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Title: Toward more realistic projections of soil carbon dynamics by Earth system models: SOIL CARBON MODELING

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 3rd to 5th 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. Firstly, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by 1st-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic 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. Secondly, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration withmore » both pool- and flux-based datasets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Thirdly, 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 datasets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.« less
Authors:
 [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [6] ;  [7] ;  [8] ;  [9] ;  [10] ;  [11] ;  [8] ;  [12] ;  [13] ;  [14] ;  [14] ;  [15] ;  [16] ;  [17] ;  [18] more »;  [19] ;  [15] ;  [20] ;  [21] ;  [22] ;  [14] ;  [23] ;  [24] ;  [25] ;  [26] ;  [27] ;  [16] ;  [28] ;  [29] ;  [30] ;  [31] ;  [32] ;  [33] ;  [34] ;  [35] ;  [36] « less
  1. Department of Microbiology and Plant Biology, University of Oklahoma, Norman Oklahoma USA; Center for Earth System Science, Tsinghua University, Beijing China
  2. Department of Earth System Science, Stanford University, Stanford California USA; Department of Physical Geography and Ecosystem Science, Lund University, Lund Sweden
  3. Department of Ecology and Evolutionary Biology, University of California, Irvine California USA; Department of Earth System Science, University of California, Irvine California USA
  4. ISRIC-World Soil Information, Wageningen Netherlands
  5. Max Planck Institute for Meteorology, Hamburg Germany
  6. 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
  7. CSIRO Land and Water National Research Flagship, Canberra Australia
  8. Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ, Gif-sur-Yvette France
  9. Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg Maryland USA
  10. Department of Biology, Boston University, Boston Massachusetts USA
  11. Department of Chemical and Biomolecular Engineering, University of California, Berkeley California USA; Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley California USA
  12. Pacific Forestry Centre, Canadian Forest Service, Victoria British Columbia Canada
  13. U.S. Geological Survey, Menlo Park California USA
  14. Department of Earth System Science, University of California, Irvine California USA
  15. Department of Microbiology and Plant Biology, University of Oklahoma, Norman Oklahoma USA
  16. Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley California USA
  17. Department of Earth System Science, Stanford University, Stanford California USA
  18. Met Office Hadley Centre, Exeter UK
  19. Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks Alaska USA
  20. U.S. Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit, Fairbanks Alaska USA; Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks Alaska USA
  21. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins Colorado USA
  22. Institute of Environment Sciences, University of Quebec at Montreal, Montreal Quebec Canada
  23. Department of Biological Sciences, Purdue University, West Lafayette Indiana USA
  24. Max Planck Institute for Biogeochemistry, Jena Germany
  25. Computational Science Laboratory, Microsoft Research, Cambridge UK
  26. International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn Alabama USA
  27. Biological Sciences Division, Pacific Northwest National Laboratory, Richland Washington USA
  28. Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff Arizona USA
  29. CSIRO Ocean and Atmosphere Flagship, Aspendale Victoria Australia
  30. Joint Global Change Research Institute, College Park Maryland USA
  31. Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge Tennessee USA
  32. National Center for Atmospheric Research, Boulder Colorado USA
  33. School of Ecological and Environmental Sciences, East China Normal University, Shanghai China
  34. Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames Iowa USA
  35. Department of Biological Science, University of Texas at El Paso, El Paso Texas USA
  36. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing China
Publication Date:
OSTI Identifier:
1255385
Report Number(s):
PNNL-SA-114616
Journal ID: ISSN 0886-6236
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Global Biogeochemical Cycles; Journal Volume: 30; Journal Issue: 1
Publisher:
American Geophysical Union (AGU)
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
Soil carbon; Earth system Models; data integration