Global satellite observations of column-averaged carbon dioxide and methane: The GHG-CCI XCO2 and XCH4 CRDP3 data set

Abstract

Carbon dioxide (CO 2) and methane (CH 4) 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-surfacesensitive atmospheric CO 2 and CH 4 observations that can be used to obtain information on CO 2 and CH 4 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 CO 2 and CH 4 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 CO 2 and CH 4 , i.e., XCO 2 and XCH 4 , 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 XCO 2 , < 10 ppb for XCH 4) and long-term stability (< 0.2 ppm/year for XCO 2 , < 2 ppb/year for XCH 4) are met for nearly all products (an exception is SCIAMACHY methane especially since 2010). For XCO 2 we present comparisons with global models using the output of two CO 2 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.

Carbon dioxide retrieval from OCO-2 satellite observations using the RemoTeC algorithm and validation with TCCON measurements

“…Possible reasons are the zero-level offset fits in RemoTeC that are not performed by ACOS or the difference in the treatment of aerosols. X CO 2 retrievals from GOSAT measurements using the RemoTeC algorithm have also been validated with TCCON data (Butz et al, 2011;Guerlet et al, 2013b;Dils et al, 2014;Buchwitz et al, 2017b). There are several improvements on the RemoTeC/GOSAT X CO 2 retrieval quality since the first report by Butz et al (2011).…”

“…In the following validation, we assume that TCCON measurements themselves are consistent over all stations with a station-tostation variability of zero. However, as discussed by Kulawik et al (2016) and Buchwitz et al (2017b), individual stations have a year-to-year variability of ∼ 0.3 ppm and the overall TCCON X CO 2 uncertainty is around 0.4 ppm (1 sigma). Although some limitations may exist, TCCON measurements are the most appropriate validation data product for satellite observations.…”

Toward Global Mapping of Methane With TROPOMI: First Results and Intersatellite Comparison to GOSAT

“…We compare TROPOMI full‐physics CH 4 data with GOSAT proxy CH 4 data that is delivered in the context of the Copernicus Atmospheric Monitoring Service (CAMS), called CAMS41 RemoTeC near‐real‐time data set. The underlying proxy retrieval method is the same as used for the European Space Agency Greenhouse Gases Climate Change Initiative CRDP3 data set described by Buchwitz et al (). The GOSAT CH 4 data used in this work are bias corrected by using high‐precision ground‐based measurements from the Total Carbon Observing Network (TCCON, Wunch et al, ) but has a remaining bias of −6.6 ppb and a standard deviation of 15.5 ppb for single soundings.…”

A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 2: Application to XCO2 Retrievals from OCO-2

“…Several XCO 2 retrieval algorithms exist for the SCIAMACHY and GOSAT observations covering the whole mission periods (e.g., [10][11][12][13][14][15][16][17][18]). Because of the sparse ground based validation sites, the analyses of an ensemble of independently developed algorithms can give important insights in the quality of the retrievals, especially, remote from the validation sites [19] and strengthen the geophysical interpretation of the data [20].…”