The ocean biogeochemistry components of two new versions
of the Canadian Earth System Model (CanESM) are presented and compared to
observations and other models. CanESM5 employs the same ocean biology model
as CanESM2, whereas CanESM5-CanOE (Canadian Ocean Ecosystem model) is a
new, more complex model developed for CMIP6, with multiple food chains,
flexible phytoplankton elemental ratios, and a prognostic iron cycle. This
new model is described in detail and the outputs (distributions of major
tracers such as oxygen, dissolved inorganic carbon, and alkalinity, the iron
and nitrogen cycles, plankton biomass, and historical trends in CO2
uptake and export production) compared to CanESM5 and CanESM2, as well as to
observations and other CMIP6 models. Both CanESM5 models show gains in skill
relative to CanESM2, which are attributed primarily to improvements in ocean
circulation. CanESM5-CanOE shows improved skill relative to CanESM5 for most
major tracers at most depths. CanESM5-CanOE includes a prognostic iron
cycle, and maintains high-nutrient/low-chlorophyll conditions in the
expected regions (in CanESM2 and CanESM5, iron limitation is specified as a
temporally static “mask”). Surface nitrate concentrations are biased low in
the subarctic Pacific and equatorial Pacific, and high in the Southern
Ocean, in both CanESM5 and CanESM5-CanOE. Export production in CanESM5-CanOE
is among the lowest for CMIP6 models; in CanESM5, it is among the highest,
but shows the most rapid decline after about 1980. CanESM5-CanOE shows some
ability to simulate aspects of plankton community structure that a
single-species model can not (e.g. seasonal dominance of large cells) but
is biased towards low concentrations of zooplankton and detritus relative to
phytoplankton. Cumulative ocean uptake of anthropogenic carbon dioxide
through 2014 is lower in both CanESM5-CanOE (122 PgC) and CanESM5 (132 PgC)
than in observation-based estimates (145 PgC) or the model ensemble mean
(144 PgC).
Introduction
The Canadian Centre for Climate Modelling and Analysis has been developing
coupled models with an interactive carbon cycle for more than a decade
(Christian et al., 2010; Arora et al., 2011). The Canadian Earth System
Model version 5 (CanESM5, Swart et al., 2019a) is an updated version of
CanESM2 (Arora et al., 2011), with a new ocean model based on the Nucleus
for European Modelling of the Ocean (NEMO) system version 3.4. The ocean
biogeochemistry modules were developed in house. CanESM5 uses the same ocean
biology model as CanESM1 (Christian et al., 2010) and CanESM2 (Arora et al.,
2011), the Canadian Model of Ocean Carbon (CMOC; Zahariev et al., 2008). An
additional model was developed for CMIP6, called the Canadian Ocean
Ecosystem model (CanOE). The biological components of CanOE are of
substantially greater complexity than CMOC, including multiple food chains,
flexible phytoplankton elemental ratios, and a prognostic iron (Fe) cycle.
The two coupled models are known as CanESM5 and CanESM5-CanOE, respectively.
The reasons for developing both models are, firstly, to evaluate the effect
of changes in ocean circulation between CanESM2 and CanESM5 on ocean
biogeochemistry by running the new climate model with the same ocean
biogeochemistry, and secondly because CanOE is substantially more expensive
computationally (19 tracers vs. 7, so the total computation time to integrate
the ocean model with biogeochemistry is approximately double). Most CMIP6
experiments were run with CanESM5 only, as ocean biogeochemistry is not
central to their purpose. Additional tracers requested by the Ocean Model
Intercomparison Project – Biogeochemistry (OMIP-BGC) including abiotic and
natural dissolved inorganic carbon (DIC), DI14C, CFCs, and SF6 (see
Orr et al., 2017) were run only in CanESM5. The CMIP6 experiments published
for CanESM5-CanOE are listed in Table S1 in the Supplement.
CMOC is a nutrient–phytoplankton–zooplankton–detritus (NPZD) model with
highly parameterized representations of phytoplankton Fe limitation,
dinitrogen (N2) fixation and denitrification, and calcification and
calcite dissolution (Zahariev et al., 2008; Fig. S1 in the Supplement).
CanESM1 and CanESM2 did not include oxygen; CanESM5 includes oxygen as a
purely “downstream” tracer that does not affect other biogeochemical
processes. In CanESM5-CanOE, denitrification is prognostic and dependent on
the concentration of oxygen. Among the less satisfactory aspects of CMOC
biogeochemistry are, firstly, that Fe limitation is specified as a static
“mask” that does not change with climate (it is calculated from the
present-day climatological distribution of nitrate, based on the assumption
that regions without iron limitation will have complete drawdown of surface
nitrate at some point in the year), and secondly, that denitrification is
parameterized so that nitrogen (N) is conserved within each vertical column,
i.e. collocated with N2 fixation in tropical and subtropical
open-ocean regions (Zahariev et al., 2008; Riche and Christian, 2018). This
latter simplification produced excessive accumulations of nitrate in Eastern
Boundary Current (EBC) regions where most denitrification occurs. CMOC also
has a tendency to produce rather stark extremes of high and low primary and
export production (Zahariev et al., 2008), a well-known problem of NPZD
models (Armstrong, 1994; Friedrichs et al., 2007). Our intent in developing
CanOE was to alleviate, or at least reduce, these biases, by including
multiple food chains, a prognostic Fe cycle, and prognostic denitrification.
Dinitrogen fixation is still parameterized, but the CanOE parameterization
includes Fe (but not P) limitation, whereas in CMOC N2 fixation tends
to grow without bound in a warming ocean as CMOC does not include P or Fe
limitation (Riche and Christian, 2018).
In this paper, we present a detailed model description for CanOE and an
evaluation of both CanESM5 and CanESM5-CanOE relative to observational data
products and other available models. CMOC has been well described previously
(Zahariev et al., 2008) and the details are not reiterated here. In some
cases, CanESM2 results are also shown to illustrate which differences in the
model solutions arise largely from the evolution of the physical climate
model, and which are specifically associated with different representations
of biogeochemistry. An overall evaluation of the CanESM5 climate including
the physical ocean is given in Swart et al. (2019a). Here, we focus on
biogeochemical variables, and we have evaluated model performance in three main
areas: (1) the distribution of major tracers like oxygen, DIC, and
alkalinity, and the resulting saturation state for CaCO3 minerals, (2)
the iron cycle and its interaction with the nitrogen cycle, and (3) plankton
community structure and the concentration and export of particulates. We
first address the major chemical species that are common to both models (and
almost all other Earth system models) to determine whether a more complex
biology model measurably improves skill, and whether the updated circulation
model improves skill relative to CanESM2. Then we examine the areas where
our two models differ: the presence of a prognostic iron cycle and multiple
food chains in CanOE. More specifically, does CanESM5-CanOE reproduce the
geographic distribution of high-nutrient/low-chlorophyll (HNLC) regions?
Does the large phytoplankton/large zooplankton food chain become dominant
under nutrient-rich conditions, and how does having multiple detrital size
classes affect particle flux and remineralization length scale? Following
this model evaluation, we present historical trends in ocean anthropogenic
CO2 uptake, export production, and total volume of low-oxygen waters
over the historical (1850–2014) experiment. Possible future changes under
Shared Socioeconomic Pathway experiments will be addressed in subsequent
publications.
Model description
CanESM5 (Swart et al., 2019a) is an updated version of CanESM2 (Arora et
al., 2011), with an entirely new ocean. The atmosphere model has the same
T63 horizontal resolution, and contains some important improvements in
atmospheric physics (Swart et al., 2019a). The land surface (Canadian Land
Surface Scheme) and terrestrial carbon cycle (Canadian Terrestrial Ecosystem
Model) models are substantially the same as in CanESM2 with minor
modifications as described by Arora et al. (2020). The CanESM5 ocean is
based on the NEMO modelling system version 3.4, with a horizontal resolution
of 1∘, telescoping to 1/3∘ in the tropics, and 45
vertical levels ranging in thickness from ∼ 6 m near the
surface to ∼ 250 m in the deep ocean (Swart et al., 2019a).
All physical climate model components are the same in CanESM5 and
CanESM5-CanOE. There are no feedbacks between biology and the physical ocean
model, so the physical climate of CanESM5 and CanESM5-CanOE is identical in
experiments with prescribed atmospheric CO2 concentration.
The NEMO system is a publicly available archive of codes based on the OPA
(Océan PArallelisé) ocean model (Madec and Imbard, 1996; Guilyardi
and Madec, 1997) and the Tracers in Ocean Paradigm (TOP) module for tracer
advection and mixing. Our ocean biogeochemistry modules are built within
TOP, using NEMO v3.4.1, but have also been implemented in NEMO 3.6 for
regional downscaling applications (Holdsworth et al., 2021).
Carbon chemistry is based on the Best Practices Guide (Dickson et al., 2007)
and the OMIP-BGC data request (Orr et al., 2017) and is identical in
CanESM5 and CanESM5-CanOE. All calculations are done on the total scale and
the recommended formulae for the equilibrium constants are employed. The
carbon chemistry solver was run for a fixed number of iterations (10 in the
surface layer and 5 in the subsurface layers in CanESM5-CanOE). CanESM5
does not solve the carbon chemistry equations in the subsurface layers.
OMIP-BGC formulations for CO2 and O2 solubility and gas exchange
are employed. It is important to note here that the carbon chemistry and gas
exchange formulations used in CanESM2 (and other CMIP5 models) are slightly
different than those used in CMIP6. However, this difference is of little
functional significance; i.e. it will have a negligible impact on the
distribution of [CO3--] compared to the differences in DIC and
alkalinity distribution. The initialization fields for nitrate, DIC and
alkalinity were also different in CanESM2. This will affect the total ocean
inventory of DIC but not the spatial distribution if the model is well
equilibrated.
The CanOE biology model is based on the cellular regulation model of Geider
et al. (1998). There are two phytoplankton size classes, and each group has
four state variables: C, N, Fe, and chlorophyll. Photosynthesis is decoupled
from cell production and photosynthetic rate is a function of the cell's
internal N and Fe quotas. Each functional group has a specified minimum and
maximum N quota and Fe quota, and nutrient uptake ceases when the maximal
cell quota is reached. Chlorophyll synthesis is a function of N uptake and
increases at low irradiance. There are also two size classes each of
zooplankton and detritus. Small zooplankton graze on small phytoplankton,
while large zooplankton graze on both large phytoplankton and small
zooplankton. Small detritus sinks at 2 m d-1 and large detritus at 30 m d-1 (in CanESM5, there is a single detrital pool with a sinking rate
of 8 m d-1). Model parameters and their values are listed in Table 1. A
schematic of the model is shown in Fig. 1.
Schematic of the CanOE biology model. Model currencies including
chlorophyll (Chl) are indicated by coloured boxes except oxygen (O2)
and carbonate (CaCO3). Arrows indicate flows of carbon (C), nitrogen
(N), and iron (Fe) between compartments containing small (S) and large (L)
phytoplankton (P), zooplankton (Z), and detritus (D) components;
counterflows of oxygen are not shown.
Ecosystem model parameters.
SymbolDescriptionUnitTrefReference temperatureK298.15EapActivation energy for photosynthesiskJ mol-137.4QminsNSmall phytoplankton minimum N quotag N g C-10.04QmaxsNSmall phytoplankton maximum N quotag N g C-10.172QminlNLarge phytoplankton minimum N quotag N g C-10.04QmaxlNLarge phytoplankton maximum N quotag N g C-10.172QminsFeSmall phytoplankton minimum Fe quotaµg Fe g C-14.65QmaxsFeSmall phytoplankton maximum Fe quotaµg Fe g C-193.QminlFeLarge phytoplankton minimum Fe quotaµg Fe g C-16.5QmaxlFeLarge phytoplankton maximum Fe quotaµg Fe g C-170.VrefNReference rate of N uptakeg N g C-1 d-10.6VrefFeReference rate of Fe uptakeµg Fe g C-1 d-179.PrefCReference rate of photosynthesisg C g C-1 d-13kXURate coefficient for exudationd-11.7kdgrRate coefficient for chlorophyll degradationd-10.02ζRespiratory cost of biosynthesisg C g N-12αchlInitial slope of P–E curveg C g CHL-1 h-1(µmol m-2 s-1)-11.08ΘmaxNMaximum chlorophyll-to-nitrogen ratiog g-10.18KNiSHalf saturation for small phytoplankton nitrate uptakemmol-1 m30.1KNaSHalf saturation for small phytoplankton ammonium uptakemmol-1 m30.05KFeSHalf saturation for small phytoplankton iron uptakenmol-1 m3100KNiLHalf saturation for large phytoplankton nitrate uptakemmol-1 m31.0KNaLHalf saturation for large phytoplankton ammonium uptakemmol-1 m30.05KFeLHalf saturation for large phytoplankton iron uptakenmol-1 m3200m1SSmall phytoplankton/zooplankton mortality rate (linear)d-10.05m2SSmall phytoplankton/zooplankton mortality coefficient (quadratic)(mmol C m-3)-1 d-10.06m1LLarge phytoplankton/zooplankton mortality rate (linear)d-10.1m2LLarge phytoplankton/zooplankton mortality coefficient (quadratic)(mmol C m-3)-1 d-10.06XminpMinimum phytoplankton concentration for linear mortalitymmol C m-30.01aLLarge zooplankton grazing parameter(mmol C m-3)-10.25GL0Large zooplankton maximum grazing rated-10.85aSSmall zooplankton grazing parameter(mmol C m-3)-10.25GS0Small zooplankton maximum grazing rated-11.7λAssimilation efficiency–0.8rzsMicrozooplankton specific respiration rate at Trefd-10.3rzlMesozooplankton specific respiration rate at Trefd-10.1r1Small detritus remineralization rate at Trefd-10.25r2Large detritus remineralization rate at Trefd-10.25EarActivation energy for detritus remineralizationkJ mol-154.0wsSmall detritus sinking speedm d-12.wlLarge detritus sinking speedm d-130.wCaCaCO3 sinking speedm d-120.PCaCaCO3 production as fraction of mortalitymol CaCO3 mol C-10.05kCaCaCO3 dissolution rated-10.0074SFe1Dissolved iron scavenging loss rate (Fe≤LFe)d-10.001SFe2Dissolved iron scavenging loss rate (Fe>LFe)d-12.5LFeLigand concentrationnmol Fe m-3600.PFePOC-dependence parameter for Fe scavenging(mmol C m-3)-10.66kNH4oxNitrification rate constantd-10.05KEHalf saturation for irradiance inhibition of nitrificationW m-21.kdnfLight and nutrient saturated rate of N2 fixation at 30 ∘Cmmol N m-3 d-10.0225aInitial slope for irradiance dependence of N2 fixation(W m-2)-10.02KFeHalf saturation for Fe dependence of N2 fixationnmol Fe m-3100.KNO3Half saturation for DIN inhibition of N2 fixationmmol m-30.1OmxdO2 concentration threshold for denitrificationmmol m-36.AfAnammox fraction of N loss to denitrification–0.25Photosynthesis and phytoplankton growth
For simplicity and clarity, the equations are shown here for a single
phytoplankton species and do not differ structurally for small and large
phytoplankton. Some parameter values differ for the two phytoplankton
groups; all parameter values are listed in Table 1.
Temperature dependence of photosynthetic activity is expressed by the
Arrhenius equation:
Tf=exp-EapR1T-1Tref,
where Eap is an enzyme activation energy that corresponds approximately
to that of RuBisCo (see Raven and Geider, 1988), R is the gas constant (8.314 J mol-1 K-1), and temperature T and reference temperature
Tref are in Kelvin. Maximal rates of nutrient (either N or Fe but
generically referred to here with the superscript X) uptake are given by
VmaxX=VrefXTfQmaxX-QXQmaxX-QminX0.05,
where VmaxX is the maximal uptake rate in mg of nutrient X per mg
of cell C, X can represent N or Fe, Q is the nutrient cell quota and
Qmin and Qmax its minimum and maximum values, and VrefX is
a (specified) basal rate at T=Tref and Q=Qmin. These maximum rates
are then reduced according to the ambient nutrient concentration, i.e.
VN=VmaxNLNH4+1-LNH4LNO3,
where LNH4=NaKNaX+Na and
LNO3=NiKNiX+Ni, with Ni and
Na indicating nitrate and ammonium respectively, and
VFe=VmaxFeFeKFeX+Fe,
where X indicates large or small phytoplankton (Table 1). The maximal
carbon-based growth rate is given by
PmaxC=PrefCTfminQN-QminNQmaxN-QminN,QFe-QminFeQmaxFe-QminFe,
where PrefC is the rate at the reference temperature Tref under
nutrient-replete conditions (Q=Qmax). The light-limited growth rate is then given by
PphotC=PmaxC1-e-αchlEθCPmaxC,
where E is irradiance and θC is the chlorophyll-to-carbon
ratio. The rate of chlorophyll synthesis is
ρchl=θmaxNPphotCEαchlθ.
These rates are then used to define a set of state equations for
phytoplankton carbon (Cp), nitrogen (Np), iron (Fep), and
chlorophyll (M).
dCpdt=(PphotC-ζVN)Cp-(G+CXS)-m1Cp-m2Cp2-kXUCINTR,
where ζ is the respiratory cost of biosynthesis, G is the grazing rate
(Eq. 12), CXS is the excess (above the ratio in grazer biomass)
carbon in grazing losses (see Eq. 16a below), m1 and m2 are
coefficients for linear and quadratic nonspecific mortality terms,
CINTR is the concentration of intracellular carbohydrate carbon in
excess of biosynthetic requirements, and kXU is a rate coefficient for
its exudation to the environment. The nonspecific mortality terms are set to
0 below 0.01 mmol C m-3 to prevent biomass from being driven to
excessively low levels in the high latitudes in winter; linear mortality
terms can result in biomass declining to levels from which recovery would
take much longer than the brief Arctic summer (Hayashida, 2018). The full
equations for phytoplankton N, Fe, and chlorophyll are
8dNpdt=VNQN-(G+m1Cp+m2Cp2)RNC-NXS9dFepdt=VFeQFe-(G+m1CP+m2Cp2)RFeC-FeXS10dMdt=ρchlVNθCM-(G+m1Cp+m2Cp2)θC-kdgrM,
where kdgr is a rate coefficient for nonspecific losses of chlorophyll
e.g. by photooxidation, in addition to losses to grazing and other
processes that also affect Cp, Np, and Fep. NXS and
FeXS are remineralization of “excess” (relative to grazer or detritus
ratios) N or Fe and are defined below (Eq. 16).
Grazing and food web interactions
Grazing rate depends on the phytoplankton carbon concentration, which most
closely represents the food concentration available to the grazer (Elser and
Urabe 1999; Loladze et al. 2000). Zooplankton biomass is also in carbon
units. State equations for small and large zooplankton are
dZsdt=λGs-(R+GZ+m1sZs+m2sZs2)dZLdt=λGL-(R+m1LZL+m2LZL2),
where
Gs=Gso(1-e-asCps)ZsGL=GL0(1-e-al(Cpl+Zs))ZL,
for small and large zooplankton, respectively, GZ is grazing of small
zooplankton by large zooplankton, R is respiration, and m1 and m2 are
non-grazing mortality rates. Large zooplankton grazing is divided into
grazing on large phytoplankton and small zooplankton in proportion to the
relative abundance of each:
GP=GLPlPl+ZsGZ=GLZsPl+Zs.
Zooplankton biomass loss to respiration is given by
R=maxrzTfZ-CXS,0,
and uses the same activation energy as photosynthesis. Respiration (R) is
assumed to consume only carbon and not result in catabolism of existing
biomass when “excess” carbon is available in the prey. In addition,
conservation of mass must be maintained by recycling to the dissolved pool
grazer consumption of elements in excess of biosynthetic requirements when
grazer and prey elemental ratios differ. In the case where the nutrient
quota (relative to carbon) exceeds the grazer fixed ratio, the excess
nutrient is remineralized to the dissolved inorganic pool. In the case where
the nutrient quota is less than the grazer ratio, the grazer intake is
reduced to what can be supported by the least abundant nutrient (relative to
the grazer biomass ratio) and excess carbon is remineralized. For the case
of two nutrients (in this case N and Fe), it is necessary to define
G′=GminNPCPRCN,FePCPRCFe,1,
where G is equal to GS (Eq. 12a) for small zooplankton and GP
(Eq. 13a) for large zooplankton, and RXY indicates the fixed ratio
of element X to element Y in grazer biomass. The “excess” carbon available
for respiration is
CXS=G′maxCPNPRNC-1,CPFePRFeC-1,0,
and the excess nutrients remineralized to their inorganic pools are
NXS=G′maxNPCP-RNC,0ε+G′maxRNCNPFePRFeN-1,01-εFeXS=G′maxFePCP-RFeC,0ε+G′maxRFeC(FePNPRNFe-1),0(1-ε),
where
ε=maxCxs,0Cxs+Δ
is a switch to prevent double counting in cases where one of the terms is
redundant (the excess relative to the least abundant element is included in
the other term) but would otherwise be nonzero (Δ is a constant
equal to 10-15, to prevent division by zero). For three elements, there
are 3! = 6 possible cases: for N greater or less than CPRNC, Fe
may be either in excess relative to both C and N, deficient relative to
both, or in excess relative to one but not the other (Table 2).
Cases where the “excess” terms are nonzero. These terms are always
greater than or equal to zero, and always zero when the phytoplankton
elemental ratio is equal to the grazer biomass ratio. A plus (+) sign
indicates that a specific term is positive. N1 and N2, Fe1
and Fe2 indicate the first and second terms in Eq. (16b) and (16c).
RNC is the grazer N/C (Redfield) ratio.
Fe in excess relative Fe in excess relative to C or N Fe deficient relative to both C and N but not both to both C and N CN1N2Fe1Fe2CN1N2Fe1Fe2CN1N2Fe1Fe2N/C>RNC++++++N/C<RNC++++++Organic and inorganic pools
There are two pools of detritus with different sinking rates but the same
fixed elemental ratios. Detrital C/N/Fe ratios are the same as zooplankton,
so zooplankton mortality or grazing of small zooplankton by large
zooplankton produce no “excess”. Phytoplankton mortality, and defecation by
zooplankton grazing on phytoplankton, produces excess nutrient or excess C
that needs to be recycled into the inorganic pool in a similar fashion as
outlined above for the assimilated fraction of grazing on phytoplankton.
The conservation equations for detrital C are
dDsdt=m1(Cps+Zs)+m2(Cps2+ZS2)-r1DsTg-wsdDsdzdDldt=m1(Cpl+ZL)+m2(Cpl2+ZL2)-r2DlTg-wldDldz,
where Tg is an Arrhenius function for temperature dependence of
remineralization and w is the sinking speed. The conservation equations for
inorganic C, N, and Fe are
dCidt=(ζVN-PphotC)Cp+R+CXS+(r1Ds+r2Dl)TgdNidt=-VNQNNp(LNO3LNO3+LNH4)+Nox-Ndentr(1-Af)dNadt=-VNQNNp(LNH4LNO3+LNH4)+RRCN+NXS+(r1Ds+r2Dl)RNCTg-Nox+Ndnf-NdentrAfdFedt=VFeQFeFep+RRCFe+FeXS+(r1Ds+r2Dl)RFeCTg,
where Nox is microbial oxidation of ammonium to nitrate (nitrification),
Ndnf and Ndentr are sources and sinks associated with dinitrogen
fixation and denitrification, and Af is the ammonium fraction of
denitrification losses, associated with anaerobic ammonium oxidation
(“anammox”). The oxygen equation is essentially the inverse of Eq. (18a),
with additional terms for oxidation and reduction of N, i.e.
dO2dt=-dCidt+2VNQNNp(LNO3LNO3+LNH4)-2Nox.
Nitrification is given by
Nox=kNH4oxNaKEKE+E(z),
where E(z) is the layer mean irradiance at depth z. Dinitrogen fixation is
parameterized as an external input of ammonium dependent on light,
temperature and Fe availability, and inhibited by high ambient
concentrations of inorganic N:
Ndnf=kdnfTdnf1-e-aEFeKFe+FeKNO3KNO3+Ni+Na,
where Tdnf=max(0, 1.962(Tf-0.773)), i.e. a linear multiple of
Eq. (1) that is 0 at T<20∘C and unity at
T=30∘C. The temperature, iron, and light limitation terms are
based on Pelagic Interactions Scheme for Carbon and Ecosystem Studies (PISCES) (Aumont et al,, 2015); the N-inhibition term is from CMOC
(Zahariev et al., 2008) (CMOC implicitly combines nitrate and ammonium into
a single inorganic N pool).
Denitrification is parameterized as a fraction of total remineralization
that increases as a linear function of oxygen concentration for
concentrations less than a threshold concentration Omxd:
Nfrxn=1-min(O2,Omxd)Omxd.
Remineralization is then divided among oxygen (1-Nfrxn), nitrate
(0.875Nfrxn), and ammonium (0.125Nfrxn) assuming an average anammox
contribution of 25 % (Babbin et al., 2014). We use this average ratio of
anammox to classical denitrification to partition fixed N losses between
NO3- and NH4+; the DIC sink and organic matter source
associated with anammox are small and are neglected here.
Calcification, calcite dissolution, and alkalinity
In CanOE, calcification is represented by a prognostic detrital calcite pool
with its own sinking rate (distinct from that of organic detritus), and
calcite burial or dissolution in the sediments depends on the saturation
state (100 % burial when ΩC≥1, 100 % dissolution when
ΩC<1). Calcification is represented by a detrital
calcium carbonate (CaCO3) state variable, but no explicit calcifier
groups. Detrital CaCO3 sinks in the same fashion as detrital
particulate organic carbon (POC), with a sinking rate independent of those
for large and small organic detritus. Calcite production is represented as a
fixed fraction of detritus production from small phytoplankton and small
zooplankton mortality:
dCadt=m1Cps+ZsPCa+m2Cps2+ZS2PCa-kCaCa-wCadCadz.
Calcite dissolution occurs throughout the water column as a first-order
process (i.e. no dependence on temperature or saturation state).
Approximately 80 % of calcite produced is exported from the euphotic zone.
In CanESM5-CanOE, burial in the sediments is represented as a simple
“on/off” switch dependent on the calcite saturation state (zero when ΩC<1 and 1 when ΩC≥1). In CanESM5,
calcification is parameterized by a temperature-dependent “rain ratio”
(Zahariev et al., 2008), and 100 % burial of calcite that reaches the
seafloor is assumed. Calcite burial in both models is balanced by an
equivalent source of DIC and alkalinity at the ocean surface (in the same
vertical column) as a crude parameterization of fluvial sources.
For each mole of calcite production, two moles of alkalinity equivalent are
lost from the dissolved phase; the reverse occurs during calcite
dissolution. There are additional sources and sinks for alkalinity
associated with phytoplankton nutrient (NH4+, NO3-)
uptake, organic matter remineralization, nitrification, denitrification, and
dinitrogen fixation (Wolf-Gladrow et al., 2007; see Table S2 in the Supplement).
The anammox reaction does not in itself contribute to alkalinity (Jetten at
al., 2001), but there is a sink associated with ammonium oxidation to
nitrite (the model does not distinguish between nitrite and nitrate).
Autotrophic production of organic matter by anammox bacteria is a net source
of alkalinity (Strous et al., 1998), but this source is extremely small
(∼ 0.03 mol molN-1) and is neglected here. Globally, the sources
and sinks of alkalinity from the N cycle offset each other such that there
is no net gain or loss as long as the global fixed N pool is conserved (see
Sect. 2.5 below). If dinitrogen fixation and denitrification are allowed to
vary freely, there will generally be a net gain or loss of fixed N and
therefore of alkalinity.
External nutrient sources and sinks
External sources and sinks consist of river inputs, aeolian deposition,
biological N2 fixation, denitrification, mobilization of Fe from
reducing sediments, loss of Fe to scavenging, and burial of calcium
carbonate in the sediments. There is no burial of organic matter; organic
matter reaching the seafloor is instantaneously remineralized. Aeolian
deposition of Fe is calculated from a climatology of mineral dust deposition
generated from offline (atmosphere-only) simulations with CanAM4 (von Salzen
et al., 2013), with an Fe mass fraction of 5 % and a fractional solubility
of 1.4 % in the surface layer. Subsurface dissolution is parameterized
based on PISCESv2 (Aumont et al., 2015); the total dissolution is 6.35 %,
with 22 % of soluble Fe input into the first vertical layer (see
the Supplement). Iron from reducing sediments is also based on
PISCES, with a constant areal flux of 1000 nmol m-2 d-1 in the
first model level, declining exponentially with increasing seafloor depth
(i.e. assuming that shelf sediments are the strongest source and the
sediments become progressively more oxygenated with increasing seafloor
depth) with an e-folding length scale of about 600 m. Scavenging of
dissolved iron is first order with a high rate (2.5 d-1) for
concentrations in excess of 0.6 nM (Johnson et al., 1997). For
concentrations below this threshold, the rate is much lower (0.001 d-1)
and is weighted by the concentration of organic detritus (Christian et al.,
2002b), i.e.
dFedt=-FeSFe1minDS+DLPFe,1,
where Fe is the dissolved iron concentration, DS and DL are the
small and large detritus concentrations, SFe1 is the first-order
scavenging rate in surface waters with abundant particulates, and PFe
is an empirical parameter to determine the dependence on particle
concentration (Table 1). The basis for this parameterization is that the
rate of scavenging must depend not only on the concentration of iron but on
the concentration of particles available for it to precipitate onto and
assumes that detrital POC is strongly positively correlated with total
particulate matter. Scavenging is treated as irreversible; i.e. scavenged
Fe is not tracked and does not re-enter the dissolved phase.
N2 fixation and denitrification vary independently in CanOE, so the
global total N pool can change. Conservation is imposed by adjusting the
global total N pool according to the difference between the gain from
N2 fixation and the loss to denitrification. A slight adjustment is
applied to the nitrate concentration at every grid point, while preserving
the overall spatial structure of the nitrate field. Adjustments are
multiplicative rather than additive to avoid producing negative
concentrations. This adjustment does not maintain (to machine precision) a
constant global N inventory but is intended to minimize long-term drift,
keeping it much smaller than the free surface error (see below). This
adjustment is applied every 10 d and has a magnitude of approximately
7×10-8 of the total N.
When the total fixed N adjustment is applied, one mole of alkalinity is
added (removed) per mole of N removed (added), to account for the alkalinity
sources associated with N2 fixation (creation of new NH4+)
and denitrification (removal of NO3-) (Wolf-Gladrow et al., 2007;
see Table S2 in the Supplement). As there is a 2 mol molN-1 sink associated with
nitrification, this formulation is globally conservative. As noted above, in
CanOE CaCO3 can dissolve or be buried in the sediments depending on the
calcite saturation state. DIC and alkalinity lost to burial are reintroduced
at the ocean surface, at the same grid point as burial occurs, providing a
crude parameterization of river inputs so that global conservation is
maintained (fresh water runoff contains no DIC or alkalinity). However, the
OPA free surface formulation is inherently imperfect with regard to tracer
conservation. Drift in total ocean alkalinity and nitrogen over time is on
the order of 0.01 % and 0.03 % per thousand years, respectively.
Ancillary data
For first-order model validation, we have relied largely on global gridded
data products rather than individual profile data. Global gridded data from
World Ocean Atlas 2018 (WOA2018) (Locarnini et al., 2018; Zweng et al.,
2018; Garcia et al., 2018a, 2018b) were used for temperature, salinity, and
oxygen and nitrate concentration. DIC and alkalinity were taken from the
GLODAPv2.2016b gridded data product (Key et al., 2015; Lauvset et al.,
2016). Offline carbon chemistry calculations were done following the Best
Practices Guide (Dickson et al., 2007) and the OMIP-BGC protocols (Orr et
al., 2017), and they are identical to those used in the models except that
constant reference concentrations were used for phosphate (1 µM) and
silicate (10 µM).
There is no global gridded data product for Fe, but we have made use of the
GEOTRACES Intermediate Data Product 2017 (Schlitzer et al., 2018), and the
data compilations from Monterey Bay Aquarium Research Institute (MBARI) (Johnson et al., 1997; 2003) and North Pacific Marine Science Organization (PICES) Working
Group 22 (Takeda et al., 2013). The latter two are concentrated in the
Pacific, while GEOTRACES is more global. The combined data sets provide more
than 10000 bottle samples from more than 1000 different locations
(Fig. S10a in the Supplement) (excluding some surface transect data that
involve frequent sampling of closely spaced locations along the ship track).
More details about model comparison to these data compilations and the list
of original references are given in the Supplement.
Satellite ocean colour estimates of surface chlorophyll were taken from the
combined SeaWiFS/MODIS climatology described by Tesdal et al. (2016).
Climatological satellite POC was downloaded from the NASA Ocean Color website and is based on the algorithm of Stramski et al. (2008) using
MODIS Aqua data. This climatology differs slightly from the chlorophyll one
in terms of years included and sensors utilized, but as only climatological
concentrations are considered and each climatology covers ∼ 15 years, these differences will have negligible effect on the results
presented. Satellite chlorophyll concentrations greater than 1 mg m-3
were excluded as these are mostly associated with coastal regions not
resolved by coarse-resolution global ocean models.
Global distribution of oxygen (O2) concentration in mmol m-3 at 400, 900, and 1400 m (rows) for CanESM5-CanOE, CanESM5, the mean
for other (non-CanESM) CMIP6 models, and World Ocean Atlas 2018 (WOA2018)
observations (columns). Numbers on the lower left are the mean model bias.
Differences from the observation-based fields are shown in
Fig. S2 in the Supplement.
Latitude–depth distribution (surface to 1750 m) of zonal mean
oxygen concentration (O2), oxygen concentration at saturation
(O2(sat)), and apparent oxygen utilization (AOU) in mmol m-3 for
CanESM5-CanOE, CanESM5, the mean for other CMIP6 models, and observations
(WOA2018). Note different colour scales for different rows. Numbers on the lower
left are the mean model bias. Differences from the observation-based fields
are shown in Fig. S2 in the Supplement.
CMIP6 model data were regridded by distance-weighted averaging using the
Climate Data Operators (https://code.mpimet.mpg.de/projects/cdo/, last access: 19 May 2022) to a
common grid (2×2∘, 33 levels) to facilitate ensemble averaging.
The vertical levels used are those used in GLODAP and in earlier (through
2009) versions of the World Ocean Atlas (e.g. Locarnini et al., 2010). For
large-scale tracer distributions, using a 1 or 2∘ grid
makes little difference (for example, the spatial pattern correlation
between CanESM5 and observed oxygen concentration at specific depths on a
1 or 2∘ grid differs by an average of 0.0011). The
years 1986–2005 of the historical experiment were averaged into
climatologies or annual means, for meaningful comparison with
observation-based data products. The CMIP6 historical experiment runs from
1850–2014 with atmospheric CO2 concentration (and other atmospheric
forcings) based on historical observed values. A single realization was used
in each case (see Table S3); 20-year averages are used to minimize the
effect of internal variability (e.g. Arguez and Vose, 2011; see Table S4).
Where time series are shown, 5-year means are used.
Sampling among CMIP6 models was somewhat opportunistic, and the exact suite
of models varies among the analyses presented. When we conducted a search
for a particular data field, we included in the search parameters all models
that published that field and repeated the search at least once for models
that were unavailable the first time the search was executed. In some cases,
model ensemble means excluded all but one model from a particular “family”
(e.g. there are three different MPI-ESM models for which ocean
biogeochemistry fields were published), as the solutions were found to be
similar and would bias the ensemble mean towards their particular climate.
The models used are ACCESS-ESM1-5, CESM2, CESM2-WACCM, CNRM-ESM2-1,
GFDL-CM4, GFDL-ESM4, IPSL-CM6A-LR, MIROC-ES2L, MPI-ESM-1-2-HAM,
MPI-ESM1-2-LR, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-LM, NorESM2-MM, and
UKESM1-0-LL. Details of which variables and realizations are used for which
models are given in Table S3 in the Supplement.
Results
We first describe the large-scale distribution of oxygen, DIC, alkalinity,
and the saturation state with respect to CaCO3 that derives from these
large-scale tracer distributions. Tracer distributions result partly from
ocean circulation and partly from biogeochemical processes. An overall
evaluation of the ocean circulation model is given in Swart et al. (2019a).
Analyzing CanESM5 and CanESM5-CanOE (with identical circulation) as well as
CanESM2 where possible (same biogeochemistry as CanESM5 but different
circulation) allows us to separate the effects of physical circulation and
biogeochemistry on evolving model skill with respect to large-scale tracer
distributions. In subsequent sections, we address the main areas where
CanESM5 and CanESM5-CanOE differ, such as the interaction of the iron and
nitrogen cycles and plankton community structure. Finally, we present some
temporal trends over the course of the historical experiment (1850–2014).
Taylor diagrams (Taylor, 2001) comparing modelled and observed
distributions of oxygen at specific depths from 100 to 3500 m. The angle from
the vertical indicates spatial pattern correlation. Distance from the origin
indicates ratio of standard deviation in modelled vs. observed (WOA2018)
fields. Red dots represent CanESM5-CanOE, blue dots CanESM5, small grey dots
other CMIP6 models, and large grey dots the model ensemble mean for all
CMIP6 models except CanESM5 and CanESM5-CanOE.
Distribution of oxygen
The spatial distribution of oxygen concentration ([O2]) at selected
intermediate depths (400, 900, and 1400 m) is shown in Fig. 2 for gridded
data from WOA2018 and differences from that observational data product for
CanESM5, CanESM5-CanOE, a model ensemble mean (MEM) of CMIP6 models
(excluding CanESM5 and CanESM5-CanOE). The depths were chosen to span the
depth range where low oxygen concentrations exist; these low-oxygen
environments are of substantial scientific and societal interest and are
sensitive to model formulation. The major features are consistent across the
models. Both CanESM models as well as the MEM show elevated oxygen
concentrations relative to observations, particularly in the North Pacific,
the North Atlantic, and the Southern Ocean. In the Indian Ocean, both CanESM
models show high oxygen concentrations in the Arabian Sea and deeper layers
of the Bay of Bengal relative to observations and the MEM; these biases are
somewhat smaller in CanESM5-CanOE than in CanESM5 (Fig. 2).
The ocean's oxygen minimum zones (OMZs) are mostly located in the eastern
Pacific Ocean, the northern North Pacific, and the northern Indian Ocean;
the spatial pattern changes with increasing depth (Fig. 2), but the OMZs
are mostly located between 200 and 2000 m depth. Biases in the EBC regions
are depth and model specific. CanESM5 shows particularly strong oxygen
depletion at 1400 m in the eastern tropical Pacific. In the southeastern
Atlantic, models tend to be biased low at the shallower depths and show
somewhat more variation at greater depths (Fig. 2). Overall, [O2]
biases tend to be positive over large areas of ocean with the exception of
some EBC regions, implying that models exaggerate the extent to which
remineralization is concentrated in these regions. An alternate version of
Fig. 2 that shows the modelled concentrations is given in
Fig. S2 in the Supplement.
Total volume of ocean with oxygen (O2) concentration less
than (a) 6 mmol m-3 (mean for last 30 years of the historical
experiment) and (b) 60 mmol m-3. Observation are from WOA2018.
Global distribution of aragonite saturation (ΩA) at
400, 900, and 3500 m for CanESM5-CanOE, CanESM5, the mean for other CMIP6
models, and observations (GLODAPv2 and WOA2018). Note different colour
scales for different depths. Numbers on the lower left are the mean model bias.
Differences from the observation-based fields are shown in
Fig. S2 in the Supplement.
The zonal mean oxygen concentration, saturation concentration, and apparent
oxygen utilization (AOU) are shown in Fig. 3 for the same four cases.
Again, the models generally show a positive bias in [O2], particularly
in high-latitude deep waters. The major ocean circulation features are
reproduced fairly well in all cases (e.g. weaker ventilation of
low-latitude subsurface waters, greater vertical extent of well-ventilated
surface waters in the subtropics). The saturation concentration (a function
of temperature and salinity) generally shows relatively little bias,
implying that the bias in [O2] arises mainly from remineralization
and/or ventilation. AOU is lower than observed over much of the subsurface
ocean. CanESM5 and CanESM5-CanOE show a high bias over much of the Northern
Hemisphere that reflects the high concentrations in the North Pacific and
North Atlantic (Fig. 2). The overall trend of bias with latitude in
CanESM5 and CanESM5-CanOE is generally similar to the MEM, but the biases
are larger. The bias in CanESM5 is generally slightly larger than in
CanESM5-CanOE, except in the Arctic Ocean. Again, Fig. S2 in the Supplement
includes a version of this plot that shows the modelled concentration
fields.
The skill of each model with respect to the distribution of O2 at
different depths is represented by Taylor diagrams (Taylor, 2001) in Fig. 4. These diagrams allow us to assess how well the model reproduces the
spatial distribution at a range of depths, because different physical and
biogeochemical processes determine the distribution in different depth
ranges. All of the CMIP6 models that were shown as an ensemble mean in
Figs. 2 and 3 are shown individually. The large blue dots represent
CanESM5, red CanESM5-CanOE, and grey the MEM; the smaller grey dots
represent the individual models. CanESM5-CanOE shows slightly higher pattern
correlation than CanESM5 at all depths. Both models compare favourably with
the full suite of CMIP6 models, with r>0.85 for CanESM5 and
r>0.9 for CanESM5-CanOE at all depths examined, and a normalized
standard deviation within ±25 % of unity.
The total volume of ocean with [O2] less than 6 mmol m-3 (the
threshold for denitrification; Devol, 2008) and 60 mmol m-3 (a
commonly used index of hypoxia) is shown in Fig. 5. The total volume is
highly variable among models (note, however, that there are several clusters
of related models with quite similar totals). CanESM5 and CanESM5-CanOE have
among the lowest total volumes (i.e. the interior ocean is relatively well
ventilated) and are among the nearest to the observed total. For [O2]<60 mmol m-3 the bias is, nonetheless, quite large (i.e. the
observed volume is underestimated by almost 50 % in both models). The
volume of water with [O2] below the denitrification threshold is
overestimated in both CanESM5 and CanESM5-CanOE; CanESM5-CanOE has a much
smaller total that is closer to the observed value. The bias in the spatial
pattern of hypoxia (not shown) is generally similar to the bias in dissolved
oxygen distribution (Fig. 2). The low-oxygen regions are generally more
concentrated in the eastern tropical Pacific in the models than in
observations, and the low-oxygen region in the northwest Pacific is not well
reproduced in CanESM models.
Distribution of DIC, alkalinity, and
CaCO3 saturation
The spatial distribution of aragonite saturation state (ΩA) at
selected depths is shown in Fig. 6. The first two depths are the same as
in Fig. 2, but a much greater depth is also included, as the length scale
for CaCO3 dissolution is greater than for organic matter
remineralization. In this case, the observations are a combination of
GLODAPv2 (Key et al., 2015; Lauvset et al., 2016) for DIC and alkalinity,
and WOA2018 for temperature and salinity. CanESM5 and CanESM5-CanOE show an
overall high saturation bias at the shallower depths, particularly in the
North Atlantic, with a low bias found mainly in the eastern Pacific. The low
saturation bias in the eastern tropical Pacific is substantially reduced in
CanESM5-CanOE compared to CanESM5. On the other hand, CanESM5 generally does
better than CanESM5-CanOE, or the MEM, at reproducing the low saturation
states in the northwestern Pacific and the Bering Sea. Both CanESM models
show a high saturation state bias in the North Atlantic and the
well-ventilated regions of the north Pacific subtropical gyre; these biases
are slightly smaller in CanESM5-CanOE. Maps of the calcite and aragonite
saturation horizon (Ω=1) depth are shown in Fig. S3 in the Supplement; these generally confirm the same biases noted in Fig. 6.
Zonal mean distributions of aragonite saturation state (ΩA),
calcite saturation state (ΩC), and carbonate ion concentration
([CO3--]) and the differences of the models from the observations
are shown in Fig. 7 (Fig. S2 in the Supplement includes versions of Figs. 6 and 7 that show the modelled fields). The models generally compare well
with the observations in the representation of the latitude–depth
distribution of high- and low-saturation waters. CanESM5 has a high
saturation bias in low-latitude surface waters that is somewhat reduced in
CanESM5-CanOE. Both CanESM5 models show a high saturation bias in Northern
Hemisphere intermediate (e.g. 200–1000 m) depth waters that is larger than
in the MEM. This is primarily a result of low Ω in the North
Atlantic Ocean (Fig. 6).
Latitude–depth distribution of zonal mean (surface to 1150 m)
aragonite saturation state (ΩA), calcite saturation state
(ΩC), and carbonate ion concentration ([CO3--]) in
mmol m-3 for CanESM5-CanOE, CanESM5, the mean for other CMIP6 models,
and observations (GLODAPv2 and WOA2018). Numbers on the lower left are the mean
model bias. Differences from the observation-based fields are shown in
Fig. S2 in the Supplement.
Taylor diagrams for a range of depths are shown for DIC in Fig. 8 and for
ΩA in Fig. 9 (for alkalinity; see Fig. S4 in the Supplement).
As expected, the MEM generally compares favourably with the individual
models (e.g. Lambert and Boer, 2001). CanESM5 and CanESM5-CanOE compare
favourably with the full suite of CMIP6 models. CanESM5-CanOE shows a gain
in skill relative to CanESM5, and both show improvement relative to CanESM2.
At 400 m, CanESM2 stands out as having extremely high variance, which is
mostly due to extremely high DIC concentrations occurring over a limited
area in the eastern equatorial Pacific (not shown). This bias is present in
CanESM5 and in CMIP6 models generally (Fig. 6) but involves much lower
concentrations spread over a larger area.
Taylor diagrams comparing modelled and observed distributions of
DIC at specific depths from 100 to 3500 m. Observations are from GLODAPv2
(Lauvset et al., 2016). Red dots represent CanESM5-CanOE, blue dots CanESM5,
magenta dots CanESM2, small grey dots other CMIP6 models, and large grey
dots the model ensemble mean for all CMIP6 models except CanESM5 and
CanESM5-CanOE.
Taylor diagrams comparing modelled and observed (GLODAPv2 and
WOA2018) distributions of ΩA at specific depths from 100 to
3500 m. Symbol colours are as in Fig. 8.
N and Fe cycles
An important difference between CanESM5 and CanESM5-CanOE is the inclusion
of a prognostic Fe cycle. The CMOC iron mask (Zahariev et al., 2008) was a
pragmatic solution in the face of resource limitations but is inherently
compromised as it can not evolve with a changing climate. The first-order
test of a model with prognostic, interacting Fe and N cycles is whether it
can reproduce the distribution of HNLC regions and the approximate surface
macronutrient concentrations within these. CanESM5-CanOE succeeded by this
standard, although the surface nitrate concentrations are biased low in the
subarctic Pacific and equatorial Pacific and high in the Southern Ocean and
in the global mean (Fig. 10).
Climatological seasonal cycle of surface nitrate concentration
averaged for selected ocean regions. The thick red line represents
CanESM5-CanOE, thick blue line CanESM5, thick black line observations
(WOA2018), thin grey lines individual CMIP6 models, and thick grey line the
model ensemble mean (excluding CanESM5 and CanESM5-CanOE). Regional
boundaries are given in Table S5 and Fig. S5.
The seasonal cycle of the zonal mean surface nitrate concentration for a
selection of CMIP6 models is shown in Fig. 11. CanESM5, CanESM5-CanOE, and
CNRM-ESM2-1 reproduce the equatorial enrichment and the low concentrations
in the tropical–subtropical latitudes fairly well. Some models either have
very weak equatorial enrichment (MPI-ESM1-2-LR) or too high a concentration
in the off-equatorial regions (UKESM1-0-LL, NorESM2-LM). UKESM1-0-LL has
very high concentrations throughout the low-latitude Pacific, which biases
the ensemble mean (Fig. 11). Figure S6 in the Supplement shows the same data
as Fig. 11 but for a more limited latitude range to better illustrate
model behaviour in the tropics. CanESM5, CanESM5-CanOE, and CNRM-ESM2-1
reproduce the seasonal cycle of tropical upwelling (e.g. Philander and
Chao, 1991), with highest concentrations in summer.
Climatological seasonal cycle of zonal mean surface nitrate
concentration for a selection of CMIP6 models, a model ensemble mean (MEM)
excluding CanESM5 and CanESM5-CanOE, and an observation-based data product
(WOA2018). An alternate version showing only latitudes <20∘ is given in Fig. S6.
The surface distribution of dissolved iron (dFe) in various CMIP6 models is
shown in Fig. 12. For Fe there is no observation-based global climatology
with which to compare the model solutions (some comparisons to available
profile data are shown in Fig. S10b–h in the Supplement). CanESM5-CanOE shows
a similar overall spatial pattern to other models, and generally falls in
the middle of the spread, particularly regarding concentrations in the
Southern Ocean. Several models show extremely high concentrations in the
tropical–subtropical North Atlantic (Sahara outflow region). CanESM5-CanOE,
along with CNRM-ESM2-1 and CESM2, has much less elevated concentrations in
this region, due to lower deposition or greater scavenging or both.
CanESM5-CanOE has its lowest concentration in the eastern subtropical South
Pacific, which is common to many models (Fig. 12). The area of strong
surface depletion is generally more spatially restricted in CanESM5-CanOE
than in other models, and surface dFe concentrations are greater over large
areas of the Pacific. Both the north–south and east–west asymmetry of
distribution in the Pacific is greater in CanESM5-CanOE than in most other
models, some of which show the South Pacific minimum extending westward
across the entire basin, and others into the Northern Hemisphere. Only in
CESM2 is this minimum similarly limited to the southeast Pacific.
Global distribution of dissolved iron (dFe) concentration (nmol m-3) at the ocean surface for CanESM5-CanOE
and other CMIP6 models that published this field. Concentrations exceeding
1000 nmol m-3 are masked in white. CanESM5 is not included because it does not have prognostic iron.
The mean depth profiles of dFe are shown in Fig. 13. Some models show more
of a “nutrient-type” (increasing with depth due to strong near-surface
biological uptake and subsequent remineralization) profile, some a more
“scavenged-type” (maximal at the surface, declining with depth) profile
(see Li, 1991; Nozaki, 2001), and others a hybrid profile (increasing
downward but with a surface enrichment). CanESM5-CanOE is at the
“nutrient-type” end of spectrum with a generally monotonic increase with
depth to a near-constant deep-water concentration of 0.6 nM and a very
slight near-surface enrichment (see also Fig. S10b, c in the Supplement).
Global mean depth profiles of dissolved iron concentration for
CanESM5-CanOE and other CMIP6 models that published this field. GFDL-CM4 is
excluded because it has very high concentrations (>2000 nmol m-3) near the surface. The thick red line represents CanESM5-CanOE, the thin
grey lines individual CMIP6 models, and the thick grey line the model
ensemble mean (excluding CanESM5-CanOE and GFDL-CM4).
Mean surface nitrate and dFe concentrations for selected ocean regions are
shown in Fig. 14. CanESM5-CanOE shows concentrations that are within the
range of CMIP6 models, although in some cases at the higher or lower end.
Surface nitrate concentrations generally compare favourably with the
observation-based climatology, but are biased low in HNLC regions other than
the Southern Ocean. These biases are not necessarily a consequence of having
too much or too little iron. For example, in the Southern Ocean
CanESM5-CanOE has among the highest surface nitrate concentrations, but it
also has some of the highest dFe concentrations, and the high nitrate bias
is present in CanESM5 as well. Comparisons with the limited GEOTRACES data
available suggest that near-surface dFe concentrations in the Southern Ocean
are biased high rather than low in CanESM5-CanOE (not shown). One region
where there does seem to be a strong correlation between surface nitrate and
dFe concentrations is the western subarctic Pacific. All but two models
(CNRM-ESM2-1, NorESM2-LM) fall along a spectrum from high Fe/low nitrate
to low Fe/high nitrate. CanESM5-CanOE falls near the high Fe/low nitrate
end of the range.
Mean surface nitrate (NO3) vs. dissolved iron (dFe)
concentrations in different oceans, including the major high-nutrient/low-chlorophyll (HNLC) regions. CanESM5-CanOE is shown as a red dot and other
CMIP6 models as grey dots (CanESM5 is not included because it does not have
iron). Observed NO3 is shown as a vertical black line as there are no
observational estimates of dFe concentration. For GFDL-CM4, nitrate is
estimated as phosphate ×16. Region definitions are given in
Table S5 and Fig. S5.
Surface nitrate concentrations along the Pacific Equator during the
upwelling season (June–October) for CanESM5 and CanESM5-CanOE are shown in
Fig. 15. The range of other CMIP6 models is not shown here because it is
large and therefore adds little information (see Figs. 11 and S6). CanESM5-CanOE better represents the east-west gradient, while
CanESM5 has slightly higher concentrations in the core upwelling region.
Both models underestimate the highest concentrations around 100∘ W. Although some localized maxima in this data product are due to
undersampling, equatorial upwelling is strong at this location (e.g. Lukas,
2001) and the spatial coherence of the data strongly suggests that this
maximum accurately reflects reality. It should be noted that CanESM5 iron
limitation is calculated from a version of the same data product; however,
the Fe mask is based on the minimum nitrate concentration over the annual
cycle, whereas the data shown here are for the upwelling season.
Surface nitrate (NO3) concentrations along the Pacific
Equator (mean from 2∘ S–2∘ N) during the upwelling
season (June–October) for CanESM5-CanOE (red), CanESM5 (blue), and WOA2018
observations (black).
Plankton biomass, detritus, and particle flux
The relative abundance of the four plankton groups is shown in Fig. 16
for a range of ocean regions. Both CanESM models mostly compare favourably
with observation-based estimates of phytoplankton biomass, except in the
tropics where CanESM5-CanOE has very high biomass. Both CanESM models have
low phytoplankton biomass in the North Atlantic. In the North Pacific and
the Southern Ocean, CanESM5-CanOE reproduces the observation-based estimates
well and CanESM5 slightly less well. The general pattern is that large and
small phytoplankton have similar abundance and are substantially more
abundant than zooplankton.
Annual mean surface ocean concentration of large and small
phytoplankton and zooplankton in CanESM5-CanOE (red) and of phytoplankton
and zooplankton in CanESM5 (blue) for the representative ocean regions shown
in Fig. 14. Observational estimates (black) are for phytoplankton biomass
calculated from satellite ocean colour estimates of surface chlorophyll
(SeaWiFS/MODIS; Tesdal et al. 2016), assuming a carbon-to-chlorophyll ratio
of 50 g g-1. Region definitions are given in Table S5 and Fig. S5.
Mean annual cycle of surface chlorophyll for the representative
ocean regions shown in Figs. 14 and 16. CanESM5-CanOE large and small
phytoplankton concentrations are shown separately and combined (red) along
with CanESM5 (blue) and observational estimates (black). Region definitions
are shown in Table S5 and Fig. S5.
Climatological surface particulate organic carbon (POC) vs.
chlorophyll for CanESM5-CanOE (red) and observations (black). Data are for
all ocean grid points (2×2∘ uniform global grid) for all months of
the year where observational data are available. Model POC is offset 17 mg m-3 for illustrative purposes. Chlorophyll concentrations >1 mg m-3 are excluded as they largely represent coastal areas poorly resolved by coarse-resolution global ocean models.
Part of the rationale for multiple food chains is that they better represent
the way that actual plankton communities adapt to different physical ocean
regimes and therefore are better able to simulate distinct ocean regions
with a single parameter set (e.g. Chisholm, 1992; Armstrong, 1994; Landry
et al., 1997; Friedrichs et al., 2007). The expectation is that small
phytoplankton will be more temporally stable and large phytoplankton will
fluctuate more strongly between high and low abundances. The mean annual
cycles of surface chlorophyll largely conform to this pattern; e.g. in the
North Atlantic and the western subarctic Pacific, large phytoplankton are
dominant in summer and much more variable over the seasons (Fig. 17).
Compared to observations, CanESM5 models underestimate the amplitude of the
seasonal cycle in the North Atlantic and overestimate it in the North
Pacific. CanESM5 shows a stronger and earlier North Atlantic spring bloom
compared to CanESM5-CanOE; the observations are in between the two in terms
of timing, and both models underestimate the amplitude (Fig. 17). In the
tropics, the seasonal cycle is weak. CanESM5-CanOE in the tropical Atlantic
shows the expected seasonal cycle but not the expected dominance of large
phytoplankton in summer. CanESM5-CanOE generally overestimates the total
near-surface chlorophyll in both the tropical Pacific and the tropical
Atlantic.
Zooplankton biomass (especially microzooplankton) is also somewhat difficult
to test against observations, but our model concentrations appear to be
biased low. Stock et al. (2014) estimated depth-integrated biomass of
phytoplankton, mesozooplankton, and microzooplankton for a range of oceanic
locations in which intensive field campaigns have occurred (estimates of
microzooplankton biomass are relatively sparse). They found that in most
locations phytoplankton and (combined) zooplankton biomass are of comparable
magnitude, whereas in CanESM5-CanOE zooplankton biomass is consistently
lower (Fig. 16). The global integral biomass of mesozooplankton is about
an order of magnitude less than the 0.19 PgC estimated by Moriarty and
O'Brien (2013). The CanESM5 total of 0.14 Pg is relatively close to the
Moriarty estimate but implicitly includes microzooplankton.
Surface chlorophyll and POC for CanESM5-CanOE and for ocean colour
observational data are shown in Fig. 18 (POC in the model is the sum of
phytoplankton, microzooplankton, and detrital carbon). The observations have
a lower limit for POC that is not present in the model (∼ 17 mgC m-3), which is unsurprising given the processes neglected in the
model; i.e. in regions of very low chlorophyll, there is still substantial
dissolved organic carbon, bacteria that consume it, and microzooplankton
that consume the bacteria and produce particulate detritus. The
observational data show a fairly linear relationship at low concentrations,
but with a curvature that implies a greater phytoplankton fraction in more
eutrophic environments (see Chisholm, 1992). The model, by contrast, shows a
fairly linear relationship over the whole range of concentrations. In other
words, the phytoplankton share of POC is higher and more constant in the
model than in the observations. The living biomass (phytoplankton and
microzooplankton) fraction of total POC in CanOE is generally in excess of
50 % (not shown), which is implausible for a real-world oceanic microbial
community (e.g. Christian and Karl, 1994) but consistent with the
relatively low rates of export from the euphotic zone.
Export production for a range of CMIP6 models is shown in Fig. 19a.
CanESM5-CanOE is at the low end of the range. Observations are not shown
because the range of observational estimates covers the entire range of
model estimates (e.g. Siegel et al., 2016). Note also that CanESM5 export
is quite a bit lower than in CanESM2, which is relatively high for CMIP5
models (not shown). The difference between CanESM2 and CanESM5 is
attributable primarily to different circulation, although the different
initialization fields for nitrate might also play a small role. The lower
rate in CanESM5-CanOE is consistent with the above results regarding
plankton community structure (e.g. the concentration of detritus is
generally low compared to living biomass), as well as the lower sinking rate
for small detritus. The latitudinal distribution of export is shown in
Fig. 19b. CanESM5 shows very high export in the midlatitudes of the
Southern Ocean, similar to CanESM2 (not shown). Both CanESM5 and
CanESM5-CanOE show latitudinal patterns consistent with the range of other
CMIP6 models. CanESM5 has slightly greater export in the equatorial zone; in
both CanESM5 and CanESM5-CanOE, the equatorial enrichment attenuates very
rapidly with latitude and the rates are low in the subtropics.
(a) Global total export production (epc100) in PgC yr-1(b)
and zonal mean export production in molC m-2 yr-1 according to
selected CMIP6 models (mean for 1985–2014 of historical experiment). The thick
red line represents CanESM5-CanOE, thick blue line CanESM5, thin grey lines
individual CMIP6 models, and thick grey line the model ensemble mean
(excluding CanESM5 and CanESM5-CanOE).
Historical trends
Cumulative ocean uptake of CO2 is shown in Fig. 20 for the historical
experiment (1850–2014). CanESM models are biased low relative to observation
based estimates (∼ 145 PgC; see Friedlingstein et al., 2020)
and the MEM (144 PgC, Fig. 20) but fall well within the spread of CMIP6
models. Some of the difference may be attributable to differences in the way
cumulative uptake is calculated in models vs. observations (Bronselaer et
al., 2017), although this should apply to other CMIP6 models as well.
CanESM5-CanOE has lower cumulative uptake than CanESM5 by ∼ 10 PgC. As the models were not fully equilibrated when the historical run was
launched, this difference does not necessarily arise from the biogeochemical
model structure; part of the difference can be attributed to differences in
the spinup (see Séférian et al., 2016). The drift in the piControl
experiment over the 165 years from the branching off of the historical
experiment is -10.0 PgC in CanESM5-CanOE and -5.1 PgC in CanESM5 (see
Table S6 in the Supplement), so drift accounts for about half (48 %) of the
difference in net ocean CO2 uptake. The spatial distribution of
anthropogenic DIC is very similar between CanESM5 and CanESM5-CanOE
(Fig. S7 in the Supplement). CanESM5 and CanESM5-CanOE show a high bias in
near-surface DIC relative to alkalinity (a measure of the ocean's capacity
to absorb CO2) in the midlatitudes of both hemispheres (Fig. S8 in the Supplement), which may in part explain the weak uptake of CO2.
Cumulative ocean uptake of carbon dioxide (CO2) as
anthropogenic dissolved inorganic carbon (AnthDIC) in PgC over the course of
the historical experiment (1850–2014). Data are shown as successive
5-year means. CMIP6 mean (thick grey line) indicates ensemble mean for
CMIP6 models (thin grey lines) excluding CanESM5 (blue) and CanESM5-CanOE
(red). An observation-based estimate of 145±20 PgC (Friedlingstein et
al., 2020) is shown for a nominal year (2014) (black).
The long-term trend in global total export production is shown in Fig. 21.
The model values must be normalized in order to compare trends, since the
differences among means are large compared to the changes over the
historical period (Fig. 19). Such trends are difficult or impossible to
meaningfully constrain with observations, but the general expectation has
been that export will decline somewhat due to increasing stratification
(e.g. Steinacher et al., 2010). CanESM5 shows a greater decline than most
other CMIP6 models, while CanESM5-CanOE is more similar to non-CanESM
models. The change in CanESM5 is geographically widespread and not
concentrated in a specific region or regions: export is maximal in the
tropics and the northern and southern midlatitudes (Fig. 19b) and
declines over the historical period in all of these regions (Fig. S9 in the Supplement). In CanESM5-CanOE, export declines in the same regions, but the
magnitude of the change is smaller, and in the Southern Ocean increases and
decreases in different latitude bands largely offset each other.
Change in export production (epc100) over the course of the
historical experiment (1850–2014), normalized to the 1850–1900 mean. Data
are shown as successive 5-year means. The thick red line represents
CanESM5-CanOE, thick blue line CanESM5, thin grey lines other CMIP6 models,
and thick grey line the ensemble mean of non-CanESM models.
The trend in the volume of ocean water with O2 concentration less than
6 or 60 mmol m-3 is shown in Fig. 22. Again, the totals are
normalized to a value close to the preindustrial, as the differences among
models are large (Fig. 5). For the volume with <60 mmol m-3,
CanESM models show relatively little change; in CanESM5, the volume actually
declines slightly, while in CanESM5-CanOE it increases, but the total change
is <1 % in each case. As with the baseline volumes, the range
among models is large, with one model showing an increase approaching 10 %
of the total volume estimated for WOA2018 (Figs. 5b and 22b). For the
volume with <6 mmol m-3 (Fig. 22a), CanESM models are among
the most stable over time. In CanESM5, the volume again declines, although
this is within the range of internal variability. Again, some models show
fairly large excursions, but in this case none show a strong secular trend
over the last half century.
(a) Change in total ocean volume with oxygen (O2)
concentration less than (a) 6 mmol m-3 and (b) 60 mmol m-3 over
the course of the historical experiment (1850–2014), normalized to the
1850–1870 mean. Data are shown as successive 5-year means. The thick red line
represents CanESM5-CanOE, thick blue line CanESM5, and thin grey lines other
CMIP6 models.
Discussion
CanESM5 and CanESM5-CanOE are new coupled ocean–atmosphere climate models
with prognostic ocean biogeochemistry. The two have the same physical
climate (in experiments with specified atmospheric CO2) and differ only
in their ocean biogeochemistry components. CanESM5-CanOE has a much more
complex biogeochemistry model including a prognostic iron cycle. We have
presented results that assess how these two models simulate the overall
distribution of major tracers like DIC, alkalinity, nitrate, and oxygen, as
well as analyses of the interaction of the iron and nitrogen cycles,
plankton community structure, export of organic matter from the euphotic
zone, and historical trends over 1850–2014.
The overall distribution of major tracers indicates that both models do a
reasonable job of simulating both biogeochemical (e.g. export and
remineralization of organic matter) and physical (e.g. deep and
intermediate ocean ventilation) processes. The volume of ocean with oxygen
concentration below 6 or 60 µM compares favourably with other CMIP6
models (Fig. 5) and is among the most stable over historical time (Fig. 22). CanESM5-CanOE has a substantially lower volume of water with
[O2]<6µM than CanESM5 and much closer to
observation-based estimates (Fig. 5). Both models are biased slightly
low in terms of historical uptake of anthropogenic CO2, which may
indicate weak Southern Ocean upwelling or too-shallow remineralization of
DIC or both (Fig. 20). The spatial distribution of anthropogenic DIC is
very similar between the two models (Fig. S7 in the Supplement), which is
expected as it is mainly a function of the physical ocean model circulation.
However, CanESM5 has higher concentrations in the main areas of
accumulation, particularly the North Atlantic and the Southern Ocean. This
probably indicates more efficient removal and export of “natural” DIC by the
plankton, particularly in the Southern Ocean upwelling zone (Fig. 19), and
deeper average remineralization, with the caveat that the preindustrial
control simulations had different degrees of equilibration when the
historical experiment was launched (see Séférian et al., 2016,
Table S6 in the Supplement).
Analyses of phytoplankton and zooplankton biomass concentrations show that
CanESM5 and CanESM5-CanOE compare somewhat favourably with available
observational data but do have distinct biases. In particular, both
zooplankton biomass and detrital organic matter concentration tend to be
very low in CanESM5-CanOE; the total biomass of the plankton community and
the standing crop of particulate organic matter are dominated by
phytoplankton (e.g. Fig. 17). Regional biases differ between the two
models, with CanESM5-CanOE showing excessively large phytoplankton biomass
in the tropics. We note, however, that the seasonal cycle of equatorial
upwelling and the formation of the equatorial Pacific HNLC are reproduced
rather well by our models (e.g. Figs. 11, 15, and S6), and that
CanESM5-CanOE is the first CanESM model to have genuinely simulated this as
an emergent property (see Sect. 3.3). In CanESM5-CanOE, decoupling of
large and small phytoplankton populations associated with seasonal upwelling
or convection (see below) is observed in some regions but not others.
Global export production is biased low, particularly in CanESM5-CanOE. This
is due in part to the biogeochemical model and in part to ocean circulation.
CanESM5 has the same ocean biology as CanESM2 but a different physical ocean
model, and global ocean export production is substantially lower in CanESM5.
It is lower still in CanESM5-CanOE (Fig. 19). We note that CanESM5
performs better than CanESM2 on most metrics of physical ocean model
evaluation (Swart et al., 2019a) and shows a more realistic distribution of
major tracers like DIC (Fig. 8). While the range of observation-based
estimates of global ocean export production is large and encompasses the
full range of CMIP5 and CMIP6 models, the change between CanESM2 and CanESM5
is large. Changes in the physical ocean are not entirely independent of the
biogeochemistry model even when the latter is ostensibly identical. In
CanESM2 and CanESM5, iron limitation is specified as a spatially static
“mask” based on the observed distribution of surface nitrate, and it is
possible that in these two models ocean upwelling occurs in different places
relative to the specified boundary of the region of Southern Ocean iron
imitation (Fig. 3 of Zahariev et al., 2008). It is also possible that the
lower export production in CanESM5-CanOE is due to low iron supply to the
surface waters of the Southern Ocean, but comparisons with available
observations do not suggest that this is the case. Several biases are common
to CanESM5 and CanESM5-CanOE that relate to Southern Ocean upwelling (high
Southern Ocean surface nitrate concentration, low export production, weak
anthropogenic CO2 uptake) and so are probably more attributable to the
physical ocean model than to the Fe submodel. The difference between CanESM2
and CanESM5 bears this out.
The development of CanOE was undertaken in response to some of the most
severe limitations of CanESM2. Many of the additional features that CanOE
introduces were already in the models published by other centres even in
CMIP5. In addition to CMOC (Zahariev et al., 2008), previous models
developed by members of our group include Denman and Peña (1999; 2002),
Christian et al. (2002a, b), Christian (2005), and Denman et al. (2006).
Christian et al. (2002a) had a prognostic Fe cycle and multiple
phytoplankton and zooplankton species but had fixed elemental ratios.
Christian (2005) incorporated a cellular-regulation model but only for a
single species and without Fe limitation. Christian (2005) had prognostic
chlorophyll, whereas Denman and Peña (1999; 2002) and Christian et al. (2002a) used an irradiance-dependent diagnostic formulation. Christian et al. (2002a) used multiplicative (Franks et al., 1986) grazing, which creates
stability in predator–prey interactions but severely limits phytoplankton
biomass accumulation under nutrient-replete conditions.
One of the most important lessons from Christian et al. (2002a, b) was
that when a fixed Fe/N ratio is employed, sensitivity to this parameter is
extreme. Because Fe cell quotas are far more variable than N, P, or Si
quotas, treating this parameter as constant results in the specified value
influencing the overall solution far more than any other parameter.
CanESM5-CanOE largely succeeded in creating a prognostic Fe–N limitation
model that produces HNLC conditions in the expected regions (Figs. 10, 11,
14, 15, S6), although surface nitrate concentration is low relative to
observation-based estimates in some cases. External Fe sources and
scavenging parameterizations will be revisited and refined in future
versions. In CanESM5-CanOE, the scavenging model is very simple, with
distinct regimes for concentrations greater or less than 0.6 nM; scavenging
rates are very high above this threshold which causes deep-water
concentrations to converge on this value. The generally nutrient-like
profile suggest that in CanOE the scavenging rate is quite low for
concentrations below 0.6 nM (Fig. 13; see also Fig. S10h in the Supplement).
We note that the aeolian mineral dust deposition field employed here is
derived from the CanESM atmosphere model; these processes are not presently
interactive but could be made so in the future.
A particular issue with CanESM2 was that extremely high concentrations of
nitrate occurred under the EBC upwelling regions. This error resulted from
spreading denitrification out over the ocean basin so that introduction of
new fixed N from N2 fixation would balance denitrification losses
within each vertical column, whereas in the real world denitrification is
highly localized in the low oxygen environments under the EBCs. CanESM2 did
not include oxygen, but CanESM5 incorporates oxygen as a “downstream” tracer
that does not feed back on other biogeochemical processes. The incorporation
of a more process-based denitrification parameterization in CanESM5-CanOE is
independent of the many other processes that are present in CanESM5-CanOE
but not in CanESM5: a CMOC-like model with prognostic denitrification is
clearly an option. We chose not to include explicit, oxygen-dependent
denitrification in CanESM5 because we wanted to maintain a CMOC-based model
as close to the CanESM2 version as possible, and because oxygen would not
then be a downstream tracer that does not affect other processes.
Plankton community structure in CanESM5-CanOE is somewhat biased toward high
concentrations of phytoplankton, low concentrations of zooplankton and
detritus, and low export (Sect. 3.4). In the development phase, a fair
number of experiments were conducted with various values of the grazing
rates and detritus sinking speeds. A wide range of values of these
parameters was tested, with no resulting improvement in the overall results.
Possibly the detrital remineralization rates are too high, although primary
production is also on the low end of the CMIP6 range (not shown) and would
probably decline further if these rates were decreased. The model was
designed around the Armstrong (1994) hypothesis of “supplementation” vs.
“replacement”; i.e. small phytoplankton and their grazers do not become
much more abundant in more nutrient-rich environments but rather stay at
about the same level and are joined by larger species that are absent in
more oligotrophic conditions (see also Chisholm, 1992; Landry et al., 1997;
Friedrichs et al., 2007). The results presented here suggest that this was
partially achieved, but further improvement is possible (Fig. 17).
As to whether the gains in skill with CanESM5-CanOE justify the extra
computational cost, Taylor diagrams (Figs. 4, 8, 9, and
Fig. S4 in the Supplement) show a modest but consistent gain in skill at simulating the
major biogeochemical species (O2, DIC, alkalinity) across variables and
depths, especially for alkalinity at middle depths (Fig. S4 in the Supplement),
for which CanESM5 displays the least skill relative to other fields or
depths. Other processes that are highly parameterized in CanESM5, such as
calcification and CaCO3 dissolution, were not addressed in detail in
this paper but are an important factor in determining the subsurface
distribution of alkalinity. Again, we emphasize that we are simulating as an
emergent property of a process-based model something that is parameterized
in CanESM5 (as previously noted for surface nitrate concentration in HNLC
regions), and doing at least as well in terms of model skill. As a general
rule, the potential for improving skill and achieving better results in
novel environments (e.g. topographically complex regional domains like the
Arctic Ocean and the boreal marginal seas) is expected to be greater in
less parameterized, more mechanistic models (e.g. Friedrichs et al., 2007;
Tesdal et al., 2016). Inclusion of a prognostic iron cycle and C/N/Fe
stoichiometry also open up additional applications and scientific
investigations that are not possible with CMOC.
An updated version of CanESM5 with prognostic denitrification is clearly
possible. However, for the reasons discussed above, a prognostic Fe cycle
with a fixed phytoplankton Fe/N remains problematic, and the model would
still have a single detritus sinking speed and remineralization length
scale. We are also developing CanOE for regional downscaling applications
(Hayashida, 2018; Holdsworth et al., 2021). The regional domains have
complex topography and prominent continental shelf and slope, and the single
remineralization length scale in CMOC may not be well suited to such an
environment. The number of tracers in CanOE is not particularly large
compared with other CMIP6 models. We expect to further refine CanOE and its
parameterizations, evaluate it against new and emerging ocean data sets
(e.g. GEOTRACES, biogeochemical Argo), and incrementally improve CMOC
(which we will maintain for a wide suite of physical climate experiments for
which ocean biogeochemistry is not central to the purpose). For CMIP6, we
chose to keep CMOC as close to the CanESM2 version as possible. This
strategy allows us to quantify how much of the improvement in model skill is
due to the physical circulation, as is illustrated by greater skill with
respect to DIC (Fig. 8) and alkalinity (Fig. S4 in the Supplement),
particularly at intermediate depths (400–900 m). The CanESM terrestrial
carbon model is also undergoing important new developments (e.g. Asaadi and
Arora, 2021) and we expect CanESM to continue to offer a credible
contribution to global carbon cycle studies, as well as advance regional
downscaling and impact science.
Code availability
The full CanESM5 source code is publicly available at https://gitlab.com/cccma/canesm (last access: 19 May 2022); within this tree, the ocean biogeochemistry code can be found at https://gitlab.com/cccma/cannemo/-/tree/v5.0.3/nemo/CONFIG/CCC_CANCPL_ORCA1_LIM_CMOC (last access: 19 May 2022) or https://gitlab.com/cccma/cannemo/-/tree/v5.0.3/nemo/CONFIG/CCC_CANCPL_ORCA1_LIM_CANOE (last access: 19 May 2022). The version of the code which can be used to produce all the simulations submitted to CMIP6, and described in this paper, is tagged as v5.0.3 and has the associated DOI: 10.5281/zenodo.3251114 (Swart et al., 2019b).
Data availability
All simulations conducted for CMIP6, including those described in this
paper, are publicly available via the Earth System Grid Federation
(source_id = CanESM5 or CanESM5-CanOE). All observational
data and other CMIP6 model data used are publicly available.
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-15-4393-2022-supplement.
Author contributions
JRC, KLD, NS, and NCS were responsible for the formulation of the overall research goals and aims. JRC, HH, AMH, WGL, OGJR, AES, and
NCS were responsible for implementation and testing of the model code. JRC, WGL, OGJR, AES, and NCS carried out the experiments. JRC, HH, AMH, AES, NS, and NCS were responsible for creation of the
published work.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
CanESM has been customized to run on the ECCC high-performance computer,
and a significant fraction of the software infrastructure used to run the
model is specific to the individual machines and architecture. While we
publicly provide the code, we cannot provide any support for migrating the
model to different machines or architectures.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work was made possible by the combined efforts of the CCCMa model
development team and computing support team. We thank all of the data
contributors to and developers of the observational data products, the NASA
Ocean Color team, and all of the CMIP6 data contributors. The Python
packages mocsy by Jim Orr and SkillMetrics by Peter Rochford were invaluable
tools in the analysis. John Dunne, William Merryfield, Anh Pham, Andrew Ross,
and several anonymous reviewers made useful comments on earlier drafts.
Fiona Davidson helped with figure preparation. This paper is dedicated to
the memory of Fouad Majaess, who supported CCCMa supercomputer users for
many years and passed away suddenly in 2020.
Review statement
This paper was edited by Riccardo Farneti and reviewed by Anh Pham, John Dunne, and two anonymous referees.
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