Carbon dioxide (CO2) and methane (CH4) are the two most important greenhouse gases emitted by mankind. Better knowledge of the surface sources and sinks of these Essential Climate Variables (ECVs) and related carbon uptake and release processes is needed for important climate change related applications such as improved climate modelling and prediction. Some satellites provide near-surface-sensitive atmospheric CO2 and CH4 observations that can be used to obtain information on CO2 and CH4 surface fluxes. The goal of the GHG-CCI project of the European Space Agency’s (ESA) Climate Change Initiative (CCI) is to use satellite data to generate atmospheric CO2 and CH4 data products meeting demanding GCOS (Global Climate Observing System) greenhouse gas (GHG) ECV requirements. To achieve this, retrieval algorithms are regularly being improved followed by annual data reprocessing and analysis cycles to generate better products in terms of extended time series and continuously improved data quality. Here we present an overview about the latest GHG-CCI data set called Climate Research Data Package No. 3 (CRDP3) focusing on the GHG-CCI core data products, which are column-averaged dry-air mole fractions of CO2 and CH4, i.e., XCO2 and XCH4, as retrieved from SCIAMACHY/ENVISAT and TANSO/GOSAT satellite radiances covering the time period end of 2002 to end of 2014. We present global maps and time series including initial validation results obtained by comparisons with Total Carbon Column Observing Network (TCCON) ground-based observations. We show that the GCOS requirements for systematic error (< 1 ppm for XCO2, < 10 ppb for XCH4) and long-term stability (< 0.2 ppm/year for XCO2, < 2 ppb/year for XCH4) are met for nearly all products (an exception is SCIAMACHY methane especially since 2010). For XCO2 we present comparisons with global models using the output of two CO2 assimilation systems (MACC version 14r2 and CarbonTracker version CT2013B). We show that overall there is reasonable consistency and agreement between all data sets (within ~ 1-2 ppm) but we also found significant differences depending on region and time period.
