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Predictable variations of the carbon sinks and atmospheric CO2 growth in a multi-model framework
  • +17
  • Tatiana Ilyina,
  • Hongmei Li,
  • Aaron Spring,
  • Wolfgang A. Müller,
  • Laurent Bopp,
  • Megumi O. Chikamoto,
  • Gokhan Danabasoglu,
  • Mikhail Dobrynin,
  • John P. Patrick Dunne,
  • Filippa Fransner,
  • Pierre Friedlingstein,
  • Woo-Sung Lee,
  • Nicole Suzanne Lovenduski,
  • William J Merryfield,
  • Juliette Mignot,
  • Jong-Yeon Park,
  • Roland Séférian,
  • Reinel Sospedra-Alfonso,
  • Michio Watanabe,
  • Stephen Yeager
Tatiana Ilyina
Max Planck Institute of Meteorology

Corresponding Author:[email protected]

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Hongmei Li
Max Planck Institute for Meteorology
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Aaron Spring
Max Planck Institute for Meteorology
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Wolfgang A. Müller
Max Planck Institute for Meteorology
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Laurent Bopp
LMD / IPSL
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Megumi O. Chikamoto
University of Texas at Austin
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Gokhan Danabasoglu
National Center for Atmospheric Research (NCAR)
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Mikhail Dobrynin
Deutscher Wetterdienst
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John P. Patrick Dunne
Geophysical Fluid Dynamics Laboratory
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Filippa Fransner
Geophysical Institute, University of Bergen
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Pierre Friedlingstein
University of Execter (UK)
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Woo-Sung Lee
Canadian Centre for Climate Modelling and Analysis
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Nicole Suzanne Lovenduski
University of Colorado Boulder
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William J Merryfield
Canadian Centre for Climate Modelling and Analysis
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Juliette Mignot
LOCEAN
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Jong-Yeon Park
Chonbuk National University
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Roland Séférian
CNRM (Météo-France/CNRS)
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Reinel Sospedra-Alfonso
Canadian Centre for Climate Modelling and Analysis
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Michio Watanabe
Japan Agency for Marine-Earth Science and Technology
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Stephen Yeager
National Center for Atmospheric Research
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Abstract

Inter-annual to decadal variability in the strength of the land and ocean carbon sinks impede accurate predictions of year-to-year atmospheric carbon dioxide (CO2) growth rate. Such information is crucial to verify the effectiveness of fossil fuel emissions reduction measures. Using a multi-model framework comprising prediction systems based on Earth system models, we find a predictive skill for the global ocean carbon sink of up to 6 years. Longer regional predictability horizons and robust spatial patterns are found across single models. On land, a predictive skill of up to 2 years is primarily maintained in the tropics and extra-tropics enabled by the initialization of the physical climate variables towards observations. We further show that anomalies of atmospheric CO2 growth rate inferred from natural variations of the land and ocean carbon sinks are predictable at lead time of 2 years and the skill is limited by the land carbon sink predictability horizon.