The nitrogen cycle and its effect on carbon uptake in the
terrestrial biosphere is a recent progression in earth system models. As
with any new component of a model, it is important to understand the
behaviour, strengths, and limitations of the various process
representations. Here we assess and compare five land surface models with
nitrogen cycles that are used as the terrestrial components of some of the
earth system models in CMIP6. The land surface models were run offline with
a common spin-up and forcing protocol. We use a historical control
simulation and two perturbations to assess the model nitrogen-related
performances: a simulation with atmospheric carbon dioxide increased by 200 ppm and one with nitrogen deposition increased by 50 kgN ha-1 yr-1. There is generally greater variability in productivity response
between models to increased nitrogen than to carbon dioxide. Across the five
models the response to carbon dioxide globally was 5 % to 20 % and the
response to nitrogen was 2 % to 24 %. The models are not evenly distributed
within the ensemble range, with two of the models having low productivity
response to nitrogen and another one with low response to elevated atmospheric
carbon dioxide, compared to the other models. In all five models individual
grid cells tend to exhibit bimodality, with either a strong response to
increased nitrogen or atmospheric carbon dioxide but rarely to both to an
equal extent. However, this local effect does not scale to either the
regional or global level. The global and tropical responses are generally
more accurately modelled than boreal, tundra, or other high-latitude areas
compared to observations. These results are due to divergent choices in the
representation of key nitrogen cycle processes. They show the need for more
observational studies to enhance understanding of nitrogen cycle processes,
especially nitrogen-use efficiency and biological nitrogen fixation.
Introduction
The terrestrial carbon (C) cycle currently removes around a third of
anthropogenic carbon emissions from the atmosphere (Friedlingstein
et al., 2019; Le Quéré et al., 2018). Changes in this uptake will
affect the allowable emissions (Seneviratne et al.,
2016) for targets such as limiting warming to 1.5 ∘C
(Millar et al., 2017;
Müller et al., 2016). Nitrogen (N) is required to synthesise new plant
tissue (biomass) out of plant-assimilated C, in differing ratios across
biomes and tissue types (McGroddy et al.,
2004). Therefore, future projections of terrestrial C uptake are dependent
on N availability, particularly under high atmospheric carbon dioxide
(CO2) concentrations (Arora
et al., 2020; Meyerholt et al., 2020; Wieder et al., 2015b; Zaehle et al.,
2014b). A key tool for projections of allowable emissions are earth system
models (ESMs), which project the responses of the coupled earth system to
perturbations in forcings (Anav
et al., 2013; Arora et al., 2013; Friedlingstein et al., 2006; Jones et al.,
2013). Of the ESMs that contributed results to the Fifth Phase of the
Coupled Model Intercomparison Project (CMIP5; Taylor et al.,
2012) only two, based on the same land component, included terrestrial N
cycling (Thornton et al., 2009).
A number of studies with stand-alone terrestrial biosphere models (Sokolov
et al., 2008; Wårlind et al., 2014; Zaehle et al., 2010; Zhang et al.,
2013) as well as post hoc assessments of CMIP5 projections suggest that
predictions of terrestrial C uptake would decrease by 37 %–58 % if ESMs
accounted for N constraints (Wieder
et al., 2015b; Zaehle et al., 2014b).
Among the latest generation of models contributing results to CMIP6
(Eyring et al., 2016), at least
10 ESMs incorporate the N cycle (Arora et
al., 2020). These models employ a range of assumptions and process
formulations, reflecting divergent theory and significant knowledge gaps
(Zaehle and Dalmonech, 2011). Initial results
imply that the inclusion of a N cycle has reduced the spread of results
across multiple ESMs (Jones and Friedlingstein,
2020). Since N availability is an important source of uncertainty for the C
cycle (Meyerholt et al.,
2020), an assessment of the sensitivity of the N cycle in these models to
changes in atmospheric CO2 and N inputs is required. Because of the
tight coupling of C and N dynamics, a direct evaluation of the N effects on
simulated C cycle dynamics using conventional model benchmarking approaches
(Collier
et al., 2018; Luo et al., 2012) is challenging. More insights into the
magnitude of a N effect can be gained by comparing model simulations against
perturbation experiments that provide evidence for the responses of
terrestrial ecosystems to changes in the C and N availability (Thomas
et al., 2013; Wieder et al., 2019; Zaehle et al., 2010).
In this study, we test five land surface models (LSMs) employed in the
latest generation of ESMs used in CMIP6. We use a set of standardised model
forcing and protocol to simulate historical changes in the C and N balance,
as well as the response to N and C perturbations. The perturbation
experiments (described in the Methods section) are designed to approximate field
experiments undertaken to understand the effects of elevated CO2 or N
(e.g. Ainsworth
and Long, 2005; LeBauer and Treseder, 2008; Song et al., 2019). These
simulations reveal the overall pattern of response of the model to these
forcings. We use a range of observations from the literature and
model-to-model comparisons to assess the behaviour and performance of the
models. The approach of assessing ESM N cycles via their
corresponding offline LSMs, driven by a standardised set of model forcing,
has the advantage of making model projections directly comparable while
giving a representative view of the latest N cycle developments.
MethodsModels
We ran simulations with five LSMs that are the land components of ESMs
taking part in CMIP6. The key N process formulations are summarised in Table 1. A brief description of each model follows.
Key nitrogen cycle algorithms applied by the models. C is Carbon;
N is Nitrogen; GPP is gross primary productivity; NPP is net primary
productivity; and PFT is plant functional type.
CLM4.5CLM5JSBACHJULES-ESLPJ-GUESSKeyreferencesOleson et al. (2010)Lawrence et al. (2019)Goll et al. (2017),Mauritsen et al.(2019)Wiltshire et al.(2020)Smith et al. (2014)N effecton GPPDownregulation of GPP tomatch stoichiometric constraint from allocable NLeaf N compartmentalisedinto different pools to co-regulate photosynthesis according to the LUNA modelNo direct effectNo direct effectReduction of Rubisco capacity in the case of N stressN effect onautotrophic respirationN content-dependenttissue-level maintenancerespirationUpdated PFT-specific N-dependent leaf respirationschemeNo direct effectN content-dependent maintenance respiration for roots andstemsN content-dependent maintenance respiration for roots andstems; leaf respiration reduced under N stressVegetation pool C : NstoichiometryFixed for all poolsFlexible for all poolsFixed for all pools except labileFlexible leafstoichiometry from which root and stem C : N are scaled with fixed fractionsFlexible for leaves and fine roots; fixed otherwiseRetrans-location ofN from shed leavesFraction of leaf N moved to mobile plant N pool prior to shedding; fraction depends on PFT-specific fixed live leaf and leaf litter C : N ratiosFraction of leaf N movedto mobile plant N priorto shedding via two pathways: a free retranslocation or a paid-for retranslocation dependent on PFT-specific dynamic leaf C : N range and minimum leaf litter C : N as well as available carbon to spend for extraction in the FUN modelFraction of leaf Nmoved to mobileplant N pool priorto sheddingFraction of leaf Nmoved to labile store with PFT-specific retranslocation coefficientFraction of leaf N moved to mobile plant N pool prior to shedding; fraction depends on N stressBiological N fixationMonotonically increasingfunction of NPPSymbiotic N fixation according to the FUN model; asymbiotic N fixation linearly dependent on evapotranspirationNon-linear function of NPPLinear function of NPP, 0.0016 kg N per kg C NPPLinear function of ecosystem evapo-transpiration, 0.102 mm yr-1 ET + 0.524 per kg N ha-1 yr-1Ecosystem N lossDenitrification loss as afraction of gross N mineralisation + fraction ofsoil inorganic N pool incase of N saturation (CLM-CN)/Denitrification as fraction of nitrification (CENTURY); leaching as a function of soil inorganic N pool size; fractional fire loss as fraction of vegetation and litter poolsDenitrification as fraction of nitrification (CENTURY); leaching as a function of soil inorganic N pool size; fractional fire loss as fraction of vegetation and litter poolsDenitrification proportional tosoil inorganicN pool and soil moisture;leaching propor-tional to soilinorganic N pooland drainageDenitrification is afixed fraction (1 %) of mineralisation flux; leaching of nitrogen is a function of soil inorganic N pool, drainage, and a parameter representing the effective solubility of nitrogenDenitrification as fixed fraction of mineralisation flux; leaching as a function of soil inorganic N pool and drainage N loss from fire eventsPlant N uptakeFunction of plant N demand, soil inorganic N availability, and competition with heterotrophsSoil uptake of inorganic Naccording to the FUN modelPlant N demand, limited bysoil inorganic N availabilityDemand based onGPP and limitedby soil inorganic N availabilityDetermined to maintain optimal leaf Nfor photosynthesis;limited by soil inorganic N availability,fine root mass, soiltemperature, and plant N status
The Community Land Model version 4.5 (CLM4.5; Koven et
al., 2013; Oleson et al., 2010) is used in the Euro-Mediterranean Centre on
Climate Change coupled climate model (CMCC-CM2; Cherchi et al., 2019) and TaiESM1. The
N component is described in
Koven et al. (2013). CLM4
is the precursor to CLM4.5 and was the first N model for ESMs used in CMIP5
(Thornton et
al., 2007, 2009). While the N cycling component of CLM4.5 is similar to
CLM4, some features of CLM4.5, such as leaf physiological traits
(Bonan et al., 2012), were modified,
and there is a vertically resolved soil biogeochemistry scheme
(Koven et al., 2013) as
opposed to the single-layer box modelling scheme for C and N in CLM4.
The Community Land Model version 5 (CLM5;
Lawrence
et al., 2019) is used in the Community Earth System Model Version 2 (CESM2;
Danabasoglu
et al., 2020) and the Norwegian Earth System Model version 2 (NorESM2;
Seland et al., 2020).
CLM5 is the latest version of CLM and represents a suite of developments on
top of CLM4.5. The N component is described in
Fisher
et al. (2010) and Shi et al. (2016). The key difference for the N cycle
compared to CLM4 is the implementation of a C cost basis for acquiring N,
derived from the Fixation and Uptake of Nitrogen (FUN) approach
(Fisher et al., 2010).
The JSBACH version 3.20 model (Goll et al., 2017) is used
in the Max Planck Earth System Model version 1.2 (MPI-ESM;
Mauritsen et al., 2019) and Alfred
Wegener Institute Earth System Model (AWI-ESM). The N component is described
in Goll et al. (2017).
The Joint UK Land Environment Simulator version 5.4 (JULES-ES;
Best et al.,
2011; Clark et al., 2011) is used in the UK Earth System Model (UKESM1;
Sellar et al., 2020).
The N component is described in
Wiltshire et al. (2020) and
Sellar et al. (2020).
The Lund-Potsdam-Jena General Ecosystem Simulator version 4.0 (LPJ-GUESS;
Olin
et al., 2015; Smith et al., 2014) is used in the European community
Earth-System Model (EC-Earth;
Hazeleger et al., 2012). The N
component is described in Smith et al. (2014).
Forcing data and model initialisation
All model pools were spun-up to equilibrium forced by pre-industrial
conditions. This comprised of a constant atmospheric CO2 concentration
of 287.14 ppm, cycling global climate data at 0.5∘×0.5∘ resolution for the years 1901–1930 from the CRU-NCEP dataset
version 7.0 (New et al., 2000),
using constant 1860 land cover from the
Hurtt et al. (2020) database, and 1860s nitrogen deposition from the Atmospheric
Chemistry and Climate Model Intercomparison Project (Lamarque
et al., 2013). Next, transient historical runs were performed for the
1861–1900 period with the same climate forcing as the spin-up but with
time-varying atmospheric CO2 concentrations from synthesised ice core
and National Oceanic and Atmospheric Administration (NOAA) measurements, as
well as annually varying land use from
Hurtt et al. (2020). The N deposition is taken from the Atmospheric Chemistry and Climate
Model Intercomparison Project (Lamarque
et al., 2013). The simulations were then continued for 1901–2015 under
all time-varying forcings, including climate.
The models applied their individual soil and vegetation spin-ups according
to their respective conventions. The goal of the spin-up procedure is to
obtain quasi-steady states of the ecosystem pools in relation to climate,
avoiding drifting pool sizes due to a lack of equilibrium, especially for
slow-turnover soil organic matter pools. Because of differences among the
models, pool sizes after spin-up are not expected to be identical.
Model experiments
In addition to the historical run described above (referred to hereafter as
the “Control”), two experiments were performed for the period 1996–2015:
increased CO2 (+CO2) and increased N (+N). These two experimental
runs are compared to the corresponding 1996–2015 simulations from the
unperturbed Control runs. Table S1 in the Supplement provides a summary of the experiments.
For the increased CO2 experiment (+CO2), the atmospheric CO2
concentration was abruptly increased to constant 550 ppm. This is almost
twice the pre-industrial atmospheric CO2 of 280 ppm or a 200 ppm
increase compared to the 1996 atmospheric CO2 of ∼350 ppm, similar to free-air CO2 enrichment experiments performed in the
1990s (Norby et al., 2005).
For the increased N experiment (+N), N deposition was abruptly increased by
50 kgN ha-1 yr-1, which is roughly equivalent to what has been
used in a number of forest N fertilisation trials
(Thomas et al., 2013) and around 5–10 times higher than typical background N deposition
(Zak et al., 2017).
Analytical framework
The response of the terrestrial productivity (and with it terrestrial C
storage) to changes in the N cycle is in principle controlled by two
components: (i) the net ecosystem balance of N, i.e. the difference between
changes in ecosystem N inputs and N losses, which determines the change in
the ecosystem N available for plant growth and immobilisation during litter
and soil organic matter decomposition, and (ii) the ratio of carbon
production per unit N availability, which can most effectively be
described as the N-use efficiency of growth.
Because the individual processes and pools considered vary between the
five models (Table 1), we use a simplified N budget to assess the annual
change in the terrestrial N store (ΔN, including soil and plants):
ΔN=Ndep+BNF-Nloss,
where Ndep is the N deposition, BNF is the biological N fixation, and
Nloss is the N lost from gaseous, leaching, and other pathways, as
declared by the models. This paradigm assumes that increased ecosystem N
input from deposition or fixation enters the soil and then becomes available
for plant uptake. In a similar way, plant N uptake (Nup) could lead to
reduced N losses, which would (assuming constant N inputs) result in an
apparent increase in the ecosystem N capital. Note that crop fertilisation
is not included here, as it is assumed to be equal in the three simulations.
Whether and how this change in N capital affects plant growth is dependent
on the magnitude of the change in plant N uptake, as well as the relationship
between Nup and NPP (whole-plant nitrogen-use efficiency, NUE;
Zaehle et al., 2014a)
NUE=NPPNup,
where Nup includes plant uptake of soil inorganic N of any origin, i.e.
atmospheric deposition, fertilisation, decomposition of plant litter, or
biological nitrogen fixation (BNF). NUE is the outcome of the product of
tissue stoichiometry and fractional allocation of NPP to different tissue
types and therefore varies with changes in the allocation fractions and
tissue C : N ratio.
Observations for comparison
We compare the models to a range of observation-based metrics at global and
regional scales, detailed in Table S2. Most of the numbers from the
literature that we cite are based on relatively small numbers of field
studies upscaled or averaged to give an approximate global value with
confidence intervals. No modification of spatial scale or averaging is done
to values used, but where the CO2 or N increase is specified, it is
scaled to 200 ppm or 50 kg ha-1 yr-1 accordingly. While these
upscaled values need to be interpreted with caution, in the absence of more
robust comparators they are useful benchmarks that can provide real-world
context in addition to field-scale comparisons and inter-model comparisons.
Where appropriate, comparisons are made at the climate-determined region
level (see Fig. S1 in the Supplement; Kottek et al., 2006).
ResultsControl run global C and N budgets
A range of pools and fluxes from the models compared to the closest
comparable observation-based data show a good performance overall and
emphasise similarities between the models at the global scale (Fig. 1). For
GPP, all the models compare well to the MTE data (Jung
et al., 2011), and when the directly comparable time period is used (see Fig. S2), the models are all within the MTE range. The global GPP value is
underlain by some regional variations between models (Figs. S2 and S3).
1996–2005 mean model estimates of the major ecosystem C and N
component pools and fluxes in comparison with observation-based estimates
from the literature. C is carbon; N is nitrogen; rh is heterotrophic
respiration; ra is autotrophic respiration; GPP is gross primary
productivity; SOM is soil organic matter; and BNF is biological nitrogen
fixation. The N uptake flux refers to root uptake of inorganic N. Ranges
shown represent the 95 % confidence intervals, standard deviation, or
similar uncertainty metrics, where available. Where observation-based ranges
or values are available, an arrow indicates that the model value is
either higher than the range or lower. Where there is no arrow, the model is within
the observation-based range or there is no observation-based range to
compare to. N loss is the loss via gaseous loss and leaching. The black
numbers indicate observation-based estimates from the literature. (a) Heterotrophic respiration: Bond-Lamberty and
Thomson (2010), soil respiration estimate for 2008. To account for the
included root respiration, we reduced the literature estimate by 33 %
according to Bowden et al. (1993); (b) autotrophic respiration:
Piao et al. (2010) and Luyssaert
et al. (2007), present-day estimate for forests from 2007; (c) GPP:
Jung
et al. (2011), averaged estimate for 1982–2011; (d) SOM + Litter and
Vegetation C: Carvalhais et al. (2014), present-day estimate
from 2014; (e) BNF: Davies-Barnard and
Friedlingstein (2020), upscaled averages for 1980–2019; (f) N deposition: Lamarque
et al. (2013), estimate for 2000; (g) C : N ratios for soil and vegetation:
Wang et al. (2018); (h) soil
nitrogen in the top 1 m and soil carbon in the top 1 m
(Batjes, 2014); (i) total ecosystem
respiration: Ballantyne et al. (2017); (j) BNF: Vitousek et al. (2013).
Like GPP, the total ecosystem respiration (TER) is similar across all the
models, and most of the models fall within the range of a top-down estimate
by Ballantyne et al. (2017) (106±12 GtC yr-1). However, the partitioning between the autotrophic and
heterotrophic respiration differs (Fig. 1). Autotrophic respiration is
overestimated in all the models (Luyssaert
et al., 2007; Piao et al., 2010), while heterotrophic respiration is
underestimated (Bond-Lamberty and Thomson,
2010). The heterotrophic value from
Bond-Lamberty and Thomson (2010) was reduced by
33 % to account for root respiration in line with
Bowden et al. (1993).
N inputs differ strongly between the models because of widely varying
biological nitrogen fixation (BNF, Fig. 1). The other major input, N
deposition, is a prescribed input with small variations resulting from
differences in the land–sea mask of the individual models. BNF, on the other
hand, has a wide range among models. An upscaled meta-analysis of BNF
covering the period of approximately 1990–2019
(Davies-Barnard and Friedlingstein, 2020) has a
range of 52–141 TgN yr-1 and only one model is outside of that
range. The three models with the highest BNF (JSBACH, CLM5, and JULES-ES)
are three of the four models that use an NPP-based function (the fourth
being CLM4.5). CLM5's process-based function uses a C cost of N acquisition
where energy from NPP can produce N based on the work by
Fisher et al. (2010).
JULES-ES, JSBACH, and CLM4.5 use an empirical large-scale correlation with
NPP (Cleveland et al., 1999).
LPJ-GUESS, the lowest BNF model, also uses an empirical correlation from
Cleveland et al. (1999), based on
evapotranspiration rather than NPP. Thus, even BNF functions based on the
same source (Cleveland et al.,
1999) can have very different results
(Wieder et al., 2015a), due to the large
range of BNF functions within the source and differences in how they are
implemented (Meyerholt et
al., 2016). BNF dominates N input variability both because of a lack of
process understanding to constrain model structures and the continued
uncertainty in available observations.
Looking at the soil and vegetation C and N pools as well as the ratios between
them, the models have a range of strengths and weaknesses, with no model
falling within the observation-constrained range for all pools. However, due
to variations in both the modelling and measurement of C and N within
different soil depths, not too much emphasis should be placed on the pool
comparisons shown in Fig. 1.
Modelled NPP responses to the +CO2 experiment
The ensemble's global modelled response of NPP to +CO2 concurs with a
meta-analysis of NPP responses to +200 ppm CO2 that suggests a positive
response of 15.6±12.8 % (Song et al.,
2019) (Table 2), with all models within that range. Other meta-analyses of
productivity (for instance, above-ground woody biomass) changes associated
with elevated CO2 give higher ranges of response (Table 2). These other
measures of productivity suggest a lower limit of around 12 %, which
encompasses all but one of the models. However, models falling within the
range of the observations may be indicative of biases and a lack of
precision in the observational estimates rather than the fidelity with which the
models can predict local and global response to elevated CO2.
Percent change in mean global NPP from perturbations. The
observations come from meta-analyses which may not be directly comparable
but which provide a useful context. ANPP represents above-ground NPP.
+CO2+NCLM4.55.4 %24.1 %CLM519.6 %22.1 %JSBACH19.3 %2.5 %JULES-ES16.7 %1.8 %LPJ-GUESS17.5 %21.7 %Mean whole-plant NPP percent changebased on meta-analyses of field-scalemeasurements15.6 % (2.8 %–28.4 %) (Song et al., 2019)6.5 % (3 %–10.5 %) (Song et al., 2019)Mean productivity value percent changebased on meta-analyses of field-scalemeasurements26 % (12.2 %–39.8 %) (Song et al., 2019) (ANPP) 22.3 % (13.9 %–31.4 %)(Baig et al., 2015) (total woody plantbiomass) 21.4 % (11 %–32.8 %) (Baig et al., 2015) (above-ground woody plant biomass)20 % (7.5 %–32.5 %) (Song et al., 2019) (ANPP) 29 % (22 %–35 %) (LeBauer and Treseder, 2008) (ANPP)
CLM4.5 has a notably lower NPP response to +CO2 than the other models
(Fig. 2), with the exception of areas where the absolute magnitude of NPP is
very low and small absolute changes (Fig. S4) already lead to large
proportional changes. However, even in these regions, the absolute changes
are consistently less than the other four models (Fig. S4). The low
response in CLM4.5 is due to a lack of mechanisms to ameliorate N limitation
when C supply increases, for instance via variable C : N ratios or increased
BNF (as is the case for CLM5) (Fisher et al., 2018; Wieder et al., 2019).
This strong limitation by the N cycle was a key reason why CESM and NorESM
in CMIP5 had lower C uptakes in response to CO2 compared to other carbon
cycle ESMs (Arora et al., 2013).
Model estimates of 1996–2005 mean net primary productivity (NPP)
response to +CO2. (a–e) Model estimates, shown as the anomaly
compared to the model control scenario. Values above 50 % are given the
50 % colour. (f) Global percent change in mean NPP.
Despite the seeming agreement of the NPP response to +CO2 at the global
scale, the regional patterns in response vary considerably for key biomes
(Fig. 2). In high-latitude tundra areas, the +CO2 response ranges from
near zero (JULES-ES) via very low (CLM4.5, JSBACH, and LPJ-Guess) to high
(CLM5). In most models, this region shows sparse vegetation cover and
N availability, allowing for only a little increase in response to
elevated CO2, whereas the increased BNF in CLM5 facilitates a response
to increasing CO2 levels. With the exception of JULES-ES, most models
predict a large +CO2 response in very dry ecosystems with marginal
productivity.
The NPP response of the equatorial region overall (Table S3 and Fig. S1)
to +CO2 ranges from 5 % for CLM4.5 to 23 % for CLM5 and JSBACH.
Looking at latitudinal averages (Fig. S4), we can see the overall patterns
are consistent across most models, and while the percent change varies a
lot, the absolute change in NPP shows considerable agreement between models,
with the exception of CLM4.5. Model responses of NPP to +CO2 in greater
Amazonia, however, do not reach a consensus. Comparing the response in the
Amazonia region with that of coastal regions of northern South America, the
JSBACH response is lower, CLM5 and LPJ-GUESS higher, and JULES-ES and CLM4.5
are approximately the same. JSBACH's dip in +CO2 NPP response at the
Equator (compared to surrounding areas) can also be seen in the absolute
values averaged by latitude (Fig. S4). The process responsible for this
spatial pattern is currently unclear but may be associated with the
strongly enhanced GPP simulated by the model for this region compared to
observation-derived estimates (Fig. S2).
Modelled NPP responses to the +N experiment
The response to +N in the models shows a binary distribution, with models
exhibiting either a high (>20 %) or low (<3 %)
response (Fig. 3) at the global scale. A meta-analysis of NPP responses to
+50 kg N ha-1 yr-1 suggests a positive response of 3 %–10.5 %
(Song et al., 2019), but none of the models are
within this range (Table 2). Other meta-analyses of productivity changes
with increased N give higher ranges of response (7.5 %–35 %),
encompassing three of the five models (Table 2). As both a percent change
and absolute change (see Fig. S5), JULES and JSBACH show much lower +N
NPP response than the other models considered here. CLM4.5 has the highest
response (24 %), on account of its high initial N limitation
(Koven et al., 2013).
Model estimates of 1996–2005 mean net primary productivity (NPP)
response to +N. (a–e) Model estimates, shown as the anomaly compared
to the model control scenario. Values above 50 % are given the 50 %
colour. (f) Globally integrated values. Global percent change in mean NPP.
The tundra biome +N response is high in CLM5 and JULES-ES and lower but
present in LPJ-GUESS and CLM4.5 (Figs. 3 and S5). If low NPP is
excluded, then the tundra mean response across models is 2 %–9 % (Table S3) and is much lower than the average of observations compiled by
LeBauer and
Treseder (2008) of 35 % (95 % confidence interval 12 %–64 %). There
is a high response to +N in Africa and Australia in CLM4.5, CLM5, and
LPJ-GUESS, despite aridity likely limiting increase in NPP in absolute, if
not relative, terms but with insufficient observations to make meaningful
comparisons. One area of agreement between the models is the lack of +N
response of the Amazonian region (Fig. 3), which is consistent with
observations which show just a 5 % +N response in tropical forests
(Schulte-Uebbing and de Vries, 2018). However,
when other tropical regions are included in the models, the +N NPP response
rises to 17 %–20 % in LPJ-GUESS, CLM4.5, and CLM5, with JULES-ES and
JSBACH remaining low (Table S3).
Comparison of NPP +N and +CO2 responses
It might be anticipated that there would be a relationship between the +N
and +CO2 responses, as an ecosystem (model) that is less N limited could
respond more strongly to increased atmospheric CO2
(Meyerholt et al., 2020).
A lack of response to N fertilisation could indicate sufficient N supply and
therefore a lacking constraint of N on the response of the vegetation to
CO2, while a strong response to N fertilisation could indicate
insufficient N supply and as a result a strong N limitation of the CO2
response. We know that response to increased N supply is globally
distributed
(LeBauer and
Treseder, 2008) and that C3 plants, which make up the majority of
vegetation worldwide, have a positive photosynthetic response to additional
atmospheric CO2 (Ainsworth and Long, 2005).
However, there is evidence that the +CO2 response would be limited by N
availability (forest NPP response to additional atmospheric CO2 is
limited by N availability; Norby et al.,
2010), and it is currently unknown whether +N would be similarly affected.
All the models are consistent with the hypothesis of either N or CO2
fertilisation at grid cell level, but the effect does not necessarily scale
to either the regional or global level. The prevalent grid cell level
spatial trend is bimodal, with grid cells either having a strong sensitivity
to +N or +CO2 but not both (see Fig. 4). Comparing percent change
emphasises the dichotomy of +N and +CO2 effects, with most values
clustered either near zero for +N or zero for +CO2, but Fig. S6 shows
that there is no positive relationship or heterogeneous distribution in the
absolute values either. The bias toward +CO2 is clear for JSBACH and
JULES-ES, with most values varying in +CO2 sensitivity but not +N (Fig. 4, also seen in the absolute anomalies in Fig. S6). A slight tendency
towards the reverse is true for CLM4.5, CLM5, and LPJ-GUESS, with more
points having a strong +N response and a weaker +CO2 response (Fig. 4).
Altogether, LPJ-GUESS and CLM5 show the most areas with both +N and +CO2
sensitivity. Wieder et al. (2019) found that there was a trade-off between +N impact and +CO2
impact in CLM4, CLM4.5, and CLM5, and this seems to be true for our ensemble
of models too.
Model estimates of 1996–2005 mean net primary productivity (NPP)
response to +N vs. +CO2, as a percent anomaly of the control scenario.
Each grid box is plotted against the corresponding grid box for the other
variable. The percent change is capped at 250 %, and values above are not
plotted. The colour of the points indicates the latitude: either north or
south.
The latitudinal distribution of response shows similarities across models,
with high latitudes (shown in purple in Fig. 4) generally more +N
sensitive and middle latitudes (red to orange in Fig. 4) more +CO2
sensitive. While negative NPP values are present in both +N and +CO2
simulations, they occur in different places, with negative NPP occurring in
hot arid areas for +N and cold arid areas for +CO2 (Figs. 2, 3, and 4).
In hot arid areas, +N increases simulate GPP and plant growth but also
plant respiration, which then exceed the additional productivity, giving a
decrease in NPP. Such model behaviour has been noted before (Meyerholt et al., 2020);
however, there is little evidence that such a process would occur in nature.
The negative values in all models except CLM4.5 also appear to have a
regional bias, with a small number of grid cells responding negatively to
both +CO2 and +N in CLM5, JSBACH, and JULES-ES in the subtropics and a
larger number of negative values in the subtropics in LPJ-GUESS (Fig. 4).
These arid areas appear to be not sensitive to +N nor +CO2, probably
due to low water availability.
We can gain further insights by considering the relationship between
responses to +CO2 and +N by forest biome (Fig. 5). The ideal for the
models is to be in the area where the observations for +N and +CO2
intersect. Two of the models achieve this partially, JSBACH and CLM5, by
having tightly clustered forest vegetation C (VegC) response to +N and
forest NPP response to +CO2. The dichotomy between +N and +CO2 NPP
response is averaged out at this scale, and the models show little of the
L-shaped relationship between the +N response and +CO2 response seen at
the grid cell level (Figs. 4 and 5).
Average 1996–2005 model predictions of woody plant NPP responses
to +CO2 (y axis) and above-ground forest vegetation C pool size responses
to nitrogen (N) addition (x axis) for each of the models (as labelled). Area
outlined in yellow indicates synthesis of observed woody plant NPP responses
to +CO2 (Baig et al., 2015). Other
coloured areas indicate biome-wise estimates of above-ground forest C change
per added N (Schulte-Uebbing and de Vries, 2018).
For +CO2, NPP is restricted to simulated vegetation with NPP >0.2 kg C m-2 yr-1 to exclude non-forest areas. For +N, forest
VegC in CLM5, CLM4.5, and LPJ-GUESS is taken from wood C and N, whereas all
C and N is included for JULES-ES and JSBACH due to model output limitations.
The biomes are allocated according to the Köppen–Geiger climate
classification (Kottek et al., 2006). The lower limits for
“Temperate” and “Boreal” +N are the same value.
According to collated N addition experiments, we would expect models to have
biome-level variation in +N response
(LeBauer
and Treseder, 2008; Schulte-Uebbing and de Vries, 2018).
Schulte-Uebbing and de Vries (2018) show that
tropical forest +N VegC response is lowest and boreal and temperate forest
response higher (Fig. 5). While LPJ-GUESS and CLM4.5 capture some variation
between averaged biomes, none of the models have the biome responses in the
correct order (Fig. 5). However, all the models except LPJ-GUESS tend toward
a lower (tropical) +N response. LPJ-GUESS, however, is the only model to
have the boreal +N response in the correct range. It is the boreal
response that seems to be the main issue, as relative to both the temperate
and tropical regions, most models show the boreal response as being lower,
whereas most of the models have the correct relative +N response for the
tropics and temperate regions. Therefore, although the global values of
model response are acceptable, the relative spatial patterns show
limitations in the reliability of all the models.
N budget responses to +N and +CO2
The model responses in different components of the N budget reflect and
affect their overall N sensitivity (Fig. 6). N inputs of BNF and N
deposition and loss (we only consider the sum of leaching and gaseous loss
so as to be consistent between models) are similar between all the models in
the Control simulation (Fig. 6a). The uptake of N by vegetation varies more
strongly between models, reflecting differing levels of N mineralisation and
assumed N requirements for growth, as also reflected by the different
amounts of C and N pools depicted in Fig. 1.
Global averaged 1996–2005 biological nitrogen fixation (BNF), N
deposition, N loss via gases and leaching, the balance of those three
inputs/losses, and the plant N uptake of the models. The top panel
represents the Control scenario, and the second and third panels represent the
response to +CO2 and +N perturbations (see Methods section). Note that the
y-axis scale is 4× smaller for +CO2 response than the Control or +N
response. All changes are relative to a nominal N pool in the terrestrial
biosphere. Gas and Leaching loss is therefore shown as a negative (a loss
from that N pool) in the Control. In the +CO2 and +N responses, a
positive change in “Gas and leach” indicates less losses than in the Control
scenario, and a negative change losses more than the Control.
Changes in the N budget components to +CO2 and +N (Fig. 6b and c) are
not straightforwardly related to changes in productivity (Figs. 2 and 3). For
instance, the weak response of NPP to +CO2 in CLM4.5 would suggest only
small changes in uptake compared to the other models (Figs. 2 and 6).
However, the +CO2-induced changes in uptake in CLM4.5 are higher than that of
LPJ-GUESS (Fig. 6b). Similarly, CLM5 has the largest increase in N balance
for +CO2 (Fig. 6b) amongst the models, but this does not correspond to a
larger response of NPP (Fig. 2f) or uptake response to elevated CO2
(Fig. 6b). Nevertheless, Fig. 6b reveals a number of important
characteristics of the N cycle response to +CO2 underlying the NPP
response presented in Sect. 3.2. For all models except CLM5, which shows a
strong response of BNF to elevated CO2, reduced N losses are an
important reason for the increased N balance of the ecosystem, which
facilitates an increase in NPP in the absence of changes in ecosystem
stoichiometry. For all models except CLM5, plant N uptake under elevated
CO2 is more enhanced than the change in the N balance of the ecosystem,
implying a net transfer of N from the soil to vegetation.
Conversely, the N uptake changes in JULES-ES and JSBACH reflect their
sensitivity of productivity to +N and +CO2 (Figs. 2, 3, and 6). For
JULES-ES, we can see that this is driven by changes in loss, particularly for
+N, which leads to a much smaller increase in N balance in JULES-ES than
the other models. In common with all the models, in JULES-ES the N loss term
is a fixed fraction of the mineralisation flux and the soil N pool size.
However, JSBACH has less than half the increase in N loss of JULES-ES in the
+N simulation (Fig. 6c), low changes in BNF compared to other models (Fig. 7b), and almost no change in NUE (Fig. 7d). This suggests that in both
JULES-ES and JSBACH there is effectively little unmet N demand in the
Control scenario.
Averaged 1996–2005 responses in biological nitrogen fixation (BNF)
and nitrogen-use efficiency (NUE; see Eq. 1) to +CO2 and +N
perturbations for the global (all vegetation types) or forest region
averages. (a) Model BNF responses to +CO2. Black line and grey area
indicate mean and 95 % CI, respectively, of the global estimate published by
Liang et al. (2016). (b) Model BNF
responses to +N. Black lines and grey areas indicate means and 95 %
confidence intervals, respectively, of the forest estimates published by Zheng
et al. (2019). (c) Model NUE responses to +CO2. (d) Model NUE responses
to +N. Forest biomes are according to the Köppen–Geiger climate
classification (Kottek et al., 2006); see Fig. S1.
BNF responses to +CO2 in the models differ in magnitude (Fig. 7a) and
mostly are smaller than a meta-analysis of CO2 manipulation suggests
(Liang et al., 2016). Only responses of JULES-ES at the global scale and the boreal response of CLM5 are within the
range of the meta-analysis of observations. CLM5 is a clear outlier, with a
large increase in BNF. CLM5 takes a C cost approach to BNF, which is
different to the other models (Table 1), and BNF can be acquired for a
relatively fixed amount of C (Houlton et al., 2008); thus, when C availability increases under +CO2, the BNF in CLM5
increases. Fisher et al. (2018)
conducted a parameter sensitivity analysis of both +CO2 and +N
fertilisation, which illustrates that both responses are sensitive to the
maximum fraction of C from NPP which is available for fixation (a proxy for
the fraction of N-fixing plants and their efficiency). However, the correct
parameterisation of this fraction of C available for fixation is not well
known and further field studies are required. The BNF +CO2 response in the
other four models is determined by their simple empirical BNF equations (see
Table 1) based on NPP or evapotranspiration. However, recent analysis suggests
that simple empirical relationships cannot represent BNF well
(Davies-Barnard and Friedlingstein, 2020).
The model BNF responses to +N show one of two responses: a small
increase in JULES-ES, CLM4.5, and JSBACH or a large decrease in CLM5 and
LPJ-GUESS (Fig. 7b). The latter models capture the correct BNF sign of
response to +N of a decrease according to the meta-analysis of
Zheng
et al. (2019), though the amplitude is too large. The former models
estimate BNF as a function of NPP resulting in increased BNF whatever the
source of the additional NPP is and even when there is sufficient N.
Observational evidence (Zheng
et al., 2019) shows that BNF reduces when N is supplied from another source, and
it is understood that this is because facultative (able to modulate) BNF reduces
and obligate BNF is out-competed (Menge et
al., 2009). Overall, there is little evidence for any of the BNF functions
performing well, primarily due to a lack of robust model parameterisations and
parameter values.
The NUE responses allow for comparison between models, though comparisons with
observations are limited by a lack of field studies. All models have an
increase in NUE with +CO2 in line with the current theory of
Walker et al. (2015), with the exception
of JULES-ES in the boreal region (Fig. 7c). It is unclear why the boreal
region is responding differently to both other regions in JULES-ES and other
models, but the boreal region reduction in NUE under +CO2 likely indicates
excess N from mineralisation, possibly triggered by the combination of soil
warming and increased atmospheric CO2. CLM4.5 has low NUE response to
+CO2 due to fixed C : N ratios, which allow for little change in NUE. The other
models allow for either more allocation to wood or flexible C : N that results in
the larger increases of NUE.
There is regional variation in model NUE responses to +N between biomes,
but all the models in our ensemble reduce NUE in response to +N (Fig. 7d).
CLM5 and LPJ-GUESS are distinct in their larger NUE response to +N
compared to the other models but do not share the same geographical spread
of response. There is little consistency between models as to which regions
have the largest change in NUE. CLM5 has the largest NUE change in the
temperate region, whereas in JULES it occurs in the boreal region. No
empirical measurements are currently available for NUE response to +N. On
the basis that scarcity encourages a more frugal use of scarce a resource, a
hypothesis could be that NUE could decrease with increased N availability,
as the models show. However, water-use efficiency suggests an alternative
hypothesis, as it tends to reduce during drought (Yu et al., 2017).
Overall, the large variations in signal and sign of BNF and NUE responses to
+N treatment between models suggest that there is considerable uncertainty in
our understanding.
Discussion
In this paper, we investigated the performance of five N-enabled land
surface models that are part of current-generation earth system models used
in the framework of CMIP6 (Eyring et al., 2016). These
new N-enabled land surface models in CMIP6 reproduce key global carbon cycle
metrics. Despite the importance of N availability for regional productivity,
there is large and unconstrained uncertainty in the magnitude of the global
and regional N fluxes (Fig. 1).
We have focused on three general components of N-enabled models that affect
the plant N uptake and eventual productivity: N inputs via BNF, NUE, and the
N losses. We find that all three show considerable heterogeneity of response
between models. Previous studies suggest that stoichiometric controls and
the processing of soil organic matter are important for a realistic +CO2
response (Zaehle et al., 2014a).
These are essentially contributory factors to NUE, where we find large
variation between models (Fig. 7). The lack of well-constrained observations
for global and biome-level NUE and N loss responses implies that these areas
need more work. N loss is particularly challenging, as there are multiple
pathways (leaching, flooding, gaseous loss, fire, land use change, etc.) and
forms (N2O, N2, etc.) of loss, and each model represents these in
different ways. More observational studies and syntheses of existing
observations are needed to quantify the N cycle in different biomes.
In particular, better constraints are needed for the N cycle response to
perturbations.
All the models show a global average productivity response to increased
atmospheric CO2 commensurate with those recorded in field studies.
However, the regional responses and mechanisms behind this response vary
widely, resulting from the interaction of the instantaneous physiological
response to elevated CO2 (e.g. Ainsworth and Long, 2005,
which is embedded in all five models; see
Rogers et al., 2017), with limitations
imposed by temperature, water, light, and nitrogen, as well as the
response time of vegetation dynamics. For instance, in LPJ-GUESS and CLM5
the response to elevated CO2 in semi-arid tropical ecosystems is
smaller than that of temperate ecosystems or other models. This suggests a
combined effect of water and nitrogen limitation on soil organic matter
decomposition in these models and thus low nitrogen availability that is
not compensated for by changes in BNF. Similarly, tundra and arctic
responses to elevated CO2 vary widely across the models and are
associated with the representation of BNF. This large regional variance
highlights the need for more comprehensive observational data to constrain
responses to elevated CO2, particularly in under-sampled regions such
as the high arctic and tropical semi-arid regions
(Song et al., 2019).
The growth response to N addition across models is more varied. Two of the
five models (JULES-ES and JSBACH) have little productivity response to
increased N availability, indicating that they do not have any significant
limitation of the C cycle by N availability (Fig. 3). There are four
substantial similarities between these two models (Table 1): (i) the use of
NPP to determine BNF; (ii) a direct control of NPP by N availability,
whereas photosynthetic C uptake (GPP) is not directly affected by N (Goll
et al., 2017; Wiltshire et al., 2020); (iii) the use of dynamic (as opposed
to prescribed) vegetation, where vegetation cover is determined by the
climate input to the model; and (iv) the assumption that N availability in
pre-industrial times was sufficient to sustain the C cycle everywhere on
land, because observed present-day N limitation is a result of anthropogenic
changes, most notably increased CO2 (Goll et al., 2017).
The hypothesis of no pre-industrial N limitation is based on the assumption
that, prior to industrial times, the conditions of natural terrestrial
ecosystems were stable for sufficient time to permit any lack of N
availability to be filled by BNF
(Thomas et al., 2015). Consequently, the
pre-industrial Control run with both N and C is very similar to the C-cycle-only version, and a C equilibrium is reached before a N equilibrium. The
disjoint between the C and N equilibriums may lead to varying levels of
simulated N availability and may affect the model responses to
perturbations. While there is evidence for wide-spread (co-)limitation of
NPP in recent decades (LeBauer
and Treseder, 2008; Song et al., 2019; Vitousek and Howarth, 1991), there is
insufficient data to test the hypothesis of no pre-industrial N limitation.
A summary by Thomas et al. (2015) suggests
reasons why pre-industrial productivity of terrestrial ecosystems was
affected by ecosystem N availability, e.g. the presence of unavoidable
losses to denitrification or the competitive exclusion of N-fixing
species as ecosystems mature. The inability of JULES-ES and JSBACH, when
initialised in the assumption that pre-industrial N availability does not
limit vegetation growth, to simulate observed N addition responses
comparable to models without this assumption suggests that this may be an
important component of the N cycle constraint on the global C cycle. No
pre-industrial N limitation also drives other model decisions (such as N
limitation not being incorporated into the GPP equation; see Table 1), which
may further contribute to the models being under-sensitive to N compared to
observations.
The models mostly represent changes in productivity from +N in high-latitude Northern Hemisphere regions less well than other parts of the world
as a percentage, as covered in the results in Sect. 3.3, Fig. 5, and Table S3. While the low NPP of these regions makes them more likely to have high
percentage increases, the mean polar +N response across the models is 8 %–59 %, which is broadly in the range of a meta-analysis of observations (12 %–64 %) (LeBauer and
Treseder, 2008). But looking at the maps of response (Fig. 3), the model
response is either too low or too high compared to the aforementioned
observational range. High-latitude tundra is an important but difficult to
model biome because of the potential for release of methane
(Nauta et al., 2015), permafrost C and N release (Anisimov,
2007; Burke et al., 2012; O'Connor et al., 2010), albedo changes with
vegetation expansion (Myers-Smith
et al., 2011), and the difficulty in representing large amounts of C stored
in soil. This complexity in C and N cycles is not always well understood or
represented in models and therefore could limit the ability of models to
provide accurate responses to perturbation. A fully integrated model that
accounts correctly for all of these is not yet possible but is necessary to
reduce uncertainties.
The greater Amazon basin is a critical area of interest for the future of
the terrestrial carbon balance under climate change. Our simulations show
that, for most models, NPP in this area increases with +CO2, but all the
models find a small or no change in NPP with +N. These regions are thought
to be phosphorus limited rather than N limited, due to depletion through weathering
over long periods. This result supports the idea that favourable climate
conditions cause a high leaf area index (LAI) in this part of the tropics,
such that there is little margin for increased NPP from +N (Fisher et al.,
2018). For +CO2, there is the potential for increased NPP because of either
increase in NUE or decreases in N losses, giving productivity increase
without an increase in LAI. Reducing the uncertainty in NPP response to
+CO2 is important, as the moist tropics represent a significant proportion
of the world's above-ground biomass; therefore, the size of the overall
terrestrial sink will be influenced by the CO2 uptake in this biome.
This experimental setup considers +N and +CO2 separately but not the
combined effects. It cannot be assumed that the effect of both +N and
+CO2 on productivity are linearly additive. It has been shown elsewhere
that LPJ-GUESS (Wårlind et al., 2014) and
BIOME-BGC (Churkina et al., 2009) have a
significant non-linear (synergetic) term between CO2 and N deposition.
An assessment of the combined effects of +N and +CO2 may show a
significantly different picture of model performance.
Part of the uncertainty in the models comes from the reanalysis climate
dataset used to drive the models. CRU-NCEP was chosen for the good spatial
and temporal coverage, but some biases exist in the data compared to
climatologies such as WATCH
(Weedon et al., 2011). Offline
simulations driven by low forcing frequency (6 hourly) CRU-NCEP data
significantly overestimate evapotranspiration in regions with convective
rainfall types and thereby could affect stomatal conductance and
photosynthesis (Fan et al., 2019). Responses to
+N and +CO2 may partially be shaped by other limiting factors such as
water availability, which will be handled differently between models,
limiting the insight into the exact processes that control model responses to
change. This does not affect all the models equally, as some are known to be
sensitive to the driving climatology. JSBACH, JULES-ES, and LPJ-GUESS may be
particularly strongly affected due to their dynamic vegetation. Lawrence
et al. (2019) show that CLM5 corresponds best to benchmarks with the GSWP3
forcing dataset (van den Hurk et al.,
2016), and work with JULES shows that climate forcing is the biggest cause of
variance of those considered (Ménard et al., 2015).
As well as uncertainty in the models, the observational data also have
uncertainties and limitations. Global benchmarks are approximate measures,
as multifaceted process mechanics are integrated over large domains and
generalised, e.g. over climate zones that are inherently variable. Of the
limited global or regional observations available, many use interpolation or
proxies such as satellite data to upscale relatively small amounts of direct
observational data. The perturbed responses may also have uncertainties
beyond the spread of the observed responses because of the small observation
basis and potential biases in the geographical sampling. Therefore, they may
suffer from leverage points and skew the data towards more accessible,
higher-income, or higher-population areas, such as western Europe, which are
not representative of where models are impacted most at the global scale.
One of the +N global responses cited is based on 126 values from
LeBauer and
Treseder (2008) but may overestimate the global response by including high
responses from young, tropical soils. The NPP response to +CO2 response for
woody plants total above-ground biomass (Fig. 5) is based on just 16
experiments (Baig et al., 2015),
making the upscaling to the biome scale less reliable than if more data were
available. These meta-analyses combine measurements from a range of time
periods and places, and different conditions (e.g. gradual or instantaneous
perturbations) and thus models run at a global scale cannot be expected to
be entirely consistent. Hence statements about the marginal issues of model
accuracy are unlikely to be robust as further observational constraints may
alter the perspective.
Conclusions
This is the first systematic comparison of the responses to increased N
(+N) and CO2 (+CO2) in LSMs with terrestrial N cycles contributing
to CMIP6. The five models considered here yield fair overall agreement with
global and tropical observations but are less robust in high-latitude
regions.
The models are not equally sensitive to either +CO2 or +N, with
individual grid cells tending to respond to either +N or +CO2. However,
at the regional and global scales this pattern is averaged away and there is
little correlation. Within this ensemble there is clear distinction between
models that show strong N limitation, e.g. CLM4.5, which has a low NPP
response to +CO2, and models that show very weak N limitation, e.g.
JULES-ES and JSBACH, which have a low NPP response to +N. The two models
with intermediate N limitation (CLM5 and LPJ-GUESS) capture the global-scale
response to +CO2 and +N reasonably well. However, although CLM5 performs
well by many metrics, it is an outlier compared to other models or
observations as its BNF and the NUE response to CO2 appear to be
larger than supported by observations. Similarly, LPJ-GUESS captures NPP
responses to +CO2 and +N well at the global level but overestimates the
vegetation C response to +N in forested tropical and temperate biomes.
The model initialisation with or without the assumption of sufficient N in
pre-industrial times is a key determinant of the differences between the
models. The presence of N limitation before the rise of atmospheric CO2
levels is an important and challenging question to resolve. While further
modern constraints on +N response may inform which approach is more
realistic, understanding from reconstructions or other data sources could
help resolve this question.
The wide range of empirical or semi-mechanistic representations for key
processes such as BNF, NUE, and N loss shows how important further process
understanding is for many parts of the N cycle. These parts of the models
are influential, but because N cycle components are a recent addition to
LSMs, fewer data are available to evaluate N cycle processes than for C
cycle components. The addition of this representation of N limitation on C
uptake is a big step forward in this generation of models, addressing the
biggest systematic bias in future projections of land C sinks. However, it
is now crucial to better constrain their behaviour at regional and process
levels. Consequently, better observational constraints are required to
understand whether models are working appropriately, even when the process
understanding is improved.
Data availability
The research data supporting this publication are openly available from the
University of Exeter's institutional repository at: 10.24378/exe.2624 (Davies-Barnard et al., 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-5129-2020-supplement.
Author contributions
SZ designed and coordinated the model simulations. The model simulations were run by VB, TDB, YF, RAF, HL, DP, BS, DW, and AJW. JM, TDB, and SZ analysed the data and made the figures. TDB wrote the paper with key contributions from all the authors: SZ, JM, PF, VB, YF, RAF, CDJ, HL, DP, BS, DW, and AJW.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Authors acknowledge the work by Johannes Meyerholt (deceased 2020) that
formed the basis of this paper.
Authors acknowledge funding from the European Union's Horizon 2020 research
and innovation programme under grant agreement no. 641816 Coordinated
Research in Earth Systems and Climate: Experiments kNowledge, Dissemination
and Outreach (CRESCENDO).
Sönke Zaehle acknowledges support by the European Union's Horizon 2020 research and
innovation programme under grant agreement no. 647204 (QUINCY).
Victor Brovkin, Pierre Friedlingstein, and Sönke Zaehle acknowledge funding from the European Union's Horizon 2020
research and innovation programme under grant agreement no. 821003 (4C
project).
Rosie A. Fisher was supported by the National Center for Atmospheric Research, which is a
major facility sponsored by the US
National Science Foundation (NSF) under cooperative agreement no. 1852977.
Benjamin Smith and David Wårlind acknowledge that this study is a contribution to the Strategic Research
Area MERGE and the Swedish strategic collaborative research programme in e-science
(eSSENCE).
Chris D. Jones and Andy J. Wiltshire were supported by the Joint UK BEIS/Defra Met Office Hadley
Centre Climate Programme (GA01101).
Financial support
This research has been supported by the European Commission, H2020 Research Infrastructures (CRESCENDO (grant no. 641816), QUINCY (grant no. 647204), and 4C (grant no. 821003)), the National Center for Atmospheric Research sponsored by the US
National Science Foundation (NSF) (cooperative agreement no. 1852977), and the Joint UK BEIS/Defra Met Office Hadley
Centre Climate Programme (grant no. GA01101).The article processing charges for this open-access publication were covered by the Max Planck Society.
Review statement
This paper was edited by Alexey V. Eliseev and reviewed by Vivek Arora, Peter Thornton, and Joshua Fisher.
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