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  Predictability horizons in the global carbon cycle inferred from a perfect-model framework

Spring, A., & Ilyina, T. (2020). Predictability horizons in the global carbon cycle inferred from a perfect-model framework. Geophysical Research Letters, 47: e2019GL085311. doi:10.1029/2019GL085311.

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 Creators:
Spring, Aaron1, 2, Author                 
Ilyina, Tatiana2, Author                 
Affiliations:
1IMPRS on Earth System Modelling, MPI for Meteorology, Max Planck Society, ou_913547              
2Ocean Biogeochemistry, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society, ou_913556              

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 Abstract: On interannual timescales the growth rate of atmospheric CO2 is largely controlled by the response of the land and ocean carbon sinks to climate variability. Yet, it is unknown to what extent this variability limits the predictability of atmospheric CO2 variations. Using perfect‐model Earth System Model simulations, we show that variations in atmospheric CO2 are potentially predictable for 3 years. We find a 2‐year predictability horizon for global oceanic CO2 flux with longer regional predictability of up to 7 years. The 2‐year predictability horizon of terrestrial CO2 flux originates in the tropics and midlatitudes. With the predictability of the isolated effects of land and ocean carbon sink on atmospheric CO2 of 5 and 12 years respectively, land dampens the overall predictability of atmospheric CO2 variations. Our research shows the potential of Earth System Model‐based predictions to forecast multiyear variations in atmospheric CO2.

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Language(s): eng - English
 Dates: 2019-082020-04-202020-05-162020-05-16
 Publication Status: Issued
 Pages: -
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 Rev. Type: Peer
 Identifiers: DOI: 10.1029/2019GL085311
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Title: Geophysical Research Letters
  Abbreviation : GRL
Source Genre: Journal
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Publ. Info: Washington, D.C. : American Geophysical Union / Wiley
Pages: - Volume / Issue: 47 Sequence Number: e2019GL085311 Start / End Page: - Identifier: ISSN: 0094-8276
CoNE: https://pure.mpg.de/cone/journals/resource/954925465217