ESD - Evaluating nitrogen cycling in terrestrial biosphere models: a disconnect between the carbon and nitrogen cycles

ESD - Evaluating nitrogen cycling in terrestrial biosphere models: a disconnect between the carbon and nitrogen cycles
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14 Aug 2023
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14 Aug 2023
Evaluating nitrogen cycling in terrestrial biosphere models: a disconnect between the carbon and nitrogen cycles
Evaluating nitrogen cycling in terrestrial biosphere models: a disconnect between the carbon and nitrogen cycles
Evaluating nitrogen cycling in terrestrial biosphere models: a disconnect between the carbon and...
Sian Kou-Giesbrecht et al.
Sian Kou-Giesbrecht
Vivek K. Arora
Christian Seiler
Almut Arneth
Stefanie Falk
Atul K. Jain
Fortunat Joos
Daniel Kennedy
Jürgen Knauer
Stephen Sitch
Michael O'Sullivan
Naiqing Pan
Qing Sun
Hanqin Tian
Nicolas Vuichard
and
Sönke Zaehle
Sian Kou-Giesbrecht
CORRESPONDING AUTHOR
sian.kougiesbrecht@dal.ca
Canadian Centre for Climate Modelling and Analysis, Climate Research Division, Environment Canada, Victoria, Canada
Department of Earth and Environmental Sciences, Dalhousie University, Halifax, Canada
Vivek K. Arora
Canadian Centre for Climate Modelling and Analysis, Climate Research Division, Environment Canada, Victoria, Canada
Christian Seiler
School of Environmental Studies, Queen's University, Kingston, Canada
Almut Arneth
Karlsruhe Institute of Technology, Atmospheric Environmental Research, Garmisch-Partenkirchen, Germany
Stefanie Falk
Department für Geographie, Ludwig Maximilian University of Munich, Munich, Germany
Atul K. Jain
Department of Atmospheric Sciences, University of Illinois
Urbana-Champaign, Urbana, Illinois, USA
Fortunat Joos
Climate and Environmental Physics, Physics Institute and Oeschger
Centre for Climate Change Research, University of Bern, Bern, Switzerland
Daniel Kennedy
National Center for Atmospheric Research, Climate and Global Dynamics, Terrestrial Sciences Section, Boulder, Colorado, USA
Jürgen Knauer
Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia
Stephen Sitch
Faculty of Environment, Science and Economy, University of Exeter,
Exeter, UK
Michael O'Sullivan
Faculty of Environment, Science and Economy, University of Exeter,
Exeter, UK
Naiqing Pan
Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, Massachusetts, USA
Qing Sun
Climate and Environmental Physics, Physics Institute and Oeschger
Centre for Climate Change Research, University of Bern, Bern, Switzerland
Hanqin Tian
Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, Massachusetts, USA
Nicolas Vuichard
Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL
(CEA-CNRS-UVSQ), Université Paris-Saclay, Gif-sur-Yvette, France
Sönke Zaehle
Max Planck Institute for Biogeochemistry, Jena, Germany
Abstract
Terrestrial carbon (C) sequestration is limited by nitrogen (N), an
empirically established constraint that could intensify under CO
fertilization and future global change. The terrestrial C sink is estimated
to currently sequester approximately a third of annual anthropogenic
CO
emissions based on an ensemble of terrestrial biosphere models,
which have been evaluated in their ability to reproduce observations of the
C, water, and energy cycles. However, their ability to reproduce
observations of N cycling and thus the regulation of terrestrial C
sequestration by N have been largely unexplored. Here, we evaluate an
ensemble of terrestrial biosphere models with coupled C–N cycling and their
performance at simulating N cycling, outlining a framework for evaluating N
cycling that can be applied across terrestrial biosphere models. We find
that models exhibit significant variability across N pools and fluxes,
simulating different magnitudes and trends over the historical period,
despite their ability to generally reproduce the historical terrestrial C
sink. Furthermore, there are no significant correlations between model
performance in simulating N cycling and model performance in simulating C
cycling, nor are there significant differences in model performance between
models with different representations of fundamental N cycling processes.
This suggests that the underlying N processes that regulate terrestrial C
sequestration operate differently across models and appear to be
disconnected from C cycling. Models tend to overestimate tropical biological
N fixation, vegetation C : N ratio, and soil C : N ratio but underestimate
temperate biological
N fixation relative to observations. However, there is
significant uncertainty associated with measurements of N cycling processes
given their scarcity (especially relative to those of C cycling processes)
and their high spatiotemporal variability. Overall, our results suggest that
terrestrial biosphere models that represent coupled C–N cycling could be
overestimating C storage per unit N, which could lead to biases in
projections of the future terrestrial C sink under CO
fertilization
and future global change (let alone those without a representation of N
cycling). More extensive observations of N cycling processes and comparisons
against experimental manipulations are crucial to evaluate N cycling and its
impact on C cycling and guide its development in terrestrial
biosphere models.
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Kou-Giesbrecht, S., Arora, V. K., Seiler, C., Arneth, A., Falk, S., Jain, A. K., Joos, F., Kennedy, D., Knauer, J., Sitch, S., O'Sullivan, M., Pan, N., Sun, Q., Tian, H., Vuichard, N., and Zaehle, S.: Evaluating nitrogen cycling in terrestrial biosphere models: a disconnect between the carbon and nitrogen cycles, Earth Syst. Dynam., 14, 767–795, https://doi.org/10.5194/esd-14-767-2023, 2023.
Received: 03 Feb 2023
Discussion started: 06 Feb 2023
Revised: 07 Jul 2023
Accepted: 12 Jul 2023
Published: 14 Aug 2023
Introduction
The terrestrial biosphere is estimated by the Global Carbon Project
(GCP) to currently sequester approximately
a third of anthropogenic CO
emissions
(Friedlingstein
et al., 2022). The GCP annually reports an estimate of the global carbon (C)
budget, which includes an estimate of the atmosphere–land CO
flux based
on simulations of an ensemble of terrestrial biosphere models – the trends
in the land carbon cycle project (TRENDY) ensemble. In recent years, the
majority of the models within the TRENDY ensemble have incorporated a
representation of coupled C and nitrogen (N) cycling given the empirically
established importance of N limitation of vegetation growth
(Elser
et al., 2007; LeBauer and Treseder, 2008; Wright et al., 2018). Whereas only
4 out of 9 models represented coupled C–N cycling in the 2013 GCP, 11 out of
16 models represented coupled C–N cycling in the 2022 GCP (Fig. 1).
Capturing N constraints on C cycling is critical for realistically
simulating the terrestrial C sink, which arises from the combined effects of
concurrently acting global change drivers that are each modulated by N.
CO
fertilization is limited by N
(Terrer et
al., 2019; S. Wang et al., 2020), intensifying N deposition increases N supply
(O'Sullivan et al.,
2019; Wang et al., 2017), rising temperature and varying precipitation
modulate decomposition and soil N availability
(Liu et al., 2017), and land use
change and associated N fertilization regimes determine N supply to crops.
Figure 1
Number of terrestrial biosphere models contributing to the Global
Carbon Project (the TRENDY ensemble) with and without coupled C–N cycling.
The TRENDY ensemble has been extensively evaluated against observations of
the C, water, and energy cycles
(Collier
et al., 2018; Friedlingstein et al., 2022; Seiler et al., 2022). Within the
GCP itself, the primary simulated C pools, C fluxes, and water fluxes are
evaluated using a skill score system developed by the International Land
Model Benchmarking (ILAMB) project that quantifies model performance by
comparing model simulations to observations
(Collier
et al., 2018; Friedlingstein et al., 2022). ILAMB scores encompass the mean
and variability of a given variable (pool or flux) over monthly to decadal
temporal scales and over grid cell to global spatial scales. However, despite its importance in
regulating C cycling, N
cycling has not been explicitly evaluated. This is in part due to the relatively recent
incorporation of N cycling in terrestrial biosphere models (Fig. 1)
(Fisher and Koven, 2020; Hungate et al., 2003) but
also due to the paucity of global observation-based datasets of N cycling. N
exists in many forms and is lost from terrestrial ecosystems via numerous
pathways (emissions of NH
, N
O, NO
, and N
, as well as
NO
and NH
leaching), N processes are generally not
measured in situ in networks such as FLUXNET, and remote sensing
methodologies for measuring N processes are still in their infancy.
Additionally, N processes exhibit extremely high spatial and temporal
variabilities and are thus challenging to measure. As such, N cycling has
commonly been evaluated by comparing simulated N pools and fluxes to global
totals based on a small number of observations that have been scaled up or
averaged to yield a value with wide confidence intervals
(Davies-Barnard et al., 2020).
N cycling is implicitly evaluated by comparing terrestrial biosphere models
without N cycling to those with coupled C–N cycling in reproducing
observations of the C, water, and energy cycles in the absence of N cycle
observations.
Results suggest that there are only minor differences between
the performance of models with and without N cycling. There is no
significant difference between the terrestrial C sink simulated by the
TRENDY models with and without N cycling
(Friedlingstein
et al., 2022) or between the terrestrial C sink simulated by the models
participating in the Multi-scale synthesis and Terrestrial Model
Intercomparison Project (MsTMIP) with and without N cycling
(Huntzinger et
al., 2017). Comparing the mean score across all C, water, and energy cycle
variables between TRENDY models with and without N cycling yielded no
significant difference (Seiler et al., 2022).
However, TRENDY models without N cycling had significantly higher scores for
net biome productivity than TRENDY models with N cycling (although all other
variables were not significantly different between TRENDY models with and
without N cycling, including vegetation C, soil C, net biome productivity,
leaf area index, latent heat flux, and runoff)
(Seiler et al., 2022). Despite this seeming
absence of a difference between models with and without coupled C–N cycling
in simulating the current terrestrial C sink, it is imperative that N
constraints on C cycling are properly represented by terrestrial biosphere
models in order to realistically simulate the terrestrial C sink under
future global change, which modifies the C–N balance through N limitation of
CO
fertilization and intensifying N deposition among other effects of
global change. As such, explicitly evaluating N cycling processes themselves
is necessary to assess the ability of terrestrial biosphere models to
capture the underlying mechanisms that determine terrestrial C sequestration
and thus to realistically project the future terrestrial C sink under global
change.
Here, we synthesize the N pools and fluxes simulated by 11 terrestrial
biosphere models in the TRENDY ensemble that participated in the 2022 GCP.
We evaluate their performance in reproducing observations of three key
variables of the N cycle: biological N fixation, vegetation C : N ratio, and
soil C : N ratio. These three variables are critical to C cycling because (1) biological N fixation is the dominant natural N supply to terrestrial
ecosystems, influencing the degree of N limitation of plant growth and thus
terrestrial C sequestration, and (2) vegetation and soil C : N ratios reflect
assimilated C per unit N and thus terrestrial C storage.
Methods
2.1
Simulation protocol
For the 2022 GCP (version 11), the TRENDY ensemble consisted of 16
terrestrial biosphere models, 11 of which represent N cycling (CABLE-POP,
CLM5.0, DLEM, ISAM, JSBACH, JULES-ES, LPJ-GUESS, LPX-Bern, OCNv2,
ORCHIDEEv3, and SDGVM). Although SDGVM includes a representation of N
cycling, its representation is simplistic and was therefore not included.
Additionally, CLASSIC contributed to the 2022 GCP without coupled C–N
cycling; the S3 simulation from the TRENDY protocol was repeated by CLASSIC
with coupled C–N cycling following the 2022 GCP protocol and was used here.
Overall, we analysed 11 models with coupled C–N cycling (Table 1).
Table 1
Terrestrial biosphere models in the TRENDY-N ensemble and
descriptions of their representations of N limitation of vegetation growth,
biological N fixation, vegetation response to N limitation (i.e., strategies
in which vegetation invests C to increase N supply in N-limited conditions),
and N limitation of decomposition.
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We analysed the S3 simulation from the TRENDY protocol, which includes
historical changes in atmospheric CO
, climate, N deposition, N
fertilization, and land use from 1851 to 2021 (see Friedlingstein et al.,
2022, for a full description of the simulation protocol). Briefly, models
were forced with atmospheric CO
from Dlugokencky and Tans (2022); the merged monthly Climate Research Unit (CRU), 6-hourly Japanese
55-year Reanalysis (JRA-55) dataset, or the monthly CRU dataset from
Harris et al. (2020); N deposition from
Hegglin et al. (2016) and Tian et al. (2022); N fertilization from the global N
O Model Intercomparison
Project (NMIP) (Tian et al., 2018); and land use from the
LUH2-GCB2022 (Land-Use Harmonization 2) dataset
(Chini
et al., 2021; Hurtt et al., 2020; Klein Goldewijk et al., 2017a, b). We
interpolated outputs from all models to a common resolution of 1
using bilinear interpolation.
2.2
Terrestrial biosphere model descriptions
The terrestrial biosphere models in the TRENDY ensemble employ a wide
variety of assumptions and formulations of N cycling processes, reflecting
knowledge gaps and divergent theories (Table 1). Here we describe four
fundamental aspects of N cycling for each terrestrial biosphere model: N
limitation of vegetation growth, biological N fixation, the response of
vegetation to N limitation (i.e., strategies in which vegetation invests C
to increase N supply in N-limited conditions), and N limitation of
decomposition. These have been identified as important challenges for
representing N cycling in terrestrial biosphere models
(Meyerholt
et al., 2020; Peng et al., 2020; Stocker et al., 2016; Wieder et al., 2015a;
Zaehle et al., 2015; Zaehle and Dalmonech, 2011).
Terrestrial biosphere models differ in how N limitation of vegetation growth
is represented (Thomas et al., 2015). Some
TRENDY models represent flexible C : N stoichiometry and modelled maximum
carboxylation rate of photosynthesis (
cmax
) decreases with decreasing
leaf N (CABLE-POP, CLASSIC, CLM5.0, LPJ-GUESS, OCNv2, ORCHIDEEv3) following
empirical evidence
(Walker et al., 2014).
Other TRENDY models represent time-invariant C : N stoichiometry and modelled
gross primary productivity (GPP) or net primary
productivity (NPP) decreases with N limitation (DLEM, ISAM, JSBACH, JULES-ES, and
LPX-Bern). Importantly, flexible vs. time-invariant C : N stoichiometry
determines terrestrial C storage per unit N.
Biological N fixation is the dominant natural N supply to terrestrial
ecosystems (Vitousek et al., 2013). In
terrestrial biosphere models, biological N fixation has generally been
represented phenomenologically as a function of either NPP or evapotranspiration (ET) (Cleveland
et al., 1999).
More recently, representations of biological N fixation have
been updated such that it is up-regulated in N-limited conditions following
empirical evidence
(Menge
et al., 2015; Vitousek et al., 2013; Zheng et al., 2019). The majority of
TRENDY models represent biological N fixation phenomenologically (ISAM,
JSBACH, JULES-ES, and LPJ-GUESS). Three TRENDY models (CLASSIC, CLM5.0, and
OCNv2) represent biological N fixation mechanistically such that it
increases with N limitation of vegetation and has an associated C cost per
unit N fixed
(Kou-Giesbrecht
and Arora, 2022; Lawrence et al., 2019; Meyerholt et al., 2016; Shi et al.,
2016; Fisher et al., 2010). These representations separate free-living
biological N fixation (via soil microbes, epiphytic microbes, lichens,
bryophytes, etc.; Reed et al., 2011) from symbiotic
biological N fixation, which is regulated by N limitation of vegetation.
DLEM derives biological N fixation as a function of soil temperature, soil
moisture, soil C, and soil N. LPX-Bern derives biological N fixation post
hoc to simulate a closed N cycle, implicitly including rock N sources
(Joos et al., 2020). Finally, CABLE-POP and ORCHIDEEv3
represent biological N fixation as a specified time-invariant input over the
historical period. Importantly, representing the regulation of biological N
fixation by N limitation not only determines biological N fixation
itself but also modulates terrestrial C sequestration: it enables vegetation
to increase N uptake in N-limited conditions, reduce N limitation, and
sustain terrestrial C sequestration. Some TRENDY models (DLEM, LPJ-GUESS,
and OCNv2) also represent increasing C allocation to roots with increasing N
limitation (Smith et al., 2014; Zaehle and
Friend, 2010) following empirical evidence
(Poorter et al., 2012). This enables
vegetation to also increase root N uptake in N-limited conditions, reduce N
limitation, and sustain terrestrial C sequestration. The response of
vegetation to N limitation, which could also include increased C allocation
to mycorrhizae (Phillips et al., 2013)
(represented in CLM5.0) or increased re-translocation of N during tissue
turnover
(Du
et al., 2020; Han et al., 2013; Kobe et al., 2005) (represented in CLM5.0),
is important for determining terrestrial C sequestration.
The decomposition rate is controlled by soil temperature, soil moisture, and N
content in litter, where increasing litter C : N ratio decreases the decomposition
rate (Cotrufo
et al., 2013). Some TRENDY
models represent this reduction in decomposition rate with increasing litter
C : N ratio (CLM5.0, DLEM, ISAM, JSBACH, JULES-ES, and OCNv2) following
empirical evidence.
2.3
Observation-based datasets
We interpolated observation-based datasets to a common resolution of
using bilinear interpolation for comparison
against model outputs. To compare model outputs against observation-based
datasets we averaged model outputs over 1980–2021, which spans the period
in which most measurements were made.
2.3.1
Biological N fixation
A biological N fixation observation-based dataset was derived from
Davies-Barnard and Friedlingstein (2020), a global
meta-analysis of field measurements of natural biological N fixation
(free-living and symbiotic) that scales biome-specific means onto the
Collection 5 MODIS Global Land Cover Type International Geosphere-Biosphere
Programme (IGBP) product (Friedl et al., 2010).
This dataset includes agricultural biological N fixation and assumes that
crop biological N fixation rates are equivalent to those of grasses.
The score of LPX-Bern in simulating biological N fixation is not analysed
because it implicitly includes rock N sources and is thus not directly
comparable to the observation-based dataset.
2.3.2
Vegetation C : N ratio
A vegetation C : N ratio observation-based dataset was derived by scaling
biome-specific means for vegetation C : N ratios from the TRY plant trait
database
(Kattge
et al., 2020) onto the Collection 5 MODIS Global Land Cover Type IGBP
product (Friedl et al., 2010) and combining it
with the remote sensing leaf N content product from
Moreno-Martínez
et al. (2018). First, we obtained N content per dry mass for leaves, roots,
and stems and C content per dry mass for leaves, roots, and stems from
the TRY plant trait database. We selected entries that reported species.
Second, we obtained the plant functional type (PFT) for each species from the
TRY plant trait database. We categorized each PFT into the IGBP land cover
types (Table A1 in the Appendix) and then used this to categorize each entry into the IGBP
land cover types using species. We averaged across entries in each IGBP land
cover type. Third, we divided mean tissue C content per tissue dry mass by
mean tissue N content per tissue dry mass for each tissue and for each IGBP
land cover type. Fourth, we weighed each tissue by its PFT-specific fraction
of total biomass from Poorter et al. (2012)
to obtain the total vegetation C : N ratio for each IGBP land cover type. Fifth,
we scaled total vegetation C : N ratio and leaf N content per dry mass for
each IGBP land cover type to the Collection 5 MODIS Global Land Cover Type
IGBP product. Sixth, we multiplied derived total vegetation C : N ratio
relative to leaf N content per dry mass by the remote sensing leaf N content
per dry mass product
(Moreno-Martínez
et al., 2018) to obtain a vegetation C : N ratio observation-based dataset.
2.3.3
Soil C : N ratio
A soil C : N ratio observation-based dataset was derived from soil C and soil
N products from SoilGrids (Poggio et al., 2021), which
provides globally gridded datasets of soil organic C and total soil N at a
250 m
250 m resolution for six layers up to a depth of 200 cm. These
estimates are derived using machine learning methods and soil observations
from 240 000 locations across the globe and over 400 environmental
covariates. We summed soil C over all layers and soil N over all layers
(using the bulk density and depth of each layer) and then obtained the soil C : N
ratio.
2.3.4
C cycling variables
In addition to evaluating N cycling variables, we also evaluated the primary
C cycling variables: GPP, net biome
productivity (NBP), vegetation C (CVEG), soil C (CSOIL), and leaf area index
(LAI). These variables have been previously evaluated in detail for the
terrestrial biosphere models in the TRENDY ensemble in Seiler et
al. (2022). Seiler et al. (2022) give further details on the
observation-based datasets used to evaluate the primary C cycling variables.
Briefly, we evaluated GPP against MODIS (Zhang
et al., 2017), GOSIF (Li
and Xiao, 2019), and FLUXCOM
(Jung et al., 2020)
products. We evaluated NBP against the CAMS
(Agustí-Panareda et al., 2019),
CarboScope (Rödenbeck et al., 2018), and CT2019
(Jacobson et al., 2020) products. We evaluated CVEG against
the GEOCARBON
(Avitabile
et al., 2016; Santoro et al., 2015), Zhang
and Liang (2020), and Huang et al. (2021) products.
We evaluated LAI against Advanced Very High Resolution Radiometer (AVHRR; Claverie et
al., 2016), Copernicus (Verger et al., 2014), and MODIS
(Myneni et al., 2002) products. We
evaluated CSOIL against Harmonized World Soil Database (HWSD; Todd-Brown et
al., 2013; Wieder, 2014) and SoilGrids
(Hengl et al.,
2017) products. These observation-based products are globally gridded.
2.4
Model evaluation with the Automated Model Benchmarking R (AMBER) package
The Automated Model Benchmarking R (AMBER) package developed by
Seiler et al. (2021) quantifies model performance in
reproducing observation-based datasets using a skill score system that is
based on ILAMB (Collier et al., 2018). Five scores
assess the simulated time mean bias (
bias
), monthly centralized
root-mean-square error (
rmse
), seasonality (
phase
), inter-annual
variability (
iav
), and spatial distribution (
dist
) in comparison
to the observation-based dataset. Scores are dimensionless and range from 0
to 1, where higher values indicate better model performance. The overall
score for each variable (
overall
) is
(1)
overall
mean
bias
rmse
phase
iav
dist
We calculated the overall score for each C and N cycling variable. Because
biological N fixation, vegetation C : N ratio, and soil C : N ratio datasets are
representative of the present-day (as a single time point) values,
rmse
phase
, and
iav
are not defined and thus do not contribute to
overall
. This also holds for vegetation C and soil C. The calculation
of each score is described in detail in Seiler et al. (2022).
2.5
Statistics
We used a Mann–Kendall trend test to assess the existence of a statistically
significant trend in the time series over the historical period for
simulated C and N cycling variables (Hipel and McLeod, 1994). We
conducted two analyses to compare model performance in simulating C cycling
vs. N cycling. First, we calculated Spearman's rank correlation coefficient
to assess the existence of statistically significant correlations between
overall scores, present-day global values, and Kendall's tau. Second, we
used a
test or ANOVA (
value
0.05) to assess the existence of
statistically significant differences between overall scores, present-day
global values, and Kendall's tau for models with different representations
of N limitation of vegetation growth, biological N fixation, vegetation
response to N limitation, and N limitation of decomposition (Table 1).
Results
3.1
Net biome productivity
Figure 2 shows NBP simulated by the TRENDY ensemble models with coupled C–N
cycling (hereafter referred to as the TRENDY-N ensemble). NBP is the
difference between the net natural atmosphere–land flux of CO
and land
use change CO
emissions. Positive values of NBP indicate a terrestrial
C sink, whereas negative values of NBP indicate a terrestrial C source. All
TRENDY-N ensemble models suggest a terrestrial C sink for the present day,
agreeing with the global carbon budget constraint from the 2022 Global Carbon Budget
with most models within 2 standard deviations of the mean (
1.5±0.6
Pg C for 2012–2021) (Fig. 2a). The TRENDY-N ensemble agrees reasonably
well with observations globally, agreeing somewhat better with CarboScope
and CT2019 than with CAMS (Fig. 2b). However, the latitudinal
distributions of the observation-based datasets display weak agreement among
themselves with opposing signs in multiple regions due to differences in the
inversion models and atmospheric CO
measurements used in each dataset
(Fig. 2b). The largest differences occur at southern latitudes and at high
northern latitudes, and this is in part due to the smaller land area at these
latitudes. The regions showing the strongest agreement are at middle to high
northern latitudes, where both the TRENDY-N ensemble and observations
suggest a terrestrial C sink (Fig. 2b).
Figure 2
Net biome productivity (NBP) simulated by the TRENDY-N ensemble.
(a)
Global NBP from 1960 to 2021. The boxes indicate the global C budget
constraint (difference between fossil fuel CO
emissions and the growth
rate of atmospheric CO
and the uptake of CO
by oceans; mean
±2
standard deviation) from the 2022 Global Carbon Budget
(Friedlingstein
et al., 2022). Thick lines indicate the moving average over 10 years, and
thin lines indicate the annual time series.
(b)
Latitudinal distribution and
global mean of NBP (averaged over 1980–2021) in comparison to three
datasets (CAMS, Agustí-Panareda et
al., 2019; CarboScope, Rödenbeck et al., 2018; and
CT2019, Jacobson et al., 2020). The boxplot shows the
median, interquartile range (box), and 80 % percentiles (whiskers) of the
global mean of NBP.
3.2
Overview of N cycling
Figure 3 shows a schematic of the N cycle alongside the primary N fluxes and
C : N ratios of the primary pools simulated by the TRENDY-N ensemble for the
present day (averaged over 1980–2021) and observation-based
estimates for these variables that have previously been used for model
evaluation (Davies-Barnard et al., 2020).
Simulated biological N fixation ranged between 20 and 566 Tg N yr
−1
(Table 2) in comparison to the observation-based estimate of 88 Tg N yr
−1
(52–130 Tg N yr
−1
). Simulated N
O emissions ranged
between 0.9 and 11.0 Tg N yr
−1
(Table 2) in comparison to the
observation-based estimate of 10.8 Tg N yr
−1
(7.1–16.0 Tg N yr
−1
(Tian
et al., 2020). Simulated N losses (which include emissions of NH
O, NO
, and N
, as well as NO
and NH
leaching) ranged between 87 and 603 Tg N yr
−1
(Table 2) in comparison
to the observation-based estimate of 293 Tg N yr
−1
(Fowler et al.,
2013). The simulated vegetation C : N ratio ranged between 103 and 222 (Table 2) in comparison to the observation-based estimate of 133
(Zechmeister-Boltenstern et al., 2015). The
simulated combined litter–soil C : N ratio ranged between 10 and 64 (Table 2)
in comparison to the observation-based estimate of 15
(Zechmeister-Boltenstern et al., 2015). Biological N
fixation has the largest inter-model spread with a coefficient of variation
of 1.06 (Table 2). Figure 4 shows the geographical distribution of the
primary N pools and fluxes simulated by the TRENDY-N ensemble for the
present day (averaged over 1980–2021), and variation across models is shown
in Appendix Fig. A1.
Figure 3
The N cycle and the primary N pools and fluxes simulated by the
TRENDY-N ensemble (averaged over 1980–2021). Horizontal black lines
indicate observation-based estimates that have previously been used for
model evaluation (biological N fixation from
Davies-Barnard and Friedlingstein, 2020; vegetation
and combined litter-soil C : N ratios from
Zechmeister-Boltenstern et al., 2015; N
emissions from
Tian
et al., 2020; and N losses from
Fowler et al., 2013). The black box indicates the terrestrial biosphere. N enters the
terrestrial biosphere via biological N fixation, N deposition, and N
fertilization (entering the organic soil N pool, the inorganic soil N pool
(ammonium (NH
) or nitrate (NO
), or the vegetation N
pool). N is transferred from the inorganic soil N pool to the vegetation N
pool via N uptake. N is transferred from the vegetation N pool to the litter
N pool via N litterfall. N is transferred from the litter N pool to the
organic soil N pool via decomposition. N is transferred from the organic
soil N pool to the inorganic soil N pool via net N mineralization. N exits
the terrestrial biosphere via N loss (which includes N leaching from soils
and N
O, NO
, NH
, and N
emissions from both soils and
land use change). Not all models provide output for each N pool or flux.
Note that biological N fixation simulated by LPX-Bern implicitly includes
rock N sources.
Figure 4
Geographical distributions of
(a)
vegetation N,
(b)
litter N,
(c)
soil N,
(d)
biological N fixation,
(e)
N uptake,
(f)
net N mineralization,
(g)
O emissions, and
(h)
N loss simulated by the TRENDY-N ensemble
(averaged across models over 1980–2021). Variation across models is shown
in Fig. A1.
Table 2
Global N pools, N fluxes, and C : N ratios simulated by the TRENDY-N
ensemble (mean and coefficient of variation across models over 1980–2021).
Download Print Version
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Figure 5 shows the time series of the change from pre-industrial levels of
the primary N pools and fluxes from 1850 to 2021 simulated by the TRENDY-N
ensemble. Figure 6 shows the corresponding Kendall's tau, which identifies
the existence of a statistically significant trend (Table A2). Over the
historical period, some models suggest decreasing vegetation N (6 out of 11 models), whereas other models suggest increasing vegetation N (2 out of 11 models)
or no trend in vegetation N (3 out of 11 models). Some models suggest decreasing
soil N (7 out of 11 models), whereas other models suggest increasing soil N (4 out of 11 models). Some models suggest increasing biological N fixation (7 out of 11 models),
whereas other models suggest decreasing biological N fixation (2 out of 11 models)
or no trend in biological N fixation (2 out of 11 models). All models suggest
increasing N uptake (10 out of 10 models). Most models suggest increasing net N
mineralization rate (9 out of 10 models) or no trend in N mineralization rate (1 out of 10 models). All models suggest increasing N
O emissions (7 out of 7 models) and
increasing N loss (10 out of 10 models).
Figure 5
Time series of the change from the pre-industrial level (averaged
over 1850–1870) of
(a)
vegetation N,
(b)
litter N,
(c)
soil N,
(d)
biological N
fixation,
(e)
N uptake,
(f)
net N mineralization,
(g)
O emissions, and
(h)
N loss simulated by the TRENDY-N ensemble from 1850 to 2021. Figure A5
shows the time series for each N pool and N flux simulated by the TRENDY-N
ensemble from 1850 to 2021.
Figure 6
Kendall's tau from the Mann–Kendall test (
value
0.05)
for each N pool and N flux time series simulated by the TRENDY-N ensemble
from 1850 to 2021 (Table A2). A positive value (red) indicates an increasing
trend and a negative value (blue) indicates a decreasing trend. Gray
indicates a statistically insignificant value, and white indicates a missing
value.
3.3
Evaluation of biological N fixation, vegetation C : N ratio, and soil C : N ratio
In comparison to the observation-based dataset from Davies-Barnard and
Friedlingstein (2020), the TRENDY-N ensemble reproduced global biological N
fixation (101.8 Tg N yr
−1
vs. 88 Tg N yr
−1
; Fig. 7a and Table 2)
but overestimated low-latitude biological N fixation and underestimated
high-latitude biological N fixation in the Northern Hemisphere (Fig. 7b).
In comparison to the observation-based dataset from the TRY plant trait
database, the TRENDY-N ensemble overestimated the global vegetation C : N
ratio (154.5 vs. 102.8; Fig. 7c and Table 2) and overestimated the
vegetation C : N ratio across latitudes while capturing its latitudinal
pattern (Fig. 7d). In comparison to the observation-based dataset from
SoilGrids, the TRENDY-N ensemble overestimated the global soil C : N ratio,
simulating a relatively constant soil C : N ratio across latitudes (11.1 vs.
8.8; Fig. 7e and Table 2). The TRENDY-N ensemble was thus unable to
capture the latitudinal pattern of the soil C : N ratio (Fig. 7f).
Figure 7
Latitudinal distributions and global means of biological N
fixation, vegetation C : N ratio, and soil C : N ratio simulated by the TRENDY-N
ensemble (averaged across models over 1980–2021) in comparison to
observations. Panels
(a, c, e)
show the latitudinal distribution of the mean and
boxplots show the global mean. Panels
(b, d, f)
show the latitudinal distribution of the
bias. Latitudinal distributions show the mean (black line) and the 50 %,
80 %, and 100 % percentiles across models. Boxplots show the median,
interquartile range (box), and 80 % percentiles (whiskers) across models.
Observation-based datasets are from Davies-Barnard and Friedlingstein (2020)
for biological N fixation, the TRY plant trait database for vegetation C : N
ratio, and SoilGrids for soil C : N ratio. LPX-Bern simulations are not shown
in
(a)
or
(b)
. Latitudinal distributions and global means of individual models in
the TRENDY-N ensemble are shown in Fig. A6.
The overall score is a metric of model performance in reproducing an
observation-based dataset. Overall scores for biological N fixation,
vegetation C : N ratio, and soil C : N ratio (0.46, 0.53, and 0.29 averaged
across models, respectively) were lower than those for C cycling variables
(0.58 averaged across all C cycling variables and across models) (Fig. 8).
The mean overall score for vegetation C : N ratio across models (0.53) was
lower than the mean overall scores for vegetation C across models (which
ranged from 0.61 to 0.69 depending on the observation-based dataset used to
derive the score). Similarly, the mean overall score for soil C : N ratio
across models (0.29) was lower than the mean overall scores for soil C
across models (which ranged from 0.39 to 0.53 depending on the
observation-based dataset used to derive the score).
Figure 8
Overall scores of the TRENDY-N ensemble in simulating C and N
cycling variables: gross primary productivity (GPP), net biome productivity
(NBP), vegetation C (CVEG), soil C (CSOIL), leaf area index (LAI),
biological N fixation (FBNF), vegetation C : N ratio (CNVEG), and soil C : N
ratio (CNSOIL). Abbreviations of the observation-based datasets are
described in Sect. 2 and in Seiler et al. (2022).
For N cycling variables, the overall score is composed of the time mean bias
score (which assesses the difference between the time mean of model
simulations and the time mean of the observation-based dataset) and the
spatial distribution score (which assesses the ability of the model to
reproduce the spatial pattern of the observation-based dataset)
(Collier
et al., 2018; Seiler
et al., 2022). For biological N fixation, the time mean bias score averaged
across models was 0.50 and the mean spatial distribution score across models
was 0.41 (Table A3). For the vegetation C : N ratio, the time mean bias
averaged score across models was 0.46 and the mean spatial distribution
score across models was 0.59 (Table A3). For the soil C : N ratio, the
time mean bias score averaged across models was 0.39 and the mean spatial
distribution score across models was 0.19 (Table A3).
Note that for C fluxes the overall score is composed of not only the
time mean bias score and the spatial distribution score but also the
monthly centralized root-mean-square-error score (which assesses the ability
of the model to reproduce the time series of the observation-based dataset),
the seasonality score (which assesses the ability of the model to reproduce
the seasonality of the observation-based dataset), and the inter-annual
variability score (which assesses the ability of the model to reproduce the
inter-annual variability of the observation-based dataset) because
observation-based datasets of C fluxes are available over time, whereas
observation-based datasets of C pools and all N cycling variables are
representative of the present day (as a single time point).
3.4
Model performance for C cycling vs. N cycling
There were no statistically significant correlations between the overall
score of NBP (as well as other primary C variables) and the overall scores
of the primary N variables across the TRENDY-N ensemble (Fig. A2).
Furthermore, there were no statistically significant correlations between
the present-day global value of NBP and the present-day global values of the
primary N variables across the TRENDY-N ensemble (Fig. A3). Finally, there
were no statistically significant correlations between Kendall's tau of NBP
and Kendall's tau of the primary N variables across the TRENDY-N ensemble
(Fig. A4).
3.5
Model performance for different representations of N cycling processes
There were no statistically significant differences in overall scores
between models with different representations of N limitation of vegetation
growth (decreasing
cmax
and flexible C : N stoichiometry vs.
decreasing NPP), different representations of biological N fixation
(function of N limitation of vegetation growth vs. function of NPP or ET vs.
time invariant), different representations of the response of vegetation to
N limitation (dynamic vs. static), or different representations of N
limitation of decomposition (function of soil N vs. N invariant) (Table A4).
However, models that represented decomposition as a function of soil N had a
significantly higher NBP score (for CT2019) than models that represented
decomposition as N invariant. Similarly, there were no statistically
significant differences between present-day global values or Kendall's tau
of primary C and N pools and fluxes between models with different
representations of N limitation of vegetation growth, biological N fixation,
vegetation response to N limitation, and N limitation of decomposition
(Tables A5 and A6). This is likely in part due to the low number of models
and the confounding influence of other process representations.
Discussion
4.1
Evaluation of N cycling in terrestrial biosphere models
Despite the ability of all TRENDY-N models to simulate the historical
terrestrial C sink in line with observations (Fig. 2), there is
substantial variation in simulated N cycling processes by the models. The
magnitudes of N pools and fluxes differ considerably between models (Figs. 3 and A1). Additionally, the historical trajectories of these N pools and
fluxes differ between models: some models simulate increasing vegetation N
and soil N, whereas others simulate decreasing vegetation N and soil N
between 1850 and 2021 (Figs. 5 and 6). These trajectories are the result
of a host of interacting global change drivers (CO
fertilization,
intensifying N deposition, rising temperature and varying precipitation, and
land use change and associated N fertilization regimes) whose effects are
challenging to disentangle without additional simulations. For example,
while intensifying N deposition and N fertilizer use could drive increasing
soil N and N uptake, land use change could increase N losses from both
vegetation N and soil N. Most models suggest increasing biological N
fixation between 1850 and 2021. This occurs as a result of either increasing
vegetation biomass or the up-regulation of biological N fixation due to N
limitation imposed by CO
fertilization or a combination thereof,
depending on the representation of biological N fixation in a given model
(Table 1). This follows observations that suggest that biological N fixation
is stimulated by CO
fertilization
(Zheng
et al., 2020; Liang et al., 2016), although its mechanism (i.e.,
up-regulated biological N fixation in N-limited conditions) may not be
captured. Similarly, most models also suggest increasing N uptake between
1850 and 2021. This also occurs as a result of increasing vegetation
biomass; increasing soil N from intensifying N deposition and N fertilizer
use; or increasing biological N fixation, mycorrhizae, and root allocation
due to N limitation imposed by CO
fertilization, which is again dependent on
the representation of the vegetation response to N limitation in a given
model (Table 1). Most models suggest increasing net N mineralization rate
between 1850 and 2021, likely due to rising temperature following
observations (Liu et al., 2017). Most
models suggest increasing N
O emissions (and N losses) between 1850 and
2021, likely due to rising temperatures and intensifying N deposition and N
fertilizer use following observations
(Tian
et al., 2020).
We focused on three key N cycling processes for evaluation: biological N
fixation, vegetation C : N ratio, and soil C : N ratio. These three key N
cycling processes have important implications for projecting the future
terrestrial C sink. Biological N fixation is the dominant natural N supply
to terrestrial ecosystems and allows vegetation to increase N uptake in
N-limited conditions, reduce N limitation, and thus sustain terrestrial C
sequestration, such as in response to N limitation imposed by CO
fertilization
(Zheng
et al., 2020; Liang et al., 2016). Vegetation and soil C : N ratios reflect
assimilated C per unit N and thus terrestrial C sequestration. They can
potentially vary, such as in response to high photosynthesis rates relative
to N uptake rates driven by CO
fertilization
(Elser et al., 2010). Overall
scores of N cycling variables, which quantify model performance in
reproducing an observation-based dataset, are lower than overall scores of
corresponding C cycling variables, suggesting that models could be less
capable of capturing N cycling processes than C cycling processes (Fig. 8). However, this could also be due to the significant uncertainty
associated with measurements of N cycling processes as discussed below.
The TRENDY-N ensemble reproduced global observation-based biological N
fixation but tended to overestimate low-latitude biological N fixation and
underestimate high-latitude biological N fixation (Fig. 7a, b). This is
likely because most models represented biological N fixation
phenomenologically as a function of a measure of vegetation activity (either
NPP or ET). Since there is higher vegetation activity at low latitudes than
at high latitudes, these models thus represent higher biological N fixation
at low latitudes than at high latitudes. However, because biological N
fixation is down-regulated in non-N-limited conditions, it is often
down-regulated at low latitudes, which are generally not (or at least less)
N-limited in nature
(Barron et
al., 2011; Batterman et al., 2013; Sullivan et al., 2014). While CLASSIC,
CLM5.0, and OCNv2 can represent the down-regulation of biological N fixation
in non-N-limited conditions, they still simulate high low-latitude
biological N fixation. This suggests that the strength of regulation of
biological N fixation could be insufficient and/or that there could be
unaccounted N sources at low latitudes. For example, rock N weathering could
be a significant N source to terrestrial ecosystems. Some estimates have
suggested that rock N weathering could be as high as 11–18 Tg N yr
−1
globally (Houlton et al., 2018) but is not
explicitly represented in the TRENDY-N ensemble (with the exception of
LPX-Bern, which calculates all external N sources post hoc to simulate a
closed N cycle, thereby implicitly including rock N sources). The discrepancy
between modelled and observed biological N fixation could also be due to
uncertainty in the observation-based dataset
given the difficulties
associated with measuring biological N fixation
(Soper et al., 2021). Ecological theory
(Hedin et al., 2009) has suggested that natural
biological N fixation should be higher at low latitudes given large N
losses, in contrast to the observation-based dataset from Davies-Barnard and
Friedlingstein (2020). Furthermore, the observation-based dataset from
Davies-Barnard and Friedlingstein (2020) did not explicitly account for
agricultural biological N fixation but rather assumed that crop biological N
fixation rates are equivalent to those of grasses, although they are likely
to be much greater (Peoples et al., 2021;
Herridge et al., 2022).
The TRENDY-N ensemble overestimated the global observation-based vegetation C : N
ratio but reproduced its latitudinal pattern (as also indicated by its
higher spatial distribution score) (Fig. 7c, d). This is because most models
represent different plant functional types (e.g., evergreen needleleaf
trees, deciduous broadleaf trees, evergreen broadleaf trees) with
different tissue C : N ratios (which can be either flexible within a
constrained range or time invariant). These
plant functional types are
geographically distributed according to similar land cover products. The
TRENDY-N ensemble overestimated global observation-based soil C : N ratio and
failed to reproduce its latitudinal pattern (as also indicated by its lower
spatial distribution score) (Fig. 7e, f). In particular, models failed to
reproduce the peak at the Equator and the peak at approximately
30
S, corresponding to tropical forests and deserts,
respectively. This is because most models represent a constant soil C : N
ratio (both temporally and spatially) and are thus unable to capture the
spatial variability in the soil C : N ratio. Improving the representation of
soil N is an important future direction for terrestrial biosphere model
development given the essential feedbacks between soil N and soil C.
4.2
Disconnect between C and N cycling in terrestrial biosphere models
The importance of N limitation of terrestrial C sequestration is empirically
established
(Elser
et al., 2007; LeBauer and Treseder, 2008; Wright et al., 2018). It has
already influenced the historical terrestrial C sink
(S. Wang et al., 2020), and it is expected to be
especially important under future CO
fertilization and global change
(Terrer et al., 2019). While all
TRENDY-N models simulate the historical terrestrial C sink in line with
observations (and are no different from TRENDY models without a
representation N cycling; Seiler et al., 2022),
our results suggest a disconnect between C and N cycling in these models.
First, the models exhibit a wide spread across simulated N pools and fluxes.
Second, there are no significant correlations between model performance in
simulating N cycling and model performance in simulating C cycling. Third,
there are no statistically significant differences between models with
different representations of fundamental N cycling processes (N limitation
of vegetation growth, biological N fixation, the response of vegetation to N
limitation, and N limitation of decomposition).
Overall, our results suggest that the underlying N cycling processes that
regulate terrestrial C sequestration operate differently across models and
may not be fully captured given that models are calibrated to C cycling. The
spread across models suggests that approaches to represent N cycling
processes vary among models and that there is no clear consensus yet on what
the best approaches are. Studies have explored the validity of different
representations of N cycling processes within a single model, suggesting
that alternative representations of a biological N fixation, ecosystem C : N
stoichiometry, and ecosystem N losses lead to substantial differences in
simulated C cycling
(Kou-Giesbrecht
and Arora, 2022; Meyerholt et al., 2020; Peng et al., 2020; Wieder et al.,
2015a). This disconnect between C and N cycling will become particularly
consequential for projecting the terrestrial C sink under future global
change, which is likely to modify the C–N balance through N limitation of
CO
fertilization and intensifying N deposition (among other effects of
global change).
4.3
Future directions
Evaluating N cycling in terrestrial biosphere models is severely restricted
by the lack of available observations of N cycling. N cycling processes are
notoriously difficult to measure, such as biological N fixation
(Soper et al., 2021) and gaseous N
losses (Barton et al., 2015). In the past, N
cycling has commonly been evaluated by comparison to estimates of global N
pools and fluxes derived from a small number of observations that have been
scaled up or averaged to yield a value with wide confidence intervals
(Davies-Barnard et al., 2020). Not only are these
global totals highly uncertain, but they also do not allow for the analysis
of spatial patterns. Here, we present an improved framework to evaluate
three key N cycling processes – biological N fixation, vegetation C : N
ratio, and soil C : N ratio – in terrestrial biosphere models. However, these
globally gridded observation-based datasets are also uncertain, given
uncertainty in the estimates of tissue C : N ratios for different plant
functional types, tissue fraction of total biomass (especially those of
roots and wood, which had a lower number of measurements in comparison to
that of leaves), and the measurements and models used to derive
soil N (Batjes et al., 2020). More
observations of these N
cycling processes are necessary to reduce uncertainty. Temporally explicit
measurements are important for assessing intra-annual and inter-annual
variability. Leveraging advances in remote sensing
(Knyazikhin
et al., 2013; Townsend et al., 2013; Cawse-Nicholson et al., 2021) and incorporating N cycling process measurements into research networks such
as FLUXNET (Vicca et al., 2018) are essential.
Multiple observation-based datasets taken from different sources and derived via
different methodologies of a given N cycling process are necessary to
evaluate observational uncertainty (Seiler et al., 2021).
Global observations of other important N cycling processes (such as N
mineralization and N losses) are necessary to fully evaluate N cycling in
terrestrial biosphere models. Additionally, hindcast simulations of the
transition from the Last Glacial Maximum to the pre-industrial period can be
used in combination with proxy-based reconstructions of past N
emissions (Fischer et al., 2019) and C stocks
(Jeltsch-Thömmes et al., 2019) for model evaluation
and can serve as a constraint for terrestrial biosphere models
(Joos et al., 2020).
Modelled experimental manipulations (such as CO
fertilization or N
fertilization experiments) are imperative to evaluate model formulations of
the underlying mechanisms of C–N cycling interactions
(Medlyn
et al., 2015; Wieder et al., 2019; Zaehle et al., 2014). Derived nutrient
limitation products (Fisher et al.,
2012) can also be applied to evaluate present-day nutrient cycling when
phosphorus (P) is accounted for
(Braghiere et al., 2022).
Evaluating the ability of models to simulate present-day N cycling
processes, as we did here, is only one method of assessing their ability to
simulate N limitation of terrestrial C sequestration. A robust test of the
simulated response to CO
fertilization and N fertilization across
models would be ideal for evaluating the ability of models to represent the
regulation of C cycling by N cycling under global change and thus their
ability to realistically simulate the future terrestrial C sink.
While some of the models in the TRENDY-N ensemble have the capability of
representing coupled C, N, and P cycling
(Goll
et al., 2012; Nakhavali et al., 2022; Sun et al., 2021; Wang et al., 2010;
Z. Wang et al., 2020; Yang et al., 2014), P cycling was not active in the model simulations
in the GCP 2022. P limitation could be important for limiting terrestrial C
sequestration, especially in low-latitude forests
(Elser
et al., 2007; Terrer et al., 2019; Wieder et al., 2015b). As more models
incorporate coupled C–N–P cycling
(Reed et al.,
2015; Braghiere et al., 2022), observation-based datasets of P will also be
necessary for model evaluation.
Conclusions
Because the TRENDY-N ensemble overestimated both vegetation and soil C : N
ratios, it is possible that models could overestimate assimilated C per unit
N and thus future terrestrial C sequestration under CO
fertilization.
Alongside discrepancies in biological N fixation, this could lead to biases
in projections of the future terrestrial C sink by the TRENDY-N ensemble.
Not to mention there are several other terrestrial biosphere models in the
TRENDY ensemble that do not represent coupled C–N cycling. While the models
are capable of reproducing the current terrestrial C sink, the spread across
the models in simulating N cycling suggests that C–N interactions operate
differently across models and may not be fully captured given that models
are calibrated to C cycling. However, these C–N interactions are critical
for projecting the terrestrial C sink under global change in the future.
Appendix A
Table A1
IGBP land cover type, corresponding TRY plant trait database PFT,
tissue C : N ratios from the TRY plant trait database
(Kattge
et al., 2020), tissue fractions (Poorter
et al., 2012), and calculated total C : N ratio.
Value from evergreen needleleaf forest.
Average of evergreen needleleaf forest, evergreen broadleaf forest,
and deciduous broadleaf forest.
Value from grasslands.
Value from croplands.
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Table A2
Kendall's tau from the Mann–Kendall test (
value
0.05)
for each N pool and N flux time series simulated by the TRENDY-N ensemble
from 1850 to 2021. NS indicates that Kendall's tau is not significant. NA
indicates that the variable was not reported by the model.
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Table A3
Time mean bias score (
bias
), spatial distribution score
dist
), and overall score (
overall
) of the TRENDY-N ensemble in
simulating biological N fixation, vegetation C : N ratio, and soil C : N ratio. NA indicates that this variable was not evaluated.
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Table A4
Overall scores of biological N fixation, vegetation C : N ratio,
soil C : N ratio, and NBP averaged across TRENDY-N ensemble models with
different representations of key N cycling processes (N limitation of
vegetation growth, biological N fixation, vegetation response to N
limitation, and N limitation of decomposition; see Table 1). The
values are
from
tests and ANOVAs assessing differences between these representations
of key N cycling processes.
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Table A5
Present-day global values of biological N fixation, vegetation C : N
ratio, and soil C : N ratio averaged across TRENDY-N ensemble models with
different representations of key N cycling processes (N limitation of
vegetation growth, biological N fixation, vegetation response to N
limitation, and N limitation of decomposition; see Table 1). The
values are
from
tests and ANOVAs assessing differences between these representations
of key N cycling processes.
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Table A6
Kendall's tau from the Mann–Kendall test (
value
0.05)
for biological N fixation, vegetation C : N ratio, and soil C : N ratio averaged
across TRENDY-N ensemble models with different representations of key N
cycling processes (N limitation of vegetation growth, biological N fixation,
vegetation response to N limitation, and N limitation of decomposition; see
Table 1). The
values are from
tests and ANOVAs assessing differences between
these representations of key N cycling processes.
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Figure A1
Geographical distributions of variation in
(a)
vegetation N,
(b)
litter N,
(c)
soil N,
(d)
biological N fixation,
(e)
N uptake,
(f)
net N
mineralization,
(g)
O emissions, and
(h)
N loss simulated by the
TRENDY-N ensemble (across models over 1980–2021).
Figure A2
Correlations between overall scores of primary C and N pools and
fluxes across TRENDY-N ensemble models: gross primary productivity (GPP),
net biome productivity (NBP), vegetation C (CVEG), soil C (CSOIL), leaf area
index (LAI), biological N fixation (FBNF), vegetation C : N ratio (CNVEG), and
soil C : N ratio (CNSOIL). Abbreviations of the observation-based datasets are
described in Sect. 2 and in Seiler et al. (2022). Spearman's rank correlation coefficient is shown for statistically
significant correlations (
value
0.05).
Figure A3
Correlations between present-day global values (averaged over
1980–2021) of primary C and N pools and fluxes across TRENDY-N ensemble
models: vegetation C (CVEG), litter C (CLITTER), soil C (CSOIL), net biome
productivity (NBP), gross primary productivity (GPP), autotrophic
respiration (RA), heterotrophic respiration (RH), leaf area index (LAI),
vegetation N (NVEG), litter N (NLITTER), soil N (NSOIL), biological N
fixation (FBNF), N uptake (NUP), net N mineralization (NETNMIN), N
emissions (N
O), N loss (NLOSS), vegetation C : N ratio (CNVEG), and soil C : N
ratio (CNSOIL). Spearman's rank correlation coefficient is shown for
statistically significant correlations (
value
0.05).
Figure A4
Correlations between Kendall's tau of primary C and N pools and
fluxes across TRENDY-N ensemble models: vegetation C (CVEG), litter C
(CLITTER), soil C (CSOIL), net biome productivity (NBP), gross primary
productivity (GPP), autotrophic respiration (RA), heterotrophic respiration
(RH), leaf area index (LAI), vegetation N (NVEG), litter N (NLITTER), soil N
(NSOIL), biological N fixation (FBNF), N uptake (NUP), net N mineralization
(NETNMIN), N
O emissions (N
O), N loss (NLOSS), vegetation C : N ratio
(CNVEG), and soil C : N ratio (CNSOIL). Spearman's rank correlation
coefficient is shown for statistically significant correlations (
value
0.05).
Figure A5
Time series of
(a)
vegetation N,
(b)
litter N,
(c)
soil N,
(d)
biological N fixation,
(e)
N uptake,
(f)
net N mineralization,
(g)
emissions, and
(h)
N loss simulated by the TRENDY-N ensemble from 1850 to
2021.
Figure A6
Latitudinal distributions and global means of
(a)
biological N
fixation,
(b)
vegetation C : N ratio, and
(c)
soil C : N ratio simulated by the
TRENDY-N ensemble (averaged across models over 1980–2021) in comparison to
observation-based datasets from Davies-Barnard and
Friedlingstein (2020) for biological N fixation, the TRY plant trait
database for vegetation C : N ratio, and SoilGrids for soil C : N ratio.
Boxplots show the median, interquartile range (box), and 80 % percentiles
(whiskers) of the global mean.
Code availability
AMBER is available at
(Seiler, 2021).
Data availability
Biological N fixation, vegetation C : N ratio, and soil C : N ratio data are
available at
(Kou-Giesbrecht, 2023).
Author contributions
SKG designed and conducted the study and prepared the initial manuscript. VKA and CS provided feedback on the initial manuscript and its subsequent revisions. AA, SF, AKJ, FJ, DK, JK, SS, MO, NP, QS, HT, NV, and SZ conducted TRENDY simulations and provided feedback on the manuscript.
Competing interests
At least one of the (co-)authors is a member of the editorial board of
Earth System Dynamics
. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors would like to thank Taraka Davies-Barnard for compiling the
observations used to evaluate biological N fixation. ORCHIDEEv3 simulations
were granted access to the HPC resources of GENCI-TGCC under the allocation
A0130106328.
Review statement
This paper was edited by Somnath Baidya Roy and reviewed by Joshua Fisher and one anonymous referee.
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Articles
Abstract
Introduction
Methods
Results
Discussion
Conclusions
Appendix A
Code availability
Data availability
Author contributions
Competing interests
Disclaimer
Acknowledgements
Review statement
References
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Short summary
Nitrogen (N) is an essential limiting nutrient to terrestrial carbon (C) sequestration. We evaluate N cycling in an ensemble of terrestrial biosphere models. We find that variability in N processes across models is large. Models tended to overestimate C storage per unit N in vegetation and soil, which could have consequences for projecting the future terrestrial C sink. However, N cycling measurements are highly uncertain, and more are necessary to guide the development of N cycling in models.
Nitrogen (N) is an essential limiting nutrient to terrestrial carbon (C) sequestration. We...
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Abstract
Introduction
Methods
Results
Discussion
Conclusions
Appendix A
Code availability
Data availability
Author contributions
Competing interests
Disclaimer
Acknowledgements
Review statement
References