| Title: |
Predictable Variations of the Carbon Sinks and Atmospheric CO2 Growth in a Multi-Model Framework |
| Authors: |
Ilyina, Tatiana; Hongmei, Li; Spring, Aaron; Müller, Wolfgang A.; Bopp, Laurent; Chikamoto, Megumi O.; Danabasoglu, Gokhan; Dobrynin, Mikhail; Dunne, John P. Patrick; Fransner, Filippa; Friedlingstein, Pierre; Lee, Woo-Sung; Lovenduski, Nicole; Merryfield, William J.; Mignot, Juliette; Park, Jong-Yeon; Séférian, Roland; Sospedra-Alfonso, Reinel; Watanabe, Michio; Yeager, Stephen G. |
| Source: |
e2020GL090695 ; Geophysical Research Letters ; 48 ; 6 |
| Publisher Information: |
Wiley |
| Publication Year: |
2021 |
| Collection: |
University of Bergen: Bergen Open Research Archive (BORA-UiB) |
| Description: |
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. ; publishedVersion |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
Norges forskningsråd: 276730; https://hdl.handle.net/11250/2986036; https://doi.org/10.1029/2020GL090695; cristin:1865933; Geophysical Research Letters. 2021, 48 (6), e2020GL090695. |
| DOI: |
10.1029/2020GL090695 |
| Availability: |
https://hdl.handle.net/11250/2986036; https://doi.org/10.1029/2020GL090695 |
| Rights: |
Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal ; http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no ; Copyright 2020. The Authors |
| Accession Number: |
edsbas.9C3D8F45 |
| Database: |
BASE |