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

We reconstructed a global continuous 8-day XCO (column-averaged CO dry air mole fraction) product (GCXCO) 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 GCXCO 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 R = 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 (R = 0.958 and R = 0.886, respectively). Compared with Total Carbon Column Observing Network (TCCON) ground station observations, the GCXCO demonstrates a better accuracy and a higher spatial resolution than the model simulation data. Based on the GCXCO product, an upward annual trend of approximately 2.105 ppm/year can be found for global XCO 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 XCO datasets and an important tool to help advance the understanding of climate change and carbon balance, but also to detect CO concentration anomalies. The dataset can be obtained publicly at doi:https://doi.org/10.5281/zenodo.10083102 (Guan and Sun, 2023).