Accurate representation of the processes governing soil carbon cycling in
high-latitude soils is crucial for reducing model uncertainty in energy and
greenhouse gas feedbacks with climate. By comparing meta-analyses based on
model output and observations, we show that two belowground biogeochemical
representations in CLM4.5 are unable to represent adequately many of the
observed high-latitude ecosystem responses to two important climate change
variables: temperature and nitrogen availability. We focus our discussion on
the potential reasons for the discrepancies in responses by highlighting (1)
the most important mechanisms currently missing from, or poorly represented
in, the models, and (2) instances where deficiencies in the experimental
approaches prohibit the data from being used to benchmark the model. We also
recommend further approaches to improve the mechanistic basis of the
belowground biogeochemistry representation in ESMs.
Response of belowground carbon<?xmltex \hack{\newline}?> cycling to warming
The observational meta-analysis suggests that elevated belowground
respiration is balanced by elevated GPP (and associated increases in soil
organic matter). We therefore conclude that the coupling of aboveground and
belowground processes resulted in these soils being carbon neutral under
modest (+1.3 ∘C) warming. The models also predicted no significant
changes to belowground SOM content under warming due to concomitant increases
in belowground respiration and GPP. However, the magnitude of the modeled
fluxes is many times larger than the observed fluxes. Therefore, the net
impact of the manipulation on SOM was predicted by the models, but with
incorrect mechanisms.
As a broader point, we believe this result illustrates a common problem among
tests of land model performance, i.e., inferences of model fidelity based on
comparisons solely with observations of emergent responses that have a low
signal-to-noise ratio. For example, it is insufficient to use net ecosystem
exchange (NEE) as a sole model benchmark (Schwalm et al., 2010), because it
ignores the fact that (1) NEE is typically a small difference between
ecosystem respiration and assimilation, and (2) models separately represent
these gross fluxes as being differently controlled by climate and antecedent
system states. We contend that representing this type of emergent ecosystem
net flux within the observational uncertainty gives little information as to
whether the model is accurately representing the underlying mechanisms
appropriately.
Nitrogen cycling under warming
Confronting the model outputs with observations showed a consistent
overestimation of key variables in the model predictions (Fig. 2a). One
potential reason for a larger modeled response is the approaches CLM-CN and
CLM-Century take to representing the nitrogen cycle, as modeled nitrogen
input, retention, and loss have been shown to have a large impact on
ecosystem carbon sequestration (Thomas et al., 2013b; Zaehle and Dalmonech,
2011). Moreover, data-based modifications to ESM nitrogen cycling mechanisms
may further improve the correspondence between observations and model output
(Thomas et al., 2013b).
CLM-CN predicts much higher rates of nitrogen loss from denitrification,
than CLM-Century, and is therefore more responsive to changing nitrogen
availability from inputs, mineralization, and losses. Under warming, CLM-CN
predicted a significant loss of soil nitrogen not predicted in CLM-Century,
which has a more closed nitrogen cycle, possibly more representative of the
nitrogen cycle in high-latitude soils (Barsdate and Alexander, 1975), where
mineralization is the main source of nitrogen for plant and microbial growth
during the growing season (Shaver et al., 1992). Depolymerization of
proteinaceous compounds, to amino acids and eventually ammonia (Jones et
al., 2009; Schimel and Bennett, 2004) is the critical step in this process
and dependent on microbial physiology and subject to the same biotic and
abiotic controls of organic matter decomposition (see discussion below).
Modeled nitrogen mineralization, however, increases under warming, with a
concomitant increase in soil nitrogen in the CLM-Century framework. CLM-CN,
with its high rates of mineral nitrogen losses, shows a very large decline in
soil nitrogen, possibly rendering the aboveground and belowground communities
nitrogen limited throughout. In our data analyses, nitrogen mineralization
declined as microbial nitrogen (i.e., immobilization) increased. The end
result in both cases (i.e., the models and observations) is the potential
limitation of plant growth over long timescales. Our data synthesis suggests
that the release of nitrogen from increased decomposition is used to meet
microbial demands or immobilized. Microbial immobilization is regulated by
the stoichiometric imbalance between the substrate being depolymerized and
the physiological nutrient demand. While analogous to carbon use efficiency (CUE), nitrogen use efficiency (NUE), which relates immobilization and mineralization to
microbial growth (Mooshammer et al., 2014), is regulated independently in
order to maintain cell stoichiometry. Some attempts have been made to
incorporate NUE controls into ecosystem models (Manzoni and Porporato, 2009),
but further experimental and modeling work is required to understand NUE's
plasticity and impacts on soil carbon dynamics.
Aboveground dynamics
The biogeochemical coupling between aboveground and belowground components of
the ecosystem is crucial for understanding high-latitude carbon cycling under
a changing climate. The meta-analysis of field measurements showed a general
stimulation of aboveground activity under warming, while previous field
studies have noted a shift in plant community composition with warming,
favoring the establishment of deciduous shrubs and graminoids and selecting
against mosses and lichens (Schuur et al., 2007; Sistla et al., 2013; Walker
et al., 2006). This shift toward more woody plants changes the ecosystem
carbon balance and nutrient dynamics (Jackson et al., 2002; Welker et al.,
2004), as shrubs tend towards higher internal carbon allocation toward woody
tissue, but also may increase belowground carbon allocation (as both litter
and exudates) relative to mosses (Street et al., 2013). This change in
belowground allocation may result in the observed relationship between GPP
and belowground respiration (Fig. 3), indicating a close coupling between
these two processes.
Current models crudely represent aboveground and belowground biogeochemical
coupling and do not represent some of the crucial roles plants play in soil
carbon dynamics (Ostle et al., 2009; Schmidt et al., 2011). Of particular
relevance to high-latitude ecosystems is the lack of any representation of
cryptogams or bryophytes in CLM4.5. These plants contribute substantially to
aboveground biomass and biogeochemical processes in tundra soils (Cornelissen
et al., 2007; Elbert et al., 2012) and are clearly important for accurate
simulations of tundra carbon dynamics. Few ESM land models (including CLM4.5)
include dynamic vegetation, and when it is included, representation tends to
be coarse (Ostle et al., 2009). Ongoing work will attempt to address some of
these deficiencies, by including representations of aboveground ecosystem
demography (Huntingford et al., 2008; Moorcroft et al., 2001), and soil
carbon dynamics (Riley et al., 2014; Tang and Riley, 2013). Integration of
these approaches into the CLM framework may improve the robustness of
long-term tundra soil simulations and reduce uncertainty associated with the
aboveground model response.
Litter decomposition
Disagreement between the observations and model predictions was also noted
for litter decomposition. Under warming, litter decomposition declined in the
observations, possibly contributing to SOM accumulation, but increased in the
models. In previous studies, the response of litter decomposition to warming
was largely dependent on the method used to increased soil temperature
(Aerts, 2006). OTCs tend to warm the soil and reduce soil moisture, limiting
litter decomposition by saprotrophic fungi. Soil moisture in the models
showed a non-significant increase with warming as the permafrost began to
thaw (Fig. 2a). The difference between the observational meta-analysis and
the models represents a potentially confounding factor in using these data to
benchmark the model. A previous meta-analysis focused solely on litter
decomposition in Arctic and Alpine tundra found that warming induced a small
increase in decomposition provided sufficient soil moisture (Aerts, 2006).
This response was not apparent in our data syntheses, but suggests the model
results, while overestimating litter decomposition, were at least in the
appropriate direction. Soil moisture is an important controller on
decomposition (Aerts, 2006; Hicks Pries et al., 2013). However, changes to
surface hydrology during permafrost thaw are dependent on thermokarst formation and topological features of the landscape (Jorgenson and Osterkamp,
2005) and may result in increased or decreased soil moisture. We identify
these issues as important for further experimental and modeling work in order
to better represent future changes in surface hydrology and the consequences
for litter decomposition.
Belowground response to warming
The observational data indicated elevated belowground respiration under
warming. The response of microbial heterotrophs to warming can partially be
explained by kinetic theory, whereby biochemical reaction rates increase with
increasing temperature (Davidson and Janssens, 2006). Hydrolytic and
oxidative extracellular enzymes, secreted to depolymerize complex organic
matter (Allison et al., 2010), are sensitive to temperature (German et al.,
2012). Structural modifications in cold ecosystems maximize their specific
activity under in situ temperatures relative to temperate ecosystems
(Hochachka and Somero, 2002), which may result in significantly enhanced
activity under warming (Koch et al., 2007). This theory fits with the
short-term (< 2 yr) data from the current meta-analysis showing
increasing belowground respiration despite no increase in microbial biomass.
However, we also identified a drop in belowground respiration in studies
lasting longer than 2 yr and shorter than 5 yr (Fig. S1b). Belowground
respiration has consistently been reported to decline under prolonged warming
(Rustad et al., 2001) and attributed to substrate limitation (Hartley et al.,
2008) or a community-level response of microbial populations to warmer
temperatures offsetting the kinetic response of individual microbes
(Bradford, 2013; Bradford et al., 2008). Given the increased GPP found in our
meta-analysis, belowground communities are unlikely to be substrate limited.
Therefore, we hypothesize that the community-level response is likely
responsible for the drop in belowground respiration under 2–5 yr of
warming.
The subsequent increase in belowground respiration over prolonged warming
(> 5 yr) could represent either the decomposition of leaf litter driven
by changes in microbial community composition, or thawing subsurface organic
matter (Dorrepaal et al., 2009). This latter hypothesis is relevant to the
long-term fate of high-latitude carbon. In the current analysis, NEE appears
balanced, with no change in SOM. However, temporal patterns of vegetation
response to warming show a transient effect of warming, with nutrient
limitation reducing plant productivity on longer timescales (Arft et al.,
1999; Chapin and Shaver, 1996). It is possible, given the large nitrogen
immobilization under warming, that belowground respiration may continue
longer than productivity, unbalancing NEE and leading to net carbon loss.
Temperature is a key factor influencing biogeochemical mechanisms in the
model. CLM models belowground respiration using static Q10 and fixed
carbon use efficiencies (CUE) for different SOM pool sizes. This approach may
result in the large modeled increase in belowground respiration. In reality,
both Q10 and CUE vary on spatial and temporal scales, and respond
nonlinearly to changes in temperature (Janssens and Pilegaard, 2003;
Sinsabaugh et al., 2013, Tang and Riley, 2014). Recent microbe-explicit
models (MEMs) that consider basic microbial physiology (e.g., Lawrence et
al., 2009) introduce direct biological control over soil carbon cycling and
different conclusions on soil carbon pool size and dynamics under warming
(Allison et al., 2010; Lawrence et al., 2009; Wieder et al., 2013). For
example, by scaling the CUE value with temperature, in accordance with
published observations (Luo et al., 2001; Melillo, 2002), the MEMs show a
decline in soil carbon turnover under warming (Li et al., 2014; Wieder et
al., 2013). Recent work using a MEM with explicit representation of internal
physiology, extracellular enzymes, and mineral surfaces (Tang and Riley
2014), demonstrates that both decomposition temperature sensitivity and CUE
are hysteretic and cannot easily be represented by a simple function of soil
temperature. However, it is also important to note that microbial CUE are not
solely temperature dependent, and other factors, some of which are already
present in CLM-CN and CLM-Century (including nutrient and soil moisture
limitations), may uncouple growth and respiration and change CUE (Manzoni et
al., 2008; Sinsabaugh et al., 2013). The predictions of the microbe-explicit
models (MEM) provide further impetus for greater representation of the
structure and function of belowground biomass.
Response of belowground carbon cycling<?xmltex \hack{\newline}?> to nitrogen addition
Our meta-analysis of field observations found that the addition of inorganic
nitrogen to traditionally nitrogen limited ecosystems enhances the carbon
sink, consistent with previous studies (Luo et al., 2012; McGuire et al.,
2012). Interactions between the carbon and nitrogen cycles resulting in soil
carbon accumulation in different ecosystems have been reported previously
(Magnani et al., 2007; Thomas et al., 2013b), and have been attributed to an
increased carbon allocation to woody tissue (Ciais et al., 2008; Tummers,
2006) and reduction in the SOM decomposition rate (Olsson et al., 2005).
Overall, our data synthesis is largely consistent with the overarching
conclusions of previous meta-analyses (Janssens et al., 2010; Knorr et al.,
2005).
A question remains, however, about the value of the responses synthesized
from studies that add fertilizer (NH4NO3 or NPK) as a source of
nitrogen far in excess of anticipated global change scenarios for
high-latitude ecosystems. The average concentration of nitrogen added to the
soils in the tundra studies (∼ 72 kg ha-1 yr-1) is
extremely high when compared with (1) estimates of nitrogen fixation
(< 10 kg ha-1 yr-1, Cleveland et al., 1999); (2) nitrogen
deposition (both current rates of deposition
0.2–0.24 kg ha-1 yr-1, Jones et al., 2005 and projections of
future deposition, Galloway et al., 2004); and (3) potential nitrogen availability from organic matter mineralization under a warming climate
(Harden et al., 2012). Consequentially, we question whether such data lend
themselves to understanding the response of the ecosystem to realistic
chronic incremental changes in nitrogen availability, and the benefit of
benchmarking the ecosystem models against such a data set. On the other hand,
if the models include the relevant underlying mechanisms, then they should
reproduce the field studies, regardless of the amount of nitrogen added. We
give further examples below of where the high nitrogen concentrations may
confound the interpretation of the experiments with respect to the model
predictions.
SOM dynamics
SOM accumulation under nitrogen addition experiments is a common feature of
both the field experiments and the model simulations. However, the underlying
mechanisms leading to SOM accumulation are very different, adding uncertainty
to the model-predicted soil carbon fate over longer timescales. In both
versions of CLM, the alleviation of nitrogen limitation stimulates a number
of ecosystem processes including aboveground primary productivity, litter
decomposition, and organic matter decomposition. The accumulation of SOM
indicates the stimulation of GPP and litter decomposition (as a source into
the SOM pools) must outweigh losses from increased belowground respiration.
The observations, on the other hand, show a significant decline in
belowground respiration and litter decomposition under nitrogen addition.
Belowground respiration depends on the decomposition and substrate
utilization capabilities of the microbial (i.e., bacterial and fungal)
community to mineralize root exudates and litter. A drop in belowground
respiration may, therefore, be attributable to several mechanisms not
included in either version of CLM, including the internal reallocation of
carbon in plants and trees that reduces the rate of root exudation to
belowground ecosystems (Janssens et al., 2010). Carbon limitation of the
microbial community may result in a decline in biomass and belowground
respiration (Janssens et al., 2010). Our empirical data show increased GPP
and vascular plant biomass that could indicate the reallocation of newly
fixed carbon in vascular plants (Ciais et al., 2008) and a drop in
belowground exudation.
Belowground response to nitrogen addition
Overall, the current observational meta-analysis found a non-significant
increase in microbial biomass (i.e., bacterial and fungal) but a significant
increase in fungal biomass under nitrogen addition. This response appears
contrary to previous studies that have recorded a drop in microbial biomass
under nitrogen addition (Treseder, 2008), but in line with fertilization
studies in tundra ecosystems (Clemmensen et al., 2006). We also note that
microbial biomass (and belowground respiration) are inversely related to the
amount of nitrogen added to the soils (Fig. 4a, b). At low nitrogen
concentrations, microbial community activity can be stimulated (Allison et
al., 2009) and decomposition elevated, as indicated by the models (Fig. 2b)
and some of the observations (Fig. 4b). Elevated nitrogen concentrations,
however, have a negative impact on microbial biomass (Treseder, 2008) and
decomposition (Janssens et al., 2010). This response can occur through the
inhibition of lignin-degrading enzymes produced by saprotrophic fungi
(Sinsabaugh et al., 2002; but see Hobbie, 2008), or the increased physical
protection of organic matter from decomposition attributed to soil carbon
undergoing condensation reactions with high concentrations of inorganic
nitrogen (Dijkstra et al., 2004). Therefore, under the high nitrogen inputs
used in the present field studies, the coupling between aboveground and
belowground ecosystems can decrease belowground respiration and litter
decomposition, resulting in an accumulation of SOM.
Whereas the warming meta-analysis yielded results that could be used to
constrain model mechanisms, the same cannot be concluded for the
nitrogen-addition studies due to the uncertainty of how high-latitude soils
will respond to lower concentrations of nitrogen. However, we suggest two
potential model changes that could rectify the different conclusions derived
from the observations and models. (1) A dynamic vegetation approach
sensitive to changes in nitrogen inventory could represent compositional
changes across the tundra with important ramifications for root biomass,
litter quality, and plant exudates that play a significant role in soil
carbon dynamics (Aerts et al., 2005). (2) Representation of discrete
belowground biomass functional groups (e.g., heterotrophic and fungal
decomposers) alongside their dependencies on soil nitrogen may help to
constrain the belowground response to nitrogen addition. Finally, while the
model mechanisms should ideally be able to reproduce the observed response
to high nitrogen loading, we believe that future manipulation studies in
high-latitude soils that use realistic nitrogen additions would be more
relevant for understanding the tundra soil response. For example, recent
studies have added nitrogen to tundra soils at magnitudes one order of
magnitude higher than measured concentrations (Lavoie et al., 2011) or
guided by soil mineralization rates (Sistla et al., 2012). The ecosystem
response is therefore more likely to reflect future responses under
anticipated mineralization or deposition scenarios (Galloway et al., 2004).
Barriers and criteria for successful<?xmltex \hack{\newline}?> experiment-based model
benchmarking
While we were able to benchmark some aspects of the model predictions using
the observational meta-analysis, we acknowledge several concerns that may
have complicated the data–model comparison. First, no general protocol
consistent with field experiments is available for setting up model
perturbations. Although different land models have different structures and
degrees of complexity, a standard approach to establishing perturbations
would be beneficial. In our model, atmospheric warming resulted in
unrealistic uniform soil warming across the study domain and therefore
underestimated the spatial heterogeneity found in passive warming experiments
(Bokhorst et al., 2012). On the other hand, solely reducing the wind speed
failed to alter the soil thermal regime, indicating a possible problem in the
formulation of CLM's surface boundary layer resistance. However, our approach
of warming via enhanced aerodynamics resistance is not transferable to models
using atmospheric temperature, rather than a surface energy balance scheme,
to force soil thermal dynamics.
Therefore, criteria need to be established to ensure, regardless of the
method used, that the experimental manipulation is reproduced in the model
with sufficient fidelity that the predicted and observed responses can be
reasonably compared. We consider the criterion used here for the warming
experiments (i.e., that the mean predicted manipulation soil temperatures are
not significantly different from the observations) to be a minimally
acceptable criterion. Ideally, the predicted response of soil temperature,
soil moisture, and radiation under warming would emerge in a statistically
similar manner to the observations. In the current study, this criterion was
not met for soil moisture, where the observations found that soil moisture
declined (by 8 ± 6 %) under warming and the model predicted large
increases (CLM-CN: 38 ± 42 %; CLM-Century: 7 ± 33 %).
This may be important, given the significant impacts moisture has on
decomposition and nitrogen cycling.
Second, the spatial discrepancy between the model predictions and
observational data is large. This mismatch arises from several sources,
including uncertainties caused by spatial heterogeneity in the site and
experimental manipulation (e.g., unequal heating within the open-top
chambers, energy leaking at the boundary with surrounding soil), and
uncertainties in the climate and environmental forcing data used to drive
the models.
Third, while we acknowledge the complexity of interpreting single-factor
manipulation experiments, the multifaceted nature of climate change calls for
more multifactorial experiments and models that can reproduce any response.
The few studies we could find measuring the response of similar variables to
combined warming and nitrogen addition (e.g., Shaver et al., 1998) found an
even larger warming response than for the single-factor experiments. However,
there were too few studies measuring complementary variables to conduct a
complete meta-analysis. Previous studies conducted in high-latitude soils
have recorded a stronger response of decomposition following perturbation by
a combination of drivers (e.g., elevated temperature and CO2) than if
those factors were considered in isolation (Fenner et al., 2007). In
contrast, Leuzinger et al. (2011) give several examples where the opposite
occurs: a combination of multiple drivers lessens the ecological response
relative to individual drivers. These contradictory results call for further
consideration of the impact of multiple drivers in high-latitude ecosystems
that might be used to benchmark model performance.
Overall recommendations
We have demonstrated here that despite some experimental drawbacks, the
underlying biogeochemical mechanisms of CLM-CN and CLM-Century are
insufficient to accurately reproduce the observations of a number of
high-latitude perturbation experiments. However, we can identify several
metrics from the meta-analyses, including nitrogen mineralization and litter
decomposition, which may serve as useful indices of model performance. The
sign and magnitude of these response ratios were incorrectly predicted by
the models in under both warming and nitrogen addition. This error in the
sign of the response also occurred for simulated belowground respiration
under nitrogen addition, where the model was unable to capture the
detrimental impact of very high nitrogen concentrations. In contrast, the
SOM response under temperature and nitrogen perturbations appears to be a
poor metric to benchmark the models, possibly owing to the large size and
undefined composition of the soil organic matter stock.
Future development of biogeochemistry representation in CLM should focus on
improvements to the nitrogen cycle. Recent work has shown that specific
modifications to different nitrogen cycle pathways (e.g., redox cycling,
plant–microbial interactions) can improve the correspondence between model
predictions and observational data (Thomas et al., 2013a). Development should
also may focus on improved kinetics (e.g., equilibrium chemistry
approximations, Tang and Riley, 2013) to regulate competition for nutrients
between biotic and abiotic sinks (e.g., plants, microbes, minerals) as an
alternative to the current allocation schemes of CLM-CN (Thornton et al.,
2007). In addition, the integration of dissolved organic nitrogen cycling as
a nutrient source for microbes and plants appears to be an important source
of nitrogen in high-latitude soils (Hobbie et al., 2009; Weintraub and
Schimel, 2005). However, insufficient data were available to include DON as a
response factor in the meta-analysis.
The lack of explicit coupling between plant functional types (PFT) and
belowground microbial ecosystems in the model fails to capture the
importance of this interaction for carbon and nutrients cycling and SOM
stability. The PFT concept could be further extended to characterize
differential belowground carbon allocation (Street et al., 2013). In
addition, symbiotic relationships between different plants and mycorrhizal
fungi can increase nutrient acquisition (Hobbie et al., 2009), by
facilitating nitrogen fixation (Nasto et al., 2014), and phosphorus
acquisition (Smith et al., 2011), thereby increasing photosynthetic rates
(Jia et al., 2004). Improving and expanding the definition of the PFT to
include these associations may serve to improve coupling between nutrient
cycling with belowground biogeochemistry.
Finally, while the magnitudes of nitrogen added to tundra soils were very
high, the threshold relationship (Fig. 4) that describes the alleviation of
nitrogen limitation and stimulation of ecosystem processes at low
concentrations from their inhibition at high nitrogen concentrations has
support from previous studies (Knorr et al., 2005). However, mechanisms have
not yet been integrated into the model to capture this range of responses.
The model should be able to reproduce the impact of high nitrogen
concentrations associated with agriculture soils, and more work is required
to further characterize this threshold effect. It is unlikely, however, that
the model-predicted linear relationship between nitrogen availability and
ecosystem processes will, in general, be true.