Atmospheric pollution over South Asia attracts special attention due to its effects on regional climate, water cycle and human health. These effects are potentially growing owing to rising trends of anthropogenic aerosol emissions. In this study, the spatio-temporal aerosol distributions over South Asia from seven global aerosol models are evaluated against aerosol retrievals from NASA satellite sensors and ground-based measurements for the period of 2000–2007. Overall, substantial underestimations of aerosol loading over South Asia are found systematically in most model simulations. Averaged over the entire South Asia, the annual mean aerosol optical depth (AOD) is underestimated by a range 15 to 44 % across models compared to MISR (Multi-angle Imaging SpectroRadiometer), which is the lowest bound among various satellite AOD retrievals (from MISR, SeaWiFS (Sea-Viewing Wide Field-of-View Sensor), MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua and Terra). In particular during the post-monsoon and wintertime periods (i.e., October–January), when agricultural waste burning and anthropogenic emissions dominate, models fail to capture AOD and aerosol absorption optical depth (AAOD) over the Indo–Gangetic Plain (IGP) compared to ground-based Aerosol Robotic Network (AERONET) sunphotometer measurements. The underestimations of aerosol loading in models generally occur in the lower troposphere (below 2 km) based on the comparisons of aerosol extinction profiles calculated by the models with those from Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Furthermore, surface concentrations of all aerosol components (sulfate, nitrate, organic aerosol (OA) and black carbon (BC)) from the models are found much lower than in situ measurements in winter. Several possible causes for these common problems of underestimating aerosols in models during the post-monsoon and wintertime periods are identified: the aerosol hygroscopic growth and formation of secondary inorganic aerosol are suppressed in the models because relative humidity (RH) is biased far too low in the boundary layer and thus foggy conditions are poorly represented in current models, the nitrate aerosol is either missing or inadequately accounted for, and emissions from agricultural waste burning and biofuel usage are too low in the emission inventories. These common problems and possible causes found in multiple models point out directions for future model improvements in this important region.
South Asia, particularly the Indo–Gangetic Plain (IGP) bounded by the
towering Himalaya (Fig. 1), is one of the global hotspots with persistent high
aerosol optical depth (AOD) routinely observed by satellite remote sensors
(e.g., Moderate Resolution Imaging Spectroradiometer – MODIS, Multi-angle Imaging SpectroRadiometer – MISR and Sea-Viewing Wide Field-of-View Sensor – SeaWiFS), as well as from ground-based measurements
(e.g., Aerosol Robotic Network – AERONET). The potential influence of
aerosols on the climate and water cycle in this region (e.g., Indian summer
monsoon) via surface dimming and atmospheric warming has been widely
discussed in the literature (e.g., Ramanathan et al., 2005; Lau et al.,
2006). The atmospheric heating due to absorbing aerosols (mainly from black
carbon – BC) is estimated to be large especially in the wintertime, about
50–70 W m
Besides these climate impacts, fine aerosol particles (PM
Topography of South Asia and the locations of the stations used in
this study. Three AERONET stations are labeled in white, eight ICARB stations
in red, and four ISRO-GBP stations in black except for Kanpur. The
topography map is obtained from
Previous studies, however, reported that global models generally underestimated aerosol loading over South Asia, especially over the IGP in winter (Reddy et al., 2004; Chin et al., 2009; Ganguly et al., 2009; Henriksson et al., 2011; Goto et al., 2011; Cherian et al., 2013; Sanap et al., 2014). Among them, Ganguly et al. (2009) reported that the GFDL-AM2 model largely underestimated the AOD over the IGP during winter by a factor of 6. Recently, AOD simulated by the regional climate model (RegCM4) showed higher correlation with AERONET AOD at stations over dust-dominated areas in south Asia than over the regions dominated by anthropogenic aerosols, i.e., 0.71 vs. 0.47 (Nair et al., 2012). Eleven out of twelve models participating in the Aerosol Comparisons between Observations and Models (AeroCom) Phase I exercise were also found to underestimate the aerosol extinction over South Asia, especially under 2 km, in comparison with the space-borne lidar measurements from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite (Koffi et al., 2012). The ability to capture surface BC concentrations over South Asia for models has also been found to be limited, with the low biases that tend to be larger in winter (Ganguly et al., 2009; Menon et al., 2010; Nair et al., 2012; Moorthy et al., 2013). A very recent study evaluating the latest generations of quasi-operational aerosol models participating in International Cooperative for Aerosol Prediction (ICAP) has shown that the models have very low skill scores in reproducing AERONET measured AOD at Kanpur, an urban city in northern India (Sessions et al., 2015). These studies underscore great challenges for current global aerosol models to adequately represent aerosols in South Asia.
Extending from previous studies and utilizing the recent model outputs from the AeroCom Phase II multi-model experiments, the present work systematically evaluates aerosol simulations in South Asia by seven global aerosol models with observations from satellites and ground-based measurements, and strives to characterize the model deficiency in reproducing observations. The outcomes of this study will help us understand the discrepancies between models and observations, thus providing directions for future model improvements in this important region.
The description of models is given in Sect. 2, followed by the introduction of observational data from satellites and ground-based measurements in Sect. 3. The model results are compared with observations in Sect. 4, including the spatial and temporal distribution of AOD and aerosol absorption optical depth (AAOD), vertical profile of aerosol extinction coefficient, and the surface BC concentration. The diversity among models is discussed in Sect. 5, and possible causes for the model underestimations of aerosol amounts are investigated in Sect. 6. Major findings are summarized in Sect. 7.
Aerosol simulations for the period of 2000–2007 from seven models, including
six models that participated in AeroCom Phase II hindcast experiment (i.e.,
AeroCom II HCA) and one additional model, GEOS5 (Goddard Earth Observing
System Model version 5), are analyzed in this paper (see Table 1 for
details). Note that the model outputs related to aerosol optical properties,
such as AOD, AAOD and extinction coefficient, are at the wavelength of
550 nm. Given that MODIS and MISR are available only after 2000, we chose
the years 2000–2007 in this study although longer time period of simulations
(starting from 1980) are available from the AeroCom models (note that
ECHAM5-HAMMOZ ended in 2005 and Hadley
Centre Global Environmental Model version 2 Earth System – HadGEM2 in 2006).
Aerosol modules in GEOS5 are based on GOCART (Goddard Chemistry Aerosol
Radiation and Transport model) with some modifications (Colarco et
al., 2010). More detailed descriptions about these models can be found in
previous studies (see references listed in Table 1 and Myhre et al., 2013).
All models include sulfate (SO
For anthropogenic emissions, which are mainly from consumption of fossil fuel and biofuel, the models use either A2-ACCMIP (AeroCom Phase II – Atmospheric Chemistry and Climate Model Inter-comparison Project) or A2-MAP (AeroCom Phase II – NASA's Modeling, Analysis and Prediction program) emission data set that are provided for the AeroCom Phase II model experiments (Diehl et al., 2012). Both A2-ACCMIP and A2-MAP were constructed by combining multiple inventories but in different ways. The annual anthropogenic emissions from A2-MAP are yearly emission data set with inter-annual variability, while those from A2-ACCMIP are without actual inter-annual variability, simply generated by linear interpolation between decadal endpoints except for biomass burning (Granier et al., 2011; Diehl et al., 2012). Over South Asia, the spatial distribution and total emission amount are somewhat different between the two emission data sets, with higher emission amount in A2-ACCMIP. Detailed information on both emission data sets can be found in Diehl et al. (2012).
Figure 2 shows the averaged annual mean (2000–2007) anthropogenic BC,
organic carbon (OC), SO
General information of models used in this study.
Spatial distribution of anthropogenic emissions of BC, OC, SO
Open biomass burning including the agricultural residue burned in the field
and forest fires contributes to 25 % of total BC (and OC) emissions over
India based on the estimation by Venkataraman et al. (2006) with the
difference between the total crop waste and that used as fuel and animal
fodder. Figure 3 shows the seasonal BC biomass burning emission based on
monthly Global Fire Emissions Database Version 2 (GFED2), which is used by
all models. The open biomass burning
displays strong spatial and seasonal variations. Pre-monsoon period (MAM) is the
most active open biomass burning season with an emission amount of 0.22 Tg C yr
Spatial distribution of biomass burning emission of BC based on
GFED2 for each season averaged for 2000–2007 (units: g C m
The major natural aerosol over South Asia is the wind-blown mineral dust
from the arid and semi-arid regions of southwest Asia, such as Iran,
Afghanistan, Pakistan, Arabian Peninsula and Thar Desert in
northwestern India. The dust emissions are calculated by each model and show
a large diversity varying from 10.6
Summary of stations in South Asia used in this study.
In this study, five satellite products are used to characterize aerosol
distribution and evaluate the model simulations. MODIS Terra and Aqua
Level-3 monthly mean AOD products at 550 nm wavelength (Collection 5.1) are
used by averaging the daily aerosol products at 1
The annual averaged mean AOD for 2000–2007 over region:
The climatology (averaged over the period of June 2006–December 2011) of
vertical extinction profiles from the CALIOP layer product version 3.01 (onboard CALIPSO satellite)
was used to evaluate the model-simulated aerosol vertical distribution in
2006 (CALIPSO, 2011; Koffi et al., 2012). Only the CALIOP observations in 532 nm channel for nighttime are used because of their better signal-to-noise
compared to daytime observations. Three aerosol parameters are used to
inter-compare model simulations with CALIOP, namely, AOD,
We also use AOD and AAOD data from the ground-based AERONET (Holben et al., 1998) sites in South Asia. Monthly mean AOD and AAOD were analyzed over Kanpur, Lahore and Karachi. Level-2 (version 2) data are used, which are cloud-screened and quality-assured aerosol products with a low uncertainty of 0.01–0.02. Locations of the three stations are shown in Fig. 1 along with 11 in situ measurement sites as described in the following Sect. 3.3. The information of all 14 ground-based measurement sites is given in Table 2.
Modeled BC concentrations are also evaluated with the surface in situ measurements from the Integrated Campaign for Aerosols gases and Radiation Budget (ICARB) field campaign in India over eight stations, which spread over Indian mainland and islands for the entire year of 2006. The BC data from the ICARB field campaign were measured by inter-compared aethalometers following a common protocol. More details of ICARB measurements can be found in previous publications (e.g., Beegum et al., 2009, and Moorthy et al., 2013).
In order to examine the aerosol chemical composition (such as surface concentrations of nitrate, sulfate, OA and BC) and meteorological conditions (such as surface relative humidity (RH) and temperature) of winter haze over IGP in multi-models, we refer to measurements from the Indian Space Research Organization Geosphere Biosphere Programme (ISRO-GBP) campaign which provided valuable information about aerosol physical, optical and chemical properties along the IGP during the wintertime of December 2004. For this study, four stations in IGP are selected because of their relatively complete measurements. They are Hisar (Ramachandran et al., 2006; Rengarajan et al., 2007; Das et al., 2008), Agra (Safai et al., 2008), Kanpur (Tripathi et al., 2006; Tare et al., 2006) and Allahabad (Ram et al., 2012a), from western to eastern IGP. Note that the in situ data used in this study are obtained from the aforementioned references.
In this section, the aerosol simulations by multi-models are evaluated in comparison to satellite data and ground-based measurements in terms of temporal variation and spatial distribution (horizontally and vertically) over South Asia.
Figure 4a shows the annual averaged mean AOD over the entire South Asia
domain (land only, shown in gray shaded area) for the period of 2000–2007.
AODs are 0.270
To further examine the details of underestimations occurring in most models, we compare the model-simulated monthly variations of AOD and AAOD with the AERONET data at three selected sites in South Asia (Fig. 5). These locations represent different aerosol environments in South Asia: Kanpur, an industrial city located in the central IGP, is influenced by high anthropogenic emissions throughout the year and by the transported dust during pre-monsoon (MAM) and early monsoon periods (JJ); Lahore, an urban city located in the western IGP, is directly influenced by biomass burning in the pre-monsoon (MAM) and post-monsoon (ON) seasons; and Karachi, an urban coastal city in Pakistan, is influenced by frequent dust outbreaks, especially from the Arabian peninsula around early summer monsoon season (JJ). A 2-year period is chosen for each site based on the availability of AERONET measurements. Three satellite data sets, namely, MODIS-Terra, MISR and SeaWiFS, are also displayed to draw inter-comparison of AOD with AERONET data.
At Kanpur (first row of Fig. 5), strong seasonal variation of AERONET AOD
(left column in Fig. 5) is evident with two peaks, one in May–July
associated with dust outbreaks and the other in October–January associated
with active open biomass burning as well as high anthropogenic emissions.
However, most models (except for HAD) only show the peak in May–July but
miss the peak in October–January. Although the HAD model simulates two
seasonal maxima, they disagree with the peak months observed from AERONET.
Overall, AOD from all models have weak or negative correlation coefficients
with AERONET data (from
At Lahore (second row of Fig. 5), AERONET data are mostly available in the year 2007, when only five model results are available (no HAD and ECH for 2007; see Table 1). Lahore is located in the Punjab region, which is an agriculture region known as the “breadbasket” for Pakistan and India. The enhanced AERONET AOD and AAOD are evident at Lahore during October–November, which is linked to the agricultural waste burning after harvest. However, all five models largely underestimate AOD and AAOD in the October–November period. This suggests that emissions from agriculture waste burning are likely underestimated in GFED2 that are used by the models (discussed in Sect. 6.4). Compared to observations, HAD again showed abnormal seasonal variation at Lahore, similar to that at Kanpur, with extremely high AOD in October though.
At Karachi (third row of Fig. 5), a unimodal seasonal distribution is revealed in AERONET AOD data, in contrast to the bimodal seasonal variation at Kanpur. The maximum AOD around July is associated with the wind-driven mineral dust from the Arabian Peninsula, which is captured by the models as indicated by relatively strong correlation from 0.58 to 0.91 (except HAD; note ECH is not available for 2006–2007). However, similar to other sites, AOD from all models is too low in late autumn to winter. Models also fail to capture the relatively higher AAOD around November that is associated with smoke transported from agriculture waste burning in northwestern IGP (i.e., the area around Lahore) (Badarinath et al., 2009a, b).
Monthly mean AOD (left column) and AAOD (right column) at three
AERONET stations in South Asia. The gray bar represents data from AERONET,
the thin lines represent results from seven models, and symbols represent
the data from three satellite retrievals. On each panel corr is correlation
coefficient of a model with AERONET; bias is relative mean bias, i.e.,
Overall, in comparison with AERONET at three sites, most models tend to significantly underestimate AOD in October–January when aerosols from agriculture waste burning and anthropogenic activities are dominant. On the other hand, the monthly variations and magnitudes of AOD from the satellites are in general similar to those from AERONET. As an exception, MODIS-Terra is biased high (up to a factor of 2) during pre-monsoon and monsoon months. This overestimation of AOD partially results from low bias of surface reflectance under dusty conditions in the MODIS Dark Target aerosol retrieval algorithm (Jethva et al., 2009).
Monthly-mean AOD of total aerosol (aer) and components (ss, so
In order to diagnose the discrepancies between models and AERONET data, the
individual component AOD from four models (HAD, GE5, SPR and GOC; unavailable
from other three models) are examined at Kanpur for 2004 in Fig. 6. We choose
the year of 2004 because the ISRO-GBP campaign took place in the same year (see
Sects. 3.3 and 6), so that we can inter-compare AERONET data with that in
ISRO-GBP campaign. In December and January, AOD from AERONET data is around
0.7, dominated by anthropogenic contributions (about 75 %, estimated by
Tripathi et al., 2006). All four models have difficulties to capture the
magnitude of AOD in December and January. Among them, AOD from HAD (upper
left panel in Fig. 6) matches relatively well with AERONET data, capturing
about half of the observed value. Interestingly, nitrate (NO
Overall, Fig. 6 demonstrates that the magnitudes and seasonal cycles of aerosol compositions are quite different across the models. Further examination of the model diversities will be discussed in Sect. 5.
In this section, we compare the spatial distributions of AOD over the entire South Asia and neighboring oceans among four satellite products (MODIS-Terra, MODIS-Aqua, MISR and SeaWiFS) and seven model simulations during the winter monsoon (DJF), pre-monsoon (MAM), summer monsoon (JJAS) and post-monsoon (ON) phases averaged over 2000–2007, shown in Fig. 7a and b. Locations of the three aforementioned AERONET stations are also labeled in the maps for reference. In general, the spatial distribution of AOD is closely associated with the emission source over South Asia, and the aerosol abundance in the atmosphere is modulated by meteorological conditions, such as efficient atmospheric dispersion associated with the strengthened westerly flow in March–July, high wet removal associated with the monsoon rainfall in June–September and stable atmospheric conditions and thus less efficient atmospheric dispersion in December–February.
During the winter season (DJF), local anthropogenic sources dominate over
dust, contributing as much as 80 % (
Starting from the pre-monsoon season (MAM), the entire South Asia is characterized by high AOD mainly due to the mineral dust transported from the arid and desert regions in southwest Asian dust sources by westerly winds, with maximum AOD over western IGP seen from most satellites (Fig. 7a). As shown in the second column of Fig. 7b, five models (GOC, SPR, GIM, GIE and GE5) partially capture this observed spatial distribution and magnitude. However, the HAD model shows high biases of AOD over northern India due to nitrate (refer to Fig. 6). A higher nitrate concentration than dust is unrealistic because the contribution of dust to the total AOD has been reported to be over 60 % during pre-monsoon season by Srivastava et al. (2012a) based on the ground-based sun/sky radiometer data. The dust source in the northwestern parts of South Asia is weak in HAD (Fig. 7b). Additionally, the ECH model shows very low AOD and little dust over IGP associated with its small dust size in coarse mode (Table 1). Despite these deficiencies, model simulations over South Asia during the pre-monsoon season are still closer to the satellite data than those during winter, with the model-averaged AOD capturing 65 % of the satellite data in the pre-monsoon season compared to only 50 % in winter.
During the monsoon season (JJAS), dust transported from the Arabian Peninsula by the strong southwesterly winds explains the high AOD over northwestern India. High AOD over the Arabian Sea and southwest Asia is evident in MODIS and MISR (Fig. 7a). As shown in the third column of Fig. 7b, most models reproduce both the spatial distribution and the magnitude of AOD during this season, implying that these models capture dust emission over the Arabian Peninsula and its transport to South Asia. However, it should be noted that during the monsoon season the monthly mean AOD from MODIS is likely to be biased high as shown earlier in Fig. 5, partly due to underestimated surface reflectance.
During the post-monsoon season (ON), the southwesterly flow significantly weakens, and thus dust transported to the Indian subcontinent is lower compared to the pre-monsoon and monsoon seasons. Based on the spatial distributions from satellite data (Fig. 7a), high AOD is found along IGP with maxima over western IGP including Punjab, Haryana and western Uttar Pradesh that are associated with biomass burning from agriculture waste fires. With the aid of northwesterly winds, aerosols are transported to the central IGP along the valley as well as the region to the south (Badarinath et al., 2009a, b). However, none of the models capture these features (the fourth column of Fig. 7b), indicating the biomass burning emissions are severely underestimated in the current inventory based on GFED2, which will be discussed further in Sect. 6.4. In contrast to the underestimations by other models, HAD overestimated AOD over IGP due to the high amount of nitrate (Fig. 6).
Figure 8 shows the comparison of aerosol extinction profile among models and
with CALIOP data in four seasons. In order to represent the latitudinal
gradient of aerosol vertical profiles, two locations are chosen, Kanpur in
northern India and Hyderabad in central India (refer two locations to Fig. 1). The CALIOP aerosol extinction profile over Kanpur
(Fig. 8a;
2
Seasonal mean of vertical profile of extinction coefficient (units:
km
Most models, especially GE5, capture the observed seasonal variation of
Monthly mean surface BC concentration at eight ICARB stations in
2006 (units:
Figure 9 shows the observed and modeled monthly surface BC concentration in
the year of 2006 (2005 from model ECH) at eight ICARB stations (refer the
locations to Fig. 1). In general, the magnitude of BC surface concentrations
is closely related to the strength of the emission source, with higher values in
northern India where higher BC anthropogenic emissions are located (refer
the spatial pattern to Fig. 2). The highest BC surface concentration is
particularly found in the largest Indian city Delhi, with a value of 27
Clearly, there is a large diversity existing among models in simulating AOD and BC concentrations as shown in Figs. 4–9, despite similar emission data sets used in these models (see Sect. 2.2 and Table 1). It is seen that models with the same emissions data sets produce quite different results. For example, at Kharagpur, shown in the upper right panel of Fig. 9, the surface concentration of BC from the SPR model is 4 times as large as that from GIM, although both models use the same anthropogenic emission (A2-ACCMIP) and biomass burning emission (GFED2). Similarly, surface concentration of BC in the HAD model is twice that of GOC, although the same emissions (A2-MAP and GFED2) are used in both models. Such substantial differences indicate that the large diversity among model simulations is due to factors other than the differences in emissions. Textor et al. (2007) also found that the differences in the model treatment of atmospheric processes (e.g., wet removal, dry deposition, cloud convection, aqueous-phase oxidation and transport), assumptions of particle size, mixture, water uptake efficiency, and optical properties are more responsible than emission for the model diversity.
The multi-model diversity (defined as the percentage of the standard
deviation to the mean of results from the seven models) over South Asia in
2006 (2005 from the model ECH) is summarized in Table 3 (monthly variations are demonstrated
in Figs. S1–3 in the Supplement). In general, on an annual basis, we found the
following features: (1) for aerosols with anthropogenic origin (i.e., BC, OA
and SO
The statistics of the aerosol parameters over South Asia
(60–95
We further examine the aerosol refractive index at the wavelength of 550 nm
for each species as listed in Table 1. The real parts of refractive indices
(representing phase velocity) at 550 nm are similar among the seven models,
but the imaginary parts (representing light absorption) are different. In the
case of BC, the most absorbing aerosol, the imaginary parts of refractive
indices are 0.44 in four models (HAD, GOC, SPR, GE5) and 0.71 in three models
(ECH, GIE, GIM). For dust, the light absorption at 550 nm is significantly
less than that of BC. The imaginary refractive index of dust ranges from
0.001 (ECH) to 0.008 (GE5), a range that is much wider than that of BC. In
order to test the sensitivity of MEE and mass absorption efficiency (MAE) to
the values of the real and imaginary refractive indices, we conduct Mie
calculation for BC and dust at 550 nm in several cases in which the
different real and imaginary parts of refractive indices are combined (see
Tables S1 and S2 in the Supplement). As for BC, we find that MEE and MAE are
enhanced by
It is noted that the function of Table 3 is to quantify the diversity of
these models over South Asia instead to reveal the discrepancies of models
from observations. SO
As shown in Sect. 4, AOD, AAOD and BC surface concentration over South Asia are consistently underestimated in seven global models used in this study, in particular during winter and the post-monsoon season. Such underestimation seems to be a common problem in other models as well (e.g., Reddy et al., 2004; Ganguly et al., 2009; Nair et al., 2012). AOD and surface BC concentrations are most severely underestimated over the IGP (the main region of anthropogenic emissions). Several possible causes for these underestimations are suggested below.
Foggy days with high near-surface RH are very common during
wintertime over IGP (Gautam et al., 2007). For example, Kanpur was subjected
to heavy fog or haze for about > 65 % days in December 2004,
with averaged surface RH of about 75 % and the surface temperature about
14.6
Figure 10 shows comparisons between models and in situ measurements
(ISRO-GBP land campaign) at four stations located in the IGP region in
December 2004. Comparisons are shown for surface meteorological conditions
(RH and temperature); surface aerosol concentrations of SO
Comparisons of seven models against ISRO-GBP campaign measurements
at four IGP stations (Hisar, Agra, Kanpur, Allahabad from western to eastern
IGP) in December 2004. The variables include meteorological fields of
surface relative humidity (RH) (1st row) and surface temperature (2nd row),
aerosol species mass concentrations of SO
In addition to favoring hygroscopic growth, foggy conditions also favor the
formation of secondary inorganic aerosol through the aqueous-phase reactions.
This phenomenon was supported by the observations of increased aerosol number
concentration and surface SO
As shown in Fig. 10, the observed surface concentrations of NO
Mass extinction efficiency (MEE) at 550 nm for individual aerosol
components (units: m
The uncertain and inadequate representations of aerosol emissions over South
Asia have been pointed out by previous studies (e.g., Sahu et al., 2008;
Ganguly et al., 2009; Nair et al., 2012; Lawrence and Lelieveld, 2010). The
results in this study further prove this issue. At Kanpur, the models
underestimate surface concentrations not only of SO
Different from other regions in Northern Hemisphere where fossil fuel
burning and industrial processes tend to dominate, biofuel and open biomass
burning in South Asia contribute two-thirds of carbon-containing aerosols to
form the dense brown clouds in winter (Gustafsson et al., 2009). Over India,
42 % of total BC emission is from biofuel, which is believed to be the
largest source of BC, with the remaining 33 % from open biomass burning
and 25 % from fossil fuel (Venkataraman et al., 2005). The percentage of
biofuel is high because residential heating and cooking (burning of wood,
paper or other solid wastes) is quite common in South Asia, especially among
the underprivileged, leading to large amount of smoke comprised mainly of
black carbon and condensed semi-volatile organics. Based on in situ
measurements, the ratios of OC
During the post-monsoon season (October–November), the extensive agriculture waste burning after harvest in northwest India (e.g., Punjab) makes a large contribution to the dense haze over South Asia based on previous observational studies (Vadrevu et al., 2011; Sharma et al., 2010). The agricultural fires in this area are evident in the MODIS fire count product. Smoke plumes from Punjab also impact the downwind regions by eastward transport along IGP and southward to central-south India (Sharma et al., 2010; Badarinath et al., 2009a, b).
Over India, the contribution from open biomass burning to the total BC emission is significant, about half of anthropogenic emissions (i.e., biofuel plus fossil fuel emissions) (Venkataraman et al., 2005). The biomass burning contribution is evident based on the AERONET data at Lahore, where AAOD is enhanced by 70 % in November (after harvest) from previous months (Fig. 5), and its contribution is also clearly seen in the MODIS-Terra and Aqua data with the maximum AOD found near Lahore in the post-monsoon season (the fourth column of Fig. 7a). BC emission from open biomass burning (based on GFED2) used by the models, however, is less than 1 % of that from anthropogenic sources (comparing Figs. 2 and 3) during the post-monsoon season, both on regional average and in areas around Lahore; therefore, it is not surprising that all models fail to capture high AAOD and AOD in this season (Figs. 5 and 7b). The underestimation of BC emission from agriculture waste burning also implies a similar degree of underestimation of OC from the same source.
The open biomass burning emission from GFED2 is derived from MODIS burned area products. It was previously reported that the small fires such as agricultural waste burning were largely missing in the GFED product (e.g., van der Werf et al., 2010; Randerson et al., 2012). The agricultural waste burning area is usually underestimated or overlooked in MODIS because the size of agriculture fires is too small to generate detectable burn scars in the 500 meter pixel resolution of MODIS product (van der Werf et al., 2010; Randerson et al., 2012).
Other factors can also cause the models to underestimate AOD. For example, the observed ratio of secondary organic carbon (SOC) to primary OC is 30–40 % in several stations located in northern India, suggesting a significant contribution from SOC (Rengarajan et al., 2007; Ram and Sarin, 2010). However, only two models include a resolved SOC chemistry. In addition, although the dust emission is minimal in winter compared to anthropogenic emission, dust sources from road traffic, soil re-suspension, and construction activity in the urban regions of the IGP (Tripathi et al., 2006; Tiwari et al., 2009) could be important, which are not considered in the current models.
Some difficulties with the models might be associated with the coarse
spatial resolution (at 1.1–2.8
Another important factor contributing to high surface aerosol concentrations
in winter over South Asia is the shallow wintertime ABL that suppresses
ventilation thereby trapping pollutants near the surface. At Kanpur, ABL
height is about 200 m in winter according to the observations (Tripathi et
al., 2006; Nair et al., 2007). However, the averaged ABL in GOC and GE5
models are 400–500 m in the study region (other models did not provide this
information), allowing more efficient vertical mixing to dilute the surface
concentrations and thus contributing to the low bias of surface aerosol
concentration (Figs. 9 and 10). Therefore, a better-constrained ABL would be
helpful to reduce the model bias of surface concentrations. Here we would
like to iterate, however, that the columnar AOD and AAOD during wintertime
are underestimated by the models as well, despite to a lesser degree than the
underestimation of surface concentration (for example, model-simulated BC
concentrations are too low by about a factor of 10, compared to the
underestimation of AAOD by a factor of
In this study, the aerosol simulations for 2000–2007 from seven global
aerosol models are evaluated with satellite data and ground-based
measurements over South Asia, in particular over IGP, one of the heaviest
polluted regions in the world. The high AOD over IGP is associated with
persistent high aerosol and precursor gas emissions (such as dust, SO
Averaged over the entire South Asia for 2000–2007, the annual mean AOD
is about 0.27–0.33 from satellites retrievals. Six out of seven global models
consistently underestimate the annual mean AOD by 15–44 % compared
to MISR, the lowest bound of four satellite data sets used in the present study.
The model performances are worse over northern India. In general, the
underestimation of aerosol loading is mainly found during the winter and
post-monsoon months when anthropogenic and open biomass burning emissions are dominant. During wintertime (DJF), six out of seven models largely underestimate
columnar AOD and AAOD over Indian subcontinent, and the underestimations of
aerosol extinction generally occur in the lower troposphere (below 2 km).
The simulated surface mass concentrations of SO During the post-monsoon season (ON), none of the models capture the
observed high AOD over western and central IGP. AAOD and BC surface concentrations
are underestimated at the stations in IGP as well. Such discrepancy is attributed
largely to the underestimation of open biomass burning in the satellite-based
emission inventory (GFED2). It is likely due to missing small agricultural waste
burning that is difficult to retrieve from satellite remote sensing. As for the inter-model diversity, the results show that the largest
diversity occurs in the treatment of dry deposition, with diversity of dry
deposition amount ranging from 41 to 46 % for BC, OA, and SO
To sum up, we have identified the major discrepancies of seven
state-of-the-art global aerosol models in simulating aerosol loading over
South Asia. Results from this study suggest directions to improve model
simulations over this important region, including improving meteorological
fields (particularly RH and fog), revising biofuel and agriculture
fire emission inventories, and adding/improving NO
We thank the ICARB, ISRO-GBP, and AERONET networks for making their data available. Site PIs and data managers of those networks are gratefully acknowledged. We also thank the Goddard Earth Science Data and Information Services Center for providing gridded satellite products of SeaWiFS, MISR, and MODIS through their Giovanni website, and the AeroCom data management for providing access to the global model output used in this study. Brigitte Koffi is appreciated for providing L3 CALIOP data. We appreciate that Hiren Jethva conducted Mie calculation for us. X. Pan is supported by an appointment to the NASA Postdoctoral Program at the GSFC, administered by Oak Ridge Associated Universities through a contract with NASA. The work by M. Chin, H. Bian, D. Kim, and P. R. Colarco are supported by the NASA MAP and ACMAP Programs. S. Bauer and K. Tsigaridis have been supported by the NASA MAP program (NN-H-04-Z-YS-008-N and NN-H-08-Z-DA-001-N). L. Pozzoli was supported by PEGASOS (FP7-ENV-2010-265148). ECHAM5-HAMMOZ simulations were supported by the Deutsches Klimarechenzentrum (DKRZ) and the Forschungszentrum Juelich. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. We are grateful to two reviewers for their constructive and helpful comments. Edited by: P. Chuang