RELATIONSHIPS BETWEEN SOIL CARBON SEQUESTRATION AND
CLIMATE CHANGE AS WELL AS ELEVATED ATMOSPHERIC CO2
CONCENTRATION
Zhongbing Lin and Renduo Zhang
School of Environmental Science and Engineering, Zhongshan (Sun Yat-Sen) University, Guangzhou, 510275, P. R. China
ABSTRACT
Soil carbon sequestration is expected to mitigate the global warming and in turn is affected by the climate change and elevated atmospheric CO 2. Effects of changes in annual average temperature
and annual precipitation as well as elevated atmospheric CO 2 on soil organic carbon (SOC) sequestration for various vegetation covers were studied based on data and simulation results of
CENTURY. Relationships were established between the relative changes of SOC and the relative changes of annual average temperature, precipitation, and atmospheric CO 2 concentration, as well as their inter-products for different vegetation covers. Results showed that the SOC was negatively related to the annual average temperature and positively related to the elevated CO 2 for
the vegetation covers, while the SOC was positively related to the precipitation changes for soybean and corn. Using the relationships, we defined a ―cutoff surfaceǁ for each of the vegetation covers, which clearly quantified the conditions for soil carbon sequestration or release
under climate change and elevated CO 2. The relationships were also applied successfully to predict the SOC with weather uncertainties.
INTRODUCTION
Soil carbon sequestration can efficiently mitigate greenhouse gas emissions (Rice, 2006). Research has been conducted to study the effects of land management practices on soil carbon sequestration (Entry et al., 2002; Sherrod et al., 2003; Birdsey et al., 2006). However, the climate
change and elevated atmospheric CO 2 concentrations in turn can affect soil carbon dynamics, which are still poorly understood. The atmospheric CO 2 concentration and global mean temperature are in a constantly increasing trend, as noted by the Intergovernmental Panel on Climate Change (IPCC, 2007). Under the elevated CO 2, the soil can be a carbon sink for the atmosphere (Jastrow et al., 2005; Kant et al., 2007). On the other hand, the global warming can
counteract the effect of elevated CO 2 in soil carbon sequestration to some extent (Fissore et al., 2008). Therefore, the soil may act either as a carbon source or sink for the atmosphere, under the conditions of climate change and elevated CO 2 (Yu et al., 2006).
To directly describe the response of SOC dynamics to climate change and elevated CO 2, it is essential to establish relationships between the SOC and the warming effect, precipitation change,
quadratic polynomial relationships between the SOC and mean annual temperature for grassland
and forestland in different annual precipitation zones. Wang et al. (2007) obtained a linear relationship between the SOC vs. annual precipitation and annual average temperature based on
historical SOC data for a Leymus chinensis meadow steppe. Fissore et al. (2008) established a linear relationship between the SOC and mean annual temperature for hardwood and pine stands.
However, important factors, such as the atmospheric CO 2 concentration and vegetation cover, have not been considered in the relationships reviewed in the literature.
The main objective of this study was to propose a simple yet comprehensive relationship, relating the SOC to the increasing annual average temperatures (warming), annual precipitation changes,
elevated atmospheric CO 2 concentrations, and vegetation covers (land use). Using the relationship, we quantified soil carbon sequestration under different conditions of the climate change, elevated CO 2, and vegetation covers. The relationship was also applied to predict the future SOC amount under climate change and elevated CO 2 with weather uncertainties.
METHODS
The SOC Relationship
A simple relationship between the SOC vs. the annual average temperature (warming), annual precipitation, and elevated atmospheric CO 2 is proposed as follows:
SOC
SOC a
T T b
P P c
CO2 CO2
d T P T P e
T CO2 T CO2
f
PCO2 P CO2
Here SOC is the SOC under the baseline climate and atmospheric CO concentration (g m ), SOC is the SOC change under the changed climate and elevated CO 2 (g m ),
T
is the baselineannual average temperature (℃),
T
is the annual average temperature change (℃),P
is thebaseline annual precipitation (cm),
P
is the annual precipitation change (cm), CO 2 is the baseline atmospheric CO 2 concentration (ppm), CO 2 is the atmospheric CO 2 concentration change (ppm), a, b, c, d, e, and f are non-dimensional parameters, changing with different vegetation covers. All the changes are the differences between the values under the changed scenarios and those under the baseline scenarios.Site Description
Nelson Farm was chosen as the study site to take the advantages of a large amount of available
data and previous research results. Nelson Farm (Latitude 34ƒ33′50″, Longitude 89ƒ57′30″) is within the Yazoo River basin with an area of 2.09 ha. During 1930-1997, the mean annual
-1
precipitation was 90.04 cm yr and the mean annual temperature was 15.7℃. Detailed land use 2
-2
(1)
and management of this site is described in Harden et al. (1999). In this study, we used the detailed input information for the CENTURY model set up by Harden et al. (1999).
The CENTURY Model
CENTURY consists of sub-models of soil organic matter, nitrogen, phosphorus, sulfur, plant production, and a simplified sub-model for water budget in the soil. The model can be used to
simulate soil carbon dynamics in the top 20 cm of soils related to processes of fertilization, irrigation, cultivation, grazing, and fire, and simulate labeled carbon, enriched CO 2 effects, and
soil incubation (Metherell et al., 1993). The effect of climate change on the SOC can be simulated using the model through input weather information.
The SOC loss processes simulated in CENTURY include soil respiration, C loss from soil erosion, and SOC leached. The SOC increase is through the net primary production, which is influenced by soil moisture, soil temperature, nutrient supply, and enriched CO 2. Soil temperature
reduces production for most plant species if the temperature is off the optimal temperature (Parton et al., 1987). The elevated atmospheric CO 2 can affect the relative plant production, potential transpiration rate, the maximum and minimum C-nutrient ratios, and the root-shoot ratio. These processes have been implemented in CENTURY. More details about CENTURY are given by Metherell et al. (1993). In this work, CENTURY 4.0 was utilized.
Parameter Determination of the SOC Relationship
To obtain the parameters of Eq. (1), we conducted simulations to generate processes of the SOC
dynamics under the baseline scenario and scenarios with changed climate as well as elevated atmospheric CO 2 concentrations, using CENTURY and the Nelson Farm data. The simulation time period of the scenarios was set from 1998 to 2100, to be consistent with the forecasting time period of climate change and rising atmospheric CO 2 concentration (IPCC, 2007). The baseline
values of SOC from 1998 to 2100 were generated using CENTURY and the baseline data of climate and atmospheric CO 2 concentration, including the annual average temperature, annual precipitation, and atmospheric CO 2 concentration of 15.7℃, 90.04 cm, and 350 ppm, respectively
(Harden et al., 1999). Based on possible changes of the future climate (IPCC, 2007), we set the
annual average temperature increases by 1 and 5℃/degrees, respectively, combined with
precipitation increase or decrease, both by 20% from 1998 to 2100 (Yu et al., 2006), which composed four scenarios. Each of the scenarios was combined with an increasing process of atmospheric CO 2 concentration, changing from 350 to 700 ppm during the simulation period (Wang et al., 2007). In addition, a separate scenario of 5℃/degree warming was composed with
2007). These settings constituted one baseline scenario and five climate change and/or elevated
CO 2 scenarios to be simulated. To analyze applicability of the relationship under different vegetation covers, we considered three types of plants (i.e., C 3 crop: soybean, C 4 crop: corn, and
C 3 grass) in the simulations.
The simulation results of the SOC for the baseline scenario and the climate change and elevated
CO 2 scenarios under different vegetation covers were used to fit Eq. (1) using multiple linear regressions to obtain the parameters. All statistics analyses were conducted employing SPSS 12.0
for Windows (SPSS Inc., 2003).
RESULTS AND DISCUSSION
Fitting Processes of the Relationship
All vegetation covers in Nelson Farm, Eq. (1) fit the simulation results very well. Coefficients of
2
determination (R ) for all fitting processes were from 0.964 to 0.995 (Table 1). Values of the fitted parameters in Eq. (1) for the different vegetation covers are listed in Table 1. The parameters showed that the relative change of SOC was negatively related to the relative change
of annual average temperature (a < 0, p < 0.001), and positively related to the relative changes of elevated CO 2 (c > 0, p < 0.001) for all the vegetation covers; while the relative change of SOC was positively related to the relative change of precipitation for soybean and corn (b > 0, p < 0.001) (Table 1).
2
Table 1. Parameters and coefficients of determination (R ) of Eq. (1) obtained by fitting the relationship with simulation results of CENTURY for Nelson Farm
Vegetation Soybean Corn Grass
‚
a -1.328 -1.228 -0.683
b 0.127 0.202
‚
0.020 c 0.265 0.086 0.120
d -1.285 -0.058 1.418
R 2
‚
e -0.638 -0.189 -0.157
f 0.424 0.239 -0.213
0.995 0.982 0.964
p 0.000 0.000 0.000 Not significant with the significant level of 0.05.
Cutoff Surfaces and SOC Calculations Based on the Relationship
Based on the combining effects of climate change and elevated CO 2 in Eq. (1), we determined the
conditions that resulted in soil carbon sequestration or soil carbon release, compared with the SOC under the baseline climate and CO 2. Setting the left-hand side of Eq. (1) as zero, we obtained a curved surface, called the cutoff surface for each of the vegetation covers. Above the
cutoff surface, the relative SOC changes are negative (soil carbon release), while below the cutoff surface, the relative SOC changes are positive (soil carbon sequestration). Using Eq. (1) with the
warming counteracted the effect of elevated CO 2 and precipitation on soil carbon sequestration. For Nelson Farm with the three vegetation covers, warming by 2.7℃ or higher (i.e., the annual
average temperature > 18.4℃) would result in a negative combining effect on the SOC (soil
[image:5.595.93.405.194.437.2]carbon release) no matter what possible changes of elevated CO 2 and precipitation.
Fig. 1: Cutoff surface for soil carbon sequestration or release under the vegetation cover of soybean.
Besides the relative SOC changes discussed above, Eq. (1) can also be used to calculate the real amount of SOC changes. To estimate the average amount of soil carbon sequestration during a time period, a mean sequestration rate (MSR) was defined as follows:
MSR SOC c(t) SOC c( 0t
)
t 0t
(2)
where
SOC c(t) SOC(t)(1 SOC R) (3)
Here
t
is time (yr); t 0 is the initial time (yr); SOC 0 is the initial SOC (g m -2); SOCc( 0t ) and -2
SOC c (t) are the SOC values (g m ) at t0 and
t
under the climate change and elevated CO2condition, respectively; SOC R is the SOC relative change calculated from Eq. (1). Without considering precipitation changes, Fig. 2A, C, and E show the MSR values during 1998 - 2100
changing with the temperature (warming) for soybean, corn, and grass, respectively. In each figure, two cases for elevated CO 2 = 0 and 350 ppm were compared. Similarly Fig. 2B, D, and F
and grass, respectively. In each figure, two cases for temperature = 0 and 5 ℃ were compared.
Figure 2 clearly indicates that the MSR values increase with the elevated CO 2 and decrease with the temperature. Under the climate change and elevated CO 2, most of the MSR values of the soil
growing soybean are negative (Figs. 2A, B), suggesting that the soil releases carbon to the atmosphere. On the contrary, the soils growing corn and grass sequestrate carbon from the atmosphere (Figs. 2C, D, E, and F). In terms of soil carbon sequestration, grass is the most efficient vegetation cover among the three.
Warming 0℃
Warming 5℃
B 4 0 -4 -8 -12 Elevated CO
2=0 ppm
Elevated CO
2=350 ppm
A
0 1 2 3 4 5 0 50 100 150 200 250 300 350 400
D C 25 20 15 10 5 0 60 54 48 42 36
0 1 2 3 4 5 0 50 100 150 200 250 300 350 400
F E
0 1 2 3 4 5
4 0 -4 -8 -12 -50 25 20 15 10 5 0 -50 60 54 48 42 36
-50 0
Warming (℃ )
50 100 150 200 250 300 350 400
[image:6.595.74.466.212.431.2]Elevated CO 2 (ppm)
Fig. 2: Mean sequestration rates of carbon during 1998 - 2100 changing with the temperature (warming) for soybean (A), corn (C), and grass (E), and elevated CO2 for soybean (B), corn (D), and grass (F) of Nelson Farm. Negative values refer to soil carbon release to the atmosphere.
SOC Predictions under Climate Change and Elevated CO 2 with Weather Uncertainties
For the simulations to obtain the parameters of Eq. (1), the input parameters for climate change
varied linearly with time without any uncertainties. However, real measurements or future weather patterns are almost always with uncertainties or fluctuations from the general trend. Therefore, we set up several scenarios of climate changes with uncertainties. The warming patterns were set from 15.7℃ in 1998 to 2100, by increasing 1.6, 2.6, 3.4℃, respectively (IPCC,
2007), and the elevated CO 2 was assumed to be double (from 350 ppm in 1998 to 700 ppm in
2100); and these two factors were linearly changed during the simulation time period. The sequential Gaussian simulation (GSLIB, Deutsch and Journel, 1992) was used to generate the random fields of the annual average temperature and annual precipitation. Coefficients of variation used in the random field generations were 3 and 10% for the annual average temperature
and annual precipitation, respectively. Combining the random fields of the annual average temperature with the warming patterns as drifts, we obtained the future warming processes with
uncertainties (Zhang, 2004). We used the following equation to calculate relative errors:
RE(t) SOC c(t) SOC s (t)
SOC s (t)
-2
in which
t
is time (yr), SOC (t) is the SOC values (g m ) calculated using Eq. (1) and the c-2
parameters in Table 1, and SOC s (t) is the SOC values (g m ) simulated using CENTURY with the temperature and precipitation uncertainties. The mean absolute relative errors between predictions using the relationship and simulation results using CENTURY for all cases were smaller than 5%. Figure 3 presents temporal distributions of relative errors between the predictions and simulation results of the SOC during the 103 years. All the absolute relative errors for grass cover were smaller than 10%, and more than 90% of absolute relative errors for
soybean and corn covers were smaller than 10%, while only one error for corn was greater than 20%. The results indicated that the relationship provided reasonably accurate predictions and was robust to scenarios with particular uncertainties.
W1.6 and DCO2 W3.4 and DCO2
A
W2.6 and DCO2
Soybean
B Corn
30 20 10 0 -10 -20 -30 30 20 10 0 -10 -20 -30 30 20 10 0 -10 -20 -30
C Grass
Fig. 3: Temporal distributions of SOC relative errors between the predictions using the relationship (Eq. (1)) and simulation results using CENTURY for vegetation covers of soybean (A), corn (B), and grass (C). W1.6, W2.6, and W3.4 refer to warming 1.6, 2.6, and 3.4
℃ respectively, and
DCO2 refers to double CO 2 concentration.
2000 2020 2040 2060 2080 2100
Time (Yr)
CONCLUSIONS
We proposed a simple yet comprehensive relationship between the SOC and the increasing annual average temperature (warming), precipitation change, elevated atmospheric CO 2, and vegetation covers (land use). Based on the relationship, a ―cutoff surfaceǁ was defined for each of the vegetation covers, which can be used to determine various conditions of climate change and
elevated CO 2 for soil carbon sequestration or soil carbon release to the atmosphere. The relationship was also applied to predict the SOC under climate change and elevated CO 2 with particular weather uncertainties. The relationship satisfactorily characterized the SOC dynamics
that changed with the air temperature, precipitation, and atmospheric CO 2 concentrations under the different vegetation covers.
ACKNOWLEDGEMENTS
This study was partly supported by a grant from the Natural Science Foundation of China (No.
50779080). We thank Ms. Cindy Keough from the Natural Resource Ecology Laboratory, Colorado State University for her valuable assistance with the CENTURY model.
REFERENCES
Birdsey, R., Pregitzer, K., Lucier, A., 2006. Forest carbon management in the United States: 1600-2100. J. Environ. Qual. 35, 1461-1469.
Deutsch, C. V., Journel, A. G., 1992. Geostatistical software library and user's guide. Oxford University Press, New York, NY, USA, pp. 164-167.
Entry, J. A., Sojka, R. E., Shewmaker, G. E., 2002. Management of irrigated agriculture to increase organic carbon storage in soils. Soil Sci. Soc. Am. J. 66, 1957-1964.
Fissore, C., Giardina, C. P., Kolka, R. K., Trettin, C. C., King, G. M., Jurgensen, M. F., Barton, C. D., Mcdowell, S. D., 2008. Temperature and vegetation effects on soil organic carbon
quality along a forested mean annual temperature gradient in North America. Global Change Biol. 14, 193-205.
Guo, Y., Gong, P., Amundson, R., Yu, Q., 2006. Analysis of factors controlling soil carbon in the conterminous United States. Soil Sci. Soc. Am. J. 70, 601-612.
Harden, J. W., Sharpe, J. M., Parton, W. J., Ojima, D. S., Fries, T. L., Huntington, T. G., Dabney, S. M., 1999. Dynamic replacement and loss of soil carbon on eroding cropland. Global Biogeochem. Cy. 13(4), 885-901.
Intergovernmental Panel on Climate Change, 2007. Climate change 2007: synthesis report. Summary for policymakers. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 5-9.
Jastrow, J. D., Miller, R. M., Matamala, R., Norby, R. J., Boutton, T. W., Rice, C. W., Owensby, C. E., 2005. Elevated atmospheric carbon dioxide increases soil carbon. Global Change Biol. 11, 2057-2064.
Kant, P. C. B., Bhadraray, S., Purakayastha, T. J., Jain, V., Pal, M., Datta, S. C., 2007. Active
atmospheric carbon dioxide concentration in a Typic Haplustept in sub-tropical India. Environ. Pollut. 147, 273-281.
Metherell, A. K., Harding, L. A., Cole, C. V., Parton, W. J., 1993. CENTURY soil organic matter model environment. Technical documentation. Agroecosystem version 4.0. Great Plains System Research Unit Technical Report No.4. USDA-ARS, Fort Collins, Colorado, USA.
Parton, W. J., Schimel, D. S., Cole, C. V., Ojima, D. S., 1987. Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Sci. Soc. Am. J. 51, 1173-1179.
Rice, C. W., 2006. Introduction to special section on greenhouse gases and carbon sequestration in agriculture and forestry. J. Environ. Qual. 35, 1338-1340.
Sherrod, L. A., Peterson, G. A., Westfall, D. G., Ahuja, L. R., 2003. Cropping intensity enhances
soil organic carbon and nitrogen in a no-till agroecosystem. Soil Sci. Soc. Am. J. 67, 1533-1543.
Wang, Y., Zhou, G., Wang, Y., 2007. Modeling responses of the meadow steppe dominated by
Leymus chinensis to climate change. Climatic Change 82, 437-452.
Yu, G., Fang, H., Gao, L., Zhang, W., 2006. Soil organic carbon budget and fertility variation of black soils in Northeast China. Ecol. Res. 21, 855-867.
