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Title: Comparison With Global Soil Radiocarbon Observations Indicates Needed Carbon Cycle Improvements in the E3SM Land Model

Abstract

We evaluated global soil organic carbon (SOC) stocks and turnover time predictions from a global land model (ELMv1-ECA) integrated in an Earth System Model (E3SM) by comparing them with observed soil bulk and Δ 14C values around the world. We analyzed observed and simulated SOC stocks and Δ 14C values using machine learning methods at the Earth System Model grid cell scale (~200 km). In grid cells with sufficient observations, the model provided reasonable estimates of soil carbon stocks across soil depth and Δ 14C values near the surface but underestimated Δ 14C at depth. Among many explanatory variables, soil albedo index, soil order, plant function type, air temperature, and SOC content were major factors affecting predicted SOC Δ 14C values. The influences of soil albedo index, soil order, and air temperature were primarily important in the shallow subsurface (≤30 cm). We also performed sensitivity studies using different vertical root distributions and decomposition turnover times and compared to observed SOC stock and Δ 14C profiles. The analyses support the role of vegetation in affecting soil carbon turnover, particularly in deep soil, possibly through supplying fresh carbon and degrading physical-chemical protection of SOC via root activities. Allowing for grid cell-specific rooting and decompositionmore » rates substantially reduced discrepancies between observed and predicted Δ 14C values and SOC content. Furthermore, our results highlight the need for more explicit representation of roots, microbes, and soil physical protection in land models.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Climate and Ecosystem Sciences Div.
  2. Univ. of California, Irvine, CA (United States). Dept. of Earth System Sciences
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1572816
Alternate Identifier(s):
OSTI ID: 1510558
Grant/Contract Number:  
AC02-05CH11231; AC02‐05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Geophysical Research. Biogeosciences
Additional Journal Information:
Journal Volume: 124; Journal Issue: 5; Journal ID: ISSN 2169-8953
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; Earth System Models; advanced land modeling; soil organic carbon; radiocarbon; statistical analysis; machine learning

Citation Formats

Chen, Jinsong, Zhu, Qing, Riley, William J., He, Yujie, Randerson, James T., and Trumbore, Susan. Comparison With Global Soil Radiocarbon Observations Indicates Needed Carbon Cycle Improvements in the E3SM Land Model. United States: N. p., 2019. Web. doi:10.1029/2018jg004795.
Chen, Jinsong, Zhu, Qing, Riley, William J., He, Yujie, Randerson, James T., & Trumbore, Susan. Comparison With Global Soil Radiocarbon Observations Indicates Needed Carbon Cycle Improvements in the E3SM Land Model. United States. doi:10.1029/2018jg004795.
Chen, Jinsong, Zhu, Qing, Riley, William J., He, Yujie, Randerson, James T., and Trumbore, Susan. Mon . "Comparison With Global Soil Radiocarbon Observations Indicates Needed Carbon Cycle Improvements in the E3SM Land Model". United States. doi:10.1029/2018jg004795.
@article{osti_1572816,
title = {Comparison With Global Soil Radiocarbon Observations Indicates Needed Carbon Cycle Improvements in the E3SM Land Model},
author = {Chen, Jinsong and Zhu, Qing and Riley, William J. and He, Yujie and Randerson, James T. and Trumbore, Susan},
abstractNote = {We evaluated global soil organic carbon (SOC) stocks and turnover time predictions from a global land model (ELMv1-ECA) integrated in an Earth System Model (E3SM) by comparing them with observed soil bulk and Δ14C values around the world. We analyzed observed and simulated SOC stocks and Δ14C values using machine learning methods at the Earth System Model grid cell scale (~200 km). In grid cells with sufficient observations, the model provided reasonable estimates of soil carbon stocks across soil depth and Δ14C values near the surface but underestimated Δ14C at depth. Among many explanatory variables, soil albedo index, soil order, plant function type, air temperature, and SOC content were major factors affecting predicted SOC Δ14C values. The influences of soil albedo index, soil order, and air temperature were primarily important in the shallow subsurface (≤30 cm). We also performed sensitivity studies using different vertical root distributions and decomposition turnover times and compared to observed SOC stock and Δ14C profiles. The analyses support the role of vegetation in affecting soil carbon turnover, particularly in deep soil, possibly through supplying fresh carbon and degrading physical-chemical protection of SOC via root activities. Allowing for grid cell-specific rooting and decomposition rates substantially reduced discrepancies between observed and predicted Δ14C values and SOC content. Furthermore, our results highlight the need for more explicit representation of roots, microbes, and soil physical protection in land models.},
doi = {10.1029/2018jg004795},
journal = {Journal of Geophysical Research. Biogeosciences},
number = 5,
volume = 124,
place = {United States},
year = {2019},
month = {4}
}

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