Ship-based measurements of aerosol and cloud condensation
nuclei (CCN) properties are presented for 2 weeks of observations in
remote marine regions of the South China Sea/East Sea during the
southwestern monsoon (SWM) season. Smoke from extensive biomass burning
throughout the Maritime Continent advected into this region during the SWM,
where it was mixed with anthropogenic continental pollution and emissions
from heavy shipping activities. Eight aerosol types were identified using a
k-means cluster analysis with data from a size-resolved CCN characterization
system. Interpretation of the clusters was supplemented by additional
onboard aerosol and meteorological measurements, satellite, and model
products for the region. A typical bimodal marine boundary layer background
aerosol population was identified and observed mixing with accumulation mode
aerosol from other sources, primarily smoke from fires in Borneo and
Sumatra. Hygroscopicity was assessed using the
In the Southeast Asian Maritime Continent (MC) and South China Sea/East Sea (SCS) aerosol particles are expected to play an important role modulating cloud development, precipitation, and radiative properties that affect heat transfer through the atmosphere (Reid et al., 2013). Assessment of aerosol properties important to understanding such processes in remote marine segments of this region has proven difficult. Extensive cloud cover confounds remote sensing and leads to a clear-sky bias in observations (Feng and Christopher, 2013; Reid et al., 2013). Aerosol monitoring has largely been confined to urban centers that are often dominated by local emissions, while in situ sampling in remote areas has been limited in duration and scope (Irwin et al., 2011; Robinson et al., 2011; Lin et al., 2014; Reid et al., 2015). Airborne measurements have provided some representation of aerosol over wider regions and at various levels (Hewitt et al., 2010; Robinson et al., 2012), but additional questions regarding the representativeness of such point measurements across larger timescales remain. Similarly, the impact of various aerosol sources on surface properties and concentrations in remote marine regions, and their relationship to expected transport pathways and the few remotely sensed column measurements that exist, is not well understood. Thus, over these remote ocean regions the aerosol optical and physical properties, their variability in time and space, and the processes controlling aerosol life cycle have not been well constrained. This uncertainty in the aerosol environment itself comes in addition to uncertainty about its impacts on meteorological processes. Aerosol concentration has been found to relate to cloud development, cloud microphysics, and precipitation formation in the region (Yu et al., 2008; Yuan et al., 2011; Wang et al., 2013), while smoke may affect cloud droplet size distributions and the onset of precipitation, similar to processes observed in other tropical regions impacted by biomass burning (Rosenfeld, 1999; Andreae et al., 2004). Improved knowledge of the aerosol environment and aerosol–cloud–climate relationships in the Southeast Asian region has therefore been identified as important regionally, and in regards to links with global climate and large-scale aerosol budgets (Reid et al., 2013).
During the May through October southwestern monsoon (SWM) season, burning throughout the MC typically reaches its greatest extent between August and early October as precipitation associated with the Intertropical Convergence Zone (ITCZ) shifts north into Indochina (Reid et al., 2012). The resulting heavy smoke mixes with urban, industrial, marine, and shipping emissions in an exceedingly complex aerosol mixture (Balasubramanian et al., 2003; Atwood et al., 2013; Reid et al., 2013). During this period, aerosol particles from surface sources are generally advected by low-level mean winds throughout the SCS, where they are scavenged by precipitation or eventually removed in the monsoonal trough east of the Philippines (Reid et al., 2012, 2015; Wang et al., 2013; Xian et al., 2013). As a result, the region of the SCS and Sulu Sea to the north and east of Borneo has been predicted to be a receptor for much of these biomass burning and pollution emissions from the greater MC during periods when air masses enter more convective phases of the SWM (Reid et al., 2012; Xian et al., 2013).
Remote marine aerosol and its impact on atmospheric processes have been studied in a number of ocean regions (Hoppel et al., 1986; Russell et al., 1994; Jensen et al., 1996; Brechtel et al., 1998; Murphy et al., 1998; Bates et al., 2000; Petters et al., 2006; Quinn et al., 2006). These studies identified a background submicron marine aerosol that is composed of two distinct modes in the number distribution, due to processing by non-precipitating clouds (Hoppel et al., 1986, 1994; Hudson et al., 2015). Bates et al. (2000) linked the differences in the average size distributions of background marine aerosol in two remote marine regions to regional meteorology, including differences in aerosol residence time and cloud processing. Increased wind speeds lead to increased flux of sea salt particles into the atmosphere, contributing submicron particles as small as 40 nm in diameter (O'Dowd and Leeuw, 2007; Russell et al., 2010; de Leeuw et al., 2011; Bates et al., 2012; Modini et al., 2015). Non-sea-salt-sulfate and organic matter from marine sources also comprise large fractions of the submicron aerosol mass loading in clean and background marine air masses (Murphy et al., 1998; Cavalli et al., 2004). As air masses from more terrestrial or anthropogenically influenced regions advect over remote marine regions, submicron size distributions and chemical compositions often diverge from background conditions (Bates et al., 2000; Quinn et al., 2006). More recent studies have further quantified the role of various processes in shaping the marine aerosol population, including primary and secondary production, aging, and mixing with non-marine sources (Allan et al., 2009; Russell et al., 2010; de Leeuw et al., 2011; Bates et al., 2012; Prather et al., 2013; Frossard et al., 2014; Modini et al., 2015). In particular, the contribution of dissolved organic components in the sea surface microlayer to aerosol produced by bubble breaking has been noted, with increasing organic enrichment as size decreases (Russell et al., 2010; Bates et al., 2012; Prather et al., 2013; Quinn et al., 2014). Additional studies into the source-dependent composition of marine aerosol have indicated non-marine sources can be important contributors to aerosol in marine regions. Shank et al. (2012) found evidence of biomass burning and combustion impacts on remote marine boundary layer (MBL) aerosol, including in nominally clean marine conditions. These authors also noted the limited importance of organic components in particulate matter in a tropical Pacific location, as compared to other regions where organics were a more important fraction of the submicron aerosol. Frossard et al. (2014) found influences on aerosol organic matter from shipping and mixing with non-marine sources in 63 % of observations across five ocean regions. Modini et al. (2015) evaluated the contribution of primary marine aerosol to cloud condensation nuclei (CCN) number concentrations and found that it accounted for less than 10 % of CCN active at 0.9 % supersaturation during low-wind conditions, with increasing importance (up to 58 % of CCN) at higher wind speeds and lower environmental supersaturations. Taken as a whole, recent understanding of marine aerosol indicates that the background marine aerosol and primary marine emissions can be complex and play an important role in cloud, radiative, and precipitation processes, and that other sources of aerosol contribute to number and mass concentrations, even in relatively clean and/or remote regions.
Two research cruises were conducted in the remote MBL of the SCS and Sulu Sea during the 2011 and 2012 SWM seasons to perform in situ aerosol and meteorological measurements, and to investigate marine aerosol and its impacts on clouds, precipitation, and climate as it reflects the complex set of sources in the region (Reid et al., 2015, 2016). In this paper, we present observations of aerosol and CCN characteristics during the second cruise, along with their relationship to aerosol source type, air mass, and meteorological phenomena. These measurements represent the first in situ observations of size-resolved CCN properties in the area and fill a gap in knowledge needed to assess aerosol–cloud–precipitation relationships in the data-poor remote marine SCS region.
The SCS research cruises occurred during the month of September in both 2011
and 2012, and took place aboard the 35 m, 186 t M/Y
A DMT passive cavity aerosol spectrometer probe (PCASP) X2 configured in an
aviation pod with heated inlet was mounted at the
Approximately 1.1 L min
The size-resolved CCN system measured CN and CCN (activated particles at a
CCNc set point supersaturation) concentrations in each of 30
quasi-monodisperse size bins between 17 and 500 nm. The CCNc was
operated at five temperature gradient settings and calibrated using ammonium
sulfate (following the methods described by Petters et al., 2009) to measure the corresponding
maximum environmental supersaturation within the CCNc column. The scan of
all 30 size bins at each supersaturation took approximately 15 min,
while a complete measurement over all five supersaturation settings took
approximately 2 h due to pauses between settings while column
temperatures stabilized. The measured CN and CCN particle counts were
inverted using the methodology of Petters et al. (2009). The inversion yielded the dry
ambient aerosol size distribution over the measured range
(d
Time lines of measured and derived variables during the
As the SCS environment tended to have relatively few particles smaller than
50 nm, only the measurements at the 0.14 and 0.38 % supersaturation
settings had complete activation curves that spanned the measured particle
diameter range. For the higher supersaturation settings, the
Additional measurements of aerosol composition were used to validate
identified source types impacting the measurements throughout the cruise. A
series of PM
A suite of weather-monitoring instruments was located on a 3 m bow mast to provide coincident meteorological measurements throughout the study. From this suite, wind speed and wind direction measurements from a Campbell sonic anemometer were used to identify gust front passage. An OTT Parsivel disdrometer was utilized to measure precipitation, from which only the rain rate measurements were used in this analysis.
Several remote-sensing and model products were used to characterize the
wider SCS atmospheric environment and to identify potential aerosol sources.
Moderate Resolution Imaging Spectroradiometer (MODIS) visible and infrared
(IR) products were used to identify convection and squall
line propagation across the SCS. The MODIS Collection 6 MOD08 Level 3 daily
aerosol optical depth (AOD) products were utilized for AOD measurements in the
region, though cloud cover obscured measurements throughout much of the
study. MODIS active fire hot spot analysis and the Fire Locating and Modeling of Burning
Emissions (FLAMBE) smoke flux product
from Terra and Aqua were used to identify the locations and times during
which fires were burning in the MC (Giglio
et al., 2003; Reid et al., 2009; Hyer et al., 2013). Simulations from
the Navy Operational Global Atmospheric Prediction System
(NOGAPS) model were used to represent surface and 700 hPa winds,
interpolated to 1
The NOAA Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT)
Version 4.9 model (Draxler, 1999; Draxler
and Hess, 1997, 1998) was used to generate daily 72 h back-trajectories
(spawned at 0 Z, 8 AM local) from the
The d
Parameters associated with each data point were then used to cluster the data points into groups based on similar observed aerosol properties. Cluster analyses have long been used to group observed aerosol size distributions into clusters of generally similar size distributions (Tunved et al., 2004), which can then be associated with various sources or atmospheric processes that shaped them (Charron et al., 2008; Beddows et al., 2009; Wegner et al., 2012). Similar cluster analyses have been utilized to classify aerosol types based on particle chemistry (Frossard et al., 2014), with Frossard et al. (2014) identifying clusters in marine aerosol observations associated with marine and non-marine aerosol types. As pointed out by those authors, the clustering approach can be superior to algorithms using simpler criteria to distinguish “clean” from “polluted” conditions, as more variables and a measure of similarity between data points are used to find the underlying population types. In this study normalized size distribution parameters were combined with total number concentration and particle composition information via hygroscopicity measurements to serve as input variables for the cluster analysis (parameters given in Table 1). As hygroscopicity data were available for at most one mode (during the 0.14 or 0.38 % supersaturation scans), Aitken and accumulation mode hygroscopicities were treated as missing for data points without this information. In order to account for missing data and adjust all clustering variables to the same scale, each variable was first standardized to a mean of 0 and standard deviation of 1, with missing data points imputed to a value of 0 (the mean value). As a result, the clustering distance function was insensitive to missing data but still included information on hygroscopicity when available.
Aerosol population type parameters used for clustering and the resulting average values (standard deviations in parentheses) for each identified population.
A hierarchical cluster analysis was first conducted using the
A nonhierarchical k-means cluster analysis was then conducted for each of
these four potential cluster numbers using the scikit-learn
The daily positions of the
Parameterized variable values for Aitken and accumulation modes
(median diameter, geometric standard deviation, modal fraction) at each of
the 15 min data points during the study, along with
Normalized d
The cluster analysis was first conducted to investigate potential aerosol population types in the dataset, followed by physical interpretation of the results against cluster aerosol properties, coincident measurements, and meteorological conditions. The parameter values input to the cluster analysis are shown for each data point and variable in Fig. 3, and colored by cluster number for the results of the eight-cluster k-means analysis. The average value and intra-cluster standard deviation for each cluster parameter and cluster are given in Table 1. Normalized size distributions for each of these eight aerosol populations are shown in Fig. 4; the average CN and CCN number concentrations and hygroscopicities are given in Table 2. Equivalent normalized volume distributions are shown in Supplement Fig. S2. The cluster number associated with each measurement is similarly shown as the background color in Fig. 2 and marker color in Fig. 5. The aerosol properties, meteorological conditions, and likely transport pathways associated with data points in each cluster were then used to provide a physical interpretation of the results and identify each population type on the basis of its likely sources as discussed below. Clusters 1–4 were the most commonly found (representing 85 % of the total observations, Table 2), while clusters 5–8 represented special cases, generally of short duration, that could be identified by specific locations or sampling conditions.
Average values (standard deviations in parentheses) for
identified aerosol population types. Shown are number of CCN system data
points classified as each type, total number concentrations for the PCASP
(125 nm–3
* Only one data point. Note that port measurements fluctuated as the
Number concentrations of coarse-mode particles (
Background marine: data points associated with this cluster occurred
throughout the study, typically following rain in the vicinity of the
Precipitation: this distribution was found during periods immediately
following extensive precipitation at or near the
Smoke: data points associated with this aerosol type occurred primarily in
two events on 14 and 25–26 September, during which back-trajectories were
at their furthest south, near burning regions in Borneo (Fig. 1a).
Normalized size distributions indicated that particles were largely
concentrated in a single accumulation mode with a tail of smaller particles.
This type was associated with the highest total particle number and
estimated submicron mass concentrations observed during the cruise, with the
exception of measurements taken in the urban plume of Puerto Princesa. The
standard deviations in the normalized size distribution parameters for the
dominant accumulation mode in this population (Fig. 4, Table 1) were
small, even while number concentration varied widely (IQR: 1802 to 2780 cm
Accumulation mode lognormal median diameters around 200 nm with a tail of smaller particles, elevated concentrations of carbon monoxide and benzene, and potassium in filter samples during this period (Reid et al., 2016, and Fig. 2c) were all consistent with expectations for aged biomass burning smoke (Yokelson et al., 2008; Akagi et al., 2011; Reid et al., 2015; Sakamoto et al., 2015). Additional examination and attribution of this event to biomass burning in Sumatra and Borneo are discussed further in Reid et al. (2016). Finally, while smoke is considered the dominant aerosol source during these periods, anthropogenic pollution may still have been co-emitted along the transport path and contributed to measured results.
Mixed marine: this population was characterized by periods during which the
background marine type mixed with other sources of aerosol. Most of the data
points associated with this type had transport pathways and biomass burning
sources similar to those for the smoke population type, but with number
concentrations and size distribution parameters between those of the
background marine and smoke types (IQR: 782 to 1160 cm
While periods of smoke mixing with a background marine air mass appeared to
constitute the majority of data points in this cluster, several other
periods point to other phenomena of interest being included in this type,
perhaps indicating this cluster was relatively more complex than other
population types. Short-lived intrusions (2 to 5 h) of accumulation
mode particles were regularly observed in both the CCN system and PCASP
datasets (e.g., 18–23 Z on 22, 23, and 24 September), after which the size
distributions quickly returned to background marine conditions. These
excursions were largely constrained to the pre-dawn hours (sunrise occurs
around 22 Z), when the boundary layer was thinnest and when precipitation was
occurring in the vicinity of the
Organic event: an approximately 4 h period starting at 1 Z on 23 September
had measured particle concentrations between 200 and 325 cm
Ultrafine event: this cluster was associated with an approximately 20 h period on 17–18 September, which included the highest concentration of particles below about 30 nm observed throughout the study (Fig. 2a) and coincided with a period of elevated VOC measurements at the start of this event (Fig. 2c). A filter during this period showed very low potassium concentrations, while benzene was among the lowest values measured during the study, indicating that biomass burning was not the likely source for this event. Anthropogenic, shipping, and marine and terrestrial biogenic emissions are known sources of such compounds; isoprene, a common biogenic VOC, was not observed during this event, and a brief period of elevated dimethyl sulfide, associated with marine emissions from phytoplankton, was observed shortly before – but not during – this event (Reid et al., 2016).
A tri-modal best fit was indicated by the Hussein et al. (2005)
algorithm for a number of these data points (Fig. 2a and Supplement Fig. S1).
The period had an overall IQR of 482 to 661 cm
Transit: this type was associated with measurements taken during a transit
away from the port of Puerto Princesa, a city with a population of over
200 000. During this period light, westerly winds advected anthropogenic
pollution out over the Sulu Sea and along the path of the
Port: this type was assigned to the measurements taken during a short period
in the port of Puerto Princesa. Local anthropogenic emissions were dominant
during this period, with number concentrations that fluctuated between 4000
and 10 000 cm
Finally, throughout the study coarse-mode particles with diameters larger than about 800 nm were consistently observed in the PCASP volume distributions (Fig. 2b). Concentrations of particles in this size range increased with increasing wind speed (Fig. 5), consistent with generation of sea spray aerosol due to bubble breaking and wave action (O'Dowd and Leeuw, 2007). While the total number concentration of coarse particles is small compared to typical CCN concentrations (Fig. 2e, f), in the cleanest conditions we measured they represented non-trivial fractions of CCN active at 0.14 and 0.38 % supersaturations. The large diameter of these particles makes them likely to activate at very low supersaturations, and they are present in more than sufficient number concentration to impact the microphysical structure and processes in stratocumulus clouds by serving as “giant CCN” (Feingold et al., 1999).
No significant relationship between wind speed and fine-mode aerosol
population type was noted. However, particles in the coarse-mode range are
not measured or accounted for in our cluster analysis (CCN system range:
17–500 nm), while submicron aerosol was often dominated by aerosol from
other sources. Modini et al. (2015)
utilized a dedicated size distribution fitting analysis that included
size-resolved observations of particles above 500 nm to examine primary submicron
marine aerosol production. They found a primary mode with a median diameter
around 200 nm and tail that extended to sizes well above 500 nm, with number
concentrations of 12
Based on this classification of the SCS remote marine boundary layer aerosol
environment, a conceptual picture emerges as to the nature and sources of
particles encountered during the
During the SWM when large amounts of biomass burning aerosol were being
advected into the SCS, a population of aged accumulation mode smoke
particles was periodically injected into the MBL, where it mixed with
existing particles. When total particle concentrations were above roughly
1500 cm
Precipitation removal of particles that had been advected into the region or
ventilation by cleaner air masses when transport pathways changed returned
the environment near the surface to its background marine state. However,
when extensive precipitation occurred, accumulation mode particles were
removed by wet deposition to a greater extent than Aitken mode particles,
leading to lower overall surface number concentrations that were dominated
by smaller particles, as evidenced by the emergence of a distinct
precipitation population type from the cluster analysis. Based on the two
In addition to these findings, several observed phenomena during the 2012 study were similar to those from the 2011 cruise (Reid et al., 2015). In particular, rapid changes in aerosol properties and source type were noted in the wake of squall lines that left clean air masses in their wake, while longer-period fluctuations on the order of days occurred as impacts from anthropogenic and smoke transport mixed with cleaner background marine and precipitation-impacted air masses. As both studies were conducted in the remote marine SCS during the biomass burning season and saw similar meteorological phenomena modulating the aerosol populations, the more detailed aerosol property results of the 2012 cruise may be representative of the general nature of changes in SCS remote marine aerosol during the SWM season. Future work in the region to compare surface properties with model results and satellite retrievals will be ultimately required to fully validate these findings.
While the cluster analysis assigned each data point to a single cluster, in reality these first four clusters could be better described as a spectrum due to the variable impacts of mixing or meteorological processes, rather than as distinct or mutually exclusive population types. As is evident in Fig. 3, overlap between these four clusters occurred in the parameter space for all nine of the measured variables used in the cluster model.
Deviations from this general picture arose when influxes of other aerosol types occurred. The additional population types each mapped out generally distinct areas in one or more of the parameters, leading to their identification by the cluster model. That such clusters corresponded to temporally distinct periods with physical and meteorological relevance ultimately justified the use of the cluster model to classify aerosol population types and assign rough population boundaries to the parameter space.
While the spectrum of mixing between population types is relevant to the
identification of impacts from various sources, additional consideration of
these aerosol types against measurements in other regions is also warranted.
Fresh sea spray particles, dominated by sodium chloride (
Aged biomass burning aerosol has often been found to have
This study reports ship-based measurements of aerosol size distributions and CCN properties conducted as part of the first extensive in situ aerosol measurement campaign in remote marine regions of the South China Sea/East Sea during the important southwestern monsoon and biomass burning season. Analysis of approximately 2 weeks of measurements found aerosol characteristics consistent with those from a previous pilot study in the region during the same season, indicating that descriptions of aerosol population types and the associated meteorological and transport phenomena that modulate changes and mixing between these populations may be representative of the wider remote marine SCS during the SWM season.
Eight aerosol population types were identified in the dataset that were associated with various impacts from background marine particles, smoke, and anthropogenic sources, as well as precipitation impacts and shorter lived events linked to influxes of VOCs or ultrafine particles. Efforts to measure or model the impact of aerosol on cloud development or atmospheric optical properties often rely on proper characterization of aerosol microphysics associated with impacts from various aerosol sources. As such, we provided population type average values and standard deviations for aerosol size distribution and hygroscopicity properties needed to model aerosol hygroscopic growth in humid environments or cloud development. Future work with this dataset will investigate the impact of the identified aerosol population types on CCN properties including supersaturation-dependent CCN concentration needed to model development of different types of clouds. Reutter et al. (2009) identified specific regimes of cloud development where aerosol number concentration was important using a cloud parcel model, while Ward et al. (2010) found such results may be further complicated by aerosol size and hygroscopic properties. Inclusion of both population type average properties and the range that they vary across into such a model may help constrain when various properties of the aerosol are relevant to cloud development in the SCS. Additionally, differences in aerosol population type are expected to be relevant to studies of radiative transfer, optical propagation through the atmosphere, and satellite retrievals in subsaturated marine environments where differences in particle number concentration, size, hygroscopicity, index of refraction, and relative humidity all affect the interaction of radiation with particles in complex ways.
Lastly, while specific observed aerosol population types were identified in this dataset, additional open questions remain regarding the relative importance of various sources and transport pathways of aerosol into remote MBL air masses and their impact on aerosol populations. Since the surface-based observations provide only a portion of the observations needed to construct a true aerosol budget for the MBL, the degree to which MBL aerosol may be impacted by mixing down from a reservoir aloft was not clear. Future airborne aerosol campaigns in the region may be useful to shed light on this important topic.
NASA MODIS AOD data were obtained from the NASA LAADS ftp site:
Funding for this research cruise and analysis was
provided from a number of sources.