EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Variations of the CO2 fluxes and atmospheric CO2 in multi-model predictions with an interactive carbon cycle

Hongmei Li1, Aaron Spring1, istvan Dunkl1, Sebastian Brune2, Raffaele Bernardello3, Laurent Bopp4, William Merryfield5, Juliette Mignot6, Reinel Sospedra-Alfonso5, Etienne Tourigny3, Michio Watanabe7,8, and Tatiana Ilyina1
Hongmei Li et al.
  • 1Max Planck Institute for Meteorology, Hamburg, Germany (
  • 2Institute of Oceanography, CEN, Universität Hamburg, Hamburg, Germany
  • 3Earth Sciences Department, Barcelona Supercomputing Center (BSC), Barcelona, Spain
  • 4Laboratoire de Météorologie Dynamique/Institut Pierre-Simon Laplace, CNRS,Ecole Normale Supérieure/Université PSL, Sorbonne Université, Ecole Polytechnique, Paris, France
  • 5Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada
  • 6Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques, Institut Pierre-Simon Laplace, Sorbonne Université/CNRS/IRD MNHN, Paris, France
  • 7Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
  • 8Atmosphere and Ocean Research Institute, the University of Tokyo, Tokyo, Japan

Variable fluxes of anthropogenic CO2 emissions into the land and the ocean and the remaining proportion in the atmosphere reflect on the global carbon budget variations and further modulate global climate change. A more accurate reconstruction of the global carbon budget in the past decades and a more reliable prediction of the variations in the next years are crucial for assessing the effectiveness of climate change mitigation policies and supporting global carbon stocktaking and monitoring in compliance with the goals of the Paris Agreement.

In this study, we investigate reconstructions and predictions of the CO2 fluxes and atmospheric CO2 growth from ensemble prediction simulations using 5 Earth System Model (ESM) - based decadal prediction systems. These novel prediction systems driven by CO2 emissions with an interactive carbon cycle enable prognostic atmospheric CO2 and represent atmospheric CO2 growth variations in response to the strength of CO2 fluxes into the ocean and the land, which are missing in the conventional concentration-driven decadal prediction systems with prescribed atmospheric CO2 concentration.

The reconstructions generated by assimilating physical ocean and atmosphere data products into the prediction systems are able to reproduce the annual mean historical variations of the CO2 fluxes and atmospheric CO2 growth. Multi-model ensemble means best match the assessments of CO2 fluxes and atmospheric CO2 growth rate from the Global Carbon Project with correlations of 0.79, 0.82, and 0.98 for atmospheric CO2 growth rate, air-land CO2 fluxes, and air-sea CO2 fluxes, respectively. The CO2 emission-driven prediction systems with an interactive carbon cycle still maintain the predictive skill of CO2 fluxes and atmospheric CO2 growth as found in conventional concentration-driven prediction systems, i.e., about 2 years for the air-land CO2 fluxes and atmospheric CO2 growth, the air-sea CO2 fluxes have higher skill up to 5 years. The ESM-based prediction systems are capable to reconstruct and predict the variations in the global carbon cycle and hence are powerful tools for supporting carbon budgeting and monitoring, especially in the decarbonization processes. Furthermore, we investigate the contribution of uncertainty in the predictions of CO2 fluxes and atmospheric CO2 growth rate from internal climate variability, different model responses, and emission-forcing reductions to identify the prominent challenge in limiting the skill of CO2 predictions. 

How to cite: Li, H., Spring, A., Dunkl, I., Brune, S., Bernardello, R., Bopp, L., Merryfield, W., Mignot, J., Sospedra-Alfonso, R., Tourigny, E., Watanabe, M., and Ilyina, T.: Variations of the CO2 fluxes and atmospheric CO2 in multi-model predictions with an interactive carbon cycle, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14765,, 2023.