During the four most recent glacial cycles, atmospheric CO
The transition from interglacials to glacial maximums is associated with a
substantial reduction in atmospheric CO
That the oceanic carbon storage increased during glacials is a well-established idea, and there are numerous studies of how and why this may have
happened
To understand the controls on the oceanic storage of CO
Changes in ocean circulation, which can be due to climate change or other
independent physical processes (e.g. tectonic and ocean gateway changes, such
as the opening of the Drake Passage; e.g.
When studying glacial ocean CO
Specifically, we aim to clarify how the initial equilibrium state, not only
in terms of ocean circulation, but also in terms of ocean carbon inventory and the
origin of the already stored carbon (e.g. biological or solubility pump), is
crucial for the outcome of a study investigating increased efficiency of the
biological pump. This will provide insight about why it is difficult to
compare results from different model studies that have attempted to simulate
and explain the glacial lowering of
In this study, we do not attempt to reproduce glacial climate. In the first
step of the modelling, we instead carry out a process study in which we change
physical parameters in the model one or two at a time, while restoring
In the second step, we enforce 100 % nutrient utilisation efficiency (see
Sect.
CO
Of the total ocean carbon reservoir, about 90 % is expected to be
preformed carbon, i.e. from the solubility pump, and the remaining 10 %
organic or regenerated carbon, i.e. from the biological pump
As described in the framework of
When the biological pump is working at maximum efficiency (when
An important component of the oceanic carbon cycle is alkalinity. It is
related to the buffer capacity of the ocean; hence, the ocean's capacity to
resist a change in pH despite the addition of an acid, such as CO
It is likely that ocean alkalinity increased during glacials due to more
weathering of carbonates caused by lower sea levels
We use the model cGENIE, an Earth system model of intermediate complexity
(EMIC), which is a computationally efficient model developed for studying the
ocean carbon cycle on timescales of 100–100 000 years. cGENIE is higher in
complexity than box models, but is still efficient enough to allow for the running of a
large ensemble to equilibrium for the carbon system. In terms of ocean carbon
system tracers, the minimum required for this type of study is
The physical ocean is modelled using a frictional geostrophic 3-D model on a
36
As our control state, we use the pre-industrial equilibrium state described
in
The stoichiometric relationships in cGENIE are based on
More recently, the stoichiometry of the production of new organic material
has been shown to be highly variable between species but also within the same
species while living under different conditions, such as nutrient
availability
In cGENIE, we also employ a set of preformed nutrients: carbon, oxygen and
alkalinity (
Preformed tracers are a recent addition to the model, which have only been
used to a limited extent in previous studies
List of sensitivity experiment equilibrium states SE1–SE12, abbreviated ensemble member description and specification of which one or two physical characteristics have been altered compared to the control PIES278. The nature of the change is specified in parenthesis.
The inventory of total carbon, TC [mol], in the equilibrium state can be
described by
Equation (
Initialising from our pre-industrial equilibrium state PIES278, we perform 12
different equilibrium experiments in which one or two physical tuning
parameters have been changed compared to the control (Fig.
The change in the total carbon inventory,
The contributions by
The changes in the inventories of TC,
Flow chart showing the experimental set-up. Grey boxes are spin-ups
and transient stages of simulations (not analysed). Coloured boxes are
equilibrium states that are analysed in this study. Throughout the study, the
pre-industrial equilibrium state PIES278 (light blue box) is used as the
control state, with which we compare the sensitivity experiment equilibrium
states SE1–SE12 (yellow box). The change in physical characteristics for
each SE state compared to the control state PIES278 is described in
Table
Finally, starting from each state SE1–SE12 and from PIES278, we run
experiments in which the NUE of biology is maximised (100 % efficiency; Fig.
The physical tuning parameters that we change are atmospheric heat
diffusivity, wind stress and ocean vertical and isopycnal diffusivity. They
are selected because they are common tuning parameters
For this study, our intention is for the ensemble to be representative of a
wide range of plausible ocean circulation states. The chosen parameter ranges
correspond to a halving and doubling of the values used in the control
simulation. Our chosen values are within the parameter space explored for a
predecessor to the GENIE model by
When comparing with models that have different available tuning parameters,
diagnostic variables such as temperature, salinity and AMOC volume transport
can indicate whether our achieved states are within the common tuning range
for ocean circulation. The IPCC AR5 WG1 report
Diagnostic variables of the pre-industrial control states (PICs) of
PMIP2 and CMIP5–PMIP3 (temperature and salinity as read from Fig. 9.18. of
WG1 in IPCC AR5 and AMOC as given in Table 1 in
In a coarse-resolution model like cGENIE, the overturning circulation, which
transports carbon to the deep ocean and back up to the surface again
The OVT measures the amount of flushing of the deep water, which is important
for the carbon storage
We use a two-step modelling approach, as explained in Sect.
Diagnostic variables for observations (Obs.), the control PIES278
(Ctrl.) and the ensemble members SE1–SE12. The variables are global ocean
averages of temperature (
Correlation coefficients of the changes in OVT (see
Sect.
In this first step, we achieve a set of pre-industrial equilibrium states –
all with pre-industrial
In panel
In PIES278 and SE1–SE12, global average salinity, total alkalinity and
PO
The control PIES278 model total carbon inventory, TC, is
The range of OVT (see Eq.
Strength of the overturning circulation in terms of OVT (see
Sect.
Figure
Strength of the overturning circulation in terms of OVT (see Section
Zonal average overturning stream function (
Although the total carbon inventory is strongly correlated with OVT
(Fig.
In the following subsections, we analyse each of the contributing terms.
Global ocean average temperature (
Sections of temperature (
Figure
In the ensemble, simulations with a weaker OVT than PIES278 tend to have a
lower
Panels showing (whole) global ocean
Example sections of
The global inventory of
When looking at
The processes influencing
Comparing panels (a) and (c) in Fig.
In AD
In this step, we use the set of equilibrium states (SEs and the control
PIES278) from step 1 as initial states for determining the drawdown
potential, DP (Fig.
The DP varies strongly between the ensemble members and is close to linearly
related to the biological efficiency, in terms of
Those experiments that have a lower
The effect on
The relationship between the changes in
In a set of idealised GCM simulations,
In experiments with stronger OVT (global and basin scale) than in PIES278,
e.g. DD
When comparing model studies, it is important to recognise differences in
biological efficiency in their control states. The pre-industrial
Few studies have simultaneously diagnosed the individual contributions by the
solubility and biological pumps and the effect of surface CO
The four different preformed model tracers (
We have shown that when comparing model simulations with the same
When attempting to simulate the glacial CO
In our ensemble members for which ocean diapycnal (i.e. near vertical)
diffusivity is halved, we achieve some glacial-like ocean characteristics:
the circulation is weaker, the global ocean temperature is colder and the
biological pump is stronger. However, it has been shown by
Since numerous studies of proxy data indicate that the global ocean was in
fact less ventilated during glacials
According to
In our ensemble of simulations, 100 % nutrient utilisation efficiency
(NUE) causes more drawdown than is necessary to reach glacial values. Future
efforts need to deduce how big an increase in NUE we could expect for a
glacial when using proxy data for e.g. iron fertilisation
In this paper, we have studied three mechanisms for ocean carbon
storage – the biological pump, the solubility pump and the contribution from
air–sea CO
We have obtained different states of equilibrium ocean circulation by varying
forcings and model parameters (listed in Table
Finally, to constrain the biological pump, we used the SE ensemble members
(Fig.
The source code for cGENIE is publicly available at
The abiotic, physical pathway, or
The biological pathway, or
Due to the difference in the chemical role of soft-tissue and hard-tissue
carbon, the biological pump is more correctly referred to as being two
separate pumps: the soft-tissue pump and the carbonate (hard-tissue) pump
Biology in the surface ocean uses a fraction of the available nutrients to
produce new organic material and binds CO
As described in the framework introduced by
Here, the overbars indicate that we are using the global average of a quantity.
If
To calculate
In this section, the calculations of the total carbon inventory described by
Eq. (
The atmospheric carbon content, which in this model is limited to its content
of CO
For one individual water parcel,
The dissociation constants used in the cGENIE calculations of solubility for
CO
Since
We calculate the first term on the right-hand side in Eq. (
The calculations in this section largely follow the Appendix in
The biogenic material also carries hard tissue, and the carbon dissolved from
this tissue is denoted
Finally,
Residual carbon,
To achieve the equilibrium states of our ensemble (SE1–SE12), we have been
restoring
In this section, we translate the observed
First, we need to know the Revelle buffer factor,
DIC
Again, we use CO2SYS, with the same control-state equilibrium parameters as
before, but now fixing DIC
In the following derivation, keep in mind that
We conduct our equilibrium-state simulation PIES278 and an ensemble of
equilibrium simulations with modified circulation. According to
Eq. (
We first study the case in which
For the hypothetical case in which we keep TC constant and allow
We seek to compute
Using Eqs. (
Changes in
Similarly, the remineralisation of organic nitrogen decreases alkalinity. This
effect is associated with the soft-tissue pump and changes
If we use Eq. (
The only remaining carbon species is
Instead of solving the carbon system equations to get the change in
MÖ, JN and KICO designed the model experiments. AR developed the cGENIE model code with the addition of new tracers. MÖ adapted the code for the experimental design, performed the model simulations and produced the figures. LB provided technical expertise for the model set-up. MÖ prepared the paper with contributions from all co-authors.
The authors declare that they have no conflict of interest.
The simulations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre, Linköping University. Malin Ödalen and Jonas Nycander would like to acknowledge the Bolin Centre for Climate Research for financial support. Kevin Oliver would like to acknowledge support from UK NERC grant NE/K002546/1, and he is grateful to the International Meteorological Institute for generous support during several visits to Stockholm. Andy Ridgwell was supported by EU grant ERC 2013-CoG-617313. This work benefited from helpful discussions with Johan Nilsson. Edited by: Victor Brovkin Reviewed by: three anonymous referees