Articles | Volume 15, issue 21
https://doi.org/10.5194/bg-15-6559-2018
© Author(s) 2018. 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-15-6559-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Nov 2018
Research article |
| 07 Nov 2018
| 07 Nov 2018
Ecosystem carbon transit versus turnover times in response to climate warming and rising atmospheric CO2 concentration
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
Center for Ecosystem Science and Society, Department
of Biological Sciences, Northern Arizona University, Flagstaff
86011, USA
CSIRO Oceans and Atmosphere, Aspendale 3195, Australia
Ying-Ping Wang
CSIRO Oceans and Atmosphere, Aspendale 3195, Australia
Center for Ecosystem Science and Society, Department
of Biological Sciences, Northern Arizona University, Flagstaff
86011, USA
Department for Earth System Science, Tsinghua University, Beijing
100084, China
Lifen Jiang
Center for Ecosystem Science and Society, Department
of Biological Sciences, Northern Arizona University, Flagstaff
86011, USA
Related authors
Shulei Zhang, Hongbin Liang, Fang Li, Xingjie Lu, and Yongjiu Dai
Hydrol. Earth Syst. Sci., 29, 3119–3143, https://doi.org/10.5194/hess-29-3119-2025, https://doi.org/10.5194/hess-29-3119-2025, 2025
Short summary
Short summary
This study enhances irrigation modeling in the Common Land Model by capturing the full irrigation process, detailing water supplies from various sources, and enabling bidirectional coupling between water demand and supply. The proposed model accurately simulates irrigation water withdrawals, energy fluxes, river flow, and crop yields. It offers insights into irrigation-related climate impacts and water scarcity, contributing to sustainable water management and improved Earth system modeling.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibo Liu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2025-1380, https://doi.org/10.5194/egusphere-2025-1380, 2025
Short summary
Short summary
Land surface models are used for simulating earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes evaluation process more comprehensive and efficient. It stands out by incorporating various factors and working with data at any scale which enabling scientists to incorporate new types of models and measurements as our understanding of Earth’s systems evolves.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 117–134, https://doi.org/10.5194/essd-17-117-2025, https://doi.org/10.5194/essd-17-117-2025, 2025
Short summary
Short summary
Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally, with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Qingliang Li, Gaosong Shi, Wei Shangguan, Vahid Nourani, Jianduo Li, Lu Li, Feini Huang, Ye Zhang, Chunyan Wang, Dagang Wang, Jianxiu Qiu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022, https://doi.org/10.5194/essd-14-5267-2022, 2022
Short summary
Short summary
SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived through machine learning trained with in situ measurements of 1789 stations, meteorological forcings, and land surface variables. It contains 10 soil layers with 10 cm intervals up to 100 cm deep. Evaluated by in situ data, the error (ubRMSE) ranges from 0.045 to 0.051, and the correlation (R) range is 0.866-0.893. Compared with ERA5-Land, SMAP-L4, and SoMo.ml, SIMI1.0 has higher accuracy and resolution.
Shuang Ma, Lifen Jiang, Rachel M. Wilson, Jeff P. Chanton, Scott Bridgham, Shuli Niu, Colleen M. Iversen, Avni Malhotra, Jiang Jiang, Xingjie Lu, Yuanyuan Huang, Jason Keller, Xiaofeng Xu, Daniel M. Ricciuto, Paul J. Hanson, and Yiqi Luo
Biogeosciences, 19, 2245–2262, https://doi.org/10.5194/bg-19-2245-2022, https://doi.org/10.5194/bg-19-2245-2022, 2022
Short summary
Short summary
The relative ratio of wetland methane (CH4) emission pathways determines how much CH4 is oxidized before leaving the soil. We found an ebullition modeling approach that has a better performance in deep layer pore water CH4 concentration. We suggest using this approach in land surface models to accurately represent CH4 emission dynamics and response to climate change. Our results also highlight that both CH4 flux and belowground concentration data are important to constrain model parameters.
Shulei Zhang, Hongbin Liang, Fang Li, Xingjie Lu, and Yongjiu Dai
Hydrol. Earth Syst. Sci., 29, 3119–3143, https://doi.org/10.5194/hess-29-3119-2025, https://doi.org/10.5194/hess-29-3119-2025, 2025
Short summary
Short summary
This study enhances irrigation modeling in the Common Land Model by capturing the full irrigation process, detailing water supplies from various sources, and enabling bidirectional coupling between water demand and supply. The proposed model accurately simulates irrigation water withdrawals, energy fluxes, river flow, and crop yields. It offers insights into irrigation-related climate impacts and water scarcity, contributing to sustainable water management and improved Earth system modeling.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, Philippe Ciais, and Daniel S. Goll
EGUsphere, https://doi.org/10.5194/egusphere-2025-2545, https://doi.org/10.5194/egusphere-2025-2545, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Accurate estimates of global soil organic carbon (SOC) content and its spatial pattern are critical for future climate change mitigation. However, the most advanced mechanistic SOC models struggle to do this task. Here we apply multiple explainable machine learning methods to identify missing variables and misrepresented relationships between environmental factors and SOC in these models, offering new insights to guide model development for more reliable SOC predictions.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibo Liu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2025-1380, https://doi.org/10.5194/egusphere-2025-1380, 2025
Short summary
Short summary
Land surface models are used for simulating earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes evaluation process more comprehensive and efficient. It stands out by incorporating various factors and working with data at any scale which enabling scientists to incorporate new types of models and measurements as our understanding of Earth’s systems evolves.
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-206, https://doi.org/10.5194/gmd-2024-206, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it’s absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 PgC in present-day Yedoma deposits and a 1–5 m shallower peat depth, 43 % less passive soil carbon in peatlands against the convention protocol.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 117–134, https://doi.org/10.5194/essd-17-117-2025, https://doi.org/10.5194/essd-17-117-2025, 2025
Short summary
Short summary
Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally, with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, and Raphael A. Viscarra Rossel
SOIL, 10, 619–636, https://doi.org/10.5194/soil-10-619-2024, https://doi.org/10.5194/soil-10-619-2024, 2024
Short summary
Short summary
Effective management of soil organic carbon (SOC) requires accurate knowledge of its distribution and factors influencing its dynamics. We identify the importance of variables in spatial SOC variation and estimate SOC stocks in Australia using various models. We find there are significant disparities in SOC estimates when different models are used, highlighting the need for a critical re-evaluation of land management strategies that rely on the SOC distribution derived from a single approach.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
Short summary
Short summary
The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Ying-Ping Wang, Julian Helfenstein, Yuanyuan Huang, and Enqing Hou
Biogeosciences, 20, 4147–4163, https://doi.org/10.5194/bg-20-4147-2023, https://doi.org/10.5194/bg-20-4147-2023, 2023
Short summary
Short summary
We identified total soil P concentration as the most important predictor of all soil P pool concentrations, except for primary mineral P concentration, which is primarily controlled by soil pH and only secondarily by total soil P concentration. We predicted soil P pools’ distributions in natural systems, which can inform assessments of the role of natural P availability for ecosystem productivity, climate change mitigation, and the functioning of the Earth system.
Kevin R. Wilcox, Scott L. Collins, Alan K. Knapp, William Pockman, Zheng Shi, Melinda D. Smith, and Yiqi Luo
Biogeosciences, 20, 2707–2725, https://doi.org/10.5194/bg-20-2707-2023, https://doi.org/10.5194/bg-20-2707-2023, 2023
Short summary
Short summary
The capacity for carbon storage (C capacity) is an attribute that determines how ecosystems store carbon in the future. Here, we employ novel data–model integration techniques to identify the carbon capacity of six grassland sites spanning the US Great Plains. Hot and dry sites had low C capacity due to less plant growth and high turnover of soil C, so they may be a C source in the future. Alternately, cooler and wetter ecosystems had high C capacity, so these systems may be a future C sink.
Jennifer A. Holm, David M. Medvigy, Benjamin Smith, Jeffrey S. Dukes, Claus Beier, Mikhail Mishurov, Xiangtao Xu, Jeremy W. Lichstein, Craig D. Allen, Klaus S. Larsen, Yiqi Luo, Cari Ficken, William T. Pockman, William R. L. Anderegg, and Anja Rammig
Biogeosciences, 20, 2117–2142, https://doi.org/10.5194/bg-20-2117-2023, https://doi.org/10.5194/bg-20-2117-2023, 2023
Short summary
Short summary
Unprecedented climate extremes (UCEs) are expected to have dramatic impacts on ecosystems. We present a road map of how dynamic vegetation models can explore extreme drought and climate change and assess ecological processes to measure and reduce model uncertainties. The models predict strong nonlinear responses to UCEs. Due to different model representations, the models differ in magnitude and trajectory of forest loss. Therefore, we explore specific plant responses that reflect knowledge gaps.
Qingliang Li, Gaosong Shi, Wei Shangguan, Vahid Nourani, Jianduo Li, Lu Li, Feini Huang, Ye Zhang, Chunyan Wang, Dagang Wang, Jianxiu Qiu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022, https://doi.org/10.5194/essd-14-5267-2022, 2022
Short summary
Short summary
SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived through machine learning trained with in situ measurements of 1789 stations, meteorological forcings, and land surface variables. It contains 10 soil layers with 10 cm intervals up to 100 cm deep. Evaluated by in situ data, the error (ubRMSE) ranges from 0.045 to 0.051, and the correlation (R) range is 0.866-0.893. Compared with ERA5-Land, SMAP-L4, and SoMo.ml, SIMI1.0 has higher accuracy and resolution.
Shuang Ma, Lifen Jiang, Rachel M. Wilson, Jeff P. Chanton, Scott Bridgham, Shuli Niu, Colleen M. Iversen, Avni Malhotra, Jiang Jiang, Xingjie Lu, Yuanyuan Huang, Jason Keller, Xiaofeng Xu, Daniel M. Ricciuto, Paul J. Hanson, and Yiqi Luo
Biogeosciences, 19, 2245–2262, https://doi.org/10.5194/bg-19-2245-2022, https://doi.org/10.5194/bg-19-2245-2022, 2022
Short summary
Short summary
The relative ratio of wetland methane (CH4) emission pathways determines how much CH4 is oxidized before leaving the soil. We found an ebullition modeling approach that has a better performance in deep layer pore water CH4 concentration. We suggest using this approach in land surface models to accurately represent CH4 emission dynamics and response to climate change. Our results also highlight that both CH4 flux and belowground concentration data are important to constrain model parameters.
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Yingping Wang, Julian Helfenstein, Yuanyuan Huang, Kailiang Yu, Zhiqiang Wang, Yongchuan Yang, and Enqing Hou
Earth Syst. Sci. Data, 13, 5831–5846, https://doi.org/10.5194/essd-13-5831-2021, https://doi.org/10.5194/essd-13-5831-2021, 2021
Short summary
Short summary
Our database of globally distributed natural soil total P (STP) concentration showed concentration ranged from 1.4 to 9630.0 (mean 570.0) mg kg−1. Global predictions of STP concentration increased with latitude. Global STP stocks (excluding Antarctica) were estimated to be 26.8 and 62.2 Pg in the topsoil and subsoil, respectively. Our global map of STP concentration can be used to constrain Earth system models representing the P cycle and to inform quantification of global soil P availability.
Juhwan Lee, Raphael A. Viscarra Rossel, Mingxi Zhang, Zhongkui Luo, and Ying-Ping Wang
Biogeosciences, 18, 5185–5202, https://doi.org/10.5194/bg-18-5185-2021, https://doi.org/10.5194/bg-18-5185-2021, 2021
Short summary
Short summary
We performed Roth C simulations across Australia and assessed the response of soil carbon to changing inputs and future climate change using a consistent modelling framework. Site-specific initialisation of the C pools with measurements of the C fractions is essential for accurate simulations of soil organic C stocks and composition at a large scale. With further warming, Australian soils will become more vulnerable to C loss: natural environments > native grazing > cropping > modified grazing.
Xin Huang, Dan Lu, Daniel M. Ricciuto, Paul J. Hanson, Andrew D. Richardson, Xuehe Lu, Ensheng Weng, Sheng Nie, Lifen Jiang, Enqing Hou, Igor F. Steinmacher, and Yiqi Luo
Geosci. Model Dev., 14, 5217–5238, https://doi.org/10.5194/gmd-14-5217-2021, https://doi.org/10.5194/gmd-14-5217-2021, 2021
Short summary
Short summary
In the data-rich era, data assimilation is widely used to integrate abundant observations into models to reduce uncertainty in ecological forecasting. However, applications of data assimilation are restricted by highly technical requirements. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module which is friendly to ecologists with limited programming skills. MIDA also supports a flexible switch of different models or observations in DA analysis.
Erqian Cui, Chenyu Bian, Yiqi Luo, Shuli Niu, Yingping Wang, and Jianyang Xia
Biogeosciences, 17, 6237–6246, https://doi.org/10.5194/bg-17-6237-2020, https://doi.org/10.5194/bg-17-6237-2020, 2020
Short summary
Short summary
Mean annual net ecosystem productivity (NEP) is related to the magnitude of the carbon sink of a specific ecosystem, while its inter-annual variation (IAVNEP) characterizes the stability of such a carbon sink. Thus, a better understanding of the co-varying NEP and IAVNEP is critical for locating the major and stable carbon sinks on land. Based on daily NEP observations from eddy-covariance sites, we found local indicators for the spatially varying NEP and IAVNEP, respectively.
Cited articles
Bolin, B. and Rodhe, H.: A note on Concepts of Age Distribution and Transit-Time in Natural Reservoirs, Tellus, 25, 58–62, 1973.
Campbell, B. D., Stafford Smith, D. M., and McKeon, G. M.: Elevated CO2 and water supply interactions in grasslands: A pastures and rangelands management perspective, Global Change Biol., 3, 177–187, 1997.
Carvalhais, N., Forkel, M., Khomik, M., Bellarby, J., Jung, M., Migliavacca, M., Mu, M. Q., Saatchi, S., Santoro, M., Thurner, M., Weber, U., Ahrens, B., Beer, C., Cescatti, A., Randerson, J. T., and Reichstein, M.: Global covariation of carbon turnover times with climate in terrestrial ecosystems, Nature, 514, 213–217, https://doi.org/10.1038/nature13731, 2014.
Chen, S. T., Huang, Y., Zou, J. W., and Shi, Y. S.: Mean residence time of global topsoil organic carbon depends on temperature, precipitation and soil nitrogen, Global Planet. Change, 100, 99–108, 2013.
Cox, P. M., Betts, R. A., Collins, M., Harris, P. P., Huntingford, C., and Jones, C. D.: Amazonian forest dieback under climate-carbon cycle projections for the 21st century, Theor. Appl. Climatol., 78, 137–156, 2004.
Cox, P. M., Pearson, D., Booth, B. B., Friedlingstein, P., Huntingford, C., Jones, C. D., and Luke, C. M.: Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability, Nature, 494, 341–344, https://doi.org/10.1038/nature11882, 2013.
Eriksson, E.: Compartment models and reservoir, Annu. Rev. Ecol. Syst., 2, 67–84, 1971.
Farquhar, G. D., Caemmerer, S. V., and Berry, J. A.: A Biochemical-Model of Photosynthetic CO2 Assimilation in Leaves of C-3 Species, Planta, 149, 78–90, https://doi.org/10.1007/Bf00386231, 1980.
Friend, A. D., Lucht, W., Rademacher, T. T., Keribin, R., Betts, R., Cadule, P., Ciais, P., Clark, D. B., Dankers, R., Falloon, P. D., Ito, A., Kahana, R., Kleidon, A., Lomas, M. R., Nishina, K., Ostberg, S., Pavlick, R., Peylin, P., Schaphoff, S., Vuichard, N., Warszawski, L., Wiltshire, A., and Woodward, F. I.: Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2, P. Natl. Acad. Sci. USA, 111, 3280–3285, https://doi.org/10.1073/pnas.1222477110, 2014.
Gaudinski, J. B., Trumbore, S. E., Davidson, E. A., and Zheng, S. H.: Soil carbon cycling in a temperate forest: radiocarbon-based estimates of residence times, sequestration rates and partitioning of fluxes, Biogeochemistry, 51, 33–69, 2000.
He, Y. J., Trumbore, S. E., Torn, M. S., Harden, J. W., Vaughn, L. J. S., 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. Y., Lu, X. J., Shi, Z., Lawrence, D., Koven, C. D., Xia, J. Y., Du, Z. G., Kluzek, E., and Luo, Y. Q.: Matrix approach to land carbon cycle modeling: A case study with the Community Land Model, Global Change Biol., 24, 1394–1404, 2018.
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J. F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and Marshall, S.: The Community Earth System Model A Framework for Collaborative Research, B. Am. Meteorol. Soc., 94, 1339–1360, https://doi.org/10.1175/Bams-D-12-00121.1, 2013.
Kelly, R. H., Parton, W. J., Hartman, M. D., Stretch, L. K., Ojima, D. S., and Schimel, D. S.: Intra-annual and interannual variability of ecosystem processes in shortgrass steppe, J. Geophys. Res.-Atmos., 105, 20093–20100, https://doi.org/10.1029/2000jd900259, 2000.
Kirilenko, A. P. and Solomon, A. M.: Modeling dynamic vegetation response to rapid climate change using bioclimatic classification, Clim. Change, 38, 15–49, 1998.
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.
Kowalczyk, E., Wang, Y., Law, R., Davies, H., McGregor, J., and Abramowitz, G.: The CSIRO Atmosphere Biosphere Land Exchange (CABLE) model for use in climate models and as an offline model, CSIRO Marine and Atmospheric Research Paper, 13, 1–37, 2006.
Leuning, R.: Temperature dependence of two parameters in a photosynthesis model, Plant Cell Environ., 25, 1205–1210, https://doi.org/10.1046/j.1365-3040.2002.00898.x, 2002.
Leuzinger, S., Luo, Y. Q., Beier, C., Dieleman, W., Vicca, S., and Korner, C.: Do global change experiments overestimate impacts on terrestrial ecosystems?, Trends Ecol. Evol., 26, 236–241, 2011.
Li, J. D., Wang, Y. P., Duan, Q. Y., Lu, X. J., Pak, B., Wiltshire, A., Robertson, E., and Ziehn, T.: Quantification and attribution of errors in the simulated annual gross primary production and latent heat fluxes by two global land surface models, J. Adv. Model. Earth Sy., 8, 1270–1288, https://doi.org/10.1002/2015ms000583, 2016.
Liang, J., Xia, J., Shi, Z., Jiang, L., Ma, S., Lu, X., Mauritz, M., Natali, S. M., Pegoraro, E., and Penton, C. R.: Biotic responses buffer warming-induced soil organic carbon loss in Arctic tundra, Global Change Biol., 24, 4946–4959, https://doi.org/10.1111/gcb.14325, 2018.
Luo, Y. and Weng, E.: Dynamic disequilibrium of the terrestrial carbon cycle under global change, Trends Ecol. Evol., 26, 96–104, https://doi.org/10.1016/j.tree.2010.11.003, 2011.
Luo, Y., Shi, Z., Lu, X., Xia, J., Liang, J., Jiang, J., Wang, Y., Smith, M. J., Jiang, L., Ahlström, A., Chen, B., Hararuk, O., Hastings, A., Hoffman, F., Medlyn, B., Niu, S., Rasmussen, M., Todd-Brown, K., and Wang, Y.-P.: Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications, Biogeosciences, 14, 145–161, https://doi.org/10.5194/bg-14-145-2017, 2017.
Luo, Y. Q., Wu, L. H., Andrews, J. A., White, L., Matamala, R., Schafer, K. V. R., and Schlesinger, W. H.: Elevated CO2 differentiates ecosystem carbon processes: Deconvolution analysis of Duke Forest FACE data, Ecol. Monogr., 71, 357–376, https://doi.org/10.1890/0012-9615(2001)071[0357:Ecdecp]2.0.CO;2, 2001.
Luo, Y. Q., Gerten, D., Le Maire, G., Parton, W. J., Weng, E. S., Zhou, X. H., Keough, C., Beier, C., Ciais, P., Cramer, W., Dukes, J. S., Emmett, B., Hanson, P. J., Knapp, A., Linder, S., Nepstad, D., and Rustad, L.: Modeled interactive effects of precipitation, temperature, and [CO2] on ecosystem carbon and water dynamics in different climatic zones, Global Change Biol., 14, 1986–1999, 2008.
Manzoni, S., Katul, G. G., and Porporato, A.: Analysis of soil carbon transit times and age distributions using network theories, J. Geophys. Res.-Biogeo., 114, G04025, https://doi.org/10.1029/2009jg001070, 2009.
Masek, J. G.: Stability of boreal forest stands during recent climate change: evidence from Landsat satellite imagery, J. Biogeogr., 28, 967–976, 2001.
McCulley, R. L., Archer, S. R., Boutton, T. W., Hons, F. M., and Zuberer, D. A.: Soil respiration and nutrient cycling in wooded communities developing in grassland, Ecology, 85, 2804–2817, https://doi.org/10.1890/03-0645, 2004.
Nir, A. and Lewis, S.: Tracer Theory in Geophysical Systems in Steady and Non-Steady State.1., Tellus, 27, 372–383, 1975.
Norby, R. J. and Luo, Y. Q.: Evaluating ecosystem responses to rising atmospheric CO2 and global warming in a multi-factor world, New Phytol, 162, 281–293, 2004.
Olson, J. S.: Energy-Storage and Balance of Producers and Decomposers in Ecological-Systems, Ecology, 44, 322–331, https://doi.org/10.2307/1932179, 1963.
Qian, T. T., Dai, A., Trenberth, K. E., and Oleson, K. W.: Simulation of global land surface conditions from 1948 to 2004. Part I: Forcing data and evaluations, J. Hydrometeorol., 7, 953–975, https://doi.org/10.1175/Jhm540.1, 2006.
Raich, J. W. and Schlesinger, W. H.: The Global Carbon-Dioxide Flux in Soil Respiration and Its Relationship to Vegetation and Climate, Tellus B, 44, 81–99, https://doi.org/10.1034/j.1600-0889.1992.t01-1-00001.x, 1992.
Rasmussen, M., Hastings, A., Smith, M. J., Agusto, F. B., Chen-Charpentier, B. M., Hoffman, F. M., Jiang, J., Todd-Brown, K. E. O., Wang, Y., Wang, Y. P., and Luo, Y. Q.: Transit times and mean ages for nonautonomous and autonomous compartmental systems, J. Math. Biol., 73, 1379–1398, 2016.
Ryan, M. G.: Effects of Climate Change on Plant Respiration, Ecol. Appl., 1, 157–167, https://doi.org/10.2307/1941808, 1991.
Sanderman, J., Amundson, R. G., and Baldocchi, D. D.: Application of eddy covariance measurements to the temperature dependence of soil organic matter mean residence time, Global Biogeochem. Cy., 17, 1061, https://doi.org/10.1029/2001gb001833, 2003.
Sierra, C. A., Muller, M., Metzler, H., Manzoni, S., and Trumbore, S. E.: The muddle of ages, turnover, transit, and residence times in the carbon cycle, Global Change Biol., 23, 1763–1773, https://doi.org/10.1111/gcb.13556, 2016.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Global Change Biol., 9, 161–185, https://doi.org/10.1046/j.1365-2486.2003.00569.x, 2003.
Soja, A. J., Tchebakova, N. M., French, N. H. F., Flannigan, M. D., Shugart, H. H., Stocks, B. J., Sukhinin, A. I., Parfenova, E. I., Chapin, F. S., and Stackhouse, P. W.: Climate-induced boreal forest change: Predictions versus current observations, Global Planet. Change, 56, 274–296, 2007.
Taylor, J. A. and Lloyd, J.: Sources and Sinks of Atmospheric CO2, Aust. J. Bot., 40, 407–418, https://doi.org/10.1071/Bt9920407, 1992.
Trumbore, S.: Age of soil organic matter and soil respiration: Radiocarbon constraints on belowground C dynamics, Ecol. Appl., 10, 399–411, 2000.
Wang, Y., Amundson, R., and Trumbore, S.: The impact of land use change on C turnover in soils, Global Biogeochem. Cy., 13, 47–57, 1999.
Wang, Y. P. and Leuning, R.: A two-leaf model for canopy conductance, photosynthesis and partitioning of available energy I: Model description and comparison with a multi-layered model, Agr. Forest. Meteorol., 91, 89–111, https://doi.org/10.1016/S0168-1923(98)00061-6, 1998.
Wang, Y. P., Law, R. M., and Pak, B.: A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere, Biogeosciences, 7, 2261–2282, https://doi.org/10.5194/bg-7-2261-2010, 2010.
Wang, Y. P., Kowalczyk, E., Leuning, R., Abramowitz, G., Raupach, M. R., Pak, B., van Gorsel, E., and Luhar, A.: Diagnosing errors in a land surface model (CABLE) in the time and frequency domains, J. Geophys. Res.-Biogeol., 116, G01034, https://doi.org/10.1029/2010JG00138, 2011.
Xia, J. Y., Luo, Y. Q., Wang, Y.-P., Weng, E. S., and Hararuk, O.: A semi-analytical solution to accelerate spin-up of a coupled carbon and nitrogen land model to steady state, Geosci. Model Dev., 5, 1259–1271, https://doi.org/10.5194/gmd-5-1259-2012, 2012.
Xu, T., White, L., Hui, D. F., and Luo, Y. Q.: Probabilistic inversion of a terrestrial ecosystem model: Analysis of uncertainty in parameter estimation and model prediction, Global Biogeochem. Cy., 20, GB2007, https://doi.org/10.1029/2005GB002468, 2006.
Yan, Y., Zhou, X., Jiang, L., and Luo, Y.: Effects of carbon turnover time on terrestrial ecosystem carbon storage, Biogeosciences, 14, 5441–5454, https://doi.org/10.5194/bg-14-5441-2017, 2017.
Zhang, Q., Wang, Y. P., Pitman, A. J., and Dai, Y. J.: Limitations of nitrogen and phosphorous on the terrestrial carbon uptake in the 20th century, Geophys. Res. Lett., 38, L22701, https://doi.org/10.1029/2011GL049244, 2011.
Zhang, X. Z., Rayner, P. J., Wang, Y. P., Silver, J. D., Lu, X. J., Pak, B., and Zheng, X. G.: Linear and nonlinear effects of dominant drivers on the trends in global and regional land carbon uptake: 1959 to 2013, Geophys. Res. Lett., 43, 1607–1614, 2016.
Zhou, T. and Luo, Y. Q.: Spatial patterns of ecosystem carbon residence time and NPP-driven carbon uptake in the conterminous United States, Global Biogeochem. Cy., 53, 1229–1240, https://doi.org/10.1029/2007GB002939, 2008.
Short summary
How long does C cycle through terrestrial ecosystems is a critical question for understanding land C sequestration capacity under future rising atmosphere [CO2] and climate warming. Under climate change, previous conventional concepts with a steady-state assumption will no longer be suitable for a non-steady state. Our results using the new concept, C transit time, suggest more significant responses in terrestrial C cycle under rising [CO2] and climate warming.
How long does C cycle through terrestrial ecosystems is a critical question for understanding...
Altmetrics
Final-revised paper
Preprint