Abstract
-
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 PackageNo. 3 (CRDP3) focusing on the GHG-CCI core data products,which are column-averaged dryair
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
timeseries including initial validation results obtained by comparisons with Total Carbon ColumnObservingNetwork
(TCCON) ground-based observations. We show that the GCOS requirements for systematic error (b1 ppm
for XCO2, b10 ppb for XCH4) and long-termstability (b0.2 ppm/year for XCO2, b2 ppb/year for XCH4) aremet 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.