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

Abstract

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 themore » 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.« less

Authors:

Zhou, Sha  ^[1];

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Liang, Junyi ^[2]; Lu, Xingjie ^[3]; Li, Qianyu ^[4]; Jiang, Lifen ^[5]; Zhang, Yao ^[6]; Schwalm, Christopher R. ^[7]; Fisher, Joshua B. ^[8]; Tjiputra, Jerry ^[9]; Sitch, Stephen ^[10]; Ahlström, Anders ^[11]; Huntzinger, Deborah N. ^[12]; Huang, Yuefei ^[13]; Wang, Guangqian ^[14]; Luo, Yiqi ^[15]

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+ Show Author Affiliations

1. Tsinghua Univ., Beijing (China). State Key Lab. of Hydroscience and Engineering, Dept. of Hydraulic Engineering
2. Univ. of Oklahoma, Norman, OK (United States). Dept. of Microbiology and Plant Biology
3. Univ. of Oklahoma, Norman, OK (United States). Dept. of Microbiology and Plant Biology; Northern Arizona Univ., Flagstaff, AZ (United States). Dept. of Biological Sciences; Northern Arizona Univ., Flagstaff, AZ (United States). Center for Ecosystem Science and Society
4. Univ. of Oklahoma, Norman, OK (United States). Dept. of Microbiology and Plant Biology; Tsinghua Univ., Beijing (China). Dept. of Earth System Science
5. Northern Arizona Univ., Flagstaff, AZ (United States). Dept. of Biological Sciences, 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, Center for Ecosystem Science and Society
8. California Inst. of Technology (CalTech), La Canada Flintridge, 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. Lund Univ., Lund (Sweden). Dept. of Physical Geography and Ecosystem Science; Stanford Univ., CA (United States). Department of Earth System Science
12. Northern Arizona Univ., Flagstaff, AZ (United States). School of Earth Sciences and Environmental Sustainability, Dept. of Civil Engineering, Construction Management, and Environmental Engineering,
13. Tsinghua Univ., Beijing (China). State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering; Qinghai Univ., Qinghai (China). College of Ecological and Environmental Engineering
14. Tsinghua Univ., Beijing (China). State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineerin
15. Northern Arizona Univ., Flagstaff, AZ (United States). Dept. of Biological Sciences, Center for Ecosystem Science and Society; Tsinghua Univ., Beijing, (China). Dept. of Earth System Science

Publication Date:

Mon Mar 12 00:00:00 EDT 2018

Research Org.:

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:

National Aeronautics and Space Administration (NASA); USDOE

OSTI Identifier:

1563947

Alternate Identifier(s):

OSTI ID: 1468035

Grant/Contract Number:  

AC02-05CH11231; AC05-00OR22725; NNX10AG01A; NNH10AN681

Resource Type:

Accepted Manuscript

Journal Name:

Journal of Climate

Additional Journal Information:

Journal Volume: 31; Journal Issue: 7; Journal ID: ISSN 0894-8755

Publisher:

American Meteorological Society

Country of Publication:

United States

Language:

English

Subject:

54 ENVIRONMENTAL SCIENCES; Carbon cycle; Land surface model; Model evaluation/performance