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Sources of Uncertainty in Modeled Land Carbon Storage within and across Three MIPs: Diagnosis with Three New Techniques

Journal Article · · Journal of Climate
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [10];  [11];  [12];  [13];  [14];  [15]
  1. Tsinghua Univ., Beijing (China). State Key Lab. of Hydroscience and Engineering, Dept. of Hydraulic Engineering; Columbia Univ., New York, NY (United States)
  2. Univ. of Oklahoma, Norman, OK (United States). Dept. of Microbiology and Plant Biology
  3. Northern Arizona Univ., Flagstaff, AZ (United States). Dept. of Biological Sciences; Northern Arizona Univ., Flagstaff, AZ (United States).Center for Ecosystem Science and Society; Univ. of Oklahoma, Norman, OK (United States). Dept. of Microbiology and Plant Biology
  4. Tsinghua Univ., Beijing (China). Dept. of Earth System Science; Univ. of Oklahoma, Norman, OK (United States). Dept. of Microbiology and Plant Biology
  5. Northern Arizona Univ., Flagstaff, AZ (United States). Dept. of Biological Sciences; Northern Arizona Univ., Flagstaff, AZ (United States).Center for Ecosystem Science and Society
  6. Univ. of Oklahoma, Norman, OK (United States). Dept. of Microbiology and Plant Biology, and Center for Spatial Analysis
  7. Woods Hole Research Center, Falmouth, MA (United States); Northern Arizona Univ., Flagstaff, AZ (United States).Center for Ecosystem Science and Society
  8. California Inst. of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Lab.
  9. Uni Research Climate, Bjerknes Centre for Climate Research, Bergen (Norway)
  10. Univ. of Exeter, Exeter (United Kingdom). College of Life and Environmental Sciences
  11. Stanford Univ., CA (United States). Dept. of Earth System Science; Lund Univ. (Sweden). Dept. of Physical Geography and Ecosystem Science
  12. Northern Arizona Univ., Flagstaff, AZ (United States). School of Earth Sciences and Environmental Sustainability; Northern Arizona Univ., Flagstaff, AZ (United States). Dept. of Civil Engineering, Construction Management, and Environmental Engineering
  13. Tsinghua Univ., Beijing (China). State Key Lab. of Hydroscience and Engineering, Dept. of Hydraulic Engineering; Qinghai Univ., Qinghai (China).College of Ecological and Environmental Engineering
  14. Tsinghua Univ., Beijing (China). State Key Lab. of Hydroscience and Engineering, Dept. of Hydraulic Engineering
  15. Northern Arizona Univ., Flagstaff, AZ (United States). Dept. of Biological Sciences; Northern Arizona Univ., Flagstaff, AZ (United States).Center for Ecosystem Science and Society; Tsinghua Univ., Beijing (China). Dept. of Earth System Science
+ Show Author Affiliations
Terrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (historical simulations), Trends in Net Land–Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. Here in this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (>90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Finally, since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level.
Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
National Aeronautic and Space Administration (NASA); USDOE
Grant/Contract Number:
AC02-05CH11231; AC05-00OR22725
OSTI ID:
1468035
Alternate ID(s):
OSTI ID: 1563947
Journal Information:
Journal of Climate, Journal Name: Journal of Climate Journal Issue: 7 Vol. 31; ISSN 0894-8755
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (5)

Matrix-Based Sensitivity Assessment of Soil Organic Carbon Storage: A Case Study from the ORCHIDEE-MICT Model journal August 2018
More replenishment than priming loss of soil organic carbon with additional carbon input journal August 2018
Divergence in land surface modeling: linking spread to structure journal October 2019
Carbon–nitrogen coupling under three schemes of model representation: a traceability analysis journal January 2018
The quasi-equilibrium framework revisited: analyzing long-term CO2 enrichment responses in plant–soil models journal January 2019

Figures / Tables (8)

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TABLE 2(p. 43)table TABLE 2
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