Evaluation of 11 terrestrial carbon-nitrogen cycle models against observations from two temperate Free-Air CO 2 Enrichment studies

Abstract

We analysed the responses of 11 ecosystem models to elevated atmospheric [CO2] (eCO2) at two temperate forest ecosystems (Duke and Oak Ridge National Laboratory (ORNL) Free-Air CO2 Enrichment (FACE) experiments) to test alternative representations of carbon (C)–nitrogen (N) cycle processes.

We decomposed the model responses into component processes affecting the response to eCO2 and confronted these with observations from the FACE experiments.

Most of the models reproduced the observed initial enhancement of net primary production (NPP) at both sites, but none was able to simulate both the sustained 10-yr enhancement at Duke and the declining response at ORNL: models generally showed signs of progressive N limitation as a result of lower than observed plant N uptake. Nonetheless, many models showed qualitative agreement with observed component processes. The results suggest that improved representation of above-ground–below-ground interactions and better constraints on plant stoichiometry are important for a predictive understanding of eCO2 effects. Improved accuracy of soil organic matter inventories is pivotal to reduce uncertainty in the observed C–N budgets.

The two FACE experiments are insufficient to fully constrain terrestrial responses to eCO2, given the complexity of factors leading to the observed diverging trends, and the consequential inability of the models to explain these trends. Nevertheless, the ecosystem models were able to capture important features of the experiments, lending some support to their projections.

Full text

Evaluation of 11 terrestrial carbon–nitrogen cycle models against
observations from two temperate Free-Air CO
2
Enrichment
studies
S€onke Zaehle
1
, Belinda E. Medlyn
2
, Martin G. De Kauwe
2
, Anthony P. Walker
3
, Michael C. Dietze
4
,
Thomas Hickler
5,6
, Yiqi Luo
7
, Ying-Ping Wang
8
, Bassil El-Masri
9
, Peter Thornton
3
, Atul Jain
9
, Shusen Wang
10
,
David Warlind
11
, Ensheng Weng
12
, William Parton
13
, Colleen M. Iversen
3
,AnneGallet-Budynek
14,15
,
Heather McCarthy
7
, Adrien Finzi
16
, Paul J. Hanson
3
, I. Colin Prentice
2,17
, Ram Oren
18,19
and Richard J. Norby
3
1
Biogeochemical Integration Department, Max Planck Institute for Biogeochemistry, Hans-Kn€oll-Str. 10, D-07745 Jena, Germany;
2
Department of Biological Science, Macquarie University,
Sydney, NSW 2109, Australia;
3
Oak Ridge National Laboratory, Environmental Sciences Division, Climate Change Science Institute, Oak Ridge, TN 37831, USA;
4
Department of Earth and
Environment, Boston University, Boston, MA 02215, USA;
5
Biodiversity and Climate Research Centre (BiK-F), Senckenberg Gesellschaft f€ur Naturforschung, D-60325 Frankfurt am Main,
Germany;
6
Department of Physical Geography, Goethe University, D-60438 Frankfurt am Main, Germany;
7
Department of Microbiology & Plant Biology, University of Oklahoma, Norman,
OK 73019, USA;
8
CSIRO Marine and Atmospheric Research, PMB 1, Aspendale, Vic. 3195 Australia;
9
Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61801, USA;
10
Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, ON K1A 0Y7, Canada;
11
Department of Physical Geography and Ecosystem Science, Lund
University, SE-22362 Lund, Sweden;
12
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA;
13
Natural Resource Ecology Laboratory, Colorado
State University, Fort Collins, CO 80523, USA;
14
INRA, UMR1220 TCEM, F-33882 Villenave d’Ornon, France;
15
Universite
´de Bordeaux, UMR1220 TCEM, F-33175 Gradignan, France;
16
Department of Biology, Boston University, Boston, MA 02215, USA;
17
AXA Chair of Biosphere and Climate Impacts, Department of Life Sciences and Grantham Institute for Climate
Change, Imperial College London, Silwood Park, Ascot SL5 7PY, UK;
18
Division of Environmental Science & Policy, Nicholas School of the Environment, Duke University, Durham, NC
27708, USA;
19
Department of Forest Ecology & Management, Swedish University of Agricultural Sciences (SLU), SE-901 83 Umea
˚, Sweden
Author for correspondence:
S€
onke Zaehle
Tel: +49 3641 57 6325
Email: szaehle@bgc-jena.mpg.de
Received: 16 September 2013
Accepted: 19 December 2013
New Phytologist (2014)
doi: 10.1111/nph.12697
Key words: carbon (C) storage, CO
2
fertilization, ecosystem modelling, elevated
CO
2
, Free-Air CO
2
Enrichment (FACE),
model evaluation, nitrogen (N) limitation,
plant physiology.
Summary
!We analysed the responses of 11 ecosystem models to elevated atmospheric [CO
2
] (eCO
2
)
at two temperate forest ecosystems (Duke and Oak Ridge National Laboratory (ORNL) Free-
Air CO
2
Enrichment (FACE) experiments) to test alternative representations of carbon
(C)–nitrogen (N) cycle processes.
!We decomposed the model responses into component processes affecting the response to
eCO
2
and confronted these with observations from the FACE experiments.
!Most of the models reproduced the observed initial enhancement of net primary production
(NPP) at both sites, but none was able to simulate both the sustained 10-yr enhancement at
Duke and the declining response at ORNL: models generally showed signs of progressive
N limitation as a result of lower than observed plant N uptake. Nonetheless, many models
showed qualitative agreement with observed component processes. The results suggest that
improved representation of above-ground–below-ground interactions and better constraints
on plant stoichiometry are important for a predictive understanding of eCO
2
effects.
Improved accuracy of soil organic matter inventories is pivotal to reduce uncertainty in the
observed C–N budgets.
!The two FACE experiments are insufficient to fully constrain terrestrial responses to eCO
2
,
given the complexity of factors leading to the observed diverging trends, and the
consequential inability of the models to explain these trends. Nevertheless, the ecosystem
models were able to capture important features of the experiments, lending some support to
their projections.
Introduction
Rising atmospheric [CO
2
] from anthropogenic fossil fuel emis-
sions fertilizes plants (Liebig, 1843; Arrhenius, 1896; Ainsworth
& Long, 2005). Biosphere models integrating the effects of
[CO
2
] on plant photosynthesis into projections of the global
terrestrial carbon (C) balance suggest that elevated atmospheric
[CO
2
] (eCO
2
) has caused a large fraction of the land C sequestra-
tion during recent decades (Cramer et al., 2001; Sitch et al.,
2013). These models also project that the CO
2
-induced land C
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Research

sequestration will continue in the future and thereby significantly
reduce the accumulation rate of anthropogenic CO
2
in the atmo-
sphere (Arora et al., 2013). However, most of these models do
not account for the limited availability of nitrogen (N) for plant
uptake and growth in many terrestrial ecosystems (Vitousek &
Howarth, 1991), which could attenuate ecosystem C storage in
response to eCO
2
: increased C sequestration as a result of eCO
2
is thought to bind N into less easily available forms of N within a
few years after the onset of CO
2
fertilization, a process referred to
as progressive N limitation (PNL; Comins & McMurtrie, 1993;
Luo et al., 2004). Terrestrial biosphere models that explicitly con-
sider the C–N cycle interaction show that future land C seques-
tration could be reduced by 50% or more because of N cycle
processes (Sokolov et al., 2008; Thornton et al., 2009; Zaehle
et al., 2010). However, estimates of the magnitude of this N
effect differ strongly among these projections as a result of uncer-
tainty in the representation of key processes determining the
strength of the N constraint on land C storage (Zaehle &
Dalmonech, 2011).
Free-Air CO
2
Enrichment (FACE) experiments in N-limited
temperate forest ecosystems provide a unique source of empirical
evidence for the ecosystem-scale response of the interacting C
and N cycle processes to eCO
2
(Oren et al., 2001; Norby et al.,
2005; Palmroth et al., 2006; Finzi et al., 2007; Iversen et al.,
2012). Specific site conditions (young, fast-growing forests estab-
lished on abandoned soils previously used for agriculture or graz-
ing) and the artificial nature of these experiments (step increase
in [CO
2
]) limit the direct application of the measurements to
estimate the N constraint on future global net primary produc-
tion (NPP) and land C uptake. Nonetheless, the fact that the
NPP enhancement resulting from experimentally elevated CO
2
at several temperate forest FACE experiments converged towards
a common response size (Norby et al., 2005) has led modellers to
attempt benchmarking exercises, to evaluate the capacity of ter-
restrial ecosystem models to simulate average multi-year effects of
CO
2
fertilization (Sitch et al., 2008; Piao et al., 2013). However,
this consistency of response to CO
2
seen during the initial years
has not been maintained as the length of the experiments
increased, showing that a single number does not capture the
complexities of ecosystem responses to eCO
2
: for instance, the
NPP response strongly declined at Oak Ridge National Labora-
tory (ORNL) FACE towards the end of the experiment, whereas
the Duke FACE site showed a sustained eCO
2
response (McCar-
thy et al., 2010; Norby et al., 2010).
In this article, we use 11 ecosystem models to investigate the
effects of N availability on the eCO
2
response of forest productiv-
ity and C storage at two forest sites with fairly similar temperate
climate (K€oppen Cfa), comparable levels of N deposition, but con-
trasting vegetation: the evergreen, needle-leaved Duke Forest
(McCarthy et al., 2010) and the deciduous, broad-leaved ORNL
Forest (Norby et al., 2010) FACE experiments. As the observed
ambient forest productivity and N requirement at the beginning of
the experiment were comparable at the two sites (see Results), our
hypothesis was that the ecosystem models should be able to explain
the diverging long-term trends based on the different processes
and time scales associated with the different vegetation types.
Our study forms part of a model intercomparison (A. P.
Walker et al., unpublished) looking at the effect of eCO
2
on
water (De Kauwe et al., 2013), C (M. G. De Kauwe et al.,
unpublished) and N cycling. Each of the participating models
incorporates the major processes by which the N cycle affects the
ecosystem’s response to eCO
2
, such as plant N uptake, net N
mineralization and the ecosystem N balance, as well as emergent
ecosystem properties, such as the N-use efficiency (NUE) of plant
production (Fig. 1). The representation of these processes varies
greatly among models (Table A1), illustrating a lack of consensus
on the nature of the mechanisms driving these processes. Our
objectives in this study were as follows:
(1) to understand the eCO
2
responses predicted by each model
for the two sites in terms of their assumptions and representations
of C–N cycle processes, and
Leaves
Wood
Roots
Labile pool
N deposition
CO2
Litter
Soil organic material
Soil
microbiota Ninorg
C
N
N2
1
2
5
4
3
N leaching
Gaseous N loss
Fig. 1 Conceptual diagram of the major nitrogen (N) and carbon (C) flows
and stores in a terrestrial ecosystem. Blue arrows denote C fluxes and red
arrows N fluxes between major plant compartments (green) and soil pools
(black). Numbers 1–5 mark important C–N cycle linkages as described in
the Evaluation framework section: 1, N-based gross primary production
(GPP
N
): the return of C assimilates per unit canopy N (Eqn 1); 2, whole-
plant nitrogen-use efficiency (NUE): the total amount of foliar, root and
woody production per unit of N taken up by plants; this process depends
on the allocation of growth between different plant compartments
(e.g. leaves, fine roots and wood) and the C : N stoichiometry of each
compartment (Eqn 2); 3, plant N uptake (fN
up
): the capacity of the plants
to take up N from the soil (Eqn 4a). The plant-available soil N is
determined by two factors: 4, net N mineralization (fN
min
): the amount of
N liberated from organic material through decomposition, which varies
with microbial activity and litter quality (Eqn 4c); and 5, the net ecosystem
nitrogen exchange (NNE): based on N inputs from biological N fixation
(fN
fix
) and atmospheric deposition (fN
dep
) and N losses from the
ecosystem as a result of leaching to groundwater (fN
leach
) and gaseous
emission (fN
gas
) (Eqn 4b). As an emergent property, the net amount of C
that can be stored in an ecosystem following an increase in CO
2
depends
on the elevated atmospheric [CO
2
] (eCO
2
) effect on the ecosystem’s N
balance and the whole-ecosystem stoichiometry, which, in turn, depends
on the change in the C : N stoichiometry of vegetation and soil, as well as
the partitioning of N between vegetation and soil (Rastetter et al., 1992).
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(2) to use experimental observations to constrain these model
projections, where possible identifying the mechanisms that are
supported vs those that are not.
Given the number and complexity of the C–N processes that
determine the observed eCO
2
responses (Fig. 1), and the imprac-
ticality of measurement of every relevant C and N flux (e.g. N
losses to leaching and gaseous emission) and stock (e.g. changes
in organic soil N) with sufficient accuracy, we aimed to identify
those process representations that lead to responses qualitatively
in agreement with the available C and N cycle observations,
rather than identifying the model best fitting the observed NPP
responses.
Materials and Methods
Experimental sites
The Duke Forest FACE site was located in a loblolly pine (Pinus
taeda L.) plantation (35.97°N, 79.08°W) established in 1983 in
an open woodland partially covered with grass harvested as fodder
(McCarthy et al., 2007). The soil is relatively nutrient poor, with
forest production showing a substantial response to N fertilization
(Oren et al., 2001; Crous et al., 2008; Maier et al., 2008), as
evidenced from separate N fertilizer experiments in subplots,
which were not analysed in the present study. At the start of the
Duke FACE experiment in August 1996, trees were 15 yr old and
c. 14 m tall, with a mean summer leaf area index (LAI) of
3–4m
2
m
"2
(for the dominant pine species). The experiment con-
sisted of three sets of paired plots (pairs of ambient and elevated
[CO
2
], each 30 m in diameter) with different levels of tree produc-
tivity related to natural variations in soil N availability, affecting
ambient NPP, LAI and the C allocation to above- vs below-ground
compartments (Finzi et al., 2002; Palmroth et al., 2006; McCarthy
et al., 2007). One of each set of plots received continuous
enhanced [CO
2
] tracking ambient conditions +200 lmol mol
"1
.
The ORNL FACE site was located in a sweetgum (Liquidam-
bar styraciflua L.) plantation (35.9°N, 84.33°W) established in
1988 on a grassland. The soil at the site had a silty clay–loam
texture, and was moderately well drained and slightly acidic
(Norby et al., 2001; Warren et al., 2011). At the start of the
experiment, the c. 90 trees per 25-m treatment plot were c. 12 m
tall and in a linear growth phase. The LAI was 5.5 m
2
m
"2
, and
the canopy was no longer expanding (Norby et al., 2002). Five
treatments plots were established at the site, in two of which
exposure to eCO
2
commenced in April 1998, and continued
during the daylight hours of each growing season (April–Novem-
ber). The average daytime [CO
2
] from 1998 to 2008 growing
seasons was 547 lmol mol
"1
in the two CO
2
-enriched plots and
395 lmol mol
"1
in the three ambient plots.
Evaluation framework
Our approach to analysing the N cycle dependence of the NPP
response to eCO
2
was to break NPP down into its component
processes, thus benefitting from the suite of supplementary obser-
vations on these processes provided at each experiment. We
investigated how each model represented these individual pro-
cesses (Table A1) and compared model outputs against relevant
observations. The key C–N cycle processes controlling the
ecosystem response to eCO
2
(Fig. 1) can be grouped into two
major categories: processes affecting NUE (see below), which has
both photosynthetic and whole-plant components, and processes
affecting N uptake (fN
up
), which include the rate of net N miner-
alization (fN
min
), the competitive strength of plant vs soil micro-
organisms for N assimilation, and the ecosystem’s balance of N
inputs and losses (net ecosystem N exchange, NNE). All variables
used in the following are listed in Table A2.
N-use efficiency The change in gross primary production
(GPP) with eCO
2
can be decomposed into the changed C return
per unit of N investment into foliage, expressed as GPP per unit
leaf N (N-based GPP; GPP
N
) and the change in the amount of
leaf N. As the models only reported canopy-integrated values of
GPP and foliar N (N
can
), and GPP and autotrophic respiration
(R
a
) could not be measured directly, we analysed the eCO
2
effect
on the relationship between NPP and N
can
at the whole-ecosys-
tem level, by analysing the N-based NPP (NPP
N
) as:
NPPN¼NPP
Ncan
¼CUE $GPPN¼NPP
GPP
GPP
Ncan
Eqn 1
where CUE is the whole-plant C-use efficiency.
NPP is related to the amount of N available for growth by the
N requirements set by the relative proportion of biomass growth
of the different plant components and their C : N stoichiometry.
We decomposed the whole-plant NUE into changes in tissue
stoichiometry, changes in tissue allocation and retranslocation as
follows:
NUE ¼NPP
fNup
¼NPP
af$nf
qfþar$nr
qrþaw$nw
qw
!"
$NPP "ftransny"1
fBf
Eqn 2
where ais the fraction of NPP allocated to foliage (f), fine roots
(r) and woody (w) biomass, nis the respective tissue N concentra-
tion and ftrans $ny"1
fBfis the amount of N resorbed from the
canopy in the previous year. Each of these terms is available from
observations, including the amount of N retranslocated, which is
calculated from the difference in N concentration between green
foliage and leaf litter. Observed fN
up
at ORNL FACE also
included an estimate of foliar N uptake from atmospheric N
deposition, a process not included in the models, at the rate of
0.6 g N m
"2
yr
"1
for both ambient and elevated plots (Norby &
Iversen, 2006).
Net changes in vegetation C : N may differ from changes in
NUE because N becomes allocated to tissues with different life-
times. The effect of such changes is reflected in changes in the
mean residence time of N in vegetation:
sNveg ¼Nveg
fNup
Eqn 3
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where N
veg
is the total N in vegetation.
Plant N uptake The plant N uptake (fN
up
) can be expressed as
the sum of three factors: the rate of net N mineralization into the
inorganic N pool from litter and soil organic matter (SOM)
decomposition (fN
min
), the depletion of the soil inorganic N
pool (DN
inorg
) and any changes in NNE:
fNup ¼fNmin þNNE "DNinorg Eqn 4a
Changes in NNE depend on inputs from biological fixation
(fN
fix
) and atmospheric deposition (fN
dep
) and losses caused by
leaching (fN
leach
) and gaseous emission (fN
gas
):
NNE ¼fNfix þfNdep "ðfNleach þfNgas ÞEqn 4b
The rate of net N mineralization (fN
min
) can also be separated
into two factors: the effect of accumulating soil N during the
course of the experiment and changes in the ratio of microbial N
immobilization to gross N mineralization as follows:
fNmin ¼NSOM
sNSOM
Eqn 4c
where N
SOM
is the size of the decomposing SOM pool, here
including the litter layer, and sNSOM is its apparent turnover time.
sNSOM is constant, as long as the ratio of gross N mineralization
to immobilization and the allocation of N to SOM pools with
different lifetimes do not change. Increasing immobilization as a
result of reduced litter quality will increase sNSOM , whereas
increased gross mineralization from increased microbial N uptake
and release will decrease sNSOM . Insufficient observations were
available to constrain the change in fN
up
component processes
during the course of the experiment (Iversen et al., 2011).
Ecosystem stoichiometry The total ecosystem C stored in a for-
est relates to the total ecosystem N as follows (Rastetter et al.,
1992):
Corg ¼fveg
Cveg
Nveg
þð1"fveg ÞCsoil
Nsoil
#$
Norg Eqn 5
where N and C are the N and C pools, respectively, for vegeta-
tion (veg), soil (soil) or total organic (org), and f
veg
is the fraction
of ecosystem N in vegetation. For the sake of simplicity, litter
pools were subsumed to the soil pools.
Observations
Observed annual changes in C and N cycle parameters were
taken from the FACE Data Management System web repository
(http://public.ornl.gov/face), as well as published literature,
where indicated below. N cycle observations from Duke FACE
were only available from 1996 to 2005, and so most of the
analyses in this article are focused on this period, although NPP
and meteorological forcing data for each treatment plot were
available until 2007. The ORNL FACE experiment ran from
1998 to 2009, and data through 2008 were available for this
study.
For Duke FACE, standing biomass and biomass production in
each plot for three plant compartments (foliage, fine roots and
woody biomass, including branches and coarse roots) were taken
from McCarthy et al. (2010), using the C and N concentration
data for each plant compartment reported by Finzi et al. (2007)
to estimate C and N stocks and fluxes. Plant N requirements and
uptake were calculated from these data following Finzi et al.
(2007). Forest floor and SOM C and N concentrations were
obtained from Lichter et al. (2008).
For ORNL FACE, standing biomass, annual biomass produc-
tion, their respective C and N concentrations, as well as inferred
N requirements and plant N uptake by plot and plant compart-
ment (foliage, fine roots and woody biomass, including branches
and coarse roots), were obtained from Norby et al. (2010). Initial
and final SOM stocks and their C and N concentrations were
obtained from Johnson et al. (2004), Jastrow et al. (2005) and
Iversen et al. (2012). Differences in sampling design and soil bulk
density measurements prevent an accurate calculation of the
change in soil C and N during the course of the experiment
(Iversen et al., 2012). Comparing the % C and N data in Johnson
et al. (2004) and Iversen et al. (2012), we estimated that
10 (21% of the greater C and N stocks in the elevated plots at
the end of the experiment (Iversen et al., 2012) were a result of
eCO
2
, whilst the rest were a result of initial differences among
the plots. Combined with the standard errors of the measure-
ments, eCO
2
led to an increase in SOM to a depth of 90 cm of
160 (188 g C m
"2
and 11.6 (24.6 g N m
"2
between the
beginning and end of the experiment.
The data analyses outlined in the Evaluation framework sec-
tion were made using data by plot and year. For Duke FACE,
responses were calculated per plot pair, and reported as the mean
and standard error across the three pairs. For ORNL FACE, the
analyses were performed with the mean and standard error across
the average of the two eCO
2
plots compared with the average of
the three ambient CO
2
plots.
Ecosystem models
In this study, we used the same set of 11 process-based ecosystem
models as described by A. P. Walker et al. (unpublished), encom-
passing stand (GDAY, DAYCENT, TECO), age/size gap
(ED2.1), land surface (CABLE, CLM4, EALCO, ISAM, OCN)
and dynamic global vegetation (LPJ-GUESS, SDGVM) models.
A detailed account of the major N cycle processes represented in
each model is given in Table A1. The model simulations covered
the time periods representative of the FACE experiments. Meteo-
rological and [CO
2
] data, as well as site history and stand charac-
teristics, were provided in a standardized manner (http://public.
ornl.gov/face).
All models (except CABLE and ED2.1) followed a similar pro-
tocol to derive the initial soil C and N pools of the sites, which
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considered the past land use, as well as the historic evolution of
atmospheric CO
2
concentration and N deposition, and site-spe-
cific meteorological driver data from during the FACE experi-
ments were used throughout the spin-up. The forest vegetation
of the plots was initialized such that the forests had the correct
age and structure, as far as considered by the model, at the begin-
ning of the eCO
2
treatment. Details of the spin-up phase varied
among models because of differences in model structure (A. P.
Walker et al., unpublished). Inherently different assumptions of
the models regarding soil C residence times and ecosystem N loss
rates, as well as pre-FACE grassland productivity and N fixation,
led to a notable spread in the initial amounts of modelled C and
N pools, net N mineralization rates and thus NPP, despite the
common initialization protocol.
Model outputs were provided at hourly or daily time steps, as
appropriate. These outputs contained estimates of the various C,
N and water fluxes and pools.
Results
Overall response to eCO
2
Observed ambient NPP and inferred fN
up
at Duke FACE were
both slightly larger than at ORNL FACE (Figs 2, 3a,b), implying
that the whole-plant NUE was similar between the sites (Fig. 4)
at 121 (2gCg
"1
N in the ambient plots (1997–2005 mean)
for Duke FACE and 129 (13 g C g
"1
N at ORNL. This simi-
larity between sites is in contrast with an earlier study (Finzi
et al., 2007), because the corrections in biomass estimates by
McCarthy et al. (2010) resulted in a downward adjustment in the
estimate of NUE at Duke Forest.
The interquartile range of the model ensemble included the
observed ambient NPP at both sites. However, there was
significant spread across the models, resulting to a large extent
from different model spin-ups, which led to different levels of N
constraints on plant production. Only a few of the models
(GDAY, OCN) captured the decline in NPP in the ORNL
ambient plots related to declining soil N availability over the
course of the experiment (Norby et al., 2010; Garten et al.,
2011). Although the models, on average, matched the inferred,
observation-based fN
up
at Duke Forest, they overestimated
fN
up
at ORNL (Fig. 3). On average, the models slightly underes-
timated NUE at Duke and more strongly at ORNL FACE
(Fig. 4). The primary cause for the underestimation was a high
bias in the simulation of the fractional (C) allocation to fine roots
at both sites (M. G. De Kauwe et al., unpublished). At ORNL
FACE, this difference was accentuated by higher modelled than
observed N concentration of the fine roots (average 1.4% mod-
elled vs 0.7% observed).
Elevated CO
2
increased NPP in the initial (first) year of the
experiments by 25 (9% and 25 (1% at Duke and ORNL
FACE, respectively, according to the measurements (Figs 2c,d,
5a,b). Most models simulated an initial (first year) increase in
NPP as a result of eCO
2
that was close to the observations. Nota-
ble exceptions were CABLE and CLM4, which systematically
underestimated the initial response at both sites, as well as
EALCO and ISAM, which overestimated the response for Duke
FACE (Fig. 5a,b). Nonetheless, no model simulated the underly-
ing changes in fN
up
and NUE correctly for both sites. At Duke
Forest, according to the measurements, the increase in NPP was
associated with a strong increase in fN
up
. The models generally
underestimated the observed increase in fN
up
and overestimated
the increase in NUE. At ORNL, according to the measurements,
the initial increase in NPP was associated with nearly equal
increases of fN
up
and NUE (Fig. 5). Some models simulated a
change in NUE in agreement with the observations (DAYCENT,
1996 1998 2000 2002 2004 2006
0
500
1000
1500
Years
1996 1998 2000 2002 2004 2006
Years
NPP (g C m−2 yr−1)
NPP (g C m−2 yr−1)
Duke FACE
0
500
1000
1500
ORNL FACE
−20
0
20
40
60
1998 2000 2002 2004 2006 2008
Years
1998 2000 2002 2004 2006 2008
Years
NPP response (%)
NPP response (%)
Duke FACE
−20
0
20
40
60 ORNL FACE
observed
multi−model mean
interquartile model range
model range
individual models
(a)
(c) (D)
(b)
Fig. 2 Ambient net primary production (NPP;
a, b) and its response to elevated CO
2
(c, d)
at the Duke (a, c) and Oak Ridge National
Laboratory (ORNL) (b, d) Free-Air CO
2
Enrichment (FACE) experiments. The
observations are across-plot averages, and
the error bars denote (1SE.
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GDAY, ISAM, LPJ-GUESS, OCN, TECO), but most models
had a tendency to underestimate the increase in fN
up
.
The observed responses at the end of the experiment differed
strongly between the two experiments (Fig. 5c,d): the CO
2
response of NPP at Duke Forest was maintained throughout the
experiment, because the initial increase in fN
up
was sustained
with little change in whole-plant NUE. At ORNL, the CO
2
response of NPP declined over time, because the initial increase
in NUE declined as a result of higher allocation to N-rich fine
roots. At the end of the experiment, NUE and fN
up
were similar
between ambient and elevated plots.
Most models showed signs of PNL (i.e. a progressively smaller
enhancement in NPP as a result of N limitation) towards the end
of the experiment at both sites (Fig. 5c,d), but with varying
strength and timing, causing an increasing spread among the
models with the duration of the experiment. At Duke FACE, the
models largely failed to capture the sustained NPP response to
11 yr of eCO
2
. The decline occurred despite increasing
1996 1998 2000 2002 2004 2006
0
5
10
15
20
Years
fNup (g N m–2 yr–1)
Duke FACE
1998 2000 2002 2004 2006 2008
0
5
10
15
20
Years
1996 1998 2000 2002 2004 2006
Years
1998 2000 2002 2004 2006 2008
Years
ORNL FACE
−20
0
20
40
60
fNup response (%)
fNup (g N m–2 yr–1)fNup response (%)
Duke FACE
−20
0
20
40
60 ORNL FACE
observed
multi−model mean
interquartile model range
model range
individual models
(a) (b)
(c) (d)
Fig. 3 Ambient plant nitrogen (N) uptake
(fN
up
; a, b) and its response to elevated CO
2
(c, d) at the Duke (a, c) and Oak Ridge
National Laboratory (ORNL) (b, d) Free-Air
CO
2
Enrichment (FACE) experiments. The
observations are across-plot averages, and
the error bars denote (1SE.
1996 1998 2000 2002 2004 2006
0
50
100
150
200
250
300
Years
NUE (g C g−1 N)
NUE (g C g−1 N)
Duke FACE
1998 2000 2002 2004 2006 2008
0
50
100
150
200
250
300
Years
1996 1998 2000 2002 2004 2006
Years
1998 2000 2002 2004 2006 2008
Years
ORNL FACE
−20
0
20
40
60
NUE response (%)
Duke FACE
−20
0
20
40
60
NUE response (%)
ORNL FACE
observed
multi−model mean
interquartile model range
model range
individual models
(a) (b)
(c) (d)
Fig. 4 Ambient whole-plant nitrogen-use
efficiency (NUE; a, b) and its response to
elevated CO
2
(c, d) at the Duke (a, c) and
Oak Ridge National Laboratory (ORNL)
(b, d) Free-Air CO
2
Enrichment (FACE)
experiments. The observations are across-
plot averages, and the error bars denote
(1SE.
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whole-plant NUE, because the models were not able to maintain
an increased fN
up
as observed (with the exception of ED2.1). At
ORNL FACE, three of the 11 models correctly simulated the
10% decline in the initial response towards the end of the experi-
ment (DAYCENT, LPJ-GUESS, SDGVM), and two models
(GDAY, OCN) showed an even stronger decline, related to an
early simulated onset of N limitation in the ambient treatment.
Two models (ED2.1 and TECO) predicted an increase in the
NPP response over time, fuelled by increases in plant N uptake,
which were supported by a large pool of easily degradable SOM
and inorganic N prescribed as initial conditions. Contrary to the
observations, NUE and vegetation C : N strongly increased at
ORNL in most models by the end of the experiment.
Processes affecting NUE
N-based GPP and NPP Models differed strongly in their initial
NPP
N
response to eCO
2
(Fig. 6), generally overestimating the
observed initial 11 (8% increase in NPP
N
at Duke FACE and
underestimating the observed 35 (4% increase at ORNL FACE.
Although N limitation did not strongly affect GPP
N
in the first
year in most models, there were substantial differences in the first
year’s response among the models, in particular at ORNL FACE.
Two models (CABLE and CLM4) showed an exceptionally low
initial response of NPP at both sites (Fig. 5). This low response
was related to a near-zero response of GPP
N
(Fig. 6a,b). In
CLM4, this response resulted from the assumption that plants
down-regulate GPP directly when N limited: CO
2
fertilization of
GPP is calculated in the absence of N limitation, and then
reduced using N-limitation scalars if fN
up
is insufficient to
support this amount of productivity. This low response did not
happen in other models that followed a similar approach (DAY-
CENT and ED2.1), because of sufficient initial N supply.
Another class of models simulated photosynthesis based on foliar
N content (CABLE, GDAY, LPJ-GUESS, OCN, SDGVM,
TECO). In these models, N limitation on GPP acts via foliar N
concentrations: limited N availability reduces foliage N, which
feeds back to limit GPP. This limitation takes time to develop,
such that it was absent or weak in the initial response, but with a
strong component of down-regulation in the longer term (Fig. 6c,d).
Model predictions of the eCO
2
effect on the other component
of NPP
N
, CUE (Eqn 1), can be readily categorized into three
groups as follows:
(1) models that assume that NPP is a fixed proportion of
GPP (GDAY and DAYCENT) showed no change in
CUE;
(2) models that estimate R
a
directly from biomass and
temperature (CABLE, CLM4, EALCO, ED2.1,
ISAM, LPJ-GUESS, SDGVM, OCN and TECO)
predicted a transient increase in CUE, because the
increase in respiration as a result of increased biomass
lagged behind the immediate eCO
2
effect on GPP.
These models generally showed that CUE returned to
its original value within the time course of the experi-
ment (10 yr). In addition to these processes;
(3) some models (CABLE, OCN) increased R
a
under
nutrient stress, when stoichiometric constraints pre-
vented allocation of the assimilated C to growth.
Initial eCO2 response (%)
Initial eCO2 response (%)
−10
0
10
20
30
40 (a) (b)
−10
0
10
20
30
40
Duke FACE
∆NPP
∆fNup
∆NUE
∆NPP
∆fNup
∆NUE∆NUE
∆NPP
∆fNup
∆NUE
∆ eCO2 response (end−initial) (%)
∆ eCO2 response (end−initial) (%)
−30
−20
−10
0
10
20
−30
−20
−10
0
10
20
−10
0
10
20
30
40
−10
0
10
20
30
40
ORNL FACE
(c) (d)
Duke FACE ORNL FACE
−30
−20
−10
0
10
20
−30
−20
−10
0
10
20
OBS
CABLE
CLM4
DAYC
EALCO
ED2.1
GDAY
ISAM
LPJ−GUESS
OCN
SDGVM
TECO
∆fNup
∆NPP
Fig. 5 First year response of net primary
production (NPP) to elevated atmospheric
[CO
2
] (eCO
2
) (a, b) and the change between
the first year and the final 5 yr of the
experiment (c, d) at the Duke and Oak Ridge
National Laboratory (ORNL) Free-Air CO
2
Enrichment (FACE) sites, respectively, as well
as the response of plant nitrogen (N) uptake
(fN
up
) and whole-plant N-use efficiency
(NUE). The grey boxes denote the mean
observed eCO
2
response (1SE.
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For example, at ORNL FACE, CUE in OCN fell noticeably
during the last years of the experiment (Fig. 6d). This change was
driven by a growing N limitation, which resulted in a build-up of
labile C. Increased respiration was used as a mechanism to
remove this excess accumulated C.
Whole-plant NUE With eCO
2
, observed NUE at Duke Forest
increased by 5 (2%, mainly because of a shift of allocation
towards lower C : N tissue (wood), whereas the 4 (3% decline
in foliar N had little effect on NUE (Fig. 7). Despite the initially
observed increase in NUE at ORNL FACE, NUE did not change
over the course of the experiment (+2(5%), as the effects of
increased tissue C : N were compensated by increased allocation
towards N-rich roots.
In the observations, the fraction of foliar N retranslocated
before leaf shedding did not change significantly with eCO
2
("1.1 (0.4% at Duke Forest, 0.0 (14.3% at ORNL
FACE), such that the retranslocation flux scaled with changes
in total canopy N (see Fig. 6). In most models (except EAL-
CO), the retranslocation fraction did not vary with foliar N
(or root N) content (Table A1), such that, in agreement with
observations, the retranslocation flux scaled with the total
foliage (and root) N change. The effect of eCO
2
on NUE
can therefore be simply separated into its effects on stoichi-
ometry and allocation (Fig. 7) for those models that produced
all of the variables required to perform these calculations.
The model ensemble includes four alternative hypothesis
combinations as to how whole-plant NUE changes with
eCO
2
, namely:
(1) assuming allocation and tissue stoichiometry to be constant
(CLM4, TECO);
(2) assuming flexible C : N ratios, but N-insensitive partitioning
fractions (CABLE, GDAY, EALCO, SDGVM);
(3) assuming constant tissue C : N ratios, but increasing root
allocation with N stress (ED2.1); and
(4) assuming the stoichiometry to be flexible and root allocation to
increase with N stress (DAYCENT, ISAM, LPJ-GUESS, OCN).
Although the modelled NUE responses differed in magnitude
among models, each model individually simulated similar trends
at both sites, such that none of the models was able to simulate
the observed difference in the NUE response between the sites, in
particular, the observation-based interannual variability of the
response at ORNL (Figs 4, 5). CABLE, which allows for the
acclimation of tissue C : N only within narrow bounds, showed
hardly any change in NUE, similar to CLM4, which simulates
fixed tissue stoichiometry and allocation fractions (Fig. 7). By
contrast, models with a large flexibility in tissue stoichiometry
(GDAY, LPJ-GUESS, OCN) consistently showed a stronger
change in NUE as a result of increases in tissue C : N ratios rather
than changes in allocation at both sites. The flexible C : N models
showed a strong decline of foliar N at both sites, leading to a
larger than observed decline in some models (Duke: CABLE,
GDAY, LPJ-GUESS, OCN; ORNL: GDAY), which contributed
to the excessive NUE response to eCO
2
of these models.
Initial eCO2 response (%)
Initial eCO2 response (%)
−20
−10
0
10
20
30
40
50
(a) (b)
(c) (d)
−20
−10
0
10
20
30
40
50
∆NPPN
∆CUE
∆GPPN
∆Ncan
∆ncan
∆NPPN
∆CUE
∆GPPN
∆Ncan
∆ncan
∆NPPN
∆CUE
∆GPPN
∆Ncan
∆ncan
∆NPPN
∆CUE
∆GPPN
Duke FACE
∆ eCO2 response (end−initial) (%)
∆ eCO2 response (end−initial) (%)
−30
−20
−10
0
10
20
30
−30
−20
−10
0
10
20
30
−20
−10
0
10
20
30
40
50
−20
−10
0
10
20
30
40
50
ORNL FACE
Duke FACE ORNL FACE
−30
−20
−10
0
10
20
30
−30
−20
−10
0
10
20
30
OBS
CABLE
CLM4
DAYC
EALCO
ED2.1
GDAY
ISAM
LPJ−GUESS
OCN
SDGVM
TECO
∆Ncan
∆ncan
Fig. 6 First year response of nitrogen (N)-
based net primary production (NPP
N
) to
elevated atmospheric [CO
2
] (eCO
2
) (a, b)
and the change between the first year and
the final 5 yr of the experiment (c, d) at the
Duke and Oak Ridge National Laboratory
(ORNL) Free-Air CO
2
Enrichment (FACE)
sites, respectively, as well as the response of
plant carbon (C)-use efficiency (CUE),
N-based gross primary production (GPP
N
)
and canopy N, expressed as total canopy N
(N
can
) and foliar N concentration (n
can
). The
grey boxes denote the mean observed eCO
2
response (1SE, where observations
corresponding to model output are available.
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The combined effect of the changes in allocation and stoichi-
ometry in most models was that sNveg first declined, as a result of
a greater growth of fast-overturning tissues (i.e. increased foliar
growth as a result of increased NPP), but increased later in the
experiment as tissue N concentration dropped and more N
became incorporated into woody tissue. This model outcome is
consistent with the observed response at Duke, but not ORNL
FACE, where the strong increase in fine root growth resulted in a
stronger decline in sNveg than suggested by the models.
In summary, models that include representations of flexible
tissue stoichiometry, photosynthesis calculations based on prog-
nostic foliar N and increasing fine root allocation under nutrient
stress were generally more consistent with the observed trends of
the component processes. However, because of difficulties in
capturing the timing and magnitude of the response of stoichi-
ometry and allocation (as well as the diverging predictions of
plant N uptake; see next section on Processes affecting plant N
uptake), these models did not appear to be generally superior to
the other models considered here in terms of predicting the CO
2
response of NPP.
Processes affecting plant N uptake
As outlined in Materials and Methods (Eqn 4), changes in mod-
elled fN
up
can be attributed to: changes in the rate of net N min-
eralization (fN
min
), which depends on the total amount of SOM
N (N
SOM
) and its turnover time (sNSOM ); changes in the rate of
depletion of the soil inorganic matter pool (DN
inorg
); and
changes in NNE.
In SDGVM, fN
up
was driven with observations and therefore
this model is not considered further in this section. Among the
other models, there are two alternative implementations of the
processes that allow for a preferential increase in fN
up
compared
with microbial N immobilization under eCO
2
, leading to
contrasting predictions (Fig. 8a,b).
The first, employed by CLM4, is to increase the relative com-
petitiveness of plants vs microbes for N. The plant’s N demand is a
function of potential GPP, which increases with eCO
2
. Conversely,
the microbial N demand does not change strongly with eCO
2
,
because CLM4 assumes fixed tissue C : N and therefore simulates
no change in litter quality with eCO
2
, which would increase the N
requirement of microbes and therefore immobilization. As a result,
CLM4 showed a sustained increase in fN
up
at Duke FACE, because
less N was immobilized than under ambient conditions (Fig. 8d).
The second mechanism is an emergent property of the
CENTURY model (used by CABLE, DAYCENT, GDAY,
LPJ-GUESS and OCN): initial increases in fN
up
as a result of
enhanced NPP lower soil inorganic N availability, which
increases the C : N ratio of the newly formed SOM according to
an empirical relationship. This reduces N immobilization during
litter decomposition, as less N needs to be sequestered for the
same amount of litter C transfer, increasing the availability of
inorganic N for fN
up
(Fig. 8e). In most of these models, the
increase was dampened or reversed within a few months or years
because the models also apply a flexible tissue C : N. Increased N
stress increased tissue (and therefore also litter) C : N ratios,
leading to higher microbial N immobilization and therefore a
reduction in the net N mineralization (fN
min
) to ambient or even
below ambient rates, reflected as an increase in sNSOM , and there-
fore a decrease in the availability of inorganic N (Fig. 8e).
A second factor affecting the eCO
2
response of fN
up
is the
initial size of the inorganic N pool. Some models simulated an
initial excess of inorganic N relative to plant N demand because
of the site history (or the spin-up procedure; ED2.1, CABLE at
Duke FACE and TECO at ORNL). An example is CABLE at
Duke Forest (Fig. 8c), in which the initial increase in fN
up
was
supported by the initially available inorganic N pool. This pool
became exhausted after a few years of the experiment, leading to
lower fN
up
relative to the ambient plots in the later years of the
OBS
CABLE
CLM4
DAYC
EALCO
ED2.1
GDAY
ISAM
LPJ−GUESS
OCN
TECO
eCO2 response (%)eCO2 response (%)
−20
−10
0
10
20
30
40
(a)
−20
−10
0
10
20
30
40
Duke FACE
(b)
OBS
CABLE
CLM4
DAYC
EALCO
ED2.1
GDAY
ISAM
LPJ−GUESS
OCN
TECO
−20
−10
0
10
20
30
40
−20
−10
0
10
20
30
40
ORNL FACE
Fig. 7 Change in nitrogen (N)-use efficiency of biomass production (NUE)
at Duke (a) and Oak Ridge National Laboratory (ORNL) (b) Free-Air CO
2
Enrichment (FACE) sites, integrated over the entire length of the
experiment (1997–2005 and 1998–2008 for Duke and ORNL FACE,
respectively). ∆NUE
alloc
denotes the change in NUE attributed to changes
in allocation to leaves, fine roots and wood, whereas ∆NUE
stoch
denotes
the change in NUE as a result of altered tissue C : N. The error bars denote
(1SE. Black bars, ∆NUE; blue bars, ∆NUE
alloc
; red bars, ∆NUE
stoch
.
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experiment. The TECO model at ORNL had a much larger
SOM pool, and with it gross N mineralization, than required by
the forest’s productivity, leading to a constant excess supply of N,
which supported fN
up
under eCO
2
.
The third factor is the ecosystem N balance (NNE), which
depends on the rates of input via deposition and fixation, and the
rates of loss via leaching and volatilization. A few models in the
ensemble (CABLE, CLM4) simulated biological N fixation
explicitly, but none suggested that eCO
2
would alter fixation
such that it would affect the net N balance. For the other models,
the principal difference affecting total ecosystem N balance was
whether the N losses were assumed to be proportional to the
amount of N mineralized (CABLE, CLM4, GDAY, TECO) or
whether they were a function of the simulated inorganic N
concentration (CABLE, CLM4, EALCO, ISAM, LPJ-GUESS,
OCN). In some of the models (CABLE, CLM4, DAYCENT,
GDAY, LPJ-GUESS, OCN), ecosystem N losses were reduced,
but the causal mechanism differed between the models: for exam-
ple, GDAY, in which fN
up
is assumed to be independent of plant
N demand, and therefore eCO
2
,fN
min
declined as a consequence
of the higher microbial immobilization (higher litter C : N),
which decreased directly the gaseous N losses in addition to
reducing N leaching, because of lower soil inorganic N. In OCN,
higher fN
up
and increased N immobilization led to lower inor-
ganic N, causing both lower gaseous and leaching losses.
In most models, the change in NNE was of the order of
1gNm
"2
over 10 yr. This reduction in N loss was not sufficient
to prevent the onset of PNL in forests that take up
8.3 (0.4 g N m
"2
yr
"1
, on average. The only exception to this
pattern was the simulation of CLM4 at Duke FACE, where larger
increases in fN
up
substantially reduced gaseous N losses during
autumn and winter, leading to a cumulative increase in fN
up
of
12 g N m
"2
(Fig. 8a). Although this sustained increase avoided
the progressive decline of fN
up
in CLM4, it was not sufficient to
explain the observed increase in vegetation N at Duke FACE.
Time-integrated effect of eCO
2
on ecosystem C and N
At Duke, c. 80% of the observed increase in cumulated NPP
(3.1 (0.6 kg C m
"2
; 1997–2005) was sequestered in vegetation
(2.5 (0.5 kg C m
"2
) and forest floor C (0.3 (0.1 kg C m
"2
),
whereas soil C declined by c. 0.2 (0.1 kg C m
"2
(Supporting
Information Fig. S1). These changes were associated with
increased vegetation N (12.2 (2.9 g N m
"2
), litter N
−20
−10
0
10
20
30
40
50
OBS
CABLE
CLM4
DAYC
EALCO
ED2.1
GDAY
ISAM
LPJ−GUESS
OCN
TECO
Cumulative ∆Nup (gN m−2)
−20
−10
0
10
20
30
40
50
(a)
Duke FACE
∆fNup
∆τNSOM
∆NSOM
∆NNE
∆Ninorg
−20
−10
0
10
20
30
40
50
OBS
CABLE
CLM4
DAYC
EALCO
ED2.1
GDAY
ISAM
LPJ−GUESS
OCN
TECO
Cumulative ∆Nup (gN m−2)
−20
−10
0
10
20
30
40
50
(b)
ORNL FACE
∆fNup
∆τNSOM
∆NSOM
∆NNE
∆Ninorg
1996
1998
2000
2002
2004
2006
−3
−2
−1
0
1
2
3
Years
∆N flux (g N yr−1)
(c)
Duke FACE − CABLE
1996
1998
2000
2002
2004
2006
−2
−1
0
1
2
Years
∆N flux (g N yr−1)
(d)
Duke FACE − CLM4
1996
1998
2000
2002
2004
2006
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5
Years
∆N flux (g N yr−1)
(e)
Duke FACE − OCN
Fig. 8 Cumulative plant nitrogen (N) uptake
as a result of elevated atmospheric [CO
2
]
(eCO
2
) over the length of the experiment,
and its assignment to different mechanisms
according to Eqns 4 and 5 at the Duke (a)
and Oak Ridge National Laboratory (ORNL)
(b) Free-Air CO
2
Enrichment (FACE) sites.
Positive values indicate an increase in plant N
uptake, and negative values a decline. (c–e)
Exemplary time courses of the net N balance
for Duke forest, as predicted by CABLE (c),
CLM4 (d) and OCN (e). ∆fN
up
, plant
nitrogen uptake; sNSOM , change in net N
mineralization caused by a change in the soil
organic N turnover time relative to the soil
organic C turnover time; ∆N
SOM
, change in
net N mineralization caused by a change in
the organic N pool; ∆NNE, change in the
ecosystem N balance (sum of N increases
from biological N fixation and atmospheric N
deposition and N losses to leaching and
gaseous emissions); ∆N
inorg
, changes in the
inorganic N pool. The error bars on the
observations denote (1SE.
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(6.8 (2.6 g N m
"2
) and decreased soil N (25.0 (7.0 g N m
"2
).
At ORNL, the observed enhancement of NPP (1.7 (
0.4 kg C m
"2
; 1998–2008) did not result in a significant change
in biomass (0.0 (0.7 kg C m
"2
and 1.2 (1.7 g N m
"2
, respec-
tively), but soil C and N pools were increased slightly
(0.2 (0.2 kg C m
"2
and 11.5 (12.3 g N m
"2
, respectively).
Most of the models suggested that a large fraction of the NPP
enhancement remained in vegetation C (Fig. S1), in agreement
with the observed trends at Duke FACE, but in disagreement
with those observed at ORNL FACE. Nevertheless, most models
underestimated vegetation C sequestration at Duke FACE,
because they underestimated the NPP enhancement and failed to
predict the decline in SOM. Most models overestimated vegeta-
tion C sequestration in ORNL FACE, mostly related to failure in
capturing accurately the allocation pattern and response (M. G.
De Kauwe et al., unpublished; Fig. S1).
The large observed increase in vegetation biomass at Duke
Forest was supported mostly by a redistribution of N from soil to
vegetation, as soil N stocks in the upper soil layers have probably
declined over the course of the experiment (Fig. 9a). However,
there were significant differences in the magnitude of the transfer
and vegetation C : N changes among the plots, causing large
uncertainty in the attribution of the observed vegetation C
increase. Although fN
up
also increased in ORNL FACE, there
was not a sustained increase in biomass N and C, because the
rapid turnover of leaves and roots did not lead to a sustained
increase in biomass N and C, which instead caused C and N
sequestration in SOM (within the detection limit; Fig. 9b). At
both sites, bulk vegetation C : N decreased slightly with eCO
2
,
despite the larger C : N in foliage, because of the larger contribu-
tion of foliage and root biomass to total biomass.
Consistent with the observations, increased organic ecosystem
N (N
org
) played a minor role in most models (Fig. 9). The excep-
tions of ED2.1 and TECO at Duke Forest were related to the
assumed initial conditions (see section on Processes affecting
plant N uptake). Changes in the ecosystem N balance, that is
reduction in N losses, led to <500 g C m
"2
additional C seques-
tration (CLM4 and CABLE at Duke Forest; DAYC and LPJ-
GUESS at ORNL FACE). Contrary to the observations, models
that assume a flexible tissue C : N ratio (CABLE, EALCO,
GDAY, LPJ-GUESS, OCN) predicted that a large fraction of the
increase in ecosystem C storage at both sites as a result of eCO
2
resulted from the increase in vegetation C : N ratios (see section
on Processes affecting NUE). Only CLM4, which assumes fixed
tissue stoichiometry, correctly predicted the decline in total vege-
tation C : N ratio at Duke Forest and the ensuing reduction in
vegetation C storage capacity; this response resulted from the
increase in foliar and root biomass. Changes in litter and soil
C : N were generally of lesser importance in absolute terms, and
roughly agreed with the observations. An exception to this was
the projected large increase in litter C : N by LPJ-GUESS at
ORNL FACE, associated with large litter fall of the deciduous
trees and a strong decline in leaf N concentrations.
At Duke Forest, most models suggested that there was a net
transfer of N to the vegetation (as a result of the increased fN
up
),
which supported C accumulation in vegetation. However, the
predicted increase was always less than half that observed. In
LPJ-GUESS, the cumulative effect was a net transfer of N to the
soil, probably related to the large fraction of C (and thus N) allo-
cated to fast-overturning tissues (M. G. De Kauwe et al., unpub-
lished). A net N transfer to vegetation initially also occurred in
most models at ORNL FACE. However, in GDAY, LPJ-GUESS
and OCN, the larger litter fall and the decreased litter C : N ratio
at the deciduous site led to increased immobilization of N during
decomposition. This provided a mechanism by which plant-
available N became trapped in the SOM pool, effectively reduc-
ing the fraction of ecosystem N stored in vegetation, consistent
with the PNL hypothesis.
Discussion
The analyses presented here have separated the eCO
2
response
into time-dependent, observable components of the C and N
cycle responses, which can be used to evaluate individual model
processes and to identify key model weaknesses, as well as to
identify the need for more observational constraints. The climate
OBS
CABLE
CLM4
DAYC
EALCO
ED2.1
GDAY
ISAM
LPJ−GUESS
OCN
TECO
Change in ecosystem C (g C m−2)
Change in ecosystem C (g C m−2)
−1000
0
1000
2000
3000
4000 (a) (b)
Duke FACE
OBS
CABLE
CLM4
DAYC
EALCO
ED2.1
GDAY
ISAM
LPJ−GUESS
OCN
TECO
−1000
0
1000
2000
3000
ORNL FACE
∆N
org
∆C : N
veg
∆C : N
soil
∆N
veg
/N
org
∆C
org
Fig. 9 Total change in ecosystem carbon
(∆C
org
) as a result of elevated atmospheric
[CO
2
] (eCO
2
) at the Duke (a) and Oak Ridge
National Laboratory (ORNL) (b) Free-Air
CO
2
Enrichment (FACE) sites resulting from
changes in the total organic ecosystem
nitrogen (N) store (∆N
org
), and vegetation
and soil C : N ratios (∆C:N
veg
and ∆C:N
soil
),
as well as changes in the fractionation of
total ecosystem N between vegetation and
soil, measured as the fraction of total
ecosystem N in vegetation (f
veg
=N
veg
/N
org
).
The error bars denote (1SE.
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and N inputs, as well as the initial ambient levels of production,
N uptake and NUE, were similar between the two sites, leading
to the expectation that the different long-term trends in the
eCO
2
response of NPP and N uptake at Duke and ORNL FACE
could be explained by processes associated with the different veg-
etation types encoded in the models. Despite the success of the
models to simulate the initial eCO
2
response of NPP at both
sites, the models did not encode the relevant processes to explain
the observed differences. Rather, most models followed the
ORNL trajectory (progressively increasing N limitation) at both
sites. In the following, we discuss the process representation of
the most important C–N cycle linkages that contribute to the site
and model–data differences.
Model responses and underlying processes
Plant N uptake and net N mineralization The increase in
fN
up
at Duke FACE was twice as large as that seen at ORNL
FACE, in absolute terms and when integrated over the time of
the experiment. This is a key factor in the observed, divergent
NPP response at the two sites. The ensemble of models generally
failed to simulate the magnitude of the observed increase in
fN
up
and the large difference between the sites, although some of
the models possess mechanisms to increase root growth, and the
specific N
inorg
uptake capacity of roots or whole plants, under N
stress. In most models, fN
up
was tightly constrained by fN
min
, but
only few ecosystem-scale observations are available for this quan-
tity (Iversen et al., 2011). At ORNL FACE, the increased fN
up
was probably related to the presence of plant-available N below
the rooting zone of trees at the beginning of the experiment,
resulting from past land use. Increased tree rooting depth and,
probably, stimulation of SOM decomposition in these layers have
added plant-accessible N (Iversen et al., 2008, 2011). The consid-
eration of SOM depth profiles is missing in most ecosystem mod-
els, but this is likely to be relevant only under site conditions in
which past land use determines the depth distribution of SOM.
Increased microbial and fungal SOM decomposition following
increased rhizodeposition (so called ‘priming’) is probably the
cause of the large N transfer from soils to plants at Duke FACE
(Drake et al., 2011); this is a further process not represented by
the model ensemble. It is an open question whether this finding
implies that models that do not incorporate such a mechanism
must also have a low NPP response to gradually increasing atmo-
spheric [CO
2
]. Under these conditions, the more gradual increase
in plant N demand (Luo & Reynolds, 1999) might be satisfied by
other mechanisms, such as the tightening of the ecosystem N bal-
ance or increased N fixation. Moreover, CENTURY-based mod-
els (DAYCENT, GDAY, OCN, LPJ-GUESS, TECO), which
mimic the net transfer of N from soils to vegetation under increasing
N stress,showed that the net N transfer based on N mining was lim-
ited. The pool of easily degradable N-rich material declined as a
result of the increased N mining and declining litter quality, suggest-
ing that ‘priming’ might be a temporary process relieving N stress.
NUE and ecosystem stoichiometry The observed initial
increase in whole-plant NUE was stronger at ORNL than at
Duke Forest, and can largely be explained by the different magni-
tude of decline in foliar N concentrations and the diverging
trends of total canopy N (Fig. 6). The NUE enhancement
decayed at ORNL FACE with increasing root allocation during
the experiment, such that there was no strong change in NUE
with eCO
2
at both sites. The inclusion of flexible C : N stoichi-
ometry, alongside increased below-ground allocation in response
to eCO
2
and increased plant N demand (M. G. De Kauwe et al.,
unpublished), appeared to be an important feature allowing the
NUE response to CO
2
to be captured because of the significant
changes in foliar N concentrations. However, models that simu-
late flexible stoichiometry tended to overestimate the whole-plant
NUE increase with eCO
2
. The probable reason for this overesti-
mation is that the predicted changes in tissue C : N are not based
on a hypothesis-driven prediction of C : N changes, but rather
the emergent model outcome, as flexible stoichiometry in these
models is the means to regulate C assimilation given plant-avail-
able N. Although the marginal change in photosynthetic capacity
can be larger than the marginal change in foliar N (Friend et al.,
1997), this does not seem to be sufficient to keep tissue C : N
within the observed bounds, as shown by an exaggerated decline
in foliar N concentrations at both sites. Other regulatory mecha-
nisms, such as the acclimation of CUE under N stress, as imple-
mented in the OCN model, can limit the reduction in tissue
C : N ratios to variations within predefined bounds, but it is
unclear whether such a mechanism exists in reality. Modelling
approaches that maximize leaf photosynthetic gain given N and
C availabilities may provide a more reliable framework to predict
stoichiometric flexibility (Medlyn, 1996a; McMurtrie et al.,
2008; Xu et al., 2012; McMurtrie & Dewar, 2013).
At both sites, the eCO
2
effect on NPP
N
according to the
measurements initially increased (more so at ORNL than Duke
FACE), but then declined to very low values of enhancement. In
deciduous trees at both sites, this decline was not associated with a
change in the relationship of photosynthetic biochemistry (V
cmax
,
the maximum rate of carboxylation; V
jmax
, the maximum rate of
electron transport at saturating irradiance) with leaf N (Norby
et al., 2010; Ellsworth et al., 2011), whereas, at Duke Forest, older
pine needles showed a reduced V
cmax
per unit leaf N (Ellsworth
et al., 2011). A number of models implement a leaf N dependence
of photosynthetic biochemistry (Table A1), and a few captured
the overall trend in foliar N and GPP
N
. However, there was a
large spread in the simulated eCO
2
response of GPP
N
, both ini-
tially and in the longer term, despite the fact that (with the excep-
tion of DAYCENT) all models inherit the CO
2
sensitivity of
photosynthesis from the Farquhar model (Farquhar et al., 1980).
As the effect of eCO
2
on GPP
N
is immediate, the uncertainty in
the modelled initial GPP
N
response is independent of the repre-
sentation of N cycle feedbacks, and therefore not affected by the
step increase in CO
2
. The differences among models were main-
tained when analysing daily data with a restricted range of meteo-
rological parameters, instead of annually integrated values, a
finding that excludes any difference caused by phenological biases
(A. P. Walker et al., unpublished) which could also affect GPP
N
.
The probable cause of these differences is alternative assumptions
about the fraction of the canopy that is limited by light
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availability vs carboxylation rate, related to the canopy scaling of
N and the depths of the canopy (Medlyn, 1996b). Varying sto-
matal responses to eCO
2
may also have played a role (De Kauwe
et al., 2013). Reducing this uncertainty requires a better represen-
tation of the changes in foliar N and the slope of the V
jmax
:V
cmax
relationship within the canopy and across different ecosystems
(Maire et al., 2012). At the ecosystem level, alternative data
sources, light response curves of net ecosystem exchange or GPP,
derived from eddy covariance measurements, could facilitate the
evaluation of the canopy-level light response across ecosystem
types (Lasslop et al., 2010; Bonan et al., 2012).
Ecosystem N balance Uncertainties in the observed changes in
soil N stocks prevent any statistically meaningful assessment of
whether eCO
2
increased N capital as a result of changes in N
inputs or outputs. Some models simulated increased plant N
availability through reduced N losses from the ecosystem.
Although these mechanisms added up to 12 g N m
"2
(accumu-
lated over the length of the experiment) in the most extreme case,
they did not contribute strongly to the simulated C sequestra-
tion. Changes in the N balance may be an important factor in
modelled eCO
2
responses (Rastetter et al., 1997), but the effect
was not very pronounced in the ensemble used in this study.
None of these N loss reduction mechanisms was sufficient to
explain the observations at Duke FACE. In agreement with pre-
vious observationally based studies (Drake et al., 2011), we con-
clude that a mechanism that increases plant N availability under
plant N stress based on the enhanced mineralization of organic
N is required for models to explain the observed trends at Duke.
Limits of the observational constraints
The process inferences above rely on uncertain observations and
implicit assumptions that require careful interpretation. The esti-
mates of plant N uptake were inferred from the biomass produc-
tion of plant tissues, their N concentrations and foliar N recovery
on leaf shedding. Estimates of NPP and fN
up
are therefore not
independent, and so the estimated whole-plant NUE should be
considered with caution. Increases in NPP without statistically
significant changes in tissue N concentrations imply an increase
in fN
up
, irrespective of whether the rhizospheric N uptake has
indeed increased, or whether changes in foliar N retention (or
perhaps labile amino acid reserves not accounted for in the
observed tissue N concentration changes) have affected the N
balance of the plants. This situation leads to uncertainty in the
fN
up
estimates for an individual year, and therefore the eCO
2
response in the initial year of the experiment. However, the error
associated with unaccounted for reserves diminishes when the
estimates are integrated over time, and, on average, the transloca-
tion fractions did not change with time in the observations,
further reducing the longer term error.
Uncertainty also results from the difficulties in measuring
below-ground biomass and production, which is a fairly small
contribution to total NPP at Duke Forest, but up to 40% of total
NPP at ORNL under eCO
2
(Iversen, 2009; McCarthy et al.,
2010). Observations of fine root biomass should give suitably
constrained estimates of the relative increase in root allocation
under eCO
2
. However, uncertainty in the absolute below-ground
C flux and, specifically, C flux to mycorrhizas propagates to
uncertainty in annual NPP –and thus in the inferred N require-
ments to sustain the eCO
2
response.
There is also substantial uncertainty in the observation-based
estimates of net SOM changes with eCO
2
, resulting from a small
signal-to-noise ratio and uncertainties in the sampling and analy-
ses of the soil data (Jastrow et al., 2005). This uncertainty is pri-
marily a result of the spatial variability of SOM, particularly for
N (Iversen et al., 2012). The uncertainty in these measurements
is sufficiently large to preclude reliable quantification of the net
eCO
2
effect on total soil and ecosystem C and N over the 10 yr
of the experiment (Figs 9, S1), as the expected change in SOM
caused by CO
2
is rather small. Therefore, the observations from
Duke and ORNL Forests do not provide a robust constraint on
the model N balance. Nonetheless, independent studies suggest
that increased microbial decomposition may have resulted in a
net transfer of N to vegetation at Duke FACE (Drake et al.,
2011, 2013; Hofmockel et al., 2011), whereas increases in micro-
bial activity with eCO
2
may have been insufficient to compensate
for the increased accumulation of N in SOM at ORNL FACE
(Iversen et al., 2012).
Year-to-year variations in meteorological parameters influence
both the ambient C and N cycling at the sites and the response to
eCO
2
. These influences range from the direct effect of temperature
on the CO
2
sensitivity of photosynthesis (Hickler et al., 2008) to
indirect effects resulting from interannual variations in the levels
of drought stress (and thus eCO
2
–water-use efficiency interac-
tions; De Kauwe et al., 2013) or N availability, following the sensi-
tivity of SOM decomposition to soil temperature and moisture
(Melillo et al., 2011). Assuming that the variability in the eCO
2
response of NPP during the first 3 yr of the experiments was pre-
dominantly influenced by meteorological conditions, and not N
availability (as suggested by most of the models), the weather-
related standard error at Duke (1.3%) is lower than the across-ring
variations (3%), whereas it is higher at ORNL (2.9% and 0.1%,
respectively). These weather-related variations add uncertainty to
our estimates of the initial response of NPP to eCO
2
, whereas they
appear to be sufficiently small to allow us to decipher the long-
term trend, which we assessed as a 5-yr mean towards the end of
the experiment. We cannot rule out, however, that extreme events,
such as the ice storm at Duke in December 2002 (McCarthy et al.,
2007), have strongly altered the forest’s C–N dynamics and
thereby obscured the expected trajectory of NPP enhancement.
Although the models’ meteorological forcing contained these
extreme events, none of the models incorporated the damage pro-
cesses associated with, for instance, ice-break or wind damage.
A further complicating factor in the model–data analyses is
that the magnitude of the N limitation of the CO
2
response
depends on various boundary conditions of the experiment,
including the magnitude of the CO
2
perturbation and the pool
of plant-available N at the beginning of the experiment. The step
increase in CO
2
is much faster than projected future transient
increases in atmospheric CO
2
. Thus, the experiment produces a
suddenly increasing plant N demand (Luo & Reynolds, 1999)
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which could: (1) lead to an overestimate of the importance of
nutrient constraints; and (2) trigger ecosystem processes that
would not have occurred otherwise. The initial pool of easily
plant-accessible N, either in the form of mineral N or readily
decomposable dead organic material, is influenced by the land
use history of the plots. It is difficult to estimate from bulk soil
SOM measurements, as the net N mineralization depends on the
partitioning of SOM into pools with different turnover times. In
the absence of suitable initialization data, most models generated
their initial condition based on site history, which caused uncer-
tainty in the amount of net N mineralization, and thus N avail-
ability for plants, at the start of the experiment. Whether or not a
model simulates PNL, and at what time scale, therefore depends
not only on the model structure, but also on the initialization
protocol. In particular, the ED2.1 model did not show signs of
N limitation, because it did not simulate N inputs or losses; thus,
the prescribed initial SOM pool provided ample inorganic N to
support the growth of the trees throughout the simulation period.
To minimize the effect of initial conditions, the models were
evaluated in terms of the compatibility of their component
processes with observations, rather than in terms of the average
modelled productivity and N uptake response to CO
2
.
Concluding remarks and recommendations for future
experiments
The two FACE experiments initially showed a similar productivity
response to eCO
2
, relative to a comparable baseline, in terms of
forest productivity and forest N use, as well as climate and atmo-
spheric N inputs. The long-term responses diverged strongly: the
cumulated NPP response to eCO
2
at the deciduous site was about
half that of the evergreen site. The primary reason for this differ-
ence was that altered SOM dynamics increased plant N availability
at Duke Forest at a rate that allowed the vegetation to maintain
elevated levels of N uptake, whereas this did not happen at a suffi-
cient rate at ORNL FACE. Furthermore, a corollary of the differ-
ent allocation responses to eCO
2
was that almost the entire NPP
enhancement remained in vegetation biomass in Duke, whereas
eCO
2
did not alter vegetation biomass at ORNL FACE.
Many models in the ensemble were capable of reproducing the
observed initial increase in NPP with eCO
2
. However, in the
majority of cases, this response resulted from compensating errors
in the underlying process responses, as the models did not correctly
simulate the magnitude of the observed initial increase in plant N
uptake at both sites, and wrongly attributed a large share of the
increased NPP to enhanced NUE. This result cautions against a
too simplistic model–data comparison and underlines the necessity
of the detailed process-level evaluation. Comparing the process
responses of ecosystem models against the observations provided
essential information on model validity: we were able to identify
component processes within particular models that were operating
well (qualitatively and quantitatively), although the overall
observed ecosystem eCO
2
response was not accurately reproduced.
Models with flexible stoichiometry and allocation patterns that
respond to N stress captured the qualitative responses observed at
both sites. Ecosystem models with flexible tissue stoichiometry
predicted a larger CO
2
response of the NPP response despite a
lower than observed CO
2
response of fN
up
, and generally overes-
timated the observed increase in vegetation C : N ratio. Despite
the conceptually increased accuracy of the results, this clearly
shows that a more explicitly process-based approach to the mod-
elling of stoichiometric flexibility is important for capturing the
eCO
2
response at these sites.
Despite the diversity of the modelling approaches employed
here, all 11 combinations of C–N cycle processes include mecha-
nisms consistent with the PNL hypothesis (Comins & McMurt-
rie, 1993; Luo et al., 2004), although the extent to which PNL was
simulated varied depending on the assumed tightness of the stoi-
chiometric constraint and the openness of the N cycle. Although
this generally agrees with the observed trends at ORNL FACE,
most models failed to simulate the sustained NPP enhancement at
the Duke FACE site, because the mechanisms to increase N avail-
ability for plant growth included in these models are insufficient
to explain the observed increases. This tendency to underestimate
the net transfer of N from soils to vegetation under elevated CO
2
at Duke calls for a better representation of below-ground pro-
cesses, in particular root allocation and microbial responses to
enhanced rhizodeposition.
Large uncertainty as to whether the observed changes in above-
ground N stocks are caused by a redistribution of N from soils or
to newly acquired N stems from the low signal-to-noise ratio in
soil N inventories. Precise inventories well below the active root-
ing depth at the beginning of the experiment (as it may increase
as the experiment progresses) would help, as would additional
regular measurements of N balance components (N leaching and
gaseous emission). Additional experiments using open-top cham-
bers may further help to reduce uncertainty with respect to the
below-ground mass balance and the net transfer of nutrients from
soil to plants. Replicated factorial manipulation of nutrient avail-
ability and atmospheric [CO
2
] treatments could help to elucidate
process interactions regarding allocation and stoichiometric
responses to altered C and N availability. The strong increase in
atmospheric CO
2
might have triggered processes that would not
have occurred if CO
2
had increased at a more gradual pace. It
would be of interest to investigate nutrient responses in ecosys-
tem-level experiments, where CO
2
is elevated more gradually to
the maximum level in instalments, allowing the ecosystem to
adjust at least partially to the new conditions. To reduce the
dependence of the experimental results on the initial state of the
ecosystem, it would also be desirable to conduct future elevated
CO
2
experiments with replication of different soil fertilities. This
model comparison exercise has also underlined the increasingly
recognized need for datasets from large-scale experiments to be
collated into a central, versioned data repository that is readily
accessible to modellers, if we are to fully capitalize on the
potential for such experiments to inform models.
The different responses of several key processes at the two
experimental sites, which cannot be explained by any of the mod-
els, imply that we should be sceptical of overarching statements
concerning the responses of ecosystems to increasing levels of
atmospheric CO
2
. There is currently insufficient knowledge to
fully constrain the eCO
2
response of global terrestrial ecosystem
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models, despite the existing body of experimental evidence.
Nevertheless, the ecosystem models were able to capture impor-
tant features of the experiments, lending some support to their
projections (e.g. Thornton et al., 2009; Zaehle et al., 2010; Zhang
et al., 2011).
Acknowledgements
This study was conducted as part of the ‘Benchmarking ecosys-
tem response models with experimental data from long-term
CO
2
enrichment experiments’ Working Group supported by the
National Center for Ecological Analysis and Synthesis, a Center
funded by the National Science Foundation (NSF) (Grant
#EF-0553768), the University of California, Santa Barbara and
the State of California. The ORNL and Duke FACE sites and
additional synthesis activities were supported by the US Depart-
ment of Energy Office of Science, Biological and Environmental
Research Program. In particular, Duke FACE research was sup-
ported under grant number FACE, DE-FG02-95ER62083). S.Z.
was supported by the FP7 people programme through grant nos
PERG02-GA-2007-224775 and 238366. M.G.D.K. and
B.E-M. were supported by ARC Discovery Grant DP1094791,
and T.H. by the research funding programme ‘LOEWE-Landes-
offensive zur Entwicklung wissenschaftlich-€okonomischer Exzel-
lenz’ of Hesse’s Ministry of Higher Education.
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Phytologist
16

Table A1 Overview of the models used and the representation of key processes in the carbon–nitrogen cycle. C, carbon; N, nitrogen; P, phosphorus; PFT,
plant functional type; T, temperature; f(x), function of x
CABLE CLM4 DAYCENT EALCO
Key reference Wang et al. (2010, 2011) Thornton &
Zimmermann (2007),
Thornton et al. (2007)
Parton et al. (2010) Wang et al. (2001)
Time step 30 min 30 min 1 d 30 min
Plant C
acquisition
Assimilation (GPP) Farquhar et al. (1980) Collatz et al. (1991) 2 9NPP
act
Farquhar et al. (1980)
N dependence of gross
photosynthesis
f(leaf N) NPP
act
/NPP
pot
None f(leaf N)
Autotrophic respiration f(tissue N, T)+
f(growth rate)
f(tissue C, T)+
f(growth rate)
0.5 9GPP f(tissue C, T)+i(growth
rate)
N dependence of
whole-plant growth
(if not GPP –R
a
)
None Potential growth
(NPP
pot
) limited by
stoichiometric N
requirement for new
tissue growth
Potential growth (f(PAR,
T, moisture, CO
2
))
limited by
stoichiometric N
requirement for new
tissue growth
None
Plant N
acquisition
Nitrogen fixation Prescribed based on Wang
& Houlton (2009)
f(NPP) Plant-associated N
fixation: f(N : P, plant N
demand); soil N
fixation: f(AET)
None
Nitrogen uptake f(plant N demand,
soil N availability)
f(relative strength of
plant and microbial N
demand, inorganic N
pool size)
f(root biomass, plant
demand, soil N
availability)
Competition of soil
mineral N between
plant and microbial
Plant growth Allocation principle
1
Fixed allocation fractions,
which vary according to
phenological state
Fixed allocation
fractions, derived from
observations at the
sites
Hierarchical allocation
factors, in which fine
roots have priority over
leaves and over wood,
with prescribed
maximum pool sizes
Fixed allocation
fractions, which vary
according to
phenological state
Maximum leaf area
1
Prescribed (LAI =8; excess
C is allocated to wood
and roots)
Predicted Predicted Prescribed from
observations at the site
N effect on allocation
1
None None Nitrogen stress increases
root allocation
None
Plant tissue C : N
stoichiometry
Flexible within 10% of the
prescribed mean C : N
Fixed Flexible within
prescribed bounds
Flexible within
prescribed bounds
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Supporting Information
Additional supporting information may be found in the online
version of this article.
Fig. S1 Cumulative effect of elevated atmospheric [CO
2
] (eCO
2
)
on carbon (C) and nitrogen (N) storage in the Duke and Oak
Ridge National Laboratory (ORNL) Free-Air CO
2
Enrichment
(FACE) sites.
Please note: Wiley Blackwell are not responsible for the content
or functionality of any supporting information supplied by the
authors. Any queries (other than missing material) should be
directed to the New Phytologist Central Office.1
Appendices
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Table A1 (Continued)
CABLE CLM4 DAYCENT EALCO
Plant N
turnover
N effect on turnover/
mortality
None Indirect via changes in
NPP
Leaf turnover increases
linearly with leaf N
concentration
None
N retention on leaf and
root shedding
50% of leaf N, 10%
of root N
Litter has a fixed C : N
(PFT specific)
50% of leaf N Retaining ratio depends
on current tissue C : N
ratio
Soil N
turnover
SOM decay (other than
dependent on soil T
and moisture)
3 litter pools (metabolic,
structural, coarse woody
debris), 3 SOM pools with
different turnover times,
1st order decay
3 litter pools, 4 SOM
pools, all with different
turnover times, 1st
order decay
3 litter pools (above and
below ground
combined), 4 SOM
pools, all with different
turnover times, 1st
order decay
3 litter pools; 4 SOM
pools with different
turnover rates, 1st
order decay
N effect on
decomposition
Lignin : N ratio affects
microbial efficiency and
decomposition rate.
Available soil mineral N
constrains immobilization
Litter decomposition
constrained by
available soil N
Lignin : N ratio affects
microbial efficiency
and decomposition
rate. Available soil
mineral N constrains
immobilization
Litter decomposition
constrained by
available soil N
Soil C : N stoichiometry Fixed for each pool Fixed for each pool f(mineral N
concentration, within
bounds)
f(mineral N
concentration, within
bounds)
Ecosystem N
losses
N leaching Proportional to
mineral N pool
f(soil water N
concentration,
drainage)
DON +N leaching =f
(precipitation, NO
3
pool size)
f(mineral N
concentration,
drainage and surface
runoff)
gaseous N loss Proportional to net N
mineralization rate
Proportional to gross N
mineralization +10%
of mineral N remaining
in the soil
NO
x
,N
2
O, N
2
fluxes, as
a function of soil N
pool size, temperature,
water
None
ED2.1 GDAY ISAM LPJ-GUESS
Key reference Medvigy et al. (2009) Comins & McMurtrie
(1993)
Yang et al. (2009) Smith et al. (2001,
2013)
Time step 15 min 1 d 30 min 1 d
Plant C
acquisition
Assimilation Farquhar et al. (1980) Sands (1995, 1996) Farquhar et al. (1980) Collatz et al. (1991),
Haxeltine & Prentice
(1996)
N dependence of gross
photosynthesis
None f(leaf N) Stoichiometric
downregulation of
V
cmax
f(leaf N)
Autotrophic respiration f(tissue C, T)+f(GPP) 0.5 9GPP f(tissue N, T)f(tissue N, T)+f(GPP)
N dependence of
whole-plant growth
(if not GPP –R
a
)
Potential growth limited
by stoichiometric N
requirement for new
tissue growth
None None None
Plant N
acquisition
Nitrogen fixation None Prescribed Predicted Prescribed
Nitrogen uptake f(root biomass, plant N
demand, soil N
availability)
Fixed proportion of the
inorganic N pool size
Michaelis–Menten
kinetics, increases with
increased plant N
demand
f(plant N demand, soil
T)
Plant growth Allocation principle
1
Functional relationships
amongst leaf and
sapwood (pipe-model),
and sapwood and fine
root biomass
Fixed allocation
fractions, derived from
observations at the
sites
Dynamic allocation
fractions, based on
light, water and
phenology
Functional relationships
amongst leaf and
sapwood (pipe-model),
and leaf and fine root
biomass
Maximum leaf area
1
Predicted Predicted Predicted Predicted
N effect on allocation
1
Nitrogen stress
decreases leaf :
root ratio
None None Nitrogen stress
decreases leaf : root
ratio
Plant C : N stoichiometry Fixed Flexible Flexible within
prescribed bounds
Flexible within
prescribed bounds
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Table A1 (Continued)
ED2.1 GDAY ISAM LPJ-GUESS
Plant N
turnover
N effect on turnover/
mortality
Indirect via changes in
NPP
None Indirect via changes in
NPP
Indirect via changes in
NPP
N retention on leaf and
root shedding
50% of N is retained
with leaf fall, but 0%
with root turnover
50% of N is retained
with leaf fall, but 0%
with root turnover
Biome dependent 50% of N is retained
Soil N
turnover
SOM decay (other than
dependent on soil T
and moisture)
Three SOM pools with
varying turnover rates
4 litter pools (above/
below metabolic and
structural litter) and 3
SOM pools with
varying turnover rates
4 litter/SOM
above-ground pools,
4 litter/SOM
below-ground pools
and one inert organic
matter pool with
different turnover rates
5 litter pools (above/
below metabolic and
structural litter, plus an
above CWD litter pool)
and 5 SOM pools with
varying turnover rates
N effect on
decomposition
Litter decomposition
constrained by
available soil N
Lignin : N ratio affects
microbial efficiency
and decomposition
rate. Available soil
mineral N constrains
immobilization
Litter decomposition
constrained by
available soil N
Litter decomposition
constrained by
available soil N
Soil C : N stoichiometry Fast pool: function of
mineral N. Slow and
structural pool: fixed
C:N
f(mineral N
concentration, within
bounds)
Fixed f(mineral N
concentration, within
bounds)
Ecosystem N
losses
N leaching None Fixed proportion of the
inorganic N pool size
f(N pool size, drainage) f(mineral N
concentration,
drainage)
gaseous N loss None None NH
3
volatilization and
denitrification losses
None
OCN SDGVM TECO
Key reference Zaehle & Friend (2010) Woodward et al. (1995) Weng & Luo (2008); updated
Time step 30 min 1 d 30 min
Plant C
acquisition
Assimilation Farquhar et al. (1980),
Kull & Kruijt (1998)
Farquhar et al. (1980),
Harley et al. (1992)
Farquhar et al. (1980)
N dependence of gross
photosynthesis
f(leaf N) f(leaf N) f(leaf N)
Autotrophic respiration f(tissue N) +f(growth rate) +excess
respiration if labile C exceeds
storage capacity, in the limits of
the labile C pool size
f(tissue N, T)f(leaf area, root and
sapwood C)
N dependence of
whole-plant growth
(if not GPP –R
a
)
f(labile C pool size, stoichiometric
N requirement for new tissue
growth)
None Surplus C under N stress is
allocated to woody biomass
Plant N
acquisition
Nitrogen fixation Prescribed None Prescribed
Nitrogen uptake Michaelis–Menten kinetics,
proportional to root biomass,
increases with increased plant N
demand
f(soil organic C and N) f(root C, plant N demand)
Plant growth Allocation principle
1
Functional relationships amongst
leaf and sapwood (pipe-model),
and leaf and fine root biomass
Leaf allocation determined as C
balance of lowest LAI layer of the
previous year. Root and wood
allocation fixed fraction if GPP >0
Resource limitations approach,
prioritizing leaf over root and
wood allocation
Maximum leaf area
1
Predicted Predicted Prescribed per plant functional
type
N effect on allocation
1
Increased plant N demand
increases root : leaf ratio
None N limitation increases allocation
to woody biomass
Plant C : N stoichiometry Flexible within prescribed bounds Foliar N is prescribed from
observations
Flexible within prescribed
narrow bounds
Plant N
turnover
N effect on turnover/
mortality
Indirect via changes in NPP None None
N retention on leaf and
root shedding
50% of N is retained None 50% of N is retained
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Table A2 List of variable names used, as well as their description and unit. Tissue types considered are foliage (f), fine roots (r) and woody (w) biomass. C,
carbon; N, nitrogen; DW, dry weight
Variable Description Unit
a
i
Fractional allocation to tissue type i–
AET actual evapotranspiration mm yr
"1
B
i
Biomass of tissue type ig DW m
"2
C
org
Ecosystem organic carbon g C m
"2
C
SOM
Soil organic matter carbon (including the litter layer) g C m
"2
C
veg
Vegetation carbon gCm
"2
CUE Carbon-use efficiency (NPP/GPP) –(g C g
"1
C)
CWD coarse woody debris gCm
"2
DON dissolved organic nitrogen g N m
"2
yr
"1
GPP Area-based gross primary production g C m
"2
yr
"1
GPP
N
N-based gross primary production g C g
"1
N
can
yr
"1
fN
dep
Atmospheric nitrogen deposition g N m
"2
yr
"1
fN
fix
Biological nitrogen fixation g N m
"2
yr
"1
fN
gas
Ecosystem loss of nitrogen through gaseous emission g N m
"2
yr
"1
fN
leach
Ecosystem loss of nitrogen through leaching g N m
"2
yr
"1
fN
min
Net nitrogen mineralization g N m
"2
yr
"1
fN
up
Plant nitrogen uptake gNm
"2
yr
"1
f
trans
Fraction of tissue N translocated before abscission –
f
veg
Fraction of organic ecosystem nitrogen in vegetation –
LAI leaf area index m
2
m
"2
n
i
Nitrogen concentration of tissue type igNg
"1
DW
N
can
Canopy nitrogen gNm
"2
N
org
Ecosystem organic nitrogen g N m
"2
N
inorg
Inorganic nitrogen in the ecosystem g N m
"2
N
SOM
Soil organic matter nitrogen (including the litter layer) g N m
"2
N
veg
Vegetation nitrogen gNm
"2
NNE Net ecosystem nitrogen exchange g N m
"2
yr
"1
NPP Area-based net primary production g C m
"2
yr
"1
NPP
N
N-based net primary production g C g
"1
N
can
yr
"1
NUE Nitrogen-use efficiency (NPP/fN
up
)gCg
"1
N
PAR photosynthetically active radiation lmol m
"2
s
"1
R
a
Autotrophic respiration gCm
"2
yr
"1
q
i
tissue carbon density gCg
"1
DW
SOM soil organic matter g C|N m
"2
sNveg Turnover time of nitrogen in vegetation yr
"1
sNSOM Turnover time of nitrogen in soil organic matter (including the litter layer) yr
"1
Table A1 (Continued)
OCN SDGVM TECO
Soil N
turnover
SOM decay (other than
dependent on soil T
and moisture)
3 litter pools; 4 SOM pools with
different turnover times, 1st order
decay
4 litter pools, 4 SOM pools, with
different turnover times, 1st order
decay
5 SOM pools (metabolic litter,
structural litter, fast SOM,
slow SOM, and passive SOM)
with different turnover rates,
1st order decay
N effect on
decomposition
Lignin : N ratio affects microbial
efficiency and decomposition
rate. Available soil mineral N
constrains immobilization
n.a.
Soil C : N stoichiometry f(mineral N concentration, within
bounds)
Fixed Flexible soil C : N ratios
Ecosystem N
losses
N leaching f(mineral N concentration,
drainage)
None f(mineralized N, runoff)
gaseous N loss f(mineral N concentration, soil T,
moisture and respiration)
None Fixed proportion of mineral N,
regulated by soil T
1
See M. G. De Kauwe et al. (unpublished) for details.
New Phytologist (2014) !2014 The Authors
New Phytologist !2014 New Phytologist Trust.
www.newphytologist.com
Research
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