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

Abstract

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.more » 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.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [10];  [11];  [8];  [12];  [13];  [3];  [3];  [14];  [15];  [16];  [17] more »;  [18];  [14];  [19];  [20];  [21];  [3];  [22];  [23];  [24];  [25];  [26];  [15];  [27];  [28];  [29];  [30];  [31];  [32];  [33];  [34];  [35] « less
+ Show Author Affiliations
  1. Univ. of Oklahoma, Norman, OK (United States); Tsinghua Univ., Beijing (China)
  2. Stanford Univ., Stanford, CA (United States); Lund Univ., Lund (Sweden)
  3. Univ. of California, Irvine, CA (United States)
  4. ISRIC-World Soil Information, Wageningen (Netherlands)
  5. Max Planck Institute for Meteorology, Hamburg (Germany)
  6. Max Planck Institute for Biogeochemistry, Jena (Germany); Univ. NOVA de Lisboa, Caparica (Portugal)
  7. CSIRO Land and Water National Research Flagship, Canberra (Australia)
  8. CEA CNRS UVSQ, Gif-sur-Yvette (France)
  9. Univ. of Maryland Center for Environmental Science, Frostburg, MD (United States)
  10. Boston Univ., Boston, MA (United States)
  11. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  12. Pacific Forestry Centre, Canadian Forest Service, Victoria BC (Canada)
  13. U.S. Geological Survey, Menlo Park, CA (United States)
  14. Univ. of Oklahoma, Norman, OK (United States)
  15. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  16. Stanford Univ., Stanford, CA (United States)
  17. Met Office Hadley Centre, Exeter (United Kingdom)
  18. Univ. of Alaska Fairbanks, Fairbanks, AK (United States)
  19. Univ. of Alaska Fairbanks, Fairbanks, AK (United States); U.S. Geological Survey, Fairbanks, AK (United States)
  20. Colorado State Univ., Fort Collins, CO (United States)
  21. Univ. of Quebec at Montreal, Montreal QC (Canada)
  22. Purdue Univ., West Lafayette, IN (United States)
  23. Max Planck Institute for Biogeochemistry, Jena (Germany)
  24. Microsoft Research, Cambridge (United Kingdom)
  25. Auburn Univ., Auburn, AL (United States)
  26. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  27. Northern Arizona Univ., Flagstaff, AZ (United States)
  28. CSIRO Ocean and Atmosphere Flagship, Aspendale VIC (Australia)
  29. Joint Global Change Research Institute, College Park, MD (United States)
  30. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  31. National Center for Atmospheric Research, Boulder, CO (United States)
  32. East China Normal Univ., Shanghai (China)
  33. Iowa State Univ., Ames, IA (United States)
  34. Univ. of Texas at El Paso, El Paso, TX (United States)
  35. Beijing Normal Univ., Beijing (China)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1335308
Alternate Identifier(s):
OSTI ID: 1785847
Grant/Contract Number:  
AC05-00OR22725; DE SC0008270; DE‐SC0006982; DE‐SC0008270; DE‐SC0014062; DE‐SC0004601; DE‐SC0010715
Resource Type:
Accepted Manuscript
Journal Name:
Global Biogeochemical Cycles
Additional Journal Information:
Journal Volume: 30; Journal Issue: 1; Journal ID: ISSN 0886-6236
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES

Citation Formats

Luo, Yiqi, Ahlstrom, Anders, Allison, Steven D., Batjes, Niels H., Brovkin, Victor, Carvalhais, Nuno, Chappell, Adrian, Ciais, Philippe, Davidson, Eric A., Finzi, Adien, Georgiou, Katerina, Guenet, Bertrand, Hararuk, Oleksandra, Harden, Jennifer W., He, Yujie, Hopkins, Francesca, Jiang, Lifen, Koven, Charlie, Jackson, Robert B., Jones, Chris D., Lara, Mark J., Liang, Junyi, McGuire, A. David, Parton, William, Peng, Changhui, Randerson, James T., Salazar, Alejandro, Sierra, Carlos A., Smith, Matthew J., Tian, Hanqin, Todd-Brown, Katherine E. O., Torn, Margaret, van Groenigen, Kees Jan, Wang, Ying Ping, West, Tristram O., Wei, Yaxing, Wieder, William R., Xia, Jianyang, Xu, Xia, Xu, Xiaofeng, and Zhou, Tao. Toward more realistic projections of soil carbon dynamics by Earth system models. United States: N. p., 2016. Web. doi:10.1002/2015GB005239.
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Luo, Yiqi, Ahlstrom, Anders, Allison, Steven D., Batjes, Niels H., Brovkin, Victor, Carvalhais, Nuno, Chappell, Adrian, Ciais, Philippe, Davidson, Eric A., Finzi, Adien, Georgiou, Katerina, Guenet, Bertrand, Hararuk, Oleksandra, Harden, Jennifer W., He, Yujie, Hopkins, Francesca, Jiang, Lifen, Koven, Charlie, Jackson, Robert B., Jones, Chris D., Lara, Mark J., Liang, Junyi, McGuire, A. David, Parton, William, Peng, Changhui, Randerson, James T., Salazar, Alejandro, Sierra, Carlos A., Smith, Matthew J., Tian, Hanqin, Todd-Brown, Katherine E. O., Torn, Margaret, van Groenigen, Kees Jan, Wang, Ying Ping, West, Tristram O., Wei, Yaxing, Wieder, William R., Xia, Jianyang, Xu, Xia, Xu, Xiaofeng, & Zhou, Tao. Toward more realistic projections of soil carbon dynamics by Earth system models. United States. https://doi.org/10.1002/2015GB005239
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Luo, Yiqi, Ahlstrom, Anders, Allison, Steven D., Batjes, Niels H., Brovkin, Victor, Carvalhais, Nuno, Chappell, Adrian, Ciais, Philippe, Davidson, Eric A., Finzi, Adien, Georgiou, Katerina, Guenet, Bertrand, Hararuk, Oleksandra, Harden, Jennifer W., He, Yujie, Hopkins, Francesca, Jiang, Lifen, Koven, Charlie, Jackson, Robert B., Jones, Chris D., Lara, Mark J., Liang, Junyi, McGuire, A. David, Parton, William, Peng, Changhui, Randerson, James T., Salazar, Alejandro, Sierra, Carlos A., Smith, Matthew J., Tian, Hanqin, Todd-Brown, Katherine E. O., Torn, Margaret, van Groenigen, Kees Jan, Wang, Ying Ping, West, Tristram O., Wei, Yaxing, Wieder, William R., Xia, Jianyang, Xu, Xia, Xu, Xiaofeng, and Zhou, Tao. Thu . "Toward more realistic projections of soil carbon dynamics by Earth system models". United States. https://doi.org/10.1002/2015GB005239. https://www.osti.gov/servlets/purl/1335308.
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@article{osti_1335308,
title = {Toward more realistic projections of soil carbon dynamics by Earth system models},
author = {Luo, Yiqi and Ahlstrom, Anders and Allison, Steven D. and Batjes, Niels H. and Brovkin, Victor and Carvalhais, Nuno and Chappell, Adrian and Ciais, Philippe and Davidson, Eric A. and Finzi, Adien and Georgiou, Katerina and Guenet, Bertrand and Hararuk, Oleksandra and Harden, Jennifer W. and He, Yujie and Hopkins, Francesca and Jiang, Lifen and Koven, Charlie and Jackson, Robert B. and Jones, Chris D. and Lara, Mark J. and Liang, Junyi and McGuire, A. David and Parton, William and Peng, Changhui and Randerson, James T. and Salazar, Alejandro and Sierra, Carlos A. and Smith, Matthew J. and Tian, Hanqin and Todd-Brown, Katherine E. O. and Torn, Margaret and van Groenigen, Kees Jan and Wang, Ying Ping and West, Tristram O. and Wei, Yaxing and Wieder, William R. and Xia, Jianyang and Xu, Xia and Xu, Xiaofeng and Zhou, Tao},
abstractNote = {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.},
doi = {10.1002/2015GB005239},
journal = {Global Biogeochemical Cycles},
number = 1,
volume = 30,
place = {United States},
year = {Thu Jan 21 00:00:00 EST 2016},
month = {Thu Jan 21 00:00:00 EST 2016}
}
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