modeling the effects of climate change and elevated co on...

J. Resour. Ecol. 2011 2(2) 168-174

DOI:10.3969/j.issn.1674-764x.2011.02.010

www.jorae.cn

June, 2011 Journal of Resources and Ecology

Received: 2011-03-07 Accepted: 2011-04-27Foundation: the National Key Research Program (2010CB951704).*Correspending author: ZHANG Xianzhou. Email: [email protected].

Vol.2 No.2

Article

1 IntroductionClimate change is a worldwide concern, and increasing CO2 concentrat ions have signif icant impacts on ecosystems. The northern hemisphere is an important terrestrial carbon sink. The implementation of emission reduction through rational land use measures could reduce CO2 emissions from land. Greenhouse gas emissions are a major driving force of climate change. According to Intergovernmental Panel on Climate Change (IPCC), if unchecked, atmospheric CO2 concentrations may increase from 650 to 970 ppm by 2100 and cause the global average temperature to rise by 1.4–5.8 °C between 1990–2100 (Houghton et al. 2001). Soil is the main source of atmospheric CO2, and the biggest carbon pool in terrestrial ecosystems (Schlesinger 1990). Soil organic carbon (SOC)

content is high in grasslands (Meersmans et al. 2008) and assessing the response of SOC to climate change is particularly important for such areas.

The effects of temperature increases on carbon storage in grasslands are uncertain. Grasslands are sensitive to climate change (Parton et al. 1994), which impacts soil carbon storage. Parton et al. (1995) have estimated global grassland productivity and soil carbon if air temperatures increased by 2–5 ℃, and predicted that SOC in grasslands will lose 3–4 Pg C in 50 years, mainly because of the increased SOC decomposition rates due to global warming. Riedo et al. (2000) have indicated that carbon is typically lost from grazed grassland with a 4 ℃ increase in temperature and increased precipitation; moreover, carbon storage increases with a 2 ℃ increase in temperature. Thornley et al. (1997) and Cao et al. (1998) have predicted

Modeling the Effects of Climate Change and Elevated CO2 on Soil Organic Carbon in an Alpine Steppe

LI Xiaojia1,2, ZHANG Xianzhou1* and ZHANG Yangjian1

1 Lasa Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;

2 Graduate University of Chinese Academy of Sciences, Beijing 100049, China

Abstract: The objective of this study was to analyze the effects of climate change and doubled atmospheric

CO2 concentrations, as well as the combined effects of climate change and doubling atmospheric CO2

concentrations on soil organic carbon (SOC) in the alpine steppe of the northern Tibetan Plateau using

the CENTURY model. The results indicate that SOC loss in climate change scenarios varied from 49.77–

52.36% in the top 20 cm. The simulation results obtained for a P1T0 scenario (increased precipitation and

unchanged temperature), P0T1 scenario (unchanged precipitation and increased temperature), and P1T1

scenario (increased precipitation and increased temperature) were similar. The alpine steppe in the P1T1

scenarios lost the greatest amount of SOC (844.40 g C m-2, representing the least amount of SOC) by the

end of the simulation. The simulation for P0T1 scenarios resulted in a 49.77% loss of SOC. However,

SOC increased 12.87% under the CO2 doubling scenario, compared with the unchanged CO2 scenario. CO2

enhancement effects on SOC were greater than the climate change effects on SOC alone. The simulation

of combined climate change and doubling atmospheric CO2 led to a decrease in SOC. This result indicated

a decrease of 52.39% in SOC for the P1T1 + 2 × CO2 scenario, 49.81% for the P0T1 + 2 × CO2 scenario,

and 52.30% for the P1T0 + 2 × CO2 scenario over the next 50 years. Therefore, SOC content in the alpine

steppe will change because of changes in precipitation, temperature and atmospheric CO2 concentrations.

Key words: soil organic carbon (SOC); modeling; CENTURY; climate change; CO2 concentration

LI Xiaojia, et al.: Modeling the Effects of Climate Change and Elevated CO2 on Soil Organic Carbon in an Alpine Steppe 169

that temperate grasslands would become a carbon sink with increasing temperature. In addition, alpine terrestrial ecosystems are extremely sensitive to global climate change (Luo et al. 2002; Zhang et al. 2007). Therefore, grasslands on the northern Tibetan Plateau, which are sensitive to climate change, have been selected to study the possible effect of climate change on SOC. The impacts of climate change on SOC were found to be negative or positive in the alpine steppe on the northern Tibetan Plateau.

Recently, many studies have successfully used the CENTURY model (Gilmanov et al. 1997), which is an ecosystem level model used to evaluate the possible impact of climatic change on grassland ecosystems (Ojima et al. 1993; Riedo et al. 1997). The model has wide applications in grasslands (Xiao et al. 1996), having been used originally in the Great Plains Grasslands (Parton et al. 1987). It divides soil organic matter pools into active, slow and passive organic matter pools (Parton et al. 1992). However, few studies have been done on SOC storage and dynamic changes in an alpine steppe, especially in the northern Tibetan Plateau.

Our objective was to model the dynamics of SOC and evaluate the potential SOC response to climate change and atmospheric CO2 changes in the alpine grasslands of the northern Tibetan Plateau for the next 50 years. These findings may serve as basis for studying the effect of climate change on the density of SOC on the Tibetan Plateau and provide a scientific reference for grassland management.

2 Materials and Methods2.1 Study areaThe site of Bange (31.39 °N, 90.31 °E) was selected as a representative site of alpine steppes on the northern Tibetan Plateau. Mean elevation is 4630 m. The land is cold and dry and has a continental highland climate typical of a semi-arid region. Mean annual precipitation is 325.25 mm, and mean annual air temperature is –0.67 ℃ (range: –27.26–19.68 ℃). Vegetation mainly consists of Stipa purpurea, Carex moorcroftii, and Leontopodium stracheyi (Table 1). The soil type is cold frozen calcium (alpine steppe soil).

2.2 MethodsField surveys and sampling were conducted in 2009. Soil samples were collected from the top 20 cm of the soil to test SOC using the potassium dichromate method. Specific climate and soil characteristics including soil texture, pH, and the soil bulk density in Bange, were used as initial conditions to initiate the CENTURY model (Tables 2 and 3). Climate data such as daily precipitation, daily maximum temperature, daily minimum temperature, and daily mean temperature data from 1980 to 2009 were obtained from the China Meteorological Data Sharing Service System and used to create a factual weather file. The alpine steppe is generally used as the main grazing region on Tibetan Plateau, and the main human activity in this region is grazing.

A grazing factor was added to fixed management scenarios in the model. The steppes are grazed from

Table 1 Vegetation survey of the Bange alpine steppe.

Height Coverage BiomassNo. Species (cm) (%) (g m-2) Dominance

1 Gentiana crenulato–truncata (Marq.) T.N.Ho 0.3 0.10 0.023 0.040 2 Artemisia duthreuil–de-rhinsi Krasch. 1.2 0.17 0.042 0.098 3 Oxytropis stracheyana Benth.ex Baker 0.7 1.20 0.547 0.113 4 Youngia simulatrix (Babc.) Babc.et Setbb. 1.2 0.17 0.046 0.119 5 Anemone obtusiloba D.Don.ssp.ovalifolia Bnühl. 1.1 1.05 0.354 0.145 6 Viola philippica Sasaki 1.2 1.20 0.043 0.146 7 Meconopsis horridula Hook.f.et Thoms. 2.0 0.30 0.057 0.194 8 Rhodiola quadrifida (Pall.) Fisch.et Mey. 2.0 0.25 0.084 0.210 9 Taraxacum cf.parvulum (Wall.) DC. 2.3 4.90 1.365 0.26110 Astragalus polycladus Bur.et Franch. 3.0 0.73 0.147 0.26911 Carex moocroftii Falc.ex Boott 3.5 1.80 0.540 0.27612 Delphinium caeruleum Jacquem.ex.Cambess. 5.0 0.80 0.499 0.36913 Leontopodium stracheyi (Hook.f.) 2.0 5.98 2.826 0.388 C.B.Clark.ex Hamsl.Var.tenuicaule Beauv.14 Poa boreali-tibetica C.Ling 4.6 1.00 0.166 0.43715 Festuca ovina Linn. 4.9 1.74 0.904 0.50616 Carex ivanovae Egorova 6.6 0.98 0.630 0.60417 Stipa purpurea Griseb. 6.0 2.90 0.847 0.610

Alpine steppe

Journal of Resources and Ecology Vol.2 No.2, 2011170

October to April of the next year and used as ungrazed land from May to September. The same land management practices were used in all simulations. Factual land management practice, mainly grazing, was used to set the schedule file. First, the CENTURY model was run 5000 years to equilibrium using random climate based on current climate conditions. The equilibrium was then used as the initial conditions for running the model, using meteorological data from 1980 to 2009. To validate the model, factual sampling data from 2009, China’s second state soil survey data from 1985 to 1990 (Regional Planning Office of Naqu 1991), and field data from 2001

to 2004 (Yang et al. 2008) were used. The SOC simulation in Bange by the CENTURY model showed good results. The Pearson correlation coefficient between the simulated values and observed data was 0.651. Finally, changes in SOC pools for the next 50 years were simulated under different climate change scenarios.

Alpine steppe is widely distributed in the northern Tibetan Plateau and is climatically sensitive. In this study, different climate change scenarios were selected to determine the potential effects of future climate change on SOC in the alpine steppe; these were then compared with an unchanged climate scenario. The changes in SOC were simulated in conjunction with possible changes in precipitation, temperature and doubling atmospheric CO2 concentrations. These are important influencing factors on SOC. Both single factor effects and combined effects on SOC were considered in order to determine the effect of climate change on SOC in detail. The climate change scenarios were determined by increasing the temperature by 2 ℃ and precipitation by 5 mm monthly to simulate the effect of climate change on SOC for the next 50 years. In addition, 2 ℃ warming scenarios were set on the average distribution of 2 ℃ for 50 years on the basis of meteorological data in unchanged climate, i.e., a monthly increase of 0.04 ℃. Brief descriptions of the four future scenarios are provided in Table 4.

Data were calculated as means with standard error using SPSS 13 (SPSS Inc., Chicago, USA). Pearson correlation coefficients between climate scenarios were also calculated.

3 Results and Discussions3.1 Effects of climate change alone on SOC densityAccording to our calculations, soil organic density in the topsoil (0–20 cm) can decrease from 1772.30 g m-2 to 846.04, 890.21, and 844.40 g m-2 in the P1T0, P0T1, and P1T1 scenarios respectively, without elevated CO2 in the next 50 years. The effects of SOC on climate change in the alpine steppe show a similar trend to grasslands in Inner Mongolia (Xiao et al. 1996).

There was a great amount of variation in SOC under climate scenarios. The content of SOC increased slightly, by 0.14% in the unchanged climate scenarios for the next 50 years. The effects of climate change on SOC in the alpine steppe were consistent in reducing the content of soil carbon in the climate change scenarios for the

Month Minimum Maximum Mean s.d. Skewness

1 –25.867 2.990 0.217 0.234 1.641 2 –23.610 3.777 0.224 0.217 1.347 3 –19.003 7.273 0.289 0.221 1.067 4 –13.477 10.297 0.624 0.428 0.774 5 –8.010 15.013 2.222 1.843 0.498 6 –2.887 18.987 5.872 2.846 0.407 7 0.263 18.637 8.353 3.317 0.567 8 –0.340 17.847 8.678 3.486 0.620 9 –3.913 15.953 5.623 2.596 0.78110 –12.087 12.673 1.117 1.021 0.72311 –19.250 5.527 0.245 0.371 2.44512 –24.227 4.033 0.180 0.149 0.371Variable tmn2m tmx2m precip prcstd prcskw

Table 2 Climate parameters in Bange from 1980 to 2009.

Notes: Variable PRECIP, PRCSTD, PRCSKW, TMN2M, and TMX2M represent the average monthly precipitation (cm), standard deviation of monthly precipitation, skewness of monthly precipitation, average month minimum air temperatures (℃), and average month maximum air temperatures (℃), respectively.

Temperatures (℃) Precipitation (cm)

PROPERTY VALUE VARIABLE

Latitude(degree) 31.390 SITLATLongitude(degree) 90.310 SITLNGSAND (fraction 0–1) 0.668 SANDSILT (fraction 0–1) 0.063 SILTCLAY (fraction 0–1) 0.065 CLAYROCK (fraction 0–1) 0.205 ROCKBULK DENSITY (g cm-3) 1.676 BULKDpH 6.713 pH

Table 3 Site position and soil parameters of the top 20 cm in Bange.

Scenarios Description Period

P0T0 Precipitation and temperature are unchanged 2010–2059P1T0 Precipitation increase by 5 mm/month; temperature unchanged 2010–2059P0T1 Precipitation unchanged; temperature increase by 2 °C 2010–2059P1T1 Precipitation increase by 5 mm/month; temperature increase 2 °C 2010–2059

Table 4 Explanation of the scenarios considered in our modelling.


next 50 years. The responses of SOC to the changes in temperature and precipitation were also similar. The impact of climate change on SOC had little difference in the P1T1, P1T0 and P0T1 scenarios. SOC decreased by 52.36% in the P1T1 scenario, and this was the maximum variation. SOC declined by 52.26% in the P1T0 scenario and declined by 49.77% in the P0T1 scenario (Table 5). Climate change alone caused SOC to decrease. The results are in agreement with Parton et al. (1995), whose study simulated SOC in seven grassland regions (cold desert steppe, temperate steppe, humid temperate, Mediterranean, dry savanna, savanna, and humid savanna), but did not include the alpine region.

The mean and standard errors of SOC were similar under the climate change scenarios. SOC in the unchanged climate (P0T0) scenario had no significant correlation with that in the other climate change scenarios (P1T0, P0T1, and P1T1). However, SOC in the P1T0, P0T1 and P1T1 scenarios were correlated (p < 0.001).

Climate warming has accelerated respiration, resulting in the reduction of carbon storage, especially soil carbon storage (Houghton and Woodwell 1989, Oechel et al. 1993, Schimel et al. 1994). Humidity and temperature changes affect the decomposition rate of soil organic matter (Alm et al. 1999, Parton et al. 1987, Schimel et al. 1994). Generally, an increase in temperature and precipitation increases the primary productivity of vegetation. The content of SOC changes as temperature increases, enhancing soil respiration and accelerating the decomposition of organic matter while also increasing evaporation. SOC density is usually low in hot and dry environments (Xie et al. 2004). The impact on SOC under

the P1T1 scenario was not significantly different from those in the P1T0 and P0T1 scenarios. The interaction of combined increasing temperature and precipitation may offset the impact on ecosystem processes.

Active SOC is composed of microbes and their metabolites. Variation in active SOC was large in climate change scenarios, although they decreased by 88% due to a quick turnover rate and the high sensitivity of the area to climate change. Slow SOC reduced by approximately 70% and was a major contributor because of more contents and variation. The variation of passive SOC was small, decreasing by about 2% due to a low sensitivity to climate change (Fig. 1).

3.2 Impact of increased atmospheric CO2 concentrations alone on soil carbon

Increasing atmospheric CO2 concentrations can increase the photosynthetic rate of plants, water use efficiency and nutrient use efficiency (Owensby et al. 1993). In addition, this can lead to increased plant productivity, plant litter and plant roots, and SOC. As a result of the CO2 fertilization effect terrestrial C stock can increase by 0.5–4.0 Gt C each year (Gifford 1994).

After simulating the effects of doubling atmospheric CO2 on soil C at the Bange alpine steppe for the next 50 years, the results showed that the content of SOC fractions in doubling atmospheric CO2 concentrations alone produced a C sink. It was significantly higher than that under the unchanged atmospheric CO2 concentration scenario (Fig. 2). The simulation results obtained for doubling CO2 concentration were similar to those by other studies regarding tropical savannas, humid savanna

Active SOCSlow SOCPassive SOCSOCActive SOCSlow SOCPassive SOCSOCActive SOCSlow SOCPassive SOCSOCActive SOCSlow SOCPassive SOCSOC

49.991210.04512.261772.3049.991210.04512.261772.3049.991210.04512.261772.3049.991210.04512.261772.30

51.901210.69512.171774.775.97339.14500.92846.046.69381.32502.20890.215.95337.58500.87844.40

+3.83+0.050.02+0.14–88.05–71.97–2.21–52.26–86.61–68.49–1.96–49.77–88.11–72.10–2.22–52.36

51.02 ± 0.281209.95 ± 0.58512.22 ± 0.011773.18 ± 0.6316.33 ± 1.61736.43 ± 40.48507.66 ± 0.511260.42 ± 42.4217.20 ± 1.63769.87 ± 38.71508.20 ± 0.461295.27 ± 40.6216.30 ± 1.61735.19 ± 40.55507.64 ± 0.511259.12 ± 42.50

Scenarios Carbon pool

Start value in simulation

(g m-2)

End value in simulation

(g m-2)

Variation during simulation

(%)

P0T0

P1T0

P0T1

P1T1

SOC(mean ± S.E.)

(g m-2)

Notes: SOC = active SOC + slow SOC + passive SOC; + increase; – decrease.

Table 5 Variation of SOC pools in the next 50 years in an alpine grassland.


Fig. 3 Linear fit of soil carbon in unchanged CO2 concentration and doubling CO2 concentrations.

regions and temperate grasslands (Ni 2001; Parton et al. 1995; Thornley et al. 1997). Compared with soil carbon fractions in unchanged CO2, the variations in doubling CO2 concentrations showed a similar trend and all increased by more than 10% (Table 6). Particularly, active SOC increased by 15.84% on average with the doubling of atmospheric CO2 concentrations for the next 50 years, increased by 13.14% for slow SOC, and increased by 11.93% for passive SOC. Compared with SOC content under unchanged CO2 concentrations, that under the doubling of an atmospheric CO2 concentration evidently increased for the next 50 years, fitted by a linear equation (Fig. 3).

3.3 Combined effect of climate change and doubling atmospheric CO2

The fractions of SOC increased under a doubling of CO2

concentration alone. However, each fraction of SOC pool proved to be significantly lower in the scenarios of combined climate change and doubling CO2. The variations of SOC in these scenarios were close. The effect of climate change on SOC offsets the CO2 fertilization effect. SOC content decreased by 52.30% in P1T0 + 2 × CO2 , by 49.81% in P0T1 + 2 × CO2, and by 52.39% in P1T1 + 2 × CO2 over the next 50 years (Table 7). Our results differ from the results of Cao and Woodward (1998),

in which a substantial increase of SOC in terrestrial ecosystems on a global scale was observed. The combined effects of doubling atmospheric CO2 and climate change on SOC in this alpine steppe ecosystem on the northern Tibetan Plateau followed an opposite trend to that of the global trend, indicating the uniqueness alpine ecosystems.

The SOC response to increased CO2 and temperature in the alpine steppe, which produced a carbon source,

Active SOC Slow SOC Passive SOC SOC

Change scenarios (g m-2) (g m-2) (g m-2) (g m-2)Unchanged CO2 51.02 ± 0.28 1209.95 ± 0.58 512.22 ± 0.01 1773.18 ± 0.63Doubling CO2 59.10 ± 0.28 1368.92 ± 1.47 573.33 ± 0.01 2001.36 ± 1.60Variation (%) +15.84 +13.14 +11.93 +12.87

Table 6 Changes in soil carbon components in an alpine steppe for the next 50 years (mean ± S.E.).


Fig. 1 Comparison of soil carbon accumulation under different climate scenarios in the next 50 years in an alpine steppe.

Fig. 2 The effect of atmospheric CO2 concentration changes on SOC in an alpine steppe for the next 50 years.

Act

ive

SOC

(g m

-2)

Slow

SO

C (g

m-2)

Pass

ive

SOC

(g m

-2)

Simlation time (year)




Pass

ive

SOC

(g m

-2)


Act

ive

SOC

(g m

-2)

SO

C (g

m-2)

Slow

SO

C (g

m-2)


Simlation time (year) Simlation time (year)


Soil

orga

nic

carb

on (g

m-2)

SO

C (g

m-2)

doubling CO2

y=497.75+0.74x (R2=0.90)

unchangable CO2

y=1841.73–0.03x (R2=0.01)


were inconsistent with research done in different regions. For example, humid and temperate grasslands are likely to be a carbon sink (Thornley et al. 1997). Therefore, the combined effects of climate change and doubling CO2 on SOC in alpine ecosystems are different to those in other regions and the responses of SOC to climate change in alpine regions requires further research.

Compared with variations on SOC pools in climate change alone scenarios, the results for the combined climate change and doubling atmospheric CO2 scenarios were a little larger. Doubled CO2 may have caused evapotranspiration to decrease, and water use efficiency to increase, partially offset by climate change (Riedo et al. 1997). The combined effects of climate change and doubling atmospheric CO2 on SOC were maximal and formed a positive feedback on atmospheric CO2. The effects of doubling CO2 alone on SOC were relatively small, although it increased the content of SOC.

4 ConclusionsSOC dynamics for the next 50 years were successfully simulated using the CENTURY model. The impact of different climate change scenarios and doubling CO2 concentrations on SOC in an alpine steppe were analyzed, the results of which vary. The SOC pool showed a slight increasing trend under the P0T0 scenario (no climate change during the simulation). Thus, negative impacts of climate alone on SOC in alpine steppes were found. SOC pools decreased significantly under various climate change scenarios. SOC density significantly increased in the doubled CO2 concentration alone and also decreased

in the combined climate change and doubling CO2

concentrations. SOC declined by 52.36% in response to climate change under the P1T1 scenario, increased by 1.93% in response to doubled CO2 alone, and declined by 52.39% in response to the combination of P1T1 and doubled CO2, as compared with an increase of 0.14% in no climate change.

AcknowledgmentsThe authors are grateful to Cindy Keough from the Natural Resources Ecology Lab-Colorado State University for running the CENTURY model. Suggestions and field assistance were provided by WU Jianshuang, WU Junxi and LU Wenjie.

ReferencesAlm J, L Schulman, J Walden, H nen Nyk, P Martikainen, et al. 1999. Carbon

balance of a boreal bog during a year with an exceptionally dry summer. Ecology, 80 (1): 161–174.

Cao M and F Woodward. 1998. Dynamic responses of terrestrial ecosystem carbon cycling to global climate change. Nature, 393 (1): 249–252.

Gifford R. 1994. The global carbon cycle: a viewpoint on the missing sink. Australian Journal of Plant Physiology ,21 (1): 1–15.

Gilmanov T G, W J Parton and D S Ojima. 1997. Testing the ‘CENTURY’ ecosystem level model on data sets from eight grassland sites in the former USSR representing a wide climatic/soil gradient. Ecological Modelling, 96:191–210.

Houghton J, Ding Y, D Griggs, M Noguer, P Van der Linden, et al. 2001. IPCC, 2001: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, 9.

Houghton R and G Woodwell. 1989. Global climatic change. Scientific American, 260 (4): 36–44.

Luo T, Li W and Zhu H. 2002. Estimated biomass and productivity of natural vegetation on the Tibetan Plateau. Ecological Applications, 12 (4): 980–997.

Meersmans J, F De Ridder, F Canters, S De Baets and M Van Molle. 2008.


Active SOCSlow SOCPassive SOCSOCActive SOCSlow SOCPassive SOCSOCActive SOCSlow SOCPassive SOCSOCActive SOCSlow SOCPassive SOCSOC

56.281354.00573.241983.5156.281354.00573.241983.5156.281354.00573.241983.5156.281354.00573.241983.51

61.441386.86573.512021.816.67379.00560.53946.217.48426.17561.97995.626.64377.25560.48944.38

+9.18+2.43+0.05+1.93–88.14–72.01–2.22–52.30–86.71–68.53–1.96–49.81–88.19–72.14–2.23–52.39

59.10 ± 0.281368.92 ± 1.47573.33 ± 0.012001.36 ± 1.6018.28 ± 1.81823.55 ± 45.33568.08 ± 0.571409.91 ± 47.5219.25 ± 1.82860.94 ± 43.35568.69 ± 0.511448.88 ± 45.4918.24 ± 1.81822.15 ± 45.42568.06 ± 0.581408.45 ± 47.61

Scenarios Carbon pool

Start value in simulation

(g m-2)

End value in simulation

(g m-2)

Variation during simulation

(%)

P0T0 +2 × CO2

P1T0 +2 × CO2

P0T1 +2 × CO2

P1T1 +2 × CO2

SOC(mean ± S.E.)

(g m-2)

Table 7 Combined effects of climate change and doubling atmospheric CO2 on SOC pools in an alpine grassland for the next 50 years.


A multiple regression approach to assess the spatial distribution of Soil Organic Carbon (SOC) at the regional scale (Flanders, Belgium). Geoderma, 143 (1–2): 1–13.

Ni J, 2001. Carbon storage in terrestrial ecosystems of China: estimates at different spatial resolutions and their responses to climate change. Climatic Change, 49 (3): 339–358.

Oechel W, S Hastings, G Vourlitis, M Jenkins, G Riechers, et al. 1993. Recent change of Arctic tundra ecosystems from a net carbon dioxide sink to a source. Nature, 361: 520–523.

Ojima D, W Parton, D Schimel, J Scurlock and T Kittel. 1993. Modeling the effects of climatic and CO2 changes on grassland storage of soil C. Water, Air, & Soil Pollution, 70 (1): 643–657.

Owensby C E, P I Coyne, J M Ham, L M Auen and A K Knapp. 1993. Biomass production in a tallgrass prairie ecosystem exposed to ambient and elevated CO2. Ecological Applications, 3 (4): 644–653.

Parton W, J Scurlock, D Ojima, D Schimel and D Hall. 1995. Impact of climate change on grassland production and soil carbon worldwide. Global Change Biology, 1 (1): 13–22.

Parton W J, B McKeown, V Kirchner and D S Ojima. 1992. CENTURY Users Manual. Ed. C S University. NREL Publication, Fort Collins, Colorado, USA.

Parton W J, D S Ojima, C V Cole and D S Schimel. 1994. Environmental change in grassland: assessment using models. Climate Change, 28: 111–141.

Parton W J, D S Schimel, C V Cole and D S Ojima. 1987. Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Science Society of America Journal, 51 (5): 1173–1179.

Regional planning office of Naqu, Soil species in Naqa region of Tibet, 1991.

Riedo M, D Gyalistras and J Fuhrer. 2000. Net primary production and carbon stocks in differently managed grasslands: simulation of site-specific sensitivity to an increase in atmospheric CO2 and to climate change. Ecological Modelling, 134 (2–3): 207–227.

Riedo M, D Gyalistras, A Grub, M Rosset and J Fuhrer. 1997. Modelling grassland responses to climate change and elevated CO2. Acta Ecologica, 18 (3): 305–311.

Schimel D, B Braswell, E Holland, R McKeown, D Ojima, et al. 1994. Climatic, edaphic, and biotic controls over storage and turnover of carbon in soils. Global Biogeochemical Cycles, 8 (3): 279–293.

Schlesinger W H. 1990. Evidence from chronosequence studies for a low carbon-storage potential of soils. Nature, 348 (6298): 232–234.

Thornley J H M and M G R Cannell. 1997. Temperate Grassland Responses to Climate Change: an Analysis using the Hurley Pasture Model. Annals of Botany, 80 (2): 205–221.

Xiao X M, Wang Y F and Chen Z Z. 1996. Dynamics of primary productivity and soil organic matter of typical steppe in the Xilin river basin of Inner Mongolia and their response to climate change. Acta Botanica Sinica 38 (1):45–52. (in Chinese)

Yang Y, Fang J, Tang Y, Ji C, Zheng C, et al. 2008. Storage, patterns and controls of soil organic carbon in the Tibetan grasslands. Global Change Biology, 14 (7): 1592–1599.

Zhang Y, Tang Y, Jiang J and Yang Y. 2007. Characterizing the dynamics of soil organic carbon in grasslands on the Qinghai–Tibetan Plateau. Science in China Series D: Earth Sciences, 50 (1): 113–120.

Xie X L, Sun B, Zhou H Z, Li Z P and Li A B. 1994. Organic carbon density and storage in soils of China and spatial analysis. Acta Pedologica Sinica, 41 (1): 35–43. (in Chinese)

模拟气候变化和CO2增加对高寒草原土壤有机碳的影响

李晓佳1,2，张宪洲1，张扬建1

1 中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室拉萨高原生态试验站，北京 100101；

2 中国科学院研究生院，北京 100049

摘要: 本研究的目标是利用CENTURY模型分析气候变化、大气CO2浓度倍增、气候变化与大气CO2浓度倍增共同作用对藏

北高原高寒草原土壤有机碳的影响。结果表明：未来50年不同气候变化情景下土壤表层（0–20cm）有机碳呈降低趋势，变化率为

49.77%–52.36%。P1T0情景（降水增加，温度不变）、P0T1情景（降水不变，温度增加）和P1T1情景（降水增加，温度增加）下

的模拟结果相近。其中P1T1情景下高寒草原土壤有机碳损失的最多（模拟结束时的土壤有机碳为844.40 g C m-2），P0T1情景下土

壤有机碳损失49.77%。相对CO2不变情景而言，CO2倍增情景下土壤有机碳增加12.87%。CO2增加对土壤有机碳的影响大于气候变

化单独作用对土壤有机碳的影响。气候变化与大气CO2浓度倍增共同作用导致土壤有机碳降低。未来50年P1T1 + 2 × CO2情景下

土壤有机碳降低52.39%，P0T1 + 2 × CO2情景下土壤有机碳降低49.81%，P1T0 + 2 × CO2情景下土壤有机碳降低52.30%。因此，

高寒草原土壤有机碳含量随降水、温度和大气CO2浓度的变化而变化。

关键词：土壤有机碳；模拟；CENTURY；气候变化；CO2浓度

modeling the effects of climate change and elevated co on...

Documents