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

Abstract

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).