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

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.