Linking Phytoplankton Activity in Polynyas and Sulfur Aerosols over Zhongshan Station, East Antarctica
1. Introduction
Dimethylsulfide (DMS) is an important marine biogenic gas; the oxidation products of DMS, such as methanesulfonic acid (MSA) and nss-SO42−, are climatically active compounds on the ocean surface and in the lower atmosphere. After being emitted into the atmosphere, DMS is primarily oxidized by OH, BrO, and NO3 radicals via addition and abstraction reaction routes (Von Glasow and Crutzen 2004). The oxidation products of DMS in the atmosphere, which primarily consist of MSA, dimethylsulfoxide (DMSO), and nss-SO42−, have significant effects on the composition of aerosols in the marine boundary layer. These aerosols are thought to affect the cloud condensation nuclei (CCN) number concentration, alter the atmospheric albedo, and regulate the climate [the Charlson, Lovelock, Andreae, and Warren (CLAW) hypothesis] (Charlson et al. 1987). Furthermore, DMS by-products can change cloud microphysics and optical properties. A negative correlation has been found between the cloud droplet effective radius and sulfur aerosol concentrations, especially in mid- and high-latitude regions (Lana et al. 2012). Observations over the Southern Ocean indicated that the seasonal cycle of the aerosol optical depth (AOD) is positively correlated with the mean surface Chl a concentration, which is thought to correspond to DMS emissions (Gabric et al. 2005). Unlike the Northern Hemisphere, where the majority of sulfur aerosols originate from anthropogenic sources, the atmospheric sulfur aerosols in Antarctica are natural. Therefore, it is particularly important to study marine biogenic sulfur emissions in the high southern latitudes, where such emissions are expected to strongly dominate the natural sulfur cycle.
Because information related to DMS emissions at high latitudes remains sparse and the physical and biological parameters related to atmospheric DMS are not clearly understood, factors that control atmospheric sulfur aerosols remain unclear. Although some studies have indicated that the atmospheric MSA and nss-SO42− variations in coastal regions of Antarctica are related to oceanic phytoplankton activity and sea ice conditions (Minikin et al. 1998; Preunkert et al. 2007), the relationships between the sulfur species remain uncertain. In addition, sea ice dynamics, such as sea ice melting during spring and early summer and sea ice formation during early fall in the seasonal ice zone or polynyas, can trigger phytoplankton blooms and affect DMS emissions (Lizotte 2001; Smith and Comiso 2008). Legrand and Pasteur (1998) demonstrated that the marine source regions influencing the sulfur aerosol cycle in the high southern latitudes are primarily located south of 50°S, with an increased contribution to the total emissions from regions located south of 60°S in summer. However, most regions in the sea ice zone (south of 60°S) are covered by sea ice with low biological activity and DMS emissions (Lana et al. 2011). Conversely, DMS concentrations as high as several hundred nanomoles per liter have been observed in polynyas along the coastal regions of Antarctica, such as the Ross Sea and Amundsen Sea (Del Valle et al. 2009; Tortell et al. 2011; Tortell et al. 2012). The polynyas in the Southern Ocean are considered the most productive regions and have the highest DMS sea–air flux in the world (Kettle et al. 1999). Polynyas have a larger effect on atmospheric sulfur aerosols than the regions covered by sea ice (Kiene et al. 2007). Therefore, studies should focus on understanding how polynyas impact atmospheric sulfur aerosols in coastal regions of Antarctica.
This study presents the seasonal and annual variations of MSA and sulfate from February 2005 to October 2008 at Zhongshan Station, East Antarctica. The nss-SO42− concentrations are also calculated. The seasonal variations of sulfur and the corresponding impact factors are discussed in this study. The influence of offshore polynyas to sulfur aerosols in coastal regions of Antarctica is evaluated. We hope that this research can provide useful information in analyzing the source of sulfur species in aerosols in coastal regions of Antarctica.
2. Experiment
a. Sampling site
Zhongshan Station (69°22′S, 76°22′E) is a Chinese research base located along the southeastern coast of Prydz Bay in East Antarctica (Fig. 1). Prydz Bay is the third largest bay in Antarctica, and the sulfur aerosol observations are conducted at Zhongshan Station to investigate the influence of marine phytoplankton activity on atmospheric sulfur species in the bay. The sampling site is located on a hill that faces upwind of the station to avoid local contamination.
Sampling location.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
Sampling location.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
Sampling location.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
Two regions are selected to analyze the source of sulfur compounds. Region A, 60°–70°S, 60°–100°E (the large-scale region), represents the area south of 60°S within the station sector. The majority of region A is covered by sea ice. Region B, 65°–70°S, 70°–85°E (the small-scale region within region A), is chosen to represent offshore regions at Zhongshan Station, which primarily contain polynyas in austral summer (i.e., after the sea ice has melted). These two regions were selected to investigate the area where significant impacts on sulfur aerosols may occur.
b. Sampling and chemical analyses
The sampling and measurement methods were the same as those described in the study by Chen et al. (2012) and Xu et al. (2013). Aerosol samples were collected with Whatman 41 filters using a high-volume bulk sampler (Model M241, University of Miami), and the sampling duration was 10–15 days with a flow rate of 1 m3 min−1. After sampling, sample filters were stored in a refrigerator at 4°C. A total of 122 high-volume bulk samples were collected from early February 2005 through the end of October 2008. A Dionex ICS-2500 ion chromatograph (IC) was used to analyze the aerosol samples for water-soluble ions (MSA, SO42−, and Mg2+). The cations were analyzed with a CS12A analytical column and CG12A guard column using MSA as the eluent, and the anions were analyzed with an AS18 analytical column and AG18 guard column with KOH as the eluent. The detection limits were 0.13 ng m−3 for SO42−, 0.016 ng m−3 for MSA, and 0.40 ng m−3 for Mg2+. The precision of the analytical procedures was <5% based on seven spiked samples.
c. Calculation of nss-SO42−
Nss-SO42− is commonly calculated using [nss-SO42−] = [SO42−]total − [Na+] × 0.252, where 0.252 is the SO42−/Na+ mass ratio in seawater. However, when using this equation, negative nss-SO42− values can be derived, especially during austral fall and winter. The reasons for such values were discussed by Hall and Wolff (1998), particularly regarding the fractionation of sulfate in aerosols by forming Na2SO4 · 10 H2O for temperatures below −8.2°C (Wagenbach et al. 1998). However, because the Na+ data were unavailable for this study, we could not use the Na+ concentration to calculate the nss-SO42− concentration. An SO42−/Cl− ratio of 0.140 and an SO42−/Cl− ratio of 0.040 were used in the studies of Legrand et al. (2001) and Minikin et al. (1998) to calculate the nss-SO42− concentration from December through March and from April through October, respectively, because of the large amount of ammonia emitted from penguin colonies, which prevented the acidification of sea-salt aerosols and the subsequent loss of HCl (Eriksson 1959). However, Cl− may be depleted at Zhongshan Station, indicating that using the Cl− concentration to calculate the nss-SO42− concentration is questionable. Therefore, we used SO42−/Mg2+ ratios of 2.096 and 0.610 to calculate the nss-SO42− concentrations from December through March and from April through November, respectively, because the Mg2+ concentration was relatively stable in the sampled aerosols and based on the ion study of frost flowers by Rankin et al. (2000).
d. Acquisition of Chl a concentrations, calculation of polynya areas, and meteorology data
Eight-day-averaged Chl a concentrations in region A (Chl a A) and region B (Chl a B) were obtained from the following website: http://oceancolor.gsfc.nasa.gov/ (Sea-WiFS L3SMI data from 2005 to 2007 and Modisa L3SMI 2008 data because some data were missing in SeaWiFS in 2008; the resolution of the data is 9 km × 9 km). These 8-day data were matched to the appropriate sampling duration.
Polynyas were defined to be present in regions with sea ice concentrations below 75% (Massom et al. 1998); the polynya areas were calculated according to this definition. Daily sea ice data were obtained from the National Snow and Ice Data Center (http://nsidc.org; resolution of 6.25 km × 6.25 km) (Spreen et al. 2008). The polynya areas in region B were calculated from 2005 to 2008, and the area data were averaged to correspond with the sampling duration.
The meteorology data were obtained from the Zhongshan Station meteorology observatory, which is operated by the Polar Research Institute of China. Three main parameters—that is, air temperature, wind direction, and wind speed—were selected and averaged daily for analysis.
3. Results and discussion
a. MSA, SO42−, Mg2+, and nss-SO42− concentrations at Zhongshan Station
The mean MSA, SO42−, Mg2+, and nss-SO42− concentrations in the studied years are presented in Fig. 2 and Table 1. The mean MSA concentrations remained relatively stable throughout the study period, varying from 18.93 to 23.36 ng m−3 (Table 1).
Concentrations of (a) nss-SO42− and Mg2+ and (b) MSA and SO42− (February 2005–October 2008).
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
Concentrations of (a) nss-SO42− and Mg2+ and (b) MSA and SO42− (February 2005–October 2008).
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
Concentrations of (a) nss-SO42− and Mg2+ and (b) MSA and SO42− (February 2005–October 2008).
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
Means and ranges of the MSA, SO42−, Mg2+, and nss-SO42− concentrations. The range is shown below the corresponding mean; n.d. means nondetectable.
The mean MSA and nss-SO42− concentrations in the different seasons were also investigated (Table 2) and compared with the other studies (Table 3). The mean MSA and nss-SO42− concentrations from January through March (44.45–83.51 ng m−3 and 112.36–233.18 ng m−3, respectively) were lower than those measured at Palmer Station (64.77°S, 64.05°W; mean MSA and nss-SO42− concentrations of 180 and 296 ng m−3, respectively, in January–February 1994) (Berresheim et al. 1998) and at Halley station (75°35′S, 26°19′W; mean MSA concentrations of 83.2 ng m−3 in January–February 2004 and 141.3 ng m−3 in January–February 2005) (Read et al. 2008). However, the concentrations identified in this study were consistent with the results obtained at Dumont d’Urille (66°40′S, 140°01′E), where the mean MSA and nss-SO42− concentrations were 73 and 247 ng m−3 in December–February 1996/97, 1999/2000, and 2001/02, and 35 and 182 ng m−3 in December–February 1997/98, 2000/01, and 2002/03 (Preunkert et al. 2007). The results of the other seasons (April–December) are also presented in Table 2. During austral winter (April–September), low MSA (4.64 ng m−3; range of 0.14–30.37 ng m−3) and nss-SO42− (51.90 ng m−3; range from nondetectable to 209.23 ng m−3) concentrations were found over the study period. In general, relatively higher sulfur species concentrations were found from October to December compared with those from April to September. In particular, extremely low mean MSA and nss-SO42− concentrations (i.e., 1.69 and 64.09 ng m−3, respectively) were found from October to December 2007.
Atmospheric MSA and nss-SO42− ion concentrations in various seasons at Zhongshan Station (February 2005–October 2008). The January–March 2005 data only contain the February–March sampling time; the October–December 2008 data only contain the October sampling time. The range is shown below the corresponding mean; n.d. means nondetectable.
Summary of mean MSA and nss-SO42− concentrations observed at various coastal Antarctica sites.
b. Seasonal variations in the MSA and nss-SO42− concentrations
Both MSA and nss-SO42− exhibited seasonal cycles, with the maximum values observed during the summer (Fig. 2). The variability of Mg2+ differed from that of the sulfur compounds, exhibiting greater variability during the study period. This difference may be due to the sea spray and biogenic components having different sources, formation mechanisms, and different atmospheric reactions; moreover, they also had different size distributions and transport processes (Saltzman 2009). The air temperature and radicals would significantly impact the DMS reaction process and the lifetime in the atmosphere. Because of the radicals’ data (OH, BrO, etc.) and because the atmospheric DMS mixing ratio is missing, it was difficult to evaluate the conversion process of the DMS reaction route.
In contrast to previous studies (Legrand et al. 2001; Minikin et al. 1998; Preunkert et al. 2007), where the maximum MSA concentrations were commonly observed in January, our results indicate that the maximum MSA concentrations occurred in early February. In addition, the MSA concentrations were generally high in austral early spring (MSA ranging from 16.63 to 41.82 ng m−3 in November). Previous studies have found that early-spring blooms in polynyas commonly occurred in the Southern Ocean (Tortell et al. 2011; Tortell and Long 2009). Such high MSA concentrations may be related to the phytoplankton blooms in the polynyas offshore Zhongshan Station. Areas with rapidly melting sea ice, such as the polynyas, have been identified as active phytoplankton bloom sites (Lizotte 2001) because of the incubation of phytoplankton species beneath the sea ice and the increasing of solar irradiation. Additionally, the release of micro- and macronutrients from the melting sea ice may be another important factor (Stabeno et al. 2010; Taylor et al. 2013). Once a phytoplankton bloom occurs, there would be an increasing of DMS concentration in seawater, and significant DMS can be emitted into the atmosphere. Subsequently, atmospheric DMS could be oxidized by radicals, such as OH and BrO, and the MSA and nss-SO42− concentrations are increased. In particular, an extremely high MSA concentration (295.58 ng m−3), which was abnormal based on the concentrations measured throughout the study period, was observed near the end of December 2006. This anomaly may be attributed to the abnormally high phytoplankton activity (Chl a concentration was 4.805 mg m−3) in the offshore water. Moreover, an extremely low MSA concentration (1.69 ng m−3, Table 2) was observed from October to December 2007, which was approximately 7–16 times lower than that of the same period in the other years. It would be possibly related to the high sea ice coverage and low primary productivity that occurred in the offshore water near the sampling site with little DMS emissions. From November to March during the sampling period, the MSA concentrations increased rapidly toward its maximum. The mean MSA concentrations from January to March did not exhibit large variability except for 2007, in which the mean MSA concentration was approximately twofold higher than those of the other years (Table 2). From April to September, little marine biogenic activity occurred in the offshore water near Zhongshan Station, as suggested by the mean Chl a concentrations being close to zero. Thus, the MSA concentrations were relatively low during that period.
The nss-SO42− concentration also exhibited a seasonal cycle, although the amplitude was larger than that of MSA (Fig. 2a). A significant positive correlation was found between the MSA and nss-SO42− concentrations (r = 0.837, n = 100, and p < 0.01; Table 4), which may be related to the presence of a common precursor (i.e., DMS) of MSA and nss-SO42−. In austral summer, high DMS emissions from marine phytoplankton were observed around coastal Antarctica (Berresheim et al. 1998), which could contribute to the observed high nss-SO42− concentrations. However, in winter, nss-SO42− was mainly derived from non-DMS sources or transported from remote regions because of the extremely low biological activity around coastal Antarctica. Minikin et al. (1998) estimated that the non-DMS sulfate sources could account for approximately 15 ng m−3 of the winter nss-SO42− levels observed in coastal Antarctic regions, and the possible sources could be related to the long-range transport of sulfate from continental areas and downward transport from the stratospheric reservoir. Legrand and Pasteur (1998) reported that nss-SO42− was likely derived from the long-range transport of by-products from marine DMS emissions near 50°S and non-DMS sulfate from the continental free troposphere in winter. Nevertheless, in winter, it was challenging to exclude a residual sea-salt sulfate level in the calculated nss-SO42− concentrations, which was partially related to an overestimation of the sea-salt fractionation phenomenon (i.e., calculating the nss-SO42− concentrations using the frost flower SO42−/Mg2+ ratio in winter).
Correlations between MSA, SO42−, nss-SO42−, Chl a A (region A), Chl a B (region B), temperature T, R (MSA/nss-SO42−), and the polynya area offshore Zhongshan Station. An asterisk indicates that the correlation is significant at the 0.01 level (two tailed). The number of samples is shown below the corresponding correlation.
c. Factors affecting atmospheric MSA and nss-SO42− variations
1) Influence of phytoplankton activity variations in polynyas
Figures 3a and 3b show typical 8-day-averaged Chl a distributions for regions A and B in austral summer. The white color represents areas where Chl a was not detected, which was covered by sea ice. The offshore open waters near the Zhongshan Station had higher phytoplankton activity than the large-scale region (region A). The open waters or polynyas along coastal Antarctica were the most productive regions (Arrigo et al. 1998), with significant DMS emissions (Kettle et al. 1999). Because the lifetime of DMS is approximately 1–2 days (Kloster et al. 2006; Read et al. 2008), the local DMS emissions may directly affect the atmospheric DMS by-products. In addition, as shown in Fig. 4, the dominant daily wind direction was northeasterly at Zhongshan Station from November to March, and the wind speed was as high as 20.2 m s−1 (average of 6.3 ± 3.1 m s−1) during the sampling period. Because the sampling site was located extremely close to the open water (<1 km), the air masses were mainly derived from offshore regions. Consequently, the variations in atmospheric sulfur species are likely related to phytoplankton activity in region B.
Typical 8-day-averaged Chl a concentration distributions for (a) region A (60°–70°S, 60°–100°E) and (b) region B (65°–70°S, 70°–85°E).
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
Typical 8-day-averaged Chl a concentration distributions for (a) region A (60°–70°S, 60°–100°E) and (b) region B (65°–70°S, 70°–85°E).
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
Typical 8-day-averaged Chl a concentration distributions for (a) region A (60°–70°S, 60°–100°E) and (b) region B (65°–70°S, 70°–85°E).
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
Daily wind rose diagram for spring and summer: (a) February–March 2005, (b) November 2005–March 2006, (c) November 2006–March 2007, and (d) November 2007–March 2008.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
Daily wind rose diagram for spring and summer: (a) February–March 2005, (b) November 2005–March 2006, (c) November 2006–March 2007, and (d) November 2007–March 2008.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
Daily wind rose diagram for spring and summer: (a) February–March 2005, (b) November 2005–March 2006, (c) November 2006–March 2007, and (d) November 2007–March 2008.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
Because the only source of MSA is derived from the oxidation of DMS, MSA can be used as an indicator of marine biogenic sulfur production (Hezel et al. 2011). The Chl a concentrations in both regions exhibited well-defined seasonal cycles and were consistent with the sulfur aerosol variations (Fig. 5a). The maximum Chl a concentrations generally occurred at the end of December or in January. The phytoplankton activity exhibited substantially different variability, which was reflected in both the Chl a concentrations and the timing of the phytoplankton blooms. The Chl a concentration in region B increased rapidly (i.e., a few weeks) to its maximum, and the MSA concentrations exhibited a slower increase.
(a) Comparison between the MSA concentrations and mean remotely sensed Chl a concentrations in region A (60°–70°S, 60°–100°E) and in region B (65°–70°S, 70°–85°E). (b) Relationships between the MSA concentration and both the temperature and polynya area offshore Zhongshan Station.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
(a) Comparison between the MSA concentrations and mean remotely sensed Chl a concentrations in region A (60°–70°S, 60°–100°E) and in region B (65°–70°S, 70°–85°E). (b) Relationships between the MSA concentration and both the temperature and polynya area offshore Zhongshan Station.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
(a) Comparison between the MSA concentrations and mean remotely sensed Chl a concentrations in region A (60°–70°S, 60°–100°E) and in region B (65°–70°S, 70°–85°E). (b) Relationships between the MSA concentration and both the temperature and polynya area offshore Zhongshan Station.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
Furthermore, the high MSA concentrations commonly followed the phytoplankton blooms during the study period; there was always a time lag between them (Fig. 4a). This time lag can be explained by the findings of Simó (2001), who reported that seawater DMS was often produced following local Chl a maxima, leading to lag periods (on the order of weeks). Previous work has shown that this phenomenon occurs primarily when DMS is produced via grazing by zooplankton on DMSP-containing phytoplankton (Stefels et al. 2007). In addition, the time lag also includes the time needed for DMS sea–air exchange, transport from the source region, and oxidation. Significant correlations between both MSA and nss-SO42− concentrations and Sea-WiFS-derived seawater DMS [estimated using Chl a (Belviso et al. 2004)]—that is, R2 = 0.70–0.83 and R2 = 0.75 for MSA and nss-SO42−, respectively—can be identified when using a 10- to 20-day lag time (Preunkert et al. 2007). Similarly, the correlation between the sulfur species and Chl a concentration in region B was further investigated by adjusting the Chl a time series by one sampling duration (approximately a 10-day lag). As shown in Table 4, significant positive correlations were identified between the Chl a concentration in region B and both the MSA concentration (r = 0.726, n = 58, and p < 0.01) and nss-SO42− concentration (r = 0.724, n = 44, and p < 0.01), indicating that the atmospheric sulfur species were closely related to the phytoplankton activity in region B. Because the atmospheric DMS mixing ratio could be affected by the ambient DMS concentrations in the seawater (Sciare et al. 2000) and the oceanic phytoplankton activity (Park et al. 2013), the DMS concentrations from offshore regions could directly affect the regional atmospheric DMS concentrations and further alter the atmospheric DMS by-products. Therefore, the phytoplankton activity in the polynyas (in region B) near Zhongshan Station had crucial impacts on the local atmospheric sulfur species.
2) Role of sea ice dynamics in polynyas and polynya areas
The melting and formation of sea ice (sea ice dynamics) can increase phytoplankton activity (Lizotte 2001). When sea ice melts, the blooms in the polynyas (Arrigo and van Dijken 2003; Tortell and Long 2009) or marginal sea ice zone (Taylor et al. 2013) are often related to the release of iron (De Baar et al. 1995; Wang et al. 2014) and algae (Boetius et al. 2013; Lizotte 2001; Loose et al. 2011) from the melting sea ice. The variation in the polynya area may reveal the sea ice dynamics during the study period. Thus, we calculated the polynya area in region B as described in section 2d and analyzed the influence of sea ice dynamics on the local phytoplankton activity and sulfur species. As shown in Fig. 5b, the polynya area also exhibited a strong seasonal cycle that followed the air temperature variations. The polynya area increased by nearly two orders of magnitude from November to February. The maximum polynya area typically occurred in February and exhibited a decreasing trend from 2005 to 2008 (from 197 762 to 96 586 km2). The increased phytoplankton activity in the polynyas occurred after the sea ice began to melt and in conjunction with an increased presence of sulfur compounds; the phytoplankton bloom generally occurred at the end of December or in January (maximum Chl a concentration) (Figs. 5a and 5b). However, the mean Chl a concentrations in the polynyas decreased after the bloom, whereas the polynya area continued to increase until February. This unsynchronized variation may result in a weak correlation between the polynya area and Chl a concentration in region B (r = 0.454, n = 52, and p < 0.01; Table 4), indicating that the melting sea ice in late summer did not have a pronounced effect on the mean Chl a concentrations. In addition, it was necessary to point out that the melting process of sea ice in the polynya might also enhance phytoplankton activity along the ice edge but not the whole region of polynyas. This weak correlation could be attributed to the depletion of nutrients after the bloom, only sustaining low levels of primary production (Takahashi et al. 1993). Furthermore, the influence of sea ice formation on phytoplankton activity was also observed in late summer, such as in early March 2007, during which a relatively high MSA concentration of 40.21 ng m−3 after the Chl a concentration increased to 1.04 mg m−3, indicating that the sea ice formation process may also impact phytoplankton activity. Thus, sea ice dynamics played an important role, although they only affect the sulfur aerosol levels by altering the phytoplankton activity.
On the other hand, the high MSA concentration corresponded to not only the phytoplankton bloom but also a relatively large polynya area (the polynya area ranged from 87 532 to 149 591 km2, which corresponded to the maximum Chl a concentrations). According to Table 4, the moderate positive correlations between the polynya area and both the MSA (r = 0.542, n = 122, and p < 0.01) and nss-SO42− (r = 0.615, n = 100, and p < 0.01) concentrations suggested that the polynya areas may have affected the sulfur species concentrations. In austral spring, although the early-spring bloom could have appeared in the polynyas, DMS emissions from relative small polynya areas (ranging from 3486 to 21 563 km2 during the early-spring phytoplankton bloom in years followed by relatively high MSA concentrations that ranged from 16.63 to 41.82 ng m−3 in November, which were one to two orders of magnitude lower than the maximum MSA concentrations) were possible, although not highly significant. The air mass may have been mixed with other air masses containing low atmospheric DMS mixing ratios, which subsequently affected the sulfur aerosols. However, the phytoplankton bloomed at the end of 2006, in which the maximum MSA concentration (295.58 ng m−3) occurred following the maximum Chl a concentration (4.80 mg m−3) within a small polynya area (46 915 km2). This phenomenon might be caused by the high wind speeds [the mean wind speed and wind direction were 12.5 ± 2.31 m s−1 and 41.3° (northeast), respectively] observed during the sampling period. The high wind speeds implied great transfer velocities, leading to relatively high sea–air DMS fluxes (Ho et al. 2006; Wanninkhof 1992). Moreover, the large polynya areas could result in a significant contribution of DMS from the ocean to the atmosphere despite the relatively low phytoplankton activity. For example, according to the observations from early February 2007 and considering the time lag, the high MSA concentration (88.42 ng m−3) was followed by a relatively low Chl a concentration (0.78 mg m−3) even though the polynya area was large (120 943 km2). Thus, the increased polynya area may have had an indirect effect on the sulfur aerosols concentrations in the mid- and late-summer periods.
3) Influence of long-range transport
Higher MSA/nss-SO42− values (>0.30) have been primarily identified during the austral summer of high-latitude regions (Berresheim et al. 1998; Legrand and Pasteur 1998), with lower values being found in low- to midlatitude regions (Chen et al. 2012; Xu and Gao 2015). The reason for this difference may be the low air temperatures in high-latitude regions and such temperatures can enhance the DMS oxidation pathway to form MSA (Arsene et al. 1999). In addition, Bates et al. (1992) reported a strong inverse relationship (R2 = 0.87) between air temperature and MSA/nss-SO42− values in submicrometer aerosol particles; that is, decreased temperatures corresponded to increased MSA/nss-SO42− values, indicating that it was possible to trace back the latitude of marine source regions influencing the nss-SO42− and MSA concentrations at a given site. The mean MSA/nss-SO42− value was 0.21 ± 0.18 over the 4 years, ranging from 0.01 to 1.08. The ratios varied and primarily exhibited high values in summer and low values in winter (Fig. 6a).
(a) MSA/nss-SO42− ratio R. (b) 5-day airmass back trajectories at a height of 1000 m for Zhongshan Station from December to February.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
(a) MSA/nss-SO42− ratio R. (b) 5-day airmass back trajectories at a height of 1000 m for Zhongshan Station from December to February.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
(a) MSA/nss-SO42− ratio R. (b) 5-day airmass back trajectories at a height of 1000 m for Zhongshan Station from December to February.
Citation: Journal of the Atmospheric Sciences 72, 12; 10.1175/JAS-D-15-0094.1
In winter (April–September), MSA/nss-SO42− values were extremely low (mainly below 0.10). The local biogenic DMS emissions were low as a result of the extensive sea ice coverage and lack of sunlight, resulting in nearly no phytoplankton activity. The low MSA/nss-SO42− values indicated that the sulfur species were transported from mid or low-latitude regions. Minikin et al. (1998) found that DMS by-products in coastal regions of Antarctica originated from regions between 50° and 60°S in austral winter; they estimated MSA/nss-SO42− values to be 0.33–0.38 (Legrand and Pasteur 1998). In the presence of long-range transport, low MSA/nss-SO42− values are suggestive of MSA being removed from the atmosphere more rapidly than nss-SO42− during the transport (Minikin et al. 1994; Mulvaney and Wolff 1994). Accordingly, the sulfur aerosols in winter at the sampling site were primarily affected by air masses derived from remote mid- or low-latitude productive regions.
During austral spring and summer, the DMS emissions from the polynyas significantly affected the atmospheric sulfur species at Zhongshan Station. To understand the possible influence of air masses from remote regions through long-range transport, we performed 5-day back trajectories at an altitude of 1000 m for Zhongshan Station from December to February. According to Fig. 6b, most of the trajectories passed through coastal and inland plateau regions, suggesting that the sulfur aerosols were mainly derived from the coastal productive regions of Antarctica in spring and summer. Previous work has shown that low concentrations of sulfur species are present in inland Antarctica (Preunkert et al. 2008), indicating that the local atmospheric sulfur compounds may be affected by mixing with those air masses. However, the air masses from the productive coastal regions or oceans may have also enhanced the atmospheric sulfur species at the sampling sites. Because the sampling duration was approximately 10–15 days, it was difficult to analyze the contributions or effects of the sulfur species transported from remote regions.
4. Conclusions
Sulfur compound data (MSA and nss-SO42−) in aerosols were collected from February 2005 to October 2008 at Zhongshan Station in East Antarctica. The mean MSA and nss-SO42− concentrations were 21.46 ng m−3 (ranging from 0.02 to 295.58 ng m−3) and 103.53 ng m−3 (ranging from the detection limit to 547.14 ng m−3), respectively, over the nearly 4-yr period. The atmospheric sulfur species exhibited a seasonal cycle that had a maximum in austral summer and a minimum in winter. The maximum MSA concentration generally occurred in early February. Significant correlations between the Chl a concentration in the polynyas offshore Zhongshan Station and both the MSA (r = 0.726, n = 52, and p < 0.01) and nss-SO42− (r = 0.724, n = 48, and p < 0.01) concentrations were found, indicating that phytoplankton activity significantly affected the sulfur aerosols. The sea ice dynamics in the polynyas during austral spring and summer could trigger the phytoplankton bloom and indirectly affected the sulfur species. The large polynya area in austral summer could also affect the sulfur aerosols emission. In austral winter, nearly no phytoplankton activity occurred in the offshore polynyas; the sulfur compounds in the atmosphere were likely derived from long-range transport sources. However, owing to the lack of seawater and atmospheric DMS observations and the low temporal resolution of the sampling adopted in this study, it was difficult to comprehensively understand the seasonal and interannual influences of oceanic DMS emissions on atmospheric sulfur compounds (atmospheric DMS and DMS by-products). Therefore, further improvements should be made in similar studies performed at Zhongshan Station.
Acknowledgments
The authors thank the staff of Zhongshan Station for supporting the field operations and Mr. Guoqiang Qiu from Xiamen University for providing assistance with the remote sensing method. This work was supported by the National Natural Science Foundation of China (NSFC) (41476172, 41230529, 40671062, and 41106168), Chinese Projects for Investigations and Assessments of the Arctic and Antarctica (CHINARE2012-15 for 01-04-02, 02-01, and 03-04-02), and International Cooperation Programs (2015DFG22010, IC201201, IC201308, and IC201513).
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