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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114, D08202, doi:10.1029/2008JD010765, 2009

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Climatology of columnar aerosol properties and the influence of synoptic conditions: First-time results from the northeastern region of India Mukunda M. Gogoi,1 K. Krishna Moorthy,1 S. Suresh Babu,1 and Pradip K. Bhuyan2 Received 11 July 2008; revised 5 February 2009; accepted 17 February 2009; published 22 April 2009.

[1] Six years of spectral aerosol optical depths (AODs), from the northeastern part of

India (Dibrugarh), are used to evolve a climatology for this region. The results indicate that the seasonal mean AODs at 500 nm go as high as 0.45 ± 0.05 during premonsoon season (March to May), decrease gradually through the monsoon (June to September) to reach the lowest value of 0.19 ± 0.06 during the retreating-monsoon season (October and November), and increase to 0.31 ± 0.04 in winter (December to February). The AOD spectra are generally flatter than those seen typically over continental sites of India (and ˚ ngstro¨m exponent a remaining below 1.0 elsewhere in the neighboring regions) with A during February through August, indicating a relatively low abundance of fine and accumulation mode aerosols. The columnar size distributions (CSD) retrieved from spectral AODs are, in general, bimodal with primary mode at 0.1 mm and secondary mode at 1.0 mm. High mass loading (309.5 ± 65.9 mg m2) and effective radius (0.40 ± 0.09 mm) occur during premonsoon and are attributed to significant abundance of coarse (natural) aerosols. Cluster analysis of air mass back trajectories indicate significant transport of mineral dust from the arid regions of west Asia and northwest India across the Indo-Gangetic plains and marine aerosols advected from the Bay of Bengal contributing largely to the coarse mode aerosols during this season. On the other hand, the peculiar topography combined with the local conditions and the widespread rainfall lead to a more pristine environment during retreating-monsoon season with quite low AODs and columnar loading. Citation: Gogoi, M. M., K. Krishna Moorthy, S. S. Babu, and P. K. Bhuyan (2009), Climatology of columnar aerosol properties and the influence of synoptic conditions: First-time results from the northeastern region of India, J. Geophys. Res., 114, D08202, doi:10.1029/2008JD010765.

1. Introduction [2] The Earth-atmosphere system is in a balance between the incoming and the outgoing energy; a perturbation to which is likely to have significant implications to global climate. Of the various factors that are responsible for climate changes, the effect of atmospheric aerosols, both natural and anthropogenic, remains most uncertain [Intergovernmental Panel on Climate Change, 2007]. Well-focused and integrated field campaigns have also been formulated, such as ACE-1 [Bates et al., 1998], ACE-2 [Raes et al., 2000], SCAR-B [Kaufman et al., 1998], TARFOX [Russell et al., 1999], INDOEX [Ramanathan et al., 2001], and ICARB [Moorthy et al., 2008]. Nevertheless, significant data gaps exist. Viewed in the backdrop of the above, the regional characterization of aerosol properties leading to a climatological feature is of great importance and relevance. 1 Space Physics Laboratory, Vikram Sarabhai Space Centre, Trivandrum, India. 2 Department of Physics, Dibrugarh University, Dibrugarh, India.

Copyright 2009 by the American Geophysical Union. 0148-0227/09/2008JD010765$09.00

[3] In the regional characterizations of atmospheric aerosols over Southeastern Asian region, the northeastern parts of India is unique owing to its characteristic dense vegetation, vast water bodies and the unique topography, with mountains in the north, east and south and the highly developed Indo-Gangetic Plains toward the west. Recent studies have shown that presence of substantial amounts of absorbing aerosols in the Himalayan region can significantly influence the Asian summer monsoon [Lau et al., 2008]. Several regional experiments are being planned to evolve regional aerosol characteristics pertinent to this region such as JAMEX [Lau et al., 2008], EAST-AIRE [Li et al., 2007], SHARE-Asia [Lau et al., 2008]. Despite these, extensive observations on aerosol characteristics leading to a climatological picture of this region of India have not yet been carried out. With a view to bridge this gap, spectral AOD measurements were initiated at Dibrugarh (27.3°N, 94.6°E, 111 m a.s.l, in extreme eastern end of India) and regular measurements have been carried out since October 2001 as a part of the aerosol radiative forcing over India (ARFI) project of ISRO-Geosphere Biosphere Program (I-GBP). The data collected for 6 years since then have been used in the present analysis to infer climatological features of

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February), premonsoon (March through May), monsoon (June through September) and retreating monsoon (October and November). During winter, fair weather prevails with occasional fog and haze. Rainfall is scarce, with a seasonal total of 103 mm (4% of annual). The minimum temperature ranges between 8°C and 10°C and the maximum between 27°C and 29°C. During premonsoon season, the ambient temperature increases (with maximum in the range 28°C to 32°C) and rainfall increases to 24% of annual. During the monsoon season, rainfall is intense and about 65% of the annual rainfall occurs during this period. The maximum temperature ranges between 33°C and 37°C. The monsoon withdraws by the end of September or beginning of October, rainfall decreases abruptly (7% of annual) and the sky becomes progressively clear.

3. Aerosol Database Figure 1. Geographical location of the measurement site Dibrugarh (DBR) over the Indian subcontinent and adjoining south Asian regions. aerosol over this region, where there exists no other measurement of aerosols. The aerosol microphysical and optical properties thus obtained are examined for region specific processes and also compared with those reported at other parts of India and south and east Asia.

2. Northeast India: Topography and Climate [4] Northeast India is the region bound between 22°N to 29.5°N and 88°E to 97.5°E, internationally bordered by China to its north and east, Myanmar to the southeast and south and Bangladesh to the southwest. Topographically, the great Himalayan ranges and Tibetan plateau to the north, the hills and mountain of Yanan to the east and the Meghalaya Plateau in the middle strongly influence the general tropical warm climate of the region. Many of these hills and mountains are quite high (between 1 km to 5 km a.s.l) and prevent the rain bearing monsoon winds from escaping this region on the one hand, while they do not allow the dry and cold winds of central Asia to enter the northeast region. The Meghalaya plateau orographically lifts the southwest monsoon winds, causing the world’s heaviest rainfall in its southern margin. The experimental site, Dibrugarh (Figure 1), is located in the northeastern corner of upper Brahmaputra valley. It is enriched with forests scattered all over and large number of rivers and their tributaries and streams flow through the area, ultimately draining into the enormous river Brahmaputra, and eventually to the Bay of Bengal. [5] Dibrugarh experiences subtropical monsoon climate with mild winter, warm, and humid summer. The average annual temperature is 23.9°C and the average annual rainfall is 2760 mm with a total number of 193 rainy days. The climatological mean rainfall at Dibrugarh peaks in July in the annuals (520 mm), and the number of rainy days (22 days). The surface winds are generally low (81% and 63%, respectively. The frequency distribution of ˚ ngstro¨m exponent too exhibits clear seasonal variation in A the distribution, with the mode changing from a low value of 0.7 during premonsoon, to 1.1 during winter through 0.9 in ret-monsoon season. During monsoon season, the dis˚ ngstro¨m tributions are quite broad for both AOD and A exponent. This further indicates the dominant influence of a coarse mode aerosol type during premonsoon and monsoon seasons, which becomes rather weak during ret-monsoon and winter. [12] In view of the strong influence of prevailing synoptic conditions in modulating the aerosol properties and to aid incorporation into regional models, we examined the climatological seasonal mean values of AOD and a. These are presented in Table 1, along with similar reports from earlier measurements by different investigators at other parts of India and countries adjoining Dibrugarh. It is apparent that ˚ ngstro¨m exponent a at Dibrugarh during winter the A (1.14) is comparable to those reported over dusty urban (Beijing and Gwangju) and coastal (Trivandrum, Gosan and Anmyon) locations, but higher than the oceanic (Arabian Sea) and pristine high-altitude (Nainital, Dunhuang) locations; indicating the strong presence of coarse mode aerosols associated with natural sources (dust or sea spray). On the other hand, industrialized urban and coastal locations such as Kanpur (urban, continental), Shirahama (urban), Pune (urban, industrial) and Goa (harbor, industrial) show comparatively higher values of a (ranging from 1.25 to 1.48) due to the strong accumulation mode sources associated with anthropogenic activities. It is also interesting to note the high values of a at Dalanzadgad, a high-altitude

rural site in Mongolia, which is due to the extremely low dominance of coarse mode aerosols. During premonsoon and monsoon seasons, the aerosol characteristics, depicting lower value of a (0.85) at Dibrugarh is similar to those seen at the IGP (Kanpur) and coastal (Trivandrum) India. Even though Beijing, Dalanzadgad and Gosan also show similar characteristics during premonsoon season, the a values at these stations remain fairly high (1.4, 1.3 and 1.2, respectively) owing to the strong industrial component. During ret-monsoon season, Dibrugarh (a = 1.12) bears a resemblance only to Kanpur while the other continental and coastal locations show comparatively higher values of a ˚ ngstro¨m (ranging from 1.2 to 1.5). The examination of A exponent a thus indicate that the values of a at Dibrugarh are lower than those obtained in many continental sites, indicating only a moderate accumulation mode loading and a fairly strong presence of coarse mode aerosols associated with natural sources. The reduced accumulation mode abundance could be attributed to remote and fairly nonindustrialized nature of this region. Extensive measurements over different geographical environments have shown that the values of a < 1.0 indicate size distributions dominated by coarse mode aerosols (radii 0.5 mm) that are typically associated with dust or sea salt [Eck et al., 1999; Schuster et al., 2006]. The high values of a (>1.3) are typically associated with the continental aerosols [Behnert et al., 2007]. The influence of continental air mass, to the increased value of a over oceans adjacent to continents, is shown in earlier literature [Hoppel et al., 1990; Satheesh and Moorthy, 1997; Moorthy et al., 2005, 2007]. Examining our results and discussions that have foregone along with Table 1, it is clear that columnar aerosol property over Dibrugarh closely resemble that of a typical remote coastal or arid environments during premonsoon and monsoon seasons; while it behaves almost like a polluted and anthropogenically influenced continental site during winter.

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Figure 6a. Frequency of occurrence of AOD (500 nm) examined separately for the distinct seasons, showing the broad nature of the distributions during premonsoon and monsoon seasons in contrast to the more sharp and skewed distributions for the other seasons. [13] The presence of dust is indicated by the fact that the largest t p(500) values correspond to the lowest a values [Holben et al., 2001]. Dey et al. [2004] have reported that maximum t p(500) > 0.8 was observed during the premonsoon season over Kanpur corresponding to the maximum decrease in a. Table 1 also clearly indicates that the highest seasonal mean AOD, but with the minimum values of a for six principal sites of East Asia occurred during March through April (or May). Over the Indian mainland, coastal and the adjoining oceanic

regions, the seasonal mean values of AOD are highest with lowest a values in the premonsoon season. Examination of our results in the light of the above clearly indicates that the high AOD and lower value of a during premonsoon season at Dibrugarh is due the loading of dust into the atmosphere. 4.4. Retrieved Columnar Size Distribution [14] The spectral AOD (t pl) values contain information of height integrated (columnar) size distribution (CSD)

˚ ngstro¨m wavelength exponent a. Figure 6b. Same as Figure 6a but for the A 6 of 16

2001 – 2005

2001 – 2005 1999 – 2000

CIMEL CIMEL CIMEL Aureolemeter

Urban site, Korea

7 of 16 0.43

1997 – 2005 2000 – 2004 2000 – 2003 2000 – 2002 2001 – 2005 2000 – 2005 1995 – 2002

CIMEL Prede Sun-sky radiometer MWR Microtop-II CIMEL CIMEL MWR MWR

Oceanic region

2000 – 2004

0.38

2002 – 2002

MWR

-

0.29

0.31

0.29

0.41

0.06

0.03

0.23

0.19

0.29

0.50

0.57

0.31

Winter

Downwind site of city, Japan Dust activity center, China High-altitude station, central Himalayas High-altitude rural site, Mongolia Urban, industrial site, west coast of India Tropical coastal site, southern India Coastal site, west coast of India Coastal background site, Korea Coastal background site, Korea Oceanic region

2004 – 2005

2001 – 2003

CIMEL

Period 2001 – 2007

Instrument MWR

Location Characteristics

Rural continental site, northeast India Urban, industrial site, Indo-Gangetic plain Urban site, China

Location

Dibrugarh (27.3°N, 94.6°E, 111m) Kanpur (26.43°N, 80.36°E, 142 m) Beijing (34.0°N, 116.40°E, 92 m) Gwangju (35.1°N, 126.50°E, 60 m) Shirahama (33.7°N, 135.4°E, 10 m) Dunhuang (40.04°N, 94.79°E, 1300 m) Nainital (29.37°N, 79.45°E, 2000 m) Dalanzadgad (43.6°N, 104.4°E, 1470 m) Pune (18.53°N, 73.85°E, 570 m) Trivandrum (8.55°N, 76.97°E, 3 m) Dona Paula, Goa (15.47°N, 73.81°E, 25 m) Gosan (33.3°N, 126.2°E, 50 m) Anmyon (36.5°N, 126.3°E, 47 m) Arabian Sea (4°N – 20°N to 50°E – 78°E) Bay of Bengal (10°N – 25°N, 77°E – 100°E) 0.48

0.47

0.55

0.40

0.48

0.40

0.42

0.18

0.16

0.29

0.34

0.46

0.80

0.54

0.45

Premonsoon

-

-

0.50

0.40

-

0.29

-

0.15

-

0.18

0.87

0.61

1.00

0.66

0.25

Monsoon

AOD

0.19

-

0.31

0.29

-

0.38

-

0.06

0.08

0.23

0.20

0.28

0.51

0.63

0.19

Ret-Monsoon

-

0.70

1.10

1.00

1.48

1.00

1.40

1.40

0.72

0.21

1.25

1.10

1.10

1.26

1.14

Winter

0.50

0.30

1.00

0.80

1.14

0.85

0.90

0.46

0.25

1.02

1.10

0.90

0.60

0.85

Premonsoon

-

-

1.40

1.20

-

0.32

-

1.30

-

0.26

1.28

1.40

1.25

0.66

0.87

Monsoon

˚ ngstro¨m Exponent A

-

-

1.30

1.40

-

1.20

-

1.50

-

0.21

1.36

1.30

1.15

1.12

1.12

Ret-Monsoon

Satheesh et al. [2006a] Satheesh et al. [2006b]

Kim et al. [2007]

Pandithurai et al. [2007] Moorthy et al. [2007] Suresh and Desa [2005] Kim et al. [2007]

Xiangao et al. [2004] Sagar et al. [2004] Kim et al. [2007]

Kim et al. [2007]

Kim et al. [2007]

Singh et al. [2004] Kim et al. [2007]

present study

Reference

˚ ngstro¨m Exponent a Over Various Indian Locations as Well as Marine Locations Adjacent to India and East Asian Locations Along With That Observed During the Table 1. AOD and A Present Study

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functions [nc(r)] of aerosols, as both are connected through the Mie integral equation, Zrb t pl ¼

pr2 Qext ðm; r; lÞnc ðrÞdr;

ð2Þ

ra

where Qext is the Mie extinction efficiency, which depends on the aerosol complex refractive index (m), radius (r) and wavelength of the incident radiation (l); nc(r) is the columnar number density of aerosols (in a vertical column of unit cross section) in an infinitesimal radius range dr centered at r. In defining nc(r) this way, it is implicitly assumed that the number size distribution is height invariant or averaged over the vertical column. The radii limits ra and rb to the integral are respectively the lower and upper cutoff radii of the particles, such that only those particles having sizes within the range ra to rb contribute significantly to Qext. [15] Under such conditions, nc(r) can be retrieved from spectral AOD estimates by numerical inversion of equation (2). Of the several methods described in the literature, we have adapted the constrained linear inversion method [King et al., 1978, King, 1982]. Since the MWR measures only the directly transmitted flux (unlike the AERONET, where almucantar measurements are also measured and used for retrieving size distributions), we used the spectral AODs and equation (2) to retrieve the columnar size distribution. The details of application of this method to the MWR data have been discussed by Moorthy et al. [1991] and Saha and Moorthy [2004]. The spectral AODs from the measurements and the corresponding errors formed the inputs. In the present case, we set ra = 0.05 mm and rb = 3.0 mm as optimal after performing a sensitivity analysis of the size range on Qext over the entire wavelength range of measurements. This range was divided into 10 coarse bins of unequal width in the numerical approach. We used wavelength-dependent complex refractive indices (1.48, .0464), (1.48, .0452), (1.48, .0437), (1.48, .043), (1.48, 0.0412), (1.48, .0415), (1.48, .0409), (1.47, .0418), (1.47, .0441), (1.47, .0464), adapted from Lubin et al. [2002], considering spherical aerosols, externally mixed with 10% soot during winter. Each parenthesis represents one complex value with the first one being its real part and the next the imaginary part. The spectral AODs are reestimated using the direct Mie equation, after each iteration, and are compared with the input AOD spectrum, and the solutions are accepted only when the reestimated values agree with those from the measurements within the measurement errors. As the measurement errors are also part of the input, the solutions are weighted by these errors, with better accuracy around size ranges sensitive to more accurate (AOD) measurements [King, 1982]. In general, stable solutions were obtained after 6 to 7 iterations, for fairly low value ( 0.4 mm. However, these measurements were made somewhat within the source regions of the dust storms. The site Dibrugarh is quite far away from active dust source regions of west Asia and central India, and surrounded by forests, dense vegetation and water bodies, which do not favor local dust production. Under such conditions, what we encounter at DBR would be advected (transported) dust, which is much finer in size than those observed near the source regions of the arids. Hess et al. [1998] have described the transported dust with a lognormal distribution having mode radii of 0.5 mm. This might account for the differences in the mode radii seen at DBR from those reported for Kanpur. The differences in the inversion schemes also might be contributing. Notwithstanding all these, the bimodal nature is persistent at all these stations. The coarse mode concentration is found to significantly decrease during monsoon associated with wash out. On the basis of multiyear measurements from the coastal location Trivandrum in south India, Moorthy et al.

Figure 12. Annual variations of (a) mode radii, rm1 and rm2, and (b) standard deviations s1 and s2 of the mode. The dashed lines represent the mean values. The striking feature is the nearly steady value of rm1 and rm2 over the years.

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Figure 13. Annual variations of (a) effective radii, reff, (b) mass loading, mL, (c) integrated content of aerosols, Nt, and (d) ratio of coarse to accumulation mode aerosol concentration, No2/No1. Highest aerosol loading mL and Reff occur in the premonsoon season showing large abundance of coarse mode aerosols. [1991] have shown that the CSDs transform from unimodal during winter season to bimodal with a coarse mode (at 0.8 mm) during monsoon season, and attributed it to the advection of sea-spray aerosols from the adjoining ocean. Similarly, analyzing the CSDs over a tiny island location Minicoy (in the Arabian Sea), Moorthy and Satheesh [2000] have reported the presence of a strong coarse particle mode rm2 0.86 mm and attributed it to sea-spray aerosols. Examining the results at Dibrugarh in the light of the above and its particular topography, allowing advection only from the Indo-Gangetic plains or Bay of Bengal, it appears that the strong presence of the coarse mode aerosols and the low value of a during most part of the year is associated with either mineral dust or marine aerosol component or both.

minimize the variability among the trajectories within a cluster and maximize the variability between the clusters. Initially each trajectory is defined to a cluster, in other words if there are N trajectories there are N clusters. Now, for every combination of trajectory pairs, the cluster spatial variance (SPVAR) is calculated. SPVAR is the sum of the squared distances between the endpoints of the cluster’s component trajectories and the mean of the trajectories in that cluster. Then the total spatial variance (TSV), the sum

5. Role of Transport [28] In order to establish the links between the synoptic air masses and the optical and microphysical properties of aerosols over Dibrugarh, the 5-day isentropic back trajectories are analyzed on all days when the MWR observations were made using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The trajectories are considered at three different height levels, namely, 500 m, 1800 m and 3600 m above ground following the considerations of Moorthy et al. [2003]. Given the regional peculiarity of Dibrugarh (section 2), only the higher trajectories would be capable of long-range transport and as such, we focus on them. For this, we separated the trajectories into different clusters, to ascertain the primary pathways that favor advection of aerosol particles originated elsewhere. The main criterion of the trajectory clustering is to

Figure 14. Total spatial variance (TSV) versus clusters in premonsoon season for trajectories at 1800 m above ground.

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Figure 15. Cluster mean trajectories along with their spread during premonsoon and monsoon seasons arriving at DBR at two different height levels from ground, but above the atmospheric boundary layer. The percentage contribution of each cluster to the total is also indicated. of all the SPVAR, is calculated. The pairs of clusters combined are the ones with the lowest increase in TSV (which is initially zero). After the first iteration, the number of clusters is N - 1. Clusters paired always stay together. For the subsequent iterations, the clusters are either individual trajectory or the cluster that were initially paired. Again, every combination is tried, and the SPVAR and TSV for each are calculated. The iterations are continued until the last two clusters are combined. [ 29 ] Initially during clustering iterations, the TSV increases faster, subsequently slowly and at a nearly constant rate. At some point in the iterations, it again increases rapidly, indicating that the clusters being combined are not very similar. This latter increase suggests where to stop the clustering and is clearly seen in a plot of percentage change in TSV versus number of clusters (Figure 14). The iterative step just before the large increase in the change of TSV gives the final number of clusters. It is readily seen from Figure 14 that the TSV increases abruptly when the number of cluster decreases to 2. The most acceptable value of cluster can thus be considered here as 4 or 3. Following the above procedure, the cluster mean trajectories and their

percent contributions are evaluated for each season at each different height. These are shown in Figure 15 (for premonsoon and monsoon) and Figure 16 (ret-monsoon and winter) respectively. It is readily noticed from Figures 15 and 16 that the cluster-mean trajectories distinctly change their orientation, traversing distinct geographic locations at different seasons, at different heights. Seven distinct cluster mean trajectories are found, which are assigned to distinct groups in accordance with their location of origin as mentioned in Table 2. The percentage contribution of each group to the total is also indicated in Figures 15 and 16. Subsequently, we grouped individual days with trajectories possessing a common cluster at all the three levels, along with the days on which the trajectories possess the same cluster at least in two levels. The values of AOD, a, and b for individual days were separated into each group and averaged and the mean values are given in Table 3. The values appearing after the ‘‘±’’ symbols represent the standard errors. The following emerge from Table 3. [30] 1. During premonsoon season, the highest value of AOD (mean = 0.45 ± 0.03) and b (mean = 0.28 ± 0.02) were contributed by the upper air westerlies from west Asia,

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Figure 16. Same as Figure 15 but for ret-monsoon and winter seasons. traversing across the IGP toward northeastern India, while the AODs remained lower associated with trajectories confined to local regions. [31] 2. During monsoon season, the highest contributions to high AOD (mean = 0.37 ± 0.08) were associated with the trajectories from central India while the advection from the east contributed 0.33 ± 0.05. Low AODs and steeper spectral dependence were encountered associated with trajectories confined to the local regions and from Bay of Bengal. It might be recalled that on the basis of AOD measurements from Port Blair in the BoB, Moorthy et al. [2003] have shown increased abundance of fine aerosols associated with advection from east Asia, which led to steeper AOD spectra.

[32] 3. The influence of long-range transport was not seen conspicuously in the ret-monsoon season. During this period, large numbers of trajectories (54%) were confined locally and 24% observations exhibited mixed characteristics. This leads to lower a and annually lowest AODs, signifying the rather pristine nature of the locale. [33] 4. During winter season, the trajectories at all levels were purely westerly, originating either at the Indian mainland or west Asian locations. However, the low surface Table 3. Mean Values of AOD, a, and b With Respect to Each Cluster of Trajectories at Four Seasons of Premonsoon, Monsoon, Ret-Monsoon, and Winter Season

Groupa

Premonsoon

L WA L MI EA WA L, BoB MI MI WA

Table 2. Cluster Groups and Region in Which They Originated Group

Region in Which They Originated

1 2 3 4 5 6 7

Local (L) Bay of Bengal (BoB) Mainland of India (MI) West Asia (WA) Southern China (SC) East Asia (EA) Mixed (M)

Monsoon Ret-Monsoon Winter a

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AOD (500 nm) 0.31 0.45 0.29 0.37 0.33 0.18 0.18 0.18 0.26 0.31

Groups defined in Table 2.

± ± ± ± ± ± ± ± ± ±

0.05 0.03 0.08 0.08 0.05 0.02 0.10 0.10 0.03 0.02

a 0.77 ± 0.07 0.85 ± 0.05 1.07 ± 0.12 0.91 ± 0.15 0.98 ± 0.09 1.05 ± 0.09 0.95 ± 0.09 1.0 ± 0.05 1.03 ± 0.06 1.14 ± 0.03

b 0.21 0.28 0.16 0.19 0.16 0.09 0.10 0.10 0.12 0.14

± ± ± ± ± ± ± ± ± ±

0.02 0.02 0.02 0.03 0.02 0.02 0.02 0.05 0.02 0.01

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temperatures and extreme winter conditions are not conducive for generation and advection of dust and consequently the AODs remain moderate and a high (1.14 ± 0.03). [34] In the light of the above, it may be concluded that during premonsoon season the increase in AOD over Dibrugarh is related, in general, to the higher-level transport of the desert and arid region of west Asia, polluted air from the industrialized and urban regions of India. Coming to monsoon season, the long-range transport of aerosols from south and east Asia, Bay of Bengal, and the mainland of India modulate the aerosol properties. However, there is also a rapid decrease in the aerosol concentration during this season due to the extensive washout. For aerosols in the size range 0.1 to 1.0 mm, wet removal is the chief removal mechanism [Flossmann et al., 1985; Moorthy et al., 2007]. During ret-monsoon season, the low rainfall coupled with low relative humidity and large diurnal variation in the ambient temperature leads to progressively drier land surfaces. The local sandy area (including the banks of the vast river Brahmaputra) also contributes to dust input along with those form the widespread vegetations and the dense tea plantations around Dibrugarh. In addition, Badarinath et al. [2007] have also reported significant correlation between forest fire occurrences and the variations in the aerosol concentrations over the northeastern India. However, these are seasonal and the abundance varies from year to year.

6. Conclusions [35] The long-term investigations of AODs and columnar size distributions over the northeastern location (Dibrugarh) of India revealed the following. [36] 1. The AOD shows clear seasonal variations with a high seasonal mean value of 0.45 ± 0.05 at 500 nm during premonsoon season, decreasing gradually through the monsoon season to reach the lowest value of 0.19 ± 0.06 during ret-monsoon season and increase to 0.31 ± 0.04 in winter. [37] 2. The AOD spectra are generally flatter than those seen typically over continental sites of India and the neighboring regions. The climatological mean value of a and b are 1.08 ± 0.29 and 0.19 ± 0.11, respectively, indicating a relatively low abundance of fine or accumulation mode aerosols as well as low columnar aerosol loading. [38] 3. The retrieved columnar size distributions are, in general, bimodal with primary mode at 0.1 mm and secondary mode at 1.0 mm. The occurrence of high mass loading and effective radius during premonsoon season is attributed to significant abundance of coarse (natural) aerosols during this season. [39] 4. Cluster analysis of air mass back trajectories indicate that the increase in AOD over Dibrugarh during premonsoon season is related, in general, to significant transport of mineral dust from the arid regions of west Asia and northwest India across the Indo-Gangetic plains. On the other hand, the peculiar topography combined with local conditions and widespread rainfall lead to low AODs and columnar loading during ret-monsoon season. [40] 5. These inferences are basically derived from the columnar AOD measurements. Other complementary measurements of aerosol microphysics and chemistry are needed for a comprehensive characterization of aerosols in this region, and this is planned in the coming years.

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[41] Acknowledgments. This study was carried out under the Aerosol Radiative Forcing over India (ARFI) project of ISRO-Geosphere Biosphere Program (ISRO-GBP). We acknowledge NOAA Air Resources Laboratory for the provision of the HYSPLIT transport and dispersion model and READY website (http://www.arl.noaa.gov/ready.html) used in this publication.

References ˚ ngstro¨m, A. (1961), Techniques of determining the turbidity of the atmoA sphere, Tellus, 13, 214 – 223. Badarinath, K. V. S., S. K. Kharol, and T. R. K. Chand (2007), Use of satellite data to study the impact of forest fires over the northeast region of India, Geosci. Remote Sens., 4(3), 485 – 489, doi:10.1109/LGRS.2007.896738. Bates, T. S., B. J. Huebert, J. L. Gras, F. B. Griffiths, and P. A. Durkee (1998), International Global Atmospheric Chemistry (IGAC) Project’s First Aerosol Characterization Experiment (ACE-1): Overview, J. Geophys. Res., 103(D13), 16,297 – 16,318, doi:10.1029/97JD03741. Behnert, I., V. Matthias, and R. Doerffer (2007), Aerosol climatology from ground-based measurements for the southern North Sea, Atmos. Res., 84, 201 – 220, doi:10.1016/j.atmosres.2006.05.006. Dey, S., S. N. Tripathi, and R. P. Singh (2004), Influence of dust storms on the aerosol optical properties over the Indo-Gangetic basin, J. Geophys. Res., 109, D20211, doi:10.1029/2004JD004924. Dubovik, O., and M. D. King (2000), A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements, J. Geophys. Res., 105(D16), 20,673 – 20,696, doi:10.1029/ 2000JD900282. Eck, T., B. N. Holben, J. Reid, O. Dubovik, A. Smirnov, N. O’Neill, I. Slutsker, and S. Kinne (1999), Wavelength dependence of the optical depth of biomass burning, urban and desert dust aerosols, J. Geophys. Res., 104(D24), 31,333 – 31,349, doi:10.1029/1999JD900923. Eck, T., B. N. Holben, O. Dubovik, A. Smirnov, J. Lobert, and V. Ramanathan (2001), Column-integrated aerosol optical properties over the Maldives during the northeast monsoon for 1998 – 2000, J. Geophys. Res., 106(D22), 28,555 – 28,566, doi:10.1029/2001JD000786. Flossmann, A. I., W. D. Hall, and H. R. Pruppacher (1985), A theoretical study of the wet removal of atmospheric pollutants, 1, The redistribution of aerosol particles captured through nucleation and impaction scavenging by growing cloud drops, J. Atmos. Sci., 42, 583 – 606, doi:10.1175/ 1520-0469(1985)0422.0.CO;2. Gogoi, M. M., P. K. Bhuyan, and K. K. Moorthy (2008), Estimation of the effect of long-range transport on seasonal variation of aerosols over northeastern India, Ann. Geophys., 26, 1 – 13. Hansen, J. E., and L. D. Travis (1974), Light scattering in planetary atmospheres, Space Sci. Rev., 16, 527 – 610. Hess, M., P. Koepke, and I. Schultz (1998), Optical properties of aerosols and clouds: The software package OPAC, Bull. Am. Meteorol. Soc., 79, 831 – 844. Holben, B. N., et al. (2001), An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET, J. Geophys. Res., 106(D11), 12,067 – 12,097, doi:10.1029/2001JD900014. Hoppel, W. A., J. W. Fitzgerald, G. M. Frick, and R. E. Larson (1990), Aerosol size distribution and optical properties found in the marine boundary layer over the Atlantic Ocean, J. Geophys. Res., 95(D4), 3659 – 3686, doi:10.1029/JD095iD04p03659. Intergovernmental Panel on Climate Change (2007), Climate Change 2007: The Physical Science Basis – Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., 996 pp., Cambridge Univ. Press, New York. Kaufman, Y. (1993), Aerosol optical thickness and path irradiance, J. Geophys. Res., 98(D2), 2677 – 2692, doi:10.1029/92JD02427. Kaufman, Y. J., et al. (1998), Smoke, Clouds, and Radiation – Brazil (SCAR-B) experiment, J. Geophys. Res., 103(D24), 31,783 – 31,808, doi:10.1029/98JD02281. Kim, S. W., S. C. Yoon, J. Kim, and S. Y. Kim (2007), Seasonal and monthly variations of columnar aerosol optical properties over east Asia determined from multi-year MODIS, LIDAR and AERONET Sun/sky radiometer measurements, Atmos. Environ., 41, 1634 – 1651, doi:10.1016/j.atmosenv.2006.10.044. King, M. D. (1982), Sensitivity of constrained linear inversion to the selection of Lagrange multiplier, J. Atmos. Sci., 39, 1356 – 1369, doi:10.1175/ 1520-0469(1982)0392.0.CO;2. King, M. D., D. M. Byrne, B. M. Herman, and J. A. Reagen (1978), Aerosol size distribution obtained by inversion of spectral optical depth measurements, J. Atmos. Sci., 35, 2153 – 2167, doi:10.1175/15200469(1978)0352.0.CO;2. Lau, K. M., et al. (2008), The Joint Aerosol-Monsoon Experiment: A new challenge for monsoon climate research, Bull. Am. Meteorol. Soc., 89, 369 – 383, doi:10.1175/BAMS-89-3-369.

15 of 16

D08202


Li, Z., et al. (2007), Preface to special section on East Asian Studies of Tropospheric Aerosols: An International Regional Experiment (EASTAIRE), J. Geophys. Res., 112, D22S00, doi:10.1029/2007JD008853. Lubin, D., S. K. Satheesh, G. McFarquar, and A. J. Heymsfield (2002), Longwave radiative forcing of Indian Ocean tropospheric aerosol, J. Geophys. Res., 107(D19), 8004, doi:10.1029/2001JD001183. Moorthy, K. K., and S. K. Satheesh (2000), Characteristics of aerosols over a remote island, Minicoy in the Arabian Sea: Optical properties and retrieved size characteristics, Q. J. R. Meteorol. Soc., 126, 81 – 109. Moorthy, K. K., P. R. Nair, and B. V. K. Murthy (1989), Multiwavelength solar radiometer network and features of aerosol optical depth at Trivandrum, Ind. J. Radio Space Phys., 18, 194 – 201. Moorthy, K. K., P. R. Nair, and B. V. K. Murthy (1991), Size distribution of coastal aerosols: Effects of local sources and sinks, J. Appl. Meteorol., 30, 844 – 852, doi:10.1175/1520-0450(1991)0302.0.CO;2. Moorthy, K. K., S. S. Babu, and S. K. Satheesh (2003), Aerosol spectral optical depths over the Bay of Bengal: Role of transport, Geophys. Res. Lett., 30(5), 1249, doi:10.1029/2002GL016520. Moorthy, K. K., S. S. Babu, and S. K. Satheesh (2005), Aerosol characteristics and radiative impacts over the Arabian Sea during inter-monsoon season: Results from the ARMEX field campaign, J. Atmos. Sci., 62, 192 – 206, doi:10.1175/JAS-3378.1. Moorthy, K. K., S. S. Babu, and S. K. Satheesh (2007), Temporal heterogeneity in aerosol characteristics and the resulting radiative impact at a tropical coastal station-Part 1: Microphysical and optical properties, Ann. Geophys., 25, 2293 – 2308. Moorthy, K. K., S. K. Satheesh, S. S. Babu, and C. B. S. Dutt (2008), Integrated Campaign for Aerosols, gases and Radiation Budget (ICARB): An overview, J. Earth Syst. Sci., 117(S1), 243 – 262, doi:10.1007/s12040008-0029-7. Nair, P. R., and K. K. Moorthy (1998), Effects of changes in the atmospheric water vapor content on the physical properties of atmospheric aerosols at a coastal station, J. Atmos. Solar Terr. Phys., 60, 563 – 572. O’Neill, N. T., T. F. Eck, B. N. Holben, A. Smirnov, O. Dubovik, and A. Royer (2001), Bimodal size distribution influences on the variation of Angstrom derivatives in spectral and optical depth space, J. Geophys. Res., 106(D9), 9787 – 9806. O’Neill, N. T., T. F. Eck, A. Smirnov, B. N. Holben, and S. Thulasiraman (2003), Spectral discrimination of coarse and fine mode optical depth, J. Geophys. Res., 108(D17), 4559, doi:10.1029/2002JD002975. Pandithurai, G., R. T. Pinker, P. C. S. Devara, T. Takamura, and K. K. Dani (2007), Seasonal asymmetry in diurnal variation of aerosol optical characteristics over Pune, western India, J. Geophys. Res., 112, D08208, doi:10.1029/2006JD007803. Pruppacher, H. R., and J. D. Klett (1978), Microphysics of Clouds and Precipitation, D. Reidel, Dordrecht, Netherlands. Raes, F., T. Bates, F. M. McGovern, and M. van Liederkerte (2000), The second Aerosol Characterization Experiment (ACE-2). General overview and main results, Tellus, Ser. B, 52, 111 – 126.

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Ramanathan, V., et al. (2001), Indian Ocean Experiment: An integrated analysis of the climate forcing and effects of the great Indo-Asian haze, J. Geophys. Res., 106(D22), 28,371 – 28,398, doi:10.1029/2001JD900133. Reid, J., T. Eck, S. Christopher, P. Hobbs, and B. N. Holben (1999), Use of ˚ ngstrom exponent to estimate the variability of optical and physical A properties of aging smoke particles in Brazil, J. Geophys. Res., 104(D22), 27,473 – 27,489, doi:10.1029/1999JD900833. Russell, P. B., P. V. Hobbs, and L. L. Stowe (1999), Aerosol properties and radiative effects in the United States East Coast haze plume: An overview of Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX), J. Geophys. Res., 104(D2), 2213 – 2222, doi:10.1029/ 1998JD200028. Sagar, R., B. Kumar, U. C. Dumka, K. K. Moorthy, and P. Pant (2004), Characteristics of aerosol spectral optical depths over Manora Peak: A high-altitude station in the central Himalayas, J. Geophys. Res., 109, D06207, doi:10.1029/2003JD003954. Saha, A., and K. K. Moorthy (2004), Impact of precipitation on aerosol spectral optical depth and retrieved size distributions: A case study, J. Appl. Meteorol., 43, 902 – 914, doi:10.1175/15200450(2004)0432.0.CO;2. Satheesh, S. K., and K. K. Moorthy (1997), Aerosols characteristics over the coastal regions of the Arabian Sea, Tellus, Ser. B, 49, 417 – 428. Satheesh, S. K., K. K. Moorthy, Y. J. Kaufman, and T. Takemura (2006a), Aerosol optical depth, physical properties and radiative forcing over the Arabian Sea, Meteorol. Atmos. Phys., 91, 45 – 62, doi:10.1007/ s00703-004-0097-4. Satheesh, S. K., J. Srinivasan, and K. K. Moorthy (2006b), Spatial and temporal heterogeneity in aerosol properties and radiative forcing over Bay of Bengal: Sources and role of aerosol transport, J. Geophys. Res., 111, D08202, doi:10.1029/2005JD006374. Schuster, G. L., O. Dubovik, and B. N. Holben (2006), Angstrom exponent and bimodal aerosol size distribution, J. Geophys. Res., 111, D07207, doi:10.1029/2005JD006328. Singh, R. P., S. Dey, S. N. Tripathy, and V. Tare (2004), Variability of aerosol parameters over Kanpur; northern India, J. Geophys. Res., 109, D23206, doi:10.1029/2004JD004966. Suresh, T., and E. Desa (2005), Seasonal variations of aerosol over Dona Paula, a coastal site on the west coast of India, Atmos. Environ., 39, 3471 – 3480, doi:10.1016/j.atmosenv.2005.01.063. Xiangao, X., C. Hongbin, and W. Pucai (2004), Aerosol properties in a Chinese semiarid region, Atmos. Environ., 38, 4571 – 4581, doi:10.1016/ j.atmosenv.2004.04.015.

S. S. Babu, M. M. Gogoi, and K. Krishna Moorthy, Space Physics Laboratory, Vikram Sarabhai Space Centre, Trivandrum 695 022, India. ([email protected]) P. K. Bhuyan, Department of Physics, Dibrugarh University, Dibrugarh 786 004, India.

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