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Title
Global soil carbon projections are improved by modelling microbial processes
Permalink
https://escholarship.org/uc/item/3hd31556
Journal
Nature Climate Change, 3(10)
ISSN
1758-678X
Authors
Wieder, William R
Bonan, Gordon B
Allison, Steven D
Publication Date
2013-10-01
DOI
10.1038/nclimate1951
Peer reviewed
Powered by the California Digital Library
University of California
Global soil carbon projections are improved by modeling microbial processes
William R. Wieder
1
Gordon B. Bonan
1
Steven D. Allison
2
1
National Center for Atmospheric Research, Boulder, CO 80307, USA
2
Department of Ecology and Evolutionary Biology & Department of Earth System Science,
University of California, Irvine, CA 92697, USA
Corresponding Author:
William R. Wieder
Phone: 303.497.1352
Fax: 303.497.1348
email:
wwieder@ucar.edu
address: TSS, CGD/NCAR
PO Box 3000
Boulder, CO 80307-3000
2
Society relies on Earth system models (ESMs) to predict future climate and carbon (C)
1
cycle feedbacks. However, the soil C response to climate change is highly uncertain in these
2
models
1,2
, and they omit key biogeochemical mechanisms
3-5
. Specifically, the traditional
3
approach in ESMs lack direct microbial control over soil C dynamics
6-8
. Thus, we tested a new
4
model that explicitly represents microbial mechanisms of soil C cycling at the global scale.
5
Compared to traditional models, the microbial model simulates soil C pools that more closely
6
match contemporary observations. It also predicts a much wider range of soil C responses to
7
climate change over the twenty-first century. Global soils accumulate C if microbial growth
8
efficiency declines with warming in the microbial model. If growth efficiency adapts to warming,
9
the microbial model predicts large soil C losses. By comparison, traditional models predict
10
modest soil C losses with global warming. Microbes also change the soil response to increased C
11
inputs, as might occur with CO
2
or nutrient fertilization. In the microbial model, microbes
12
consume these additional inputs; whereas in traditional models, additional inputs lead to C
13
storage. Our results indicate that ESMs should simulate microbial physiology in order to more
14
accurately project climate change feedbacks.
15
Contemporary ESMs use traditional soil C models, which implicitly simulate microbial
16
decomposition via first-order kinetics that determine turnover rates of soil C pools
1,2
. Although
17
such models can replicate extant soil C pools at various scales
9,10
, their ability to predict soil C
18
response in a changing environment remains unresolved
11,12
. In the past 30 years, researchers
19
have identified key processes and feedbacks that could be important for accurately simulating
20
future C cycle—climate feedbacks. For example, traditional models neglect microbial
21
physiological processes that transform and stabilize soil C inputs
3-5
. In contrast, recent microbial
22
models explicitly simulate microbial biomass pools that catalyze soil C mineralization
6,8
and
23
3
produce notably different results in transient simulations
6
. By representing microbial
24
physiological responses, such models may provide a better fit to observations, especially in a
25
changing environment
13,14
. Yet to date, no modeling studies have tested the relevance of
26
microbial mechanisms for soil C responses to climate change at the global scale.
27
We created a new soil biogeochemistry module for use in the Community Land Model
28
that explicitly simulates microbial biomass pools (hereafter referred to as the CLM microbial
29
model; Fig. 1; modified from ref.
6
). The CLM microbial model represents aboveground and
30
belowground processes and separates belowground pools into surface (0-30 cm) and subsurface
31
(30-100 cm) horizons. Microbes in this model directly catalyze the mineralization of litter and
32
soil C pools according to Michaelis-Menten kinetics. In this formulation, decomposition losses
33
can be limited by both substrate availability (the organic C pools) and the microbial biomass,
34
which is assumed to be the source of enzymatic activity. This structure differs from traditional
35
models in which decomposition losses depend only on first-order decay of substrate (soil C)
36
pools
6
.
37
Temperature affects three key microbial parameters in our model. The Michaelis-Menten
38
relationship requires two parameters: K
m
, the substrate half-saturation constant, and V
max
, the
39
maximal reaction velocity (Fig. 1). We used observational data to constrain these parameters
40
and their temperature sensitivities, which generally follow an exponential form
15
. The third key
41
parameter is microbial growth efficiency (MGE), which determines how much microbial
42
biomass is produced per unit of substrate consumed
16
. MGE probably declines with increasing
43
temperature, although the magnitude of the response is uncertain
17
. Consequently, C
44
decomposition depends on temperature, substrate availability, and the size of the microbial
45
biomass pool.
46
4
After running to steady-state, we compared soil C pools from the CLM microbial model
47
to soil C pools from two traditional models (illustrated with model parameterizations from
48
CLM4cn
18
and DAYCENT
10
). We also compared model outputs to observations from the
49
globally gridded Harmonized World Soils Database
19
. Global simulations were forced with
50
observationally-derived litter inputs (see methods) and with soil temperature and moisture from a
51
20
th
century simulation
18
. Overall, the CLM microbial model explained 50% of the spatial
52
variation in the soil C observations, whereas the traditional models explained 28-30% of the
53
variation and showed greater average deviations from soil C observations (Fig. 2).
54
Other traditional models perform even worse than the two reported here. For example, a
55
prior version of CLM4cn, using modeled litter inputs, explained only ~2% of the spatial
56
variation in observed soil C stocks at the 1º grid scale, and no other ESM explained more than
57
16% of the variation
2
. Some of this poor performance may be due to ESM errors in simulating
58
litter inputs. We avoided these errors by using litterfall observations for our current analysis.
59
Still, the CLM microbial model explained 20% more soil C variation than traditional CLM4cn
60
with observed litterfall, an improvement rivaling the entire explanatory power of previous
61
models. Moreover, the CLM microbial model accurately simulates observed soil C pools in both
62
surface soil layers (0-30 cm) and total soil profiles (0-100 cm;
r
= 0.75 and 0.71, respectively; SI
63
Fig. 1).
64
A closer examination of regional patterns illustrates specific gaps in our representation of
65
processes driving soil C cycling (Fig. 2). Some regions, especially in the tropics, have low
66
predicted soil C densities compared to soil C observations. These low biases suggest systematic
67
problems with modeling the physiochemical soil environment. Specifically, the CLM microbial
68
model does not simulate the physical protection of soil C or pH effects on soil microbial activity.
69
5
These mechanisms should be a focus for future model development, especially in tropical soils.
70
Additionally, simulating processes that build and maintain organic soils remains a challenge in
71
ESMs
20
. In the Arctic, the CLM microbial model generates higher soil C densities than
72
traditional modeling approaches (Fig. 2). However, there are poor spatial correlations between
73
our modeled soil C pools and observational datasets (SI Fig. 2). Also, all of the Arctic datasets
74
show a high degree of spatial heterogeneity in soil C, a feature clearly absent from our model
75
simulations (SI Fig. 2). Improved hydrologic and moisture controls over soil C turnover will
76
likely be needed to simulate this heterogeneity in the Arctic. In addition to model improvements,
77
measurement efforts should address the wide discrepancies in empirical estimates of Arctic soil
78
C (SI Fig. 2).
79
Accurate simulation of current soil C stocks is essential, but the main goal of ESMs is to
80
project carbon – climate feedbacks in the future. When the environment changes, the CLM
81
microbial model makes projections that differ from traditional soil biogeochemistry models (Fig.
82
3). For example, perturbations like elevated CO
2
or N deposition may increase plant productivity
83
and C inputs to soils. In the CLM microbial model, increasing global litter inputs by 20% results
84
in an ephemeral accumulation of soil C, which concurrently increases microbial biomass. Larger
85
microbial biomass pools then accelerate rates of soil C turnover and increase rates of
86
heterotrophic respiration. The net effect is no change in soil C pools after 30 years (Fig. 3a). In
87
contrast, increasing litterfall inputs to traditional models causes soil C accumulation. The
88
difference is due to the joint dependence of soil C loss on substrate pool size and microbial
89
biomass in the microbial model.
90
On balance, projections from the CLM microbial model show better agreement with
91
observations from leaf litter manipulations
21,22
and CO
2
enrichment studies
23
. Increasing litter
92
6
inputs generally increase rates of soil respiration, but elicit no change in soil C storage (but see
93
ref.
24
). Although the mechanisms underlying these observations are not well understood, several
94
studies emphasize the importance of the priming effect. Priming occurs when increased inputs of
95
fresh organic substrates accelerate microbial decomposition of existing soil C
25
. Typically,
96
priming is driven by increased microbial demand for nutrients from soil organic matter, or
97
increased microbial growth and enzyme production in response to substrate addition. Only the
98
latter mechanism operated in our simulations because the CLM microbial model does not include
99
C-N interactions.
100
We use both microbial and traditional models to simulate soil C responses to global
101
warming (Fig. 3b). In the microbial model, elevated temperatures accelerate enzyme kinetics,
102
which generally leads to soil C loss. However, this effect can be completely offset if MGE
103
declines with warming and reduces the microbial biomass that controls decomposition. If MGE
104
does not change with warming, then enzyme kinetics dominate and soils lose up to 300 Pg C.
105
Consequently, global soil C losses over the 21
st
century could be negligible, or massive,
106
depending on the thermal response of MGE. Empirical studies suggest that MGE declines with
107
increasing temperature, at least in the short term
16,17
. Still, the MGE response to temperature is
108
poorly constrained, and adaptive processes in microbial communities could stabilize MGE in a
109
warming world. In traditional models, MGE is a fixed constant. Accordingly, warming
110
temperatures only affect kinetic constants in traditional models, which predict modest and
111
similar soil C losses in the warming scenario (Fig. 3b). Thus, traditional ESMs miss an important
112
element of global climate sensitivity driven by microbial control over soil C cycling.
113
Despite better agreement with soil C observations, nearly 50% of the spatial variation in
114
global soil C pools remains to be explained. Our work is just the first step toward a new
115
7
generation of models that includes key biological and physical mechanisms in the soil C cycle.
116
For example, shifts in microbial community structure could alter the temperature sensitivity of
117
heterotrophic respiration
26
, such that soils respire less CO
2
than expected for a given amount of
118
warming. Enzyme K
m
, and enzyme V
max
could also adapt to climate warming, such that enzyme
119
catalytic rates increase more than expected at warmer temperatures
14,15
. Some of these
120
parameters may also shift with changes in N availability, possibly as a result of shifts in
121
microbial community structure
27
. Accounting for these mechanisms not only holds promise for
122
improved simulation of current soil C distributions, but should also increase confidence in the
123
prediction of soil C responses to future climate change. However, the magnitude of microbial
124
adaptation to climate change remains controversial
28
, and more empirical studies are needed to
125
determine the mechanisms underlying adaptation, including physiological acclimation, microbial
126
community shifts, and evolutionary processes. Nonetheless our analysis suggests that soil C
127
predictions from current ESMs will remain questionable until they can account for critical
128
microbial mechanisms that affect soil carbon dynamics.
129
Another key shortcoming in the CLM microbial model is the lack of soil mineral
130
interactions. In particular, there is no physiochemical protection of soil organic matter on
131
mineral surfaces or within aggregates, yet physical protection is known to affect soil C
132
storage
4,7,29
. This omission is also relevant because minerals and aggregates are involved in soil
133
C responses to perturbations
3,7,29
. For example, soil mineralogy may influence the stabilization
134
of microbial byproducts and the temperature sensitivity of organic matter sorption and
135
desorption. These mechanisms should be high priorities for future model development.
136
Our results have broad implications because society relies on ESMs to predict future
137
atmospheric CO
2
levels and climate. Our model comparison shows that traditional ESMs omit
138
8
key microbial mechanisms that determine soil C responses to global climate change. Clearly
139
additional mechanisms should be included, but our model is a crucial first step toward a new
140
generation of global models that integrates microbial physiology. Soil biogeochemistry models
141
in ESMs deserve further investigation, development, and more rigorous benchmarking with data,
142
but we contend that an explicitly microbial approach, like the one presented here, has several
143
advantages. Simple microbial models should help bring ESMs into better alignment with our
144
theoretical understanding of processes controlling turnover and stabilization of soil C, without
145
adding undue computational expense. Additionally, key parameters in the CLM microbial model
146
can be measured, a feature that should facilitate future model development, evaluation, and
147
validation. Finally, this approach represents biological mechanisms responsible for carbon
148
turnover in soils and will likely generate more accurate predictions of soil C feedbacks on
149
climate change.
150
151
Methods
152
Equilibrium soil C pools were calculated for CLM4cn and DAYCENT models using an
153
analytical solution
30
with globally gridded input datasets for mean annual soil moisture and
154
temperature
18
, soil texture and pH
19
, litter chemistry
31
, and litterfall inputs derived from
155
observations
32
(described in ref.
33
). We forced the model with these litterfall data to reduce error
156
and biases associated with ESMs’ predictions of net primary productivity, plant C allocation, and
157
associated litter fluxes. This modification substantially improves soil C estimates in
158
conventional soil biogeochemistry models
33
. Additionally, DAYCENT parameterizations were
159
modified to simulate deeper soil horizons and minimize error between modeled and observed
160
soil C pools
33
. In its current configuration, the CLM microbial model has no structure allowing
161
9
for the decomposition of coarse woody debris. Accordingly, coarse woody debris inputs were
162
omitted from the litterfall inputs used to force all three models evaluated here. For conventional
163
models, soil C pools reported here are the sums of all pools (Fig. 2b, 2c).
164
Using the same soil temperature and litterfall inputs, we calculated equilibrium soil C
165
pools for the CLM microbial model using a traditional spin-up (~1500 y run at hourly time
166
steps). For vertically resolved soils in the CLM microbial model, we allocated 65% of root litter
167
inputs to surface soils (0-30 cm) and the remaining 35% to subsurface horizons (30-100 cm).
168
Soil C pools reported for the CLM microbial model represent the sum of SOC and microbial
169
biomass, although at equilibrium, microbial biomass pools are only ~1% of total soil C pools.
170
We compared modeled soil C pools with observations from the Harmonized World Soils
171
Database
19
using sample cross-correlation and area weighted root-mean-square-error (RMSE).
172
We assumed Michaelis-Menten kinetics parameters (V
max
and K
m
) and MGE were
173
temperature sensitive, using parameter values reported in refs.
6,15
. Median values used to
174
calculate the relationship between temperature and enzyme kinetics produced plausible global
175
soil C pools (SI Fig. 3), although high RMSE, large litter pools, and large soil C pools suggested
176
that C turnover was too slow, especially at high latitudes. Therefore we used the upper and
177
lower bounds for the temperature sensitivity of V
max
and K
m
, respectively, in the CLM microbial
178
model to simulate equilibrium soil C pools that minimized RMSE with observations (Fig. 2d, SI
179
Fig. 1).
180
To examine model behaviors in response to future global change, we took steady-state
181
soil C estimates generated for each model and perturbed litter inputs or soil temperature. In both
182
perturbation experiments, control simulations were forced with observationally-derived litter
183
inputs evenly distributed throughout the year, and mean monthly soil temperature and soil
184
10
moisture data from 1985-2005 from a single Community Earth System Model (CESM) ensemble
185
member from archived CMIP5 experiments (publically available online at
186
http://www.earthsystemgrid.org). In year 5 of the litter manipulation experiment, we increased
187
global litter fluxes 20% for 30 years, calculating the difference in global soil C pools between
188
control and increased litter simulations (Fig. 3a). Using CESM soil temperature projections from
189
an archived CMIP5 experiment for RCP 8.5 from 2006 to 2100, we calculated the change in soil
190
C pools predicted by 4.8
°
C warming by the end of this century for each model (Fig. 3b). The
191
CLM microbial model has temperature sensitive MGE. We explored the implications of
192
assumptions made about changes in MGE with increasing soil temperatures, allowing: 1)
193
instantaneous decreases in MGE with warming soil temperatures (Fig. 3b, solid green line); or 2)
194
instantaneous adaptation of microbial community MGE, so that MGE does not decrease with
195
warming (dashed green line). Data presented in Fig. 3b are a subset of results from these
196
warming experiments showing the range of possible outcomes with different parameters and
197
initial soil C pools. More information is available in SI Fig. 4.
198
199
Acknowledgements
We appreciate the suggestions from three anonymous reviewers whose
200
input clarified and improved this manuscript. The National Center for Atmospheric Research is
201
sponsored by the National Science Foundation. This work was supported by National Science
202
Foundation grant AGS-1020767, the NSF Advancing Theory in Biology Program, and the Office
203
of Science (BER), US Department of Energy.
204
205
11
Author Contributions
W.R.W and S.D.A. conceived the project and built the model. W.R.W.
206
and G.B. assembled input and model evaluation data sets. W.R.W. conducted model runs. All
207
authors contributed to writing the paper.
208
209
Additional Information
The authors declare no competing financial interests. Correspondence
210
and requests for materials should be addressed to W.R.W.
211
212
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Figure 1
|
Diagram of the CLM microbial model.
The model explicitly simulates microbial-
302
driven soil C cycling in above ground, surface (0-30 cm) and sub-surface (30-100 cm) soil
303
horizons. Ovals represent pools for litter (Lit), microbial biomass (Mic), and soil organic carbon
304
(SOC). Fluxes between pools are shown with arrows. Plant inputs enter leaf and root litter
305
pools (solid black arrows). A small fraction of litter flux (F
i
) enters SOC pools without passing
306
through microbial biomass (dashed black arrows). Otherwise, litter and SOC pools pass through
307
microbial biomass, with rates determined by the size of the microbial biomass pool and
308
temperature sensitive Michaelis-Menten kinetic parameters (V
max
and K
m,
red arrows), based on
309
observations
15
(SI Table 1). Microbial respiration is also assumed to be temperature sensitive,
310
and equal to 1 – MGE (heavy black arrows). Currently, MGE declines linearly with soil
311
temperature, but parameters for this relationship are not well constrained by observations (see
312
also ref
15
). Microbial turnover (i.e., mortality;
τ
) converts microbial biomass to SOC pools (blue
313
arrows). In the current parameterization,
τ
= 0.0005 h
-1
and F
i
= 0.02 h
-1
(SI Table 1).
314
315
Figure 2 | Global distribution of soil C pools
(0-100 cm) from observations
19
and models
.
(a)
316
Observations, global total = 1259 Pg C,
(b)
CLM4cn, global total = 691 Pg C [spatial correlation
317
16
with observations (
r
) = 0.55, model-weighted root mean square error (RMSE) = 7.1 kg C m
-2
];
318
(c)
DAYCENT, global total = 939 Pg C [
r
= 0.53, RMSE = 7.6]; and
(d)
the CLM microbial
319
model, global total = 1310 Pg C [
r
= 0.71, RMSE = 5.3].
320
321
Figure 3
|
Divergent model responses of global soil C pools in global change simulations.
322
Response of steady-state soil C pools for conventional soil biogeochemistry models [CLM4cn
323
(black) and DAYCENT (blue)] and the CLM microbial model (green) to:
(a)
20% global
324
increase in litterfall beginning in year 5;
(b)
4.8
o
C mean increase in global temperature by 2100,
325
predicted by ensemble member one of CESM simulations for RCP 8.5 used in CMIP5
326
experiments from 2006-2100. For the microbial model, MGE changes with temperature (solid
327
line) or microbial communities adapt to increasing temperatures without changing MGE (dashed
328
line).
329
