Predictable variations of the carbon sinks and atmospheric CO2 growth in a multi‐model framework

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 initialized by the observed state of the physical climate, we find a predictive skill for the global ocean carbon sink of up to 6 years for some models. Longer regional predictability horizons 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. 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.

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Author Ilyina, T.
Li, H.
Spring, A.
Müller, W. A.
Bopp, L.
Chikamoto, M. O.
Danabasoglu, Gokhan
Dobrynin, M.
Dunne, J.
Fransner, F.
Friedlingstein, P.
Lee, W.
Lovenduski, N. S.
Merryfield, W.J.
Mignot, J.
Park, J.Y.
Séférian, R.
Sospedra‐Alfonso, R.
Watanabe, M.
Yeager, Stephen
Publisher UCAR/NCAR - Library
Publication Date 2021-03-28T00:00:00
Digital Object Identifier (DOI) Not Assigned
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Resource Version N/A
Topic Category geoscientificInformation
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Metadata Date 2023-08-18T18:29:07.345118
Metadata Record Identifier edu.ucar.opensky::articles:24286
Metadata Language eng; USA
Suggested Citation Ilyina, T., Li, H., Spring, A., Müller, W. A., Bopp, L., Chikamoto, M. O., Danabasoglu, Gokhan, Dobrynin, M., Dunne, J., Fransner, F., Friedlingstein, P., Lee, W., Lovenduski, N. S., Merryfield, W.J., Mignot, J., Park, J.Y., Séférian, R., Sospedra‐Alfonso, R., Watanabe, M., Yeager, Stephen. (2021). Predictable variations of the carbon sinks and atmospheric CO2 growth in a multi‐model framework. UCAR/NCAR - Library. http://n2t.net/ark:/85065/d73b63j6. Accessed 17 June 2025.

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