Satellite retrievals of column-averaged dry-air mole
fractions of carbon dioxide (

Carbon dioxide (

Overview of the presented

As Fig. 1 but for

The C3S satellite greenhouse gas (GHG) data set consists of single-sensor
satellite data products and of merged (i.e., combined multi-sensor,
multi-algorithm) data products. Here we present the latest version, version
4.1, of the merged Level 2 (L2) and merged Level 3 (L3)

Figure 1 provides an overview of the resulting merged

Satellite

In this section, we present an overview about the input data used to
generate and validate the new

The input satellite data used to generate the merged satellite data products
are individual satellite sensor Level 2 (L2) data products. Table 1 provides
an overview about the satellite

Individual satellite sensor

All listed satellites perform nadir (down-looking) and glint observations
and provide radiance spectra covering the relevant

As Table 1 but for

TCCON sites used for the validation of the

Table 2 and Fig. 4 provide an overview about the satellite

As Fig. 3 but

The satellite data products have been validated by comparison with the

In Sect. 4.3, we present annual

In order to generate the merged L2 products, the ensemble median algorithm is used, which is described in detail in Reuter et al. (2013).
Therefore, we limit the description given here to a short overview of the
latest version of the EMMA algorithm. To be specific, we initially describe
the EMMA

The EMMA

This means that EMMA selects for each month and each

However, some modifications are applied. In order to remove (or at least to
minimize) the impact of different a priori assumptions, all products are converted to
common a priori

Global bias correction as applied by EMMA to the
individual satellite

SECM2018 is also used to correct for potential offsets between the
individual data products by adding or subtracting a global offset (i.e., by
using one constant offset value for each individual product applied globally
and for the full time series). Time series of the individual data products
before and after offset correction are shown in Fig. 5. Note that in Fig. 5
all data are relative to SECM2018, which is a very simple

Another modification applied to the individual L2 input products is a potential scaling of their reported uncertainty for the individual L2 soundings. The scaling factor has been chosen such that on average the uncertainty of the reported error is consistent with the standard deviation of satellite minus ground-based validation data differences (see Sect. 4.1 for the validation of the reported uncertainties via the uncertainty ratio).

In order to avoid that an individual input product, which has much more
observations than the other products (such as OCO-2 compared to GOSAT),
entirely dominates the EMMA product, a method has been implemented to
prevent overweighting the contributions from individual L2 input data
products. The method is based on limiting the number of L2 data points. For
each grid cell and month, we perform the following steps: first, we compute
SEOM for each algorithm. From these values, we compute the 25th
percentile and divide it by

Relative data weight

In addition to the L2 information of the selected data products, EMMA stores the following diagnostic information for each selected sounding: identifier for the selected L2 algorithm and inter-algorithm spread (IAS) within the grid box of the sounding. Within each grid box, IAS is defined as the algorithm-to-algorithm standard deviation of the grid box averages.

By how much each individual satellite

As Fig. 5 but for

The EMMA L2

As Fig. 6 but for

Figure 8a shows the RDW, and Fig. 8b shows the number of
soundings per month for all individual sensor

April 2011

The version 4.1 L3 XCO2_OBS4MIPS and XCH4_OBS4MIPS data products have been obtained by gridding (averaging) the
version 4.1 L2, i.e., XCO2_EMMA and XCH4_EMMA,
products using monthly time and

For each individual product, the gridding is based on computing an
arithmetic, unweighted average of all soundings falling in a grid box.For
each grid box, the standard error of the mean is computed using the
uncertainties contained in the corresponding EMMA product files. In order to
reduce noise at least two individual observations must be present and the
resulting standard error of the mean must be less than 1.6 ppm for

Besides

As Fig. 9 but for April 2015. Note that
the SCIAMACHY/BESD map (top left) is empty because this product ended in
April 2012 (see Fig. 9 for SCIAMACHY/BESD

The validation of the merged satellite-derived

Both methods used for the validation of the L2 EMMA products are based on
colocating each individual satellite

Overview validation results at TCCON sites for data product XCO2_EMMA (version 4.1).

Average

The first validation method is the EMMA quality assessment method, which
is described in Reuter et al. (2013). Note that EMMA is not only a merging
method but also a data quality assessment method, as the assessment of
the quality of all satellite input data (listed in Tables 1 and 2) is a key
aspect of EMMA. The second method is the quality assessment/quality
control (QA/QC) method (Buchwitz et al., 2017b), which is applied to all
satellite

Key differences between the QA/QC method and the EMMA method are listed as follows.

Colocation criteria: QA/QC used

Filtering criterion surface elevation: EMMA requires a surface elevation difference of less than 250 m between a TCCON site and satellite footprints, whereas the QA/QC does not use this filtering criterion.

A priori correction: both methods correct for the use of different a priori

Approach to quantify seasonal bias and linear bias trend: the EMMA method is based on fitting a trend model, which includes an offset term, a slope term, and a sine term for seasonal fluctuations (see Reuter et al., 2019c) and computes the seasonal bias from the standard deviation of the fitted seasonal fluctuation term and obtains the bias trend and its uncertainty from the fitted slope term. The QA/QC method (Buchwitz et al., 2019a) uses (only) a linear fit to obtain the bias trend and its uncertainty and computes the seasonal bias from the standard deviation of the seasonal biases (as also done by Dils et al., 2014, for their quantity seasonality).

Criteria for enough data: both algorithms use several different thresholds for the required minimum number of colocations per TCCON site and minimum length of overlapping TCCON time series.

When generating an EMMA product, a set of standard figures are generated
such as Figs. 5 and 6 already discussed but also maps of the EMMA product
and of the various input data products for all months of the 2003–2018 time
period. Two of these figures are shown here, namely the figures for April 2011 (Fig. 9) and April 2015 (Fig. 10) (note that 2011 is the last full year
with data from SCIAMACHY and that 2015 is the first full year with OCO-2
data). The maps in the first four rows of Figs. 9 and 10 show the individual
sensor/algorithm L2 input data. As can be seen, the spatial

The average IAS for the entire time period 2003–2018 is shown in Fig. 11. As
can be seen, the scatter is typically in the range 0.6–1.1 ppm with the
exception of parts of the tropics, in particular central Africa, the
Himalayas, parts of southeast Asia, and high latitudes. High latitudes
typically correspond to large solar zenith angles, which is a challenge for
accurate satellite

Detailed validation results for all individual sensors and the EMMA

Table 4 lists all TCCON sites, which fulfill either the EMMA method or the
QA/QC method criteria with respect to a minimum number of colocations and
length of time series. Listed are the numerical values (in ppm), which have
been computed for several figures of merit. This includes (i) the overall
estimation of the single-observation random error computed as the standard
deviation of the satellite minus TCCON differences; (ii) the uncertainty
ratio, which is the ratio of the mean value of the reported (

Validation summary for data product XCO2_EMMA (version 4.1).

Table 5 lists (i) the mean value of the single-observation random error, (ii) the global bias computed as the mean value of the biases at the various TCCON sites, (iii) the regional bias computed as the standard deviation of the biases at the various TCCON sites, (iv) the mean seasonal bias, and (v) the spatiotemporal bias computed as the root sum square of the regional and of the seasonal bias. The spatiotemporal bias is used to quantify the achieved performance for relative accuracy, which characterizes the spatially and temporally varying component of the bias (i.e., neglects a possible global bias (global offset), which is reported separately).

The linear bias trend has also been computed by fitting a line to the
satellite–TCCON differences (not shown here). The mean value of the
linear trend (slope) and its uncertainty (

As can be seen from Table 5, the values computed independently using the EMMA and the QA/QC assessment methods are quite similar, which gives not only confidence in the overall quality assessment summary documented in Table 5 but also in the products and the used validation methods.

TCCON

Assessment method DP is the method used by the data
provider. For

Note however that the quality of the satellite data (at least at TCCON
sites) is very likely better than Table 5 suggests (i) because the TCCON
retrievals are not free of errors (the

Overview validation results at TCCON sites for data product XCH4_EMMA (version 4.1).

The XCO2_OBS4MIPS product has also been directly compared with TCCON using a comparison method based on the comparison of the monthly satellite product with TCCON monthly mean values. The results are shown in Fig. 13. As can be seen, the mean difference (satellite – TCCON) is 0.18 ppm (which is close to the mean value of the global bias of 0.20 ppm listed in Table 5), the standard deviation is 1.18 ppm (as expected, because of the spatiotemporal averaging, which is somewhat smaller than the value of 1.29 ppm obtained for the XCO2_EMMA product listed in Table 5), and the linear correlation coefficient is 0.99. The spatiotemporal bias, computed as the standard deviation of 3-monthly averages at the TCCON sites listed in Fig. 13, is 0.7 ppm.

Figure 1 presents an overview of the

As for

Detailed validation results are shown in Appendix A (Fig. A2), and the
validation results are summarized in Tables 7 and 8, which have the same
structure as the corresponding

Average

Summary of the comparison of product XCO2_OBS4MIPS with TCCON monthly mean

September 2010

Figure 16 shows the TCCON station

As Fig. 14 but for September 2018. Note that the
SCIAMACHY/WFMD map (top left) is empty because this product ended in April 2012 (see Fig. 14 for SCIAMACHY/WFMD

Validation summary for data product XCH4_EMMA (version 4.1).

The XCH4_OBS4MIPS product has also been directly compared
with TCCON (Fig. 17) using the same method as also used for product
XCO2_OBS4MIPS (Fig. 13). As can be seen from Fig. 17, the
mean difference (satellite – TCCON) is

As Fig. 12 but for

Summary of the comparison of product XCH4_OBS4MIPS with TCCON monthly mean

Figure 2 presents an overview of the

Finally, we present an update and extension of the year 2003–2016 annual

The growth rate of atmospheric methane is also an important quantity (e.g.,
Nisbet et al., 2019). The method of Buchwitz et al. (2018) has now also been
used to compute annual

Satellite-derived ensemble

From January 2003 to March 2009 the products are based on SCIAMACHY/ENVISAT,
and from April 2009 onwards the products use an ensemble of one SCIAMACHY (until early
2012) and several GOSAT products. The

The EMMA algorithm selects for each month and each

Detailed quality assessment results based on comparisons with TCCON
ground-based retrievals have been presented. We found that the

The new data sets have also been used to derive annual

An important application for the EMMA products is to use them together with
inverse modeling to obtain improved information on regional-scale

The EMMA and OBS4MIPS

MR generated the EMMA and OBS4MIPS

The authors declare that they have no conflict of interest.

The generation of the EMMA Level 2 and OBS4MIPS Level 3 data sets and the
corresponding data analysis has been funded primarily by the European Union
(EU) via the Copernicus Climate Change Service (C3S,

The work presented here strongly benefited from additional funding by the
European Space Agency (ESA) via ESA's Climate Change Initiative (CCI,

The further development of the FOCAL retrieval algorithm used to generate
the OCO-2/FOCAL

We thank several space agencies for making available satellite Level 1 (L1) input data: ESA/DLR for SCIAMACHY L1 data, JAXA for GOSAT Level 1B data, and NASA for the OCO-2 L1 data product. We also thank ESA for making the GOSAT L1 product available via the ESA Third Party Mission (TPM) archive.

We thank NIES for the operational GOSAT

TCCON data were obtained from the TCCON Data Archive, hosted by CaltechDATA,
California Institute of Technology (

The TCCON stations Ascension Island, Bremen, Garmisch, Karlsruhe, and Ny-Ålesund have been supported by the German Bundesministerium für Wirtschaft und Energie (BMWi) via the DLR under grants 50EE1711A-E. We thank the ESA Ariane tracking station at North East Bay, Ascension Island, for hosting and local support. Nicholas M. Deutscher is supported by an ARC Future Fellowship, FT180100327. The TCCON site at Réunion island is operated by the Royal Belgian Institute for Space Aeronomy with financial support in 2014, 2015, 2016, 2017, 2018, and 2019 under the EU project ICOS-Inwire and the ministerial decree for ICOS (FR/35/IC2) and local activities supported by LACy/UMR8105 – Université de La Réunion. The TCCON stations at Tsukuba and Burgos are supported in part by the GOSAT series project. Local support for Burgos is provided by the Energy Development Corporation (EDC, Philippines). The Paris TCCON site has received funding from Sorbonne Université, the French research center CNRS, the French space agency CNES, and Région Île-de-France.

We also thank NOAA for access to the surface

We also thank Peter Bergamaschi for providing MACC-II project inversion
system

This research has been supported by the European Union (Copernicus Climate Change Service project C3S_312b_Lot2, via contract with DLR, contract no. D/565/67260504; CHE, grant agreement no. 776186; VERIFY, grant agreement no. 776810; CCiCC, grant agreement no. 821003) and the European Space Agency (ESA; grant no. Project GHG-CCI+).The article processing charges for this open-access publication were covered by the University of Bremen.

This paper was edited by John Worden and reviewed by Ray Nassar and two anonymous referees.