CAMS background methane mole fraction curtains - South America

Background methane mole fraction "curtains" around the South American domain [61.0° S - 22.3° N, 91.3° W - 24.8° W], used in Keshtmand et al. (2026) (code hosted at https://github.com/GATES-Lab/GATES_LPDM_emulator). They were created by Tunnicliffe et al. (2020), extracted from the Copernicus Atmosphere Monitoring Service (CAMS) CH4 flux inversion product v17r1, surface only (available from the Copernicus Datasets portal), and processed with the codebase from the University of Bristol's Atmospheric Chemistry Research Group . The curtains provide the monthly boundary mole fraction fields at each side (north, south, east, west) of the South American domain used for dispersion modelling [61.0° S - 22.3° N, 91.3° W - 24.8° W], at 20 level heights. They are designed to be combined with LPDM-derived boundary condition sensitivities to compute the background mole fraction contributions. The Zenodo datasets containing the boundary sensitivities are at https://doi.org/10.5281/zenodo.16748754 and https://doi.org/10.5281/zenodo.21031381. The datasets are provided as netCDF (.nc) arrays, which can be opened and explored in python using the xarray library. Each file contains curtains (under variable "vmr_x", where x is designated as n for north, s for south, e for east, or w for west) which span 20 vertical levels from 500 metres to 19,500 metres at 1000 metre intervals. The north/south curtains have a horizontal resolution of 0.352°, whilst east/west have a horizontal resolution of 0.234°. Tunnicliffe et al. (2020). Quantifying sources of Brazil's CH4 emissions between 2010 and 2018 from satellite data. Atmospheric Chemistry and Physics, 20(21), 13041–13067. https://doi.org/10.5194/acp-20-13041-2020 Keshtmand et al. (2026). GATESBackground: Emulating background greenhouse gas mole fractions for regional atmospheric inverse modelling with graph neural networks. Preprint egusphere-2026-3361. Fillola, E. and Tunnicliffe, R.: LPDM footprint dataset – Brazil, Zenodo [data set], https://doi.org/10.5281/zenodo.16748754, 2025. Keshtmand, N. and Tunnicliffe, R.: LPDM footprint dataset - Brazil 2017, Zenodo [data set], https://doi.org/10.5281/zenodo.21031381, 2026.