Inter-annual variations in natural and anthropogenic aerosol loadings over the seas adjoining India using a hybrid approach

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1 ATOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 15: (2014) Published online 17 October 2013 in iley Online Library (wileyonlinelibrary.com) DOI: /asl2.469 Inter-annual variations in natural and anthropogenic aerosol loadings over the seas adjoining India using a hybrid approach Sushant Das, Sagnik Dey* and S. K. Dash Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi , India *Correspondence to: S. Dey, Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi , India. sagnik@cas.iitd.ac.in Received: 13 arch 2013 Revised: 23 September 2013 Accepted: 23 September 2013 Abstract e report the inter-annual variations of natural and anthropogenic aerosol loading over the Arabian Sea (AS) and Bay of Bengal (BoB) for the period using a hybrid approach. ean (±1 standard deviation) decadal contributions of maritime, dust and anthropogenic aerosols to aerosol optical depth, τ are 16.6 ± 4.2 and 17.5 ± 4.6%, 44.3 ± 22.6 and 37.4 ± 20% and 39 ± 23.4 and 45 ± 20.5%, respectively, over the AS and BoB. Significant increase in τ (>0.007 or 2.3% per year) is mainly driven by anthropogenic aerosols. The results can be used for improved estimates of aerosol radiative forcing in these regions. Keywords: variability natural aerosols; anthropogenic aerosols; remote sensing; Indian Ocean; 1. Introduction Aerosols perturb the Earth s radiation budget directly by scattering and absorbing solar radiation and indirectly by modifying cloud properties. Numerous studies have been carried out to quantify these effects at various spatial and temporal scales (e.g. Yu et al have summarized some of these studies), but considerable uncertainty exists at regional scale. Large uncertainties in emission inventories of anthropogenic and natural aerosols, their mixing and redistribution by wind make it difficult for the climate models to reproduce the observed space-time variability of aerosols in the Indian subcontinent. Limited groundbased measurements of aerosol chemical properties are inadequate to address this problem. easurement of aerosol optical depth (τ) cannot be directly used to separate out the anthropogenic and natural components. To tackle this issue, two major approaches were adopted. Combinations of aerosol properties were used to qualitatively estimate the anthropogenic and natural fraction. For example, Bellouin et al. (2005) have separated the anthropogenic and natural aerosols based on the accumulation mode fraction threshold values. A combination of accumulation mode fraction and single scattering albedo (SSA) was utilized by Srivastava et al. (2012) to infer the dominant aerosol types in the Indo-Gangetic Basin. Another popular way to infer about dominant aerosol types includes analysis of τ and Angstrom Exponent (e.g. Kaskaoutis et al., 2010, 2011a). In another approach, representative aerosol composition has been derived by constraining the model-simulated spectral optical properties with in-situ observations (e.g. Dey and Tripathi, 2008). Both these approaches rely on aerosol properties, primarily spectral τ, and hence have limitations for applicability at regional scale because of unavailability of a dense network of long-term groundbased measurements. oreover, such observations are mostly confined to the Indian landmass, while data over the oceans are available only for short periods typical of campaign mode mostly during the winter (e.g. oorthy et al., 2010) and pre-monsoon (e.g. Kaskaoutis et al., 2010) seasons. In-situ measurements over the oceans are difficult to carry out during the monsoon season due to weather condition, thus limiting the applicability of the above two approaches. τ shows strong seasonal cycle over the Arabian Sea (AS) and Bay of Bengal (BoB), adjacent to the Indian subcontinent (Dey and Di Girolamo, 2010; Kaskaoutis et al., 2010; Satheesh et al. (2006a, 2006b)). Every year, during the pre-monsoon (arch ay) and monsoon (June September) seasons, mineral dust is transported to the oceans from the Great Indian Desert, est Asia and Northeast African dust sources (Kaskaoutis et al., 2011b). Anthropogenic particles are transported to these regions from the postmonsoon to winter seasons (oorthy et al., 2010). Thus, it is important to quantify the inter-annual variations of relative contributions of natural (dust and maritime) and anthropogenic aerosols to τ for further improving the estimates of aerosol radiative forcing. Here, we report a hybrid approach (by combining satellite aerosol products and regional climate model simulations) to differentiate optical depths of dust (τ d ) and maritime (τ ma ) particles from anthropogenic (τ an ) component over the seas adjoining India. The variability of τ ma, τ d and τ an for the period is examined and the climatic implications are discussed Royal eteorological Society

2 Variability of natural and anthropogenic aerosols over Indian Ocean particles with an increase in wind speed (Satheesh et al., 2006a, 2006b). Then τ ma can be estimated as function of U by: (3) τma = exp (0.10 U ) 1 τ =0.062exp(0.10U ) 0.5 ODIS-τ ind Speeed (m s-1) Figure 1. ind-dependence of ODIS-τ with wind speed U at 1000 hpa. 2. Hybrid approach τ = τan + τd + τma (1) e chose southern Indian Ocean (bounded within E and S) to derive an empirical relation for wind-dependent production of τ ma following Satheesh et al. (2006a) and further partitioning into fine and coarse mode. This region is far away from the landmass and is assumed to contain negligible dust and anthropogenic aerosols (Babu et al., 2010). ODIS-τ showed exponential relationship with mean wind speed (U ) at 1000 hpa from NCEP reanalysis data set (Figure 1) with a high degree of correlation (R = 0.74, statistically significant at 95% confidence level following t-test) and a root mean square error of 0.04: τ = exp (0.10 U ) As, no other source of maritime particle production exists, this empirical relation is assumed to be valid for the AS and BoB as well and utilized to estimate τ ma during the entire study period from corresponding NCEP-derived U at 1000 hpa. The empirical regression constant 0.10 in Equation (1) is very close to the value (0.09) reported in Satheesh et al. (2006a), whereas the regression constant is slightly smaller and may be attributed to a longer period of data compared to limited period and smaller area considered in the previous study. Now, fine mode optical depth can be expressed as: τ ff = τma fma,f + τd fd,f + τan fan,f, e used Terra-oderate Resolution Imaging Spectroradiometer (ODIS) retrieved τ (C005, Level 3) at 550 nm wavelength. ODIS is a large swath ( 2330 km) multi-spectral sensor, routinely monitoring τ at km resolution across the globe. Details of ODIS aerosol retrieval are well documented in the literature (Remer et al., 2008) and hence are not repeated here. The quality of ODIS-τ was globally validated and utilized by numerous researchers to study the variability of aerosol characteristics at global as well as regional scales including India (e.g. Ramachandran and Cherian, 2008). Here, analysis was carried out over the AS (6 19 N and E) and BoB (12 21 N and E). ODIS-τ can be represented as sum of three components: (2) The empirical constant represents the nonmaritime background τ in absence of wind. Natural sulfate particles from marine planktons may contribute to this background τ and the relative contribution of sulfate to τ decreases relative to that of maritime 2013 Royal eteorological Society 59 (4) where f f is the total fine mode fraction and f ma,f, f d,f and f an,f are the fractions of τ ma, τ d and τ an in the fine mode, respectively. Substituting for τ an from Equation (1) to Equation (4), τ d can be estimated as: τ fan,f ff τma fan,f fma,f τd = (5) fan,f fd,f Assuming entire anthropogenic contribution in the fine mode, Equation (5) can be rewritten as: τ (1 ff ) τma 1 fma,f τd = (6) 1 fd,f provided f ma,f, f f and f d,f are known for our study regions. f ma,f and f f are derived from ODIS aerosol product. ean (±1σ ) f ma,f calculated over the southern Indian ocean (where f ma,f = f f ) is found to be 0.38 ± 0.08 and close to the value 0.31 ± 0.1 derived from AERONET data in Amsterdam Island (77.57 E, S). e used ICTP-Regional Climate odel, RegC4.1 to calculate f d,f in absence of any in-situ data from these regions. RegC4.1 is a hydrostatic model with sigma-p vertical coordinates that includes an inbuilt aerosol module for mineral dust (Zakey et al., 2006). Dust emission results from saltation causing horizontal flux and sand blasting, which creates a vertical flux of dust into the atmosphere depending on the wind strength. Dust loading is calculated at four size bins ranging from 0.1 to 1.0 µm, 1.0 to 2.5 µm, 2.5 to 5.0 µm and 5.0 to 20 µm (Das et al., 2013). The soil textures are taken from United States Department of Agriculture data set. RegC4.1 is driven by initial and 6-hourly updating lateral boundary conditions from National Centers for Environmental ediction (NCEP)-National Center for Atmospheric Research (NCAR) reanalysis data. Simulations are carried out Atmos. Sci. Let. 15: (2014)

3 60 S. Das, S. Dey and S. K. Dash at 30-km horizontal resolution and 18 vertical layers with the model top set at 50 hpa considering Holtslag planetary boundary layer scheme and Grell cumulus parameterization scheme (Giorgi et al., 2012). The model captures the spatial variability of wind field and precipitation reasonably well, but with a high bias in 850 hpa wind over the AS and high bias in monsoon precipitation over the BoB. This may lead to a high bias in f d,f over the AS and BoB during the premonsoon and monsoon seasons, respectively, that is estimated by the ratio of τ d simulated using the first two size bins and all four bins (Das et al., 2013). Discussion is warranted about the magnitude of uncertainty in the hybrid approach. Uncertainty may arise due to errors in the regression constants of Equation (3) and absolute value of U. τ ma is less sensitive to an increase in U until U exceeds 6 m s 1 (Figure 1). Even beyond 6 m s 1, τ ma varies by for a change of U by 6 m s 1. Hence a large error in U is required to induce significant uncertainty in estimated τ ma and τ d. An error of ±10% in the empirical constant in the Equation (3) translates to an error of ±7.6% (±11%) in τ d for U lower (greater) than 6 m s 1. The uncertainty in estimated τ d is <2% for an error as large as 50% in the constant 0.10 of the Equation (3). These factors may be important only in the monsoon season (when U is large), but given the large mean seasonal f d values (Table I), the error will not invalidate the overall conclusions. Error due to absolute errors in ODIS-τ and f f retrievals over oceans due to cloud contamination and uncertainties regarding aerosol microphysics is another factor to consider. As τ ma is estimated using Equation (3), any error in ODIS-τ and f f will lead to error in estimated τ d only. Overestimations of ODIS-τ and f f tend to overestimate and underestimate τ d, respectively. Global validation of ODIS C005 aerosol product suggests better retrieval quality over ocean than over land. For example, ODIS-τ at 550 nm shows strong correlation (R = 0.91) with AERONET-τ with a slope of 0.94 and very low (0.005) intercept for the best-fit regression line (Remer et al., 2008). Standard error in ODIS-τ is <0.01 for τ<0.5 (Shi et al., 2011), a condition observed in our study region. This translates into a maximum possible underestimation of 0.01 in estimated τ d. f f agrees to within 25% of the AERONET-retrieved values over ocean for τ>0.15 (Remer et al., 2002). For a ±25% error in ODIS-f f (since τ in our study area always exceeds, Table I), estimated τ d has an uncertainty envelope of 30%. It is more likely that ODIS-f f is underestimated as the dust particles are mostly considered in the coarse mode in the retrieval algorithm. e note that overestimation of τ d due to the underestimation of f f may partially be compensated by the overestimation due to an error in ODIS-τ. This may lead to an underestimation of τ an values shown in Figure 1. The third factor is the error in simulated f d,f. Sensitivity study reveals that 10% error in f d,f translates into an error τ d inter AS e-monsoon AS onsoon AS st-monsoon AS inter BoB e-monsoon BoB onsoon BoB st-monsoon BoB Figure 2. Error in mean seasonal dust optical depth ( τ d )due to deviation from the assumption of f an,f = 1 (solid and dotted lines are for the AS and BoB, respectively). of 0.01 in estimated τ d. In general, τ d will be overestimated for an overestimation in f d,f, which is the case since the model does not consider any local sources (Das et al., 2013). Finally, error may creep in due to the assumption of entire anthropogenic fraction in fine mode. The underestimation in estimated mean seasonal τ d over both the regions increases (Figure 2) with an increase in fraction of τ an in coarse mode with larger sensitivity observed during the seasons dominated by anthropogenic particles. Size-segregated data of anthropogenic aerosols are unavailable from these regions. Considering f an,f = 0.83 ± 0.05 (following Bellouin et al., 2005, where f f > 0.83 was considered to be of mainly anthropogenic sources and hence f f may be considered equal to f an,f ), the error in mean seasonal τ d is maximum (>0.04, which translates into >50%) during these seasons; whereas it is minimum (0.03 translating to 15%) during the monsoon season. This also leads to an overestimation of τ d. To summarize, all these factors lead to an underestimation of τ an and hence the mean seasonal estimates of f an (summarized in Table I) are on the lower side. Size-segregated chemical measurements are required to estimate regional mean seasonal values of f d,f and f an,f. Until then, we proceed to examine the seasonal and inter-annual variations of τ ma, τ d and τ an in view of these uncertainties and conservative estimates of τ an (Figure 3). 3. Results The temporal variability of mean (±1 standard deviation, σ ) seasonal relative fractions of dust (f d ), maritime (f ma ) and anthropogenic (f an ) aerosols over the AS and BoB during the 10-year period as derived by the hybrid approach is shown in Figure 1 along with total τ. τ shows similar seasonal variation over the two ocean basins with maxima in the monsoon season and minima in the post-monsoon season. ean (±1σ ) wintertime f ma, f d and f an over the AS (0.192 ± 0.03, 33 ± 0.03 and ± 0.05, f an,f

4 Variability of natural and anthropogenic aerosols over Indian Ocean 61 Table I. ean seasonal τ as retrieved by ODIS and f ma, f d and f an as derived by the hybrid approach over the Arabian Sea (1st value) and Bay of Bengal (2nd value) during Year Season τ f ma f d f an 2001 inter 12, 55 1, , 7 2, 0.55 e-monsoon 69, , , , 0.36 onsoon 14, , 6 9, , 0.19 st-monsoon 55, , , , inter 24, 65 3, , , 0.57 e-monsoon 79, , , , 0.30 onsoon 0.386, , 3 7, , 0.15 st-monsoon 59, , , , inter 21, 60 1, , , 0 e-monsoon 91, , , 3 3, 5 onsoon 08, , 6 8, , 0.18 st-monsoon 53, , , , inter 26, 70 0, , , 1 e-monsoon 0.313, , , , 8 onsoon 11, , , , 0.19 st-monsoon 21, , , , inter 06, 62 3, , , 0 e-monsoon 51, , , , 0.39 onsoon 21, , 4 6, , 2 st-monsoon 36, , 1 6, , inter 43, 48 0, , , 0.56 e-monsoon 67, , , , 1 onsoon 0.394, 46 3, 1 9, , 0.16 st-monsoon 20, , , , inter 65, , , , 2 e-monsoon 0.326, , , , 3 onsoon 23, , , , 0.31 st-monsoon 86, , 5 8, , inter 63, , , , 3 e-monsoon 0.304, , , , 0.37 onsoon 0.553, , , , 4 st-monsoon 0.315, , , , inter 82, , , 1 6, 6 e-monsoon 0.375, , , , 0.37 onsoon 17, 07 1, , , 1 st-monsoon 83, , , 2 2, inter 64, , , 2 5, 4 e-monsoon 0.356, , , , 0.38 onsoon 0.386, , , , 3 st-monsoon 61, , 0 7, , 2 respectively) are similar to the corresponding values (0.146 ± 0.02, 63 ± 0.05 and ± 0.06, respectively) during the post-monsoon season. Over the BoB, mean (±1σ ) f ma, f d and f an for the winter season are ± 0.02, 31 ± 0.02 and 02 ± 0.03, respectively, whereas the corresponding values of the postmonsoon season are 02 ± 0.06, ± 0.05 and 33 ± 0.06 respectively. In the pre-monsoon season, mean (±1σ ) f d increases to ± 0.05 and ± 0.05 over the AS and BoB, at the expense of anthropogenic particles for which f an reduces to ± 0.06 and ± 0.05, respectively. Detection of mixed type aerosols as the most frequent type in the analysis of ship-borne measurements of spectral τ and Angstrom Exponent in nm range (Kalapureddy et al., 2009) further attests the necessity of the estimates of the relative proportions of natural and anthropogenic components to quantify aerosol forcing. The AS is closer to the dust sources (i.e. est Asia, Africa and Great Indian Desert) than the BoB. However, dust from the Great Indian Desert is transported across the Indo-Gangetic Basin by predominantly north-westerly winds in this season to the northern BoB. On the other hand, the estern Ghats along the west coast of Indian peninsula channels the dust-laden air mass to the southern BoB (Dey and Di Girolamo, 2010). This combined influence of topography and meteorology leads to almost similar f d over the two ocean basins. Dust loading continues to be high in the monsoon season, but f d is larger in the AS (88 ± 0.03) than the BoB (0.560 ± 0.06) due to its proximity to the dust sources. Overall reduction in dust loading over BoB in the monsoon season is attributed to change in wind pattern that somewhat restricts the transport along the oceanic pathway and washout by precipitation. evious estimates of f ma = over the northern AS (Satheesh et al., 2006a) during the monsoon months of the year are very close

5 62 S. Das, S. Dey and S. K. Dash (a) τ (b) AS BoB Relative Contribution to τ 0.8 τ ma τ d τ an 0.0 (c) 1.0 Relative Contribution to τ τ ma τ d τ an Figure 3. Inter-annual variations of mean seasonal τ (top panel) and relative contributions of τ ma, τ d and τ an to total τ over the Arabian Sea (middle panel) and the Bay of Bengal (bottom panel).,, and in the x-axis represent winter, pre-monsoon, monsoon and post-monsoon seasons, respectively. The corresponding years are also mentioned in the x-axis. The deficit monsoon seasons in the years 2002, 2004 and 2009 are indicated by arrow. to our estimates of f ma = 1. However, f ma in our estimates during the post-monsoon to winter season is lower by 10% than the previous estimates. Next, we focus on the inter-annual variations of f ma, f d and f an over these two regions (Figure 1 and Table I). τ an has increased at the rate of and (significant at 95% confidence level following t-test) per year in the winter and and per year in the pre-monsoon season over the AS and BoB, respectively. Larger f an in the last half of the last decade (Figure 1) suggests an increase in anthropogenic pollution load over the ocean. However, no statistically significant change in synoptic wind (Dey and Di Girolamo, 2011) means that transport strength remains more or less same. Hence, emission from anthropogenic sources over the landmass may have increased over the years. In the pre-monsoon season, the significant increase in τ an is not reflected in f an due to larger variability of natural aerosols (Figure 1). τ an increases by per year over the BoB in the monsoon season and by per year over the AS in the post-monsoon season. τ d and τ ma do not show any trend and hence the significant increase in τ an drives the increasing trend of τ over the last decade. Note that the signal of increasing τ an is strong enough to be detected despite of the conservative estimates (as discussed in previous section) in our approach and is also supported by satellite-based estimates (Dey and Di Girolamo, 2011; Kaskaoutis et al., 2011b). f d does not show any significant difference over the AS during the El Niño ( and ) and La Niña years ( ). However, f d is much larger in the El Niño years (0.587) relative to the La Niña years (70) over the BoB. This implies an enhanced dust transport to the BoB during the El Niño years mostly through the Indo-Gangetic Basin route

6 Variability of natural and anthropogenic aerosols over Indian Ocean 63 and not over the oceans along the Indian coastline, which is also observed in the satellite data of absorbing index (Abish and ohanakumar, 2013). Larger f ma over the AS during the monsoon season of El Niño years than in the La Niña years is attributed to a stronger zonal wind. However, over the BoB, a larger increase in τ d outweighs the increase in τ ma during the El Niño years. Also note that f an is higher during the La Niña years relative to El Niño years. Thus the inter-annual variations of natural and anthropogenic aerosols over the oceans are highly influenced regional scale circulation. However, how much the zonal and meridional circulations are influenced by aerosols need further investigation. Also, f d is lower in the deficit years than in the normal monsoon years (all other years are normal years in the last decade except the three deficit years marked in Figure 1), probably due to larger transport of dust from East Africa by stronger Somali jet. Larger aerosol loading over the oceans during the deficit years was also observed by Rahul et al. (2009). This is in contrast to the observations over land (e.g. Kaskaoutis et al., 2012), where higher dust loading was observed during the deficit years (with respect to normal years) because of larger emission of local soil dust and dust transport from the Great Indian Desert. This further indicates the different dynamics of aerosol transport over the land and the oceans that must be understood in order to explain the observed inter-annual variations of natural and anthropogenic aerosols in the subcontinent. 4. Discussion and conclusions In this study, a hybrid approach (by combining satellite data and climate model simulations) is adopted to examine inter-annual variations of natural and anthropogenic aerosols over the seas surrounding the Indian landmass. Our approach complements the lack of continuous observation of aerosol composition in these regions. The seasonal and inter-annual variability of the three components over the two ocean basins as discussed here are qualitatively supported by the satellitebased and ship-borne studies in the literature. The seasonal statistics (keeping in mind the uncertainties and the assumption of entire anthropogenic load in fine mode) may be used to evaluate simulations of aerosol distribution by chemical transport models. The estimates of radiative forcing may further be improved by considering the representative values of non-spherical and spherical dust. For example, optical properties of non-spherical dust differ from spherical dust of similar composition (ishra et al., 2008). Recently, Dey and Di Girolamo (2010) have reported mean seasonal nonspherical fraction to τ (i.e. f nsp ) to be 0.39 (7) and 0.51 (0.37) for the AS (BoB) in the pre-monsoon and monsoon seasons, respectively, based on ultiangle Imaging SpectroRadiometer (ISR) data. Both ISR climatology and this approach show an increase in relative abundance of dust in the monsoon season from the pre-monsoon season and larger values over the AS than BoB. In the ISR algorithm, f nsp is entirely contributed only by dust, whereas the spherical fraction is contributed by all types including dust. Considering mean f nsp from Dey and Di Girolamo (2010) and f d from this study, 67.6 and 74.1% (i.e. f nsp /f d )of the dust particles are estimated to be non-spherical in the pre-monsoon and monsoon seasons over the AS. The corresponding values over the BoB are 51.9 and 66.1%, respectively. These values may help in estimating the aerosol radiative forcing more accurately in view of the sensitivity study by ishra et al. (2008). In the other two seasons, f d is low and hence perhaps the assumption of dusts as spherical particles may not lead to any significant error in the estimated radiative forcing. The major conclusions are as follows: 1. Annually, maritime, dust and anthropogenic particles contribute 16.6% (17.5%), 44.3% (37.4%) and 39% (44.9%) to τ over the AS (BoB) with a strong seasonal cycle. 2. In the last decade, a significant rise (2.3% per year) in τ in these regions is driven by anthropogenic particles (the trend may be even higher given the conservative estimates in this hybrid approach). 3. Combining previous estimates of non-spherical fraction from Dey and Di Girolamo (2010) and the present approach, non-spherical particles are estimated to contribute 67.6% (51.9%) to dust optical depth over the AS (BoB) in the premonsoon season, which further increases to 74.1% (66.1%) in the monsoon season. Acknowledgements The work is supported by research grant from Department of Science and Technology, Govt. of India under contract SR/FTP/ES-191/2010 (Fast Track Scheme) through a research project operational at IIT Delhi (IITD/IRD/RP2509). The authors acknowledge ICTP for providing the RegC4.1 odel. The first author is thankful to CSIR for providing scholarship to carry out research work in IIT Delhi. NCEP reanalysis data are obtained from NOAA CIRES Climate Diagnostics Centre. ODIS data are downloaded from Atmospheric Science Data Centre. 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