LuoAhlstroemAllisonEtAl2016

Référence

Luo, Y., Ahlström, A., Allison, S.D., Batjes, N.H., Brovkin, V., Carvalhais, N., Chappell, A., Ciais, P., Davidson, E.A., Finzi, A., Georgiou, K., Guenet, B., Hararuk, O., Harden, J.W., He, Y., Hopkins, F., Jiang, L., Koven, C., Jackson, R.B., Jones, C.D., Lara, M.J., Liang, J., McGuire, A.D., Parton, W., Peng, C., Randerson, J.T., Salazar, A., Sierra, C.A., Smith, M.J., Tian, H., Todd-Brown, K.E.O., Torn, M., Van Groenigen, K.J., Wang, Y.P., West, T.O., Wei, Y., Wieder, W.R., Xia, J., Xu, X., Xu, X., Zhou, T. (2016) Toward more realistic projections of soil carbon dynamics by Earth system models. Global Biogeochemical Cycles, 30(1):40-56. (Scopus )

Résumé

Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields. ©2015. American Geophysical Union. All Rights Reserved.

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@ARTICLE { LuoAhlstroemAllisonEtAl2016,
    AUTHOR = { Luo, Y. and Ahlström, A. and Allison, S.D. and Batjes, N.H. and Brovkin, V. and Carvalhais, N. and Chappell, A. and Ciais, P. and Davidson, E.A. and Finzi, A. and Georgiou, K. and Guenet, B. and Hararuk, O. and Harden, J.W. and He, Y. and Hopkins, F. and Jiang, L. and Koven, C. and Jackson, R.B. and Jones, C.D. and Lara, M.J. and Liang, J. and McGuire, A.D. and Parton, W. and Peng, C. and Randerson, J.T. and Salazar, A. and Sierra, C.A. and Smith, M.J. and Tian, H. and Todd-Brown, K.E.O. and Torn, M. and Van Groenigen, K.J. and Wang, Y.P. and West, T.O. and Wei, Y. and Wieder, W.R. and Xia, J. and Xu, X. and Xu, X. and Zhou, T. },
    TITLE = { Toward more realistic projections of soil carbon dynamics by Earth system models },
    JOURNAL = { Global Biogeochemical Cycles },
    YEAR = { 2016 },
    VOLUME = { 30 },
    NUMBER = { 1 },
    PAGES = { 40-56 },
    NOTE = { cited By 90 },
    ABSTRACT = { Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields. ©2015. American Geophysical Union. All Rights Reserved. },
    AFFILIATION = { Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, United States; Center for Earth System Science, Tsinghua University, Beijing, China; Department of Earth System Science, Stanford University, Stanford, CA, United States; Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden; Department of Ecology and Evolutionary Biology, University of California, Irvine, CA, United States; Department of Earth System Science, University of California, Irvine, CA, United States; ISRIC-World Soil Information, Wageningen, Netherlands; Max Planck Institute for Meteorology, Hamburg, Germany; Max Planck Institute for Biogeochemistry, Jena, Germany; CENSE, Departamento de Ciências e Engenharia Do Ambiente, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Caparica, Portugal; CSIRO Land and Water National Research Flagship, Canberra, Australia; Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ, Gif-sur-Yvette, France; Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD, United States; Department of Biology, Boston University, Boston, MA, United States; Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, United States; Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States; Pacific Forestry Centre, Canadian Forest Service, Victoria, BC, Canada; U.S. Geological Survey, Menlo Park, CA, United States; Met Office Hadley Centre, Exeter, United Kingdom; Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, United States; U.S. Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit, Fairbanks, AK, United States; Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, United States; Institute of Environment Sciences, University of Quebec at Montreal, Montreal, QC, Canada; Department of Biological Sciences, Purdue University, West Lafayette, IN, United States; Computational Science Laboratory, Microsoft Research, Cambridge, United Kingdom; International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, United States; Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States; Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, United States; CSIRO Ocean and Atmosphere Flagship, Aspendale, VIC, Australia; Joint Global Change Research Institute, College Park, MD, United States; Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States; National Center for Atmospheric Research, Boulder, CO, United States; School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China; Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IW, United States; Department of Biological Science, University of Texas at El Paso, El Paso, TX, United States; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China },
    AUTHOR_KEYWORDS = { CMIP5; Earth system models; realistic projections; recommendations; soil carbon dynamics },
    DOCUMENT_TYPE = { Conference Paper },
    DOI = { 10.1002/2015GB005239 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84956641600&doi=10.1002%2f2015GB005239&partnerID=40&md5=a7b09522ae24233c6931ddc220c5c6ac },
}

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