The eastern boundary region of the southeastern Pacific Ocean hosts one of
the world's most dynamic and productive upwelling systems with an associated
oxygen minimum zone (OMZ). The variability in downward export fluxes in this
region, with strongly varying surface productivity, upwelling intensities
and water column oxygen content, is however poorly understood. Thorium-234
(234Th) is a powerful tracer to study the dynamics of export fluxes of
carbon and other elements, yet intense advection and diffusion in nearshore
environments impact the assessment of depth-integrated 234Th fluxes
when not properly evaluated. Here we use vessel-mounted acoustic Doppler current
profiler (VmADCP) current velocities,
satellite wind speed and in situ microstructure measurements to determine the
magnitude of advective and diffusive fluxes over the entire 234Th flux
budget at 25 stations from 11 to 16∘ S in the
Peruvian OMZ. Contrary to findings along the GEOTRACES P16 eastern section,
our results showed that weak surface wind speed during our cruises induced
low upwelling rates and minimal upwelled 234Th fluxes, whereas vertical
diffusive 234Th fluxes were important only at a few shallow shelf
stations. Horizontal advective and diffusive 234Th fluxes were
negligible because of small alongshore 234Th gradients. Our data
indicated a poor correlation between seawater 238U activity and
salinity. Assuming a linear relationship between the two would lead to
significant underestimations of the total 234Th flux by up to 40 % in
our study. Proper evaluation of both physical transport and variability in
238U activity is thus crucial in coastal 234Th flux studies.
Finally, we showed large temporal variations on 234Th residence times
across the Peruvian upwelling zone and cautioned future carbon export
studies to take these temporal variabilities into consideration while
evaluating carbon export efficiency.
Introduction
Isotopes of thorium (Th) are widely used as tracers for particle cycling in
the oceans (Waples et al., 2006). In particular, 234Th has
been extensively used to trace particle dynamics and export fluxes in the
upper ocean and to quantify the marine budgets of important macro- and
micronutrients such as carbon (C), nitrogen (N), phosphorus (P) and iron
(Fe) (e.g., Bhat et al., 1968; Buesseler et al., 1992; Coale and Bruland,
1987; Lee et al., 1998; Le Moigne et al., 2013; Cochran and Masqué,
2003; Van Der Loeff et al., 2006; Black et al., 2019). 234Th has a
relatively short half-life (τ1/2=24.1 d) that allows
studies of biological and physical processes occurring on timescales of days
to weeks. Unlike its radioactive parent uranium-238 (238U, τ1/2=4.47 Ga) that is soluble in seawater, 234Th is highly
particle reactive with a particle–water partition coefficient of 103 to
108 (Santschi et al., 2006, and references therein) and is
thus strongly scavenged by particles (Bhat et al., 1968). Generally,
a deficit of 234Th relative to 238U is observed in the surface
ocean and reflects net removal of 234Th due to particle sinking,
whereas secular equilibrium between 234Th and 238U is observed for
intermediate and deep waters. Integrating this surface 234Th deficit
with depth yields the sinking flux of 234Th and, if
elemental : 234Th ratios are known, the sinking flux of elements such as
C, N, P, Si and trace metals (e.g., Bhat et al., 1968; Buesseler et al.,
1998, 1992, 2006; Coale and Bruland, 1987; Weinstein and Moran,
2005; Owens et al., 2015; Black et al., 2019;
Puigcorbé et al., 2020).
Various 234Th models have been put forward to study
adsorption–desorption, aggregation and export, but single box models that
assume negligible 234Th fluxes due to physical transport are commonly
used to calculate oceanic 234Th-derived particle fluxes (see
detailed review by Savoye et al., 2006). This assumption is typically
appropriate in open-ocean settings where 234Th fluxes due to advection
and diffusion are small relative to the downward fluxes of 234Th
associated with particle sinking. However, in upwelling regions such as the
equatorial Pacific and coastal systems, advective and diffusive 234Th
fluxes may become increasingly important (e.g., Bacon et al., 1996;
Buesseler et al., 1998, 1995; Dunne and Murray, 1999). For
example, in the equatorial Pacific, strong upwelling post El Niño could
account for ∼50 % of the total 234Th fluxes (Bacon
et al., 1996; Buesseler et al., 1995). Ignoring the upwelling term could
thus lead to an underestimation of 234Th fluxes by a factor of 2.
Conversely, horizontal diffusion carrying recently upwelled,
234Th-replete waters has been shown to balance the upwelled 234Th
fluxes in the central equatorial Pacific (Dunne and Murray, 1999). To
the contrary, advective and diffusive 234Th fluxes were minimal off the
Crozet Islands in the Southern Ocean due to limited horizontal 234Th
gradients, long residence time of water masses, and low upwelling rates and
diffusivities (Morris et al., 2007).
The dynamic nature of coastal processes requires that physical terms
be included in 234Th flux calculation whenever possible. Accurate
measurements of current velocities and diffusivities are however challenging,
and thus direct observations of the effects of physical processes on
234Th distributions in coastal regions are scarce. Limited studies have
incorporated advection and diffusion in the nearshore zones of the Arabian
Sea (Buesseler et al., 1998), Gulf of Maine (Gustafsson et al.,
1998; Benitez-Nelson et al., 2000), South China Sea (Cai et al.,
2008) and Peruvian oxygen minimum zone (OMZ) (Black et al., 2018).
In the Arabian Sea, coastal upwelling during the southwest monsoon season
could account for over 50 % of the total 234Th flux
(Buesseler et al., 1998). Horizontal advection has been shown to be
substantial in the inner Casco Bay of the Gulf of Maine
(Gustafsson et al., 1998), whereas offshore advection and
diffusion are only important in late summer (Benitez-Nelson et al.,
2000). Therefore, the importance of physical processes on the 234Th
flux estimate is highly dependent on the seasonal and spatial variability of
the current velocities, diffusivities and 234Th gradients. In terms of
the Peruvian OMZ, Black et al. (2018) showed that coastal
upwelling accounts for >50 % of total 234Th fluxes at
12∘ S; however, how upwelling 234Th fluxes vary seasonally
and spatially in this region is unclear.
Another uncertainty in 234Th flux calculations in such regions stems from variations on dissolved 238U activities. Generally speaking, U
behaves conservatively under open-ocean oxic conditions and is linearly
correlated with salinity (Chen et al., 1986; Ku et al., 1977; Owens et
al., 2011). However, numerous studies have shown that such a correlation
breaks down in various marine environments including the tropical Atlantic
(Owens et al., 2011), Mediterranean Sea (Schmidt and Reyss,
1991) and Arabian Sea (Rengarajan et al., 2003). Although it is
generally accepted that deviations from the linear 238U–salinity correlation
will lead to differences in the final calculated 234Th fluxes, there is
currently little knowledge on how significant these differences could be.
In this study, we report vertical profiles of 234Th and 238U along
four transects perpendicular to the coastline of Peru (i.e., shelf–offshore
transects). We evaluate the 238U–salinity correlation in low-oxygen waters and
how deviations from this correlation impact final 234Th flux estimates.
We also assess the spatial and temporal importance of advection and
diffusion on 234Th flux estimates.
Sampling and methodsSeawater sampling and analysis
Seawater samples were collected at 25 stations along four shelf–offshore
transects between 11 and 16∘ S in the Peruvian OMZ
during two cruises, M136 and M138, on board the R/V
Meteor (Fig. 1). Cruise
M136 took place in austral autumn (11 April to 3 May 2017) along two main
transects at 12 and 14∘ S (Dengler and
Sommer, 2017). Two stations from M136 (stations 458 and 495) were reoccupied
within a week (repeat stations 508 and 516, respectively) to evaluate the
steady-state assumption in the 234Th flux calculation. The surface
sample of the repeat station 508 (reoccupied 4.5 d after station 458) was
missing so only results from repeat stations 495 and 516 (occupation
interval 1.5 d) were compared and discussed in terms of the non-steady-state model (Sect. 3.3). 234Th sampling during cruise M138 was
carried out in austral winter (1 June to 4 July 2017) and focused on four
shelf–offshore transects at 11, 12, 14 and 16∘ S.
Maps showing (a) locations of each station from M136 (white
squares) and M138 (grey circles) and (b) monthly-averaged current field in
the top 15 m from 16 April to 15 May 2017 derived from altimetry
measurements (http://marine.copernicus.eu/, last access: 8 June 2020; product ID:
MULTIOBS_GLO-PHY_REP_015_004). Color boxes in (a) schematically divide the four
shelf–offshore transects. Map (a) was created with Ocean Data View
(Schlitzer, 2014). The white box in (b) highlights our study area.
At each station, a stainless-steel rosette with Niskin bottles (Ocean Test
Equipment®) was deployed for sampling of total
234Th in unfiltered seawater and dissolved 238U (0.2 µm pore
size, AcroPak® polycarbonate membrane). High-vertical-resolution sampling was performed in the upper 200 m where most of the
biological activity occurs; additional depths were sampled down to 600 m, or
50 m above the seafloor. Deep seawater at 1000, 1500, and 2000 m was
sampled at three stations to determine the absolute β counting
efficiency. Salinity, temperature, oxygen concentrations and fluorescence
data (Table S1 in the Supplement) were derived from the sensors (Seabird
Electronics® 9plus system) mounted on the CTD
frame (Krahmann, 2018; Lüdke et al., 2020).
Sample collection and subsequent chemical processing and analysis for total
234Th followed protocols by Pike et al. (2005) and the SCOR working
group RiO5 cookbook (https://cmer.whoi.edu/, last access: 27 March 2019). Briefly, a 230Th yield
tracer (1 dpm) was added to each sample (4 L) before Th was extracted with
MnO2 precipitates. Precipitates were filtered onto 25 mm quartz
microfiber filters (Whatman® QMA, 2.2 µm
nominal pore size) and dried overnight at 50 ∘C, after which they
were counted at sea on a Risø® low-level beta
GM multicounter until uncertainty was below 3 % and again 6 months later
at the home laboratory for background 234Th activities. After the second
beta counting, filters were digested in an 8 M HNO3/ 10 %
H2O2 solution (Carl Roth®, trace metal
grade). A total of 10 dpm of 229Th was added to each sample at the beginning of
digestion to achieve a 1 : 1 atom ratio between 229Th and 230Th.
Digested samples were diluted in a 2.5 % HNO3/ 0.01 % HF mixture, and
229Th/230Th ratios were measured using an inductively coupled plasma mass spectrometer (ICP-MS)
(ThermoFisher® Element XR) to determine the
chemistry yield and final 234Th activities. The average yield was
calculated to be 97 % ± 6 % (n=247). For a subset of samples
(marked in Table S1) whose analysis failed during initial ICP-MS
measurement, anion chromatography (Bio-Rad® AG1x8, 100–200 mesh, Poly-Prep columns) was performed to remove Mn from the sample matrix
before another ICP-MS analysis. This subset of samples also included three
samples (marked in Table S1) whose initial ICP-MS measurement was
successful to test whether anion chromatography affects final ICP-MS
results. Identical 229Th/230Th ratios were measured for samples
with and without column chromatography (see Table S1 footnotes for details).
Each 238U sample was acidified to pH∼1.6 at sea and
transported home for analysis. Samples of dissolved 238U were diluted
20 times in 1 N HNO3 at the home laboratory and spiked with an appropriate
amount of 236U spike to achieve 236U:238U∼ 1 : 1. Ratios of 236U:238U were analyzed by ICP-MS (ThermoFisher
Element XR), and activities of 238U were calculated using isotope
dilution. Seawater certified reference materials (CRMs), CASS-6 and NASS-7,
and the International Association for the Physical Sciences of the Oceans
(IAPSO) standard seawater were analyzed routinely for uranium
concentrations.
Flux calculation
Assuming a one-box model, the temporal change of 234Th activities is
balanced by production from 238U, radioactive decay of 234Th,
removal of 234Th onto sinking particles, and transport into or out of
the box by advection and diffusion (Bhat et al., 1968; Savoye et al.,
2006; and references therein):
∂ATh∂t=λ(AU-ATh)-P+V,
where AU and ATh are respectively the activities of dissolved
238U and total 234Th, λ is the decay constant of
234Th, P is the net removal flux of 234Th, and V is the sum of
advective and diffusive fluxes. It is recommended that the time interval
between station occupations should be >2 weeks in order to
adequately capture the temporal variability of the mean spatial gradients
rather than small local changes (Resplandy et al.,
2012). The solution of Eq. (1) (Savoye et al., 2006) is
P=λAU1-e-λΔt+ATh1⋅e-λΔt-ATh21-e-λΔt,
where Δt is the time interval between repeat occupations of a
station, and ATh1 and ATh2 are respectively total 234Th
activities during the first and second occupation. At times when repeat
sampling is not possible within an adequate cruise timeframe, steady-state
conditions are generally assumed, i.e., ∂ATh∂t=0. In this case, Eq. (1) is simplified into
P=∫0zλ(AU-ATh)dz+V.
The vertical flux of 234Th, P (dpm m-2 d-1), is integrated to
the depth of interest. Earlier studies generally used arbitrarily fixed
depths (e.g., the base of mixed layer or ML, and 100 m) for 234Th and
particulate organic carbon (POC) flux estimates (e.g., Bacon et al., 1996; Buesseler et al., 1992).
Recent studies emphasized the need to normalize POC flux to the depth of
the euphotic zone (EZ), which separates the particle production layer in the
surface from the flux attenuation layer below (Black et al., 2018;
Buesseler and Boyd, 2009; Rosengard et al., 2015). In the open ocean, the
depth of EZ is generally similar to ML depth. The PAR (photosynthetically
active radiation) sensor was not available during both of our cruises, so
it was not possible to identify the base of the EZ. For the purpose of
this study, the slight difference of the exact depth chosen (ML vs. EZ) was
of little relevance to the significance of physical processes and 238U
variability. Due to sampling logistics, however, we did not sample at the
base of the ML but 5–20 m below the ML. This depth corresponded closely to
the EZ depth used in Black et al. (2018) in the same study area
during austral spring 2013. For the purpose of comparison with earlier
studies which reported 234Th fluxes at 100 m, we also calculated
234Th fluxes at 100 m in this study.
Quantification of the physical fluxes
The physical term V in Eq. (2) is expressed as follows:
V=∫0zw∂Th∂z-u∂Th∂x-v∂Th∂ydz+∫0zKx∂2Th∂x2+Ky∂2Th∂y2-Kz∂2Th∂z2dz,
where w is the vertical (i.e., upwelling) velocity (m s-1); u and v are respectively the zonal and meridional current velocities (m s-1); and
Kx, Ky, and Kz represent eddy diffusivities (m2 s-1) in zonal, meridional and vertical directions, respectively.
∂Th∂z, ∂Th∂x and
∂Th∂y are vertical and horizontal 234Th
gradients (dpm L-1 m-1); and ∂2Th∂x2, ∂2Th∂y2 and ∂2Th∂z2 are respectively the second derivative of
234Th (dpm L-1 m-2) on the zonal, meridional and vertical
directions.
Estimation of upwelling velocities
In the Mauritanian and Peruvian coastal upwelling regions, there is strong
evidence that upwelling velocities in the mixed layer derived from satellite
scatterometer winds and Ekman divergence (Gill, 1982) agree well with those
from helium isotope disequilibrium (Steinfeldt et al.,
2015). The parameterization by Gill (1982) considers the baroclinic response
of winds blowing parallel to a coastline in a two-layer ocean. Vertical
velocity (w) at the interface yields
w=τρfae-x/a,
where τ is the wind stress (kg m-1 s-2) parallel to the
coast line, ρ the water density (1023 kg m-3), f the Coriolis
parameter (s-1) as a function of latitude, a the first baroclinic Rossby
radius (km) and X the distance (km) to the coast.
Upwelling velocities were calculated at stations within 60 nautical miles of the coast, where upwelling is the most significant
(Steinfeldt et al., 2015). We used a=15 km for all
stations based on the results reported by Steinfeldt et al. (2015) for the same study area. The magnitude of monthly wind stress was
estimated from the monthly wind velocities (Smith, 1988):
τ=ρairCDU2,
where ρair is the air density above the sea surface (1.225 kg m-3), CD the drag coefficient (10-3 for wind speed <6 m s-1) and U the wind speed.
Monthly wind speed (m s-1) fields from the MetOp-A ASCAT scatterometer
sensor with a spatial resolution of 0.25∘ (Bentamy and
Croize-Fillon, 2010) were retrieved from the Centre de Recherche et
d'Exploitation Satellitaire (CERSAT), at IFREMER, Plouzané (France)
(data version numbers L3-MWF-GLO-20170903175636-01.0 and
L3-MWF-GLO-20170903194638-01.0). We assumed a linear decrease of w from the base of the mixed layer toward both the ocean surface and 240 m depth (bottom
depth of our shallowest station). Upwelling rates at any depth between 0 and
240 m at individual stations could thus be determined once w was estimated.
Following Rapp et al. (2019), an error of 50 % was
assigned to estimated upwelling velocities to account for uncertainties
associated with the spatial structure and temporal variability of the wind
field, as well as the satellite wind product near the coast.
Estimation of upper-ocean velocities
During both cruises a phased-array vessel-mounted acoustic Doppler current
profiler (VmADCP; 75 kHz Ocean Surveyor, Teledyne RD Instruments)
continuously measured zonal and meridional velocities in the upper 700 m of
the water column (Lüdke et al., 2020). Postprocessing of the velocity data included water track calibration
and bottom editing. After calibration, the remaining uncertainty of hourly
averages of horizontal velocities is smaller than 3 cm s-1
(e.g., Fischer et al., 2003). For the horizontal advective flux
calculation (Eq. 3), velocities collected within a 10 km radian at inshore
stations (stations 353, 428, 458, 475, 508, 904 and 907) and within a 50 km
radian at offshore stations (Lüdke et al., 2020) were averaged. Data collected at the same positions within 5 d due to station repeats were also included in the velocity average. As
representative for the near-surface flow, we extracted the velocity data
from the top 30 m for M136 stations and top 50 m for M138 stations (defined
as the “top layer” hereafter); these depths correspond to 5–20 m below
the base of the ML during each cruise.
Estimation of vertical and horizontal eddy diffusivities
While the strength of ocean turbulence determines the magnitude of diapycnal
or vertical eddy diffusivities, the intensity of meso- and submesoscale
eddies determines the magnitude of lateral eddy diffusivities. During the R/V
Meteor cruise (M136) and the follow-up cruise (M137) in the same region, the
strength of upper-ocean turbulence was measured using shear probes mounted
to a microstructure profiler. The loosely tethered profiler was optimized to
sink at a rate of 0.55 m s-1 and equipped with three shear sensors; a
fast-response temperature sensor; an acceleration sensor; two tilt sensors;
and conductivity, temperature, depth sensors sampling with a lower response
time. On transit between each CTD station 3 to 9 microstructure profiles
were collected. Standard processing procedures were used to determine the
dissipation rate of turbulent kinetic energy (ε) in the water column
(see Schafstall et al., 2010, for a detailed description). Subsequently,
turbulent vertical diffusivities Kz were determined from Kz=ΓεN-2 (Osborn, 1980), where N is
stratification and Γ is the mixing efficiency for which a
value of 0.2 was used following Gregg et al. (2018). Stratification
(buoyancy frequency) was calculated using CTD data retrieved from
microstructure profilers and following the gsw_Nsquared
function from the Gibbs Sea Water library (McDougall et al.,
2009; Roquet et al., 2015). A running mean of 10 dbar was applied to avoid
including unstable events due to turbulent overturns. The 95 % confidence
intervals for averaged Kz values were determined from Gaussian error
propagation following Schafstall et al. (2010).
Altogether, 189 microstructure profiles were collected during M136 (Thomsen
and Lüdke, 2018) and 258 profiles during the follow-up cruise M137
(unpublished data; 6–29 May 2017). An average turbulent vertical
diffusivity profile was calculated from all inshore (<500 m
water depth) profiles and another one from all offshore (>500 m water depth) profiles
(Fig. S1 in the Supplement). Microstructure profiles collected during cruise M138 were not available, but there were very small variations amongst the cruise average
inshore and offshore microstructure profiles from M136 and M137 despite
the drastic change in the intensities of the poleward Peru–Chile Undercurrent
(Lüdke et al., 2020). It thus
appears appropriate to apply these average vertical diffusivities also to
stations during M138.
Horizontal eddy diffusivity could not be determined from data collected
during the cruises. Surface eddy diffusivities in the North Atlantic OMZ
were estimated to be on the order of a few 1000 m2 s-1, which
decrease exponentially with depth (Hahn et al., 2014). A similar
magnitude of eddy diffusivities was estimated for the eastern equatorial South Pacific based on surface
drifter data and satellite altimetry (Abernathey and Marshall, 2013;
Zhurbas and Oh, 2004). We thus consider an eddy diffusivity of 1000 m2 s-1 as a good approximation in this study for the evaluation of
horizontal diffusive 234Th fluxes.
Residence time of 234Th
The residence time (τTh) of total 234Th represents a
combination of the time required for the partition of dissolved 234Th
onto particulate matter and that for particle removal. In a one-box model,
the residence time of an element of interest can be estimated by determining
the standing stock of this element and the rates of elemental input to the
ocean or the rate of elemental removal from seawater to sediments
(Bewers and Yeats, 1977; Zimmerman, 1976):
τTh=ATh(mean)⋅ZP.
For the case of 234Th, ATh(mean) is the averaged 234Th
activities of the surface layer, Z is the depth of top layer and P is the
removal flux of 234Th.
ResultsProfiles of dissolved 238U, total 234Th, oxygen and fluorescence
The vertical profiles of 238U and 234Th activities are shown in
Fig. 2 and tabulated in Table S1. Data from station 508 were reported in
Fig. 2 and Table S1 but excluded in the Discussion section, because the
surface sample at 5 m from this station was missing, which prevents any flux
calculation. Also tabulated in Table S1 are temperature, salinity, and
concentrations of oxygen and fluorescence obtained from the CTD sensors.
Uranium concentrations of CRMs and the IAPSO standard seawater are reported
in Table S2.
Profiles of 238U (black) and 234Th (orange squares –
M136; orange circles – M138) along with concentrations of oxygen (grey) and
fluorescence (green). Profiles are organized by cruises, transects, and
distance to shore from left to right and top to bottom, indicated by east
(E) to west (W) arrows. Error bars for both 238U and 234Th are
indicated. Dashed red lines indicate the depth of the mixed layer. The start
of the oxygen-deficient zone is where oxygen diminishes. Bottom depths are
indicated for stations whose bottom depths are shallower than 600 m.
Activities of 238U showed small to negligible variations with depth,
averaging 2.54±0.05 dpm L-1 (or 3.28±0.07 ng g-1, 1 SD, n=247) at all stations. The vertical distributions of 238U did not
appear to be affected by water column oxygen concentrations or the extent of
surface fluorescence maxima (Fig. 2). Average U concentrations of both
CASS-6 (2.77±0.04 ng g-1, 1 SD, n=5) and NASS-7 (2.86±0.05 ng g-1, 1 SD, n=5) measured in this study agreed well with
certified values (2.86±0.42 ng g-1 and 2.81±0.16 ng g-1, respectively). Average 238U concentration measured in our
IAPSO standard seawater (OSIL batch P156) (3.24±0.06 ng g-1,
1 SD, n=27) is slightly higher than that reported in Owens et al. (2011)
(3.11±0.03 ng g-1, 1 SD, n=10, OSIL P149) and may reflect
slight differences in U concentrations between different OSIL batches.
Total 234Th activities varied from 0.63 to 2.89 dpm L-1 (Fig. 2). All stations showed large 234Th deficits in surface waters with
234Th/238U ratios as low as 0.25 (Fig. 3). The extent of surface
234Th deficits did not vary as a function of depths of either mixed
layer or the upper oxic–anoxic interface or as a function of the magnitude of surface
fluorescence concentrations (Table 1, Fig. 2). 234Th at all stations
generally reached equilibrium with 238U at depths between 30 and 250 m (Table 1). The equilibrium depths were slightly shallower toward the shelf
at the 11, 12 and 16∘ S transects (Fig. 3). At
station 912, deficits of 234Th extended beyond 600 m depth (Fig. 2). The
following stations (stations 428, 879, 898, 906, 907, 915, 919) displayed a
secondary 234Th deficit below the equilibrium depth, indicative of
234Th removal processes. A small 234Th excess at depth was only
observed for station 559 at 100 m. Ratios of 234Th/238U for deep
samples at 1000, 1500, and 2000 m varied between 0.95 and 1.02 (1.00±0.04, 1 SD, n=11), suggesting that 234Th was at equilibrium
with 238U at these depths.
234Th fluxes due to production and decay, upwelling, and vertical diffusion below the mixed layer and at 100 m. Horizontal advective fluxes were not quantified at 100 m. Refer to text for details.
CruiseStationCastMixedUpperMaximumEquili-234Th flux 5–20 m below the ML 234Th flux at 100 m layeroxyclinefluore-briumDepthProductionUpwellingDiffusionFinal total1 SDProductionUpwellingDiffusionFinal total1 SDdepthdepthscencedepthand decayfluxand decayfluxmmµg L-1mmdpm m-2 d-1dpm m-2 d-1dpm m-2 d-1dpm m-2 d-1dpm m-2 d-1dpm m-2 d-1dpm m-2 d-1dpm m-2 d-1dpm m-2 d-1dpm m-2 d-1M1363531251021.201003090752-36923671422-1421410188M1363801261290.87803011450-4111055416370-11637132M1364021241297.51100308080-75732681234021236111M136428110764.113030983-1284931348210177233-3901415194M136445117642.0710030820-10168265816215361681167M13645815551.61100301012-181611155812101-111452235137M136472111297.4120040188715-291872533315-1263336692M136495118501.132003011491-1911303531952-5319289M136516116453.7720030614016154822292-42227109M136547122481.28150307910858776525100-152495118M136559120791.7085506263-961763854-42852121M136567121502.401503015930-2315705330110-11300086M138879343932.242006012490-1612669017020-51697111M13888210392112.68150501321-716133163226419-12227282M13888312102201.3125030683-84-15975885178231-1211692102M1388887411271.591505013640-12012448318130-4180986M13889214471281.0510060139533-1181309911743-31174199M1388981381011.42605010990-1910801031091001091125M1389041612723.63150208122750108714026430-9263479M1389061832811.732004017960417994131000-1310077M13890711311001.2960601594-88131518113178767-21853125M138912337702.75>6005019600-7918815129750-3297277M138915126993.51200401628022165038275200275293M138919119794.4615030131604913653232490-8324185
Shelf–offshore distributions of 234Th/238U along the
four studied transects, as shown in Fig. 1, for M136 (a) and M138 (b). White dots denote station location.
Vertical and horizontal 234Th gradients
Discrete vertical 234Th gradients in each profile (or the curvature of
the profile) were estimated by the difference in 234Th activities and
that in sampling depths. As such, vertical 234Th gradients varied
greatly amongst stations and were larger at shallow depths ranging from
0.003 to 0.085 dpm L-1 m-1 (median 0.013 dpm L-1 m-1). Vertical 234Th gradients were essentially
negligible at and below equilibrium depths.
While calculation of the vertical 234Th gradient is straightforward,
the same is hardly true for the determination of horizontal 234Th
gradient. Mean 234Th activities in the top layer (see Sect. 2.3.2 for
depth definition) of the water column are highly variable amongst stations
(Table 3, Fig. 4) and likely reflect variations occurring at small
temporal and spatial scales in the Peruvian OMZ. Quantification of the
horizontal 234Th gradient between individual station may thus not be
adequate to evaluate large-scale advection and eddy diffusion across the
study area. Therefore, alongshore 234Th gradients on a larger spatial
scale (1∘ apart) were instead calculated by grouping stations into
1∘ by 1∘ grids and averaging 234Th activities of
each grid for the top layer. Alongshore 234Th gradients in the top
layer at nearshore stations for M138 are fairly consistent, ranging from 1.5×10-6 to 1.7×10-6 dpm L-1 m-1,
with a slightly stronger gradient in the north compared to the south. The
net difference in alongshore 234Th gradient is merely 2×10-7 dpm L-1 m-1. A slightly smaller alongshore 234Th gradient of 4.8×10-7 dpm L-1 m-1 was observed for M136. The magnitude of
the net difference in alongshore 234Th gradient for M136 cannot be
adequately quantified, due to smaller spatial sampling coverage. Judging on
the similarity in the spatial distributions of mean 234Th between
cruises M136 and M138 (Fig. 4), it is reasonable to assume that the net
difference in alongshore 234Th gradient remained similar during both
cruises.
Distributions of averaged 234Th activities during M136 (a,
top 30 m) and M138 (b, top 50 m).
Steady-state vs. non-steady-state models
The relative importance of 234Th fluxes due to advection and diffusion was assessed here assuming steady-state conditions, which assume negligible
temporal 234Th variability. But how valid is this assumption in the
Peruvian upwelling zone? Profiles of temperature and oxygen at repeat
stations 458 and 508 showed that a lightly cooler and oxygen-depleted water
mass dominated at the upper 50 m at station 508 (Fig. 5). However, an
assessment of the 234Th fluxes at these two stations was not possible
as the surface sample from station 508 was missing. Repeat stations 495 and
516 show substantial temporal variations in 234Th activities at each
sampled depth in the top 200 m, while temperature and salinity profiles
confirmed that similar water masses were sampled during both occupations
(Fig. 5). Particularly, the surface 234Th deficit was more intense at
station 495 (234Th/238U=0.44) compared to station 516
(234Th/238U=0.73). Correspondingly, 234Th fluxes
decreased substantially from station 495 to station 516. At 100 m, the difference in
234Th fluxes between these two stations was ∼30 %
(3200±90 dpm m-2 d-1 at station 495 and 2230±110 dpm m-2 d-1 at station 516). At 200 m where 234Th resumed equilibrium
with 238U at both stations, the 234Th flux difference was
∼25 % (4510±220 dpm m-2 d-1 at station 495
and 3455±200 dpm m-2 d-1 at station 516). Taking the
non-steady-state term in Eq. (1) into consideration (see details in
Resplandy et al., 2012, and Savoye et al., 2006, for the derivation of flux formulation and error propagation)
increased total 234Th at station 516 by 40 % to 3110±1870 dpm m-2 d-1 at 100 m (or 45 % to 5040±2290 dpm m-2 d-1 at 200 m), which is indistinguishable within error from fluxes at
station 495. The large errors associated with the non-steady-state calculation
due to the short duration between station occupations prevent a meaningful
application of this model in the current study (also see discussion in
Resplandy et al., 2012). As estimation of the physical fluxes is independent
of the models chosen between steady and non-steady states, the following
results and discussion sections regarding physical effects on the 234Th
flux estimates are based on the steady-state model only.
Profiles of temperature (solid lines) and salinity (dashed lines)
for repeated stations (a) 458 (purple) and 508 (yellow) and (d) 495 (blue)
and 516 (orange). Panels (b) and (c) are respectively profiles for stations 458 and 508
of 238U (black), 234Th (color squares), and concentrations of
oxygen (grey) and fluorescence (green). Panels (e) and (f) are respectively profiles
for stations 495 and 516 of 238U (black), 234Th (color squares),
and concentrations of oxygen (grey) and fluorescence (green).
Export fluxes of 234Th
Fluxes of 234Th due to radioactive production and decay (hereafter
“production flux”), upwelling, and vertical diffusion were reported in Table 1 and Fig. 6 for both depths 5–20 m below the ML and at 100 m. The
production fluxes of 234Th at 5–20 m below the ML ranged from 560 to 1880 dpm m-2 d-1, whereas at 100 m they were
much higher at 850 to 3370 dpm m-2 d-1.
There is no discernable trend regarding the production fluxes between the
shelf and offshore stations, similar to those seen along the eastern GP16
transect (Black et al., 2018).
Bar charts of 234Th fluxes due to production and decay
(blue), upwelling (orange) and vertical diffusion (grey) for the depths at
5–20 m below the ML (top) and 100 m below sea surface (bottom). Color
boxes corresponds to individual transects in Fig. 1. Within each transect,
stations from west (offshore) to east (nearshore) are listed from left to
right. Error bars (1 SE) are indicated.
Alongshore winds were unusually weak off Peru preceding and during our
sampling campaign as a result of the 2017 coastal El Niño (Echevin et
al., 2018; Lüdke et al., 2020; Peng et al., 2019), which
resulted in nominal upwelling in the water column. At nearshore stations,
upwelling rates at the base of the ML varied between 1.3×10-7 and 9.7×10-6 m s-1, whereas
upwelling rates at offshore stations were on the order of 10-10 to 10-8 m s-1 and essentially negligible. As a result,
upwelled 234Th fluxes at 5–20 m below the ML were only significant at
stations closest to shore; these stations were 428 (130 dpm m-2 d-1), 883–12 (80 dpm m-2 d-1) and 904–16 (280 dpm m-2 d-1), whose upwelled 234Th fluxes accounted for 10 %, 11 % and
25 % of the total 234Th fluxes respectively (Fig. 6). Upwelled
234Th fluxes at the rest of the stations accounted for less than 2 %
of the total 234Th fluxes (6 % at stations 353 and 907–11) and were
insignificant. At 100 m, both vertical 234Th gradients and upwelling
rates were significantly smaller compared to shallower depths. As a result,
upwelled 234Th fluxes were less than 70 dpm m-2 d-1, or less
than 4 % of total 234Th fluxes.
Similarly, vertical diffusivities, shown as running mean over 20 m in Fig. S1, were an order of magnitude higher at shallow stations (3.2×10-4±1.7×10-4 m2 s-1; 1 SD, 27 to
100 m below sea surface) compared to those at deep stations (1.7×10-5±0.6×10-5 m2 s-1; 1 SD; 34–100 m below sea surface). Within the upper 27 to 33 m layer at offshore deep
stations, vertical diffusivities decreased exponentially by an order of
magnitude within a few meters; below this depth, vertical diffusivities
remained relatively stable (Fig. S1). This is not surprising as
wind-driven turbulence is most significant at the ocean surface
(Buckingham et al., 2019). In this study, the sampling
depths immediately below the ML were generally 30 and 60 m. A few high-vertical-diffusivity values around 30 m at deep stations were not likely representative for the 30–60 m water column layer. We thus opted to
only apply vertical diffusivities below 33 m at deep stations. Relative
standard errors (RSEs) associated with diffusivity estimates varied from
35 % to 55 %. Vertical diffusive 234Th fluxes at 5–20 m below the
ML, determined using both vertical diffusivity and vertical 234Th
gradient, varied greatly amongst stations. At shallow stations 428, 458 and
883–12, vertical diffusive 234Th fluxes made up 37 % (490 dpm m-2 d-1), 14 % (160 dpm m-2 d-1) and 21 % (160 dpm m-2 d-1) of total 234Th fluxes, respectively (Fig. 6). At
the rest of the stations, vertical diffusive 234Th fluxes appeared to
be insignificant, ranging between 1 % and 10 % in the total 234Th
flux budget. At 100 m, vertical diffusive 234Th fluxes at station 428,
458 and 883–12 remained high at 390, 150 and 120 dpm m-2 d-1, respectively, whereas those at the rest
of the stations accounted for <2 % of the total 234Th flux.
Horizontal advective and diffusive 234Th fluxes were both very small.
Average alongshore current velocities (Lüdke
et al., 2020) for the top layer varied from 0.06 to
0.34 m s-1. At the periphery of a freshly formed anticyclonic eddy
(station 915-1), alongshore current velocities could be as high as 0.53 m s-1. Taking the mean alongshore velocity of 0.2 m s-1 and the net
difference in the alongshore 234Th gradient of 2×10-7 dpm L-1 m-1, the resulting net horizontal advective 234Th flux at the top
layer is ∼50 dpm m-2 d-1, a mere 3 %–9 % of the
total 234Th fluxes.
Horizontal diffusive 234Th flux was estimated using an average eddy
diffusivity of 1000 m2 s-1 (see methods Sect. 2.3.3) and the
alongshore 234Th gradient. A maximum value of 10 dpm m-2 d-1
was calculated, which accounted for <1 % of total 234Th flux
at all stations. Note that the horizontal advective and lateral diffusive
fluxes presented here are a rough estimate and should only provide an idea
of their order of magnitude. Due to the uncertainty inherent to the
estimates, we refrain from adding these values to Table 1.
DiscussionLack of linear 238U–salinity correlation in the Peruvian OMZ
The water column profiles of 238U in the Peruvian OMZ (Fig. 2) are
similar to those seen in the open ocean (see compilations in
Owens et al., 2011, and Van Der Loeff et al., 2006, and references therein).
It thus appears that water column suboxic/anoxic conditions alone are not
sufficient to remove U, in contrast to sedimentary U studies underlying low-oxygen waters where soluble U(VI) diffuses downward into subsurface
sediments and is reduced to insoluble U(IV) (Anderson et al., 1989;
Böning et al., 2004; Scholz et al., 2011). Our inference is in accord
with water column 238U studies in intense OMZs in the eastern tropical
North Pacific (Nameroff et al., 2002) and the Arabian Sea
(Rengarajan et al., 2003), where 238U concentrations remain
constant over the entire upper water column studied.
Dissolved 238U and salinity across the entire Peruvian OMZ displayed
poor linear correlation regardless of seawater oxygen concentrations (Fig. 7a–b). The general consensus is that U behaves conservatively in oxic
seawater in the open ocean, and early observations have shown that 238U
activities can be calculated from salinity based on a simple linear
correlation between the two (e.g., Chen et al., 1986; Ku et al., 1977).
Compilations in Van Der Loeff et al. (2006) and Owens et
al. (2011) further demonstrated that the majority of uranium data points in
the global seawater dataset follow a linear correlation with seawater
salinity. The 238U-salinity formulations from either Chen et al. (1986)
or Owens et al. (2011) are thus generally appropriate for open-ocean
conditions and have been widely used in 234Th flux studies. However,
this linear 238U–salinity correlation breaks down in the Peruvian OMZ.
Furthermore, the measured 238U activities in this study correlated
poorly with those calculated from salinity using the Owens formulation
regardless of water column oxygen concentrations (Table S2, Fig. 7c), with
the former significantly higher than the projected values and differences up
to 10 %. This evidence suggested that non-conservative processes have
introduced significant amount of dissolved U into the water column.
Cross plots of measured 238U activities vs. salinity for M136 (a) and M138 (b), showing poor linear relationship between 238U and
salinity. Panel (c) shows a direct comparison between measured and salinity-based
238U to further highlight the large difference between the two. The
solid blue line indicates the 1 : 1 ratio between measured and projected
238U. Dashed blue lines indicate the ± errors reported in Owens
et al. (2011). Error bars for measured 238U activities are smaller than
symbols.
It is likely that this poor 238U–salinity correlation in the water
column is not a unique feature off the coast of Peru. Poor correlations
between dissolved 238U and salinity have been previously observed in open-ocean settings such as the Arabian Sea (Rengarajan et al.,
2003) and the Pacific Ocean (Ku et al., 1977), as well as shelf–estuary
systems such as the Amazon shelf (McKee et al., 1987; Swarzenski et al.,
2004). It is possible that the narrow range of salinity within any single
ocean basin precludes a meaningful 238U–salinity correlation (Ku et
al., 1977; Owens et al., 2011). For the Peruvian shelf system, two possible
scenarios may further explain the lack of linear 238U–salinity
correlation in the water column. Firstly, authigenic U within the sediments
may be remobilized under El Niño–Southern Oscillation (ENSO)-related oxygenation events. In reducing pore
water, U reduction and removal from pore water is usually seen within the Fe
reduction zone (Barnes and Cochran, 1990; Barnes and Cochran, 1991;
Scholz et al., 2011). As such, a downward diffusive flux of U across the
water–sediment interface is expected in reducing sedimentary environment.
However, pore water and bottom water geochemistry measurements during two
previous cruises (M77-1 and M77-2) along an 11∘ S transect off
Peru showed large diffusive fluxes of U out of the Peruvian shelf sediments
despite the fact that both Fe reduction and U reduction took place in the top
centimeters of sediments (Scholz et al., 2011). It was suggested
that a minute increase in bottom water oxygen concentration induced by El
Niño events would be sufficient in shifting the U(VI)/U(IV) boundary by
a few centimeters and remobilize authigenic U (Scholz et al.,
2011). Preceding and during our sampling campaign, a coastal El Niño
event, with coastal precipitation as strong as the 1997–1998 El Niño
event, had developed rapidly and unexpectedly in January and disappeared by
May 2017 during cruise M136 (Echevin et al., 2018; Garreaud, 2018; Peng
et al., 2019). This strong coastal El Niño event could induce an
oxygenation event large enough to remobilize authigenic U along the
Peruvian shelf. Secondly, resuspension of bottom sediments and subsequent
desorption of U from ferric oxyhydroxides could affect the
238U-salinity relationship, similar to that seen on the Amazon shelf at
salinity above 10 (McKee et al., 1987) and in laboratory
experiments (Barnes and Cochran, 1993). Fe reduction and release
from the Peruvian shelf sediments (Noffke et al., 2012; Scholz et al.,
2014) could release additional U to overlying waters. The magnitude of such,
however, has not been quantified.
The consequence of the notable difference between measured 238U in this
study and salinity-based 238U to 234Th flux according to Eq. (2)
is neither linear nor straightforward, because the vertical gradients of
both 238U and 234Th strongly affects the impacts of 238U
variations on 234Th fluxes. In this study, 234Th fluxes at 100 m
derived from salinity-based 238U lead to significant underestimation of
234Th fluxes by an average of 20 % and as high as 40 % (Table 2).
These differences in 234Th fluxes will have direct consequences for
234Th-derived elemental fluxes such as C, N, P and trace metals. It is
thus important to note that U concentrations in coastal systems are highly
sensitive to bottom water oxygen concentrations and redox-related U
addition, the variability of which is expected to intensify with future climate
change (Shepherd et al., 2017). Relatively minor variations in dissolved
238U could account for substantial overestimation/underestimation of
the depth-integrated 234Th fluxes. We thus encourage future 234Th
flux studies in such environments to include seawater 238U analysis.
Comparison of 234Th fluxes at 100 m calculated with measured 238U activities and those with salinity-based 238U.
CruiseStationCast234Th fluxes at 100 m*Differencemeasuredpredicteddpm m-2 d-1dpm m-2 d-1%M1363531142213208M13638011637130426M1364021123486543M13642811772144323M13644511621136519M13645812101185913M1364721331530738M1364951319530584M1365161222921404M1365471251023139M136559185475114M1365671301128795M13887931702151512M138882102264187521M138883121782135232M13888871813144126M138892141743125739M1388981109177042M138904162643228016M138906183100267316M138907111787130837M13891232975257216M13891512752238016M13891913249286214
* For the purpose of evaluating flux difference due to uranium variability, we report here 234Th fluxes only due to radioactive production and decay.
Dynamic advective and diffusive 234Th fluxes
The significance of advection and diffusion in the total 234Th flux
budget highly depends on the upwelling rate, current velocity, vertical
diffusivity, and 234Th gradient on the horizontal and vertical
directions. Our results demonstrated that physical processes off Peru during
and after the 2017 coastal El Niño have very limited impact on the
downward fluxes of 234Th (Fig. 6).
Our findings are in reasonable agreement with those from the GEOTRACES GP16
eastern section along 12∘ S from Peru to Tahiti, in which
Black et al. (2018) quantified both horizontal and vertical
advective 234Th fluxes. Horizontal advective fluxes for the upper 30 m
water column estimated during GP16 were ∼180 dpm m-2 d-1 for all nearshore and offshore stations, similar in magnitude to
those estimated in our study (∼50 dpm m-2 d-1).
Upwelling fluxes along GP16 eastern section were suggested to account for
50 % to 80 % of total 234Th fluxes at the base of the euphotic zone
(Black et al., 2018), a depth similar to or slightly deeper than
ML depths in the current study where upwelling fluxes accounted for less
than 25 % of total 234Th fluxes. Total 234Th fluxes along the
GP16 eastern section, ranging from 4000 to 5000 dpm m-2 d-1 at the
base of the euphotic zone, were much higher than those in our study (560 to
1900 dpm m-2 d-1 5–20 m below the ML). This difference could be
related to the period of sampling (austral autumn and winter 2017 in our
study vs. austral spring 2013 for the GP16 section). We note that the estimated
vertical mixing rates based on 7Be isotope at the base of the euphotic
zone along the GP16 section (Kadko, 2017) were at least an order of
magnitude higher than the upwelling rates at the base of the ML at nearby
stations in our study. This difference could stem from different methods
used to estimate upwelling rates at different timescales and may also
reflect the dynamic upwelling system off Peru in which upwelling rates vary
greatly seasonally and interannually. During cruises M136 and M138,
upwelling favorable easterly winds off Peru were weak, resulting in
negligible coastal upwelling. Coastal upwelling in the same general area was
also suggested to be negligible in austral summer 2013 during cruise M92 due
to nominal surface wind stress (Thomsen et al., 2016). Results from
studies conducted in the same year (October to December 2013, Kadko,
2017; December 2012, Steinfeldt et al., 2015; January 2013, Thomsen et al.,
2016) indicate that seasonal upwelling rates vary drastically in the
Peruvian upwelling zone. The seasonal dynamics of coastal upwelling off Peru
are similar to those seen in the Arabian Sea, where large upwelled
234Th fluxes only occurred during the middle-to-late southwest monsoon at
stations close to shore (Buesseler et al., 1998). Our findings lend
further support to earlier studies that advection and diffusion are
seasonally important for 234Th fluxes in regions with high upwelling
velocities and diffusivities such as the equatorial Pacific (Bacon et
al., 1996; Buesseler et al., 1995; Dunne and Murray, 1999) and coastal sites
such as the Arabian Sea (Buesseler et al., 1998) and offshore Peru
(Black et al., 2018; this study).
Residence time of 234Th in the Peruvian OMZ
The residence time calculated using Eq. (6) was based on a simplified
one-dimension (1D) model of Zimmerman (1976). This 1D steady-state
model is obviously an oversimplification of a multi-dimensional process; it
however provides a good first-order estimate for understanding the highly
dynamic nature of the 234Th residence time. It also provides a
reasonable value that can be directly compared to values estimated in
earlier 234Th flux studies that did not consider the physical
processes. Furthermore, we showed in the Discussion (Sect. 4.2) that
physical processes, namely upwelling and vertical diffusion, are only
important at a few shelf stations. We thus consider this simple 1D model
robust in estimating the residence time of total 234Th.
In this study, residence time of total 234Th in the top layer varied
from 20 d at shallow stations to 95 d at deep stations (mean τ=51±23 d, 1 SD, n=24; Table 3). These values were similar
to those estimated within the California Current (Coale and
Bruland, 1985) and the residence times of particulate organic carbon (POC)
and nitrogen (PON) (Murray et al., 1989) but were much longer
than predicted in nearshore shelf waters where residence times of total
234Th were on the order of a few days (Kaufman et al., 1981; Kim et
al., 1999; and references therein). The longer residence times estimated in
our study could reflect a combination of weak surface 234Th deficits
(234Th=0.63 to 1.82 dpm L-1) (Fig. 3) and low export fluxes
(800 to 2000 dpm m-2 d-1, Fig. 7). Nearshore seawater samples
during GP16 (Black et al., 2018) featured similar surface
234Th deficits (234Th= 0.63 to 1.33 dpm L-1) but much
higher downward 234Th fluxes (4000 to 5000 dpm m-2 d-1) as a
result of strong upwelling, implying that residence times of total
234Th in the Peruvian OMZ during GP16 occupation would be 3–6 times
shorter. Indeed, a quick reassessment of the GP16 data predicted a shorter
residence time of total 234Th of 5–23 d within the euphotic zone
of the coastal Peruvian OMZ.
Residence time of total 234Th in the top layers of Peruvian OMZ.
CruiseStationCastAverage 234Th inResidence timethe top layer*dpm L-1daysM13635311.4846M13638011.3535M13640211.6461M13642811.5735M13644511.6461M13645811.4538M13647210.9320M13649511.2031M13651611.7485M13654711.6763M13655911.7594M13656711.4145M13887931.5975M138882101.8169M138883121.8774M13888871.6867M138892141.6965M13889811.6692M138904161.3224M138906181.1525M138907111.0441M13891231.2533M13891511.1628M13891911.1726
* Here “the top layer” refers to the top 30 m during M136 and top 50 m during M138.
These temporal variations on the residence times of total 234Th have
important implications for the estimation of POC fluxes and quantification
of carbon export efficiency. Firstly, seasonal changes in Th residence times
reflect variations in particle removal over different integrated timescales.
For example, POC produced in surface waters during GP16 (austral spring
2013) (Black et al., 2018) would have been exported out of the
euphotic zone 3–6 times faster than during austral autumn 2017 (this
study). Secondly, to properly evaluate carbon export efficiency, surface net
primary production (NPP) should be averaged over a timescale similar to the
residence time of total 234Th during station occupation. Applying a
16 d averaged NPP for the export efficiency estimate (Black et al., 2018;
Henson et al., 2011) would likely not be appropriate in the current study in
which total 234Th fluxes integrated timescales of several weeks.
234Th residence times should thus be properly quantified in coastal
studies before deriving export efficiencies over varying NPP integration
timescales.
Conclusions and implications for coastal 234Th flux studies
Advection and diffusion are important in coastal and upwelling regions with
respect to 234Th export fluxes (Bacon et al., 1996; Buesseler et
al., 1995, 1998; Dunne and Murray, 1999). Our findings
show that their significance is subject to the seasonal variability of the
current and upwelling velocities, diffusivities, and 234Th gradients and should be evaluated on a case-by-case basis. Advective fluxes are
perhaps the most straightforward to estimate as current velocities can be
obtained routinely from shipboard ADCP measurements and upwelling rates
calculated from satellite wind stress (Steinfeldt et al., 2015; Bacon et
al., 1996). Horizontal and vertical velocities derived from general ocean
circulation models also provide a good first-order estimate for advective
234Th fluxes; this approach has been successfully demonstrated in a few
studies (Buesseler et al., 1995, 1998). In addition,
the anthropogenic SF6 tracer and radium isotopes, widely used to
quantify nutrient and Fe fluxes (Charette et al., 2007; Law et al.,
2001), as well as 7Be isotope (Kadko, 2017), could be used
independently to constrain horizontal and vertical exchange rates of
234Th (Morris et al., 2007; Charette et al., 2007; Buesseler et al.,
2005). When in situ microstructure measurements are available (this study),
vertical diffusivity can be directly calculated to estimate the vertical
diffusive 234Th fluxes. Yet, microstructure analysis is not a routine
measurement on oceanographic cruises. Earlier studies in the equatorial
Pacific and the Gulf of Maine have shown that general ocean circulation
models and a simple assumption on dissipation coefficients could provide a
robust estimate on vertical and horizontal diffusivities (Benitez-Nelson
et al., 2000; Gustafsson et al., 1998; Charette et al., 2001). Therefore,
the calculation of physical fluxes is possible, though challenging, and
234Th fluxes due to physical processes should be carefully considered
when conducting research in a coastal and upwelling systems.
A striking finding in this study is that the assumption of a linear
238U–salinity correlation could lead to one of the largest errors in
234Th flux estimates. In our study, using the salinity-based 238U
activities resulted in significant underestimation of total 234Th
fluxes by as much as 40 %. Because the translation of 238U activities
to 234Th fluxes is not linear, larger differences between measured and
salinity-based 238U do not necessarily contribute to greater
overestimation or underestimation of 234Th fluxes. For example,
a moderate difference of 3 %–6 % in 238U throughout the upper 100 m at
station 898 leads to a 40 % difference in the final 234Th flux, while a
5 %–9 % difference in 238U at station 906 only resulted in a 16 %
234Th flux difference (Tables 2, S2). We would thus stress the
importance of 238U measurements in future 234Th flux studies,
particularly in coastal and shelf regions.
Finally, our study showed that the residence times of total 234Th in
the Peruvian nearshore waters varied seasonally. Tropical OMZs are important
hotspots for carbon sequestration from the atmosphere and enhanced
sedimentary carbon preservation (Arthur et al., 1998; Suess et al.,
1987). These OMZs are projected to intensify as a result of future climate
change (Keeling and Garcia, 2002; Schmidtko et al., 2017; Stramma et al.,
2008). Future studies should take into consideration the large temporal
variations of the residence times of total 234Th in order to properly evaluate how carbon biogeochemical cycles and carbon export efficiency in
these OMZs will respond to continuing ocean deoxygenation.
Data availability
Data are available in supplementary tables and archived at 10.1594/PANGAEA.921917 (Xie et al., 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-4919-2020-supplement.
Author contributions
RCX, FACLM and EAP designed the study. RCX carried out sampling, onboard
beta counting of 234Th, and drafted the manuscript. IR conducted
234Th and 238U analyses at the home laboratory. JL computed current
velocities and vertical diffusivities respectively from VmADCP and
microstructure profiler data. All coauthors had a chance to review the
manuscript and contributed to the discussion and interpretation of the data
presented.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Ocean deoxygenation: drivers and consequences – past, present and future (BG/CP/OS inter-journal SI)”. It is a result of the International Conference on Ocean Deoxygenation, Kiel, Germany, 3–7 September 2018.
Acknowledgements
We thank the crew and science party on board M136 and M138 for their help in
sample collection and instrument operation. Thank you to SiaoJean Ko,
Dominik Jasinski, André Mutzberg and Mario Esposito for their laboratory
assistance. We thank two anonymous reviewers and the associate editor,
Marilaure Grégoire, for their constructive comments. The project,
cruises, Insa Rapp, Jan Lüdke and Ruifang C. Xie were funded by the German SFB 754 program
(Climate-Biogeochemistry Interactions in the Tropical Ocean); Ruifang C. Xie
additionally by a DFG research grant (project number 432469432); and Frédéric A. C. Le Moigne
by a DFG fellowship of the excellence cluster “The Future Ocean” (CP1403).
This manuscript benefited from stimulating discussions at the BIARRITZ
(“bridging international activity and related research into the twilight
zone”) workshop held in Southampton, UK in 2019.
Financial support
This research has been supported by the DFG SFB 754 (project no. 27542298) and a DFG grant to Ruifang C. Xie (project no. 432469432).
The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.
Review statement
This paper was edited by Marilaure Grégoire and reviewed by two anonymous referees.
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