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A new global atmospheric carbon dioxide (CO₂) real-time forecast is now available as part of the pre-operational Monitoring of Atmospheric Composition and Climate – Interim Implementation (MACC-II) service using the infrastructure of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). One of the strengths of the CO₂ forecasting system is that the land surface, including vegetation CO₂ fluxes, is modelled online within the IFS. Other CO₂ fluxes are prescribed from inventories and from off-line statistical and physical models. The CO₂ forecast also benefits from the transport modelling from a state-of-the-art numerical weather prediction (NWP) system initialized daily with a wealth of meteorological observations. This paper describes the capability of the forecast in modelling the variability of CO₂ on different temporal and spatial scales compared to observations. The modulation of the amplitude of the CO₂ diurnal cycle by near-surface winds and boundary layer height is generally well represented in the forecast. The CO₂ forecast also has high skill in simulating day-to-day synoptic variability. In the atmospheric boundary layer, this skill is significantly enhanced by modelling the day-to-day variability of the CO₂ fluxes from vegetation compared to using equivalent monthly mean fluxes with a diurnal cycle. However, biases in the modelled CO₂ fluxes also lead to accumulating errors in the CO₂ forecast. These biases vary with season with an underestimation of the amplitude of the seasonal cycle both for the CO₂ fluxes compared to total optimized fluxes and the atmospheric CO₂ compared to observations. The largest biases in the atmospheric CO₂ forecast are found in spring, corresponding to the onset of the growing season in the Northern Hemisphere. In the future, the forecast will be re-initialized regularly with atmospheric CO₂ analyses based on the assimilation of CO₂ products retrieved from satellite measurements and CO₂ in situ observations, as they become available in near-real time. In this way, the accumulation of errors in the atmospheric CO₂ forecast will be reduced. Improvements in the CO₂ forecast are also expected with the continuous developments in the operational IFS.