GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-9-2357-2016Constraining the strength of the terrestrial CO2 fertilization effect
in the Canadian Earth system model version 4.2 (CanESM4.2)AroraVivek K.vivek.arora@ec.gc.caScinoccaJohn F.Canadian Centre for Climate Modelling and Analysis, Environment and Climate
Change Canada, University of Victoria, Victoria, B.C., V8W 2Y2, CanadaVivek K. Arora (vivek.arora@ec.gc.ca)7July2016972357237617November201515January201626May201629May2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://gmd.copernicus.org/articles/9/2357/2016/gmd-9-2357-2016.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/9/2357/2016/gmd-9-2357-2016.pdf
Earth system models (ESMs) explicitly simulate the interactions between the
physical climate system components and biogeochemical cycles. Physical and
biogeochemical aspects of ESMs are routinely compared against their
observation-based counterparts to assess model performance and to evaluate
how this performance is affected by ongoing model development. Here, we
assess the performance of version 4.2 of the Canadian Earth system model
against four land carbon-cycle-focused, observation-based determinants of
the global carbon cycle and the historical global carbon budget over the
1850–2005 period. Our objective is to constrain the strength of the
terrestrial CO2 fertilization effect, which is known to be the most
uncertain of all carbon-cycle feedbacks. The observation-based determinants
include (1) globally averaged atmospheric CO2 concentration,
(2) cumulative atmosphere–land CO2 flux, (3) atmosphere–land CO2
flux for the decades of 1960s, 1970s, 1980s, 1990s, and 2000s, and (4) the
amplitude of the globally averaged annual CO2 cycle and its increase
over the 1980 to 2005 period. The optimal simulation that satisfies
constraints imposed by the first three determinants yields a net primary productivity (NPP) increase from ∼ 58 Pg C year-1 in 1850 to
about ∼ 74 Pg C year-1 in 2005; an increase of ∼ 27 %
over the 1850–2005 period. The simulated loss in the global soil carbon
amount due to anthropogenic land use change (LUC) over the historical period is
also broadly consistent with empirical estimates. Yet, it remains possible
that these determinants of the global carbon cycle are insufficient to
adequately constrain the historical carbon budget, and consequently the
strength of terrestrial CO2 fertilization effect as it is represented in
the model, given the large uncertainty associated with LUC emissions over the
historical period.
Introduction
The evolution of the atmospheric CO2 concentration in response to
anthropogenic fossil fuel CO2 emissions is determined by the rate at
which a fraction of these emissions is taken up by the land and ocean. Had
the land and ocean not provided this “ecosystem service” since the start of
the industrial era, and not removed about 50 % of CO2 emissions from
the atmosphere (Knorr, 2009), the present concentration of CO2 in the
atmosphere would have been around 500 ppm, compared to its current value of
around 400 ppm. Over land, temperate and boreal forests as well as forests
in the tropical region are known to be sinks of atmospheric carbon (Ciais et
al., 2013; Gourdji et al., 2012; Schimel et al., 2015). The sink in the
tropical forests is, however, countered by anthropogenic land use change
emissions (Phillips and Lewis, 2014). Over ocean, the uptake of anthropogenic carbon is observed to be larger in the high latitudes than in
the tropical and subtropical regions (Khatiwala et al., 2009). The manner in
which the land and ocean will continue to provide this ecosystem service in
the future is of both scientific and policy relevance.
Future projections of atmospheric CO2 concentration, [CO2], in
response to continued anthropogenic CO2 emissions, or alternatively
projections of CO2 emissions compatible with a given future [CO2]
pathway, are based primarily on comprehensive Earth system models (ESMs),
which include interactive land and ocean carbon-cycle components (Jones et
al., 2013). The land and ocean carbon-cycle components in ESMs respond both
to increases in [CO2] as well as the associated changes in climate.
These carbon components also respond to changes in climate associated with
other forcings including changes in concentration of non-CO2 greenhouse
gases and aerosols, to nitrogen deposition, and over land to anthropogenic
land use change (LUC).
The response of land and ocean carbon-cycle components to changes in
[CO2] and the associated change in climate is most simply characterized
in the framework of the 140-year long 1 % per year increasing CO2
(1pctCO2) experiment, in which [CO2] increases at a rate of 1 % per
year from pre-industrial value of about 285 ppm until concentration
quadruples to about 1140 ppm. The 1pctCO2 experiment has been recognized as
a standard experiment by the coupled model intercomparison project (CMIP),
which serves to quantify the response of several climate and Earth system
metrics to increasing CO2. These metrics include the transient climate
response (TCR) and the transient climate response to cumulative emissions
(TCRE; Gillett et al., 2013). Arora et al. (2013) analysed results from
fully , biogeochemically and radiatively coupled versions of the 1pctCO2
experiment from eight ESMs that participated in the phase five of the CMIP
(CMIP5). They calculated the response of land and ocean carbon-cycle
components to changes in [CO2] and the associated change in climate
expressed in terms of carbon-concentration and carbon–climate feedbacks,
respectively. Arora et al. (2013) found that of all the carbon-cycle
feedbacks, the carbon-concentration feedback over land, which is primarily
determined by the strength of the terrestrial CO2 fertilization effect,
is the most uncertain across models. They found that while the uncertainty in
the carbon-concentration feedback over land (expressed in terms of the
standard deviation of the magnitude of the feedbacks) had somewhat reduced
since the first coupled carbon-cycle climate model intercomparison project
(C4MIP) (Friedlingstein et al., 2006), its uncertainty remained the
largest of all carbon-cycle feedbacks. The comparison of the actual
magnitudes of the carbon-cycle feedbacks over land is, however, not
straightforward between the Arora et al. (2013) and Friedlingstein et
al. (2006) studies because they used different CO2 scenarios.
The reason for this large uncertainty is that it is fairly difficult at
present to constrain the strength of the terrestrial CO2 fertilization
effect at the global scale. The net atmosphere–land CO2 flux since the
start of the industrial era has not only been influenced by the changes in
[CO2] but also the associated change in climate (due both to changes in
[CO2] and other climate forcers), nitrogen deposition, and more
importantly land use change – the contribution of which itself remains
highly uncertain. Since it is difficult to estimate the observed magnitude of
net atmosphere–land CO2 flux since the start of the industrial era,
attributable only to the increase in [CO2], it is consequently difficult to
estimate the strength of the terrestrial CO2 fertilization effect.
Measurements at Free-Air CO2 Enrichment (FACE) sites in which
vegetation is exposed to elevated levels of [CO2] help to assess some
aspects of CO2 fertilization and how nutrients constraints regulate
photosynthesis at elevated [CO2] (Medlyn et al., 1999; McGuire et al.,
1995). However, FACE results cannot be easily extrapolated to the global
scale and the response of vegetation corresponds to a step increase in
[CO2] not the gradual increase, which the real-world vegetation is
experiencing.
As part of the ongoing evaluation of carbon cycle in ESMs, the model
simulated aspects of the global carbon cycle are routinely evaluated against
their observation-based counterparts. These evaluations also provide the
opportunity to adjust physical processes that influence the strength of the
terrestrial CO2 fertilization effect to provide the best comparison with
observation-based aspects of the global carbon cycle. Here, we present
results from such an evaluation for a new version of the Canadian Earth
system model (CanESM4.2). An earlier version of the Canadian Earth system
model (CanESM2; Arora et al., 2011) participated in the CMIP5 (Taylor et al.,
2012) and its results also contributed to the fifth assessment report (AR5)
of the Intergovernmental Panel on Climate Change (IPCC). We evaluate the
response of CanESM4.2, for three different strengths of the terrestrial
CO2 fertilization effect, against four observation-based determinants of
the global carbon cycle and the historical global carbon budget over the
1850–2005 period, with a focus on the land carbon-cycle component. These
determinants include (1) globally averaged atmospheric CO2
concentration, (2) cumulative atmosphere–land CO2 flux,
(3) atmosphere–land CO2 flux for the decades of 1960s, 1970s, 1980s,
1990s, and 2000s, and (4) the amplitude of the globally averaged annual
CO2 cycle and its increase over the 1980 to 2005 period.
The strength of the CO2 fertilization effect influences all four of
these determinants of the global carbon cycle and the historical carbon
budget. A stronger CO2 fertilization effect, of course, implies a larger
carbon uptake by land and consequently a lower rate of increase of [CO2]
in response to anthropogenic fossil fuel emissions. However, the strength of
the CO2 fertilization effect also influences the amplitude of the annual
[CO2] cycle, which is primarily controlled by the Northern Hemisphere's
biospheric activity. The amplitude of the annual [CO2] cycle has been
observed to increase over the past 5 decades, suggesting a gradual increase
in photosynthesis in association with a strengthening of the CO2
fertilization effect (Keeling et al., 1996; Randerson et al., 1997) and thus
possibly can help to constrain the strength of the terrestrial CO2
fertilization effect in Earth system models.
The coupled climate-carbon system and CanESM4.2The coupled climate-carbon system
The globally averaged and vertically integrated carbon budget for the
combined atmosphere–land–ocean system may be written as
dHGdt=dHAdt+dHLdt+dHOdt=EF,
where the global carbon pool HG=HA+HL+HO is the sum of carbon in the atmosphere, land, and ocean components,
respectively (Pg C), and EF is the rate of anthropogenic CO2
emissions (Pg C year-1) into the atmosphere. The equations for the
atmosphere, land, and ocean components are written as
dHAdt=FA+EF=-FL-FO+EF=-(Fl-EL)-FO+EF=-Fl-FO+EF+ELdHLdt=FL=Fl-ELdHOdt=FO,
where (FL+FO)=-FA are the fluxes
(Pg C year-1)
between the atmosphere and the underlying land and ocean, taken to be
positive into the components. The net atmosphere–land CO2 flux
FL=Fl-EL is composed of LUC emission rate
EL (Pg C year-1) as well as the remaining global
“natural” CO2 flux Fl that is often referred to as the
residual or missing land sink in the context of the historical carbon budget
(Le Quéré et al., 2015). The emissions associated with LUC occur when
natural vegetation, for example, is deforested and replaced by croplands
resulting in net loss of carbon from land to the atmosphere (i.e. positive
EL). Conversely, when croplands are abandoned and gradually
replaced by forests then carbon is gained from atmosphere into the land (i.e.
negative EL).
Over land, the rate of change of carbon is reflected in the model's three
land pools (vegetation, V; soil, S; and litter or detritus, D)dHLdt=FL=Fl-EL=dHVdt+dHSdt+dHDdt=(G-RA)-RH-EL=N-RH-EL,
where G is the gross primary productivity (Pg C year-1), which
represents the rate of carbon uptake by vegetation through photosynthesis,
and RA and RH are the autotrophic and heterotrophic
respiratory fluxes (Pg C year-1) from living vegetation and dead
litter and soil carbon pools, respectively. N=G-RA is the net
primary productivity (NPP), which represents the carbon uptake by vegetation
after autotrophic respiratory costs have been taken into account. The
heterotrophic respiration RH=RH,D+RH,S is
composed of respiration from the litter and soil carbon pools. The rate of
change in carbon in model's litter (HD) and soil (HS)
pools is written as
dHDdt=DL+DS+DR-CD→S-RH,DdHSdt=CD→S-RH,S,
where Di,i=L,S,R is the litter fall from the model's leaf, stem
and root components into the model's litter pool. CD→S
is the transfer of humidified litter into the soil carbon pool calculated as
a fraction of the litter respiration (RH,D)CD→S=χRH,D
and χ is the humification factor.
Integrating Eqs. (2) and (3) in time with ∫t0t(dH/dt)dt=H(t)-H(t0)=ΔH(t) and ∫t0tFdt=F̃(t) (Pg C) gives
ΔHA=-F̃O+F̃l+ẼF+ẼLΔHO=F̃OΔHL=F̃L=F̃l-ẼL;=ΔHV+ΔHS+ΔHD=F̃l-ẼL=Ñ-R̃H-ẼLΔHl=F̃lΔH=ẼF.
The cumulative change in the atmosphere, the ocean and the land carbon pools
is written as
ΔHA+ΔHO+ΔHl-ẼL=ẼFΔHA+ΔHO+ΔHl=ẼF+ẼL=Ẽ,
where Ẽ (Pg C) is the cumulative sum of the anthropogenic
emissions from fossil fuel consumption and land use change. When emissions
associated with LUC are zero, Eq. (7) becomes
ΔHA+ΔHO+ΔHL=ẼF=Ẽ,
which indicates how cumulative emissions are parsed into changes in
atmospheric carbon burden and carbon uptake by the ocean and land components.
Canadian Earth system model version 4.2Physical components
At the Canadian Centre for Climate Modelling and Analysis (CCCma), the Earth system model, CanESM2, has undergone further development since its use for
CMIP5. This version of the model has been equivalently labelled CanESM4.0 in
an effort to rationalize the ESM naming convention to better reflect the fact
that this model version employs the fourth generation atmosphere component,
CanAM4, (Von Salzen et al., 2013) and the fourth generation ocean component,
CanOM4 (Arora et al., 2011). The version of the CCCma Earth system model used
for this study is CanESM4.2 and therefore represents two full cycles of model
development on all of its components. Similar to CanESM2, the physical ocean
component of CanESM4.2 (CanOM4.2) has 40 levels with approximately 10 m
resolution in the upper ocean while the horizontal ocean resolution is
approximately 1.41∘ (longitude) × 0.94∘ (latitude).
The majority of development in CanESM4.2, relative to CanESM2, has occurred
on its atmospheric component CanAM4.2. CanAM4.2 is a spectral model employing
T63 triangular truncation with physical tendencies calculated on a
128 × 64 (∼ 2.81∘) horizontal linear grid with
49 layers in the vertical, whose thicknesses increase monotonically with
height to 1 hPa. Relative to CanAM4, CanAM4.2 includes a new version of the
Canadian Land Surface Scheme, CLASS3.6, which models the energy and water
fluxes at the atmosphere–land boundary by tracking energy and water through
the soil, snow, and vegetation canopy components (Verseghy, 2012). CLASS
models the land surface energy and water balance and calculates liquid and
frozen soil moisture, and soil temperature for three soil layers (with
thicknesses 0.1, 0.25 and 3.75 m). The thickness of the third layer depends
on the depth to bedrock (and is in many places less than 3.75 m) based on
the Zobler (1986) soil data set. Changes to CLASS primarily include
improvements to the simulation of snow at the land surface. These incorporate
new formulations for vegetation interception of snow (Bartlett et al., 2006),
for unloading of snow from vegetation (Hedstrom and Pomeroy, 1998), for the
albedo of snow-covered canopies (Bartlett and Verseghy, 2015), for limiting
snow density as a function of depth (Tabler et al., 1990; Brown et al.,
2006), and for the thermal conductivity of snow (Sturm et al., 1997). Water
retention in snowpacks has also been incorporated. CanAM4.2 also includes an
aerosol microphysics scheme (von Salzen, 2006; Ma et al., 2008; Peng et al.,
2012), a higher vertical resolution in the upper troposphere, a reduced solar
constant (1361 W m-2) and an improved treatment of the solar continuum
used in the radiative transfer. CanAM4.2 also considers natural and
anthropogenic aerosols and their emissions, transport, gas-phase and
aqueous-phase chemistry, and dry and wet deposition as summarized in Namazi
et al. (2015)
Land and ocean carbon-cycle components
The ocean and land carbon-cycle components of CanESM4.2, are similar to
CanESM2, and represented by the Canadian Model of Ocean Carbon (CMOC)
(Christian et al., 2010) and the Canadian Terrestrial Ecosystem Model (CTEM)
(Arora et al., 2009; Arora and Boer, 2010), respectively.
LUC emissions in CTEM are modelled interactively on the basis of changes in
land cover, which are determined by changes in crop area. The historical land
cover used in the simulations presented here is reconstructed using the
linear approach of Arora and Boer (2010) and is the same as that used for CMIP5
simulations; as the fraction of crop area in a grid cell changes, the
fraction of non-crop plant functional types (PFTs) is adjusted linearly in
proportion to their existing coverage. The historical changes in crop area
are based on the data set provided for CMIP5 simulations as explained in
Arora and Boer (2014). When the fraction of crop area in a grid cell
increases then the fractional coverage of other PFTs is reduced, which results
in deforested biomass. The deforested biomass is allocated to three
components that are (i) burnt instantaneously and contribute to
(ii) short-term
(paper) and (iii) long-term (wood products) pools (Arora and Boer, 2010). The
deforested biomass corresponding to paper and wood products is transferred to
model's litter and soil carbon pools, respectively. When the fraction of crop
area decreases, the fractional coverage of non-crop PFTs increases and their
vegetation biomass is spread over a larger area reducing vegetation density.
Carbon is sequestered until a new equilibrium is reached providing a carbon
sink associated with regrowth as the abandoned areas revert back to natural
vegetation.
The LUC emissions term (EL) in the Eqs. (1) through (8) is not
easily defined or calculated. Pongratz et al. (2014) discuss the multiple
definitions and methods of calculating EL. When EL is
calculated using models, it is most usually defined as the difference in
FL between simulations with and without LUC. This is also the
basic definition used by Pongratz et al. (2014). Calculating EL
thus requires performing additional simulations without land use change in
which land cover is held constant at its pre-industrial state. For a
simulation without LUC, Eq. (3) becomes
dHL′dt=FL′=Fl′
and an estimate of EL, and its cumulative values
ẼL, is obtained as
EL=FL′-FLẼL=F̃L′-F̃L.
Over the historical period, globally, FL′ is expected to be higher
than FL (both considered positive downwards) due, at least, to two
processes: (1) fraction of deforested biomass that is burnt and which
contributes to short- and long-term product pools all release carbon to the
atmosphere, albeit at different timescales, (2) the area that is deforested
and put under agricultural use loses soil carbon and cannot sequester carbon
in response to increase [CO2] since crops are frequently harvested. As a
result EL is positive.
Relative to CanESM2, the version of CTEM employed in CanESM4.2, CTEM4.2,
includes changes to the humification factor (χ; see Eqs. 4 and 5), which
determines what fraction of the humidified litter is transferred from litter
(HD) to the soil carbon pool (HS). The value of χ
employed in CTEM4.2 has been changed for crop PFTs from 0.45 to 0.10, which
decreases the transfer of the humidified litter to the soil carbon pool. As a
result, a decrease in global soil carbon over the historical period is
obtained as natural vegetation is replaced by croplands as is seen in
empirical measurements (Wei et al., 2014). This change in humification factor
was required despite the higher litter decomposition rates over croplands and
is discussed in more detail later in the results section. In addition, in
CTEM4.2 the sensitivity of photosynthesis to soil moisture is reduced for
coupling to CLASS 3.6, especially for the broadleaf evergreen PFT (which
exists mainly in the tropics) to somewhat account for deep roots, for
example in the Amazonian region (e.g. see da Rocha et al., 2004).
CTEM has always included a parameterization of photosynthesis
down-regulation, which represents acclimatization to elevated CO2 in the
form of a decline in maximum photosynthetic rate. In the absence of explicit
coupling of terrestrial carbon and nitrogen cycles this parameterization
yields a mechanism to reduce photosynthesis rates as [CO2] increases.
The photosynthesis down-regulation parameterization is described in detail in
Arora et al. (2009) and is based on earlier simpler models, which expressed
net or gross primary productivity (NPP or GPP) as a logarithmic function of
atmospheric CO2 concentration (e.g. Cao et al., 2001; Alexandrov and
Oikawa, 2002).
G(t)=G01+γplnC(t)C0,
where GPP at any given time, G(t), is a function of its initial value
G0, atmospheric CO2 concentration at time t, C(t), and its
initial value C0. The rate of increase of GPP is determined by the
parameter γp (where p indicates the “potential” rate of
increase of GPP with CO2). The ratio of GPP in two different versions of
a model in which GPP increases at different rates (γp and
γd) is given by
ξ(C)=1+γdlnC/C01+γplnC/C0,
where t is omitted for clarity. When γd<γp,
the modelled potential gross photosynthesis rate (Gp), which is not
constrained by nutrient limitation, can be multiplied by the scalar ξ(C)
(Eq. 12), which yields the gross primary productivity (G) used in Eq. (3)
that now increases in response to CO2 increases at a rate determined by
the value of γd (the subscript d indicates
down-regulation).
G=ξ(C)Gp.
A lower value of γd than γp yields a value of
ξ(C) that is less than one. As the concentration of CO2, expressed
as C in Eq. (12), increases above its pre-industrial level C0
(285 ppm), ξ(C) progressively decreases resulting in a gross primary
productivity G, which is less than its potential value Gp.
Figure 1 shows the behaviour of ξ(C) for γp=0.95 and
three values of γd (0.25, 0.4, and 0.55) corresponding to
three different strengths of the terrestrial CO2 fertilization effect. A
value of γd=0.25 was used for CanESM2 to best simulate the
globally averaged surface CO2 concentration and cumulative 1850–2005
atmosphere–land CO2 flux. CanESM2, however, was not as rigourously
evaluated as we have attempted here for CanESM4.2. Through the parameter
γd, the physical process of down-regulation has a direct
influence on the strength of the terrestrial CO2 fertilization effect.
In practice, different combinations of γd and
γp are able to yield very similar values of ξ(C). Arora
et al. (2009) calculated the value of γd based on results
from six studies, two of which were meta-analyses each based on 15 and 77
individual studies, which grow plants in ambient and elevated CO2
environments. Their results are equivalent to γd=0.46 with a
range from 0.22 to 0.63 for γp=0.95.
The behaviour of terrestrial photosynthesis down-regulation scalar
ξ(C) (Eq. 12) for γp=0.95 and values of
γd equal to 0.25, 0.4 and 0.55 that are used in CanESM4.2
simulations.
In Fig. 1, while ξ(C) decreases with an increase in atmospheric
CO2, indicating progressive decline in photosynthesis due to nutrient
limitation, the slope dξdC also decreases.
Although a second-order effect, this is a limitation of the current
formulation of ξ(C). A decreasing ξ(C) as CO2 increases can
eventually also lead to a decrease in GPP although we have not seen this
behaviour up to CO2 concentration of around 1000 ppm in simulations
performed with CanESM2 (see Arora and Boer, 2014). Whereas γd
is used to model down-regulation of photosynthesis it may also be used as a
measure of the strength of the CO2 fertilization effect. Lower values of
γd indicate higher down-regulation (see Fig. 1) so higher
values of γd imply higher strength of the CO2
fertilization effect. Finally, γd is specific to CTEM and as
such the value of this parameter is irrelevant to other models. More relevant
for comparison with other models is the simulated rate of increase of NPP
over the historical period that a given value of γd yields.
Treatment of CO2 in the atmosphere
The land and ocean components of the carbon cycle in CanESM4.2 are operable
for two experimental designs – (1) an emissions-driven mode, where the
atmospheric CO2 concentration is a freely evolving three-dimensional (3-D) tracer in the
model and (2) a concentration-driven mode, where the atmospheric CO2
concentration is prescribed externally.
In the emissions-driven mode the anthropogenic CO2 emissions
(EF) are specified and since the interactive land and ocean carbon-cycle components simulate the FL and FO terms,
respectively, the model is able to simulate the evolution of [CO2]
through the HA term, which represents the atmospheric carbon
burden, in Eq. (2). This is referred to as the interactively simulated
[CO2], or “free-CO2” configuration. In this case, the model simulates
the transport of CO2 in the atmosphere producing 3-D structure, an
annual cycle, and inter-annual variability.
Components of the carbon budget Eq. (8) that make up
cumulative diagnosed emissions based on results from the fully coupled
1pctCO2 experiment. Results shown are from eight CMIP5 models that
participated in the Arora et al. (2013) study and from three CanESM4.2
simulations (shown in darker colours) for three different strengths of the
terrestrial CO2 fertilization effect.
In the concentration-driven mode, the land and ocean CO2 fluxes,
FL and FO, remain interactively determined so model
results can be used to diagnose the EF term (based on Eq. 2) that
is compatible with a given [CO2] pathway at the global scale. The
concentration-driven mode can be executed in two CanESM4.2 configurations.
In the first configuration, a single scalar value of [CO2], which may be
time evolving, is imposed at all geographical and vertical locations in the
model. This follows the CMIP5 prescription for concentration-driven
simulations and we refer to it here as “specified-CO2”
concentration-driven mode. In the second configuration, a new approach for
specifying CO2 concentration has been implemented in CanESM4.2. In this
new approach, only the globally averaged concentration of CO2 in the
lowest model level is constrained by the prescribed value. The geographical
and vertical distribution of CO2 in the atmosphere and its annual cycle
in this second configuration is otherwise free to evolve in the same manner
as in the emissions-driven, free-CO2, configuration. A relaxation timescale
of 1 day is employed in this new configuration and a fixed annual cycle,
derived from the free-CO2 pre-industrial control simulation, is imposed on the
reference value of [CO2]. The reference value of [CO2] may
additionally be specified as time evolving. We refer to this configuration as
the “relaxed-CO2” concentration-driven mode. Aside from the
relaxational
constraint on the global-mean surface value of [CO2], the atmospheric
configuration for relaxed-CO2 is identical to that for free-CO2 with zero
emissions. As a consequence, the relaxed-CO2 configuration allows for the same
non-linearlity in the atmosphere–surface exchange of CO2 as the free-CO2 configuration leading to nearly identical spatial distribution and
seasonal cycle of atmosphere CO2 concentrations. In this regard, the
relaxed-CO2 configuration is physically more realistic than the specified-CO2
configuration.
There are practical advantages to using the relaxed-CO2 configuration over
the specified-CO2 configuration for concentration-driven simulations. When
spinning up land and ocean carbon pools in a pre-industrial control
simulation, the model is executed in concentration-driven mode to bring
these pools into equilibrium with a prescribed CO2 concentration. In
earlier versions of the CanESM, a specified-CO2 configuration was used for
this purpose. Beginning with version 4.1, the relaxed-CO2 configuration is
used for this purpose because it produces little or no drift when used to
initialize the free-CO2 pre-industrial control simulations. In fact, a
relaxed-CO2 pre-industrial control simulation may be used as the control
simulation for both emissions-driven and (relaxed-CO2) concentration-driven
experiments. This is not the case when the specified-CO2 is used as the
configuration for concentration-driven experiments.
Summary of simulations performed for this study and the forcings
used.
Simulation1pctCO2esmhistoricalesmhistorical_nolucSimulation details1 % per year increasing CO2 simulation1850–2005 historical simulation based on CMIP5 protocol1850–2005 historical simulation based on CMIP5 protocol, but with no anthropogenic land use changePurposeTo allow for comparison of CanESM4.2 with CMIP5 models especially in terms of its land carbon uptakeTo compare simulated aspects of the global carbon cycle and historical carbon budget with observation-based estimatesTo diagnose LUC emissions by differencing atmosphere–land CO2 flux between historical simulations with and without LUC.Length140 years156 years CO2 forcing285 ppm at the start of the simulation and 1140 ppm after 140 years.Historical CO2 forcing Land cover forcingLand cover corresponds to its 1850 stateLand cover evolution is based on increase in crop area over the historical periodLand cover corresponds to its 1850 stateNon-CO2 greenhousegases (GHGs) forcingConcentration of non-CO2 GHGs is specified at their 1850 levels.Concentration of non-CO2 GHGs is specified and evolves over the historical period based on the CMIP5 protocol Aerosols forcingEmissions of aerosols and their precursors are specified at their 1850 levels.Emissions of aerosols and their precursors are specified and evolve over the historical period based on the CMIP5 protocol
CanESM2 (panel a) and CanESM4.2 (panel b; γd=0.40)
precipitation anomalies compared to the observation-based estimates from CPC
Merged Analysis of Precipitation (CMAP) based on Xie and Arkin (1997)
averaged over the 1979–1998 period.
Experimental set-up
Three different kinds of experiments are performed for this study. The first
is the standard 1 % per year increasing CO2 experiment (1pctCO2)
performed for three different strengths of the terrestrial CO2
fertilization effect. The 1pctCO2 is a concentration-driven experiment and we
use the relaxed-CO2 configuration to specify CO2 in the atmosphere.
The second experiment is the CMIP5 1850–2005 historical experiment, referred
to as “esmhistorical” following CMIP5 terminology, which is performed with
specified anthropogenic CO2 emissions (i.e. in emissions-driven, or
free-CO2, mode), where [CO2] is simulated interactively.
Concentrations of non-CO2 greenhouse gases and emissions of aerosols and
their precursors are specified in the esmhistorical experiment following the
CMIP5 protocol. The third experiment is same as the esmhistorical experiment
but LUC is not permitted and the land cover remains at its 1850 value;
referred to as the esmhistorical_noluc experiment. Two ensemble members
are performed for each of the three versions of the esmhistorical and
esmhistorical_noluc experiments corresponding to three different strengths
of the terrestrial CO2 fertilization effect. The rationale for
performing historical simulations without LUC is to be able to quantify LUC
emissions EL using Eq. (10). Table 1 summarizes all the
simulations performed.
The 1pctCO2 simulations with relaxed CO2 for three different
strengths of the terrestrial CO2 fertilization effect are initialized
from a corresponding pre-industrial control simulation with CO2
specified at ∼ 285 ppm and all other forcings at their 1850 values.
The esmhistorical and esmhistorical_noluc simulations are initialized from
a pre-industrial control simulation with free CO2 and zero
anthropogenic CO2 emissions.
Results1 % per year increasing CO2 experiments
Figure 2 shows the carbon budget components of Eq. (8); ΔHA,
ΔHO, and ΔHL, i.e. the change in atmospheric
carbon burden and cumulative atmosphere–ocean and atmosphere–land CO2
flux, which together make up the cumulative diagnosed emissions (Ẽ)
based on results from the fully coupled 1pctCO2 experiment. Results are shown
from eight CMIP5 models that participated in the Arora et al. (2013) study,
including CanESM2, which used γd=0.25, together with those
from CanESM4.2 for three different strengths of the terrestrial CO2
fertilization effect. The cumulative atmosphere–land CO2 flux across
models varies much more than the cumulative atmosphere–ocean CO2 flux
across the CMIP5 models as already noted in Arora et al. (2013). The results
for CanESM4.2 indicate that the influence of γd (Eq. 12) on
the strength of the model's terrestrial CO2 fertilization effect allows
CanESM4.2's cumulative diagnosed emissions to essentially span the range of
the other CMIP5 models. For the three different strengths of the terrestrial
CO2 fertilization effect, γd=0.25, 0.4, and 0.55, the
γd values of 0.4 and 0.55 yield cumulative atmosphere–land
CO2 flux that is higher than all the CMIP5 models. The basis for
choosing these values of γd within the range 0.4 ± 0.15
is that they span the observation-based estimates of various quantities
reasonably well as shown later.
Atmosphere–land CO2 flux (FL)(a) and its
cumulative values F̃L(b) from CanESM2 and the three
CanESM4.2 historical 1850–2005 simulations for different strengths of the
terrestrial CO2 fertilization effect. In (a) the observation-based
estimates of FL and their uncertainty (shown via boxes) for the
decades of 1960, 1970, 1980, 1990, and 2000 are reproduced from Le
Quéré et al. (2015). The bold lines in (a) are the 10-year
moving averages of the annual FL values, which are shown in light
colours. The results from CanESM2 and CanESM4.2 are the average of the two
ensemble members.
The cumulative atmosphere–land CO2 flux ΔHL for
CanESM4.2 for the simulation with γd=0.25 is higher than that
for CanESM2, which also uses γd=0.25, because of the changes
made to soil moisture sensitivity of photosynthesis and because ΔHL also depends on the model climate. In particular, the CanESM2
bias of low precipitation over the Amazonian region has been reduced in
CanESM4.2, as shown in Fig. 3. The increased precipitation over the Amazonian
region causes increased carbon uptake with increasing [CO2]. The
improved precipitation bias of CanESM4.2 in this region is in part caused by
the decreased sensitivity of photosynthesis to soil moisture in CTEM4.2,
especially for broadleaf evergreen PFT, which helps to increase
evapotranspiration and in turn increase precipitation over the region.
Change in and absolute values of global soil carbon and vegetation
biomass amounts from CanESM2 and the three CanESM4.2 historical 1850–2005
simulations with different strengths of the terrestrial CO2
fertilization effect. The results shown in all panels are the average of the
two ensemble members.
Historical simulations with LUC
The results presented in this section evaluate the model against four
observation-based determinants of the global carbon cycle and the historical
global carbon budget over the 1850–2005 period mentioned earlier. Simulated
atmosphere–ocean CO2 fluxes are also compared with observation-based
estimates although, of course, they are not directly affected by the strength
of the terrestrial CO2 fertilization effect.
Components of land carbon budget
In Fig. 4, time series of instantaneous (FL panel a) and cumulative
(F̃L panel b) atmosphere–land CO2 flux over the period
1850–2005 are displayed for CanESM2 (which contributed results to CMIP5) and
CanESM4.2 for the three different strengths of the terrestrial CO2
fertilization effect. The observation-based estimates of FL=(Fl-EL) in Fig. 4a for the decades of 1960, 1970, 1980,
1990, and 2000 are reproduced from Le Quéré et al. (2015), who derive
the FL=(Fl-EL) term as residual of the carbon
budget equation dHA/dt=-(Fl-EL)-FO+EF using observation-based estimates of change in
atmospheric carbon budget (dHA/dt), atmosphere–ocean
CO2 flux (FO) and fossil fuel emissions (EF). The
observation-based estimate of -11 ± 47 Pg C in Fig. 4b for
F̃L over the period 1850–2005 is from Arora et al. (2011)
(their Table 1).
The primary difference between CanESM2 and CanESM4.2 simulations in Fig. 4 is
that F̃L for CanESM2 generally stays positive throughout the
historical period, whereas for CanESM4.2 it first becomes negative
(indicating that land is losing carbon) and then becomes positive (indicating
that land is gaining carbon) towards the end of the 20th century, depending
on the strength of the CO2 fertilization effect. The behaviour of
F̃L for CanESM4.2 is considered to be more realistic. As
the land responds to anthropogenic land use change, associated with an
increase in crop area early in the historical period, it causes a decrease in
vegetation and soil carbon (see Fig. 5). Later in the 20th century, the
CO2 fertilization effect causes the land to become a sink for carbon
resulting in both vegetation and soil carbon increases. This behaviour is
consistent with the mean model response of the 15 CMIP5 models analysed by
Hoffman et al. (2013) (their Fig. 2b). In contrast, CanESM2 shows a gradual
increase in the global soil carbon amount (Fig. 5a) over the historical
period. In Fig. 5, it can be seen that the effect of CO2 fertilization
in the second half of the 20th century is delayed for soil carbon compared to
that for vegetation. This is primarily because of the lag introduced by the
turnover time of vegetation (i.e. increased NPP inputs have to go through
vegetation pool first) and the longer turnover timescale of the soil carbon
pool. The more reasonable response of soil carbon to anthropogenic land use
change, in Fig. 5a for CanESM4.2, is achieved by changing the humification
factor from 0.45 (in CanESM2) to 0.10 (in CanESM4.2) in Eq. (5), which yields
a reduction in global soil carbon amount in response to land use change up
until the time that the effect of CO2 fertilization starts to take
effect. In Fig. 4a, CanESM4.2 is also able to simulate continuously
increasing FL during the period 1960 to 2005, depending on the
strength of the CO2 fertilization effect, whereas CanESM2 simulates near
constant or decreasing FL from about 1990 onwards, as is also seen
in Fig. 4b for F̃L. This behaviour of FL is not
consistent with observation-based estimates from Le Quéré et
al. (2015), which show continued strengthening of the land carbon sink since
1960s.
Absolute values of (a) and change in (b) net primary
productivity (NPP) from CanESM2 and the three CanESM4.2 historical 1850–2005
simulations with different strengths of the terrestrial CO2
fertilization effect. The thin lines show the ensemble-mean based on results
from the two ensemble members and the bold lines are their 10-year moving
averages.
In Fig. 4a, amongst the three versions of the CanESM4.2, the simulation with
γd=0.4 (blue line) yields the best comparison with
observation-based estimates of FL from Le Quéré et
al. (2015), while the simulations with γd=0.25 (green line)
and γd=0.55 (red line) yield FL values that are lower
and higher, respectively, than observation-based estimates. In Fig. 4b, the
cumulative atmosphere–land CO2 flux F̃L over the
1850–2005 period from the simulations with γd=0.25 and 0.4
(green and blue lines, respectively) lies within the uncertainty of
observation-based estimates, while the simulation with γd=0.55 (red line) yields F̃L value that is high relative to
observation-based estimate.
Figure 6 shows the change in and absolute values of NPP from CanESM2 and the
simulations made with CanESM4.2 for three different strengths of the CO2
fertilization effect. Consistent with 1pctCO2 simulations, the rate of
increase of NPP in CanESM4.2 with γd=0.25 is higher than
that in CanESM2, which also uses γd=0.25. This is because the
underlying model climate is different in CanESM2 and CanESM4.2, as mentioned
earlier, and the fact that photosynthesis sensitivity to soil moisture has
also been reduced. The rates of increase of NPP for γd=0.40
and 0.50 are, of course, even higher. The CanESM4.2 simulation with γd=0.40, which yields the best comparison with observation-based
estimates of FL for the decade of 1960 through 2000 (Fig. 4a) as
well as F̃L for the period 1850–2005 (Fig. 4b), yields an
increase in NPP of ∼ 16 Pg C year-1 over the 1850–2005 period.
A caveat here is that part of this increase is also caused by increase in the
crop area over the historical period that is realized in the model regardless
of the strength of the CO2 fertilization effect. In CTEM, the maximum
photosynthetic capacity of crops is higher than for other PFTs to account for
the fact that agricultural areas are generally fertilized. As a result,
increase in crop area also increases global NPP. The increasing crop
productivity has been suggested to contribute to the increase in amplitude of
the annual [CO2] cycle since 1960s (Zeng et al., 2014). However, in the
absence of an explicit representation of terrestrial N cycle (and thus
fertilization of cropped areas) or a representation of increase in crop yield
per unit area due to genetic modifications, the only processes in CTEM that
contribute to changes in crop yield are the change in crop area itself and
the increase in crop NPP due to the CO2 fertilization effect.
Globally averaged [CO2]
Figure 7 shows the simulated globally averaged surface [CO2] from the
emissions-driven esmhistorical simulation of CanESM2 and that of CanESM4.2
for three different strengths of the CO2 fertilization effect. The
observation-based time series of [CO2] is illustrated by the heavy black
line. The CanESM2 (γd=0.25) simulation yields a reasonable
comparison with observation-based [CO2]. Amongst the versions of
CanESM4.2 with different strengths of the CO2 fertilization effect, the
version with γd=0.40 yield the best comparison. The CanESM4.2
version with γd=0.25 (weaker strength of the CO2
fertilization effect) and 0.55 (stronger CO2 fertilization effect) yield
CO2 concentrations that are respectively higher and lower than the
observational estimate from roughly mid-20th century onward. The reason
CanESM4.2 (γd=0.40) requires a stronger CO2
fertilization effect than CanESM2 (γd=0.25) for simulating
the observation-based increase in atmospheric CO2 burden over the
historical period is the enhanced impact of LUC in CanESM4.2 due to its
increased humification factor and the associated response of the global soil
carbon pool, as discussed in the previous section. The differences in
simulated [CO2] in Fig. 7 from CanESM4.2 are due only to differences in
the strength of the CO2 fertilization effect. Although, of course, since
in these simulations [CO2] is simulated interactively, the simulated
atmosphere–land flux FL and [CO2] both respond to and affect
each other.
Simulated globally averaged surface atmospheric CO2
concentration from CanESM2 and the three CanESM4.2 historical 1850–2005
simulations with different strengths of the terrestrial CO2
fertilization effect. The observation-based concentration is shown in black.
Also shown is the CO2 concentration of 284.6 ppm used in CanESM4.2's
pre-industrial simulation in the relaxed-CO2 configuration and the simulated
concentration from the pre-industrial CanESM4.2 simulation with
interactively determined CO2.
Atmosphere–ocean CO2 flux (FO)(a) and its
cumulative values F̃O(b) from CanESM2 and the three
CanESM4.2 historical 1850–2005 simulations for three different strengths of
the terrestrial CO2 fertilization effect. In (a) the
observation-based estimates of FO and their uncertainty (show via
boxes) for the decades of 1960, 1970, 1980, 1990, and 2000 are reproduced from
Le Quéré et al. (2015). The bold lines in (a) are the 10-year
moving averages of the annual FL values, which are shown in light
colours. The results from CanESM2 and CanESM4.2 are the average of the two
ensemble members.
Both CanESM2 and CanESM4.2 underpredict [CO2] relative to observational
estimates over the period 1850–1930, and are also unable to reproduce the
near-zero rate of increase of [CO2] around 1940. Possible reasons for
these discrepancies include (1) the possibility that the carbon cycle before 1850
was not in true equilibrium and this aspect cannot be captured since the
model is spun up to equilibrium for 1850 conditions, (2) the uncertainties
associated with anthropogenic emissions for the late 19th and early 20th
century that are used to drive the model, and (3) the uncertainties
associated with pre-Mauna Loa [CO2] observations.
Atmosphere–ocean CO2 flux
Figure 8a and b, respectively, show time series of instantaneous
(FO) and cumulative (F̃O) atmosphere–ocean
CO2 fluxes over the period 1850–2005 for the set of emissions-driven
simulations presented in Fig. 7. The strength of the terrestrial CO2
fertilization effect has little or no impact on the ocean biogeochemical
processes. The differences in values of FO and F̃O for the three versions CanESM4.2 are, therefore, primarily due to the
differences in [CO2]. The observation-based estimates of FO
in Fig. 8a for the decades of 1960, 1970, 1980, 1990, and 2000 are from
Le Quéré et al. (2015). The observation-based estimate of
F̃O of 141 ± 27 Pg C in Fig. 8b for the period
1850–2005 is from Arora et al. (2011) (their Table 1).
Both CanESM2 and the CanESM4.2 simulation for γd=0.40 (which
provides the best comparison with observation-based estimate for [CO2];
blue line in Fig. 7) yield lower F̃O compared to
observation-based values. The FO value from CanESM2 and the
CanESM4.2 simulation for γd=0.40 are lower than the mean
estimates from Le Quéré et al. (2015) for the decades of 1960s
through 2000s, although still within their uncertainty range. The family of
ESMs from CCCma, all of which have the same physical ocean model, including
CanESM1 (Arora et al., 2009), CanESM2 (Arora et al., 2011) and now CanESM4.2,
yield lower than observed ocean carbon uptake over the historical period.
Recent analyses of these model versions suggest that the primary reason for
their low carbon uptake is a negative bias in near-surface wind speeds over
the Southern Ocean and an iron limitation in the same region, which is too
strong (personal communication, Neil Swart, Canadian Centre for Climate
Modelling and Analysis). The CanESM4.2 simulation with γd=0.25 (green line in Fig. 8) yields a better comparison with
observation-based estimates of FO and F̃O but
that is because of the higher simulated [CO2] in that simulation
associated with lower carbon uptake by land.
Amplitude of the annual CO2 cycle
The annual CO2 cycle is influenced strongly by the terrestrial
biospheric activity of the Northern Hemisphere (Keeling et al., 1996;
Randerson et al., 1997). Higher than normal biospheric uptake of carbon
during a Northern Hemisphere's growing season, for example, will yield lower
than normal [CO2] by the end of the growing season, around September
when [CO2] is at its lowest level (see Fig. 9a). Similarly, during the
Northern Hemisphere's dormant season, increased respiration from live
vegetation and decomposition of dead carbon, including leaf litter, that may
be associated with increased carbon uptake during the last growing season,
will yield higher than normal [CO2] during April when [CO2] is at
its highest level. Both processes increase the amplitude of the annual
[CO2] cycle. Given this strong control, the rate of change of the
amplitude of the annual [CO2] cycle can potentially help to constrain
the strength of the terrestrial CO2 fertilization effect.
Figure 9a compares the annual cycle of the trend-adjusted globally averaged
near-surface monthly [CO2] anomalies from CanESM2 and the versions of
CanESM4.2 for three different strengths of the CO2 fertilization effect
with observation-based estimates for the 1991–2000 period. Figure 9b shows
the time series of the amplitude of the annual cycle of the trend-adjusted
globally averaged near-surface monthly [CO2] anomalies (referred to as
ΦCO2) from CanESM2 and CanEM4.2, as well as observation-based
estimates going back to 1980s. While CO2 measurements at Mauna Loa
started in 1959, observation-based globally averaged near-surface [CO2]
values are only available since 1980s
(ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_gl.txt). In
Fig. 9b, consistent with the strengthening of the CO2 fertilization
effect, associated with the increase in [CO2], the observation-based
estimate of ΦCO2 shows an increase from 1980s to the
present. Both CanESM2 and versions of CanESM4.2 also show an increase in the
amplitude of ΦCO2 over the period 1850–2005. However, the
absolute values of ΦCO2 are lower in CanESM2 than in
CanESM4.2 (Fig. 9b). Of course, in the absence of an observation-based
estimate of pre-industrial value of ΦCO2 it is difficult to
say which value is more correct. However, when considering the present-day
values of ΦCO2, the three versions of CanESM4.2 yield better
comparison with observation-based estimate as also shown in Fig. 9a. The
increase in the value of ΦCO2 from CanESM2 to CanESM4.2,
which now yields better comparison with observation-based value of ΦCO2, is most likely caused by the change in the land surface
scheme from CLASS 2.7 (that is implemented in CanESM2) to CLASS 3.6
(implemented in CanESM4.2), since the atmospheric component of the model
has not changed substantially. It is, however, difficult to attribute the
cause of this improvement in the present-day value of ΦCO2
in CanESM4.2 to a particular aspect of the new version of the land surface
scheme. The annual [CO2] cycle is driven primarily by the response of
the terrestrial biosphere to the annual cycle of temperature and the
associated greening of the biosphere every summer in the Northern Hemisphere.
However, the simulated amplitude of the annual cycle of near-surface
temperature has not changed substantially from CanESM2 to CanESM4.2 (not
shown).
The annual cycle of trend-adjusted globally averaged near-surface
monthly [CO2] anomalies from CanESM2, the versions of CanESM4.2 for
three different strengths of the CO2 fertilization effect and
observation-based estimates for the 1991–2000 period (a).
Panel (b) shows the time series of the amplitude of the annual cycle of the trend-adjusted globally averaged near-surface monthly [CO2] anomalies for
corresponding model and observation-based estimates. The bold lines are
10-year moving averages and the thin lines for model results are the average
of results from two ensemble members.
In Fig. 9b, the simulated values of ΦCO2 for the CanESM4.2
simulations with γd=0.25, 0.40, and 0.55 are 4.41, 4.69, and
4.85 ppm, respectively, averaged over the period 1991–2000, compared to
observation-based value of ΦCO2 of 4.36 ppm. Here,
CanESM4.2 simulation with γd=0.25 yields the best comparison
with observation-based value of ΦCO2. An increase in the
strength of the CO2 fertilization effect increases the amplitude of the
annual [CO2] cycle so a larger value of γd yields a
larger value of ΦCO2. The increase in the amplitude of the
annual [CO2] cycle comes both from lower [CO2] at the end of the
growing season in September as well as higher [CO2] at the start of the
Northern Hemisphere's growing season in April (see Fig. 9a), as mentioned
earlier in this section.
Comparison of CanESM4.2 simulations with and without
implementation of anthropogenic land use change over the historical period
for three different strengths of the terrestrial CO2 fertilization
effect: (a) globally averaged annual surface atmospheric CO2 concentration,
(b) net primary productivity, (c) global vegetation biomass, and (c) global
soil carbon mass. All lines are the average of results from two ensemble
members. Additionally, in (b) the bold lines are the 10-year moving
averages.
More important than the absolute value of ΦCO2 is its rate
of increase over time, which is a measure of the strength of the terrestrial
CO2 fertilization effect. Figure 9b also shows the trend in ΦCO2 over the 1980–2005 overlapping period for which for both
the model and observation-based estimates of ΦCO2 are
available. The magnitude of trend for observation-based estimate of
ΦCO2 is 0.142 ± 0.08 ppm 10-years-1 (mean ± standard deviation,
x¯±σx), implying that over the 26-year 1980–2005 period
the amplitude of annual [CO2] cycle has increased by
0.37 ± 0.21 ppm. The calculated mean and standard deviation of the
observation-based trend, however, does not take into account the uncertainty
associated with the observation-based estimates of [CO2], consideration
of which will increase the calculated standard deviation even more. The
magnitudes of trend in ΦCO2 simulated by CanESM2
(γd=0.25) and CanESM4.2 (for γd=0.25) are
0.103 ± 0.05 and 0.153 ± 0.031, respectively, and statistically
not different from the trend in the observation-based value of
ΦCO2 implying an increase of 0.27 ± 0.13 and
0.40 ± 0.08 ppm, respectively, in ΦCO2 over the
1980–2005 period. The statistical difference is calculated on the basis of
x¯±1.385σx range, which corresponds to 83.4 %
confidence intervals; the estimates from two sources are statistically not
different at the 95 % confidence level if this range overlaps (Knol et
al., 2011). The magnitudes of the trend in ΦCO2 over the
1980–2005 period for CanESM4.2 simulations with γd=0.4 and
0.55 (0.328 ± 0.038 and 0.314 ± 0.034 ppm 10-years-1,
respectively) are, however, more than twice, as well as statistically different
from,
the observation-based estimate (0.142 ± 0.08 ppm 10-years-1).
Overall, the CanESM4.2 simulation with γd=0.25 yields the
amplitude of the globally average annual CO2 cycle and its rate of
increase over the 1980–2005 period that compares best with observation-based
estimates.
Comparison of simulated cumulative atmosphere–land CO2 flux
from CanESM4.2 simulations with and without implementation of anthropogenic
land use change over the historical period for three different strengths of
the terrestrial CO2 fertilization (a). Panel (b) shows the
cumulative diagnosed LUC emissions calculated using Eq. (10) as the
difference between cumulative atmosphere–land CO2 flux from simulations
with and without LUC shown in (a). All lines are the average of
results from two ensemble members.
Historical simulations without LUC
Figures 10 and 11 show results from CanESM4.2 emissions-driven simulations
for three different strengths of the CO2 fertilization effect that do
not implement anthropogenic LUC over the historical period and compare them
to their corresponding simulations with LUC.
Figure 10a compares the simulated [CO2]; as expected in the absence of
anthropogenic LUC the simulated [CO2] is lower since LUC emissions do
not contribute to increase in [CO2]. The difference in [CO2] at the
end of the simulation, in year 2005, between simulations with and without LUC
is 29.0, 23.6, and 19.0 ppm for γd=0.25, 0.40, and 0.55. The
simulations with the lowest strength of the CO2 fertilization effect
(γd=0.25) yield the largest difference because these
simulations also have the largest [CO2] amongst their set of simulations
with and without LUC. The CO2 fertilization of the terrestrial biosphere
implies that the effect of deforestation will be higher, because of reduced
carbon uptake by deforested vegetation, if background [CO2] is higher.
Figure 10b compares the simulated NPP from CanESM4.2 simulations with and
without LUC. The increase in simulated NPP, regardless of the strength of the
CO2 fertilization effect, is lower over the historical period in
simulations without LUC for two apparent reasons. First, the rate of increase
of [CO2] is itself lower and second, in the absence of LUC, there is no
contribution from increasing crop area to NPP. Overall, the increase in NPP
over the 1850–2005 period in simulations with LUC is a little more than
twice that in simulations without LUC. Figure 10c and d compare the changes
in global vegetation biomass and soil carbon mass, over the historical
period, from simulations with and without LUC. As expected, in the absence of
LUC, global vegetation biomass, and soil carbon mass more or less show a
continuous increase, associated with the increase in NPP, which itself is due
to the increase in [CO2]. Consequently, in Fig. 11a, the cumulative
atmosphere–land CO2 flux F̃L in simulations without
LUC also shows a more or less continuous increase over the historical period.
Finally, Fig. 11b shows the diagnosed cumulative LUC emissions
ẼL calculated as the difference between cumulative
F̃L, following Eq. (10), from simulations with and without
LUC. The diagnosed ẼL in this manner are equal to 95, 81, and
67 Pg C, over the 1850–2005 period, for γd=0.25, 0.40, and
0.55. The calculated diagnosed ẼL are highest for γd=0.25 associated with the highest background simulated [CO2]
in these simulations, as mentioned earlier. For comparison, LUC emissions
estimated by Houghton (2008) for the period 1850–2005, based on a
bookkeeping approach, are 156 Pg C but these estimates are generally
believed to be ±50 % uncertain (see Fig. 1 of Ramankutty et al.,
2007). LUC emissions, when calculated by differencing FL from
simulations with and without LUC, also depend on the type of simulations
performed – in particular, if simulations are driven with specified CO2
concentrations or specified CO2 emissions. Had our simulations been
concentration driven, in contrast to being emissions driven, then both with
and without LUC simulations would have experienced the same specified
observed CO2 concentration over the historical period and the simulated
LUC emissions would have been higher. Arora and Boer (2010) found that
diagnosed LUC emissions in the first version of the Canadian Earth system model (CanESM1) increased from 71 Pg C (for emissions-driven simulations)
to 124 Pg C (for concentration-driven simulations). Concentration-driven
simulations, however, cannot be evaluated against observation-based amplitude
of the annual CO2 cycle and its increase over the historical period.
These simulations either ignore the annual cycle of CO2 (our
specified-CO2 case) or use a specified amplitude of the CO2 annual cycle
(our relaxed-CO2 case).
Discussion and conclusions
This study evaluates the ability of four observation-based determinants of
the global carbon cycle and the historical carbon budget to constrain the
parameterization of photosynthesis down-regulation, which directly determines
the strength of the CO2 fertilization effect, over the historical period
1850–2005. The key parameter that controls the strength of the CO2
fertilization effect in CTEM, γd, was varied in the latest
version of CCCma's Earth system model CanESM4.2. Comparing simulated and
observation-based estimates of (1) globally averaged atmospheric CO2
concentration, (2) cumulative atmosphere–land CO2 flux, and
(3) atmosphere–land CO2 flux for the decades of 1960s, 1970s, 1980s,
1990s, and 2000s, it is found that the CanESM4.2 version with γd=0.40 yields the best comparison.
The evaluation of CTEM within the framework of CanESM4.2 presented here is
based on an emergent model property at the global scale and may be
considered as a top-down approach of model evaluation. In contrast, the
bottom-up approaches of model evaluation typically evaluate model results
and processes against observations of primary atmosphere–land carbon and/or
nitrogen fluxes and sizes of the vegetation, litter and soil carbon/nitrogen
pools (e.g. Zaehle et al., 2014). Indeed, CTEM has been evaluated at point
(e.g. Arora and Boer, 2005; Melton et al., 2015), regional (e.g. Peng et
al., 2014; Garnaud et al., 2014), and global (e.g. Arora and Boer, 2010;
Melton and Arora, 2014) scales in a number of studies when driven with
observation-based reanalysis data. Both top-down and bottom-up approaches of
model evaluation are complimentary to each other and allow to evaluate
different aspects of the model at different spatial and temporal scales.
For the top-down approach used here, CanESM4.2 simulates globally averaged
near-surface [CO2] of 400, 381 and 368 ppm for
γd=0.25, 0.40, and 0.55, respectively, compared to
the observation-based estimate of 379 ppm for year 2005. The
cumulative atmosphere–land CO2 flux of 18 Pg C for the period
1850–2005 for γd=0.40 lies within the range of the
observation-based estimate of -11 ± 47 Pg C in Fig. 4b, and so do
the average atmosphere–land CO2 flux for the decades of 1960s through to
2000s in Fig. 4a when compared to observation-based estimates from Le
Quéré et al. (2015). γd=0.25 and 0.55 yield average
atmosphere–land CO2 flux for the decades of 1960s through to 2000s that
are lower and higher, respectively, than the observation-based estimates from
Le Quéré et al. (2015). The only determinant against which
γd=0.40 does not yield the best comparison with
observation-based estimates is the amplitude of the globally averaged annual
CO2 cycle and its increase over the 1980 to 2005 period. For this
determinant, γd=0.25 seems to yield the best comparison
(Fig. 9). The value of γd=0.40 that yields best overall
comparison with observation-based determinants of the global carbon cycle and
the historical carbon budget is also broadly consistent with Arora et
al. (2009), who derived a value of γd=0.46 based on results
from FACE studies (as mentioned in Sect. 2.2.2).
The caveat with the analyses presented here, or for any model for that
matter, is that the strength of the terrestrial CO2 fertilization effect
is dependent on the processes included in the model and the parameter values
associated with them. The primary example of this is the adjustment to the
humification factor in CTEM4.2, which leads to reduction in the global soil
carbon amount as anthropogenic LUC becomes significant towards the mid-20th
century. This response of soil carbon was not present in the model's
configuration of CTEM and historical simulations made with CanESM2. The
representation of soil carbon loss, in response to anthropogenic LUC in
CanESM4.2, implies that a stronger CO2 fertilization effect (or weaker
photosynthesis down-regulation) should be required to reproduce realistic
atmosphere–land CO2 flux over the historical period and this was found
to be the case in Fig. 4a. Despite this dependence on processes included in
the model, the response of the land carbon cycle, over the historical period,
to the two primary forcings of increased [CO2] and anthropogenic land
use change must be sufficiently realistic in the model to satisfy all the
four determinants of the global carbon cycle and the historical global carbon
budget.
The simulated loss in soil carbon in response to anthropogenic LUC over the
historical period may also be assessed against observation-based estimates
from Wei et al. (2014). Using data from 453 sites that were converted from
forest to agricultural land, Wei et al. (2014) found that the soil organic
carbon stocks decreased by an average of 43.1 ± 1.1 % for all
sites. Based on the HYDE v3.1 data set from which the changes in crop area
are derived (Hurtt et al., 2011), LUC as implemented in CanESM4.2 yields an
increase in crop area from about 5 million km2 in 1850 to about
15 million km2 in 2005. Assuming an initial soil carbon amount of
10 kg C m-2 (see
Fig. 2c of Melton and Arora, 2014) and an average 40 % decrease in soil
carbon amount, based on Wei et al. (2014), implies that the increase in crop
area of about 10 million km2 over the historical period has likely
yielded a global soil organic carbon loss of 40 Pg C. The loss in soil
carbon in Fig. 5a is simulated to 18 Pg C for CanESM4.2 simulations with
γd=0.40, the simulation that yields the best comparison with
observation-based determinants of the global carbon cycle and the historical
carbon budget. This loss of 18 Pg C is expected to be less than the
40 Pg C because the model estimates also include an increase associated
with the increase in NPP due to the CO2 fertilization effect from
non-crop areas. The effect of LUC on global soil carbon loss may also by
estimated by differencing global soil carbon amounts from simulations with
and without LUC from Fig. 10d at the end of the simulation in year 2005.
For CanESM4.2 simulation with γd=0.40, this amounts to
around 50 Pg C. Both these estimates of soil carbon loss are broadly
consistent with the back-of-the-envelope calculation of 40 Pg C soil carbon
loss, based on Wei et al. (2014) estimates, indicating that the soil carbon
loss simulated in response to anthropogenic LUC over the historical period is
not grossly over or underestimated.
The CanESM4.2 simulation with γd=0.40, however, fails to
satisfy the rate of increase of the amplitude of the globally averaged annual
CO2 cycle over the 1980–2005 period implying that there are still
limitations in the model structure and/or parameter values. Of course, the
fact that the amplitude of the globally averaged annual CO2 cycle is
also affected by the atmosphere–ocean CO2 fluxes makes it more difficult
to attribute the changes in the amplitude of the globally averaged annual
CO2 cycle solely to atmosphere–land CO2 fluxes. Additionally, the
increase in crop area as well as crop yield per unit area over the historical
period have been suggested by Zeng et al. (2014) to contribute towards the
observed increase in the amplitude of annual CO2 cycle. Based on their
sensitivity tests, Zeng et al. (2014) attributed 45, 29, and 26 percent of the
observed increase in the seasonal-cycle amplitude of the CO2 cycle to
LUC, climate variability and change (including factors such as the
lengthening of the growing season), and increased productivity due to CO2
fertilization, respectively. Comparison of the rate of increase of NPP in
CanESM4.2 experiments with and without LUC (Fig. 10b), as a measure of
increase in the strength of the CO2 fertilization effect, suggests that
the contribution of anthropogenic LUC to the increase in the seasonal-cycle
amplitude is 52 %, which is broadly consistent with the 45 % value
obtained by Zeng et al. (2014).
While CanESM4.2 simulation with γd=0.40 is able to simulate
a realistic rate of increase of [CO2] over the period 1960 to 2005, the
modelled atmosphere–ocean CO2 fluxes for this and the CanESM2 version
are lower than observational estimates of this quantity (Fig. 8). This
implies that if the modelled atmosphere–ocean CO2 flux were to increase
and become more consistent with observation-based estimates then the modelled
atmosphere–land CO2 flux must decrease to still be able to yield
sufficiently realistic rate of increase of [CO2]. This implies that the
strength of the terrestrial CO2 fertilization effect should likely be
somewhat lower than what is obtained by γd=0.40 or the
simulated atmosphere–land CO2 flux is higher because of some other
reason, most likely lower LUC emissions. Indeed, the required decrease in
modelled atmosphere–land CO2 flux is consistent with the fact that the
modelled LUC emissions for γd=0.40 (81 Pg C) are about
half the estimate from Houghton (2008) (156 Pg C) with the caveat, of
course, that Houghton's estimates themselves have an uncertainty of roughly
±50 %. The LUC module of CTEM currently only accounts for changes in
crop area and does not take into account changes associated with pasture area
given their ambiguous definition (pasture may or may not be grasslands). The
model also does not take into account wood harvesting, which amongst other
uses is also used as a biofuel. Treatment of these additional processes will
increase modelled LUC emissions.
Although the CanESM4.2 simulation with γd=0.40 satisfies
three out of four constraints placed by the chosen determinants of the global
carbon cycle and the historical carbon budget, and also simulates reasonable
soil carbon loss in response to anthropogenic LUC, the model now yields the
highest land carbon uptake, in the 1pctCO2 experiment, amongst the CMIP5
models that were compared by Arora et al. (2013) as seen in Fig. 2. Of
course, the 1pctCO2 experiment is in no way indicative of models' performance
over the historical period, nor is being an outlier amongst CMIP5 models a
conclusive evaluation of CanESM4.2's land carbon uptake. However, it remains
possible that the chosen determinants of the global carbon cycle and the
historical carbon budget are not able to constrain the model sufficiently,
given the especially large uncertainty associated with LUC emissions.
Nevertheless, these observation-based constraints of the carbon cycle and
historical carbon budget are essentially the only means to evaluate carbon-cycle aspects of the ESMs at the global scale, including the strength of the
terrestrial CO2 fertilization effect. In the near future, availability
of model output from the sixth phase of CMIP (CMIP6) will allow for a comparison
of the simulated aspects of the global carbon cycle and the historical carbon
budget from ESMs to observations-based estimates for the 1850–2014 period.
These data will allow for a comparison of the rate of increase of the amplitude
of globally averaged surface [CO2] in models with observation-based
estimates over a longer period. This should help better constrain the
strength of the terrestrial CO2 fertilization effect, as it is
represented in models, in a somewhat more robust manner.
Source code and data availability
Source code for the complete CanESM4.2 model is an extremely complex set of
FORTRAN subroutines, with C pre-processor (CPP) directives, which reside in
CCCma libraries. Unix shell scripts process the model code for compilation
based on CPP directives and several other switches (e.g. those related to
free-CO2, specified-CO2, and relaxed-CO2 settings). As such, it is extremely
difficult to make the full model code available. However, selected model
subroutines related to specific physical and biogeochemical processes can be
made available by either author (vivek.arora@canada.ca,
john.scinocca@canada.ca) upon agreeing to Environment and Climate Change
Canada's software licensing agreement available at
http://collaboration.cmc.ec.gc.ca/science/rpn.comm/license.html. Data
used to produce plots and figures can be obtained from the first author
(vivek.arora@canada.ca).
We would like to thank Joe Melton and Neil Swart for providing comments on an
earlier version of this paper. We also thank the three anonymous reviewers
for their constructive and helpful comments.
Edited by: J. Kala
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