BGBiogeosciencesBGBiogeosciences1726-4189Copernicus PublicationsGöttingen, Germany10.5194/bg-15-7205-2018Southern Ocean controls of the vertical marine δ13C gradient –
a modelling studySouthern Ocean controls of the vertical marine δ13C gradientMoréeAnne L.anne.moree@uib.nohttps://orcid.org/0000-0002-0283-5947SchwingerJörghttps://orcid.org/0000-0002-7525-6882HeinzeChristophGeophysical Institute, University of Bergen and Bjerknes Centre for
Climate Research, 5007 Bergen, NorwayUni Research Climate, Bjerknes Centre for Climate Research, 5007
Bergen, NorwayAnne L. Morée (anne.moree@uib.no)4December201815237205722321February201826February201826October201826November2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://bg.copernicus.org/articles/15/7205/2018/bg-15-7205-2018.htmlThe full text article is available as a PDF file from https://bg.copernicus.org/articles/15/7205/2018/bg-15-7205-2018.pdf
δ13C, the standardised 13C/12C ratio
expressed in per mille, is a widely used ocean tracer to study changes in ocean
circulation, water mass ventilation, atmospheric pCO2, and
the biological carbon pump on timescales ranging from decades to tens of
millions of years. δ13C data derived from ocean sediment core
analysis provide information on δ13C of dissolved inorganic
carbon and the vertical δ13C gradient (i.e. Δδ13C) in past oceans. In order to correctly interpret
δ13C and Δδ13C variations, a good
understanding is needed of the influence from ocean circulation, air–sea gas
exchange and biological productivity on these variations. The Southern Ocean
is a key region for these processes, and we show here that Δδ13C in all ocean basins is sensitive to changes in the
biogeochemical state of the Southern Ocean. We conduct a set of idealised
sensitivity experiments with the ocean biogeochemistry general circulation
model HAMOCC2s to explore the effect of biogeochemical state changes of the
Southern and Global Ocean on atmospheric δ13C,
pCO2, and marine δ13C and Δδ13C. The experiments cover changes in air–sea gas exchange rates,
particulate organic carbon sinking rates, sea ice cover, and nutrient uptake
efficiency in an unchanged ocean circulation field. Our experiments show
that global mean Δδ13C varies by up to about ±0.35 ‰ around the pre-industrial model reference (1.2 ‰) in response to biogeochemical change. The amplitude
of this sensitivity can be larger at smaller scales, as seen from a maximum
sensitivity of about -0.6 ‰ on ocean basin scale. The
ocean's oldest water (North Pacific) responds most to biological changes, the
young deep water (North Atlantic) responds strongly to air–sea gas exchange
changes, and the vertically well-mixed water (SO) has a low or even reversed
Δδ13C sensitivity compared to the other basins. This
local Δδ13C sensitivity depends on the local
thermodynamic disequilibrium and the Δδ13C sensitivity
to local POC export production changes. The direction of both glacial
(intensification of Δδ13C) and interglacial (weakening of
Δδ13C) Δδ13C change matches the
direction of the sensitivity of biogeochemical processes associated with
these periods. This supports the idea that biogeochemistry likely explains
part of the reconstructed variations in Δδ13C, in
addition to changes in ocean circulation.
Introduction
The vertical marine δ13C gradient (Δδ13C)
is the surface-to-deep difference in δ13C of dissolved
inorganic carbon (DIC), where the standardised 13C/12C ratio
(δ13C) is expressed in per mille (Zeebe and Wolf-Gladrow, 2001):
δ13C=13C/12C13C/12Cstandard-1×1000‰.
Here, 13C/12Cstandard is the Pee Dee Belemnite standard
(0.0112372) (Craig, 1957). 13C is slightly heavier than the 12C
isotope, which causes a fractionation effect during air–sea gas exchange and
photosynthesis, thereby changing δ13C and Δδ13C (Laws et al., 1997; Zhang et al., 1995;
Mackenzie and Lerman, 2006). Photosynthetic fractionation increases the 13C/12C ratio of
surface ocean DIC (i.e. a δ13C increase) due to the preferred
uptake of the lighter 12C into biogenic matter (which therefore has a
low δ13C). The deep sea DIC has a relatively low δ13C
signature as a result of the remineralisation of low-δ13C biogenic matter at depth. The resulting vertical δ13C
gradient is in addition influenced by air–sea gas exchange and circulation
(Zeebe and Wolf-Gladrow, 2001; Emerson and Hedges, 2008; Ziegler et al.,
2013). Both deep sea and surface ocean δ13C signatures are
archived in the calcareous shells of foraminifera in the sediments. Such
records of δ13C from planktic and benthic foraminiferal shell
material cover tens of millions of years (Hilting et al., 2008). Using this
archive, δ13C and Δδ13C have been used to
reconstruct, for example, atmospheric CO2 concentration, ocean
circulation, and the strength of the biological pump (Broecker, 1982; Shackleton and Pisias, 1985; Zahn et al., 1986;
Oppo et al., 1990; Hollander and McKenzie, 1991; Keir, 1991;
Crucifix, 2005; Curry and Oppo, 2005; Lisiecki, 2010; Broecker and McGee,
2013; Ziegler et al., 2013; Hoogakker et al., 2015; Bauska et al., 2016). Δδ13C is independent of
whole-ocean δ13C shifts (due to terrestrial influences),
because such influences would affect δ13C equally everywhere.
This makes Δδ13C a valuable proxy to study the marine
carbon cycle independent of changes in carbon storage on land. Besides the
use of δ13C for understanding the past ocean, contemporary
measurements of δ13C of DIC support the quantification of
anthropogenic carbon uptake by the oceans as well as the study of the
effects of biology and ocean circulation on tracer distributions (Kroopnick, 1980, 1985; Gruber and Keeling, 2001; Quay et al., 2003; Holden et al., 2013; Eide et
al., 2017b). However, major uncertainties remain in
the interpretation of foraminiferal δ13C records and Δδ13C (Oliver et al., 2010; Broecker and McGee, 2013) as well
as in the interpretation of the present-day δ13C data (Eide et
al., 2017b).
This article addresses part of these uncertainties by exploring the
pre-industrial sensitivity of δ13C and Δδ13C to biogeochemical change in idealised model experiments. By doing
so we can investigate a number of biogeochemical mechanisms that could
explain (part of) the observed changes in δ13C and Δδ13C. We focus on the Southern Ocean (SO), the ocean south of
45∘ S, because the SO plays an important role in the global carbon
cycle by regulating atmospheric CO2 concentrations and uptake of
anthropogenic CO2 (Broecker and Maier-Reimer, 1992; Heinze, 2002;
Marinov et al., 2006) as well as influencing the global efficiency of the
biological pump, global primary production, and preformed nutrients (Primeau
et al., 2013).
Variations in Δδ13C over the past few 100 000 years
show that Δδ13C is generally increased during glacial
periods, due to a higher contrast of deep δ13C with surface and
mid-depth δ13C (Broecker, 1982; Shackleton and Pisias, 1985; Boyle, 1988; Charles et al.,
2010; Oliver et al., 2010). Long-term δ13C and Δδ13C variations have been explained by
ocean circulation changes (Duplessy et al., 1988; Jansen, 2017; Oppo et al.,
1990; Toggweiler, 1999; Menviel et al., 2016). However, Δδ13C variability cannot be explained by ocean
stratification or circulation changes alone: an interaction between
biogeochemical and physical processes must be at play (Keir, 1991; Boyle, 1988; Mulitza et al., 1998; Charles
et al., 2010; Ziegler et al., 2013; Schmittner and Somes, 2016). Δδ13C has been used in different
ways over time: in earlier studies as the contrast between surface and deep
water δ13C, derived from planktic versus benthic foraminifera
(Broecker, 1982; Boyle, 1988; Shackleton et al.,
1983; Duplessy et al., 1988), and now increasingly as the contrast of deep ocean (benthic) δ13C with thermocline or intermediate ocean δ13C (Mulitza et al., 1998; Charles
et al., 2010; Lisiecki, 2010).
Here, we explore the sensitivity of δ13C and Δδ13C to changes in the biogeochemical state of the Global Ocean and
Southern Ocean under a constant circulation field, to support the
paleo-oceanographic interpretation of δ13C and Δδ13C as well as to improve the understanding of the SO role in global
carbon cycling and its variability and sensitivity. In order to study
biogeochemical mechanisms that could influence δ13C and Δδ13C, a set of sensitivity experiments is conducted with the
ocean biogeochemistry general circulation model HAMOCC2s (Heinze et al.,
2016). We first estimate the contribution of biology versus air–sea gas
exchange to marine δ13C of DIC (Sect. 3.2). The experiments
focus on one or more of the biogeochemical aspects assumed to be important
for δ13C and Δδ13C, e.g. the biological
pump efficiency and/or equilibration at the air–sea interface (Sect. 3.3.1–3.3.4). Together these experiments provide a broad spectrum of
biogeochemical changes that could influence local and global δ13C
and Δδ13C. The modelling results of Sect. 3.3.1–3.3.4 are discussed in context of observational data from sediment
cores (Sect. 3.4). As δ13C and Δδ13C are
used to study changes in atmospheric pCO2 (pCO2atm), a final
section will cover the relationship between atmospheric δ13C,
Δδ13C, and pCO2atm under different marine
biogeochemical states (Sect. 3.5).
Methods
In this study we employ the ocean biogeochemistry general circulation model
HAMOCC2s (Heinze et al., 1999, 2016), which simulates the inorganic and organic carbon cycle in the water column
and in the sediments. The horizontal resolution of the model is
3.5∘×3.5∘ and there are 11 depth layers in
the ocean. HAMOCC2s has an annual time step and an annually averaged fixed
circulation field, as well as a one-layer atmosphere component (permitting
exchange of O2, 13CO2, and CO2 with the ocean
component), which is assumed to be longitudinally well-mixed. The model is
computationally very economic and thus an ideal tool for sensitivity
experiments over long integration times. Biogenic particulate matter in the
model is represented as particulate organic carbon (POC), calcium carbonate
(CaCO3), and biogenic silica (opal). These biogenic particles are only
modelled as export production due to the annual time step of the model. POC
and opal export production are described by Michaelis–Menten kinetics for
nutrient uptake, limited by phosphate and silicic acid respectively (Sect. 1A in the Supplement).
CaCO3 export production depends on the ratio between opal and POC
production. POC is carried as a tracer as well as transported downwards
according to a set of mass balance equations that describe POC gain through
surface-layer POC production and POC losses through constant sinking and
remineralisation rates (Sect. 1A in the Supplement). This is done similarly for opal and
CaCO3 sinking and dissolution. As the model has an annual time step,
sea ice is always present south of ∼60∘ S and north
of ∼70∘ N in the control run (Fig. S1 in the Supplement). A more
detailed model description is provided in previous studies using a similar
configuration of HAMOCC2s (Heinze, 2002; Heinze et al., 2016), as well as in
Sect. 1A in the Supplement.
Fractionation between 13C and 12C during photosynthesis is set to
a constant value of -20 ‰ (Lynch-Stieglitz et al., 1995;
Tagliabue and Bopp, 2008), as model results are little influenced by the
chosen parameterisation (Schmittner et al., 2013; Jahn et al., 2015). The
fractionation during air–sea gas exchange depends on temperature according
to ε=-9.483×103/T+ 23.89 ‰, where temperature (T) is measured in Kelvin (K) (Mook, 1986), causing stronger fractionation at lower temperatures (i.e. at
high latitudes). Fractionation during CaCO3 formation is omitted from
the model, as was done in previous studies (Lynch-Stieglitz et al., 1995; Marchal
et al., 1998; Schmittner et al., 2013), as its size is uncertain but likely
minor (∼1 ‰) and effects on δ13C and Δδ13C are small (Shackleton and Pisias,
1985). In the version of HAMOCC2s used in this study, a fixed weathering
input is used for 13C to tune the ocean inventory to values comparable
to observations. The weathering flux of 13C into the ocean was
determined by an iterative procedure: The model was run over 100 000 years, replacing the weathering rate with the
diagnosed burial rate for 13C continuously.
After this, the model 13C inventory was recalibrated such that the
atmospheric value for δ13C arrived at -6.5 ‰.
This procedure was repeated over three iterations. Afterwards, the
weathering rate of 13C was fixed to the last diagnosed value
(0.36 Tmol 13C yr-1) – which results in a weathering flux
δ13C of DIC of -11 ‰ . Another 100 000-year run was
carried out with this constant input rate in order to check whether the
global 13C distribution was stable in all reservoirs. The sensitivity
experiments were then re-started from the result of that run. Weathering
fluxes are added homogeneously over the first ocean layer as dissolved
matter and in a fixed stoichiometric ratio for CaCO3, organic carbon,
PO43-, alkalinity, and Si. Annual weathering fluxes (Tmol) are 27
for CaCO3, 5 for organic carbon, 5/rC:P for PO43-,
2 ×CaCO3-rN:P×PO43- for alkalinity, and 4.5 for Si (with
rC:P=122 and rN:P=16). These values are within the
uncertainties of observational estimates for Si (5.6 Tmol yr-1; Tréguer, 2002), CaCO3 (∼32 Tmol yr-1; Milliman and Droxler, 1996), and organic carbon (4 Tmol yr-1; Broecker and
Peng, 1987) and have been adjusted to improve the fit of the respective
modelled marine tracer distributions as well as burial fluxes to
observational estimates. The spin-up procedure described here created a model
set-up with close-to-observed marine and atmospheric δ13C
(δ13Catm) values and freely evolving atmospheric
pCO2 and δ13C. This equilibrated model version is referred
to as the “control run” in the remainder of this article. We define the
vertical δ13C gradient (Δδ13C) as follows:
Δδ13C=δ13Csurface-δ13Cdeep,
where δ13Csurface and δ13Cdeep are the
volume-weighted mean δ13C of DIC in the surface ocean
(< 50 m depth, i.e. the model photic zone) and the deep ocean
(lowermost wet layer in the model, if the top of the layer is > 3 km depth),
respectively. By doing so, we can compare the Δδ13C
summarised as one number between the different sensitivity experiments.
Description of the sensitivity experiments. The sensitivity
experiments on the CO2 gas exchange rate and the biological pump have
been done twice, once for the Global Ocean and once only making changes in
the Southern Ocean (south of 45∘ S).
ExperimentExperiment set-upGas fastCO2 gas exchange rate multiplied by 4Gas slowCO2 gas exchange rate divided by 4Efficient biological pumpPOC sinking rate doubled to 6 m d-1Inefficient biological pumpPOC sinking rate halved to 1.5 m d-1VmaxHigh nutrient uptake rate (control × 5) in the Southern OceanIce largeSouthern Ocean sea ice cover south of 50∘ SIce smallSouthern Ocean sea ice cover south of 70∘ S
We conducted a set of sensitivity experiments to explore changes in air–sea
gas exchange rate, sea ice extent (influencing both biological production
and the air–sea gas exchange of carbon), and the efficiency of the biological
pump through the POC sinking rate and nutrient uptake rate (Table 1). We
employ the term “efficiency of the biological pump” as a measure of the
success of phytoplankton to maintain low nutrient concentrations in the
surface ocean. All experiments are run for 2000 model years starting from
the end of the spin-up. These runtimes allowed for atmospheric
quasi-equilibrium to establish (Fig. S5), with an exception for the long-term
effects caused by POC sinking rate changes (as studied in more detail by
Roth et al., 2014). The equilibration timescale of Δδ13C is much shorter than that of atmospheric δ13C: this
is the case because (1) the long-term weathering-burial equilibration of
δ13C affects the whole ocean reservoir simultaneously – thus
keeping Δδ13C constant – and (2) the processes that
potentially influence Δδ13C (changes in biological
production and air–sea gas exchange) affect Δδ13C on
shorter (centennial to millennial) timescales. In order to compare effects
of SO change and global change, the gas exchange rate and POC sinking rate
experiments are done twice – once changing the respective model parameter
for the Global Ocean and once for the Southern Ocean only (SO-only). The
model parameters were changed in a way that marine biogeochemical tracer
distributions (e.g. PO43-, δ13C) remained reasonable
but did provide an estimate of the sensitivity of the respective tracer to
biogeochemical change. The model has a constant sea ice cover (Fig. S1),
which permits gas transfer through the ice depending on ice cover thickness
(the transfer rate is divided by ice thickness in centimetres) while light transfer
is inhibited at ice thicknesses over 0.01 cm. The maximum and minimum sea
ice cover experiments (“ice large” and “ice small”, Table 1) approximate the
Last Glacial Maximum winter extent and the modern summer extent of SO sea
ice, respectively (Crosta, 2009 and Fig. A22 therein) and assume full
inhibition of gas and light transfer through ice for simplicity. The
experiment on nutrient drawdown (Vmax) alters the Michaelis–Menten
kinetics of POC production by changing the maximum nutrient (i.e.
PO43-) uptake rate (VmaxPOC in Sect. 1A in the Supplement). The gas exchange
experiments alter the specific gas exchange rate kw, as described in
more detail in Sect. 1B in the Supplement. The POC sinking rate experiments change the sinking
velocity constant wPOC in the POC mass balance equations (Sect. 1A in the Supplement).
The contribution of biological processes versus air–sea gas exchange to
δ13C is calculated using the method of Broecker and
Maier-Reimer (1992) as done for observations by Eide et al. (2017b) and in a
modelling context by Sonnerup and Quay (2012):
δ13Cbio[‰]=εphotoDIC‾×rC:P×PO4-PO4‾+δ13C‾,
where εphoto=-20‰, rC:P=122 and
the following model control run mean values are used:
DIC‾=2332.284µmol kg-1, PO4‾=2.409µmol kg-1 and δ13C‾=0.656 ‰.
These values result in the modelled δ13Cbio:PO43- relationship
δ13Cbio=3.18-1.05×PO43-. The constant 3.18 is
somewhat higher than estimated for observed δ13C, for which a
constant of 2.8 was found by Eide et al. (2017b). This higher constant
originates from the over-prediction of the model mean δ13C and
PO43- at depth, as seen in other models (Sonnerup and Quay, 2012).
Equation () assumes a constant biological fractionation as well as a constant
rC:P ratio, and these assumptions will introduce some error in the
partition of biological and air–sea gas exchange signatures derived from
observed δ13C to PO43- ratios (e.g. Eide et al.
2017b). For the purpose of determining δ13Cbio in our
model, these assumptions are unproblematic, since rC:P and εphoto are actually taken to be constant in the model formulation. The
air–sea gas signature δ13CAS is approximated as the
residual (δ13CAS=δ13Cmodel-δ13Cbio).δ13CAS is 0 ‰ when δ13Cmodel=δ13Cbio, i.e. when the δ13C can be explained by
biology only. We express δ13Cbio as a percentage to aid
interpretation of the results (denoted δ13Cbioperc),
because the values of δ13Cbio in per mille
depend strongly on the chosen “reference” values, i.e. mean DIC,
PO43-, and δ13C (compare Broecker and Maier-Reimer, 1992; Lynch-Stieglitz et al., 1995;
Sonnerup and Quay, 2012; Schmittner et al., 2013; Eide et al., 2017b). The conversion from
δ13Cbio to a percentage is calculated as follows:
δ13Cbioperc%=δ13Cbioδ13Cbio+δ13CAS×100%.
In our analysis, we define the total amount of air–sea carbon exchange as
Fu+d=Fup+Fdown, with Fup as the upward annual carbon
flux from the ocean into the atmosphere and Fdown its downward
counterpart (Sect. 1B in the Supplement and Heinze and Maier-Reimer, 1999). Fu+d is relevant for
understanding the sensitivity of δ13C. The net carbon exchange
is defined as Fnet=Fup-Fdown. The sign of Fnet indicates
whether a region is a source or a sink for carbon and is relevant for
understanding changes in pCO2atm.
Modelled δ13C of DIC
(‰) distribution for the model control run: (a)δ13C at 25 m depth,
(b) Pacific transect of δ13C, (c) zonal transect of δ13C
at 26∘ S, and (d) Atlantic transect of δ13C.
Results and discussionModel control run
The model reproduces the main features of observed marine δ13C,
as shown in Figs. 1 and S2. The modelled global mean surface ocean
δ13Csurface of DIC is higher (1.88 ‰)
than deep ocean δ13Cdeep (0.67 ‰),
creating a mean ocean Δδ13C of 1.21 ‰. In the North Atlantic and SO, Δδ13C is the least pronounced (0.9 ‰ and 0.8 ‰
respectively) due to vertical mixing between surface and deep water during
deep water formation and upwelling (Duplessy et al., 1988). Δδ13C increases with water mass age as expected from the increased
imprint of remineralisation on δ13C. The mean modelled ocean
δ13C is higher by 0.16 ‰ relative to
observations (Eide et al., 2017b), which is especially pronounced in the
oldest water masses (Fig. S2). This is observed in other models as well and
attributed to the model's relative contribution of deep water production in
the North Atlantic and Southern Ocean (Sonnerup and Quay, 2012). The
modelled global export POC production is 9.6 Gt C yr-1, of which 18 %
is produced in the SO, which is within the uncertainty of observational
estimates (MacCready and Quay, 2001; Schlitzer, 2002; Dunne et al.,
2007; Lutz et al., 2007; Nevison et al., 2012). The atmosphere has a modelled
equilibrium pCO2atm of 279 ppm and a δ13Catm of
-6.50 ‰ , which developed in the model from the
“best-fit” weathering value Feqw as described above in
Sect. 2. Net air–sea gas exchange is close to zero (ventilating
∼2×10-7 Gt of carbon to the atmosphere
annually). The resulting drift of the model control over 2000 years is
7×10-7 ‰ for δ13Catm,
and +2×10-4 ppm for pCO2atm.
δ13Cbioperc, the contribution
of biology to the local δ13C signal (%), as calculated using
Eq. () at (a) 25 m depth and (b) a Pacific transect. The remainder
of 100 % is attributed to air–sea gas exchange. The δ13Cbio
and δ13CAS values in per mille are very
similar to the values found by Schmittner et al. (2013).
Air–sea gas exchange versus biology
The contribution of biology based on Eqs. () and () to the
δ13C distribution is presented in Fig. 2, broadly in agreement with
previous studies (Kroopnick, 1985; Schmittner et al., 2013). The
contribution of biology to the modelled δ13C distribution is
generally below 45 % and has a steep gradient from the surface to the
deep ocean. The (thermodynamic) fractionation effect of air–sea gas exchange
on δ13C is strongly impeded by the long equilibration time of
13C, which leaves room for biological processes to contribute
significantly to δ13C and Δδ13C (Lynch-Stieglitz et al., 1995; Murnane and Sarmiento, 2000;
Schmittner et al., 2013; Eide et al., 2017a). In the ocean below 250 m, the influence of biology
increases to 35 %–45 % due to the remineralisation of POC, with the
exception of the Arctic Ocean, where no POC production is modelled due to the
sea ice cover (Figs. 2b and S1). δ13Cbioperc is close
to 50 % around 1000 m depth in the northern Pacific and Indian oceans,
due to the old water masses located there, which have accumulated a large
fraction of remineralised DIC. At the surface, air–sea gas exchange
dominates the δ13C signature of DIC, as is visible from the low
δ13Cbioperc (Fig. 2a). The only exception at the surface is
in upwelling regions, where a relatively high δ13Cbioperc is expected due to high POC production and upwelled remineralised carbon.
High δ13Cbiopercgenerally corresponds to a low-δ13C water mass (compare Figs. 1 and 2), as expected from the
upwelling of 13C-depleted DIC and modelled and observed close to the
Antarctic continent (Fig. 1a and observations by Eide et al., 2017a). The
results presented in Fig. 2 appear to be quite robust as δ13Cbioperc typically does not change by more than 5 %–10 % for
the wide range of biogeochemical states, as is explored in the sensitivity
experiments presented below.
Sensitivity of Δδ13C and δ13CAir–sea gas exchange of carbon
Atmospheric and marine carbon isotopic ratios are generally in thermodynamic
disequilibrium due to their relatively long equilibration timescales compared to the time of contact of a water parcel with the atmosphere.
CO2 equilibration through the air–sea interface takes ∼4
months (Jones et al., 2014) and is inversely related to the Revelle buffer
factor, and slowed down by a factor of ∼20 compared to inert
gases due to carbon speciation (i.e. the adjustment of the bicarbonate
pool). Again ∼10 times slower than CO2, the air–sea
equilibration of the atmospheric isotope ratio
13CO2/12CO2 (i.e. δ13Catm) with marine
DI13C/ DI12C (i.e. δ13C) takes ∼4 years
(Jones et al., 2014). The equilibration timescale of the carbon isotopes is
not facilitated by the buffering reaction of CO2 with H2O,
but instead depends on the DIC :CO2 ratio of seawater (Broecker and Peng, 1974; Jones et al.,
2014; Galbraith et al., 2015). Over 90 % of the
surface ocean δ13C signature is set by air–sea gas exchange
outside of upwelling regions across the world's oceans (Fig. 2), making the
equilibration across the air–sea interface important for surface ocean
δ13C. Understanding the effects of equilibration across the
air–sea interface is thus key to understanding global surface ocean δ13C signatures. Here we explore two extreme cases, very slow but
non-zero gas exchange (“gas slow”, gas exchange rate divided by 4) and very
fast gas exchange to bring the air–sea equilibration close to equilibrium
(“gas fast”, gas exchange rate multiplied by 4).
Our results show that the effects of changes in air–sea gas exchange on
δ13C mainly depend on the prior disequilibrium δ13Cdiseq
(δ13Cdiseq=δ13C-δ13Ceq, where δ13Ceq
represents the δ13C value a water parcel would have had if it would have fully
equilibrated with the atmosphere; see also Gruber et al., 1999). Full
isotopic equilibrium with the atmosphere results in a δ13Csurface of ∼0.5 to
∼4 ‰ at low and high latitudes, respectively (Menviel et al.,
2015), where the range is caused by the temperature-dependent fractionation
(Mook et al., 1986; Zhang et al., 1995). In this study, modelled
ε is between 7.7 ‰ and 11 ‰ . This
thermodynamic effect increases the difference between δ13Csurface and δ13Ceq at the poles (Menviel et
al., 2015), thus increasing the potential of high-latitude surface water to
be affected by air–sea gas exchange fluxes.
δ13C of DIC (‰) difference after 2000
years for the fast gas exchange experiments (experiment - control): global
experiments (a, c) and SO-only experiments (b, d), at 25 m depth
(a, b) and as a Pacific transect of δ13C difference (c, d).
Our gas exchange experiments (Table 1) show a transient phase where the net
global air–sea gas exchange flux Fnet is non-zero, which affects
pCO2atm until a new quasi-equilibrium is established (Fig. S3). We
find an increase in pCO2atm by 10 ppm (slow gas exchange) and a
decrease by 4 ppm (fast gas exchange) after 2000 years, respectively. If gas
exchange is only changed in the SO (i.e. for 22 % of the global ice-free
ocean area), an effect of 3.7 and -0.7 ppm is found after 2000 years (Table 2). Gas exchange in the SO can thus cause a disproportionate response
(∼30 % of the sensitivity) in pCO2atm. These
changes occur in the first ∼600 years of the sensitivity
experiments (Fig. S3), with the strongest changes occurring after
∼100 years. The air–sea pCO2 difference is smaller at
increased gas exchange rates and larger at reduced gas exchange rates (Fig. S4). δ13Catm decreases by 0.3 ‰ during
fast gas exchange and increases by 0.2 ‰ when the gas
exchange rate is reduced. This is explained by the increase in the global
amount of air–sea gas exchange Fu+d in the fast gas exchange experiment
(4 times more, at 542 Gt C yr-1). Such an increase leads to a smaller
thermodynamic disequilibrium, which increases the mean marine δ13C and lowers δ13Catm. Slow gas exchange reduces
Fu+d (by 73 % to 36 Gt C yr-1), thus decreasing the role of
air–sea gas exchange on surface ocean δ13C. This results in an
increased contrast between atmospheric and surface ocean δ13C,
which explains the increase in δ13Catm. Moreover, our
SO-only experiments show that these effects on δ13Catm are
more pronounced if gas exchange only changes in the SO. This indicates that
the remainder of the ocean offsets part of the atmospheric sensitivity to SO
change.
Results of pCO2atm (ppm) and δ13Catm
(‰) for all sensitivity experiments.
Global experiments SO-only experiments pCO2atmδ13CatmpCO2atmδ13CatmControl279-6.5– Gas exchangeFast275-6.8278-7.0Slow289-6.3283-6.2Biological pumpPOC: efficient256-6.3275-6.5POC: inefficient292-6.7282-6.5Vmax– 235-6.1IceLarge– 287-6.2Small– 272-6.6
Volume-weighted basin mean anomaly profiles of δ13C
after 2000 years, with respect to the control profiles (upper row). Δδ13C denoted per basin in the lower right (control) and lower
left (sensitivity experiments) corner of each subgraph. Results are
presented for the global gas exchange and POC sinking experiments. Basin
extent is visualised in Fig. S8.
δ13C shows a different response in high latitudes compared to
the lower latitudes in the surface ocean (Figs. 3a and S5): an increased
air–sea gas exchange rate lowers the surface ocean δ13C of DIC
by 0.2 ‰ to 0.9 ‰ at the lower latitudes and increases
surface ocean δ13C at high latitudes by 0.2 ‰–0.5 ‰ (Figs. 3 and 4). The direction of the response
indicates whether δ13Cdiseq is positive or negative, and
is in line with previous studies (Schmittner et al., 2013; Galbraith et al.,
2015) that show that the disequilibrium is negative (δ13C
< δ13Ceq) at high latitudes and in low-latitude
upwelling regions, and positive elsewhere. The sign of δ13Cdiseq, and thus the direction of the δ13C
response, is understood from the difference between the natural δ13C distribution (Fig. 1) and the δ13Ceq, which
depends on thermodynamic fractionation (Sect. 2). At increased gas
exchange rates (i.e. closer to equilibrium), δ13C has to
increase in cool high-latitude surface waters and has to decrease in warm
low-latitude surface waters in order to get closer to equilibrium (Menviel
et al., 2015; Murnane and Sarmiento, 2000). The net effect of a slower gas
exchange rate on surface ocean δ13C is less pronounced than the
effect of an increased gas exchange rate (Figs. S5, 3). The smaller
effects seen for slow gas exchange indicate that the control model ocean is
a “slow ocean”, i.e. closer to (very) slow gas exchange than to
thermodynamic equilibrium (infinitely fast gas exchange).
The effect of the gas exchange rate on marine δ13C is mostly
established in the top 250 to 1000 m of the water column (Figs. 3c, d, 4). Recording this air–sea gas exchange signal thus strongly depends on the
reliability of planktic δ13C-based δ13Csurface reconstructions and knowledge of the living depth
represented by the planktic foraminifera. The signal penetrates deepest (to
∼2000 m depth) into the North Atlantic (Fig. 4), where
δ13C is strongly influenced by air–sea gas exchange (Fig. 2a).
However, the interpretation of variations in North Atlantic benthic δ13C as coming partly from air–sea gas exchange (Lear et al., 2016) is
not supported by our experiment. Due to the limited export of the δ13C signal to depth, the sensitivity of Δδ13C to
the gas exchange rate mainly comes from surface ocean δ13C.
Globally, the Δδ13C is weakened to 0.87 ‰ when the thermodynamic disequilibrium is decreased
(i.e. “Gas fast”, Fig. 5) and Δδ13C strengthens to 1.32 ‰ when the thermodynamic disequilibrium is increased
(“Gas slow”, Fig. 5). The extent to which thermodynamic equilibrium can
develop is thus an efficient way to change the biologically induced Δδ13C (Murnane and Sarmiento, 2000); however, this is only true
at lower latitudes where δ13Cdiseq is positive: the
direction of the high-latitude SO Δδ13C sensitivity
mirrors the sensitivity of the low-latitude regions (Fig. 4) as well as the
global mean due to its negative δ13Cdiseq.
Global mean Δδ13C after 2000 years for the
different sensitivity experiments (Table 1). “Bio – Efficient” represents the
high POC sinking rate experiment, “Bio – Inefficient” the slow POC sinking
rate experiment. The results for the Southern Ocean-only experiments (Sect. 2) are described in the text.
The biological pump: POC sinking rate
The net effect of a regionally changed biological pump efficiency depends on
the sequestration efficiency, which depends on the interplay between the
biological pump and ocean circulation (DeVries et al., 2012). A more
efficient biological pump (here, a higher POC sinking rate) leads to a loss
of carbon to the sediments, which affects pCO2atm and δ13Catm on millennial timescales. Here we present results from a
2000-year simulation (as for the other experiments), which are thus
transient results. A full equilibrium of the system could take as long as
200 000 years (Roth et al., 2014). On these long timescales other processes
and feedbacks would occur (Tschumi et al., 2011), which complicates the
attribution of changes to a primary trigger. A fast POC sinking rate leads
to a pCO2atm decrease of 23 ppm and higher (+0.2 ‰) atmospheric δ13C after 2000 years
(Table 2, Fig. S3) as well as an increase in mean ocean δ13C of
0.15 ‰ , caused by the transient sediment burial of
low-δ13C POC. The transient imbalance between weathering and
burial fluxes can thus cause profound changes in both marine and atmospheric
δ13C, and moreover provides an important feedback for the
long-term marine carbon cycle (Tschumi et al., 2011; Roth et al., 2014). In
our experiment, an efficient biological pump leads to a global
∼6 % decrease in the amount of air–sea gas exchange
Fu+d because of efficient export of carbon to depth, thereby lowering
the net upward advection of carbon. A mirrored but weaker response is
modelled for a decrease in biological pump efficiency: halving the POC
sinking rate leads to a 13 ppm increase in pCO2atm (of which 23 %
can be explained by the SO) and a more negative atmospheric δ13C (-6.7 ‰ ) and 7 % increased Fu+d (Table 2, Fig. S3).
δ13C of DIC (‰) difference after
2000 years for the POC sinking rate experiments (experiment - control):
(a) the global efficient biological pump (high POC sinking rate) experiment for
a Pacific transect and (b) the SO-only efficient biological pump experiment
for a Pacific transect and (c) the global efficient biological pump
experiment at 3000 m depth. Note the different scales.
Surface ocean δ13C is mostly influenced by the changes in
productivity and the vertical displacement of the POC remineralisation
depth. The deepening of the remineralisation depth has been extensively
discussed in the literature (Boyle, 1988; Keir, 1991; Mulitza et al., 1998;
Roth et al., 2014) and likely explains lowered mid-depth glacial δ13C together with changes in ocean circulation (for example,
Toggweiler, 1999). POC sinking removes nutrients and preferentially light
12C carbon from the surface ocean, while exporting them to the deep
ocean. If POC sinking rates are high, this increases the mean surface ocean
δ13C (contributing to the δ13Catm increase)
by 0.12 ‰ and lowers mean deep ocean δ13C
by 0.01 ‰ (Fig. 6) – with values corrected for the
overall 0.15 ‰ increase in marine δ13C,
which occurs due to transient imbalance between weathering and sediment
burial. Therefore, even though the absolute export production is globally
reduced by 26 %, the biological pump is more efficient as any new
nutrients in the surface ocean will immediately be used and exported. With a
halving of the POC sinking rate, the remineralisation is confined closer to
the surface ocean (Fig. 4). The net effect is that surface ocean δ13C is reduced (by a mean of 0.21 ‰ – corrected
for the mean ocean change of 0.04 ‰) throughout the
ocean (Fig. 4), because the fractionation effect during photosynthesis is
counteracted by the remineralisation of POC (which would normally have
occurred at depth). The SO plays a relatively minor role in the sensitivity
to the POC experiments (Fig. 6b). Changes in deep ocean δ13C
depend on the water mass age (Fig. 6c): old water (North Pacific) has a
larger remineralisation signal when the biological pump is efficient.
Independent of the biological pump efficiency, the relatively young waters
of the deep North Atlantic generally adopt about the same δ13C
signal as the surface ocean δ13C, which is set by air–sea gas
exchange. This is in agreement with a relatively low δ13Cbioperc estimate for the deep North Atlantic (∼30 %).
The sensitivity of Δδ13C to changes in POC sinking rate
depends strongly on location (Figs. 4 and 6). In general, the Δδ13C is strengthened for an increased biological pump efficiency
(Fig. 5), and this effect is stronger with water mass age (Figs. 6c and 4).
The downward shift of the remineralisation depth of low-δ13C
POC drives this increase in Δδ13C, a mechanism
discussed by Boyle (1988) and Mulitza et al. (1998), among others, to
understand glacial Δδ13C increase. Our results show
that the vertical displacement of the δ13C profile is most
pronounced in the North and South Pacific (Fig. 4). The North Atlantic
Δδ13C is much less affected as these waters are mostly
influenced by air–sea gas exchange. Instead, the entire North Atlantic
profile is shifted more than in the other ocean basins (Fig. 4). Δδ13C
weakens for a reduced biological pump efficiency (Figs. 4 and 5), especially in older water where δ13Cbioperc is
higher (Fig. 2a). It is worth noting, however, that the changes in Δδ13C in the SO are comparably small because the vertical
mixing in the SO of the low-δ13C deep water mostly causes
shifts in the entire δ13C profile, not a change in the gradient
(Fig. 4).
δ13C of DIC (‰) difference after 2000
years for the Vmax nutrient depletion experiment (experiment - control):
(a) at 25 m depth and for (b) an Atlantic transect and (c) a Pacific
transect.
The biological pump: SO nutrient depletion
Consistent with previous studies (Sarmiento et al., 2004; Marinov et al., 2006; Primeau et al., 2013), we find a large atmospheric impact of our SO
nutrient depletion experiment. The high SO nutrient uptake efficiency (i.e.
an efficient biological pump) causes a 44 ppm reduction in
pCO2atm after 2000 years. The Vmax experiment reaches
quasi-equilibrium after ∼800 years, as seen from the time
evolution of pCO2atm and δ13Catm (Fig. S3).
δ13Catm increases to -6.1 ‰ due to the
increased surface ocean δ13C (Fig. 7a). This 0.4 ‰ increase is high
compared to the results of Menviel et al. (2015), who found a δ13Catm
sensitivity of 0.1 ‰–0.2 ‰ in response to changes in SO nutrient utilisation. The
different development time compared to the fast POC sinking rate
experiment is explained by the absence of long-term loss of carbon to the
sediments in the Vmax experiment, because transport and water-column
remineralisation within the SO limits an increase in POC burial there. In
the SO, net carbon uptake (Fnet) increases fourfold to 1.5 Gt C yr-1 (Fig. S6) because the high nutrient and carbon consumption
transport carbon into the ocean interior and do not permit CO2 to
escape to the atmosphere.
SO export production of POC is increased (Fig. S7) by a factor of 2.4, causing
global POC export production to increase by 15 %, albeit reducing
lower-latitude productivity (non-SO up to ∼35∘ N)
by 13 %. This relocation of global POC export production in response to
SO increased nutrient uptake efficiency is in agreement with earlier studies
(Marinov et al., 2006; Primeau et al., 2013). The increased surface ocean
δ13C signature everywhere north of the SO sea ice edge (Fig. 7a) is in the SO attributed to increased POC export production counteracted
by a decreased Fu+d (which would reduce δ13Csurface in
the SO because of the negative δ13Cdiseq; Figs. 3 and S5).
At lower latitudes, the decreased Fu+d (which increases δ13Csurface in
lower latitudes because of the positive δ13Cdiseq; Figs. 3 and S5) dominates the effect of the 13 %
lower POC export production on δ13Csurface. At depth and
under the sea ice in the Antarctic, where deep water upwells, the imprint of
additional POC remineralisation at depth decreases δ13C of DIC
(Fig. 7). This decrease in δ13C is only visible in water masses
downstream of the SO (Fig. 7b and c) and is most pronounced in the deep North
Pacific (Fig. 7c). The increased nutrient uptake rate in the SO increases
global mean Δδ13C by 0.4 ‰ (Fig. 5)
as well as increasing Δδ13C in all ocean basins (Fig. 4), as
seen for the fast POC sinking rate experiment. Δδ13C is affected more in older waters, where a more pronounced
remineralisation imprint has developed (Fig. 4). Besides effects on the
δ13C distribution (Fig. 7), the O2 and PO43-
distributions change as well: the O2 distribution is reorganised such
that surface ocean O2 is increased (by up to 20 µmol kg-1,
with largest changes in the SO), while deep ocean O2 is reduced
downstream of the SO (by up to 40 µmol kg-1). Surface ocean
PO43- is reduced mostly in the SO (by up to 0.8 µmol kg-1). This signal is, however, too small to significantly increase mean
deep ocean PO43-. This implies a reduction in global preformed
phosphate governed by the efficient nutrient uptake in the SO; see also
Primeau et al. (2013). SO nutrient drawdown can thus cause negligible mean
(deep) ocean PO43- and δ13C changes, despite causing
large changes in local δ13C and Δδ13C
through the interplay between biology and air–sea gas exchange. Interesting
in light of glacial proxy interpretation, the fit to the δ13C:PO43- relationship is improved throughout the ocean for
the Vmax experiment, similar to the effects of the “Gas slow”
experiment (Fig. 8). Changes in the goodness of fit of δ13C and
PO43- data to the δ13C:PO43- relationship (i.e. δ13Cbio=3.18-1.05×PO43-,
Sect. 2) are usually interpreted as changes in ventilation or air–sea gas
exchange (Lear et al., 2016; Eide et al., 2017b). However, here we show that
changes in the fit represent the relative importance of biology and air–sea
gas exchange in determining δ13C, as both changes in δ13Cdiseq (i.e. gas exchange rate experiments) and changes
in the biological pump can affect the goodness of fit to the δ13C:PO43- relationship (Fig. 8).
δ13C versus PO43- for the control,
Vmax, “gas slow” and “gas fast” experiments for all ocean basins except
the Nordic Seas (i.e. basins A to F in Fig. S8). Dark blue = A/North
Atlantic, Light blue = B/South Atlantic, Red = C/Southern Ocean,
Yellow = D/South Pacific, Green = E/North Pacific, Orange = F/Indian Ocean. The black line is the
δ13Cbio=3.18-1.05×PO43- relationship, i.e. the relationship between
δ13C and PO43- expected if only biology affected δ13C (Sect. 2). Deviations from the black line represent the relative
importance of air–sea gas exchange compared to biology for δ13C.
Southern Ocean sea ice cover
The sea ice cover of the SO changes considerably over glacial–interglacial
cycles, as well as on seasonal timescales (Crosta, 2009) and Fig. A22
therein). In general, the model sea ice cover will inhibit light penetration
into the surface ocean and limit air–sea gas exchange based on its thickness
(Sect. 2). In the sensitivity experiments we assume complete inhibition of
both light and air–sea carbon exchange by the sea ice. In this section we
thus explore the effect of both biological production and air–sea gas
exchange in two extreme cases, (i) the largest realistic sea ice cover based
on the glacial maximum winter extreme (50∘ S) and (ii) the smallest
sea ice cover based on the contemporary summer minimum sea ice extent
(70∘ S). Note that there is a constant sea ice cover about north
of 70∘ N and south of 60∘ S in the control run of the
model. Therefore, the strongest marine δ13C change is expected
south of 60∘ S for a decreased sea ice cover and between
50 and 60∘ S for an increased sea ice cover, as this is the area where
ice cover is altered relative to the control run. Ocean circulation changes
that could result from a changed sea ice cover are not taken into account,
as we want to study the potential isolated effect of sea ice on δ13C through biological and air–sea gas exchange changes.
δ13C of DIC (‰) difference after 2000
years for the Antarctic sea ice cover experiments (experiment - control):
the effect of a large (a, c) and small (b, d) Antarctic sea ice cover, for
25 m depth (a, b) and an Atlantic transect (c, d).
Both local and global air–sea carbon fluxes are influenced by a change in
the SO sea ice cover, which results in changes in pCO2atm and
δ13Catm. In our experiment, pCO2atm increases by
9 ppm for an increased sea ice cover and decreases by 6 ppm for a decreased
sea ice cover (Table 2, Fig. S3). As noted in Sect. 3.3.1, a change in
pCO2atm is governed by a transient change in the net air–sea gas
exchange flux Fnet until a new equilibrium is established. An extended
ice cover causes more CO2 to remain in the atmosphere because the
additional ice covers a part of the SO that is a sink for CO2 (Fig. S4
– Control). As the net global air–sea gas exchange Fnet approaches
equilibrium, the non-SO ocean therefore becomes a smaller source for carbon.
This reduces the net gas exchange Fnet inside and outside of the SO by
∼40 %–50 %. Our results show that the effects of a changed
sea ice cover on pCO2atm are yet to be fully understood: Stephens
and Keeling (2000) for example modelled a strong decrease in
pCO2atm in response to an increased sea ice cover south of the
Antarctic Polar Front, because they mostly cover a part of the SO that is a
source of carbon to the atmosphere. In our study, the reduction in
pCO2atm by 6 ppm due to a reduced sea ice cover is attributable to
the POC production in the previously ice-covered area between
∼60 and 70∘ S. In a sensitivity
experiment where the ice cover influences air–sea gas exchange only, the sea
ice retreat leads to an increase in pCO2atm because the region
below the ice is strongly supersaturated in carbon with respect to the
atmosphere. The increased sea ice cover leads to a complete suppression of
air–sea gas exchange south of 50∘ S. Since this region is in
negative carbon isotopic disequilibrium with the atmosphere (δ13C < δ13Ceq), the ice cover inhibits a
δ13C flux into the ocean. As a result, pCO2atm increases to -6.2 ‰ , while the
opposite happens for a reduced sea ice cover, leading to a lowered pCO2atm (-6.6 ‰).
The increased sea ice cover over the SO results in a surface ocean δ13C reduction relative to the control of -0.5 ‰ to
-0.1 ‰ in the SO (Figs. 4 and 9), while δ13C increases outside of the SO by 0 ‰–0.2 ‰
(Fig. 9a). The reduction is especially pronounced between 40 and 60∘ S. The
∼40 % reduced POC export production in the SO due to light
inhibition by the sea ice cover causes a major part of the SO surface
δ13C reduction, as the absence of photosynthesis will cause
lower surface ocean δ13C. Next to that, the reduced air–sea gas
exchange Fu+d in the SO also leads to a lowered surface ocean δ13C signature. About the opposite happens when we simulate a strongly
decreased sea ice cover (only ice south of 70∘ S): a small
reduction of δ13C is modelled outside the SO, but the SO
δ13Csurface locally becomes up to ∼0.8 ‰ higher relative to the control (Fig. 9b) as the
increased amount of air–sea gas exchange Fu+d decreases the carbon
isotopic disequilibrium and increases POC production in the newly exposed
area, both acting to increase δ13C of DIC.
The effect of a changed ice cover on deep ocean δ13C is less
than ∼0.1 ‰ (Fig. 9c, d) as the surface
signal is diluted while it follows the general ocean circulation. As for
air–sea gas exchange (Sect. 3.3.1), no pronounced deep ocean δ13C signal is found outside of the SO due to sea ice cover changes
(this is opposed to interpretations by Lear et al., 2016). Global mean Δδ13C is not significantly affected by changes in the SO sea
ice cover (Fig. 5) because the low- and high-latitude effects on δ13Csurface
cancel each other out. The SO Δδ13C, however, weakens considerably to 0.4 ‰ when the
50–60∘ S region becomes covered with sea ice and strengthens to 1 ‰ if the sea ice is removed between 60 and 70∘ S
(Fig. 4). The presence or absence of a sea ice cover should thus be clearly
visible in especially planktic SO δ13C sediment records. The
effect on Δδ13C spreads downstream of the SO, where
Δδ13C is increased by up to 0.2 ‰
in the Pacific Ocean for an increased SO sea ice cover (Fig. 4).
Modelled versus observed Δδ13C variations
The variations in Δδ13C on glacial–interglacial
timescales provide researchers with a tracer to study the biogeochemical
state of the past global ocean, under the condition that we can interpret
(variations in) Δδ13C. The idealised perturbations made
to the (Southern) Ocean in this study show that global mean Δδ13C is particularly sensitive to an increased gas exchange rate and
changes in the efficiency of the biological pump. Global mean Δδ13C varies by up to about ±0.35 ‰
around the pre-industrial model reference (1.2 ‰ ) in
response to biogeochemical change (Fig. 5) – under the assumption of a
constant ocean circulation. However, the sensitivity of Δδ13C to biogeochemical changes depends strongly on location for all
sensitivity experiments (Fig. 4), possibly explaining part of the
incoherency of reconstructed planktic and benthic foraminiferal δ13C from sediment cores (Oliver et al., 2010). In general, such
Δδ13C reconstructions show Δδ13C
variations in ∼1 ‰ over the past 350 000
years (Shackleton et al., 1983; Shackleton and Pisias, 1985; Boyle, 1988;
Charles et al., 2010; Oliver et al., 2010; Ziegler et al., 2013). Ocean
circulation changes explain at least part of these variations in Δδ13C
(Oppo et al., 1990; Heinze et al., 1991; Heinze and Hasselmann, 1993; Toggweiler 1999; Charles et al., 2010; Jansen, 2017). However,
the changes in the biogeochemical state of the ocean imposed in our
experiments show that variations in Δδ13C could be
strongly influenced by (SO) biogeochemistry as well. Δδ13C is increased during glacials and reduced during interglacials
across a large set of sediment cores (Boyle, 1988; Charles et al., 2010;
Oliver et al., 2010; Ziegler et al., 2013). Rapid and large changes have
been documented for SO Δδ13C records (Ziegler et al.,
2013), and here we show that biogeochemical changes in the SO affect Δδ13C globally. Our results show that an increase in mean
Δδ13C could biogeochemically result from slower gas
exchange, increased POC sinking rates, or an increased nutrient uptake rate
in the SO (Figs. 4 and 5). Such biogeochemical changes have been associated
with glacial periods (for example, Ziegler et al., 2013) and are potential
candidates to explain part of the Δδ13C increase in
interplay with stronger ocean stratification. Sediment-core reconstructions
of Δδ13C show that an increased Δδ13C can originate from a downward shift of the metabolic imprint of
low-δ13C POC, which would increase shallow δ13C
(Boyle, 1988; Mulitza et al., 1998; Toggweiler, 1999; Charles et al., 2010),
and/or a deep ocean δ13C decrease (Broecker, 1982; Boyle, 1988;
Oliver et al., 2010), with little variation recorded for surface ocean
δ13C. The absence of a clear surface δ13C signal
could in the SO be the net effect of an increased sea ice cover (locally
decreasing δ13C; Figs. 4 and 9a) and an increased biological
pump efficiency (locally increasing δ13Csurface; Figs. 6a and b, 7a) or increased SO thermodynamic equilibrium (Fig. 3a and b) –
if these opposing signals get mixed. A pronounced deep ocean δ13C decrease is associated with an efficient biological pump and older
water masses in our study (Fig. 4). Interestingly, large local changes in
deep ocean δ13C and Δδ13C do not
necessarily imply changes in mean deep ocean PO43- (Sect. 3.3.3).
The local character of the Δδ13C sensitivity (Fig. 4)
implies that correlations between sediment core Δδ13C/δ13C variations and global parameters (e.g.
pCO2) are not easily extrapolated to other sediment cores over large
distances. Analysis of SO Δδ13C reconstructions from
sediment cores, at 42 and 46∘ S (Charles et al., 2010)
for example, shows that there is a strong correlation between these cores and
Northern Hemisphere climate fluctuations. We expect that this strong
correlation does not exist for cores further south in the SO because our
results indicate that the SO south of ∼50–60∘ S
often has a different Δδ13C response to biogeochemical
change than the rest of the ocean.
Interglacial periods are generally thought to be associated with a decrease
in the efficiency of the biological pump and increased deep-ocean
ventilation via southern-sourced water masses (Gottschalk et al., 2016).
Increased deep-ocean ventilation might be driven by increased winds (Tschumi
et al., 2011), which would (apart from changing the SO circulation pattern)
also increase gas exchange rates. Each of these processes indeed reduces mean
Δδ13C in our experiments (Fig. 5), although
this is less pronounced in the SO (Fig. 4). However, the interglacial reduction
of Δδ13C seems to originate from a deep ocean δ13C increase compared to the glacial deep ocean δ13C
(Broecker, 1982; Charles et al., 2010; Oliver et al., 2010). Our results
show that neither an inefficient biological pump nor fast gas exchange can
be associated with a pronounced deep sea δ13C increase relative
to our control, because their effects are restricted to the surface ocean.
On the other hand, the interglacial decrease in Δδ13C
is a decrease compared to the glacial state: if glacial SO nutrient
uptake was higher (Vmax), a return to the “normal” state (i.e. the
model control run) would result in a major decrease in Δδ13C (Figs. 4 and 5).
Relationships between global mean Δδ13C,
δ13Catm, and pCO2atm. (a) Global mean Δδ13C versus δ13Catm of the different
sensitivity experiments. R2 of the best-fit line is 0.71, and the line
is described by y=1.3x-8.1. (b) Global mean Δδ13C
versus pCO2atm of the different sensitivity experiments. R2 of
the best-fit line is 0.39, and the line is described by y=-54x+341.
The relationship between Δδ13C, δ13Catm, and pCO2atm
One would expect variations in δ13Catm as well as Δδ13C to correlate with variations in pCO2atm, because
similar processes (biology and air–sea gas exchange) steer their
distribution and concentrations (Shackleton and Pisias, 1985; this article).
Δδ13C is considered a promising proxy for
reconstructions of pCO2atm for times predating ice-core records
(Lisiecki, 2010). Here we show that a positive linear relationship between
δ13Catm and global mean Δδ13C (Fig. 10a) holds over a wide range of biogeochemical states, as simulated in the
sensitivity experiments. However, the negative linear relationship between
pCO2atm and global mean Δδ13C (Fig. 10b) is
weak (R2=0.39). Yet, previous studies do show the existence of a
correlation between local Δδ13C and pCO2atm
(such as found by for example Dickson et al., 2008), and correlation of
modified Δδ13C between ocean basins with
pCO2atm (Lisiecki, 2010). The effects of ocean circulation on
glacial–interglacial δ13Catm changes, not studied here,
are most pronounced in response to Antarctic Bottom Water formation rate
variations and are of the order of 0 ‰–0.15 ‰ (Menviel et
al., 2015). Our results show that δ13Catm varies by up to
about ±0.5 ‰ in response to biogeochemical
changes (Table 2). Changes in the POC sinking rate lie approximately along a
line in δ13Catm:Δδ13C space (Fig. 10a), suggesting that changes in the biological pump efficiency are
important for the δ13Catm:Δδ13C
relationship. Likewise, both the gas exchange rate and biological pump
experiments lie along an approximate lines in pCO2atm:Δδ13C space (Fig. 10b, albeit a different one – leading to a
low total correlation). Changes in air–sea gas exchange (as simulated in the
gas exchange and sea ice cover experiments) affect δ13Catm
more than Δδ13C. This confirms the idea that Δδ13C is governed by biological processes and will also set
δ13Catm, unless air–sea gas exchange gets the chance to
dominate δ13Catm. The high potential of SO air–sea gas
exchange to change δ13Catm (Table 2: sea ice and gas
exchange rate experiments) complements recent studies showing that increased
SO ventilation of deep ocean carbon is a likely candidate for
glacial–interglacial δ13Catm excursions – which are of
the order of 0.5 ‰ (Lourantou et al., 2010; Menviel et al., 2015; Bauska et al., 2016; Eggleston et
al., 2016).
Summary and conclusions
This study addresses the sensitivity of modelled marine and atmospheric
δ13C and Δδ13C to changes in
biogeochemical parameters under constant ocean circulation, focusing on the
contribution of the SO (the ocean south of 45∘ S, 22 % of the
global ice-free ocean area). Variations in Δδ13C
recorded in sediment records are sensitive to ocean circulation changes as
shown in previous studies, but here we show that the biogeochemical state of
the (Southern) Ocean can also have large effects on Δδ13C across all ocean basins. Using the ocean biogeochemistry general
circulation model HAMOCC2s, a set of sensitivity experiments was carried
out, which focuses on the biogeochemical aspects known to be important for
δ13C and Δδ13C. Specifically, the
experiments explore changes in air–sea gas exchange rate, sea ice extent
(influencing both biological production and the air–sea gas exchange of
carbon), and the efficiency of the biological pump through the POC sinking
rate and nutrient uptake rate.
The results show the important role of the SO in determining global δ13C and Δδ13C sensitivities, as well as the
strong spatial differences in these. A new quasi-equilibrium state developed
mostly within the first 100–800 years of the sensitivity experiments, except
for the POC sinking experiment, where an imbalance between weathering and
burial causes a long-term drift. The δ13C signature is governed
by different processes depending on location: air–sea gas exchange sets
surface ocean δ13C in all ocean basins, contributing 60 %–100 % to the δ13C signature. At depth and with increasing water
mass age, the influence of biology increases to 50 % in the oldest water
masses (North Pacific) due to POC remineralisation. This spatial pattern
behind the δ13C signature of a water parcel results in a
non-uniform sensitivity of δ13C to biogeochemical change.
Global mean Δδ13C varies by up to about ±0.35 ‰ due to biogeochemical state changes in our experiments
(at a constant ocean circulation) (Fig. 5). This amplitude is almost half of
the reconstructed variation in Δδ13C on
glacial–interglacial timescales (1 ‰), and could thus
contribute to variations in Δδ13C together with ocean
circulation changes. However, Δδ13C can have a
different response on a basin scale: the ocean's oldest water (North
Pacific) responds most to biological changes, the young deep water (North
Atlantic) responds strongly to air–sea gas exchange changes, and the
vertically well-mixed water (SO) has a low or even reversed Δδ13C sensitivity compared to the other basins. The amplitude of the
Δδ13C sensitivity can be higher at decreasing scale, as
seen from a maximum sensitivity of about -0.6 ‰ on ocean
basin scale (Fig. 4). Interestingly, the direction of both glacial
(intensification of Δδ13C) and interglacial (weakening
of Δδ13C) Δδ13C change matches
changes in biogeochemical processes thought to be associated with these
periods. This supports the idea that biogeochemistry explains part of the
reconstructed variations in Δδ13C, in addition to
changes in ocean circulation.
An increased gas exchange rate has the potential to reduce the
biologically induced Δδ13C through the reduction of
surface ocean and atmospheric δ13C. Increased gas exchange,
however, only reduces Δδ13C in the low latitudes: in
high latitudes, increased gas exchange will increase Δδ13C (by increasing δ13Csurface) because of the
negative disequilibrium δ13Cdiseq (i.e. δ13C
< δ13Ceq) in this region, and thus potentially
increase δ13Csurface (Sect. 3.3.1). Notably,
pCO2atm, δ13Catm, and marine δ13C are
shown to be disproportionally sensitive to SO gas exchange rate changes.
Changes in the efficiency of the biological pump also have a major potential
to alter Δδ13C as well as pCO2atm and
δ13Catm. The globally increased POC sinking rate experiment shows
that Δδ13C strengthens in low latitudes (and more so in
older waters) by deepening the low-δ13C signature of
remineralised POC, while SO Δδ13C is not very sensitive
to POC sinking rates. The SO effects are comparably small because the
vertical mixing in the SO of the low-δ13C deep water only
causes shifts in the entire δ13C profile, not a change in the
gradient (Fig. 4). Increased POC sinking causes a long-term imbalance
between weathering and sediment burial which leads to an increase in mean
δ13C and δ13Catm (of about 0.15 ‰) after 2000 years. Increased nutrient uptake in the
SO (Vmax experiment) results in 13 % lower non-SO POC export
production up to ∼35∘ N, in agreement with
previous studies on the role of the SO biological pump in lower-latitude
productivity. Interestingly, the increase in Δδ13C in
all ocean basins occurs without significantly changing mean (deep) ocean
PO43-, which advocates for increased SO nutrient uptake to explain
(part of) glacial–interglacial Δδ13C variations.
Furthermore, our results show that improved goodness of fit of the model
data to the δ13C:PO43- relationship can be driven by
reduced gas exchange as well as biological uptake efficiency in the SO,
since both increase the importance of biology relative to air–sea gas
exchange for δ13C. Caution should thus be exercised when
interpreting changes in the fit of observations to the δ13C:PO43- relationship as changes in ocean ventilation or
air–sea gas exchange alone.
A significant linear relationship was found across the sensitivity
experiments between δ13Catm and Δδ13C
(R2=0.71), and a weaker one (R2=0.39) for pCO2atm and
Δδ13C. This result shows that paleo-reconstructions of
δ13Catm based on Δδ13C could be valid
for a wide range of biogeochemical states. Previous studies have shown good
correlation between pCO2atm and local Δδ13C,
but our results suggest that the relationship may not be valid if both
biological and gas exchange rate changes occur. The maximum response of
δ13Catm to the biogeochemical changes imposed in our
experiments (up to 0.5 ‰) is larger than the idealised
maximum effect of ocean circulation changes on δ13Catm
(0 ‰–0.15 ‰; Menviel et al., 2015), which underlines the
potential importance of biogeochemical processes for variations in
δ13Catm. The high potential of SO air–sea gas exchange to steer
δ13Catm (Table 2: sea ice and gas exchange rate
experiments) complements recent studies showing that increased SO
ventilation of deep ocean carbon is a likely candidate for
glacial–interglacial δ13Catm excursions.
As an outlook, the use of a more complex model with a higher horizontal and
vertical resolution and a shorter time step (resolving seasonal variations)
could provide valuable additional information. For example, the role of
different regions within the SO on the global δ13C distribution
could be better studied with a more complex model. Sediment core-based
reconstructions of the global carbon cycle could possibly be aided by a more
complex model with a finer grid and higher time resolution, by providing
more detailed information on the contribution of biogeochemical processes to
local ocean tracers. Next to that, exploring the effect on Δδ13C of a glacial model circulation field could provide a way to
quantify the maximum combined effect of circulation and biogeochemical
change on Δδ13C.
The model and model output can be made available upon request to the main
author.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-15-7205-2018-supplement.
CH provided the HAMOCC2s model. ALM and CH designed the model experiments
and ALM carried them out. ALM analysed the results and prepared the
paper, with feedback and supervision from JS and CH.
The authors declare that they have no conflict of interest.
This article is part of the special issue “The 10th
International Carbon Dioxide Conference (ICDC10) and the 19th WMO/IAEA
Meeting on Carbon Dioxide, other Greenhouse Gases and Related Measurement
Techniques (GGMT-2017) (AMT/ACP/BG/CP/ESD inter-journal SI)”. It is a result
of the 10th International Carbon Dioxide Conference, Interlaken, Switzerland,
21–25 August 2017.
Acknowledgements
The authors would like to thank two anonymous reviewers for their
constructive and helpful comments, which improved this paper. This
study is a contribution to the project “Earth system modelling of climate
variations in the Anthropocene” (EVA; grant no. 229771) as well as the
project “Overturning circulation and its implications for global carbon
cycle in coupled models” (ORGANIC; grant no. 239965) which are both funded
by the Research Council of Norway. This is a contribution to the Bjerknes
Centre for Climate Research (Bergen, Norway). Storage resources were
provided by the Norwegian storage infrastructure of Sigma2 (NorStore project
ns2980k). Anne L. Morée is grateful for PhD funding through the Faculty for
Mathematics and Natural Sciences of the University of Bergen and the Meltzer
Foundation. Christoph Heinze acknowledges sabbatical support from the
Faculty for Mathematics and Natural Sciences of the University of Bergen.
Edited by: Fortunat Joos
Reviewed by: two anonymous referees
ReferencesBauska, T. K., Baggenstos, D., Brook, E. J., Mix, A. C., Marcott, S. A.,
Petrenko, V. V., Schaefer, H., Severinghaus, J. P., and Lee, J. E.: Carbon
isotopes characterize rapid changes in atmospheric carbon dioxide during the
last deglaciation, P. Natl. Acad. Sci. USA, 113, 3465–3470, 10.1073/pnas.1513868113, 2016.Boyle, E. A.: The role of vertical chemical fractionation in controlling late
Quaternary atmospheric carbon dioxide, J. Geophys. Res.-Oceans, 93, 15701-15714, 10.1029/JC093iC12p15701, 1988.Broecker, W. S.: Ocean chemistry during glacial time, Geochim. Cosmochim. Ac., 46, 1689–1705,
10.1016/0016-7037(82)90110-7, 1982.Broecker, W. S. and Maier-Reimer, E.: The influence of air and sea exchange
on the carbon isotope distribution in the sea, Global Biogeochem. Cy.,
6, 315–320, 10.1029/92GB01672, 1992.Broecker, W. S. and McGee, D.: The 13C record for atmospheric CO2: What is
it trying to tell us?, Earth Planet. Sci. Lett., 368, 175–182,
10.1016/j.epsl.2013.02.029, 2013.Broecker W, S. and Peng, T. H.: Gas exchange rates between air and sea,
Tellus, 26, 21–35, 10.1111/j.2153-3490.1974.tb01948.x, 1974.Broecker, W. S. and Peng, T.-H.: The role of CaCO3 compensation in the
glacial to interglacial atmospheric CO2 change, Global Biogeochem. Cy.,
1, 15–29, 10.1029/GB001i001p00015, 1987.Charles, C. D., Pahnke, K., Zahn, R., Mortyn, P. G., Ninnemann, U., and
Hodell, D. A.: Millennial scale evolution of the Southern Ocean chemical
divide, Quaternary Science Reviews, 29, 399-409, 10.1016/j.quascirev.2009.09.021, 2010.Craig, H.: Isotopic standards for carbon and oxygen and correction factors
for mass-spectrometric analysis of carbon dioxide, Geochim. Cosmochim. Ac., 12, 133–149, 10.1016/0016-7037(57)90024-8, 1957.
Crosta, X.: Antarctic Sea Ice History, Late Quaternary, in: Encyclopedia of
Paleoclimatology and Ancient Environments, edited by: Gornitz, V., Springer
Netherlands, Dordrecht, the Netherlands, 31–34, 2009.Crucifix, M.: Distribution of carbon isotopes in the glacial ocean: A model
study, Paleoceanography, 20, PA4020, 10.1029/2005PA001131, 2005.Curry, W. B. and Oppo, D. W.: Glacial water mass geometry and the
distribution of δ13C of ΣCO2 in the western Atlantic Ocean,
Paleoceanography, 20, PA1017, 10.1029/2004PA001021, 2005.DeVries, T., Primeau, F., and Deutsch, C.: The sequestration efficiency of
the biological pump, Geophys. Res. Lett., 39, L13601, 10.1029/2012GL051963, 2012.Dickson, A. J., Leng, M. J., and Maslin, M. A.: Mid-depth South Atlantic
Ocean circulation and chemical stratification during MIS-10 to 12:
implications for atmospheric CO2, Clim. Past, 4, 333–344,
10.5194/cp-4-333-2008, 2008.Dunne, J. P., Sarmiento, J. L., and Gnanadesikan, A.: A synthesis of global
particle export from the surface ocean and cycling through the ocean interior
and on the seafloor, Global Biogeochem. Cy., 21, GB4006, 10.1029/2006GB002907, 2007.Duplessy, J. C., Shackleton, N. J., Fairbanks, R. G., Labeyrie, L., Oppo, D.,
and Kallel, N.: Deepwater source variations during the last climatic cycle
and their impact on the global deepwater circulation, Paleoceanography, 3,
343–360, 10.1029/PA003i003p00343, 1988.Eggleston, S., Schmitt, J., Bereiter, B., Schneider, R., and Fischer, H.:
Evolution of the stable carbon isotope composition of atmospheric CO2 over
the last glacial cycle, Paleoceanography, 31, 434–452, 10.1002/2015PA002874,
2016.Eide, M., Olsen, A., Ninnemann, U. S., and Eldevik, T.: A global estimate of
the full oceanic 13C Suess effect since the preindustrial, Global Biogeochem. Cy., 31, 492–514, 10.1002/2016GB005472, 2017a.Eide, M., Olsen, A., Ninnemann, U. S., and Johannessen, T.: A global ocean
climatology of preindustrial and modern ocean δ13C, Global Biogeochem. Cy., 31, 515–534, 10.1002/2016GB005473, 2017b.
Emerson, S. and Hedges, J.: Chemical oceanography and the marine carbon
cycle, Cambridge University Press, Cambridge, UK, xi, 453 pp. of col.
plates pp., 2008.Galbraith, E. D., Kwon, E. Y., Bianchi, D., Hain, M. P., and Sarmiento, J.
L.: The impact of atmospheric pCO2 on carbon isotope ratios of the
atmosphere and ocean, Global Biogeochem. Cy., 29, 307–324, 10.1002/2014GB004929, 2015.Gottschalk, J., Skinner, L. C., Lippold, J., Vogel, H., Frank, N., Jaccard,
S. L., and Waelbroeck, C.: Biological and physical controls in the Southern
Ocean on past millennial-scale atmospheric CO2 changes, Nat. Commun., 7, 11539, 10.1038/ncomms11539,
2016.Gruber, N. and Keeling, C. D.: An improved estimate of the isotopic air-sea
disequilibrium of CO2: Implications for the oceanic uptake of anthropogenic
CO2, Geophys. Res. Lett., 28, 555–558, 10.1029/2000GL011853, 2001.Gruber, N., Keeling, C. D., Bacastow, R. B., Guenther, P. R., Lueker, T. J.,
Wahlen, M., Meijer, H. A. J., Mook, W. G., and Stocker, T. F.: Spatiotemporal
patterns of carbon-13 in the global surface oceans and the oceanic suess
effect, Global Biogeochem. Cy., 13, 307–335, 10.1029/1999GB900019,
1999.Heinze, C.: Assessing the importance of the Southern Ocean for natural
atmospheric pCO2 variations with a global biogeochemical general
circulation model, DeepSea Res. Pt. II, 49, 3105–3125, 10.1016/S0967-0645(02)00074-7,
2002.Heinze, C., and Hasselmann, K.: Inverse Multiparameter Modeling of
Paleoclimate Carbon Cycle Indices, Quaternary Res., 40, 281–296,
10.1006/qres.1993.1082, 1993.
Heinze, C. and Maier-Reimer, E.: The Hamburg Oceanic Carbon Cycle
Circulation Model Version “HAMOCC2s” for long time integrations,
Max-Planck-Institut für Meteorologie, Hamburg REPORT 20, 1999.Heinze, C., Maier-Reimer, E., and Winn, K.: Glacial pCO2 Reduction
by the World Ocean: Experiments With the Hamburg Carbon Cycle Model,
Paleoceanography, 6, 395–430, 10.1029/91PA00489, 1991.Heinze, C., Hoogakker, B. A. A., and Winguth, A.: Ocean carbon cycling during
the past 130 000 years – a pilot study on inverse palaeoclimate record
modelling, Clim. Past, 12, 1949–1978,
10.5194/cp-12-1949-2016, 2016.Hilting, A. K., Kump, L. R., and Bralower, T. J.: Variations in the oceanic
vertical carbon isotope gradient and their implications for the
Paleocene-Eocene biological pump, Paleoceanography, 23, PA3222, 10.1029/2007PA001458, 2008.Holden, P. B., Edwards, N. R., Müller, S. A., Oliver, K. I. C., Death, R.
M., and Ridgwell, A.: Controls on the spatial distribution of oceanic
δ13CDIC, Biogeosciences, 10, 1815–1833,
10.5194/bg-10-1815-2013, 2013.Hollander, D. J. and McKenzie, J. A.: CO2 control on carbon-isotope
fractionation during aqueous photosynthesis: A paleo-pCO2 barometer,
Geology, 19, 929–932, 10.1130/0091-7613(1991)019<0929:ccocif>2.3.co;2, 1991.Hoogakker, B. A. A., Elderfield, H., Schmiedl, G., McCave, I. N., and
Rickaby, R. E. M.: Glacial-interglacial changes in bottom-water oxygen
content on the Portuguese margin, Nat. Geosci., 8, 40–43, 10.1038/ngeo2317,
2015.Jahn, A., Lindsay, K., Giraud, X., Gruber, N., Otto-Bliesner, B. L., Liu, Z.,
and Brady, E. C.: Carbon isotopes in the ocean model of the Community Earth
System Model (CESM1), Geosci. Model Dev., 8, 2419–2434,
10.5194/gmd-8-2419-2015, 2015.Jansen, M. F.: Glacial ocean circulation and stratification explained by
reduced atmospheric temperature, P. Natl. Acad. Sci. USA, 114, 45–50, 10.1073/pnas.1610438113, 2017.Jones, D. C., Ito, T., Takano, Y., and Hsu, W.-C.: Spatial and seasonal
variability of the air-sea equilibration timescale of carbon dioxide,
Global Biogeochem. Cy., 28, 1163–1178, 10.1002/2014GB004813, 2014.Keir, R. S.: The effect of vertical nutrient redistribution on surface ocean
δ13C, Global Biogeochem. Cy., 5, 351–358,
10.1029/91GB01913, 1991.Kroopnick, P.: The distribution of 13C in the Atlantic Ocean, Earth Planet. Sc. Lett., 49, 469–484,
10.1016/0012-821X(80)90088-6, 1980.Kroopnick, P. M.: The distribution of 13C of ΣCO2 in the world
oceans, Deep-Sea Res., 32, 57–84,
10.1016/0198-0149(85)90017-2, 1985.Laws, E. A., Bidigare, R., R., and Popp, B. N.: Effects of growth rate and
CO2 concentration on carbon isotopic fractionation by the marine diatom
Phaeodactylum tricornutum, Limnol. Oceanogr., 42, 1552–1560, 1997.Lear, C. H., Billups, K., Rickaby, R. E. M., Diester-Haass, L., Mawbey, E.
M., and Sosdian, S. M.: Breathing more deeply: Deep ocean carbon storage
during the mid-Pleistocene climate transition, Geology, 44, 1035–1038, 10.1130/G38636.1, 2016.Lisiecki, L. E.: A benthic δ13C-based proxy for atmospheric pCO2 over
the last 1.5 Myr, Geophys. Res. Lett., 37, L21708, 10.1029/2010GL045109,
2010.Lourantou, A., Lavrič Jošt, V., Köhler, P., Barnola, J. M.,
Paillard, D., Michel, E., Raynaud, D., and Chappellaz, J.: Constraint of the
CO2 rise by new atmospheric carbon isotopic measurements during the last
deglaciation, Global Biogeochem. Cy., 24, GB2015, 10.1029/2009GB003545, 2010.Lutz, M. J., Caldeira, K., Dunbar, R. B., and Behrenfeld, M. J.: Seasonal
rhythms of net primary production and particulate organic carbon flux to
depth describe the efficiency of biological pump in the global ocean, J. Geophys. Res.-Oceans, 112, C10011, 10.1029/2006JC003706, 2007.Lynch-Stieglitz, J., Stocker, T. F., Broecker, W. S., and Fairbanks, R. G.:
The influence of air-sea exchange on the isotopic composition of oceanic
carbon: Observations and modeling, Global Biogeochem. Cy., 9, 653–665, 10.1029/95GB02574, 1995.MacCready, P. and Quay, P.: Biological export flux in the Southern Ocean
estimated from a climatological nitrate budget, Deep-Sea Res. Pt. II, 48, 4299–4322,
10.1016/S0967-0645(01)00090-X, 2001.
Mackenzie, F. T. and Lerman, A.: Isotopic Fractionation of Carbon: Inorganic
and Biological Processes, in: Carbon in the Geobiosphere – Earth's Outer
Shell –, edited by: Mackenzie, F. T. and Lerman, A., Springer Netherlands,
Dordrecht, the Netherlands, 165–191, 2006.Marchal, O., Stocker, T. F., and Joos, F.: A latitude-depth,
circulation-biogeochemical ocean model for paleoclimate studies. Development
and sensitivities, Tellus B, 50, 290–316, 10.1034/j.1600-0889.1998.t01-2-00006.x, 1998.
Marinov, I., Gnanadesikan, A., Toggweiler, J. R., and Sarmiento, J. L.: The
Southern Ocean biogeochemical divide, Nature, 441, 964–967,
2006.Menviel, L., Mouchet, A., Meissner, K. J., Joos, F., and England, M. H.:
Impact of oceanic circulation changes on atmospheric δ13CO2, Global Biogeochem. Cy., 29, 1944–1961, 10.1002/2015GB005207, 2015.Menviel, L., Yu, J., Joos, F., Mouchet, A., Meissner, K. J., and England, M.
H.: Poorly ventilated deep ocean at the Last Glacial Maximum inferred from
carbon isotopes: A data-model comparison study, Paleoceanography, 32, 2–17,
10.1002/2016PA003024, 2016.Milliman, J. D. and Droxler, A. W.: Neritic and pelagic carbonate
sedimentation in the marine environment: ignorance is not bliss, Geol.
Rundsch., 85, 496–504, 10.1007/BF02369004, 1996.Mook, W. G.: 13C in atmospheric CO2, Neth. J. Sea Res., 20,
211–223, 10.1016/0077-7579(86)90043-8, 1986.Mulitza, S., Rühlemann, C., Bickert, T., Hale, W., Pätzold, J., and
Wefer, G.: Late Quaternary δ13C gradients and carbonate accumulation
in the western equatorial Atlantic, Earth Planet Sci. Lett., 155,
237–249, 10.1016/S0012-821X(98)00012-0, 1998.Murnane, R. J. and Sarmiento, J. L.: Roles of biology and gas exchange in
determining the δ13C distribution in the ocean and the preindustrial
gradient in atmospheric δ13C, Global Biogeochem. Cy., 14,
389–405, 10.1029/1998GB001071, 2000.Nevison, C. D., Keeling, R. F., Kahru, M., Manizza, M., Mitchell, B. G., and
Cassar, N.: Estimating net community production in the Southern Ocean based
on atmospheric potential oxygen and satellite ocean color data, Global Biogeochem. Cy., 26, GB1020, 10.1029/2011GB004040, 2012.Oliver, K. I. C., Hoogakker, B. A. A., Crowhurst, S., Henderson, G. M.,
Rickaby, R. E. M., Edwards, N. R., and Elderfield, H.: A synthesis of marine
sediment core δ13C data over the last 150 000 years, Clim.
Past, 6, 645–673, 10.5194/cp-6-645-2010, 2010.Oppo, D. W., Fairbanks, R. G., and Gordon, A. L.: Late Pleistocene Southern
Ocean δ13C variability, Paleoceanography, 5, 43–54,
10.1029/PA005i001p00043, 1990.Primeau, F. W., Holzer, M., and DeVries, T.: Southern Ocean nutrient trapping
and the efficiency of the biological pump, J. Geophys. Res.-Oceans, 118, 2547–2564, 10.1002/jgrc.20181, 2013.Quay, P., Sonnerup, R., Westby, T., Stutsman, J., and McNichol, A.: Changes
in the 13C/12C of dissolved inorganic carbon in the ocean as a tracer of
anthropogenic CO2 uptake, Global Biogeochem. Cy., 17, 4-1–4-20, 10.1029/2001GB001817, 2003.Roth, R., Ritz, S. P., and Joos, F.: Burial-nutrient feedbacks amplify the
sensitivity of atmospheric carbon dioxide to changes in organic matter
remineralisation, Earth Syst. Dynam., 5, 321–343, 10.5194/esd-5-321-2014, 2014.
Sarmiento, J. L., Gruber, N., Brzezinski, M. A., and Dunne, J. P.:
High-latitude controls of thermocline nutrients and low latitude biological
productivity, Nature, 427, 56–60, 2004.
Schlitzer, R.: Carbon Export Fluxes in the Southern Ocean: Results from
Inverse Modeling and Comparison with Satellite Estimates, Deep-Sea Res.,
2, 1623–1644, 2002.Schmittner, A. and Somes, C. J.: Complementary constraints from carbon (13C)
and nitrogen (15N) isotopes on the glacial ocean's soft-tissue biological
pump, Paleoceanography, 31, 669–693, 10.1002/2015PA002905, 2016.Schmittner, A., Gruber, N., Mix, A. C., Key, R. M., Tagliabue, A., and
Westberry, T. K.: Biology and air–sea gas exchange controls on the
distribution of carbon isotope ratios (δ13C) in the ocean, Biogeosciences,
10, 5793–5816, 10.5194/bg-10-5793-2013, 2013.Shackleton, N. J., Hall, M. A., Line, J., and Shuxi, C.: Carbon isotope data
in core V19-30 confirm reduced carbon dioxide concentration in the ice age
atmosphere, Nature, 306, 319–322, 10.1038/306319a0, 1983.Shackleton, N. J. and Pisias, N. G.: Atmospheric carbon dioxide, orbital
forcing, and climate, in: The Carbon cycle and atmospheric CO2: natural
variations archean to present, edited by: Sundquist, E. T., and Broecker, W.
S., Geophysical Monograph, American Geophysical Union, Washington, USA, 303–317,
1985.Sonnerup, R. E. and Quay, P. D.: 13C constraints on ocean carbon cycle
models, Global Biogeochem. Cy., 26, GB2014, 10.1029/2010GB003980, 2012.Stephens, B. B. and Keeling, R. F.: The influence of Antarctic sea ice on
glacial–interglacial CO2 variations, Nature, 404, 171–174, 10.1038/35004556,
2000.Tagliabue, A. and Bopp, L.: Towards understanding global variability in
ocean carbon-13, Global Biogeochem. Cy., 22, GB1025, 10.1029/2007GB003037, 2008.Toggweiler, J. R.: Variation of atmospheric CO2 by ventilation of the ocean's
deepest water, Paleoceanography, 14, 571–588, 10.1029/1999PA900033, 1999.Tréguer, P.: Silica and the cycle of carbon in the ocean, C. R.
Geosci., 334, 3–11, 10.1016/S1631-0713(02)01680-2, 2002.Tschumi, T., Joos, F., Gehlen, M., and Heinze, C.: Deep ocean ventilation,
carbon isotopes, marine sedimentation and the deglacial CO2 rise, Clim. Past,
7, 771–800, 10.5194/cp-7-771-2011, 2011.Zahn, R., Winn, K., and Sarnthein, M.: Benthic foraminiferal δ13C and
accumulation rates of organic carbon: Uvigerina Peregrina group and
Cibicidoides Wuellerstorfi, Paleoceanography, 1, 27–42, 10.1029/PA001i001p00027, 1986.Zeebe, R. and Wolf-Gladrow, D.: CO2 in Seawater: Equilibrium, Kinetics,
Isotopes, Elsevier Oceanography Series, edited by: Halpern, D., Elsevier
Science B.V., Amsterdam, the Netherlands, 346 pp., 2001.Zhang, J., Quay, P. D., and Wilbur, D. O.: Carbon isotope fractionation
during gas-water exchange and dissolution of CO2, Geochim. Cosmochim. Ac., 59, 107–114, 10.1016/0016-7037(95)91550-D, 1995.Ziegler, M., Diz, P., Hall, I. R., and Zahn, R.: Millennial-scale changes in
atmospheric CO2 levels linked to the Southern Ocean carbon isotope gradient
and dust flux, Nat. Geosci., 6, 457–461, 10.1038/ngeo1782,
2013.