Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets

Wei Li; Philippe Ciais; Yilong Wang; Shushi Peng; Grégoire Broquet; Ashley P. Ballantyne; Josep G. Canadell; Leila Cooper; Pierre Friedlingstein; Corinne Le Quéré; Ranga B. Myneni; Glen P. Peters; Shilong Piao; Julia Pongratz

doi:10.1073/pnas.1603956113

Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets

Wei Li, Philippe Ciais, Yilong Wang, Shushi Peng, Grégoire Broquet, Ashley P. Ballantyne, Josep G. Canadell, Leila Cooper, Pierre Friedlingstein, Corinne Le Quéré, Ranga B. Myneni, Glen P. Peters, Shilong Piao, Julia Pongratz

Ecosystem and Conservation Sciences

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

34 Scopus citations

Abstract

Conventional calculations of the global carbon budget infer the land sink as a residual between emissions, atmospheric accumulation, and the ocean sink. Thus, the land sink accumulates the errors from the other flux terms and bears the largest uncertainty. Here, we present a Bayesian fusion approach that combines multiple observations in different carbon reservoirs to optimize the land (B) and ocean (O) carbon sinks, land use change emissions (L), and indirectly fossil fuel emissions (F) from 1980 to 2014. Compared with the conventional approach, Bayesian optimization decreases the uncertainties in B by 41% and in O by 46%. The L uncertainty decreases by 47%, whereas F uncertainty is marginally improved through the knowledge of natural fluxes. Both ocean and net land uptake (B + L) rates have positive trends of 29 ± 8 and 37 ± 17 Tg C·y^-2 since 1980, respectively. Our Bayesian fusion of multiple observations reduces uncertainties, thereby allowing us to isolate important variability in global carbon cycle processes.

Original language	English
Pages (from-to)	13104-13108
Number of pages	5
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	113
Issue number	46
DOIs	https://doi.org/10.1073/pnas.1603956113
State	Published - Nov 15 2016

Funding

This paper builds on an analysis started by the late Michael R. Raupach and is a contribution to the work of the Global Carbon Project to understand and better constrain the human perturbation of the carbon budget. We thank Alessandro Anav for providing the data from CMIP5 and Peter Landschützer and Christian Rödenbeck for sharing their ocean flux data. We are grateful to the scientists from NOAA/ESRL and the Scripps O2 Program for making the invaluable data from the long-term measurements of CGR and O2/N2 available to the community. W.L. is supported by the European Commission-funded project LUC4C (603542). P.C. and S. Peng acknowledge support from the European Research Council through Synergy Grant ERC-2013-SyG-610028 "IMBALANCE-P." J.G.C. is supported by the Australian Climate Change Science Program, G.P.P. is supported by the Norwegian Research Council (236296), and J.P. is supported by the German Research Foundation's Emmy Noether Program.

Funders	Funder number
603542
1443108, 1633831
National Oceanic and Atmospheric Administration
Natural Environment Research Council	NE/I03002X/1
ERC-2013-SyG-610028 "IMBALANCE-P
236296

Keywords

Bayesian fusion
Carbon cycle
Decadal variations
Global carbon budget

UN SDGs

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

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10.1073/pnas.1603956113

Cite this

Li, W., Ciais, P., Wang, Y., Peng, S., Broquet, G., Ballantyne, A. P., Canadell, J. G., Cooper, L., Friedlingstein, P., Le Quéré, C., Myneni, R. B., Peters, G. P., Piao, S., & Pongratz, J. (2016). Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets. Proceedings of the National Academy of Sciences of the United States of America, 113(46), 13104-13108. https://doi.org/10.1073/pnas.1603956113

@article{66e7119f8cfe44908b283c5d201b821f,

title = "Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets",

abstract = "Conventional calculations of the global carbon budget infer the land sink as a residual between emissions, atmospheric accumulation, and the ocean sink. Thus, the land sink accumulates the errors from the other flux terms and bears the largest uncertainty. Here, we present a Bayesian fusion approach that combines multiple observations in different carbon reservoirs to optimize the land (B) and ocean (O) carbon sinks, land use change emissions (L), and indirectly fossil fuel emissions (F) from 1980 to 2014. Compared with the conventional approach, Bayesian optimization decreases the uncertainties in B by 41\% and in O by 46\%. The L uncertainty decreases by 47\%, whereas F uncertainty is marginally improved through the knowledge of natural fluxes. Both ocean and net land uptake (B + L) rates have positive trends of 29 ± 8 and 37 ± 17 Tg C·y-2 since 1980, respectively. Our Bayesian fusion of multiple observations reduces uncertainties, thereby allowing us to isolate important variability in global carbon cycle processes.",

keywords = "Bayesian fusion, Carbon cycle, Decadal variations, Global carbon budget",

author = "Wei Li and Philippe Ciais and Yilong Wang and Shushi Peng and Gr{\'e}goire Broquet and Ballantyne, \{Ashley P.\} and Canadell, \{Josep G.\} and Leila Cooper and Pierre Friedlingstein and \{Le Qu{\'e}r{\'e}\}, Corinne and Myneni, \{Ranga B.\} and Peters, \{Glen P.\} and Shilong Piao and Julia Pongratz",

note = "Funding Information: This paper builds on an analysis started by the late Michael R. Raupach and is a contribution to the work of the Global Carbon Project to understand and better constrain the human perturbation of the carbon budget. We thank Alessandro Anav for providing the data from CMIP5 and Peter Landsch{\"u}tzer and Christian R{\"o}denbeck for sharing their ocean flux data. We are grateful to the scientists from NOAA/ESRL and the Scripps O2 Program for making the invaluable data from the long-term measurements of CGR and O2/N2 available to the community. W.L. is supported by the European Commission-funded project LUC4C (603542). P.C. and S. Peng acknowledge support from the European Research Council through Synergy Grant ERC-2013-SyG-610028 {"}IMBALANCE-P.{"} J.G.C. is supported by the Australian Climate Change Science Program, G.P.P. is supported by the Norwegian Research Council (236296), and J.P. is supported by the German Research Foundation's Emmy Noether Program.",

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language = "English",

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Li, W, Ciais, P, Wang, Y, Peng, S, Broquet, G, Ballantyne, AP, Canadell, JG, Cooper, L, Friedlingstein, P, Le Quéré, C, Myneni, RB, Peters, GP, Piao, S & Pongratz, J 2016, 'Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets', Proceedings of the National Academy of Sciences of the United States of America, vol. 113, no. 46, pp. 13104-13108. https://doi.org/10.1073/pnas.1603956113

TY - JOUR

T1 - Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets

AU - Li, Wei

AU - Ciais, Philippe

AU - Wang, Yilong

AU - Peng, Shushi

AU - Broquet, Grégoire

AU - Ballantyne, Ashley P.

AU - Canadell, Josep G.

AU - Cooper, Leila

AU - Friedlingstein, Pierre

AU - Le Quéré, Corinne

AU - Myneni, Ranga B.

AU - Peters, Glen P.

AU - Piao, Shilong

AU - Pongratz, Julia

N1 - Funding Information: This paper builds on an analysis started by the late Michael R. Raupach and is a contribution to the work of the Global Carbon Project to understand and better constrain the human perturbation of the carbon budget. We thank Alessandro Anav for providing the data from CMIP5 and Peter Landschützer and Christian Rödenbeck for sharing their ocean flux data. We are grateful to the scientists from NOAA/ESRL and the Scripps O2 Program for making the invaluable data from the long-term measurements of CGR and O2/N2 available to the community. W.L. is supported by the European Commission-funded project LUC4C (603542). P.C. and S. Peng acknowledge support from the European Research Council through Synergy Grant ERC-2013-SyG-610028 "IMBALANCE-P." J.G.C. is supported by the Australian Climate Change Science Program, G.P.P. is supported by the Norwegian Research Council (236296), and J.P. is supported by the German Research Foundation's Emmy Noether Program.

PY - 2016/11/15

Y1 - 2016/11/15

N2 - Conventional calculations of the global carbon budget infer the land sink as a residual between emissions, atmospheric accumulation, and the ocean sink. Thus, the land sink accumulates the errors from the other flux terms and bears the largest uncertainty. Here, we present a Bayesian fusion approach that combines multiple observations in different carbon reservoirs to optimize the land (B) and ocean (O) carbon sinks, land use change emissions (L), and indirectly fossil fuel emissions (F) from 1980 to 2014. Compared with the conventional approach, Bayesian optimization decreases the uncertainties in B by 41% and in O by 46%. The L uncertainty decreases by 47%, whereas F uncertainty is marginally improved through the knowledge of natural fluxes. Both ocean and net land uptake (B + L) rates have positive trends of 29 ± 8 and 37 ± 17 Tg C·y-2 since 1980, respectively. Our Bayesian fusion of multiple observations reduces uncertainties, thereby allowing us to isolate important variability in global carbon cycle processes.

AB - Conventional calculations of the global carbon budget infer the land sink as a residual between emissions, atmospheric accumulation, and the ocean sink. Thus, the land sink accumulates the errors from the other flux terms and bears the largest uncertainty. Here, we present a Bayesian fusion approach that combines multiple observations in different carbon reservoirs to optimize the land (B) and ocean (O) carbon sinks, land use change emissions (L), and indirectly fossil fuel emissions (F) from 1980 to 2014. Compared with the conventional approach, Bayesian optimization decreases the uncertainties in B by 41% and in O by 46%. The L uncertainty decreases by 47%, whereas F uncertainty is marginally improved through the knowledge of natural fluxes. Both ocean and net land uptake (B + L) rates have positive trends of 29 ± 8 and 37 ± 17 Tg C·y-2 since 1980, respectively. Our Bayesian fusion of multiple observations reduces uncertainties, thereby allowing us to isolate important variability in global carbon cycle processes.

KW - Bayesian fusion

KW - Carbon cycle

KW - Decadal variations

KW - Global carbon budget

UR - https://www.scopus.com/pages/publications/84995653359

U2 - 10.1073/pnas.1603956113

DO - 10.1073/pnas.1603956113

M3 - Article

AN - SCOPUS:84995653359

SN - 0027-8424

VL - 113

SP - 13104

EP - 13108

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

IS - 46

ER -

Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets

Abstract

Funding

Keywords

UN SDGs

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