1. health and fitness
2. disease
3. cholesterol

GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 18, GB1022, doi:10.1029/2003GB002119, 2004
Fast labile carbon turnover obscures sensitivity of heterotrophic
respiration from soil to temperature: A model analysis
Lianhong Gu, Wilfred M. Post, and Anthony W. King
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
Received 3 July 2003; revised 22 November 2003; accepted 15 December 2003; published 5 February 2004.
[1] Labile carbon, although often a small fraction of soil organic carbon (SOC),
significantly affects heterotrophic respiration at short timescales because of its rapid
decomposition. However, in the current literature, most soil respiration measurements are
interpreted without simultaneous information on labile carbon pool dynamics. Sensitivity
of soil respiration to temperature is routinely derived directly from field observations,
and such relationships have been used to extrapolate effects of global change (e.g.,
warming) on carbon emission from SOC. Here we use a multipool SOC model to
demonstrate the impacts of seasonal fluctuations of labile carbon pools on interpretation
of soil respiration measurements. We find that labile carbon pool sizes vary widely in
response to seasonal changes in representative plant material inputs and temperature even
though the model is operating at equilibrium in terms of annual means. Convolution of
the dynamics of fast turnover carbon pools and temporal progression in temperature lead
to misrepresentation and misinterpretation of the heterotrophic respiration-temperature
relationships estimated from bulk soil CO2 exchanges. Temperature sensitivity is
overestimated when the variations of labile carbon pools and temperature are in phase and
underestimated when they are out of phase. Furthermore, with normally used observation
time windows (weeks to a year), temperature sensitivity is more likely to be
underestimated. A distortion of temperature sensitivity (Q10) from 2 (actual, sensitive
dependence on temperature) to nearly 1 (false, no dependence on temperature) is shown.
Applying estimated temperature sensitivity parameter back into the model considerably
overestimates soil carbon storage at equilibrium. Our findings indicate that caution
must be taken when soil respiration-temperature relationships are evaluated based on bulk
soil observations and when sensitivity of soil respiration to temperature estimated directly
INDEX
under field conditions is used to predict future carbon cycle-climate feedbacks.
TERMS: 1615 Global Change: Biogeochemical processes (4805); 0315 Atmospheric Composition and
Structure: Biosphere/atmosphere interactions; 3210 Mathematical Geophysics: Modeling; KEYWORDS:
carbon cycling, soil respiration, soil organic carbon, temperature sensitivity (Q10), labile carbon, soil carbon
dynamics
Citation: Gu, L., W. M. Post, and A. W. King (2004), Fast labile carbon turnover obscures sensitivity of heterotrophic respiration
from soil to temperature: A model analysis, Global Biogeochem. Cycles, 18, GB1022, doi:10.1029/2003GB002119.
1. Introduction
[2] SOC contains a mixture of dead plant and animal
material derived substances with highly variable physical
and chemical properties. Most SOC is stored in the top
meter of soil [Jobba´gy and Jackson, 2000]. Globally, this
layer of soil contains about 1500 Pg of carbon, which is
more than the amount of carbon stored in the living plants
and the atmosphere combined [Post et al., 1982; Sundquist,
1993; Schlesinger, 1997; Amundson, 2001]. Carbon emission (heterotrophic respiration) from this reservoir is the
dominant flux balancing the carbon entering into the terrestrial biosphere through photosynthesis at various timeThis paper is not subject to U.S. Copyright. Published in 2004 by the
American Geophysical Union.
scales [Gu et al., 1999; Schlesinger and Andrews, 2000;
Valentini et al., 2000]. How heterotrophic respiration
responds to variations in environmental variables, particularly temperature, will critically determine the importance of
climate-carbon cycle feedbacks in affecting future atmospheric CO2 concentration [Cox et al., 2000; Friedlingstein
et al., 2001; Jones et al., 2003; Friedlingstein et al., 2003].
[3] Seasonal and annual cycles in plant carbon inputs
and variations in activities of microorganisms (decomposers)
in soil, coupled with diurnal, seasonal, and interannual
variations of environmental variables, make SOC a
dynamic carbon reservoir. A convenient way to describe
these dynamics is to represent SOC with several carbon
pools characterized by distinct turnover times. For example, SOC models such as Century [Parton et al., 1987] and
ROTHC [Jenkinson, 1990] often divide SOC into different
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carbon pools with turnover times ranging from days to
years to millennia. These characteristic turnover times are
generally modified by temperature and other environmental factors. Currently, there is no satisfactory approach to
physically or chemically separate the components of SOC
according to different turnover rates [Trumbore, 1997], yet
dynamic changes in the sizes of different carbon pools and
their relative proportions can greatly complicate the relationship between temperature and respiration observed
from bulk soil. Kirschbaum [1995] warned that the input
of easily decomposable material is not evenly distributed
through time and this could make difficult analysis of
responses of decomposition to temperature that is based on
seasonal variations of soil respiration. For deciduous forest
ecosystems or grasslands that have summer as growing
season, most fresh aboveground litter material becomes
available during the cool time of the year. In these seasons,
the higher quantity and quality of plant substrates lead to
higher respiration rates that could be underestimated if a
temperature-respiration relationship developed under warm
growing season conditions is used. The reverse could be
true for Mediterranean systems where vegetation activities
and temperature regimes are out of phase (if moisture
condition is taken into account). Trumbore [2000] emphasized that proper treatment of the heterogeneity in SOC
decomposability is a premise for reliable evaluation of
responses of soil carbon to climate change. She pointed
out that an assumption of homogeneous SOC will lead to
either underestimation or overestimation of responses of
soil carbon reservoir to changes in plant material inputs
and decomposition rates, depending on the timescales
involved.
[4] Given the complex nature of SOC and its dynamics,
it is perhaps not surprising that presently there is considerable debate and inconsistency about the sensitivity of soil
respiration to temperature. Results from field experiments,
laboratory incubation, and modeling studies are often at
odds with each other [e.g., Kirschbaum, 1995; Trumbore et
al., 1996; Bird et al., 1996; Townsend et al., 1997; Liski et
al., 1999; Giardina and Ryan, 2000; Luo et al., 2001;
Melillo et al., 2002; Jones et al., 2003]. While the Q10
(exponential) function has been widely used to describe
responses of soil respiration to temperature, Lloyd and
Taylor [1994] and Kirschbaum [1995], among others, have
argued against its use. These researchers extracted data
from published papers and showed that the Q10 itself
decreased with temperature. Support for the suggestion of
decreasing Q10 with temperature is provided by incubation
experiments of Dalias et al. [2001], which showed that
higher incubation temperature led to lower Q10 for straw
carbon mineralization in a variety of forest soils. Further
support comes from Xu and Qi [2001] and Janssens and
Pilegaard [2003], who used chambers to measure CO2
efflux from soil surfaces and found that Q10 tended to be
smaller in summer than in winter. However, Ka¨tterer et al.
[1998] also used data published in the literature but found
that different models, including the Q10 function, produced
similar fit to the compiled data and better results were
obtained when two-component models were used, indicating that proper representation of carbon pool composition
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is more important than choosing a (marginally) better
temperature response form.
[5] Another important issue is whether carbon pools
with different turnover times have similar sensitivity to
temperature. Townsend et al. [1997] used incubation experiments to show that recalcitrant SOC is as sensitive to
temperature as labile SOC. Consistent with these results,
SOC models such as Century [Parton et al., 1987] and
ROTHC [Jenkinson, 1990] adopt this notion of uniform
temperature sensitivity and apply the same temperature
modifiers to decomposition rates in different carbon pools.
However, this appears to be in contradiction to results from
field warming experiments [e.g., Luo et al., 2001; Melillo et
al., 2002; Wan and Luo, 2003]. In these experiments,
warming either failed to enhance soil respiration substantially or only produced a response that was limited to a very
short initial time period. Rapid exhaustion of labile carbon
pools has been used to explain the observed patterns. An
implication of this explanation is that recalcitrant carbon,
which is left after labile carbon is gone, is less or not
sensitive to temperature change. In fact, Luo et al. [2001]
and Wan and Luo [2003] reported that warming reduced
temperature sensitivity. Giardina and Ryan [2000] presented an extreme argument that warming alone will not
accelerate soil organic carbon decomposition, based on
their finding of unvaried organic carbon decomposition
rates across climate gradients in mean annual temperature.
They suggested that substrate quality and quantity affect
heterotrophic microbial activities and therefore can modify
temperature-organic carbon decomposition relationships.
[6] Until these inconsistencies and controversies are
resolved, the role of global SOC in climate change will
remain unclear and large uncertainties in the prediction of
future atmospheric CO2 concentration will continue to exist
[Jones et al., 2003]. The objective of this present study is
to use a modeling approach to examine how the dynamics
of labile carbon pools affect the representation and interpretation of the temperature-respiration relationships based
on CO2 efflux measurements conducted on soil surfaces.
The questions that we intend to answer include the
following: How variable are different carbon pools at
timescales ranging from several days to a year? How do
variations in carbon pools affect heterotrophic respiration
rates? Can temperature sensitivity of SOC decomposition
be accurately inferred from CO2 efflux measurements
conducted on soil surfaces? How well do the temperature-soil respiration relationships estimated with a homogeneous SOC assumption depict CO 2 efflux from
heterogeneous SOC? How reliable are such relationships
for predicting long-term soil carbon budgets? Answers to
these questions can lead to an understanding of the current
debate on responses of SOC decomposition to temperature
changes and insights into the role of SOC in affecting
atmospheric CO2 concentration.
2. Modeling Approach
2.1. Strategy
[7] We explore the complexity in the soil respirationtemperature relationship caused by SOC pool dynamics. We
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employ a multipool SOC model to simulate a time series of
bulk soil respiration rates and then use the simulated time
series to retrieve the temperature sensitivity parameter used
in the model through regression as would be done under
field conditions. Any difference between the retrieved value
and the original value would represent distortion of sensitivity of soil respiration to temperature by SOC pool
dynamics. Under field conditions, soil respiration is a
combination of autotrophic respiration from roots and
heterotrophic respiration from microbes, fungi, bacteria,
etc. Both respond similarly to temperature and can be
reinforced by such processes as nutrient cycling. Root
growth phenology may be influenced by nutrient availability after periods of rapid decomposition. Root respiration is
strongly linked to aboveground production and allocation
over rapid timescales [Lee et al., 2003], while heterotrophic
respiration is strongly influenced by the amount of decomposable plant material [Parkin and Kaspar, 2003]. Therefore these two sources of respiration may be largely
independent. In this modeling study, we focus on the soil
organic carbon pool dynamics and their effects on the
representation and interpretation of responses of bulk SOC
decomposition to variations in temperature. The presence of
autotrophic respiration can complicate but not alter the
nature of the issues interested in this study. Therefore we
exclude root respiration from analysis for the sake of
simplicity. Throughout this paper, when we speak of soil
respiration in the modeling context, we always mean
heterotrophic respiration.
2.2. Rothamsted SOC Model (ROTHC)
[8] We use the ROTHC model to simulate decomposition of litter and soil organic carbon and the associated
release of CO2 in heterotrophic respiration [Jenkinson,
1990; Coleman and Jenkinson, 1999]. The model splits
soil organic carbon into four active pools and a small inert
pool. The active pools are Decomposable Plant Material
(DPM), Resistant Plant Material (RPM), Microbial Biomass (BIO), and Humified organic carbon (HUM). Incoming organic carbon passes through the decomposable and
resistant plant material pools once (20% to DPM, 80% to
RPM). Both DPM and RPM decompose to CO2 (which is
lost to the atmosphere), BIO, and HUM. When BIO and
HUM decompose, CO2 is released and new microbial
biomass and humus are formed. The microbial biomass
and humus are subject to continued decomposition. A
small inert pool is assumed to be immune to biological
attack. The dynamics of the four active carbon pools are
described by a system of ordinary differential equations.
The nominal decay rates of DPM, RPM, BIO, and HUM
are set at 10.0, 0.3, 0.66, and 0.02 year1, corresponding to
turnover times of 0.1, 3.3, 1.5, and 50 years, respectively.
These nominal rates represent averages modified by environmental factors of temperature and soil moisture deficit.
Decay rates and turnover times change at each time step as
a result of changing environmental conditions and may be
considerably larger or smaller than these nominal values.
In particular, the temperature modifier may vary from 0 to
over 4.0 and so turnover times may vary from less than
one fourth to as much as twice their nominal values as
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temperature changes. The original ROTHC temperature
modifier is given by:
f ðTs Þ ¼
47:9
;
106
1 þ exp
Ts þ 18:3
ð1Þ
where Ts is soil temperature (C). The temperature
sensitivity represented by this function depends on
temperature. This is not a desirable feature for the purpose
of this analysis because our interest is to find out how
carbon pool turnovers affect temperature sensitivity estimation. For the same reason, functions such as those advocated
by Lloyd and Taylor [1994] and Kirschbaum [1995] also are
not suitable. Therefore the original ROTHC temperature
function is replaced by the following exponential function
having a constant Q10 for all temperatures:
Ts 10
f ðTs Þ ¼ 1:05Q1010 :
ð2Þ
To produce the simulation data set, we set Q10 = 2. We note
that the temperature sensitivity we choose to use here is less
than that of the original ROTHC temperature function
(equation (1)) but in line with the value often used in
climate studies [Jones et al., 2003; Jones and Cox, 2001].
The factor 1.05 is determined by matching the above Q10
function with the original ROTHC function at 9.25C (mean
annual temperature at the Rothamsted Experimental Station,
UK, f (Ts = 9.25C) = 1). The new temperature modifier is
uniformly applied to all four active carbon pools as in the
original model.
2.3. Simulation Procedures
[9] In general, SOC models are run at large time steps
(months or longer). However, field chamber measurements
of soil respiration are often taken at much higher resolutions. To simulate soil respiration measurements, we run the
ROTHC model at half-hourly intervals. The system of
ordinary differential equations is solved numerically using
the Crank-Nicholson Method. The model is driven by plant
material inputs and temperature. No moisture stress is
applied to avoid confounding effects of temperature and
moisture on soil respiration. Two plant material input
scenarios (Scenario 1 and Scenario 2) are used in the
simulation (Figure 1a). Information on how total plant litter
(leaves, twigs, reproductive plant parts, and roots) changes
over season is extremely scarce. Both scenarios represent a
combination of our qualitative understanding of seasonal
dynamics in plant litter production and available data in the
literature. Scenario 1 is modeled after observations of
litterfall in a deciduous forest in east Tennessee [Grizzard
et al., 1976] with an addition of assumed root litter input
(the smaller hump in Scenario 1). Scenario 2 follows the
observed distribution of leaf fall in a Loblolly pine stand in
South Carolina [Van Lear and Goebel, 1976]. The annual
total plant material input to SOC for both scenarios is about
840 g C m2, which is roughly the amount of net primary
production predicted by the Miami model for a mean annual
temperature of 15C with sufficient precipitation [Lieth,
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dynamics [Hanson et al., 1993, 2000; Wang et al., 2003]. It
does not have to equal 1.05 mmol m2 s1 as in equation (2).
Equation (2) (or equation (1)) is just a standard modifier for
the nominal decomposition rates. However, an accurate
retrieval procedure should give Q10 = 2, the value used in
producing the simulation data set. Error characteristics of
the obtained regression relationship in representation of
instantaneous soil respiration rates is examined. We also
apply the retrieved temperature sensitivity back into the
ROTHC model with a new spinning-up process to see how
the mean annual carbon contents of different pools at
equilibrium deviate from their original value.
3. Results
Figure 1. Scenarios of (a) plant litter inputs and (b) soil
temperature used to generate the simulation data set.
1975]. To capture natural variability of temperature
regimes, we use a half-hourly time series of soil temperature
(Figure 1b) measured at a depth of 4 cm in 1995 at the
Walker Branch eddy covariance flux tower site, located in
Oak Ridge, Tennessee [Baldocchi and Wilson, 2001].
[10] The soil carbon contents at the beginning of the
simulation are determined by spinning up the model until
an ‘‘equilibrium steady state’’ is reached (the mean annual
carbon content in each pool remains constant). After the
mean annual carbon contents are stabilized, we run the
model for 1 year to produce time series of SOC carbon pool
sizes and bulk heterotrophic soil respiration rates. Then we
try to retrieve the value of Q10 by regressing simulated
respiration rates against soil temperature using different
time windows (2 weeks, 4 weeks, irregular window, and
whole year) based on the same relationship as equation (2),
except that the factor 1.05 now becomes a free parameter,
Ts 10
R ¼ R10 Q1010 ;
ð3Þ
where R10 is the respiration rate of the bulk SOC at 10C.
Note that R10 in equation (3) should vary with carbon pool
3.1. Simulated Seasonal Patterns of Carbon Pools and
Heterotrophic Soil Respiration
[11] Even though the mean annual carbon contents of the
four active carbon pools are stabilized after the model spin
up, large seasonal variations in fast turnover pools still exist
(Figures 2a, 2b, 2d, and 2e). DPM has the shortest turnover
time. As a result of this and also because it directly receives
fresh litter as input, it is the most dynamic pool in view of
its annual mean pool size (Figures 2a and 2d). It reaches
minimum in late summer just before annual plant senescence and mortality start to rapidly transfer litter materials
into SOC. It then increases as the litter input outpaces the
decomposition. DPM eventually peaks in late fall or early
winter after the litter input reaches its maximum. Steady
decline in DPM then occurs as the decomposition outpaces
the litter input. Over much of the winter and following
spring, DPM continues to decline even though the temperature is low, until the next year’s production starts to
replenish it. RPM, which also receives fresh litter inputs
directly, decomposes more slowly than DPM. However, it
has a much larger pool size. Consequently, RPM has a
greater absolute magnitude of seasonal cycle (note that the
y axis scales are different in Figures 2b and 2e and in
Figures 2a and 2d). BIO also varies seasonally (Figures 2a
and 2d), but the variation is less conspicuous than DPM and
RPM, albeit its turnover time is between DPM and RPM.
This is because it is an internally cycling pool, which
receives only inputs from the four carbon pools (including
itself). These results indicate that fast turnover carbon pools
can fluctuate widely at the seasonal timescale, even though
the annual mean carbon pool sizes remain unvaried as the
simulation is conducted at the equilibrium steady state. Only
HUM, which is also an internally cycling pool and has a
turnover time of half a century, keeps unchanged within the
time framework under investigation (Figures 2b and 2e).
[12] The simulated heterotrophic soil respiration shows
seasonal patterns that are somewhat different between the
two plant material input scenarios (compare Figures 2c
and 2f ). Both the maximum respiration rate in summer
and the minimum respiration rate in winter are larger in
Scenario 2 than in Scenario 1. Scenario 2 also has a sharper
late summer heterotrophic respiration peak than Scenario 1.
The discrepancy exhibits more clearly in Figure 3, in which
the differences between the two scenarios in heterotrophic
respiration, DPM and RPM are shown together (note that
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Figure 2. (a, b, d, e) Simulated SOC carbon pools and (c, f ) bulk SOC decomposition rates under the
two plant litter input scenarios. In Figures 2c and 2f the ranges of respiration variations are also shown.
the difference in RPM is scaled by a factor of 1/6 so that it
can be shown in the same figure). The difference in
heterotrophic respiration is synchronized with the difference
in DPM and slightly out of phase with the difference in
RPM, indicating a bigger role played by DPM in causing
the divergence between the two scenarios. This divergence
is important because the respiratory processes of the
two scenarios are driven by the same temperature regime
(Figure 1b) and they have the same total annual plant
material input. It arises purely from dissimilar seasonal
distributions of plant material inputs (Figure 1a).
3.2. Retrieval of the Soil Respiration-Temperature
Relationship
[13] Figure 4 depicts changes of the retrieved Q10 value
and respiration rate at 10C (R10) with time for Scenario 1.
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Figure 3. Seasonal variations in the differences in
heterotrophic respiration rates and DPM and RPM pools
between the two plant litter input scenarios (Scenario 1 –
Scenario 2). RPM is scaled with a factor 1/6.
Figure 5 is for Scenario 2. For easy comparison, seasonal
patterns of DPM, RPM, and temperature are shown alongside (note that in order to display DPM and RPM in the
same plot, RPM is rescaled as (actual RPM 1200)/8). Our
simulation experiments show that the Q10 value retrieved
from respiration rates of the bulk SOC is quite variable. It
varies with time and the size of the time window from
which the data are taken for the regression. This is not
expected, because in our simulation run, the Q10 value is
fixed at 2 for all carbon pools. Note that our simulation is a
deterministic process. The higher-frequency, noise-like features in the seasonal variations of calculated soil respiration
rates are due to fluctuations in the soil temperature used in
driving the model. Therefore variations in the retrieved Q10
are not caused by environmental noises.
[14] Careful examination of Figures 4 and 5 reveals that
for a given regression time window, the retrieval process
can either overestimate or underestimate the actual Q10
value, depending on the phase relation between the trends
of soil temperature and DPM and to a slightly lesser extent,
RMP as well, within the window. If the trends of these fast
carbon pools and soil temperature are in phase, Q10 is
overestimated. Conversely, if they are out of phase, Q10 is
underestimated. This can be seen more clearly from variations of estimated Q10 using irregular windows marked by
vertical lines in Figures 4a and 5a. These irregular windows
are set so that trends of DPM (overall, seasonal patterns of
DPM and RPM are similar, except for some short periods)
and temperature are either in phase or out of phase. In
Figure 4b, the Q 10 estimated from days 260 – 340
approaches 1, falsely indicating nearly no dependence of
soil respiration to temperature. In this window, DPM and
RPM increase while soil temperature decreases. In contrast,
for the 2-week window around day 270 in both Figures 4b
and 5b ( points marked by asterisks), DPM, RPM, and soil
temperature all have a trend of increase, leading to an
overestimated Q10. The indication that the distortion in
Q10 is caused more by dynamics of DPM than by that of
RPM comes from the window roughly from days 100–
160 in Figure 4. In this window, Q10 is overestimated as
both DPM and temperature increase, but RPM decreases.
Figure 4. (b) Q10 and (c) bulk SOC decomposition rates at
10C (R10), calculated using different regression windows
for the plant litter input Scenario 1. (a) Seasonal patterns of
DPM, RPM, and soil temperature are also shown. The RPM
curve represents (actual RPM 1200)/8. Irregular windows, which are determined largely according to phase
relations between DPM and temperature, are marked in
Figure 4a. The star symbol in Figures 4a and 4b denotes the
location of a 2-week regression window in which Q10 is
overestimated. Note that during this window, temperature
increases, and this increase is embedded in a broad,
decreasing trend of temperature. During this time period,
however, DPM increases.
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using the whole year as a single regression window and find
that for both scenarios, the sensitivity of soil respiration to
temperature is underestimated (Q10 = 1.50 for Scenario 1
and 1.53 for Scenario 2, as compared to the actual Q10 = 2).
[16] Seasonal patterns in estimated respiration rates at
10C (R10) follow the DPM and RPM pool dynamics rather
closely although large deviations occur when regression time
window increases (Figures 4c and 5c). This indicates that
while bulk soil respiration rates cannot be used to reliably
estimate the sensitivity of soil respiration to temperature,
they can be used to infer general seasonal patterns of the
labile carbon dynamics if the regression window is sufficiently small and if respiration rates are normalized to the
same temperature. In Scenario 1, estimated respiration rates
at 10C fluctuate from 1 to 2.6 mmol m2 s1. In Scenario 2,
this range is between 1.2 and 2.3 mmol m2 s1. For
comparison, using the whole year as a regression window,
the estimated respiration rate at 10C is 1.81 mmol m2 s1
for Scenarios 1 and 1.79 mmol m2 s1 for Scenario 2 thus
falls within the ranges of values estimated from smaller
time windows.
Figure 5. Same as in Figure 4, except it is for the plant
litter input Scenario 2.
Nevertheless, we point out that SOC is a continuum;
dividing it into distinctive pools is somewhat artificial and
largely for the convenience of modeling; it is sufficient to
attribute the distortion of Q10 to the dynamics of the labile
component of SOC.
[15] Using smaller time windows for regression can
somewhat reduce variations in retrieved Q10 values. However, small windows do not guarantee reliable estimates.
Large distortion can still occur if both DPM and temperature change rapidly (see the points marked with an asterisk
in Figures 4b and 5b). While both underestimation and
overestimation of Q10 can occur, underestimation is more
likely to happen as the regression time window increases.
Also, the magnitude of departure from the actual value is
larger for underestimation than for overestimation. We also
calculate the Q10 value of the bulk SOC respiration rate
3.3. Consequences of Applying Inappropriately
Derived Temperature Sensitivity
[17] Clearly, soil respiration is not just a function of
environmental variables; it is also a function of carbon pool
sizes. Simple regression functions that disregard the temporal dynamics and heterogeneous nature of SOC cannot
adequately describe CO2 efflux from soil, yet such relationships have been commonly used in the literature as a
framework to interpret field observations, to fill gaps in
measurements, and to extrapolate in future predictions. To
further illustrate the consequences of this practice, we
compare the heterotrophic respiration rates calculated from
equation (3) with the simulated ROTHC values. In this
examination, the regression window is the whole year
because this is the time span used in most soil respiration
analyses. Figure 6 shows the seasonal patterns of the error
(equation (3), ROTHC) for both input scenarios. Again the
error is related to the seasonal patterns of the DPM and
RPM pool sizes. Variations in the error of the regression
function and in the sizes of DPM and RPM tend to be out of
phase. This indicates that regression functions that do not
account for the dynamics of heterogeneous SOC will
introduce systematic errors in the estimation of soil respiration and in any secondary products that depend on soil
respiration estimates (gross primary production calculated
from eddy covariance measurements, for example). This
applies not only to the Q10 (exponential) type of functions
that do not account for pool size fluctuations but also to any
model that treats SOC as a single homogeneous component.
[18] An interesting, important question arises: How does
inappropriately derived temperature sensitivity parameter
affect long-term prediction of the soil carbon budget? This
question cannot be directly answered here because we
have forced the model to operate at equilibrium, meaning
that annual production, which is fixed in this study, is
balanced by annual respiration. However, we can examine
the consequence on the soil carbon pool sizes at equilibrium by applying the retrieved Q10 back into the ROTHC
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scales smaller than a year, in response to the imbalance
between fresh plant litter inputs and the decomposition
process. Convolution of the dynamics of carbon pools and
natural variations in temperature regimes can greatly distort
the apparent sensitivity of soil respiration to temperature as
estimated from measurements of respiration rates of bulk
soils. In-phase changes between fast turnover pools and
temperature regimes lead to overestimation of temperature
sensitivity, whereas out-of-phase variations lead to underestimation. We have found that the simple regression
relationship established from measured respiration rates of
the bulk soil and temperature over a year has an error
pattern related to seasonal variations in labile carbon pools.
We have inferred, albeit indirectly, that the use of temperature sensitivity derived directly from measurements of
respiration rates of bulk soils can significantly overestimate
soil carbon storage and thus underestimate the potential of
soil organic carbon as a source of CO2 for the atmosphere in
long-term climate predictions.
[20] Different seasonal distributions in plant litter inputs
to SOC will influence the time when the temperature
sensitivity estimation is distorted as well as the magnitude
of distortion. However, distortion is likely unavoidable for
any productive terrestrial ecosystem. To illustrate this point,
we list the equations describing the dynamics of DPM and
RPM in the ROTHC model here,
Figure 6. Difference between the bulk SOC decomposition rates as estimated from the regression relationship
(using the annual time window) and the original ROTHC
simulation values. Seasonal patterns of DPM and RPM
pools are also shown. The RPM curves represent (actual
RPM 1200)/8.
and re-spinning the model up. The difference in the soil
carbon pool sizes between the original Q10(= 2) and the
retrieved Q10 represents the accumulated difference in soil
respiration over the spinning-up period. To make the
equilibrium carbon pool sizes comparable between the
original and retrieved Q10, we adjust the factor 1.05 in
equation (2) exactly as is done for the original simulation
so that the temperature modifier also equals 1 at the
Rothamsted mean annual temperature (9.25C) for the
retrieval simulation. All other conditions are kept unchanged.
Figure 7 compares the original mean annual total active
SOC at equilibrium with that obtained with the retrieved
Q10 for both plant litter input scenarios. In both cases, the
new Q10 leads to overestimation of total active SOC by
about 22%. Applied globally and assuming a global soil
carbon reservoir of 1500 Gt [Amundson, 2001], this would
be equivalent to 330 Gt of carbon or about 44% of carbon
currently in the atmosphere (here the small inert carbon
pool in soil is neglected).
4. Discussion
[19] In this modeling study, we have demonstrated that
fast turnover pools of SOC can fluctuate widely at time-
dcDPM
¼ ð1 d ÞI kDPM f ðTs Þf ðwÞf ð pÞcDPM
dt
dcRPM
¼ dI kRPM f ðTs Þf ðwÞf ð pÞcRPM ;
dt
ð4Þ
where cDPM and cRPM are the pool sizes of DPM and RPM,
respectively; kDPM and kRPM are the nominal decay
Figure 7. Mean annual total active SOC at equilibrium in
the original ROTHC simulation (Q10 = 2) compared with
those in a new simulation after the standard temperature
modifier is changed using the Q10 values obtained from the
annual regressions (1.50 for Scenario 1 and 1.53 for
Scenario 2). Base respiration rates at 10C are also adjusted
to make the simulations comparable. The small inert carbon
pool is neglected in this analysis.
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GU ET AL.: LABILE CARBON DYNAMICS
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constants of DPM and RPM, respectively; d is the fraction
of litter input to RPM; f (Ts), f (w), and f ( p) are the modifiers
for temperature, moisture, and plant retainment, respectively; I is plant litter input. Accurate estimation of
temperature sensitivity from bulk soil respiration measurements requires cDPM and cRPM to be constant over time.
However, this is a condition that cannot hold under field
conditions at subannual timescales, because if they hold, we
have
ð1 d Þ
I
kDPM
f ðTs Þf ðwÞf ð pÞ
d
I
:
¼
kRPM f ðTs Þf ðwÞf ð pÞ
cDPM ¼
cRPM
ð5Þ
Since Ts always changes with time, the condition that cDPM
and cRPM are constant over time implies that
I / f ðTs Þ:
ð6Þ
Condition (6) requires that plant litter input responds to
temperature just like the decomposition process responds to
temperature. This restraint may have some validity over
many years at one location and across biomes as ecosystems
reach equilibrium with their local climate and total soil
respiration becomes a function of annual litterfall [Raich and
Nadelhoffer, 1989; Davidson, 2002]. However, condition (6)
cannot be true for timescales shorter than a few years
because within this time framework, plant litter production is
a function and integration of plant growth activities and
cannot be in synchronization with temperature regimes,
which can change rapidly.
[21] Using short time observation windows for regression
may allow us to obtain better results. However, under field
conditions where temperature is not controllable, we almost
always have to use large time windows so that we can
have sufficient temperature range for reliable regression.
Measurement errors and environmental noises provide
additional requirement for longer regression time windows.
This means that temperature sensitivity estimated from in
situ measurements may be intrinsically unreliable. Furthermore, under natural conditions, out-of-phase variations in
DPM and RPM and temperature are probably more likely to
happen than in-phase variations between them. The warming trend in temperature (spring and early summer) is
generally associated with recovery of vegetation activities
from dormancy and lack of litter inputs to soil, whereas the
cooling trend (late summer and fall) is generally associated
with recession of photosynthesis and accumulation of fresh
litter materials. Thus for most of the time during a year the
warming trend tends to occur simultaneously with a depleting trend of DPM and RPM, whereas the cooling trend
tends to take place at the same time as an accumulating
trend of DPM and RPM. Therefore we can probably
conclude that sensitivity of soil respiration to temperature
is more likely to be underestimated than overestimated
under field conditions.
[22] In this study we only examine how co-variations in
labile carbon pools and temperature regimes distort the
sensitivity of soil respiration to temperature. Other factors,
particularly variations in soil moisture, may also affect
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estimation of temperature sensitivity. For example, during
a period when soil moisture decreases while temperature
increases, the limiting effect of decreasing soil moisture will
be transferred to a lower apparent Q10 if one tries to estimate
Q10 by conducting regression between measured respiration
and temperature. Temperature sensitivity would also be
underestimated for periods when increase in soil moisture
is accompanied by decrease in temperature. The opposite
would be true if soil moisture and temperature vary in
phase.
[23] The composition of plant litter material may affect the
magnitude of distortion in the estimation of sensitivity of soil
respiration to temperature. A higher volatile component in
the litter input will lead to greater seasonality of soil labile
carbon pools and thus larger distortion of the estimated
temperature sensitivity. Both our plant litter input scenarios
are for forests, which have lower DPM:RPM ratios than
crops or grasses. For croplands and grasslands, the uncertainties in temperature sensitivity estimated directly from
bulk soil respiration measurements may be even larger than
found here.
5. Conclusions
[24] Our findings raise serious questions about the appropriateness of using in situ soil respiration measurements to
evaluate broad relationships between soil respiration and
temperature without information on labile carbon pool
dynamics. In situ soil chamber measurements are now
conducted routinely at numerous sites over the world. These
measurements represent carbon exchanges of bulk soils
with the atmosphere. Simple, single-pool temperature
response functions are widely used to fit them. Changes
in Q10 with season [Xu and Qi, 2001; Janssens and
Pilegaard, 2003] and with warming [Luo et al., 2001] have
been reported. These findings have been used as evidence of
acclimation of soil respiration to temperature [Rustad,
2001]. However, without knowing how labile carbon pools
were changing when these measurements were taken, there
is no way to know whether these changes in estimated
temperature sensitivity were real or just a distortion caused
by labile carbon pool dynamics. Measurements of seasonal
dynamics of carbon pool sizes along with seasonal dynamics of autotrophic contribution from roots are critical for
appropriate interpretation of bulk soil respiration observations. The potential of automated chambers [Parkin and
Kaspar, 2003; Edwards and Riggs, 2003] for understanding
soil carbon dynamics can be fully realized only when
respiration measurements are related to not only air and
soil temperature but also rainfall, water content, root activities, labile carbon pools, and soil physical and chemical
properties.
[25] Unfortunately, there are not yet standard approaches
for accomplishing measurements of imprecisely defined
conceptual soil organic carbon pools and fluxes [Trumbore,
1997; Hanson et al., 2000; Six et al., 2000a]. Method
improvements for isolating root and microbial respiration
in the field are yielding useful results for separating these
processes that have substantially different controlling factors and dynamics at a range of timescales [Lee et al., 2003].
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GU ET AL.: LABILE CARBON DYNAMICS
A variety of methods are showing considerable promise in
quantifying organic carbon pools that may be regarded as
decomposable plant carbon. These include separation of
particulate organic carbon from mineral-associated organic
carbon [Jastrow, 1996], isolating organic carbon fractions
from mineral soil aggregates of different size and stability
[Six et al., 2000b, 2002], and short- or long-term laboratory
incubations [Paul et al., 2001, 2003]. Experience with field
manipulations and physical and chemical analyses in the
field and laboratory to augment soil CO2 flux measurement
combined with modeling studies [Fang and Moncrieff,
2001] will be required to develop a better understanding
of processes that determine soil CO2 patterns and dynamics.
[26] Acknowledgments. We thank Paul Hanson, Stan Wullschleger,
and two anonymous reviewers for detailed, insightful comments on the
manuscript and Dennis Baldocchi of University of California at Berkeley
for providing soil temperature measurements. The study was carried out at
Oak Ridge National Laboratory (ORNL) with support from U.S. Department of Energy, Office of Science, Biological and Environmental Research
Program, Environmental Science Division. ORNL is managed by UTBattelle, LLC, for the U.S. Department of Energy under contract DEAC05-00OR22725.
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