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

Research output: Contribution to journalArticle

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.

Details

Authors
  • Yiqi Luo
  • Steven D. Allison
  • Niels H. Batjes
  • Victor Brovkin
  • Nuno Carvalhais
  • Adrian Chappell
  • Philippe Ciais
  • Eric A. Davidson
  • Adien Finzi
  • Katerina Georgiou
  • Bertrand Guenet
  • Oleksandra Hararuk
  • Jennifer W. Harden
  • Yujie He
  • Francesca Hopkins
  • Lifen Jiang
  • Charlie Koven
  • Robert B. Jackson
  • Chris D. Jones
  • Mark J. Lara
  • Junyi Liang
  • A. David McGuire
  • William Parton
  • Changhui Peng
  • James T. Randerson
  • Alejandro Salazar
  • Carlos A. Sierra
  • Matthew J. Smith
  • Hanqin Tian
  • Katherine E.O. Todd-Brown
  • Margaret Torn
  • Kees Jan Van Groenigen
  • Ying Ping Wang
  • Tristram O. West
  • Yaxing Wei
  • William R. Wieder
  • Jianyang Xia
  • Xia Xu
  • Xiaofeng Xu
  • Tao Zhou
Organisations
External organisations
  • University of Oklahoma
  • Tsinghua University
  • Stanford University
  • University of California, Irvine
  • ISRIC - World Soil Information
  • Max Planck Institute for Meteorology
  • Max Planck Institute for Biogeochemistry
  • New University of Lisbon
  • CSIRO Land & Water Flagship
  • Versailles Saint-Quentin-en-Yvelines University
  • University of Maryland
  • Boston University
  • University of California, Berkeley
  • Lawrence Berkeley National Laboratory
  • Pacific Forestry Centre
  • United States Geological Survey Western Region
  • Met Office
  • University of Alaska Fairbanks
  • Northern Rocky Mountain Science Center
  • Colorado State University
  • University Of Quebec In Montreal
  • Purdue University
  • Microsoft Research Ltd, UK
  • Auburn University
  • Pacific Northwest National Laboratory
  • Northern Arizona University
  • CSIRO Oceans and Atmosphere, Canberra
  • Joint Global Change Research Institute
  • Oak Ridge National Laboratory
  • National Center for Atmospheric Research
  • East China Normal University
  • Iowa State University
  • University of Texas at El Paso
  • Beijing Normal University
Research areas and keywords

Keywords

  • CMIP5, Earth system models, realistic projections, recommendations, soil carbon dynamics
Original languageEnglish
Pages (from-to)40-56
Number of pages17
JournalGlobal Biogeochemical Cycles
Volume30
Issue number1
Publication statusPublished - 2016 Jan 1
Publication categoryResearch
Peer-reviewedYes