Articles | Volume 16, issue 4
https://doi.org/10.5194/bg-16-917-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-16-917-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Feb 2019
Research article |
| 27 Feb 2019
| 27 Feb 2019
Evaluating the simulated mean soil carbon transit times by Earth system models using observations
Jing Wang
Zhejiang Tiantong Forest Ecosystem National Observation and Research
Station, Shanghai Key Lab for Urban Ecological Processes and
Eco-Restoration, School of Ecological and Environmental Sciences, East China
Normal University, Shanghai 200241, China
Jianyang Xia
CORRESPONDING AUTHOR
Zhejiang Tiantong Forest Ecosystem National Observation and Research
Station, Shanghai Key Lab for Urban Ecological Processes and
Eco-Restoration, School of Ecological and Environmental Sciences, East China
Normal University, Shanghai 200241, China
State Key Laboratory of Estuarine and Coastal Research, Research
Center for Global Change and Ecological Forecasting, East China Normal
University, Shanghai 200241, China
Xuhui Zhou
Zhejiang Tiantong Forest Ecosystem National Observation and Research
Station, Shanghai Key Lab for Urban Ecological Processes and
Eco-Restoration, School of Ecological and Environmental Sciences, East China
Normal University, Shanghai 200241, China
State Key Laboratory of Estuarine and Coastal Research, Research
Center for Global Change and Ecological Forecasting, East China Normal
University, Shanghai 200241, China
Kun Huang
Zhejiang Tiantong Forest Ecosystem National Observation and Research
Station, Shanghai Key Lab for Urban Ecological Processes and
Eco-Restoration, School of Ecological and Environmental Sciences, East China
Normal University, Shanghai 200241, China
Jian Zhou
Zhejiang Tiantong Forest Ecosystem National Observation and Research
Station, Shanghai Key Lab for Urban Ecological Processes and
Eco-Restoration, School of Ecological and Environmental Sciences, East China
Normal University, Shanghai 200241, China
Yuanyuan Huang
Laboratoire des Sciences du Climat et de l'Environnement, 91191
Gif-sur-Yvette, France
Lifen Jiang
Center for ecosystem science and society, Northern Arizona University,
Arizona, Flagstaff, AZ 86011, USA
Xia Xu
College of Biology and the Environment, Nanjing Forestry University,
Nanjing 210037, China
Junyi Liang
Environmental Sciences Division & Climate Change Science Institute,
Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, USA
Ying-Ping Wang
CSIRO Ocean and Atmosphere, PMB 1, Aspendale, Victoria 3195,
Australia
Xiaoli Cheng
Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074,
Hubei Province, China
Center for ecosystem science and society, Northern Arizona University,
Arizona, Flagstaff, AZ 86011, USA
Department of Earth System Science, Tsinghua University, Beijing
100084, China
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Yan Sun, Daniel S. Goll, Jinfeng Chang, Philippe Ciais, Betrand Guenet, Julian Helfenstein, Yuanyuan Huang, Ronny Lauerwald, Fabienne Maignan, Victoria Naipal, Yilong Wang, Hui Yang, and Haicheng Zhang
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We evaluated the performance of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 against remote sensing, ground-based measurement networks and ecological databases. The simulated carbon, nitrogen and phosphorus fluxes among different spatial scales are generally in good agreement with data-driven estimates. However, the recent carbon sink in the Northern Hemisphere is substantially underestimated. Potential causes and model development priorities are discussed.
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Cited articles
Ahlström, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A., Jung,
M., Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K., and
Kato, E.: The dominant role of semi-arid ecosystems in the trend and
variability of the land CO2 sink, Science, 348, 895–899,
https://doi.org/10.1126/science.aaa1668, 2015.
Allison, S. D., Matthew, D. W., and Mark, A. B.: Soil-carbon response to
warming dependent on microbial physiology, Nat. Geosci., 3, 336–340, https://doi.org/10.1038/ngeo846,
2010.
Balesdent, J., Mariotti, A., and Guillet, B.: Natural 13C abundance
as a tracer for studies of soil organic matter dynamics, Soil Biol. Biochem.,
19, 25–30, https://doi.org/10.1016/0038-0717(87)90120-9, 1987.
Bernstein, L., Bosch, P., Canziani, O., Chen, Z., Christ, R., and Riahi,
K.: IPCC, 2007: Climate Change 2007: Synthesis Report, 2008.
Bloom, A. A., Exbrayat, J. F., van der Velde, I. R., Feng, L., and Williams,
M.: The decadal state of the terrestrial carbon cycle: Global retrievals of
terrestrial carbon allocation, pools, and residence times, P. Natl. Acad.
Sci. USA, 113, 1285–1290, https://doi.org/10.1073/pnas.1515160113, 2016.
Bolin, B. and Henning, R.: A note on the concepts of age distribution and
transit time in natural reservoirs. Tellus, 25, 58–62,
https://doi.org/10.1111/j.2153-3490.1973.tb01594.x, 1973.
Bolker, B. M., Pacala, S. W., and Parton Jr., W. J.: Linear analysis of
soil decomposition: insights from the century model, Ecol.
Appl., 8, 425–439, 1998.
Bradford, M. A., Wieder, W. R., Bonan, G. B., Fierer, N., Raymond, P. A., and
Crowther, T. W.: Managing uncertainty in soil carbon feedbacks to climate
change, Nat. Clim. Change, 6, 751–758, https://doi.org/10.1038/nclimate3071, 2016.
Carvalhais, N., Forkel, M., Khomik, M., Bellarby, J., Jung, M., Migliavacca,
M., Mu, M., Saatchi, S., Santoro, M., Thurner, M., and Weber, U.: Global
covariation of carbon turnover times with climate in terrestrial ecosystems,
Nature, 514, 213–217, https://doi.org/10.1038/nature13731, 2014.
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, J., and Thornton, P.:
Climate Change 2013: the physical science basis. Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K, Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge Univ. Press, 465–570,
2013.
Feng, W., Shi, Z., Jiang, J., Xia, J., Liang, J., Zhou, J., and Luo, Y.:
Methodological uncertainty in estimating carbon turnover times of soil
fractions, Soil Biol. Biochem., 100, 118–124,
https://doi.org/10.1016/j.soilbio.2016.06.003, 2016.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N.,
Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: Algorithm
refinements and characterization of new datasets, Remote Sens. Environ., 114,
168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010.
Friedlingstein, P., Cox, P., Betts, R., Bopp, L., von Bloh, W., Brovkin, V.,
Cadule, P., Doney, S., Eby, M., Fung, I., and Bala, G.: Climate-carbon cycle
feedback analysis: Results from the C4MIP model intercomparison, J. Clim.,
19, 3337–3353, https://doi.org/10.1175/JCLI3800.1, 2006.
Fröberg, M., Tipping, E., Stendahl, J., Clarke, N., and Bryant, C.: Mean
residence time of O horizon carbon along a climatic gradient in Scandinavia
estimated by 14C measurements of archived soils, Biogeochemistry,
104, 227–236, https://doi.org/10.1007/s10533-010-9497-3, 2011.
Gerber, S., Hedin, L. O., Oppenheimer, M., Pacala, S. W., and Shevliakova,
E.: Nitrogen cycling and feedbacks in a global dynamic land model, Global
Biogeochem. Cy., 24, GB1001, https://doi.org/10.1029/2008GB003336, 2010.
He, Y., Trumbore, S. E., Torn, M. S., Harden, J. W., Vaughn, L. J., Allison,
S. D., and Randerson, J. T.: Radiocarbon constraints imply reduced carbon
uptake by soils during the 21st century, Science, 353, 1419–1424,
https://doi.org/10.1126/science.aad4273, 2016.
Huang, Y., Lu, X., Shi, Z., Lawrence, D., Koven, C.D., Xia, J., Du, Z.,
Kluzek, E., and Luo, Y.: Matrix approach to land carbon cycle modeling: A
case study with Community Land Model, Glob. Change Biol., 24, 1394–1404,
https://doi.org/10.1111/gcb.13948, 2017.
Hutchinson, M. F. and Xu, T.: Anusplin version 4.2 user guide. Centre for
Resource and Environmental Studies, The Australian National University,
Canberra, 54, 2004.
Ji, M., Huang, J., Xie, Y., and Liu, J.: Comparison of dryland climate change
in observations and CMIP5 simulations, Adv. Atmos. Sci., 32, 1565–1574,
https://doi.org/10.1007/s00376-015-4267-8, 2015.
Jones, C. D., Arora, V., Friedlingstein, P., Bopp, L., Brovkin, V., Dunne,
J., Graven, H., Hoffman, F., Ilyina, T., John, J. G., Jung, M., Kawamiya, M.,
Koven, C., Pongratz, J., Raddatz, T., Randerson, J. T., and Zaehle, S.: C4MIP
– The Coupled Climate-Carbon Cycle Model Intercomparison Project:
experimental protocol for CMIP6, Geosci. Model Dev., 9, 2853–2880,
https://doi.org/10.5194/gmd-9-2853-2016, 2016.
Koven, C. D., Riley, W. J., Subin, Z. M., Tang, J. Y., Torn, M. S., Collins,
W. D., Bonan, G. B., Lawrence, D. M., and Swenson, S. C.: The effect of
vertically resolved soil biogeochemistry and alternate soil C and N models on
C dynamics of CLM4, Biogeosciences, 10, 7109–7131,
https://doi.org/10.5194/bg-10-7109-2013, 2013.
Koven, C. D., Hugelius, G., Lawrence, D. M., and Wieder, W. R.: Higher
climatological temperature sensitivity of soil carbon in cold than warm
climates, Nat. Clim. Change, 7, 817–822, https://doi.org/10.1038/nclimate3421, 2017.
Liang, J., Li, D., Shi, Z., Tiedje, J. M., Zhou, J., Schuur, E. A. G.,
Konstantinidis, K. T., and Luo, Y.: Methods for estimating temperature
sensitivity of soil organic matter based on incubation data: A comparative
evaluation, Soil Biol. Biochem., 80, 127–135,
https://doi.org/10.1016/j.soilbio.2014.10.005, 2015.
Lu, X., Wang, Y.-P., Luo, Y., and Jiang, L.: Ecosystem carbon transit versus
turnover times in response to climate warming and rising atmospheric CO2
concentration, Biogeosciences, 15, 6559–6572,
https://doi.org/10.5194/bg-15-6559-2018, 2018.
Luo, Y., White, L. W., Canadell, J. G., DeLucia, E. H., Ellsworth, D. S.,
Finzi, A., Lichter, J., and Schlesinger, W. H.: Sustainability of terrestrial
carbon sequestration: A case study in Duke Forest with inversion approach,
Global Biogeochem. Cy., 17, 12101–2113, https://doi.org/10.1029/2002GB001923, 2003.
Luo, Y., Ahlström, A., Allison, S. D., Batjes, N. H., Brovkin, V.,
Carvalhais, N., Chappell, A., Ciais, P., Davidson, E. A., Finzi, A., and
Georgiou, K.: Toward more realistic projections of soil carbon dynamics by
Earth system models, Glob. Biogeochem. Cy., 30, 40–56,
https://doi.org/10.1002/2015GB005239, 2016.
Metzler, H. and Sierra, C. A.: Linear autonomous compartmental models as
continuous-time Markov chains: transit-time and age distributions, Math.
Geosci., 50, 1–34, 2018.
Metzler, H., Müller, M., and Sierra, C. A.: Transit-time and age
distributions for nonlinear time-dependent compartmental systems, P. Natl.
Acad. Sci. USA, 22, 201705296, https://doi.org/10.1073/pnas.1705296115, 2018.
Mishra, U., Drewniak, B., Jastrow, J. D., Matamala, R. M., and Vitharana, U.
W. A.: Spatial representation of organic carbon and active-layer thickness of
high latitude soils in CMIP5 earth system models, Geoderma, 300, 55–63,
https://doi.org/10.1016/j.geoderma.2016.04.017, 2017.
NASA LP DAAC Land Cover Type Yearly L3 Global 0.05 Deg CMG (MCD12C1),
USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls,
South Dakota, available at: https://lpdaac.usgs.gov/products/modis products table/land cover/yearly l3 global 0.05 deg cmg/mcd12c1 (last access: 14 April 2014), 2008.
Parry, M., Parry, M. L., Canziani, O., Palutikof, J., Van der Linden, P., and
Hanson, C.: Climate Change 2007: Impacts, Adaptation and Vulnerability,
Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge
Univ. Press, Cambridge, UK, 211–272, 2007.
Poulter, B., Frank, D., Ciais, P., Myneni, R. B., Andela, N., Bi, J.,
Broquet, G., Canadell, J. G., Chevallier, F., Liu, Y. Y., and Running, S. W.:
Contribution of semi-arid ecosystems to interannual variability of the global
carbon cycle, Nature, 509, 600–603, https://doi.org/10.1038/nature13376, 2014.
Rasmussen, M., Hastings, A., Smith, M. J., Agusto, F. B., Chen-Charpentier,
B. M., Hoffman, F. M., Jiang, J., Todd-Brown, K. E., Wang, Y., Wang, Y. P.,
and Luo, Y.: Transit times and mean ages for nonautonomous and autonomous
compartmental systems, J. Math. Biol., 73, 1379–1398,
https://doi.org/10.1007/s00285-016-0990-8, 2016.
Sanderman, J. Ronald, G. A., and Dennis, D. B.: Application of eddy
covariance measurements to the temperature dependence of soil organic matter
mean residence time, Glob. Biogeochem. Cy., 17, 301–3015,
https://doi.org/10.1029/2001GB001833, 2003.
Saoudi, S., Ghorbel, F., and Hillion, A.: Some statistical properties of the
kernel – diffeomorphism estimator, Appl. Stoch. Model Data Anal., 13,
39–58,
https://doi.org/10.1002/(SICI)1099-0747(199703)13:1<39::AID-ASM292>3.0.CO;2-J,
1997.
Schmidt, M. W., Torn, M. S., Abiven, S., Dittmar, T., Guggenberger, G.,
Janssens, I. A., Kleber, M., Kögel-Knabner, I., Lehmann, J., Manning, D.
A., and Nannipieri, P.: Persistence of soil organic matter as an ecosystem
property, Nature, 478, 49–56, https://doi.org/10.1038/nature10386, 2011.
Schuur, E. A. G., McGuire, A. D., Schädel, C., Grosse, G., Harden, J. W.,
Hayes, D. J., Hugelius, G., Koven, C. D., Kuhry, P., Lawrence, D. M., and
Natali, S. M.: Climate change and the permafrost carbon feedback, Nature,
520, 171–179, https://doi.org/10.1038/nature14338, 2015.
Shao, P., Zeng, X., Sakaguchi, K., Monson, R. K., and Zeng, X.: Terrestrial
carbon cycle: climate relations in eight CMIP5 earth system models, J. Clim.,
26, 8744–8764, https://doi.org/10.1175/JCLI-D-12-00831.1, 2013.
Sheather, S. J. and Marron, J. S.: Kernel quantile estimators, J. Am. Stat.
Assoc., 85, 410–416, https://doi.org/10.1080/01621459.1990.10476214, 1990.
Sierra, C. A. and Markus, M.: A general mathematical framework for
representing soil organic matter dynamics, Ecol. Monogr., 85, 505–524,
https://doi.org/10.1890/15-0361.1, 2015.
Sierra, C. A., Müller, M., Metzler, H., Manzoni, S., and Trumbore, S. E.:
The muddle of ages, turnover, transit, and residence times in the carbon
cycle, Glob. Change Biol., 23, 1763–1773, https://doi.org/10.1111/gcb.13556, 2017.
Sierra, C. A., Ceballos-Núñez, V., Metzler, H., and Müler, M.:
Representing and understanding the carbon cycle using the theory of
compartmental dynamical systems, J. Adv. Model. Earth Sy., 10, 1729–1734,
https://doi.org/10.1029/2018MS001360, 2018.
Six, J. and Jastrow, J. D.: Organic matter turnover, Encycl. of Soil Science,
2002,
936–942, 2002.
Smith, W. K., Reed, S. C., Cleveland, C. C., Ballantyne, A. P., Anderegg, W.
R., Wieder, W. R., Liu, Y. Y., and Running, S. W.: Large divergence of
satellite and Earth system model estimates of global terrestrial CO2
fertilization, Nat. Clim. Change, 6, 306–310, https://doi.org/10.1038/nclimate2879,
2016.
Spohn, M. and Sierra, C. A.: How long do elements cycle in terrestrial
ecosystems?, Biogeochemistry, 139, 69–83,
https://doi.org/10.1007/s10533-018-0452-z, 2018.
Stewart, C. E., Paustian, K., Conant, R. T., Plante, A. F., and Six, J.: Soil
carbon saturation: evaluation and corroboration by long-term incubations,
Soil Biol. Biochem., 40, 1741–1750, https://doi.org/10.1016/j.soilbio.2008.02.014,
2008.
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M.,
Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in
soil carbon simulations from CMIP5 Earth system models and comparison with
observations, Biogeosciences, 10, 1717–1736,
https://doi.org/10.5194/bg-10-1717-2013, 2013.
Trumbore, S. E.: Comparison of carbon dynamics in tropical and temperate
soils using radiocarbon measurements, Glob. Biogeochem. Cy., 7, 275–290,
https://doi.org/10.1029/93GB00468, 1993.
Trumbore, S. E., Chadwick, O. A., and Amundson, R.: Rapid exchange between
soil carbon and atmospheric carbon dioxide driven by temperature change,
Science, 272, 393–396, https://doi.org/10.1126/science.272.5260.393, 1996.
Wieder, W. R., Bonan, G. B., and Allison, S. D.: Global soil carbon
projections are improved by modelling microbial processes, Nat. Clim. Change,
3, 909–912, https://doi.org/10.1038/nclimate1951, 2013.
Xia, J., Luo, Y., Wang, Y. P., and Hararuk, O.: Traceable components of
terrestrial carbon storage capacity in biogeochemical models, Glob. Change
Biol., 19, 2104–2116, https://doi.org/10.1111/gcb.12172, 2013.
Xia, J., McGuire, A. D., Lawrence, D., Burke, E., Chen, G., Chen, X., Delire,
C., Koven, C., MacDougall, A., Peng, S., and Rinke, A.: Terrestrial ecosystem
model performance in simulating productivity and its vulnerability to climate
change in the northern permafrost region, J. Geophys. Res., 122, 430–446,
https://doi.org/10.1002/2016JG003384, 2017.
Xu, X., Shi, Z., Li, D., Rey, A., Ruan, H., Craine, J. M., Liang, J., Zhou,
J., and Luo, Y.: Soil properties control decomposition of soil organic
carbon: results from dataassimilation analysis, Geoderma, 262, 235–242,
https://doi.org/10.1016/j.geoderma.2015.08.038, 2016.
Zhang, K., Dang, H., Zhang, Q., and Cheng, X.: Soil carbon dynamics following
landuse change varied with temperature and precipitation gradients: evidence
from stable isotopes, Glob. Change Biol., 21, 2762–2772,
https://doi.org/10.1111/gcb.12886, 2015.
Short summary
Soil is critical in mitigating climate change mainly because soil carbon turns over much slower in soils than vegetation and the atmosphere. However, Earth system models (ESMs) have large uncertainty in simulating carbon dynamics due to their biased estimation of soil carbon transit time (τsoil). Here, the τsoil estimates from 12 ESMs that participated in CMIP5 were evaluated by a database of measured τsoil. We detected a large spatial variation in measured τsoil across the globe.
Soil is critical in mitigating climate change mainly because soil carbon turns over much slower...
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