, 2008). Observationalstudies have documented the importance of absorbing aerosolseven over remote oceanic locations. The solar heating by absorbingaerosol (soot and dust) layers would alter the vertical temperaturestructure of the atmosphere and hence the atmospheric stability(Gautam et al., 2009;Moorthy et al., 2009). Studies have shown thatabsorbing aerosols can lead to lower atmospheric warming therebyreduce low level cloud fraction and hence can lead to net warmingwhose magnitude can exceed cooling by aerosols (Ackerman et al.,2000; Koch and Del Genio, 2010).

It is widely believed that the gaseous absorption and scatteringin the atmosphere is well understood (IPCC, 2007). However thereare observations of “anomalous or excess absorption” (Halthoreet al., 1998) where atmosphere absorbs more than predicted bymodels. Several investigators have speculated that an un-modeledabsorbing species might be the source of such discrepancies, butwere limited in their ability to verify this hypothesis either by use ofbroadband observations and calculations (Mlawer et al., 2000).

Table 1Details of the observational instruments used to make aerosol and radiationmeasurements.

S No Instrument Measured parameter From To Remark

1 Multi-WavelengthRadiometer (MWR)

Aerosol opticaldepth (10 channels)

2003 2008 continuousautomatic (every 2min on clear days)

2 Microtops sunphotometer

Aerosol opticaldepth (5 channels)

2001 2008 hand heldinstrument (clearsky only)

3 Magee ScientificAethalometer

Black Carbonmass concentration

2003 2008 5 min/4 LPMflow rate

4 Yankee ScientificShadow bandRadiometer

Direct, diffuse andtotal solar irradiance(W m�2) at surface

2004 2005 1 min interval(0.3e1.1 mmspectral interval)

S.K. Satheesh et al. / Atmospheric Environment 44 (2010) 5295e53045296

Black carbon aerosols and their presence in the atmosphere withdiffering states of mixing (external/internal/core-shell) is one of themajor absorbing components of aerosols (Jacobson, 2001; Duboviket al., 2002; Chandra et al., 2004). Large uncertainties prevail in itsglobal effect due to insufficient data and its high spatial andtemporal variability. Presently, a majority of aerosol radiativeimpact assessments are based on models (eg., Yu et al., 2006; IPCC,2007) both on local and global scales, which incorporate measuredaerosol properties. However, this approach involves severalassumptions, which would lead to significant uncertainties. On theother hand the determination of aerosol radiative effect directlyfrom measured radiative fluxes has advantages over model esti-mates, as the assumptions involved are minimal (Satheesh et al.,2006). The only assumption is with regard to the aerosol-freeatmosphere, with no assumptions made regarding the aerosolparameter (Satheesh et al., 1999, 2006).

Indian region due to its unique geographical location andweather, characterized by the reversing monsoonal circulation,transports the highly absorbing carbonaceous aerosols (of fossil fueland biomass burning origin) as well as those produced by desertdust to the oceanic regions during the winter seasons and advectmarine aerosols towards the continental region during summer(Kaskaoutis et al., 2009; Badarinath et al., 2010). In this paper, wehave used independent ground-based radiation measurements(radiative flux) made simultaneously with comprehensivemeasurements of aerosol microphysical and optical properties tomake assessments of aerosol radiative impact. Two approacheshave been followed one directly from solar fluxes and second frommicrophysical properties incorporated into radiation models. Thedata sets are used to quantify surface changes in irradiance (DFS,where subscript S represents the surface) or aerosol radiativeimpact at the Earth’s surface. The aerosol-induced surface changesin irradiance obtained from radiative fluxes have been comparedwith those estimated using a radiation model, which employedmeasured aerosol microphysical and optical properties. In bothcases, the same aerosol-free atmosphere was considered.

2. Site description and local meteorology

Measurements of the optical and microphysical properties ofaerosols concurrently with radiation flux were carried out at thenorthern Bangalore (13�N, 77.6�E) in India. The measurementswere made at a height of w10 m above ground level. Bangalore isone among the 50 largest cities by population around the worldwith a growth rate of 4.9% per annum making it one of the fastestgrowing cities in south Asia. It is located in southern centralpeninsular India at an altitude of w920 m above mean sea level(MSL). The population of the Bangalore metropolitan area is over5.7 million (and with the inclusion of the Bangalore rural, it reachesw6.8 million). With a very large vehicular population which standsat w2.8 million and the motorized 2-wheelers being the maingrowth category among vehicular population, they are a majorsource of anthropogenic emissions.

Measurements of meteorological parameters like temperature,pressure, relative humidity, wind speed and wind direction weremade continuously using the automatic weather station at the site.The climatological mean monthly rainfall pattern over Bangaloreshows that the monsoon commence significantly fromMay and theintensity peaks to a maximum (w170e180 mm) in September/October (Vinoj et al., 2004). As such, MayeOctober may beconsidered the rainy season in Bangalore (with rainfall greater than500 mm) and NovembereApril as the dry months. The stationreceives rainfall during both the summer (south-west) andretreating (or north-east) monsoon seasons with the highest rain-fall occurring during the latter (September and October period).

Winds are mostly westerly or easterly depending on season. Theweather at Bangalore is typical of tropics with the minimumtemperature varying between 16 and 21 �C and maximumtemperature between 26 and 33 �C. Being a tropical station, theannual variation of temperature in Bangalore is smaller than that ofthe diurnal variation. The diurnal variability lies withinw7.5 �Cew13 �C, whereas the annual variation on an average is lessthan 7 �C. The maximum temperature range (difference betweenthe maximum and minimum temperature) is observed duringJanuary and February and the minimum during JuneeAugust asa consequence of the heavy rainfall and a lower temperaturemaxima. The highest monthly mean temperature w27 �C occursduring April thereafter, the temperature decreases to a minimum ofw15 �C during winter.

3. Data and instrumentation

A summary of instruments used in this study are provided inTable 1. The aerosol optical depth (AOD) measurements wereregularly made using a multi-wavelength radiometer (MWR)designed and developed at the Space Physics Laboratory, VikramSarabhai Space Centre, India (Moorthy et al., 1999) as part of theIndian Space Research Organization-Geosphere Biosphere Program(ISRO-GBP). The MWR measures the field limited (2�) direct solarflux at 10 different wavelength channels centered at 380, 400, 450,500, 600, 650, 750, 850, 945 and 1025 nm (with a nominal fullwidth half maximum (FWHM) bandwidth of 5 nm) as a function ofthe solar zenith angle. More information on this instrument is givenelsewhere (Moorthy et al., 1997, 1998). In addition to MWR, a handheld portable Microtops Sun photometer was also used to makeAOD measurements, which yielded AOD at five wavelength chan-nels centered at 340, 380, 500, 675 and 870 nm with an FWHMbandwidth of 2e10 nm at different channels. The instrument hasa FOV of w2.5�. The measurements were made during clear solarvisibility conditions, when no clouds were present close to theSun’s disk. The Langley technique was employed (Shaw et al., 1973)to obtain AOD (as mean for forenoon and afternoon parts) usingMWR, while Microtops used its internal calibration. The overalluncertainty associated with the AOD retrieval using MWR is in therange of 0.02e0.05. The Microtops instrument is calibrated regu-larly at Mauna Loa observatory, Hawaii. The overall uncertaintyassociated with the AOD retrieval using Microtops instrument is inthe range of 0.01e0.03. Additional details on Microtops are avail-able elsewhere (Ichoku et al., 2002).

The black carbon (BC) mass concentration (ng m�3) measure-ments were made continuously using an Aethalometer (model AE-2 of Magee Scientific, USA) operating at a time base of 5 min andflow rate of 4 L min�1. It used a 10 mm sharp cut cyclone at the airintake part of the inlet tube. The instrument measures the

S.K. Satheesh et al. / Atmospheric Environment 44 (2010) 5295e5304 5297

attenuation of light transmitted through the aerosols which areaccumulated on a quartz fiber filter that acts as a perfect diffusescattering matrix with light absorbing particles embedded in it(Hansen et al., 1984; Hansen et al., 1993). There are several recentpapers dealing with the inherent uncertainties in the aethalometerderived BC mass estimates (for example, Babu et al., 2008). Theamplification of attenuation due to multiple scattering of lightpassing through the filter (which leads to over estimation of massloading) and the shadowing effects especially at high particle load(which occurs when higher attenuation thresholds are used) in thefilter tape (called the ‘C’ factor and ‘R’ factor respectively) are themajor issues (Weingartner et al., 2003; Arnott et al., 2005; Corrigenet al., 2006). In this respect, Weingartner et al. (2003) showed thatthe ‘R factor’ is quite significant for pure soot particles, whereas it isalmost negligible for aged aerosols. The maximum attenuation waskept at 50% with a view to reducing the effect due to loading of thetape so that effect of ‘R’ factor is very small. By inter-comparison ofaethalometer with other techniques Hitzenberger et al. (2006) hasshown that the average concentrations inferred by various tech-niques to measure BC (including the aethalometer) agrees wellwithin the instrumental uncertainties for aged aerosols (away fromsources). In the present study, the BC mass measurements weremade at a height of w10 m.

The surface reaching solar irradiance flux (W m�2) measure-ments were made using a rotating shadow band Single DetectorRadiometer (SDR) of Yankee Environmental Systems (YES) Inc. Therotating shadow band radiometer is a field instrument thatmeasures the global, diffuse and direct components of solar irra-diance. A microprocessor controlled shadow band alternatelyshades and exposes the instrument diffuser, enabling the system tomeasure all three irradiance components using the same detector.The shadow band is a strip of metal formed into a circular arc andmounted along a celestial meridian, with the instruments entranceaperture at the centre of the arc. The shadow band blocks a strip ofsky with a 3.3� umbral angle sufficient to block the Sun. It can bepositioned at an accuracy of 0.4� by the microprocessor controlledstepper motor. Once the instrument is aligned, no furthermechanical adjustment is necessary and the instrument operatesfor extended periods of time. The instrument used in the presentstudy is equipped with one broadband channel (0.3e1.1 mm). Table2 gives the mechanical and operational characteristics of theinstrument.

At each measurement interval, the instrument computes thesolar position using an approximation of the solar ephemeris. Thefirst measurement is made with the band rotated to its nadirposition (also called the home position) to obtain the global or totalirradiance. The band is then rotated to make three moremeasurements. The second measurement is made with the Suncompletely blocked; the other two measurements are made withthe band rotated 9� to either side of the Sun. These side measure-ments permit the system to correct for the excess sky that isblocked by the shadow band during the second measurement. The

Table 2SDR-1 instrument specification.

Characteristics Description

Spectral response 0.3e1.1 mmCosine response Better than 5% for 0e80� zenith

angle and 1% with correctionsTemperature range �30 to 50 �C (all photo diodes and

sensitive electronic components areheld inside a temperaturecontrolled enclosure

Accuracy of band positioning 0.4�

Umbral angle and shadow band 3.3�

corrected diffuse component value is subtracted from the globalirradiance to obtain the direct horizontal component. The directhorizontal component is then divided by the cosine of the solarzenith angle (qz) (available from the ephemeris calculation) tocompute the direct normal component. The instrument is pro-grammed to provide measurements at every 1 min interval.Detailed description of the instrument and its working principle isgiven elsewhere (Harrison et al., 1994; Kaskaoutis et al., 2008). SDR-1 measures the global and the diffuse flux by alternatively shadingthe diffuser from the direct radiation. The three components ofatmospheric radiation are related by

Iglobal ¼ Idiffuse þ IdirectcosðqzÞ (1)

where qz is the solar zenith angle. The direct flux is then calculatedusing the above relation.

4. Determination of aerosol radiative forcing: methodology

Aerosol radiative effect or forcing is the perturbation to theradiative fluxes of the Eartheatmosphere system caused by theaerosols. Direct aerosol radiative forcing is defined as the differencebetween net (downward minus upward) radiative flux for a clearsky atmosphere with aerosols and without aerosol. The aerosoldirect forcing (DF) is defined as,

DF ¼�FYa � F[a

���FYn � F[n

�(2)

where FYa and F[a are downward and upward solar fluxes at theEarth’s surface in the presence of aerosols and FYn and F[n are thesame quantities but for no aerosols case. We use terminology“diurnal aerosol forcing” to indicate the 24-h-averaged aerosolforcing. The value for DF is calculated for both surface as well as topof the atmosphere (TOA) when models are used. The atmosphericforcing is defined as,

DFAtmosphere ¼ DFTOA � DFSurface (3)

The radiative effects of aerosols are being widely reported basedon measurements made at the Earth’s surface or aircraft basedmeasurements and subsequently incorporated in models. Theseform one of themost important contributions to reduce the presentuncertainty in the aerosol radiative effect over the globe. Ona global scale, the aerosols tend to cool the planet, however,regionally their effects would vary largely (Satheesh and KrishnaMoorthy, 2005). Aerosol radiative forcing depends heavily on thescattering and absorption properties of aerosols which areprimarily governed by their size distribution and chemicalcomposition. Thus accuracy of modeling aerosol radiative forcingdepends on the aerosol model used in the estimation.

The most basic measured aerosol property with respect toradiative effect is the AOD, other important properties being thesingle scattering albedo (u), phase function (P), asymmetry factor(g) and their spectral variations. In addition, aerosol radiativeforcing is also dependent on the surface albedo and on the aerosolvertical distribution (McComiskey et al., 2008; Moorthy et al.,2009). Aerosol vertical distribution becomes more importantdepending on their absorption efficiency. A sensitivity analysisdemonstrating the role of some of the important factors, whichinfluence the aerosol radiative effect such as single scatteringalbedo, surface albedo, aerosol vertical distribution and relativehumidity are presented in this section. The surface reflectance orsurface albedo is one of the most important situational variables(depending on the geographical location) in assessing the aerosolradiative forcing. The presence of absorbing aerosols over high-

S.K. Satheesh et al. / Atmospheric Environment 44 (2010) 5295e53045298

reflecting surface would lead to positive TOA forcing (warming)(Hatzianastassiou et al., 2004).

In this sensitivity analysis, MODIS-derived land surface producton bidirectional reflectance distribution function products (Moodyet al., 2005) like black-sky albedo (BSA) (direct reflectance) andwhite-sky albedo (WSA) (bi-hemispherical reflectance) have beenused to model the surface reflectance characteristics required forthe radiative transfer calculation. MODIS provides WSA and BSA for7 different channels (0.47, 0.555, 0.659, 0.858, 1.240, 1.640 and2.100 mm) as well as for three broadbands (0.30e0.87 mm,0.70e5.0 mm and 0.30e5.0 mm). We here use the broad band0.30e5.0 mm to calculate the albedo at Bangalore for differentmonths. Actual albedo was calculated from BSA and WSA using thefollowing relation,

Actual Albedo ¼ WSA*DSF þ BSA (1 � DSF) (4)

where diffuse sky fraction (DSF) is a function of aerosol loading(aerosol optical depth) and solar zenith angle. Observed monthlymean AODs were used to derive DSF using a look-up table providedby MODIS land team (Schaaf et al., 2002).

The effect of surface reflectance with a constant aerosol model(continental polluted as described in Hess et al., 1998) was used(Fig. 1a). Relative humidity was kept constant at 50% and simula-tions were made for a tropical atmosphere and for the wavelengthrange from 0.25 to 4.0 mm. It was found that the TOA forcingchanges sign from negative to positive with increase in surfacereflectance. The effect of differing single scattering albedo (repre-sentative of different absorption characteristics of aerosols) and thecritical single scattering albedo where forcing changes sign topositive is shown (Fig. 1b). It brings out the important point that

Fig. 1. (a) The effect of surface reflectance on radiative forcing efficiency. Continental polluteconstant at 50% and simulations were made for a tropical atmosphere and for the wavelengchanges sign from negative to positive (at TOA) and thereby warming. (c) The effect of elevatmostly absorbing and sulphate aerosol which is a purely scattering in nature). (d) The efatmosphere and top of the atmosphere.

even mildly absorbing aerosols would lead to atmospheric warm-ing while located over highly reflective surfaces such as deserts orsnow (from Haywood and Shine, 1995; Chylek and Wong, 1995;McComiskey et al., 2008).

Over highly reflective surfaces the vertical distribution of aero-sols also plays an important role especially when the aerosols arehighly absorbing (such as black carbon). Under such conditions, thepresence of same aerosol layer at different altitudes changes theforcing characteristics (Fig. 1c). For a layer with highly absorbingaerosols, a moderate change in surface albedo can change the TOAforcing by as much as w50% while less than 3% at surface.

Aerosol radiative forcing is also dependent on the ambientrelative humidity. It is an important parameter that determines thedirect aerosol radiative forcing by its effect on AOD, single scat-tering albedo and scattering phase function by modifying theaerosol liquid water content, size and hence extinction coefficientand refractive indices. It is known that higher relative humidityenhances the hygroscopic aerosol growth (Pilinis et al., 1995;Kotchenruther et al., 1999; Kay and Box, 2000; Smirnov et al.,2000; Holben et al., 2001). Fig. 1(d) shows that the increase inrelative humidity leads to decrease in forcing efficiency (forcing perunit aerosol optical depth) both at the surface and atmosphere. Butthe corresponding aerosol growth due to the water uptake leads toincrease in the AODwhich leads to increase in absolute value of theforcing with increase in relative humidity.

4.1. Aerosol model for Bangalore

To estimate the aerosol radiative forcing from microphysicalmeasurements, an appropriate aerosol model needs to be evolved.This relies on information like aerosol chemical composition, size

d aerosol model as described in Hess et al. (1998) was used. Relative humidity was keptth range from 0.25 to 4.0 mm. (b) The critical single scattering albedo at which forcinged aerosol layers on surface and TOA forcing for two typical aerosol types (Black carbon,fect of increasing relative humidity on aerosol radiative forcing efficiency at surface,

S.K. Satheesh et al. / Atmospheric Environment 44 (2010) 5295e5304 5299

distribution and altitude distribution. Usually a hybrid approach isfollowed using the observations to constrain the aerosol model(Babu et al., 2002; Satheesh and Srinivasan, 2006). This initialassumption of aerosol model is based on standard models outlinedin Optical Properties of Aerosols and Clouds (OPAC) (Hess et al.,1998). The number density of each component is adjusted whilemaintaining themeasured parameters intact with the observations.This method has been widely used and reported in the literature(Babu et al., 2002; Satheesh, 2002; Vinoj and Satheesh, 2003; Vinojet al., 2004; Satheesh and Srinivasan, 2006 are examples). In thepresent study, we have used the measured aerosol black carbonmass (using aethalometer) as an anchoring point. Various aerosolspecies were iteratively varied until agreement (within 5%) wasreached between modeled and measured spectral AODs. Aerosolmodel was thus obtained, which is capable of reproducing themeasured spectral AODs and having the same amount of blackcarbon as measured. Aerosol spectral optical depths as well asspectral values of single scattering albedo (u) and phase function P(q) are used as inputs in Santa Barbara DISORT Atmospheric Radi-ative Transfer (SBDART) model (Ricchiazzi et al., 1998) to simulatesurface reaching solar irradiance for different solar zenith angles.The simulated irradiances are then used to calculate the diurnallyaveraged aerosol radiative forcing. The detailed methodology fol-lowed is available in literature (Babu et al., 2002; Satheesh, 2002;Satheesh and Srinivasan, 2002).

Aerosol models have thus been developed for the followingseasons; winter (December, January, February, DJF), pre-monsoon(March, April, May, MAM), monsoon (June, July, August, JJA) andpost-monsoon (September, October, November, SON). It wasobserved that during DJF, the aerosol BC contributes to around 5%,water soluble species (WASO) contributes to 30%, insoluble (INSO)to 21% and dust 44% to the composite aerosol mass (see Fig. 2).

Fig. 2. The mass fraction of different aero

Though the BC contributed only to 5% to the aerosol mass loading,its contribution to the composite aerosol optical depth (at 500 nm)is w14% (Fig. 3). This shows the importance of aerosol BC mass onthe optical and radiative effect over Bangalore. During the summer(JJA), the contribution of BC to the total mass (AOD) is 2% (7%).Though the aerosol BC is present only in very small fractions, theireffect on short wave radiation is significant.

The aerosol surface forcing (estimated using these aerosolmodels) was in the range from �40 to �30 W m�2 for differentseasons (Fig. 4). The highest surface forcing as well as atmosphericforcing was observed during the pre-monsoon period (MAM). Thisperiod is characterized by large aerosol AOD coupled with largesurface BC mass concentration. The lowest forcing (both surfaceand TOA) was observed during winter period (DJF). The moststriking feature is the positive TOA forcing during all the seasons. Ithas been shown in earlier sections that the presence of absorbingaerosols over more reflective surfaces (such as land) can change thesign of the TOA forcing. In the present study, it can be seen thatirrespective of the season, TOA forcing always remained positiveshowing the warming nature of the aerosols and consequently theatmospheric forcing remains always greater than 30 W m�2. Evenduring the monsoon season, in-spite of the removal of aerosols bywet scavenging by the wide spread rainfall, the TOA forcing ispositive. This is due to the fact that, BC aerosols which is the maincontributor to the absorption in the atmosphere is not completelyremoved due to the presence of a persistent local source (vehicularexhaust for example) which replenishes the BC mass loading in theatmosphere rather quickly. A similar exercise on a monthly scaleand the radiative forcing thus obtained is given in Fig. 5. Eventhough no clear seasonal variation trend in forcing was observed inFig. 4, the highest surface forcing as well as atmospheric forcingwas observed during April as evidenced in Fig. 5.

sol components for different seasons.

Fig. 3. The contribution of different aerosol components to total aerosol optical depth (at 500 nm).

S.K. Satheesh et al. / Atmospheric Environment 44 (2010) 5295e53045300

On a monthly scale, surface radiative forcing was as large as(w45 W m�2). The highest forcing was observed during April andlowest forcing was observed during June (w20 W m�2). It may benoted that the values discussed are based on the climatologicalobservations spanning a period from 2001 to 2008. Large changesin aerosol properties on inter-annual scale were observed at Ban-galore and this implies that the estimated values are only

DJF MAM JJA SON

-50

-40

-30

-20

-10

0

10

20

30

40

50

mW

(g

ni

cr

oF

ev

it

ai

da

Rl

os

or

eA

2-

)

Fig. 4. The aerosol radiative forcing for different seasons. (Surface forcing (black), TOA(white) and Atmospheric forcing (Grey).)

representative for different months, but can vary depending on thespecific nature of the aerosols during different years.

4.2. Observational estimate of aerosol radiative effect

With the above model-derived information in the backdrop, weexamine the direct measurements of surface radiative fluxes. The

Fig. 5. Monthly aerosol radiative forcing estimated for Bangalore at surface, TOA andatmosphere.

42.3 42.4 42.5 42.6 42.7 42.8

0

200

400

600

800

Time (Day)

m

W

(

e

c

n

a

i

d

a

r

r

I

t

c

e

r

i

D

2

- )

raw data

screened

& colocated

Fig. 6. A typical case of cloud screened data from SDR-1. Data collected on 14thNovember 2004.

S.K. Satheesh et al. / Atmospheric Environment 44 (2010) 5295e5304 5301

SDR-1 data (global, diffuse and direct irradiance) collocated withthe AOD data were used for the analysis to estimate the aerosolradiative forcing efficiency from measurements. The SDR-1 irradi-ance flux data is screened for cloud contamination followingSatheesh et al. (1999). The cloud-screened data is then collocatedwith the AOD measurements for further analysis (see Fig. 6).

The collocated aerosol optical depth and the radiation flux atsurface are used to estimate the surface aerosol forcing efficiency(or forcing per unit optical depth). Because of calibration uncer-tainties in surface radiation measurements, we followed slopemethod to calculate the aerosol forcing efficiency (method

Fig. 7. An example of the estimation of aerosol radiative

described in detail in Satheesh and Ramanathan, 2000). In thismethod the mean diurnal aerosol forcing (calculated from thedifference between measured and modeled aerosol-free fluxes) isplotted as a function of the daily mean aerosol optical depth. Theslope of the linear fit defines the aerosol forcing efficiency, whichcan be used to obtain the mean aerosol forcing for individual daysby multiplying with individual day AODs. This method assumesthat the aerosol optical properties (within a day) remain invariantduring the daytime. The advantage of this method is that aerosolforcing estimated is not influenced by calibration uncertainties. Itmay be noted that the assumption of invariant aerosol propertieswithin a day (especially AOD) is valid for this location as it has beenobserved that the variability in AOD within the daytime is less thanw5%, which is closer to the observational uncertainty of AODmeasurements.

The aerosol forcing efficiency (DF/Ds) has also been calculated forall the three radiation components reaching the surface (see Fig. 7for example). A large change in surface reaching irradiancecomponents with time is possible for similar values of AOD due tothe change in solar zenith angle within a day. Therefore, the surfaceflux measurements were normalized using the cosine of the solarzenith angle. Hereafter in this paper, the surface fluxes wouldmeannormalized surface fluxes corrected for change in solar zenith angle.These corrected irradiance components (global, diffuse and direct)along with AOD measurements are used to obtain the aerosolradiative forcing at the surface. The plot of aerosol optical depthversus aerosol radiative forcing yielded a straight line of slopewhichis a measure of aerosol forcing efficiency at surface (Fig. 8).

4.3. Aerosol radiative impact: measurements versus models

A comparison betweenmeasured andmodeled aerosol radiativeforcing efficiency is shown in Figs. 8 and 9 for two distinct seasons;

forcing efficiency for winter (Dec 2004eFeb 2005).

Fig. 9. Same as in Fig. 8 but for summer and pre-monsoon (MarcheMay 2005). Theobserved (dark line) and modeled (grey line) value of forcing efficiency.

S.K. Satheesh et al. / Atmospheric Environment 44 (2010) 5295e53045302

winter (Fig. 8) and summer and pre-monsoon (Fig. 9). It may benoted that aerosol forcing presented in Figs. 8 and 9 uses globalirradiance component (direct þ diffuse) and we followed ‘slopemethod’ described in Section 4.2, second paragraph. It was foundthat there exists large discrepancy in observed and modeled valuesof the forcing efficiency. The experimentally measured values wereconsistently larger than the modeled values for both the seasonsconsidered. In other words, model under estimates the rate ofincrease of aerosol forcing with aerosol optical depth. We examinethe possible causes for this and is discussed below.

These large differences between modeled and observed aerosolsurface radiative impact are not a unique feature of the Indianregion. Halthore et al. (1998) have shown that radiative transfermodels consistently overestimate diffuse downward irradiance atthe surface in a cloud-free atmosphere by 9e40%, while correctlycalculating direct normal solar irradiance. Chandra et al. (2004)reported that modeled surface diffuse radiative fluxes over-estimate the observations by �23 W m�2 over the tropical IndianOcean during the summer monsoon season. Comparison ofobserved surface diffuse fluxes in the present study with thoseestimated using radiation models have revealed that the modelconsistently overestimates diffuse fluxes by as much as35e45 W m�2. Simulations have shown that a single scatteringalbedo (SSA) as low as 0.60e0.65 is needed to reproduce theobserved (inferred frommeasured surface radiative fluxes) aerosol-induced flux changes. This is much lower (which means theabsorption is more) than the observed SSA at this location. Insummary, the radiation model (which employs observed aerosolmicrophysical and optical properties) consistently under estimatesaerosol-induced changes in irradiance while overestimating thediffuse fluxes at the Earth’s surface. Therefore it can be concludedthat aerosols over Bangalore absorb much more than predicted bythe radiation model even though models incorporate simulta-neously observed aerosol microphysical and optical properties.

This discrepancy between measured and modeled impact couldbe due to inadequate description of aerosols in radiation models.Jacobson (2001) has pointed out that when smaller aerosols accu-mulate over larger ones the radiative impact is significantlydifferent compared to that of an external mixture. Jacobson (2001)has also demonstrated that for a model in which aerosols aretreated in the form of ‘core-shells’ agrees well with observedsurface irradiance at Riverside and Claremont, USA. The possibility

Fig. 8. The aerosol-induced changes in irradiance at the Earth’s surface (0.3e1.1 mm) asa function of columnar aerosol optical depth (at 0.5 mm wavelength). The vertical barsrepresent uncertainties. The solid line is the least-square fitted line. The aerosol-induced changes in irradiance per unit optical depth (aerosol forcing efficiency) are theslopes. The correlation coefficient (r) is 0.89. Grey line represents modeled values ofirradiance as a function of aerosol optical depth.

of such a mixing of smaller anthropogenic aerosols on larger onescannot be ruled out in an urban environment. This shows thatadequate description of aerosol state of mixing could be one of theimportant factors to be considered while estimating aerosol radi-ative forcing over highly polluted urban environments.

It was also noticed that forcing efficiency values (estimated fromradiative flux measurements) differed by as much as 20% betweenwinter and summer. Previous investigations have revealed that theBC mass fraction is a more important parameter than its absolutevalue in the estimation of aerosol radiative effect (Babu et al., 2004).It was found that BC mass fraction was 3.6% during the winter incomparison to 5.2% during summer. The difference of w1.6% isinsufficient to make a difference ofw20% in forcing efficiency. Hereit may be noted that estimation of aerosol radiative impact frommodels incorporate aerosol microphysical and optical propertiesmade at the surface and therefore model assume homogeneousaerosol in the vertical column. Thus it is apparent that the presenceof elevated aerosols in summer and pre-monsoon season could beanother contributor to the difference between measured andmodeled values.

We examine the contribution of aerosols at different levels tothe column optical depth using data from a micro pulse lidar beingoperational at this site. In Table 3, we show quantitatively thecontribution to the aerosol optical depth above three differentheights (1, 2 and 3 km) for winter and summer seasons. A signifi-cant amount of aerosol optical depth is contributed by aerosolsabove 1 km. Thus, the presence of aerosols aloft with optical andradiative properties different from that at the surface can also bea reason for difference in observed aerosol forcing efficiencybetween winter and summer. Definitely more investigations onaerosol ‘state of mixing’ and vertical structure of aerosols areneeded in order to understand such ‘anomalous’ absorption byaerosols.

Table 3Percentage contribution of aerosol extinction to the column optical depth.

Range Level Winter Summer

1 km Below 31 12Above 69 88

2 km Below 74 41Above 26 59

3 km Below 88 64Above 12 36

S.K. Satheesh et al. / Atmospheric Environment 44 (2010) 5295e5304 5303

5. Summary and conclusions

Ground-based radiometer measurements made simultaneouslywith comprehensive measurements of aerosol microphysical andoptical properties at a highly populated urban site, Bangalore insouthern India as a part of dedicated campaign during winter of2004 and summer and pre-monsoon season of 2005. We have alsoused longer term measurements carried out at this site to presentgeneral features of aerosols over this region. The aerosol radiativeimpact assessments were made from direct measurements ofground reaching irradiance as well as by incorporating measuredaerosol properties into a radiative transfer model. Major conclu-sions are listed below.

1. Large discrepancies were observed between measured andmodeled (by incorporating measured aerosol properties)radiative impacts at the Earth’s surface.

2. It appears that the presence of elevated aerosol layers and (or)inappropriate description of aerosol state of mixing are (is)responsible for the discrepancies between measured andmodeled aerosol forcing.

3. Large increase in aerosol-induced surface radiative impact(estimated based on measurements of radiative fluxes) wasobserved fromwinter to summer and vary from 30Wm�2 to asmuch as 65 W m�2.

4. Our investigations re-iterate the inadequacy of aerosolmeasurements at surface alone and importance of representingcolumn properties (using vertical profiles) accurately in orderto obtain aerosol-induced climate changes accurately.

5. Our study also indicates that description of aerosol state ofmixing in radiative transfer models is important for accurateassessment of aerosol radiative impact over Indian region,which is unique in aerosol perspective as natural and anthro-pogenic aerosols co-exist.

Acknowledgement

Authors thank ARFI project of ISRO-GBP for supporting thiswork. One of the authors (SKS) would like to thank DST, New Delhifor Swarna Jayanti Fellowship awarded to him.

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