Multimodel Reconstructions and Predictions of the CO₂ Fluxes and Atmospheric CO₂ Variations

Attributing atmospheric carbon dioxide (CO₂) growth to anthropogenic emissions in the presence of natural climate variability, both in the past and in the near future, is critical for assessing the impact of climate policies on the Earth system toward establishing early warnings for the carbon cycle and climate extremes. Using ensemble simulations from six novel prediction systems based on Earth system models (ESMs), we investigate reconstructions and predictions of the CO₂ fluxes and atmospheric CO₂ growth. These systems enable predictions of atmospheric CO₂ growth variations in response to air–sea and air–land CO₂ fluxes via activating the interactive carbon cycle, which is missing in the conventional decadal prediction systems with prescribed atmospheric CO₂ concentration. The reconstructions from assimilation runs, integrating physical atmosphere and ocean data products, reproduce the annual mean observed variations in the CO₂ fluxes and atmospheric CO₂ growth to a large degree. The emission-driven prediction systems show predictive skill for up to 2 years for the air–land CO₂ fluxes and atmospheric CO₂ growth, while the air–sea CO₂ fluxes have higher skill for up to 5 years, indicating that the predictive skill of atmospheric CO₂ growth is limited by the air–land CO₂ fluxes. While predictions of air–land CO₂ fluxes are linearly linked to El Niño–Southern Oscillation (ENSO), predictions of air–sea CO₂ fluxes are less so. The ESMs’ ability to predict variations in CO₂ fluxes regardless of their linear relationship with ENSO merits further study of the mechanisms regulating the predictive skill toward improving our predictive capability in a closed Earth system. SIGNIFICANCE STATEMENT: This study aims to reconstruct variations in carbon dioxide (CO₂) fluxes and atmospheric CO₂ growth over the past decades and predict the changes in the next years. This has urgent implications for informing decarbonization and climate mitigation policy, as the global carbon cycle is affected by anthropogenic CO₂ emissions and interacts with the changing climate. Six novel prediction systems based on Earth system models (ESMs) provide comprehensive carbon cycle estimates within a closed budget while enabling process-based attribution of the variations. We demonstrate that these initialized ESMs can predict the variations in atmospheric CO₂ growth for 2 years. The outcomes have recently contributed to the Global Carbon Budget annual updates and the World Meteorological Organization Lead Centre for Annual-to-Decadal Climate Prediction.