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

  • Author(s): Luo, Y
  • Ahlström, A
  • Allison, SD
  • Batjes, NH
  • Brovkin, V
  • Carvalhais, N
  • Chappell, A
  • Ciais, P
  • Davidson, EA
  • Finzi, A
  • Georgiou, K
  • Guenet, B
  • Hararuk, O
  • Harden, JW
  • He, Y
  • Hopkins, F
  • Jiang, L
  • Koven, C
  • Jackson, RB
  • Jones, CD
  • Lara, MJ
  • Liang, J
  • McGuire, AD
  • Parton, W
  • Peng, C
  • Randerson, JT
  • Salazar, A
  • Sierra, CA
  • Smith, MJ
  • Tian, H
  • Todd-Brown, KEO
  • Torn, M
  • Van Groenigen, KJ
  • Wang, YP
  • West, TO
  • Wei, Y
  • Wieder, WR
  • Xia, J
  • Xu, X
  • Xu, X
  • Zhou, T
  • et al.
Abstract

©2015. American Geophysical Union. All Rights Reserved. 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.

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