The isotopes of carbon (δ13C) and nitrogen
(δ15N) are commonly used proxies for understanding the ocean.
When used in tandem, they provide powerful insight into physical and
biogeochemical processes. Here, we detail the implementation of
δ13C and δ15N in the ocean component of an
Earth system model. We evaluate our simulated δ13C and
δ15N against contemporary measurements, place the model's
performance alongside other isotope-enabled models and document the response
of δ13C and δ15N to changes in ecosystem
functioning. The model combines the Commonwealth Scientific and Industrial
Research Organisation Mark 3L (CSIRO Mk3L) climate system model with the
Carbon of the Ocean, Atmosphere and Land (COAL) biogeochemical model. The
oceanic component of CSIRO Mk3L-COAL has a resolution of 1.6∘
latitude × 2.8∘ longitude and resolves multimillennial
timescales, running at a rate of ∼400 years per day. We show that this
coarse-resolution, computationally efficient model adequately reproduces
water column and core-top δ13C and δ15N
measurements, making it a useful tool for palaeoceanographic research.
Changes to ecosystem function involve varying phytoplankton stoichiometry,
varying CaCO3 production based on calcite saturation state and
varying N2 fixation via iron limitation. We find that large changes
in CaCO3 production have little effect on δ13C and
δ15N, while changes in N2 fixation and phytoplankton
stoichiometry have substantial and complex effects. Interpretations of
palaeoceanographic records are therefore open to multiple lines of
interpretation where multiple processes imprint on the isotopic signature,
such as in the tropics, where denitrification, N2 fixation and
nutrient utilisation influence δ15N. Hence, there is
significant scope for isotope-enabled models to provide more robust
interpretations of the proxy records.
Introduction
Elements that are involved in reactions of interest, such as exchanges of
carbon and nutrients, experience isotopic fractionation. Typically, the
heavier isotope will be enriched in the reactant during kinetic
fractionation, in more oxidised compounds during equilibrium fractionation
and in the denser form during phase state fractionation (i.e. evaporation).
Because fractionation against one isotope relative to the other is minuscule,
the isotopic content of a sample is conventionally expressed as a δ
value (δhE), where the ratio of the heavy to light element
in solution (hE:lE) is compared to a standard ratio
(hEstd:lEstd) in units of per mille
(‰).
δhE=(hE:lEhEstd:lEstd-1)⋅1000
The strength of fractionation against the heavier isotope during a given
reaction, ϵ, is also expressed in per mille notation. Fractionation
with an ϵ equal to 10 ‰, for example, will involve 990
units of hE for every 1000 units of lE at a
hypothetical standard ratio
(hEstd:lEstd) of 1:1. At more
realistic standard ratios ⋘1:1, say 0.0112372:1 for a
δ13C value of 0 ‰, a fractionation at 10 ‰
would involve ∼0.0111123(0.010⋅0.01123721.0112372) units of 13C per unit of
12C. Slightly greater preference of one isotope over another in
this case involves a preference for the lighter carbon isotope
(12C) over the heavier (13C), which enriches the
remaining dissolved inorganic carbon (DIC) in 13C and depletes the
product. Certain isotopic preferences, or strengths of fractionation,
therefore allow certain reactions to be detected in the environment.
The measurement of the stable isotopes of carbon (δ13C) and
nitrogen (δ15N) have been fundamental for understanding how these
important elements cycle within the ocean e.g.. We will now briefly introduce each
isotope in turn.
The distribution of δ13C is dependent on air–sea gas exchange,
ocean circulation and organic matter cycling. These contributions make the
δ13C signature difficult to interpret, and several modelling
studies have attempted to elucidate their roles . These studies have shown that preferential uptake of
12C over 13C by biology in surface waters enforces strong
horizontal and vertical gradients in δ13C of DIC
(δ13CDIC), greatly enriching surface waters, particularly
in subtropical gyres where vertical exchange with deeper waters is restricted
. Meanwhile, air–sea gas exchange and
carbon speciation control the δ13CDIC reservoir over longer
timescales . Because air–sea and speciation
fractionation are temperature dependent, such that cooler conditions tend to
elevate the δ13CDIC of surface waters, they also tend to
smooth the gradients produced by biology by working antagonistically to them.
Despite this smoothing, biological fractionation drives strong gradients at
the surface, which imparts unique δ13C signatures to the water
masses that are carried into the interior. These insights have provided clear
evidence of reduced ventilation rates in the deep ocean during glacial
climates .
δ15N is determined by biological processes that add or remove
fixed forms of nitrogen. It therefore records the relative rates of sources
and sinks within the marine nitrogen cycle . Dinitrogen
(N2) fixation is the largest source of fixed nitrogen to the ocean,
the bulk of which occurs in warm, sunlit surface waters and introduces
nitrogen with a δ15N of approximately -1 ‰
. Denitrification is the largest sink of fixed nitrogen and
occurs in deoxygenated water columns and sediments. Denitrification
fractionates strongly against 15N at ∼25 ‰
. Fractionation during denitrification is most strongly
expressed in the water column where ample nitrate (NO3) is available,
making water column denitrification responsible for elevating global mean
δ15N above the -1 ‰ of N2 fixers
. Meanwhile, denitrification occurring in the sediments
only weakly fractionates against 15N, providing
only a slight enrichment of δ15N above that introduced by
N2 fixation. Variations in δ15N can therefore tell us
about global changes in the ratio of sedimentary to water column
denitrification, with increases in δ15N associated with
increases in the proportion of denitrification occurring in the water column
, but it can also reflect regional changes in N2
fixation and denitrification .
However, nitrogen isotopes are also subject to the effect of utilisation,
which makes the interpretation of δ15N more complicated.
Basically, when nitrogen is abundant, the preference for 14N over
15N increases but when nitrogen is limited this preference
disappears . Complete utilisation of nitrogen therefore
reduces fractionation to 0 ‰. While this adds complexity, it also
imbues δ15N as a proxy of nutrient utilisation by
phytoplankton. As nitrogen supply to phytoplankton is controlled by physical
delivery from below, changes in δ15N can be interpreted as
changes in the physical supply . Phytoplankton fractionate
against 15N at ∼5 ‰ when
bioavailable nitrogen is abundant. If nitrogen is utilised to completion,
which occurs in much of the low to midlatitude ocean, then no fractionation
will occur and the δ15N of organic matter will reflect the
δ15N of the nitrogen that was supplied .
However, in the case where nitrogen is not consumed towards completion, which
occurs in zones of strong upwelling/mixing near coastlines, the Equator and
high latitudes, the bioavailable nitrogen pool will be enriched in
15N as phytoplankton preferentially consume 14N. As the
remaining bioavailable N is continually enriched in 15N, the organic
matter that settles into sediments beneath a zone of incomplete nutrient
utilisation will bear this enriched δ15N signal. In
combination with modelling , the δ15N
record is able to provide evidence for a more efficient utilisation of
bioavailable nitrogen during glacial times and a less efficient one during the Holocene .
Complimentary measurements of δ13C and δ15N
provide powerful, multi-focal insights into oceanographic processes.
δ13C is largely a reflection of how water masses mix away the
strong vertical and horizontal gradients enforced by biology, while
δ15N simultaneously reflects changes in the major sources and
sinks of the marine nitrogen cycle and how effectively nutrients are consumed
at the surface. However, the interpretation of these isotopes is often
difficult. They are subject to considerable uncertainty because there are
multiple processes that imprint on the measured values. Our goal is to equip
version 1.0 of the Commonwealth Scientific and Industrial
Research Organisation Mark 3L (CSIRO Mk3L) climate system model with the
Carbon of the Ocean, Atmosphere and Land (COAL) Earth system model with oceanic
δ13C and δ15N such that this model can be used
for interpreting palaeoceanographic records. First, we introduce CSIRO
Mk3L-COAL. Second, we detail the equations that govern the implementation of
carbon and nitrogen isotopes. Third, we assess our simulated isotopes against
contemporary measurements from both the water column and sediments and
compare the model performance against other isotope-enabled models. Finally,
as a first test of the model, we take the opportunity to document how changes
in ecosystem functioning affect δ13C and δ15N.
CSIRO Mk3L-COAL v1.0
The CSIRO Mk3L-COAL couples a computationally efficient climate system model
with biogeochemical cycles in the ocean, atmosphere and
land. The model is therefore based on the CSIRO Mk3L climate system model,
where the “L” denotes that it is a low-resolution version of the CSIRO Mk3
model that contributed towards the third phase of Coupled Model Intercomparison Project
and the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change . See
for a complete discussion of the CSIRO family of climate
models. The land biogeochemical component represents carbon, nitrogen and
phosphorus cycles in the Community Atmosphere Biosphere Land Exchange (CABLE)
. The ocean component currently represents carbon, alkalinity,
oxygen, nitrogen, phosphorus and iron cycles. The atmospheric component
conserves carbon and alters its radiative properties according to changes in
its carbon content. For this paper, we focus on the ocean biogeochemical model
(OBGCM).
Previous versions of the OBGCM have explored changes in oceanic properties
under past , present and future
scenarios . These studies demonstrate that the
model can reproduce observed features of the global carbon cycle, nutrient
cycling and organic matter cycling in the ocean. The OBGCM offers highly
efficient simulations of these processes at computational speeds of
∼400 years per day when the ocean general circulation model (OGCM) is
run offline (compared to ∼10 years per day in fully coupled mode). The
ocean is made up of grid cells of 1.6∘ in latitude by 2.8∘ in
longitude, with 21 vertical depth levels spaced by 25 m at the surface and
450 m in the deep ocean (Table ). The OGCM time step is 1 h,
while the OBGCM time step is 1 d. The ability of the OBGCM to reproduce
large-scale dynamical and biogeochemical properties of the ocean coupled with
its fast computational speed makes the OBGCM useful as a tool for
palaeoceanographic research.
Ocean biogeochemical model (OBGCM)
The OBGCM is equipped with 13 prognostic tracers (Fig. ).
These can be grouped into carbon chemistry fields, oxygen fields, nutrient
fields, age tracers and nitrous oxide (N2O). Carbon chemistry fields
include DIC, alkalinity (ALK), DI13C
and radiocarbon (14C). Radiocarbon is simulated according to
. Oxygen fields include dissolved oxygen (O2)
and abiotic dissolved oxygen (O2abio), a purely physical tracer
from which true oxygen utilisation (TOU) can be calculated
. Nutrient fields include phosphate (PO4),
dissolved bioavailable iron (Fe), nitrate (NO3) and 15NO3.
Although we define the phosphorus and nitrogen tracers as their dominant
species, being PO4 and NO3, these tracers can also be thought
of as total dissolved inorganic phosphorus and nitrogen pools.
Remineralisation, for instance, implicitly accounts for the process of
nitrification from ammonium (NH4) to NO3
and therefore implicitly includes NH4 and nitrite (NO2)
within the NO3 tracer. Age tracer fields include years since
subduction from the surface (Agegbl) and years since entering a
suboxic zone where O2 concentrations are less than 10 mmol m-3
(Ageomz). Finally, N2O in µmolm-3 is
produced via nitrification and denitrification according to the
temperature-dependent equations of . All air–sea gas
exchanges (CO2, 13CO2, O2 and N2O)
and carbon speciation reactions are computed according to the Ocean Modelling
Intercomparison Project phase 6 protocol .
A conceptual representation of the ocean biogeochemical model
(OBGCM). The bottom panel shows organic matter cycling involving the isotopes
of carbon and nitrogen. (1) Carbon chemistry reactions. (2) Air–sea gas
exchange. (3) Biological uptake of nutrients and production of organic and
inorganic matter. Particulate organic carbon (POC) is produced by the general
phytoplankton group and N2 fixers (diazotrophs), while particulate
inorganic carbon (PIC) as calcium carbonate (CaCO3) is produced by
calcifiers. Export of POC by the general (G) phytoplankton group and
N2 fixers (D; diazotrophs) is herein referred to as
CorgG and CorgD (see
Sect. in the Appendix), respectively. (4) Remineralisation of sinking organic
matter under oxic and suboxic conditions. (5) Sedimentary oxic and suboxic
remineralisation. (6) Nitrous oxide production and consumption.
Because the isotopes of carbon and nitrogen are influenced by biological
processes and there is as yet no accepted standard for ecosystem model
parameterisation in the community seefor a more detailed
discussion, we provide a thorough description of the ecosystem
component of the OBGCM in Sect. in the Appendix. Default parameters for the
OBGCM are further provided in Sect. in the Appendix. Briefly, the
ecosystem model simulates the production, remineralisation and stoichiometry
(elemental composition) of three types of primary producers: a general
phytoplankton group, diazotrophs (N2 fixers) and calcifiers.
Carbon and nitrogen isotope equationsδ13C
The OBGCM explicitly simulates the fractionation of 13C from the
total DIC pool, where for simplicity we make the assumption that the total
DIC pool represents the light isotope of carbon and is therefore
DI12C. Fractionation occurs during air–sea gas exchange,
equilibrium reactions and biological consumption in the euphotic zone.
The air–sea gas exchange of 13CO2 is calculated as
the exchange of CO2 with additional fractionation factors applied to
the sea–air and air–sea components . The flux of
13CO2 across the air–sea interface,
F(13CO2), therefore takes the form of CO2 with
additional terms that convert to units of 13C in both environments.
Without any isotopic fractionation, the equation requires the gas piston
velocity of carbon dioxide in m s-1 (kCO2), the
concentration of aqueous CO2 in both mediums at the air–sea interface
in mmol m-3 (CO2air and CO2sea) and the ratios
of 13C:12C in both mediums (Ratm and
Rsea):
2F(13CO2)=kCO2⋅(CO2air⋅Ratm-CO2sea⋅RDIC),whereRDIC=DI13CDI13C+DI12CRatm=13CO213CO2+12CO2=0.011164381.
A transfer of 13C into the ocean is therefore positive and an
outgassing is negative. The Ratm is set to a preindustrial
atmospheric δ13C of -6.48 ‰ .
The fractionation of carbon isotopes during air–sea exchange involves three
components. These are (ϵk13C), a kinetic
fractionation that occurs during transfer of gaseous CO2 into or out
of the ocean; (ϵaq←g13C), a fractionation
that occurs as gaseous CO2 becomes aqueous CO2 (is dissolved
in solution); and (ϵDIC←g13C), an
equilibrium isotopic fractionation as carbon speciates into DIC constituents (H2CO2⇔HCO3-⇔CO32-). The kinetic fractionation
during transfer, ϵk13C, is constant at 0.99912, thus
reducing the δ13C of carbon entering the ocean by
0.88 ‰. Conversely, carbon outgassing increases the
δ13C of the ocean. The fractionations during dissolution
(ϵaq←g13C) and speciation
(ϵDIC←g13C) are both dependent on
temperature. Fractionation during speciation is also dependent on the
fraction of CO32- relative to total DIC (fCO32-).
These fractionation factors are parameterised as
3ϵaq←g13C=0.0049⋅T-1.311000+14ϵDIC←g13C=0.0144⋅T⋅fCO32--0.107⋅T+10.531000+1.
Dissolution of CO2 into seawater (ϵaq←g13C) therefore preferences the lighter isotope and lowers
δ13C by between 1.32 ‰ and 1.14 ‰, while the
speciation of gaseous CO2 into DIC instead prefers the heavier
isotope and raises δ13C by between 10.7 ‰ and
6.8 ‰ for temperatures between -2 and 35 ∘C.
These fractionation factors are applied to the gaseous exchange of
CO2 (Eq. ) to calculate carbon isotopic fractionation.
F(13CO2)=k⋅ϵk13C⋅ϵaq←g13C5⋅(CO2air⋅Ratm-CO2sea⋅RDICϵDIC←g13C)
Because fractionation to aqueous CO2 from DIC
(ϵaq←DIC13C) is equal to
ϵaq←g13CϵDIC←g13C, a strong preference to
hold the heavy isotope in solution exists (ϵaq←DIC13C=-11.9 ‰ to -7.9 ‰ between -2 and
35 ∘C). Aqueous carbon that is transferred to the atmosphere is
hence depleted in 13C. It is therefore the equilibrium
fractionation associated with carbon speciation that is largely responsible
for bolstering the oceanic δ13C signature above the
atmospheric signature, as it tends to shift 13C towards the
oxidised species (CO32-), a tendency that strengthens under cooler
conditions.
In the default version of CSIRO Mk3L-COAL v1.0, the fractionation of carbon
during biological uptake (ϵbio13C) is
set at 21 ‰ for general phytoplankton, 12 ‰ for diazotrophs
e.g. and at 2 ‰ for the formation of
calcite . However, a variable fractionation rate for the
general phytoplankton group may be activated and depends on the aqueous
CO2 concentration (CO2(aq) in mmol m-3) and the growth
rate (µ in d-1, as a function of temperature and limiting
resources) following :
ϵbio13C=(0.371-μCO2(aq))/0.015.
An upper bound of 25 ‰ exists within Eq. () when
μCO2(aq) approaches zero but a lower bound does not.
We chose to limit ϵbio13C to a minimum of
15 ‰ given the reported variations of
ϵbio13C from culture studies
e.g..
ϵbio13C=max(15,ϵbio13C)
Biological fractionation of 13C is then applied to the uptake and
release of organic carbon.
ΔDI13C=RDIC⋅Corg⋅(1-ϵbio13C1000)
Because biological fractionation is strong for the general phytoplankton
group, which dominates export production throughout most of the ocean, this
imparts a negative δ13C signature to the deep ocean.
Subsequent remineralisation releases DIC with no fractionation. Finally, the
concentration of DI13C is converted into a δ13C via
δ13C=(DI13CDIC⋅10.0112372-1)⋅1000,
where 0.0112372 is the Pee Dee Belemnite standard .
δ15N
The OBGCM explicitly simulates the fractionation of 15N from the
pool of bioavailable nitrogen. For simplicity, we treat this bioavailable pool
as nitrate (NO3), where total NO3 is the sum of
15NO3 and 14NO3. We therefore chose to ignore
fractionation during reactions involving ammonium, nitrite and dissolved
organic nitrogen, which can vary in their isotopic composition independent of
NO3 but represent a small fraction of the bioavailable pool of
nitrogen.
The isotopic signatures of N2 fixation and atmospheric deposition,
and the fractionation during water column denitrification
(ϵwc15N) and sedimentary denitrification
(ϵsed15N) determine the global δ15N of
NO3. Biological assimilation
(ϵbio15N) and remineralisation are internal exchanges
of the oceanic nitrogen cycle and affect the distribution of
δ15NO3. N2 fixation and atmospheric deposition
introduce 15NO3 to the ocean with δ15N values of
-1 ‰ and -2 ‰, respectively, while biological
assimilation, water column denitrification and sedimentary denitrification
fractionate against 15NO3 at 5 ‰, 20 ‰ and
3 ‰, respectively Fig. .
The accepted standard 15N:14N ratio used to measure variations
in nature is the average atmospheric 15N:14N ratio of 0.0036765.
To minimise numerical errors caused by the OGCM, we set the atmospheric
standard to 1. This scales up the 15NO3 such that a
δ15N value of 0 ‰ was equivalent to a
15N:14N ratio of 1:1.
Because we simulate NO3 and 15NO3 as tracers, our
calculations require solving for an implicit pool of 14NO3 during
each reaction involving 15NO3. The introduction of NO3 at
a fixed δ15NNO3 of -1 ‰ due to remineralisation
of N2 fixer biomass provides a simple example with which we can begin
to describe our equations. Setting the isotopic value of newly fixed
NO3 to -1 ‰ is simple because it removes any complications
associated with fractionation. We note, however, that in reality the
nitrogenase enzyme does fractionate during its conversion of aqueous
N2 (+0.7 ‰) to ammonium and the biomass that is
subsequently produced can vary substantially depending of the type of
nitrogenase enzyme used (vanadium versus molybdenum based)
. However, we choose to implicitly account for these
transformations and considerably simplify them by setting the
δ15N of N2 fixer biomass equal to -1 ‰,
which reflects the biomass of N2 fixers associated with the more
common Mo-nitrogenase.
A δ15NNO3 of -1 ‰ is equivalent to a
15N:14N ratio of 0.999 in our approach, where 0 ‰ equals
a 1:1 ratio of 15N:14N. If the amount of NO3 being
added is known alongside its 15N:14N ratio, in this case 0.999
for N2 fixation, we are able to calculate how much 15NO3
is added. We begin with two equations that describe the system.
10NO3=15NO3+14NO311δ15NNO3=(15NO3/14NO315Nstd/14Nstd-1)⋅1000
Ultimately, we need to solve for the change in 15NO3 associated
with an introduction of NO3 by N2 fixation. Our known variables are
the change in NO3, the δ15N of that NO3 and
the 15Nstd/14Nstd. Our two unknowns are 15NO3
and 14NO3. We must solve for 14NO3 implicitly by
describing it according to 15NO3 by rearranging
Eq. ().
14NO3=15NO3/((δ15NNO31000+1)⋅15Nstd/14Nstd)
This allows us to replace the 14NO3 term in Eq. (),
such that
NO3=15NO3+15NO3/((δ15NNO31000+1)⋅15Nstd/14Nstd).
In our example of N2 fixation, we know the δ15N of the
newly added NO3 as being -1 ‰. We also know
15Nstd/14Nstd as equal to 1:1, or 1. Our equation is
simplified.
NO3=15NO3+15NO3/0.999
We can now solve for 15NO3 by rearranging the equation.
15NO3=0.999⋅NO31+0.999
The same calculation is applied to NO3 addition via
atmospheric deposition, except at a constant fraction of 0.998
(δ15N=-2 ‰), and can be applied to any addition
or subtraction of 15NO3 relative to NO3 where the isotopic
signature is known.
Fractionating against 15NO3 during biological
assimilation (ϵbio15N), water column
denitrification (ϵwc15N) and sedimentary
denitrification (ϵsed15N) involves more
considerations because we must account for the preference of 14NO3
over 15NO3. We begin with an ϵ of 5 ‰ for
biological assimilation. This is equivalent to a 15NO3:14NO3
ratio of 0.995 when our atmospheric standard is equal to 1:1 using the
following equation.
ϵ=(15N/14N15Nstd/14Nstd-1)⋅1000
Note that a positive ϵ value returns a 15NO3:14NO3
ratio <1, while a negative δ15N in the previous example
with N2 fixation also returned a 15NO3:14NO3 ratio <1. This works because the reactions are in opposite directions. N2
fixation adds NO3, while assimilation removes NO3. This means
that 0.995 units of 15NO3 are assimilated per unit of
14NO3. As we have seen, a more useful way to quantify this is per
unit of NO3 assimilated into organic matter. Using
Eq. (), we find that ∼0.4987 units of 15NO3
and ∼0.5013 units of 14NO3 are assimilated per unit (1.0) of
NO3 when ϵ equals 5 ‰. Biological assimilation
therefore leaves slightly more 15N in the unused NO3 pool
relative to 14N, which increases the δ15N of
NO3 while creating more 15N-deplete organic matter
(δ15Norg).
However, we must also account for the effect that NO3 availability
has on fractionation. The preference of 14NO3 over
15NO3 strongly depends on the availability of NO3, such
that when NO3 is abundant the preference for the lighter isotope will
be strongest. This preference (fractionation) becomes weaker as NO3
is depleted because cells will absorb any NO3 that is available
irrespective of its isotopic composition . Thus, as
NO3 is utilised, u, towards 100 % of its availability (u=1),
the fractionation against 15NO3 decreases to an ϵ of
0 ‰. This means that when u is equal to 1, no fractionation occurs
and equal parts 15N and 14N (0.5:0.5 per unit
NO3) are assimilated. As we are interested in long timescales, we
chose the accumulated product equations to approximate
this process, where
17u=min(0.999,max(0.001,NorgNO3))18ϵu=ϵ⋅1-uu⋅ln(1-u).
For numerical reasons, we limited the domain of u to (0.001,0.999) rather
than (0,1), such that the utilisation-affected ϵu has a
range of -4.997 to -0.035 ‰ for an ϵ of 5 ‰.
ϵu is then converted into ratio units by dividing by 1000
and added to the ambient 15N:14N of NO3 in the reactant
pool to determine the 15N:14N of the product. In this case, it
is the 15N:14N of newly created organic matter but could also
be unused NO3 effluxed from denitrifying cells in the case for
denitrification.
15Norg:14Norg=15NO3:14NO3+ϵu
We then solve for how much 15NO3 is assimilated into organic
matter using Eq. () because we now know the change in
NO3 (ΔNO3) and the 15N:14N of the product,
which is 15Norg/14Norg in our example of biological
assimilation.
Δ15NO3=15Norg/14Norg⋅ΔNO31+15Norg/14Norg
Here, the change in 15NO3 is equivalent to that assimilated into
organic matter. Following assimilation into organic matter, the release of
15NO3 through the water column during remineralisation occurs with
no fractionation, such that the same δ15N signature is
released throughout the water column.
We apply these calculations to each reaction in the nitrogen cycle that
involves fractionation (assimilation, water column denitrification and
sedimentary denitrification). They could be applied to any form of
fractionation process with knowledge of ϵ, the isotopic ratio of the
reactant, the amount of reactant that is used and the total amount of
reactant available.
Model performance
CSIRO Mk3L-COAL adequately reproduces the large-scale thermohaline properties
and circulation of the ocean under preindustrial conditions in numerous prior
studies . Rather
than reproduce these studies, we concentrate here on how the biogeochemical
model performs relative to measurements of δ13C and
δ15N in the water column δ15NNO3
data courtesy of the Sigman Lab at Princeton University and in the
sediments . We make these model–data
comparisons alongside other isotope-enabled ocean general circulation models
(Table ).
All analyses of model performance were undertaken using the default
parameterisation of the biogeochemical model, which is summarised in the
tables of Sect. in the Appendix. Each experiment was run towards
steady state under preindustrial atmospheric conditions over many thousands
of years. All results presented in this paper therefore reflect tracers that
have achieved an equilibrium solution. We present annual averages of the
equilibrium state in the following analysis.
Models assessed against isotope data. The University of Victoria – Model of Ocean Biogeochemistry and Isotopes (UVic-MOBI) fields
taken from . Pelagic Interactions Scheme for Carbon and Ecosystem Studies (PISCES)
fields provided by Laurent Bopp.
LOch–Vecode-Ecbilt-CLio-agIsm Model (LOVECLIM) fields taken from . The isotope-enabled Community Earth System Model
(iCESM) fields for δ13C (low resolution) provided by Alexandra Jahn and those for δ15N
(high resolution) provided by Simon Yang . PISCES and CESM model resolutions have a range of
longitude–latitude spacings to reflect regions of finer resolution, including the Equator and polar regions.
ModelGroupLong × latVertical levelsCSIRO Mk3L-COALCommonwealth Scientific and Industrial Research Organisation2.8125∘×∼1.6∘21UVic-MOBIOregon State University/GEOMAR Kiel3.6∘×1.8∘19PISCESNucleus for European Modelling of the Ocean1∘×∼0.05-0.95∘75LOVECLIMUniversité catholique de Louvain3∘×3∘20iCESM-lowNational Center for Atmospheric Research≤3.4∘×∼3.6∘60iCESM-highNational Center for Atmospheric Research≤1.1∘×≤0.6∘60δ13C of dissolved inorganic carbon (δ13CDIC)
The recent reconstruction of preindustrial δ13CDIC by
provides a large dataset for comparison. We chose this
dataset over the compilation of point location water column data of
because it offers a gridded product where short-term
and small-scale variability are smoothed, making for more appropriate
comparison with model output.
Predicted values of δ13CDIC from CSIRO Mk3L-COAL broadly
replicated the preindustrial distribution. The predicted global mean of
0.41 ‰ reflected that of the reconstructed mean of 0.42 ‰
(Table ). Spatial agreement was acceptable with a global
correlation of 0.80 (G marker in Fig. ). Regionally, the
Southern Ocean performed well with the lowest root mean square (rms) error of 0.42 ‰,
while a greater degree of disagreement in the values of
δ13CDIC existed in the middle and lower latitudes of each
major basin, particularly in the Atlantic where model–data agreement
(correlation, rms error and normalised standard deviation) was poorest.
Subsurface δ13CDIC was too low in the tropics of the major
basins by ∼0.2 ‰ and too high in the North Pacific and North
Atlantic by 0.4 ‰ to 0.6 ‰ (Fig. ).
Comparison of global and region mean δ13CDIC
between observations and model simulations. Means are annual
averages and do not include the Arctic or the upper 200 m of the water
column. All data were regridded onto the CSIRO Mk3L-COAL grid space.
Global and regional fits between data and simulated δ13C of
dissolved inorganic carbon displayed as Taylor diagrams . Shading of the markers
represents normalised bias. G indicates global; S indicates Southern Ocean (90–40∘ S);
A indicates Atlantic (40∘ S–70∘ N); P indicates
Pacific (40∘ S–70∘ N); I indicates Indian Ocean (40∘ S–70∘ N).
Measures of fit do not include the Arctic or the upper 200 m of the water column. All data
were regridded onto the CSIRO Mk3L-COAL grid space before comparison.
Zonal mean observed (a, b, c) and modelled (d, e, f)δ13C of DIC produced by CSIRO Mk3L-COAL for each major basin.
The red dashed line marks the upper 175 m and is used for comparison between
observed and modelled distributions. Replicate figures for the other models
are available in the Supplement.
These inconsistencies were likely related to physical and biological
limitations of CSIRO Mk3L-COAL. δ13CDIC in subsurface
tropical waters was too low because restricted horizontal mixing and high
carbon export drove very negative δ13CDIC values. The very
negative δ13C values were associated with very large oxygen
minimum zones and were thus a product of poorly represented, fine-scale
equatorial dynamics. Coarse-resolution OGCMs are known to have weak
equatorial undercurrents that lead to oxygen minimum zones that are too large
and CSIRO Mk3L-COAL is no exception.
Alternatively, the large oxygen minimum zones could be due to our
conservative treatment of organic matter remineralisation
(Sect. in the Appendix), where remineralisation is prevented when O2
and NO3 are unavailable. Organic matter therefore falls deeper into
the interior through oxygen-deficient zones, leading to their vertical
expansion. Almost certainly, however, it was the poorly represented dynamics
within the Pacific basin that were responsible for high
δ13CDIC in the subsurface North Pacific, which contains low
O2 and low δ13CDIC water due to northward transport
from the tropics.
Another inconsistency was a positive bias in the upper 200–500 m, with
values exceeding 2 ‰ in many areas of the lower latitudes. However,
values as high as 2 ‰ have been measured in the upper 500 m of the
Indo-Pacific . Given the difficulties associated with
accounting for the Suess effect (invasion of isotopically light fossil fuel
CO2), it is possible that the upper ocean values of
underestimate the preindustrial δ13CDIC surface field.
It is also equally possible that a fixed biological fractionation
(ϵbio13C) of 21 ‰ may have driven
unrealistic enrichment in the simulated field. High growth rates are thought to lower the strength of
fractionation during carbon fixation . To explore the
possibility of model–data mismatch caused by our choice to fix
ϵbio13C at 21 ‰, we implemented biological
fractionation that is dependent on phytoplankton growth rate and aqueous
CO2 concentration (Eq. ). We found the implementation
of a variable ϵbio13C reduced high values in the
upper part of the low-latitude ocean but that this reduction was small
(Fig. ). The overwhelming effect was an increase in
δ13CDIC throughout the interior, itself caused by weaker
fractionation in the tropical ocean. Global mean δ13CDIC
subsequently increased by 0.25 ‰. Meanwhile, model skill was
unaffected (see CSIRO Mk3L-COAL (vary-ϵbio13C)
in Fig. ). Neither fixed nor variable biological
fractionation could reproduce the low upper ocean values of the data.
The introduction of variable carbon fractionation by
phytoplankton (a) and the consequent change in
δ13CDIC represented as a zonal mean (b) relative
to a case where ϵbio13C is fixed at
21 ‰.
It is helpful to place our predicted δ13CDIC alongside
those of other global ocean models (Fig. ;
Table ), both for skill assessment and to further understand the
cause of the positive bias in the upper ocean. We take annually averaged,
preindustrial δ13CDIC distributions from the UVic-MOBI,
PISCES, LOVECLIM and iCESM-low biogeochemical models, most of which have been
used in significant palaeoceanographic modelling studies . Predicted δ13CDIC performs
adequately in CSIRO Mk3L-COAL relative to these state-of-the-art models.
LOVECLIM showed good fit in terms of global and regional means
(Table ) but had lower correlations (Fig. ),
suggesting that its values were accurate but its distribution biased.
UVic-MOBI had high correlations, but it consistently overestimated the
preindustrial field by ∼0.2 ‰. Interestingly, the bias of
UVic-MOBI, which treats biological fractionation as a function of growth rate
and aqueous CO2, is similar to CSIRO Mk3L-COAL when this form of
fractionation was activated (vary-ϵbio13C).
PISCES and iCESM-low were the best performing models, equally demonstrating
high correlations, low biases, accurate regional and global means and the
lowest rms errors. This is perhaps not surprising considering the
significantly finer vertical resolutions of these OGCMs and their more
complex horizontal grid structure that enables an improved representation of
ocean dynamics (Table ). However, all models performed most
poorly in the Atlantic Ocean, with poor correlations, high variability and
greater biases.
Returning to the consistent positive bias in the upper ocean, most models
(except iCESM-low) predicted upper ocean
δ13CDIC≥ 2.0 ‰ (Figs. S1, S2,
S3 and S4 in the Supplement) similar to CSIRO Mk3L-COAL. As each model has a unique
representation of the marine ecosystem and consequently a unique treatment of
biological fractionation, the common prediction of high upper ocean
δ13C once more suggests that the upper ocean values between
200 and 500 m of may be too low. The underestimation of
δ13CDIC may be due to a neglect of biology introducing
anthropogenic, isotopically depleted carbon to surface and subsurface layers
via remineralisation (the biological Suess effect). This would in turn
suggest that a higher global mean of 0.73 ‰ generated from a global
compilation of foraminiferal δ13C is
perhaps a more accurate representation of preindustrial δ13C
values.
Overall, CSIRO Mk3L-COAL performed acceptably in terms of its mean values and
correlations but had consistently greater rms errors in major basins outside
of the Southern Ocean. This indicates that CSIRO Mk3L-COAL exaggerated
regional minima and maxima as discussed. Despite the regional biases of CSIRO
Mk3L-COAL, the comparison demonstrates that all models have strengths and
weaknesses. Given its low resolution and computational efficiency, CSIRO
Mk3L-COAL performs adequately among other biogeochemical models in its
simulation of δ13CDIC.
δ13C of Cibicides foraminifera (δ13CCib)
We extended our assessment of modelled δ13CDIC by comparing
it to a compilation of benthic δ13C measured within the
calcite of foraminifera from the genus Cibicides, a genus on which much of the palaeoceanographic
δ13C records are based. For this comparison, we adjusted our
predicted δ13CDIC to predicted δ13CCib
using the linear dependence on carbonate ion concentration and depth
suggested by :
δ13CCib=0.45+δ13CDIC-2.2×10-321⋅CO3-6.6×10-5⋅z.
This adjustment accounts for slight fractionation during incorporation of DIC
into foraminiferal calcite and is found to be partly explained by the
concentration of CO32- ions and pressure. A one-to-one comparison
between δ13CDIC and δ13CCib hence
introduces some degree of error since this fractionation is not accounted
for. Because we are interested in applying simulated
δ13CDIC to a palaeoceanographic context, we must first be
able to convert our simulated δ13CDIC to
δ13CCib in an effort to make better comparisons,
particularly as the distribution of CO32- is subject to change. By
adjusting our three-dimensional δ13CDIC output using
Eq. (), we attain predicted δ13CCib (see
inset titled “calibration” in Fig. ). For good
measure, we also computed measures of statistical fit for a traditional one-to-one
comparison between δ13CDIC and
δ13CCib to assess the benefit of the calibration.
Measured versus modelled δ13CCib (N=690) of
CSIRO Mk3L-COAL coloured by latitude. Red shading about the 1:1 line is an
estimate of the variability implicit in the relationship between
δ13CCib and δ13CDIC of
. The inset on the bottom right shows the effect of the
calibration of Eq. ().
Measured δ13CCib data from were
binned into model grid boxes and averaged for the comparison. Those
measurements that fell within the OGCM's land mask were excluded. Transfer
and averaging onto the coarse-resolution OGCM grid reduced the number of
points for comparison from 1763 to 690, lowered the mean of measured
δ13CCib from 0.76 ‰ to 0.52 ‰ and reduced
the absolute range from -0.9→2.1 to -0.7→2.1.
Adjusted δ13CCib using Eq. () showed
good fit to measured δ13CCib given the sparsity of data,
with a global correlation of 0.64, a mean of 0.57 ‰ and an rms error
of 0.63 ‰ (Table ). If a one-to-one relationship
between δ13CDIC and δ13CCib was used, the
global correlation was not affected and only slightly worse skill was
detected in mean, rms error and standard deviation. Accounting for the
regional influence of carbonate ion concentration and depth was therefore
beneficial, likely because very negative and positive values were slightly
adjusted towards the mid-range (inset in Fig. ), but
this was not necessary for an adequate comparison. This conclusion was also
reached by . Likewise, implementing variable
fractionation by phytoplankton (ϵbio13C) had
little effect except to increase values and slightly improve measures of
skill (Table ). Of the 690 data points used in the
comparison, 419 fell within the error around what could be considered a good
fit (shaded red area in Fig. ). The error was taken as
0.29 ‰ and represents the standard deviation associated with the
relationship between δ13CDIC and δ13CCib
measurements .
Statistical comparison of core-top δ13CCib with predicted values produced by the CSIRO Mk3L-COAL ocean
model.
Some notable over and underestimation occurred in the adjusted
δ13CCib output that more or less mirrored those
inconsistencies previously discussed for δ13CDIC. Values as
low as -1.9 ‰, well below measured δ13CCib
minima of -0.7 ‰, existed in the equatorial subsurface Pacific and
Indian oceans (i.e. where the oxygen minimum zones existed). This can be seen
in Fig. , where some values in the equatorial band are
well below the shaded region of good fit. Meanwhile, very high values of
δ13CCib were predicted in Arctic surface waters. The
exaggeration of these local minima and maxima reflects those found in the
modelled δ13CDIC distribution. Despite these local
inconsistencies, CSIRO Mk3L-COAL shows good potential for direct comparisons
to palaeoceanographic data sets of foraminiferal δ13C with or
without calibration.
δ15N of nitrate (δ15NNO3)
We produced univariate measures of fit by comparing measurements of
δ15NNO3 with equivalent values from CSIRO Mk3L-COAL at the
nearest point (Fig. ; Table ). Measured
δ15NNO3 values were collected over a 30-year period using a
variety of collection and measurement methods with a distinct bias towards
the Atlantic Ocean. To try and remove some temporal and spatial bias, we
binned and averaged measurements into equivalent model grids.
Comparison of global and region mean δ15NNO3
between observations and model simulations. Model means are annual averages.
All data were regridded onto the CSIRO Mk3L-COAL grid space. The
δ15N data (5330 measurements courtesy of the Sigman Lab,
Princeton University) were binned into corresponding grid boxes and averaged
for direct comparison, which reduced the data to 2532 points. More than one
data point of δ15N may therefore contribute to each simulated
value.
Global and regional fits between observations and simulated
δ15NNO3 displayed as Taylor diagrams .
Shading of the markers represent normalised bias. G indicates global; S indicates
Southern Ocean (90–40∘ S); A indicates Atlantic
(40∘ S–70∘ N); P indicates Pacific
(40∘ S–70∘ N); I indicates Indian Ocean
(40∘ S–70∘ N). The δ15N data (5330
measurements courtesy of the Sigman Lab, Princeton University) were binned
into corresponding grid boxes and averaged for direct comparison, which
reduced the data to 2532 points. More than one data point of
δ15N may therefore contribute to each simulated value.
CSIRO Mk3L-COAL adequately reproduced the global patterns of
δ15NNO3. We found excellent agreement in the
volume-weighted means of δ15NNO3 (Table ).
Tight agreement in the means was a consequence of reproducing similar values
where the majority of observed data existed. Most δ15NNO3
measurements have been taken from the upper 1000 m in the North Atlantic
where values cluster at just under 5 ‰ (see Fig. a, c, e). Closer inspection of the Atlantic using depth
and zonally averaged sections (Figs.
and ) revealed that the model adequately reproduced the low
δ15N signature of ∼4 ‰ caused by N2
fixation occurring in the tropical Atlantic . A basin-wide
rate of Atlantic N2 fixation equal to ∼33 Tg N yr-1
lowered Atlantic values below 5 ‰ and was fundamental for
reproducing the observations. Outside the Atlantic, where data are more sparse,
the model successfully reproduced the meridional gradients across the
Antarctic, Subantarctic and subtropical zones, the subsurface
δ15NNO3 maxima in the tropics of all major basins and the
tongues of high and low values in surface waters of the Pacific consistent
with changes in nitrate utilisation (Figs.
and ).
Observed (a, c, e) and modelled (b, d, f)δ15N of NO3 data (N=5004) plotted against depth
(a, b), latitude (c, d) and longitude (e, f).
Colour shading represents the density of data, such that the darker a mass of
data points is, the more data are represented there.
Depth-averaged sections of modelled (colour contours) and observed (overlaid markers) δ15NNO3.
Zonally averaged sections of modelled (colour contours) and observed (overlaid markers)
δ15NNO3. The global zonal average encompasses all basins.
Some important regional inconsistencies between the simulated and measured
values did exist (refer to Figs. and )
and degraded the correlation. Much like the high values of
δ13CDIC that were transported too deeply into the North
Atlantic interior, a low δ15NNO3 signature was transported
too far into the deep North Atlantic. CSIRO Mk3L-COAL therefore
underestimated deep δ15NNO3 before mixing through to the
South Atlantic restored values towards the measurements. Subsurface values in
the North Pacific were also underestimated, which can be attributed to the
inability of the coarse-resolution OGCM to transport low O2, high
δ15NNO3 water northwards from the eastern tropical
Pacific. Simulated values in the Indian Ocean, specifically near the
Arabian Sea, significantly underestimated the data because the suboxic zone
was misrepresented in the Bay of Bengal. Misrepresentation of the northern Indian
Ocean was responsible for very poor model–data fit in the Indian Ocean
(Fig. ). Meanwhile, the deep (>1500 m) eastern tropical
Pacific tended to overestimate the data, due to a large, deep, unimodal
suboxic zone. These physically driven inconsistencies in the oxygen field are
common to other coarse-resolution models and, like the δ13C distribution, were the main
cause of the misfit between simulated and observed
δ15NNO3. The correlations reflected these regional under-
and overestimations, particularly in the Indian Ocean.
Finally, we placed CSIRO Mk3L-COAL in the context of other isotope-enabled
global models: UVic-MOBI, PISCES and iCESM-high (Table ).
This comparison demonstrated that the modelled distribution of
δ15NNO3 was adequately placed among the current generation
of models. The global and regional means were more accurately reproduced by
CSIRO Mk3L-COAL than for UVic-MOBI, PISCES and iCESM-high
(Table ; also see shading in Fig. ). Atlantic
δ15NNO3 was best reproduced by CSIRO Mk3L-COAL. Meanwhile,
correlations tended to be slightly lower for CSIRO Mk3L-COAL than UVic-MOBI
and iCESM-high, and consistently lower than PISCES (Fig. ).
UVic-MOBI underestimated the data but produced high correlations in the
Southern Ocean and globally. Regionally, PISCES was best correlated to the
measurements of δ15NNO3 of the three models, although it
had a consistent positive bias. iCESM-high was acceptably correlated with the
data in the global sense but was highest in rms errors, particularly in the
Pacific. CSIRO Mk3L-COAL therefore showed an acceptable measure of fit to the
noisy and sparse δ15NNO3 data and reproduced most regional
patterns, albeit with misrepresentation in the Indian Ocean and some
exaggerations of local minima/maxima as discussed. Future model–data
comparisons with CSIRO Mk3L-COAL should therefore take these limitations into
account. Overall, however, we find that CSIRO Mk3L-COAL broadly reproduced
the δ15NNO3 data. Annual rates of N2 fixation,
water column denitrification and sedimentary denitrification at roughly 122,
52 and 78 Tg N yr-1, respectively, produced this agreement.
An important caveat to the δ15NNO3 routines of CSIRO
Mk3L-COAL should be noted. CSIRO Mk3L-COAL underwent significant tuning of
water column and sedimentary denitrification parameterisations in order to
reproduce known values of δ15NNO3 during development. One
important parameter is the lower threshold of NO3 concentration at
which point water column denitrification is shut off (Sect. ).
In CSIRO Mk3L-COAL, this is set at 30 mmol m-3, which is an arbitrary
limit that was implemented to prevent water column denitrification from
reducing NO3 to zero in the large suboxic zones. Hence, a caveat of
the current model is an inability for water column and sedimentary
denitrification to realistically adjust as suboxia changes. However, the
parameterisation does allow for targeted experiments where the ratio of water
column to sedimentary denitrification can be controlled if, for instance, it
is unclear how water column and sedimentary denitrification respond to
certain conditions. This is currently the case during the Last Glacial
Maximum, where expansive suboxic zones in the Pacific
were counter-intuitively associated with reduced rates of water column
denitrification . We have, in this version, chosen to
keep this parameterisation and note that future developments will focus on
dynamic responses to variations in suboxia.
δ15N of organic matter (δ15Norg)
CSIRO Mk3L-COAL tracks the δ15N signature of organic matter
(δ15Norg) that is deposited in the sediments. We compared
the simulated δ15Norg to the core-top compilation of
with 2176 records of δ15Norg. These
records were binned and averaged onto the CSIRO Mk3L-COAL ocean grid, such
that the 2176 records became 592. When comparing sediment core-top
measurements of δ15N to that of the model, it is necessary to
consider how δ15Norg is altered by early burial. As records
in the compilation of are from bulk nitrogen, we can
assume that the “diagenetic offset” as described by is
active. The diagenetic offset involves an increase in the δ15N
of sedimentary nitrogen of between 0.5 ‰ and 4.1 ‰ relative to that of
sinking particulate organic matter and appears to be related to pressure
, although the reasoning behind this relationship remains
to be defined.
In light of the diagenetic offset, we make three comparisons with the
compilation of . A raw comparison is made, alongside an
attempt to account for the diagenetic offset using two depth-dependent
corrections (Table and Fig. ):
22δ15Norgcor:1=δ15Norg,ifz(km)<1kmδ15Norg+(1⋅z(km)+1),ifz(km)≥1km23δ15Norgcor:2=δ15Norg+0.9⋅z(km).
The first correction (δ15Norgcor:1) is taken from
, while the second (δ15Norgcor:2)
originates from how treated sedimentary nitrogen
isotope data in their study of the Last Glacial Maximum. Both are based on
the observation that the diagenetic offset increases with pressure, in this
case represented by depth (z) in kilometres (km).
Statistical comparison of core-top δ15Norg with
predicted values of the CSIRO Mk3L-COAL ocean model. The corrected vales
(δ15Norgcor:1 and δ15Norgcor:2)
account for alteration during early diagenesis following burial.
Following binning and averaging onto the model grid, the raw comparison
immediately showed a consistent underestimation of the core-top data, with a
predicted mean of 2.7 ‰ well below the observed mean of
4.7 ‰. Our correlation was 0.27, which indicates a limited ability
to replicate regional patterns. This underestimation and low correlation is
easily seen when predicted values are compared directly to the core-top data
in Fig. . Like the nitrogen isotope model of
, we find that the offset between simulated and observed
core-top bulk δ15Norg is roughly equivalent to the observed
average diagenetic offset of ∼2.3±1.8 ‰. This indicates that
diagenetic alteration of δ15Norg is active during early
burial in the core-top data.
Including a diagenetic offset therefore improved agreement between our
predicted δ15Norg and the core-top data considerably
(Table and Fig. ). Both corrections
accounted for the enrichment of δ15N in deeper regions and the
minor diagenetic alteration in areas of high sedimentation that typically
occurs in shallower sediments. The average δ15Norg increased
to 4.5 ‰ for δ15Norgcor:1 and 5.2 ‰
for δ15Norgcor:2. Correlations increased from 0.27 to
0.47 and 0.53, respectively. The improvement was clearly observed in the
Southern Ocean, where both the magnitude and spatial pattern of
δ15Norg were well replicated by the model. Changes in the
Southern Ocean over glacial–interglacial cycles reflect shifts in the global
marine nitrogen cycle and nutrient utilisation , and the ability of CSIRO Mk3L-COAL to account for these patterns
in the core-top data is encouraging for future study. We suggest that future
palaeoceanographic model–data comparisons of δ15Norg use
the depth correction of as it provided the best
correlations and reproduced Southern Ocean δ15Norg at
0.5 ‰ greater than the global mean (see Table ).
Direct comparison of observed versus modelled
δ15Norg incident on the sediments. Panels (a), (c) and (e) show
spatial distribution of simulated δ15Norg overlain by
core-top data from the compilation of . Panels (b), (d) and (f)
compare all core-top data against simulated δ15Norg. Panels (a) and (b) depict raw output of the model,
while panels (c)–(f)
depict the predicted values of the model following two depth-dependent
offsets (Eqs. and ) that account for
diagenetic alteration.
Ecosystem effects
As a first test of the isotope-enabled ocean model, we undertook simple
ecosystem experiments to assess the effect on δ13C and
δ15N. For reference, the assessment of model performance
described above used model output with variable stoichiometry activated, a
fixed 8 % rain ratio of CaCO3 to organic carbon and a strong iron
limitation of N2 fixers that enforced a low degree of spatial
coupling between N2 fixers and denitrification zones. A summary of
the biogeochemical effects of the different experiments is provided in
Table .
Summary of the biogeochemical effects of the different treatments of
the ecosystem in CSIRO Mk3L-COAL. Corg is the total organic carbon
exported from the euphotic zone composed of both general and diazotrophic
phytoplankton groups (CorgG+CorgD; see
Sect. in the Appendix), while CCaCO3 is the total export of
CaCO3 out of the euphotic zone. The sum of Corg and
CCaCO3 are equal to the global rate of carbon export referred to in
the text. Sed : WC refers to the sedimentary to water column
denitrification ratio. Note that the global mean δ13CDIC is
higher than reported in Table because it includes the upper
200 m and the Arctic.
CorgCCaCO3N2 fixSed : WCO2SuboxiaDICδ13CDICδ15NNO3Pg C yr-1Tg N yr-1ratiommol m-3% oceanPg C‰ Variable versus Redfieldian stoichiometry (Sect. ) Redfield7.080.521071.51871.533 9080.475.1Variable7.420.541221.51932.133 8700.515.6Calcifier dependence on calcite saturation state (Sect. ) Fixed (8 % of CorgG)7.420.541221.51932.133 8700.515.6Variable (η=0.53)7.410.321221.51932.134 0100.525.6Variable (η=0.81)7.410.471221.51932.133 9160.505.6Variable (η=1.09)7.420.681221.51932.133 7830.485.6Strength of coupling between N2 fixation and denitrification (Sect. ) Weak7.420.541221.51932.133 8700.515.6Moderate7.720.481441.91882.534 0790.455.2Strong7.590.461542.11872.734 1820.425.0Variable versus Redfieldian stoichiometry
Enabling variable stoichiometry (see Sect. in the Appendix) of the general
phytoplankton group (PorgG) over a Redfieldian ratio
(C:N:P:O2rem:NO3rem=106:16:1:-138:-94.4) altered
the rate and distribution of organic matter export. Organic matter had more
carbon and nitrogen per unit phosphorus in regions with low PO4, such
as the Atlantic Ocean (Fig. a), which elevated O2
and NO3 demand during oxic and suboxic remineralisation
(denitrification), respectively. Lower ratios were produced in eutrophic
regions such as the subarctic Pacific, Southern Ocean and tropical zones of
upwelling. Overall, global mean C:P increased from the Redfieldian
106:1 to 117:1 and caused an increase in carbon export from 7.6 to
8.0 Pg C yr-1. Approximately 0.1 Pg C yr-1, or 25 % of the
increase, was attributed purely to organic carbon export from N2
fixation, which increased from 107 to 122 Tg N yr-1 as higher
N:P ratios in the tropics broadened their competitive niche. The total
contribution of N2 fixation to the increase in carbon export was
likely greater than 25 %, as NO3 also became more available to
NO3-limited ecosystems in the lower latitudes . The
increase in carbon export under variable stoichiometry as compared to a
Redfieldian ocean was therefore felt largely in the lower latitudes between
40∘ S and 40∘ N (Fig. b). Export
production decreased poleward of 40∘, particularly in the Southern
Ocean, because C:P ratios were lower than the 106:1 Redfield ratio
(Fig. a).
Distributions of both isotopes were affected by the change in carbon export
and the marine nitrogen cycle. Global mean δ13CDIC
increased from 0.47 ‰ to 0.51 ‰ and
δ15NNO3 increased from 5.1 ‰ to 5.6 ‰.
These are not great changes on the global scale and they had little influence
on model–data measures of fit. However, the spatial distribution of these
isotopes was significantly altered. Intermediate waters leaving the Southern
Ocean were depleted in δ13CDIC by up to 0.1 ‰ and
δ15NNO3 by up to 1 ‰, while the deep ocean,
particularly the Pacific, was enriched in both isotopes to a similar degree
(Fig. ). Depletion of both isotopes in waters subducted
between 40 and 60∘ S reflected the local loss in export production
as a result of lower C:P and N:P ratios, such that biological
fractionation was unable to enrich DIC and NO3 in the heavier isotope
to the same degree as surface waters travelled north. Enrichment of
δ13C in the deep ocean was the result of reduced carbon export
in the Antarctic zone due to low C:P ratios, while enrichment of
δ15N in the deep ocean was the result of increased tropical
production that increased water column denitrification
(ϵwc15N=20 ‰). Lower C:P and
N:P ratios in both the Antarctic and Subantarctic zones therefore
elicited divergent isotope effects in deep and intermediate waters leaving
the Southern Ocean.
Meanwhile, each isotope showed a different response in the suboxic zones of
the tropics where variable stoichiometry increased the volume of suboxia
(O2<10 mmol m-3) by 0.5 %. The increase in water column
denitrification caused by the expansion of suboxia increased
δ15NNO3, while the local increase in carbon export that
drove the increase in water column denitrification reduced
δ13CDIC in the same waters (Fig. ).
Overall, the increase in low-latitude carbon export caused an expansion of
water column suboxia and elicited diverging behaviours in the isotopes,
whereby δ15NNO3 increased and δ13CDIC
decreased.
Simulated difference in the C:P ratio of exported organic
matter due to variable stoichiometry as compared to Redfield stoichiometry
(a) and the resulting change in carbon export out of the euphotic
zone (b).
Differences in δ13CDIC(a) and
δ15NNO3(b) as a result of variable stoichiometry
as compared to Redfield stoichiometry. Values are zonal means.
Calcifier dependence on calcite saturation state
The rate of calcification of planktonic foraminifera and coccolithophores is
dependent on the calcite saturation state . In previous
experiments, the production of CaCO3 was fixed at a rate of 8 % per
unit of organic carbon produced in accordance with the modelling study of
and produced 0.54 Pg CaCO3 yr-1. Now we
investigate how spatial variations in the CaCO3:Corg ratio
(RCaCO3 in Eq. ) affected
δ13CDIC and δ13CCib (see
Sect. in the Appendix). We applied three different values of η to
Eq. () to alter the quantity of CaCO3 produced per
unit of organic carbon (CorgG) given the calcite saturation
state (Ωca). The η coefficients were 0.53, 0.81 and
1.09. These numbers are equivalent to those in the experiments of
.
Global distribution of CaCO3 export as a percentage of
organic carbon (Corg) export (a) and the change in the
CaCO3 production field as a result of making CaCO3 production
dependent on calcite saturation state (η=1.09) compared to when it was
a fixed 8 % of Corg(b). Areas where export production
does not occur due to severely nutrient limited conditions are masked out.
Changes in the distribution of carbon isotopes
(δ13CDIC and δ13CCib; a) and
carbon chemistry (dissolved inorganic carbon and alkalinity; b) as a
result of increasing CaCO3 production in surface waters between
40∘ S and 40∘ N.
Mean RCaCO3 was 4.5 %, 6.6 % and 9.5 %, and annual CaCO3
production was 0.32, 0.47 and 0.68 Pg CaCO3 yr-1 in the three
experiments. Although different in total CaCO3 production, the three
experiments shared the same spatial patterns. Low-latitude waters were high
in RCaCO3, particularly the oligotrophic subtropical gyres, while
high latitudes were low, particularly the Antarctic zone where mixing of old
waters into the surface depressed the calcite saturation state
(Fig. ). These regional patterns in RCaCO3
therefore had the largest effect in areas of high export production.
Productive, high-latitude areas like the Southern Ocean, subpolar Pacific and
North Atlantic waters all produced less CaCO3 when compared to an
enforced 8 % rain ratio, while CaCO3 production between
40∘ S and 40∘ N relative to a fixed RCaCO3 of
8 % was dependent on η. The highest η coefficient of 1.09
achieved greater export of CaCO3 in the mid- to lower-latitude regions
of high export production (Fig. ). The consequence of
increasing CaCO3 production in the middle–lower latitudes was a loss of
upper ocean alkalinity, subsequent outgassing of CO2 and losses in
the DIC inventory. Losses in global DIC were 95 and 130 Pg C as
RCaCO3 increased from 4.6→6.6→9.5 %
(Table ), equivalent to one-fifth of the glacial increase in
oceanic carbon .
Despite the significant changes associated with the implementation of
Ωca-dependent CaCO3 production, effects were
negligible on both δ13CDIC and δ13CCib.
Global mean δ13CDIC was 0.51 ‰, when
RCaCO3 was fixed at 8 %, and this changed to 0.52 ‰,
0.50 ‰ and 0.48 ‰ under η coefficients of 0.53, 0.81
and 1.09 (Table ). Likewise, global mean
δ13CCib was 0.59 ‰, when RCaCO3 was
fixed at 8 %, and this changed to 0.60 ‰, 0.58 ‰ and
0.55 ‰. Minimal change in δ13CCib indicated
minimal change in the CO32- concentration (see
Eq. ), which varied by ≤2 mmol m-3 between
experiments. Visual inspection of the change in δ13CDIC and
δ13CCib distributions showed an enrichment of these
isotopes in the upper ocean north of 40∘ S. Subsequent increases in
η, which increased low-latitude CaCO3 production, magnified the
enrichment. Enrichment of δ13CDIC and
δ13CCib was caused by outgassing of CO2 as surface
alkalinity decreased in response to greater CaCO3 production
(Fig. ). The change, however, was at most 0.1 ‰,
which lies well within 1 standard deviation of variability known in the
proxy data . We therefore find little scope for
recognising even large variations in global CaCO3 production (0.32 to
0.68 Pg CaCO3 yr-1) in the signature of carbon isotopes
despite considerable effects on the oceanic inventory of DIC.
However, we stress that version 1.0 of CSIRO Mk3L-COAL does not include
CaCO3 burial or dissolution from the sediments according the calcite
saturation state of overlying water . To neglect
ocean–sediment CaCO3 cycling is to neglect of an important aspect of
the global carbon cycle active on millennial timescales .
Changes in CaCO3 burial and dissolution could have a non-negligible
effect on δ13C through altering whole ocean alkalinity and
thereby air–sea gas exchange of CO2, which would in turn affect
surface δ13C as we have seen. While we do not address these
effects here, we aim to do so in upcoming versions of the model equipped with
carbon compensation dynamics.
Changes in the distribution of marine N2 fixation caused by
altering how limiting iron is to the growth of N2 fixers via the
coefficient KDFe in Eq. (). Iron limitation is
sequentially relaxed from top to bottom.
Strength of coupling between N2 fixation and denitrification
The degree to which N2 fixers are spatially coupled to the tropical
denitrification zones is controlled by altering the degree to which
N2 fixers are limited by iron (KDFe) in
Eq. () (see Sect. in the Appendix). Decreasing
KDFe ensures that N2 fixation becomes less dependent
on iron supply and as such is released from regions of high aeolian
deposition, such as the North Atlantic, to inhabit areas of low
NO3:PO4 ratios. Areas of low NO3:PO4 exist in the tropics
proximal to water column denitrification zones. Releasing N2 fixers
from Fe limitation therefore increases the spatial coupling between
N2 fixation and water column denitrification and increases the global
rate of N2 fixation.
Change in δ15NNO3 caused by a stronger coupling
between N2 fixation and tropical regions of low NO3:PO4
concentrations (i.e. tropical upwelling zones with active water column
denitrification). Panel (a) shows the global zonal mean change,
while panel (b) shows the average change in the euphotic zone, here
defined as the top 100 m. Areas with very low NO3 (<0.1 mmol m-3) are masked out.
We steadily decreased iron limitation (KDFe) to increase the
strength of spatial coupling between N2 fixers and the tropical
denitrification zones (Fig. ). As N2 fixers
coupled more strongly to regions of low NO3:PO4, the rate of
N2 fixation increased from 122 to 144 to 154 Tg N yr-1
(Table ). An expansion of suboxia from 2.1 % to 2.5 % to
2.7 % of global ocean volume in the tropics accompanied the increase in
N2 fixation, as did a decrease in global mean
δ13CDIC of 0.06 ‰ and 0.1 ‰, since
greater rates of N2 fixation stimulated tropical export production.
Due to the expansion of the already large suboxic zones, which occurred in
both horizontal and vertical directions, the amount of organic carbon that
reached tropical sediments (20∘ S to 20∘ N) increased from
0.35 to 0.46 to 0.51 Pg C yr-1.
The overarching consequence for δ15NNO3 due to an
expansion of the suboxic zones was an increase in the sedimentary to water
column denitrification ratio from 1.5 to 1.9 to 2.2, which decreased mean
δ15NNO3 from 5.6 ‰ to 5.2 ‰ to
5.0 ‰ (Table ). The increase in N2 fixation
(δ15Norg=-1‰) and sedimentary denitrification
(ϵsed15N=3‰) in the tropics was felt
globally for δ15NNO3 (Fig. ). Lower
δ15NNO3 by 0.5 ‰ and 0.9 ‰ permeated
water columns in the Southern Ocean and tropics, respectively. Meanwhile,
δ15NNO3 was up to 10 ‰ lower in surface waters of
the tropical and subtropical Pacific, which is where the greatest increase in
N2 fixation and sedimentary denitrification occurred. The dramatic
reduction in surface δ15NNO3 was subsequently conveyed to
the sediments as δ15Norg±1 ‰–2 ‰.
These simple experiments demonstrate that the insights garnered from
sedimentary records of δ15N are open to multiple lines of
interpretation. An expansion of the suboxic zones, normally associated with
an increase in δ15NNO3, could
instead cause a decrease in δ15NNO3 if more organic matter
reached the sediments to stimulate sedimentary denitrification. There is good
evidence that the suboxic zones might have undergone a vertical expansion
and that more organic matter reached the tropical
sediments under glacial conditions . The glacial
decrease in bulk δ15Norg recorded in the eastern tropical
Pacific therefore does not necessarily mean a
decrease in suboxia. Rather, our experiments show that lower
δ15Norg might also be caused by an increase in local
N2 fixation and sedimentary denitrification. The decrease in
δ15NNO3 associated with more sedimentary denitrification
and local N2 fixation demonstrates the complexity of interpreting
sedimentary δ15Norg records in the lower latitudes.
Conclusions
The stable isotopes of carbon (δ13C) and nitrogen
(δ15N) are proxies that have been fundamental for
understanding the ocean. We have included both isotopes into the ocean
component of an Earth system model, CSIRO Mk3L-COAL, to enable future studies
with the capability for direct model–proxy data comparisons. We detailed how
these isotopes are simulated, how to conduct model–data comparisons using
both water column and sedimentary data and some basic assessment of changes
caused by altered ecosystem functioning. We made three overall findings.
First, CSIRO Mk3L-COAL performs well alongside a number of isotope-enabled
global ocean GCMs. Second, alteration of δ13C during formation
of foraminiferal calcite does not jeopardise simple one-to-one comparisons
with simulated δ13C of DIC, while diagenetic alteration of
bulk organic δ15N during early burial must be accounted for in
model–data comparisons. Third, changes in how marine ecosystems function can
have significant and complex effects on δ13C and
δ15N. Our idealised experiments hence showed that the
interpretation of palaeoceanographic records may suffer from multiple lines
of interpretation, particularly records from the lower latitudes where
multiple processes imprint on the isotopic signatures laid down in sediments.
Future work will involve palaeoceanographic simulations of CSIRO Mk3L-COAL
that seek to understand how the oceanic carbon and nitrogen cycles respond to
and influence important climate transitions.
Data availability
All model output is provided for download on Australia's National Computing Infrastructure
(NCI) at
https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search\#/metadata/f3048_7378_3224_4737
(last access: 12 April 2019)
and is citable with 10.25914/5c6643f64446c. Nitrogen isotope data are available by request to Dario M. Marconi and Daniel M. Sigman at
Princeton University. LOVECLIM data are freely available for download at 10.4225/41/58192cb8bff06.
UVic-MOBI data were provided by Christopher Somes, PISCES data by Laurent Bopp, iCESM-high data from Simon Yang and iCESM-low data by Alexandra Jahn.
Code availability
The source code for CSIRO Mk3L-COAL is shared via a
repository located at
http://svn.tpac.org.au/repos/CSIRO_Mk3L/branches/CSIRO_Mk3L-COAL/ (last access: 12 April 2019). Access to the repository may be obtained by following
the instructions at
https://www.tpac.org.au/csiro-mk3l-access-request/ (last access: 12 April 2019). Access to the source code is subject to a bespoke
license that does not permit commercial usage but is otherwise unrestricted.
An “out-of-the-box” run directory is also available for download with all
files required to run the model in the configuration used in this study,
although users will need to modify the runscript according to their
computing infrastructure.
Ecosystem component of the OBGCMExport productionGeneral phytoplankton group (G)
The production of organic matter by the general phytoplankton group
(PorgG) is measured in units of mmol phosphorus (P)
m-3 d-1 and is dependent on temperature (T), nutrients
(PO4, NO3 and Fe) and irradiance (I):
A1PorgG=SGE:P⋅μ(T)G⋅min(PlimG,NlimG,FelimG,F(I)),whereSGE:P=0.005mmolPO4m3A2μ(T)G=0.59⋅1.0635TA3F(I)=1-eL(I)A4L(I)=I⋅α⋅PARμ(T).
In the example above, SE:P converts growth rates in units of d-1 to
mmol PO4 m-3 d-1. SE:P conceptually
represents the export to production ratio, and for simplicity we assume it
does not change. μ(T) is the temperature-dependent maximum daily growth
rate of phytoplankton (doubling per day), as defined by
. The light limitation term (F(I)) is the productivity
versus irradiance equation used to describe phytoplankton growth defined by
and is dependent on I, the daily averaged shortwave
incident radiation (W m-2), α, the initial slope of the
productivity versus radiance curve (d-1 (W m-2)-1) and PAR, the fraction of shortwave radiation
that is photosynthetically active.
The nutrient limitation terms (PlimG, NlimG
and FelimG) may be calculated in two ways.
If the option for static nutrient limitation is true, then
Michaelis–Menten kinetics is used:
A5PlimG=PO4PO4+KPO4GA6NlimG=NO3NO3+KNO3GA7FelimG=FeFe+KFeG.
Half-saturation coefficients (KGnutrient) show a large range
across phytoplankton species e.g., and so for
simplicity, we set KGPO4=0.1 mmol PO4 m-3, KGNO3=0.75 mmol NO3 m-3 and KGFe=0.1µmol Fe m-3.
If the option for variable nutrient limitation is true (default),
then optimal uptake kinetics is used:
A8PlimG=PO4/(PO41-fA+V/AfA⋅N:P)A9NlimG=NO3/(NO31-fA+V/AfA)A10FelimG=FeFe+KFe,wherefA=max[(1+[NO3]V/A)-1,A11(1+[PO4]⋅N:PV/A)-1].
Optimal uptake kinetics varies the two terms in the denominator of the
Michaelis–Menten form according to the availability of nutrients. It
therefore accounts for different phytoplankton communities with different
abilities for nutrient uptake and does so using the fA term. The V/A
term represents the maximum potential nutrient uptake, V, over the cellular
affinity for that nutrient, A, and is set at 0.1.
Diazotrophs (D; N2 fixers)
Organic matter produced by diazotrophs (PorgD) is also
measured in units of mmol phosphorus (P) m-3 d-1 and is
calculated in the same form of Eq. (), but using the maximum
growth rate μ(T)D of , notable changes in the
limitation terms and minimum thresholds that ensure the nitrogen fixation
occurs everywhere in the ocean, except under sea ice. PorgD is
calculated via
PorgD=SDE:P⋅μ(T)D⋅max(0.01,min(NlimD,Plim,FelimD))A12⋅(1-ico),whereA13μ(T)D=max(0.01,-0.0042T2+0.2253T-2.7819)A14NlimD=e-NO3A15PlimD=PO4PO4+KDPO4A16FelimD=max(0.0,tanh(2Fe-KDFe)).
The half-saturation values for PO4 and Fe limitation are set at
0.1 mmol m-1 and 0.5 µmolm-1, respectively, in the
default parameterisation. The motivation for making N2 fixers
strongly limited by Fe was the high cellular requirements of Fe for
diazotrophy seeand references therein. A dependency on
light is omitted from the limitation term when PorgD is
produced. The omission of light is justified by its strong correlation with
sea surface temperature and its negligible effect on nitrogen
fixation in the Atlantic Ocean . Finally, the
fractional area coverage of sea ice (ico) is included to ensure that cold
water N2 fixation does not occur under ice, since
a light dependency is omitted.
Calcifiers
The calcifying group produces calcium carbonate (CaCO3) in units of
mmol carbon (C) m-3 d-1. The production of CaCO3 is
always a proportion of the organic carbon export of the general phytoplankton
group (CorgG), according to
CaCO3=CorgG⋅RCaCO3.
The ratio of CaCO3 to CorgG (RCaCO3) can be
calculated in two ways.
If the option for fixed RCaCO3 is true (default), then
RCaCO3 is set to 0.08 as informed by the experiments of
. The production of CaCO3 is thus 8 % of
CorgG everywhere.
If the option for variable RCaCO3 is true, then
RCaCO3 varies as a function of the saturation state of calcite
(Ωca) according to , where
RCaCO3=0.022⋅(Ωca-1)η.
The exponent (η) is easily modified consistent with the
parameterisations of and controls the rate of CaCO3
production at a given value of Ωca.
RemineralisationGeneral phytoplankton group (G)
Organic matter produced by the general phytoplankton group (in units of
phosphorus: PorgG) at the surface is instantaneously
remineralised each time step at depth levels beneath the euphotic zone using a
power law scaled to depth . This power law defines the
concentration of organic matter remaining at a given depth
(PorgG,z) as a function of organic matter at
the surface (PorgG,0) and depth itself (z).
Its form is as follows:
PorgG,z=PorgG,0⋅(zzrem)b,
where zrem in the denominator represents the depth at which
remineralisation begins and is set to be 100 m everywhere. The OBGCM
therefore does not consider sinking speeds or an interaction between
organic matter and physical mixing. However, variations in the b exponent
affect the steepness of the curve, thereby emulating sinking speeds and
affecting the transfer and release of nutrients from the surface to the deep
ocean.
Remineralisation of PorgG through the water
column is therefore dependent on the exponent b value in
Eq. (). The b exponent is calculated in two ways.
If the option for static remineralisation is true, then b is set
to -0.858 according to .
If the option for variable remineralisation is true (default), then
b is dependent on the component fraction of picoplankton
(Fpico) in the ecosystem. The Fpico shows a strong
inverse relationship to the transfer efficiency (Teff) of organic
matter from beneath the euphotic zone to 1000 m depth .
Because Fpico is not explicitly simulated in OBGCM, we estimate
Fpico from the export production field in units of carbon
(CorgG), calculate Teff using the parameterisation
of and subsequently calculate the b exponent:
A20Fpico=0.51-0.26⋅CorgG(mgCm-2h-1)CorgG,max(mgCm-2h-1)A21Teff=0.47-0.81⋅FpicoA22b=log(Teff)log(1000100)=log(Teff).
Diazotrophs (D)
Remineralisation of diazotrophs (PorgD) is calculated in the
same way as the general phytoplankton group
(PorgG), with the exception that the depth at
which remineralisation occurs is raised from 100 to 25 m in
Eq. (). This alteration emulates the release of NO3
from N2 fixers well within the euphotic zone, which in some cases can
exceed the physical supply from below . Release of their N-
and C-rich organic matter (see stoichiometry Sect. ) therefore
occurs higher in the water column than the general phytoplankton group.
Suboxic environments
The remineralisation of PorgG and
PorgD will typically require O2 to be removed, except
for in regions where oxygen concentrations are less than a particular
threshold (DenO2lim), which is set to
7.5 mmol O2 m-3 and represents the onset of suboxia. In these
regions, the remineralisation of organic matter begins to consume NO3
via the process of denitrification. We calculate the fraction of organic
matter that is remineralised by denitrification (Fden) via
Fden=(1-e-0.5⋅DenlimO2+eO2-0.5⋅DenlimO2)-1,
such that Fden rises and plateaus at 100 % in a sigmoidal
function as O2 is depleted from 7.5 to 0 mmol m-3.
Following this, the strength of denitrification is reduced if the ambient
concentration of NO3 is deemed to be limiting. Denitrification within
the modern oxygen minimum zones only depletes NO3 towards
concentrations between 15 and 40 mmol m-3. Without an additional constraint that weakens denitrification as
NO3 is drawn down, here defined as rden, NO3
concentrations quickly go to zero in simulated suboxic zones
. We weaken denitrification by prescribing a lower
bound at which NO3 can no longer be consumed via denitrification,
DenlimNO3, which is set at 30 mmol NO3 m-3.
A24rden=0.5+0.5⋅tanh(0.25⋅NO3-0.25⋅DenNO3lim-2.5)A25ifFden>rden,thenFden=rdenFden is therefore reduced if NO3 is deemed to be
limiting and subsequently applied against both PorgG and
PorgD to get the proportion of organic matter to be
remineralised by O2 and NO3.
If the availability of O2 and NO3 is insufficient to
remineralise all the organic matter at a given depth level, z, then the
unremineralised organic matter will pass into the next depth level.
Unremineralised organic matter will continue to pass into lower depth levels
until the final depth level is reached, at which point all organic matter is
remineralised by either water column or sedimentary processes. This version
of CSIRO Mk3L-COAL does not consider burial of organic matter.
Calcifiers
The dissolution of CaCO3 is calculated using an e-folding
depth-dependent decay, where the amount of CaCO3 at a given depth z
is defined by
CaCO3z=CaCO30⋅e-zzdis,
where zdis represents the depth at which e-1 of
CaCO3 (∼0.37) produced at the surface remains undissolved.
Calcifiers are not susceptible to oxygen-limited remineralisation or the
concentration of carbonate ion because the dissolution of CaCO3
depends solely on this depth-dependent decay. All CaCO3 reaching
the final depth level is remineralised without considering burial. Future
work will include a full representation of carbonate compensation.
Stoichiometry
The elemental constitution, or stoichiometry, of organic matter affects the
biogeochemistry of the water column through uptake (production) and release
(remineralisation). The general phytoplankton group and diazotrophs both
affect carbon chemistry, O2 and nutrients (PO4, NO3
and Fe), while the calcifiers only affect carbon chemistry tracers (DIC,
DI13C and ALK).
Alkalinity ratios for both the general and nitrogen-fixing groups are the
negative of the N:P ratio, such that for a loss of 1 mmol of
NO3, alkalinity will increase at 1 mmol eq m-3.
General phytoplankton group (G)
The stoichiometry of the general phytoplankton group is calculated in two
ways.
If the option for static stoichiometry is true, then the
C:N:Fe:P ratio is set according to the Redfield ratio of
106:16:0.00032:1.
If the option for variable stoichiometry is true (default), then the
C:N:P ratio of PorgG is made dependent on the ambient
nutrient concentration according to :
A27C:P=(6.9⋅[PO4]+61000)-1A28N:C=0.125+0.03⋅[NO3]0.32+[NO3]A29N:P=C:P⋅N:C.
Thus, the stoichiometry of PorgG varies across the ocean
according to the nutrient concentration, and the uptake and release of
carbon, nutrients and oxygen (see Sect. ) are dependent on the
concentration of surface PO4 and NO3. The ratio of iron to
phosphorus (Fe:P) remains fixed at 0.00032, such that 0.32 µmol of Fe is consumed per mmol of PO4. We chose to maintain a fixed
Fe:P ratio because phytoplankton communities from subtropical to
Antarctic waters appear to show similar iron content ,
despite changes in C:N:P. However, the ratio of C:N:Fe does
change as a result of varying C:N:P ratios, with higher C:Fe in
oligotrophic environments and lower C:Fe in eutrophic regions.
Diazotrophs (D)
The stoichiometry of diazotrophs is fixed at a C:N:P:Fe ratio of
331:50:1:0.00064, which represents values reported in the literature
. Diazotrophs do not consume
NO3; rather, they consume N2, which is assumed to be of
unlimited supply, and release NO3 during remineralisation.
Calcifiers
Calcifying organisms produce CaCO3, which includes DIC,
DI13C and ALK, and these tracers are consumed and released at a
ratio of 1:0.998:2, respectively, relative to organic carbon. Thus, the
ratio of C:DI13C:ALK relative to each unit of phosphorus consumed
by the general phytoplankton group is equal to the rain ratio of
CaCO3 to organic phosphorus multiplied by 106:105.8:212. This group
has no effect on nutrient tracers or oxygen values.
Stoichiometry of remineralisation
The requirements for oxygen (O2rem:P) and nitrate
(NO3rem:P) during oxic and suboxic remineralisation, respectively,
are calculated from the C:N:P ratios of organic matter via the
equations of . Additional knowledge of the hydrogen and
oxygen content of the organic matter is also required to calculate
O2rem:P and NO3rem:P. However, the hydrogen and oxygen
content of phytoplankton depends strongly on the proportions of lipids,
carbohydrates and proteins that constitute the cell. As there is no empirical
model for predicting these physiological components based on environmental
variables, we continue Redfield's legacy by assuming that all organic matter
is a carbohydrate of the form CH2O. Future work, however, should
address this obvious bias.
To calculate O2rem:P and NO3rem:P, we therefore need to
first calculate the amount of hydrogen and oxygen in organic matter via
A30H:P=2C:P+3N:P+3A31O:P=C:P+4.
Once a C:N:P:H:O ratio for organic matter is known, we calculate
O2rem:P and NO3rem:P in units of
mmol m-3 P-1 using the equations of :
O2rem:P=-(C:P+0.25H:P-0.5O:P-0.75N:PA32+1.25)-2N:PNO3rem:P=-(0.8C:P+0.25H:P-0.5O:P-0.75N:PA33+1.25)+0.6N:P.
The calculation of O2rem:P accounts for the oxygen that is also
needed to oxidise ammonium to nitrate.
From these calculations, we find the following requirements of oxic and
suboxic remineralisation, assuming the static stoichiometry option for the
general phytoplankton group:
O2rem:PorgG=138NO3rem:PorgG=94.4O2rem:PorgD=431NO3rem:PorgD=294.8.
These numbers change dynamically alongside C:N:P ratios when the
stoichiometry of organic matter is allowed to vary.
Sedimentary processes
The remineralisation of organic matter within the sediments is provided as an
option in the OBGCM. Sedimentary denitrification, and its slight preference
for the light isotopes of fixed nitrogen (ϵsed15N=3 ‰), is an important component of the marine nitrogen cycle and
its isotopes. It acts as an additional sink of NO3 and reduces the
δ15N value of the global ocean by offsetting the strong
fractionation of water column denitrification (ϵwc15N=20 ‰).
If sedimentary processes are active, the empirical model of
is used to estimate the rate of sedimentary
denitrification, where the removal of NO3 is dependent on the rate of
particulate organic carbon (CorgG + CorgD)
arriving at the sediments and the ambient concentrations of oxygen and
nitrate. In the following, we assume that the concentrations of NO3
and O2 that are available in the sediments are two-thirds of the
concentration in the overlying water column based on observations of transport
across the diffusive boundary layer .
A34ΔNO3(sed)=(α+β⋅0.98(O2-NO3))⋅(CorgG+CorgD)A35where α=0.04andβ=0.1
In the example above, both the α and β values were halved from the
values of to raise global mean NO3 concentrations
and lower the sedimentary to water column denitrification ratio to between 1
and 2. If NO3 is not available, the remaining organic matter is
remineralised using oxygen if the environment is sufficiently oxygenated. An
additional limitation is set for sediments underlying hypoxic waters
(O2<40 mmol m-3), where oxic remineralisation is weakened
towards zero according to a hyperbolic tangent function (0.5+0.5⋅tanh(0.2⋅O2-5)). If oxygen is also limiting, the remaining organic
matter is remineralised via sulfate reduction. As sulfate is not explicitly
simulated, we assumed that sulfate is always available to account for the
remaining organic matter.
Thus, sedimentary denitrification is heavily dependent on the rate of organic
matter arriving at the sediments. However, a large amount of sedimentary
remineralisation is not captured using only these parameterisations because
the coarse resolution of the OGCM enables it to resolve only the largest
continental shelves, such as the shallow Indonesian seas. Many small areas of
raised bathymetry in pelagic environments are also unresolved by the OGCM. To
address this insufficiency and increase the global rate of sedimentation and
sedimentary denitrification, we coupled a subgrid-scale bathymetry to the
coarse-resolution OGCM following the methodology of using
the Earth topography 5 min grid (ETOPO5) 1/12∘ dataset. For each latitude-by-longitude grid
point, we calculated the fraction of area that would be represented by
shallower levels in the OGCM if this finer-resolution bathymetry were used.
At each depth level above the deepest level, the fractional area represented
by sediments on the subgrid-scale bathymetry can be used to remineralise all
forms of exported matter (CorgG, CorgD and
CaCO3) via sedimentary processes.
Also following the methodology of , we included an option to
amplify sedimentary denitrification in the upper 250 m to account for narrow
continental shelves that are not resolved by the OGCM. Narrow shelves
experience strong rates of upwelling and productivity, and hence high rates
of sedimentary denitrification . To amplify shallow rates
of sedimentary denitrification, we included an optional acceleration factor
(Γsed), set to 3.0 in the default parameterisation, dependent on
the total fraction of shallower depths not covered by the subgrid-scale
bathymetry:
ΔNO3(sed)=ΔNO3(sed)⋅((1-Fsgb)⋅Γsed+1).
For those grids with a low fraction covered by the subgrid-scale bathymetry
(Fsgb), the amplification of sedimentary denitrification is
therefore greatest.
Parameterisation of the OBGCM ecosystem component
Default parameters for the marine ecosystem component of CSIRO Mk3L-COAL are
outlined in Tables , and
. The values presented in these tables are required as
input when running the ocean model.
Parameter values controlling export production in the ecosystem
component of the CSIRO Mk3L-COAL ocean model. Default settings:
Michaelis–Menten == False, Optimal Uptake == True,
fix == True and Vary CaCO3 == False.
ParameterActionValueActive whenGeneral phytoplankton (G) SGE:PExport-to-production ratio0.005 mmol P m-3 d-1AlwaysαInitial slope of production versus irradiance curve0.025 d-1 (W m-2)-1AlwaysPARFraction of shortwave radiation that is photosynthetically active0.5AlwaysKGPO4Half-saturation coefficient for phosphate0.1 mmol P m-3Michaelis–Menten == TrueKGNO3Half-saturation coefficient for nitrate0.75 mmol N m-3Michaelis–Menten == TrueKGFeHalf-saturation coefficient for iron0.1 µmolFem-3Michaelis–Menten == TrueV/AMaximum potential uptake over affinity for nutrient0.1Optimal Uptake == TrueDiazotrophs (D) SDE:PExport-to-production ratio0.005 mmol P m-3 d-1fix == TrueKDFeHalf-saturation coefficient for iron0.5 µmolFem-3fix == TrueKDFeHalf-saturation coefficient for phosphate0.1 mmol PO4 m-3fix == TrueCalcifiers RCaCO3Ratio of CaCO3 produced per unit carbon of PorgG0.08Vary CaCO3 == FalseRCaCO3Ratio of CaCO3 produced per unit carbon of PorgG0.022Vary CaCO3 == TrueηExponent varying RCaCO3 due to calcite saturation0.53, 0.81 or 1.09Vary CaCO3 == True
Parameter values controlling remineralisation in the ecosystem
component of the CSIRO Mk3L-COAL ocean model. Default settings:
ReminPico == True, fix == True, den == True and
sedfluxes == True.
ParameterActionValueActive whenGeneral phytoplankton (G) bRemineralisation profile exponent-0.858ReminPico == FalsebRemineralisation profile exponent-0.7 to -1.2ReminPico == TruezremDepth at which remineralisation begins100 mAlwaysDiazotrophs (D) zremDepth at which remineralisation begins25 mfix == TrueCalcifiers zdisDepth at which e-1CaCO3 remains undissolved3500 mAlwaysSuboxic environments (affects G and D) DenlimO2Dissolved oxygen concentration when denitrification begins7.5 mmol m-3den == TrueDenlimO2Nitrate concentration when denitrification is limited30.0 mmol m-3den == TrueSediments (affects G and D) αFirst constant in Bohlen Eq. ()0.04sedfluxes == TrueβSecond constant in Bohlen Eq. ()0.1sedfluxes == TrueΓsedSedimentary remineralisation amplification factor3.0sedfluxes == True
Parameter values controlling stoichiometry in the ecosystem
component of the CSIRO Mk3L-COAL ocean model. Default settings: Vary Stoich == True, Vary frac13 == False and fix == True.
ParameterActionValueActive whenGeneral phytoplankton (G) C:ALK:N:Fe:PNutrient stoichiometry of general phytoplankton106:-16:16:0.00032:1Vary Stoich == FalseO2rem:NO3rem:PRemineralisation requirements of O2 and NO3-138:-94.4:1Vary Stoich == FalseC:PCarbon stoichiometry of general phytoplankton(50to170):1Vary Stoich == TrueN:PNitrogen stoichiometry of general phytoplankton(7to26):1Vary Stoich == TrueO2rem:PRemineralisation requirements of O2(-220to-60):1Vary Stoich == TrueNO3rem:PRemineralisation requirements of NO3(-42to-150):1Vary Stoich == Trueϵbio13CCarbon isotope fractionation during biological assimilation21 ‰Vary frac13 == Falseϵbio13CCarbon isotope fractionation during biological assimilation15 ‰ to 25 ‰Vary frac13 == TrueDiazotrophs (D) C:13C:ALK:N:Fe:PNutrient stoichiometry of general phytoplankton331:327:-50:50:0.00064:1fix == TrueO2rem:NO3rem:PRemineralisation requirements of O2 and NO3-431:-294.8:1fix == TrueCalcifiers C:13C:ALK:PNutrient stoichiometry of calcifiers106:105.8:212:1Always
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-12-1491-2019-supplement.
Author contributions
PJB designed the study, undertook model development, ran the experiments,
analysed model output and wrote the manuscript. RJM designed the study,
provided instruction on development, aided in analysis and edited the
manuscript. ZC designed the study, aided in analysis and edited the
manuscript. SJP aided in model development and edited the manuscript. NLB
aided in interpretation of results and edited the manuscript.
Competing interests
The authors declare that there is no conflict of
interest.
Acknowledgements
The Australian Research Council's Centre of Excellence for Climate System
Science and the Tasmanian Partnership for Advanced Computing (TPAC) were
instrumental for this research. This research was supported under the
Australian Research Council's Special Research Initiative for the Antarctic
Gateway Partnership (project ID SR140300001). The authors wish to acknowledge
the use of the Ferret programme for the analysis undertaken in this work.
Ferret is a product of NOAA's Pacific Marine Environmental Laboratory
(information is available at
http://ferret.pmel.noaa.gov/Ferret/, last access: 12 April 2019). The Matplotlib package and the cmocean package
were used for producing the figures. We are indebted to
Kristen Karsh, Daniel Sigman, Dario Marconi and Eric Raes for discussions
that focussed this work. Special thanks are given to Christopher Somes for
correspondence in some development steps and revisions that improved the
manuscript. Finally, the lead author is indebted to an Australian Fulbright
postgraduate scholarship, which supported him at the Princeton Geosciences
department during the writing of the manuscript.
Review statement
This paper was edited by Andrew Yool and reviewed by Christopher Somes and two anonymous referees.
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