Tracking methane across South America: an inversion of TROPOMI satellite observations to quantify emissions and sectoral contributions

Methane (CH₄) emissions from South America have been estimated to account for approximately 15% of global emissions over the past decade. While natural emissions are predominantly driven by wetlands, anthropogenic emissions include contributions from livestock and landfills. However, bottom-up estimates remain highly uncertain, particularly for wetland contributions. The top-down approach, based on atmospheric transport inverse modeling, offers a critical tool for enhancing the monitoring of regional CH₄ emissions. Given the sparse network of in-situ measurements and limited aircraft campaigns in the region, satellite observations of total column methane mixing ratios (XCH₄) provide a valuable source of observations for inverse modeling.

The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor (S5P) satellite, launched in 2017, provides XCH₄ with global daily coverage and a relatively high (5.5×7 km²) horizontal resolution. Three different products are derived from the raw spectra measurements and are used in this study: the official product by SRON, the WFMD product by the University of Bremen and the BLENDED product by the University of Harvard. While widely used for detecting localized methane plumes linked to super-emitters, TROPOMI CH₄ data also support regional and global flux inversions, enabling improved mapping of CH₄ emissions. In 2019, TROPOMI provided over 4 million observations across South America, though with uneven spatial coverage, particularly limited over the tropical region due to cloud cover.

We assimilate the TROPOMI XCH₄observations into regional atmospheric inversions of CH₄ emissions over South America at a 0.2°×0.2° resolution, for 2019. The inversions are performed with the CHIMERE transport model coupled with the inverse modeling platform Community Inversion Framework (CIF). We first compare prior emission dataset, evaluating sector-specific uncertainties and spatial-temporal correlations within the background error covariance (B). The study then assesses system sensitivity to key input datasets and parametrization, including deep convection modeling, prior datasets and TROPOMI product selection, and optimization parameters. Additionally, the response of simulated XCH₄ to sectoral contributions is analyzed. Particular focus is given over the tropical region and especially the Amazon basin, where extensive wetland emissions and low satellite observation coverage pose significant challenges. Finally, posterior CH₄ emission budgets are presented at local, country, and regional scale, with detailed analysis of sectoral contributions from livestock, landfills, and wetlands, offering insights into the drivers of South America’s methane emissions.