Aerosol particle vertical distributions and optical properties over Singapore

Atmospheric Environment 79 (2013) 599e613

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Aerosol particle vertical distributions and optical properties over Singapore Boon Ning Chew a, *, James R. Campbell b, Santo V. Salinas a, Chew Wai Chang a, Jeffrey S. Reid b, Ellsworth J. Welton c, Brent N. Holben d, Soo Chin Liew a a Centre for Remote Imaging, Sensing and Processing, National University of Singapore, Block S17, Level 2, 10 Lower Kent Ridge Road, Singapore 119076, Singapore b Naval Research Laboratory, Marine Meteorology Division, 7 Grace Hopper Avenue Stop 2, Monterey, CA 93943-5502, USA c Micro-Pulse Lidar Network, Code 612, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA d Code 618, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

h i g h l i g h t s
Aerosol vertical profiles & optical properties over Singapore are examined.
Seasonal aerosol vertical extinction, lidar ratios & Ångström exponents are shown.
Significant aerosol loading is found above 1.5 km MSL.
PCA identifies 5 primary aerosol vertical profile types over Singapore.
An off-season biomass burning smoke event is identified with PCA & examined.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 February 2013 Received in revised form 6 June 2013 Accepted 11 June 2013

As part of the Seven Southeast Asian Studies (7SEAS) program, an Aerosol Robotic Network (AERONET) sun photometer and a Micro-Pulse Lidar Network (MPLNET) instrument have been deployed at Singapore to study the regional aerosol environment of the Maritime Continent (MC). Using coincident AERONET Level 2.0 and MPLNET Level 2.0a data from 24 September 2009 to 31 March 2011, the seasonal variability of aerosol particle vertical distributions and optical properties is examined. On average, the bulk (w65%) of aerosol extinction is found below 1.5 km with substantial aerosol loading (w35%) above. Possibly due to the transition from El Niño to La Niña conditions and subsequent reduction in fire events, the MPLNET mean integrated aerosol extinction is observed to be the lowest for JulyeSeptember 2010, which coincides with the typical MC biomass burning season. On the other hand, the highest mean integrated extinctions are derived for JanuaryeMarch 2010 and 2011, which can be attributed to off-season MC biomass burning smoke and anthropogenic pollution. The seasonal lidar ratios also show higher occurrences 60 sr, which are indicative of biomass burning smoke, for October 2009eJune 2010, but such occurrences decrease from July 2010 to March 2011 when La Niña conditions prevail. In addition, principal component analysis (PCA) identifies five primary aerosol vertical profile types over Singapore, i.e. strongly-capped/deep near-surface layer (SCD; 0e1.35 km), enhanced mid-level layer (EML; 1.35e2.4 km), enhanced upper-level layer (EUL; 2.4e3.525 km), deep contiguous layer (DCL; 3.525e4.95 km) and deep multi-layer (DML; >4.95 km). PCA also identifies an off-season MC biomass burning smoke event from 22 February to 8 March 2010, which is subsequently examined in detail. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: AERONET ENSO MPLNET Lidar Maritime Continent Southeast Asia

1. Introduction

* Corresponding author. Tel.: þ65 65166396; fax: þ65 67757717. E-mail address: [email protected] (B.N. Chew). 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.06.026

In tropical Southeast Asia (SEA), or the so-called Maritime Continent (MC), which includes Brunei, Indonesia, Malaysia, Singapore, Timor-Leste and the surrounding open waters, regional air pollution is a major environmental concern due to increasing emissions from industrialization and urbanization (e.g., Reid et al.,

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2013). While forest fires rarely occur naturally in the MC (Goldammer, 2006), anthropogenic biomass burning, which is used for land preparation and forest clearance, has increased dramatically over the last 30 years (Field et al., 2009). During prolonged dry periods, these fires can develop into uncontrollable wildfires (Stolle and Lambin, 2003), resulting in transboundary smoke that affects air quality in both source and surrounding regions (e.g., Hyer and Chew, 2010; Salinas et al., 2012, 2013). Given abundant tropical solar radiation, different aerosol particle types from various sources react photo-chemically with important consequences for regional environmental quality (e.g., He et al., 2010). Aerosol particles influence planetary radiative energy balance through the scattering and absorption of solar radiation (e.g., Ramanathan et al., 2001), and their impacts on regional climate are dependent on their optical properties and vertical distributions in the atmosphere. The vertical distribution of aerosol particles affects surface and top-of-atmosphere radiative equilibrium (e.g., Haywood and Ramaswamy, 1998), and induces diabatic heating of the atmospheric column (e.g., Johnson et al., 2008). Of the limited observations of aerosol particle vertical distributions over the MC reported in literature, evidence indicates that the bulk of columnar aerosol mass concentration, including smoke, is confined to within 2e3 km of the surface, and usually lower, approaching 1e2 km, over open waters (Campbell et al., 2013). Surface fuel sources, especially peat, burn at relatively low temperatures, generating equally low buoyancy when compared with biomass burning in other regions (Duncan et al., 2003). Therefore, plume injection of MC smoke emissions is commonly confined to the boundary layer (Tosca et al., 2011). However, it is hypothesized that the prevalence of both small- and large-scale tropical convective processes, as well as the development of internal coastal boundary layers, serve as secondary means for lofting smoke particles into the convective boundary layer and free troposphere (Atwood et al., 2013; Reid et al., 2012, 2013). Therefore, there is sufficient aerosol variability with height, which can induce uncertainties in determining columnar aerosol radiative forcing over the region if not well characterized. As part of the Seven Southeast Asian Studies (7SEAS; Reid et al., 2013) program, an Aerosol Robotic Network (AERONET; Holben et al., 1998) sun photometer and a Micro-Pulse Lidar Network (MPLNET; Welton et al., 2001) instrument from the National Aeronautics and Space Administration (NASA) have been deployed at an atmospheric measurement supersite established at the National University of Singapore (1.30 N, 103.77 E; 0.079 km above mean sea level; MSL) to study the regional aerosol, cloud and radiation environment of the MC (Chew et al., 2009). Situated off the southern tip of the Malay Peninsula and north of the Indonesian Archipelago (Fig. 1), the supersite is ideally positioned for monitoring anthropogenic pollution and biomass burning emissions from surrounding regions (Atwood et al., 2013). In this study, Singapore AERONET and MPLNET datasets are considered for quantifying and characterizing the seasonal variability of aerosol particle vertical distributions and optical properties from 24 September 2009 to 31 March 2011. In addition, principal component analysis (PCA; Tabachnick and Fidell, 2007) is applied to extract key aerosol vertical profile features and distribution scenarios, with case studies illustrating the dominant types. Finally, an off-season MC biomass burning event from 22 February to 8 March 2010 is identified with PCA and subsequently examined in greater detail. By determining the most frequently observed profiles for aerosol particle extinction, and their corresponding optical characteristics, our broader goal is to better constrain the vertical depiction of particle transport within the MC in order to motivate studies of regional aerosol radiative forcing. Fundamental characterization of the aerosol extinction profile will also lead to

Fig. 1. The location of Singapore within the MC. The directions for the monsoonal winds are indicated.

improved modeling simulations that better isolate and quantify direct radiative forcing and diabatic heating rates in this complex region (e.g., Wang et al., 2007). 2. Instruments and method 2.1. AERONET and MPLNET The Cimel Electronique CE-318 sun photometer deployed at Singapore performs direct solar measurements in eight spectral bands (with center wavelengths between 0.340 mm and 1.640 mm) every 30 s within an approximately one-minute period to produce a triplet measurement used to compute aerosol optical depth (AOD; Holben et al., 1998). Cloud-screened (Smirnov et al., 2000) and quality-assured Level 2.0 AOD data from 24 September 2009 to 31 March 2011 are used. Fine- and coarse-mode AOD, their associated relative fractions (FMF and CMF) and Ångström exponents (a) at 0.500 mm are computed with the Spectral Deconvolution Algorithm (SDA) based on a second-order polynomial fitting to the AOD spectra from 0.380 to 0.870 mm (O’Neill et al., 2001a, 2001b). Uncertainties for fine- and coarse-mode optical parameters calculated with the SDA method are described by O’Neill et al. (2003). The collocated MPLNET instrument (0.527 mm; Spinhirne, 1993; Spinhirne et al., 1995) is a compact and eye-safe lidar capable of profiling aerosols and clouds by transmitting a short pulse of laser energy into the atmosphere and measuring the time-of-flight return for the backscattered signal. Instrument calibrations and a processing algorithm for MPLNET Level 1.0 normalized relative backscatter product (NRB), and its uncertainties, have been described in detail (Campbell et al., 2002; Welton and Campbell, 2002), and are briefly summarized here. The instrument collects profiles of backscattered photon counts that are converted to Level 1.0 NRB signals by correcting for detector deadtime effects and subtracting solar background noise. The resultant profile is then range- and energy-normalized. Calibration procedures include corrections for detector dark noise, laser-detector cross talk (afterpulse) and the lidar overlap (near-field signal loss).

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2.2. Method Over SEA, cirrus cloud frequencies are particularly high as they are enhanced by the proximity to the tropical Western Pacific warm pool (e.g., Virts and Wallace, 2010), where regional convection, and the production of ice through gravity wave breaking and diurnal cooling in the tropical tropopause region, are endemic (Kang et al., 1999). Optically-thin cirrus cloud contamination in AERONET measurements can bias regional AOD high when unscreened (Huang et al., 2011); a finding reinforced by a study conducted at Singapore using AERONET Level 2.0 data with the bulk of MPLNET data considered here (Chew et al., 2011). Therefore, in addition to the standard AERONET cloud screening (Smirnov et al., 2000), we set a threshold of a (0.440e0.870 mm) > 0.75 to exclude cases of cloud contamination from spatially homogeneous clouds (Eck et al., 2009). Individual one-minute MPLNET Level 1.0 NRB profiles are first screened using a simple signal threshold to identify and remove those where low-level liquid water cloud presence are considered likely (e.g., Clothiaux et al.,1998). Corresponding with each AERONET Level 2.0 observation in the sampling period, vertically-resolved NRB data, when available, are averaged in 20-minute segments centered on the reported AERONET time. Vertical profiles of aerosol particle extinction coefficient are then derived from the cloud-screened and averaged lidar profiles with the backward Fernald two-component solution (Fernald, 1984), as detailed in Welton et al. (2000). For our study, MPLNET Level 2.0a aerosol data, which use AERONET Level 2.0 AOD as retrieval constrains, are applied with the following quality assurance procedures. Only MPLNET data acquired within an optimal temperature range (23  5  C for this particular instrument) are used. Profiles with derived extinctionto-backscatter ratio error >30% or include < 80% of the nominal 20-minute average are discarded as potentially erroneous (Welton et al., 2000). The accuracy of MPLNET 2.0a aerosol extinction coefficient values has been evaluated to be within 20% (Schmid et al., 2006). 3. Meteorological and fire overview SEA experiences two primary monsoon seasons: the Northeast Monsoon during the boreal winter (DecembereMarch) and the Southwest Monsoon (JuneeSeptember) during boreal summer (Fig. 1), which are regulated by the movement of the Intertropical Convergence Zone (ITCZ). These two seasons are separated by relatively shorter inter-monsoon transitional periods (AprileMay and OctobereNovember) (e.g., Chang et al., 2005). During winter, the ITCZ and a secondary South Pacific convergence zone result in dual zonal rain bands embedded within trade winds in the Central Pacific (Masunaga and L’Ecuyer, 2010). The associated monsoonal trough pivots from being a zonal feature across the MC in winter to a diagonal one extending into the Northern South China Sea and anchoring over Indochina (IC; Thailand, Cambodia, Laos, Myanmar, Vietnam, etc) in summer (Reid et al., 2012). The maximums for SEA biomass burning activities generally anti-correlate with the monsoonal trough as it oscillates over the course of the year (Reid et al., 2013). During the Northeast Monsoon Season in winter, the IC Peninsula experiences a dry season while the MC experiences a relatively wet one. IC biomass burning generally starts in January and February, and intensifies from March to May (Reid et al., 2012, 2013). Regional smoke can be entrained into the free-tropospheric westerlies, and in fact has been observed as far as Hawaii (Reid et al., 2009). Smoke particles may also be advected toward the MC in northeasterly transport. However, rainfall rates over the MC are relatively high during this season, thus inducing wet particle deposition and limiting particle airborne lifetimes (Xian et al., 2013).

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IC biomass burning gradually ends during the transitional months (AprileMay), leading into the Southwest Monsoon Season in summer. Here, the monsoonal trough migrates over IC, inducing rainy conditions over the region and the annual dry season over the MC. Significant biomass burning usually begins in Sumatra from July, progressing eastward through the MC till November. Smoke particles are advected northward in southwesterly transport over Peninsular Malaysia and Singapore from biomass burning in Sumatra, and into the South China Sea from Kalimantan (Xian et al., 2013). MC biomass burning usually ends during the transitional months (OctobereNovember), leading back into the Northeast Monsoon Season. It is also important to note that off-season fires in parts of the MC (e.g., Peninsular Malaysia and Sumatra) can occur at any time of the year, even during the MC wet season, due to foreste clearing activities, especially after experiencing a prolonged dry spell (Balasubramanian et al., 2003; Field and Shen, 2008; Reid et al., 2012, 2013). An example of transported smoke from offseason MC biomass burning from 22 February to 8 March 2010 is later presented in Section 4.2.1. Warm phase El Niño/Southern Oscillation (ENSO) or El Niño conditions result in negative summertime precipitation anomalies in the MC, leading to extensive biomass burning (McBride et al., 2003). La Niña conditions, on the other hand, result in extensive summertime precipitation, thus potentially suppressing biomass burning during our study period. The monthly Multivariate ENSO Index (MEI; Wolter and Timlin, 1998) and daily hotspots, detected over SEA by the Moderate Resolution Imaging Spectroradiometer (MODIS; Davies et al., 2009) on board both NASA Terra and Aqua satellite platforms, are plotted in Fig. 2a and b respectively. Numerical values of MEI are also shown in Table 1. MEI is strongly positive for October 2009eMarch 2010, indicating strong El Niño conditions. However, it begins transitioning from El Niño to La Niña conditions from April to June 2010, and is at historic lows as considered since 1950s from July to October, indicating extreme La Niña conditions. MEI remains highly negative from November 2010 to March 2011. The effect of El Niño and La Niña conditions is reflected in the daily MODIS hotspots over Sumatra and Kalimantan during the Southwest Monsoon Season, and over IC during the Northeast Monsoon Season. The number of hotspots detected over Sumatra and Kalimantan during September 2009, corresponding with high MEI, is much higher than that detected from July to September 2010, in line with historical MEI lows. The impact of biomass burning smoke on air quality over Singapore can be seen in the daily PM10 concentrations and AERONET Level 2.0 AOD (Fig. 2c and d). Note that all available AERONET Level 2.0 AOD data, instead of only those coincident with MPLNET Level 2.0a data, are shown to give an overview of Singapore’s aerosol environment. Although the MPLNET instrument is not operational till the end of September 2009, high AERONET total and fine-mode AOD averages for JulyeSeptember 2009 are shown in Table 1, and indicate the severity of MC biomass burning, which is also described in Atwood et al. (2013). In general, air quality in Singapore deteriorates with increasing biomass burning smoke transport from Indonesia from July to September 2009, and does not appear to be significantly affected by IC biomass burning from January to April 2010. The average PM10 concentration and AOD in JulyeSeptember 2009 is 39 mg m3 and 0.52 respectively, which is higher than that of 32 mg m3 and 0.20 from July to September 2010, and in line with the trend of decreasing MEI. An atypical biomass burning smoke haze event occurred from w16 to 24 October 2010. The drier conditions induced in Sumatra due to the passage of Typhoon Megi through SEA may have provided an opportunity for extensive agricultural burning (Reid et al., 2012, 2013; Wang et al., 2013). Extreme cases of biomass burning smoke may be excluded from AERONET Level 2.0 data, which again

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Fig. 2. (a) Multivariate ENSO Index (MEI), (b) daily MODIS hotspots detected over MC and IC, (c) daily PM10 concentrations, (d) AERONET daily AOD and (e) daily-averaged lidar profiles over Singapore from September 2009 to March 2011. Note that Fig. 1d includes all AERONET Level 2.0 data, instead of only those coincident with MPLNET Level 2.0a data, to give an overview of Singapore’s aerosol environment during the entire study period.

are used as retrieval constrains for MPLNET Level 2.0a data, due to misidentification of smoke as clouds with AERONET cloudscreening algorithm (O’Neill et al., 2006). Furthermore, most retrievals during this period of time do not fulfill the quality assurance requirements of MPLNET Level 2.0a data, and are hence excluded from this study. However, selected MPLNET Level 1.5a profiles and carefully-screened AERONET Level 1.0 data can be used to interpret and analyze the smoke haze event as shown in Salinas et al. (2012).

4. Results and discussion 4.1. Seasonal variability of aerosol vertical distributions and columnar optical properties A total of 1115 AERONET Level 2.0 data coincident with cloudfree MPLNET-derived vertical profiles of aerosol particle extinction is available for analysis. The breakdown of the dataset according to seasons, including the number of available profiles and

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Table 1 Multivariate ENSO Index (MEI) from October 2009 to March 2011. Mean (without parentheses) and median value (with parentheses) for the lidar ratios, AOD, fine-mode AOD, coarse-mode AOD and Ångström exponents according to seasons. Note that aerosol optical properties for July to September 2009 are based solely on AERONET Level 2.0 data as coincident MPLNET Level 2.0a data are not available till end of September 2009.

MEI

No. of profiles No. of days Lidar ratio (sr) Ångström Exponent AOD Fine-mode AOD Coarse-mode AOD

JuleSep 2009

OcteDec 2009

JaneMar 2010

ApreJun 2010

JuleSep 2010

OcteDec 2010

JaneMar 2011

0.938 (Jul) 0.944 (Aug) 0.764 (Sep) NA 43 NA 1.3 (1.3) 0.52 (0.42) 0.42 (0.34) 0.11 (0.080)

1.018 (Oct) 1.061 (Nov) 1.007 (Dec) 93 21 53 (53) 1.4 (1.4) 0.24 (0.18) 0.17 (0.11) 0.072 (0.059)

1.148 (Jan) 1.518 (Feb) 1.395 (Mar) 91 21 51 (51) 1.3 (1.3) 0.39 (0.30) 0.33 (0.27) 0.056 (0.054)

0.862 (Apr) 0.548 (May) 0.466 (Jun) 175 30 51 (51) 1.5 (1.5) 0.29 (0.24) 0.24 (0.18) 0.055 (0.045)

1.217 (Jul) 1.849 (Aug) 2.037 (Sep) 399 31 46 (45) 1.7 (1.7) 0.20 (0.18) 0.16 (0.13) 0.043 (0.041)

1.948 (Oct) 1.606 (Nov) 1.580 (Dec) 136 16 43 (43) 1.5 (1.5) 0.34 (0.26) 0.28 (0.20) 0.060 (0.053)

1.654 (Jan) 1.552 (Feb) 1.548 (Mar) 221 23 44 (42) 1.3 (1.3) 0.39 (0.34) 0.32 (0.28) 0.066 (0.062)

the number of days on which profiles are available, is shown in Table 1. The daily-averaged vertical distributions of aerosol extinction coefficient at 0.527 mm from October 2009 to March 2011 are plotted in Fig. 2e. On average, 96% of columnar aerosol extinction is found below 3 km, 91% below 2.5 km, 65% below 1.5 km and 27% below 0.5 km. Therefore, relatively significant aerosol loading

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processes with regional biomass burning processes mentioned above. The horizontal bars in Fig. 3 represent one standard deviation from the mean profile with range. In each of the seasonal plots, the bulk (w96%) of aerosol extinction is found below 3 km MSL, with a distinct near-surface layer (0e1 km MSL) and elevated/stratified structures from w1 to 3 km MSL. The aerosol particle vertical distributions identified with the MPLNET instrument in this study exhibit some structural differences from those identified by the NASA Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP; Campbell et al., 2013), though both instruments depict the bulk of extinction being confined very near the surface. They further compare averages of annual and seasonal MPLNET-derived extinction coefficient with CALIOP and find similar differences. For instance, elevated structures from 1 to 3 km MSL as observed by the MPLNET instrument in Fig. 3 are not evident in CALIOP vertical profiles. However, the sampling periods for the MPLNET profiles are different from Campbell et al. (2013), and the spatial averaging necessary for producing corresponding CALIOP averages requires inclusion of both over-land and overocean data points that may smooth out important structures. Representativeness bias is likely occurring. The highest mean integrated extinction coefficients are derived during JFM 2010/2011, while JAS 2010 and OND 2009/2010 exhibit relatively lower values. Biomass burning in Sumatra generally starts in July, and peaks in August and September when the Southwest Monsoon is favorable for transport from Sumatra to Singapore. The lowered mean integrated extinction in JAS 2010 (Fig. 3d) is attributable to La Niña conditions and reduced fire events (Fig. 2b). This can be contrasted against the much higher average AOD and other columnar aerosol optical parameters for JAS 2009 during El Niño conditions (Table 1), though coincident MPLNET Level 2.0a data are not available. As biomass burning ceases in Sumatra during October, and monsoonal transition occurs in OctobereNovember, OND 2009/2010 (Fig. 3a and e) show lower mean integrated extinction within expectation. Although this is a topic that requires further study, we believe it is likely that anthropogenic pollution in/around Singapore and Peninsular Malaysia is a likely cause for higher extinction profiles in JFM 2010/ 2011 and AMJ 2010 (Fig. 3b, c and f), when northeasterly winds are predominant. In addition, smoke from off-season fires in Sumatra and Peninsular Malaysia during JFM 2010/2011 may be transported to Singapore (Balasubramanian et al., 2003; Field and Shen, 2008; Reid et al., 2012, 2013). Elastic-scattering lidar measurements are a function of two unknown terms: particulate backscatter and extinction coefficients. A unique solution is not possible without a-priori knowledge of one or both (e.g., Fernald, 1984). The extinction-to-backscatter ratio, or so-called “lidar ratio”, relates the two in a single parameter, and is inversely proportional to the product of single-scattering albedo and 180 backscattering phase function (e.g., Ackermann, 1998). Iterative solutions to the lidar equation are based on an assumption of aerosol particle turbidity within the atmospheric column (Fernald, 1984). However, it is common to observe multiple particle layers and types over Singapore (Salinas et al., 2009; Chew et al., 2011), and may result in representativeness error in the derived solution (Welton et al., 2000, 2002). Fig. 4 depicts MPLNET-derived seasonal lidar ratio distributions, with mean and median values noted in Table 1. Burton et al. (2012) describe lidar ratios based on the airborne High Spectral Resolution Lidar (HSRL; Grund and Eloranta, 1991) measurements at 0.532 mm for various global aerosol types (i.e., maritime aerosols: 15e25 sr; polluted maritime air mass: 35e 45 sr; urban aerosols: 50e70 sr; advected biomass burning aerosols: 60e80 sr). These findings are also qualitatively in agreement with Müller et al. (2007), who describe 10-year lidar ratio values

derived with Raman lidar observations at 0.532 mm at various sites in Europe, Asia and Africa. Therefore, we generalize our results based upon the above classifications as clean and polluted maritime aerosol particles or dust (20e40 sr), urban aerosols (40e 60 sr) and biomass burning aerosols (60e80 sr). We maintain that this classification is strictly qualitative. The high humidity environment in Singapore causes hygroscopic growth of aerosols, possibly resulting in higher lidar ratios and mixing between different classification groups (Ackermann, 1998). In cases of two or more aerosol species present within the atmospheric column, lidar ratios will be calculated as a columnar average, and are dependent of the relative loading of each aerosol species (Welton et al., 2000). OND 2009 exhibits the highest mean seasonal lidar ratio of 53 sr, indicating a likely urban environment, with 31% of occurrences 60 sr (Fig. 4a), believed to indicate the influence of MC biomass burning enhanced by El Niño conditions. Similar results are found for JFM and AMJ 2010, both with seasonal-mean lidar ratios of 51 sr, and 31% and 21% of occurrences 60 sr (Fig. 4b and c) respectively. JAS 2010 corresponds with the lowest mean integrated extinction coefficient profile (Fig. 3d), and a relatively low seasonal-mean lidar ratio of 46 sr is also found, with 14% of cases  60 sr (Fig. 4d). This is found due to La Niña conditions prevailing from July to October 2010, with likely precipitation suppression indicated by negative MEI values during JAS and OND 2010. The percentage occurrence of cases 60 sr (14%) for JAS 2010 is significantly lower when compared with OND 2009. As La Niña conditions continued from OND 2010 to JFM 2011, seasonal-mean lidar ratios drop to 43 sr and 44 sr, with only 2% and 11% of cases 60 sr for the two seasons (Fig. 4e and f), respectively. We reiterate here that extreme cases of biomass burning smoke during October 2010 are absent from our dataset. Salinas et al. (2012) considers these cases using provincial MPLNET Level 1.5a profiles and carefully-screened AERONET Level 1.0 data. Seasonal a at 0.500 mm retrieved from AERONET Level 2.0 data are shown in Fig. 5, with mean and median values noted in Table 1. OND 2009 exhibits a mean a of 1.4 with 72% of occurrences < 1.5, indicating a mixture of fine- and coarse-mode particulates (Fig. 5a). A predominance of fine-mode particulates during MC biomass burning season is clearly present during AMJ, JAS and OND 2010, with high mean a of w1.5e1.7 and 61, 86 and 52% of occurrences 1.5 (Fig. 5c, d and e). On the other hand, lower a are observed during JFM 2010/2011. Both average approximately 1.3, with 85 and 97% of cases <1.5 (Fig. 5b and f). Fine-mode particle growth from such mechanisms as coagulation, secondary particle production, and haze formation in high humidity can result in a lower a (Reid et al., 2005; Salinas et al., 2012), especially during possible longrange transport from IC biomass burning sources (Reid et al., 2013). However, as corroborated by Campbell et al. (2013), the confinement of most aerosol particle mass very near the surface likely represents relatively limited transport, likelier sedimentation effects, and the impact of wet deposition processes. It is more likely that anthropogenic pollution and transported smoke from MC offseason fires mix with salt-rich maritime air masses arriving from the South China Sea via northeasterly transport during JFM, resulting in the lower a. 4.2. Principal component analysis In Fig. 3aef, standard deviations corresponding with extinction coefficients solved at each lidar range bin are relatively high, indicating significant variability present within the means. Therefore, these profiles may in fact be made up of multiple transport modes. Indeed, even cursory examination of retrieved extinction in Fig. 2e time series demonstrates reasonable variability for altitudes below

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Fig. 4. Seasonal lidar ratio distributions detected by MPLNET instrument at 0.527 mm over Singapore for (a) OND 2009, (b) JFM, (c) AMJ, (d) JAS, (e) OND 2010 and (f) JFM 2011. Bin sizes for all panels are 5 sr.

4 km. In order to extract key profile features, PCA (Tabachnick and Fidell, 2007) was performed on the aerosol extinction coefficient dataset for each 75-m range bin up to w6 km. Prior to performing PCA, the suitability of dataset was assessed. The Bartlett’s Test of Sphericity (Bartlett, 1954), which is used to validate the hypothesis that a given correlation matrix is an identity matrix, reached statistical significance (p < 0.001). The KaisereMeyereOlkin test (Kaiser, 1970, 1974), which is used to check sampling adequacy by calculating partial correlations among the variables, determined a value of 0.877, exceeding the recommended value of 0.6. These two tests supported the factorability of the correlation matrix derived from the MPLNET data.

Primary profile features at five different height ranges are isolated by PCA of the MPLNET sample. The rotated factor scores for each profile type are plotted in Fig. 6aee, with corresponding representative case studies shown in Fig. 7bef. Fig. 7a shows a representative example of the boundary layer (0e0.7 km; 00:22 UTC 26 October 2009) for comparison with the five profile types. The five profile types include: strongly-capped/deep near-surface layer (SCD; 0e1.35 km; 08:12 UTC 16 October 2010; Fig. 7b), enhanced mid-level layer (EML; 1.35e2.4 km; 10:02 UTC 14 September 2010; Fig. 7c), enhanced upper-level layer (EUL; 2.4e 3.535 km; 06:34 UTC 20 October 2009; Fig. 7d), deep contiguous layer (DCL; 3.525e4.95 km; 06:35 UTC 13 July 2010; Fig. 7e) and

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Fig. 5. Seasonal Ångström exponent distributions by coincident AERONET Level 2.0 measurements at 0.500 mm over Singapore for (a) OND 2009, (b) JFM, (c) AMJ, (d) JAS, (e) OND 2010 and (f) JFM 2011. Bin sizes are 0.1.

deep multi-layer (DML; >4.95 km; 03:15 UTC 14 February 2011; Fig. 7f). Observations of transported aerosols at various heights over Singapore are consistent with Tosca et al. (2011), who describe smoke plumes originating from MC biomass burning being confined to 1 km within the boundary layer with mean plume heights of 749  24 m over Sumatra and 709  14 m over Borneo. Plume injection of fire emissions into the free troposphere does not seem to be an important mechanism for vertical mixing of aerosols, as many fires in the MC region occur over peat land and are expected to have lower intensities due to the smothering nature of burning (Duncan et al., 2003). Further, as Campbell et al. (2013) hypothesize, cloud processing likely plays a significant role in particle suppression. Therefore, smoke plumes transported directly from Sumatra during the Southwest Monsoon most strongly influence composition in the near-surface boundary (Fig. 7a) and SCD layers (Fig. 7b) over Singapore. Tosca et al. (2011) has also shown smoke layers to be between 1 km and 2 km with occasional thin smoke layers up to roughly 4.5 km. Convection is an important mechanism for the lofting of aerosols within the troposphere, and their elevation to higher altitudes (Mori et al., 2004). Diurnal evolution of the boundary layer may draw smoke plumes upward along isentropic trajectories during daytime (Yu et al., 2002). Strong near-surface layer capping will similarly result from its receding

overnight (Schafer et al., 2001), which may further induce EML, EUL, DCL and DML scenarios (Fig. 7cef) over Singapore. The sources for these elevated aerosol layers are highly variable, compared with those for the near-surface boundary and SCD layers, due to the substantial vertical wind shear over Singapore (Atwood et al., 2013). The representative PCA-derived profiles occur both during the MC (Fig. 7aee; dates above) and IC biomass burning seasons (Fig. 7f). Corresponding smoke AOD composites over the MC resolved by the Navy Aerosol Analysis and Prediction System (NAAPS; Christensen, 1997) are shown in Fig. 8. The NAAPS model uses global meteorological fields from the Navy Operational Global Atmospheric Prediction System (NOGAPS; Hogan and Rosmond, 1991; Hogan and Brody, 1993), and forecasts smoke, dust and sulfate fractional AOD at a 1  1 grid, 6-h intervals and twenty-four vertical levels up to 100 mb. The Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System e Aerosol Optical Depth (NAVDAS-AOD; Zhang et al., 2008) is utilized to improve NAAPS simulations by assimilating MODIS observational data within the analysis fields. Although Fig. 8 depicts smoke aerosols impacting Singapore in every case, the deterioration of air quality at ground level only occurs for 26 October 2009 and 16 October 2010 (Fig. 7a and b), with aerosol particle mass concentrated near ground level and

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Fig. 6. PCA rotated factor scores for (a) strongly-capped/deep near-surface layer (SCD; 0e1.35 km), (b) enhanced mid-level layer (EML; 1.35e2.4 km), (c) enhanced upper-level layer (EUL; 2.4e3.525 km), (d) deep contiguous layer (DCL; 3.525e4.95 km) and (e) deep multi-layer (DML; >4.95 km) from September 2009 to March 2011. (f) Daily-averaged lidar profiles over Singapore; similar to Fig. 2e. Red circles correspond to profile types shown in Fig. 7. Red dashed box marks the period of off-season biomass burning in Sumatra and Peninsular Malaysia described in Section 4.2.1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

hourly PM10 measurements of 50 and 63 mg m3, respectively. The corresponding MPLNET lidar ratios are 46 and 53 sr, with AERONET a of 1.5 and 1.6. Fig. 7a and b likely depict biomass burning aerosols mixed with polluted marine and urban aerosols at the surface. Hourly PM10 measurements for 14 September 2010, 20 October

2009 and 13 July 2010 (Fig. 7cee) are 25, 20 and 15 mg m3 respectively. Corresponding lidar ratios are 70, 44 and 57 sr, and a are 1.7, 1.1 and 1.6, again each respectively. Therefore, it is likely that biomass burning aerosols exist in the elevated layers depicted in Fig. 7c and e, though not necessarily at the surface in high

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Fig. 7. (a) Typical boundary layer over Singapore (00:22 UTC 26 October 2009) and five primary aerosol vertical profile types identified with PCA: (b) SCD (0e1.35 km; 08:12 UTC 16 October 2010), (c) EML (1.35e2.4 km; 10:02 UTC 14 September 2010), (d) EUL (2.4e3.525 km; 06:34 UTC 20 October 2009), (e) DCL (3.525e4.95 km; 06:35 UTC 13 July 2010) and (f) DML (>4.95 km; 03:15 UTC 14 February 2011).

concentrations. For Fig. 7d, the low a is likely indicative of columnar spectral mixing between coarse-mode polluted marine aerosols and urban aerosols at the surface, with elevated biomass burning aerosols. The hourly PM10 measurement for 14 February 2011 (Fig. 7f) is 49 mg m3, with a lidar ratio of 49 sr and a of 1.3. Therefore, urban aerosols are mostly probably dominant at the surface, as the extinction coefficient in the elevated aerosol layer is not relatively significant. 4.2.1. Off-season biomass burning smoke event in JFM 2010 From PCA, transported biomass burning smoke over Singapore is identified from 22 February to 8 March 2010, which is the offseason period for MC biomass burning, with elevated factor scores corresponding to the SCD layer and EML (see red dashed box in Fig. 6). Off-season MC biomass burning is an exception to the simple seasonal SEA biomass burning model (Reid et al., 2012). Offseason burning activities are not often discussed in climate literature, but have been reported in air quality studies (Balasubramanian et al., 2003), factor analyses (Field and Shen, 2008) and remote sensing/modeling studies (Reid et al., 2012). Despite relatively high cloud coverage during the 2010 Northeast Monsoon Season, MODIS retrievals consistently identified fire

hotspots over Sumatra and Peninsular Malaysia. Examples of fire activities in these two areas on 24 February 2010 and 7 March 2010 are shown in Fig. 9a and b respectively. Three-day Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT; Draxler and Hess, 1997) back trajectories, driven by the Global Data Assimilation System (GDAS1) 1  1 meteorological dataset provided by the National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory, are generated at two height levels (500 m for air masses arriving at the near-surface boundary and SCD layers, and 2000 m for air masses arriving at EML over Singapore). One back trajectory per day (at 00:00 UTC) is generated from 22 February to 8 March 2010. The resultant residence time analysis (Fig. 9c and d) for these back trajectories indicates air masses predominantly arrive from the South China Sea at both 500 m and 2000 m, consistent with northeasterly transport. However, possible pathways over Peninsular Malaysia and Sumatra at 2000 m are also identified, and consistent with fire source regions identified in Fig. 9a and b. Smoke aerosols within EML may have been elevated due to the transport mechanisms mentioned above, i.e. convection and diurnal evolution of the boundary layer. The AERONET total, fine- and coarse-mode AOD (Fig. 10a), total and fine-mode a (Fig. 10b), fine-mode fraction (Fig. 10c), as well as

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Fig. 8. NAAPS smoke AOD over the MC corresponding to Fig. 7. (a) 00:00 UTC 26 October 2009, (c) 06:00 UTC 16 October 2010, (c) 12:00 UTC 14 September 2010, (d) 06:00 UTC 20 October 2009, (e) 06:00 UTC 13 July 2010 and (f) 00:00 UTC 14 February 2011. Red circle marks the position of Singapore. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 9. Fire activities (indicated by small red squares) over Sumatra and Peninsular Malaysia detected by (a) Terra (24 February 2010) and (b) Aqua (7 March 2010) MODIS. HYSPLIT residence time analysis for 3-day back trajectories at (c) 500 m and (d) 2000 m from 22 February to 8 March 2010. Red circle marks the position of Singapore. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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MPLNET lidar ratios and daily PM10 concentrations (Fig. 10d) during the transported smoke event (22 Februarye8 March 2010) are shown. Total and fine-mode AOD generally increase from 22 to 28 February with consistently low coarse-mode AOD. Fine-mode a are generally high, with an average of 1.6, while an average total a of 1.3 over this period indicates the likely mixing of fine- and coarsemode particles. Fire activities decrease from 1 to 3 March, likely suppressed by widespread precipitation over the MC. Wet deposition of aerosols is likely enhanced during this period of time as well, as can be seen from the decrease in total and fine-mode AOD. From 4 to 8 March, total and fine-mode AOD increase again. For this period, average AOD of 0.8 is high, with an equally high mean finemode AOD of 0.7. The average total and fine-mode a are 1.4 and 1.5 respectively. The fine-mode fraction also reaches 0.9 and is sustained from 4 to 8 March, which is indicative of biomass burning fine-mode particles (Salinas et al., 2012). The average PM10 concentration from 22 February to 8 March is 36 mg m3, compared to the JFM 2010/2011 average of 31 and 29 mg m3 respectively. MPLNET data depict clear cases of biomass burning smoke on 24, 28 February and 4, 8 March, with lidar ratios

exceeding roughly 60 sr (see red dashed boxes in Fig. 10d). Aerosol particle extinction profiles with the highest lidar ratios corresponding to the above-mentioned dates are also shown in Fig. 11, depicting the bulk of the aerosol mass concentration to be above the boundary layer, thus not affecting the surface air quality significantly. Although IC biomass burning was relatively high for JFM 2010, there is no indication of biomass burning smoke transport to Singapore from the region, resulting in significant impacts on surface air quality. It is also important to note that Campbell et al. (2013) did not find significant outflow structure from IC southward toward Singapore in their analysis of CALIOP profiles over SEA in late boreal winter/spring when northeasterlies are dominant. In addition, Liew et al. (2009) found that transported IC biomass burning smoke primarily impacted the northern part of Peninsular Malaysia. Therefore, it is likely that IC biomass burning smoke contributes minimally to poor air quality in Singapore over this period of time. Instead, off-season biomass burning smoke from Sumatra and Peninsular Malaysia is more likely to impact air quality significantly due to the proximity of these sources.

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5. Conclusions In this study, we examine the seasonal variability of aerosol particle vertical distributions and columnar optical properties over Singapore using coincident AERONET Level 2.0 and MPLNET Level 2.0a datasets from 24 September 2009 to 31 March 2011. On average, substantial aerosol loading (w35%) occurs above 1.5 km and the boundary layer. Possibly due to the transition from El Niño to La Niña conditions during the study period and subsequent reduction in fire events, the MPLNET integrated columnar aerosol extinction is observed to be the lowest for JulyeSeptember 2010, which coincides with the typical MC biomass burning season. On the other hand, the highest mean integrated extinction coefficients are derived for JanuaryeMarch 2010/2011, which can be attributed to off-season MC biomass burning and anthropogenic pollution in/around Singapore and Peninsular Malaysia. The effect of the transition from El Niño to La Niña conditions is also substantiated by the reduced seasonal-mean lidar ratios with decreased occurrences 60 sr through the study period. PCA applied to MPLNET-derived aerosol particle extinction profiles results in five primary vertical profile types. They are: strongly-capped/deep near-surface layer (SCD; 0e1.35 km), enhanced mid-level layer (EML; 1.35e2.4 km), enhanced upperlevel layer (EUL; 2.4e3.535 km), deep contiguous layer (DCL; 3.535e4.95 km) and deep multi-layer (DML; >4.95 km). Such observations are consistent with Tosca et al. (2011) and Campbell et al. (2013), which indicate that smoke are generally confined within 2e

3 km of the surface in the MC. Case studies corresponding to the various profile types are also presented to illustrate the aerosol vertical distributions over Singapore with varying impacts on surface air quality. An example of transported smoke from off-season MC biomass burning is also identified with PCA. Off-season MC biomass burning is an exception to the simple seasonal SEA biomass burning (Reid et al., 2012). Such burning activities are not often discussed in climate literature, but have been reported in air quality studies (Balasubramanian et al., 2003), factor analyses (Field and Shen, 2008) and remote sensing/modeling studies (Reid et al., 2012). Therefore, the aerosol particle extinction profiles and optical properties, i.e. AERONET total, fine- and coarse-mode AOD, total and finemode a, fine-mode fractions, for this particular event are presented. The seasonal characterization of the aerosol particle extinction profiles, as well as determination of the most frequently observed features, allows a better vertical depiction of particle transport within the MC. As particles are generally confined within 3 km of the surface, long-range aerosol transport from the MC is not likely. Therefore, aerosol radiative forcing is also likely to be confined within or near the source regions. Acknowledgments AERONET and MPLNETare supported with funding from the NASA Earth Observing System and Radiation Sciences Programs. The AERONET and MPLNET instruments are deployed at Singapore as

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part of the Seven Southeast Asian Studies (7SEAS) field campaign, as sponsored by the Office of Naval Research (ONR), ONR Global and NASA. Dr. Campbell acknowledges the support of NASA Interagency Agreement NNG12HG05I on behalf of NASA MPLNET. Dr. Reid's participation is supported by the NRL 6.1 Base Program. The authors would like to thank the Department of Civil and Environmental Engineering, and NUS Environmental Research Institute (NERI) at the National University of Singapore for hosting the 7SEAS atmospheric measurement supersite, and Singapore’s National Environment Agency for collecting and archiving the surface air quality data.

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