Long-term (2000-2020) global 0.05° continuous atmospheric carbon dioxide mapping combining OCO-2 observations and...

We reconstructed a global continuous 8-day XCO₂ (column-averaged CO₂ dry air mole fraction) product (GCXCO₂) at a spatial resolution of 0.05° from 2000 to 2020, combining terrestrial/marine remote sensing data and model simulations based on developed and tested stacking machine learning method. The GCXCO₂ product has the similar spatial pattern with OCO-2 satellite observations but with global seamless coverage, showing a higher spatial resolution and accuracy than CarbonTracker and CAMS model simulation data. A novel dynamic normalization strategy was developed to handle the great temporal variation issue and ensure the temporal expansion of the prediction model. The sampled based 10-fold cross-validation shows an overall satisfactory result at a global scale, with R² = 0.974 and root-mean-square error (RMSE) = 0.551 ppm (parts per million). Further spatial extension and temporal prediction experiments also proved that dependable results could be obtained in the regions and time periods without valid OCO-2 satellite observations (R² = 0.958 and R² = 0.886, respectively). Compared with Total Carbon Column Observing Network (TCCON) ground station observations, the GCXCO₂ demonstrates a better accuracy and a higher spatial resolution than the model simulation data. Based on the GCXCO₂ product, an upward annual trend of approximately 2.105 ppm/year can be found for global XCO₂ between 2000 and 2020, with greater seasonal fluctuations in the Northern Hemisphere than in the Southern Hemisphere. This product is one of some remote sensing-based global high-precision long-term XCO₂ datasets and an important tool to help advance the understanding of climate change and carbon balance, but also to detect CO₂ concentration anomalies. The dataset can be obtained publicly at doi:https://doi.org/10.5281/zenodo.10083102 (Guan and Sun, 2023).