Introduction
The sea-surface microlayer (SML) is the uppermost layer of the water column
and the interface between the ocean and the atmosphere. The accumulation of
organic matter, distinct physical and chemical properties and a specific
organismal community (neuston) distinguish the SML as a unique
biogeochemical and ecological system. It has been suggested that the SML has
a gel-like nature (Cunliffe and Murrell, 2009; Sieburth, 1983) of varying
thickness (20–150 µm, Cunliffe et al., 2013) with
dissolved polymeric carbohydrates and amino acids present as well as gel
particles, such as transparent exopolymer particles (TEP) of polysaccharidic
composition, and Coomassie stainable particles (CSPs) of proteinaceous
composition. These gelatinous compounds originate from high molecular weight
polymers that are released form phytoplankton and bacterial cells by
exudation and cell break up (Chin et al., 1998; Engel et al., 2004;
Verdugo et al., 2004). Polysaccharide-rich gels, like TEP, were attributed
mainly to phytoplankton exudation (Passow, 2002), while the production
of protein-containing gels, such as CSPs, has been related to cell lysis and
decomposition, as well as to the absorption of proteins onto
non-proteinaceous particles (Long and Azam, 1996). Gels are
transported to the SML by rising bubbles (Azetsu-Scott and
Passow, 2004; Zhou et al., 1998) or are produced from dissolved precursors
directly at the air–sea interface during surface wave action
(Wurl et al., 2011). Gel particles can promote
microbial biofilm formation (Bar-Zeev et al., 2012) and mediate
vertical organic matter transport, either to the atmosphere (Leck and
Bigg, 2005; Orellana et al., 2011) or to the deep ocean (Passow,
2002).
Accumulation of organic matter in the SML may be tightly coupled to
phytoplankton abundance in the water column (Bigg et al., 2004; Galgani
et al., 2014; Gao et al., 2012; Matrai et al., 2008). Thus, organic matter
accumulation and composition in the SML may also reflect the sensitivity of
marine microorganisms in the surface ocean to environmental changes, which
was shown during previous mesocosms studies (Engel et al., 2013;
Riebesell et al., 2009; Schulz et al., 2013). Distinct from the SML and on
top of it lies the nanolayer, a monomolecular film, which, like the SML,
shows seasonality features with carbohydrate-rich polymeric material being
most abundant during the summer months and possibly related to a combination
of primary production (phytoplankton abundance) and photochemical and/or
microbial reworking of organic matter (Laß et al., 2013).
Maps of stations where sampling for the sea-surface microlayer (SML)
and underlying seawater (ULW) was conducted during the SOPRAN Meteor 91
cruise along the coastal upwelling area off the coast of Peru in 2012.
In our study we focused on the upper micrometers of the water–air interface
that we operationally define as SML, whose compositional changes and
accumulation of organic matter may influence two air–sea interface processes
necessary to understand oceanic feedbacks on the atmosphere: sea-spray
aerosol (SSA) emission and air–sea gas exchange (Cunliffe et al., 2013).
During biologically productive periods, a high amount of SSA with a
predominant organic composition is emitted from the ocean's surface
(O'Dowd et al., 2004). These compounds primarily reveal a
polysaccharidic, gel-like composition, suggesting that the abundance and
size of dissolved polysaccharides and marine gels in the sea surface may
influence the organic fraction of SSA (Orellana et al., 2011; Russell et
al., 2010). It has also been shown that the presence of biogenic surface
active substances (surfactants) in the SML leads to capillary wave damping,
alters the molecular diffusion of gases (Frew et al., 1990;
Liss and Duce, 2005) and thereby affects gas exchange rates particularly
at lower wind speed (Jähne and Haußecker, 1998). In
this respect, the understanding of sources, composition and fate of
biological components in the SML becomes of particular relevance for
environments, where biological productivity is high like in coastal
upwelling regimes.
Off the coast of Peru, the coastal upwelling region extends between approximately
4 and 40∘ S. In this area, upwelling processes are
sustained by winds throughout the year but feature high inter-annual
variability induced by the El Niño–Southern Oscillation (ENSO) cycle
(Tarazona and Arntz, 2001). Eastern boundary upwelling systems
(EBUSs) like the system off the coast of Peru are characterized by high biological
productivity supported by deep upwelling of nutrients and often associated
with subsurface oxygen minimum zones (OMZs). The supply of oxygen to the
OMZ is largely controlled by physical (i.e., diffusive and advective)
mechanisms, whereas biological processes (i.e., respiration of organic
matter) provide sinks (Lachkar and Gruber, 2011).
OMZs are significant source regions for major climate-relevant gases such
as carbon dioxide, methane, hydrogen sulfide and nitrous oxide (Paulmier
et al., 2008, 2011). Processes affecting gas exchange in
these regions need to be understood in order to accurately estimate trace
gas fluxes from the ocean to the atmosphere and consequences on climate. In
2008, the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment
(VOCALS-REx) and the Chilean Upwelling Experiment (VOCALS-CUpEx) conducted
between Southern Peru and Northern Chile focused on the link between
aerosols, clouds and precipitation as well as on physical and chemical
couplings between the upper ocean and the lower atmosphere (Garreaud et
al., 2011; Wood et al., 2011). During the SOPRAN cruise METEOR91 (M91), we
studied organic matter components at the very sea surface since properties
of the SML may represent a major uncertainty for gas, heat and aerosol
fluxes in this specific region and in other oceanic environments. During our
cruise, organic matter concentration and composition of the SML and the
underlying seawater were studied on 37 different stations, providing the
first SML data set for the upwelling system off the coast of Peru, including the first
data set on gel particles in EBUSs so far.
Material and methods
Field information and sampling
The R/V METEOR cruise M91 studied the upwelling region off the coast of Peru
(Bange, 2013). Samples were collected between 4.59∘ S
and 82.0∘ W, and 15.4∘ S and 77.5∘ W from
3 to 23 December in 2012. The overall goal of M91 was to conduct an
integrated biogeochemical study on the upwelling region off the coast of Peru in order to
assess the importance of OMZs for the sea-air exchange of various
climate-relevant trace gases and for tropospheric chemistry. Salinity and
temperature were measured with a CTD at each station. Global and UV
radiation and wind speed data were retrieved from the DShip database for the
time of sampling based on the sensors installed on board.
On 37 different stations between 5 and 16∘ S off the
Peruvian coast (Fig. 1), a total of 39 SML samples was collected from a
rubber boat using a glass plate sampler according to the original approach
described by Harvey and Burzell (1972). Different methods have
been developed to sample and investigate the SML. These methods do not only
differ in terms of application but also with respect to the thickness of the
SML sampled as well as to selective removal of certain components. Several
studies evaluated these methods against each other. A recent summary can be
found in the “Guide to best practices to study the ocean's surface”
(Cunliffe and Wurl, 2014). During this study, we applied the glass
plate technique because it allows for sampling of a relatively large volume
needed to analyze different organic components while keeping the
simultaneous sampling of ULW minimal. Two stations were sampled twice in a
time frame of 24 h (stations 12_1 and 12_3, 16_2 and 16_3). Our glass plate with the
dimensions of 500 mm (length) × 250 mm (width) × 5 mm (thickness) was made
of borosilicate glass and had an effective sampling surface area of 2000 cm2 (considering both sides). For each sample, the glass plate was
inserted into the water perpendicular to the surface and withdrawn slowly at
a rate of approximately 20 cm s-1. The sample, retained on the glass
because of surface tension, was removed with the help of a Teflon wiper.
Samples were collected as far upwind of the ship as possible and away from
the path taken by the ship to avoid contamination. For each sample the glass
plate was dipped and wiped about 20 times. The exact number of dips and
the total volume collected were recorded. Samples were collected into acid-cleaned (HCl, 10 %) and Milli-Q-washed glass bottles, and the first
milliliters were used to rinse the bottles and then discarded. Prior to each
sampling, both the glass plate and wiper were washed with HCl (10 %) and
intensively rinsed with Milli-Q water. At the sampling site, both
instruments were copiously rinsed with seawater in order to minimize any
possible contamination with alien material while handling or transporting
the devices.
The apparent thickness (d) of the layer sampled with the glass plate was
determined as follows:
d=V/(A×n),
where V is the SML volume collected (i.e., 60–140 mL) A is the sampling area of
the glass plate (A= 2000 cm2) and n is the number of dips
(Cunliffe and Wurl, 2014). We will use d (µm) as an operational
estimate for the thickness of the SML.
At the same stations, after sampling the SML, about 500 mL samples were
collected from the underlying seawater (ULW) at ∼ 20 cm depth
by holding an acid-cleaned (HCl 10 %) and Milli-Q-rinsed borosilicate
glass bottle. The bottle was open and closed underwater to avoid
simultaneous sampling of SML water. For safety reasons, sampling for the SML
from a rubber boat could be made only during daylight hours.
Chemical and biological analyses
Total organic carbon (TOC) and dissolved organic carbon (DOC)
Samples for TOC and DOC (20 mL) were collected in combusted glass ampoules,
DOC after filtration through combusted GF/F filters (8 h,
500 ∘C). Samples were acidified with 80 µL of 85 %
phosphoric acid, heat sealed immediately, and stored at 4 ∘C in
the dark until analysis. DOC and TOC samples were analyzed by applying the
high-temperature catalytic oxidation method (TOC-VCSH, Shimadzu) modified
from Sugimura and Suzuki (1988). The instrument was calibrated
every 8–10 days by measuring standard solutions of 0, 500, 1000, 1500, 2500
and 5000 µg C L-1, prepared from a potassium hydrogen phthalate
standard (Merck 109017). Every measurement day, ultrapure (MilliQ) water was
used to determine the instrument blank, which was accepted for values
< 1 µmol C L-1. TOC analysis was validated on every
measurement day with deep seawater reference (DSR) material provided by the
Consensus Reference Materials Project of RSMAS (University of Miami)
yielding values within the certified range of 42–45 µmol C L-1.
Additionally, two internal standards with DOC within the range of those in
samples were prepared each measurement day using a potassium hydrogen
phthalate (Merck 109017). DOC and TOC concentration was determined in each
sample from 5 to 8 injections. The precision was < 4 % estimated
as the standard deviation of replicate measurements divided by the mean.
Particulate organic carbon (POC) was determined as the difference between
TOC and DOC.
Total nitrogen (TN) and total dissolved nitrogen (TDN)
TN and TDN were determined simultaneously with TOC and DOC, respectively,
using the TNM-1 detector on the Shimadzu analyzer. Nitrogen in the samples
is combusted and converted to NOx, which chemiluminesces when mixed with
ozone and can be detected using a photomultiplier
(Dickson et al., 2007). Calibration of the instrument was
done every 8–10 days by measuring standard solutions of 0, 100, 250, 500 and
800 µg N L-1, prepared with potassium nitrate
Suprapur® (Merck 105065). Particulate nitrogen (PN) was
determined as the difference between TN and TDN. Deep seawater reference
(DSR) material provided by the Consensus Reference Materials Project of
RSMAS (University of Miami) was used on every measurement day and yielded
values within the certified range of 31–33 µmol N L-1. The
precision was < 2 % estimated as the standard deviation of 5–8
measurements divided by the mean.
Total, dissolved and free amino acids
For total hydrolysable amino acids (THAA), 5 mL of sample were filled into
pre-combusted glass vials (8 h, 500 ∘C) and stored at -20 ∘C until analysis. Samples for dissolved hydrolysable (DHAA) and
free amino acids (FAA) were additionally filtered through 0.45 µm
Millipore Acrodisc® syringe filters and then stored in the
same way as samples for THAA. Analysis was performed according to Lindroth
and Mopper (1979) and Dittmar et al. (2009) with some
modifications. Duplicate samples were hydrolyzed for 20 h at 100 ∘C
with hydrochloric acid (suprapur, Merck) and neutralized by acid evaporation
under vacuum in a microwave at 60 ∘C. Samples were washed with
water to remove remaining acid. Analysis was performed on a 1260 HPLC system
(Agilent). Thirteen different amino acids were separated with a C18 column
(Phenomenex Kinetex, 2.6 µm, 150 × 4.6 mm) after in-line
derivatization with o-phthaldialdehyde and mercaptoethanol. The following
standard amino acids were used: aspartic acid (AsX), glutamic acid (GlX),
serine (Ser), arginine (Arg), glycine (Gly), threonine (Thr), alanine (Ala),
tyrosine (Tyr), valine (Val), phenylalanine (Phe), isoleucine (Ileu),
leucine (Leu), γ- amino butyric acid (GABA). α- amino
butyric acid was used as an internal standard to account for losses during
handling. Solvent A was 5 % acetonitrile (LiChrosolv, Merck, HPLC gradient
grade) in sodium-di-hydrogen-phosphate (Merck, suprapur) buffer (PH 7.0).
Solvent B was acetonitrile. A gradient was run from 100 % solvent A to
78 % solvent A in 50 min. FAA were determined from DHAA samples
without prior hydrolysis in separate analyses. Particulate hydrolysable
amino acids (PHAA) were determined by subtracting DHAA from THAA. The
detection limit for individual amino acids was 2 nmol monomer L-1. The
precision was < 5 %, estimated as the standard deviation of
replicate measurements divided by the mean.
Total and dissolved combined carbohydrates
For total and dissolved combined carbohydrates > 1 kDa (TCCHO and
DCCHO), 20 mL were filled into pre-combusted glass vials (8 h, 500 ∘C) and kept frozen at -20 ∘C until analysis. Samples
for DCCHO were additionally filtered through 0.45 µm Pall
Acrodisc® syringe filters. The analysis was conducted
according to Engel and Händel (2011) applying high
performance anion exchange chromatography coupled with pulsed amperometric
detection (HPAEC-PAD) on a Dionex ICS 3000. Samples were desalinated by
membrane dialysis (1 kDa MWCO, Spectra Por) for 5 h at 1 ∘C,
hydrolyzed for 20 h at 100 ∘C with 0.4 M HCl final concentration,
and neutralized through acid evaporation under vacuum and nitrogen
atmosphere (1 h, 60 ∘C). Two replicate samples were analyzed. The
retention of carbohydrates on exchange columns, and thus the reproducibility
of results are highly sensitive to changes in temperature
(Panagiotopoulos et al., 2001; Yu and Mou, 2006). For our system, best
resolution of sugars was obtained at 25 ∘C and therefore applied
constantly during all analyses. In order to minimize degradation of samples
before analysis, the temperature in the auto-sampler was kept at 4 ∘C. The system was calibrated with a mixed sugar standard
solution including (a) the neutral sugars: fucose (4.6 µM, Fuc),
rhamnose (3.1 µM, Rha), arabinose (2.0 µM, Ara), galactose
(2.4 µM, Gal), xylose/ mannose (3.1 µM, Xyl/ Man), glucose
(2.4 µM, Glc), (b) the amino sugars: galactosamine (2.0 µM,
GalN), glucosamine (2.8 µM, GlcN), and (c) the acidic sugars:
galacturonic acid (2.8 µM, Gal-URA), gluconic acid (5.1 µM,
Glu-Ac), glucuronic acid (3.0 µM, Glc-URA) and muramic acid (1.9 µM, Mur-Ac).
Regular calibration was performed by injecting 12.5, 15.0, 17.5 and 20 µL of mixed standard
solution. Linearity of the calibration curves of individual sugar standards
was verified in the concentration range 10 nM–10 µM. Therefore, the
standard mixture was diluted 10, 20, 50, and 100 fold with Milli-Q water.
Injection volume for samples and for the blank was 17.5 µL. To check
the performance of carbohydrate analysis and stability of the HPLC-PAD
system, a 17.5 µL standard solution was analyzed after every second
sample. The detection limit was 10 nM for each sugar with a standard
deviation between replicate runs of < 2 %. Milli-Q water was used
as blank to account for potential contamination during sample handling.
Blanks were treated and analyzed in the same way as the samples. Blank
concentration was subtracted from sample concentration if above the
detection limit. Particulate combined carbohydrates (PCCHO) were determined
as the difference between TCCHO and DCCHO.
Gel particles
Total area, particle numbers and equivalent spherical diameter (dp) of
gel particles were determined by microscopy after Engel (2009).
Therefore, 20 to 30 mL were filtered onto 0.4 µm Nuclepore membranes
(Whatmann) and stained with 1 mL Alcian Blue solution for polysaccharidic
gels (i.e., transparent exopolymer particles (TEP)) and 1 mL Coomassie
Brilliant Blue G (CBBG) working solution for proteinaceous gels (i.e.,
Coomassie stainable particles (CSP)). Filters were mounted onto
Cytoclear©slides and stored at -20 ∘C until microscopy
analysis. The size–frequency distribution of gel particles was described by the following:
dNddp=kdpδ,
where dN is the number of particles per unit water volume in the size range
dp to (dp+d(dp); Mari and Kiørboe, 1996). The factor
k is a constant that depends on the total number of particles per volume, and δ (δ < 0) describes the spectral slope of the size
distribution. The value δ is related to the slope of the cumulative
size distribution N=adpβ by δ=β+1, where N is
the total number of particles per unit water volume. The less negative is
δ, the greater is the fraction of larger gels. Both δ and k
were derived from regressions of log(dN/d(dp)) vs. log(dp) over the
size range 1.05–14.14 µm ESD.
Formation of exopolymeric gel particles (e.g., TEP) can be described in terms
of coagulation kinetics (Engel et al., 2004; Mari and Burd, 1998).
Aggregates can be described using a fractal scaling relationship (e.g., M ∼ LD), where M is the mass of the aggregate, L the size
and D is the fractal dimension, which is controlled by the size of particles
that form the aggregate as well as by the processes of particle collision,
e.g Brownian motion, shear, or differential settlement (Meakin, 1991).
Assuming that TEP are formed by shear-induced coagulation D can be estimated
from δ (Mari and Burd, 1998):
D=(64-∂)26.2.
Heterotrophic bacteria
For bacterial cell numbers, 4 mL samples were fixed with 200 µL
glutaraldehyde (25 % final concentration) and stored at -20 ∘C
until enumeration. Samples were stained with SYBR Green I (molecular
probes). Heterotrophic bacteria were enumerated using a flow cytometer
(Becton & Dickinson FACSCalibur) equipped with a laser emitting at 488 nm
and detected by their signature in a plot of side scatter (SSC) vs. green
fluorescence (FL1). Heterotrophic bacteria were distinguished from
photosynthetic prokaryotes (e.g., Prochlorococcus) by their signature in a plot of red
fluorescence (FL2) vs. green fluorescence (FL 1). Yellow-green latex
beads (Polysciences, 0.5 µm) were used as internal standard. Sampling
bacterioneuston with a glass plate does not bias cell abundance measurements
(Stolle et al., 2009).
Phytoplankton
For photoautotrophic cell numbers < 20 µm, 4 mL samples were
fixed with 20 µL glutaraldehyde (25 % final concentration), and
stored at -80 ∘C until enumeration. Phytoplankton counts were
performed with a FACSCalibur flow-cytometer (Becton Dickinson) equipped with
an air-cooled laser providing 15 mW at 488 nm and with a standard filter
set-up. The cells were analyzed at high flow rate (∼ 39–41 µL min-1) with the addition of 1 µm-fluorescent beads
(Trucount, BD). Autotrophic groups were discriminated on the basis of their
forward or right angle light scatter (FALS, RALS) as well as from
chlorophyll and phycoerythrin (characteristic for cyanobacterial, mainly Synechococcus
populations) fluorescence. Cell counts were analyzed using BD CellQuest
Pro-Software. Two groups were distinguished: non-cyanobacterial-type
phytoplankton (NCPL) and cyanobacterial-type phytoplankton (CPL).
Hydrographic conditions encountered during SML sampling off the coast of Peru in
2012
(M91). Data on air temperature, wind speed, global and UV radiation were
obtained from
the ship's DShip database for the time of sampling.
Temperature
Salinity
Air temperature
Wind speed
Global radiation
UV radiation
(∘C)
(∘C)
(m s-1)
(W m-2)
(W m-2)
Average
19.25
34.87
19.67
5.66
570
37 935
SD
1.70
0.50
0.89
2.14
366
23 384
Min
15.91
32.02
17.30
0.60
10
0.812
Max
21.90
35.32
21.50
9.00
1103
71.10
(a) Surface water (1 m depth) temperature (∘C) and wind
speed (m s-1) (b) during M91.
Data analysis
The relative concentration of a substance A in the SML was compared to the
underlying water (ULW) by the enrichment factor (EF), defined by the following:
EF=(A)SML/(A)ULW,
where (A) is the concentration of a given parameter in the SML or ULW,
respectively (GESAMP, 1995). Because the concentration of a component is
normalized to its values in the underlying water, EFs for different
components can be readily compared. Enrichment of a component is indicated
by EF > 1, depletion by EF < 1.
Differences in data as revealed by statistical tests (t test) were accepted
as significant for p < 0.05. Average values for total concentrations
are given by their arithmetic mean, averages for ratios by their geometric
mean. Average values are reported with ±1 standard deviation
(SD). Calculations, statistical tests and illustration of the data were
performed with the software packages Microsoft Office Excel 2010, Sigma Plot
12.0 (Systat) and Ocean Data View (Schlitzer, 2013). Weighted-average
gridding was used in ODV to display data in the SML according to data
coverage with automatic scale lengths (53 permille x scale length, 40
permille y scale length).
Results
The physical environment
Coastal upwelling off the coast of Peru can occur throughout the year (Carr
and Kearns, 2003). During the M91 cruise upwelling and upwelling velocities
were determined from 3He/4He disequilibrium
(Steinfeldt et al., 2015). High upwelling velocities of
> 3 × 10-5 m s-1 were observed south of Lima (stations
10, 14, 15; Fig. 1). The coastal upwelling of deep water resulted in
strong gradients of surface seawater temperature and salinity along the
Peruvian shelf as well as with increasing distance to the shelf during M91.
Salinity measured at about 1 m depth corresponding to the ship's keel varied
between 32 and 35 with the lowest values occurring close to the coast at
stations 10_1 to 10_4, 14_1 and
14_2 and 15_1 to 15_3. Here,
temperatures were below the average of all surface stations (19.25 ± 1.7 ∘C), indicating the colder, upwelling deep water (Table 1,
Fig. 2). Wind speed encountered during the cruise ranged between 0.6 and
9.0 m s-1 with the lower wind speeds also observed closer to the coast,
i.e., between 12 and 14∘ S and at the northern stations
(Fig. 2). Thus, higher wind speed was observed at the more off-shore
stations having higher surface water temperatures, leading to significant
co-variation between surface water temperature and wind speed (Fig. 3).
Global radiation and UV radiation varied between 10 and 1103 W m-2, and
between 0.8 and 71 W m-2, respectively, with no significant impact of
SML organic matter accumulation.
Direct relationship between surface water temperature and wind
speed during M91 SML sampling, p < 0.001, r= 0.58, n= 37. Data between 16 and 18∘ were selected for analysis of wind speed effects at similar
temperatures, see Fig. 7.
SML properties and organic matter accumulation
Estimates for SML thickness are depending on the method applied to sample
the SML (Carlson, 1982; Zhang et al., 1998). For
the glass plate technique, Zhang et al. (1998) showed that SML thickness
decreases with increasing withdrawal rates; i.e., from 50–60 µm for a
withdrawal rate of 20 cm s-1, to 10–20 µm at rate of
5–6 cm s-1. Their results confirmed earlier studies that generally revealed
thinner SML layers at slower withdrawal rates (Carlson, 1982; Harvey and
Burzell, 1972; Hatcher and Parker, 1974). During this study, the SML was
sampled with the glass plate at ∼ 20 cm s-1, yielding a
thickness between 45 and 60 µm, with an overall mean value of
49 ± 8.89 µm (n= 39). This value is in good accordance with the
proposed apparent sampling thickness of 50 ± 10 µm
(Zhang et al., 1998) and fits well to previous
observations for the SML sampled with a glass plate at the same withdrawal
rate (Cunliffe et al., 2011; Galgani and Engel, 2013; Galgani et al.,
2014; Zhang et al., 1998; Zhang, 2003). Using direct pH microelectrode
measurements, Zhang (2003) later confirmed an in situ thickness of
∼ 60 µm for the SML, which they defined as the layer of
sudden change of physico-chemical properties.
Concentration of various organic components in the SML during M91,
given as average (avg.) and standard deviation (SD), as well as minimum
(min) and maximum (max); n= number of observations. For abbreviations see
text.
Unit
Avg.
SD
min
max
n
DOC
µmol L-1
94
13
71
122
39
TOC
µmol L-1
127
33
82
199
39
POC
µmol L-1
33
25
2.3
96
39
TEP number
× 106 L-1
19
15
1.8
63
39
TEP area
mm2 L-1
100
106
6.9
408
39
DCCHO
nmol L-1
1111
550
507
2668
39
PCCHO
nmol L-1
1084
1300
41
5156
34
TN
µmol L-1
16
4.9
8.7
28
39
TDN
µmol L-1
12.5
4.0
7.7
25
39
PN
µmol L-1
3.3
3.7
bd
16
39
CSP number
×106 L-1
118
72
19
311
39
CSP area
mm2 L-1
1024
728
137
3051
39
FAA
nmol L-1
151
104
49
531
37
DHAA
nmol L-1
770
359
423
2017
30
PHAA
nmol L-1
1176
774
208
3956
29
NCPL
× 103 mL-1
45
53
5.4
300
35
CPL
× 103 mL-1
27
35
3.7
193
35
Het. bacteria
× 104 mL-1
195
206
3
854
36
We therefore assume that samples obtained from the SML during this study
well represented the SML, as defined by Zhang (2003). Thickness of
the SML as determined during this study increased significantly with amount
of organic substances in the SML, determined as TOC concentration
(p < 0.005; n= 39). This corroborates earlier findings from
experimental studies showing that organic matter produced by phytoplankton
increases the thickness of SML sampled with a glass plate (Galgani
and Engel, 2013). No correlation instead was observed between SML thickness
and wind speed (r=-0.11, n= 39) or between SML thickness and temperature
(r=-0.06; n= 39).
Unless stated otherwise, all observations described in this paragraph relate
to the SML. In general, concentration of organic components in the SML
showed spatial distribution patterns resembling those of temperature and
wind speed (Figs. 3, 4, 5). Highest concentration values for nearly all
organic components were observed at the upwelling stations 10_1 to 10_4, 14_1 and 14_2 and
15_1 to 15_3 (Fig. 1) in accordance with
high estimated primary production rates (Steinfeldt et al.,
2015) and high Chl a concentrations (Hu et al., 2015)
determined in surface waters at these sites during M91.
Phytoneuston abundances (< 20 µm) varied between 3.7 × 103
and 1.9,× 105 mL-1 for cyanobacterial-type phytoplankton (CPL; mainly Synechococcus spp.) and between 5.4 × 103 and 3.0 × 105 mL-1 for other
non-cyanobacterial-type phytoplankton (NCPL). Generally, highest abundance
was determined on and close to the upwelling stations (Fig. 4). On all
other stations, cell abundance of CPL and NCPL differed spatially, with
higher abundance of NCPL at the southern stations and higher numbers of CPL
at the northern stations (Fig. 4). NCPL and CPL were closely related to
cell abundance in the ULW (Table 3).
Correlation coefficients (r) between concentrations of various
organic components in the SML and their concentration in the underlying
seawater (ULW), temperature (T, ∘C), and wind speed (U, m s-1) at the time of sampling. Correlations yielding a significance level
of p < 0.01 are marked bold. For abbreviations see text.
SML
rULW
rT
rU
n
DOC
0.75
-0.04
0.06
39
TOC
0.79
-0.53
-0.35
39
POC
0.68
-0.67
-0.48
39
TEP number
0.51
-0.58
-0.69
39
TEP area
0.58
-0.65
-0.69
39
DCCHO
0.94
-0.44
-0.29
39
PCCHO
0.77
-0.59
-0.38
34
TDN
0.24
-0.18
-0.05
39
PN
0.59
-0.55
-0.43
39
CSP number
0.53
-0.04
0.15
39
CSP area
0.68
-0.36
-0.31
39
FAA
0.34
-0.34
0.19
37
DHAA
0.30
-0.47
-0.37
30
PHAA
0.56
-0.64
-0.53
29
NCPL
0.70*
-0.24
-0.21
35
CPL
0.90*
-0.21
-0.31
35
Het. bacteria
0.92
-0.33
-0.37
36
* Only 30
samples were analyzed for NCPL and CPL from the ULW.
Heterotrophic bacteria were determined in abundances between 3.0 × 104
and 8.5 × 106 mL-1 with the highest numbers observed at the upwelling
stations and southeast of the upwelling (Fig. 4). Heterotrophic bacteria
in the SML were highly positively correlated to abundances in the ULW
(r= 0.94; n= 36; p < 0.001) and negatively influenced by wind speed,
although less clearly (r=-0.37; n= 36; p= 0.01). No significant influence on
heterotrophic bacteria abundance was detected with respect to global
radiation or UV radiation.
Phyto- and bacterioneuston (< 20 µm) abundance
(number mL-1) in the SML off the coast of Peru during M93: NCPL:
“Non-cyanobacterial-type” phytoplankton; CPL: “cyanobacterial-type”
phytoplankton; HPL: heterotrophic bacterioplankton.
Surface distribution patterns of organic matter concentrations in
the SML during M91 showing particulate organic carbon (POC, µmol L-1),
dissolved organic carbon (DOC, µmol L-1) dissolved
combined carbohydrates (DCCHO, nmol L-1), dissolved hydrolysable
amino acids (DHAA, nmol L-1) and abundance of TEP (L-1) and CSP
(L-1).
TOC concentration ranged between 82 and 199 µmol L-1, and was
clearly higher than DOC concentration on all stations. Particulate Organic
Carbon (POC) concentration was calculated as the difference between TOC and
DOC and ranged from 2.3 to 96 µmol L-1. Highest POC
concentration was observed at the upwelling stations (Fig. 5). In general,
POC concentration was highly correlated to temperature (r=-0.67, n= 39 p < 0.001) and
to wind speed (r=-0.48, n= 39 p < 0.001; Table 3). DOC
concentration ranged between 71 and 122 µmol L-1 (Table 2) and,
in contrast to POC, was not significantly related to temperature or wind
speed (Table 3). Relatively high DOC concentrations of about 100 µmol L-1 were observed at stations 9 and 9_2 (Fig. 5), but
excluding these stations from analysis did not reveal a correlation to
temperature or wind speed either. DOC is a bulk measure and is
quantitatively dominated by refractory compounds that are independent from
recent biological productivity. More closely linked to productivity and
likely stimulated by the upwelling of nutrients along the Peruvian coast are
labile and semi-labile compounds such as dissolved combined carbohydrates
and amino acids. Indeed, both DCCHO and DHAA reached highest concentrations
at the upwelling stations (Fig. 5). Thereby, maximum concentration of
DCCHO of 2670 nmol L-1 (mean: 1110 ± 550 nmol L-1) was
observed at station 15_2, slightly south of the station
14_1 exhibiting highest DHAA concentrations of 2020 nmol L-1 (mean: 770 ± 360 nmol L-1; Table 2). In general high
DCCHO concentration was more focused to the upwelling, and exhibited strong
horizontal gradients to the northern and southern stations.
DHAA concentration was on average lower than DCCHO concentration (Table 2)
and horizontal differences were less pronounced than for DCCHO. Both
components of semi-labile DOC were inversely correlated to temperature
(DCCHO r=-0.44, n= 39, p < 0.001; DHAA: r=-0.47, n= 30, p < 0.001),
linking their accumulation in the SML to productivity in the cold
upwelling waters.
Concentrations of carbohydrates and amino acid in particles, and in gels
(i.e., TEP, CSP) in particular, were highest at the coastal upwelling
stations also. Particulate carbohydrates and amino acids (PCCHO, PHAA) were
highly correlated to POC concentrations (PCCHO: r= 0.70, n= 39, p < 0.001; PHAA: r= 0.81, n= 30, p < 0.001).
In general, numerical abundance as well as total area were about 10-fold
higher for CSP than for TEP (Table 2). Spatial variability of gel particles
abundance was high, and yielded lowest values of total TEP area of 6.9 mm2 L-1 at station 13_1 and highest
values of 408 mm2 L-1 at station 15_1, about 100 nautical miles
apart. The highest abundance of both TEP and CSP was observed close to the
coastal upwelling, but apart from these stations, the distribution of TEP in
the SML clearly differed from that of CSP (Fig. 5). While higher TEP
abundance was observed at the northern stations, CSP abundance was more
pronounced at the southern stations. Moreover, stations of highest and
lowest concentration of CSP were different from those of TEP. Lowest value
of CSP total area of 137 mm2 L-1 was observed at station
11_1 and highest values of 3051 mm2 L-1 at station
14_1.
Box and whisker plot of enrichment factors (EFs) calculated for
various particulate and dissolved components during M91. Each box encloses
50 % of the data with the median value of the variable displayed as a
line. The bottom of the box marks the 25 %, and the top the 75 % limit,
of data. The lines extending from the top and bottom of each box marks the
10 and 90 % percentiles within the data set and the filled circles
indicate the data outside of this range. For abbreviations, see text.
Accumulation patterns in the SML
For almost all components investigated during this study, concentration in
the SML was significantly related to the respective concentration in the ULW
(Table 3). Thereby, correlations between SML and ULW were strongest for
combined carbohydrates, particularly for DCCHO. Close correlations were also
observed for bulk organic carbon measurements, i.e., TOC and DOC, and POC is a combination thereof. For dissolved nitrogenous compounds (i.e., TDN, FAA and
DHAA), no relationship between SML and ULW concentrations was observed, suggesting
that loss or gain of these compounds in the SML were faster than exchange
processes with the ULW. Temperature had an effect on most organic compounds
in the SML, with generally higher concentrations at lower temperature (Table 3). This can largely be attributed to the higher production of organic
matter at the colder upwelling sites. Concentrations of particulate
components POC, TEP, PHCCHO, PHAA and particulate nitrogen (PN) were also
inversely related to wind speed, whereas DCCHO and DHAA were inversely
related to temperature but not to wind speed. Clear differences were
observed for the two different gel particle types determined in this study.
In contrast to TEP, neither abundance nor total area of CSP were related to
wind speed, nor to seawater temperature. Instead abundance of CSP in the SML
was mostly related to their abundance in ULW. However, with the exception of
CSP, particulate components in the SML were affected by changes in wind
speed more than concentration of dissolved compounds (Table 3).
Enrichment factors indicated a general accumulation of organic matter in the
SML with respect to the ULW (Fig. 6), which happened at most stations.
Thereby, clear differences were observed between EF values of different
components. The highest enrichment was observed for FAA that were enriched
more than 10-fold at some stations. Moreover, FAA were consistently enriched
in the SML, except for one station where the lowest FAA concentration was
determined (49 nmol L-1). The largest variability of EF was observed
for abundance and total area of gel particles. For TEP total area, values of
EF ranged between 0.2–12, with highest EF observed at the coastal upwelling
station 14_1, where the wind speed recorded was 0.6 m s-1. In proximity of this station, the lowest EF of TEP was determined
(station 15_3) indicating a clear depletion at wind speed of
7 m s-1. The EFs of CSP total area ranged between 0.4 and 4.8. Thus
highest EF of CSP was clearly lower than for TEP, and in contrast to TEP it
was observed at the more offshore station 18_2 at a higher
wind speed rate of 9.2 m s-1. Total and dissolved hydrolysable amino
acids (THAA, DHAA) were enriched in the SML at almost all stations (Fig. 6), with EFs in the range 0.8–4.6 (DHAA) and 0.4–3.4 (THAA). Median EFs
were 1.7 and 1.4 for DHAA and THAA, respectively.
Influence of wind speed (m s-1) on the total area
concentration of TEP (mm2 L-1) in the SML at all
stations (a) and relationship between TEP enrichment factors (EFs) and wind
speed (m s-1) for only those stations that showed similar sea surface
temperature as indicated in Fig. 3. Filled dots indicated data from
stations of similar sea surface temperature. Data in plot (b) were fitted by
a power law function.
Concentration of TCCHO and DCCHO in the SML were often similar to the ULW,
with EF values ranging between 0.6 and 1.4 (DCCHO) and between 0.3 and 1.7
(TCCHO), respectively.
In general, variability of EFs was smaller for dissolved than for particulate
organic compounds, suggesting differences in the accumulation dynamics.
In contrast to all organic chemical compounds, bacteria were found to be
depleted in the SML at almost all stations (Fig. 6), having a median EF of
0.8
Size distribution of gel particles within the SML
Abundance of gel particles in the SML and ULW decreased with increasing
particle size according to the power law function given in Eq. 2 (Fig. 8).
The parameter δ describes the slope of the particles size spectrum.
Lower values of δ indicate relatively higher abundance of smaller
particles. Data fits to the function were very well described for each
sample with r2 > 0.90, yielding a standard error
for δ of < 20 %. For TEP, δ varied between -2.63
and -1.38 (mean value: -1.86, SD: 0.27) for particles in the SML and between
-2.25 and -1.25 (mean value: -1.70, SD: 0.30) for particles in the ULW. To
compare the size distribution of TEP in the SML and the ULW, we calculated
the slope ratio (δ∗=δSML/δULW; Fig. 9). Size distributions of TEP in the SML and ULW were generally quite
similar yielding δTEP∗ in the range of 0.78–1.42, with
a median value of 1.1. Nevertheless, spatial differences were observed, with
δTEP∗ < 0.95 at the more coastal northern
stations and δTEP∗ > 1.1 more offshore at
the southern stations (Fig. 9). At the upwelling stations with high TEP
abundance slopes of SML and ULW were very similar, yielding δTEP∗ in the range 0.95–1.1. This showed a relatively higher
abundance of smaller TEP in the SML at the offshore stations, whereas
relatively more, larger-sized TEP were present close to the coast in the
northern part of the study region. This comparison also showed that sampling
of TEP from the SML with a glass plate does not bias TEP size distribution, e.g., by inducing particle aggregation during sampling. Such a bias would be
expected, especially at stations where TEP were highly abundant, like at the
upwelling stations. However, particularly at those stations, no differences
in size distributions of TEP in the SML and ULW were observed. Fractal
scaling exponents of TEP were estimated from Eq. 3 and yielded D= 2.51 for
both SML and ULW samples (DSML= 2.51 ± 0.015;
DULW= 2.51 ± 0.011). The very similar fractal dimension for TEP in
the SML and ULW suggests that TEP in the SML and in the bulk water are
formed by similar aggregation processes. The value of D= 2.51 estimated in
this study is close to 2.55 proposed by Mari and Burd (1998) for seawater
TEP.
Size–frequency distribution of TEP (a) and CSP (b) observed
during the M91 cruise for samples collected from the SML (open symbols) and
in the ULW (filled symbols) at the stations with lowest wind speed of 0.6 m s-1 (circles) and highest wind speed of 9.0 m s-1 (triangles).
Linear regression of log(dN/d(dp)) vs. log(dp) was fitted to the particles in the size
range of 1.05–14.14 µm ESD.
In the SML, the number of TEP in the smallest size class (1.25–1.77 µm) ranged from 96 to 1.38 × 104 mL-1, and included on average
61 ± 5.2 % of all TEP. For CSP, variability of abundance in the
1.25–1.77 µm size class was much smaller and ranged between
1.46 × 104 and 2.33 × 105 mL-1. Although CSP thus represented the
largest fraction of small gel particles, the relative abundance of CSP in
the smallest size fraction was lower, yielding an average contribution of
52 ± 6.0 % of all CSP. Similar to TEP, size distribution of CSP
followed the power law relationship of Eq. (2), yielding δ values
between -1.12 and -2.01 (mean value: -1.44, SD: 0.20) for particles in the
SML and between -1.11 and -1.88 (mean value: -1.39, SD: 0.17) for particles
in the ULW. With D= 2.50 ± 0.008, the fractal dimension of CSP was
almost identical to that of TEP, suggesting that similar processes, i.e.,
shear-induced aggregation, are responsible for CSP formation. The slope
ratio, δ∗, for CSP varied between 0.77 and 1.32, with a median
value of 1.0. No spatial pattern was observed for the distribution of
δ∗CSP. Slopes of the size distribution of CSP in the SML
and ULW were not significantly different (p= 0.176, n= 39, paired t test),
indicating that CSP size distribution, similarly to TEP, is not biased by
the sampling approach of the glass plate.
No overall relationship was established between the slope of the size
distribution of TEP and wind velocity (δTEP vs. wind speed:
r=-0.19, n= 37, p= 0.20). However, TEP size distribution was much steeper
at the station with highest wind speed compared to the one with lowest wind
velocity (δTEP at 0.6 m s-1=-1.51,
r2 =0.95, n= 7; δTEP at 9.0 m s-1=-2.31, r2 = 0.95, n= 7; Fig. 8a). In particular, at the
high wind speed a loss of larger TEP, i.e., > 7 µm was
observed in the SML compared to the ULW and relative to the low wind speed
station.
For CSP a significant inverse relationship was observed between the slope
δ and wind speed (δCSP vs. wind speed: r=-0.61,
n= 37, p < 0.001). A loss of larger CSP was also observed by direct
comparison between low and high wind speed stations (δCSP at
0.6 m s-1=-1.12, r2 =0.92, n= 7; δTEP
at 9.0 m s-1=-1.45, r2 =0.97, n= 7; Fig. 8b).
Spatial distribution of the slope ratio, δ*, for TEP in
the upwelling region off the coast of Peru during M91.
Discussion
It has been suggested that the presence of organic matter in the SML
influences a series of processes relevant to air–sea exchange of gases,
dissolved and particulate components. EBUSs are characterized by high
biological productivity and strong across shelf gradients of organic matter
concentration (Capone and Hutchins, 2013). Therefore EBUSs
are ideal model systems to study the linkages of biological productivity and
SML properties, with respect to characteristics of organic matter
composition and factors controlling organic matter enrichment in the SML.
Organic matter characteristics of the SML in the upwelling region
off the coast of Peru
Strong horizontal gradients in organic matter concentration of the SML were
observed for the coastal and shelf-break region off the coast of Peru with generally
higher organic matter concentrations in the SML towards the area of
upwelling of colder, nutrient-rich deep water. Hence, increasing ecosystem
productivity is one likely factor responsible for higher concentrations of
organic components in the SML. Significant correlations between organic
matter concentration in the SML and in the ULW were determined and showed
that the SML basically reflects the underlying seawater system. The close
connectivity between SML organic properties and biological development was
also shown during a recent mesocosm study, indicating that ecosystem changes
impact SML organic matter composition and concentration
(Galgani et al., 2014). Despite this finding that relates to a
more general characteristic of the SML, clear differences in the
accumulation behavior of different organic matter components were determined
during this study and are in good accordance with previous observations. A
generally higher SML accumulation was observed for amino acids compared to
carbohydrates. Significant enrichment of amino acids in the SML has been
determined previously for coastal as well as open ocean sites, and higher
accumulation of FAA compared to DHAA and THAA, as also observed during this
study, appears to be a consistent SML feature (Carlucci et al., 1992;
Henrichs and Williams, 1985; Kuznetsova and Lee, 2002, 2001; Kuznetsova et
al., 2004; Reinthaler et al., 2008). As for this study, wind velocity and
temperature have not been identified as physical factors responsible for
amino acid enrichment in the past (Kuznetsova et al., 2004). FAA and
DHAA are labile to semi-labile substrates and taken-up by heterotrophic
microorganisms (Keil and Kirchman, 1992). Turnover times of these
components in the water column are usually in the range of minutes to days
(Benner, 2002; Fuhrman and Ferguson, 1986). The observed
accumulation of FAA and DHAA in the SML may therefore be related to a
reduced activity of bacteria. For different coastal Baltic Sea sites, Stolle
et al. (2009) determined a lowered bacterial biomass production in
the SML, despite bacterial cell numbers being similar to those in the ULW.
During M91 bacteria were mostly depleted in the SML compared to the ULW
supporting the idea of the SML being an “extreme environment” for bacteria.
Earlier studies showed that some bacteria may be adapted to UV radiation in
the SML as well as in the ULW (Agogué et al., 2005; Carlucci et al.,
1985). Amino acid consumption by bacterioneuston under UV-B stress may be
reduced (Santos et al., 2012), which may give
an explanation for the higher concentrations of FAA and DHAA in the SML
during M91. However, no significant correlation between bacterial abundance
and UV radiation or between UV radiation and amino acid concentrations in
the different pools was observed during this study, suggesting that at most
stations history rather than instantaneous UV radiation, if at
all, responsible for controlling bacteria and organic matter components in the
SML.
SML thickness during this study was significantly related to TOC
concentration, but not to wind speed. A thickening of the SML with
increasing wind speed up to 8 m s-1 has been observed by Falkowska (1999) from samples collected in the Baltic Sea and explained by
increased advective transport of organic matter to the SML (e.g., through
bubble adsorption) at higher turbulence. During M91, accumulation of organic
matter in the SML was higher at the upwelling stations where wind speed
often was quite low. Hence, a higher source of organic matter in the ULW may
have counterbalanced the wind speed effect.
Wind speed, however, was determined as a factor controlling accumulation of
particulate material, in particular TEP, in the SML in addition to the
dynamics occurring in the ULW. TEP are marine gel particles hypothesized to
be neutrally or positively buoyant thanks to their high water content
(Azetsu-Scott and Passow, 2004; Engel and Schartau, 1999).
TEP were moreover suggested to form within the SML, either by wind-shear-induced aggregation of precursors or due to coalescence of pre-cursor
molecules, primarily polysaccharides, when entrained air bubbles burst at
the sear surface (Wurl et al., 2011). Adsorption of
DOM onto bubble surfaces and TEP formation by bubble bursting have been
determined during experimental flotation and bubbling studies using surface
seawater from different locations (Wallace and Duce,
1978; Zhou et al., 1998). Bubble scavenging of DOM in the upper water column
may thus be responsible for high concentrations of TEP at the SML, because
more TEP precursors are lifted up the water column (Gao et al., 2012;
Wurl et al., 2011). In addition, compression and dilatation of the SML due
to capillary waves may increase the rate of polymer collision, subsequently
facilitating gel aggregation (Carlson, 1993). During M91, TEP
enrichment in the SML was inversely related to wind speed, supporting
earlier observations of Wurl and colleagues (Wurl et al., 2009, 2011). However, in contrast to earlier observations showing EF values
> 1 for TEP in the SML also at higher wind speed, we found the
SML to be depleted of TEP at wind speed of ∼ 5 m s-1 and
above. It has been suggested that TEP aggregation rates in the SML are
higher than in the ULW, due to enhance collision rates by shear or bubble
bursting. TEP have been shown to control coagulation efficiencies of solid
particles, such as diatoms and coccolithophores (Chow et al., 2015;
Engel, 2000; Logan et al., 1995). At higher wind speed, increased
aggregation rates of TEP with solid particles, eventually containing mineral
ballast, may thus favor the formation of aggregates that become negatively
buoyant and sink out of the SML. This, may explain the observed loss of
larger TEP (> 7 µm) from the SML relative to the ULW and
to the SML at low wind speed. Enhanced aggregation rates could then also
explain the inverse relationship between POC and wind speed, observed during
this study.
In contrast to TEP, no impact of wind speed was determined for CSP
accumulation, or for CSP enrichment in the SML. Moreover, clear spatial
differences were observed for the distribution of TEP and CSP in the SML.
Although both TEP and CSP are gel particles that form from dissolved organic
precursors released by microorganisms, their spatial and temporal occurrence
in marine systems can be quite different (e.g., TEP accumulate towards the
end of phytoplankton blooms, while CSP rather co-occur with maximum
phytoplankton abundance (Cisternas-Novoa et al., 2015; Engel et al.,
2015). Moreover, the depth distribution of TEP and CSP was shown to be
different for open ocean sites (Cisternas-Novoa et al.,
2015). These spatial and temporal differences in the occurrence of TEP and
CSP in the water column may explain the spatial separation of both types of
marine gels in the SML observed during this study. However, the observed
differences in relation to wind speed suggest that additional factors
control the enrichment of TEP and CSP in the SML. It has been shown that CSP
are less prone to aggregation than TEP (Engel et al., 2015; Prieto et
al., 2002). Similarly, CSP may be less involved in aggregation formation and
sinking out of the SML at higher wind speed. Yet, similarly to TEP, larger
CSP were observed in the SML at low wind speed suggesting that both kind of
gels may be involved in slick formation that becomes disrupted when wind
speed increases.
Implications of organic matter accumulation in EBUSs
Air–sea gas exchange
Although the SML and surface active substances (surfactants) within are
widely believed affecting the exchange of gases and heat at the air–sea
interface (Davies, 1966; Frew, 1997; Salter et al., 2011), particularly
at lower wind speed (Liss, 1983), we still have little quantitative
knowledge on how natural organic components at the immediate sea-surface
alter the gas transfer velocity in water (kw). Our data showed a depletion of
the SML with respect to TEP and POC at wind speeds > 5 m s-1, suggesting that an effect of these “insoluble” components on gas
exchange is, if any, operating only at low wind speed. Due to their fractal
scaling, gel particles have a relatively large surface to volume ratio and
may act as a cover, reducing molecular diffusion rates at the interface
between air and sea.
Accumulation of dissolved organic components in the SML during M91 was not
related to wind speed. DCCHO and DHAA concentration representing fresh DOM
were highest at the upwelling sites and therefore negatively related to
seawater temperature. DOM, such as DCCHO and chromophoric dissolved organic
matter (CDOM), have demonstrated surfactant properties and reduced gas
transfer velocity in water (kw) at low wind speed in laboratory and field
experiments (Frew et al., 2004, 1990). The reduction of kw is
thereby believed to be related to a dampening of small, capillary waves.
Salter et al. (2011) recently showed that artificial
surfactants can suppress gas transfer velocity by up to 55 % at sea.
Suppression of k666 (i.e., kw normalized to a Schmidt number of 666) during
their field study was dependent on wind speed, but was detected up to 11 m s-1, encompassing the full range of wind speed determined during M91.
Thus, accumulation of natural DOM particularly in upwelling regimes with
high biological production and coastal wind shelter as observed during this
study may have an influence on gas exchanges rates as well.
Across the SML, the diffusivity of climate-relevant gases such as methane
(CH4), has been proposed being mediated by SML bacteria, as possible
sink (Upstill-Goddard et al., 2003) or source of this compound
(Cunliffe et al., 2013). About ∼ 30 % of
the atmospheric concentration of nitrous oxide (N2O), one of the
strongest greenhouse gases and responsible for ozone depletion, is supported
by oceanic sources (Solomon et al., 2007). Of total oceanic
N2O production, oxygen minimum zones (OMZs) contribute about 25–75 %
(Bange et al., 2001). In EBUSs, high primary production and
induced high aerobic remineralization associated with large-scale
circulation maintain the presence of OMZs (Gutknecht et al., 2013;
Paulmier and Ruiz-Pino, 2009), which, in the last decades, have been
expanding and intensifying due to enhanced stratification and reduced
ventilation (Keeling et al., 2010; Stramma et al.,
2008). During M91, N2O concentration in surface waters was highly
supersaturated at the upwelling sites and in particular at station
14_1 (Arevalo-Martinez et al., 2015).
Although a direct influence of organic matter in the SML on gas-exchange was
not investigated during M91, it can be assumed that the high enrichment of
organic components in the SML observed the upwelling sites was one factor
contributing to N2O supersaturation.
Our study was intended to understand how organic matter accumulates in the
SML, which might mediate the transfer rate of trace- and greenhouse gases
such as N2O in oceanic regions like OMZs affected by a changing
climate. A recent laboratory study reported π non-covalent interactions
of N2O with phenols, suggesting a possible important role of N2O
in biological processes by specifically binding to phenolic groups as those
of the amino acids tyrosine and phenylalanine (Cao et
al., 2014). Tyrosine and phenylalanine in the SML of our study represented a
small molar percentage of total amino acids pool (data not shown), but were
present. As we found evidence of overall accumulation of amino acids in the SML during
our cruise, for those amino acids in particular the median EF both in the
total (THAA) and in the dissolved (DHAA) fraction was > 1,
suggesting a possible interaction of specific SML organics with N2O in
the coastal upwelling region off the coast of Peru. Although the experiment conducted by
Cao and colleagues cannot be directly translated to our setting, it provides
interesting ideas for the interaction of N2O with biological
macromolecules worth further investigation.
Overall, our results showed that accumulation of organic substances occurs
in EBUSs and is related to the increased biological production. Hence, the
organic SML may play a particularly important role for exchange of climate-relevant gases that are associated to high organic matter production and
resulting anoxia in upwelling systems like the one off the coast of Peru.
Organic aerosol production
The structure of sea-spray aerosols (SSA), originating by bubble bursting at
the sea surface, is a function of biological, chemical and physical
properties of the SML, which may comprise a vast array of organic
surface-active compounds, microorganisms and exopolymer gels (Leck and
Bigg, 2005; Quinn and Bates, 2011; Wilson et al., 2015). Despite recent
evidences showing that high levels of chlorophyll a are not directly related
to the organic carbon content of SSA (Quinn et al.,
2014), still organic SSA largely derive from the oceanic surface layer and
therefore are also subject to the effects of climate change on marine
systems (Andreae and Crutzen, 1997). Polysaccharides and
polysaccharidic nanogels (Orellana et al., 2011; Russell et al., 2010) as
well as particulate amino acids and proteinaceous compounds
(Kuznetsova et al., 2005) are present in organic SSA particles.
During M91, we found a different accumulation behavior of TEP and CSP in the
SML. TEP showed a close inverse relationship to wind speed, being depleted
in the SML above 5 m s-1, while particulate proteinaceous compounds
(CSP) accumulated independently of wind speed. Submicron gels embedded in
sea spray may represent an important source for primary organic aerosols in
the more offshore wind exposed regions. TEP as well as dissolved
polysaccharides include sugars with carboxylic groups such as uronic acids
and may contribute to the relatively high fraction of carboxylic acid that
was observed in the organic matter component of marine aerosols
(Hawkins et al., 2010). In the upwelling region off the coast of Peru
the wind-driven export of polysaccharidic components to the atmosphere thus
might represent a loss-pathway of these organic compounds from the SML that
would then contribute to a larger extent to the organic SSA mass.
Proteinaceous compounds, including CSP, are probably more stable at the sea
surface and may contribute to organic mass in aerosols even at higher wind
speed.
However, future studies that investigate gel particles within the SML and in
SSA are needed to clarify if the observed loss of TEP from the SML at higher
wind speeds is indeed related to a transport of TEP to the atmosphere, or if
CSP contribute to organic aerosol mass.
The accumulation of organic matter in the SML, and the distinct behavior of
certain compounds at the water–air interface is certainly an important issue
for all exchange processes between the ocean and the atmosphere that needs
to be further exploited.