Worldwide Evaluation of CAMS-EGG4 CO₂ Data Re-Analysis at the Surface Level

This study systematically examines the global uncertainties and biases in the carbon dioxide (CO₂) mixing ratio provided by the Copernicus Atmosphere Monitoring Service (CAMS). The global greenhouse gas re-analysis (EGG4) data product from the European Centre for Medium-Range Weather Forecasts (ECMWF) was evaluated against ground-based in situ measurements from more than 160 of stations across the world. The evaluation shows that CO₂ re-analysis can capture the general features in the tracer distributions, including the CO₂ seasonal cycle and its strength at different latitudes, as well as the global CO₂ trend. The emissions and natural fluxes of CO₂ at the surface are evaluated on a wide range of scales, from diurnal to interannual. The results highlight re-analysis compliance, reproducing biogenic fluxes as well the observed CO₂ patterns in remote environments. CAMS consistently reproduces observations at marine and remote regions with low CO₂ fluxes and smooth variability. However, the model's weaknesses were observed in continental areas, regions with complex sources, transport circulations and large CO₂ fluxes. A strong variation in the accuracy and bias are displayed among those stations with different flux profiles, with the largest uncertainties in the continental regions with high CO₂ anthropogenic fluxes. Displaying biased estimation and root-mean-square error (RMSE) ranging from values below one ppmv up to 70 ppmv, the results reveal a poor response from re-analysis to high CO₂ mixing ratio, showing larger uncertainty of the product in the boundaries where the CAMS system misses solving sharp flux variability. The mismatch at regions with high fluxes of anthropogenic emission indicate large uncertainties in inventories and constrained physical parameterizations in the CO₂ at boundary conditions. The current study provides a broad uncertainty assessment for the CAMS CO₂ product worldwide, suggesting deficiencies and methods that can be used in the future to overcome failures and uncertainties in regional CO₂ mixing ratio and flux estimates.