Introduction
Quantifying the sources and sinks of carbon at the land surface is key to an
accurate carbon balance and to the overall assessment of where
anthropogenically released fossilized carbon ends up in the Earth system.
While current estimates suggest that the land absorbs the equivalent of
about a quarter of anthropogenic CO2 emissions (IPCC, 2014), the
uncertainty in the global carbon budget associated with terrestrial
ecosystem processes is large (Le Quéré et al., 2016). For example,
studies disagree on the partitioning of the land carbon sink between the
tropics and the extratropics. Some studies consider tropical ecosystems to
be carbon sinks (Stephens et al., 2007; Lewis et al., 2009; Schimel et al.,
2015) and others consider them to be carbon sources (Baccini et al., 2017;
Houghton et al., 2018). A substantial interannual variability is found in
the tropical carbon balance, primarily in response to climate-driven
variations (Baker et al., 2006; Cleveland et al., 2015; Fu et al., 2017);
indeed, tropical ecosystems represent a large fraction of the uncertainty in
estimates of the total land carbon sink and its future trajectory (Pan et
al., 2011; Wang et al., 2014). Carbon fluxes in boreal ecosystems also
remain highly uncertain and are likely to be strongly influenced by changes
in climate and the length of growing season. Warming over northern lands may
lead to an increase in vegetation productivity (Xu et al., 2013) and to a
greater amplitude of seasonal CO2 exchange (Forkel et al., 2016) via
climate-induced changes in phenological seasonal cycles (e.g., earlier
vegetation “green-up”).
Because terrestrial carbon dynamics are greatly influenced by atmospheric
forcing (e.g., air temperature, precipitation, radiation, humidity, CO2
concentration), quantifying the sensitivity of surface carbon fluxes to
variations in atmospheric drivers is critical to obtaining accurate flux
estimates. Such quantification helps identify model processes and
assumptions that are responsible for the uncertainty. It indeed promotes
essential understanding regarding what controls these fluxes, understanding
that should, in turn, lead to improved models of terrestrial carbon
processes. Only with accurate models can we obtain reasonably accurate
projections of climate under different emission scenarios.
While the impacts of some aspects of atmospheric variability, such as that
of temperature and precipitation, on global land carbon fluxes have been
explored extensively (e.g., Beer et al., 2010; Poulter et al., 2014;
Ahlström et al., 2015), the impact of atmospheric CO2 variability
on the fluxes is relatively understudied and is in fact generally ignored in
recent flux estimation exercises. In most land surface model (LSM) or
terrestrial biosphere model (TBM) simulations, the atmospheric CO2
applied is annually and/or spatially uniform (e.g., TRENDY project; Sitch et
al., 2015) or allowed to vary only on a monthly and/or zonal basis (e.g.,
Multi-scale Terrestrial Model Intercomparison Project (MsTMIP); Huntzinger
et al., 2013; Wei et al., 2014; Ito et al., 2016). Potential time variations
in the carbon fluxes associated with the diurnal and day-to-day variability,
if monthly CO2 is applied, and also with the seasonal variability, if
annual CO2 is applied, are not represented in these modeling studies.
Likewise, the regional flux response to spatial variations in CO2 is
only partially represented with the latitudinal CO2 driver and not at
all with the spatially uniform CO2 driver.
Such simplifications neglect lessons from decades of in situ measurements
showing that CO2 concentrations vary widely on different time and space
scales. During the growing season, daytime (nighttime) CO2 at the
canopy level can be significantly smaller (larger) than the daily mean
CO2 due to the diurnal cycle of photosynthesis. Summertime
measurements, for example, at an 11 m tower in northern Wisconsin indicate
that the atmospheric CO2 concentration fluctuates by approximately 70 ppm
over the course of a day, from 350 ppm during the day to 420 ppm at
night (Yi et al., 2000); indeed, the day–night difference is comparable to
the global atmospheric CO2 growth of the last few decades
(∼63 ppm since 1980). In addition to large diurnal
variations, many stations observe strong seasonal variations in CO2
concentrations; for example, such variations are as large as 30 ppm at the
Hegyhátsál monitoring site in western Hungary (e.g., Haszpra et al.,
2008).
Spatial variations in CO2 are also known to be significant.
Concentrations of CO2 contain large spatial gradients with higher
annual mean values found in the Northern Hemisphere than in the Southern
Hemisphere due to the higher level of fossil fuel emissions (Tans et al.,
1989). Higher annual mean concentrations are evident over land masses,
particularly those with large anthropogenic emissions. In addition, the
covariance between flux processes and atmospheric transport results in a
phenomenon called the “rectifier effect” wherein substantial spatial
variations are introduced into simulated CO2 fields, even when an
annually balanced biosphere flux is assumed (Denning et al., 1995, 1999).
In light of such known variations, the Coupled Model Intercomparison Project
(CMIP6) is now encouraging modeling groups to force their offline models
with CO2 concentrations that vary in space and time (Eyring et al.,
2016). Ostensibly this makes sense, given that relevant datasets on temporal
and spatial CO2 variations are available for use (Meinshausen et al.,
2017). Nevertheless, it seems appropriate at the outset of such efforts to
quantify the potential usefulness of this added complexity. It is still
arguably unknown how much the uncertainty in estimated terrestrial carbon
fluxes will decrease through the explicit consideration of CO2
variations.
In a recent study, Liu et al. (2016) begin to address this issue – they use
a TBM to show that the explicit consideration of the seasonal variation in
CO2 in modeling studies can lower the estimated terrestrial gross primary production (GPP) by 0.4 PgCyear-1
globally, and they also show that the consideration of the
spatial variability in CO2 can increase mean global GPP estimates by
2.1 PgCyear-1. There are, however, additional facets of CO2
variability that are worth exploring. In particular, diurnal variations in
CO2 are known to be large (e.g., ∼70 ppm in the central
US and ∼50 ppm in central Europe), and it is worth
determining if, in ignoring these particular variations, process-based
models produce significant errors in carbon flux estimation.
In this paper we provide an analysis of carbon flux sensitivity to spatial
and temporal variations in atmospheric CO2 that is duly comprehensive.
We employ in this study a particular process-based terrestrial biosphere
model, the Catchment-CN model of NASA's Global Modeling and Assimilation
Office (GMAO). We first evaluate the ability of the model to reproduce
observationally informed carbon flux estimates. This evaluation includes a
test of our model's response to artificially enriched CO2 – an imposed
surplus of 200 ppm, mimicking the surplus applied in an established field
experiment. Then, in a carefully designed suite of simulation experiments,
we quantify the sensitivity of monthly simulated GPP and net biome production (NBP) to different
temporal and spatial scales of atmospheric CO2 variability. The paper
concludes with some discussion on the implications of the results for future
carbon cycle research.
Methods
Catchment-CN model
The NASA Catchment-CN model (Koster et al., 2014) is a hybrid of two
existing models: the NASA Catchment model (Koster et al., 2000) and the
NCAR Community Land Model version 4 (CLM4) (Oleson et al., 2010). The hybrid
utilizes the code from the Catchment model that performs water and energy
budget calculations. The carbon and nitrogen dynamics from CLM4 provides to
the hybrid all of the carbon reservoir and carbon flux calculations as well
as photosynthesis-based estimates of canopy conductance for use in the
Catchment model's energy balance equations. Unlike most land surface models,
the surface element for Catchment-CN is the hydrological catchment (with a
typical spatial dimension of about 20 km); model equations further provide a
separation of each catchment into three separate dynamic hydrological
regimes, each with its own set of energy balance calculations. There are 19
available plant functional types (PFTs) (Table S1 in the Supplement), and up to four PFTs are
allowed in each of three static sub-areas loosely tied to the three
hydrological regimes. The model used a 10 min time step for the energy
and water balance calculations and a 90 min time step for the carbon
calculations. This model's ability to capture the observed sensitivity of
phenological variables to moisture variations was demonstrated in Koster et
al. (2014).
The environmental variables (temperature, precipitation, radiation,
humidity, wind and atmospheric CO2 concentrations) directly affect leaf
photosynthesis (A) in Catchment-CN (as in NCAR CLM4 (Oleson et al., 2010);
see also Farquhar et al. (1980) and Collatz et al. (1991) for the C3 plant
model, and Collatz et al. (1992) for the C4 plant model), which is
predicted to be the minimum value (Eq. 1) of Rubisco-limited
photosynthesis (ωc, Eq. 2), light-limited photosynthesis
(ωj, Eq. 3) and export-limited photosynthesis (ωe, Eq. 4):
A=minωc,ωj,ωe,ωc=Vcmax(ci-Γ∗)ci+Kc(1+oiKo) for C3 plantsVcmax for C4 plants,ωj=ci-Γ∗4.6ϕαCi+2Γ∗ for C3 plants4.6ϕα for C4 plants,ωe=0.5Vcmax for C3 plants4000VcmaxciPatm for C4 plants,
where ci is the internal leaf CO2 partial pressure (Pa) and
oi is the O2 partial pressure (Pa). Kc and Ko are the
Michaelis–Menten parameters (Pa) for CO2 and O2, respectively, and
vary according to the leaf temperature. Γ∗
is the CO2 compensation point (Pa), α is quantum efficiency,
ϕ is absorbed photosynthetically active radiation (APAR) (Wm-2),
and Vcmax is the maximum rate of carboxylation (µmolCO2m-2s-1),
which varies according to the leaf temperature, soil
water and day length. Photosynthesis calculations of the type represented by
Eqs. (1)–(4) are common in process-based LSMs,
including, for example, the Joint UK Land Environment Simulator (JULES)
model (Walters et al., 2014) and the Organising Carbon and Hydrology In
Dynamic Ecosystems (ORCHIDEE) model (Krinner et al., 2005).
Leaf photosynthesis (µmolCO2m-2s-1; denoted as A) can
also be expressed in terms of the diffusion gradient and stomatal
conductance for CO2 among the ambient atmosphere, the leaf surface
and the internal leaf:
A=ca-ci1.37rb+1.65rsPatmbetween atmosphere and internal leaf,=ca-cs1.37rbPatmbetween atmosphere and leaf surface,=cs-ci(1.65rs)Patmbetween leaf surface and internal leaf,
where rb is boundary layer resistance and rs is leaf stomatal
resistance (m2sµmol-1), and where ca is the CO2
partial pressure of ambient atmosphere and cs is the pressure at leaf
surface (note that Eq. 5a is a consequence of the others, Eq. 5b and c).
Using the Ball–Berry model of stomatal conductance (Ball et al., 1987;
Collatz et al., 1991), rs is expressed as a function of A, cs and
vapor pressures (es, the vapor pressure at the leaf surface, and
ei, the saturation vapor pressure inside the leaf):
1rs=mAcseseiPatm+b,
where m is a parameter dependent upon PFT (m=5 for C4
grass, 6 for needleleaf trees, and 9 for all other types), and b is the
minimum stomatal conductance (20 000 µmolm-2s-1). Assuming
the initial value of ci to be 0.7 ca (for C3 plants) or 0.4
ca (for C4 plants), the Catchment-CN model simultaneously computes the
leaf photosynthesis (A) from Eqs. (1)–(4). This value of A is then used to
estimate cs in Eq. (5b) and rs in Eq. (6), as well as ci in
Eq. (5c), which is inserted back into Eqs. (2)–(4) for another calculation of
A. The iteration cycle proceeds three times to obtain the final value of A.
A grid-level GPP is tied directly to the computed photosynthesis by taking a
tile-based (i.e., delineated catchment) area-weighted average of A.
NBP is calculated as
NBP=GPP-Ra-Rh-F,
where Ra is the autotrophic respiration (through plant growth and
maintenance), Rh is the heterotrophic respiration (through litter and
soil decomposition) and F is fire carbon flux. Positive (negative) NBP
values mean that the land surface is a carbon sink (source). The respiration
terms Ra and Rh were calculated as in NCAR CLM4, except for a
modification to Rh, imposed here, that prohibits decomposition if the
soil water is frozen. With this modification, the Catchment-CN's NBP showed
a better agreement with atmospheric inversion estimates in the northern high-latitude regions during December through February. The fire term (F) is
controlled by the amount of available fuel and the status of soil moisture.
Note that our study did not consider carbon flux changes associated with
land use (e.g., deforestation).
Datasets for model evaluation and comparison
Given that no direct measurements of GPP exist at the global scale (Anav et
al., 2015), we evaluate the GPP values produced in our control simulation
against GPP estimates from the data-derived FLUXNET Multi-Tree Ensemble
(MTE) GPP project (hereafter referred to as MTE-GPP) (https://www.bgc-jena.mpg.de/geodb/projects/Home.php,
last access: May 2013). This global-scale,
monthly, gridded dataset effectively consists of upscaled observations from
the eddy-covariance towers of the FLUXNET network; the upscaling utilizes
the the MTE approach with inputs of (i) meteorological data, (ii) the
fraction of absorbed photosynthetically active radiation (fPAR) derived from
the Global Inventory Modeling and Mapping Studies (GIMMS) normalized
difference vegetation index (NDVI) and (iii) land cover information (i.e.,
vegetation type) (Jung et al., 2009, 2011). The flux partitioning method
utilized was from Lasslop et al. (2010). This dataset is widely used for
performance evaluation of TBMs including CLM (e.g., Bonan et al., 2011).
The net carbon fluxes (i.e., NBP) of the Catchment-CN model were evaluated
against estimates from three atmospheric inversions: Monitoring Atmospheric
Composition and Climate (MACC) v14r2 (Chevallier et al., 2011;
http://macc.copernicus-atmosphere.eu/, last access: June 2017),
CarbonTracker 2015 (Peters et al., 2007, with updates documented at
http://carbontracker.noaa.gov, last access: August 2016) and Jena
CarboScope v3.8 (Rödenbeck et al., 2003;
http://www.bgc-jena.mpg.de/CarboScope/, last access: March 2017). The
atmospheric inversion methods use atmospheric CO2 concentration
measurements in conjunction with an atmospheric transport model to provide a
range of estimates of net carbon fluxes between the atmosphere and biosphere.
The net carbon fluxes of the Catchment-CN model were also compared with
fluxes estimated by the diagnostic Carnegie–Ames–Stanford Approach (CASA)
Global Fire Emission Database (GFED, version 3) (Ott et al., 2015; van der
Werf et al., 2010). CASA GFED3 is a widely used dataset that is heavily
constrained by satellite observations, including GIMMS
FPAR, as well as by MERRA-2 meteorology. The mean NBP of the
11 years (2004–2014) overlapping our control simulation were evaluated.
Experimental design
In all simulations examined in this study, the Catchment-CN model is driven
with atmospheric fields from NASA's Modern-Era Retrospective analysis for
Research and Applications, version 2 (MERRA-2) reanalysis (Gelaro et al.,
2017, and also available at http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/, last access: August 2015). Since MERRA-2 fields are
provided on a 0.5∘×0.625∘ resolution grid,
the forcing values for a given Catchment-CN tile are taken from the MERRA-2
grid cell whose center is closest to the tile's centroid. Precipitation
forcing is the same as that used in the production of the Soil Moisture
Active Passive (SMAP) level 4 product (Reichle et al., 2016); this
precipitation is scaled to agree with rain gauge observations where
available.
Our control case imposes a maximum level of CO2 variability. In the
control simulation, the model is forced with time-varying (at 3-hourly
resolution) and spatially varying (at 3∘ longitude × 2∘ latitude
resolution) global fields of CO2 concentration
over the period 2001–2014. The surface CO2 fields are extracted from
the NOAA CarbonTracker database (Peters et al., 2007) for this period
(CT2015, http://www.esrl.noaa.gov/gmd/ccgg/carbontracker/molefractions.php,
last access: August 2016).
We achieved reasonable initial land carbon states for 1 January 2001 using
a two-step approach. First, starting with carbon prognostic states already
equilibrated over multiple millennia with a somewhat different
modeling–forcing combination (including the use of present-day CO2
concentrations), the Catchment-CN model was run for at least 2000
additional simulation years under a spatially and temporally uniform
CO2 concentration of 280 ppm to mimic the preindustrial era (i.e.,
before 1850), with meteorological forcing consisting of repeated cycles of
the 1981–2015 MERRA-2 dataset. In the second step, the period from 1850 to
2000 was simulated using CO2 concentrations that varied diurnally,
seasonally, and spatially and that grew linearly in time to match the
observed CO2 conditions (see below). The meteorological forcing applied
during this time was also the cycled 1981–2015 MERRA-2 forcing and thus was
also not tied to true year-specific forcing (except for within the final
1981–2000 period); such meteorological information is unavailable for the
earlier part of the industrial period, and in any case, the main point of
the exercise was to allow the carbon reservoirs in the land surface to
respond to the gradual increase in CO2 concentrations. The resulting
status of the land ecosystem on 1 January 2001 was used as the initial
condition for the control simulation and for all experiments.
Schematic of the six
simulations examined in this study, which were designed to isolate the
impacts of the different facets' spatiotemporal CO2 variability on
simulated carbon fluxes. The CO2 concentrations were reconstructed
from the NOAA CarbonTracker 3-hourly global CO2 data.
The CO2 concentration fields used during the 1850–2000 spin-up period
were constructed as follows. First, the 3-hourly, spatially varying
CarbonTracker CO2 fields were averaged over 2001–2014 and over each
month into a climatological 3-hourly diurnal cycle for each of the 12 months
of the year (i.e., 96 fields – eight 3-hourly fields for each month at each
grid location). The 12 diurnal cycles were then assigned to the middle of
each month and linear interpolation to each day of year produced 365
climatological diurnal cycles of CO2 concentration. We applied these
daily diurnal cycles in each year of 1850–2000 after scaling them with a
year-specific scaling factor that forced the annual, global mean CO2
concentration to increase linearly in time from 280 ppm in 1850 to 311 ppm in
1950 and then from this value to 375.5 ppm in 2000 (to approximate the growth
in CO2 seen in the historical record; see
http://www.eea.europa.eu/data-and-maps/figures/atmospheric-concentration-of-co2-ppm-1, last access: April 2016).
All of the interpolation was performed in the time dimension only; the
global spatial variation contained within the CarbonTracker data was
retained.
The strategy behind our experiments is described in Fig. 1. We performed a
series of six experiments covering the period 2001–2014 (applying the same
meteorology except for the atmospheric CO2 concentrations and using the
same 2001 initial conditions as the control), with each experiment removing,
in turn, one facet of the spatiotemporal variability in atmospheric
CO2 concentration. In the first experiment (referred to as dCO2), the
3-hourly CO2 diurnal cycle was averaged into a single daily value at
every tile, and these daily-averaged values were then used to force the
Catchment-CN model. Comparing the results of this experiment to those of the
control thus illustrates the impact of ignoring diurnal CO2 variability
on the modeled carbon fluxes. In the second experiment (mCO2), day-to-day
variability in CO2 was removed – the daily CO2 concentrations
used in dCO2 were averaged into monthly values, which were then linearly
interpolated (as in the spin-up procedure) into a temporally smoothed
version of the daily fields. Note that through the interpolation, the global
average of CO2 is conserved in essence. In the third experiment
(maCO2), seasonality in CO2 was removed – the annual average CO2
from CarbonTracker above a surface element was applied to that element. Note
that the annual fields used for maCO2 still retain the spatial variability
in CO2 inherent in the CarbonTracker data; this spatial variability was
removed in the fourth experiment (magCO2), in which the globally uniform but
yearly varying mean annual CO2 fields were used. This experiment
(magCO2) replicates the commonly used CO2 forcing fields applied in
many other land modeling experiments. Finally, in the fifth and sixth
experiments, different facets of the interannual variability in CO2
were removed. In the fifth experiment (magtCO2), year-to-year variations in
globally averaged CO2 were removed while retaining the overall mean
trend; this was achieved by regressing the 14 annual mean values used in
magCO2 against the year index and then using the resulting regression line
to assign the annual values. In the sixth experiment (cC02), the long-term
trend was also removed by averaging the 14 annual values into a single
number – in cCO2, a constant CO2 concentration (392.34 ppm) was
applied everywhere, every 10 min.
All of our analyses were performed on tile-based fluxes. This efficiently
excludes coastal water and lake water impacts and thus allows for an
accurate estimation of the aggregated land-based global carbon fluxes. We
computed mean global GPP by multiplying tile-based fluxes (in units of
gCm-2s-1)
by the associated tile area and then aggregating the areal
totals over global land (excluding Greenland and Antarctica). The mean
global NBP was estimated in the same way.
Spatial patterns of 2002–2011 mean GPP (gCm-2day-1) from (a) Catchment-CN
GPP and (b) MTE-GPP as well as (c) zonal mean GPP and (d) the annual
cycle of GPP (solid blue: Catchment-CN model; dotted black: MTE-GPP).
Results
We evaluate in Sect. 3.1 and 3.2 the ability of the control simulation to
produce reasonable GPP and NBP fluxes, and we examine in Sect. 3.3 the
model's initial response to CO2 enrichment. With this overview of model
performance in hand, we analyze in Sect. 3.4 the results of the
experiments outlined in Fig. 1.
Evaluation of simulated GPP against the MTE-GPP dataset
The spatial pattern of the mean annual GPP simulated by the Catchment-CN in
the control simulation (i.e., the case forced with spatially varying,
3-hourly atmospheric CO2 fields) is broadly consistent with the MTE-GPP
data over the period of 2002–2011 (Fig. 2a and b). The generally higher
values seen in the tropics for Catchment-CN are not surprising given that
higher values were also found for CLM4 (Bonan et al., 2011), the parent
model of Catchment-CN's carbon code. Also note that because the MTE-GPP
dataset is more reliable in regions with denser observations, and because
measurement stations in the tropics are limited, MTE-GPP estimates in the
tropics are subject to particular uncertainty (Anav et al., 2015). Outside
the tropics, the model produces higher GPP values in southeastern China,
southeastern Brazil and the North American boreal region but slightly lower
values in western Europe. The zonal means of the simulated GPP data and the
MTE-GPP product in fact agree well (Fig. 2c), though the seasonal mean of
the simulated GPP is slightly more evenly distributed over the year than the
MTE-GPP (Fig. 2d). The zonal means of the Catchment-CN GPP for each season
agree reasonably well with the MTE-GPP product (Fig. S1 in the Supplement).
Averaged over the full simulation period (2001–2014), the Catchment-CN model
predicts a mean global GPP of 127.5 PgCyear-1. This value is
essentially in the range, though at the high end, of estimates from MTE-GPP:
119±6 PgCyear-1 for the period 1982–2008 (Jung et al., 2011)
and 123 PgCyear-1 for the period 1998–2005 (Beer et al., 2010). The
Catchment-CN's GPP estimate also lies within the range of mean global GPP
predicted by other process-based LSMs or TBMs. CLM4, from which the
Catchment-CN model's carbon modules were procured, produces an estimate of
165 PgCyear-1 (Bonan et al., 2011). We found that the majority of GPP
difference between the Catchment-CN of this study and the original CLM4 is
attributable to the choice of meteorological forcing. A version of the CLM
model with revised treatments (which were adopted later in CLM4.5) of
canopy radiation, leaf photosynthesis, stomatal conductance and canopy
scaling produces a value of 130 PgCyear-1 for the period of 1982–2004
(Bonan et al., 2011). The JULES model (Slevin et al., 2017) produces a value
of 140 PgCyear-1 for 2001–2010.
Monthly mean of terrestrial NBP of the Catchment-CN model (blue), of
the CASA GFED3 model (red) and of three atmospheric inversions (dotted
lines), for the period of 2004–2014. Positive (negative) NBP values indicate
that land is a carbon sink (source).
Evaluation of simulated NBP against multiple datasets
The mean global net carbon fluxes from our control simulation were compared
with the CASA GFED3 model estimates (which, in fact, serve as a prior to
CarbonTracker; CarbonTracker Documentation CT2015 Release, 2016) as well as
against the three aforementioned atmospheric inversion estimates (MACC
v14r2, CarbonTracker 2015 and Jena CarboScope v3.8). In Fig. 3, the phase
of the climatological NBP from the Catchment-CN model (solid blue) agrees
well with that of the inversions (dotted curves). These datasets agree, for
example, on the time during spring at which the land shifts from being a
carbon source to a carbon sink. The CASA GFED3 model (solid red) shows a
delay in the shift, a feature noted in previous studies (e.g., Ott et al.,
2015).
The annual NBP from Catchment-CN (+0.53 PgCyear-1) indicates that
the land is a carbon sink, though the value is smaller than the mean of the
sinks estimated by the three atmospheric inversions (+3.2 PgCyear-1).
The reason for the smaller value is unclear; we note only that
the sink strength produced by the model reflects the net effect of a
multitude of physical processes (underlying GPP, respirations and fire) in
the model, processes that can interact with each other in complex ways.
The seasonal and zonal dependence of the Catchment-CN NBP is, in any case,
within the spread of the inversions and the CASA GFED3 model (Fig. S2). The
boreal summer (JJA) global carbon sink of Catchment-CN is approximately
three-quarters of the inversion estimates (Fig. 3) and is relatively weak in
the northern boreal ecosystem (Fig. S2c). This weaker summer global carbon
sink is caused, in part, by the underestimated summer GPP (Fig. 2d) and
perhaps also by the respiration values produced (Fig. S3). During December, January and February, the
model NBP agrees with the inversions and the CASA GFED3 model estimates in
the Northern Hemisphere, but it mostly follows the MACC v14r2 inversion in
the Southern Hemisphere tropics where the inversions show disagreement in
sign (Fig. S2a). The spring and fall NBP values from Catchment-CN lie within
the range of the inversion estimates (MAM in Fig. S2b; SON in Fig. S2d).
Sensitivity of Catchment-CN fluxes to enrichment of
CO2
Our analysis in Sect. 3.4 will focus on how simulated GPP responds to
various facets of the spatiotemporal character of the imposed atmospheric
CO2 forcing. It is thus particularly appropriate to evaluate the
model's sensitivity to CO2 variations.
The Large-Scale Free-air CO2 Enrichment (FACE) experiments provide
valuable data for such an evaluation. In these experiments, CO2 is
released into the air and advected by natural wind over the vegetation
within experimental plots; the resulting CO2 concentrations were
increased by about 200 ppm above ambient conditions. Net primary productivity
(NPP) observations over the FACE plots were compared to those over control
plots with no CO2 increase (e.g., Ainsworth and Long, 2004; Norby et
al., 2005; Norby and Zak, 2011). Here we focus on two particular temperate
forest FACE experiments: Duke FACE (35.58∘ N, 79.5∘ W)
(Hendrey et al., 1999) and Oak Ridge National Laboratory (ORNL) FACE
(35.54∘ N, 84.20∘ W) (Norby et al., 2001),
well-documented field experiments that have been used in previous model–data
comparison studies (e.g., Hickler et al., 2008; Piao et al., 2013; Zaehle et
al., 2014; Walker et al., 2014).
To mimic these FACE experiments, we performed a supplemental numerical
experiment with the Catchment-CN model (beyond the experiments outlined in
Sect. 2.3): the control simulation was repeated but with the atmospheric
CO2 forcing increased artificially by 200 ppm. In this supplemental
experiment, the CO2 enrichment was applied globally starting on 1 January 2001,
though we focus here on the simulated increases in NPP
(relative to the control simulation, 3hCO2) within the land elements
containing the Duke and ORNL FACE sites (i.e., the closest tile for each
site). Because the original CLM4's NPP increase was found in a past study
(with a similar experiment) to be low after the first year of the CO2
enrichment, presumably due to an insufficient supply of mineralized nitrogen
in the model for the plants' increased nitrogen demand associated with the
CO2-induced increase in the rate of photosynthesis (Zaehle et al.,
2014), we evaluate here only the first year's simulation of NPP. Note that
we started the CO2 enrichment in 2001, whereas the actual FACE
experiments began in earlier years (August 1996 for Duke and April 1998 for
ORNL).
(a) Change in mean global GPP (PgCmonth-1) due to removal of
diurnal variability in atmospheric CO2 concentration (i.e., GPP from
the dCO2 experiment minus that from the control). (b) Map of time-averaged
GPP changes as a percentage (%). The tile-based model GPP values were
aggregated to 2∘×2.5∘ for visualization purposes.
In this CO2-enriched simulation, the Catchment-CN model produces an
18 % increase in NPP during the first year for the Duke site and a 15 %
increase for the ORNL site. These results are at the low end of the
observations for the Duke site (25±9 %) and underestimate the
observed response at the ORNL site (25±1 %); the model does not
capture the full sensitivity measured in the experiments. This
underestimation must be kept in mind when interpreting our main results in
the following section. For example, we forced our model with MERRA-2
meteorology instead of the site meteorology, and we applied the CO2
stepwise increase in different years compared to the FACE experiment. In
any case, our model results are still relevant to the interpretation and
evaluation of the bottom-up
estimates of GPP and NBP based on a dynamic global vegetation model (DGVM) found in the literature. For example, the average
increase in NPP across the 11 DGVMs participating in a similar
experiment was about 26 % (ranging from 9 % to 35 %) for the Duke site
and 20 % (ranging from 7 % to 30 %) for the ORNL site (Zaehle et al.,
2014; in their Fig. 5), somewhat similar to the increases found with our
model. We can infer, then, that the sensitivities uncovered with our model
experiments likely also apply to other models, including those providing
global GPP and NBP estimates to the scientific community.
Changes in mean global GPP and NBP for 2001–2014, resulting from a
series of simulations representing the removal of temporal and spatial
variability in atmospheric CO2 concentrations. Delta (Δ)
indicates the difference due to removal of spatial–temporal variability
(see Fig. 1 for description).
Case
GPP
NBP
Missing variability
ΔGPP
ΔNBP
(PgCyear-1)
(PgCyear-1)
(PgCyear-1)
(PgCyear-1)
3hCO2
127.545
0.527
–
–
–
dCO2
128.038
0.626
No diurnal variability (dCO2–3hCO2)
0.492
0.099
mCO2
128.040
0.627
No day-to-day variability (mCO2–dCO2)
0.003
0.001
maCO2
128.059
0.632
No seasonal variability (maCO2–mCO2)
0.019
0.005
magCO2
128.007
0.620
No spatial variability (magCO2–maCO2)
-0.052
-0.012
magtCO2
128.004
0.618
No interannual variability (anomalies) (magtCO2–magCO2)
-0.003
-0.002
cCO2
128.082
0.616
No interannual variability (trend) (cCO2–magtCO2)
0.078
-0.002
Global-scale sensitivity of carbon fluxes to imposed CO2
variability
Here we present the results of the experiments outlined in Fig. 1, with
each facet of variability considered separately.
Diurnal variability in CO2 (dCO2–3hCO2)
Figure 4 compares the results of dCO2 to those of the control simulation,
thereby revealing the impact of the CO2 diurnal cycle on simulated GPP
and NBP. Figure 4a shows the time series of global mean GPP differences
(dCO2 minus control) over the 14-year period; removing the diurnal
variability clearly increases GPP, and the effect is particularly large in
boreal summer (0.07 PgCmonth-1, equivalent to 0.8 PgCyear-1).
Figure 4b shows that most of the increases are in the tropics and in the far
eastern areas of the Northern Hemisphere continents. Almost no region shows
a decrease in GPP associated with the removal of the CO2 diurnal cycle.
As indicated in Table 1, removing the CO2 diurnal cycle leads to an
overall increase in global mean GPP of 0.497 PgCyear-1 and a change in
the global mean NBP of 0.100 PgCyear-1.
The changes evident in Fig. 4 make sense in the context of the daily
variations in atmospheric CO2 noted in many studies (e.g., Denning et
al., 1995, 1999). In nature (and as captured in the control simulation), the
nighttime atmospheric CO2 within the planetary boundary layer is higher
than the daily mean value due to the shutdown of photosynthetic activity.
Correspondingly, midday CO2 concentrations are lower near the surface
due to the plants' photosynthetic uptake of CO2. In experiment dCO2,
applying the daily mean CO2 concentration at all hours of the day has
the effect of imposing a higher CO2 concentration during the daytime, when
photosynthesis occurs, and this has the effect of artificially
“fertilizing” the surface – the extra CO2 imposed during the daytime
makes photosynthesis more productive, increasing GPP. The GPP change in the
tropics accounts for about two-thirds of the mean global GPP change, which
is not surprising given the region's high productivity over the whole year.
Day-to-day variability in CO2 (mCO2–dCO2)
The day-to-day variability in CO2, as influenced, for example, by
synoptic-scale weather and its impacts on atmospheric transport, is removed
in experiment mCO2 relative to experiment dCO2. Table 1 indicates a
negligible impact of this modification on the simulated global GPP and NBP
compared to the impact of sub-daily CO2 variations. The impacts on the
temporal changes in the carbon fluxes and on the spatial distribution of the
fluxes are similarly minimal (not shown).
(a) Change in mean global GPP (PgCmonth-1) due to removal of
seasonal variability in atmospheric CO2 concentration (i.e., GPP from
the maCO2 experiment minus that from the mCO2 experiment). (b) Map of
time-averaged GPP changes as a percentage (%).
Seasonal variability in CO2 (maCO2–mCO2)
The maCO2 experiment forces the land surface with yearly averaged, but
spatially varying, atmospheric CO2. The resulting increases in GPP
(maCO2 minus mCO2) in Fig. 5a thus reflect the impact of seasonal CO2
variations. By applying the yearly averaged CO2 concentration all year
long, vegetation outside of the tropics experiences higher CO2
concentrations during the spring and summer seasons, when photosynthesis is
highest, than it would have otherwise; in nature photosynthetic drawdown of
atmospheric CO2 acts to reduce warm season CO2 concentrations
below the annual mean. The artificial warm season fertilization of the
vegetation in the maCO2 case leads to an increase in growing season GPP
(Fig. 5a).
A comparison of Figs. 4 and 5 shows that the influence of seasonal CO2
variations is smaller than that of diurnal variations, which is consistent
with the fact that the amplitude of the CO2 seasonal cycle is about
10–20 ppm while that of the diurnal cycle is about 5 times
larger (up to ∼120 ppm) in boreal summer (Fig. S4). The
response of GPP to the seasonal variability in atmospheric CO2 is
highest in the Northern Hemisphere high latitudes (Fig. 5b), for which the
distinction between cold season and warm season photosynthesis is largest.
The regional- and seasonal-scale impact of this variability is further
discussed in Sect. 3.5.
(a) Change in mean global GPP (PgCmonth-1) due to removal of
spatial variability in atmospheric CO2 concentration (i.e., GPP from
the magCO2 experiment minus that from the maCO2 experiment). (b) Map of
time-averaged GPP changes as a percentage (%).
Spatial variability in CO2 (magCO2–maCO2)
Figure 6 shows the impact of applying in experiment magCO2 a globally
uniform yearly averaged atmospheric CO2 rather than a spatially varying
distribution (e.g., with the inter-hemisphere gradient). In contrast to the
above impacts of reducing temporal variability, the loss of spatial
variability in atmospheric CO2 leads to a global GPP decrease (Fig. 6a,
showing results for magCO2 minus maCO2). This decrease in fact tends to
partially offset the global GPP increases seen in the other experiments.
Loss of spatial variability in CO2 results in an overall reduction in
global mean GPP of -0.052 PgCyear-1 and a change in the global mean
NBP of -0.012 PgCyear-1 (Table 1).
Notably, the sign of the GPP change associated with the removal of CO2
spatial variability is not globally uniform (Fig. 6b). In the absence of the
large-scale inter-hemispheric gradient (Fig. S5), the GPP change is mostly
negative in the densely vegetated areas of the Northern Hemisphere
continents and positive in the Southern Hemisphere. GPP decreases are
especially large in Europe, in the eastern US, in eastern China, and in
tropical regions (e.g., the southeast Asia, Amazon and Congo rainforests),
and these changes are only partially compensated for by GPP increases in
extratropical Southern Hemisphere land areas such as the South American
Atlantic forests and Cerrado. For densely vegetated areas, the pattern of
the GPP change correlates well with changes in the imposed atmospheric
CO2 (Fig. S5); the agreement is less evident in areas with sparse
vegetation.
(a) Change in mean global GPP (PgCmonth-1) due to removal of
the trend in the interannual variability in atmospheric CO2
concentration (i.e., GPP from the cCO2 experiment minus that from the magtCO2
experiment). (b) Map of time-averaged GPP changes in percent (%).
Interannual variability in CO2 (magtCO2–magCO2 and
cCO2–magtCO2)
Finally, in experiments magtCO2 and cCO2, the interannual variability in
atmospheric CO2 is removed in a stepwise manner. First, in magtCO2,
year-to-year variations in CO2 are removed while retaining the longer-term
growth trend. This causes little change in global mean GPP and NBP (Table 1).
The impacts on the temporal and spatial distribution of the fluxes are
also negligible (not shown).
Conversely, when the observed long-term trend in atmospheric CO2
is also removed (cCO2), increases in the global GPP are seen early in the
simulation (2001–2008), and decreases are seen in the later part (2009–2014)
(Fig. 7a, showing results for cCO2 minus magtCO2). In Fig. 7b, the removal
of the long-term trend is seen to affect GPP mostly in the tropics, leading
to an additional change in global mean GPP of 0.078 PgCyear-1 (Table 1).
While this time-mean change is smaller than that associated with
neglecting diurnal variability, the differences at the beginning and end of
the period (1.4 PgCyear-1 between year 2001 and year 2014) are
comparable to, or even larger than, the diurnal variability impact. These
larger differences may have relevance to some period-specific model-based
GPP estimates in the literature.
Regional- and seasonal-scale sensitivity of carbon fluxes to imposed
CO2 variability
The Atmospheric Tracer Transport Model Intercomparison Project (TransCom) 3 experiment (Gurney et al., 2000) defined a number of land and ocean
source–sink regions of interest for the estimation of uncertainty in
atmospheric inversion-based carbon flux estimates. The 11 terrestrial
regional boundaries shown in their basis function map
(http://transcom.project.asu.edu/transcom03_protocol_basisMap.php, last
access: November 2017) offer a convenient framework for characterizing, in
one place, the relative impacts of the different facets of spatiotemporal
CO2 variability on carbon fluxes and how the relative importance of
these different facets varies across the globe. Such a characterization is
presented here in the form of histograms (Fig. 8); together, the histograms
succinctly capture our regional and seasonal findings.
Regional- and seasonal-scale impacts of spatiotemporal CO2
variabilities on GPP. Incremental change in GPP associated with each added
facet of CO2 variability is shown as a percentage of the previous
experiment's regional GPP. The map in (l) shows the regional
boundaries of TransCom land regions (reconstructed from the basis function
map in http://transcom.project.asu.edu/transcom03_protocol_basisMap.php, last access: November 2017).
Figure 8 shows, for example, that ignoring the diurnal variation in
atmospheric CO2 results in the overestimation of GPP in all seasons and
in all TransCom regions except for Australia, where it slightly reduces GPP
and where the influence of the spatial CO2 variability is dominant.
Spatial CO2 variability is also found to partially compensate for
diurnal variability in the Northern Hemisphere temperate regions (North
America and Eurasia; see Fig. 8b and h) and in North Africa (Fig. 8e).
Seasonal CO2 variations are found to be particularly important in
Northern Hemisphere high-latitude regions; during fall, the GPP change
induced by seasonal CO2 variations is comparable to (and in the same
direction as) that caused by diurnal variations (Fig. 8a and g).
Similarly, seasonal variations have an important impact on GPP in Europe
during fall (i.e., SON in Fig. 8k), presumably due to the presence of mixed
(boreal and temperate) forests there; this impact is large enough to offset
the fall GPP reduction induced by ignoring spatial CO2 variations
(Fig. 8b and k). Day-to-day and year-to-year variations in atmospheric
CO2 have little impact anywhere, reaffirming our global-scale analysis.
The long-term trend in CO2, however, has a relatively large percentage
impact in the two African regions (Fig. 8e and f) – ignoring this trend
in CO2 in these regions leads to increased GPP. While diurnal CO2
variations are important for all seasons across nearly all regions, the
interplay among seasonal variations, spatial variations and long-term trends
appears to be crucial to certain seasonal and/or regional GPP estimations.
Discussion
Overall, our results indicate that ignoring temporal variability in
atmospheric CO2 in the bottom-up estimation of carbon fluxes with a
representative offline model can lead to overestimates of global GPP of up
to 0.5 PgCyear-1 (see Table 1). The corresponding estimates of the
strength of the land carbon sink may be too high by about 0.1 PgCyear-1.
The most important facets of temporal CO2 variability are
found to be diurnal variability and the trend in interannual variability;
ignoring them contributes 0.5 and 0.08 PgCyear-1,
respectively, to the global GPP overestimate. Conversely, ignoring
spatial variability in atmospheric CO2 reduces the mean global GPP by
0.05 PgCyear-1 (Table 1); that is, ignoring this spatial variability
contributes to an underestimation of global GPP.
Liu et al. (2016) performed, in essence, a subset of the experiments
examined here. In agreement with our findings, they show that the seasonal
variation in CO2 lowers global GPP and that the spatial variation
in
CO2 increases it. The authors in fact suggest that ignoring spatial
variability in CO2 largely compensates for ignoring the temporal
variability, though they admit that the use of marine background CO2
concentrations in their baseline simulation, which are lower than the
surface-layer CO2 values seen by plants, may have exaggerated the
spatial-variability-related GPP reduction. Our more comprehensive set of
experiments allows us to examine, in addition, the effects of diurnal and
interannual CO2 variability on global carbon fluxes, which turn out to
be more important than the effects of either seasonal or spatial CO2
variability. Note that the neglect of diurnal variability may partially
explain the overestimate (relative to observation-based datasets) noted in
the literature regarding tropical GPP simulated by CLM4 (Bonan et al.,
2011). Also note that because the Catchment-CN model underestimates the
response to CO2 fertilization seen in the FACE experiments, the impact
of diurnal variability at work in nature could be somewhat larger than our
estimate here.
Differences in mean global GPP and NBP compared to the case
that uses the most popular atmospheric CO2 forcing
(magCO2). The values are the global mean of 2001–2014.
Case
GPP
ΔGPP to magCO2
NBP
ΔNBP to magCO2
(PgCyear-1)
(PgCyear-1)
(PgCyear-1)
(PgCyear-1)
3hCO2
127.545
-0.461
0.527
-0.093
dCO2
128.038
0.031
0.626
0.007
mCO2
128.040
0.033
0.627
0.007
maCO2
128.059
0.052
0.632
0.012
magCO2
128.007
–
0.620
–
magtCO2
128.004
-0.003
0.618
-0.001
cCO2
128.082
0.075
0.616
-0.004
Again, the overestimation of the global carbon sink associated with ignoring
the temporal variability in atmospheric CO2 is 0.1 PgCyear-1
(Table 1). This, again, is a small deviation relative to estimates of the
overall land sink; Le Quéré et al. (2016, their Fig. 2), for
example, cite an estimate of 3.1 PgCyear-1 for this sink. This small
sensitivity has relevance to the ongoing CMIP6 project. Through our
experiments we quantify in effect the expected impacts of the minimum
requirement recommended by CMIP6 for historical simulations (Eyring et al.,
2016), namely, that of globally uniform annual mean CO2 with
interannual variations and of the CMIP6 option of including latitudinal and
seasonal variations (Meinshausen et al., 2017). The small sensitivities we
uncover suggest that these recommendations, while not harmful, will
nevertheless have little impact on the global-scale fluxes produced in
CMIP6. Note again that the first approach, that of using globally uniform
annual mean CO2 with interannual variations, was effectively used in
our magCO2 experiment; as shown in Table 2 and Fig. S6a, the global mean
fluxes produced in our other experiments are indeed similar to those
produced in magCO2. The land modeling and carbon cycle community need not
have been too concerned over the years about the global impacts of CO2
variability finer than what has commonly been applied in past studies (i.e.,
annually increasing transient CO2).
This, however, may be an overstatement. It is worth noting that the bias of
0.1 PgCyear-1 associated with spatiotemporal CO2 variability is
in fact a significant fraction of the uncertainty in this value (listed by
Le Quéré et al. (2016) as ±0.9 PgCyear-1). Also,
various model intercomparison studies, e.g., CMIP6, TRENDY and MsTMIP, may
need to consider the full range of spatiotemporal CO2 variability when
estimating terrestrial productivity and net sink size on regional and
seasonal scales (Fig. 8), for which the impacts can be larger. The
growing-season NBP bias can be as large as -6 % from our analysis (MAM in
Fig. S7b), and the local impact well exceeds the global impact (Fig. S6b).
It is thus sensible to impose, if at all possible, realistic CO2
variability in carbon budget analyses.
Our results have some broader implications. They suggest that the diurnal
rectifier effect, the substantial CO2 covariations that are
introduced with daily variations in photosynthesis and boundary layer
turbulence, in a DGVM-based NBP may need to be considered in future
atmospheric inversion studies that use it as a prior, given that biases in
the prior can propagate into errors in the inversion products. Furthermore,
they suggest that if the land carbon component of an Earth modeling system
is not coupled to its atmospheric component with a sub-daily time step
(e.g., in a climate change study), the bias can be carried into the
evolution of regional and seasonal land carbon dynamics, albeit the global
effect may be minor. Finally, our results indicate a negligible impact of
spatiotemporal CO2 variability on water cycle variations through their
impacts on stomatal conductance and thus evapotranspiration (not shown). The
interaction between the water and carbon cycles in this study is thus
limited; more careful analysis in a fully coupled modeling system, however,
may reveal some interesting connections.
Conclusions
In summary, the key results from this study are as follows.
The carbon flux estimates of the Catchment-CN model generally agree with
other statistics-based and model-based estimates. The GPP estimates from our
control simulation (which utilized the full complement of atmospheric
CO2 variability contained within the CarbonTracker dataset) validate
reasonably well with the MTE-GPP dataset, a widely used product for model
evaluation, and our NBP estimates are also consistent to the first order with
results from the diagnostic CASA GFED3 model (a bottom-up approach) and the
atmospheric inversions (a top-down approach). The agreement supports our use
of the Catchment-CN model in the experiments outlined in Fig. 1.
Ignoring the various facets of temporal variability in CO2 leads to
increases in the mean global GPP simulated by the process-based model. The
diurnal component of the variability is particularly important; ignoring it
increases the estimated mean global GPP by 0.5 PgCyear-1.
Ignoring the spatial variability in atmospheric CO2, however,
leads to a decrease in mean global GPP, with decreases in the Northern
Hemisphere and increases in the Southern Hemisphere. The overall decrease of
0.05 PgCyear-1 is smaller than the increase associated with ignoring
temporal variability.
For estimating multiyear mean GPP, the effect of neglecting interannual
variations in atmospheric CO2 is small. Ignoring the long-term trend,
however, can have important implications; the differences at the beginning
and end of the period (up to a 1.4 PgCyear-1 difference between the year 2001
and the year 2014 in this study) can be much greater than the effect of
ignoring the diurnal CO2 variation.
The impacts of ignoring temporal and spatial variability vary with region.
The sensitivity in the tropics tends to be the largest. The seasonal
variability in atmospheric CO2 plays a particularly important role in
the NH boreal regions during fall. Spatial variability in CO2 is
important in temperate regions, offsetting the local impacts of temporal
variability on GPP.
The magnitude of the sensitivities found is small, particularly at the
global scale. The proper imposition of realistic CO2 variability in
offline studies will incur only slight modifications to the terrestrial
carbon fluxes computed. This said, the imposition of realistic CO2
variability is straightforward and could have more significant impacts on
quantified regional and seasonal fluxes.
The carbon flux estimation sensitivities highlighted herein are, of course,
model dependent. The sensitivities are subject to model-specific assumptions
and parameters (see the MsTMIP inter-model comparison study; Ito et al.,
2016) and to the selection of the meteorological inputs (Poulter et al.,
2011). Still, as noted in Sect. 3.3, the sensitivity of GPP to CO2
increases in the Catchment-CN model is similar to that in other
state-of-the-art models, suggesting that the results herein are broadly
applicable and that DGVM-based estimates in the literature of global GPP may
be subject to the noted biases, small as they are found to be here.