Satellite-derived XCO2 and XCH4 climate data records generated with the latest version of the EnseMble Median Algorithm (EMMA)

Carbon dioxide (CO₂) and methane (CH₄) are the two most important anthropogenic greenhouse gases and are the primary drivers of ongoing climate change. Satellite-based remote sensing of their column-average dry-air mole fractions (XCO2 and XCH4) contributes to an improved understanding of the climate system and natural carbon fluxes, enables the quantification of anthropogenic emissions, and supports the monitoring of emission reduction measures. Many of these applications have demanding requirements on the accuracy of the underlying satellite data. In particular, climate studies benefit from long-term climate data records with high inter-sensor consistency.

For decades, climate modellers use ensemble approaches to calculate the ensemble median and to estimate uncertainties of climate projections where no ground-truth is available. Following this concept, the EnseMble Median Algorithm (EMMA) enables the combination of multiple XCO2 and XCH4 data sets from different satellite instruments into a single, consistent data product with high accuracy and quantified uncertainties. Since 2016, EMMA-based XCO2 and XCH₄ climate data records have been generated and made publicly available within the framework of the Copernicus Climate Change Service (C3S).

The latest EMMA version, v5.1, represents a significant update of the algorithm. It enables, for the first time, the meaningful integration of very large data sets, such as those from Sentinel-5P, and allows the generation of data products suitable also for the analysis of small-scale emission sources. Our presentation, will introduce the updated algorithm, the generated XCO2 and XCH4 climate data records, and present validation results.