Cobalt (Co) is an important bioactive trace metal that is the metal
cofactor in cobalamin (vitamin B12) which can limit or co-limit
phytoplankton growth in many regions of the ocean. Total dissolved and
labile Co measurements in the Canadian sector of the Arctic Ocean during the
U.S. GEOTRACES Arctic expedition (GN01) and the Canadian International Polar
Year GEOTRACES expedition (GIPY14) revealed a dynamic biogeochemical cycle
for Co in this basin. The major sources of Co in the Arctic were from shelf
regions and rivers, with only minimal contributions from other freshwater
sources (sea ice, snow) and eolian deposition. The most striking feature
was the extremely high concentrations of dissolved Co in the upper 100 m,
with concentrations routinely exceeding 800 pmol L-1 over the shelf
regions. This plume of high Co persisted throughout the Arctic basin and
extended to the North Pole, where sources of Co shifted from primarily
shelf-derived to riverine, as freshwater from Arctic rivers was entrained in
the Transpolar Drift. Dissolved Co was also strongly organically complexed
in the Arctic, ranging from 70 % to 100 % complexed in the surface and deep
ocean, respectively. Deep-water concentrations of dissolved Co were
remarkably consistent throughout the basin (∼55 pmol L-1), with concentrations reflecting those of deep Atlantic water and
deep-ocean scavenging of dissolved Co. A biogeochemical model of Co cycling
was used to support the hypothesis that the majority of the high surface Co
in the Arctic was emanating from the shelf. The model showed that the high
concentrations of Co observed were due to the large shelf area of the
Arctic, as well as to dampened scavenging of Co by manganese-oxidizing (Mn-oxidizing)
bacteria due to the lower temperatures. The majority of this scavenging
appears to have occurred in the upper 200 m, with minimal additional
scavenging below this depth. Evidence suggests that both dissolved Co (dCo) and labile Co (LCo) are increasing over time on the Arctic shelf, and these limited temporal results are consistent
with other tracers in the Arctic. These
elevated surface concentrations of Co likely lead to a net flux of Co out of
the Arctic, with implications for downstream biological uptake of Co in the
North Atlantic and elevated Co in North Atlantic Deep Water. Understanding
the current distributions of Co in the Arctic will be important for
constraining changes to Co inputs resulting from regional intensification of
freshwater fluxes from ice and permafrost melt in response to ongoing
climate change.
Introduction
Cobalt (Co) is an essential micronutrient in the ocean. It is utilized by
eukaryotic phytoplankton as a substitute for zinc (Zn) in the metalloenzyme
carbonic anhydrase (Lane and Morel, 2000; Sunda and Huntsman,
1995; Yee and Morel, 1996), and cyanobacteria have an absolute requirement
for Co (Hawco and Saito, 2018; Saito et al., 2002; Sunda
and Huntsman, 1995). Co is also the metal center in the micronutrient
cobalamin, or vitamin B12. In most ocean basins, dissolved Co (dCo;
<0.2µm) is extremely scarce in surface waters (<10 pmol L-1) and is strongly complexed by a pool of thus far
uncharacterized organic Co-binding ligands (Saito et al.,
2005; Saito and Moffett, 2001). Due to its low concentrations, strong
organic complexation, and presence in cobalamin, dCo has
been found to be a limiting or co-limiting nutrient for phytoplankton growth
in several regions (Bertrand
et al., 2007, 2015; Browning et al., 2017; Hawco et al., 2020; Martin et
al., 1989; Moore et al., 2013; Panzeca et al., 2008; Saito et al., 2005).
Growth limitation can be due to a lack of dCo, or cobalamin (Bertrand
et al., 2012, 2007; Browning et al., 2017), as cobalamin is
only synthesized by cyanobacteria and some archaea (Doxey et
al., 2015). However, many phytoplankton utilize cobalamin for the synthesis
of methionine (Yee and Morel, 1996; Zhang et al., 2009) and
therefore must obtain it from the natural environment (Heal
et al., 2017).
Co is taken up as a micronutrient by phytoplankton in surface waters and is
regenerated from sinking organic matter at depth, but it is also prone to
intense scavenging throughout the mesopelagic ocean (Dulaquais
et al., 2014b; Hawco et al., 2018; Saito et al., 2017). The strongest
removal mechanism for dissolved Co (dCo) is through co-precipitation of dCo
with manganese (Mn) by Mn-oxidizing bacteria, due to their similar redox
properties and ionic radii (Cowen
and Bruland, 1985; Moffett and Ho, 1996; Sunda and Huntsman, 1988). Several
sources of Co to the ocean have been identified, including rivers
(Tovar-Sánchez et al., 2004; Zhang et al., 1990),
coastal sediments (Dulaquais
et al., 2014a, 2017; Hawco et al., 2016; Noble et al., 2012, 2017), and to a
lesser extent hydrothermal and eolian inputs (Shelley et
al., 2012; Thuróczy et al., 2010). The largest reservoirs of dCo thus
far have been seen in oxygen-deficient zones, likely due to a combination of
low oxygen concentrations at the sediment–water interface and advection from
reducing sediments, as well as to enhanced regeneration in low-oxygen waters (Dulaquais
et al., 2014b; Hawco et al., 2016; Noble et al., 2012, 2017). These oxygen
minimum zone sources of dCo exert an important control on the inventory of
dCo, which is likely sensitive to small perturbations in bottom-water oxygen
concentrations (Hawco et
al., 2018; Tagliabue et al., 2018).
It is important to understand the sources and sinks and internal cycling of
dCo due to its key role as a micronutrient. However, Co has one of the most
complex biogeochemical cycles of all of the trace metals. Thousands of
measurements of both total dCo and weakly complexed and/or inorganic or
“labile” Co (LCo) and particulate Co (pCo) now exist from the ocean, greatly
improving our understanding of Co cycling, and have facilitated the
representation of the biogeochemical model of Co to be included in global
ocean models (Tagliabue et al., 2018).
Several observational zonal transects have been generated by large-scale
programs including the international GEOTRACES program, among others. Large
datasets now exist in the North Atlantic (Baars
and Croot, 2015; Dulaquais et al., 2014a, b; Noble et
al., 2017), South Atlantic (Noble et al., 2012), South
Pacific (Hawco et al., 2016), Southern Ocean
(Bown et al., 2011; Saito et al., 2010), and
Mediterranean Sea (Dulaquais et
al., 2017).
Although the global coverage of Co measurements has greatly improved over
the last decade, no published measurements to our knowledge have been made
in the Arctic Ocean. The Arctic Ocean is arguably the most dynamic of the
ocean basins and is changing rapidly due to warmer temperatures affecting
the maximal sea ice extent (Screen and Simmonds, 2010;
Stroeve et al., 2012), the melting of permafrost (Jorgenson et
al., 2006), and additional inputs of meltwater and river water
(Johannessen et al., 2004; Serreze and Barry, 2011). The
Arctic Ocean is also likely distinct in terms of Co cycling compared to
other ocean basins due to its large shelf area, restricted circulation, and
potentially distinct Co sources including sea ice, snow, and highly seasonal
riverine inputs. The Arctic Ocean is known to have high concentrations of
dissolved organic matter (DOM), which could influence the organic
complexation of Co in this ocean basin. This study examined dCo, LCo, and
pCo in two different transects in the Canadian sector of the Arctic Ocean.
We then used a Co biogeochemical model (Tagliabue et al., 2018) in order to
evaluate hypotheses about the role of external sources and internal cycling
in the observed Co distributions and the potential of the Arctic to be a net
source of Co to the North Atlantic and to identify Co sources and sinks
that may be sensitive to future changes in this rapidly changing ocean
basin.
MethodsSample collection and handlingWater column samples
Samples were collected on two expeditions in the Canadian section of the
Arctic Ocean (Fig. 1). The first set of samples (n=107) were collected on
board the CCGS Amundsen from 27 August to 12 September 2009 in the Beaufort
Sea as part of the Canadian IPY-GEOTRACES program (ArcticNet 0903; GIPY14).
The second set of samples (n=361) were collected on board the USCGC
Healy (HLY1502) on the U.S. GEOTRACES Arctic expedition (GN01) from 9 August to 12 October 2015. The Canadian GEOTRACES expedition sampled along the
shelf and slope in the Beaufort Sea. The U.S. GEOTRACES expedition sailed in
and out of Dutch Harbor, Alaska, and traversed the Bering Shelf and
Makarov Basin before reaching the North Pole on 5 September 2015 and
returning south across the Canada Basin. Samples from the Canadian GEOTRACES
expedition were collected using a trace metal rosette system fitted with 12×12 L GO-FLO bottles (General Oceanics), and only the dCo and LCo samples
collected in the water column from this study are discussed here. All other
metadata from this expedition can be found at
http://www.bodc.ac.uk/geotraces/data/ (last access: 10 August 2020). Samples from the U.S. GEOTRACES
expedition were collected using the U.S. GEOTRACES trace metal clean rosette
outfitted with twenty-four 12 L GO-FLO bottles and a Vectran conducting
hydrowire (Cutter and Bruland, 2012). Two
GO-FLO bottles were triggered at each depth during the trace metal
hydrocasts. One bottle was used for particulate trace metal sampling, and
the other was used for all dissolved metal and macronutrient analyses. Upon
recovery of the sampling system, the GO-FLO bottles were immediately brought
inside a 20 ft (6.1 m) ISO container van. Sampling for bulk particulate trace
metal samples has been described in detail elsewhere
(Twining et al., 2015). The filters for
particulate analyses were stored in trace metal clean centrifuge tubes and
frozen at -20∘C until analysis
(Twining et al., 2015). Dissolved trace
metal and nutrient samples were filtered with a 0.2 µm capsule filter
(AcroPak 200, VWR International) under pressurized filtered air
(Cutter and Bruland, 2012). Samples for dCo and
LCo from the Canadian GEOTRACES expedition were collected similarly but
were unfiltered. Samples for dCo were placed in two separate 60 mL
Citranox-soaked (1 %) and acid-cleaned low-density polyethylene (LDPE)
bottles and were filled until there was no headspace (Noble
et al., 2012, 2017). One sample was used for LCo analyses, and
the other was used for total dCo analyses. Nutrient samples were analyzed
immediately on board by the Oceanographic Data Facility at Scripps Institution of
Oceanography.
Standard CTD sampling stations (green) and trace metal rosette
(TM) sampling stations (blue) from the GN01 expedition in 2015, and trace
metal sampling locations from the GIPY14 expedition in 2009 (red).
Ice hole samples
Ice hole samples were only analyzed from the U.S. GEOTRACES cruise (GN01).
Seawater from ice holes for Co analyses was collected using Teflon-coated
Tygon tubing and a rotary pump with plastic wetted parts (IWAKI magnetic
drive pump, model WMD-30LFY-115) from a hole at the station's sea ice floe.
The hole was made with an ice corer (Kovacs 9 cm diameter Mark II corer) and
allowed to sit undisturbed for ∼1 h under a canvas tent
prior to sampling. Samples were collected from 1, 5, or 20 m at several
sites. Seawater was filtered in-line with a 0.2 µm filter
(AcroPak 200 capsule filter) and dispensed into a carboy, where it was
homogenized and brought back to the clean lab on board the ship. Subsamples
were taken for dCo from this carboy and stored as described below for other
water column dissolved samples. Additional details on ice hole samples can
be found elsewhere (Marsay et al., 2018).
Sample storage
Total dCo and LCo samples were stored in two distinct ways. Oxygen
concentrations have been found to have a significant effect on storage of
dCo samples (Noble
et al., 2017). Although the mechanism has not been fully explained, loss of
some dCo species has been observed in the presence of oxygen in both
acidified and nonacidified samples across regions with active biological
gradients (Hawco
et al., 2016; Noble et al., 2012, 2017, 2008). Since dCo and
LCo analyses were not able to be performed at sea on either expedition,
groups of six dCo samples from the US expedition from a single cast were
double-bagged and stored in a gas-impermeable plastic bag (Ampac) along with
three to four gas-absorbing satchels (Mitsubishi Gas Chemical, model RP-3K). This
outer bag was heat-sealed, and samples were kept refrigerated (4 ∘C) and unacidified until analysis (Hawco
et al., 2016, 2018; Noble et al., 2017). LCo samples were double-bagged and
stored at 4 ∘C and unacidified until analysis. Samples were
hand-carried at the termination of the GN01 expedition to Woods Hole
Oceanographic Institution, and all samples were analyzed within 3 months. Samples from the Canadian GEOTRACES expedition (GIPY14) were
initially collected as unfiltered samples prior to filtration and analysis
and were not stored in gas-impermeable bags prior to analysis, as the
effects of oxygen on dCo loss were not known at the time of the expedition.
It is possible there could have been some loss of dCo during the time
between sample collection and analyses (approximately 1 year), and thus
these concentrations could be underestimated. Additional discussion on how
storage may have impacted these results is discussed in Sect. 4.3.
Reagent preparation
All reagents were prepared in acid-cleaned plastic bottles and in large
batches in order to have consistent reagent batches for all sample analyses.
For dCo and LCo analyses, a 0.5 mol L-1 EPPS
(N-(2-hydroxyethyl)piperazine-N-(3-propanesulfonic acid)) buffer and a 1.5 M NaNO2 solution were prepared in Milli-Q (18 MΩ) and chelexed
(Chelex 100, Bio-Rad) to remove trace metal contaminants. Dimethylglyoxime
(DMG) was prepared by first making a 10-3 mol L-1 EDTA solution
in Milli-Q and adding 1.2 g of DMG. This solution was warmed by carefully
microwaving at 50 % power to prevent boiling, until the DMG was fully
dissolved. The solution was placed on ice and left at 4 ∘C to
recrystallize overnight. The supernatant was decanted, and the remaining
crystals were poured into an acid-cleaned plastic weigh boat, and the
remaining liquid was left to evaporate overnight in a Class 100 clean hood.
Once dry, the remaining DMG was added to an Optima methanol solution for a
final concentration of 0.1 mol L-1 DMG. A 1.5 mol L-1 solution
of sodium nitrite was prepared by placing sodium nitrite in Milli-Q and
chelexing the solution before use to remove trace metal contaminants. A Co
standard solution was prepared weekly by adding 29.5 µL of a 1 mg L-1 Co AA standard (SPEX CertiPrep) to 100 mL of Milli-Q in a
volumetric flask. For each new Co standard that was prepared during sample
runs, an approximately 1 mL aliquot was saved for later analyses to ensure
no variation was seen between batches. More information on reagent
preparations can be found at
https://www.protocols.io/researchers/randie-bundy/publications (last access: 10 August 2020).
Dissolved and labile cobalt determinations
The dCo and LCo measurements were determined using a modified cathodic
stripping voltammetry method (Saito and Moffett,
2001) for the GIPY14 samples and a fully automated method based on Hawco et
al. (2016) for the GN01 samples. Measurements for both sample sets were performed using a Metrohm 663
VA stand connected to an Eco Chemie µAutolabIII system. Peak
determinations for samples collected on GIPY14 were completed as described
in Noble et al. (2012). Sample automation and data acquisition for samples
from GN01 were completed using NOVA 1.8 software (Metrohm Autolab), and peak
determination was completed using a custom MATLAB code (see Sect. 2.6).
The dCo samples were UV-irradiated for 1 h in a temperature-controlled
UV system prior to analysis to remove any strong organic ligands that may
prevent DMG from effectively binding the entire dCo pool. For the GIPY14
samples, a modified temperature-controlled UV system (Metrohm 705 Digester)
was used (Hawco et al., 2016), while for GN01
samples an integrated temperature-controlled (18 ∘C) digestor was
used (Metrohm 909 Digester). In both cases samples were placed in
acid-cleaned and Milli-Q-conditioned 15 mL quartz tubes. After irradiation,
11 mL of each sample was placed into acid-cleaned and sample-rinsed 15 mL
polypropylene tubes. For GIPY14 samples a final concentration of 353 µmol L-1 DMG and 3 mmol L-1 EPPS was added to each sample before
analysis (Noble et al., 2017),
and for GN01 samples a final concentration of 400 µmol L-1 DMG
and 7.6 mmol L-1 EPPS was added to each sample before analysis.
Samples were then inverted several times before either being analyzed
individually or being placed on the autosampler (Metrohm 858 Sample
Processor). For autosampler analyses, the system was flushed with Milli-Q,
and 2 mL of sample was used to condition the tubing and the Teflon analysis
cup. Then 8.5 mL of sample was dosed into the cup automatically by a 800 Dosino burette (Metrohm), along with a 1.5 mL addition of 1.5 M NaNO2 for
a final analysis volume of 10 mL. Samples were purged for 180 s with N2 (high purity, >99.99 %) and conditioned at -0.6 V for 90 s.
The inorganic Co in the sample that was complexed by DMG (logKcond=11.5±0.3) forms a bis complex with Co2+ that absorbs to the
hanging mercury drop electrode (Saito and Moffett,
2001). The Co2+ and the DMG are both reduced at the electrode surface
using a fast linear sweep (from -0.6 to -1.4 V at 10 V s-1), and the
height of the Co(DMG)2 reduction peak that appears at -1.15 V is
proportional to the dCo concentration in the sample. The dCo was quantified
by triplicate scans of the sample, followed by four standard additions of
either 25 or 50 pmol L-1 per addition that were dosed directly into the
Teflon analysis cup. The slope of the linear regression of these additions
and triplicate “zero” scans were used to calculate the individual
sample-specific sensitivity (nA pmol-1 L-1). The average of the
three “zero addition” scans was then divided by the sensitivity and then
corrected for the volume of the reagent and the blank (see Sect. 2.5). In
between sample batches, or before analyzing LCo samples, the entire
auto-sampling system was rinsed with 10 % HCl and then Milli-Q.
LCo measurements were made similarly to the dCo measurements, with the
following amendments. LCo samples were not UV-irradiated, and 400 µmol L-1 DMG was added to 11 mL of sample and was equilibrated for at
least 8 h (overnight) in conditioned 15 mL polypropylene tubes.
Immediately prior to placement of the sample on the autosampler, EPPS was
added, and the samples were analyzed as described above for dCo analyses. LCo
measurements are thus operationally defined as the fraction of dCo that is
labile to 400 µmol L-1 DMG over the equilibration period
(Hawco et
al., 2016; Noble et al., 2012).
Blanks and standards
The blank for GN01 samples was prepared by UV-irradiating low-dCo seawater
for 1 h. After UV-irradiation, the seawater was passed slowly through a
Chelex 100 column to remove any metals. The clean seawater was then
UV-irradiated a second time before being analyzed. The blank used for GIPY14
samples was analyzed at the beginning and the end of the sample analyses to
ensure the blank was consistent between runs. GEOTRACES consensus reference
materials were also analyzed along with GIPY14 samples, the results of which
are reported elsewhere (Noble et
al., 2017).
For the GN01 samples, enough seawater was prepared in order to use the same
blank seawater for all of the subsequent sample analyses, and the blank was
analyzed regularly with each batch of samples (every 10–20 samples). A
combination of consensus reference materials and an in-house seawater
consistency standard were used throughout the sample analyses (Table 1).
SAFe and GEOTRACES standards were analyzed to ensure the accuracy of the
sample measurements and were slowly neutralized dropwise with 1 N ammonium
hydroxide (Optima, Fisher Scientific) until reaching a pH of approximately
8. Aliquots of the SAFe and GEOTRACES samples were then placed in
conditioned quartz tubes and UV-irradiated for 1 h, before being
analyzed as described above for dCo measurements. The consistency standard
was prepared by UV-irradiating 2 L of Southern Ocean trace metal clean
seawater as described above and was analyzed with each batch of samples to
ensure consistency between sample runs.
Average dCo concentrations from blank, internal standard, and
consensus standard runs.
ndCo (pmol L-1)SDBlank292.50.7Internal standard2650.37.6SAFe D1347.92.1SAFe D2345.22.1GSP32.41.8GSC377.92.8Dissolved and labile cobalt data processing
Peak heights for the dCo and LCo samples for the GIPY14 dataset were
determined with NOVA 1.8 software (Noble et al., 2017). All dCo and
LCo peaks from the GN01 dataset were calculated using custom MATLAB code
available on GitHub (https://github.com/rmbundy/voltammetry, last access: 15 July 2020). Text files of
the data output from the NOVA 1.8 software were saved automatically from each
scan and processed in MATLAB to determine the dCo and LCo peak heights. The
signal was smoothed using the Savitzky–Golay smoothing function (span 5,
degree 3), and the first derivative of the voltammetric signal between -1.4
and -1.1 V was calculated in order to find the start and end of the
Co(DMG)2 peak. The baseline was drawn and linearly interpolated between
the start and the end of the peak. The final peak height was determined by
finding the maximum of the signal and subtracting it from the baseline. Peak
heights from the “zero addition” scans were plotted along with the
standard additions, and a linear regression was computed from all seven
scans. Data were flagged if the r2 of the slope was <0.97,
and samples were reanalyzed.
Dissolved and particulate manganese measurements
The 0.2 µm filtered seawater samples for dissolved manganese (dMn)
were acidified to pH 2 using sub-boiling distilled HCl. The filtered
subsamples were drawn into acid prewashed 125 mL polymethylpentene bottles
after three sample rinses, and the sample bottles were stored in
polyethylene bags in the dark at room temperature before analyses, which was
usually within 24 h of collection. Prior to analysis, samples for manganese
(dMn) were acidified by adding 125 µL of sub-boiling distilled 6 N HCl.
Since the samples were used to determine dissolved iron (dFe) as well, the
obtained samples were then microwaved in groups of four for 3 min in a 900 W
microwave oven to achieve a temperature of 60±10∘C in an
effort to release dFe from complexation in the samples. Samples were allowed
to cool for at least 1 h prior to flow injection analysis. The dMn
measurements were determined in the filtered, acidified, microwave-treated
subsamples using a shipboard flow injection analysis (FIA) method
(Resing and Mottl, 1992). Samples were analyzed in groups of eight,
and the samples collected at each station were generally analyzed together
during the same day. A 3 min preconcentration of sample (∼9 mL) onto an 8-hydroxyquinoline (8-HQ) resin column yielded a detection
limit of 0.55 nmol L-1 and a precision of 1.16 % at 2.7 nmol L-1.
Particulate trace element concentrations were determined through a total
digestion procedure as described in Ohnemus et al. (2014) and Twining et al. (2015). Briefly, approximately 7 L of contamination-free seawater was
filtered directly from Teflon-coated GO-FLO sampling bottles over
acid-washed 47 mm (shelf stations) or 25 mm (open basin stations) PES Supor
filters. Filters were divided in half, and one-half was digested for 3 h
at 100–120 ∘C in sealed Teflon vials containing 4 M HCl, 4 M HNO3, and 4 M HF (Fisher Optima), which digests the marine suspended
particulate matter (SPM) but leaves the PES filter mostly intact. The PES
filters were rinsed with ultrahigh-purity water (18.2 MΩ cm-1) and removed from the digestion vials, and 60 µL of
sulfuric acid (Optima) and 20 µL of hydrogen peroxide (Fisher Optima)
were added to the vials to digest any filter fragments. The digest solution
was taken to dryness at ∼210∘C (8–24 h). The
digest residue was redissolved in 4 mL of 0.32 M HNO3 before
measuring the total particulate Co, Mn, and phosphorous (pCo, pMn, pP)
concentrations by inductively coupled plasma mass spectrometry (ICP-MS;
Thermo Element 2, National High Magnetic Field Laboratory, Tallahassee,
Florida). Major and trace element concentrations were calibrated using an
external multielement standard curve and corrected for instrument drift
using a 10 ppb indium internal standard
(Twining et al., 2019).
Biogeochemical modeling of Co in the Arctic
Modeling runs in the Arctic Ocean were completed using a previously
published biogeochemical model for Co (Tagliabue et al., 2018). Briefly, the
Co model is part of the PISCES-v2 model and has an additional six tracers
for Co, including dCo, scavenged Co (associated with Mn oxides), Co within diatoms, Co in nanoplankton, small particulate organic Co, and large
particulate organic Co (Tagliabue et
al., 2018). Phytoplankton uptake of Co in the model allows for variable Co/C
ratios and is based on a maximum cellular quota. The PISCES model is an
excellent platform for these studies as it has a detailed representation of
ocean biogeochemical cycling and has been used for a range of different
studies. Measured pCo is equal to the sum of all of the particulate Co
tracers in the model (including living and nonliving pools). Excretion of
Co is also simulated in a similar manner to Fe in PISCES-v2, with a fixed
Co/C ratio in both micro- and mesozooplankton that sets the excretion of dCo
as a function of the Co content of their food
(Tagliabue et al., 2018). The background
biogeochemical model presented in Tagliabue et al. (2018) was modified
slightly for this work, most notably with an improved particle flux scheme
(Aumont et al., 2017), with the Co-specific parameterizations
left unchanged. We used the model to assess the role of different processes
by conducting sensitivity tests whereby the sedimentary Co source was
eliminated, the riverine Co source was eliminated, the slowdown of Co
scavenging at lower oxygen levels was removed (meaning oxygen did not affect Co
scavenging), and the change in Co scavenging due to variations in bacterial
biomass was instead set to a constant value. By comparing the results of
these four sensitivity experiments to the control model, we were able to
quantify the relative contributions of different external sources and
internal cycling processes.
ResultsOceanographic context
The Arctic Ocean is a unique ocean basin. The surface circulation in the
Arctic is characterized by a clockwise current that entrains shelf water
from the Chukchi and Eurasian shelves, before being swept across the North
Pole by the Transpolar Drift (TPD; Fig. 1). This current is distinguished by
its low salinity and elevated concentrations of dissolved organic carbon
(DOC; Klunder
et al., 2012; Wheeler et al., 1997). The Arctic Ocean is a highly stratified
system, with little mixing between the main water masses
(Steele et al., 2004). The major water masses that enter the
Arctic through the Bering Strait are the upper modified Pacific water (mPW)
and the Pacific halocline water (PHW). The mPW includes inputs from the
Bering Shelf, as well as freshwater inputs from rivers, sea ice melt, and
glacially modified waters. PHW includes some influences from Bering Sea
water (BSW; including both summer and winter water; Steele et
al., 2004). Atlantic water (AW) comprises the bulk of the intermediate and
deep waters of the Arctic basin. These major water masses (mPW, PHW, AW) can
be distinguished from the high-resolution nutrient, oxygen, and salinity data
from the conventional CTD rosette stations in the sampling region (Fig. 2).
The mPW is characteristic of low salinity (31<S<32) and
nutrients (Fig. 2) and contains contributions from Alaska Coastal Water
(Steele et al., 2004), as well as from other modified water masses
from the shelf. The PHW can be clearly identified from the elevated
macronutrient concentrations (Fig. 2d) and temperature maximum within the
salinity range of 31–33 (Steele et al., 2004; Steele and
Boyd, 1998; Fig. 2a, c). The AW comprises a relatively uniform deep layer
throughout the entire Arctic basin. AW enters the Arctic through the Fram
Strait and Barents Sea and cycles in a cyclonic direction around the
Eurasian Basin and Canada Basin (Aagaard and Carmack, 1989;
Carmack et al., 1995) and is characterized by higher salinities
(>33), its temperature (∼-1.0∘C), and
lower nutrient concentrations (silicate < 5 µmol L-1).
In situ temperature (a), nitrate (b), salinity (c), phosphate (d), oxygen (e), and silicate (f) with neutral density anomaly contours from the
northern and southern legs of the GN01 transect as shown in Fig. 1. Major
water masses are labeled as modified Pacific water (mPW), Pacific halocline
water (PHW), and Atlantic water (AW).
Dissolved cobalt distributionsElevated dissolved cobalt in surface waters
Blank and consensus values for the GIPY14 dataset are reported elsewhere
(Noble et al., 2017), and the dCo
blanks and standards for the GN01 analyses are reported in Table 1. The dCo
profiles in the Arctic resembled a “scavenged-like” profile throughout the
majority of the transect and were distinct from recent U.S. GEOTRACES
efforts in the North Atlantic (Noble et al., 2017) and eastern
tropical South Pacific (Hawco et al., 2016;
Fig. 3). When median dCo concentrations from this study are binned by depth,
the upper 50 m in the Arctic contains a median dCo concentration
approximately 10 times higher than that of surface waters in the North
Atlantic or South Pacific (Dulaquais
et al., 2014a; Hawco et al., 2016; Noble et al., 2017, 2012). Profiles in the
Arctic also show no perceptible mid-depth maximum analogous to either the
Atlantic or the Pacific (Fig. 3), and instead dCo concentrations rapidly decline
until reaching values of approximately 50–60 pmol L-1. These
concentrations in deep waters are slightly lower than those of the deep Atlantic and
closer to background Pacific levels (∼ 30–40 pmol L-1).
Median dCo concentrations at specific depth intervals from the
Arctic Ocean (this study; red circles), Atlantic Ocean (blue triangles), and
the Pacific Ocean (orange squares). Shaded regions indicate the upper and
lower quartiles of the data in each dataset.
The dCo concentrations were highly elevated in surface waters (<100 m) in the shelf regions (Fig. 4a–c, p–r), and these high concentrations
persisted into the basin in the vicinity of the North Pole (Fig. 4f–h). In
the Bering Sea, dCo in surface waters ranged from 131 to 156 pmol L-1 in
the upper 40 m, with an apparent surface or subsurface minimum associated
with biological drawdown (Fig. 4a). Concentrations notably increased in
stations near the Bering Strait (stations 2–6; Fig. 4b), where dCo reached
up to 457 pmol L-1 in surface waters (Figs. 4b, 5), and was even
higher in bottom waters, sometimes exceeding 1.5 nmol L-1 (Figs. 4b,
5). Surface enrichment of dCo was even more pronounced on the Chukchi
Shelf, where concentrations consistently exceeded 800 pmol L-1 (Figs. 4q, 5). The dCo and LCo concentrations from the Canadian GEOTRACES
expedition in 2009 also had near-surface maxima in dCo and LCo, with up to
300 pmol L-1 dCo (Fig. 4r). These concentrations were lower than those of nearby
samples collected in 2015 (Fig. 4p, q), which contained up to 3 times
more dCo in the upper 100 m.
Dissolved cobalt (dCo; black circles) and labile cobalt (LCo; open
circles) from all stations from the 2015 (a–q) and 2009 (r) studies.
(a) dCo concentrations and (b) LCo concentrations in the Arctic
Ocean.
The elevated dCo concentrations on both shelves traversed by the US
expedition persisted throughout the marginal ice zone (MIZ; stations 12–17,
51–54) and into the Canada Basin (stations 12–26), following similar
patterns in dFe and dMn (Laramie Jensen and Mariko Hatta, personal communication, 2020). Water mass
fractions and sea ice melt in the MIZ in this study were determined based
on δ18O data (Newton et al., 2013). Some
high concentrations of dCo were observed in the region of the MIZ and in
samples with pronounced influence from meltwater (>1.5 % sea
ice melt; Table 2) in the upper 30 m, with median dCo concentrations equal
to 358 pmol L-1 in the MIZ, though with large variability (range
26–546 pmol L-1) likely due to surface drawdown and additional dCo
sources. Surface concentrations in this region ranged from approximately
100 to 500 pmol L-1 (Fig. 4d–f, m–n). The dCo in surface waters decreased
slightly in the Makarov Basin and reached some of the lowest observed
concentrations at the North Pole (210 pmol L-1; Figs. 4h, 5),
though concentrations were still slightly higher than at station 1, the only
Pacific station (Fig. 4a). Although some elements such as dFe showed
noticeable elevated concentrations in the vicinity of the North Pole in surface
waters compared to surrounding waters (Laramie Jensen, personal communication, 2020), dCo remained
lower than on the shelf and in the MIZ (Fig. 5). Surface dCo at the North
Pole was approximately 250 pmol L-1, nearly half the concentrations
observed in the Canada Basin (Fig. 4h).
Median, maximum, and minimum concentrations of total dissolved cobalt (dCo)
and labile cobalt (LCo) in samples with representative water masses and
sources in the Arctic Ocean. Median concentrations were determined in each
water mass type by using water masses that contained >95 %
Atlantic water, >95 % Pacific water, >10 %
meteoric water, and >1.5 % sea ice melt. Shelf stations were
stations 2–10 and 60–66; MIZ stations were 10–17 and 51–57 (<30 m); and
North Pole stations were 27–36 (<200 m). Ice hole samples were sampled
from 1 and 5 m. The notation “nd” means not determined.
dCo (pmol L-1)MaxMinnLCo (pmol L-1)MaxMinnAtlantic61.6126.336.9372.25.80.227Pacific269.6687.364.14145.8133.82.535Meteoric266.1497.264.12777.5139.811.625Shelf526.01852.125.930148.0578.76.130MIZ357.5546.225.919117.0158.66.119North Pole139.8280.264.21410.322.01.514Sea ice melt526.01021.5207.33151.1233.048.83Ice hole281.1316.2259.44ndndnd4Dissolved cobalt in Pacific halocline and deep waters
While silicate (SiO3) and phosphate (PO43-) concentrations
were indicative of the advection of PHW (Fig. 2e, f), dCo did not show a
prominent enhancement within this feature (Fig. 5a), likely due to the lower
relative concentrations of dCo in Pacific waters compared to shelf waters
(station 1; Fig. 4a). Median concentrations of dCo in waters dominated by
Pacific water (>95 %) were 270 pmol L-1 (range 64–687 pmol L-1), while on the shelf they were 526 pmol L-1 (Table 2).
Any elevated dCo concentrations observed within the PHW density layer
(σθ= 26.2–27.2; Steele et al., 2004) were
likely added along the flow path of Pacific water across the Bering Shelf
(Fig. 4b). Thus, stronger relationships were observed with other elements
which are also elevated on the shelf (e.g., dFe and dMn; Mariko Hatta, personal communication, 2020) than with SiO3 or other macronutrients (e.g., PO43-).
The dCo was remarkably constant within the deep Arctic, reflective of both
AW and deep Arctic bottom water (Fig. 5a; Swift et al.,
1983). Concentrations in AW (>95 % AW and all depths
>500 m) had a median value of 62 pmol L-1 (Table 2), in
between the average deep-water dCo concentrations found in the Pacific and
Atlantic (Fig. 3). The near-bottom sample from some profiles also showed
slightly lower dCo (<5 pmol L-1) than the sample immediately
above it (Fig. 4c, d, f), perhaps indicating some influence of the weak
nepheloid layers on bottom-water scavenging of dCo in the Arctic
(Noble et al., 2017).
Labile cobalt distributionsLabile cobalt in surface waters
LCo is the fraction of total dCo that is either not organically complexed or
weakly bound by organic ligands and represents the labile fraction of
the total dCo pool in terms of either biological uptake or scavenging (Saito
et al., 2004; Saito and Moffett, 2001). LCo distributions looked remarkably
similar to dCo distributions in the upper water column (Figs. 4, 5). Concentrations were
lower than those of dCo, ranging from 0 (not detectable) to 600 pmol L-1 on
the Canadian side of the Chukchi Shelf (station 61, 66). LCo comprised
20 %–35 % of the total dCo pool in the upper water column (Fig. 6), with the
highest percentage of LCo found over the Chukchi Shelf and approximately
20 % LCo in Pacific waters (station 1; Fig. 6). LCo decreased more rapidly
with respect to distance from the shelf than dCo in the Canada Basin and
towards the North Pole, with the North Pole region containing significantly
lower median concentrations of LCo (10 pmol L-1, p<0.05) than surrounding waters (148 and 117 pmol L-1 on the shelf and in the MIZ,
respectively; Table 2). The majority of the LCo appeared to be
removed via either scavenging or biological uptake in the upper water column in the
Canada Basin and along the Lomonosov Ridge. Some of the highest median LCo
concentrations were observed in the upper 30 m in the MIZ and in waters
containing significant sea ice melt (>1.5 %, Table 2), with
median concentrations rivaling those on the shelf (Table 2). The LCo in
these samples had a large range in many cases (49 to 233 pmol L-1 in
samples with >1.5 % sea ice melt), suggesting that sea ice may
be a source of LCo and that it is taken up quickly in surface waters after
input from meltwater.
The ratio of LCo (pmol L-1) to total dCo (pmol L-1)
along the transect from south to north in the upper 1000 m.
Labile cobalt in Pacific halocline waters and deep waters
LCo was extremely low, and often undetectable, in the deep waters of the
Arctic (Fig. 4). Any detectable LCo at these depths represented less than
10 % of total dCo (Fig. 6), and the majority of the dCo in the deep Arctic
was strongly organically complexed. Similar to dCo, there was no observable
enhancement of LCo in PHW, with LCo distributions closely following those of
dCo and other shelf-enhanced trace metals such as dFe and dMn (Laramie Jensen, personal communication, 2020; Jensen et al., 2019; Tonnard et
al., 2020). LCo decreased below the upper 250 m, and the median
concentration of LCo in the Atlantic layer was 2 pmol L-1 (Table 2),
virtually equal to the detection limit of the method, suggesting scavenging
or uptake of LCo in the upper water column and little to no detectable LCo
in deep waters of the Arctic.
Dissolved and particulate manganese and particulate cobalt distributions
DCo and dMn had very similar distributions across the transect. The pCo and
pMn concentrations were slightly decoupled from the dissolved
concentrations, with a subsurface peak in both (Fig. 7), as opposed to the
surface peak observed in dCo and dMn. The maximum in pCo and pMn occurred at
depths of approximately 200–300 m, corresponding to a region of
significantly elevated concentrations of particulate Mn oxides (Phoebe Lam, personal communication, 2020). Overall, pCo and pMn concentrations were the highest on the shelf,
with visible increases at the base of the profiles near the sediment–water
interface (Fig. 7b, c). Concentrations of pCo and pMn declined by almost an
order of magnitude from the shelves into the Arctic basin, with
concentrations ranging from 20 to 40 pmol L-1 and 1 to 10 nmol L-1 for pCo and pMn, respectively. Deep-water (>1000 m)
particulate concentrations for both metals were extremely consistent, with
concentrations varying slightly over the entire Arctic basin (Fig. 7d, h).
These deep-water pMn and pCo concentrations are notably higher than in other
regions, such as deep Pacific waters (Lee et al., 2018).
Particulate manganese (pMn; open circles) and particulate cobalt
(pCo; ×) from several stations along the northern (a–d) and southern (e–h)
legs of the transect, with the same station designations as in Fig. 4.
Modeling sensitivity experiments
The control model run agreed well with the data over a number of different
depth strata (Fig. 8). In the surface layer (0–50 m), the model output was
most consistent with the observations (Fig. 8a), although in general, the
model tends to produce maximum levels of dCo that underestimate the highest
dCo concentrations observed. Part of this is likely due to the fact that the
model is comparing an annual mean output against the synoptic scale of the
in situ observations. However, the model may underestimate sources of dCo in
the Arctic. Below 50 m, there is also good agreement with observations (Fig. 8b), with the model capturing the much lower dCo characteristic of these
waters and in particular the contrast between our data in the Arctic and
other data from the North Atlantic (Dulaquais et al.,
2014a). In the deepest layers (Fig. 8c and d), the model again is able to
reproduce the decline in dCo to ∼60 pmol L-1 and the
consistency between the deep Arctic and North Atlantic.
Model output (colors) compared to observations (dots) from 0 to 50 m (a), 50 to 150 m (b), 700 to 800 m (c), and 1500 to 2000 m (d).
In order to explore the major processes contributing to the modeled dCo
sources and sinks, the proportion of the dCo signal in two distinct depth
horizons was further investigated using a set of sensitivity experiments. In
the 0–50 m depth range (Fig. 9), rivers in the model were shown to have no
large-scale impact on the Arctic-wide dCo signal (Fig. 9a), while removing
sediment margin sources reduced dCo by over 80 % (Fig. 9b). Enhanced
sediment Co supply under low oxygen also had no impact in this region.
Similarly, modulating the effect of oxygen on Co scavenging had little
impact in the Arctic (Fig. 9c). It was notable that in sensitivity
experiments where bacteria scavenging due to Mn-oxide formation was kept
constant (e.g., by eliminating the effect of bacterial biomass on scavenging),
the dCo concentrations were reduced by over 60 % in surface waters in some
regions, indicating that lower rates of scavenging were also contributing to
the high concentrations of dCo in the surface ocean (Fig. 9d). Thus, our
model experiments suggest that the high levels of dCo in the Arctic surface
waters are due to high supply from sediments, combined with reduced
scavenging rates due to lower metabolic activity of Mn-oxidizing bacteria
due to the colder temperatures. In the 700–800 m depth horizon, we similarly
found that changing sediment supply was more important than rivers (Fig. 10a
and b) but that the effect of sediments was reduced at these depths
compared to the surface. Equally, retardation of Co scavenging under low
oxygen had a minor role in the ocean interior (Fig. 10c), with bacterial
biomass again having a significant effect on the dCo signal (Fig. 10d).
Thus, in contrast with the surface, we find that in the 700–800 m stratum
there is a roughly equal role played by sediment Co supply and low rates of
Co removal by Mn-oxidizing bacteria in maintaining the dCo concentrations.
(a) Model output of the proportion of the dCo signal from 0 to 50 m
that is controlled by (a) rivers, (b) sediment input, (c) oxygen
concentrations, and (d) removal by Mn oxidation from Mn-oxidizing bacteria.
(a) Model output of the proportion of the dCo signal from 700 to 800 m that is controlled by (a) rivers, (b) sediment input, (c) oxygen
concentrations, and (d) removal by Mn oxidation from Mn-oxidizing bacteria.
DiscussionQuantifying external sources of cobalt to the Arctic Ocean
The coherence of the dCo and LCo distributions with that of dMn, along with
evidence from the model output, suggests that shelf sediments are one of the
primary sources of Co in the Canadian sector of the Arctic Ocean (Figs. 5,
9). Mn is known to be an excellent tracer of sediment input due to the high
solubility of reduced Mn from anoxic sediments (Johnson
et al., 1992; März et al., 2011; McManus et al., 2012; Noble et al.,
2012), though there was also a limited source of dMn from rivers in this
region (Charette et al., 2020). By using the dMn concentrations as a tracer
for shelf input, we can quantify the proportion of the variance in the dCo
and LCo observations that are explained by this shelf proxy. Linear
regressions between dCo or LCo distributions and dMn in the upper 200 m
across all of the stations explained 67 % and 72 % of the variance in
the dCo and LCo concentrations, respectively (Fig. 11a; p<0.05).
This trend is driven primarily by the data in the upper 50 m. The variance
explained decreases, however, if only the shelf stations (stations 2–10,
57–66) are included in the analysis (data not shown), suggesting that some
process other than shelf inputs couples the dMn and Co distributions within
the basin. The amount of the variance in the Co distributions that is
explained by shelf inputs as indicated by dMn is slightly less than that
observed in the model (Fig. 9b), though both agree that shelf inputs are the
dominant source.
dCo (closed circles) and LCo (open circles) in the upper 200 m
plotted against (a) dMn in shelf stations only (stations 2–10, 57–66), as
well as (b) salinity from only the stations influenced by the Transpolar
Drift (stations 30–43).
The modeling results suggest that nearly all of the dCo in the upper 50 m
can be accounted for by a combination of a sediment source and diminished
scavenging in the Arctic relative to other ocean basins (Fig. 9b and d; Tagliabue et al., 2018). However, the
observations suggest that 20 %–30 % of the variance cannot be explained by a
shelf source alone. If the dCo and LCo is examined against salinity for all
stations from GN01 in the upper 200 m, then salinity can explain 24 % and
28 % of the variance for dCo and LCo, respectively (data not shown). This
relationship is improved if only the stations in the central Arctic basin
are included (stations 30–43), and then salinity explains 47 % of the dCo
and 57 % of the LCo distributions (Fig. 11b). The coherence of dCo and LCo
with salinity across the dataset, and particularly in this region, appears
to be due to a contribution of low-salinity water from rivers, rather than
from sea ice melt (Fig. 12c), as no relationship was observed with the
fraction of sea ice melt determined from δ18O isotopic
measurements of seawater (Bauch et al., 2005;
Cooper et al., 1997, 2005; Newton et al., 2013). Instead, the relationship
with salinity is driven by freshwater inputs from rivers, as a strong
relationship is observed with the fraction of meteoric water (Fig. 12d).
These stations correspond to a region of anomalously high dFe and DOC
concentrations (Charette et al., 2020), interpreted to be
indicative of river inputs carried across the basin in the Transpolar Drift
(TPD)
(Gascard
et al., 2008; Klunder et al., 2012; Middag et al., 2011; Wheeler et al.,
1997). This is supported by measurements of 228Ra, which has been used
as a tracer of shelf inputs throughout the Arctic (Kipp et
al., 2018; van der Loeff et al., 2018). A similar relationship was also
observed with salinity in the North Atlantic, supporting the role of rivers
as a source of dCo (Dulaquais
et al., 2014a; Noble et al., 2017; Saito and Moffett, 2001). In our model
sensitivity experiments, we found a small effect of rivers on dCo (Figs. 9a,
10a), and the Co/N river endmember in the model was similar to that measured
by the Arctic Great Rivers Observatory (Holmes et
al., 2018). It appears that the data suggest a larger role for rivers than
what is captured by the model, which could imply that gross riverine fluxes
are underestimated by our model. However it is difficult to disentangle
riverine processes from other processes happening on the shelf like
groundwater inputs (Charette et al., 2020). It is possible that
there is some mixing of river and sediment dCo occurring in the coastal zone
or that our global-scale model is not able to properly account for the
physical transport of fluvial signals into the open basin.
dCo and LCo from select stations versus (a) the fraction of
Atlantic water (Fatl; all stations <500 m), (b) the fraction
of Pacific water (Fpac; all stations <500 m), (c) the fraction of
sea ice melt (Fice; <100 m and south of 84∘ N),
and (d) the fraction of meteoric water (Fmet; <500 m and north
of 84∘ N).
The presence of such high concentrations of trace elements and isotopes at
the North Pole was surprising, yet several tracers indicate that this is an
area significantly influenced by river and shelf input from the surrounding
continents (Charette et al., 2020; Colombo et
al., 2020; Kipp et al., 2018; van der Loeff et al., 2018). The elevated
concentrations of dCo at great distances from the continental shelf are also
likely partially due to the enhanced organic complexation of dCo in TPD
waters. Averaged over the entire dataset, dCo is 79±13 % organically
complexed (21±13 % labile) in the upper 200 m of the water column.
However, at TPD-influenced stations (stations 29–34; Charette
et al., 2020), dCo is 92±6 % organically complexed, significantly
higher than in the rest of the transect (paired-sample t test, p<0.05). This
suggests that elevated concentrations of DOC from Arctic rivers entrained in
the TPD or ligands produced in situ may play a role in stabilizing a portion
of the dCo pool during transport towards the North Pole, as has been
observed for other metals such as dFe (Slagter
et al., 2017, 2019) and dissolved copper (Nixon et al.,
2019). Although the exact character of the organic dCo-binding ligands in
seawater is unknown, in the Arctic it is likely that humic-like substances
contribute some portion of the organic complexation observed, due to the
presence of elevated colored DOM (CDOM) in the TPD (Wheeler et
al., 1997), consistent with the presence of humic substances (Del
Vecchio and Blough, 2004). Despite the presence of humic substances, it
seems somewhat unlikely that humics account for all of the ligands
complexing dCo in this region. Our analytical method distinguishes
organically bound Co as the fraction of total dCo that is more strongly
complexed than our competing ligand (DMG). The complexation of humic and
fulvic-like substances with Co has been shown to be much weaker than the
Co(DMG)2 complex (logKCo(HS)cond∼8 versus
logKCo(DMG)2cond=11.5±0.3;
Yang and Van Den Berg, 2009). Ligands similar
to those suspected to complex Co in open ocean waters of the Atlantic or
Pacific could be responsible for Co stabilization in the TPD waters
(Saito and Moffett,
2001). These ligands are presumed to have functional groups similar to
cobalamin (vitamin B12), with a Co atom tightly bound inside a corrin
ring. Cyanobacteria and some archaea are known cobalamin producers
(Bertrand et al., 2007; Doxey
et al., 2015; Heal, 2018; Heal et al., 2017; Lionheart, 2017), and both are
found in the Arctic (archaea – Cottrell and Kirchman,
2009; cyanobacteria – Waleron et al., 2007; Zakhia et al., 2008), although in
very low abundance. The nature of the organic molecules binding dCo in this
region will be interesting to explore further in future studies.
Overall, both the modeling results and observations agree that the dominant
source of Co in the Arctic is from the extensive shelf sediments surrounding
the Arctic Ocean, with additional contributions from Arctic rivers. The
observations, however, show that sources vary in importance in space, with
sediment sources clearly dominating in stations close to the shelf and
river sources dominating in the central Arctic basin through the influence
of the TPD. The interaction between rivers and shelves requires further
inquiry, as the shelf sediments might behave as a “capacitor” for dCo,
accumulating Co from rivers and sinking organic matter and then releasing Co
to the overlying water during reductive dissolution in the sediments
(Bruland et
al., 2001; Chase et al., 2007). Although the mechanism is uncertain, it is
clear that the riverine source dominates the distribution observed near the
North Pole where dCo and LCo concentrations remain high despite the distance
from land and that organic complexation likely plays a role in the distal
transport of this dCo (Charette et al., 2020).
Cobalt scavenging and internal cycling
A striking feature of the dCo and LCo dataset is the vertical transition in
the water column from very high to low Co concentrations throughout the deep
Arctic (Fig. 5). The question remains (1) whether or not this elevated dCo is
scavenged at a shallow depth horizon, (2) if the high dCo concentrations
in the surface layer (<200 m) are simply physically isolated from
deeper water masses, or if a combination of (1) and (2) is the case. This would suggest that
the Atlantic water characteristic of the deep Arctic does not mix with the
modified surface Arctic water containing high concentrations of Co. We
examined both hypotheses within a modeling framework and compared this to
the observations. In the model, the dCo is scavenged primarily in the upper
50 m with almost no scavenging below 200 m (data not shown). The dCo
scavenging in the model is primarily controlled by Mn-oxidizing bacteria,
which have a strong temperature dependence in the model
(Tebo et al., 2004). The cold temperatures in the majority
of the Arctic prevent enhanced scavenging of dCo by this mechanism compared
to in other basins (Hawco
et al., 2018; Saito et al., 2017; Tagliabue et al., 2018). However,
relatively warmer temperatures on the shallow shelves suggest that
scavenging is enhanced in this region (Fig. 4), and the coherence of the pCo
and pMn peaks in the upper 200–250 m (Fig. 7) support this mechanism of
upper-ocean scavenging. Evidence from 234Th data shows very little
particulate organic carbon (POC) flux in the upper water column along this
transect; however strong lateral transport from the shelves to the basin was
observed (Black, 2018). This lateral transport was observed both in
the upper water column and at depth, suggesting fast-moving currents through
the deep canyons may be significant in transporting material from the shelf
into the basin (Black, 2018). It is possible that additional
scavenging of Co may occur along this flow path. Some of the profiles
observed in the deep basin also show evidence for bottom-water scavenging in
the Atlantic water (e.g., Fig. 4e, h, p).
Additional insights on Co scavenging in this basin can be observed by
exploring the dCo: phosphate (P) ratios (pmol L-1 : µmol L-1)
along the transect (Fig. 13). The relationship between dCo and P in the
Arctic water column yields insights into biological uptake and regeneration
processes acting on the dCo inventory, as well as into scavenging. An analysis
completed by Saito et al. (2017) showed that positive slopes in the dCo : P
relationship were indicative of regeneration, while negative slopes were
indicative of biological uptake or scavenging (Saito et al., 2017). The high
dCo in the Arctic yields a unique dCo : P relationship compared to the North
Atlantic (Fig. 13a; Saito et al., 2017). When dCo : P slopes (r2>0.6) are binned according to whether they are positive (Fig. 13b)
or negative (Fig. 13c) and then plotted versus depth (Fig. 13d), a few
patterns are apparent. Positive dCo : P slopes are observed largely within a
confined depth layer in the PHW (Fig. 13d). This is not surprising, given
that deep Pacific waters carry a strong regeneration signal. However, at
most other depths the dCo : P slopes are negative, showing that scavenging is
occurring to some extent throughout the water column (Fig. 13d). With one
exception, the magnitudes of the negative dCo : P slopes are greater in the
upper water column, supporting the model results and our interpretations of
the pCo profiles that most of the scavenging occurs in the upper water
column but also continues to occur throughout the deep Arctic. The negative
slopes at the base of the profiles could also represent the dilution of dCo
in the deep Arctic with lower-dCo Atlantic water, as noted in the western
Atlantic Ocean (Dulaquais et al., 2014b). However, it is unlikely that
dilution alone accounts for the negative slopes observed throughout the
water column.
(a) The dCo (pmol L-1) compared to phosphate (dP; µmol L-1) from the GN01 dataset. (b) five-point two-way linear regression
of positive dCo : P slopes (r2>0.6). (c) five-point two-way
linear regression of negative dCo : P slopes (r2<-0.6). (d) Depths where either a positive (blue) or a negative (red) dCo : P slope was
identified in the GN01 dataset. Additional details on the regression
analysis can be found in Saito et al. (2017).
This evidence, combined with the coinciding maxima observed in pCo and pMn,
suggests that scavenging occurs in the upper water column but that
additional scavenging continues to occur in deeper waters. The elevated pCo
concentrations in the deep Arctic compared to other regions
(Lee et al., 2018) suggest that scavenging over long
timescales continues to add to the pCo pool. The strong stratification in
the Arctic likely prevents high concentrations of dCo from mixing between
the modified surface waters, the PHW, and the deep Atlantic water
(Steele et al., 2004). Thus, it is likely a combination of
limited upper-ocean scavenging and strong stratification between water
masses that keeps the elevated dCo and LCo confined to the surface waters
in the Arctic, yielding the intense scavenged-like profile of Co in this region
compared to other basins (Fig. 3).
Increases in Co inventories over time in the Canadian sector of the Arctic Ocean
Samples collected on the shelf in the Beaufort Sea in 2009 in proximity to
the U.S. GEOTRACES transect in 2015 (Fig. 1) had significantly lower dCo
(paired t test, p<0.05) than shelf samples from 2015 (Fig. 14). Shelf
samples for dCo from 2015 were approximately 4 times higher than the dCo in 2009,
and for LCo in 2015 they were approximately 8 times higher than the LCo in 2009 (Fig. 14c). The
maximum dCo concentration measured in 2009 was 301 pmol L-1, while in
2015 it was 1852 pmol L-1. The dCo and LCo concentrations below 150 m
agreed very well, however, between the 2 years (Fig. 14a, b). Several
factors could account for the higher dCo and LCo observed in 2015 compared
to 2009. The Co samples from 2009 were initially unfiltered and were not
stored with gas-absorbing satchels like the samples from 2015. Recently,
loss of dCo has been observed in the presence of oxygen during storage;
however this loss was most pronounced for samples in low-oxygen regions
(Noble, 2012). The mechanism of the dCo loss is unknown and is
difficult to quantify from these samples; however the waters are well
oxygenated in this region (Fig. 2b), and thus the loss due to storage was
likely minimal. However, we cannot say for certain how much of the observed
increase in dCo over time is due to a storage artifact. Previous work has
shown a maximum loss of dCo of 40 % after 5 months of storage
(Noble, 2012). If we consider that 40 % of the dCo could have
been lost in the samples collected from 2009, the data from 2015 still show
an increase in dCo of approximately 400 %. Some of the samples from 2009
were also collected over a narrower region of the shelf compared to those in
2015, so shelf width could also be an important factor in the observed
increase in dCo. Thus, although we cannot quantify with certainty the
percent increase in dCo over time in the Canadian sector of the Arctic, it
is possible that an increase in dCo was observed.
The dCo on the shelf measured in 2009 (GIPY14; black triangles)
and 2015 (GN01; blue circles) in the upper 3500 m (a) and upper 500 m (b).
Average dCo and LCo in the upper 150 m from 2009 (grey) and 2015 (blue; c).
Error bars represent the standard deviation, and * denotes a significant
difference.
The increase in dCo over time in the Arctic is interesting and has been
documented for other tracers in the Arctic. Kipp et al. (2018) and van der
Loeff et al. (2018) noted that 228Ra has increased over time in the
central Arctic. They suggest that increases in shelf and/or river inputs
from thawing permafrost are the source of this elevated 228Ra
(Kipp et al., 2018; van der Loeff et al., 2018). A similar
mechanism is likely increasing metal inventories over time on Arctic
shelves. The majority of the variance (∼70 %) in dCo in the
upper 100 m on the U.S. GEOTRACES transect could be explained by a shelf
source, and the remainder was likely associated with river inputs (Fig. 11).
If these sources are similar to the sources of dCo in 2009, then an increase
in either a shelf or river flux could be responsible for the dramatic
increase in dCo over time. While there are not enough data to state whether
the river dCo flux has in fact changed over time in the Arctic and the
observed changes could be due to seasonal or interannual variability,
several other studies have documented an increase in river discharge due to
increases in permafrost melt over time (Doxaran et al., 2015; Drake et al.,
2018; Kipp et al., 2018; van der Loeff et al., 2018; Tank et al., 2016;
Toohey et al., 2016). The increase in river discharge has the potential to
considerably increase trace metal inventories in the future Arctic Ocean,
perhaps particularly for those metals that are strongly organically
complexed, thus protecting against scavenging in the estuarine mixing zone
(Bundy et al.,
2015). We recognize these two Arctic dCo datasets are limited in temporal
coverage and have methodological differences; however, we felt a
responsibility to transparently present these observations of dCo increases
in the Arctic Ocean to raise community awareness of this potential
environmental change. These increases in metals over time may have
implications for metal stoichiometries and phytoplankton growth in a
changing Arctic Ocean.
Implications of the Arctic as a net source of Co to the North Atlantic Ocean
The concentrations of dCo and LCo in this region of the Arctic are some of
the highest that have been observed thus far in the ocean. In some cases,
the dCo was almost 10 times higher than in the low-oxygen region of the
eastern Pacific (Hawco et al., 2016). Although the Arctic is considered to
be a macronutrient-poor system, in contrast to other oligotrophic regions
the Arctic is quite enriched in micronutrients (Charette
et al., 2020; Colombo et al., 2020; Jensen et al., 2019; Marsay et al.,
2018; Slagter et al., 2017). These distinct micronutrient ratios may have
implications for Arctic phytoplankton communities, as well as for communities in
the North Atlantic that are influenced by inputs from the Arctic.
(a) The ratio of LCo to dCo (colors) from this study and the
western portion of the GA03 North Atlantic transect (Noble et al., 2017)
along with dCo concentrations (b) in temperature–salinity space, with
Labrador Sea Water (LSW) source waters (solid black box) and signature in
the Atlantic (dashed box) are highlighted. (c) Sampling region in this study
and the stations used from Noble et al. (2017).
Median dCo concentrations (a), dissolved Zn concentrations (b),
and dCo/dZn ratios (c) in the upper 200 m in the Arctic (this study), North
Atlantic (Noble et al., 2017), and the southern eastern Pacific (Hawco et
al., 2016). (d)Co/Zn ratios in phytoplankton from the Arctic and North
Atlantic. Whiskers represent the lower (25 %)
and upper (75 %) quartiles.
Arctic waters are thought to primarily exit the basin and impact the North
Atlantic via the Canadian archipelago and the Fram and Davis straits
(Talley, 2008). The organic complexation and stabilization as well as the
high concentrations of dCo suggest that some of this dCo might exit the
Arctic and impact nutrient distributions in the North Atlantic. Noble et al. (2017) noted a plume of elevated dCo in the western portion of the U.S.
GEOTRACES North Atlantic (GA03) transect that did not correspond with a
signature from reducing sediments as on the North Atlantic eastern boundary.
Noble et al. (2017) postulated that high dCo in Labrador Sea Water (LSW) was
the source of this signal, due to the presence of a corresponding signature
of low silica that is characteristic of this water mass. The authors noted
this anomalously high dCo could be from elevated dCo in Arctic waters, or
due to high dCo on the shelf that is picked up along the flow path of the
LSW, or a combination of the two (Dulaquais et al.,
2014a; Noble et al., 2017). This observation was also noted by Dulaquais et
al. (2014b) in the GEOTRACES GA02 section (Dulaquais et al.,
2014a, b). Our data suggest that a combination of the high dCo
observed in this study and additional Co entrained on the shelf in the
Labrador Sea likely contribute to that signal, and when observed in temperature and
salinity space the data support this hypothesis (Fig. 15). The Arctic source
waters that contribute to the formation of LSW have a low-salinity
signature and are likely significantly modified as they exit the Canadian
archipelago, Fram Strait, and Davis Straits (Myers, 2005). From
these data we cannot quantitatively connect the elevated dCo and LCo observed
in the Arctic source waters to the LSW seen in the western Atlantic
(Dulaquais et al.,
2014a; Noble et al., 2017), given the complex history (e.g., transformation,
mixing) of source waters in the Labrador Sea region (Le Bras et
al., 2017). However, it is apparent that the low-salinity Arctic waters
contain high Co (Fig. 15), which given the advective pathways of these water
masses from the Arctic, suggests that they may act as a source of Co to
lower-latitude waters. Interestingly, the high dCo in the Arctic has a
distinct LCo/dCo signature compared to that observed in the western North
Atlantic (Fig. 15a). Due to the significant impact that Arctic shelves and
rivers have on the dCo signature observed in this study, it is likely that
additional Co may be added to these waters as they pass through the Canadian
archipelago. The fate of these waters and their Co as they exit via the Fram
and Davis straits is unknown. Constraining these Arctic endmembers and how
they contribute to dCo distributions in the North Atlantic deserves further
attention, as it has interesting implications for nutrient resource ratios
for North Atlantic phytoplankton communities.
The possibility that elevated micronutrient concentrations from the Arctic
are being exported to the North Atlantic could have implications for
phytoplankton nutrient utilization and community composition. The dCo and
dZn for example, which can be interchanged within carbonic anhydrase in some
eukaryotes (Lane and Morel, 2000; Sunda and Huntsman, 1995;
Yee and Morel, 1996), are elevated in the Arctic
(Jensen et al., 2019) compared to the North Atlantic
and South Pacific (Fig. 16a, b; Schlitzer et al., 2018). The
higher concentrations of both metals result in a dCo/dZn ratio that is
quite similar to that observed in the North Atlantic; however the range in
this ratio is large (Fig. 16c). Small changes in the sources of each of
these metals could manifest as big impacts on the ratio of these
micronutrients in surface waters, which laboratory studies have shown to
have significant effects on growth (Hawco and Saito, 2018;
Kellogg et al., 2020; Sunda and Huntsman, 1995). The cellular Co/Zn ratios
are also slightly higher in the Arctic compared to the North Atlantic but
span a similar range (Fig. 16d). However, if river
inputs continue to increase with an increase in permafrost thawing in the
warming Arctic (Jorgenson et al., 2006) and similar increases
in dCo are observed over time as seen in this work, then the inventory of
dCo in the Arctic may begin to influence the North Atlantic to a greater
extent. These increases in metal sources may disproportionately affect Co
compared to Zn, whose primary source was found to be from a regeneration
signal on the shelf rather than from river input (Jensen et al., 2019), and
the total Co inventory is small compared to Zn. For example, diatoms that
have enhanced growth rates when metabolically substituting Co for Zn may be
favored in surface waters with higher dCo/dZn ratios (Kellogg
et al., 2020), although there are no experimental data to our knowledge
examining the influence of Zn and Co on Arctic phytoplankton. Understanding
how future changes in metal sources in the Arctic may impact the North
Atlantic or shifts in phytoplankton community structure will be important to
constrain.
Conclusions
The unique dissolved and labile Co distributions observed in the Arctic
compared to in other open ocean basins have potential implications for future
changes in micronutrients in the warming Arctic Ocean. Sediment and river
inputs to the Arctic appear to be the dominant mechanisms for the input of
dCo to the Arctic, and these elevated signals persist over a broad area of
the western Arctic far from their source regions. In part, this appears to
be due to relatively slow scavenging of Co in the Arctic, highlighting the
impact of lower temperatures and slower kinetics of Mn-oxide formation in
this basin. The dCo in the Arctic is also strongly organically complexed,
which may also prevent scavenging and lead to the persistently high
concentrations observed in surface waters. Notably, Co was also suggested to
be increasing over time on the shelf in the Canadian Arctic, likely due to
increases in river inputs from thawing permafrost, consistent with other
Arctic tracers. The increase in the inventory of dCo over time in the Arctic
may have downstream impacts on dCo/dZn ratios in North Atlantic waters, as
the dCo inventory will be disproportionately magnified relative to dZn with
additional future increases from Arctic rivers. Higher dCo/dZn ratios in the
Arctic and North Atlantic may also favor organisms that have elevated growth
rates if Co is metabolically substituted for Zn. These ecological impacts
are likely to become increasingly important in the future, with increased
warming and changes to Co sources in the Arctic basin.
Data availability
The metadata for this paper are available through the BCO-DMO for GN01
(https://www.bco-dmo.org/project/638812; Landing et al., 2019) and through the BODC for
GIPY14 (https://www.bodc.ac.uk/geotraces/data/inventories/0903/; Adjou, 2020). The dissolved
and labile cobalt data for GN01 specifically are available at https://www.bco-dmo.org/dataset/722472 (Saito and Rauch, 2020).
Author contributions
RMB analyzed the samples and wrote the manuscript. MRC developed the data-processing code and helped write the manuscript. MAS designed the study and
helped write the manuscript. AT, NJH, PLM, BST, MH, AEN, SGJ, and JTC
contributed data and helped write the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to thank the captain and crew of the USGC Healy; Gabi Weiss and
Simone Moos for sampling; Dawn Moran, Noelle Held, and Matt McIlvin for
help with sample preparations and analyses; Ana Aguilar-Islas and
Robert Rember for small-boat and sea ice hole operations; the Oceanographic Data
Facility at Scripps Institution of Oceanography for macronutrient, oxygen,
and salinity measurements; Sara Rauschenberg for sample collection; and Peter Schlosser, Robert Newton, Tobias Koffman, and Angelica Pasqualini for water mass fraction
data.
Financial support
This work was supported by National Science Foundation Ocean Sciences (NSF OCE) grants (grant nos. 1435056, 1736599, and 1924554) to
Mak A. Saito, as well as by a Woods Hole Oceanographic Institution Postdoctoral
Scholar grant to Randelle M. Bundy and Mattias R. Cape. Mariko Hatta was supported by NSF OCE
grant no. 1439253. Alessandro Tagliabue was supported by the European Research Council
(ERC) under the European Union's Horizon 2020 research and innovation
program (BYONIC, grant no. 724289). Benjamin S. Twining was supported by NSF OCE
grant no. 1435862. Peter L. Morton was supported by NSF OCE grant no. 1436019, and a portion of the
work was completed at the NHMFL, which is supported by the National Science Foundation through
DMR-1644779 and the State of Florida. Jay T. Cullen was supported by the
Natural Sciences and Engineering Research Council (NSERC) of Canada and an
International Polar Year (IPY) Canada grant.
Review statement
This paper was edited by Peter Landschützer and reviewed by three anonymous referees.
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