EGU22-8031
https://doi.org/10.5194/egusphere-egu22-8031
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Multi-model comparison of carbon cycle predictability in initialized perfect-model simulations

Aaron Spring1, Hongmei Li1, Tatiana Ilyina1, Raffaele Bernardello2, Yohan Ruprich-Robert2, Etienne Tourigny2, Juliette Mignot3, Filippa Fransner4, Jerry Tjiputra5, Reinel Sospedra-Alfonso6, Thomas Frölicher7,8, and Michio Watanabe9
Aaron Spring et al.
  • 1Max-Planck-Institute for Meteorology, Hamburg, Germany
  • 2Barcelona Supercomputing Center, Barcelona, Spain
  • 3LOCEAN/IPSL, Paris, France
  • 4University of Bergen, Bjerknes Centre for Climate Research, Bergen, Norway
  • 5NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway
  • 6Canadian Centre for Climate Modelling and Analysis, Victoria, Canada
  • 7Climate and Environmental Physcis, Physics Institute, University of Bern, Bern, Switzerland
  • 8Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
  • 9Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Predicting carbon fluxes and atmospheric CO2 can constrain the expected next-year atmospheric CO2 growth rate and thereby allow to independently monitor total anthropogenic CO2 emission rates. Several studies have established predictive skill in retrospective forecasts of carbon fluxes. These studies are usually backed by perfect-model simulations of single models showing the origins of predictive skill in carbon fluxes and atmospheric CO2 concentration. Yet, a comprehensive multi-model comparison of perfect-model predictions, which can be valuable in explaining differences in retrospective predictions, is still lacking. Moreover, as of now, we don't have sufficient understanding of how well do the models predict their own integrated carbon cycles and how congruent this predictability is across models.

Here, we show the predictive skill of land and ocean carbon fluxes as well as atmospheric CO2 concentration in seven Earth-System-Models. Our first results indicate predictive skill of globally aggregated carbon fluxes of 2±1 years and atmospheric CO2 of 3±2 years. However, the regional patterns, hotspots and origins of predictive skill diverge among models. This heterogeneity explains the regional differences found in existing retrospective forecasts and backs the overall consistent predictability time-scales at global scale.

How to cite: Spring, A., Li, H., Ilyina, T., Bernardello, R., Ruprich-Robert, Y., Tourigny, E., Mignot, J., Fransner, F., Tjiputra, J., Sospedra-Alfonso, R., Frölicher, T., and Watanabe, M.: Multi-model comparison of carbon cycle predictability in initialized perfect-model simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8031, https://doi.org/10.5194/egusphere-egu22-8031, 2022.

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