Quantifying Human-Mediated Carbon Cycle Feedbacks

Andrew D. Jones; Katherine V. Calvin; Xiaoying Shi; Alan V. Di Vittorio; Ben Bond-Lamberty; Peter E. Thornton; William D. Collins

doi:10.1029/2018GL079350

Quantifying Human-Mediated Carbon Cycle Feedbacks

Andrew D. Jones, Katherine V. Calvin, Xiaoying Shi, Alan V. Di Vittorio, Ben Bond-Lamberty, Peter E. Thornton, William D. Collins

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

Changes in land and ocean carbon storage in response to elevated atmospheric carbon dioxide concentrations and associated climate change, known as the concentration-carbon and climate-carbon feedbacks, are principal controls on the response of the climate system to anthropogenic greenhouse gas emissions. Such feedbacks have typically been quantified in the context of natural ecosystems, but land management activities are also responsive to future atmospheric carbon and climate changes. Here we show that inclusion of such human-driven responses within an Earth system model shifts both the terrestrial concentration-carbon and climate-carbon feedbacks toward increased carbon storage. We introduce a conceptual framework for decomposing these changes into separate concentration-land cover, climate-land cover, and land cover-carbon effects, providing a parsimonious means to diagnose sources of variation across numerical models capable of estimating such feedbacks.

Original language	English
Pages (from-to)	11,370-11,379
Journal	Geophysical Research Letters
Volume	45
Issue number	20
DOIs	https://doi.org/10.1029/2018GL079350
State	Published - Oct 28 2018

Funding

This research was supported as part of the Accelerated Climate Modeling for Energy (ACME) project, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. The iESM model code used in this paper can be accessed at https://github.com/E3SM-Project/iESM. Data associated with each figure in the paper are provided in the supporting information. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-05CH11231. This research also used resources of the Oak Ridge Leadership Computing Facility, which is a U.S. Department of Energy Office of Science User Facility supported under contract DE-AC05-00OR22725. This work used the Community Earth System Model, CESM, and the Global Change Assessment Model, GCAM. The National Science Foundation and the Office of Science of the U.S. Department of Energy support the CESM project. The authors acknowledge long-term support for GCAM development from the Integrated Assessment Research Program in the Office of Science of the U.S. Department of Energy. The opinions expressed in this paper are the authors' alone. This research was supported as part of the Accelerated Climate Modeling for Energy (ACME) project, funded by the U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research. The iESM model code used in this paper can be accessed at https://github.com/E3SM- Project/iESM. Data associated with each figure in the paper are provided in the supporting information. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-05CH11231. This research also used resources of the Oak Ridge Leadership Computing Facility, which is a U.S. Department of Energy Office of Science User Facility supported under contract DE-AC05-00OR22725. This work used the Community Earth System Model, CESM, and the Global Change Assessment Model, GCAM. The National Science Foundation and the Office of Science of the U.S. Department of Energy support the CESM project. The authors acknowledge long-term support for GCAM development from the Integrated Assessment Research Program in the Office of Science of the U.S. Department of Energy. The opinions expressed in this paper are the authors’ alone.

Funders	Funder number
DOE Office of Science
GCAM
Global Change Assessment Model
Office of Biological and Environmental Research
U. S. Department of Energy
U.S. Department of Energy Office of Science	DE-AC05-00OR22725
National Science Foundation
U.S. Department of Energy	DE-AC02-05CH11231
Office of Science

Keywords

carbon cycle feedbacks
climate impacts on agriculture
coupled human and natural systems

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1029/2018GL079350

Cite this

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Quantifying Human-Mediated Carbon Cycle Feedbacks

Abstract

Funding

Keywords

UN SDGs

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