Dust absorption over the Great Indian Desert inferred using ground-based and satellite remote sensing

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112,, doi: /2006jd007690, 2007 Dust absorption over the Great Indian Desert inferred using ground-based and satellite remote sensing K. Krishna Moorthy, 1 S. Suresh Babu, 1 S. K. Satheesh, 2 J. Srinivasan, 2 and C. B. S. Dutt 3 Received 22 June 2006; revised 2 November 2006; accepted 28 December 2006; published 5 May [1] Mineral dust is the single largest contributor of natural aerosols over land. Dust aerosols exhibit high variability in their radiative effects because their composition varies locally. This arises because of the regional distinctiveness of the soil characteristics as well as the accumulation of other aerosol species, such as black carbon, on dust while airborne. To accurately estimate the climate impact of dust, spatial and temporal distribution of its radiative properties are essential. However, this is poorly understood over many regions of the world, including the Indian region. In this paper, infrared (IR) radiance ( mm) acquired from METEOSAT-5 satellite (5-km resolution) is used to retrieve dust aerosol characteristics over the Great Indian Desert and adjacent regions. The infrared radiance depression on account of the presence of dust in the atmosphere has been used as an index of dust load, called the Infrared Difference Dust Index (IDDI). Simultaneous, ground-based spectral optical depths estimated at visible and near-infrared wavelengths (using a multiwavelength solar radiometer) are used along with the IDDI to infer the dust absorption. The inferred single scattering albedo of dust was in the range of We infer that dust over the Indian desert is of more absorbing nature (compared with African dust). Seasonally, the absorption is least in summer and most in winter. The large dust absorption leads to lower atmospheric warming of K day 1. Citation: Moorthy, K. K., S. S. Babu, S. K. Satheesh, J. Srinivasan, and C. B. S. Dutt (2007), Dust absorption over the Great Indian Desert inferred using ground-based and satellite remote sensing, J. Geophys. Res., 112,, doi: /2006jd Introduction [2] Mineral dust aerosols constitute a major fraction of global aerosol abundance especially over the landmass and have an important role in regulating global climate [Tanré and Legrand, 1991; Prospero et al., 2002; Tanre et al., 2003; Zender et al., 2003; Tegen et al., 2004; Deepshikha et al., 2006]. Unlike other aerosol species (such as sulfates or sea salt), optical properties of dust aerosol vary spatially and temporally with consequent effects on the radiation budget of the earth-atmospheric system [Seinfeld et al., 2004]. This arises mainly because (1) the soil characteristics vary regionally and (2) other aerosol species, such as black carbon (BC), adhere to the dust while it is airborne or is being transported to long distances over varying geographical regions. Though dust is produced mainly from desert and arid regions, these are not homogeneous sources [Tanre et al., 2003; Tegen et al., 2004]. Thus to accurately assess the climate impact of dust, knowledge of the spatial and 1 Space Physics Laboratory, Vikram Sarabhai Space Centre, Trivandrum, India. 2 Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore, India. 3 Indian Space Research Organisation Headquarters, Bangalore, India. Copyright 2007 by the American Geophysical Union /07/2006JD temporal distribution of its radiative properties is essential. This is nonexistent over many regions of the world, including India, with vast desert areas to its west. As of now, no global models treat absorbing properties of dust as a function of space and time. [3] Particles originating from the arid regions are usually mineral aerosols and are produced by weathering of soil [d Almeida et al., 1991]. The long-range transport of continental-derived particles by the combined action of convection currents and general circulation systems makes these particles a significant constituent even at locations far from their sources and over remote oceans [Prospero et al., 2002], where they play an important role in many marine biogeochemical processes. Dust is a source of Fe (iron), which in some oceanic regions may be a limiting nutrient for phytoplankton [Falkowski et al., 1998; Fung et al., 2000]. Consequently, dust could modulate the global carbon cycle. Moreover, certain species of cyanobacteria that utilize Fe in their metabolism could significantly influence the nitrogen chemistry of the ocean; the rate of production of nitrate and ammonium by these organisms could be strongly controlled by the rate of input of mineral dust to the oceans [Michaels et al., 1996; Falkowski et al., 1998]. [4] Soil-derived particles are among the largest aerosols with radii ranging from below 0.1 mm to asmuch as100 mm. Because the composition of the soil varies locally, these aerosols exhibit large variability in the imaginary part of 1of10

2 Figure 1. Schematic of the study region, with the location of Jodhpur marked on it. Its position over India is shown in the inset. their refractive index, which mainly determines their climate forcing through atmospheric absorption [Ginoux et al., 2001; Zender et al., 2003; Seinfeld et al., 2004; Tegen et al., 2004]. Moreover, during their atmospheric lifetime, other aerosol species get accumulated over pure dust leading to significant changes in their radiative effects. Consequently, the magnitude and even the sign of the direct radiative forcing of dust remain uncertain [Tegen et al., 2004]. [5] There are some difficulties in the measurement of dust using direct measurements or remote sensing using visible wavelengths. Continuous, in situ sampling of dust aerosol is not feasible over desert regions because of logistical reasons, and the number of such sites are limited. Remote sensing using visible wavelengths is further complicated because of the high and varying surface reflection properties of the soil over the deserts as well as arid regions. Recently, Hsu et al. [2004] have demonstrated that aerosol optical depths (AODs) can be derived using Moderate Resolution Imaging Spectroradiometer (MODIS) data over bright surfaces. Martonchik et al. [2004] have compared MISR-derived optical depths with those measured by AERONET sites over deserts and found agreement. Dust aerosols are, however, known to be nonspherical in shape. This can cause errors in the remote sensing of dust aerosols from visible wavelengths. However, infrared (IR) remote sensing provides a powerful tool for retrieving the regional characteristics of mineral dust aerosols from the space [e.g., Legrand et al., 1994, 2001; Deepshikha et al., 2005, 2006]. [6] In this paper, we have inferred the dust absorption using simultaneous satellite (thermal IR) and ground-based (visible and near IR) remote sensing and have investigated its seasonal characteristics based on observations carried out over the western desert/arid region of India. 2. Study Region [7] The investigations were centered about Jodhpur (JDR), located in the northwestern part of India, geographically within the Great Indian Desert or the Thar Desert (as it is known widely) of Rajasthan. The Thar Desert, with an area of approximately km 2, bordering the northwestern India (with a part of it in Pakistan), has an undulating topography and is classified as a monsoon desert. As the monsoon currents cross India, they lose moisture on the eastern slopes of the Aravalli Ranges (Figure 1), resulting in highly depleted precipitation to its west. More details on the physical features of this region are available elsewhere [e.g., Yadav and Rajamani, 2004]. Though Jodhpur is located toward the eastern end of Thar Desert, the area is well within the arid zone and exhibits all the characteristics of a desert/arid region. The most important among these are the strong dust raising winds occurring from April to July and the scanty rainfall (<300 mm annually). The Aravalli hills (having a mean elevation of m and running all the way to the north from the southern boundary of Rajasthan) lie a few hundred kilometers to the east of Jodhpur and act as a barrier separating this western desert region from the eastern plains of India. A schematic of the study region, with the location of Jodhpur (from where the ground-based measurements were carried out) marked on it, is shown in Figure 1 (latitudes and longitudes are marked inside the box). 3. Database 3.1. Spectral Aerosol Optical Depth (AOD) [8] Spectral aerosol optical depths (AODs) at 10 narrow wavelength bands centered at 380, 400, 450, 500, 600, 650, 750, 850, 935, and 1025 nm were estimated regularly over Jodhpur (26.27 N, E; 236 m above mean sea level) using a multiwavelength solar radiometer (MWR), which formed part of the national network of MWRs operated under the Geosphere Biosphere Program of the Indian Space Research Organization (ISRO-GBP). The desired wavelengths were selected using interference filters having nominal full width at half maximum (FWHM) bandwidths of 5 nm. The instrument had a field-of-view of 2. More details of the instrument, the data analysis techniques, and the error budget are given in several earlier publications [e.g., Moorthy et al., 1997; Saha and Moorthy, 2004] and hence are not repeated. The instrument was mounted 5 m above the ground level, on the rooftop of the building (of the regional remote sensing service center, Jodhpur), located about 6 km to the west of the town center within the vast campus of the Central Arid Zone Research Institute (CAZRI). Measurements were made mostly on clear days and partly on cloudy days when the area of the sky of 10 with the solar disc at its center is free from any visible clouds. The raw data were analyzed following the Langley plot technique [e.g., Shaw et al., 1973] and subtracting the molecular contributions from the total optical depth [Saha and Moorthy, 2004]. For this, the data obtained during 1 day are considered as a single set unless the Langley plot revealed presence of two slopes, and in such instances, the data were separated to forenoon and afternoon parts and each part was considered as a separate set. In general, the uncertainties in the AOD estimates are in the range The mean AODs, thus estimated at each wavelength for each data set, formed 2of10

3 radiance depression (DR) at the top of the atmosphere (TOA) due to the presence of dust layer is, DR ¼ R " t R " b ð1þ where R " t is the radiance leaving the TOA (satellite measured) under normal, dusty conditions, and R " b is the radiance leaving the TOA ideally, without dust or more practically under very clear periods of extremely low dust loading. The raw data from satellite (in the form of counts) were corrected for space count and then converted to radiance values using the calibration coefficients, following the relation Radiance ¼ C f * ðc a C s Þ ð2þ Figure 2. Original image (top) and reference image (bottom) generated from METEOSAT data for deducing IDDI. Details are given in the text. the database. The MWR data are available for 3 years from November 1998 to October Of this, the data for the year 1999 (January to December) are simultaneous with the METOSAT-5 data over this region, when the latter was placed over the Indian Ocean during the Indian Ocean Experiment (INDOEX). These data composed of 45 independent spectral AOD estimates spread over the year. However, no data could be obtained during the months of July, September, and October 1999 when the sky has been extensively cloudy and not conducive for making the MWR measurements Infrared Difference Dust Index (IDDI) [9] Infrared radiance (in the band mm) from the METEOSAT satellite (of European Space Agency, ESA) was used to retrieve dust aerosol characteristics with a spatial resolution of 5 5 km. The principle behind IDDI is that the presence of dust layer reduces the IR radiance received by the satellite over arid and semiarid regions. The resulting depression in the retrieved IR brightness temperature (IRBT), called Infrared Difference Dust Index (IDDI [Legrand et al., 2001]), has been extensively used globally as an index of dust load. The where C a is the actual radiance count at the satellite, C s is the space count, and C f is the calibration coefficient provided by METEOSAT along with the radiometric counts. Radiance data are converted into brightness temperature (IRBT) data using inverse Planck function. This IRBT forms the original image, and it contains all the radiative information about surface and atmosphere [Legrand et al., 2001]. A typical of such image is shown in Figure 2 (top panel), obtained using the data for 5 June The IRBT is expressed in Kelvin (K). The next step was to create the reference image representing clear-sky conditions with very low dust loading for consecutive, nonoverlapping periods whose duration is short enough to ignore the seasonal effects but long enough to ensure that the clear-sky or near clear-sky conditions exist, at least, for one measurement for each pixel. Such a reference image generated for the first half of June 1999 is shown in the bottom panel of Figure 2. The purpose of reference image is to separate the land effect from original image. A 15-day reference period is used here. This operation for a pixel (i, j) and day number k is given by P r ij ¼ max ðp1 ij ;...; Pk ij ;...; Pn ij Þ The superscript r means the reference value of k associated with the highest value of P k ij. For a given pixel, the maximum in the daily values of IRBT within a given reference period is assumed to represent the characteristics of the target pixel as in cloud-free and dust-free/very low dust (background) condition. [10] Next, a difference image exhibiting the clouds and dust patterns separated from the permanent surface features is obtained by subtracting individual day original image (or IRBT) from reference image for that period. P k ij ¼ Pr ij Pk ij The difference image (Figure 3) shows only the variable atmospheric radiative effects related to both clouds and dust. Difference values represent the reduction in IRBT due to dust aerosols, provided cloudy pixels are identified and screened. Since the permanent surface patterns in the difference image are eliminated, clouds are observed over ð3þ ð4þ 3of10

4 Figure 3. Difference image obtained by subtracting the reference image from the original image for 9 June continental regions against a smooth, somewhat ocean-like background. This makes it easier to identify clouds in a difference image than in the original image. Cloudy pixels were screened using the spatial coherence method [Coakley and Bretherton, 1982]. More details on data analysis including construction of IDDI images and cloud screening are available in literature [Legrand et al., 2001; Deepshikha et al., 2005, 2006] and are not repeated here. [11] In a recent study, Deepshikha et al. [2005] have demonstrated the use of IDDI and aerosol optical depth (t a ) at the visible wavelengths to estimate the dust-absorbing efficiency (D AE ) defined as and T min ), the relative humidity (RH max and RH min ), and the monthly total rainfall. The averages of the relevant parameters for the respective months are obtained from daily data for the period recorded at the meteorological facility of the CAZRI. The strikingly low and highly seasonal nature of the rainfall is clearly seen, with most of it confined to the months of July and August (which account for 75% of the annual rainfall). It also shows considerable interannual variation in the quantum of rain. Winds are generally weak (3 6 m s 1 ) from September to March. The speed increases from April to reach the peak (10 m s 1 ) in June, when the intense winds raise large amount of dust (dust storms) reducing the visibility significantly and interrupting ground-based MWR observations. Monthly mean T max increases from 25 in January to reach peak values of 40 C in May and June and remains high (>35 C until October) before rapidly decreasing to the winter. The minimum temperatures go as low as 10 C in winter (on a few days, it dips down to even 5 C) and increase gradually to reach high values 25 C or more by May. This large variation in temperature and scanty rainfall for a prolonged period cause weathering of the soil, and the loose dust is picked by the winds as they grow stronger. RH (Figure 4) remains generally very low, as is characteristic to the arid zone. Its maximum value remains <60% throughout except during the rainy months of June, July, and August. Even in those months, the minimum values of RH go as low as 25 30%. [13] The climatological monthly mean column water vapor content for Jodhpur is shown in Figure 5. Water vapor content was estimated using a 935-nm channel of MWR following the method described by Leckner [1978]. More details of technique are given by Nair and Moorthy [1998]. It may be noted that irrespective of significant rainfall during July and August, water vapor content rarely exceeds 2.5 g cm 2. This is because rainfall over Jodhpur was sporadic and not spread over the whole month but D AE ¼ IDDI t a ð5þ where t a is the AOD at 500 nm. The D AE is a measure of infrared brightness temperature depression per unit AOD (at 500 nm). Thus an aerosol system with larger D AE indicates higher absorption efficiency due to dust [Deepshikha et al., 2005]. In this study, we have used the simultaneous METEOSAT-retrieved IDDI data for the year 1999, when the satellite was moved over to the Indian Ocean (as a part of the INDOEX), simultaneous with the ground-based estimates of spectral AOD using the MWR to infer on the absorption characteristics of dust over the Thar desert, keeping Jodhpur as the central location, and to examine its spatiotemporal variations. 4. Meteorology [12] The general meteorology that prevailed over Jodhpur during the study period is shown in Figure 4, in which we have plotted the variation of the monthly mean wind speed, the maximum and minimum values of temperature (T max Figure 4. Annual variations of surface meteorological parameters at Jodhpur based on the data for the period , obtained from the MET facility of CAZRI. The points are the mean value (over the 3-year period) for the month, and vertical bars are the standard deviation of the mean. For rainfall, the average monthly total values are given. 4of10

5 Figure 5. Climatological monthly mean column water vapor content (g cm 2 ). occurred only a few days in a month. When rainfall occurred, optical depth (from MWR) data were not available and hence DAE. Legrand et al. [2001] have made detailed sensitivity analysis on the role of atmospheric water vapor in contaminating IDDI. They reported that when water vapor amount is in the range between 0.5 and 3gcm 2, maximum variation in simulated METEOSAT infrared response (IDDI) is not greater than 1 K. When water vapor amount exceeds 3 g cm 2, the effect of water vapor becomes important. Over arid and semiarid locations, water vapor amount is usually less than 3 g cm 2 during most of the time in a year as is the case with Jodhpur (see Figure 5). Over locations experiencing water vapor amount exceeding 3 g cm 2, water vapor absorption contributes to IDDI. We have made estimates of the effect of water vapor on IDDI (based on column water vapor content shown in Figure 5), which shows that even during August when water vapor content is maximum (but around 2.5 g cm 2 only), the effect of water vapor on IDDI is around 1 K. Deepshikha et al. [2006] have examined the regional distribution of clear-sky column water content (from MODIS data) derived using near-infrared method during both dry and monsoon months, and they have found that over Rajasthan desert (observational site in this paper), the effect of water vapor on IDDI is less than 1 K during March to May (MAM) and not more than 2 K during June to August (JJA). The climatological wind vectors (from National Centers for Environmental Prediction) at 850-hPa level over this region is shown in Figure 6, for January (left panel) and for June (right panel), as representatives of winter and summer seasons, respectively. The position of Jodhpur (JDR) is marked in the figures by the solid circles. In January, a weak anticyclonic circulation prevails over this region, favoring subsidence and leading to dry conditions. The winds are extremely weak. In contrast, a strong cyclonic circulation forms in summer Figure 6. The climatological wind fields at 850 hpa over the region derived from NCEP/NCAR reanalysis data for January (left panel) and for June (right panel). 5of10

6 Figure 7. Annual variations of the AODs, at two representative wavelengths 500 nm (mid-visible) and 1025 nm (near IR) estimated from the MWR measurements. Each point represents the mean value for the month (obtained using the entire data from 1988 to 2001 to get a fairly long-term average), and vertical bars are the standard deviations of the means. due northwest of JDR, and the associated winds are strong westerlies/southwesterlies. Geographically (Figure 1), the regions north and west of JDR are arid regions. The strong cyclonic circulation and the associated wind drafts are thus conducive for picking up the lose dust and transporting it from the arid regions lying to the west and north. The strong thermal convections prevailing over this region during summer help in significant vertical transport of aerosols. The strong westerlies then carry these dust to far east, as far as Delhi and Kanpur and even beyond [e.g., Yadav and Rajamani, 2004]. 5. Results 5.1. Spectral AOD From the MWR Measurements [14] Annual variations of the AODs estimated from the MWR measurements are shown in Figure 7, at two representative wavelengths 500 nm (mid-visible) and 1025 nm (near IR, and the longest wavelength available in the MWR). Each point represents the mean value for the month (obtained using the entire data from 1988 to 2001 to get a fairly long-term average representative of the mean prevailing conditions), and the vertical bars are the standard deviations of the means. High AODs occur during the period June to October, and during this period, the AOD spectra are nearly flat or at times even inverse with the (AOD) 1025 > (AOD) 500, indicating the large dominance of coarse mode particles. It should be borne in mind that the MWR observations were confined only to clear days, and that during intense dust storm periods, the instrument was kept covered. Even during the rest of the year, when the AODs are comparatively lower, (AOD) 1025 remains comparable to that at 500 nm except in the winter months of December through February. The extreme winter conditions during these months might be making the desert almost inactive as far as dust production is concerned (low temperatures, low convective turbulence, very low winds, and the prevailing anticyclonic upper air winds; Figure 6, left panel). As such, during winter, the AODs showed a wavelength dependence similar to that seen over continents, and the spectra showed a rapid decrease in AOD with l, with the Angstrom wavelength exponent lying in the range 1.5 3, showing the large depletion in the relative abundance of coarse mode particles. The accumulation mode particles leading to the steep AOD spectrum in this season might be arising from the local and regional anthropogenic activities, which include the automobile traffic in the urban and semiurban regions around the desert, power plants, and other industries Association Between IDDI and AODs [15] With the above picture as background, we examined the simultaneous spectral AOD (from MWR) and IDDI estimated from METOSAT for the 45 days from March to August of In Figure 8, we show a scatterplot of IDDI against AOD for the above period, at four wavelengths (380, 500, 750, and 1025 nm), two in the visible and two in the near IR, and thus include the extreme wavelengths used in the MWR. [16] A fairly good positive correlation is seen between IDDI and AODs at 380, 500, 750, and 1025 nm with correlation coefficients (g) of 0.77, 0.74, 0.70, and 0.76, respectively. All these coefficients are significant at P < 0.001, where P is the significance level for acceptance. Following equation (5), the slope of the linear least-squares fitted line gives the dust absorption efficiencies D AE. At the four different wavelengths considered here, D AE ranges from 6 to 13.5, with its highest value at 750 nm. The mean values and the errors (square root of the variance of the least-squares fit) and the corresponding correlation coefficients (g) obtained for March to May (MAM) and June to August (JJA) are listed in Table 1. [17] As the dust absorption characteristics can differ depending on the extent of mixing with other constituents [such as black carbon (BC) for example] and as this would be different in different seasons (depending on the wind pattern, dust abundance, BC abundance, and so on), we examined the above association separately for two seasons: March to May (MAM) and June to August (JJA), when the databases are relatively strong. The results are shown in Figure 9. The figure clearly indicates the change in the dust absorption characteristics; the D AE remaining comparatively low and almost insensitive to the wavelength (or weakly decreases with wavelength) in MAM but doing the reverse in JJA, increasing sharply with wavelength to reach a peak at 750 nm and then gradually decreasing. It may be noticed that D AE for the full year resembles the pattern seen during summer season, when the dust loading is highest along the northwestern region of India. Not only the values but also the wavelength dependence does so. During MAM, it appears that the abundance of dust is not very high (as seen from the relatively low AODs in Figure 7 and the moderate winds and other meteorological parameters). Nevertheless, it contributes significantly to the coarse aerosol burden as evidenced from the flat spectral variation of AOD. The winds are still only moderate, and the convective turbulence and the thermal stress are yet well below the peak (which occur in JJA). Thus, the aerosols still will be mixed with other anthropogenic species. As such, 6of10

7 Figure 8. Scatterplot of IDDI estimated from METOSAT data against simultaneous AODs obtained from the MWRs at four wavelengths (380, 500, 750, and 1025 nm) for the year D AE remains almost wavelength independent. In JJA, the dust load is at its extreme and is perhaps purer. D AE increases with l; the peak at 750 nm might perhaps be caused by the dominant mode in the (columnar) aerosol size distribution. The seasonal and whole year values of D AE as a function of l and the correlation between AOD and IDDI are listed in Table Association Between IDDI and Angstrom Exponent (a) [18] Spectral variation of aerosol optical depth can be expressed by using Angstrom relation 1964; Shaw et al., 1973]. Lower values of a indicate larger abundance of coarse mode aerosols [Satheesh and Moorthy, 1997]. Performing a regression analysis between t a and l in a log-log scale, a and b are evaluated for each MWR data set. Since IDDI depends on the size distribution of dust, we have examined its association with Angstrom wavelength exponent (a) by constructing a scatterplot (Figure 10) of IDDI (in log scale) with the corresponding values of a (for the simultaneous data sets). [19] The points indicate an anticorrelation between the two. The least-squares fitted line (dashed line in Figure 10) indicates the mean trend of the form, t al ¼ bl a ð6þ lnðiddiþ ¼0:99 0:22a ð7þ where a is the wavelength exponent indicating the size distribution, b is the turbidity parameter indicating the aerosol loading, and l is the wavelength in mm [Ångström, with a correlation coefficient of It indicates that the IDDI tends to be lower when there is a relative increase in Table 1. Dust-Absorbing Efficiency (DAE) for Different Seasons and Wavelengths 380 nm 500 nm 750 nm 1025 nm Season D AE g D AE g D AE g D AE g MAM 8.3 ± ± ± ± JJA 5.9 ± ± ± ± Whole Year 6.0 ± ± ± ± of10

8 Figure 9. Seasonal distinctiveness of DAE. It may be noted that during July, there were no data because of cloudiness. the abundance of fine, accumulation mode particles. Most of these particles are likely to be associated with anthropogenic activities and not directly related to dust. [20] Recent investigations [Legrand et al., 2001; Deepshikha et al., 2005] have demonstrated that IDDI is linearly related to aerosol optical depth. Since logarithm of optical depth is linearly related to a following equation (6), the linear variation of the logarithm of the IDDI with a is only logical. [21] The regional distribution of dust-absorbing efficiency is shown in Figure 11 over the Indian desert and the adjoining regions for two representative (dry) months influenced by dust. These maps are constructed using the IDDI derived from METEOSAT radiances and the AOD derived from TOMS data for this region. For this analysis, optical depths from TOMS were compared with MWR optical depths and found agreements within Since the spatial resolution of Figure 10. Scatter plot of IDDI against the Angstrom exponent a. The dashed line is least-squares fitted. Figure 11. Regional map of dust-absorbing efficiency (DAE) for April and May IDDI image was higher than the TOMS optical depth image, the former was rescaled to match the TOMS resolution. The figure clearly shows the following: 1. Regionally, the D AE is higher (by nearly a factor of 2) over the Great Indian Desert and the adjoining regions, indicating higher absorption compared with those in west Asian/Arabian regions. 2. Seasonally, the dust absorption is high during April to May period and decreases thereafter, when dust generation is intense and dust storms are quite frequent, possibly because the dust is more nascent during this period. [22] In an earlier study following similar approach, Deepshikha et al. [2005] have shown that dust over northeastern Africa and Afghanistan exhibit higher absorption compared with the more pure dust over Saudi Arabia. They attributed the enhanced absorption of dust to the deposition of black carbon aerosols on the dust over Africa. Viewed in the light of this, the D AE values reported in this paper also indicate the presence of mixed aerosols over the Thar Desert region (probably with black carbon). 6. Implications [23] The sensitivity analysis given by Deepshikha et al. [2005] has demonstrated how to infer the single scattering albedo (SSA) of dust (which strongly influences its radiative impacts) from D AE values. The dust-absorbing efficiencies observed in this study corresponds to single scattering albedo (in the visible region) in the range of The higher values are observed during JJA, when frequent dust episodes occur leading to large abundance of fresh dust and the dust is less mixed with other aerosol species such as black carbon (BC). On the other hand, in MAM and winter 8of10

9 (November to February), when the dust abundance is relatively low and the chance of mixing with other species is higher so that the dust becomes dirtier, the inferred SSA tends to be lower, implying increased absorption. The SSA of dust reported by various studies also has shown a large range of values. For example, Kaufman et al. [2001], using remote sensing, inferred SSA of Saharan dust as 0.97 at 0.55 mm, while Haywood et al. [2001] have shown that the SSA of Saharan dust at 0.55 mm is in the range of Recent studies [Jacobson, 2001] show that the significantly lower SSA reported in the past (Hess et al. [1998], for example, reported SSA of 0.78) could be due to the possible mixing of Saharan dust with aerosols from biomass burning. The investigations as part of Aerosol Characterization Experiments (ACE-Asia) have demonstrated that aerosols indeed are in mixed state [Seinfeld et al., 2004]. This mixing changes dust aerosol radiative effects in many ways, adding black carbon and other aerosols to the mineral particles [Chandra et al., 2004]. Recent studies show that dust transported from East Asia to the Pacific does not absorb as much light as the dark aerosol from South Asia or some previous measurements of dust from the Sahara Desert [Seinfeld et al., 2004]. [24] Studies by Parungo et al. [1987] have demonstrated that dust particles originating from China s desert are coated by sulfates or soot after passing through polluted industrial regions downwind of the desert. In contrast, Saharan dust transported over the Atlantic Ocean is often coated by sea salt. Dust particles internally mixed with soot, sulfates, nitrates, or aqueous solutions can have drastically different properties from those that are evident at the dust source. The ability of dust particles to scatter and absorb light can be altered in different ways depending on which species aggregate with dust particles. [25] The low correlation between IDDI and AOD in winter would be partly due to the fact that in winter, the amount of pure dust to the total aerosol abundance will be very small because of the low wind speeds, the extremely low temperatures, the weak convection, and the consequent shallow boundary layer experienced at this location. Based on chemical analysis of Aeolian dust from several locations along the west-east corridor of this desert region, Yadav and Rajamani [2004] have reported that the samples are compositionally homogeneous with large abundance of upper continental crustal matter, except in winter when large deviations are observed. They attributed it to the secondary, nonsilicate sources and meteorological processes. The anthropogenic aerosols produced in the neighboring locations (Punjab in the north, where extensive burning of agricultural waste occurs, and the urban regions to the east (Jodhpur, Jaipur, etc.), where the urban activities occur) add to the aerosol burden significantly during this season. The weak winds and the anticyclone are nonconducive to pick up mineral dust and to carry them to long distances. The convective turbulence is weak to cause significant vertical mixing, unlike in summer, when the strong winds along with strong convections produce a remarkable homogeneity in other seasons [Yadav and Rajamani, 2004]. [26] The aerosol spectral optical depth and the dust optical properties deduced from D AE are incorporated in a Discrete Ordinate Radiative Transfer (RT) model developed by University of Santa Barbara (SBDART) [Ricchiazzi et al., 1998] to estimate the aerosol impact (forcing) on the short-wave (SW) fluxes (for details, see Satheesh et al. [1999]). The radiative forcing values reported here are diurnally averaged (averaged over all zenith angles). The surface reflection was obtained from MODIS Albedo Product (MODIS/Terra Albedo; 16-day; Level-3 Global 1-km Grid). The seasonal changes in surface reflectance are taken into account using MODIS surface reflection products for different months. Atmospheric forcing component is estimated by subtracting the surface forcing from that at the top of the atmosphere. The atmospheric heating rate due to the atmospheric forcing component (DF) of dust is ¼ g c p DF is the heating rate (K day 1 ), g is the acceleration due to gravity, c p is the specific heat capacity of air at constant pressure (1006 J kg 1 K 1 ), and P is the atmospheric pressure, respectively [e.g., Moorthy et al., 2005]. Estimates show that the dust absorption over Indian desert would lead to lower atmospheric warming of K day Conclusions [27] 1. The infrared radiance depression on account of the presence of dust in the atmosphere has been used as an index of dust load (known as Infrared Difference Dust Index). [28] 2. The infrared radiance ( mm), acquired from METEOSAT-5 satellite (5 km resolution), was used to retrieve dust aerosol characteristics over Great Indian Desert and adjacent regions. [29] 3. Simultaneous, ground-based spectral optical depths estimated at visible and near IR wavelengths (using a multiwavelength solar radiometer) are used along with the IDDI, retrieved from satellite data, to infer the dust absorption. [30] 4. The inferred single scattering albedo of dust was in the range of Our studies indicate that dust over the Indian desert is of more absorbing nature compared with pure dust. [31] 5. This large dust absorption over vast desert regions would lead to lower atmospheric warming of K day 1. [32] Acknowledgments. This work was carried out under the Aerosol Climatology project of ISRO-GBP. 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(krishnamoorthy_k@vssc.org) S. K. Satheesh and J. Srinivasan, Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore , India. (satheesh@ caos.iisc.ernet.in) C. B. S. Dutt, Indian Space Research Organisation Headquarters, New BEL Road, Bangalore , India. 10 of 10