Global Estimates of Daily Gapless Atmospheric XCH₄ Concentrations From Satellite and Reanalysis Data During 2003–2020

Methane is a potent greenhouse gas that significantly drives climate change and affects human health and ecosystems by promoting surface ozone formation. However, the spatial continuity of current satellite-derived column-averaged dry-air mole fraction of methane (XCH<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub>) data remains limited, and the sparse distribution of ground-based observations further hinders our understanding of methane sources and trends. To address this, we developed a novel spatiotemporal Transformer model to fill gaps in satellite retrievals and correct biases, thereby generating a global, daily, gapless XCH<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> dataset at 0.1° × 0.1° (∼10 × 10 km near the equator) grid resolution from 2003 to 2020. This model integrates XCH<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> retrievals from the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) and Greenhouse Gases Observing Satellite (GOSAT), together with XCH<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> reanalysis from the Copernicus Atmosphere Monitoring Service (CAMS), meteorological variables, and other auxiliary datasets. Our fused daily XCH<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> dataset yielded a mean coefficient of determination (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) value of 0.93, a root-mean-square error (RMSE) of 13.10 ppb, and a bias of 7.45 ppb when independently compared with measurements from the Total Carbon Column Observing Network (TCCON). Furthermore, we performed bias correction on the reconstructed XCH<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> data using the TCCON network and supplementary variables to improve consistency across satellites and enhance accuracy, achieving a mean cross-validation R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (CV-R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) of 0.97, an RMSE of 7.51 ppb, and a bias of 0.51 ppb. We observed a global annual growth rate of 6.09 ppb/yr (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.001) from 2003 to 2020, with the average annual trends in developed countries being much lower than those in developing countries, at 5.86 ppb/yr and 6.16 ppb/yr (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.001), respectively. Bangladesh exhibited the highest annual average concentration and trend, with average values of 1850.21 ± 41.85 ppb and 8.07 ppb/yr (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.001), respectively. This unique global gapless daily XCH<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> dataset enables the precise detection of local emissions and provides valuable support for informing methane emission management policies and enhancing our understanding of global methane sources and their spatiotemporal variations.