Vertical variability of seawater DMS in the South Pacific Ocean and its implication for atmospheric and surface seawater DMS

Chemosphere 78 (2010) 1063–1070

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Vertical variability of seawater DMS in the South Pacific Ocean and its implication for atmospheric and surface seawater DMS Gangwoong Lee a, Jooyoung Park a, Yuwoon Jang a, Meehye Lee b,*, Kyung-Ryul Kim c, Jae-Ryoung Oh d, Dongseon Kim d, Hi-Il Yi d, Tong-Yup Kim e a

Department of Environmental Science, Hankuk University of Foreign Studies, Yongin, South Korea Department of Earth and Environmental Sciences, Korea University, Seoul, South Korea School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea d Korea Ocean Research & Development Institute, Ansan, South Korea e Korea Polar Research Institute, Songdo, South Korea b c

a r t i c l e

i n f o

Article history: Received 29 April 2009 Received in revised form 20 October 2009 Accepted 23 October 2009 Available online 15 January 2010 Keywords: Dimethyl sulfide Dissolved DMSP Particulate DMSP South Pacific Sea-to-air flux Mixed layer depth

a b s t r a c t Shipboard measurements of atmospheric dimethylsulfide (DMS) and sea surface water DMS were performed aboard the R/V Onnuri across the South Pacific from Santiago, Chile to Fiji in February 2000. Hydrographic profiles of DMS, dissolved dimethylsulfoniopropionate (DMSPd), and particulate DMSPp in the upper 200 m were obtained at 16 stations along the track. Atmospheric and sea surface water DMS concentrations ranged from 3 to 442 pptv and from 0.1 to 19.9 nM, respectively; the mean values of 61 pptv and 2.1 nM, respectively, were comparable to those from previous studies in the South Pacific. The South Pacific Gyre was distinguished by longitudinal-vertical distributions of DMS, DMSPd, and DMSPp, which was thought to be associated with the characteristic modification of biological activities that occurs mainly due to significant change in water temperature. The averaged DMS maximum appeared at 40 m depth, whereas DMSPp and DMSPd maxima coincided with that of dissolved oxygen content at 60–80 m. The sea-to-air fluxes of DMS were estimated to be 0.4–11.3 lmol d1 m2 (mean = 2.8 lmol d1 m2). A fairly good correlation between atmospheric DMS and sea-to-air DMS flux indicated that atmospheric DMS concentration was more sensitive to change in physical parameters than its photochemical removal process or surface seawater DMS concentrations. Ó 2009 Published by Elsevier Ltd.

1. Introduction Dimethylsulfide (DMS), the most dominant sulfur species throughout the ocean, is formed by enzymatic cleavage of dimethylsulfoniopropionate (DMSP), which is produced by a variety of phytoplankton species. Most DMSP is consumed by bacteria and only a fraction is used to produce DMS (Kiene, 1996). Although DMS is removed by bacterial consumption (Kiene and Bates, 1990), sea surface layers are always supersaturated with it, which implies a net flux of DMS to the atmosphere (Huebert et al., 2004). As a result, approximately 1% of the DMSP produced in sea water is transported to the air in the form of DMS through sea-to-air flux (Bates et al., 1994; Simó and Pedrós-Alió, 1999). After being released into the atmosphere, DMS is readily oxidized to non-sea-salt sulfate (nss-SO2 4 ) and methane sulfonate (MSA) in the atmospheric boundary layer. Atmospheric DMS is mainly oxidized by OH during the day and nitrate radical (NO3) at night. The oxidation of atmospheric DMS seems to contribute * Corresponding author. Tel.: +82 2 3290 3178; fax: +82 2 3290 3189. E-mail address: [email protected] (M. Lee). 0045-6535/$ - see front matter Ó 2009 Published by Elsevier Ltd. doi:10.1016/j.chemosphere.2009.10.054

largely to the formation of aerosol containing nss-SO2 4 in the marine troposphere. According to the CLAW hypothesis (Charlson et al., 1987), sulfate and MSA produced from oceanic DMS affect the Earth’s radiation balance through the formation of cloud condensation nuclei (CCN), thereby altering cloud properties. The overall effect of these couplings on climate is negative feedback, meaning that it tends to stabilize the climate. In recent studies, DMS was positively correlated with atmospheric CCN (Vallina et al., 2006, 2007) and solar radiation (Toole and Siegel, 2004; Vallina and Simó, 2007) over most of the global ocean, which supports the DMS–climate feedback loop for open-ocean environments. Researchers estimate that the gaseous DMS flux from the ocean to the atmosphere lies between 23 and 35 Tg S yr1 (Kettle and Andreae, 2000; Simó and Dachs, 2002; Kloster et al., 2006). The oceanic DMS flux compromises 30% of global sulfur sources (IPCC, 2001) and its contribution to global nss-SO2 4 is 27%, both of which are similar in magnitude (Kloster et al., 2006). The mean annual contribution of DMS to the climate-relevant nss-SO2 4 column burden is the greatest (43%) in the relatively pristine Southern Hemisphere, where a lower oxidative capacity of the atmosphere, a larger sea-to-air transfer of DMS, and a larger surface area of

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emission lead to an elevated atmospheric DMS burden (Gondwe et al., 2003). Therefore, the vast area of the South Pacific is the key region in which to test the validity of the CLAW hypothesis. DMS flux from the ocean has been estimated through the parameterization of wind fields and the maps of DMS concentrations in the global ocean (Liss and Merlivat, 1986; Wanninkhof, 1992; Kettle et al., 1999). Although Kettle et al. (1999) compiled a seawater DMS and DMSP database of more than 15 000 measurements over the global ocean, the temporal and spatial coverage of DMS is still poor. To obtain a global view of DMS distribution by time, determining oceanic DMS concentration has to be approached using various empirical parameterizations of field observation datasets, such as chlorophyll a distribution (Anderson et al., 2001), climatological mixed layer depth (Simó and Dachs, 2002), and SeaWiFS ocean color measurements (Belviso et al., 2006). Kettle et al. (1999) found no significant correlations between DMS and other oceanographic parameters and no simple algorithm to create temporal fields of sea surface DMS concentrations based on these parameters. Thus, to reduce the great uncertainty inherent in estimates of DMS flux, more measurements with greater temporal and spatial resolution are necessary. This is particularly true for the South Pacific, where measurements of DMS and DMSP are still very sparse. DMS in the South Pacific has been studied most extensively by Bates and his group (Bates and Quinn, 1997; Bates et al., 1998; Bates, 2004). Most of their research, however, has been concentrated on the equatorial Pacific, which is a region that exhibits relatively high DMS emissions throughout the year (Bates and Quinn, 1997). Unlike the equatorial Pacific, the central South Pacific (20–50°S) should have large seasonal and spatial variations of DMS levels due to distinct seasonality and latitudinal variations in sea surface temperature. The central South Pacific has an area of 30  106 km2 and covers about 8% of all oceans and seas worldwide, yet only four sets of latitudinal transit data are available for this region in the Global Surface Seawater DMS Database (Bates, 2004). Convincing evidence also exists for the seasonality of DMSP and DMS concentrations and DMS flux in the Southern Ocean (Simó and Dachs, 2002; Vallina et al., 2007). Kettle et al. (1999) reported that having DMS measurements is not enough to explain the global DMS distribution, particularly in the South Pacific and Indian Ocean. To evaluate the role of DMS in climate change at regional to global scales also requires measurements of atmospheric and sea water DMS concentrations, quantification of its sea-to-air flux, and identification of the factors that control them. In this experiment, we concurrently measured sea water DMS, dissolved DMSP, and particulate DMSP at various depths, mainly within the thermocline, and the atmospheric DMS along the ship track from Chile to Fiji. We then characterized the behaviors of atmospheric DMS and its sea-to-air flux with regard to various factors such as sea surface DMS and DMSP, momentum flux, and mixed layer depth MLD. The results of this study will be useful to evaluate global ecosystem models for DMS production and to accurately determine the global DMS budget.

2. Methods As a part of the Southern Pacific Ocean Dynamic Studies, measurements of atmospheric DMS and surface water DMS were made on board the research vessel (R/V) Onnuri, which left Punta Arenas, Chile on February 5, 2000 and arrived at Fiji on March 4, 2000. The study area lies between 20°S and 50°S and runs from the equatorial Pacific to the Southern Ocean, in which scarce sets of DMS and DMSP measurements are available. Fig. 1 shows the ship track and the stations at which hydrocast samples were taken. On February 20 and 21, hydrocast sampling was cancelled in order to keep the ship on schedule.

Fig. 1. Ship track and stations (closed circles) for vertical seawater sampling. Numbers above and below each station indicate the station number and date in February 2000, respectively.

For vertical profiles of DMS and DMSP, sea water samples were collected using 11 Niskin bottles from 3 m below the surface down to 200 m at 16 stations. During each hydrocast, conductivity, temperature, and depth were continuously determined with a CTD along with dissolved oxygen content. Water samples were taken from 3 m below surface down to 200 m at each station: seven samples between 3 m and 100 m, and four samples between 100 m and 200 m at a 25 m interval. The hydrocasts were conducted at dawn so that bacterial and phytoplankton populations would be minimally affected upon exposure to ultraviolet radiation (Kiene and Linn, 2000). Upon retrieval of the bottles, sea water was gently drawn from each Niskin bottle into a 130 mL DO bottle through tygon tubing; the new bottle was overflowed with sea water 2–3 times so that no air would be trapped inside. At each hydrocast station, surface water was collected using a bucket for the surface DMS measurement. Along the cruise track, surface seawater also was sampled 4–6 times a day for DMS analysis using the continuous seawater pumping system on the ship. To measure sea water DMS, an aliquot of 30 mL was withdrawn from the DO bottle with a 50 mL syringe. The water-filled syringe then was connected to an air-tight filter holder equipped with a 47 mm Whatman GF/F glass fiber filter. By applying gentle pressure to syringe, the water sample was filtered into a 100 mL gas stripper bottle. Next, the filtrate was purged with high purity helium at 100 mL min1 for 20 min and the purged air was passed through a Nafion dryer (Perma-Pure, Inc., USA) to remove water vapor. Finally, DMS was captured in a carbosieve 300 adsorption trap (Supelco, USA). The dissolved DMSP (DMSPd) in the purged filtrate and the particulate DMSP (DMSPp) on the filter were converted to DMS using a strong alkaline solution for 1–2 h and then measured as DMS (Kim and Andreae, 1987). A common practice is to leave the particulate DMSP samples in the alkaline solution at least 12 h. As a result, our DMSPp values were likely underestimated due to incomplete hydrolysis of DMSPp. DMS and DMSP in sea water were measured right after the water samples were collected from the upper depths. Some samples from lower depths, however, could not be analyzed immediately and had to wait a maximum of 4 h; during this time they remained in the dark at

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3. Results and discussion 3.1. Distribution of atmospheric and surface seawater DMS Fig. 2 shows the variations of atmospheric and seawater DMS, momentum fluxes, and wind speeds over the course of the study. The atmospheric DMS concentrations ranged from 3 to 442 pptv with a mean of 61 pptv. For the first week of the experiment, the atmospheric DMS concentrations remained below 50 pptv. Most of the high atmospheric DMS concentrations (>100 pptv) occurred between February 14 and 24 and between stations 4 (37°590 S, 101°580 W) and 13 (28°510 S, 153°330 W), which is the subtropical region of the South Pacific. Although atmospheric DMS over the ocean originates from the ocean, we did not find a significant correlation between atmospheric and surface seawater DMS concentrations in this study. Although sea surface and atmospheric DMS concentrations were raised over the same period, their variations clearly differed from each other. Watanabe et al. (1995) observed a strong positive correlation between the spatial distributions of surface seawater and air DMS over the temperate North Pacific. However, other previous studies conducted in various marine environments failed to find similar correlations due to complexities in sea-air transfer mechanisms of DMS (Berresheim et al., 1991; Church et al., 1991) and removal processes of atmospheric DMS (Kieber et al., 1996).

(a)

500

Atmospheric DMS

pptv

400 300 200 100 0

(b)

16

Sea surface DMS

nM

12 8 4 0

(c) N/m2

1.2

Momentum Flux

0.8 0.4 0 12

(d) m/sec

room temperature. Traps containing seawater DMS were stored in a freezer at 70 °C until gas chromatography (GC) analysis could be conducted. Air DMS was collected in adsorption traps (carbosieve 300) every 3–6 h (4–6 times a day) at the upper bridge deck of the ship. Air was passed through a Nafion dryer filled with silica gel and a KI trap at 100 mL min1 (1 atm, 25 °C) ahead of an adsorption tube to remove water vapor and oxidants, respectively. Atmospheric DMS was analyzed immediately after being collected. The DMS in a trap was desorbed by rapid heating to 300 °C using a thermal desorption unit (Supelco, USA) for 3 min and injected directly into a HP 6890 GC equipped with a 30-in. packed super Q column (Alltech, USA) and a sulfur chemiluminescence detector (Siever, USA). The flow rate of carrier gas (He) was set at 30 mL min1. The oven temperature was programmed to initiate at 60 °C for 3 min, then the temperature was raised to 200 °C at a rate of 15 °C min1, held there for 5 min. Detection limits of atmospheric and seawater DMS were 1.5 pptv and 0.01 nM, respectively. DMS concentrations were calibrated using a permeation device (13 ng min1 at 30 °C, VICI, USA). The analytical precisions of atmospheric and seawater DMS measurements were 12% and 13%, respectively, which accounted for uncertainties associated with variance of daily DMS standards, air flow, and water volume over the course of the experiment. Meteorological data, such as wind speed, wind direction, air temperature, and pressure, were obtained from the shipboard automated weather system (Vaisala, Finland), which was designed for accurate measurement of true wind direction and speed. However, the data for the first 2 weeks of the experiment were lost because they were overwritten daily under the automatic saving mode. After February 16, meteorological data were manually saved. Modeled wind speeds and momentum fluxes were derived from the FNL archive produced by the National Centers for Environmental Prediction (NCEP). The FNL model data used in this study were archived in the Air Resources Laboratory of the National Oceanic and Atmospheric Administration (NOAA, http:// www.arl.noaa.gov/ready). While the FNL wind speed and direction and those observed on the ship were similar in their pattern of variation, their one-to-one correlation was poor.

Wind Speed

8 4 0 6-Feb

11-Feb

16-Feb

21-Feb

26-Feb

2-Mar

Fig. 2. Variations of (a) atmospheric DMS, (b) surface water DMS, (c) momentum flux, and (d) wind speeds during the course of the experiment.

In this study, meteorological parameters affecting sea-to-air flux played a more pronounced role in controlling atmospheric DMS than did surface water DMS concentrations. The period of elevated atmospheric DMS concentrations, especially between February 17 and 24, was characterized by higher wind speed (>5 m s1). Although wind data were lost and the overall correlation was not available for the first 10 d of the experiment, atmospheric DMS concentrations were closely related to the momentum fluxes. However, the poor correlations between observed wind and FNL data hinder further discussion about the relationship between atmospheric DMS and the calculated momentum flux. Nonetheless, it is evident that the intimate coupling of sea-to-air flux with wind speed and momentum flux played a significant role in determining atmospheric DMS concentrations over the study region. The surface seawater DMS concentrations varied considerably from 0.1 to 19.9 nM (mean = 2.1 nM) and were elevated concurrently with atmospheric DMS at stations 4–13 in the subtropical region. Our results indicate that in summer, seawater DMS concentrations of the subtropical region in the South Pacific clearly differed from those of tropical and temperate regions. These spatial differences in surface seawater DMS will be examined in detail in a later section. Our measurements of atmospheric and surface seawater DMS are in reasonable agreement with DMS concentrations from previous studies conducted in the Pacific Ocean. For atmospheric DMS, our mean was close to that obtained by Nguyen et al. (1983) during the spring, but their measurements (40–139 pptv) were less scattered than ours (3–442 pptv); our values also fall within the ranges observed at a coastal site (36°160 S) in New Zealand (Wylie and de Mora, 1996) and in the North Pacific (Watanabe et al., 1995). Over the South Pacific Subtropical Gyre, the levels of atmospheric and seawater DMS are highest during February and March (3–4 nM), based on the global database of measurements (Kettle

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et al., 1999; Kettle and Andreae, 2000) and calculations based on satellite observations of primary production (Longhurst et al., 1995). For surface seawater DMS, our mean of 2.1 nM lies near the lower end of all measurements throughout the Pacific Ocean. Excluding temperate and tropical regions, however, the mean concentration of 2.9 nM for the subtropics is comparable to that from the compiled database. It is noteworthy that the overall mean of 2.1 nM in our study was very similar to that calculated (2.4 nM) with an algorithm using mean MLD (Simó and Dachs, 2002), which will be discussed later. These results show that the measured DMS concentrations in this study were in accordance with those estimated from the global database (Kettle et al., 1999; Simó and Dachs, 2002).

the detection limit at 200 m. A water mass with high concentrations of DMS was distinguishable between 105°W (38°S) and 150°W (30°S); across these boundaries, the abundance of phytoplankton species and productivity significantly changed as well. In the region between 30°S and 40°S, chlorophytes are the dominant species during summer. This area lies in the transition zone from diatom-dominated Antarctica to the cyanobacteria-dominated southern central Gyre (Gregg et al., 2003). As we did not analyze phytoplankton species, we could not confirm that the distinctively high DMS concentrations to the north of 38°S was due mainly to the change from diatoms to chlorophytes. However, the abrupt change in DMS distribution likely was related with biological activity, which depends greatly on phytoplankton species. We also detected a discernable change in the depths of DMS maxima between two regions: at higher latitudes, the DMS maximum occurred 20 m, whereas it was deeper in the central Gyre (40 m). The variation in the depth of DMS maxima suggests spatial differences in biological activity. In the transition zone between 105°W and 150°W where relatively high sub-surface DMS concentrations occurred, DMSPp concentrations also were enhanced. In contrast, DMSPd concentrations were consistently low in this region. If we consider the change in the DMSPd/DMS ratio is indicative of bacterial conversion of DMSPd

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Longitude Fig. 3. Contoured vertical distributions of (a) DMS, (b) dissolved DMSP, and (c) particulate DMSP.

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The hydrographic profiles of DMS, DMSPd, and DMSPp were obtained at 16 stations along the cruise track, and we used these data to construct longitude–depth distributions (Fig. 3). In general, DMS maxima occurred 20–40 m below the sea surface. Such sub-surface maxima are commonly observed in the open ocean (Dacey et al., 1998; Scarratt et al., 2002; Wong et al., 2005). Below the maxima, DMS concentrations decreased exponentially and almost reached

3

3.2. Vertical distribution of water DMS

0.06

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to DMS (Jones et al., 1998), the bacterial conversion of DMSPd would be greatest over the transition region. While the levels of DMS and DMSPp within the water column rapidly decreased moving north from 30°S (west of 150°W), DMSPd increased noticeably in this subtropical oligotrophic region; this indicates that the rate of bacterial conversion of DMSPd to DMS or the direct release of DMS from phytoplankton likely decreased. Evidently, the changes

Concentration (nM) 0

(a)

1

2

3

4

5

0

Depth (m)

40

80

3.3. DMS flux into the air 120

We estimated the sea-to-air fluxes of DMS using the following gas exchange model for the 12 stations for which wind speed and water temperature data were available:

160

DMS Dissolved DMSP Particulate DMSP 200

Sigma-θ 24

24

25

25

26

26

27

Temperature oC

(b)

in biological activity, including phytoplankton composition and bacterial activities, played a crucial role in the distribution of DMS over the research area. Further investigation is needed to clarify the mechanisms responsible for this finding and their relative importance to DMS and DMSP distributions. In Fig. 4a, vertical profiles at all 16 stations were averaged for DMS, DMSPd, and DMSPp. The maximum depth appeared at 40 m for DMS and 60–80 m for DMSPp and DMSPd, which is within the depth range of the Chl a maximum reported for the South Pacific (Maritorena et al., 2002; Grob et al., 2007). As shown in typical profiles of density, temperature, salinity, and oxygen contents (Fig. 4b), the surface mixed layer was well developed throughout the study region, even though the depth of the mixed layer varied slightly from station to station (mainly due to meteorological and oceanographic conditions). The depth for maximum dissolved oxygen coincided with the depth at which the maximum concentrations of DMSPp and DMSPd were found. Considering that dissolved oxygen content is an indication of biological production within the euphotic zone, the conversion of DMSPd to DMS would be higher within the mixed layer. Indeed, the variations in DMS and DMSPd were out of phase between 20 m and 60 m (Fig. 4a).

8

12

16

20

24

0

40

80

Depth

Temperature Salinity Sigma-θ Oxygen

FluxDMS ¼ kl ðC l  C g =HÞ ffi kl  C l where kl is the gas transfer velocity; Cl and Cg are the concentrations of gas in seawater and the atmosphere, respectively; and H is the solubility of the gas in seawater. Because seawater is supersaturated relative to atmospheric DMS, the flux of DMS can be expressed as the product of surface water concentrations and the transfer velocity of DMS in the interface of sea and air. Various methods have been employed to parameterize kl using wind speed and physio-chemical properties of surface water (Liss and Merlivat, 1986; Wanninkhof, 1992; Nightingale et al., 2000). Here, we used the parameterization schemes of Liss and Merlivat (1986) (hereinafter LM86) and Wanninkhof (1992) (hereinafter W92) that have been most widely used in calculations of gas transfer velocity. In their schemes, the gas transfer velocity is regarded as being dependent on wind speed at 10 m height (u10) and the Schmidt number (ScDMS), which is expressed as a function of temperature (Saltzman et al., 1993):

ScDMS ¼ 2674:0  147:12t þ 3:726t2  0:038t 3 where t is water temperature (°C). In LM86, the gas transfer velocity is defined differently according to wind speed:

120

1

K l;DMS ¼ 0:17u10 ð660=ScDMS Þ2=3 ½cm h  u10 6 3:6 ðsmooth regimeÞ

160

1

K l;DMS ¼ ð2:85u10  9:65Þð660=ScDMS Þ1=2 ½cm h  3:6 < u10 6 13 ðrough regimeÞ 1

K l;DMS ¼ ð5:9u10  49:3Þð660=ScDMS Þ1=2 ½cm h  200 34.0

34.2

34.4

34.6

34.8

35.0

In comparison, the gas transfer velocity given by W92 is simpler than that of LM86:

Salinity %o 5

5

6

u10 P 13 ðwave-breaking regimeÞ

6

6

Oxygen mg/l Fig. 4. Vertical profiles of (a) DMS and dissolved and particulate DMSP concentrations (averaged for all stations) and (b) density (sigma h), temperature, salinity, and oxygen contents at station 8.

1

K l;DMS ¼ 0:31u210 ð660=ScDMS Þ1 ½cm h  Using these equations, we calculated the DMS fluxes for each station and the results are given in Table 1. The sea-to-air fluxes of DMS by LM86 ranged from 0.4 to 11.3, with a mean of

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2.8 mol d1 m2. In comparison, DMS fluxes by W92 were greater by almost a factor of two. Recent studies reported that the most defensible gas exchange velocities fall between those of LM86 and W92 (Nightingale et al., 2000; Huebert et al., 2004), so the mean flux of DMS in this study would be between 2.8 mol d1 m2 (LM86) and 4.5 mol d1 m2 (W92). Using the LM86 method, Ket-

Table 1 Calculated DMS fluxes using observed and model-derived winds. DMS fluxes (lmol d1 m2)

Stations

Surface water DMS (nM)

Water temperature (°C)

Wind speed (m s1)

W92

LM86

1 3 6 7 9 10 11 12 13 14 15 16

0.8 4.2 1.0 1.2 1.4 3.1 3.0 0.7 1.3 0.6 1.2 1.3

15.8 16.5 20.1 21.4 22 22 23.7 24.4 26 26.5 27.3 27.2

7.0 3.5 3.2 5.7 7.8 9.4 6.8 4.0 5.0 6.3 6.2 6.9

2.1 3.0 0.6 2.5 5.4 18.0 9.6 0.8 2.2 1.8 3.3 4.7

1.4 0.4 0.1 1.6 3.5 11.3 6.5 0.3 1.3 1.2 2.2 3.2

Means

2.1

21.6

6.0

4.5

2.8

(a)

tle and Andreae (2000) estimated the mean DMS flux as 4– 5 mol d1 m2 at latitudes from 30°S to 40°S using global climatological wind fields and compiled surface seawater DMS. Their higher flux value can be attributed in part to their higher concentrations of surface DMS. Our data included measurements from some tropical and temperate regions where water DMS levels were relatively low. If we limited our analysis to the same latitudinal zone studied by Kettle and Andreae (stations 6–13), the mean flux of DMS would increase to 3.5 mol d1 m2. Considering the uncertainties in two studies, we found no significant difference. Although wind is the most important physical factor controlling DMS flux, field studies rarely report that atmospheric DMS concentrations were dependent on wind speeds (Huebert et al., 2004). In our study, we also found a reasonable quadratic correlation between atmospheric DMS and wind speed (Fig. 5a). Andreae et al. (1995) and Ayers et al. (1995) reported a similar relationship. Because the surface DMS concentrations were determined along the ship track as well as at hydrocast stations, we calculated sea-toair DMS fluxes for all available surface seawater DMS measurements using the LM86 method. Sea water temperatures were not measured during transit and interpolated from those obtained near hydrocast stations. Fig. 5b illustrates that air DMS was well correlated with DMS flux, which is of great consequence considering the poor correlation between surface seawater and atmospheric DMS concentrations. Some systematic deviations occur in Fig. 5b and

(b) −

×

×

×

−

(c)

−

−

(d)

Fig. 5. Correlations between atmospheric DMS and (a) observed wind speeds and (b) DMS fluxes, and diurnal variations of (a) atmospheric and (b) surface seawater DMS. Boxes and bars represent 25–75 percentiles and 5–95 percentiles, respectively. Lines and diamonds inside the box denote median and mean, respectively. No surface water samples were collected from midnight to 6 am.

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(a)

0

8

40

10

6

30

4

20

2

10

0

MLD m

0

DMS MLD

MLD m

50

10

Surface Water DMS nM

Surface Water DMS nM

(b)

20

2

4

6

8

10

R2 = 0.443834 Y = 9.819 * ln(X) + 23.830

30 40

0

50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Station number Fig. 6. (a) The spatial variations of surface DMS and mixed layer depth (MLD) and (b) the relationship between surface DMS concentrations and MLD. The best fitted line was expressed as a logarithmic equation. MLD was defined as the depth at which water density increased by 0.03 kg m3 or temperature dropped by 0.1°C.

the atmospheric concentration is a direct result of the competition between seawater source and atmospheric sinks. However, this correlation implies that levels of atmospheric DMS were more subject to change in physical parameters (such as wind speed) than surface seawater DMS concentration. Watanabe et al. (1995) pointed that with fast removal of atmospheric DMS, a proportional correlation between atmospheric DMS and flux should be possible. In the open ocean, the most important process of removal of atmospheric DMS is its oxidation by OH radicals (Chen et al., 2000). Because OH radicals are produced by sunlight, the removal of atmospheric DMS is most efficient during the day. All atmospheric DMS measurements throughout the cruise were plotted against local times in Fig. 5c. In our experiment, diurnal variations of atmospheric DMS showed a general tendency toward low concentrations during the day. Most studies that have reported a typical pattern of atmospheric DMS variation with minima in the afternoon were conducted at island sites or in areas in which oceanic and atmospheric conditions were relatively homogeneous and persistent (Ayers et al., 1995; Bandy et al., 1996; Sciare et al., 2000). In fact, Cooper and Saltzman (1991) reported that diurnal variations were not evident in open oceans due to large-scale changes in seawater and atmospheric conditions, which effectively masked any effects due to oxidation processes. We found no significant diurnal variation in seawater DMS (Fig. 5d), mainly because its short-term variability was coupled not only with the variability of solar radiation, but also with other physical parameters such as wind speed and mixing (Toole and Siegel, 2004). 3.4. MLD vs. surface seawater DMS Simó and Dachs (2002) constructed the following straightforward but meaningful algorithm to estimate surface seawater DMS concentrations [nM] using MLD [m] and Chl a [mg m3] by combining more than 2000 DMS data points with concurrent surface Chl a measurements and climatological MLDs:

DMS ¼ LnðMLDÞ þ 5:7

for Chl a=MLD < 0:02

DMS ¼ 55:8ðChl a=MLDÞ þ 0:6 for Chl a=MLD 6 0:02

ð1Þ

Although we did not measure Chl a in our study, it is not likely that Chl a concentrations were higher than 1 mg m3 based on results reported by Gregg and Casey (2004). Thus, ratios of Chl a/MLD in this experiment would be smaller than 0.02 with the observed maximum MLD of 45 m. Therefore, it is reasonable to assume that our data fall into the first regime, where MLD is the sole factor that determines the surface seawater DMS concentration. Using the

equation above, our mean MLD of 28 m resulted in 2.4 nM of DMS, which was just 10% higher than the overall observed mean of 2.1 nM for surface seawater DMS. Thus, Simó and Dachs (2002) empirical equation produced a reasonable mean value when applied to large areas of the South Pacific. The logarithmic equation above suggests a negative correlation between surface seawater DMS concentration and MLD. In this study, however, surface seawater DMS and MLD for each station were positively correlated (Fig. 6). This disagreement can be resolved by considering the vertical distribution of seawater DMS and the physical properties of the water column (Fig. 4). While the mean depth of water DMS maxima was 40 m, most MLDs were shallower than 40 m (mean = 28 m). Under such conditions, DMS concentrations in the surface water would be enhanced by a deeper MLD, which would enable the surface water to be mixed with water enriched in DMS below the MLD. Because Simó and Dachs (2002) equation was derived from global measurements of data combined with climatological MLD, cautions should be exercised when applying the MLD algorithm to estimate water DMS variations at a certain region for a short period of time.

4. Conclusions We successfully measured atmospheric DMS and seawater DMS, DMSPd, and DMSPp along an east–west transect cruise in the South Pacific during February 2000. The mean concentrations of 61 pptv and 2.11 nM for atmospheric and surface seawater DMS, respectively, agreed well with those from previous studies over similar latitudes and times of the year. Our results also agreed well with water DMS concentrations estimated from the global database over the South Pacific in summertime. The South Pacific Gyre was identified by characteristic distributions of DMS, DMSPd, and DMSPp along latitude and depth. The distinctive variations of these species were closely coupled with characteristic modification in geophysical and biological parameters. Averaged DMS maxima of whole study appeared at 40 m, which was deeper than the mean MLD of 28 m. The DMSPp and DMSPd maxima occurred at 60–80 m, where dissolved oxygen content also was at a maximum. The seato-air fluxes of DMS varied from 0.4 to 11.3, with a mean of 2.8 mol d1 m2. For the region of the South Pacific Gyre (30– 40°S), the mean flux was 3.5 lmol d1 m2, which was lower than that estimated by Kettle et al. (1999) (4–5 lmol d1 m2) using the same parameterization with global climatological wind fields and compiled surface seawater DMS. Neither clear correlation between atmospheric DMS and surface seawater DMS nor significant

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diurnal variations of atmospheric DMS were observed in our study. Instead, we found a reasonable correlation between atmospheric DMS and sea-to-air DMS flux. Finally, Simó and Dachs’ (2002) estimated sea surface DMS concentration calculated using mixed layer depth agreed well with the observed mean in our study. Because of inherent differences in methodology, however, extreme care should be exercised when estimating short-term variations of DMS using only MLD. Acknowledgements This work was funded by the Korea Meteorological Administration Research and Development Program under Grant CATER 20073204. References Anderson, T.R., Spall, S.A., Yool, A., Cipollini, P., Challenor, P.G., Fasham, M.J.R., 2001. Global fields of sea surface dimethylsulfide predicted from chlorophyll, nutrients and light. J. Mar. Syst. 30, 1–20. Andreae, M.O., Elbert, W., de Mora, S.J., 1995. Biogenic sulfur emissions and aerosols over the tropical South Atlantic 3. Atmospheric dimethylsulfide, aerosols and cloud condensation nuclei. J. Geophys. Res. 100, 11335–11356. Ayers, G.P., Gillett, R.W., Ivey, J.P., Schäfer, B., Gabric, A., 1995. Short-term variability in marine atmospheric dimethylsulfide concentration. Geophys. Res. Lett. 22 (18), 2513–2516. Bandy, A.R., Thornton, D.C., Blomquist, B.W., Chen, S., Wade, T.P., Ianni, J.C., Mitchell, G.M., Nadler, W., 1996. Chemistry of dimethyl sulfide in the equatorial Pacific atmosphere. Geophys. Res. Lett. 23, 741–744. Bates, T.S., 2004. Global surface seawater dimethylsulfide (DMS) database. In: Paper Presented at 8th International Global and Atmospheric Chemistry Conference, Christchurch, New Zealand. Bates, T.S., Quinn, P.K., 1997. Dimethylsulfide (DMS) in the equatorial Pacific Ocean (1982 to 1996): evidence of a climate feedback. Geophys. Res. Lett. 24, 861–864. Bates, T.S., Kiene, R.P., Wolfe, G.V., Matrai, P.A., Chavez, F.P., Buck, K.R., Blomquist, B.W., Cuhel, R.L., 1994. The cycling of sulfur in surface seawater of the Northeast Pacific. J. Geophys. Res. 99, 7835–7843. Bates, T.S., Kapustin, V.N., Quinn, P.K., Covert, D.S., Coffman, D.J., Mari, C., Durkee, P.A., DeBruyn, W., Saltzman, E., 1998. Processes controlling the distribution of aerosol panicles in the lower marine boundary layer during the First Aerosol Characterization Experiment (ACE-I). J. Geophys. Res. 103, 16369–16383. Belviso, S., Thouaeau, G., Schmidt, S., Reigstad, M., Wassmann, P., Arashkevich, E., Stefels, J., 2006. Significance of vertical flux as a sink for surface water DMSP and as a source for the sediment surface in coastal zones of northern Europe. Estuar. Coast. Shelf Sci. 68, 473–488. Berresheim, H., Andreae, M.O., Iverson, R.L., Li, S.M., 1991. Seasonal variations of dimethylsulfide emissions and atmospheric sulfur and nitrogen species over the western North Atlantic Ocean. Tellus B 43, 353–372. Charlson, R.J., Lovelock, J.E., Andreae, M.O., Warren, S.G., 1987. Oceanic phytoplankton, atmospheric sulfur, cloud albedo and climate. Nature 295, 655–661. Chen, G., Davis, D., Kasibhatla, P., Bandy, A., Thornton, D., Huebert, B., Clarke, A., 2000. A study of DMS oxidation in the tropics: comparison of Christmas Island field observations of DMS, SO2, and DMSO with model simulations. J. Atmos. Chem. 37, 137–160. Church, T.M., Tramontano, J.M., Whelpdale, D.M., Andreae, M.O., Galloway, J.N., Keene, W.C., Knap, A.H., Tokos, J., 1991. Atmospheric and precipitation chemistry over the North Atlantic Ocean: shipboard results, April–May 1984. J. Geophys. Res. 96 (D10), 18705–18726. Cooper, D.J., Saltzman, E.S., 1991. Measurements of atmospheric dimethyl sulfide and carbon disulfide in the western Atlantic boundary layer. J. Atmos. Chem. 12, 153–168. Dacey, J.W.H., Howse, F.A., Michaels, A.F., Wakeham, S.G., 1998. Temporal variability of dimethylsulfide and dimethylsulfoniopropionate in the Sargasso sea. DeepSea Res. I 45, 2085–2104. Gondwe, M., Krol, M., Gieskes, W., Klaassen, W., de Baar, H., 2003. The contribution of ocean-leaving DMS to the global atmospheric burdens of DMS, MSA, SO2, and NSS SO2 4 . Global Biogeochem. Cycles 17 (2), 1056. doi:10.1029/2002GB001937. Gregg, W.W., Casey, N.W., 2004. Global and regional evaluation of the SeaWiFS chlorophyll data set. Remote Sens. Environ. 93, 463–479. Gregg, W.W., Ginoux, P., Schopf, P.S., Casey, N.W., 2003. Phytoplankton and iron: validation of a global three-dimensional ocean biogeochemical model. DeepSea Res. II 50, 3143–3169. Grob, C., Ulloa, O., Li, W.K.W., Alarcón, G., Fukasawa, M., Watanabe, S., 2007. Picoplankton abundance and biomass across the eastern South Pacific Ocean along latitude 32.5°S. Mar. Ecol. Prog. Ser. 332, 53–62.

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