A large amount of soil carbon in northern temperate and boreal regions could
be emitted as greenhouse gases in a warming future. However, lacking
detailed microbial processes such as microbial dormancy in current
biogeochemistry models might have biased the quantification of the regional
carbon dynamics. Here the effect of microbial dormancy was incorporated into
a biogeochemistry model to improve the quantification for the last century and this
century. Compared with the previous model without considering the microbial
dormancy, the new model estimated the regional soils stored 75.9 Pg more C
in the terrestrial ecosystems during the last century and will store 50.4 and 125.2 Pg more C under the RCP8.5 and RCP2.6 scenarios,
respectively, in this century. This study highlights the importance of the
representation of microbial dormancy in earth system models to adequately
quantify the carbon dynamics in the northern temperate and boreal natural
terrestrial ecosystems.
Introduction
The land ecosystems in northern temperate and boreal regions (>45∘ N) occupy 22 % of the global surface and store over
40 % of the global soil organic carbon (SOC) (McGuire and Hobbie, 1997;
Melillo et al., 1993; Tarnocai et al., 2009; Hugelius et al., 2014). During
the past decades, a greening accompanying a warming in the region has been
documented (Zhou et al., 2001; Lloyd et al., 2002; Stow et al., 2004;
Callaghan et al., 2005; Tape et al., 2006). The regional carbon dynamics are
expected to loom large in the global carbon cycle and exert large feedbacks
to the global climate system (McGuire et al., 2009; Davidson and Janssens,
2006; Bond-Lamberty and Thomson, 2010).
To date, numerous ecosystem models have been developed to project the
feedbacks between terrestrial ecosystem carbon cycling and climate (Raich et
al., 1991; Zhuang et al., 2001, 2002, 2015; Parton et al., 1993; Knorr et
al., 2005; Running and Coughlan, 1988), but they can bias their
quantifications due to missing detailed microbial mechanisms in these models
(Schmidt et al., 2011; Todd-Brown et al., 2013; Conant et al., 2011;
Treseder et al., 2011). Microorganisms play a central role in decomposition
of litter and soil organic carbon, which further governs the global carbon
cycling and climate change (Xu et al., 2014; Treseder et al., 2011; Wang et
al., 2015). An emerging field of research has begun to incorporate microbial
ecology into existing process-based models to represent decomposition in
ways that include important microbial processes that were previously ignored
(Zha and Zhuang, 2018; Schimel and Weintraub, 2003; Allison et al., 2010;
German et al., 2012). These microbe-based models tend to better reproduce
field and satellite observations than traditional ones that treat soil
decomposition as a first-order decay process without considering microbial
activities (Treseder et al., 2011; Wieder et al., 2013; Todd-Brown et al.,
2011; Lawrence et al., 2009; Moorhead and Sinsabaugh, 2006). However, some vital
microbial traits such as microbial dormancy and community shifts are still
rarely explicitly considered in large-scale ecosystem models (Wieder et al.,
2015), and this may introduce notable uncertainties (Graham et al., 2014,
2016; Wang et al., 2015; Bouskill et al., 2012; Kaiser et al., 2014).
Dormancy is broadly recognized as a strategy for microorganisms to cope with
periodical environmental stresses (Harder and Dijkhuizen, 1983). When
environmental conditions are unfavorable for growth, microbes switch to a
dormant state, which is a reversible state of low to zero metabolic
activity (Stolpovsky et al., 2011; Lennon and Jones, 2011). In this
state, biogeochemical processes such as soil decomposition are slow
(Blagodatskaya and Kuzyakov, 2013). At any given time, there is only a
fraction of, likely below 50 %, metabolically active microbes in natural
soils (Wang et al., 2015; Stolpovsky et al., 2011). Soil decomposition and
nutrient cycling mainly depend on these active microbes because only active
ones can consume organic matter and replicate themselves (Wang et al., 2015;
Blagodatskaya et al., 2014). To date, most existing biogeochemistry
models use total rather than active microbial biomass as an indicator of
microbial activities (Wieder et al., 2015), which could bias the estimates
of soil decomposition and ecosystem carbon budget (Hagerty et al., 2014; He
et al., 2015). In particular, the northern temperate and boreal terrestrial
ecosystems are nitrogen-limited; neglecting microbial dormancy will lead to
incorrect estimates of nitrogen availability through soil decomposition,
failing to capture nitrogen feedbacks to carbon dynamics (Wang et al., 2015;
Stolpovsky et al., 2011; Thullner et al., 2005). Furthermore, these
ecosystems have experienced a marked seasonality of active and dormant
microbial cycles and the above-global-average warming, which might have
increased the proportion of active microbes in soils (He et al., 2015).
Thus, incorporating dormancy effects will improve model realism to provide a
better projection of the northern temperate and boreal terrestrial ecosystem
carbon dynamics.
This study incorporated the effects of the microbial dormancy trait into an
extant process-based biogeochemistry model (MIC-TEM) (Zha and Zhuang, 2018;
He et al., 2015). The dormant and active microbial physiology has been
considered explicitly in the new version of the model (MIC-TEM-dormancy). The
revised model was parameterized, validated, and then applied to evaluate the
carbon dynamics during the last century and this century in the northern temperate
and boreal terrestrial ecosystems (north 45∘ N
above). By comparing the results of MIC-TEM-dormancy and MIC-TEM, we can
show that incorporating microbial dormancy may produce a much different
prediction in the historical and future carbon budget.
MethodsOverview
Due to the importance of microbial dormancy, some recent work has been done
to consider the metabolic activation and deactivation of microbes in soil
and its effects on soil carbon (C) dynamics and climate feedbacks. For
example, Wang et al. (2015) have incorporated transformation processes
between active and dormant states to develop two versions of MEND, that is,
MEND with and without dormancy. The two versions of the model have been
applied to quantify the carbon decomposition in laboratory incubations of
four soils. Salazar et al. (2018) have also taken microbially dormancy into
account to compare their predictions of microbial biomass and soil
heterotrophic respiration (RH) under simulated cycles of stressful
(dryness) and favorable (wet pulses) conditions. Our study extends those
modeling studies to the northern temperate and boreal terrestrial ecosystems
by developing a more detailed biogeochemistry model considering the dormancy
impacts. Below, we first describe how we developed the new model
(MIC-TEM-dormancy) by incorporating the microbial dormancy trait into an
existing microbe-based biogeochemistry model (MIC-TEM). Second, we discuss
how parameterization and validation of the MIC-TEM-dormancy model were conducted
using observed net ecosystem exchange data and heterotrophic respiration
data at representative sites. Third, we presented how the model was applied
to natural ecosystems in the region (above 45∘ N)
for the 20th and 21st centuries and discussed the dormancy effects
on their regional carbon budget.
Model description
A non-dormancy version of the biogeochemistry model (MIC-TEM) has been developed
by incorporating a microbial module (Allison et al., 2010) into an extant
large-scale biogeochemical model (TEM) to explicitly (Zhuang et al., 2015)
consider the effects of microbial dynamics and enzyme kinetics on carbon
dynamics (Zha and Zhuang, 2018). Here we further advanced the MIC-TEM by
incorporating algorithms that describe the effects of microbial dormancy
dynamics based on He et al. (2015). Different from He et al. (2015), in
which the microbial module was driven with existing data of carbon stocks and
fluxes, our study incorporated the microbial module into an extant MIC-TEM
that simulates carbon data dynamically. This coupling enables us to
extrapolate our model to northern temperate and boreal terrestrial
ecosystems, rather than only for temperate forest regions in He et al. (2015). In our new model (MIC-TEM-dormancy), the microbial biomass pool was
divided into two fractions, including the dormant and active microbial
biomass pools. The two microbial biomass pools and the reversible transition
between them have been considered explicitly in the new model (Fig. 1) but were ignored in MIC-TEM.
Framework of the dormancy model: microbial biomass is split into
two parts, active microbial biomass and dormant microbial biomass (shown in
the green dashed circle). Maintenance respiration from these two parts and
the CO2 production through microbial assimilation contribute to
heterotrophic respiration. The model was revised based on Zha and Zhuang
(2018).
In previous MIC-TEM, heterotrophic respiration (RH) is simply
calculated as the product of ASSIM and CUE, which are microbial assimilation and carbon use efficiency,
respectively. For detailed carbon dynamics in MIC-TEM, see Zha and Zhuang
(2018).
Here we revised MIC-TEM by incorporating microbial dormancy dynamics
according to He et al. (2015). In MIC-TEM-dormancy, the soil heterotrophic
respiration RH is revised to include three parts: the maintenance
respiration from the active and dormant microorganisms and the CO2
production through the process of microbial assimilation (He et al., 2015):
RH=mRQ10 mictemp-1510Ba+βmRQ10 mictemp-1510Bd+CO2,
where the first two terms are maintenance respiration from the active and
dormant microorganisms. The last term is the CO2 produced
during the process of microbial assimilation.
For the first two terms, Ba and Bd represent the active and dormant
microbial biomass pool, respectively. The parameter mR denotes the
specific maintenance rate in an active state (h-1), and β is the
ratio of dormant maintenance rate to active maintenance rate. Thus, βmR denotes the maximum specific maintenance rate in a dormant state.
Temperature sensitivity was expressed as the Q10 function
Q10temp-1510, where
temp is soil temperature in the top 20 cm (degrees Celsius).
For the third term, the CO2 produced through microbial assimilation is
calculated as in He et al. (2015) and Allison et al. (2010):
CO2=ASSIM×(1-Yg),
where ASSIM represents the microbial assimilation, and the parameter Yg
represents carbon use efficiency. Microbial assimilation (ASSIM) is
calculated as in He et al. (2015):
ASSIM=1YgΦαmRQ10enztemp-1510BaCNsoilCNmic0.6.
Here parameter α is maintenance weight (h-1); CNsoil and
CNmic denote the C:N ratios of soil and that of microbial biomass.
In addition, Φ is the substrate saturation level and defined as
in He et al. (2015) and Wang et al. (2014):
Φ=SKs+S,
where Ks is the half saturation constant for substrate uptake as
indicated by the Michaelis–Menten kinetic, and S is soluble C substrates
that are directly accessible for microbial assimilation (Wang et al., 2014).
Here we quantified concentration of soluble C substrates that are directly
accessible for microbial assimilation by using the conceptual framework from
Davidson et al. (2012):
S=solubleC×Dliq×θ3.
The term “soluble C” denotes the state variable of the soluble carbon pool.
Dliq is the diffusion coefficient of the substrate in the liquid phase
and is formulated as
Dliq=1/(1-BD/PD)3,
where BD is the bulk density and PD is the soil particle density.
θ is the volumetric soil moisture.
Different from MIC-TEM, the transitions between active and dormant microbial
biomass are included in MIC-TEM-dormancy.
7Ba→d=(1-Φ)mRQ10mictemp-1510Ba,8Bd→a=ΦmRQ10mictemp-1510Bd,
where Ba→d and Bd→a
denote the transition from the active to dormant microbe and from the
dormant to active microbe, respectively (He et al., 2015; Wang et al.,
2014). Thus, dormancy rate is affected by active and dormant biomass, soil
temperature (temp), and soil moisture (θ in Φ).
The active microbial biomass (Ba) is modeled as (He et al., 2015; Wang
et al., 2014)
dBadt=ASSIM×Yg-mRQ10mictemp-1510Ba-Ba→d+Bd→a-DEATH-EPROD,
where DEATH and EPROD denote microbial biomass death and enzyme production,
which are modeled as proportional to active microbial biomass with constant
rates rdeath and rEnzProd (Allison et al., 2010):
10DEATH=rdeath×Ba,11EPROD=rEnzProd×Ba,
where rdeath and rEnzProd are the rate constants of microbial
death and enzyme production, respectively.
The dormant microbial biomass (Bd) is modeled as (He et al., 2015; Wang
et al., 2014)
dBddt=-βmRQ10mictemp-1510Bd+Ba→d-Bd→a.
The soluble C pool is modeled as (He et al., 2015; Allison et al., 2010)
dSolubleCdt=DECAY-ASSIM+ELOSS+DEATH,
where DECAY represents the enzymatic decay of soil organic carbon (SOC), and
ELOSS represents the loss of enzyme.
DECAY is regulated by enzyme biomass (ENZ), soil organic carbon (SOC), soil
temperature, and substrate quality (He et al., 2015):
DECAY=Vmax×Q10enztemp-1510×ENZ×SOCKmuptake+SOC×(120-CNsoil),
where Vmax is the maximum SOC decay rate, and
Kmuptake is the half-saturation constant for enzymatic
decay.
ELOSS is modeled as a first-order process (Allison et al., 2010) to
represent enzyme turnover:
ELOSS=renzloss×ENZ,
where renzloss is the rate constant of enzyme loss.
The soil organic carbon pool (SOC) is modeled as
dSOCdt=litterfall-DECAY,
where litterfall is estimated as a function of vegetation carbon (Zhuang et
al., 2010).
Last, the enzyme pool (ENZ) is modeled as
dENZdt=EPROD-ELOSS.
With the modification of microbial carbon dynamics by considering microbial
life history traits, soil decomposition is changed since it is controlled by
microbes. When microbial dormancy is considered, the number of active
microbes that participate in soil decomposition is much less. The changes in
soil decomposition directly influence the amount of soil respiration and
further influence soil nitrogen (N) mineralization that determines soil N
availability for plants, affecting gross primary production (GPP). Since
both GPP and RH can be affected by microbial dormancy, net ecosystem
production (NEP) will also be affected.
Parameters associated with detailed microbial dormancy in
MIC-TEM-dormancy.
ParameterUnitDescriptionParameter rangeReferencesmRh-1Specific maintenance rate at active state[0.001, 0.08]Wang et al. (2014)Q10 mic–Temperature effects on microbial metabolic activity (rate change per 10 ∘C increase in temperature); based on 0.65 eV activation energy for soils[1.5, 3.5]He et al. (2015)Q10 enz–Temperature effects on enzyme activity (rate change per 10 ∘C increase in temperature); based on 6 % rate increase per degree Celsius1.79He et al. (2015)α–The ratio of mR to the sum of maximum specific growth rate[0.01, 0.5]Wang et al. (2014)β–Ratio of dormant microbial maintenance rate to mR[0.0005, 0.005]Wang et al. (2014)Yg–Carbon use efficiency[0.3, 0.7]He et al. (2015)KsmgC cm-2Half-saturation constant for directly accessible substrate[0.01, 10]Wang et al. (2014)KmuptakemgC cm-2Half-saturation constant for enzymatic decay of SOC[200, 1000]He et al. (2015)rdeathh-1Potential rate of microbial death[2×10-4, 2×10-3]Allison et al. (2010)rEnzProdh-1Enzyme production rate of microbe[1×10-4, 8×10-4]He et al. (2015)renzlossh-1Enzyme loss rate[0.0005, 0.002]Allison et al. (2010)VmaxmgC cm-2 h-1Maximum SOC decay rate[1×10-4, 5×10-3]He et al. (2015)Model parameterization and validation
The detailed description of parameters that are related to microbial
dormancy can be found in He et al. (2015) (Table 1). Here we calibrated the
MIC-TEM-dormancy at six representative sites with gap-filled monthly net
ecosystem productivity (NEP, gC m-2 per month) data in northern
temperate and boreal regions (Table 2). Site-level climatic data and soil
texture data were organized for driving the model. All site information can be
found on the AmeriFlux network (Davidson et al., 2000). The results for model
parameterization were presented in Fig. 2. We conducted the
parameterization using a global optimization algorithm known as the SCE-UA
(Shuffled complex evolution) method (Duan et al., 1994). An ensemble of 50
independent sets of parameters were performed based on prior ranges from
literature (Table 1) to minimize the difference between the monthly
simulated and measured NEP at the chosen sites. The cost function of the
minimization is
Obj=∑i=1k(NEPobs,i-NEPsim,i)2,
where NEPobs,i and
NEPsim,i are the observed and simulated NEP,
respectively. k is the number of data pairs for comparison. Except for the
parameters of microbial dormancy, other parameters are derived directly from
MIC-TEM (Zha and Zhuang, 2018). The optimized parameters were used for
model validation and regional simulations.
Site description and measured NEP data used to calibrate
MIC-TEM-dormancy.
Site name Location (latitude (degrees) /longitude (degrees))Ele-vation (m)Vegetation typeDescriptionData rangeCitationsUniv. of Mich. Biological Station45.56 N/ 84.71 W234Temperate deciduous forestLocated within a protected forest owned by the University of Michigan. Mean annual temperature is 5.83 ∘C with mean annual precipitation of 803 mm.01/2005–12/2006Gough et al. (2013)Howland Forest (main tower)45.20 N/ 68.74 W60Temperate coniferous forestClosed coniferous forest, minimal disturbance.01/2004–12/2004Davidson et al. (2006)UCI-1964 burn site55.91 N/ 98.3 8 W260Boreal forestLocated in a continental boreal forest, dominated by black spruce trees, within the BOREAS northern study area in central Manitoba, Canada.01/2004–10/2005Goulden et al. (2006)KUOM turf- grass field45.0 N/ 93.19 W301GrasslandA low-maintenance lawn consisting of cool-season turf grasses.01/2006–12/2008Hiller et al. (2010)Atqasuk70.47 N/ 157.41 W15Wet tundra100 km south of Utqiaġvik (formerly known as Barrow), Alaska. Variety of moist-wet coastal sedge tundra and moist-tussock tundra surfaces in the more well-drained upland.01/2005–12/2006Oechel et al. (2014)Ivotuk68.49 N/ 155.75 W568Alpine tundra300 km south of Utqiaġvik and is located at the foothills of the Brooks Range and is classified as tussock sedge, dwarf-shrub, moss tundra.01/2004–12/2004McEwing et al. (2015)
For model validation, we chose another six sites that contain monthly NEP
data from the AmeriFlux network (Table 3). Four of these six sites were also
used for parameterization (Table 2). However, we used the data of different
observation periods for model validation for those overlapped sites.
Moreover, we also conducted site-level validations with monthly soil
respiration data from the AmeriFlux network and Fluxnet dataset. The site
information was provided in Table 4. For these sites, we assumed 50 % of
soil respiration was heterotrophic respiration (RH) for forest (Hanson
et al., 2000) and 60 % and 70 % of that was RH for grassland (Wang et
al., 2009) and tundra (Billings et al., 1977). Because there is a limited
amount of available heterotrophic respiration (RH) data, we could not conduct a regional validation for
all pixels in northern temperate and boreal regions. Instead, we extracted
61 sites providing data of average annual heterotrophic respiration from the
ORNL global Soil Respiration Dataset
(https://daac.ornl.gov/SOILS/guides/SRDB_V4.html, last access: 9 December 2020,
Bond-Lamberty et al., 2018) for model validation. The site-level observed
average annual RH was used to compare with simulated annual RH by MIC-TEM-dormancy and MIC-TEM. The MIC-TEM-dormancy was run at monthly
time steps to keep consistent with the time step of MIC-TEM. Although
microbial dynamics occur at fine temporal scales (Tang and Riley, 2014), we
can still quantify the cumulative impacts of microbial dynamics on carbon
and nitrogen cycling at monthly time steps by not changing the model structure.
Site description and measured NEP data used to validate
MIC-TEM-dormancy.
Site name Location (latitude (degrees) /longitude (degrees))Ele-vation (m)Vegetation typeDescriptionData rangeCitationsBartlett Experimental Forest44.06 N/ 71.29 W272Temperate deciduous forestLocated within the White Mountains National Forest in north-central New Hampshire, USA, with mean annual temperature of 5.61 ∘C and mean annual precipitation of 1246 mm.01/2005– 12/2006Jenkins et al. (2007); Richardson et al. (2007)Howland Forest (main tower)45.20 N/ 68.74 W60Temperate coniferous forestClosed coniferous forest, minimal disturbance.01/2003– 12/2003Davidson et al. (2006)UCI-1964 burn site55.91 N/ 98.38 W260Boreal forestLocated in a continental boreal forest, dominated by black spruce trees, within the BOREAS northern study area in central Manitoba, Canada.01/2002– 12/2003Goulden et al. (2006)Brookings44.35 N/ 96.84 W510GrasslandLocated in a private pasture, belonging to the Northern Great Plains Rangelands, the grassland is representative of many in the north central United States, with seasonal winter conditions and a wet growing season.01/2005– 12/2006Gilmanov et al. (2005)Atqasuk70.47 N/ 157.41 W15Wet tundra100 km south of Utqiaġvik, Alaska. Variety of moist-wet coastal sedge tundra and moist-tussock tundra surfaces in the more well-drained upland.01/2003– 12/2004Oechel et al. (2014)Ivotuk68.49 N/ 155.75 W568Alpine tundra300 km south of Utqiaġvik and is located at the foothills of the Brooks Range and is classified as tussock sedge, dwarf-shrub, moss tundra.01/2005– 12/2005McEwing et al. (2015)Spatial extrapolation
For historical simulations during the 20th century, two sets of
regional simulations using MIC-TEM-dormancy and MIC-TEM at a spatial
resolution of 0.5∘ latitude × 0.5∘ longitude
were conducted. Our model simulation contains two parts: spin-up and
transient simulation. A typical spin-up was conducted to get the model to a
steady state for each spatial location, which will be used as initial
conditions for transient simulations (McGuire et al., 1992). During spin-up
procedure, cyclic forcing data were used to force the model run and repeated
continuously until dynamic equilibrium was achieved at which the modeled
state variables show a cyclic pattern or become constant. Specifically, this
study used the monthly historical climate data from 1900 to 1940 to
repeatedly drive the model for the spin-up. Before spin-up procedure, the
model was initialized with default built-in carbon stocks (Raich et al.,
1991). During transient simulations, the calibrated ecosystem-specific
parameters were used for regional simulations. The previous dynamic
equilibrium was used as an initial value for transient simulation. The
historical climatic forcing data, including the monthly air temperature,
precipitation, cloudiness, and atmospheric CO2 concentrations, were
organized from the Climatic Research Unit (CRU TS3.1) from the University of
East Anglia (Harris et al., 2014). We also used gridded data of soil texture
(Zhuang et al., 2015), elevation (Zhuang et al., 2015), and potential
natural vegetation (Melillo et al., 1993) from literature. In our model, we
assumed that soil texture, elevation, and potential natural vegetation data
only vary spatially, not vary over time (Zhuang et al., 2015).
Comparison between observed and simulated NEP (gC m-2 per month) at (a) Ivotuk (alpine tundra), (b) UCI-1964 burn site
(boreal forest), (c) Howland Forest (main tower) (temperate coniferous
forest), (d) Univ. of Mich. Biological Station (temperate deciduous forest),
(e) KUOM turf-grass field (grassland), and (f) Atqasuk (wet tundra). Note:
scales are different. Error bars represent standard errors among daily
measured data in 1 month.
In addition, regional simulations over the 21st century were
conducted under two Intergovernmental Panel on Climate Change (IPCC) climate
scenarios (RCP2.6 and RCP8.5). The future climatic forcing data under
these two climate change scenarios were derived from the HadGEM2-ES model,
which is a member of CMIP5project213
(https://esgf-node.llnl.gov/search/cmip5/, last access: 9 December 2020). Then the regional estimations
were obtained by summing up the gridded outputs for our study region. The
positive simulated NEP represents a CO2 sink from the atmosphere to
terrestrial ecosystems, while a negative value represents a source of
CO2 from terrestrial ecosystems to the atmosphere.
Parameter equifinality effects
Our previous studies using TEM have demonstrated that equifinality derived
from site-level parameterization will affect the uncertainty in the
estimation of regional carbon dynamics (Tang and Zhuang, 2008, 2009). Here
equifinality refers to the fact that a number of sets of parameters result in model
simulations that all match the data similarly well. To quantify this effect
on our simulation uncertainty, we conducted ensemble regional simulations
with 50 sets of parameters for both historical and future studies. The 50
sets of parameters were obtained according to the method in Tang and Zhuang
(2008).
Box plot of parameter posterior distributions that are obtained
after ensemble inverse modeling for MIC-TEM-dormancy for all six sites: US-Ivo:
Ivotuk (alpine tundra), CA-NS3: UCI-1964 burn site (boreal forest), US-Ho1:
Howland Forest (temperate coniferous forest), US-UMB: Univ. of Mich.
Biological Station (temperate deciduous forest), US-KUT: KUOM turf-grass
field (grassland), US-Atq: Atqasuk (wet tundra).
ResultsInversed model parameters and model validation
Using the SCE-UA ensemble method, 50 independent sets of parameters were
converged to minimize the objective function. Then the optimized parameters
are calculated as the mean of these 50 sets of inversed parameters. The
box plot of parameter posterior distributions reflects different ecosystem
properties at these sites (Fig. 3). For instance, growth yield was higher
in tundra types than in forests, meaning microorganisms in environments with
higher energy limitation tend to enhance the efficiency of energy
transportation. In addition, alpha, the maintenance weight, was also higher in
tundra types than in forests. From the plot for the parameter beta, the ratio of
dormant maintenance rate to specific maintenance rate for active biomass in
tundra types is lower than that in forest types. Other microbially related
parameters did not differentiate much among different vegetation types.
Comparison between observed and simulated NEP (gC m-2 per month) at (a) Ivotuk (alpine tundra), (b) UCI-1964 burn site
(boreal forest), (c) Howland Forest (main tower) (temperate coniferous
forest), (d) Bartlett Experimental Forest (temperate deciduous forest), (e) Brookings (grassland), and (f) Atqasuk (wet tundra). Note: scales are
different.
Site description and measured RH data used to validate the
MIC-TEM-dormancy model.
Site Location (latitude (degrees)/longitude (degrees))Elevation (m)Vegetation typeData rangeCitationsUS-EML63.88 N/ 149.25 W700Alpine tundra01/2009–12/2013Belshe et al. (2012)CA-SJ253.95 N/ 104.65 W580Borealforest01/2004–12/2008Coursolle et al. (2006)US-Ho245.21 N/ 68.75 W91Temperate coniferous forest01/2000–12/2004Davidson et al. (2006)US-UMB45.56 N/ 84.71 W234Temperate deciduous forest01/2005–12/2006Gough et al. (2013)US-Ro444.68 N/ 93.07 W274Grasslands01/2016–12/2017Griffis et al. (2011)RU-Che68.61 N/ 161.34 E6Wet tundra01/2002–12/2005Merbold et al. (2009)
After parameterization, the MIC-TEM-dormancy was validated with monthly NEP
data for six representative ecosystems, and the comparisons between monthly
observed NEP and simulated NEP were presented in Fig. 4. With the
optimized parameters, the dormancy-based model was used to reproduce NEP to
compare with the measured NEP (Table 5). The R2 ranges from
0.67 for Atqasuk to 0.93 for Bartlett Experimental Forest (Table 5).
Generally, our new model performs better for forest ecosystems than for
tundra ecosystems. Compared with MIC-TEM, the dormancy model performs better for
alpine tundra, temperate coniferous forest, and grassland. For other sites,
both models show similar performance (Table 5). In addition, a set of monthly
soil respiration data were selected to evaluate the estimated RH. The
comparisons between monthly observed RH and simulated RH from
two contrasting models were conducted (Fig. 5). MIC-TEM-dormancy has
higher R2 and lower root-mean-square error (RMSE) (Table 6). Sixty-one
sites with average annual RH in northern temperate and boreal regions
were used to further evaluate the new model performance. The dormancy model
has lower intercept and slope with R2 of 0.45, while R2 of MIC-TEM
is 0.3 (Fig. 6). These analyses indicate that the new model is more realistic
in representing RH by considering microbial dormancy.
Model validation statistics for the dormancy model and MIC-TEM at six
sites with NEP data.
Comparison between observed and simulated RH (gC m-2 per month) at (a) US-EML (alpine tundra), (b) CA-SJ2 (boreal
forest), (c) US-Ho2 (temperate coniferous forest), (d) US-UMB (temperate
deciduous forest), (e) US-Ro4 (grassland), and (f) RU-Che (wet tundra).
Note: scales are different.
Linear regression between simulated and observed annual RH (gC m-2 yr-1) for (a) MIC-TEM-dormancy and (b) MIC-TEM.
Regional carbon dynamics during the 20th century
Regional extrapolation with both models estimated a regional terrestrial
ecosystem carbon sink but with different magnitudes (Fig. 7c). With
optimized parameters, MIC-TEM estimated a regional carbon sink of 77.6 Pg
with the interannual standard deviation of 0.21 Pg C yr-1 during the
20th century. However, MIC-TEM-dormancy nearly doubles the sink at
153.5 Pg with the interannual standard deviation of 0.12 Pg C yr-1
during the last century (Fig. 7c). At the end of the century, MIC-TEM
estimated that NEP reaches 1.0 Pg C yr-1 in comparison with
MIC-TEM-dormancy estimates of 1.5 Pg C yr-1 (Fig. 7c). Both models
simulated similar trends for regional net primary production (NPP), RH, and NEP (Fig. 7).
Generally, they show an increasing trend in the 20th century (Fig. 7).
Meanwhile, with optimized parameters, MIC-TEM-dormancy estimated NPP and
RH at 7.94 and 6.4 Pg C yr-1, which are 5.8 % and
16.3 % less than the estimations from MIC-TEM, respectively (Fig. 7a
and b). This pronounced difference of NEP between two models comes from the
disparity between the simulated NPP and RH with them since NEP is
calculated as the difference between NPP and RH. Without considering
dormancy, MIC-TEM estimates more active microbial biomass, hence
overestimating both RH and NPP (due to higher simulated N
mineralization and uptake by plants), but resulting in lower NEP than that
calculated by MIC-TEM-dormancy.
Simulated annual net primary production (NPP, a),
heterotrophic respiration (RH, b), and net ecosystem
production (NEP, c) during the 20th century by the dormancy
model and MIC-TEM.
Model validation statistics for the dormancy model and MIC-TEM at six
sites with RH data.
Temporally, both models projected higher NPP and RH in summer than in
winter (Fig. 8a and b) due to higher soil temperature and moisture
(McGuire et al., 1992). Setting the RH projection from MIC-TEM as a
baseline, MIC-TEM-dormancy projected 33 % less RH in summer (May to
September) and 30 % more in winter (other months) (Fig. 8b), indicating
that without dormancy, the model tends to estimate lower soil respiration due to
ignorance of dormant respiration in winter but higher soil respiration due
to higher active biomass in summer. NEP seasonality estimated with two
models are close to each other (Fig. 8c), but the dormancy model projected
slightly higher NEP in summer.
Regional annual seasonal pattern of simulated (a) net primary
production (NPP, a), (b) heterotrophic respiration (RH, b), and (c) net ecosystem production (NEP, c) during the 1990s
from the dormancy model and MIC-TEM. The region is all land areas north of 45∘ N.
Regional carbon dynamics during the 21st century
Under the RCP8.5 scenario, both models estimated the regional natural
terrestrial ecosystems act as a carbon sink (Fig. 9). The MIC-TEM-dormancy
predicted a C accumulation of 129.9 Pg by the end of this century, with the
interannual standard deviation of 0.13 Pg C yr-1, whereas MIC-TEM
estimates a C accumulation of 79.5 Pg with the interannual standard
deviation of 0.37 Pg C yr-1 during the 21st century (Fig. 9).
Thus, MIC-TEM-dormancy estimates an increase of 50.4 Pg regional carbon
sequestration relative to MIC-TEM, with less interannual variation (Fig. 9). Under this scenario, both models predict similar temporal trends for
NEP, namely increasing from the 2000s and then decreasing from the 2070s
onward (Fig. 9). MIC-TEM-dormancy predicts that the carbon sink reaches 1.36 Pg C yr-1 in the 2090s, which is 0.26 Pg C yr-1 more than the
projection of MIC-TEM. Moreover, MIC-TEM-dormancy estimated NPP and RH
at 10.2 and 8.9 Pg C yr-1, which are 1.3
and 1.8 Pg C yr-1 less than the estimations from MIC-TEM, respectively
(Fig. 9).
Predicted changes in carbon fluxes: (i) NPP, (ii)RH, and
(iii) NEP for all land areas north of 45∘ N in response to
transient climate change under the RCP8.5 scenario (left panels) and RCP2.6
scenario (right panels) with the dormancy model and MIC-TEM, respectively. The
decadal running mean is applied.
Under the RCP2.6 scenario, the cumulative NEP from two models diverged by
125.2 Pg C by 2100. The trajectory of interannual NEP estimated with the
two models also diverged. The MIC-TEM predicted the region fluctuates
between carbon sinks and sources and totally acts as a carbon source of 1.6 Pg C with the interannual standard deviation of 0.24 Pg C yr-1 during
the 21st century. In contrast, MIC-TEM-dormancy projected the region
acts as a carbon sink of 123.6 Pg C with an interannual standard deviation
of 0.1 Pg C yr-1 (Fig. 9). MIC-TEM-dormancy estimates NPP and RH
at 9.9 and 8.7 Pg C yr-1, which are 0.5
and 1.7 Pg C yr-1 less than the estimations from MIC-TEM, respectively
(Fig. 9). Moreover, simulations under the two contrasting climate
scenarios (RCP2.6 and RCP8.5) exhibit a large difference of 81.1 Pg C of
cumulative NEP during the 21st century by MIC-TEM, but only 6.3 Pg C of
that by MIC-TEM-dormancy.
Regional annual seasonal pattern of simulated net primary
production (NPP, a, d), heterotrophic respiration (RH, b, e), and net ecosystem production (NEP, c, f) during the 2090s
from the dormancy model and MIC-TEM under (a, b, c) the RCP2.6 scenario and
(d, e, f) the RCP8.5 scenario. The region is all land areas north of
45∘ N.
MIC-TEM-dormancy estimated higher RH in winter, but lower RH in
summer under both future scenarios in the 2090s (Fig. 10). NPP is the same
in winter with or without dormancy, and in the late summer it is higher than
that without dormancy, especially in the RCP8.5 scenario. The combined
flattening patterns of NPP and RH result in different patterns for NEP.
Under the RCP2.6 scenario, MIC-TEM-dormancy predicts higher NEP from June
to October but lower NEP from January to April compared to MIC-TEM (Fig. 10). Under the RCP8.5 scenario, MIC-TEM-dormancy predicts higher NEP from
June to September but much lower NEP in other months than MIC-TEM (Fig. 10).
Simulated annual net primary production (NPP, a),
heterotrophic respiration (RH, b), and net ecosystem
production (NEP, c) by MIC-TEM-dormancy with an ensemble of
parameters.
Regional uncertainty considering equifinality effects during 20th
and 21st centuries
The ensemble simulations for the 20th century are shown in Fig. 11.
Given the uncertainty in parameters, MIC-TEM-dormancy predicts that the
regional cumulative carbon ranges from a carbon loss of 28.2 Pg to a carbon
sink of 362.1 Pg by different ensemble members, with a mean of 71.2±54.8 Pg (Fig. 11). For the 21st century, MIC-TEM-dormancy predicts
that the region acts from a carbon source of 49.3 Pg C to a carbon sink of
296.5 Pg C, with a mean of 112.7±116.5 Pg under RCP2.6 scenario
(Fig. 12). Under the RCP8.5 scenario, MIC-TEM-dormancy predicts that the
region acts from a carbon source of 27.1 Pg C to a carbon sink of 401.3 Pg C, with a mean of 143.1±162.5 Pg (Fig. 12).
Discussion
Our regional simulations with two contrasting models (MIC-TEM,
MIC-TEM-dormancy) indicate the regional natural terrestrial ecosystems acted
as a carbon sink in past decades, which is consistent with results from
other process-based models (White et al., 2000;
McGuire et al., 2009; Schimel, 2013). However, the magnitudes of this sink
are quite different in two models. Moreover, MIC-TEM-dormancy predicts the
sink will decrease under both RCP8.5 and RCP2.6 scenarios during the
21st century, while MIC-TEM projects that the sink will increase under
the RCP8.5 but change to a carbon source under the RCP2.6 scenario.
Estimations based on models without dormancy could fit observations of
RH as well as estimations with dormancy, but at the cost of
underestimating microbial biomass (Wang et al., 2014). Differences in
predicted RH with and without dormancy increase with temperature and
with the length of the dry periods between wetting events (Salazar et al.,
2018). The large difference in the two models suggests the importance of
incorporating microbial dormancy effects.
Simulated annual net primary production (NPP, a, d),
heterotrophic respiration (RH, b, e), and net ecosystem
production (NEP, c, f) under the RCP8.5 scenario (a–c) and RCP2.6 scenario (d–f) by MIC-TEM-dormancy with an ensemble of parameters.
The decadal running mean is applied. The grey area represents the upper and
lower bounds of simulations.
The large bias between dormancy and non-dormancy models mainly comes from
two parts. First, many important microbial activities such as soil organic
carbon decomposition and nutrient cycling largely depend on the active
fraction of microbial communities, not total microbial biomass (Wang et al.,
2014; Blagodatsky et al., 2000). However, only a small part (about
0.1 %–2 %, seldom exceed 5 %) of the total soil microbial biomass is
recognized to be active under natural conditions (Blagodatsky et al., 2011;
Werf and Verstraete, 1987). Thus, dormancy could be a prominent feature in
soil systems (Wang et al., 2014). Without considering dormancy, the
“effective” microbial biomass for soil decomposition could be
overestimated, resulting in overestimation of heterotrophic respiration (He
et al., 2015). He et al. (2015) predicted total soil RH of all
temperate forests (25–50∘ N) from the dormancy model
amounted to 7.28 and 8.83 Pg C yr-1 from a no-dormancy
model, which is 21.3 % higher than the dormancy model. Although their
study region and simulation period are different from our study, the results
can still be comparable. Both studies indicated that the magnitude of
RH from the no-dormancy model is higher than for dormancy models. Second, high
soil respiration stimulates N mineralization in soils (Zhuang et al., 2001,
2002), making more nutrients for photosynthesis of plants (Raich et al.,
1991; McGuire et al., 1995; Zhuang et al., 2015; Zha and Zhuang, 2018;
Thullner et al., 2005).
Therefore, NPP will be higher due to the N enrichment from higher RH.
However, how NEP will change is still unclear. Our estimates of the northern
extratropical NEP in the 1980s (1.61 Pg C yr-1 with MIC-TEM-dormancy
and 0.84 Pg C yr-1 with MIC-TEM) are within ranges (0.6 to 2.3 Pg C yr-1) reported in the literature for northern regions (Schimel et
al., 2001). Moreover, our predicted time trajectory of NEP in the 21st
century under the RCP2.6 scenario is very similar to the finding of White
et al. (2000), indicating that NEP increases from the 2000s to the 2070s
and then decreases in the 2090s. Although our dormancy model can project
reasonable carbon fluxes and indicates the importance of incorporating
microbial dormancy when compared with MIC-TEM (Zha and Zhuang et al.,
2018), there are some other microbial traits that have not yet been considered in
our model. For instance, one vital common evolutionary trait of microbes is
the community shift (Wang et al., 2015) with changing environment, including
warming, N fertilization, and precipitation (Treseder et al., 2011; Frey et
al., 2013; Allison et al., 2009; Evans and Wallenstein, 2011). Community
shift will influence microbial physiology, temperature sensitivity, and
growth rates (Classen et al., 2015), which will further affect the rate of
soil decomposition and other carbon dynamics (Treseder et al., 2011; Schimel
and Schaeffer, 2012; Todd-Brown et al., 2011). In addition, microbial community
composition was ignored in our model. We did not separate among functional
microbial groups, but gather microbes into one “box”. However, microbial
community composition could influence ecosystem functioning, and their
variance in responses to environmental conditions could alter the prediction
of the rates of decomposition of organic material (Balser et al., 2002;
Fierer et al., 2007). In particular, some narrowly distributed functions can be
more sensitive to microbial community composition, and these might benefit
most from explicit consideration of distinguishing functional groups in
ecosystem models (McGuire and Treseder, 2010; Schimel, 1995). Thus,
functional dissimilarity in microbial communities can be considered the next
step for model development (Strickland et al., 2009; Moorhead et al., 2006).
Moreover, microbial acclimation, a mechanism of adaption to a new
temperature regime, is another important trait to affect soil decomposition.
Recent studies have found that the warming-induced elevated respiration of
the microbial community could decrease over time because of acclimation
(Melillo et al., 1993; Todd-Brown et al., 2011). This mechanism shall be
factored into future soil decomposition analysis.
Except for the model limitations mentioned above, additional uncertainties may
come from inadequate model parameterization and model assumptions. For
example, a critical microbial parameter, carbon use efficiency (CUE), is a
primary control to soil CO2 efflux. Higher CUE indicates more microbial
growth and more carbon uptake by plants, while lower CUE indicates higher
soil decomposition (Manzoni et al., 2012). Theoretical and empirical
studies have suggested that CUE depends on both temperature and substrate
quality (Frey et al., 2013) and decreases as temperature increases and
nutrient availability decreases (Manzoni et al., 2012). Our study
considered the CUE sensitivity to temperature, but not nutrient
availability. On the other hand, some model assumptions can also cause
uncertainties. For example, we assumed that vegetation will not change
during the transient simulation. However, over the past few decades in
northern temperate and boreal regions, temperature increases have led to
vegetation shift from one type to another (Hansen et al., 2006; White
et al., 2000). The vegetation changes will affect carbon cycling in these
ecosystems.
While our analysis suggests it is important to incorporate microbial
dormancy dynamics into a process-based biogeochemistry model to more
adequately simulate carbon dynamics in northern temperate and boreal
regions, we do confront modeling dilemmas. First, our process-based models
have a relatively large number of parameters, which unavoidably creates the
“equifinality” problem as recognized in our previous studies for the model
(e.g., Tang and Zhuang, 2008, 2009). To alleviate this problem in this
analysis, we have conducted parameter ensemble simulations at both site and
regional levels and presented our results with uncertainties, which could be
a standard approach for process-based complex biogeochemistry modeling
analyses. Second, incorporating more ecosystem processes increases the
number of parameters in our model, inducing even larger uncertainties for
both site-level and regional simulations. On the one hand, the more complex
model to a certain degree helps capture observations; on the other hand, the
model uncertainty has not been constrained or even enlarged. We highlight
the need to further investigate this trade-off within the modeling research
community.
Conclusions
This study incorporated microbial dormancy into a detailed microbe-based
soil decomposition biogeochemistry model to examine the fate of large soil
carbon storage in northern temperate and boreal natural terrestrial
ecosystems under changing climate conditions. Regional simulations using
MIC-TEM-dormancy indicated that, over the 20th century, the region is a
carbon sink of 166.8±97.7 Pg. This sink could decrease to 175.9±105.4 Pg under the RCP8.5 scenario or 125.4±85.5 Pg under
the RCP2.6 scenario during the 21st century. Whether considering
microbial dormancy or not can cause large differences in soil decomposition
estimation between two models. Meanwhile, due to available nitrogen affected
by soil decomposition, net primary production is consequently influenced in
these two centuries. The combined changes in soil decomposition and net
primary production led to large differences in carbon budget estimation
between two models. Compared with MIC-TEM, MIC-TEM-dormancy projected 75.9 Pg more C stored in the terrestrial ecosystems over the last century and 50.4 and 125.2 Pg more C under the RCP8.5 and RCP2.6 scenarios,
respectively. This study highlights the importance of the representation of
microbial dormancy in earth system models in order to adequately quantify
the carbon dynamics of natural terrestrial ecosystems in northern temperate
and boreal regions.
Data availability
All data used in this study are available from the
authors upon request.
Author contributions
QZ designed the study. JZ conducted model
development, simulation, and analysis. JZ and QZ wrote the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This research was supported by a NSF project (IIS-1027955), a DOE project
(DE-SC0008092), and a NASA LCLUC project (NNX09AI26G) to Qianlai Zhuang. We
acknowledge the Rosen High Performance Computing Center at Purdue for
computing support. We thank the National Snow and Ice Data center for
providing Global Monthly EASE-Grid snow water equivalent data and the National
Oceanic and Atmospheric Administration for North American Regional
Reanalysis (NARR). We also acknowledge the World Climate Research
Programme's Working Group on Coupled Modeling Intercomparison Project CMIP5,
and we thank the climate modeling groups for producing and making available
their model output. The data presented in this paper can be accessed through
our research website (http://www.eaps.purdue.edu/ebdl/, last access: 9 December 2020).
Financial support
This research has been supported by the NSF (grant no. IIS-1027955), the DOE (grant no. DE-SC0008092), NASA LCLUC (grant no. NNX09AI26G), and NASA (NNX17AK20G).
Review statement
This paper was edited by Jens-Arne Subke and reviewed by Thomas Wutzler, Alejandro Salazar, and one anonymous referee.
ReferencesAllison, E. H., Perry, A. L., Badjeck, M.-C., Neil Adger, W., Brown, K.,
Conway, D., Halls, A. S., Pilling, G. M., Reynolds, J. D., Andrew, N. L.,
and Dulvy, N. K.: Vulnerability of national economies to the impacts of
climate change on fisheries, Fish Fish., 10, 173–196,
10.1111/j.1467-2979.2008.00310.x, 2009.Allison, S. D., Wallenstein, M. D., and Bradford, M. A.: Soil-carbon
response to warming dependent on microbial physiology, Nat. Geosci., 3,
336–340, 10.1038/ngeo846, 2010.
Balser, T. C., Kinzig, A. P., and Firestone, M. K.: Linking soil microbial
communities and ecosystem functioning, The functional consequences of
biodiversity: Empirical progress and theoretical extensions, Biogeochemistry, 73, 265–293, 2002.Billings, W. D., Peterson, K. M., Shaver, G. R., and Trent, A. W.: Root Growth,
Respiration, and Carbon Dioxide Evolution in an Arctic Tundra Soil, Arctic
Alpine Res., 9, 129–137, 10.1080/00040851.1977.12003908, 1977.Blagodatskaya, E. and Kuzyakov, Y.: Active microorganisms in soil: Critical
review of estimation criteria and approaches, Soil Biol. Biochem.,
67, 192–211, 10.1016/j.soilbio.2013.08.024, 2013.Blagodatskaya, E., Khomyakov, N., Myachina, O., Bogomolova, I., Blagodatsky,
S., and Kuzyakov, Y.: Microbial interactions affect sources of priming
induced by cellulose, Soil Biol. Biochem., 74, 39–49,
10.1016/j.soilbio.2014.02.017, 2014.Blagodatsky, S., Grote, R., Kiese, R., Werner, C., and Butterbach-Bahl, K.:
Modelling of microbial carbon and nitrogen turnover in soil with special
emphasis on N-trace gases emission, Plant Soil, 346, 297–330,
10.1007/s11104-011-0821-z, 2011.
Blagodatsky, S. A., Heinemeyer, O., and Richter, J.: Estimating the active
and total soil microbial biomass by kinetic respiration analysis, Biol.
Fertil. Soils, 32, 73–81, 2000.Bond-Lamberty, B. and Thomson, A.: Temperature-associated increases in the
global soil respiration record, Nature, 464, 579–582, 10.1038/nature08930,
2010.Bond-Lamberty, B., Bailey, V. L., Chen, M., Gough, C. M., and Vargas, R.:
Globally rising soil heterotrophic respiration over recent decades, Nature,
560, 80–83, 10.1038/s41586-018-0358-x, 2018.Bouskill, N. J., Tang, J., Riley, W. J., and Brodie, E. L.: Trait-based
representation of biological nitrification: model development, testing, and
predicted community composition, Front. Microb., 3, 364,
10.3389/fmicb.2012.00364, 2012.
Callaghan, T., Björn, L. O., Chernov, Y., Chapin, T., Christensen, T.
R., Huntley, B., Ims, R., Jolly, D., Jonasson, S., Matveyeva, N., Panikov,
N., Oechel, W., and Shaver, G.: Arctic tundra and polar desert ecosystems,
Arctic climate impact assessment, Cambridge University Press, New York, 243–352, 2005.Classen, A. T., Sundqvist, M. K., Henning, J. A., Newman, G. S., Moore, J.
A. M., Cregger, M. A., Moorhead, L. C., and Patterson, C. M.: Direct and
indirect effects of climate change on soil microbial and soil
microbial-plant interactions: What lies ahead?, Ecosphere, 6, 130,
10.1890/es15-00217.1, 2015.Conant, R. T., Ryan, M. G., Ågren, G. I., Birge, H. E., Davidson, E. A.,
Eliasson, P. E., Evans, S. E., Frey, S. D., Giardina, C. P., Hopkins, F. M.,
Hyvönen, R., Kirschbaum, M. U. F., Lavallee, J. M., Leifeld, J., Parton,
W. J., Megan Steinweg, J., Wallenstein, M. D., Martin Wetterstedt, J.
Å., and Bradford, M. A.: Temperature and soil organic matter
decomposition rates – synthesis of current knowledge and a way forward,
Glob. Change Biol., 17, 3392–3404, 10.1111/j.1365-2486.2011.02496.x,
2011.Coursolle, C., Margolis, H. A., Barr, A. G., Black, T. A., Amiro, B. D.,
McCaughey, J. H., Flanagan, L. B., Lafleur, P. M., Roulet, N. T., Bourque,
C. P. A., Arain, M. A., Wofsy, S. C., Dunn, A., Morgenstern, K., Orchansky,
A. L., Bernier, P. Y., Chen, J. M., Kidston, J., Saigusa, N., and Hedstrom,
N.: Late-summer carbon fluxes from Canadian forests and peatlands along an
east–west continental transect, Can. J. Forest Res., 36,
783–800, 10.1139/x05-270, 2006.Davidson, E. A., Trumbore, S. E., and Amundson, R.: Biogeochemistry: soil
warming and organic carbon content, Nature, 408, 789–790, 10.1038/35048672, 2000.Davidson, E. A. and Janssens, I. A.: Temperature sensitivity of soil carbon
decomposition and feedbacks to climate change, Nature, 440, 165–173,
10.1038/nature04514, 2006.Davidson, E. A., Janssens, I. A., and Luo, Y.: On the variability of
respiration in terrestrial ecosystems: moving beyond Q10, Glob. Change Biol., 12, 154–164, 10.1111/j.1365-2486.2005.01065.x, 2006.Davidson, E. A., Samanta, S., Caramori, S. S., and Savage, K.: The Dual
Arrhenius and Michaelis-Menten kinetics model for decomposition of soil
organic matter at hourly to seasonal time scales, Glob. Change Biol., 18,
371–384, 10.1111/j.1365-2486.2011.02546.x, 2012.
Duan, Q., Sorooshian, S., and Gupta, V. K.: Optimal use of the SCE-UA global
optimization method for calibrating watershed models, J. Hydrol.,
158, 265–284, 1994.Evans, S. E. and Wallenstein, M. D.: Soil microbial community response to
drying and rewetting stress: does historical precipitation regime matter?,
Biogeochemistry, 109, 101–116, 10.1007/s10533-011-9638-3, 2011.
Fierer, N., Morse, J. L., Berthrong, S. T., Bernhardt, E. S., and Jackson,
R. B.: Environmental controls on the landscape – scale biogeography of
stream bacterial communities, Ecology, 88, 2162–2173, 2007.Frey, S. D., Lee, J., Melillo, J. M., and Six, J.: The temperature response
of soil microbial efficiency and its feedback to climate, Nat. Clim. Change, 3, 395–398, 10.1038/nclimate1796, 2013.German, D. P., Marcelo, K. R. B., Stone, M. M., and Allison, S. D.: The
Michaelis-Menten kinetics of soil extracellular enzymes in response to
temperature: a cross-latitudinal study, Glob. Change Biol., 18,
1468–1479, 10.1111/j.1365-2486.2011.02615.x, 2012.Gilmanov, T. G., Tieszen, L. L., Wylie, B. K., Flanagan, L. B., Frank, A.
B., Haferkamp, M. R., Meyers, T. P., and Morgan, J. A.: Integration of
CO2flux and remotely-sensed data for primary production and ecosystem
respiration analyses in the Northern Great Plains: potential for
quantitative spatial extrapolation, Glob. Ecol. Biogeogr., 14,
271–292, 10.1111/j.1466-822X.2005.00151.x, 2005.
Gough, C. M., Hardiman, B. S., Nave, L. E., Bohrer, G., Maurer, K. D.,
Vogel, C. S., Nadelhoffer, K. J., and Curtis, P. S.: Sustained carbon uptake
and storage following moderate disturbancein a Great Lakes forest,
Ecol. Appl., 23, 1202–1215, 2013.Goulden, M. L., Winston, G. C., McMillan, A. M. S., Litvak, M. E., Read, E.
L., Rocha, A. V., and Rob Elliot, J.: An eddy covariance mesonet to measure
the effect of forest age on land-atmosphere exchange, Glob. Change Biol.,
12, 2146–2162, 10.1111/j.1365-2486.2006.01251.x, 2006.Graham, E. B., Wieder, W. R., Leff, J. W., Weintraub, S. R., Townsend, A.
R., Cleveland, C. C., Philippot, L., and Nemergut, D. R.: Do we need to
understand microbial communities to predict ecosystem function? A comparison
of statistical models of nitrogen cycling processes, Soil Biol. Biochem., 68, 279–282, 10.1016/j.soilbio.2013.08.023, 2014.Graham, E. B., Knelman, J. E., Schindlbacher, A., Siciliano, S., Breulmann,
M., Yannarell, A., Beman, J. M., Abell, G., Philippot, L., Prosser, J.,
Foulquier, A., Yuste, J. C., Glanville, H. C., Jones, D. L., Angel, R.,
Salminen, J., Newton, R. J., Burgmann, H., Ingram, L. J., Hamer, U.,
Siljanen, H. M., Peltoniemi, K., Potthast, K., Baneras, L., Hartmann, M.,
Banerjee, S., Yu, R. Q., Nogaro, G., Richter, A., Koranda, M., Castle, S.
C., Goberna, M., Song, B., Chatterjee, A., Nunes, O. C., Lopes, A. R., Cao,
Y., Kaisermann, A., Hallin, S., Strickland, M. S., Garcia-Pausas, J., Barba,
J., Kang, H., Isobe, K., Papaspyrou, S., Pastorelli, R., Lagomarsino, A.,
Lindstrom, E. S., Basiliko, N., and Nemergut, D. R.: Microbes as Engines of
Ecosystem Function: When Does Community Structure Enhance Predictions of
Ecosystem Processes?, Front. Microb., 7, 214,
10.3389/fmicb.2016.00214, 2016.Griffis, T. J., Lee, X., Baker, J. M., Billmark, K., Schultz, N., Erickson,
M., Zhang, X., Fassbinder, J., Xiao, W., and Hu, N.: Oxygen isotope
composition of evapotranspiration and its relation to C4photosynthetic
discrimination, J. Geophys. Res., 116, 10.1029/2010jg001514,
2011.Hagerty, S. B., van Groenigen, K. J., Allison, S. D., Hungate, B. A.,
Schwartz, E., Koch, G. W., Kolka, R. K., and Dijkstra, P.: Accelerated
microbial turnover but constant growth efficiency with warming in soil,
Nat. Clim. Change, 4, 903–906, 10.1038/nclimate2361, 2014.
Hanson, P. J., Edwards, N. T., Garten, C. T., and Andrews, J. A.: Separating root and soil
microbial contributions to soil respiration: A review of methods and
observations, Biogeochemistry, 48, 115–146, 2000.Hansen, J., Sato, M., Ruedy, R., Lo, K., Lea, D. W., and Medina-Elizade, M.:
Global temperature change, P. Natl. Acad. Sci. USA, 103, 14288–14293, 10.1073/pnas.0606291103,
2006.
Harder, W. and Dijkhuizen, L.: Physiological responses to nutrient
limitation, Annu. Rev. Microb., 37, 1–23, 1983.Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated
high-resolution grids of monthly climatic observations – the CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642,
10.1002/joc.3711, 2014.He, Y., Yang, J., Zhuang, Q., Harden, J. W., McGuire, A. D., Liu, Y., Wang,
G., and Gu, L.: Incorporating microbial dormancy dynamics into soil
decomposition models to improve quantification of soil carbon dynamics of
northern temperate forests, J. Geophys. Res.-Biogeo.,
120, 2596–2611, 10.1002/2015jg003130, 2015.Hiller, R. V., McFadden, J. P., and Kljun, N.: Interpreting CO2 Fluxes Over
a Suburban Lawn: The Influence of Traffic Emissions, Bound.-Lay. Meteorol., 138, 215–230, 10.1007/s10546-010-9558-0, 2010.Houghton, R. A.: Balancing the Global Carbon Budget, Annu. Rev. Earth
Pl. Sc., 35, 313–347, 10.1146/annurev.earth.35.031306.140057,
2007.Hugelius, G., Strauss, J., Zubrzycki, S., Harden, J. W., Schuur, E. A. G., Ping, C.-L., Schirrmeister, L., Grosse, G., Michaelson, G. J., Koven, C. D., O'Donnell, J. A., Elberling, B., Mishra, U., Camill, P., Yu, Z., Palmtag, J., and Kuhry, P.: Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps, Biogeosciences, 11, 6573–6593, 10.5194/bg-11-6573-2014, 2014.Jenkins, J. P., Richardson, A. D., Braswell, B. H., Ollinger, S. V.,
Hollinger, D. Y., and Smith, M. L.: Refining light-use efficiency
calculations for a deciduous forest canopy using simultaneous tower-based
carbon flux and radiometric measurements, Agr. Forest
Meteorol., 143, 64–79, 10.1016/j.agrformet.2006.11.008, 2007.Kaiser, C., Franklin, O., Dieckmann, U., and Richter, A.: Microbial
community dynamics alleviate stoichiometric constraints during litter decay,
Ecol. Lett., 17, 680–690, 10.1111/ele.12269, 2014.Knorr, W., Prentice, I. C., House, J. I., and Holland, E. A.: Long-term
sensitivity of soil carbon turnover to warming, Nature, 433, 298–301, 10.1038/nature03226, 2005.Lawrence, C. R., Neff, J. C., and Schimel, J. P.: Does adding microbial
mechanisms of decomposition improve soil organic matter models?, A comparison
of four models using data from a pulsed rewetting experiment, Soil Biol. Biochem., 41, 1923–1934, 10.1016/j.soilbio.2009.06.016, 2009.
Lennon, J. T. and Jones, S. E.: Microbial seed banks: the ecological and
evolutionary implications of dormancy, Nature reviews, Microbiology, 9,
119–130, 10.1038/nrmicro2504, 2011.Lloyd, A. H., Rupp, T. S., Fastie, C. L., and Starfield, A. M.: Patterns and
dynamics of treeline advance on the Seward Peninsula, Alaska, J. Geophys. Res., 108, 8161, 10.1029/2001jd000852, 2002.Manzoni, S., Taylor, P., Richter, A., Porporato, A., and Agren, G. I.:
Environmental and stoichiometric controls on microbial carbon-use efficiency
in soils, New Phytol., 196, 79–91, 10.1111/j.1469-8137.2012.04225.x,
2012.McEwing, K. R., Fisher, J. P., and Zona, D.: Environmental and vegetation
controls on the spatial variability of CH4 emission from wet-sedge and
tussock tundra ecosystems in the Arctic, Plant Soil, 388, 37–52,
10.1007/s11104-014-2377-1, 2015.
McGuire, A. D., Melillo, J. M., Joyce, L. A., Kicklighter, D. W., Grace, A.
L., III, B. M., and Vorosmarty, C. J.: Interactions between carbon and
nitrogen dynamics in estimating net primary productivity for potential
vegetation in North America, Global Biogeochem. Cy., 6, 101–124, 1992.
McGuire, A. D., Melillo, J. M., Kicklighter, D. W., and Joyce, L. A.:
Equilibrium responses of soil carbon to climate change: Empirical and
process-based estimates, J. Biogeogr., 22, 785–796, 1995.
McGuire, A. D. and Hobbie, J. E.: Global climate change and the equilibrium
responses of carbon storage in arctic and subarctic regions, in: Modeling the
Arctic system: A workshop report on the state of modeling in the Arctic
System Science program, 53–54, 1997.
McGuire, A. D., Anderson, L. G., Christensen, T. R., Dallimore, S., Guo, L.,
Hayes, D. J., Heimann, M., Lorenson, T. D., Macdonald, R. W., and Roulet,
N.: Sensitivity of the carbon cycle in the Arctic to climate change,
Ecol. Monogr., 79, 523–555, 2009.McGuire, K. L. and Treseder, K. K.: Microbial communities and their
relevance for ecosystem models: Decomposition as a case study, Soil Biol. Biochem., 42, 529–535, 10.1016/j.soilbio.2009.11.016, 2010.Melillo, J. M., McGuire, A. D., Kicklighter, D. W., Moore III, B., Vorosmarty,
C. J., and Schloss, A. L.: Global climate change and terrestrial net primary
production, Nature, 363, 234–240,
10.1038/363234a0, 1993.Merbold, L., Kutsch, W. L., Corradi, C., Kolle, O., Rebmann, C., Stoy, P.
C., Zimov, S. A., and Schulze, E. D.: Artificial drainage and associated
carbon fluxes (CO2/CH4) in a tundra ecosystem, Glob. Change Biol., 15,
2599–2614, 10.1111/j.1365-2486.2009.01962.x, 2009.
Moorhead, D. L. and Sinsabaugh, R. L.: A theoretical model of litter decay
and microbial interaction, Ecol. Monogr., 76, 151–174, 2006.Oechel, W. C., Laskowski, C. A., Burba, G., Gioli, B., and Kalhori, A. A.
M.: Annual patterns and budget of CO2flux in an Arctic tussock tundra
ecosystem, J. Geophys. Res.-Biogeo., 119, 323–339,
10.1002/2013jg002431, 2014.
Parton, W. J., Scurlock, J. M. O., Ojima, D. S., Gilmanov, T. G., Scholes,
R. J., Schimel, D. S., Kirchner, T., Menaut, J. C., Seastedt, T., Moya, E.
G., Kamnalrut, A., and Kinyamario, J. I.: Observations and modeling of
biomass and soil organic matter dynamics for the grassland biome worldwide,
Global Biogeochem. Cy., 7, 785–809, 1993.Raich, J. W., Rastetter, E. B., Melillo, J. M., Kicklighter, D. W.,
Steudler, P. A., Peterson, B. J., Grace, A. L., Moore, B., and Vorosmary,
C. J.: Potential net primary productivity in South America: application of a
global model, Ecol. Appl., 1, 399–429,
10.2307/1941899, 1991.Richardson, A. D., Jenkins, J. P., Braswell, B. H., Hollinger, D. Y.,
Ollinger, S. V., and Smith, M. L.: Use of digital webcam images to track
spring green-up in a deciduous broadleaf forest, Oecologia, 152, 323–334,
10.1007/s00442-006-0657-z, 2007.
Running, S. W. and Coughlan, J. C.: A general model of forest ecosystem
processes for regional applications I. Hydrologic balance, canopy gas
exchange and primary production processes, Ecol. Modell., 42,
125–154, 1988.Salazar, A., Sulman, B. N., and Dukes, J. S.: Microbial dormancy promotes microbial biomass and respiration across pulses of drying-wetting stress, Soil Biol. Biochem., 116, 237–244, 10.1016/j.soilbio.2017.10.017, 2018.
Schimel, D. S.: Terrestrial ecosystems and the carbon cycle, Glob. Change Biol., 1, 77–91, 1995.Schimel, D. S., House, J. I., Hibbard, K. A., Bousquet, P., Ciais, P.,
Peylin, P., Braswell, B. H., Apps, M. J., Baker, D., Bondeau, A., Canadell,
J., Churkina, G., Cramer, W., Denning, A. S., Field, C. B., Friedlingstein,
P., Goodale, C., Heimann, M., Houghton, R. A., Melillo, J. M., III, B. M.,
Murdiyarso, D., Noble, I., Pacala, S. W., Prentice, I. C., Raupach, M. R.,
Rayner, P. J., Scholes, R. J., Steffen, W. L., and Wirth, C.: Recent
patterns and mechanisms of carbon exchange by terrestrial ecosystems,
Nature, 414, 169–172, 10.1038/3510250, 2001.Schimel, J.: Microbes and global carbon, Nat. Clim. Change, 3, 867–868,
10.1038/nclimate2015, 2013.Schimel, J. P. and Weintraub, M. N.: The implications of exoenzyme activity
on microbial carbon and nitrogen limitation in soil: a theoretical model,
Soil Biol. Biochem., 35, 549–563, 10.1016/s0038-0717(03)00015-4,
2003.Schimel, J. P. and Schaeffer, S. M.: Microbial control over carbon cycling
in soil, Front. Microb., 3, 348, 10.3389/fmicb.2012.00348, 2012.Schmidt, M. W., Torn, M. S., Abiven, S., Dittmar, T., Guggenberger, G.,
Janssens, I. A., Kleber, M., Kogel-Knabner, I., Lehmann, J., Manning, D. A.,
Nannipieri, P., Rasse, D. P., Weiner, S., and Trumbore, S. E.: Persistence
of soil organic matter as an ecosystem property, Nature, 478, 49–56,
10.1038/nature10386, 2011.Stolpovsky, K., Martinez-Lavanchy, P., Heipieper, H. J., Van Cappellen, P.,
and Thullner, M.: Incorporating dormancy in dynamic microbial community
models, Ecol. Modell., 222, 3092–3102,
10.1016/j.ecolmodel.2011.07.006, 2011.Stow, D. A., Hope, A., McGuire, D., Verbyla, D., Gamon, J., Huemmrich, F.,
Houston, S., Racine, C., Sturm, M., Tape, K., Hinzman, L., Yoshikawa, K.,
Tweedie, C., Noyle, B., Silapaswan, C., Douglas, D., Griffith, B., Jia, G.,
Epstein, H., Walker, D., Daeschner, S., Petersen, A., Zhou, L., and Myneni,
R.: Remote sensing of vegetation and land-cover change in Arctic Tundra
Ecosystems, Remote Sens. Environ., 89, 281–308,
10.1016/j.rse.2003.10.018, 2004.
Strickland, M. S., Lauber, C., Fierer, N., and Bradford, M. A.: Testing the
functional signi?canceof microbial community composition, Ecology, 90,
441–451, 2009.Tang, J. and Zhuang, Q.: Equifinality in parameterization of process-based
biogeochemistry models: A significant uncertainty source to the estimation
of regional carbon dynamics, J. Geophys. Res., 113, G04010,
10.1029/2008JG000757, 2008.Tang, J. and Zhuang, Q.: A global sensitivity analysis and Bayesian
inference framework for improving the parameter estimation and prediction of
a process-based Terrestrial Ecosystem Model, J. Geophys. Res., 114, D15303,
10.1029/2009JD011724, 2009.Tang, J. and Riley, W. J.: Weaker soil carbon–climate feedbacks resulting
from microbial and abiotic interactions, Nat. Clim. Change, 5, 56–60,
10.1038/nclimate2438, 2014.Tape, K. E. N., Sturm, M., and Racine, C.: The evidence for shrub expansion
in Northern Alaska and the Pan-Arctic, Glob. Change Biol., 12, 686–702,
10.1111/j.1365-2486.2006.01128.x, 2006.Tarnocai, C., Canadell, J. G., Schuur, E. A. G., Kuhry, P., Mazhitova, G.,
and Zimov, S.: Soil organic carbon pools in the northern circumpolar
permafrost region, Global Biogeochem. Cy., 23, GB2023,
10.1029/2008gb003327, 2009.Thullner, M., Van Cappellen, P., and Regnier, P.: Modeling the impact of
microbial activity on redox dynamics in porous media, Geochim.
Cosmochim. Ac., 69, 5005–5019, 10.1016/j.gca.2005.04.026, 2005.Todd-Brown, K. E. O., Hopkins, F. M., Kivlin, S. N., Talbot, J. M., and
Allison, S. D.: A framework for representing microbial decomposition in
coupled climate models, Biogeochemistry, 109, 19–33,
10.1007/s10533-011-9635-6, 2011.Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, 10.5194/bg-10-1717-2013, 2013.Treseder, K. K., Balser, T. C., Bradford, M. A., Brodie, E. L., Dubinsky, E.
A., Eviner, V. T., Hofmockel, K. S., Lennon, J. T., Levine, U. Y.,
MacGregor, B. J., Pett-Ridge, J., and Waldrop, M. P.: Integrating microbial
ecology into ecosystem models: challenges and priorities, Biogeochemistry,
109, 7–18, 10.1007/s10533-011-9636-5, 2011.Wang, G., Mayes, M. A., Gu, L., and Schadt, C. W.: Representation of
Dormant and Active Microbial Dynamics for Ecosystem Modeling, Public Library
of Science, 9, e89252, 10.1371/journal.pone.0089252.g001, 2014.Wang, G., Jagadamma, S., Mayes, M. A., Schadt, C. W., Steinweg, J. M., Gu,
L., and Post, W. M.: Microbial dormancy improves development and
experimental validation of ecosystem model, ISME J., 9, 226–237,
10.1038/ismej.2014.120, 2015.Wang, W., Feng, J., and Oikawa, T.: Contribution of Root and Microbial Respiration
to Soil CO2 Efflux and Their Environmental Controls in a Humid Temperate
Grassland of Japan, Pedosphere, 19, 31–39, 2009.
Werf, H. V. D. and Verstraete, W.: Estimation of active soil microbial
biomass by mathematical analysis of respiration curves: relation to
conventional estimation of total biomass, Soil Biol. Biochem., 19,
267–271, 1987.
White, A., Cannell, M. G. R., and Friend, A. D.: The high-latitude
terrestrial carbon sink: a model analysis Glob. Change Biol., 6, 227–245,
2000.Wieder, W. R., Allison, S. D., Davidson, E. A., Georgiou, K., Hararuk, O., He, Y., Hopkins, F., Luo, Y., Smith, M. J., Sulman, B., Todd-Brown, K., Wang, Y. P., Xia, J., and Xu, X.: Explicitly representing soil microbial processes in Earth system models, Global Biogeochem. Cy., 29, 1782–1800, 10.1002/2015GB005188, 2015.Wieder, W. R., Bonan, G. B., and Allison, S. D.: Global soil carbon
projections are improved by modelling microbial processes, Nat. Clim. Change, 3, 909–912, 10.1038/nclimate1951, 2013.Xu, X., Schimel, J. P., Thornton, P. E., Song, X., Yuan, F., and Goswami,
S.: Substrate and environmental controls on microbial assimilation of soil
organic carbon: a framework for Earth system models, Ecol. Lett., 17,
547–555, 10.1111/ele.12254, 2014.
Zha, J. and Zhuang, Q.: Microbial decomposition processes and vulnerable arctic soil organic carbon in the 21st century, Biogeosciences, 15, 5621–5634, 10.5194/bg-15-5621-2018, 2018.Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., and
Myneni, R. B.: Variations in northern vegetation activity inferred from
satellite data of vegetation index during 1981 to 1999, J. Geophys. Res.-Atmos., 106, 20069–20083, 10.1029/2000jd000115,
2001.Zhuang, Q., Romanovsky, V. E., and McGuire, A. D.: Incorporation of a
permafrost model into a large-scale ecosystem model: Evaluation of temporal
and spatial scaling issues in simulating soil thermal dynamics, J. Geophys. Res.:-Atmos., 106, 33649–33670, 10.1029/2001jd900151,
2001.Zhuang, Q., McGuire, A. D., O'Neill, K. P., Harden, J. W., Romanovsky, V.
E., and Yarie, J.: Modeling soil thermal and carbon dynamics of a fire
chronosequence in interior Alaska, J. Geophys. Res., 108, FFR 3-1-FFR-3-26, 10.1029/2001jd001244, 2002.Zhuang, Q., He, J., Lu, Y., Ji, L., Xiao, J., and Luo, T.: Carbon dynamics
of terrestrial ecosystems on the Tibetan Plateau during the 20th century: An
analysis with a process-based biogeochemical model, Glob. Ecol. Biogeogr., 19, 649–662, 10.1111/j.1466-8238.2010.00559.x, 2010.Zhuang, Q., Zhu, X., He, Y., Prigent, C., Melillo, J. M., David McGuire, A.,
Prinn, R. G., and Kicklighter, D. W.: Influence of changes in wetland
inundation extent on net fluxes of carbon dioxide and methane in northern
high latitudes from 1993 to 2004, Environ. Res. Lett., 10,
095009, 10.1088/1748-9326/10/9/095009, 2015.Zhuang, Q., McGuire, A. D., Melillo, J. M., Clein, J. S., Dargaville, R. J.,
Kicklighter, D. W., Myneni, R. B., Dong, J., Romanovsky, V. E., Harden, J.,
and Hobbie, J. E.: Carbon cycling in extratropical terrestrial ecosystems of
the Northern Hemisphere during the 20th century: a modeling analysis of the
influences of soil thermal dynamics, Tellus B, 55, 751–776, 10.3402/tellusb.v55i3.16368, 2016.