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Chapter 5

Estimation of Aerosol radiative forcing

Radiative forcing due to aerosols is one of the major uncertainties in estimating

anthropogenic climatic perturbations (Charlson et al., 1992; Houghton et al., 1995; IPCC

2001). This is mainly due to the large scale spatial and temporal variabilities of aerosols

and the lack of adequate database existing on their radiative properties (Russell et al.,

1997; Bates et al., 2006; Bhawar and Devara, 2010). Aerosol radiative forcing is

generally classified as direct and indirect in which the direct aerosol radiative forcing

(DRF) is induced by scattering and absorption of solar radiation in a cloud free sky.

Light scattered by aerosols results in a negative radiative forcing (cooling effect) and

light absorbed by the particles lead to a positive forcing (heating effect) (Haywood and

Shine, 1995; Ramanathan et al., 2001a, 2001b). On a global average basis, the DRF at

the top of the atmosphere induces a negative forcing, offsetting the positive radiative

forcing produced by greenhouse gases. For greenhouse gases, their concentration,

distribution and radiative properties are well known, while the quantification of aerosols

is still uncertain (IPCC, 2007). In the indirect radiative forcing aerosols modify the

microphysical properties of clouds and thereby modulate the radiative properties of

clouds and cloud life time (Twomey, 1977; Penner et al., 2004). If absorbing aerosols are

present in higher atmospheric layers radiative heating on these layers can change the

temperature gradient, and low level clouds are evaporated (Hansen et al., 1997). The

gravity of this effect depends on the altitude of clouds (Satheesh, 2002; Satheesh and

Moorthy, 2005). When a cloud layer is present above aerosols most of the incident

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radiation will be reflected back and only a small fraction interact with aerosols, while the

cloud layer is below the aerosol layer, the aerosols modify not only the incident radiation

but also the reflected radiation from the cloud layer below. The various radiative

mechanisms associated with cloud mechanisms are depicted in figure.5.1.

Figure 5.1: Diagrammatic representation of radiative mechanisms associated with cloud effects. The small black dots represent aerosols and open circles cloud droplets. CDNC refers to the cloud droplet number concentration and LWC the liquid water content (IPCC, 2007)

Aerosol radiative forcing at any layer is defined as the difference in the net fluxes

(down minus up) with and without aerosols, present in that layer. The effect of aerosols

on the top of the atmosphere (TOA) radiative flux is TOA radiative flux. Similarly, the

effect of aerosols on the surface radiative flux is surface (S) radiative forcing. The

difference between TOA forcing and surface forcing is atmospheric radiative forcing.

Hence

, , ,( ) ( )S TOA a a S TOA o o S TOAF f f f fΔ = ↓ − ↑ − ↓ − ↑ (1)

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Where fa↓ and fa↑ denotes the downwelling and upwelling irradiance (W m-2) with

aerosols and fo↓ and fo↑ denotes the respective quantities (in W m-2) without aerosols

and,

( )TOA S ATMF F FΔ −Δ = Δ (2)

There are several methods for the estimation of aerosol radiative forcing. Aerosol

samples collected on filter papers are chemically analyzed to obtain the mass

concentration of various aerosol species. These are then converted to number distribution

and subsequently to optical depths using Mie scattering theories and forcing are

calculated using suitable radiative models (Satheesh and Srinivasan, 2006). Since the

surface aerosol properties are quite different from column aerosol properties the

estimated forcing magnitudes can cause large errors. Alternately the measured radiant

flux under clear sky conditions is subtracted from the stimulated aerosol free radiative

flux to estimate aerosol radiative forcing. Once the aerosol properties like AOD,SSA and

ASP are precisely retrieved aerosol radiative forcing can be computed with the aid of

efficient software tools like SBDART model (Ricchiazzi et al., 1998) or RRTM_SW

(Rapid Radiative Transfer Model Shortwave), (Clough et al., 2005). Aerosol radiative

forcing primarily depends on variables affecting the environment like surface albedo and

on the vertical distribution of aerosols. Several studies have revealed global radiative

forcing by individual aerosol components, such as sulphate (Penner et al., 1998),

carbonaceous aerosols (Chung and Seinfeld, 2002), sea salt (Gong et al., 1997), and

mineral dust (Tegen 2003). Multiple aerosol components have also been simulated

simultaneously in global models (Jacobson, 2001). In this section we present the

instantaneous aerosol radiative forcing and its sensitiveness to various parameters with a

simple analytical model as suggested by Haywood and Shine (1995). The direct Aerosol

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Radiative Forcing in the short wave range (0.25 - 4µm) has also been estimated on clear

sky days in the month of April, August, October and December using SBDART model.

5.1 Simple analytical model

An aerosol layer can scatter and absorb solar radiation. Scattering aerosols cool

the atmosphere whereas the absorbing aerosols heat it. The overall effect of the aerosol

layer is decided by the surface reflectance and the nature of aerosols.

5.1.1 Theory

For an aerosol layer of optical depth τ and if we assume that the solar beam is

directly overhead, the fraction of the incident beam transmitted through the layer is .

The fraction of light getting reflected back in the direction of the beam

(1 )zr e ωβ−= − (3)

where ‘ω’ is the single scattering albedo and ‘β’ the back scattering fraction. The fraction

of light absorbed within the layer

(1 )(1 )za eω −= − − (4)

and the fraction scattered downward is

(1 )(1 )zs eω β −= − − (5)

The total fraction of radiation that transmits downward is

(1 )(1 )z zt e eω β− −= + − − (6)

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If Rs is the reflectance of the Earth’s surface, the fraction of radiation reflected is Rs * t.

On the first upward pass of the reflected beam the fraction transmitted is also‘t’ and

hence The fraction of this beam reflected back to Earth is ‘r* Rs * t’, and transmitted

through the aerosol layer is t2 Rs. This process continues and for Rs and r < 1, the

change in reflectance due to the presence of aerosol layer is

2

1s

p ss

t RR r RR r

⎡ ⎤Δ = + −⎢ ⎥−⎣ ⎦ (7)

The sign of the change in planetary albedo due to presence of aerosol layer ∆Rp

determines whether the forcing is negative (cooling) or positive (heating). The key

parameter leading to cooling or heating is the SSA (ω) and the magnitude of ω at which

∆Rp = 0 defines the boundary between heating and cooling. For small τ it can be shown

that this boundary value ω crit is

2

22 (1 )

scrit

s s

RR R

ωβ

=+ − (8)

Figure 5.2 shows the dependence of ω crit on surface reflectance Rs and backscattering

factor β. For a mean surface albedo about 0.3, and for a representative value of the

spectrally and solar zenith angle averaged β of about 0.29, the critical value of ω is about

0.65. If Ta denotes the fractional atmospheric transmittance above the aerosol layer

which differ from unity due to Rayleigh scattering and absorption by ozone and other

gases of the atmosphere and F0 the incident flux, the change in the outgoing radiative

flux as a result of an aerosol layer underlying an atmospheric layer is

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Surface Reflectance0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Sin

gle

Sca

tterin

g A

lbed

o

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4β=0.1 β=0.2 β=0.3β=0.4 β=0.5

Figure 5.2: Critical single scattering albedo as a function of surface reflectance and back scattering factor

22

0 [( ) ]1

sa s

s

t RF F T r RR r

Δ = + −− (9)

For purely scattering aerosols and for small AOD, (τ«1) under cloud free conditions the

direct aerosol radiative forcing at the top of the atmosphere is (Charlson et al., 1991,

1992)

2 2

0 ( ) ( ) (1 ( ))TOA a sF F T Rλ λ βτ λΔ = − − (10)

and for the combination of absorbing and non-absorbing aerosols, equation(10) can be

approximated (Haywood and Shine, 1995) to

2 2

0 ( ) ( )[ (1 ( )) 2 (1 )]TOA a s sF F T R Rλ λ ωβτ λ τ ωΔ = − − − − (11)

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The first term in the equation is the negative forcing (cooling) due to up scattering,

whereas the second term represents positive forcing (heating) owing to the absorption by

the aerosols. ∆F can either be positive or negative depending on the relative value of

single scattering albedo, surface reflectance or back scattering fraction. The sensitiveness

of ∆FTOA to the controlling factors like τ, ω and Rs is given by

( )22

0 1 2 (1 )TOAa s s

F F T R Rβω ωτ

∂ ⎡ ⎤= − − − −⎣ ⎦∂ (12)

( )22

0 1 2TOAa s s

F F T R Rτ βω

∂ ⎡ ⎤= − − +⎣ ⎦∂ (13)

( )2

02 1 (1 )TOAa s

s

F F T RR

τ βω ω∂= − + −⎡ ⎤⎣ ⎦∂ (14)

In contrast to the forcing which can either be positive or negative, ∂F/∂ω is always

negative and ∂F/∂Rs is always positive. The radiant flux absorbed by the aerosol layer

can be approximated to

0 (1 ) (1 )ATM a sF T F Rω τΔ = − +

(15)

The sensitiveness of ∆F ATM to the respective controlling factors are

[ ]0 (1 ) 1ATM

a sF T F Rωτ

∂= − +

∂ (16)

[ ]0 1A TM

a sF T F Rτω

∂= − +

∂ (17)

0 (1 )A T M

as

F T FR

ω τ∂= −

∂ (18)

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The magnitude of the instantaneous forcing can be greater than that for diurnally

averaged forcing, mainly due to the absence of forcing during night time.

5.1.2 Model specification

By employing the model described above the instantaneous aerosol radiative

forcing is computed for 12.00 noon in the months April, August, October and December.

The model is reliable only when the optical depth τ« 1. The key parameters required to

run the model is SSA, AOD, back scattering factor, surface reflectance and the solar flux

at the top of the atmosphere. To estimate the radiative effects of aerosols, it can be

mainly grouped into four components sulphate/ nitrate group, sea salt group, black

carbon group and mineral dust group. Refractive index of aerosol particles is a vital

parameter essential for determining its radiative effects. The real part determines its

scattering properties and imaginary part describes its absorption characteristics. The real

part of refractive index usually lies in the range 1.3 to 1.7 and imaginary part varies over

several orders of magnitude. Particles originating from combustion processes usually

have high absorption properties with high imaginary part. The real and imaginary parts

of refractive indices of common aerosol groups are shown in table 5.1.

Aerosol group Refractive index

Real part Imaginary part

Sulphate/Nitrate, etc. 1.44 -0.00265

Sea salt 1.38 -4.5E-9

Soil dust 1.53 -0.0078

Soot 1.75 -0.45 Table 5.1: Refractive index of various aerosol components at 500nm (from Hess et al 1998)

97

Aerosol Index is a measure of how much the wavelength dependence of backscattered

UV radiation from an atmosphere containing aerosols differs from that of the atmosphere

containing molecules only. Quantitatively Aerosol Index is defined as

331 331

10 10360 360

100 log logmeas calc

I IAII I

⎡ ⎤⎛ ⎞ ⎛ ⎞= − −⎢ ⎥⎜ ⎟ ⎜ ⎟

⎢ ⎥⎝ ⎠ ⎝ ⎠⎣ ⎦ (19)

where Imeas is the measured backscatter radiance and I cal is the calculated irradiance for a

pure Raleigh atmosphere. Always AI is positive for absorbing aerosols and negative for

non-absorbing aerosols. The magnitude of AI mainly depends on aerosol properties like

AOD, SSA, particle distribution etc. A simple aerosol model has been developed for this

location after analyzing the inorganic chemical composition of aerosols and the Aerosol

Index (AI) available at http://gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=

omi. The mean AI estimated for the month April is 0.832, August and October are 0.699

and December is 0.933. These values reveal that absorbing aerosols are maximum in

December and minimum in August and October. To formulate an aerosol model for this

location we utilized the inorganic analysis results (Section 4.5) and the aerosol index

values. To account for the presence of BC resulting from excessive vehicular emission a

fixed value is assigned in the model.

5.1.3 Estimation of SSA

The SSA associated with the composite aerosol is the weighted average of the

SSA of the individual species. The SSA and ASP of important aerosol groups are shown

in table 5.2. For the winter the weighted average of SSA of the composite aerosol is 0.91

(30% sea salt species, 32% sulphate species, 32% dust and organic aerosols and 6%

soot), for summer it is 0.92 (30% sea salt species, 30% sulphate species, 35% dust and

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organic aerosols and 5% soot) and during monsoon and post monsoon its magnitude is

0.95 (40% sea salt species, 40% sulphate species, 20% dust and organic species and 5%

soot).

Species

Single Scattering albedo Asymmetry parameter

Dry 70% RH dry 70% RH

Water soluble sulphate/nitrate, etc 0.968 0.984 0.621 0.697

Sea salt 1.00 1.00 0.746 0.814

Soil dust 0.83 0.83 0.755 0.755

Soot 0.23 0.23 0.353 0.353 Table 5.2: SSA and asymmetry parameter for major aerosol species (from Hess et al., 1998)

The AOD measured with MICROTOPS II was used to evaluate the AOD at 500 nm

using Angstroms exponential law.

5.1.4 Estimation of surface reflection

Surface reflection was estimated as the area weighted average of constituent

reflectance. For this site it was assumed that soil (30%) and vegetation (70%) make up

land surface. For this combination surface reflectance is 0.115 for visible range of

wavelength, 0.373 for near IR wavelength and 0.267 for IR region (Satheesh and

Srinivasan, 2006). So we assigned a value of 0.25 to surface reflectance.

5.1.5 Estimation of upscatter fraction

The up scatter fraction β (fraction of light scattered into the upward hemisphere

relative to local horizon) depends on the particle size, wavelength and solar zenith angle

(Pilinis et al., 1995). As the sun at the horizon (θ = 90o) β = 0.5, which is independent of

99

particle size, because of the symmetry of the phase function. For zenith angle decreasing

from 90o, forward scattering leads to a decrease in the value of β, although the decrease

is fairly low for the tiny particle. (Nemesure et al., 1995) Calculations of β were

previously described by Wiscombe and Grams (1976) as an integral of scattering phase

function. The IPCC report (2001) suggests that average upscatter fraction is 0.25 for

polluted continental aerosols and 0.21 for clean continental aerosols. The global average

β value approaches to 0.5 for the smallest particle and decreases to a value of about 0.2

for the biggest particle. Thus we adopted a value of 0.23 in our calculation.

5.1.6 Estimation of Solar flux

The amount of solar radiation reaching earth’s atmosphere is a function of the

solar zenith angle. The solar flux at the top of the atmosphere on the 15th of each month

was computed by using SBDART solar flux calculation model. SBDART will compute

the solar zenith angle for a particular date and time when latitude and longitude are

entered. The annual average solar flux over Kannur at 12.00 noon is 1058 W m-2.

Maximum solar flux (1132 W m-2) was found in the month of April and minimum (924

W m-2) in the month of December. The variation of solar flux at the top of the

atmosphere at 12:00 hrs for each month is shown in figure 5.3.

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MonthJan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Sola

r flu

x (W

/m2 )

850

900

950

1000

1050

1100

1150

1200

Figure 5.3: The variation of solar flux at the top of the atmosphere at 12:00 hrs for for different months over Kannur 5.1.7 Results and discussion

The instantaneous aerosol radiative forcing and their associated sensitiveness at

12 noon for 15th of April, August, October and December have been computed using the

simple analytical aerosol extinction model specified above and the results are tabulated

in table 5.3. The four months represent summer, monsoon, post monsoon and winter

seasons at this site. According to this model instantaneous aerosol forcing at the top of

the atmosphere is always negative. It is maximum during August (- 21.4 W m-2) and

minimum (- 9.98 W m-2) in December. The absorption due to aerosol is maximum (33.53

W m-2) in April and minimum (13.53 W m-2) in October. The surface forcing is

maximum (-54.23 W m-2) in April and minimum (- 27.01 W m-2) in December.

Moreover, the radiative forcing at the top of the atmosphere (scattering) and in the

atmosphere (absorption) were found to be more sensitive to the SSA, than the AOD and

surface reflectance during the whole period of observation.

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Year 2010 April August October December AOD (at 500nm) 0.375 0.276 0.262 0.189 SSA 0.92 0.95 0.95 0.91 ∆F TOA (Wm-2) -20.7 -21.4 -19.01 -9.98 ∆F ATM (Wm-2) 33.53 15.25 13.53 17.03 ∆F SUR (Wm-2) -54.23 -36.65 -32.54 -27.01 ∂FTOA/∂τ (Wm-2/τ) -55.8 -67.8 -63.38 -39.74 ∂FTOA/∂ω (Wm-2/ω) -166.74 -121.4 -107.71 -71.63 ∂FTOA/∂Rs (Wm-2/Rs) 84.51 101.4 90.05 82.63 ∂FATM/∂τ(Wm-2/τ) 87.58 55.3 51.75 86.33 ∂FATM/∂ω(Wm-2/ω) -417.58 -305.3 -271 -179.3 ∂FATM/∂Rs (Wm-2/Rs) 26.8 12.21 10.83 13.4

Table 5.3: Instantaneous aerosol radiative forcing and its sensitiveness to controlling parameters

For a parameter with large sensitivity, if the value is not exact the uncertainty in

forcing will be significant. The major limitation of the extinction model mentioned above

is that it does not include the wavelength dependence of AOD and surface reflectance.

Moreover it ignores the multiple scattering and variation of AOD with height. Since the

model is relatively easy to run it is commonly used for sensitivity studies.

5.2 Radiative transfer models

The azimuthally integrated form of the Radiative transfer equation for plane

parallel atmosphere is (Zdunkowski et al., 2007)

10

1

00 0 0

0

( , ) ( , ) ( , ) ( , )2

e x p ( , ) (1 ) ( )4

d I I P I dd

S P B

ωμ τ μ τ μ μ μ τ μ μτ

ω τ μ μ ω τπ μ

−

′ ′ ′= −

⎛ ⎞−− − − −⎜ ⎟

⎝ ⎠

∫

where I (τ, μ) is the total radiation field, µ = cos θ ,τ the optical depth, ω0 the SSA, B(τ)

the Planks function and P (μ, μ’) is the azimuthally integrated form of phase function .

The three terms describe multiple scattering, primary scattering of solar radiation and

thermal emission. Various techniques have been used to solve the radiative transfer

102

equations. Although these techniques employ different mathematical models, they all

produce very accurate solutions. Even though they produce accurate solutions, the

mathematical and computational complexity is a real challenge. The most common

solution schemes adopted are (1) The matrix operator method (Plass et al., 1973), (2) The

Discrete Ordinate method (Stamnens et al., 1988), (3) The spherical harmonics method

(Zdunkowski et al., 1998) and the Monte Carlo Method (Davis et al., 1979).

5.2.1 SBDART: A Research tool for plane parallel Radiative transfer in the

Earth’s Atmosphere

The code that we have used for the calculation of upwelling and downwelling

radiations at the surface and top of the atmosphere is the Radiative Transfer Model often

termed as SBDART which was originally presented by Ricchiazzi et al.,(1998). It is a

FORTRAN program which uses a DISORT code to solve the radiative transfer equation

for a vertically inhomogeneous plane-parallel atmosphere. It was designed and

developed by the University of California at Santa Barbara, USA. The program is mainly

based on a collection of highly developed and reliable physical models, which have been

developed by researchers of atmospheric science over the past few decades. Six standard

atmospheric profiles, are projected to model the following prototypical climatic

conditions like tropical, mid latitude summer, mid latitude winter, sub artic summer and

sub artic winter and US 62 (which represents typical conditions over United states

(McClatchey et al., 1972) are separately included in SBDART. Standard ground

reflectance models like Ocean water (Tanre et al., 1990) Lake water (Kondratyev, 1969),

Vegetation (Reeves et al., 1975) Snow (Wiscombe, 1980) and Sand (Staetter and

Schroeder, 1978) are used to parameterize the surface albedo. Users can specify a mixed

surface consisting of the above five, as well. The model incorporates some

parameterization to account for the effects of the atmospheric constituents, gases,

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aerosols etc. Also, users can choose standard atmospheres or put in their own

atmospheric profiles. In SBDART, several types of aerosols present in lower and upper

atmosphere like rural, urban or maritime conditions can be simulated using the standard

aerosol model developed by Shettle and Fenn (1995). These models differ from one

another in the values of extinction efficiency, single scattering albedo, asymmetry factor

and in the percentage of humidity.

To execute SBDART, the user prepares a main file named INPUT. The file

contains a single name list option, which can be modified from default values. Also other

input files are sometimes required by SBDART, a particularly important one is

atmos.dat, where the user introduces an atmospheric profile. Other required files are

aerosol.dat, with spectral aerosol characteristics, albedo.dat, with the spectral surface

albedo, filter.dat, where a sensor filter function can be specified. In Solar.dat, user can

supply solar spectrum and in usrcld.dat, the user can specify the cloud vertical profile,

including cloud properties such as the effective droplet radius, which are required for

advanced calculations.

5.2.2 Model specifications

For the estimation of Direct Aerosol Radiative forcing a tropical model

atmosphere was assumed. The measured AOD values, total column of ozone obtained

from TOMS as well as OMI satellites and PWC obtained from MODIS site were

provided. The SSA and asymmetry parameter, derived from the aerosol model over this

region were also provided as inputs. An example of the input file for clear sky conditions

used in the study with some brief explanation is given in table 5.4. For each simulation

the spectral downward irradiance has been calculated in the range 0.25 to 4.0µm. in steps

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of 0.005 μm increase in wavelength. The source code was compiled in and run under

LINUX operating systems.

Parameter and value applied Explanation isat = 0 Wlinf to Wlsup with filter function =1.

wlinf = 0.25 Lower wave length limit is 0.25 µm.

wlsup = 4.0 Upper wavelength limit is 4.0 µm.

wlinc = 0 Wavelength increment is 0.005 µm or 1/10th of the wavelength range whichever is less.

idatm = 1 Use tropical atmospheric profile.

nf = 2 LOWTRAN_7 solar spectrum (20 cm-1 resolution for 0 to 28780 cm-1)

iday = 15 15th day of the year

time = 6.30 Time in hours GMT

alat = 11.9 Latitude on Earth.

alon = 75.4 Longitude on Earth. islab = 0 Albdeo = albcon.

albcon = 0.25 Spectrally uniform surface albedo = 0.25

jaer=0 Stratospheric aerosols neglected

iaer = 5 User defined boundary layer aerosol. If iaer = 0, no boundary layer aerosol.

wlbaer = .340,.440,.675,.870,1.020 Aerosol optical depths are measured at five short band wavelength 340,440,657,870 and 1020 nm

gbaer = .412,.218,.147,.130,.110 AOD at wavelengths 340, 440, 675, 870 and 1020 nm

wbaer = 5*.9 Single scattering albedo 0.9

gbaer = 5*.8 Asymmetry parameter for the five wavelengths mentioned above, calculated from linear fit

zout=0,100 Get output at the surface and top of the atmosphere

iout = 10

One output record per run, integrated over wavelength. Output quantities are (integration by trapezoid rule) WLINF, WLSUP, FFEW, TOPDN, TOPUP, TOPDIR, BOTDN, BOTUP, BOTDIR

nstr =4 Number of discrete ordinates in DISORT (four polar angles and four azimuthal mode)

Table 5.4: Example of input.dat files for SBDART

105

where,

WLINF is the lower wavelength limit in microns.

WLSUP, the upper wavelength limit in microns.

FFEW, filter function equivalent width in microns.

TOPDN, total downward flux at ZOUT (100) km. in W/m2.

TOPUP, total upward flux at ZOUT (100) km. in W/m2.

TOPDIR, direct downward flux at ZOUT (100) km. in W/m2.

BOTDN, total downward flux at the surface. in W/m2.

BOTUP, total upward flux at the surface. in W/m2 and

BOTDIR, direct downward flux at the surface. in W/m2.

Additional input parameters like integrated water vapor amount (UW), integrated ozone

concentration (UO3), cloud parameters, cloud radius, etc. can be provided as input

parameters. The downward and upward SW flux at the surface and top of the atmosphere

were computed with and without aerosols. Thus Shortwave, clear sky radiative forcing at

the surface(S) and the top of the atmosphere (TOA) are estimated as

, , ,( ) ( )S TOA a a S TOA o o S TOAF f f f fΔ = ↓ − ↑ − ↓ − ↑ (21)

(∆FTOA - ∆FS) gives ∆FATM the net atmospheric forcing. This energy gets converted into

heat thereby resulting in atmospheric heating, which is the indicator of climatic impact of

aerosols. The atmospheric heating rate have been calculated (Liou, 2002) as

106

ATM

p

FT gt C P

Δ∂=

∂ Δ (22)

where T/ t is the heating rate, g the acceleration due to gravity Cp the specific heat

capacity at constant pressure and ∆P the change in atmospheric pressure.

5.2.3 Results and discussion

The aerosol radiative forcing in the short wave region ranging from 0.25 -4.0µm

under clear sky days have been computed for four representative months in 2010.

Calculations of aerosol radiative forcing have been performed separately, with and

without aerosols at hourly intervals and 24 hour averages have been taken to estimate the

direct radiative forcing. The four months April, August, October and December are the

representatives of summer, monsoon, post-monsoon and winter seasons. The radiative

forcing results and the atmospheric absorption translated into atmospheric heating have

been shown in table 5.5.

Month Aerosol radiative forcing Heating rate K/day

Top of the atmosphere (W m-2)

Surface (W m-2)

Atmosphere (W m-2)

April -1.5±0.4 -23.7±2.3 22.2±2.7 0.62 August -2.58±0.6 -14.80±1.3 12.22±1.9 0.34 October -2.68±0.4 -14.98±1.1 12.3±1.5 0.34

December 0.09±0.07 -18.12±1.8 18.91±1.89 0.53 Table 5.5: Aerosol radiative forcing over Kannur and corresponding heating rate/day ARF at any location is dependent on many parameters like total columnar AOD,

their vertical distribution, SSA, their size distribution, asymmetry factor, surface

reflectance, relative humidity and many other factors (George, 2001). The magnitude of

TOA forcing is slightly positive (0.09 W m-2) in December, and negative in April (-1.5

W m-2) August (-2.58 W m-2) and October (-2.68 W m-2).The surface forcing is negative

107

in all the four months. It varies from -14.80 W m-2 in August to -23.7 W m-2 in April.

Nearly equal forcing has been estimated in the Month of August and October. The

seasonal variation of aerosol forcing at the top of the atmosphere, atmosphere, and

surface is depicted in figure 5.4. The forcing at the surface leads to cooling of the

surface, but within the atmosphere, it results in heating. The significant difference

between the TOA and surface forcing is due to the absorptive properties of the aerosols,

and is a measure of the heating rate of the atmosphere. The maximum surface forcing in

the month of April may be attributed to maximum values of AOD during these months.

This variation in AOD is mainly due to the aerosol loading over this area from the

neighbouring polluted areas and also due to some local influences like firework festivals.

Almost equal values of aerosol forcing have been identified in the monsoon and post

monsoon seasons because the rain continues from June to November and wash out of

aerosols takes place. Moreover during these months the marine influence dominates and

more sea salts aerosols are injected into the atmosphere. In the month of December even

though the AOD values are low,the slightly low value of SSA make the aerosol forcing

positive at the top of the atmosphere.

The atmospheric absorption translates into atmospheric heating show that the

heating rate is 0.62Kday-1 in April and and is about half during ( 0.34Kday-1 ) monsoon

season. Even though the heating rate is small this is capable of influencing the monsoon

pattern (Manoj et al., 2010). Aerosol radiative forcing is a strong function of aerosol

optical depth. Hence it is significant to calculate the forcing efficiency that is the rate at

which the atmosphere is forced per unit optical depth. It is calculated by dividing the

forcing value by AOD at 500 nm and is an indicator of the forcing potential of the

composite aerosols. The values of forcing efficiency during different months are shown

in table 5.6.

108

Month

Radiative forcing efficiency (W m-2 τ-1)

TOA Surface Atmosphere

April -4 -63.2 59.2

August -9.34 -53.62 44.2

October -10.22 -57.17 49.9

December +2 -95.87 100 .

Table 5.6: Radiative forcing efficiency for different seasons

Month

Rad

iativ

e fo

rcin

g (W

/m2 )

-30

-20

-10

0

10

20

30Top of the atmosphere Surface

Atmosphere

April August October December

Figure 5.4: Seasonal variations in aerosol radiative forcing

The atmospheric forcing efficiency is maximum in the winter month December and

minimum in the summer month April, which may be attributed to the low value of AOD

in winter than summer. The forcing efficiency at the top of the Atmosphere is maximum

in the month October and minimum during the winter month December. This is due to

domination of sea salt aerosols during the monsoon season over this region.

109

5.2.4 Comparison with other geographical locations

Comparison of aerosol radiative forcing at different locations is shown in the table 5.7.

Location Period TOA (Wm-2)

Surface (Wm-2)

ATM (Wm-2) Reference

Mohal-Kullu

April-July -6.55 -40.51 33.96

Guleria et al., 2010

Aug.-Sept -6.89 -32.36 25.47

Oct.-Nov 10.62 -29.11 18.48

Dec.-Mar -10.36 -28.31 18.00

Vishakapatanam

Mar-May 3.99 -16.8 20.78

Sreekanth et al., 2007

June-Aug 2.36 -9.9 12.26

Sept.-Oct 0.7 -2.81 3.51

Nov-Feb 8.4 -35.78 44.18

Ahmedabad

April-May 8 ± 2 -41.4±5 48±7 Ganguly and Jayaraman 2006

June-Sept 14 ± 4 -41 ± 11 55.5 ± 15

Oct-Nov -22 ± 3 -63 ± 10 40 ± 11

Dec-Mar -26 ± 3 -54 ± 6 28 ± 9

Trivandrum

April-May 0.3 to -1.4 -37.4 to 34.2 37.6 to 32.8 Babu et al.,

2007 June-Sept -1.4 to -2.6 -26.9 to 24.4 25.5 to 21.8

Oct-Nov -1.5 to -2.8 -30.2 to 27.8 28.7 to 25

Dec-Mar 4.1 to 1.8 -48.9 to 44.8 52.9 to 46.6

Table 5.7: Aerosol radiative forcing at different locations

Comparing our results with those reported from other geographical areas of India,

it is seen that TOA forcing is negative in Mohal Kullu, mostly negative in Trivandrum,

whereas it was positive over Vishakapattanam for all the seasons. In Ahmadabad it is

positive during summer seasons and negative during post monsoon and winter seasons.

The magnitude of TOA forcing, is more or less comparable over the four locations,

except at Ahemadabad. The minimum and maximum values of TOA, surface and

atmospheric forcing are lower than that at other regions except that at Vishakapattanam.

110

This variation is attributed not only to the diversity of aerosols, but also to the surface

reflectance and weather conditions, which in turn highly influence the behavior or

aerosols, also.

Aerosol radiative forcing during the dust events over New Delhi indicates a

consistent increase in surface cooling ranging from -39 W m-2(March) to -99 W m-2

(June) and an increase in heating of the atmosphere from 27 W m-2(March) to123 W m-2

(June) (Pandithurai et al., 2008). This was attributed due to the rise in AOD from 0.55

(March) to 1.2 (June) at 500nm and the decrease in SSA from 0.84 to 0.74. The results

indicate a strong influence of absorbing aerosols over these regions during summer.